From 05e0ec30a632339a8533082476f27bda31ccde16 Mon Sep 17 00:00:00 2001 From: Chunseok Lee Date: Thu, 30 Jul 2020 11:32:26 +0900 Subject: [PATCH] Imported Upstream version 1.7.0 --- .readthedocs.yml | 22 + COPYRIGHT | 2 +- Makefile.template | 37 +- README.md | 73 +- compiler/adtidas/README.md | 1 + compiler/angkor/src/ADT/feature/Buffer.test.cpp | 10 +- compiler/angkor/src/ADT/feature/CHWLayout.test.cpp | 6 +- compiler/angkor/src/ADT/feature/HWCLayout.test.cpp | 6 +- compiler/angkor/src/ADT/feature/Layout.test.cpp | 10 +- compiler/angkor/src/ADT/feature/Overlay.test.cpp | 14 +- compiler/angkor/src/ADT/feature/Shape.test.cpp | 8 +- compiler/angkor/src/ADT/kernel/Buffer.test.cpp | 12 +- compiler/angkor/src/ADT/kernel/Layout.test.cpp | 10 +- compiler/angkor/src/ADT/kernel/NCHWLayout.test.cpp | 8 +- compiler/angkor/src/ADT/kernel/NHWCLayout.test.cpp | 8 +- compiler/angkor/src/ADT/kernel/Overlay.test.cpp | 16 +- compiler/angkor/src/ADT/kernel/Shape.test.cpp | 10 +- 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tests/nnapi/specs/V1_2/prelu_float_1_nnfw.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/prelu_quant8_1_nnfw.mod.py rename tests/nnapi/specs/{skip => }/V1_2/reduce_all.mod.py (100%) mode change 100755 => 100644 create mode 100644 tests/nnapi/specs/V1_2/reduce_all_2D_nnfw.mod.py create mode 100644 tests/nnapi/specs/V1_2/reduce_all_4D_nnfw.mod.py create mode 100644 tests/nnapi/specs/V1_2/reduce_all_dynamic_nnfw.mod.py rename tests/nnapi/specs/{skip => }/V1_2/reduce_any.mod.py (100%) mode change 100755 => 100644 create mode 100644 tests/nnapi/specs/V1_2/reduce_any_2D_nnfw.mod.py create mode 100644 tests/nnapi/specs/V1_2/reduce_any_4D_nnfw.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/reduce_max.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/reduce_max_2D_float_nnfw.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/reduce_max_2D_int32_nnfw.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/reduce_max_4D_float_reducing_C_nnfw.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/reduce_max_4D_float_reducing_HW_nnfw.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/reduce_max_float_1_nnfw.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/reduce_max_float_2_nnfw.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/reduce_max_float_nnfw.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/reduce_max_quant8_1_nnfw.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/reduce_max_quant8_2_nnfw.mod.py create mode 100644 tests/nnapi/specs/V1_2/reduce_mean_dynamic_1_nnfw.mod.py create mode 100644 tests/nnapi/specs/V1_2/reduce_mean_dynamic_2_nnfw.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/reduce_min.mod.py create mode 100644 tests/nnapi/specs/V1_2/reduce_min_dynamic_nnfw.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/reduce_min_float_1_nnfw.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/reduce_min_float_2_nnfw.mod.py mode change 100755 => 100644 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create mode 100644 tests/nnapi/specs/V1_2/split_dynamic_float_nnfw.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/split_float_1.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/split_float_2.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/split_float_3.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/split_float_4.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/split_float_5.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/split_int32_1.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/split_int32_2.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/split_int32_3.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/split_int32_4.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/split_quant8_1.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/split_quant8_2.mod.py mode change 100755 => 100644 tests/nnapi/specs/V1_2/split_quant8_3.mod.py mode change 100755 => 100644 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+# .readthedocs.yml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 1.4 + +# Build documentation in the docs/ directory with Sphinx +sphinx: + builder: html + configuration: docs/conf.py + fail_on_warning: false + +# Optionally build your docs in additional formats such as PDF +formats: + - pdf + +# Optionally set the version of Python and requirements required to build your docs +python: + version: 3.7 + install: + - requirements: docs/requirements.txt diff --git a/COPYRIGHT b/COPYRIGHT index 3c78815..17f325b 100644 --- a/COPYRIGHT +++ b/COPYRIGHT @@ -1 +1 @@ -Copyright (c) <2019> All Rights Reserved. +Copyright (c) 2020 Samsung Electronics Co., Ltd. & Contributors All Rights Reserved. diff --git a/Makefile.template b/Makefile.template index 588a300..6c919f3 100644 --- a/Makefile.template +++ b/Makefile.template @@ -1,10 +1,9 @@ -HOST_ARCH?=$(shell uname -p) -TARGET_ARCH?=$(shell uname -p) +HOST_ARCH?=$(shell uname -m) +TARGET_ARCH?=$(shell uname -m) BUILD_TYPE?=Debug CROSS_BUILD?=0 HOST_OS?=linux TARGET_OS?=linux -PARALLEL_BUILD?=1 COVERAGE_BUILD?=0 BENCHMARK_ACL_BUILD?=0 OPTIONS?= @@ -49,9 +48,9 @@ ifeq ($(BENCHMARK_ACL_BUILD),1) OPTIONS+= -DBUILD_BENCHMARK_ACL=1 endif -ifneq ($(EXT_ACL_FOLDER),) - OPTIONS+= -DBUILD_ARMCOMPUTE=OFF - OPTIONS+= -DARMCompute_EXTDIR=$(EXT_ACL_FOLDER) +ifneq ($(EXT_HDF5_DIR),) + $(info Hello $(EXT_HDF5_DIR)) + OPTIONS+= -DEXT_HDF5_DIR=$(EXT_HDF5_DIR) endif ifneq ($(EXTERNAL_VOLUME),) @@ -78,14 +77,12 @@ ifneq ($(ANDROID_BOOST_ROOT),) OPTIONS+= -DANDROID_BOOST_ROOT=$(ANDROID_BOOST_ROOT) endif -ifeq ($(PARALLEL_BUILD),1) - # Get number of processors (linux only for now) - ifeq ($(HOST_OS),linux) - NPROCS?=$(shell grep -c ^processor /proc/cpuinfo) - endif +ifeq ($(HOST_OS),linux) + NPROCS?=$(shell grep -c ^processor /proc/cpuinfo) +else + NPROCS?=1 endif -NPROCS?=1 WORKHOME=$(CURDIR)/Product WORKFOLDER=$(TARGET_ARCH_LC)-$(TARGET_OS).$(BUILD_TYPE_LC) WORKSPACE=$(WORKHOME)/$(WORKFOLDER) @@ -125,14 +122,22 @@ distclean: ### Command (internal) ### configure_internal: +# TODO Remove setting EXT_ACL_FOLDER +# Construct overlay folder directly outside (with headers?) +ifneq ($(EXT_ACL_FOLDER),) + mkdir -p $(OVERLAY_FOLDER)/lib + cp $(EXT_ACL_FOLDER)/* $(OVERLAY_FOLDER)/lib +endif + NNFW_WORKSPACE="$(WORKSPACE)" NNFW_INSTALL_PREFIX=$(INSTALL_PATH) ./nnfw configure \ -DCMAKE_BUILD_TYPE=$(BUILD_TYPE_LC) \ -DNNFW_OVERLAY_DIR=$(OVERLAY_FOLDER) \ + -DACL_BUILD_THREADS=$(NPROCS) \ $(OPTIONS) touch $(TIMESTAMP_CONFIGURE) build_internal: $(BUILD_FOLDER) - NNFW_WORKSPACE="$(WORKSPACE)" NPROCS="$(NPROCS)" PARALLEL_BUILD="1" ./nnfw build + NNFW_WORKSPACE="$(WORKSPACE)" ./nnfw build -j $(NPROCS) rm -rf $(BUILD_ALIAS) ln -s $(BUILD_FOLDER) $(BUILD_ALIAS) touch $(TIMESTAMP_BUILD) @@ -144,8 +149,10 @@ install_internal: touch $(TIMESTAMP_INSTALL) runtime_tar_internal: $(TIMESTAMP_BUILD) install_internal - tar -zcf nnfw-package.tar.gz -C $(INSTALL_PATH) lib include - mv nnfw-package.tar.gz $(INSTALL_PATH)/. + tar -zcf nnfw-package.tar.gz -C $(INSTALL_PATH) lib + tar -zcf nnfw-dev-package.tar.gz -C $(INSTALL_PATH) include/nnfw + tar -zcf nnfw-internal-dev-package.tar.gz -C $(INSTALL_PATH) include/onert + mv nnfw-*package.tar.gz $(INSTALL_PATH)/. install_internal_acl: # Workaround to install acl for test (ignore error when there is no file to copy) diff --git a/README.md b/README.md index dfa91af..bbeb66c 100644 --- a/README.md +++ b/README.md @@ -1,40 +1,40 @@ -# nnfw +[![GitHub release (latest +SemVer)](https://img.shields.io/github/v/release/Samsung/ONE)](https://github.com/Samsung/ONE/releases) +[![Documentation Status](https://readthedocs.org/projects/nnfw/badge/?version=latest)](https://nnfw.readthedocs.io/en/latest/?badge=latest) +![GitHub commit activity](https://img.shields.io/github/commit-activity/w/Samsung/ONE?color=light%20green) +[![Gitter](https://img.shields.io/gitter/room/Samsung/ONE?color=orange)](https://gitter.im/Samsung/ONE) -A high-performance, on-device neural network inference framework +# **ONE** (On-device Neural Engine) -## Goal +ONE Logo -This project _nnfw_ aims at providing a high-performance, on-device neural network (NN) inference -framework that performs inference of a given NN model on processors, such as CPU, GPU, or NPU, in -the target platform, such as the Linux kernel based OS including Tizen. +A high-performance, on-device neural network inference framework. -## Project Documents +## Goal -- [Roadmap](docs/nnfw/roadmap.md) -- [SW Requirement Specification](docs/nnfw/project/2019_requirement_specification.md) -- [SW High Level Design](docs/nnfw/project/2018_high_level_design.md) +This project **ONE** aims at providing a high-performance, on-device neural network (NN) inference +framework that performs inference of a given NN model on processors, such as CPU, GPU, DSP or NPU. -## Getting started +We develop a runtime that runs on a Linux kernel-based OS platform such as Ubuntu, Tizen, or +Android, and a compiler toolchain to support NN models created using various NN training frameworks such +as Tensorflow or PyTorch in a unified form at runtime. -- For the contribution, please refer to our [contribution guide](docs/HowToContribute.md). -- You can also find how-to documents [HERE](docs/nnfw/howto.md). +## Overview -## Maintainers +- [Background](docs/overview/background.md) +- [Roadmap](docs/overview/roadmap.md) +- [Overall Architecture](docs/overview/overall-architecture.md) -- Sung-Jae Lee <> -- Chunseok Lee <> - -## Committers +## Getting started -- Hyeongseok Oh <> -- Hanjoung Lee <> -- Sharan Allur <> +- For the contribution, please refer to our [contribution guide](docs/howto/how-to-contribute.md). +- You can also find various how-to documents [here](docs/howto). -## Feature Request (NEW) +## Feature Request -You can suggest development of nnfw's features that are not yet available. +You can suggest development of **ONE**'s features that are not yet available. -The functions requested so far can be checked in the [popular feature request](https://github.com/Samsung/ONE/issues?utf8=%E2%9C%93&q=is%3Aopen+is%3Aissue+label%3AFEATURE_REQUEST+sort%3Areactions-%2B1-desc) list. +The functions requested so far can be checked in the [popular feature request](https://github.com/Samsung/ONE/issues?q=label%3AFEATURE_REQUEST+) list. - If the feature you want is on the list, :+1: to the body of the issue. The feature with the most :+1: is placed at the top of the list. When adding new features, we will prioritize them with this reference. @@ -45,30 +45,17 @@ Sooner or later, the maintainer will tag the `FEATURE_REQUEST` label and appear We expect one of the most frequent feature requests would be the operator kernel implementation. It is good to make a request, but it is better if you contribute by yourself. See the following guide, -[How to Implement Operator Kernel](docs/nnfw/howto/HowToAddNewOperation.md), for help. +[How to add a new operation](docs/howto/how-to-add-a-new-operation.md), for help. We are looking forward to your participation. Thank you in advance! -# nncc -Re-targetable neural network (NN) model compilation framework - -## Goals -nncc, which stands for neural network compiler collection, aims to provide a general framework for -compiling a given NN model to an artifact that runs on various target devices such as CPU, GPU, or -NPU. - -## Maintainers - -- Sung-Jae Lee <> -- Jonghyun Park <> +## How to Contact -## Committers +- Please post questions, issues, or suggestions into [Issues](https://github.com/Samsung/ONE/issues). This is the best way to communicate with the developer. +- You can also have an open discussion with community members through [gitter.im](https://gitter.im/Samsung/ONE) channel. -- Saehie Park <> -- Hyeongseok Oh <> -- Efimov Alexander <> +## Hall of Fame -## How to Contact +[![](https://sourcerer.io/fame/lemmaa/Samsung/ONE/images/0)](https://sourcerer.io/fame/lemmaa/Samsung/ONE/links/0)[![](https://sourcerer.io/fame/lemmaa/Samsung/ONE/images/1)](https://sourcerer.io/fame/lemmaa/Samsung/ONE/links/1)[![](https://sourcerer.io/fame/lemmaa/Samsung/ONE/images/2)](https://sourcerer.io/fame/lemmaa/Samsung/ONE/links/2)[![](https://sourcerer.io/fame/lemmaa/Samsung/ONE/images/3)](https://sourcerer.io/fame/lemmaa/Samsung/ONE/links/3)[![](https://sourcerer.io/fame/lemmaa/Samsung/ONE/images/4)](https://sourcerer.io/fame/lemmaa/Samsung/ONE/links/4)[![](https://sourcerer.io/fame/lemmaa/Samsung/ONE/images/5)](https://sourcerer.io/fame/lemmaa/Samsung/ONE/links/5)[![](https://sourcerer.io/fame/lemmaa/Samsung/ONE/images/6)](https://sourcerer.io/fame/lemmaa/Samsung/ONE/links/6)[![](https://sourcerer.io/fame/lemmaa/Samsung/ONE/images/7)](https://sourcerer.io/fame/lemmaa/Samsung/ONE/links/7) -- Please post questions, issues, or suggestions into [Issues](https://github.com/Samsung/ONE/issues). diff --git a/compiler/adtidas/README.md b/compiler/adtidas/README.md new file mode 100644 index 0000000..df427d3 --- /dev/null +++ b/compiler/adtidas/README.md @@ -0,0 +1 @@ +# adtidas diff --git a/compiler/angkor/src/ADT/feature/Buffer.test.cpp b/compiler/angkor/src/ADT/feature/Buffer.test.cpp index 1e44302..f61b724 100644 --- a/compiler/angkor/src/ADT/feature/Buffer.test.cpp +++ b/compiler/angkor/src/ADT/feature/Buffer.test.cpp @@ -30,9 +30,9 @@ TEST(ADT_FEATURE_BUFFER, ctor) const Shape shape{4, 6, 3}; auto buffer = make_buffer(shape); - ASSERT_EQ(buffer.shape().depth(), shape.depth()); - ASSERT_EQ(buffer.shape().height(), shape.height()); - ASSERT_EQ(buffer.shape().width(), shape.width()); + ASSERT_EQ(shape.depth(), buffer.shape().depth()); + ASSERT_EQ(shape.height(), buffer.shape().height()); + ASSERT_EQ(shape.width(), buffer.shape().width()); } TEST(ADT_FEATURE_BUFFER, access) @@ -40,9 +40,9 @@ TEST(ADT_FEATURE_BUFFER, access) const Shape shape{4, 6, 3}; auto buffer = make_buffer(shape); - ASSERT_EQ(buffer.at(3, 5, 2), 0); + ASSERT_EQ(0, buffer.at(3, 5, 2)); buffer.at(3, 5, 2) = 4; // Casting is introduced to use 'const T &at(...) const' method - ASSERT_EQ(static_cast &>(buffer).at(3, 5, 2), 4); + ASSERT_EQ(4, static_cast &>(buffer).at(3, 5, 2)); } diff --git a/compiler/angkor/src/ADT/feature/CHWLayout.test.cpp b/compiler/angkor/src/ADT/feature/CHWLayout.test.cpp index 5610df8..72aef22 100644 --- a/compiler/angkor/src/ADT/feature/CHWLayout.test.cpp +++ b/compiler/angkor/src/ADT/feature/CHWLayout.test.cpp @@ -25,7 +25,7 @@ TEST(ADT_FEATURE_CHW_LAYOUT, col_increase) const Shape shape{4, 3, 6}; const CHWLayout l; - ASSERT_EQ(l.offset(shape, 1, 2, 1) + 1, l.offset(shape, 1, 2, 2)); + ASSERT_EQ(l.offset(shape, 1, 2, 2), l.offset(shape, 1, 2, 1) + 1); } TEST(ADT_FEATURE_CHW_LAYOUT, row_increase) @@ -33,7 +33,7 @@ TEST(ADT_FEATURE_CHW_LAYOUT, row_increase) const Shape shape{4, 3, 6}; const CHWLayout l; - ASSERT_EQ(l.offset(shape, 1, 1, 1) + 6, l.offset(shape, 1, 2, 1)); + ASSERT_EQ(l.offset(shape, 1, 2, 1), l.offset(shape, 1, 1, 1) + 6); } TEST(ADT_FEATURE_CHW_LAYOUT, ch_increase) @@ -41,5 +41,5 @@ TEST(ADT_FEATURE_CHW_LAYOUT, ch_increase) const Shape shape{4, 3, 6}; const CHWLayout l; - ASSERT_EQ(l.offset(shape, 1, 1, 1) + 6 * 3, l.offset(shape, 2, 1, 1)); + ASSERT_EQ(l.offset(shape, 2, 1, 1), l.offset(shape, 1, 1, 1) + 6 * 3); } diff --git a/compiler/angkor/src/ADT/feature/HWCLayout.test.cpp b/compiler/angkor/src/ADT/feature/HWCLayout.test.cpp index d1f3597..1cfb29c 100644 --- a/compiler/angkor/src/ADT/feature/HWCLayout.test.cpp +++ b/compiler/angkor/src/ADT/feature/HWCLayout.test.cpp @@ -29,7 +29,7 @@ TEST(ADT_FEATURE_HWC_LAYOUT, C_increase) const Shape shape{C, H, W}; const HWCLayout l; - ASSERT_EQ(l.offset(shape, 1, 1, 1) + 1, l.offset(shape, 2, 1, 1)); + ASSERT_EQ(l.offset(shape, 2, 1, 1), l.offset(shape, 1, 1, 1) + 1); } TEST(ADT_FEATURE_HWC_LAYOUT, W_increase) @@ -41,7 +41,7 @@ TEST(ADT_FEATURE_HWC_LAYOUT, W_increase) const Shape shape{C, H, W}; const HWCLayout l; - ASSERT_EQ(l.offset(shape, 1, 2, 1) + C, l.offset(shape, 1, 2, 2)); + ASSERT_EQ(l.offset(shape, 1, 2, 2), l.offset(shape, 1, 2, 1) + C); } TEST(ADT_FEATURE_HWC_LAYOUT, H_increase) @@ -53,5 +53,5 @@ TEST(ADT_FEATURE_HWC_LAYOUT, H_increase) const Shape shape{C, H, W}; const HWCLayout l; - ASSERT_EQ(l.offset(shape, 1, 1, 1) + W * C, l.offset(shape, 1, 2, 1)); + ASSERT_EQ(l.offset(shape, 1, 2, 1), l.offset(shape, 1, 1, 1) + W * C); } diff --git a/compiler/angkor/src/ADT/feature/Layout.test.cpp b/compiler/angkor/src/ADT/feature/Layout.test.cpp index 023594e..de91cb3 100644 --- a/compiler/angkor/src/ADT/feature/Layout.test.cpp +++ b/compiler/angkor/src/ADT/feature/Layout.test.cpp @@ -28,7 +28,7 @@ TEST(ADT_FEATURE_LAYOUT, ctor) { Layout l{offset_0}; - ASSERT_EQ(l.offset(Shape{4, 3, 6}, 1, 1, 1), 0); + ASSERT_EQ(0, l.offset(Shape{4, 3, 6}, 1, 1, 1)); } TEST(ADT_FEATURE_LAYOUT, copy) @@ -36,11 +36,11 @@ TEST(ADT_FEATURE_LAYOUT, copy) Layout orig{offset_0}; Layout copy{offset_1}; - ASSERT_EQ(copy.offset(Shape{4, 3, 6}, 1, 1, 1), 1); + ASSERT_EQ(1, copy.offset(Shape{4, 3, 6}, 1, 1, 1)); copy = orig; - ASSERT_EQ(copy.offset(Shape{4, 3, 6}, 1, 1, 1), 0); + ASSERT_EQ(0, copy.offset(Shape{4, 3, 6}, 1, 1, 1)); } TEST(ADT_FEATURE_LAYOUT, move) @@ -48,9 +48,9 @@ TEST(ADT_FEATURE_LAYOUT, move) Layout orig{offset_0}; Layout move{offset_1}; - ASSERT_EQ(move.offset(Shape{4, 3, 6}, 1, 1, 1), 1); + ASSERT_EQ(1, move.offset(Shape{4, 3, 6}, 1, 1, 1)); move = std::move(orig); - ASSERT_EQ(move.offset(Shape{4, 3, 6}, 1, 1, 1), 0); + ASSERT_EQ(0, move.offset(Shape{4, 3, 6}, 1, 1, 1)); } diff --git a/compiler/angkor/src/ADT/feature/Overlay.test.cpp b/compiler/angkor/src/ADT/feature/Overlay.test.cpp index c8e2943..8ba28bf 100644 --- a/compiler/angkor/src/ADT/feature/Overlay.test.cpp +++ b/compiler/angkor/src/ADT/feature/Overlay.test.cpp @@ -34,9 +34,9 @@ TEST(ADT_FEATURE_OVERLAY, ctor) }; auto overlay = make_overlay(shape, data); - ASSERT_EQ(overlay.shape().depth(), shape.depth()); - ASSERT_EQ(overlay.shape().height(), shape.height()); - ASSERT_EQ(overlay.shape().width(), shape.width()); + ASSERT_EQ(shape.depth(), overlay.shape().depth()); + ASSERT_EQ(shape.height(), overlay.shape().height()); + ASSERT_EQ(shape.width(), overlay.shape().width()); } TEST(ADT_FEATURE_OVERLAY, read) @@ -50,9 +50,9 @@ TEST(ADT_FEATURE_OVERLAY, read) CHWLayout layout{}; - ASSERT_EQ(data[layout.offset(shape, 3, 5, 2)], 0); + ASSERT_EQ(0, data[layout.offset(shape, 3, 5, 2)]); data[layout.offset(shape, 3, 5, 2)] = 2; - ASSERT_EQ(overlay.at(3, 5, 2), 2); + ASSERT_EQ(2, overlay.at(3, 5, 2)); } TEST(ADT_FEATURE_OVERLAY, access) @@ -66,7 +66,7 @@ TEST(ADT_FEATURE_OVERLAY, access) CHWLayout layout{}; - ASSERT_EQ(data[layout.offset(shape, 3, 5, 2)], 0); + ASSERT_EQ(0, data[layout.offset(shape, 3, 5, 2)]); overlay.at(3, 5, 2) = 4; - ASSERT_EQ(data[layout.offset(shape, 3, 5, 2)], 4); + ASSERT_EQ(4, data[layout.offset(shape, 3, 5, 2)]); } diff --git a/compiler/angkor/src/ADT/feature/Shape.test.cpp b/compiler/angkor/src/ADT/feature/Shape.test.cpp index 9216182..460561b 100644 --- a/compiler/angkor/src/ADT/feature/Shape.test.cpp +++ b/compiler/angkor/src/ADT/feature/Shape.test.cpp @@ -26,9 +26,9 @@ TEST(ADT_FEATURE_SHAPE, ctor) nncc::core::ADT::feature::Shape shape{C, H, W}; - ASSERT_EQ(shape.depth(), C); - ASSERT_EQ(shape.height(), H); - ASSERT_EQ(shape.width(), W); + ASSERT_EQ(C, shape.depth()); + ASSERT_EQ(H, shape.height()); + ASSERT_EQ(W, shape.width()); } TEST(ADT_FEATURE_SHAPE, num_elements) @@ -40,7 +40,7 @@ TEST(ADT_FEATURE_SHAPE, num_elements) using nncc::core::ADT::feature::Shape; using nncc::core::ADT::feature::num_elements; - ASSERT_EQ(num_elements(Shape{C, H, W}), C * H * W); + ASSERT_EQ(C * H * W, num_elements(Shape{C, H, W})); } TEST(ADT_FEATURE_SHAPE, operator_eq) diff --git a/compiler/angkor/src/ADT/kernel/Buffer.test.cpp b/compiler/angkor/src/ADT/kernel/Buffer.test.cpp index da34459..a3d92a6 100644 --- a/compiler/angkor/src/ADT/kernel/Buffer.test.cpp +++ b/compiler/angkor/src/ADT/kernel/Buffer.test.cpp @@ -30,10 +30,10 @@ TEST(ADT_KERNEL_BUFFER, ctor) const Shape shape{2, 4, 6, 3}; auto buffer = make_buffer(shape); - ASSERT_EQ(buffer.shape().count(), shape.count()); - ASSERT_EQ(buffer.shape().depth(), shape.depth()); - ASSERT_EQ(buffer.shape().height(), shape.height()); - ASSERT_EQ(buffer.shape().width(), shape.width()); + ASSERT_EQ(shape.count(), buffer.shape().count()); + ASSERT_EQ(shape.depth(), buffer.shape().depth()); + ASSERT_EQ(shape.height(), buffer.shape().height()); + ASSERT_EQ(shape.width(), buffer.shape().width()); } TEST(ADT_KERNEL_BUFFER, access) @@ -41,9 +41,9 @@ TEST(ADT_KERNEL_BUFFER, access) const Shape shape{2, 4, 6, 3}; auto buffer = make_buffer(shape); - ASSERT_EQ(buffer.at(1, 3, 5, 2), 0); + ASSERT_EQ(0, buffer.at(1, 3, 5, 2)); buffer.at(1, 3, 5, 2) = 4; // Casting is introduced to use 'const T &at(...) const' method - ASSERT_EQ(static_cast &>(buffer).at(1, 3, 5, 2), 4); + ASSERT_EQ(4, static_cast &>(buffer).at(1, 3, 5, 2)); } diff --git a/compiler/angkor/src/ADT/kernel/Layout.test.cpp b/compiler/angkor/src/ADT/kernel/Layout.test.cpp index 94885cd..36234d2 100644 --- a/compiler/angkor/src/ADT/kernel/Layout.test.cpp +++ b/compiler/angkor/src/ADT/kernel/Layout.test.cpp @@ -28,7 +28,7 @@ TEST(ADT_KERNEL_LAYOUT, ctor) { Layout l{offset_0}; - ASSERT_EQ(l.offset(Shape{4, 3, 6, 5}, 1, 1, 1, 1), 0); + ASSERT_EQ(0, l.offset(Shape{4, 3, 6, 5}, 1, 1, 1, 1)); } TEST(ADT_KERNEL_LAYOUT, copy) @@ -36,11 +36,11 @@ TEST(ADT_KERNEL_LAYOUT, copy) Layout orig{offset_0}; Layout copy{offset_1}; - ASSERT_EQ(copy.offset(Shape{4, 3, 6, 5}, 1, 1, 1, 1), 1); + ASSERT_EQ(1, copy.offset(Shape{4, 3, 6, 5}, 1, 1, 1, 1)); copy = orig; - ASSERT_EQ(copy.offset(Shape{4, 3, 6, 5}, 1, 1, 1, 1), 0); + ASSERT_EQ(0, copy.offset(Shape{4, 3, 6, 5}, 1, 1, 1, 1)); } TEST(ADT_KERNEL_LAYOUT, move) @@ -48,9 +48,9 @@ TEST(ADT_KERNEL_LAYOUT, move) Layout orig{offset_0}; Layout move{offset_1}; - ASSERT_EQ(move.offset(Shape{4, 3, 6, 5}, 1, 1, 1, 1), 1); + ASSERT_EQ(1, move.offset(Shape{4, 3, 6, 5}, 1, 1, 1, 1)); move = std::move(orig); - ASSERT_EQ(move.offset(Shape{4, 3, 6, 5}, 1, 1, 1, 1), 0); + ASSERT_EQ(0, move.offset(Shape{4, 3, 6, 5}, 1, 1, 1, 1)); } diff --git a/compiler/angkor/src/ADT/kernel/NCHWLayout.test.cpp b/compiler/angkor/src/ADT/kernel/NCHWLayout.test.cpp index ba03b7b..578bc58 100644 --- a/compiler/angkor/src/ADT/kernel/NCHWLayout.test.cpp +++ b/compiler/angkor/src/ADT/kernel/NCHWLayout.test.cpp @@ -25,7 +25,7 @@ TEST(ADT_KERNEL_KERNEL_NCHW_LAYOUT, col_increment) const Shape shape{4, 3, 6, 5}; const NCHWLayout l; - ASSERT_EQ(l.offset(shape, 1, 1, 1, 1) + 1, l.offset(shape, 1, 1, 1, 2)); + ASSERT_EQ(l.offset(shape, 1, 1, 1, 2), l.offset(shape, 1, 1, 1, 1) + 1); } TEST(ADT_KERNEL_KERNEL_NCHW_LAYOUT, row_increment) @@ -33,7 +33,7 @@ TEST(ADT_KERNEL_KERNEL_NCHW_LAYOUT, row_increment) const Shape shape{4, 3, 6, 5}; const NCHWLayout l; - ASSERT_EQ(l.offset(shape, 1, 1, 1, 1) + 5, l.offset(shape, 1, 1, 2, 1)); + ASSERT_EQ(l.offset(shape, 1, 1, 2, 1), l.offset(shape, 1, 1, 1, 1) + 5); } TEST(ADT_KERNEL_KERNEL_NCHW_LAYOUT, ch_increment) @@ -41,7 +41,7 @@ TEST(ADT_KERNEL_KERNEL_NCHW_LAYOUT, ch_increment) const Shape shape{4, 3, 6, 5}; const NCHWLayout l; - ASSERT_EQ(l.offset(shape, 1, 1, 1, 1) + 6 * 5, l.offset(shape, 1, 2, 1, 1)); + ASSERT_EQ(l.offset(shape, 1, 2, 1, 1), l.offset(shape, 1, 1, 1, 1) + 6 * 5); } TEST(ADT_KERNEL_KERNEL_NCHW_LAYOUT, n_increment) @@ -49,5 +49,5 @@ TEST(ADT_KERNEL_KERNEL_NCHW_LAYOUT, n_increment) const Shape shape{4, 3, 6, 5}; const NCHWLayout l; - ASSERT_EQ(l.offset(shape, 1, 1, 1, 1) + 3 * 6 * 5, l.offset(shape, 2, 1, 1, 1)); + ASSERT_EQ(l.offset(shape, 2, 1, 1, 1), l.offset(shape, 1, 1, 1, 1) + 3 * 6 * 5); } diff --git a/compiler/angkor/src/ADT/kernel/NHWCLayout.test.cpp b/compiler/angkor/src/ADT/kernel/NHWCLayout.test.cpp index 2c5df7d..184e107 100644 --- a/compiler/angkor/src/ADT/kernel/NHWCLayout.test.cpp +++ b/compiler/angkor/src/ADT/kernel/NHWCLayout.test.cpp @@ -31,7 +31,7 @@ TEST(ADT_KERNEL_KERNEL_NHWC_LAYOUT, ch_increment) const Shape shape{N, C, H, W}; const NHWCLayout l; - ASSERT_EQ(l.offset(shape, 1, 1, 1, 1) + 1, l.offset(shape, 1, 2, 1, 1)); + ASSERT_EQ(l.offset(shape, 1, 2, 1, 1), l.offset(shape, 1, 1, 1, 1) + 1); } TEST(ADT_KERNEL_KERNEL_NHWC_LAYOUT, col_increment) @@ -44,7 +44,7 @@ TEST(ADT_KERNEL_KERNEL_NHWC_LAYOUT, col_increment) const Shape shape{N, C, H, W}; const NHWCLayout l; - ASSERT_EQ(l.offset(shape, 1, 1, 1, 1) + C, l.offset(shape, 1, 1, 1, 2)); + ASSERT_EQ(l.offset(shape, 1, 1, 1, 2), l.offset(shape, 1, 1, 1, 1) + C); } TEST(ADT_KERNEL_KERNEL_NHWC_LAYOUT, row_increment) @@ -57,7 +57,7 @@ TEST(ADT_KERNEL_KERNEL_NHWC_LAYOUT, row_increment) const Shape shape{N, C, H, W}; const NHWCLayout l; - ASSERT_EQ(l.offset(shape, 1, 1, 1, 1) + C * W, l.offset(shape, 1, 1, 2, 1)); + ASSERT_EQ(l.offset(shape, 1, 1, 2, 1), l.offset(shape, 1, 1, 1, 1) + C * W); } TEST(ADT_KERNEL_KERNEL_NHWC_LAYOUT, n_increment) @@ -70,5 +70,5 @@ TEST(ADT_KERNEL_KERNEL_NHWC_LAYOUT, n_increment) const Shape shape{N, C, H, W}; const NHWCLayout l; - ASSERT_EQ(l.offset(shape, 1, 1, 1, 1) + H * W * C, l.offset(shape, 2, 1, 1, 1)); + ASSERT_EQ(l.offset(shape, 2, 1, 1, 1), l.offset(shape, 1, 1, 1, 1) + H * W * C); } diff --git a/compiler/angkor/src/ADT/kernel/Overlay.test.cpp b/compiler/angkor/src/ADT/kernel/Overlay.test.cpp index e80ebbc..4e9bd8d 100644 --- a/compiler/angkor/src/ADT/kernel/Overlay.test.cpp +++ b/compiler/angkor/src/ADT/kernel/Overlay.test.cpp @@ -34,10 +34,10 @@ TEST(ADT_KERNEL_OVERLAY, ctor) }; auto overlay = make_overlay(shape, data); - ASSERT_EQ(overlay.shape().count(), shape.count()); - ASSERT_EQ(overlay.shape().depth(), shape.depth()); - ASSERT_EQ(overlay.shape().height(), shape.height()); - ASSERT_EQ(overlay.shape().width(), shape.width()); + ASSERT_EQ(shape.count(), overlay.shape().count()); + ASSERT_EQ(shape.depth(), overlay.shape().depth()); + ASSERT_EQ(shape.height(), overlay.shape().height()); + ASSERT_EQ(shape.width(), overlay.shape().width()); } TEST(ADT_KERNEL_OVERLAY, read) @@ -51,9 +51,9 @@ TEST(ADT_KERNEL_OVERLAY, read) NCHWLayout layout{}; - ASSERT_EQ(data[layout.offset(shape, 1, 3, 5, 2)], 0); + ASSERT_EQ(0, data[layout.offset(shape, 1, 3, 5, 2)]); data[layout.offset(shape, 1, 3, 5, 2)] = 2; - ASSERT_EQ(overlay.at(1, 3, 5, 2), 2); + ASSERT_EQ(2, overlay.at(1, 3, 5, 2)); } TEST(ADT_KERNEL_OVERLAY, access) @@ -67,7 +67,7 @@ TEST(ADT_KERNEL_OVERLAY, access) NCHWLayout layout{}; - ASSERT_EQ(data[layout.offset(shape, 1, 3, 5, 2)], 0); + ASSERT_EQ(0, data[layout.offset(shape, 1, 3, 5, 2)]); overlay.at(1, 3, 5, 2) = 4; - ASSERT_EQ(data[layout.offset(shape, 1, 3, 5, 2)], 4); + ASSERT_EQ(4, data[layout.offset(shape, 1, 3, 5, 2)]); } diff --git a/compiler/angkor/src/ADT/kernel/Shape.test.cpp b/compiler/angkor/src/ADT/kernel/Shape.test.cpp index da608fb..b2bd13c 100644 --- a/compiler/angkor/src/ADT/kernel/Shape.test.cpp +++ b/compiler/angkor/src/ADT/kernel/Shape.test.cpp @@ -27,10 +27,10 @@ TEST(ADT_KERNEL_SHAPE, ctor) nncc::core::ADT::kernel::Shape shape{N, C, H, W}; - ASSERT_EQ(shape.count(), N); - ASSERT_EQ(shape.depth(), C); - ASSERT_EQ(shape.height(), H); - ASSERT_EQ(shape.width(), W); + ASSERT_EQ(N, shape.count()); + ASSERT_EQ(C, shape.depth()); + ASSERT_EQ(H, shape.height()); + ASSERT_EQ(W, shape.width()); } TEST(ADT_KERNEL_SHAPE, num_elements) @@ -43,7 +43,7 @@ TEST(ADT_KERNEL_SHAPE, num_elements) using nncc::core::ADT::kernel::Shape; using nncc::core::ADT::kernel::num_elements; - ASSERT_EQ(num_elements(Shape{N, C, H, W}), N * C * H * W); + ASSERT_EQ(N * C * H * W, num_elements(Shape{N, C, H, W})); } TEST(ADT_KERNEL_SHAPE, operator_eq) diff --git a/compiler/angkor/src/ADT/tensor/Buffer.test.cpp b/compiler/angkor/src/ADT/tensor/Buffer.test.cpp index c2b6a99..39d0a80 100644 --- a/compiler/angkor/src/ADT/tensor/Buffer.test.cpp +++ b/compiler/angkor/src/ADT/tensor/Buffer.test.cpp @@ -31,7 +31,7 @@ TEST(ADT_TENSOR_BUFFER, ctor) const Shape shape{2, 3}; auto buffer = make_buffer(shape); - ASSERT_EQ(buffer.shape(), shape); + ASSERT_EQ(shape, buffer.shape()); } TEST(ADT_TENSOR_BUFFER, access) @@ -41,9 +41,9 @@ TEST(ADT_TENSOR_BUFFER, access) const Index index{1, 2}; - ASSERT_EQ(buffer.at(index), 0); + ASSERT_EQ(0, buffer.at(index)); buffer.at(index) = 4; // Casting is introduced to use 'const T &at(...) const' method - ASSERT_EQ(static_cast &>(buffer).at(index), 4); + ASSERT_EQ(4, static_cast &>(buffer).at(index)); } diff --git a/compiler/angkor/src/ADT/tensor/Index.test.cpp b/compiler/angkor/src/ADT/tensor/Index.test.cpp index 2306028..53dbd41 100644 --- a/compiler/angkor/src/ADT/tensor/Index.test.cpp +++ b/compiler/angkor/src/ADT/tensor/Index.test.cpp @@ -22,19 +22,19 @@ TEST(ADT_TENSOR_INDEX, ctor) { nncc::core::ADT::tensor::Index index; - ASSERT_EQ(index.rank(), 0); + ASSERT_EQ(0, index.rank()); } TEST(ADT_TENSOR_INDEX, ctor_initializer_list) { const nncc::core::ADT::tensor::Index index{1, 3, 5, 7}; - ASSERT_EQ(index.rank(), 4); + ASSERT_EQ(4, index.rank()); - ASSERT_EQ(index.at(0), 1); - ASSERT_EQ(index.at(1), 3); - ASSERT_EQ(index.at(2), 5); - ASSERT_EQ(index.at(3), 7); + ASSERT_EQ(1, index.at(0)); + ASSERT_EQ(3, index.at(1)); + ASSERT_EQ(5, index.at(2)); + ASSERT_EQ(7, index.at(3)); } TEST(ADT_TENSOR_INDEX, operator_add) @@ -43,10 +43,10 @@ TEST(ADT_TENSOR_INDEX, operator_add) nncc::core::ADT::tensor::Index index2{5, 6, 7, 8}; nncc::core::ADT::tensor::Index result{index1 + index2}; - ASSERT_EQ(result.at(0), 6); - ASSERT_EQ(result.at(1), 8); - ASSERT_EQ(result.at(2), 10); - ASSERT_EQ(result.at(3), 12); + ASSERT_EQ(6, result.at(0)); + ASSERT_EQ(8, result.at(1)); + ASSERT_EQ(10, result.at(2)); + ASSERT_EQ(12, result.at(3)); } TEST(ADT_TENSOR_INDEX, operator_eqaul) @@ -75,7 +75,7 @@ TEST(ADT_TENSOR_INDEX, resize) index.resize(4); - ASSERT_EQ(index.rank(), 4); + ASSERT_EQ(4, index.rank()); } TEST(ADT_TENSOR_INDEX, at) @@ -89,7 +89,7 @@ TEST(ADT_TENSOR_INDEX, at) for (uint32_t axis = 0; axis < 4; ++axis) { index.at(axis) = indices[axis]; - ASSERT_EQ(index.at(axis), indices[axis]); + ASSERT_EQ(indices[axis], index.at(axis)); } } @@ -98,11 +98,11 @@ TEST(ADT_TENSOR_INDEX, copy) const nncc::core::ADT::tensor::Index original{3, 5, 2, 7}; const nncc::core::ADT::tensor::Index copied{original}; - ASSERT_EQ(original.rank(), copied.rank()); + ASSERT_EQ(copied.rank(), original.rank()); for (uint32_t axis = 0; axis < 4; ++axis) { - ASSERT_EQ(original.at(axis), copied.at(axis)); + ASSERT_EQ(copied.at(axis), original.at(axis)); } } @@ -112,8 +112,8 @@ TEST(ADT_TENSOR_INDEX, fill) index.fill(3); - ASSERT_EQ(index.rank(), 2); + ASSERT_EQ(2, index.rank()); - ASSERT_EQ(index.at(0), 3); - ASSERT_EQ(index.at(1), 3); + ASSERT_EQ(3, index.at(0)); + ASSERT_EQ(3, index.at(1)); } diff --git a/compiler/angkor/src/ADT/tensor/IndexEnumerator.test.cpp b/compiler/angkor/src/ADT/tensor/IndexEnumerator.test.cpp index 204a8aa..54cc2e9 100644 --- a/compiler/angkor/src/ADT/tensor/IndexEnumerator.test.cpp +++ b/compiler/angkor/src/ADT/tensor/IndexEnumerator.test.cpp @@ -40,7 +40,7 @@ TEST(ADT_TENSOR_INDEX_ENUMERATOR, iterate_full_range) { const auto &ind = e.current(); - ASSERT_EQ(ind.rank(), 2); + ASSERT_EQ(2, ind.rank()); count.at(ind.at(0) * W + ind.at(1)) += 1; } diff --git a/compiler/angkor/src/ADT/tensor/Layout.test.cpp b/compiler/angkor/src/ADT/tensor/Layout.test.cpp index 145adfe..6d5b3fe 100644 --- a/compiler/angkor/src/ADT/tensor/Layout.test.cpp +++ b/compiler/angkor/src/ADT/tensor/Layout.test.cpp @@ -28,7 +28,7 @@ TEST(ADT_TENSOR_LAYOUT, ctor) { nncc::core::ADT::tensor::Layout l{offset_0}; - ASSERT_EQ(l.offset(Shape{4, 3, 6}, Index{1, 1, 1}), 0); + ASSERT_EQ(0, l.offset(Shape{4, 3, 6}, Index{1, 1, 1})); } TEST(ADT_TENSOR_LAYOUT, copy) @@ -36,11 +36,11 @@ TEST(ADT_TENSOR_LAYOUT, copy) nncc::core::ADT::tensor::Layout orig{offset_0}; nncc::core::ADT::tensor::Layout copy{offset_1}; - ASSERT_EQ(copy.offset(Shape{4, 3, 6}, Index{1, 1, 1}), 1); + ASSERT_EQ(1, copy.offset(Shape{4, 3, 6}, Index{1, 1, 1})); copy = orig; - ASSERT_EQ(copy.offset(Shape{4, 3, 6}, Index{1, 1, 1}), 0); + ASSERT_EQ(0, copy.offset(Shape{4, 3, 6}, Index{1, 1, 1})); } TEST(ADT_TENSOR_LAYOUT, move) @@ -48,9 +48,9 @@ TEST(ADT_TENSOR_LAYOUT, move) nncc::core::ADT::tensor::Layout orig{offset_0}; nncc::core::ADT::tensor::Layout move{offset_1}; - ASSERT_EQ(move.offset(Shape{4, 3, 6}, Index{1, 1, 1}), 1); + ASSERT_EQ(1, move.offset(Shape{4, 3, 6}, Index{1, 1, 1})); move = std::move(orig); - ASSERT_EQ(move.offset(Shape{4, 3, 6}, Index{1, 1, 1}), 0); + ASSERT_EQ(0, move.offset(Shape{4, 3, 6}, Index{1, 1, 1})); } diff --git a/compiler/angkor/src/ADT/tensor/LexicalLayout.test.cpp b/compiler/angkor/src/ADT/tensor/LexicalLayout.test.cpp index 8f9b729..0acaa3a 100644 --- a/compiler/angkor/src/ADT/tensor/LexicalLayout.test.cpp +++ b/compiler/angkor/src/ADT/tensor/LexicalLayout.test.cpp @@ -28,7 +28,7 @@ TEST(ADT_TENSOR_LEXICAL_LAYOUT, last) const nncc::core::ADT::tensor::LexicalLayout l; - ASSERT_EQ(l.offset(shape, curr) + 1, l.offset(shape, next)); + ASSERT_EQ(l.offset(shape, next), l.offset(shape, curr) + 1); } TEST(ADT_TENSOR_LEXICAL_LAYOUT, lexical_middle) @@ -39,7 +39,7 @@ TEST(ADT_TENSOR_LEXICAL_LAYOUT, lexical_middle) const nncc::core::ADT::tensor::LexicalLayout l; - ASSERT_EQ(l.offset(shape, curr) + 6, l.offset(shape, next)); + ASSERT_EQ(l.offset(shape, next), l.offset(shape, curr) + 6); } TEST(ADT_TENSOR_LEXICAL_LAYOUT, lexical_first) @@ -50,5 +50,5 @@ TEST(ADT_TENSOR_LEXICAL_LAYOUT, lexical_first) const nncc::core::ADT::tensor::LexicalLayout l; - ASSERT_EQ(l.offset(shape, curr) + 6 * 3, l.offset(shape, next)); + ASSERT_EQ(l.offset(shape, next), l.offset(shape, curr) + 6 * 3); } diff --git a/compiler/angkor/src/ADT/tensor/Overlay.test.cpp b/compiler/angkor/src/ADT/tensor/Overlay.test.cpp index aacb5a9..57cd1e6 100644 --- a/compiler/angkor/src/ADT/tensor/Overlay.test.cpp +++ b/compiler/angkor/src/ADT/tensor/Overlay.test.cpp @@ -35,7 +35,7 @@ TEST(ADT_TENSOR_OVERLAY, ctor) }; auto view = make_overlay(shape, data); - ASSERT_EQ(view.shape(), shape); + ASSERT_EQ(shape, view.shape()); } TEST(ADT_TENSOR_OVERLAY, read) @@ -51,9 +51,9 @@ TEST(ADT_TENSOR_OVERLAY, read) const Index index{1, 2}; - ASSERT_EQ(data[layout.offset(shape, index)], 0); + ASSERT_EQ(0, data[layout.offset(shape, index)]); data[layout.offset(shape, index)] = 2; - ASSERT_EQ(view.at(index), 2); + ASSERT_EQ(2, view.at(index)); } TEST(ADT_TENSOR_OVERLAY, access) @@ -69,7 +69,7 @@ TEST(ADT_TENSOR_OVERLAY, access) const Index index{1, 2}; - ASSERT_EQ(data[layout.offset(shape, index)], 0); + ASSERT_EQ(0, data[layout.offset(shape, index)]); view.at(index) = 4; - ASSERT_EQ(data[layout.offset(shape, index)], 4); + ASSERT_EQ(4, data[layout.offset(shape, index)]); } diff --git a/compiler/angkor/src/ADT/tensor/Shape.test.cpp b/compiler/angkor/src/ADT/tensor/Shape.test.cpp index 711ae3d..9915e78 100644 --- a/compiler/angkor/src/ADT/tensor/Shape.test.cpp +++ b/compiler/angkor/src/ADT/tensor/Shape.test.cpp @@ -22,19 +22,19 @@ TEST(ADT_TENSOR_SHAPE, ctor) { nncc::core::ADT::tensor::Shape shape; - ASSERT_EQ(shape.rank(), 0); + ASSERT_EQ(0, shape.rank()); } TEST(ADT_TENSOR_SHAPE, ctor_initializer_list) { nncc::core::ADT::tensor::Shape shape{1, 3, 5, 7}; - ASSERT_EQ(shape.rank(), 4); + ASSERT_EQ(4, shape.rank()); - ASSERT_EQ(shape.dim(0), 1); - ASSERT_EQ(shape.dim(1), 3); - ASSERT_EQ(shape.dim(2), 5); - ASSERT_EQ(shape.dim(3), 7); + ASSERT_EQ(1, shape.dim(0)); + ASSERT_EQ(3, shape.dim(1)); + ASSERT_EQ(5, shape.dim(2)); + ASSERT_EQ(7, shape.dim(3)); } TEST(ADT_TENSOR_SHAPE, resize) @@ -43,7 +43,7 @@ TEST(ADT_TENSOR_SHAPE, resize) shape.resize(4); - ASSERT_EQ(shape.rank(), 4); + ASSERT_EQ(4, shape.rank()); } TEST(ADT_TENSOR_SHAPE, dim) @@ -57,7 +57,7 @@ TEST(ADT_TENSOR_SHAPE, dim) for (uint32_t axis = 0; axis < 4; ++axis) { shape.dim(axis) = dims[axis]; - ASSERT_EQ(shape.dim(axis), dims[axis]); + ASSERT_EQ(dims[axis], shape.dim(axis)); } } @@ -66,11 +66,11 @@ TEST(ADT_TENSOR_SHAPE, copy) const nncc::core::ADT::tensor::Shape original{3, 5, 2, 7}; const nncc::core::ADT::tensor::Shape copied{original}; - ASSERT_EQ(original.rank(), copied.rank()); + ASSERT_EQ(copied.rank(), original.rank()); for (uint32_t axis = 0; axis < 4; ++axis) { - ASSERT_EQ(original.dim(axis), copied.dim(axis)); + ASSERT_EQ(copied.dim(axis), original.dim(axis)); } } @@ -81,7 +81,7 @@ TEST(ADT_TENSOR_SHAPE, num_elements_rank_0) Shape rank_0_shape; - ASSERT_EQ(num_elements(rank_0_shape), 1); + ASSERT_EQ(1, num_elements(rank_0_shape)); } TEST(ADT_TENSOR_SHAPE, num_elements_zero) @@ -89,7 +89,7 @@ TEST(ADT_TENSOR_SHAPE, num_elements_zero) using nncc::core::ADT::tensor::Shape; using nncc::core::ADT::tensor::num_elements; - ASSERT_EQ(num_elements(Shape{0, 0, 0, 0}), 0); + ASSERT_EQ(0, num_elements(Shape{0, 0, 0, 0})); } TEST(ADT_TENSOR_SHAPE, num_elements_nonzero) @@ -97,7 +97,7 @@ TEST(ADT_TENSOR_SHAPE, num_elements_nonzero) using nncc::core::ADT::tensor::Shape; using nncc::core::ADT::tensor::num_elements; - ASSERT_EQ(num_elements(Shape{2, 3}), 6); + ASSERT_EQ(6, num_elements(Shape{2, 3})); } TEST(ADT_TENSOR_SHAPE, num_elements_nulldim) @@ -105,7 +105,7 @@ TEST(ADT_TENSOR_SHAPE, num_elements_nulldim) using nncc::core::ADT::tensor::Shape; using nncc::core::ADT::tensor::num_elements; - ASSERT_EQ(num_elements(Shape{2, 0, 3}), 0); + ASSERT_EQ(0, num_elements(Shape{2, 0, 3})); } TEST(ADT_TENSOR_SHAPE, squeeze_neg) @@ -115,10 +115,10 @@ TEST(ADT_TENSOR_SHAPE, squeeze_neg) auto squeezed = squeeze(Shape{3, 5, 2}); - ASSERT_EQ(squeezed.rank(), 3); - ASSERT_EQ(squeezed.dim(0), 3); - ASSERT_EQ(squeezed.dim(1), 5); - ASSERT_EQ(squeezed.dim(2), 2); + ASSERT_EQ(3, squeezed.rank()); + ASSERT_EQ(3, squeezed.dim(0)); + ASSERT_EQ(5, squeezed.dim(1)); + ASSERT_EQ(2, squeezed.dim(2)); } TEST(ADT_TENSOR_SHAPE, squeeze_neg_0) @@ -128,10 +128,10 @@ TEST(ADT_TENSOR_SHAPE, squeeze_neg_0) auto squeezed = squeeze(Shape{3, 0, 2}); - ASSERT_EQ(squeezed.rank(), 3); - ASSERT_EQ(squeezed.dim(0), 3); - ASSERT_EQ(squeezed.dim(1), 0); - ASSERT_EQ(squeezed.dim(2), 2); + ASSERT_EQ(3, squeezed.rank()); + ASSERT_EQ(3, squeezed.dim(0)); + ASSERT_EQ(0, squeezed.dim(1)); + ASSERT_EQ(2, squeezed.dim(2)); } TEST(ADT_TENSOR_SHAPE, squeeze_pos) @@ -141,9 +141,9 @@ TEST(ADT_TENSOR_SHAPE, squeeze_pos) auto squeezed = squeeze(Shape{3, 1, 2}); - ASSERT_EQ(squeezed.rank(), 2); - ASSERT_EQ(squeezed.dim(0), 3); - ASSERT_EQ(squeezed.dim(1), 2); + ASSERT_EQ(2, squeezed.rank()); + ASSERT_EQ(3, squeezed.dim(0)); + ASSERT_EQ(2, squeezed.dim(1)); } TEST(ADT_TENSOR_SHAPE, squeeze_nested) @@ -155,9 +155,9 @@ TEST(ADT_TENSOR_SHAPE, squeeze_nested) shape.squeeze().squeeze(); - ASSERT_EQ(shape.rank(), 2); - ASSERT_EQ(shape.dim(0), 3); - ASSERT_EQ(shape.dim(1), 2); + ASSERT_EQ(2, shape.rank()); + ASSERT_EQ(3, shape.dim(0)); + ASSERT_EQ(2, shape.dim(1)); } TEST(ADT_TENSOR_SHAPE, eq_negative_on_unmatched_rank) diff --git a/compiler/angkor/src/TensorIndex.test.cpp b/compiler/angkor/src/TensorIndex.test.cpp index 68cf391..dcfc4d3 100644 --- a/compiler/angkor/src/TensorIndex.test.cpp +++ b/compiler/angkor/src/TensorIndex.test.cpp @@ -22,19 +22,19 @@ TEST(TensorIndexTest, ctor) { angkor::TensorIndex index; - ASSERT_EQ(index.rank(), 0); + ASSERT_EQ(0, index.rank()); } TEST(TensorIndexTest, ctor_initializer_list) { const angkor::TensorIndex index{1, 3, 5, 7}; - ASSERT_EQ(index.rank(), 4); + ASSERT_EQ(4, index.rank()); - ASSERT_EQ(index.at(0), 1); - ASSERT_EQ(index.at(1), 3); - ASSERT_EQ(index.at(2), 5); - ASSERT_EQ(index.at(3), 7); + ASSERT_EQ(1, index.at(0)); + ASSERT_EQ(3, index.at(1)); + ASSERT_EQ(5, index.at(2)); + ASSERT_EQ(7, index.at(3)); } TEST(TensorIndexTest, resize) @@ -43,7 +43,7 @@ TEST(TensorIndexTest, resize) index.resize(4); - ASSERT_EQ(index.rank(), 4); + ASSERT_EQ(4, index.rank()); } TEST(TensorIndexTest, at) @@ -57,7 +57,7 @@ TEST(TensorIndexTest, at) for (uint32_t axis = 0; axis < 4; ++axis) { index.at(axis) = indices[axis]; - ASSERT_EQ(index.at(axis), indices[axis]); + ASSERT_EQ(indices[axis], index.at(axis)); } } @@ -66,11 +66,11 @@ TEST(TensorIndexTest, copy) const angkor::TensorIndex original{3, 5, 2, 7}; const angkor::TensorIndex copied{original}; - ASSERT_EQ(original.rank(), copied.rank()); + ASSERT_EQ(copied.rank(), original.rank()); for (uint32_t axis = 0; axis < 4; ++axis) { - ASSERT_EQ(original.at(axis), copied.at(axis)); + ASSERT_EQ(copied.at(axis), original.at(axis)); } } @@ -80,8 +80,8 @@ TEST(TensorIndexTest, fill) index.fill(3); - ASSERT_EQ(index.rank(), 2); + ASSERT_EQ(2, index.rank()); - ASSERT_EQ(index.at(0), 3); - ASSERT_EQ(index.at(1), 3); + ASSERT_EQ(3, index.at(0)); + ASSERT_EQ(3, index.at(1)); } diff --git a/compiler/angkor/src/TensorShape.test.cpp b/compiler/angkor/src/TensorShape.test.cpp index 5e6766a..3b96bb8 100644 --- a/compiler/angkor/src/TensorShape.test.cpp +++ b/compiler/angkor/src/TensorShape.test.cpp @@ -22,19 +22,19 @@ TEST(TensorShapeTest, ctor) { angkor::TensorShape shape; - ASSERT_EQ(shape.rank(), 0); + ASSERT_EQ(0, shape.rank()); } TEST(TensorShapeTest, ctor_initializer_list) { angkor::TensorShape shape{1, 3, 5, 7}; - ASSERT_EQ(shape.rank(), 4); + ASSERT_EQ(4, shape.rank()); - ASSERT_EQ(shape.dim(0), 1); - ASSERT_EQ(shape.dim(1), 3); - ASSERT_EQ(shape.dim(2), 5); - ASSERT_EQ(shape.dim(3), 7); + ASSERT_EQ(1, shape.dim(0)); + ASSERT_EQ(3, shape.dim(1)); + ASSERT_EQ(5, shape.dim(2)); + ASSERT_EQ(7, shape.dim(3)); } TEST(TensorShapeTest, resize) @@ -43,7 +43,7 @@ TEST(TensorShapeTest, resize) shape.resize(4); - ASSERT_EQ(shape.rank(), 4); + ASSERT_EQ(4, shape.rank()); } TEST(TensorShapeTest, dim) @@ -57,7 +57,7 @@ TEST(TensorShapeTest, dim) for (uint32_t axis = 0; axis < 4; ++axis) { shape.dim(axis) = dims[axis]; - ASSERT_EQ(shape.dim(axis), dims[axis]); + ASSERT_EQ(dims[axis], shape.dim(axis)); } } @@ -66,11 +66,11 @@ TEST(TensorShapeTest, copy) const angkor::TensorShape original{3, 5, 2, 7}; const angkor::TensorShape copied{original}; - ASSERT_EQ(original.rank(), copied.rank()); + ASSERT_EQ(copied.rank(), original.rank()); for (uint32_t axis = 0; axis < 4; ++axis) { - ASSERT_EQ(original.dim(axis), copied.dim(axis)); + ASSERT_EQ(copied.dim(axis), original.dim(axis)); } } diff --git a/compiler/ann-api/README.md b/compiler/ann-api/README.md new file mode 100644 index 0000000..0c14116 --- /dev/null +++ b/compiler/ann-api/README.md @@ -0,0 +1 @@ +# ann-api diff --git a/compiler/arser/CMakeLists.txt b/compiler/arser/CMakeLists.txt new file mode 100644 index 0000000..63d19f5 --- /dev/null +++ b/compiler/arser/CMakeLists.txt @@ -0,0 +1,15 @@ +add_library(arser INTERFACE) + +# It specifies INTERFACE so that future targets linked with arser library will inherit its include directory. +# It means that a developer who want to link arser just need to add one line. +# target_link_library(another-users-target arser) +target_include_directories(arser INTERFACE include/) + +if(NOT ENABLE_TEST) + return() +endif(NOT ENABLE_TEST) + +nnas_find_package(GTest REQUIRED) +set(TESTS "${CMAKE_CURRENT_SOURCE_DIR}/tests/arser.test.cpp") +GTest_AddTest(arser_test ${TESTS}) +target_include_directories(arser_test PRIVATE include) diff --git a/compiler/arser/README.md b/compiler/arser/README.md new file mode 100644 index 0000000..e853e7e --- /dev/null +++ b/compiler/arser/README.md @@ -0,0 +1,3 @@ +# arser + +This is an Argument parser for c++. See [`arser.test.cpp`](tests/arser.test.cpp) for details on how to use diff --git a/compiler/arser/include/arser/arser.h b/compiler/arser/include/arser/arser.h new file mode 100644 index 0000000..64bb557 --- /dev/null +++ b/compiler/arser/include/arser/arser.h @@ -0,0 +1,507 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include + +#include +#include + +#include +#include +#include +#include +#include +#include + +#include + +namespace +{ + +template T lexical_cast(const std::string &str) +{ + std::istringstream ss; + ss.str(str); + T data; + ss >> data; + return data; +} + +template <> bool lexical_cast(const std::string &str) +{ + bool data = true; + if (str == "false" || str == "False" || str == "FALSE" || str == "0") + data = false; + return data; +} + +template inline std::string to_string(const T value) { return std::to_string(value); } + +template <> inline std::string to_string(const char *value) { return std::string(value); } + +template <> inline std::string to_string(const bool value) { return value ? "true" : "false"; } + +} // namespace + +namespace arser +{ + +// TypeName declaration +template struct TypeName +{ + static const char *Get() { return typeid(T).name(); } +}; +template <> struct TypeName +{ + static const char *Get() { return "int"; } +}; +template <> struct TypeName> +{ + static const char *Get() { return "vector"; } +}; +template <> struct TypeName +{ + static const char *Get() { return "float"; } +}; +template <> struct TypeName> +{ + static const char *Get() { return "vector"; } +}; +template <> struct TypeName +{ + static const char *Get() { return "bool"; } +}; +template <> struct TypeName +{ + static const char *Get() { return "string"; } +}; +template <> struct TypeName> +{ + static const char *Get() { return "vector"; } +}; +template <> struct TypeName +{ + static const char *Get() { return "string"; } +}; +template <> struct TypeName> +{ + static const char *Get() { return "vector"; } +}; + +// supported DataType +enum class DataType +{ + INT32, + INT32_VEC, + FLOAT, + FLOAT_VEC, + BOOL, + STR, + STR_VEC, +}; + +class Arser; + +class Argument +{ +public: + explicit Argument(const std::string &arg_name) : _name{arg_name} {} + + Argument &nargs(uint32_t num) + { + if (num == 0) + { + _type = "bool"; + } + _nargs = num; + return *this; + } + + Argument &type(DataType type) + { + switch (type) + { + case DataType::INT32: + _type = "int"; + break; + case DataType::INT32_VEC: + _type = "vector"; + break; + case DataType::FLOAT: + _type = "float"; + break; + case DataType::FLOAT_VEC: + _type = "vector"; + break; + case DataType::BOOL: + _type = "bool"; + break; + case DataType::STR: + _type = "string"; + break; + case DataType::STR_VEC: + _type = "vector"; + break; + default: + throw std::runtime_error("NYI DataType"); + } + return *this; + } + + Argument &required(void) + { + _is_required = true; + return *this; + } + + Argument &required(bool value) + { + _is_required = value; + return *this; + } + + Argument &help(std::string help_message) + { + _help_message = help_message; + return *this; + } + + Argument &exit_with(const std::function &func) + { + _func = func; + return *this; + } + + template Argument &default_value(const T value) + { + if ((_nargs <= 1 && TypeName::Get() == _type) || + (_nargs > 1 && TypeName>::Get() == _type)) + _values.emplace_back(::to_string(value)); + else + { + throw std::runtime_error("Type mismatch. " + "You called default_value() method with a type different " + "from the one you specified. " + "Please check the type of what you specified in " + "add_argument() method."); + } + return *this; + } + + template Argument &default_value(const T value, const Ts... values) + { + if ((_nargs <= 1 && TypeName::Get() == _type) || + (_nargs > 1 && TypeName>::Get() == _type)) + { + _values.emplace_back(::to_string(value)); + default_value(values...); + } + else + { + throw std::runtime_error("Type mismatch. " + "You called default_value() method with a type different " + "from the one you specified. " + "Please check the type of what you specified in " + "add_argument() method."); + } + return *this; + } + +private: + std::string _name; + std::string _type; + std::string _help_message; + std::function _func; + uint32_t _nargs{1}; + bool _is_required{false}; + std::vector _values; + + friend class Arser; + friend std::ostream &operator<<(std::ostream &, const Arser &); +}; + +class Arser +{ +public: + explicit Arser(const std::string &program_description = {}) + : _program_description{program_description} + { + add_argument("--help").help("Show help message and exit").nargs(0); + } + + Argument &add_argument(const std::string &arg_name) + { + if (arg_name.at(0) != '-') + { + _positional_arg_vec.emplace_back(arg_name); + _arg_map[arg_name] = &_positional_arg_vec.back(); + } + else + { + _optional_arg_vec.emplace_back(arg_name); + _arg_map[arg_name] = &_optional_arg_vec.back(); + } + return *_arg_map[arg_name]; + } + + void parse(int argc, char **argv) + { + _program_name = argv[0]; + _program_name.erase(0, _program_name.find_last_of("/\\") + 1); + if (argc >= 2) + { + if (!std::strcmp(argv[1], "--help")) + { + std::cout << *this; + std::exit(0); + } + else + { + for (const auto &arg : _arg_map) + { + const auto &func = arg.second->_func; + if (func && !std::strcmp(argv[1], arg.second->_name.c_str())) + { + func(); + std::exit(0); + } + } + } + } + /* + ** ./program_name [optional argument] [positional argument] + */ + // get the number of positioanl argument + size_t parg_num = _positional_arg_vec.size(); + // get the number of "required" optional argument + size_t required_oarg_num = 0; + for (auto arg : _optional_arg_vec) + { + if (arg._is_required) + required_oarg_num++; + } + // parse argument + for (int c = 1; c < argc;) + { + std::string arg_name{argv[c++]}; + auto arg = _arg_map.find(arg_name); + // check whether arg is positional or not + if (arg == _arg_map.end()) + { + if (parg_num) + { + auto it = _positional_arg_vec.begin(); + std::advance(it, _positional_arg_vec.size() - parg_num); + (*it)._values.clear(); + (*it)._values.emplace_back(arg_name); + parg_num--; + } + else + throw std::runtime_error("Invalid argument. " + "You've given more positional argument than necessary."); + } + else // optional argument + { + // check whether arg is required or not + if (arg->second->_is_required) + required_oarg_num--; + arg->second->_values.clear(); + for (uint32_t n = 0; n < arg->second->_nargs; n++) + { + if (c >= argc) + throw std::runtime_error("Invalid argument. " + "You must have missed some argument."); + arg->second->_values.emplace_back(argv[c++]); + } + if (arg->second->_nargs == 0) + { + // TODO std::boolalpha for true or false + arg->second->_values.emplace_back("1"); + } + } + } + if (parg_num || required_oarg_num) + throw std::runtime_error("Invalid argument. " + "You must have missed some argument."); + } + + bool operator[](const std::string &arg_name) + { + auto arg = _arg_map.find(arg_name); + if (arg == _arg_map.end()) + return false; + + return arg->second->_values.size() > 0 ? true : false; + } + + template T get_impl(const std::string &arg_name, T *); + + template std::vector get_impl(const std::string &arg_name, std::vector *); + + template T get(const std::string &arg_name); + +private: + std::string _program_name; + std::string _program_description; + std::list _positional_arg_vec; + std::list _optional_arg_vec; + std::map _arg_map; + + friend std::ostream &operator<<(std::ostream &, const Arser &); +}; + +template T Arser::get_impl(const std::string &arg_name, T *) +{ + auto arg = _arg_map.find(arg_name); + if (arg == _arg_map.end()) + throw std::runtime_error("Invalid argument. " + "There is no argument you are looking for."); + + if (arg->second->_type != TypeName::Get()) + throw std::runtime_error("Type mismatch. " + "You called get() method with a type different " + "from the one you specified. " + "Please check the type of what you specified in " + "add_argument() method."); + + if (arg->second->_values.size() == 0) + throw std::runtime_error("Wrong access. " + "You must make sure that the argument is given before accessing it. " + "You can do it by calling arser[\"argument\"]."); + + return ::lexical_cast(arg->second->_values[0]); +} + +template std::vector Arser::get_impl(const std::string &arg_name, std::vector *) +{ + auto arg = _arg_map.find(arg_name); + if (arg == _arg_map.end()) + throw std::runtime_error("Invalid argument. " + "There is no argument you are looking for."); + + if (arg->second->_type != TypeName>::Get()) + throw std::runtime_error("Type mismatch. " + "You called get using a type different from the one you specified."); + + std::vector data; + std::transform(arg->second->_values.begin(), arg->second->_values.end(), std::back_inserter(data), + [](std::string str) -> T { return ::lexical_cast(str); }); + return data; +} + +template T Arser::get(const std::string &arg_name) +{ + return get_impl(arg_name, static_cast(nullptr)); +} + +std::ostream &operator<<(std::ostream &stream, const Arser &parser) +{ + // print description + if (!parser._program_description.empty()) + { + stream << "What " << parser._program_name << " does: " << parser._program_description << "\n\n"; + } + /* + ** print usage + */ + stream << "Usage: ./" << parser._program_name << " "; + // required optional argument + for (const auto &arg : parser._optional_arg_vec) + { + if (!arg._is_required) + continue; + stream << arg._name << " "; + std::string arg_name = arg._name.substr(2); + std::for_each(arg_name.begin(), arg_name.end(), + [&stream](const char &c) { stream << static_cast(::toupper(c)); }); + stream << " "; + } + // rest of the optional argument + for (const auto &arg : parser._optional_arg_vec) + { + if (arg._is_required) + continue; + stream << "[" << arg._name; + if (arg._nargs) + { + stream << " "; + std::string arg_name = arg._name.substr(2); + std::for_each(arg_name.begin(), arg_name.end(), + [&stream](const char &c) { stream << static_cast(::toupper(c)); }); + } + stream << "]" + << " "; + } + // positional arguement + for (const auto &arg : parser._positional_arg_vec) + { + stream << arg._name << " "; + } + stream << "\n\n"; + /* + ** print argument list and its help message + */ + // get the length of the longest argument + size_t length_of_longest_arg = 0; + for (const auto &arg : parser._positional_arg_vec) + { + length_of_longest_arg = std::max(length_of_longest_arg, arg._name.length()); + } + for (const auto &arg : parser._optional_arg_vec) + { + length_of_longest_arg = std::max(length_of_longest_arg, arg._name.length()); + } + + const size_t message_width = 60; + // positional argument + if (!parser._positional_arg_vec.empty()) + { + stream << "[Positional argument]" << std::endl; + for (const auto &arg : parser._positional_arg_vec) + { + stream.width(length_of_longest_arg); + stream << std::left << arg._name << "\t"; + for (size_t i = 0; i < arg._help_message.length(); i += message_width) + { + if (i) + stream << std::string(length_of_longest_arg, ' ') << "\t"; + stream << arg._help_message.substr(i, message_width) << std::endl; + } + } + std::cout << std::endl; + } + // optional argument + if (!parser._optional_arg_vec.empty()) + { + stream << "[Optional argument]" << std::endl; + for (const auto &arg : parser._optional_arg_vec) + { + stream.width(length_of_longest_arg); + stream << std::left << arg._name << "\t"; + for (size_t i = 0; i < arg._help_message.length(); i += message_width) + { + if (i) + stream << std::string(length_of_longest_arg, ' ') << "\t"; + stream << arg._help_message.substr(i, message_width) << std::endl; + } + } + } + + return stream; +} + +} // namespace arser diff --git a/compiler/arser/tests/arser.test.cpp b/compiler/arser/tests/arser.test.cpp new file mode 100644 index 0000000..28bee42 --- /dev/null +++ b/compiler/arser/tests/arser.test.cpp @@ -0,0 +1,344 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include +#include +#include + +#include + +#include "arser/arser.h" + +using namespace arser; + +class Prompt +{ +public: + Prompt(const std::string &command) + { + std::istringstream iss(command); + std::vector token(std::istream_iterator{iss}, + std::istream_iterator()); + _arg = std::move(token); + _argv.reserve(_arg.size()); + for (const auto &t : _arg) + { + _argv.push_back(const_cast(t.data())); + } + } + int argc(void) const { return _argv.size(); } + char **argv(void) { return _argv.data(); } + +private: + std::vector _argv; + std::vector _arg; +}; + +TEST(BasicTest, option) +{ + /* arrange */ + Arser arser; + + arser.add_argument("--verbose") + .nargs(0) + .help("It provides additional details as to what the executable is doing"); + + Prompt prompt("./executable --verbose"); + /* act */ + arser.parse(prompt.argc(), prompt.argv()); + /* assert */ + EXPECT_TRUE(arser["--verbose"]); + EXPECT_TRUE(arser.get("--verbose")); +} + +TEST(BasicTest, OptionalArgument) +{ + /* arrange */ + Arser arser; + + arser.add_argument("--volume") + .nargs(1) + .type(arser::DataType::INT32) + .help("Set a volume as you provided."); + arser.add_argument("--frequency") + .nargs(1) + .type(arser::DataType::FLOAT) + .help("Set a frequency as you provided."); + + Prompt prompt("./radio --volume 5 --frequency 128.5"); + /* act */ + arser.parse(prompt.argc(), prompt.argv()); + /* assert */ + EXPECT_TRUE(arser["--volume"]); + EXPECT_EQ(5, arser.get("--volume")); + + EXPECT_TRUE(arser["--frequency"]); + EXPECT_FLOAT_EQ(128.5, arser.get("--frequency")); + + EXPECT_FALSE(arser["--price"]); + EXPECT_THROW(arser.get("--volume"), std::runtime_error); +} + +TEST(BasicTest, NonRequiredOptionalArgument) +{ + /* arrange */ + Arser arser; + + arser.add_argument("--weight") + .nargs(1) + .type(arser::DataType::INT32) + .help("Set a volume as you provided."); + + Prompt prompt("./radio"); // empty argument + /* act */ + arser.parse(prompt.argc(), prompt.argv()); + /* assert */ + EXPECT_FALSE(arser["--volume"]); + EXPECT_THROW(arser.get("--weight"), std::runtime_error); +} + +TEST(BasicTest, RequiredOptionalArgument) +{ + /* arrange */ + Arser arser; + + arser.add_argument("--volume") + .nargs(1) + .type(arser::DataType::INT32) + .required() + .help("Set a volume as you provided."); + + Prompt prompt("./radio"); + /* act */ /* assert */ + EXPECT_THROW(arser.parse(prompt.argc(), prompt.argv()), std::runtime_error); +} + +TEST(BasicTest, OptionalMultipleArgument) +{ + /* arrange */ + Arser arser; + + arser.add_argument("--add").nargs(2).type(arser::DataType::INT32_VEC).help("Add two numbers."); + + Prompt prompt("./calculator --add 3 5"); + /* act */ + arser.parse(prompt.argc(), prompt.argv()); + /* assert */ + EXPECT_TRUE(arser["--add"]); + std::vector values = arser.get>("--add"); + EXPECT_EQ(3, values.at(0)); + EXPECT_EQ(5, values.at(1)); + + EXPECT_THROW(arser.get>("--add"), std::runtime_error); +} + +TEST(BasicTest, MultipleOptionalArgument) +{ + /* arrange */ + Arser arser; + + arser.add_argument("--input_path") + .nargs(1) + .type(arser::DataType::STR) + .help("input path of this program.") + .required(); + arser.add_argument("--output_path") + .nargs(1) + .type(arser::DataType::STR) + .help("output path of this program.") + .required(true); + arser.add_argument("--training_data") + .nargs(5) + .type(arser::DataType::INT32_VEC) + .help("give traning data to this program.") + .required(); + + Prompt prompt("./ml --input_path /I/am/in.put --output_path I/am/out.put " + "--training_data 2 43 234 3 334"); + /* act */ + arser.parse(prompt.argc(), prompt.argv()); + /* assert */ + EXPECT_TRUE(arser["--input_path"]); + EXPECT_EQ("/I/am/in.put", arser.get("--input_path")); + EXPECT_TRUE(arser["--output_path"]); + EXPECT_EQ("I/am/out.put", arser.get("--output_path")); + EXPECT_TRUE(arser["--training_data"]); + std::vector data = arser.get>("--training_data"); + EXPECT_EQ(2, data.at(0)); + EXPECT_EQ(43, data.at(1)); + EXPECT_EQ(234, data.at(2)); + EXPECT_EQ(3, data.at(3)); + EXPECT_EQ(334, data.at(4)); +} + +TEST(BasicTest, MultipleFloatValue) +{ + /* arrange */ + Arser arser; + + arser.add_argument("--add_float") + .nargs(2) + .type(arser::DataType::FLOAT_VEC) + .help("Add two float numbers."); + + Prompt prompt("./calculator --add_float 3.2 5.4"); + /* act */ + arser.parse(prompt.argc(), prompt.argv()); + /* assert */ + EXPECT_TRUE(arser["--add_float"]); + std::vector values = arser.get>("--add_float"); + EXPECT_FLOAT_EQ(3.2, values.at(0)); + EXPECT_FLOAT_EQ(5.4, values.at(1)); + + EXPECT_THROW(arser.get>("--add_float"), std::runtime_error); +} + +TEST(BasicTest, MultipleStringValue) +{ + /* arrange */ + Arser arser; + + arser.add_argument("--three_color") + .nargs(3) + .type(arser::DataType::STR_VEC) + .help("insert your three favorite color"); + + Prompt prompt("./color_factory --three_color red blue yellow"); + /* act */ + arser.parse(prompt.argc(), prompt.argv()); + /* assert */ + EXPECT_TRUE(arser["--three_color"]); + std::vector values = arser.get>("--three_color"); + EXPECT_EQ("red", values.at(0)); + EXPECT_EQ("blue", values.at(1)); + EXPECT_EQ("yellow", values.at(2)); + + EXPECT_THROW(arser.get>("--color"), std::runtime_error); +} + +void printBiography(void) { std::cerr << "When I was young.." << std::endl; } + +TEST(BasicTest, ExitWithFunctionCall) +{ + /* arrange */ + Arser arser; + + arser.add_argument("--history").help("Show history and exit").exit_with(printBiography); + + arser.add_argument("--name").nargs(1).type(arser::DataType::STR).help("Name your hero"); + + Prompt prompt("./hero --history"); + /* act */ /* assert */ + EXPECT_EXIT(arser.parse(prompt.argc(), prompt.argv()), testing::ExitedWithCode(0), + "When I was young.."); +} + +void printVersion(std::string version) { std::cerr << "arser version : " << version << std::endl; } + +TEST(BasicTest, ExitWithFunctionCallWithBind) +{ + /* arrange */ + Arser arser; + + arser.add_argument("--version") + .help("Show version and exit") + .exit_with(std::bind(printVersion, "1.2.0")); + + Prompt prompt("./arser --version"); + /* act */ /* assert */ + EXPECT_EXIT(arser.parse(prompt.argc(), prompt.argv()), testing::ExitedWithCode(0), + "arser version : 1.2.0"); +} + +TEST(BasicTest, ExitWithFunctionCallWithLamda) +{ + /* arrange */ + Arser arser; + + arser.add_argument("--shutdown").help("Shut down your computer").exit_with([](void) { + std::cerr << "Good bye.." << std::endl; + }); + + arser.add_argument("OS").nargs(1).type(arser::DataType::STR).help("The OS you want to boot"); + + Prompt prompt("./computer --shutdown"); + /* act */ /* assert */ + EXPECT_EXIT(arser.parse(prompt.argc(), prompt.argv()), testing::ExitedWithCode(0), "Good bye.."); +} + +TEST(BasicTest, DefaultValue) +{ + /* arrange */ + Arser arser; + + arser.add_argument("--delivery") + .nargs(3) + .type(arser::DataType::STR_VEC) + .default_value("pizza", "chicken", "hamburger") + .help("Enter three foods that you want to deliver"); + arser.add_argument("--assistant") + .type(arser::DataType::STR) + .default_value("Bixby") + .help("Enter name of your assistant"); + arser.add_argument("--sound") + .type(arser::DataType::BOOL) + .nargs(1) + .default_value(true) + .help("Sound on/off"); + arser.add_argument("--number") + .type(arser::DataType::INT32_VEC) + .nargs(4) + .default_value(1, 2, 3, 4) + .help("Enter the number that you want to call"); + arser.add_argument("--time") + .type(arser::DataType::INT32_VEC) + .nargs(3) + .default_value(0, 0, 0) + .help("Current time(H/M/S)"); + arser.add_argument("--name") + .type(arser::DataType::STR) + .nargs(1) + .default_value("no name") + .help("Enter your name"); + + Prompt prompt("/phone --time 1 52 34 --name arser"); + /* act */ + arser.parse(prompt.argc(), prompt.argv()); + /* assert */ + // 3 strings, no argument + std::vector delivery = arser.get>("--delivery"); + EXPECT_EQ("pizza", delivery.at(0)); + EXPECT_EQ("chicken", delivery.at(1)); + EXPECT_EQ("hamburger", delivery.at(2)); + // 1 string, no argument + EXPECT_EQ("Bixby", arser.get("--assistant")); + // 1 bool, no argument + EXPECT_EQ(true, arser.get("--sound")); + // 4 integer, no argument + std::vector number = arser.get>("--number"); + EXPECT_EQ(1, number.at(0)); + EXPECT_EQ(2, number.at(1)); + EXPECT_EQ(3, number.at(2)); + EXPECT_EQ(4, number.at(3)); + // 3 integer, 3 arguments + std::vector time = arser.get>("--time"); + EXPECT_EQ(1, time.at(0)); + EXPECT_EQ(52, time.at(1)); + EXPECT_EQ(34, time.at(2)); + // 1 string, 1 argument + EXPECT_EQ("arser", arser.get("--name")); +} diff --git a/compiler/caffe2circle/requires.cmake b/compiler/caffe2circle/requires.cmake index cc05edd..b16a511 100644 --- a/compiler/caffe2circle/requires.cmake +++ b/compiler/caffe2circle/requires.cmake @@ -1,3 +1,3 @@ -require("mir-onnx-importer") +require("mir") require("mir2loco") require("exo") diff --git a/compiler/circle-inspect/CMakeLists.txt b/compiler/circle-inspect/CMakeLists.txt index 222f8cb..d0775ea 100644 --- a/compiler/circle-inspect/CMakeLists.txt +++ b/compiler/circle-inspect/CMakeLists.txt @@ -8,6 +8,7 @@ file(GLOB_RECURSE SOURCES "src/*.cpp") add_executable(circle-inspect ${DRIVER} ${SOURCES}) target_include_directories(circle-inspect PRIVATE src) +target_link_libraries(circle-inspect arser) +target_link_libraries(circle-inspect foder) target_link_libraries(circle-inspect mio_circle) target_link_libraries(circle-inspect safemain) -target_link_libraries(circle-inspect stdex) diff --git a/compiler/circle-inspect/driver/Driver.cpp b/compiler/circle-inspect/driver/Driver.cpp index d23cd0f..72cfa28 100644 --- a/compiler/circle-inspect/driver/Driver.cpp +++ b/compiler/circle-inspect/driver/Driver.cpp @@ -14,74 +14,63 @@ * limitations under the License. */ -#include "Model.h" #include "Dump.h" -#include +#include +#include #include #include #include +#include #include #include -using OptionHook = std::function(void)>; - int entry(int argc, char **argv) { - if (argc < 3) + arser::Arser arser{ + "circle-inspect allows users to retrieve various information from a Circle model file"}; + arser.add_argument("--operators").nargs(0).help("Dump operators in circle file"); + arser.add_argument("--conv2d_weight") + .nargs(0) + .help("Dump Conv2D series weight operators in circle file"); + arser.add_argument("--op_version").nargs(0).help("Dump versions of the operators in circle file"); + arser.add_argument("circle").type(arser::DataType::STR).help("Circle file to inspect"); + + try + { + arser.parse(argc, argv); + } + catch (const std::runtime_error &err) { - std::cerr << "ERROR: Failed to parse arguments" << std::endl; - std::cerr << std::endl; - std::cerr << "USAGE: " << argv[0] << " [options] [circle]" << std::endl; - std::cerr << " --operators : dump operators in circle file" << std::endl; - std::cerr << " --conv2d_weight : dump Conv2D series weight operators in circle file" - << std::endl; + std::cout << err.what() << std::endl; + std::cout << arser; return 255; } - // Simple argument parser (based on map) - std::map argparse; - - argparse["--operators"] = [&](void) { - // dump all operators - return std::move(stdex::make_unique()); - }; - - argparse["--conv2d_weight"] = [&](void) { - // dump Conv2D, DepthwiseConv2D weight operators - return std::move(stdex::make_unique()); - }; + if (!arser["--operators"] && !arser["--conv2d_weight"] && !arser["--op_version"]) + { + std::cout << "At least one option must be specified" << std::endl; + std::cout << arser; + return 255; + } std::vector> dumps; - for (int n = 1; n < argc - 1; ++n) - { - const std::string tag{argv[n]}; - - auto it = argparse.find(tag); - if (it == argparse.end()) - { - std::cerr << "Option '" << tag << "' is not supported" << std::endl; - return 255; - } - auto dump = it->second(); - assert(dump != nullptr); - dumps.push_back(std::move(dump)); - } + if (arser["--operators"]) + dumps.push_back(std::make_unique()); + if (arser["--conv2d_weight"]) + dumps.push_back(std::make_unique()); + if (arser["--op_version"]) + dumps.push_back(std::make_unique()); - std::string model_file = argv[argc - 1]; + std::string model_file = arser.get("circle"); // Load Circle model from a circle file - auto model = circleinspect::load_circle(model_file); - if (model == nullptr) - { - std::cerr << "ERROR: Failed to load circle '" << model_file << "'" << std::endl; - return 255; - } - - const circle::Model *circlemodel = model->model(); - if (circlemodel == nullptr) + foder::FileLoader fileLoader{model_file}; + std::vector modelData = fileLoader.load(); + const circle::Model *circleModel = circle::GetModel(modelData.data()); + if (circleModel == nullptr) { std::cerr << "ERROR: Failed to load circle '" << model_file << "'" << std::endl; return 255; @@ -89,7 +78,7 @@ int entry(int argc, char **argv) for (auto &dump : dumps) { - dump->run(std::cout, circlemodel); + dump->run(std::cout, circleModel); } return 0; diff --git a/compiler/circle-inspect/requires.cmake b/compiler/circle-inspect/requires.cmake index b090dbd..81e0f0d 100644 --- a/compiler/circle-inspect/requires.cmake +++ b/compiler/circle-inspect/requires.cmake @@ -1,3 +1,3 @@ +require("arser") require("mio-circle") require("safemain") -require("stdex") diff --git a/compiler/circle-inspect/src/Dump.cpp b/compiler/circle-inspect/src/Dump.cpp index fbc092b..5c71afb 100644 --- a/compiler/circle-inspect/src/Dump.cpp +++ b/compiler/circle-inspect/src/Dump.cpp @@ -26,19 +26,22 @@ void DumpOperators::run(std::ostream &os, const circle::Model *model) { circleinspect::Reader reader(model); - assert(reader.num_subgraph() == 1); - reader.select_subgraph(0); - - auto ops = reader.operators(); + const uint32_t subgraph_size = reader.num_subgraph(); - // dump operators - for (uint32_t i = 0; i < ops->Length(); ++i) + for (uint32_t g = 0; g < subgraph_size; g++) { - const auto op = ops->Get(i); + reader.select_subgraph(g); + auto ops = reader.operators(); - auto op_name = reader.opcode_name(op); + // dump operators + for (uint32_t i = 0; i < ops->Length(); ++i) + { + const auto op = ops->Get(i); + + auto op_name = reader.opcode_name(op); - os << op_name << std::endl; + os << op_name << std::endl; + } } } @@ -92,43 +95,82 @@ void DumpConv2DWeight::run(std::ostream &os, const circle::Model *model) { circleinspect::Reader reader(model); + const uint32_t subgraph_size = reader.num_subgraph(); + + for (uint32_t g = 0; g < subgraph_size; g++) + { + reader.select_subgraph(g); + auto ops = reader.operators(); + + // dump Conv2D, DepthwiseConv2D and its weight input operator + for (uint32_t i = 0; i < ops->Length(); ++i) + { + const auto op = ops->Get(i); + auto bc = reader.builtin_code(op); + + if (bc == circle::BuiltinOperator_CONV_2D || bc == circle::BuiltinOperator_DEPTHWISE_CONV_2D) + { + const std::vector &inputs = circleinspect::as_index_vector(op->inputs()); + if (inputs.size() < 2) + { + throw std::runtime_error("Operator has invalid input"); + } + auto weight_input = inputs[1]; // Tensor ID of weight input + + const auto op_weight = operator_match_output(reader, weight_input); + const auto buffer_size = tensor_buffer_size(reader, weight_input); + + std::string weight_op_name = "?"; + + if (op_weight == nullptr && buffer_size > 0) + { + weight_op_name = "CONST"; + } + else if (op_weight != nullptr) + { + weight_op_name = reader.opcode_name(op_weight); + } + + auto op_name = reader.opcode_name(op); + os << op_name << "," << weight_op_name << std::endl; + } + } + } +} + +} // namespace circleinspect + +namespace circleinspect +{ + +void DumpOperatorVersion::run(std::ostream &os, const circle::Model *model) +{ + std::map op_version_map; + + circleinspect::Reader reader(model); + + // This assert is subject to be changed later assert(reader.num_subgraph() == 1); reader.select_subgraph(0); auto ops = reader.operators(); - // dump Conv2D, DepthwiseConv2D and its weight input operator + // Dump operators' version for (uint32_t i = 0; i < ops->Length(); ++i) { const auto op = ops->Get(i); - auto bc = reader.builtin_code(op); - - if (bc == circle::BuiltinOperator_CONV_2D || bc == circle::BuiltinOperator_DEPTHWISE_CONV_2D) - { - const std::vector &inputs = circleinspect::as_index_vector(op->inputs()); - if (inputs.size() < 2) - { - throw std::runtime_error("Operator has invalid input"); - } - auto weight_input = inputs[1]; // Tensor ID of weight input - - const auto op_weight = operator_match_output(reader, weight_input); - const auto buffer_size = tensor_buffer_size(reader, weight_input); - std::string weight_op_name = "?"; + auto op_name = reader.opcode_name(op); + auto op_version = reader.opcodes().at(op->opcode_index())->version(); - if (op_weight == nullptr && buffer_size > 0) - { - weight_op_name = "CONST"; - } - else if (op_weight != nullptr) - { - weight_op_name = reader.opcode_name(op_weight); - } + if (op_version_map.find(op_name) == op_version_map.end() || + op_version_map[op_name] < op_version) + op_version_map[op_name] = op_version; + } - auto op_name = reader.opcode_name(op); - os << op_name << "," << weight_op_name << std::endl; - } + for (auto op : op_version_map) + { + os << op.first << "," << op.second << std::endl; } } diff --git a/compiler/circle-inspect/src/Dump.h b/compiler/circle-inspect/src/Dump.h index 6afba83..996c421 100644 --- a/compiler/circle-inspect/src/Dump.h +++ b/compiler/circle-inspect/src/Dump.h @@ -51,6 +51,15 @@ public: void run(std::ostream &os, const circle::Model *model); }; +class DumpOperatorVersion final : public DumpInterface +{ +public: + DumpOperatorVersion() = default; + +public: + void run(std::ostream &os, const circle::Model *model); +}; + } // namespace circleinspect #endif // __DUMP_H__ diff --git a/compiler/circle-inspect/src/Model.cpp b/compiler/circle-inspect/src/Model.cpp deleted file mode 100644 index 1924bfa..0000000 --- a/compiler/circle-inspect/src/Model.cpp +++ /dev/null @@ -1,143 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Model.h" - -#include -#include -#include -#include - -namespace -{ - -class MemoryMappedModel final : public circleinspect::Model -{ -public: - /** - * @require fd and data SHOULD be valid - */ - explicit MemoryMappedModel(int fd, void *data, size_t size) : _fd{fd}, _data{data}, _size{size} - { - // DO NOTHING - } - -public: - ~MemoryMappedModel() - { - munmap(_data, _size); - close(_fd); - } - -public: - MemoryMappedModel(const MemoryMappedModel &) = delete; - MemoryMappedModel(MemoryMappedModel &&) = delete; - -public: - const ::circle::Model *model(void) const override { return ::circle::GetModel(_data); } - -private: - int _fd = -1; - void *_data = nullptr; - size_t _size = 0; -}; - -class FileDescriptor final -{ -public: - FileDescriptor(int value) : _value{value} - { - // DO NOTHING - } - -public: - // NOTE Copy is not allowed - FileDescriptor(const FileDescriptor &) = delete; - -public: - // NOTE Move is allowed - FileDescriptor(FileDescriptor &&fd) { _value = fd.release(); } - -public: - ~FileDescriptor() - { - if (_value != -1) - { - // Close on descturction - close(_value); - } - } - -public: - int value(void) const { return _value; } - -public: - int release(void) - { - auto res = _value; - _value = -1; - return res; - } - -private: - int _value = -1; -}; - -} // namespace - -namespace circleinspect -{ - -std::unique_ptr load_circle(const std::string &path) -{ - FileDescriptor fd = open(path.c_str(), O_RDONLY); - - if (fd.value() == -1) - { - // Return nullptr on open failure - return nullptr; - } - - struct stat st; - if (fstat(fd.value(), &st) == -1) - { - // Return nullptr on fstat failure - return nullptr; - } - - auto size = st.st_size; - auto data = mmap(nullptr, size, PROT_READ, MAP_SHARED, fd.value(), 0); - - if (data == MAP_FAILED) - { - // Return nullptr on mmap failure - return nullptr; - } - - // Check if file is a valid Flatbuffer file - const uint8_t *u8data = reinterpret_cast(data); - flatbuffers::Verifier verifier{u8data, static_cast(size)}; - if (!circle::VerifyModelBuffer(verifier)) - { - munmap(data, size); - close(fd.release()); - return nullptr; - } - - return std::unique_ptr{new MemoryMappedModel(fd.release(), data, size)}; -} - -} // namespace circleinspect diff --git a/compiler/circle-inspect/src/Model.h b/compiler/circle-inspect/src/Model.h deleted file mode 100644 index 8206ed3..0000000 --- a/compiler/circle-inspect/src/Model.h +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __MODEL_H__ -#define __MODEL_H__ - -#include - -#include - -namespace circleinspect -{ - -struct Model -{ - virtual ~Model() = default; - - virtual const ::circle::Model *model(void) const = 0; -}; - -/** - * @brief Load Circle model (as a raw Model) from a given path - * - * @note May return a nullptr - */ -std::unique_ptr load_circle(const std::string &path); - -} // namespace circleinspect - -#endif // __MODEL_H__ diff --git a/compiler/circle-inspect/src/Reader.cpp b/compiler/circle-inspect/src/Reader.cpp index dbbc7c7..7807db3 100644 --- a/compiler/circle-inspect/src/Reader.cpp +++ b/compiler/circle-inspect/src/Reader.cpp @@ -50,7 +50,10 @@ std::string opcode_name(const circle::OperatorCode *opcode) if (!opcode->custom_code()) return "(invalid custom)"; - return opcode->custom_code()->c_str(); + std::string custom_op = "CUSTOM("; + custom_op += opcode->custom_code()->c_str(); + custom_op += ")"; + return custom_op; } circle::BuiltinOperator code = opcode->builtin_code(); diff --git a/compiler/circle-quantizer/CMakeLists.txt b/compiler/circle-quantizer/CMakeLists.txt new file mode 100644 index 0000000..1335057 --- /dev/null +++ b/compiler/circle-quantizer/CMakeLists.txt @@ -0,0 +1,17 @@ +file(GLOB_RECURSE SOURCES "src/*.cpp") + +add_executable(circle-quantizer "${SOURCES}") +target_include_directories(circle-quantizer PRIVATE include) +target_include_directories(circle-quantizer PRIVATE src) +target_link_libraries(circle-quantizer foder) +target_link_libraries(circle-quantizer safemain) +target_link_libraries(circle-quantizer oops) +target_link_libraries(circle-quantizer loco) +target_link_libraries(circle-quantizer mio_circle) +target_link_libraries(circle-quantizer luci_import) +target_link_libraries(circle-quantizer luci_service) +target_link_libraries(circle-quantizer luci_pass) +target_link_libraries(circle-quantizer luci_export) +target_link_libraries(circle-quantizer arser) + +install(TARGETS circle-quantizer DESTINATION bin) diff --git a/compiler/circle-quantizer/README.md b/compiler/circle-quantizer/README.md new file mode 100644 index 0000000..2666c84 --- /dev/null +++ b/compiler/circle-quantizer/README.md @@ -0,0 +1,3 @@ +# circle-quantizer + +_circle-quantizer_ provides post-training quantization functionalities for Circle models diff --git a/compiler/circle-quantizer/include/CircleExpContract.h b/compiler/circle-quantizer/include/CircleExpContract.h new file mode 100644 index 0000000..e888e4a --- /dev/null +++ b/compiler/circle-quantizer/include/CircleExpContract.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __CIRCLEQUANTIZER_CIRCLEXPCONTRACT_H__ +#define __CIRCLEQUANTIZER_CIRCLEXPCONTRACT_H__ + +#include +#include +#include + +#include +#include + +struct CircleExpContract : public luci::CircleExporter::Contract +{ +public: + CircleExpContract(luci::Module *module, const std::string &filename) + : _module(module), _filepath(filename) + { + // NOTHING TO DO + } + virtual ~CircleExpContract() = default; + +public: + loco::Graph *graph(void) const final { return nullptr; } + luci::Module *module(void) const final { return _module; }; + +public: + bool store(const char *ptr, const size_t size) const final; + +private: + luci::Module *_module; + const std::string _filepath; +}; + +#endif // __CIRCLEQUANTIZER_CIRCLEXPCONTRACT_H__ diff --git a/compiler/circle-quantizer/requires.cmake b/compiler/circle-quantizer/requires.cmake new file mode 100644 index 0000000..2293e53 --- /dev/null +++ b/compiler/circle-quantizer/requires.cmake @@ -0,0 +1,7 @@ +require("foder") +require("loco") +require("locop") +require("safemain") +require("luci") +require("oops") +require("arser") diff --git a/compiler/circle-quantizer/src/CircleExpContract.cpp b/compiler/circle-quantizer/src/CircleExpContract.cpp new file mode 100644 index 0000000..b56b7ee --- /dev/null +++ b/compiler/circle-quantizer/src/CircleExpContract.cpp @@ -0,0 +1,33 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "CircleExpContract.h" + +#include + +#include +#include + +bool CircleExpContract::store(const char *ptr, const size_t size) const +{ + if (!ptr) + INTERNAL_EXN("Graph was not serialized by FlatBuffer for some reason"); + + std::ofstream fs(_filepath.c_str(), std::ofstream::binary); + fs.write(ptr, size); + + return fs.good(); +} diff --git a/compiler/circle-quantizer/src/CircleQuantizer.cpp b/compiler/circle-quantizer/src/CircleQuantizer.cpp new file mode 100644 index 0000000..b56b547 --- /dev/null +++ b/compiler/circle-quantizer/src/CircleQuantizer.cpp @@ -0,0 +1,155 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "CircleExpContract.h" + +#include + +#include +#include +#include +#include + +#include +#include + +#include +#include +#include +#include + +using OptionHook = std::function; + +using Algorithms = luci::CircleOptimizer::Options::Algorithm; +using AlgorithmParameters = luci::CircleOptimizer::Options::AlgorithmParameters; + +int entry(int argc, char **argv) +{ + // Simple argument parser (based on map) + std::map argparse; + luci::CircleOptimizer optimizer; + + auto options = optimizer.options(); + + const std::string qdqw = "--quantize_dequantize_weights"; + const std::string qwmm = "--quantize_with_minmax"; + + arser::Arser arser("circle-quantizer provides circle model quantization"); + + arser.add_argument(qdqw) + .nargs(3) + .type(arser::DataType::STR_VEC) + .required(false) + .help("Quantize-dequantize weight values required action before quantization. " + "Three arguments required: input_dtype(float32) " + "output_dtype(uint8) granularity(layer)"); + + arser.add_argument(qwmm) + .nargs(3) + .type(arser::DataType::STR_VEC) + .required(false) + .help("Quantize with min/max values. " + "Three arguments required: input_dtype(float32) " + "output_dtype(uint8) granularity(layer)"); + + arser.add_argument("input").nargs(1).type(arser::DataType::STR).help("Input circle model"); + arser.add_argument("output").nargs(1).type(arser::DataType::STR).help("Output circle model"); + + try + { + arser.parse(argc, argv); + } + catch (const std::runtime_error &err) + { + std::cout << err.what() << std::endl; + std::cout << arser; + return 255; + } + + if (arser[qdqw]) + { + auto values = arser.get>(qdqw); + if (values.size() != 3) + { + std::cerr << arser; + return 255; + } + options->enable(Algorithms::QuantizeDequantizeWeights); + + options->param(AlgorithmParameters::Quantize_input_dtype, values.at(0)); + options->param(AlgorithmParameters::Quantize_output_dtype, values.at(1)); + options->param(AlgorithmParameters::Quantize_granularity, values.at(2)); + } + + if (arser[qwmm]) + { + auto values = arser.get>(qwmm); + if (values.size() != 3) + { + std::cerr << arser; + return 255; + } + options->enable(Algorithms::QuantizeWithMinMax); + + options->param(AlgorithmParameters::Quantize_input_dtype, values.at(0)); + options->param(AlgorithmParameters::Quantize_output_dtype, values.at(1)); + options->param(AlgorithmParameters::Quantize_granularity, values.at(2)); + } + + std::string input_path = arser.get("input"); + std::string output_path = arser.get("output"); + + // Load model from the file + foder::FileLoader file_loader{input_path}; + std::vector model_data = file_loader.load(); + const circle::Model *circle_model = circle::GetModel(model_data.data()); + if (circle_model == nullptr) + { + std::cerr << "ERROR: Failed to load circle '" << input_path << "'" << std::endl; + return EXIT_FAILURE; + } + + // Import from input Circle file + luci::Importer importer; + auto module = importer.importModule(circle_model); + + for (size_t idx = 0; idx < module->size(); ++idx) + { + auto graph = module->graph(idx); + + // quantize the graph + optimizer.quantize(graph); + + if (!luci::validate(graph)) + { + std::cerr << "ERROR: Quantized graph is invalid" << std::endl; + return 255; + } + } + + // Export to output Circle file + luci::CircleExporter exporter; + + CircleExpContract contract(module.get(), output_path); + + if (!exporter.invoke(&contract)) + { + std::cerr << "ERROR: Failed to export '" << output_path << "'" << std::endl; + return 255; + } + + return 0; +} diff --git a/compiler/circle-tensordump/CMakeLists.txt b/compiler/circle-tensordump/CMakeLists.txt new file mode 100644 index 0000000..e55901f --- /dev/null +++ b/compiler/circle-tensordump/CMakeLists.txt @@ -0,0 +1,23 @@ +if(NOT TARGET mio_circle) + return() +endif(NOT TARGET mio_circle) + +nnas_find_package(HDF5 QUIET) + +if(NOT HDF5_FOUND) + message(STATUS "Build circle-tensordump: FAILED (missing HDF5)") + return() +endif(NOT HDF5_FOUND) + +set(DRIVER "driver/Driver.cpp") + +file(GLOB_RECURSE SOURCES "src/*.cpp") + +add_executable(circle-tensordump ${DRIVER} ${SOURCES}) +target_include_directories(circle-tensordump PRIVATE src) +target_include_directories(circle-tensordump PRIVATE ${HDF5_INCLUDE_DIRS}) +target_link_libraries(circle-tensordump PRIVATE ${HDF5_CXX_LIBRARIES}) +target_link_libraries(circle-tensordump PRIVATE arser) +target_link_libraries(circle-tensordump PRIVATE foder) +target_link_libraries(circle-tensordump PRIVATE mio_circle) +target_link_libraries(circle-tensordump PRIVATE safemain) diff --git a/compiler/circle-tensordump/README.md b/compiler/circle-tensordump/README.md new file mode 100644 index 0000000..dcb05d5 --- /dev/null +++ b/compiler/circle-tensordump/README.md @@ -0,0 +1,73 @@ +# circle-tensordump + +_circle-tensordump_ allows users to retrieve tensor information from a Circle model file + +## options + +**--tensors** + +dump tensors in circle file + +``` +$ ./circle-tensordump --tensors ../luci/tests/Conv2D_000.circle + +---------------------------------------------------------------------- +[ifm] + └── shape : (1, 3, 3, 2) + +---------------------------------------------------------------------- +[ker] + ├── shape : (1, 1, 1, 2) + └── buffer +    ├── index : 3 +    ├── size : 8 +    └── data : 0.727939, 0.320132, + +---------------------------------------------------------------------- +[bias] + ├── shape : (1) + └── buffer +    ├── index : 4 +    ├── size : 4 +    └── data : -0.794465, + +---------------------------------------------------------------------- +[ofm] + └── shape : (1, 3, 3, 1) +``` + +**--tensors_to_hdf5** + +dump tensors in circle file to hdf5 file + +``` +$ ./circle-tensordump --tensors_to_hdf5 ../luci/tests/Conv2D_000.circle output_path.h5 +$ h5dump output_path.h5 + +HDF5 "output_path.h5" { +GROUP "/" { + GROUP "bias" { + DATASET "weights" { + DATATYPE H5T_IEEE_F32LE + DATASPACE SIMPLE { ( 1 ) / ( 1 ) } + DATA { + (0): -0.794465 + } + } + } + GROUP "ifm" { + } + GROUP "ker" { + DATASET "weights" { + DATATYPE H5T_IEEE_F32LE + DATASPACE SIMPLE { ( 1, 1, 1, 2 ) / ( 1, 1, 1, 2 ) } + DATA { + (0,0,0,0): 0.727939, 0.320132 + } + } + } + GROUP "ofm" { + } +} +} +``` diff --git a/compiler/circle-tensordump/driver/Driver.cpp b/compiler/circle-tensordump/driver/Driver.cpp new file mode 100644 index 0000000..a55cd45 --- /dev/null +++ b/compiler/circle-tensordump/driver/Driver.cpp @@ -0,0 +1,79 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Dump.h" + +#include +#include + +#include +#include +#include +#include +#include +#include + +int entry(int argc, char **argv) +{ + arser::Arser arser{ + "circle-tensordump allows users to retrieve tensor information from a Circle model file"}; + + arser.add_argument("circle").nargs(1).type(arser::DataType::STR).help("Circle file path to dump"); + arser.add_argument("--tensors").nargs(0).help("Dump to console"); + arser.add_argument("--tensors_to_hdf5") + .nargs(1) + .type(arser::DataType::STR) + .help("Dump to hdf5 file. Specify hdf5 file path to be dumped"); + + try + { + arser.parse(argc, argv); + } + catch (const std::runtime_error &err) + { + std::cout << err.what() << std::endl; + std::cout << arser; + return 0; + } + + std::unique_ptr dump; + + std::string model_file = arser.get("circle"); + std::string output_path; + if (arser["--tensors_to_hdf5"]) + { + dump = std::move(std::make_unique()); + output_path = arser.get("--tensors_to_hdf5"); + } + if (arser["--tensors"]) + { + dump = std::move(std::make_unique()); + } + + // Load Circle model from a circle file + foder::FileLoader fileLoader{model_file}; + std::vector modelData = fileLoader.load(); + const circle::Model *circleModel = circle::GetModel(modelData.data()); + if (circleModel == nullptr) + { + std::cerr << "ERROR: Failed to load circle '" << model_file << "'" << std::endl; + return EXIT_FAILURE; + } + + dump->run(std::cout, circleModel, output_path); + + return EXIT_SUCCESS; +} diff --git a/compiler/circle-tensordump/requires.cmake b/compiler/circle-tensordump/requires.cmake new file mode 100644 index 0000000..1c754f5 --- /dev/null +++ b/compiler/circle-tensordump/requires.cmake @@ -0,0 +1,4 @@ +require("arser") +require("foder") +require("mio-circle") +require("safemain") diff --git a/compiler/circle-tensordump/src/Dump.cpp b/compiler/circle-tensordump/src/Dump.cpp new file mode 100644 index 0000000..dfa78f0 --- /dev/null +++ b/compiler/circle-tensordump/src/Dump.cpp @@ -0,0 +1,325 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Dump.h" +#include "Reader.h" + +#include + +#include +#include +#include +#include + +namespace +{ + +template +void print_comma_sepearted(std::ostream &os, const flatbuffers::Vector *vec) +{ + if (vec == nullptr) + return; + for (auto iter = vec->begin(); iter != vec->end(); iter++) + { + if (iter != vec->begin()) + os << ", "; + os << *iter; + } +} + +void print_buffer(std::ostream &os, uint32_t buff_idx, const flatbuffers::Vector *data_ptr, + const circle::TensorType &type) +{ + if (data_ptr == nullptr) + return; + + os << " └── buffer" << std::endl; + os << "    ├── index : " << buff_idx << std::endl; + size_t buff_size = data_ptr->size(); + os << "    ├── size : " << buff_size << std::endl; + os << "    └── data : "; + switch (type) + { + case circle::TensorType_UINT8: + { + const uint8_t *buff_data_ui8 = reinterpret_cast(data_ptr->data()); + for (uint32_t idx = 0; idx < buff_size / sizeof(uint8_t); idx++) + { + os << static_cast(buff_data_ui8[idx]) << ", "; + } + break; + } + case circle::TensorType_INT32: + { + const int32_t *buff_data_i32 = reinterpret_cast(data_ptr->data()); + for (uint32_t idx = 0; idx < buff_size / sizeof(int32_t); idx++) + { + os << buff_data_i32[idx] << ", "; + } + break; + } + case circle::TensorType_INT64: + { + const int64_t *buff_data_i64 = reinterpret_cast(data_ptr->data()); + for (uint32_t idx = 0; idx < buff_size / sizeof(int64_t); idx++) + { + os << buff_data_i64[idx] << ", "; + } + break; + } + case circle::TensorType_FLOAT32: + { + const float *buff_data_f32 = reinterpret_cast(data_ptr->data()); + for (uint32_t idx = 0; idx < buff_size / sizeof(float); idx++) + { + os << buff_data_f32[idx] << ", "; + } + break; + } + default: + throw std::runtime_error("NYI tensor type : " + std::to_string(type)); + } + os << std::endl; +} + +} // namespace + +namespace circletensordump +{ + +void DumpTensors::run(std::ostream &os, const circle::Model *model, const std::string &) +{ + circletensordump::Reader reader(model); + uint32_t num_subgraph = reader.num_subgraph(); + auto buffers = reader.buffers(); + + for (uint32_t subgraph_idx = 0; subgraph_idx < num_subgraph; subgraph_idx++) + { + reader.select_subgraph(subgraph_idx); + + auto tensors = reader.tensors(); + for (const auto &tensor : *tensors) + { + os << std::string(70, '-') << std::endl; + os << "[" << tensor->name()->str() << "]" << std::endl; + auto buff_idx = tensor->buffer(); + auto buff_data_ptr = reader.buffers()->Get(buff_idx)->data(); + auto quant_param = tensor->quantization(); + std::string print_format = (!buff_data_ptr && !quant_param) ? "└──" : "├──"; + + // shape + auto shape = tensor->shape(); + os << " " + print_format + " shape : ("; + ::print_comma_sepearted(os, shape); + os << ")" << std::endl; + + // quantization paramters + if (quant_param) + { + std::string print_format1 = buff_data_ptr ? "├──" : "└──"; + std::string print_format2 = buff_data_ptr ? "│" : " "; + os << " " + print_format1 + " quantization" << std::endl; + auto min = quant_param->min(); + auto max = quant_param->max(); + auto scale = quant_param->scale(); + auto zero_point = quant_param->zero_point(); + + os << " " + print_format2 + "   ├── min : "; + ::print_comma_sepearted(os, min); + os << std::endl; + os << " " + print_format2 + "   ├── max : "; + ::print_comma_sepearted(os, max); + os << std::endl; + os << " " + print_format2 + "   ├── scale : "; + ::print_comma_sepearted(os, scale); + os << std::endl; + os << " " + print_format2 + "   └── zero_point : "; + ::print_comma_sepearted(os, zero_point); + os << std::endl; + } + + // buffer + print_buffer(os, buff_idx, buff_data_ptr, tensor->type()); + os << std::endl; + } + } +} + +} // namespace circletensordump + +namespace +{ + +// HDF5 forbids the inclusion of '/' in the name. +std::string mangle(const std::string &name) +{ + std::string ret{name}; + std::replace(ret.begin(), ret.end(), '/', '_'); + return ret; +} + +H5::PredType hdf5_dtype_cast(const circle::TensorType &circle_type) +{ + switch (circle_type) + { + case circle::TensorType_UINT8: + { + return H5::PredType::NATIVE_UINT8; + } + case circle::TensorType_INT32: + { + return H5::PredType::NATIVE_INT32; + } + case circle::TensorType_INT64: + { + return H5::PredType::NATIVE_INT64; + } + case circle::TensorType_FLOAT32: + { + return H5::PredType::NATIVE_FLOAT; + } + default: + throw std::runtime_error("NYI tensor type : " + std::to_string(circle_type)); + } +} + +/** + * In order to create a dataspace, its rank and dimensions are required as hsize_t type. + * This function converts flatbuffers::Vector to std::vector. + * + * If "dims" parameter is passed, the parameter will be converted. However, if + * not passed(nullptr), data is considered as a rank 1 vector. + */ +template +std::vector hdf5_dims_cast(const flatbuffers::Vector *data, + const flatbuffers::Vector *dims = nullptr) +{ + std::vector ret; + if (data != nullptr) + { + if (dims == nullptr) + { + ret.resize(1); + ret.at(0) = data->size(); + } + else + { + const uint32_t rank = dims->size(); + ret.resize(rank); + for (uint32_t d = 0; d < rank; d++) + { + ret.at(d) = dims->Get(d); + } + } + } + return ret; +} + +/** + * This function writes data to given hdf5 file like below. + * + * GROUP "group_name" + * ㄴDATATYPE "type" + * ㄴDATASET "dataset_name" + * ㄴDATASPACE "dims" + * ㄴDATA "data" + */ +template +void write_data_to_hdf5(H5::H5File &file, std::string &group_name, std::string dataset_name, + const H5::PredType &type, const flatbuffers::Vector *data, + std::vector dims) +{ + if (data == nullptr) + return; + auto dataspace = std::make_unique(dims.size(), dims.data()); + auto dataset = std::make_unique( + file.createDataSet(group_name + "/" + dataset_name, type, *dataspace)); + dataset->write(data->data(), type); +} + +} // namespace + +namespace circletensordump +{ + +/** + * HDF5 layout is like below + * + * GROUP "/" + * ㄴGROUP "tensor name" + * ㄴDATASET "weights" : Shape (x, y, ...), type(uint8, int16) + * ㄴDATASET "min" : Shape (n) + * ㄴDATASET "max" : Shape (n) + * ㄴDATASET "scale" : Shape (m) + * ㄴDATASET "zero_point" : Shape (m) + * + * NOTE All Dataset is optional. It means that if tensor doesn't have the data, it won't be created + * as a Dataset + * + */ +void DumpTensorsToHdf5::run(std::ostream &os, const circle::Model *model, + const std::string &output_path) +{ + // loads a circle model + circletensordump::Reader reader(model); + uint32_t num_subgraph = reader.num_subgraph(); + + // create a hdf5 file + H5::H5File file{output_path, H5F_ACC_TRUNC}; + + for (uint32_t subgraph_idx = 0; subgraph_idx < num_subgraph; subgraph_idx++) + { + reader.select_subgraph(subgraph_idx); + + auto tensors = reader.tensors(); + for (const auto &tensor : *tensors) + { + // create a group for each tensor whose name is its tensor name + std::string group_name = ::mangle(tensor->name()->c_str()); + std::unique_ptr tensor_group = + std::make_unique(file.createGroup(group_name)); + + // write a buffer data + uint32_t buff_idx = tensor->buffer(); + auto buff_data_ptr = reader.buffers()->Get(buff_idx)->data(); + if (buff_data_ptr) + { + ::write_data_to_hdf5(file, group_name, "weights", ::hdf5_dtype_cast(tensor->type()), + buff_data_ptr, ::hdf5_dims_cast(buff_data_ptr, tensor->shape())); + } + + // write quantization parameters + auto quant_param = tensor->quantization(); + if (quant_param) + { + auto min = quant_param->min(); + ::write_data_to_hdf5(file, group_name, "min", H5::PredType::NATIVE_FLOAT, min, + ::hdf5_dims_cast(min)); + auto max = quant_param->max(); + ::write_data_to_hdf5(file, group_name, "max", H5::PredType::NATIVE_FLOAT, max, + ::hdf5_dims_cast(max)); + auto scale = quant_param->scale(); + ::write_data_to_hdf5(file, group_name, "scale", H5::PredType::NATIVE_FLOAT, scale, + ::hdf5_dims_cast(scale)); + auto zero_point = quant_param->zero_point(); + ::write_data_to_hdf5(file, group_name, "zero_point", H5::PredType::NATIVE_INT64, zero_point, + ::hdf5_dims_cast(zero_point)); + } + } + } +} + +} // namespace circletensordump diff --git a/compiler/circle-tensordump/src/Dump.h b/compiler/circle-tensordump/src/Dump.h new file mode 100644 index 0000000..5dfa59d --- /dev/null +++ b/compiler/circle-tensordump/src/Dump.h @@ -0,0 +1,57 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __CIRCLE_TENSORDUMP_DUMP_H__ +#define __CIRCLE_TENSORDUMP_DUMP_H__ + +#include + +#include + +namespace circletensordump +{ + +class DumpInterface +{ +public: + virtual ~DumpInterface() = default; + +public: + virtual void run(std::ostream &os, const circle::Model *model, + const std::string &output_path = {}) = 0; +}; + +class DumpTensors final : public DumpInterface +{ +public: + DumpTensors() = default; + +public: + void run(std::ostream &os, const circle::Model *model, const std::string &) override; +}; + +class DumpTensorsToHdf5 final : public DumpInterface +{ +public: + DumpTensorsToHdf5() = default; + +public: + void run(std::ostream &os, const circle::Model *model, const std::string &output_path) override; +}; + +} // namespace circletensordump + +#endif // __CIRCLE_TENSORDUMP_DUMP_H__ diff --git a/compiler/circle-tensordump/src/Reader.cpp b/compiler/circle-tensordump/src/Reader.cpp new file mode 100644 index 0000000..429736b --- /dev/null +++ b/compiler/circle-tensordump/src/Reader.cpp @@ -0,0 +1,169 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Reader.h" + +#include +#include + +namespace circletensordump +{ + +bool is_valid(const circle::OperatorCode *opcode) +{ + circle::BuiltinOperator code = opcode->builtin_code(); + return (circle::BuiltinOperator_MIN <= code && code <= circle::BuiltinOperator_MAX); +} + +bool is_custom(const circle::OperatorCode *opcode) +{ + circle::BuiltinOperator code = opcode->builtin_code(); + return (code == circle::BuiltinOperator_CUSTOM); +} + +std::string opcode_name(const circle::OperatorCode *opcode) +{ + assert(opcode); + + if (!is_valid(opcode)) + { + std::ostringstream oss; + oss << "(invalid)"; + return oss.str(); + } + + if (is_custom(opcode)) + { + if (!opcode->custom_code()) + return "(invalid custom)"; + + std::string custom_op = "CUSTOM("; + custom_op += opcode->custom_code()->c_str(); + custom_op += ")"; + return custom_op; + } + + circle::BuiltinOperator code = opcode->builtin_code(); + return circle::EnumNameBuiltinOperator(code); +} + +const char *tensor_type(const circle::Tensor *tensor) +{ + return circle::EnumNameTensorType(tensor->type()); +} + +const char *tensor_name(const circle::Tensor *tensor) +{ + static const char *kEmptyTensorName = "(noname)"; + + auto name = tensor->name(); + if (name) + return name->c_str(); + + return kEmptyTensorName; +} + +Reader::Reader(const circle::Model *model) +{ + _subgraphs = model->subgraphs(); + _buffers = model->buffers(); + + auto opcodes = model->operator_codes(); + for (const ::circle::OperatorCode *opcode : *opcodes) + { + _op_codes.push_back(opcode); + } +} + +size_t Reader::buffer_info(uint32_t buf_idx, const uint8_t **buff_data) +{ + if (buff_data != nullptr) + { + *buff_data = nullptr; + } + + if (buf_idx == 0) + return 0; + + if (auto *buffer = (*_buffers)[buf_idx]) + { + if (auto *array = buffer->data()) + { + if (size_t size = array->size()) + { + if (buff_data != nullptr) + { + *buff_data = reinterpret_cast(array->data()); + } + return size; + } + } + } + + return 0; +} + +circle::BuiltinOperator Reader::builtin_code(const circle::Operator *op) const +{ + uint32_t index = op->opcode_index(); + assert(index < _op_codes.size()); + const circle::OperatorCode *opcode = _op_codes.at(index); + + return opcode->builtin_code(); +} + +std::string Reader::opcode_name(const circle::Operator *op) const +{ + uint32_t index = op->opcode_index(); + assert(index < _op_codes.size()); + const circle::OperatorCode *opcode = _op_codes.at(index); + + if (!is_valid(opcode)) + { + std::ostringstream oss; + oss << "(invalid: " << index << ")"; + return oss.str(); + } + + return circletensordump::opcode_name(opcode); +} + +bool Reader::select_subgraph(uint32_t sgindex) +{ + _tensors = nullptr; + _operators = nullptr; + + _inputs.clear(); + _outputs.clear(); + + if (_subgraphs->Length() <= sgindex) + { + assert(false); + return false; + } + + const circle::SubGraph *subgraph = (*_subgraphs)[sgindex]; + + _tensors = subgraph->tensors(); + _operators = subgraph->operators(); + + _inputs = as_index_vector(subgraph->inputs()); + _outputs = as_index_vector(subgraph->outputs()); + + return true; +} + +} // namespace circletensordump diff --git a/compiler/circle-tensordump/src/Reader.h b/compiler/circle-tensordump/src/Reader.h new file mode 100644 index 0000000..bbb0395 --- /dev/null +++ b/compiler/circle-tensordump/src/Reader.h @@ -0,0 +1,91 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __CIRCLE_TENSORDUMP_READER_H__ +#define __CIRCLE_TENSORDUMP_READER_H__ + +#include + +#include +#include +#include + +namespace circletensordump +{ + +template std::vector as_index_vector(const flatbuffers::Vector *flat_array) +{ + std::vector ret(flat_array->Length()); + for (uint32_t i = 0; i < flat_array->Length(); i++) + { + ret[i] = flat_array->Get(i); + } + return ret; +} + +bool is_valid(const circle::OperatorCode *opcode); +bool is_custom(const circle::OperatorCode *opcode); +std::string opcode_name(const circle::OperatorCode *opcode); +const char *tensor_type(const circle::Tensor *tensor); +const char *tensor_name(const circle::Tensor *tensor); + +/** + * @brief Loads Circle file and provides helpers to access attributes + */ +class Reader +{ +private: + using CircleSubGraphs_t = flatbuffers::Vector>; + using CircleBuffers_t = flatbuffers::Vector>; + using CircleTensors_t = flatbuffers::Vector>; + using CircleOperators_t = flatbuffers::Vector>; + +public: + Reader(const circle::Model *model); + + Reader() = delete; + +public: + const std::vector &opcodes() { return _op_codes; } + const CircleBuffers_t *buffers() { return _buffers; } + const CircleTensors_t *tensors() { return _tensors; } + const CircleOperators_t *operators() { return _operators; } + const std::vector &inputs() const { return _inputs; } + const std::vector &outputs() const { return _outputs; } + + uint32_t num_subgraph() const { return _subgraphs->Length(); } + + size_t buffer_info(uint32_t buf_idx, const uint8_t **buff_data); + circle::BuiltinOperator builtin_code(const circle::Operator *op) const; + std::string opcode_name(const circle::Operator *op) const; + +public: + bool select_subgraph(uint32_t subgraph); + +private: + const CircleSubGraphs_t *_subgraphs{nullptr}; + const CircleBuffers_t *_buffers{nullptr}; + const CircleTensors_t *_tensors{nullptr}; + const CircleOperators_t *_operators{nullptr}; + + std::vector _op_codes; + std::vector _inputs; + std::vector _outputs; +}; + +} // namespace circletensordump + +#endif // __CIRCLE_TENSORDUMP_READER_H__ diff --git a/compiler/circle-verify/CMakeLists.txt b/compiler/circle-verify/CMakeLists.txt index 2e19951..f221748 100644 --- a/compiler/circle-verify/CMakeLists.txt +++ b/compiler/circle-verify/CMakeLists.txt @@ -6,7 +6,8 @@ file(GLOB_RECURSE SOURCES "src/*.cpp") add_executable(circle-verify ${SOURCES}) target_include_directories(circle-verify PRIVATE src) +target_link_libraries(circle-verify arser) target_link_libraries(circle-verify mio_circle) target_link_libraries(circle-verify safemain) target_link_libraries(circle-verify cwrap) -target_link_libraries(circle-verify stdex) +target_link_libraries(circle-verify foder) diff --git a/compiler/circle-verify/requires.cmake b/compiler/circle-verify/requires.cmake index 2509b69..e1b7fb2 100644 --- a/compiler/circle-verify/requires.cmake +++ b/compiler/circle-verify/requires.cmake @@ -1,4 +1,5 @@ +require("arser") require("mio-circle") require("safemain") require("cwrap") -require("stdex") +require("foder") diff --git a/compiler/circle-verify/src/Driver.cpp b/compiler/circle-verify/src/Driver.cpp index ad13e50..1af31d9 100644 --- a/compiler/circle-verify/src/Driver.cpp +++ b/compiler/circle-verify/src/Driver.cpp @@ -16,23 +16,31 @@ #include "VerifyFlatBuffers.h" -#include +#include #include +#include #include int entry(int argc, char **argv) { - if (argc != 2) + arser::Arser arser; + arser.add_argument("circle").type(arser::DataType::STR).help("Circle file path to verify"); + + try + { + arser.parse(argc, argv); + } + catch (const std::runtime_error &err) { - std::cerr << "ERROR: Failed to parse arguments" << std::endl; - std::cerr << std::endl; - std::cerr << "USAGE: " << argv[0] << " [circle]" << std::endl; - return 255; + std::cout << err.what() << std::endl; + std::cout << arser; + return 0; } - auto verifier = stdex::make_unique(); - std::string model_file = argv[argc - 1]; + auto verifier = std::make_unique(); + + std::string model_file = arser.get("circle"); std::cout << "[ RUN ] Check " << model_file << std::endl; diff --git a/compiler/circle-verify/src/Model.cpp b/compiler/circle-verify/src/Model.cpp deleted file mode 100644 index efac121..0000000 --- a/compiler/circle-verify/src/Model.cpp +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Model.h" - -#include - -#include -#include -#include -#include - -namespace -{ - -class MemoryMappedModel final : public ModelData -{ -public: - /** - * @require fd and data SHOULD be valid - */ - explicit MemoryMappedModel(int fd, void *data, size_t size) : _fd{fd}, _data{data}, _size{size} - { - // DO NOTHING - } - -public: - ~MemoryMappedModel() - { - munmap(_data, _size); - close(_fd); - } - -public: - MemoryMappedModel(const MemoryMappedModel &) = delete; - MemoryMappedModel(MemoryMappedModel &&) = delete; - -public: - const void *data(void) const override { return _data; }; - const size_t size(void) const override { return _size; }; - -private: - int _fd = -1; - void *_data = nullptr; - size_t _size = 0; -}; - -} // namespace - -std::unique_ptr load_modeldata(const std::string &path) -{ - cwrap::Fildes fd(open(path.c_str(), O_RDONLY)); - - if (fd.get() == -1) - { - // Return nullptr on open failure - return nullptr; - } - - struct stat st; - if (fstat(fd.get(), &st) == -1) - { - // Return nullptr on fstat failure - return nullptr; - } - - auto size = st.st_size; - auto data = mmap(nullptr, size, PROT_READ, MAP_SHARED, fd.get(), 0); - - if (data == MAP_FAILED) - { - // Return nullptr on mmap failure - return nullptr; - } - - return std::unique_ptr{new MemoryMappedModel(fd.release(), data, size)}; -} diff --git a/compiler/circle-verify/src/Model.h b/compiler/circle-verify/src/Model.h deleted file mode 100644 index e1bd839..0000000 --- a/compiler/circle-verify/src/Model.h +++ /dev/null @@ -1,38 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __MODEL_H__ -#define __MODEL_H__ - -#include -#include - -struct ModelData -{ - virtual ~ModelData() = default; - - virtual const void *data(void) const = 0; - virtual const size_t size(void) const = 0; -}; - -/** - * @brief Load Circle model (as a raw data) from a given path - * - * @note May return a nullptr - */ -std::unique_ptr load_modeldata(const std::string &path); - -#endif // __MODEL_H__ diff --git a/compiler/circle-verify/src/VerifyFlatBuffers.cpp b/compiler/circle-verify/src/VerifyFlatBuffers.cpp index 36b1668..e8557d2 100644 --- a/compiler/circle-verify/src/VerifyFlatBuffers.cpp +++ b/compiler/circle-verify/src/VerifyFlatBuffers.cpp @@ -16,16 +16,16 @@ #include "VerifyFlatBuffers.h" -#include "Model.h" - +#include #include int VerifyFlatbuffers::run(const std::string &model_file) { - auto modeldata = load_modeldata(model_file); + foder::FileLoader fileLoader{model_file}; + std::vector modeldata = fileLoader.load(); - const uint8_t *data = reinterpret_cast(modeldata->data()); - flatbuffers::Verifier verifier{data, modeldata->size()}; + const uint8_t *data = reinterpret_cast(modeldata.data()); + flatbuffers::Verifier verifier{data, modeldata.size()}; if (!circle::VerifyModelBuffer(verifier)) { diff --git a/compiler/circle2circle-dredd-recipe-test/CMakeLists.txt b/compiler/circle2circle-dredd-recipe-test/CMakeLists.txt new file mode 100644 index 0000000..6663cb9 --- /dev/null +++ b/compiler/circle2circle-dredd-recipe-test/CMakeLists.txt @@ -0,0 +1,185 @@ +nnas_include(TargetRequire) + +unset(REQUIRED_TARGETS) +list(APPEND REQUIRED_TARGETS circlechef) +list(APPEND REQUIRED_TARGETS circle-inspect) +list(APPEND REQUIRED_TARGETS circle-verify) +list(APPEND REQUIRED_TARGETS circle2circle) +list(APPEND REQUIRED_TARGETS dredd_rule_lib) +list(APPEND REQUIRED_TARGETS tflchef) +list(APPEND REQUIRED_TARGETS tflite2circle) +TargetRequire_Return(${REQUIRED_TARGETS}) + +nncc_find_resource(TensorFlowLiteRecipes) +nncc_find_resource(CircleRecipes) + +set(TFLITE_RECIPE_REPO "${TensorFlowLiteRecipes_DIR}") +set(CIRCLE_RECIPE_REPO "${CircleRecipes_DIR}") +unset(RECIPE_REPO) + +set(TEST_RECIPE_FILENAME "test.recipe") +set(TEST_RULE_FILENAME "test.rule") + +unset(TEST_DEPS) +unset(TEST_NAMES) + +set(options "") +set(oneValueArgs "") +set(multiValueArgs PASS) + +macro(Add RECIPE) + if(NOT EXISTS "${TFLITE_RECIPE_REPO}/${RECIPE}/test.recipe") + if(NOT EXISTS "${CIRCLE_RECIPE_REPO}/${RECIPE}/test.recipe") + message(FATAL_ERROR "Missing recipe of '${RECIPE}' test") + else() + set(RECIPE_REPO ${CIRCLE_RECIPE_REPO}) + endif() + else() + set(RECIPE_REPO ${TFLITE_RECIPE_REPO}) + endif() + + if(NOT EXISTS "${RECIPE_REPO}/${RECIPE}/test.rule") + message(FATAL_ERROR "Missing rule of '${RECIPE}' test") + endif() + + cmake_parse_arguments(ARG "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + unset(OPT_OPTIONS) + foreach(src ${ARG_PASS}) + # option = "--${src}" + list(APPEND OPT_OPTIONS "--${src}") + endforeach(src ${ARG_PASS}) + + set(RECIPE_FILE "${RECIPE}.recipe") + set(RECIPE_SOURCE_PATH "${RECIPE_REPO}/${RECIPE}/${TEST_RECIPE_FILENAME}") + set(RECIPE_BINARY_PATH "${CMAKE_CURRENT_BINARY_DIR}/${RECIPE_FILE}") + + set(RULE_FILE "${RECIPE}.rule") + set(RULE_SOURCE_PATH "${RECIPE_REPO}/${RECIPE}/${TEST_RULE_FILENAME}") + set(RULE_BINARY_PATH "${CMAKE_CURRENT_BINARY_DIR}/${RULE_FILE}") + + set(TFLITE_FILE "${RECIPE}.tflite") + set(TFLITE_OUTPUT_PATH "${CMAKE_CURRENT_BINARY_DIR}/${TFLITE_FILE}") + + set(CIRCLE_FILE "${RECIPE}.circle") + set(CIRCLE_OUTPUT_PATH "${CMAKE_CURRENT_BINARY_DIR}/${CIRCLE_FILE}") + + set(OPT_CIRCLE_FILE "${RECIPE}.opt.circle") + set(OPT_CIRCLE_OUTPUT_PATH "${CMAKE_CURRENT_BINARY_DIR}/${OPT_CIRCLE_FILE}") + + # Copy .recipe + add_custom_command(OUTPUT ${RECIPE_BINARY_PATH} + COMMAND ${CMAKE_COMMAND} -E copy "${RECIPE_SOURCE_PATH}" "${RECIPE_BINARY_PATH}" + DEPENDS ${RECIPE_SOURCE_PATH} + COMMENT "Generate ${RECIPE_FILE}" + ) + + # Copy .rule + add_custom_command(OUTPUT ${RULE_BINARY_PATH} + COMMAND ${CMAKE_COMMAND} -E copy "${RULE_SOURCE_PATH}" "${RULE_BINARY_PATH}" + DEPENDS ${RULE_SOURCE_PATH} + COMMENT "Generate ${RULE_FILE}" + ) + + if(${RECIPE_REPO} STREQUAL ${TFLITE_RECIPE_REPO}) + # Generate .tflite + add_custom_command(OUTPUT ${TFLITE_OUTPUT_PATH} + COMMAND $ ${RECIPE_BINARY_PATH} ${TFLITE_OUTPUT_PATH} + DEPENDS $ ${RECIPE_BINARY_PATH} + COMMENT "Generate ${TFLITE_FILE}" + ) + + # Generate .circle + add_custom_command(OUTPUT ${CIRCLE_OUTPUT_PATH} + COMMAND $ ${TFLITE_OUTPUT_PATH} ${CIRCLE_OUTPUT_PATH} + DEPENDS $ ${TFLITE_OUTPUT_PATH} + COMMENT "Generate ${CIRCLE_FILE}" + ) + + list(APPEND TEST_DEPS ${TFLITE_OUTPUT_PATH}) + else() + # Generate .circle + add_custom_command(OUTPUT ${CIRCLE_OUTPUT_PATH} + COMMAND $ ${RECIPE_BINARY_PATH} ${CIRCLE_OUTPUT_PATH} + DEPENDS $ ${RECIPE_BINARY_PATH} + COMMENT "Generate ${CIRCLE_FILE}" + ) + endif() + + # Generate optimized .circle + add_custom_command(OUTPUT ${OPT_CIRCLE_OUTPUT_PATH} + COMMAND $ ${OPT_OPTIONS} ${CIRCLE_OUTPUT_PATH} ${OPT_CIRCLE_OUTPUT_PATH} + DEPENDS $ ${CIRCLE_OUTPUT_PATH} + COMMENT "Generate ${OPT_CIRCLE_FILE}" + ) + + list(APPEND TEST_DEPS ${RECIPE_BINARY_PATH} ${RULE_BINARY_PATH} + ${CIRCLE_OUTPUT_PATH} ${OPT_CIRCLE_OUTPUT_PATH}) + list(APPEND TEST_NAMES ${RECIPE}) +endmacro(Add) + +# Read "test.lst" +include("test.lst") + +## +## Copy testall +## +set(TEST_RUNNER "${CMAKE_CURRENT_BINARY_DIR}/testall.sh") +set(TEST_RUNNER_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/testall.sh") + +add_custom_command( + OUTPUT ${TEST_RUNNER} + COMMAND ${CMAKE_COMMAND} -E copy "${TEST_RUNNER_SOURCE}" "${TEST_RUNNER}" + DEPENDS ${TEST_RUNNER_SOURCE} + COMMENT "Generate test runner" +) + +list(APPEND TEST_DEPS "${TEST_RUNNER}") + +### +### Generate test.config +### +set(TEST_CONFIG "${CMAKE_CURRENT_BINARY_DIR}/test.config") + +add_custom_command( + OUTPUT ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E remove -f ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E echo 'CIRCLE_INSPECT_PATH=\"$\"' >> ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E echo 'CIRCLE_VERIFY_PATH=\"$\"' >> ${TEST_CONFIG} + DEPENDS + circle-inspect + circle-verify + COMMENT "Generate test configuration" +) + +list(APPEND TEST_DEPS "${TEST_CONFIG}") + +# +# copy rule-lib.sh (a library of shell script functions) +# + +# getting path for rule-lib.sh in dredd-rule-lib +get_target_property(DREDD_RULE_LIB_DIR dredd_rule_lib BINARY_DIR) + +set(RULE_LIB_SOURCE_PATH "${DREDD_RULE_LIB_DIR}/rule-lib.sh") +set(RULE_LIB_BINARY_PATH "${CMAKE_CURRENT_BINARY_DIR}/rule-lib.sh") + +add_custom_command( + OUTPUT ${RULE_LIB_BINARY_PATH} + COMMAND ${CMAKE_COMMAND} -E copy "${RULE_LIB_SOURCE_PATH}" "${RULE_LIB_BINARY_PATH}" + DEPENDS ${RULE_LIB_SOURCE_PATH} + COMMENT "Generate rule lib" +) + +list(APPEND TEST_DEPS "${RULE_LIB_BINARY_PATH}") + +# Generate dependencies +add_custom_target(circle2circle_dredd_recipe_test ALL DEPENDS ${TEST_DEPS}) + +# Run tests +add_test( + NAME circle2circle_dredd_recipe_test + COMMAND "${TEST_RUNNER}" + "${TEST_CONFIG}" + "${CMAKE_CURRENT_BINARY_DIR}" + ${TEST_NAMES} +) diff --git a/compiler/circle2circle-dredd-recipe-test/README.md b/compiler/circle2circle-dredd-recipe-test/README.md new file mode 100644 index 0000000..85140a8 --- /dev/null +++ b/compiler/circle2circle-dredd-recipe-test/README.md @@ -0,0 +1,21 @@ +# circle2circle-dredd-recipe-test + +It tests the non-functional conditions of the optimized circle binary resulting from circle2circle. + +This test basically refers to the _TensorFlowLiteRecipes_ resource. So you should add what you want to test to both of the resource and `test.lst`. + +## Example + +``` +# TensorFlowLiteRecipes +res/TensorFlowLiteRecipes/BatchMatMulV2_000 +├── test.recipe # What you want to test +└── test.rule # Non-functional conditions to be satisfied + +# test.lst +... +Add(BatchMatMulV2_000 PASS resolve_customop_batchmatmul) +... +``` + +For more information on the rules, see _dredd-rule-lib_ module. diff --git a/compiler/circle2circle-dredd-recipe-test/requires.cmake b/compiler/circle2circle-dredd-recipe-test/requires.cmake new file mode 100644 index 0000000..e4a5b71 --- /dev/null +++ b/compiler/circle2circle-dredd-recipe-test/requires.cmake @@ -0,0 +1,7 @@ +require("circlechef") +require("circle2circle") +require("circle-inspect") +require("circle-verify") +require("dredd-rule-lib") +require("tflchef") +require("tflite2circle") diff --git a/compiler/circle2circle-dredd-recipe-test/test.lst b/compiler/circle2circle-dredd-recipe-test/test.lst new file mode 100644 index 0000000..202f669 --- /dev/null +++ b/compiler/circle2circle-dredd-recipe-test/test.lst @@ -0,0 +1,20 @@ +## EXAMPLE +# +# Add(RECIPE_REPO PASS pass1 pass2 ...) +# +## SUPPORTED PASS +# +# fuse_instnorm +# resolve_customop_batchmatmul +# resolve_customop_matmul + +## TFLITE RECIPE + +Add(Net_InstanceNorm_001 PASS fuse_instnorm) +# Add(Net_InstanceNorm_002 PASS fuse_instnorm) +Add(BatchMatMulV2_000 PASS resolve_customop_batchmatmul) +Add(MatMul_000 PASS resolve_customop_matmul) + +## CIRCLE RECIPE + +Add(CircleBatchMatMul_000) diff --git a/compiler/circle2circle-dredd-recipe-test/testall.sh b/compiler/circle2circle-dredd-recipe-test/testall.sh new file mode 100755 index 0000000..33a2036 --- /dev/null +++ b/compiler/circle2circle-dredd-recipe-test/testall.sh @@ -0,0 +1,98 @@ +#!/bin/bash + +# Need at least 2 arguments +if [[ $# -lt 2 ]]; then + echo "USAGE: $0 ..." + echo + echo "ARGUMENTS:" + echo " [test.config path]" + echo " [WORKDIR]" + echo " [Prefix1]" + echo " [Prefix2]" + echo " ..." + exit 255 +fi + +CONFIG_PATH="$1"; shift +WORKDIR="$1"; shift + +source "${CONFIG_PATH}" + +echo "-- Found circle-inspect: ${CIRCLE_INSPECT_PATH}" +echo "-- Found circle-verify: ${CIRCLE_VERIFY_PATH}" +echo "-- Found circle2circle: ${CIRCLE2CIRCLE_PATH}" +echo "-- Found workdir: ${WORKDIR}" + +TESTED=() +PASSED=() +FAILED=() + +pushd "${WORKDIR}" +while [[ $# -ne 0 ]]; do + PREFIX="$1"; shift + + TESTED+=("${PREFIX}") + + PASSED_TAG="${PREFIX}.passed" + + rm -f "${PASSED_TAG}" + + cat > "${PREFIX}.log" <( + exec 2>&1 + + echo "-- Found tflite: ${PREFIX}.tflite" + + # Exit immediately if any command fails + set -e + # Show commands + set -x + + # + # Check if rule is satisfied + # + + # Note: turn off 'command printing'. Otherwise printing will be so messy + set +x + + # (COMPILED_FILE, INSPECT_PROG_PATH, VERIFY_PROG_PATH, ERROR_LOG) must be set for rule-lib.sh + COMPILED_FILE="${WORKDIR}/${PREFIX}.opt.circle" + INSPECT_PROG_PATH=${CIRCLE_INSPECT_PATH} + VERIFY_PROG_PATH=${CIRCLE_VERIFY_PATH} + ERROR_LOG="${PREFIX}.error" + + rm -f "${ERROR_LOG}" + + # in case error while running rule-lib.sh, prints error msg + trap 'echo "** ERROR **" ; cat "${ERROR_LOG}"' ERR + + source rule-lib.sh + source "${PREFIX}.rule" + + # unset + trap - ERR + set -x + + # At this point, the exit code of all commands is 0 + # If not 0, execution of this script ends because of "set -e" + touch "${PASSED_TAG}" + ) + + if [[ -f "${PASSED_TAG}" ]]; then + PASSED+=("$PREFIX") + else + FAILED+=("$PREFIX") + fi +done +popd + +if [[ ${#TESTED[@]} -ne ${#PASSED[@]} ]]; then + echo "FAILED" + for TEST in "${FAILED[@]}" + do + echo "- ${TEST}" + done + exit 255 +fi + +echo "PASSED" +exit 0 diff --git a/compiler/circle2circle/CMakeLists.txt b/compiler/circle2circle/CMakeLists.txt index 6441799..7b2bf9b 100644 --- a/compiler/circle2circle/CMakeLists.txt +++ b/compiler/circle2circle/CMakeLists.txt @@ -5,18 +5,20 @@ list(REMOVE_ITEM SOURCES ${TESTS}) add_executable(circle2circle "${SOURCES}") target_include_directories(circle2circle PRIVATE include) target_include_directories(circle2circle PRIVATE src) +target_link_libraries(circle2circle foder) target_link_libraries(circle2circle nncc_common) target_link_libraries(circle2circle safemain) -target_link_libraries(circle2circle stdex) target_link_libraries(circle2circle oops) target_link_libraries(circle2circle hermes) target_link_libraries(circle2circle hermes_std) target_link_libraries(circle2circle loco) target_link_libraries(circle2circle mio_circle) +target_link_libraries(circle2circle luci_env) target_link_libraries(circle2circle luci_import) target_link_libraries(circle2circle luci_service) target_link_libraries(circle2circle luci_pass) target_link_libraries(circle2circle luci_export) +target_link_libraries(circle2circle arser) install(TARGETS circle2circle DESTINATION bin) @@ -29,14 +31,16 @@ nnas_find_package(GTest REQUIRED) GTest_AddTest(circle2circle_test ${TESTS} ${SOURCES}) target_include_directories(circle2circle_test PRIVATE include) target_include_directories(circle2circle_test PRIVATE src) +target_link_libraries(circle2circle_test foder) target_link_libraries(circle2circle_test nncc_common) -target_link_libraries(circle2circle_test stdex) target_link_libraries(circle2circle_test oops) target_link_libraries(circle2circle_test hermes) target_link_libraries(circle2circle_test hermes_std) target_link_libraries(circle2circle_test loco) target_link_libraries(circle2circle_test mio_circle) +target_link_libraries(circle2circle_test luci_env) target_link_libraries(circle2circle_test luci_import) target_link_libraries(circle2circle_test luci_service) target_link_libraries(circle2circle_test luci_pass) target_link_libraries(circle2circle_test luci_export) +target_link_libraries(circle2circle_test arser) diff --git a/compiler/circle2circle/README.md b/compiler/circle2circle/README.md index 7bc1b7f..3e94d25 100644 --- a/compiler/circle2circle/README.md +++ b/compiler/circle2circle/README.md @@ -1,3 +1,3 @@ # circle2circle -_circle2circle_ provides Circle optimizations and quantizations as executable tool +_circle2circle_ provides Circle optimizations as executable tool diff --git a/compiler/circle2circle/requires.cmake b/compiler/circle2circle/requires.cmake index 5b1e657..8cbb90d 100644 --- a/compiler/circle2circle/requires.cmake +++ b/compiler/circle2circle/requires.cmake @@ -1,10 +1,11 @@ +require("foder") require("loco") require("locop") require("logo-core") -require("stdex") require("safemain") require("mio-circle") require("oops") require("hermes") require("hermes-std") require("luci") +require("arser") diff --git a/compiler/circle2circle/src/Circle2Circle.cpp b/compiler/circle2circle/src/Circle2Circle.cpp index 781825f..6888d26 100644 --- a/compiler/circle2circle/src/Circle2Circle.cpp +++ b/compiler/circle2circle/src/Circle2Circle.cpp @@ -14,91 +14,145 @@ * limitations under the License. */ -#include "Model.h" #include "CircleExpContract.h" +#include + #include #include #include #include +#include -#include #include +#include #include #include -#include #include -using OptionHook = std::function; - using Algorithms = luci::CircleOptimizer::Options::Algorithm; - -void print_help(const char *progname) -{ - std::cerr << "USAGE: " << progname << " [options] input output" << std::endl; - std::cerr << " --fuse_instnorm : Enable FuseInstanceNormalization Pass" << std::endl; - std::cerr << std::endl; -} +using AlgorithmParameters = luci::CircleOptimizer::Options::AlgorithmParameters; int entry(int argc, char **argv) { - if (argc < 3) - { - std::cerr << "ERROR: Failed to parse arguments" << std::endl; - std::cerr << std::endl; - print_help(argv[0]); - return 255; - } - // Simple argument parser (based on map) - std::map argparse; luci::CircleOptimizer optimizer; auto options = optimizer.options(); - - // TODO merge this with help message - argparse["--fuse_instnorm"] = [&options](const char **) { - options->enable(Algorithms::FuseInstanceNorm); - return 0; - }; - - for (int n = 1; n < argc - 2; ++n) + auto settings = luci::UserSettings::settings(); + + arser::Arser arser("circle2circle provides circle model optimization and transformations"); + + arser.add_argument("--all").nargs(0).required(false).default_value(false).help( + "Enable all optimize options"); + + arser.add_argument("--fuse_bcq") + .nargs(0) + .required(false) + .default_value(false) + .help("This will fuse operators and apply Binary Coded Quantization"); + + arser.add_argument("--fuse_instnorm") + .nargs(0) + .required(false) + .default_value(false) + .help("This will fuse operators to InstanceNorm operator"); + + arser.add_argument("--resolve_customop_add") + .nargs(0) + .required(false) + .default_value(false) + .help("This will convert Custom(Add) to Add operator"); + + arser.add_argument("--resolve_customop_batchmatmul") + .nargs(0) + .required(false) + .default_value(false) + .help("This will convert Custom(BatchMatmul) to BatchMatmul operator"); + + arser.add_argument("--resolve_customop_matmul") + .nargs(0) + .required(false) + .default_value(false) + .help("This will convert Custom(Matmul) to Matmul operator"); + + arser.add_argument("--mute_warnings") + .nargs(0) + .required(false) + .default_value(false) + .help("This will turn off warning messages"); + + arser.add_argument("--disable_validation") + .nargs(0) + .required(false) + .default_value(false) + .help("This will turn off operator vaidations. May help input model investigation."); + + arser.add_argument("input").nargs(1).type(arser::DataType::STR).help("Input circle model"); + arser.add_argument("output").nargs(1).type(arser::DataType::STR).help("Output circle model"); + + try { - const std::string tag{argv[n]}; - auto it = argparse.find(tag); - if (it == argparse.end()) - { - std::cerr << "Option '" << tag << "' is not supported" << std::endl; - std::cerr << std::endl; - print_help(argv[0]); - return 255; - } + arser.parse(argc, argv); + } + catch (const std::runtime_error &err) + { + std::cout << err.what() << std::endl; + std::cout << arser; + return 255; + } - n += it->second((const char **)&argv[n + 1]); + if (arser.get("--all")) + { + options->enable(Algorithms::FuseBCQ); + options->enable(Algorithms::FuseInstanceNorm); + options->enable(Algorithms::ResolveCustomOpAdd); + options->enable(Algorithms::ResolveCustomOpBatchMatMul); + options->enable(Algorithms::ResolveCustomOpMatMul); } + if (arser.get("--fuse_bcq")) + options->enable(Algorithms::FuseBCQ); + if (arser.get("--fuse_instnorm")) + options->enable(Algorithms::FuseInstanceNorm); + if (arser.get("--resolve_customop_add")) + options->enable(Algorithms::ResolveCustomOpAdd); + if (arser.get("--resolve_customop_batchmatmul")) + options->enable(Algorithms::ResolveCustomOpBatchMatMul); + if (arser.get("--resolve_customop_matmul")) + options->enable(Algorithms::ResolveCustomOpMatMul); + + if (arser.get("--mute_warnings")) + settings->set(luci::UserSettings::Key::MuteWarnings, true); + if (arser.get("--disable_validation")) + settings->set(luci::UserSettings::Key::DisableValidation, true); - std::string input_path = argv[argc - 2]; - std::string output_path = argv[argc - 1]; + std::string input_path = arser.get("input"); + std::string output_path = arser.get("output"); // Load model from the file - std::unique_ptr model = luci::load_model(input_path); - if (model == nullptr) + foder::FileLoader file_loader{input_path}; + std::vector model_data; + + try { - std::cerr << "ERROR: Failed to load '" << input_path << "'" << std::endl; - return 255; + model_data = file_loader.load(); } - - const circle::Model *input_model = model->model(); - if (input_model == nullptr) + catch (const std::runtime_error &err) { - std::cerr << "ERROR: Failed to read '" << input_path << "'" << std::endl; - return 255; + std::cerr << err.what() << std::endl; + return EXIT_FAILURE; + } + const circle::Model *circle_model = circle::GetModel(model_data.data()); + if (circle_model == nullptr) + { + std::cerr << "ERROR: Failed to load circle '" << input_path << "'" << std::endl; + return EXIT_FAILURE; } // Import from input Circle file luci::Importer importer; - auto module = importer.importModule(input_model); + auto module = importer.importModule(circle_model); for (size_t idx = 0; idx < module->size(); ++idx) { @@ -108,7 +162,10 @@ int entry(int argc, char **argv) optimizer.optimize(graph); if (!luci::validate(graph)) + { + std::cerr << "ERROR: Optimized graph is invalid" << std::endl; return 255; + } } // Export to output Circle file @@ -116,5 +173,11 @@ int entry(int argc, char **argv) CircleExpContract contract(module.get(), output_path); - return exporter.invoke(&contract) ? 0 : 255; + if (!exporter.invoke(&contract)) + { + std::cerr << "ERROR: Failed to export '" << output_path << "'" << std::endl; + return 255; + } + + return 0; } diff --git a/compiler/circle2circle/src/Circle2Circle.test.cpp b/compiler/circle2circle/src/Circle2Circle.test.cpp index 015358a..d8a3ed0 100644 --- a/compiler/circle2circle/src/Circle2Circle.test.cpp +++ b/compiler/circle2circle/src/Circle2Circle.test.cpp @@ -25,5 +25,5 @@ TEST(Circle2CircleTest, NoArg_NEG) ::testing::internal::CaptureStdout(); int result = entry(1, argv.argv()); - ASSERT_EQ(result, 255); + ASSERT_EQ(255, result); } diff --git a/compiler/circle2circle/src/Model.cpp b/compiler/circle2circle/src/Model.cpp deleted file mode 100644 index 20d55a1..0000000 --- a/compiler/circle2circle/src/Model.cpp +++ /dev/null @@ -1,78 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Model.h" - -#include -#include - -#include -#include -#include - -namespace -{ - -class FileModel final : public luci::Model -{ -public: - explicit FileModel(const std::string &filename) : _filename(filename) {} - -public: - FileModel(const FileModel &) = delete; - FileModel(FileModel &&) = delete; - -public: - const ::circle::Model *model(void) override - { - std::ifstream file(_filename, std::ios::binary | std::ios::in); - if (!file.good()) - return nullptr; - - file.unsetf(std::ios::skipws); - - std::streampos fileSize; - file.seekg(0, std::ios::end); - fileSize = file.tellg(); - file.seekg(0, std::ios::beg); - - // reserve capacity - _data.reserve(fileSize); - - // read the data - file.read(_data.data(), fileSize); - if (file.fail()) - return nullptr; - - return ::circle::GetModel(_data.data()); - } - -private: - const std::string _filename; - std::vector _data; -}; - -} // namespace - -namespace luci -{ - -std::unique_ptr load_model(const std::string &path) -{ - return std::unique_ptr{new FileModel(path)}; -} - -} // namespace luci diff --git a/compiler/circlechef/CMakeLists.txt b/compiler/circlechef/CMakeLists.txt new file mode 100644 index 0000000..cba7d0a --- /dev/null +++ b/compiler/circlechef/CMakeLists.txt @@ -0,0 +1,21 @@ +nnas_find_package(Protobuf QUIET) + +if(NOT Protobuf_FOUND) + return() +endif(NOT Protobuf_FOUND) + +if(NOT TARGET mio_circle) + return() +endif(NOT TARGET mio_circle) + +# Recipe Parser +add_subdirectory(proto) +# Log +add_subdirectory(log) +# Core Library +add_subdirectory(core) +# Circle Library +add_subdirectory(circle) +# Tools +add_subdirectory(tools) +add_subdirectory(tests) diff --git a/compiler/circlechef/README.md b/compiler/circlechef/README.md new file mode 100644 index 0000000..1871a06 --- /dev/null +++ b/compiler/circlechef/README.md @@ -0,0 +1,8 @@ +# circlechef + +## What is circlechef? + +Do you need a circle model for testing? Ask it to _circlechef_. +Given a recipe, _circlechef_ will cook a circle model for you. + +**NOTE** _circlechef_ covers only what _tflchef_ does not cover. This is to support ops that exist only in circle shema, and other things can be made using _tflchef_ and _tflite2circle_. diff --git a/compiler/circlechef/circle/CMakeLists.txt b/compiler/circlechef/circle/CMakeLists.txt new file mode 100644 index 0000000..75165ad --- /dev/null +++ b/compiler/circlechef/circle/CMakeLists.txt @@ -0,0 +1,9 @@ +file(GLOB_RECURSE SOURCES "src/*.cpp") + +add_library(circlechef_circle STATIC ${SOURCES}) +target_include_directories(circlechef_circle PUBLIC include) +target_include_directories(circlechef_circle PRIVATE src) +target_link_libraries(circlechef_circle circlechef_proto) +target_link_libraries(circlechef_circle mio_circle) +target_link_libraries(circlechef_circle stdex) +target_link_libraries(circlechef_circle cwrap) diff --git a/compiler/circlechef/circle/include/circlechef/RecipeChef.h b/compiler/circlechef/circle/include/circlechef/RecipeChef.h new file mode 100644 index 0000000..d3cafa2 --- /dev/null +++ b/compiler/circlechef/circle/include/circlechef/RecipeChef.h @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __RECIPE_CHEF_H__ +#define __RECIPE_CHEF_H__ + +#include +#include + +#include +#include + +namespace circlechef +{ + +/** + * @brief Create ModelRecipe from circle::Model + */ +std::unique_ptr generate_recipe(const circle::Model *model); + +/** + * @brief Write ModelRecipe to file with given name + */ +bool write_recipe(const std::string &filename, std::unique_ptr &recipe); + +} // namespace circlechef + +#endif // __RECIPE_CHEF_H__ diff --git a/compiler/circlechef/circle/src/CircleImport.cpp b/compiler/circlechef/circle/src/CircleImport.cpp new file mode 100644 index 0000000..e970fbc --- /dev/null +++ b/compiler/circlechef/circle/src/CircleImport.cpp @@ -0,0 +1,145 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "CircleImport.h" + +#include "Convert.h" + +#include + +namespace circlechef +{ + +const char *kEmptyTensorName = "(noname)"; + +const char *tensor_type(const circle::Tensor *tensor) +{ + return circle::EnumNameTensorType(tensor->type()); +} + +const char *tensor_name(const circle::Tensor *tensor) +{ + auto name = tensor->name(); + if (name) + return name->c_str(); + return kEmptyTensorName; +} + +bool is_valid(const circle::OperatorCode *opcode) +{ + circle::BuiltinOperator code = opcode->builtin_code(); + return (circle::BuiltinOperator_MIN <= code && code <= circle::BuiltinOperator_MAX); +} + +bool is_custom(const circle::OperatorCode *opcode) +{ + circle::BuiltinOperator code = opcode->builtin_code(); + return (code == circle::BuiltinOperator_CUSTOM); +} + +CircleImport::CircleImport(const circle::Model *model) +{ + _subgraphs = model->subgraphs(); + _buffers = model->buffers(); + + auto opcodes = model->operator_codes(); + for (const ::circle::OperatorCode *opcode : *opcodes) + { + _op_codes.push_back(opcode); + } +} + +bool CircleImport::select_sub_graph(uint32_t sgindex) +{ + _tensors = nullptr; + _operators = nullptr; + _inputs.clear(); + _outputs.clear(); + + if (_subgraphs->Length() <= sgindex) + { + assert(false); + return false; + } + + const circle::SubGraph *subgraph = (*_subgraphs)[sgindex]; + + _tensors = subgraph->tensors(); + _operators = subgraph->operators(); + + _inputs = as_index_vector(subgraph->inputs()); + _outputs = as_index_vector(subgraph->outputs()); + + return true; +} + +circle::BuiltinOperator CircleImport::builtin_code(const circle::Operator *op) const +{ + uint32_t index = op->opcode_index(); + assert(index < _op_codes.size()); + const circle::OperatorCode *opcode = _op_codes.at(index); + + return opcode->builtin_code(); +} + +std::string CircleImport::opcode_name(const circle::Operator *op) const +{ + uint32_t index = op->opcode_index(); + assert(index < _op_codes.size()); + const circle::OperatorCode *opcode = _op_codes.at(index); + + if (!is_valid(opcode)) + { + std::ostringstream oss; + oss << "(invalid: " << index << ")"; + return oss.str(); + } + + if (is_custom(opcode)) + { + if (!opcode->custom_code()) + return "(invalid custom)"; + + return opcode->custom_code()->c_str(); + } + + circle::BuiltinOperator code = opcode->builtin_code(); + return EnumNameBuiltinOperator(code); +} + +size_t CircleImport::buffer_info(const circle::Tensor *tensor, const uint8_t **buff_data) +{ + *buff_data = nullptr; + + if (tensor->buffer() == 0) + return 0; + + if (auto *buffer = (*_buffers)[tensor->buffer()]) + { + if (auto *array = buffer->data()) + { + if (size_t size = array->size()) + { + *buff_data = reinterpret_cast(array->data()); + return size; + } + } + } + + return 0; +} + +} // namespace circlechef diff --git a/compiler/circlechef/circle/src/CircleImport.h b/compiler/circlechef/circle/src/CircleImport.h new file mode 100644 index 0000000..a8ef3ee --- /dev/null +++ b/compiler/circlechef/circle/src/CircleImport.h @@ -0,0 +1,140 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __CIRCLE_IMPORT_H__ +#define __CIRCLE_IMPORT_H__ + +#include + +#include + +#include +#include + +namespace circlechef +{ + +using CircleSubGraphs_t = flatbuffers::Vector>; +using CircleTensors_t = flatbuffers::Vector>; +using CircleBuffers_t = flatbuffers::Vector>; +using CircleOperators_t = flatbuffers::Vector>; + +const char *tensor_type(const circle::Tensor *tensor); +const char *tensor_name(const circle::Tensor *tensor); +bool is_valid(const circle::OperatorCode *opcode); +bool is_custom(const circle::OperatorCode *opcode); + +/** + * @brief Loads TF lite file and provides helpers to access attributes + */ +class CircleImport +{ +public: + CircleImport(const circle::Model *model); + + CircleImport() = delete; + +public: + bool select_sub_graph(uint32_t subgraph); + +public: + const CircleBuffers_t *buffers() { return _buffers; } + const CircleTensors_t *tensors() { return _tensors; } + const CircleOperators_t *operators() { return _operators; } + const std::vector &inputs() const { return _inputs; } + const std::vector &outputs() const { return _outputs; } + + uint32_t num_subgraph() const { return _subgraphs->Length(); } + + circle::BuiltinOperator builtin_code(const circle::Operator *op) const; + std::string opcode_name(const circle::Operator *op) const; + size_t buffer_info(const circle::Tensor *tensor, const uint8_t **buff_data); + + /** + * @brief This will record the tensor by index, if it needs filler option, + * such as kernel, bias. + */ + void set_tensor_filler(uint32_t tensor_index) { _tensor_filler[tensor_index] = true; } + + /** + * @brief This will store int32 filler values such as reshape information for the tensor + */ + void set_tensor_filler(uint32_t tensor_index, std::vector &expvalues) + { + _tensor_filler_vint32[tensor_index] = expvalues; + } + + void set_tensor_filler(uint32_t tensor_index, std::vector &expvalues) + { + _tensor_filler_vfloat[tensor_index] = expvalues; + } + + /** + * @brief This will return true if the tensor by index, needs a filler option. + */ + bool get_tensor_filler(uint32_t tensor_index) + { + auto it = _tensor_filler.find(tensor_index); + if (it != _tensor_filler.end()) + { + return it->second; + } + return false; + } + + /** + * @brief This will return true if the tensor by index, needs a int array filler option. + */ + bool get_tensor_filler(uint32_t tensor_index, std::vector &expvalues) + { + auto it = _tensor_filler_vint32.find(tensor_index); + if (it != _tensor_filler_vint32.end()) + { + expvalues = it->second; + return true; + } + return false; + } + + bool get_tensor_filler(uint32_t tensor_index, std::vector &expvalues) + { + auto it = _tensor_filler_vfloat.find(tensor_index); + if (it != _tensor_filler_vfloat.end()) + { + expvalues = it->second; + return true; + } + return false; + } + +private: + const CircleSubGraphs_t *_subgraphs{nullptr}; + const CircleBuffers_t *_buffers{nullptr}; + const CircleTensors_t *_tensors{nullptr}; + const CircleOperators_t *_operators{nullptr}; + + std::vector _op_codes{}; + std::vector _inputs{}; + std::vector _outputs{}; + + std::map _tensor_filler{}; + std::map> _tensor_filler_vint32{}; + std::map> _tensor_filler_vfloat{}; +}; + +} // namespace circlechef + +#endif // __CIRCLE_IMPORT_H__ diff --git a/compiler/circlechef/circle/src/CircleOpChef.h b/compiler/circlechef/circle/src/CircleOpChef.h new file mode 100644 index 0000000..a3bcd97 --- /dev/null +++ b/compiler/circlechef/circle/src/CircleOpChef.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __CIRCLE_OP_CHEF_H__ +#define __CIRCLE_OP_CHEF_H__ + +#include + +#include + +#include "CircleImport.h" + +namespace circlechef +{ + +/** + * @brief Interface for each operators to build circlechef + */ +class CircleOpChef +{ +public: + virtual void filler(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const = 0; + virtual ::circlechef::Operation *build(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const = 0; + virtual ~CircleOpChef() {} +}; + +} // namespace circlechef + +#endif // __CIRCLE_OP_CHEF_H__ diff --git a/compiler/circlechef/circle/src/CircleOpChefs.h b/compiler/circlechef/circle/src/CircleOpChefs.h new file mode 100644 index 0000000..6a0ce5d --- /dev/null +++ b/compiler/circlechef/circle/src/CircleOpChefs.h @@ -0,0 +1,26 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __CIRCLE_OP_CHEFS_H__ +#define __CIRCLE_OP_CHEFS_H__ + +// In alphabet order +#include "Op/BatchMatMul.h" +#include "Op/BCQFullyConnected.h" +#include "Op/BCQGather.h" +#include "Op/InstanceNorm.h" + +#endif // __CIRCLE_OP_CHEFS_H__ diff --git a/compiler/circlechef/circle/src/CircleOpRegistry.h b/compiler/circlechef/circle/src/CircleOpRegistry.h new file mode 100644 index 0000000..2bf1e19 --- /dev/null +++ b/compiler/circlechef/circle/src/CircleOpRegistry.h @@ -0,0 +1,71 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __CIRCLE_OP_REGISTRY_H__ +#define __CIRCLE_OP_REGISTRY_H__ + +#include "CircleOpChef.h" +#include "CircleOpChefs.h" + +#include + +namespace circlechef +{ + +/** + * @brief circlechef operator registry + */ +class CircleOpRegistry +{ +public: + /** + * @brief Returns registered CircleOpChef pointer for BuiltinOperator or + * nullptr if not registered + */ + const CircleOpChef *lookup(circle::BuiltinOperator op) const + { + if (_circleop_map.find(op) == _circleop_map.end()) + return nullptr; + + return _circleop_map.at(op).get(); + } + + static CircleOpRegistry &get() + { + static CircleOpRegistry me; + return me; + } + +private: + CircleOpRegistry() + { +#define REG_TFL_OP(OPCODE, CLASS) \ + _circleop_map[circle::BuiltinOperator_##OPCODE] = std::make_unique() + + REG_TFL_OP(BATCH_MATMUL, CircleOpBatchMatMul); + REG_TFL_OP(BCQ_FULLY_CONNECTED, CircleOpBCQFullyConnected); + REG_TFL_OP(BCQ_GATHER, CircleOpBCQGather); + REG_TFL_OP(INSTANCE_NORM, CircleOpInstanceNorm); +#undef REG_TFL_OP + } + +private: + std::map> _circleop_map; +}; + +} // namespace circlechef + +#endif // __CIRCLE_OP_REGISTRY_H__ diff --git a/compiler/circlechef/circle/src/Convert.cpp b/compiler/circlechef/circle/src/Convert.cpp new file mode 100644 index 0000000..77614d9 --- /dev/null +++ b/compiler/circlechef/circle/src/Convert.cpp @@ -0,0 +1,78 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Convert.h" + +namespace circlechef +{ + +circlechef::TensorType as_circlechef_type(const circle::TensorType type) +{ + switch (type) + { + case circle::TensorType_FLOAT32: + return circlechef::FLOAT32; + case circle::TensorType_INT32: + return circlechef::INT32; + case circle::TensorType_INT64: + return circlechef::INT64; + case circle::TensorType_UINT8: + return circlechef::UINT8; + case circle::TensorType_BOOL: + return circlechef::BOOL; + // TODO handle other types + // TensorType_FLOAT16 + // TensorType_STRING + // TensorType_INT16 + // TensorType_COMPLEX64 + default: + throw std::runtime_error{"unsupported tensor type"}; + } +} + +circlechef::Activation as_circlechef_activation(const circle::ActivationFunctionType type) +{ + switch (type) + { + case circle::ActivationFunctionType_NONE: + return circlechef::NONE; + case circle::ActivationFunctionType_RELU: + return circlechef::RELU; + case circle::ActivationFunctionType_RELU6: + return circlechef::RELU6; + // TODO handle other types + // ActivationFunctionType_RELU_N1_TO_1 + // ActivationFunctionType_TANH + // ActivationFunctionType_SIGN_BIT + default: + throw std::runtime_error{"unsupported activation type"}; + } +} + +circlechef::Padding as_circlechef_padding(const circle::Padding padding) +{ + switch (padding) + { + case circle::Padding_SAME: + return circlechef::SAME; + case circle::Padding_VALID: + return circlechef::VALID; + default: + throw std::runtime_error{"unsupported padding"}; + } +} + +} // namespace circlechef diff --git a/compiler/circlechef/circle/src/Convert.h b/compiler/circlechef/circle/src/Convert.h new file mode 100644 index 0000000..7842c4b --- /dev/null +++ b/compiler/circlechef/circle/src/Convert.h @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __CONVERT_H__ +#define __CONVERT_H__ + +#include + +#include + +namespace circlechef +{ + +circlechef::TensorType as_circlechef_type(const circle::TensorType type); +circlechef::Activation as_circlechef_activation(const circle::ActivationFunctionType type); +circlechef::Padding as_circlechef_padding(const circle::Padding padding); + +/** + * @brief extract buffer data to std::vector
+ */ +template std::vector
extract_buffer(const circle::Buffer *buffer) +{ + auto buffer_length = buffer->data()->size(); + auto num_elements = buffer_length / sizeof(DT); + std::vector
result(num_elements); + std::memcpy(result.data(), buffer->data()->data(), buffer_length); + return result; +} + +template std::vector as_index_vector(const flatbuffers::Vector *flat_array) +{ + if (flat_array == nullptr) + throw std::runtime_error("flat_array is nullptr"); + + std::vector ret(flat_array->Length()); + for (uint32_t i = 0; i < flat_array->Length(); i++) + { + ret[i] = flat_array->Get(i); + } + return ret; +} + +} // namespace circlechef + +#endif // __CONVERT_H__ diff --git a/compiler/circlechef/circle/src/Op/BCQFullyConnected.cpp b/compiler/circlechef/circle/src/Op/BCQFullyConnected.cpp new file mode 100644 index 0000000..0e85f39 --- /dev/null +++ b/compiler/circlechef/circle/src/Op/BCQFullyConnected.cpp @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "BCQFullyConnected.h" + +#include "Convert.h" + +namespace circlechef +{ + +void CircleOpBCQFullyConnected::filler(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const +{ + const std::vector &inputs = as_index_vector(op->inputs()); + + import->set_tensor_filler(inputs[1]); + import->set_tensor_filler(inputs[3]); + + const circle::Tensor *tensor2 = import->tensors()->Get(inputs[2]); + assert(tensor2->type() == circle::TensorType::TensorType_INT32); + const circle::Buffer *buffer2 = import->buffers()->Get(tensor2->buffer()); + auto vec2 = extract_buffer(buffer2); + import->set_tensor_filler(inputs[2], vec2); + + const circle::Tensor *tensor4 = import->tensors()->Get(inputs[4]); + assert(tensor4->type() == circle::TensorType::TensorType_INT32); + const circle::Buffer *buffer4 = import->buffers()->Get(tensor4->buffer()); + auto vec4 = extract_buffer(buffer4); + import->set_tensor_filler(inputs[4], vec4); +} + +circlechef::Operation *CircleOpBCQFullyConnected::build(const circle::Operator *op, + CircleImport *import, + circlechef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_BCQFullyConnectedOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("BCQFullyConnected"); + + auto op_options = operation->mutable_bcq_fully_connected_options(); + + op_options->set_weights_hidden_size(op_params->weights_hidden_size()); + op_options->set_activation(as_circlechef_activation(op_params->fused_activation_function())); + + return operation; +} + +} // namespace circlechef diff --git a/compiler/circlechef/circle/src/Op/BCQFullyConnected.h b/compiler/circlechef/circle/src/Op/BCQFullyConnected.h new file mode 100644 index 0000000..c0ea581 --- /dev/null +++ b/compiler/circlechef/circle/src/Op/BCQFullyConnected.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __CIRCLE_OP_BCQFULLYCONNECTED_H__ +#define __CIRCLE_OP_BCQFULLYCONNECTED_H__ + +#include "CircleOpChef.h" + +namespace circlechef +{ + +/** + * @brief circlechef operator builder for BCQFullyConnected + */ +class CircleOpBCQFullyConnected : public CircleOpChef +{ +public: + void filler(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const override; + circlechef::Operation *build(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const override; +}; + +} // namespace circlechef + +#endif // __CIRCLE_OP_BCQFULLYCONNECTED_H__ diff --git a/compiler/circlechef/circle/src/Op/BCQGather.cpp b/compiler/circlechef/circle/src/Op/BCQGather.cpp new file mode 100644 index 0000000..cde345a --- /dev/null +++ b/compiler/circlechef/circle/src/Op/BCQGather.cpp @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "BCQGather.h" + +#include "Convert.h" + +namespace circlechef +{ + +void CircleOpBCQGather::filler(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const +{ + const std::vector &inputs = as_index_vector(op->inputs()); + + import->set_tensor_filler(inputs[0]); + + const circle::Tensor *tensor1 = import->tensors()->Get(inputs[1]); + assert(tensor1->type() == circle::TensorType::TensorType_INT32); + const circle::Buffer *buffer1 = import->buffers()->Get(tensor1->buffer()); + auto vec1 = extract_buffer(buffer1); + import->set_tensor_filler(inputs[1], vec1); + + const circle::Tensor *tensor2 = import->tensors()->Get(inputs[2]); + assert(tensor2->type() == circle::TensorType::TensorType_INT32); + const circle::Buffer *buffer2 = import->buffers()->Get(tensor2->buffer()); + auto vec2 = extract_buffer(buffer2); + import->set_tensor_filler(inputs[2], vec2); + + const circle::Tensor *tensor3 = import->tensors()->Get(inputs[3]); + assert(tensor3->type() == circle::TensorType::TensorType_INT32); + const circle::Buffer *buffer3 = import->buffers()->Get(tensor3->buffer()); + auto vec3 = extract_buffer(buffer3); + import->set_tensor_filler(inputs[3], vec3); +} + +circlechef::Operation *CircleOpBCQGather::build(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_BCQGatherOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("BCQGather"); + + auto op_options = operation->mutable_bcq_gather_options(); + + op_options->set_input_hidden_size(op_params->input_hidden_size()); + op_options->set_axis(op_params->axis()); + + return operation; +} + +} // namespace circlechef diff --git a/compiler/circlechef/circle/src/Op/BCQGather.h b/compiler/circlechef/circle/src/Op/BCQGather.h new file mode 100644 index 0000000..0ff0405 --- /dev/null +++ b/compiler/circlechef/circle/src/Op/BCQGather.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __CIRCLE_OP_BCQGATHER_H__ +#define __CIRCLE_OP_BCQGATHER_H__ + +#include "CircleOpChef.h" + +namespace circlechef +{ + +/** + * @brief circlechef operator builder for BCQGather + */ +class CircleOpBCQGather : public CircleOpChef +{ +public: + void filler(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const override; + circlechef::Operation *build(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const override; +}; + +} // namespace circlechef + +#endif // __CIRCLE_OP_BCQGATHER_H__ diff --git a/compiler/circlechef/circle/src/Op/BatchMatMul.cpp b/compiler/circlechef/circle/src/Op/BatchMatMul.cpp new file mode 100644 index 0000000..bcf2188 --- /dev/null +++ b/compiler/circlechef/circle/src/Op/BatchMatMul.cpp @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "BatchMatMul.h" + +#include "Convert.h" + +namespace circlechef +{ + +void CircleOpBatchMatMul::filler(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +circlechef::Operation *CircleOpBatchMatMul::build(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_BatchMatMulOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("BatchMatMul"); + + auto op_options = operation->mutable_batch_matmul_options(); + + op_options->set_adjoint_lhs(op_params->adjoint_lhs()); + op_options->set_adjoint_rhs(op_params->adjoint_rhs()); + + return operation; +} + +} // namespace circlechef diff --git a/compiler/circlechef/circle/src/Op/BatchMatMul.h b/compiler/circlechef/circle/src/Op/BatchMatMul.h new file mode 100644 index 0000000..3d40368 --- /dev/null +++ b/compiler/circlechef/circle/src/Op/BatchMatMul.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __CIRCLE_OP_BATCHMATMUL_H__ +#define __CIRCLE_OP_BATCHMATMUL_H__ + +#include "CircleOpChef.h" + +namespace circlechef +{ + +/** + * @brief circlechef operator builder for batchmatmul + */ +class CircleOpBatchMatMul : public CircleOpChef +{ +public: + void filler(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const override; + circlechef::Operation *build(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const override; +}; + +} // namespace circlechef + +#endif // __CIRCLE_OP_BATCHMATMUL_H__ diff --git a/compiler/circlechef/circle/src/Op/InstanceNorm.cpp b/compiler/circlechef/circle/src/Op/InstanceNorm.cpp new file mode 100644 index 0000000..a1395a5 --- /dev/null +++ b/compiler/circlechef/circle/src/Op/InstanceNorm.cpp @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "InstanceNorm.h" + +#include "Convert.h" + +namespace circlechef +{ + +void CircleOpInstanceNorm::filler(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const +{ + // index 1 and 2 maybe constant + const std::vector &inputs = as_index_vector(op->inputs()); + assert(inputs.size() == 3); + + import->set_tensor_filler(inputs[1]); // set gaussian filler + import->set_tensor_filler(inputs[2]); +} + +circlechef::Operation *CircleOpInstanceNorm::build(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("InstanceNorm"); + + auto op_options = operation->mutable_instance_norm_options(); + + auto op_params = op->builtin_options_as_InstanceNormOptions(); + assert(op_params != nullptr); + + op_options->set_epsilon(op_params->epsilon()); + op_options->set_activation(as_circlechef_activation(op_params->fused_activation_function())); + + return operation; +} + +} // namespace circlechef diff --git a/compiler/circlechef/circle/src/Op/InstanceNorm.h b/compiler/circlechef/circle/src/Op/InstanceNorm.h new file mode 100644 index 0000000..9cb48e1 --- /dev/null +++ b/compiler/circlechef/circle/src/Op/InstanceNorm.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __CIRCLE_OP_INSTANCE_NORM_H__ +#define __CIRCLE_OP_INSTANCE_NORM_H__ + +#include "CircleOpChef.h" + +namespace circlechef +{ + +/** + * @brief circlechef operator builder for INSTANCE_NORM + */ +class CircleOpInstanceNorm : public CircleOpChef +{ +public: + void filler(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const override; + circlechef::Operation *build(const circle::Operator *op, CircleImport *import, + circlechef::ModelRecipe *model_recipe) const override; +}; + +} // namespace circlechef + +#endif // __CIRCLE_OP_INSTANCE_NORM_H__ diff --git a/compiler/circlechef/circle/src/RecipeChef.cpp b/compiler/circlechef/circle/src/RecipeChef.cpp new file mode 100644 index 0000000..17ef1be --- /dev/null +++ b/compiler/circlechef/circle/src/RecipeChef.cpp @@ -0,0 +1,248 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include + +#include "Convert.h" +#include "CircleImport.h" +#include "CircleOpChef.h" +#include "CircleOpChefs.h" +#include "CircleOpRegistry.h" + +#include +#include + +namespace circlechef +{ + +void set_inputs(CircleImport *import, circlechef::Operation *operation, const circle::Operator *op) +{ + auto tensors = import->tensors(); + const std::vector &inputs = as_index_vector(op->inputs()); + + for (auto input : inputs) + { + if (input == -1) + { + operation->add_input(""); + } + else + { + auto tensor = tensors->Get(input); + std::string name = tensor_name(tensor); + operation->add_input(name); + } + } +} + +void set_outputs(CircleImport *import, circlechef::Operation *operation, const circle::Operator *op) +{ + auto tensors = import->tensors(); + const std::vector &outputs = as_index_vector(op->outputs()); + + for (auto output : outputs) + { + auto tensor = tensors->Get(output); + std::string name = tensor_name(tensor); + operation->add_output(name); + } +} + +/** + * @brief This will build ModelRecipe from circle::Model + * First to check operand filler options by scanning all operators, + * then translate all operands and operators. + * Last will set network inputs and outputs. + */ +std::unique_ptr generate_recipe(const circle::Model *model) +{ + std::unique_ptr model_recipe{new ModelRecipe()}; + + CircleImport circle_import(model); + + assert(circle_import.num_subgraph() == 1); + circle_import.select_sub_graph(0); + + auto tensors = circle_import.tensors(); + auto buffers = circle_import.buffers(); + auto operators = circle_import.operators(); + + // operand fillers for adding all operators + for (uint32_t i = 0; i < operators->Length(); ++i) + { + const auto *op = operators->Get(i); + circle::BuiltinOperator builtincode = circle_import.builtin_code(op); + + if (const auto *graph_builder = CircleOpRegistry::get().lookup(builtincode)) + { + graph_builder->filler(op, &circle_import, model_recipe.get()); + } + else + { + std::string opcodename = circle_import.opcode_name(op); + throw std::runtime_error{"Not supported: " + opcodename}; + } + } + + // add all operands(tensors) + for (uint32_t i = 0; i < tensors->Length(); ++i) + { + auto tensor = tensors->Get(i); + + // check buffer + if (tensor->buffer() >= buffers->size()) + throw std::runtime_error{"file load failed"}; + + ::circlechef::Operand *operand = model_recipe->add_operand(); + + operand->set_name(tensor_name(tensor)); + operand->set_type(as_circlechef_type(tensor->type())); + + std::vector dims = as_index_vector(tensor->shape()); + ::circlechef::TensorShape *shape = operand->mutable_shape(); + for (auto dim : dims) + { + shape->add_dim(dim); + } + + // filler for weights, bias and so on + std::vector expvalues; + std::vector expfvalues; + if (circle_import.get_tensor_filler(i)) + { + circlechef::TensorFiller *filler = operand->mutable_filler(); + // Note: it is OK to use random weights for functionality validation + filler->set_tag("gaussian"); + filler->add_arg("0.0"); // average + filler->add_arg("0.1"); // standard deviation + } + else if (circle_import.get_tensor_filler(i, expvalues)) + { + circlechef::TensorFiller *filler = operand->mutable_filler(); + filler->set_tag("explicit"); + for (auto value : expvalues) + { + std::ostringstream ss; + ss << value; + filler->add_arg(ss.str()); + } + } + else if (circle_import.get_tensor_filler(i, expfvalues)) + { + circlechef::TensorFiller *filler = operand->mutable_filler(); + filler->set_tag("explicit"); + for (auto value : expfvalues) + { + std::ostringstream ss; + ss << value; + filler->add_arg(ss.str()); + } + } + + auto quant = tensor->quantization(); + if (quant != nullptr) + { + // Note: Calling 'operand->mutable_quant()' will create empty 'quant' node + // in the recipe file. We want this only when valid parameter exist. + if (quant->min() != nullptr && quant->min()->size() > 0) + { + circlechef::TensorQuantization *chef_quant = operand->mutable_quant(); + for (uint32_t idx = 0; idx < quant->min()->size(); ++idx) + chef_quant->add_min(quant->min()->Get(idx)); + } + if (quant->max() != nullptr && quant->max()->size() > 0) + { + circlechef::TensorQuantization *chef_quant = operand->mutable_quant(); + for (uint32_t idx = 0; idx < quant->max()->size(); idx++) + chef_quant->add_max(quant->max()->Get(idx)); + } + if (quant->scale() != nullptr && quant->scale()->size() > 0) + { + circlechef::TensorQuantization *chef_quant = operand->mutable_quant(); + for (uint32_t idx = 0; idx < quant->scale()->size(); ++idx) + chef_quant->add_scale(quant->scale()->Get(idx)); + } + if (quant->zero_point() != nullptr && quant->zero_point()->size() > 0) + { + circlechef::TensorQuantization *chef_quant = operand->mutable_quant(); + for (uint32_t idx = 0; idx < quant->zero_point()->size(); ++idx) + chef_quant->add_zero_point(quant->zero_point()->Get(idx)); + } + } + } + + // add all operators + for (uint32_t i = 0; i < operators->Length(); ++i) + { + const auto *op = operators->Get(i); + circle::BuiltinOperator builtincode = circle_import.builtin_code(op); + + if (const auto *graph_builder = CircleOpRegistry::get().lookup(builtincode)) + { + auto operation = graph_builder->build(op, &circle_import, model_recipe.get()); + + // common for all operators: inputs, outputs + set_inputs(&circle_import, operation, op); + set_outputs(&circle_import, operation, op); + } + else + { + std::string opcodename = circle_import.opcode_name(op); + throw std::runtime_error{"Not supported: " + opcodename}; + } + } + + // network inputs/outputs + const std::vector &inputs = circle_import.inputs(); + const std::vector &outputs = circle_import.outputs(); + + for (const auto input : inputs) + { + auto tensor = tensors->Get(input); + std::string name = tensor_name(tensor); + + model_recipe->add_input(name); + } + for (const auto output : outputs) + { + auto tensor = tensors->Get(output); + std::string name = tensor_name(tensor); + + model_recipe->add_output(name); + } + + return std::move(model_recipe); +} + +bool write_recipe(const std::string &filename, std::unique_ptr &recipe) +{ + std::fstream fo(filename, std::ios::binary | std::ios::out); + + if (!fo.is_open()) + { + throw std::runtime_error{"file store failed"}; + } + + // Note: SerializeToString() or SerializeToOstream() writes in binary mode + // DebugString() and Utf8DebugString() will print as a human readable text + fo << recipe->Utf8DebugString(); + + fo.close(); + + return true; +} + +} // namespace circlechef diff --git a/compiler/circlechef/core/CMakeLists.txt b/compiler/circlechef/core/CMakeLists.txt new file mode 100644 index 0000000..54b3ea5 --- /dev/null +++ b/compiler/circlechef/core/CMakeLists.txt @@ -0,0 +1,9 @@ +file(GLOB_RECURSE SOURCES "src/*.cpp") + +add_library(circlechef_core STATIC ${SOURCES}) +target_include_directories(circlechef_core PUBLIC include) +target_include_directories(circlechef_core PRIVATE src) +target_link_libraries(circlechef_core circlechef_proto) +target_link_libraries(circlechef_core circlechef_log) +target_link_libraries(circlechef_core mio_circle) +target_link_libraries(circlechef_core souschef) diff --git a/compiler/circlechef/core/include/circlechef/ModelChef.h b/compiler/circlechef/core/include/circlechef/ModelChef.h new file mode 100644 index 0000000..6432617 --- /dev/null +++ b/compiler/circlechef/core/include/circlechef/ModelChef.h @@ -0,0 +1,56 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __MODEL_CHEF_H__ +#define __MODEL_CHEF_H__ + +#include + +#include + +namespace circlechef +{ + +class GeneratedModel final +{ +public: + struct Impl + { + virtual ~Impl() = default; + + virtual const char *base(void) const = 0; + virtual size_t size(void) const = 0; + }; + +public: + GeneratedModel(std::unique_ptr &&impl) : _impl{std::move(impl)} + { + // DO NOTHING + } + +public: + const char *base(void) const { return _impl->base(); } + size_t size(void) const { return _impl->size(); } + +private: + std::unique_ptr _impl; +}; + +GeneratedModel cook(const ModelRecipe &model_recipe); + +} // namespace circlechef + +#endif // __MODEL_CHEF_H__ diff --git a/compiler/circlechef/core/src/Arguments.h b/compiler/circlechef/core/src/Arguments.h new file mode 100644 index 0000000..9fe7bbb --- /dev/null +++ b/compiler/circlechef/core/src/Arguments.h @@ -0,0 +1,34 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __ARGUMENTS_H__ +#define __ARGUMENTS_H__ + +#include +#include + +/** + * @brief Read-only string sequence view + */ +struct Arguments +{ + virtual ~Arguments() = default; + + virtual uint32_t count(void) const = 0; + virtual const std::string &value(uint32_t n) const = 0; +}; + +#endif // __ARGUMENTS_H__ diff --git a/compiler/circlechef/core/src/Convert.cpp b/compiler/circlechef/core/src/Convert.cpp new file mode 100644 index 0000000..2db0a62 --- /dev/null +++ b/compiler/circlechef/core/src/Convert.cpp @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Convert.h" + +#include + +circle::Padding as_circle_padding(const circlechef::Padding &value) +{ + switch (value) + { + case circlechef::SAME: + return circle::Padding_SAME; + case circlechef::VALID: + return circle::Padding_VALID; + default: + break; + } + + throw std::runtime_error{"Unknown padding value"}; +} + +circle::ActivationFunctionType as_circle_activation(const circlechef::Activation &value) +{ + switch (value) + { + case circlechef::NONE: + return circle::ActivationFunctionType_NONE; + case circlechef::RELU: + return circle::ActivationFunctionType_RELU; + case circlechef::RELU6: + return circle::ActivationFunctionType_RELU6; + default: + break; + } + + throw std::runtime_error{"Unknown activation"}; +} + +circle::TensorType as_circle_tensortype(const circlechef::TensorType &value) +{ + switch (value) + { + case circlechef::FLOAT32: + return circle::TensorType_FLOAT32; + case circlechef::INT32: + return circle::TensorType_INT32; + case circlechef::UINT8: + return circle::TensorType_UINT8; + case circlechef::INT64: + return circle::TensorType_INT64; + case circlechef::BOOL: + return circle::TensorType_BOOL; + default: + break; + } + + throw std::runtime_error{"Unknown tensor type"}; +} diff --git a/compiler/circlechef/core/src/Convert.h b/compiler/circlechef/core/src/Convert.h new file mode 100644 index 0000000..766098d --- /dev/null +++ b/compiler/circlechef/core/src/Convert.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @file Convert.h + * @brief This header declares various as_circle_TYPE functions + */ +#ifndef __CONVERT_H__ +#define __CONVERT_H__ + +#include +#include + +circle::Padding as_circle_padding(const circlechef::Padding &value); +circle::ActivationFunctionType as_circle_activation(const circlechef::Activation &value); +circle::TensorType as_circle_tensortype(const circlechef::TensorType &value); + +#endif // __CONVERT_H__ diff --git a/compiler/circlechef/core/src/ModelChef.cpp b/compiler/circlechef/core/src/ModelChef.cpp new file mode 100644 index 0000000..76aeacd --- /dev/null +++ b/compiler/circlechef/core/src/ModelChef.cpp @@ -0,0 +1,631 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "circlechef/ModelChef.h" +#include +#include + +#include "Convert.h" + +#include + +#include "OpChef.h" +#include "OpChefs.h" + +#include + +#include "Log.h" + +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +namespace +{ + +using namespace souschef; + +template std::vector as_vector(const ::google::protobuf::RepeatedPtrField &field) +{ + std::vector res; + for (const auto &elem : field) + { + res.emplace_back(elem); + } + return res; +} + +template Dataset as_dataset(const ::google::protobuf::RepeatedPtrField &field) +{ + return Dataset(as_vector(field)); +} + +} // namespace + +namespace +{ + +template using Dims = std::vector; + +Dims as_dims(const circlechef::TensorShape &shape) +{ + std::vector res; + + for (auto &dim : shape.dim()) + { + res.emplace_back(static_cast(dim)); + } + + return res; +} + +int32_t element_count(const Dims &dims) +{ + return std::accumulate(dims.begin(), dims.end(), 1, std::multiplies()); +} + +} // namespace + +namespace +{ + +class GeneratedModelImpl final : public circlechef::GeneratedModel::Impl +{ +public: + GeneratedModelImpl(std::unique_ptr &&builder) + : _builder{std::move(builder)} + { + // DO NOTHING + } + +public: + const char *base(void) const override + { + // Return the base address of generated flatbuffer model + return reinterpret_cast(_builder->GetBufferPointer()); + } + +public: + size_t size(void) const override + { + // Return the size of generated flatbuffer model + return _builder->GetSize(); + } + +private: + std::unique_ptr _builder; +}; + +} // namespace + +namespace +{ + +struct DataChefRegistry final : public Registry +{ +}; + +DataChefRegistry &data_chef_registry(const circlechef::TensorType &type) +{ + static DataChefRegistry s32; + static DataChefRegistry s64; + static DataChefRegistry fp32; + static DataChefRegistry u8; + static DataChefRegistry boolean; + + switch (type) + { + case circlechef::INT32: + return s32; + case circlechef::INT64: + return s64; + case circlechef::FLOAT32: + return fp32; + case circlechef::UINT8: + return u8; + case circlechef::BOOL: + return boolean; + default: + break; + } + + throw std::runtime_error{"Unknown tensor type"}; +} + +struct OpChefRegistry final : public Registry +{ +}; + +OpChefRegistry &op_chef_registry(void) +{ + static OpChefRegistry registry; + return registry; +} + +/// @brief This will prepare a map of unique builtin codes in the model recipe +std::map +gather_builtincode_map(const ::circlechef::ModelRecipe &model_recipe) +{ + // Key and value of the map are BuiltinOperator and operator version + std::map builtin_map; + + for (const auto &operation : model_recipe.operation()) + { + auto op_chef = op_chef_registry().lookup(operation.type()).create(&operation); + if (op_chef->code() == circle::BuiltinOperator_CUSTOM) + continue; + + // Various operation version is unified as the highest version among them + if (builtin_map.find(op_chef->code()) == builtin_map.end() || + builtin_map[op_chef->code()] < operation.version()) + builtin_map[op_chef->code()] = operation.version(); + } + + // Add ops used in Graphs(subgraphs) + for (int g = 0; g < model_recipe.graph_size(); ++g) + { + const auto &graph = model_recipe.graph(g); + for (const auto &operation : graph.operation()) + { + auto op_chef = op_chef_registry().lookup(operation.type()).create(&operation); + if (op_chef->code() == circle::BuiltinOperator_CUSTOM) + continue; + + // Various operation version is unified as the highest version among them + if (builtin_map.find(op_chef->code()) == builtin_map.end() || + builtin_map[op_chef->code()] < operation.version()) + builtin_map[op_chef->code()] = operation.version(); + } + } + + return builtin_map; +} + +/// @brief This will prepare a set of unique custom codes in the mode recipe +std::set gather_customcode_set(const ::circlechef::ModelRecipe &model_recipe) +{ + std::set customcode_set; + for (const auto &operation : model_recipe.operation()) + { + auto op_chef = op_chef_registry().lookup(operation.type()).create(&operation); + if (op_chef->code() == circle::BuiltinOperator_CUSTOM) + customcode_set.insert(operation.type()); + } + + // Add ops used in Graphs(subgraphs) + for (int g = 0; g < model_recipe.graph_size(); ++g) + { + const auto &graph = model_recipe.graph(g); + for (const auto &operation : graph.operation()) + { + auto op_chef = op_chef_registry().lookup(operation.type()).create(&operation); + if (op_chef->code() == circle::BuiltinOperator_CUSTOM) + customcode_set.insert(operation.type()); + } + } + + return customcode_set; +} + +} // namespace + +namespace +{ + +struct CookParams +{ + std::vector> &buffer_vec; + std::vector> &code_vec; + std::vector> &subgraph_vec; + std::unique_ptr &flatbuffer_builder; + std::map &builtin_code_map; + std::string noname; +}; + +template void cook_graph(const T &graph, CookParams &cp) +{ + LOGGER(l); + + std::vector> &buffer_vec = cp.buffer_vec; + std::vector> &code_vec = cp.code_vec; + std::vector> &subgraph_vec = cp.subgraph_vec; + std::unique_ptr &flatbuffer_builder = cp.flatbuffer_builder; + std::map &builtin_code_map = cp.builtin_code_map; + + // Operand-related + std::vector> tensor_vec; + + // Operation-related + std::vector> operator_vec; + + // default name for graph + std::string graph_name = cp.noname; + if (graph.has_name()) + graph_name = graph.name(); + + // Tensor Name -> Tensor ID mapping (per Graph) + std::map symbol_table; + + auto lookup = [&symbol_table, &graph_name](const std::string &name) { + if (symbol_table.find(name) != symbol_table.end()) + return symbol_table.at(name); + else if (name == "") + return -1; // -1 in circle means that optional input tensor is empty. + else + { + std::string msg = "circlechef : input not found in " + graph_name + " graph"; + throw std::runtime_error(msg.c_str()); + } + }; + + int32_t buffer_start = buffer_vec.size(); + int32_t buffer_index = 0; + + // Create buffer(s) 1~n(I) for input(s) + const auto size_input = graph.input_size(); + for (int ci = 0; ci < size_input; ++ci) + { + circle::BufferBuilder buffer_builder{*flatbuffer_builder}; + buffer_vec.emplace_back(buffer_builder.Finish()); + } + // Create buffer(s) n(I)+1~n(I)+n(O) for output(s) + const auto size_output = graph.output_size(); + for (int co = 0; co < size_output; ++co) + { + circle::BufferBuilder buffer_builder{*flatbuffer_builder}; + buffer_vec.emplace_back(buffer_builder.Finish()); + } + + auto input_names = as_dataset(graph.input()).vectorize(); + auto output_names = as_dataset(graph.output()).vectorize(); + + for (const auto &operand : graph.operand()) + { + assert(operand.has_name()); + + assert(operand.has_type()); + + flatbuffers::Offset> shape; + std::vector dims; + if (operand.has_shape()) + { + dims = as_dims(operand.shape()); + shape = flatbuffer_builder->CreateVector(dims); + } + + auto name = flatbuffer_builder->CreateString(operand.name()); + + buffer_index = 0; + + // Create Buffer if filler is specified + if (operand.has_filler()) + { + const auto &filler = operand.filler(); + + assert(filler.has_tag()); + + auto args = ranged_arguments(filler.arg().begin(), filler.arg().end()); + auto chef = data_chef_registry(operand.type()).lookup(filler.tag()).create(args); + + assert(chef != nullptr); + + // Create Data + int32_t count = (element_count(dims) > 0) ? element_count(dims) : filler.arg_size(); + auto data_vec = chef->generate(count); + auto data = flatbuffer_builder->CreateVector(data_vec); + + // Create Buffer + circle::BufferBuilder buffer_builder{*flatbuffer_builder}; + buffer_builder.add_data(data); + auto buffer = buffer_builder.Finish(); + + // Update Buffer Index & Vector + buffer_index = buffer_vec.size(); + buffer_vec.emplace_back(buffer); + } + else + { + // if this is input or output, assign to that buffer_index + int idx = 0; + for (auto it = input_names.begin(); it != input_names.end(); ++it, ++idx) + { + if (*it == operand.name()) + { + buffer_index = buffer_start + idx; + break; + } + } + if (buffer_index == 0) + { + idx = 0; + for (auto it = output_names.begin(); it != output_names.end(); ++it, ++idx) + { + if (*it == operand.name()) + { + buffer_index = buffer_start + size_input + idx; + break; + } + } + } + if (buffer_index == 0) + { + // we couldn't find the buffer; create an empty buffer for this tensor + buffer_index = buffer_vec.size(); + + circle::BufferBuilder buffer_builder{*flatbuffer_builder}; + buffer_vec.emplace_back(buffer_builder.Finish()); + } + } + assert(buffer_index != 0); + + flatbuffers::Offset quant_index; + + // Create QuantizationParameters if quant is specified + if (operand.has_quant()) + { + const auto &quant = operand.quant(); + + // Create each parameters + // NOTE if some parameters are not given, those will be set to default value + std::vector quant_max_vec(quant.max_size()); + std::vector quant_min_vec(quant.min_size()); + std::vector quant_scale_vec(quant.scale_size()); + std::vector quant_zero_point_vec(quant.zero_point_size()); + + for (uint32_t i = 0; i < quant.max_size(); ++i) + quant_max_vec.at(i) = quant.max(i); + for (uint32_t i = 0; i < quant.min_size(); ++i) + quant_min_vec.at(i) = quant.min(i); + for (uint32_t i = 0; i < quant.scale_size(); ++i) + quant_scale_vec.at(i) = quant.scale(i); + for (uint32_t i = 0; i < quant.zero_point_size(); ++i) + quant_zero_point_vec.at(i) = quant.zero_point(i); + + auto quant_max = flatbuffer_builder->CreateVector(quant_max_vec); + auto quant_min = flatbuffer_builder->CreateVector(quant_min_vec); + auto quant_scale = flatbuffer_builder->CreateVector(quant_scale_vec); + auto quant_zero_point = flatbuffer_builder->CreateVector(quant_zero_point_vec); + + // Create QuantizationParameters + circle::QuantizationParametersBuilder quant_builder{*flatbuffer_builder}; + quant_builder.add_max(quant_max); + quant_builder.add_min(quant_min); + quant_builder.add_scale(quant_scale); + quant_builder.add_zero_point(quant_zero_point); + + // Update QuantizationParameters Index + quant_index = quant_builder.Finish(); + } + + // Create Tensor + circle::TensorBuilder tensor_builder{*flatbuffer_builder}; + + tensor_builder.add_shape(shape); + tensor_builder.add_type(as_circle_tensortype(operand.type())); + tensor_builder.add_buffer(buffer_index); + tensor_builder.add_name(name); + if (operand.has_quant()) + tensor_builder.add_quantization(quant_index); + + // Append! + tensor_vec.emplace_back(tensor_builder.Finish()); + + // Update Tensor Name -> Tensor Index Map + int32_t tensor_index = symbol_table.size(); + const auto &tensor_name = operand.name(); + + INFO(l) << "Symbol [" << tensor_name << "] = Tensor " << tensor_index << std::endl; + + symbol_table[tensor_name] = tensor_index; + } + + // Create Operator + for (const auto &operation : graph.operation()) + { + assert(operation.has_type()); + + auto op_chef = op_chef_registry().lookup(operation.type()).create(&operation); + + // Create 'inputs' + std::vector input_vec = as_dataset(operation.input()).map(lookup).vectorize(); + auto inputs = flatbuffer_builder->CreateVector(input_vec); + + // Create 'outputs' + std::vector output_vec = as_dataset(operation.output()).map(lookup).vectorize(); + auto outputs = flatbuffer_builder->CreateVector(output_vec); + + // Create Option + auto options = op_chef->value(*flatbuffer_builder); + + // Create Custom option + auto circle_custom_options = op_chef->custom_value(*flatbuffer_builder); + + // Create Operator + circle::OperatorBuilder op_builder{*flatbuffer_builder}; + + // Get operator code index from builtin_code_map with assumption, order of + // builtin_code_map is same as that of code_vec + auto op_it = builtin_code_map.find(op_chef->code()); + assert(op_it != builtin_code_map.end()); + uint32_t opcode_index = std::distance(builtin_code_map.begin(), op_it); + + op_builder.add_opcode_index(opcode_index); + op_builder.add_inputs(inputs); + op_builder.add_outputs(outputs); + op_builder.add_builtin_options_type(op_chef->type()); + op_builder.add_builtin_options(options); + op_builder.add_custom_options(circle_custom_options); + op_builder.add_custom_options_format(circle::CustomOptionsFormat_FLEXBUFFERS); + // Append Operator + operator_vec.emplace_back(op_builder.Finish()); + } + + // Create network input/output vector + std::vector input_vec = as_dataset(graph.input()).map(lookup).vectorize(); + std::vector output_vec = as_dataset(graph.output()).map(lookup).vectorize(); + + // Create "SubGraph" arguments + auto tensors = flatbuffer_builder->CreateVector(tensor_vec); + auto inputs = flatbuffer_builder->CreateVector(input_vec); + auto outputs = flatbuffer_builder->CreateVector(output_vec); + auto operators = flatbuffer_builder->CreateVector(operator_vec); + auto name = flatbuffer_builder->CreateString(graph_name); + + circle::SubGraphBuilder subgraph_builder{*flatbuffer_builder}; + + subgraph_builder.add_tensors(tensors); + subgraph_builder.add_inputs(inputs); + subgraph_builder.add_outputs(outputs); + subgraph_builder.add_operators(operators); + subgraph_builder.add_name(name); + + subgraph_vec.emplace_back(subgraph_builder.Finish()); +} + +} // namespace + +namespace circlechef +{ + +/** + * @brief Generate a (in-memory) TensorFlow Lite model from a given model recipe + */ +GeneratedModel cook(const ::circlechef::ModelRecipe &model_recipe) +{ +// Initialize Op Chef Registry +#define OP_CHEF(NAME, FACTORY_CLASS) \ + op_chef_registry().add(#NAME, std::unique_ptr(new FACTORY_CLASS())); +#include "OpChef.def" +#undef OP_CHEF + +// Initialize Data Chef Registry +#define DATA_CHEF(TYPE, NAME, FACTORY_CLASS) \ + data_chef_registry(::circlechef::TYPE) \ + .add(#NAME, std::unique_ptr(new FACTORY_CLASS())); +#include +#undef DATA_CHEF + + // + // Create FlatBufferBuilder + // + auto flatbuffer_builder = + std::unique_ptr(new flatbuffers::FlatBufferBuilder(1024)); + + // Operand-related + std::vector> buffer_vec; + + // Operation-related + std::vector> code_vec; + + // Graphs-related + std::vector> subgraph_vec; + + // Create OperatorCode with Builtin Operator + std::map builtin_code_map = + gather_builtincode_map(model_recipe); + for (auto const &opcode : builtin_code_map) + { + circle::OperatorCodeBuilder code_builder{*flatbuffer_builder}; + code_builder.add_builtin_code(opcode.first); + code_builder.add_version(opcode.second); + auto code = code_builder.Finish(); + // Update OperatorCode vector + code_vec.emplace_back(code); + } + + // Create OperatorCode with Custom Operator + std::set custom_code_set = gather_customcode_set(model_recipe); + if (custom_code_set.size() && + builtin_code_map.find(circle::BuiltinOperator_CUSTOM) == builtin_code_map.end()) + builtin_code_map[circle::BuiltinOperator_CUSTOM] = 1; + + for (auto opcode : custom_code_set) + { + auto custom_code = flatbuffer_builder->CreateString(opcode); + circle::OperatorCodeBuilder code_builder{*flatbuffer_builder}; + code_builder.add_builtin_code(circle::BuiltinOperator_CUSTOM); + code_builder.add_custom_code(custom_code); + auto code = code_builder.Finish(); + // Update OperatorCode vector + code_vec.emplace_back(code); + } + + // Create an Empty Buffer + // + // Buffer 0 SHOULD be an empty buffer in TensorFlow Lite model file + // (Please refer to the comment for Tensor.buffer field in schema) + { + circle::BufferBuilder buffer_builder{*flatbuffer_builder}; + buffer_vec.emplace_back(buffer_builder.Finish()); + } + + // + // Create Main graph + // + CookParams cp{buffer_vec, code_vec, subgraph_vec, flatbuffer_builder, builtin_code_map, "main"}; + + cook_graph<::circlechef::ModelRecipe>(model_recipe, cp); + + // + // Create subgraphs if exist + // + for (int g = 0; g < model_recipe.graph_size(); ++g) + { + const auto &graph = model_recipe.graph(g); + + std::ostringstream stringStream; + stringStream << "sub_" << (g + 1); + + CookParams cp{buffer_vec, code_vec, subgraph_vec, + flatbuffer_builder, builtin_code_map, stringStream.str()}; + + cook_graph<::circlechef::Graph>(graph, cp); + } + + // Create "Model" arguments + auto buffers = flatbuffer_builder->CreateVector(buffer_vec); + auto operator_codes = flatbuffer_builder->CreateVector(code_vec); + auto subgraphs = flatbuffer_builder->CreateVector(subgraph_vec); + auto description = flatbuffer_builder->CreateString("Generated by circlechef"); + + // Create "Model" + circle::ModelBuilder model_builder{*flatbuffer_builder}; + + model_builder.add_version(3); + model_builder.add_operator_codes(operator_codes); + model_builder.add_subgraphs(subgraphs); + model_builder.add_description(description); + model_builder.add_buffers(buffers); + + auto model = model_builder.Finish(); + + // Finalize + ::circle::FinishModelBuffer(*flatbuffer_builder, model); + + // Return "GenerateModel" + return GeneratedModel{ + std::unique_ptr(new GeneratedModelImpl(std::move(flatbuffer_builder)))}; +} + +} // namespace circlechef diff --git a/compiler/circlechef/core/src/Op/BCQFullyConnected.cpp b/compiler/circlechef/core/src/Op/BCQFullyConnected.cpp new file mode 100644 index 0000000..4c82c52 --- /dev/null +++ b/compiler/circlechef/core/src/Op/BCQFullyConnected.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "BCQFullyConnected.h" + +#include "Convert.h" + +flatbuffers::Offset BCQFullyConnectedChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_bcq_fully_connected_options()); + + circle::BCQFullyConnectedOptionsBuilder bcq_fully_connected_options_builder{fbb}; + bcq_fully_connected_options_builder.add_weights_hidden_size( + operation.bcq_fully_connected_options().weights_hidden_size()); + bcq_fully_connected_options_builder.add_fused_activation_function( + as_circle_activation(operation.bcq_fully_connected_options().activation())); + + return bcq_fully_connected_options_builder.Finish().Union(); +} + +std::unique_ptr +BCQFullyConnectedChefFactory::create(const circlechef::Operation *operation) const +{ + return std::unique_ptr{new BCQFullyConnectedChef{operation}}; +} diff --git a/compiler/circlechef/core/src/Op/BCQFullyConnected.h b/compiler/circlechef/core/src/Op/BCQFullyConnected.h new file mode 100644 index 0000000..41e6b53 --- /dev/null +++ b/compiler/circlechef/core/src/Op/BCQFullyConnected.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_BCQFULLYCONNECTED_H__ +#define __OP_BCQFULLYCONNECTED_H__ + +#include "OpChef.h" + +class BCQFullyConnectedChef final : public OpChef +{ +public: + explicit BCQFullyConnectedChef(const circlechef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + circle::BuiltinOperator code(void) const override + { + return circle::BuiltinOperator_BCQ_FULLY_CONNECTED; + } + + circle::BuiltinOptions type(void) const override + { + return circle::BuiltinOptions_BCQFullyConnectedOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const circlechef::Operation *_operation; +}; + +struct BCQFullyConnectedChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const circlechef::Operation *operation) const override; +}; + +#endif // __OP_BCQFULLYCONNECTED_H__ diff --git a/compiler/circlechef/core/src/Op/BCQGather.cpp b/compiler/circlechef/core/src/Op/BCQGather.cpp new file mode 100644 index 0000000..08f6f61 --- /dev/null +++ b/compiler/circlechef/core/src/Op/BCQGather.cpp @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "BCQGather.h" + +flatbuffers::Offset BCQGatherChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_bcq_gather_options()); + + circle::BCQGatherOptionsBuilder bcq_gather_options_builder{fbb}; + bcq_gather_options_builder.add_input_hidden_size( + operation.bcq_gather_options().input_hidden_size()); + bcq_gather_options_builder.add_axis(operation.bcq_gather_options().axis()); + + return bcq_gather_options_builder.Finish().Union(); +} + +std::unique_ptr BCQGatherChefFactory::create(const circlechef::Operation *operation) const +{ + return std::unique_ptr{new BCQGatherChef{operation}}; +} diff --git a/compiler/circlechef/core/src/Op/BCQGather.h b/compiler/circlechef/core/src/Op/BCQGather.h new file mode 100644 index 0000000..24a797a --- /dev/null +++ b/compiler/circlechef/core/src/Op/BCQGather.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_BCQGATHER_H__ +#define __OP_BCQGATHER_H__ + +#include "OpChef.h" + +class BCQGatherChef final : public OpChef +{ +public: + explicit BCQGatherChef(const circlechef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + circle::BuiltinOperator code(void) const override { return circle::BuiltinOperator_BCQ_GATHER; } + + circle::BuiltinOptions type(void) const override + { + return circle::BuiltinOptions_BCQGatherOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const circlechef::Operation *_operation; +}; + +struct BCQGatherChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const circlechef::Operation *operation) const override; +}; + +#endif // __OP_BCQGATHER_H__ diff --git a/compiler/circlechef/core/src/Op/BatchMatMul.cpp b/compiler/circlechef/core/src/Op/BatchMatMul.cpp new file mode 100644 index 0000000..d98c080 --- /dev/null +++ b/compiler/circlechef/core/src/Op/BatchMatMul.cpp @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "BatchMatMul.h" + +flatbuffers::Offset BatchMatMulChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_batch_matmul_options()); + + circle::BatchMatMulOptionsBuilder batch_matmul_options_options_builder{fbb}; + batch_matmul_options_options_builder.add_adjoint_lhs( + operation.batch_matmul_options().adjoint_lhs()); + batch_matmul_options_options_builder.add_adjoint_rhs( + operation.batch_matmul_options().adjoint_rhs()); + + return batch_matmul_options_options_builder.Finish().Union(); +} + +std::unique_ptr BatchMatMulChefFactory::create(const circlechef::Operation *operation) const +{ + return std::unique_ptr{new BatchMatMulChef{operation}}; +} diff --git a/compiler/circlechef/core/src/Op/BatchMatMul.h b/compiler/circlechef/core/src/Op/BatchMatMul.h new file mode 100644 index 0000000..fbb411e --- /dev/null +++ b/compiler/circlechef/core/src/Op/BatchMatMul.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_BATCH_MATMUL_H__ +#define __OP_BATCH_MATMUL_H__ + +#include "OpChef.h" + +class BatchMatMulChef final : public OpChef +{ +public: + explicit BatchMatMulChef(const circlechef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + circle::BuiltinOperator code(void) const override { return circle::BuiltinOperator_BATCH_MATMUL; } + + circle::BuiltinOptions type(void) const override + { + return circle::BuiltinOptions_BatchMatMulOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const circlechef::Operation *_operation; +}; + +struct BatchMatMulChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const circlechef::Operation *operation) const override; +}; + +#endif // __OP_BATCH_MATMUL_H__ diff --git a/compiler/circlechef/core/src/Op/InstanceNorm.cpp b/compiler/circlechef/core/src/Op/InstanceNorm.cpp new file mode 100644 index 0000000..115ecef --- /dev/null +++ b/compiler/circlechef/core/src/Op/InstanceNorm.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "InstanceNorm.h" + +#include "Convert.h" + +flatbuffers::Offset InstanceNormChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_instance_norm_options()); + auto circle_activation = as_circle_activation(operation.instance_norm_options().activation()); + + circle::InstanceNormOptionsBuilder options_builder{fbb}; + options_builder.add_epsilon(operation.instance_norm_options().epsilon()); + options_builder.add_fused_activation_function(circle_activation); + + return options_builder.Finish().Union(); +} + +std::unique_ptr +InstanceNormChefFactory::create(const circlechef::Operation *operation) const +{ + return std::unique_ptr{new InstanceNormChef{operation}}; +} diff --git a/compiler/circlechef/core/src/Op/InstanceNorm.h b/compiler/circlechef/core/src/Op/InstanceNorm.h new file mode 100644 index 0000000..f36b5d7 --- /dev/null +++ b/compiler/circlechef/core/src/Op/InstanceNorm.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_INSTANCE_NORM_H__ +#define __OP_INSTANCE_NORM_H__ + +#include "OpChef.h" + +class InstanceNormChef final : public OpChef +{ +public: + explicit InstanceNormChef(const circlechef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + circle::BuiltinOperator code(void) const override + { + return circle::BuiltinOperator_INSTANCE_NORM; + } + + circle::BuiltinOptions type(void) const override + { + return circle::BuiltinOptions_InstanceNormOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const circlechef::Operation *_operation; +}; + +struct InstanceNormChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const circlechef::Operation *operation) const override; +}; + +#endif // __OP_INSTANCE_NORM_H__ diff --git a/compiler/circlechef/core/src/OpChef.def b/compiler/circlechef/core/src/OpChef.def new file mode 100644 index 0000000..3128d3b --- /dev/null +++ b/compiler/circlechef/core/src/OpChef.def @@ -0,0 +1,10 @@ +#ifndef OP_CHEF +#error "Define OP first" +#endif // OP_CHEF + +// Please keep the list in alphabetical order +// OP_CHEF(NAME, FACTORY_CLASS) +OP_CHEF(BatchMatMul, BatchMatMulChefFactory) +OP_CHEF(BCQFullyConnected, BCQFullyConnectedChefFactory) +OP_CHEF(BCQGather, BCQGatherChefFactory) +OP_CHEF(InstanceNorm, InstanceNormChefFactory) diff --git a/compiler/circlechef/core/src/OpChef.h b/compiler/circlechef/core/src/OpChef.h new file mode 100644 index 0000000..3479e51 --- /dev/null +++ b/compiler/circlechef/core/src/OpChef.h @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_CHEF_H__ +#define __OP_CHEF_H__ + +#include +#include + +#include + +struct OpChef +{ + virtual ~OpChef() = default; + + virtual circle::BuiltinOperator code(void) const = 0; + virtual circle::BuiltinOptions type(void) const = 0; + virtual flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const = 0; + + // TODO Find a way to place this method in a better place + virtual flatbuffers::Offset> + custom_value(flatbuffers::FlatBufferBuilder &fbb) const + { + return flatbuffers::Offset>(); + } +}; + +struct OpChefFactory +{ + virtual ~OpChefFactory() = default; + + virtual std::unique_ptr create(const circlechef::Operation *operation) const = 0; +}; + +#endif // __OP_CHEF_H__ diff --git a/compiler/circlechef/core/src/OpChefs.h b/compiler/circlechef/core/src/OpChefs.h new file mode 100644 index 0000000..e13c5e0 --- /dev/null +++ b/compiler/circlechef/core/src/OpChefs.h @@ -0,0 +1,25 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_CHEFS_H__ +#define __OP_CHEFS_H__ + +#include "Op/BatchMatMul.h" +#include "Op/BCQFullyConnected.h" +#include "Op/BCQGather.h" +#include "Op/InstanceNorm.h" + +#endif // __OP_CHEFS_H__ diff --git a/compiler/circlechef/log/CMakeLists.txt b/compiler/circlechef/log/CMakeLists.txt new file mode 100644 index 0000000..6527ca7 --- /dev/null +++ b/compiler/circlechef/log/CMakeLists.txt @@ -0,0 +1,7 @@ +# TODO Find how to test logging framework +file(GLOB_RECURSE SOURCES "src/*.cpp") + +add_library(circlechef_log STATIC ${SOURCES}) +target_include_directories(circlechef_log PUBLIC include) +target_link_libraries(circlechef_log PUBLIC hermes) +target_link_libraries(circlechef_log PRIVATE hermes_std) diff --git a/compiler/circlechef/log/include/Log.h b/compiler/circlechef/log/include/Log.h new file mode 100644 index 0000000..ef00b26 --- /dev/null +++ b/compiler/circlechef/log/include/Log.h @@ -0,0 +1,75 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __CIRCLECHEF_LOG_H__ +#define __CIRCLECHEF_LOG_H__ + +#include + +namespace circlechef +{ + +/** + * @brief Logger Implementation + */ +class Logger final : public hermes::Source +{ +public: + Logger(hermes::Context *ctx); + ~Logger(); +}; + +/** + * @brief Logger Configuration + * + * Users are able to turn logging on/off via CIRCLECHEF_LOG environment variable. + */ +class LoggerConfig final : public hermes::Config +{ +public: + LoggerConfig(); + +public: + void configure(const hermes::Source *, hermes::Source::Setting &) const final; + void configure(const Logger *, hermes::Source::Setting &) const; + +private: + bool _enabled; +}; + +} // namespace circlechef + +#include "LoggingContext.h" + +/** + * HOW TO USE: + * + * LOGGER(l); + * + * INFO(l) << "Hello, World" << std::endl; + * + */ +#define LOGGER(name) ::circlechef::Logger name{::circlechef::LoggingContext::get()}; + +// TODO Support FATAL, ERROR, WARN, and VERBOSE +#define INFO(name) HERMES_INFO(name) + +// WARNING! +// +// THE CURRENT IMPLEMENTATION IS NOT THREAD SAFE. +// + +#endif // __CIRCLECHEF_LOG_H__ diff --git a/compiler/circlechef/log/include/LoggingContext.h b/compiler/circlechef/log/include/LoggingContext.h new file mode 100644 index 0000000..1282cfd --- /dev/null +++ b/compiler/circlechef/log/include/LoggingContext.h @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __CIRCLECHEF_LOGGING_CONTEXT_H__ +#define __CIRCLECHEF_LOGGING_CONTEXT_H__ + +#include + +namespace circlechef +{ + +/** + * @brief Global logging context + */ +struct LoggingContext +{ + static hermes::Context *get(void); +}; + +} // namespace circlechef + +#endif // __CIRCLECHEF_LOGGING_CONTEXT_H__ diff --git a/compiler/circlechef/log/src/Log.cpp b/compiler/circlechef/log/src/Log.cpp new file mode 100644 index 0000000..11a60fb --- /dev/null +++ b/compiler/circlechef/log/src/Log.cpp @@ -0,0 +1,87 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Log.h" + +#include +#include +#include + +// TODO Extract these lexical conversion routines as a library +namespace +{ + +/** + * @brief Convert C-string as a value of type T + * + * safecast(s, v) returns v if s is nullptr. + */ +template T safecast(const char *, const T &); + +template <> bool safecast(const char *s, const bool &value) +{ + return (s == nullptr) ? value : (std::stoi(s) != 0); +} + +} // namespace + +// +// Logger +// +namespace circlechef +{ + +Logger::Logger(hermes::Context *ctx) { activate(ctx->sources(), ctx->bus()); } +Logger::~Logger() { deactivate(); } + +} // namespace circlechef + +// +// LoggerConfig +// +namespace circlechef +{ + +LoggerConfig::LoggerConfig() +{ + // Turn on logging if CIRCLECHEF_LOG is set as non-zero value + _enabled = safecast(std::getenv("CIRCLECHEF_LOG"), false); +} + +void LoggerConfig::configure(const hermes::Source *source, hermes::Source::Setting &setting) const +{ + // Let's ignore hermes::Sources if that is not a moco logger + if (auto logger = dynamic_cast(source)) + { + configure(logger, setting); + } +} + +void LoggerConfig::configure(const Logger *, hermes::Source::Setting &setting) const +{ + if (_enabled) + { + // Enable all catagories + setting.accept_all(); + } + else + { + // Disable all catagories + setting.reject_all(); + } +} + +} // namespace circlechef diff --git a/compiler/circlechef/log/src/LoggingContext.cpp b/compiler/circlechef/log/src/LoggingContext.cpp new file mode 100644 index 0000000..b64bd3f --- /dev/null +++ b/compiler/circlechef/log/src/LoggingContext.cpp @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LoggingContext.h" +#include "Log.h" + +#include + +#include + +namespace circlechef +{ + +hermes::Context *LoggingContext::get(void) +{ + static hermes::Context *ctx = nullptr; + + if (ctx == nullptr) + { + ctx = new hermes::Context; + ctx->sinks()->append(std::make_unique()); + ctx->config(std::make_unique()); + } + + return ctx; +} + +} // namespace circlechef diff --git a/compiler/circlechef/proto/CMakeLists.txt b/compiler/circlechef/proto/CMakeLists.txt new file mode 100644 index 0000000..3cc26de --- /dev/null +++ b/compiler/circlechef/proto/CMakeLists.txt @@ -0,0 +1,5 @@ +Protobuf_Generate(CIRCLECHEF_PROTO "${CMAKE_CURRENT_BINARY_DIR}/generated" "${CMAKE_CURRENT_SOURCE_DIR}" "circlechef.proto") + +add_library(circlechef_proto STATIC ${CIRCLECHEF_PROTO_SOURCES}) +target_include_directories(circlechef_proto PUBLIC ${CIRCLECHEF_PROTO_INCLUDE_DIRS}) +target_link_libraries(circlechef_proto libprotobuf) diff --git a/compiler/circlechef/proto/circlechef.proto b/compiler/circlechef/proto/circlechef.proto new file mode 100644 index 0000000..b8c009b --- /dev/null +++ b/compiler/circlechef/proto/circlechef.proto @@ -0,0 +1,110 @@ +syntax = "proto2"; + +package circlechef; + +// +// Initial version +// - Our initial version +// +// Version 1 +// - Backward compatible with Initial version +// - Added Graph to represent sub graphs +// - Added name, version(default as 1), graph in ModelRecipe +// + +// This enum value corresponds to TensorType in TensorFlow Lite schema +enum TensorType { + FLOAT32 = 0; + INT32 = 2; + UINT8 = 3; + INT64 = 4; + BOOL = 6; +} + +message TensorShape { + repeated uint32 dim = 3; +} + +message TensorFiller { + optional string tag = 1; + repeated string arg = 2; +} + +message TensorQuantization { + repeated float min = 1; + repeated float max = 2; + repeated float scale = 3; + repeated int64 zero_point = 4; +} + +message Operand { + optional string name = 1; + optional TensorType type = 2; + optional TensorShape shape = 3; + optional TensorFiller filler = 4; + optional TensorQuantization quant = 5; +} + +// This enum value corresponds to Padding in TensorFlow Lite schema +enum Padding { + SAME = 0; + VALID = 1; +} + +// This enum value corresponds to ActivationFunctionType in TensorFlow Lite schema +enum Activation { + NONE = 0; + RELU = 1; + RELU6 = 3; +} + +message BatchMatMulOptions { + optional bool adjoint_lhs = 1 [default = false]; + optional bool adjoint_rhs = 2 [default = false]; +} + +message InstanceNormOptions { + optional float epsilon = 1 [default = 1e-05]; + optional Activation activation = 2 [default = NONE]; +} + +message BCQFullyConnectedOptions { + optional int32 weights_hidden_size = 1 [default = 0]; + optional Activation activation = 2 [default = NONE]; +} + +message BCQGatherOptions { + optional int32 input_hidden_size = 1 [default = 0]; + optional int32 axis = 2 [default = 0]; +} + +message Operation { + optional string type = 1; + repeated string input = 2; + repeated string output = 3; + optional int32 version = 4 [default = 1]; + + optional BatchMatMulOptions batch_matmul_options = 100; + optional InstanceNormOptions instance_norm_options = 101; + optional BCQFullyConnectedOptions bcq_fully_connected_options = 102; + optional BCQGatherOptions bcq_gather_options = 103; +} + +// For additional subgraphs +message Graph { + repeated Operand operand = 1; + repeated Operation operation = 2; + repeated string input = 3; + repeated string output = 4; + optional string name = 5; +} + +message ModelRecipe { + repeated Operand operand = 1; + repeated Operation operation = 2; + repeated string input = 3; + repeated string output = 4; + optional string name = 5; + optional uint32 version = 6 [default = 1]; + repeated Graph graph = 7; +} diff --git a/compiler/circlechef/requires.cmake b/compiler/circlechef/requires.cmake new file mode 100644 index 0000000..2106146 --- /dev/null +++ b/compiler/circlechef/requires.cmake @@ -0,0 +1,9 @@ +require("arser") +require("nnkit") +require("cwrap") +require("mio-circle") +require("safemain") +require("hermes") +require("hermes-std") +require("foder") +require("souschef") diff --git a/compiler/circlechef/tests/CMakeLists.txt b/compiler/circlechef/tests/CMakeLists.txt new file mode 100644 index 0000000..4dc58ad --- /dev/null +++ b/compiler/circlechef/tests/CMakeLists.txt @@ -0,0 +1,70 @@ +nncc_find_resource(CircleRecipes) +set(CIRCLERECIPES_DIR "${CircleRecipes_DIR}") + +file(GLOB RECIPES RELATIVE ${CIRCLERECIPES_DIR} "${CIRCLERECIPES_DIR}/*/test.recipe") + +foreach(RECIPE IN ITEMS ${RECIPES}) + get_filename_component(RECIPE_PREFIX ${RECIPE} DIRECTORY) + + set(RECIPE_SOURCE_FILE "${RECIPE_PREFIX}.recipe") + set(RECIPE_OUTPUT_FILE "${RECIPE_PREFIX}.circle") + + # Copy .recipe + add_custom_command(OUTPUT ${RECIPE_SOURCE_FILE} + COMMAND ${CMAKE_COMMAND} -E copy_if_different + "${CIRCLERECIPES_DIR}/${RECIPE}" ${RECIPE_SOURCE_FILE} + DEPENDS "${CIRCLERECIPES_DIR}/${RECIPE}" + COMMENT "Generating ${RECIPE_SOURCE_FILE}") + + # Generate .circle + add_custom_command(OUTPUT ${RECIPE_OUTPUT_FILE} + COMMAND circlechef-file ${RECIPE_SOURCE_FILE} ${RECIPE_OUTPUT_FILE} + DEPENDS circlechef-file ${RECIPE_SOURCE_FILE} + COMMENT "Generating ${RECIPE_OUTPUT_FILE}") + + list(APPEND TESTS ${RECIPE_PREFIX}) + list(APPEND TESTFILES ${RECIPE_OUTPUT_FILE}) +endforeach(RECIPE) + +#Test circlechef-reverse +file(GLOB GEN_CIRCLEFILES RELATIVE ${CIRCLERECIPES_DIR} "${CIRCLERECIPES_DIR}/*/test.reverse") +# Note: While in development, circlechef-reverse may not handle the operator. +# To separate this linkage scan empty test.reverse for test targets for circlechef-reverse. + +foreach(CIRCLEFILE IN ITEMS ${GEN_CIRCLEFILES}) + get_filename_component(CIRCLE_PREFIX ${CIRCLEFILE} DIRECTORY) + + # file from above circlechef-file block + # use circle file as input of circlechef-reverse generated from circlechef-file + set(RECIPE_OUTPUT_FILE "${CIRCLE_PREFIX}.circle") + set(RECIPE_GEN_OUTPUT_FILE "${CIRCLE_PREFIX}.gen.recipe") + set(RECIPE_GEN_OUTPUT_FILE2 "${CIRCLE_PREFIX}.gen.circle") + + # Generate .gen.recipe from generated .circle + add_custom_command(OUTPUT ${RECIPE_GEN_OUTPUT_FILE} + COMMAND circlechef-reverse ${RECIPE_OUTPUT_FILE} ${RECIPE_GEN_OUTPUT_FILE} + DEPENDS circlechef-reverse ${RECIPE_OUTPUT_FILE} + COMMENT "Generating ${RECIPE_GEN_OUTPUT_FILE}") + + # now we are going to generate .gen.circle from .gen.recipe + # to check generated .gen.recipe file is correct by using it. + # as weight values may be different, binary comparision is not acceptable. + add_custom_command(OUTPUT ${RECIPE_GEN_OUTPUT_FILE2} + COMMAND circlechef-file ${RECIPE_GEN_OUTPUT_FILE} ${RECIPE_GEN_OUTPUT_FILE2} + DEPENDS circlechef-file ${RECIPE_GEN_OUTPUT_FILE} + COMMENT "Generating ${RECIPE_GEN_OUTPUT_FILE2}") + + list(APPEND TESTS ${CIRCLE_PREFIX}.gen) + list(APPEND TESTFILES ${RECIPE_GEN_OUTPUT_FILE2}) +endforeach(CIRCLEFILE) + +# Add a dummy target to create a target-level dependency. +# TODO Find a way to create a dependency between circlechef_test and generated testfiles. +add_custom_target(circlechef_testfiles ALL DEPENDS ${TESTFILES}) + +# Using circle_verify for temporary as it only calls flatbuffer validate +# TODO do testing with running the model with runtime/interpreter +add_test(NAME circlechef_test + COMMAND "${CMAKE_CURRENT_SOURCE_DIR}/runvalidate.sh" + $ + ${TESTS}) diff --git a/compiler/circlechef/tests/runvalidate.sh b/compiler/circlechef/tests/runvalidate.sh new file mode 100755 index 0000000..46ad125 --- /dev/null +++ b/compiler/circlechef/tests/runvalidate.sh @@ -0,0 +1,56 @@ +#!/bin/bash + +if [[ $# -le 2 ]]; then + echo "USAGE: $0 [circle-verify path] [prefix 0] " + exit 255 +fi + +CIRCLE_VERIFY_PATH="$1"; shift + +echo "-- Found circle-verify: ${CIRCLE_VERIFY_PATH}" + +TESTED=() +PASSED=() +FAILED=() + +pushd "${WORKDIR}" +while [[ $# -ne 0 ]]; do + PREFIX="$1"; shift + + TESTED+=("${PREFIX}") + + PASSED_TAG="${PREFIX}.passed" + + rm -f "${PASSED_TAG}" + + cat > "${PREFIX}.log" <( + exec 2>&1 + + echo "'${CIRCLE_VERIFY_PATH}' '${PREFIX}.circle'" + "${CIRCLE_VERIFY_PATH}" "${PREFIX}.circle" + + if [[ $? -eq 0 ]]; then + touch "${PASSED_TAG}" + fi + ) + + if [[ -f "${PASSED_TAG}" ]]; then + PASSED+=("$PREFIX") + else + FAILED+=("$PREFIX") + fi +done +popd + +echo "SUMMARY: ${#PASSED[@]} PASS AND ${#FAILED[@]} FAIL AMONG ${#TESTED[@]} TESTS" + +if [[ ${#TESTED[@]} -ne ${#PASSED[@]} ]]; then + echo "FAILED" + for TEST in "${FAILED[@]}" + do + echo "- ${TEST}" + done + exit 255 +fi + +exit 0 diff --git a/compiler/circlechef/tools/CMakeLists.txt b/compiler/circlechef/tools/CMakeLists.txt new file mode 100644 index 0000000..c958614 --- /dev/null +++ b/compiler/circlechef/tools/CMakeLists.txt @@ -0,0 +1,6 @@ +# Console-based tool (circlechef) +add_subdirectory(console) +# File-based tool (circlechef-file) +add_subdirectory(file) +# Reverse tool to generate recipe from circle (circlechef-reverse) +add_subdirectory(reverse) diff --git a/compiler/circlechef/tools/console/CMakeLists.txt b/compiler/circlechef/tools/console/CMakeLists.txt new file mode 100644 index 0000000..10168fc --- /dev/null +++ b/compiler/circlechef/tools/console/CMakeLists.txt @@ -0,0 +1,3 @@ +add_executable(circlechef Driver.cpp) +target_link_libraries(circlechef circlechef_core) +target_link_libraries(circlechef safemain) diff --git a/compiler/circlechef/tools/console/Driver.cpp b/compiler/circlechef/tools/console/Driver.cpp new file mode 100644 index 0000000..0909f59 --- /dev/null +++ b/compiler/circlechef/tools/console/Driver.cpp @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "circlechef/ModelChef.h" + +#include +#include +#include + +#include + +int entry(int argc, char **argv) +{ + int32_t model_version = 1; + + ::circlechef::ModelRecipe model_recipe; + + // Read a model recipe from standard input + { + google::protobuf::io::IstreamInputStream iis{&std::cin}; + if (!google::protobuf::TextFormat::Parse(&iis, &model_recipe)) + { + std::cerr << "ERROR: Failed to parse recipe" << std::endl; + return 255; + } + + if (model_recipe.has_version()) + { + model_version = model_recipe.version(); + } + } + + if (model_version > 1) + { + std::cerr << "ERROR: Unsupported recipe version: " << model_version << std::endl; + return 255; + } + + auto generated_model = circlechef::cook(model_recipe); + + // Write a generated model into standard output + std::cout.write(generated_model.base(), generated_model.size()); + + return 0; +} diff --git a/compiler/circlechef/tools/file/CMakeLists.txt b/compiler/circlechef/tools/file/CMakeLists.txt new file mode 100644 index 0000000..2524a65 --- /dev/null +++ b/compiler/circlechef/tools/file/CMakeLists.txt @@ -0,0 +1,4 @@ +add_executable(circlechef-file Driver.cpp) +target_link_libraries(circlechef-file arser) +target_link_libraries(circlechef-file circlechef_core) +target_link_libraries(circlechef-file safemain) diff --git a/compiler/circlechef/tools/file/Driver.cpp b/compiler/circlechef/tools/file/Driver.cpp new file mode 100644 index 0000000..a15da40 --- /dev/null +++ b/compiler/circlechef/tools/file/Driver.cpp @@ -0,0 +1,85 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "circlechef/ModelChef.h" + +#include +#include +#include + +#include + +#include +#include + +int entry(int argc, char **argv) +{ + arser::Arser arser; + arser.add_argument("recipe") + .type(arser::DataType::STR) + .help("Source recipe file path to convert"); + arser.add_argument("circle").type(arser::DataType::STR).help("Target circle file path"); + + try + { + arser.parse(argc, argv); + } + catch (const std::runtime_error &err) + { + std::cout << err.what() << std::endl; + std::cout << arser; + return 0; + } + + int32_t model_version = 1; + + ::circlechef::ModelRecipe model_recipe; + + std::string recipe_path = arser.get("recipe"); + // Load model recipe from a file + { + std::ifstream is{recipe_path}; + google::protobuf::io::IstreamInputStream iis{&is}; + if (!google::protobuf::TextFormat::Parse(&iis, &model_recipe)) + { + std::cerr << "ERROR: Failed to parse recipe '" << recipe_path << "'" << std::endl; + return 255; + } + + if (model_recipe.has_version()) + { + model_version = model_recipe.version(); + } + } + + if (model_version > 1) + { + std::cerr << "ERROR: Unsupported recipe version: " << model_version << ", '" << recipe_path + << "'" << std::endl; + return 255; + } + + auto generated_model = circlechef::cook(model_recipe); + + std::string circle_path = arser.get("circle"); + // Dump generated model into a file + { + std::ofstream os{circle_path, std::ios::binary}; + os.write(generated_model.base(), generated_model.size()); + } + + return 0; +} diff --git a/compiler/circlechef/tools/reverse/CMakeLists.txt b/compiler/circlechef/tools/reverse/CMakeLists.txt new file mode 100644 index 0000000..a1606c9 --- /dev/null +++ b/compiler/circlechef/tools/reverse/CMakeLists.txt @@ -0,0 +1,5 @@ +add_executable(circlechef-reverse Driver.cpp) +target_link_libraries(circlechef-reverse arser) +target_link_libraries(circlechef-reverse circlechef_circle) +target_link_libraries(circlechef-reverse safemain) +target_link_libraries(circlechef-reverse foder) diff --git a/compiler/circlechef/tools/reverse/Driver.cpp b/compiler/circlechef/tools/reverse/Driver.cpp new file mode 100644 index 0000000..9c0b9ea --- /dev/null +++ b/compiler/circlechef/tools/reverse/Driver.cpp @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include + +#include +#include + +#include +#include + +int entry(int argc, char **argv) +{ + arser::Arser arser; + arser.add_argument("circle") + .type(arser::DataType::STR) + .help("Source circle file path to convert"); + arser.add_argument("recipe").type(arser::DataType::STR).help("Target recipe file path"); + + try + { + arser.parse(argc, argv); + } + catch (const std::runtime_error &err) + { + std::cout << err.what() << std::endl; + std::cout << arser; + return 0; + } + + std::string circle_path = arser.get("circle"); + // Load TF lite model from a circle file + const foder::FileLoader fileLoader{circle_path}; + std::vector modelData = fileLoader.load(); + const circle::Model *circlemodel = circle::GetModel(modelData.data()); + if (circlemodel == nullptr) + { + std::cerr << "ERROR: Failed to load circle '" << circle_path << "'" << std::endl; + return 255; + } + + // Generate ModelRecipe recipe + std::unique_ptr recipe = circlechef::generate_recipe(circlemodel); + if (recipe.get() == nullptr) + { + std::cerr << "ERROR: Failed to generate recipe" << std::endl; + return 255; + } + + std::string recipe_path = arser.get("recipe"); + // Save to a file + bool result = circlechef::write_recipe(recipe_path, recipe); + if (!result) + { + std::cerr << "ERROR: Failed to write to recipe '" << recipe_path << "'" << std::endl; + return 255; + } + return 0; +} diff --git a/compiler/circledump/CMakeLists.txt b/compiler/circledump/CMakeLists.txt index a117e72..fb72b1d 100644 --- a/compiler/circledump/CMakeLists.txt +++ b/compiler/circledump/CMakeLists.txt @@ -8,7 +8,7 @@ file(GLOB_RECURSE SOURCES "src/*.cpp") add_executable(circledump ${DRIVER} ${SOURCES}) target_include_directories(circledump PRIVATE include) +target_link_libraries(circledump arser) target_link_libraries(circledump mio_circle) target_link_libraries(circledump safemain) -target_link_libraries(circledump stdex) target_link_libraries(circledump flatbuffers) diff --git a/compiler/circledump/driver/Driver.cpp b/compiler/circledump/driver/Driver.cpp index 8ed88e1..b8f561f 100644 --- a/compiler/circledump/driver/Driver.cpp +++ b/compiler/circledump/driver/Driver.cpp @@ -14,6 +14,7 @@ * limitations under the License. */ +#include #include #include @@ -21,30 +22,37 @@ int entry(int argc, char **argv) { - if (argc != 2) + arser::Arser arser; + arser.add_argument("circle").type(arser::DataType::STR).help("Circle file path to dump"); + + try { - std::cerr << "ERROR: Failed to parse arguments" << std::endl; - std::cerr << std::endl; - std::cerr << "USAGE: " << argv[0] << " [circle]" << std::endl; - return 255; + arser.parse(argc, argv); + } + catch (const std::runtime_error &err) + { + std::cout << err.what() << '\n'; + std::cout << arser; + return 0; } + std::string circle_path = arser.get("circle"); // Load Circle model from a circle file - std::unique_ptr model = circleread::load_circle(argv[1]); + std::unique_ptr model = circleread::load_circle(circle_path); if (model == nullptr) { - std::cerr << "ERROR: Failed to load circle '" << argv[1] << "'" << std::endl; + std::cerr << "ERROR: Failed to load circle '" << circle_path << "'" << std::endl; return 255; } const circle::Model *circlemodel = model->model(); if (circlemodel == nullptr) { - std::cerr << "ERROR: Failed to load circle '" << argv[1] << "'" << std::endl; + std::cerr << "ERROR: Failed to load circle '" << circle_path << "'" << std::endl; return 255; } - std::cout << "Dump: " << argv[1] << std::endl << std::endl; + std::cout << "Dump: " << circle_path << std::endl << std::endl; std::cout << circlemodel << std::endl; diff --git a/compiler/circledump/requires.cmake b/compiler/circledump/requires.cmake index b090dbd..81e0f0d 100644 --- a/compiler/circledump/requires.cmake +++ b/compiler/circledump/requires.cmake @@ -1,3 +1,3 @@ +require("arser") require("mio-circle") require("safemain") -require("stdex") diff --git a/compiler/circledump/src/Dump.cpp b/compiler/circledump/src/Dump.cpp index 3d99189..c695b07 100644 --- a/compiler/circledump/src/Dump.cpp +++ b/compiler/circledump/src/Dump.cpp @@ -123,8 +123,8 @@ void dump_sub_graph(std::ostream &os, circleread::Reader &reader) os << std::endl; // dump operands(tensors) - os << "Operands: T(subgraph index : tensor index) TYPE (shape) B(buffer index) OperandName" - << std::endl; + os << "Operands: T(subgraph index : tensor index) TYPE (shape) (shape_signature) " + << "B(buffer index) OperandName" << std::endl; for (uint32_t i = 0; i < tensors->Length(); ++i) { // TODO refactor to some better structure @@ -137,6 +137,11 @@ void dump_sub_graph(std::ostream &os, circleread::Reader &reader) os << "T(" << reader.subgraph_index() << ":" << i << ") " << circleread::tensor_type(tensor) << " "; os << "(" << dims << ") "; + if (tensor->shape_signature()) + { + std::vector dims_sig = circleread::as_index_vector(tensor->shape_signature()); + os << "(" << dims_sig << ") "; + } os << "B(" << tensor->buffer() << ") "; os << circleread::tensor_name(tensor) << std::endl; @@ -167,7 +172,12 @@ void dump_sub_graph(std::ostream &os, circleread::Reader &reader) os << std::endl << strqindent; } if (q_params->zero_point()) + { os << "zeropt(" << q_params->zero_point() << ") "; + if (q_params->zero_point()->size() > 1) + os << std::endl << strqindent; + } + os << "quantized_dimension(" << q_params->quantized_dimension() << ")"; os << std::endl; } @@ -199,7 +209,7 @@ void dump_sub_graph(std::ostream &os, circleread::Reader &reader) for (auto input : inputs) { - os << " I T(" << input << ") "; + os << " I T(" << reader.subgraph_index() << ":" << input << ") "; if (input >= 0) { auto tensor = tensors->Get(input); @@ -209,7 +219,7 @@ void dump_sub_graph(std::ostream &os, circleread::Reader &reader) } for (auto output : outputs) { - os << " O T(" << output << ") "; + os << " O T(" << reader.subgraph_index() << ":" << output << ") "; if (output >= 0) { auto tensor = tensors->Get(output); @@ -227,14 +237,14 @@ void dump_sub_graph(std::ostream &os, circleread::Reader &reader) { auto tensor = tensors->Get(input); std::string name = circleread::tensor_name(tensor); - os << "I T(" << input << ") " << name << std::endl; + os << "I T(" << reader.subgraph_index() << ":" << input << ") " << name << std::endl; } for (const auto output : reader.outputs()) { auto tensor = tensors->Get(output); std::string name = circleread::tensor_name(tensor); - os << "O T(" << output << ") " << name << std::endl; + os << "O T(" << reader.subgraph_index() << ":" << output << ") " << name << std::endl; } os << std::endl; diff --git a/compiler/circledump/src/OpPrinter.cpp b/compiler/circledump/src/OpPrinter.cpp index f9daab4..2c03203 100644 --- a/compiler/circledump/src/OpPrinter.cpp +++ b/compiler/circledump/src/OpPrinter.cpp @@ -17,11 +17,11 @@ #include "OpPrinter.h" #include "Read.h" -#include +#include #include -using stdex::make_unique; +using std::make_unique; namespace circledump { @@ -60,6 +60,51 @@ public: } }; +class ArgMinPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_ArgMinOptions()) + { + os << " "; + os << "OutputType(" << EnumNameTensorType(params->output_type()) << ") "; + os << std::endl; + } + } +}; + +class BatchMatMulPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_BatchMatMulOptions()) + { + os << " "; + os << std::boolalpha; + os << "adjoint_lhs(" << params->adjoint_lhs() << ") "; + os << "adjoint_rhs(" << params->adjoint_rhs() << ") "; + os << std::endl; + } + } +}; + +class CastPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto cast_params = op->builtin_options_as_CastOptions()) + { + os << " "; + os << "in_data_type(" << circle::EnumNameTensorType(cast_params->in_data_type()) << ") "; + os << "out_data_type(" << circle::EnumNameTensorType(cast_params->out_data_type()) << ") "; + os << std::endl; + } + } +}; + class Conv2DPrinter : public OpPrinter { public: @@ -71,6 +116,8 @@ public: os << "Padding(" << conv_params->padding() << ") "; os << "Stride.W(" << conv_params->stride_w() << ") "; os << "Stride.H(" << conv_params->stride_h() << ") "; + os << "Dilation.W(" << conv_params->dilation_w_factor() << ") "; + os << "Dilation.H(" << conv_params->dilation_h_factor() << ") "; os << "Activation(" << EnumNameActivationFunctionType(conv_params->fused_activation_function()) << ")"; os << std::endl; @@ -78,6 +125,20 @@ public: } }; +class DepthToSpacePrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *std_params = op->builtin_options_as_DepthToSpaceOptions()) + { + os << " "; + os << "BlockSize(" << std_params->block_size() << ")"; + os << std::endl; + } + } +}; + class DivPrinter : public OpPrinter { public: @@ -130,6 +191,20 @@ public: } }; +class ReducerPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto reducer_params = op->builtin_options_as_ReducerOptions()) + { + os << " "; + os << "keep_dims(" << reducer_params->keep_dims() << ") "; + os << std::endl; + } + } +}; + class ReshapePrinter : public OpPrinter { public: @@ -145,6 +220,52 @@ public: } }; +class ResizeBilinearPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *resize_params = op->builtin_options_as_ResizeBilinearOptions()) + { + os << " "; + os << std::boolalpha; + os << "align_corners(" << resize_params->align_corners() << ")"; + os << "half_pixel_centers(" << resize_params->half_pixel_centers() << ")"; + os << std::endl; + } + } +}; + +class ResizeNearestNeighborPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *resize_params = op->builtin_options_as_ResizeNearestNeighborOptions()) + { + os << " "; + os << std::boolalpha; + os << "align_corners(" << resize_params->align_corners() << ")"; + os << std::endl; + } + } +}; + +class ReverseSequencePrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_ReverseSequenceOptions()) + { + os << " "; + os << "seq_dim(" << params->seq_dim() << ") "; + os << "batch_dim(" << params->batch_dim() << ") "; + os << std::endl; + } + } +}; + class DepthwiseConv2DPrinter : public OpPrinter { public: @@ -184,6 +305,95 @@ public: } }; +class GatherPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_GatherOptions()) + { + os << " "; + os << "Axis(" << params->axis() << ") "; + + os << std::endl; + } + } +}; + +class IfPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_IfOptions()) + { + os << " "; + os << "then_subgraph_index(" << params->then_subgraph_index() << ") "; + os << "else_subgraph_index(" << params->else_subgraph_index() << ") "; + os << std::endl; + } + } +}; + +class L2NormPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_L2NormOptions()) + { + os << " "; + os << "Activation(" << EnumNameActivationFunctionType(params->fused_activation_function()) + << ") "; + os << std::endl; + } + } +}; + +class LeakyReluPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_LeakyReluOptions()) + { + os << " "; + os << "alpha(" << params->alpha() << ") "; + } + } +}; + +class LocalResponseNormalizationPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_LocalResponseNormalizationOptions()) + { + os << " "; + os << "radius(" << params->radius() << ") "; + os << "bias(" << params->bias() << ") "; + os << "alpha(" << params->alpha() << ") "; + os << "beta(" << params->beta() << ") "; + os << std::endl; + } + } +}; + +class MirrorPadPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_MirrorPadOptions()) + { + os << " "; + os << "mode(" << EnumNameMirrorPadMode(params->mode()) << ") "; + os << std::endl; + } + } +}; + class MulPrinter : public OpPrinter { public: @@ -199,6 +409,21 @@ public: } }; +class OneHotPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_OneHotOptions()) + { + os << " "; + os << "Axis(" << params->axis() << ") "; + + os << std::endl; + } + } +}; + class PackPrinter : public OpPrinter { public: @@ -214,6 +439,20 @@ public: } }; +class ShapePrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_ShapeOptions()) + { + os << " "; + os << "out_type(" << EnumNameTensorType(params->out_type()) << ") "; + os << std::endl; + } + } +}; + class SoftmaxPrinter : public OpPrinter { public: @@ -228,6 +467,101 @@ public: } }; +class SpaceToDepthPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *std_params = op->builtin_options_as_SpaceToDepthOptions()) + { + os << " "; + os << "BlockSize(" << std_params->block_size() << ")"; + os << std::endl; + } + } +}; + +class SparseToDensePrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *std_params = op->builtin_options_as_SparseToDenseOptions()) + { + os << " "; + os << "ValidateIndices(" << std_params->validate_indices() << ")"; + os << std::endl; + } + } +}; + +class SplitPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_SplitOptions()) + { + os << " "; + os << "num_splits(" << params->num_splits() << ") "; + os << std::endl; + } + } +}; + +class SplitVPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_SplitVOptions()) + { + os << " "; + os << "num_splits(" << params->num_splits() << ") "; + os << std::endl; + } + } +}; + +class SqueezePrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_SqueezeOptions()) + { + os << " "; + os << "SqueezeDims("; + for (int i = 0; i < params->squeeze_dims()->size(); ++i) + { + if (i != 0) + os << ", "; + os << params->squeeze_dims()->Get(i); + } + os << ")"; + os << std::endl; + } + } +}; + +class StridedSlicePrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *strided_slice_params = op->builtin_options_as_StridedSliceOptions()) + { + os << " "; + os << "begin_mask(" << strided_slice_params->begin_mask() << ") "; + os << "end_mask(" << strided_slice_params->end_mask() << ") "; + os << "ellipsis_mask(" << strided_slice_params->ellipsis_mask() << ") "; + os << "new_axis_mask(" << strided_slice_params->new_axis_mask() << ") "; + os << "shrink_axis_mask(" << strided_slice_params->shrink_axis_mask() << ") "; + os << std::endl; + } + } +}; + class SubPrinter : public OpPrinter { public: @@ -243,6 +577,37 @@ public: } }; +class TransposeConvPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto conv_params = op->builtin_options_as_TransposeConvOptions()) + { + os << " "; + os << "Padding(" << conv_params->padding() << ") "; + os << "Stride.W(" << conv_params->stride_w() << ") "; + os << "Stride.H(" << conv_params->stride_h() << ") "; + os << std::endl; + } + } +}; + +class WhilePrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_WhileOptions()) + { + os << " "; + os << "cond_subgraph_index(" << params->cond_subgraph_index() << ") "; + os << "body_subgraph_index(" << params->body_subgraph_index() << ") "; + os << std::endl; + } + } +}; + class CustomOpPrinter : public OpPrinter { public: @@ -283,25 +648,108 @@ public: } }; +class BCQFullyConnectedPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_BCQFullyConnectedOptions()) + { + os << " "; + os << "Activation(" << EnumNameActivationFunctionType(params->fused_activation_function()) + << ") "; + os << "weights_hidden_size(" << params->weights_hidden_size() << ") "; + os << std::endl; + } + } +}; + +class BCQGatherPrinter : public OpPrinter +{ +public: + void options(const circle::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_BCQGatherOptions()) + { + os << " "; + os << "axis(" << params->axis() << ") "; + os << "weights_hidden_size(" << params->input_hidden_size() << ") "; + os << std::endl; + } + } +}; + OpPrinterRegistry::OpPrinterRegistry() { _op_map[circle::BuiltinOperator_ADD] = make_unique(); + // There is no Option for ADD_N _op_map[circle::BuiltinOperator_ARG_MAX] = make_unique(); + _op_map[circle::BuiltinOperator_ARG_MIN] = make_unique(); _op_map[circle::BuiltinOperator_AVERAGE_POOL_2D] = make_unique(); + _op_map[circle::BuiltinOperator_BATCH_MATMUL] = make_unique(); + _op_map[circle::BuiltinOperator_CAST] = make_unique(); + // There is no Option for CEIL _op_map[circle::BuiltinOperator_CONCATENATION] = make_unique(); _op_map[circle::BuiltinOperator_CONV_2D] = make_unique(); + _op_map[circle::BuiltinOperator_DEPTH_TO_SPACE] = make_unique(); _op_map[circle::BuiltinOperator_DEPTHWISE_CONV_2D] = make_unique(); _op_map[circle::BuiltinOperator_DIV] = make_unique(); + // There is no Option for FLOOR + // There is no Option for FLOOR_MOD _op_map[circle::BuiltinOperator_FULLY_CONNECTED] = make_unique(); + _op_map[circle::BuiltinOperator_GATHER] = make_unique(); + _op_map[circle::BuiltinOperator_IF] = make_unique(); + _op_map[circle::BuiltinOperator_L2_NORMALIZATION] = make_unique(); + _op_map[circle::BuiltinOperator_L2_POOL_2D] = make_unique(); + _op_map[circle::BuiltinOperator_LEAKY_RELU] = make_unique(); + _op_map[circle::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION] = + make_unique(); + // There is no Option for LOG + // There is no Option for LOGISTIC + // There is no Option for LOG_SOFTMAX _op_map[circle::BuiltinOperator_MAX_POOL_2D] = make_unique(); + _op_map[circle::BuiltinOperator_MIRROR_PAD] = make_unique(); _op_map[circle::BuiltinOperator_MUL] = make_unique(); + _op_map[circle::BuiltinOperator_ONE_HOT] = make_unique(); _op_map[circle::BuiltinOperator_PACK] = make_unique(); - // There is no Option for Pad - // There is no Option for ReLU and ReLU6 + // There is no Option for PAD + // There is no Option for PRELU + // There is no Option for RELU + // There is no Option for RELU6 + // There is no Option for RELU_N1_TO_1 + _op_map[circle::BuiltinOperator_REDUCE_ANY] = make_unique(); + _op_map[circle::BuiltinOperator_REDUCE_MAX] = make_unique(); + _op_map[circle::BuiltinOperator_REDUCE_MIN] = make_unique(); + _op_map[circle::BuiltinOperator_REDUCE_PROD] = make_unique(); _op_map[circle::BuiltinOperator_RESHAPE] = make_unique(); + _op_map[circle::BuiltinOperator_RESIZE_BILINEAR] = make_unique(); + _op_map[circle::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR] = + make_unique(); + _op_map[circle::BuiltinOperator_REVERSE_SEQUENCE] = make_unique(); + // There is no Option for ROUND + // There is no Option for SELECT + // There is no Option for SELECT_V2 + _op_map[circle::BuiltinOperator_SHAPE] = make_unique(); + // There is no Option for SIN + // There is no Option for SLICE _op_map[circle::BuiltinOperator_SOFTMAX] = make_unique(); + _op_map[circle::BuiltinOperator_SPACE_TO_DEPTH] = make_unique(); + // There is no Option for SPACE_TO_BATCH_ND + _op_map[circle::BuiltinOperator_SPARSE_TO_DENSE] = make_unique(); + _op_map[circle::BuiltinOperator_SPLIT] = make_unique(); + _op_map[circle::BuiltinOperator_SPLIT_V] = make_unique(); + _op_map[circle::BuiltinOperator_SQUEEZE] = make_unique(); + _op_map[circle::BuiltinOperator_STRIDED_SLICE] = make_unique(); _op_map[circle::BuiltinOperator_SUB] = make_unique(); + _op_map[circle::BuiltinOperator_SUM] = make_unique(); + _op_map[circle::BuiltinOperator_TRANSPOSE_CONV] = make_unique(); + // There is no Option for TOPK_V2 + _op_map[circle::BuiltinOperator_WHILE] = make_unique(); _op_map[circle::BuiltinOperator_CUSTOM] = make_unique(); + + // Circle only + _op_map[circle::BuiltinOperator_BCQ_FULLY_CONNECTED] = make_unique(); + _op_map[circle::BuiltinOperator_BCQ_GATHER] = make_unique(); } } // namespace circledump diff --git a/compiler/common-artifacts/CMakeLists.txt b/compiler/common-artifacts/CMakeLists.txt new file mode 100644 index 0000000..ee4191d --- /dev/null +++ b/compiler/common-artifacts/CMakeLists.txt @@ -0,0 +1,258 @@ +#[[ Generate common python virtual enviornment ]] +find_package(PythonInterp 3 QUIET) +find_package(PythonLibs 3 QUIET) + +if(NOT ${PYTHONINTERP_FOUND}) + message(STATUS "Build common-artifacts: FALSE (Python3 is missing)") + return() +endif() + +if(${PYTHON_VERSION_MINOR} LESS 3) + message(STATUS "Build common-artifacts: FALSE (You need to install Python version higher than 3.3)") + return() +endif() + +# Create python virtual environment with tensorflow 1.13.2 +set(VIRTUALENV_OVERLAY "${NNCC_OVERLAY_DIR}/venv_1_13_2") + +add_custom_command( + OUTPUT ${VIRTUALENV_OVERLAY} + COMMAND ${PYTHON_EXECUTABLE} -m venv ${VIRTUALENV_OVERLAY} +) + +# Create requirements.txt and install required pip packages +set(REQUIREMENTS_FILE "requirements.txt") +set(REQUIREMENTS_OVERLAY_PATH "${NNCC_OVERLAY_DIR}/${REQUIREMENTS_FILE}") + +add_custom_command( + OUTPUT ${REQUIREMENTS_OVERLAY_PATH} + COMMAND ${CMAKE_COMMAND} -E echo "tensorflow==1.13.2" > ${REQUIREMENTS_OVERLAY_PATH} + COMMAND ${VIRTUALENV_OVERLAY}/bin/python -m pip --default-timeout=1000 install --upgrade pip setuptools + COMMAND ${VIRTUALENV_OVERLAY}/bin/python -m pip --default-timeout=1000 install -r ${REQUIREMENTS_OVERLAY_PATH} --upgrade + DEPENDS ${VIRTUALENV_OVERLAY} ${REQUIREMENTS_OVERLAY_PATH} +) + +add_custom_target(common_artifacts_python_deps ALL + DEPENDS ${VIRTUALENV_OVERLAY} ${REQUIREMENTS_OVERLAY_PATH} +) + +# TODO Create python virtual environment with tensorflow 2.3.0-rc0 + +#[[ Generate common resources ]] +# TODO add pbtxt +nnas_find_package(HDF5 QUIET) + +if(NOT HDF5_FOUND) + message(STATUS "Build common-artifacts: FAILED (missing HDF5)") + return() +endif(NOT HDF5_FOUND) + +set(SOURCES src/TestDataGenerator.cpp) + +add_executable(testDataGenerator ${SOURCES}) +target_include_directories(testDataGenerator PRIVATE ${HDF5_INCLUDE_DIRS}) +target_link_libraries(testDataGenerator PRIVATE ${HDF5_CXX_LIBRARIES}) +target_link_libraries(testDataGenerator PRIVATE foder) +target_link_libraries(testDataGenerator PRIVATE luci_import) +target_link_libraries(testDataGenerator PRIVATE luci_interpreter) +target_link_libraries(testDataGenerator PRIVATE mio_circle) +target_link_libraries(testDataGenerator PRIVATE safemain) + +unset(TEST_DEPS) + +# Include recipe repo +nncc_find_resource(TensorFlowLiteRecipes) +nncc_find_resource(CircleRecipes) +set(TFLITE_RECIPE_REPO "${TensorFlowLiteRecipes_DIR}") +set(CIRCLE_RECIPE_REPO "${CircleRecipes_DIR}") +set(TEST_RECIPE_FILENAME "test.recipe") +set(TEST_RULE_FILENAME "test.rule") + +set(MODEL2NNPKG "${NNAS_PROJECT_SOURCE_DIR}/tools/nnpackage_tool/model2nnpkg/model2nnpkg.sh") +# Get test case list +unset(RECIPES) +file(GLOB TFLITE_SUBDIR RELATIVE ${TFLITE_RECIPE_REPO} ${TFLITE_RECIPE_REPO}/*) +foreach(DIR IN ITEMS ${TFLITE_SUBDIR}) + if(IS_DIRECTORY ${TFLITE_RECIPE_REPO}/${DIR}) + list(APPEND RECIPES ${DIR}) + endif() +endforeach() +file(GLOB CIRCLE_SUBDIR RELATIVE ${CIRCLE_RECIPE_REPO} ${CIRCLE_RECIPE_REPO}/*) +foreach(DIR IN ITEMS ${CIRCLE_SUBDIR}) + if(IS_DIRECTORY ${CIRCLE_RECIPE_REPO}/${DIR}) + list(APPEND RECIPES ${DIR}) + endif() +endforeach() + +macro(circlize NAME) + set(NO_CIRCLIZE_${NAME} TRUE) + set(NO_OPTIMIZE_${NAME} TRUE) + set(NO_TCGEN_${NAME} TRUE) +endmacro() +macro(optimize NAME) + set(NO_OPTIMIZE_${NAME} TRUE) +endmacro() +macro(tcgenerate NAME) + set(NO_TCGEN_${NAME} TRUE) +endmacro() + +include("exclude.lst") + +foreach(RECIPE IN ITEMS ${RECIPES}) + unset(OPT_FORMAT) + unset(MODEL_FORMAT) + + set(RECIPE_FILE "${RECIPE}.recipe") + set(RULE_FILE "${RECIPE}.rule") + set(TFLITE_RECIPE_SOURCE_PATH "${TFLITE_RECIPE_REPO}/${RECIPE}/${TEST_RECIPE_FILENAME}") + set(CIRCLE_RECIPE_SOURCE_PATH "${CIRCLE_RECIPE_REPO}/${RECIPE}/${TEST_RECIPE_FILENAME}") + + if(NOT EXISTS "${TFLITE_RECIPE_SOURCE_PATH}") + if(NOT EXISTS "${CIRCLE_RECIPE_SOURCE_PATH}") + message(FATAL_ERROR "Missing recipe of '${RECIPE}' test") + else() + # circle recipe + set(MODEL_FORMAT "circle") + set(RECIPE_SOURCE_PATH ${CIRCLE_RECIPE_SOURCE_PATH}) + endif() + else() + # tflite recipe + set(MODEL_FORMAT "tflite") + set(RECIPE_SOURCE_PATH ${TFLITE_RECIPE_SOURCE_PATH}) + endif() + + set(TFLITE_RULE_SOURCE_PATH "${TFLITE_RECIPE_REPO}/${RECIPE}/${TEST_RULE_FILENAME}") + set(CIRCLE_RULE_SOURCE_PATH "${CIRCLE_RECIPE_REPO}/${RECIPE}/${TEST_RULE_FILENAME}") + + unset(RULE_SOURCE_PATH) + if(EXISTS "${TFLITE_RULE_SOURCE_PATH}") + set(RULE_SOURCE_PATH ${TFLITE_RULE_SOURCE_PATH}) + endif() + if(EXISTS "${CIRCLE_RULE_SOURCE_PATH}") + set(RULE_SOURCE_PATH ${CIRCLE_RULE_SOURCE_PATH}) + endif() + + set(RECIPE_BINARY_PATH "${CMAKE_CURRENT_BINARY_DIR}/${RECIPE_FILE}") + set(RULE_BINARY_PATH "${CMAKE_CURRENT_BINARY_DIR}/${RULE_FILE}") + + set(TFLITE_FILE "${RECIPE}.tflite") + set(TFLITE_OUTPUT_PATH "${CMAKE_CURRENT_BINARY_DIR}/${TFLITE_FILE}") + set(CIRCLE_FILE "${RECIPE}.circle") + set(CIRCLE_OUTPUT_PATH "${CMAKE_CURRENT_BINARY_DIR}/${CIRCLE_FILE}") + + # Copy .recipe + add_custom_command(OUTPUT ${RECIPE_BINARY_PATH} + COMMAND ${CMAKE_COMMAND} -E copy "${RECIPE_SOURCE_PATH}" "${RECIPE_BINARY_PATH}" + DEPENDS ${RECIPE_SOURCE_PATH} + COMMENT "Generate ${RECIPE_FILE}" + ) + list(APPEND TEST_DEPS ${RECIPE_BINARY_PATH}) + + if(DEFINED RULE_SOURCE_PATH) + # Copy .rule + add_custom_command(OUTPUT ${RULE_BINARY_PATH} + COMMAND ${CMAKE_COMMAND} -E copy "${RULE_SOURCE_PATH}" "${RULE_BINARY_PATH}" + DEPENDS ${RULE_SOURCE_PATH} + COMMENT "Generate ${RULE_FILE}" + ) + list(APPEND TEST_DEPS ${RULE_BINARY_PATH}) + endif() + + if(${MODEL_FORMAT} STREQUAL "tflite") + # Generate .tflite + add_custom_command(OUTPUT ${TFLITE_OUTPUT_PATH} + COMMAND $ ${RECIPE_BINARY_PATH} ${TFLITE_OUTPUT_PATH} + DEPENDS $ ${RECIPE_BINARY_PATH} + COMMENT "Generate ${TFLITE_FILE}" + ) + list(APPEND TEST_DEPS ${TFLITE_OUTPUT_PATH}) + + if(NOT DEFINED NO_CIRCLIZE_${RECIPE}) + # Generate .circle + add_custom_command(OUTPUT ${CIRCLE_OUTPUT_PATH} + COMMAND $ ${TFLITE_OUTPUT_PATH} ${CIRCLE_OUTPUT_PATH} + DEPENDS $ ${TFLITE_OUTPUT_PATH} + COMMENT "Generate ${CIRCLE_FILE}" + ) + set(MODEL_FORMAT "circle") + list(APPEND TEST_DEPS ${CIRCLE_OUTPUT_PATH}) + endif() + else() + # Generate .circle + add_custom_command(OUTPUT ${CIRCLE_OUTPUT_PATH} + COMMAND $ ${RECIPE_BINARY_PATH} ${CIRCLE_OUTPUT_PATH} + DEPENDS $ ${RECIPE_BINARY_PATH} + COMMENT "Generate ${CIRCLE_FILE}" + ) + list(APPEND TEST_DEPS ${CIRCLE_OUTPUT_PATH}) + endif() + + set(OPT_CIRCLE_FILE "${RECIPE}.opt.circle") + set(OPT_CIRCLE_OUTPUT_PATH "${CMAKE_CURRENT_BINARY_DIR}/${OPT_CIRCLE_FILE}") + + if(NOT DEFINED NO_OPTIMIZE_${RECIPE}) + # Generate optimized .circle + add_custom_command(OUTPUT ${OPT_CIRCLE_OUTPUT_PATH} + COMMAND $ --all ${CIRCLE_OUTPUT_PATH} ${OPT_CIRCLE_OUTPUT_PATH} + DEPENDS $ ${CIRCLE_OUTPUT_PATH} + COMMENT "Generate ${OPT_CIRCLE_FILE}" + ) + set(OPT_FORMAT ".opt") + list(APPEND TEST_DEPS ${OPT_CIRCLE_OUTPUT_PATH}) + endif() + + set(MODEL_FILE "${RECIPE}${OPT_FORMAT}.${MODEL_FORMAT}") + set(MODEL_PATH "${CMAKE_CURRENT_BINARY_DIR}/${MODEL_FILE}") + set(NNPKG_FILE "${RECIPE}${OPT_FORMAT}") + set(NNPKG_PATH "${CMAKE_CURRENT_BINARY_DIR}/${NNPKG_FILE}") + + add_custom_command(OUTPUT ${NNPKG_PATH} + COMMAND ${MODEL2NNPKG} ${MODEL_PATH} + DEPENDS ${MODEL2NNPKG} ${MODEL_PATH} + COMMENT "Generate ${RECIPE} nnpackage" + ) + list(APPEND TEST_DEPS ${NNPKG_PATH}) + + set(INPUT_HDF5_FILE "${RECIPE}${OPT_FORMAT}.input.h5") + set(INPUT_BIN_PATH "${CMAKE_CURRENT_BINARY_DIR}/${INPUT_HDF5_FILE}") + + set(EXPECTED_HDF5_FILE "${RECIPE}${OPT_FORMAT}.expected.h5") + set(EXPECTED_BIN_PATH "${CMAKE_CURRENT_BINARY_DIR}/${EXPECTED_HDF5_FILE}") + + if(NOT DEFINED NO_TCGEN_${RECIPE}) + # Generate input.h5, expected.h5 + add_custom_command(OUTPUT ${INPUT_BIN_PATH} ${EXPECTED_BIN_PATH} + COMMAND $ ${MODEL_FILE} + DEPENDS $ ${MODEL_FILE} + COMMENT "Generate ${INPUT_BIN_PATH} and ${EXPECTED_BIN_PATH}" + ) + + # Generate test directory + set(TC_DIRECTORY "${NNPKG_PATH}/metadata/tc") + add_custom_command(OUTPUT ${TC_DIRECTORY} + COMMAND ${CMAKE_COMMAND} -E make_directory ${TC_DIRECTORY} + DEPENDS ${NNPKG_PATH} + COMMENT "Generate ${RECIPE} nnpackage test directory" + ) + + # Move input hdf5 file to test directory + set(INPUT_NNPKG_PATH "${TC_DIRECTORY}/input.h5") + add_custom_command(OUTPUT ${INPUT_NNPKG_PATH} + COMMAND ${CMAKE_COMMAND} -E rename ${INPUT_BIN_PATH} ${INPUT_NNPKG_PATH} + DEPENDS ${INPUT_BIN_PATH} ${TC_DIRECTORY} + COMMENT "Move ${INPUT_HDF5_FILE} to nnpackage" + ) + + # Move expected hdf5 file to test directory + set(EXPECTED_NNPKG_PATH "${TC_DIRECTORY}/expected.h5") + add_custom_command(OUTPUT ${EXPECTED_NNPKG_PATH} + COMMAND ${CMAKE_COMMAND} -E rename ${EXPECTED_BIN_PATH} ${EXPECTED_NNPKG_PATH} + DEPENDS ${EXPECTED_BIN_PATH} ${TC_DIRECTORY} + COMMENT "Move ${EXPECTED_HDF5_FILE} to nnpackage" + ) + list(APPEND TEST_DEPS ${TC_DIRECTORY} ${INPUT_BIN_PATH} ${EXPECTED_BIN_PATH} + ${INPUT_NNPKG_PATH} ${EXPECTED_NNPKG_PATH}) + endif() +endforeach() + +add_custom_target(common_artifacts_deps ALL DEPENDS ${TEST_DEPS}) diff --git a/compiler/common-artifacts/README.md b/compiler/common-artifacts/README.md new file mode 100644 index 0000000..cb6ba40 --- /dev/null +++ b/compiler/common-artifacts/README.md @@ -0,0 +1,37 @@ +# common-artifacts + +`common-artifacts` is a module that produces intermediate artifacts that are commonly generated for compiler testing. + +There are four modules used here. +- tflchef : recipe -> tflite +- tflite2circle : tflite -> circle (circlize) +- circle2circle : circle -> circle (optimize) +- TestDataGenerator : generate input.h5 and expected.h5 (tcgenerate) + +## List of intermediate artifacts +- recipe +- tflite +- circle +- circle (applied all optimizations in circle2circle) +- input data for nnpackage (.h5) +- expected output data for nnpackage (.h5) + +## How to exclude from resource generation +Sometimes a specific module that generates a resource does not support the generation of the resource, so exclusion is sometimes required. + +There is a `exclude.lst` that performs the function. If you enter the name of steps(circlize, optimize, tcgenerate) and operator you want to exclude there, you can omit the module's step. + +e.g. +``` +$ cat exclude.lst +# circlize : Exclude from tflite-to-circle conversion(tflite2circle) + +# optimize : Exclude from circle optimization(circle2circle) +optimize(ReLU6_000) +optimize(Where_000) +optimize(Where_001) + +# tcgenerate : Exclude from test data generation(TestDataGenerator) +tcgenerate(Abs_000) +tcgenerate(AddN_000) +``` diff --git a/compiler/common-artifacts/exclude.lst b/compiler/common-artifacts/exclude.lst new file mode 100644 index 0000000..b614b71 --- /dev/null +++ b/compiler/common-artifacts/exclude.lst @@ -0,0 +1,191 @@ +#[[ circlize : Exclude from tflite-to-circle conversion(tflite2circle) ]] +## TensorFlowLiteRecipes + +## CircleRecipes + +#[[ optimize : Exclude from circle optimization(circle2circle) ]] +## TensorFlowLiteRecipes +optimize(ReLU6_000) +optimize(Where_000) +optimize(Where_001) + +## CircleRecipes + +#[[ tcgenerate : Exclude from test data generation(TestDataGenerator) ]] +## TensorFlowLiteRecipes +tcgenerate(Abs_000) +tcgenerate(AddN_000) +tcgenerate(Add_001) # runtime doesn't support +tcgenerate(Add_U8_000) +tcgenerate(All_000) +tcgenerate(ArgMax_U8_000) +tcgenerate(ArgMax_U8_001) +tcgenerate(ArgMax_U8_002) +tcgenerate(ArgMax_U8_003) +tcgenerate(ArgMin_000) +tcgenerate(ArgMin_001) +tcgenerate(ArgMin_002) +tcgenerate(ArgMin_003) +tcgenerate(ArgMin_U8_000) +tcgenerate(ArgMin_U8_001) +tcgenerate(ArgMin_U8_002) +tcgenerate(ArgMin_U8_003) +tcgenerate(BatchMatMul_000) +tcgenerate(BatchMatMulV2_000) +tcgenerate(BatchMatMulV2_001) +tcgenerate(BatchToSpaceND_000) +tcgenerate(Cast_000) +tcgenerate(Cast_001) +tcgenerate(Ceil_000) +tcgenerate(Concatenation_U8_000) +tcgenerate(Conv2D_003) # runtime doesn't support dilation +tcgenerate(Conv2D_U8_000) +tcgenerate(Conv2D_U8_001) +tcgenerate(Cos_000) +tcgenerate(DepthToSpace_000) +tcgenerate(DepthwiseConv2D_001) # runtime doesn't support dilation +tcgenerate(DepthwiseConv2D_003) # runtime doesn't support dilation +tcgenerate(DepthwiseConv2D_U8_000) +tcgenerate(Div_000) +tcgenerate(ELU_000) +tcgenerate(Equal_000) +tcgenerate(Exp_000) +tcgenerate(ExpandDims_000) +tcgenerate(ExpandDims_001) +tcgenerate(ExpandDims_002) +tcgenerate(ExpandDims_003) +tcgenerate(Fill_000) +tcgenerate(Fill_001) +tcgenerate(Floor_000) +tcgenerate(FloorDiv_000) +tcgenerate(FloorDiv_001) +tcgenerate(FloorMod_000) +tcgenerate(FloorMod_001) +tcgenerate(FullyConnected_002) +tcgenerate(FullyConnected_U8_000) +tcgenerate(Gather_000) +tcgenerate(GatherNd_000) +tcgenerate(GatherNd_001) +tcgenerate(Greater_000) +tcgenerate(GreaterEqual_000) +tcgenerate(If_000) +tcgenerate(If_001) +tcgenerate(L2Normalize_000) # runtime doesn't support +tcgenerate(L2Pool2D_000) # runtime doesn't support +tcgenerate(L2Pool2D_U8_000) +tcgenerate(LeakyRelu_000) # runtime doesn't support +tcgenerate(Less_000) +tcgenerate(LessEqual_000) +tcgenerate(LocalResponseNormalization_000) # runtime doesn't support +tcgenerate(Log_000) +tcgenerate(LogicalAnd_000) +tcgenerate(LogicalNot_000) +tcgenerate(LogicalOr_000) +tcgenerate(LogSoftmax_000) +tcgenerate(MatMul_000) +tcgenerate(MatrixBandPart_000) +tcgenerate(MatrixDiag_000) +tcgenerate(MatrixSetDiag_000) +tcgenerate(Maximum_000) +tcgenerate(MaxPool2D_U8_000) +tcgenerate(Mean_U8_000) +tcgenerate(Minimum_000) +tcgenerate(MirrorPad_000) +tcgenerate(Mul_U8_000) +tcgenerate(Neg_000) +tcgenerate(Net_Dangle_001) +tcgenerate(Net_InstanceNorm_001) +tcgenerate(Net_InstanceNorm_002) +tcgenerate(Net_ZeroDim_001) # fix luci +tcgenerate(NotEqual_000) +tcgenerate(OneHot_000) +tcgenerate(OneHot_001) +tcgenerate(OneHot_002) +tcgenerate(OneHot_003) +tcgenerate(Pack_000) +tcgenerate(Pack_U8_000) +tcgenerate(Pad_U8_000) +tcgenerate(Pow_000) +tcgenerate(PRelu_000) +tcgenerate(Range_000) +tcgenerate(Rank_000) +tcgenerate(ReduceAny_000) +tcgenerate(ReduceAny_001) +tcgenerate(ReduceAny_002) +tcgenerate(ReduceAny_003) +tcgenerate(ReduceMax_000) +tcgenerate(ReduceMin_000) +tcgenerate(ReduceProd_000) +tcgenerate(ReduceProd_001) +tcgenerate(ReduceProd_002) +tcgenerate(ReduceProd_003) +tcgenerate(ReLU_000) +tcgenerate(ReLU6_000) # luci NYI +tcgenerate(ReLUN1To1_000) +tcgenerate(Reshape_003) # fix luci +tcgenerate(Reshape_U8_000) +tcgenerate(ResizeBilinear_000) +tcgenerate(ResizeNearestNeighbor_000) +tcgenerate(ReverseSequence_000) +tcgenerate(ReverseV2_000) +tcgenerate(Round_000) +tcgenerate(Rsqrt_000) +tcgenerate(ScatterNd_000) +tcgenerate(SegmentSum_000) +tcgenerate(Select_000) +tcgenerate(Select_001) +tcgenerate(Select_002) +tcgenerate(SelectV2_000) +tcgenerate(SelectV2_001) +tcgenerate(SelectV2_002) +tcgenerate(Shape_000) +tcgenerate(Sin_000) +tcgenerate(Slice_000) +tcgenerate(Softmax_U8_000) +tcgenerate(SpaceToBatchND_000) +tcgenerate(SpaceToBatchND_001) +tcgenerate(SpaceToBatchND_002) +tcgenerate(SpaceToBatchND_003) +tcgenerate(SpaceToDepth_000) +tcgenerate(SparseToDense_000) +tcgenerate(SplitV_000) # fix luci +tcgenerate(Sqrt_000) +tcgenerate(Square_000) +tcgenerate(SquaredDifference_000) +tcgenerate(Squeeze_000) +tcgenerate(StridedSlice_000) +tcgenerate(StridedSlice_001) +tcgenerate(StridedSlice_002) +tcgenerate(Sub_000) +tcgenerate(Sub_001) +tcgenerate(Sub_U8_000) +tcgenerate(Sum_000) +tcgenerate(Sum_001) +tcgenerate(Tanh_000) +tcgenerate(Tile_000) +tcgenerate(Tile_U8_000) +tcgenerate(TopKV2_000) # fix luci +tcgenerate(TopKV2_001) # fix luci +tcgenerate(TransposeConv_000) # fix interpreter +tcgenerate(Unique_000) +tcgenerate(Unique_001) +tcgenerate(Unique_002) +tcgenerate(Unique_003) +tcgenerate(Unique_U8_000) +tcgenerate(Unique_U8_001) +tcgenerate(Where_000) # luci NYI +tcgenerate(Where_001) # luci NYI +tcgenerate(While_000) # fix luci +tcgenerate(While_001) +tcgenerate(While_002) +tcgenerate(While_003) +tcgenerate(YUV_TO_RGB_000) # fix luci +tcgenerate(YUV_TO_RGB_U8_000) +tcgenerate(ZerosLike_000) + +## CircleRecipes +tcgenerate(BCQFullyConnected_000) +tcgenerate(BCQFullyConnected_001) +tcgenerate(BCQGather_000) +tcgenerate(CircleBatchMatMul_000) +tcgenerate(InstanceNorm_000) diff --git a/compiler/common-artifacts/requires.cmake b/compiler/common-artifacts/requires.cmake new file mode 100644 index 0000000..8c27565 --- /dev/null +++ b/compiler/common-artifacts/requires.cmake @@ -0,0 +1,8 @@ +require("circle2circle") +require("circlechef") +require("foder") +require("luci") +require("luci-interpreter") +require("mio-circle") +require("safemain") +require("tflchef") diff --git a/compiler/common-artifacts/src/TestDataGenerator.cpp b/compiler/common-artifacts/src/TestDataGenerator.cpp new file mode 100644 index 0000000..739300d --- /dev/null +++ b/compiler/common-artifacts/src/TestDataGenerator.cpp @@ -0,0 +1,234 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include +#include +#include + +#include + +#include +#include +#include +#include +#include + +namespace +{ + +uint32_t element_num(std::vector &vec) +{ + return static_cast( + std::accumulate(std::begin(vec), std::end(vec), 1, std::multiplies())); +} + +H5::PredType hdf5_dtype_cast(const loco::DataType loco_dtype) +{ + switch (loco_dtype) + { + case loco::DataType::U8: + return H5::PredType::NATIVE_UINT8; + case loco::DataType::S32: + return H5::PredType::NATIVE_INT32; + case loco::DataType::S64: + return H5::PredType::NATIVE_INT64; + case loco::DataType::FLOAT32: + return H5::PredType::NATIVE_FLOAT; + default: + throw std::runtime_error("NYI data type."); + } +} + +template void geneate_random_data(std::mt19937 &gen, void *data, uint32_t size) +{ + std::normal_distribution distrib(0, 2); // mean(0), stddev(2) + for (uint32_t i = 0; i < size; i++) + { + static_cast(data)[i] = static_cast(distrib(gen)); + } +} + +void fill_random_data(void *data, uint32_t size, loco::DataType dtype) +{ + std::random_device rd; // used to obtain a seed for the random number engine + std::mt19937 gen(rd()); // standard mersenne_twister_engine seeded with rd() + + switch (dtype) + { + case loco::DataType::U8: + geneate_random_data(gen, data, size); + break; + case loco::DataType::S32: + geneate_random_data(gen, data, size); + break; + case loco::DataType::S64: + geneate_random_data(gen, data, size); + break; + case loco::DataType::FLOAT32: + geneate_random_data(gen, data, size); + break; + default: + break; + } +} + +} // namespace + +int entry(int argc, char **argv) +{ + std::string circle_file{argv[1]}; + size_t last_dot_index = circle_file.find_last_of("."); + std::string prefix = circle_file.substr(0, last_dot_index); + + // load circle file + foder::FileLoader file_loader{circle_file}; + std::vector model_data = file_loader.load(); + const circle::Model *circle_model = circle::GetModel(model_data.data()); + if (circle_model == nullptr) + { + std::cerr << "ERROR: Failed to load circle '" << circle_file << "'" << std::endl; + return EXIT_FAILURE; + } + + // load luci module + std::unique_ptr module = luci::Importer().importModule(circle_model); + luci_interpreter::Interpreter interpreter(module.get()); + + /** + * HDF5 layout is like below + * + * GROUP "/" + * ㄴGROUP "name" + * ㄴATTRIBUTE "0" + * ㄴDATA (0): "input_01:0" + * ㄴATTRIBUTE "1" + * ㄴDATA (0): "input_02:0" + * ㄴGROUP "value" + * ㄴDATASET "0" + * ㄴDATA ... + * ㄴDATASET "1" + * ㄴDATA ... + */ + // create random data and dump into hdf5 file + H5::H5File input_file{prefix + ".input.h5", H5F_ACC_TRUNC}; + std::unique_ptr input_name_group = + std::make_unique(input_file.createGroup("name")); + std::unique_ptr input_value_group = + std::make_unique(input_file.createGroup("value")); + + H5::H5File output_file{prefix + ".expected.h5", H5F_ACC_TRUNC}; + std::unique_ptr output_name_group = + std::make_unique(output_file.createGroup("name")); + std::unique_ptr output_value_group = + std::make_unique(output_file.createGroup("value")); + + uint32_t input_index = 0; + for (uint32_t g = 0; g < circle_model->subgraphs()->size(); g++) + { + const auto input_nodes = loco::input_nodes(module->graph(g)); + for (const auto &node : input_nodes) + { + const auto *input_node = dynamic_cast(node); + std::string name = input_node->name(); + if (name.find(":") == std::string::npos) + name += ":0"; + + // create attribute + H5::DataSpace name_dataspace(H5S_SCALAR); + H5::StrType name_datatype(H5::PredType::C_S1, name.size()); + + auto name_attr = input_name_group->createAttribute(std::to_string(input_index), name_datatype, + name_dataspace); + + name_attr.write(name_datatype, name); + + // create value + std::vector dims(input_node->rank()); + for (uint32_t d = 0; d < input_node->rank(); d++) + { + dims.at(d) = input_node->dim(d).value(); + assert(dims.at(d) >= 0); + } + auto dataspace = std::make_unique(dims.size(), dims.data()); + auto dtype = hdf5_dtype_cast(input_node->dtype()); + auto dataset = std::make_unique( + input_file.createDataSet("value/" + std::to_string(input_index), dtype, *dataspace)); + + auto data_size = ::element_num(dims); + auto dtype_size = loco::size(input_node->dtype()); + auto byte_size = dtype_size * data_size; + std::vector data(byte_size); + + // generate random data + fill_random_data(data.data(), data_size, input_node->dtype()); + + dataset->write(data.data(), dtype); + + interpreter.writeInputTensor(input_node, data.data(), byte_size); + + input_index++; + } + } + + interpreter.interpret(); + + // dump output data into hdf5 file + uint32_t output_index = 0; + for (uint32_t g = 0; g < circle_model->subgraphs()->size(); g++) + { + const auto output_nodes = loco::output_nodes(module->graph(g)); + for (const auto &node : output_nodes) + { + const auto *output_node = dynamic_cast(node); + std::string name = output_node->name(); + if (name.find(":") == std::string::npos) + name += ":0"; + + // create attribute + H5::DataSpace name_dataspace(H5S_SCALAR); + H5::StrType name_datatype(H5::PredType::C_S1, name.size()); + + auto name_attr = output_name_group->createAttribute(std::to_string(output_index), + name_datatype, name_dataspace); + + name_attr.write(name_datatype, name); + + // create value + std::vector dims(output_node->rank()); + for (uint32_t d = 0; d < output_node->rank(); d++) + { + dims.at(d) = output_node->dim(d).value(); + assert(dims.at(d) >= 0); + } + auto dataspace = std::make_unique(dims.size(), dims.data()); + auto dtype = hdf5_dtype_cast(output_node->dtype()); + auto dataset = std::make_unique( + output_file.createDataSet("value/" + std::to_string(output_index), dtype, *dataspace)); + + uint32_t tensor_bytesize = loco::size(output_node->dtype()); + tensor_bytesize *= ::element_num(dims); + std::vector output_data(tensor_bytesize); + interpreter.readOutputTensor(output_node, output_data.data(), output_data.size()); + + dataset->write(output_data.data(), dtype); + + output_index++; + } + } + + return EXIT_SUCCESS; +} diff --git a/compiler/cwrap/include/cwrap/Fildes.h b/compiler/cwrap/include/cwrap/Fildes.h index f1061cc..22a95ef 100644 --- a/compiler/cwrap/include/cwrap/Fildes.h +++ b/compiler/cwrap/include/cwrap/Fildes.h @@ -47,7 +47,7 @@ public: int release(void); private: - int _value; + int _value{-1}; }; bool valid(const Fildes &); diff --git a/compiler/cwrap/src/Fildes.test.cpp b/compiler/cwrap/src/Fildes.test.cpp index 08e1e2a..f9fa20f 100644 --- a/compiler/cwrap/src/Fildes.test.cpp +++ b/compiler/cwrap/src/Fildes.test.cpp @@ -32,7 +32,9 @@ namespace int make_temp(char *name_template) { + mode_t mask = umask(S_IRWXU); int fd = mkstemp(name_template); + umask(mask); if (fd == -1) { @@ -55,9 +57,13 @@ TEST(FildesTest, value_constructor) { DECLARE_TEMPLATE(name_template); - cwrap::Fildes fildes{make_temp(name_template)}; + { + cwrap::Fildes fildes{make_temp(name_template)}; + + ASSERT_TRUE(cwrap::valid(fildes)); + } - ASSERT_TRUE(cwrap::valid(fildes)); + unlink(name_template); } TEST(FildesTest, move_constructor) @@ -68,19 +74,24 @@ TEST(FildesTest, move_constructor) int src_fd = make_temp(src_template); int dst_fd = make_temp(dst_template); - cwrap::Fildes src{src_fd}; - cwrap::Fildes dst{dst_fd}; + { + cwrap::Fildes src{src_fd}; + cwrap::Fildes dst{dst_fd}; - dst = std::move(src); + dst = std::move(src); - ASSERT_FALSE(cwrap::valid(src)); - ASSERT_TRUE(cwrap::valid(dst)); + ASSERT_FALSE(cwrap::valid(src)); + ASSERT_TRUE(cwrap::valid(dst)); - ASSERT_EQ(dst.get(), src_fd); + ASSERT_EQ(dst.get(), src_fd); - // "src_fd" SHOULD be valid, and "dst_fd" SHOULD be closed - ASSERT_NE(fcntl(src_fd, F_GETFD), -1); - ASSERT_EQ(fcntl(dst_fd, F_GETFD), -1); + // "src_fd" SHOULD be valid, and "dst_fd" SHOULD be closed + ASSERT_NE(fcntl(src_fd, F_GETFD), -1); + ASSERT_EQ(fcntl(dst_fd, F_GETFD), -1); + } + + unlink(src_template); + unlink(dst_template); } TEST(FildesTest, destructor) @@ -94,4 +105,6 @@ TEST(FildesTest, destructor) cwrap::Fildes fildes{fd}; } ASSERT_EQ(fcntl(fd, F_GETFD), -1); + + unlink(name_template); } diff --git a/compiler/dredd-rule-lib/rule-lib.sh b/compiler/dredd-rule-lib/rule-lib.sh index 8ebe3d7..9254cc9 100755 --- a/compiler/dredd-rule-lib/rule-lib.sh +++ b/compiler/dredd-rule-lib/rule-lib.sh @@ -200,4 +200,21 @@ verify_file_format() echo ${ACTUAL} } +op_version() +{ + argc_check $# 1 + file_path_check ${COMPILED_FILE} + file_path_check ${INSPECT_PROG_PATH} + + set -o pipefail + + ACTUAL=`init_error_log ; \ + ${INSPECT_PROG_PATH} --op_version ${COMPILED_FILE} | \ + awk -F, -v opname="$1" '{ if ($1 == opname) print $2}'` + + check_success_exit_code $? 0 + + echo ${ACTUAL} +} + # TODO define more qullity test function diff --git a/compiler/enco-intf/README.md b/compiler/enco-intf/README.md new file mode 100644 index 0000000..5f265bc --- /dev/null +++ b/compiler/enco-intf/README.md @@ -0,0 +1 @@ +# enco-intf diff --git a/compiler/exo/src/Circle/CircleTensorExporter.cpp b/compiler/exo/src/Circle/CircleTensorExporter.cpp index efceae5..6adc316 100644 --- a/compiler/exo/src/Circle/CircleTensorExporter.cpp +++ b/compiler/exo/src/Circle/CircleTensorExporter.cpp @@ -60,8 +60,8 @@ public: private: std::string _name; - circle::TensorType _dtype; - ShapeDescription _shape; + circle::TensorType _dtype{circle::TensorType_FLOAT32}; + ShapeDescription _shape{}; // TODO Find a better design loco::ConstGen *_content = nullptr; // TODO deprecate diff --git a/compiler/exo/src/Conversion/DepthwiseConv2DConverter.cpp b/compiler/exo/src/Conversion/DepthwiseConv2DConverter.cpp index 5959fcc..e3884c3 100644 --- a/compiler/exo/src/Conversion/DepthwiseConv2DConverter.cpp +++ b/compiler/exo/src/Conversion/DepthwiseConv2DConverter.cpp @@ -56,7 +56,7 @@ bool DepthwiseConv2DConverter::convert(loco::DepthwiseConv2D *origin) tfl_dw_conv2d->fusedActivationFunction(locoex::FusedActFunc::NONE); uint32_t multiplier = filter_shape.multiplier().value(); - EXO_ASSERT(multiplier < std::numeric_limits::max(), + EXO_ASSERT(multiplier < static_cast(std::numeric_limits::max()), "Multiplier is too big that casting may occur unintended behavior") tfl_dw_conv2d->depthMultiplier(static_cast(multiplier)); diff --git a/compiler/exo/src/Conversion/TensorBroadcastConverter.cpp b/compiler/exo/src/Conversion/TensorBroadcastConverter.cpp index 5323327..daccbe6 100644 --- a/compiler/exo/src/Conversion/TensorBroadcastConverter.cpp +++ b/compiler/exo/src/Conversion/TensorBroadcastConverter.cpp @@ -139,7 +139,7 @@ bool TensorBroadcastConverter::run(loco::Graph *graph) { if (node->dialect() == locoex::TFLDialect::get()) { - auto tfl_node = dynamic_cast(node); + auto tfl_node = loco::must_cast(node); tfl_node->accept(&collector); } } diff --git a/compiler/exo/src/Dialect/IR/CircleDialect.test.cpp b/compiler/exo/src/Dialect/IR/CircleDialect.test.cpp index 6132eb3..6c85b67 100644 --- a/compiler/exo/src/Dialect/IR/CircleDialect.test.cpp +++ b/compiler/exo/src/Dialect/IR/CircleDialect.test.cpp @@ -27,5 +27,5 @@ TEST(CircleDialectTest, get) // get() SHOULD return a valid(non-null) pointer ASSERT_NE(d, nullptr); // The return value SHOULD be stable across multiple invocations - ASSERT_EQ(d, CircleDialect::get()); + ASSERT_EQ(CircleDialect::get(), d); } diff --git a/compiler/exo/src/Dialect/IR/CircleNodeImpl.h b/compiler/exo/src/Dialect/IR/CircleNodeImpl.h index d9f4871..8c8b99b 100644 --- a/compiler/exo/src/Dialect/IR/CircleNodeImpl.h +++ b/compiler/exo/src/Dialect/IR/CircleNodeImpl.h @@ -18,7 +18,6 @@ #define __LOCOEX_IR_CIRCLENODEIMPL_H__ #include "CircleNodes.h" -#include "CircleNodeVisitor.h" #include diff --git a/compiler/exo/src/Dialect/IR/CircleNodeVisitor.h b/compiler/exo/src/Dialect/IR/CircleNodeVisitor.h index fc70c9e..f5c28a1 100644 --- a/compiler/exo/src/Dialect/IR/CircleNodeVisitor.h +++ b/compiler/exo/src/Dialect/IR/CircleNodeVisitor.h @@ -18,7 +18,6 @@ #define __LOCOEX_IR_CIRCLENODE_VISITOR_H__ #include "CircleNode.h" -#include "CircleNodes.h" #include diff --git a/compiler/exo/src/Dialect/IR/CircleNodes.test.cpp b/compiler/exo/src/Dialect/IR/CircleNodes.test.cpp index b63e7cc..3011bdc 100644 --- a/compiler/exo/src/Dialect/IR/CircleNodes.test.cpp +++ b/compiler/exo/src/Dialect/IR/CircleNodes.test.cpp @@ -25,12 +25,12 @@ TEST(CircleInstanceNormTest, constructor) { locoex::CircleInstanceNorm instance_norm; - ASSERT_EQ(instance_norm.dialect(), locoex::CircleDialect::get()); - ASSERT_EQ(instance_norm.opcode(), locoex::CircleOpcode::INSTANCE_NORM); + ASSERT_EQ(locoex::CircleDialect::get(), instance_norm.dialect()); + ASSERT_EQ(locoex::CircleOpcode::INSTANCE_NORM, instance_norm.opcode()); - ASSERT_EQ(instance_norm.input(), nullptr); - ASSERT_EQ(instance_norm.gamma(), nullptr); - ASSERT_EQ(instance_norm.beta(), nullptr); - ASSERT_FLOAT_EQ(instance_norm.epsilon(), 1e-05); - ASSERT_EQ(instance_norm.fusedActivationFunction(), locoex::FusedActFunc::UNDEFINED); + ASSERT_EQ(nullptr, instance_norm.input()); + ASSERT_EQ(nullptr, instance_norm.gamma()); + ASSERT_EQ(nullptr, instance_norm.beta()); + ASSERT_FLOAT_EQ(1e-05, instance_norm.epsilon()); + ASSERT_EQ(locoex::FusedActFunc::UNDEFINED, instance_norm.fusedActivationFunction()); } diff --git a/compiler/exo/src/Dialect/IR/NodeMixins.h b/compiler/exo/src/Dialect/IR/NodeMixins.h index c35daeb..9490ea1 100644 --- a/compiler/exo/src/Dialect/IR/NodeMixins.h +++ b/compiler/exo/src/Dialect/IR/NodeMixins.h @@ -58,7 +58,7 @@ protected: loco::Use *at(unsigned n) const { return _args.at(n).get(); } private: - std::array, N> _args; + std::array, N> _args{}; }; } // namespace locoex diff --git a/compiler/exo/src/Dialect/IR/TFLDialect.test.cpp b/compiler/exo/src/Dialect/IR/TFLDialect.test.cpp index 136721e..1267540 100644 --- a/compiler/exo/src/Dialect/IR/TFLDialect.test.cpp +++ b/compiler/exo/src/Dialect/IR/TFLDialect.test.cpp @@ -27,5 +27,5 @@ TEST(TFLDialectTest, get) // get() SHOULD return a valid(non-null) pointer ASSERT_NE(d, nullptr); // The return value SHOULD be stable across multiple invocations - ASSERT_EQ(d, TFLDialect::get()); + ASSERT_EQ(TFLDialect::get(), d); } diff --git a/compiler/exo/src/Dialect/IR/TFLNodeImpl.h b/compiler/exo/src/Dialect/IR/TFLNodeImpl.h index 6338827..2ec94a2 100644 --- a/compiler/exo/src/Dialect/IR/TFLNodeImpl.h +++ b/compiler/exo/src/Dialect/IR/TFLNodeImpl.h @@ -18,7 +18,6 @@ #define __LOCOEX_IR_TFLNODEIMPL_H__ #include "TFLNodes.h" -#include "TFLNodeVisitor.h" #include diff --git a/compiler/exo/src/Dialect/IR/TFLNodeVisitor.h b/compiler/exo/src/Dialect/IR/TFLNodeVisitor.h index e1f5959..147b673 100644 --- a/compiler/exo/src/Dialect/IR/TFLNodeVisitor.h +++ b/compiler/exo/src/Dialect/IR/TFLNodeVisitor.h @@ -18,7 +18,6 @@ #define __LOCOEX_IR_TFLNODE_VISITOR_H__ #include "TFLNode.h" -#include "TFLNodes.h" #include diff --git a/compiler/exo/src/Dialect/IR/TFLNodes.h b/compiler/exo/src/Dialect/IR/TFLNodes.h index 5f521a0..41a11e7 100644 --- a/compiler/exo/src/Dialect/IR/TFLNodes.h +++ b/compiler/exo/src/Dialect/IR/TFLNodes.h @@ -183,7 +183,7 @@ public: void axis(uint32_t axis) { _axis = axis; } private: - uint32_t _axis; + uint32_t _axis{0}; }; /** @@ -540,7 +540,7 @@ public: Stride *stride(void) { return &_stride; } private: - Padding _padding; + Padding _padding{Padding::UNDEFINED}; Stride _stride; }; diff --git a/compiler/exo/src/Dialect/IR/TFLNodes.test.cpp b/compiler/exo/src/Dialect/IR/TFLNodes.test.cpp index 09c5c83..f17898b 100644 --- a/compiler/exo/src/Dialect/IR/TFLNodes.test.cpp +++ b/compiler/exo/src/Dialect/IR/TFLNodes.test.cpp @@ -25,11 +25,11 @@ TEST(TFLAddTest, constructor) { locoex::TFLAdd add_node; - ASSERT_EQ(add_node.dialect(), locoex::TFLDialect::get()); - ASSERT_EQ(add_node.opcode(), locoex::TFLOpcode::ADD); + ASSERT_EQ(locoex::TFLDialect::get(), add_node.dialect()); + ASSERT_EQ(locoex::TFLOpcode::ADD, add_node.opcode()); - ASSERT_EQ(add_node.x(), nullptr); - ASSERT_EQ(add_node.y(), nullptr); + ASSERT_EQ(nullptr, add_node.x()); + ASSERT_EQ(nullptr, add_node.y()); } // TODO TFLAveragePool2D @@ -38,14 +38,14 @@ TEST(TFLConcatTest, constructor) { locoex::TFLConcatenation concat_node(3); - ASSERT_EQ(concat_node.dialect(), locoex::TFLDialect::get()); - ASSERT_EQ(concat_node.opcode(), locoex::TFLOpcode::CONCATENATION); + ASSERT_EQ(locoex::TFLDialect::get(), concat_node.dialect()); + ASSERT_EQ(locoex::TFLOpcode::CONCATENATION, concat_node.opcode()); - ASSERT_EQ(concat_node.numValues(), 3); - ASSERT_EQ(concat_node.values(0), nullptr); - ASSERT_EQ(concat_node.values(1), nullptr); - ASSERT_EQ(concat_node.values(2), nullptr); - ASSERT_EQ(concat_node.fusedActivationFunction(), locoex::FusedActFunc::UNDEFINED); + ASSERT_EQ(3, concat_node.numValues()); + ASSERT_EQ(nullptr, concat_node.values(0)); + ASSERT_EQ(nullptr, concat_node.values(1)); + ASSERT_EQ(nullptr, concat_node.values(2)); + ASSERT_EQ(locoex::FusedActFunc::UNDEFINED, concat_node.fusedActivationFunction()); } // TODO TFLConv2D @@ -54,28 +54,28 @@ TEST(TFLDepthwiseConv2DTest, constructor) { locoex::TFLDepthwiseConv2D dw_conv2d_node; - ASSERT_EQ(dw_conv2d_node.dialect(), locoex::TFLDialect::get()); - ASSERT_EQ(dw_conv2d_node.opcode(), locoex::TFLOpcode::DEPTHWISE_CONV_2D); - - ASSERT_EQ(dw_conv2d_node.input(), nullptr); - ASSERT_EQ(dw_conv2d_node.filter(), nullptr); - ASSERT_EQ(dw_conv2d_node.bias(), nullptr); - ASSERT_EQ(dw_conv2d_node.padding(), locoex::Padding::UNDEFINED); - ASSERT_EQ(dw_conv2d_node.stride()->h(), 1); - ASSERT_EQ(dw_conv2d_node.stride()->w(), 1); - ASSERT_EQ(dw_conv2d_node.depthMultiplier(), 0); - ASSERT_EQ(dw_conv2d_node.fusedActivationFunction(), locoex::FusedActFunc::UNDEFINED); + ASSERT_EQ(locoex::TFLDialect::get(), dw_conv2d_node.dialect()); + ASSERT_EQ(locoex::TFLOpcode::DEPTHWISE_CONV_2D, dw_conv2d_node.opcode()); + + ASSERT_EQ(nullptr, dw_conv2d_node.input()); + ASSERT_EQ(nullptr, dw_conv2d_node.filter()); + ASSERT_EQ(nullptr, dw_conv2d_node.bias()); + ASSERT_EQ(locoex::Padding::UNDEFINED, dw_conv2d_node.padding()); + ASSERT_EQ(1, dw_conv2d_node.stride()->h()); + ASSERT_EQ(1, dw_conv2d_node.stride()->w()); + ASSERT_EQ(0, dw_conv2d_node.depthMultiplier()); + ASSERT_EQ(locoex::FusedActFunc::UNDEFINED, dw_conv2d_node.fusedActivationFunction()); } TEST(TFLDivTest, constructor) { locoex::TFLDiv div_node; - ASSERT_EQ(div_node.dialect(), locoex::TFLDialect::get()); - ASSERT_EQ(div_node.opcode(), locoex::TFLOpcode::DIV); + ASSERT_EQ(locoex::TFLDialect::get(), div_node.dialect()); + ASSERT_EQ(locoex::TFLOpcode::DIV, div_node.opcode()); - ASSERT_EQ(div_node.x(), nullptr); - ASSERT_EQ(div_node.y(), nullptr); + ASSERT_EQ(nullptr, div_node.x()); + ASSERT_EQ(nullptr, div_node.y()); } // TODO TFLMaxPool2D @@ -84,21 +84,21 @@ TEST(TFLMulTest, constructor) { locoex::TFLMul mul_node; - ASSERT_EQ(mul_node.dialect(), locoex::TFLDialect::get()); - ASSERT_EQ(mul_node.opcode(), locoex::TFLOpcode::MUL); + ASSERT_EQ(locoex::TFLDialect::get(), mul_node.dialect()); + ASSERT_EQ(locoex::TFLOpcode::MUL, mul_node.opcode()); - ASSERT_EQ(mul_node.x(), nullptr); - ASSERT_EQ(mul_node.y(), nullptr); + ASSERT_EQ(nullptr, mul_node.x()); + ASSERT_EQ(nullptr, mul_node.y()); } TEST(TFLReluTest, constructor) { locoex::TFLRelu relu_node; - ASSERT_EQ(relu_node.dialect(), locoex::TFLDialect::get()); - ASSERT_EQ(relu_node.opcode(), locoex::TFLOpcode::RELU); + ASSERT_EQ(locoex::TFLDialect::get(), relu_node.dialect()); + ASSERT_EQ(locoex::TFLOpcode::RELU, relu_node.opcode()); - ASSERT_EQ(relu_node.features(), nullptr); + ASSERT_EQ(nullptr, relu_node.features()); } // TODO TFLRelu6 @@ -107,12 +107,12 @@ TEST(TFLReshapeTest, constructor) { locoex::TFLReshape reshape; - ASSERT_EQ(reshape.dialect(), locoex::TFLDialect::get()); - ASSERT_EQ(reshape.opcode(), locoex::TFLOpcode::RESHAPE); + ASSERT_EQ(locoex::TFLDialect::get(), reshape.dialect()); + ASSERT_EQ(locoex::TFLOpcode::RESHAPE, reshape.opcode()); - ASSERT_EQ(reshape.tensor(), nullptr); - ASSERT_EQ(reshape.shape(), nullptr); - ASSERT_EQ(reshape.newShape()->rank(), 0); + ASSERT_EQ(nullptr, reshape.tensor()); + ASSERT_EQ(nullptr, reshape.shape()); + ASSERT_EQ(0, reshape.newShape()->rank()); } TEST(TFLReshapeTest, alloc_new_shape) @@ -120,14 +120,14 @@ TEST(TFLReshapeTest, alloc_new_shape) locoex::TFLReshape reshape; reshape.newShape()->rank(2); - ASSERT_EQ(reshape.newShape()->rank(), 2); + ASSERT_EQ(2, reshape.newShape()->rank()); reshape.newShape()->dim(0) = 0; reshape.newShape()->dim(1) = 1; auto &const_reshape = const_cast(reshape); - ASSERT_EQ(const_reshape.newShape()->dim(0), 0); - ASSERT_EQ(const_reshape.newShape()->dim(1), 1); + ASSERT_EQ(0, const_reshape.newShape()->dim(0)); + ASSERT_EQ(1, const_reshape.newShape()->dim(1)); } // TODO TFLSoftmax @@ -138,11 +138,11 @@ TEST(TFLSubTest, constructor) { locoex::TFLSub sub_node; - ASSERT_EQ(sub_node.dialect(), locoex::TFLDialect::get()); - ASSERT_EQ(sub_node.opcode(), locoex::TFLOpcode::SUB); + ASSERT_EQ(locoex::TFLDialect::get(), sub_node.dialect()); + ASSERT_EQ(locoex::TFLOpcode::SUB, sub_node.opcode()); - ASSERT_EQ(sub_node.x(), nullptr); - ASSERT_EQ(sub_node.y(), nullptr); + ASSERT_EQ(nullptr, sub_node.x()); + ASSERT_EQ(nullptr, sub_node.y()); } // TODO TFLTanh @@ -151,9 +151,9 @@ TEST(TFLTransposeTest, constructor) { locoex::TFLTranspose tr_node; - ASSERT_EQ(tr_node.dialect(), locoex::TFLDialect::get()); - ASSERT_EQ(tr_node.opcode(), locoex::TFLOpcode::TRANSPOSE); + ASSERT_EQ(locoex::TFLDialect::get(), tr_node.dialect()); + ASSERT_EQ(locoex::TFLOpcode::TRANSPOSE, tr_node.opcode()); - ASSERT_EQ(tr_node.a(), nullptr); - ASSERT_EQ(tr_node.perm(), nullptr); + ASSERT_EQ(nullptr, tr_node.a()); + ASSERT_EQ(nullptr, tr_node.perm()); } diff --git a/compiler/exo/src/Dialect/Service/CircleTypeInferenceRule.cpp b/compiler/exo/src/Dialect/Service/CircleTypeInferenceRule.cpp index 6bc95a1..dd64eac 100644 --- a/compiler/exo/src/Dialect/Service/CircleTypeInferenceRule.cpp +++ b/compiler/exo/src/Dialect/Service/CircleTypeInferenceRule.cpp @@ -49,7 +49,7 @@ bool CircleTypeInferenceRule::infer(const loco::Node *node, loco::DataType &dtyp TypeInferenceAlgorithm alg; - dtype = dynamic_cast(node)->accept(&alg); + dtype = loco::must_cast(node)->accept(&alg); assert(dtype != loco::DataType::Unknown); return true; diff --git a/compiler/exo/src/Dialect/Service/TFLShapeInferenceRule.test.cpp b/compiler/exo/src/Dialect/Service/TFLShapeInferenceRule.test.cpp index 35c8f0b..b68728b 100644 --- a/compiler/exo/src/Dialect/Service/TFLShapeInferenceRule.test.cpp +++ b/compiler/exo/src/Dialect/Service/TFLShapeInferenceRule.test.cpp @@ -60,12 +60,12 @@ TEST(TFLShapeInferenceRuleTest, minimal_with_TFLRelu) // Verify { ASSERT_TRUE(loco::shape_known(tfl_node)); - ASSERT_EQ(loco::shape_get(tfl_node).domain(), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(tfl_node).domain()); auto shape = loco::shape_get(tfl_node).as(); - ASSERT_EQ(shape.rank(), 2); - ASSERT_EQ(shape.dim(0), 3); - ASSERT_EQ(shape.dim(1), 4); + ASSERT_EQ(2, shape.rank()); + ASSERT_EQ(3, shape.dim(0)); + ASSERT_EQ(4, shape.dim(1)); } } @@ -105,14 +105,14 @@ TEST(TFLShapeInferenceRuleTest, avgpool2d_valid) // Verify { ASSERT_TRUE(loco::shape_known(tfl_node)); - ASSERT_EQ(loco::shape_get(tfl_node).domain(), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(tfl_node).domain()); auto shape = loco::shape_get(tfl_node).as(); - ASSERT_EQ(shape.rank(), 4); - ASSERT_EQ(shape.dim(0).value(), 1); - ASSERT_EQ(shape.dim(1).value(), 2); - ASSERT_EQ(shape.dim(2).value(), 1); - ASSERT_EQ(shape.dim(3).value(), 1); + ASSERT_EQ(4, shape.rank()); + ASSERT_EQ(1, shape.dim(0).value()); + ASSERT_EQ(2, shape.dim(1).value()); + ASSERT_EQ(1, shape.dim(2).value()); + ASSERT_EQ(1, shape.dim(3).value()); } } @@ -152,14 +152,14 @@ TEST(TFLShapeInferenceRuleTest, avgpool2d_same) // Verify { ASSERT_TRUE(loco::shape_known(tfl_node)); - ASSERT_EQ(loco::shape_get(tfl_node).domain(), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(tfl_node).domain()); auto shape = loco::shape_get(tfl_node).as(); - ASSERT_EQ(shape.rank(), 4); - ASSERT_EQ(shape.dim(0).value(), 1); - ASSERT_EQ(shape.dim(1).value(), 2); - ASSERT_EQ(shape.dim(2).value(), 2); - ASSERT_EQ(shape.dim(3).value(), 1); + ASSERT_EQ(4, shape.rank()); + ASSERT_EQ(1, shape.dim(0).value()); + ASSERT_EQ(2, shape.dim(1).value()); + ASSERT_EQ(2, shape.dim(2).value()); + ASSERT_EQ(1, shape.dim(3).value()); } } @@ -227,13 +227,13 @@ TEST(TFLShapeInferenceRuleTest, TFAdd_shapeinf_different) // Verify { ASSERT_TRUE(loco::shape_known(tfl_node)); - ASSERT_EQ(loco::shape_get(tfl_node).domain(), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(tfl_node).domain()); auto shape = loco::shape_get(tfl_node).as(); - ASSERT_EQ(shape.rank(), 3); - ASSERT_EQ(shape.dim(0), 2); - ASSERT_EQ(shape.dim(1), 3); - ASSERT_EQ(shape.dim(2), 5); + ASSERT_EQ(3, shape.rank()); + ASSERT_EQ(2, shape.dim(0)); + ASSERT_EQ(3, shape.dim(1)); + ASSERT_EQ(5, shape.dim(2)); } } @@ -268,10 +268,10 @@ TEST(TFLShapeInferenceRuleTest, TFLTranspose_simple) ASSERT_TRUE(loco::shape_known(g.tfl_transpose)); auto shape = loco::shape_get(g.tfl_transpose).as(); - ASSERT_EQ(shape.rank(), 4); - ASSERT_EQ(shape.dim(0), 30); - ASSERT_EQ(shape.dim(1), 40); - ASSERT_EQ(shape.dim(2), 10); - ASSERT_EQ(shape.dim(3), 20); + ASSERT_EQ(4, shape.rank()); + ASSERT_EQ(30, shape.dim(0)); + ASSERT_EQ(40, shape.dim(1)); + ASSERT_EQ(10, shape.dim(2)); + ASSERT_EQ(20, shape.dim(3)); } } diff --git a/compiler/exo/src/Dialect/Service/TFLTypeInferenceRule.cpp b/compiler/exo/src/Dialect/Service/TFLTypeInferenceRule.cpp index 3f123a6..b0fecff 100644 --- a/compiler/exo/src/Dialect/Service/TFLTypeInferenceRule.cpp +++ b/compiler/exo/src/Dialect/Service/TFLTypeInferenceRule.cpp @@ -132,7 +132,7 @@ bool TFLTypeInferenceRule::infer(const loco::Node *node, loco::DataType &dtype) TypeInferenceAlgorithm alg; - dtype = dynamic_cast(node)->accept(&alg); + dtype = loco::must_cast(node)->accept(&alg); assert(dtype != loco::DataType::Unknown); return true; diff --git a/compiler/exo/src/Dialect/Service/TFLTypeInferenceRule.test.cpp b/compiler/exo/src/Dialect/Service/TFLTypeInferenceRule.test.cpp index dd1f93c..9326e5e 100644 --- a/compiler/exo/src/Dialect/Service/TFLTypeInferenceRule.test.cpp +++ b/compiler/exo/src/Dialect/Service/TFLTypeInferenceRule.test.cpp @@ -53,5 +53,5 @@ TEST(TFLTypeInferenceRuleTest, minimal_with_TFLRelu) // Verify ASSERT_TRUE(loco::dtype_known(tfl_node)); auto type = loco::dtype_get(tfl_node); - ASSERT_EQ(type, loco::DataType::S32); + ASSERT_EQ(loco::DataType::S32, type); } diff --git a/compiler/exo/src/Pass/FuseBiasAddPass.cpp b/compiler/exo/src/Pass/FuseBiasAddPass.cpp index aab8209..6338dff 100644 --- a/compiler/exo/src/Pass/FuseBiasAddPass.cpp +++ b/compiler/exo/src/Pass/FuseBiasAddPass.cpp @@ -166,7 +166,7 @@ template locoex::TFLConst *Fuser::create_fused_bias_con { // we have to create a new bias const by adding/substracting bias and const node (of TFLAdd or // TFLSub) - auto bias = dynamic_cast(_former->bias()); + auto bias = loco::must_cast(_former->bias()); assert(bias->dtype() == loco::DataType::FLOAT32 && _const_node->dtype() == loco::DataType::FLOAT32); @@ -344,7 +344,7 @@ bool FuseBiasAddPass::run(loco::Graph *g) { if (node->dialect() == locoex::TFLDialect::get()) { - auto tfl_node = dynamic_cast(node); + auto tfl_node = loco::must_cast(node); tfl_node->accept(&collector); } } diff --git a/compiler/exo/src/Pass/FuseBiasAddPass.test.cpp b/compiler/exo/src/Pass/FuseBiasAddPass.test.cpp index 6ba728d..c464d76 100644 --- a/compiler/exo/src/Pass/FuseBiasAddPass.test.cpp +++ b/compiler/exo/src/Pass/FuseBiasAddPass.test.cpp @@ -263,8 +263,8 @@ TEST(FuseBiasAddPassTest, Conv2D_Sub_02_fusing_will_not_performed) ASSERT_TRUE(a_bias != nullptr); ASSERT_TRUE(a_bias->dim(0) == 2); - ASSERT_FLOAT_EQ(a_bias->at(0), 0); - ASSERT_FLOAT_EQ(a_bias->at(1), 0); + ASSERT_FLOAT_EQ(0, a_bias->at(0)); + ASSERT_FLOAT_EQ(0, a_bias->at(1)); auto a_sub = exo::test::find_first_node_bytype(g.graph()); ASSERT_TRUE(a_sub != nullptr); diff --git a/compiler/exo/src/Pass/FuseReluPass.cpp b/compiler/exo/src/Pass/FuseReluPass.cpp index d7af0c5..ae430bc 100644 --- a/compiler/exo/src/Pass/FuseReluPass.cpp +++ b/compiler/exo/src/Pass/FuseReluPass.cpp @@ -97,7 +97,7 @@ bool FuseReluPass::run(loco::Graph *g) { if (node->dialect() == locoex::TFLDialect::get()) { - auto tfl_node = dynamic_cast(node); + auto tfl_node = loco::must_cast(node); tfl_node->accept(&collector); } } diff --git a/compiler/exo/src/TFLite/TFLExporterImpl.test.cpp b/compiler/exo/src/TFLite/TFLExporterImpl.test.cpp index 7d74223..866ede6 100644 --- a/compiler/exo/src/TFLite/TFLExporterImpl.test.cpp +++ b/compiler/exo/src/TFLite/TFLExporterImpl.test.cpp @@ -221,18 +221,18 @@ TEST(TFLExporterImplTest, Transpose_simple) auto perm = operators->Get(n)->inputs()->Get(1); auto perm_tensor = model->subgraphs()->Get(0)->tensors()->Get(perm); - ASSERT_EQ(perm_tensor->type(), tflite::TensorType::TensorType_INT32); - ASSERT_EQ(perm_tensor->shape()->size(), 1); - ASSERT_EQ(perm_tensor->shape()->Get(0), 4); + ASSERT_EQ(tflite::TensorType::TensorType_INT32, perm_tensor->type()); + ASSERT_EQ(1, perm_tensor->shape()->size()); + ASSERT_EQ(4, perm_tensor->shape()->Get(0)); auto bufs = (model->buffers()); auto *perm_buf = reinterpret_cast(bufs->Get(perm_tensor->buffer())->data()->data()); - ASSERT_EQ(perm_buf[0], 1); - ASSERT_EQ(perm_buf[1], 2); - ASSERT_EQ(perm_buf[2], 3); - ASSERT_EQ(perm_buf[3], 0); + ASSERT_EQ(1, perm_buf[0]); + ASSERT_EQ(2, perm_buf[1]); + ASSERT_EQ(3, perm_buf[2]); + ASSERT_EQ(0, perm_buf[3]); } } @@ -279,17 +279,17 @@ TEST(TFLExporterImplTest, Transpose_from_FilterEncode_FilterDecode) auto perm = operators->Get(n)->inputs()->Get(1); auto perm_tensor = model->subgraphs()->Get(0)->tensors()->Get(perm); - ASSERT_EQ(perm_tensor->type(), tflite::TensorType::TensorType_INT32); - ASSERT_EQ(perm_tensor->shape()->size(), 1); - ASSERT_EQ(perm_tensor->shape()->Get(0), 4); + ASSERT_EQ(tflite::TensorType::TensorType_INT32, perm_tensor->type()); + ASSERT_EQ(1, perm_tensor->shape()->size()); + ASSERT_EQ(4, perm_tensor->shape()->Get(0)); auto bufs = (model->buffers()); auto *perm_buf = reinterpret_cast(bufs->Get(perm_tensor->buffer())->data()->data()); - ASSERT_EQ(perm_buf[0], 3); - ASSERT_EQ(perm_buf[1], 0); - ASSERT_EQ(perm_buf[2], 1); - ASSERT_EQ(perm_buf[3], 2); + ASSERT_EQ(3, perm_buf[0]); + ASSERT_EQ(0, perm_buf[1]); + ASSERT_EQ(1, perm_buf[2]); + ASSERT_EQ(2, perm_buf[3]); } } @@ -317,8 +317,8 @@ TEST_F(TFLExporterImplTests, Regression_0000) auto decode = exo::make_feature_decode(relu); auto push = make_node(); - ASSERT_EQ(maxpool->window()->vertical(), 1); - ASSERT_EQ(maxpool->window()->horizontal(), 1); + ASSERT_EQ(1, maxpool->window()->vertical()); + ASSERT_EQ(1, maxpool->window()->horizontal()); maxpool->ifm(encode); relu->input(maxpool); @@ -350,11 +350,11 @@ TEST_F(TFLExporterImplTests, Regression_0000) switch (model->operator_codes()->Get(opcode_index)->builtin_code()) { case tflite::BuiltinOperator_RELU: - ASSERT_EQ(relu_exeuction_index, -1); + ASSERT_EQ(-1, relu_exeuction_index); relu_exeuction_index = static_cast(n); break; case tflite::BuiltinOperator_MAX_POOL_2D: - ASSERT_EQ(maxpool_execution_index, -1); + ASSERT_EQ(-1, maxpool_execution_index); maxpool_execution_index = static_cast(n); break; default: @@ -397,17 +397,17 @@ TEST_F(TFLExporterImplTests, Regression_0001) auto buffers = model->buffers(); // 0'th empty buffer + ConstGen data + ConstGen node output - ASSERT_EQ(buffers->Length(), 3); + ASSERT_EQ(3, buffers->Length()); // 0'th should be empty buffer auto buffer_0 = (*buffers)[0]; auto array_0 = buffer_0->data(); - ASSERT_EQ(array_0, nullptr); + ASSERT_EQ(nullptr, array_0); // 1'st should be ConstGen data which is two float auto buffer_1 = (*buffers)[1]; auto array_1 = buffer_1->data(); size_t size_1 = array_1->size(); - ASSERT_EQ(size_1, 2 * sizeof(float)); + ASSERT_EQ(2 * sizeof(float), size_1); } } diff --git a/compiler/exo/src/TFLite/TFLExporterUtils.test.cpp b/compiler/exo/src/TFLite/TFLExporterUtils.test.cpp index d19f87d..ec9714d 100644 --- a/compiler/exo/src/TFLite/TFLExporterUtils.test.cpp +++ b/compiler/exo/src/TFLite/TFLExporterUtils.test.cpp @@ -46,7 +46,7 @@ TEST(ExporterUtilsTests, getOpPadding) ofm._dims[1] = 2; ofm._dims[2] = 2; - ASSERT_EQ(getOpPadding(&pad, &stride, ifm, ofm), tflite::Padding_VALID); + ASSERT_EQ(tflite::Padding_VALID, getOpPadding(&pad, &stride, ifm, ofm)); } // SAME padding @@ -65,7 +65,7 @@ TEST(ExporterUtilsTests, getOpPadding) ofm._dims[1] = 3; ofm._dims[2] = 3; - ASSERT_EQ(getOpPadding(&pad, &stride, ifm, ofm), tflite::Padding_SAME); + ASSERT_EQ(tflite::Padding_SAME, getOpPadding(&pad, &stride, ifm, ofm)); } // Custom padding 1 - Not supported by tflite diff --git a/compiler/exo/src/TFLite/TFLTensorExporter.cpp b/compiler/exo/src/TFLite/TFLTensorExporter.cpp index 66854ef..23c810e 100644 --- a/compiler/exo/src/TFLite/TFLTensorExporter.cpp +++ b/compiler/exo/src/TFLite/TFLTensorExporter.cpp @@ -60,8 +60,8 @@ public: private: std::string _name; - tflite::TensorType _dtype; - ShapeDescription _shape; + tflite::TensorType _dtype{TensorType_FLOAT32}; + ShapeDescription _shape{}; // TODO Find a better design loco::ConstGen *_content = nullptr; // TODO deprecate @@ -80,7 +80,7 @@ struct NoOpDetector final : public loco::CanonicalNodeMutableVisitor bool visit(loco::FilterEncode *node) final { - auto encoder = dynamic_cast *>(node->encoder()); + auto encoder = loco::must_cast *>(node->encoder()); auto perm = encoder->perm(); return isNHWC(perm); @@ -88,14 +88,16 @@ struct NoOpDetector final : public loco::CanonicalNodeMutableVisitor bool visit(loco::FeatureEncode *node) final { - auto encoder = dynamic_cast *>(node->encoder()); + auto encoder = + loco::must_cast *>(node->encoder()); auto perm = encoder->perm(); return isNHWC(perm); } bool visit(loco::FeatureDecode *node) final { - auto decoder = dynamic_cast *>(node->decoder()); + auto decoder = + loco::must_cast *>(node->decoder()); auto perm = decoder->perm(); return isNHWC(perm); } @@ -133,7 +135,8 @@ void allocateTFLiteTensor(loco::Node *node, TFLTensorContext &ctx) tensor_info.dtype(TypeInference::get(node)); tensor_info.shape(ShapeInference::get(node)); - tensor_info.tfl_content(dynamic_cast(node)); + if (auto const_node = dynamic_cast(node)) + tensor_info.tfl_content(const_node); set_tensor_index(node, tensor_index); diff --git a/compiler/exo/src/TFLite/TFLTypeInference.test.cpp b/compiler/exo/src/TFLite/TFLTypeInference.test.cpp index 0712f0a..8a3a08d 100644 --- a/compiler/exo/src/TFLite/TFLTypeInference.test.cpp +++ b/compiler/exo/src/TFLite/TFLTypeInference.test.cpp @@ -91,7 +91,7 @@ private: private: loco::Graph _graph; - loco::Node *_last; + loco::Node *_last{nullptr}; }; struct TypeInferenceTest : public Sequential, public ::testing::Test @@ -113,6 +113,6 @@ TEST_F(TypeInferenceTest, Regression_0000) TypeInferencePass type_inf_pass; type_inf_pass.run(graph()); - ASSERT_EQ(TypeInference::get(relu), tflite::TensorType_INT8); - ASSERT_EQ(TypeInference::get(push), tflite::TensorType_INT8); + ASSERT_EQ(tflite::TensorType_INT8, TypeInference::get(relu)); + ASSERT_EQ(tflite::TensorType_INT8, TypeInference::get(push)); } diff --git a/compiler/fipe/README.md b/compiler/fipe/README.md new file mode 100644 index 0000000..7f3c74a --- /dev/null +++ b/compiler/fipe/README.md @@ -0,0 +1 @@ +# fipe diff --git a/compiler/foder/CMakeLists.txt b/compiler/foder/CMakeLists.txt new file mode 100644 index 0000000..6a413c6 --- /dev/null +++ b/compiler/foder/CMakeLists.txt @@ -0,0 +1,2 @@ +add_library(foder INTERFACE) +target_include_directories(foder INTERFACE include) diff --git a/compiler/foder/README.md b/compiler/foder/README.md new file mode 100644 index 0000000..2bb1635 --- /dev/null +++ b/compiler/foder/README.md @@ -0,0 +1,13 @@ +# foder + +_foder_ is a header only library that loads files. + +## Example + +```cpp +foder::FileLoader fileloader{input_path}; + +std::vector data = fileloader.load(); + +DO_SOMETHING_WITH(data); +``` diff --git a/compiler/foder/include/foder/FileLoader.h b/compiler/foder/include/foder/FileLoader.h new file mode 100644 index 0000000..e2143ec --- /dev/null +++ b/compiler/foder/include/foder/FileLoader.h @@ -0,0 +1,69 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include + +namespace foder +{ + +class FileLoader +{ +private: + using DataBuffer = std::vector; + +public: + explicit FileLoader(const std::string &path) : _path(path) {} + +public: + FileLoader(const FileLoader &) = delete; + FileLoader(FileLoader &&) = delete; + +public: + DataBuffer load(void) const + { + std::ifstream file(_path, std::ios::binary | std::ios::in); + if (!file.good()) + { + std::string errmsg = "ERROR: Failed to open file: " + _path; + throw std::runtime_error(errmsg.c_str()); + } + + file.unsetf(std::ios::skipws); + + file.seekg(0, std::ios::end); + auto fileSize = file.tellg(); + file.seekg(0, std::ios::beg); + + // reserve capacity + DataBuffer data(fileSize); + + // read the data + file.read(data.data(), fileSize); + if (file.fail()) + { + std::string errmsg = "ERROR: Failed to read file: " + _path; + throw std::runtime_error(errmsg.c_str()); + } + + return data; + } + +private: + const std::string _path; +}; + +} // namespace foder diff --git a/compiler/gen-core/CMakeLists.txt b/compiler/gen-core/CMakeLists.txt index 3732f49..3cee4ce 100644 --- a/compiler/gen-core/CMakeLists.txt +++ b/compiler/gen-core/CMakeLists.txt @@ -1,4 +1,4 @@ -find_package(HDF5 COMPONENTS CXX QUIET) +nnas_find_package(HDF5 QUIET) if(NOT HDF5_FOUND) return() diff --git a/compiler/i5diff/CMakeLists.txt b/compiler/i5diff/CMakeLists.txt index 321ae49..c310a66 100644 --- a/compiler/i5diff/CMakeLists.txt +++ b/compiler/i5diff/CMakeLists.txt @@ -1,4 +1,4 @@ -find_package(HDF5 COMPONENTS CXX QUIET) +nnas_find_package(HDF5 QUIET) if(NOT HDF5_FOUND) return() diff --git a/compiler/imgdata2hdf5/CMakeLists.txt b/compiler/imgdata2hdf5/CMakeLists.txt new file mode 100644 index 0000000..e2d9154 --- /dev/null +++ b/compiler/imgdata2hdf5/CMakeLists.txt @@ -0,0 +1,13 @@ +set(imgdata2hdf5_FILE "imgdata2hdf5.py") +set(imgdata2hdf5_SRC "${CMAKE_CURRENT_SOURCE_DIR}/${imgdata2hdf5_FILE}") +set(imgdata2hdf5_BIN "${CMAKE_CURRENT_BINARY_DIR}/${imgdata2hdf5_FILE}") + +add_custom_command(OUTPUT ${imgdata2hdf5_BIN} + COMMAND ${CMAKE_COMMAND} -E copy "${imgdata2hdf5_SRC}" "${imgdata2hdf5_BIN}" + DEPENDS ${imgdata2hdf5_SRC} + COMMENT "Generate ${imgdata2hdf5_BIN}" + ) + +add_custom_target(imgdata2hdf5 ALL DEPENDS ${imgdata2hdf5_BIN}) + +install(FILES ${imgdata2hdf5_BIN} DESTINATION bin) diff --git a/compiler/imgdata2hdf5/README.md b/compiler/imgdata2hdf5/README.md new file mode 100644 index 0000000..54743e0 --- /dev/null +++ b/compiler/imgdata2hdf5/README.md @@ -0,0 +1,24 @@ +# imgdata2hdf5 + +_imgdata2hdf5_ is a tool to convert raw image data (assumed to be pre-processed) to an hdf5 file. + +## Prerequisite +- Raw image data pre-processed for the corresponding DNN model +- List of data to convert (saved in the text file) +- Python installed with _numpy_ and _h5py_ (See docs/how-to-prepare-virtualenv.txt) + +## Example +``` +python imgdata2hdf5.py \ +> --data_list=tmp/imgdata/datalist.txt +> --output_path=tmp/imgdata/imgdata.hdf5 +``` + +## Arguments +``` + -h, --help Show this help message and exit + -l DATA_LIST, --data_list DATA_LIST + Path to the text file which lists the absolute paths of the raw image data files to be converted. + -o OUTPUT_PATH, --output_path OUTPUT_PATH + Path to the output hdf5 file. +``` diff --git a/compiler/imgdata2hdf5/imgdata2hdf5.py b/compiler/imgdata2hdf5/imgdata2hdf5.py new file mode 100755 index 0000000..1ff912a --- /dev/null +++ b/compiler/imgdata2hdf5/imgdata2hdf5.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python3 +import h5py as h5 +import numpy as np +import argparse +import glob +import os + +parser = argparse.ArgumentParser() +parser.add_argument( + "-l", + "--data_list", + type=str, + help= + "Path to the text file which lists the absolute paths of the raw image data files to be converted.", + required=True) +parser.add_argument( + "-o", "--output_path", type=str, help="Path to the output hdf5 file.", required=True) + +args = parser.parse_args() +data_list = args.data_list +output_path = args.output_path + +# Create h5 file +h5_file = h5.File(output_path, 'w') +group = h5_file.create_group("value") +# We assume the raw input data have the correct type/shape for the corresponding model +# If this flag is set in the hdf5 file, record-minmax will skip type/shape check +group.attrs['rawData'] = '1' + +if os.path.isfile(data_list) == False: + raise SystemExit("No such file. " + data_list) + +# Data list +datalist = [] +with open(data_list, 'r') as f: + lines = f.readlines() + for line in lines: + if line.strip(): + filename = line.rstrip() + if os.path.isfile(filename): + datalist.append(filename) + else: + raise SystemExit("No such file. " + filename) + +# Input files +num_converted = 0 +for imgdata in datalist: + with open(imgdata, 'rb') as f: + sample = group.create_group(str(num_converted)) + num_converted += 1 + filename = os.path.basename(imgdata) + sample.attrs['desc'] = filename + raw_data = bytearray(f.read()) + # The target model is DNN for handling an input image + sample.create_dataset('0', data=raw_data) + +h5_file.close() + +print("Raw image data have been packaged to " + output_path) +print("Number of packaged data: " + str(num_converted)) diff --git a/compiler/loco/include/loco/IR/CastHelpers.h b/compiler/loco/include/loco/IR/CastHelpers.h new file mode 100644 index 0000000..0dcd92d --- /dev/null +++ b/compiler/loco/include/loco/IR/CastHelpers.h @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LOCO_IR_CAST_HELPERS_H__ +#define __LOCO_IR_CAST_HELPERS_H__ + +#include +#include +#include + +namespace loco +{ + +// TODO move to somewhere appropriate +template T _must_cast(ARG arg) +{ + auto cast_arg = dynamic_cast(arg); + if (cast_arg == nullptr) + { + std::string msg = "loco::must_cast() failed to cast: "; + msg += typeid(T).name(); + throw std::invalid_argument(msg.c_str()); + } + return cast_arg; +} + +} // namespace loco + +#endif // __LOCO_IR_CAST_HELPERS_H__ diff --git a/compiler/loco/include/loco/IR/DataTypeTraits.h b/compiler/loco/include/loco/IR/DataTypeTraits.h index c4479e5..c186300 100644 --- a/compiler/loco/include/loco/IR/DataTypeTraits.h +++ b/compiler/loco/include/loco/IR/DataTypeTraits.h @@ -46,18 +46,43 @@ template <> struct DataTypeImpl using Type = uint8_t; }; +template <> struct DataTypeImpl +{ + // Use C++ int16_t type for 16bit integer + using Type = int16_t; +}; + template <> struct DataTypeImpl { // Use C++ int32_t type for 32bit integer using Type = int32_t; }; +template <> struct DataTypeImpl +{ + // Use C++ uint32_t type for unsigned 32bit integer + using Type = uint32_t; +}; + +template <> struct DataTypeImpl +{ + // Use C++ int64_t type for 64bit integer + using Type = int64_t; +}; + template <> struct DataTypeImpl { // Use C++ float type for IEEE 32-bit floating-point numbers using Type = float; }; +// NOTE DataTypeImpl for BOOL is subject to change +template <> struct DataTypeImpl +{ + // Use C++ uint8_t type for bool + using Type = uint8_t; +}; + /** * @brief Returns the size of the data type. * @note If you need the size at compile time, use `sizeof(typename DataTypeImpl
::Type)`. @@ -70,10 +95,18 @@ inline uint32_t size(DataType data_type) return sizeof(DataTypeImpl::Type); case DataType::U8: return sizeof(DataTypeImpl::Type); + case DataType::S16: + return sizeof(DataTypeImpl::Type); case DataType::S32: return sizeof(DataTypeImpl::Type); + case DataType::U32: + return sizeof(DataTypeImpl::Type); + case DataType::S64: + return sizeof(DataTypeImpl::Type); case DataType::FLOAT32: return sizeof(DataTypeImpl::Type); + case DataType::BOOL: + return sizeof(DataTypeImpl::Type); default: // TODO Support remaining data types. assert(false); diff --git a/compiler/loco/include/loco/IR/Dimension.h b/compiler/loco/include/loco/IR/Dimension.h index 7b5d594..a939f1a 100644 --- a/compiler/loco/include/loco/IR/Dimension.h +++ b/compiler/loco/include/loco/IR/Dimension.h @@ -36,18 +36,18 @@ private: }; public: - // @brief Construct an "unknown" dimension + /// @brief Construct an "unknown" dimension Dimension() = default; - // @brief Construct a "known" dimension + /// @brief Construct a "known" dimension Dimension(uint32_t value) { set(value); } public: - // @brief Return whether the value is known (or not) + /// @brief Return whether the value is known (or not) bool known(void) const { return _kind == Kind::Known; } - // @brief Return the value - // @note This value is meaningful only for known dimension + /// @brief Return the value + /// @note This value is meaningful only for known dimension uint32_t value(void) const { return _value; } void set(uint32_t value) @@ -77,7 +77,7 @@ bool operator==(const Dimension &, const Dimension &); bool operator==(const Dimension &, uint32_t); bool operator==(uint32_t, const Dimension &); -// @brief Make an "unknown" dimension +/// @brief Make an "unknown" dimension Dimension make_dimension(void); } // namespace loco diff --git a/compiler/loco/include/loco/IR/FeatureCodec.h b/compiler/loco/include/loco/IR/FeatureCodec.h index 93094e1..c5c4656 100644 --- a/compiler/loco/include/loco/IR/FeatureCodec.h +++ b/compiler/loco/include/loco/IR/FeatureCodec.h @@ -23,6 +23,8 @@ #include "loco/IR/TensorShape.h" #include "loco/IR/TensorIndex.h" +#include "loco/IR/CastHelpers.h" + #include namespace loco @@ -72,6 +74,29 @@ struct FeatureDecoder virtual std::unique_ptr clone(void) const = 0; }; +/** + * @brief A helper dynamic_cast that throws when failed + */ +template T must_cast(FeatureEncoder *node) +{ + return _must_cast(node); +} + +template T must_cast(const FeatureEncoder *node) +{ + return _must_cast(node); +} + +template T must_cast(FeatureDecoder *node) +{ + return _must_cast(node); +} + +template T must_cast(const FeatureDecoder *node) +{ + return _must_cast(node); +} + } // namespace loco #endif // __LOCO_IR_FEATURE_CODEC_H__ diff --git a/compiler/loco/include/loco/IR/FilterCodec.h b/compiler/loco/include/loco/IR/FilterCodec.h index 3ff548d..cf13dee 100644 --- a/compiler/loco/include/loco/IR/FilterCodec.h +++ b/compiler/loco/include/loco/IR/FilterCodec.h @@ -23,6 +23,8 @@ #include "loco/IR/TensorShape.h" #include "loco/IR/TensorIndex.h" +#include "loco/IR/CastHelpers.h" + namespace loco { @@ -56,6 +58,21 @@ struct FilterDecoder virtual FilterIndex value(const TensorIndex &index) const = 0; }; +/** + * @brief A helper dynamic_cast that throws when failed + */ +template T must_cast(FilterEncoder *node) +{ + return _must_cast(node); +} + +template T must_cast(const FilterEncoder *node) +{ + return _must_cast(node); +} + +// TODO add must_cast for FilterDecoder + } // namespace loco #endif // __LOCO_IR_FILTER_CODEC_H__ diff --git a/compiler/loco/include/loco/IR/Node.h b/compiler/loco/include/loco/IR/Node.h index ef0bf23..28689b7 100644 --- a/compiler/loco/include/loco/IR/Node.h +++ b/compiler/loco/include/loco/IR/Node.h @@ -23,6 +23,7 @@ #include "loco/IR/Dialect.h" #include "loco/IR/NodePool.forward.h" #include "loco/IR/Graph.forward.h" +#include "loco/IR/CastHelpers.h" #include #include @@ -142,6 +143,13 @@ private: Subst replace(Node *node); +/** + * @brief A helper dynamic_cast that throws when failed + */ +template T must_cast(Node *node) { return _must_cast(node); } + +template T must_cast(const Node *node) { return _must_cast(node); } + } // namespace loco #endif // __LOCO_IR_NODE_H__ diff --git a/compiler/loco/include/loco/IR/Nodes.h b/compiler/loco/include/loco/IR/Nodes.h index 9aac48b..fecfad2 100644 --- a/compiler/loco/include/loco/IR/Nodes.h +++ b/compiler/loco/include/loco/IR/Nodes.h @@ -632,7 +632,7 @@ public: private: TensorAxisSet _axes; - ReduceFunc _func; + ReduceFunc _func{ReduceFunc::Mean}; }; /** diff --git a/compiler/loco/include/loco/IR/TensorShape.h b/compiler/loco/include/loco/IR/TensorShape.h index af1066d..44eca67 100644 --- a/compiler/loco/include/loco/IR/TensorShape.h +++ b/compiler/loco/include/loco/IR/TensorShape.h @@ -57,6 +57,11 @@ private: */ uint32_t element_count(const loco::TensorShape *tensor_shape); +/** + * @brief '==' operator for TensorShape + */ +bool operator==(const TensorShape &lhs, const TensorShape &rhs); + } // namespace loco #endif // __LOCO_IR_TENSOR_SHAPE_H__ diff --git a/compiler/loco/src/ADT/AnnotatedItem.test.cpp b/compiler/loco/src/ADT/AnnotatedItem.test.cpp index 42113ff..45ca87d 100644 --- a/compiler/loco/src/ADT/AnnotatedItem.test.cpp +++ b/compiler/loco/src/ADT/AnnotatedItem.test.cpp @@ -41,15 +41,15 @@ TEST(AnnotatedItemTest, annotation) { loco::AnnotatedItem<::Annotation> item; - ASSERT_EQ(item.annot>(), nullptr); + ASSERT_EQ(nullptr, item.annot>()); item.annot(DerivedAnnotation<0>::make()); ASSERT_NE(item.annot>(), nullptr); - ASSERT_EQ(item.annot>(), nullptr); + ASSERT_EQ(nullptr, item.annot>()); item.annot>(nullptr); - ASSERT_EQ(item.annot>(), nullptr); + ASSERT_EQ(nullptr, item.annot>()); // Below check guarantees that "annot(nullptr)" is allowed even when there is no annotation. // This guarantee allows us to simplify code for some cases. diff --git a/compiler/loco/src/IR/Algorithm.test.cpp b/compiler/loco/src/IR/Algorithm.test.cpp index f0a3585..c60ae14 100644 --- a/compiler/loco/src/IR/Algorithm.test.cpp +++ b/compiler/loco/src/IR/Algorithm.test.cpp @@ -50,9 +50,9 @@ TEST(AlgorithmTest, postorder_traversal) auto seq = loco::postorder_traversal({push}); - ASSERT_EQ(seq.size(), 2); - ASSERT_EQ(seq.at(0), pull_1); - ASSERT_EQ(seq.at(1), push); + ASSERT_EQ(2, seq.size()); + ASSERT_EQ(pull_1, seq.at(0)); + ASSERT_EQ(push, seq.at(1)); } TEST(AlgorithmTest, postorder_traversal_visit_once) @@ -74,7 +74,7 @@ TEST(AlgorithmTest, postorder_traversal_visit_once) auto seq = loco::postorder_traversal({push_1, push_2}); - ASSERT_EQ(seq.size(), 3); + ASSERT_EQ(3, seq.size()); ASSERT_TRUE(contains(seq, pull)); ASSERT_TRUE(contains(seq, push_1)); ASSERT_TRUE(contains(seq, push_2)); @@ -97,9 +97,9 @@ TEST(AlgorithmTest, postorder_traversal_incomplte_graph) auto seq = loco::postorder_traversal({concat}); - ASSERT_EQ(seq.size(), 2); - ASSERT_EQ(seq.at(0), pull); - ASSERT_EQ(seq.at(1), concat); + ASSERT_EQ(2, seq.size()); + ASSERT_EQ(pull, seq.at(0)); + ASSERT_EQ(concat, seq.at(1)); } TEST(AlgorithmTest, active_nodes) @@ -116,7 +116,7 @@ TEST(AlgorithmTest, active_nodes) auto s = loco::active_nodes({push}); - ASSERT_EQ(s.size(), 2); + ASSERT_EQ(2, s.size()); ASSERT_TRUE(contains(s, pull)); ASSERT_TRUE(contains(s, push)); } diff --git a/compiler/loco/src/IR/CanonicalDialect.test.cpp b/compiler/loco/src/IR/CanonicalDialect.test.cpp index 96b4821..e479aed 100644 --- a/compiler/loco/src/IR/CanonicalDialect.test.cpp +++ b/compiler/loco/src/IR/CanonicalDialect.test.cpp @@ -25,5 +25,5 @@ TEST(CanonicalDialectTest, get) // get() SHOULD return a valid(non-null) pointer ASSERT_NE(d, nullptr); // The return value SHOULD be stable across multiple invocations - ASSERT_EQ(d, loco::CanonicalDialect::get()); + ASSERT_EQ(loco::CanonicalDialect::get(), d); } diff --git a/compiler/loco/src/IR/CanonicalNode.test.cpp b/compiler/loco/src/IR/CanonicalNode.test.cpp index cb61b5e..dddb158 100644 --- a/compiler/loco/src/IR/CanonicalNode.test.cpp +++ b/compiler/loco/src/IR/CanonicalNode.test.cpp @@ -34,8 +34,8 @@ TEST(CanonicalNodeTest, visitor_with_user_default_impl) MyVisitor v; - ASSERT_EQ(forward.accept(&v), 128); - ASSERT_EQ(constgen.accept(&v), 256); + ASSERT_EQ(128, forward.accept(&v)); + ASSERT_EQ(256, constgen.accept(&v)); } TEST(CanonicalNodeTest, visitor) @@ -50,7 +50,7 @@ TEST(CanonicalNodeTest, visitor) CountingVisitor v; - ASSERT_EQ(node.accept(&v), 1); + ASSERT_EQ(1, node.accept(&v)); } TEST(CanonicalNodeTest, mutable_visitor) @@ -68,5 +68,5 @@ TEST(CanonicalNodeTest, mutable_visitor) ResetForward v; forward_node.accept(&v); - ASSERT_EQ(forward_node.input(), nullptr); + ASSERT_EQ(nullptr, forward_node.input()); } diff --git a/compiler/loco/src/IR/DataTypeTraits.test.cpp b/compiler/loco/src/IR/DataTypeTraits.test.cpp index 76d2515..d33d994 100644 --- a/compiler/loco/src/IR/DataTypeTraits.test.cpp +++ b/compiler/loco/src/IR/DataTypeTraits.test.cpp @@ -25,5 +25,5 @@ TEST(DataTypeTraitsTest, FLOAT32) auto obtained = std::type_index(typeid(loco::DataTypeImpl::Type)); auto expected = std::type_index(typeid(float)); - ASSERT_EQ(obtained, expected); + ASSERT_EQ(expected, obtained); } diff --git a/compiler/loco/src/IR/DepthwiseFilterIndex.test.cpp b/compiler/loco/src/IR/DepthwiseFilterIndex.test.cpp index 202647c..f62c067 100644 --- a/compiler/loco/src/IR/DepthwiseFilterIndex.test.cpp +++ b/compiler/loco/src/IR/DepthwiseFilterIndex.test.cpp @@ -23,10 +23,10 @@ TEST(DepthwiseFilterIndexTest, default_constructor) loco::DepthwiseFilterIndex index; // All the values are 0 at the beginning - ASSERT_EQ(index.channel(), 0); - ASSERT_EQ(index.nth(), 0); - ASSERT_EQ(index.row(), 0); - ASSERT_EQ(index.column(), 0); + ASSERT_EQ(0, index.channel()); + ASSERT_EQ(0, index.nth()); + ASSERT_EQ(0, index.row()); + ASSERT_EQ(0, index.column()); } TEST(DepthwiseFilterIndexTest, settet_and_getter) @@ -36,32 +36,32 @@ TEST(DepthwiseFilterIndexTest, settet_and_getter) // Set depth index.channel() = 2; - ASSERT_EQ(index.channel(), 2); - ASSERT_EQ(index.nth(), 0); - ASSERT_EQ(index.row(), 0); - ASSERT_EQ(index.column(), 0); + ASSERT_EQ(2, index.channel()); + ASSERT_EQ(0, index.nth()); + ASSERT_EQ(0, index.row()); + ASSERT_EQ(0, index.column()); // Set multiplier index.nth() = 3; - ASSERT_EQ(index.channel(), 2); - ASSERT_EQ(index.nth(), 3); - ASSERT_EQ(index.row(), 0); - ASSERT_EQ(index.column(), 0); + ASSERT_EQ(2, index.channel()); + ASSERT_EQ(3, index.nth()); + ASSERT_EQ(0, index.row()); + ASSERT_EQ(0, index.column()); // Set height index.row() = 4; - ASSERT_EQ(index.channel(), 2); - ASSERT_EQ(index.nth(), 3); - ASSERT_EQ(index.row(), 4); - ASSERT_EQ(index.column(), 0); + ASSERT_EQ(2, index.channel()); + ASSERT_EQ(3, index.nth()); + ASSERT_EQ(4, index.row()); + ASSERT_EQ(0, index.column()); // Set width index.column() = 5; - ASSERT_EQ(index.channel(), 2); - ASSERT_EQ(index.nth(), 3); - ASSERT_EQ(index.row(), 4); - ASSERT_EQ(index.column(), 5); + ASSERT_EQ(2, index.channel()); + ASSERT_EQ(3, index.nth()); + ASSERT_EQ(4, index.row()); + ASSERT_EQ(5, index.column()); } diff --git a/compiler/loco/src/IR/DepthwiseFilterShape.test.cpp b/compiler/loco/src/IR/DepthwiseFilterShape.test.cpp index 2b9518c..56bad5e 100644 --- a/compiler/loco/src/IR/DepthwiseFilterShape.test.cpp +++ b/compiler/loco/src/IR/DepthwiseFilterShape.test.cpp @@ -40,7 +40,7 @@ TEST(DepthwiseFilterShapeTest, settet_and_getter) ASSERT_FALSE(shape.height().known()); ASSERT_FALSE(shape.width().known()); - ASSERT_EQ(shape.depth(), 2); + ASSERT_EQ(2, shape.depth()); // Set multiplier shape.multiplier() = 3; @@ -50,8 +50,8 @@ TEST(DepthwiseFilterShapeTest, settet_and_getter) ASSERT_FALSE(shape.height().known()); ASSERT_FALSE(shape.width().known()); - ASSERT_EQ(shape.depth(), 2); - ASSERT_EQ(shape.multiplier(), 3); + ASSERT_EQ(2, shape.depth()); + ASSERT_EQ(3, shape.multiplier()); // Set height shape.height() = 4; @@ -61,9 +61,9 @@ TEST(DepthwiseFilterShapeTest, settet_and_getter) ASSERT_TRUE(shape.height().known()); ASSERT_FALSE(shape.width().known()); - ASSERT_EQ(shape.depth(), 2); - ASSERT_EQ(shape.multiplier(), 3); - ASSERT_EQ(shape.height(), 4); + ASSERT_EQ(2, shape.depth()); + ASSERT_EQ(3, shape.multiplier()); + ASSERT_EQ(4, shape.height()); // Set width shape.width() = 5; @@ -73,8 +73,8 @@ TEST(DepthwiseFilterShapeTest, settet_and_getter) ASSERT_TRUE(shape.height().known()); ASSERT_TRUE(shape.width().known()); - ASSERT_EQ(shape.depth(), 2); - ASSERT_EQ(shape.multiplier(), 3); - ASSERT_EQ(shape.height(), 4); - ASSERT_EQ(shape.width(), 5); + ASSERT_EQ(2, shape.depth()); + ASSERT_EQ(3, shape.multiplier()); + ASSERT_EQ(4, shape.height()); + ASSERT_EQ(5, shape.width()); } diff --git a/compiler/loco/src/IR/Dialect.test.cpp b/compiler/loco/src/IR/Dialect.test.cpp index 312bb52..3af3033 100644 --- a/compiler/loco/src/IR/Dialect.test.cpp +++ b/compiler/loco/src/IR/Dialect.test.cpp @@ -36,6 +36,6 @@ TEST(DialectTest, service) MockDialect dialect; - ASSERT_EQ(dialect.service(), nullptr); + ASSERT_EQ(nullptr, dialect.service()); ASSERT_NE(dialect.service(), nullptr); } diff --git a/compiler/loco/src/IR/Dimension.test.cpp b/compiler/loco/src/IR/Dimension.test.cpp index 4faf78a..aa3a8d6 100644 --- a/compiler/loco/src/IR/Dimension.test.cpp +++ b/compiler/loco/src/IR/Dimension.test.cpp @@ -44,7 +44,7 @@ TEST_F(DimensionTest, value_constructor) loco::Dimension dim{value()}; ASSERT_TRUE(dim.known()); - ASSERT_EQ(dim.value(), value()); + ASSERT_EQ(value(), dim.value()); } TEST_F(DimensionTest, set) @@ -54,7 +54,7 @@ TEST_F(DimensionTest, set) dim.set(value()); ASSERT_TRUE(dim.known()); - ASSERT_EQ(dim.value(), value()); + ASSERT_EQ(value(), dim.value()); } TEST_F(DimensionTest, unset) diff --git a/compiler/loco/src/IR/FeatureIndex.test.cpp b/compiler/loco/src/IR/FeatureIndex.test.cpp index 82b5639..0ed6b4f 100644 --- a/compiler/loco/src/IR/FeatureIndex.test.cpp +++ b/compiler/loco/src/IR/FeatureIndex.test.cpp @@ -23,10 +23,10 @@ TEST(FeatureIndexTest, default_constructor) loco::FeatureIndex index; // All the values are 0 at the beginning - ASSERT_EQ(index.batch(), 0); - ASSERT_EQ(index.channel(), 0); - ASSERT_EQ(index.row(), 0); - ASSERT_EQ(index.column(), 0); + ASSERT_EQ(0, index.batch()); + ASSERT_EQ(0, index.channel()); + ASSERT_EQ(0, index.row()); + ASSERT_EQ(0, index.column()); } TEST(FeatureIndexTest, settet_and_getter) @@ -36,32 +36,32 @@ TEST(FeatureIndexTest, settet_and_getter) // Set count index.batch() = 2; - ASSERT_EQ(index.batch(), 2); - ASSERT_EQ(index.channel(), 0); - ASSERT_EQ(index.row(), 0); - ASSERT_EQ(index.column(), 0); + ASSERT_EQ(2, index.batch()); + ASSERT_EQ(0, index.channel()); + ASSERT_EQ(0, index.row()); + ASSERT_EQ(0, index.column()); // Set channel index.channel() = 3; - ASSERT_EQ(index.batch(), 2); - ASSERT_EQ(index.channel(), 3); - ASSERT_EQ(index.row(), 0); - ASSERT_EQ(index.column(), 0); + ASSERT_EQ(2, index.batch()); + ASSERT_EQ(3, index.channel()); + ASSERT_EQ(0, index.row()); + ASSERT_EQ(0, index.column()); // Set height index.row() = 4; - ASSERT_EQ(index.batch(), 2); - ASSERT_EQ(index.channel(), 3); - ASSERT_EQ(index.row(), 4); - ASSERT_EQ(index.column(), 0); + ASSERT_EQ(2, index.batch()); + ASSERT_EQ(3, index.channel()); + ASSERT_EQ(4, index.row()); + ASSERT_EQ(0, index.column()); // Set width index.column() = 5; - ASSERT_EQ(index.batch(), 2); - ASSERT_EQ(index.channel(), 3); - ASSERT_EQ(index.row(), 4); - ASSERT_EQ(index.column(), 5); + ASSERT_EQ(2, index.batch()); + ASSERT_EQ(3, index.channel()); + ASSERT_EQ(4, index.row()); + ASSERT_EQ(5, index.column()); } diff --git a/compiler/loco/src/IR/FeatureShape.test.cpp b/compiler/loco/src/IR/FeatureShape.test.cpp index 59e25ac..b0414be 100644 --- a/compiler/loco/src/IR/FeatureShape.test.cpp +++ b/compiler/loco/src/IR/FeatureShape.test.cpp @@ -40,7 +40,7 @@ TEST(FeatureShapeTest, settet_and_getter) ASSERT_FALSE(shape.height().known()); ASSERT_FALSE(shape.width().known()); - ASSERT_EQ(shape.count(), 2); + ASSERT_EQ(2, shape.count()); // Set depth shape.depth() = 3; @@ -50,8 +50,8 @@ TEST(FeatureShapeTest, settet_and_getter) ASSERT_FALSE(shape.height().known()); ASSERT_FALSE(shape.width().known()); - ASSERT_EQ(shape.count(), 2); - ASSERT_EQ(shape.depth(), 3); + ASSERT_EQ(2, shape.count()); + ASSERT_EQ(3, shape.depth()); // Set height shape.height() = 4; @@ -61,9 +61,9 @@ TEST(FeatureShapeTest, settet_and_getter) ASSERT_TRUE(shape.height().known()); ASSERT_FALSE(shape.width().known()); - ASSERT_EQ(shape.count(), 2); - ASSERT_EQ(shape.depth(), 3); - ASSERT_EQ(shape.height(), 4); + ASSERT_EQ(2, shape.count()); + ASSERT_EQ(3, shape.depth()); + ASSERT_EQ(4, shape.height()); // Set width shape.width() = 5; @@ -73,8 +73,8 @@ TEST(FeatureShapeTest, settet_and_getter) ASSERT_TRUE(shape.height().known()); ASSERT_TRUE(shape.width().known()); - ASSERT_EQ(shape.count(), 2); - ASSERT_EQ(shape.depth(), 3); - ASSERT_EQ(shape.height(), 4); - ASSERT_EQ(shape.width(), 5); + ASSERT_EQ(2, shape.count()); + ASSERT_EQ(3, shape.depth()); + ASSERT_EQ(4, shape.height()); + ASSERT_EQ(5, shape.width()); } diff --git a/compiler/loco/src/IR/FilterIndex.test.cpp b/compiler/loco/src/IR/FilterIndex.test.cpp index 58f3871..4edf62c 100644 --- a/compiler/loco/src/IR/FilterIndex.test.cpp +++ b/compiler/loco/src/IR/FilterIndex.test.cpp @@ -23,10 +23,10 @@ TEST(FilterIndexTest, default_constructor) loco::FilterIndex index; // All the values are 0 at the beginning - ASSERT_EQ(index.nth(), 0); - ASSERT_EQ(index.channel(), 0); - ASSERT_EQ(index.row(), 0); - ASSERT_EQ(index.column(), 0); + ASSERT_EQ(0, index.nth()); + ASSERT_EQ(0, index.channel()); + ASSERT_EQ(0, index.row()); + ASSERT_EQ(0, index.column()); } TEST(FilterIndexTest, settet_and_getter) @@ -36,32 +36,32 @@ TEST(FilterIndexTest, settet_and_getter) // Set count index.nth() = 2; - ASSERT_EQ(index.nth(), 2); - ASSERT_EQ(index.channel(), 0); - ASSERT_EQ(index.row(), 0); - ASSERT_EQ(index.column(), 0); + ASSERT_EQ(2, index.nth()); + ASSERT_EQ(0, index.channel()); + ASSERT_EQ(0, index.row()); + ASSERT_EQ(0, index.column()); // Set channel index.channel() = 3; - ASSERT_EQ(index.nth(), 2); - ASSERT_EQ(index.channel(), 3); - ASSERT_EQ(index.row(), 0); - ASSERT_EQ(index.column(), 0); + ASSERT_EQ(2, index.nth()); + ASSERT_EQ(3, index.channel()); + ASSERT_EQ(0, index.row()); + ASSERT_EQ(0, index.column()); // Set height index.row() = 4; - ASSERT_EQ(index.nth(), 2); - ASSERT_EQ(index.channel(), 3); - ASSERT_EQ(index.row(), 4); - ASSERT_EQ(index.column(), 0); + ASSERT_EQ(2, index.nth()); + ASSERT_EQ(3, index.channel()); + ASSERT_EQ(4, index.row()); + ASSERT_EQ(0, index.column()); // Set width index.column() = 5; - ASSERT_EQ(index.nth(), 2); - ASSERT_EQ(index.channel(), 3); - ASSERT_EQ(index.row(), 4); - ASSERT_EQ(index.column(), 5); + ASSERT_EQ(2, index.nth()); + ASSERT_EQ(3, index.channel()); + ASSERT_EQ(4, index.row()); + ASSERT_EQ(5, index.column()); } diff --git a/compiler/loco/src/IR/FilterShape.test.cpp b/compiler/loco/src/IR/FilterShape.test.cpp index ccb60ed..8033027 100644 --- a/compiler/loco/src/IR/FilterShape.test.cpp +++ b/compiler/loco/src/IR/FilterShape.test.cpp @@ -40,7 +40,7 @@ TEST(FilterShapeTest, settet_and_getter) ASSERT_FALSE(shape.height().known()); ASSERT_FALSE(shape.width().known()); - ASSERT_EQ(shape.count(), 2); + ASSERT_EQ(2, shape.count()); // Set depth shape.depth() = 3; @@ -50,8 +50,8 @@ TEST(FilterShapeTest, settet_and_getter) ASSERT_FALSE(shape.height().known()); ASSERT_FALSE(shape.width().known()); - ASSERT_EQ(shape.count(), 2); - ASSERT_EQ(shape.depth(), 3); + ASSERT_EQ(2, shape.count()); + ASSERT_EQ(3, shape.depth()); // Set height shape.height() = 4; @@ -61,9 +61,9 @@ TEST(FilterShapeTest, settet_and_getter) ASSERT_TRUE(shape.height().known()); ASSERT_FALSE(shape.width().known()); - ASSERT_EQ(shape.count(), 2); - ASSERT_EQ(shape.depth(), 3); - ASSERT_EQ(shape.height(), 4); + ASSERT_EQ(2, shape.count()); + ASSERT_EQ(3, shape.depth()); + ASSERT_EQ(4, shape.height()); // Set width shape.width() = 5; @@ -73,8 +73,8 @@ TEST(FilterShapeTest, settet_and_getter) ASSERT_TRUE(shape.height().known()); ASSERT_TRUE(shape.width().known()); - ASSERT_EQ(shape.count(), 2); - ASSERT_EQ(shape.depth(), 3); - ASSERT_EQ(shape.height(), 4); - ASSERT_EQ(shape.width(), 5); + ASSERT_EQ(2, shape.count()); + ASSERT_EQ(3, shape.depth()); + ASSERT_EQ(4, shape.height()); + ASSERT_EQ(5, shape.width()); } diff --git a/compiler/loco/src/IR/Graph.cpp b/compiler/loco/src/IR/Graph.cpp index 1d87522..8073d45 100644 --- a/compiler/loco/src/IR/Graph.cpp +++ b/compiler/loco/src/IR/Graph.cpp @@ -37,7 +37,7 @@ std::unique_ptr make_tensor_shape(std::initializer_list mixin; - ASSERT_EQ(mixin.dtype(), loco::DataType::Unknown); + ASSERT_EQ(loco::DataType::Unknown, mixin.dtype()); } TEST(DataTypedMixinTest, setter_and_getter) @@ -56,7 +56,7 @@ TEST(DataTypedMixinTest, setter_and_getter) loco::Mixin mixin; mixin.dtype(loco::DataType::FLOAT32); - ASSERT_EQ(mixin.dtype(), loco::DataType::FLOAT32); + ASSERT_EQ(loco::DataType::FLOAT32, mixin.dtype()); } TEST(TensorShapedMixinTest, setter_and_getter) @@ -65,11 +65,11 @@ TEST(TensorShapedMixinTest, setter_and_getter) mixin.shape({1, 2, 3, 4}); ASSERT_NE(mixin.shape(), nullptr); - ASSERT_EQ(mixin.shape()->rank(), 4); - ASSERT_EQ(mixin.shape()->dim(0), 1); - ASSERT_EQ(mixin.shape()->dim(1), 2); - ASSERT_EQ(mixin.shape()->dim(2), 3); - ASSERT_EQ(mixin.shape()->dim(3), 4); + ASSERT_EQ(4, mixin.shape()->rank()); + ASSERT_EQ(1, mixin.shape()->dim(0)); + ASSERT_EQ(2, mixin.shape()->dim(1)); + ASSERT_EQ(3, mixin.shape()->dim(2)); + ASSERT_EQ(4, mixin.shape()->dim(3)); } TEST(GraphTest, create_and_destroy_node) @@ -89,8 +89,8 @@ TEST(GraphTest, create_input) auto input = g->inputs()->create(); // TODO Add more checks - ASSERT_EQ(input->shape(), nullptr); - ASSERT_EQ(input->index(), 0); + ASSERT_EQ(nullptr, input->shape()); + ASSERT_EQ(0, input->index()); } TEST(GraphTest, create_output) @@ -100,8 +100,8 @@ TEST(GraphTest, create_output) auto output = g->outputs()->create(); // TODO Add more checks - ASSERT_EQ(output->shape(), nullptr); - ASSERT_EQ(output->index(), 0); + ASSERT_EQ(nullptr, output->shape()); + ASSERT_EQ(0, output->index()); } namespace @@ -132,10 +132,10 @@ TEST(GraphTest, consturctor_with_param_node) auto test_node = g->nodes()->create(22, 11.11); - ASSERT_EQ(test_node->graph(), g.get()); - ASSERT_EQ(const_cast(test_node)->graph(), g.get()); + ASSERT_EQ(g.get(), test_node->graph()); + ASSERT_EQ(g.get(), const_cast(test_node)->graph()); - ASSERT_EQ(test_node->i(), 22); + ASSERT_EQ(22, test_node->i()); ASSERT_FLOAT_EQ(test_node->f(), 11.11); ASSERT_NO_THROW(g->nodes()->destroy(test_node)); @@ -170,7 +170,7 @@ TEST(GraphTest, graph_node_enumeration) // Returns true if "nodes" includes a given node auto member = [&nodes](loco::Node *node) { return nodes.find(node) != nodes.end(); }; - ASSERT_EQ(nodes.size(), 2); + ASSERT_EQ(2, nodes.size()); ASSERT_TRUE(member(pull_1)); ASSERT_TRUE(member(push_1)); } @@ -197,9 +197,9 @@ TEST(GraphTest, graph_inout_enumeration) auto output_nodes = loco::output_nodes(g.get()); - ASSERT_EQ(output_nodes.size(), 2); - ASSERT_EQ(output_nodes.at(0), push_1); - ASSERT_EQ(output_nodes.at(1), push_3); + ASSERT_EQ(2, output_nodes.size()); + ASSERT_EQ(push_1, output_nodes.at(0)); + ASSERT_EQ(push_3, output_nodes.at(1)); } TEST(GraphTest, graph_name) diff --git a/compiler/loco/src/IR/MockupNode.h b/compiler/loco/src/IR/MockupNode.h index ec56c90..16eaccf 100644 --- a/compiler/loco/src/IR/MockupNode.h +++ b/compiler/loco/src/IR/MockupNode.h @@ -32,7 +32,7 @@ struct MockDialect final : public loco::Dialect } }; -// @brief Mockup node for internal testing +/// @brief Mockup node for internal testing class MockupNode final : public loco::Node { public: @@ -53,6 +53,21 @@ private: loco::Use _arg{this}; }; +/// @brief Mockup2Node node for internal testing +class Mockup2Node final : public loco::Node +{ +public: + Mockup2Node() = default; + +public: + const loco::Dialect *dialect(void) const final { return MockDialect::get(); } + uint32_t opnum(void) const final { return 1; } + + uint32_t arity(void) const final { return 0; } + Node *arg(uint32_t) const final { return nullptr; } + void drop(void) final {} +}; + } // namespace #endif // __LOCO_IR_MOCKUP_NODE_H__ diff --git a/compiler/loco/src/IR/Node.test.cpp b/compiler/loco/src/IR/Node.test.cpp index 00e4444..7c3eb3f 100644 --- a/compiler/loco/src/IR/Node.test.cpp +++ b/compiler/loco/src/IR/Node.test.cpp @@ -29,7 +29,7 @@ TEST(NodeTest, preds) auto preds = loco::preds(&node); - ASSERT_EQ(preds.size(), 1); + ASSERT_EQ(1, preds.size()); ASSERT_NE(preds.find(&arg), preds.end()); } @@ -44,7 +44,7 @@ TEST(NodeTest, succs) auto succs = loco::succs(&node); - ASSERT_EQ(succs.size(), 2); + ASSERT_EQ(2, succs.size()); ASSERT_NE(succs.find(&succ_1), succs.end()); ASSERT_NE(succs.find(&succ_2), succs.end()); } @@ -63,8 +63,8 @@ TEST(NodeTest, replace_with) // The following holds at this point // - node_3 USE node_1 // - node_4 USE node_2 - ASSERT_EQ(node_3.in(), &node_1); - ASSERT_EQ(node_4.in(), &node_2); + ASSERT_EQ(&node_1, node_3.in()); + ASSERT_EQ(&node_2, node_4.in()); // Replace all the usage of node_1 with node_2 replace(&node_1).with(&node_2); @@ -72,8 +72,8 @@ TEST(NodeTest, replace_with) // The following holds at this point // - node_3 USE node_2 // - node_4 USE node_2 - ASSERT_EQ(node_3.in(), &node_2); - ASSERT_EQ(node_4.in(), &node_2); + ASSERT_EQ(&node_2, node_3.in()); + ASSERT_EQ(&node_2, node_4.in()); } TEST(NodeTest, constructor) @@ -81,7 +81,7 @@ TEST(NodeTest, constructor) MockupNode node; // graph() SHOULD return nullptr if node is not constructed through "Graph" - ASSERT_EQ(node.graph(), nullptr); + ASSERT_EQ(nullptr, node.graph()); } // TODO Rewrite this as a FixedAritry mix-in test @@ -96,7 +96,23 @@ TEST(FixedArityNodeTest, constructor) DerivedNode node; - ASSERT_EQ(node.arity(), 1); - ASSERT_EQ(node.arg(0), nullptr); + ASSERT_EQ(1, node.arity()); + ASSERT_EQ(nullptr, node.arg(0)); } #endif + +TEST(NodeTest, cast_with_must_NEG) +{ + Mockup2Node mockupnode; + loco::Node *node = &mockupnode; + + ASSERT_THROW(loco::must_cast(node), std::invalid_argument); +} + +TEST(NodeTest, cast_with_const_must_NEG) +{ + Mockup2Node mockupnode; + const loco::Node *node = &mockupnode; + + ASSERT_THROW(loco::must_cast(node), std::invalid_argument); +} diff --git a/compiler/loco/src/IR/NodeShape.test.cpp b/compiler/loco/src/IR/NodeShape.test.cpp index 4f092e0..8bf7900 100644 --- a/compiler/loco/src/IR/NodeShape.test.cpp +++ b/compiler/loco/src/IR/NodeShape.test.cpp @@ -22,7 +22,7 @@ TEST(NodeShapeTest, default_constructor) { loco::NodeShape node_shape; - ASSERT_EQ(node_shape.domain(), loco::Domain::Unknown); + ASSERT_EQ(loco::Domain::Unknown, node_shape.domain()); } TEST(NodeShapeTest, bias_shape_constructor) @@ -33,8 +33,8 @@ TEST(NodeShapeTest, bias_shape_constructor) loco::NodeShape node_shape{bias_shape}; - ASSERT_EQ(node_shape.domain(), loco::Domain::Bias); - ASSERT_EQ(node_shape.as().length(), 4); + ASSERT_EQ(loco::Domain::Bias, node_shape.domain()); + ASSERT_EQ(4, node_shape.as().length()); } TEST(NodeShapeTest, dwfilter_shape_constructor) @@ -48,11 +48,11 @@ TEST(NodeShapeTest, dwfilter_shape_constructor) loco::NodeShape node_shape{dwfilter_shape}; - ASSERT_EQ(node_shape.domain(), loco::Domain::DepthwiseFilter); - ASSERT_EQ(node_shape.as().depth(), 2); - ASSERT_EQ(node_shape.as().multiplier(), 3); - ASSERT_EQ(node_shape.as().height(), 4); - ASSERT_EQ(node_shape.as().width(), 5); + ASSERT_EQ(loco::Domain::DepthwiseFilter, node_shape.domain()); + ASSERT_EQ(2, node_shape.as().depth()); + ASSERT_EQ(3, node_shape.as().multiplier()); + ASSERT_EQ(4, node_shape.as().height()); + ASSERT_EQ(5, node_shape.as().width()); } TEST(NodeShapeTest, feature_shape_constructor) @@ -66,11 +66,11 @@ TEST(NodeShapeTest, feature_shape_constructor) loco::NodeShape node_shape{feature_shape}; - ASSERT_EQ(node_shape.domain(), loco::Domain::Feature); - ASSERT_EQ(node_shape.as().count(), 2); - ASSERT_EQ(node_shape.as().depth(), 3); - ASSERT_EQ(node_shape.as().height(), 4); - ASSERT_EQ(node_shape.as().width(), 5); + ASSERT_EQ(loco::Domain::Feature, node_shape.domain()); + ASSERT_EQ(2, node_shape.as().count()); + ASSERT_EQ(3, node_shape.as().depth()); + ASSERT_EQ(4, node_shape.as().height()); + ASSERT_EQ(5, node_shape.as().width()); } TEST(NodeShapeTest, filter_shape_constructor) @@ -84,11 +84,11 @@ TEST(NodeShapeTest, filter_shape_constructor) loco::NodeShape node_shape{filter_shape}; - ASSERT_EQ(node_shape.domain(), loco::Domain::Filter); - ASSERT_EQ(node_shape.as().count(), 2); - ASSERT_EQ(node_shape.as().depth(), 3); - ASSERT_EQ(node_shape.as().height(), 4); - ASSERT_EQ(node_shape.as().width(), 5); + ASSERT_EQ(loco::Domain::Filter, node_shape.domain()); + ASSERT_EQ(2, node_shape.as().count()); + ASSERT_EQ(3, node_shape.as().depth()); + ASSERT_EQ(4, node_shape.as().height()); + ASSERT_EQ(5, node_shape.as().width()); } TEST(NodeShapeTest, tensor_shape_constructor) @@ -101,10 +101,10 @@ TEST(NodeShapeTest, tensor_shape_constructor) loco::NodeShape node_shape{tensor_shape}; - ASSERT_EQ(node_shape.domain(), loco::Domain::Tensor); - ASSERT_EQ(node_shape.as().rank(), 2); - ASSERT_EQ(node_shape.as().dim(0), 4); - ASSERT_EQ(node_shape.as().dim(1), 5); + ASSERT_EQ(loco::Domain::Tensor, node_shape.domain()); + ASSERT_EQ(2, node_shape.as().rank()); + ASSERT_EQ(4, node_shape.as().dim(0)); + ASSERT_EQ(5, node_shape.as().dim(1)); } TEST(NodeShapeTest, copy_constructible) @@ -118,8 +118,8 @@ TEST(NodeShapeTest, copy_constructible) loco::NodeShape orig{tensor_shape}; loco::NodeShape copy{orig}; // Call Copy Constructor - ASSERT_EQ(copy.domain(), loco::Domain::Tensor); - ASSERT_EQ(copy.as().rank(), 2); - ASSERT_EQ(copy.as().dim(0), 4); - ASSERT_EQ(copy.as().dim(1), 5); + ASSERT_EQ(loco::Domain::Tensor, copy.domain()); + ASSERT_EQ(2, copy.as().rank()); + ASSERT_EQ(4, copy.as().dim(0)); + ASSERT_EQ(5, copy.as().dim(1)); } diff --git a/compiler/loco/src/IR/Nodes.test.cpp b/compiler/loco/src/IR/Nodes.test.cpp index cd51f46..0b22103 100644 --- a/compiler/loco/src/IR/Nodes.test.cpp +++ b/compiler/loco/src/IR/Nodes.test.cpp @@ -23,8 +23,8 @@ TEST(PushTest, constructor) { loco::Push push_node; - ASSERT_EQ(push_node.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(push_node.opcode(), loco::CanonicalOpcode::Push); + ASSERT_EQ(loco::CanonicalDialect::get(), push_node.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::Push, push_node.opcode()); ASSERT_FALSE(push_node.indexed()); } @@ -37,24 +37,24 @@ TEST(PushTest, shape) push_node.shape({dims[0], dims[1], dims[2], dims[3]}); - ASSERT_EQ(push_node.rank(), dims.size()); - ASSERT_EQ(push_node.dim(0), dims[0]); - ASSERT_EQ(push_node.dim(1), dims[1]); - ASSERT_EQ(push_node.dim(2), dims[2]); - ASSERT_EQ(push_node.dim(3), dims[3]); + ASSERT_EQ(dims.size(), push_node.rank()); + ASSERT_EQ(dims[0], push_node.dim(0)); + ASSERT_EQ(dims[1], push_node.dim(1)); + ASSERT_EQ(dims[2], push_node.dim(2)); + ASSERT_EQ(dims[3], push_node.dim(3)); } TEST(PullTest, constructor) { loco::Pull pull_node; - ASSERT_EQ(pull_node.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(pull_node.opcode(), loco::CanonicalOpcode::Pull); + ASSERT_EQ(loco::CanonicalDialect::get(), pull_node.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::Pull, pull_node.opcode()); ASSERT_FALSE(pull_node.indexed()); - ASSERT_EQ(pull_node.dtype(), loco::DataType::Unknown); - ASSERT_EQ(pull_node.rank(), 0); + ASSERT_EQ(loco::DataType::Unknown, pull_node.dtype()); + ASSERT_EQ(0, pull_node.rank()); } TEST(PullTest, shape) @@ -65,58 +65,58 @@ TEST(PullTest, shape) pull_node.shape({dims[0], dims[1], dims[2], dims[3]}); - ASSERT_EQ(pull_node.rank(), dims.size()); - ASSERT_EQ(pull_node.dim(0), dims[0]); - ASSERT_EQ(pull_node.dim(1), dims[1]); - ASSERT_EQ(pull_node.dim(2), dims[2]); - ASSERT_EQ(pull_node.dim(3), dims[3]); + ASSERT_EQ(dims.size(), pull_node.rank()); + ASSERT_EQ(dims[0], pull_node.dim(0)); + ASSERT_EQ(dims[1], pull_node.dim(1)); + ASSERT_EQ(dims[2], pull_node.dim(2)); + ASSERT_EQ(dims[3], pull_node.dim(3)); } TEST(ForwardTest, constructor) { loco::Forward forward_node; - ASSERT_EQ(forward_node.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(forward_node.opcode(), loco::CanonicalOpcode::Forward); + ASSERT_EQ(loco::CanonicalDialect::get(), forward_node.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::Forward, forward_node.opcode()); - ASSERT_EQ(forward_node.input(), nullptr); + ASSERT_EQ(nullptr, forward_node.input()); } TEST(ReLUTest, constructor) { loco::ReLU relu_node; - ASSERT_EQ(relu_node.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(relu_node.opcode(), loco::CanonicalOpcode::ReLU); + ASSERT_EQ(loco::CanonicalDialect::get(), relu_node.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::ReLU, relu_node.opcode()); - ASSERT_EQ(relu_node.input(), nullptr); + ASSERT_EQ(nullptr, relu_node.input()); } TEST(ReLU6Test, constructor) { loco::ReLU6 relu6_node; - ASSERT_EQ(relu6_node.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(relu6_node.opcode(), loco::CanonicalOpcode::ReLU6); + ASSERT_EQ(loco::CanonicalDialect::get(), relu6_node.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::ReLU6, relu6_node.opcode()); - ASSERT_EQ(relu6_node.input(), nullptr); + ASSERT_EQ(nullptr, relu6_node.input()); } TEST(ConstGenTest, constructor) { loco::ConstGen constgen_node; - ASSERT_EQ(constgen_node.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(constgen_node.opcode(), loco::CanonicalOpcode::ConstGen); + ASSERT_EQ(loco::CanonicalDialect::get(), constgen_node.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::ConstGen, constgen_node.opcode()); - ASSERT_EQ(constgen_node.dtype(), loco::DataType::Unknown); - ASSERT_EQ(constgen_node.rank(), 0); + ASSERT_EQ(loco::DataType::Unknown, constgen_node.dtype()); + ASSERT_EQ(0, constgen_node.rank()); constgen_node.dtype(loco::DataType::FLOAT32); - ASSERT_EQ(constgen_node.dtype(), loco::DataType::FLOAT32); + ASSERT_EQ(loco::DataType::FLOAT32, constgen_node.dtype()); constgen_node.rank(2); - ASSERT_EQ(constgen_node.rank(), 2); + ASSERT_EQ(2, constgen_node.rank()); constgen_node.dim(0) = 2; constgen_node.dim(1) = 3; @@ -124,12 +124,12 @@ TEST(ConstGenTest, constructor) ASSERT_TRUE(constgen_node.dim(0).known()); ASSERT_TRUE(constgen_node.dim(1).known()); - ASSERT_EQ(constgen_node.dim(0), 2); - ASSERT_EQ(constgen_node.dim(1), 3); + ASSERT_EQ(2, constgen_node.dim(0)); + ASSERT_EQ(3, constgen_node.dim(1)); constgen_node.size(6); - ASSERT_EQ(constgen_node.size(), 6); + ASSERT_EQ(6, constgen_node.size()); constgen_node.at(0) = 0.0f; // Set 0,0 constgen_node.at(1) = 1.0f; // Set 0,1 @@ -138,26 +138,26 @@ TEST(ConstGenTest, constructor) constgen_node.at(4) = 4.0f; // Set 1,1 constgen_node.at(5) = 5.0f; // Set 1,2 - ASSERT_EQ(constgen_node.at(0), 0.0f); - ASSERT_EQ(constgen_node.at(1), 1.0f); - ASSERT_EQ(constgen_node.at(2), 2.0f); - ASSERT_EQ(constgen_node.at(3), 3.0f); - ASSERT_EQ(constgen_node.at(4), 4.0f); - ASSERT_EQ(constgen_node.at(5), 5.0f); + ASSERT_EQ(0.0f, constgen_node.at(0)); + ASSERT_EQ(1.0f, constgen_node.at(1)); + ASSERT_EQ(2.0f, constgen_node.at(2)); + ASSERT_EQ(3.0f, constgen_node.at(3)); + ASSERT_EQ(4.0f, constgen_node.at(4)); + ASSERT_EQ(5.0f, constgen_node.at(5)); } TEST(ConstGenTest, constructor_s32) { loco::ConstGen constgen_node; - ASSERT_EQ(constgen_node.dtype(), loco::DataType::Unknown); - ASSERT_EQ(constgen_node.rank(), 0); + ASSERT_EQ(loco::DataType::Unknown, constgen_node.dtype()); + ASSERT_EQ(0, constgen_node.rank()); constgen_node.dtype(loco::DataType::S32); - ASSERT_EQ(constgen_node.dtype(), loco::DataType::S32); + ASSERT_EQ(loco::DataType::S32, constgen_node.dtype()); constgen_node.rank(2); - ASSERT_EQ(constgen_node.rank(), 2); + ASSERT_EQ(2, constgen_node.rank()); constgen_node.dim(0) = 2; constgen_node.dim(1) = 3; @@ -165,12 +165,12 @@ TEST(ConstGenTest, constructor_s32) ASSERT_TRUE(constgen_node.dim(0).known()); ASSERT_TRUE(constgen_node.dim(1).known()); - ASSERT_EQ(constgen_node.dim(0), 2); - ASSERT_EQ(constgen_node.dim(1), 3); + ASSERT_EQ(2, constgen_node.dim(0)); + ASSERT_EQ(3, constgen_node.dim(1)); constgen_node.size(6); - ASSERT_EQ(constgen_node.size(), 6); + ASSERT_EQ(6, constgen_node.size()); constgen_node.at(0) = 0; // Set 0,0 constgen_node.at(1) = 1; // Set 0,1 @@ -179,33 +179,33 @@ TEST(ConstGenTest, constructor_s32) constgen_node.at(4) = -4; // Set 1,1 constgen_node.at(5) = -5; // Set 1,2 - ASSERT_EQ(constgen_node.at(0), 0); - ASSERT_EQ(constgen_node.at(1), 1); - ASSERT_EQ(constgen_node.at(2), 2); - ASSERT_EQ(constgen_node.at(3), -3); - ASSERT_EQ(constgen_node.at(4), -4); - ASSERT_EQ(constgen_node.at(5), -5); + ASSERT_EQ(0, constgen_node.at(0)); + ASSERT_EQ(1, constgen_node.at(1)); + ASSERT_EQ(2, constgen_node.at(2)); + ASSERT_EQ(-3, constgen_node.at(3)); + ASSERT_EQ(-4, constgen_node.at(4)); + ASSERT_EQ(-5, constgen_node.at(5)); } TEST(MaxPool2DTest, constructor) { loco::MaxPool2D maxpool_node; - ASSERT_EQ(maxpool_node.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(maxpool_node.opcode(), loco::CanonicalOpcode::MaxPool2D); + ASSERT_EQ(loco::CanonicalDialect::get(), maxpool_node.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::MaxPool2D, maxpool_node.opcode()); - ASSERT_EQ(maxpool_node.ifm(), nullptr); + ASSERT_EQ(nullptr, maxpool_node.ifm()); - ASSERT_EQ(maxpool_node.pad()->top(), 0); - ASSERT_EQ(maxpool_node.pad()->bottom(), 0); - ASSERT_EQ(maxpool_node.pad()->left(), 0); - ASSERT_EQ(maxpool_node.pad()->right(), 0); + ASSERT_EQ(0, maxpool_node.pad()->top()); + ASSERT_EQ(0, maxpool_node.pad()->bottom()); + ASSERT_EQ(0, maxpool_node.pad()->left()); + ASSERT_EQ(0, maxpool_node.pad()->right()); - ASSERT_EQ(maxpool_node.window()->vertical(), 1); - ASSERT_EQ(maxpool_node.window()->horizontal(), 1); + ASSERT_EQ(1, maxpool_node.window()->vertical()); + ASSERT_EQ(1, maxpool_node.window()->horizontal()); - ASSERT_EQ(maxpool_node.stride()->vertical(), 1); - ASSERT_EQ(maxpool_node.stride()->horizontal(), 1); + ASSERT_EQ(1, maxpool_node.stride()->vertical()); + ASSERT_EQ(1, maxpool_node.stride()->horizontal()); } TEST(MaxPool2DTest, pad) @@ -218,71 +218,71 @@ TEST(MaxPool2DTest, pad) loco::MaxPool2D maxpool_node; maxpool_node.pad()->top(t); - ASSERT_EQ(maxpool_node.pad()->top(), t); + ASSERT_EQ(t, maxpool_node.pad()->top()); maxpool_node.pad()->bottom(b); - ASSERT_EQ(maxpool_node.pad()->bottom(), b); + ASSERT_EQ(b, maxpool_node.pad()->bottom()); maxpool_node.pad()->left(l); - ASSERT_EQ(maxpool_node.pad()->left(), l); + ASSERT_EQ(l, maxpool_node.pad()->left()); maxpool_node.pad()->right(r); - ASSERT_EQ(maxpool_node.pad()->right(), r); + ASSERT_EQ(r, maxpool_node.pad()->right()); } TEST(AvgPool2DTest, constructor) { loco::AvgPool2D avgpool_node; - ASSERT_EQ(avgpool_node.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(avgpool_node.opcode(), loco::CanonicalOpcode::AvgPool2D); + ASSERT_EQ(loco::CanonicalDialect::get(), avgpool_node.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::AvgPool2D, avgpool_node.opcode()); - ASSERT_EQ(avgpool_node.ifm(), nullptr); + ASSERT_EQ(nullptr, avgpool_node.ifm()); - ASSERT_EQ(avgpool_node.convention(), loco::AvgPool2D::Convention::Unknown); + ASSERT_EQ(loco::AvgPool2D::Convention::Unknown, avgpool_node.convention()); - ASSERT_EQ(avgpool_node.pad()->top(), 0); - ASSERT_EQ(avgpool_node.pad()->bottom(), 0); - ASSERT_EQ(avgpool_node.pad()->left(), 0); - ASSERT_EQ(avgpool_node.pad()->right(), 0); + ASSERT_EQ(0, avgpool_node.pad()->top()); + ASSERT_EQ(0, avgpool_node.pad()->bottom()); + ASSERT_EQ(0, avgpool_node.pad()->left()); + ASSERT_EQ(0, avgpool_node.pad()->right()); - ASSERT_EQ(avgpool_node.window()->vertical(), 1); - ASSERT_EQ(avgpool_node.window()->horizontal(), 1); + ASSERT_EQ(1, avgpool_node.window()->vertical()); + ASSERT_EQ(1, avgpool_node.window()->horizontal()); - ASSERT_EQ(avgpool_node.stride()->vertical(), 1); - ASSERT_EQ(avgpool_node.stride()->horizontal(), 1); + ASSERT_EQ(1, avgpool_node.stride()->vertical()); + ASSERT_EQ(1, avgpool_node.stride()->horizontal()); } TEST(FeatureEncodeTest, constructor) { loco::FeatureEncode feature_encode; - ASSERT_EQ(feature_encode.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(feature_encode.opcode(), loco::CanonicalOpcode::FeatureEncode); + ASSERT_EQ(loco::CanonicalDialect::get(), feature_encode.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::FeatureEncode, feature_encode.opcode()); - ASSERT_EQ(feature_encode.input(), nullptr); - ASSERT_EQ(feature_encode.encoder(), nullptr); + ASSERT_EQ(nullptr, feature_encode.input()); + ASSERT_EQ(nullptr, feature_encode.encoder()); } TEST(FeatureDecodeTest, constructor) { loco::FeatureDecode feature_decode; - ASSERT_EQ(feature_decode.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(feature_decode.opcode(), loco::CanonicalOpcode::FeatureDecode); + ASSERT_EQ(loco::CanonicalDialect::get(), feature_decode.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::FeatureDecode, feature_decode.opcode()); - ASSERT_EQ(feature_decode.input(), nullptr); - ASSERT_EQ(feature_decode.decoder(), nullptr); + ASSERT_EQ(nullptr, feature_decode.input()); + ASSERT_EQ(nullptr, feature_decode.decoder()); } TEST(Reshape_Fixed_Test, constructor) { loco::Reshape reshape; - ASSERT_EQ(reshape.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(reshape.opcode(), loco::CanonicalOpcode::FixedReshape); + ASSERT_EQ(loco::CanonicalDialect::get(), reshape.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::FixedReshape, reshape.opcode()); - ASSERT_EQ(reshape.rank(), 0); + ASSERT_EQ(0, reshape.rank()); } TEST(Reshape_Fixed_Test, shape) @@ -290,153 +290,153 @@ TEST(Reshape_Fixed_Test, shape) loco::Reshape reshape; reshape.shape({2, 3}); - ASSERT_EQ(reshape.rank(), 2); - ASSERT_EQ(reshape.dim(0), 2); - ASSERT_EQ(reshape.dim(1), 3); + ASSERT_EQ(2, reshape.rank()); + ASSERT_EQ(2, reshape.dim(0)); + ASSERT_EQ(3, reshape.dim(1)); } TEST(FilterEncodeTest, constructor) { loco::FilterEncode filter_encode; - ASSERT_EQ(filter_encode.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(filter_encode.opcode(), loco::CanonicalOpcode::FilterEncode); + ASSERT_EQ(loco::CanonicalDialect::get(), filter_encode.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::FilterEncode, filter_encode.opcode()); - ASSERT_EQ(filter_encode.input(), nullptr); - ASSERT_EQ(filter_encode.encoder(), nullptr); + ASSERT_EQ(nullptr, filter_encode.input()); + ASSERT_EQ(nullptr, filter_encode.encoder()); } TEST(FilterDecodeTest, constructor) { loco::FilterDecode filter_decode; - ASSERT_EQ(filter_decode.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(filter_decode.opcode(), loco::CanonicalOpcode::FilterDecode); + ASSERT_EQ(loco::CanonicalDialect::get(), filter_decode.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::FilterDecode, filter_decode.opcode()); - ASSERT_EQ(filter_decode.input(), nullptr); - ASSERT_EQ(filter_decode.decoder(), nullptr); + ASSERT_EQ(nullptr, filter_decode.input()); + ASSERT_EQ(nullptr, filter_decode.decoder()); } TEST(DepthwiseFilterEncodeTest, constructor) { loco::DepthwiseFilterEncode dw_filter_encode; - ASSERT_EQ(dw_filter_encode.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(dw_filter_encode.opcode(), loco::CanonicalOpcode::DepthwiseFilterEncode); + ASSERT_EQ(loco::CanonicalDialect::get(), dw_filter_encode.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::DepthwiseFilterEncode, dw_filter_encode.opcode()); - ASSERT_EQ(dw_filter_encode.input(), nullptr); - ASSERT_EQ(dw_filter_encode.encoder(), nullptr); + ASSERT_EQ(nullptr, dw_filter_encode.input()); + ASSERT_EQ(nullptr, dw_filter_encode.encoder()); } TEST(DepthwiseFilterDecodeTest, constructor) { loco::DepthwiseFilterDecode dw_filter_decode; - ASSERT_EQ(dw_filter_decode.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(dw_filter_decode.opcode(), loco::CanonicalOpcode::DepthwiseFilterDecode); + ASSERT_EQ(loco::CanonicalDialect::get(), dw_filter_decode.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::DepthwiseFilterDecode, dw_filter_decode.opcode()); - ASSERT_EQ(dw_filter_decode.input(), nullptr); - ASSERT_EQ(dw_filter_decode.decoder(), nullptr); + ASSERT_EQ(nullptr, dw_filter_decode.input()); + ASSERT_EQ(nullptr, dw_filter_decode.decoder()); } TEST(TensorConcatTest, constructor) { loco::TensorConcat tensor_concat; - ASSERT_EQ(tensor_concat.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(tensor_concat.opcode(), loco::CanonicalOpcode::TensorConcat); + ASSERT_EQ(loco::CanonicalDialect::get(), tensor_concat.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::TensorConcat, tensor_concat.opcode()); - ASSERT_EQ(tensor_concat.lhs(), nullptr); - ASSERT_EQ(tensor_concat.rhs(), nullptr); - ASSERT_EQ(tensor_concat.axis(), 0); + ASSERT_EQ(nullptr, tensor_concat.lhs()); + ASSERT_EQ(nullptr, tensor_concat.rhs()); + ASSERT_EQ(0, tensor_concat.axis()); tensor_concat.axis(3); - ASSERT_EQ(tensor_concat.axis(), 3); + ASSERT_EQ(3, tensor_concat.axis()); } TEST(Conv2DTest, constructor) { loco::Conv2D conv2d; - ASSERT_EQ(conv2d.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(conv2d.opcode(), loco::CanonicalOpcode::Conv2D); + ASSERT_EQ(loco::CanonicalDialect::get(), conv2d.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::Conv2D, conv2d.opcode()); - ASSERT_EQ(conv2d.ifm(), nullptr); - ASSERT_EQ(conv2d.ker(), nullptr); + ASSERT_EQ(nullptr, conv2d.ifm()); + ASSERT_EQ(nullptr, conv2d.ker()); ASSERT_NE(conv2d.pad(), nullptr); - ASSERT_EQ(conv2d.pad()->top(), 0); - ASSERT_EQ(conv2d.pad()->bottom(), 0); - ASSERT_EQ(conv2d.pad()->left(), 0); - ASSERT_EQ(conv2d.pad()->right(), 0); + ASSERT_EQ(0, conv2d.pad()->top()); + ASSERT_EQ(0, conv2d.pad()->bottom()); + ASSERT_EQ(0, conv2d.pad()->left()); + ASSERT_EQ(0, conv2d.pad()->right()); ASSERT_NE(conv2d.stride(), nullptr); - ASSERT_EQ(conv2d.stride()->vertical(), 1); - ASSERT_EQ(conv2d.stride()->horizontal(), 1); + ASSERT_EQ(1, conv2d.stride()->vertical()); + ASSERT_EQ(1, conv2d.stride()->horizontal()); } TEST(DepthwiseConv2DTest, constructor) { loco::DepthwiseConv2D dw_conv2d; - ASSERT_EQ(dw_conv2d.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(dw_conv2d.opcode(), loco::CanonicalOpcode::DepthwiseConv2D); + ASSERT_EQ(loco::CanonicalDialect::get(), dw_conv2d.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::DepthwiseConv2D, dw_conv2d.opcode()); - ASSERT_EQ(dw_conv2d.ifm(), nullptr); - ASSERT_EQ(dw_conv2d.ker(), nullptr); + ASSERT_EQ(nullptr, dw_conv2d.ifm()); + ASSERT_EQ(nullptr, dw_conv2d.ker()); ASSERT_NE(dw_conv2d.pad(), nullptr); - ASSERT_EQ(dw_conv2d.pad()->top(), 0); - ASSERT_EQ(dw_conv2d.pad()->bottom(), 0); - ASSERT_EQ(dw_conv2d.pad()->left(), 0); - ASSERT_EQ(dw_conv2d.pad()->right(), 0); + ASSERT_EQ(0, dw_conv2d.pad()->top()); + ASSERT_EQ(0, dw_conv2d.pad()->bottom()); + ASSERT_EQ(0, dw_conv2d.pad()->left()); + ASSERT_EQ(0, dw_conv2d.pad()->right()); ASSERT_NE(dw_conv2d.stride(), nullptr); - ASSERT_EQ(dw_conv2d.stride()->vertical(), 1); - ASSERT_EQ(dw_conv2d.stride()->horizontal(), 1); + ASSERT_EQ(1, dw_conv2d.stride()->vertical()); + ASSERT_EQ(1, dw_conv2d.stride()->horizontal()); } TEST(TransposedConv2DTest, constructor) { loco::TransposedConv2D tr_conv2d; - ASSERT_EQ(tr_conv2d.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(tr_conv2d.opcode(), loco::CanonicalOpcode::TransposedConv2D); + ASSERT_EQ(loco::CanonicalDialect::get(), tr_conv2d.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::TransposedConv2D, tr_conv2d.opcode()); - ASSERT_EQ(tr_conv2d.ifm(), nullptr); - ASSERT_EQ(tr_conv2d.ker(), nullptr); + ASSERT_EQ(nullptr, tr_conv2d.ifm()); + ASSERT_EQ(nullptr, tr_conv2d.ker()); ASSERT_NE(tr_conv2d.pad(), nullptr); - ASSERT_EQ(tr_conv2d.pad()->top(), 0); - ASSERT_EQ(tr_conv2d.pad()->bottom(), 0); - ASSERT_EQ(tr_conv2d.pad()->left(), 0); - ASSERT_EQ(tr_conv2d.pad()->right(), 0); + ASSERT_EQ(0, tr_conv2d.pad()->top()); + ASSERT_EQ(0, tr_conv2d.pad()->bottom()); + ASSERT_EQ(0, tr_conv2d.pad()->left()); + ASSERT_EQ(0, tr_conv2d.pad()->right()); ASSERT_NE(tr_conv2d.stride(), nullptr); - ASSERT_EQ(tr_conv2d.stride()->vertical(), 1); - ASSERT_EQ(tr_conv2d.stride()->horizontal(), 1); + ASSERT_EQ(1, tr_conv2d.stride()->vertical()); + ASSERT_EQ(1, tr_conv2d.stride()->horizontal()); } TEST(BiasEncodeTest, constructor) { loco::BiasEncode bias_encode; - ASSERT_EQ(bias_encode.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(bias_encode.opcode(), loco::CanonicalOpcode::BiasEncode); + ASSERT_EQ(loco::CanonicalDialect::get(), bias_encode.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::BiasEncode, bias_encode.opcode()); - ASSERT_EQ(bias_encode.input(), nullptr); + ASSERT_EQ(nullptr, bias_encode.input()); } TEST(TensorBiasAddTest, constructor) { loco::BiasAdd bias_add; - ASSERT_EQ(bias_add.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(bias_add.opcode(), loco::CanonicalOpcode::TensorBiasAdd); + ASSERT_EQ(loco::CanonicalDialect::get(), bias_add.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::TensorBiasAdd, bias_add.opcode()); - ASSERT_EQ(bias_add.value(), nullptr); - ASSERT_EQ(bias_add.bias(), nullptr); - ASSERT_EQ(bias_add.axis(), 0); + ASSERT_EQ(nullptr, bias_add.value()); + ASSERT_EQ(nullptr, bias_add.bias()); + ASSERT_EQ(0, bias_add.axis()); } TEST(TensorBiasAddTest, alias) @@ -450,11 +450,11 @@ TEST(FeatureBiasAddTest, constructor) { loco::BiasAdd bias_add; - ASSERT_EQ(bias_add.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(bias_add.opcode(), loco::CanonicalOpcode::FeatureBiasAdd); + ASSERT_EQ(loco::CanonicalDialect::get(), bias_add.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::FeatureBiasAdd, bias_add.opcode()); - ASSERT_EQ(bias_add.value(), nullptr); - ASSERT_EQ(bias_add.bias(), nullptr); + ASSERT_EQ(nullptr, bias_add.value()); + ASSERT_EQ(nullptr, bias_add.bias()); } TEST(FeatureBiasAddTest, alias) @@ -503,74 +503,74 @@ TEST(EltwiseSqrtTest, constructor) { loco::EltwiseSqrt sqrt_node; - ASSERT_EQ(sqrt_node.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(sqrt_node.opcode(), loco::CanonicalOpcode::EltwiseSqrt); + ASSERT_EQ(loco::CanonicalDialect::get(), sqrt_node.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::EltwiseSqrt, sqrt_node.opcode()); - ASSERT_EQ(sqrt_node.input(), nullptr); + ASSERT_EQ(nullptr, sqrt_node.input()); } TEST(TensorBroadcastTest, constructor) { loco::TensorBroadcast tensor_broadcast_node; - ASSERT_EQ(tensor_broadcast_node.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(tensor_broadcast_node.opcode(), loco::CanonicalOpcode::TensorBroadcast); + ASSERT_EQ(loco::CanonicalDialect::get(), tensor_broadcast_node.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::TensorBroadcast, tensor_broadcast_node.opcode()); - ASSERT_EQ(tensor_broadcast_node.input(), nullptr); + ASSERT_EQ(nullptr, tensor_broadcast_node.input()); } TEST(TensorBroadcastTest, mapping) { loco::TensorBroadcast tensor_broadcast_node; - ASSERT_EQ(tensor_broadcast_node.mapping()->defined(0), false); + ASSERT_EQ(false, tensor_broadcast_node.mapping()->defined(0)); tensor_broadcast_node.mapping()->dim(0) = 3; - ASSERT_EQ(tensor_broadcast_node.mapping()->defined(0), true); - ASSERT_EQ(tensor_broadcast_node.mapping()->dim(0), 3); + ASSERT_EQ(true, tensor_broadcast_node.mapping()->defined(0)); + ASSERT_EQ(3, tensor_broadcast_node.mapping()->dim(0)); } TEST(MatrixEncodeTest, constructor) { loco::MatrixEncode matrix_encode; - ASSERT_EQ(matrix_encode.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(matrix_encode.opcode(), loco::CanonicalOpcode::MatrixEncode); + ASSERT_EQ(loco::CanonicalDialect::get(), matrix_encode.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::MatrixEncode, matrix_encode.opcode()); - ASSERT_EQ(matrix_encode.input(), nullptr); + ASSERT_EQ(nullptr, matrix_encode.input()); } TEST(MatrixDecodeTest, constructor) { loco::MatrixDecode matrix_decode; - ASSERT_EQ(matrix_decode.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(matrix_decode.opcode(), loco::CanonicalOpcode::MatrixDecode); + ASSERT_EQ(loco::CanonicalDialect::get(), matrix_decode.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::MatrixDecode, matrix_decode.opcode()); - ASSERT_EQ(matrix_decode.input(), nullptr); + ASSERT_EQ(nullptr, matrix_decode.input()); } TEST(MatMulTest, constructor) { loco::MatMul mat_mul; - ASSERT_EQ(mat_mul.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(mat_mul.opcode(), loco::CanonicalOpcode::MatMul); + ASSERT_EQ(loco::CanonicalDialect::get(), mat_mul.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::MatMul, mat_mul.opcode()); - ASSERT_EQ(mat_mul.lhs(), nullptr); - ASSERT_EQ(mat_mul.rhs(), nullptr); + ASSERT_EQ(nullptr, mat_mul.lhs()); + ASSERT_EQ(nullptr, mat_mul.rhs()); } TEST(TransposeTest, constructor) { loco::TensorTranspose transpose; - ASSERT_EQ(transpose.dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(transpose.opcode(), loco::CanonicalOpcode::TensorTranspose); + ASSERT_EQ(loco::CanonicalDialect::get(), transpose.dialect()); + ASSERT_EQ(loco::CanonicalOpcode::TensorTranspose, transpose.opcode()); - ASSERT_EQ(transpose.input(), nullptr); - ASSERT_EQ(transpose.perm()->size(), 0); + ASSERT_EQ(nullptr, transpose.input()); + ASSERT_EQ(0, transpose.perm()->size()); } TEST(TransposeTest, perm) @@ -582,7 +582,7 @@ TEST(TransposeTest, perm) transpose.perm()->axis(1) = 2; transpose.perm()->axis(2) = 0; - ASSERT_EQ(transpose.perm()->axis(0), 1); - ASSERT_EQ(transpose.perm()->axis(1), 2); - ASSERT_EQ(transpose.perm()->axis(2), 0); + ASSERT_EQ(1, transpose.perm()->axis(0)); + ASSERT_EQ(2, transpose.perm()->axis(1)); + ASSERT_EQ(0, transpose.perm()->axis(2)); } diff --git a/compiler/loco/src/IR/Padding2D.test.cpp b/compiler/loco/src/IR/Padding2D.test.cpp index 2e3d4af..b919f26 100644 --- a/compiler/loco/src/IR/Padding2D.test.cpp +++ b/compiler/loco/src/IR/Padding2D.test.cpp @@ -22,8 +22,8 @@ TEST(PadTest, default_constructor_2D) { loco::Padding2D pad; - ASSERT_EQ(pad.top(), 0); - ASSERT_EQ(pad.bottom(), 0); - ASSERT_EQ(pad.left(), 0); - ASSERT_EQ(pad.right(), 0); + ASSERT_EQ(0, pad.top()); + ASSERT_EQ(0, pad.bottom()); + ASSERT_EQ(0, pad.left()); + ASSERT_EQ(0, pad.right()); } diff --git a/compiler/loco/src/IR/PaddingND.test.cpp b/compiler/loco/src/IR/PaddingND.test.cpp index 0e20406..8c384c0 100644 --- a/compiler/loco/src/IR/PaddingND.test.cpp +++ b/compiler/loco/src/IR/PaddingND.test.cpp @@ -26,7 +26,7 @@ TEST(PaddingNDTest, default_constructor_ND) padding.front(0) = 1; padding.back(0) = 2; - ASSERT_EQ(padding.rank(), 1); - ASSERT_EQ(padding.front(0), 1); - ASSERT_EQ(padding.back(0), 2); + ASSERT_EQ(1, padding.rank()); + ASSERT_EQ(1, padding.front(0)); + ASSERT_EQ(2, padding.back(0)); } diff --git a/compiler/loco/src/IR/PermutingCodec.test.cpp b/compiler/loco/src/IR/PermutingCodec.test.cpp index 2eff286..f8f7541 100644 --- a/compiler/loco/src/IR/PermutingCodec.test.cpp +++ b/compiler/loco/src/IR/PermutingCodec.test.cpp @@ -43,10 +43,10 @@ TEST(PemutationTest, feature) ASSERT_TRUE(perm.mapped(FeatureAxis::Width)); // Check the value - ASSERT_EQ(perm[FeatureAxis::Count], 5); - ASSERT_EQ(perm[FeatureAxis::Depth], 6); - ASSERT_EQ(perm[FeatureAxis::Height], 7); - ASSERT_EQ(perm[FeatureAxis::Width], 8); + ASSERT_EQ(5, perm[FeatureAxis::Count]); + ASSERT_EQ(6, perm[FeatureAxis::Depth]); + ASSERT_EQ(7, perm[FeatureAxis::Height]); + ASSERT_EQ(8, perm[FeatureAxis::Width]); } TEST(PemutationTest, filter) @@ -72,10 +72,10 @@ TEST(PemutationTest, filter) ASSERT_TRUE(perm.mapped(FilterAxis::Width)); // Check the value - ASSERT_EQ(perm[FilterAxis::Count], 5); - ASSERT_EQ(perm[FilterAxis::Depth], 6); - ASSERT_EQ(perm[FilterAxis::Height], 7); - ASSERT_EQ(perm[FilterAxis::Width], 8); + ASSERT_EQ(5, perm[FilterAxis::Count]); + ASSERT_EQ(6, perm[FilterAxis::Depth]); + ASSERT_EQ(7, perm[FilterAxis::Height]); + ASSERT_EQ(8, perm[FilterAxis::Width]); } TEST(PemutationTest, depthwise_filter) @@ -101,10 +101,10 @@ TEST(PemutationTest, depthwise_filter) ASSERT_TRUE(perm.mapped(DepthwiseFilterAxis::Width)); // Check the value - ASSERT_EQ(perm[DepthwiseFilterAxis::Depth], 5); - ASSERT_EQ(perm[DepthwiseFilterAxis::Multiplier], 6); - ASSERT_EQ(perm[DepthwiseFilterAxis::Height], 7); - ASSERT_EQ(perm[DepthwiseFilterAxis::Width], 8); + ASSERT_EQ(5, perm[DepthwiseFilterAxis::Depth]); + ASSERT_EQ(6, perm[DepthwiseFilterAxis::Multiplier]); + ASSERT_EQ(7, perm[DepthwiseFilterAxis::Height]); + ASSERT_EQ(8, perm[DepthwiseFilterAxis::Width]); } TEST(PermutingEncoderTest, feature) @@ -147,10 +147,10 @@ TEST(PermutingEncoderTest, feature) // Get the feature shape corresponding to a given image auto feature_shape = enc.shape(tensor_shape); - ASSERT_EQ(feature_shape.count(), 1); - ASSERT_EQ(feature_shape.depth(), 3); - ASSERT_EQ(feature_shape.height(), 720); - ASSERT_EQ(feature_shape.width(), 1280); + ASSERT_EQ(1, feature_shape.count()); + ASSERT_EQ(3, feature_shape.depth()); + ASSERT_EQ(720, feature_shape.height()); + ASSERT_EQ(1280, feature_shape.width()); // Let's find a source tensor index! FeatureIndex feature_index; @@ -162,10 +162,10 @@ TEST(PermutingEncoderTest, feature) auto tensor_index = enc.value(feature_index); - ASSERT_EQ(tensor_index.at(0), 0); // BATCH(COUNT) - ASSERT_EQ(tensor_index.at(1), 2); // ROW(HEIGHT) - ASSERT_EQ(tensor_index.at(2), 3); // COLUMN(WIDTH) - ASSERT_EQ(tensor_index.at(3), 1); // CHANNEL(DEPTH) + ASSERT_EQ(0, tensor_index.at(0)); // BATCH(COUNT) + ASSERT_EQ(2, tensor_index.at(1)); // ROW(HEIGHT) + ASSERT_EQ(3, tensor_index.at(2)); // COLUMN(WIDTH) + ASSERT_EQ(1, tensor_index.at(3)); // CHANNEL(DEPTH) } TEST(PermutingEncoderTest, feature_clone) @@ -180,7 +180,7 @@ TEST(PermutingEncoderTest, feature_clone) src_perm->axis(FeatureAxis::Width) = 2; auto dst_enc = src_enc.clone(); - auto dst_perm = dynamic_cast *>(dst_enc.get())->perm(); + auto dst_perm = loco::must_cast *>(dst_enc.get())->perm(); EXPECT_EQ(dst_perm->axis(FeatureAxis::Count), src_perm->axis(FeatureAxis::Count)); EXPECT_EQ(dst_perm->axis(FeatureAxis::Depth), src_perm->axis(FeatureAxis::Depth)); @@ -233,10 +233,10 @@ TEST(PermutingEncoderTest, filter) // Get the corresponding filter shape auto filter_shape = enc.shape(tensor_shape); - ASSERT_EQ(filter_shape.count(), 8); - ASSERT_EQ(filter_shape.depth(), 4); - ASSERT_EQ(filter_shape.height(), 1); - ASSERT_EQ(filter_shape.width(), 7); + ASSERT_EQ(8, filter_shape.count()); + ASSERT_EQ(4, filter_shape.depth()); + ASSERT_EQ(1, filter_shape.height()); + ASSERT_EQ(7, filter_shape.width()); // Let's find a source tensor index! FilterIndex filter_index; @@ -248,10 +248,10 @@ TEST(PermutingEncoderTest, filter) auto tensor_index = enc.value(filter_index); - ASSERT_EQ(tensor_index.at(0), 1); // NTH(COUNT) - ASSERT_EQ(tensor_index.at(1), 0); // ROW(HEIGHT) - ASSERT_EQ(tensor_index.at(2), 3); // COLUMN(WIDTH) - ASSERT_EQ(tensor_index.at(3), 2); // CHANNEL(DEPTH) + ASSERT_EQ(1, tensor_index.at(0)); // NTH(COUNT) + ASSERT_EQ(0, tensor_index.at(1)); // ROW(HEIGHT) + ASSERT_EQ(3, tensor_index.at(2)); // COLUMN(WIDTH) + ASSERT_EQ(2, tensor_index.at(3)); // CHANNEL(DEPTH) } TEST(PermutingEncoderTest, depthwise_filter) @@ -293,10 +293,10 @@ TEST(PermutingEncoderTest, depthwise_filter) // Get the corresponding depthwise filter shape auto filter_shape = enc.shape(tensor_shape); - ASSERT_EQ(filter_shape.depth(), 8); - ASSERT_EQ(filter_shape.multiplier(), 4); - ASSERT_EQ(filter_shape.height(), 1); - ASSERT_EQ(filter_shape.width(), 7); + ASSERT_EQ(8, filter_shape.depth()); + ASSERT_EQ(4, filter_shape.multiplier()); + ASSERT_EQ(1, filter_shape.height()); + ASSERT_EQ(7, filter_shape.width()); // Let's find a source tensor index! DepthwiseFilterIndex filter_index; @@ -308,10 +308,10 @@ TEST(PermutingEncoderTest, depthwise_filter) auto tensor_index = enc.value(filter_index); - ASSERT_EQ(tensor_index.at(0), 1); // CHANNEL(DEPTH) - ASSERT_EQ(tensor_index.at(1), 0); // ROW(HEIGHT) - ASSERT_EQ(tensor_index.at(2), 3); // COLUMN(WIDTH) - ASSERT_EQ(tensor_index.at(3), 2); // NTH(MULTIPLIER) + ASSERT_EQ(1, tensor_index.at(0)); // CHANNEL(DEPTH) + ASSERT_EQ(0, tensor_index.at(1)); // ROW(HEIGHT) + ASSERT_EQ(3, tensor_index.at(2)); // COLUMN(WIDTH) + ASSERT_EQ(2, tensor_index.at(3)); // NTH(MULTIPLIER) } TEST(PermutingEncoderTest, depthwisefilter_init) @@ -379,11 +379,11 @@ TEST(PermutingDecoderTest, feature) // Get the tensor shape corresponding to a given image auto tensor_shape = dec.shape(feature_shape); - ASSERT_EQ(tensor_shape.rank(), 4); - ASSERT_EQ(tensor_shape.dim(0), 1); // COUNT - ASSERT_EQ(tensor_shape.dim(1), 720); // HEIGHT - ASSERT_EQ(tensor_shape.dim(2), 1280); // WIDTH - ASSERT_EQ(tensor_shape.dim(3), 3); // DEPTH + ASSERT_EQ(4, tensor_shape.rank()); + ASSERT_EQ(1, tensor_shape.dim(0)); // COUNT + ASSERT_EQ(720, tensor_shape.dim(1)); // HEIGHT + ASSERT_EQ(1280, tensor_shape.dim(2)); // WIDTH + ASSERT_EQ(3, tensor_shape.dim(3)); // DEPTH // Let's find a source feature index! TensorIndex tensor_index; @@ -397,10 +397,10 @@ TEST(PermutingDecoderTest, feature) auto feature_index = dec.value(tensor_index); - ASSERT_EQ(feature_index.batch(), 0); - ASSERT_EQ(feature_index.channel(), 1); - ASSERT_EQ(feature_index.row(), 2); - ASSERT_EQ(feature_index.column(), 3); + ASSERT_EQ(0, feature_index.batch()); + ASSERT_EQ(1, feature_index.channel()); + ASSERT_EQ(2, feature_index.row()); + ASSERT_EQ(3, feature_index.column()); } TEST(PermutingDecoderTest, feature_clone) @@ -415,7 +415,7 @@ TEST(PermutingDecoderTest, feature_clone) src_perm->axis(FeatureAxis::Width) = 2; auto dst_enc = src_enc.clone(); - auto dst_perm = dynamic_cast *>(dst_enc.get())->perm(); + auto dst_perm = loco::must_cast *>(dst_enc.get())->perm(); EXPECT_EQ(dst_perm->axis(FeatureAxis::Count), src_perm->axis(FeatureAxis::Count)); EXPECT_EQ(dst_perm->axis(FeatureAxis::Depth), src_perm->axis(FeatureAxis::Depth)); @@ -468,11 +468,11 @@ TEST(PermutingDecoderTest, filter) // Get the tensor shape corresponding to a given image auto tensor_shape = dec.shape(filter_shape); - ASSERT_EQ(tensor_shape.rank(), 4); - ASSERT_EQ(tensor_shape.dim(0), 10); // COUNT - ASSERT_EQ(tensor_shape.dim(1), 6); // HEIGHT - ASSERT_EQ(tensor_shape.dim(2), 8); // WIDTH - ASSERT_EQ(tensor_shape.dim(3), 3); // DEPTH + ASSERT_EQ(4, tensor_shape.rank()); + ASSERT_EQ(10, tensor_shape.dim(0)); // COUNT + ASSERT_EQ(6, tensor_shape.dim(1)); // HEIGHT + ASSERT_EQ(8, tensor_shape.dim(2)); // WIDTH + ASSERT_EQ(3, tensor_shape.dim(3)); // DEPTH // Let's find a source filter index! TensorIndex tensor_index; @@ -486,10 +486,10 @@ TEST(PermutingDecoderTest, filter) auto filter_index = dec.value(tensor_index); - ASSERT_EQ(filter_index.nth(), 0); - ASSERT_EQ(filter_index.channel(), 1); - ASSERT_EQ(filter_index.row(), 2); - ASSERT_EQ(filter_index.column(), 3); + ASSERT_EQ(0, filter_index.nth()); + ASSERT_EQ(1, filter_index.channel()); + ASSERT_EQ(2, filter_index.row()); + ASSERT_EQ(3, filter_index.column()); } TEST(PermutingDecoderTest, depthwise_filter) @@ -530,10 +530,10 @@ TEST(PermutingDecoderTest, depthwise_filter) // Get the corresponding depthwise filter shape auto tensor_shape = dec.shape(dw_filter_shape); - ASSERT_EQ(tensor_shape.dim(0).value(), 8); - ASSERT_EQ(tensor_shape.dim(1).value(), 7); - ASSERT_EQ(tensor_shape.dim(2).value(), 4); - ASSERT_EQ(tensor_shape.dim(3).value(), 1); + ASSERT_EQ(8, tensor_shape.dim(0).value()); + ASSERT_EQ(7, tensor_shape.dim(1).value()); + ASSERT_EQ(4, tensor_shape.dim(2).value()); + ASSERT_EQ(1, tensor_shape.dim(3).value()); // Let's find a source tensor index! TensorIndex tensor_index; @@ -546,8 +546,8 @@ TEST(PermutingDecoderTest, depthwise_filter) auto dw_filter_index = dec.value(tensor_index); - ASSERT_EQ(dw_filter_index.channel(), 4); - ASSERT_EQ(dw_filter_index.nth(), 0); - ASSERT_EQ(dw_filter_index.row(), 2); - ASSERT_EQ(dw_filter_index.column(), 1); + ASSERT_EQ(4, dw_filter_index.channel()); + ASSERT_EQ(0, dw_filter_index.nth()); + ASSERT_EQ(2, dw_filter_index.row()); + ASSERT_EQ(1, dw_filter_index.column()); } diff --git a/compiler/loco/src/IR/Stride.test.cpp b/compiler/loco/src/IR/Stride.test.cpp index 60deb5c..9cc88f3 100644 --- a/compiler/loco/src/IR/Stride.test.cpp +++ b/compiler/loco/src/IR/Stride.test.cpp @@ -22,8 +22,8 @@ TEST(StrideTest, default_constructor_2D) { loco::Stride<2> stride; - ASSERT_EQ(stride.vertical(), 1); - ASSERT_EQ(stride.horizontal(), 1); + ASSERT_EQ(1, stride.vertical()); + ASSERT_EQ(1, stride.horizontal()); } TEST(StrideTest, setter_and_getter_2D) @@ -32,11 +32,11 @@ TEST(StrideTest, setter_and_getter_2D) stride.vertical(2); - ASSERT_EQ(stride.vertical(), 2); - ASSERT_EQ(stride.horizontal(), 1); + ASSERT_EQ(2, stride.vertical()); + ASSERT_EQ(1, stride.horizontal()); stride.horizontal(3); - ASSERT_EQ(stride.vertical(), 2); - ASSERT_EQ(stride.horizontal(), 3); + ASSERT_EQ(2, stride.vertical()); + ASSERT_EQ(3, stride.horizontal()); } diff --git a/compiler/loco/src/IR/TensorShape.cpp b/compiler/loco/src/IR/TensorShape.cpp index ad30dcb..e9d4f7e 100644 --- a/compiler/loco/src/IR/TensorShape.cpp +++ b/compiler/loco/src/IR/TensorShape.cpp @@ -37,3 +37,20 @@ uint32_t element_count(const loco::TensorShape *tensor_shape) } } // namespace loco + +namespace loco +{ + +bool operator==(const TensorShape &lhs, const TensorShape &rhs) +{ + if (lhs.rank() != rhs.rank()) + return false; + for (uint32_t axis = 0; axis < lhs.rank(); ++axis) + { + if (!(lhs.dim(axis) == rhs.dim(axis))) + return false; + } + return true; +} + +} // namespace loco diff --git a/compiler/loco/src/IR/TensorShape.test.cpp b/compiler/loco/src/IR/TensorShape.test.cpp index ce03ccb..ca7af72 100644 --- a/compiler/loco/src/IR/TensorShape.test.cpp +++ b/compiler/loco/src/IR/TensorShape.test.cpp @@ -22,20 +22,20 @@ TEST(TensorShapeTest, default_constructor) { loco::TensorShape tensor_shape; - ASSERT_EQ(tensor_shape.rank(), 0); + ASSERT_EQ(0, tensor_shape.rank()); } TEST(TensorShapeTest, initializer_list_constructor) { loco::TensorShape tensor_shape{3, 5}; - ASSERT_EQ(tensor_shape.rank(), 2); + ASSERT_EQ(2, tensor_shape.rank()); ASSERT_TRUE(tensor_shape.dim(0).known()); ASSERT_TRUE(tensor_shape.dim(1).known()); - ASSERT_EQ(tensor_shape.dim(0).value(), 3); - ASSERT_EQ(tensor_shape.dim(1).value(), 5); + ASSERT_EQ(3, tensor_shape.dim(0).value()); + ASSERT_EQ(5, tensor_shape.dim(1).value()); } TEST(TensorShapeTest, rank) @@ -44,7 +44,7 @@ TEST(TensorShapeTest, rank) tensor_shape.rank(2); - ASSERT_EQ(tensor_shape.rank(), 2); + ASSERT_EQ(2, tensor_shape.rank()); ASSERT_FALSE(tensor_shape.dim(0).known()); ASSERT_FALSE(tensor_shape.dim(1).known()); } @@ -60,7 +60,7 @@ TEST(TensorShapeTest, dim) ASSERT_TRUE(tensor_shape.dim(0).known()); ASSERT_FALSE(tensor_shape.dim(1).known()); - ASSERT_EQ(tensor_shape.dim(0), 3); + ASSERT_EQ(3, tensor_shape.dim(0)); } TEST(TensorShapeTest, rank_update) @@ -78,7 +78,7 @@ TEST(TensorShapeTest, rank_update) ASSERT_FALSE(tensor_shape.dim(2).known()); ASSERT_FALSE(tensor_shape.dim(3).known()); - ASSERT_EQ(tensor_shape.dim(1), 3); + ASSERT_EQ(3, tensor_shape.dim(1)); } TEST(TensorShapeTest, copy) @@ -92,12 +92,12 @@ TEST(TensorShapeTest, copy) dst = src; - ASSERT_EQ(dst.rank(), 2); + ASSERT_EQ(2, dst.rank()); ASSERT_FALSE(dst.dim(0).known()); ASSERT_TRUE(dst.dim(1).known()); - ASSERT_EQ(dst.dim(1), 3); + ASSERT_EQ(3, dst.dim(1)); } TEST(TensorShapeTest, element_count) @@ -105,5 +105,36 @@ TEST(TensorShapeTest, element_count) // Check Rank-0 case loco::TensorShape src; - ASSERT_EQ(loco::element_count(&src), 1); + ASSERT_EQ(1, loco::element_count(&src)); +} + +TEST(TensorShapeTest, equal_operator) +{ + loco::TensorShape lhs, rhs; + + lhs.rank(2); + lhs.dim(0) = 1; + lhs.dim(1) = 3; + + rhs.rank(1); + rhs.dim(0) = 1; + + EXPECT_FALSE(lhs == rhs); + + rhs.rank(2); + rhs.dim(0) = 1; + rhs.dim(1) = 3; + + EXPECT_TRUE(lhs == rhs); + + // for unknown + loco::TensorShape lhs_u, rhs_u; + + lhs_u.rank(2); + lhs_u.dim(0) = 1; + + rhs_u.rank(2); + rhs_u.dim(0) = 1; + + EXPECT_FALSE(lhs == rhs_u); } diff --git a/compiler/loco/src/IR/Use.test.cpp b/compiler/loco/src/IR/Use.test.cpp index 4a2f1cc..251e3b4 100644 --- a/compiler/loco/src/IR/Use.test.cpp +++ b/compiler/loco/src/IR/Use.test.cpp @@ -25,8 +25,8 @@ TEST(UseTest, constructor) MockupNode user; loco::Use use{&user}; - ASSERT_EQ(use.user(), &user); - ASSERT_EQ(use.node(), nullptr); + ASSERT_EQ(&user, use.user()); + ASSERT_EQ(nullptr, use.node()); } TEST(UseTest, link_node) @@ -37,6 +37,6 @@ TEST(UseTest, link_node) use.node(&def); - ASSERT_EQ(use.user(), &user); - ASSERT_EQ(use.node(), &def); + ASSERT_EQ(&user, use.user()); + ASSERT_EQ(&def, use.node()); } diff --git a/compiler/loco/src/IR/Verifier.test.cpp b/compiler/loco/src/IR/Verifier.test.cpp index 247a593..8c40a50 100644 --- a/compiler/loco/src/IR/Verifier.test.cpp +++ b/compiler/loco/src/IR/Verifier.test.cpp @@ -58,7 +58,7 @@ TEST(VerifierTest, valid_error_reporter) std::vector> errors; ASSERT_FALSE(loco::valid(g.get(), make_unique(&errors))); - ASSERT_EQ(errors.size(), 1); - ASSERT_EQ(errors.at(0).node(), push); - ASSERT_EQ(errors.at(0).index(), 0); + ASSERT_EQ(1, errors.size()); + ASSERT_EQ(push, errors.at(0).node()); + ASSERT_EQ(0, errors.at(0).index()); } diff --git a/compiler/loco/src/IR/Window.test.cpp b/compiler/loco/src/IR/Window.test.cpp index c112e0f..4cf7183 100644 --- a/compiler/loco/src/IR/Window.test.cpp +++ b/compiler/loco/src/IR/Window.test.cpp @@ -22,8 +22,8 @@ TEST(WindowTest, default_constructor_2D) { loco::Window<2> window; - ASSERT_EQ(window.vertical(), 1); - ASSERT_EQ(window.horizontal(), 1); + ASSERT_EQ(1, window.vertical()); + ASSERT_EQ(1, window.horizontal()); } TEST(WindowTest, setter_and_getter_2D) @@ -32,11 +32,11 @@ TEST(WindowTest, setter_and_getter_2D) window.vertical(2); - ASSERT_EQ(window.vertical(), 2); - ASSERT_EQ(window.horizontal(), 1); + ASSERT_EQ(2, window.vertical()); + ASSERT_EQ(1, window.horizontal()); window.horizontal(3); - ASSERT_EQ(window.vertical(), 2); - ASSERT_EQ(window.horizontal(), 3); + ASSERT_EQ(2, window.vertical()); + ASSERT_EQ(3, window.horizontal()); } diff --git a/compiler/loco/src/Service/CanonicalShapeInferenceRule.cpp b/compiler/loco/src/Service/CanonicalShapeInferenceRule.cpp index d30a827..6d5adc5 100644 --- a/compiler/loco/src/Service/CanonicalShapeInferenceRule.cpp +++ b/compiler/loco/src/Service/CanonicalShapeInferenceRule.cpp @@ -766,7 +766,7 @@ void CanonicalShapeInferenceRule::infer(const Context *ctx, const Node *node, Si assert(dynamic_cast(node) != nullptr); ForwardShapeInferenceAlgorithm alg{ctx}; - auto shape = dynamic_cast(node)->accept(&alg); + auto shape = loco::must_cast(node)->accept(&alg); sink->okay(shape); } diff --git a/compiler/loco/src/Service/CanonicalShapeInferenceRule.test.cpp b/compiler/loco/src/Service/CanonicalShapeInferenceRule.test.cpp index 5cc8c38..e88872b 100644 --- a/compiler/loco/src/Service/CanonicalShapeInferenceRule.test.cpp +++ b/compiler/loco/src/Service/CanonicalShapeInferenceRule.test.cpp @@ -35,12 +35,12 @@ TEST(CanonicalShapeInferenceRuleTest, minimal) // Verify! ASSERT_TRUE(loco::shape_known(testcase.push_node)); - ASSERT_EQ(loco::shape_get(testcase.push_node).domain(), loco::Domain::Tensor); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().rank(), 4); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().dim(0), 1); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().dim(1), 2); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().dim(2), 3); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().dim(3), 4); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(testcase.push_node).domain()); + ASSERT_EQ(4, loco::shape_get(testcase.push_node).as().rank()); + ASSERT_EQ(1, loco::shape_get(testcase.push_node).as().dim(0)); + ASSERT_EQ(2, loco::shape_get(testcase.push_node).as().dim(1)); + ASSERT_EQ(3, loco::shape_get(testcase.push_node).as().dim(2)); + ASSERT_EQ(4, loco::shape_get(testcase.push_node).as().dim(3)); } TEST(CanonicalShapeInferenceRuleTest, const_gen) @@ -58,10 +58,10 @@ TEST(CanonicalShapeInferenceRuleTest, const_gen) // Verify! ASSERT_TRUE(loco::shape_known(testcase.push_node)); - ASSERT_EQ(loco::shape_get(testcase.push_node).domain(), loco::Domain::Tensor); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().rank(), 2); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().dim(0), 1); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().dim(1), 2); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(testcase.push_node).domain()); + ASSERT_EQ(2, loco::shape_get(testcase.push_node).as().rank()); + ASSERT_EQ(1, loco::shape_get(testcase.push_node).as().dim(0)); + ASSERT_EQ(2, loco::shape_get(testcase.push_node).as().dim(1)); } TEST(CanonicalShapeInferenceRuleTest, relu) @@ -78,12 +78,12 @@ TEST(CanonicalShapeInferenceRuleTest, relu) // Verify! ASSERT_TRUE(loco::shape_known(testcase.push_node)); - ASSERT_EQ(loco::shape_get(testcase.push_node).domain(), loco::Domain::Tensor); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().rank(), 4); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().dim(0), 1); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().dim(1), 2); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().dim(2), 3); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().dim(3), 4); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(testcase.push_node).domain()); + ASSERT_EQ(4, loco::shape_get(testcase.push_node).as().rank()); + ASSERT_EQ(1, loco::shape_get(testcase.push_node).as().dim(0)); + ASSERT_EQ(2, loco::shape_get(testcase.push_node).as().dim(1)); + ASSERT_EQ(3, loco::shape_get(testcase.push_node).as().dim(2)); + ASSERT_EQ(4, loco::shape_get(testcase.push_node).as().dim(3)); } TEST(CanonicalShapeInferenceRuleTest, feature_codec) @@ -100,15 +100,15 @@ TEST(CanonicalShapeInferenceRuleTest, feature_codec) // Verify! ASSERT_TRUE(loco::shape_known(testcase.encode_node)); - ASSERT_EQ(loco::shape_get(testcase.encode_node).domain(), loco::Domain::Feature); + ASSERT_EQ(loco::Domain::Feature, loco::shape_get(testcase.encode_node).domain()); ASSERT_TRUE(loco::shape_known(testcase.decode_node)); - ASSERT_EQ(loco::shape_get(testcase.decode_node).domain(), loco::Domain::Tensor); - ASSERT_EQ(loco::shape_get(testcase.decode_node).as().rank(), 4); - ASSERT_EQ(loco::shape_get(testcase.decode_node).as().dim(0), 1); - ASSERT_EQ(loco::shape_get(testcase.decode_node).as().dim(1), 2); - ASSERT_EQ(loco::shape_get(testcase.decode_node).as().dim(2), 3); - ASSERT_EQ(loco::shape_get(testcase.decode_node).as().dim(3), 4); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(testcase.decode_node).domain()); + ASSERT_EQ(4, loco::shape_get(testcase.decode_node).as().rank()); + ASSERT_EQ(1, loco::shape_get(testcase.decode_node).as().dim(0)); + ASSERT_EQ(2, loco::shape_get(testcase.decode_node).as().dim(1)); + ASSERT_EQ(3, loco::shape_get(testcase.decode_node).as().dim(2)); + ASSERT_EQ(4, loco::shape_get(testcase.decode_node).as().dim(3)); } TEST(CanonicalShapeInferenceRuleTest, avgpool2d) @@ -141,11 +141,11 @@ TEST(CanonicalShapeInferenceRuleTest, avgpool2d) // // NOTE AvgPool2D testcase assumes NHWC layout ASSERT_TRUE(loco::shape_known(testcase.avgpool2d_node)); - ASSERT_EQ(loco::shape_get(testcase.avgpool2d_node).domain(), loco::Domain::Feature); - ASSERT_EQ(loco::shape_get(testcase.avgpool2d_node).as().count(), 1); - ASSERT_EQ(loco::shape_get(testcase.avgpool2d_node).as().depth(), 3); - ASSERT_EQ(loco::shape_get(testcase.avgpool2d_node).as().height(), 4); - ASSERT_EQ(loco::shape_get(testcase.avgpool2d_node).as().width(), 2); + ASSERT_EQ(loco::Domain::Feature, loco::shape_get(testcase.avgpool2d_node).domain()); + ASSERT_EQ(1, loco::shape_get(testcase.avgpool2d_node).as().count()); + ASSERT_EQ(3, loco::shape_get(testcase.avgpool2d_node).as().depth()); + ASSERT_EQ(4, loco::shape_get(testcase.avgpool2d_node).as().height()); + ASSERT_EQ(2, loco::shape_get(testcase.avgpool2d_node).as().width()); } TEST(CanonicalShapeInferenceRuleTest, depthwiseconv2d) @@ -172,11 +172,11 @@ TEST(CanonicalShapeInferenceRuleTest, depthwiseconv2d) // // NOTE DepthwiseConv2D testcase assumes NHWC layout ASSERT_TRUE(loco::shape_known(testcase.depthwiseconv2d_node)); - ASSERT_EQ(loco::shape_get(testcase.depthwiseconv2d_node).domain(), loco::Domain::Feature); - ASSERT_EQ(loco::shape_get(testcase.depthwiseconv2d_node).as().count(), 1); - ASSERT_EQ(loco::shape_get(testcase.depthwiseconv2d_node).as().depth(), 6); - ASSERT_EQ(loco::shape_get(testcase.depthwiseconv2d_node).as().height(), 3); - ASSERT_EQ(loco::shape_get(testcase.depthwiseconv2d_node).as().width(), 3); + ASSERT_EQ(loco::Domain::Feature, loco::shape_get(testcase.depthwiseconv2d_node).domain()); + ASSERT_EQ(1, loco::shape_get(testcase.depthwiseconv2d_node).as().count()); + ASSERT_EQ(6, loco::shape_get(testcase.depthwiseconv2d_node).as().depth()); + ASSERT_EQ(3, loco::shape_get(testcase.depthwiseconv2d_node).as().height()); + ASSERT_EQ(3, loco::shape_get(testcase.depthwiseconv2d_node).as().width()); } TEST(CanonicalShapeInferenceRuleTest, transposedconv2d) @@ -206,11 +206,11 @@ TEST(CanonicalShapeInferenceRuleTest, transposedconv2d) // Verify! ASSERT_TRUE(loco::shape_known(testcase.tr_conv2d_node)); - ASSERT_EQ(loco::shape_get(testcase.tr_conv2d_node).domain(), loco::Domain::Feature); - ASSERT_EQ(loco::shape_get(testcase.tr_conv2d_node).as().count(), 1); - ASSERT_EQ(loco::shape_get(testcase.tr_conv2d_node).as().height(), 540); - ASSERT_EQ(loco::shape_get(testcase.tr_conv2d_node).as().width(), 960); - ASSERT_EQ(loco::shape_get(testcase.tr_conv2d_node).as().depth(), 12); + ASSERT_EQ(loco::Domain::Feature, loco::shape_get(testcase.tr_conv2d_node).domain()); + ASSERT_EQ(1, loco::shape_get(testcase.tr_conv2d_node).as().count()); + ASSERT_EQ(540, loco::shape_get(testcase.tr_conv2d_node).as().height()); + ASSERT_EQ(960, loco::shape_get(testcase.tr_conv2d_node).as().width()); + ASSERT_EQ(12, loco::shape_get(testcase.tr_conv2d_node).as().depth()); } TEST(CanonicalShapeInferenceRuleTest, maxpool2d) @@ -243,11 +243,11 @@ TEST(CanonicalShapeInferenceRuleTest, maxpool2d) // // NOTE MaxPool2D testcase assumes NHWC layout ASSERT_TRUE(loco::shape_known(testcase.maxpool2d_node)); - ASSERT_EQ(loco::shape_get(testcase.maxpool2d_node).domain(), loco::Domain::Feature); - ASSERT_EQ(loco::shape_get(testcase.maxpool2d_node).as().count(), 1); - ASSERT_EQ(loco::shape_get(testcase.maxpool2d_node).as().depth(), 3); - ASSERT_EQ(loco::shape_get(testcase.maxpool2d_node).as().height(), 4); - ASSERT_EQ(loco::shape_get(testcase.maxpool2d_node).as().width(), 2); + ASSERT_EQ(loco::Domain::Feature, loco::shape_get(testcase.maxpool2d_node).domain()); + ASSERT_EQ(1, loco::shape_get(testcase.maxpool2d_node).as().count()); + ASSERT_EQ(3, loco::shape_get(testcase.maxpool2d_node).as().depth()); + ASSERT_EQ(4, loco::shape_get(testcase.maxpool2d_node).as().height()); + ASSERT_EQ(2, loco::shape_get(testcase.maxpool2d_node).as().width()); } TEST(CanonicalShapeInferenceRuleTest, tensor_concat) @@ -268,11 +268,11 @@ TEST(CanonicalShapeInferenceRuleTest, tensor_concat) // Verify! ASSERT_TRUE(loco::shape_known(testcase.concat_node)); - ASSERT_EQ(loco::shape_get(testcase.concat_node).domain(), loco::Domain::Tensor); - ASSERT_EQ(loco::shape_get(testcase.concat_node).as().rank(), 3); - ASSERT_EQ(loco::shape_get(testcase.concat_node).as().dim(0), 1); - ASSERT_EQ(loco::shape_get(testcase.concat_node).as().dim(1), 6); - ASSERT_EQ(loco::shape_get(testcase.concat_node).as().dim(2), 3); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(testcase.concat_node).domain()); + ASSERT_EQ(3, loco::shape_get(testcase.concat_node).as().rank()); + ASSERT_EQ(1, loco::shape_get(testcase.concat_node).as().dim(0)); + ASSERT_EQ(6, loco::shape_get(testcase.concat_node).as().dim(1)); + ASSERT_EQ(3, loco::shape_get(testcase.concat_node).as().dim(2)); } TEST(CanonicalShapeInferenceRuleTest, fixed_reshape) @@ -290,10 +290,10 @@ TEST(CanonicalShapeInferenceRuleTest, fixed_reshape) // Verify! ASSERT_TRUE(loco::shape_known(testcase.push_node)); - ASSERT_EQ(loco::shape_get(testcase.push_node).domain(), loco::Domain::Tensor); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().rank(), 2); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().dim(0), 4); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().dim(1), 9); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(testcase.push_node).domain()); + ASSERT_EQ(2, loco::shape_get(testcase.push_node).as().rank()); + ASSERT_EQ(4, loco::shape_get(testcase.push_node).as().dim(0)); + ASSERT_EQ(9, loco::shape_get(testcase.push_node).as().dim(1)); } TEST(CanonicalShapeInferenceRuleTest, tensor_broadcast) @@ -310,10 +310,10 @@ TEST(CanonicalShapeInferenceRuleTest, tensor_broadcast) // Verify! ASSERT_TRUE(loco::shape_known(testcase.push_node)); - ASSERT_EQ(loco::shape_get(testcase.push_node).domain(), loco::Domain::Tensor); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().rank(), 2); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().dim(0), 4); - ASSERT_EQ(loco::shape_get(testcase.push_node).as().dim(1), 2); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(testcase.push_node).domain()); + ASSERT_EQ(2, loco::shape_get(testcase.push_node).as().rank()); + ASSERT_EQ(4, loco::shape_get(testcase.push_node).as().dim(0)); + ASSERT_EQ(2, loco::shape_get(testcase.push_node).as().dim(1)); } TEST(CanonicalShapeInferenceRuleTest, tensor_transpose) @@ -336,12 +336,12 @@ TEST(CanonicalShapeInferenceRuleTest, tensor_transpose) // Verify! ASSERT_TRUE(loco::shape_known(tc.push_node)); - ASSERT_EQ(loco::shape_get(tc.push_node).domain(), loco::Domain::Tensor); - ASSERT_EQ(loco::shape_get(tc.push_node).as().rank(), 4); - ASSERT_EQ(loco::shape_get(tc.push_node).as().dim(0), 30); - ASSERT_EQ(loco::shape_get(tc.push_node).as().dim(1), 40); - ASSERT_EQ(loco::shape_get(tc.push_node).as().dim(2), 10); - ASSERT_EQ(loco::shape_get(tc.push_node).as().dim(3), 20); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(tc.push_node).domain()); + ASSERT_EQ(4, loco::shape_get(tc.push_node).as().rank()); + ASSERT_EQ(30, loco::shape_get(tc.push_node).as().dim(0)); + ASSERT_EQ(40, loco::shape_get(tc.push_node).as().dim(1)); + ASSERT_EQ(10, loco::shape_get(tc.push_node).as().dim(2)); + ASSERT_EQ(20, loco::shape_get(tc.push_node).as().dim(3)); } namespace @@ -393,8 +393,8 @@ TEST(CanonicalShapeInferenceRuleTest, infer_v2) rule.infer(&ctx, relu_2, &sink); - ASSERT_EQ(sink.shape.domain(), loco::Domain::Tensor); - ASSERT_EQ(sink.shape.as().rank(), 2); - ASSERT_EQ(sink.shape.as().dim(0), 4); - ASSERT_EQ(sink.shape.as().dim(1), 5); + ASSERT_EQ(loco::Domain::Tensor, sink.shape.domain()); + ASSERT_EQ(2, sink.shape.as().rank()); + ASSERT_EQ(4, sink.shape.as().dim(0)); + ASSERT_EQ(5, sink.shape.as().dim(1)); } diff --git a/compiler/loco/src/Service/GraphBuilder.test.cpp b/compiler/loco/src/Service/GraphBuilder.test.cpp index 7b2ea51..8129648 100644 --- a/compiler/loco/src/Service/GraphBuilder.test.cpp +++ b/compiler/loco/src/Service/GraphBuilder.test.cpp @@ -41,7 +41,7 @@ TEST(GraphBuilderTest, Usecase_000) auto node = gbuilder->pop(); - ASSERT_EQ(g->nodes()->size(), 1); - ASSERT_EQ(node->dialect(), loco::CanonicalDialect::get()); - ASSERT_EQ(node->opnum(), static_cast(loco::CanonicalOpcode::ConstGen)); + ASSERT_EQ(1, g->nodes()->size()); + ASSERT_EQ(loco::CanonicalDialect::get(), node->dialect()); + ASSERT_EQ(static_cast(loco::CanonicalOpcode::ConstGen), node->opnum()); } diff --git a/compiler/loco/src/Service/GraphTestcase.h b/compiler/loco/src/Service/GraphTestcase.h index 6743b9a..27b011f 100644 --- a/compiler/loco/src/Service/GraphTestcase.h +++ b/compiler/loco/src/Service/GraphTestcase.h @@ -59,7 +59,7 @@ template <> loco::Permutation make_NHWC_perm(void) template loco::Permutation make_HWCN_perm(void); -// @note Also known as HWIO permutation +/// @note Also known as HWIO permutation template <> loco::Permutation make_HWCN_perm(void) { loco::Permutation perm; diff --git a/compiler/loco/src/Service/MultiDialectShapeInferenceRule.test.cpp b/compiler/loco/src/Service/MultiDialectShapeInferenceRule.test.cpp index ffa9ee5..3d5a11a 100644 --- a/compiler/loco/src/Service/MultiDialectShapeInferenceRule.test.cpp +++ b/compiler/loco/src/Service/MultiDialectShapeInferenceRule.test.cpp @@ -120,15 +120,15 @@ TEST(MultiDialectShapeInferenceRuleTest, test1) // Verify! ASSERT_TRUE(loco::shape_known(t23_node)); auto t23_shape = loco::shape_get(t23_node); - ASSERT_EQ(t23_shape.domain(), loco::Domain::Tensor); - ASSERT_EQ(t23_shape.as().rank(), 2); - ASSERT_EQ(t23_shape.as().dim(0), 2); - ASSERT_EQ(t23_shape.as().dim(1), 3); + ASSERT_EQ(loco::Domain::Tensor, t23_shape.domain()); + ASSERT_EQ(2, t23_shape.as().rank()); + ASSERT_EQ(2, t23_shape.as().dim(0)); + ASSERT_EQ(3, t23_shape.as().dim(1)); ASSERT_TRUE(loco::shape_known(t45_node)); auto t45_shape = loco::shape_get(t45_node); - ASSERT_EQ(t45_shape.domain(), loco::Domain::Tensor); - ASSERT_EQ(t45_shape.as().rank(), 2); - ASSERT_EQ(t45_shape.as().dim(0), 4); - ASSERT_EQ(t45_shape.as().dim(1), 5); + ASSERT_EQ(loco::Domain::Tensor, t45_shape.domain()); + ASSERT_EQ(2, t45_shape.as().rank()); + ASSERT_EQ(4, t45_shape.as().dim(0)); + ASSERT_EQ(5, t45_shape.as().dim(1)); } diff --git a/compiler/loco/src/Service/ShapeInference.test.cpp b/compiler/loco/src/Service/ShapeInference.test.cpp index e10b988..2085703 100644 --- a/compiler/loco/src/Service/ShapeInference.test.cpp +++ b/compiler/loco/src/Service/ShapeInference.test.cpp @@ -71,16 +71,16 @@ TEST(ShapeInferenceTest, framework) loco::apply(&rule).to(testcase.graph()); // Framework SHOULD visit all the nodes - ASSERT_EQ(nodes.size(), 2); + ASSERT_EQ(2, nodes.size()); // Framework SHOULD visit "pull" before "push" - ASSERT_EQ(nodes.at(0), testcase.pull_node); - ASSERT_EQ(nodes.at(1), testcase.push_node); + ASSERT_EQ(testcase.pull_node, nodes.at(0)); + ASSERT_EQ(testcase.push_node, nodes.at(1)); // Framework SHOULD make an annotation if "rule" returns TRUE ASSERT_TRUE(loco::shape_known(testcase.pull_node)); - ASSERT_EQ(loco::shape_get(testcase.pull_node).domain(), loco::Domain::Tensor); - ASSERT_EQ(loco::shape_get(testcase.pull_node).as().rank(), 1); - ASSERT_EQ(loco::shape_get(testcase.pull_node).as().dim(0), 4); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(testcase.pull_node).domain()); + ASSERT_EQ(1, loco::shape_get(testcase.pull_node).as().rank()); + ASSERT_EQ(4, loco::shape_get(testcase.pull_node).as().dim(0)); // Framework SHOULD NOT make any annotation if "rule" returns FALSE ASSERT_FALSE(loco::shape_known(testcase.push_node)); diff --git a/compiler/loco/src/Service/TypeInference.cpp b/compiler/loco/src/Service/TypeInference.cpp index fbf0033..27d7d9a 100644 --- a/compiler/loco/src/Service/TypeInference.cpp +++ b/compiler/loco/src/Service/TypeInference.cpp @@ -182,7 +182,7 @@ bool CanonicalTypeInferenceRule::infer(const Node *node, DataType &dtype) const assert(dynamic_cast(node) != nullptr); CanonicalTypeForwardAlgorithm alg; - dtype = dynamic_cast(node)->accept(&alg); + dtype = loco::must_cast(node)->accept(&alg); return true; } diff --git a/compiler/loco/src/Service/TypeInference.test.cpp b/compiler/loco/src/Service/TypeInference.test.cpp index 4660401..13bcfa5 100644 --- a/compiler/loco/src/Service/TypeInference.test.cpp +++ b/compiler/loco/src/Service/TypeInference.test.cpp @@ -88,14 +88,14 @@ TEST(TypeInferenceTest, framework) loco::apply(&rule).to(g.get()); - ASSERT_EQ(nodes.size(), 2); // Framework SHOULD visit all the nodes - ASSERT_EQ(nodes.at(0), pull_node); // Framework SHOULD visit "pull" before "push" - ASSERT_EQ(nodes.at(1), push_node); + ASSERT_EQ(2, nodes.size()); // Framework SHOULD visit all the nodes + ASSERT_EQ(pull_node, nodes.at(0)); // Framework SHOULD visit "pull" before "push" + ASSERT_EQ(push_node, nodes.at(1)); // Framework SHOULD NOT make any annotation if "rule" returns FALSE ASSERT_TRUE(loco::dtype_known(pull_node)); // Framework SHOULD make an annotation if "rule" returns TRUE - ASSERT_EQ(loco::dtype_get(pull_node), loco::DataType::U8); + ASSERT_EQ(loco::DataType::U8, loco::dtype_get(pull_node)); ASSERT_FALSE(loco::dtype_known(push_node)); } @@ -129,7 +129,7 @@ TEST(CanonicalTypeInferenceRuleTest, minimal) // Verify! ASSERT_TRUE(loco::dtype_known(push_node)); - ASSERT_EQ(loco::dtype_get(push_node), loco::DataType::U8); + ASSERT_EQ(loco::DataType::U8, loco::dtype_get(push_node)); } TEST(CanonicalTypeInferenceRuleTest, relu6) @@ -166,7 +166,7 @@ TEST(CanonicalTypeInferenceRuleTest, relu6) // Verify! ASSERT_TRUE(loco::dtype_known(relu6_node)); - ASSERT_EQ(loco::dtype_get(relu6_node), loco::DataType::FLOAT32); + ASSERT_EQ(loco::DataType::FLOAT32, loco::dtype_get(relu6_node)); } TEST(CanonicalTypeInferenceRuleTest, tensor_broadcast) @@ -183,7 +183,7 @@ TEST(CanonicalTypeInferenceRuleTest, tensor_broadcast) // Verify! ASSERT_TRUE(loco::dtype_known(testcase.push_node)); - ASSERT_EQ(loco::dtype_get(testcase.push_node), loco::DataType::U8); + ASSERT_EQ(loco::DataType::U8, loco::dtype_get(testcase.push_node)); } // mockup for MultiDialectTypeInferenceRule @@ -275,8 +275,8 @@ TEST(MultiDialectTypeInferenceRuleTest, test1) // Verify! ASSERT_TRUE(loco::dtype_known(s8_node)); - ASSERT_EQ(loco::dtype_get(s8_node), loco::DataType::S8); + ASSERT_EQ(loco::DataType::S8, loco::dtype_get(s8_node)); ASSERT_TRUE(loco::dtype_known(u8_node)); - ASSERT_EQ(loco::dtype_get(u8_node), loco::DataType::U8); + ASSERT_EQ(loco::DataType::U8, loco::dtype_get(u8_node)); } diff --git a/compiler/loco/src/loco.test.cpp b/compiler/loco/src/loco.test.cpp index 4c4f51a..e5668b6 100644 --- a/compiler/loco/src/loco.test.cpp +++ b/compiler/loco/src/loco.test.cpp @@ -58,11 +58,11 @@ TEST(LOCO, identity_network) loco::link(graph_output, push_node); // loco::link SHOULD update "index" - ASSERT_EQ(pull_node->index(), 0); - ASSERT_EQ(graph_input->dtype(), loco::DataType::FLOAT32); + ASSERT_EQ(0, pull_node->index()); + ASSERT_EQ(loco::DataType::FLOAT32, graph_input->dtype()); // loco::link SHOULD update "index" - ASSERT_EQ(push_node->index(), 0); + ASSERT_EQ(0, push_node->index()); } #if 0 @@ -99,10 +99,10 @@ TEST(LOCO, identity_network_V2) push_node->index(0); push_node->from(pull_node); - ASSERT_EQ(pull_node->dtype(), loco::DataType::FLOAT32); + ASSERT_EQ(loco::DataType::FLOAT32, pull_node->dtype()); // TODO Check Shape of pull_node // TODO Check Shape of push_node - ASSERT_EQ(loco::pull_node(g.get(), 0), pull_node); - ASSERT_EQ(loco::push_node(g.get(), 0), push_node); + ASSERT_EQ(pull_node, loco::pull_node(g.get(), 0)); + ASSERT_EQ(push_node, loco::push_node(g.get(), 0)); } diff --git a/compiler/locoex-customop/src/Service/COpShapeInferenceRule.cpp b/compiler/locoex-customop/src/Service/COpShapeInferenceRule.cpp index 4dc8f46..cf9decc 100644 --- a/compiler/locoex-customop/src/Service/COpShapeInferenceRule.cpp +++ b/compiler/locoex-customop/src/Service/COpShapeInferenceRule.cpp @@ -37,7 +37,7 @@ bool COpShapeInferenceRule::infer(const loco::Node *node, loco::NodeShape &shape assert(node->dialect() == COpDialect::get()); assert(dynamic_cast(node) != nullptr); - auto cop_call = dynamic_cast(node); + auto cop_call = loco::must_cast(node); // Note that the shape of custom op is considered as TensorShape // TODO Decide how to deal with this shape error cases diff --git a/compiler/locomotiv/src/Node/AvgPool2D.cpp b/compiler/locomotiv/src/Node/AvgPool2D.cpp index ad603ba..5fdf1e7 100644 --- a/compiler/locomotiv/src/Node/AvgPool2D.cpp +++ b/compiler/locomotiv/src/Node/AvgPool2D.cpp @@ -129,7 +129,8 @@ nncc::core::ADT::tensor::Buffer avgPool2D(const loco::AvgPool2D *avgpool2d, } } - assert(filter_ele_count > 0); + if (filter_ele_count <= 0) + throw std::runtime_error("The number of filter element must be greater than zero."); output_buf.at(Index({batch, out_y, out_x, channel})) = total / filter_ele_count; } } @@ -141,10 +142,12 @@ nncc::core::ADT::tensor::Buffer avgPool2D(const loco::AvgPool2D *avgpool2d, } // namespace -namespace locomotiv +namespace { -void NodeExecution::execute(loco::AvgPool2D *avgpool2d) +using namespace locomotiv; + +void exectute_node(loco::AvgPool2D *avgpool2d) { auto ifm_data = annot_data(avgpool2d->ifm()); @@ -176,4 +179,11 @@ void NodeExecution::execute(loco::AvgPool2D *avgpool2d) annot_domain(avgpool2d, loco::Domain::Feature); } +} // namespace + +namespace locomotiv +{ + +void NodeExecution::execute(loco::AvgPool2D *avgpool2d) { exectute_node(avgpool2d); } + } // namespace locomotiv diff --git a/compiler/locomotiv/src/Node/AvgPool2D.test.cpp b/compiler/locomotiv/src/Node/AvgPool2D.test.cpp index 89e10a3..f9863b4 100644 --- a/compiler/locomotiv/src/Node/AvgPool2D.test.cpp +++ b/compiler/locomotiv/src/Node/AvgPool2D.test.cpp @@ -88,10 +88,10 @@ void run_test(const float *ifm, const float *expected_ofm, const Shape &ifm_shap for (nncc::core::ADT::tensor::IndexEnumerator e{ofm_shape}; e.valid(); e.advance()) { const auto &ind = e.current(); - ASSERT_FLOAT_EQ(avgpool2d_data->as_f32_bufptr()->at(ind), ofm_overlay.at(ind)); + ASSERT_FLOAT_EQ(ofm_overlay.at(ind), avgpool2d_data->as_f32_bufptr()->at(ind)); } - ASSERT_EQ(locomotiv::annot_domain(avgpool2d), loco::Domain::Feature); + ASSERT_EQ(loco::Domain::Feature, locomotiv::annot_domain(avgpool2d)); } } // namespace diff --git a/compiler/locomotiv/src/Node/BiasAdd.cpp b/compiler/locomotiv/src/Node/BiasAdd.cpp index 0724fb7..dfe32ca 100644 --- a/compiler/locomotiv/src/Node/BiasAdd.cpp +++ b/compiler/locomotiv/src/Node/BiasAdd.cpp @@ -46,6 +46,8 @@ namespace locomotiv void NodeExecution::execute(loco::BiasAdd *bias_add) { + validate(bias_add, "BiasAdd is nullptr"); + auto input_data = locomotiv::annot_data(bias_add->value()); auto bias_data = locomotiv::annot_data(bias_add->bias()); @@ -63,6 +65,8 @@ void NodeExecution::execute(loco::BiasAdd *bias_add) void NodeExecution::execute(loco::BiasAdd *bias_add) { + validate(bias_add, "BiasAdd is nullptr"); + auto input_data = locomotiv::annot_data(bias_add->value()); auto bias_data = locomotiv::annot_data(bias_add->bias()); diff --git a/compiler/locomotiv/src/Node/BiasAdd.test.cpp b/compiler/locomotiv/src/Node/BiasAdd.test.cpp index 0ca8266..cba2d41 100644 --- a/compiler/locomotiv/src/Node/BiasAdd.test.cpp +++ b/compiler/locomotiv/src/Node/BiasAdd.test.cpp @@ -107,16 +107,16 @@ TEST(NodeExecution_TensorBiasAdd, f32) // comparing the result ASSERT_NE(bias_add_data, nullptr); - ASSERT_EQ(bias_add_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(bias_add_data->shape()), Shape({1, 3, 3, 2})); + ASSERT_EQ(loco::DataType::FLOAT32, bias_add_data->dtype()); + ASSERT_EQ(Shape({1, 3, 3, 2}), *(bias_add_data->shape())); uint32_t n = 0; for (IndexEnumerator e{*(bias_add_data->shape())}; e.valid(); e.advance()) { - ASSERT_FLOAT_EQ(bias_add_data->as_f32_bufptr()->at(e.current()), out_val[n++]); + ASSERT_FLOAT_EQ(out_val[n++], bias_add_data->as_f32_bufptr()->at(e.current())); } - ASSERT_EQ(locomotiv::annot_domain(bias_add), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(bias_add)); } /* @@ -191,14 +191,14 @@ TEST(NodeExecution_FeatureBiasAdd, f32) // comparing the result ASSERT_NE(bias_add_data, nullptr); - ASSERT_EQ(bias_add_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(bias_add_data->shape()), Shape({1, 3, 3, 2})); + ASSERT_EQ(loco::DataType::FLOAT32, bias_add_data->dtype()); + ASSERT_EQ(Shape({1, 3, 3, 2}), *(bias_add_data->shape())); uint32_t n = 0; for (IndexEnumerator e{*(bias_add_data->shape())}; e.valid(); e.advance()) { - ASSERT_FLOAT_EQ(bias_add_data->as_f32_bufptr()->at(e.current()), out_val[n++]); + ASSERT_FLOAT_EQ(out_val[n++], bias_add_data->as_f32_bufptr()->at(e.current())); } - ASSERT_EQ(locomotiv::annot_domain(feature_bias_add), loco::Domain::Feature); + ASSERT_EQ(loco::Domain::Feature, locomotiv::annot_domain(feature_bias_add)); } diff --git a/compiler/locomotiv/src/Node/BiasEncode.test.cpp b/compiler/locomotiv/src/Node/BiasEncode.test.cpp index 73e2af8..cdb255c 100644 --- a/compiler/locomotiv/src/Node/BiasEncode.test.cpp +++ b/compiler/locomotiv/src/Node/BiasEncode.test.cpp @@ -82,11 +82,11 @@ template void test() auto bias_enc_data = locomotiv::annot_data(bias_enc); ASSERT_NE(bias_enc_data, nullptr); - ASSERT_EQ(bias_enc_data->dtype(), loco_dtype()); - ASSERT_EQ(*(bias_enc_data->shape()), Shape{1}); - ASSERT_EQ(as_bufptr(bias_enc_data)->at(Index{0}), pull_buf.at(Index{0})); + ASSERT_EQ(loco_dtype(), bias_enc_data->dtype()); + ASSERT_EQ(Shape{1}, *(bias_enc_data->shape())); + ASSERT_EQ(pull_buf.at(Index{0}), as_bufptr(bias_enc_data)->at(Index{0})); - ASSERT_EQ(locomotiv::annot_domain(bias_enc), loco::Domain::Bias); + ASSERT_EQ(loco::Domain::Bias, locomotiv::annot_domain(bias_enc)); } } // namespace diff --git a/compiler/locomotiv/src/Node/ConstGen.test.cpp b/compiler/locomotiv/src/Node/ConstGen.test.cpp index 838f4c1..382cc77 100644 --- a/compiler/locomotiv/src/Node/ConstGen.test.cpp +++ b/compiler/locomotiv/src/Node/ConstGen.test.cpp @@ -53,16 +53,16 @@ TEST(NodeExecution_ConstGen, s32) // test auto data = locomotiv::annot_data(&constgen); ASSERT_NE(data, nullptr); - ASSERT_EQ(data->dtype(), loco::DataType::S32); - ASSERT_EQ(*data->shape(), Shape({2, 3})); - ASSERT_EQ(data->as_s32_bufptr()->at(Index{0, 0}), 0); - ASSERT_EQ(data->as_s32_bufptr()->at(Index{0, 1}), 1); - ASSERT_EQ(data->as_s32_bufptr()->at(Index{0, 2}), 2); - ASSERT_EQ(data->as_s32_bufptr()->at(Index{1, 0}), -3); - ASSERT_EQ(data->as_s32_bufptr()->at(Index{1, 1}), -4); - ASSERT_EQ(data->as_s32_bufptr()->at(Index{1, 2}), -5); - - ASSERT_EQ(locomotiv::annot_domain(&constgen), loco::Domain::Tensor); + ASSERT_EQ(loco::DataType::S32, data->dtype()); + ASSERT_EQ(Shape({2, 3}), *data->shape()); + ASSERT_EQ(0, data->as_s32_bufptr()->at(Index{0, 0})); + ASSERT_EQ(1, data->as_s32_bufptr()->at(Index{0, 1})); + ASSERT_EQ(2, data->as_s32_bufptr()->at(Index{0, 2})); + ASSERT_EQ(-3, data->as_s32_bufptr()->at(Index{1, 0})); + ASSERT_EQ(-4, data->as_s32_bufptr()->at(Index{1, 1})); + ASSERT_EQ(-5, data->as_s32_bufptr()->at(Index{1, 2})); + + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(&constgen)); } TEST(NodeExecution_ConstGen, f32) @@ -87,14 +87,14 @@ TEST(NodeExecution_ConstGen, f32) // test auto data = locomotiv::annot_data(&constgen); ASSERT_NE(data, nullptr); - ASSERT_EQ(data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*data->shape(), Shape({2, 3})); - ASSERT_FLOAT_EQ(data->as_f32_bufptr()->at(Index{0, 0}), 0.0f); - ASSERT_FLOAT_EQ(data->as_f32_bufptr()->at(Index{0, 1}), 1.0f); - ASSERT_FLOAT_EQ(data->as_f32_bufptr()->at(Index{0, 2}), 2.0f); - ASSERT_FLOAT_EQ(data->as_f32_bufptr()->at(Index{1, 0}), 3.0f); - ASSERT_FLOAT_EQ(data->as_f32_bufptr()->at(Index{1, 1}), 4.0f); - ASSERT_FLOAT_EQ(data->as_f32_bufptr()->at(Index{1, 2}), 5.0f); - - ASSERT_EQ(locomotiv::annot_domain(&constgen), loco::Domain::Tensor); + ASSERT_EQ(loco::DataType::FLOAT32, data->dtype()); + ASSERT_EQ(Shape({2, 3}), *data->shape()); + ASSERT_FLOAT_EQ(0.0f, data->as_f32_bufptr()->at(Index{0, 0})); + ASSERT_FLOAT_EQ(1.0f, data->as_f32_bufptr()->at(Index{0, 1})); + ASSERT_FLOAT_EQ(2.0f, data->as_f32_bufptr()->at(Index{0, 2})); + ASSERT_FLOAT_EQ(3.0f, data->as_f32_bufptr()->at(Index{1, 0})); + ASSERT_FLOAT_EQ(4.0f, data->as_f32_bufptr()->at(Index{1, 1})); + ASSERT_FLOAT_EQ(5.0f, data->as_f32_bufptr()->at(Index{1, 2})); + + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(&constgen)); } diff --git a/compiler/locomotiv/src/Node/Conv2D.test.cpp b/compiler/locomotiv/src/Node/Conv2D.test.cpp index 83d7fc2..66e947a 100644 --- a/compiler/locomotiv/src/Node/Conv2D.test.cpp +++ b/compiler/locomotiv/src/Node/Conv2D.test.cpp @@ -101,10 +101,10 @@ void run_test(const float *ifm, const float *ker, const float *expected_ofm, con for (nncc::core::ADT::tensor::IndexEnumerator e{ofm_shape}; e.valid(); e.advance()) { const auto &ind = e.current(); - ASSERT_FLOAT_EQ(conv2d_result->as_f32_bufptr()->at(ind), ofm_overlay.at(ind)); + ASSERT_FLOAT_EQ(ofm_overlay.at(ind), conv2d_result->as_f32_bufptr()->at(ind)); } - ASSERT_EQ(locomotiv::annot_domain(conv2d), loco::Domain::Feature); + ASSERT_EQ(loco::Domain::Feature, locomotiv::annot_domain(conv2d)); } } // namespace diff --git a/compiler/locomotiv/src/Node/DepthwiseConv2D.test.cpp b/compiler/locomotiv/src/Node/DepthwiseConv2D.test.cpp index 48824c2..1ff333b 100644 --- a/compiler/locomotiv/src/Node/DepthwiseConv2D.test.cpp +++ b/compiler/locomotiv/src/Node/DepthwiseConv2D.test.cpp @@ -101,10 +101,10 @@ void run_test(const float *ifm, const float *ker, const float *expected_ofm, con for (nncc::core::ADT::tensor::IndexEnumerator e{ofm_shape}; e.valid(); e.advance()) { const auto &ind = e.current(); - ASSERT_FLOAT_EQ(dw_conv2d_result->as_f32_bufptr()->at(ind), ofm_overlay.at(ind)); + ASSERT_FLOAT_EQ(ofm_overlay.at(ind), dw_conv2d_result->as_f32_bufptr()->at(ind)); } - ASSERT_EQ(locomotiv::annot_domain(dw_conv2d), loco::Domain::Feature); + ASSERT_EQ(loco::Domain::Feature, locomotiv::annot_domain(dw_conv2d)); } } // namespace diff --git a/compiler/locomotiv/src/Node/DepthwiseFilterEncode.test.cpp b/compiler/locomotiv/src/Node/DepthwiseFilterEncode.test.cpp index db828c0..5b2ec93 100644 --- a/compiler/locomotiv/src/Node/DepthwiseFilterEncode.test.cpp +++ b/compiler/locomotiv/src/Node/DepthwiseFilterEncode.test.cpp @@ -77,14 +77,14 @@ TEST(NodeExecution_DepthwiseFilterEncode, f32) auto enc_data = locomotiv::annot_data(enc); ASSERT_NE(enc_data, nullptr); - ASSERT_EQ(enc_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(enc_data->shape()), (Shape{H, W, C, M})); // locomotiv depthwise filter is HWCM + ASSERT_EQ(loco::DataType::FLOAT32, enc_data->dtype()); + ASSERT_EQ((Shape{H, W, C, M}), *(enc_data->shape())); // locomotiv depthwise filter is HWCM auto enc_buf = enc_data->as_f32_bufptr(); for (uint32_t h = 0; h < H; ++h) for (uint32_t w = 0; w < W; ++w) for (uint32_t c = 0; c < C; ++c) for (uint32_t m = 0; m < M; ++m) - ASSERT_FLOAT_EQ(pull_buf.at(Index{m, h, w, c}), enc_buf->at(Index{h, w, c, m})); + ASSERT_FLOAT_EQ(enc_buf->at(Index{h, w, c, m}), pull_buf.at(Index{m, h, w, c})); - ASSERT_EQ(locomotiv::annot_domain(enc), loco::Domain::DepthwiseFilter); + ASSERT_EQ(loco::Domain::DepthwiseFilter, locomotiv::annot_domain(enc)); } diff --git a/compiler/locomotiv/src/Node/EltwiseAdd.test.cpp b/compiler/locomotiv/src/Node/EltwiseAdd.test.cpp index 2899dcc..2873a65 100644 --- a/compiler/locomotiv/src/Node/EltwiseAdd.test.cpp +++ b/compiler/locomotiv/src/Node/EltwiseAdd.test.cpp @@ -108,14 +108,14 @@ TEST(NodeExecution_EltwiseAdd, f32) // comparing the result ASSERT_NE(eltwise_add_data, nullptr); - ASSERT_EQ(eltwise_add_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(eltwise_add_data->shape()), Shape({1, 3, 3, 2})); + ASSERT_EQ(loco::DataType::FLOAT32, eltwise_add_data->dtype()); + ASSERT_EQ(Shape({1, 3, 3, 2}), *(eltwise_add_data->shape())); uint32_t n = 0; for (IndexEnumerator e{*(eltwise_add_data->shape())}; e.valid(); e.advance()) { - ASSERT_FLOAT_EQ(eltwise_add_data->as_f32_bufptr()->at(e.current()), out_val[n++]); + ASSERT_FLOAT_EQ(out_val[n++], eltwise_add_data->as_f32_bufptr()->at(e.current())); } - ASSERT_EQ(locomotiv::annot_domain(eltwise_add), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(eltwise_add)); } diff --git a/compiler/locomotiv/src/Node/EltwiseDiv.test.cpp b/compiler/locomotiv/src/Node/EltwiseDiv.test.cpp index 60950c1..cc50450 100644 --- a/compiler/locomotiv/src/Node/EltwiseDiv.test.cpp +++ b/compiler/locomotiv/src/Node/EltwiseDiv.test.cpp @@ -108,14 +108,14 @@ TEST(NodeExecution_EltwiseDiv, f32) // comparing the result ASSERT_NE(eltwise_div_data, nullptr); - ASSERT_EQ(eltwise_div_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(eltwise_div_data->shape()), Shape({1, 3, 3, 2})); + ASSERT_EQ(loco::DataType::FLOAT32, eltwise_div_data->dtype()); + ASSERT_EQ(Shape({1, 3, 3, 2}), *(eltwise_div_data->shape())); uint32_t n = 0; for (IndexEnumerator e{*(eltwise_div_data->shape())}; e.valid(); e.advance()) { - ASSERT_FLOAT_EQ(eltwise_div_data->as_f32_bufptr()->at(e.current()), out_val[n++]); + ASSERT_FLOAT_EQ(out_val[n++], eltwise_div_data->as_f32_bufptr()->at(e.current())); } - ASSERT_EQ(locomotiv::annot_domain(eltwise_div), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(eltwise_div)); } diff --git a/compiler/locomotiv/src/Node/EltwiseMax.test.cpp b/compiler/locomotiv/src/Node/EltwiseMax.test.cpp index c64db89..94c3982 100644 --- a/compiler/locomotiv/src/Node/EltwiseMax.test.cpp +++ b/compiler/locomotiv/src/Node/EltwiseMax.test.cpp @@ -108,14 +108,14 @@ TEST(NodeExecution_EltwiseMax, f32) // comparing the result ASSERT_NE(eltwise_max_data, nullptr); - ASSERT_EQ(eltwise_max_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(eltwise_max_data->shape()), Shape({1, 3, 3, 2})); + ASSERT_EQ(loco::DataType::FLOAT32, eltwise_max_data->dtype()); + ASSERT_EQ(Shape({1, 3, 3, 2}), *(eltwise_max_data->shape())); uint32_t n = 0; for (IndexEnumerator e{*(eltwise_max_data->shape())}; e.valid(); e.advance()) { - ASSERT_FLOAT_EQ(eltwise_max_data->as_f32_bufptr()->at(e.current()), out_val[n++]); + ASSERT_FLOAT_EQ(out_val[n++], eltwise_max_data->as_f32_bufptr()->at(e.current())); } - ASSERT_EQ(locomotiv::annot_domain(eltwise_max), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(eltwise_max)); } diff --git a/compiler/locomotiv/src/Node/EltwiseMul.test.cpp b/compiler/locomotiv/src/Node/EltwiseMul.test.cpp index b768883..bbe51bc 100644 --- a/compiler/locomotiv/src/Node/EltwiseMul.test.cpp +++ b/compiler/locomotiv/src/Node/EltwiseMul.test.cpp @@ -111,14 +111,14 @@ TEST(NodeExecution_EltwiseMul, f32) // comparing the result ASSERT_NE(eltwise_mul_data, nullptr); - ASSERT_EQ(eltwise_mul_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(eltwise_mul_data->shape()), Shape({1, 3, 3, 2})); + ASSERT_EQ(loco::DataType::FLOAT32, eltwise_mul_data->dtype()); + ASSERT_EQ(Shape({1, 3, 3, 2}), *(eltwise_mul_data->shape())); uint32_t n = 0; for (IndexEnumerator e{*(eltwise_mul_data->shape())}; e.valid(); e.advance()) { - ASSERT_FLOAT_EQ(eltwise_mul_data->as_f32_bufptr()->at(e.current()), out_val[n++]); + ASSERT_FLOAT_EQ(out_val[n++], eltwise_mul_data->as_f32_bufptr()->at(e.current())); } - ASSERT_EQ(locomotiv::annot_domain(eltwise_mul), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(eltwise_mul)); } diff --git a/compiler/locomotiv/src/Node/EltwiseSqrt.test.cpp b/compiler/locomotiv/src/Node/EltwiseSqrt.test.cpp index adb1b85..44d0ca6 100644 --- a/compiler/locomotiv/src/Node/EltwiseSqrt.test.cpp +++ b/compiler/locomotiv/src/Node/EltwiseSqrt.test.cpp @@ -58,12 +58,12 @@ TEST(NodeExecution_EltwiseSqrt, f32) auto sqrt_data = locomotiv::annot_data(sqrt); ASSERT_NE(sqrt_data, nullptr); - ASSERT_EQ(sqrt_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(sqrt_data->shape()), Shape{4}); - ASSERT_FLOAT_EQ(sqrt_data->as_f32_bufptr()->at(Index{0}), 2.0f); - ASSERT_FLOAT_EQ(sqrt_data->as_f32_bufptr()->at(Index{1}), 3.0f); - ASSERT_FLOAT_EQ(sqrt_data->as_f32_bufptr()->at(Index{2}), 0.0f); + ASSERT_EQ(loco::DataType::FLOAT32, sqrt_data->dtype()); + ASSERT_EQ(Shape{4}, *(sqrt_data->shape())); + ASSERT_FLOAT_EQ(2.0f, sqrt_data->as_f32_bufptr()->at(Index{0})); + ASSERT_FLOAT_EQ(3.0f, sqrt_data->as_f32_bufptr()->at(Index{1})); + ASSERT_FLOAT_EQ(0.0f, sqrt_data->as_f32_bufptr()->at(Index{2})); ASSERT_TRUE(std::isnan(sqrt_data->as_f32_bufptr()->at(Index{3}))); - ASSERT_EQ(locomotiv::annot_domain(sqrt), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(sqrt)); } diff --git a/compiler/locomotiv/src/Node/EltwiseSub.test.cpp b/compiler/locomotiv/src/Node/EltwiseSub.test.cpp index 7eff90f..94dc9c9 100644 --- a/compiler/locomotiv/src/Node/EltwiseSub.test.cpp +++ b/compiler/locomotiv/src/Node/EltwiseSub.test.cpp @@ -108,14 +108,14 @@ TEST(NodeExecution_EltwiseSub, f32) // comparing the result ASSERT_NE(eltwise_sub_data, nullptr); - ASSERT_EQ(eltwise_sub_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(eltwise_sub_data->shape()), Shape({1, 3, 3, 2})); + ASSERT_EQ(loco::DataType::FLOAT32, eltwise_sub_data->dtype()); + ASSERT_EQ(Shape({1, 3, 3, 2}), *(eltwise_sub_data->shape())); uint32_t n = 0; for (IndexEnumerator e{*(eltwise_sub_data->shape())}; e.valid(); e.advance()) { - ASSERT_FLOAT_EQ(eltwise_sub_data->as_f32_bufptr()->at(e.current()), out_val[n++]); + ASSERT_FLOAT_EQ(out_val[n++], eltwise_sub_data->as_f32_bufptr()->at(e.current())); } - ASSERT_EQ(locomotiv::annot_domain(eltwise_sub), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(eltwise_sub)); } diff --git a/compiler/locomotiv/src/Node/FeatureCodec.test.cpp b/compiler/locomotiv/src/Node/FeatureCodec.test.cpp index c35f0e6..1b6b06c 100644 --- a/compiler/locomotiv/src/Node/FeatureCodec.test.cpp +++ b/compiler/locomotiv/src/Node/FeatureCodec.test.cpp @@ -128,16 +128,16 @@ TEST_F(NodeExecution_FeatureCodec, s32) // Test FeatureEncode auto enc_data = locomotiv::annot_data(enc); ASSERT_NE(enc_data, nullptr); - ASSERT_EQ(enc_data->dtype(), loco::DataType::S32); - ASSERT_EQ(*(enc_data->shape()), (Shape{N, H, W, C})); // locomotiv feature is NHWC + ASSERT_EQ(loco::DataType::S32, enc_data->dtype()); + ASSERT_EQ((Shape{N, H, W, C}), *(enc_data->shape())); // locomotiv feature is NHWC auto enc_buf = enc_data->as_s32_bufptr(); for (uint32_t n = 0; n < N; ++n) for (uint32_t h = 0; h < H; ++h) for (uint32_t w = 0; w < W; ++w) for (uint32_t c = 0; c < C; ++c) - ASSERT_EQ(pull_buf.at(Index{n, c, h, w}), enc_buf->at(Index{n, h, w, c})); + ASSERT_EQ(enc_buf->at(Index{n, h, w, c}), pull_buf.at(Index{n, c, h, w})); - ASSERT_EQ(locomotiv::annot_domain(enc), loco::Domain::Feature); + ASSERT_EQ(loco::Domain::Feature, locomotiv::annot_domain(enc)); // FeatureDecode auto dec = feature_decode_layer(enc, NCHW); @@ -146,16 +146,16 @@ TEST_F(NodeExecution_FeatureCodec, s32) // Test FeatureDecode: Encode -> Decode == identity auto dec_data = locomotiv::annot_data(dec); ASSERT_NE(dec_data, nullptr); - ASSERT_EQ(dec_data->dtype(), loco::DataType::S32); - ASSERT_EQ(*(dec_data->shape()), (Shape{N, C, H, W})); + ASSERT_EQ(loco::DataType::S32, dec_data->dtype()); + ASSERT_EQ((Shape{N, C, H, W}), *(dec_data->shape())); auto dec_buf = dec_data->as_s32_bufptr(); for (uint32_t n = 0; n < N; ++n) for (uint32_t h = 0; h < H; ++h) for (uint32_t w = 0; w < W; ++w) for (uint32_t c = 0; c < C; ++c) - ASSERT_EQ(pull_buf.at(Index{n, c, h, w}), dec_buf->at(Index{n, c, h, w})); + ASSERT_EQ(dec_buf->at(Index{n, c, h, w}), pull_buf.at(Index{n, c, h, w})); - ASSERT_EQ(locomotiv::annot_domain(dec), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(dec)); } TEST_F(NodeExecution_FeatureCodec, f32) @@ -192,16 +192,16 @@ TEST_F(NodeExecution_FeatureCodec, f32) // Test FeatureEncode auto enc_data = locomotiv::annot_data(enc); ASSERT_NE(enc_data, nullptr); - ASSERT_EQ(enc_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(enc_data->shape()), (Shape{N, H, W, C})); // locomotiv feature is NHWC + ASSERT_EQ(loco::DataType::FLOAT32, enc_data->dtype()); + ASSERT_EQ((Shape{N, H, W, C}), *(enc_data->shape())); // locomotiv feature is NHWC auto enc_buf = enc_data->as_f32_bufptr(); for (uint32_t n = 0; n < N; ++n) for (uint32_t h = 0; h < H; ++h) for (uint32_t w = 0; w < W; ++w) for (uint32_t c = 0; c < C; ++c) - ASSERT_FLOAT_EQ(pull_buf.at(Index{c, h, n, w}), enc_buf->at(Index{n, h, w, c})); + ASSERT_FLOAT_EQ(enc_buf->at(Index{n, h, w, c}), pull_buf.at(Index{c, h, n, w})); - ASSERT_EQ(locomotiv::annot_domain(enc), loco::Domain::Feature); + ASSERT_EQ(loco::Domain::Feature, locomotiv::annot_domain(enc)); // FeatureDecode auto dec = feature_decode_layer(enc, CHNW); @@ -210,14 +210,14 @@ TEST_F(NodeExecution_FeatureCodec, f32) // Test FeatureDecode: Encode -> Decode == identity auto dec_data = locomotiv::annot_data(dec); ASSERT_NE(dec_data, nullptr); - ASSERT_EQ(dec_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(dec_data->shape()), (Shape{C, H, N, W})); + ASSERT_EQ(loco::DataType::FLOAT32, dec_data->dtype()); + ASSERT_EQ((Shape{C, H, N, W}), *(dec_data->shape())); auto dec_buf = dec_data->as_f32_bufptr(); for (uint32_t n = 0; n < N; ++n) for (uint32_t h = 0; h < H; ++h) for (uint32_t w = 0; w < W; ++w) for (uint32_t c = 0; c < C; ++c) - ASSERT_FLOAT_EQ(pull_buf.at(Index{c, h, n, w}), dec_buf->at(Index{c, h, n, w})); + ASSERT_FLOAT_EQ(dec_buf->at(Index{c, h, n, w}), pull_buf.at(Index{c, h, n, w})); - ASSERT_EQ(locomotiv::annot_domain(dec), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(dec)); } diff --git a/compiler/locomotiv/src/Node/FilterEncode.test.cpp b/compiler/locomotiv/src/Node/FilterEncode.test.cpp index 79b8308..dcca949 100644 --- a/compiler/locomotiv/src/Node/FilterEncode.test.cpp +++ b/compiler/locomotiv/src/Node/FilterEncode.test.cpp @@ -77,16 +77,16 @@ TEST(NodeExecution_FilterEncode, s32) auto enc_data = locomotiv::annot_data(enc); ASSERT_NE(enc_data, nullptr); - ASSERT_EQ(enc_data->dtype(), loco::DataType::S32); - ASSERT_EQ(*(enc_data->shape()), (Shape{N, H, W, C})); // locomotiv filter is NHWC + ASSERT_EQ(loco::DataType::S32, enc_data->dtype()); + ASSERT_EQ((Shape{N, H, W, C}), *(enc_data->shape())); // locomotiv filter is NHWC auto enc_buf = enc_data->as_s32_bufptr(); for (uint32_t n = 0; n < N; ++n) for (uint32_t h = 0; h < H; ++h) for (uint32_t w = 0; w < W; ++w) for (uint32_t c = 0; c < C; ++c) - ASSERT_EQ(pull_buf.at(Index{n, c, h, w}), enc_buf->at(Index{n, h, w, c})); + ASSERT_EQ(enc_buf->at(Index{n, h, w, c}), pull_buf.at(Index{n, c, h, w})); - ASSERT_EQ(locomotiv::annot_domain(enc), loco::Domain::Filter); + ASSERT_EQ(loco::Domain::Filter, locomotiv::annot_domain(enc)); } TEST(NodeExecution_FilterEncode, f32) @@ -131,14 +131,14 @@ TEST(NodeExecution_FilterEncode, f32) auto enc_data = locomotiv::annot_data(enc); ASSERT_NE(enc_data, nullptr); - ASSERT_EQ(enc_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(enc_data->shape()), (Shape{N, H, W, C})); // locomotiv filter is NHWC + ASSERT_EQ(loco::DataType::FLOAT32, enc_data->dtype()); + ASSERT_EQ((Shape{N, H, W, C}), *(enc_data->shape())); // locomotiv filter is NHWC auto enc_buf = enc_data->as_f32_bufptr(); for (uint32_t n = 0; n < N; ++n) for (uint32_t h = 0; h < H; ++h) for (uint32_t w = 0; w < W; ++w) for (uint32_t c = 0; c < C; ++c) - ASSERT_FLOAT_EQ(pull_buf.at(Index{c, h, n, w}), enc_buf->at(Index{n, h, w, c})); + ASSERT_FLOAT_EQ(enc_buf->at(Index{n, h, w, c}), pull_buf.at(Index{c, h, n, w})); - ASSERT_EQ(locomotiv::annot_domain(enc), loco::Domain::Filter); + ASSERT_EQ(loco::Domain::Filter, locomotiv::annot_domain(enc)); } diff --git a/compiler/locomotiv/src/Node/Forward.test.cpp b/compiler/locomotiv/src/Node/Forward.test.cpp index 73d3713..5116a95 100644 --- a/compiler/locomotiv/src/Node/Forward.test.cpp +++ b/compiler/locomotiv/src/Node/Forward.test.cpp @@ -52,11 +52,11 @@ TEST(NodeExecution_Forward, s32) auto forward_data = locomotiv::annot_data(forward); ASSERT_NE(forward_data, nullptr); - ASSERT_EQ(forward_data->dtype(), loco::DataType::S32); - ASSERT_EQ(*(forward_data->shape()), Shape{1}); - ASSERT_EQ(forward_data->as_s32_bufptr()->at(Index{0}), pull_buf.at(Index{0})); + ASSERT_EQ(loco::DataType::S32, forward_data->dtype()); + ASSERT_EQ(Shape{1}, *(forward_data->shape())); + ASSERT_EQ(pull_buf.at(Index{0}), forward_data->as_s32_bufptr()->at(Index{0})); - ASSERT_EQ(locomotiv::annot_domain(forward), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(forward)); } TEST(NodeExecution_Forward, f32) @@ -80,9 +80,9 @@ TEST(NodeExecution_Forward, f32) auto forward_data = locomotiv::annot_data(forward); ASSERT_NE(forward_data, nullptr); - ASSERT_EQ(forward_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(forward_data->shape()), Shape{1}); - ASSERT_FLOAT_EQ(forward_data->as_f32_bufptr()->at(Index{0}), pull_buf.at(Index{0})); + ASSERT_EQ(loco::DataType::FLOAT32, forward_data->dtype()); + ASSERT_EQ(Shape{1}, *(forward_data->shape())); + ASSERT_FLOAT_EQ(pull_buf.at(Index{0}), forward_data->as_f32_bufptr()->at(Index{0})); - ASSERT_EQ(locomotiv::annot_domain(forward), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(forward)); } diff --git a/compiler/locomotiv/src/Node/MatMul.test.cpp b/compiler/locomotiv/src/Node/MatMul.test.cpp index bd480f7..f1f3a52 100644 --- a/compiler/locomotiv/src/Node/MatMul.test.cpp +++ b/compiler/locomotiv/src/Node/MatMul.test.cpp @@ -92,14 +92,14 @@ void run_test(const T *lhs, const T *rhs, const T *expected_output, const Shape { const auto &ind = e.current(); if (expected_datatype == loco::DataType::FLOAT32) - ASSERT_FLOAT_EQ(mat_mul_result->as_f32_bufptr()->at(ind), out_overlay.at(ind)); + ASSERT_FLOAT_EQ(out_overlay.at(ind), mat_mul_result->as_f32_bufptr()->at(ind)); else if (expected_datatype == loco::DataType::S32) - ASSERT_EQ(mat_mul_result->as_s32_bufptr()->at(ind), out_overlay.at(ind)); + ASSERT_EQ(out_overlay.at(ind), mat_mul_result->as_s32_bufptr()->at(ind)); else throw std::runtime_error("NYI for these DataTypes"); } - ASSERT_EQ(locomotiv::annot_domain(mat_mul), loco::Domain::Matrix); + ASSERT_EQ(loco::Domain::Matrix, locomotiv::annot_domain(mat_mul)); } } // namespace diff --git a/compiler/locomotiv/src/Node/MatrixCodec.test.cpp b/compiler/locomotiv/src/Node/MatrixCodec.test.cpp index 8fc5d59..da4afed 100644 --- a/compiler/locomotiv/src/Node/MatrixCodec.test.cpp +++ b/compiler/locomotiv/src/Node/MatrixCodec.test.cpp @@ -124,14 +124,14 @@ TEST_F(NodeExecution_MatrixCodec, HW_s32) // Test MatrixEncode auto enc_data = locomotiv::annot_data(enc); ASSERT_NE(enc_data, nullptr); - ASSERT_EQ(enc_data->dtype(), loco::DataType::S32); - ASSERT_EQ(*(enc_data->shape()), (Shape{H, W})); // locomotiv matrix is HW + ASSERT_EQ(loco::DataType::S32, enc_data->dtype()); + ASSERT_EQ((Shape{H, W}), *(enc_data->shape())); // locomotiv matrix is HW auto enc_buf = enc_data->as_s32_bufptr(); for (uint32_t h = 0; h < H; ++h) for (uint32_t w = 0; w < W; ++w) - ASSERT_EQ(pull_buf.at(Index{h, w}), enc_buf->at(Index{h, w})); + ASSERT_EQ(enc_buf->at(Index{h, w}), pull_buf.at(Index{h, w})); - ASSERT_EQ(locomotiv::annot_domain(enc), loco::Domain::Matrix); + ASSERT_EQ(loco::Domain::Matrix, locomotiv::annot_domain(enc)); // MatrixDecode auto dec = matrix_decode_layer(enc, HW); @@ -140,14 +140,14 @@ TEST_F(NodeExecution_MatrixCodec, HW_s32) // Test MatrixDecode: Encode -> Decode == identity auto dec_data = locomotiv::annot_data(dec); ASSERT_NE(dec_data, nullptr); - ASSERT_EQ(dec_data->dtype(), loco::DataType::S32); - ASSERT_EQ(*(dec_data->shape()), (Shape{H, W})); + ASSERT_EQ(loco::DataType::S32, dec_data->dtype()); + ASSERT_EQ((Shape{H, W}), *(dec_data->shape())); auto dec_buf = dec_data->as_s32_bufptr(); for (uint32_t h = 0; h < H; ++h) for (uint32_t w = 0; w < W; ++w) - ASSERT_EQ(pull_buf.at(Index{h, w}), dec_buf->at(Index{h, w})); + ASSERT_EQ(dec_buf->at(Index{h, w}), pull_buf.at(Index{h, w})); - ASSERT_EQ(locomotiv::annot_domain(dec), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(dec)); } TEST_F(NodeExecution_MatrixCodec, WH_f32) @@ -180,14 +180,14 @@ TEST_F(NodeExecution_MatrixCodec, WH_f32) // Test MatrixEncode auto enc_data = locomotiv::annot_data(enc); ASSERT_NE(enc_data, nullptr); - ASSERT_EQ(enc_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(enc_data->shape()), (Shape{H, W})); // locomotiv matrix is HW + ASSERT_EQ(loco::DataType::FLOAT32, enc_data->dtype()); + ASSERT_EQ((Shape{H, W}), *(enc_data->shape())); // locomotiv matrix is HW auto enc_buf = enc_data->as_f32_bufptr(); for (uint32_t h = 0; h < H; ++h) for (uint32_t w = 0; w < W; ++w) - ASSERT_FLOAT_EQ(pull_buf.at(Index{w, h}), enc_buf->at(Index{h, w})); + ASSERT_FLOAT_EQ(enc_buf->at(Index{h, w}), pull_buf.at(Index{w, h})); - ASSERT_EQ(locomotiv::annot_domain(enc), loco::Domain::Matrix); + ASSERT_EQ(loco::Domain::Matrix, locomotiv::annot_domain(enc)); // MatrixDecode auto dec = matrix_decode_layer(enc, WH); @@ -196,12 +196,12 @@ TEST_F(NodeExecution_MatrixCodec, WH_f32) // Test MatrixDecode: Encode -> Decode == identity auto dec_data = locomotiv::annot_data(dec); ASSERT_NE(dec_data, nullptr); - ASSERT_EQ(dec_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(dec_data->shape()), (Shape{W, H})); + ASSERT_EQ(loco::DataType::FLOAT32, dec_data->dtype()); + ASSERT_EQ((Shape{W, H}), *(dec_data->shape())); auto dec_buf = dec_data->as_f32_bufptr(); for (uint32_t h = 0; h < H; ++h) for (uint32_t w = 0; w < W; ++w) - ASSERT_FLOAT_EQ(pull_buf.at(Index{w, h}), dec_buf->at(Index{w, h})); + ASSERT_FLOAT_EQ(dec_buf->at(Index{w, h}), pull_buf.at(Index{w, h})); - ASSERT_EQ(locomotiv::annot_domain(dec), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(dec)); } diff --git a/compiler/locomotiv/src/Node/MaxPool2D.test.cpp b/compiler/locomotiv/src/Node/MaxPool2D.test.cpp index 9d877a9..5046d4a 100644 --- a/compiler/locomotiv/src/Node/MaxPool2D.test.cpp +++ b/compiler/locomotiv/src/Node/MaxPool2D.test.cpp @@ -86,10 +86,10 @@ void run_test(const float *ifm, const float *expected_ofm, const Shape &ifm_shap for (nncc::core::ADT::tensor::IndexEnumerator e{ofm_shape}; e.valid(); e.advance()) { const auto &ind = e.current(); - ASSERT_FLOAT_EQ(maxpool2d_data->as_f32_bufptr()->at(ind), ofm_overlay.at(ind)); + ASSERT_FLOAT_EQ(ofm_overlay.at(ind), maxpool2d_data->as_f32_bufptr()->at(ind)); } - ASSERT_EQ(locomotiv::annot_domain(maxpool2d), loco::Domain::Feature); + ASSERT_EQ(loco::Domain::Feature, locomotiv::annot_domain(maxpool2d)); } } // namespace diff --git a/compiler/locomotiv/src/Node/Push.test.cpp b/compiler/locomotiv/src/Node/Push.test.cpp index be8f1e4..e9f5605 100644 --- a/compiler/locomotiv/src/Node/Push.test.cpp +++ b/compiler/locomotiv/src/Node/Push.test.cpp @@ -52,11 +52,11 @@ TEST(NodeExecution_Push, s32) auto push_data = locomotiv::annot_data(push); ASSERT_NE(push_data, nullptr); - ASSERT_EQ(push_data->dtype(), loco::DataType::S32); - ASSERT_EQ(*(push_data->shape()), Shape{1}); - ASSERT_EQ(push_data->as_s32_bufptr()->at(Index{0}), pull_buf.at(Index{0})); + ASSERT_EQ(loco::DataType::S32, push_data->dtype()); + ASSERT_EQ(Shape{1}, *(push_data->shape())); + ASSERT_EQ(pull_buf.at(Index{0}), push_data->as_s32_bufptr()->at(Index{0})); - ASSERT_EQ(locomotiv::annot_domain(push), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(push)); } TEST(NodeExecution_Push, f32) @@ -80,9 +80,9 @@ TEST(NodeExecution_Push, f32) auto push_data = locomotiv::annot_data(push); ASSERT_NE(push_data, nullptr); - ASSERT_EQ(push_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(push_data->shape()), Shape{1}); - ASSERT_FLOAT_EQ(push_data->as_f32_bufptr()->at(Index{0}), pull_buf.at(Index{0})); + ASSERT_EQ(loco::DataType::FLOAT32, push_data->dtype()); + ASSERT_EQ(Shape{1}, *(push_data->shape())); + ASSERT_FLOAT_EQ(pull_buf.at(Index{0}), push_data->as_f32_bufptr()->at(Index{0})); - ASSERT_EQ(locomotiv::annot_domain(push), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(push)); } diff --git a/compiler/locomotiv/src/Node/ReLU.test.cpp b/compiler/locomotiv/src/Node/ReLU.test.cpp index 0ddd01d..d2f928d 100644 --- a/compiler/locomotiv/src/Node/ReLU.test.cpp +++ b/compiler/locomotiv/src/Node/ReLU.test.cpp @@ -53,10 +53,10 @@ TEST(NodeExecution_ReLU, f32) auto relu_data = locomotiv::annot_data(relu); ASSERT_NE(relu_data, nullptr); - ASSERT_EQ(relu_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(relu_data->shape()), Shape{2}); - ASSERT_FLOAT_EQ(relu_data->as_f32_bufptr()->at(Index{0}), 0.0f); - ASSERT_FLOAT_EQ(relu_data->as_f32_bufptr()->at(Index{1}), 10.0f); + ASSERT_EQ(loco::DataType::FLOAT32, relu_data->dtype()); + ASSERT_EQ(Shape{2}, *(relu_data->shape())); + ASSERT_FLOAT_EQ(0.0f, relu_data->as_f32_bufptr()->at(Index{0})); + ASSERT_FLOAT_EQ(10.0f, relu_data->as_f32_bufptr()->at(Index{1})); - ASSERT_EQ(locomotiv::annot_domain(relu), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(relu)); } diff --git a/compiler/locomotiv/src/Node/ReLU6.test.cpp b/compiler/locomotiv/src/Node/ReLU6.test.cpp index 07f6af2..b2362b1 100644 --- a/compiler/locomotiv/src/Node/ReLU6.test.cpp +++ b/compiler/locomotiv/src/Node/ReLU6.test.cpp @@ -55,12 +55,12 @@ TEST(NodeExecution_ReLU6, f32) auto relu6_data = locomotiv::annot_data(relu6); ASSERT_NE(relu6_data, nullptr); - ASSERT_EQ(relu6_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(relu6_data->shape()), Shape({2, 2})); - ASSERT_FLOAT_EQ(relu6_data->as_f32_bufptr()->at(Index{0, 0}), 0.0f); - ASSERT_FLOAT_EQ(relu6_data->as_f32_bufptr()->at(Index{0, 1}), 6.0f); - ASSERT_FLOAT_EQ(relu6_data->as_f32_bufptr()->at(Index{1, 0}), 6.0f); - ASSERT_FLOAT_EQ(relu6_data->as_f32_bufptr()->at(Index{1, 1}), 0.0f); + ASSERT_EQ(loco::DataType::FLOAT32, relu6_data->dtype()); + ASSERT_EQ(Shape({2, 2}), *(relu6_data->shape())); + ASSERT_FLOAT_EQ(0.0f, relu6_data->as_f32_bufptr()->at(Index{0, 0})); + ASSERT_FLOAT_EQ(6.0f, relu6_data->as_f32_bufptr()->at(Index{0, 1})); + ASSERT_FLOAT_EQ(6.0f, relu6_data->as_f32_bufptr()->at(Index{1, 0})); + ASSERT_FLOAT_EQ(0.0f, relu6_data->as_f32_bufptr()->at(Index{1, 1})); - ASSERT_EQ(locomotiv::annot_domain(relu6), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(relu6)); } diff --git a/compiler/locomotiv/src/Node/Reshape.test.cpp b/compiler/locomotiv/src/Node/Reshape.test.cpp index 8e54a16..8aeb465 100644 --- a/compiler/locomotiv/src/Node/Reshape.test.cpp +++ b/compiler/locomotiv/src/Node/Reshape.test.cpp @@ -56,12 +56,12 @@ TEST(NodeExecution_Reshape, f32) auto reshape_data = locomotiv::annot_data(reshape); ASSERT_NE(reshape_data, nullptr); - ASSERT_EQ(reshape_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(reshape_data->shape()), (Shape{2, 2})); - ASSERT_FLOAT_EQ(reshape_data->as_f32_bufptr()->at(Index{0, 0}), 0.0f); - ASSERT_FLOAT_EQ(reshape_data->as_f32_bufptr()->at(Index{0, 1}), 1.1f); - ASSERT_FLOAT_EQ(reshape_data->as_f32_bufptr()->at(Index{1, 0}), 2.2f); - ASSERT_FLOAT_EQ(reshape_data->as_f32_bufptr()->at(Index{1, 1}), 3.3f); + ASSERT_EQ(loco::DataType::FLOAT32, reshape_data->dtype()); + ASSERT_EQ((Shape{2, 2}), *(reshape_data->shape())); + ASSERT_FLOAT_EQ(0.0f, reshape_data->as_f32_bufptr()->at(Index{0, 0})); + ASSERT_FLOAT_EQ(1.1f, reshape_data->as_f32_bufptr()->at(Index{0, 1})); + ASSERT_FLOAT_EQ(2.2f, reshape_data->as_f32_bufptr()->at(Index{1, 0})); + ASSERT_FLOAT_EQ(3.3f, reshape_data->as_f32_bufptr()->at(Index{1, 1})); - ASSERT_EQ(locomotiv::annot_domain(reshape), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(reshape)); } diff --git a/compiler/locomotiv/src/Node/Softmax.test.cpp b/compiler/locomotiv/src/Node/Softmax.test.cpp index 21d2402..2572793 100644 --- a/compiler/locomotiv/src/Node/Softmax.test.cpp +++ b/compiler/locomotiv/src/Node/Softmax.test.cpp @@ -57,12 +57,12 @@ TEST(NodeExecution_Softmax, f32) auto kShape = Shape{2, 2}; auto softmax_data = locomotiv::annot_data(softmax); ASSERT_NE(softmax_data, nullptr); - ASSERT_EQ(softmax_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(softmax_data->shape()), kShape); - ASSERT_FLOAT_EQ(softmax_data->as_f32_bufptr()->at(Index{0, 0}), 0.5f); - ASSERT_FLOAT_EQ(softmax_data->as_f32_bufptr()->at(Index{0, 1}), 0.5f); - ASSERT_FLOAT_EQ(softmax_data->as_f32_bufptr()->at(Index{1, 0}), 0.5f); - ASSERT_FLOAT_EQ(softmax_data->as_f32_bufptr()->at(Index{1, 1}), 0.5f); + ASSERT_EQ(loco::DataType::FLOAT32, softmax_data->dtype()); + ASSERT_EQ(kShape, *(softmax_data->shape())); + ASSERT_FLOAT_EQ(0.5f, softmax_data->as_f32_bufptr()->at(Index{0, 0})); + ASSERT_FLOAT_EQ(0.5f, softmax_data->as_f32_bufptr()->at(Index{0, 1})); + ASSERT_FLOAT_EQ(0.5f, softmax_data->as_f32_bufptr()->at(Index{1, 0})); + ASSERT_FLOAT_EQ(0.5f, softmax_data->as_f32_bufptr()->at(Index{1, 1})); - ASSERT_EQ(locomotiv::annot_domain(softmax), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(softmax)); } diff --git a/compiler/locomotiv/src/Node/Tanh.test.cpp b/compiler/locomotiv/src/Node/Tanh.test.cpp index 78c3a13..96c1e7f 100644 --- a/compiler/locomotiv/src/Node/Tanh.test.cpp +++ b/compiler/locomotiv/src/Node/Tanh.test.cpp @@ -54,11 +54,11 @@ TEST(NodeExecution_Tanh, f32) auto tanh_data = locomotiv::annot_data(tanh); ASSERT_NE(tanh_data, nullptr); - ASSERT_EQ(tanh_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(tanh_data->shape()), Shape{3}); - ASSERT_FLOAT_EQ(tanh_data->as_f32_bufptr()->at(Index{0}), 0.0f); - ASSERT_FLOAT_EQ(tanh_data->as_f32_bufptr()->at(Index{1}), 0.761594f); - ASSERT_FLOAT_EQ(tanh_data->as_f32_bufptr()->at(Index{2}), -0.761594f); + ASSERT_EQ(loco::DataType::FLOAT32, tanh_data->dtype()); + ASSERT_EQ(Shape{3}, *(tanh_data->shape())); + ASSERT_FLOAT_EQ(0.0f, tanh_data->as_f32_bufptr()->at(Index{0})); + ASSERT_FLOAT_EQ(0.761594f, tanh_data->as_f32_bufptr()->at(Index{1})); + ASSERT_FLOAT_EQ(-0.761594f, tanh_data->as_f32_bufptr()->at(Index{2})); - ASSERT_EQ(locomotiv::annot_domain(tanh), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(tanh)); } diff --git a/compiler/locomotiv/src/Node/TensorBroadcast.test.cpp b/compiler/locomotiv/src/Node/TensorBroadcast.test.cpp index e8347d7..52f7c85 100644 --- a/compiler/locomotiv/src/Node/TensorBroadcast.test.cpp +++ b/compiler/locomotiv/src/Node/TensorBroadcast.test.cpp @@ -54,10 +54,10 @@ TEST(NodeExecution_TensorBroadcast, f32) auto broadcast_data = locomotiv::annot_data(broadcast); ASSERT_NE(broadcast_data, nullptr); - ASSERT_EQ(broadcast_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ((*(broadcast_data->shape())), (Shape{2, 1})); - ASSERT_FLOAT_EQ(broadcast_data->as_f32_bufptr()->at(Index{0, 0}), -1.0f); - ASSERT_FLOAT_EQ(broadcast_data->as_f32_bufptr()->at(Index{1, 0}), -1.0f); + ASSERT_EQ(loco::DataType::FLOAT32, broadcast_data->dtype()); + ASSERT_EQ((Shape{2, 1}), (*(broadcast_data->shape()))); + ASSERT_FLOAT_EQ(-1.0f, broadcast_data->as_f32_bufptr()->at(Index{0, 0})); + ASSERT_FLOAT_EQ(-1.0f, broadcast_data->as_f32_bufptr()->at(Index{1, 0})); - ASSERT_EQ(locomotiv::annot_domain(broadcast), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(broadcast)); } diff --git a/compiler/locomotiv/src/Node/TensorConcat.cpp b/compiler/locomotiv/src/Node/TensorConcat.cpp index 5097e55..3187a7f 100644 --- a/compiler/locomotiv/src/Node/TensorConcat.cpp +++ b/compiler/locomotiv/src/Node/TensorConcat.cpp @@ -40,6 +40,8 @@ namespace locomotiv void NodeExecution::execute(loco::TensorConcat *tensor_concat) { + validate(tensor_concat, "TensorConcat is nullptr"); + auto lhs_data = annot_data(tensor_concat->lhs()); auto rhs_data = annot_data(tensor_concat->rhs()); auto axis = tensor_concat->axis(); diff --git a/compiler/locomotiv/src/Node/TensorConcat.test.cpp b/compiler/locomotiv/src/Node/TensorConcat.test.cpp index d71b515..e9060e3 100644 --- a/compiler/locomotiv/src/Node/TensorConcat.test.cpp +++ b/compiler/locomotiv/src/Node/TensorConcat.test.cpp @@ -65,14 +65,14 @@ TEST(NodeExecution_TensorConcat, f32) auto concat_data = locomotiv::annot_data(tconcat); ASSERT_NE(concat_data, nullptr); - ASSERT_EQ(concat_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ((*(concat_data->shape())), (Shape{2, 2})); - ASSERT_FLOAT_EQ(concat_data->as_f32_bufptr()->at(Index{0, 0}), -1.0f); - ASSERT_FLOAT_EQ(concat_data->as_f32_bufptr()->at(Index{0, 1}), -2.0f); - ASSERT_FLOAT_EQ(concat_data->as_f32_bufptr()->at(Index{1, 0}), 3.0f); - ASSERT_FLOAT_EQ(concat_data->as_f32_bufptr()->at(Index{1, 1}), 4.0f); - - ASSERT_EQ(locomotiv::annot_domain(tconcat), loco::Domain::Tensor); + ASSERT_EQ(loco::DataType::FLOAT32, concat_data->dtype()); + ASSERT_EQ((Shape{2, 2}), (*(concat_data->shape()))); + ASSERT_FLOAT_EQ(-1.0f, concat_data->as_f32_bufptr()->at(Index{0, 0})); + ASSERT_FLOAT_EQ(-2.0f, concat_data->as_f32_bufptr()->at(Index{0, 1})); + ASSERT_FLOAT_EQ(3.0f, concat_data->as_f32_bufptr()->at(Index{1, 0})); + ASSERT_FLOAT_EQ(4.0f, concat_data->as_f32_bufptr()->at(Index{1, 1})); + + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(tconcat)); } TEST(NodeExecution_TensorConcat, f32_2) @@ -113,16 +113,16 @@ TEST(NodeExecution_TensorConcat, f32_2) auto concat_data = locomotiv::annot_data(tconcat); ASSERT_NE(concat_data, nullptr); - ASSERT_EQ(concat_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ((*(concat_data->shape())), (Shape{4, 2})); - ASSERT_FLOAT_EQ(concat_data->as_f32_bufptr()->at(Index{0, 0}), -1.0f); - ASSERT_FLOAT_EQ(concat_data->as_f32_bufptr()->at(Index{0, 1}), -2.0f); - ASSERT_FLOAT_EQ(concat_data->as_f32_bufptr()->at(Index{1, 0}), 3.0f); - ASSERT_FLOAT_EQ(concat_data->as_f32_bufptr()->at(Index{1, 1}), 4.0f); - ASSERT_FLOAT_EQ(concat_data->as_f32_bufptr()->at(Index{2, 0}), -3.0f); - ASSERT_FLOAT_EQ(concat_data->as_f32_bufptr()->at(Index{2, 1}), -4.0f); - ASSERT_FLOAT_EQ(concat_data->as_f32_bufptr()->at(Index{3, 0}), 5.0f); - ASSERT_FLOAT_EQ(concat_data->as_f32_bufptr()->at(Index{3, 1}), 6.0f); - - ASSERT_EQ(locomotiv::annot_domain(tconcat), loco::Domain::Tensor); + ASSERT_EQ(loco::DataType::FLOAT32, concat_data->dtype()); + ASSERT_EQ((Shape{4, 2}), (*(concat_data->shape()))); + ASSERT_FLOAT_EQ(-1.0f, concat_data->as_f32_bufptr()->at(Index{0, 0})); + ASSERT_FLOAT_EQ(-2.0f, concat_data->as_f32_bufptr()->at(Index{0, 1})); + ASSERT_FLOAT_EQ(3.0f, concat_data->as_f32_bufptr()->at(Index{1, 0})); + ASSERT_FLOAT_EQ(4.0f, concat_data->as_f32_bufptr()->at(Index{1, 1})); + ASSERT_FLOAT_EQ(-3.0f, concat_data->as_f32_bufptr()->at(Index{2, 0})); + ASSERT_FLOAT_EQ(-4.0f, concat_data->as_f32_bufptr()->at(Index{2, 1})); + ASSERT_FLOAT_EQ(5.0f, concat_data->as_f32_bufptr()->at(Index{3, 0})); + ASSERT_FLOAT_EQ(6.0f, concat_data->as_f32_bufptr()->at(Index{3, 1})); + + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(tconcat)); } diff --git a/compiler/locomotiv/src/Node/TensorConstantPad.cpp b/compiler/locomotiv/src/Node/TensorConstantPad.cpp index 989afaf..cd81a3a 100644 --- a/compiler/locomotiv/src/Node/TensorConstantPad.cpp +++ b/compiler/locomotiv/src/Node/TensorConstantPad.cpp @@ -36,6 +36,8 @@ namespace locomotiv void NodeExecution::execute(loco::TensorConstantPad *pad) { + validate(pad, "TensorConstantPad is nullptr"); + auto input_data = annot_data(pad->input()); auto input_domain = annot_domain(pad->input()); validate(input_data, "Input not ready"); diff --git a/compiler/locomotiv/src/Node/TensorConstantPad.test.cpp b/compiler/locomotiv/src/Node/TensorConstantPad.test.cpp index 0f60c5f..64b9130 100644 --- a/compiler/locomotiv/src/Node/TensorConstantPad.test.cpp +++ b/compiler/locomotiv/src/Node/TensorConstantPad.test.cpp @@ -74,16 +74,16 @@ TEST(NodeExecution_Pad, tensor_constant_pad_4_dim) auto pad_data = locomotiv::annot_data(pad); ASSERT_NE(pad_data, nullptr); - ASSERT_EQ(pad_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(pad_data->shape()), Shape({1, 6, 4, 1})); + ASSERT_EQ(loco::DataType::FLOAT32, pad_data->dtype()); + ASSERT_EQ(Shape({1, 6, 4, 1}), *(pad_data->shape())); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{0, 3, 1, 0}), 1.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{0, 3, 2, 0}), 2.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{0, 4, 1, 0}), 3.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{0, 4, 2, 0}), 4.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{0, 0, 0, 0}), 0.0f); + ASSERT_FLOAT_EQ(1.0f, pad_data->as_f32_bufptr()->at(Index{0, 3, 1, 0})); + ASSERT_FLOAT_EQ(2.0f, pad_data->as_f32_bufptr()->at(Index{0, 3, 2, 0})); + ASSERT_FLOAT_EQ(3.0f, pad_data->as_f32_bufptr()->at(Index{0, 4, 1, 0})); + ASSERT_FLOAT_EQ(4.0f, pad_data->as_f32_bufptr()->at(Index{0, 4, 2, 0})); + ASSERT_FLOAT_EQ(0.0f, pad_data->as_f32_bufptr()->at(Index{0, 0, 0, 0})); - ASSERT_EQ(locomotiv::annot_domain(pad), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(pad)); } TEST(NodeExecution_Pad, tensor_constant_pad_1_dim) @@ -122,17 +122,17 @@ TEST(NodeExecution_Pad, tensor_constant_pad_1_dim) auto pad_data = locomotiv::annot_data(pad); ASSERT_NE(pad_data, nullptr); - ASSERT_EQ(pad_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(pad_data->shape()), Shape({6})); + ASSERT_EQ(loco::DataType::FLOAT32, pad_data->dtype()); + ASSERT_EQ(Shape({6}), *(pad_data->shape())); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{0}), 0.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1}), 0.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{2}), 1.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{3}), 5.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{4}), 3.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{5}), 0.0f); + ASSERT_FLOAT_EQ(0.0f, pad_data->as_f32_bufptr()->at(Index{0})); + ASSERT_FLOAT_EQ(0.0f, pad_data->as_f32_bufptr()->at(Index{1})); + ASSERT_FLOAT_EQ(1.0f, pad_data->as_f32_bufptr()->at(Index{2})); + ASSERT_FLOAT_EQ(5.0f, pad_data->as_f32_bufptr()->at(Index{3})); + ASSERT_FLOAT_EQ(3.0f, pad_data->as_f32_bufptr()->at(Index{4})); + ASSERT_FLOAT_EQ(0.0f, pad_data->as_f32_bufptr()->at(Index{5})); - ASSERT_EQ(locomotiv::annot_domain(pad), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(pad)); } TEST(NodeExecution_Pad, tensor_constant_pad_6_dim) @@ -200,19 +200,19 @@ TEST(NodeExecution_Pad, tensor_constant_pad_6_dim) auto pad_data = locomotiv::annot_data(pad); ASSERT_NE(pad_data, nullptr); - ASSERT_EQ(pad_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(pad_data->shape()), Shape({4, 1, 6, 5, 1, 5})); - - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 1, 2, 0, 1}), 1.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 1, 2, 0, 2}), 2.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 1, 3, 0, 1}), 3.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 1, 3, 0, 2}), 4.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 2, 2, 0, 1}), 5.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 2, 2, 0, 2}), 6.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 2, 3, 0, 1}), 7.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 2, 3, 0, 2}), 8.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 3, 2, 0, 1}), 9.0f); - ASSERT_FLOAT_EQ(pad_data->as_f32_bufptr()->at(Index{1, 0, 3, 2, 0, 2}), 10.0f); - - ASSERT_EQ(locomotiv::annot_domain(pad), loco::Domain::Tensor); + ASSERT_EQ(loco::DataType::FLOAT32, pad_data->dtype()); + ASSERT_EQ(Shape({4, 1, 6, 5, 1, 5}), *(pad_data->shape())); + + ASSERT_FLOAT_EQ(1.0f, pad_data->as_f32_bufptr()->at(Index{1, 0, 1, 2, 0, 1})); + ASSERT_FLOAT_EQ(2.0f, pad_data->as_f32_bufptr()->at(Index{1, 0, 1, 2, 0, 2})); + ASSERT_FLOAT_EQ(3.0f, pad_data->as_f32_bufptr()->at(Index{1, 0, 1, 3, 0, 1})); + ASSERT_FLOAT_EQ(4.0f, pad_data->as_f32_bufptr()->at(Index{1, 0, 1, 3, 0, 2})); + ASSERT_FLOAT_EQ(5.0f, pad_data->as_f32_bufptr()->at(Index{1, 0, 2, 2, 0, 1})); + ASSERT_FLOAT_EQ(6.0f, pad_data->as_f32_bufptr()->at(Index{1, 0, 2, 2, 0, 2})); + ASSERT_FLOAT_EQ(7.0f, pad_data->as_f32_bufptr()->at(Index{1, 0, 2, 3, 0, 1})); + ASSERT_FLOAT_EQ(8.0f, pad_data->as_f32_bufptr()->at(Index{1, 0, 2, 3, 0, 2})); + ASSERT_FLOAT_EQ(9.0f, pad_data->as_f32_bufptr()->at(Index{1, 0, 3, 2, 0, 1})); + ASSERT_FLOAT_EQ(10.0f, pad_data->as_f32_bufptr()->at(Index{1, 0, 3, 2, 0, 2})); + + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(pad)); } diff --git a/compiler/locomotiv/src/Node/TensorReduce.cpp b/compiler/locomotiv/src/Node/TensorReduce.cpp index fae7a75..a60ebd8 100644 --- a/compiler/locomotiv/src/Node/TensorReduce.cpp +++ b/compiler/locomotiv/src/Node/TensorReduce.cpp @@ -121,9 +121,8 @@ namespace locomotiv void NodeExecution::execute(loco::TensorReduce *node) { auto input_data = annot_data(node->input()); - auto input_shape = input_data->shape(); - validate(input_data, "Input not ready"); + auto input_shape = input_data->shape(); validate(annot_domain(node->input()) == loco::Domain::Tensor, "Input domain of TensorReduce is not Tensor"); diff --git a/compiler/locomotiv/src/Node/TensorReduce.test.cpp b/compiler/locomotiv/src/Node/TensorReduce.test.cpp index 68398ca..d0e73a2 100644 --- a/compiler/locomotiv/src/Node/TensorReduce.test.cpp +++ b/compiler/locomotiv/src/Node/TensorReduce.test.cpp @@ -60,12 +60,12 @@ TEST(NodeExecution_Fixed_Reduce_Mean, f32_0) auto kShape = Shape{1, 1, 2}; auto reduce_data = locomotiv::annot_data(reduce_node); ASSERT_NE(reduce_data, nullptr); - ASSERT_EQ(reduce_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(reduce_data->shape()), kShape); - ASSERT_FLOAT_EQ(reduce_data->as_f32_bufptr()->at(Index{0, 0, 0}), 3.3f); - ASSERT_FLOAT_EQ(reduce_data->as_f32_bufptr()->at(Index{0, 0, 1}), 4.4f); + ASSERT_EQ(loco::DataType::FLOAT32, reduce_data->dtype()); + ASSERT_EQ(kShape, *(reduce_data->shape())); + ASSERT_FLOAT_EQ(3.3f, reduce_data->as_f32_bufptr()->at(Index{0, 0, 0})); + ASSERT_FLOAT_EQ(4.4f, reduce_data->as_f32_bufptr()->at(Index{0, 0, 1})); - ASSERT_EQ(locomotiv::annot_domain(reduce_node), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(reduce_node)); } TEST(NodeExecution_Fixed_Reduce_Mean, f32_1) @@ -96,9 +96,9 @@ TEST(NodeExecution_Fixed_Reduce_Mean, f32_1) auto kShape = Shape{1, 1, 1}; auto reduce_data = locomotiv::annot_data(reduce_node); ASSERT_NE(reduce_data, nullptr); - ASSERT_EQ(reduce_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(reduce_data->shape()), kShape); - ASSERT_FLOAT_EQ(reduce_data->as_f32_bufptr()->at(Index{0, 0, 0}), 3.85f); + ASSERT_EQ(loco::DataType::FLOAT32, reduce_data->dtype()); + ASSERT_EQ(kShape, *(reduce_data->shape())); + ASSERT_FLOAT_EQ(3.85f, reduce_data->as_f32_bufptr()->at(Index{0, 0, 0})); - ASSERT_EQ(locomotiv::annot_domain(reduce_node), loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, locomotiv::annot_domain(reduce_node)); } diff --git a/compiler/locomotiv/src/Node/TransposedConv2D.test.cpp b/compiler/locomotiv/src/Node/TransposedConv2D.test.cpp index bd955a0..ef759f5 100644 --- a/compiler/locomotiv/src/Node/TransposedConv2D.test.cpp +++ b/compiler/locomotiv/src/Node/TransposedConv2D.test.cpp @@ -101,10 +101,10 @@ void run_test(const float *ifm, const float *ker, const float *expected_ofm, con for (nncc::core::ADT::tensor::IndexEnumerator e{ofm_shape}; e.valid(); e.advance()) { const auto &ind = e.current(); - ASSERT_FLOAT_EQ(conv2d_result->as_f32_bufptr()->at(ind), ofm_overlay.at(ind)); + ASSERT_FLOAT_EQ(ofm_overlay.at(ind), conv2d_result->as_f32_bufptr()->at(ind)); } - ASSERT_EQ(locomotiv::annot_domain(tr_conv2d), loco::Domain::Feature); + ASSERT_EQ(loco::Domain::Feature, locomotiv::annot_domain(tr_conv2d)); } } // namespace diff --git a/compiler/locomotiv/src/NodeData.test.cpp b/compiler/locomotiv/src/NodeData.test.cpp index b1c9832..65bd3e1 100644 --- a/compiler/locomotiv/src/NodeData.test.cpp +++ b/compiler/locomotiv/src/NodeData.test.cpp @@ -35,9 +35,9 @@ TEST(NodeData, as_s32_buffer_wrapper) auto data = locomotiv::make_data(buf); - ASSERT_EQ(data->dtype(), loco::DataType::S32); - ASSERT_EQ(*(data->shape()), shape); - ASSERT_EQ(data->as_s32_bufptr()->at(Index{0}), 42); + ASSERT_EQ(loco::DataType::S32, data->dtype()); + ASSERT_EQ(shape, *(data->shape())); + ASSERT_EQ(42, data->as_s32_bufptr()->at(Index{0})); } TEST(NodeData, as_f32_buffer_wrapper) @@ -48,7 +48,7 @@ TEST(NodeData, as_f32_buffer_wrapper) auto data = locomotiv::make_data(buf); - ASSERT_EQ(data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(data->shape()), shape); - ASSERT_FLOAT_EQ(data->as_f32_bufptr()->at(Index{0}), 3.14f); + ASSERT_EQ(loco::DataType::FLOAT32, data->dtype()); + ASSERT_EQ(shape, *(data->shape())); + ASSERT_FLOAT_EQ(3.14f, data->as_f32_bufptr()->at(Index{0})); } diff --git a/compiler/locomotiv/src/NodeDataImpl.test.cpp b/compiler/locomotiv/src/NodeDataImpl.test.cpp index b859560..3fb0cc2 100644 --- a/compiler/locomotiv/src/NodeDataImpl.test.cpp +++ b/compiler/locomotiv/src/NodeDataImpl.test.cpp @@ -39,7 +39,7 @@ TEST(NodeDataImpl, as_annotation) auto g = loco::make_graph(); auto node = g->nodes()->create(); - ASSERT_EQ(locomotiv::annot_data(node), nullptr); + ASSERT_EQ(nullptr, locomotiv::annot_data(node)); // Set annotation locomotiv::annot_data(node, std::move(data)); @@ -48,11 +48,11 @@ TEST(NodeDataImpl, as_annotation) const locomotiv::NodeData *obtained = locomotiv::annot_data(node); ASSERT_NE(obtained, nullptr); - ASSERT_EQ(obtained->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(obtained->shape()), shape); - ASSERT_FLOAT_EQ(obtained->as_f32_bufptr()->at(Index{0}), 3.14f); + ASSERT_EQ(loco::DataType::FLOAT32, obtained->dtype()); + ASSERT_EQ(shape, *(obtained->shape())); + ASSERT_FLOAT_EQ(3.14f, obtained->as_f32_bufptr()->at(Index{0})); // Erase annotation locomotiv::erase_annot_data(node); - ASSERT_EQ(locomotiv::annot_data(node), nullptr); + ASSERT_EQ(nullptr, locomotiv::annot_data(node)); } diff --git a/compiler/locomotiv/src/NodeDomain.test.cpp b/compiler/locomotiv/src/NodeDomain.test.cpp index 9cfcf2e..87c8135 100644 --- a/compiler/locomotiv/src/NodeDomain.test.cpp +++ b/compiler/locomotiv/src/NodeDomain.test.cpp @@ -22,16 +22,16 @@ TEST(NodeDomain, as_annotation) { loco::Pull node; - ASSERT_EQ(locomotiv::annot_domain(&node), loco::Domain::Unknown); + ASSERT_EQ(loco::Domain::Unknown, locomotiv::annot_domain(&node)); // Set annotation locomotiv::annot_domain(&node, loco::Domain::Tensor); // Get annotation const loco::Domain obtained = locomotiv::annot_domain(&node); - ASSERT_EQ(obtained, loco::Domain::Tensor); + ASSERT_EQ(loco::Domain::Tensor, obtained); // Erase annotation locomotiv::erase_annot_domain(&node); - ASSERT_EQ(locomotiv::annot_domain(&node), loco::Domain::Unknown); + ASSERT_EQ(loco::Domain::Unknown, locomotiv::annot_domain(&node)); } diff --git a/compiler/locomotiv/src/NodeExecution.cpp b/compiler/locomotiv/src/NodeExecution.cpp index e532b5a..2a86971 100644 --- a/compiler/locomotiv/src/NodeExecution.cpp +++ b/compiler/locomotiv/src/NodeExecution.cpp @@ -72,9 +72,9 @@ void NodeExecution::eltwise_unary(loco::Node *node, const UnaryFunc &f) auto input_node = node->arg(0); auto input_domain = annot_domain(input_node); auto input_data = annot_data(input_node); + validate(input_data, "Input is not ready"); auto input_dtype = input_data->dtype(); - validate(input_data, "Input is not ready"); validate(input_domain != loco::Domain::Unknown, "Input domain is unknown"); auto output_node = node; diff --git a/compiler/locomotiv/src/Session.test.cpp b/compiler/locomotiv/src/Session.test.cpp index 6d4a241..b73e4fa 100644 --- a/compiler/locomotiv/src/Session.test.cpp +++ b/compiler/locomotiv/src/Session.test.cpp @@ -57,8 +57,8 @@ TEST(Session, graph_IO_size) // Make session locomotiv::Session s(g.get()); - ASSERT_EQ(s.input_size(), inputs); - ASSERT_EQ(s.output_size(), outputs); + ASSERT_EQ(inputs, s.input_size()); + ASSERT_EQ(outputs, s.output_size()); } TEST(Session, set_input) @@ -173,9 +173,9 @@ TEST(Session, inference_identity) auto output_data = s.get_output(0); ASSERT_NE(output_data, nullptr); - ASSERT_EQ(output_data->dtype(), loco::DataType::FLOAT32); - ASSERT_EQ(*(output_data->shape()), Shape{1}); - ASSERT_EQ(output_data->as_f32_bufptr()->at(Index{0}), 3.14f); + ASSERT_EQ(loco::DataType::FLOAT32, output_data->dtype()); + ASSERT_EQ(Shape{1}, *(output_data->shape())); + ASSERT_EQ(3.14f, output_data->as_f32_bufptr()->at(Index{0})); } } @@ -234,63 +234,63 @@ TEST(Session, session_for_subgraph) { // Session to get t1 only locomotiv::Session s(g.get(), {t1}); - ASSERT_EQ(s.output_size(), 1); - ASSERT_EQ(s.get_output_node(0), dynamic_cast(t1)); + ASSERT_EQ(1, s.output_size()); + ASSERT_EQ(dynamic_cast(t1), s.get_output_node(0)); s.infer(); auto t1_data = s.get_output(0); ASSERT_NE(t1_data, nullptr); - ASSERT_EQ(*(t1_data->shape()), Shape{2}); + ASSERT_EQ(Shape{2}, *(t1_data->shape())); auto t1_buf = t1_data->as_f32_bufptr(); - ASSERT_EQ(t1_buf->at({0}), 0.1f); - ASSERT_EQ(t1_buf->at({1}), 0.2f); + ASSERT_EQ(0.1f, t1_buf->at({0})); + ASSERT_EQ(0.2f, t1_buf->at({1})); } { // Session to get t2 only locomotiv::Session s(g.get(), {t2}); - ASSERT_EQ(s.output_size(), 1); - ASSERT_EQ(s.get_output_node(0), dynamic_cast(t2)); + ASSERT_EQ(1, s.output_size()); + ASSERT_EQ(dynamic_cast(t2), s.get_output_node(0)); s.infer(); auto t2_data = s.get_output(0); ASSERT_NE(t2_data, nullptr); - ASSERT_EQ(*(t2_data->shape()), Shape{2}); + ASSERT_EQ(Shape{2}, *(t2_data->shape())); auto t2_buf = t2_data->as_f32_bufptr(); - ASSERT_EQ(t2_buf->at({0}), 0.3f); - ASSERT_EQ(t2_buf->at({1}), 0.4f); + ASSERT_EQ(0.3f, t2_buf->at({0})); + ASSERT_EQ(0.4f, t2_buf->at({1})); } { // Session to get t2 and push locomotiv::Session s(g.get(), {t2, push}); - ASSERT_EQ(s.output_size(), 2); - ASSERT_EQ(s.get_output_node(0), dynamic_cast(t2)); - ASSERT_EQ(s.get_output_node(1), dynamic_cast(push)); + ASSERT_EQ(2, s.output_size()); + ASSERT_EQ(dynamic_cast(t2), s.get_output_node(0)); + ASSERT_EQ(dynamic_cast(push), s.get_output_node(1)); s.infer(); auto t2_data = s.get_output(0); ASSERT_NE(t2_data, nullptr); - ASSERT_EQ(*(t2_data->shape()), Shape{2}); + ASSERT_EQ(Shape{2}, *(t2_data->shape())); auto t2_buf = t2_data->as_f32_bufptr(); - ASSERT_EQ(t2_buf->at({0}), 0.3f); - ASSERT_EQ(t2_buf->at({1}), 0.4f); + ASSERT_EQ(0.3f, t2_buf->at({0})); + ASSERT_EQ(0.4f, t2_buf->at({1})); auto push_data = s.get_output(1); ASSERT_NE(push_data, nullptr); - ASSERT_EQ(*(push_data->shape()), Shape{4}); + ASSERT_EQ(Shape{4}, *(push_data->shape())); auto push_buf = push_data->as_f32_bufptr(); - ASSERT_EQ(push_buf->at({0}), 0.1f); - ASSERT_EQ(push_buf->at({1}), 0.2f); - ASSERT_EQ(push_buf->at({2}), 0.3f); - ASSERT_EQ(push_buf->at({3}), 0.4f); + ASSERT_EQ(0.1f, push_buf->at({0})); + ASSERT_EQ(0.2f, push_buf->at({1})); + ASSERT_EQ(0.3f, push_buf->at({2})); + ASSERT_EQ(0.4f, push_buf->at({3})); } } @@ -321,19 +321,19 @@ TEST(Session, ctor_by_range) auto constgen_data = s.get_output(0); ASSERT_NE(constgen_data, nullptr); - ASSERT_EQ(*(constgen_data->shape()), Shape{2}); + ASSERT_EQ(Shape{2}, *(constgen_data->shape())); auto constgen_buf = constgen_data->as_f32_bufptr(); - ASSERT_EQ(constgen_buf->at({0}), 0.1f); - ASSERT_EQ(constgen_buf->at({1}), -0.1f); + ASSERT_EQ(0.1f, constgen_buf->at({0})); + ASSERT_EQ(-0.1f, constgen_buf->at({1})); auto push_data = s.get_output(1); ASSERT_NE(push_data, nullptr); - ASSERT_EQ(*(push_data->shape()), Shape{2}); + ASSERT_EQ(Shape{2}, *(push_data->shape())); auto push_buf = push_data->as_f32_bufptr(); - ASSERT_EQ(push_buf->at({0}), 0.1f); - ASSERT_EQ(push_buf->at({1}), 0.0f); + ASSERT_EQ(0.1f, push_buf->at({0})); + ASSERT_EQ(0.0f, push_buf->at({1})); } // Below here is internal test for locomotiv, i.e. not public usage of locomotiv @@ -363,17 +363,17 @@ TEST(Session, dtor) s.set_input(0, std::move(data)); auto data_annotated = locomotiv::annot_data(pull); - ASSERT_EQ(data_annotated, nullptr); + ASSERT_EQ(nullptr, data_annotated); auto user_data_annotated = locomotiv::user_data(pull); ASSERT_NE(user_data_annotated, nullptr); auto domain_annotated = locomotiv::annot_domain(pull); - ASSERT_EQ(domain_annotated, loco::Domain::Unknown); + ASSERT_EQ(loco::Domain::Unknown, domain_annotated); } auto data_annotated = locomotiv::annot_data(pull); - ASSERT_EQ(data_annotated, nullptr); + ASSERT_EQ(nullptr, data_annotated); auto user_data_annotated = locomotiv::user_data(pull); - ASSERT_EQ(user_data_annotated, nullptr); + ASSERT_EQ(nullptr, user_data_annotated); auto domain_annotated = locomotiv::annot_domain(pull); - ASSERT_EQ(domain_annotated, loco::Domain::Unknown); + ASSERT_EQ(loco::Domain::Unknown, domain_annotated); } diff --git a/compiler/locop/requires.cmake b/compiler/locop/requires.cmake new file mode 100644 index 0000000..d314ae5 --- /dev/null +++ b/compiler/locop/requires.cmake @@ -0,0 +1 @@ +require("pp") diff --git a/compiler/locop/src/CanonicalNodeSummaryBuilder.cpp b/compiler/locop/src/CanonicalNodeSummaryBuilder.cpp index b962f49..61d9e8a 100644 --- a/compiler/locop/src/CanonicalNodeSummaryBuilder.cpp +++ b/compiler/locop/src/CanonicalNodeSummaryBuilder.cpp @@ -71,9 +71,7 @@ std::string opname(const loco::Node *node) { if (node->dialect() == loco::CanonicalDialect::get()) { - auto canonical_node = dynamic_cast(node); - - assert(canonical_node != nullptr); + auto canonical_node = loco::must_cast(node); switch (canonical_node->opcode()) { @@ -288,8 +286,7 @@ bool CanonicalNodeSummaryBuilder::build(const loco::Node *node, locop::NodeSumma return false; } - auto canonical_node = dynamic_cast(node); - assert(canonical_node != nullptr); + auto canonical_node = loco::must_cast(node); out = canonical_node_desc(*_tbl, canonical_node); return true; } diff --git a/compiler/locop/src/FormattedGraph.cpp b/compiler/locop/src/FormattedGraph.cpp index 84de1e8..bf41757 100644 --- a/compiler/locop/src/FormattedGraph.cpp +++ b/compiler/locop/src/FormattedGraph.cpp @@ -67,6 +67,9 @@ std::string str(const loco::DataType &dtype) case loco::DataType::FLOAT64: return "FLOAT64"; + case loco::DataType::BOOL: + return "BOOL"; + default: break; }; diff --git a/compiler/logo/include/logo/DeadNodeQueryService.h b/compiler/logo/include/logo/DeadNodeQueryService.h new file mode 100644 index 0000000..e74a4bc --- /dev/null +++ b/compiler/logo/include/logo/DeadNodeQueryService.h @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LOGO_DEAD_NODE_QUERY_SERVICE_H__ +#define __LOGO_DEAD_NODE_QUERY_SERVICE_H__ + +#include +#include + +namespace logo +{ + +struct DeadNodeQueryService : public ::loco::DialectService +{ + virtual ~DeadNodeQueryService() = default; + /// @brief Check if the node is dead node + virtual bool isDeadNode(loco::Node *node) = 0; +}; + +} // namespace logo + +#endif // __LOGO_DEAD_NODE_QUERY_SERVICE_H__ diff --git a/compiler/logo/include/logo/RemoveDeadNodeWithQueryPass.h b/compiler/logo/include/logo/RemoveDeadNodeWithQueryPass.h new file mode 100644 index 0000000..de08671 --- /dev/null +++ b/compiler/logo/include/logo/RemoveDeadNodeWithQueryPass.h @@ -0,0 +1,34 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LOGO_REMOVE_DEAD_NODE_WITH_QUERY_PASS_H__ +#define __LOGO_REMOVE_DEAD_NODE_WITH_QUERY_PASS_H__ + +#include + +namespace logo +{ + +struct RemoveDeadNodeWithQueryPass final : public Pass +{ + const char *name(void) const final { return "RemoveDeadNodeWithQueryPass"; } + + bool run(loco::Graph *g); +}; + +} // namespace logo + +#endif // __LOGO_REMOVE_DEAD_NODE_WITH_QUERY_PASS_H__ diff --git a/compiler/logo/src/Passes/ConstantFoldingPass.test.cpp b/compiler/logo/src/Passes/ConstantFoldingPass.test.cpp index 8240277..b9c4942 100644 --- a/compiler/logo/src/Passes/ConstantFoldingPass.test.cpp +++ b/compiler/logo/src/Passes/ConstantFoldingPass.test.cpp @@ -82,7 +82,7 @@ TEST(ConstantFolding, const_relu_to_const) } auto push = logo::test::find_first_node_by_type(graph.get()); - auto const_gen = dynamic_cast(push->from()); + auto const_gen = loco::must_cast(push->from()); ASSERT_NE(const_gen, nullptr); ASSERT_EQ(const_gen->size(), 2); @@ -168,7 +168,7 @@ TEST(ConstantFolding, const_relu_to_concat) } auto push = logo::test::find_first_node_by_type(graph.get()); - auto const_gen = dynamic_cast(push->from()); + auto const_gen = loco::must_cast(push->from()); ASSERT_NE(const_gen, nullptr); ASSERT_EQ(const_gen->size(), 4); diff --git a/compiler/logo/src/Passes/RemoveDeadNodeWithQueryPass.cpp b/compiler/logo/src/Passes/RemoveDeadNodeWithQueryPass.cpp new file mode 100644 index 0000000..5c74521 --- /dev/null +++ b/compiler/logo/src/Passes/RemoveDeadNodeWithQueryPass.cpp @@ -0,0 +1,70 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include + +#include +#include +#include + +#include + +namespace logo +{ + +bool RemoveDeadNodeWithQueryPass::run(loco::Graph *g) +{ + // Let's enumerate nodes required to compute output nodes + auto active_nodes = loco::active_nodes(loco::output_nodes(g)); + + // List dead(= non-active) nodes candidates + std::set candidates; + + for (auto node : loco::all_nodes(g)) + { + if (active_nodes.find(node) == active_nodes.end()) + { + candidates.insert(node); + } + } + + // Find the nodes that should not be dead node in candidates + for (auto node : candidates) + { + if (auto service = node->dialect()->service()) + { + if (!service->isDeadNode(node)) + { + candidates.erase(node); + } + } + } + + for (auto node : candidates) + { + node->drop(); + } + + for (auto node : candidates) + { + g->nodes()->destroy(node); + } + + return candidates.size() > 0; +} + +} // namespace logo diff --git a/compiler/logo/src/Passes/RemoveForwardNodePass.cpp b/compiler/logo/src/Passes/RemoveForwardNodePass.cpp index c951cfa..966b270 100644 --- a/compiler/logo/src/Passes/RemoveForwardNodePass.cpp +++ b/compiler/logo/src/Passes/RemoveForwardNodePass.cpp @@ -47,7 +47,7 @@ bool RemoveForwardNodePass::run(loco::Graph *g) { if (node->dialect() == loco::CanonicalDialect::get()) { - auto canonical_node = dynamic_cast(node); + auto canonical_node = loco::must_cast(node); canonical_node->accept(&collector); } } diff --git a/compiler/logo/src/Passes/ReorderDecodePass.cpp b/compiler/logo/src/Passes/ReorderDecodePass.cpp index 724db57..863f180 100644 --- a/compiler/logo/src/Passes/ReorderDecodePass.cpp +++ b/compiler/logo/src/Passes/ReorderDecodePass.cpp @@ -37,49 +37,54 @@ bool isReLU(const loco::Node *node) return node->opnum() == static_cast(loco::CanonicalOpcode::ReLU); } -} // namespace - -namespace logo -{ - -bool ReorderDecodePass::run(loco::Graph *g) +// Update queue +class Collector final : public loco::CanonicalNodeMutableVisitor { - std::queue q; - - // Update queue - class Collector final : public loco::CanonicalNodeMutableVisitor +public: + Collector(std::queue *out) : _out{out} { - public: - Collector(std::queue *out) : _out{out} - { - // DO NOTHING - } + // DO NOTHING + } - void visit(loco::FeatureDecode *node) final + void visit(loco::FeatureDecode *node) final + { + if (node->input() != nullptr) { - if (node->input() != nullptr) - { - _out->push(node); - } + _out->push(node); } + } - void visit(loco::Node *) final { return; } + void visit(loco::Node *) final { return; } - private: - // TODO This definition should be revised to support other decode operations - std::queue *_out; - }; +private: + // TODO This definition should be revised to support other decode operations + std::queue *_out; +}; +void gather_candidates(loco::Graph *g, std::queue &q) +{ Collector collector{&q}; for (auto node : loco::all_nodes(g)) { if (node->dialect() == loco::CanonicalDialect::get()) { - auto canonical_node = dynamic_cast(node); + auto canonical_node = loco::must_cast(node); canonical_node->accept(&collector); } } +} + +} // namespace + +namespace logo +{ + +bool ReorderDecodePass::run(loco::Graph *g) +{ + std::queue q; + + gather_candidates(g, q); bool changed = false; @@ -125,9 +130,7 @@ bool ReorderDecodePass::run(loco::Graph *g) // Q. Is it better to create an independent transform for this rewriting rule? if (isTensorBiasAdd(u)) { - auto old_badd = dynamic_cast(u); - - assert(old_badd != nullptr); + auto old_badd = loco::must_cast(u); /** * Let us consider the following example: @@ -182,40 +185,7 @@ bool ReorderDecodePass::run(loco::Graph *g) { std::queue q; - // Update queue - class Collector final : public loco::CanonicalNodeMutableVisitor - { - public: - Collector(std::queue *out) : _out{out} - { - // DO NOTHING - } - - void visit(loco::FeatureDecode *node) final - { - if (node->input() != nullptr) - { - _out->push(node); - } - } - - void visit(loco::Node *) final { return; } - - private: - // TODO This definition should be revised to support other decode operations - std::queue *_out; - }; - - Collector collector{&q}; - - for (auto node : loco::all_nodes(g)) - { - if (node->dialect() == loco::CanonicalDialect::get()) - { - auto canonical_node = dynamic_cast(node); - canonical_node->accept(&collector); - } - } + gather_candidates(g, q); bool changed = false; diff --git a/compiler/logo/src/Passes/ResolveDuplicateReshapePass.cpp b/compiler/logo/src/Passes/ResolveDuplicateReshapePass.cpp index d3c74cb..94ff629 100644 --- a/compiler/logo/src/Passes/ResolveDuplicateReshapePass.cpp +++ b/compiler/logo/src/Passes/ResolveDuplicateReshapePass.cpp @@ -61,7 +61,7 @@ bool is_duplicate_reshape(loco::Node *node) */ void remap_input(loco::FixedReshape *reshape) { - auto input_reshape = dynamic_cast(reshape->input()); + auto input_reshape = loco::must_cast(reshape->input()); auto volume = [](loco::FixedReshape *node) { uint32_t vol = 1; @@ -94,7 +94,7 @@ bool ResolveDuplicateReshapePass::run(loco::Graph *graph) { if (is_duplicate_reshape(node)) { - auto node_as_reshape = dynamic_cast(node); + auto node_as_reshape = loco::must_cast(node); remap_input(node_as_reshape); diff --git a/compiler/logo/src/Passes/SimplifyDomainConversionPass.cpp b/compiler/logo/src/Passes/SimplifyDomainConversionPass.cpp index 9b7a8d1..0bda85b 100644 --- a/compiler/logo/src/Passes/SimplifyDomainConversionPass.cpp +++ b/compiler/logo/src/Passes/SimplifyDomainConversionPass.cpp @@ -411,7 +411,7 @@ bool SimplifyDomainConversionPass::run(loco::Graph *g) { if (node->dialect() == loco::CanonicalDialect::get()) { - auto canonical_node = dynamic_cast(node); + auto canonical_node = loco::must_cast(node); canonical_node->accept(&collector); } } diff --git a/compiler/logo/src/Passes/SimplifyDomainConversionPass.test.cpp b/compiler/logo/src/Passes/SimplifyDomainConversionPass.test.cpp index 6bd93c1..9a05763 100644 --- a/compiler/logo/src/Passes/SimplifyDomainConversionPass.test.cpp +++ b/compiler/logo/src/Passes/SimplifyDomainConversionPass.test.cpp @@ -227,7 +227,7 @@ TEST(SimplifyDomainConversionPass, FilterEncode_FilterDecode_equal_perms) ASSERT_EQ(loco::output_nodes(graph.get()).size(), 1); loco::Node *output_node = loco::output_nodes(graph.get())[0]; - auto forward = dynamic_cast(output_node->arg(0)); + auto forward = loco::must_cast(output_node->arg(0)); ASSERT_NE(forward, nullptr); auto const_gen = dynamic_cast(forward->arg(0)); ASSERT_NE(const_gen, nullptr); diff --git a/compiler/luci-interpreter/CMakeLists.txt b/compiler/luci-interpreter/CMakeLists.txt new file mode 100644 index 0000000..33fdc52 --- /dev/null +++ b/compiler/luci-interpreter/CMakeLists.txt @@ -0,0 +1,4 @@ +set(LUCI_INTERPRETER_INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include") +set(LUCI_INTERPRETER_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/src") + +add_subdirectory(src) diff --git a/compiler/luci-interpreter/include/luci_interpreter/Interpreter.h b/compiler/luci-interpreter/include/luci_interpreter/Interpreter.h new file mode 100644 index 0000000..7a14bf6 --- /dev/null +++ b/compiler/luci-interpreter/include/luci_interpreter/Interpreter.h @@ -0,0 +1,78 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_INTERPRETER_H +#define LUCI_INTERPRETER_INTERPRETER_H + +#include "luci_interpreter/core/Tensor.h" + +#include +#include + +#include + +#include +#include +#include + +namespace luci_interpreter +{ + +class ExecutionObserver +{ +public: + virtual ~ExecutionObserver(); + + // Called when the value of a tensor has been updated during execution. + virtual void postTensorWrite(const luci::CircleNode *node, const Tensor *tensor); + + // Called before / after executing an operator. + // Note that these methods are not called for auxiliary operators (CircleInput, CircleOutput, + // CircleConst and Circle*Out). + virtual void preOperatorExecute(const luci::CircleNode *node); + virtual void postOperatorExecute(const luci::CircleNode *node); +}; + +class Interpreter +{ +public: + explicit Interpreter(const luci::Module *module); + + ~Interpreter(); + + void writeInputTensor(const luci::CircleInput *input_node, const void *data, size_t data_size); + + void readOutputTensor(const luci::CircleOutput *output_node, void *data, size_t data_size); + + void interpret(); + + void attachObserver(ExecutionObserver *observer); + + const Tensor *getTensor(const loco::Node *node) { return _node_to_tensor[node]; } + +private: + std::unique_ptr _runtime_module; + + // Observer functionality support. + std::unique_ptr _runtime_to_ir; + std::unordered_map _node_to_tensor; + std::unique_ptr _event_notifier; + std::vector _observers; +}; + +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_INTERPRETER_H diff --git a/compiler/luci-interpreter/include/luci_interpreter/core/DataType.h b/compiler/luci-interpreter/include/luci_interpreter/core/DataType.h new file mode 100644 index 0000000..27bf719 --- /dev/null +++ b/compiler/luci-interpreter/include/luci_interpreter/core/DataType.h @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_CORE_DATATYPE_H +#define LUCI_INTERPRETER_CORE_DATATYPE_H + +#include +#include + +#include + +namespace luci_interpreter +{ + +using DataType = loco::DataType; + +template using DataTypeImpl = loco::DataTypeImpl
; + +inline size_t getDataTypeSize(DataType data_type) { return loco::size(data_type); } + +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_CORE_DATATYPE_H diff --git a/compiler/luci-interpreter/include/luci_interpreter/core/Tensor.h b/compiler/luci-interpreter/include/luci_interpreter/core/Tensor.h new file mode 100644 index 0000000..9987898 --- /dev/null +++ b/compiler/luci-interpreter/include/luci_interpreter/core/Tensor.h @@ -0,0 +1,133 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_CORE_TENSOR_H +#define LUCI_INTERPRETER_CORE_TENSOR_H + +#include "luci_interpreter/core/DataType.h" + +#include +#include +#include +#include +#include +#include + +namespace luci_interpreter +{ + +class Shape +{ +public: + explicit Shape(int rank) : _dims(rank, 0) {} + + Shape(std::initializer_list dims) : _dims(dims.begin(), dims.end()) {} + + int num_dims() const { return _dims.size(); } + + int32_t dim(int i) const + { + assert(i >= 0 && i < static_cast(_dims.size())); + return _dims[i]; + } + + int32_t &dim(int i) + { + assert(i >= 0 && i < static_cast(_dims.size())); + return _dims[i]; + } + + int32_t num_elements() const + { + int32_t result = 1; + for (const int32_t dim : _dims) + { + result *= dim; + } + return result; + } + + bool operator==(const Shape &other) const { return _dims == other._dims; } + + bool operator!=(const Shape &other) const { return !operator==(other); } + +private: + std::vector _dims; +}; + +// Tensor affine quantization parameters. +// +// The relationship between real and quantized values: +// real_value = (quantized_value - zero_point) * scale +// +// In per-tensor case, 'scale' and 'zero_point' are one element each. +// In per-channel case, 'scale' and 'zero_point' are N elements each, where N is the size +// of the quantized dimension. +// +// Note that due to historical and performance reasons, per-tensor quantization uses unsigned +// integer types, while per-channel uses signed types assuming 'zero_point' == 0. +// +// TODO Add 'quantized_dimension' field for per-channel case when IR provides it. +struct AffineQuantization +{ + std::vector scale; + std::vector zero_point; +}; + +class Tensor +{ +public: + Tensor(DataType element_type, Shape shape, AffineQuantization quantization, std::string name); + + DataType element_type() const { return _element_type; } + + const Shape &shape() const { return _shape; } + + float scale() const + { + assert(_quantization.scale.size() == 1); + return _quantization.scale[0]; + } + + float zero_point() const + { + assert(_quantization.zero_point.size() == 1); + return _quantization.zero_point[0]; + } + + template const T *data() const { return reinterpret_cast(_data.get()); } + + template T *data() { return reinterpret_cast(_data.get()); } + + const std::string &name() const { return _name; } + + void readData(void *data_ptr, size_t data_size) const; + + void writeData(const void *data_ptr, size_t data_size); + + void resize(const Shape &new_shape); + +private: + DataType _element_type; + Shape _shape; + AffineQuantization _quantization; + std::unique_ptr _data; + std::string _name; +}; + +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_CORE_TENSOR_H diff --git a/compiler/luci-interpreter/requires.cmake b/compiler/luci-interpreter/requires.cmake new file mode 100644 index 0000000..f411f38 --- /dev/null +++ b/compiler/luci-interpreter/requires.cmake @@ -0,0 +1 @@ +require(luci) diff --git a/compiler/luci-interpreter/src/CMakeLists.txt b/compiler/luci-interpreter/src/CMakeLists.txt new file mode 100644 index 0000000..6a66f14 --- /dev/null +++ b/compiler/luci-interpreter/src/CMakeLists.txt @@ -0,0 +1,35 @@ +nnas_find_package(TensorFlowSource EXACT 2.1.0 QUIET) +nnas_find_package(TensorFlowGEMMLowpSource EXACT 2.1.0 QUIET) +nnas_find_package(TensorFlowEigenSource EXACT 2.1.0 QUIET) + +if (NOT TensorFlowSource_FOUND) + message(STATUS "Skipping luci-interpreter: TensorFlow not found") + return() +endif () + +if (NOT TensorFlowGEMMLowpSource_FOUND) + message(STATUS "Skipping luci-interpreter: gemmlowp not found") + return() +endif () + +if (NOT TensorFlowEigenSource_FOUND) + message(STATUS "Skipping luci-interpreter: Eigen not found") + return() +endif () + +add_subdirectory(core) +add_subdirectory(kernels) +add_subdirectory(loader) + +set(SOURCES + "${LUCI_INTERPRETER_INCLUDE_DIR}/luci_interpreter/Interpreter.h" + Interpreter.cpp) + +add_library(luci_interpreter SHARED ${SOURCES}) +target_include_directories(luci_interpreter PUBLIC "${LUCI_INTERPRETER_INCLUDE_DIR}") +target_include_directories(luci_interpreter PRIVATE "${LUCI_INTERPRETER_SOURCE_DIR}") +target_link_libraries(luci_interpreter + PUBLIC luci_lang luci_interpreter_loader luci_interpreter_core + PRIVATE nncc_common) + +install(TARGETS luci_interpreter DESTINATION lib) diff --git a/compiler/luci-interpreter/src/Interpreter.cpp b/compiler/luci-interpreter/src/Interpreter.cpp new file mode 100644 index 0000000..639ffc1 --- /dev/null +++ b/compiler/luci-interpreter/src/Interpreter.cpp @@ -0,0 +1,126 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci_interpreter/Interpreter.h" + +#include "loader/ModuleLoader.h" + +#include + +namespace luci_interpreter +{ + +namespace +{ + +class EventNotifierImpl final : public EventNotifier +{ +public: + EventNotifierImpl(const RuntimeToIR &runtime_to_ir, + const std::vector &observers) + : _runtime_to_ir(runtime_to_ir), _observers(observers) + { + } + + void postTensorWrite(const Tensor *tensor) override + { + assert(tensor != nullptr); + for (const auto &observer : _observers) + { + observer->postTensorWrite(_runtime_to_ir.tensor_to_node.at(tensor), tensor); + } + } + + void preOperatorExecute(const Kernel *kernel) override + { + assert(kernel != nullptr); + for (const auto &observer : _observers) + { + observer->preOperatorExecute(_runtime_to_ir.kernel_to_node.at(kernel)); + } + } + + void postOperatorExecute(const Kernel *kernel) override + { + assert(kernel != nullptr); + for (const auto &observer : _observers) + { + observer->postOperatorExecute(_runtime_to_ir.kernel_to_node.at(kernel)); + } + } + +private: + const RuntimeToIR &_runtime_to_ir; + const std::vector &_observers; +}; + +} // namespace + +Interpreter::Interpreter(const luci::Module *module) +{ + _runtime_to_ir = std::make_unique(); + _event_notifier = std::make_unique(*_runtime_to_ir, _observers); + _runtime_module = std::make_unique(_event_notifier.get()); + ModuleLoader loader(module, _runtime_module.get(), *_runtime_to_ir, _node_to_tensor); + loader.load(); +} + +Interpreter::~Interpreter() = default; + +void Interpreter::writeInputTensor(const luci::CircleInput *input_node, const void *data, + size_t data_size) +{ + Tensor *tensor = _runtime_module->getInputTensors()[input_node->index()]; + if (tensor == nullptr) + { + const std::string &name = input_node->name(); + throw std::runtime_error("Cannot find tensor for input node named \"" + name + "\"."); + } + if (data != nullptr) + tensor->writeData(data, data_size); +} + +void Interpreter::readOutputTensor(const luci::CircleOutput *output_node, void *data, + size_t data_size) +{ + Tensor *tensor = _runtime_module->getOutputTensors()[output_node->index()]; + if (tensor == nullptr) + { + const std::string &name = output_node->name(); + throw std::runtime_error("Cannot find tensor for output node named \"" + name + "\"."); + } + if (data != nullptr) + tensor->readData(data, data_size); +} + +void Interpreter::interpret() { _runtime_module->execute(); } + +void Interpreter::attachObserver(ExecutionObserver *observer) +{ + if (std::find(_observers.cbegin(), _observers.cend(), observer) != _observers.cend()) + throw std::runtime_error("Observer is already attached."); + _observers.push_back(observer); +} + +ExecutionObserver::~ExecutionObserver() = default; + +void ExecutionObserver::postTensorWrite(const luci::CircleNode *, const Tensor *) {} + +void ExecutionObserver::preOperatorExecute(const luci::CircleNode *) {} + +void ExecutionObserver::postOperatorExecute(const luci::CircleNode *) {} + +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/core/CMakeLists.txt b/compiler/luci-interpreter/src/core/CMakeLists.txt new file mode 100644 index 0000000..e576dbd --- /dev/null +++ b/compiler/luci-interpreter/src/core/CMakeLists.txt @@ -0,0 +1,17 @@ +set(SOURCES + "${LUCI_INTERPRETER_INCLUDE_DIR}/luci_interpreter/core/DataType.h" + "${LUCI_INTERPRETER_INCLUDE_DIR}/luci_interpreter/core/Tensor.h" + EventNotifier.h + Kernel.h + KernelParams.h + RuntimeGraph.h + RuntimeGraph.cpp + RuntimeModule.h + Tensor.cpp) + +add_library(luci_interpreter_core STATIC ${SOURCES}) +set_target_properties(luci_interpreter_core PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_include_directories(luci_interpreter_core PUBLIC "${LUCI_INTERPRETER_INCLUDE_DIR}") +target_include_directories(luci_interpreter_core PUBLIC "${LUCI_INTERPRETER_SOURCE_DIR}") +target_link_libraries(luci_interpreter_core PUBLIC luci_lang) +target_link_libraries(luci_interpreter_core PRIVATE nncc_common) diff --git a/compiler/luci-interpreter/src/core/EventNotifier.h b/compiler/luci-interpreter/src/core/EventNotifier.h new file mode 100644 index 0000000..5c4fbd3 --- /dev/null +++ b/compiler/luci-interpreter/src/core/EventNotifier.h @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_CORE_EVENTNOTIFIER_H +#define LUCI_INTERPRETER_CORE_EVENTNOTIFIER_H + +namespace luci_interpreter +{ + +// Used at execution stage to tell the interpreter that the runtime state has changed in some way. +class EventNotifier +{ +public: + virtual ~EventNotifier() = default; + + virtual void postTensorWrite(const Tensor *tensor) = 0; + virtual void preOperatorExecute(const Kernel *kernel) = 0; + virtual void postOperatorExecute(const Kernel *kernel) = 0; +}; + +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_CORE_EVENTNOTIFIER_H diff --git a/compiler/luci-interpreter/src/core/Kernel.h b/compiler/luci-interpreter/src/core/Kernel.h new file mode 100644 index 0000000..5f5efb2 --- /dev/null +++ b/compiler/luci-interpreter/src/core/Kernel.h @@ -0,0 +1,75 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_CORE_KERNEL_H +#define LUCI_INTERPRETER_CORE_KERNEL_H + +#include "luci_interpreter/core/Tensor.h" + +#include + +namespace luci_interpreter +{ + +// Base class for all kernels. +class Kernel +{ +protected: + Kernel(std::vector inputs, std::vector outputs) + : _inputs(std::move(inputs)), _outputs(std::move(outputs)) + { + } + +public: + virtual ~Kernel() = default; + + std::vector getInputTensors() const { return _inputs; } + std::vector getOutputTensors() const { return _outputs; } + + // Configures the kernel. + // This function is currently called once for each kernel during interpreter construction, + // which makes it a convenient place for preparing (resizing) output tensors. + virtual void configure() = 0; + + // Executes the kernel. + virtual void execute() const = 0; + +protected: + // NOTE Prefer not to use these in derived classes. + const std::vector _inputs; + const std::vector _outputs; +}; + +// Base class for kernels with parameters. +template class KernelWithParams : public Kernel +{ +protected: + KernelWithParams(std::vector inputs, std::vector outputs, + const Params ¶ms) + : Kernel(std::move(inputs), std::move(outputs)), _params(params) + { + } + +public: + const Params ¶ms() const { return _params; } + +protected: + const Params _params; +}; + +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_CORE_KERNEL_H diff --git a/compiler/luci-interpreter/src/core/KernelParams.h b/compiler/luci-interpreter/src/core/KernelParams.h new file mode 100644 index 0000000..a32e0d4 --- /dev/null +++ b/compiler/luci-interpreter/src/core/KernelParams.h @@ -0,0 +1,151 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_CORE_KERNELPARAMS_H +#define LUCI_INTERPRETER_CORE_KERNELPARAMS_H + +#include +#include +#include + +#include +#include + +namespace luci_interpreter +{ + +// Inject commonly used types into `luci_interpreter` namespace for convenience. +using Activation = luci::FusedActFunc; +using Padding = luci::Padding; + +struct AddParams +{ + Activation activation; +}; + +struct ArgMaxParams +{ + DataType output_type; +}; + +struct ConcatenationParams +{ + int axis; +}; + +struct Conv2DParams +{ + Padding padding; + int32_t stride_height; + int32_t stride_width; + int32_t dilation_height_factor; + int32_t dilation_width_factor; + Activation activation; +}; + +struct DepthwiseConv2DParams +{ + Padding padding; + int32_t depth_multiplier; // TODO Remove, as it can be calculated. + int32_t stride_height; + int32_t stride_width; + int32_t dilation_height_factor; + int32_t dilation_width_factor; + Activation activation; +}; + +struct FullyConnectedParams +{ + Activation activation; +}; + +struct L2NormParams +{ + Activation activation; +}; + +struct LeakyReluParams +{ + float alpha; +}; + +struct LocalResponseNormalizationParams +{ + int32_t radius; + float bias; + float alpha; + float beta; +}; + +struct MulParams +{ + Activation activation; +}; + +struct Pool2DParams +{ + Padding padding; + int32_t filter_height; + int32_t filter_width; + int32_t stride_height; + int32_t stride_width; + Activation activation; +}; + +struct ReducerParams +{ + bool keep_dims; +}; + +struct SpaceToDepthParams +{ + int block_size; +}; + +struct SoftmaxParams +{ + float beta; +}; + +struct StridedSliceParams +{ + int32_t begin_mask; + int32_t end_mask; + int32_t ellipsis_mask; + int32_t new_axis_mask; + int32_t shrink_axis_mask; +}; + +struct SqueezeParams +{ + std::vector squeeze_dims; +}; + +struct TransposeConvParams +{ + Padding padding; + int32_t stride_height; + int32_t stride_width; +}; + +struct UnpackParams +{ + int axis; +}; + +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_CORE_KERNELPARAMS_H diff --git a/compiler/luci-interpreter/src/core/RuntimeGraph.cpp b/compiler/luci-interpreter/src/core/RuntimeGraph.cpp new file mode 100644 index 0000000..06f0fed --- /dev/null +++ b/compiler/luci-interpreter/src/core/RuntimeGraph.cpp @@ -0,0 +1,93 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "core/RuntimeGraph.h" + +#include "core/RuntimeModule.h" + +#include + +namespace luci_interpreter +{ + +Tensor *RuntimeGraph::addTensor(std::unique_ptr &&tensor) +{ + assert(tensor != nullptr); + _tensors.push_back(std::move(tensor)); + return _tensors.back().get(); +} + +void RuntimeGraph::setInputTensors(const std::vector &input_tensors) +{ + assert(std::all_of(input_tensors.cbegin(), input_tensors.cend(), + [](Tensor *tensor) { return tensor != nullptr; })); + _input_tensors = input_tensors; +} + +void RuntimeGraph::setOutputTensors(const std::vector &output_tensors) +{ + assert(std::all_of(output_tensors.cbegin(), output_tensors.cend(), + [](Tensor *tensor) { return tensor != nullptr; })); + _output_tensors = output_tensors; +} + +void RuntimeGraph::addKernel(std::unique_ptr &&kernel) +{ + assert(kernel != nullptr); + _kernels.push_back(std::move(kernel)); +} + +void RuntimeGraph::execute() const +{ + EventNotifier *event_notifier = _owning_module->getEventNotifier(); + + // Notify the observers that the input tensors have changed. + if (event_notifier != nullptr) + { + for (const Tensor *input_tensor : getInputTensors()) + { + event_notifier->postTensorWrite(input_tensor); + } + } + + for (const auto &kernel : _kernels) + { + if (event_notifier != nullptr) + { + event_notifier->preOperatorExecute(kernel.get()); + } + + // TODO The `configure` method should only be called if the outputs of an operator need to be + // resized. + kernel->configure(); + kernel->execute(); + + if (event_notifier != nullptr) + { + event_notifier->postOperatorExecute(kernel.get()); + } + + for (const Tensor *tensor : kernel->getOutputTensors()) + { + if (event_notifier != nullptr) + { + event_notifier->postTensorWrite(tensor); + } + } + } +} + +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/core/RuntimeGraph.h b/compiler/luci-interpreter/src/core/RuntimeGraph.h new file mode 100644 index 0000000..6ddbea4 --- /dev/null +++ b/compiler/luci-interpreter/src/core/RuntimeGraph.h @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_CORE_RUNTIMEGRAPH_H +#define LUCI_INTERPRETER_CORE_RUNTIMEGRAPH_H + +#include "luci_interpreter/core/Tensor.h" +#include "core/Kernel.h" + +#include +#include + +namespace luci_interpreter +{ + +class RuntimeModule; + +class RuntimeGraph +{ +public: + explicit RuntimeGraph(RuntimeModule *owning_module) : _owning_module(owning_module) {} + + Tensor *addTensor(std::unique_ptr &&tensor); + + void setInputTensors(const std::vector &input_tensors); + void setOutputTensors(const std::vector &output_tensors); + + const std::vector &getInputTensors() const { return _input_tensors; } + const std::vector &getOutputTensors() const { return _output_tensors; } + + void addKernel(std::unique_ptr &&kernel); + + void execute() const; + +private: + RuntimeModule *_owning_module; + std::vector> _tensors; + std::vector _input_tensors; + std::vector _output_tensors; + + // Kernels in execution order. + std::vector> _kernels; +}; + +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_CORE_RUNTIMEGRAPH_H diff --git a/compiler/luci-interpreter/src/core/RuntimeModule.h b/compiler/luci-interpreter/src/core/RuntimeModule.h new file mode 100644 index 0000000..dccc3a1 --- /dev/null +++ b/compiler/luci-interpreter/src/core/RuntimeModule.h @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_CORE_RUNTIMEMODULE_H +#define LUCI_INTERPRETER_CORE_RUNTIMEMODULE_H + +#include "core/RuntimeGraph.h" +#include "core/EventNotifier.h" + +#include +#include + +namespace luci_interpreter +{ + +class RuntimeModule +{ +public: + explicit RuntimeModule(EventNotifier *event_notifier) : _event_notifier(event_notifier) {} + + EventNotifier *getEventNotifier() const { return _event_notifier; } + + RuntimeGraph *addGraph() + { + _graphs.push_back(std::make_unique(this)); + return _graphs.back().get(); + } + + const std::vector &getInputTensors() const { return getMainGraph()->getInputTensors(); } + const std::vector &getOutputTensors() const + { + return getMainGraph()->getOutputTensors(); + } + + void execute() const { getMainGraph()->execute(); } + +private: + RuntimeGraph *getMainGraph() const { return _graphs[0].get(); } + + EventNotifier *const _event_notifier; + std::vector> _graphs; +}; + +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_CORE_RUNTIMEMODULE_H diff --git a/compiler/luci-interpreter/src/core/Tensor.cpp b/compiler/luci-interpreter/src/core/Tensor.cpp new file mode 100644 index 0000000..4fe7479 --- /dev/null +++ b/compiler/luci-interpreter/src/core/Tensor.cpp @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci_interpreter/core/Tensor.h" + +#include +#include + +namespace luci_interpreter +{ + +Tensor::Tensor(DataType element_type, Shape shape, AffineQuantization quantization, + std::string name) + : _element_type(element_type), _shape(std::move(shape)), _quantization(std::move(quantization)), + _name(std::move(name)) +{ + const size_t element_size = getDataTypeSize(_element_type); + const int32_t num_elements = _shape.num_elements(); + _data = std::make_unique(num_elements * element_size); +} + +void Tensor::readData(void *data_ptr, size_t data_size) const +{ + const size_t element_size = getDataTypeSize(element_type()); + const int32_t num_elements = shape().num_elements(); + if (data_size != num_elements * element_size) + { + throw std::invalid_argument("Invalid data size."); + } + assert(data_ptr != nullptr); + std::memcpy(data_ptr, data(), data_size); +} + +void Tensor::writeData(const void *data_ptr, size_t data_size) +{ + const size_t element_size = getDataTypeSize(element_type()); + const int32_t num_elements = shape().num_elements(); + if (data_size != num_elements * element_size) + { + throw std::invalid_argument("Invalid data size."); + } + assert(data_ptr != nullptr); + std::memcpy(data(), data_ptr, data_size); +} + +void Tensor::resize(const Shape &new_shape) +{ + _shape = new_shape; + const size_t element_size = getDataTypeSize(_element_type); + const int32_t num_elements = _shape.num_elements(); + // NOTE: _data can be nullptr for empty tensors + _data = std::make_unique(num_elements * element_size); +} + +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Add.cpp b/compiler/luci-interpreter/src/kernels/Add.cpp new file mode 100644 index 0000000..9b93347 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Add.cpp @@ -0,0 +1,141 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Add.h" + +#include "kernels/Utils.h" + +#include +#include + +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +Add::Add(const Tensor *input1, const Tensor *input2, Tensor *output, const AddParams ¶ms) + : KernelWithParams({input1, input2}, {output}, params) +{ +} + +void Add::configure() +{ + assert(input1()->element_type() == input2()->element_type()); + output()->resize(calculateShapeForBroadcast(input1()->shape(), input2()->shape())); +} + +void Add::execute() const +{ + switch (input1()->element_type()) + { + case DataType::FLOAT32: + evalFloat(); + break; + case DataType::U8: + evalQuantized(); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +void Add::evalFloat() const +{ + float activation_min{}; + float activation_max{}; + calculateActivationRange(_params.activation, &activation_min, &activation_max); + + tflite::ArithmeticParams params{}; + params.float_activation_min = activation_min; + params.float_activation_max = activation_max; + + const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes( + getTensorShape(input1()), getTensorShape(input2()), ¶ms); + + if (need_broadcast) + { + tflite::reference_ops::BroadcastAdd4DSlow( + params, getTensorShape(input1()), getTensorData(input1()), getTensorShape(input2()), + getTensorData(input2()), getTensorShape(output()), getTensorData(output())); + } + else + { + tflite::reference_ops::Add(params, getTensorShape(input1()), getTensorData(input1()), + getTensorShape(input2()), getTensorData(input2()), + getTensorShape(output()), getTensorData(output())); + } +} + +void Add::evalQuantized() const +{ + const auto input1_scale = static_cast(input1()->scale()); + const auto input2_scale = static_cast(input2()->scale()); + const auto output_scale = static_cast(output()->scale()); + + const int left_shift = 20; + const double twice_max_input_scale = 2 * std::max(input1_scale, input2_scale); + const double real_input1_multiplier = input1_scale / twice_max_input_scale; + const double real_input2_multiplier = input2_scale / twice_max_input_scale; + const double real_output_multiplier = twice_max_input_scale / ((1 << left_shift) * output_scale); + + int32_t input1_multiplier{}, input2_multiplier{}, output_multiplier{}; + int input1_shift{}, input2_shift{}, output_shift{}; + quantizeMultiplierSmallerThanOneExp(real_input1_multiplier, &input1_multiplier, &input1_shift); + quantizeMultiplierSmallerThanOneExp(real_input2_multiplier, &input2_multiplier, &input2_shift); + quantizeMultiplierSmallerThanOneExp(real_output_multiplier, &output_multiplier, &output_shift); + + int32_t activation_min{}; + int32_t activation_max{}; + calculateActivationRangeQuantized(_params.activation, output(), &activation_min, &activation_max); + + tflite::ArithmeticParams params{}; + params.left_shift = left_shift; + // The kernel expects inputs' zero points to be negated. + params.input1_offset = -input1()->zero_point(); // Note the '-'. + params.input1_multiplier = input1_multiplier; + params.input1_shift = input1_shift; + params.input2_offset = -input2()->zero_point(); // Note the '-'. + params.input2_multiplier = input2_multiplier; + params.input2_shift = input2_shift; + params.output_offset = output()->zero_point(); + params.output_multiplier = output_multiplier; + params.output_shift = output_shift; + params.quantized_activation_min = activation_min; + params.quantized_activation_max = activation_max; + + const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes( + getTensorShape(input1()), getTensorShape(input2()), ¶ms); + + if (need_broadcast) + { + tflite::reference_ops::BroadcastAdd4DSlow( + params, getTensorShape(input1()), getTensorData(input1()), + getTensorShape(input2()), getTensorData(input2()), getTensorShape(output()), + getTensorData(output())); + } + else + { + tflite::reference_ops::Add(params, getTensorShape(input1()), getTensorData(input1()), + getTensorShape(input2()), getTensorData(input2()), + getTensorShape(output()), getTensorData(output())); + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Add.h b/compiler/luci-interpreter/src/kernels/Add.h new file mode 100644 index 0000000..a1f7e04 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Add.h @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_ADD_H +#define LUCI_INTERPRETER_KERNELS_ADD_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class Add : public KernelWithParams +{ +public: + Add(const Tensor *input1, const Tensor *input2, Tensor *output, const AddParams ¶ms); + + const Tensor *input1() const { return _inputs[0]; } + const Tensor *input2() const { return _inputs[1]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + void evalFloat() const; + void evalQuantized() const; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_ADD_H diff --git a/compiler/luci-interpreter/src/kernels/Add.test.cpp b/compiler/luci-interpreter/src/kernels/Add.test.cpp new file mode 100644 index 0000000..54e1cc6 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Add.test.cpp @@ -0,0 +1,174 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Add.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +// for quantized Add, the error shouldn't exceed step +float GetTolerance(float min, float max) +{ + float kQuantizedStep = (max - min) / 255.0; + return kQuantizedStep; +} + +TEST(AddTest, Uint8) +{ + std::initializer_list base_shape = {2, 3, 1, 2}; + std::initializer_list base_data = {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, + 1.2f, 2.8f, -1.6f, 0.0f, 0.7f, -2.2f}; + std::initializer_list test_shapes[] = { + {1, 1, 3, 2}, {1, 3, 1, 2}, {2, 1, 3, 1}, {2, 3, 1, 1}}; + std::initializer_list test_data = {0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f}; + std::initializer_list output_shapes[] = { + {2, 3, 3, 2}, {2, 3, 1, 2}, {2, 3, 3, 2}, {2, 3, 1, 2}}; + std::vector> output_data = { + {-0.1f, 2.6f, -0.7f, 2.8f, 0.7f, 3.0f, 1.1f, 0.8f, 0.5f, 1.0f, 1.9f, 1.4f, + 1.0f, -0.8f, 0.4f, -0.6f, 1.8f, -0.2f, 1.4f, 3.0f, 0.8f, 3.0f, 2.2f, 3.0f, + -1.4f, 0.3f, -2.0f, 0.5f, -0.6f, 0.9f, 0.9f, -1.9f, 0.3f, -1.7f, 1.7f, -1.3f}, + {-0.1f, 2.6f, 0.5f, 1.0f, 1.8f, -0.2f, 1.4f, 3.0f, -2.0f, 0.5f, 1.7f, -1.3f}, + {-0.1f, 2.5f, 0.0f, 2.6f, -0.7f, 1.9f, 1.1f, 0.7f, 1.2f, 0.8f, 0.5f, 0.1f, + 1.0f, -0.9f, 1.1f, -0.8f, 0.4f, -1.5f, 1.7f, 3.0f, 2.2f, 3.0f, 2.1f, 3.0f, + -1.1f, 0.5f, -0.6f, 1.0f, -0.7f, 0.9f, 1.2f, -1.7f, 1.7f, -1.2f, 1.6f, -1.3f}, + {-0.1f, 2.5f, 1.2f, 0.8f, 0.4f, -1.5f, 1.7f, 3.0f, -0.6f, 1.0f, 1.6f, -1.3f}}; + float kQuantizedTolerance = GetTolerance(-3.f, 3.f); + std::pair quant_param = quantizationParams(-3.f, 3.f); + for (int i = 0; i < output_data.size(); i++) + { + Tensor input1_tensor{ + getElementType(), base_shape, {{quant_param.first}, {quant_param.second}}, ""}; + Tensor input2_tensor{ + getElementType(), test_shapes[i], {{quant_param.first}, {quant_param.second}}, ""}; + std::vector quantized_input1_value = + quantize(base_data, quant_param.first, quant_param.second); + std::vector quantized_input2_value = + quantize(test_data, quant_param.first, quant_param.second); + input1_tensor.writeData(quantized_input1_value.data(), + quantized_input1_value.size() * sizeof(uint8_t)); + input2_tensor.writeData(quantized_input2_value.data(), + quantized_input2_value.size() * sizeof(uint8_t)); + Tensor output_tensor = + makeOutputTensor(getElementType(), quant_param.first, quant_param.second); + + AddParams params{}; + params.activation = Activation::NONE; + + Add kernel(&input1_tensor, &input2_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(dequantize(extractTensorData(output_tensor), + output_tensor.scale(), output_tensor.zero_point()), + ElementsAreArray(ArrayFloatNear(output_data[i], kQuantizedTolerance))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shapes[i])); + } + // Re-run with exchanged inputs. + for (int i = 0; i < output_data.size(); i++) + { + Tensor input1_tensor{ + getElementType(), test_shapes[i], {{quant_param.first}, {quant_param.second}}, ""}; + Tensor input2_tensor{ + getElementType(), base_shape, {{quant_param.first}, {quant_param.second}}, ""}; + std::vector quantized_input1_value = + quantize(test_data, quant_param.first, quant_param.second); + std::vector quantized_input2_value = + quantize(base_data, quant_param.first, quant_param.second); + input1_tensor.writeData(quantized_input1_value.data(), + quantized_input1_value.size() * sizeof(uint8_t)); + input2_tensor.writeData(quantized_input2_value.data(), + quantized_input2_value.size() * sizeof(uint8_t)); + Tensor output_tensor = + makeOutputTensor(getElementType(), quant_param.first, quant_param.second); + + AddParams params{}; + params.activation = Activation::NONE; + + Add kernel(&input1_tensor, &input2_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(dequantize(extractTensorData(output_tensor), + output_tensor.scale(), output_tensor.zero_point()), + ElementsAreArray(ArrayFloatNear(output_data[i], kQuantizedTolerance))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shapes[i])); + } +} + +TEST(AddTest, Float) +{ + Shape base_shape = {2, 3, 1, 2}; + std::vector test_shapes{{1, 1, 3, 2}, {1, 3, 1, 2}, {2, 1, 3, 1}, {2, 3, 1, 1}}; + std::vector> test_outputs = { + {0.0f, 2.6f, 0.0f, 2.8f, 0.7f, 3.2f, 1.1f, 0.8f, 0.5f, 1.0f, 1.9f, 1.4f, + 1.0f, 0.0f, 0.4f, 0.0f, 1.8f, 0.0f, 1.4f, 3.1f, 0.8f, 3.3f, 2.2f, 3.7f, + 0.0f, 0.3f, 0.0f, 0.5f, 0.0f, 0.9f, 0.9f, 0.0f, 0.3f, 0.0f, 1.7f, 0.0f}, + {0.0f, 2.6f, 0.5f, 1.0f, 1.8f, 0.0f, 1.4f, 3.1f, 0.0f, 0.5f, 1.7f, 0.0f}, + {0.0f, 2.5f, 0.0f, 2.6f, 0.0f, 1.9f, 1.1f, 0.7f, 1.2f, 0.8f, 0.5f, 0.1f, + 1.0f, 0.0f, 1.1f, 0.0f, 0.4f, 0.0f, 1.7f, 3.3f, 2.2f, 3.8f, 2.1f, 3.7f, + 0.0f, 0.5f, 0.0f, 1.0f, 0.0f, 0.9f, 1.2f, 0.0f, 1.7f, 0.0f, 1.6f, 0.0f}, + {0.0f, 2.5f, 1.2f, 0.8f, 0.4f, 0.0f, 1.7f, 3.3f, 0.0f, 1.0f, 1.6f, 0.0f}}; + std::vector input1_data{-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, + 1.2f, 2.8f, -1.6f, 0.0f, 0.7f, -2.2f}; + std::vector input2_data{0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f}; + for (size_t i = 0; i < test_shapes.size(); ++i) + { + Tensor input1_tensor = makeInputTensor(base_shape, input1_data); + Tensor input2_tensor = makeInputTensor(test_shapes[i], input2_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + AddParams params{}; + params.activation = Activation::RELU; + + Add kernel(&input1_tensor, &input2_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), + ::testing::ElementsAreArray(ArrayFloatNear(test_outputs[i], 0.0001f))) + << "With shape number " << i; + } + // Re-run with exchanged inputs. + for (size_t i = 0; i < test_shapes.size(); ++i) + { + Tensor input1_tensor = makeInputTensor(test_shapes[i], input2_data); + Tensor input2_tensor = makeInputTensor(base_shape, input1_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + AddParams params{}; + params.activation = Activation::RELU; + + Add kernel(&input1_tensor, &input2_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), + ::testing::ElementsAreArray(ArrayFloatNear(test_outputs[i], 0.0001f))) + << "With shape number " << i; + } +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/ArgMax.cpp b/compiler/luci-interpreter/src/kernels/ArgMax.cpp new file mode 100644 index 0000000..5c464ed --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/ArgMax.cpp @@ -0,0 +1,140 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/ArgMax.h" +#include "kernels/Utils.h" +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +ArgMax::ArgMax(const Tensor *input, const Tensor *axis, Tensor *output, const ArgMaxParams ¶ms) + : KernelWithParams({input, axis}, {output}, params) +{ +} + +void ArgMax::configure() +{ + assert(axis()->element_type() == DataType::S32 || axis()->element_type() == DataType::S64); + assert(input()->shape().num_dims() >= 1); + const Shape &input_shape = input()->shape(); + const int num_dims = input_shape.num_dims(); + Shape output_shape(num_dims - 1); + + // If axis value is negative, then update by adding input_shape's num_dims. + // If updated value also negative, then assert. + assert(axis()->shape().num_elements() == 1); + int axis_value = getTensorData(axis())[0]; + if (axis_value < 0) + axis_value = axis_value + num_dims; + assert(axis_value >= 0); + + int j = 0; + for (int i = 0; i < num_dims; i++) + { + if (i == axis_value) + continue; + output_shape.dim(j++) = input_shape.dim(i); + } + + assert(output()->element_type() == _params.output_type); + + output()->resize(output_shape); +} + +void ArgMax::execute() const +{ + +#define TF_LITE_ARG_MAX(data_type, axis_type, output_type) \ + tflite::optimized_ops::ArgMinMax(getTensorShape(input()), getTensorData(input()), \ + getTensorData(axis()), getTensorShape(output()), \ + getTensorData(output()), \ + std::greater()) + if (axis()->element_type() == DataType::S32) + { + switch (_params.output_type) + { + case DataType::S32: + switch (input()->element_type()) + { + case DataType::FLOAT32: + TF_LITE_ARG_MAX(float, int32_t, int32_t); + break; + case DataType::U8: + TF_LITE_ARG_MAX(uint8_t, int32_t, int32_t); + break; + default: + throw std::runtime_error("Unsupported input type."); + } + break; + case DataType::S64: + switch (input()->element_type()) + { + case DataType::FLOAT32: + TF_LITE_ARG_MAX(float, int32_t, int64_t); + break; + case DataType::U8: + TF_LITE_ARG_MAX(uint8_t, int32_t, int64_t); + break; + default: + throw std::runtime_error("Unsupported input type."); + } + break; + default: + throw std::runtime_error("Unsupported output type."); + } + } + else + { + switch (_params.output_type) + { + case DataType::S32: + switch (input()->element_type()) + { + case DataType::FLOAT32: + TF_LITE_ARG_MAX(float, int64_t, int32_t); + break; + case DataType::U8: + TF_LITE_ARG_MAX(uint8_t, int64_t, int32_t); + break; + default: + throw std::runtime_error("Unsupported input type."); + } + break; + case DataType::S64: + switch (input()->element_type()) + { + case DataType::FLOAT32: + TF_LITE_ARG_MAX(float, int64_t, int64_t); + break; + case DataType::U8: + TF_LITE_ARG_MAX(uint8_t, int64_t, int64_t); + break; + default: + throw std::runtime_error("Unsupported input type."); + } + break; + default: + throw std::runtime_error("Unsupported output type."); + } + } +#undef TF_LITE_ARG_MAX +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/ArgMax.h b/compiler/luci-interpreter/src/kernels/ArgMax.h new file mode 100644 index 0000000..c851b58 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/ArgMax.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_ARGMAX_H +#define LUCI_INTERPRETER_KERNELS_ARGMAX_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class ArgMax : public KernelWithParams +{ +public: + ArgMax(const Tensor *input, const Tensor *axis, Tensor *output, const ArgMaxParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + const Tensor *axis() const { return _inputs[1]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_ARGMAX_H diff --git a/compiler/luci-interpreter/src/kernels/ArgMax.test.cpp b/compiler/luci-interpreter/src/kernels/ArgMax.test.cpp new file mode 100644 index 0000000..5ac3b2f --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/ArgMax.test.cpp @@ -0,0 +1,98 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/ArgMax.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +template +void Check(std::initializer_list input_shape, + std::initializer_list dimension_shape, + std::initializer_list output_shape, std::initializer_list input_data, + std::initializer_list dimension_data, std::initializer_list output_data) +{ + + Tensor input_tensor{getElementType(), input_shape, {}, ""}; + input_tensor.writeData(input_data.begin(), input_data.size() * sizeof(T1)); + Tensor dimension_tensor{DataType::S32, dimension_shape, {}, ""}; + dimension_tensor.writeData(dimension_data.begin(), dimension_data.size() * sizeof(int32_t)); + + Tensor output_tensor = makeOutputTensor(getElementType()); + + ArgMaxParams params{}; + params.output_type = getElementType(); + ArgMax kernel(&input_tensor, &dimension_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), ::testing::ElementsAreArray(output_data)); + EXPECT_THAT(extractTensorShape(output_tensor), output_shape); +} + +template class ArgMaxTest : public ::testing::Test +{ +}; + +using DataTypes = ::testing::Types; +TYPED_TEST_CASE(ArgMaxTest, DataTypes); + +TYPED_TEST(ArgMaxTest, Simple) +{ + Check(/*input_shape=*/{1, 1, 1, 4}, /*dimension_shape=*/{}, + /*output_shape=*/{1, 1, 1}, + /*input_data=*/ + { + 1, 9, 7, 3, + }, + /*dimension_data=*/{3}, /*output_data=*/{1}); + Check(/*input_shape=*/{1, 1, 1, 4}, /*dimension_shape=*/{}, + /*output_shape=*/{1, 1, 1}, + /*input_data=*/ + { + 1, 9, 7, 3, + }, + /*dimension_data=*/{3}, /*output_data=*/{1}); +} + +TYPED_TEST(ArgMaxTest, MultiDimensions) +{ + Check(/*input_shape=*/{1, 1, 2, 4}, /*dimension_shape=*/{}, + /*output_shape=*/{1, 1, 2}, + /*input_data=*/ + { + 1, 2, 7, 8, 1, 9, 7, 3, + }, + /*dimension_data=*/{3}, /*output_data=*/{3, 1}); + Check(/*input_shape=*/{1, 1, 2, 4}, /*dimension_shape=*/{}, + /*output_shape=*/{1, 1, 2}, + /*input_data=*/ + { + 1, 2, 7, 8, 1, 9, 7, 3, + }, + /*dimension_data=*/{3}, /*output_data=*/{3, 1}); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/AveragePool2D.cpp b/compiler/luci-interpreter/src/kernels/AveragePool2D.cpp new file mode 100644 index 0000000..6d1b8ea --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/AveragePool2D.cpp @@ -0,0 +1,115 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/AveragePool2D.h" + +#include "kernels/Utils.h" + +#include + +#include + +namespace luci_interpreter +{ + +namespace kernels +{ + +AveragePool2D::AveragePool2D(const Tensor *input, Tensor *output, const Pool2DParams ¶ms) + : KernelWithParams({input}, {output}, params) +{ +} + +void AveragePool2D::configure() +{ + const Shape &input_shape = input()->shape(); + + const int32_t batches = input_shape.dim(0); + const int32_t input_height = input_shape.dim(1); + const int32_t input_width = input_shape.dim(2); + const int32_t depth = input_shape.dim(3); + + const int32_t output_height = computeOutputSize(_params.padding, input_height, + _params.filter_height, _params.stride_height); + const int32_t output_width = + computeOutputSize(_params.padding, input_width, _params.filter_width, _params.stride_width); + + _padding_height = + computePadding(_params.stride_height, 1, input_height, _params.filter_height, output_height); + _padding_width = + computePadding(_params.stride_width, 1, input_width, _params.filter_width, output_width); + + output()->resize({batches, output_height, output_width, depth}); +} + +void AveragePool2D::execute() const +{ + switch (input()->element_type()) + { + case DataType::FLOAT32: + evalFloat(); + break; + case DataType::U8: + evalQuantized(); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +void AveragePool2D::evalFloat() const +{ + float activation_min{}; + float activation_max{}; + calculateActivationRange(_params.activation, &activation_min, &activation_max); + + tflite::PoolParams params{}; + params.padding_values.height = _padding_height; + params.padding_values.width = _padding_width; + params.stride_height = _params.stride_height; + params.stride_width = _params.stride_width; + params.filter_height = _params.filter_height; + params.filter_width = _params.filter_width; + params.float_activation_min = activation_min; + params.float_activation_max = activation_max; + + tflite::reference_ops::AveragePool(params, getTensorShape(input()), getTensorData(input()), + getTensorShape(output()), getTensorData(output())); +} + +void AveragePool2D::evalQuantized() const +{ + int32_t activation_min{}; + int32_t activation_max{}; + calculateActivationRangeQuantized(_params.activation, output(), &activation_min, &activation_max); + + tflite::PoolParams params{}; + params.padding_values.height = _padding_height; + params.padding_values.width = _padding_width; + params.stride_height = _params.stride_height; + params.stride_width = _params.stride_width; + params.filter_height = _params.filter_height; + params.filter_width = _params.filter_width; + params.quantized_activation_min = activation_min; + params.quantized_activation_max = activation_max; + + tflite::reference_ops::AveragePool(params, getTensorShape(input()), + getTensorData(input()), getTensorShape(output()), + getTensorData(output())); +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/AveragePool2D.h b/compiler/luci-interpreter/src/kernels/AveragePool2D.h new file mode 100644 index 0000000..91f212b --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/AveragePool2D.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_AVERAGEPOOL2D_H +#define LUCI_INTERPRETER_KERNELS_AVERAGEPOOL2D_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class AveragePool2D : public KernelWithParams +{ +public: + AveragePool2D(const Tensor *input, Tensor *output, const Pool2DParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + void evalFloat() const; + void evalQuantized() const; + +private: + int32_t _padding_height{}; + int32_t _padding_width{}; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_AVERAGEPOOL2D_H diff --git a/compiler/luci-interpreter/src/kernels/AveragePool2D.test.cpp b/compiler/luci-interpreter/src/kernels/AveragePool2D.test.cpp new file mode 100644 index 0000000..7160e49 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/AveragePool2D.test.cpp @@ -0,0 +1,127 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/AveragePool2D.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +TEST(AveragePool2DTest, Float) +{ + Shape input_shape{1, 3, 5, 1}; + std::vector input_data{ + -4, -3, -2, -1, 0, // + 1, 2, 3, 4, 5, // + 6, 7, 8, 9, 10, // + }; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Pool2DParams params{}; + params.padding = Padding::VALID; + params.filter_height = 2; + params.filter_width = 3; + params.stride_height = 1; + params.stride_width = 2; + params.activation = Activation::RELU6; + + AveragePool2D kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{ + 0, 1.5, // + 4.5, 6, // + }; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 2, 2, 1})); +} + +TEST(AveragePool2DTest, Uint8_0) +{ + std::pair quant_param = quantizationParams(-15.9375f, 15.9375f); + Tensor input_tensor{DataType::U8, {1, 2, 4, 1}, {{quant_param.first}, {quant_param.second}}, ""}; + Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second); + + std::vector quant_input = quantize( + { + 0, -6, 12, 4, // + -3, -2, 10, 7, // + }, + quant_param.first, quant_param.second); + input_tensor.writeData(quant_input.data(), quant_input.size() * sizeof(uint8_t)); + + Pool2DParams params{}; + params.padding = Padding::VALID; + params.filter_height = 2; + params.filter_width = 2; + params.stride_height = 2; + params.stride_width = 2; + params.activation = Activation::RELU6; + + AveragePool2D kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(dequantize(extractTensorData(output_tensor), output_tensor.scale(), + output_tensor.zero_point()), + ElementsAreArray(ArrayFloatNear({0.0, 6.0}))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 1, 2, 1})); +} + +TEST(AveragePool2DTest, Uint8_1) +{ + std::pair quant_param = quantizationParams(-15.9375f, 15.9375f); + Tensor input_tensor{DataType::U8, {1, 2, 4, 1}, {{quant_param.first}, {quant_param.second}}, ""}; + Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second); + + std::vector quant_input = quantize( + { + 0, 6, 12, 4, // + 3, 2, 10, 7, // + }, + quant_param.first, quant_param.second); + input_tensor.writeData(quant_input.data(), quant_input.size() * sizeof(uint8_t)); + + Pool2DParams params{}; + params.padding = Padding::VALID; + params.filter_height = 2; + params.filter_width = 2; + params.stride_height = 2; + params.stride_width = 2; + params.activation = Activation::RELU6; + + AveragePool2D kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(dequantize(extractTensorData(output_tensor), output_tensor.scale(), + output_tensor.zero_point()), + ElementsAreArray(ArrayFloatNear({2.75, 6.0}))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 1, 2, 1})); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/CMakeLists.txt b/compiler/luci-interpreter/src/kernels/CMakeLists.txt new file mode 100644 index 0000000..fe36231 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/CMakeLists.txt @@ -0,0 +1,106 @@ +find_package(Threads REQUIRED) +nnas_find_package(GTest REQUIRED) + +set(SOURCES + Add.h + Add.cpp + ArgMax.h + ArgMax.cpp + AveragePool2D.h + AveragePool2D.cpp + Concatenation.h + Concatenation.cpp + Conv2D.h + Conv2D.cpp + DepthwiseConv2D.h + DepthwiseConv2D.cpp + Elu.h + Elu.cpp + FullyConnected.h + FullyConnected.cpp + If.h + If.cpp + L2Normalize.h + L2Normalize.cpp + L2Pool2D.h + L2Pool2D.cpp + LeakyRelu.h + LeakyRelu.cpp + LocalResponseNormalization.h + LocalResponseNormalization.cpp + Logistic.h + Logistic.cpp + MaxPool2D.h + MaxPool2D.cpp + Mean.h + Mean.cpp + Mul.h + Mul.cpp + Pad.h + Pad.cpp + Reshape.h + Reshape.cpp + Softmax.h + Softmax.cpp + SpaceToDepth.h + SpaceToDepth.cpp + Split.h + Split.cpp + StridedSlice.h + StridedSlice.cpp + Squeeze.h + Squeeze.cpp + Transpose.h + Transpose.cpp + TransposeConv.h + TransposeConv.cpp + Unpack.h + Unpack.cpp) + +list(APPEND SOURCES Utils.h Utils.cpp) + +add_library(luci_interpreter_kernels STATIC ${SOURCES}) +set_target_properties(luci_interpreter_kernels PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_include_directories(luci_interpreter_kernels PUBLIC ${LUCI_INTERPRETER_SOURCE_DIR}) +target_include_directories(luci_interpreter_kernels SYSTEM PRIVATE + "${TensorFlowGEMMLowpSource_DIR}" + "${TensorFlowEigenSource_DIR}" + "${TensorFlowSource_DIR}") +target_link_libraries(luci_interpreter_kernels + PUBLIC luci_interpreter_core + PRIVATE nncc_common Threads::Threads) + + +set(TEST_SOURCES + Add.test.cpp + ArgMax.test.cpp + AveragePool2D.test.cpp + Concatenation.test.cpp + Conv2D.test.cpp + DepthwiseConv2D.test.cpp + Elu.test.cpp + FullyConnected.test.cpp + If.test.cpp + L2Normalize.test.cpp + L2Pool2D.test.cpp + LeakyRelu.test.cpp + LocalResponseNormalization.test.cpp + Logistic.test.cpp + MaxPool2D.test.cpp + Mean.test.cpp + Mul.test.cpp + Pad.test.cpp + Reshape.test.cpp + Softmax.test.cpp + SpaceToDepth.test.cpp + Split.test.cpp + StridedSlice.test.cpp + Squeeze.test.cpp + Transpose.test.cpp + TransposeConv.test.cpp + Unpack.test.cpp) + +list(APPEND TEST_SOURCES TestUtils.h TestUtils.cpp) + +GTest_AddTest(luci_interpreter_kernels_test ${TEST_SOURCES}) +target_link_libraries(luci_interpreter_kernels_test luci_interpreter_kernels) diff --git a/compiler/luci-interpreter/src/kernels/Concatenation.cpp b/compiler/luci-interpreter/src/kernels/Concatenation.cpp new file mode 100644 index 0000000..812ab76 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Concatenation.cpp @@ -0,0 +1,136 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Concatenation.h" +#include "kernels/Utils.h" + +#include + +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +Concatenation::Concatenation(std::vector inputs, Tensor *output, + const ConcatenationParams ¶ms) + : KernelWithParams(std::move(inputs), {output}, params) +{ +} + +void Concatenation::configure() +{ + const int num_inputs = _inputs.size(); + assert(num_inputs > 0); + const Tensor *t0 = _inputs[0]; + + int axis = _params.axis; + if (axis < 0) + axis += t0->shape().num_dims(); + assert(axis >= 0 && axis < t0->shape().num_dims()); + + int32_t sum_axis = t0->shape().dim(axis); + for (int i = 1; i < num_inputs; ++i) + { + const Tensor *tensor = _inputs[i]; + assert(tensor->element_type() == t0->element_type()); + assert(tensor->shape().num_dims() == t0->shape().num_dims()); + for (int d = 0; d < t0->shape().num_dims(); ++d) + { + if (d == axis) + { + sum_axis += tensor->shape().dim(axis); + } + else + { + assert(tensor->shape().dim(d) == t0->shape().dim(d)); + } + } + } + + Shape output_shape = t0->shape(); + output_shape.dim(axis) = sum_axis; + + // TODO S8 type needs more checking: quantization parameters of all input tensors and the output + // tensor should be the same. Note that there is no such requirement for U8 type. + if (t0->element_type() == DataType::S8) + throw std::runtime_error("Unsupported type."); + + output()->resize(output_shape); +} + +void Concatenation::execute() const +{ + switch (_inputs[0]->element_type()) + { + case DataType::FLOAT32: + evalGeneric(); + break; + case DataType::U8: + evalQuantized(); + break; + case DataType::S8: + evalGeneric(); + break; + case DataType::S32: + evalGeneric(); + break; + case DataType::S64: + evalGeneric(); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +template void Concatenation::evalGeneric() const +{ + int axis = _params.axis; + if (axis < 0) + axis += output()->shape().num_dims(); + + VectorOfTensors inputs(_inputs); + tflite::ConcatenationParams params{}; + params.axis = axis; + params.inputs_count = _inputs.size(); + tflite::reference_ops::Concatenation(params, inputs.shapes(), inputs.data(), + getTensorShape(output()), getTensorData(output())); +} + +void Concatenation::evalQuantized() const +{ + int axis = _params.axis; + if (axis < 0) + axis += output()->shape().num_dims(); + + VectorOfQuantizedTensors inputs(_inputs); + tflite::ConcatenationParams params{}; + params.axis = axis; + params.input_zeropoint = inputs.zero_point(); + params.input_scale = inputs.scale(); + params.inputs_count = _inputs.size(); + params.output_zeropoint = output()->zero_point(); + params.output_scale = output()->scale(); + + tflite::reference_ops::ConcatenationWithScaling(params, inputs.shapes(), inputs.data(), + getTensorShape(output()), + getTensorData(output())); +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Concatenation.h b/compiler/luci-interpreter/src/kernels/Concatenation.h new file mode 100644 index 0000000..b48c8ed --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Concatenation.h @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_CONCATENATION_H +#define LUCI_INTERPRETER_KERNELS_CONCATENATION_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class Concatenation : public KernelWithParams +{ +public: + Concatenation(std::vector inputs, Tensor *output, + const ConcatenationParams ¶ms); + + const Tensor *input(int index) const { return _inputs[index]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + template void evalGeneric() const; + void evalQuantized() const; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_CONCATENATION_H diff --git a/compiler/luci-interpreter/src/kernels/Concatenation.test.cpp b/compiler/luci-interpreter/src/kernels/Concatenation.test.cpp new file mode 100644 index 0000000..d9a7097 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Concatenation.test.cpp @@ -0,0 +1,83 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Concatenation.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +TEST(ConcatenationTest, Float) +{ + std::vector input1_data{1, 2, 3, 4, 5, 6}; + std::vector input2_data{7, 8, 9, 10, 11, 12}; + Tensor input1_tensor = makeInputTensor({2, 3}, input1_data); + Tensor input2_tensor = makeInputTensor({2, 3}, input2_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + ConcatenationParams params{}; + + // Try different 'axis' and expect different results. + { + params.axis = 0; + + Concatenation kernel({&input1_tensor, &input2_tensor}, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}))); + } + { + params.axis = -2; // Same as '0'. + + Concatenation kernel({&input1_tensor, &input2_tensor}, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}))); + } + { + params.axis = 1; + + Concatenation kernel({&input1_tensor, &input2_tensor}, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear({1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12}))); + } + { + params.axis = -1; // Same as '1'. + + Concatenation kernel({&input1_tensor, &input2_tensor}, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear({1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12}))); + } +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Conv2D.cpp b/compiler/luci-interpreter/src/kernels/Conv2D.cpp new file mode 100644 index 0000000..60e6134 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Conv2D.cpp @@ -0,0 +1,194 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Conv2D.h" + +#include "kernels/Utils.h" + +#include + +#include +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +Conv2D::Conv2D(const Tensor *input, const Tensor *filter, const Tensor *bias, Tensor *output, + const Conv2DParams ¶ms) + : KernelWithParams({input, filter, bias}, {output}, params) +{ +} + +void Conv2D::configure() +{ + // TensorFlow Lite (as of v2.2.0) supports the following combinations of types: + // | input filter bias output | + // ----+---------------------------+ + // (1) | float float float float | + // (2) | float int8 float float | hybrid + // (3) | uint8 uint8 int32 uint8 | quantized + // (4) | int8 int8 int32 int8 | quantized per channel + // + // We only support (1) and (3) for now. + if (input()->element_type() == DataType::FLOAT32 && filter()->element_type() == DataType::FLOAT32) + { + assert(bias() == nullptr || bias()->element_type() == DataType::FLOAT32); + } + else if (input()->element_type() == DataType::U8 && filter()->element_type() == DataType::U8) + { + assert(bias() == nullptr || bias()->element_type() == DataType::S32); + } + else + { + throw std::runtime_error("Unsupported type."); + } + assert(output()->element_type() == input()->element_type()); + + const Shape &input_shape = input()->shape(); + const Shape &filter_shape = filter()->shape(); + assert(input_shape.num_dims() == 4 && filter_shape.num_dims() == 4); + + const int32_t batches = input_shape.dim(0); + const int32_t input_height = input_shape.dim(1); + const int32_t input_width = input_shape.dim(2); + const int32_t output_depth = filter_shape.dim(0); + const int32_t filter_height = filter_shape.dim(1); + const int32_t filter_width = filter_shape.dim(2); + assert(filter_shape.dim(3) == input_shape.dim(3)); + + assert(bias() == nullptr || + (bias()->shape().num_dims() == 1 && bias()->shape().dim(0) == output_depth)); + + const int32_t output_height = + computeOutputSize(_params.padding, input_height, filter_height, _params.stride_height, + _params.dilation_height_factor); + const int32_t output_width = + computeOutputSize(_params.padding, input_width, filter_width, _params.stride_width, + _params.dilation_width_factor); + + _padding_height = computePadding(_params.stride_height, _params.dilation_height_factor, + input_height, filter_height, output_height); + _padding_width = computePadding(_params.stride_width, _params.dilation_width_factor, input_width, + filter_width, output_width); + + output()->resize({batches, output_height, output_width, output_depth}); + + // Allocate tensor for Im2Col, if needed. + // The checks here should be aligned with the actual implementation. + const bool need_dilated_im2col = + _params.dilation_height_factor != 1 || _params.dilation_width_factor != 1; + const bool need_non_dilated_im2col = _params.stride_height != 1 || _params.stride_width != 1 || + filter_height != 1 || filter_width != 1; + const bool need_im2col = need_dilated_im2col || need_non_dilated_im2col; + if (need_im2col) + { + const int input_depth = input_shape.dim(3); + Shape im2col_shape{batches, output_height, output_width, + input_depth * filter_height * filter_width}; + _im2col = + std::make_unique(input()->element_type(), im2col_shape, AffineQuantization{}, ""); + } +} + +void Conv2D::execute() const +{ + switch (input()->element_type()) + { + case DataType::FLOAT32: + if (filter()->element_type() == DataType::FLOAT32) + { + evalFloat(); + break; + } + throw std::runtime_error("Unsupported type."); + case DataType::U8: + evalQuantized(); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +void Conv2D::evalFloat() const +{ + float activation_min{}; + float activation_max{}; + calculateActivationRange(_params.activation, &activation_min, &activation_max); + + tflite::ConvParams params{}; + params.padding_values.height = _padding_height; + params.padding_values.width = _padding_width; + params.stride_height = _params.stride_height; + params.stride_width = _params.stride_width; + params.dilation_height_factor = _params.dilation_height_factor; + params.dilation_width_factor = _params.dilation_width_factor; + params.float_activation_min = activation_min; + params.float_activation_max = activation_max; + + tflite::optimized_ops::Conv(params, getTensorShape(input()), getTensorData(input()), + getTensorShape(filter()), getTensorData(filter()), + getTensorShape(bias()), getTensorData(bias()), + getTensorShape(output()), getTensorData(output()), + getTensorShape(_im2col.get()), getTensorData(_im2col.get())); +} + +void Conv2D::evalQuantized() const +{ + const auto input_scale = static_cast(input()->scale()); + const auto filter_scale = static_cast(filter()->scale()); + const auto output_scale = static_cast(output()->scale()); + + const double real_multiplier = input_scale * filter_scale / output_scale; + int32_t output_multiplier{}; + int output_shift{}; + quantizeMultiplier(real_multiplier, &output_multiplier, &output_shift); + + int32_t activation_min{}; + int32_t activation_max{}; + calculateActivationRangeQuantized(_params.activation, output(), &activation_min, &activation_max); + + tflite::ConvParams params{}; + params.padding_values.height = _padding_height; + params.padding_values.width = _padding_width; + params.stride_height = _params.stride_height; + params.stride_width = _params.stride_width; + params.dilation_height_factor = _params.dilation_height_factor; + params.dilation_width_factor = _params.dilation_width_factor; + // The kernel expects input and filter zero points to be negated. + params.input_offset = -input()->zero_point(); // Note the '-'. + params.weights_offset = -filter()->zero_point(); // Note the '-'. + params.output_offset = output()->zero_point(); + params.output_multiplier = output_multiplier; + params.output_shift = output_shift; + params.quantized_activation_min = activation_min; + params.quantized_activation_max = activation_max; + + // TODO This should only be done once (although it takes only a few microseconds). + // Also, the user should be able to adjust the number of threads. + auto gemmlowp_context = std::make_unique(); + gemmlowp_context->set_max_num_threads(static_cast(std::thread::hardware_concurrency())); + + tflite::optimized_ops::Conv( + params, getTensorShape(input()), getTensorData(input()), getTensorShape(filter()), + getTensorData(filter()), getTensorShape(bias()), getTensorData(bias()), + getTensorShape(output()), getTensorData(output()), getTensorShape(_im2col.get()), + getTensorData(_im2col.get()), gemmlowp_context.get()); +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Conv2D.h b/compiler/luci-interpreter/src/kernels/Conv2D.h new file mode 100644 index 0000000..69e3098 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Conv2D.h @@ -0,0 +1,57 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_CONV2D_H +#define LUCI_INTERPRETER_KERNELS_CONV2D_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +class Conv2D : public KernelWithParams +{ +public: + Conv2D(const Tensor *input, const Tensor *filter, const Tensor *bias, Tensor *output, + const Conv2DParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + const Tensor *filter() const { return _inputs[1]; } + const Tensor *bias() const { return _inputs[2]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + void evalFloat() const; + void evalQuantized() const; + +private: + std::unique_ptr _im2col; + int32_t _padding_height{}; + int32_t _padding_width{}; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_CONV2D_H diff --git a/compiler/luci-interpreter/src/kernels/Conv2D.test.cpp b/compiler/luci-interpreter/src/kernels/Conv2D.test.cpp new file mode 100644 index 0000000..ef9ace9 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Conv2D.test.cpp @@ -0,0 +1,185 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Conv2D.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +TEST(Conv2DTest, Float) +{ + Shape input_shape{1, 4, 3, 2}; + Shape filter_shape{2, 2, 2, 2}; + Shape bias_shape{2}; + std::vector input_data{ + 1, 2, 3, 4, 5, 6, // row = 0 + 7, 8, 9, 10, 11, 12, // row = 1 + 13, 14, 15, 16, 17, 18, // row = 2 + 19, 20, 21, 22, 23, 24, // row = 3 + }; + std::vector filter_data{ + 1, 2, -3, -4, // out = 0, row = 0 + -5, 6, -7, 8, // out = 1, row = 0 + 4, -2, 3, -1, // out = 0, row = 1 + -8, -6, 7, 5, // out = 1, row = 1 + }; + std::vector bias_data{1, 2}; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor filter_tensor = makeInputTensor(filter_shape, filter_data); + Tensor bias_tensor = makeInputTensor(bias_shape, bias_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Conv2DParams params{}; + params.padding = Padding::VALID; + params.stride_height = 2; + params.stride_width = 1; + params.dilation_height_factor = 1; + params.dilation_width_factor = 1; + params.activation = Activation::RELU; + + Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{ + 11, 16, 7, 20, // row = 0 + 0, 40, 0, 44, // row = 1 + }; + std::vector ref_output_shape{1, 2, 2, 2}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape)); +} + +TEST(Conv2DTest, FloatCheck) +{ + Shape input_shape{2, 2, 4, 1}; + Shape filter_shape{3, 2, 2, 1}; + Shape bias_shape{3}; + std::vector input_data{ + // First batch + 1, 1, 1, 1, // row = 1 + 2, 2, 2, 2, // row = 2 + // Second batch + 1, 2, 3, 4, // row = 1 + 1, 2, 3, 4, // row = 2 + }; + std::vector filter_data{ + 1, 2, 3, 4, // first 2x2 filter + -1, 1, -1, 1, // second 2x2 filter + -1, -1, 1, 1, // third 2x2 filter + }; + std::vector bias_data{1, 2, 3}; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor filter_tensor = makeInputTensor(filter_shape, filter_data); + Tensor bias_tensor = makeInputTensor(bias_shape, bias_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Conv2DParams params{}; + params.padding = Padding::VALID; + params.stride_height = 2; + params.stride_width = 2; + params.dilation_height_factor = 1; + params.dilation_width_factor = 1; + params.activation = Activation::NONE; + + Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{ + 18, 2, 5, // first batch, left + 18, 2, 5, // first batch, right + 17, 4, 3, // second batch, left + 37, 4, 3, // second batch, right + }; + std::vector ref_output_shape{2, 1, 2, 3}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape)); +} + +TEST(Conv2DTest, Uint8) +{ + std::pair input_quant_param = quantizationParams(-63.5, 64); + std::pair output_quant_param = quantizationParams(-127, 128); + Shape bias_shape = {3}; + Tensor input_tensor{ + DataType::U8, {2, 2, 4, 1}, {{input_quant_param.first}, {input_quant_param.second}}, ""}; + Tensor filter_tensor{ + DataType::U8, {3, 2, 2, 1}, {{input_quant_param.first}, {input_quant_param.second}}, ""}; + Tensor bias_tensor{ + DataType::S32, bias_shape, {{input_quant_param.first * input_quant_param.first}, {0}}, ""}; + Tensor output_tensor = + makeOutputTensor(DataType::U8, output_quant_param.first, output_quant_param.second); + std::vector quantized_input = quantize( + { + // First batch + 1, 1, 1, 1, // row = 1 + 2, 2, 2, 2, // row = 2 + // Second batch + 1, 2, 3, 4, // row = 1 + 1, 2, 3, 4, // row = 2 + }, + input_quant_param.first, input_quant_param.second); + std::vector quantized_filter = quantize( + { + 1, 2, 3, 4, // first 2x2 filter + -1, 1, -1, 1, // second 2x2 filter + -1, -1, 1, 1, // third 2x2 filter + }, + input_quant_param.first, input_quant_param.second); + std::vector bias_data = + quantize({1, 2, 3}, input_quant_param.first * input_quant_param.first, 0); + input_tensor.writeData(quantized_input.data(), quantized_input.size() * sizeof(uint8_t)); + filter_tensor.writeData(quantized_filter.data(), quantized_filter.size() * sizeof(uint8_t)); + bias_tensor.writeData(bias_data.data(), bias_data.size() * sizeof(int32_t)); + + Conv2DParams params{}; + params.padding = Padding::VALID; + params.stride_height = 2; + params.stride_width = 2; + params.dilation_height_factor = 1; + params.dilation_width_factor = 1; + params.activation = Activation::NONE; + + Conv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{ + 18, 2, 5, // first batch, left + 18, 2, 5, // first batch, right + 17, 4, 3, // second batch, left + 37, 4, 3, // second batch, right + }; + std::vector ref_output_shape{2, 1, 2, 3}; + EXPECT_THAT(dequantize(extractTensorData(output_tensor), + output_quant_param.first, output_quant_param.second), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape)); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/DepthwiseConv2D.cpp b/compiler/luci-interpreter/src/kernels/DepthwiseConv2D.cpp new file mode 100644 index 0000000..b01a5e0 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/DepthwiseConv2D.cpp @@ -0,0 +1,175 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/DepthwiseConv2D.h" + +#include "kernels/Utils.h" + +#include +#include + +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +DepthwiseConv2D::DepthwiseConv2D(const Tensor *input, const Tensor *filter, const Tensor *bias, + Tensor *output, const DepthwiseConv2DParams ¶ms) + : KernelWithParams({input, filter, bias}, {output}, params) +{ +} + +void DepthwiseConv2D::configure() +{ + // TensorFlow Lite (as of v2.2.0) supports the following combinations of types: + // | input filter bias output | + // ----+---------------------------+ + // (1) | float float float float | + // (2) | float int8 float float | hybrid + // (3) | uint8 uint8 int32 uint8 | quantized + // (4) | int8 int8 int32 int8 | quantized per channel + // (5) | int16 int8 int64 int16 | quantized per channel 16x8 + // + // We only support (1) and (3) for now. + if (input()->element_type() == DataType::FLOAT32 && filter()->element_type() == DataType::FLOAT32) + { + assert(bias() == nullptr || bias()->element_type() == DataType::FLOAT32); + } + else if (input()->element_type() == DataType::U8 && filter()->element_type() == DataType::U8) + { + assert(bias() == nullptr || bias()->element_type() == DataType::S32); + } + else + { + throw std::runtime_error("Unsupported type."); + } + assert(output()->element_type() == input()->element_type()); + + const Shape &input_shape = input()->shape(); + const Shape &filter_shape = filter()->shape(); + assert(input_shape.num_dims() == 4 && filter_shape.num_dims() == 4); + + const int32_t batches = input_shape.dim(0); + const int32_t input_height = input_shape.dim(1); + const int32_t input_width = input_shape.dim(2); + // Filter format: [1, H, W, O]. + assert(filter_shape.dim(0) == 1); + const int32_t filter_height = filter_shape.dim(1); + const int32_t filter_width = filter_shape.dim(2); + const int32_t channels_out = filter_shape.dim(3); + + assert(bias() == nullptr || + (bias()->shape().num_dims() == 1 && bias()->shape().dim(0) == channels_out)); + + const int32_t output_height = + computeOutputSize(_params.padding, input_height, filter_height, _params.stride_height, + _params.dilation_height_factor); + const int32_t output_width = + computeOutputSize(_params.padding, input_width, filter_width, _params.stride_width, + _params.dilation_width_factor); + + _padding_height = computePadding(_params.stride_height, _params.dilation_height_factor, + input_height, filter_height, output_height); + _padding_width = computePadding(_params.stride_width, _params.dilation_width_factor, input_width, + filter_width, output_width); + + output()->resize({batches, output_height, output_width, channels_out}); +} + +void DepthwiseConv2D::execute() const +{ + switch (input()->element_type()) + { + case DataType::FLOAT32: + if (filter()->element_type() == DataType::FLOAT32) + { + evalFloat(); + break; + } + throw std::runtime_error("Unsupported type."); + case DataType::U8: + evalQuantized(); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +void DepthwiseConv2D::evalFloat() const +{ + float activation_min{}; + float activation_max{}; + calculateActivationRange(_params.activation, &activation_min, &activation_max); + + tflite::DepthwiseParams params{}; + params.padding_values.height = _padding_height; + params.padding_values.width = _padding_width; + params.stride_height = _params.stride_height; + params.stride_width = _params.stride_width; + params.dilation_height_factor = _params.dilation_height_factor; + params.dilation_width_factor = _params.dilation_width_factor; + params.depth_multiplier = _params.depth_multiplier; + params.float_activation_min = activation_min; + params.float_activation_max = activation_max; + + tflite::reference_ops::DepthwiseConv( + params, getTensorShape(input()), getTensorData(input()), getTensorShape(filter()), + getTensorData(filter()), getTensorShape(bias()), getTensorData(bias()), + getTensorShape(output()), getTensorData(output())); +} + +void DepthwiseConv2D::evalQuantized() const +{ + const auto input_scale = static_cast(input()->scale()); + const auto filter_scale = static_cast(filter()->scale()); + const auto output_scale = static_cast(output()->scale()); + + const double real_multiplier = input_scale * filter_scale / output_scale; + int32_t output_multiplier{}; + int output_shift{}; + quantizeMultiplier(real_multiplier, &output_multiplier, &output_shift); + + int32_t activation_min{}; + int32_t activation_max{}; + calculateActivationRangeQuantized(_params.activation, output(), &activation_min, &activation_max); + + tflite::DepthwiseParams params{}; + params.padding_values.height = _padding_height; + params.padding_values.width = _padding_width; + params.stride_height = _params.stride_height; + params.stride_width = _params.stride_width; + params.dilation_height_factor = _params.dilation_height_factor; + params.dilation_width_factor = _params.dilation_width_factor; + params.depth_multiplier = _params.depth_multiplier; + // The kernel expects input and filter zero points to be negated. + params.input_offset = -input()->zero_point(); // Note the '-'. + params.weights_offset = -filter()->zero_point(); // Note the '-'. + params.output_offset = output()->zero_point(); + params.output_multiplier = output_multiplier; + params.output_shift = output_shift; + params.quantized_activation_min = activation_min; + params.quantized_activation_max = activation_max; + + tflite::reference_ops::DepthwiseConv( + params, getTensorShape(input()), getTensorData(input()), getTensorShape(filter()), + getTensorData(filter()), getTensorShape(bias()), getTensorData(bias()), + getTensorShape(output()), getTensorData(output())); +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/DepthwiseConv2D.h b/compiler/luci-interpreter/src/kernels/DepthwiseConv2D.h new file mode 100644 index 0000000..62f4bff --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/DepthwiseConv2D.h @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_DEPTHWISECONV2D_H +#define LUCI_INTERPRETER_KERNELS_DEPTHWISECONV2D_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class DepthwiseConv2D : public KernelWithParams +{ +public: + DepthwiseConv2D(const Tensor *input, const Tensor *filter, const Tensor *bias, Tensor *output, + const DepthwiseConv2DParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + const Tensor *filter() const { return _inputs[1]; } + const Tensor *bias() const { return _inputs[2]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + void evalFloat() const; + void evalQuantized() const; + +private: + int32_t _padding_height{}; + int32_t _padding_width{}; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_DEPTHWISECONV2D_H diff --git a/compiler/luci-interpreter/src/kernels/DepthwiseConv2D.test.cpp b/compiler/luci-interpreter/src/kernels/DepthwiseConv2D.test.cpp new file mode 100644 index 0000000..a9b43d8 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/DepthwiseConv2D.test.cpp @@ -0,0 +1,135 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/DepthwiseConv2D.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +TEST(DepthwiseConv2DTest, Float) +{ + Shape input_shape{1, 4, 2, 2}; + Shape filter_shape{1, 2, 2, 4}; + Shape bias_shape{4}; + std::vector input_data{ + 1, 2, 7, 8, // + 3, 4, 9, 10, // + 5, 6, 11, 12, // + 13, 14, 15, 16, // + }; + std::vector filter_data{ + 1, 2, 3, 4, // + -9, 10, -11, 12, // + 5, 6, 7, 8, // + 13, -14, 15, -16, // + }; + std::vector bias_data{1, 2, 3, 4}; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor filter_tensor = makeInputTensor(filter_shape, filter_data); + Tensor bias_tensor = makeInputTensor(bias_shape, bias_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + DepthwiseConv2DParams params{}; + params.padding = Padding::VALID; + params.depth_multiplier = 2; + params.stride_height = 2; + params.stride_width = 1; + params.dilation_height_factor = 1; + params.dilation_width_factor = 1; + params.activation = Activation::RELU; + + DepthwiseConv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{ + 71, 0, 99, 0, // + 167, 0, 227, 28, // + }; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 2, 1, 4})); +} + +TEST(DepthwiseConv2DTest, Uint8) +{ + std::pair input_quant_param = quantizationParams(-63.5, 64); + std::pair output_quant_param = quantizationParams(-127, 128); + + Tensor input_tensor{ + DataType::U8, {1, 3, 2, 2}, {{input_quant_param.first}, {input_quant_param.second}}, ""}; + Tensor filter_tensor{ + DataType::U8, {1, 2, 2, 4}, {{input_quant_param.first}, {input_quant_param.second}}, ""}; + Tensor bias_tensor{ + DataType::S32, {4}, {{input_quant_param.first * input_quant_param.first}, {0}}, ""}; + Tensor output_tensor = + makeOutputTensor(DataType::U8, output_quant_param.first, output_quant_param.second); + + std::vector quant_input = quantize( + { + 1, 2, 7, 8, // column 1 + 3, 4, 9, 10, // column 2 + 5, 6, 11, 12, // column 3 + }, + input_quant_param.first, input_quant_param.second); + std::vector quant_filter = quantize( + { + 1, 2, 3, 4, // + -9, 10, -11, 12, // + 5, 6, 7, 8, // + 13, -14, 15, -16, // + }, + input_quant_param.first, input_quant_param.second); + std::vector quant_bias = + quantize({1, 2, 3, 4}, input_quant_param.first * input_quant_param.first, 0); + + input_tensor.writeData(quant_input.data(), quant_input.size() * sizeof(uint8_t)); + filter_tensor.writeData(quant_filter.data(), quant_filter.size() * sizeof(uint8_t)); + bias_tensor.writeData(quant_bias.data(), quant_bias.size() * sizeof(int32_t)); + + DepthwiseConv2DParams params{}; + params.padding = Padding::VALID; + params.depth_multiplier = 2; + params.stride_height = 1; + params.stride_width = 1; + params.dilation_height_factor = 1; + params.dilation_width_factor = 1; + params.activation = Activation::NONE; + + DepthwiseConv2D kernel(&input_tensor, &filter_tensor, &bias_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{ + 71, -34, 99, -20, // + 91, -26, 127, -4, // + }; + EXPECT_THAT(dequantize(extractTensorData(output_tensor), output_tensor.scale(), + output_tensor.zero_point()), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 2, 1, 4})); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Elu.cpp b/compiler/luci-interpreter/src/kernels/Elu.cpp new file mode 100644 index 0000000..5de4a1f --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Elu.cpp @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Elu.h" +#include "kernels/Utils.h" + +#include + +#include + +namespace luci_interpreter +{ + +namespace kernels +{ + +Elu::Elu(const Tensor *input, Tensor *output) : Kernel({input}, {output}) {} + +void Elu::configure() +{ + assert(input()->element_type() == output()->element_type()); + output()->resize(input()->shape()); +} + +void Elu::execute() const +{ + switch (input()->element_type()) + { + case DataType::FLOAT32: + tflite::optimized_ops::Elu(getTensorShape(input()), getTensorData(input()), + getTensorShape(output()), getTensorData(output())); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Elu.h b/compiler/luci-interpreter/src/kernels/Elu.h new file mode 100644 index 0000000..c844ab5 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Elu.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_ELU_H +#define LUCI_INTERPRETER_KERNELS_ELU_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class Elu : public Kernel +{ +public: + Elu(const Tensor *input, Tensor *output); + + const Tensor *input() const { return _inputs[0]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_ELU_H diff --git a/compiler/luci-interpreter/src/kernels/Elu.test.cpp b/compiler/luci-interpreter/src/kernels/Elu.test.cpp new file mode 100644 index 0000000..52444cb --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Elu.test.cpp @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Elu.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +void Check(std::initializer_list input_shape, std::initializer_list output_shape, + std::initializer_list input_data, std::initializer_list output_data) +{ + Tensor input_tensor{DataType::FLOAT32, input_shape, {}, ""}; + input_tensor.writeData(input_data.begin(), input_data.size() * sizeof(float)); + + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Elu kernel(&input_tensor, &output_tensor); + kernel.configure(); + kernel.execute(); + + (void)output_shape; + EXPECT_THAT(extractTensorData(output_tensor), + ::testing::ElementsAreArray(ArrayFloatNear(output_data))); +} + +TEST(EluTest, SimpleElu) +{ + Check( + /*input_shape=*/{1, 2, 4, 1}, /*output_shape=*/{1, 2, 4, 1}, + /*input_data=*/ + { + 0, -6, 2, -4, // + 3, -2, 10, -0.1, // + }, + /*output_data=*/ + { + 0.0, -0.997521, 2.0, -0.981684, // + 3.0, -0.864665, 10.0, -0.0951626, // + }); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/FullyConnected.cpp b/compiler/luci-interpreter/src/kernels/FullyConnected.cpp new file mode 100644 index 0000000..6529c5e --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/FullyConnected.cpp @@ -0,0 +1,79 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/FullyConnected.h" + +#include "kernels/Utils.h" + +#include + +#include + +namespace luci_interpreter +{ + +namespace kernels +{ + +FullyConnected::FullyConnected(const Tensor *input, const Tensor *weights, const Tensor *bias, + Tensor *output, const FullyConnectedParams ¶ms) + : KernelWithParams({input, weights, bias}, {output}, params) +{ +} + +void FullyConnected::configure() +{ + if (weights()->element_type() != DataType::FLOAT32) + throw std::runtime_error("Unsupported type."); + + assert(input()->element_type() == DataType::FLOAT32); + assert(weights()->element_type() == DataType::FLOAT32); + assert(bias() == nullptr || bias()->element_type() == DataType::FLOAT32); + + const Shape &input_shape = input()->shape(); + const Shape &weights_shape = weights()->shape(); + + assert(weights_shape.num_dims() == 2); + assert(bias() == nullptr || bias()->shape().num_elements() == weights_shape.dim(0)); + + assert(input_shape.num_elements() % weights_shape.dim(1) == 0); + const int32_t batch_size = input_shape.num_elements() / weights_shape.dim(1); + const int32_t num_units = weights_shape.dim(0); + + output()->resize({batch_size, num_units}); +} + +void FullyConnected::execute() const { evalFloat(); } + +void FullyConnected::evalFloat() const +{ + float activation_min{}; + float activation_max{}; + calculateActivationRange(_params.activation, &activation_min, &activation_max); + + tflite::FullyConnectedParams params{}; + params.float_activation_min = activation_min; + params.float_activation_max = activation_max; + params.weights_format = tflite::FullyConnectedWeightsFormat::kDefault; + + tflite::reference_ops::FullyConnected( + params, getTensorShape(input()), getTensorData(input()), getTensorShape(weights()), + getTensorData(weights()), getTensorShape(bias()), getTensorData(bias()), + getTensorShape(output()), getTensorData(output())); +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/FullyConnected.h b/compiler/luci-interpreter/src/kernels/FullyConnected.h new file mode 100644 index 0000000..2e3174c --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/FullyConnected.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_FULLYCONNECTED_H +#define LUCI_INTERPRETER_KERNELS_FULLYCONNECTED_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class FullyConnected : public KernelWithParams +{ +public: + FullyConnected(const Tensor *input, const Tensor *weights, const Tensor *bias, Tensor *output, + const FullyConnectedParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + const Tensor *weights() const { return _inputs[1]; } + const Tensor *bias() const { return _inputs[2]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + void evalFloat() const; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_FULLYCONNECTED_H diff --git a/compiler/luci-interpreter/src/kernels/FullyConnected.test.cpp b/compiler/luci-interpreter/src/kernels/FullyConnected.test.cpp new file mode 100644 index 0000000..8077fcb --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/FullyConnected.test.cpp @@ -0,0 +1,67 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/FullyConnected.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +TEST(FullyConnectedTest, Float) +{ + Shape input_shape{3, 2, 2, 1}; + std::vector input_data{ + -3, -5, 5, 4, 9, -2, // batch = 0 + -3, -2, -4, 9, -8, 1, // batch = 1 + }; + Shape weights_shape{3, 6}; + std::vector weights_data{ + -3, -7, 4, -4, -6, 4, // unit = 0 + 3, 5, 2, 3, -3, -8, // unit = 1 + -3, 7, 4, 9, 0, -5, // unit = 2 + }; + Shape bias_shape{3}; + std::vector bias_data{-1, -5, -8}; + + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor weights_tensor = makeInputTensor(weights_shape, weights_data); + Tensor bias_tensor = makeInputTensor(bias_shape, bias_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + FullyConnectedParams params{}; + params.activation = Activation::RELU; + + FullyConnected kernel(&input_tensor, &weights_tensor, &bias_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{ + 0, 0, 32, // batch = 0 + 22, 11, 47, // batch = 1 + }; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/If.cpp b/compiler/luci-interpreter/src/kernels/If.cpp new file mode 100644 index 0000000..e6bdee3 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/If.cpp @@ -0,0 +1,89 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/If.h" + +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +static std::vector joinInputs(const Tensor *cond, + const std::vector &inputs) +{ + std::vector result{cond}; + result.insert(result.cend(), inputs.cbegin(), inputs.cend()); + return result; +} + +If::If(const Tensor *cond, const std::vector &inputs, std::vector outputs, + RuntimeGraph *then_graph, RuntimeGraph *else_graph) + : Kernel(joinInputs(cond, inputs), std::move(outputs)), _then_graph(then_graph), + _else_graph(else_graph) +{ +} + +void If::configure() +{ + assert(cond()->element_type() == DataType::BOOL); + assert(cond()->shape().num_elements() == 1); + + for (RuntimeGraph *graph : {_then_graph, _else_graph}) + { + (void)graph; + assert(graph->getInputTensors().size() == getInputTensors().size() - 1); + assert(graph->getOutputTensors().size() == getOutputTensors().size()); + } +} + +void If::execute() const +{ + const bool cond_value = cond()->data()[0]; + + RuntimeGraph *active_graph = cond_value ? _then_graph : _else_graph; + const auto &graph_inputs = active_graph->getInputTensors(); + const auto &graph_outputs = active_graph->getOutputTensors(); + + // Copy kernel inputs to active graph inputs. + for (size_t i = 0; i < getInputTensors().size() - 1; ++i) + { + assert(graph_inputs[i]->element_type() == input(i)->element_type()); + graph_inputs[i]->resize(input(i)->shape()); + + const int32_t num_elements = input(i)->shape().num_elements(); + const std::size_t element_size = getDataTypeSize(input(i)->element_type()); + std::memcpy(graph_inputs[i]->data(), input(i)->data(), num_elements * element_size); + } + + active_graph->execute(); + + // Copy graph outputs to kernel outputs. + for (size_t i = 0; i < getOutputTensors().size(); ++i) + { + assert(graph_outputs[i]->element_type() == output(i)->element_type()); + output(i)->resize(graph_outputs[i]->shape()); + + const int32_t num_elements = output(i)->shape().num_elements(); + const std::size_t element_size = getDataTypeSize(output(i)->element_type()); + std::memcpy(output(i)->data(), graph_outputs[i]->data(), + num_elements * element_size); + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/If.h b/compiler/luci-interpreter/src/kernels/If.h new file mode 100644 index 0000000..fa6ab37 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/If.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_IF_H +#define LUCI_INTERPRETER_KERNELS_IF_H + +#include "core/Kernel.h" +#include "core/RuntimeGraph.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class If : public Kernel +{ +public: + If(const Tensor *cond, const std::vector &inputs, std::vector outputs, + RuntimeGraph *then_graph, RuntimeGraph *else_graph); + + const Tensor *cond() const { return _inputs[0]; } + const Tensor *input(int index) const { return _inputs[1 + index]; } + Tensor *output(int index) const { return _outputs[index]; } + + void configure() override; + void execute() const override; + +private: + RuntimeGraph *const _then_graph; + RuntimeGraph *const _else_graph; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_IF_H diff --git a/compiler/luci-interpreter/src/kernels/If.test.cpp b/compiler/luci-interpreter/src/kernels/If.test.cpp new file mode 100644 index 0000000..9b3857c --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/If.test.cpp @@ -0,0 +1,111 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "core/RuntimeModule.h" +#include "kernels/Add.h" +#include "kernels/If.h" +#include "kernels/Mul.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +RuntimeGraph *buildAddSubgraph(RuntimeModule *module) +{ + RuntimeGraph *graph = module->addGraph(); + Tensor *input1 = graph->addTensor( + std::make_unique(DataType::FLOAT32, Shape{}, AffineQuantization{}, "")); + Tensor *input2 = graph->addTensor( + std::make_unique(DataType::FLOAT32, Shape{}, AffineQuantization{}, "")); + Tensor *output = graph->addTensor( + std::make_unique(DataType::FLOAT32, Shape{}, AffineQuantization{}, "")); + + graph->setInputTensors({input1, input2}); + graph->setOutputTensors({output}); + + AddParams params{}; + params.activation = Activation::NONE; + graph->addKernel(std::make_unique(input1, input2, output, params)); + + return graph; +} + +RuntimeGraph *buildMulSubgraph(RuntimeModule *module) +{ + RuntimeGraph *graph = module->addGraph(); + Tensor *input1 = graph->addTensor( + std::make_unique(DataType::FLOAT32, Shape{}, AffineQuantization{}, "")); + Tensor *input2 = graph->addTensor( + std::make_unique(DataType::FLOAT32, Shape{}, AffineQuantization{}, "")); + Tensor *output = graph->addTensor( + std::make_unique(DataType::FLOAT32, Shape{}, AffineQuantization{}, "")); + + graph->setInputTensors({input1, input2}); + graph->setOutputTensors({output}); + + MulParams params{}; + params.activation = Activation::NONE; + graph->addKernel(std::make_unique(input1, input2, output, params)); + + return graph; +} + +TEST(IfTest, CondTrue) +{ + Tensor cond = makeInputTensor({1}, {true}); + Tensor input1 = makeInputTensor({2}, {5, 7}); + Tensor input2 = makeInputTensor({1, 2}, {1, 2}); + Tensor output = makeOutputTensor(DataType::FLOAT32); + + RuntimeModule module(nullptr); + RuntimeGraph *then_graph = buildAddSubgraph(&module); + RuntimeGraph *else_graph = buildMulSubgraph(&module); + + If kernel(&cond, {&input1, &input2}, {&output}, then_graph, else_graph); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output), ElementsAreArray(ArrayFloatNear({6, 9}))); +} + +TEST(IfTest, CondFalse) +{ + Tensor cond = makeInputTensor({1}, {false}); + Tensor input1 = makeInputTensor({2}, {5, 7}); + Tensor input2 = makeInputTensor({1, 2}, {1, 2}); + Tensor output = makeOutputTensor(DataType::FLOAT32); + + RuntimeModule module(nullptr); + RuntimeGraph *then_graph = buildAddSubgraph(&module); + RuntimeGraph *else_graph = buildMulSubgraph(&module); + + If kernel(&cond, {&input1, &input2}, {&output}, then_graph, else_graph); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output), ElementsAreArray(ArrayFloatNear({5, 14}))); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/L2Normalize.cpp b/compiler/luci-interpreter/src/kernels/L2Normalize.cpp new file mode 100644 index 0000000..cfa5350 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/L2Normalize.cpp @@ -0,0 +1,74 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/L2Normalize.h" +#include "kernels/Utils.h" + +#include + +#include + +namespace luci_interpreter +{ + +namespace kernels +{ + +L2Normalize::L2Normalize(const Tensor *input, Tensor *output, const L2NormParams ¶ms) + : KernelWithParams({input}, {output}, params) +{ +} + +void L2Normalize::configure() +{ + assert(input()->shape().num_dims() <= 4); + assert(output()->element_type() == DataType::FLOAT32 || output()->element_type() == DataType::U8); + assert(input()->element_type() == output()->element_type()); + if (output()->element_type() == DataType::U8) + { + assert(output()->scale() == (1. / 128.)); + assert(output()->zero_point() == 128); + } + assert(params().activation == Activation::NONE); + output()->resize(input()->shape()); +} + +void L2Normalize::execute() const +{ + switch (output()->element_type()) + { + case DataType::FLOAT32: + eval(0); + break; + case DataType::U8: + eval(input()->zero_point()); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +template void L2Normalize::eval(int32_t zero_point) const +{ + tflite::L2NormalizationParams op_params{}; + op_params.input_zero_point = zero_point; + tflite::optimized_ops::L2Normalization(op_params, getTensorShape(input()), + getTensorData(input()), getTensorShape(output()), + getTensorData(output())); +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/L2Normalize.h b/compiler/luci-interpreter/src/kernels/L2Normalize.h new file mode 100644 index 0000000..6c7dac6 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/L2Normalize.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_L2NORMALIZE_H +#define LUCI_INTERPRETER_KERNELS_L2NORMALIZE_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class L2Normalize : public KernelWithParams +{ +public: + L2Normalize(const Tensor *input, Tensor *output, const L2NormParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + template void eval(int32_t zero_point) const; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_L2NORMALIZE_H diff --git a/compiler/luci-interpreter/src/kernels/L2Normalize.test.cpp b/compiler/luci-interpreter/src/kernels/L2Normalize.test.cpp new file mode 100644 index 0000000..fad450d --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/L2Normalize.test.cpp @@ -0,0 +1,57 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "kernels/L2Normalize.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +TEST(L2NormalizeTest, Float) +{ + std::vector input_data = {-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}; + + Tensor input_tensor = makeInputTensor({1, 1, 1, 6}, input_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + L2NormParams params{}; + params.activation = Activation::NONE; + + L2Normalize kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{-0.55, 0.3, 0.35, 0.6, -0.35, 0.05}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); +} + +TEST(L2NormalizeTest, Uint8Quantized) +{ + // TODO + // Implement GetDequantizedOutput Function. + // Create Test for Uint8 Case +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/L2Pool2D.cpp b/compiler/luci-interpreter/src/kernels/L2Pool2D.cpp new file mode 100644 index 0000000..37a6dde --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/L2Pool2D.cpp @@ -0,0 +1,88 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/L2Pool2D.h" + +#include "kernels/Utils.h" + +#include + +#include + +namespace luci_interpreter +{ + +namespace kernels +{ + +L2Pool2D::L2Pool2D(const Tensor *input, Tensor *output, const Pool2DParams ¶ms) + : KernelWithParams({input}, {output}, params) +{ +} + +void L2Pool2D::configure() +{ + assert(input()->shape().num_dims() == 4); + assert(input()->element_type() == output()->element_type()); + + int batches = input()->shape().dim(0); + int height = input()->shape().dim(1); + int width = input()->shape().dim(2); + int channels_out = input()->shape().dim(3); + + // Matching GetWindowedOutputSize in TensorFlow. + auto padding = params().padding; + int out_width, out_height; + out_width = computeOutputSize(padding, width, params().filter_width, params().stride_width, 1); + out_height = + computeOutputSize(padding, height, params().filter_height, params().stride_height, 1); + _padding_width = + computePadding(params().stride_width, 1, width, params().filter_width, out_width); + _padding_height = + computePadding(params().stride_height, 1, height, params().filter_height, out_height); + + assert(input()->element_type() == DataType::FLOAT32); + output()->resize({batches, out_height, out_width, channels_out}); +} + +void L2Pool2D::execute() const +{ + switch (input()->element_type()) + { + case DataType::FLOAT32: + float activation_min, activation_max; + calculateActivationRange(params().activation, &activation_min, &activation_max); + tflite::PoolParams op_params; + op_params.stride_height = params().stride_height; + op_params.stride_width = params().stride_width; + op_params.filter_height = params().filter_height; + op_params.filter_width = params().filter_width; + op_params.padding_values.height = _padding_height; + op_params.padding_values.width = _padding_width; + op_params.float_activation_min = activation_min; + op_params.float_activation_max = activation_max; + tflite::optimized_ops::L2Pool(op_params, getTensorShape(input()), + getTensorData(input()), getTensorShape(output()), + getTensorData(output())); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/L2Pool2D.h b/compiler/luci-interpreter/src/kernels/L2Pool2D.h new file mode 100644 index 0000000..d40f5f4 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/L2Pool2D.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_L2POOL2D_H +#define LUCI_INTERPRETER_KERNELS_L2POOL2D_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +class L2Pool2D : public KernelWithParams +{ +public: + L2Pool2D(const Tensor *input, Tensor *output, const Pool2DParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + int32_t _padding_height = 0; + int32_t _padding_width = 0; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_L2POOL2D_H diff --git a/compiler/luci-interpreter/src/kernels/L2Pool2D.test.cpp b/compiler/luci-interpreter/src/kernels/L2Pool2D.test.cpp new file mode 100644 index 0000000..06bb938 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/L2Pool2D.test.cpp @@ -0,0 +1,228 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/L2Pool2D.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +TEST(L2Pool2DTest, FloatNone) +{ + Shape input_shape{1, 2, 4, 1}; + std::vector input_data{ + 0, 6, 2, 4, // + 3, 2, 10, 7, // + }; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Pool2DParams params{}; + params.padding = Padding::VALID; + params.activation = Activation::NONE; + params.filter_height = 2; + params.filter_width = 2; + params.stride_height = 2; + params.stride_width = 2; + + L2Pool2D kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{3.5, 6.5}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + // TODO make a Shape checking of output_tensor. +} + +TEST(L2Pool2DTest, FloatRelu) +{ + Shape input_shape{1, 2, 4, 1}; + std::vector input_data{ + -1, -6, 2, 4, // + -3, -2, 10, 7, // + }; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Pool2DParams params{}; + params.padding = Padding::VALID; + params.activation = Activation::RELU; + params.filter_height = 2; + params.filter_width = 2; + params.stride_height = 2; + params.stride_width = 2; + + L2Pool2D kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{3.53553, 6.5}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + // TODO make a Shape checking of output_tensor. +} + +TEST(L2Pool2DTest, FloatRelu1) +{ + Shape input_shape{1, 2, 4, 1}; + std::vector input_data{ + -0.1, -0.6, 2, 4, // + -0.3, -0.2, 10, 7, // + }; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Pool2DParams params{}; + params.padding = Padding::VALID; + params.activation = Activation::RELU_N1_TO_1; + params.filter_height = 2; + params.filter_width = 2; + params.stride_height = 2; + params.stride_width = 2; + + L2Pool2D kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{0.353553, 1.0}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + // TODO make a Shape checking of output_tensor. +} + +TEST(L2Pool2DTest, FloatRelu6) +{ + Shape input_shape{1, 2, 4, 1}; + std::vector input_data{ + -0.1, -0.6, 2, 4, // + -0.3, -0.2, 10, 7, // + }; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Pool2DParams params{}; + params.padding = Padding::VALID; + params.activation = Activation::RELU6; + params.filter_height = 2; + params.filter_width = 2; + params.stride_height = 2; + params.stride_width = 2; + + L2Pool2D kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{0.353553, 6.0}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + // TODO make a Shape checking of output_tensor. +} + +TEST(L2Pool2DTest, FloatPaddingSame) +{ + Shape input_shape{1, 2, 4, 1}; + std::vector input_data{ + 0, 6, 2, 4, // + 3, 2, 10, 7, // + }; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Pool2DParams params{}; + params.padding = Padding::SAME; + params.activation = Activation::NONE; + params.filter_height = 2; + params.filter_width = 2; + params.stride_height = 2; + params.stride_width = 2; + + L2Pool2D kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{3.5, 6.5}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + // TODO make a Shape checking of output_tensor. +} + +TEST(L2Pool2DTest, FloatPaddingSameSlide1) +{ + Shape input_shape{1, 2, 4, 1}; + std::vector input_data{ + 0, 6, 2, 4, // + 3, 2, 10, 7, // + }; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Pool2DParams params{}; + params.padding = Padding::SAME; + params.activation = Activation::NONE; + params.filter_height = 2; + params.filter_width = 2; + params.stride_height = 1; + params.stride_width = 1; + + L2Pool2D kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{3.5, 6.0, 6.5, 5.70088, 2.54951, 7.2111, 8.63134, 7.0}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + // TODO make a Shape checking of output_tensor. +} + +TEST(L2Pool2DTest, FloatPaddingValidSlide1) +{ + Shape input_shape{1, 2, 4, 1}; + std::vector input_data{ + 0, 6, 2, 4, // + 3, 2, 10, 7, // + }; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Pool2DParams params{}; + params.padding = Padding::VALID; + params.activation = Activation::NONE; + params.filter_height = 2; + params.filter_width = 2; + params.stride_height = 1; + params.stride_width = 1; + + L2Pool2D kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{3.5, 6.0, 6.5}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + // TODO make a Shape checking of output_tensor. +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/LeakyRelu.cpp b/compiler/luci-interpreter/src/kernels/LeakyRelu.cpp new file mode 100644 index 0000000..fce01a6 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/LeakyRelu.cpp @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/LeakyRelu.h" + +#include "kernels/Utils.h" + +#include +#include + +#include + +namespace luci_interpreter +{ + +namespace kernels +{ + +LeakyRelu::LeakyRelu(const Tensor *input, Tensor *output, const LeakyReluParams ¶ms) + : KernelWithParams({input}, {output}, params) +{ +} + +void LeakyRelu::configure() +{ + assert(input()->element_type() == output()->element_type()); + if (input()->element_type() == DataType::U8) + { + _q_alpha = static_cast(std::max( + std::numeric_limits::min(), + std::min(std::numeric_limits::max(), + std::round(input()->zero_point() + (params().alpha / input()->scale()))))); + double real_multiplier = input()->scale() * input()->scale() / output()->scale(); + quantizeMultiplierSmallerThanOneExp(real_multiplier, &_output_multiplier, &_output_shift); + } + output()->resize(input()->shape()); +} + +void LeakyRelu::execute() const +{ + switch (input()->element_type()) + { + case DataType::FLOAT32: + evalFloat(); + break; + case DataType::U8: + evalQuantized(); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +void LeakyRelu::evalFloat() const +{ + tflite::LeakyReluParams op_params{}; + op_params.alpha = params().alpha; + tflite::optimized_ops::LeakyRelu(op_params, getTensorShape(input()), + getTensorData(input()), getTensorShape(output()), + getTensorData(output())); +} + +void LeakyRelu::evalQuantized() const +{ + tflite::LeakyReluParams op_params{}; + op_params.input_offset = input()->zero_point(); + op_params.alpha_offset = input()->zero_point(); + op_params.output_offset = output()->zero_point(); + + op_params.output_multiplier = _output_multiplier; + op_params.output_shift = _output_shift; + + tflite::reference_ops::QuantizeLeakyRelu( + op_params, _q_alpha, getTensorShape(input()), getTensorData(input()), + getTensorShape(output()), getTensorData(output())); +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/LeakyRelu.h b/compiler/luci-interpreter/src/kernels/LeakyRelu.h new file mode 100644 index 0000000..dcc2be9 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/LeakyRelu.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_LEAKYRELU_H +#define LUCI_INTERPRETER_KERNELS_LEAKYRELU_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class LeakyRelu : public KernelWithParams +{ +public: + LeakyRelu(const Tensor *input, Tensor *output, const LeakyReluParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + void evalFloat() const; + void evalQuantized() const; + +private: + uint8_t _q_alpha = 0; + int32_t _output_multiplier = 0; + int _output_shift = 0; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_LEAKYRELU_H diff --git a/compiler/luci-interpreter/src/kernels/LeakyRelu.test.cpp b/compiler/luci-interpreter/src/kernels/LeakyRelu.test.cpp new file mode 100644 index 0000000..b0c06e7 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/LeakyRelu.test.cpp @@ -0,0 +1,75 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/LeakyRelu.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +template +void Check(std::initializer_list input_shape, std::initializer_list output_shape, + std::initializer_list input_data, std::initializer_list output_data, float alpha, + DataType element_type) +{ + Tensor input_tensor{element_type, input_shape, {}, ""}; + input_tensor.writeData(input_data.begin(), input_data.size() * sizeof(T)); + + Tensor output_tensor = makeOutputTensor(element_type); + + LeakyReluParams params{}; + params.alpha = alpha; + + LeakyRelu kernel(&input_tensor, &output_tensor, params); + + kernel.configure(); + kernel.execute(); + + (void)output_shape; + EXPECT_THAT(extractTensorData(output_tensor), ::testing::ElementsAreArray(output_data)); +} + +TEST(LeakReluTest, FloatSimple) +{ + Check(/*input_shape=*/{2, 3}, /*output_shape=*/{2, 3}, /*input_data=*/ + { + 0.0f, 1.0f, 3.0f, // Row 1 + 1.0f, -1.0f, -2.0f, // Row 2 + }, + /*output_data=*/ + { + 0.0f, 1.0f, 3.0f, // Row 1 + 1.0f, -0.5f, -1.0f, // Row 2 + }, + /*alpha=*/0.5f, getElementType()); +} + +TEST(LeakReluTest, Uint8Simple) +{ + // TODO + // Implement GetDequantizedOutput Function. + // Create Test for Uint8 Case +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/LocalResponseNormalization.cpp b/compiler/luci-interpreter/src/kernels/LocalResponseNormalization.cpp new file mode 100644 index 0000000..08efa1d --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/LocalResponseNormalization.cpp @@ -0,0 +1,65 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/LocalResponseNormalization.h" + +#include "kernels/Utils.h" + +#include + +#include + +namespace luci_interpreter +{ + +namespace kernels +{ + +LocalResponseNormalization::LocalResponseNormalization( + const Tensor *input, Tensor *output, const LocalResponseNormalizationParams ¶ms) + : KernelWithParams({input}, {output}, params) +{ +} + +void LocalResponseNormalization::configure() +{ + assert(input()->shape().num_dims() == 4); + assert(output()->element_type() == DataType::FLOAT32); + assert(input()->element_type() == output()->element_type()); + output()->resize(input()->shape()); +} + +void LocalResponseNormalization::execute() const +{ + switch (output()->element_type()) + { + case DataType::FLOAT32: + tflite::LocalResponseNormalizationParams op_params; + op_params.range = params().radius; + op_params.bias = params().bias; + op_params.alpha = params().alpha; + op_params.beta = params().beta; + tflite::optimized_ops::LocalResponseNormalization( + op_params, getTensorShape(input()), getTensorData(input()), + getTensorShape(output()), getTensorData(output())); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/LocalResponseNormalization.h b/compiler/luci-interpreter/src/kernels/LocalResponseNormalization.h new file mode 100644 index 0000000..60408a1 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/LocalResponseNormalization.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_LOCALRESPONSENORMALIZATION_H +#define LUCI_INTERPRETER_KERNELS_LOCALRESPONSENORMALIZATION_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class LocalResponseNormalization : public KernelWithParams +{ +public: + LocalResponseNormalization(const Tensor *input, Tensor *output, + const LocalResponseNormalizationParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_LOCALRESPONSENORMALIZATION_H diff --git a/compiler/luci-interpreter/src/kernels/LocalResponseNormalization.test.cpp b/compiler/luci-interpreter/src/kernels/LocalResponseNormalization.test.cpp new file mode 100644 index 0000000..4191bdb --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/LocalResponseNormalization.test.cpp @@ -0,0 +1,113 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/LocalResponseNormalization.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +TEST(LocalResponseNormalizationTest, SameAsL2Norm) +{ + Tensor input_tensor = + makeInputTensor({1, 1, 1, 6}, {-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + LocalResponseNormalizationParams params{}; + params.radius = 20; + params.bias = 0.0; + params.alpha = 1.0; + params.beta = 0.5; + + LocalResponseNormalization kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear({-0.55, 0.3, 0.35, 0.6, -0.35, 0.05}))); +} + +TEST(LocalResponseNormalizationTest, WithAlpha) +{ + Tensor input_tensor = + makeInputTensor({1, 1, 1, 6}, {-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + LocalResponseNormalizationParams params{}; + params.radius = 20; + params.bias = 0.0; + params.alpha = 4.0; + params.beta = 0.5; + + LocalResponseNormalization kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear({-0.275, 0.15, 0.175, 0.3, -0.175, 0.025}))); +} + +TEST(LocalResponseNormalizationTest, WithBias) +{ + Tensor input_tensor = + makeInputTensor({1, 1, 1, 6}, {-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + LocalResponseNormalizationParams params{}; + params.radius = 20; + params.bias = 9.0; + params.alpha = 4.0; + params.beta = 0.5; + + LocalResponseNormalization kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear({-0.22, 0.12, 0.14, 0.24, -0.14, 0.02}))); +} + +TEST(LocalResponseNormalizationTest, SmallRadius) +{ + Tensor input_tensor = + makeInputTensor({1, 1, 1, 6}, {-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + LocalResponseNormalizationParams params{}; + params.radius = 2; + params.bias = 9.0; + params.alpha = 4.0; + params.beta = 0.5; + + LocalResponseNormalization kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray( + ArrayFloatNear({-0.264926, 0.125109, 0.140112, 0.267261, -0.161788, 0.0244266}))); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Logistic.cpp b/compiler/luci-interpreter/src/kernels/Logistic.cpp new file mode 100644 index 0000000..c7d4561 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Logistic.cpp @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Logistic.h" + +#include "kernels/Utils.h" + +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +Logistic::Logistic(const Tensor *input, Tensor *output) : Kernel({input}, {output}) {} + +void Logistic::configure() +{ + assert(input()->element_type() == output()->element_type()); + if (input()->element_type() == DataType::U8) + { + assert(output()->scale() == 1. / 256); + populateLookupTable(); + } + output()->resize(input()->shape()); +} + +void Logistic::execute() const +{ + switch (input()->element_type()) + { + case DataType::FLOAT32: + evalFloat(); + break; + case DataType::U8: + evalQuantized(); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +void Logistic::evalFloat() const +{ + tflite::reference_ops::Logistic(getTensorShape(input()), getTensorData(input()), + getTensorShape(output()), getTensorData(output())); +} + +void Logistic::evalQuantized() const +{ + const int size = tflite::MatchingFlatSize(getTensorShape(input()), getTensorShape(output())); + uint8_t *output_data = getTensorData(output()); + const uint8_t *input_data = getTensorData(input()); + for (int i = 0; i < size; ++i) + { + output_data[i] = getTableValue(input_data[i]); + } +} + +void Logistic::populateLookupTable() +{ + const auto input_scale = static_cast(input()->scale()); + const auto input_zero_point = static_cast(input()->zero_point()); + const auto output_scale = static_cast(output()->scale()); + const auto output_zero_point = static_cast(output()->zero_point()); + const float inverse_scale = 1 / output_scale; + int32_t maxval = std::numeric_limits::max(); + int32_t minval = std::numeric_limits::min(); + for (int32_t val = minval; val <= maxval; ++val) + { + const float dequantized = input_scale * (val - input_zero_point); + const float transformed = 1.0f / (1.0f + std::exp(-dequantized)); + const float rescaled = std::round(transformed * inverse_scale); + const int32_t quantized = static_cast(rescaled + output_zero_point); + setTableValue(static_cast(std::max(std::min(maxval, quantized), minval)), + static_cast(val)); + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Logistic.h b/compiler/luci-interpreter/src/kernels/Logistic.h new file mode 100644 index 0000000..31de6ad --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Logistic.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_LOGISTIC_H +#define LUCI_INTERPRETER_KERNELS_LOGISTIC_H + +#include "core/Kernel.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class Logistic : public Kernel +{ +public: + Logistic(const Tensor *input, Tensor *output); + + const Tensor *input() const { return _inputs[0]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + void evalFloat() const; + void evalQuantized() const; + void populateLookupTable(); + void setTableValue(uint8_t value, uint8_t idx) { _table[idx] = value; }; + uint8_t getTableValue(uint8_t idx) const { return _table[idx]; }; + +private: + uint8_t _table[256]{}; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_LOGISTIC_H diff --git a/compiler/luci-interpreter/src/kernels/Logistic.test.cpp b/compiler/luci-interpreter/src/kernels/Logistic.test.cpp new file mode 100644 index 0000000..17456a4 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Logistic.test.cpp @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Logistic.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +TEST(LogisticTest, Float) +{ + Shape input_shape{1, 2, 4, 1}; + std::vector input_data{ + 0, -6, 2, 4, // + 3, -2, 10, 1, // + }; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Logistic kernel(&input_tensor, &output_tensor); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{ + 0.5, 0.002473, 0.880797, 0.982014, // + 0.952574, 0.119203, 0.999955, 0.731059, // + }; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + // TODO make a Shape checking of output_tensor. +} + +TEST(LogisticTest, Uint8) +{ + // Need to Implement GetDequantizedOutput Function. +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/MaxPool2D.cpp b/compiler/luci-interpreter/src/kernels/MaxPool2D.cpp new file mode 100644 index 0000000..afecf90 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/MaxPool2D.cpp @@ -0,0 +1,120 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/MaxPool2D.h" + +#include "kernels/Utils.h" + +#include + +#include + +namespace luci_interpreter +{ + +namespace kernels +{ + +MaxPool2D::MaxPool2D(const Tensor *input, Tensor *output, const Pool2DParams ¶ms) + : KernelWithParams({input}, {output}, params) +{ +} + +void MaxPool2D::configure() +{ + assert(input()->element_type() == output()->element_type()); + assert(input()->shape().num_dims() == 4); + const Shape &input_shape = input()->shape(); + const int32_t batches = input_shape.dim(0); + const int32_t input_height = input_shape.dim(1); + const int32_t input_width = input_shape.dim(2); + const int32_t depth = input_shape.dim(3); + + const int32_t output_height = computeOutputSize(_params.padding, input_height, + _params.filter_height, _params.stride_height); + const int32_t output_width = + computeOutputSize(_params.padding, input_width, _params.filter_width, _params.stride_width); + + _padding_height = + computePadding(_params.stride_height, 1, input_height, _params.filter_height, output_height); + _padding_width = + computePadding(_params.stride_width, 1, input_width, _params.filter_width, output_width); + + output()->resize({batches, output_height, output_width, depth}); + if (input()->element_type() == DataType::U8 || input()->element_type() == DataType::S8) + { + assert(input()->scale() == output()->scale()); + assert(input()->zero_point() == output()->zero_point()); + } +} + +void MaxPool2D::execute() const +{ + switch (input()->element_type()) + { + case DataType::FLOAT32: + evalFloat(); + break; + case DataType::U8: + evalQuantized(); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +void MaxPool2D::evalFloat() const +{ + float activation_min{}; + float activation_max{}; + calculateActivationRange(_params.activation, &activation_min, &activation_max); + + tflite::PoolParams params{}; + params.padding_values.height = _padding_height; + params.padding_values.width = _padding_width; + params.stride_height = _params.stride_height; + params.stride_width = _params.stride_width; + params.filter_height = _params.filter_height; + params.filter_width = _params.filter_width; + params.float_activation_min = activation_min; + params.float_activation_max = activation_max; + + tflite::reference_ops::MaxPool(params, getTensorShape(input()), getTensorData(input()), + getTensorShape(output()), getTensorData(output())); +} + +void MaxPool2D::evalQuantized() const +{ + int32_t activation_min{}; + int32_t activation_max{}; + calculateActivationRangeQuantized(_params.activation, output(), &activation_min, &activation_max); + + tflite::PoolParams params{}; + params.padding_values.height = _padding_height; + params.padding_values.width = _padding_width; + params.stride_height = _params.stride_height; + params.stride_width = _params.stride_width; + params.filter_height = _params.filter_height; + params.filter_width = _params.filter_width; + params.quantized_activation_min = activation_min; + params.quantized_activation_max = activation_max; + + tflite::reference_ops::MaxPool(params, getTensorShape(input()), getTensorData(input()), + getTensorShape(output()), getTensorData(output())); +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/MaxPool2D.h b/compiler/luci-interpreter/src/kernels/MaxPool2D.h new file mode 100644 index 0000000..7a59ff0 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/MaxPool2D.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_MAXPOOL2D_H +#define LUCI_INTERPRETER_KERNELS_MAXPOOL2D_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class MaxPool2D : public KernelWithParams +{ +public: + MaxPool2D(const Tensor *input, Tensor *output, const Pool2DParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + void evalFloat() const; + void evalQuantized() const; + +private: + int32_t _padding_height{}; + int32_t _padding_width{}; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_MAXPOOL2D_H diff --git a/compiler/luci-interpreter/src/kernels/MaxPool2D.test.cpp b/compiler/luci-interpreter/src/kernels/MaxPool2D.test.cpp new file mode 100644 index 0000000..390255d --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/MaxPool2D.test.cpp @@ -0,0 +1,97 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/MaxPool2D.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +TEST(MaxPool2DTest, Float) +{ + Shape input_shape{1, 3, 5, 1}; + std::vector input_data{ + 1, -1, 0, -2, 2, // + -7, -6, -5, -4, -3, // + 5, 4, 3, 6, 7, // + }; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Pool2DParams params{}; + params.padding = Padding::VALID; + params.filter_height = 2; + params.filter_width = 3; + params.stride_height = 1; + params.stride_width = 2; + params.activation = Activation::RELU6; + + MaxPool2D kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{ + 1, 2, // + 5, 6, // + }; + std::initializer_list ref_output_shape{1, 2, 2, 1}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape)); +} + +TEST(MaxPool2DTest, Uint8) +{ + std::pair quant_param = quantizationParams(-15.9375, 15.9375); + std::vector input_data{ + 0, -6, 12, 4, // + -3, -2, 10, 7, // + }; + Tensor input_tensor{DataType::U8, {1, 2, 4, 1}, {{quant_param.first}, {quant_param.second}}, ""}; + Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second); + std::vector quantize_input = + quantize(input_data, quant_param.first, quant_param.second); + input_tensor.writeData(quantize_input.data(), quantize_input.size() * sizeof(uint8_t)); + + Pool2DParams params{}; + params.padding = Padding::VALID; + params.filter_height = 2; + params.filter_width = 2; + params.stride_height = 2; + params.stride_width = 2; + params.activation = Activation::RELU6; + + MaxPool2D kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{0.0, 6.0}; + std::initializer_list ref_output_shape{1, 1, 2, 1}; + EXPECT_THAT(dequantize(extractTensorData(output_tensor), output_tensor.scale(), + output_tensor.zero_point()), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape)); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Mean.cpp b/compiler/luci-interpreter/src/kernels/Mean.cpp new file mode 100644 index 0000000..2394e2c --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Mean.cpp @@ -0,0 +1,249 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Mean.h" + +#include "kernels/Utils.h" + +#include + +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +static void resolveAxes(const int *axes_data, int num_axes, tflite::MeanParams *params) +{ + params->axis_count = num_axes; + for (int i = 0; i < num_axes; ++i) + { + params->axis[i] = static_cast(axes_data[i]); + } + for (int i = num_axes; i < 4; ++i) + { + params->axis[i] = 1; + } +} + +// Returns the number of axes that will be reduced. Removes duplicates. +static int getAxisReductionCount(const int *axes_data, int num_axes, int input_num_dims) +{ + int reduction_count = num_axes; + for (int i = 0; i < num_axes; ++i) + { + int current = axes_data[i] >= 0 ? axes_data[i] : axes_data[i] + input_num_dims; + assert(current >= 0 && current < input_num_dims); + for (int j = 0; j < i; j++) + { + int previous = axes_data[j] >= 0 ? axes_data[j] : axes_data[j] + input_num_dims; + // This checks for duplicate axis + if (current == previous) + { + --reduction_count; + break; + } + } + } + return reduction_count; +} + +static Shape getOutputShape(const Shape &input_shape, const int *axes_data, int num_axes, + bool keep_dims) +{ + int input_num_dims = input_shape.num_dims(); + if (input_num_dims == 0) + { + return Shape(0); + } + + if (keep_dims) + { + Shape output_shape(input_num_dims); + for (int idx = 0; idx < input_num_dims; ++idx) + { + bool is_axis = false; + for (int axis_idx = 0; axis_idx < num_axes; ++axis_idx) + { + if (axes_data[axis_idx] == idx || axes_data[axis_idx] + input_num_dims == idx) + { + is_axis = true; + break; + } + } + if (is_axis) + { + output_shape.dim(idx) = 1; + } + else + { + output_shape.dim(idx) = input_shape.dim(idx); + } + } + return output_shape; + } + else + { + int num_reduce_axes = getAxisReductionCount(axes_data, num_axes, input_num_dims); + Shape output_shape(input_num_dims - num_reduce_axes); + int num_skip_axes = 0; + for (int idx = 0; idx < input_num_dims; ++idx) + { + bool is_axis = false; + for (int axis_idx = 0; axis_idx < num_axes; ++axis_idx) + { + if (axes_data[axis_idx] == idx || axes_data[axis_idx] + input_num_dims == idx) + { + ++num_skip_axes; + is_axis = true; + break; + } + } + if (!is_axis) + { + output_shape.dim(idx - num_skip_axes) = input_shape.dim(idx); + } + } + return output_shape; + } +} + +Mean::Mean(const Tensor *input, const Tensor *axes, Tensor *output, const ReducerParams ¶ms) + : KernelWithParams({input, axes}, {output}, params) +{ +} + +void Mean::configure() +{ + assert(input()->element_type() == output()->element_type()); + assert(axes()->element_type() == DataType::S32); + const Shape &input_shape = input()->shape(); + int input_num_dims = input_shape.num_dims(); + + const auto *axes_data = getTensorData(axes()); + int num_axes = axes()->shape().num_elements(); + assert(num_axes <= 4); + + Shape output_shape = getOutputShape(input_shape, axes_data, num_axes, _params.keep_dims); + output()->resize(output_shape); + + tflite::MeanParams params{}; + resolveAxes(axes_data, num_axes, ¶ms); + const bool need_temporaries = + !(_params.keep_dims && input_num_dims == 4 && params.axis_count == 2 && + ((params.axis[0] == 1 && params.axis[1] == 2) || + (params.axis[0] == 2 && params.axis[1] == 1))); + if (need_temporaries) + { + _temp_index = + std::make_unique(DataType::S32, Shape(input_num_dims), AffineQuantization{}, ""); + _resolved_axes = + std::make_unique(DataType::S32, Shape(num_axes), AffineQuantization{}, ""); + _temp_sum = std::make_unique(input()->element_type(), output()->shape(), + AffineQuantization{}, ""); + } +} + +void Mean::execute() const +{ + switch (input()->element_type()) + { + case DataType::FLOAT32: + evalFloat(); + break; + case DataType::U8: + evalQuantized(); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +void Mean::evalFloat() const +{ + const Shape &input_shape = input()->shape(); + int input_num_dims = input_shape.num_dims(); + const auto *axes_data = getTensorData(axes()); + int num_axes = axes()->shape().num_elements(); + + tflite::MeanParams params{}; + resolveAxes(axes_data, num_axes, ¶ms); + + // Defer to specialized implementation for 4D Mean across axes 1 & 2. + if (_params.keep_dims && input_num_dims == 4 && params.axis_count == 2 && + ((params.axis[0] == 1 && params.axis[1] == 2) || + (params.axis[0] == 2 && params.axis[1] == 1))) + { + tflite::reference_ops::Mean(params, getTensorShape(input()), getTensorData(input()), + getTensorShape(output()), getTensorData(output())); + } + else + { + tflite::reference_ops::Mean( + getTensorData(input()), getTensorShape(input()).DimsData(), + input()->shape().num_dims(), getTensorData(output()), + getTensorShape(output()).DimsData(), output()->shape().num_dims(), axes_data, num_axes, + _params.keep_dims, getTensorData(_temp_index.get()), + getTensorData(_resolved_axes.get()), getTensorData(_temp_sum.get())); + } +} + +void Mean::evalQuantized() const +{ + const Shape &input_shape = input()->shape(); + int input_num_dims = input_shape.num_dims(); + const auto *axes_data = getTensorData(axes()); + int num_axes = axes()->shape().num_elements(); + + tflite::MeanParams params{}; + resolveAxes(axes_data, num_axes, ¶ms); + + // Defer to specialized implementation for 4D Mean across axes 1 & 2. + if (_params.keep_dims && input_num_dims == 4 && params.axis_count == 2 && + ((params.axis[0] == 1 && params.axis[1] == 2) || + (params.axis[0] == 2 && params.axis[1] == 1))) + { + tflite::reference_ops::Mean(params, getTensorShape(input()), getTensorData(input()), + input()->zero_point(), input()->scale(), getTensorShape(output()), + getTensorData(output()), output()->zero_point(), + output()->scale()); + } + else if (input()->zero_point() == output()->zero_point() && input()->scale() == output()->scale()) + { + tflite::reference_ops::Mean( + getTensorData(input()), getTensorShape(input()).DimsData(), + input()->shape().num_dims(), getTensorData(output()), + getTensorShape(output()).DimsData(), output()->shape().num_dims(), axes_data, num_axes, + _params.keep_dims, getTensorData(_temp_index.get()), + getTensorData(_resolved_axes.get()), getTensorData(_temp_sum.get())); + } + else + { + tflite::reference_ops::QuantizedMeanOrSum<>( + getTensorData(input()), input()->zero_point(), input()->scale(), + getTensorShape(input()).DimsData(), input()->shape().num_dims(), + getTensorData(output()), output()->zero_point(), output()->scale(), + getTensorShape(output()).DimsData(), output()->shape().num_dims(), axes_data, num_axes, + _params.keep_dims, getTensorData(_temp_index.get()), + getTensorData(_resolved_axes.get()), getTensorData(_temp_sum.get()), + /*compute_sum=*/false); + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Mean.h b/compiler/luci-interpreter/src/kernels/Mean.h new file mode 100644 index 0000000..9cc793c --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Mean.h @@ -0,0 +1,55 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_MEAN_H +#define LUCI_INTERPRETER_KERNELS_MEAN_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +class Mean : public KernelWithParams +{ +public: + Mean(const Tensor *input, const Tensor *axes, Tensor *output, const ReducerParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + const Tensor *axes() const { return _inputs[1]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + void evalFloat() const; + void evalQuantized() const; + +private: + std::unique_ptr _temp_index; + std::unique_ptr _resolved_axes; + std::unique_ptr _temp_sum; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_MEAN_H diff --git a/compiler/luci-interpreter/src/kernels/Mean.test.cpp b/compiler/luci-interpreter/src/kernels/Mean.test.cpp new file mode 100644 index 0000000..f4e411c --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Mean.test.cpp @@ -0,0 +1,165 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Mean.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +TEST(MeanTest, FloatKeepDims) +{ + std::vector input_data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + + std::vector axis_data{0, 2}; + Tensor input_tensor = makeInputTensor({4, 3, 2}, input_data); + Tensor axis_tensor = makeInputTensor({2}, axis_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + ReducerParams params{}; + params.keep_dims = true; + + Mean kernel(&input_tensor, &axis_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{10.5, 12.5, 14.5}; + std::initializer_list ref_output_shape{1, 3, 1}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape)); +} + +TEST(MeanTest, FloatKeepDims4DMean) +{ + std::vector input_data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + + std::vector axis_data{1, 2}; + Tensor input_tensor = makeInputTensor({2, 2, 3, 2}, input_data); + Tensor axis_tensor = makeInputTensor({2}, axis_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + ReducerParams params{}; + params.keep_dims = true; + + Mean kernel(&input_tensor, &axis_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{6, 7, 18, 19}; + std::initializer_list ref_output_shape{2, 1, 1, 2}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape)); +} + +TEST(MeanTest, FloatNotKeepDims) +{ + std::vector input_data = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, + 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, + 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}; + + std::vector axis_data{1, 0, -3, -3}; + Tensor input_tensor = makeInputTensor({4, 3, 2}, input_data); + Tensor axis_tensor = makeInputTensor({4}, axis_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + ReducerParams params{}; + params.keep_dims = false; + + Mean kernel(&input_tensor, &axis_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{12, 13}; + std::initializer_list ref_output_shape{2}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape)); +} + +TEST(MeanTest, Uint8KeepDims) +{ + float kQuantizedTolerance = getTolerance(-1.0, 1.0, 255); + std::vector input_data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; + std::pair quant_param = quantizationParams(-1.0f, 1.0f); + + std::vector axis_data{1}; + Tensor input_tensor{DataType::U8, {3, 2}, {{quant_param.first}, {quant_param.second}}, ""}; + Tensor axis_tensor = makeInputTensor({1}, axis_data); + Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second); + std::vector quantize_input = + quantize(input_data, quant_param.first, quant_param.second); + input_tensor.writeData(quantize_input.data(), quantize_input.size() * sizeof(uint8_t)); + + ReducerParams params{}; + params.keep_dims = true; + + Mean kernel(&input_tensor, &axis_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{0.3, 0.35, 0.55}; + std::initializer_list ref_output_shape{3, 1}; + EXPECT_THAT(dequantize(extractTensorData(output_tensor), output_tensor.scale(), + output_tensor.zero_point()), + ElementsAreArray(ArrayFloatNear(ref_output_data, kQuantizedTolerance))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape)); +} + +TEST(MeanTest, Uint8NotKeepDims) +{ + float kQuantizedTolerance = getTolerance(-1.0, 1.0, 255); + std::vector input_data = {0.4, 0.2, 0.3, 0.4, 0.5, 0.6}; + std::pair quant_param = quantizationParams(-1.0f, 1.0f); + + std::vector axis_data{1}; + Tensor input_tensor{DataType::U8, {1, 3, 2}, {{quant_param.first}, {quant_param.second}}, ""}; + Tensor axis_tensor = makeInputTensor({1}, axis_data); + Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second); + std::vector quantize_input = + quantize(input_data, quant_param.first, quant_param.second); + input_tensor.writeData(quantize_input.data(), quantize_input.size() * sizeof(uint8_t)); + + ReducerParams params{}; + params.keep_dims = false; + + Mean kernel(&input_tensor, &axis_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{0.4, 0.4}; + std::initializer_list ref_output_shape{1, 2}; + EXPECT_THAT(dequantize(extractTensorData(output_tensor), output_tensor.scale(), + output_tensor.zero_point()), + ElementsAreArray(ArrayFloatNear(ref_output_data, kQuantizedTolerance))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape)); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Mul.cpp b/compiler/luci-interpreter/src/kernels/Mul.cpp new file mode 100644 index 0000000..a6e721a --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Mul.cpp @@ -0,0 +1,82 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Mul.h" + +#include "kernels/Utils.h" + +#include + +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +Mul::Mul(const Tensor *input1, const Tensor *input2, Tensor *output, const MulParams ¶ms) + : KernelWithParams({input1, input2}, {output}, params) +{ +} + +void Mul::configure() +{ + assert(input1()->element_type() == input2()->element_type()); + output()->resize(calculateShapeForBroadcast(input1()->shape(), input2()->shape())); +} + +void Mul::execute() const +{ + switch (input1()->element_type()) + { + case DataType::FLOAT32: + evalFloat(); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +void Mul::evalFloat() const +{ + float activation_min{}; + float activation_max{}; + calculateActivationRange(_params.activation, &activation_min, &activation_max); + + tflite::ArithmeticParams params{}; + params.float_activation_min = activation_min; + params.float_activation_max = activation_max; + + const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes( + getTensorShape(input1()), getTensorShape(input2()), ¶ms); + + if (need_broadcast) + { + tflite::reference_ops::BroadcastMul4DSlow( + params, getTensorShape(input1()), getTensorData(input1()), getTensorShape(input2()), + getTensorData(input2()), getTensorShape(output()), getTensorData(output())); + } + else + { + tflite::reference_ops::Mul(params, getTensorShape(input1()), getTensorData(input1()), + getTensorShape(input2()), getTensorData(input2()), + getTensorShape(output()), getTensorData(output())); + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Mul.h b/compiler/luci-interpreter/src/kernels/Mul.h new file mode 100644 index 0000000..e46160b --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Mul.h @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_MUL_H +#define LUCI_INTERPRETER_KERNELS_MUL_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +#include +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +class Mul : public KernelWithParams +{ +public: + Mul(const Tensor *input1, const Tensor *input2, Tensor *output, const MulParams ¶ms); + + const Tensor *input1() const { return _inputs[0]; } + const Tensor *input2() const { return _inputs[1]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + void evalFloat() const; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_MUL_H diff --git a/compiler/luci-interpreter/src/kernels/Mul.test.cpp b/compiler/luci-interpreter/src/kernels/Mul.test.cpp new file mode 100644 index 0000000..f2255ac --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Mul.test.cpp @@ -0,0 +1,85 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Mul.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +TEST(MulTest, Float) +{ + Shape base_shape = {2, 3, 1, 2}; + std::vector test_shapes{{1, 1, 3, 2}, {1, 3, 1, 2}, {2, 1, 3, 1}, {2, 3, 1, 1}}; + std::vector> test_outputs = { + {0.00f, 0.69f, 0.12f, 1.15f, 0.00f, 2.07f, 0.18f, 0.15f, 0.00f, 0.25f, 0.90f, 0.45f, + 0.16f, 0.00f, 0.00f, 0.00f, 0.80f, 0.00f, 0.24f, 0.84f, 0.00f, 1.40f, 1.20f, 2.52f, + 0.00f, 0.00f, 0.64f, 0.00f, 0.00f, 0.00f, 0.14f, 0.00f, 0.00f, 0.00f, 0.70f, 0.00f}, + {0.00f, 0.69f, 0.00f, 0.25f, 0.80f, 0.00f, 0.24f, 0.84f, 0.64f, 0.00f, 0.70f, 0.00f}, + {0.00f, 0.46f, 0.00f, 0.69f, 0.12f, 0.00f, 0.18f, 0.10f, 0.27f, 0.15f, 0.00f, 0.00f, + 0.16f, 0.00f, 0.24f, 0.00f, 0.00f, 0.44f, 0.60f, 1.40f, 1.20f, 2.80f, 1.08f, 2.52f, + 0.00f, 0.00f, 0.00f, 0.00f, 0.00f, 0.00f, 0.35f, 0.00f, 0.70f, 0.00f, 0.63f, 0.00f}, + {0.00f, 0.46f, 0.27f, 0.15f, 0.00f, 0.44f, 0.60f, 1.40f, 0.00f, 0.00f, 0.63f, 0.00f}}; + std::vector input1_data{-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, + 1.2f, 2.8f, -1.6f, 0.0f, 0.7f, -2.2f}; + std::vector input2_data{0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f}; + for (size_t i = 0; i < test_shapes.size(); ++i) + { + Tensor input1_tensor = makeInputTensor(base_shape, input1_data); + Tensor input2_tensor = makeInputTensor(test_shapes[i], input2_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + MulParams params{}; + params.activation = Activation::RELU; + + Mul kernel(&input1_tensor, &input2_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), + ::testing::ElementsAreArray(ArrayFloatNear(test_outputs[i], 0.0001f))) + << "With shape number " << i; + } + // Re-run with exchanged inputs. + for (size_t i = 0; i < test_shapes.size(); ++i) + { + Tensor input1_tensor = makeInputTensor(test_shapes[i], input2_data); + Tensor input2_tensor = makeInputTensor(base_shape, input1_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + MulParams params{}; + params.activation = Activation::RELU; + + Mul kernel(&input1_tensor, &input2_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), + ::testing::ElementsAreArray(ArrayFloatNear(test_outputs[i], 0.0001f))) + << "With shape number " << i; + } +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Pad.cpp b/compiler/luci-interpreter/src/kernels/Pad.cpp new file mode 100644 index 0000000..bdf3a2a --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Pad.cpp @@ -0,0 +1,102 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Pad.h" + +#include "kernels/Utils.h" + +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +Pad::Pad(const Tensor *input, const Tensor *paddings, Tensor *output) + : Kernel({input, paddings}, {output}) +{ +} + +void Pad::configure() +{ + const Shape &input_shape = input()->shape(); + const int num_dims = input_shape.num_dims(); + + if (num_dims > 4) + throw std::runtime_error("Unsupported number of dimensions."); + + assert(output()->element_type() == input()->element_type()); + assert(paddings()->element_type() == DataType::S32); + // Paddings shape should be [N, 2]. + assert(paddings()->shape().num_dims() == 2); + assert(paddings()->shape().dim(0) == num_dims); + assert(paddings()->shape().dim(1) == 2); + + Shape output_shape(num_dims); + const auto *paddings_data = getTensorData(paddings()); + for (int i = 0; i < num_dims; ++i) + { + const int32_t padding_before = paddings_data[i * 2]; + const int32_t padding_after = paddings_data[i * 2 + 1]; + assert(padding_before >= 0 && padding_after >= 0); + output_shape.dim(i) = input_shape.dim(i) + padding_before + padding_after; + } + + output()->resize(output_shape); +} + +void Pad::execute() const +{ + const int num_dims = input()->shape().num_dims(); + + tflite::PadParams params{}; + params.left_padding_count = num_dims; + params.right_padding_count = num_dims; + + const auto *paddings_data = getTensorData(paddings()); + for (int i = num_dims - 1; i >= 0; --i) + { + params.left_padding[i] = paddings_data[i * 2]; + params.right_padding[i] = paddings_data[i * 2 + 1]; + } + + switch (input()->element_type()) + { + case DataType::FLOAT32: + { + const float pad_value = 0.0f; + tflite::reference_ops::Pad(params, getTensorShape(input()), getTensorData(input()), + &pad_value, getTensorShape(output()), + getTensorData(output())); + break; + } + case DataType::U8: + { + assert(output()->zero_point() >= std::numeric_limits::min()); + assert(output()->zero_point() <= std::numeric_limits::max()); + const auto pad_value = static_cast(output()->zero_point()); + tflite::reference_ops::Pad(params, getTensorShape(input()), getTensorData(input()), + &pad_value, getTensorShape(output()), + getTensorData(output())); + break; + } + default: + throw std::runtime_error("Unsupported type."); + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Pad.h b/compiler/luci-interpreter/src/kernels/Pad.h new file mode 100644 index 0000000..e05b47f --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Pad.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_PAD_H +#define LUCI_INTERPRETER_KERNELS_PAD_H + +#include "core/Kernel.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class Pad : public Kernel +{ +public: + Pad(const Tensor *input, const Tensor *paddings, Tensor *output); + + const Tensor *input() const { return _inputs[0]; } + const Tensor *paddings() const { return _inputs[1]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_PAD_H diff --git a/compiler/luci-interpreter/src/kernels/Pad.test.cpp b/compiler/luci-interpreter/src/kernels/Pad.test.cpp new file mode 100644 index 0000000..15fcd0d --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Pad.test.cpp @@ -0,0 +1,79 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Pad.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +float GetTolerance(float min, float max) { return (max - min) / 255.0; } + +TEST(Pad, Uint8) +{ + float kQuantizedTolerance = GetTolerance(-1.0, 1.0); + std::pair quant_param = quantizationParams(-1.0f, 1.0f); + std::vector input_data{-0.8, 0.2, 0.9, 0.7, 0.1, -0.3}; + std::vector paddings_data{0, 0, 0, 2, 1, 3, 0, 0}; + Tensor input_tensor{DataType::U8, {1, 2, 3, 1}, {{quant_param.first}, {quant_param.second}}, ""}; + Tensor paddings_tensor = makeInputTensor({4, 2}, paddings_data); + Tensor output_tensor = makeOutputTensor(DataType::U8, quant_param.first, quant_param.second); + std::vector quantize_input = + quantize(input_data, quant_param.first, quant_param.second); + input_tensor.writeData(quantize_input.data(), quantize_input.size() * sizeof(uint8_t)); + + Pad kernel(&input_tensor, &paddings_tensor, &output_tensor); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{0, -0.8, 0.2, 0.9, 0, 0, 0, 0, 0.7, 0.1, -0.3, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; + EXPECT_THAT(dequantize(extractTensorData(output_tensor), output_tensor.scale(), + output_tensor.zero_point()), + ElementsAreArray(ArrayFloatNear(ref_output_data, kQuantizedTolerance))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray({1, 4, 7, 1})); +} + +TEST(Pad, Float) +{ + std::vector input_data{1, 2, 3, 4, 5, 6}; + std::vector paddings_data{1, 0, 0, 2, 0, 3, 0, 0}; + Tensor input_tensor = makeInputTensor({1, 2, 3, 1}, input_data); + Tensor paddings_tensor = makeInputTensor({4, 2}, paddings_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Pad kernel(&input_tensor, &paddings_tensor, &output_tensor); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0, 0, 0, 4, 5, + 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; + std::initializer_list ref_output_shape{2, 4, 6, 1}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(ref_output_shape)); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Reshape.cpp b/compiler/luci-interpreter/src/kernels/Reshape.cpp new file mode 100644 index 0000000..d88b539 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Reshape.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Reshape.h" + +#include +#include + +namespace luci_interpreter +{ + +namespace kernels +{ + +static Shape extractShapeFromTensor(const Tensor *tensor) +{ + assert(tensor->element_type() == DataType::S32); + Shape shape(tensor->shape().num_elements()); + const auto *shape_data = tensor->data(); + for (int i = 0; i < tensor->shape().num_elements(); ++i) + { + shape.dim(i) = shape_data[i]; + } + return shape; +} + +static void resolveUnknownDimension(const Shape &input_shape, Shape *output_shape) +{ + const int32_t num_input_elements = input_shape.num_elements(); + int32_t num_output_elements = 1; + int unknown_dim_index = -1; + for (int i = 0; i < output_shape->num_dims(); ++i) + { + const int32_t value = output_shape->dim(i); + if (value == -1) + { + assert(unknown_dim_index == -1); + unknown_dim_index = i; + } + else + { + num_output_elements *= value; + } + } + if (unknown_dim_index != -1) + { + output_shape->dim(unknown_dim_index) = num_input_elements / num_output_elements; + num_output_elements *= output_shape->dim(unknown_dim_index); + } + assert(num_output_elements == num_input_elements); +} + +Reshape::Reshape(const Tensor *input, const Tensor *shape, Tensor *output) + : Kernel({input, shape}, {output}) +{ +} + +void Reshape::configure() +{ + Shape output_shape = extractShapeFromTensor(shape()); + resolveUnknownDimension(input()->shape(), &output_shape); + output()->resize(output_shape); +} + +void Reshape::execute() const +{ + const auto *input_data = input()->data(); + auto *output_data = output()->data(); + + const size_t element_size = getDataTypeSize(input()->element_type()); + const int32_t num_elements = input()->shape().num_elements(); + std::memcpy(output_data, input_data, num_elements * element_size); +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Reshape.h b/compiler/luci-interpreter/src/kernels/Reshape.h new file mode 100644 index 0000000..99b947f --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Reshape.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_RESHAPE_H +#define LUCI_INTERPRETER_KERNELS_RESHAPE_H + +#include "core/Kernel.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class Reshape : public Kernel +{ +public: + Reshape(const Tensor *input, const Tensor *shape, Tensor *output); + + const Tensor *input() const { return _inputs[0]; } + const Tensor *shape() const { return _inputs[1]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_RESHAPE_H diff --git a/compiler/luci-interpreter/src/kernels/Reshape.test.cpp b/compiler/luci-interpreter/src/kernels/Reshape.test.cpp new file mode 100644 index 0000000..7255b81 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Reshape.test.cpp @@ -0,0 +1,69 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Reshape.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +// TODO Test types other than FLOAT32. + +TEST(ReshapeTest, Regular) +{ + Shape input_shape{1, 2, 2, 3}; + std::vector input_data{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}; + Shape shape_shape{2}; + std::vector shape_data{3, 4}; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor shape_tensor = makeInputTensor(shape_shape, shape_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Reshape kernel(&input_tensor, &shape_tensor, &output_tensor); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(input_data))); +} + +TEST(ReshapeTest, UnknownDimension) +{ + Shape input_shape{2, 1, 2, 3}; + std::vector input_data{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}; + Shape shape_shape{3}; + std::vector shape_data{2, -1, 2}; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor shape_tensor = makeInputTensor(shape_shape, shape_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + Reshape kernel(&input_tensor, &shape_tensor, &output_tensor); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(input_data))); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Softmax.cpp b/compiler/luci-interpreter/src/kernels/Softmax.cpp new file mode 100644 index 0000000..2fb7f3f --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Softmax.cpp @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Softmax.h" + +#include "kernels/Utils.h" + +#include + +#include + +namespace luci_interpreter +{ + +namespace kernels +{ + +Softmax::Softmax(const Tensor *input, Tensor *output, const SoftmaxParams ¶ms) + : KernelWithParams({input}, {output}, params) +{ +} + +void Softmax::configure() +{ + assert(input()->element_type() == output()->element_type()); + output()->resize(input()->shape()); +} + +void Softmax::execute() const +{ + switch (input()->element_type()) + { + case DataType::FLOAT32: + evalFloat(); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +void Softmax::evalFloat() const +{ + tflite::SoftmaxParams params{}; + params.beta = _params.beta; + + tflite::reference_ops::Softmax(params, getTensorShape(input()), getTensorData(input()), + getTensorShape(output()), getTensorData(output())); +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Softmax.h b/compiler/luci-interpreter/src/kernels/Softmax.h new file mode 100644 index 0000000..2e4eda4 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Softmax.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_SOFTMAX_H +#define LUCI_INTERPRETER_KERNELS_SOFTMAX_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class Softmax : public KernelWithParams +{ +public: + Softmax(const Tensor *input, Tensor *output, const SoftmaxParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + void evalFloat() const; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_SOFTMAX_H diff --git a/compiler/luci-interpreter/src/kernels/Softmax.test.cpp b/compiler/luci-interpreter/src/kernels/Softmax.test.cpp new file mode 100644 index 0000000..2193c3e --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Softmax.test.cpp @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Softmax.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +TEST(SoftmaxTest, Float) +{ + Shape input_shape{2, 1, 2, 3}; + std::vector input_data{ + 5, -9, 8, // + -7, 2, -4, // + 1, -2, 9, // + 3, -6, -1, // + }; + Tensor input_tensor = makeInputTensor(input_shape, input_data); + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + SoftmaxParams params{}; + params.beta = 0.1; + + Softmax kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + std::vector ref_output_data{ + 0.38514, 0.09497, 0.51989, // + 0.20792, 0.51141, 0.28067, // + 0.25212, 0.18678, 0.56110, // + 0.48149, 0.19576, 0.32275, // + }; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(ref_output_data))); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/SpaceToDepth.cpp b/compiler/luci-interpreter/src/kernels/SpaceToDepth.cpp new file mode 100644 index 0000000..6a5bd7c --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/SpaceToDepth.cpp @@ -0,0 +1,79 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SpaceToDepth.h" +#include "Utils.h" +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +SpaceToDepth::SpaceToDepth(const Tensor *input, Tensor *output, const SpaceToDepthParams ¶ms) + : KernelWithParams({input}, {output}, params) +{ +} + +void SpaceToDepth::configure() +{ + assert(input()->shape().num_dims() == 4); + assert(output()->element_type() == DataType::FLOAT32 || + output()->element_type() == DataType::U8 || output()->element_type() == DataType::S8 || + output()->element_type() == DataType::S32 || output()->element_type() == DataType::S64); + assert(input()->element_type() == output()->element_type()); + + const int block_size = params().block_size; + const int32_t input_height = input()->shape().dim(1); + const int32_t input_width = input()->shape().dim(2); + int32_t output_height = input_height / block_size; + int32_t output_width = input_width / block_size; + + assert(input_height == output_height * block_size); + assert(input_width == output_width * block_size); + + Shape output_shape(4); + output_shape.dim(0) = input()->shape().dim(0); + output_shape.dim(1) = output_height; + output_shape.dim(2) = output_width; + output_shape.dim(3) = input()->shape().dim(3) * block_size * block_size; + + output()->resize(output_shape); +} + +void SpaceToDepth::execute() const +{ + tflite::SpaceToDepthParams op_params{}; + op_params.block_size = params().block_size; + switch (input()->element_type()) + { + case DataType::FLOAT32: + tflite::optimized_ops::SpaceToDepth(op_params, getTensorShape(input()), + getTensorData(input()), getTensorShape(output()), + getTensorData(output())); + break; + case DataType::U8: + tflite::optimized_ops::SpaceToDepth(op_params, getTensorShape(input()), + getTensorData(input()), getTensorShape(output()), + getTensorData(output())); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/SpaceToDepth.h b/compiler/luci-interpreter/src/kernels/SpaceToDepth.h new file mode 100644 index 0000000..e66316b --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/SpaceToDepth.h @@ -0,0 +1,45 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_SPACETODEPTH_H +#define LUCI_INTERPRETER_KERNELS_SPACETODEPTH_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +class SpaceToDepth : public KernelWithParams +{ +public: + SpaceToDepth(const Tensor *input, Tensor *output, const SpaceToDepthParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_SPACETODEPTH_H diff --git a/compiler/luci-interpreter/src/kernels/SpaceToDepth.test.cpp b/compiler/luci-interpreter/src/kernels/SpaceToDepth.test.cpp new file mode 100644 index 0000000..e4a0fd6 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/SpaceToDepth.test.cpp @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/SpaceToDepth.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +template class SpaceToDepthTest : public ::testing::Test +{ +}; + +using DataTypes = ::testing::Types; +TYPED_TEST_CASE(SpaceToDepthTest, DataTypes); + +TYPED_TEST(SpaceToDepthTest, SimpleCase) +{ + std::vector input_data{1, 5, 6, 7, 2, 3, 4, 8}; + Shape input_shape{1, 2, 2, 2}; + Tensor input_tensor{getElementType(), input_shape, {{}, {}}, ""}; + input_tensor.writeData(input_data.data(), input_data.size() * sizeof(TypeParam)); + std::vector output_data{1, 5, 6, 7, 2, 3, 4, 8}; + std::vector output_shape{1, 1, 1, 8}; + Tensor output_tensor = makeOutputTensor(getElementType()); + + SpaceToDepthParams params{}; + params.block_size = 2; + + SpaceToDepth kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), + ::testing::ElementsAreArray(output_data)); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shape)); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Split.cpp b/compiler/luci-interpreter/src/kernels/Split.cpp new file mode 100644 index 0000000..325b1c2 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Split.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Split.h" + +#include "Utils.h" + +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +Split::Split(const Tensor *axis, const Tensor *input, std::vector outputs) + : Kernel({axis, input}, std::move(outputs)) +{ +} + +void Split::configure() +{ + assert(axis()->shape().num_elements() == 1); + _axis_value = getTensorData(axis())[0]; + if (_axis_value < 0) + _axis_value += input()->shape().num_dims(); + assert(_axis_value >= 0 && _axis_value < input()->shape().num_dims()); + + const int32_t input_size = input()->shape().dim(_axis_value); + assert(input_size % _outputs.size() == 0); + const int32_t slice_size = input_size / _outputs.size(); + + Shape output_shape = input()->shape(); + output_shape.dim(_axis_value) = slice_size; + for (Tensor *output : _outputs) + { + output->resize(output_shape); + } +} + +void Split::execute() const +{ + tflite::SplitParams params{}; + params.num_split = _outputs.size(); + params.axis = _axis_value; + +#define TF_LITE_SPLIT(scalar) \ + { \ + VectorOfTensors all_outputs(_outputs); \ + tflite::optimized_ops::Split(params, getTensorShape(input()), getTensorData(input()), \ + all_outputs.shapes(), all_outputs.data()); \ + } + + switch (input()->element_type()) + { + case DataType::FLOAT32: + TF_LITE_SPLIT(float); + break; + case DataType::U8: + TF_LITE_SPLIT(uint8_t); + break; + default: + throw std::runtime_error("Unsupported type."); + } +#undef TF_LITE_SPLIT +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Split.h b/compiler/luci-interpreter/src/kernels/Split.h new file mode 100644 index 0000000..9542b1e --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Split.h @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_SPLIT_H +#define LUCI_INTERPRETER_KERNELS_SPLIT_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class Split : public Kernel +{ +public: + Split(const Tensor *axis, const Tensor *input, std::vector outputs); + + const Tensor *axis() const { return _inputs[0]; } + const Tensor *input() const { return _inputs[1]; } + Tensor *output(int index) const { return _outputs[index]; } + + void configure() override; + void execute() const override; + +private: + int32_t _axis_value{}; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_SPLIT_H diff --git a/compiler/luci-interpreter/src/kernels/Split.test.cpp b/compiler/luci-interpreter/src/kernels/Split.test.cpp new file mode 100644 index 0000000..11d0b1e --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Split.test.cpp @@ -0,0 +1,126 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2018 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Split.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +template +void Check(int axis, int num_splits, std::initializer_list input_shape, + std::initializer_list output_shape, std::initializer_list input_data, + std::vector> output_data, DataType element_type) +{ + Tensor axis_tensor = makeInputTensor({}, {axis}); + Tensor input_tensor{element_type, input_shape, {}, ""}; + input_tensor.writeData(input_data.begin(), input_data.size() * sizeof(T)); + + std::vector output_tensors; + output_tensors.reserve(num_splits); + for (int i = 0; i < num_splits; ++i) + { + output_tensors.emplace_back(makeOutputTensor(element_type)); + } + + std::vector output_tensor_ptrs(num_splits); + for (int i = 0; i < num_splits; ++i) + { + output_tensor_ptrs[i] = &output_tensors[i]; + } + + Split kernel(&axis_tensor, &input_tensor, std::move(output_tensor_ptrs)); + kernel.configure(); + kernel.execute(); + + for (int i = 0; i < num_splits; ++i) + { + EXPECT_THAT(extractTensorData(output_tensors[i]), + ::testing::ElementsAreArray(output_data[i])); + } +} + +template class SplitTest : public ::testing::Test +{ +}; + +using DataTypes = ::testing::Types; +TYPED_TEST_CASE(SplitTest, DataTypes); + +TYPED_TEST(SplitTest, FourDimensional) +{ + Check(/*axis=*/0, /*num_splits=*/2, {2, 2, 2, 2}, {1, 2, 2, 2}, + {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, + { + {1, 2, 3, 4, 5, 6, 7, 8}, // + {9, 10, 11, 12, 13, 14, 15, 16}, // + }, + getElementType()); + Check( + /*axis=*/1, /*num_splits=*/2, {2, 2, 2, 2}, {2, 1, 2, 2}, + {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, + { + {1, 2, 3, 4, 9, 10, 11, 12}, // + {5, 6, 7, 8, 13, 14, 15, 16}, // + }, + getElementType()); + Check( + /*axis=*/2, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 1, 2}, + {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, + { + {1, 2, 5, 6, 9, 10, 13, 14}, // + {3, 4, 7, 8, 11, 12, 15, 16}, // + }, + getElementType()); + Check( + /*axis=*/3, /*num_splits=*/2, {2, 2, 2, 2}, {2, 2, 2, 1}, + {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, + { + {1, 3, 5, 7, 9, 11, 13, 15}, // + {2, 4, 6, 8, 10, 12, 14, 16}, // + }, + getElementType()); +} + +TYPED_TEST(SplitTest, OneDimensional) +{ + Check( + /*axis=*/0, /*num_splits=*/8, {8}, {1}, {1, 2, 3, 4, 5, 6, 7, 8}, + {{1}, {2}, {3}, {4}, {5}, {6}, {7}, {8}}, getElementType()); +} + +TYPED_TEST(SplitTest, NegativeAxis) +{ + Check( + /*axis=*/-4, /*num_splits=*/2, {2, 2, 2, 2}, {1, 2, 2, 2}, + {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, + { + {1, 2, 3, 4, 5, 6, 7, 8}, // + {9, 10, 11, 12, 13, 14, 15, 16}, + }, + getElementType()); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Squeeze.cpp b/compiler/luci-interpreter/src/kernels/Squeeze.cpp new file mode 100644 index 0000000..ce43ef7 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Squeeze.cpp @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2018 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Squeeze.h" + +#include "kernels/Utils.h" + +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +Squeeze::Squeeze(const Tensor *input, Tensor *output, const SqueezeParams ¶ms) + : KernelWithParams({input}, {output}, params) +{ +} + +void Squeeze::configure() +{ + int input_num_dims = input()->shape().num_dims(); + int num_squeeze_dims = params().squeeze_dims.size(); + assert(input_num_dims <= 8); + bool should_squeeze[8] = {false}; + int num_squeezed_dims = 0; + if (num_squeeze_dims == 0) + { + for (int idx = 0; idx < input_num_dims; ++idx) + { + if (input()->shape().dim(idx) == 1) + { + should_squeeze[idx] = true; + ++num_squeezed_dims; + } + } + } + else + { + for (int idx = 0; idx < num_squeeze_dims; ++idx) + { + int current = params().squeeze_dims[idx] < 0 ? params().squeeze_dims[idx] + input_num_dims + : params().squeeze_dims[idx]; + assert(current >= 0 && current < input_num_dims && input()->shape().dim(current) == 1); + if (!should_squeeze[current]) + ++num_squeezed_dims; + should_squeeze[current] = true; + } + } + Shape output_shape(input_num_dims - num_squeezed_dims); + for (int in_idx = 0, out_idx = 0; in_idx < input_num_dims; ++in_idx) + { + if (!should_squeeze[in_idx]) + { + output_shape.dim(out_idx++) = input()->shape().dim(in_idx); + } + } + output()->resize(output_shape); +} + +void Squeeze::execute() const +{ + assert(input()->shape().num_elements() == output()->shape().num_elements()); + + const auto *input_data = input()->data(); + auto *output_data = output()->data(); + std::memcpy(output_data, input_data, + getDataTypeSize(input()->element_type()) * input()->shape().num_elements()); +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Squeeze.h b/compiler/luci-interpreter/src/kernels/Squeeze.h new file mode 100644 index 0000000..687af51 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Squeeze.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2018 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_SQUEEZE_H +#define LUCI_INTERPRETER_KERNELS_SQUEEZE_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class Squeeze : public KernelWithParams +{ +public: + Squeeze(const Tensor *input, Tensor *output, const SqueezeParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_SQUEEZE_H diff --git a/compiler/luci-interpreter/src/kernels/Squeeze.test.cpp b/compiler/luci-interpreter/src/kernels/Squeeze.test.cpp new file mode 100644 index 0000000..3a34284 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Squeeze.test.cpp @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Squeeze.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +template +void Check(std::initializer_list input_shape, std::initializer_list output_shape, + std::initializer_list input_data, std::initializer_list output_data, + DataType element_type, std::vector squeeze_dims) +{ + Tensor input_tensor{element_type, input_shape, {}, ""}; + input_tensor.writeData(input_data.begin(), input_data.size() * sizeof(T)); + Tensor output_tensor = makeOutputTensor(element_type); + + SqueezeParams params{}; + for (size_t i = 0; i < squeeze_dims.size(); i++) + { + params.squeeze_dims.push_back(squeeze_dims.at(i)); + } + + Squeeze kernel(&input_tensor, &output_tensor, params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), ::testing::ElementsAreArray(output_data)); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shape)); +} + +template class SqueezeTest : public ::testing::Test +{ +}; + +using DataTypes = ::testing::Types; +TYPED_TEST_CASE(SqueezeTest, DataTypes); + +TYPED_TEST(SqueezeTest, TotalTest) +{ + Check( + /*input_shape=*/{1, 24, 1}, /*output_shape=*/{24}, + /*input_data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, + 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}, + /*output_data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, + 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}, + getElementType(), {-1, 0}); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/StridedSlice.cpp b/compiler/luci-interpreter/src/kernels/StridedSlice.cpp new file mode 100644 index 0000000..6794854 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/StridedSlice.cpp @@ -0,0 +1,145 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/StridedSlice.h" + +#include "kernels/Utils.h" + +#include + +#include + +namespace luci_interpreter +{ + +namespace kernels +{ + +StridedSlice::StridedSlice(const Tensor *input, const Tensor *begin, const Tensor *end, + const Tensor *strides, Tensor *output, const StridedSliceParams ¶ms) + : KernelWithParams({input, begin, end, strides}, {output}, params) +{ +} + +void StridedSlice::configure() +{ + assert(begin()->shape().num_dims() == 1); + assert(end()->shape().num_dims() == 1); + assert(strides()->shape().num_dims() == 1); + assert(input()->element_type() == output()->element_type()); + assert(begin()->element_type() == DataType::S32); + assert(end()->element_type() == DataType::S32); + assert(strides()->element_type() == DataType::S32); + assert(input()->shape().num_dims() <= 4); + if (params().ellipsis_mask != 0) + { + throw std::runtime_error("ellipsis_mask is not implemented yet."); + } + if (params().new_axis_mask != 0) + { + throw std::runtime_error("new_axis_mask is not implemented yet."); + } + if (input()->element_type() == DataType::U8) + { + assert(input()->scale() == output()->scale()); + assert(input()->zero_point() == output()->zero_point()); + } + tflite::StridedSliceParams op_params{}; + op_params.start_indices_count = input()->shape().num_dims(); + op_params.stop_indices_count = input()->shape().num_dims(); + op_params.strides_count = input()->shape().num_dims(); + + for (int i = 0; i < input()->shape().num_dims(); i++) + { + op_params.start_indices[i] = getTensorData(begin())[i]; + op_params.stop_indices[i] = getTensorData(end())[i]; + op_params.strides[i] = getTensorData(strides())[i]; + } + op_params.begin_mask = params().begin_mask; + op_params.ellipsis_mask = 0; + op_params.end_mask = params().end_mask; + op_params.new_axis_mask = 0; + op_params.shrink_axis_mask = params().shrink_axis_mask; + std::vector output_shape_vector; + for (int i = 0; i < input()->shape().num_dims(); i++) + { + int idx = input()->shape().num_dims() - i - 1; + int32_t stride = getTensorData(strides())[idx]; + assert(stride != 0); + int32_t begin = ::tflite::strided_slice::StartForAxis(op_params, getTensorShape(input()), idx); + int32_t end = + ::tflite::strided_slice::StopForAxis(op_params, getTensorShape(input()), idx, begin); + + const bool shrink_axis = params().shrink_axis_mask & (1 << idx); + if (shrink_axis) + { + end = begin + 1; + } + + int32_t dim_shape = std::ceil((end - begin) / static_cast(stride)); + dim_shape = dim_shape < 0 ? 0 : dim_shape; + if (!shrink_axis) + { + output_shape_vector.push_back(dim_shape); + } + } + Shape output_shape = Shape(output_shape_vector.size()); + for (size_t i = 0; i < output_shape_vector.size(); i++) + { + output_shape.dim(i) = output_shape_vector[output_shape_vector.size() - i - 1]; + } + output()->resize(output_shape); +} + +void StridedSlice::execute() const +{ + tflite::StridedSliceParams op_params{}; + op_params.start_indices_count = input()->shape().num_dims(); + op_params.stop_indices_count = input()->shape().num_dims(); + op_params.strides_count = input()->shape().num_dims(); + + for (int i = 0; i < input()->shape().num_dims(); i++) + { + op_params.start_indices[i] = getTensorData(begin())[i]; + op_params.stop_indices[i] = getTensorData(end())[i]; + op_params.strides[i] = getTensorData(strides())[i]; + } + op_params.begin_mask = params().begin_mask; + op_params.ellipsis_mask = 0; + op_params.end_mask = params().end_mask; + op_params.new_axis_mask = 0; + op_params.shrink_axis_mask = params().shrink_axis_mask; + + switch (input()->element_type()) + { + case DataType::FLOAT32: + tflite::reference_ops::StridedSlice(op_params, getTensorShape(input()), + getTensorData(input()), getTensorShape(output()), + getTensorData(output())); + break; + case DataType::U8: + tflite::reference_ops::StridedSlice(op_params, getTensorShape(input()), + getTensorData(input()), getTensorShape(output()), + getTensorData(output())); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/StridedSlice.h b/compiler/luci-interpreter/src/kernels/StridedSlice.h new file mode 100644 index 0000000..fc96893 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/StridedSlice.h @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_STRIDEDSLICE_H +#define LUCI_INTERPRETER_KERNELS_STRIDEDSLICE_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class StridedSlice : public KernelWithParams +{ +public: + StridedSlice(const Tensor *input, const Tensor *begin, const Tensor *end, const Tensor *strides, + Tensor *output, const StridedSliceParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + const Tensor *begin() const { return _inputs[1]; } + const Tensor *end() const { return _inputs[2]; } + const Tensor *strides() const { return _inputs[3]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_STRIDEDSLICE_H diff --git a/compiler/luci-interpreter/src/kernels/StridedSlice.test.cpp b/compiler/luci-interpreter/src/kernels/StridedSlice.test.cpp new file mode 100644 index 0000000..5ab06e2 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/StridedSlice.test.cpp @@ -0,0 +1,113 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/StridedSlice.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +TEST(StridedSliceTest, Float) +{ + Shape input_shape{2, 3, 2}; + std::vector input_data{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}; + Shape begin_shape{3}; + std::vector begin_data{0, 0, 0}; + Shape end_shape{3}; + std::vector end_data{1, 3, 2}; + Shape strides_shape{3}; + std::vector strides_data{1, 1, 1}; + Tensor input_tensor{DataType::FLOAT32, input_shape, {}, ""}; + Tensor begin_tensor{DataType::S32, begin_shape, {}, ""}; + Tensor end_tensor{DataType::S32, end_shape, {}, ""}; + Tensor strides_tensor{DataType::S32, strides_shape, {}, ""}; + Tensor output_tensor = makeOutputTensor(DataType::FLOAT32); + + input_tensor.writeData(input_data.data(), input_data.size() * sizeof(float)); + begin_tensor.writeData(begin_data.data(), begin_data.size() * sizeof(int32_t)); + end_tensor.writeData(end_data.data(), end_data.size() * sizeof(int32_t)); + strides_tensor.writeData(strides_data.data(), strides_data.size() * sizeof(int32_t)); + + StridedSliceParams params{}; + params.begin_mask = 0; + params.end_mask = 0; + params.ellipsis_mask = 0; + params.new_axis_mask = 0; + params.shrink_axis_mask = 1; + + StridedSlice kernel(&input_tensor, &begin_tensor, &end_tensor, &strides_tensor, &output_tensor, + params); + kernel.configure(); + kernel.execute(); + + std::vector output_shape{3, 2}; + std::vector output_data{1, 2, 3, 4, 5, 6}; + EXPECT_THAT(extractTensorData(output_tensor), + ElementsAreArray(ArrayFloatNear(output_data))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shape)); +} + +TEST(StridedSliceTest, Uint8) +{ + Shape input_shape{2, 3, 2}; + std::vector input_data{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}; + std::vector quant_input_data = quantize(input_data, 1.0f, 0); + Shape begin_shape{3}; + std::vector begin_data{0, 0, 0}; + Shape end_shape{3}; + std::vector end_data{1, 3, 2}; + Shape strides_shape{3}; + std::vector strides_data{1, 1, 1}; + Tensor input_tensor{DataType::U8, input_shape, {{1.0f}, {0}}, ""}; + Tensor begin_tensor{DataType::S32, begin_shape, {}, ""}; + Tensor end_tensor{DataType::S32, end_shape, {}, ""}; + Tensor strides_tensor{DataType::S32, strides_shape, {}, ""}; + Tensor output_tensor = makeOutputTensor(DataType::U8, 1.0f, 0); + + input_tensor.writeData(quant_input_data.data(), quant_input_data.size() * sizeof(uint8_t)); + begin_tensor.writeData(begin_data.data(), begin_data.size() * sizeof(int32_t)); + end_tensor.writeData(end_data.data(), end_data.size() * sizeof(int32_t)); + strides_tensor.writeData(strides_data.data(), strides_data.size() * sizeof(int32_t)); + + StridedSliceParams params{}; + params.begin_mask = 0; + params.end_mask = 0; + params.ellipsis_mask = 0; + params.new_axis_mask = 0; + params.shrink_axis_mask = 1; + + StridedSlice kernel(&input_tensor, &begin_tensor, &end_tensor, &strides_tensor, &output_tensor, + params); + kernel.configure(); + kernel.execute(); + + std::vector output_shape{3, 2}; + std::vector output_data{1, 2, 3, 4, 5, 6}; + EXPECT_THAT(dequantize(extractTensorData(output_tensor), output_tensor.scale(), + output_tensor.zero_point()), + ElementsAreArray(ArrayFloatNear(output_data))); + EXPECT_THAT(extractTensorShape(output_tensor), ::testing::ElementsAreArray(output_shape)); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/TestUtils.cpp b/compiler/luci-interpreter/src/kernels/TestUtils.cpp new file mode 100644 index 0000000..2c8a6ae --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/TestUtils.cpp @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace testing +{ + +using ::testing::FloatNear; +using ::testing::Matcher; + +Tensor makeOutputTensor(DataType element_type) { return Tensor(element_type, {}, {}, ""); } + +Tensor makeOutputTensor(DataType element_type, float scale, int32_t zero_point) +{ + return Tensor(element_type, {}, {{scale}, {zero_point}}, ""); +} + +std::vector> ArrayFloatNear(const std::vector &values, float max_abs_error) +{ + std::vector> matchers; + matchers.reserve(values.size()); + for (const float v : values) + { + matchers.emplace_back(FloatNear(v, max_abs_error)); + } + return matchers; +} + +std::vector extractTensorShape(const Tensor &tensor) +{ + std::vector result; + int dims = tensor.shape().num_dims(); + for (int i = 0; i < dims; i++) + { + result.push_back(tensor.shape().dim(i)); + } + return result; +} + +} // namespace testing +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/TestUtils.h b/compiler/luci-interpreter/src/kernels/TestUtils.h new file mode 100644 index 0000000..5311a19 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/TestUtils.h @@ -0,0 +1,183 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_TESTUTILS_H +#define LUCI_INTERPRETER_KERNELS_TESTUTILS_H + +#include "luci_interpreter/core/Tensor.h" + +#include + +#include +#include + +namespace luci_interpreter +{ +namespace kernels +{ +namespace testing +{ + +template +Tensor makeInputTensor(const Shape &shape, const std::vector::Type> &data) +{ + Tensor tensor(DT, shape, {}, ""); + tensor.writeData(data.data(), data.size() * sizeof(typename DataTypeImpl
::Type)); + return tensor; +} + +Tensor makeOutputTensor(DataType element_type); +Tensor makeOutputTensor(DataType element_type, float scale, int32_t zero_point); + +std::vector extractTensorShape(const Tensor &tensor); + +// Returns the corresponding DataType given the type T. +template constexpr DataType getElementType() +{ + if (std::is_same::value) + return DataType::FLOAT32; + if (std::is_same::value) + return DataType::U8; + if (std::is_same::value) + return DataType::S32; + if (std::is_same::value) + return DataType::S64; + return DataType::Unknown; +} + +template std::vector extractTensorData(const Tensor &tensor) +{ + const auto *data_ptr = tensor.data(); + return std::vector(data_ptr, data_ptr + tensor.shape().num_elements()); +} + +std::vector<::testing::Matcher> ArrayFloatNear(const std::vector &values, + float max_abs_error = 1.0e-5f); + +template +inline std::vector quantize(const std::vector &data, float scale, int32_t zero_point) +{ + assert(!std::is_floating_point::value); + std::vector q; + for (const auto &f : data) + { + q.push_back(static_cast(std::max( + std::numeric_limits::lowest(), + std::min(std::numeric_limits::max(), std::round(zero_point + (f / scale)))))); + } + return q; +} + +template +inline std::vector dequantize(const std::vector &data, float scale, int32_t zero_point) +{ + assert(!std::is_floating_point::value); + std::vector f; + for (const T &q : data) + { + f.push_back(scale * (q - zero_point)); + } + return f; +} + +template std::pair quantizationParams(float f_min, float f_max) +{ + if (std::is_floating_point::value) + { + return {1.0f, 0}; + } + int32_t zero_point = 0; + double scale = 0; + const T qmin = std::numeric_limits::lowest(); + const T qmax = std::numeric_limits::max(); + const double qmin_double = qmin; + const double qmax_double = qmax; + // 0 should always be a representable value. Let's assume that the initial + // min,max range contains 0. + assert(f_max >= 0); + assert(f_min <= 0); + if (f_min == f_max) + { + // Special case where the min,max range is a point. Should be {0}. + assert(f_max == 0); + assert(f_min == 0); + return {scale, zero_point}; + } + + // General case. + // + // First determine the scale. + scale = (f_max - f_min) / (qmax_double - qmin_double); + + // Zero-point computation. + // First the initial floating-point computation. The zero-point can be + // determined from solving an affine equation for any known pair + // (real value, corresponding quantized value). + // We know two such pairs: (rmin, qmin) and (rmax, qmax). + // The arithmetic error on the zero point computed from either pair + // will be roughly machine_epsilon * (sum of absolute values of terms) + // so we want to use the variant that adds the smaller terms. + const double zero_point_from_min = qmin_double - f_min / scale; + const double zero_point_from_max = qmax_double - f_max / scale; + + const double zero_point_from_min_error = std::abs(qmin_double) + std::abs(f_min / scale); + + const double zero_point_from_max_error = std::abs(qmax_double) + std::abs(f_max / scale); + + const double zero_point_double = zero_point_from_min_error < zero_point_from_max_error + ? zero_point_from_min + : zero_point_from_max; + + // Now we need to nudge the zero point to be an integer + // (our zero points are integer, and this is motivated by the requirement + // to be able to represent the real value "0" exactly as a quantized value, + // which is required in multiple places, for example in Im2col with SAME + // padding). + + T nudged_zero_point = 0; + if (zero_point_double < qmin_double) + { + nudged_zero_point = qmin; + } + else if (zero_point_double > qmax_double) + { + nudged_zero_point = qmax; + } + else + { + nudged_zero_point = static_cast(std::round(zero_point_double)); + } + + // The zero point should always be in the range of quantized value, + // // [qmin, qmax]. + assert(qmax >= nudged_zero_point); + assert(qmin <= nudged_zero_point); + zero_point = nudged_zero_point; + // finally, return the values + return {static_cast(scale), zero_point}; +} + +inline float getTolerance(float min, float max, int quantize_steps) +{ + return ((max - min) / quantize_steps); +} + +} // namespace testing +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_TESTUTILS_H diff --git a/compiler/luci-interpreter/src/kernels/Transpose.cpp b/compiler/luci-interpreter/src/kernels/Transpose.cpp new file mode 100644 index 0000000..8265d99 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Transpose.cpp @@ -0,0 +1,84 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Transpose.h" + +#include "kernels/Utils.h" + +#include + +#include + +namespace luci_interpreter +{ + +namespace kernels +{ + +Transpose::Transpose(const Tensor *input, const Tensor *perm, Tensor *output) + : Kernel({input, perm}, {output}) +{ +} + +void Transpose::configure() +{ + // Transpose op only supports 1D-4D input arrays. + int dims = input()->shape().num_dims(); + const int *perm_data = getTensorData(perm()); + + assert(input()->shape().num_dims() <= 4); + assert(input()->element_type() == output()->element_type()); + + assert(perm()->shape().num_dims() == 1); + assert(perm()->shape().dim(0) == dims); + + Shape output_shape(dims); + for (int i = 0; i < dims; i++) + { + assert(perm_data[i] < dims && perm_data[i] >= 0); + output_shape.dim(i) = input()->shape().dim(perm_data[i]); + } + + output()->resize(output_shape); +} + +void Transpose::execute() const +{ + tflite::TransposeParams params{}; + const int *perm_data = getTensorData(perm()); + const int size = perm()->shape().dim(0); + params.perm_count = size; + for (int i = 0; i < size; i++) + params.perm[i] = perm_data[i]; + switch (input()->element_type()) + { + case DataType::FLOAT32: + tflite::reference_ops::Transpose(params, getTensorShape(input()), + getTensorData(input()), getTensorShape(output()), + getTensorData(output())); + break; + case DataType::U8: + tflite::reference_ops::Transpose(params, getTensorShape(input()), + getTensorData(input()), getTensorShape(output()), + getTensorData(output())); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Transpose.h b/compiler/luci-interpreter/src/kernels/Transpose.h new file mode 100644 index 0000000..d6f89c3 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Transpose.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_TRANSPOSE_H +#define LUCI_INTERPRETER_KERNELS_TRANSPOSE_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class Transpose : public Kernel +{ +public: + Transpose(const Tensor *input, const Tensor *perm, Tensor *output); + + const Tensor *input() const { return _inputs[0]; } + const Tensor *perm() const { return _inputs[1]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_TRANSPOSE_H diff --git a/compiler/luci-interpreter/src/kernels/Transpose.test.cpp b/compiler/luci-interpreter/src/kernels/Transpose.test.cpp new file mode 100644 index 0000000..87e6e2a --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Transpose.test.cpp @@ -0,0 +1,117 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Transpose.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +template +void Check(std::initializer_list input_shape, std::initializer_list perm_shape, + std::initializer_list output_shape, std::initializer_list input_data, + std::initializer_list perm_data, std::initializer_list output_data, + DataType element_type) +{ + Tensor input_tensor{element_type, input_shape, {}, ""}; + input_tensor.writeData(input_data.begin(), input_data.size() * sizeof(T)); + + Tensor perm_tensor{DataType::S32, perm_shape, {}, ""}; + perm_tensor.writeData(perm_data.begin(), perm_data.size() * sizeof(int32_t)); + Tensor output_tensor = makeOutputTensor(element_type); + + Transpose kernel(&input_tensor, &perm_tensor, &output_tensor); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), ::testing::ElementsAreArray(output_data)); +} + +template class TransposeTest : public ::testing::Test +{ +}; + +using DataTypes = ::testing::Types; +TYPED_TEST_CASE(TransposeTest, DataTypes); + +TYPED_TEST(TransposeTest, Small3D) +{ + Check(/*input_shape=*/{2, 3, 4}, /*perm_shape=*/{3}, /*output_shape=*/{4, 2, 3}, + /*input_data=*/{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, + 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23}, + /*perm_data=*/{2, 0, 1}, + /*output_data=*/{0, 4, 8, 12, 16, 20, 1, 5, 9, 13, 17, 21, + 2, 6, 10, 14, 18, 22, 3, 7, 11, 15, 19, 23}, + getElementType()); +} + +TYPED_TEST(TransposeTest, Large4D) +{ + Check( + /*input_shape=*/{2, 3, 4, 5}, /*perm_shape=*/{4}, /*output_shape=*/{4, 2, 3, 5}, + /*input_data=*/{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, + 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, + 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, + 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, + 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, + 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, + 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, + 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119}, + /*perm_data=*/{2, 0, 1, 3}, + /*output_data=*/{0, 1, 2, 3, 4, 20, 21, 22, 23, 24, 40, 41, 42, 43, 44, + 60, 61, 62, 63, 64, 80, 81, 82, 83, 84, 100, 101, 102, 103, 104, + 5, 6, 7, 8, 9, 25, 26, 27, 28, 29, 45, 46, 47, 48, 49, + 65, 66, 67, 68, 69, 85, 86, 87, 88, 89, 105, 106, 107, 108, 109, + 10, 11, 12, 13, 14, 30, 31, 32, 33, 34, 50, 51, 52, 53, 54, + 70, 71, 72, 73, 74, 90, 91, 92, 93, 94, 110, 111, 112, 113, 114, + 15, 16, 17, 18, 19, 35, 36, 37, 38, 39, 55, 56, 57, 58, 59, + 75, 76, 77, 78, 79, 95, 96, 97, 98, 99, 115, 116, 117, 118, 119}, + getElementType()); +} + +TYPED_TEST(TransposeTest, Large2D) +{ + Check( + /*input_shape=*/{10, 12}, /*perm_shape=*/{2}, /*output_shape=*/{12, 10}, + /*input_data=*/{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, + 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, + 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, + 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, + 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, + 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, + 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, + 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119}, + /*perm_data=*/{1, 0}, + /*output_data=*/{0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 1, 13, 25, 37, 49, + 61, 73, 85, 97, 109, 2, 14, 26, 38, 50, 62, 74, 86, 98, 110, + 3, 15, 27, 39, 51, 63, 75, 87, 99, 111, 4, 16, 28, 40, 52, + 64, 76, 88, 100, 112, 5, 17, 29, 41, 53, 65, 77, 89, 101, 113, + 6, 18, 30, 42, 54, 66, 78, 90, 102, 114, 7, 19, 31, 43, 55, + 67, 79, 91, 103, 115, 8, 20, 32, 44, 56, 68, 80, 92, 104, 116, + 9, 21, 33, 45, 57, 69, 81, 93, 105, 117, 10, 22, 34, 46, 58, + 70, 82, 94, 106, 118, 11, 23, 35, 47, 59, 71, 83, 95, 107, 119}, + getElementType()); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/TransposeConv.cpp b/compiler/luci-interpreter/src/kernels/TransposeConv.cpp new file mode 100644 index 0000000..46380e2 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/TransposeConv.cpp @@ -0,0 +1,153 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/TransposeConv.h" + +#include "kernels/Utils.h" + +#include + +#include + +namespace luci_interpreter +{ + +namespace kernels +{ + +TransposeConv::TransposeConv(const Tensor *output_shape, const Tensor *filter, const Tensor *input, + Tensor *output, const TransposeConvParams ¶ms) + : KernelWithParams({output_shape, filter, input}, {output}, params) +{ +} + +void TransposeConv::configure() +{ + assert(output_shape()->shape().num_dims() == 1); + assert(input()->shape().num_dims() == 4); + assert(filter()->shape().num_dims() == 4); + assert(input()->element_type() == DataType::FLOAT32 || input()->element_type() == DataType::U8); + assert(input()->element_type() == output()->element_type()); + assert(input()->shape().dim(3) == filter()->shape().dim(3)); + if (input()->element_type() == DataType::U8) + { + _scratch_tensor = + std::make_unique(DataType::S32, output()->shape(), AffineQuantization{}, ""); + double real_multiplier = 0.0; + const double input_product_scale = input()->scale() * filter()->scale(); + assert(input_product_scale >= 0); + real_multiplier = input_product_scale / output()->scale(); + int exponent; + quantizeMultiplier(real_multiplier, &_output_multiplier, &exponent); + _output_shift = -exponent; + } + + const int num_dims = output_shape()->shape().dim(0); + Shape out_shape(num_dims); + const auto *shape_data = getTensorData(output_shape()); + for (int i = 0; i < num_dims; i++) + out_shape.dim(i) = shape_data[i]; + output()->resize(out_shape); +} + +void TransposeConv::execute() const +{ + switch (input()->element_type()) + { + case DataType::FLOAT32: + evalFloat(); + break; + case DataType::U8: + evalQuantized(); + break; + default: + throw std::runtime_error("Unsupported type."); + } +} + +void TransposeConv::evalFloat() const +{ + const int width = output()->shape().dim(2); + const int height = output()->shape().dim(1); + + const int filter_width = filter()->shape().dim(2); + const int filter_height = filter()->shape().dim(1); + + int unused_output_height, unused_output_width; + unused_output_width = + computeOutputSize(params().padding, width, filter_width, params().stride_width, 1); + unused_output_height = + computeOutputSize(params().padding, height, filter_height, params().stride_height, 1); + int32_t offset = 0; + tflite::ConvParams op_params{}; + op_params.padding_type = tflite::PaddingType::kSame; + op_params.padding_values.height = computePaddingWithOffset( + params().stride_height, 1, height, filter_height, unused_output_height, &offset); + op_params.padding_values.height_offset = offset; + op_params.padding_values.width = computePaddingWithOffset( + params().stride_width, 1, width, filter_width, unused_output_width, &offset); + op_params.padding_values.width_offset = offset; + op_params.stride_height = params().stride_height; + op_params.stride_width = params().stride_width; + op_params.output_multiplier = _output_multiplier; + tflite::reference_ops::TransposeConv( + op_params, getTensorShape(input()), getTensorData(input()), getTensorShape(filter()), + getTensorData(filter()), getTensorShape(output()), getTensorData(output()), + tflite::RuntimeShape(), (float *)nullptr); +} + +void TransposeConv::evalQuantized() const +{ + int32_t input_offset = -input()->zero_point(); + int32_t filter_offset = -filter()->zero_point(); + int32_t output_offset = filter()->zero_point(); + const int width = output()->shape().dim(2); + const int height = output()->shape().dim(1); + + const int filter_width = filter()->shape().dim(2); + const int filter_height = filter()->shape().dim(1); + + int unused_output_height, unused_output_width; + unused_output_width = + computeOutputSize(params().padding, width, filter_width, params().stride_width, 1); + unused_output_height = + computeOutputSize(params().padding, height, filter_height, params().stride_height, 1); + int32_t offset = 0; + tflite::ConvParams op_params{}; + op_params.padding_type = tflite::PaddingType::kSame; + op_params.padding_values.height = computePaddingWithOffset( + params().stride_height, 1, height, filter_height, unused_output_height, &offset); + op_params.padding_values.width = computePaddingWithOffset( + params().stride_width, 1, width, filter_width, unused_output_width, &offset); + op_params.stride_height = params().stride_height; + op_params.stride_width = params().stride_width; + op_params.input_offset = input_offset; + op_params.output_offset = output_offset; + op_params.weights_offset = filter_offset; + op_params.output_multiplier = _output_multiplier; + op_params.output_shift = -_output_shift; + op_params.quantized_activation_min = std::numeric_limits::min(); + op_params.quantized_activation_max = std::numeric_limits::max(); + + tflite::reference_ops::TransposeConv( + op_params, getTensorShape(input()), getTensorData(input()), getTensorShape(filter()), + getTensorData(filter()), getTensorShape(output()), getTensorData(output()), + tflite::RuntimeShape(), (uint8 *)nullptr, getTensorData(_scratch_tensor.get())); +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/TransposeConv.h b/compiler/luci-interpreter/src/kernels/TransposeConv.h new file mode 100644 index 0000000..d73e939 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/TransposeConv.h @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_TRANSPOSECONV_H +#define LUCI_INTERPRETER_KERNELS_TRANSPOSECONV_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class TransposeConv : public KernelWithParams +{ +public: + TransposeConv(const Tensor *output_shape, const Tensor *filter, const Tensor *input, + Tensor *output, const TransposeConvParams ¶ms); + + const Tensor *output_shape() const { return _inputs[0]; } + const Tensor *filter() const { return _inputs[1]; } + const Tensor *input() const { return _inputs[2]; } + Tensor *output() const { return _outputs[0]; } + + void configure() override; + void execute() const override; + +private: + void evalFloat() const; + void evalQuantized() const; + +private: + std::unique_ptr _scratch_tensor; + + // The scaling factor from input to output (aka the 'real multiplier') can + // be represented as a fixed point multiplier plus a left shift. + int32_t _output_multiplier = 0; + int _output_shift = 0; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_TRANSPOSECONV_H diff --git a/compiler/luci-interpreter/src/kernels/TransposeConv.test.cpp b/compiler/luci-interpreter/src/kernels/TransposeConv.test.cpp new file mode 100644 index 0000000..3386d36 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/TransposeConv.test.cpp @@ -0,0 +1,102 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/TransposeConv.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +template +void Check(std::initializer_list output_shape_shape, + std::initializer_list weight_shape, + std::initializer_list input_data_shape, + std::initializer_list output_shape, + std::initializer_list output_shape_data, std::initializer_list weight_data, + std::initializer_list input_data_data, std::initializer_list output_data, + luci::Padding padding, int32_t stride_height, int32_t stride_width, + DataType element_type) +{ + Tensor output_shape_tensor{element_type, output_shape_shape, {}, ""}; + output_shape_tensor.writeData(output_shape_data.begin(), output_shape_data.size() * sizeof(T)); + Tensor weight_tensor{element_type, weight_shape, {}, ""}; + weight_tensor.writeData(weight_data.begin(), weight_data.size() * sizeof(T)); + Tensor input_data_tensor{element_type, input_data_shape, {}, ""}; + input_data_tensor.writeData(input_data_data.begin(), input_data_data.size() * sizeof(T)); + + Tensor output_tensor = makeOutputTensor(element_type); + + TransposeConvParams params{}; + params.padding = padding; + params.stride_height = stride_height; + params.stride_width = stride_width; + + TransposeConv kernel(&output_shape_tensor, &weight_tensor, &input_data_tensor, &output_tensor, + params); + kernel.configure(); + kernel.execute(); + + EXPECT_THAT(extractTensorData(output_tensor), ::testing::ElementsAreArray(output_data)); +} + +TEST(TransposeConvTest, FloatSimple) +{ + Check( + /*outputShape_shape=*/{4}, /*weight_shape=*/{1, 3, 3, 1}, /*input_shape=*/{1, 4, 4, 1}, + /*output_shape=*/{1, 4, 4, 1}, /*outputShape_data=*/{1, 4, 4, 1}, + /*weight_data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9}, + /*input_data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, + /*output_data=*/{29, 62, 83, 75, 99, 192, 237, 198, 207, 372, 417, 330, 263, 446, 485, 365}, + /*params.padding=*/luci::Padding::SAME, /*stride_height=*/1, /*stride_width=*/1, + getElementType()); +} + +TEST(TransposeConvTest, FloatTwoFiltersTest) +{ + Check( + /*outputShape_shape=*/{4}, /*weight_shape=*/{1, 3, 3, 2}, /*input_shape=*/{1, 4, 4, 2}, + /*output_shape=*/{1, 4, 4, 1}, /*outputShape_data=*/{1, 4, 4, 1}, + /*weight_data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18}, + /*input_data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32}, + /*output_data=*/{184, 412, 568, 528, 678, 1347, 1689, 1434, 1494, 2715, 3057, 2442, 1968, + 3352, 3652, 2760}, + /*params.padding=*/luci::Padding::SAME, /*stride_height=*/1, /*stride_width=*/1, + getElementType()); +} + +TEST(TransposeConvTest, Uint8Simple) +{ + // TODO + // Implement GetDequantizedOutput Function. + // Create Test for Uint8 Case +} +TEST(TransposeConvTest, Uint8FiltersTest) +{ + // TODO + // Implement GetDequantizedOutput Function. + // Create Test for Uint8 Case +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Unpack.cpp b/compiler/luci-interpreter/src/kernels/Unpack.cpp new file mode 100644 index 0000000..834b799 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Unpack.cpp @@ -0,0 +1,84 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Unpack.h" + +#include "kernels/Utils.h" + +#include + +#include + +namespace luci_interpreter +{ + +namespace kernels +{ + +Unpack::Unpack(const Tensor *input, std::vector outputs, const UnpackParams ¶ms) + : KernelWithParams({input}, std::move(outputs), params) +{ +} + +void Unpack::configure() +{ + const Shape &input_shape = input()->shape(); + + int axis = _params.axis; + if (axis < 0) + axis += input()->shape().num_dims(); + assert(axis >= 0 && axis < input_shape.num_dims()); + + Shape output_shape(input_shape.num_dims() - 1); + int out_index = 0; + for (int in_index = 0; in_index < input_shape.num_dims(); ++in_index) + { + if (in_index != axis) + output_shape.dim(out_index++) = input_shape.dim(in_index); + } + + for (Tensor *output : _outputs) + { + assert(output->element_type() == input()->element_type()); + output->resize(output_shape); + } +} + +template void Unpack::executeImpl() const +{ + tflite::UnpackParams params{}; + params.axis = _params.axis; + params.num_split = _outputs.size(); + VectorOfTensors all_outputs(_outputs); + tflite::reference_ops::Unpack(params, getTensorShape(input()), getTensorData(input()), + **all_outputs.shapes(), all_outputs.data()); +} + +void Unpack::execute() const +{ + switch (input()->element_type()) + { + case DataType::FLOAT32: + return executeImpl(); + case DataType::U8: + return executeImpl(); + default: + throw std::runtime_error("Unsupported type."); + } +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Unpack.h b/compiler/luci-interpreter/src/kernels/Unpack.h new file mode 100644 index 0000000..f4a44ec --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Unpack.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_UNPACK_H +#define LUCI_INTERPRETER_KERNELS_UNPACK_H + +#include "core/Kernel.h" +#include "core/KernelParams.h" + +namespace luci_interpreter +{ +namespace kernels +{ + +class Unpack : public KernelWithParams +{ +public: + Unpack(const Tensor *input, std::vector outputs, const UnpackParams ¶ms); + + const Tensor *input() const { return _inputs[0]; } + Tensor *output(int index) const { return _outputs[index]; } + + void configure() override; + void execute() const override; + +private: + template void executeImpl() const; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_UNPACK_H diff --git a/compiler/luci-interpreter/src/kernels/Unpack.test.cpp b/compiler/luci-interpreter/src/kernels/Unpack.test.cpp new file mode 100644 index 0000000..f70c584 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Unpack.test.cpp @@ -0,0 +1,141 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2018 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Unpack.h" +#include "kernels/TestUtils.h" + +namespace luci_interpreter +{ +namespace kernels +{ +namespace +{ + +using namespace testing; + +template +void Check(int axis, Shape input_shape, std::initializer_list input_data, + const std::vector> &exp_output_shape, + std::vector> exp_output_data) +{ + constexpr DataType element_type = getElementType(); + const int num_outputs = input_shape.dim(axis < 0 ? axis + input_shape.num_dims() : axis); + + Tensor input_tensor = makeInputTensor(input_shape, input_data); + std::vector output_tensors; + output_tensors.reserve(num_outputs); + for (int i = 0; i < num_outputs; ++i) + { + output_tensors.push_back(makeOutputTensor(element_type)); + } + + std::vector output_tensor_ptrs(num_outputs); + for (int i = 0; i < num_outputs; ++i) + { + output_tensor_ptrs[i] = &output_tensors[i]; + } + + UnpackParams params{}; + params.axis = axis; + + Unpack kernel(&input_tensor, std::move(output_tensor_ptrs), params); + kernel.configure(); + kernel.execute(); + + for (int i = 0; i < num_outputs; ++i) + { + EXPECT_THAT(extractTensorData(output_tensors[i]), + ::testing::ElementsAreArray(exp_output_data[i])); + } +} + +template class UnpackTest : public ::testing::Test +{ +}; + +using DataTypes = ::testing::Types; +TYPED_TEST_CASE(UnpackTest, DataTypes); + +TYPED_TEST(UnpackTest, ThreeOutputs) +{ + Check(/*axis=*/0, /*input_shape=*/{3, 2}, + /*input_data=*/{1, 2, 3, 4, 5, 6}, + /*exp_output_shape=*/{{2}, {2}, {2}}, + /*exp_output_data=*/{{1, 2}, {3, 4}, {5, 6}}); +} + +TYPED_TEST(UnpackTest, ThreeOutputsAxisOne) +{ + Check(/*axis=*/1, /*input_shape=*/{3, 2}, + /*input_data=*/{1, 2, 3, 4, 5, 6}, + /*exp_output_shape=*/{{3}, {3}}, + /*exp_output_data=*/{{1, 3, 5}, {2, 4, 6}}); +} + +TYPED_TEST(UnpackTest, ThreeOutputsNegativeAxisOne) +{ + Check(/*axis=*/-1, /*input_shape=*/{3, 2}, + /*input_data=*/{1, 2, 3, 4, 5, 6}, + /*exp_output_shape=*/{{3}, {3}}, + /*exp_output_data=*/{{1, 3, 5}, {2, 4, 6}}); +} + +TYPED_TEST(UnpackTest, ThreeOutputsNegativeAxisTwo) +{ + Check(/*axis=*/-2, /*input_shape=*/{3, 2}, + /*input_data=*/{1, 2, 3, 4, 5, 6}, + /*exp_output_shape=*/{{2}, {2}, {2}}, + /*exp_output_data=*/{{1, 2}, {3, 4}, {5, 6}}); +} + +TYPED_TEST(UnpackTest, OneOutput) +{ + Check(/*axis=*/0, /*input_shape=*/{1, 6}, + /*input_data=*/{1, 2, 3, 4, 5, 6}, + /*exp_output_shape=*/{{6}}, + /*exp_output_data=*/{{1, 2, 3, 4, 5, 6}}); +} + +TYPED_TEST(UnpackTest, ThreeDimensionsTwoOutputs) +{ + Check(/*axis=*/2, /*input_shape=*/{2, 2, 2}, + /*input_data=*/{1, 2, 3, 4, 5, 6, 7, 8}, + /*exp_output_shape=*/{{2, 2}, {2, 2}}, + /*exp_output_data=*/{{1, 3, 5, 7}, {2, 4, 6, 8}}); +} + +TYPED_TEST(UnpackTest, FiveDimensionsTwoOutputs) +{ + Check( + /*axis=*/2, /*input_shape=*/{2, 2, 2, 2, 1}, + /*input_data=*/{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}, + /*exp_output_shape=*/{{2, 2, 2, 1}, {2, 2, 2, 1}}, + /*exp_output_data=*/ + {{1, 2, 5, 6, 9, 10, 13, 14}, {3, 4, 7, 8, 11, 12, 15, 16}}); +} + +TYPED_TEST(UnpackTest, VectorToScalar) +{ + Check(/*axis=*/0, /*input_shape=*/{5}, + /*input_data=*/{1, 2, 3, 4, 5}, + /*exp_output_shape=*/{{}, {}, {}, {}, {}}, + /*exp_output_data=*/{{1}, {2}, {3}, {4}, {5}}); +} + +} // namespace +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Utils.cpp b/compiler/luci-interpreter/src/kernels/Utils.cpp new file mode 100644 index 0000000..b9e7738 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Utils.cpp @@ -0,0 +1,182 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "kernels/Utils.h" + +#include +#include +#include +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +void calculateActivationRange(Activation activation, float *activation_min, float *activation_max) +{ + switch (activation) + { + case Activation::NONE: + *activation_min = std::numeric_limits::lowest(); + *activation_max = std::numeric_limits::max(); + break; + case Activation::RELU: + *activation_min = 0; + *activation_max = std::numeric_limits::max(); + break; + case Activation::RELU_N1_TO_1: + *activation_min = -1; + *activation_max = 1; + break; + case Activation::RELU6: + *activation_min = 0; + *activation_max = 6; + break; + default: + throw std::runtime_error("Unsupported activation."); + } +} + +static void calculateActivationRangeQuantizedImpl(Activation activation, int32_t qmin, int32_t qmax, + const Tensor *output, int32_t *activation_min, + int32_t *activation_max) +{ + const float scale = output->scale(); + const int32_t zero_point = output->zero_point(); + + auto quantize = [scale, zero_point](float x) { + return zero_point + static_cast(std::round(x / scale)); + }; + + switch (activation) + { + case Activation::NONE: + *activation_min = qmin; + *activation_max = qmax; + break; + case Activation::RELU: + *activation_min = std::max(qmin, quantize(0.0f)); + *activation_max = qmax; + break; + case Activation::RELU_N1_TO_1: + *activation_min = std::max(qmin, quantize(-1.0f)); + *activation_max = std::min(qmax, quantize(1.0f)); + break; + case Activation::RELU6: + *activation_min = std::max(qmin, quantize(0.0f)); + *activation_max = std::min(qmax, quantize(6.0f)); + break; + default: + throw std::runtime_error("Unsupported activation."); + } +} + +void calculateActivationRangeQuantized(Activation activation, const Tensor *output, + int32_t *activation_min, int32_t *activation_max) +{ + int32_t qmin{}; + int32_t qmax{}; + switch (output->element_type()) + { + case DataType::U8: + qmin = std::numeric_limits::min(); + qmax = std::numeric_limits::max(); + break; + case DataType::S8: + qmin = std::numeric_limits::min(); + qmax = std::numeric_limits::max(); + break; + case DataType::S16: + qmin = std::numeric_limits::min(); + qmax = std::numeric_limits::max(); + break; + default: + throw std::runtime_error("Unsupported type."); + } + + calculateActivationRangeQuantizedImpl(activation, qmin, qmax, output, activation_min, + activation_max); +} + +void quantizeMultiplier(double double_multiplier, int32_t *quantized_multiplier, int *shift) +{ + if (double_multiplier == 0.0) + { + *quantized_multiplier = 0; + *shift = 0; + return; + } + + const double q = std::frexp(double_multiplier, shift); + auto q_fixed = static_cast(std::round(q * (INT64_C(1) << 31))); + + if (q_fixed == (INT64_C(1) << 31)) + { + q_fixed /= 2; + ++*shift; + } + assert(q_fixed <= std::numeric_limits::max()); + // A shift amount smaller than -31 would cause all bits to be shifted out + // and thus all results would be zero. We implement that instead with + // q_fixed==0, so as to avoid hitting issues with right-shift + // operations with shift amounts greater than 31. Note that this happens + // roughly when abs(double_multiplier) < 2^-31 and the present handling means + // that we're effectively flushing tiny double_multiplier's to zero. + // We could conceivably handle values in the range (roughly) [32, 63] + // as 'denormals' i.e. (shift==0, q_fixed < 2^30). In that point of view + // the present handling is just doing 'flush denormals to zero'. We could + // reconsider and actually generate nonzero denormals if a need arises. + if (*shift < -31) + { + *shift = 0; + q_fixed = 0; + } + *quantized_multiplier = static_cast(q_fixed); +} + +void quantizeMultiplierSmallerThanOneExp(double double_multiplier, int32_t *quantized_multiplier, + int *left_shift) +{ + assert(double_multiplier < 1.0); + assert(double_multiplier > 0.0); + int shift; + quantizeMultiplier(double_multiplier, quantized_multiplier, &shift); + assert(shift <= 0); + *left_shift = shift; +} + +Shape calculateShapeForBroadcast(const Shape &input1_shape, const Shape &input2_shape) +{ + const int num_input1_dims = input1_shape.num_dims(); + const int num_input2_dims = input2_shape.num_dims(); + const int num_out_dims = std::max(num_input1_dims, num_input2_dims); + Shape output_shape(num_out_dims); + + for (int i = 0; i < num_out_dims; ++i) + { + const int32_t input1_dim = i < num_input1_dims ? input1_shape.dim(num_input1_dims - i - 1) : 1; + const int32_t input2_dim = i < num_input2_dims ? input2_shape.dim(num_input2_dims - i - 1) : 1; + assert(input1_dim == input2_dim || input1_dim == 1 || input2_dim == 1); + output_shape.dim(num_out_dims - i - 1) = std::max(input1_dim, input2_dim); + } + + return output_shape; +} + +} // namespace kernels +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/kernels/Utils.h b/compiler/luci-interpreter/src/kernels/Utils.h new file mode 100644 index 0000000..3c2cc84 --- /dev/null +++ b/compiler/luci-interpreter/src/kernels/Utils.h @@ -0,0 +1,194 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_KERNELS_UTILS_H +#define LUCI_INTERPRETER_KERNELS_UTILS_H + +#include "core/KernelParams.h" +#include "luci_interpreter/core/Tensor.h" + +#include + +#include +#include + +namespace luci_interpreter +{ +namespace kernels +{ + +inline int32_t computePadding(int32_t stride, int32_t dilation_rate, int32_t in_size, + int32_t filter_size, int32_t out_size) +{ + const int32_t effective_filter_size = (filter_size - 1) * dilation_rate + 1; + const int32_t padding = ((out_size - 1) * stride + effective_filter_size - in_size) / 2; + return padding > 0 ? padding : 0; +} + +inline int32_t computePaddingWithOffset(int32_t stride, int32_t dilation_rate, int32_t in_size, + int32_t filter_size, int32_t out_size, int32_t *offset) +{ + int32_t effective_filter_size = (filter_size - 1) * dilation_rate + 1; + int32_t total_padding = ((out_size - 1) * stride + effective_filter_size - in_size); + total_padding = total_padding > 0 ? total_padding : 0; + *offset = total_padding % 2; + return total_padding / 2; +} + +inline int32_t computeOutputSize(Padding padding, int32_t image_size, int32_t filter_size, + int32_t stride, int32_t dilation_rate = 1) +{ + const int32_t effective_filter_size = (filter_size - 1) * dilation_rate + 1; + switch (padding) + { + case Padding::SAME: + return (image_size + stride - 1) / stride; + case Padding::VALID: + return (image_size + stride - effective_filter_size) / stride; + default: + assert(false); + return 0; + } +} + +void calculateActivationRange(Activation activation, float *activation_min, float *activation_max); + +void calculateActivationRangeQuantized(Activation activation, const Tensor *output, + int32_t *activation_min, int32_t *activation_max); + +// Decompose a double multiplier into a Q0.31 int32 representation of its +// significand, and shift representation of its exponent. +// +// Handles an arbitrary positive multiplier. The 'shift' output-value is +// basically the 'floating-point exponent' of the multiplier: +// Negative for a right-shift (when the multiplier is <1), positive for a +// left-shift (when the multiplier is >1) +void quantizeMultiplier(double double_multiplier, int32_t *quantized_multiplier, int *shift); + +// Decompose a double multiplier into a Q0.31 int32 representation of its +// significand, and shift representation of NEGATIVE its exponent --- +// this is intended as a RIGHT-shift. +// +// Restricted to the case where the multiplier < 1 (and non-negative). +void quantizeMultiplierSmallerThanOneExp(double double_multiplier, int32_t *quantized_multiplier, + int *left_shift); + +Shape calculateShapeForBroadcast(const Shape &input1_shape, const Shape &input2_shape); + +inline tflite::RuntimeShape getTensorShape(const Tensor *tensor) +{ + if (tensor == nullptr) + return tflite::RuntimeShape(); + + const Shape &shape = tensor->shape(); + tflite::RuntimeShape runtime_shape(shape.num_dims()); + for (int i = 0; i < shape.num_dims(); ++i) + { + runtime_shape.SetDim(i, shape.dim(i)); + } + return runtime_shape; +} + +template const T *getTensorData(const Tensor *tensor) +{ + return tensor != nullptr ? tensor->data() : nullptr; +} + +template T *getTensorData(Tensor *tensor) +{ + return tensor != nullptr ? tensor->data() : nullptr; +} + +// A list of tensors in a format that can be used by kernels like split and +// concatenation. +template class VectorOfTensors +{ +public: + using ElementT = typename std::conditional::type; + using TensorT = typename std::conditional::type; + + // Build with the tensors in 'tensor_list'. + explicit VectorOfTensors(const std::vector &tensor_list) + { + const int num_tensors = tensor_list.size(); + + all_data_.reserve(num_tensors); + all_shape_.reserve(num_tensors); + all_shape_ptr_.reserve(num_tensors); + + for (TensorT *tensor : tensor_list) + { + all_data_.push_back(getTensorData(tensor)); + all_shape_.push_back(getTensorShape(tensor)); + } + + // Taking the pointer from inside a std::vector is only OK if the vector is + // never modified, so we populate all_shape in the previous loop and then we + // are free to grab iterators here. + for (tflite::RuntimeShape &shape : all_shape_) + { + all_shape_ptr_.push_back(&shape); + } + } + // Return a pointer to the data pointers of all tensors in the list. For + // example: + // float* const* f = v.data(); + // f[0][1] is the second element of the first tensor. + ElementT *const *data() const { return all_data_.data(); } + + // Return a pointer the shape pointers of all tensors in the list. For + // example: + // const RuntimeShape* const* d = v.dims(); + // dims[1] are the dimensions of the second tensor in the list. + const tflite::RuntimeShape *const *shapes() const { return all_shape_ptr_.data(); } + +private: + std::vector all_data_; + std::vector all_shape_; + std::vector all_shape_ptr_; +}; + +// A list of quantized tensors in a format that can be used by kernels like +// split and concatenation. +template class VectorOfQuantizedTensors : public VectorOfTensors +{ +public: + using typename VectorOfTensors::TensorT; + + // Build with the tensors in 'tensor_list'. + explicit VectorOfQuantizedTensors(const std::vector &tensor_list) + : VectorOfTensors(tensor_list) + { + for (TensorT *tensor : tensor_list) + { + zero_point_.push_back(tensor->zero_point()); + scale_.push_back(tensor->scale()); + } + } + + const float *scale() const { return scale_.data(); } + const int32_t *zero_point() const { return zero_point_.data(); } + +private: + std::vector zero_point_; + std::vector scale_; +}; + +} // namespace kernels +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_KERNELS_UTILS_H diff --git a/compiler/luci-interpreter/src/loader/CMakeLists.txt b/compiler/luci-interpreter/src/loader/CMakeLists.txt new file mode 100644 index 0000000..fb36c4a --- /dev/null +++ b/compiler/luci-interpreter/src/loader/CMakeLists.txt @@ -0,0 +1,15 @@ +set(SOURCES + GraphLoader.h + GraphLoader.cpp + KernelBuilder.h + KernelBuilder.cpp + ModuleLoader.h + ModuleLoader.cpp + RuntimeToIR.h) + +add_library(luci_interpreter_loader STATIC ${SOURCES}) +set_target_properties(luci_interpreter_loader PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_include_directories(luci_interpreter_loader PUBLIC "${LUCI_INTERPRETER_SOURCE_DIR}") +target_link_libraries(luci_interpreter_loader + PUBLIC luci_lang luci_interpreter_core + PRIVATE luci_interpreter_kernels nncc_common) diff --git a/compiler/luci-interpreter/src/loader/GraphLoader.cpp b/compiler/luci-interpreter/src/loader/GraphLoader.cpp new file mode 100644 index 0000000..779fa06 --- /dev/null +++ b/compiler/luci-interpreter/src/loader/GraphLoader.cpp @@ -0,0 +1,205 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "loader/GraphLoader.h" + +#include "loader/ModuleLoader.h" +#include "loader/KernelBuilder.h" + +#include + +namespace luci_interpreter +{ +namespace +{ + +template Shape getNodeShape(const NodeT *node) +{ + Shape shape(node->rank()); + for (uint32_t i = 0; i < node->rank(); ++i) + { + shape.dim(i) = node->dim(i).value(); + } + return shape; +} + +template const void *getNodeDataImpl(const luci::CircleConst *node, size_t *data_size) +{ + const size_t element_size = getDataTypeSize(DT); + const int32_t num_elements = node->size
(); + + *data_size = num_elements * element_size; + if (*data_size > 0) + { + // FIXME There is no good way to get the pointer to the data currently. + return &node->at
(0); + } + return nullptr; +} + +const void *getNodeData(const luci::CircleConst *node, size_t *data_size) +{ + switch (node->dtype()) + { + case DataType::U8: + return getNodeDataImpl(node, data_size); + case DataType::FLOAT32: + return getNodeDataImpl(node, data_size); + case DataType::S32: + return getNodeDataImpl(node, data_size); + default: + throw std::runtime_error("Unsupported type."); + } +} + +bool isExecutableNode(const luci::CircleNode *node) +{ + switch (node->opcode()) + { + // These nodes denote inputs / outputs of a graph. + case luci::CircleOpcode::CONST: + case luci::CircleOpcode::CIRCLEINPUT: + case luci::CircleOpcode::CIRCLEOUTPUT: + // The following nodes denote outputs of multiple-output nodes. + case luci::CircleOpcode::CIRCLEIFOUT: + case luci::CircleOpcode::CIRCLESPLITOUT: + case luci::CircleOpcode::CIRCLEUNPACKOUT: + return false; + default: + return true; + } +} + +bool isTensorProducingNode(const luci::CircleNode *node) +{ + switch (node->opcode()) + { + // Output nodes do not produce tensors. + case luci::CircleOpcode::CIRCLEOUTPUT: + // The following nodes are multiple-output nodes. They do not produce tensors, the tensors + // are produced by the corresponding *Out nodes instead. + case luci::CircleOpcode::IF: + case luci::CircleOpcode::SPLIT: + case luci::CircleOpcode::UNPACK: + return false; + default: + return true; + } +} + +} // namespace + +GraphLoader::GraphLoader(const ModuleLoader &module_loader, const loco::Graph *graph, + RuntimeGraph *runtime_graph, RuntimeToIR &runtime_to_ir, + std::unordered_map &node_to_tensor) + : _module_loader(module_loader), _graph(graph), _runtime_graph(runtime_graph), + _runtime_to_ir(runtime_to_ir), _node_to_tensor(node_to_tensor) +{ +} + +void GraphLoader::loadTensors() +{ + for (uint32_t i = 0; i < _graph->nodes()->size(); ++i) + { + const auto *node = loco::must_cast(_graph->nodes()->at(i)); + + if (!isTensorProducingNode(node)) + continue; + + // Only Input and Const nodes have shapes. Shapes of intermediate tensors will be inferred. + Shape shape{}; + if (const auto *input_node = dynamic_cast(node)) + { + shape = getNodeShape(input_node); + } + else if (const auto *const_node = dynamic_cast(node)) + { + shape = getNodeShape(const_node); + } + + AffineQuantization quantization; + if (node->quantparam() != nullptr) + { + const luci::CircleQuantParam *params = node->quantparam(); + quantization.scale.assign(params->scale.cbegin(), params->scale.cend()); + quantization.zero_point.assign(params->zerop.cbegin(), params->zerop.cend()); + } + + auto tensor = std::make_unique(node->dtype(), std::move(shape), std::move(quantization), + node->name()); + + if (const auto *const_node = dynamic_cast(node)) + { + size_t data_size{}; + const void *const_data = getNodeData(const_node, &data_size); + if (const_data != nullptr) + tensor->writeData(const_data, data_size); + } + + _node_to_tensor.emplace(node, tensor.get()); + _runtime_to_ir.tensor_to_node.emplace(tensor.get(), node); + + _runtime_graph->addTensor(std::move(tensor)); + } +} + +void GraphLoader::initInputOutputTensors() const +{ + auto input_nodes = loco::input_nodes(_graph); + std::vector input_tensors(input_nodes.size()); + for (size_t i = 0; i < input_nodes.size(); ++i) + { + input_tensors[i] = _node_to_tensor.at(input_nodes[i]); + } + _runtime_graph->setInputTensors(input_tensors); + + auto output_nodes = loco::output_nodes(const_cast(_graph)); + std::vector output_tensors(output_nodes.size()); + for (size_t i = 0; i < output_nodes.size(); ++i) + { + const auto *node = loco::must_cast(output_nodes[i]); + output_tensors[i] = _node_to_tensor.at(node->from()); + } + _runtime_graph->setOutputTensors(output_tensors); +} + +void GraphLoader::loadOperators() +{ + KernelBuilder kernel_builder(_module_loader, *this); + + // Create kernels for executable nodes. This has to be done in execution order. + for (const loco::Node *loco_node : + loco::postorder_traversal(loco::output_nodes(const_cast(_graph)))) + { + const auto *node = loco::must_cast(loco_node); + + if (isExecutableNode(node)) + { + std::unique_ptr kernel = node->accept(&kernel_builder); + _runtime_to_ir.kernel_to_node.emplace(kernel.get(), node); + _runtime_graph->addKernel(std::move(kernel)); + } + } +} + +void GraphLoader::load() +{ + loadTensors(); + initInputOutputTensors(); + loadOperators(); +} + +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/loader/GraphLoader.h b/compiler/luci-interpreter/src/loader/GraphLoader.h new file mode 100644 index 0000000..e0adc0f --- /dev/null +++ b/compiler/luci-interpreter/src/loader/GraphLoader.h @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_LOADER_GRAPHLOADER_H +#define LUCI_INTERPRETER_LOADER_GRAPHLOADER_H + +#include "core/RuntimeGraph.h" +#include "loader/RuntimeToIR.h" + +#include + +#include + +namespace luci_interpreter +{ + +class ModuleLoader; + +class GraphLoader +{ +public: + GraphLoader(const ModuleLoader &module_loader, const loco::Graph *graph, + RuntimeGraph *runtime_graph, RuntimeToIR &runtime_to_ir, + std::unordered_map &node_to_tensor); + + void load(); + + Tensor *getTensorForNode(const loco::Node *node) const { return _node_to_tensor.at(node); } + +private: + void loadOperators(); + void initInputOutputTensors() const; + void loadTensors(); + + const ModuleLoader &_module_loader; + const loco::Graph *_graph; + RuntimeGraph *_runtime_graph; + RuntimeToIR &_runtime_to_ir; + + std::unordered_map &_node_to_tensor; +}; + +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_LOADER_GRAPHLOADER_H diff --git a/compiler/luci-interpreter/src/loader/KernelBuilder.cpp b/compiler/luci-interpreter/src/loader/KernelBuilder.cpp new file mode 100644 index 0000000..56da961 --- /dev/null +++ b/compiler/luci-interpreter/src/loader/KernelBuilder.cpp @@ -0,0 +1,529 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "loader/KernelBuilder.h" + +#include "kernels/Add.h" +#include "kernels/ArgMax.h" +#include "kernels/AveragePool2D.h" +#include "kernels/Concatenation.h" +#include "kernels/Conv2D.h" +#include "kernels/DepthwiseConv2D.h" +#include "kernels/Elu.h" +#include "kernels/FullyConnected.h" +#include "kernels/If.h" +#include "kernels/L2Normalize.h" +#include "kernels/L2Pool2D.h" +#include "kernels/LeakyRelu.h" +#include "kernels/LocalResponseNormalization.h" +#include "kernels/Logistic.h" +#include "kernels/MaxPool2D.h" +#include "kernels/Mean.h" +#include "kernels/Mul.h" +#include "kernels/Pad.h" +#include "kernels/Reshape.h" +#include "kernels/Softmax.h" +#include "kernels/SpaceToDepth.h" +#include "kernels/Split.h" +#include "kernels/StridedSlice.h" +#include "kernels/Squeeze.h" +#include "kernels/Unpack.h" +#include "kernels/Transpose.h" +#include "kernels/TransposeConv.h" +#include "loader/GraphLoader.h" +#include "loader/ModuleLoader.h" + +#include + +namespace luci_interpreter +{ + +template +static std::vector collectOutputNodes(const luci::CircleNode *node) +{ + std::vector output_nodes; + for (const loco::Node *loco_node : loco::succs(node)) + { + output_nodes.push_back(loco::must_cast(loco_node)); + } + std::sort(output_nodes.begin(), output_nodes.end(), + [](const CircleNodeOut *node1, const CircleNodeOut *node2) { + return node1->index() < node2->index(); + }); + return {output_nodes.cbegin(), output_nodes.cend()}; +} + +const Tensor *KernelBuilder::getInputTensor(const loco::Node *node) const +{ + const Tensor *tensor = _graph_loader.getTensorForNode(node); + assert(tensor != nullptr); + return tensor; +} + +const Tensor *KernelBuilder::getOptionalInputTensor(const loco::Node *node) const +{ + // TODO Revise this when optional inputs are implemented in the IR. + return getInputTensor(node); +} + +Tensor *KernelBuilder::getOutputTensor(const loco::Node *node) const +{ + Tensor *tensor = _graph_loader.getTensorForNode(node); + assert(tensor != nullptr); + return tensor; +} + +std::vector +KernelBuilder::getOutputTensors(const std::vector &nodes) const +{ + std::vector tensors; + tensors.reserve(nodes.size()); + for (const loco::Node *node : nodes) + tensors.push_back(getOutputTensor(node)); + return tensors; +} + +RuntimeGraph *KernelBuilder::getRuntimeGraph(const loco::Graph *graph) const +{ + RuntimeGraph *runtime_graph = _module_loader.getRuntimeGraph(graph); + assert(runtime_graph != nullptr); + return runtime_graph; +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleAdd *node) +{ + assert(node->arity() == 2); + + const Tensor *input1 = getInputTensor(node->x()); + const Tensor *input2 = getInputTensor(node->y()); + Tensor *output = getOutputTensor(node); + + AddParams params{}; + params.activation = node->fusedActivationFunction(); + + return std::make_unique(input1, input2, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleArgMax *node) +{ + assert(node->arity() == 2); + const Tensor *input1 = getInputTensor(node->input()); + const Tensor *input2 = getInputTensor(node->dimension()); + Tensor *output = getOutputTensor(node); + + ArgMaxParams params{}; + params.output_type = node->output_type(); + + return std::make_unique(input1, input2, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleAveragePool2D *node) +{ + assert(node->arity() == 1); + + const Tensor *input = getInputTensor(node->value()); + Tensor *output = getOutputTensor(node); + + Pool2DParams params{}; + params.padding = node->padding(); + params.filter_height = node->filter()->h(); + params.filter_width = node->filter()->w(); + params.stride_height = node->stride()->h(); + params.stride_width = node->stride()->w(); + params.activation = node->fusedActivationFunction(); + + return std::make_unique(input, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleConcatenation *node) +{ + std::vector inputs(node->numValues()); + for (uint32_t i = 0; i < node->numValues(); ++i) + { + inputs[i] = getInputTensor(node->values(i)); + } + Tensor *output = getOutputTensor(node); + + ConcatenationParams params{}; + params.axis = node->axis(); + + return std::make_unique(std::move(inputs), output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleConst *) +{ + throw std::runtime_error("Const node cannot be executed."); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleConv2D *node) +{ + assert(node->arity() == 3); + + const Tensor *input = getInputTensor(node->input()); + const Tensor *filter = getInputTensor(node->filter()); + const Tensor *bias = getInputTensor(node->bias()); + Tensor *output = getOutputTensor(node); + + Conv2DParams params{}; + params.padding = node->padding(); + params.stride_height = node->stride()->h(); + params.stride_width = node->stride()->w(); + params.dilation_height_factor = node->dilation()->h(); + params.dilation_width_factor = node->dilation()->w(); + params.activation = node->fusedActivationFunction(); + + return std::make_unique(input, filter, bias, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleDepthwiseConv2D *node) +{ + assert(node->arity() == 3); + + const Tensor *input = getInputTensor(node->input()); + const Tensor *filter = getInputTensor(node->filter()); + const Tensor *bias = getInputTensor(node->bias()); + Tensor *output = getOutputTensor(node); + + DepthwiseConv2DParams params{}; + params.padding = node->padding(); + params.depth_multiplier = node->depthMultiplier(); + params.stride_height = node->stride()->h(); + params.stride_width = node->stride()->w(); + params.dilation_height_factor = node->dilation()->h(); + params.dilation_width_factor = node->dilation()->w(); + params.activation = node->fusedActivationFunction(); + + return std::make_unique(input, filter, bias, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleElu *node) +{ + assert(node->arity() == 1); + + const Tensor *input = getInputTensor(node->features()); + Tensor *output = getOutputTensor(node); + + return std::make_unique(input, output); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleFullyConnected *node) +{ + assert(node->arity() == 3); + + const Tensor *input = getInputTensor(node->input()); + const Tensor *filter = getInputTensor(node->weights()); + const Tensor *bias = getOptionalInputTensor(node->bias()); + Tensor *output = getOutputTensor(node); + + FullyConnectedParams params{}; + params.activation = node->fusedActivationFunction(); + + return std::make_unique(input, filter, bias, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleIf *node) +{ + auto output_nodes = collectOutputNodes(node); + assert(node->arity() == 1 + node->input_count()); + assert(output_nodes.size() == static_cast(node->output_count())); + + const Tensor *cond = getInputTensor(node->cond()); + std::vector inputs(node->input_count()); + for (uint32_t i = 0; i < node->input_count(); ++i) + { + inputs[i] = getInputTensor(node->input(i)); + } + std::vector outputs = getOutputTensors(output_nodes); + + RuntimeGraph *then_graph = getRuntimeGraph(node->then_graph()); + RuntimeGraph *else_graph = getRuntimeGraph(node->else_graph()); + + return std::make_unique(cond, std::move(inputs), std::move(outputs), then_graph, + else_graph); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleL2Normalize *node) +{ + assert(node->arity() == 1); + + const Tensor *input = getInputTensor(node->x()); + Tensor *output = getOutputTensor(node); + + L2NormParams params{}; + params.activation = node->fusedActivationFunction(); + + return std::make_unique(input, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleL2Pool2D *node) +{ + assert(node->arity() == 1); + + const Tensor *input = getInputTensor(node->value()); + Tensor *output = getOutputTensor(node); + + Pool2DParams params{}; + params.padding = node->padding(); + params.filter_height = node->filter()->h(); + params.filter_width = node->filter()->w(); + params.stride_height = node->stride()->h(); + params.stride_width = node->stride()->w(); + params.activation = node->fusedActivationFunction(); + + return std::make_unique(input, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleLeakyRelu *node) +{ + assert(node->arity() == 1); + const Tensor *input = getInputTensor(node->features()); + Tensor *output = getOutputTensor(node); + + LeakyReluParams params{}; + params.alpha = node->alpha(); + + return std::make_unique(input, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleLocalResponseNormalization *node) +{ + assert(node->arity() == 1); + const Tensor *input = getInputTensor(node->input()); + Tensor *output = getOutputTensor(node); + + LocalResponseNormalizationParams params{}; + params.radius = node->radius(); + params.bias = node->bias(); + params.alpha = node->alpha(); + params.beta = node->beta(); + + return std::make_unique(input, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleLogistic *node) +{ + assert(node->arity() == 1); + + const Tensor *input = getInputTensor(node->x()); + Tensor *output = getOutputTensor(node); + + return std::make_unique(input, output); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleInput *) +{ + throw std::runtime_error("Input node cannot be executed."); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleMaxPool2D *node) +{ + assert(node->arity() == 1); + + const Tensor *input = getInputTensor(node->value()); + Tensor *output = getOutputTensor(node); + + Pool2DParams params{}; + params.padding = node->padding(); + params.filter_height = node->filter()->h(); + params.filter_width = node->filter()->w(); + params.stride_height = node->stride()->h(); + params.stride_width = node->stride()->w(); + params.activation = node->fusedActivationFunction(); + + return std::make_unique(input, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleMean *node) +{ + assert(node->arity() == 2); + + const Tensor *input = getInputTensor(node->input()); + const Tensor *axes = getInputTensor(node->reduction_indices()); + Tensor *output = getOutputTensor(node); + + ReducerParams params{}; + params.keep_dims = node->keep_dims(); + + return std::make_unique(input, axes, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleMul *node) +{ + assert(node->arity() == 2); + + const Tensor *input1 = getInputTensor(node->x()); + const Tensor *input2 = getInputTensor(node->y()); + Tensor *output = getOutputTensor(node); + + MulParams params{}; + params.activation = node->fusedActivationFunction(); + + return std::make_unique(input1, input2, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleOutput *) +{ + throw std::runtime_error("Output node cannot be executed."); +} + +std::unique_ptr KernelBuilder::visit(const luci::CirclePad *node) +{ + assert(node->arity() == 2); + + const Tensor *input = getInputTensor(node->input()); + const Tensor *paddings = getInputTensor(node->paddings()); + Tensor *output = getOutputTensor(node); + + return std::make_unique(input, paddings, output); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleReshape *node) +{ + assert(node->arity() == 2); + + const Tensor *input = getInputTensor(node->tensor()); + const Tensor *shape = getInputTensor(node->shape()); + Tensor *output = getOutputTensor(node); + + // NOTE 'newShape' attribute is ignored. + return std::make_unique(input, shape, output); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleSoftmax *node) +{ + assert(node->arity() == 1); + + const Tensor *input = getInputTensor(node->logits()); + Tensor *output = getOutputTensor(node); + + SoftmaxParams params{}; + params.beta = node->beta(); + + return std::make_unique(input, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleSpaceToDepth *node) +{ + assert(node->arity() == 1); + const Tensor *input = getInputTensor(node->input()); + + Tensor *output = getOutputTensor(node); + + SpaceToDepthParams params{}; + params.block_size = node->block_size(); + + return std::make_unique(input, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleSplit *node) +{ + auto output_nodes = collectOutputNodes(node); + assert(node->arity() == 2); + assert(output_nodes.size() == static_cast(node->num_split())); + + const Tensor *axis = getInputTensor(node->split_dim()); + const Tensor *input = getInputTensor(node->input()); + std::vector outputs = getOutputTensors(output_nodes); + + // NOTE 'num_splits' attribute is ignored. + return std::make_unique(axis, input, std::move(outputs)); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleStridedSlice *node) +{ + assert(node->arity() == 4); + + const Tensor *input = getInputTensor(node->input()); + const Tensor *begin = getInputTensor(node->begin()); + const Tensor *end = getInputTensor(node->end()); + const Tensor *strides = getInputTensor(node->strides()); + + Tensor *output = getOutputTensor(node); + + StridedSliceParams params{}; + params.begin_mask = node->begin_mask(); + params.ellipsis_mask = node->ellipsis_mask(); + params.end_mask = node->end_mask(); + params.new_axis_mask = node->new_axis_mask(); + params.shrink_axis_mask = node->shrink_axis_mask(); + + return std::make_unique(input, begin, end, strides, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleSqueeze *node) +{ + assert(node->arity() == 1); + + const Tensor *input = getInputTensor(node->input()); + Tensor *output = getOutputTensor(node); + + SqueezeParams params{}; + assert(node->squeeze_dims().size() <= 4); + for (size_t i = 0; i < node->squeeze_dims().size(); i++) + { + params.squeeze_dims.push_back(node->squeeze_dims().at(i)); + } + + return std::make_unique(input, output, params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleTransposeConv *node) +{ + assert(node->arity() == 3); + + const Tensor *input_sizes = getInputTensor(node->inputSizes()); + const Tensor *filter = getInputTensor(node->filter()); + const Tensor *out_backprop = getInputTensor(node->outBackprop()); + + Tensor *output = getOutputTensor(node); + + TransposeConvParams params{}; + params.padding = node->padding(); + params.stride_height = node->stride()->h(); + params.stride_width = node->stride()->w(); + + return std::make_unique(input_sizes, filter, out_backprop, output, + params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleUnpack *node) +{ + auto output_nodes = collectOutputNodes(node); + assert(node->arity() == 1); + assert(output_nodes.size() == static_cast(node->num())); + + const Tensor *input = getInputTensor(node->value()); + std::vector outputs = getOutputTensors(output_nodes); + + UnpackParams params{}; + params.axis = node->axis(); + + // NOTE 'num' attribute is ignored. + return std::make_unique(input, std::move(outputs), params); +} + +std::unique_ptr KernelBuilder::visit(const luci::CircleTranspose *node) +{ + assert(node->arity() == 2); + + const Tensor *input = getInputTensor(node->a()); + const Tensor *perm = getInputTensor(node->perm()); + Tensor *output = getOutputTensor(node); + + return std::make_unique(input, perm, output); +} + +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/loader/KernelBuilder.h b/compiler/luci-interpreter/src/loader/KernelBuilder.h new file mode 100644 index 0000000..7e30d39 --- /dev/null +++ b/compiler/luci-interpreter/src/loader/KernelBuilder.h @@ -0,0 +1,91 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_LOADER_KERNELBUILDER_H +#define LUCI_INTERPRETER_LOADER_KERNELBUILDER_H + +#include "core/Kernel.h" +#include "core/RuntimeGraph.h" + +#include + +#include +#include + +namespace luci_interpreter +{ + +class GraphLoader; +class ModuleLoader; + +class KernelBuilder : public luci::CircleNodeVisitor> +{ +public: + KernelBuilder(const ModuleLoader &module_loader, const GraphLoader &graph_loader) + : _module_loader(module_loader), _graph_loader(graph_loader) + { + } + + std::unique_ptr visit(const luci::CircleAdd *node) override; + std::unique_ptr visit(const luci::CircleArgMax *node) override; + std::unique_ptr visit(const luci::CircleAveragePool2D *node) override; + std::unique_ptr visit(const luci::CircleConcatenation *node) override; + std::unique_ptr visit(const luci::CircleConv2D *node) override; + std::unique_ptr visit(const luci::CircleConst *node) override; + std::unique_ptr visit(const luci::CircleDepthwiseConv2D *node) override; + std::unique_ptr visit(const luci::CircleElu *node) override; + std::unique_ptr visit(const luci::CircleFullyConnected *node) override; + std::unique_ptr visit(const luci::CircleIf *node) override; + std::unique_ptr visit(const luci::CircleL2Normalize *node) override; + std::unique_ptr visit(const luci::CircleL2Pool2D *node) override; + std::unique_ptr visit(const luci::CircleLeakyRelu *node) override; + std::unique_ptr visit(const luci::CircleLocalResponseNormalization *node) override; + std::unique_ptr visit(const luci::CircleLogistic *node) override; + std::unique_ptr visit(const luci::CircleInput *node) override; + std::unique_ptr visit(const luci::CircleMaxPool2D *node) override; + std::unique_ptr visit(const luci::CircleMean *node) override; + std::unique_ptr visit(const luci::CircleMul *node) override; + std::unique_ptr visit(const luci::CircleOutput *node) override; + std::unique_ptr visit(const luci::CirclePad *node) override; + std::unique_ptr visit(const luci::CircleReshape *node) override; + std::unique_ptr visit(const luci::CircleSoftmax *node) override; + std::unique_ptr visit(const luci::CircleSpaceToDepth *node) override; + std::unique_ptr visit(const luci::CircleSplit *node) override; + std::unique_ptr visit(const luci::CircleStridedSlice *node) override; + std::unique_ptr visit(const luci::CircleSqueeze *node) override; + std::unique_ptr visit(const luci::CircleTranspose *node) override; + std::unique_ptr visit(const luci::CircleTransposeConv *node) override; + std::unique_ptr visit(const luci::CircleUnpack *node) override; + +private: + const Tensor *getInputTensor(const loco::Node *node) const; + + const Tensor *getOptionalInputTensor(const loco::Node *node) const; + + Tensor *getOutputTensor(const loco::Node *node) const; + + std::vector getOutputTensors(const std::vector &nodes) const; + + RuntimeGraph *getRuntimeGraph(const loco::Graph *graph) const; + +private: + const ModuleLoader &_module_loader; + const GraphLoader &_graph_loader; +}; + +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_LOADER_KERNELBUILDER_H diff --git a/compiler/luci-interpreter/src/loader/ModuleLoader.cpp b/compiler/luci-interpreter/src/loader/ModuleLoader.cpp new file mode 100644 index 0000000..7780a61 --- /dev/null +++ b/compiler/luci-interpreter/src/loader/ModuleLoader.cpp @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ModuleLoader.h" + +#include "GraphLoader.h" + +namespace luci_interpreter +{ + +ModuleLoader::ModuleLoader(const luci::Module *module, RuntimeModule *runtime_module, + RuntimeToIR &runtime_to_ir, + std::unordered_map &node_to_tensor) + : _module(module), _runtime_module(runtime_module), _runtime_to_ir(runtime_to_ir), + _node_to_tensor(node_to_tensor) +{ +} + +void ModuleLoader::load() +{ + // Runtime graphs have to be created in advance, because they will be needed during the loading + // process for control flow nodes. + for (size_t i = 0; i < _module->size(); ++i) + { + _graph_to_runtime_graph.emplace(_module->graph(i), _runtime_module->addGraph()); + } + for (size_t i = 0; i < _module->size(); ++i) + { + const loco::Graph *graph = _module->graph(i); + RuntimeGraph *runtime_graph = _graph_to_runtime_graph.at(graph); + GraphLoader loader(*this, graph, runtime_graph, _runtime_to_ir, _node_to_tensor); + loader.load(); + } +} + +} // namespace luci_interpreter diff --git a/compiler/luci-interpreter/src/loader/ModuleLoader.h b/compiler/luci-interpreter/src/loader/ModuleLoader.h new file mode 100644 index 0000000..954dbfb --- /dev/null +++ b/compiler/luci-interpreter/src/loader/ModuleLoader.h @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_LOADER_MODULELOADER_H +#define LUCI_INTERPRETER_LOADER_MODULELOADER_H + +#include "core/RuntimeModule.h" +#include "loader/RuntimeToIR.h" + +#include + +#include + +namespace luci_interpreter +{ + +class ModuleLoader +{ +public: + ModuleLoader(const luci::Module *module, RuntimeModule *runtime_module, + RuntimeToIR &runtime_to_ir, + std::unordered_map &node_to_tensor); + + void load(); + + RuntimeGraph *getRuntimeGraph(const loco::Graph *graph) const + { + return _graph_to_runtime_graph.at(graph); + } + +private: + const luci::Module *_module; + RuntimeModule *_runtime_module; + RuntimeToIR &_runtime_to_ir; + std::unordered_map &_node_to_tensor; + std::unordered_map _graph_to_runtime_graph; +}; + +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_LOADER_MODULELOADER_H diff --git a/compiler/luci-interpreter/src/loader/RuntimeToIR.h b/compiler/luci-interpreter/src/loader/RuntimeToIR.h new file mode 100644 index 0000000..9ea8b1f --- /dev/null +++ b/compiler/luci-interpreter/src/loader/RuntimeToIR.h @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef LUCI_INTERPRETER_LOADER_RUNTIMETOIR_H +#define LUCI_INTERPRETER_LOADER_RUNTIMETOIR_H + +#include "luci_interpreter/core/Tensor.h" + +#include + +#include + +namespace luci_interpreter +{ + +// Maps runtime entities back to IR entities. It is used to implement observing functionality. +struct RuntimeToIR +{ + std::unordered_map tensor_to_node; + std::unordered_map kernel_to_node; +}; + +} // namespace luci_interpreter + +#endif // LUCI_INTERPRETER_LOADER_RUNTIMETOIR_H diff --git a/compiler/luci-value-test/CMakeLists.txt b/compiler/luci-value-test/CMakeLists.txt new file mode 100644 index 0000000..3a5c42b --- /dev/null +++ b/compiler/luci-value-test/CMakeLists.txt @@ -0,0 +1,25 @@ +unset(LUCI_VALUE_TESTS) + +macro(addeval NAME) + list(APPEND LUCI_VALUE_TESTS ${NAME}) +endmacro(addeval) + +# Read "test.lst" +include("test.lst") +# Read "test.local.lst" if exists +include("test.local.lst" OPTIONAL) + +# Generate dependencies +add_custom_target(luci_eval_testfiles ALL DEPENDS ${TESTFILES}) + +add_subdirectory(tester) + +get_target_property(ARTIFACTS_BIN_PATH testDataGenerator BINARY_DIR) + +add_test(NAME luci_value_test + COMMAND "${CMAKE_CURRENT_SOURCE_DIR}/evalverify.sh" + "${CMAKE_CURRENT_BINARY_DIR}" + "${ARTIFACTS_BIN_PATH}" + "${NNCC_OVERLAY_DIR}/venv_1_13_2" + ${LUCI_VALUE_TESTS} +) diff --git a/compiler/luci-value-test/README.md b/compiler/luci-value-test/README.md new file mode 100644 index 0000000..90e9283 --- /dev/null +++ b/compiler/luci-value-test/README.md @@ -0,0 +1,15 @@ +# luci-value-test + +`luci-value-test` validates luci IR graph model file (.circle) + +The test proceeds as follows + +Step 1: Generate tflite files and circle files from TFLite recipes (listsed in test.lst). +"TFLite recipe" -> tflchef -> "tflite file" -> tflite2circle -> "circle file" + +Step 2: Run TFLite interpreter and luci-interpreter for the generated tflite and circle, respectively. +(with the same input tensors filled with random values) +circle file -> luci-interpreter -------> Execution result 1 +tflite file -> TFLite interpreter -----> Execution result 2 + +Step 3: Compare the execution result 1 and 2. The result must be the same. diff --git a/compiler/luci-value-test/evalverify.sh b/compiler/luci-value-test/evalverify.sh new file mode 100755 index 0000000..dfd55a6 --- /dev/null +++ b/compiler/luci-value-test/evalverify.sh @@ -0,0 +1,61 @@ +#!/bin/bash + +# This script verifies the basic behavior of luci interpreter +# +# HOW TO USE +# +# ./evalverify.sh ... +# work_dir : build directory of luci-value-test (ex: build/compiler/luci-value-test) + +VERIFY_SOURCE_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +VERIFY_SCRIPT_PATH="${VERIFY_SOURCE_PATH}/luci_eval_verifier.py" +BINDIR="$1"; shift +WORKDIR="$1"; shift +VIRTUALENV="$1"; shift +INTERPRETER_DRIVER_PATH="${BINDIR}/tester/luci_eval_tester" + +TESTED=() +PASSED=() +FAILED=() + +for TESTCASE in "$@"; do + TESTED+=("${TESTCASE}") + + TESTCASE_FILE="${WORKDIR}/${TESTCASE}" + TEST_RESULT_FILE="${BINDIR}/${TESTCASE}" + + PASSED_TAG="${TEST_RESULT_FILE}.passed" + rm -f "${PASSED_TAG}" + + cat > "${TEST_RESULT_FILE}.log" <( + exec 2>&1 + set -ex + + source "${VIRTUALENV}/bin/activate" + "${VIRTUALENV}/bin/python" "${VERIFY_SCRIPT_PATH}" \ + --driver "${INTERPRETER_DRIVER_PATH}" \ + --model "${TESTCASE_FILE}" + + if [[ $? -eq 0 ]]; then + touch "${PASSED_TAG}" + fi + ) + + if [[ -f "${PASSED_TAG}" ]]; then + PASSED+=("${TESTCASE}") + else + FAILED+=("${TESTCASE}") + fi +done + +if [[ ${#TESTED[@]} -ne ${#PASSED[@]} ]]; then + echo "FAILED" + for TEST in "${FAILED[@]}" + do + echo "- ${TEST}" + done + exit 255 +fi + +echo "PASSED" +exit 0 diff --git a/compiler/luci-value-test/luci_eval_verifier.py b/compiler/luci-value-test/luci_eval_verifier.py new file mode 100755 index 0000000..6999110 --- /dev/null +++ b/compiler/luci-value-test/luci_eval_verifier.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python3 +import numpy as np +import tensorflow as tf +import subprocess +import argparse +import traceback + +# +# This script compares the execution result of luci-interpreter with that of TFLite interpreter +# +# Basic usage: +# eval_verifier.py --driver build/compiler/luci-value-test/tester/luci_eval_tester +# --model inception_v3 +parser = argparse.ArgumentParser() +parser.add_argument('--driver', type=str, required=True) +parser.add_argument('--model', type=str, required=True) +args = parser.parse_args() + +driver = args.driver +tflite_model = args.model + ".tflite" +circle_model = args.model + ".circle" + +# Build TFLite interpreter. +interpreter = tf.lite.Interpreter(tflite_model) +interpreter.allocate_tensors() + +# Generate random input data. +num_inputs = len(interpreter.get_input_details()) +for i in range(num_inputs): + input_details = interpreter.get_input_details()[i] + if input_details["dtype"] == np.float32: + input_data = np.array( + np.random.random_sample(input_details["shape"]), input_details["dtype"]) + elif input_details["dtype"] == np.uint8: + input_data = np.array( + np.random.randint(0, 256, size=input_details["shape"]), + input_details["dtype"]) + else: + raise SystemExit("Unsupported input dtype") + + interpreter.set_tensor(input_details["index"], input_data) + input_data.tofile(circle_model + ".input" + str(i)) + +# Do inference +interpreter.invoke() + +# Get reference output data. +assert len(interpreter.get_output_details()) == 1 # TODO: Support multiple outputs +output_details = interpreter.get_output_details()[0] +ref_output_data = interpreter.get_tensor(output_details["index"]) + +# Execute luci interpreter. +subprocess.run( + [ + driver, circle_model, + str(num_inputs), circle_model + ".input", circle_model + ".output" + ], + check=True) +output_data = np.fromfile(circle_model + ".output", output_details["dtype"]) +shape_file = open(circle_model + ".output.shape", 'r') +output_shape = [int(i) for i in shape_file.read().split(',')] +shape_file.close() +luci_output_data = np.reshape(output_data, output_shape) + +# Compare the results. +try: + if output_details["dtype"] == np.uint8: + if np.allclose(luci_output_data, ref_output_data, rtol=0, atol=0) == False: + raise SystemExit("Execution result of " + tflite_model + + " does not match with " + circle_model) + elif output_details["dtype"] == np.float32: + if np.allclose( + luci_output_data, ref_output_data, rtol=1.e-5, atol=1.e-5) == False: + raise SystemExit("Execution result of " + tflite_model + + " does not match with " + circle_model) + else: + raise SystemExit("Unsupported data type: ", output_details["dtype"]) +except: + print(traceback.format_exc()) + quit(255) + +quit(0) diff --git a/compiler/luci-value-test/requires.cmake b/compiler/luci-value-test/requires.cmake new file mode 100644 index 0000000..f8af5f2 --- /dev/null +++ b/compiler/luci-value-test/requires.cmake @@ -0,0 +1,6 @@ +require("common-artifacts") +require("luci") +require("luci-interpreter") +require("safemain") +require("oops") +require("loco") diff --git a/compiler/luci-value-test/test.lst b/compiler/luci-value-test/test.lst new file mode 100644 index 0000000..6a332f9 --- /dev/null +++ b/compiler/luci-value-test/test.lst @@ -0,0 +1,81 @@ +#addeval(Abs_000) +addeval(Add_000) +addeval(Add_U8_000) +#addeval(ArgMax_000) +#addeval(ArgMax_001) +#addeval(ArgMax_002) +#addeval(ArgMax_003) +#addeval(ArgMax_U8_000) +#addeval(ArgMax_U8_001) +#addeval(ArgMax_U8_002) +#addeval(ArgMax_U8_003) +addeval(AveragePool2D_000) +#addeval(BatchMatMulV2_000) +#addeval(BatchMatMulV2_001) +#addeval(BatchToSpaceND_000) +#addeval(Cast_000) +addeval(Concatenation_000) +addeval(Concatenation_U8_000) +addeval(Conv2D_000) +addeval(Conv2D_001) +addeval(Conv2D_002) +addeval(Conv2D_U8_000) +addeval(Conv2D_U8_001) +#addeval(Cos_000) +addeval(DepthwiseConv2D_000) +addeval(DepthwiseConv2D_U8_000) +#addeval(Div_000) +#addeval(Equal_000) +#addeval(Exp_000) +addeval(FullyConnected_000) +addeval(FullyConnected_001) +#addeval(FullyConnected_002) +#addeval(FullyConnected_U8_000) +#addeval(Gather_000) +#addeval(If_000) +#addeval(If_001) +#addeval(LogicalNot_000) +#addeval(LogicalOr_000) +#addeval(Logistic_000) +addeval(MaxPool2D_000) +addeval(MaxPool2D_U8_000) +addeval(Mean_000) +addeval(Mean_001) +addeval(Mean_U8_000) +addeval(Mul_000) +#addeval(Mul_U8_000) +#addeval(Pack_000) +#addeval(Pack_U8_000) +addeval(Pad_000) +addeval(Pad_U8_000) +#addeval(ReduceProd_000) +#addeval(ReduceProd_001) +#addeval(ReduceProd_002) +#addeval(ReduceProd_003) +#addeval(ReLU_000) +addeval(Reshape_000) +addeval(Reshape_001) +addeval(Reshape_002) +#addeval(Reshape_003) +addeval(Reshape_U8_000) +#addeval(Rsqrt_000) +#addeval(Sin_000) +addeval(Softmax_000) +#addeval(Softmax_U8_000) +#addeval(SpaceToBatchND_000) +#addeval(SpaceToBatchND_001) +#addeval(SpaceToBatchND_002) +#addeval(SpaceToBatchND_003) +#addeval(StridedSlice_000) +#addeval(StridedSlice_001) +#addeval(Sub_000) +#addeval(Sub_U8_000) +#addeval(Tanh_000) +#addeval(Tile_000) +#addeval(Tile_U8_000) +#addeval(Transpose_000) +#addeval(Unpack_000) +#addeval(Unpack_001) +#addeval(Unpack_002) +#addeval(While_000) +#addeval(While_001) diff --git a/compiler/luci-value-test/tester/CMakeLists.txt b/compiler/luci-value-test/tester/CMakeLists.txt new file mode 100644 index 0000000..f3b6dfc --- /dev/null +++ b/compiler/luci-value-test/tester/CMakeLists.txt @@ -0,0 +1,15 @@ + +set(SRCS_EVAL_TESTER + src/EvalTester.cpp + src/CircleExpContract.h + src/CircleExpContract.cpp + ) + +add_executable(luci_eval_tester ${SRCS_EVAL_TESTER}) +target_link_libraries(luci_eval_tester PRIVATE oops) +target_link_libraries(luci_eval_tester PRIVATE loco) +target_link_libraries(luci_eval_tester PRIVATE luci_import) +target_link_libraries(luci_eval_tester PRIVATE luci_export) +target_link_libraries(luci_eval_tester PRIVATE luci_lang) +target_link_libraries(luci_eval_tester PRIVATE luci_interpreter) +target_link_libraries(luci_eval_tester PRIVATE safemain) diff --git a/compiler/luci-value-test/tester/src/CircleExpContract.cpp b/compiler/luci-value-test/tester/src/CircleExpContract.cpp new file mode 100644 index 0000000..b56b7ee --- /dev/null +++ b/compiler/luci-value-test/tester/src/CircleExpContract.cpp @@ -0,0 +1,33 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "CircleExpContract.h" + +#include + +#include +#include + +bool CircleExpContract::store(const char *ptr, const size_t size) const +{ + if (!ptr) + INTERNAL_EXN("Graph was not serialized by FlatBuffer for some reason"); + + std::ofstream fs(_filepath.c_str(), std::ofstream::binary); + fs.write(ptr, size); + + return fs.good(); +} diff --git a/compiler/luci-value-test/tester/src/CircleExpContract.h b/compiler/luci-value-test/tester/src/CircleExpContract.h new file mode 100644 index 0000000..4d08fb8 --- /dev/null +++ b/compiler/luci-value-test/tester/src/CircleExpContract.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_VALUE_TEST_CIRCLEXPCONTRACT_H__ +#define __LUCI_VALUE_TEST_CIRCLEXPCONTRACT_H__ + +#include +#include +#include + +#include +#include + +struct CircleExpContract : public luci::CircleExporter::Contract +{ +public: + CircleExpContract(luci::Module *module, const std::string &filename) + : _module(module), _filepath(filename) + { + // NOTHING TO DO + } + virtual ~CircleExpContract() = default; + +public: + loco::Graph *graph(void) const final { return nullptr; } + luci::Module *module(void) const final { return _module; }; + +public: + bool store(const char *ptr, const size_t size) const final; + +private: + luci::Module *_module; + const std::string _filepath; +}; + +#endif // __LUCI_VALUE_TEST_CIRCLEXPCONTRACT_H__ diff --git a/compiler/luci-value-test/tester/src/EvalTester.cpp b/compiler/luci-value-test/tester/src/EvalTester.cpp new file mode 100644 index 0000000..58f62f5 --- /dev/null +++ b/compiler/luci-value-test/tester/src/EvalTester.cpp @@ -0,0 +1,164 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "CircleExpContract.h" + +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace +{ + +void readDataFromFile(const std::string &filename, char *data, size_t data_size) +{ + std::ifstream fs(filename, std::ifstream::binary); + if (fs.fail()) + throw std::runtime_error("Cannot open file \"" + filename + "\".\n"); + if (fs.read(data, data_size).fail()) + throw std::runtime_error("Failed to read data from file \"" + filename + "\".\n"); +} + +void writeDataToFile(const std::string &filename, const char *data, size_t data_size) +{ + std::ofstream fs(filename, std::ofstream::binary); + if (fs.fail()) + throw std::runtime_error("Cannot open file \"" + filename + "\".\n"); + if (fs.write(data, data_size).fail()) + { + throw std::runtime_error("Failed to write data to file \"" + filename + "\".\n"); + } +} + +std::unique_ptr importModel(const std::string &filename) +{ + std::ifstream fs(filename, std::ifstream::binary); + if (fs.fail()) + { + throw std::runtime_error("Cannot open model file \"" + filename + "\".\n"); + } + std::vector model_data((std::istreambuf_iterator(fs)), + std::istreambuf_iterator()); + return luci::Importer().importModule(circle::GetModel(model_data.data())); +} + +template size_t getTensorSize(const NodeT *node) +{ + uint32_t tensor_size = loco::size(node->dtype()); + for (uint32_t i = 0; i < node->rank(); ++i) + tensor_size *= node->dim(i).value(); + return tensor_size; +} + +} // namespace + +/* + * @brief EvalTester main + * + * Driver for testing luci-inerpreter + * + */ +int entry(int argc, char **argv) +{ + if (argc != 5) + { + std::cerr + << "Usage: " << argv[0] + << " \n"; + return EXIT_FAILURE; + } + + const char *filename = argv[1]; + const int32_t num_inputs = atoi(argv[2]); + const char *input_prefix = argv[3]; + const char *output_file = argv[4]; + const std::string intermediate_filename = std::string(filename) + ".inter.circle"; + + // Load model from the file + std::unique_ptr initial_module = importModel(filename); + if (initial_module == nullptr) + { + std::cerr << "ERROR: Failed to load '" << filename << "'" << std::endl; + return EXIT_FAILURE; + } + + // Export to a Circle file + luci::CircleExporter exporter; + CircleExpContract contract(initial_module.get(), intermediate_filename); + if (!exporter.invoke(&contract)) + { + std::cerr << "ERROR: Failed to export '" << intermediate_filename << "'" << std::endl; + return EXIT_FAILURE; + } + + // Import model again + std::unique_ptr module = importModel(intermediate_filename); + if (module == nullptr) + { + std::cerr << "ERROR: Failed to load '" << intermediate_filename << "'" << std::endl; + return EXIT_FAILURE; + } + + // Create interpreter. + luci_interpreter::Interpreter interpreter(module.get()); + + // Set input. + // Data for n'th input is read from ${input_prefix}n + // (ex: Add.circle.input0, Add.circle.input1 ..) + const auto input_nodes = loco::input_nodes(module->graph()); + assert(num_inputs == input_nodes.size()); + for (int32_t i = 0; i < num_inputs; i++) + { + const auto *input_node = dynamic_cast(input_nodes[i]); + std::vector input_data(getTensorSize(input_node)); + readDataFromFile(std::string(input_prefix) + std::to_string(i), input_data.data(), + input_data.size()); + interpreter.writeInputTensor(input_node, input_data.data(), input_data.size()); + } + + // Do inference. + interpreter.interpret(); + + // Get output. + const auto output_nodes = loco::output_nodes(module->graph()); + // TODO: Support multiple outputs + assert(output_nodes.size() == 1); + const auto *output_node = dynamic_cast(output_nodes[0]); + std::vector output_data(getTensorSize(output_node)); + interpreter.readOutputTensor(output_node, output_data.data(), output_data.size()); + + // Output data is written in ${output_file} + // (ex: Add.circle.output) + // Output shape is written in ${output_file}.shape + // (ex: Add.circle.output.shape) + // TODO: Use HDF5 file format + writeDataToFile(output_file, output_data.data(), output_data.size()); + auto shape_str = std::to_string(output_node->dim(0).value()); + for (int i = 1; i < output_node->rank(); i++) + { + shape_str += ","; + shape_str += std::to_string(output_node->dim(i).value()); + } + writeDataToFile(std::string(output_file) + ".shape", shape_str.c_str(), shape_str.size()); + return EXIT_SUCCESS; +} diff --git a/compiler/luci/CMakeLists.txt b/compiler/luci/CMakeLists.txt index 387c224..214a1bb 100644 --- a/compiler/luci/CMakeLists.txt +++ b/compiler/luci/CMakeLists.txt @@ -1,3 +1,4 @@ +add_subdirectory(env) add_subdirectory(log) add_subdirectory(lang) add_subdirectory(service) diff --git a/compiler/luci/env/CMakeLists.txt b/compiler/luci/env/CMakeLists.txt new file mode 100644 index 0000000..3d8387a --- /dev/null +++ b/compiler/luci/env/CMakeLists.txt @@ -0,0 +1,18 @@ +file(GLOB_RECURSE SOURCES "src/*.cpp") +file(GLOB_RECURSE TESTS "src/*.test.cpp") +list(REMOVE_ITEM SOURCES ${TESTS}) + +add_library(luci_env SHARED ${SOURCES}) +target_include_directories(luci_env PUBLIC include) +target_link_libraries(luci_env PRIVATE nncc_common) +install(TARGETS luci_env DESTINATION lib) + +if(NOT ENABLE_TEST) + return() +endif(NOT ENABLE_TEST) + +nnas_find_package(GTest REQUIRED) + +GTest_AddTest(luci_env_test ${TESTS}) +target_include_directories(luci_env_test PRIVATE src) +target_link_libraries(luci_env_test luci_env) diff --git a/compiler/luci/env/README.md b/compiler/luci/env/README.md new file mode 100644 index 0000000..cda0078 --- /dev/null +++ b/compiler/luci/env/README.md @@ -0,0 +1,3 @@ +# luci-env + +_luci-env_ provides user environment settings that control _luci_ diff --git a/compiler/luci/env/include/luci/UserSettings.h b/compiler/luci/env/include/luci/UserSettings.h new file mode 100644 index 0000000..bcfd160 --- /dev/null +++ b/compiler/luci/env/include/luci/UserSettings.h @@ -0,0 +1,45 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_USER_SETTINGS__ +#define __LUCI_USER_SETTINGS__ + +// NOTE Revise the logic if we find a better way not using global status + +namespace luci +{ + +/** + * @brief UserSettings provides user settings by key-value + */ +struct UserSettings +{ + enum Key + { + Undefined, + MuteWarnings, + DisableValidation, + }; + + static UserSettings *settings(); + + virtual void set(const Key key, bool value) = 0; + virtual bool get(const Key key) const = 0; +}; + +} // namespace luci + +#endif // __LUCI_USER_SETTINGS__ diff --git a/compiler/luci/env/src/UserSettings.cpp b/compiler/luci/env/src/UserSettings.cpp new file mode 100644 index 0000000..27dec76 --- /dev/null +++ b/compiler/luci/env/src/UserSettings.cpp @@ -0,0 +1,77 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/UserSettings.h" + +#include + +namespace luci +{ + +class UserSettingsImpl : public UserSettings +{ +public: + void set(const Key key, bool value) override; + bool get(const Key key) const override; + +private: + bool _MuteWarnings{false}; + bool _DisableValidation{false}; +}; + +void UserSettingsImpl::set(const Key key, bool value) +{ + switch (key) + { + case Key::MuteWarnings: + _MuteWarnings = value; + break; + case Key::DisableValidation: + _DisableValidation = value; + break; + default: + throw std::runtime_error("Invalid key in boolean set"); + break; + } +} + +bool UserSettingsImpl::get(const Key key) const +{ + switch (key) + { + case Key::MuteWarnings: + return _MuteWarnings; + case Key::DisableValidation: + return _DisableValidation; + default: + throw std::runtime_error("Invalid key in boolean get"); + break; + } + return false; +} + +} // namespace luci + +namespace luci +{ + +UserSettings *UserSettings::settings() +{ + static UserSettingsImpl _this; + return &_this; +} + +} // namespace luci diff --git a/compiler/luci/env/src/UserSettings.test.cpp b/compiler/luci/env/src/UserSettings.test.cpp new file mode 100644 index 0000000..8d9d187 --- /dev/null +++ b/compiler/luci/env/src/UserSettings.test.cpp @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/UserSettings.h" + +#include + +TEST(UserSettings, instance) +{ + auto settings = luci::UserSettings::settings(); + ASSERT_NE(nullptr, settings); + + auto s2 = luci::UserSettings::settings(); + ASSERT_EQ(s2, settings); +} + +TEST(UserSettings, MuteWarnings) +{ + auto settings = luci::UserSettings::settings(); + ASSERT_NE(nullptr, settings); + + settings->set(luci::UserSettings::Key::MuteWarnings, false); + ASSERT_FALSE(settings->get(luci::UserSettings::Key::MuteWarnings)); + + settings->set(luci::UserSettings::Key::MuteWarnings, true); + ASSERT_TRUE(settings->get(luci::UserSettings::Key::MuteWarnings)); +} + +TEST(UserSettings, DisableValidation) +{ + auto settings = luci::UserSettings::settings(); + ASSERT_NE(nullptr, settings); + + settings->set(luci::UserSettings::Key::DisableValidation, false); + ASSERT_FALSE(settings->get(luci::UserSettings::Key::DisableValidation)); + + settings->set(luci::UserSettings::Key::DisableValidation, true); + ASSERT_TRUE(settings->get(luci::UserSettings::Key::DisableValidation)); +} + +TEST(UserSettings, undefined_set_NEG) +{ + auto settings = luci::UserSettings::settings(); + ASSERT_NE(nullptr, settings); + + ASSERT_THROW(settings->set(luci::UserSettings::Key::Undefined, true), std::exception); +} + +TEST(UserSettings, undefined_get_NEG) +{ + auto settings = luci::UserSettings::settings(); + ASSERT_NE(nullptr, settings); + + ASSERT_THROW(settings->get(luci::UserSettings::Key::Undefined), std::exception); +} diff --git a/compiler/luci/export/CMakeLists.txt b/compiler/luci/export/CMakeLists.txt index e32eca3..fe4382e 100644 --- a/compiler/luci/export/CMakeLists.txt +++ b/compiler/luci/export/CMakeLists.txt @@ -10,6 +10,7 @@ target_link_libraries(luci_export PRIVATE luci_lang) target_link_libraries(luci_export PRIVATE luci_service) target_link_libraries(luci_export PRIVATE luci_pass) target_link_libraries(luci_export PRIVATE mio_circle) +target_link_libraries(luci_export PRIVATE luci_env) target_link_libraries(luci_export PRIVATE luci_log) target_link_libraries(luci_export PRIVATE luci_logex) target_link_libraries(luci_export PRIVATE nncc_common) diff --git a/compiler/luci/export/src/CircleExporterImpl.cpp b/compiler/luci/export/src/CircleExporterImpl.cpp index 81109ee..860cebf 100644 --- a/compiler/luci/export/src/CircleExporterImpl.cpp +++ b/compiler/luci/export/src/CircleExporterImpl.cpp @@ -16,6 +16,7 @@ #include "CircleExporterImpl.h" #include "Optimize.h" +#include "TypeBridge.h" #include "CircleTensorExporter.h" #include "CircleOperationExporter.h" #include "CircleExporterUtils.h" @@ -36,11 +37,11 @@ luci::CircleInput *input_node(loco::Graph *g, const loco::GraphInputIndex &index { for (uint32_t n = 0; n < g->nodes()->size(); ++n) { - if (auto pull = dynamic_cast(g->nodes()->at(n))) + if (auto input = dynamic_cast(g->nodes()->at(n))) { - if (pull->indexed() && pull->index() == index) + if (input->indexed() && input->index() == index) { - return pull; + return input; } } } @@ -51,11 +52,11 @@ luci::CircleOutput *output_node(loco::Graph *g, const loco::GraphOutputIndex &in { for (uint32_t n = 0; n < g->nodes()->size(); ++n) { - if (auto push = dynamic_cast(g->nodes()->at(n))) + if (auto output = dynamic_cast(g->nodes()->at(n))) { - if (push->indexed() && push->index() == index) + if (output->indexed() && output->index() == index) { - return push; + return output; } } } @@ -80,6 +81,13 @@ void registerGraphOutputTensors(loco::Graph *graph, luci::SubGraphContext &ctx) assert(push != nullptr); auto node = push->from(); assert(node != nullptr); + + // Do not export CircleOutput when it's input is CircleOutputExclude + if (dynamic_cast(push->from()) != nullptr) + { + continue; + } + ctx._outputs.push_back(luci::get_tensor_index(node)); } } @@ -93,8 +101,7 @@ using namespace circle; using namespace flatbuffers; Offset>> -encodeOperatorCodes(FlatBufferBuilder &builder, std::unordered_map &opcodes, - std::unordered_map &custom_opcodes) +encodeOperatorCodes(FlatBufferBuilder &builder, std::unordered_map &opcodes) { std::vector> operator_codes_vec(opcodes.size()); for (auto it : opcodes) @@ -102,19 +109,15 @@ encodeOperatorCodes(FlatBufferBuilder &builder, std::unordered_mapsecond)); + CreateOperatorCode(builder, it.first.opcode, builder.CreateString(it.first.custom_code)); } } + return builder.CreateVector(operator_codes_vec); } @@ -136,8 +139,9 @@ CircleExporterImpl::exportSubgraph(SerializedGraphData &gd) auto inputs = _builder.CreateVector(gd._inputs); auto outputs = _builder.CreateVector(gd._outputs); auto operators = _builder.CreateVector(gd._operators); + auto name = _builder.CreateString(gd._name); auto df = gd._data_format; - auto subgraph = CreateSubGraph(_builder, tensors, inputs, outputs, operators, df); + auto subgraph = CreateSubGraph(_builder, tensors, inputs, outputs, operators, name, df); return subgraph; } @@ -146,6 +150,9 @@ void CircleExporterImpl::exportGraph(loco::Graph *graph) // do graph optimization optimize(graph); + // copy shape/dtype inference data to CircleNode + copy_shape_dtype(graph); + _builder.Clear(); SerializedModelData md; @@ -154,6 +161,9 @@ void CircleExporterImpl::exportGraph(loco::Graph *graph) // This version is taken from comment in fbs constexpr uint32_t version = 0; + // set Subgraph name + gd._name = graph->name(); + // TODO set this value properly gd._data_format = circle::DataFormat::DataFormat_CHANNELS_LAST; @@ -170,8 +180,7 @@ void CircleExporterImpl::exportGraph(loco::Graph *graph) exportNodes(graph, _builder, md, gd); // encode operator codes - auto operator_codes = - encodeOperatorCodes(_builder, md._operator_codes, md._custom_operator_codes); + auto operator_codes = encodeOperatorCodes(_builder, md._operator_codes); // Subgraphs Offset subgraph = exportSubgraph(gd); @@ -203,6 +212,9 @@ void CircleExporterImpl::exportModule(Module *module) _builder.Clear(); + // prepare model data + prepareModelData(_builder, md); + std::vector> subgraph_vec; for (size_t g = 0; g < module->size(); ++g) @@ -211,8 +223,14 @@ void CircleExporterImpl::exportModule(Module *module) optimize(graph); + // copy shape/dtype inference data to CircleNode + copy_shape_dtype(graph); + SerializedGraphData gd; + // set Subgraph name + gd._name = graph->name(); + // TODO set this value properly gd._data_format = circle::DataFormat::DataFormat_CHANNELS_LAST; @@ -233,8 +251,7 @@ void CircleExporterImpl::exportModule(Module *module) auto subgraphs = _builder.CreateVector(std::vector>{subgraph_vec}); // encode operator codes - auto operator_codes = - encodeOperatorCodes(_builder, md._operator_codes, md._custom_operator_codes); + auto operator_codes = encodeOperatorCodes(_builder, md._operator_codes); // Description std::string description_str = "nnpackage"; diff --git a/compiler/luci/export/src/CircleExporterUtils.cpp b/compiler/luci/export/src/CircleExporterUtils.cpp index 1272fac..f097e71 100644 --- a/compiler/luci/export/src/CircleExporterUtils.cpp +++ b/compiler/luci/export/src/CircleExporterUtils.cpp @@ -70,28 +70,49 @@ circle::TensorType to_circle_tensortype(loco::DataType type) } } +circle::MirrorPadMode to_circle_mirrorpadmode(luci::MirrorPadMode mode) +{ + switch (mode) + { + case luci::MirrorPadMode::REFLECT: + return circle::MirrorPadMode::MirrorPadMode_REFLECT; + case luci::MirrorPadMode::SYMMETRIC: + return circle::MirrorPadMode::MirrorPadMode_SYMMETRIC; + default: + INTERNAL_EXN_V("trying to convert unsupported luci::MirrorPadMode", oops::to_uint32(mode)); + } +} + } // namespace luci namespace luci { -uint32_t SerializedModelData::registerBuiltinOpcode(circle::BuiltinOperator builtin_code) +uint32_t SerializedModelData::registerBuiltinOpcode(circle::BuiltinOperator builtin_code, + const int32_t op_version) { - auto it = _operator_codes.find(OpCode{builtin_code}); + assert(op_version > 0); + + auto it = _operator_codes.find(OpCode{builtin_code, "", op_version}); if (it != _operator_codes.end()) { return it->second; } auto idx = static_cast(_operator_codes.size()); - _operator_codes.emplace(OpCode{builtin_code}, idx); + _operator_codes.emplace(OpCode{builtin_code, "", op_version}, idx); return idx; } -uint32_t SerializedModelData::registerCustomOpcode(const std::string &custom_op) +uint32_t SerializedModelData::registerCustomOpcode(const std::string &custom_code) { - circle::BuiltinOperator custom_code = circle::BuiltinOperator_CUSTOM; - auto idx = registerBuiltinOpcode(custom_code); - _custom_operator_codes.emplace(OpCode{custom_code}, custom_op); + const circle::BuiltinOperator builtin_code = circle::BuiltinOperator_CUSTOM; + auto it = _operator_codes.find(OpCode{builtin_code, custom_code}); + if (it != _operator_codes.end()) + { + return it->second; + } + auto idx = static_cast(_operator_codes.size()); + _operator_codes.emplace(OpCode{builtin_code, custom_code}, idx); return idx; } diff --git a/compiler/luci/export/src/CircleExporterUtils.h b/compiler/luci/export/src/CircleExporterUtils.h index 6b970fd..f9ce6d2 100644 --- a/compiler/luci/export/src/CircleExporterUtils.h +++ b/compiler/luci/export/src/CircleExporterUtils.h @@ -31,6 +31,7 @@ namespace luci circle::ActivationFunctionType to_circle_actfunc(luci::FusedActFunc func); circle::TensorType to_circle_tensortype(loco::DataType type); +circle::MirrorPadMode to_circle_mirrorpadmode(luci::MirrorPadMode mode); } // namespace luci diff --git a/compiler/luci/export/src/CircleOperationExporter.cpp b/compiler/luci/export/src/CircleOperationExporter.cpp index ad9c7fd..3c01b67 100644 --- a/compiler/luci/export/src/CircleOperationExporter.cpp +++ b/compiler/luci/export/src/CircleOperationExporter.cpp @@ -22,6 +22,8 @@ #include #include #include +#include +#include #include #include @@ -49,42 +51,125 @@ public: public: void visit(luci::CircleAbs *) final; void visit(luci::CircleAdd *) final; + void visit(luci::CircleAddN *) final; void visit(luci::CircleArgMax *) final; + void visit(luci::CircleArgMin *) final; void visit(luci::CircleAveragePool2D *) final; + void visit(luci::CircleBatchMatMul *) final; void visit(luci::CircleBatchToSpaceND *) final; + void visit(luci::CircleCast *) final; + void visit(luci::CircleCeil *) final; void visit(luci::CircleConcatenation *) final; void visit(luci::CircleConst *) final{/* skip, everything is done in exportOpDefinedTensors */}; void visit(luci::CircleConv2D *) final; void visit(luci::CircleCos *) final; + void visit(luci::CircleCustom *) final; + void visit(luci::CircleDepthToSpace *) final; void visit(luci::CircleDepthwiseConv2D *) final; void visit(luci::CircleDiv *) final; - void visit(luci::CircleExp *) final; + void visit(luci::CircleElu *) final; void visit(luci::CircleEqual *) final; + void visit(luci::CircleExp *) final; + void visit(luci::CircleExpandDims *) final; + void visit(luci::CircleFill *) final; + void visit(luci::CircleFloor *) final; + void visit(luci::CircleFloorDiv *) final; + void visit(luci::CircleFloorMod *) final; void visit(luci::CircleFullyConnected *) final; + void visit(luci::CircleGather *) final; + void visit(luci::CircleGatherNd *) final; + void visit(luci::CircleGreater *) final; + void visit(luci::CircleGreaterEqual *) final; + void visit(luci::CircleIf *) final; + void visit(luci::CircleL2Normalize *) final; + void visit(luci::CircleL2Pool2D *) final; + void visit(luci::CircleLeakyRelu *) final; + void visit(luci::CircleLess *) final; + void visit(luci::CircleLessEqual *) final; + void visit(luci::CircleLocalResponseNormalization *) final; + void visit(luci::CircleLog *) final; + void visit(luci::CircleLogicalAnd *) final; void visit(luci::CircleLogicalNot *) final; void visit(luci::CircleLogicalOr *) final; + void visit(luci::CircleLogistic *) final; + void visit(luci::CircleLogSoftmax *) final; + void visit(luci::CircleMatrixDiag *) final; + void visit(luci::CircleMatrixSetDiag *) final; void visit(luci::CircleMaximum *) final; void visit(luci::CircleMaxPool2D *) final; void visit(luci::CircleMean *) final; + void visit(luci::CircleMinimum *) final; + void visit(luci::CircleMirrorPad *) final; void visit(luci::CircleMul *) final; + void visit(luci::CircleNeg *) final; + void visit(luci::CircleNotEqual *) final; + void visit(luci::CircleOneHot *) final; void visit(luci::CirclePack *) final; void visit(luci::CirclePad *) final; + void visit(luci::CirclePow *) final; + void visit(luci::CirclePRelu *) final; + void visit(luci::CircleRange *) final; + void visit(luci::CircleRank *) final; + void visit(luci::CircleReduceAny *) final; + void visit(luci::CircleReduceMax *) final; + void visit(luci::CircleReduceMin *) final; + void visit(luci::CircleReduceProd *) final; void visit(luci::CircleRelu *) final; void visit(luci::CircleRelu6 *) final; + void visit(luci::CircleReluN1To1 *) final; void visit(luci::CircleReshape *) final; + void visit(luci::CircleResizeBilinear *) final; + void visit(luci::CircleResizeNearestNeighbor *) final; + void visit(luci::CircleReverseSequence *) final; + void visit(luci::CircleReverseV2 *) final; + void visit(luci::CircleRound *) final; void visit(luci::CircleRsqrt *) final; + void visit(luci::CircleScatterNd *) final; + void visit(luci::CircleSegmentSum *) final; + void visit(luci::CircleSelect *) final; + void visit(luci::CircleSelectV2 *) final; + void visit(luci::CircleShape *) final; + void visit(luci::CircleSin *) final; + void visit(luci::CircleSlice *) final; void visit(luci::CircleSoftmax *) final; + void visit(luci::CircleSpaceToBatchND *) final; + void visit(luci::CircleSpaceToDepth *) final; + void visit(luci::CircleSparseToDense *) final; + void visit(luci::CircleSplit *) final; + void visit(luci::CircleSplitV *) final; void visit(luci::CircleSqrt *) final; + void visit(luci::CircleSquare *) final; void visit(luci::CircleSquaredDifference *) final; + void visit(luci::CircleSqueeze *) final; + void visit(luci::CircleStridedSlice *) final; void visit(luci::CircleSub *) final; - // TODO CircleTanh + void visit(luci::CircleSum *) final; + void visit(luci::CircleTanh *) final; + void visit(luci::CircleTile *) final; + void visit(luci::CircleTopKV2 *) final; void visit(luci::CircleTranspose *) final; void visit(luci::CircleTransposeConv *) final; + void visit(luci::CircleUnpack *) final; + void visit(luci::CircleWhere *) final; + void visit(luci::CircleWhile *) final; + void visit(luci::CircleZerosLike *) final; // Circle only + void visit(luci::CircleBCQFullyConnected *) final; + void visit(luci::CircleBCQGather *) final; void visit(luci::CircleInstanceNorm *) final; // Virtual void visit(luci::CircleInput *) final {} void visit(luci::CircleOutput *) final {} + void visit(luci::CircleOutputDummy *) final {} + void visit(luci::CircleOutputExclude *) final {} + // Virtual for multiple-outputs + void visit(luci::CircleCustomOut *) final {} + void visit(luci::CircleIfOut *) final {} + void visit(luci::CircleSplitOut *) final {} + void visit(luci::CircleSplitVOut *) final {} + void visit(luci::CircleTopKV2Out *) final {} + void visit(luci::CircleUnpackOut *) final {} + void visit(luci::CircleWhileOut *) final {} private: /** @@ -95,6 +180,17 @@ private: template void export_pool_2d(CirclePool2D *node, circle::BuiltinOperator builtin_op); + /** + * @brief export simple nodes + */ + void export_simple(loco::Node *node, circle::BuiltinOperator bop, circle::BuiltinOptions bot, + flatbuffers::Offset options_offset); + + /** + * @brief export simple nodes having void options + */ + void export_simple(loco::Node *node, circle::BuiltinOperator bop); + private: FlatBufferBuilder &builder; SerializedModelData &md; @@ -105,11 +201,12 @@ template void OperationExporter::export_pool_2d(CirclePool2D *node, circle::BuiltinOperator builtin_op) { LUCI_ASSERT(builtin_op == circle::BuiltinOperator_MAX_POOL_2D || + builtin_op == circle::BuiltinOperator_L2_POOL_2D || builtin_op == circle::BuiltinOperator_AVERAGE_POOL_2D, - "Should be MaxPool or AvgPool"); + "Should be L2Pool, MaxPool or AvgPool"); LUCI_ASSERT(node->padding() != luci::Padding::UNDEFINED, "Padding is not set"); - uint32_t op_idx = md.registerBuiltinOpcode(builtin_op); + uint32_t op_idx = md.registerBuiltinOpcode(builtin_op, node->op_version()); std::vector inputs_vec{get_tensor_index(node->value())}; std::vector outputs_vec{get_tensor_index(static_cast(node))}; auto inputs = builder.CreateVector(inputs_vec); @@ -125,54 +222,122 @@ void OperationExporter::export_pool_2d(CirclePool2D *node, circle::BuiltinOperat gd._operators.push_back(op_offset); } -void OperationExporter::visit(luci::CircleAbs *node) +void OperationExporter::export_simple(loco::Node *node, circle::BuiltinOperator bop, + circle::BuiltinOptions bot, + flatbuffers::Offset options_offset) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_ABS); - std::vector inputs_vec{get_tensor_index(node->x())}; + uint32_t op_idx = + md.registerBuiltinOpcode(bop, loco::must_cast(node)->op_version()); + std::vector inputs_vec; + std::vector outputs_vec{get_tensor_index(node)}; + for (uint32_t i = 0; i < node->arity(); ++i) + inputs_vec.push_back(get_tensor_index(node->arg(i))); + auto inputs = builder.CreateVector(inputs_vec); + auto outputs = builder.CreateVector(outputs_vec); + auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, bot, options_offset); + gd._operators.push_back(op_offset); +} + +void OperationExporter::export_simple(loco::Node *node, circle::BuiltinOperator bop) +{ + uint32_t op_idx = + md.registerBuiltinOpcode(bop, loco::must_cast(node)->op_version()); + std::vector inputs_vec; std::vector outputs_vec{get_tensor_index(static_cast(node))}; + for (uint32_t i = 0; i < node->arity(); ++i) + inputs_vec.push_back(get_tensor_index(node->arg(i))); auto inputs = builder.CreateVector(inputs_vec); auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateAbsOptions(builder); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_AbsOptions, options.Union()); + auto op_offset = CreateOperator(builder, op_idx, inputs, outputs); gd._operators.push_back(op_offset); } +void OperationExporter::visit(luci::CircleAbs *node) +{ + export_simple(node, circle::BuiltinOperator_ABS, circle::BuiltinOptions_AbsOptions, + CreateAbsOptions(builder).Union()); +} + void OperationExporter::visit(luci::CircleAdd *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_ADD); - std::vector inputs_vec{get_tensor_index(node->x()), get_tensor_index(node->y())}; + export_simple( + node, circle::BuiltinOperator_ADD, circle::BuiltinOptions_AddOptions, + CreateAddOptions(builder, to_circle_actfunc(node->fusedActivationFunction())).Union()); +} + +void OperationExporter::visit(luci::CircleAddN *node) +{ + uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_ADD_N, node->op_version()); + std::vector inputs_vec; std::vector outputs_vec{get_tensor_index(static_cast(node))}; + + for (uint32_t i = 0; i < node->arity(); ++i) + inputs_vec.push_back(get_tensor_index(node->inputs(i))); + auto inputs = builder.CreateVector(inputs_vec); auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateAddOptions(builder, to_circle_actfunc(node->fusedActivationFunction())); + auto options = CreateAddNOptions(builder); auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_AddOptions, options.Union()); + circle::BuiltinOptions_AddNOptions, options.Union()); gd._operators.push_back(op_offset); } void OperationExporter::visit(luci::CircleArgMax *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_ARG_MAX); - std::vector inputs_vec{get_tensor_index(node->input()), - get_tensor_index(node->dimension())}; + export_simple(node, circle::BuiltinOperator_ARG_MAX, circle::BuiltinOptions_ArgMaxOptions, + CreateArgMaxOptions(builder, to_circle_tensortype(node->output_type())).Union()); +} + +void OperationExporter::visit(luci::CircleArgMin *node) +{ + export_simple(node, circle::BuiltinOperator_ARG_MIN, circle::BuiltinOptions_ArgMinOptions, + CreateArgMinOptions(builder, to_circle_tensortype(node->output_type())).Union()); +} + +void OperationExporter::visit(luci::CircleAveragePool2D *node) +{ + export_pool_2d(node, circle::BuiltinOperator_AVERAGE_POOL_2D); +} + +void OperationExporter::visit(luci::CircleBatchMatMul *node) +{ + export_simple(node, circle::BuiltinOperator_BATCH_MATMUL, + circle::BuiltinOptions_BatchMatMulOptions, + CreateBatchMatMulOptions(builder, node->adj_x(), node->adj_y()).Union()); +} + +void OperationExporter::visit(luci::CircleCast *node) +{ + uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_CAST, node->op_version()); + std::vector inputs_vec{get_tensor_index(node->x())}; std::vector outputs_vec{get_tensor_index(static_cast(node))}; auto inputs = builder.CreateVector(inputs_vec); auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateArgMaxOptions(builder, to_circle_tensortype(node->output_type())); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_ArgMaxOptions, options.Union()); + + flatbuffers::Offset op_offset; + if (node->out_data_type() != loco::DataType::Unknown) + { + auto options = CreateCastOptions(builder, to_circle_tensortype(node->in_data_type()), + to_circle_tensortype(node->out_data_type())); + op_offset = CreateOperator(builder, op_idx, inputs, outputs, circle::BuiltinOptions_CastOptions, + options.Union()); + } + else + { + op_offset = CreateOperator(builder, op_idx, inputs, outputs); + } gd._operators.push_back(op_offset); } -void OperationExporter::visit(luci::CircleAveragePool2D *node) +void OperationExporter::visit(luci::CircleCeil *node) { - export_pool_2d(node, circle::BuiltinOperator_AVERAGE_POOL_2D); + export_simple(node, circle::BuiltinOperator_CEIL); } void OperationExporter::visit(luci::CircleConcatenation *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_CONCATENATION); + uint32_t op_idx = + md.registerBuiltinOpcode(circle::BuiltinOperator_CONCATENATION, node->op_version()); std::vector inputs_vec; std::vector outputs_vec{get_tensor_index(static_cast(node))}; @@ -190,169 +355,304 @@ void OperationExporter::visit(luci::CircleConcatenation *node) void OperationExporter::visit(luci::CircleBatchToSpaceND *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_BATCH_TO_SPACE_ND); - std::vector inputs_vec{get_tensor_index(node->input()), - get_tensor_index(node->block_shape()), - get_tensor_index(node->crops())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; - - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateBatchToSpaceNDOptions(builder); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_BatchToSpaceNDOptions, options.Union()); - gd._operators.push_back(op_offset); + export_simple(node, circle::BuiltinOperator_BATCH_TO_SPACE_ND, + circle::BuiltinOptions_BatchToSpaceNDOptions, + CreateBatchToSpaceNDOptions(builder).Union()); } void OperationExporter::visit(luci::CircleConv2D *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_CONV_2D); - - // Make input, output and options for operator - std::vector inputs_vec{get_tensor_index(node->input()), get_tensor_index(node->filter()), - get_tensor_index(node->bias())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - circle::Padding padding = getOpPadding(node->padding()); - auto options = CreateConv2DOptions(builder, padding, node->stride()->w(), node->stride()->h(), - to_circle_actfunc(node->fusedActivationFunction())); - - // Make CONV_2D operator - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_Conv2DOptions, options.Union()); - gd._operators.push_back(op_offset); + export_simple(node, circle::BuiltinOperator_CONV_2D, circle::BuiltinOptions_Conv2DOptions, + CreateConv2DOptions(builder, getOpPadding(node->padding()), node->stride()->w(), + node->stride()->h(), + to_circle_actfunc(node->fusedActivationFunction()), + node->dilation()->w(), node->dilation()->h()) + .Union()); } void OperationExporter::visit(luci::CircleCos *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_COS); + export_simple(node, circle::BuiltinOperator_COS, circle::BuiltinOptions_CosOptions, + CreateCosOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleCustom *node) +{ + auto custom_outputs = loco::succs(node); + + uint32_t op_idx = md.registerCustomOpcode(node->custom_code()); + std::vector inputs_vec; + std::vector outputs_vec; + + for (uint32_t index = 0; index < node->numInputs(); index++) + { + inputs_vec.push_back(get_tensor_index(node->inputs(index))); + } + for (uint32_t index = 0; index < custom_outputs.size(); index++) + { + // store in order of index + bool found = false; + for (auto out : custom_outputs) + { + auto custom_out = loco::must_cast(out); + if (custom_out->index() == static_cast(index)) + { + outputs_vec.push_back(get_tensor_index(custom_out)); + found = true; + break; + } + } + if (!found) + { + INTERNAL_EXN("Invalid Custom output"); + } + } - // Make input, output and options for operator - std::vector inputs_vec{get_tensor_index(node->x())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; auto inputs = builder.CreateVector(inputs_vec); auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateCosOptions(builder); - - // Make COS operator - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_CosOptions, options.Union()); + flatbuffers::Offset> circle_custom_options; + std::vector custom_options_vec{node->custom_options().begin(), + node->custom_options().end()}; + circle_custom_options = builder.CreateVector(custom_options_vec); + auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, circle::BuiltinOptions_NONE, + flatbuffers::Offset(), circle_custom_options); gd._operators.push_back(op_offset); } +void OperationExporter::visit(luci::CircleDepthToSpace *node) +{ + export_simple(node, circle::BuiltinOperator_DEPTH_TO_SPACE, + circle::BuiltinOptions_DepthToSpaceOptions, + CreateDepthToSpaceOptions(builder, node->block_size()).Union()); +} + void OperationExporter::visit(luci::CircleDepthwiseConv2D *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_DEPTHWISE_CONV_2D); + export_simple(node, circle::BuiltinOperator_DEPTHWISE_CONV_2D, + circle::BuiltinOptions_DepthwiseConv2DOptions, + CreateDepthwiseConv2DOptions(builder, getOpPadding(node->padding()), + node->stride()->w(), node->stride()->h(), + node->depthMultiplier(), + to_circle_actfunc(node->fusedActivationFunction()), + node->dilation()->w(), node->dilation()->h()) + .Union()); +} - // Make input, output and options for operator - std::vector inputs_vec{get_tensor_index(node->input()), get_tensor_index(node->filter()), - get_tensor_index(node->bias())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - circle::Padding padding = getOpPadding(node->padding()); - auto options = CreateDepthwiseConv2DOptions(builder, padding, node->stride()->w(), - node->stride()->h(), node->depthMultiplier(), - to_circle_actfunc(node->fusedActivationFunction())); +void OperationExporter::visit(luci::CircleDiv *node) +{ + export_simple( + node, circle::BuiltinOperator_DIV, circle::BuiltinOptions_DivOptions, + CreateDivOptions(builder, to_circle_actfunc(node->fusedActivationFunction())).Union()); +} - // Make DEPTHWISE_CONV_2D operator - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_DepthwiseConv2DOptions, options.Union()); - gd._operators.push_back(op_offset); +void OperationExporter::visit(luci::CircleElu *node) +{ + export_simple(node, circle::BuiltinOperator_ELU); } -void OperationExporter::visit(luci::CircleDiv *node) +void OperationExporter::visit(luci::CircleEqual *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_DIV); - std::vector inputs_vec{get_tensor_index(node->x()), get_tensor_index(node->y())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateDivOptions(builder, to_circle_actfunc(node->fusedActivationFunction())); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_DivOptions, options.Union()); - gd._operators.push_back(op_offset); + export_simple(node, circle::BuiltinOperator_EQUAL, circle::BuiltinOptions_EqualOptions, + CreateEqualOptions(builder).Union()); } void OperationExporter::visit(luci::CircleExp *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_EXP); - std::vector inputs_vec{get_tensor_index(node->x())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateAbsOptions(builder); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_ExpOptions, options.Union()); - gd._operators.push_back(op_offset); + export_simple(node, circle::BuiltinOperator_EXP, circle::BuiltinOptions_ExpOptions, + CreateExpOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleExpandDims *node) +{ + export_simple(node, circle::BuiltinOperator_EXPAND_DIMS, circle::BuiltinOptions_ExpandDimsOptions, + CreateExpandDimsOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleFill *node) +{ + export_simple(node, circle::BuiltinOperator_FILL, circle::BuiltinOptions_FillOptions, + CreateFillOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleFloor *node) +{ + export_simple(node, circle::BuiltinOperator_FLOOR); +} + +void OperationExporter::visit(luci::CircleFloorDiv *node) +{ + export_simple(node, circle::BuiltinOperator_FLOOR_DIV, circle::BuiltinOptions_FloorDivOptions, + CreateFloorDivOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleFloorMod *node) +{ + export_simple(node, circle::BuiltinOperator_FLOOR_MOD, circle::BuiltinOptions_FloorModOptions, + CreateFloorModOptions(builder).Union()); } void OperationExporter::visit(luci::CircleFullyConnected *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_FULLY_CONNECTED); + export_simple( + node, circle::BuiltinOperator_FULLY_CONNECTED, circle::BuiltinOptions_FullyConnectedOptions, + CreateFullyConnectedOptions(builder, to_circle_actfunc(node->fusedActivationFunction())) + .Union()); +} - // Make input, output and options for operator - std::vector inputs_vec{get_tensor_index(node->input()), - get_tensor_index(node->weights()), - get_tensor_index(node->bias())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - auto options = - CreateFullyConnectedOptions(builder, to_circle_actfunc(node->fusedActivationFunction())); +void OperationExporter::visit(luci::CircleGather *node) +{ + export_simple(node, circle::BuiltinOperator_GATHER, circle::BuiltinOptions_GatherOptions, + CreateGatherOptions(builder, node->axis()).Union()); +} - // Make FULLY_CONNECTED operator - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_FullyConnectedOptions, options.Union()); - gd._operators.push_back(op_offset); +void OperationExporter::visit(luci::CircleGatherNd *node) +{ + export_simple(node, circle::BuiltinOperator_GATHER_ND, circle::BuiltinOptions_GatherNdOptions, + CreateGatherNdOptions(builder).Union()); } -void OperationExporter::visit(luci::CircleLogicalNot *node) +void OperationExporter::visit(luci::CircleGreater *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_LOGICAL_NOT); + export_simple(node, circle::BuiltinOperator_GREATER, circle::BuiltinOptions_GreaterOptions, + CreateGreaterOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleGreaterEqual *node) +{ + export_simple(node, circle::BuiltinOperator_GREATER_EQUAL, + circle::BuiltinOptions_GreaterEqualOptions, + CreateGreaterEqualOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleIf *node) +{ + auto if_outs = loco::succs(node); + assert(if_outs.size() == node->output_count()); + + uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_IF, node->op_version()); + std::vector inputs_vec; + std::vector outputs_vec; + + inputs_vec.push_back(get_tensor_index(node->cond())); + for (uint32_t idx = 0; idx < node->input_count(); ++idx) + inputs_vec.push_back(get_tensor_index(node->input(idx))); + + for (uint32_t idx = 0; idx < node->output_count(); ++idx) + { + // store in order of index + bool found = false; + for (auto out : if_outs) + { + auto if_out = loco::must_cast(out); + if (if_out->index() == static_cast(idx)) + { + outputs_vec.push_back(get_tensor_index(if_out)); + found = true; + break; + } + } + if (!found) + { + INTERNAL_EXN("Invalid CircleIf output"); + } + } - // Make input, output and options for operator - std::vector inputs_vec{get_tensor_index(node->x())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; auto inputs = builder.CreateVector(inputs_vec); auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateLogicalNotOptions(builder); - - // Make LOGICAL_NOT operator + auto options = CreateIfOptions(builder, node->then_branch(), node->else_branch()); auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_LogicalNotOptions, options.Union()); + circle::BuiltinOptions_IfOptions, options.Union()); gd._operators.push_back(op_offset); } +void OperationExporter::visit(luci::CircleL2Normalize *node) +{ + export_simple( + node, circle::BuiltinOperator_L2_NORMALIZATION, circle::BuiltinOptions_L2NormOptions, + CreateL2NormOptions(builder, to_circle_actfunc(node->fusedActivationFunction())).Union()); +} + +void OperationExporter::visit(luci::CircleL2Pool2D *node) +{ + export_pool_2d(node, circle::BuiltinOperator_L2_POOL_2D); +} + +void OperationExporter::visit(luci::CircleLeakyRelu *node) +{ + export_simple(node, circle::BuiltinOperator_LEAKY_RELU, circle::BuiltinOptions_LeakyReluOptions, + CreateLeakyReluOptions(builder, node->alpha()).Union()); +} + +void OperationExporter::visit(luci::CircleLess *node) +{ + export_simple(node, circle::BuiltinOperator_LESS, circle::BuiltinOptions_LessOptions, + CreateLessOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleLessEqual *node) +{ + export_simple(node, circle::BuiltinOperator_LESS_EQUAL, circle::BuiltinOptions_LessEqualOptions, + CreateLessEqualOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleLocalResponseNormalization *node) +{ + export_simple(node, circle::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, + circle::BuiltinOptions_LocalResponseNormalizationOptions, + CreateLocalResponseNormalizationOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleLog *node) +{ + export_simple(node, circle::BuiltinOperator_LOG); +} + +void OperationExporter::visit(luci::CircleLogicalAnd *node) +{ + export_simple(node, circle::BuiltinOperator_LOGICAL_AND, circle::BuiltinOptions_LogicalAndOptions, + CreateLogicalAndOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleLogicalNot *node) +{ + export_simple(node, circle::BuiltinOperator_LOGICAL_NOT, circle::BuiltinOptions_LogicalNotOptions, + CreateLogicalNotOptions(builder).Union()); +} + void OperationExporter::visit(luci::CircleLogicalOr *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_LOGICAL_OR); + export_simple(node, circle::BuiltinOperator_LOGICAL_OR, circle::BuiltinOptions_LogicalOrOptions, + CreateLogicalOrOptions(builder).Union()); +} - // Make input, output and options for operator - std::vector inputs_vec{get_tensor_index(node->x()), get_tensor_index(node->y())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateLogicalOrOptions(builder); +void OperationExporter::visit(luci::CircleLogistic *node) +{ + export_simple(node, circle::BuiltinOperator_LOGISTIC); +} - // Make LOGICAL_OR operator - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_LogicalOrOptions, options.Union()); - gd._operators.push_back(op_offset); +void OperationExporter::visit(luci::CircleLogSoftmax *node) +{ + export_simple(node, circle::BuiltinOperator_LOG_SOFTMAX, circle::BuiltinOptions_LogSoftmaxOptions, + CreateLogSoftmaxOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleMatrixDiag *node) +{ + export_simple(node, circle::BuiltinOperator_MATRIX_DIAG, circle::BuiltinOptions_MatrixDiagOptions, + CreateMatrixDiagOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleMatrixSetDiag *node) +{ + export_simple(node, circle::BuiltinOperator_MATRIX_SET_DIAG, + circle::BuiltinOptions_MatrixSetDiagOptions, + CreateMatrixSetDiagOptions(builder).Union()); } void OperationExporter::visit(luci::CircleMaximum *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_MAXIMUM); - std::vector inputs_vec{get_tensor_index(node->x()), get_tensor_index(node->y())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateMaximumMinimumOptions(builder); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_MaximumMinimumOptions, options.Union()); - gd._operators.push_back(op_offset); + export_simple(node, circle::BuiltinOperator_MAXIMUM, circle::BuiltinOptions_MaximumMinimumOptions, + CreateMaximumMinimumOptions(builder).Union()); } void OperationExporter::visit(luci::CircleMaxPool2D *node) @@ -362,259 +662,568 @@ void OperationExporter::visit(luci::CircleMaxPool2D *node) void OperationExporter::visit(luci::CircleMean *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_MEAN); - std::vector inputs_vec{get_tensor_index(node->input()), - get_tensor_index(node->reduction_indices())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateReducerOptions(builder, node->keep_dims()); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_ReducerOptions, options.Union()); - gd._operators.push_back(op_offset); + export_simple(node, circle::BuiltinOperator_MEAN, circle::BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder, node->keep_dims()).Union()); +} + +void OperationExporter::visit(luci::CircleMinimum *node) +{ + export_simple(node, circle::BuiltinOperator_MINIMUM, circle::BuiltinOptions_MaximumMinimumOptions, + CreateMaximumMinimumOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleMirrorPad *node) +{ + export_simple(node, circle::BuiltinOperator_MIRROR_PAD, circle::BuiltinOptions_MirrorPadOptions, + CreateMirrorPadOptions(builder, to_circle_mirrorpadmode(node->mode())).Union()); } void OperationExporter::visit(luci::CircleMul *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_MUL); - std::vector inputs_vec{get_tensor_index(node->x()), get_tensor_index(node->y())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateMulOptions(builder, to_circle_actfunc(node->fusedActivationFunction())); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_MulOptions, options.Union()); - gd._operators.push_back(op_offset); + export_simple( + node, circle::BuiltinOperator_MUL, circle::BuiltinOptions_MulOptions, + CreateMulOptions(builder, to_circle_actfunc(node->fusedActivationFunction())).Union()); } -void OperationExporter::visit(luci::CirclePack *node) +void OperationExporter::visit(luci::CircleNeg *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_PACK); - std::vector inputs_vec; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; + export_simple(node, circle::BuiltinOperator_NEG, circle::BuiltinOptions_NegOptions, + CreateNegOptions(builder).Union()); +} - for (uint32_t i = 0; i < node->values_count(); ++i) - inputs_vec.push_back(get_tensor_index(node->values(i))); +void OperationExporter::visit(luci::CircleNotEqual *node) +{ + export_simple(node, circle::BuiltinOperator_NOT_EQUAL, circle::BuiltinOptions_NotEqualOptions, + CreateNotEqualOptions(builder).Union()); +} - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - auto options = CreatePackOptions(builder, node->values_count(), node->axis()); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_PackOptions, options.Union()); - gd._operators.push_back(op_offset); +void OperationExporter::visit(luci::CircleOneHot *node) +{ + export_simple(node, circle::BuiltinOperator_ONE_HOT, circle::BuiltinOptions_OneHotOptions, + CreateOneHotOptions(builder, node->axis()).Union()); +} + +void OperationExporter::visit(luci::CirclePack *node) +{ + export_simple(node, circle::BuiltinOperator_PACK, circle::BuiltinOptions_PackOptions, + CreatePackOptions(builder, node->values_count(), node->axis()).Union()); } void OperationExporter::visit(luci::CirclePad *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_PAD); - std::vector inputs_vec{get_tensor_index(node->input()), - get_tensor_index(node->paddings())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - auto options = CreatePadOptions(builder); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_PadOptions, options.Union()); - gd._operators.push_back(op_offset); + export_simple(node, circle::BuiltinOperator_PAD, circle::BuiltinOptions_PadOptions, + CreatePadOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CirclePow *node) +{ + export_simple(node, circle::BuiltinOperator_POW, circle::BuiltinOptions_PowOptions, + CreatePowOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CirclePRelu *node) +{ + export_simple(node, circle::BuiltinOperator_PRELU); +} + +void OperationExporter::visit(luci::CircleRange *node) +{ + export_simple(node, circle::BuiltinOperator_RANGE, circle::BuiltinOptions_RangeOptions, + CreateRangeOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleRank *node) +{ + export_simple(node, circle::BuiltinOperator_RANK, circle::BuiltinOptions_RankOptions, + CreateRankOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleReduceAny *node) +{ + export_simple(node, circle::BuiltinOperator_REDUCE_ANY, circle::BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder, node->keep_dims()).Union()); +} + +void OperationExporter::visit(luci::CircleReduceMax *node) +{ + export_simple(node, circle::BuiltinOperator_REDUCE_MAX, circle::BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder, node->keep_dims()).Union()); +} + +void OperationExporter::visit(luci::CircleReduceMin *node) +{ + export_simple(node, circle::BuiltinOperator_REDUCE_MIN, circle::BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder, node->keep_dims()).Union()); +} + +void OperationExporter::visit(luci::CircleReduceProd *node) +{ + export_simple(node, circle::BuiltinOperator_REDUCE_PROD, circle::BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder, node->keep_dims()).Union()); } void OperationExporter::visit(luci::CircleRelu *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_RELU); - std::vector inputs_vec{get_tensor_index(node->features())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs); - gd._operators.push_back(op_offset); + export_simple(node, circle::BuiltinOperator_RELU); } void OperationExporter::visit(luci::CircleRelu6 *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_RELU6); - std::vector inputs_vec{get_tensor_index(node->features())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs); - gd._operators.push_back(op_offset); + export_simple(node, circle::BuiltinOperator_RELU6); } -void OperationExporter::visit(luci::CircleReshape *node) +void OperationExporter::visit(luci::CircleReluN1To1 *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_RESHAPE); - - // Create inputs and outputs. - std::vector inputs_vec{get_tensor_index(node->tensor()), - get_tensor_index(node->shape())}; - std::vector outputs_vec{get_tensor_index(node)}; - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); + export_simple(node, circle::BuiltinOperator_RELU_N1_TO_1); +} - // Create options. +void OperationExporter::visit(luci::CircleReshape *node) +{ auto new_shape = builder.CreateVector( node->newShape()->rank(), [node](size_t i) { return node->newShape()->dim(i); }); - auto options = CreateReshapeOptions(builder, new_shape); - // Create the operator. - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_ReshapeOptions, options.Union()); - gd._operators.push_back(op_offset); + export_simple(node, circle::BuiltinOperator_RESHAPE, circle::BuiltinOptions_ReshapeOptions, + CreateReshapeOptions(builder, new_shape).Union()); } -void OperationExporter::visit(luci::CircleRsqrt *node) +void OperationExporter::visit(luci::CircleResizeBilinear *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_RSQRT); - std::vector inputs_vec{get_tensor_index(node->x())}; + export_simple( + node, circle::BuiltinOperator_RESIZE_BILINEAR, circle::BuiltinOptions_ResizeBilinearOptions, + CreateResizeBilinearOptions(builder, node->align_corners(), node->half_pixel_centers()) + .Union()); +} + +void OperationExporter::visit(luci::CircleResizeNearestNeighbor *node) +{ + export_simple(node, circle::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR, + circle::BuiltinOptions_ResizeNearestNeighborOptions, + CreateResizeNearestNeighborOptions(builder, node->align_corners()).Union()); +} + +void OperationExporter::visit(luci::CircleReverseSequence *node) +{ + export_simple( + node, circle::BuiltinOperator_REVERSE_SEQUENCE, circle::BuiltinOptions_ReverseSequenceOptions, + CreateReverseSequenceOptions(builder, node->seq_axis(), node->batch_axis()).Union()); +} + +void OperationExporter::visit(luci::CircleReverseV2 *node) +{ + uint32_t op_idx = + md.registerBuiltinOpcode(circle::BuiltinOperator_REVERSE_V2, node->op_version()); + std::vector inputs_vec{get_tensor_index(node->tensor()), get_tensor_index(node->axis())}; std::vector outputs_vec{get_tensor_index(static_cast(node))}; auto inputs = builder.CreateVector(inputs_vec); auto outputs = builder.CreateVector(outputs_vec); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs); + auto options = CreateReverseV2Options(builder); + auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, + circle::BuiltinOptions_ReverseSequenceOptions, options.Union()); gd._operators.push_back(op_offset); } +void OperationExporter::visit(luci::CircleRound *node) +{ + export_simple(node, circle::BuiltinOperator_ROUND); +} + +void OperationExporter::visit(luci::CircleRsqrt *node) +{ + export_simple(node, circle::BuiltinOperator_RSQRT); +} + +void OperationExporter::visit(luci::CircleScatterNd *node) +{ + export_simple(node, circle::BuiltinOperator_SCATTER_ND, circle::BuiltinOptions_ScatterNdOptions, + CreateScatterNdOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleSegmentSum *node) +{ + export_simple(node, circle::BuiltinOperator_SEGMENT_SUM, circle::BuiltinOptions_SegmentSumOptions, + CreateSegmentSumOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleSelect *node) +{ + export_simple(node, circle::BuiltinOperator_SELECT, circle::BuiltinOptions_SelectOptions, + CreateSelectOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleSelectV2 *node) +{ + export_simple(node, circle::BuiltinOperator_SELECT_V2, circle::BuiltinOptions_SelectV2Options, + CreateSelectV2Options(builder).Union()); +} + +void OperationExporter::visit(luci::CircleShape *node) +{ + export_simple(node, circle::BuiltinOperator_SHAPE, circle::BuiltinOptions_ShapeOptions, + CreateShapeOptions(builder, to_circle_tensortype(node->out_type())).Union()); +} + +void OperationExporter::visit(luci::CircleSin *node) +{ + export_simple(node, circle::BuiltinOperator_SIN); +} + +void OperationExporter::visit(luci::CircleSlice *node) +{ + export_simple(node, circle::BuiltinOperator_SLICE, circle::BuiltinOptions_SliceOptions, + CreateSliceOptions(builder).Union()); +} + void OperationExporter::visit(luci::CircleSoftmax *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_SOFTMAX); - std::vector inputs_vec{get_tensor_index(node->logits())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; + export_simple(node, circle::BuiltinOperator_SOFTMAX, circle::BuiltinOptions_SoftmaxOptions, + CreateSoftmaxOptions(builder, node->beta()).Union()); +} + +void OperationExporter::visit(luci::CircleSpaceToBatchND *node) +{ + export_simple(node, circle::BuiltinOperator_SPACE_TO_BATCH_ND, + circle::BuiltinOptions_SpaceToBatchNDOptions, + CreateSpaceToBatchNDOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleSpaceToDepth *node) +{ + export_simple(node, circle::BuiltinOperator_SPACE_TO_DEPTH, + circle::BuiltinOptions_SpaceToDepthOptions, + CreateSpaceToDepthOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleSparseToDense *node) +{ + export_simple(node, circle::BuiltinOperator_SPARSE_TO_DENSE, + circle::BuiltinOptions_SparseToDenseOptions, + CreateSparseToDenseOptions(builder, node->validate_indices()).Union()); +} + +void OperationExporter::visit(luci::CircleSplit *node) +{ + auto split_outs = loco::succs(node); + assert(int32_t(split_outs.size()) == node->num_split()); + + uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_SPLIT, node->op_version()); + // NOTE BuiltinOperator_SPLIT input is placed at second position + std::vector inputs_vec{get_tensor_index(node->split_dim()), + get_tensor_index(node->input())}; + std::vector outputs_vec; + + for (int32_t index = 0; index < node->num_split(); index++) + { + // store in order of index + bool found = false; + for (auto out : split_outs) + { + auto split_out = loco::must_cast(out); + if (split_out->index() == index) + { + outputs_vec.push_back(get_tensor_index(split_out)); + found = true; + break; + } + } + if (!found) + { + INTERNAL_EXN("Invalid Split output"); + } + } + auto inputs = builder.CreateVector(inputs_vec); auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateSoftmaxOptions(builder, node->beta()); + auto options = CreateSplitOptions(builder, node->num_split()); auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_SoftmaxOptions, options.Union()); + circle::BuiltinOptions_SplitOptions, options.Union()); gd._operators.push_back(op_offset); } -void OperationExporter::visit(luci::CircleSqrt *node) +void OperationExporter::visit(luci::CircleSplitV *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_SQRT); - std::vector inputs_vec{get_tensor_index(node->x())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; + auto split_outs = loco::succs(node); + assert(int32_t(split_outs.size()) == node->num_split()); + + uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_SPLIT_V, node->op_version()); + std::vector inputs_vec{get_tensor_index(node->input()), + get_tensor_index(node->size_splits()), + get_tensor_index(node->split_dim())}; + std::vector outputs_vec; + + for (int32_t index = 0; index < node->num_split(); index++) + { + // store in order of index + bool found = false; + for (auto out : split_outs) + { + auto split_out = loco::must_cast(out); + if (split_out->index() == index) + { + outputs_vec.push_back(get_tensor_index(split_out)); + found = true; + break; + } + } + if (!found) + { + INTERNAL_EXN("Invalid SplitV output"); + } + } + auto inputs = builder.CreateVector(inputs_vec); auto outputs = builder.CreateVector(outputs_vec); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs); + auto options = CreateSplitVOptions(builder, node->num_split()); + auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, + circle::BuiltinOptions_SplitVOptions, options.Union()); gd._operators.push_back(op_offset); } +void OperationExporter::visit(luci::CircleSqrt *node) +{ + export_simple(node, circle::BuiltinOperator_SQRT); +} + +void OperationExporter::visit(luci::CircleSquare *node) +{ + export_simple(node, circle::BuiltinOperator_SQUARE, circle::BuiltinOptions_SquareOptions, + CreateSquareOptions(builder).Union()); +} + void OperationExporter::visit(luci::CircleSquaredDifference *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_SQUARED_DIFFERENCE); - std::vector inputs_vec{get_tensor_index(node->x()), get_tensor_index(node->y())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateSquaredDifferenceOptions(builder); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_SquaredDifferenceOptions, options.Union()); - gd._operators.push_back(op_offset); + export_simple(node, circle::BuiltinOperator_SQUARED_DIFFERENCE, + circle::BuiltinOptions_SquaredDifferenceOptions, + CreateSquaredDifferenceOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleSqueeze *node) +{ + auto squeeze_dims = builder.CreateVector(node->squeeze_dims()); + export_simple(node, circle::BuiltinOperator_SQUEEZE, circle::BuiltinOptions_SqueezeOptions, + CreateSqueezeOptions(builder, squeeze_dims).Union()); +} + +void OperationExporter::visit(luci::CircleStridedSlice *node) +{ + export_simple(node, circle::BuiltinOperator_STRIDED_SLICE, + circle::BuiltinOptions_StridedSliceOptions, + CreateStridedSliceOptions(builder, node->begin_mask(), node->end_mask(), + node->ellipsis_mask(), node->new_axis_mask(), + node->shrink_axis_mask()) + .Union()); } void OperationExporter::visit(luci::CircleSub *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_SUB); - std::vector inputs_vec{get_tensor_index(node->x()), get_tensor_index(node->y())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; - auto inputs = builder.CreateVector(inputs_vec); - auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateSubOptions(builder, to_circle_actfunc(node->fusedActivationFunction())); - auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_SubOptions, options.Union()); - gd._operators.push_back(op_offset); + export_simple( + node, circle::BuiltinOperator_SUB, circle::BuiltinOptions_SubOptions, + CreateSubOptions(builder, to_circle_actfunc(node->fusedActivationFunction())).Union()); } -// TODO CircleTanh +void OperationExporter::visit(luci::CircleSum *node) +{ + export_simple(node, circle::BuiltinOperator_SUM, circle::BuiltinOptions_ReducerOptions, + CreateReducerOptions(builder, node->keep_dims()).Union()); +} -void OperationExporter::visit(luci::CircleTranspose *node) +void OperationExporter::visit(luci::CircleTanh *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_TRANSPOSE); - std::vector inputs_vec{get_tensor_index(node->arg(0)), get_tensor_index(node->arg(1))}; - std::vector outputs_vec{get_tensor_index(node)}; + export_simple(node, circle::BuiltinOperator_TANH); +} + +void OperationExporter::visit(luci::CircleTile *node) +{ + export_simple(node, circle::BuiltinOperator_TILE, circle::BuiltinOptions_TileOptions, + CreateTileOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleTopKV2 *node) +{ + auto topkv2_outs = loco::succs(node); + int outs_count = int32_t(topkv2_outs.size()); + assert(outs_count == 2); + + uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_TOPK_V2, node->op_version()); + std::vector inputs_vec{get_tensor_index(node->input()), get_tensor_index(node->k())}; + std::vector outputs_vec; + + for (int32_t index = 0; index < outs_count; index++) + { + // store in order of index + bool found = false; + for (auto out : topkv2_outs) + { + auto topkv2_out = loco::must_cast(out); + if (topkv2_out->index() == index) + { + outputs_vec.push_back(get_tensor_index(topkv2_out)); + found = true; + break; + } + } + if (!found) + { + INTERNAL_EXN("Invalid TopKV2 output"); + } + } auto inputs = builder.CreateVector(inputs_vec); auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateTransposeOptions(builder); - - auto op_offset = - CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions::BuiltinOptions_TransposeOptions, options.Union()); + auto options = CreateTopKV2Options(builder); + auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, + circle::BuiltinOptions_TopKV2Options, options.Union()); gd._operators.push_back(op_offset); } +void OperationExporter::visit(luci::CircleTranspose *node) +{ + export_simple(node, circle::BuiltinOperator_TRANSPOSE, circle::BuiltinOptions_TransposeOptions, + CreateTransposeOptions(builder).Union()); +} + void OperationExporter::visit(luci::CircleTransposeConv *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_TRANSPOSE_CONV); + export_simple(node, circle::BuiltinOperator_TRANSPOSE_CONV, + circle::BuiltinOptions_TransposeConvOptions, + CreateTransposeConvOptions(builder, getOpPadding(node->padding()), + node->stride()->w(), node->stride()->h()) + .Union()); +} + +void OperationExporter::visit(luci::CircleUnpack *node) +{ + LOGGER(l); + auto settings = luci::UserSettings::settings(); + + auto unpack_outs = loco::succs(node); + // NOTE real models may not use all of the outputs + if (static_cast(unpack_outs.size()) != node->num()) + { + if (settings->get(luci::UserSettings::Key::DisableValidation)) + { + WARN(l) << "Warning: export Unpack(" << node->name() << ") 'num' not same as outputs"; + } + else + assert(false); + } + + uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_UNPACK, node->op_version()); + std::vector inputs_vec{get_tensor_index(node->value())}; + std::vector outputs_vec; + + for (int32_t index = 0; index < node->num(); index++) + { + // store in order of index + bool found = false; + for (auto out : unpack_outs) + { + auto unpack_out = loco::must_cast(out); + if (unpack_out->index() == index) + { + outputs_vec.push_back(get_tensor_index(unpack_out)); + found = true; + break; + } + } + // NOTE real models may not use all of the outputs + if (!found) + { + if (settings->get(luci::UserSettings::Key::DisableValidation)) + { + WARN(l) << "Warning: export Unpack(" << node->name() << ") output " << index << " not used"; + } + else + assert(false); + } + } - // Make input, output and options for operator - std::vector inputs_vec{get_tensor_index(node->inputSizes()), - get_tensor_index(node->filter()), - get_tensor_index(node->outBackprop())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; auto inputs = builder.CreateVector(inputs_vec); auto outputs = builder.CreateVector(outputs_vec); - circle::Padding padding = getOpPadding(node->padding()); - auto options = - CreateTransposeConvOptions(builder, padding, node->stride()->w(), node->stride()->h()); - - // Make TRANSPOSE_CONV operator + auto options = CreateUnpackOptions(builder, node->num(), node->axis()); auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_TransposeConvOptions, options.Union()); + circle::BuiltinOptions_UnpackOptions, options.Union()); gd._operators.push_back(op_offset); } -void OperationExporter::visit(luci::CircleInstanceNorm *node) +void OperationExporter::visit(luci::CircleWhere *node) { - uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_INSTANCE_NORM); - std::vector inputs_vec{get_tensor_index(node->input()), get_tensor_index(node->gamma()), - get_tensor_index(node->beta())}; - std::vector outputs_vec{get_tensor_index(static_cast(node))}; + export_simple(node, circle::BuiltinOperator_WHERE, circle::BuiltinOptions_WhereOptions, + CreateWhereOptions(builder).Union()); +} + +void OperationExporter::visit(luci::CircleWhile *node) +{ + auto while_outs = loco::succs(node); + assert(while_outs.size() == node->output_count()); + + uint32_t op_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_WHILE, node->op_version()); + std::vector inputs_vec; + std::vector outputs_vec; + + for (uint32_t idx = 0; idx < node->input_count(); ++idx) + inputs_vec.push_back(get_tensor_index(node->input(idx))); + + for (uint32_t idx = 0; idx < node->output_count(); ++idx) + { + // store in order of index + bool found = false; + for (auto out : while_outs) + { + auto while_out = loco::must_cast(out); + if (while_out->index() == static_cast(idx)) + { + outputs_vec.push_back(get_tensor_index(while_out)); + found = true; + break; + } + } + if (!found) + { + INTERNAL_EXN("Invalid CircleWhile output"); + } + } + auto inputs = builder.CreateVector(inputs_vec); auto outputs = builder.CreateVector(outputs_vec); - auto options = CreateInstanceNormOptions(builder, node->epsilon(), - to_circle_actfunc(node->fusedActivationFunction())); + auto options = CreateWhileOptions(builder, node->cond_branch(), node->body_branch()); auto op_offset = CreateOperator(builder, op_idx, inputs, outputs, - circle::BuiltinOptions_InstanceNormOptions, options.Union()); + circle::BuiltinOptions_WhileOptions, options.Union()); gd._operators.push_back(op_offset); } -void OperationExporter::visit(luci::CircleEqual *node) +void OperationExporter::visit(luci::CircleZerosLike *node) { - uint32_t opcode_idx = md.registerBuiltinOpcode(circle::BuiltinOperator_EQUAL); - std::vector inputs{get_tensor_index(node->x()), get_tensor_index(node->y())}; - std::vector outputs{get_tensor_index(node)}; - - auto fb_inputs = builder.CreateVector(inputs); - auto fb_outputs = builder.CreateVector(outputs); + export_simple(node, circle::BuiltinOperator_ZEROS_LIKE, circle::BuiltinOptions_ZerosLikeOptions, + CreateZerosLikeOptions(builder).Union()); +} - auto options = CreateEqualOptions(builder); +void OperationExporter::visit(luci::CircleBCQFullyConnected *node) +{ + export_simple(node, circle::BuiltinOperator_BCQ_FULLY_CONNECTED, + circle::BuiltinOptions_BCQFullyConnectedOptions, + CreateBCQFullyConnectedOptions(builder, node->weights_hidden_size(), + to_circle_actfunc(node->fusedActivationFunction())) + .Union()); +} - auto op_offset = CreateOperator(builder, opcode_idx, fb_inputs, fb_outputs, - circle::BuiltinOptions_EqualOptions, options.Union()); +void OperationExporter::visit(luci::CircleBCQGather *node) +{ + export_simple(node, circle::BuiltinOperator_BCQ_GATHER, circle::BuiltinOptions_BCQGatherOptions, + CreateBCQGatherOptions(builder, node->input_hidden_size(), node->axis()).Union()); +} - gd._operators.push_back(op_offset); +void OperationExporter::visit(luci::CircleInstanceNorm *node) +{ + export_simple(node, circle::BuiltinOperator_INSTANCE_NORM, + circle::BuiltinOptions_InstanceNormOptions, + CreateInstanceNormOptions(builder, node->epsilon(), + to_circle_actfunc(node->fusedActivationFunction())) + .Union()); } void exportNode(loco::Node *node, flatbuffers::FlatBufferBuilder &builder, SerializedModelData &md, SerializedGraphData &gd) { - // TODO Use explicit tagging to prevent possible mistake - auto isNoOp = [](loco::Node *node) { - // If there is only one input and the TensorIndex for the input is same - // as the TensorIndex of the output then this node is just a dummy node - if (node->arity() == 1) - { - assert(node->arg(0) != nullptr); - return get_tensor_index(node) == get_tensor_index(node->arg(0)); - } - return false; - }; - - if (isNoOp(node)) - { - // Skip if a given node is marked as NoOp (op with no effect) before - return; - } - if (auto circle_node = dynamic_cast(node)) { OperationExporter exporter{builder, md, gd}; diff --git a/compiler/luci/export/src/CircleTensorExporter.cpp b/compiler/luci/export/src/CircleTensorExporter.cpp index ef9b9d7..5cad392 100644 --- a/compiler/luci/export/src/CircleTensorExporter.cpp +++ b/compiler/luci/export/src/CircleTensorExporter.cpp @@ -15,6 +15,7 @@ */ #include "CircleTensorExporter.h" +#include "TypeBridge.h" #include #include @@ -52,6 +53,9 @@ public: const ShapeDescription &shape(void) const { return _shape; } void shape(const ShapeDescription &shape) { _shape = shape; } + luci::ShapeStatus shape_status(void) const { return _shape_status; } + void shape_status(luci::ShapeStatus ss) { _shape_status = ss; } + public: luci::CircleConst *content(void) const { return _content; } void content(luci::CircleConst *c) { _content = c; } @@ -62,8 +66,9 @@ public: private: std::string _name; - circle::TensorType _dtype; - ShapeDescription _shape; + circle::TensorType _dtype{circle::TensorType_FLOAT32}; + ShapeDescription _shape{}; + luci::ShapeStatus _shape_status{luci::ShapeStatus::UNDEFINED}; luci::CircleConst *_content = nullptr; luci::CircleQuantParam *_quantparam = nullptr; @@ -76,30 +81,16 @@ struct NoOpDetector final : public luci::CircleNodeMutableVisitor // Input is Virtual but does produce a Tensor // Output is Virtual that does not produce any Tensor bool visit(luci::CircleOutput *) final { return true; } + bool visit(luci::CircleOutputExclude *) final { return true; } // Return false by default bool visit(luci::CircleNode *) final { return false; } }; -void allocateCircleTensor(CircleNode *node, CircleTensorContext &ctx) +void allocateCircleTensorInfo(CircleNode *node, CircleTensorContext &ctx) { LOGGER(l); - auto isNoOp = [](loco::Node *node) { - if (auto circle_node = dynamic_cast(node)) - { - NoOpDetector d; - return circle_node->accept(&d); - } - return false; - }; - - if (isNoOp(node)) - { - set_tensor_index(node, get_tensor_index(node->arg(0))); - return; - } - auto tensor_index = static_cast(ctx.size()); // TODO Use Graph-level metadata for Input & Output // auto tensor_name = "t_" + std::to_string(tensor_index); @@ -111,8 +102,10 @@ void allocateCircleTensor(CircleNode *node, CircleTensorContext &ctx) CircleTensoInfo tensor_info; tensor_info.name(tensor_name); - tensor_info.dtype(TypeInference::get(node)); - tensor_info.shape(ShapeInference::get(node)); + tensor_info.dtype(to_circle_tensortype(luci::node_dtype(node))); + if (node->shape_status() == ShapeStatus::VALID) + tensor_info.shape(to_shape_description(luci::node_shape(node))); + tensor_info.shape_status(node->shape_status()); tensor_info.content(dynamic_cast(node)); tensor_info.quantparam(node->quantparam()); @@ -122,6 +115,108 @@ void allocateCircleTensor(CircleNode *node, CircleTensorContext &ctx) ctx.emplace_back(tensor_info); } +class MultiOutputDetector final : public luci::CircleNodeMutableVisitor +{ +public: + MultiOutputDetector(CircleTensorContext &ctx) : _ctx(ctx) {} + +private: + void store_outputs(luci::CircleNode *node, uint32_t count) + { + auto outs = loco::succs(node); + assert(outs.size() == count); + (void)count; // for unused variable error in release build + for (auto out : outs) + { + auto circle_out = loco::must_cast(out); + allocateCircleTensorInfo(circle_out, _ctx); + } + set_tensor_index(node, -1); + } + +public: + bool visit(luci::CircleIfOut *) final { return true; } + bool visit(luci::CircleSplitOut *) final { return true; } + bool visit(luci::CircleSplitVOut *) final { return true; } + bool visit(luci::CircleTopKV2Out *) final { return true; } + bool visit(luci::CircleUnpackOut *) final { return true; } + bool visit(luci::CircleWhileOut *) final { return true; } + + bool visit(luci::CircleIf *node) final + { + store_outputs(node, node->output_count()); + return true; + } + + bool visit(luci::CircleSplit *node) final + { + store_outputs(node, uint32_t(node->num_split())); + return true; + } + + bool visit(luci::CircleSplitV *node) final + { + store_outputs(node, uint32_t(node->num_split())); + return true; + } + + bool visit(luci::CircleTopKV2 *node) final + { + store_outputs(node, 2); + return true; + } + + bool visit(luci::CircleUnpack *node) final + { + store_outputs(node, node->num()); + return true; + } + + bool visit(luci::CircleWhile *node) final + { + store_outputs(node, node->output_count()); + return true; + } + + // Return false by default + bool visit(luci::CircleNode *) final { return false; } + +private: + CircleTensorContext &_ctx; +}; + +void allocateCircleTensor(CircleNode *node, CircleTensorContext &ctx) +{ + if (node == nullptr) + throw std::runtime_error("allocateCIrcleTensor Failed : node is nullptr"); + + auto isNoOp = [](loco::Node *node) { + if (auto circle_node = dynamic_cast(node)) + { + NoOpDetector d; + return circle_node->accept(&d); + } + return false; + }; + + if (isNoOp(node)) + { + set_tensor_index(node, -1); + return; + } + + // TODO revise this when loco supports multiple outputs + // NOTE this will store all virtual output tensors and skip for the real node + if (auto circle_node = dynamic_cast(node)) + { + MultiOutputDetector d(ctx); + if (circle_node->accept(&d)) + return; + } + + allocateCircleTensorInfo(node, ctx); +} + } // namespace namespace @@ -166,18 +261,22 @@ flatbuffers::Offset encodeOpBufferByDType(FlatBufferBuilder &bui template <> flatbuffers::Offset encodeOpBuffer(FlatBufferBuilder &builder, luci::CircleConst *c) { - // TODO use switch - if (c->dtype() == loco::DataType::FLOAT32) + switch (c->dtype()) { - return encodeOpBufferByDType(builder, c); - } - else if (c->dtype() == loco::DataType::S32) - { - return encodeOpBufferByDType(builder, c); - } - else if (c->dtype() == loco::DataType::U8) - { - return encodeOpBufferByDType(builder, c); + case loco::DataType::FLOAT32: + return encodeOpBufferByDType(builder, c); + case loco::DataType::S16: + return encodeOpBufferByDType(builder, c); + case loco::DataType::S32: + return encodeOpBufferByDType(builder, c); + case loco::DataType::S64: + return encodeOpBufferByDType(builder, c); + case loco::DataType::U8: + return encodeOpBufferByDType(builder, c); + case loco::DataType::BOOL: + return encodeOpBufferByDType(builder, c); + default: + break; } INTERNAL_EXN_V("Unsupported datatype", oops::to_uint32(c->dtype())); @@ -210,7 +309,9 @@ void exportOpDefinedTensor(const CircleTensoInfo &info, FlatBufferBuilder &build SerializedModelData &md, SerializedGraphData &gd) { // Create and register output tensor shape - auto shape_offset = encodeShape(builder, info.shape()); + flatbuffers::Offset> shape_offset; + if (info.shape_status() == ShapeStatus::VALID) + shape_offset = encodeShape(builder, info.shape()); // encode and register output tensor buffer auto buffer = @@ -249,9 +350,21 @@ void exportOpDefinedTensors(loco::Graph *g, FlatBufferBuilder &builder, Serializ { CircleTensorContext tensor_ctx; + // NOTE There may exist dangle CircleInput that is not visited with postorder_traversal() + // All dangle CircleOutput should be visited by postorder_traversal() + auto nodes = g->nodes(); + for (uint32_t n = 0; n < nodes->size(); ++n) + { + auto node = dynamic_cast(nodes->at(n)); + if (node != nullptr) + allocateCircleTensor(node, tensor_ctx); + } + for (auto node : loco::postorder_traversal(loco::output_nodes(g))) { - CircleNode *circle_node = dynamic_cast(node); + CircleNode *circle_node = loco::must_cast(node); + if (dynamic_cast(circle_node) != nullptr) + continue; allocateCircleTensor(circle_node, tensor_ctx); } diff --git a/compiler/luci/export/src/ProgressReporter.cpp b/compiler/luci/export/src/ProgressReporter.cpp index ac9c3d9..216bd3f 100644 --- a/compiler/luci/export/src/ProgressReporter.cpp +++ b/compiler/luci/export/src/ProgressReporter.cpp @@ -49,36 +49,36 @@ namespace luci void ProgressReporter::notify(const logo::PhaseEventInfo *) { - LOGGER(prime); + LOGGER(l); - INFO(prime) << "=============================================================="; - INFO(prime) << "luci::PhaseRunner<" << to_str(strategy()) << ">"; - INFO(prime) << "Initial graph"; - INFO(prime) << fmt(graph()); + VERBOSE(l, 4) << "=============================================================="; + VERBOSE(l, 4) << "luci::PhaseRunner<" << to_str(strategy()) << ">"; + VERBOSE(l, 4) << "Initial graph"; + VERBOSE(l, 4) << fmt(graph()); } void ProgressReporter::notify(const logo::PhaseEventInfo *) { - LOGGER(prime); + LOGGER(l); - INFO(prime) << "luci::PhaseRunner<" << to_str(strategy()) << "> - done"; + VERBOSE(l, 4) << "luci::PhaseRunner<" << to_str(strategy()) << "> - done"; } void ProgressReporter::notify(const logo::PhaseEventInfo *info) { - LOGGER(prime); + LOGGER(l); - INFO(prime) << "--------------------------------------------------------------"; - INFO(prime) << "Before " << logo::pass_name(info->pass()); + VERBOSE(l, 4) << "--------------------------------------------------------------"; + VERBOSE(l, 4) << "Before " << logo::pass_name(info->pass()); } void ProgressReporter::notify(const logo::PhaseEventInfo *info) { - LOGGER(prime); + LOGGER(l); - INFO(prime) << "After " << logo::pass_name(info->pass()) - << " (changed: " << to_char(info->changed()) << ")"; - INFO(prime) << fmt(graph()); + VERBOSE(l, 4) << "After " << logo::pass_name(info->pass()) + << " (changed: " << to_char(info->changed()) << ")"; + VERBOSE(l, 4) << fmt(graph()); } } // namespace luci diff --git a/compiler/luci/export/src/SerializedData.h b/compiler/luci/export/src/SerializedData.h index 8424965..251daa0 100644 --- a/compiler/luci/export/src/SerializedData.h +++ b/compiler/luci/export/src/SerializedData.h @@ -29,8 +29,20 @@ namespace luci struct OpCode { circle::BuiltinOperator opcode; - - bool operator==(const OpCode &rhs) const { return opcode == rhs.opcode; } + std::string custom_code{""}; + int32_t version = 1; + + bool operator==(const OpCode &rhs) const + { + if (opcode == circle::BuiltinOperator_CUSTOM) + { + return custom_code == rhs.custom_code; + } + else + { + return opcode == rhs.opcode; + } + } }; } // namespace luci @@ -53,11 +65,13 @@ namespace luci */ struct SubGraphContext { + /// @brief SubGraph name + std::string _name; /// @brief SubGraph input tensor id std::vector _inputs; /// @brief SubGraph output tensor id std::vector _outputs; - /// @DataFormat for SubGraph + /// @brief DataFormat for SubGraph circle::DataFormat _data_format{circle::DataFormat::DataFormat_CHANNELS_LAST}; }; @@ -68,7 +82,6 @@ struct SerializedModelData final SerializedModelData(const SerializedModelData &) = delete; std::unordered_map _operator_codes; - std::unordered_map _custom_operator_codes; std::vector> _buffers; /** @@ -76,7 +89,7 @@ struct SerializedModelData final * @param builtin_code * @return idx of opcode in table of opcodes (see schema) */ - uint32_t registerBuiltinOpcode(circle::BuiltinOperator builtin_code); + uint32_t registerBuiltinOpcode(circle::BuiltinOperator builtin_code, const int32_t op_version); uint32_t registerCustomOpcode(const std::string &custom_op); }; diff --git a/compiler/luci/export/src/TypeBridge.cpp b/compiler/luci/export/src/TypeBridge.cpp new file mode 100644 index 0000000..9ccd523 --- /dev/null +++ b/compiler/luci/export/src/TypeBridge.cpp @@ -0,0 +1,105 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "TypeBridge.h" + +#include "CircleExporterUtils.h" + +#include +#include +#include +#include + +#include +#include + +namespace +{ + +/** + * @brief CopySelector will return condition of copy shape/type inference to node + */ +struct CopySelector final : public luci::CircleNodeVisitor +{ + // return false(don't copy) for nodes that provides shape/type from nature + bool visit(const luci::CircleInput *) final { return false; } + bool visit(const luci::CircleConst *) final { return false; } + + // default is copy attributes + bool visit(const luci::CircleNode *) { return true; } +}; + +} // namespace + +namespace luci +{ + +loco::TensorShape node_shape(CircleNode *node) +{ + loco::TensorShape shape; + + shape.rank(node->rank()); + for (uint32_t r = 0; r < node->rank(); ++r) + { + shape.dim(r) = loco::Dimension(node->dim(r).value()); + } + return shape; +} + +loco::DataType node_dtype(CircleNode *node) { return node->dtype(); } + +void copy_shape_dtype(loco::Graph *graph) +{ + /** + * @note We will iterate all the nodes in the graph to include dangle nodes + */ + auto nodes = graph->nodes(); + for (uint32_t n = 0; n < nodes->size(); ++n) + { + auto node = loco::must_cast(nodes->at(n)); + + CopySelector cs; + if (node->accept(&cs)) + { + // NOTE not all nodes have infered shape/dtype: multiple outs may not be + // visited when outputs are not used + // TODO fix shape inference traversal + // NOTE when loco supports multiple outputs in nature this issue should be + // resolved also + + if (loco::dtype_known(node)) + { + node->dtype(loco::dtype_get(node)); + } + + if (loco::shape_known(node)) + { + auto shape = loco::shape_get(node).as(); + node->rank(shape.rank()); + for (uint32_t r = 0; r < shape.rank(); ++r) + { + node->dim(r) = loco::Dimension(shape.dim(r).value()); + } + + // ShapeStatus should be update only when the status was UNDEFINED + if (node->shape_status() == ShapeStatus::UNDEFINED) + node->shape_status(ShapeStatus::VALID); + } + } + } +} + +} // namespace luci diff --git a/compiler/luci/export/src/TypeBridge.h b/compiler/luci/export/src/TypeBridge.h new file mode 100644 index 0000000..a63fbce --- /dev/null +++ b/compiler/luci/export/src/TypeBridge.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TYPE_BRIDGE_H__ +#define __TYPE_BRIDGE_H__ + +#include + +#include + +namespace luci +{ + +/** + * @brief node_shape() will return loco::TensorShape of CircleNode + */ +loco::TensorShape node_shape(CircleNode *node); + +/** + * @brief node_dtype() will return loco::DataType of CircleNode + */ +loco::DataType node_dtype(CircleNode *node); + +/** + * @brief copy_shape_dtype() will copy shape and dtype inference data to CircleNode + */ +void copy_shape_dtype(loco::Graph *graph); + +} // namespace luci + +#endif // __TYPE_BRIDGE_H__ diff --git a/compiler/luci/import/CMakeLists.txt b/compiler/luci/import/CMakeLists.txt index bc9a915..2ae00b8 100644 --- a/compiler/luci/import/CMakeLists.txt +++ b/compiler/luci/import/CMakeLists.txt @@ -7,6 +7,7 @@ target_include_directories(luci_import PRIVATE src) target_include_directories(luci_import PUBLIC include) target_link_libraries(luci_import PUBLIC luci_lang) target_link_libraries(luci_import PUBLIC mio_circle) +target_link_libraries(luci_import PRIVATE luci_env) target_link_libraries(luci_import PRIVATE luci_log) target_link_libraries(luci_import PRIVATE luci_logex) target_link_libraries(luci_import PRIVATE nncc_common) diff --git a/compiler/luci/import/include/luci/Import/CircleReader.h b/compiler/luci/import/include/luci/Import/CircleReader.h index fcbe09c..3d85b9e 100644 --- a/compiler/luci/import/include/luci/Import/CircleReader.h +++ b/compiler/luci/import/include/luci/Import/CircleReader.h @@ -21,6 +21,7 @@ #include #include +#include #include #include @@ -42,9 +43,13 @@ const circle::QuantizationParametersT *tensor_quantization(const circle::TensorT loco::DataType luci_datatype(circle::TensorType type); FusedActFunc luci_actfunc(const circle::ActivationFunctionType type); Padding luci_padding(const circle::Padding padding); +MirrorPadMode luci_mirrorpad_mode(const circle::MirrorPadMode mode); std::unique_ptr luci_quantparam(const circle::QuantizationParametersT *quantization); +/// @brief Copy common tensor attributes such as name, type, etc. to node. +void copy_tensor_attributes(const circle::TensorT &tensor, CircleNode *node); + /** * @brief Loads Circle file and provides helpers to access attributes */ @@ -56,6 +61,9 @@ private: using CircleOperators_t = std::vector>; using CircleOperatorCodes_t = std::vector>; + using CircleSubGraphsPtr_t = flatbuffers::Vector>; + using CircleTensorsPtr_t = flatbuffers::Vector>; + public: CircleReader() = default; @@ -68,6 +76,8 @@ public: const std::vector &outputs() const { return _current_subgraph->outputs; } const std::string &name() const { return _current_subgraph->name; } + const CircleTensorsPtr_t *tensors_ptr() const { return _tensors_ptr; } + uint32_t num_subgraph() const { return _model->subgraphs.size(); } circle::BuiltinOperator builtin_code(const circle::OperatorT &op) const; @@ -80,6 +90,9 @@ public: private: std::unique_ptr _model; const circle::SubGraphT *_current_subgraph{nullptr}; + + const circle::Model *_model_ptr{nullptr}; + const CircleTensorsPtr_t *_tensors_ptr{nullptr}; }; } // namespace luci diff --git a/compiler/luci/import/include/luci/Import/GraphBuilder.h b/compiler/luci/import/include/luci/Import/GraphBuilder.h index 61f673f..548264d 100644 --- a/compiler/luci/import/include/luci/Import/GraphBuilder.h +++ b/compiler/luci/import/include/luci/Import/GraphBuilder.h @@ -18,6 +18,7 @@ #define __LUCI_IMPORT_GRAPH_BUILDER_H__ #include "GraphBuilderContext.h" +#include "GraphBuilderBase.h" #include @@ -25,25 +26,14 @@ namespace luci { /** - * @brief Interface of convert circle:: NodeDef to loco::Node (e.g., Conv2DGraphBuilder) + * @brief Base of general single output graph builder(e.g., Conv2DGraphBuilder) */ -class GraphBuilder +class GraphBuilder : public GraphBuilderBase { public: - struct ValidateArgs - { - ValidateArgs(const circle::OperatorT &o, const CircleReader &r) : op(o), reader(r) {} - - const circle::OperatorT &op; - const CircleReader &reader; - }; - -public: virtual ~GraphBuilder() = default; - virtual bool validate(const ValidateArgs &) const = 0; - - void build(const circle::OperatorT &op, GraphBuilderContext *context) const; + void build(const circle::OperatorT &op, GraphBuilderContext *context) const final; private: virtual CircleNode *build_node(const circle::OperatorT &op, diff --git a/compiler/luci/import/include/luci/Import/GraphBuilderBase.h b/compiler/luci/import/include/luci/Import/GraphBuilderBase.h new file mode 100644 index 0000000..a0cd008 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/GraphBuilderBase.h @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_GRAPH_BUILDER_BASE_H__ +#define __LUCI_IMPORT_GRAPH_BUILDER_BASE_H__ + +#include "GraphBuilderContext.h" + +#include + +namespace luci +{ + +/** + * @brief Interface of convert circle::OperatorT to CircleNode + */ +struct GraphBuilderBase +{ + struct ValidateArgs + { + ValidateArgs(const circle::OperatorT &o, const CircleReader &r) : op(o), reader(r) {} + + const circle::OperatorT &op; + const CircleReader &reader; + }; + + virtual bool validate(const ValidateArgs &) const = 0; + virtual void build(const circle::OperatorT &op, GraphBuilderContext *context) const = 0; + + virtual ~GraphBuilderBase() = default; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_GRAPH_BUILDER_BASE_H__ diff --git a/compiler/luci/import/include/luci/Import/GraphBuilderContext.h b/compiler/luci/import/include/luci/Import/GraphBuilderContext.h index 8d46418..72e237a 100644 --- a/compiler/luci/import/include/luci/Import/GraphBuilderContext.h +++ b/compiler/luci/import/include/luci/Import/GraphBuilderContext.h @@ -24,6 +24,7 @@ #include #include +#include namespace luci { @@ -48,13 +49,29 @@ private: }; /** + * @brief Set of Tensor Index of outputs of operators + * including graph input nodes + */ +class IndexTensorOutputs +{ +public: + void enroll(TensorIndex idx); + + bool find(TensorIndex idx); + +private: + std::set _set; +}; + +/** * @brief Class to store context to build loco graph IR from TensorFlow */ class GraphBuilderContext { public: - GraphBuilderContext(loco::Graph *g, CircleReader *reader, IndexNodeFinder *nodefinder) - : _g(g), _reader(reader), _indexnodefinder(nodefinder) + GraphBuilderContext(loco::Graph *g, CircleReader *reader, IndexNodeFinder *nodefinder, + IndexTensorOutputs *tensoroutputs) + : _g(g), _reader(reader), _indexnodefinder(nodefinder), _indextensoroutputs(tensoroutputs) { // DO NOTHING } @@ -67,11 +84,13 @@ public: CircleReader *reader() { return _reader; } IndexNodeFinder *nodefinder() { return _indexnodefinder; } + IndexTensorOutputs *tensoroutputs() { return _indextensoroutputs; } private: loco::Graph *_g; CircleReader *_reader; IndexNodeFinder *_indexnodefinder; + IndexTensorOutputs *_indextensoroutputs; }; } // namespace luci diff --git a/compiler/luci/import/include/luci/Import/GraphBuilderRegistry.h b/compiler/luci/import/include/luci/Import/GraphBuilderRegistry.h index 99054e7..b8dc22f 100644 --- a/compiler/luci/import/include/luci/Import/GraphBuilderRegistry.h +++ b/compiler/luci/import/include/luci/Import/GraphBuilderRegistry.h @@ -17,7 +17,7 @@ #ifndef __LUCI_IMPORT_GRAPH_BUILDER_REGISTRY_H__ #define __LUCI_IMPORT_GRAPH_BUILDER_REGISTRY_H__ -#include "GraphBuilder.h" +#include "GraphBuilderBase.h" #include @@ -31,11 +31,11 @@ struct GraphBuilderSource /** * @brief Returns registered GraphBuilder pointer for operator (nullptr if not present) */ - virtual const GraphBuilder *lookup(const circle::BuiltinOperator &op) const = 0; + virtual const GraphBuilderBase *lookup(const circle::BuiltinOperator &op) const = 0; }; /** - * @brief Class to return graph builder for TF nodes + * @brief Class to return graph builder for Circle nodes */ class GraphBuilderRegistry final : public GraphBuilderSource { @@ -53,7 +53,7 @@ public: * @brief Returns registered GraphBuilder pointer for operator or * nullptr if not registered */ - const GraphBuilder *lookup(const circle::BuiltinOperator &op) const final + const GraphBuilderBase *lookup(const circle::BuiltinOperator &op) const final { if (_builder_map.find(op) == _builder_map.end()) return (_parent == nullptr) ? nullptr : _parent->lookup(op); @@ -68,7 +68,7 @@ public: } public: - void add(const circle::BuiltinOperator op, std::unique_ptr &&builder) + void add(const circle::BuiltinOperator op, std::unique_ptr &&builder) { _builder_map[op] = std::move(builder); } @@ -77,7 +77,7 @@ private: const GraphBuilderSource *_parent = nullptr; private: - std::map> _builder_map; + std::map> _builder_map; }; } // namespace luci diff --git a/compiler/luci/import/include/luci/Import/Nodes.h b/compiler/luci/import/include/luci/Import/Nodes.h index 381d02b..2719a5a 100644 --- a/compiler/luci/import/include/luci/Import/Nodes.h +++ b/compiler/luci/import/include/luci/Import/Nodes.h @@ -19,30 +19,110 @@ #include "Nodes/CircleAbs.h" #include "Nodes/CircleAdd.h" +#include "Nodes/CircleAddN.h" #include "Nodes/CircleArgMax.h" +#include "Nodes/CircleArgMin.h" #include "Nodes/CircleAveragePool2D.h" +#include "Nodes/CircleBatchMatMul.h" #include "Nodes/CircleBatchToSpaceND.h" +#include "Nodes/CircleBCQFullyConnected.h" +#include "Nodes/CircleBCQGather.h" +#include "Nodes/CircleCast.h" +#include "Nodes/CircleCeil.h" #include "Nodes/CircleConcatenation.h" #include "Nodes/CircleConst.h" #include "Nodes/CircleConv2D.h" #include "Nodes/CircleCos.h" +#include "Nodes/CircleCustom.h" +#include "Nodes/CircleDepthToSpace.h" #include "Nodes/CircleDepthwiseConv2D.h" #include "Nodes/CircleDiv.h" +#include "Nodes/CircleElu.h" #include "Nodes/CircleEqual.h" #include "Nodes/CircleExp.h" +#include "Nodes/CircleExpandDims.h" +#include "Nodes/CircleFill.h" +#include "Nodes/CircleFloor.h" +#include "Nodes/CircleFloorDiv.h" +#include "Nodes/CircleFloorMod.h" #include "Nodes/CircleFullyConnected.h" +#include "Nodes/CircleGather.h" +#include "Nodes/CircleGatherNd.h" +#include "Nodes/CircleGreater.h" +#include "Nodes/CircleGreaterEqual.h" +#include "Nodes/CircleIf.h" +#include "Nodes/CircleInstanceNorm.h" +#include "Nodes/CircleL2Normalize.h" +#include "Nodes/CircleL2Pool2D.h" +#include "Nodes/CircleLeakyRelu.h" +#include "Nodes/CircleLess.h" +#include "Nodes/CircleLessEqual.h" +#include "Nodes/CircleLocalResponseNormalization.h" +#include "Nodes/CircleLog.h" +#include "Nodes/CircleLogicalAnd.h" #include "Nodes/CircleLogicalNot.h" #include "Nodes/CircleLogicalOr.h" +#include "Nodes/CircleLogistic.h" +#include "Nodes/CircleLogSoftmax.h" +#include "Nodes/CircleMatrixSetDiag.h" +#include "Nodes/CircleMaximum.h" #include "Nodes/CircleMaxPool2D.h" +#include "Nodes/CircleMatrixDiag.h" #include "Nodes/CircleMean.h" +#include "Nodes/CircleMinimum.h" +#include "Nodes/CircleMirrorPad.h" #include "Nodes/CircleMul.h" +#include "Nodes/CircleNeg.h" +#include "Nodes/CircleNotEqual.h" +#include "Nodes/CircleOneHot.h" #include "Nodes/CirclePack.h" #include "Nodes/CirclePad.h" +#include "Nodes/CirclePow.h" +#include "Nodes/CirclePRelu.h" +#include "Nodes/CircleRange.h" +#include "Nodes/CircleRank.h" +#include "Nodes/CircleReduceAny.h" +#include "Nodes/CircleReduceMax.h" +#include "Nodes/CircleReduceMin.h" +#include "Nodes/CircleReduceProd.h" #include "Nodes/CircleRelu.h" +#include "Nodes/CircleRelu6.h" +#include "Nodes/CircleReluN1To1.h" #include "Nodes/CircleReshape.h" +#include "Nodes/CircleResizeBilinear.h" +#include "Nodes/CircleResizeNearestNeighbor.h" +#include "Nodes/CircleReverseSequence.h" +#include "Nodes/CircleReverseV2.h" +#include "Nodes/CircleRound.h" #include "Nodes/CircleRsqrt.h" +#include "Nodes/CircleScatterNd.h" +#include "Nodes/CircleSegmentSum.h" +#include "Nodes/CircleSelect.h" +#include "Nodes/CircleSelectV2.h" +#include "Nodes/CircleShape.h" +#include "Nodes/CircleSin.h" +#include "Nodes/CircleSlice.h" #include "Nodes/CircleSoftmax.h" +#include "Nodes/CircleSpaceToBatchND.h" +#include "Nodes/CircleSpaceToDepth.h" +#include "Nodes/CircleSparseToDense.h" +#include "Nodes/CircleSplit.h" +#include "Nodes/CircleSplitV.h" +#include "Nodes/CircleSqrt.h" +#include "Nodes/CircleSquare.h" +#include "Nodes/CircleSquaredDifference.h" +#include "Nodes/CircleSqueeze.h" +#include "Nodes/CircleStridedSlice.h" #include "Nodes/CircleSub.h" +#include "Nodes/CircleSum.h" +#include "Nodes/CircleTanh.h" +#include "Nodes/CircleTile.h" +#include "Nodes/CircleTopKV2.h" #include "Nodes/CircleTranspose.h" +#include "Nodes/CircleTransposeConv.h" +#include "Nodes/CircleUnpack.h" +#include "Nodes/CircleWhere.h" +#include "Nodes/CircleWhile.h" +#include "Nodes/CircleZerosLike.h" #endif // __LUCI_IMPORT_NODES_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleAddN.h b/compiler/luci/import/include/luci/Import/Nodes/CircleAddN.h new file mode 100644 index 0000000..3ec6b2a --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleAddN.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_ADD_N_H__ +#define __LUCI_IMPORT_OP_CIRCLE_ADD_N_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleAddNGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_ADD_N_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleArgMin.h b/compiler/luci/import/include/luci/Import/Nodes/CircleArgMin.h new file mode 100644 index 0000000..746f528 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleArgMin.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_ARGMIN_H__ +#define __LUCI_IMPORT_OP_CIRCLE_ARGMIN_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleArgMinGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_ARGMIN_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleBCQFullyConnected.h b/compiler/luci/import/include/luci/Import/Nodes/CircleBCQFullyConnected.h new file mode 100644 index 0000000..be58acd --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleBCQFullyConnected.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_BCQFULLYCONNECTED_H__ +#define __LUCI_IMPORT_OP_CIRCLE_BCQFULLYCONNECTED_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleBCQFullyConnectedGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_BCQFULLYCONNECTED_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleBCQGather.h b/compiler/luci/import/include/luci/Import/Nodes/CircleBCQGather.h new file mode 100644 index 0000000..ff1c1f7 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleBCQGather.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_BCQGATHER_H__ +#define __LUCI_IMPORT_OP_CIRCLE_BCQGATHER_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleBCQGatherGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_BCQGATHER_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleBatchMatMul.h b/compiler/luci/import/include/luci/Import/Nodes/CircleBatchMatMul.h new file mode 100644 index 0000000..b46a871 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleBatchMatMul.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_BATCHMATMUL_H__ +#define __LUCI_IMPORT_OP_CIRCLE_BATCHMATMUL_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleBatchMatMulGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_BATCHMATMUL_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleCast.h b/compiler/luci/import/include/luci/Import/Nodes/CircleCast.h new file mode 100644 index 0000000..1cd850b --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleCast.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_CAST_H__ +#define __LUCI_IMPORT_OP_CIRCLE_CAST_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleCastGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_CAST_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleCeil.h b/compiler/luci/import/include/luci/Import/Nodes/CircleCeil.h new file mode 100644 index 0000000..f1bdf23 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleCeil.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_CEIL_H__ +#define __LUCI_IMPORT_OP_CIRCLE_CEIL_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleCeilGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_CEIL_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleCustom.h b/compiler/luci/import/include/luci/Import/Nodes/CircleCustom.h new file mode 100644 index 0000000..65745be --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleCustom.h @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_CUSTOM_H__ +#define __LUCI_IMPORT_OP_CIRCLE_CUSTOM_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleCustomGraphBuilder : public GraphBuilderBase +{ +public: + bool validate(const ValidateArgs &args) const final; + + void build(const circle::OperatorT &op, GraphBuilderContext *context) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_CUSTOM_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleDepthToSpace.h b/compiler/luci/import/include/luci/Import/Nodes/CircleDepthToSpace.h new file mode 100644 index 0000000..a479cbd --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleDepthToSpace.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_DEPTHTOSPACE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_DEPTHTOSPACE_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleDepthToSpaceGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_DEPTHTOSPACE_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleElu.h b/compiler/luci/import/include/luci/Import/Nodes/CircleElu.h new file mode 100644 index 0000000..2ec5642 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleElu.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_ELU_H__ +#define __LUCI_IMPORT_OP_CIRCLE_ELU_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleEluGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_ELU_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleExpandDims.h b/compiler/luci/import/include/luci/Import/Nodes/CircleExpandDims.h new file mode 100644 index 0000000..acbfe7a --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleExpandDims.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_EXPAND_DIMS_H__ +#define __LUCI_IMPORT_OP_CIRCLE_EXPAND_DIMS_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleExpandDimsGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_EXPAND_DIMS_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleFill.h b/compiler/luci/import/include/luci/Import/Nodes/CircleFill.h new file mode 100644 index 0000000..3539dcd --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleFill.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_FILL_H__ +#define __LUCI_IMPORT_OP_CIRCLE_FILL_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleFillGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_FILL_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleFloor.h b/compiler/luci/import/include/luci/Import/Nodes/CircleFloor.h new file mode 100644 index 0000000..0578008 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleFloor.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_FLOOR_H__ +#define __LUCI_IMPORT_OP_CIRCLE_FLOOR_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleFloorGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_FLOOR_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleFloorDiv.h b/compiler/luci/import/include/luci/Import/Nodes/CircleFloorDiv.h new file mode 100644 index 0000000..ddc2ab2 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleFloorDiv.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_FLOOR_DIV_H__ +#define __LUCI_IMPORT_OP_CIRCLE_FLOOR_DIV_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleFloorDivGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_FLOOR_DIV_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleFloorMod.h b/compiler/luci/import/include/luci/Import/Nodes/CircleFloorMod.h new file mode 100644 index 0000000..1d6aa87 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleFloorMod.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_FLOOR_MOD_H__ +#define __LUCI_IMPORT_OP_CIRCLE_FLOOR_MOD_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleFloorModGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_FLOOR_MOD_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleGather.h b/compiler/luci/import/include/luci/Import/Nodes/CircleGather.h new file mode 100644 index 0000000..0680c94 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleGather.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_GATHER_H__ +#define __LUCI_IMPORT_OP_CIRCLE_GATHER_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleGatherGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_GATHER_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleGatherNd.h b/compiler/luci/import/include/luci/Import/Nodes/CircleGatherNd.h new file mode 100644 index 0000000..be96b7d --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleGatherNd.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_GATHER_ND_H__ +#define __LUCI_IMPORT_OP_CIRCLE_GATHER_ND_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleGatherNdGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_GATHER_ND_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleGreater.h b/compiler/luci/import/include/luci/Import/Nodes/CircleGreater.h new file mode 100644 index 0000000..87f0a8d --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleGreater.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_GREATER_H__ +#define __LUCI_IMPORT_OP_CIRCLE_GREATER_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleGreaterGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_GREATER_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleGreaterEqual.h b/compiler/luci/import/include/luci/Import/Nodes/CircleGreaterEqual.h new file mode 100644 index 0000000..4d24314 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleGreaterEqual.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_GREATEREQUAL_H__ +#define __LUCI_IMPORT_OP_CIRCLE_GREATEREQUAL_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleGreaterEqualGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_GREATEREQUAL_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleIf.h b/compiler/luci/import/include/luci/Import/Nodes/CircleIf.h new file mode 100644 index 0000000..8faf09c --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleIf.h @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_IF_H__ +#define __LUCI_IMPORT_OP_CIRCLE_IF_H__ + +#include "luci/Import/GraphBuilderBase.h" + +namespace luci +{ + +class CircleIfGraphBuilder : public GraphBuilderBase +{ +public: + bool validate(const ValidateArgs &args) const final; + + void build(const circle::OperatorT &op, GraphBuilderContext *context) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_IF_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleInstanceNorm.h b/compiler/luci/import/include/luci/Import/Nodes/CircleInstanceNorm.h new file mode 100644 index 0000000..5fd8f14 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleInstanceNorm.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_INSTANCE_NORM_H__ +#define __LUCI_IMPORT_OP_CIRCLE_INSTANCE_NORM_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleInstanceNormGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_INSTANCE_NORM_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleL2Normalize.h b/compiler/luci/import/include/luci/Import/Nodes/CircleL2Normalize.h new file mode 100644 index 0000000..116605f --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleL2Normalize.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_L2_NORMALIZE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_L2_NORMALIZE_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleL2NormalizeGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_L2_NORMALIZE_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleL2Pool2D.h b/compiler/luci/import/include/luci/Import/Nodes/CircleL2Pool2D.h new file mode 100644 index 0000000..2211c47 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleL2Pool2D.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_L2_POOL2D_H__ +#define __LUCI_IMPORT_OP_CIRCLE_L2_POOL2D_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleL2Pool2DGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_L2_POOL2D_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleLeakyRelu.h b/compiler/luci/import/include/luci/Import/Nodes/CircleLeakyRelu.h new file mode 100644 index 0000000..b7fa41f --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleLeakyRelu.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_LEAKY_RELU_H__ +#define __LUCI_IMPORT_OP_CIRCLE_LEAKY_RELU_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleLeakyReluGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_LEAKY_RELU_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleLess.h b/compiler/luci/import/include/luci/Import/Nodes/CircleLess.h new file mode 100644 index 0000000..b93155b --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleLess.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_LESS_H__ +#define __LUCI_IMPORT_OP_CIRCLE_LESS_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleLessGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_LESS_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleLessEqual.h b/compiler/luci/import/include/luci/Import/Nodes/CircleLessEqual.h new file mode 100644 index 0000000..e54a4cb --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleLessEqual.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_LESS_EQUAL_H__ +#define __LUCI_IMPORT_OP_CIRCLE_LESS_EQUAL_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleLessEqualGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_LESS_EQUAL_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleLocalResponseNormalization.h b/compiler/luci/import/include/luci/Import/Nodes/CircleLocalResponseNormalization.h new file mode 100644 index 0000000..95e6ea8 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleLocalResponseNormalization.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_LOCAL_RESPONSE_NORMALIZATION_H__ +#define __LUCI_IMPORT_OP_CIRCLE_LOCAL_RESPONSE_NORMALIZATION_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleLocalResponseNormalizationGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_LOCAL_RESPONSE_NORMALIZATION_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleLog.h b/compiler/luci/import/include/luci/Import/Nodes/CircleLog.h new file mode 100644 index 0000000..5b33210 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleLog.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_LOG_H__ +#define __LUCI_IMPORT_OP_CIRCLE_LOG_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleLogGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_LOG_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleLogSoftmax.h b/compiler/luci/import/include/luci/Import/Nodes/CircleLogSoftmax.h new file mode 100644 index 0000000..ef29833 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleLogSoftmax.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_LOG_SOFTMAX_H__ +#define __LUCI_IMPORT_OP_CIRCLE_LOG_SOFTMAX_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleLogSoftmaxGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_LOG_SOFTMAX_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleLogicalAnd.h b/compiler/luci/import/include/luci/Import/Nodes/CircleLogicalAnd.h new file mode 100644 index 0000000..9336f4a --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleLogicalAnd.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_LOGICALAND_H__ +#define __LUCI_IMPORT_OP_CIRCLE_LOGICALAND_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleLogicalAndGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_LOGICALAND_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleLogistic.h b/compiler/luci/import/include/luci/Import/Nodes/CircleLogistic.h new file mode 100644 index 0000000..67c6c1f --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleLogistic.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_LOGISTIC_H__ +#define __LUCI_IMPORT_OP_CIRCLE_LOGISTIC_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleLogisticGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_LOGISTIC_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleMatrixDiag.h b/compiler/luci/import/include/luci/Import/Nodes/CircleMatrixDiag.h new file mode 100644 index 0000000..e038c3e --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleMatrixDiag.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_MATRIX_DIAG_H__ +#define __LUCI_IMPORT_OP_CIRCLE_MATRIX_DIAG_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleMatrixDiagGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_MATRIX_DIAG_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleMatrixSetDiag.h b/compiler/luci/import/include/luci/Import/Nodes/CircleMatrixSetDiag.h new file mode 100644 index 0000000..a9ea0ac --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleMatrixSetDiag.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_MATRIX_SET_DIAG_H__ +#define __LUCI_IMPORT_OP_CIRCLE_MATRIX_SET_DIAG_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleMatrixSetDiagGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_MATRIX_SET_DIAG_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleMaximum.h b/compiler/luci/import/include/luci/Import/Nodes/CircleMaximum.h new file mode 100644 index 0000000..9705d3a --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleMaximum.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_MAXIMUM_H__ +#define __LUCI_IMPORT_OP_CIRCLE_MAXIMUM_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleMaximumGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_MAXIMUM_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleMinimum.h b/compiler/luci/import/include/luci/Import/Nodes/CircleMinimum.h new file mode 100644 index 0000000..d9546ec --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleMinimum.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_MINIMUM_H__ +#define __LUCI_IMPORT_OP_CIRCLE_MINIMUM_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleMinimumGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_MINIMUM_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleMirrorPad.h b/compiler/luci/import/include/luci/Import/Nodes/CircleMirrorPad.h new file mode 100644 index 0000000..7f512cd --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleMirrorPad.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_MIRROR_PAD_H__ +#define __LUCI_IMPORT_OP_CIRCLE_MIRROR_PAD_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleMirrorPadGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_MIRROR_PAD_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleNeg.h b/compiler/luci/import/include/luci/Import/Nodes/CircleNeg.h new file mode 100644 index 0000000..3d0bac1 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleNeg.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_NEG_H__ +#define __LUCI_IMPORT_OP_CIRCLE_NEG_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleNegGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_NEG_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleNotEqual.h b/compiler/luci/import/include/luci/Import/Nodes/CircleNotEqual.h new file mode 100644 index 0000000..10c79b7 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleNotEqual.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_NOTEQUAL_H__ +#define __LUCI_IMPORT_OP_CIRCLE_NOTEQUAL_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleNotEqualGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_NOTEQUAL_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleOneHot.h b/compiler/luci/import/include/luci/Import/Nodes/CircleOneHot.h new file mode 100644 index 0000000..8d9526d --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleOneHot.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_ONEHOT_H__ +#define __LUCI_IMPORT_OP_CIRCLE_ONEHOT_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleOneHotGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_ONEHOT_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CirclePRelu.h b/compiler/luci/import/include/luci/Import/Nodes/CirclePRelu.h new file mode 100644 index 0000000..822862c --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CirclePRelu.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_PRELU_H__ +#define __LUCI_IMPORT_OP_CIRCLE_PRELU_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CirclePReluGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_PRELU_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CirclePow.h b/compiler/luci/import/include/luci/Import/Nodes/CirclePow.h new file mode 100644 index 0000000..284aa9b --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CirclePow.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_POW_H__ +#define __LUCI_IMPORT_OP_CIRCLE_POW_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CirclePowGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_POW_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleRange.h b/compiler/luci/import/include/luci/Import/Nodes/CircleRange.h new file mode 100644 index 0000000..bc63286 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleRange.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_RANGE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_RANGE_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleRangeGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_RANGE_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleRank.h b/compiler/luci/import/include/luci/Import/Nodes/CircleRank.h new file mode 100644 index 0000000..43a7fdb --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleRank.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_RANK_H__ +#define __LUCI_IMPORT_OP_CIRCLE_RANK_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleRankGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_RANK_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleReduceAny.h b/compiler/luci/import/include/luci/Import/Nodes/CircleReduceAny.h new file mode 100644 index 0000000..5ee5179 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleReduceAny.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_REDUCE_ANY_H__ +#define __LUCI_IMPORT_OP_CIRCLE_REDUCE_ANY_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleReduceAnyGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_REDUCE_ANY_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleReduceMax.h b/compiler/luci/import/include/luci/Import/Nodes/CircleReduceMax.h new file mode 100644 index 0000000..0bc7021 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleReduceMax.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_REDUCE_MAX_H__ +#define __LUCI_IMPORT_OP_CIRCLE_REDUCE_MAX_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleReduceMaxGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_REDUCE_MAX_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleReduceMin.h b/compiler/luci/import/include/luci/Import/Nodes/CircleReduceMin.h new file mode 100644 index 0000000..0c05457 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleReduceMin.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_REDUCE_MIN_H__ +#define __LUCI_IMPORT_OP_CIRCLE_REDUCE_MIN_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleReduceMinGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_REDUCE_MIN_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleReduceProd.h b/compiler/luci/import/include/luci/Import/Nodes/CircleReduceProd.h new file mode 100644 index 0000000..446bc78 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleReduceProd.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_REDUCE_PROD_H__ +#define __LUCI_IMPORT_OP_CIRCLE_REDUCE_PROD_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleReduceProdGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_REDUCE_PROD_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleRelu6.h b/compiler/luci/import/include/luci/Import/Nodes/CircleRelu6.h new file mode 100644 index 0000000..d17b4e2 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleRelu6.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_RELU6_H__ +#define __LUCI_IMPORT_OP_CIRCLE_RELU6_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleRelu6GraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_RELU6_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleReluN1To1.h b/compiler/luci/import/include/luci/Import/Nodes/CircleReluN1To1.h new file mode 100644 index 0000000..0594315 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleReluN1To1.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_RELU_N1_TO_1_H__ +#define __LUCI_IMPORT_OP_CIRCLE_RELU_N1_TO_1_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleReluN1To1GraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_RELU_N1_TO_1_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleResizeBilinear.h b/compiler/luci/import/include/luci/Import/Nodes/CircleResizeBilinear.h new file mode 100644 index 0000000..8c20ecc --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleResizeBilinear.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_RESIZE_BILINEAR_H__ +#define __LUCI_IMPORT_OP_CIRCLE_RESIZE_BILINEAR_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleResizeBilinearGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_RESIZE_BILINEAR_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleResizeNearestNeighbor.h b/compiler/luci/import/include/luci/Import/Nodes/CircleResizeNearestNeighbor.h new file mode 100644 index 0000000..5b06471 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleResizeNearestNeighbor.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_RESIZE_NEAREST_NEIGHBOR_H__ +#define __LUCI_IMPORT_OP_CIRCLE_RESIZE_NEAREST_NEIGHBOR_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleResizeNearestNeighborGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_RESIZE_NEAREST_NEIGHBOR_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleReverseSequence.h b/compiler/luci/import/include/luci/Import/Nodes/CircleReverseSequence.h new file mode 100644 index 0000000..cbeed30 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleReverseSequence.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_REVERSE_SEQUENCE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_REVERSE_SEQUENCE_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleReverseSequenceGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_REVERSE_SEQUENCE_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleReverseV2.h b/compiler/luci/import/include/luci/Import/Nodes/CircleReverseV2.h new file mode 100644 index 0000000..f354298 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleReverseV2.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_REVERSE_V2_H__ +#define __LUCI_IMPORT_OP_CIRCLE_REVERSE_V2_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleReverseV2GraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_REVERSE_V2_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleRound.h b/compiler/luci/import/include/luci/Import/Nodes/CircleRound.h new file mode 100644 index 0000000..8b027d7 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleRound.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_ROUND_H__ +#define __LUCI_IMPORT_OP_CIRCLE_ROUND_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleRoundGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_ROUND_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleScatterNd.h b/compiler/luci/import/include/luci/Import/Nodes/CircleScatterNd.h new file mode 100644 index 0000000..8fa7a2f --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleScatterNd.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SCATTER_ND_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SCATTER_ND_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleScatterNdGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SCATTER_ND_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleSegmentSum.h b/compiler/luci/import/include/luci/Import/Nodes/CircleSegmentSum.h new file mode 100644 index 0000000..7c33dee --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleSegmentSum.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SEGMENT_SUM_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SEGMENT_SUM_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleSegmentSumGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SEGMENT_SUM_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleSelect.h b/compiler/luci/import/include/luci/Import/Nodes/CircleSelect.h new file mode 100644 index 0000000..87bd1a7 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleSelect.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SELECT_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SELECT_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleSelectGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SELECT_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleSelectV2.h b/compiler/luci/import/include/luci/Import/Nodes/CircleSelectV2.h new file mode 100644 index 0000000..28c73b0 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleSelectV2.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SELECT_V2_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SELECT_V2_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleSelectV2GraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SELECT_V2_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleShape.h b/compiler/luci/import/include/luci/Import/Nodes/CircleShape.h new file mode 100644 index 0000000..3002084 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleShape.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SHAPE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SHAPE_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleShapeGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SHAPE_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleSin.h b/compiler/luci/import/include/luci/Import/Nodes/CircleSin.h new file mode 100644 index 0000000..605f5a5 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleSin.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SIN_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SIN_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleSinGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SIN_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleSlice.h b/compiler/luci/import/include/luci/Import/Nodes/CircleSlice.h new file mode 100644 index 0000000..3bb4c51 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleSlice.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SLICE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SLICE_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleSliceGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SLICE_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleSpaceToBatchND.h b/compiler/luci/import/include/luci/Import/Nodes/CircleSpaceToBatchND.h new file mode 100644 index 0000000..b872309 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleSpaceToBatchND.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SPACETOBATCHND_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SPACETOBATCHND_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleSpaceToBatchNDGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SPACETOBATCHND_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleSpaceToDepth.h b/compiler/luci/import/include/luci/Import/Nodes/CircleSpaceToDepth.h new file mode 100644 index 0000000..75a54dd --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleSpaceToDepth.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SPACETODEPTH_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SPACETODEPTH_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleSpaceToDepthGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SPACETODEPTH_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleSparseToDense.h b/compiler/luci/import/include/luci/Import/Nodes/CircleSparseToDense.h new file mode 100644 index 0000000..baf2409 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleSparseToDense.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SPARSETODENSE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SPARSETODENSE_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleSparseToDenseGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SPARSETODENSE_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleSplit.h b/compiler/luci/import/include/luci/Import/Nodes/CircleSplit.h new file mode 100644 index 0000000..3395e40 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleSplit.h @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SPLIT_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SPLIT_H__ + +#include "luci/Import/GraphBuilderBase.h" + +namespace luci +{ + +class CircleSplitGraphBuilder : public GraphBuilderBase +{ +public: + bool validate(const ValidateArgs &args) const final; + + void build(const circle::OperatorT &op, GraphBuilderContext *context) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SPLIT_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleSplitV.h b/compiler/luci/import/include/luci/Import/Nodes/CircleSplitV.h new file mode 100644 index 0000000..3e53df3 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleSplitV.h @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SPLIT_V_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SPLIT_V_H__ + +#include "luci/Import/GraphBuilderBase.h" + +namespace luci +{ + +class CircleSplitVGraphBuilder : public GraphBuilderBase +{ +public: + bool validate(const ValidateArgs &args) const final; + + void build(const circle::OperatorT &op, GraphBuilderContext *context) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SPLIT_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleSqrt.h b/compiler/luci/import/include/luci/Import/Nodes/CircleSqrt.h new file mode 100644 index 0000000..4fd7995 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleSqrt.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SQRT_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SQRT_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleSqrtGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SQRT_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleSquare.h b/compiler/luci/import/include/luci/Import/Nodes/CircleSquare.h new file mode 100644 index 0000000..3a12991 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleSquare.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SQUARE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SQUARE_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleSquareGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SQUARE_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleSquaredDifference.h b/compiler/luci/import/include/luci/Import/Nodes/CircleSquaredDifference.h new file mode 100644 index 0000000..95f0841 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleSquaredDifference.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SQUAREDDIFFERENCE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SQUAREDDIFFERENCE_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleSquaredDifferenceGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SQUAREDDIFFERENCE_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleSqueeze.h b/compiler/luci/import/include/luci/Import/Nodes/CircleSqueeze.h new file mode 100644 index 0000000..4f0dfb5 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleSqueeze.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SQUEEZE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SQUEEZE_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleSqueezeGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SQUEEZE_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleStridedSlice.h b/compiler/luci/import/include/luci/Import/Nodes/CircleStridedSlice.h new file mode 100644 index 0000000..f535c3a --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleStridedSlice.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_STRIDED_SLICE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_STRIDED_SLICE_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleStridedSliceGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_STRIDED_SLICE_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleSum.h b/compiler/luci/import/include/luci/Import/Nodes/CircleSum.h new file mode 100644 index 0000000..e65dd46 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleSum.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_SUM_H__ +#define __LUCI_IMPORT_OP_CIRCLE_SUM_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleSumGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_SUM_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleTanh.h b/compiler/luci/import/include/luci/Import/Nodes/CircleTanh.h new file mode 100644 index 0000000..b3795ac --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleTanh.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_TANH_H__ +#define __LUCI_IMPORT_OP_CIRCLE_TANH_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleTanhGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_TANH_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleTile.h b/compiler/luci/import/include/luci/Import/Nodes/CircleTile.h new file mode 100644 index 0000000..1da6cdb --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleTile.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_TILE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_TILE_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleTileGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_TILE_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleTopKV2.h b/compiler/luci/import/include/luci/Import/Nodes/CircleTopKV2.h new file mode 100644 index 0000000..8ec3f33 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleTopKV2.h @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_TOPK_V2_H__ +#define __LUCI_IMPORT_OP_CIRCLE_TOPK_V2_H__ + +#include "luci/Import/GraphBuilderBase.h" + +namespace luci +{ + +class CircleTopKV2GraphBuilder : public GraphBuilderBase +{ +public: + bool validate(const ValidateArgs &args) const final; + + void build(const circle::OperatorT &op, GraphBuilderContext *context) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_TOPK_V2_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleTransposeConv.h b/compiler/luci/import/include/luci/Import/Nodes/CircleTransposeConv.h new file mode 100644 index 0000000..2614d0d --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleTransposeConv.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_TRANSPOSE_CONV_H__ +#define __LUCI_IMPORT_OP_CIRCLE_TRANSPOSE_CONV_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleTransposeConvGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_TRANSPOSE_CONV_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleUnpack.h b/compiler/luci/import/include/luci/Import/Nodes/CircleUnpack.h new file mode 100644 index 0000000..f1a21de --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleUnpack.h @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_UNPACK_H__ +#define __LUCI_IMPORT_OP_CIRCLE_UNPACK_H__ + +#include "luci/Import/GraphBuilderBase.h" + +namespace luci +{ + +class CircleUnpackGraphBuilder : public GraphBuilderBase +{ +public: + bool validate(const ValidateArgs &args) const final; + + void build(const circle::OperatorT &op, GraphBuilderContext *context) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_UNPACK_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleWhere.h b/compiler/luci/import/include/luci/Import/Nodes/CircleWhere.h new file mode 100644 index 0000000..72f98ef --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleWhere.h @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_WHERE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_WHERE_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleWhereGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const override; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_WHERE_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleWhile.h b/compiler/luci/import/include/luci/Import/Nodes/CircleWhile.h new file mode 100644 index 0000000..68c56b3 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleWhile.h @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_WHILE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_WHILE_H__ + +#include "luci/Import/GraphBuilderBase.h" + +namespace luci +{ + +class CircleWhileGraphBuilder : public GraphBuilderBase +{ +public: + bool validate(const ValidateArgs &args) const final; + + void build(const circle::OperatorT &op, GraphBuilderContext *context) const final; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_WHILE_H__ diff --git a/compiler/luci/import/include/luci/Import/Nodes/CircleZerosLike.h b/compiler/luci/import/include/luci/Import/Nodes/CircleZerosLike.h new file mode 100644 index 0000000..2a34103 --- /dev/null +++ b/compiler/luci/import/include/luci/Import/Nodes/CircleZerosLike.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IMPORT_OP_CIRCLE_ZEROS_LIKE_H__ +#define __LUCI_IMPORT_OP_CIRCLE_ZEROS_LIKE_H__ + +#include "luci/Import/GraphBuilder.h" + +namespace luci +{ + +class CircleZerosLikeGraphBuilder : public GraphBuilder +{ +public: + bool validate(const ValidateArgs &args) const final; + +private: + CircleNode *build_node(const circle::OperatorT &op, const std::vector &inputs, + loco::Graph *graph) const override; +}; + +} // namespace luci + +#endif // __LUCI_IMPORT_OP_CIRCLE_ZEROS_LIKE_H__ diff --git a/compiler/luci/import/include/luci/Importer.h b/compiler/luci/import/include/luci/Importer.h index 246df9f..f08ddcd 100644 --- a/compiler/luci/import/include/luci/Importer.h +++ b/compiler/luci/import/include/luci/Importer.h @@ -51,4 +51,4 @@ private: } // namespace luci -#endif // __MOCO_IMPORTER_H__ +#endif // __LUCI_IMPORTER_H__ diff --git a/compiler/luci/import/src/CircleReader.cpp b/compiler/luci/import/src/CircleReader.cpp index ead0093..81e945d 100644 --- a/compiler/luci/import/src/CircleReader.cpp +++ b/compiler/luci/import/src/CircleReader.cpp @@ -136,6 +136,19 @@ Padding luci_padding(const circle::Padding padding) return Padding::UNDEFINED; } +MirrorPadMode luci_mirrorpad_mode(const circle::MirrorPadMode mode) +{ + switch (mode) + { + case circle::MirrorPadMode::MirrorPadMode_REFLECT: + return MirrorPadMode::REFLECT; + case circle::MirrorPadMode::MirrorPadMode_SYMMETRIC: + return MirrorPadMode::SYMMETRIC; + } + assert(false); + return MirrorPadMode::UNDEFINED; +} + std::unique_ptr luci_quantparam(const circle::QuantizationParametersT *quantization) { @@ -159,6 +172,27 @@ luci_quantparam(const circle::QuantizationParametersT *quantization) return nullptr; } +void copy_tensor_attributes(const circle::TensorT &tensor, CircleNode *node) +{ + node->name(tensor_name(tensor)); + node->dtype(luci_datatype(tensor.type)); + + std::vector dims = tensor.shape; // in NHWC + node->rank(dims.size()); + for (uint32_t r = 0; r < dims.size(); ++r) + { + node->dim(r) = loco::Dimension(dims[r]); + } + + const auto *quantization = tensor.quantization.get(); + if (quantization != nullptr) + { + auto quantparam = luci_quantparam(quantization); + if (quantparam) + node->quantparam(std::move(quantparam)); + } +} + circle::BuiltinOperator CircleReader::builtin_code(const circle::OperatorT &op) const { const auto &op_codes = opcodes(); @@ -192,6 +226,9 @@ bool CircleReader::parse(const circle::Model *model) _model.reset(model->UnPack()); + // for direct pointer access + _model_ptr = model; + return true; } @@ -205,6 +242,12 @@ bool CircleReader::select_subgraph(uint32_t sgindex) _current_subgraph = _model->subgraphs[sgindex].get(); + // for direct pointer access + auto subgraphs = _model_ptr->subgraphs(); + const circle::SubGraph *subgraph = (*subgraphs)[sgindex]; + + _tensors_ptr = subgraph->tensors(); + return true; } diff --git a/compiler/luci/import/src/GraphBuilder.cpp b/compiler/luci/import/src/GraphBuilder.cpp index e0ec9de..80a9f98 100644 --- a/compiler/luci/import/src/GraphBuilder.cpp +++ b/compiler/luci/import/src/GraphBuilder.cpp @@ -16,21 +16,39 @@ #include "luci/Import/GraphBuilder.h" +#include + namespace luci { void GraphBuilder::build(const circle::OperatorT &op, GraphBuilderContext *context) const { + LOGGER(l); + assert(context != nullptr); const std::vector &inputs = op.inputs; const std::vector &outputs = op.outputs; const auto &tensors = context->reader()->tensors(); + const auto &opcodes = context->reader()->opcodes(); + auto tensors_ptr = context->reader()->tensors_ptr(); + assert(tensors_ptr != nullptr); std::vector input_nodes; for (const int32_t input_tensor_index : inputs) { - input_nodes.push_back(context->nodefinder()->node(input_tensor_index)); + if (input_tensor_index >= 0) + { + auto input = context->nodefinder()->node(input_tensor_index); + if (input == nullptr) + INFO(l) << "[luci] Warning: input node is null " << input_tensor_index << std::endl; + input_nodes.push_back(input); + } + else + { + // If there is no tensor, insert CircleOutputExclude. + input_nodes.push_back(context->graph()->nodes()->create()); + } } CircleNode *node = build_node(op, input_nodes, context->graph()); @@ -39,16 +57,15 @@ void GraphBuilder::build(const circle::OperatorT &op, GraphBuilderContext *conte assert(outputs.size() == 1); { const circle::TensorT &output_tensor = *tensors[outputs[0]]; + copy_tensor_attributes(output_tensor, node); + // mark shape_status + if (tensors_ptr->Get(outputs[0])->shape() == nullptr) + node->shape_status(ShapeStatus::NOSHAPE); + else + node->shape_status(ShapeStatus::VALID); - node->name(tensor_name(output_tensor)); - - auto quantization = tensor_quantization(output_tensor); - if (quantization) - { - auto quantparam = luci_quantparam(quantization); - if (quantparam) - node->quantparam(std::move(quantparam)); - } + // mark operator version + node->op_version(opcodes[op.opcode_index].get()->version); } // Register node's only output. diff --git a/compiler/luci/import/src/GraphBuilderContext.cpp b/compiler/luci/import/src/GraphBuilderContext.cpp index a5162ce..21adfa7 100644 --- a/compiler/luci/import/src/GraphBuilderContext.cpp +++ b/compiler/luci/import/src/GraphBuilderContext.cpp @@ -25,10 +25,12 @@ namespace luci void IndexNodeFinder::enroll(TensorIndex idx, CircleNode *node) { - if (_table.find(idx) != _table.end()) + auto iter = _table.find(idx); + if (iter != _table.end()) { LOGGER(l); - INFO(l) << "[luci] NodeFinder SKIP (" << idx << ") " << node << std::endl; + INFO(l) << "[luci] NodeFinder SKIP (" << idx << ") " << node << ":" << node->name() + << " existing: " << iter->second << ":" << iter->second->name() << std::endl; return; } @@ -39,9 +41,22 @@ CircleNode *IndexNodeFinder::node(TensorIndex idx) const { MapIndexNode_t::const_iterator iter = _table.find(idx); - assert(iter != _table.end() && iter->second != nullptr); + // dangle output node may exist that are not enrolled + return (iter != _table.end()) ? iter->second : nullptr; +} - return iter->second; +void IndexTensorOutputs::enroll(TensorIndex idx) +{ + auto iter = _set.find(idx); + if (iter != _set.end()) + { + LOGGER(l); + INFO(l) << "[luci] TensorOutputs SKIP (" << idx << ") existing" << std::endl; + return; + } + _set.insert(idx); } +bool IndexTensorOutputs::find(TensorIndex idx) { return (_set.find(idx) != _set.end()); } + } // namespace luci diff --git a/compiler/luci/import/src/GraphBuilderRegistry.cpp b/compiler/luci/import/src/GraphBuilderRegistry.cpp index 929b71a..d29557f 100644 --- a/compiler/luci/import/src/GraphBuilderRegistry.cpp +++ b/compiler/luci/import/src/GraphBuilderRegistry.cpp @@ -27,137 +27,140 @@ GraphBuilderRegistry::GraphBuilderRegistry() { #define CIRCLE_NODE(OPCODE, CLASS) add(circle::BuiltinOperator_##OPCODE, std::make_unique()); - CIRCLE_NODE(ABS, CircleAbsGraphBuilder); // 101 - CIRCLE_NODE(ADD, CircleAddGraphBuilder); // 0 - CIRCLE_NODE(ARG_MAX, CircleArgMaxGraphBuilder); // 56 - CIRCLE_NODE(AVERAGE_POOL_2D, CircleAveragePool2DGraphBuilder); // 1 - CIRCLE_NODE(BATCH_TO_SPACE_ND, CircleBatchToSpaceNDGraphBuilder); // 37 - CIRCLE_NODE(CONCATENATION, CircleConcatenationGraphBuilder); // 2 - CIRCLE_NODE(CONV_2D, CircleConv2DGraphBuilder); // 3 - CIRCLE_NODE(COS, CircleCosGraphBuilder); // 108 - CIRCLE_NODE(DEPTHWISE_CONV_2D, CircleDepthwiseConv2DGraphBuilder); // 4 - CIRCLE_NODE(DIV, CircleDivGraphBuilder); // 42 - CIRCLE_NODE(EQUAL, CircleEqualGraphBuilder); // 71 - CIRCLE_NODE(EXP, CircleExpGraphBuilder); // 47 - CIRCLE_NODE(FULLY_CONNECTED, CircleFullyConnectedGraphBuilder); // 9 - CIRCLE_NODE(LOGICAL_NOT, CircleLogicalNotGraphBuilder); // 87 - CIRCLE_NODE(LOGICAL_OR, CircleLogicalOrGraphBuilder); // 84 - CIRCLE_NODE(MAX_POOL_2D, CircleMaxPool2DGraphBuilder); // 17 - CIRCLE_NODE(MEAN, CircleMeanGraphBuilder); // 40 - CIRCLE_NODE(MUL, CircleMulGraphBuilder); // 18 - CIRCLE_NODE(PACK, CirclePackGraphBuilder); // 83 - CIRCLE_NODE(PAD, CirclePadGraphBuilder); // 34 - CIRCLE_NODE(RELU, CircleReluGraphBuilder); // 19 - CIRCLE_NODE(RESHAPE, CircleReshapeGraphBuilder); // 22 - CIRCLE_NODE(RSQRT, CircleRsqrtGraphBuilder); // 76 - CIRCLE_NODE(SOFTMAX, CircleSoftmaxGraphBuilder); // 25 - CIRCLE_NODE(SUB, CircleSubGraphBuilder); // 41 - CIRCLE_NODE(TRANSPOSE, CircleTransposeGraphBuilder); // 39 + CIRCLE_NODE(ABS, CircleAbsGraphBuilder); // 101 + CIRCLE_NODE(ADD, CircleAddGraphBuilder); // 0 + CIRCLE_NODE(ADD_N, CircleAddNGraphBuilder); // 106 + CIRCLE_NODE(ARG_MAX, CircleArgMaxGraphBuilder); // 56 + CIRCLE_NODE(ARG_MIN, CircleArgMinGraphBuilder); // 79 + CIRCLE_NODE(AVERAGE_POOL_2D, CircleAveragePool2DGraphBuilder); // 1 + CIRCLE_NODE(BATCH_MATMUL, CircleBatchMatMulGraphBuilder); // 126 + CIRCLE_NODE(BATCH_TO_SPACE_ND, CircleBatchToSpaceNDGraphBuilder); // 37 + CIRCLE_NODE(BCQ_FULLY_CONNECTED, CircleBCQFullyConnectedGraphBuilder); // 253 + CIRCLE_NODE(BCQ_GATHER, CircleBCQGatherGraphBuilder); // 252 + CIRCLE_NODE(CAST, CircleCastGraphBuilder); // 53 + CIRCLE_NODE(CEIL, CircleCeilGraphBuilder); // 104 + CIRCLE_NODE(CUSTOM, CircleCustomGraphBuilder); // 32 + CIRCLE_NODE(CONCATENATION, CircleConcatenationGraphBuilder); // 2 + CIRCLE_NODE(CONV_2D, CircleConv2DGraphBuilder); // 3 + CIRCLE_NODE(COS, CircleCosGraphBuilder); // 108 + CIRCLE_NODE(DEPTH_TO_SPACE, CircleDepthToSpaceGraphBuilder); // 5 + CIRCLE_NODE(DEPTHWISE_CONV_2D, CircleDepthwiseConv2DGraphBuilder); // 4 + CIRCLE_NODE(DIV, CircleDivGraphBuilder); // 42 + CIRCLE_NODE(ELU, CircleEluGraphBuilder); // 111 + CIRCLE_NODE(EQUAL, CircleEqualGraphBuilder); // 71 + CIRCLE_NODE(EXP, CircleExpGraphBuilder); // 47 + CIRCLE_NODE(EXPAND_DIMS, CircleExpandDimsGraphBuilder); // 70 + CIRCLE_NODE(FILL, CircleFillGraphBuilder); // 94 + CIRCLE_NODE(FLOOR, CircleFloorGraphBuilder); // 8 + CIRCLE_NODE(FLOOR_DIV, CircleFloorDivGraphBuilder); // 90 + CIRCLE_NODE(FLOOR_MOD, CircleFloorModGraphBuilder); // 95 + CIRCLE_NODE(FULLY_CONNECTED, CircleFullyConnectedGraphBuilder); // 9 + CIRCLE_NODE(GATHER, CircleGatherGraphBuilder); // 36 + CIRCLE_NODE(GATHER_ND, CircleGatherNdGraphBuilder); // 107 + CIRCLE_NODE(GREATER, CircleGreaterGraphBuilder); // 61 + CIRCLE_NODE(GREATER_EQUAL, CircleGreaterEqualGraphBuilder); // 62 + CIRCLE_NODE(IF, CircleIfGraphBuilder); // 118 + CIRCLE_NODE(INSTANCE_NORM, CircleInstanceNormGraphBuilder); // 254 + CIRCLE_NODE(L2_NORMALIZATION, CircleL2NormalizeGraphBuilder); // 11 + CIRCLE_NODE(L2_POOL_2D, CircleL2Pool2DGraphBuilder); // 12 + CIRCLE_NODE(LEAKY_RELU, CircleLeakyReluGraphBuilder); // 98, + CIRCLE_NODE(LESS, CircleLessGraphBuilder); // 58 + CIRCLE_NODE(LESS_EQUAL, CircleLessEqualGraphBuilder); // 63 + CIRCLE_NODE(LOCAL_RESPONSE_NORMALIZATION, CircleLocalResponseNormalizationGraphBuilder); // 13 + CIRCLE_NODE(LOG, CircleLogGraphBuilder); // 73 + CIRCLE_NODE(LOGICAL_AND, CircleLogicalAndGraphBuilder); // 86 + CIRCLE_NODE(LOGICAL_NOT, CircleLogicalNotGraphBuilder); // 87 + CIRCLE_NODE(LOGICAL_OR, CircleLogicalOrGraphBuilder); // 84 + CIRCLE_NODE(LOGISTIC, CircleLogisticGraphBuilder); // 14 + CIRCLE_NODE(LOG_SOFTMAX, CircleLogSoftmaxGraphBuilder); // 50 + CIRCLE_NODE(MATRIX_DIAG, CircleMatrixDiagGraphBuilder); // 113 + CIRCLE_NODE(MATRIX_SET_DIAG, CircleMatrixSetDiagGraphBuilder); // 115 + CIRCLE_NODE(MAXIMUM, CircleMaximumGraphBuilder); // 55 + CIRCLE_NODE(MAX_POOL_2D, CircleMaxPool2DGraphBuilder); // 17 + CIRCLE_NODE(MEAN, CircleMeanGraphBuilder); // 40 + CIRCLE_NODE(MINIMUM, CircleMinimumGraphBuilder); // 57 + CIRCLE_NODE(MIRROR_PAD, CircleMirrorPadGraphBuilder); // 100 + CIRCLE_NODE(MUL, CircleMulGraphBuilder); // 18 + CIRCLE_NODE(NEG, CircleNegGraphBuilder); // 59 + CIRCLE_NODE(NOT_EQUAL, CircleNotEqualGraphBuilder); // 72 + CIRCLE_NODE(ONE_HOT, CircleOneHotGraphBuilder); // 85 + CIRCLE_NODE(PACK, CirclePackGraphBuilder); // 83 + CIRCLE_NODE(PAD, CirclePadGraphBuilder); // 34 + CIRCLE_NODE(POW, CirclePowGraphBuilder); // 78 + CIRCLE_NODE(PRELU, CirclePReluGraphBuilder); // 54, + CIRCLE_NODE(RANGE, CircleRangeGraphBuilder); // 96 + CIRCLE_NODE(RANK, CircleRankGraphBuilder); // 110 + CIRCLE_NODE(REDUCE_ANY, CircleReduceAnyGraphBuilder); // 91 + CIRCLE_NODE(REDUCE_MAX, CircleReduceMaxGraphBuilder); // 82 + CIRCLE_NODE(REDUCE_MIN, CircleReduceMinGraphBuilder); // 89 + CIRCLE_NODE(REDUCE_PROD, CircleReduceProdGraphBuilder); // 81 + CIRCLE_NODE(RELU, CircleReluGraphBuilder); // 19 + CIRCLE_NODE(RELU6, CircleRelu6GraphBuilder); // 21 + CIRCLE_NODE(RELU_N1_TO_1, CircleReluN1To1GraphBuilder); // 20 + CIRCLE_NODE(RESHAPE, CircleReshapeGraphBuilder); // 22 + CIRCLE_NODE(RESIZE_BILINEAR, CircleResizeBilinearGraphBuilder); // 23 + CIRCLE_NODE(RESIZE_NEAREST_NEIGHBOR, CircleResizeNearestNeighborGraphBuilder); // 97 + CIRCLE_NODE(REVERSE_SEQUENCE, CircleReverseSequenceGraphBuilder); // 112 + CIRCLE_NODE(REVERSE_V2, CircleReverseV2GraphBuilder); // 105 + CIRCLE_NODE(ROUND, CircleRoundGraphBuilder); // 116 + CIRCLE_NODE(RSQRT, CircleRsqrtGraphBuilder); // 76 + CIRCLE_NODE(SCATTER_ND, CircleScatterNdGraphBuilder); // 122 + CIRCLE_NODE(SEGMENT_SUM, CircleSegmentSumGraphBuilder); // 125 + CIRCLE_NODE(SELECT, CircleSelectGraphBuilder); // 64 + CIRCLE_NODE(SELECT_V2, CircleSelectV2GraphBuilder); // 123 + CIRCLE_NODE(SHAPE, CircleShapeGraphBuilder); // 77 + CIRCLE_NODE(SIN, CircleSinGraphBuilder); // 66 + CIRCLE_NODE(SLICE, CircleSliceGraphBuilder); // 65 + CIRCLE_NODE(SOFTMAX, CircleSoftmaxGraphBuilder); // 25 + CIRCLE_NODE(SPACE_TO_BATCH_ND, CircleSpaceToBatchNDGraphBuilder); // 38 + CIRCLE_NODE(SPACE_TO_DEPTH, CircleSpaceToDepthGraphBuilder); // 26 + CIRCLE_NODE(SPARSE_TO_DENSE, CircleSparseToDenseGraphBuilder); // 68 + CIRCLE_NODE(SPLIT, CircleSplitGraphBuilder); // 49 + CIRCLE_NODE(SPLIT_V, CircleSplitVGraphBuilder); // 102 + CIRCLE_NODE(SQRT, CircleSqrtGraphBuilder); // 75 + CIRCLE_NODE(SQUARE, CircleSquareGraphBuilder); // 92 + CIRCLE_NODE(SQUARED_DIFFERENCE, CircleSquaredDifferenceGraphBuilder); // 99 + CIRCLE_NODE(SQUEEZE, CircleSqueezeGraphBuilder); // 43 + CIRCLE_NODE(STRIDED_SLICE, CircleStridedSliceGraphBuilder); // 45 + CIRCLE_NODE(SUB, CircleSubGraphBuilder); // 41 + CIRCLE_NODE(SUM, CircleSumGraphBuilder); // 74 + CIRCLE_NODE(TANH, CircleTanhGraphBuilder); // 28 + CIRCLE_NODE(TILE, CircleTileGraphBuilder); // 69 + CIRCLE_NODE(TOPK_V2, CircleTopKV2GraphBuilder); // 48 + CIRCLE_NODE(TRANSPOSE, CircleTransposeGraphBuilder); // 39 + CIRCLE_NODE(TRANSPOSE_CONV, CircleTransposeConvGraphBuilder); // 67 + CIRCLE_NODE(UNPACK, CircleUnpackGraphBuilder); // 88 + CIRCLE_NODE(WHERE, CircleWhereGraphBuilder); // 109 + CIRCLE_NODE(WHILE, CircleWhileGraphBuilder); // 119 + CIRCLE_NODE(ZEROS_LIKE, CircleZerosLikeGraphBuilder); // 93 #undef CIRCLE_NODE // BuiltinOperator_DEQUANTIZE = 6, // BuiltinOperator_EMBEDDING_LOOKUP = 7, - // BuiltinOperator_FLOOR = 8, // BuiltinOperator_HASHTABLE_LOOKUP = 10, - // BuiltinOperator_L2_NORMALIZATION = 11, - // BuiltinOperator_L2_POOL_2D = 12, - // BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION = 13, - // BuiltinOperator_LOGISTIC = 14, // BuiltinOperator_LSH_PROJECTION = 15, // BuiltinOperator_LSTM = 16, - // BuiltinOperator_RELU_N1_TO_1 = 20, - // BuiltinOperator_RELU6 = 21, - // BuiltinOperator_RESIZE_BILINEAR = 23, // BuiltinOperator_RNN = 24, - // BuiltinOperator_SPACE_TO_DEPTH = 26, // BuiltinOperator_SVDF = 27, - // BuiltinOperator_TANH = 28, // BuiltinOperator_CONCAT_EMBEDDINGS = 29, // BuiltinOperator_SKIP_GRAM = 30, // BuiltinOperator_CALL = 31, - // BuiltinOperator_CUSTOM = 32, // BuiltinOperator_EMBEDDING_LOOKUP_SPARSE = 33, // BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN = 35, - // BuiltinOperator_GATHER = 36, - // BuiltinOperator_SPACE_TO_BATCH_ND = 38, - // BuiltinOperator_SQUEEZE = 43, // BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM = 44, - // BuiltinOperator_STRIDED_SLICE = 45, // BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN = 46, - // BuiltinOperator_TOPK_V2 = 48, - // BuiltinOperator_SPLIT = 49, - // BuiltinOperator_LOG_SOFTMAX = 50, // BuiltinOperator_DELEGATE = 51, // BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM = 52, - // BuiltinOperator_CAST = 53, - // BuiltinOperator_PRELU = 54, - // BuiltinOperator_MAXIMUM = 55, // BuiltinOperator_ARG_MAX = 56, - // BuiltinOperator_MINIMUM = 57, - // BuiltinOperator_LESS = 58, - // BuiltinOperator_NEG = 59, // BuiltinOperator_PADV2 = 60, - // BuiltinOperator_GREATER = 61, - // BuiltinOperator_GREATER_EQUAL = 62, - // BuiltinOperator_LESS_EQUAL = 63, - // BuiltinOperator_SELECT = 64, - // BuiltinOperator_SLICE = 65, - // BuiltinOperator_SIN = 66, - // BuiltinOperator_TRANSPOSE_CONV = 67, - // BuiltinOperator_SPARSE_TO_DENSE = 68, - // BuiltinOperator_TILE = 69, - // BuiltinOperator_EXPAND_DIMS = 70, - // BuiltinOperator_NOT_EQUAL = 72, - // BuiltinOperator_LOG = 73, - // BuiltinOperator_SUM = 74, - // BuiltinOperator_SQRT = 75, - // BuiltinOperator_SHAPE = 77, - // BuiltinOperator_POW = 78, - // BuiltinOperator_ARG_MIN = 79, // BuiltinOperator_FAKE_QUANT = 80, - // BuiltinOperator_REDUCE_PROD = 81, - // BuiltinOperator_REDUCE_MAX = 82, - // BuiltinOperator_ONE_HOT = 85, - // BuiltinOperator_LOGICAL_AND = 86, - // BuiltinOperator_UNPACK = 88, - // BuiltinOperator_REDUCE_MIN = 89, - // BuiltinOperator_FLOOR_DIV = 90, - // BuiltinOperator_REDUCE_ANY = 91, - // BuiltinOperator_SQUARE = 92, - // BuiltinOperator_ZEROS_LIKE = 93, - // BuiltinOperator_FILL = 94, - // BuiltinOperator_FLOOR_MOD = 95, - // BuiltinOperator_RANGE = 96, - // BuiltinOperator_RESIZE_NEAREST_NEIGHBOR = 97, - // BuiltinOperator_LEAKY_RELU = 98, - // BuiltinOperator_SQUARED_DIFFERENCE = 99, - // BuiltinOperator_MIRROR_PAD = 100, - // BuiltinOperator_SPLIT_V = 102, // BuiltinOperator_UNIQUE = 103, - // BuiltinOperator_CEIL = 104, - // BuiltinOperator_REVERSE_V2 = 105, - // BuiltinOperator_ADD_N = 106, - // BuiltinOperator_GATHER_ND = 107, - // BuiltinOperator_WHERE = 109, - // BuiltinOperator_RANK = 110, - // BuiltinOperator_ELU = 111, - // BuiltinOperator_REVERSE_SEQUENCE = 112, - // BuiltinOperator_MATRIX_DIAG = 113, // BuiltinOperator_QUANTIZE = 114, - // BuiltinOperator_MATRIX_SET_DIAG = 115, - // BuiltinOperator_ROUND = 116, // BuiltinOperator_HARD_SWISH = 117, - // BuiltinOperator_IF = 118, - // BuiltinOperator_WHILE = 119, // BuiltinOperator_NON_MAX_SUPPRESSION_V4 = 120, // BuiltinOperator_NON_MAX_SUPPRESSION_V5 = 121, - // BuiltinOperator_SCATTER_ND = 122, - // BuiltinOperator_SELECT_V2 = 123, // BuiltinOperator_DENSIFY = 124, - // BuiltinOperator_SEGMENT_SUM = 125, - // BuiltinOperator_BATCH_MATMUL = 126, - // BuiltinOperator_INSTANCE_NORM = 254, } } // namespace luci diff --git a/compiler/luci/import/src/Importer.cpp b/compiler/luci/import/src/Importer.cpp index 964c476..ab89f35 100644 --- a/compiler/luci/import/src/Importer.cpp +++ b/compiler/luci/import/src/Importer.cpp @@ -15,6 +15,7 @@ */ #include "luci/Importer.h" +#include "PostImport.h" #include "luci/Import/GraphBuilder.h" #include "luci/Import/GraphBuilderContext.h" @@ -27,6 +28,7 @@ #include #include +#include #include #include @@ -40,11 +42,28 @@ void convert_graph(const luci::GraphBuilderSource &source, luci::CircleReader &r LOGGER(l); auto nodefinder = std::make_unique(); + auto tensoroutputs = std::make_unique(); - luci::GraphBuilderContext gb_context(graph, &reader, nodefinder.get()); + luci::GraphBuilderContext gb_context(graph, &reader, nodefinder.get(), tensoroutputs.get()); const auto &operators = reader.operators(); const auto &tensors = reader.tensors(); + auto tensors_ptr = reader.tensors_ptr(); + assert(tensors_ptr != nullptr); + + // build a cache to identify if a tensor is output of an operator + // if this is set, we should not create a CircleConst for this tensor + for (uint32_t i = 0; i < operators.size(); ++i) + { + const circle::OperatorT &op = *operators[i]; + const auto &outputs = op.outputs; + + for (uint32_t j = 0; j < outputs.size(); ++j) + { + auto tidx = outputs[j]; + tensoroutputs->enroll(tidx); + } + } // graph inputs; there are no input nodes in TFlite but just Tensors // creating virtual input nodes will make possible to connect nodes that uses them @@ -55,51 +74,43 @@ void convert_graph(const luci::GraphBuilderSource &source, luci::CircleReader &r assert(input_node != nullptr); const circle::TensorT &tensor = *tensors[input]; - auto tname = luci::tensor_name(tensor); - input_node->name(tname); - auto quantization = luci::tensor_quantization(tensor); - if (quantization) - { - auto quantparam = luci::luci_quantparam(quantization); - if (quantparam.get()) - input_node->quantparam(std::move(quantparam)); - } + luci::copy_tensor_attributes(tensor, input_node); + if (tensors_ptr->Get(input)->shape() == nullptr) + input_node->shape_status(luci::ShapeStatus::NOSHAPE); + else + input_node->shape_status(luci::ShapeStatus::VALID); INFO(l) << "[luci] NodeFinder INPUT(" << input << ") = " << input_node << std::endl; nodefinder->enroll(input, input_node); - // Shape of Input - const std::vector &input_dims = tensor.shape; // in NHWC - input_node->rank(input_dims.size()); - for (uint32_t r = 0; r < input_dims.size(); ++r) - input_node->dim(r) = loco::Dimension(input_dims[r]); - - // Data type of Input - auto dtype = luci::luci_datatype(tensor.type); - input_node->dtype(dtype); + // input_node is also an output to a tensor + tensoroutputs->enroll(input); // Name auto graph_input = graph->inputs()->create(); - graph_input->name(tname); + graph_input->name(input_node->name()); // Set GraphInputOutputIndex for graph input_node->index(graph_input->index()); // Data type - graph_input->dtype(dtype); + graph_input->dtype(input_node->dtype()); + + // Shape of GraphInput + auto input_shape = std::make_unique(); + const std::vector &input_dims = tensor.shape; // in NHWC + input_shape->rank(input_dims.size()); + for (uint32_t r = 0; r < input_dims.size(); ++r) + input_shape->dim(r) = loco::Dimension(input_dims[r]); + graph_input->shape(std::move(input_shape)); } // Create CircleConst nodes for constant tensors. - const auto &buffers = reader.buffers(); for (uint32_t i = 0; i < tensors.size(); ++i) { - const circle::TensorT &tensor = *tensors[i]; - const std::vector &buffer = buffers[tensor.buffer]->data; - if (!buffer.empty()) - { - luci::CircleConst *const_node = luci::create_circleconst(&gb_context, i); + luci::CircleConst *const_node = luci::create_circleconst(&gb_context, i); + if (const_node != nullptr) nodefinder->enroll(i, const_node); - } } // Import the operators. @@ -130,18 +141,38 @@ void convert_graph(const luci::GraphBuilderSource &source, luci::CircleReader &r // graph outputs for (auto output : reader.outputs()) { + const circle::TensorT &tensor = *tensors[output]; + auto output_node = graph->nodes()->create(); assert(output_node != nullptr); - output_node->from(nodefinder->node(output)); + auto output_from = nodefinder->node(output); + if (output_from != nullptr) + output_node->from(output_from); + else + { + // NOTE loco::Graph requires all input node(s) to a node should exist. + // Here, CircleOutput needs an input node. + // We add a dummy node to make it happy. + auto output_dummy = graph->nodes()->create(); + assert(output_dummy != nullptr); + output_node->from(output_dummy); + + luci::copy_tensor_attributes(tensor, output_dummy); + if (tensors_ptr->Get(output)->shape() == nullptr) + output_dummy->shape_status(luci::ShapeStatus::NOSHAPE); + else + output_dummy->shape_status(luci::ShapeStatus::VALID); + } INFO(l) << "[luci] NodeFinder OUTPUT(" << output << ") = " << output_node << std::endl; // set the graph output name and node object - const circle::TensorT &tensor = *tensors[output]; auto graph_output = graph->outputs()->create(); std::string tname = luci::tensor_name(tensor); graph_output->name("output_" + tname); + luci::copy_tensor_attributes(tensor, output_node); + // Set GraphInputOutputIndex for graph output_node->index(graph_output->index()); @@ -195,8 +226,10 @@ std::unique_ptr Importer::import(const circle::Model *model) const if (!reader.parse(model)) return nullptr; - // TODO support multiple subgraph when Circle supports - assert(reader.num_subgraph() == 1); + if (reader.num_subgraph() != 1) + { + INTERNAL_EXN("Use 'importModule()' for multiple subgraphs"); + } if (!reader.select_subgraph(0)) return nullptr; @@ -204,11 +237,14 @@ std::unique_ptr Importer::import(const circle::Model *model) const convert_graph(*source_ptr, reader, graph.get()); LOGGER(l); - INFO(l) << fmt(graph.get()); + VERBOSE(l, 3) << "--- graph dump begin -------------------------------------------"; + VERBOSE(l, 3) << "Name: " << graph->name(); + VERBOSE(l, 3) << fmt(graph.get()); + VERBOSE(l, 3) << "--- graph dump end ---------------------------------------------"; assert(loco::valid(graph.get(), std::make_unique())); - return std::move(graph); + return graph; } std::unique_ptr Importer::importModule(const circle::Model *model) const @@ -240,14 +276,19 @@ std::unique_ptr Importer::importModule(const circle::Model *model) const convert_graph(*source_ptr, reader, graph.get()); LOGGER(l); - INFO(l) << fmt(graph.get()); + VERBOSE(l, 3) << "--- graph dump begin -------------------------------------------"; + VERBOSE(l, 3) << "Name: " << graph->name(); + VERBOSE(l, 3) << fmt(graph.get()); + VERBOSE(l, 3) << "--- graph dump end ---------------------------------------------"; assert(loco::valid(graph.get(), std::make_unique())); module->add(std::move(graph)); } - return std::move(module); + post_import_graph(module.get(), reader); + + return module; } } // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleAddN.cpp b/compiler/luci/import/src/Nodes/CircleAddN.cpp new file mode 100644 index 0000000..2f1716e --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleAddN.cpp @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleAddN.h" + +#include + +#include + +namespace luci +{ + +bool CircleAddNGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() < 1) + return false; + + if (args.op.outputs.size() != 1) + return false; + + return true; +} + +CircleNode *CircleAddNGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(inputs.size()); + for (uint32_t i = 0; i < inputs.size(); ++i) + { + node->inputs(i, inputs[i]); + } + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleArgMin.cpp b/compiler/luci/import/src/Nodes/CircleArgMin.cpp new file mode 100644 index 0000000..4d85bbf --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleArgMin.cpp @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleArgMin.h" + +#include + +#include + +namespace luci +{ + +bool CircleArgMinGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 2) + return false; + + return true; +} + +CircleNode *CircleArgMinGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->dimension(inputs[1]); + + const auto *options = op.builtin_options.AsArgMinOptions(); + node->output_type(luci_datatype(options->output_type)); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleBCQFullyConnected.cpp b/compiler/luci/import/src/Nodes/CircleBCQFullyConnected.cpp new file mode 100644 index 0000000..7cc077e --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleBCQFullyConnected.cpp @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleBCQFullyConnected.h" + +#include + +#include + +namespace luci +{ + +bool CircleBCQFullyConnectedGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 5) + return false; + + return true; +} + +CircleNode *CircleBCQFullyConnectedGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + + node->input(inputs[0]); + node->weights_scales(inputs[1]); + node->weights_binary(inputs[2]); + node->bias(inputs[3]); + node->weights_clusters(inputs[4]); + + // TODO Find and move to appropriate place for setting optional input + if (auto bias = dynamic_cast(node->bias())) + { + // bias is not used for type inference, but node itself should have a type + bias->dtype(loco::DataType::FLOAT32); + + // bias is not used for shape inference + } + + const auto *options = op.builtin_options.AsBCQFullyConnectedOptions(); + node->weights_hidden_size(options->weights_hidden_size); + node->fusedActivationFunction(luci_actfunc(options->fused_activation_function)); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleBCQGather.cpp b/compiler/luci/import/src/Nodes/CircleBCQGather.cpp new file mode 100644 index 0000000..c6d2ab5 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleBCQGather.cpp @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleBCQGather.h" + +#include + +#include + +namespace luci +{ + +bool CircleBCQGatherGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 4) + return false; + + return true; +} + +CircleNode *CircleBCQGatherGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + + node->input_scales(inputs[0]); + node->input_binary(inputs[1]); + node->indices(inputs[2]); + node->input_clusters(inputs[3]); + + const auto *options = op.builtin_options.AsBCQGatherOptions(); + node->input_hidden_size(options->input_hidden_size); + node->axis(options->axis); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleBatchMatMul.cpp b/compiler/luci/import/src/Nodes/CircleBatchMatMul.cpp new file mode 100644 index 0000000..6026b2a --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleBatchMatMul.cpp @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleBatchMatMul.h" + +#include + +namespace luci +{ + +bool CircleBatchMatMulGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 2) + return false; + + return true; +} + +CircleNode *CircleBatchMatMulGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + node->y(inputs[1]); + + const auto *options = op.builtin_options.AsBatchMatMulOptions(); + node->adj_x(options->adjoint_lhs); + node->adj_y(options->adjoint_rhs); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleCast.cpp b/compiler/luci/import/src/Nodes/CircleCast.cpp new file mode 100644 index 0000000..a4d09b5 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleCast.cpp @@ -0,0 +1,99 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleCast.h" + +#include + +#include +#include + +#include + +namespace luci +{ + +bool CircleCastGraphBuilder::validate(const ValidateArgs &args) const +{ + LOGGER(l); + + auto settings = luci::UserSettings::settings(); + + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + if (inputs.size() != 1) + return false; + if (outputs.size() != 1) + return false; + + // NOTE real models do have type mismatch + const auto *options = args.op.builtin_options.AsCastOptions(); + if (options != nullptr) + { + const auto &tensors = args.reader.tensors(); + const circle::TensorT &output_tensor = *tensors[outputs[0]]; + auto name = tensor_name(output_tensor); + + const auto &tensor_in = tensors.at(inputs[0]); + if (tensor_in->type != options->in_data_type) + { + if (settings->get(luci::UserSettings::Key::DisableValidation)) + { + WARN(l) << "Warning: import Cast(" << name << ") dtype mismatch"; + } + else + return false; + } + const auto &tensor_out = tensors.at(outputs[0]); + if (tensor_out->type != options->out_data_type) + { + if (settings->get(luci::UserSettings::Key::DisableValidation)) + { + WARN(l) << "Warning: import Cast(" << name << ") dtype mismatch"; + } + else + return false; + } + } + + return true; +} + +CircleNode *CircleCastGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + + const auto *options = op.builtin_options.AsCastOptions(); + if (options != nullptr) + { + node->in_data_type(luci_datatype(options->in_data_type)); + node->out_data_type(luci_datatype(options->out_data_type)); + } + else + { + node->in_data_type(inputs[0]->dtype()); + node->out_data_type(loco::DataType::Unknown); + // type inference should use node->dtype() for Unknown + // export should use BuiltinOptions_NONE for Unknown + } + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleCeil.cpp b/compiler/luci/import/src/Nodes/CircleCeil.cpp new file mode 100644 index 0000000..d3d6cd9 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleCeil.cpp @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleCeil.h" + +#include + +#include + +namespace luci +{ + +bool CircleCeilGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + if (inputs.size() != 1) + return false; + if (outputs.size() != 1) + return false; + + // TODO dtype check + + return true; +} + +CircleNode *CircleCeilGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleConst.cpp b/compiler/luci/import/src/Nodes/CircleConst.cpp index 1d79898..7131dc1 100644 --- a/compiler/luci/import/src/Nodes/CircleConst.cpp +++ b/compiler/luci/import/src/Nodes/CircleConst.cpp @@ -24,6 +24,24 @@ #include +namespace +{ + +std::ostream &operator<<(std::ostream &os, const std::vector &vect) +{ + uint32_t seq = 0; + for (auto &v : vect) + { + if (seq) + os << ", "; + os << v; + seq++; + } + return os; +} + +} // namespace + namespace luci { @@ -53,55 +71,73 @@ CircleConst *create_circleconst(GraphBuilderContext *context, int32_t tensor_ind auto graph = context->graph(); auto reader = context->reader(); const auto &tensors = reader->tensors(); - - // (1) create CircleConst - auto const_node = graph->nodes()->create(); const circle::TensorT &const_tensor = *tensors[tensor_index]; - const_node->name(tensor_name(const_tensor)); - auto quantization = luci::tensor_quantization(const_tensor); - if (quantization) + + const std::vector &buffer = reader->buffers()[const_tensor.buffer]->data; + std::vector const_dims = const_tensor.shape; // in NHWC + if (const_dims.size() == 0 && buffer.empty()) { - auto quantparam = luci::luci_quantparam(quantization); - if (quantparam.get()) - const_node->quantparam(std::move(quantparam)); + // unknown shape tensor + return nullptr; } - INFO(l) << "[luci] NodeFinder const_node(" << tensor_index << ") -> " << const_node << std::endl; - - // (2) set data_type to CircleConst - const_node->dtype(luci_datatype(const_tensor.type)); + // if tensor_index is used as output to some other operator, this is not a constant + auto tensoroutputs = context->tensoroutputs(); + if (tensoroutputs->find(tensor_index)) + { + // other operator output tensor + return nullptr; + } - // (3) set shape to CicleConst - std::vector const_dims = const_tensor.shape; // in NHWC - const_node->rank(const_dims.size()); uint32_t num_elements = 1; for (uint32_t r = 0; r < const_dims.size(); ++r) { - const_node->dim(r) = loco::Dimension(const_dims[r]); num_elements = num_elements * const_dims[r]; } - // (4) constant values from circle buffer - const std::vector &buffer = reader->buffers()[const_tensor.buffer]->data; - if (buffer.empty()) - throw oops::UserExn("Empty buffer"); - - switch (luci_datatype(const_tensor.type)) + if (buffer.empty() && num_elements > 0) { - case loco::DataType::FLOAT32: - copy_data(buffer, num_elements, const_node); - break; - - case loco::DataType::U8: - copy_data(buffer, num_elements, const_node); - break; - - case loco::DataType::S32: - copy_data(buffer, num_elements, const_node); - break; + // normal empty tensor + return nullptr; + } - default: - throw oops::UserExn("Unsupported tensor type", circle::EnumNameTensorType(const_tensor.type)); + auto const_node = graph->nodes()->create(); + copy_tensor_attributes(const_tensor, const_node); + const_node->shape_status(luci::ShapeStatus::VALID); + INFO(l) << "[luci] NodeFinder const_node(" << tensor_index << ") -> " << const_node << " " + << const_dims << std::endl; + if (num_elements > 0) + { + switch (luci_datatype(const_tensor.type)) + { + case loco::DataType::FLOAT32: + copy_data(buffer, num_elements, const_node); + break; + + case loco::DataType::U8: + copy_data(buffer, num_elements, const_node); + break; + + case loco::DataType::S16: + copy_data(buffer, num_elements, const_node); + break; + + case loco::DataType::S32: + copy_data(buffer, num_elements, const_node); + break; + + case loco::DataType::S64: + copy_data(buffer, num_elements, const_node); + break; + + case loco::DataType::BOOL: + copy_data(buffer, num_elements, const_node); + break; + + default: + throw oops::UserExn("Unsupported tensor type", + circle::EnumNameTensorType(const_tensor.type)); + } } return const_node; diff --git a/compiler/luci/import/src/Nodes/CircleConv2D.cpp b/compiler/luci/import/src/Nodes/CircleConv2D.cpp index ec9dce0..42c5c26 100644 --- a/compiler/luci/import/src/Nodes/CircleConv2D.cpp +++ b/compiler/luci/import/src/Nodes/CircleConv2D.cpp @@ -50,7 +50,8 @@ CircleNode *CircleConv2DGraphBuilder::build_node(const circle::OperatorT &op, node->stride()->w(options->stride_w); node->stride()->h(options->stride_h); node->fusedActivationFunction(luci_actfunc(options->fused_activation_function)); - // FIXME Check dilation_w_factor, dilation_h_factor. + node->dilation()->w(options->dilation_w_factor); + node->dilation()->h(options->dilation_h_factor); return node; } diff --git a/compiler/luci/import/src/Nodes/CircleCustom.cpp b/compiler/luci/import/src/Nodes/CircleCustom.cpp new file mode 100644 index 0000000..d541ee8 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleCustom.cpp @@ -0,0 +1,88 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleCustom.h" + +#include + +namespace luci +{ + +bool CircleCustomGraphBuilder::validate(const ValidateArgs &) const +{ + // DO NOTHING + return true; +} + +void CircleCustomGraphBuilder::build(const circle::OperatorT &op, + GraphBuilderContext *context) const +{ + assert(context != nullptr); + + auto graph = context->graph(); + + const std::vector &inputs = op.inputs; + const std::vector &outputs = op.outputs; + const auto &tensors = context->reader()->tensors(); + auto tensors_ptr = context->reader()->tensors_ptr(); + assert(tensors_ptr != nullptr); + + // Create CircleCustom + const auto &opcodes = context->reader()->opcodes(); + const uint32_t opcode_index = op.opcode_index; + const circle::OperatorCodeT &opcode = *opcodes[opcode_index]; + + auto *node = graph->nodes()->create(inputs.size()); + uint32_t input_idx = 0; + for (const int32_t input_tensor_index : inputs) + { + node->inputs(input_idx++, context->nodefinder()->node(input_tensor_index)); + } + node->custom_options(std::vector{op.custom_options.begin(), op.custom_options.end()}); + node->custom_code(opcode.custom_code); + // Operator version of custom is always 1, so do nothing + + uint32_t output_count = outputs.size(); + + assert(output_count > 0); + { + // Let's use attributes from output 0 for this node + const circle::TensorT &output_tensor = *tensors[outputs[0]]; + node->name(tensor_name(output_tensor)); + node->dtype(luci_datatype(output_tensor.type)); + } + + // Create virtual outputs of Custom + for (uint32_t n = 0; n < output_count; ++n) + { + const circle::TensorT &output_tensor = *tensors[outputs[n]]; + + auto *nodeout = graph->nodes()->create(); + copy_tensor_attributes(output_tensor, nodeout); + // mark shape_status + if (tensors_ptr->Get(outputs[n])->shape() == nullptr) + nodeout->shape_status(ShapeStatus::NOSHAPE); + else + nodeout->shape_status(ShapeStatus::VALID); + + nodeout->input(node); + nodeout->index(n); + + context->nodefinder()->enroll(outputs[n], nodeout); + } +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleDepthToSpace.cpp b/compiler/luci/import/src/Nodes/CircleDepthToSpace.cpp new file mode 100644 index 0000000..827b634 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleDepthToSpace.cpp @@ -0,0 +1,67 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleDepthToSpace.h" + +#include + +#include + +#include + +namespace luci +{ + +bool CircleDepthToSpaceGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + const auto *options = args.op.builtin_options.AsDepthToSpaceOptions(); + + if (inputs.size() != 1) + return false; + + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + + if (tensors[outputs[0]]->type != tensors[inputs[0]]->type) + { + return false; + } + + if (options->block_size < 2) + return false; + + return true; +} + +CircleNode *CircleDepthToSpaceGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + + const auto *options = op.builtin_options.AsDepthToSpaceOptions(); + node->block_size(options->block_size); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleDepthwiseConv2D.cpp b/compiler/luci/import/src/Nodes/CircleDepthwiseConv2D.cpp index c6d3b1f..2b13f9e 100644 --- a/compiler/luci/import/src/Nodes/CircleDepthwiseConv2D.cpp +++ b/compiler/luci/import/src/Nodes/CircleDepthwiseConv2D.cpp @@ -52,7 +52,8 @@ CircleNode *CircleDepthwiseConv2DGraphBuilder::build_node(const circle::Operator node->stride()->h(options->stride_h); node->depthMultiplier(options->depth_multiplier); node->fusedActivationFunction(luci_actfunc(options->fused_activation_function)); - // FIXME Check dilation_w_factor, dilation_h_factor. + node->dilation()->w(options->dilation_w_factor); + node->dilation()->h(options->dilation_h_factor); return node; } diff --git a/compiler/luci/import/src/Nodes/CircleElu.cpp b/compiler/luci/import/src/Nodes/CircleElu.cpp new file mode 100644 index 0000000..37a290c --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleElu.cpp @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleElu.h" + +#include + +#include + +namespace luci +{ + +bool CircleEluGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 1) + return false; + + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[0]); + + switch (tensor->type) + { + case circle::TensorType_FLOAT32: + break; + default: + return false; + } + + if (tensors[outputs[0]]->type != tensor->type) + return false; + + return true; +} + +CircleNode *CircleEluGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->features(inputs[0]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleExp.cpp b/compiler/luci/import/src/Nodes/CircleExp.cpp index 44fc93d..a328514 100644 --- a/compiler/luci/import/src/Nodes/CircleExp.cpp +++ b/compiler/luci/import/src/Nodes/CircleExp.cpp @@ -16,7 +16,7 @@ #include "luci/Import/Nodes/CircleExp.h" -#include +#include #include @@ -50,7 +50,7 @@ CircleNode *CircleExpGraphBuilder::build_node(const circle::OperatorT &, const std::vector &inputs, loco::Graph *graph) const { - auto *node = graph->nodes()->create(); + auto *node = graph->nodes()->create(); node->x(inputs[0]); return node; diff --git a/compiler/luci/import/src/Nodes/CircleExpandDims.cpp b/compiler/luci/import/src/Nodes/CircleExpandDims.cpp new file mode 100644 index 0000000..1cef67a --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleExpandDims.cpp @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleExpandDims.h" + +#include + +#include + +namespace luci +{ + +bool CircleExpandDimsGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + + if (inputs.size() != 2) + { + return false; + } + + const auto &tensors = args.reader.tensors(); + + return tensors[inputs[1]]->type == circle::TensorType_INT32; +} + +CircleNode *CircleExpandDimsGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->axis(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleFill.cpp b/compiler/luci/import/src/Nodes/CircleFill.cpp new file mode 100644 index 0000000..6c3d3a2 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleFill.cpp @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleFill.h" + +#include + +namespace luci +{ + +bool CircleFillGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 2) + return false; + + if (args.op.outputs.size() != 1) + return false; + + return true; +} + +CircleNode *CircleFillGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->dims(inputs[0]); + node->value(inputs[1]); + + const auto *options = op.builtin_options.AsFillOptions(); + (void)options; + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleFloor.cpp b/compiler/luci/import/src/Nodes/CircleFloor.cpp new file mode 100644 index 0000000..302a9ea --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleFloor.cpp @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleFloor.h" + +#include + +#include + +namespace luci +{ + +bool CircleFloorGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + if (inputs.size() != 1) + return false; + if (outputs.size() != 1) + return false; + + // TODO dtype check + + return true; +} + +CircleNode *CircleFloorGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleFloorDiv.cpp b/compiler/luci/import/src/Nodes/CircleFloorDiv.cpp new file mode 100644 index 0000000..8751978 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleFloorDiv.cpp @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleFloorDiv.h" + +#include + +#include + +namespace luci +{ + +bool CircleFloorDivGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + { + return false; + } + + if (outputs.size() != 1) + { + return false; + } + + const auto &tensors = args.reader.tensors(); + const auto &tensor_in_0 = tensors.at(inputs[0]); + const auto &tensor_in_1 = tensors.at(inputs[1]); + const auto &tensor_out = tensors.at(outputs[0]); + + if (tensor_in_0->type != tensor_in_1->type) + return false; + + if (tensor_out->type != tensor_in_1->type) + { + return false; + } + + return true; +} + +CircleNode *CircleFloorDivGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + node->y(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleFloorMod.cpp b/compiler/luci/import/src/Nodes/CircleFloorMod.cpp new file mode 100644 index 0000000..3ccdce0 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleFloorMod.cpp @@ -0,0 +1,57 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleFloorMod.h" + +#include + +#include + +namespace luci +{ + +bool CircleFloorModGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + if (inputs.size() != 2) + return false; + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor_in_0 = tensors.at(inputs[0]); + const auto &tensor_in_1 = tensors.at(inputs[1]); + if (tensor_in_0->type != tensor_in_1->type) + return false; + + // TODO dtype check + + return true; +} + +CircleNode *CircleFloorModGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + node->y(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleFullyConnected.cpp b/compiler/luci/import/src/Nodes/CircleFullyConnected.cpp index 8f74fe9..8937e78 100644 --- a/compiler/luci/import/src/Nodes/CircleFullyConnected.cpp +++ b/compiler/luci/import/src/Nodes/CircleFullyConnected.cpp @@ -17,6 +17,7 @@ #include "luci/Import/Nodes/CircleFullyConnected.h" #include +#include #include #include @@ -39,7 +40,16 @@ CircleNode *CircleFullyConnectedGraphBuilder::build_node(const circle::OperatorT auto *node = graph->nodes()->create(); node->input(inputs[0]); node->weights(inputs[1]); - node->bias(inputs[2]); + node->bias(inputs[2]); // bias is optional + + // TODO Find and move to appropriate place for setting optional input + if (auto bias = dynamic_cast(node->bias())) + { + // bias is not used for type inference, but node itself should have a type + bias->dtype(loco::DataType::FLOAT32); + + // bias is not used for shape inference + } const auto *options = op.builtin_options.AsFullyConnectedOptions(); node->fusedActivationFunction(luci_actfunc(options->fused_activation_function)); diff --git a/compiler/luci/import/src/Nodes/CircleGather.cpp b/compiler/luci/import/src/Nodes/CircleGather.cpp new file mode 100644 index 0000000..1caa05e --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleGather.cpp @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleGather.h" + +#include + +#include +#include + +namespace luci +{ + +bool CircleGatherGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + const auto *options = args.op.builtin_options.AsGatherOptions(); + + int32_t axis = options->axis; + + if (inputs.size() != 2) + return false; + + if (outputs.size() != 1) + return false; + + if (axis < 0) + axis += inputs.size(); + + if (axis < 0) + return false; + + // TODO do indices type check + // TODO do axis check when shape information is given + + return true; +} + +CircleNode *CircleGatherGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + + node->params(inputs[0]); + node->indices(inputs[1]); + + const auto *options = op.builtin_options.AsGatherOptions(); + node->axis(options->axis); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleGatherNd.cpp b/compiler/luci/import/src/Nodes/CircleGatherNd.cpp new file mode 100644 index 0000000..621d4ae --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleGatherNd.cpp @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleGatherNd.h" + +#include + +#include +#include +#include + +namespace luci +{ + +bool CircleGatherNdGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + return false; + + if (outputs.size() != 1) + return false; + + auto &indices_tensor = args.reader.tensors()[inputs[1]]; + + if (!(indices_tensor->type == circle::TensorType::TensorType_INT32 || + indices_tensor->type == circle::TensorType::TensorType_INT64)) + { + return false; + } + + return true; +} + +CircleNode *CircleGatherNdGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + + node->params(inputs[0]); + node->indices(inputs[1]); + + // GatherNd options empty + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleGreater.cpp b/compiler/luci/import/src/Nodes/CircleGreater.cpp new file mode 100644 index 0000000..8810758 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleGreater.cpp @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleGreater.h" + +#include + +#include +#include + +#include + +namespace luci +{ + +bool CircleGreaterGraphBuilder::validate(const ValidateArgs &args) const +{ + LOGGER(l); + + auto settings = luci::UserSettings::settings(); + + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + return false; + + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + + if (tensors[inputs[0]]->type != tensors[inputs[1]]->type) + return false; + + // NOTE: real models do have output dtype NOT BOOL + if (tensors[outputs[0]]->type != circle::TensorType_BOOL) + { + if (settings->get(luci::UserSettings::Key::DisableValidation)) + { + const circle::TensorT &output_tensor = *tensors[outputs[0]]; + auto name = tensor_name(output_tensor); + WARN(l) << "Warning: import Greater(" << name << ") output dtype is not boolean"; + } + else + return false; + } + + return true; +} + +CircleNode *CircleGreaterGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + node->y(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleGreaterEqual.cpp b/compiler/luci/import/src/Nodes/CircleGreaterEqual.cpp new file mode 100644 index 0000000..dff1510 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleGreaterEqual.cpp @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleGreaterEqual.h" + +#include + +#include + +namespace luci +{ + +bool CircleGreaterEqualGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + { + return false; + } + + if (outputs.size() != 1) + { + return false; + } + + const auto &tensors = args.reader.tensors(); + + if (tensors[inputs[0]]->type != tensors[inputs[1]]->type) + { + return false; + } + + return tensors[outputs[0]]->type == circle::TensorType::TensorType_BOOL; +} + +CircleNode *CircleGreaterEqualGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + node->y(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleIf.cpp b/compiler/luci/import/src/Nodes/CircleIf.cpp new file mode 100644 index 0000000..d609064 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleIf.cpp @@ -0,0 +1,138 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleIf.h" + +#include +#include + +#include +#include + +namespace luci +{ + +bool CircleIfGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto *options = args.op.builtin_options.AsIfOptions(); + + if (inputs.size() < 2) // cond + input + return false; + if (args.op.outputs.size() < 1) // output + return false; + + auto num_graphs = static_cast(args.reader.num_subgraph()); + if (options->then_subgraph_index >= num_graphs) + return false; + if (options->else_subgraph_index >= num_graphs) + return false; + + // input 0 should be BOOL type + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[0]); + if (tensor->type != circle::TensorType_BOOL) + return false; + + const auto &shape = tensor->shape; + if (shape.size() != 1 && shape.size() != 0) + return false; + + return true; +} + +/** + * @brief If Node builder + * + * @note Current loco does not provide multiple outputs + * We will create multiple CircleIfOut nodes to emulate this + * For two outputs that may look like this + * + * --- CircleIf --- Node --- + * \- Node --- + * + * will be created like this + * + * --- CircleIf --- CircleIfOut --- Node --- + * \- CircleIfOut --- Node --- + */ + +void CircleIfGraphBuilder::build(const circle::OperatorT &op, GraphBuilderContext *context) const +{ + assert(context != nullptr); + + auto graph = context->graph(); + + const std::vector &inputs = op.inputs; + const std::vector &outputs = op.outputs; + const auto &tensors = context->reader()->tensors(); + const auto &opcodes = context->reader()->opcodes(); + auto tensors_ptr = context->reader()->tensors_ptr(); + assert(tensors_ptr != nullptr); + + std::vector input_nodes; + for (const int32_t input_tensor_index : inputs) + { + input_nodes.push_back(context->nodefinder()->node(input_tensor_index)); + } + + uint32_t input_count = inputs.size() - 1; + uint32_t output_count = outputs.size(); + + // Create CircleIf + CircleIf *node = graph->nodes()->create(input_count, output_count); + + node->cond(input_nodes[0]); + for (uint32_t idx = 0; idx < input_count; ++idx) + { + node->input(idx, input_nodes[idx + 1]); + } + + const auto *options = op.builtin_options.AsIfOptions(); + node->then_branch(options->then_subgraph_index); + node->else_branch(options->else_subgraph_index); + + assert(outputs.size() > 0); + { + // Lets use name of output 0 as If name + const circle::TensorT &output_tensor = *tensors[outputs[0]]; + node->name(tensor_name(output_tensor)); + node->op_version(opcodes[op.opcode_index].get()->version); + + // NOTE We don't set quantization for If itself but to virtual outputs + } + + // Create virtual outputs of If + for (uint32_t n = 0; n < output_count; ++n) + { + const circle::TensorT &output_tensor = *tensors[outputs[n]]; + + auto *nodeout = graph->nodes()->create(); + copy_tensor_attributes(output_tensor, nodeout); + // mark shape_status + if (tensors_ptr->Get(outputs[n])->shape() == nullptr) + nodeout->shape_status(ShapeStatus::NOSHAPE); + else + nodeout->shape_status(ShapeStatus::VALID); + + nodeout->input(node); + nodeout->index(n); + + context->nodefinder()->enroll(outputs[n], nodeout); + } +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleInstanceNorm.cpp b/compiler/luci/import/src/Nodes/CircleInstanceNorm.cpp new file mode 100644 index 0000000..b95c54c --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleInstanceNorm.cpp @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleInstanceNorm.h" + +#include + +#include + +namespace luci +{ + +bool CircleInstanceNormGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 3) + return false; + + // TODO check dtypes + + return true; +} + +CircleNode *CircleInstanceNormGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->gamma(inputs[1]); + node->beta(inputs[2]); + + const auto *options = op.builtin_options.AsInstanceNormOptions(); + node->epsilon(options->epsilon); + node->fusedActivationFunction(luci_actfunc(options->fused_activation_function)); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleL2Normalize.cpp b/compiler/luci/import/src/Nodes/CircleL2Normalize.cpp new file mode 100644 index 0000000..fe10a85 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleL2Normalize.cpp @@ -0,0 +1,56 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleL2Normalize.h" + +#include + +#include + +namespace luci +{ + +bool CircleL2NormalizeGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 1) + { + return false; + } + + if (outputs.size() != 1) + { + return false; + } + + return true; +} + +CircleNode *CircleL2NormalizeGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + const auto *options = op.builtin_options.AsL2NormOptions(); + node->fusedActivationFunction(luci_actfunc(options->fused_activation_function)); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleL2Pool2D.cpp b/compiler/luci/import/src/Nodes/CircleL2Pool2D.cpp new file mode 100644 index 0000000..0232066 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleL2Pool2D.cpp @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleL2Pool2D.h" + +#include + +#include + +namespace luci +{ + +bool CircleL2Pool2DGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 1) + return false; + + // TODO check dtypes + + return true; +} + +CircleNode *CircleL2Pool2DGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->value(inputs[0]); + + const auto *options = op.builtin_options.AsPool2DOptions(); + node->padding(luci_padding(options->padding)); + node->stride()->w(options->stride_w); + node->stride()->h(options->stride_h); + node->filter()->w(options->filter_width); + node->filter()->h(options->filter_height); + node->fusedActivationFunction(luci_actfunc(options->fused_activation_function)); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleLeakyRelu.cpp b/compiler/luci/import/src/Nodes/CircleLeakyRelu.cpp new file mode 100644 index 0000000..4957cea --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleLeakyRelu.cpp @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleLeakyRelu.h" + +#include + +#include + +namespace luci +{ + +bool CircleLeakyReluGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 1) + return false; + + if (args.op.outputs.size() != 1) + return false; + + return true; +} + +CircleNode *CircleLeakyReluGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->features(inputs[0]); + + const auto *options = op.builtin_options.AsLeakyReluOptions(); + node->alpha(options->alpha); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleLess.cpp b/compiler/luci/import/src/Nodes/CircleLess.cpp new file mode 100644 index 0000000..40ad28c --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleLess.cpp @@ -0,0 +1,78 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleLess.h" + +#include + +#include + +namespace luci +{ + +bool CircleLessGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + { + return false; + } + + if (outputs.size() != 1) + { + return false; + } + + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[0]); + + switch (tensor->type) + { + case circle::TensorType_FLOAT32: + case circle::TensorType_FLOAT64: + case circle::TensorType_INT32: + case circle::TensorType_UINT8: + case circle::TensorType_INT16: + case circle::TensorType_INT8: + case circle::TensorType_INT64: + case circle::TensorType_FLOAT16: + break; + default: + return false; + } + + if (tensors[inputs[1]]->type != tensor->type) + { + return false; + } + + return tensors[outputs[0]]->type == circle::TensorType_BOOL; +} + +CircleNode *CircleLessGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + node->y(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleLessEqual.cpp b/compiler/luci/import/src/Nodes/CircleLessEqual.cpp new file mode 100644 index 0000000..13e9950 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleLessEqual.cpp @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleLessEqual.h" + +#include + +#include + +namespace luci +{ + +bool CircleLessEqualGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + { + return false; + } + + if (outputs.size() != 1) + { + return false; + } + + const auto &tensors = args.reader.tensors(); + + if (tensors[inputs[0]]->type != tensors[inputs[1]]->type) + { + return false; + } + + return tensors[outputs[0]]->type == circle::TensorType::TensorType_BOOL; +} + +CircleNode *CircleLessEqualGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + node->y(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleLocalResponseNormalization.cpp b/compiler/luci/import/src/Nodes/CircleLocalResponseNormalization.cpp new file mode 100644 index 0000000..7b1f0db --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleLocalResponseNormalization.cpp @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleLocalResponseNormalization.h" + +#include + +#include + +namespace luci +{ + +bool CircleLocalResponseNormalizationGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 1) + return false; + + // TODO do attribute checks + + return true; +} + +CircleNode *CircleLocalResponseNormalizationGraphBuilder::build_node( + const circle::OperatorT &op, const std::vector &inputs, loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + + const auto *options = op.builtin_options.AsLocalResponseNormalizationOptions(); + node->radius(options->radius); + node->bias(options->bias); + node->alpha(options->alpha); + node->beta(options->beta); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleLog.cpp b/compiler/luci/import/src/Nodes/CircleLog.cpp new file mode 100644 index 0000000..2140832 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleLog.cpp @@ -0,0 +1,65 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleLog.h" + +#include + +#include + +namespace luci +{ + +bool CircleLogGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + if (inputs.size() != 1) + return false; + if (args.op.outputs.size() != 1) + return false; + + // input type check + // Must be one of bfloat16, half, float32, float64, complex64, complex128. + // Currently circle supports half(float16), float32, float64, complex64. + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[0]); + switch (tensor->type) + { + case circle::TensorType_FLOAT16: + case circle::TensorType_FLOAT32: + case circle::TensorType_FLOAT64: + case circle::TensorType_COMPLEX64: + break; + default: + return false; + } + + return true; +} + +CircleNode *CircleLogGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + + // No options for Log + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleLogSoftmax.cpp b/compiler/luci/import/src/Nodes/CircleLogSoftmax.cpp new file mode 100644 index 0000000..e738c4a --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleLogSoftmax.cpp @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleLogSoftmax.h" + +#include + +#include + +namespace luci +{ + +bool CircleLogSoftmaxGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 1) + return false; + + // TODO do attribute checks + + return true; +} + +CircleNode *CircleLogSoftmaxGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->logits(inputs[0]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleLogicalAnd.cpp b/compiler/luci/import/src/Nodes/CircleLogicalAnd.cpp new file mode 100644 index 0000000..8509dba --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleLogicalAnd.cpp @@ -0,0 +1,55 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleLogicalAnd.h" + +#include + +#include + +namespace luci +{ + +bool CircleLogicalAndGraphBuilder::validate(const ValidateArgs &args) const +{ + // Only BOOL type is allowed for inputs + const auto &inputs = args.op.inputs; + if (inputs.size() != 2) + return false; + + const auto &tensors = args.reader.tensors(); + for (auto input : inputs) + { + const auto &tensor = tensors.at(input); + if (tensor->type != circle::TensorType::TensorType_BOOL) + return false; + } + + return true; +} + +CircleNode *CircleLogicalAndGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + node->y(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleLogistic.cpp b/compiler/luci/import/src/Nodes/CircleLogistic.cpp new file mode 100644 index 0000000..85e7e55 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleLogistic.cpp @@ -0,0 +1,66 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleLogistic.h" + +#include + +#include + +namespace luci +{ + +bool CircleLogisticGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + if (inputs.size() != 1) + return false; + const auto &outputs = args.op.outputs; + if (outputs.size() != 1) + return false; + + // Must be one of the following types + // float16, float32, float64, complex64, or complex128 + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[0]); + switch (tensor->type) + { + case circle::TensorType_FLOAT16: + case circle::TensorType_FLOAT32: + case circle::TensorType_FLOAT64: + case circle::TensorType_COMPLEX64: + break; + default: + return false; + } + + if (tensors.at(inputs[0])->type != tensors.at(outputs[0])->type) + return false; + + return true; +} + +CircleNode *CircleLogisticGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleMatrixDiag.cpp b/compiler/luci/import/src/Nodes/CircleMatrixDiag.cpp new file mode 100644 index 0000000..f4ae03c --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleMatrixDiag.cpp @@ -0,0 +1,56 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleMatrixDiag.h" + +#include + +#include + +namespace luci +{ + +bool CircleMatrixDiagGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 1) + return false; + + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[0]); + + if (tensors[outputs[0]]->type != tensor->type) + return false; + + return true; +} + +CircleNode *CircleMatrixDiagGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->diagonal(inputs[0]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleMatrixSetDiag.cpp b/compiler/luci/import/src/Nodes/CircleMatrixSetDiag.cpp new file mode 100644 index 0000000..d6f6aee --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleMatrixSetDiag.cpp @@ -0,0 +1,57 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleMatrixSetDiag.h" + +#include + +#include + +namespace luci +{ + +bool CircleMatrixSetDiagGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + return false; + + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[0]); + + if (tensors[outputs[0]]->type != tensor->type) + return false; + + return true; +} + +CircleNode *CircleMatrixSetDiagGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->diagonal(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleMaximum.cpp b/compiler/luci/import/src/Nodes/CircleMaximum.cpp new file mode 100644 index 0000000..6ca7e40 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleMaximum.cpp @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleMaximum.h" + +#include + +#include + +namespace luci +{ + +bool CircleMaximumGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + return false; + + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[0]); + + switch (tensor->type) + { + case circle::TensorType_FLOAT16: + case circle::TensorType_FLOAT32: + case circle::TensorType_FLOAT64: + case circle::TensorType_INT32: + case circle::TensorType_INT64: + break; + default: + return false; + } + + if (tensors[inputs[1]]->type != tensor->type) + return false; + + if (tensors[outputs[0]]->type != tensor->type) + return false; + + return true; +} + +CircleNode *CircleMaximumGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + node->y(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleMinimum.cpp b/compiler/luci/import/src/Nodes/CircleMinimum.cpp new file mode 100644 index 0000000..b770f36 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleMinimum.cpp @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleMinimum.h" + +#include + +#include + +namespace luci +{ + +bool CircleMinimumGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + return false; + + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[0]); + + switch (tensor->type) + { + case circle::TensorType_FLOAT16: + case circle::TensorType_FLOAT32: + case circle::TensorType_FLOAT64: + case circle::TensorType_INT32: + case circle::TensorType_INT64: + break; + default: + return false; + } + + if (tensors[inputs[1]]->type != tensor->type) + return false; + + if (tensors[outputs[0]]->type != tensor->type) + return false; + + return true; +} + +CircleNode *CircleMinimumGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + node->y(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleMirrorPad.cpp b/compiler/luci/import/src/Nodes/CircleMirrorPad.cpp new file mode 100644 index 0000000..41b5e5d --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleMirrorPad.cpp @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleMirrorPad.h" + +#include + +#include + +namespace luci +{ + +bool CircleMirrorPadGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 2) + return false; + + // TODO check others + + return true; +} + +CircleNode *CircleMirrorPadGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->paddings(inputs[1]); + + const auto *options = op.builtin_options.AsMirrorPadOptions(); + node->mode(luci_mirrorpad_mode(options->mode)); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleNeg.cpp b/compiler/luci/import/src/Nodes/CircleNeg.cpp new file mode 100644 index 0000000..3d3079c --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleNeg.cpp @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleNeg.h" + +#include + +#include + +namespace luci +{ +bool CircleNegGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 1) + return false; + + // TODO Support type check + return true; +} + +CircleNode *CircleNegGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleNotEqual.cpp b/compiler/luci/import/src/Nodes/CircleNotEqual.cpp new file mode 100644 index 0000000..5b04856 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleNotEqual.cpp @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleNotEqual.h" + +#include + +#include + +namespace luci +{ + +bool CircleNotEqualGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + { + return false; + } + + if (outputs.size() != 1) + { + return false; + } + + const auto &tensors = args.reader.tensors(); + + if (tensors[inputs[0]]->type != tensors[inputs[1]]->type) + { + return false; + } + + return tensors[outputs[0]]->type == circle::TensorType::TensorType_BOOL; +} + +CircleNode *CircleNotEqualGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + node->y(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleOneHot.cpp b/compiler/luci/import/src/Nodes/CircleOneHot.cpp new file mode 100644 index 0000000..9fdbfa8 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleOneHot.cpp @@ -0,0 +1,77 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleOneHot.h" + +#include + +#include +#include + +namespace luci +{ + +bool CircleOneHotGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + const auto *options = args.op.builtin_options.AsOneHotOptions(); + + // Only 4 Input come refered from + if (inputs.size() != 4) + return false; + + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &indices = tensors.at(inputs[0]); + const auto &depth = tensors.at(inputs[1]); + const auto &on_value = tensors.at(inputs[2]); + const auto &off_value = tensors.at(inputs[3]); + + if (options->axis < -1 || options->axis > static_cast(indices->shape.size())) + return false; + if (depth->shape.size() != 0) + return false; + if (on_value->shape.size() != 0) + return false; + if (off_value->shape.size() != 0) + return false; + if (on_value->type != off_value->type) + return false; + + return true; +} + +CircleNode *CircleOneHotGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + + node->indices(inputs[0]); + node->depth(inputs[1]); + node->on_value(inputs[2]); + node->off_value(inputs[3]); + + const auto *options = op.builtin_options.AsOneHotOptions(); + node->axis(options->axis); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CirclePRelu.cpp b/compiler/luci/import/src/Nodes/CirclePRelu.cpp new file mode 100644 index 0000000..0d87cd4 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CirclePRelu.cpp @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CirclePRelu.h" + +#include + +#include + +namespace luci +{ + +bool CirclePReluGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 2) + return false; + + if (args.op.outputs.size() != 1) + return false; + + return true; +} + +CircleNode *CirclePReluGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->alpha(inputs[1]); + + // PRelu options are empty + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CirclePow.cpp b/compiler/luci/import/src/Nodes/CirclePow.cpp new file mode 100644 index 0000000..ff98331 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CirclePow.cpp @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CirclePow.h" + +#include + +#include + +namespace luci +{ + +bool CirclePowGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 2) + return false; + + if (args.op.outputs.size() != 1) + return false; + + return true; +} + +CircleNode *CirclePowGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + node->y(inputs[1]); + + // Pow options are empty + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleRange.cpp b/compiler/luci/import/src/Nodes/CircleRange.cpp new file mode 100644 index 0000000..c211916 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleRange.cpp @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleRange.h" + +#include + +#include + +namespace luci +{ +bool CircleRangeGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 3) + return false; + + // TODO Support type check + return true; +} + +CircleNode *CircleRangeGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->start(inputs[0]); + node->limit(inputs[1]); + node->delta(inputs[2]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleRank.cpp b/compiler/luci/import/src/Nodes/CircleRank.cpp new file mode 100644 index 0000000..705ae01 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleRank.cpp @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleRank.h" + +#include + +#include + +namespace luci +{ +bool CircleRankGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 1) + return false; + + if (args.op.outputs.size() != 1) + return false; + + return true; +} + +CircleNode *CircleRankGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleReduceAny.cpp b/compiler/luci/import/src/Nodes/CircleReduceAny.cpp new file mode 100644 index 0000000..030c530 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleReduceAny.cpp @@ -0,0 +1,69 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleReduceAny.h" + +#include + +namespace luci +{ + +bool CircleReduceAnyGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + if (inputs.size() != 2) + return false; + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor_0 = tensors.at(inputs[0]); + const auto &tensor_1 = tensors.at(inputs[1]); + const auto &tensor_o = tensors.at(outputs[0]); + + if (tensor_0->type != circle::TensorType_BOOL) + return false; + if (tensor_o->type != circle::TensorType_BOOL) + return false; + + switch (tensor_1->type) + { + case circle::TensorType_INT32: + case circle::TensorType_INT64: + break; + default: + return false; + } + + return true; +} + +CircleNode *CircleReduceAnyGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->reduction_indices(inputs[1]); + + const auto *options = op.builtin_options.AsReducerOptions(); + node->keep_dims(options->keep_dims); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleReduceMax.cpp b/compiler/luci/import/src/Nodes/CircleReduceMax.cpp new file mode 100644 index 0000000..8ca8e2e --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleReduceMax.cpp @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleReduceMax.h" + +#include + +namespace luci +{ + +bool CircleReduceMaxGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + return false; + + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor_axis = tensors.at(inputs[1]); + + switch (tensor_axis->type) + { + case circle::TensorType_INT32: + case circle::TensorType_INT64: + break; + default: + return false; + } + + return true; +} + +CircleNode *CircleReduceMaxGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->reduction_indices(inputs[1]); + + const auto *options = op.builtin_options.AsReducerOptions(); + node->keep_dims(options->keep_dims); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleReduceMin.cpp b/compiler/luci/import/src/Nodes/CircleReduceMin.cpp new file mode 100644 index 0000000..3020c37 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleReduceMin.cpp @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleReduceMin.h" + +#include + +namespace luci +{ + +bool CircleReduceMinGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + return false; + + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor_axis = tensors.at(inputs[1]); + + switch (tensor_axis->type) + { + case circle::TensorType_INT32: + case circle::TensorType_INT64: + break; + default: + return false; + } + + return true; +} + +CircleNode *CircleReduceMinGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->reduction_indices(inputs[1]); + + const auto *options = op.builtin_options.AsReducerOptions(); + node->keep_dims(options->keep_dims); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleReduceProd.cpp b/compiler/luci/import/src/Nodes/CircleReduceProd.cpp new file mode 100644 index 0000000..2bb43f6 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleReduceProd.cpp @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleReduceProd.h" + +#include + +namespace luci +{ + +bool CircleReduceProdGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + if (inputs.size() != 2) + return false; + if (args.op.outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor_1 = tensors.at(inputs[1]); + + // TODO check input types + + // Check for reduction_indices types + switch (tensor_1->type) + { + case circle::TensorType_INT32: + case circle::TensorType_INT64: + break; + default: + return false; + } + + return true; +} + +CircleNode *CircleReduceProdGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->reduction_indices(inputs[1]); + + const auto *options = op.builtin_options.AsReducerOptions(); + node->keep_dims(options->keep_dims); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleRelu6.cpp b/compiler/luci/import/src/Nodes/CircleRelu6.cpp new file mode 100644 index 0000000..5b44399 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleRelu6.cpp @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleRelu6.h" + +#include + +#include + +namespace luci +{ + +bool CircleRelu6GraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 1) + return false; + + if (args.op.outputs.size() != 1) + return false; + + return true; +} + +CircleNode *CircleRelu6GraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->features(inputs[0]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleReluN1To1.cpp b/compiler/luci/import/src/Nodes/CircleReluN1To1.cpp new file mode 100644 index 0000000..edf662f --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleReluN1To1.cpp @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleReluN1To1.h" + +#include + +#include + +namespace luci +{ + +bool CircleReluN1To1GraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 1) + return false; + + if (args.op.outputs.size() != 1) + return false; + + // TODO check dtypes + + return true; +} + +CircleNode *CircleReluN1To1GraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->features(inputs[0]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleReshape.cpp b/compiler/luci/import/src/Nodes/CircleReshape.cpp index c83f143..f72c152 100644 --- a/compiler/luci/import/src/Nodes/CircleReshape.cpp +++ b/compiler/luci/import/src/Nodes/CircleReshape.cpp @@ -66,7 +66,14 @@ CircleNode *CircleReshapeGraphBuilder::build_node(const circle::OperatorT &op, if (shape_node == nullptr) { const auto *options = op.builtin_options.AsReshapeOptions(); - shape_node = create_shape_node(options->new_shape, graph); + if (options != nullptr) + shape_node = create_shape_node(options->new_shape, graph); + else + { + shape_node = graph->nodes()->create(); + shape_node->dtype(loco::DataType::S32); + shape_node->rank(0); + } } auto *node = graph->nodes()->create(); @@ -74,7 +81,8 @@ CircleNode *CircleReshapeGraphBuilder::build_node(const circle::OperatorT &op, node->shape(shape_node); const auto *options = op.builtin_options.AsReshapeOptions(); - setup_shape_attribute(options->new_shape, node); + if (options) + setup_shape_attribute(options->new_shape, node); return node; } diff --git a/compiler/luci/import/src/Nodes/CircleResizeBilinear.cpp b/compiler/luci/import/src/Nodes/CircleResizeBilinear.cpp new file mode 100644 index 0000000..6128f1b --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleResizeBilinear.cpp @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleResizeBilinear.h" + +#include +#include + +namespace luci +{ + +bool CircleResizeBilinearGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 2) + return false; + + if (args.op.outputs.size() != 1) + return false; + + return true; +} + +CircleNode *CircleResizeBilinearGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->size(inputs[1]); + + const auto *options = op.builtin_options.AsResizeBilinearOptions(); + node->align_corners(options->align_corners); + node->half_pixel_centers(options->half_pixel_centers); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleResizeNearestNeighbor.cpp b/compiler/luci/import/src/Nodes/CircleResizeNearestNeighbor.cpp new file mode 100644 index 0000000..a1f1ef0 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleResizeNearestNeighbor.cpp @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleResizeNearestNeighbor.h" + +#include +#include + +namespace luci +{ + +bool CircleResizeNearestNeighborGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 2) + return false; + + if (args.op.outputs.size() != 1) + return false; + + return true; +} + +CircleNode *CircleResizeNearestNeighborGraphBuilder::build_node( + const circle::OperatorT &op, const std::vector &inputs, loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->size(inputs[1]); + + const auto *options = op.builtin_options.AsResizeNearestNeighborOptions(); + node->align_corners(options->align_corners); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleReverseSequence.cpp b/compiler/luci/import/src/Nodes/CircleReverseSequence.cpp new file mode 100644 index 0000000..72d3b15 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleReverseSequence.cpp @@ -0,0 +1,71 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleReverseSequence.h" + +#include + +#include + +namespace luci +{ + +bool CircleReverseSequenceGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + return false; + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor_in = tensors.at(inputs[0]); + const auto &tensor_lengths = tensors.at(inputs[1]); + const auto &tensor_out = tensors.at(outputs[0]); + + switch (tensor_lengths->type) + { + case circle::TensorType_INT32: + case circle::TensorType_INT64: + break; + default: + return false; + } + + if (tensor_in->type != tensor_out->type) + return false; + + return true; +} + +CircleNode *CircleReverseSequenceGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->seq_lengths(inputs[1]); + + const auto *options = op.builtin_options.AsReverseSequenceOptions(); + node->seq_axis(options->seq_dim); + node->batch_axis(options->batch_dim); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleReverseV2.cpp b/compiler/luci/import/src/Nodes/CircleReverseV2.cpp new file mode 100644 index 0000000..cd18128 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleReverseV2.cpp @@ -0,0 +1,67 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleReverseV2.h" + +#include + +#include + +namespace luci +{ + +bool CircleReverseV2GraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + return false; + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor_in = tensors.at(inputs[0]); + const auto &tensor_axis = tensors.at(inputs[1]); + const auto &tensor_out = tensors.at(outputs[0]); + + switch (tensor_axis->type) + { + case circle::TensorType_INT32: + case circle::TensorType_INT64: + break; + default: + return false; + } + + if (tensor_out->type != tensor_in->type) + return false; + + return true; +} + +CircleNode *CircleReverseV2GraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->tensor(inputs[0]); + node->axis(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleRound.cpp b/compiler/luci/import/src/Nodes/CircleRound.cpp new file mode 100644 index 0000000..8964895 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleRound.cpp @@ -0,0 +1,71 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleRound.h" + +#include + +#include + +namespace luci +{ + +bool CircleRoundGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 1) + return false; + if (outputs.size() != 1) + return false; + + // Must be one of the following types + // bfloat16, half (float16), float32, float64, complex64, complex128 + // Currently, circle supports float16, float32, complex64 + const auto &tensors = args.reader.tensors(); + const auto &tensor_in = tensors.at(inputs[0]); + const auto &tensor_out = tensors.at(outputs[0]); + + switch (tensor_in->type) + { + case circle::TensorType_FLOAT16: + case circle::TensorType_FLOAT32: + case circle::TensorType_FLOAT64: + case circle::TensorType_INT32: + case circle::TensorType_INT64: + break; + default: + return false; + } + + if (tensor_out->type != tensor_in->type) + return false; + + return true; +} + +CircleNode *CircleRoundGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleScatterNd.cpp b/compiler/luci/import/src/Nodes/CircleScatterNd.cpp new file mode 100644 index 0000000..adcaa00 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleScatterNd.cpp @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleScatterNd.h" + +#include + +#include + +namespace luci +{ + +bool CircleScatterNdGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + if (inputs.size() != 3) + return false; + + // indices must have the same type as shape + const auto &tensors = args.reader.tensors(); + + if (tensors[inputs[0]]->type != tensors[inputs[2]]->type) + return false; + + // indices must be either int32 or int64 + if (tensors[inputs[0]]->type != circle::TensorType_INT32 && + tensors[inputs[0]]->type != circle::TensorType_INT64) + return false; + + return true; +} + +CircleNode *CircleScatterNdGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->indices(inputs[0]); + node->updates(inputs[1]); + node->shape(inputs[2]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSegmentSum.cpp b/compiler/luci/import/src/Nodes/CircleSegmentSum.cpp new file mode 100644 index 0000000..1122bdc --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleSegmentSum.cpp @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleSegmentSum.h" + +#include + +#include + +namespace luci +{ + +bool CircleSegmentSumGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + if (inputs.size() != 2) + return false; + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor_in = tensors.at(inputs[0]); + const auto &tensor_out = tensors.at(outputs[0]); + const auto &tensor_ids = tensors.at(inputs[1]); + + switch (tensor_ids->type) + { + case circle::TensorType_INT32: + case circle::TensorType_INT64: + break; + default: + return false; + } + + if (tensor_out->type != tensor_in->type) + { + return false; + } + + return true; +} + +CircleNode *CircleSegmentSumGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->segment_ids(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSelect.cpp b/compiler/luci/import/src/Nodes/CircleSelect.cpp new file mode 100644 index 0000000..ff94212 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleSelect.cpp @@ -0,0 +1,56 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleSelect.h" + +#include + +#include + +namespace luci +{ + +bool CircleSelectGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + if (inputs.size() != 3) + return false; + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[0]); + if (tensor->type != circle::TensorType_BOOL) + return false; + // TODO check dtypes for input 1, 2 + + return true; +} + +CircleNode *CircleSelectGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->condition(inputs[0]); + node->t(inputs[1]); + node->e(inputs[2]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSelectV2.cpp b/compiler/luci/import/src/Nodes/CircleSelectV2.cpp new file mode 100644 index 0000000..78b2e64 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleSelectV2.cpp @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleSelectV2.h" + +#include + +#include + +namespace luci +{ + +bool CircleSelectV2GraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + if (inputs.size() != 3) + return false; + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &condition = tensors.at(inputs[0]); + if (condition->type != circle::TensorType_BOOL) + return false; + + const auto &t = tensors.at(inputs[1]); + const auto &e = tensors.at(inputs[2]); + if (t->type != e->type) + return false; + + return true; +} + +CircleNode *CircleSelectV2GraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->condition(inputs[0]); + node->t(inputs[1]); + node->e(inputs[2]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleShape.cpp b/compiler/luci/import/src/Nodes/CircleShape.cpp new file mode 100644 index 0000000..864b5eb --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleShape.cpp @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleShape.h" + +#include + +#include + +namespace luci +{ + +bool CircleShapeGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + if (inputs.size() != 1) + return false; + if (outputs.size() != 1) + return false; + + // TODO check shape, dtype + + return true; +} + +CircleNode *CircleShapeGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + + const auto *options = op.builtin_options.AsShapeOptions(); + node->out_type(luci_datatype(options->out_type)); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSin.cpp b/compiler/luci/import/src/Nodes/CircleSin.cpp new file mode 100644 index 0000000..61d60c7 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleSin.cpp @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleSin.h" + +#include + +#include + +namespace luci +{ + +bool CircleSinGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + if (inputs.size() != 1) + return false; + if (args.op.outputs.size() != 1) + return false; + + // input type check + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[0]); + switch (tensor->type) + { + case circle::TensorType_FLOAT16: + case circle::TensorType_FLOAT32: + case circle::TensorType_FLOAT64: + break; + // TODO support TensorType_COMPLEX64, complex128, bfloat16 + default: + return false; + } + + return true; +} + +CircleNode *CircleSinGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + + // No options for Sin + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSlice.cpp b/compiler/luci/import/src/Nodes/CircleSlice.cpp new file mode 100644 index 0000000..313c355 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleSlice.cpp @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleSlice.h" + +#include + +#include + +#include + +namespace luci +{ + +bool CircleSliceGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 3) + return false; + if (args.op.outputs.size() != 1) + return false; + + // TODO check shapes and types + + return true; +} + +CircleNode *CircleSliceGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->begin(inputs[1]); + node->size(inputs[2]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSpaceToBatchND.cpp b/compiler/luci/import/src/Nodes/CircleSpaceToBatchND.cpp new file mode 100644 index 0000000..f1361fb --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleSpaceToBatchND.cpp @@ -0,0 +1,80 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleSpaceToBatchND.h" + +#include + +#include + +#include + +namespace luci +{ + +bool CircleSpaceToBatchNDGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + if (inputs.size() != 3) + return false; + + // input 1 and 2 should have INT32/INT64 type + const auto &tensors = args.reader.tensors(); + const auto &tensor_1 = tensors.at(inputs[1]); + switch (tensor_1->type) + { + case circle::TensorType_INT32: + case circle::TensorType_INT64: + break; + default: + return false; + } + const auto &tensor_2 = tensors.at(inputs[2]); + switch (tensor_2->type) + { + case circle::TensorType_INT32: + case circle::TensorType_INT64: + break; + default: + return false; + } + + // Only support input shape dimension 3 and 4 only + const auto &tensor_0 = tensors.at(inputs[0]); + const auto t_0_s = tensor_0->shape.size(); + if (t_0_s != 3 && t_0_s != 4) + return false; + + // TODO check input shape + + return true; +} + +CircleNode *CircleSpaceToBatchNDGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->block_shape(inputs[1]); + node->paddings(inputs[2]); + + // No options for SpaceToBatchND + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSpaceToDepth.cpp b/compiler/luci/import/src/Nodes/CircleSpaceToDepth.cpp new file mode 100644 index 0000000..b612c9a --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleSpaceToDepth.cpp @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleSpaceToDepth.h" + +#include + +#include + +#include + +namespace luci +{ + +bool CircleSpaceToDepthGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + if (inputs.size() != 1) + return false; + + // TODO do attribute checks + + return true; +} + +CircleNode *CircleSpaceToDepthGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + + const auto *options = op.builtin_options.AsSpaceToDepthOptions(); + node->block_size(options->block_size); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSparseToDense.cpp b/compiler/luci/import/src/Nodes/CircleSparseToDense.cpp new file mode 100644 index 0000000..bfe790f --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleSparseToDense.cpp @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleSparseToDense.h" + +#include + +#include + +namespace luci +{ + +bool CircleSparseToDenseGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 4) + return false; + + return true; +} + +CircleNode *CircleSparseToDenseGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->indices(inputs[0]); + node->output_shape(inputs[1]); + node->values(inputs[2]); + node->default_value(inputs[3]); + + const auto *options = op.builtin_options.AsSparseToDenseOptions(); + node->validate_indices(options->validate_indices); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSplit.cpp b/compiler/luci/import/src/Nodes/CircleSplit.cpp new file mode 100644 index 0000000..07b6cc9 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleSplit.cpp @@ -0,0 +1,119 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleSplit.h" + +#include +#include + +#include +#include + +namespace luci +{ + +bool CircleSplitGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + const auto *options = args.op.builtin_options.AsSplitOptions(); + + if (inputs.size() != 2) + return false; + + if (static_cast(outputs.size()) != options->num_splits) + return false; + + // TODO check types + + return true; +} + +/** + * @brief Split Node builder + * + * @note Current loco does not provide multiple outputs + * We will create multiple CircleSplitOut nodes to emulate this + * For two outputs that may look like this + * + * --- CircleSplit --- FullyConnected --- + * \- FullyConnected --- + * + * will be created like this + * + * --- CircleSplit --- CircleSplitOut --- FullyConnected --- + * \- CircleSplitOut --- FullyConnected --- + */ + +void CircleSplitGraphBuilder::build(const circle::OperatorT &op, GraphBuilderContext *context) const +{ + assert(context != nullptr); + + auto graph = context->graph(); + + const std::vector &inputs = op.inputs; + const std::vector &outputs = op.outputs; + const auto &tensors = context->reader()->tensors(); + const auto &opcodes = context->reader()->opcodes(); + auto tensors_ptr = context->reader()->tensors_ptr(); + assert(tensors_ptr != nullptr); + + std::vector input_nodes; + for (const int32_t input_tensor_index : inputs) + { + input_nodes.push_back(context->nodefinder()->node(input_tensor_index)); + } + + // Create CircleSplit + auto node = graph->nodes()->create(); + node->split_dim(input_nodes[0]); + node->input(input_nodes[1]); + + const auto *options = op.builtin_options.AsSplitOptions(); + node->num_split(options->num_splits); + + assert(outputs.size() > 0); + assert(int32_t(outputs.size()) == options->num_splits); + { + // Let's use name of output 0 as Split name + const circle::TensorT &output_tensor = *tensors[outputs[0]]; + node->name(tensor_name(output_tensor)); + node->op_version(opcodes[op.opcode_index].get()->version); + + // NOTE We don't set quantization for Split itself but to virtual outputs + } + + // Create virtual outputs of Split + for (int32_t n = 0; n < options->num_splits; ++n) + { + const circle::TensorT &output_tensor = *tensors[outputs[n]]; + + auto *nodeout = graph->nodes()->create(); + copy_tensor_attributes(output_tensor, nodeout); + // mark shape_status + if (tensors_ptr->Get(outputs[n])->shape() == nullptr) + nodeout->shape_status(ShapeStatus::NOSHAPE); + else + nodeout->shape_status(ShapeStatus::VALID); + + nodeout->input(node); + nodeout->index(n); + + context->nodefinder()->enroll(outputs[n], nodeout); + } +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSplitV.cpp b/compiler/luci/import/src/Nodes/CircleSplitV.cpp new file mode 100644 index 0000000..7c6e83e --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleSplitV.cpp @@ -0,0 +1,121 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleSplitV.h" + +#include +#include + +#include +#include + +namespace luci +{ + +bool CircleSplitVGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + const auto *options = args.op.builtin_options.AsSplitVOptions(); + + if (inputs.size() != 3) + return false; + + if (static_cast(outputs.size()) != options->num_splits) + return false; + + // TODO check types + + return true; +} + +/** + * @brief SplitV Node builder + * + * @note Current loco does not provide multiple outputs + * We will create multiple CircleSplitVOut nodes to emulate this + * For two outputs that may look like this + * + * --- CircleSplitV --- FullyConnected --- + * \- FullyConnected --- + * + * will be created like this + * + * --- CircleSplitV --- CircleSplitVOut --- FullyConnected --- + * \- CircleSplitVOut --- FullyConnected --- + */ + +void CircleSplitVGraphBuilder::build(const circle::OperatorT &op, + GraphBuilderContext *context) const +{ + assert(context != nullptr); + + auto graph = context->graph(); + + const std::vector &inputs = op.inputs; + const std::vector &outputs = op.outputs; + const auto &tensors = context->reader()->tensors(); + const auto &opcodes = context->reader()->opcodes(); + auto tensors_ptr = context->reader()->tensors_ptr(); + assert(tensors_ptr != nullptr); + + std::vector input_nodes; + for (const int32_t input_tensor_index : inputs) + { + input_nodes.push_back(context->nodefinder()->node(input_tensor_index)); + } + + // Create CircleSplitV + auto node = graph->nodes()->create(); + node->input(input_nodes[0]); + node->size_splits(input_nodes[1]); + node->split_dim(input_nodes[2]); + + const auto *options = op.builtin_options.AsSplitVOptions(); + node->num_split(options->num_splits); + + assert(outputs.size() > 0); + assert(int32_t(outputs.size()) == options->num_splits); + { + // Let's use name of output 0 as Split name + const circle::TensorT &output_tensor = *tensors[outputs[0]]; + node->name(tensor_name(output_tensor)); + node->op_version(opcodes[op.opcode_index].get()->version); + + // NOTE We don't set quantization for Split itself but to virtual outputs + } + + // Create virtual outputs of Split + for (int32_t n = 0; n < options->num_splits; ++n) + { + const circle::TensorT &output_tensor = *tensors[outputs[n]]; + + auto *nodeout = graph->nodes()->create(); + copy_tensor_attributes(output_tensor, nodeout); + // mark shape_status + if (tensors_ptr->Get(outputs[n])->shape() == nullptr) + nodeout->shape_status(ShapeStatus::NOSHAPE); + else + nodeout->shape_status(ShapeStatus::VALID); + + nodeout->input(node); + nodeout->index(n); + + context->nodefinder()->enroll(outputs[n], nodeout); + } +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSqrt.cpp b/compiler/luci/import/src/Nodes/CircleSqrt.cpp new file mode 100644 index 0000000..8a90f66 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleSqrt.cpp @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleSqrt.h" + +#include + +#include + +namespace luci +{ + +bool CircleSqrtGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 1) + return false; + + return true; +} + +CircleNode *CircleSqrtGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSquare.cpp b/compiler/luci/import/src/Nodes/CircleSquare.cpp new file mode 100644 index 0000000..8398548 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleSquare.cpp @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleSquare.h" + +#include + +#include + +namespace luci +{ + +bool CircleSquareGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + if (inputs.size() != 1) + return false; + + // Must be one of the following types + // bfloat16, half (float16), float32, float64, complex64, complex128 + // Currently, circle supports float16, float32, complex64 + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[0]); + switch (tensor->type) + { + case circle::TensorType_INT32: + case circle::TensorType_INT64: + case circle::TensorType_FLOAT16: + case circle::TensorType_FLOAT32: + case circle::TensorType_FLOAT64: + case circle::TensorType_COMPLEX64: + break; + default: + return false; + } + + return true; +} + +CircleNode *CircleSquareGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSquaredDifference.cpp b/compiler/luci/import/src/Nodes/CircleSquaredDifference.cpp new file mode 100644 index 0000000..93ce959 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleSquaredDifference.cpp @@ -0,0 +1,77 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleSquaredDifference.h" + +#include + +#include + +namespace luci +{ + +bool CircleSquaredDifferenceGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + return false; + + if (outputs.size() != 1) + return false; + + // Inputs must be one of the following types + // bfloat16, half(float16), float32, float64, int32, int64, complex64, complex128 + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[0]); + switch (tensor->type) + { + case circle::TensorType_FLOAT16: + case circle::TensorType_FLOAT32: + case circle::TensorType_FLOAT64: + case circle::TensorType_INT32: + case circle::TensorType_INT64: + case circle::TensorType_COMPLEX64: + break; + // TODO support bfloat16, complex128 + default: + return false; + } + + // Input types must match + if (tensors.at(inputs[0])->type != tensors.at(inputs[1])->type) + return false; + + // Input and output types must match + if (tensors.at(inputs[0])->type != tensors.at(outputs[0])->type) + return false; + + return true; +} + +CircleNode *CircleSquaredDifferenceGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + node->y(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSqueeze.cpp b/compiler/luci/import/src/Nodes/CircleSqueeze.cpp new file mode 100644 index 0000000..a5252d0 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleSqueeze.cpp @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleSqueeze.h" + +#include +#include + +namespace luci +{ + +bool CircleSqueezeGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 1) + return false; + + if (args.op.outputs.size() != 1) + return false; + + return true; +} + +CircleNode *CircleSqueezeGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + + const auto *options = op.builtin_options.AsSqueezeOptions(); + assert(options); + + node->squeeze_dims(options->squeeze_dims); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleStridedSlice.cpp b/compiler/luci/import/src/Nodes/CircleStridedSlice.cpp new file mode 100644 index 0000000..95e4467 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleStridedSlice.cpp @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleStridedSlice.h" + +#include + +#include + +#include + +namespace luci +{ + +bool CircleStridedSliceGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 4) + return false; + if (args.op.outputs.size() != 1) + return false; + + // TODO check shapes and types + + return true; +} + +CircleNode *CircleStridedSliceGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->begin(inputs[1]); + node->end(inputs[2]); + node->strides(inputs[3]); + + const auto *options = op.builtin_options.AsStridedSliceOptions(); + node->begin_mask(options->begin_mask); + node->end_mask(options->end_mask); + node->ellipsis_mask(options->ellipsis_mask); + node->new_axis_mask(options->new_axis_mask); + node->shrink_axis_mask(options->shrink_axis_mask); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleSum.cpp b/compiler/luci/import/src/Nodes/CircleSum.cpp new file mode 100644 index 0000000..b4865de --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleSum.cpp @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleSum.h" + +#include + +namespace luci +{ + +bool CircleSumGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 2) + return false; + + return true; +} + +CircleNode *CircleSumGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->reduction_indices(inputs[1]); + + const auto *options = op.builtin_options.AsReducerOptions(); + node->keep_dims(options->keep_dims); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleTanh.cpp b/compiler/luci/import/src/Nodes/CircleTanh.cpp new file mode 100644 index 0000000..8986378 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleTanh.cpp @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleTanh.h" + +#include + +#include + +namespace luci +{ + +bool CircleTanhGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + if (inputs.size() != 1) + return false; + + // Must be one of the following types + // bfloat16, half (float16), float32, float64, complex64, complex128 + // Currently, circle supports float16, float32, complex64 + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[0]); + switch (tensor->type) + { + case circle::TensorType_FLOAT16: + case circle::TensorType_FLOAT32: + case circle::TensorType_COMPLEX64: + break; + default: + return false; + } + + return true; +} + +CircleNode *CircleTanhGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->x(inputs[0]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleTile.cpp b/compiler/luci/import/src/Nodes/CircleTile.cpp new file mode 100644 index 0000000..91054ce --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleTile.cpp @@ -0,0 +1,68 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleTile.h" + +#include + +#include + +namespace luci +{ + +bool CircleTileGraphBuilder::validate(const ValidateArgs &args) const +{ + auto inputs = args.op.inputs; + auto outputs = args.op.outputs; + + if (inputs.size() != 2) + return false; + + if (outputs.size() != 1) + return false; + + // Multiples (inputs[1]) must be one of the following types + // int32, int64 + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[1]); + switch (tensor->type) + { + case circle::TensorType_INT32: + case circle::TensorType_INT64: + break; + default: + return false; + } + + // Type of input and output must be the same + if (tensors.at(inputs[0])->type != tensors.at(outputs[0])->type) + return false; + + return true; +} + +CircleNode *CircleTileGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + node->multiples(inputs[1]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleTopKV2.cpp b/compiler/luci/import/src/Nodes/CircleTopKV2.cpp new file mode 100644 index 0000000..5c1051c --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleTopKV2.cpp @@ -0,0 +1,117 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleTopKV2.h" + +#include +#include + +#include +#include + +namespace luci +{ + +bool CircleTopKV2GraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 2) + return false; + if (outputs.size() != 2) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[1]); + if (tensor->type != circle::TensorType_INT32) + return false; + + return true; +} + +/** + * @brief TopKV2 Node builder + * + * @note Current loco does not provide multiple outputs + * We will create multiple CircleTopKV2Out nodes to emulate this + * For two outputs that may look like this + * + * --- CircleTopKV2--- FullyConnected --- + * \- FullyConnected --- + * + * will be created like this + * + * --- CircleTopKV2 --- CircleTopKV2Out --- FullyConnected --- + * \- CircleTopKV2Out --- FullyConnected --- + */ + +void CircleTopKV2GraphBuilder::build(const circle::OperatorT &op, + GraphBuilderContext *context) const +{ + assert(context != nullptr); + + auto graph = context->graph(); + + const std::vector &inputs = op.inputs; + const std::vector &outputs = op.outputs; + const auto &tensors = context->reader()->tensors(); + const auto &opcodes = context->reader()->opcodes(); + auto tensors_ptr = context->reader()->tensors_ptr(); + assert(tensors_ptr != nullptr); + + std::vector input_nodes; + for (const int32_t input_tensor_index : inputs) + { + input_nodes.push_back(context->nodefinder()->node(input_tensor_index)); + } + + // Create CircleTopKV2 + auto node = graph->nodes()->create(); + node->input(input_nodes[0]); + node->k(input_nodes[1]); + + assert(outputs.size() == 2); + { + // Let's use name of output 0 as TopKV2 name + const circle::TensorT &output_tensor = *tensors[outputs[0]]; + node->name(tensor_name(output_tensor)); + node->op_version(opcodes[op.opcode_index].get()->version); + + // NOTE We don't set quantization for TopKV2 itself but to virtual outputs + } + + // Create virtual outputs of TopKV2 + for (size_t n = 0; n < outputs.size(); ++n) + { + const circle::TensorT &output_tensor = *tensors[outputs[n]]; + + auto *nodeout = graph->nodes()->create(); + copy_tensor_attributes(output_tensor, nodeout); + // mark shape_status + if (tensors_ptr->Get(outputs[n])->shape() == nullptr) + nodeout->shape_status(ShapeStatus::NOSHAPE); + else + nodeout->shape_status(ShapeStatus::VALID); + + nodeout->input(node); + nodeout->index(n); + + context->nodefinder()->enroll(outputs[n], nodeout); + } +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleTransposeConv.cpp b/compiler/luci/import/src/Nodes/CircleTransposeConv.cpp new file mode 100644 index 0000000..7bdf46d --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleTransposeConv.cpp @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleTransposeConv.h" + +#include + +#include + +#include + +namespace luci +{ + +bool CircleTransposeConvGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 3) + return false; + + return true; +} + +CircleNode *CircleTransposeConvGraphBuilder::build_node(const circle::OperatorT &op, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + + node->inputSizes(inputs[0]); + node->filter(inputs[1]); + node->outBackprop(inputs[2]); + + const auto *options = op.builtin_options.AsTransposeConvOptions(); + node->padding(luci_padding(options->padding)); + node->stride()->w(options->stride_w); + node->stride()->h(options->stride_h); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleUnpack.cpp b/compiler/luci/import/src/Nodes/CircleUnpack.cpp new file mode 100644 index 0000000..c4282e2 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleUnpack.cpp @@ -0,0 +1,151 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleUnpack.h" + +#include +#include + +#include +#include + +#include +#include + +namespace luci +{ + +bool CircleUnpackGraphBuilder::validate(const ValidateArgs &args) const +{ + LOGGER(l); + + auto settings = luci::UserSettings::settings(); + + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + const auto *options = args.op.builtin_options.AsUnpackOptions(); + + if (inputs.size() != 1) + return false; + + // NOTE real models may have mismatch + if (static_cast(outputs.size()) != options->num) + { + if (settings->get(luci::UserSettings::Key::DisableValidation)) + { + const auto &tensors = args.reader.tensors(); + const circle::TensorT &output_tensor = *tensors[outputs[0]]; + auto name = tensor_name(output_tensor); + WARN(l) << "Warning: import Unpack(" << name << ") 'num' is not same as outputs used"; + } + else + return false; + } + + if (options->num < 0) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor = tensors.at(inputs[0]); + const auto &shape = tensor->shape; + auto shape_size = static_cast(shape.size()); + if (shape_size > 0) + { + // NOTE for unknown shape, shape_size is 0 + if (options->axis < -shape_size || options->axis >= shape_size) + return false; + } + + return true; +} + +/** + * @brief Unpack Node builder + * + * @note Current loco does not provide multiple outputs + * We will create multiple CircleUnpackOut nodes to emulate this + * For two outputs that may look like this + * + * --- CircleUnpack --- FullyConnected --- + * \- FullyConnected --- + * + * will be created like this + * + * --- CircleUnpack --- CircleUnpackOut --- FullyConnected --- + * \- CircleUnpackOut --- FullyConnected --- + */ + +void CircleUnpackGraphBuilder::build(const circle::OperatorT &op, + GraphBuilderContext *context) const +{ + assert(context != nullptr); + + auto graph = context->graph(); + + const std::vector &inputs = op.inputs; + const std::vector &outputs = op.outputs; + const auto &tensors = context->reader()->tensors(); + const auto &opcodes = context->reader()->opcodes(); + auto tensors_ptr = context->reader()->tensors_ptr(); + assert(tensors_ptr != nullptr); + + // NOTE Unpack has only one input so running a loop is not necessary + // This is provided as a reference for other Ops as a reference + std::vector input_nodes; + for (const int32_t input_tensor_index : inputs) + { + input_nodes.push_back(context->nodefinder()->node(input_tensor_index)); + } + + // Create CircleUnpack + CircleUnpack *node = graph->nodes()->create(); + node->value(input_nodes[0]); + + const auto *options = op.builtin_options.AsUnpackOptions(); + node->num(options->num); + node->axis(options->axis); + + assert(outputs.size() > 0); + { + // Let's use name of output 0 as Unpack name + const circle::TensorT &output_tensor = *tensors[outputs[0]]; + node->name(tensor_name(output_tensor)); + node->op_version(opcodes[op.opcode_index].get()->version); + + // NOTE We don't set quantization for Unpack itself but to virtual outputs + } + + // Create virtual outputs of Unpack + for (int32_t n = 0; n < options->num; ++n) + { + const circle::TensorT &output_tensor = *tensors[outputs[n]]; + + auto *nodeout = graph->nodes()->create(); + copy_tensor_attributes(output_tensor, nodeout); + // mark shape_status + if (tensors_ptr->Get(outputs[n])->shape() == nullptr) + nodeout->shape_status(ShapeStatus::NOSHAPE); + else + nodeout->shape_status(ShapeStatus::VALID); + + nodeout->input(node); + nodeout->index(n); + + context->nodefinder()->enroll(outputs[n], nodeout); + } +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleWhere.cpp b/compiler/luci/import/src/Nodes/CircleWhere.cpp new file mode 100644 index 0000000..a13c4d6 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleWhere.cpp @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleWhere.h" + +#include + +#include + +namespace luci +{ + +bool CircleWhereGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto &outputs = args.op.outputs; + + if (inputs.size() != 1) + return false; + + if (outputs.size() != 1) + return false; + + const auto &tensors = args.reader.tensors(); + const auto &tensor_condition = tensors.at(inputs[0]); + const auto &tensor_out = tensors.at(outputs[0]); + + if (tensor_condition->type != circle::TensorType_BOOL) + return false; + + if (tensor_out->type != circle::TensorType_INT64) + return false; + + return true; +} + +CircleNode *CircleWhereGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->condition(inputs[0]); + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleWhile.cpp b/compiler/luci/import/src/Nodes/CircleWhile.cpp new file mode 100644 index 0000000..aead250 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleWhile.cpp @@ -0,0 +1,123 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleWhile.h" + +#include +#include + +#include +#include + +namespace luci +{ + +bool CircleWhileGraphBuilder::validate(const ValidateArgs &args) const +{ + const auto &inputs = args.op.inputs; + const auto *options = args.op.builtin_options.AsWhileOptions(); + + if (inputs.size() != args.op.outputs.size()) + return false; + + auto num_graphs = static_cast(args.reader.num_subgraph()); + if (options->cond_subgraph_index >= num_graphs) + return false; + if (options->body_subgraph_index >= num_graphs) + return false; + + return true; +} + +/** + * @brief While Node builder + * + * @note Current loco does not provide multiple outputs + * We will create multiple CircleWhileOut nodes to emulate this + * For two outputs that may look like this + * + * --- CircleWhile --- Node --- + * \- Node --- + * + * will be created like this + * + * --- CircleWhile --- CircleWhileOut --- Node --- + * \- CircleWhileOut --- Node --- + */ + +void CircleWhileGraphBuilder::build(const circle::OperatorT &op, GraphBuilderContext *context) const +{ + assert(context != nullptr); + + auto graph = context->graph(); + + const std::vector &inputs = op.inputs; + const std::vector &outputs = op.outputs; + const auto &tensors = context->reader()->tensors(); + const auto &opcodes = context->reader()->opcodes(); + + std::vector input_nodes; + for (const int32_t input_tensor_index : inputs) + { + auto input_node = context->nodefinder()->node(input_tensor_index); + assert(input_node != nullptr); + input_nodes.push_back(input_node); + } + + uint32_t input_count = inputs.size(); + uint32_t output_count = outputs.size(); + + // Create CircleWhile + CircleWhile *node = graph->nodes()->create(input_count, output_count); + + for (uint32_t idx = 0; idx < input_count; ++idx) + { + node->input(idx, input_nodes[idx]); + } + + const auto *options = op.builtin_options.AsWhileOptions(); + node->cond_branch(options->cond_subgraph_index); + node->body_branch(options->body_subgraph_index); + + assert(outputs.size() > 0); + { + // Lets use name of output 0 as While name + const circle::TensorT &output_tensor = *tensors[outputs[0]]; + node->name(tensor_name(output_tensor)); + node->op_version(opcodes[op.opcode_index].get()->version); + + // NOTE We don't set quantization for While itself but to virtual outputs + } + + // Create virtual outputs of While + for (uint32_t n = 0; n < output_count; ++n) + { + const circle::TensorT &output_tensor = *tensors[outputs[n]]; + + auto *nodeout = graph->nodes()->create(); + + nodeout->input(node); + nodeout->index(n); + + copy_tensor_attributes(output_tensor, nodeout); + + // Note: leave shape_status to UNKNOWN to run shape inference + + context->nodefinder()->enroll(outputs[n], nodeout); + } +} + +} // namespace luci diff --git a/compiler/luci/import/src/Nodes/CircleZerosLike.cpp b/compiler/luci/import/src/Nodes/CircleZerosLike.cpp new file mode 100644 index 0000000..4362925 --- /dev/null +++ b/compiler/luci/import/src/Nodes/CircleZerosLike.cpp @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Import/Nodes/CircleZerosLike.h" + +#include + +#include + +namespace luci +{ + +bool CircleZerosLikeGraphBuilder::validate(const ValidateArgs &args) const +{ + if (args.op.inputs.size() != 1) + return false; + + if (args.op.outputs.size() != 1) + return false; + + return true; +} + +CircleNode *CircleZerosLikeGraphBuilder::build_node(const circle::OperatorT &, + const std::vector &inputs, + loco::Graph *graph) const +{ + auto *node = graph->nodes()->create(); + node->input(inputs[0]); + + // ZerosLikeOptinos are empty + + return node; +} + +} // namespace luci diff --git a/compiler/luci/import/src/PostImport.cpp b/compiler/luci/import/src/PostImport.cpp new file mode 100644 index 0000000..f436b48 --- /dev/null +++ b/compiler/luci/import/src/PostImport.cpp @@ -0,0 +1,354 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "PostImport.h" + +#include "luci/Import/CircleReader.h" + +#include +#include +#include +#include + +#include +#include + +namespace +{ + +/** + * @brief FixInterGraphNodes will fix inter graph connections for each Nodes + */ +class FixInterGraphNodes final : public luci::CircleNodeMutableVisitor +{ +public: + FixInterGraphNodes(const luci::Module *m, const luci::CircleReader &r) : _module(m), _reader(r) {} + + /** + * @note This will set Graph* to every CircleIf nodes 'else' and 'then' + */ + void visit(luci::CircleIf *node) final + { + LOGGER(l); + INFO(l) << "CircleIf " << node->name() << std::endl; + + auto then_branch = node->then_branch(); + auto else_branch = node->else_branch(); + auto num_graphs = static_cast(_module->size()); + (void)num_graphs; + + assert(num_graphs > 0); + assert(then_branch >= 0 && then_branch < num_graphs); + assert(else_branch >= 0 && else_branch < num_graphs); + + auto then_graph = _module->graph(then_branch); + auto else_graph = _module->graph(else_branch); + assert(then_graph != nullptr); + assert(else_graph != nullptr); + + node->then_graph(then_graph); + node->else_graph(else_graph); + } + + void visit(luci::CircleWhile *node) final + { + LOGGER(l); + INFO(l) << "CircleWhile " << node->name() << std::endl; + + auto cond_branch = node->cond_branch(); + auto body_branch = node->body_branch(); + auto num_graphs = static_cast(_module->size()); + (void)num_graphs; + + assert(num_graphs > 0); + assert(cond_branch >= 0 && cond_branch < num_graphs); + assert(body_branch >= 0 && body_branch < num_graphs); + + auto cond_graph = _module->graph(cond_branch); + auto body_graph = _module->graph(body_branch); + assert(cond_graph != nullptr); + assert(body_graph != nullptr); + + node->cond_graph(cond_graph); + node->body_graph(body_graph); + } + + void visit(luci::CircleNode *) final + { + // DO NOTHING + } + +private: + const luci::Module *_module; + const luci::CircleReader &_reader; +}; + +/** + * @brief FixInterGraph will fix inter graph connections + */ +class FixInterGraph final +{ +public: + void run(loco::Graph *g, const luci::Module *m, const luci::CircleReader &r) + { + for (auto node : loco::postorder_traversal(loco::output_nodes(g))) + { + if (recognize(node->dialect())) + { + auto cn = loco::must_cast(node); + + fix(cn, m, r); + } + } + } + +private: + bool recognize(const loco::Dialect *dialect) { return (dialect == luci::CircleDialect::get()); } + + void fix(luci::CircleNode *node, const luci::Module *module, const luci::CircleReader &reader) + { + FixInterGraphNodes fix(module, reader); + node->accept(&fix); + } +}; + +} // namespace + +namespace +{ +/** + * @brief ValidateNodeProp will validate inter graph connections for each Nodes + */ +class ValidateNodeProp final : public luci::CircleNodeMutableVisitor +{ +public: + ValidateNodeProp(const luci::Module *m, const luci::CircleReader &r) : _module(m), _reader(r) {} + + /** + * @note Validate CircleIf node 'else' and 'then' graph input/output count + * shape and type + */ + void visit(luci::CircleIf *node) final + { + LOGGER(l); + INFO(l) << "CircleIf " << node->name() << std::endl; + + auto then_graph = node->then_graph(); + auto else_graph = node->else_graph(); + assert(then_graph != nullptr); + assert(else_graph != nullptr); + + // TODO support for differnt shape; but how? + // NODE Shape/Type inference assume below conditions + + // Check both "then" and "else" subgraph outputs are same in count + auto then_outputs = loco::output_nodes(then_graph); // CircleOutput nodes + auto else_outputs = loco::output_nodes(else_graph); + if (then_outputs.size() != else_outputs.size()) + { + INTERNAL_EXN("CircleIf THEN and ELSE Graph are not same in size"); + } + + // check outputs have same shape and dtype + auto then_graph_outputs = then_graph->outputs(); // loco::GraphOutput items + auto else_graph_outputs = else_graph->outputs(); + for (size_t idx = 0; idx < then_outputs.size(); ++idx) + { + auto then_out = loco::must_cast(then_outputs.at(idx)); + auto else_out = loco::must_cast(else_outputs.at(idx)); + + auto then_graph_output = then_graph_outputs->at(then_out->index()); + auto else_graph_output = else_graph_outputs->at(else_out->index()); + if (!(*then_graph_output->shape() == *else_graph_output->shape())) + { + INTERNAL_EXN_V("CircleIf THEN and ELSE Graph Output shape mismatch ", idx); + } + if (then_graph_output->dtype() != else_graph_output->dtype()) + { + INTERNAL_EXN_V("CircleIf THEN and ELSE Graph Output type mismatch ", idx); + } + } + } + + /** + * @note Validate CircleWhile node 'cond' and 'body' graph input/output count + * shape and type + */ + void visit(luci::CircleWhile *node) final + { + LOGGER(l); + INFO(l) << "CircleWhile " << node->name() << std::endl; + + auto cond_graph = node->cond_graph(); + auto body_graph = node->body_graph(); + assert(cond_graph != nullptr); + assert(body_graph != nullptr); + + // Check input of "cond" and input/output of "body" subgraph have the same size + auto cond_inputs = loco::input_nodes(cond_graph); + auto cond_outputs = loco::output_nodes(cond_graph); + auto body_inputs = loco::input_nodes(body_graph); + auto body_outputs = loco::output_nodes(body_graph); + if (cond_inputs.size() != body_outputs.size()) + { + INTERNAL_EXN("CircleWhile COND input and BODY output have different sizes"); + } + if (cond_inputs.size() != body_inputs.size()) + { + INTERNAL_EXN("CircleWhile COND input and BODY input have different sizes"); + } + if (cond_outputs.size() != 1) + { + INTERNAL_EXN("CircleWhile COND output must have size 1"); + } + auto cond_out = loco::must_cast(cond_outputs.at(0)); + if (cond_out->dtype() != loco::DataType::BOOL) + { + INTERNAL_EXN("CircleWhile COND output must have bool type"); + } + + // input of "cond" and input/output of "body" subgraph must have the same shape and type + // First we compare input of "cond" with input of "body" + auto cond_graph_inputs = cond_graph->inputs(); + auto body_graph_inputs = body_graph->inputs(); + for (size_t idx = 0; idx < cond_inputs.size(); ++idx) + { + auto cond_in = loco::must_cast(cond_inputs.at(idx)); + auto body_in = loco::must_cast(body_inputs.at(idx)); + + auto cond_graph_input = cond_graph_inputs->at(cond_in->index()); + auto body_graph_input = body_graph_inputs->at(body_in->index()); + if ((cond_in->rank() != body_in->rank())) + { + INTERNAL_EXN_V("CircleWhile COND input and BODY input shape mismatch ", idx); + } + if (cond_in->rank() > 0 && body_in->rank() > 0) + { + if (!(*cond_graph_input->shape() == *body_graph_input->shape())) + { + INTERNAL_EXN_V("CircleWhile COND input and BODY input shape mismatch ", idx); + } + } + if (cond_in->dtype() != body_in->dtype()) + { + INTERNAL_EXN_V("CircleWhile COND input and BODY input type mismatch ", idx); + } + } + + // Next we compare input of "cond" with output of "body" + auto body_graph_outputs = body_graph->outputs(); + for (size_t idx = 0; idx < cond_inputs.size(); ++idx) + { + auto cond_in = loco::must_cast(cond_inputs.at(idx)); + auto body_out = loco::must_cast(body_outputs.at(idx)); + + auto cond_graph_input = cond_graph_inputs->at(cond_in->index()); + auto body_graph_output = body_graph_outputs->at(body_out->index()); + if ((cond_in->rank() != body_out->rank())) + { + INTERNAL_EXN_V("CircleWhile COND input and BODY output shape mismatch ", idx); + } + if (cond_in->rank() > 0 && body_out->rank() > 0) + { + if (!(*cond_graph_input->shape() == *body_graph_output->shape())) + { + INTERNAL_EXN_V("CircleWhile COND input and BODY output shape mismatch ", idx); + } + } + if (cond_in->dtype() != body_out->dtype()) + { + INTERNAL_EXN_V("CircleWhile COND input and BODY output type mismatch ", idx); + } + } + } + + void visit(luci::CircleNode *) final + { + // DO NOTHING + } + +private: + const luci::Module *_module; + const luci::CircleReader &_reader; +}; + +/** + * @brief ValidateGraphProp will validate inter graph node properties + */ +class ValidateGraphProp final +{ +public: + void run(loco::Graph *g, const luci::Module *m, const luci::CircleReader &r) + { + for (auto node : loco::postorder_traversal(loco::output_nodes(g))) + { + if (recognize(node->dialect())) + { + auto cn = loco::must_cast(node); + + eval(cn, m, r); + } + } + } + +private: + bool recognize(const loco::Dialect *dialect) { return (dialect == luci::CircleDialect::get()); } + + void eval(luci::CircleNode *node, const luci::Module *module, const luci::CircleReader &reader) + { + ValidateNodeProp val(module, reader); + node->accept(&val); + } +}; + +} // namespace + +namespace luci +{ + +/** + * @brief Do post import actions + */ +void post_import_graph(luci::Module *module, const luci::CircleReader &reader) +{ + LOGGER(l); + + auto count = module->size(); + + for (size_t s = 0; s < count; ++s) + { + auto g = module->graph(s); + assert(g != nullptr); + + INFO(l) << "--- FixInterGraph " << g->name() << "-------------------------"; + FixInterGraph fix; + fix.run(g, module, reader); + } + + for (size_t s = 0; s < count; ++s) + { + auto g = module->graph(s); + assert(g != nullptr); + + INFO(l) << "--- ValidateGraphProp " << g->name() << "---------------------"; + ValidateGraphProp prop; + prop.run(g, module, reader); + } + + INFO(l) << "--- post_import_graph done -------------------------------------"; +} + +} // namespace luci diff --git a/compiler/luci/import/src/PostImport.h b/compiler/luci/import/src/PostImport.h new file mode 100644 index 0000000..c719c58 --- /dev/null +++ b/compiler/luci/import/src/PostImport.h @@ -0,0 +1,34 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_POST_IMPORT_H__ +#define __LUCI_POST_IMPORT_H__ + +#include "luci/Import/CircleReader.h" + +#include "luci/IR/Module.h" + +namespace luci +{ + +/** + * @brief Do post import actions + */ +void post_import_graph(luci::Module *module, const luci::CircleReader &reader); + +} // namespace luci + +#endif // __LUCI_POST_IMPORT_H__ diff --git a/compiler/luci/lang/CMakeLists.txt b/compiler/luci/lang/CMakeLists.txt index 564e777..32d0a89 100644 --- a/compiler/luci/lang/CMakeLists.txt +++ b/compiler/luci/lang/CMakeLists.txt @@ -7,6 +7,7 @@ target_include_directories(luci_lang PRIVATE src) target_include_directories(luci_lang PUBLIC include) target_link_libraries(luci_lang PUBLIC loco) target_link_libraries(luci_lang PUBLIC oops) +target_link_libraries(luci_lang PRIVATE logo) target_link_libraries(luci_lang PRIVATE nncc_common) install(TARGETS luci_lang DESTINATION lib) @@ -20,3 +21,4 @@ nnas_find_package(GTest REQUIRED) GTest_AddTest(luci_lang_test ${TESTS}) target_include_directories(luci_lang_test PRIVATE src) target_link_libraries(luci_lang_test luci_lang) +target_link_libraries(luci_lang_test logo) diff --git a/compiler/luci/lang/include/luci/IR/AttrDilation.h b/compiler/luci/lang/include/luci/IR/AttrDilation.h new file mode 100644 index 0000000..c2b28d7 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/AttrDilation.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_ATTRDILATION_H__ +#define __LUCI_IR_ATTRDILATION_H__ + +#include + +namespace luci +{ + +class Dilation final +{ +public: + Dilation() : _w(1), _h(1) {} + + int32_t w() const { return _w; } + void w(int32_t w) { _w = w; } + + int32_t h() const { return _h; } + void h(int32_t h) { _h = h; } + +private: + int32_t _w; + int32_t _h; +}; + +} // namespace luci + +#endif // __LUCI_IR_ATTRDILATION_H__ diff --git a/compiler/luci/lang/include/luci/IR/AttrMirrorPadMode.h b/compiler/luci/lang/include/luci/IR/AttrMirrorPadMode.h new file mode 100644 index 0000000..7ca9d5d --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/AttrMirrorPadMode.h @@ -0,0 +1,33 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_ATTR_MIRROR_PAD_MODE_H__ +#define __LUCI_IR_ATTR_MIRROR_PAD_MODE_H__ + +namespace luci +{ + +enum class MirrorPadMode +{ + UNDEFINED, // This is not defined by Circle. This was added to prevent programming error. + + REFLECT, + SYMMETRIC, +}; + +} // namespace luci + +#endif // __LUCI_IR_ATTR_MIRROR_PAD_MODE_H__ diff --git a/compiler/luci/lang/include/luci/IR/CircleNodeDecl.h b/compiler/luci/lang/include/luci/IR/CircleNodeDecl.h index b87bdf9..967103e 100644 --- a/compiler/luci/lang/include/luci/IR/CircleNodeDecl.h +++ b/compiler/luci/lang/include/luci/IR/CircleNodeDecl.h @@ -17,8 +17,10 @@ #ifndef __LUCI_IR_CIRCLENODEDECL_H__ #define __LUCI_IR_CIRCLENODEDECL_H__ -#include #include +#include +#include +#include #include "CircleOpcode.h" #include "CircleNodeVisitor.forward.h" @@ -31,7 +33,9 @@ namespace luci using NodeName = std::string; -struct CircleNode : public loco::Node +struct CircleNode : public loco::Node, + public loco::NodeMixin, + public loco::NodeMixin { virtual ~CircleNode() = default; @@ -50,9 +54,17 @@ struct CircleNode : public loco::Node _quantparam = std::move(quantparam); } + ShapeStatus shape_status(void) const { return _shape_status; } + void shape_status(ShapeStatus ss) { _shape_status = ss; } + + int32_t op_version(void) const { return _op_version; } + void op_version(int32_t op_version) { _op_version = op_version; } + private: NodeName _name; std::unique_ptr _quantparam; + ShapeStatus _shape_status{ShapeStatus::UNDEFINED}; + int32_t _op_version = 1; }; template struct CircleNodeImpl : public CircleNode diff --git a/compiler/luci/lang/include/luci/IR/CircleNodeImpl.h b/compiler/luci/lang/include/luci/IR/CircleNodeImpl.h index bdcfc9c..a6b9488 100644 --- a/compiler/luci/lang/include/luci/IR/CircleNodeImpl.h +++ b/compiler/luci/lang/include/luci/IR/CircleNodeImpl.h @@ -18,7 +18,6 @@ #define __LUCI_IR_CIRCLENODEIMPL_H__ #include "CircleNodes.h" -#include "CircleNodeVisitor.h" #include diff --git a/compiler/luci/lang/include/luci/IR/CircleNodes.h b/compiler/luci/lang/include/luci/IR/CircleNodes.h index cc82284..3b31149 100644 --- a/compiler/luci/lang/include/luci/IR/CircleNodes.h +++ b/compiler/luci/lang/include/luci/IR/CircleNodes.h @@ -19,42 +19,124 @@ #include "Nodes/CircleAbs.h" #include "Nodes/CircleAdd.h" +#include "Nodes/CircleAddN.h" #include "Nodes/CircleArgMax.h" +#include "Nodes/CircleArgMin.h" #include "Nodes/CircleAveragePool2D.h" +#include "Nodes/CircleBatchMatMul.h" #include "Nodes/CircleBatchToSpaceND.h" +#include "Nodes/CircleCast.h" +#include "Nodes/CircleCeil.h" #include "Nodes/CircleConcatenation.h" #include "Nodes/CircleConst.h" #include "Nodes/CircleConv2D.h" #include "Nodes/CircleCos.h" +#include "Nodes/CircleCustom.h" +#include "Nodes/CircleDepthToSpace.h" #include "Nodes/CircleDepthwiseConv2D.h" #include "Nodes/CircleDiv.h" +#include "Nodes/CircleElu.h" #include "Nodes/CircleEqual.h" #include "Nodes/CircleExp.h" +#include "Nodes/CircleExpandDims.h" +#include "Nodes/CircleFill.h" +#include "Nodes/CircleFloor.h" +#include "Nodes/CircleFloorDiv.h" +#include "Nodes/CircleFloorMod.h" #include "Nodes/CircleFullyConnected.h" #include "Nodes/CircleGather.h" +#include "Nodes/CircleGatherNd.h" +#include "Nodes/CircleGreater.h" +#include "Nodes/CircleGreaterEqual.h" +#include "Nodes/CircleIf.h" +#include "Nodes/CircleL2Normalize.h" +#include "Nodes/CircleL2Pool2D.h" +#include "Nodes/CircleLeakyRelu.h" +#include "Nodes/CircleLess.h" +#include "Nodes/CircleLessEqual.h" +#include "Nodes/CircleLocalResponseNormalization.h" +#include "Nodes/CircleLog.h" +#include "Nodes/CircleLogicalAnd.h" #include "Nodes/CircleLogicalNot.h" #include "Nodes/CircleLogicalOr.h" +#include "Nodes/CircleLogistic.h" +#include "Nodes/CircleLogSoftmax.h" +#include "Nodes/CircleMatrixDiag.h" +#include "Nodes/CircleMatrixSetDiag.h" #include "Nodes/CircleMaximum.h" #include "Nodes/CircleMaxPool2D.h" #include "Nodes/CircleMean.h" +#include "Nodes/CircleMinimum.h" +#include "Nodes/CircleMirrorPad.h" #include "Nodes/CircleMul.h" +#include "Nodes/CircleNeg.h" +#include "Nodes/CircleNotEqual.h" +#include "Nodes/CircleOneHot.h" #include "Nodes/CirclePack.h" #include "Nodes/CirclePad.h" -#include "Nodes/CircleRelu6.h" +#include "Nodes/CirclePow.h" +#include "Nodes/CirclePRelu.h" +#include "Nodes/CircleRange.h" +#include "Nodes/CircleRank.h" +#include "Nodes/CircleReduceAny.h" +#include "Nodes/CircleReduceMax.h" +#include "Nodes/CircleReduceMin.h" +#include "Nodes/CircleReduceProd.h" #include "Nodes/CircleRelu.h" +#include "Nodes/CircleRelu6.h" +#include "Nodes/CircleReluN1To1.h" #include "Nodes/CircleReshape.h" +#include "Nodes/CircleResizeBilinear.h" +#include "Nodes/CircleResizeNearestNeighbor.h" +#include "Nodes/CircleReverseSequence.h" +#include "Nodes/CircleReverseV2.h" +#include "Nodes/CircleRound.h" #include "Nodes/CircleRsqrt.h" +#include "Nodes/CircleScatterNd.h" +#include "Nodes/CircleSegmentSum.h" +#include "Nodes/CircleSelect.h" +#include "Nodes/CircleSelectV2.h" +#include "Nodes/CircleShape.h" +#include "Nodes/CircleSin.h" +#include "Nodes/CircleSlice.h" #include "Nodes/CircleSoftmax.h" +#include "Nodes/CircleSpaceToBatchND.h" +#include "Nodes/CircleSpaceToDepth.h" +#include "Nodes/CircleSparseToDense.h" +#include "Nodes/CircleSplit.h" +#include "Nodes/CircleSplitV.h" #include "Nodes/CircleSqrt.h" +#include "Nodes/CircleSquare.h" #include "Nodes/CircleSquaredDifference.h" +#include "Nodes/CircleSqueeze.h" +#include "Nodes/CircleStridedSlice.h" #include "Nodes/CircleSub.h" -#include "Nodes/CircleTransposeConv.h" +#include "Nodes/CircleSum.h" +#include "Nodes/CircleTanh.h" +#include "Nodes/CircleTile.h" +#include "Nodes/CircleTopKV2.h" #include "Nodes/CircleTranspose.h" +#include "Nodes/CircleTransposeConv.h" +#include "Nodes/CircleUnpack.h" +#include "Nodes/CircleWhere.h" +#include "Nodes/CircleWhile.h" +#include "Nodes/CircleZerosLike.h" // Circle only +#include "Nodes/CircleBCQFullyConnected.h" +#include "Nodes/CircleBCQGather.h" #include "Nodes/CircleInstanceNorm.h" // Virtual nodes #include "Nodes/CircleInput.h" #include "Nodes/CircleOutput.h" +#include "Nodes/CircleCustomOut.h" +#include "Nodes/CircleIfOut.h" +#include "Nodes/CircleUnpackOut.h" +#include "Nodes/CircleSplitOut.h" +#include "Nodes/CircleSplitVOut.h" +#include "Nodes/CircleTopKV2Out.h" +#include "Nodes/CircleWhileOut.h" + +#include namespace luci { @@ -68,6 +150,18 @@ namespace luci */ void set_new_shape(CircleReshape *node, int32_t *base, uint32_t size); +/// @brief Link GraphOutput with CircleOutput node +void link(loco::GraphOutput *, CircleOutput *); + +/// @brief Link GraphInput with CircleInput node +void link(loco::GraphInput *, CircleInput *); + +/// @brief Find a CircleOutput node with a given output index +CircleOutput *output_node(loco::Graph *g, const loco::GraphOutputIndex &index); + +/// @brief Find a Pull node with a given input index +CircleInput *input_node(loco::Graph *g, const loco::GraphInputIndex &index); + } // namespace luci #endif // __LUCI_IR_CIRCLENODES_H__ diff --git a/compiler/luci/lang/include/luci/IR/CircleNodes.lst b/compiler/luci/lang/include/luci/IR/CircleNodes.lst index ca3f7fb..488dcfb 100644 --- a/compiler/luci/lang/include/luci/IR/CircleNodes.lst +++ b/compiler/luci/lang/include/luci/IR/CircleNodes.lst @@ -13,40 +13,121 @@ CIRCLE_NODE(ABS, luci::CircleAbs) CIRCLE_NODE(ADD, luci::CircleAdd) +CIRCLE_NODE(ADD_N, luci::CircleAddN) CIRCLE_NODE(ARG_MAX, luci::CircleArgMax) +CIRCLE_NODE(ARG_MIN, luci::CircleArgMin) CIRCLE_NODE(AVERAGE_POOL_2D, luci::CircleAveragePool2D) CIRCLE_NODE(BATCH_TO_SPACE_ND, luci::CircleBatchToSpaceND) +CIRCLE_NODE(BATCHMATMUL, luci::CircleBatchMatMul) +CIRCLE_NODE(CAST, luci::CircleCast) +CIRCLE_NODE(CEIL, luci::CircleCeil) CIRCLE_NODE(CONCATENATION, luci::CircleConcatenation) CIRCLE_NODE(CONST, luci::CircleConst) CIRCLE_NODE(CONV_2D, luci::CircleConv2D) CIRCLE_NODE(COS, luci::CircleCos) +CIRCLE_NODE(CUSTOM, luci::CircleCustom) +CIRCLE_NODE(DEPTH_TO_SPACE, luci::CircleDepthToSpace) CIRCLE_NODE(DEPTHWISE_CONV_2D, luci::CircleDepthwiseConv2D) CIRCLE_NODE(DIV, luci::CircleDiv) +CIRCLE_NODE(ELU, luci::CircleElu) CIRCLE_NODE(EQUAL, luci::CircleEqual) CIRCLE_NODE(EXP, luci::CircleExp) +CIRCLE_NODE(EXPAND_DIMS, luci::CircleExpandDims) +CIRCLE_NODE(FILL, luci::CircleFill) +CIRCLE_NODE(FLOOR, luci::CircleFloor) +CIRCLE_NODE(FLOOR_DIV, luci::CircleFloorDiv) +CIRCLE_NODE(FLOOR_MOD, luci::CircleFloorMod) CIRCLE_NODE(FULLY_CONNECTED, luci::CircleFullyConnected) CIRCLE_NODE(GATHER, luci::CircleGather) +CIRCLE_NODE(GATHER_ND, luci::CircleGatherNd) +CIRCLE_NODE(GREATER, luci::CircleGreater) +CIRCLE_NODE(GREATER_EQUAL, luci::CircleGreaterEqual) +CIRCLE_NODE(IF, luci::CircleIf) +CIRCLE_NODE(L2_NORMALIZATION, luci::CircleL2Normalize) +CIRCLE_NODE(L2_POOL_2D, luci::CircleL2Pool2D) +CIRCLE_NODE(LEAKY_RELU, luci::CircleLeakyRelu) +CIRCLE_NODE(LESS, luci::CircleLess) +CIRCLE_NODE(LESS_EQUAL, luci::CircleLessEqual) +CIRCLE_NODE(LOCAL_RESPONSE_NORMALIZATION, luci::CircleLocalResponseNormalization) +CIRCLE_NODE(LOG, luci::CircleLog) +CIRCLE_NODE(LOGICAL_AND, luci::CircleLogicalAnd) CIRCLE_NODE(LOGICAL_NOT, luci::CircleLogicalNot) CIRCLE_NODE(LOGICAL_OR, luci::CircleLogicalOr) -CIRCLE_NODE(MAXIMUM, luci::CircleMaximum) +CIRCLE_NODE(LOGISTIC, luci::CircleLogistic) +CIRCLE_NODE(LOG_SOFTMAX, luci::CircleLogSoftmax) +CIRCLE_NODE(MATRIX_DIAG, luci::CircleMatrixDiag) CIRCLE_NODE(MAX_POOL_2D, luci::CircleMaxPool2D) +CIRCLE_NODE(MATRIX_SET_DIAG, luci::CircleMatrixSetDiag) +CIRCLE_NODE(MAXIMUM, luci::CircleMaximum) CIRCLE_NODE(MEAN, luci::CircleMean) +CIRCLE_NODE(MINIMUM, luci::CircleMinimum) +CIRCLE_NODE(MIRROR_PAD, luci::CircleMirrorPad) CIRCLE_NODE(MUL, luci::CircleMul) +CIRCLE_NODE(NEG, luci::CircleNeg) +CIRCLE_NODE(NOT_EQUAL, luci::CircleNotEqual) +CIRCLE_NODE(ONE_HOT, luci::CircleOneHot) CIRCLE_NODE(PACK, luci::CirclePack) CIRCLE_NODE(PAD, luci::CirclePad) +CIRCLE_NODE(POW, luci::CirclePow) +CIRCLE_NODE(PRELU, luci::CirclePRelu) +CIRCLE_NODE(RANGE, luci::CircleRange) +CIRCLE_NODE(RANK, luci::CircleRank) +CIRCLE_NODE(REDUCE_ANY, luci::CircleReduceAny) +CIRCLE_NODE(REDUCE_MAX, luci::CircleReduceMax) +CIRCLE_NODE(REDUCE_MIN, luci::CircleReduceMin) +CIRCLE_NODE(REDUCE_PROD, luci::CircleReduceProd) CIRCLE_NODE(RELU, luci::CircleRelu) CIRCLE_NODE(RELU6, luci::CircleRelu6) +CIRCLE_NODE(RELU_N1_TO_1, luci::CircleReluN1To1) CIRCLE_NODE(RESHAPE, luci::CircleReshape) +CIRCLE_NODE(RESIZE_BILINEAR, luci::CircleResizeBilinear) +CIRCLE_NODE(RESIZE_NEAREST_NEIGHBOR, luci::CircleResizeNearestNeighbor) +CIRCLE_NODE(REVERSE_SEQUENCE, luci::CircleReverseSequence) +CIRCLE_NODE(REVERSE_V2, luci::CircleReverseV2) +CIRCLE_NODE(ROUND, luci::CircleRound) CIRCLE_NODE(RSQRT, luci::CircleRsqrt) +CIRCLE_NODE(SCATTER_ND, luci::CircleScatterNd) +CIRCLE_NODE(SEGMENT_SUM, luci::CircleSegmentSum) +CIRCLE_NODE(SELECT, luci::CircleSelect) +CIRCLE_NODE(SELECT_V2, luci::CircleSelectV2) +CIRCLE_NODE(SHAPE, luci::CircleShape) +CIRCLE_NODE(SIN, luci::CircleSin) +CIRCLE_NODE(SLICE, luci::CircleSlice) CIRCLE_NODE(SOFTMAX, luci::CircleSoftmax) +CIRCLE_NODE(SPACE_TO_BATCH_ND, luci::CircleSpaceToBatchND) +CIRCLE_NODE(SPACE_TO_DEPTH, luci::CircleSpaceToDepth) +CIRCLE_NODE(SPARSE_TO_DENSE, luci::CircleSparseToDense) +CIRCLE_NODE(SPLIT, luci::CircleSplit) +CIRCLE_NODE(SPLIT_V, luci::CircleSplitV) CIRCLE_NODE(SQRT, luci::CircleSqrt) +CIRCLE_NODE(SQUARE, luci::CircleSquare) CIRCLE_NODE(SQUARED_DIFFERENCE, luci::CircleSquaredDifference) +CIRCLE_NODE(SQUEEZE, luci::CircleSqueeze) +CIRCLE_NODE(STRIDED_SLICE, luci::CircleStridedSlice) CIRCLE_NODE(SUB, luci::CircleSub) -// TODO TFLTanh +CIRCLE_NODE(SUM, luci::CircleSum) +CIRCLE_NODE(TANH, luci::CircleTanh) +CIRCLE_NODE(TILE, luci::CircleTile) +CIRCLE_NODE(TOPK_V2, luci::CircleTopKV2) CIRCLE_NODE(TRANSPOSE, luci::CircleTranspose) CIRCLE_NODE(TRANSPOSE_CONV, luci::CircleTransposeConv) +CIRCLE_NODE(UNPACK, luci::CircleUnpack) +CIRCLE_NODE(WHERE, luci::CircleWhere) +CIRCLE_NODE(WHILE, luci::CircleWhile) +CIRCLE_NODE(ZEROS_LIKE, luci::CircleZerosLike) // Circle Only +CIRCLE_NODE(BCQ_FULLY_CONNECTED, luci::CircleBCQFullyConnected) +CIRCLE_NODE(BCQ_GATHER, luci::CircleBCQGather) CIRCLE_NODE(INSTANCE_NORM, luci::CircleInstanceNorm) // Virtual node(s) CIRCLE_NODE(CIRCLEINPUT, luci::CircleInput) CIRCLE_NODE(CIRCLEOUTPUT, luci::CircleOutput) +CIRCLE_NODE(CIRCLEOUTPUTDUMMY, luci::CircleOutputDummy) +CIRCLE_NODE(CIRCLEOUTPUTEXCLUDE, luci::CircleOutputExclude) +CIRCLE_NODE(CIRCLECUSTOMOUT, luci::CircleCustomOut) +CIRCLE_NODE(CIRCLEIFOUT, luci::CircleIfOut) +CIRCLE_NODE(CIRCLESPLITOUT, luci::CircleSplitOut) +CIRCLE_NODE(CIRCLESPLITVOUT, luci::CircleSplitVOut) +CIRCLE_NODE(CIRCLETOPKV2OUT, luci::CircleTopKV2Out) +CIRCLE_NODE(CIRCLEUNPACKOUT, luci::CircleUnpackOut) +CIRCLE_NODE(CIRCLEWHILEOUT, luci::CircleWhileOut) diff --git a/compiler/luci/lang/include/luci/IR/LuciNodeMixins.h b/compiler/luci/lang/include/luci/IR/LuciNodeMixins.h index b18ac5d..c1bb0db 100644 --- a/compiler/luci/lang/include/luci/IR/LuciNodeMixins.h +++ b/compiler/luci/lang/include/luci/IR/LuciNodeMixins.h @@ -22,6 +22,8 @@ #include #include +#include + namespace luci { @@ -70,6 +72,7 @@ template class FixedArityNode : public Base public: FixedArityNode() { + _args.resize(N); for (uint32_t n = 0; n < N; ++n) { _args[n] = std::make_unique(this); @@ -96,7 +99,7 @@ protected: loco::Use *at(unsigned n) const { return _args.at(n).get(); } private: - std::array, N> _args; + std::vector> _args{}; }; } // namespace luci diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleAddN.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleAddN.h new file mode 100644 index 0000000..6ba4a96 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleAddN.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCEL_ADD_N_H__ +#define __LUCI_IR_CIRCEL_ADD_N_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/VariadicArityNode.h" + +namespace luci +{ + +/** + * @brief ADD_N in Circle + */ +class CircleAddN final : public VariadicArityNode> +{ +public: + CircleAddN(uint32_t arity) : VariadicArityNode>(arity) + { + assert(arity >= 1); + } + +public: + Node *inputs(uint32_t index) const { return at(index)->node(); } + void inputs(uint32_t index, Node *node) { at(index)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCEL_ADD_N_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleArgMin.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleArgMin.h new file mode 100644 index 0000000..8cb5619 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleArgMin.h @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCELARGMIN_H__ +#define __LUCI_IR_CIRCELARGMIN_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief ARG_Min in Circle + */ +class CircleArgMin final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *dimension(void) const { return at(1)->node(); } + void dimension(loco::Node *node) { at(1)->node(node); } + +public: + loco::DataType output_type(void) const { return _output_type; } + void output_type(loco::DataType ot) { _output_type = ot; } + +private: + loco::DataType _output_type{loco::DataType::S64}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCELARGMIN_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleBCQFullyConnected.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleBCQFullyConnected.h new file mode 100644 index 0000000..7d12d59 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleBCQFullyConnected.h @@ -0,0 +1,66 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLEBCQFULLYCONNECTED_H__ +#define __LUCI_IR_CIRCLEBCQFULLYCONNECTED_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/AttrFusedActFunc.h" +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief BCQ_FULLY_CONNECTED in Circle + */ +class CircleBCQFullyConnected final + : public FixedArityNode<5, CircleNodeImpl>, + public LuciNodeMixin, + public LuciNodeMixin +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *weights_scales(void) const { return at(1)->node(); } + void weights_scales(loco::Node *node) { at(1)->node(node); } + + loco::Node *weights_binary(void) const { return at(2)->node(); } + void weights_binary(loco::Node *node) { at(2)->node(node); } + + loco::Node *bias(void) const override { return at(3)->node(); } + void bias(loco::Node *node) override { at(3)->node(node); } + + loco::Node *weights_clusters(void) const { return at(4)->node(); } + void weights_clusters(loco::Node *node) { at(4)->node(node); } + +public: + int32_t weights_hidden_size(void) const { return _weights_hidden_size; } + void weights_hidden_size(int32_t weights_hidden_size) + { + _weights_hidden_size = weights_hidden_size; + } + +private: + int32_t _weights_hidden_size = 0; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLEBCQFULLYCONNECTED_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleBCQGather.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleBCQGather.h new file mode 100644 index 0000000..f763826 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleBCQGather.h @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLEBCQGATHER_H__ +#define __LUCI_IR_CIRCLEBCQGATHER_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief BCQ_GATHER in Circle + */ +class CircleBCQGather final : public FixedArityNode<4, CircleNodeImpl> +{ +public: + loco::Node *input_scales(void) const { return at(0)->node(); } + void input_scales(loco::Node *node) { at(0)->node(node); } + + loco::Node *input_binary(void) const { return at(1)->node(); } + void input_binary(loco::Node *node) { at(1)->node(node); } + + loco::Node *indices(void) const { return at(2)->node(); } + void indices(loco::Node *node) { at(2)->node(node); } + + loco::Node *input_clusters(void) const { return at(3)->node(); } + void input_clusters(loco::Node *node) { at(3)->node(node); } + +public: + int32_t axis(void) const { return _axis; } + void axis(int32_t axis) { _axis = axis; } + + int32_t input_hidden_size(void) const { return _input_hidden_size; } + void input_hidden_size(int32_t input_hidden_size) { _input_hidden_size = input_hidden_size; } + +private: + int32_t _axis = 0; + int32_t _input_hidden_size = 0; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLEBCQGATHER_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleBatchMatMul.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleBatchMatMul.h new file mode 100644 index 0000000..1999992 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleBatchMatMul.h @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCELBATCHMATMUL_H__ +#define __LUCI_IR_CIRCELBATCHMATMUL_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief BATCHMATMUL in Circle + */ +class CircleBatchMatMul final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } + + loco::Node *y(void) const { return at(1)->node(); } + void y(loco::Node *node) { at(1)->node(node); } + +public: + bool adj_x(void) const { return _adj_x; } + void adj_x(bool arg) { _adj_x = arg; } + + bool adj_y(void) const { return _adj_y; } + void adj_y(bool arg) { _adj_y = arg; } + +private: + bool _adj_x = false; + bool _adj_y = false; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCELBATCHMATMUL_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleCast.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleCast.h new file mode 100644 index 0000000..9a89d0b --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleCast.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCELECAST_H__ +#define __LUCI_IR_CIRCELECAST_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief CAST in Circle + */ +class CircleCast final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } + +public: + loco::DataType in_data_type(void) const { return _in_data_type; } + void in_data_type(loco::DataType it) { _in_data_type = it; } + + loco::DataType out_data_type(void) const { return _out_data_type; } + void out_data_type(loco::DataType ot) { _out_data_type = ot; } + +private: + loco::DataType _in_data_type{loco::DataType::FLOAT32}; + loco::DataType _out_data_type{loco::DataType::FLOAT32}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCELECAST_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleCeil.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleCeil.h new file mode 100644 index 0000000..8a8715d --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleCeil.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_CEIL_H__ +#define __LUCI_IR_CIRCLE_CEIL_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief CEIL in Circle + */ +class CircleCeil final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_CEIL_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleConcatenation.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleConcatenation.h index 8a6778a..dea1a46 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleConcatenation.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleConcatenation.h @@ -64,7 +64,7 @@ public: void axis(int32_t axis) { _axis = axis; } private: - int32_t _axis; + int32_t _axis{0}; }; } // namespace luci diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleConst.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleConst.h index 089836e..fc67174 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleConst.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleConst.h @@ -20,7 +20,6 @@ #include "luci/IR/CircleNodeDecl.h" #include "luci/IR/CircleOpcode.h" -#include "luci/IR/AttrFusedActFunc.h" #include "luci/IR/LuciNodeMixins.h" #include @@ -32,9 +31,7 @@ namespace luci * @brief Class to build tensor data * @note This will not be exported as a specific op */ -class CircleConst final : public FixedArityNode<0, CircleNodeImpl>, - public loco::NodeMixin, - public loco::NodeMixin +class CircleConst final : public FixedArityNode<0, CircleNodeImpl> { public: CircleConst() = default; diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleConv2D.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleConv2D.h index 54318e6..13657ce 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleConv2D.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleConv2D.h @@ -22,6 +22,7 @@ #include "luci/IR/AttrPadding.h" #include "luci/IR/AttrStride.h" +#include "luci/IR/AttrDilation.h" #include "luci/IR/AttrFusedActFunc.h" #include "luci/IR/LuciNodeMixins.h" @@ -52,9 +53,13 @@ public: const Stride *stride(void) const { return &_stride; } Stride *stride(void) { return &_stride; } + const Dilation *dilation(void) const { return &_dilation; } + Dilation *dilation(void) { return &_dilation; } + private: Padding _padding = Padding::UNDEFINED; Stride _stride; + Dilation _dilation; }; } // namespace luci diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleCustom.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleCustom.h new file mode 100644 index 0000000..6c722b7 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleCustom.h @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLECUSTOM_H__ +#define __LUCI_IR_CIRCLECUSTOM_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/VariadicArityNode.h" + +namespace luci +{ + +/** + * @brief CUSTOM in Circle + */ +class CircleCustom final : public VariadicArityNode> +{ +public: + CircleCustom(uint32_t arity) : VariadicArityNode>(arity) + { + // TODO Support when arity is 0 + assert(arity >= 1); + } + +public: + uint32_t numInputs(void) const { return arity(); } + +public: + Node *inputs(uint32_t index) const { return at(index)->node(); } + void inputs(uint32_t index, Node *node) { at(index)->node(node); } + + const std::vector &custom_options(void) const { return _custom_options; } + void custom_options(const std::vector &custom_options) + { + _custom_options = custom_options; + } + + const std::string &custom_code(void) const { return _custom_code; } + void custom_code(const std::string &custom_code) { _custom_code = custom_code; } + +private: + std::vector _custom_options; + std::string _custom_code; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLECUSTOM_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleCustomOut.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleCustomOut.h new file mode 100644 index 0000000..36b8e4a --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleCustomOut.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_CUSTOMOUT_H__ +#define __LUCI_IR_CIRCLE_CUSTOMOUT_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief Virtual CIRCLECUSTOMOUT in Circle + */ +class CircleCustomOut final + : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleCustomOut() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + +public: + int32_t index(void) const { return _index; } + void index(int32_t index) { _index = index; } + +private: + int32_t _index{-1}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_CUSTOMOUT_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleDepthToSpace.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleDepthToSpace.h new file mode 100644 index 0000000..e19282b --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleDepthToSpace.h @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_DEPTHTOSPACE_H__ +#define __LUCI_IR_CIRCLE_DEPTHTOSPACE_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief DEPTH_TO_SPACE in Circle + */ +class CircleDepthToSpace final + : public FixedArityNode<1, CircleNodeImpl> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + +public: + int block_size(void) const { return _block_size; } + void block_size(int block_size) { _block_size = block_size; } + +private: + int _block_size{0}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_DEPTHTOSPACE_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleDepthwiseConv2D.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleDepthwiseConv2D.h index 15ee62b..eb058ce 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleDepthwiseConv2D.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleDepthwiseConv2D.h @@ -20,6 +20,7 @@ #include "luci/IR/CircleNodeDecl.h" #include "luci/IR/CircleOpcode.h" +#include "luci/IR/AttrDilation.h" #include "luci/IR/AttrFilter.h" #include "luci/IR/AttrPadding.h" #include "luci/IR/AttrStride.h" @@ -57,10 +58,14 @@ public: int32_t depthMultiplier(void) const { return _depth_multiplier; } void depthMultiplier(int32_t arg) { _depth_multiplier = arg; } + const Dilation *dilation(void) const { return &_dilation; } + Dilation *dilation(void) { return &_dilation; } + private: Padding _padding = Padding::UNDEFINED; Stride _stride; int32_t _depth_multiplier = 0; + Dilation _dilation; }; } // namespace luci diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleElu.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleElu.h new file mode 100644 index 0000000..fbb2f35 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleElu.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLEELU_H__ +#define __LUCI_IR_CIRCLEELU_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief ELU in Circle + */ +class CircleElu final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleElu() = default; + +public: + loco::Node *features(void) const { return at(0)->node(); } + void features(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLEELU_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleExpandDims.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleExpandDims.h new file mode 100644 index 0000000..f702196 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleExpandDims.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLEEXPAND_DIMS_H__ +#define __LUCI_IR_CIRCLEEXPAND_DIMS_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief EXPAND_DIMS in Circle + */ +class CircleExpandDims final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + CircleExpandDims() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *axis(void) const { return at(1)->node(); } + void axis(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLEEXPAND_DIMS_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleFill.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleFill.h new file mode 100644 index 0000000..bfc6527 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleFill.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCEFILL_H__ +#define __LUCI_IR_CIRCEFILL_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief FILL in Circle + */ +class CircleFill final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *dims(void) const { return at(0)->node(); } + void dims(loco::Node *node) { at(0)->node(node); } + + loco::Node *value(void) const { return at(1)->node(); } + void value(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCEFILL_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleFloor.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleFloor.h new file mode 100644 index 0000000..7e10547 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleFloor.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_FLOOR_H__ +#define __LUCI_IR_CIRCLE_FLOOR_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief FLOOR in Circle + */ +class CircleFloor final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_FLOOR_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleFloorDiv.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleFloorDiv.h new file mode 100644 index 0000000..ba9db01 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleFloorDiv.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_FLOOR_DIV_H__ +#define __LUCI_IR_CIRCLE_FLOOR_DIV_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief FLOOR_DIV in Circle + */ +class CircleFloorDiv final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } + + loco::Node *y(void) const { return at(1)->node(); } + void y(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_FLOOR_DIV_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleFloorMod.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleFloorMod.h new file mode 100644 index 0000000..4d13717 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleFloorMod.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_FLOOR_MOD_H__ +#define __LUCI_IR_CIRCLE_FLOOR_MOD_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief FLOOR_MOD in Circle + */ +class CircleFloorMod final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } + + loco::Node *y(void) const { return at(1)->node(); } + void y(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_FLOOR_MOD_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleGather.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleGather.h index 489596c..1e8c498 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleGather.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleGather.h @@ -20,7 +20,6 @@ #include "luci/IR/CircleNodeDecl.h" #include "luci/IR/CircleOpcode.h" -#include "luci/IR/AttrFusedActFunc.h" #include "luci/IR/LuciNodeMixins.h" namespace luci @@ -32,11 +31,11 @@ namespace luci class CircleGather final : public FixedArityNode<2, CircleNodeImpl> { public: - loco::Node *input(void) const { return at(0)->node(); } - void input(loco::Node *node) { at(0)->node(node); } + loco::Node *params(void) const { return at(0)->node(); } + void params(loco::Node *node) { at(0)->node(node); } - loco::Node *positions(void) const { return at(1)->node(); } - void positions(loco::Node *node) { at(1)->node(node); } + loco::Node *indices(void) const { return at(1)->node(); } + void indices(loco::Node *node) { at(1)->node(node); } public: int32_t axis(void) const { return _axis; } diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleGatherNd.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleGatherNd.h new file mode 100644 index 0000000..3423a82 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleGatherNd.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLEGATHER_ND_H__ +#define __LUCI_IR_CIRCLEGATHER_ND_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief GATHER_ND in Circle + */ +class CircleGatherNd final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *params(void) const { return at(0)->node(); } + void params(loco::Node *node) { at(0)->node(node); } + + loco::Node *indices(void) const { return at(1)->node(); } + void indices(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLEGATHER_ND_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleGreater.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleGreater.h new file mode 100644 index 0000000..040a4e3 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleGreater.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_GREATER_H__ +#define __LUCI_IR_CIRCLE_GREATER_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief Greater in Circle + */ +class CircleGreater final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } + + loco::Node *y(void) const { return at(1)->node(); } + void y(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_GREATER_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleGreaterEqual.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleGreaterEqual.h new file mode 100644 index 0000000..82bdab2 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleGreaterEqual.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_GREATEREQUAL_H__ +#define __LUCI_IR_CIRCLE_GREATEREQUAL_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief GREATER EQUAL in Circle + */ +class CircleGreaterEqual final + : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } + + loco::Node *y(void) const { return at(1)->node(); } + void y(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_GREATEREQUAL_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleIf.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleIf.h new file mode 100644 index 0000000..2f9eac2 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleIf.h @@ -0,0 +1,79 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_IF_H__ +#define __LUCI_IR_CIRCLE_IF_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/VariadicArityNode.h" + +#include + +namespace luci +{ + +/** + * @brief IF in Circle + */ +class CircleIf final : public VariadicArityNode> +{ +public: + CircleIf(uint32_t arity, uint32_t out) + : VariadicArityNode>(arity + 1), _output_count(out) + { + assert(arity > 0); + assert(out > 0); + } + +public: + uint32_t input_count(void) const { return arity() - 1; } + uint32_t output_count(void) const { return _output_count; } + +public: + Node *cond(void) const { return at(0)->node(); } + void cond(Node *node) { at(0)->node(node); } + + Node *input(uint32_t index) const { return at(index + 1)->node(); } + void input(uint32_t index, Node *node) { at(index + 1)->node(node); } + +public: + int32_t then_branch(void) const { return _then_branch; } + void then_branch(int32_t then_branch) { _then_branch = then_branch; } + + int32_t else_branch(void) const { return _else_branch; } + void else_branch(int32_t else_branch) { _else_branch = else_branch; } + +public: + loco::Graph *then_graph(void) const { return _then_graph; } + void then_graph(loco::Graph *then_graph) { _then_graph = then_graph; } + + loco::Graph *else_graph(void) const { return _else_graph; } + void else_graph(loco::Graph *else_graph) { _else_graph = else_graph; } + +private: + uint32_t _output_count{0}; + int32_t _then_branch{-1}; + int32_t _else_branch{-1}; + + loco::Graph *_then_graph{nullptr}; + loco::Graph *_else_graph{nullptr}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_IF_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleIfOut.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleIfOut.h new file mode 100644 index 0000000..3654e94 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleIfOut.h @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_IFOUT_H__ +#define __LUCI_IR_CIRCLE_IFOUT_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief Virtual CIRCLEIFOUT in Circle + */ +class CircleIfOut final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleIfOut() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + +public: + int32_t index(void) const { return _index; } + void index(int32_t index) { _index = index; } + +private: + int32_t _index{-1}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_IFOUT_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleInput.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleInput.h index 2c4d602..4a7d36a 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleInput.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleInput.h @@ -20,7 +20,6 @@ #include "luci/IR/CircleNodeDecl.h" #include "luci/IR/CircleOpcode.h" -#include "luci/IR/AttrFusedActFunc.h" #include "luci/IR/LuciNodeMixins.h" #include @@ -33,9 +32,7 @@ namespace luci * @brief CircleNode used for Input of the Graph * @note This will not be exported as a specific op */ -class CircleInput final : public FixedArityNode<0, CircleNodeImpl>, - public loco::NodeMixin, - public loco::NodeMixin +class CircleInput final : public FixedArityNode<0, CircleNodeImpl> { public: CircleInput() = default; diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleL2Normalize.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleL2Normalize.h new file mode 100644 index 0000000..efa932d --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleL2Normalize.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCEL2NORMALIZE_H__ +#define __LUCI_IR_CIRCEL2NORMALIZE_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/AttrFusedActFunc.h" +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief L2_NORMALIZATION in Circle + */ +class CircleL2Normalize final + : public FixedArityNode<1, CircleNodeImpl>, + public LuciNodeMixin +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCEL2NORMALIZE_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleL2Pool2D.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleL2Pool2D.h new file mode 100644 index 0000000..7c76ee5 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleL2Pool2D.h @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_L2_POOL2D_H__ +#define __LUCI_IR_CIRCLE_L2_POOL2D_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/AttrFilter.h" +#include "luci/IR/AttrPadding.h" +#include "luci/IR/AttrStride.h" +#include "luci/IR/AttrFusedActFunc.h" +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief L2_POOL_2D in Circle + */ +class CircleL2Pool2D final : public FixedArityNode<1, CircleNodeImpl>, + public LuciNodeMixin +{ +public: + CircleL2Pool2D() : _padding(Padding::UNDEFINED) { /* empty */} + +public: + loco::Node *value(void) const { return at(0)->node(); } + void value(loco::Node *node) { at(0)->node(node); } + + Padding padding() const { return _padding; } + void padding(Padding padding) { _padding = padding; } + + const Filter *filter(void) const { return &_filter; } + Filter *filter(void) { return &_filter; } + + const Stride *stride(void) const { return &_stride; } + Stride *stride(void) { return &_stride; } + +private: + Padding _padding; + Stride _stride; + Filter _filter; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_L2_POOL2D_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleLeakyRelu.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleLeakyRelu.h new file mode 100644 index 0000000..d6ac97f --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleLeakyRelu.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_LEAKY_RELU_H__ +#define __LUCI_IR_CIRCLE_LEAKY_RELU_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief LEAKY_RELU in Circle + */ +class CircleLeakyRelu final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleLeakyRelu() = default; + +public: + loco::Node *features(void) const { return at(0)->node(); } + void features(loco::Node *node) { at(0)->node(node); } + + float alpha() const { return _alpha; } + void alpha(float alpha) { _alpha = alpha; } + +private: + float _alpha = 0.2f; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_LEAKY_RELU_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleLess.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleLess.h new file mode 100644 index 0000000..cd6cf18 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleLess.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_LESS_H__ +#define __LUCI_IR_CIRCLE_LESS_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief LESS in Circle + */ +class CircleLess final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } + + loco::Node *y(void) const { return at(1)->node(); } + void y(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_LESS_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleLessEqual.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleLessEqual.h new file mode 100644 index 0000000..4c7c6a4 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleLessEqual.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_LESSEQUAL_H__ +#define __LUCI_IR_CIRCLE_LESSEQUAL_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief LESS_EQUAL in Circle + */ +class CircleLessEqual final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } + + loco::Node *y(void) const { return at(1)->node(); } + void y(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_LESSEQUAL_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleLocalResponseNormalization.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleLocalResponseNormalization.h new file mode 100644 index 0000000..8ad2b40 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleLocalResponseNormalization.h @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLELOCAL_RESPONSE_NORMALIZATION_H__ +#define __LUCI_IR_CIRCLELOCAL_RESPONSE_NORMALIZATION_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief LOCAL_RESPONSE_NORMALIZATION in Circle + */ +class CircleLocalResponseNormalization final + : public FixedArityNode<1, CircleNodeImpl> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + +public: + int32_t radius(void) const { return _radius; } + void radius(int32_t radius) { _radius = radius; } + + float bias(void) const { return _bias; } + void bias(float bias) { _bias = bias; } + + float alpha(void) const { return _alpha; } + void alpha(float alpha) { _alpha = alpha; } + + float beta(void) const { return _beta; } + void beta(float beta) { _beta = beta; } + +private: + int32_t _radius{5}; + float _bias{1.0f}; + float _alpha{1.0f}; + float _beta{0.5f}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLELOCAL_RESPONSE_NORMALIZATION_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleLog.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleLog.h new file mode 100644 index 0000000..aeb13fe --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleLog.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_LOG_H__ +#define __LUCI_IR_CIRCLE_LOG_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief LOG in Circle + */ +class CircleLog final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_LOG_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleLogSoftmax.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleLogSoftmax.h new file mode 100644 index 0000000..5dfd2c1 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleLogSoftmax.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_LOG_SOFTMAX_H__ +#define __LUCI_IR_CIRCLE_LOG_SOFTMAX_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief LOG_SOFTMAX in Circle + */ +class CircleLogSoftmax final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + loco::Node *logits(void) const { return at(0)->node(); } + void logits(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_LOG_SOFTMAX_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleLogicalAnd.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleLogicalAnd.h new file mode 100644 index 0000000..975f6db --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleLogicalAnd.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_LOGICALAND_H__ +#define __LUCI_IR_CIRCLE_LOGICALAND_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief LOGICAL_AND in Circle + */ +class CircleLogicalAnd final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } + + loco::Node *y(void) const { return at(1)->node(); } + void y(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_LOGICALAND_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleLogistic.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleLogistic.h new file mode 100644 index 0000000..8328cb3 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleLogistic.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_LOGISTIC_H__ +#define __LUCI_IR_CIRCLE_LOGISTIC_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief LOGISTIC in Circle + */ +class CircleLogistic final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleLogistic() = default; + +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_LOGISTIC_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleMatrixDiag.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleMatrixDiag.h new file mode 100644 index 0000000..dca6538 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleMatrixDiag.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLEMATRIXDIAG_H__ +#define __LUCI_IR_CIRCLEMATRIXDIAG_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief MATRIX_DIAG in Circle + */ +class CircleMatrixDiag final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + loco::Node *diagonal(void) const { return at(0)->node(); } + void diagonal(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLEMATRIXDIAG_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleMatrixSetDiag.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleMatrixSetDiag.h new file mode 100644 index 0000000..c1f5f30 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleMatrixSetDiag.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLEMATRIXSETDIAG_H__ +#define __LUCI_IR_CIRCLEMATRIXSETDIAG_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief MATRIX_SET_DIAG in Circle + */ +class CircleMatrixSetDiag final + : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *diagonal(void) const { return at(1)->node(); } + void diagonal(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLEMATRIXSETDIAG_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleMaximum.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleMaximum.h index cf7305e..6f789bc 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleMaximum.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleMaximum.h @@ -20,7 +20,6 @@ #include "luci/IR/CircleNodeDecl.h" #include "luci/IR/CircleOpcode.h" -#include "luci/IR/AttrFusedActFunc.h" #include "luci/IR/LuciNodeMixins.h" namespace luci diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleMean.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleMean.h index 6fd7914..7f8aeb5 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleMean.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleMean.h @@ -20,7 +20,6 @@ #include "luci/IR/CircleNodeDecl.h" #include "luci/IR/CircleOpcode.h" -#include "luci/IR/AttrFusedActFunc.h" #include "luci/IR/LuciNodeMixins.h" namespace luci diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleMinimum.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleMinimum.h new file mode 100644 index 0000000..79d5a6f --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleMinimum.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLEMINIMUM_H__ +#define __LUCI_IR_CIRCLEMINIMUM_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief MINIMUM in Circle + */ +class CircleMinimum final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } + + loco::Node *y(void) const { return at(1)->node(); } + void y(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLEMINIMUM_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleMirrorPad.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleMirrorPad.h new file mode 100644 index 0000000..68db8f6 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleMirrorPad.h @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_MIRRORPAD_H__ +#define __LUCI_IR_CIRCLE_MIRRORPAD_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" +#include "luci/IR/AttrMirrorPadMode.h" + +namespace luci +{ + +/** + * @brief MIRROR_PAD in Circle + */ +class CircleMirrorPad final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + CircleMirrorPad() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *paddings(void) const { return at(1)->node(); } + void paddings(loco::Node *node) { at(1)->node(node); } + +public: + MirrorPadMode mode(void) const { return _mode; } + void mode(MirrorPadMode mode) { _mode = mode; } + +private: + MirrorPadMode _mode{MirrorPadMode::REFLECT}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_MIRRORPAD_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleNeg.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleNeg.h new file mode 100644 index 0000000..4149ac4 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleNeg.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLENEG_H__ +#define __LUCI_IR_CIRCLENEG_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief NEG in Circle + */ +class CircleNeg final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLENEG_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleNotEqual.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleNotEqual.h new file mode 100644 index 0000000..cca7a5e --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleNotEqual.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_NOTEQUAL_H__ +#define __LUCI_IR_CIRCLE_NOTEQUAL_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief NOT EQUAL in Circle + */ +class CircleNotEqual final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } + + loco::Node *y(void) const { return at(1)->node(); } + void y(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_NOTEQUAL_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleOneHot.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleOneHot.h new file mode 100644 index 0000000..665e01d --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleOneHot.h @@ -0,0 +1,56 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLEONEHOT_H__ +#define __LUCI_IR_CIRCLEONEHOT_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief ONEHOT in Circle + */ +class CircleOneHot final : public FixedArityNode<4, CircleNodeImpl> +{ +public: + loco::Node *indices(void) const { return at(0)->node(); } + void indices(loco::Node *node) { at(0)->node(node); } + + loco::Node *depth(void) const { return at(1)->node(); } + void depth(loco::Node *node) { at(1)->node(node); } + + loco::Node *on_value(void) const { return at(2)->node(); } + void on_value(loco::Node *node) { at(2)->node(node); } + + loco::Node *off_value(void) const { return at(3)->node(); } + void off_value(loco::Node *node) { at(3)->node(node); } + +public: + int32_t axis(void) const { return _axis; } + void axis(int32_t axis) { _axis = axis; } + +private: + int32_t _axis = -1; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLEONEHOT_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleOutput.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleOutput.h index c65317a..67e55f1 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleOutput.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleOutput.h @@ -20,7 +20,6 @@ #include "luci/IR/CircleNodeDecl.h" #include "luci/IR/CircleOpcode.h" -#include "luci/IR/AttrFusedActFunc.h" #include "luci/IR/LuciNodeMixins.h" #include @@ -50,6 +49,27 @@ private: int64_t _index = -1; // Uninitialized }; +/** + * @brief Temporary DummyNode used with dangle CircleNode + */ +// TODO remove CircleOutputDummy +class CircleOutputDummy final + : public FixedArityNode<0, CircleNodeImpl> +{ +public: + CircleOutputDummy() = default; +}; + +/** + * @brief CircleOutputExclude is used to specifying not exported nodes + */ +class CircleOutputExclude final + : public FixedArityNode<0, CircleNodeImpl> +{ +public: + CircleOutputExclude() = default; +}; + } // namespace luci #endif // __LUCI_IR_CIRCLEOUTPUT_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CirclePRelu.h b/compiler/luci/lang/include/luci/IR/Nodes/CirclePRelu.h new file mode 100644 index 0000000..6937775 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CirclePRelu.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_PRELU_H__ +#define __LUCI_IR_PRELU_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief PRelu in Circle + */ +class CirclePRelu final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + CirclePRelu() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *alpha(void) const { return at(1)->node(); } + void alpha(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_PRELU_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CirclePow.h b/compiler/luci/lang/include/luci/IR/Nodes/CirclePow.h new file mode 100644 index 0000000..006e3dd --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CirclePow.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_POW_H__ +#define __LUCI_IR_POW_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief POW in Circle + */ +class CirclePow final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + CirclePow() = default; + +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } + + loco::Node *y(void) const { return at(1)->node(); } + void y(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_POW_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleRange.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleRange.h new file mode 100644 index 0000000..977a37a --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleRange.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLERANGE_H__ +#define __LUCI_IR_CIRCLERANGE_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief RANGE in Circle + */ +class CircleRange final : public FixedArityNode<3, CircleNodeImpl> +{ +public: + loco::Node *start(void) const { return at(0)->node(); } + void start(loco::Node *node) { at(0)->node(node); } + + loco::Node *limit(void) const { return at(1)->node(); } + void limit(loco::Node *node) { at(1)->node(node); } + + loco::Node *delta(void) const { return at(2)->node(); } + void delta(loco::Node *node) { at(2)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLERANGE_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleRank.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleRank.h new file mode 100644 index 0000000..ba6d67f --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleRank.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLERANK_H__ +#define __LUCI_IR_CIRCLERANK_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief RANK in Circle + */ +class CircleRank final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLERANK_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleReduceAny.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleReduceAny.h new file mode 100644 index 0000000..0456be8 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleReduceAny.h @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_REDUCE_ANY_H__ +#define __LUCI_IR_CIRCLE_REDUCE_ANY_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief REDUCE_ANY in Circle + */ +class CircleReduceAny final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *reduction_indices(void) const { return at(1)->node(); } + void reduction_indices(loco::Node *node) { at(1)->node(node); } + +public: + bool keep_dims(void) const { return _keep_dims; } + void keep_dims(bool keep_dims) { _keep_dims = keep_dims; } + +private: + bool _keep_dims = false; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_REDUCE_ANY_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleReduceMax.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleReduceMax.h new file mode 100644 index 0000000..925c977 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleReduceMax.h @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_REDUCE_MAX_H__ +#define __LUCI_IR_CIRCLE_REDUCE_MAX_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief REDUCE_MAX in Circle + */ +class CircleReduceMax final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *reduction_indices(void) const { return at(1)->node(); } + void reduction_indices(loco::Node *node) { at(1)->node(node); } + +public: + bool keep_dims(void) const { return _keep_dims; } + void keep_dims(bool keep_dims) { _keep_dims = keep_dims; } + +private: + bool _keep_dims = false; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_REDUCE_MAX_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleReduceMin.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleReduceMin.h new file mode 100644 index 0000000..fd789ae --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleReduceMin.h @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_REDUCE_MIN_H__ +#define __LUCI_IR_CIRCLE_REDUCE_MIN_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief REDUCE_MIN in Circle + */ +class CircleReduceMin final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *reduction_indices(void) const { return at(1)->node(); } + void reduction_indices(loco::Node *node) { at(1)->node(node); } + +public: + bool keep_dims(void) const { return _keep_dims; } + void keep_dims(bool keep_dims) { _keep_dims = keep_dims; } + +private: + bool _keep_dims = false; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_REDUCE_MIN_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleReduceProd.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleReduceProd.h new file mode 100644 index 0000000..b7d2262 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleReduceProd.h @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_REDUCE_PROD_H__ +#define __LUCI_IR_CIRCLE_REDUCE_PROD_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief REDUCE_PROD in Circle + */ +class CircleReduceProd final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *reduction_indices(void) const { return at(1)->node(); } + void reduction_indices(loco::Node *node) { at(1)->node(node); } + +public: + bool keep_dims(void) const { return _keep_dims; } + void keep_dims(bool keep_dims) { _keep_dims = keep_dims; } + +private: + bool _keep_dims = false; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_REDUCE_PROD_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleRelu.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleRelu.h index afb2c66..91272d2 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleRelu.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleRelu.h @@ -20,7 +20,6 @@ #include "luci/IR/CircleNodeDecl.h" #include "luci/IR/CircleOpcode.h" -#include "luci/IR/AttrFusedActFunc.h" #include "luci/IR/LuciNodeMixins.h" namespace luci diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleRelu6.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleRelu6.h index b313a55..b4274de 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleRelu6.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleRelu6.h @@ -20,7 +20,6 @@ #include "luci/IR/CircleNodeDecl.h" #include "luci/IR/CircleOpcode.h" -#include "luci/IR/AttrFusedActFunc.h" #include "luci/IR/LuciNodeMixins.h" namespace luci diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleReluN1To1.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleReluN1To1.h new file mode 100644 index 0000000..a5c5710 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleReluN1To1.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_RELU_N1_TO_1_H__ +#define __LUCI_IR_CIRCLE_RELU_N1_TO_1_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief RELU_N1_TO_1 in Circle + */ +class CircleReluN1To1 final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleReluN1To1() = default; + +public: + loco::Node *features(void) const { return at(0)->node(); } + void features(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_RELU_N1_TO_1_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleReshape.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleReshape.h index a3a2a3f..b13144f 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleReshape.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleReshape.h @@ -20,7 +20,6 @@ #include "luci/IR/CircleNodeDecl.h" #include "luci/IR/CircleOpcode.h" -#include "luci/IR/AttrFusedActFunc.h" #include "luci/IR/LuciNodeMixins.h" namespace luci @@ -38,8 +37,8 @@ public: loco::Node *tensor(void) const { return at(0)->node(); } void tensor(loco::Node *node) { at(0)->node(node); } - // TODO Make this input optional. That is, loco system does not emit error - // with this input being null + // NOTE shape is optional and can be CircleConst or any other type + // and also can be CircleOutputDummy when reshape option does not exist loco::Node *shape(void) const { return at(1)->node(); } void shape(loco::Node *node) { at(1)->node(node); } diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleResizeBilinear.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleResizeBilinear.h new file mode 100644 index 0000000..3c82233 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleResizeBilinear.h @@ -0,0 +1,57 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLERESIZE_BILINEAR_H__ +#define __LUCI_IR_CIRCLERESIZE_BILINEAR_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief RESIZE_BILINEAR in Circle + */ +class CircleResizeBilinear final + : public FixedArityNode<2, CircleNodeImpl> +{ +public: + CircleResizeBilinear() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *size(void) const { return at(1)->node(); } + void size(loco::Node *node) { at(1)->node(node); } + + bool align_corners() const { return _align_corners; } + void align_corners(bool value) { _align_corners = value; } + + bool half_pixel_centers() const { return _half_pixel_centers; } + void half_pixel_centers(bool value) { _half_pixel_centers = value; } + +private: + bool _align_corners = false; + bool _half_pixel_centers = false; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLERESIZE_BILINEAR_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleResizeNearestNeighbor.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleResizeNearestNeighbor.h new file mode 100644 index 0000000..dc32ebe --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleResizeNearestNeighbor.h @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLERESSIZE_NEAREST_NEIGHBOR_H__ +#define __LUCI_IR_CIRCLERESSIZE_NEAREST_NEIGHBOR_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief RESIZE_NEAREST_NEIGHBOR in Circle + */ +class CircleResizeNearestNeighbor final + : public FixedArityNode<2, CircleNodeImpl> +{ +public: + CircleResizeNearestNeighbor() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *size(void) const { return at(1)->node(); } + void size(loco::Node *node) { at(1)->node(node); } + + bool align_corners() const { return _align_corners; } + void align_corners(bool value) { _align_corners = value; } + +private: + bool _align_corners = false; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLERESSIZE_NEAREST_NEIGHBOR_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleReverseSequence.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleReverseSequence.h new file mode 100644 index 0000000..b0766dd --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleReverseSequence.h @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLEREVERSESEQUENCE_H__ +#define __LUCI_IR_CIRCLEREVERSESEQUENCE_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief REVERSE_SEQUENCE in Circle + */ +class CircleReverseSequence final + : public FixedArityNode<2, CircleNodeImpl> +{ +public: + CircleReverseSequence() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *seq_lengths(void) const { return at(1)->node(); } + void seq_lengths(loco::Node *node) { at(1)->node(node); } + +public: + int seq_axis(void) const { return _seq_axis; } + void seq_axis(int seq_axis) { _seq_axis = seq_axis; } + + int batch_axis(void) const { return _batch_axis; } + void batch_axis(int batch_axis) { _batch_axis = batch_axis; } + +private: + int _seq_axis{0}; + int _batch_axis{0}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLEREVERSESEQUENCE_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleReverseV2.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleReverseV2.h new file mode 100644 index 0000000..71d9f65 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleReverseV2.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLEREVERSE_V2_H__ +#define __LUCI_IR_CIRCLEREVERSE_V2_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief ReverseV2 in Circle + */ +class CircleReverseV2 final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *tensor(void) const { return at(0)->node(); } + void tensor(loco::Node *node) { at(0)->node(node); } + + loco::Node *axis(void) const { return at(1)->node(); } + void axis(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLEREVERSE_V2_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleRound.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleRound.h new file mode 100644 index 0000000..30296ce --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleRound.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLEROUND_H__ +#define __LUCI_IR_CIRCLEROUND_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief ROUND in Circle + */ +class CircleRound final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleRound() = default; + +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLEROUND_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleRsqrt.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleRsqrt.h index 44d22ef..873397b 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleRsqrt.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleRsqrt.h @@ -20,7 +20,6 @@ #include "luci/IR/CircleNodeDecl.h" #include "luci/IR/CircleOpcode.h" -#include "luci/IR/AttrFusedActFunc.h" #include "luci/IR/LuciNodeMixins.h" namespace luci diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleScatterNd.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleScatterNd.h new file mode 100644 index 0000000..9f93a0a --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleScatterNd.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLESCATTER_ND_H__ +#define __LUCI_IR_CIRCLESCATTER_ND_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief SCATTER_ND in Circle + */ +class CircleScatterNd final : public FixedArityNode<3, CircleNodeImpl> +{ +public: + loco::Node *indices(void) const { return at(0)->node(); } + void indices(loco::Node *node) { at(0)->node(node); } + + loco::Node *updates(void) const { return at(1)->node(); } + void updates(loco::Node *node) { at(1)->node(node); } + + loco::Node *shape(void) const { return at(2)->node(); } + void shape(loco::Node *node) { at(2)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLESCATTER_ND_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSegmentSum.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSegmentSum.h new file mode 100644 index 0000000..416d617 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSegmentSum.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_SEGMENT_SUM_H__ +#define __LUCI_IR_CIRCLE_SEGMENT_SUM_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief SEGMENT_SUM in Circle + */ +class CircleSegmentSum final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + CircleSegmentSum() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *segment_ids(void) const { return at(1)->node(); } + void segment_ids(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_SEGMENT_SUM_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSelect.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSelect.h new file mode 100644 index 0000000..7276471 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSelect.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_SELECT_H__ +#define __LUCI_IR_CIRCLE_SELECT_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief SELECT in Circle + */ +class CircleSelect final : public FixedArityNode<3, CircleNodeImpl> +{ +public: + CircleSelect() = default; + +public: + loco::Node *condition(void) const { return at(0)->node(); } + void condition(loco::Node *node) { at(0)->node(node); } + + loco::Node *t(void) const { return at(1)->node(); } + void t(loco::Node *node) { at(1)->node(node); } + + loco::Node *e(void) const { return at(2)->node(); } + void e(loco::Node *node) { at(2)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_SELECT_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSelectV2.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSelectV2.h new file mode 100644 index 0000000..7ac3c05 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSelectV2.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_SELECT_V2_H__ +#define __LUCI_IR_CIRCLE_SELECT_V2_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief SELECT_V2 in Circle + */ +class CircleSelectV2 final : public FixedArityNode<3, CircleNodeImpl> +{ +public: + CircleSelectV2() = default; + +public: + loco::Node *condition(void) const { return at(0)->node(); } + void condition(loco::Node *node) { at(0)->node(node); } + + loco::Node *t(void) const { return at(1)->node(); } + void t(loco::Node *node) { at(1)->node(node); } + + loco::Node *e(void) const { return at(2)->node(); } + void e(loco::Node *node) { at(2)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_SELECT_V2_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleShape.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleShape.h new file mode 100644 index 0000000..ff20ce6 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleShape.h @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_SHAPE_H__ +#define __LUCI_IR_CIRCLE_SHAPE_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief SHAPE in Circle + */ +class CircleShape final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleShape() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + +public: + loco::DataType out_type(void) const { return _out_type; } + void out_type(loco::DataType ot) { _out_type = ot; } + +private: + loco::DataType _out_type{loco::DataType::S32}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_SHAPE_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSin.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSin.h new file mode 100644 index 0000000..5624db2 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSin.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_SIN_H__ +#define __LUCI_IR_CIRCLE_SIN_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief SIN in Circle + */ +class CircleSin final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_SIN_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSlice.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSlice.h new file mode 100644 index 0000000..a211364 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSlice.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_SLICE_H__ +#define __LUCI_IR_SLICE_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief SLICE in Circle + */ +class CircleSlice final : public FixedArityNode<3, CircleNodeImpl> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *begin(void) const { return at(1)->node(); } + void begin(loco::Node *node) { at(1)->node(node); } + + loco::Node *size(void) const { return at(2)->node(); } + void size(loco::Node *node) { at(2)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_SLICE_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSoftmax.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSoftmax.h index 4ea3c4b..7166a32 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleSoftmax.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSoftmax.h @@ -39,7 +39,7 @@ public: void beta(float beta) { _beta = beta; } private: - float _beta; + float _beta{0.0f}; }; } // namespace luci diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSpaceToBatchND.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSpaceToBatchND.h new file mode 100644 index 0000000..042ebff --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSpaceToBatchND.h @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_SPACETOBATCHND_H__ +#define __LUCI_IR_CIRCLE_SPACETOBATCHND_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief SPACE_TO_BATCH_ND in Circle + */ +class CircleSpaceToBatchND final + : public FixedArityNode<3, CircleNodeImpl> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *block_shape(void) const { return at(1)->node(); } + void block_shape(loco::Node *node) { at(1)->node(node); } + + loco::Node *paddings(void) const { return at(2)->node(); } + void paddings(loco::Node *node) { at(2)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_SPACETOBATCHND_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSpaceToDepth.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSpaceToDepth.h new file mode 100644 index 0000000..420a4cb --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSpaceToDepth.h @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_SPACETODEPTH_H__ +#define __LUCI_IR_CIRCLE_SPACETODEPTH_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief SPACE_TO_DEPTH in Circle + */ +class CircleSpaceToDepth final + : public FixedArityNode<1, CircleNodeImpl> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + +public: + int block_size(void) const { return _block_size; } + void block_size(int block_size) { _block_size = block_size; } + +private: + int _block_size{0}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_SPACETODEPTH_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSparseToDense.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSparseToDense.h new file mode 100644 index 0000000..9f50513 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSparseToDense.h @@ -0,0 +1,57 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCELSPARSETODENSE_H__ +#define __LUCI_IR_CIRCELSPARSETODENSE_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief SPARSE_TO_DENSE in Circle + */ +class CircleSparseToDense final + : public FixedArityNode<4, CircleNodeImpl> +{ +public: + loco::Node *indices(void) const { return at(0)->node(); } + void indices(loco::Node *node) { at(0)->node(node); } + + loco::Node *output_shape(void) const { return at(1)->node(); } + void output_shape(loco::Node *node) { at(1)->node(node); } + + loco::Node *values(void) const { return at(2)->node(); } + void values(loco::Node *node) { at(2)->node(node); } + + loco::Node *default_value(void) const { return at(3)->node(); } + void default_value(loco::Node *node) { at(3)->node(node); } + +public: + bool validate_indices(void) const { return _validate_indices; } + void validate_indices(bool validate_indices) { _validate_indices = validate_indices; } + +private: + bool _validate_indices{true}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCELSPARSETODENSE_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSplit.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSplit.h new file mode 100644 index 0000000..0eda195 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSplit.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_SPLIT_H__ +#define __LUCI_IR_CIRCLE_SPLIT_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief SPLIT in Circle + */ +class CircleSplit final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *split_dim(void) const { return at(0)->node(); } + void split_dim(loco::Node *node) { at(0)->node(node); } + + loco::Node *input(void) const { return at(1)->node(); } + void input(loco::Node *node) { at(1)->node(node); } + +public: + // NOTE it is num_split() not num_splits() as we follow TF name + int32_t num_split(void) const { return _num_split; } + void num_split(int32_t num_split) { _num_split = num_split; } + +private: + int32_t _num_split{0}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_SPLIT_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSplitOut.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSplitOut.h new file mode 100644 index 0000000..6bf4a9f --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSplitOut.h @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_SPLITOUT_H__ +#define __LUCI_IR_CIRCLE_SPLITOUT_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief Virtual CIRCLESPLITOUT in Circle + */ +class CircleSplitOut final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleSplitOut() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + +public: + int32_t index(void) const { return _index; } + void index(int32_t index) { _index = index; } + +private: + int32_t _index{-1}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_SPLITOUT_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSplitV.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSplitV.h new file mode 100644 index 0000000..1b7d555 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSplitV.h @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_SPLIT_V_H__ +#define __LUCI_IR_CIRCLE_SPLIT_V_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief SPLIT_V in Circle + */ +class CircleSplitV final : public FixedArityNode<3, CircleNodeImpl> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *size_splits(void) const { return at(1)->node(); } + void size_splits(loco::Node *node) { at(1)->node(node); } + + loco::Node *split_dim(void) const { return at(2)->node(); } + void split_dim(loco::Node *node) { at(2)->node(node); } + +public: + // NOTE it is num_split() not num_splits() as we follow TF name + int32_t num_split(void) const { return _num_split; } + void num_split(int32_t num_split) { _num_split = num_split; } + +private: + int32_t _num_split{0}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_SPLIT_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSplitVOut.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSplitVOut.h new file mode 100644 index 0000000..d3b2f1e --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSplitVOut.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_SPLITVOUT_H__ +#define __LUCI_IR_CIRCLE_SPLITVOUT_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief Virtual CIRCLESPLITVOUT in Circle + */ +class CircleSplitVOut final + : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleSplitVOut() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + +public: + int32_t index(void) const { return _index; } + void index(int32_t index) { _index = index; } + +private: + int32_t _index{-1}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_SPLITVOUT_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSqrt.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSqrt.h index bc1f39d..c96ca84 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleSqrt.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSqrt.h @@ -20,7 +20,6 @@ #include "luci/IR/CircleNodeDecl.h" #include "luci/IR/CircleOpcode.h" -#include "luci/IR/AttrFusedActFunc.h" #include "luci/IR/LuciNodeMixins.h" namespace luci diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSquare.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSquare.h new file mode 100644 index 0000000..a29edfe --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSquare.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLESQUARE_H__ +#define __LUCI_IR_CIRCLESQUARE_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief SQUARE in Circle + */ +class CircleSquare final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleSquare() = default; + +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLESQUARE_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSquaredDifference.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSquaredDifference.h index ff337df..b5b39f9 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleSquaredDifference.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSquaredDifference.h @@ -20,7 +20,6 @@ #include "luci/IR/CircleNodeDecl.h" #include "luci/IR/CircleOpcode.h" -#include "luci/IR/AttrFusedActFunc.h" #include "luci/IR/LuciNodeMixins.h" namespace luci diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSqueeze.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSqueeze.h new file mode 100644 index 0000000..f175f14 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSqueeze.h @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLESQUEEZE_H__ +#define __LUCI_IR_CIRCLESQUEEZE_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief SQUEEZE in Circle + */ +class CircleSqueeze final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleSqueeze() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + +public: + const std::vector &squeeze_dims() const { return _squeeze_dims; } + void squeeze_dims(const std::vector &squeeze_dims) { _squeeze_dims = squeeze_dims; }; + +private: + std::vector _squeeze_dims{}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLESQUEEZE_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleStridedSlice.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleStridedSlice.h new file mode 100644 index 0000000..98799fe --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleStridedSlice.h @@ -0,0 +1,73 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_STRIDEDSLICE_H__ +#define __LUCI_IR_STRIDEDSLICE_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief STRIDED_SLICE in Circle + */ +class CircleStridedSlice final + : public FixedArityNode<4, CircleNodeImpl> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *begin(void) const { return at(1)->node(); } + void begin(loco::Node *node) { at(1)->node(node); } + + loco::Node *end(void) const { return at(2)->node(); } + void end(loco::Node *node) { at(2)->node(node); } + + loco::Node *strides(void) const { return at(3)->node(); } + void strides(loco::Node *node) { at(3)->node(node); } + +public: + int32_t begin_mask() const { return _begin_mask; } + void begin_mask(int32_t mask) { _begin_mask = mask; } + + int32_t end_mask() const { return _end_mask; } + void end_mask(int32_t mask) { _end_mask = mask; } + + int32_t ellipsis_mask() const { return _ellipsis_mask; } + void ellipsis_mask(int32_t mask) { _ellipsis_mask = mask; } + + int32_t new_axis_mask() const { return _new_axis_mask; } + void new_axis_mask(int32_t mask) { _new_axis_mask = mask; } + + int32_t shrink_axis_mask() const { return _shrink_axis_mask; } + void shrink_axis_mask(int32_t mask) { _shrink_axis_mask = mask; } + +private: + int32_t _begin_mask{0}; + int32_t _end_mask{0}; + int32_t _ellipsis_mask{0}; + int32_t _new_axis_mask{0}; + int32_t _shrink_axis_mask{0}; +}; + +} // namespace luci + +#endif // __LUCI_IR_STRIDEDSLICE_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleSum.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleSum.h new file mode 100644 index 0000000..21faa76 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleSum.h @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLESUM_H__ +#define __LUCI_IR_CIRCLESUM_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief SUM in Circle + */ +class CircleSum final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *reduction_indices(void) const { return at(1)->node(); } + void reduction_indices(loco::Node *node) { at(1)->node(node); } + +public: + bool keep_dims(void) const { return _keep_dims; } + void keep_dims(bool keep_dims) { _keep_dims = keep_dims; } + +private: + bool _keep_dims = false; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLESUM_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleTanh.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleTanh.h new file mode 100644 index 0000000..f744492 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleTanh.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLETANH_H__ +#define __LUCI_IR_CIRCLETANH_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief TANH in Circle + */ +class CircleTanh final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleTanh() = default; + +public: + loco::Node *x(void) const { return at(0)->node(); } + void x(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLETANH_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleTile.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleTile.h new file mode 100644 index 0000000..96e1f69 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleTile.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLETILE_H__ +#define __LUCI_IR_CIRCLETILE_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief TILE in Circle + */ +class CircleTile final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + CircleTile() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *multiples(void) const { return at(1)->node(); } + void multiples(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLETILE_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleTopKV2.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleTopKV2.h new file mode 100644 index 0000000..3b2b5ab --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleTopKV2.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_TOPK_V2_H__ +#define __LUCI_IR_CIRCLE_TOPK_V2_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief TOPK_V2 in Circle + */ +class CircleTopKV2 final : public FixedArityNode<2, CircleNodeImpl> +{ +public: + CircleTopKV2() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + + loco::Node *k(void) const { return at(1)->node(); } + void k(loco::Node *node) { at(1)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_TOPK_V2_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleTopKV2Out.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleTopKV2Out.h new file mode 100644 index 0000000..5a6dd0c --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleTopKV2Out.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_TOPK_V2_OUT_H__ +#define __LUCI_IR_CIRCLE_TOPK_V2_OUT_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief Virtual CIRCLETOPKV2OUT in Circle + */ +class CircleTopKV2Out final + : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleTopKV2Out() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + +public: + int32_t index(void) const { return _index; } + void index(int32_t index) { _index = index; } + +private: + int32_t _index{-1}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_TOPK_V2_OUT_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleTranspose.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleTranspose.h index 198b56a..095cd67 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleTranspose.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleTranspose.h @@ -20,7 +20,6 @@ #include "luci/IR/CircleNodeDecl.h" #include "luci/IR/CircleOpcode.h" -#include "luci/IR/AttrFusedActFunc.h" #include "luci/IR/LuciNodeMixins.h" namespace luci diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleTransposeConv.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleTransposeConv.h index 54a0d01..fc638d4 100644 --- a/compiler/luci/lang/include/luci/IR/Nodes/CircleTransposeConv.h +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleTransposeConv.h @@ -22,7 +22,6 @@ #include "luci/IR/AttrPadding.h" #include "luci/IR/AttrStride.h" -#include "luci/IR/AttrFusedActFunc.h" #include "luci/IR/LuciNodeMixins.h" namespace luci @@ -55,7 +54,7 @@ public: Stride *stride(void) { return &_stride; } private: - Padding _padding; + Padding _padding{Padding::UNDEFINED}; Stride _stride; }; diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleUnpack.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleUnpack.h new file mode 100644 index 0000000..cb91d7e --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleUnpack.h @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_UNPACK_H__ +#define __LUCI_IR_CIRCLE_UNPACK_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief UNPACK in Circle + */ +class CircleUnpack final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleUnpack() = default; + +public: + loco::Node *value(void) const { return at(0)->node(); } + void value(loco::Node *node) { at(0)->node(node); } + +public: + int32_t num(void) const { return _num; } + void num(int32_t num) { _num = num; } + + int32_t axis(void) const { return _axis; } + void axis(int32_t axis) { _axis = axis; } + +private: + int32_t _num{0}; + int32_t _axis{0}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_UNPACK_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleUnpackOut.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleUnpackOut.h new file mode 100644 index 0000000..6f24578 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleUnpackOut.h @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_UNPACKOUT_H__ +#define __LUCI_IR_CIRCLE_UNPACKOUT_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief Virtual CIRCLEUNPACKOUT in Circle + */ +class CircleUnpackOut final + : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleUnpackOut() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + +public: + int32_t index(void) const { return _index; } + void index(int32_t index) { _index = index; } + +private: + int32_t _index{0}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_UNPACKOUT_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleWhere.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleWhere.h new file mode 100644 index 0000000..51eda3d --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleWhere.h @@ -0,0 +1,45 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_WHERE_H__ +#define __LUCI_IR_CIRCLE_WHERE_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +#include + +namespace luci +{ + +/** + * @brief WHERE in Circle + */ +class CircleWhere final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleWhere() = default; + +public: + loco::Node *condition() const { return at(0)->node(); } + void condition(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_WHERE_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleWhile.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleWhile.h new file mode 100644 index 0000000..40ec964 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleWhile.h @@ -0,0 +1,79 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_WHILE_H__ +#define __LUCI_IR_CIRCLE_WHILE_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/VariadicArityNode.h" + +#include + +namespace luci +{ + +/** + * @brief WHILE in Circle + */ +class CircleWhile final : public VariadicArityNode> +{ +public: + CircleWhile(uint32_t arity, uint32_t out) + : VariadicArityNode>(arity), _output_count(out) + { + assert(arity > 0); + assert(out > 0); + + // input and output must have the same size + assert(arity == out); + } + +public: + uint32_t input_count(void) const { return arity(); } + uint32_t output_count(void) const { return _output_count; } + +public: + Node *input(uint32_t index) const { return at(index)->node(); } + void input(uint32_t index, Node *node) { at(index)->node(node); } + +public: + int32_t cond_branch(void) const { return _cond_branch; } + void cond_branch(int32_t cond_branch) { _cond_branch = cond_branch; } + + int32_t body_branch(void) const { return _body_branch; } + void body_branch(int32_t body_branch) { _body_branch = body_branch; } + +public: + loco::Graph *cond_graph(void) const { return _cond_graph; } + void cond_graph(loco::Graph *cond_graph) { _cond_graph = cond_graph; } + + loco::Graph *body_graph(void) const { return _body_graph; } + void body_graph(loco::Graph *body_graph) { _body_graph = body_graph; } + +private: + uint32_t _output_count{0}; + int32_t _cond_branch{-1}; + int32_t _body_branch{-1}; + + loco::Graph *_cond_graph{nullptr}; + loco::Graph *_body_graph{nullptr}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_WHILE_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleWhileOut.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleWhileOut.h new file mode 100644 index 0000000..cdf6178 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleWhileOut.h @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_WHILEOUT_H__ +#define __LUCI_IR_CIRCLE_WHILEOUT_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief Virtual CIRCLEWHILEOUT in Circle + */ +class CircleWhileOut final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleWhileOut() = default; + +public: + loco::Node *input(void) const { return at(0)->node(); } + void input(loco::Node *node) { at(0)->node(node); } + +public: + int32_t index(void) const { return _index; } + void index(int32_t index) { _index = index; } + +private: + int32_t _index{-1}; +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_WHILEOUT_H__ diff --git a/compiler/luci/lang/include/luci/IR/Nodes/CircleZerosLike.h b/compiler/luci/lang/include/luci/IR/Nodes/CircleZerosLike.h new file mode 100644 index 0000000..d3b6d27 --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/Nodes/CircleZerosLike.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_CIRCLE_ZEROS_LIKE_H__ +#define __LUCI_IR_CIRCLE_ZEROS_LIKE_H__ + +#include "luci/IR/CircleNodeDecl.h" +#include "luci/IR/CircleOpcode.h" + +#include "luci/IR/LuciNodeMixins.h" + +namespace luci +{ + +/** + * @brief ZEROS_LIKE in Circle + */ +class CircleZerosLike final : public FixedArityNode<1, CircleNodeImpl> +{ +public: + CircleZerosLike() = default; + +public: + /// @brief Get the input node + loco::Node *input(void) const { return at(0)->node(); } + + /// @brief Set the input node + void input(loco::Node *node) { at(0)->node(node); } +}; + +} // namespace luci + +#endif // __LUCI_IR_CIRCLE_ZEROS_LIKE_H__ diff --git a/compiler/luci/lang/include/luci/IR/PropertyShapeStatus.h b/compiler/luci/lang/include/luci/IR/PropertyShapeStatus.h new file mode 100644 index 0000000..179a8ab --- /dev/null +++ b/compiler/luci/lang/include/luci/IR/PropertyShapeStatus.h @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_IR_PROPERTY_SHAPE_STATUS_H__ +#define __LUCI_IR_PROPERTY_SHAPE_STATUS_H__ + +namespace luci +{ + +/** + * @brief ShapeStatus is to remember circle node shape status. + * @note This is not an attribute from the file but inner status of a node. + * Shape with [] is scalar but sometimes it acts as dynamic shape. + */ +enum class ShapeStatus +{ + UNDEFINED, // Shape status is undefined + + NOSHAPE, // shape is unknown; to distinguish from scalar + VALID, // shape is valid +}; + +} // namespace luci + +#endif // __LUCI_IR_PROPERTY_SHAPE_STATUS_H__ diff --git a/compiler/luci/lang/include/luci/IR/VariadicArityNode.h b/compiler/luci/lang/include/luci/IR/VariadicArityNode.h index a4814ee..e83d909 100644 --- a/compiler/luci/lang/include/luci/IR/VariadicArityNode.h +++ b/compiler/luci/lang/include/luci/IR/VariadicArityNode.h @@ -46,11 +46,7 @@ public: public: uint32_t arity(void) const final { return _args.size(); } - loco::Node *arg(uint32_t n) const final - { - assert(n < _args.size()); - return _args.at(n)->node(); - } + loco::Node *arg(uint32_t n) const final { return _args.at(n)->node(); } void drop(void) final { @@ -62,11 +58,7 @@ public: protected: // This API allows inherited classes to access "_args" field. - loco::Use *at(uint32_t n) const - { - assert(n < _args.size()); - return _args.at(n).get(); - } + loco::Use *at(uint32_t n) const { return _args.at(n).get(); } private: std::vector> _args; diff --git a/compiler/luci/lang/src/CircleDialect.cpp b/compiler/luci/lang/src/CircleDialect.cpp index e1c925d..42ca3c9 100644 --- a/compiler/luci/lang/src/CircleDialect.cpp +++ b/compiler/luci/lang/src/CircleDialect.cpp @@ -22,6 +22,8 @@ #include #include +#include "DeadNodeQueryService.h" + #include #include @@ -42,8 +44,7 @@ struct GiiQueryServiceImpl final : public loco::GraphInputIndexQueryService loco::GraphOutputIndex index(const loco::Node *node) const final { assert(associated(node)); - auto circleinput = dynamic_cast(node); - assert(circleinput != nullptr); + auto circleinput = loco::must_cast(node); return circleinput->index(); } }; @@ -62,8 +63,7 @@ struct GoiQueryServiceImpl final : public loco::GraphOutputIndexQueryService loco::GraphOutputIndex index(const loco::Node *node) const final { assert(associated(node)); - auto circleoutput = dynamic_cast(node); - assert(circleoutput != nullptr); + auto circleoutput = loco::must_cast(node); return circleoutput->index(); } }; @@ -77,6 +77,7 @@ CircleDialect::CircleDialect() { service(std::make_unique()); service(std::make_unique()); + service(std::make_unique()); } loco::Dialect *CircleDialect::get(void) diff --git a/compiler/luci/lang/src/CircleDialect.test.cpp b/compiler/luci/lang/src/CircleDialect.test.cpp index 78221f1..a09c105 100644 --- a/compiler/luci/lang/src/CircleDialect.test.cpp +++ b/compiler/luci/lang/src/CircleDialect.test.cpp @@ -15,6 +15,10 @@ */ #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodes.h" + +#include +#include #include @@ -25,10 +29,59 @@ TEST(CircleDialectTest, get_P) // get() SHOULD return a valid(non-null) pointer ASSERT_NE(d, nullptr); // The return value SHOULD be stable across multiple invocations - ASSERT_EQ(d, luci::CircleDialect::get()); + ASSERT_EQ(luci::CircleDialect::get(), d); } -TEST(CircleDialectTest, get_N) +TEST(CircleDialectTest, check_if_dead_node_service) { - // TBD + /** + * [CircleInput1] [CircleInput2] [CircleInput3] + * \ / (dangling input) + * \ / + * [CircleAdd] [CircleBatchMatMul] + * | (dangling node) + * | + * [CircleOutput1] [CircleOutput2] + * (dangling output) + */ + auto g = loco::make_graph(); + + auto graph_input1 = g->inputs()->create(); + auto circle_input1 = g->nodes()->create(); + circle_input1->index(graph_input1->index()); + + auto graph_input2 = g->inputs()->create(); + auto circle_input2 = g->nodes()->create(); + circle_input2->index(graph_input2->index()); + + // dangling output + auto graph_input3 = g->inputs()->create(); + auto dangling_input = g->nodes()->create(); + dangling_input->index(graph_input3->index()); + + auto active_node = g->nodes()->create(); + active_node->x(circle_input1); + active_node->y(circle_input2); + + auto dangling_node = g->nodes()->create(); + + auto graph_output1 = g->outputs()->create(); + auto circle_output1 = g->nodes()->create(); + circle_output1->index(graph_output1->index()); + circle_output1->from(active_node); + + // dangling output + auto graph_output2 = g->outputs()->create(); + auto circle_output2 = g->nodes()->create(); + circle_output2->index(graph_output2->index()); + + auto service = active_node->dialect()->service(); + + ASSERT_TRUE(service->isDeadNode(dangling_node)); + ASSERT_FALSE(service->isDeadNode(dangling_input)); + ASSERT_FALSE(service->isDeadNode(active_node)); + ASSERT_FALSE(service->isDeadNode(circle_input1)); + ASSERT_FALSE(service->isDeadNode(circle_input2)); + ASSERT_FALSE(service->isDeadNode(circle_output1)); + ASSERT_FALSE(service->isDeadNode(circle_output2)); } diff --git a/compiler/luci/lang/src/CircleNodeShapeDtype.test.cpp b/compiler/luci/lang/src/CircleNodeShapeDtype.test.cpp new file mode 100644 index 0000000..61eab4b --- /dev/null +++ b/compiler/luci/lang/src/CircleNodeShapeDtype.test.cpp @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/CircleNodes.h" + +#include + +TEST(CircleNodeShapeDTypeTest, constructor) +{ + luci::CircleAdd node; + + ASSERT_EQ(loco::DataType::Unknown, node.dtype()); + ASSERT_EQ(0, node.rank()); +} + +TEST(CircleNodeShapeDTypeTest, values) +{ + luci::CircleAdd node; + + node.dtype(loco::DataType::FLOAT32); + ASSERT_EQ(loco::DataType::FLOAT32, node.dtype()); + + node.rank(4); + ASSERT_EQ(4, node.rank()); + ASSERT_FALSE(node.dim(0).known()); + + node.dim(0) = loco::Dimension(1); + ASSERT_TRUE(node.dim(0).known()); + ASSERT_EQ(1, node.dim(0).value()); +} + +TEST(CircleNodeShapeDTypeTest, values_NEG) +{ + luci::CircleAdd node; + + node.rank(4); + EXPECT_ANY_THROW(node.dim(100).known()); + EXPECT_ANY_THROW(node.dim(100) = loco::Dimension(1)); +} diff --git a/compiler/luci/lang/src/CircleNodes.cpp b/compiler/luci/lang/src/CircleNodes.cpp index 76ff7ec..c77c068 100644 --- a/compiler/luci/lang/src/CircleNodes.cpp +++ b/compiler/luci/lang/src/CircleNodes.cpp @@ -37,6 +37,7 @@ void set_new_shape(CircleReshape *node, int32_t *base, uint32_t size) const_shape_node->dim(0) = size; const_shape_node->dtype(S32); const_shape_node->size(size); + const_shape_node->shape_status(luci::ShapeStatus::VALID); for (uint32_t axis = 0; axis < size; ++axis) const_shape_node->at(axis) = base[axis]; node->shape(const_shape_node); @@ -47,4 +48,38 @@ void set_new_shape(CircleReshape *node, int32_t *base, uint32_t size) node->newShape()->dim(axis) = base[axis]; } +void link(loco::GraphOutput *output, CircleOutput *node) { node->index(output->index()); } + +CircleOutput *output_node(loco::Graph *g, const loco::GraphOutputIndex &index) +{ + for (uint32_t n = 0; n < g->nodes()->size(); ++n) + { + if (auto output = dynamic_cast(g->nodes()->at(n))) + { + if (output->indexed() && output->index() == index) + { + return output; + } + } + } + return nullptr; +} + +void link(loco::GraphInput *input, CircleInput *node) { node->index(input->index()); } + +CircleInput *input_node(loco::Graph *g, const loco::GraphInputIndex &index) +{ + for (uint32_t n = 0; n < g->nodes()->size(); ++n) + { + if (auto input = dynamic_cast(g->nodes()->at(n))) + { + if (input->indexed() && input->index() == index) + { + return input; + } + } + } + return nullptr; +} + } // namespace luci diff --git a/compiler/luci/lang/src/DeadNodeQueryService.cpp b/compiler/luci/lang/src/DeadNodeQueryService.cpp new file mode 100644 index 0000000..a22574c --- /dev/null +++ b/compiler/luci/lang/src/DeadNodeQueryService.cpp @@ -0,0 +1,74 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "DeadNodeQueryService.h" + +#include "luci/IR/CircleNodeVisitor.h" + +#include + +namespace luci +{ + +struct VirtualOutputDetector final : public luci::CircleNodeMutableVisitor +{ + bool visit(luci::CircleIfOut *) final { return true; } + bool visit(luci::CircleSplitOut *) final { return true; } + bool visit(luci::CircleSplitVOut *) final { return true; } + bool visit(luci::CircleTopKV2Out *) final { return true; } + bool visit(luci::CircleUnpackOut *) final { return true; } + bool visit(luci::CircleWhileOut *) final { return true; } + // TODO add more nodes that multi output virtual nodes + + // default is false + bool visit(luci::CircleNode *) final { return false; } +}; + +bool DeadNodeQueryServiceImpl::isDeadNode(loco::Node *node) +{ + auto g = node->graph(); + auto input_nodes_vec = loco::input_nodes(g); + auto output_nodes_vec = loco::output_nodes(g); + + auto input_nodes = std::set(input_nodes_vec.begin(), input_nodes_vec.end()); + auto output_nodes = std::set(output_nodes_vec.begin(), output_nodes_vec.end()); + auto active_nodes = loco::active_nodes(output_nodes_vec); + + if (active_nodes.find(node) != active_nodes.end()) + return false; + // input and output nodes are not dead node even if it is not active. + if (input_nodes.find(node) != input_nodes.end()) + return false; + + // if node is one of virtual mulitple outputs, we need to ask the real node + if (auto circle_node = dynamic_cast(node)) + { + VirtualOutputDetector d; + if (circle_node->accept(&d)) + { + assert(node->arity() == 1); + loco::Node *real_node = node->arg(0); + if (active_nodes.find(real_node) != active_nodes.end()) + return false; + if (input_nodes.find(real_node) != input_nodes.end()) + return false; + } + } + + return true; +} + +} // namespace luci diff --git a/compiler/luci/lang/src/DeadNodeQueryService.h b/compiler/luci/lang/src/DeadNodeQueryService.h new file mode 100644 index 0000000..d106966 --- /dev/null +++ b/compiler/luci/lang/src/DeadNodeQueryService.h @@ -0,0 +1,34 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_LANG_DEADNODEQUERYSERVICE_H__ +#define __LUCI_LANG_DEADNODEQUERYSERVICE_H__ + +#include + +#include + +namespace luci +{ + +struct DeadNodeQueryServiceImpl final : public logo::DeadNodeQueryService +{ + bool isDeadNode(loco::Node *node) final; +}; + +} // namespace luci + +#endif // __LUCI_LANG_DEADNODEQUERYSERVICE_H__ diff --git a/compiler/luci/lang/src/Module.cpp b/compiler/luci/lang/src/Module.cpp index e52d897..80ef619 100644 --- a/compiler/luci/lang/src/Module.cpp +++ b/compiler/luci/lang/src/Module.cpp @@ -43,4 +43,4 @@ loco::Graph *Module::graph(size_t idx) const std::unique_ptr make_module(void) { return std::make_unique(); } -} // namespace loco +} // namespace luci diff --git a/compiler/luci/lang/src/Module.test.cpp b/compiler/luci/lang/src/Module.test.cpp index f603199..26bf073 100644 --- a/compiler/luci/lang/src/Module.test.cpp +++ b/compiler/luci/lang/src/Module.test.cpp @@ -33,8 +33,8 @@ TEST(ModuleTest, add) m->add(std::move(g)); - ASSERT_EQ(m->graph(), g_ptr); - ASSERT_EQ(m->graph(0), g_ptr); + ASSERT_EQ(g_ptr, m->graph()); + ASSERT_EQ(g_ptr, m->graph(0)); } TEST(ModuleTest, add_more) @@ -51,11 +51,11 @@ TEST(ModuleTest, add_more) m->add(std::move(g2)); m->add(std::move(g3)); - ASSERT_EQ(m->size(), 3); - ASSERT_EQ(m->graph(), g1_ptr); - ASSERT_EQ(m->graph(0), g1_ptr); - ASSERT_EQ(m->graph(1), g2_ptr); - ASSERT_EQ(m->graph(2), g3_ptr); + ASSERT_EQ(3, m->size()); + ASSERT_EQ(g1_ptr, m->graph()); + ASSERT_EQ(g1_ptr, m->graph(0)); + ASSERT_EQ(g2_ptr, m->graph(1)); + ASSERT_EQ(g3_ptr, m->graph(2)); } TEST(ModuleTest, add_nullptr_NEG) diff --git a/compiler/luci/lang/src/Nodes/CircleAbs.test.cpp b/compiler/luci/lang/src/Nodes/CircleAbs.test.cpp index 847f150..f97becb 100644 --- a/compiler/luci/lang/src/Nodes/CircleAbs.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleAbs.test.cpp @@ -17,15 +17,78 @@ #include "luci/IR/Nodes/CircleAbs.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include +#include + TEST(CircleAbsTest, constructor) { luci::CircleAbs abs_node; - ASSERT_EQ(abs_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(abs_node.opcode(), luci::CircleOpcode::ABS); + ASSERT_EQ(luci::CircleDialect::get(), abs_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::ABS, abs_node.opcode()); + + ASSERT_EQ(nullptr, abs_node.x()); +} + +TEST(CircleAbsTest, common_NEG) +{ + luci::CircleAbs abs_node; + + abs_node.name("name"); + ASSERT_EQ("name", abs_node.name()); + + auto q = std::make_unique(); + abs_node.quantparam(std::move(q)); + ASSERT_NE(nullptr, abs_node.quantparam()); + + ASSERT_EQ(luci::ShapeStatus::UNDEFINED, abs_node.shape_status()); + abs_node.shape_status(luci::ShapeStatus::NOSHAPE); + ASSERT_NE(luci::ShapeStatus::UNDEFINED, abs_node.shape_status()); +} + +TEST(CircleAbsTest, input_NEG) +{ + luci::CircleAbs abs_node; + luci::CircleAbs node; + + abs_node.x(&node); + ASSERT_NE(nullptr, abs_node.x()); + + abs_node.x(nullptr); + ASSERT_EQ(nullptr, abs_node.x()); +} + +TEST(CircleAbsTest, arity_NEG) +{ + luci::CircleAbs abs_node; + + ASSERT_NO_THROW(abs_node.arg(0)); + ASSERT_THROW(abs_node.arg(1), std::out_of_range); +} + +TEST(CircleAbsTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleAbs abs_node; + + TestVisitor tv; + ASSERT_THROW(abs_node.accept(&tv), std::exception); +} + +TEST(CircleAbsTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleAbs abs_node; - ASSERT_EQ(abs_node.x(), nullptr); + TestVisitor tv; + ASSERT_THROW(abs_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleAdd.test.cpp b/compiler/luci/lang/src/Nodes/CircleAdd.test.cpp index a770196..382faa5 100644 --- a/compiler/luci/lang/src/Nodes/CircleAdd.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleAdd.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleAdd.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,9 +25,57 @@ TEST(CircleAddTest, constructor_P) { luci::CircleAdd add_node; - ASSERT_EQ(add_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(add_node.opcode(), luci::CircleOpcode::ADD); + ASSERT_EQ(luci::CircleDialect::get(), add_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::ADD, add_node.opcode()); - ASSERT_EQ(add_node.x(), nullptr); - ASSERT_EQ(add_node.y(), nullptr); + ASSERT_EQ(nullptr, add_node.x()); + ASSERT_EQ(nullptr, add_node.y()); +} + +TEST(CircleAddTest, input_NEG) +{ + luci::CircleAdd add_node; + luci::CircleAdd node; + + add_node.x(&node); + add_node.y(&node); + ASSERT_NE(nullptr, add_node.x()); + ASSERT_NE(nullptr, add_node.y()); + + add_node.x(nullptr); + add_node.y(nullptr); + ASSERT_EQ(nullptr, add_node.x()); + ASSERT_EQ(nullptr, add_node.y()); +} + +TEST(CircleAddTest, arity_NEG) +{ + luci::CircleAdd add_node; + + ASSERT_NO_THROW(add_node.arg(1)); + ASSERT_THROW(add_node.arg(2), std::out_of_range); +} + +TEST(CircleAddTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleAdd add_node; + + TestVisitor tv; + ASSERT_THROW(add_node.accept(&tv), std::exception); +} + +TEST(CircleAddTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleAdd add_node; + + TestVisitor tv; + ASSERT_THROW(add_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleAddN.test.cpp b/compiler/luci/lang/src/Nodes/CircleAddN.test.cpp new file mode 100644 index 0000000..399d8cb --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleAddN.test.cpp @@ -0,0 +1,91 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleAddN.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleAddNTest, constructor) +{ + luci::CircleAddN add_node(3); + + ASSERT_EQ(luci::CircleDialect::get(), add_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::ADD_N, add_node.opcode()); + + ASSERT_EQ(nullptr, add_node.inputs(0)); + ASSERT_EQ(nullptr, add_node.inputs(1)); + ASSERT_EQ(nullptr, add_node.inputs(2)); +} + +TEST(CircleAddNTest, input_NEG) +{ + luci::CircleAddN add_node(3); + luci::CircleAddN node(2); + + add_node.inputs(0, &node); + add_node.inputs(1, &node); + add_node.inputs(2, &node); + ASSERT_NE(nullptr, add_node.inputs(0)); + ASSERT_NE(nullptr, add_node.inputs(1)); + ASSERT_NE(nullptr, add_node.inputs(2)); + + add_node.inputs(0, nullptr); + add_node.inputs(1, nullptr); + add_node.inputs(2, nullptr); + ASSERT_EQ(nullptr, add_node.inputs(0)); + ASSERT_EQ(nullptr, add_node.inputs(1)); + ASSERT_EQ(nullptr, add_node.inputs(2)); +} + +TEST(CircleAddNTest, arity_NEG) +{ + luci::CircleAddN add_node(3); + luci::CircleAddN node(2); + + ASSERT_NO_THROW(add_node.inputs(2, &node)); + ASSERT_NO_THROW(add_node.inputs(2, nullptr)); + ASSERT_THROW(add_node.inputs(3, &node), std::out_of_range); + + ASSERT_NO_THROW(add_node.arg(2)); + ASSERT_THROW(add_node.arg(3), std::out_of_range); +} + +TEST(CircleAddNTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleAddN add_node(2); + + TestVisitor tv; + ASSERT_THROW(add_node.accept(&tv), std::exception); +} + +TEST(CircleAddNTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleAddN add_node(2); + + TestVisitor tv; + ASSERT_THROW(add_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleArgMax.test.cpp b/compiler/luci/lang/src/Nodes/CircleArgMax.test.cpp index 6b2cff1..375a74c 100644 --- a/compiler/luci/lang/src/Nodes/CircleArgMax.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleArgMax.test.cpp @@ -17,16 +17,65 @@ #include "luci/IR/Nodes/CircleArgMax.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include TEST(CircleArgMaxTest, constructor_P) { - luci::CircleArgMax add_node; + luci::CircleArgMax argmax_node; - ASSERT_EQ(add_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(add_node.opcode(), luci::CircleOpcode::ARG_MAX); + ASSERT_EQ(luci::CircleDialect::get(), argmax_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::ARG_MAX, argmax_node.opcode()); - ASSERT_EQ(add_node.input(), nullptr); - ASSERT_EQ(add_node.dimension(), nullptr); + ASSERT_EQ(nullptr, argmax_node.input()); + ASSERT_EQ(nullptr, argmax_node.dimension()); +} + +TEST(CircleArgMaxTest, input_NEG) +{ + luci::CircleArgMax argmax_node; + luci::CircleArgMax node; + + argmax_node.input(&node); + argmax_node.dimension(&node); + ASSERT_NE(nullptr, argmax_node.input()); + ASSERT_NE(nullptr, argmax_node.dimension()); + + argmax_node.input(nullptr); + argmax_node.dimension(nullptr); + ASSERT_EQ(nullptr, argmax_node.input()); + ASSERT_EQ(nullptr, argmax_node.dimension()); +} + +TEST(CircleArgMaxTest, arity_NEG) +{ + luci::CircleArgMax argmax_node; + + ASSERT_NO_THROW(argmax_node.arg(1)); + ASSERT_THROW(argmax_node.arg(2), std::out_of_range); +} + +TEST(CircleArgMaxTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleArgMax argmax_node; + + TestVisitor tv; + ASSERT_THROW(argmax_node.accept(&tv), std::exception); +} + +TEST(CircleArgMaxTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleArgMax argmax_node; + + TestVisitor tv; + ASSERT_THROW(argmax_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleArgMin.test.cpp b/compiler/luci/lang/src/Nodes/CircleArgMin.test.cpp new file mode 100644 index 0000000..6607bf8 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleArgMin.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleArgMin.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleArgMinTest, constructor_P) +{ + luci::CircleArgMin argmin_node; + + ASSERT_EQ(luci::CircleDialect::get(), argmin_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::ARG_MIN, argmin_node.opcode()); + + ASSERT_EQ(nullptr, argmin_node.input()); + ASSERT_EQ(nullptr, argmin_node.dimension()); +} + +TEST(CircleArgMinTest, input_NEG) +{ + luci::CircleArgMin argmin_node; + luci::CircleArgMin node; + + argmin_node.input(&node); + argmin_node.dimension(&node); + ASSERT_NE(nullptr, argmin_node.input()); + ASSERT_NE(nullptr, argmin_node.dimension()); + + argmin_node.input(nullptr); + argmin_node.dimension(nullptr); + ASSERT_EQ(nullptr, argmin_node.input()); + ASSERT_EQ(nullptr, argmin_node.dimension()); +} + +TEST(CircleArgMinTest, arity_NEG) +{ + luci::CircleArgMin argmin_node; + + ASSERT_NO_THROW(argmin_node.arg(1)); + ASSERT_THROW(argmin_node.arg(2), std::out_of_range); +} + +TEST(CircleArgMinTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleArgMin argmin_node; + + TestVisitor tv; + ASSERT_THROW(argmin_node.accept(&tv), std::exception); +} + +TEST(CircleArgMinTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleArgMin argmin_node; + + TestVisitor tv; + ASSERT_THROW(argmin_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleAveragePool2D.test.cpp b/compiler/luci/lang/src/Nodes/CircleAveragePool2D.test.cpp new file mode 100644 index 0000000..fc7265c --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleAveragePool2D.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleAveragePool2D.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleAveragePool2DTest, constructor_P) +{ + luci::CircleAveragePool2D average_pool_2d_node; + + ASSERT_EQ(luci::CircleDialect::get(), average_pool_2d_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::AVERAGE_POOL_2D, average_pool_2d_node.opcode()); + + ASSERT_EQ(nullptr, average_pool_2d_node.value()); + ASSERT_EQ(luci::Padding::UNDEFINED, average_pool_2d_node.padding()); + ASSERT_EQ(1, average_pool_2d_node.filter()->h()); + ASSERT_EQ(1, average_pool_2d_node.filter()->w()); + ASSERT_EQ(1, average_pool_2d_node.stride()->h()); + ASSERT_EQ(1, average_pool_2d_node.stride()->w()); +} + +TEST(CircleAveragePool2DTest, input_NEG) +{ + luci::CircleAveragePool2D avgpool_node; + luci::CircleAveragePool2D node; + + avgpool_node.value(&node); + ASSERT_NE(nullptr, avgpool_node.value()); + + avgpool_node.value(nullptr); + ASSERT_EQ(nullptr, avgpool_node.value()); + + avgpool_node.filter()->h(2); + avgpool_node.filter()->w(2); + avgpool_node.stride()->h(2); + avgpool_node.stride()->w(2); + ASSERT_NE(1, avgpool_node.filter()->h()); + ASSERT_NE(1, avgpool_node.filter()->w()); + ASSERT_NE(1, avgpool_node.stride()->h()); + ASSERT_NE(1, avgpool_node.stride()->w()); +} + +TEST(CircleAveragePool2DTest, arity_NEG) +{ + luci::CircleAveragePool2D avgpool_node; + + ASSERT_NO_THROW(avgpool_node.arg(0)); + ASSERT_THROW(avgpool_node.arg(1), std::out_of_range); +} + +TEST(CircleAveragePool2DTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleAveragePool2D avgpool_node; + + TestVisitor tv; + ASSERT_THROW(avgpool_node.accept(&tv), std::exception); +} + +TEST(CircleAveragePool2DTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleAveragePool2D avgpool_node; + + TestVisitor tv; + ASSERT_THROW(avgpool_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleBCQFullyConnected.test.cpp b/compiler/luci/lang/src/Nodes/CircleBCQFullyConnected.test.cpp new file mode 100644 index 0000000..35c9ab9 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleBCQFullyConnected.test.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleBCQFullyConnected.h" + +#include "luci/IR/CircleDialect.h" + +#include + +TEST(CircleBCQFullyConnectedTest, constructor) +{ + luci::CircleBCQFullyConnected bcq_FC_node; + + ASSERT_EQ(luci::CircleDialect::get(), bcq_FC_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::BCQ_FULLY_CONNECTED, bcq_FC_node.opcode()); + + ASSERT_EQ(nullptr, bcq_FC_node.input()); + ASSERT_EQ(nullptr, bcq_FC_node.weights_scales()); + ASSERT_EQ(nullptr, bcq_FC_node.weights_binary()); + ASSERT_EQ(nullptr, bcq_FC_node.bias()); + ASSERT_EQ(nullptr, bcq_FC_node.weights_clusters()); + + ASSERT_EQ(luci::FusedActFunc::UNDEFINED, bcq_FC_node.fusedActivationFunction()); + ASSERT_EQ(0, bcq_FC_node.weights_hidden_size()); +} diff --git a/compiler/luci/lang/src/Nodes/CircleBCQGather.test.cpp b/compiler/luci/lang/src/Nodes/CircleBCQGather.test.cpp new file mode 100644 index 0000000..c187a90 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleBCQGather.test.cpp @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleBCQGather.h" + +#include "luci/IR/CircleDialect.h" + +#include + +TEST(CircleBCQGatherTest, constructor) +{ + luci::CircleBCQGather bcq_gather_node; + + ASSERT_EQ(luci::CircleDialect::get(), bcq_gather_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::BCQ_GATHER, bcq_gather_node.opcode()); + + ASSERT_EQ(nullptr, bcq_gather_node.input_scales()); + ASSERT_EQ(nullptr, bcq_gather_node.input_binary()); + ASSERT_EQ(nullptr, bcq_gather_node.indices()); + ASSERT_EQ(nullptr, bcq_gather_node.input_clusters()); + + ASSERT_EQ(0, bcq_gather_node.axis()); + ASSERT_EQ(0, bcq_gather_node.input_hidden_size()); +} diff --git a/compiler/luci/lang/src/Nodes/CircleBatchMatMul.test.cpp b/compiler/luci/lang/src/Nodes/CircleBatchMatMul.test.cpp new file mode 100644 index 0000000..d7712c8 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleBatchMatMul.test.cpp @@ -0,0 +1,84 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleBatchMatMul.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleBatchMatMulTest, constructor) +{ + luci::CircleBatchMatMul batchmatmul_node; + + ASSERT_EQ(luci::CircleDialect::get(), batchmatmul_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::BATCHMATMUL, batchmatmul_node.opcode()); + + ASSERT_EQ(nullptr, batchmatmul_node.x()); + ASSERT_EQ(nullptr, batchmatmul_node.y()); + + ASSERT_FALSE(batchmatmul_node.adj_x()); + ASSERT_FALSE(batchmatmul_node.adj_y()); +} + +TEST(CircleBatchMatMulTest, input_NEG) +{ + luci::CircleBatchMatMul batchmatmul_node; + luci::CircleBatchMatMul node; + + batchmatmul_node.x(&node); + batchmatmul_node.y(&node); + ASSERT_NE(nullptr, batchmatmul_node.x()); + ASSERT_NE(nullptr, batchmatmul_node.y()); + + batchmatmul_node.x(nullptr); + batchmatmul_node.y(nullptr); + ASSERT_EQ(nullptr, batchmatmul_node.x()); + ASSERT_EQ(nullptr, batchmatmul_node.y()); +} + +TEST(CircleBatchMatMulTest, arity_NEG) +{ + luci::CircleBatchMatMul batchmatmul_node; + + ASSERT_NO_THROW(batchmatmul_node.arg(1)); + ASSERT_THROW(batchmatmul_node.arg(2), std::out_of_range); +} + +TEST(CircleBatchMatMulTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleBatchMatMul batchmatmul_node; + + TestVisitor tv; + ASSERT_THROW(batchmatmul_node.accept(&tv), std::exception); +} + +TEST(CircleBatchMatMulTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleBatchMatMul batchmatmul_node; + + TestVisitor tv; + ASSERT_THROW(batchmatmul_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleBatchToSpaceND.test.cpp b/compiler/luci/lang/src/Nodes/CircleBatchToSpaceND.test.cpp index e995718..0374fe0 100644 --- a/compiler/luci/lang/src/Nodes/CircleBatchToSpaceND.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleBatchToSpaceND.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleBatchToSpaceND.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,10 +25,62 @@ TEST(CircleBatchToSpaceNDTest, constructor) { luci::CircleBatchToSpaceND bts_node; - ASSERT_EQ(bts_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(bts_node.opcode(), luci::CircleOpcode::BATCH_TO_SPACE_ND); + ASSERT_EQ(luci::CircleDialect::get(), bts_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::BATCH_TO_SPACE_ND, bts_node.opcode()); - ASSERT_EQ(bts_node.input(), nullptr); - ASSERT_EQ(bts_node.block_shape(), nullptr); - ASSERT_EQ(bts_node.crops(), nullptr); + ASSERT_EQ(nullptr, bts_node.input()); + ASSERT_EQ(nullptr, bts_node.block_shape()); + ASSERT_EQ(nullptr, bts_node.crops()); +} + +TEST(CircleBatchToSpaceNDTest, input_NEG) +{ + luci::CircleBatchToSpaceND bts_node; + luci::CircleBatchToSpaceND node; + + bts_node.input(&node); + bts_node.block_shape(&node); + bts_node.crops(&node); + ASSERT_NE(nullptr, bts_node.input()); + ASSERT_NE(nullptr, bts_node.block_shape()); + ASSERT_NE(nullptr, bts_node.crops()); + + bts_node.input(nullptr); + bts_node.block_shape(nullptr); + bts_node.crops(nullptr); + ASSERT_EQ(nullptr, bts_node.input()); + ASSERT_EQ(nullptr, bts_node.block_shape()); + ASSERT_EQ(nullptr, bts_node.crops()); +} + +TEST(CircleBatchToSpaceNDTest, arity_NEG) +{ + luci::CircleBatchToSpaceND bts_node; + + ASSERT_NO_THROW(bts_node.arg(2)); + ASSERT_THROW(bts_node.arg(3), std::out_of_range); +} + +TEST(CircleBatchToSpaceNDTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleBatchToSpaceND bts_node; + + TestVisitor tv; + ASSERT_THROW(bts_node.accept(&tv), std::exception); +} + +TEST(CircleBatchToSpaceNDTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleBatchToSpaceND bts_node; + + TestVisitor tv; + ASSERT_THROW(bts_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleCast.test.cpp b/compiler/luci/lang/src/Nodes/CircleCast.test.cpp new file mode 100644 index 0000000..b58bf96 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleCast.test.cpp @@ -0,0 +1,78 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleCast.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleCastTest, constructor) +{ + luci::CircleCast cast_node; + + ASSERT_EQ(luci::CircleDialect::get(), cast_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::CAST, cast_node.opcode()); + + ASSERT_EQ(nullptr, cast_node.x()); + ASSERT_EQ(loco::DataType::FLOAT32, cast_node.in_data_type()); + ASSERT_EQ(loco::DataType::FLOAT32, cast_node.out_data_type()); +} + +TEST(CircleCastTest, input_NEG) +{ + luci::CircleCast cast_node; + luci::CircleCast node; + + cast_node.x(&node); + ASSERT_NE(nullptr, cast_node.x()); + + cast_node.x(nullptr); + ASSERT_EQ(nullptr, cast_node.x()); +} + +TEST(CircleCastTest, arity_NEG) +{ + luci::CircleCast cast_node; + + ASSERT_NO_THROW(cast_node.arg(0)); + ASSERT_THROW(cast_node.arg(1), std::out_of_range); +} + +TEST(CircleCastTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleCast cast_node; + + TestVisitor tv; + ASSERT_THROW(cast_node.accept(&tv), std::exception); +} + +TEST(CircleCastTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleCast cast_node; + + TestVisitor tv; + ASSERT_THROW(cast_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleCeil.test.cpp b/compiler/luci/lang/src/Nodes/CircleCeil.test.cpp new file mode 100644 index 0000000..efac614 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleCeil.test.cpp @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleCeil.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleCeilTest, constructor) +{ + luci::CircleCeil ceil_node; + + ASSERT_EQ(luci::CircleDialect::get(), ceil_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::CEIL, ceil_node.opcode()); + + ASSERT_EQ(nullptr, ceil_node.x()); +} + +TEST(CircleCeilTest, input_NEG) +{ + luci::CircleCeil ceil_node; + luci::CircleCeil node; + + ceil_node.x(&node); + ASSERT_NE(nullptr, ceil_node.x()); + + ceil_node.x(nullptr); + ASSERT_EQ(nullptr, ceil_node.x()); +} + +TEST(CircleCeilTest, arity_NEG) +{ + luci::CircleCeil ceil_node; + + ASSERT_NO_THROW(ceil_node.arg(0)); + ASSERT_THROW(ceil_node.arg(1), std::out_of_range); +} + +TEST(CircleCeilTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleCeil ceil_node; + + TestVisitor tv; + ASSERT_THROW(ceil_node.accept(&tv), std::exception); +} + +TEST(CircleCeilTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleCeil ceil_node; + + TestVisitor tv; + ASSERT_THROW(ceil_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleConcatenation.test.cpp b/compiler/luci/lang/src/Nodes/CircleConcatenation.test.cpp index 7167682..9f219a3 100644 --- a/compiler/luci/lang/src/Nodes/CircleConcatenation.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleConcatenation.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleConcatenation.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,12 +25,60 @@ TEST(CircleConcatenationTest, constructor_P) { luci::CircleConcatenation concat_node(3); - ASSERT_EQ(concat_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(concat_node.opcode(), luci::CircleOpcode::CONCATENATION); + ASSERT_EQ(luci::CircleDialect::get(), concat_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::CONCATENATION, concat_node.opcode()); - ASSERT_EQ(concat_node.numValues(), 3); - ASSERT_EQ(concat_node.values(0), nullptr); - ASSERT_EQ(concat_node.values(1), nullptr); - ASSERT_EQ(concat_node.values(2), nullptr); - ASSERT_EQ(concat_node.fusedActivationFunction(), luci::FusedActFunc::UNDEFINED); + ASSERT_EQ(3, concat_node.numValues()); + ASSERT_EQ(nullptr, concat_node.values(0)); + ASSERT_EQ(nullptr, concat_node.values(1)); + ASSERT_EQ(nullptr, concat_node.values(2)); + ASSERT_EQ(luci::FusedActFunc::UNDEFINED, concat_node.fusedActivationFunction()); +} + +TEST(CircleConcatenationTest, input_NEG) +{ + luci::CircleConcatenation concat_node(2); + luci::CircleConcatenation node(2); + + concat_node.values(0, &node); + concat_node.values(1, &node); + ASSERT_NE(nullptr, concat_node.values(0)); + ASSERT_NE(nullptr, concat_node.values(1)); + + concat_node.values(0, nullptr); + concat_node.values(1, nullptr); + ASSERT_EQ(nullptr, concat_node.values(0)); + ASSERT_EQ(nullptr, concat_node.values(1)); +} + +TEST(CircleConcatenationTest, arity_NEG) +{ + luci::CircleConcatenation concat_node(5); + + ASSERT_NO_THROW(concat_node.arg(4)); + ASSERT_THROW(concat_node.arg(5), std::out_of_range); +} + +TEST(CircleConcatenationTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleConcatenation concat_node(2); + + TestVisitor tv; + ASSERT_THROW(concat_node.accept(&tv), std::exception); +} + +TEST(CircleConcatenationTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleConcatenation concat_node(2); + + TestVisitor tv; + ASSERT_THROW(concat_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleConst.cpp b/compiler/luci/lang/src/Nodes/CircleConst.cpp index 1c46884..17ff853 100644 --- a/compiler/luci/lang/src/Nodes/CircleConst.cpp +++ b/compiler/luci/lang/src/Nodes/CircleConst.cpp @@ -70,9 +70,12 @@ template typename loco::DataTypeImpl
::Type &CircleConst: template const typename loco::DataTypeImpl
::Type &CircleConst::scalar
(void) const; \ template typename loco::DataTypeImpl
::Type &CircleConst::scalar
(void); +INSTANTIATE(loco::DataType::S64); INSTANTIATE(loco::DataType::S32); +INSTANTIATE(loco::DataType::S16); INSTANTIATE(loco::DataType::FLOAT32); INSTANTIATE(loco::DataType::U8); +INSTANTIATE(loco::DataType::BOOL); #undef INSTANTIATE diff --git a/compiler/luci/lang/src/Nodes/CircleConv2D.test.cpp b/compiler/luci/lang/src/Nodes/CircleConv2D.test.cpp index 7931c7e..7fcc71d 100644 --- a/compiler/luci/lang/src/Nodes/CircleConv2D.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleConv2D.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleConv2D.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,10 +25,84 @@ TEST(CircleConv2Dest, constructor_P) { luci::CircleConv2D conv2d_node; - ASSERT_EQ(conv2d_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(conv2d_node.opcode(), luci::CircleOpcode::CONV_2D); + ASSERT_EQ(luci::CircleDialect::get(), conv2d_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::CONV_2D, conv2d_node.opcode()); - ASSERT_EQ(conv2d_node.input(), nullptr); - ASSERT_EQ(conv2d_node.filter(), nullptr); - ASSERT_EQ(conv2d_node.bias(), nullptr); + ASSERT_EQ(nullptr, conv2d_node.input()); + ASSERT_EQ(nullptr, conv2d_node.filter()); + ASSERT_EQ(nullptr, conv2d_node.bias()); + ASSERT_EQ(luci::Padding::UNDEFINED, conv2d_node.padding()); + ASSERT_EQ(1, conv2d_node.stride()->h()); + ASSERT_EQ(1, conv2d_node.stride()->w()); + ASSERT_EQ(1, conv2d_node.dilation()->h()); + ASSERT_EQ(1, conv2d_node.dilation()->w()); + ASSERT_EQ(luci::FusedActFunc::UNDEFINED, conv2d_node.fusedActivationFunction()); +} + +TEST(CircleConv2Dest, input_NEG) +{ + luci::CircleConv2D conv2d_node; + luci::CircleConv2D node; + + conv2d_node.input(&node); + conv2d_node.filter(&node); + conv2d_node.bias(&node); + ASSERT_NE(nullptr, conv2d_node.input()); + ASSERT_NE(nullptr, conv2d_node.filter()); + ASSERT_NE(nullptr, conv2d_node.bias()); + + conv2d_node.input(nullptr); + conv2d_node.filter(nullptr); + conv2d_node.bias(nullptr); + ASSERT_EQ(nullptr, conv2d_node.input()); + ASSERT_EQ(nullptr, conv2d_node.filter()); + ASSERT_EQ(nullptr, conv2d_node.bias()); + + conv2d_node.padding(luci::Padding::SAME); + ASSERT_NE(luci::Padding::UNDEFINED, conv2d_node.padding()); + + conv2d_node.stride()->h(2); + conv2d_node.stride()->w(2); + ASSERT_EQ(2, conv2d_node.stride()->h()); + ASSERT_EQ(2, conv2d_node.stride()->w()); + + conv2d_node.dilation()->h(2); + conv2d_node.dilation()->w(2); + ASSERT_EQ(2, conv2d_node.dilation()->h()); + ASSERT_EQ(2, conv2d_node.dilation()->w()); + + conv2d_node.fusedActivationFunction(luci::FusedActFunc::RELU); + ASSERT_NE(luci::FusedActFunc::UNDEFINED, conv2d_node.fusedActivationFunction()); +} + +TEST(CircleConv2Dest, arity_NEG) +{ + luci::CircleConv2D conv2d_node; + + ASSERT_NO_THROW(conv2d_node.arg(2)); + ASSERT_THROW(conv2d_node.arg(3), std::out_of_range); +} + +TEST(CircleConv2Dest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleConv2D conv2d_node; + + TestVisitor tv; + ASSERT_THROW(conv2d_node.accept(&tv), std::exception); +} + +TEST(CircleConv2Dest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleConv2D conv2d_node; + + TestVisitor tv; + ASSERT_THROW(conv2d_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleCos.test.cpp b/compiler/luci/lang/src/Nodes/CircleCos.test.cpp index 34c2cfd..55438d3 100644 --- a/compiler/luci/lang/src/Nodes/CircleCos.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleCos.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleCos.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,8 +25,52 @@ TEST(CircleCosTest, constructor_P) { luci::CircleCos cos_node; - ASSERT_EQ(cos_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(cos_node.opcode(), luci::CircleOpcode::COS); + ASSERT_EQ(luci::CircleDialect::get(), cos_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::COS, cos_node.opcode()); - ASSERT_EQ(cos_node.x(), nullptr); + ASSERT_EQ(nullptr, cos_node.x()); +} + +TEST(CircleCosTest, input_NEG) +{ + luci::CircleCos cos_node; + luci::CircleCos node; + + cos_node.x(&node); + ASSERT_NE(nullptr, cos_node.x()); + + cos_node.x(nullptr); + ASSERT_EQ(nullptr, cos_node.x()); +} + +TEST(CircleCosTest, arity_NEG) +{ + luci::CircleCos cos_node; + + ASSERT_NO_THROW(cos_node.arg(0)); + ASSERT_THROW(cos_node.arg(1), std::out_of_range); +} + +TEST(CircleCosTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleCos cos_node; + + TestVisitor tv; + ASSERT_THROW(cos_node.accept(&tv), std::exception); +} + +TEST(CircleCosTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleCos cos_node; + + TestVisitor tv; + ASSERT_THROW(cos_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleCustom.test.cpp b/compiler/luci/lang/src/Nodes/CircleCustom.test.cpp new file mode 100644 index 0000000..74ea82c --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleCustom.test.cpp @@ -0,0 +1,45 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleCustom.h" + +#include "luci/IR/CircleDialect.h" + +#include + +TEST(CircleCustomTest, constructor) +{ + luci::CircleCustom custom_node(2); + + ASSERT_EQ(luci::CircleDialect::get(), custom_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::CUSTOM, custom_node.opcode()); + + ASSERT_EQ(2, custom_node.arity()); + ASSERT_EQ(nullptr, custom_node.arg(0)); + ASSERT_EQ(nullptr, custom_node.arg(1)); + + ASSERT_EQ(2, custom_node.numInputs()); + ASSERT_EQ(0, custom_node.custom_code().size()); +} + +TEST(CircleCustomTest, constructor_NEG) { ASSERT_DEBUG_DEATH(luci::CircleCustom{0}, ""); } + +TEST(CircleCustomTest, invalidIndex_NEG) +{ + luci::CircleCustom custom_node(2); + + EXPECT_ANY_THROW(custom_node.arg(5)); +} diff --git a/compiler/luci/lang/src/Nodes/CircleCustomOut.test.cpp b/compiler/luci/lang/src/Nodes/CircleCustomOut.test.cpp new file mode 100644 index 0000000..8b63f97 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleCustomOut.test.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleCustomOut.h" + +#include "luci/IR/CircleDialect.h" + +#include + +TEST(CircleCustomOutTest, constructor) +{ + luci::CircleCustomOut customout_node; + + ASSERT_EQ(luci::CircleDialect::get(), customout_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::CIRCLECUSTOMOUT, customout_node.opcode()); + + ASSERT_EQ(nullptr, customout_node.input()); + ASSERT_EQ(-1, customout_node.index()); +} diff --git a/compiler/luci/lang/src/Nodes/CircleDepthToSpace.test.cpp b/compiler/luci/lang/src/Nodes/CircleDepthToSpace.test.cpp new file mode 100644 index 0000000..9e3bbb7 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleDepthToSpace.test.cpp @@ -0,0 +1,80 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleDepthToSpace.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleDepthToSpaceTest, constructor_P) +{ + luci::CircleDepthToSpace std_node; + + ASSERT_EQ(luci::CircleDialect::get(), std_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::DEPTH_TO_SPACE, std_node.opcode()); + + ASSERT_EQ(nullptr, std_node.input()); + ASSERT_EQ(0, std_node.block_size()); +} + +TEST(CircleDepthToSpaceTest, input_NEG) +{ + luci::CircleDepthToSpace std_node; + luci::CircleDepthToSpace node; + + std_node.input(&node); + ASSERT_NE(nullptr, std_node.input()); + + std_node.input(nullptr); + ASSERT_EQ(nullptr, std_node.input()); + + std_node.block_size(2); + ASSERT_EQ(2, std_node.block_size()); +} + +TEST(CircleDepthToSpaceTest, arity_NEG) +{ + luci::CircleDepthToSpace std_node; + + ASSERT_NO_THROW(std_node.arg(0)); + ASSERT_THROW(std_node.arg(1), std::out_of_range); +} + +TEST(CircleDepthToSpaceTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleDepthToSpace std_node; + + TestVisitor tv; + ASSERT_THROW(std_node.accept(&tv), std::exception); +} + +TEST(CircleDepthToSpaceTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleDepthToSpace std_node; + + TestVisitor tv; + ASSERT_THROW(std_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleDepthwiseConv2D.test.cpp b/compiler/luci/lang/src/Nodes/CircleDepthwiseConv2D.test.cpp index bbc1ea5..5761775 100644 --- a/compiler/luci/lang/src/Nodes/CircleDepthwiseConv2D.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleDepthwiseConv2D.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleDepthwiseConv2D.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,15 +25,85 @@ TEST(CircleDepthwiseConv2DTest, constructor_P) { luci::CircleDepthwiseConv2D dw_conv2d_node; - ASSERT_EQ(dw_conv2d_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(dw_conv2d_node.opcode(), luci::CircleOpcode::DEPTHWISE_CONV_2D); - - ASSERT_EQ(dw_conv2d_node.input(), nullptr); - ASSERT_EQ(dw_conv2d_node.filter(), nullptr); - ASSERT_EQ(dw_conv2d_node.bias(), nullptr); - ASSERT_EQ(dw_conv2d_node.padding(), luci::Padding::UNDEFINED); - ASSERT_EQ(dw_conv2d_node.stride()->h(), 1); - ASSERT_EQ(dw_conv2d_node.stride()->w(), 1); - ASSERT_EQ(dw_conv2d_node.depthMultiplier(), 0); - ASSERT_EQ(dw_conv2d_node.fusedActivationFunction(), luci::FusedActFunc::UNDEFINED); + ASSERT_EQ(luci::CircleDialect::get(), dw_conv2d_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::DEPTHWISE_CONV_2D, dw_conv2d_node.opcode()); + + ASSERT_EQ(nullptr, dw_conv2d_node.input()); + ASSERT_EQ(nullptr, dw_conv2d_node.filter()); + ASSERT_EQ(nullptr, dw_conv2d_node.bias()); + ASSERT_EQ(luci::Padding::UNDEFINED, dw_conv2d_node.padding()); + ASSERT_EQ(1, dw_conv2d_node.stride()->h()); + ASSERT_EQ(1, dw_conv2d_node.stride()->w()); + ASSERT_EQ(1, dw_conv2d_node.dilation()->h()); + ASSERT_EQ(1, dw_conv2d_node.dilation()->w()); + ASSERT_EQ(0, dw_conv2d_node.depthMultiplier()); + ASSERT_EQ(luci::FusedActFunc::UNDEFINED, dw_conv2d_node.fusedActivationFunction()); +} + +TEST(CircleDepthwiseConv2DTest, input_NEG) +{ + luci::CircleDepthwiseConv2D dw_conv2d_node; + luci::CircleDepthwiseConv2D node; + + dw_conv2d_node.input(&node); + dw_conv2d_node.filter(&node); + dw_conv2d_node.bias(&node); + ASSERT_NE(nullptr, dw_conv2d_node.input()); + ASSERT_NE(nullptr, dw_conv2d_node.filter()); + ASSERT_NE(nullptr, dw_conv2d_node.bias()); + + dw_conv2d_node.input(nullptr); + dw_conv2d_node.filter(nullptr); + dw_conv2d_node.bias(nullptr); + ASSERT_EQ(nullptr, dw_conv2d_node.input()); + ASSERT_EQ(nullptr, dw_conv2d_node.filter()); + ASSERT_EQ(nullptr, dw_conv2d_node.bias()); + + dw_conv2d_node.padding(luci::Padding::SAME); + ASSERT_NE(luci::Padding::UNDEFINED, dw_conv2d_node.padding()); + + dw_conv2d_node.stride()->h(2); + dw_conv2d_node.stride()->w(2); + ASSERT_EQ(2, dw_conv2d_node.stride()->h()); + ASSERT_EQ(2, dw_conv2d_node.stride()->w()); + + dw_conv2d_node.dilation()->h(2); + dw_conv2d_node.dilation()->w(2); + ASSERT_EQ(2, dw_conv2d_node.dilation()->h()); + ASSERT_EQ(2, dw_conv2d_node.dilation()->w()); + + dw_conv2d_node.fusedActivationFunction(luci::FusedActFunc::RELU); + ASSERT_NE(luci::FusedActFunc::UNDEFINED, dw_conv2d_node.fusedActivationFunction()); +} + +TEST(CircleDepthwiseConv2DTest, arity_NEG) +{ + luci::CircleDepthwiseConv2D dw_conv2d_node; + + ASSERT_NO_THROW(dw_conv2d_node.arg(2)); + ASSERT_THROW(dw_conv2d_node.arg(3), std::out_of_range); +} + +TEST(CircleDepthwiseConv2DTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleDepthwiseConv2D dw_conv2d_node; + + TestVisitor tv; + ASSERT_THROW(dw_conv2d_node.accept(&tv), std::exception); +} + +TEST(CircleDepthwiseConv2DTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleDepthwiseConv2D dw_conv2d_node; + + TestVisitor tv; + ASSERT_THROW(dw_conv2d_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleDiv.test.cpp b/compiler/luci/lang/src/Nodes/CircleDiv.test.cpp index e950cc6..d0b632c 100644 --- a/compiler/luci/lang/src/Nodes/CircleDiv.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleDiv.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleDiv.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,9 +25,57 @@ TEST(CircleDivTest, constructor_P) { luci::CircleDiv div_node; - ASSERT_EQ(div_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(div_node.opcode(), luci::CircleOpcode::DIV); + ASSERT_EQ(luci::CircleDialect::get(), div_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::DIV, div_node.opcode()); - ASSERT_EQ(div_node.x(), nullptr); - ASSERT_EQ(div_node.y(), nullptr); + ASSERT_EQ(nullptr, div_node.x()); + ASSERT_EQ(nullptr, div_node.y()); +} + +TEST(CircleDivTest, input_NEG) +{ + luci::CircleDiv div_node; + luci::CircleDiv node; + + div_node.x(&node); + div_node.y(&node); + ASSERT_NE(nullptr, div_node.x()); + ASSERT_NE(nullptr, div_node.y()); + + div_node.x(nullptr); + div_node.y(nullptr); + ASSERT_EQ(nullptr, div_node.x()); + ASSERT_EQ(nullptr, div_node.y()); +} + +TEST(CircleDivTest, arity_NEG) +{ + luci::CircleDiv div_node; + + ASSERT_NO_THROW(div_node.arg(1)); + ASSERT_THROW(div_node.arg(2), std::out_of_range); +} + +TEST(CircleDivTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleDiv div_node; + + TestVisitor tv; + ASSERT_THROW(div_node.accept(&tv), std::exception); +} + +TEST(CircleDivTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleDiv div_node; + + TestVisitor tv; + ASSERT_THROW(div_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleElu.test.cpp b/compiler/luci/lang/src/Nodes/CircleElu.test.cpp new file mode 100644 index 0000000..2a044d7 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleElu.test.cpp @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleElu.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleEluTest, constructor_P) +{ + luci::CircleElu elu_node; + + ASSERT_EQ(luci::CircleDialect::get(), elu_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::ELU, elu_node.opcode()); + + ASSERT_EQ(nullptr, elu_node.features()); +} + +TEST(CircleEluTest, input_NEG) +{ + luci::CircleElu elu_node; + luci::CircleElu node; + + elu_node.features(&node); + ASSERT_NE(nullptr, elu_node.features()); + + elu_node.features(nullptr); + ASSERT_EQ(nullptr, elu_node.features()); +} + +TEST(CircleEluTest, arity_NEG) +{ + luci::CircleElu elu_node; + + ASSERT_NO_THROW(elu_node.arg(0)); + ASSERT_THROW(elu_node.arg(1), std::out_of_range); +} + +TEST(CircleEluTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleElu elu_node; + + TestVisitor tv; + ASSERT_THROW(elu_node.accept(&tv), std::exception); +} + +TEST(CircleEluTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleElu elu_node; + + TestVisitor tv; + ASSERT_THROW(elu_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleEqual.test.cpp b/compiler/luci/lang/src/Nodes/CircleEqual.test.cpp index e2757f0..2ae1529 100644 --- a/compiler/luci/lang/src/Nodes/CircleEqual.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleEqual.test.cpp @@ -17,16 +17,65 @@ #include "luci/IR/Nodes/CircleEqual.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include TEST(CircleEqualTest, constructor_P) { - luci::CircleEqual or_node; + luci::CircleEqual equ_node; - ASSERT_EQ(or_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(or_node.opcode(), luci::CircleOpcode::EQUAL); + ASSERT_EQ(luci::CircleDialect::get(), equ_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::EQUAL, equ_node.opcode()); - ASSERT_EQ(or_node.x(), nullptr); - ASSERT_EQ(or_node.y(), nullptr); + ASSERT_EQ(nullptr, equ_node.x()); + ASSERT_EQ(nullptr, equ_node.y()); +} + +TEST(CircleEqualTest, input_NEG) +{ + luci::CircleEqual equ_node; + luci::CircleEqual node; + + equ_node.x(&node); + equ_node.y(&node); + ASSERT_NE(nullptr, equ_node.x()); + ASSERT_NE(nullptr, equ_node.y()); + + equ_node.x(nullptr); + equ_node.y(nullptr); + ASSERT_EQ(nullptr, equ_node.x()); + ASSERT_EQ(nullptr, equ_node.y()); +} + +TEST(CircleEqualTest, arity_NEG) +{ + luci::CircleEqual equ_node; + + ASSERT_NO_THROW(equ_node.arg(1)); + ASSERT_THROW(equ_node.arg(2), std::out_of_range); +} + +TEST(CircleEqualTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleEqual equ_node; + + TestVisitor tv; + ASSERT_THROW(equ_node.accept(&tv), std::exception); +} + +TEST(CircleEqualTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleEqual equ_node; + + TestVisitor tv; + ASSERT_THROW(equ_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleExp.test.cpp b/compiler/luci/lang/src/Nodes/CircleExp.test.cpp index db10d0b..5ca90e9 100644 --- a/compiler/luci/lang/src/Nodes/CircleExp.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleExp.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleExp.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,8 +25,52 @@ TEST(CircleExpTest, constructor) { luci::CircleExp exp_node; - ASSERT_EQ(exp_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(exp_node.opcode(), luci::CircleOpcode::EXP); + ASSERT_EQ(luci::CircleDialect::get(), exp_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::EXP, exp_node.opcode()); - ASSERT_EQ(exp_node.x(), nullptr); + ASSERT_EQ(nullptr, exp_node.x()); +} + +TEST(CircleExpTest, input_NEG) +{ + luci::CircleExp exp_node; + luci::CircleExp node; + + exp_node.x(&node); + ASSERT_NE(nullptr, exp_node.x()); + + exp_node.x(nullptr); + ASSERT_EQ(nullptr, exp_node.x()); +} + +TEST(CircleExpTest, arity_NEG) +{ + luci::CircleExp exp_node; + + ASSERT_NO_THROW(exp_node.arg(0)); + ASSERT_THROW(exp_node.arg(1), std::out_of_range); +} + +TEST(CircleExpTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleExp exp_node; + + TestVisitor tv; + ASSERT_THROW(exp_node.accept(&tv), std::exception); +} + +TEST(CircleExpTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleExp exp_node; + + TestVisitor tv; + ASSERT_THROW(exp_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleExpandDims.test.cpp b/compiler/luci/lang/src/Nodes/CircleExpandDims.test.cpp new file mode 100644 index 0000000..754ef01 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleExpandDims.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleExpandDims.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleExpandDimsTest, constructor_P) +{ + luci::CircleExpandDims expand_dims; + + ASSERT_EQ(luci::CircleDialect::get(), expand_dims.dialect()); + ASSERT_EQ(luci::CircleOpcode::EXPAND_DIMS, expand_dims.opcode()); + + ASSERT_EQ(nullptr, expand_dims.input()); + ASSERT_EQ(nullptr, expand_dims.axis()); +} + +TEST(CircleExpandDimsTest, input_NEG) +{ + luci::CircleExpandDims expand_dims; + luci::CircleExpandDims node; + + expand_dims.input(&node); + expand_dims.axis(&node); + ASSERT_NE(nullptr, expand_dims.input()); + ASSERT_NE(nullptr, expand_dims.axis()); + + expand_dims.input(nullptr); + expand_dims.axis(nullptr); + ASSERT_EQ(nullptr, expand_dims.input()); + ASSERT_EQ(nullptr, expand_dims.axis()); +} + +TEST(CircleExpandDimsTest, arity_NEG) +{ + luci::CircleExpandDims expand_dims; + + ASSERT_NO_THROW(expand_dims.arg(1)); + ASSERT_THROW(expand_dims.arg(2), std::out_of_range); +} + +TEST(CircleExpandDimsTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleExpandDims expand_dims; + + TestVisitor tv; + ASSERT_THROW(expand_dims.accept(&tv), std::exception); +} + +TEST(CircleExpandDimsTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleExpandDims expand_dims; + + TestVisitor tv; + ASSERT_THROW(expand_dims.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleFill.test.cpp b/compiler/luci/lang/src/Nodes/CircleFill.test.cpp new file mode 100644 index 0000000..4555da1 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleFill.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleFill.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleFillTest, constructor_P) +{ + luci::CircleFill fill; + + ASSERT_EQ(fill.dialect(), luci::CircleDialect::get()); + ASSERT_EQ(fill.opcode(), luci::CircleOpcode::FILL); + + ASSERT_EQ(nullptr, fill.dims()); + ASSERT_EQ(nullptr, fill.value()); +} + +TEST(CircleFillTest, input_NEG) +{ + luci::CircleFill fill_node; + luci::CircleFill node; + + fill_node.dims(&node); + fill_node.value(&node); + ASSERT_NE(nullptr, fill_node.dims()); + ASSERT_NE(nullptr, fill_node.value()); + + fill_node.dims(nullptr); + fill_node.value(nullptr); + ASSERT_EQ(nullptr, fill_node.dims()); + ASSERT_EQ(nullptr, fill_node.value()); +} + +TEST(CircleFillTest, arity_NEG) +{ + luci::CircleFill fill_node; + + ASSERT_NO_THROW(fill_node.arg(1)); + ASSERT_THROW(fill_node.arg(2), std::out_of_range); +} + +TEST(CircleFillTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleFill fill_node; + + TestVisitor tv; + ASSERT_THROW(fill_node.accept(&tv), std::exception); +} + +TEST(CircleFillTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleFill fill_node; + + TestVisitor tv; + ASSERT_THROW(fill_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleFloor.test.cpp b/compiler/luci/lang/src/Nodes/CircleFloor.test.cpp new file mode 100644 index 0000000..38d38a0 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleFloor.test.cpp @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleFloor.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleFloorTest, constructor) +{ + luci::CircleFloor floor_node; + + ASSERT_EQ(luci::CircleDialect::get(), floor_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::FLOOR, floor_node.opcode()); + + ASSERT_EQ(nullptr, floor_node.x()); +} + +TEST(CircleFloorTest, input_NEG) +{ + luci::CircleFloor floor_node; + luci::CircleFloor node; + + floor_node.x(&node); + ASSERT_NE(nullptr, floor_node.x()); + + floor_node.x(nullptr); + ASSERT_EQ(nullptr, floor_node.x()); +} + +TEST(CircleFloorTest, arity_NEG) +{ + luci::CircleFloor floor_node; + + ASSERT_NO_THROW(floor_node.arg(0)); + ASSERT_THROW(floor_node.arg(1), std::out_of_range); +} + +TEST(CircleFloorTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleFloor floor_node; + + TestVisitor tv; + ASSERT_THROW(floor_node.accept(&tv), std::exception); +} + +TEST(CircleFloorTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleFloor floor_node; + + TestVisitor tv; + ASSERT_THROW(floor_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleFloorDiv.test.cpp b/compiler/luci/lang/src/Nodes/CircleFloorDiv.test.cpp new file mode 100644 index 0000000..6c52eee --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleFloorDiv.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleFloorDiv.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleFloorDivTest, constructor_P) +{ + luci::CircleFloorDiv floordiv_node; + + ASSERT_EQ(luci::CircleDialect::get(), floordiv_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::FLOOR_DIV, floordiv_node.opcode()); + + ASSERT_EQ(nullptr, floordiv_node.x()); + ASSERT_EQ(nullptr, floordiv_node.y()); +} + +TEST(CircleFloorDivTest, input_NEG) +{ + luci::CircleFloorDiv floordiv_node; + luci::CircleFloorDiv node; + + floordiv_node.x(&node); + floordiv_node.y(&node); + ASSERT_NE(nullptr, floordiv_node.x()); + ASSERT_NE(nullptr, floordiv_node.y()); + + floordiv_node.x(nullptr); + floordiv_node.y(nullptr); + ASSERT_EQ(nullptr, floordiv_node.x()); + ASSERT_EQ(nullptr, floordiv_node.y()); +} + +TEST(CircleFloorDivTest, arity_NEG) +{ + luci::CircleFloorDiv floordiv_node; + + ASSERT_NO_THROW(floordiv_node.arg(1)); + ASSERT_THROW(floordiv_node.arg(2), std::out_of_range); +} + +TEST(CircleFloorDivTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleFloorDiv floordiv_node; + + TestVisitor tv; + ASSERT_THROW(floordiv_node.accept(&tv), std::exception); +} + +TEST(CircleFloorDivTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleFloorDiv floordiv_node; + + TestVisitor tv; + ASSERT_THROW(floordiv_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleFloorMod.test.cpp b/compiler/luci/lang/src/Nodes/CircleFloorMod.test.cpp new file mode 100644 index 0000000..c3fa187 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleFloorMod.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleFloorMod.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleFloorModTest, constructor) +{ + luci::CircleFloorMod floormod_node; + + ASSERT_EQ(luci::CircleDialect::get(), floormod_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::FLOOR_MOD, floormod_node.opcode()); + + ASSERT_EQ(nullptr, floormod_node.x()); + ASSERT_EQ(nullptr, floormod_node.y()); +} + +TEST(CircleFloorModTest, input_NEG) +{ + luci::CircleFloorMod floormod_node; + luci::CircleFloorMod node; + + floormod_node.x(&node); + floormod_node.y(&node); + ASSERT_NE(nullptr, floormod_node.x()); + ASSERT_NE(nullptr, floormod_node.y()); + + floormod_node.x(nullptr); + floormod_node.y(nullptr); + ASSERT_EQ(nullptr, floormod_node.x()); + ASSERT_EQ(nullptr, floormod_node.y()); +} + +TEST(CircleFloorModTest, arity_NEG) +{ + luci::CircleFloorMod floormod_node; + + ASSERT_NO_THROW(floormod_node.arg(1)); + ASSERT_THROW(floormod_node.arg(2), std::out_of_range); +} + +TEST(CircleFloorModTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleFloorMod floormod_node; + + TestVisitor tv; + ASSERT_THROW(floormod_node.accept(&tv), std::exception); +} + +TEST(CircleFloorModTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleFloorMod floormod_node; + + TestVisitor tv; + ASSERT_THROW(floormod_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleFullyConnected.test.cpp b/compiler/luci/lang/src/Nodes/CircleFullyConnected.test.cpp index 994dcd2..bb0e3c5 100644 --- a/compiler/luci/lang/src/Nodes/CircleFullyConnected.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleFullyConnected.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleFullyConnected.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,11 +25,66 @@ TEST(CircleFullyConnectedTest, constructor) { luci::CircleFullyConnected fc_node; - ASSERT_EQ(fc_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(fc_node.opcode(), luci::CircleOpcode::FULLY_CONNECTED); + ASSERT_EQ(luci::CircleDialect::get(), fc_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::FULLY_CONNECTED, fc_node.opcode()); - ASSERT_EQ(fc_node.input(), nullptr); - ASSERT_EQ(fc_node.weights(), nullptr); - ASSERT_EQ(fc_node.bias(), nullptr); - ASSERT_EQ(fc_node.fusedActivationFunction(), luci::FusedActFunc::UNDEFINED); + ASSERT_EQ(nullptr, fc_node.input()); + ASSERT_EQ(nullptr, fc_node.weights()); + ASSERT_EQ(nullptr, fc_node.bias()); + ASSERT_EQ(luci::FusedActFunc::UNDEFINED, fc_node.fusedActivationFunction()); +} + +TEST(CircleFullyConnectedTest, input_NEG) +{ + luci::CircleFullyConnected fc_node; + luci::CircleFullyConnected node; + + fc_node.input(&node); + fc_node.weights(&node); + fc_node.bias(&node); + ASSERT_NE(nullptr, fc_node.input()); + ASSERT_NE(nullptr, fc_node.weights()); + ASSERT_NE(nullptr, fc_node.bias()); + + fc_node.input(nullptr); + fc_node.weights(nullptr); + fc_node.bias(nullptr); + ASSERT_EQ(nullptr, fc_node.input()); + ASSERT_EQ(nullptr, fc_node.weights()); + ASSERT_EQ(nullptr, fc_node.bias()); + + fc_node.fusedActivationFunction(luci::FusedActFunc::RELU); + ASSERT_NE(luci::FusedActFunc::UNDEFINED, fc_node.fusedActivationFunction()); +} + +TEST(CircleFullyConnectedTest, arity_NEG) +{ + luci::CircleFullyConnected fc_node; + + ASSERT_NO_THROW(fc_node.arg(2)); + ASSERT_THROW(fc_node.arg(3), std::out_of_range); +} + +TEST(CircleFullyConnectedTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleFullyConnected fc_node; + + TestVisitor tv; + ASSERT_THROW(fc_node.accept(&tv), std::exception); +} + +TEST(CircleFullyConnectedTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleFullyConnected fc_node; + + TestVisitor tv; + ASSERT_THROW(fc_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleGather.test.cpp b/compiler/luci/lang/src/Nodes/CircleGather.test.cpp index 4eace9a..5194d6b 100644 --- a/compiler/luci/lang/src/Nodes/CircleGather.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleGather.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleGather.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,10 +25,61 @@ TEST(CircleGatherTest, constructor) { luci::CircleGather gather_node; - ASSERT_EQ(gather_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(gather_node.opcode(), luci::CircleOpcode::GATHER); + ASSERT_EQ(luci::CircleDialect::get(), gather_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::GATHER, gather_node.opcode()); - ASSERT_EQ(gather_node.input(), nullptr); - ASSERT_EQ(gather_node.positions(), nullptr); - ASSERT_EQ(gather_node.axis(), 0); + ASSERT_EQ(nullptr, gather_node.params()); + ASSERT_EQ(nullptr, gather_node.indices()); + ASSERT_EQ(0, gather_node.axis()); +} + +TEST(CircleGatherTest, input_NEG) +{ + luci::CircleGather gather_node; + luci::CircleGather node; + + gather_node.params(&node); + gather_node.indices(&node); + ASSERT_NE(nullptr, gather_node.params()); + ASSERT_NE(nullptr, gather_node.indices()); + + gather_node.params(nullptr); + gather_node.indices(nullptr); + ASSERT_EQ(nullptr, gather_node.params()); + ASSERT_EQ(nullptr, gather_node.indices()); + + gather_node.axis(1); + ASSERT_NE(0, gather_node.axis()); +} + +TEST(CircleGatherTest, arity_NEG) +{ + luci::CircleGather gather_node; + + ASSERT_NO_THROW(gather_node.arg(1)); + ASSERT_THROW(gather_node.arg(2), std::out_of_range); +} + +TEST(CircleGatherTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleGather gather_node; + + TestVisitor tv; + ASSERT_THROW(gather_node.accept(&tv), std::exception); +} + +TEST(CircleGatherTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleGather gather_node; + + TestVisitor tv; + ASSERT_THROW(gather_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleGatherNd.test.cpp b/compiler/luci/lang/src/Nodes/CircleGatherNd.test.cpp new file mode 100644 index 0000000..1402e6e --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleGatherNd.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleGatherNd.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleGatherNdTest, constructor) +{ + luci::CircleGatherNd gather_nd_node; + + ASSERT_EQ(luci::CircleDialect::get(), gather_nd_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::GATHER_ND, gather_nd_node.opcode()); + + ASSERT_EQ(nullptr, gather_nd_node.params()); + ASSERT_EQ(nullptr, gather_nd_node.indices()); +} + +TEST(CircleGatherNdTest, input_NEG) +{ + luci::CircleGatherNd gather_nd_node; + luci::CircleGatherNd node; + + gather_nd_node.params(&node); + gather_nd_node.indices(&node); + ASSERT_NE(nullptr, gather_nd_node.params()); + ASSERT_NE(nullptr, gather_nd_node.indices()); + + gather_nd_node.params(nullptr); + gather_nd_node.indices(nullptr); + ASSERT_EQ(nullptr, gather_nd_node.params()); + ASSERT_EQ(nullptr, gather_nd_node.indices()); +} + +TEST(CircleGatherNdTest, arity_NEG) +{ + luci::CircleGatherNd gather_nd_node; + + ASSERT_NO_THROW(gather_nd_node.arg(1)); + ASSERT_THROW(gather_nd_node.arg(2), std::out_of_range); +} + +TEST(CircleGatherNdTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleGatherNd gather_nd_node; + + TestVisitor tv; + ASSERT_THROW(gather_nd_node.accept(&tv), std::exception); +} + +TEST(CircleGatherNdTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleGatherNd gather_nd_node; + + TestVisitor tv; + ASSERT_THROW(gather_nd_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleGreater.test.cpp b/compiler/luci/lang/src/Nodes/CircleGreater.test.cpp new file mode 100644 index 0000000..9a2b5f9 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleGreater.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleGreater.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleGreaterTest, constructor_P) +{ + luci::CircleGreater greater_node; + + ASSERT_EQ(luci::CircleDialect::get(), greater_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::GREATER, greater_node.opcode()); + + ASSERT_EQ(nullptr, greater_node.x()); + ASSERT_EQ(nullptr, greater_node.y()); +} + +TEST(CircleGreaterTest, input_NEG) +{ + luci::CircleGreater greater_node; + luci::CircleGreater node; + + greater_node.x(&node); + greater_node.y(&node); + ASSERT_NE(nullptr, greater_node.x()); + ASSERT_NE(nullptr, greater_node.y()); + + greater_node.x(nullptr); + greater_node.y(nullptr); + ASSERT_EQ(nullptr, greater_node.x()); + ASSERT_EQ(nullptr, greater_node.y()); +} + +TEST(CircleGreaterTest, arity_NEG) +{ + luci::CircleGreater greater_node; + + ASSERT_NO_THROW(greater_node.arg(1)); + ASSERT_THROW(greater_node.arg(2), std::out_of_range); +} + +TEST(CircleGreaterTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleGreater greater_node; + + TestVisitor tv; + ASSERT_THROW(greater_node.accept(&tv), std::exception); +} + +TEST(CircleGreaterTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleGreater greater_node; + + TestVisitor tv; + ASSERT_THROW(greater_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleGreaterEqual.test.cpp b/compiler/luci/lang/src/Nodes/CircleGreaterEqual.test.cpp new file mode 100644 index 0000000..51c22b7 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleGreaterEqual.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleGreaterEqual.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleGreaterEqualTest, constructor_P) +{ + luci::CircleGreaterEqual greater_equal_node; + + ASSERT_EQ(luci::CircleDialect::get(), greater_equal_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::GREATER_EQUAL, greater_equal_node.opcode()); + + ASSERT_EQ(nullptr, greater_equal_node.x()); + ASSERT_EQ(nullptr, greater_equal_node.y()); +} + +TEST(CircleGreaterEqualTest, input_NEG) +{ + luci::CircleGreaterEqual greater_equal_node; + luci::CircleGreaterEqual node; + + greater_equal_node.x(&node); + greater_equal_node.y(&node); + ASSERT_NE(nullptr, greater_equal_node.x()); + ASSERT_NE(nullptr, greater_equal_node.y()); + + greater_equal_node.x(nullptr); + greater_equal_node.y(nullptr); + ASSERT_EQ(nullptr, greater_equal_node.x()); + ASSERT_EQ(nullptr, greater_equal_node.y()); +} + +TEST(CircleGreaterEqualTest, arity_NEG) +{ + luci::CircleGreaterEqual greater_equal_node; + + ASSERT_NO_THROW(greater_equal_node.arg(1)); + ASSERT_THROW(greater_equal_node.arg(2), std::out_of_range); +} + +TEST(CircleGreaterEqualTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleGreaterEqual greater_equal_node; + + TestVisitor tv; + ASSERT_THROW(greater_equal_node.accept(&tv), std::exception); +} + +TEST(CircleGreaterEqualTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleGreaterEqual greater_equal_node; + + TestVisitor tv; + ASSERT_THROW(greater_equal_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleIf.test.cpp b/compiler/luci/lang/src/Nodes/CircleIf.test.cpp new file mode 100644 index 0000000..e3c8c9f --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleIf.test.cpp @@ -0,0 +1,87 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleIf.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleIfTest, constructor) +{ + luci::CircleIf if_node(2, 2); + + ASSERT_EQ(luci::CircleDialect::get(), if_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::IF, if_node.opcode()); + + ASSERT_EQ(2, if_node.input_count()); + ASSERT_EQ(2, if_node.output_count()); + + ASSERT_EQ(nullptr, if_node.input(0)); + ASSERT_EQ(nullptr, if_node.input(1)); + + ASSERT_EQ(-1, if_node.then_branch()); + ASSERT_EQ(-1, if_node.else_branch()); +} + +TEST(CircleIfTestDeath, invalid_arity_NEG) +{ + ASSERT_DEBUG_DEATH(luci::CircleIf very_long_name_if_node(0, 1), ""); +} + +TEST(CircleIfTestDeath, invalid_output_count_NEG) +{ + ASSERT_DEBUG_DEATH(luci::CircleIf if_node(2, 0), ""); +} + +TEST(CircleIfTestDeath, invalid_input_get_index_NEG) +{ + luci::CircleIf if_node(2, 2); + + EXPECT_ANY_THROW(if_node.input(100)); +} + +TEST(CircleIfTestDeath, invalid_input_set_index_NEG) +{ + luci::CircleIf if_node(2, 2); + + EXPECT_ANY_THROW(if_node.input(100, nullptr)); +} + +TEST(CircleIfTestDeath, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleIf if_node(2, 2); + + TestVisitor tv; + ASSERT_THROW(if_node.accept(&tv), std::exception); +} + +TEST(CircleIfTestDeath, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleIf if_node(2, 2); + + TestVisitor tv; + ASSERT_THROW(if_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleIfOut.test.cpp b/compiler/luci/lang/src/Nodes/CircleIfOut.test.cpp new file mode 100644 index 0000000..5154b6b --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleIfOut.test.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleIfOut.h" + +#include "luci/IR/CircleDialect.h" + +#include + +TEST(CircleIfOutTest, constructor) +{ + luci::CircleIfOut ifout_node; + + ASSERT_EQ(luci::CircleDialect::get(), ifout_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::CIRCLEIFOUT, ifout_node.opcode()); + + ASSERT_EQ(nullptr, ifout_node.input()); + ASSERT_EQ(-1, ifout_node.index()); +} diff --git a/compiler/luci/lang/src/Nodes/CircleInstanceNorm.test.cpp b/compiler/luci/lang/src/Nodes/CircleInstanceNorm.test.cpp index b87e817..88a5b8c 100644 --- a/compiler/luci/lang/src/Nodes/CircleInstanceNorm.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleInstanceNorm.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleInstanceNorm.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,12 +25,67 @@ TEST(CircleInstanceNormTest, constructor) { luci::CircleInstanceNorm instance_norm; - ASSERT_EQ(instance_norm.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(instance_norm.opcode(), luci::CircleOpcode::INSTANCE_NORM); + ASSERT_EQ(luci::CircleDialect::get(), instance_norm.dialect()); + ASSERT_EQ(luci::CircleOpcode::INSTANCE_NORM, instance_norm.opcode()); - ASSERT_EQ(instance_norm.input(), nullptr); - ASSERT_EQ(instance_norm.gamma(), nullptr); - ASSERT_EQ(instance_norm.beta(), nullptr); + ASSERT_EQ(nullptr, instance_norm.input()); + ASSERT_EQ(nullptr, instance_norm.gamma()); + ASSERT_EQ(nullptr, instance_norm.beta()); ASSERT_FLOAT_EQ(instance_norm.epsilon(), 1e-05); - ASSERT_EQ(instance_norm.fusedActivationFunction(), luci::FusedActFunc::UNDEFINED); + ASSERT_EQ(luci::FusedActFunc::UNDEFINED, instance_norm.fusedActivationFunction()); +} + +TEST(CircleInstanceNormTest, input_NEG) +{ + luci::CircleInstanceNorm instance_norm; + luci::CircleInstanceNorm node; + + instance_norm.input(&node); + instance_norm.gamma(&node); + instance_norm.beta(&node); + ASSERT_NE(nullptr, instance_norm.input()); + ASSERT_NE(nullptr, instance_norm.gamma()); + ASSERT_NE(nullptr, instance_norm.beta()); + + instance_norm.input(nullptr); + instance_norm.gamma(nullptr); + instance_norm.beta(nullptr); + ASSERT_EQ(nullptr, instance_norm.input()); + ASSERT_EQ(nullptr, instance_norm.gamma()); + ASSERT_EQ(nullptr, instance_norm.beta()); + + instance_norm.fusedActivationFunction(luci::FusedActFunc::RELU); + ASSERT_NE(luci::FusedActFunc::UNDEFINED, instance_norm.fusedActivationFunction()); +} + +TEST(CircleInstanceNormTest, arity_NEG) +{ + luci::CircleInstanceNorm instance_norm; + + ASSERT_NO_THROW(instance_norm.arg(2)); + ASSERT_THROW(instance_norm.arg(3), std::out_of_range); +} + +TEST(CircleInstanceNormTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleInstanceNorm instance_norm; + + TestVisitor tv; + ASSERT_THROW(instance_norm.accept(&tv), std::exception); +} + +TEST(CircleInstanceNormTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleInstanceNorm instance_norm; + + TestVisitor tv; + ASSERT_THROW(instance_norm.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleL2Pool2D.test.cpp b/compiler/luci/lang/src/Nodes/CircleL2Pool2D.test.cpp new file mode 100644 index 0000000..cb779ef --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleL2Pool2D.test.cpp @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleL2Pool2D.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleL2Pool2DTest, constructor) +{ + luci::CircleL2Pool2D l2pool2d_node; + + ASSERT_EQ(luci::CircleDialect::get(), l2pool2d_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::L2_POOL_2D, l2pool2d_node.opcode()); + + ASSERT_EQ(nullptr, l2pool2d_node.value()); + ASSERT_EQ(1, l2pool2d_node.filter()->h()); + ASSERT_EQ(1, l2pool2d_node.filter()->w()); + ASSERT_EQ(1, l2pool2d_node.stride()->h()); + ASSERT_EQ(1, l2pool2d_node.stride()->w()); + ASSERT_EQ(luci::FusedActFunc::UNDEFINED, l2pool2d_node.fusedActivationFunction()); +} + +TEST(CircleL2Pool2DTest, input_NEG) +{ + luci::CircleL2Pool2D l2pool2d_node; + luci::CircleL2Pool2D node; + + l2pool2d_node.value(&node); + ASSERT_NE(nullptr, l2pool2d_node.value()); + + l2pool2d_node.value(nullptr); + ASSERT_EQ(nullptr, l2pool2d_node.value()); + + l2pool2d_node.stride()->h(2); + l2pool2d_node.stride()->w(2); + ASSERT_EQ(2, l2pool2d_node.stride()->h()); + ASSERT_EQ(2, l2pool2d_node.stride()->w()); + + l2pool2d_node.filter()->h(2); + l2pool2d_node.filter()->w(2); + ASSERT_EQ(2, l2pool2d_node.filter()->h()); + ASSERT_EQ(2, l2pool2d_node.filter()->w()); + + l2pool2d_node.fusedActivationFunction(luci::FusedActFunc::RELU); + ASSERT_NE(luci::FusedActFunc::UNDEFINED, l2pool2d_node.fusedActivationFunction()); +} + +TEST(CircleL2Pool2DTest, arity_NEG) +{ + luci::CircleL2Pool2D l2pool2d_node; + + ASSERT_NO_THROW(l2pool2d_node.arg(0)); + ASSERT_THROW(l2pool2d_node.arg(1), std::out_of_range); +} + +TEST(CircleL2Pool2DTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleL2Pool2D l2pool2d_node; + + TestVisitor tv; + ASSERT_THROW(l2pool2d_node.accept(&tv), std::exception); +} + +TEST(CircleL2Pool2DTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleL2Pool2D l2pool2d_node; + + TestVisitor tv; + ASSERT_THROW(l2pool2d_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleLeakyRelu.test.cpp b/compiler/luci/lang/src/Nodes/CircleLeakyRelu.test.cpp new file mode 100644 index 0000000..bacb444 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleLeakyRelu.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleLeakyRelu.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleLeakyReluTest, constructor) +{ + luci::CircleLeakyRelu relu_node; + + ASSERT_EQ(luci::CircleDialect::get(), relu_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::LEAKY_RELU, relu_node.opcode()); + + ASSERT_EQ(nullptr, relu_node.features()); + + ASSERT_EQ(0.2f, relu_node.alpha()); +} + +TEST(CircleLeakyReluTest, input_NEG) +{ + luci::CircleLeakyRelu relu_node; + luci::CircleLeakyRelu node; + + relu_node.features(&node); + ASSERT_NE(nullptr, relu_node.features()); + + relu_node.features(nullptr); + ASSERT_EQ(nullptr, relu_node.features()); + + relu_node.alpha(1.2f); + ASSERT_NE(0.2f, relu_node.alpha()); +} + +TEST(CircleLeakyReluTest, arity_NEG) +{ + luci::CircleLeakyRelu relu_node; + + ASSERT_NO_THROW(relu_node.arg(0)); + ASSERT_THROW(relu_node.arg(1), std::out_of_range); +} + +TEST(CircleLeakyReluTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleLeakyRelu relu_node; + + TestVisitor tv; + ASSERT_THROW(relu_node.accept(&tv), std::exception); +} + +TEST(CircleLeakyReluTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleLeakyRelu relu_node; + + TestVisitor tv; + ASSERT_THROW(relu_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleLess.test.cpp b/compiler/luci/lang/src/Nodes/CircleLess.test.cpp new file mode 100644 index 0000000..ec454df --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleLess.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleLess.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleLessTest, constructor_P) +{ + luci::CircleLess less_node; + + ASSERT_EQ(luci::CircleDialect::get(), less_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::LESS, less_node.opcode()); + + ASSERT_EQ(nullptr, less_node.x()); + ASSERT_EQ(nullptr, less_node.y()); +} + +TEST(CircleLessTest, input_NEG) +{ + luci::CircleLess less_node; + luci::CircleLess node; + + less_node.x(&node); + less_node.y(&node); + ASSERT_NE(nullptr, less_node.x()); + ASSERT_NE(nullptr, less_node.y()); + + less_node.x(nullptr); + less_node.y(nullptr); + ASSERT_EQ(nullptr, less_node.x()); + ASSERT_EQ(nullptr, less_node.y()); +} + +TEST(CircleLessTest, arity_NEG) +{ + luci::CircleLess less_node; + + ASSERT_NO_THROW(less_node.arg(1)); + ASSERT_THROW(less_node.arg(2), std::out_of_range); +} + +TEST(CircleLessTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleLess less_node; + + TestVisitor tv; + ASSERT_THROW(less_node.accept(&tv), std::exception); +} + +TEST(CircleLessTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleLess less_node; + + TestVisitor tv; + ASSERT_THROW(less_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleLessEqual.test.cpp b/compiler/luci/lang/src/Nodes/CircleLessEqual.test.cpp new file mode 100644 index 0000000..baa9202 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleLessEqual.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleLessEqual.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleLessEqualTest, constructor_P) +{ + luci::CircleLessEqual less_equal_node; + + ASSERT_EQ(luci::CircleDialect::get(), less_equal_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::LESS_EQUAL, less_equal_node.opcode()); + + ASSERT_EQ(nullptr, less_equal_node.x()); + ASSERT_EQ(nullptr, less_equal_node.y()); +} + +TEST(CircleLessEqualTest, input_NEG) +{ + luci::CircleLessEqual less_equal_node; + luci::CircleLessEqual node; + + less_equal_node.x(&node); + less_equal_node.y(&node); + ASSERT_NE(nullptr, less_equal_node.x()); + ASSERT_NE(nullptr, less_equal_node.y()); + + less_equal_node.x(nullptr); + less_equal_node.y(nullptr); + ASSERT_EQ(nullptr, less_equal_node.x()); + ASSERT_EQ(nullptr, less_equal_node.y()); +} + +TEST(CircleLessEqualTest, arity_NEG) +{ + luci::CircleLessEqual less_equal_node; + + ASSERT_NO_THROW(less_equal_node.arg(1)); + ASSERT_THROW(less_equal_node.arg(2), std::out_of_range); +} + +TEST(CircleLessEqualTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleLessEqual less_equal_node; + + TestVisitor tv; + ASSERT_THROW(less_equal_node.accept(&tv), std::exception); +} + +TEST(CircleLessEqualTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleLessEqual less_equal_node; + + TestVisitor tv; + ASSERT_THROW(less_equal_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleLocalResponseNormalization.test.cpp b/compiler/luci/lang/src/Nodes/CircleLocalResponseNormalization.test.cpp new file mode 100644 index 0000000..1b1bf67 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleLocalResponseNormalization.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleLocalResponseNormalization.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleLocalResponseNormalizationTest, constructor_P) +{ + luci::CircleLocalResponseNormalization local_response_normalization_node; + + ASSERT_EQ(luci::CircleDialect::get(), local_response_normalization_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::LOCAL_RESPONSE_NORMALIZATION, + local_response_normalization_node.opcode()); + + ASSERT_EQ(nullptr, local_response_normalization_node.input()); + ASSERT_EQ(5, local_response_normalization_node.radius()); + ASSERT_EQ(1.0f, local_response_normalization_node.bias()); + ASSERT_EQ(1.0f, local_response_normalization_node.alpha()); + ASSERT_EQ(0.5f, local_response_normalization_node.beta()); +} + +TEST(CircleLocalResponseNormalizationTest, input_NEG) +{ + luci::CircleLocalResponseNormalization local_response_normalization_node; + luci::CircleLocalResponseNormalization node; + + local_response_normalization_node.input(&node); + ASSERT_NE(nullptr, local_response_normalization_node.input()); + + local_response_normalization_node.input(nullptr); + ASSERT_EQ(nullptr, local_response_normalization_node.input()); + + local_response_normalization_node.radius(100); + local_response_normalization_node.bias(100.0f); + local_response_normalization_node.alpha(100.0f); + local_response_normalization_node.beta(100.0f); + ASSERT_NE(5, local_response_normalization_node.radius()); + ASSERT_NE(1.0f, local_response_normalization_node.bias()); + ASSERT_NE(1.0f, local_response_normalization_node.alpha()); + ASSERT_NE(0.5f, local_response_normalization_node.beta()); +} + +TEST(CircleLocalResponseNormalizationTest, arity_NEG) +{ + luci::CircleLocalResponseNormalization local_response_normalization_node; + + ASSERT_NO_THROW(local_response_normalization_node.arg(0)); + ASSERT_THROW(local_response_normalization_node.arg(1), std::out_of_range); +} + +TEST(CircleLocalResponseNormalizationTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleLocalResponseNormalization local_response_normalization_node; + + TestVisitor tv; + ASSERT_THROW(local_response_normalization_node.accept(&tv), std::exception); +} + +TEST(CircleLocalResponseNormalizationTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleLocalResponseNormalization local_response_normalization_node; + + TestVisitor tv; + ASSERT_THROW(local_response_normalization_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleLog.test.cpp b/compiler/luci/lang/src/Nodes/CircleLog.test.cpp new file mode 100644 index 0000000..0bb9ee7 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleLog.test.cpp @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleLog.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleLogTest, constructor) +{ + luci::CircleLog log_node; + + ASSERT_EQ(luci::CircleDialect::get(), log_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::LOG, log_node.opcode()); + + ASSERT_EQ(nullptr, log_node.x()); +} + +TEST(CircleLogTest, input_NEG) +{ + luci::CircleLog log_node; + luci::CircleLog node; + + log_node.x(&node); + ASSERT_NE(nullptr, log_node.x()); + + log_node.x(nullptr); + ASSERT_EQ(nullptr, log_node.x()); +} + +TEST(CircleLogTest, arity_NEG) +{ + luci::CircleLog log_node; + + ASSERT_NO_THROW(log_node.arg(0)); + ASSERT_THROW(log_node.arg(1), std::out_of_range); +} + +TEST(CircleLogTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleLog log_node; + + TestVisitor tv; + ASSERT_THROW(log_node.accept(&tv), std::exception); +} + +TEST(CircleLogTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleLog log_node; + + TestVisitor tv; + ASSERT_THROW(log_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleLogSoftmax.test.cpp b/compiler/luci/lang/src/Nodes/CircleLogSoftmax.test.cpp new file mode 100644 index 0000000..70977ae --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleLogSoftmax.test.cpp @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleLogSoftmax.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleLogSoftmaxTest, constructor) +{ + luci::CircleLogSoftmax log_softmax_node; + + ASSERT_EQ(luci::CircleDialect::get(), log_softmax_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::LOG_SOFTMAX, log_softmax_node.opcode()); + + ASSERT_EQ(nullptr, log_softmax_node.logits()); +} + +TEST(CircleLogSoftmaxTest, input_NEG) +{ + luci::CircleLogSoftmax log_softmax_node; + luci::CircleLogSoftmax node; + + log_softmax_node.logits(&node); + ASSERT_NE(nullptr, log_softmax_node.logits()); + + log_softmax_node.logits(nullptr); + ASSERT_EQ(nullptr, log_softmax_node.logits()); +} + +TEST(CircleLogSoftmaxTest, arity_NEG) +{ + luci::CircleLogSoftmax log_softmax_node; + + ASSERT_NO_THROW(log_softmax_node.arg(0)); + ASSERT_THROW(log_softmax_node.arg(1), std::out_of_range); +} + +TEST(CircleLogSoftmaxTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleLogSoftmax log_softmax_node; + + TestVisitor tv; + ASSERT_THROW(log_softmax_node.accept(&tv), std::exception); +} + +TEST(CircleLogSoftmaxTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleLogSoftmax log_softmax_node; + + TestVisitor tv; + ASSERT_THROW(log_softmax_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleLogicalAnd.test.cpp b/compiler/luci/lang/src/Nodes/CircleLogicalAnd.test.cpp new file mode 100644 index 0000000..db378f0 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleLogicalAnd.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleLogicalAnd.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleLogicalAndTest, constructor_P) +{ + luci::CircleLogicalAnd and_node; + + ASSERT_EQ(luci::CircleDialect::get(), and_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::LOGICAL_AND, and_node.opcode()); + + ASSERT_EQ(nullptr, and_node.x()); + ASSERT_EQ(nullptr, and_node.y()); +} + +TEST(CircleLogicalAndTest, input_NEG) +{ + luci::CircleLogicalAnd and_node; + luci::CircleLogicalAnd node; + + and_node.x(&node); + and_node.y(&node); + ASSERT_NE(nullptr, and_node.x()); + ASSERT_NE(nullptr, and_node.y()); + + and_node.x(nullptr); + and_node.y(nullptr); + ASSERT_EQ(nullptr, and_node.x()); + ASSERT_EQ(nullptr, and_node.y()); +} + +TEST(CircleLogicalAndTest, arity_NEG) +{ + luci::CircleLogicalAnd and_node; + + ASSERT_NO_THROW(and_node.arg(1)); + ASSERT_THROW(and_node.arg(2), std::out_of_range); +} + +TEST(CircleLogicalAndTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleLogicalAnd and_node; + + TestVisitor tv; + ASSERT_THROW(and_node.accept(&tv), std::exception); +} + +TEST(CircleLogicalAndTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleLogicalAnd and_node; + + TestVisitor tv; + ASSERT_THROW(and_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleLogicalNot.test.cpp b/compiler/luci/lang/src/Nodes/CircleLogicalNot.test.cpp index 360dd47..0c2c029 100644 --- a/compiler/luci/lang/src/Nodes/CircleLogicalNot.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleLogicalNot.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleLogicalNot.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,8 +25,52 @@ TEST(CircleLogicalNotTest, constructor_P) { luci::CircleLogicalNot not_node; - ASSERT_EQ(not_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(not_node.opcode(), luci::CircleOpcode::LOGICAL_NOT); + ASSERT_EQ(luci::CircleDialect::get(), not_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::LOGICAL_NOT, not_node.opcode()); - ASSERT_EQ(not_node.x(), nullptr); + ASSERT_EQ(nullptr, not_node.x()); +} + +TEST(CircleLogicalNotTest, input_NEG) +{ + luci::CircleLogicalNot not_node; + luci::CircleLogicalNot node; + + not_node.x(&node); + ASSERT_NE(nullptr, not_node.x()); + + not_node.x(nullptr); + ASSERT_EQ(nullptr, not_node.x()); +} + +TEST(CircleLogicalNotTest, arity_NEG) +{ + luci::CircleLogicalNot not_node; + + ASSERT_NO_THROW(not_node.arg(0)); + ASSERT_THROW(not_node.arg(1), std::out_of_range); +} + +TEST(CircleLogicalNotTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleLogicalNot not_node; + + TestVisitor tv; + ASSERT_THROW(not_node.accept(&tv), std::exception); +} + +TEST(CircleLogicalNotTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleLogicalNot not_node; + + TestVisitor tv; + ASSERT_THROW(not_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleLogicalOr.test.cpp b/compiler/luci/lang/src/Nodes/CircleLogicalOr.test.cpp index 039db4a..a08b863 100644 --- a/compiler/luci/lang/src/Nodes/CircleLogicalOr.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleLogicalOr.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleLogicalOr.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,9 +25,57 @@ TEST(CircleLogicalOrTest, constructor_P) { luci::CircleLogicalOr or_node; - ASSERT_EQ(or_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(or_node.opcode(), luci::CircleOpcode::LOGICAL_OR); + ASSERT_EQ(luci::CircleDialect::get(), or_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::LOGICAL_OR, or_node.opcode()); - ASSERT_EQ(or_node.x(), nullptr); - ASSERT_EQ(or_node.y(), nullptr); + ASSERT_EQ(nullptr, or_node.x()); + ASSERT_EQ(nullptr, or_node.y()); +} + +TEST(CircleLogicalOrTest, input_NEG) +{ + luci::CircleLogicalOr or_node; + luci::CircleLogicalOr node; + + or_node.x(&node); + or_node.y(&node); + ASSERT_NE(nullptr, or_node.x()); + ASSERT_NE(nullptr, or_node.y()); + + or_node.x(nullptr); + or_node.y(nullptr); + ASSERT_EQ(nullptr, or_node.x()); + ASSERT_EQ(nullptr, or_node.y()); +} + +TEST(CircleLogicalOrTest, arity_NEG) +{ + luci::CircleLogicalOr or_node; + + ASSERT_NO_THROW(or_node.arg(1)); + ASSERT_THROW(or_node.arg(2), std::out_of_range); +} + +TEST(CircleLogicalOrTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleLogicalOr or_node; + + TestVisitor tv; + ASSERT_THROW(or_node.accept(&tv), std::exception); +} + +TEST(CircleLogicalOrTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleLogicalOr or_node; + + TestVisitor tv; + ASSERT_THROW(or_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleLogistic.test.cpp b/compiler/luci/lang/src/Nodes/CircleLogistic.test.cpp new file mode 100644 index 0000000..18efd86 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleLogistic.test.cpp @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleLogistic.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleLogisticTest, constructor) +{ + luci::CircleLogistic logistic_node; + + ASSERT_EQ(luci::CircleDialect::get(), logistic_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::LOGISTIC, logistic_node.opcode()); + + ASSERT_EQ(nullptr, logistic_node.x()); +} + +TEST(CircleLogisticTest, input_NEG) +{ + luci::CircleLogistic logistic_node; + luci::CircleLogistic node; + + logistic_node.x(&node); + ASSERT_NE(nullptr, logistic_node.x()); + + logistic_node.x(nullptr); + ASSERT_EQ(nullptr, logistic_node.x()); +} + +TEST(CircleLogisticTest, arity_NEG) +{ + luci::CircleLogistic logistic_node; + + ASSERT_NO_THROW(logistic_node.arg(0)); + ASSERT_THROW(logistic_node.arg(1), std::out_of_range); +} + +TEST(CircleLogisticTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleLogistic logistic_node; + + TestVisitor tv; + ASSERT_THROW(logistic_node.accept(&tv), std::exception); +} + +TEST(CircleLogisticTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleLogistic logistic_node; + + TestVisitor tv; + ASSERT_THROW(logistic_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleMatrixDiag.test.cpp b/compiler/luci/lang/src/Nodes/CircleMatrixDiag.test.cpp new file mode 100644 index 0000000..9209cf1 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleMatrixDiag.test.cpp @@ -0,0 +1,78 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleMatrixDiag.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleMatrixDiagTest, constructor_P) +{ + luci::CircleMatrixDiag matrix_diag_node; + + ASSERT_EQ(luci::CircleDialect::get(), matrix_diag_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::MATRIX_DIAG, matrix_diag_node.opcode()); + + ASSERT_EQ(nullptr, matrix_diag_node.diagonal()); +} + +TEST(CircleMatrixDiagTest, input_NEG) +{ + luci::CircleMatrixDiag matrix_diag_node; + luci::CircleMatrixDiag node; + + matrix_diag_node.diagonal(&node); + + ASSERT_NE(nullptr, matrix_diag_node.diagonal()); + + matrix_diag_node.diagonal(nullptr); + + ASSERT_EQ(nullptr, matrix_diag_node.diagonal()); +} + +TEST(CircleMatrixDiagTest, arity_NEG) +{ + luci::CircleMatrixDiag matrix_diag_node; + + ASSERT_NO_THROW(matrix_diag_node.arg(0)); + ASSERT_THROW(matrix_diag_node.arg(1), std::out_of_range); +} + +TEST(CircleMatrixDiagTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleMatrixDiag matrix_diag_node; + + TestVisitor tv; + ASSERT_THROW(matrix_diag_node.accept(&tv), std::exception); +} + +TEST(CircleMatrixDiagTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleMatrixDiag matrix_diag_node; + + TestVisitor tv; + ASSERT_THROW(matrix_diag_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleMatrixSetDiag.test.cpp b/compiler/luci/lang/src/Nodes/CircleMatrixSetDiag.test.cpp new file mode 100644 index 0000000..9dea985 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleMatrixSetDiag.test.cpp @@ -0,0 +1,84 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleMatrixSetDiag.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleMatrixSetDiagTest, constructor_P) +{ + luci::CircleMatrixSetDiag matrix_set_diag_node; + + ASSERT_EQ(luci::CircleDialect::get(), matrix_set_diag_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::MATRIX_SET_DIAG, matrix_set_diag_node.opcode()); + + ASSERT_EQ(nullptr, matrix_set_diag_node.input()); + ASSERT_EQ(nullptr, matrix_set_diag_node.diagonal()); +} + +TEST(CircleMatrixSetDiagTest, input_NEG) +{ + luci::CircleMatrixSetDiag matrix_set_diag_node; + luci::CircleMatrixSetDiag node; + + matrix_set_diag_node.input(&node); + matrix_set_diag_node.diagonal(&node); + + ASSERT_NE(nullptr, matrix_set_diag_node.input()); + ASSERT_NE(nullptr, matrix_set_diag_node.diagonal()); + + matrix_set_diag_node.input(nullptr); + matrix_set_diag_node.diagonal(nullptr); + + ASSERT_EQ(nullptr, matrix_set_diag_node.input()); + ASSERT_EQ(nullptr, matrix_set_diag_node.diagonal()); +} + +TEST(CircleMatrixSetDiagTest, arity_NEG) +{ + luci::CircleMatrixSetDiag matrix_set_diag_node; + + ASSERT_NO_THROW(matrix_set_diag_node.arg(0)); + ASSERT_NO_THROW(matrix_set_diag_node.arg(1)); + ASSERT_THROW(matrix_set_diag_node.arg(2), std::out_of_range); +} + +TEST(CircleMatrixSetDiagTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleMatrixSetDiag matrix_set_diag_node; + + TestVisitor tv; + ASSERT_THROW(matrix_set_diag_node.accept(&tv), std::exception); +} + +TEST(CircleMatrixSetDiagTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleMatrixSetDiag matrix_set_diag_node; + + TestVisitor tv; + ASSERT_THROW(matrix_set_diag_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleMaxPool2D.test.cpp b/compiler/luci/lang/src/Nodes/CircleMaxPool2D.test.cpp index 874ecec..cb6c016 100644 --- a/compiler/luci/lang/src/Nodes/CircleMaxPool2D.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleMaxPool2D.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleMaxPool2D.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,10 +25,66 @@ TEST(CircleMaxPool2DTest, constructor_P) { luci::CircleMaxPool2D maxpool2d_node; - ASSERT_EQ(maxpool2d_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(maxpool2d_node.opcode(), luci::CircleOpcode::MAX_POOL_2D); + ASSERT_EQ(luci::CircleDialect::get(), maxpool2d_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::MAX_POOL_2D, maxpool2d_node.opcode()); - ASSERT_EQ(maxpool2d_node.value(), nullptr); - ASSERT_NE(maxpool2d_node.filter(), nullptr); - ASSERT_NE(maxpool2d_node.stride(), nullptr); + ASSERT_EQ(nullptr, maxpool2d_node.value()); + ASSERT_EQ(luci::Padding::UNDEFINED, maxpool2d_node.padding()); + ASSERT_EQ(1, maxpool2d_node.filter()->h()); + ASSERT_EQ(1, maxpool2d_node.filter()->w()); + ASSERT_EQ(1, maxpool2d_node.stride()->h()); + ASSERT_EQ(1, maxpool2d_node.stride()->w()); +} + +TEST(CircleMaxPool2DTest, input_NEG) +{ + luci::CircleMaxPool2D maxpool2d_node; + luci::CircleMaxPool2D node; + + maxpool2d_node.value(&node); + ASSERT_NE(nullptr, maxpool2d_node.value()); + + maxpool2d_node.value(nullptr); + ASSERT_EQ(nullptr, maxpool2d_node.value()); + + maxpool2d_node.filter()->h(2); + maxpool2d_node.filter()->w(2); + maxpool2d_node.stride()->h(2); + maxpool2d_node.stride()->w(2); + ASSERT_NE(1, maxpool2d_node.filter()->h()); + ASSERT_NE(1, maxpool2d_node.filter()->w()); + ASSERT_NE(1, maxpool2d_node.stride()->h()); + ASSERT_NE(1, maxpool2d_node.stride()->w()); +} + +TEST(CircleMaxPool2DTest, arity_NEG) +{ + luci::CircleMaxPool2D maxpool2d_node; + + ASSERT_NO_THROW(maxpool2d_node.arg(0)); + ASSERT_THROW(maxpool2d_node.arg(1), std::out_of_range); +} + +TEST(CircleMaxPool2DTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleMaxPool2D maxpool2d_node; + + TestVisitor tv; + ASSERT_THROW(maxpool2d_node.accept(&tv), std::exception); +} + +TEST(CircleMaxPool2DTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleMaxPool2D maxpool2d_node; + + TestVisitor tv; + ASSERT_THROW(maxpool2d_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleMaximum.test.cpp b/compiler/luci/lang/src/Nodes/CircleMaximum.test.cpp index efe62f1..3fc6f11 100644 --- a/compiler/luci/lang/src/Nodes/CircleMaximum.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleMaximum.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleMaximum.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,9 +25,57 @@ TEST(CircleMaximumTest, constructor_P) { luci::CircleMaximum max_node; - ASSERT_EQ(max_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(max_node.opcode(), luci::CircleOpcode::MAXIMUM); + ASSERT_EQ(luci::CircleDialect::get(), max_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::MAXIMUM, max_node.opcode()); - ASSERT_EQ(max_node.x(), nullptr); - ASSERT_EQ(max_node.y(), nullptr); + ASSERT_EQ(nullptr, max_node.x()); + ASSERT_EQ(nullptr, max_node.y()); +} + +TEST(CircleMaximumTest, input_NEG) +{ + luci::CircleMaximum max_node; + luci::CircleMaximum node; + + max_node.x(&node); + max_node.y(&node); + ASSERT_NE(nullptr, max_node.x()); + ASSERT_NE(nullptr, max_node.y()); + + max_node.x(nullptr); + max_node.y(nullptr); + ASSERT_EQ(nullptr, max_node.x()); + ASSERT_EQ(nullptr, max_node.y()); +} + +TEST(CircleMaximumTest, arity_NEG) +{ + luci::CircleMaximum max_node; + + ASSERT_NO_THROW(max_node.arg(1)); + ASSERT_THROW(max_node.arg(2), std::out_of_range); +} + +TEST(CircleMaximumTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleMaximum max_node; + + TestVisitor tv; + ASSERT_THROW(max_node.accept(&tv), std::exception); +} + +TEST(CircleMaximumTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleMaximum max_node; + + TestVisitor tv; + ASSERT_THROW(max_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleMean.test.cpp b/compiler/luci/lang/src/Nodes/CircleMean.test.cpp new file mode 100644 index 0000000..7430639 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleMean.test.cpp @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleMean.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleMeanTest, constructor) +{ + luci::CircleMean mean_node; + + ASSERT_EQ(luci::CircleDialect::get(), mean_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::MEAN, mean_node.opcode()); + + ASSERT_EQ(nullptr, mean_node.input()); + ASSERT_EQ(nullptr, mean_node.reduction_indices()); + + ASSERT_FALSE(mean_node.keep_dims()); +} + +TEST(CircleMeanTest, input_NEG) +{ + luci::CircleMean mean_node; + luci::CircleMean node; + + mean_node.input(&node); + mean_node.reduction_indices(&node); + ASSERT_NE(nullptr, mean_node.input()); + ASSERT_NE(nullptr, mean_node.reduction_indices()); + + mean_node.input(nullptr); + mean_node.reduction_indices(nullptr); + ASSERT_EQ(nullptr, mean_node.input()); + ASSERT_EQ(nullptr, mean_node.reduction_indices()); + + mean_node.keep_dims(true); + ASSERT_TRUE(mean_node.keep_dims()); +} + +TEST(CircleMeanTest, arity_NEG) +{ + luci::CircleMean mean_node; + + ASSERT_NO_THROW(mean_node.arg(1)); + ASSERT_THROW(mean_node.arg(2), std::out_of_range); +} + +TEST(CircleMeanTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleMean mean_node; + + TestVisitor tv; + ASSERT_THROW(mean_node.accept(&tv), std::exception); +} + +TEST(CircleMeanTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleMean mean_node; + + TestVisitor tv; + ASSERT_THROW(mean_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleMinimum.test.cpp b/compiler/luci/lang/src/Nodes/CircleMinimum.test.cpp new file mode 100644 index 0000000..19fe69f --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleMinimum.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleMinimum.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleMinimumTest, constructor_P) +{ + luci::CircleMinimum min_node; + + ASSERT_EQ(luci::CircleDialect::get(), min_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::MINIMUM, min_node.opcode()); + + ASSERT_EQ(nullptr, min_node.x()); + ASSERT_EQ(nullptr, min_node.y()); +} + +TEST(CircleMinimumTest, input_NEG) +{ + luci::CircleMinimum min_node; + luci::CircleMinimum node; + + min_node.x(&node); + min_node.y(&node); + ASSERT_NE(nullptr, min_node.x()); + ASSERT_NE(nullptr, min_node.y()); + + min_node.x(nullptr); + min_node.y(nullptr); + ASSERT_EQ(nullptr, min_node.x()); + ASSERT_EQ(nullptr, min_node.y()); +} + +TEST(CircleMinimumTest, arity_NEG) +{ + luci::CircleMinimum min_node; + + ASSERT_NO_THROW(min_node.arg(1)); + ASSERT_THROW(min_node.arg(2), std::out_of_range); +} + +TEST(CircleMinimumTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleMinimum min_node; + + TestVisitor tv; + ASSERT_THROW(min_node.accept(&tv), std::exception); +} + +TEST(CircleMinimumTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleMinimum min_node; + + TestVisitor tv; + ASSERT_THROW(min_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleMirrorPad.test.cpp b/compiler/luci/lang/src/Nodes/CircleMirrorPad.test.cpp new file mode 100644 index 0000000..9ba6bf5 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleMirrorPad.test.cpp @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleMirrorPad.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleMirrorPadTest, constructor_P) +{ + luci::CircleMirrorPad pad_node; + + ASSERT_EQ(luci::CircleDialect::get(), pad_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::MIRROR_PAD, pad_node.opcode()); + + ASSERT_EQ(nullptr, pad_node.input()); + ASSERT_EQ(nullptr, pad_node.paddings()); + + ASSERT_EQ(luci::MirrorPadMode::REFLECT, pad_node.mode()); +} + +TEST(CircleMirrorPadTest, input_NEG) +{ + luci::CircleMirrorPad pad_node; + luci::CircleMirrorPad node; + + pad_node.input(&node); + pad_node.paddings(&node); + ASSERT_NE(nullptr, pad_node.input()); + ASSERT_NE(nullptr, pad_node.paddings()); + + pad_node.input(nullptr); + pad_node.paddings(nullptr); + ASSERT_EQ(nullptr, pad_node.input()); + ASSERT_EQ(nullptr, pad_node.paddings()); + + pad_node.mode(luci::MirrorPadMode::SYMMETRIC); + ASSERT_NE(luci::MirrorPadMode::REFLECT, pad_node.mode()); +} + +TEST(CircleMirrorPadTest, arity_NEG) +{ + luci::CircleMirrorPad pad_node; + + ASSERT_NO_THROW(pad_node.arg(1)); + ASSERT_THROW(pad_node.arg(2), std::out_of_range); +} + +TEST(CircleMirrorPadTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleMirrorPad pad_node; + + TestVisitor tv; + ASSERT_THROW(pad_node.accept(&tv), std::exception); +} + +TEST(CircleMirrorPadTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleMirrorPad pad_node; + + TestVisitor tv; + ASSERT_THROW(pad_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleMul.test.cpp b/compiler/luci/lang/src/Nodes/CircleMul.test.cpp index f9eca42..3c26d08 100644 --- a/compiler/luci/lang/src/Nodes/CircleMul.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleMul.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleMul.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,9 +25,57 @@ TEST(CircleMulTest, constructor_P) { luci::CircleMul mul_node; - ASSERT_EQ(mul_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(mul_node.opcode(), luci::CircleOpcode::MUL); + ASSERT_EQ(luci::CircleDialect::get(), mul_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::MUL, mul_node.opcode()); - ASSERT_EQ(mul_node.x(), nullptr); - ASSERT_EQ(mul_node.y(), nullptr); + ASSERT_EQ(nullptr, mul_node.x()); + ASSERT_EQ(nullptr, mul_node.y()); +} + +TEST(CircleMulTest, input_NEG) +{ + luci::CircleMul mul_node; + luci::CircleMul node; + + mul_node.x(&node); + mul_node.y(&node); + ASSERT_NE(nullptr, mul_node.x()); + ASSERT_NE(nullptr, mul_node.y()); + + mul_node.x(nullptr); + mul_node.y(nullptr); + ASSERT_EQ(nullptr, mul_node.x()); + ASSERT_EQ(nullptr, mul_node.y()); +} + +TEST(CircleMulTest, arity_NEG) +{ + luci::CircleMul mul_node; + + ASSERT_NO_THROW(mul_node.arg(1)); + ASSERT_THROW(mul_node.arg(2), std::out_of_range); +} + +TEST(CircleMulTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleMul mul_node; + + TestVisitor tv; + ASSERT_THROW(mul_node.accept(&tv), std::exception); +} + +TEST(CircleMulTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleMul mul_node; + + TestVisitor tv; + ASSERT_THROW(mul_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleNeg.test.cpp b/compiler/luci/lang/src/Nodes/CircleNeg.test.cpp new file mode 100644 index 0000000..4bcfa48 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleNeg.test.cpp @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleNeg.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleNegTest, constructor) +{ + luci::CircleNeg neg_node; + + ASSERT_EQ(luci::CircleDialect::get(), neg_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::NEG, neg_node.opcode()); + + ASSERT_EQ(nullptr, neg_node.x()); +} + +TEST(CircleNegTest, input_NEG) +{ + luci::CircleNeg neg_node; + luci::CircleNeg node; + + neg_node.x(&node); + ASSERT_NE(nullptr, neg_node.x()); + + neg_node.x(nullptr); + ASSERT_EQ(nullptr, neg_node.x()); +} + +TEST(CircleNegTest, arity_NEG) +{ + luci::CircleNeg neg_node; + + ASSERT_NO_THROW(neg_node.arg(0)); + ASSERT_THROW(neg_node.arg(1), std::out_of_range); +} + +TEST(CircleNegTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleNeg neg_node; + + TestVisitor tv; + ASSERT_THROW(neg_node.accept(&tv), std::exception); +} + +TEST(CircleNegTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleNeg neg_node; + + TestVisitor tv; + ASSERT_THROW(neg_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleNotEqual.test.cpp b/compiler/luci/lang/src/Nodes/CircleNotEqual.test.cpp new file mode 100644 index 0000000..e464a7b --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleNotEqual.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleNotEqual.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleNotEqualTest, constructor_P) +{ + luci::CircleNotEqual not_equal_node; + + ASSERT_EQ(luci::CircleDialect::get(), not_equal_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::NOT_EQUAL, not_equal_node.opcode()); + + ASSERT_EQ(nullptr, not_equal_node.x()); + ASSERT_EQ(nullptr, not_equal_node.y()); +} + +TEST(CircleNotEqualTest, input_NEG) +{ + luci::CircleNotEqual not_equal_node; + luci::CircleNotEqual node; + + not_equal_node.x(&node); + not_equal_node.y(&node); + ASSERT_NE(nullptr, not_equal_node.x()); + ASSERT_NE(nullptr, not_equal_node.y()); + + not_equal_node.x(nullptr); + not_equal_node.y(nullptr); + ASSERT_EQ(nullptr, not_equal_node.x()); + ASSERT_EQ(nullptr, not_equal_node.y()); +} + +TEST(CircleNotEqualTest, arity_NEG) +{ + luci::CircleNotEqual not_equal_node; + + ASSERT_NO_THROW(not_equal_node.arg(1)); + ASSERT_THROW(not_equal_node.arg(2), std::out_of_range); +} + +TEST(CircleNotEqualTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleNotEqual not_equal_node; + + TestVisitor tv; + ASSERT_THROW(not_equal_node.accept(&tv), std::exception); +} + +TEST(CircleNotEqualTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleNotEqual not_equal_node; + + TestVisitor tv; + ASSERT_THROW(not_equal_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleOneHot.test.cpp b/compiler/luci/lang/src/Nodes/CircleOneHot.test.cpp new file mode 100644 index 0000000..18e1045 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleOneHot.test.cpp @@ -0,0 +1,95 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleOneHot.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleOneHotTest, constructor) +{ + luci::CircleOneHot one_hot_node; + + ASSERT_EQ(luci::CircleDialect::get(), one_hot_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::ONE_HOT, one_hot_node.opcode()); + + ASSERT_EQ(nullptr, one_hot_node.indices()); + ASSERT_EQ(nullptr, one_hot_node.depth()); + ASSERT_EQ(nullptr, one_hot_node.on_value()); + ASSERT_EQ(nullptr, one_hot_node.off_value()); + ASSERT_EQ(-1, one_hot_node.axis()); +} + +TEST(CircleOneHotTest, input_NEG) +{ + luci::CircleOneHot one_hot_node; + luci::CircleOneHot node; + + one_hot_node.indices(&node); + one_hot_node.depth(&node); + one_hot_node.on_value(&node); + one_hot_node.off_value(&node); + ASSERT_NE(nullptr, one_hot_node.indices()); + ASSERT_NE(nullptr, one_hot_node.depth()); + ASSERT_NE(nullptr, one_hot_node.on_value()); + ASSERT_NE(nullptr, one_hot_node.off_value()); + + one_hot_node.indices(nullptr); + one_hot_node.depth(nullptr); + one_hot_node.on_value(nullptr); + one_hot_node.off_value(nullptr); + ASSERT_EQ(nullptr, one_hot_node.indices()); + ASSERT_EQ(nullptr, one_hot_node.depth()); + ASSERT_EQ(nullptr, one_hot_node.on_value()); + ASSERT_EQ(nullptr, one_hot_node.off_value()); + + one_hot_node.axis(1); + ASSERT_NE(-1, one_hot_node.axis()); +} + +TEST(CircleOneHotTest, arity_NEG) +{ + luci::CircleOneHot one_hot_node; + + ASSERT_NO_THROW(one_hot_node.arg(3)); + ASSERT_THROW(one_hot_node.arg(4), std::out_of_range); +} + +TEST(CircleOneHotTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleOneHot one_hot_node; + + TestVisitor tv; + ASSERT_THROW(one_hot_node.accept(&tv), std::exception); +} + +TEST(CircleOneHotTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleOneHot one_hot_node; + + TestVisitor tv; + ASSERT_THROW(one_hot_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CirclePRelu.test.cpp b/compiler/luci/lang/src/Nodes/CirclePRelu.test.cpp new file mode 100644 index 0000000..8355c6d --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CirclePRelu.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CirclePRelu.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CirclePReluTest, constructor_P) +{ + luci::CirclePRelu prelu_node; + + ASSERT_EQ(luci::CircleDialect::get(), prelu_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::PRELU, prelu_node.opcode()); + + ASSERT_EQ(nullptr, prelu_node.input()); + ASSERT_EQ(nullptr, prelu_node.alpha()); +} + +TEST(CirclePReluTest, input_NEG) +{ + luci::CirclePRelu prelu_node; + luci::CirclePRelu node; + + prelu_node.input(&node); + prelu_node.alpha(&node); + ASSERT_NE(nullptr, prelu_node.input()); + ASSERT_NE(nullptr, prelu_node.alpha()); + + prelu_node.input(nullptr); + prelu_node.alpha(nullptr); + ASSERT_EQ(nullptr, prelu_node.input()); + ASSERT_EQ(nullptr, prelu_node.alpha()); +} + +TEST(CirclePReluTest, arity_NEG) +{ + luci::CirclePRelu prelu_node; + + ASSERT_NO_THROW(prelu_node.arg(1)); + ASSERT_THROW(prelu_node.arg(2), std::out_of_range); +} + +TEST(CirclePReluTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CirclePRelu prelu_node; + + TestVisitor tv; + ASSERT_THROW(prelu_node.accept(&tv), std::exception); +} + +TEST(CirclePReluTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CirclePRelu prelu_node; + + TestVisitor tv; + ASSERT_THROW(prelu_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CirclePack.test.cpp b/compiler/luci/lang/src/Nodes/CirclePack.test.cpp index 5c9a96f..5e64f0d 100644 --- a/compiler/luci/lang/src/Nodes/CirclePack.test.cpp +++ b/compiler/luci/lang/src/Nodes/CirclePack.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CirclePack.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,12 +25,60 @@ TEST(CirclePackTest, constructor) { luci::CirclePack pack_node(3); - ASSERT_EQ(pack_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(pack_node.opcode(), luci::CircleOpcode::PACK); + ASSERT_EQ(luci::CircleDialect::get(), pack_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::PACK, pack_node.opcode()); - ASSERT_EQ(pack_node.axis(), 0); - ASSERT_EQ(pack_node.values_count(), 3); - ASSERT_EQ(pack_node.values(0), nullptr); - ASSERT_EQ(pack_node.values(1), nullptr); - ASSERT_EQ(pack_node.values(2), nullptr); + ASSERT_EQ(0, pack_node.axis()); + ASSERT_EQ(3, pack_node.values_count()); + ASSERT_EQ(nullptr, pack_node.values(0)); + ASSERT_EQ(nullptr, pack_node.values(1)); + ASSERT_EQ(nullptr, pack_node.values(2)); +} + +TEST(CirclePackTest, input_NEG) +{ + luci::CirclePack pack_node(2); + luci::CirclePack node(2); + + pack_node.values(0, &node); + pack_node.values(1, &node); + ASSERT_NE(nullptr, pack_node.values(0)); + ASSERT_NE(nullptr, pack_node.values(1)); + + pack_node.values(0, nullptr); + pack_node.values(1, nullptr); + ASSERT_EQ(nullptr, pack_node.values(0)); + ASSERT_EQ(nullptr, pack_node.values(1)); +} + +TEST(CirclePackTest, arity_NEG) +{ + luci::CirclePack pack_node(5); + + ASSERT_NO_THROW(pack_node.arg(4)); + ASSERT_THROW(pack_node.arg(5), std::out_of_range); +} + +TEST(CirclePackTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CirclePack pack_node(2); + + TestVisitor tv; + ASSERT_THROW(pack_node.accept(&tv), std::exception); +} + +TEST(CirclePackTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CirclePack pack_node(2); + + TestVisitor tv; + ASSERT_THROW(pack_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CirclePad.test.cpp b/compiler/luci/lang/src/Nodes/CirclePad.test.cpp index 3a23fa0..12c66b7 100644 --- a/compiler/luci/lang/src/Nodes/CirclePad.test.cpp +++ b/compiler/luci/lang/src/Nodes/CirclePad.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CirclePad.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,9 +25,57 @@ TEST(CirclePadTest, constructor_P) { luci::CirclePad pad_node; - ASSERT_EQ(pad_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(pad_node.opcode(), luci::CircleOpcode::PAD); + ASSERT_EQ(luci::CircleDialect::get(), pad_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::PAD, pad_node.opcode()); - ASSERT_EQ(pad_node.input(), nullptr); - ASSERT_EQ(pad_node.paddings(), nullptr); + ASSERT_EQ(nullptr, pad_node.input()); + ASSERT_EQ(nullptr, pad_node.paddings()); +} + +TEST(CirclePadTest, input_NEG) +{ + luci::CirclePad pad_node; + luci::CirclePad node; + + pad_node.input(&node); + pad_node.paddings(&node); + ASSERT_NE(nullptr, pad_node.input()); + ASSERT_NE(nullptr, pad_node.paddings()); + + pad_node.input(nullptr); + pad_node.paddings(nullptr); + ASSERT_EQ(nullptr, pad_node.input()); + ASSERT_EQ(nullptr, pad_node.paddings()); +} + +TEST(CirclePadTest, arity_NEG) +{ + luci::CirclePad pad_node; + + ASSERT_NO_THROW(pad_node.arg(1)); + ASSERT_THROW(pad_node.arg(2), std::out_of_range); +} + +TEST(CirclePadTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CirclePad pad_node; + + TestVisitor tv; + ASSERT_THROW(pad_node.accept(&tv), std::exception); +} + +TEST(CirclePadTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CirclePad pad_node; + + TestVisitor tv; + ASSERT_THROW(pad_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CirclePow.test.cpp b/compiler/luci/lang/src/Nodes/CirclePow.test.cpp new file mode 100644 index 0000000..67ba088 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CirclePow.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CirclePow.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CirclePowTest, constructor_P) +{ + luci::CirclePow pow_node; + + ASSERT_EQ(luci::CircleDialect::get(), pow_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::POW, pow_node.opcode()); + + ASSERT_EQ(nullptr, pow_node.x()); + ASSERT_EQ(nullptr, pow_node.y()); +} + +TEST(CirclePowTest, input_NEG) +{ + luci::CirclePow pow_node; + luci::CirclePow node; + + pow_node.x(&node); + pow_node.y(&node); + ASSERT_NE(nullptr, pow_node.x()); + ASSERT_NE(nullptr, pow_node.y()); + + pow_node.x(nullptr); + pow_node.y(nullptr); + ASSERT_EQ(nullptr, pow_node.x()); + ASSERT_EQ(nullptr, pow_node.y()); +} + +TEST(CirclePowTest, arity_NEG) +{ + luci::CirclePow pow_node; + + ASSERT_NO_THROW(pow_node.arg(1)); + ASSERT_THROW(pow_node.arg(2), std::out_of_range); +} + +TEST(CirclePowTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CirclePow pow_node; + + TestVisitor tv; + ASSERT_THROW(pow_node.accept(&tv), std::exception); +} + +TEST(CirclePowTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CirclePow pow_node; + + TestVisitor tv; + ASSERT_THROW(pow_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleRange.test.cpp b/compiler/luci/lang/src/Nodes/CircleRange.test.cpp new file mode 100644 index 0000000..dd54dfd --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleRange.test.cpp @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleRange.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleRangeTest, constructor) +{ + luci::CircleRange range_node; + + ASSERT_EQ(luci::CircleDialect::get(), range_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::RANGE, range_node.opcode()); + + ASSERT_EQ(nullptr, range_node.start()); + ASSERT_EQ(nullptr, range_node.limit()); + ASSERT_EQ(nullptr, range_node.delta()); +} + +TEST(CircleRangeTest, input_NEG) +{ + luci::CircleRange range_node; + luci::CircleRange node; + + range_node.start(&node); + range_node.limit(&node); + range_node.delta(&node); + ASSERT_NE(nullptr, range_node.start()); + ASSERT_NE(nullptr, range_node.limit()); + ASSERT_NE(nullptr, range_node.delta()); + + range_node.start(nullptr); + range_node.limit(nullptr); + range_node.delta(nullptr); + ASSERT_EQ(nullptr, range_node.start()); + ASSERT_EQ(nullptr, range_node.limit()); + ASSERT_EQ(nullptr, range_node.delta()); +} + +TEST(CircleRangeTest, arity_NEG) +{ + luci::CircleRange range_node; + + ASSERT_NO_THROW(range_node.arg(2)); + ASSERT_THROW(range_node.arg(3), std::out_of_range); +} + +TEST(CircleRangeTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleRange range_node; + + TestVisitor tv; + ASSERT_THROW(range_node.accept(&tv), std::exception); +} + +TEST(CircleRangeTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleRange range_node; + + TestVisitor tv; + ASSERT_THROW(range_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleRank.test.cpp b/compiler/luci/lang/src/Nodes/CircleRank.test.cpp new file mode 100644 index 0000000..e64eae2 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleRank.test.cpp @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleRank.h" + +#include "luci/IR/CircleDialect.h" + +#include + +TEST(CircleRankTest, constructor_P) +{ + luci::CircleRank rank_node; + + ASSERT_EQ(luci::CircleDialect::get(), rank_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::RANK, rank_node.opcode()); + + ASSERT_EQ(nullptr, rank_node.input()); +} diff --git a/compiler/luci/lang/src/Nodes/CircleReduceAny.test.cpp b/compiler/luci/lang/src/Nodes/CircleReduceAny.test.cpp new file mode 100644 index 0000000..cd5c6b7 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleReduceAny.test.cpp @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleReduceAny.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleReduceAnyTest, constructor) +{ + luci::CircleReduceAny reduce_any_node; + + ASSERT_EQ(luci::CircleDialect::get(), reduce_any_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::REDUCE_ANY, reduce_any_node.opcode()); + + ASSERT_EQ(nullptr, reduce_any_node.input()); + ASSERT_EQ(nullptr, reduce_any_node.reduction_indices()); + + ASSERT_FALSE(reduce_any_node.keep_dims()); +} + +TEST(CircleReduceAnyTest, input_NEG) +{ + luci::CircleReduceAny reduce_any_node; + luci::CircleReduceAny node; + + reduce_any_node.input(&node); + reduce_any_node.reduction_indices(&node); + ASSERT_NE(nullptr, reduce_any_node.input()); + ASSERT_NE(nullptr, reduce_any_node.reduction_indices()); + + reduce_any_node.input(nullptr); + reduce_any_node.reduction_indices(nullptr); + ASSERT_EQ(nullptr, reduce_any_node.input()); + ASSERT_EQ(nullptr, reduce_any_node.reduction_indices()); + + reduce_any_node.keep_dims(true); + ASSERT_TRUE(reduce_any_node.keep_dims()); +} + +TEST(CircleReduceAnyTest, arity_NEG) +{ + luci::CircleReduceAny reduce_any_node; + + ASSERT_NO_THROW(reduce_any_node.arg(1)); + ASSERT_THROW(reduce_any_node.arg(2), std::out_of_range); +} + +TEST(CircleReduceAnyTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleReduceAny reduce_any_node; + + TestVisitor tv; + ASSERT_THROW(reduce_any_node.accept(&tv), std::exception); +} + +TEST(CircleReduceAnyTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleReduceAny reduce_any_node; + + TestVisitor tv; + ASSERT_THROW(reduce_any_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleReduceMax.test.cpp b/compiler/luci/lang/src/Nodes/CircleReduceMax.test.cpp new file mode 100644 index 0000000..bdd1818 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleReduceMax.test.cpp @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleReduceMax.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleReduceMaxTest, constructor_P) +{ + luci::CircleReduceMax reduce_max_node; + + ASSERT_EQ(luci::CircleDialect::get(), reduce_max_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::REDUCE_MAX, reduce_max_node.opcode()); + + ASSERT_EQ(nullptr, reduce_max_node.input()); + ASSERT_EQ(nullptr, reduce_max_node.reduction_indices()); + + ASSERT_FALSE(reduce_max_node.keep_dims()); +} + +TEST(CircleReduceMaxTest, input_NEG) +{ + luci::CircleReduceMax reduce_max_node; + luci::CircleReduceMax node; + + reduce_max_node.input(&node); + reduce_max_node.reduction_indices(&node); + ASSERT_NE(nullptr, reduce_max_node.input()); + ASSERT_NE(nullptr, reduce_max_node.reduction_indices()); + + reduce_max_node.input(nullptr); + reduce_max_node.reduction_indices(nullptr); + ASSERT_EQ(nullptr, reduce_max_node.input()); + ASSERT_EQ(nullptr, reduce_max_node.reduction_indices()); + + reduce_max_node.keep_dims(true); + ASSERT_TRUE(reduce_max_node.keep_dims()); +} + +TEST(CircleReduceMaxTest, arity_NEG) +{ + luci::CircleReduceMax reduce_max_node; + + ASSERT_NO_THROW(reduce_max_node.arg(1)); + ASSERT_THROW(reduce_max_node.arg(2), std::out_of_range); +} + +TEST(CircleReduceMaxTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleReduceMax reduce_max_node; + + TestVisitor tv; + ASSERT_THROW(reduce_max_node.accept(&tv), std::exception); +} + +TEST(CircleReduceMaxTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleReduceMax reduce_max_node; + + TestVisitor tv; + ASSERT_THROW(reduce_max_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleReduceMin.test.cpp b/compiler/luci/lang/src/Nodes/CircleReduceMin.test.cpp new file mode 100644 index 0000000..ba99ae6 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleReduceMin.test.cpp @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleReduceMin.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleReduceMinTest, constructor_P) +{ + luci::CircleReduceMin reduce_min_node; + + ASSERT_EQ(luci::CircleDialect::get(), reduce_min_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::REDUCE_MIN, reduce_min_node.opcode()); + + ASSERT_EQ(nullptr, reduce_min_node.input()); + ASSERT_EQ(nullptr, reduce_min_node.reduction_indices()); + + ASSERT_FALSE(reduce_min_node.keep_dims()); +} + +TEST(CircleReduceMinTest, input_NEG) +{ + luci::CircleReduceMin reduce_min_node; + luci::CircleReduceMin node; + + reduce_min_node.input(&node); + reduce_min_node.reduction_indices(&node); + ASSERT_NE(nullptr, reduce_min_node.input()); + ASSERT_NE(nullptr, reduce_min_node.reduction_indices()); + + reduce_min_node.input(nullptr); + reduce_min_node.reduction_indices(nullptr); + ASSERT_EQ(nullptr, reduce_min_node.input()); + ASSERT_EQ(nullptr, reduce_min_node.reduction_indices()); + + reduce_min_node.keep_dims(true); + ASSERT_TRUE(reduce_min_node.keep_dims()); +} + +TEST(CircleReduceMinTest, arity_NEG) +{ + luci::CircleReduceMin reduce_min_node; + + ASSERT_NO_THROW(reduce_min_node.arg(1)); + ASSERT_THROW(reduce_min_node.arg(2), std::out_of_range); +} + +TEST(CircleReduceMinTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleReduceMin reduce_min_node; + + TestVisitor tv; + ASSERT_THROW(reduce_min_node.accept(&tv), std::exception); +} + +TEST(CircleReduceMinTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleReduceMin reduce_min_node; + + TestVisitor tv; + ASSERT_THROW(reduce_min_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleReduceProd.test.cpp b/compiler/luci/lang/src/Nodes/CircleReduceProd.test.cpp new file mode 100644 index 0000000..f60b290 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleReduceProd.test.cpp @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleReduceProd.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleReduceProdTest, constructor) +{ + luci::CircleReduceProd reduce_prod_node; + + ASSERT_EQ(luci::CircleDialect::get(), reduce_prod_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::REDUCE_PROD, reduce_prod_node.opcode()); + + ASSERT_EQ(nullptr, reduce_prod_node.input()); + ASSERT_EQ(nullptr, reduce_prod_node.reduction_indices()); + + ASSERT_FALSE(reduce_prod_node.keep_dims()); +} + +TEST(CircleReduceProdTest, input_NEG) +{ + luci::CircleReduceProd reduce_prod_node; + luci::CircleReduceProd node; + + reduce_prod_node.input(&node); + reduce_prod_node.reduction_indices(&node); + ASSERT_NE(nullptr, reduce_prod_node.input()); + ASSERT_NE(nullptr, reduce_prod_node.reduction_indices()); + + reduce_prod_node.input(nullptr); + reduce_prod_node.reduction_indices(nullptr); + ASSERT_EQ(nullptr, reduce_prod_node.input()); + ASSERT_EQ(nullptr, reduce_prod_node.reduction_indices()); + + reduce_prod_node.keep_dims(true); + ASSERT_TRUE(reduce_prod_node.keep_dims()); +} + +TEST(CircleReduceProdTest, arity_NEG) +{ + luci::CircleReduceProd reduce_prod_node; + + ASSERT_NO_THROW(reduce_prod_node.arg(1)); + ASSERT_THROW(reduce_prod_node.arg(2), std::out_of_range); +} + +TEST(CircleReduceProdTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleReduceProd reduce_prod_node; + + TestVisitor tv; + ASSERT_THROW(reduce_prod_node.accept(&tv), std::exception); +} + +TEST(CircleReduceProdTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleReduceProd reduce_prod_node; + + TestVisitor tv; + ASSERT_THROW(reduce_prod_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleRelu.test.cpp b/compiler/luci/lang/src/Nodes/CircleRelu.test.cpp index 19ea88a..3579650 100644 --- a/compiler/luci/lang/src/Nodes/CircleRelu.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleRelu.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleRelu.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,8 +25,52 @@ TEST(CircleReluTest, constructor_P) { luci::CircleRelu relu_node; - ASSERT_EQ(relu_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(relu_node.opcode(), luci::CircleOpcode::RELU); + ASSERT_EQ(luci::CircleDialect::get(), relu_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::RELU, relu_node.opcode()); - ASSERT_EQ(relu_node.features(), nullptr); + ASSERT_EQ(nullptr, relu_node.features()); +} + +TEST(CircleReluTest, input_NEG) +{ + luci::CircleRelu relu_node; + luci::CircleRelu node; + + relu_node.features(&node); + ASSERT_NE(nullptr, relu_node.features()); + + relu_node.features(nullptr); + ASSERT_EQ(nullptr, relu_node.features()); +} + +TEST(CircleReluTest, arity_NEG) +{ + luci::CircleRelu relu_node; + + ASSERT_NO_THROW(relu_node.arg(0)); + ASSERT_THROW(relu_node.arg(1), std::out_of_range); +} + +TEST(CircleReluTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleRelu relu_node; + + TestVisitor tv; + ASSERT_THROW(relu_node.accept(&tv), std::exception); +} + +TEST(CircleReluTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleRelu relu_node; + + TestVisitor tv; + ASSERT_THROW(relu_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleRelu6.test.cpp b/compiler/luci/lang/src/Nodes/CircleRelu6.test.cpp index 74bf2e8..647a5d7 100644 --- a/compiler/luci/lang/src/Nodes/CircleRelu6.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleRelu6.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleRelu6.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,8 +25,52 @@ TEST(CircleRelu6Test, constructor_P) { luci::CircleRelu6 relu6_node; - ASSERT_EQ(relu6_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(relu6_node.opcode(), luci::CircleOpcode::RELU6); + ASSERT_EQ(luci::CircleDialect::get(), relu6_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::RELU6, relu6_node.opcode()); - ASSERT_EQ(relu6_node.features(), nullptr); + ASSERT_EQ(nullptr, relu6_node.features()); +} + +TEST(CircleRelu6Test, input_NEG) +{ + luci::CircleRelu6 relu6_node; + luci::CircleRelu6 node; + + relu6_node.features(&node); + ASSERT_NE(nullptr, relu6_node.features()); + + relu6_node.features(nullptr); + ASSERT_EQ(nullptr, relu6_node.features()); +} + +TEST(CircleRelu6Test, arity_NEG) +{ + luci::CircleRelu6 relu6_node; + + ASSERT_NO_THROW(relu6_node.arg(0)); + ASSERT_THROW(relu6_node.arg(1), std::out_of_range); +} + +TEST(CircleRelu6Test, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleRelu6 relu6_node; + + TestVisitor tv; + ASSERT_THROW(relu6_node.accept(&tv), std::exception); +} + +TEST(CircleRelu6Test, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleRelu6 relu6_node; + + TestVisitor tv; + ASSERT_THROW(relu6_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleReluN1To1.test.cpp b/compiler/luci/lang/src/Nodes/CircleReluN1To1.test.cpp new file mode 100644 index 0000000..8de84ac --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleReluN1To1.test.cpp @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleReluN1To1.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleReluN1To1Test, constructor) +{ + luci::CircleReluN1To1 relu_node; + + ASSERT_EQ(luci::CircleDialect::get(), relu_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::RELU_N1_TO_1, relu_node.opcode()); + + ASSERT_EQ(nullptr, relu_node.features()); +} + +TEST(CircleReluN1To1Test, input_NEG) +{ + luci::CircleReluN1To1 relu_node; + luci::CircleReluN1To1 node; + + relu_node.features(&node); + ASSERT_NE(nullptr, relu_node.features()); + + relu_node.features(nullptr); + ASSERT_EQ(nullptr, relu_node.features()); +} + +TEST(CircleReluN1To1Test, arity_NEG) +{ + luci::CircleReluN1To1 relu_node; + + ASSERT_NO_THROW(relu_node.arg(0)); + ASSERT_THROW(relu_node.arg(1), std::out_of_range); +} + +TEST(CircleReluN1To1Test, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleReluN1To1 relu_node; + + TestVisitor tv; + ASSERT_THROW(relu_node.accept(&tv), std::exception); +} + +TEST(CircleReluN1To1Test, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleReluN1To1 relu_node; + + TestVisitor tv; + ASSERT_THROW(relu_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleReshape.test.cpp b/compiler/luci/lang/src/Nodes/CircleReshape.test.cpp index 7bc2d32..236fde2 100644 --- a/compiler/luci/lang/src/Nodes/CircleReshape.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleReshape.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleReshape.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,12 +25,12 @@ TEST(CircleReshapeTest, constructor_P) { luci::CircleReshape reshape; - ASSERT_EQ(reshape.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(reshape.opcode(), luci::CircleOpcode::RESHAPE); + ASSERT_EQ(luci::CircleDialect::get(), reshape.dialect()); + ASSERT_EQ(luci::CircleOpcode::RESHAPE, reshape.opcode()); - ASSERT_EQ(reshape.tensor(), nullptr); - ASSERT_EQ(reshape.shape(), nullptr); - ASSERT_EQ(reshape.newShape()->rank(), 0); + ASSERT_EQ(nullptr, reshape.tensor()); + ASSERT_EQ(nullptr, reshape.shape()); + ASSERT_EQ(0, reshape.newShape()->rank()); } TEST(CircleReshapeTest, alloc_new_shape_P) @@ -37,12 +38,60 @@ TEST(CircleReshapeTest, alloc_new_shape_P) luci::CircleReshape reshape; reshape.newShape()->rank(2); - ASSERT_EQ(reshape.newShape()->rank(), 2); + ASSERT_EQ(2, reshape.newShape()->rank()); reshape.newShape()->dim(0) = 0; reshape.newShape()->dim(1) = 1; auto &const_reshape = const_cast(reshape); - ASSERT_EQ(const_reshape.newShape()->dim(0), 0); - ASSERT_EQ(const_reshape.newShape()->dim(1), 1); + ASSERT_EQ(0, const_reshape.newShape()->dim(0)); + ASSERT_EQ(1, const_reshape.newShape()->dim(1)); +} + +TEST(CircleReshapeTest, input_NEG) +{ + luci::CircleReshape reshape_node; + luci::CircleReshape node; + + reshape_node.tensor(&node); + reshape_node.shape(&node); + ASSERT_NE(nullptr, reshape_node.tensor()); + ASSERT_NE(nullptr, reshape_node.shape()); + + reshape_node.tensor(nullptr); + reshape_node.shape(nullptr); + ASSERT_EQ(nullptr, reshape_node.tensor()); + ASSERT_EQ(nullptr, reshape_node.shape()); +} + +TEST(CircleReshapeTest, arity_NEG) +{ + luci::CircleReshape reshape_node; + + ASSERT_NO_THROW(reshape_node.arg(1)); + ASSERT_THROW(reshape_node.arg(2), std::out_of_range); +} + +TEST(CircleReshapeTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleReshape reshape_node; + + TestVisitor tv; + ASSERT_THROW(reshape_node.accept(&tv), std::exception); +} + +TEST(CircleReshapeTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleReshape reshape_node; + + TestVisitor tv; + ASSERT_THROW(reshape_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleResizeBilinear.test.cpp b/compiler/luci/lang/src/Nodes/CircleResizeBilinear.test.cpp new file mode 100644 index 0000000..a1481a6 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleResizeBilinear.test.cpp @@ -0,0 +1,88 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleResizeBilinear.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleResizeBilinearTest, constructor) +{ + luci::CircleResizeBilinear resize; + + ASSERT_EQ(luci::CircleDialect::get(), resize.dialect()); + ASSERT_EQ(luci::CircleOpcode::RESIZE_BILINEAR, resize.opcode()); + + ASSERT_EQ(nullptr, resize.input()); + ASSERT_EQ(nullptr, resize.size()); + ASSERT_FALSE(resize.align_corners()); + ASSERT_FALSE(resize.half_pixel_centers()); +} + +TEST(CircleResizeBilinearTest, input_NEG) +{ + luci::CircleResizeBilinear resize_node; + luci::CircleResizeBilinear node; + + resize_node.input(&node); + resize_node.size(&node); + ASSERT_NE(nullptr, resize_node.input()); + ASSERT_NE(nullptr, resize_node.size()); + + resize_node.input(nullptr); + resize_node.size(nullptr); + ASSERT_EQ(nullptr, resize_node.input()); + ASSERT_EQ(nullptr, resize_node.size()); + + resize_node.align_corners(true); + ASSERT_TRUE(resize_node.align_corners()); + resize_node.half_pixel_centers(true); + ASSERT_TRUE(resize_node.half_pixel_centers()); +} + +TEST(CircleResizeBilinearTest, arity_NEG) +{ + luci::CircleResizeBilinear resize_node; + + ASSERT_NO_THROW(resize_node.arg(1)); + ASSERT_THROW(resize_node.arg(2), std::out_of_range); +} + +TEST(CircleResizeBilinearTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleResizeBilinear resize_node; + + TestVisitor tv; + ASSERT_THROW(resize_node.accept(&tv), std::exception); +} + +TEST(CircleResizeBilinearTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleResizeBilinear resize_node; + + TestVisitor tv; + ASSERT_THROW(resize_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleResizeNearestNeighbor.test.cpp b/compiler/luci/lang/src/Nodes/CircleResizeNearestNeighbor.test.cpp new file mode 100644 index 0000000..00e0ae9 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleResizeNearestNeighbor.test.cpp @@ -0,0 +1,85 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleResizeNearestNeighbor.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleResizeNearestNeightborTest, constructor) +{ + luci::CircleResizeNearestNeighbor resize; + + ASSERT_EQ(luci::CircleDialect::get(), resize.dialect()); + ASSERT_EQ(luci::CircleOpcode::RESIZE_NEAREST_NEIGHBOR, resize.opcode()); + + ASSERT_EQ(nullptr, resize.input()); + ASSERT_EQ(nullptr, resize.size()); + ASSERT_FALSE(resize.align_corners()); +} + +TEST(CircleResizeNearestNeightborTest, input_NEG) +{ + luci::CircleResizeNearestNeighbor resize_node; + luci::CircleResizeNearestNeighbor node; + + resize_node.input(&node); + resize_node.size(&node); + ASSERT_NE(nullptr, resize_node.input()); + ASSERT_NE(nullptr, resize_node.size()); + + resize_node.input(nullptr); + resize_node.size(nullptr); + ASSERT_EQ(nullptr, resize_node.input()); + ASSERT_EQ(nullptr, resize_node.size()); + + resize_node.align_corners(true); + ASSERT_TRUE(resize_node.align_corners()); +} + +TEST(CircleResizeNearestNeightborTest, arity_NEG) +{ + luci::CircleResizeNearestNeighbor resize_node; + + ASSERT_NO_THROW(resize_node.arg(1)); + ASSERT_THROW(resize_node.arg(2), std::out_of_range); +} + +TEST(CircleResizeNearestNeightborTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleResizeNearestNeighbor resize_node; + + TestVisitor tv; + ASSERT_THROW(resize_node.accept(&tv), std::exception); +} + +TEST(CircleResizeNearestNeightborTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleResizeNearestNeighbor resize_node; + + TestVisitor tv; + ASSERT_THROW(resize_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleReverseSequence.test.cpp b/compiler/luci/lang/src/Nodes/CircleReverseSequence.test.cpp new file mode 100644 index 0000000..b1cc6d6 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleReverseSequence.test.cpp @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleReverseSequence.h" + +#include "luci/IR/CircleDialect.h" + +#include + +TEST(CircleReverseSequenceTest, constructor_P) +{ + luci::CircleReverseSequence std_node; + + ASSERT_EQ(luci::CircleDialect::get(), std_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::REVERSE_SEQUENCE, std_node.opcode()); + + ASSERT_EQ(nullptr, std_node.input()); + ASSERT_EQ(nullptr, std_node.seq_lengths()); + + ASSERT_EQ(0, std_node.seq_axis()); + ASSERT_EQ(0, std_node.batch_axis()); +} diff --git a/compiler/luci/lang/src/Nodes/CircleReverseV2.test.cpp b/compiler/luci/lang/src/Nodes/CircleReverseV2.test.cpp new file mode 100644 index 0000000..cc568e8 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleReverseV2.test.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleReverseV2.h" + +#include "luci/IR/CircleDialect.h" + +#include + +TEST(CircleReverseV2, constructor_P) +{ + luci::CircleReverseV2 std_node; + + ASSERT_EQ(luci::CircleDialect::get(), std_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::REVERSE_V2, std_node.opcode()); + + ASSERT_EQ(nullptr, std_node.tensor()); + ASSERT_EQ(nullptr, std_node.axis()); +} diff --git a/compiler/luci/lang/src/Nodes/CircleRound.test.cpp b/compiler/luci/lang/src/Nodes/CircleRound.test.cpp new file mode 100644 index 0000000..2f4518d --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleRound.test.cpp @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleRound.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleRoundTest, constructor_P) +{ + luci::CircleRound round_node; + + ASSERT_EQ(luci::CircleDialect::get(), round_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::ROUND, round_node.opcode()); + + ASSERT_EQ(nullptr, round_node.x()); +} + +TEST(CircleRoundTest, input_NEG) +{ + luci::CircleRound round_node; + luci::CircleRound node; + + round_node.x(&node); + ASSERT_NE(nullptr, round_node.x()); + + round_node.x(nullptr); + ASSERT_EQ(nullptr, round_node.x()); +} + +TEST(CircleRoundTest, arity_NEG) +{ + luci::CircleRound round_node; + + ASSERT_NO_THROW(round_node.arg(0)); + ASSERT_THROW(round_node.arg(1), std::out_of_range); +} + +TEST(CircleRoundTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleRound round_node; + + TestVisitor tv; + ASSERT_THROW(round_node.accept(&tv), std::exception); +} + +TEST(CircleRoundTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleRound round_node; + + TestVisitor tv; + ASSERT_THROW(round_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleRsqrt.test.cpp b/compiler/luci/lang/src/Nodes/CircleRsqrt.test.cpp index 51f6bab..d038979 100644 --- a/compiler/luci/lang/src/Nodes/CircleRsqrt.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleRsqrt.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleRsqrt.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,8 +25,52 @@ TEST(CircleRsqrtTest, constructor) { luci::CircleRsqrt rsqrt_node; - ASSERT_EQ(rsqrt_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(rsqrt_node.opcode(), luci::CircleOpcode::RSQRT); + ASSERT_EQ(luci::CircleDialect::get(), rsqrt_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::RSQRT, rsqrt_node.opcode()); - ASSERT_EQ(rsqrt_node.x(), nullptr); + ASSERT_EQ(nullptr, rsqrt_node.x()); +} + +TEST(CircleRsqrtTest, input_NEG) +{ + luci::CircleRsqrt rsqrt_node; + luci::CircleRsqrt node; + + rsqrt_node.x(&node); + ASSERT_NE(nullptr, rsqrt_node.x()); + + rsqrt_node.x(nullptr); + ASSERT_EQ(nullptr, rsqrt_node.x()); +} + +TEST(CircleRsqrtTest, arity_NEG) +{ + luci::CircleRsqrt rsqrt_node; + + ASSERT_NO_THROW(rsqrt_node.arg(0)); + ASSERT_THROW(rsqrt_node.arg(1), std::out_of_range); +} + +TEST(CircleRsqrtTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleRsqrt rsqrt_node; + + TestVisitor tv; + ASSERT_THROW(rsqrt_node.accept(&tv), std::exception); +} + +TEST(CircleRsqrtTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleRsqrt rsqrt_node; + + TestVisitor tv; + ASSERT_THROW(rsqrt_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleScatterNd.test.cpp b/compiler/luci/lang/src/Nodes/CircleScatterNd.test.cpp new file mode 100644 index 0000000..165f26b --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleScatterNd.test.cpp @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleScatterNd.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleScatterNdTest, constructor_P) +{ + luci::CircleScatterNd scatter_nd_node; + + ASSERT_EQ(luci::CircleDialect::get(), scatter_nd_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SCATTER_ND, scatter_nd_node.opcode()); + + ASSERT_EQ(nullptr, scatter_nd_node.indices()); + ASSERT_EQ(nullptr, scatter_nd_node.updates()); + ASSERT_EQ(nullptr, scatter_nd_node.shape()); +} + +TEST(CircleScatterNdTest, input_NEG) +{ + luci::CircleScatterNd scatter_nd_node; + luci::CircleScatterNd node; + + scatter_nd_node.indices(&node); + scatter_nd_node.updates(&node); + scatter_nd_node.shape(&node); + ASSERT_NE(nullptr, scatter_nd_node.indices()); + ASSERT_NE(nullptr, scatter_nd_node.updates()); + ASSERT_NE(nullptr, scatter_nd_node.shape()); + + scatter_nd_node.indices(nullptr); + scatter_nd_node.updates(nullptr); + scatter_nd_node.shape(nullptr); + ASSERT_EQ(nullptr, scatter_nd_node.indices()); + ASSERT_EQ(nullptr, scatter_nd_node.updates()); + ASSERT_EQ(nullptr, scatter_nd_node.shape()); +} + +TEST(CircleScatterNdTest, arity_NEG) +{ + luci::CircleScatterNd scatter_nd_node; + + ASSERT_NO_THROW(scatter_nd_node.arg(2)); + ASSERT_THROW(scatter_nd_node.arg(3), std::out_of_range); +} + +TEST(CircleScatterNdTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleScatterNd scatter_nd_node; + + TestVisitor tv; + ASSERT_THROW(scatter_nd_node.accept(&tv), std::exception); +} + +TEST(CircleScatterNdTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleScatterNd scatter_nd_node; + + TestVisitor tv; + ASSERT_THROW(scatter_nd_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleSegmentSum.test.cpp b/compiler/luci/lang/src/Nodes/CircleSegmentSum.test.cpp new file mode 100644 index 0000000..90469b7 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleSegmentSum.test.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleSegmentSum.h" + +#include "luci/IR/CircleDialect.h" + +#include + +TEST(CircleSegmentSumTest, constructor) +{ + luci::CircleSegmentSum segment_sum_node; + + ASSERT_EQ(luci::CircleDialect::get(), segment_sum_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SEGMENT_SUM, segment_sum_node.opcode()); + + ASSERT_EQ(nullptr, segment_sum_node.input()); + ASSERT_EQ(nullptr, segment_sum_node.segment_ids()); +} diff --git a/compiler/luci/lang/src/Nodes/CircleSelect.test.cpp b/compiler/luci/lang/src/Nodes/CircleSelect.test.cpp new file mode 100644 index 0000000..7eeb538 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleSelect.test.cpp @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleSelect.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleSelectTest, constructor) +{ + luci::CircleSelect select_node; + + ASSERT_EQ(luci::CircleDialect::get(), select_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SELECT, select_node.opcode()); + + ASSERT_EQ(nullptr, select_node.condition()); + ASSERT_EQ(nullptr, select_node.t()); + ASSERT_EQ(nullptr, select_node.e()); +} + +TEST(CircleSelectTest, input_NEG) +{ + luci::CircleSelect select_node; + luci::CircleSelect node; + + select_node.condition(&node); + select_node.t(&node); + select_node.e(&node); + ASSERT_NE(nullptr, select_node.condition()); + ASSERT_NE(nullptr, select_node.t()); + ASSERT_NE(nullptr, select_node.e()); + + select_node.condition(nullptr); + select_node.t(nullptr); + select_node.e(nullptr); + ASSERT_EQ(nullptr, select_node.condition()); + ASSERT_EQ(nullptr, select_node.t()); + ASSERT_EQ(nullptr, select_node.e()); +} + +TEST(CircleSelectTest, arity_NEG) +{ + luci::CircleSelect select_node; + + ASSERT_NO_THROW(select_node.arg(2)); + ASSERT_THROW(select_node.arg(3), std::out_of_range); +} + +TEST(CircleSelectTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSelect select_node; + + TestVisitor tv; + ASSERT_THROW(select_node.accept(&tv), std::exception); +} + +TEST(CircleSelectTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSelect select_node; + + TestVisitor tv; + ASSERT_THROW(select_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleSelectV2.test.cpp b/compiler/luci/lang/src/Nodes/CircleSelectV2.test.cpp new file mode 100644 index 0000000..eea5fb8 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleSelectV2.test.cpp @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleSelectV2.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleSelectV2Test, constructor) +{ + luci::CircleSelectV2 select_node; + + ASSERT_EQ(luci::CircleDialect::get(), select_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SELECT_V2, select_node.opcode()); + + ASSERT_EQ(nullptr, select_node.condition()); + ASSERT_EQ(nullptr, select_node.t()); + ASSERT_EQ(nullptr, select_node.e()); +} + +TEST(CircleSelectV2Test, input_NEG) +{ + luci::CircleSelectV2 select_v2_node; + luci::CircleSelectV2 node; + + select_v2_node.condition(&node); + select_v2_node.t(&node); + select_v2_node.e(&node); + ASSERT_NE(nullptr, select_v2_node.condition()); + ASSERT_NE(nullptr, select_v2_node.t()); + ASSERT_NE(nullptr, select_v2_node.e()); + + select_v2_node.condition(nullptr); + select_v2_node.t(nullptr); + select_v2_node.e(nullptr); + ASSERT_EQ(nullptr, select_v2_node.condition()); + ASSERT_EQ(nullptr, select_v2_node.t()); + ASSERT_EQ(nullptr, select_v2_node.e()); +} + +TEST(CircleSelectV2Test, arity_NEG) +{ + luci::CircleSelectV2 select_v2_node; + + ASSERT_NO_THROW(select_v2_node.arg(2)); + ASSERT_THROW(select_v2_node.arg(3), std::out_of_range); +} + +TEST(CircleSelectV2Test, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSelectV2 select_v2_node; + + TestVisitor tv; + ASSERT_THROW(select_v2_node.accept(&tv), std::exception); +} + +TEST(CircleSelectV2Test, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSelectV2 select_v2_node; + + TestVisitor tv; + ASSERT_THROW(select_v2_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleShape.test.cpp b/compiler/luci/lang/src/Nodes/CircleShape.test.cpp new file mode 100644 index 0000000..18271d2 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleShape.test.cpp @@ -0,0 +1,80 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleShape.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleShapeTest, constructor) +{ + luci::CircleShape shape_node; + + ASSERT_EQ(luci::CircleDialect::get(), shape_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SHAPE, shape_node.opcode()); + + ASSERT_EQ(nullptr, shape_node.input()); + ASSERT_EQ(loco::DataType::S32, shape_node.out_type()); +} + +TEST(CircleShapeTest, input_NEG) +{ + luci::CircleShape shape_node; + luci::CircleShape node; + + shape_node.input(&node); + ASSERT_NE(nullptr, shape_node.input()); + + shape_node.input(nullptr); + ASSERT_EQ(nullptr, shape_node.input()); + + shape_node.out_type(loco::DataType::U8); + ASSERT_NE(loco::DataType::S32, shape_node.out_type()); +} + +TEST(CircleShapeTest, arity_NEG) +{ + luci::CircleShape shape_node; + + ASSERT_NO_THROW(shape_node.arg(0)); + ASSERT_THROW(shape_node.arg(1), std::out_of_range); +} + +TEST(CircleShapeTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleShape shape_node; + + TestVisitor tv; + ASSERT_THROW(shape_node.accept(&tv), std::exception); +} + +TEST(CircleShapeTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleShape shape_node; + + TestVisitor tv; + ASSERT_THROW(shape_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleSin.test.cpp b/compiler/luci/lang/src/Nodes/CircleSin.test.cpp new file mode 100644 index 0000000..e01932d --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleSin.test.cpp @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleSin.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleSinTest, constructor) +{ + luci::CircleSin sin_node; + + ASSERT_EQ(luci::CircleDialect::get(), sin_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SIN, sin_node.opcode()); + + ASSERT_EQ(nullptr, sin_node.x()); +} + +TEST(CircleSinTest, input_NEG) +{ + luci::CircleSin sin_node; + luci::CircleSin node; + + sin_node.x(&node); + ASSERT_NE(nullptr, sin_node.x()); + + sin_node.x(nullptr); + ASSERT_EQ(nullptr, sin_node.x()); +} + +TEST(CircleSinTest, arity_NEG) +{ + luci::CircleSin sin_node; + + ASSERT_NO_THROW(sin_node.arg(0)); + ASSERT_THROW(sin_node.arg(1), std::out_of_range); +} + +TEST(CircleSinTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSin sin_node; + + TestVisitor tv; + ASSERT_THROW(sin_node.accept(&tv), std::exception); +} + +TEST(CircleSinTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSin sin_node; + + TestVisitor tv; + ASSERT_THROW(sin_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleSlice.test.cpp b/compiler/luci/lang/src/Nodes/CircleSlice.test.cpp new file mode 100644 index 0000000..5563a34 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleSlice.test.cpp @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleSlice.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleSliceTest, constructor) +{ + luci::CircleSlice s_node; + + ASSERT_EQ(luci::CircleDialect::get(), s_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SLICE, s_node.opcode()); + + ASSERT_EQ(nullptr, s_node.input()); + ASSERT_EQ(nullptr, s_node.begin()); + ASSERT_EQ(nullptr, s_node.size()); +} + +TEST(CircleSliceTest, input_NEG) +{ + luci::CircleSlice s_node; + luci::CircleSlice node; + + s_node.input(&node); + s_node.begin(&node); + s_node.size(&node); + ASSERT_NE(nullptr, s_node.input()); + ASSERT_NE(nullptr, s_node.begin()); + ASSERT_NE(nullptr, s_node.size()); + + s_node.input(nullptr); + s_node.begin(nullptr); + s_node.size(nullptr); + ASSERT_EQ(nullptr, s_node.input()); + ASSERT_EQ(nullptr, s_node.begin()); + ASSERT_EQ(nullptr, s_node.size()); +} + +TEST(CircleSliceTest, arity_NEG) +{ + luci::CircleSlice s_node; + + ASSERT_NO_THROW(s_node.arg(2)); + ASSERT_THROW(s_node.arg(3), std::out_of_range); +} + +TEST(CircleSliceTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSlice s_node; + + TestVisitor tv; + ASSERT_THROW(s_node.accept(&tv), std::exception); +} + +TEST(CircleSliceTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSlice s_node; + + TestVisitor tv; + ASSERT_THROW(s_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleSoftmax.test.cpp b/compiler/luci/lang/src/Nodes/CircleSoftmax.test.cpp index 7e99449..b15c009 100644 --- a/compiler/luci/lang/src/Nodes/CircleSoftmax.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleSoftmax.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleSoftmax.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,8 +25,52 @@ TEST(CircleSoftmaxTest, constructor_P) { luci::CircleSoftmax softmax_node; - ASSERT_EQ(softmax_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(softmax_node.opcode(), luci::CircleOpcode::SOFTMAX); + ASSERT_EQ(luci::CircleDialect::get(), softmax_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SOFTMAX, softmax_node.opcode()); - ASSERT_EQ(softmax_node.logits(), nullptr); + ASSERT_EQ(nullptr, softmax_node.logits()); +} + +TEST(CircleSoftmaxTest, input_NEG) +{ + luci::CircleSoftmax softmax_node; + luci::CircleSoftmax node; + + softmax_node.logits(&node); + ASSERT_NE(nullptr, softmax_node.logits()); + + softmax_node.logits(nullptr); + ASSERT_EQ(nullptr, softmax_node.logits()); +} + +TEST(CircleSoftmaxTest, arity_NEG) +{ + luci::CircleSoftmax softmax_node; + + ASSERT_NO_THROW(softmax_node.arg(0)); + ASSERT_THROW(softmax_node.arg(1), std::out_of_range); +} + +TEST(CircleSoftmaxTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSoftmax softmax_node; + + TestVisitor tv; + ASSERT_THROW(softmax_node.accept(&tv), std::exception); +} + +TEST(CircleSoftmaxTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSoftmax softmax_node; + + TestVisitor tv; + ASSERT_THROW(softmax_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleSpaceToBatchND.test.cpp b/compiler/luci/lang/src/Nodes/CircleSpaceToBatchND.test.cpp new file mode 100644 index 0000000..8b4ac8f --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleSpaceToBatchND.test.cpp @@ -0,0 +1,86 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleSpaceToBatchND.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleSpaceToBatchNDTest, constructor) +{ + luci::CircleSpaceToBatchND stb_node; + + ASSERT_EQ(luci::CircleDialect::get(), stb_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SPACE_TO_BATCH_ND, stb_node.opcode()); + + ASSERT_EQ(nullptr, stb_node.input()); + ASSERT_EQ(nullptr, stb_node.block_shape()); + ASSERT_EQ(nullptr, stb_node.paddings()); +} + +TEST(CircleSpaceToBatchNDTest, input_NEG) +{ + luci::CircleSpaceToBatchND stb_node; + luci::CircleSpaceToBatchND node; + + stb_node.input(&node); + stb_node.block_shape(&node); + stb_node.paddings(&node); + ASSERT_NE(nullptr, stb_node.input()); + ASSERT_NE(nullptr, stb_node.block_shape()); + ASSERT_NE(nullptr, stb_node.paddings()); + + stb_node.input(nullptr); + stb_node.block_shape(nullptr); + stb_node.paddings(nullptr); + ASSERT_EQ(nullptr, stb_node.input()); + ASSERT_EQ(nullptr, stb_node.block_shape()); + ASSERT_EQ(nullptr, stb_node.paddings()); +} + +TEST(CircleSpaceToBatchNDTest, arity_NEG) +{ + luci::CircleSpaceToBatchND stb_node; + + ASSERT_NO_THROW(stb_node.arg(2)); + ASSERT_THROW(stb_node.arg(3), std::out_of_range); +} + +TEST(CircleSpaceToBatchNDTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSpaceToBatchND stb_node; + + TestVisitor tv; + ASSERT_THROW(stb_node.accept(&tv), std::exception); +} + +TEST(CircleSpaceToBatchNDTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSpaceToBatchND stb_node; + + TestVisitor tv; + ASSERT_THROW(stb_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleSpaceToDepth.test.cpp b/compiler/luci/lang/src/Nodes/CircleSpaceToDepth.test.cpp new file mode 100644 index 0000000..d49a2ce --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleSpaceToDepth.test.cpp @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleSpaceToDepth.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleSpaceToDepthTest, constructor) +{ + luci::CircleSpaceToDepth std_node; + + ASSERT_EQ(luci::CircleDialect::get(), std_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SPACE_TO_DEPTH, std_node.opcode()); + + ASSERT_EQ(nullptr, std_node.input()); +} + +TEST(CircleSpaceToDepthTest, input_NEG) +{ + luci::CircleSpaceToDepth std_node; + luci::CircleSpaceToDepth node; + + std_node.input(&node); + ASSERT_NE(nullptr, std_node.input()); + + std_node.input(nullptr); + ASSERT_EQ(nullptr, std_node.input()); +} + +TEST(CircleSpaceToDepthTest, arity_NEG) +{ + luci::CircleSpaceToDepth std_node; + + ASSERT_NO_THROW(std_node.arg(0)); + ASSERT_THROW(std_node.arg(1), std::out_of_range); +} + +TEST(CircleSpaceToDepthTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSpaceToDepth std_node; + + TestVisitor tv; + ASSERT_THROW(std_node.accept(&tv), std::exception); +} + +TEST(CircleSpaceToDepthTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSpaceToDepth std_node; + + TestVisitor tv; + ASSERT_THROW(std_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleSparseToDense.test.cpp b/compiler/luci/lang/src/Nodes/CircleSparseToDense.test.cpp new file mode 100644 index 0000000..de3cf6e --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleSparseToDense.test.cpp @@ -0,0 +1,93 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleSparseToDense.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleSparseToDenseTest, constructor) +{ + luci::CircleSparseToDense stb_node; + + ASSERT_EQ(luci::CircleDialect::get(), stb_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SPARSE_TO_DENSE, stb_node.opcode()); + + ASSERT_EQ(nullptr, stb_node.indices()); + ASSERT_EQ(nullptr, stb_node.output_shape()); + ASSERT_EQ(nullptr, stb_node.values()); + ASSERT_EQ(nullptr, stb_node.default_value()); + + ASSERT_EQ(true, stb_node.validate_indices()); +} + +TEST(CircleSparseToDenseTest, input_NEG) +{ + luci::CircleSparseToDense stb_node; + luci::CircleSparseToDense node; + + stb_node.indices(&node); + stb_node.output_shape(&node); + stb_node.values(&node); + stb_node.default_value(&node); + ASSERT_NE(nullptr, stb_node.indices()); + ASSERT_NE(nullptr, stb_node.output_shape()); + ASSERT_NE(nullptr, stb_node.values()); + ASSERT_NE(nullptr, stb_node.default_value()); + + stb_node.indices(nullptr); + stb_node.output_shape(nullptr); + stb_node.values(nullptr); + stb_node.default_value(nullptr); + ASSERT_EQ(nullptr, stb_node.indices()); + ASSERT_EQ(nullptr, stb_node.output_shape()); + ASSERT_EQ(nullptr, stb_node.values()); + ASSERT_EQ(nullptr, stb_node.default_value()); +} + +TEST(CircleSparseToDenseTest, arity_NEG) +{ + luci::CircleSparseToDense stb_node; + + ASSERT_NO_THROW(stb_node.arg(3)); + ASSERT_THROW(stb_node.arg(4), std::out_of_range); +} + +TEST(CircleSparseToDenseTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSparseToDense stb_node; + + TestVisitor tv; + ASSERT_THROW(stb_node.accept(&tv), std::exception); +} + +TEST(CircleSparseToDenseTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSparseToDense stb_node; + + TestVisitor tv; + ASSERT_THROW(stb_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleSplit.test.cpp b/compiler/luci/lang/src/Nodes/CircleSplit.test.cpp new file mode 100644 index 0000000..acf8c44 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleSplit.test.cpp @@ -0,0 +1,85 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleSplit.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleSplitTest, constructor) +{ + luci::CircleSplit split_node; + + ASSERT_EQ(luci::CircleDialect::get(), split_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SPLIT, split_node.opcode()); + + ASSERT_EQ(nullptr, split_node.input()); + ASSERT_EQ(nullptr, split_node.split_dim()); + ASSERT_EQ(0, split_node.num_split()); +} + +TEST(CircleSplitTest, input_NEG) +{ + luci::CircleSplit split_node; + luci::CircleSplit node; + + split_node.input(&node); + split_node.split_dim(&node); + ASSERT_NE(nullptr, split_node.input()); + ASSERT_NE(nullptr, split_node.split_dim()); + + split_node.input(nullptr); + split_node.split_dim(nullptr); + ASSERT_EQ(nullptr, split_node.input()); + ASSERT_EQ(nullptr, split_node.split_dim()); + + split_node.num_split(100); + ASSERT_NE(0, split_node.num_split()); +} + +TEST(CircleSplitTest, arity_NEG) +{ + luci::CircleSplit split_node; + + ASSERT_NO_THROW(split_node.arg(1)); + ASSERT_THROW(split_node.arg(2), std::out_of_range); +} + +TEST(CircleSplitTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSplit split_node; + + TestVisitor tv; + ASSERT_THROW(split_node.accept(&tv), std::exception); +} + +TEST(CircleSplitTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSplit split_node; + + TestVisitor tv; + ASSERT_THROW(split_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleSplitOut.test.cpp b/compiler/luci/lang/src/Nodes/CircleSplitOut.test.cpp new file mode 100644 index 0000000..e937158 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleSplitOut.test.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleSplitOut.h" + +#include "luci/IR/CircleDialect.h" + +#include + +TEST(CircleSplitOutTest, constructor) +{ + luci::CircleSplitOut vout_node; + + ASSERT_EQ(luci::CircleDialect::get(), vout_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::CIRCLESPLITOUT, vout_node.opcode()); + + ASSERT_EQ(nullptr, vout_node.input()); + ASSERT_EQ(-1, vout_node.index()); +} diff --git a/compiler/luci/lang/src/Nodes/CircleSplitV.test.cpp b/compiler/luci/lang/src/Nodes/CircleSplitV.test.cpp new file mode 100644 index 0000000..1f01608 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleSplitV.test.cpp @@ -0,0 +1,90 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleSplitV.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleSplitVTest, constructor) +{ + luci::CircleSplitV splitv_node; + + ASSERT_EQ(luci::CircleDialect::get(), splitv_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SPLIT_V, splitv_node.opcode()); + + ASSERT_EQ(nullptr, splitv_node.input()); + ASSERT_EQ(nullptr, splitv_node.size_splits()); + ASSERT_EQ(nullptr, splitv_node.split_dim()); + ASSERT_EQ(0, splitv_node.num_split()); +} + +TEST(CircleSplitVTest, input_NEG) +{ + luci::CircleSplitV splitv_node; + luci::CircleSplitV node; + + splitv_node.input(&node); + splitv_node.size_splits(&node); + splitv_node.split_dim(&node); + ASSERT_NE(nullptr, splitv_node.input()); + ASSERT_NE(nullptr, splitv_node.size_splits()); + ASSERT_NE(nullptr, splitv_node.split_dim()); + + splitv_node.input(nullptr); + splitv_node.size_splits(nullptr); + splitv_node.split_dim(nullptr); + ASSERT_EQ(nullptr, splitv_node.input()); + ASSERT_EQ(nullptr, splitv_node.size_splits()); + ASSERT_EQ(nullptr, splitv_node.split_dim()); + + splitv_node.num_split(100); + ASSERT_NE(0, splitv_node.num_split()); +} + +TEST(CircleSplitVTest, arity_NEG) +{ + luci::CircleSplitV splitv_node; + + ASSERT_NO_THROW(splitv_node.arg(2)); + ASSERT_THROW(splitv_node.arg(3), std::out_of_range); +} + +TEST(CircleSplitVTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSplitV splitv_node; + + TestVisitor tv; + ASSERT_THROW(splitv_node.accept(&tv), std::exception); +} + +TEST(CircleSplitVTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSplitV splitv_node; + + TestVisitor tv; + ASSERT_THROW(splitv_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleSplitVOut.test.cpp b/compiler/luci/lang/src/Nodes/CircleSplitVOut.test.cpp new file mode 100644 index 0000000..2a4fe32 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleSplitVOut.test.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleSplitVOut.h" + +#include "luci/IR/CircleDialect.h" + +#include + +TEST(CircleSplitVOutTest, constructor) +{ + luci::CircleSplitVOut vout_node; + + ASSERT_EQ(luci::CircleDialect::get(), vout_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::CIRCLESPLITVOUT, vout_node.opcode()); + + ASSERT_EQ(nullptr, vout_node.input()); + ASSERT_EQ(-1, vout_node.index()); +} diff --git a/compiler/luci/lang/src/Nodes/CircleSqrt.test.cpp b/compiler/luci/lang/src/Nodes/CircleSqrt.test.cpp index 6cfb3bc..f4222fd 100644 --- a/compiler/luci/lang/src/Nodes/CircleSqrt.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleSqrt.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleSqrt.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,8 +25,52 @@ TEST(CircleSqrtTest, constructor_P) { luci::CircleSqrt sqrt_node; - ASSERT_EQ(sqrt_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(sqrt_node.opcode(), luci::CircleOpcode::SQRT); + ASSERT_EQ(luci::CircleDialect::get(), sqrt_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SQRT, sqrt_node.opcode()); - ASSERT_EQ(sqrt_node.x(), nullptr); + ASSERT_EQ(nullptr, sqrt_node.x()); +} + +TEST(CircleSqrtTest, input_NEG) +{ + luci::CircleSqrt sqrt_node; + luci::CircleSqrt node; + + sqrt_node.x(&node); + ASSERT_NE(nullptr, sqrt_node.x()); + + sqrt_node.x(nullptr); + ASSERT_EQ(nullptr, sqrt_node.x()); +} + +TEST(CircleSqrtTest, arity_NEG) +{ + luci::CircleSqrt sqrt_node; + + ASSERT_NO_THROW(sqrt_node.arg(0)); + ASSERT_THROW(sqrt_node.arg(1), std::out_of_range); +} + +TEST(CircleSqrtTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSqrt sqrt_node; + + TestVisitor tv; + ASSERT_THROW(sqrt_node.accept(&tv), std::exception); +} + +TEST(CircleSqrtTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSqrt sqrt_node; + + TestVisitor tv; + ASSERT_THROW(sqrt_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleSquare.test.cpp b/compiler/luci/lang/src/Nodes/CircleSquare.test.cpp new file mode 100644 index 0000000..3b0a86e --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleSquare.test.cpp @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleSquare.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleSquareTest, constructor_P) +{ + luci::CircleSquare square_node; + + ASSERT_EQ(luci::CircleDialect::get(), square_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SQUARE, square_node.opcode()); + + ASSERT_EQ(nullptr, square_node.x()); +} + +TEST(CircleSquareTest, input_NEG) +{ + luci::CircleSquare square_node; + luci::CircleSquare node; + + square_node.x(&node); + ASSERT_NE(nullptr, square_node.x()); + + square_node.x(nullptr); + ASSERT_EQ(nullptr, square_node.x()); +} + +TEST(CircleSquareTest, arity_NEG) +{ + luci::CircleSquare square_node; + + ASSERT_NO_THROW(square_node.arg(0)); + ASSERT_THROW(square_node.arg(1), std::out_of_range); +} + +TEST(CircleSquareTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSquare square_node; + + TestVisitor tv; + ASSERT_THROW(square_node.accept(&tv), std::exception); +} + +TEST(CircleSquareTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSquare square_node; + + TestVisitor tv; + ASSERT_THROW(square_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleSquaredDifference.test.cpp b/compiler/luci/lang/src/Nodes/CircleSquaredDifference.test.cpp index 71df189..ea63221 100644 --- a/compiler/luci/lang/src/Nodes/CircleSquaredDifference.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleSquaredDifference.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleSquaredDifference.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,9 +25,57 @@ TEST(CircleSquaredDifferenceTest, constructor_P) { luci::CircleSquaredDifference sd_node; - ASSERT_EQ(sd_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(sd_node.opcode(), luci::CircleOpcode::SQUARED_DIFFERENCE); + ASSERT_EQ(luci::CircleDialect::get(), sd_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SQUARED_DIFFERENCE, sd_node.opcode()); - ASSERT_EQ(sd_node.x(), nullptr); - ASSERT_EQ(sd_node.y(), nullptr); + ASSERT_EQ(nullptr, sd_node.x()); + ASSERT_EQ(nullptr, sd_node.y()); +} + +TEST(CircleSquaredDifferenceTest, input_NEG) +{ + luci::CircleSquaredDifference sd_node; + luci::CircleSquaredDifference node; + + sd_node.x(&node); + sd_node.y(&node); + ASSERT_NE(nullptr, sd_node.x()); + ASSERT_NE(nullptr, sd_node.y()); + + sd_node.x(nullptr); + sd_node.y(nullptr); + ASSERT_EQ(nullptr, sd_node.x()); + ASSERT_EQ(nullptr, sd_node.y()); +} + +TEST(CircleSquaredDifferenceTest, arity_NEG) +{ + luci::CircleSquaredDifference sd_node; + + ASSERT_NO_THROW(sd_node.arg(1)); + ASSERT_THROW(sd_node.arg(2), std::out_of_range); +} + +TEST(CircleSquaredDifferenceTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSquaredDifference sd_node; + + TestVisitor tv; + ASSERT_THROW(sd_node.accept(&tv), std::exception); +} + +TEST(CircleSquaredDifferenceTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSquaredDifference sd_node; + + TestVisitor tv; + ASSERT_THROW(sd_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleSqueeze.test.cpp b/compiler/luci/lang/src/Nodes/CircleSqueeze.test.cpp new file mode 100644 index 0000000..6dc3d03 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleSqueeze.test.cpp @@ -0,0 +1,87 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleSqueeze.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleSqueezeTest, constructor_P) +{ + luci::CircleSqueeze squeeze; + + ASSERT_EQ(luci::CircleDialect::get(), squeeze.dialect()); + ASSERT_EQ(luci::CircleOpcode::SQUEEZE, squeeze.opcode()); + + ASSERT_EQ(nullptr, squeeze.input()); + ASSERT_EQ(0, squeeze.squeeze_dims().size()); +} + +TEST(CircleSqueezeTest, squeeze_dims) +{ + luci::CircleSqueeze squeeze; + + squeeze.squeeze_dims({1, 2}); + + ASSERT_EQ(1, squeeze.squeeze_dims().at(0)); + ASSERT_EQ(2, squeeze.squeeze_dims().at(1)); +} + +TEST(CircleSqueezeTest, input_NEG) +{ + luci::CircleSqueeze squeeze_node; + luci::CircleSqueeze node; + + squeeze_node.input(&node); + ASSERT_NE(nullptr, squeeze_node.input()); + + squeeze_node.input(nullptr); + ASSERT_EQ(nullptr, squeeze_node.input()); +} + +TEST(CircleSqueezeTest, arity_NEG) +{ + luci::CircleSqueeze squeeze_node; + + ASSERT_NO_THROW(squeeze_node.arg(0)); + ASSERT_THROW(squeeze_node.arg(1), std::out_of_range); +} + +TEST(CircleSqueezeTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSqueeze squeeze_node; + + TestVisitor tv; + ASSERT_THROW(squeeze_node.accept(&tv), std::exception); +} + +TEST(CircleSqueezeTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSqueeze squeeze_node; + + TestVisitor tv; + ASSERT_THROW(squeeze_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleStridedSlice.test.cpp b/compiler/luci/lang/src/Nodes/CircleStridedSlice.test.cpp new file mode 100644 index 0000000..1982e7b --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleStridedSlice.test.cpp @@ -0,0 +1,108 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleStridedSlice.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleStridedSliceTest, constructor) +{ + luci::CircleStridedSlice ss_node; + + ASSERT_EQ(luci::CircleDialect::get(), ss_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::STRIDED_SLICE, ss_node.opcode()); + + ASSERT_EQ(nullptr, ss_node.input()); + ASSERT_EQ(nullptr, ss_node.begin()); + ASSERT_EQ(nullptr, ss_node.end()); + ASSERT_EQ(nullptr, ss_node.strides()); + + ASSERT_EQ(0, ss_node.begin_mask()); + ASSERT_EQ(0, ss_node.end_mask()); + ASSERT_EQ(0, ss_node.ellipsis_mask()); + ASSERT_EQ(0, ss_node.new_axis_mask()); + ASSERT_EQ(0, ss_node.shrink_axis_mask()); +} + +TEST(CircleStridedSliceTest, input_NEG) +{ + luci::CircleStridedSlice ss_node; + luci::CircleStridedSlice node; + + ss_node.input(&node); + ss_node.begin(&node); + ss_node.end(&node); + ss_node.strides(&node); + ASSERT_NE(nullptr, ss_node.input()); + ASSERT_NE(nullptr, ss_node.begin()); + ASSERT_NE(nullptr, ss_node.end()); + ASSERT_NE(nullptr, ss_node.strides()); + + ss_node.input(nullptr); + ss_node.begin(nullptr); + ss_node.end(nullptr); + ss_node.strides(nullptr); + ASSERT_EQ(nullptr, ss_node.input()); + ASSERT_EQ(nullptr, ss_node.begin()); + ASSERT_EQ(nullptr, ss_node.end()); + ASSERT_EQ(nullptr, ss_node.strides()); + + ss_node.begin_mask(1); + ss_node.end_mask(1); + ss_node.ellipsis_mask(1); + ss_node.new_axis_mask(1); + ss_node.shrink_axis_mask(1); + ASSERT_NE(0, ss_node.begin_mask()); + ASSERT_NE(0, ss_node.end_mask()); + ASSERT_NE(0, ss_node.ellipsis_mask()); + ASSERT_NE(0, ss_node.new_axis_mask()); + ASSERT_NE(0, ss_node.shrink_axis_mask()); +} + +TEST(CircleStridedSliceTest, arity_NEG) +{ + luci::CircleStridedSlice ss_node; + + ASSERT_NO_THROW(ss_node.arg(3)); + ASSERT_THROW(ss_node.arg(4), std::out_of_range); +} + +TEST(CircleStridedSliceTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleStridedSlice ss_node; + + TestVisitor tv; + ASSERT_THROW(ss_node.accept(&tv), std::exception); +} + +TEST(CircleStridedSliceTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleStridedSlice ss_node; + + TestVisitor tv; + ASSERT_THROW(ss_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleSub.test.cpp b/compiler/luci/lang/src/Nodes/CircleSub.test.cpp index ebb2944..92c674b 100644 --- a/compiler/luci/lang/src/Nodes/CircleSub.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleSub.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleSub.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,9 +25,57 @@ TEST(CircleSubTest, constructor_P) { luci::CircleSub sub_node; - ASSERT_EQ(sub_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(sub_node.opcode(), luci::CircleOpcode::SUB); + ASSERT_EQ(luci::CircleDialect::get(), sub_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SUB, sub_node.opcode()); - ASSERT_EQ(sub_node.x(), nullptr); - ASSERT_EQ(sub_node.y(), nullptr); + ASSERT_EQ(nullptr, sub_node.x()); + ASSERT_EQ(nullptr, sub_node.y()); +} + +TEST(CircleSubTest, input_NEG) +{ + luci::CircleSub sub_node; + luci::CircleSub node; + + sub_node.x(&node); + sub_node.y(&node); + ASSERT_NE(nullptr, sub_node.x()); + ASSERT_NE(nullptr, sub_node.y()); + + sub_node.x(nullptr); + sub_node.y(nullptr); + ASSERT_EQ(nullptr, sub_node.x()); + ASSERT_EQ(nullptr, sub_node.y()); +} + +TEST(CircleSubTest, arity_NEG) +{ + luci::CircleSub sub_node; + + ASSERT_NO_THROW(sub_node.arg(1)); + ASSERT_THROW(sub_node.arg(2), std::out_of_range); +} + +TEST(CircleSubTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSub sub_node; + + TestVisitor tv; + ASSERT_THROW(sub_node.accept(&tv), std::exception); +} + +TEST(CircleSubTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSub sub_node; + + TestVisitor tv; + ASSERT_THROW(sub_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleSum.test.cpp b/compiler/luci/lang/src/Nodes/CircleSum.test.cpp new file mode 100644 index 0000000..84b51d6 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleSum.test.cpp @@ -0,0 +1,85 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleSum.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleSumTest, constructor_P) +{ + luci::CircleSum sum_node; + + ASSERT_EQ(luci::CircleDialect::get(), sum_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::SUM, sum_node.opcode()); + + ASSERT_EQ(nullptr, sum_node.input()); + ASSERT_EQ(nullptr, sum_node.reduction_indices()); + ASSERT_EQ(false, sum_node.keep_dims()); +} + +TEST(CircleSumTest, input_NEG) +{ + luci::CircleSum sum_node; + luci::CircleSum node; + + sum_node.input(&node); + sum_node.reduction_indices(&node); + ASSERT_NE(nullptr, sum_node.input()); + ASSERT_NE(nullptr, sum_node.reduction_indices()); + + sum_node.input(nullptr); + sum_node.reduction_indices(nullptr); + ASSERT_EQ(nullptr, sum_node.input()); + ASSERT_EQ(nullptr, sum_node.reduction_indices()); + + sum_node.keep_dims(true); + ASSERT_TRUE(sum_node.keep_dims()); +} + +TEST(CircleSumTest, arity_NEG) +{ + luci::CircleSum sum_node; + + ASSERT_NO_THROW(sum_node.arg(1)); + ASSERT_THROW(sum_node.arg(2), std::out_of_range); +} + +TEST(CircleSumTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleSum sum_node; + + TestVisitor tv; + ASSERT_THROW(sum_node.accept(&tv), std::exception); +} + +TEST(CircleSumTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleSum sum_node; + + TestVisitor tv; + ASSERT_THROW(sum_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleTanh.test.cpp b/compiler/luci/lang/src/Nodes/CircleTanh.test.cpp new file mode 100644 index 0000000..257ecb2 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleTanh.test.cpp @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleTanh.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleTanhTest, constructor) +{ + luci::CircleTanh tanh_node; + + ASSERT_EQ(luci::CircleDialect::get(), tanh_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::TANH, tanh_node.opcode()); + + ASSERT_EQ(nullptr, tanh_node.x()); +} + +TEST(CircleTanhTest, input_NEG) +{ + luci::CircleTanh neg_node; + luci::CircleTanh node; + + neg_node.x(&node); + ASSERT_NE(nullptr, neg_node.x()); + + neg_node.x(nullptr); + ASSERT_EQ(nullptr, neg_node.x()); +} + +TEST(CircleTanhTest, arity_NEG) +{ + luci::CircleTanh neg_node; + + ASSERT_NO_THROW(neg_node.arg(0)); + ASSERT_THROW(neg_node.arg(1), std::out_of_range); +} + +TEST(CircleTanhTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleTanh neg_node; + + TestVisitor tv; + ASSERT_THROW(neg_node.accept(&tv), std::exception); +} + +TEST(CircleTanhTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleTanh neg_node; + + TestVisitor tv; + ASSERT_THROW(neg_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleTile.test.cpp b/compiler/luci/lang/src/Nodes/CircleTile.test.cpp new file mode 100644 index 0000000..1695165 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleTile.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleTile.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleTileTest, constructor) +{ + luci::CircleTile tile_node; + + ASSERT_EQ(luci::CircleDialect::get(), tile_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::TILE, tile_node.opcode()); + + ASSERT_EQ(nullptr, tile_node.input()); + ASSERT_EQ(nullptr, tile_node.multiples()); +} + +TEST(CircleTileTest, input_NEG) +{ + luci::CircleTile tile_node; + luci::CircleTile node; + + tile_node.input(&node); + tile_node.multiples(&node); + ASSERT_NE(nullptr, tile_node.input()); + ASSERT_NE(nullptr, tile_node.multiples()); + + tile_node.input(nullptr); + tile_node.multiples(nullptr); + ASSERT_EQ(nullptr, tile_node.input()); + ASSERT_EQ(nullptr, tile_node.multiples()); +} + +TEST(CircleTileTest, arity_NEG) +{ + luci::CircleTile tile_node; + + ASSERT_NO_THROW(tile_node.arg(1)); + ASSERT_THROW(tile_node.arg(2), std::out_of_range); +} + +TEST(CircleTileTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleTile tile_node; + + TestVisitor tv; + ASSERT_THROW(tile_node.accept(&tv), std::exception); +} + +TEST(CircleTileTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleTile tile_node; + + TestVisitor tv; + ASSERT_THROW(tile_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleTopKV2.test.cpp b/compiler/luci/lang/src/Nodes/CircleTopKV2.test.cpp new file mode 100644 index 0000000..31478d3 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleTopKV2.test.cpp @@ -0,0 +1,81 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleTopKV2.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleTopKV2Test, constructor) +{ + luci::CircleTopKV2 topkv2_node; + + ASSERT_EQ(luci::CircleDialect::get(), topkv2_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::TOPK_V2, topkv2_node.opcode()); + + ASSERT_EQ(nullptr, topkv2_node.input()); + ASSERT_EQ(nullptr, topkv2_node.k()); +} + +TEST(CircleTopKV2Test, input_NEG) +{ + luci::CircleTopKV2 topkv2_node; + luci::CircleTopKV2 node; + + topkv2_node.input(&node); + topkv2_node.k(&node); + ASSERT_NE(nullptr, topkv2_node.input()); + ASSERT_NE(nullptr, topkv2_node.k()); + + topkv2_node.input(nullptr); + topkv2_node.k(nullptr); + ASSERT_EQ(nullptr, topkv2_node.input()); + ASSERT_EQ(nullptr, topkv2_node.k()); +} + +TEST(CircleTopKV2Test, arity_NEG) +{ + luci::CircleTopKV2 topkv2_node; + + ASSERT_NO_THROW(topkv2_node.arg(1)); + ASSERT_THROW(topkv2_node.arg(2), std::out_of_range); +} + +TEST(CircleTopKV2Test, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleTopKV2 topkv2_node; + + TestVisitor tv; + ASSERT_THROW(topkv2_node.accept(&tv), std::exception); +} + +TEST(CircleTopKV2Test, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleTopKV2 topkv2_node; + + TestVisitor tv; + ASSERT_THROW(topkv2_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleTopKV2Out.test.cpp b/compiler/luci/lang/src/Nodes/CircleTopKV2Out.test.cpp new file mode 100644 index 0000000..d0835a2 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleTopKV2Out.test.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleTopKV2Out.h" + +#include "luci/IR/CircleDialect.h" + +#include + +TEST(CircleTopKV2OutTest, constructor) +{ + luci::CircleTopKV2Out topout_node; + + ASSERT_EQ(luci::CircleDialect::get(), topout_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::CIRCLETOPKV2OUT, topout_node.opcode()); + + ASSERT_EQ(nullptr, topout_node.input()); + ASSERT_EQ(-1, topout_node.index()); +} diff --git a/compiler/luci/lang/src/Nodes/CircleTranspose.test.cpp b/compiler/luci/lang/src/Nodes/CircleTranspose.test.cpp index 7233869..f4db3f3 100644 --- a/compiler/luci/lang/src/Nodes/CircleTranspose.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleTranspose.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleTranspose.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,9 +25,57 @@ TEST(CircleTransposeTest, constructor_P) { luci::CircleTranspose tr_node; - ASSERT_EQ(tr_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(tr_node.opcode(), luci::CircleOpcode::TRANSPOSE); + ASSERT_EQ(luci::CircleDialect::get(), tr_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::TRANSPOSE, tr_node.opcode()); - ASSERT_EQ(tr_node.a(), nullptr); - ASSERT_EQ(tr_node.perm(), nullptr); + ASSERT_EQ(nullptr, tr_node.a()); + ASSERT_EQ(nullptr, tr_node.perm()); +} + +TEST(CircleTransposeTest, input_NEG) +{ + luci::CircleTranspose tr_node; + luci::CircleTranspose node; + + tr_node.a(&node); + tr_node.perm(&node); + ASSERT_NE(nullptr, tr_node.a()); + ASSERT_NE(nullptr, tr_node.perm()); + + tr_node.a(nullptr); + tr_node.perm(nullptr); + ASSERT_EQ(nullptr, tr_node.a()); + ASSERT_EQ(nullptr, tr_node.perm()); +} + +TEST(CircleTransposeTest, arity_NEG) +{ + luci::CircleTranspose tr_node; + + ASSERT_NO_THROW(tr_node.arg(1)); + ASSERT_THROW(tr_node.arg(2), std::out_of_range); +} + +TEST(CircleTransposeTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleTranspose tr_node; + + TestVisitor tv; + ASSERT_THROW(tr_node.accept(&tv), std::exception); +} + +TEST(CircleTransposeTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleTranspose tr_node; + + TestVisitor tv; + ASSERT_THROW(tr_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleTransposeConv.test.cpp b/compiler/luci/lang/src/Nodes/CircleTransposeConv.test.cpp index 9615082..4291697 100644 --- a/compiler/luci/lang/src/Nodes/CircleTransposeConv.test.cpp +++ b/compiler/luci/lang/src/Nodes/CircleTransposeConv.test.cpp @@ -17,6 +17,7 @@ #include "luci/IR/Nodes/CircleTransposeConv.h" #include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" #include @@ -24,10 +25,74 @@ TEST(CircleTransposeConvTest, constructor_P) { luci::CircleTransposeConv trc_node; - ASSERT_EQ(trc_node.dialect(), luci::CircleDialect::get()); - ASSERT_EQ(trc_node.opcode(), luci::CircleOpcode::TRANSPOSE_CONV); + ASSERT_EQ(luci::CircleDialect::get(), trc_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::TRANSPOSE_CONV, trc_node.opcode()); - ASSERT_EQ(trc_node.inputSizes(), nullptr); - ASSERT_EQ(trc_node.filter(), nullptr); - ASSERT_EQ(trc_node.outBackprop(), nullptr); + ASSERT_EQ(nullptr, trc_node.inputSizes()); + ASSERT_EQ(nullptr, trc_node.filter()); + ASSERT_EQ(nullptr, trc_node.outBackprop()); + + ASSERT_EQ(luci::Padding::UNDEFINED, trc_node.padding()); + ASSERT_EQ(1, trc_node.stride()->h()); + ASSERT_EQ(1, trc_node.stride()->w()); +} + +TEST(CircleTransposeConvTest, input_NEG) +{ + luci::CircleTransposeConv trc_node; + luci::CircleTransposeConv node; + + trc_node.inputSizes(&node); + trc_node.filter(&node); + trc_node.outBackprop(&node); + ASSERT_NE(nullptr, trc_node.inputSizes()); + ASSERT_NE(nullptr, trc_node.filter()); + ASSERT_NE(nullptr, trc_node.outBackprop()); + + trc_node.inputSizes(nullptr); + trc_node.filter(nullptr); + trc_node.outBackprop(nullptr); + ASSERT_EQ(nullptr, trc_node.inputSizes()); + ASSERT_EQ(nullptr, trc_node.filter()); + ASSERT_EQ(nullptr, trc_node.outBackprop()); + + trc_node.padding(luci::Padding::SAME); + ASSERT_NE(luci::Padding::UNDEFINED, trc_node.padding()); + + trc_node.stride()->h(2); + trc_node.stride()->w(2); + ASSERT_EQ(2, trc_node.stride()->h()); + ASSERT_EQ(2, trc_node.stride()->w()); +} + +TEST(CircleTransposeConvTest, arity_NEG) +{ + luci::CircleTransposeConv trc_node; + + ASSERT_NO_THROW(trc_node.arg(2)); + ASSERT_THROW(trc_node.arg(3), std::out_of_range); +} + +TEST(CircleTransposeConvTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleTransposeConv trc_node; + + TestVisitor tv; + ASSERT_THROW(trc_node.accept(&tv), std::exception); +} + +TEST(CircleTransposeConvTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleTransposeConv trc_node; + + TestVisitor tv; + ASSERT_THROW(trc_node.accept(&tv), std::exception); } diff --git a/compiler/luci/lang/src/Nodes/CircleUnpack.test.cpp b/compiler/luci/lang/src/Nodes/CircleUnpack.test.cpp new file mode 100644 index 0000000..4323028 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleUnpack.test.cpp @@ -0,0 +1,83 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleUnpack.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleUnpackTest, constructor) +{ + luci::CircleUnpack unpack_node; + + ASSERT_EQ(luci::CircleDialect::get(), unpack_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::UNPACK, unpack_node.opcode()); + + ASSERT_EQ(nullptr, unpack_node.value()); + ASSERT_EQ(0, unpack_node.num()); + ASSERT_EQ(0, unpack_node.axis()); +} + +TEST(CircleUnpackTest, input_NEG) +{ + luci::CircleUnpack unpack_node; + luci::CircleUnpack node; + + unpack_node.value(&node); + ASSERT_NE(nullptr, unpack_node.value()); + + unpack_node.value(nullptr); + ASSERT_EQ(nullptr, unpack_node.value()); + + unpack_node.num(1); + unpack_node.axis(1); + ASSERT_NE(0, unpack_node.num()); + ASSERT_NE(0, unpack_node.axis()); +} + +TEST(CircleUnpackTest, arity_NEG) +{ + luci::CircleUnpack unpack_node; + + ASSERT_NO_THROW(unpack_node.arg(0)); + ASSERT_THROW(unpack_node.arg(1), std::out_of_range); +} + +TEST(CircleUnpackTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleUnpack unpack_node; + + TestVisitor tv; + ASSERT_THROW(unpack_node.accept(&tv), std::exception); +} + +TEST(CircleUnpackTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleUnpack unpack_node; + + TestVisitor tv; + ASSERT_THROW(unpack_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleUnpackOut.test.cpp b/compiler/luci/lang/src/Nodes/CircleUnpackOut.test.cpp new file mode 100644 index 0000000..7b8a41b --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleUnpackOut.test.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleUnpackOut.h" + +#include "luci/IR/CircleDialect.h" + +#include + +TEST(CircleUnpackOutTest, constructor) +{ + luci::CircleUnpackOut unpackout_node; + + ASSERT_EQ(luci::CircleDialect::get(), unpackout_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::CIRCLEUNPACKOUT, unpackout_node.opcode()); + + ASSERT_EQ(nullptr, unpackout_node.input()); + ASSERT_EQ(0, unpackout_node.index()); +} diff --git a/compiler/luci/lang/src/Nodes/CircleWhere.test.cpp b/compiler/luci/lang/src/Nodes/CircleWhere.test.cpp new file mode 100644 index 0000000..287eda4 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleWhere.test.cpp @@ -0,0 +1,33 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleWhere.h" +#include "luci/IR/Nodes/CircleInput.h" + +#include "luci/IR/CircleDialect.h" + +#include + +TEST(CircleWhereTest, constructor_P) +{ + luci::CircleWhere where_node; + + ASSERT_EQ(luci::CircleDialect::get(), where_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::WHERE, where_node.opcode()); + + ASSERT_EQ(1, where_node.arity()); + ASSERT_EQ(nullptr, where_node.condition()); +} diff --git a/compiler/luci/lang/src/Nodes/CircleWhile.test.cpp b/compiler/luci/lang/src/Nodes/CircleWhile.test.cpp new file mode 100644 index 0000000..19290c0 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleWhile.test.cpp @@ -0,0 +1,87 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleWhile.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleWhileTest, constructor) +{ + luci::CircleWhile while_node(2, 2); + + ASSERT_EQ(luci::CircleDialect::get(), while_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::WHILE, while_node.opcode()); + + ASSERT_EQ(2, while_node.input_count()); + ASSERT_EQ(2, while_node.output_count()); + + ASSERT_EQ(nullptr, while_node.input(0)); + ASSERT_EQ(nullptr, while_node.input(1)); + + ASSERT_EQ(-1, while_node.cond_branch()); + ASSERT_EQ(-1, while_node.body_branch()); +} + +TEST(CircleWhileTestDeath, invalid_arity_NEG) +{ + ASSERT_DEBUG_DEATH(luci::CircleWhile very_long_name_while_node(0, 1), ""); +} + +TEST(CircleWhileTestDeath, invalid_output_count_NEG) +{ + ASSERT_DEBUG_DEATH(luci::CircleWhile while_node(2, 0), ""); +} + +TEST(CircleWhileTestDeath, invalid_input_get_index_NEG) +{ + luci::CircleWhile while_node(2, 2); + + EXPECT_ANY_THROW(while_node.input(100)); +} + +TEST(CircleWhileTestDeath, invalid_input_set_index_NEG) +{ + luci::CircleWhile while_node(2, 2); + + EXPECT_ANY_THROW(while_node.input(100, nullptr)); +} + +TEST(CircleWhileTestDeath, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleWhile while_node(2, 2); + + TestVisitor tv; + ASSERT_THROW(while_node.accept(&tv), std::exception); +} + +TEST(CircleWhileTestDeath, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleWhile while_node(2, 2); + + TestVisitor tv; + ASSERT_THROW(while_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/lang/src/Nodes/CircleWhileOut.test.cpp b/compiler/luci/lang/src/Nodes/CircleWhileOut.test.cpp new file mode 100644 index 0000000..1800e40 --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleWhileOut.test.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleWhileOut.h" + +#include "luci/IR/CircleDialect.h" + +#include + +TEST(CircleWhileOutTest, constructor) +{ + luci::CircleWhileOut whileout_node; + + ASSERT_EQ(luci::CircleDialect::get(), whileout_node.dialect()); + ASSERT_EQ(luci::CircleOpcode::CIRCLEWHILEOUT, whileout_node.opcode()); + + ASSERT_EQ(nullptr, whileout_node.input()); + ASSERT_EQ(-1, whileout_node.index()); +} diff --git a/compiler/luci/lang/src/Nodes/CircleZerosLike.test.cpp b/compiler/luci/lang/src/Nodes/CircleZerosLike.test.cpp new file mode 100644 index 0000000..3368c8e --- /dev/null +++ b/compiler/luci/lang/src/Nodes/CircleZerosLike.test.cpp @@ -0,0 +1,76 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/IR/Nodes/CircleZerosLike.h" + +#include "luci/IR/CircleDialect.h" +#include "luci/IR/CircleNodeVisitor.h" + +#include + +TEST(CircleZerosLikeTest, constructor_P) +{ + luci::CircleZerosLike node; + + ASSERT_EQ(luci::CircleDialect::get(), node.dialect()); + ASSERT_EQ(luci::CircleOpcode::ZEROS_LIKE, node.opcode()); + + ASSERT_EQ(nullptr, node.input()); +} + +TEST(CircleZerosLikeTest, input_NEG) +{ + luci::CircleZerosLike zeros_node; + luci::CircleZerosLike node; + + zeros_node.input(&node); + ASSERT_NE(nullptr, zeros_node.input()); + + zeros_node.input(nullptr); + ASSERT_EQ(nullptr, zeros_node.input()); +} + +TEST(CircleZerosLikeTest, arity_NEG) +{ + luci::CircleZerosLike zeros_node; + + ASSERT_NO_THROW(zeros_node.arg(0)); + ASSERT_THROW(zeros_node.arg(1), std::out_of_range); +} + +TEST(CircleZerosLikeTest, visit_mutable_NEG) +{ + struct TestVisitor final : public luci::CircleNodeMutableVisitor + { + }; + + luci::CircleZerosLike zeros_node; + + TestVisitor tv; + ASSERT_THROW(zeros_node.accept(&tv), std::exception); +} + +TEST(CircleZerosLikeTest, visit_NEG) +{ + struct TestVisitor final : public luci::CircleNodeVisitor + { + }; + + luci::CircleZerosLike zeros_node; + + TestVisitor tv; + ASSERT_THROW(zeros_node.accept(&tv), std::exception); +} diff --git a/compiler/luci/log/CMakeLists.txt b/compiler/luci/log/CMakeLists.txt index af2e7a7..5e82287 100644 --- a/compiler/luci/log/CMakeLists.txt +++ b/compiler/luci/log/CMakeLists.txt @@ -6,4 +6,5 @@ target_include_directories(luci_log PUBLIC include) target_link_libraries(luci_log PUBLIC hermes) target_link_libraries(luci_log PRIVATE hermes_std) target_link_libraries(luci_log PRIVATE nncc_common) +target_link_libraries(luci_log PRIVATE luci_env) install(TARGETS luci_log DESTINATION lib) diff --git a/compiler/luci/log/include/luci/Log.h b/compiler/luci/log/include/luci/Log.h index 51299a0..e148810 100644 --- a/compiler/luci/log/include/luci/Log.h +++ b/compiler/luci/log/include/luci/Log.h @@ -35,7 +35,7 @@ public: /** * @brief Logger Configuration * - * Users are able to turn logging on/off via MOCO_LOG environment variable. + * Users are able to turn logging on/off via LUCI_LOG environment variable. */ class LoggerConfig final : public hermes::Config { @@ -47,7 +47,9 @@ public: void configure(const Logger *, hermes::Source::Setting &) const; private: - bool _enabled; + bool _show_warn = true; + bool _show_info = false; + int _show_verbose = 0; }; } // namespace luci @@ -64,8 +66,10 @@ private: */ #define LOGGER(name) ::luci::Logger name{::luci::LoggingContext::get()}; -// TODO Support FATAL, ERROR, WARN, and VERBOSE +// TODO Support FATAL, ERROR #define INFO(name) HERMES_INFO(name) +#define WARN(name) HERMES_WARN(name) +#define VERBOSE(name, lv) HERMES_VERBOSE(name, lv) // WARNING! // diff --git a/compiler/luci/log/src/Log.cpp b/compiler/luci/log/src/Log.cpp index 7e16340..c26bf30 100644 --- a/compiler/luci/log/src/Log.cpp +++ b/compiler/luci/log/src/Log.cpp @@ -16,6 +16,8 @@ #include "luci/Log.h" +#include + #include #include #include @@ -36,6 +38,11 @@ template <> bool safecast(const char *s, const bool &value) return (s == nullptr) ? value : (std::stoi(s) != 0); } +template <> int safecast(const char *s, const int &value) +{ + return (s == nullptr) ? value : std::stoi(s); +} + } // namespace // @@ -57,8 +64,16 @@ namespace luci LoggerConfig::LoggerConfig() { - // Turn on logging if LUCI_LOG is set as non-zero value - _enabled = safecast(std::getenv("LUCI_LOG"), false); + auto settings = luci::UserSettings::settings(); + + _show_warn = !settings->get(luci::UserSettings::Key::MuteWarnings); + + // Turn on info logging if LUCI_LOG is set as non-zero value + _show_info = safecast(std::getenv("LUCI_LOG"), false); + + // Turn on verbose logging if LUCI_LOG is set to some level + // VERBOSE(l, 1) will be visible with LUCI_LOG=2 and VERBOSE(l, 2) with LUCI_LOG=3 and so on + _show_verbose = safecast(std::getenv("LUCI_LOG"), 0); } void LoggerConfig::configure(const hermes::Source *source, hermes::Source::Setting &setting) const @@ -72,15 +87,24 @@ void LoggerConfig::configure(const hermes::Source *source, hermes::Source::Setti void LoggerConfig::configure(const Logger *, hermes::Source::Setting &setting) const { - if (_enabled) + setting.filter(hermes::SeverityCategory::FATAL).reject_all(); + setting.filter(hermes::SeverityCategory::ERROR).reject_all(); + setting.filter(hermes::SeverityCategory::WARN).reject_all(); + setting.filter(hermes::SeverityCategory::INFO).reject_all(); + setting.filter(hermes::SeverityCategory::VERBOSE).reject_all(); + + // TODO enable FATAL and ERROR + if (_show_warn) + { + setting.filter(hermes::SeverityCategory::WARN).accept_all(); + } + if (_show_info) { - // Enable all catagories - setting.accept_all(); + setting.filter(hermes::SeverityCategory::INFO).accept_all(); } - else + if (_show_verbose) { - // Disable all catagories - setting.reject_all(); + setting.filter(hermes::SeverityCategory::VERBOSE).accept_upto(_show_verbose); } } diff --git a/compiler/luci/logex/src/FormattedGraph.cpp b/compiler/luci/logex/src/FormattedGraph.cpp index 894ebc1..4725ee3 100644 --- a/compiler/luci/logex/src/FormattedGraph.cpp +++ b/compiler/luci/logex/src/FormattedGraph.cpp @@ -78,6 +78,8 @@ const char *to_str(loco::DataType type) } } +const char *to_str(bool value) { return value ? "true" : "false"; } + const char *to_str(luci::FusedActFunc fused) { switch (fused) @@ -108,6 +110,19 @@ const char *to_str(luci::Padding padding) } } +const char *to_str(luci::MirrorPadMode mode) +{ + switch (mode) + { + case luci::MirrorPadMode::REFLECT: + return "REFLECT"; + case luci::MirrorPadMode::SYMMETRIC: + return "SYMMETRIC"; + default: + return "Error"; + } +} + std::string to_str(const luci::Stride *stride) { return pepper::str(stride->h(), ",", stride->w()); @@ -180,43 +195,187 @@ private: #define IMPLEMENT(CLASS) bool summary(const CLASS *, locop::NodeSummary &) const final; IMPLEMENT(luci::CircleAbs) IMPLEMENT(luci::CircleAdd) + IMPLEMENT(luci::CircleAddN) IMPLEMENT(luci::CircleArgMax) + IMPLEMENT(luci::CircleArgMin) IMPLEMENT(luci::CircleAveragePool2D) + IMPLEMENT(luci::CircleBatchMatMul) IMPLEMENT(luci::CircleBatchToSpaceND) + IMPLEMENT(luci::CircleCast) + IMPLEMENT(luci::CircleCeil) IMPLEMENT(luci::CircleConcatenation) IMPLEMENT(luci::CircleConst) IMPLEMENT(luci::CircleConv2D) IMPLEMENT(luci::CircleCos) + IMPLEMENT(luci::CircleCustom) + IMPLEMENT(luci::CircleDepthToSpace) IMPLEMENT(luci::CircleDepthwiseConv2D) IMPLEMENT(luci::CircleDiv) + IMPLEMENT(luci::CircleElu) IMPLEMENT(luci::CircleExp) + IMPLEMENT(luci::CircleExpandDims) + IMPLEMENT(luci::CircleFill) + IMPLEMENT(luci::CircleFloor) + IMPLEMENT(luci::CircleFloorDiv) + IMPLEMENT(luci::CircleFloorMod) IMPLEMENT(luci::CircleFullyConnected) + IMPLEMENT(luci::CircleGather) + IMPLEMENT(luci::CircleGatherNd) + IMPLEMENT(luci::CircleGreater) + IMPLEMENT(luci::CircleGreaterEqual) + IMPLEMENT(luci::CircleIf) + IMPLEMENT(luci::CircleL2Normalize) + IMPLEMENT(luci::CircleLeakyRelu) + IMPLEMENT(luci::CircleLess) + IMPLEMENT(luci::CircleLessEqual) + IMPLEMENT(luci::CircleLocalResponseNormalization) + IMPLEMENT(luci::CircleLog) + IMPLEMENT(luci::CircleLogicalAnd) IMPLEMENT(luci::CircleLogicalNot) IMPLEMENT(luci::CircleLogicalOr) + IMPLEMENT(luci::CircleLogistic) + IMPLEMENT(luci::CircleLogSoftmax) + IMPLEMENT(luci::CircleMatrixDiag) + IMPLEMENT(luci::CircleMatrixSetDiag) IMPLEMENT(luci::CircleMaximum) IMPLEMENT(luci::CircleMaxPool2D) IMPLEMENT(luci::CircleMean) + IMPLEMENT(luci::CircleMinimum) + IMPLEMENT(luci::CircleMirrorPad) IMPLEMENT(luci::CircleMul) + IMPLEMENT(luci::CircleNeg) + IMPLEMENT(luci::CircleNotEqual) + IMPLEMENT(luci::CircleOneHot) IMPLEMENT(luci::CirclePack) IMPLEMENT(luci::CirclePad) + IMPLEMENT(luci::CirclePow) + IMPLEMENT(luci::CirclePRelu) + IMPLEMENT(luci::CircleRange) + IMPLEMENT(luci::CircleRank) + IMPLEMENT(luci::CircleReduceAny) + IMPLEMENT(luci::CircleReduceMax) + IMPLEMENT(luci::CircleReduceMin) + IMPLEMENT(luci::CircleReduceProd) IMPLEMENT(luci::CircleRelu) IMPLEMENT(luci::CircleRelu6) + IMPLEMENT(luci::CircleReluN1To1) IMPLEMENT(luci::CircleReshape) + IMPLEMENT(luci::CircleResizeBilinear) + IMPLEMENT(luci::CircleResizeNearestNeighbor) + IMPLEMENT(luci::CircleReverseSequence) + IMPLEMENT(luci::CircleReverseV2) + IMPLEMENT(luci::CircleRound) IMPLEMENT(luci::CircleRsqrt) + IMPLEMENT(luci::CircleScatterNd) + IMPLEMENT(luci::CircleSegmentSum) + IMPLEMENT(luci::CircleSelect) + IMPLEMENT(luci::CircleSelectV2) + IMPLEMENT(luci::CircleShape) + IMPLEMENT(luci::CircleSin) + IMPLEMENT(luci::CircleSlice) IMPLEMENT(luci::CircleSoftmax) + IMPLEMENT(luci::CircleSpaceToBatchND) + IMPLEMENT(luci::CircleSpaceToDepth) + IMPLEMENT(luci::CircleSparseToDense) + IMPLEMENT(luci::CircleSplit) + IMPLEMENT(luci::CircleSplitV) IMPLEMENT(luci::CircleSqrt) + IMPLEMENT(luci::CircleSquare) IMPLEMENT(luci::CircleSquaredDifference) + IMPLEMENT(luci::CircleSqueeze) + IMPLEMENT(luci::CircleStridedSlice) IMPLEMENT(luci::CircleSub) + IMPLEMENT(luci::CircleSum) + IMPLEMENT(luci::CircleTanh) + IMPLEMENT(luci::CircleTile) + IMPLEMENT(luci::CircleTopKV2) IMPLEMENT(luci::CircleTranspose) IMPLEMENT(luci::CircleTransposeConv) + IMPLEMENT(luci::CircleUnpack) + IMPLEMENT(luci::CircleWhere) + IMPLEMENT(luci::CircleWhile) + IMPLEMENT(luci::CircleZerosLike) // Circle Only + IMPLEMENT(luci::CircleBCQFullyConnected) + IMPLEMENT(luci::CircleBCQGather) IMPLEMENT(luci::CircleInstanceNorm) // Virtual nodes IMPLEMENT(luci::CircleInput) IMPLEMENT(luci::CircleOutput) + IMPLEMENT(luci::CircleIfOut) + IMPLEMENT(luci::CircleSplitOut) + IMPLEMENT(luci::CircleSplitVOut) + IMPLEMENT(luci::CircleTopKV2Out) + IMPLEMENT(luci::CircleUnpackOut) + IMPLEMENT(luci::CircleWhileOut) #undef IMPLEMENT }; +template +bool use_x(const locop::SymbolTable *tbl, const CIRCLENODE *node, locop::NodeSummary &s) +{ + s.args().append("x", tbl->lookup(node->x())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +template +bool use_input(const locop::SymbolTable *tbl, const CIRCLENODE *node, locop::NodeSummary &s) +{ + s.args().append("input", tbl->lookup(node->input())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +template +bool use_features(const locop::SymbolTable *tbl, const CIRCLENODE *node, locop::NodeSummary &s) +{ + s.args().append("features", tbl->lookup(node->features())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +template +bool use_xy(const locop::SymbolTable *tbl, const CIRCLENODE *node, locop::NodeSummary &s) +{ + s.args().append("x", tbl->lookup(node->x())); + s.args().append("y", tbl->lookup(node->y())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +template +bool use_xy_act(const locop::SymbolTable *tbl, const CIRCLENODE *node, locop::NodeSummary &s) +{ + assert(node->fusedActivationFunction() != luci::FusedActFunc::UNDEFINED); + + s.args().append("x", tbl->lookup(node->x())); + s.args().append("y", tbl->lookup(node->y())); + s.args().append("fused_activation_function", to_str(node->fusedActivationFunction())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +template +bool use_reducer(const locop::SymbolTable *tbl, const CIRCLENODE *node, locop::NodeSummary &s) +{ + s.args().append("input", tbl->lookup(node->input())); + s.args().append("reduction_indices", tbl->lookup(node->reduction_indices())); + s.args().append("keep_dims", node->keep_dims() ? "true" : "false"); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +template +bool use_ido(const locop::SymbolTable *tbl, const CIRCLENODE *node, locop::NodeSummary &s) +{ + s.args().append("input", tbl->lookup(node->input())); + s.args().append("dimension", tbl->lookup(node->dimension())); + s.args().append("output_type", to_str(node->output_type())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + bool CircleNodeSummaryBuilderBase::build(const loco::Node *node, locop::NodeSummary &s) const { if (node->dialect() != luci::CircleDialect::get()) @@ -236,29 +395,31 @@ bool CircleNodeSummaryBuilderBase::build(const loco::Node *node, locop::NodeSumm bool CircleNodeSummaryBuilder::summary(const luci::CircleAbs *node, locop::NodeSummary &s) const { - s.args().append("x", tbl()->lookup(node->x())); - s.state(locop::NodeSummary::State::Complete); - return true; + return use_x(tbl(), node, s); } bool CircleNodeSummaryBuilder::summary(const luci::CircleAdd *node, locop::NodeSummary &s) const { - assert(node->fusedActivationFunction() != luci::FusedActFunc::UNDEFINED); + return use_xy_act(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleAddN *node, locop::NodeSummary &s) const +{ + for (uint32_t i = 0; i < node->arity(); ++i) + s.args().append("inputs", tbl()->lookup(node->inputs(i))); - s.args().append("x", tbl()->lookup(node->x())); - s.args().append("y", tbl()->lookup(node->y())); - s.args().append("fused_activation_function", to_str(node->fusedActivationFunction())); s.state(locop::NodeSummary::State::Complete); return true; } bool CircleNodeSummaryBuilder::summary(const luci::CircleArgMax *node, locop::NodeSummary &s) const { - s.args().append("input", tbl()->lookup(node->input())); - s.args().append("dimension", tbl()->lookup(node->dimension())); - s.args().append("output_type", to_str(node->output_type())); - s.state(locop::NodeSummary::State::Complete); - return true; + return use_ido(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleArgMin *node, locop::NodeSummary &s) const +{ + return use_ido(tbl(), node, s); } bool CircleNodeSummaryBuilder::summary(const luci::CircleAveragePool2D *node, @@ -277,6 +438,17 @@ bool CircleNodeSummaryBuilder::summary(const luci::CircleAveragePool2D *node, return true; } +bool CircleNodeSummaryBuilder::summary(const luci::CircleBatchMatMul *node, + locop::NodeSummary &s) const +{ + s.args().append("x", tbl()->lookup(node->x())); + s.args().append("y", tbl()->lookup(node->y())); + s.args().append("adj_x", to_str(node->adj_x())); + s.args().append("adj_y", to_str(node->adj_y())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + bool CircleNodeSummaryBuilder::summary(const luci::CircleBatchToSpaceND *node, locop::NodeSummary &s) const { @@ -289,6 +461,20 @@ bool CircleNodeSummaryBuilder::summary(const luci::CircleBatchToSpaceND *node, return true; } +bool CircleNodeSummaryBuilder::summary(const luci::CircleCast *node, locop::NodeSummary &s) const +{ + s.args().append("x", tbl()->lookup(node->x())); + s.args().append("in_data_type", to_str(node->in_data_type())); + s.args().append("out_data_type", to_str(node->out_data_type())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleCeil *node, locop::NodeSummary &s) const +{ + return use_x(tbl(), node, s); +} + bool CircleNodeSummaryBuilder::summary(const luci::CircleConcatenation *node, locop::NodeSummary &s) const { @@ -318,6 +504,8 @@ bool CircleNodeSummaryBuilder::summary(const luci::CircleConv2D *node, locop::No s.args().append("bias", tbl()->lookup(node->bias())); s.args().append("stride(h,w)", to_str(node->stride())); + s.args().append("dilation(h,w)", to_str(node->dilation())); + s.args().append("padding", to_str(node->padding())); s.args().append("fused", to_str(node->fusedActivationFunction())); @@ -328,8 +516,28 @@ bool CircleNodeSummaryBuilder::summary(const luci::CircleConv2D *node, locop::No bool CircleNodeSummaryBuilder::summary(const luci::CircleCos *node, locop::NodeSummary &s) const { - s.args().append("x", tbl()->lookup(node->x())); + return use_x(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleCustom *node, locop::NodeSummary &s) const +{ + for (uint32_t i = 0; i < node->numInputs(); i++) + { + s.args().append("input" + std::to_string(i), tbl()->lookup(node->inputs(i))); + } + s.args().append("custom_code", node->custom_code()); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleDepthToSpace *node, + locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("block_size", std::to_string(node->block_size())); + s.state(locop::NodeSummary::State::Complete); + return true; } @@ -344,6 +552,7 @@ bool CircleNodeSummaryBuilder::summary(const luci::CircleDepthwiseConv2D *node, s.args().append("bias", tbl()->lookup(node->bias())); s.args().append("stride(h,w)", to_str(node->stride())); + s.args().append("dilation(h,w)", to_str(node->dilation())); s.args().append("padding", to_str(node->padding())); s.args().append("depthMultiplier", std::to_string(node->depthMultiplier())); s.args().append("fused", to_str(node->fusedActivationFunction())); @@ -355,15 +564,49 @@ bool CircleNodeSummaryBuilder::summary(const luci::CircleDepthwiseConv2D *node, bool CircleNodeSummaryBuilder::summary(const luci::CircleDiv *node, locop::NodeSummary &s) const { - s.args().append("x", tbl()->lookup(node->x())); - s.args().append("y", tbl()->lookup(node->y())); + return use_xy(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleElu *node, locop::NodeSummary &s) const +{ + return use_features(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleExp *node, locop::NodeSummary &s) const +{ + return use_x(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleExpandDims *node, + locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("axis", tbl()->lookup(node->axis())); s.state(locop::NodeSummary::State::Complete); return true; } -bool CircleNodeSummaryBuilder::summary(const luci::CircleExp *node, locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleFloor *node, locop::NodeSummary &s) const { - s.args().append("x", tbl()->lookup(node->x())); + return use_x(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleFloorDiv *node, + locop::NodeSummary &s) const +{ + return use_xy(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleFloorMod *node, + locop::NodeSummary &s) const +{ + return use_xy(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleFill *node, locop::NodeSummary &s) const +{ + s.args().append("dims", tbl()->lookup(node->dims())); + s.args().append("value", tbl()->lookup(node->value())); s.state(locop::NodeSummary::State::Complete); return true; } @@ -383,184 +626,741 @@ bool CircleNodeSummaryBuilder::summary(const luci::CircleFullyConnected *node, return true; } -bool CircleNodeSummaryBuilder::summary(const luci::CircleLogicalNot *node, - locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleGather *node, locop::NodeSummary &s) const { - s.args().append("x", tbl()->lookup(node->x())); + s.args().append("params", tbl()->lookup(node->params())); + s.args().append("indices", tbl()->lookup(node->indices())); + s.args().append("axis", pepper::str(node->axis())); + s.state(locop::NodeSummary::State::Complete); return true; } -bool CircleNodeSummaryBuilder::summary(const luci::CircleLogicalOr *node, +bool CircleNodeSummaryBuilder::summary(const luci::CircleGatherNd *node, locop::NodeSummary &s) const { - s.args().append("x", tbl()->lookup(node->x())); - s.args().append("y", tbl()->lookup(node->y())); + s.args().append("params", tbl()->lookup(node->params())); + s.args().append("indices", tbl()->lookup(node->indices())); s.state(locop::NodeSummary::State::Complete); return true; } -bool CircleNodeSummaryBuilder::summary(const luci::CircleMaximum *node, locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleGreater *node, locop::NodeSummary &s) const { - s.args().append("x", tbl()->lookup(node->x())); - s.args().append("y", tbl()->lookup(node->y())); - s.state(locop::NodeSummary::State::Complete); - return true; + return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleMaxPool2D *node, +bool CircleNodeSummaryBuilder::summary(const luci::CircleGreaterEqual *node, locop::NodeSummary &s) const { - assert(node->fusedActivationFunction() != luci::FusedActFunc::UNDEFINED); + return use_xy(tbl(), node, s); +} - s.args().append("value", tbl()->lookup(node->value())); - s.args().append("filter(h,w)", to_str(node->filter())); - s.args().append("stride(h,w)", to_str(node->stride())); - s.args().append("padding", to_str(node->padding())); - s.args().append("fused", to_str(node->fusedActivationFunction())); +bool CircleNodeSummaryBuilder::summary(const luci::CircleIf *node, locop::NodeSummary &s) const +{ + s.args().append("cond", tbl()->lookup(node->cond())); + for (uint32_t i = 0; i < node->input_count(); ++i) + s.args().append("input", tbl()->lookup(node->input(i))); - s.state(locop::NodeSummary::State::Complete); + if (node->then_graph() != nullptr) + s.args().append("then_graph", node->then_graph()->name()); + else + s.args().append("then_branch", pepper::str(node->then_branch())); - return true; -} + if (node->else_graph() != nullptr) + s.args().append("else_graph", node->else_graph()->name()); + else + s.args().append("else_branch", pepper::str(node->else_branch())); -bool CircleNodeSummaryBuilder::summary(const luci::CircleMean *node, locop::NodeSummary &s) const -{ - s.args().append("input", tbl()->lookup(node->input())); - s.args().append("reduction_indices", tbl()->lookup(node->reduction_indices())); - s.args().append("keep_dims", node->keep_dims() ? "true" : "false"); s.state(locop::NodeSummary::State::Complete); + return true; } -bool CircleNodeSummaryBuilder::summary(const luci::CircleMul *node, locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleL2Normalize *node, + locop::NodeSummary &s) const { - assert(node->fusedActivationFunction() != luci::FusedActFunc::UNDEFINED); - s.args().append("x", tbl()->lookup(node->x())); - s.args().append("y", tbl()->lookup(node->y())); s.args().append("fused_activation_function", to_str(node->fusedActivationFunction())); s.state(locop::NodeSummary::State::Complete); return true; } -bool CircleNodeSummaryBuilder::summary(const luci::CirclePack *node, locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleLess *node, locop::NodeSummary &s) const { - for (uint32_t i = 0; i < node->values_count(); ++i) - s.args().append("values", tbl()->lookup(node->values(i))); - s.args().append("values_count", pepper::str(node->values_count())); - s.args().append("axis", pepper::str(node->axis())); - s.state(locop::NodeSummary::State::Complete); - return true; + return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CirclePad *node, locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleLessEqual *node, + locop::NodeSummary &s) const { - s.args().append("input", tbl()->lookup(node->input())); - s.args().append("paddings", tbl()->lookup(node->paddings())); - s.state(locop::NodeSummary::State::Complete); - return true; + return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleRelu *node, locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleLeakyRelu *node, + locop::NodeSummary &s) const { s.args().append("features", tbl()->lookup(node->features())); + s.args().append("alpha", std::to_string(node->alpha())); s.state(locop::NodeSummary::State::Complete); return true; } -bool CircleNodeSummaryBuilder::summary(const luci::CircleRelu6 *node, locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleLocalResponseNormalization *node, + locop::NodeSummary &s) const { - s.args().append("features", tbl()->lookup(node->features())); + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("radius", pepper::str(node->radius())); + s.args().append("bias", pepper::str(node->bias())); + s.args().append("alpha", pepper::str(node->alpha())); + s.args().append("beta", pepper::str(node->beta())); s.state(locop::NodeSummary::State::Complete); return true; } -bool CircleNodeSummaryBuilder::summary(const luci::CircleReshape *node, locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleLog *node, locop::NodeSummary &s) const { - s.args().append("tensor", tbl()->lookup(node->tensor())); - s.args().append("shape", tbl()->lookup(node->shape())); - // TODO Show newShape info - s.state(locop::NodeSummary::State::PartiallyKnown); - return true; + return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleRsqrt *node, locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleLogicalAnd *node, + locop::NodeSummary &s) const { - s.args().append("x", tbl()->lookup(node->x())); - s.state(locop::NodeSummary::State::Complete); - return true; + return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSoftmax *node, locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleLogicalNot *node, + locop::NodeSummary &s) const { - s.args().append("logits", tbl()->lookup(node->logits())); - s.args().append("beta", pepper::str(node->beta())); - s.state(locop::NodeSummary::State::Complete); - return true; + return use_x(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSqrt *node, locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleLogicalOr *node, + locop::NodeSummary &s) const { - s.args().append("x", tbl()->lookup(node->x())); - s.state(locop::NodeSummary::State::Complete); - return true; + return use_xy(tbl(), node, s); } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSquaredDifference *node, +bool CircleNodeSummaryBuilder::summary(const luci::CircleLogistic *node, locop::NodeSummary &s) const { - s.args().append("x", tbl()->lookup(node->x())); - s.args().append("y", tbl()->lookup(node->y())); + return use_x(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleLogSoftmax *node, + locop::NodeSummary &s) const +{ + s.args().append("logits", tbl()->lookup(node->logits())); s.state(locop::NodeSummary::State::Complete); return true; } -bool CircleNodeSummaryBuilder::summary(const luci::CircleSub *node, locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleMatrixDiag *node, + locop::NodeSummary &s) const { - s.args().append("x", tbl()->lookup(node->x())); - s.args().append("y", tbl()->lookup(node->y())); + s.args().append("diagonal", tbl()->lookup(node->diagonal())); s.state(locop::NodeSummary::State::Complete); return true; } -// TODO TFLTanh - -bool CircleNodeSummaryBuilder::summary(const luci::CircleTranspose *node, +bool CircleNodeSummaryBuilder::summary(const luci::CircleMatrixSetDiag *node, locop::NodeSummary &s) const { - s.args().append("a", tbl()->lookup(node->a())); - s.args().append("perm", tbl()->lookup(node->perm())); + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("diagonal", tbl()->lookup(node->diagonal())); s.state(locop::NodeSummary::State::Complete); return true; } -bool CircleNodeSummaryBuilder::summary(const luci::CircleTransposeConv *node, - locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleMaximum *node, locop::NodeSummary &s) const { - assert(node->padding() != luci::Padding::UNDEFINED); + return use_xy(tbl(), node, s); +} - s.args().append("inputSizes", tbl()->lookup(node->inputSizes())); - s.args().append("filter", tbl()->lookup(node->filter())); - s.args().append("outBackprop", tbl()->lookup(node->outBackprop())); +bool CircleNodeSummaryBuilder::summary(const luci::CircleMaxPool2D *node, + locop::NodeSummary &s) const +{ + assert(node->fusedActivationFunction() != luci::FusedActFunc::UNDEFINED); + s.args().append("value", tbl()->lookup(node->value())); + s.args().append("filter(h,w)", to_str(node->filter())); s.args().append("stride(h,w)", to_str(node->stride())); s.args().append("padding", to_str(node->padding())); + s.args().append("fused", to_str(node->fusedActivationFunction())); s.state(locop::NodeSummary::State::Complete); return true; } -bool CircleNodeSummaryBuilder::summary(const luci::CircleInput *, locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleMean *node, locop::NodeSummary &s) const +{ + return use_reducer(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleMinimum *node, locop::NodeSummary &s) const +{ + return use_xy(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleMirrorPad *node, + locop::NodeSummary &s) const { + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("paddings", tbl()->lookup(node->paddings())); + s.args().append("mode", to_str(node->mode())); s.state(locop::NodeSummary::State::Complete); return true; } -bool CircleNodeSummaryBuilder::summary(const luci::CircleOutput *node, locop::NodeSummary &s) const +bool CircleNodeSummaryBuilder::summary(const luci::CircleMul *node, locop::NodeSummary &s) const { - s.args().append("from", tbl()->lookup(node->from())); + return use_xy_act(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleNeg *node, locop::NodeSummary &s) const +{ + return use_x(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleNotEqual *node, + locop::NodeSummary &s) const +{ + return use_xy(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleOneHot *node, locop::NodeSummary &s) const +{ + s.args().append("indices", tbl()->lookup(node->indices())); + s.args().append("depth", tbl()->lookup(node->depth())); + s.args().append("on_value", tbl()->lookup(node->on_value())); + s.args().append("off_value", tbl()->lookup(node->off_value())); + s.args().append("axis", pepper::str(node->axis())); + + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CirclePack *node, locop::NodeSummary &s) const +{ + for (uint32_t i = 0; i < node->values_count(); ++i) + s.args().append("values", tbl()->lookup(node->values(i))); + s.args().append("values_count", pepper::str(node->values_count())); + s.args().append("axis", pepper::str(node->axis())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CirclePad *node, locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("paddings", tbl()->lookup(node->paddings())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CirclePow *node, locop::NodeSummary &s) const +{ + return use_xy(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CirclePRelu *node, locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("alpha", tbl()->lookup(node->alpha())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleRange *node, locop::NodeSummary &s) const +{ + s.args().append("start", tbl()->lookup(node->start())); + s.args().append("limit", tbl()->lookup(node->limit())); + s.args().append("delta", tbl()->lookup(node->delta())); + + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleRank *node, locop::NodeSummary &s) const +{ + return use_input(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleReduceAny *node, + locop::NodeSummary &s) const +{ + return use_reducer(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleReduceMax *node, + locop::NodeSummary &s) const +{ + return use_reducer(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleReduceMin *node, + locop::NodeSummary &s) const +{ + return use_reducer(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleReduceProd *node, + locop::NodeSummary &s) const +{ + return use_reducer(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleRelu *node, locop::NodeSummary &s) const +{ + return use_features(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleRelu6 *node, locop::NodeSummary &s) const +{ + return use_features(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleReluN1To1 *node, + locop::NodeSummary &s) const +{ + return use_features(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleReshape *node, locop::NodeSummary &s) const +{ + s.args().append("tensor", tbl()->lookup(node->tensor())); + s.args().append("shape", tbl()->lookup(node->shape())); + // TODO Show newShape info + s.state(locop::NodeSummary::State::PartiallyKnown); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleResizeBilinear *node, + locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("size", tbl()->lookup(node->size())); + s.args().append("align_corners", node->align_corners() ? "true" : "false"); + s.args().append("half_pixel_centers", node->half_pixel_centers() ? "true" : "false"); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleResizeNearestNeighbor *node, + locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("size", tbl()->lookup(node->size())); + s.args().append("align_corners", node->align_corners() ? "true" : "false"); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleReverseSequence *node, + locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("seq_lengths", tbl()->lookup(node->seq_lengths())); + s.args().append("seq_axis", std::to_string(node->seq_axis())); + s.args().append("batch_axis", std::to_string(node->batch_axis())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleReverseV2 *node, + locop::NodeSummary &s) const +{ + s.args().append("tensor", tbl()->lookup(node->tensor())); + s.args().append("axis", tbl()->lookup(node->axis())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleRound *node, locop::NodeSummary &s) const +{ + return use_x(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleRsqrt *node, locop::NodeSummary &s) const +{ + return use_x(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleScatterNd *node, + locop::NodeSummary &s) const +{ + s.args().append("indices", tbl()->lookup(node->indices())); + s.args().append("updates", tbl()->lookup(node->updates())); + s.args().append("shape", tbl()->lookup(node->shape())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSegmentSum *node, + locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("segment_ids", tbl()->lookup(node->segment_ids())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSelect *node, locop::NodeSummary &s) const +{ + s.args().append("condition", tbl()->lookup(node->condition())); + s.args().append("t", tbl()->lookup(node->t())); + s.args().append("e", tbl()->lookup(node->e())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSelectV2 *node, + locop::NodeSummary &s) const +{ + s.args().append("condition", tbl()->lookup(node->condition())); + s.args().append("t", tbl()->lookup(node->t())); + s.args().append("e", tbl()->lookup(node->e())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleShape *node, locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("out_type", to_str(node->out_type())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSin *node, locop::NodeSummary &s) const +{ + return use_x(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSlice *node, locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("begin", tbl()->lookup(node->begin())); + s.args().append("size", tbl()->lookup(node->size())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSoftmax *node, locop::NodeSummary &s) const +{ + s.args().append("logits", tbl()->lookup(node->logits())); + s.args().append("beta", pepper::str(node->beta())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSpaceToBatchND *node, + locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("block_shape", tbl()->lookup(node->block_shape())); + s.args().append("paddings", tbl()->lookup(node->paddings())); + + s.state(locop::NodeSummary::State::Complete); + + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSpaceToDepth *node, + locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("block_size", pepper::str(node->block_size())); + + s.state(locop::NodeSummary::State::Complete); + + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSparseToDense *node, + locop::NodeSummary &s) const +{ + s.args().append("indices", tbl()->lookup(node->indices())); + s.args().append("output_shape", tbl()->lookup(node->output_shape())); + s.args().append("values", tbl()->lookup(node->values())); + s.args().append("default_value", tbl()->lookup(node->default_value())); + + s.args().append("Validate_indices", pepper::str(node->validate_indices())); + + s.state(locop::NodeSummary::State::Complete); + + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSplit *node, locop::NodeSummary &s) const +{ + s.args().append("split_dim", tbl()->lookup(node->split_dim())); + s.args().append("input", tbl()->lookup(node->input())); + + s.args().append("num_split", pepper::str(node->num_split())); + + s.state(locop::NodeSummary::State::Complete); + + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSplitV *node, locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("size_splits", tbl()->lookup(node->size_splits())); + s.args().append("split_dim", tbl()->lookup(node->split_dim())); + + s.args().append("num_split", pepper::str(node->num_split())); + + s.state(locop::NodeSummary::State::Complete); + + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSqrt *node, locop::NodeSummary &s) const +{ + return use_x(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSquare *node, locop::NodeSummary &s) const +{ + return use_x(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSquaredDifference *node, + locop::NodeSummary &s) const +{ + return use_xy(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSqueeze *node, locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + + std::stringstream ss{"("}; + for (size_t i = 0; i < node->squeeze_dims().size(); ++i) + { + if (i != 0) + ss << ", "; + ss << node->squeeze_dims()[i]; + } + ss << ")"; + + s.args().append("squeeze_dims", ss.str()); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleStridedSlice *node, + locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("begin", tbl()->lookup(node->begin())); + s.args().append("end", tbl()->lookup(node->end())); + s.args().append("strides", tbl()->lookup(node->strides())); + + s.args().append("begin_mask", pepper::str(node->begin_mask())); + s.args().append("end_mask", pepper::str(node->end_mask())); + s.args().append("ellipsis_mask", pepper::str(node->ellipsis_mask())); + s.args().append("new_axis_mask", pepper::str(node->new_axis_mask())); + s.args().append("shrink_axis_mask", pepper::str(node->shrink_axis_mask())); + + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSub *node, locop::NodeSummary &s) const +{ + return use_xy(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSum *node, locop::NodeSummary &s) const +{ + return use_reducer(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleTanh *node, locop::NodeSummary &s) const +{ + return use_x(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleTile *node, locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("multiples", tbl()->lookup(node->multiples())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleTopKV2 *node, locop::NodeSummary &s) const +{ + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("k", tbl()->lookup(node->k())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleTranspose *node, + locop::NodeSummary &s) const +{ + s.args().append("a", tbl()->lookup(node->a())); + s.args().append("perm", tbl()->lookup(node->perm())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleTransposeConv *node, + locop::NodeSummary &s) const +{ + assert(node->padding() != luci::Padding::UNDEFINED); + + s.args().append("inputSizes", tbl()->lookup(node->inputSizes())); + s.args().append("filter", tbl()->lookup(node->filter())); + s.args().append("outBackprop", tbl()->lookup(node->outBackprop())); + + s.args().append("stride(h,w)", to_str(node->stride())); + s.args().append("padding", to_str(node->padding())); + + s.state(locop::NodeSummary::State::Complete); + + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleUnpack *node, locop::NodeSummary &s) const +{ + s.args().append("value", tbl()->lookup(node->value())); + + s.args().append("num", pepper::str(node->num())); + s.args().append("axis", pepper::str(node->axis())); + + s.state(locop::NodeSummary::State::Complete); + + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleWhere *node, locop::NodeSummary &s) const +{ + s.args().append("condition", tbl()->lookup(node->condition())); + s.state(locop::NodeSummary::State::Complete); + + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleWhile *node, locop::NodeSummary &s) const +{ + for (uint32_t i = 0; i < node->input_count(); ++i) + s.args().append("input", tbl()->lookup(node->input(i))); + + if (node->cond_graph() != nullptr) + s.args().append("cond_graph", node->cond_graph()->name()); + else + s.args().append("cond_branch", pepper::str(node->cond_branch())); + + if (node->body_graph() != nullptr) + s.args().append("body_graph", node->body_graph()->name()); + else + s.args().append("body_branch", pepper::str(node->body_branch())); + + s.state(locop::NodeSummary::State::Complete); + + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleZerosLike *node, + locop::NodeSummary &s) const +{ + return use_input(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSplitOut *node, + locop::NodeSummary &s) const +{ + return use_input(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleSplitVOut *node, + locop::NodeSummary &s) const +{ + return use_input(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleTopKV2Out *node, + locop::NodeSummary &s) const +{ + s.args().append("topkv2", tbl()->lookup(node->input())); + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleUnpackOut *node, + locop::NodeSummary &s) const +{ + s.args().append("unpack", tbl()->lookup(node->input())); + + s.state(locop::NodeSummary::State::Complete); + + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleIfOut *node, locop::NodeSummary &s) const +{ + return use_input(tbl(), node, s); +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleWhileOut *node, + locop::NodeSummary &s) const +{ + s.args().append("while", tbl()->lookup(node->input())); + + s.state(locop::NodeSummary::State::Complete); + + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleInput *, locop::NodeSummary &s) const +{ + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleOutput *node, locop::NodeSummary &s) const +{ + s.args().append("from", tbl()->lookup(node->from())); + + s.state(locop::NodeSummary::State::Complete); + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleBCQFullyConnected *node, + locop::NodeSummary &s) const +{ + assert(node->fusedActivationFunction() != luci::FusedActFunc::UNDEFINED); + + s.args().append("input", tbl()->lookup(node->input())); + s.args().append("weights_scales", tbl()->lookup(node->weights_scales())); + s.args().append("weights_binary", tbl()->lookup(node->weights_binary())); + s.args().append("bias", tbl()->lookup(node->bias())); + s.args().append("weights_clusters", tbl()->lookup(node->weights_clusters())); + + s.args().append("fused", to_str(node->fusedActivationFunction())); + s.args().append("weights_hidden_size", pepper::str(node->weights_hidden_size())); + + s.state(locop::NodeSummary::State::Complete); + + return true; +} + +bool CircleNodeSummaryBuilder::summary(const luci::CircleBCQGather *node, + locop::NodeSummary &s) const +{ + s.args().append("input_scales", tbl()->lookup(node->input_scales())); + s.args().append("input_binary", tbl()->lookup(node->input_binary())); + s.args().append("indices", tbl()->lookup(node->indices())); + s.args().append("input_clusters", tbl()->lookup(node->input_clusters())); + + s.args().append("axis", pepper::str(node->axis())); + s.args().append("input_hidden_size", pepper::str(node->input_hidden_size())); s.state(locop::NodeSummary::State::Complete); return true; @@ -603,4 +1403,4 @@ bool NodeSummaryBuilder::build(const loco::Node *node, locop::NodeSummary &s) co return false; } -} // namespace exo +} // namespace luci diff --git a/compiler/luci/pass/CMakeLists.txt b/compiler/luci/pass/CMakeLists.txt index 93130ce..2c5fb34 100644 --- a/compiler/luci/pass/CMakeLists.txt +++ b/compiler/luci/pass/CMakeLists.txt @@ -1,6 +1,6 @@ file(GLOB_RECURSE SOURCES "src/*.cpp") -#file(GLOB_RECURSE TESTS "src/*.test.cpp") -#list(REMOVE_ITEM SOURCES ${TESTS}) +file(GLOB_RECURSE TESTS "src/*.test.cpp") +list(REMOVE_ITEM SOURCES ${TESTS}) add_library(luci_pass SHARED ${SOURCES}) target_include_directories(luci_pass PRIVATE src) @@ -16,14 +16,14 @@ target_link_libraries(luci_pass PRIVATE nncc_common) target_link_libraries(luci_pass PRIVATE oops) install(TARGETS luci_pass DESTINATION lib) -# TODO enable for tests -#if(NOT ENABLE_TEST) -# return() -#endif(NOT ENABLE_TEST) -# -#nnas_find_package(GTest REQUIRED) -# -#GTest_AddTest(luci_pass_test ${TESTS}) -#target_include_directories(luci_pass_test PRIVATE src) -#target_link_libraries(luci_pass_test luci_pass) +if(NOT ENABLE_TEST) + return() +endif(NOT ENABLE_TEST) + +nnas_find_package(GTest REQUIRED) + +GTest_AddTest(luci_pass_test ${TESTS}) +target_include_directories(luci_pass_test PRIVATE src) +target_link_libraries(luci_pass_test luci_pass) +target_link_libraries(luci_pass_test luci_lang) #target_link_libraries(luci_pass_test oops) diff --git a/compiler/luci/pass/include/luci/CircleOptimizer.h b/compiler/luci/pass/include/luci/CircleOptimizer.h index a969cca..312749f 100644 --- a/compiler/luci/pass/include/luci/CircleOptimizer.h +++ b/compiler/luci/pass/include/luci/CircleOptimizer.h @@ -32,11 +32,28 @@ public: { enum Algorithm { + FuseBCQ, FuseInstanceNorm, + ResolveCustomOpAdd, + ResolveCustomOpBatchMatMul, + ResolveCustomOpMatMul, + QuantizeDequantizeWeights, + QuantizeWithMinMax, }; + enum AlgorithmParameters + { + Quantize_input_dtype, + Quantize_output_dtype, + Quantize_granularity // layer-wise or channel-wise + }; + + virtual ~Options() = default; + virtual void enable(Algorithm) = 0; virtual bool query(Algorithm) = 0; + virtual void param(AlgorithmParameters, const std::string &) = 0; + virtual const std::string param(AlgorithmParameters) const = 0; }; public: @@ -46,6 +63,8 @@ public: public: void optimize(loco::Graph *) const; + void quantize(loco::Graph *) const; + private: std::unique_ptr _options; }; diff --git a/compiler/luci/pass/include/luci/Pass/FuseBCQPass.h b/compiler/luci/pass/include/luci/Pass/FuseBCQPass.h new file mode 100644 index 0000000..4404a9f --- /dev/null +++ b/compiler/luci/pass/include/luci/Pass/FuseBCQPass.h @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_FUSE_BCQ_PASS_H__ +#define __LUCI_FUSE_BCQ_PASS_H__ + +#include + +namespace luci +{ + +/** + * @brief Class to fuse certain pattern of subgraph into CircleBCQFullyConnected or CircleBCQGather + * + */ +struct FuseBCQPass final : public logo::Pass +{ + const char *name(void) const final { return "luci::FuseBCQPass"; } + + bool run(loco::Graph *g) final; +}; + +} // namespace luci + +#endif // __LUCI_FUSE_BCQ_PASS_H__ diff --git a/compiler/luci/pass/include/luci/Pass/QuantizationParameters.h b/compiler/luci/pass/include/luci/Pass/QuantizationParameters.h new file mode 100644 index 0000000..5c9cd42 --- /dev/null +++ b/compiler/luci/pass/include/luci/Pass/QuantizationParameters.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_QUANTIZATION_PARAMETERS_H__ +#define __LUCI_QUANTIZATION_PARAMETERS_H__ + +namespace luci +{ + +enum QuantizationGranularity +{ + LayerWise = 0, + ChannelWise = 1, +}; + +} // namespace luci + +#endif // __LUCI_QUANTIZATION_PARAMETERS_H__ diff --git a/compiler/luci/pass/include/luci/Pass/QuantizeDequantizeWeightsPass.h b/compiler/luci/pass/include/luci/Pass/QuantizeDequantizeWeightsPass.h new file mode 100644 index 0000000..713b88f --- /dev/null +++ b/compiler/luci/pass/include/luci/Pass/QuantizeDequantizeWeightsPass.h @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_QUANTIZE_DEQUANTIZE_WEIGHTS_PASS_H__ +#define __LUCI_QUANTIZE_DEQUANTIZE_WEIGHTS_PASS_H__ + +#include + +#include + +#include + +namespace luci +{ + +/** + * @brief Pass to quantize weights + */ +class QuantizeDequantizeWeightsPass : public logo::Pass +{ +public: + QuantizeDequantizeWeightsPass(loco::DataType input_dtype, loco::DataType output_dtype, + QuantizationGranularity granularity) + : _input_dtype{input_dtype}, _output_dtype{output_dtype}, _granularity{granularity} + { + // DO NOTHING + } + virtual const char *name(void) const { return "luci::QuantizeDequantizeWeightsPass"; } + +public: + bool run(loco::Graph *graph); + +private: + loco::DataType _input_dtype; + loco::DataType _output_dtype; + QuantizationGranularity _granularity; +}; + +} // namespace luci + +#endif //__LUCI_QUANTIZE_DEQUANTIZE_WEIGHTS_PASS_H__ diff --git a/compiler/luci/pass/include/luci/Pass/QuantizeWithMinMaxPass.h b/compiler/luci/pass/include/luci/Pass/QuantizeWithMinMaxPass.h new file mode 100644 index 0000000..bb0d0ff --- /dev/null +++ b/compiler/luci/pass/include/luci/Pass/QuantizeWithMinMaxPass.h @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_QUANTIZE_WITH_MINMAX_PASS_H__ +#define __LUCI_QUANTIZE_WITH_MINMAX_PASS_H__ + +#include + +#include + +#include + +namespace luci +{ + +/** + * @brief Pass to quantize activation, weights, and bias + */ +class QuantizeWithMinMaxPass : public logo::Pass +{ +public: + QuantizeWithMinMaxPass(loco::DataType input_dtype, loco::DataType output_dtype, + QuantizationGranularity granularity) + : _input_dtype{input_dtype}, _output_dtype{output_dtype}, _granularity{granularity} + { + // DO NOTHING + } + virtual const char *name(void) const { return "luci::QuantizeWithMinMaxPass"; } + +public: + bool run(loco::Graph *graph); + +private: + loco::DataType _input_dtype; + loco::DataType _output_dtype; + QuantizationGranularity _granularity; +}; + +} // namespace luci + +#endif //__LUCI_QUANTIZE_WITH_MINMAX_PASS_H__ diff --git a/compiler/luci/pass/include/luci/Pass/ResolveCustomOpAddPass.h b/compiler/luci/pass/include/luci/Pass/ResolveCustomOpAddPass.h new file mode 100644 index 0000000..35a3350 --- /dev/null +++ b/compiler/luci/pass/include/luci/Pass/ResolveCustomOpAddPass.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_RESOLVE_CUSTOM_OP_ADD_PASS_H__ +#define __LUCI_RESOLVE_CUSTOM_OP_ADD_PASS_H__ + +#include + +namespace luci +{ + +/** + * @brief Class to resolve certain custom op of subgraph into add op in circle schema. + */ +struct ResolveCustomOpAddPass final : public logo::Pass +{ + const char *name(void) const final { return "luci::ResolveCustomOpAddPass"; } + + bool run(loco::Graph *g) final; +}; + +} // namespace luci + +#endif // __LUCI_RESOLVE_CUSTOM_OP_ADD_PASS_H__ diff --git a/compiler/luci/pass/include/luci/Pass/ResolveCustomOpBatchMatMulPass.h b/compiler/luci/pass/include/luci/Pass/ResolveCustomOpBatchMatMulPass.h new file mode 100644 index 0000000..7c48c8d --- /dev/null +++ b/compiler/luci/pass/include/luci/Pass/ResolveCustomOpBatchMatMulPass.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_RESOLVE_CUSTOM_OP_BATCHMATMUL_PASS_H__ +#define __LUCI_RESOLVE_CUSTOM_OP_BATCHMATMUL_PASS_H__ + +#include + +namespace luci +{ + +/** + * @brief Class to resolve certain custom op of subgraph into batchmatmul op in circle schema. + */ +struct ResolveCustomOpBatchMatMulPass final : public logo::Pass +{ + const char *name(void) const final { return "luci::ResolveCustomOpBatchMatMulPass"; } + + bool run(loco::Graph *g) final; +}; + +} // namespace luci + +#endif // __LUCI_RESOLVE_CUSTOM_OP_BATCHMATMUL_PASS_H__ diff --git a/compiler/luci/pass/include/luci/Pass/ResolveCustomOpMatMulPass.h b/compiler/luci/pass/include/luci/Pass/ResolveCustomOpMatMulPass.h new file mode 100644 index 0000000..701deba --- /dev/null +++ b/compiler/luci/pass/include/luci/Pass/ResolveCustomOpMatMulPass.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_RESOLVE_CUSTOM_OP_MATMUL_PASS_H__ +#define __LUCI_RESOLVE_CUSTOM_OP_MATMUL_PASS_H__ + +#include + +namespace luci +{ + +/** + * @brief Class to resolve certain custom op of subgraph into matmul op in circle schema. + */ +struct ResolveCustomOpMatMulPass final : public logo::Pass +{ + const char *name(void) const final { return "luci::ResolveCustomOpMatMulPass"; } + + bool run(loco::Graph *g) final; +}; + +} // namespace luci + +#endif // __LUCI_RESOLVE_CUSTOM_OP_MATMUL_PASS_H__ diff --git a/compiler/luci/pass/src/CircleOptimizer.cpp b/compiler/luci/pass/src/CircleOptimizer.cpp index dcb05a0..90fbe90 100644 --- a/compiler/luci/pass/src/CircleOptimizer.cpp +++ b/compiler/luci/pass/src/CircleOptimizer.cpp @@ -16,16 +16,23 @@ #include "luci/CircleOptimizer.h" +#include "luci/Pass/FuseBCQPass.h" #include "luci/Pass/FuseInstanceNormPass.h" +#include "luci/Pass/ResolveCustomOpAddPass.h" +#include "luci/Pass/ResolveCustomOpBatchMatMulPass.h" +#include "luci/Pass/ResolveCustomOpMatMulPass.h" +#include "luci/Pass/QuantizeWithMinMaxPass.h" +#include "luci/Pass/QuantizeDequantizeWeightsPass.h" // TODO add more passes #include "luci/Pass/ShapeInferencePass.h" #include "luci/Pass/TypeInferencePass.h" // logo passes -#include +#include #include "ProgressReporter.h" +#include "CircleOptimizerUtils.h" #include @@ -36,18 +43,39 @@ namespace using namespace luci; -class OptimizeOptionsImpl : public luci::CircleOptimizer::Options +class OptimizeOptionsImpl final : public luci::CircleOptimizer::Options { public: void enable(Algorithm) final; + void param(AlgorithmParameters, const std::string &) final; + const std::string param(AlgorithmParameters) const final; bool query(Algorithm) final; private: std::vector _algorithms; + std::map _algorithm_params; }; void OptimizeOptionsImpl::enable(Algorithm algo) { _algorithms.push_back(algo); } +void OptimizeOptionsImpl::param(AlgorithmParameters param, const std::string &str) +{ + _algorithm_params.insert(std::pair(param, str)); +} + +const std::string OptimizeOptionsImpl::param(AlgorithmParameters param) const +{ + auto param_str = _algorithm_params.find(param); + if (param_str != _algorithm_params.end()) + { + return param_str->second; + } + else + { + return std::string(); + } +} + bool OptimizeOptionsImpl::query(Algorithm algo) { std::vector::iterator it = std::find(_algorithms.begin(), _algorithms.end(), algo); @@ -77,14 +105,31 @@ void CircleOptimizer::optimize(loco::Graph *g) const logo::Phase phase; /* TRANSFORM DECLARATION BEGIN */ + if (_options->query(Options::Algorithm::ResolveCustomOpAdd)) + { + phase.emplace_back(std::make_unique()); + } + if (_options->query(Options::Algorithm::ResolveCustomOpBatchMatMul)) + { + phase.emplace_back(std::make_unique()); + } + if (_options->query(Options::Algorithm::ResolveCustomOpMatMul)) + { + phase.emplace_back(std::make_unique()); + } if (_options->query(Options::Algorithm::FuseInstanceNorm)) { phase.emplace_back(std::make_unique()); } + if (_options->query(Options::Algorithm::FuseBCQ)) + { + phase.emplace_back(std::make_unique()); + } + // Shape inference is needed for added nodes doing above transformations phase.emplace_back(std::make_unique()); phase.emplace_back(std::make_unique()); - phase.emplace_back(std::make_unique()); + phase.emplace_back(std::make_unique()); /* TRANSFORM DECLARATION END */ ProgressReporter prog(g, logo::PhaseStrategy::Saturate); @@ -93,4 +138,74 @@ void CircleOptimizer::optimize(loco::Graph *g) const phase_runner.run(phase); } +void CircleOptimizer::quantize(loco::Graph *g) const +{ + // Fake quantization of weights + if (_options->query(Options::Algorithm::QuantizeDequantizeWeights)) + { + static const std::vector fakeq_supported_input_dtype{"float32"}; + static const std::vector fakeq_supported_output_dtype{"uint8"}; + static const std::vector fakeq_supported_granularity{"layer"}; + + auto input_dtype = _options->param(Options::AlgorithmParameters::Quantize_input_dtype); + auto output_dtype = _options->param(Options::AlgorithmParameters::Quantize_output_dtype); + auto granularity = _options->param(Options::AlgorithmParameters::Quantize_granularity); + + if (!in_array(to_lower_case(input_dtype), fakeq_supported_input_dtype)) + throw std::runtime_error("Unsupported input type. List of supported input type: " + + to_string(fakeq_supported_input_dtype)); + + if (!in_array(to_lower_case(output_dtype), fakeq_supported_output_dtype)) + throw std::runtime_error("Unsupported output type. List of supported output type: " + + to_string(fakeq_supported_output_dtype)); + + if (!in_array(to_lower_case(granularity), fakeq_supported_granularity)) + throw std::runtime_error("Unsupported granularity. List of supported granularity: " + + to_string(fakeq_supported_granularity)); + + luci::QuantizeDequantizeWeightsPass fake_quantizer( + str_to_dtype(input_dtype), str_to_dtype(output_dtype), str_to_granularity(granularity)); + fake_quantizer.run(g); + } + + // Actual quantization of weights, bias, and activation + if (_options->query(Options::Algorithm::QuantizeWithMinMax)) + { + static const std::vector qwmm_supported_input_dtype{"float32"}; + static const std::vector qwmm_supported_output_dtype{"uint8"}; + static const std::vector qwmm_supported_granularity{"layer"}; + + auto input_dtype = _options->param(Options::AlgorithmParameters::Quantize_input_dtype); + auto output_dtype = _options->param(Options::AlgorithmParameters::Quantize_output_dtype); + auto granularity = _options->param(Options::AlgorithmParameters::Quantize_granularity); + + if (!in_array(to_lower_case(input_dtype), qwmm_supported_input_dtype)) + throw std::runtime_error("Unsupported input type. List of supported input types: " + + to_string(qwmm_supported_input_dtype)); + + if (!in_array(to_lower_case(output_dtype), qwmm_supported_output_dtype)) + throw std::runtime_error("Unsupported output type. List of supported output types: " + + to_string(qwmm_supported_output_dtype)); + + if (!in_array(to_lower_case(granularity), qwmm_supported_granularity)) + throw std::runtime_error("Unsupported granularity. List of supported granularity: " + + to_string(qwmm_supported_granularity)); + + luci::QuantizeWithMinMaxPass quantizer(str_to_dtype(input_dtype), str_to_dtype(output_dtype), + str_to_granularity(granularity)); + quantizer.run(g); + } + + logo::Phase phase; + + // Do Shape/Type inference + phase.emplace_back(std::make_unique()); + phase.emplace_back(std::make_unique()); + + ProgressReporter prog(g, logo::PhaseStrategy::Saturate); + logo::PhaseRunner phase_runner{g}; + phase_runner.attach(&prog); + phase_runner.run(phase); +} + } // namespace luci diff --git a/compiler/luci/pass/src/CircleOptimizerUtils.cpp b/compiler/luci/pass/src/CircleOptimizerUtils.cpp new file mode 100644 index 0000000..ffc3723 --- /dev/null +++ b/compiler/luci/pass/src/CircleOptimizerUtils.cpp @@ -0,0 +1,89 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "CircleOptimizerUtils.h" + +namespace luci +{ + +bool in_array(const std::string &str, const std::vector &array) +{ + return std::find(array.begin(), array.end(), str) != array.end(); +} + +std::string to_string(const std::vector &strings) +{ + assert(!strings.empty()); + + std::string res; + for (unsigned int i = 0; i < strings.size() - 1; i++) + res += strings[i] + ", "; + + res += strings[strings.size() - 1]; + return res; +} + +std::string to_lower_case(std::string s) +{ + std::transform(s.begin(), s.end(), s.begin(), [](unsigned char c) { return std::tolower(c); }); + return s; +} + +loco::DataType str_to_dtype(const std::string &str) +{ + if (to_lower_case(str).compare("uint8") == 0) + return loco::DataType::U8; + if (to_lower_case(str).compare("uint16") == 0) + return loco::DataType::U16; + if (to_lower_case(str).compare("uint32") == 0) + return loco::DataType::U32; + if (to_lower_case(str).compare("uint64") == 0) + return loco::DataType::U64; + + if (to_lower_case(str).compare("int8") == 0) + return loco::DataType::S8; + if (to_lower_case(str).compare("int16") == 0) + return loco::DataType::S16; + if (to_lower_case(str).compare("int32") == 0) + return loco::DataType::S32; + if (to_lower_case(str).compare("int64") == 0) + return loco::DataType::S64; + + if (to_lower_case(str).compare("float16") == 0) + return loco::DataType::FLOAT16; + if (to_lower_case(str).compare("float32") == 0) + return loco::DataType::FLOAT32; + if (to_lower_case(str).compare("float64") == 0) + return loco::DataType::FLOAT64; + + if (to_lower_case(str).compare("bool") == 0) + return loco::DataType::BOOL; + + return loco::DataType::Unknown; +} + +QuantizationGranularity str_to_granularity(const std::string &str) +{ + if (to_lower_case(str).compare("layer") == 0) + return QuantizationGranularity::LayerWise; + + if (to_lower_case(str).compare("channel") == 0) + return QuantizationGranularity::ChannelWise; + + throw std::runtime_error("Quantization granularity must be either 'layer' or 'channel'"); +} + +} // namespace luci diff --git a/compiler/luci/pass/src/CircleOptimizerUtils.h b/compiler/luci/pass/src/CircleOptimizerUtils.h new file mode 100644 index 0000000..7e577a0 --- /dev/null +++ b/compiler/luci/pass/src/CircleOptimizerUtils.h @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_CIRCLE_OPTIMIZER_UTILS_H__ +#define __LUCI_CIRCLE_OPTIMIZER_UTILS_H__ + +#include "luci/Pass/QuantizeDequantizeWeightsPass.h" +#include "luci/Pass/QuantizeWithMinMaxPass.h" + +#include + +#include + +namespace luci +{ + +bool in_array(const std::string &, const std::vector &); + +std::string to_string(const std::vector &); + +std::string to_lower_case(std::string); + +loco::DataType str_to_dtype(const std::string &); + +QuantizationGranularity str_to_granularity(const std::string &); + +} // namespace luci + +#endif // __LUCI_CIRCLE_OPTIMIZER_UTILS_H__ diff --git a/compiler/luci/pass/src/FuseBCQPass.cpp b/compiler/luci/pass/src/FuseBCQPass.cpp new file mode 100644 index 0000000..b81db88 --- /dev/null +++ b/compiler/luci/pass/src/FuseBCQPass.cpp @@ -0,0 +1,405 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/FuseBCQPass.h" + +#include + +#include +#include +#include + +namespace +{ + +/** + * @brief Circle nodes including BCQ information and a circle node to which BCQ will be applied + * are connected with their name. And their names include common prefix. + * However, after pb file is converted to tflite file, some nodes' name are changed. + * Thus this function will return original common prefix. + * + * @note All the re-naming rule of TFLite converter is not figured out. + * Therefore, if new naming rule is detected, this function should be updated. + */ +const std::string node_name_prefix(luci::NodeName node_name) +{ + std::string prefix = node_name; + + if (prefix.find("ReadVariableOp/resource/") != std::string::npos) + { + const auto start_index = prefix.find("ReadVariableOp/resource/"); + + const auto left_prefix = prefix.substr(0, start_index); + const auto right_prefix = prefix.substr(start_index + 24); + + prefix = left_prefix + right_prefix; + } + + if (prefix.find("Tensordot/") != std::string::npos) + { + const auto index = prefix.find("Tensordot/"); + prefix = prefix.substr(0, index - 1); + } + else if (prefix.find("kernel/") != std::string::npos) + { + const auto index = prefix.find("kernel/"); + prefix = prefix.substr(0, index - 1); + } + else if (prefix.find("/bcqinfo_") != std::string::npos) + { + const auto index = prefix.find("/bcqinfo_"); + prefix = prefix.substr(0, index); + } + + return prefix; +} + +} // namespace + +namespace +{ + +class BCQConverter final +{ +public: + void add_BCQ_info_node(luci::CircleConst *node) + { + const auto node_name = node->name(); + const auto prefix = node_name_prefix(node_name); + + // If bcqinfo_* nodes are held by Reshape operation, + // shape of bcqinfo_* nodes are copied to `shape` input of Reshape operation. + // Then the name becomes bcqinfo_*_copy_shape. + // We should prevent this node not to added to bcq information. + if (node_name.find("_copy_shape") != std::string::npos) + return; + + if (node_name.find("bcqinfo_do_w_x") != std::string::npos) + _do_w_x[prefix] = node; + else if (node_name.find("bcqinfo_alpha") != std::string::npos) + _alpha[prefix] = node; + else if (node_name.find("bcqinfo_packed_binary_code") != std::string::npos) + _packed_binary_code[prefix] = node; + else if (node_name.find("bcqinfo_number_of_clusters") != std::string::npos) + _number_of_clusters[prefix] = node; + else if (node_name.find("bcqinfo_size_of_clusters") != std::string::npos) + _size_of_clusters[prefix] = node; + else if (node_name.find("bcqinfo_qbits_of_clusters") != std::string::npos) + _qbits_of_clusters[prefix] = node; + else if (node_name.find("bcqinfo_dequant_weight") != std::string::npos) + _dequant_weight[prefix] = node; + } + + bool has_BCQ_info(luci::CircleConst *node) + { + const auto prefix = node_name_prefix(node->name()); + bool has_info = true; + + has_info &= (_do_w_x.find(prefix) != _do_w_x.end()); + has_info &= (_alpha.find(prefix) != _alpha.end()); + has_info &= (_packed_binary_code.find(prefix) != _packed_binary_code.end()); + has_info &= (_number_of_clusters.find(prefix) != _number_of_clusters.end()); + has_info &= (_size_of_clusters.find(prefix) != _size_of_clusters.end()); + has_info &= (_qbits_of_clusters.find(prefix) != _qbits_of_clusters.end()); + // bcqinfo_dequant_weight is just for validation, so not always exists. + + return has_info; + } + + bool do_w_x(luci::CircleConst *node) + { + const auto prefix = node_name_prefix(node->name()); + + if (_do_w_x[prefix]->dtype() == loco::DataType::S32) + return _do_w_x[prefix]->at(0) == 1; + else if (_do_w_x[prefix]->dtype() == loco::DataType::BOOL) + return _do_w_x[prefix]->at(0); + else + throw std::runtime_error("do_w_x should be int or bool"); + } + + luci::CircleConst *get_alpha(luci::CircleConst *node) + { + const auto prefix = node_name_prefix(node->name()); + return _alpha[prefix]; + } + + luci::CircleConst *get_packed_binary_code(luci::CircleConst *node) + { + const auto prefix = node_name_prefix(node->name()); + return _packed_binary_code[prefix]; + } + + luci::CircleConst *get_number_of_clusters(luci::CircleConst *node) + { + const auto prefix = node_name_prefix(node->name()); + return _number_of_clusters[prefix]; + } + + luci::CircleConst *get_size_of_clusters(luci::CircleConst *node) + { + const auto prefix = node_name_prefix(node->name()); + return _size_of_clusters[prefix]; + } + + luci::CircleConst *get_qbits_of_clusters(luci::CircleConst *node) + { + const auto prefix = node_name_prefix(node->name()); + return _qbits_of_clusters[prefix]; + } + + luci::CircleConst *packed_clusters(luci::CircleConst *node) + { + auto graph = node->graph(); + auto qbits_of_clusters = get_qbits_of_clusters(node); + auto size_of_clusters = get_size_of_clusters(node); + const auto number_of_clusters = get_number_of_clusters(node)->at(0); + + auto packed_clusters = graph->nodes()->create(); + packed_clusters->dtype(loco::DataType::S32); + packed_clusters->size(number_of_clusters * 2); + packed_clusters->rank(2); + packed_clusters->dim(0) = number_of_clusters; + packed_clusters->dim(1) = 2; + packed_clusters->shape_status(luci::ShapeStatus::VALID); + + for (int i = 0; i < number_of_clusters; ++i) + { + packed_clusters->at(i * 2) = + qbits_of_clusters->at(i); + packed_clusters->at(i * 2 + 1) = + size_of_clusters->at(i); + } + + return packed_clusters; + } + + /** + * @brief Exclude BCQ information nodes which are used for fusing BCQ operations + * from graph output by using CircleOutputExclude + */ + void clear_BCQ_nodes() + { + auto createNoOp = [](luci::CircleNode *circle_node) { + auto graph = circle_node->graph(); + auto noOp = graph->nodes()->create(); + + if (circle_node->shape_status() == luci::ShapeStatus::VALID) + { + noOp->dtype(circle_node->dtype()); + noOp->rank(circle_node->rank()); + for (uint32_t i = 0; i < circle_node->rank(); ++i) + noOp->dim(i) = circle_node->dim(i); + } + else + { + // For type inference + noOp->dtype(loco::DataType::FLOAT32); + } + + return noOp; + }; + + auto clear_nodes = [createNoOp](std::map &nodes) { + for (auto &n : nodes) + { + auto node = n.second; + + for (auto s : loco::succs(node)) + { + if (auto outnode = dynamic_cast(s)) + { + outnode->from(createNoOp(node)); + } + else if (auto reshape_node = dynamic_cast(s)) + { + for (auto o : loco::succs(reshape_node)) + { + auto circle_output = loco::must_cast(o); + circle_output->from(createNoOp(reshape_node)); + } + } + } + } + }; + + clear_nodes(_do_w_x); + clear_nodes(_alpha); + clear_nodes(_packed_binary_code); + clear_nodes(_number_of_clusters); + clear_nodes(_size_of_clusters); + clear_nodes(_qbits_of_clusters); + clear_nodes(_dequant_weight); + } + +private: + std::map _do_w_x; + std::map _alpha; + std::map _packed_binary_code; + std::map _number_of_clusters; + std::map _size_of_clusters; + std::map _qbits_of_clusters; + std::map _dequant_weight; +}; + +} // namespace + +namespace luci +{ + +bool FuseBCQPass::run(loco::Graph *g) +{ + BCQConverter converter; + + bool changed = false; + + for (auto node : loco::all_nodes(g)) + { + if (auto circle_const = dynamic_cast(node)) + { + converter.add_BCQ_info_node(circle_const); + } + } + + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + if (auto gather = dynamic_cast(node)) + { + auto params = dynamic_cast(gather->params()); + if (params != nullptr && converter.has_BCQ_info(params)) + { + auto bcq_gather = g->nodes()->create(); + + bcq_gather->input_scales(converter.get_alpha(params)); + bcq_gather->input_binary(converter.get_packed_binary_code(params)); + bcq_gather->indices(gather->indices()); + bcq_gather->input_clusters(converter.packed_clusters(params)); + + const auto binary_hidden_size = + loco::must_cast(bcq_gather->input_binary())->dim(1).value() * 32; + bcq_gather->input_hidden_size(binary_hidden_size); + + if (converter.do_w_x(params)) + { + bcq_gather->axis(gather->axis()); + } + else + { + const auto axis_transpose = (gather->axis() == 0) ? 1 : 0; + bcq_gather->axis(axis_transpose); + } + + loco::replace(gather).with(bcq_gather); + + changed = true; + } + } + else if (auto fully_connected = dynamic_cast(node)) + { + auto weights = dynamic_cast(fully_connected->weights()); + if (weights != nullptr && converter.has_BCQ_info(weights)) + { + auto bcq_fc = g->nodes()->create(); + + bcq_fc->weights_scales(converter.get_alpha(weights)); + bcq_fc->weights_binary(converter.get_packed_binary_code(weights)); + bcq_fc->bias(fully_connected->bias()); + bcq_fc->weights_clusters(converter.packed_clusters(weights)); + bcq_fc->fusedActivationFunction(fully_connected->fusedActivationFunction()); + + loco::Node *bcq_input = fully_connected->input(); + int32_t batch_rank = 0; + + // If input of BCQFullyConnected has more than rank 2, we should reshape it as rank 2 + const auto original_input = loco::must_cast(fully_connected->input()); + if (original_input->shape_status() == ShapeStatus::VALID && original_input->rank() > 2) + { + auto new_shape = g->nodes()->create(); + new_shape->dtype(loco::DataType::S32); + new_shape->size(2); + new_shape->rank(1); + new_shape->dim(0) = 2; + + auto batch_size = 1; + for (uint32_t i = 0; i < original_input->rank() - 1; ++i) + batch_size *= original_input->dim(i).value(); + + new_shape->at(0) = batch_size; + new_shape->at(1) = + original_input->dim(original_input->rank() - 1).value(); + new_shape->shape_status(ShapeStatus::VALID); + + auto reshape = g->nodes()->create(); + reshape->tensor(original_input); + reshape->shape(new_shape); + + bcq_input = reshape; + batch_rank = original_input->rank() - 2; + } + + // If x_w formation, we should insert Transpose in front and back of BCQFullyConnected + if (converter.do_w_x(weights)) + { + const auto binary_hidden_size = + loco::must_cast(fully_connected->input()) + ->dim(batch_rank) + .value(); + bcq_fc->weights_hidden_size(binary_hidden_size); + bcq_fc->input(bcq_input); + loco::replace(fully_connected).with(bcq_fc); + } + else + { + const auto binary_hidden_size = + loco::must_cast(fully_connected->input()) + ->dim(1 + batch_rank) + .value(); + bcq_fc->weights_hidden_size(binary_hidden_size); + + auto perm = g->nodes()->create(); + perm->dtype(loco::DataType::S32); + perm->size(2); + perm->rank(1); + perm->dim(0) = 2; + perm->at(0) = 1; + perm->at(1) = 0; + perm->shape_status(ShapeStatus::VALID); + + auto input_transpose = g->nodes()->create(); + input_transpose->a(bcq_input); + input_transpose->perm(perm); + + bcq_fc->input(input_transpose); + + auto output_transpose = g->nodes()->create(); + output_transpose->a(bcq_fc); + output_transpose->perm(perm); + + loco::replace(fully_connected).with(output_transpose); + } + + changed = true; + } + } + } + + if (changed) + converter.clear_BCQ_nodes(); + + return changed; +} + +} // namespace luci diff --git a/compiler/luci/pass/src/FuseInstanceNormPass.cpp b/compiler/luci/pass/src/FuseInstanceNormPass.cpp index 180b5bb..ad8765c 100644 --- a/compiler/luci/pass/src/FuseInstanceNormPass.cpp +++ b/compiler/luci/pass/src/FuseInstanceNormPass.cpp @@ -15,6 +15,7 @@ */ #include "luci/Pass/FuseInstanceNormPass.h" +#include "FuseInstanceNormPassInternal.h" #include @@ -114,8 +115,6 @@ bool NodeFiller::with_commutative_args_of(const COMM_NOD } // namespace // Helper to check detail -namespace -{ /// @return true When node has shape of '1 x .. x 1 x depth' bool is_1D_with_dummy_dim(luci::CircleConst *node, uint32_t depth) @@ -130,7 +129,23 @@ bool is_1D_with_dummy_dim(luci::CircleConst *node, uint32_t depth) return node->dim(axis).value() == depth; } -bool is_instance_mean(luci::CircleMean *mean) +/// @return true if node shape consists of ones, except the one before the last dim: 1,...1,depth,1 +bool is_quasi_1D_with_dummy_dim(luci::CircleConst *node, uint32_t depth) +{ + auto rank = node->rank(); + // minimal accepted shape is [1 x depth x 1] + if (rank < 3) + return false; + const auto depth_axis = rank - 2; + for (uint32_t axis = 0; axis < rank; ++axis) + { + if (axis != depth_axis && node->dim(axis).value() != 1) + return false; + } + return node->dim(depth_axis).value() == depth; +} + +bool is_instance_mean_v0(luci::CircleMean *mean) { // // CHECK 1) input is rank 4 @@ -175,7 +190,53 @@ bool is_instance_mean(luci::CircleMean *mean) return mean->keep_dims(); } -} // namespace +bool is_instance_mean_v1(luci::CircleMean *mean) +{ + // + // CHECK 1) input is rank 5 (NHWCX) + // + auto input = mean->input(); + if (not loco::shape_known(input)) + return false; + auto input_shape = loco::shape_get(input).as(); + if (input_shape.rank() != 5) + return false; + + // + // CHECK 2) 'reduction indices' is CircleConst of value [1,2,4], that is HWX of NHWCX input shape + // + // TODO Support equivalent case, like [-3,-2] + // TODO Support non-Const case? + // TODO What if input is NCHW format in Circle? + auto red_indices = dynamic_cast(mean->reduction_indices()); + if (not red_indices) + return false; + if (red_indices->rank() != 1) + return false; + std::set red_indices_set; + + // TODO Currently only support S32, support other types + if (red_indices->dtype() != loco::DataType::S32) + return false; + for (uint32_t i = 0; i < red_indices->dim(0).value(); ++i) + red_indices_set.insert(red_indices->at(i)); + + if (red_indices_set.size() != 3) + return false; + if (red_indices_set.find(1) == red_indices_set.end()) + return false; + if (red_indices_set.find(2) == red_indices_set.end()) + return false; + if (red_indices_set.find(4) == red_indices_set.end()) + return false; + + // + // CHECK 3) keep_dims == true (?) + // + // We only have case of 'keep_dims == true' so far, but it might be okay with 'keep_dims == false' + // TODO Check this fact, and if true, return true regardless of keep_dims + return mean->keep_dims(); +} // Helper to fuse Instance Norm namespace @@ -227,14 +288,61 @@ namespace * | * V * [Out] + *------------------------------------------------------------------- + * [In] + * | + * V + * ifm + * | + * V + * +---------reshape_of_ifm ----+ (reduction indicies) + * | | | | + * | | V V + * | | mean_of_reshape -------------+ + * | V | | + * | sqdiff <--+ (reduction indicies) | + * | | | | + * | V | | + * | mean_as_variance <---+ const_as_epsilon | + * | | | | + * | V | | + * | add_as_variance <--------+ | + * | | | + * | V | + * | rsqrt const_as_gamma | + * | | | | + * | V | | + * | mul_gamma <--+ | + * | | | | + * V V V | + * mul_as_scaled_reshape mul_as_scaled_mean <-----------+ + * | | + * | const_as_beta | + * | | V + * | +------> sub + * V | + * add_as_terminal <----------+ + * | + * V + * reshape_as_terminal + * | + * V + * [Out] */ class InstanceNormPattern final { public: - InstanceNormPattern(luci::CircleAdd *candidate) + enum PatternVersion + { + Version_0, + Version_1 + }; + + InstanceNormPattern(luci::CircleAdd *candidate, PatternVersion pv) { assert(candidate); add_as_terminal = candidate; + _pv = pv; } public: @@ -244,7 +352,9 @@ public: public: // Context loco::Node *ifm = nullptr; + luci::CircleReshape *reshape_of_ifm = nullptr; luci::CircleMean *mean_of_ifm = nullptr; + luci::CircleMean *mean_of_reshape = nullptr; luci::CircleSquaredDifference *sqdiff = nullptr; luci::CircleMean *mean_as_variance = nullptr; luci::CircleConst *const_as_epsilon = nullptr; @@ -254,12 +364,14 @@ public: luci::CircleMul *mul_gamma = nullptr; luci::CircleMul *mul_as_scaled_ifm = nullptr; luci::CircleMul *mul_as_scaled_mean = nullptr; + luci::CircleMul *mul_as_scaled_reshape = nullptr; luci::CircleConst *const_as_beta = nullptr; luci::CircleSub *sub = nullptr; luci::CircleAdd *add_as_terminal = nullptr; private: bool _matched = false; + PatternVersion _pv; }; bool InstanceNormPattern::matched() @@ -273,8 +385,18 @@ bool InstanceNormPattern::matched() // Check order is DFS - CHECK_OR_FALSE(fill(&mul_as_scaled_ifm, &sub).with_commutative_args_of(add_as_terminal)); - CHECK_OR_FALSE(fill(&ifm, &mul_gamma).with_commutative_args_of(mul_as_scaled_ifm)); + if (_pv == PatternVersion::Version_0) + { + CHECK_OR_FALSE(fill(&mul_as_scaled_ifm, &sub).with_commutative_args_of(add_as_terminal)); + CHECK_OR_FALSE(fill(&ifm, &mul_gamma).with_commutative_args_of(mul_as_scaled_ifm)); + } + if (_pv == PatternVersion::Version_1) + { + CHECK_OR_FALSE(fill(&mul_as_scaled_reshape, &sub).with_commutative_args_of(add_as_terminal)); + CHECK_OR_FALSE( + fill(&reshape_of_ifm, &mul_gamma).with_commutative_args_of(mul_as_scaled_reshape)); + ifm = reshape_of_ifm->tensor(); + } CHECK_OR_FALSE(loco::shape_known(ifm)); auto ifm_shape = loco::shape_get(ifm); @@ -284,7 +406,15 @@ bool InstanceNormPattern::matched() uint32_t ifm_channel_depth = ifm_tensor_shape.dim(3).value(); CHECK_OR_FALSE(fill(&rsqrt, &const_as_gamma).with_commutative_args_of(mul_gamma)); - CHECK_OR_FALSE(is_1D_with_dummy_dim(const_as_gamma, ifm_channel_depth)); + + if (_pv == PatternVersion::Version_0) + { + CHECK_OR_FALSE(is_1D_with_dummy_dim(const_as_gamma, ifm_channel_depth)); + } + if (_pv == PatternVersion::Version_1) + { + CHECK_OR_FALSE(is_quasi_1D_with_dummy_dim(const_as_gamma, ifm_channel_depth)); + } add_as_variance = dynamic_cast(rsqrt->x()); CHECK_OR_FALSE(add_as_variance); @@ -296,29 +426,69 @@ bool InstanceNormPattern::matched() // TODO Support regarding broadcast CHECK_OR_FALSE(const_as_epsilon->size() == 1); - CHECK_OR_FALSE(is_instance_mean(mean_as_variance)); + if (_pv == PatternVersion::Version_0) + { + CHECK_OR_FALSE(is_instance_mean_v0(mean_as_variance)); + } + if (_pv == PatternVersion::Version_1) + { + CHECK_OR_FALSE(is_instance_mean_v1(mean_as_variance)); + } + sqdiff = dynamic_cast(mean_as_variance->input()); CHECK_OR_FALSE(sqdiff); - loco::Node *ifm_should_be = nullptr; - CHECK_OR_FALSE(fill(&ifm_should_be, &mean_of_ifm).with_commutative_args_of(sqdiff)); - CHECK_OR_FALSE(ifm == ifm_should_be); - CHECK_OR_FALSE(is_instance_mean(mean_of_ifm)); - CHECK_OR_FALSE(ifm == mean_of_ifm->input()); + if (_pv == PatternVersion::Version_0) + { + loco::Node *ifm_should_be = nullptr; + CHECK_OR_FALSE(fill(&ifm_should_be, &mean_of_ifm).with_commutative_args_of(sqdiff)); + CHECK_OR_FALSE(ifm == ifm_should_be); + CHECK_OR_FALSE(is_instance_mean_v0(mean_of_ifm)); + CHECK_OR_FALSE(ifm == mean_of_ifm->input()); + } + if (_pv == PatternVersion::Version_1) + { + loco::Node *reshape_should_be = nullptr; + CHECK_OR_FALSE(fill(&reshape_should_be, &mean_of_reshape).with_commutative_args_of(sqdiff)); + CHECK_OR_FALSE(reshape_of_ifm == reshape_should_be); + CHECK_OR_FALSE(is_instance_mean_v1(mean_of_reshape)); + CHECK_OR_FALSE(reshape_of_ifm == mean_of_reshape->input()); + } const_as_beta = dynamic_cast(sub->x()); CHECK_OR_FALSE(const_as_beta); - CHECK_OR_FALSE(is_1D_with_dummy_dim(const_as_beta, ifm_channel_depth)); + + if (_pv == PatternVersion::Version_0) + { + CHECK_OR_FALSE(is_1D_with_dummy_dim(const_as_beta, ifm_channel_depth)); + } + if (_pv == PatternVersion::Version_1) + { + CHECK_OR_FALSE(is_quasi_1D_with_dummy_dim(const_as_beta, ifm_channel_depth)); + } mul_as_scaled_mean = dynamic_cast(sub->y()); CHECK_OR_FALSE(mul_as_scaled_mean); luci::CircleMul *mul_gamma_should_be = nullptr; luci::CircleMean *mean_of_ifm_should_be = nullptr; - CHECK_OR_FALSE(fill(&mul_gamma_should_be, &mean_of_ifm_should_be) - .with_commutative_args_of(mul_as_scaled_mean)); - CHECK_OR_FALSE(mul_gamma == mul_gamma_should_be); - CHECK_OR_FALSE(mean_of_ifm == mean_of_ifm_should_be); + luci::CircleMean *mean_of_reshape_should_be = nullptr; + + if (_pv == PatternVersion::Version_0) + { + CHECK_OR_FALSE(fill(&mul_gamma_should_be, &mean_of_ifm_should_be) + .with_commutative_args_of(mul_as_scaled_mean)); + CHECK_OR_FALSE(mul_gamma == mul_gamma_should_be); + CHECK_OR_FALSE(mean_of_ifm == mean_of_ifm_should_be); + } + if (_pv == PatternVersion::Version_1) + { + CHECK_OR_FALSE(fill(&mul_gamma_should_be, &mean_of_reshape_should_be) + .with_commutative_args_of(mul_as_scaled_mean)); + CHECK_OR_FALSE(mul_gamma == mul_gamma_should_be); + CHECK_OR_FALSE(mean_of_reshape == mean_of_reshape_should_be); + } + #undef CHECK_OR_FALSE _matched = true; return true; @@ -381,13 +551,28 @@ namespace luci bool FuseInstanceNormPass::run(loco::Graph *g) { bool changed = false; + luci::CircleAdd *add; + InstanceNormPattern::PatternVersion pv; + for (auto node : loco::active_nodes(loco::output_nodes(g))) { - auto add = dynamic_cast(node); - if (not add) - continue; + auto reshape = dynamic_cast(node); + if (not reshape) + { + add = dynamic_cast(node); + if (not add) + continue; + pv = InstanceNormPattern::PatternVersion::Version_0; + } + else + { + add = dynamic_cast(reshape->tensor()); + if (not add) + continue; + pv = InstanceNormPattern::PatternVersion::Version_1; + } - InstanceNormPattern pattern(add); + InstanceNormPattern pattern(add, pv); if (not pattern.matched()) continue; diff --git a/compiler/luci/pass/src/FuseInstanceNormPass.test.cpp b/compiler/luci/pass/src/FuseInstanceNormPass.test.cpp new file mode 100644 index 0000000..3037f3d --- /dev/null +++ b/compiler/luci/pass/src/FuseInstanceNormPass.test.cpp @@ -0,0 +1,64 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "FuseInstanceNormPassInternal.h" + +#include + +#include + +namespace +{ + +void setShape(luci::CircleNode &node, const std::vector &v) +{ + node.rank(v.size()); + for (int i = 0; i < v.size(); ++i) + { + node.dim(i) = v[i]; + } +} + +} // namespace + +TEST(FuseInstanceNormPass, is_quasi_1D_with_dummy_dim) +{ + luci::CircleConst const_node; + + setShape(const_node, {}); + EXPECT_FALSE(is_quasi_1D_with_dummy_dim(&const_node, 8)); + + setShape(const_node, {1}); + EXPECT_FALSE(is_quasi_1D_with_dummy_dim(&const_node, 8)); + + setShape(const_node, {8}); + EXPECT_FALSE(is_quasi_1D_with_dummy_dim(&const_node, 8)); + + setShape(const_node, {1, 2, 1, 8, 1}); + EXPECT_FALSE(is_quasi_1D_with_dummy_dim(&const_node, 8)); + + setShape(const_node, {8, 3}); + EXPECT_FALSE(is_quasi_1D_with_dummy_dim(&const_node, 8)); + + setShape(const_node, {8, 1}); + EXPECT_FALSE(is_quasi_1D_with_dummy_dim(&const_node, 8)); + + setShape(const_node, {1, 8, 1}); + EXPECT_TRUE(is_quasi_1D_with_dummy_dim(&const_node, 8)); + + setShape(const_node, {1, 1, 1, 8, 1}); + EXPECT_TRUE(is_quasi_1D_with_dummy_dim(&const_node, 8)); +} diff --git a/compiler/luci/pass/src/FuseInstanceNormPassInternal.h b/compiler/luci/pass/src/FuseInstanceNormPassInternal.h new file mode 100644 index 0000000..32b638b --- /dev/null +++ b/compiler/luci/pass/src/FuseInstanceNormPassInternal.h @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_CIRCLE_FUSE_INSTANCE_NORM_PASS_INTERNAL_H__ +#define __LUCI_CIRCLE_FUSE_INSTANCE_NORM_PASS_INTERNAL_H__ + +#include + +/// @return true When node has shape of '1 x .. x 1 x depth' +bool is_1D_with_dummy_dim(luci::CircleConst *node, uint32_t depth); + +/// @return true When node has shape of '1 x .. x depth x 1' +bool is_quasi_1D_with_dummy_dim(luci::CircleConst *node, uint32_t depth); + +#endif // __LUCI_CIRCLE_FUSE_INSTANCE_NORM_PASS_INTERNAL_H__ diff --git a/compiler/luci/pass/src/QuantizationUtils.cpp b/compiler/luci/pass/src/QuantizationUtils.cpp new file mode 100644 index 0000000..6726ce7 --- /dev/null +++ b/compiler/luci/pass/src/QuantizationUtils.cpp @@ -0,0 +1,172 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "QuantizationUtils.h" + +#include + +#include +#include + +namespace luci +{ + +void compute_sym_scale_zp(float min, float max, float &scaling_factor, int64_t &zp, + float &nudged_min, float &nudged_max) +{ + assert(min != max); + + const int32_t kMaxScale = std::numeric_limits::max(); + const int32_t kMinScale = -kMaxScale; + const double qmin_double = kMinScale; + const double qmax_double = kMaxScale; + const double rmin = std::fmin(0, min); + const double rmax = std::fmax(0, max); + double scale_factor_from_min_side{0}; + double scale_factor_from_max_side{0}; + + if ((qmin_double * rmin) > 0) + scale_factor_from_min_side = rmin / qmin_double; + + if ((qmax_double * rmax) > 0) + scale_factor_from_max_side = rmax / qmax_double; + + scaling_factor = scale_factor_from_min_side > scale_factor_from_max_side + ? scale_factor_from_min_side + : scale_factor_from_max_side; + zp = 0; + nudged_min = static_cast(qmin_double * scaling_factor); + nudged_max = static_cast(qmax_double * scaling_factor); +} + +void compute_asym_scale_zp(float min, float max, float &scaling_factor, int64_t &zp, + float &nudged_min, float &nudged_max) +{ + LOGGER(l); + + assert(min <= max); + const int32_t kMinScale = 0; + const int32_t kMaxScale = 255; + const double qmin_double = kMinScale; + const double qmax_double = kMaxScale; + const double rmin = std::fmin(0, min); + const double rmax = std::fmax(0, max); + + double scale = (rmax - rmin) / (qmax_double - qmin_double); + double zero_point_double = 0; + uint8_t nudged_zero_point = 0; + if (scale == 0) + { + WARN(l) << "The minimum and maximum values are the same." << std::endl; + if (min >= 0 && max >= 0) + zero_point_double = kMinScale; + else + zero_point_double = kMaxScale; + } + else + zero_point_double = qmin_double - rmin / scale; + if (zero_point_double <= qmin_double) + { + assert(min >= 0 && max >= 0); + nudged_zero_point = kMinScale; + scale = max / (qmax_double - qmin_double); + if (min > 0 && max > 0) + WARN(l) << "The minimum and maximum values are all positive." << std::endl; + } + else if (zero_point_double >= qmax_double) + { + assert(min < 0 && max < 0); + nudged_zero_point = kMaxScale; + scale = -min / (qmax_double - qmin_double); + WARN(l) << "The minimum and maximum values are all negative." << std::endl; + } + else + { + assert(min < 0 && max >= 0); + nudged_zero_point = static_cast(std::round(zero_point_double)); + } + + nudged_min = static_cast((qmin_double - nudged_zero_point) * scale); + nudged_max = static_cast((qmax_double - nudged_zero_point) * scale); + + scaling_factor = scale; + zp = nudged_zero_point; +} + +bool get_channel_dim_index(CircleConst *node, loco::TensorShape &dimension, int &channel_dim_index) +{ + auto succs = loco::succs(node); + if (succs.size() != 1) // assume weights is used by only one node + return false; + + for (auto out : succs) + { + auto conv = dynamic_cast(out); + auto dw_conv = dynamic_cast(out); + auto tw_conv = dynamic_cast(out); + auto fc = dynamic_cast(out); + + // Refer to https://github.com/Samsung/ONE/pull/2448. + if ((conv != nullptr && conv->filter() == node) || + (tw_conv != nullptr && tw_conv->filter() == node)) // OHWI + { + assert(node->rank() == 4); + dimension.dim(0).set(node->dim(0).value()); + dimension.dim(1).set(node->dim(1).value()); + dimension.dim(2).set(node->dim(2).value()); + dimension.dim(3).set(node->dim(3).value()); + channel_dim_index = 0; // Set channel_dim_index based on "O" + return true; + } + else if (dw_conv != nullptr && dw_conv->filter() == node) // IHWC + { + assert(node->rank() == 4); + dimension.dim(0).set(node->dim(0).value()); + dimension.dim(1).set(node->dim(1).value()); + dimension.dim(2).set(node->dim(2).value()); + dimension.dim(3).set(node->dim(3).value()); + channel_dim_index = 3; // Set channel_dim_index based on "C" + return true; + } + else if (fc != nullptr && fc->weights() == node) // OI + { + assert(node->rank() == 2); + dimension.dim(0).set(node->dim(0).value()); + dimension.dim(1).set(1); // Set FC layer like CONV + dimension.dim(2).set(1); + dimension.dim(3).set(node->dim(1).value()); + channel_dim_index = 0; // Set channel_dim_index based on "O" + return true; + } + else + { + // node does not support channle-wise quantization + assert(false); + } + } + + return false; +} + +uint32_t cal_offset(loco::TensorShape &dimension, uint32_t *indices) +{ + return indices[0] * dimension.dim(1).value() * dimension.dim(2).value() * + dimension.dim(3).value() + + indices[1] * dimension.dim(2).value() * dimension.dim(3).value() + + indices[2] * dimension.dim(3).value() + indices[3]; +} + +} // namespace luci diff --git a/compiler/luci/pass/src/QuantizationUtils.h b/compiler/luci/pass/src/QuantizationUtils.h new file mode 100644 index 0000000..ec0e86d --- /dev/null +++ b/compiler/luci/pass/src/QuantizationUtils.h @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __LUCI_QUANTIZATION_UTILS_H__ +#define __LUCI_QUANTIZATION_UTILS_H__ + +#include +#include + +namespace luci +{ + +void compute_sym_scale_zp(float min, float max, float &scaling_factor, int64_t &zp, + float &nudged_min, float &nudged_max); + +void compute_asym_scale_zp(float min, float max, float &scaling_factor, int64_t &zp, + float &nudged_min, float &nudged_max); + +bool get_channel_dim_index(CircleConst *node, loco::TensorShape &dimension, int &channel_dim_index); + +uint32_t cal_offset(loco::TensorShape &dimension, uint32_t *indices); + +} // namespace luci + +#endif // __LUCI_QUANTIZATION_UTILS_H__ diff --git a/compiler/luci/pass/src/QuantizeDequantizeWeightsPass.cpp b/compiler/luci/pass/src/QuantizeDequantizeWeightsPass.cpp new file mode 100644 index 0000000..c492234 --- /dev/null +++ b/compiler/luci/pass/src/QuantizeDequantizeWeightsPass.cpp @@ -0,0 +1,495 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/QuantizeDequantizeWeightsPass.h" +#include "QuantizationUtils.h" + +#include +#include +#include +#include + +#include +#include + +namespace luci +{ + +namespace +{ + +void cal_minmax_per_channel(CircleConst *node, std::vector &min, std::vector &max) +{ + loco::TensorShape dimension; + dimension.rank(4); + uint32_t indices[4] = { + 0, + }; + int channel_dim_index{0}; + int size{0}; + + if (!get_channel_dim_index(node, dimension, channel_dim_index)) + { + assert(false); + return; + } + size = dimension.dim(channel_dim_index).value(); + + std::vector has_min_max_value(size, false); + min.resize(size); + max.resize(size); + for (indices[0] = 0; indices[0] < dimension.dim(0).value(); indices[0]++) + { + for (indices[1] = 0; indices[1] < dimension.dim(1).value(); indices[1]++) + { + for (indices[2] = 0; indices[2] < dimension.dim(2).value(); indices[2]++) + { + for (indices[3] = 0; indices[3] < dimension.dim(3).value(); indices[3]++) + { + int channel_idx = indices[channel_dim_index]; + auto data = node->at(cal_offset(dimension, indices)); + if (has_min_max_value[channel_idx]) + { + min[channel_idx] = data < min[channel_idx] ? data : min[channel_idx]; + max[channel_idx] = data > max[channel_idx] ? data : max[channel_idx]; + } + else + { + min[channel_idx] = data; + max[channel_idx] = data; + has_min_max_value[channel_idx] = true; + } + } + } + } + } +} + +void sym_wquant_per_channel(CircleConst *node, std::vector &min, std::vector &max, + std::vector &scaling_factor, std::vector &zp, + std::vector &nudged_min, std::vector &nudged_max) +{ + assert(node->dtype() == loco::DataType::FLOAT32); + const int32_t kMaxScale = std::numeric_limits::max(); + const int32_t kMinScale = -kMaxScale; + + uint32_t size = node->size(); + std::vector quantized_values(size); + + for (size_t i = 0; i < min.size(); ++i) + { + compute_sym_scale_zp(min[i], max[i], scaling_factor[i], zp[i], nudged_min[i], nudged_max[i]); + } + + loco::TensorShape dimension; + dimension.rank(4); + uint32_t indices[4] = { + 0, + }; + int channel_dim_index{0}; + + if (!get_channel_dim_index(node, dimension, channel_dim_index)) + { + assert(false); + return; + } + + for (indices[0] = 0; indices[0] < dimension.dim(0).value(); indices[0]++) + { + for (indices[1] = 0; indices[1] < dimension.dim(1).value(); indices[1]++) + { + for (indices[2] = 0; indices[2] < dimension.dim(2).value(); indices[2]++) + { + for (indices[3] = 0; indices[3] < dimension.dim(3).value(); indices[3]++) + { + int channel_idx = indices[channel_dim_index]; + const float scaling_factor_inv = 1.0 / scaling_factor[channel_idx]; + auto data = node->at(cal_offset(dimension, indices)); + data = data < nudged_min[channel_idx] ? nudged_min[channel_idx] : data; + data = data > nudged_max[channel_idx] ? nudged_max[channel_idx] : data; + quantized_values[cal_offset(dimension, indices)] = + static_cast(std::round(data * scaling_factor_inv)); + } + } + } + } + + node->dtype(loco::DataType::S16); // change the type of tensor + node->size(size); // resize tensor + for (uint32_t i = 0; i < size; ++i) + { + node->at(i) = + std::min(kMaxScale, std::max(kMinScale, quantized_values[i])); + } +} + +void sym_wdequant_per_channel(CircleConst *node, std::vector &scaling_factor) +{ + assert(node->dtype() == loco::DataType::S16); + uint32_t size = node->size(); + std::vector dequantized_values(size); + + loco::TensorShape dimension; + dimension.rank(4); + uint32_t indices[4] = { + 0, + }; + int channel_dim_index{0}; + + if (!get_channel_dim_index(node, dimension, channel_dim_index)) + { + assert(false); + return; + } + + for (indices[0] = 0; indices[0] < dimension.dim(0).value(); indices[0]++) + { + for (indices[1] = 0; indices[1] < dimension.dim(1).value(); indices[1]++) + { + for (indices[2] = 0; indices[2] < dimension.dim(2).value(); indices[2]++) + { + for (indices[3] = 0; indices[3] < dimension.dim(3).value(); indices[3]++) + { + int channel_idx = indices[channel_dim_index]; + auto data = node->at(cal_offset(dimension, indices)); + dequantized_values[cal_offset(dimension, indices)] = + static_cast(data) * scaling_factor[channel_idx]; + } + } + } + } + + node->dtype(loco::DataType::FLOAT32); // change the type of tensor + node->size(size); // resize tensor + for (uint32_t i = 0; i < size; ++i) + { + node->at(i) = dequantized_values[i]; + } +} + +void asymmetric_wquant_per_channel(CircleConst *node, std::vector &min, + std::vector &max, std::vector &scaling_factor, + std::vector &zp, std::vector &nudged_min, + std::vector &nudged_max) +{ + assert(node->dtype() == loco::DataType::FLOAT32); + + const int32_t kMinScale = 0; + const int32_t kMaxScale = 255; + + uint32_t size = node->size(); + std::vector quantized_values(size); + + for (size_t i = 0; i < min.size(); ++i) + { + compute_asym_scale_zp(min[i], max[i], scaling_factor[i], zp[i], nudged_min[i], nudged_max[i]); + } + + loco::TensorShape dimension; + dimension.rank(4); + uint32_t indices[4] = { + 0, + }; + int channel_dim_index{0}; + + if (!get_channel_dim_index(node, dimension, channel_dim_index)) + { + assert(false); + return; + } + + for (indices[0] = 0; indices[0] < dimension.dim(0).value(); indices[0]++) + { + for (indices[1] = 0; indices[1] < dimension.dim(1).value(); indices[1]++) + { + for (indices[2] = 0; indices[2] < dimension.dim(2).value(); indices[2]++) + { + for (indices[3] = 0; indices[3] < dimension.dim(3).value(); indices[3]++) + { + int channel_idx = indices[channel_dim_index]; + const float scaling_factor_inv = 1.0 / scaling_factor[channel_idx]; + auto data = node->at(cal_offset(dimension, indices)); + data = data < nudged_min[channel_idx] ? nudged_min[channel_idx] : data; + data = data > nudged_max[channel_idx] ? nudged_max[channel_idx] : data; + quantized_values[cal_offset(dimension, indices)] = static_cast( + std::round((data - nudged_min[channel_idx]) * scaling_factor_inv)); + } + } + } + } + + node->dtype(loco::DataType::U8); // change the type of tensor + node->size(size); // resize tensor + for (uint32_t i = 0; i < size; ++i) + { + node->at(i) = std::min(kMaxScale, std::max(kMinScale, quantized_values[i])); + } +} + +void asymmetric_wdequant_per_channel(CircleConst *node, std::vector &scaling_factor, + std::vector &nudged_min) +{ + assert(node->dtype() == loco::DataType::U8); + uint32_t size = node->size(); + std::vector dequantized_values(size); + + loco::TensorShape dimension; + dimension.rank(4); + uint32_t indices[4] = { + 0, + }; + int channel_dim_index{0}; + + if (!get_channel_dim_index(node, dimension, channel_dim_index)) + { + assert(false); + return; + } + + for (indices[0] = 0; indices[0] < dimension.dim(0).value(); indices[0]++) + { + for (indices[1] = 0; indices[1] < dimension.dim(1).value(); indices[1]++) + { + for (indices[2] = 0; indices[2] < dimension.dim(2).value(); indices[2]++) + { + for (indices[3] = 0; indices[3] < dimension.dim(3).value(); indices[3]++) + { + int channel_idx = indices[channel_dim_index]; + auto data = node->at(cal_offset(dimension, indices)); + dequantized_values[cal_offset(dimension, indices)] = + static_cast(data) * scaling_factor[channel_idx] + nudged_min[channel_idx]; + } + } + } + } + + node->dtype(loco::DataType::FLOAT32); // change the type of tensor + node->size(size); // resize tensor + for (uint32_t i = 0; i < size; ++i) + { + node->at(i) = dequantized_values[i]; + } +} + +void asymmetric_wquant_with_minmax_per_layer(CircleConst *node, float min, float max, + float &scaling_factor, int64_t &zp, float &nudged_min, + float &nudged_max) +{ + + const int32_t kMinScale = 0; + const int32_t kMaxScale = 255; + + uint32_t size = node->size(); + compute_asym_scale_zp(min, max, scaling_factor, zp, nudged_min, nudged_max); + const float scaling_factor_inv = 1.0 / scaling_factor; + std::vector quantized_values(size); + for (uint32_t i = 0; i < size; ++i) + { + // clipping + auto data = node->at(i); + data = data < nudged_min ? nudged_min : data; + data = data > nudged_max ? nudged_max : data; + quantized_values[i] = + static_cast(std::round((data - nudged_min) * scaling_factor_inv)); + } + + node->dtype(loco::DataType::U8); // change the type of tensor + node->size(size); // resize tensor + for (uint32_t i = 0; i < size; ++i) + { + node->at(i) = std::min(kMaxScale, std::max(kMinScale, quantized_values[i])); + } +} + +void asymmetric_wdequant_with_minmax_per_layer(CircleConst *node, float scaling_factor, + float nudged_min) +{ + uint32_t size = node->size(); + std::vector dequantized_values(size); + for (uint32_t i = 0; i < size; ++i) + { + auto data = node->at(i); + dequantized_values[i] = static_cast(data) * scaling_factor + nudged_min; + } + + node->dtype(loco::DataType::FLOAT32); // change the type of tensor + node->size(size); // resize tensor + for (uint32_t i = 0; i < size; ++i) + { + node->at(i) = dequantized_values[i]; + } +} + +bool is_quantized(const CircleNode *node) +{ + return node->dtype() == loco::DataType::U8 || // activation, weight + node->dtype() == loco::DataType::S16 || // activation, weight + node->dtype() == loco::DataType::S32; // bias +} + +// Check if node is weights of conv2d, transepose_conv2d, depthwise_conv2d, or fully_connected layer +bool is_weights(CircleNode *node) +{ + auto circle_const = dynamic_cast(node); + if (circle_const == nullptr) + return false; + + auto succs = loco::succs(node); + if (succs.size() != 1) // assume weights is used by only one node + return false; + + for (auto out : succs) + { + auto conv = dynamic_cast(out); + if (conv != nullptr && conv->filter() == circle_const && circle_const->rank() == 4) + return true; + + auto dw_conv = dynamic_cast(out); + if (dw_conv != nullptr && dw_conv->filter() == circle_const && circle_const->rank() == 4) + return true; + + auto tw_conv = dynamic_cast(out); + if (tw_conv != nullptr && tw_conv->filter() == circle_const && circle_const->rank() == 4) + return true; + + auto fc = dynamic_cast(out); + if (fc != nullptr && fc->weights() == circle_const && circle_const->rank() == 2) + return true; + } + return false; +} + +/** + * @brief QuantizeDequantizeWeights quantizes and dequantizes tensors for weights + * @details Find min/max values on the fly, quantize the model, and dequantize the model + */ +struct QuantizeDequantizeWeights final : public luci::CircleNodeMutableVisitor +{ + QuantizeDequantizeWeights(loco::DataType input, loco::DataType output, + QuantizationGranularity granularity) + : input_type(input), output_type(output), granularity(granularity) + { + } + + loco::DataType input_type; + loco::DataType output_type; + QuantizationGranularity granularity; + + // Quantize and dequantize input tensors of each node + bool visit(luci::CircleNode *node) + { + assert(output_type == loco::DataType::U8 || output_type == loco::DataType::S16); + LOGGER(l); + INFO(l) << "QuantizeDequantizeWeights visit node: " << node->name() << std::endl; + auto arity = node->arity(); + for (uint32_t i = 0; i < arity; i++) + { + auto input_node = node->arg(i); + auto circle_node = loco::must_cast(input_node); + + // Check if this is already quantized + if (is_quantized(circle_node)) + continue; + + if (is_weights(circle_node)) + { + auto circle_const = loco::must_cast(circle_node); + + // Find min/max per channel-wise + if (granularity == QuantizationGranularity::ChannelWise) + { + std::vector min; + std::vector max; + + cal_minmax_per_channel(circle_const, min, max); + + std::vector nudged_min(min.size()); + std::vector nudged_max(min.size()); + std::vector scaling_factor(min.size()); + std::vector zp(min.size()); + + if (output_type == loco::DataType::U8) + { + asymmetric_wquant_per_channel(circle_const, min, max, scaling_factor, zp, nudged_min, + nudged_max); + asymmetric_wdequant_per_channel(circle_const, scaling_factor, nudged_min); + } + else + { + sym_wquant_per_channel(circle_const, min, max, scaling_factor, zp, nudged_min, + nudged_max); + sym_wdequant_per_channel(circle_const, scaling_factor); + } + + auto quantparam = std::make_unique(); + quantparam->min = nudged_min; + quantparam->max = nudged_max; + quantparam->scale = scaling_factor; + quantparam->zerop = zp; + circle_node->quantparam(std::move(quantparam)); + } + // Find min/max per layer-wise + else + { + float min = std::numeric_limits::max(); + float max = std::numeric_limits::lowest(); + for (uint32_t i = 0; i < circle_const->size(); i++) + { + auto data = circle_const->at(i); + min = data < min ? data : min; + max = data > max ? data : max; + } + float scaling_factor{0}; + int64_t zp{0}; + float nudged_min{0}; + float nudged_max{0}; + + asymmetric_wquant_with_minmax_per_layer(circle_const, min, max, scaling_factor, zp, + nudged_min, nudged_max); + asymmetric_wdequant_with_minmax_per_layer(circle_const, scaling_factor, nudged_min); + auto quantparam = std::make_unique(); + quantparam->min.push_back(nudged_min); + quantparam->max.push_back(nudged_max); + quantparam->scale.push_back(scaling_factor); + quantparam->zerop.push_back(zp); + circle_node->quantparam(std::move(quantparam)); + } + } + } + return false; + } +}; + +} // namespace + +bool QuantizeDequantizeWeightsPass::run(loco::Graph *g) +{ + LOGGER(l); + INFO(l) << "QuantizeDequantizeWeightsPass Start" << std::endl; + + // Quantize weights + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + QuantizeDequantizeWeights qw(_input_dtype, _output_dtype, _granularity); + auto circle_node = loco::must_cast(node); + circle_node->accept(&qw); + } + + INFO(l) << "QuantizeDequantizeWeightsPass End" << std::endl; + return false; // one time run +} + +} // namespace luci diff --git a/compiler/luci/pass/src/QuantizeWithMinMaxPass.cpp b/compiler/luci/pass/src/QuantizeWithMinMaxPass.cpp new file mode 100644 index 0000000..f8abee7 --- /dev/null +++ b/compiler/luci/pass/src/QuantizeWithMinMaxPass.cpp @@ -0,0 +1,551 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/QuantizeWithMinMaxPass.h" +#include "QuantizationUtils.h" + +#include +#include +#include + +#include + +#include +#include + +namespace luci +{ + +namespace +{ + +// Check if the node is the bias of Conv2D, DepthwiseConv2D, or FullyConnected layer +// If true, return pair of the successor node (used to quantize bias) +// If flase, return +std::pair get_input_weight_of_bias(CircleNode *node) +{ + auto circle_const = dynamic_cast(node); + if (circle_const == nullptr) + return std::make_pair(nullptr, nullptr); + + auto succs = loco::succs(node); + if (succs.size() != 1) // assume bias is used by only one node + return std::make_pair(nullptr, nullptr); + + for (auto out : succs) + { + auto conv = dynamic_cast(out); + if (conv != nullptr && conv->bias() == circle_const) + { + assert(conv->input() != nullptr); + assert(conv->filter() != nullptr); + return std::make_pair(conv->input(), conv->filter()); + } + auto dw_conv = dynamic_cast(out); + if (dw_conv != nullptr && dw_conv->bias() == circle_const) + { + assert(dw_conv->input() != nullptr); + assert(dw_conv->filter() != nullptr); + return std::make_pair(dw_conv->input(), dw_conv->filter()); + } + auto fc = dynamic_cast(out); + if (fc != nullptr && fc->bias() == circle_const) + { + assert(fc->input() != nullptr); + assert(fc->weights() != nullptr); + return std::make_pair(fc->input(), fc->weights()); + } + } + return std::make_pair(nullptr, nullptr); +} + +void asym_quant_bias_per_layer(CircleConst *node, float input_scale, float weight_scale, + float *scaling_factor, int64_t *zp) +{ + float scale = input_scale * weight_scale; + const float scaling_factor_inv = (scale == 0) ? 0 : 1.0 / scale; + + uint32_t size = node->size(); + std::vector quantized_values(size); + for (uint32_t i = 0; i < size; ++i) + { + quantized_values[i] = + static_cast(std::round(node->at(i) * scaling_factor_inv)); + } + + node->dtype(loco::DataType::S32); // change the type of tensor + node->size(size); // resize tensor + const int32_t kMinScale = std::numeric_limits::lowest(); + const int32_t kMaxScale = std::numeric_limits::max(); + for (uint32_t i = 0; i < size; ++i) + { + node->at(i) = + std::min(kMaxScale, std::max(kMinScale, quantized_values[i])); + } + *scaling_factor = scale; + *zp = 0; +} + +void quant_bias_per_channel(CircleConst *node, float input_scale, std::vector &weight_scale, + std::vector &scaling_factor, std::vector &zp) +{ + float scaling_factor_inv{0}; + + uint32_t size = node->size(); + std::vector quantized_values(size); + + for (uint32_t i = 0; i < size; ++i) + { + scaling_factor[i] = input_scale * weight_scale[i]; + scaling_factor_inv = (scaling_factor[i] == 0) ? 0 : 1.0 / scaling_factor[i]; + quantized_values[i] = + static_cast(std::round(node->at(i) * scaling_factor_inv)); + zp[i] = 0; + } + + node->dtype(loco::DataType::S32); // change the type of tensor + node->size(size); // resize tensor + const int32_t kMinScale = std::numeric_limits::lowest(); + const int32_t kMaxScale = std::numeric_limits::max(); + for (uint32_t i = 0; i < size; ++i) + { + node->at(i) = + std::min(kMaxScale, std::max(kMinScale, quantized_values[i])); + } +} + +bool has_min_max(const CircleNode *node) +{ + return node->quantparam() && !node->quantparam()->min.empty() && !node->quantparam()->max.empty(); +} + +bool is_quantized(const CircleNode *node) +{ + return node->dtype() == loco::DataType::U8 || // activation, weight + node->dtype() == loco::DataType::S32; // bias +} + +void sym_wquant_per_channel(CircleConst *node, std::vector &scaling_factor) +{ + assert(node->dtype() == loco::DataType::FLOAT32); + + const int32_t kMaxScale = std::numeric_limits::max(); + const int32_t kMinScale = -kMaxScale; + + uint32_t size = node->size(); + std::vector quantized_values(size); + + loco::TensorShape dimension; + dimension.rank(4); + uint32_t indices[4] = { + 0, + }; + int channel_dim_index{0}; + + if (!get_channel_dim_index(node, dimension, channel_dim_index)) + { + assert(false); + return; + } + + for (indices[0] = 0; indices[0] < dimension.dim(0).value(); indices[0]++) + { + for (indices[1] = 0; indices[1] < dimension.dim(1).value(); indices[1]++) + { + for (indices[2] = 0; indices[2] < dimension.dim(2).value(); indices[2]++) + { + for (indices[3] = 0; indices[3] < dimension.dim(3).value(); indices[3]++) + { + int channel_idx = indices[channel_dim_index]; + const float scaling_factor_inv = 1.0 / scaling_factor[channel_idx]; + auto data = node->at(cal_offset(dimension, indices)); + quantized_values[cal_offset(dimension, indices)] = + static_cast(std::round(data * scaling_factor_inv)); + } + } + } + } + + node->dtype(loco::DataType::S16); // change the type of tensor + node->size(size); // resize tensor + for (uint32_t i = 0; i < size; ++i) + { + node->at(i) = + std::min(kMaxScale, std::max(kMinScale, quantized_values[i])); + } +} + +void asym_wquant_per_channel(CircleConst *node, std::vector &min, + std::vector &scaling_factor) +{ + assert(node->dtype() == loco::DataType::FLOAT32); + + const int32_t kMinScale = 0; + const int32_t kMaxScale = 255; + + uint32_t size = node->size(); + std::vector quantized_values(size); + + loco::TensorShape dimension; + dimension.rank(4); + uint32_t indices[4] = { + 0, + }; + int channel_dim_index{0}; + + if (!get_channel_dim_index(node, dimension, channel_dim_index)) + { + assert(false); + return; + } + + for (indices[0] = 0; indices[0] < dimension.dim(0).value(); indices[0]++) + { + for (indices[1] = 0; indices[1] < dimension.dim(1).value(); indices[1]++) + { + for (indices[2] = 0; indices[2] < dimension.dim(2).value(); indices[2]++) + { + for (indices[3] = 0; indices[3] < dimension.dim(3).value(); indices[3]++) + { + int channel_idx = indices[channel_dim_index]; + const float scaling_factor_inv = 1.0 / scaling_factor[channel_idx]; + auto data = node->at(cal_offset(dimension, indices)); + quantized_values[cal_offset(dimension, indices)] = + static_cast(std::round((data - min[channel_idx]) * scaling_factor_inv)); + } + } + } + } + + node->dtype(loco::DataType::U8); // change the type of tensor + node->size(size); // resize tensor + for (uint32_t i = 0; i < size; ++i) + { + node->at(i) = std::min(kMaxScale, std::max(kMinScale, quantized_values[i])); + } +} + +void asym_wquant_per_layer(CircleConst *node, float min, float scaling_factor) +{ + const int32_t kMinScale = 0; + const int32_t kMaxScale = 255; + + uint32_t size = node->size(); + + const float scaling_factor_inv = 1.0 / scaling_factor; + std::vector quantized_values(size); + for (uint32_t i = 0; i < size; ++i) + { + auto data = node->at(i); + quantized_values[i] = static_cast(std::round((data - min) * scaling_factor_inv)); + } + + node->dtype(loco::DataType::U8); // change the type of tensor + node->size(size); // resize tensor + for (uint32_t i = 0; i < size; ++i) + { + node->at(i) = std::min(kMaxScale, std::max(kMinScale, quantized_values[i])); + } +} + +// Check if node is weights of conv2d, depthwise_conv2d, or fully_connected layer +bool is_weights(CircleNode *node) +{ + auto circle_const = dynamic_cast(node); + if (circle_const == nullptr) + return false; + + auto succs = loco::succs(node); + if (succs.size() != 1) // assume weights is used by only one node + return false; + + for (auto out : succs) + { + auto conv = dynamic_cast(out); + if (conv != nullptr && conv->filter() == circle_const) + return true; + + auto dw_conv = dynamic_cast(out); + if (dw_conv != nullptr && dw_conv->filter() == circle_const) + return true; + + auto fc = dynamic_cast(out); + if (fc != nullptr && fc->weights() == circle_const) + return true; + } + return false; +} + +/** + * @brief QuantizeActivation quantizes tensors for activations + * @details Quantize using recorded min/max values + */ +struct QuantizeActivation final : public luci::CircleNodeMutableVisitor +{ + QuantizeActivation(loco::DataType input, loco::DataType output) + : input_type(input), output_type(output) + { + } + + loco::DataType input_type; + loco::DataType output_type; + + // Quantize input tensors of each node + bool visit(luci::CircleNode *node) + { + LOGGER(l); + INFO(l) << "QuantizeActivation visit node: " << node->name() << std::endl; + auto arity = node->arity(); + for (uint32_t i = 0; i < arity; i++) + { + auto input_node = node->arg(i); + auto circle_node = loco::must_cast(input_node); + + // Check if this is already quantized + if (is_quantized(circle_node)) + continue; + + // Check if this is bias (bias is quantized later) + auto iw = get_input_weight_of_bias(circle_node); + if (iw.first != nullptr && iw.second != nullptr) + continue; + + // Check if this is activation + // We assume min/max are recorded only for activations + if (has_min_max(circle_node) && !is_weights(circle_node)) + { + // Quantize using recorded min/max + auto quantparam = circle_node->quantparam(); + assert(quantparam->min.size() == 1); // only support layer-wise quant + assert(quantparam->max.size() == 1); // only support layer-wise quant + auto min = quantparam->min[0]; + auto max = quantparam->max[0]; + + float scaling_factor{0}; + int64_t zp{0}; + float nudged_min{0}; + float nudged_max{0}; + + if (output_type == loco::DataType::U8) + { + compute_asym_scale_zp(min, max, scaling_factor, zp, nudged_min, nudged_max); + circle_node->dtype(loco::DataType::U8); + } + else + { + compute_sym_scale_zp(min, max, scaling_factor, zp, nudged_min, nudged_max); + circle_node->dtype(loco::DataType::S16); + } + + circle_node->quantparam()->max[0] = nudged_max; + circle_node->quantparam()->min[0] = nudged_min; + circle_node->quantparam()->scale.push_back(scaling_factor); + circle_node->quantparam()->zerop.push_back(zp); + } + } + return false; + } +}; + +struct QuantizeBias final : public luci::CircleNodeMutableVisitor +{ + QuantizeBias(loco::DataType input, loco::DataType output, QuantizationGranularity gr) + : input_type(input), output_type(output), granularity(gr) + { + } + + loco::DataType input_type; + loco::DataType output_type; + QuantizationGranularity granularity; + + // Quantize bias node + bool visit(luci::CircleNode *node) + { + // Check if this is already quantized + if (is_quantized(node)) + return false; + + // Check if this is bias + auto iw = get_input_weight_of_bias(node); + if (iw.first == nullptr || iw.second == nullptr) + return false; + + auto input = loco::must_cast(iw.first); + auto weight = loco::must_cast(iw.second); + + if (granularity == QuantizationGranularity::ChannelWise) + { + assert(input->quantparam()->scale.size() == 1); // input scale's layer-wise + auto input_scale = input->quantparam()->scale[0]; + + assert(weight->quantparam() != nullptr); // weight scale's channel-wise + auto weight_scale = weight->quantparam()->scale; + + auto circle_const = loco::must_cast(node); + + uint32_t size = circle_const->size(); + assert(size == weight_scale.size()); + std::vector scaling_factor(size); + std::vector zp(size); + + quant_bias_per_channel(circle_const, input_scale, weight_scale, scaling_factor, zp); + + auto quantparam = std::make_unique(); + quantparam->scale = scaling_factor; + quantparam->zerop = zp; + assert(circle_const->quantparam() == nullptr); // bias should not be quantized before + circle_const->quantparam(std::move(quantparam)); + } + else + { + assert(input->quantparam()->scale.size() == 1); // Only support per-layer quant + auto input_scale = input->quantparam()->scale[0]; + + assert(weight->quantparam()->scale.size() == 1); // Only support per-layer quant + auto weight_scale = weight->quantparam()->scale[0]; + + auto circle_const = loco::must_cast(node); + float scaling_factor{0}; + int64_t zp{0}; + asym_quant_bias_per_layer(circle_const, input_scale, weight_scale, &scaling_factor, &zp); + auto quantparam = std::make_unique(); + quantparam->scale.push_back(scaling_factor); + quantparam->zerop.push_back(zp); + assert(circle_const->quantparam() == nullptr); // bias should not be quantized before + circle_const->quantparam(std::move(quantparam)); + } + return false; + } +}; + +/** + * @brief QuantizeWeights quantizes tensors for weights + * @details Find min/max values on the fly and then quantize + */ +struct QuantizeWeights final : public luci::CircleNodeMutableVisitor +{ + QuantizeWeights(loco::DataType input, loco::DataType output, QuantizationGranularity gr) + : input_type(input), output_type(output), granularity(gr) + { + } + + loco::DataType input_type; + loco::DataType output_type; + QuantizationGranularity granularity; + + // Quantize input tensors of each node + bool visit(luci::CircleNode *node) + { + LOGGER(l); + INFO(l) << "QuantizeWeights visit node: " << node->name() << std::endl; + auto arity = node->arity(); + for (uint32_t i = 0; i < arity; i++) + { + auto input_node = node->arg(i); + auto circle_node = loco::must_cast(input_node); + + // Check if this is already quantized + if (is_quantized(circle_node)) + continue; + + if (is_weights(circle_node)) + { + auto circle_const = loco::must_cast(circle_node); + + // Find min/max per channel-wise + if (granularity == QuantizationGranularity::ChannelWise) + { + auto quantparam = circle_node->quantparam(); + assert(quantparam != nullptr); + auto min = quantparam->min; + auto scaling_factor = quantparam->scale; + + if (output_type == loco::DataType::U8) + { + asym_wquant_per_channel(circle_const, min, scaling_factor); + } + else + { + sym_wquant_per_channel(circle_const, scaling_factor); + } + } + // Find min/max per layer-wise + else + { + // Quantize using recorded quantparam + auto quantparam = circle_node->quantparam(); + assert(quantparam != nullptr); + assert(quantparam->min.size() == 1); // only support layer-wise quant + assert(quantparam->scale.size() == 1); // only support layer-wise quant + auto min = quantparam->min[0]; + auto scaling_factor = quantparam->scale[0]; + asym_wquant_per_layer(circle_const, min, scaling_factor); + } + } + } + return false; + } +}; + +} // namespace + +bool QuantizeWithMinMaxPass::run(loco::Graph *g) +{ + LOGGER(l); + INFO(l) << "QuantizeWithMinMaxPass Start" << std::endl; + + // Quantize activation + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + QuantizeActivation qa(_input_dtype, _output_dtype); + auto circle_node = loco::must_cast(node); + circle_node->accept(&qa); + } + + // Quantize weights + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + QuantizeWeights qw(_input_dtype, _output_dtype, _granularity); + auto circle_node = loco::must_cast(node); + circle_node->accept(&qw); + } + + // Quantize bias + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + QuantizeBias qb(_input_dtype, _output_dtype, _granularity); + auto circle_node = loco::must_cast(node); + circle_node->accept(&qb); + } + + // Update output dtype + auto graph_outputs = g->outputs(); + for (auto node : loco::output_nodes(g)) + { + auto circle_node = loco::must_cast(node); + if (static_cast(circle_node->from())->dtype() == _output_dtype) + { + circle_node->dtype(_output_dtype); + auto graph_output = graph_outputs->at(circle_node->index()); + graph_output->dtype(_output_dtype); + } + } + + INFO(l) << "QuantizeWithMinMaxPass End" << std::endl; + return false; // one time run +} + +} // namespace luci diff --git a/compiler/luci/pass/src/ResolveCustomOpAddPass.cpp b/compiler/luci/pass/src/ResolveCustomOpAddPass.cpp new file mode 100644 index 0000000..e52d667 --- /dev/null +++ b/compiler/luci/pass/src/ResolveCustomOpAddPass.cpp @@ -0,0 +1,124 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/ResolveCustomOpAddPass.h" + +#include "flatbuffers/flexbuffers.h" + +#include +#include + +namespace +{ + +/// @brief Returns the index of BroadcastTo node among cop's inputs. +// NOTE This function assumes there is only one BroadcastTo node among its inputs. +int32_t get_broadcastTo_index_among_inputs_of(luci::CircleCustom *cop) +{ + for (uint32_t idx = 0; idx < cop->numInputs(); idx++) + { + auto input = dynamic_cast(cop->inputs(idx)); + if (input) + { + auto broadcastTo = loco::must_cast(input->input()); + if (broadcastTo->custom_code() == "BroadcastTo") + return idx; + } + } + + return -1; +} + +/** BEFORE + * [CircleConst] + * | + * [CircleNode] [BroadcastTo(CircleCustom)] + * \ | + * \ [CircleCustomOUt] + * \ / + * [AddV2(CircleCustom)] + * AFTER + * + * [CircleConst] [CircleNode] + * \ / + * \ / + * [CircleAdd] + */ +bool resolve_with_BroadcastTo(luci::CircleCustom *addv2) +{ + int32_t broadcastTo_idx = get_broadcastTo_index_among_inputs_of(addv2); + + if (broadcastTo_idx == -1) + return false; + + auto input = loco::must_cast(addv2->inputs(broadcastTo_idx)); + auto broadcastTo = loco::must_cast(input->input()); + + auto add = addv2->graph()->nodes()->create(); + add->fusedActivationFunction(luci::FusedActFunc::NONE); + add->x(addv2->inputs(1 - broadcastTo_idx)); + add->y(broadcastTo->inputs(0)); + auto customOut = loco::succs(addv2); + assert(customOut.size() == 1); + replace(*customOut.begin()).with(add); + + return true; +} + +bool resolve_custom_op(luci::CircleCustom *addv2) +{ + const std::string custom_code = addv2->custom_code(); + const std::vector custom_options = addv2->custom_options(); + + if (custom_code != "AddV2") + return false; + + if (resolve_with_BroadcastTo(addv2)) + return true; + + auto add = addv2->graph()->nodes()->create(); + add->fusedActivationFunction(luci::FusedActFunc::NONE); + add->x(addv2->inputs(0)); + add->y(addv2->inputs(1)); + auto customOut = loco::succs(addv2); + assert(customOut.size() == 1); + replace(*customOut.begin()).with(add); + + return true; +} + +} // namespace + +namespace luci +{ + +bool ResolveCustomOpAddPass::run(loco::Graph *g) +{ + bool changed = false; + + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + auto cop = dynamic_cast(node); + if (not cop) + continue; + + changed |= resolve_custom_op(cop); + } + + return changed; +} + +} // namespace luci diff --git a/compiler/luci/pass/src/ResolveCustomOpBatchMatMulPass.cpp b/compiler/luci/pass/src/ResolveCustomOpBatchMatMulPass.cpp new file mode 100644 index 0000000..145e9cb --- /dev/null +++ b/compiler/luci/pass/src/ResolveCustomOpBatchMatMulPass.cpp @@ -0,0 +1,69 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/ResolveCustomOpBatchMatMulPass.h" + +#include "flatbuffers/flexbuffers.h" + +#include + +namespace +{ + +bool resolve_custom_op(luci::CircleCustom *cop) +{ + const std::string custom_code = cop->custom_code(); + const std::vector custom_options = cop->custom_options(); + + if (custom_code == "BatchMatMulV2") + { + auto batch_matmul = cop->graph()->nodes()->create(); + // input + batch_matmul->x(cop->inputs(0)); + batch_matmul->y(cop->inputs(1)); + // TODO find much better way of parsing custom_options + // adj + auto map = flexbuffers::GetRoot(custom_options).AsMap(); + batch_matmul->adj_x(map["adj_x"].AsBool()); + batch_matmul->adj_y(map["adj_y"].AsBool()); + + replace(cop).with(batch_matmul); + return true; + } + return false; +} + +} // namespace + +namespace luci +{ + +bool ResolveCustomOpBatchMatMulPass::run(loco::Graph *g) +{ + bool changed = false; + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + auto cop = dynamic_cast(node); + if (not cop) + continue; + + changed |= resolve_custom_op(cop); + } + + return changed; +} + +} // namespace luci diff --git a/compiler/luci/pass/src/ResolveCustomOpMatMulPass.cpp b/compiler/luci/pass/src/ResolveCustomOpMatMulPass.cpp new file mode 100644 index 0000000..547fd22 --- /dev/null +++ b/compiler/luci/pass/src/ResolveCustomOpMatMulPass.cpp @@ -0,0 +1,185 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "luci/Pass/ResolveCustomOpMatMulPass.h" + +#include "flatbuffers/flexbuffers.h" +#include + +#include + +#include +#include +#include +#include + +namespace +{ + +template +luci::CircleConst *create_const_node(loco::Graph *g, const loco::DataType dtype, + const std::vector &shape, + const std::vector &values) +{ + auto node = g->nodes()->create(); + node->dtype(dtype); + node->rank(shape.size()); + + uint32_t size = 1; + for (uint32_t i = 0; i < shape.size(); ++i) + { + node->dim(i) = shape.at(i); + size *= shape.at(i); + } + +#define INIT_VALUES(DT) \ + { \ + node->size
(size); \ + for (uint32_t i = 0; i < values.size(); ++i) \ + node->at
(i) = values[i]; \ + } + + switch (dtype) + { + case loco::DataType::U8: + INIT_VALUES(loco::DataType::U8); + break; + case loco::DataType::S16: + INIT_VALUES(loco::DataType::S16); + break; + case loco::DataType::S32: + INIT_VALUES(loco::DataType::S32); + break; + case loco::DataType::FLOAT32: + INIT_VALUES(loco::DataType::FLOAT32) + break; + default: + INTERNAL_EXN("create_const_node called with unsupported type"); + break; + } + return node; +} + +bool resolve_matmul(luci::CircleCustom *cop) +{ +#define CHECK_OR_FALSE(condition) \ + if (not(condition)) \ + return false; +#define CHECK_OR_THROW(condition, message) \ + if (not(condition)) \ + INTERNAL_EXN(message); + + auto graph = cop->graph(); + const std::vector custom_options = cop->custom_options(); + auto map = flexbuffers::GetRoot(custom_options).AsMap(); + const auto U8 = loco::DataType::U8; + const auto S16 = loco::DataType::S16; + const auto S32 = loco::DataType::S32; + const auto FLOAT32 = loco::DataType::FLOAT32; + + bool transpose_a = map["transpose_a"].AsBool(); + bool transpose_b = map["transpose_b"].AsBool(); + + loco::Node *lhs = cop->inputs(0); + loco::Node *rhs = cop->inputs(1); + + // Check that the type of the first input is known + CHECK_OR_FALSE(loco::dtype_known(lhs)); + auto lhs_dtype = loco::dtype_get(cop->inputs(0)); + + // If transpose of first input is requested, its shape must be known + CHECK_OR_FALSE(!transpose_a || loco::shape_known(lhs)); + // and its rank should be at least 2 + CHECK_OR_FALSE(!transpose_a || loco::shape_get(lhs).as().rank() >= 2); + // Check that the shape of the 2nd input is known + CHECK_OR_FALSE(loco::shape_known(rhs)); + // TODO as of 06/23/20 TFLite only supports rank 2 for 2nd input. Fix this once that changes! + CHECK_OR_FALSE(loco::shape_get(rhs).as().rank() == 2); + // Check that input data type is supported + CHECK_OR_THROW(lhs_dtype == U8 || lhs_dtype == S16 || lhs_dtype == FLOAT32, + "Only UInt8, Int16 and Float32 data types are supported by MatMul"); + + if (transpose_a) + { + auto a_shape = loco::shape_get(lhs).as(); + // Create a permutation constant node + std::vector perm; + for (uint32_t i = 0; i < a_shape.rank(); ++i) + perm.push_back(i); + std::swap(perm[a_shape.rank() - 1], perm[a_shape.rank() - 2]); + auto perm_node = create_const_node(graph, S32, {a_shape.rank()}, perm); + // Now make a transpose node + auto transpose_node = graph->nodes()->create(); + transpose_node->a(lhs); + transpose_node->perm(perm_node); + lhs = transpose_node; + } + + // Transpose the second input if needed. TFLite FullyConnected operator + // assumes the second input is in column-major order, but the input is + // in row-major order, thus we need to convert between them. + if (!transpose_b) + { + const std::vector perm{1, 0}; + auto perm_node = create_const_node(graph, S32, {2}, perm); + auto transpose_node = graph->nodes()->create(); + transpose_node->a(rhs); + transpose_node->perm(perm_node); + rhs = transpose_node; + } + + // Make a constant zero-filled bias node + auto b_shape = loco::shape_get(cop->inputs(1)).as(); + uint32_t bias_size = b_shape.dim(transpose_b ? 1 : 0).value(); + const std::vector val(bias_size, .0f); + auto bias_node = create_const_node(graph, lhs_dtype, {bias_size}, val); + auto fc_node = graph->nodes()->create(); + fc_node->input(lhs); + fc_node->weights(rhs); + fc_node->bias(bias_node); + fc_node->fusedActivationFunction(luci::FusedActFunc::NONE); + + replace(cop).with(fc_node); + return true; +} + +} // namespace + +namespace luci +{ + +bool ResolveCustomOpMatMulPass::run(loco::Graph *g) +{ + bool changed = false; + for (auto node : loco::active_nodes(loco::output_nodes(g))) + { + auto cop = dynamic_cast(node); + if (not cop) + continue; + + if (cop->custom_code() != "MatMul") + continue; + + if (!resolve_matmul(cop)) + continue; + + changed = true; + } + + return changed; +} + +} // namespace luci diff --git a/compiler/luci/requires.cmake b/compiler/luci/requires.cmake index e88dabd..e52523d 100644 --- a/compiler/luci/requires.cmake +++ b/compiler/luci/requires.cmake @@ -1,5 +1,7 @@ +require("foder") require("loco") require("locop") +require("logo") require("logo-core") require("mio-circle") require("oops") diff --git a/compiler/luci/service/src/CircleShapeInference.cpp b/compiler/luci/service/src/CircleShapeInference.cpp index fdcfa76..0732849 100644 --- a/compiler/luci/service/src/CircleShapeInference.cpp +++ b/compiler/luci/service/src/CircleShapeInference.cpp @@ -27,11 +27,8 @@ namespace luci ShapeDescription ShapeInference::get(loco::Node *node) { - // TODO Adjust indentation level - { - assert(loco::shape_known(node)); - return to_shape_description(loco::shape_get(node)); - } + assert(loco::shape_known(node)); + return to_shape_description(loco::shape_get(node)); } } // namespace luci diff --git a/compiler/luci/service/src/CircleShapeInferenceRule.cpp b/compiler/luci/service/src/CircleShapeInferenceRule.cpp index c8e872b..a291cfe 100644 --- a/compiler/luci/service/src/CircleShapeInferenceRule.cpp +++ b/compiler/luci/service/src/CircleShapeInferenceRule.cpp @@ -17,6 +17,8 @@ #include "luci/Service/CircleShapeInferenceRule.h" #include "Check.h" +#include "ShapeInfer_StridedSlice.h" + #include #include #include @@ -26,11 +28,25 @@ #include #include +#include #include namespace { +std::ostream &operator<<(std::ostream &os, const loco::TensorShape &tensor_shape) +{ + os << "["; + for (uint32_t r = 0; r < tensor_shape.rank(); ++r) + { + if (r) + os << ","; + os << tensor_shape.dim(r).value(); + } + os << "]"; + return os; +} + // Call this for CircleAvgPool2D and CircleMaxPool2D only template loco::NodeShape infer_pool_2d_shape(const Pool2DType *node) { @@ -176,6 +192,157 @@ loco::TensorShape broadcast_shape(const loco::TensorShape &x, const loco::Tensor return output_shape; } +// BatchMatMulV2 supports broadcasting in the batch dimensions(BatchMatMul doesn't) +// TODO Distinguish BatchMatMul and BatchMatMulV2 +loco::NodeShape infer_batchmatmul_shape(const loco::TensorShape &x_shape, + const loco::TensorShape &y_shape, bool adj_x, bool adj_y) +{ + uint32_t x_rank = x_shape.rank(); + uint32_t y_rank = y_shape.rank(); + assert(x_rank >= 2 && y_rank >= 2); + + loco::TensorShape output_shape; + output_shape.rank(x_shape.rank()); + // Braodcast in the batch dimension + if (x_rank > 2 || y_rank > 2) + { + loco::TensorShape dummy_x = x_shape; + loco::TensorShape dummy_y = y_shape; + expand_rank(dummy_x, dummy_y); + if (x_rank < y_rank) + expand_rank(output_shape, dummy_y); + + for (uint32_t d = 0; d < output_shape.rank() - 2; d++) + { + uint32_t max_dim = std::max(dummy_x.dim(d).value(), dummy_y.dim(d).value()); + if (dummy_x.dim(d) == dummy_y.dim(d) || + dummy_x.dim(d).value() * dummy_y.dim(d).value() == max_dim) + output_shape.dim(d).set(max_dim); + else + INTERNAL_EXN("BatchMatMul has wrong shape"); + } + } + + loco::Dimension x_lhs = adj_x ? x_shape.dim(x_rank - 1) : x_shape.dim(x_rank - 2); + loco::Dimension x_rhs = adj_x ? x_shape.dim(x_rank - 2) : x_shape.dim(x_rank - 1); + loco::Dimension y_lhs = adj_y ? y_shape.dim(y_rank - 1) : y_shape.dim(y_rank - 2); + loco::Dimension y_rhs = adj_y ? y_shape.dim(y_rank - 2) : y_shape.dim(y_rank - 1); + + if (not(x_rhs == y_lhs)) + INTERNAL_EXN("x_rhs and y_lhs should be same"); + + uint32_t out_rank = output_shape.rank(); + output_shape.dim(out_rank - 2) = x_lhs; + output_shape.dim(out_rank - 1) = y_rhs; + + return loco::NodeShape{output_shape}; +} + +loco::TensorShape own_shape(const luci::CircleNode *node) +{ + loco::TensorShape shape; + shape.rank(node->rank()); + for (uint32_t r = 0; r < node->rank(); ++r) + shape.dim(r) = loco::Dimension(node->dim(r).value()); + return shape; +} + +loco::TensorShape infer_reducer(const loco::Node *input, const loco::Node *indices, bool keep_dims) +{ + const loco::DataType S32 = loco::DataType::S32; + + auto input_shape = loco::shape_get(input).as(); + auto reduction_indices = loco::must_cast(indices); + + { // Exceptions + // TODO support non-const case + // TODO support other data type + LUCI_ASSERT(reduction_indices->dtype() == S32, "Only support int 32"); + } + + std::vector reduction_values; + + for (uint32_t i = 0; i < reduction_indices->size(); ++i) + { + int32_t axis = reduction_indices->at(i); + if (axis < 0) + axis += input_shape.rank(); + if (not(0 <= axis and axis < static_cast(input_shape.rank()))) + INTERNAL_EXN_V("Invalid reduction axis for REDUCER", oops::to_uint32(axis)); + reduction_values.push_back(axis); + } + + loco::TensorShape output_shape; + + if (keep_dims) + { + output_shape.rank(input_shape.rank()); + for (uint32_t i = 0; i < input_shape.rank(); ++i) + output_shape.dim(i) = input_shape.dim(i); + for (uint32_t i = 0; i < reduction_values.size(); ++i) + output_shape.dim(reduction_values.at(i)) = 1; + } + else + { + std::vector check_reduce(input_shape.rank(), false); + for (uint32_t i = 0; i < reduction_values.size(); ++i) + check_reduce.at(reduction_values.at(i)) = true; + + uint32_t reduce_cnt = 0; + for (uint32_t i = 0; i < check_reduce.size(); ++i) + if (check_reduce.at(i)) + ++reduce_cnt; + + output_shape.rank(input_shape.rank() - reduce_cnt); + for (uint32_t i = 0, j = 0; i < check_reduce.size(); ++i) + if (check_reduce.at(i) == false) + output_shape.dim(j++) = input_shape.dim(i); + } + + return output_shape; +} + +/** + * @brief vector_from_constant will return int64_t vector from CircleConst node + */ +template std::vector vector_from_constant(luci::CircleConst *const_node) +{ + std::vector result; + + for (uint32_t idx = 0; idx < const_node->size(); ++idx) + result.push_back(const_node->at(idx)); + + return result; +} + +template loco::NodeShape broadcast_xy(const CIRCLENODE *node) +{ + auto x_shape = loco::shape_get(node->x()).template as(); + auto y_shape = loco::shape_get(node->y()).template as(); + + auto output_shape = broadcast_shape(x_shape, y_shape); + + return loco::NodeShape{output_shape}; +} + +template loco::NodeShape use_x(const CIRCLENODE *node) +{ + auto x_shape = loco::shape_get(node->x()).template as(); + return loco::NodeShape{x_shape}; +} + +template loco::NodeShape use_logits(const CIRCLENODE *node) +{ + auto shape = loco::shape_get(node->logits()).template as(); + return loco::NodeShape{shape}; +} + +loco::NodeShape use_own(const luci::CircleNode *node) +{ + loco::TensorShape shape = own_shape(node); + return loco::NodeShape{shape}; +} + /** * @brief Class to infer the shape of CircleNode * @@ -184,20 +351,24 @@ loco::TensorShape broadcast_shape(const loco::TensorShape &x, const loco::Tensor class ShapeInferenceAlgorithm final : public luci::CircleNodeVisitor { public: - loco::NodeShape visit(const luci::CircleAbs *node) final - { - auto x_shape = loco::shape_get(node->x()).as(); - return loco::NodeShape{x_shape}; - } + loco::NodeShape visit(const luci::CircleAbs *node) final { return use_x(node); } - loco::NodeShape visit(const luci::CircleAdd *node) final + loco::NodeShape visit(const luci::CircleAdd *node) final { return broadcast_xy(node); } + + loco::NodeShape visit(const luci::CircleAddN *node) final { - auto x_shape = loco::shape_get(node->x()).as(); - auto y_shape = loco::shape_get(node->y()).as(); + auto shape = loco::shape_get(node->inputs(0)).as(); - auto output_shape = broadcast_shape(x_shape, y_shape); + for (uint32_t idx = 1; idx < node->arity(); ++idx) + { + auto shape_idx = loco::shape_get(node->inputs(idx)).as(); + if (!(shape == shape_idx)) + { + INTERNAL_EXN_V("ADD_N shape not same as the first input: ", idx); + } + } - return loco::NodeShape{output_shape}; + return loco::NodeShape{shape}; } loco::NodeShape visit(const luci::CircleArgMax *node) final @@ -211,8 +382,7 @@ public: // Only support node's shape() is CircleConst with S32/S64 // Support S32 for now. - auto const_shape_node = dynamic_cast(node->dimension()); - LUCI_ASSERT(const_shape_node, "Only support CircleConst for shape of CircleArgMax"); + auto const_shape_node = loco::must_cast(node->dimension()); LUCI_ASSERT(const_shape_node->dtype() == loco::DataType::S32, "Only support int32 CircleConst for CircleArgMax"); @@ -240,11 +410,58 @@ public: return loco::NodeShape{shape_output}; } + loco::NodeShape visit(const luci::CircleArgMin *node) final + { + auto input_shape = loco::shape_get(node->input()).as(); + auto dimension_shape = loco::shape_get(node->dimension()).as(); + + int64_t select_axis = 0; + { + LUCI_ASSERT(node->dimension(), "2nd input dimension() should not be nullptr"); + + // Only support node's shape() is CircleConst with S32/S64 + // Support S32 for now. + auto const_shape_node = loco::must_cast(node->dimension()); + LUCI_ASSERT(const_shape_node->dtype() == loco::DataType::S32, + "Only support int32 CircleConst for CircleArgMin"); + + if (const_shape_node->rank() > 1) + INTERNAL_EXN_V("Only support rank 0/1 CircleConst", + oops::to_uint32(const_shape_node->rank())); + + select_axis = const_shape_node->scalar(); + } + assert(select_axis < input_shape.rank()); + assert(select_axis >= 0); // TODO support minus of this breaks + + // NOTE select_axis is removed + loco::TensorShape shape_output; + uint32_t rank = input_shape.rank(); + uint32_t shrink = static_cast(select_axis); + assert(rank > 0); + shape_output.rank(rank - 1); + for (uint32_t r = 0, d = 0; r < rank; ++r) + { + if (r == shrink) + continue; + shape_output.dim(d++) = input_shape.dim(r); + } + return loco::NodeShape{shape_output}; + } + loco::NodeShape visit(const luci::CircleAveragePool2D *node) final { return infer_pool_2d_shape(node); } + loco::NodeShape visit(const luci::CircleBatchMatMul *node) final + { + auto x_shape = loco::shape_get(node->x()).as(); + auto y_shape = loco::shape_get(node->y()).as(); + + return infer_batchmatmul_shape(x_shape, y_shape, node->adj_x(), node->adj_y()); + } + loco::NodeShape visit(const luci::CircleBatchToSpaceND *node) final { const loco::DataType S32 = loco::DataType::S32; @@ -254,14 +471,12 @@ public: assert(input_shape.rank() == 3 || input_shape.rank() == 4); // Only support block_shape() with S32 type CircleConst for now - auto const_block_shape = dynamic_cast(node->block_shape()); - LUCI_ASSERT(const_block_shape, "Only support CircleConst for block_shape"); + auto const_block_shape = loco::must_cast(node->block_shape()); LUCI_ASSERT(const_block_shape->dtype() == loco::DataType::S32, "Only support int32 block_shape"); // Only support crops() with S32 type CircleConst for now - auto const_crops = dynamic_cast(node->crops()); - LUCI_ASSERT(const_crops, "Only support CircleConst for crops"); + auto const_crops = loco::must_cast(node->crops()); LUCI_ASSERT(const_crops->dtype() == loco::DataType::S32, "Only support int32 crops"); auto const_block_shape_shape = loco::shape_get(const_block_shape).as(); @@ -295,6 +510,10 @@ public: return loco::NodeShape{shape_output}; } + loco::NodeShape visit(const luci::CircleCast *node) final { return use_x(node); } + + loco::NodeShape visit(const luci::CircleCeil *node) final { return use_x(node); } + loco::NodeShape visit(const luci::CircleConcatenation *node) final { // TODO Support when CircleConcatenation has 0 input @@ -330,16 +549,7 @@ public: return loco::NodeShape{output_shape}; } - loco::NodeShape visit(const luci::CircleConst *node) final - { - loco::TensorShape shape; - - shape.rank(node->rank()); - for (uint32_t axis = 0; axis < node->rank(); axis++) - shape.dim(axis) = node->dim(axis); - - return loco::NodeShape{shape}; - } + loco::NodeShape visit(const luci::CircleConst *node) final { return use_own(node); } loco::NodeShape visit(const luci::CircleConv2D *node) final { @@ -361,8 +571,8 @@ public: uint32_t stride_width = node->stride()->w(); uint32_t ker_height = ker_shape.dim(1).value(); uint32_t ker_width = ker_shape.dim(2).value(); - uint32_t dilation_height = 1; - uint32_t dilation_width = 1; + uint32_t dilation_height = node->dilation()->h(); + uint32_t dilation_width = node->dilation()->w(); uint32_t effective_ker_height = dilation_height * (ker_height - 1) + 1; uint32_t effective_ker_width = dilation_width * (ker_width - 1) + 1; @@ -392,11 +602,40 @@ public: return loco::NodeShape{ofm_shape}; } - loco::NodeShape visit(const luci::CircleCos *node) final + loco::NodeShape visit(const luci::CircleCos *node) final { return use_x(node); } + + loco::NodeShape visit(const luci::CircleCustom *node) final { return use_own(node); } + + loco::NodeShape visit(const luci::CircleDepthToSpace *node) final { - auto x_shape = loco::shape_get(node->x()).as(); + auto input_shape = loco::shape_get(node->input()).as(); + LUCI_ASSERT(input_shape.rank() == 4, "Only input rank 4 is supported"); + + // Only data format NHWC is supported + // TODO need to clarify what to do with layout in this operator + int32_t height = input_shape.dim(1).value(); + int32_t width = input_shape.dim(2).value(); + int32_t depth = input_shape.dim(3).value(); + + int block_size = node->block_size(); + + if (block_size < 2) + INTERNAL_EXN("Block size must be >= 2"); + + if (depth % (block_size * block_size)) + { + INTERNAL_EXN("The input tensor's depth must be divisible by block_size^2"); + } - return loco::NodeShape{x_shape}; + loco::TensorShape output_shape; + output_shape.rank(4); + + output_shape.dim(0) = input_shape.dim(0).value(); + output_shape.dim(1) = height * block_size; + output_shape.dim(2) = width * block_size; + output_shape.dim(3) = depth / (block_size * block_size); + + return loco::NodeShape{output_shape}; } loco::NodeShape visit(const luci::CircleDepthwiseConv2D *node) final @@ -414,8 +653,8 @@ public: uint32_t stride_width = node->stride()->w(); uint32_t ker_height = ker_shape.dim(1).value(); uint32_t ker_width = ker_shape.dim(2).value(); - uint32_t dilation_height = 1; - uint32_t dilation_width = 1; + uint32_t dilation_height = node->dilation()->h(); + uint32_t dilation_width = node->dilation()->w(); uint32_t effective_ker_height = dilation_height * (ker_height - 1) + 1; uint32_t effective_ker_width = dilation_width * (ker_width - 1) + 1; @@ -445,30 +684,87 @@ public: return loco::NodeShape{ofm_shape}; } - loco::NodeShape visit(const luci::CircleDiv *node) final - { - auto x_shape = loco::shape_get(node->x()).as(); - auto y_shape = loco::shape_get(node->y()).as(); + loco::NodeShape visit(const luci::CircleDiv *node) final { return broadcast_xy(node); } - auto output_shape = broadcast_shape(x_shape, y_shape); + loco::NodeShape visit(const luci::CircleElu *node) final + { + auto input_shape = loco::shape_get(node->features()).as(); - return loco::NodeShape{output_shape}; + return loco::NodeShape{input_shape}; } - loco::NodeShape visit(const luci::CircleEqual *node) final + loco::NodeShape visit(const luci::CircleEqual *node) final { return broadcast_xy(node); } + + loco::NodeShape visit(const luci::CircleExp *node) final { return use_x(node); } + + loco::NodeShape visit(const luci::CircleExpandDims *node) final { - const auto x_shape = loco::shape_get(node->x()).as(); - const auto y_shape = loco::shape_get(node->y()).as(); - loco::TensorShape output_shape = broadcast_shape(x_shape, y_shape); + const loco::DataType S32 = loco::DataType::S32; + auto x_shape = loco::shape_get(node->input()).as(); + if (x_shape.rank() == 0) + { + // This maybe for unknown shape. We use shape from the node itself. + return use_own(node); + } + auto const_axis = loco::must_cast(node->axis()); + LUCI_ASSERT(const_axis->dtype() == S32, "Only support int32 CircleConst for axis"); + if (const_axis->rank() != 0 && const_axis->rank() != 1) + { + INTERNAL_EXN_V("Non-scalar axis in OP", node->opnum()); + } + int32_t axis = const_axis->at(0); + LUCI_ASSERT((axis <= static_cast(x_shape.rank())) && + (axis >= -1 - static_cast(x_shape.rank())), + "Axis has to be between [-(D+1), D], where D is rank of input."); + size_t positive_axis = axis < 0 ? x_shape.rank() + axis + 1 : axis; + loco::TensorShape output_shape; + output_shape.rank(x_shape.rank() + 1); + size_t i = 0; + for (; i < positive_axis; i++) + output_shape.dim(i) = x_shape.dim(i); + output_shape.dim(i) = loco::Dimension(1); + for (; i < x_shape.rank(); i++) + output_shape.dim(i + 1) = x_shape.dim(i); return loco::NodeShape{output_shape}; } - loco::NodeShape visit(const luci::CircleExp *node) final + loco::NodeShape visit(const luci::CircleFill *node) final { - auto x_shape = loco::shape_get(node->x()).as(); - return loco::NodeShape{x_shape}; + loco::TensorShape shape; + { + LUCI_ASSERT(node->dims(), "dims input should not be nullptr"); + + auto dims_node = dynamic_cast(node->dims()); + if (dims_node != nullptr) + { + // Only support node with S32 + LUCI_ASSERT(dims_node->dtype() == loco::DataType::S32, "Only support int32 CircleConst"); + + if (dims_node->rank() != 1) + INTERNAL_EXN_V("Only support rank 1 CircleConst", oops::to_uint32(dims_node->rank())); + + shape.rank(dims_node->dim(0).value()); + + for (uint32_t axis = 0; axis < shape.rank(); ++axis) + { + shape.dim(axis) = dims_node->at(axis); + } + } + else + { + shape = own_shape(node); + } + } + + return loco::NodeShape{shape}; } + loco::NodeShape visit(const luci::CircleFloor *node) final { return use_x(node); } + + loco::NodeShape visit(const luci::CircleFloorDiv *node) final { return broadcast_xy(node); } + + loco::NodeShape visit(const luci::CircleFloorMod *node) final { return broadcast_xy(node); } + loco::NodeShape visit(const luci::CircleFullyConnected *node) final { auto input_shape = loco::shape_get(node->input()).as(); @@ -478,8 +774,11 @@ public: // Input: a tensor of at least rank 2 [D1, D2, ... Dn] // Weight: [# of units, K] // Output: [D1 * D2 * ... * Dn / K, # of units] - LUCI_ASSERT(input_shape.rank() >= 2, "Input rank should be at least 2"); - LUCI_ASSERT(weights_shape.rank() == 2, "Incompatible weights rank for fully connected"); + if (input_shape.rank() < 2 || weights_shape.rank() != 2) + { + // Return node own shape if shape inference is not possible + return use_own(node); + } uint32_t input_size = 1; for (uint32_t i = 0; i < input_shape.rank(); i++) @@ -495,133 +794,290 @@ public: return loco::NodeShape{out_shape}; } - loco::NodeShape visit(const luci::CircleLogicalNot *node) final + loco::NodeShape visit(const luci::CircleGather *node) final { - const auto input_shape = loco::shape_get(node->x()).as(); - return loco::NodeShape{input_shape}; - } + loco::TensorShape output_shape; - loco::NodeShape visit(const luci::CircleLogicalOr *node) final - { - const auto input_shape = loco::shape_get(node->x()).as(); - return loco::NodeShape{input_shape}; - } + const auto input_shape = loco::shape_get(node->params()).as(); + const auto positions_shape = loco::shape_get(node->indices()).as(); + int32_t axis = node->axis(); - loco::NodeShape visit(const luci::CircleMaximum *node) final - { - auto x_shape = loco::shape_get(node->x()).as(); - auto y_shape = loco::shape_get(node->y()).as(); + // If CircleGather input has a dynamic shape, it can't inference this shape. So, it returns the + // shape that node already has. + if (input_shape.rank() == 0 || positions_shape.rank() == 0) + return use_own(node); - auto output_shape = broadcast_shape(x_shape, y_shape); + if (axis < 0) + axis += input_shape.rank(); + + output_shape.rank(input_shape.rank() - 1 + positions_shape.rank()); + int32_t outdim_index = 0; + for (int32_t i = 0; i < axis; ++i) + output_shape.dim(outdim_index++) = input_shape.dim(i); + for (uint32_t i = 0; i < positions_shape.rank(); ++i) + output_shape.dim(outdim_index++) = positions_shape.dim(i); + for (uint32_t i = axis + 1; i < input_shape.rank(); ++i) + output_shape.dim(outdim_index++) = input_shape.dim(i); return loco::NodeShape{output_shape}; } - loco::NodeShape visit(const luci::CircleMaxPool2D *node) final + loco::NodeShape visit(const luci::CircleGatherNd *node) final { - return infer_pool_2d_shape(node); - } + loco::TensorShape output_shape; - loco::NodeShape visit(const luci::CircleMean *node) final - { - const loco::DataType S32 = loco::DataType::S32; + const auto params_shape = loco::shape_get(node->params()).as(); + const auto indices_shape = loco::shape_get(node->indices()).as(); - auto input_shape = loco::shape_get(node->input()).as(); - auto reduction_indices = dynamic_cast(node->reduction_indices()); + const auto params_rank = params_shape.rank(); + const auto indices_rank = indices_shape.rank(); - { // Exceptions - // TODO support non-const case - LUCI_ASSERT(reduction_indices, "Only support constant reduction_indices"); - // TODO support other data type - LUCI_ASSERT(reduction_indices->dtype() == S32, "Only support int 32"); - } + // see https://www.tensorflow.org/api_docs/python/tf/gather_nd + // output.shape = indices.shape[:-1] + params.shape[indices.shape[-1]:] + // batch_dims isn't supported in tflite - std::vector reduction_values; + // TODO: replace exceptions with setting shape to unknown? - for (uint32_t i = 0; i < reduction_indices->size(); ++i) - { - int32_t axis = reduction_indices->at(i); - if (axis < 0) - axis += input_shape.rank(); - if (not(0 <= axis and axis < static_cast(input_shape.rank()))) - INTERNAL_EXN_V("Invalid reduction axis for MEAN", oops::to_uint32(axis)); - reduction_values.push_back(axis); - } + if (!indices_shape.dim(indices_rank - 1).known()) + INTERNAL_EXN("Last indices dimension is unknown"); - loco::TensorShape output_shape; + auto indices_last_dim = indices_shape.dim(indices_rank - 1).value(); + + if (indices_last_dim > params_rank) + INTERNAL_EXN("Last indices dimension should be <= params rank"); + + const uint32_t output_rank = indices_rank + params_rank - indices_last_dim - 1; - if (node->keep_dims()) + output_shape.rank(output_rank); + + uint32_t output_index = 0; + for (uint32_t i = 0; i < indices_rank - 1; ++i) { - output_shape.rank(input_shape.rank()); - for (uint32_t i = 0; i < input_shape.rank(); ++i) - output_shape.dim(i) = input_shape.dim(i); - for (uint32_t i = 0; i < reduction_values.size(); ++i) - output_shape.dim(reduction_values.at(i)) = 1; + auto &dim = indices_shape.dim(i); + if (!dim.known()) + INTERNAL_EXN("Unknown indices dimension is unsupported"); + output_shape.dim(output_index++).set(dim.value()); } - else - { - std::vector check_reduce(input_shape.rank(), false); - for (uint32_t i = 0; i < reduction_values.size(); ++i) - check_reduce.at(reduction_values.at(i)) = true; - uint32_t reduce_cnt = 0; - for (uint32_t i = 0; i < check_reduce.size(); ++i) - if (check_reduce.at(i)) - ++reduce_cnt; - - output_shape.rank(input_shape.rank() - reduce_cnt); - for (uint32_t i = 0, j = 0; i < check_reduce.size(); ++i) - if (check_reduce.at(i) == false) - output_shape.dim(j++) = i; + for (uint32_t i = indices_last_dim; i < params_rank; ++i) + { + auto &dim = params_shape.dim(i); + if (!dim.known()) + INTERNAL_EXN("Unknown params dimension is unsupported"); + output_shape.dim(output_index++).set(dim.value()); } return loco::NodeShape{output_shape}; } - loco::NodeShape visit(const luci::CircleMul *node) final + loco::NodeShape visit(const luci::CircleGreater *node) final { return broadcast_xy(node); } + + loco::NodeShape visit(const luci::CircleGreaterEqual *node) final { return broadcast_xy(node); } + + loco::NodeShape visit(const luci::CircleIf *node) final { - auto x_shape = loco::shape_get(node->x()).as(); - auto y_shape = loco::shape_get(node->y()).as(); + // Shape of CircleIf is not used. Just use input 0 + assert(node->input_count() > 0); + const auto input_shape = loco::shape_get(node->input(0)).as(); + return loco::NodeShape{input_shape}; + } - auto output_shape = broadcast_shape(x_shape, y_shape); + loco::NodeShape visit(const luci::CircleL2Normalize *node) final { return use_x(node); } - return loco::NodeShape{output_shape}; + loco::NodeShape visit(const luci::CircleL2Pool2D *node) final + { + return infer_pool_2d_shape(node); } - loco::NodeShape visit(const luci::CirclePack *node) final + loco::NodeShape visit(const luci::CircleLeakyRelu *node) final { - LUCI_ASSERT(node->values_count() > 0, "Only support one or more inputs"); + const auto input_shape = loco::shape_get(node->features()).as(); + return loco::NodeShape{input_shape}; + } - auto first_shape = loco::shape_get(node->values(0)).as(); - // Make sure all inputs have the same shape. - for (uint32_t i = 1; i < node->values_count(); ++i) - { - auto in_shape = loco::shape_get(node->values(i)).as(); - LUCI_ASSERT(loco::NodeShape{first_shape} == loco::NodeShape{in_shape}, - "All inputs must have the same shape"); - } + loco::NodeShape visit(const luci::CircleLess *node) final { return broadcast_xy(node); } - // Checking shape capability for pack layer - // Input: tensors [D1, D2, ... Dn] - // Axis: K - // Output: [D1, D2, ... , D_K-1, n, D_K+1, ... Dn] - auto axis = node->axis(); - if (axis < 0) - axis += first_shape.rank() + 1; + loco::NodeShape visit(const luci::CircleLessEqual *node) final { return broadcast_xy(node); } - LUCI_ASSERT(0 <= axis, "Axis is out of range"); - LUCI_ASSERT(static_cast(axis) <= first_shape.rank(), "Axis is out of range"); + loco::NodeShape visit(const luci::CircleLocalResponseNormalization *node) final + { + const auto input_shape = loco::shape_get(node->input()).as(); + return loco::NodeShape{input_shape}; + } - loco::TensorShape output_shape; - output_shape.rank(first_shape.rank() + 1); + loco::NodeShape visit(const luci::CircleLog *node) final { return use_x(node); } - uint32_t j = 0; - for (uint32_t i = 0; i < output_shape.rank(); ++i) - { - if (i == static_cast(axis)) - { - output_shape.dim(i) = node->values_count(); - } + loco::NodeShape visit(const luci::CircleLogicalAnd *node) final { return use_x(node); } + + loco::NodeShape visit(const luci::CircleLogicalNot *node) final { return use_x(node); } + + loco::NodeShape visit(const luci::CircleLogicalOr *node) final { return use_x(node); } + + loco::NodeShape visit(const luci::CircleLogistic *node) final { return use_x(node); } + + loco::NodeShape visit(const luci::CircleMatrixSetDiag *node) final + { + auto input_shape = loco::shape_get(node->input()).as(); + auto diagonal_shape = loco::shape_get(node->diagonal()).as(); + + auto rank = diagonal_shape.rank(); + + LUCI_ASSERT(rank == input_shape.rank() - 1, "diagonal rank = input rank - 1"); + + for (uint32_t i = 0; i < rank - 1; i++) + { + LUCI_ASSERT(diagonal_shape.dim(i) == input_shape.dim(i), "diagonal dims = input dims"); + } + + auto dim = std::min(input_shape.dim(rank - 1).value(), input_shape.dim(rank).value()); + + LUCI_ASSERT(dim == diagonal_shape.dim(rank - 1), "Max diag len error"); + + return loco::NodeShape{input_shape}; + } + + loco::NodeShape visit(const luci::CircleLogSoftmax *node) final { return use_logits(node); } + + loco::NodeShape visit(const luci::CircleMatrixDiag *node) final + { + loco::TensorShape output_shape; + + auto diagonal_shape = loco::shape_get(node->diagonal()).as(); + auto rank = diagonal_shape.rank(); + + output_shape.rank(rank + 1); + + for (uint32_t i = 0; i < rank; i++) + { + output_shape.dim(i) = diagonal_shape.dim(i); + } + + output_shape.dim(rank) = diagonal_shape.dim(rank - 1); + + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleMaximum *node) final { return broadcast_xy(node); } + + loco::NodeShape visit(const luci::CircleMaxPool2D *node) final + { + return infer_pool_2d_shape(node); + } + + loco::NodeShape visit(const luci::CircleMean *node) final + { + auto output_shape = infer_reducer(node->input(), node->reduction_indices(), node->keep_dims()); + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleMinimum *node) final { return broadcast_xy(node); } + + loco::NodeShape visit(const luci::CircleMirrorPad *node) final + { + const loco::DataType S32 = loco::DataType::S32; + + auto input_shape = loco::shape_get(node->input()).as(); + auto paddings = loco::must_cast(node->paddings()); + + // TODO support non-const case + // TODO support other data type + LUCI_ASSERT(paddings->dtype() == S32, "Only support int 32 for now"); + LUCI_ASSERT(paddings->rank() == 2, "paddings should be rank 2") + + int32_t n = paddings->dim(0).value(); + int32_t v = paddings->dim(1).value(); + + LUCI_ASSERT(v == 2, "paddings should be [n, 2]"); + LUCI_ASSERT(n == int32_t(input_shape.rank()), + "paddings [n, 2] should have same value of input rank"); + + loco::TensorShape output_shape; + + output_shape.rank(input_shape.rank()); + for (int32_t ni = 0; ni < n; ++ni) + { + int32_t idx = ni * 2; + int value = input_shape.dim(ni).value(); + value += paddings->at(idx + 0); // left + value += paddings->at(idx + 1); // right + output_shape.dim(ni) = value; + } + + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleMul *node) final { return broadcast_xy(node); } + + loco::NodeShape visit(const luci::CircleNeg *node) final { return use_x(node); } + + loco::NodeShape visit(const luci::CircleNotEqual *node) final { return broadcast_xy(node); } + + loco::NodeShape visit(const luci::CircleOneHot *node) final + { + const loco::DataType S32 = loco::DataType::S32; + auto indices_shape = loco::shape_get(node->indices()).as(); + // Only support OneHot node's depth() is CircleConst with type S32 + // TODO support depth with other types + auto depth = loco::must_cast(node->depth()); + LUCI_ASSERT(depth->dtype() == S32, "Only support int32 CircleConst"); + if (depth->rank() != 0) + INTERNAL_EXN_V("Only support rank 0 CircleOneHot in Depth", oops::to_uint32(depth->rank())); + loco::TensorShape output_shape; + output_shape.rank(indices_shape.rank() + 1); + auto axis = node->axis(); + if (axis < 0) + axis += indices_shape.rank() + 1; + LUCI_ASSERT(0 <= axis, "Axis is out of range"); + LUCI_ASSERT(static_cast(axis) <= indices_shape.rank(), "Axis is out of range"); + uint32_t j = 0; + for (uint32_t i = 0; i < output_shape.rank(); i++) + { + if (i == static_cast(axis)) + { + output_shape.dim(i) = depth->at(0); + } + else + { + output_shape.dim(i) = indices_shape.dim(j++); + } + } + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CirclePack *node) final + { + LUCI_ASSERT(node->values_count() > 0, "Only support one or more inputs"); + + auto first_shape = loco::shape_get(node->values(0)).as(); + // Make sure all inputs have the same shape. + for (uint32_t i = 1; i < node->values_count(); ++i) + { + auto in_shape = loco::shape_get(node->values(i)).as(); + LUCI_ASSERT(loco::NodeShape{first_shape} == loco::NodeShape{in_shape}, + "All inputs must have the same shape"); + } + + // Checking shape capability for pack layer + // Input: tensors [D1, D2, ... Dn] + // Axis: K + // Output: [D1, D2, ... , D_K-1, n, D_K+1, ... Dn] + auto axis = node->axis(); + if (axis < 0) + axis += first_shape.rank() + 1; + + LUCI_ASSERT(0 <= axis, "Axis is out of range"); + LUCI_ASSERT(static_cast(axis) <= first_shape.rank(), "Axis is out of range"); + + loco::TensorShape output_shape; + output_shape.rank(first_shape.rank() + 1); + + uint32_t j = 0; + for (uint32_t i = 0; i < output_shape.rank(); ++i) + { + if (i == static_cast(axis)) + { + output_shape.dim(i) = node->values_count(); + } else { output_shape.dim(i) = first_shape.dim(j++); @@ -636,10 +1092,9 @@ public: const loco::DataType S32 = loco::DataType::S32; auto input_shape = loco::shape_get(node->input()).as(); - auto paddings = dynamic_cast(node->paddings()); + auto paddings = loco::must_cast(node->paddings()); // TODO support non-const case - LUCI_ASSERT(paddings, "Only support constant reduction_indices"); // TODO support other data type LUCI_ASSERT(paddings->dtype() == S32, "Only support int 32 for now"); LUCI_ASSERT(paddings->rank() == 2, "paddings should be rank 2") @@ -666,6 +1121,93 @@ public: return loco::NodeShape{output_shape}; } + loco::NodeShape visit(const luci::CirclePow *node) final { return broadcast_xy(node); } + + loco::NodeShape visit(const luci::CirclePRelu *node) final + { + auto input_shape = loco::shape_get(node->input()).as(); + auto alpha_shape = loco::shape_get(node->alpha()).as(); + + auto output_shape = broadcast_shape(input_shape, alpha_shape); + + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleRange *node) final + { + loco::TensorShape output_shape; + output_shape.rank(1); + + auto start_node = dynamic_cast(node->start()); + auto limit_node = dynamic_cast(node->limit()); + auto delta_node = dynamic_cast(node->delta()); + + if (start_node == nullptr || limit_node == nullptr || delta_node == nullptr) + { + return use_own(node); + } + + double start = 0, limit = 0, delta = 0; + +#define GET_RANGE_PARAM(DT) \ + start = start_node->scalar
(); \ + limit = limit_node->scalar
(); \ + delta = delta_node->scalar
(); + + switch (start_node->dtype()) + { + case loco::DataType::FLOAT32: + GET_RANGE_PARAM(loco::DataType::FLOAT32) + break; + case loco::DataType::S32: + GET_RANGE_PARAM(loco::DataType::S32) + break; + default: + INTERNAL_EXN("Range data type not supported"); + } + +#undef GET_RANGE_PARAM + + if (delta == 0) + INTERNAL_EXN("Delta can not be zero"); + + output_shape.dim(0) = ceil((limit - start) / delta); + + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleRank *) final + { + loco::TensorShape shape_output; + shape_output.rank(0); + + return loco::NodeShape{shape_output}; + } + + loco::NodeShape visit(const luci::CircleReduceAny *node) final + { + auto output_shape = infer_reducer(node->input(), node->reduction_indices(), node->keep_dims()); + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleReduceMax *node) final + { + auto output_shape = infer_reducer(node->input(), node->reduction_indices(), node->keep_dims()); + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleReduceMin *node) final + { + auto output_shape = infer_reducer(node->input(), node->reduction_indices(), node->keep_dims()); + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleReduceProd *node) final + { + auto output_shape = infer_reducer(node->input(), node->reduction_indices(), node->keep_dims()); + return loco::NodeShape{output_shape}; + } + loco::NodeShape visit(const luci::CircleRelu *node) final { auto input_shape = loco::shape_get(node->features()).as(); @@ -680,15 +1222,24 @@ public: return loco::NodeShape{input_shape}; } + loco::NodeShape visit(const luci::CircleReluN1To1 *node) final + { + auto input_shape = loco::shape_get(node->features()).as(); + + return loco::NodeShape{input_shape}; + } + /** * @note CircleReshape has new shape info in two places: 2nd input and attribute. - * This shape inference forces both to exist, and match each other. - * When this condition satisfied, it return the inferred shape + * This shape inference uses shape from input 'shape' node when it's constant. + * If not, shape will be from node itself. shape from attribute is not used. * * TODO Change this policy when not appropriate */ loco::NodeShape visit(const luci::CircleReshape *node) final { + LOGGER(l); + const loco::DataType S32 = loco::DataType::S32; loco::TensorShape shape_by_input; @@ -698,18 +1249,21 @@ public: // Only support node's shape() is CircleConst with S32 // TODO support other node with other types auto const_shape_node = dynamic_cast(node->shape()); - LUCI_ASSERT(const_shape_node, "Only support CircleConst for shape of CircleReshape"); - LUCI_ASSERT(const_shape_node->dtype() == S32, "Only support int32 CircleConst"); - - if (const_shape_node->rank() != 1) - INTERNAL_EXN_V("Only support rank 1 CircleConst", - oops::to_uint32(const_shape_node->rank())); + if (const_shape_node != nullptr) + { + LUCI_ASSERT(const_shape_node->dtype() == S32, "Only support int32 CircleConst"); - shape_by_input.rank(const_shape_node->dim(0).value()); + shape_by_input.rank(const_shape_node->size()); - for (uint32_t axis = 0; axis < shape_by_input.rank(); ++axis) + for (uint32_t axis = 0; axis < shape_by_input.rank(); ++axis) + { + shape_by_input.dim(axis) = const_shape_node->at(axis); + } + } + else { - shape_by_input.dim(axis) = const_shape_node->at(axis); + // We use shape from the node itself + shape_by_input = own_shape(node); } } @@ -723,8 +1277,12 @@ public: } } - LUCI_ASSERT(shape_by_input == shape_by_attr, - "Warning: Two new shape information mismatched for CircleReshape"); + if (!(shape_by_input == shape_by_attr)) + { + INFO(l) << "CircleReshape: Two new shape information mismatched : " << std::endl; + INFO(l) << " shape_by_input : " << shape_by_input << std::endl; + INFO(l) << " shape_by_attr : " << shape_by_attr << std::endl; + } loco::TensorShape output_shape = shape_by_input; @@ -754,94 +1312,517 @@ public: return loco::NodeShape{output_shape}; } - loco::NodeShape visit(const luci::CircleRsqrt *node) final + loco::NodeShape visit(const luci::CircleResizeBilinear *node) final { - auto input_shape = loco::shape_get(node->x()).as(); + auto input_shape = loco::shape_get(node->input()).as(); - return loco::NodeShape{input_shape}; + if (input_shape.rank() != 4) + INTERNAL_EXN("Expected ResizeBilinear input to have rank 4"); + + auto *const_node = loco::must_cast(node->size()); + + if (const_node->dtype() != loco::DataType::S32) + INTERNAL_EXN("Only S32 datatype is supported for ResizeBilinear size"); + + if (const_node->rank() != 1) + INTERNAL_EXN("Expected size tensor of rank 1"); + + if (const_node->dim(0).value() != 2) + INTERNAL_EXN("Expected size tensor with shape [2]"); + + loco::TensorShape output_shape; + output_shape.rank(4); + output_shape.dim(0) = input_shape.dim(0); + output_shape.dim(1) = const_node->at(0); + output_shape.dim(2) = const_node->at(1); + output_shape.dim(3) = input_shape.dim(3); + + return loco::NodeShape{output_shape}; } - loco::NodeShape visit(const luci::CircleSoftmax *node) final + loco::NodeShape visit(const luci::CircleResizeNearestNeighbor *node) final { - auto input_shape = loco::shape_get(node->logits()).as(); + auto input_shape = loco::shape_get(node->input()).as(); - return loco::NodeShape{input_shape}; + if (input_shape.rank() != 4) + INTERNAL_EXN("Expected ResizeNearesNeighbor input to have rank 4"); + + auto *const_node = loco::must_cast(node->size()); + + if (const_node->dtype() != loco::DataType::S32) + INTERNAL_EXN("Only S32 datatype is supported for ResizeNearesNeighbor size"); + + if (const_node->rank() != 1) + INTERNAL_EXN("Expected size tensor of rank 1"); + + if (const_node->dim(0).value() != 2) + INTERNAL_EXN("Expected size tensor with shape [2]"); + + loco::TensorShape output_shape; + output_shape.rank(4); + output_shape.dim(0) = input_shape.dim(0); + output_shape.dim(1) = const_node->at(0); + output_shape.dim(2) = const_node->at(1); + output_shape.dim(3) = input_shape.dim(3); + + return loco::NodeShape{output_shape}; } - loco::NodeShape visit(const luci::CircleSqrt *node) final + loco::NodeShape visit(const luci::CircleReverseSequence *node) final { - auto input_shape = loco::shape_get(node->x()).as(); + auto input_shape = loco::shape_get(node->input()).as(); return loco::NodeShape{input_shape}; } - loco::NodeShape visit(const luci::CircleSquaredDifference *node) final + loco::NodeShape visit(const luci::CircleRound *node) final { return use_x(node); } + + loco::NodeShape visit(const luci::CircleReverseV2 *node) final { - auto x_shape = loco::shape_get(node->x()).as(); - auto y_shape = loco::shape_get(node->y()).as(); + auto input_shape = loco::shape_get(node->tensor()).as(); - auto output_shape = broadcast_shape(x_shape, y_shape); + LUCI_ASSERT(loco::shape_get(node->axis()).as().rank() == 1, + "Tensor must be 1-D"); - return loco::NodeShape{output_shape}; + return loco::NodeShape{input_shape}; } - loco::NodeShape visit(const luci::CircleSub *node) final + loco::NodeShape visit(const luci::CircleRsqrt *node) final { return use_x(node); } + + loco::NodeShape visit(const luci::CircleScatterNd *node) final { - auto x_shape = loco::shape_get(node->x()).as(); - auto y_shape = loco::shape_get(node->y()).as(); + loco::TensorShape output_shape; + + auto shape_node = loco::must_cast(node->shape()); - auto output_shape = broadcast_shape(x_shape, y_shape); + const loco::DataType S32 = loco::DataType::S32; + const loco::DataType S64 = loco::DataType::S64; + + std::vector vect_shape; + + if (shape_node->dtype() == S32) + vect_shape = vector_from_constant(shape_node); + else if (shape_node->dtype() == S64) + vect_shape = vector_from_constant(shape_node); + else + LUCI_ASSERT(false, "Only support int32/int64 for shape()"); + + output_shape.rank(vect_shape.size()); + for (uint32_t i = 0; i < vect_shape.size(); ++i) + output_shape.dim(i) = vect_shape[i]; return loco::NodeShape{output_shape}; } - // TODO CircleTanh - - /// @brief Returns output shape of transpose. Use loco::ConstGen and luci::CircleConst for ConstT. - template - loco::TensorShape output_shape_of_transpose(loco::TensorShape input_shape, - const ConstT *perm_node) + loco::NodeShape visit(const luci::CircleSegmentSum *node) final { + auto input_shape = loco::shape_get(node->input()).as(); + auto segment_shape = loco::shape_get(node->segment_ids()).as(); + + LUCI_ASSERT(segment_shape.rank() == 1, "segment_ids must be 1-D tensor"); + LUCI_ASSERT(segment_shape.dim(0).value() == input_shape.dim(0).value(), + "segment_ids size must be equal to the size of data's first dimension"); + + auto ids_shape_value = loco::must_cast(node->segment_ids()); + + std::vector vect_ids; + + if (ids_shape_value->dtype() == loco::DataType::S32) + vect_ids = vector_from_constant(ids_shape_value); + + LUCI_ASSERT(std::is_sorted(vect_ids.begin(), vect_ids.end()), + "segment_ids values should be sorted") + loco::TensorShape output_shape; + output_shape.rank(input_shape.rank()); - assert(perm_node->dtype() == loco::DataType::S32); - assert(input_shape.rank() == perm_node->template size()); + for (uint32_t i = 1; i < input_shape.rank(); ++i) + output_shape.dim(i) = input_shape.dim(i); - for (uint32_t out_axis = 0; out_axis < output_shape.rank(); out_axis++) + output_shape.dim(0) = vect_ids.back() + 1; + + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleSelect *node) final + { + auto t_shape = loco::shape_get(node->t()).as(); + assert(t_shape == loco::shape_get(node->e()).as()); + + // condition shape validation + auto c_shape = loco::shape_get(node->condition()).as(); + if (c_shape.rank() != t_shape.rank()) { - auto in_axis = perm_node->template at(out_axis); - output_shape.dim(out_axis) = input_shape.dim(in_axis); + if (c_shape.rank() != 0 && c_shape.rank() != 1) + INTERNAL_EXN_V("CircleSelect condition rank is not 0 nor 1: ", c_shape.rank()); + + if (c_shape.rank() == 1) + { + if (c_shape.dim(0).value() != t_shape.dim(0).value()) + INTERNAL_EXN("CircleSelect condition dim(0) should match with t.dim(0)"); + } } - return output_shape; + return loco::NodeShape{t_shape}; } - loco::NodeShape visit(const luci::CircleTranspose *node) final + loco::NodeShape visit(const luci::CircleSelectV2 *node) final { - auto input_shape = loco::shape_get(node->a()).as(); + auto c_shape = loco::shape_get(node->condition()).as(); + auto t_shape = loco::shape_get(node->t()).as(); + auto e_shape = loco::shape_get(node->e()).as(); - auto canon_perm = dynamic_cast(node->perm()); - auto circle_perm = dynamic_cast(node->perm()); + // validate ability to broadcast shapes to each other + auto b_shape = broadcast_shape(broadcast_shape(c_shape, t_shape), e_shape); + return loco::NodeShape{b_shape}; + } - if (canon_perm) + loco::NodeShape visit(const luci::CircleShape *node) final + { + auto input_shape = loco::shape_get(node->input()).as(); + + loco::TensorShape output_shape; + + output_shape.rank(1); + output_shape.dim(0) = input_shape.rank(); + + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleSin *node) final { return use_x(node); } + + loco::NodeShape visit(const luci::CircleSlice *node) final + { + const loco::DataType S32 = loco::DataType::S32; + const loco::DataType S64 = loco::DataType::S64; + + auto input_shape = loco::shape_get(node->input()).as(); + + auto const_begin = loco::must_cast(node->begin()); + auto const_size = loco::must_cast(node->size()); + + loco::TensorShape output_shape; + std::vector vect_begin; // to hold both S32/S64, we use int64_t + std::vector vect_size; + + if (const_begin->dtype() == S32) + vect_begin = vector_from_constant(const_begin); + else if (const_begin->dtype() == S64) + vect_begin = vector_from_constant(const_begin); + else + LUCI_ASSERT(false, "Only support int32/int64 for begin()"); + + if (const_size->dtype() == S32) + vect_size = vector_from_constant(const_size); + else if (const_size->dtype() == S64) + vect_size = vector_from_constant(const_size); + else + LUCI_ASSERT(false, "Only support int32/int64 for size()"); + + assert(input_shape.rank() == vect_begin.size()); + assert(input_shape.rank() == vect_size.size()); + + output_shape.rank(vect_begin.size()); + for (uint32_t idx = 0; idx < vect_begin.size(); ++idx) + { + auto size = vect_size.at(idx); + if (size == -1) + { + size = input_shape.dim(idx).value() - vect_begin.at(idx); + } + output_shape.dim(idx) = size; + } + + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleSoftmax *node) final { return use_logits(node); } + + loco::NodeShape visit(const luci::CircleSpaceToBatchND *node) final + { + const loco::DataType S32 = loco::DataType::S32; + + auto input_shape = loco::shape_get(node->input()).as(); + // Support only input rank is 3 and 4 + assert(input_shape.rank() == 3 || input_shape.rank() == 4); + + // Only support block_shape() with S32 type CircleConst for now + auto const_block_shape = loco::must_cast(node->block_shape()); + LUCI_ASSERT(const_block_shape->dtype() == S32, "Only support int32 block_shape"); + + // Only support paddings() with S32 type CircleConst for now + auto const_paddings = loco::must_cast(node->paddings()); + LUCI_ASSERT(const_paddings->dtype() == S32, "Only support int32 paddings"); + + auto const_block_shape_shape = loco::shape_get(const_block_shape).as(); + auto const_paddings_shape = loco::shape_get(const_paddings).as(); + assert(const_block_shape_shape.rank() == 1); + assert(const_paddings_shape.rank() == 2); + + int32_t input_spatial_dim = input_shape.rank() - 2; + assert(const_block_shape_shape.dim(0) == input_spatial_dim); + assert(const_paddings_shape.dim(0) == input_spatial_dim); + assert(const_paddings_shape.dim(1) == 2); + + // Check all values of block_shape >= 1 + uint32_t ele_count = const_block_shape->size(); + for (uint32_t e = 0; e < ele_count; ++e) + { + auto val = const_block_shape->at(e); + if (val < 1) + { + INTERNAL_EXN_V("All values of block_shape >= 1: ", e); + } + } + + loco::TensorShape shape_output; + + shape_output.rank(input_shape.rank()); + + int32_t output_batch_size = input_shape.dim(0).value(); + for (int32_t dim = 0; dim < input_spatial_dim; ++dim) + { + int dim_size = input_shape.dim(dim + 1).value(); + dim_size += const_paddings->at(dim * 2); + dim_size += const_paddings->at(dim * 2 + 1); + shape_output.dim(dim + 1) = dim_size / const_block_shape->at(dim); + + assert(dim_size % const_block_shape->at(dim) == 0); + output_batch_size = output_batch_size * const_block_shape->at(dim); + } + shape_output.dim(0) = output_batch_size; + shape_output.dim(input_shape.rank() - 1) = input_shape.dim(input_shape.rank() - 1); + + return loco::NodeShape{shape_output}; + } + + loco::NodeShape visit(const luci::CircleSpaceToDepth *node) final + { + auto input_shape = loco::shape_get(node->input()).as(); + LUCI_ASSERT(input_shape.rank() == 4, "Only input rank 4 is supported"); + + // Only data format NHWC is supported + int32_t height = input_shape.dim(1).value(); + int32_t width = input_shape.dim(2).value(); + int32_t depth = input_shape.dim(3).value(); + + int block_size = node->block_size(); + + if (block_size < 2) + INTERNAL_EXN("Block size must be >= 2"); + + if ((height % block_size) || (width % block_size)) + { + INTERNAL_EXN("The input tensor's height and width must be divisible by block_size"); + } + + loco::TensorShape output_shape; + output_shape.rank(4); + + output_shape.dim(0) = input_shape.dim(0).value(); + output_shape.dim(1) = height / block_size; + output_shape.dim(2) = width / block_size; + output_shape.dim(3) = block_size * block_size * depth; + + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleSparseToDense *node) final + { + loco::TensorShape shape; { - return loco::NodeShape{output_shape_of_transpose(input_shape, canon_perm)}; + LUCI_ASSERT(node->output_shape(), "dims input should not be nullptr"); + + auto output_shape_node = dynamic_cast(node->output_shape()); + if (output_shape_node != nullptr) + { + // Only support node with S32 + LUCI_ASSERT(output_shape_node->dtype() == loco::DataType::S32, + "Only support int32 CircleConst"); + + if (output_shape_node->rank() != 1) + INTERNAL_EXN_V("Only support rank 1 CircleConst", + oops::to_uint32(output_shape_node->rank())); + + shape.rank(output_shape_node->dim(0).value()); + + for (uint32_t axis = 0; axis < shape.rank(); ++axis) + { + shape.dim(axis) = output_shape_node->at(axis); + } + } + else + { + shape = own_shape(node); + } } - else if (circle_perm) + + return loco::NodeShape{shape}; + } + + loco::NodeShape visit(const luci::CircleSplit *node) final + { + // We'll set Split output as same as input so that SplitOut can handle it's own shape + auto input_shape = loco::shape_get(node->input()).as(); + return loco::NodeShape{input_shape}; + } + + loco::NodeShape visit(const luci::CircleSplitV *node) final + { + // We'll set SplitV output as same as input so that SplitOut can handle it's own shape + auto input_shape = loco::shape_get(node->input()).as(); + return loco::NodeShape{input_shape}; + } + + loco::NodeShape visit(const luci::CircleSqrt *node) final { return use_x(node); } + + loco::NodeShape visit(const luci::CircleSquare *node) final { return use_x(node); } + + loco::NodeShape visit(const luci::CircleSquaredDifference *node) final + { + return broadcast_xy(node); + } + + loco::NodeShape visit(const luci::CircleStridedSlice *node) final + { + auto begin_node = dynamic_cast(node->begin()); + auto end_node = dynamic_cast(node->end()); + auto strides_node = dynamic_cast(node->strides()); + + if (begin_node == nullptr || end_node == nullptr || strides_node == nullptr) { - return loco::NodeShape{output_shape_of_transpose(input_shape, circle_perm)}; + return use_own(node); + } + + loco::TensorShape shape = infer_output_shape(node); + return loco::NodeShape{shape}; + } + + loco::NodeShape visit(const luci::CircleSqueeze *node) final + { + auto input_shape = loco::shape_get(node->input()).as(); + + // TODO input shape may be unknown before runtime + std::vector do_squeeze(input_shape.rank(), false); + uint32_t num_squeezed = 0; + + if (!node->squeeze_dims().empty()) + { + // SqueezeDims not empty, squeeze only dims specified + for (int32_t raw_dim : node->squeeze_dims()) + { + int32_t dim = raw_dim < 0 ? raw_dim + input_shape.rank() : raw_dim; + + if (dim < 0 || static_cast(dim) >= input_shape.rank() || + input_shape.dim(dim).value() != 1) + { + INTERNAL_EXN("invalid dimention specified to Squeeze"); + } + + if (!do_squeeze[dim]) + ++num_squeezed; + do_squeeze[dim] = true; + } } else - INTERNAL_EXN("perm of CircleTranspose should be either ConstGen or CircleConst"); + { + // SqueezeDims empty, squeeze any dims with size == 1 + for (uint32_t dim = 0; dim < input_shape.rank(); ++dim) + { + if (input_shape.dim(dim) == 1) + { + do_squeeze[dim] = true; + ++num_squeezed; + } + } + } + + loco::TensorShape output_shape; + output_shape.rank(input_shape.rank() - num_squeezed); + + for (uint32_t in_dim = 0, out_dim = 0; in_dim < input_shape.rank(); ++in_dim) + { + if (!do_squeeze[in_dim]) + { + output_shape.dim(out_dim++) = input_shape.dim(in_dim); + } + } + + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleSub *node) final { return broadcast_xy(node); } + + loco::NodeShape visit(const luci::CircleSum *node) final + { + auto output_shape = infer_reducer(node->input(), node->reduction_indices(), node->keep_dims()); + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleTanh *node) final { return use_x(node); } + + loco::NodeShape visit(const luci::CircleTile *node) final + { + const loco::DataType S32 = loco::DataType::S32; + + auto input_shape = loco::shape_get(node->input()).as(); + auto multiples = loco::must_cast(node->multiples()); + + // TODO support non-const case + // TODO support S64 type + LUCI_ASSERT(multiples->dtype() == S32, "Only support int32 multiples"); + LUCI_ASSERT(multiples->rank() == 1, "multiples should be rank 1") + + uint32_t n = multiples->dim(0).value(); + + LUCI_ASSERT(n == input_shape.rank(), "length of multiples should be the same with input rank"); + + loco::TensorShape output_shape; + + output_shape.rank(input_shape.rank()); + for (uint32_t ni = 0; ni < n; ++ni) + { + int32_t multiple = multiples->at(ni); + output_shape.dim(ni) = input_shape.dim(ni).value() * static_cast(multiple); + } + + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleTopKV2 *node) final + { + // set shape of this node as same as input + const auto input_shape = loco::shape_get(node->input()).as(); + return loco::NodeShape{input_shape}; + } + + loco::NodeShape visit(const luci::CircleTranspose *node) final + { + auto input_shape = loco::shape_get(node->a()).as(); + + auto perm_node = loco::must_cast(node->perm()); + + loco::TensorShape output_shape; + output_shape.rank(input_shape.rank()); + + assert(perm_node->dtype() == loco::DataType::S32); + assert(input_shape.rank() == perm_node->template size()); + + for (uint32_t out_axis = 0; out_axis < output_shape.rank(); out_axis++) + { + auto in_axis = perm_node->template at(out_axis); + output_shape.dim(out_axis) = input_shape.dim(in_axis); + } + + return output_shape; } loco::NodeShape visit(const luci::CircleTransposeConv *node) final { // TransposeConv's output shape is written in its 'inputSizes' argument - auto input_sizes_const = dynamic_cast(node->inputSizes()); - LUCI_ASSERT(input_sizes_const, - "Only support when CircleTransposeConv's inputSizes is CircleConst") + auto input_sizes_const = loco::must_cast(node->inputSizes()); + // TODO support non-const type LUCI_ASSERT(input_sizes_const->dtype() == loco::DataType::S32, "Only support S32 dtype") LUCI_ASSERT(input_sizes_const->rank() == 1 && input_sizes_const->dim(0).value() == 4, "Only support rank 1 with 4 entries") @@ -855,7 +1836,115 @@ public: return loco::NodeShape{shape}; } + loco::NodeShape visit(const luci::CircleUnpack *node) final + { + // CircleUnpack provides list(array) of Tensors which has one less dimension of the input + // We'll set shape of CircleUnpack to shape of actual outputs + // TODO fix this if any problem rises + auto value_shape = loco::shape_get(node->value()).as(); + + auto axis = node->axis(); + auto num = node->num(); + auto rank = static_cast(value_shape.rank()); + + if (rank == 0) + { + // Unknown shape + return use_own(node); + } + + LUCI_ASSERT(-rank <= axis && axis < rank, "Axis is out of range"); + + if (axis < 0) + axis += rank; + + LUCI_ASSERT(num == static_cast(value_shape.dim(axis).value()), + "num, axis maybe incorrect"); + + loco::TensorShape output_shape; + output_shape.rank(rank - 1); + + for (int32_t i = 0, o = 0; i < rank; ++i) + { + if (i != axis) + output_shape.dim(o++) = value_shape.dim(i); + } + + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleWhere *node) final { return use_own(node); } + + loco::NodeShape visit(const luci::CircleWhile *node) final + { + // Shape of CircleWhile is not used. Just use input 0 + assert(node->arity() > 0); + const auto input_shape = loco::shape_get(node->input(0)).as(); + return loco::NodeShape{input_shape}; + } + + loco::NodeShape visit(const luci::CircleZerosLike *node) final + { + auto input_shape = loco::shape_get(node->input()).as(); + + return loco::NodeShape{input_shape}; + } + // Circle Only + loco::NodeShape visit(const luci::CircleBCQFullyConnected *node) final + { + loco::TensorShape out_shape; + + auto input_shape = loco::shape_get(node->input()).as(); + auto weights_clusters = loco::must_cast(node->weights_clusters()); + + LUCI_ASSERT(input_shape.rank() == 2, "Input rank of BCQFullyConnected should be 2"); + + int32_t qbits_sum = 0; + for (uint32_t i = 0; i < weights_clusters->dim(0).value(); ++i) + { + qbits_sum += weights_clusters->at(i * 2 + 1); + } + + out_shape.rank(2); + out_shape.dim(0) = qbits_sum; + out_shape.dim(1) = input_shape.dim(1); + + return loco::NodeShape{out_shape}; + } + + loco::NodeShape visit(const luci::CircleBCQGather *node) final + { + loco::TensorShape input_shape; + loco::TensorShape output_shape; + + const auto input_binary_shape = loco::shape_get(node->input_binary()).as(); + const auto indices_shape = loco::shape_get(node->indices()).as(); + auto axis = node->axis(); + + auto input_clusters = loco::must_cast(node->input_clusters()); + auto qbits_sum = 0; + for (uint32_t i = 0; i < input_clusters->dim(0).value(); ++i) + { + qbits_sum += input_clusters->at(i * 2 + 1); + } + + input_shape.rank(2); + input_shape.dim(0) = qbits_sum; + input_shape.dim(1) = input_binary_shape.dim(1).value() * 32; + + output_shape.rank(input_shape.rank() - 1 + indices_shape.rank()); + int32_t outdim_index = 0; + for (int32_t i = 0; i < axis; ++i) + output_shape.dim(outdim_index++) = input_shape.dim(i); + for (uint32_t i = 0; i < indices_shape.rank(); ++i) + output_shape.dim(outdim_index++) = indices_shape.dim(i); + for (uint32_t i = axis + 1; i < input_shape.rank(); ++i) + output_shape.dim(outdim_index++) = input_shape.dim(i); + + return loco::NodeShape{output_shape}; + } + loco::NodeShape visit(const luci::CircleInstanceNorm *node) final { auto input_shape = loco::shape_get(node->input()).as(); @@ -877,9 +1966,230 @@ public: loco::NodeShape visit(const luci::CircleOutput *node) final { - auto from_shape = loco::shape_get(node->from()).as(); + auto graph_outputs = node->graph()->outputs(); + auto graph_output = graph_outputs->at(node->index()); + auto output_shape = graph_output->shape(); + + return loco::NodeShape{*output_shape}; + } + + loco::NodeShape visit(const luci::CircleOutputDummy *node) final { return use_own(node); } + + loco::NodeShape visit(const luci::CircleOutputExclude *node) final { return use_own(node); } + + loco::NodeShape visit(const luci::CircleCustomOut *node) final { return use_own(node); } + + loco::NodeShape visit(const luci::CircleIfOut *node) final + { + /** + * @note IF operator type and shape are that of the "then" and "else" + * Graph Outputs. + */ + auto circle_if = dynamic_cast(node->input()); + if (circle_if == nullptr) + { + INTERNAL_EXN("CircleIf IR is not configured correctly"); + } + + auto index = node->index(); + auto then_graph = circle_if->then_graph(); + auto else_graph = circle_if->else_graph(); + assert(then_graph != nullptr); + assert(else_graph != nullptr); + + // shape and type are assumed to be same + // these are checked at post_import_graph() in Import + auto then_outputs = loco::output_nodes(then_graph); + auto else_outputs = loco::output_nodes(else_graph); + assert(then_outputs.size() == else_outputs.size()); + assert(index < static_cast(then_outputs.size())); + + auto then_out = loco::must_cast(then_outputs.at(index)); + auto else_out = loco::must_cast(else_outputs.at(index)); + + auto then_graph_outputs = then_graph->outputs(); // loco::GraphOutput items + auto else_graph_outputs = else_graph->outputs(); + assert(then_graph_outputs->size() == else_graph_outputs->size()); + + auto then_graph_output = then_graph_outputs->at(then_out->index()); + auto else_graph_output = else_graph_outputs->at(else_out->index()); + (void)else_graph_output; // make compiler happy for unused variable warnings + assert(*then_graph_output->shape() == *else_graph_output->shape()); + + return loco::NodeShape{*then_graph_output->shape()}; + } + + loco::NodeShape visit(const luci::CircleSplitOut *node) final + { + const loco::DataType S32 = loco::DataType::S32; + + auto split = dynamic_cast(node->input()); + if (split == nullptr) + INTERNAL_EXN("CircleSplit IR is not configured correctly"); + + loco::NodeShape unknown; + + auto split_shape = loco::shape_get(split).as(); + + auto split_dim = dynamic_cast(split->split_dim()); + if (split_dim == nullptr) + return unknown; // we need CircleConst for split_dim + LUCI_ASSERT(split_dim->dtype() == S32, "Only support int32 for split_dim"); + + assert(split_dim->size() == 1); + auto split_dim_axis = split_dim->at(0); + if (split_dim_axis < 0) + split_dim_axis += split_shape.rank(); - return loco::NodeShape{from_shape}; + auto split_dim_value = split_shape.dim(split_dim_axis).value(); + assert(split_dim_value % split->num_split() == 0); + const int split_depth = split_dim_value / split->num_split(); + + loco::TensorShape output_shape = split_shape; + + // All shapes are equally same + output_shape.dim(split_dim_axis) = loco::Dimension(split_depth); + + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleSplitVOut *node) final + { + const loco::DataType S32 = loco::DataType::S32; + + auto split = dynamic_cast(node->input()); + if (split == nullptr) + INTERNAL_EXN("CircleSplit IR is not configured correctly"); + + loco::NodeShape unknown; + + auto split_shape = loco::shape_get(split).as(); + + auto size_splits = dynamic_cast(split->size_splits()); + if (size_splits == nullptr) + return unknown; // we need CircleConst for size_splits + LUCI_ASSERT(size_splits->dtype() == S32, "Only support int32 for size_splits"); + + auto split_dim = dynamic_cast(split->split_dim()); + if (split_dim == nullptr) + return unknown; // we need CircleConst for split_dim + LUCI_ASSERT(split_dim->dtype() == S32, "Only support int32 for split_dim"); + + // fetch axis + assert(split_dim->size() == 1); + auto split_dim_axis = split_dim->at(0); + if (split_dim_axis < 0) + split_dim_axis += split_shape.rank(); + + // interpret size_splits values + int32_t size_splits_count = static_cast(size_splits->size()); + assert(size_splits_count == split->num_split()); + + int64_t minus_one_count = 0, size_splits_sum = 0; + for (int32_t idx = 0; idx < size_splits_count; ++idx) + { + auto size = size_splits->at(idx); + assert(size >= -1); + if (size == -1) + ++minus_one_count; + else + size_splits_sum += size; + } + if (minus_one_count > 1) + INTERNAL_EXN("CircleSplitV size_splits has more than two -1 values"); + + // calcuate this SplitVOut shape + auto input_size = split_shape.dim(split_dim_axis).value(); + assert(size_splits_sum <= input_size); + + auto index_this = node->index(); + assert(0 <= index_this && index_this < split->num_split()); + auto split_depth = size_splits->at(index_this); + if (split_depth == -1) + split_depth = input_size - size_splits_sum; + + loco::TensorShape output_shape = split_shape; + + output_shape.dim(split_dim_axis) = loco::Dimension(split_depth); + + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleTopKV2Out *node) final + { + const loco::DataType S32 = loco::DataType::S32; + + auto topkv2 = dynamic_cast(node->input()); + if (topkv2 == nullptr) + INTERNAL_EXN("CircleSplit IR is not configured correctly"); + + // shape of topkv2 is same as topkv2->input() + auto input_shape = loco::shape_get(topkv2).as(); + + auto node_k = loco::must_cast(topkv2->k()); + LUCI_ASSERT(node_k->dtype() == S32, "Only support Int32"); + assert(node_k->size() == 1); + + loco::TensorShape output_shape; + + output_shape.rank(input_shape.rank()); + for (uint32_t idx = 0; idx < input_shape.rank() - 1; ++idx) + { + output_shape.dim(idx) = input_shape.dim(idx); + } + output_shape.dim(input_shape.rank() - 1) = node_k->at(0); + + return loco::NodeShape{output_shape}; + } + + loco::NodeShape visit(const luci::CircleUnpackOut *node) final + { + auto unpack = dynamic_cast(node->input()); + if (unpack == nullptr) + { + INTERNAL_EXN("CircleUnpack IR is not configured correctly"); + } + + auto unpack_shape = loco::shape_get(unpack).as(); + + return loco::NodeShape{unpack_shape}; + } + + loco::NodeShape visit(const luci::CircleWhileOut *node) final + { + /** + * @note WHILE operator's shape is the same with the "cond" + * Graph input. + */ + auto circle_while = dynamic_cast(node->input()); + if (circle_while == nullptr) + { + INTERNAL_EXN("CircleWhile IR is not configured correctly"); + } + + auto index = node->index(); + auto cond_graph = circle_while->cond_graph(); + assert(cond_graph != nullptr); + + // Assumption: the index of CircleWhileOut matches with the index of input nodes returned by + // loco::input_nodes + auto cond_inputs = loco::input_nodes(cond_graph); + auto cond_in = loco::must_cast(cond_inputs.at(index)); + + auto cond_graph_inputs = cond_graph->inputs(); + auto cond_graph_input = cond_graph_inputs->at(cond_in->index()); + + auto cond_graph_input_shape = *cond_graph_input->shape(); + auto this_shape = own_shape(node); + + if (!(this_shape == cond_graph_input_shape)) + { + LOGGER(l); + WARN(l) << "Warning: CircleWhileOut '" << node->name() << "' shape mispatch " << this_shape + << " vs " << cond_graph_input_shape; + } + + return loco::NodeShape{this_shape}; } }; @@ -895,11 +2205,30 @@ bool CircleShapeInferenceRule::recognize(const loco::Dialect *d) const bool CircleShapeInferenceRule::infer(const loco::Node *node, loco::NodeShape &shape) const { + LOGGER(l); + assert(node->dialect() == CircleDialect::get()); - assert(dynamic_cast(node) != nullptr); ShapeInferenceAlgorithm alg; - shape = dynamic_cast(node)->accept(&alg); + auto circle_node = loco::must_cast(node); + + bool is_shape_undefined = (circle_node->shape_status() == ShapeStatus::UNDEFINED); + bool is_shape_none = (circle_node->shape_status() == ShapeStatus::NOSHAPE); + bool is_scalar = (circle_node->rank() == 0); + + if (is_shape_undefined) + shape = circle_node->accept(&alg); + else + { + if (is_shape_none || is_scalar) + shape = own_shape(circle_node); + else + shape = circle_node->accept(&alg); + } + + VERBOSE(l, 1) << "[luci] shape: " << circle_node->name(); + VERBOSE(l, 1) << " own_shape: " << own_shape(circle_node) + << " -> infer: " << shape.as(); return true; } diff --git a/compiler/luci/service/src/CircleShapeInferenceRule.test.cpp b/compiler/luci/service/src/CircleShapeInferenceRule.test.cpp index 0374251..ac27db3 100644 --- a/compiler/luci/service/src/CircleShapeInferenceRule.test.cpp +++ b/compiler/luci/service/src/CircleShapeInferenceRule.test.cpp @@ -26,6 +26,8 @@ #include #include +#include + #include #include @@ -51,38 +53,39 @@ TEST(CircleShapeInferenceRuleTest, minimal_with_CircleRelu) { // Create a simple network luci::test::TestGraph graph; - auto tfl_node = graph.append(graph.pull); - graph.complete(tfl_node); + auto relu_node = graph.append(graph.input_node); + graph.complete(relu_node); // set shape { - graph.pull->rank(2); - graph.pull->dim(0) = 3; - graph.pull->dim(1) = 4; + graph.input_node->rank(2); + graph.input_node->dim(0) = 3; + graph.input_node->dim(1) = 4; + + graph.output_node->rank(2); + graph.output_node->dim(0) = 3; + graph.output_node->dim(1) = 4; + + luci::test::graph_input_shape(graph.input_node); + luci::test::graph_output_shape(graph.output_node); } // pre-check - ASSERT_FALSE(loco::shape_known(tfl_node)); + ASSERT_FALSE(loco::shape_known(relu_node)); // shape inference - luci::CircleShapeInferenceRule tfl_rule; - loco::CanonicalShapeInferenceRule canonical_rule; - loco::MultiDialectShapeInferenceRule rules; - - rules.bind(loco::CanonicalDialect::get(), &canonical_rule) - .bind(luci::CircleDialect::get(), &tfl_rule); - - loco::apply(&rules).to(graph.g.get()); + while (shape_pass(graph.graph()) == true) + ; // Verify { - ASSERT_TRUE(loco::shape_known(tfl_node)); - ASSERT_EQ(loco::shape_get(tfl_node).domain(), loco::Domain::Tensor); + ASSERT_TRUE(loco::shape_known(relu_node)); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(relu_node).domain()); - auto shape = loco::shape_get(tfl_node).as(); - ASSERT_EQ(shape.rank(), 2); - ASSERT_EQ(shape.dim(0), 3); - ASSERT_EQ(shape.dim(1), 4); + auto shape = loco::shape_get(relu_node).as(); + ASSERT_EQ(2, shape.rank()); + ASSERT_EQ(3, shape.dim(0)); + ASSERT_EQ(4, shape.dim(1)); } } @@ -91,85 +94,92 @@ TEST(CircleShapeInferenceRuleTest, minimal_with_CircleRelu) TEST(CircleShapeInferenceRuleTest, avgpool2d_valid) { luci::test::TestGraph graph; - auto tfl_node = graph.append(graph.pull); + auto avg_node = graph.append(graph.input_node); graph.complete(); - auto pull = graph.pull; + auto input_node = graph.input_node; { - pull->shape({1, 4, 3, 1}); + input_node->shape({1, 4, 3, 1}); + luci::test::graph_input_shape(input_node); + } + auto output_node = graph.output_node; + { + output_node->shape({1, 2, 1, 1}); + luci::test::graph_output_shape(output_node); } // setting CircleAveragePool2D { - tfl_node->filter()->h(2); - tfl_node->filter()->w(2); - tfl_node->stride()->h(2); - tfl_node->stride()->w(2); - tfl_node->fusedActivationFunction(luci::FusedActFunc::NONE); - tfl_node->padding(luci::Padding::VALID); + avg_node->filter()->h(2); + avg_node->filter()->w(2); + avg_node->stride()->h(2); + avg_node->stride()->w(2); + avg_node->fusedActivationFunction(luci::FusedActFunc::NONE); + avg_node->padding(luci::Padding::VALID); } - ASSERT_FALSE(loco::shape_known(tfl_node)); + ASSERT_FALSE(loco::shape_known(avg_node)); // shape inference - luci::CircleShapeInferenceRule tfl_rule; - loco::CanonicalShapeInferenceRule canonical_rule; - loco::MultiDialectShapeInferenceRule rules; - - rules.bind(loco::CanonicalDialect::get(), &canonical_rule) - .bind(luci::CircleDialect::get(), &tfl_rule); - - loco::apply(&rules).to(graph.g.get()); + while (shape_pass(graph.graph()) == true) + ; // Verify { - ASSERT_TRUE(loco::shape_known(tfl_node)); - ASSERT_EQ(loco::shape_get(tfl_node).domain(), loco::Domain::Tensor); - - auto shape = loco::shape_get(tfl_node).as(); - ASSERT_EQ(shape.rank(), 4); - ASSERT_EQ(shape.dim(0).value(), 1); - ASSERT_EQ(shape.dim(1).value(), 2); - ASSERT_EQ(shape.dim(2).value(), 1); - ASSERT_EQ(shape.dim(3).value(), 1); + ASSERT_TRUE(loco::shape_known(avg_node)); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(avg_node).domain()); + + auto shape = loco::shape_get(avg_node).as(); + ASSERT_EQ(4, shape.rank()); + ASSERT_EQ(1, shape.dim(0).value()); + ASSERT_EQ(2, shape.dim(1).value()); + ASSERT_EQ(1, shape.dim(2).value()); + ASSERT_EQ(1, shape.dim(3).value()); } } TEST(CircleShapeInferenceRuleTest, avgpool2d_same) { luci::test::TestGraph graph; - auto tfl_node = graph.append(graph.pull); + auto avg_node = graph.append(graph.input_node); graph.complete(); - auto pull = graph.pull; + auto input_node = graph.input_node; { - pull->shape({1, 4, 3, 1}); + input_node->shape({1, 4, 3, 1}); + luci::test::graph_input_shape(input_node); + } + auto output_node = graph.output_node; + { + output_node->shape({1, 2, 2, 1}); + luci::test::graph_output_shape(output_node); } // setting CircleAveragePool2D { - tfl_node->filter()->h(2); - tfl_node->filter()->w(2); - tfl_node->stride()->h(2); - tfl_node->stride()->w(2); - tfl_node->fusedActivationFunction(luci::FusedActFunc::NONE); - tfl_node->padding(luci::Padding::SAME); + avg_node->filter()->h(2); + avg_node->filter()->w(2); + avg_node->stride()->h(2); + avg_node->stride()->w(2); + avg_node->fusedActivationFunction(luci::FusedActFunc::NONE); + avg_node->padding(luci::Padding::SAME); } - ASSERT_FALSE(loco::shape_known(tfl_node)); + ASSERT_FALSE(loco::shape_known(avg_node)); // shape inference - shape_pass(graph.g.get()); + while (shape_pass(graph.graph()) == true) + ; // Verify { - ASSERT_TRUE(loco::shape_known(tfl_node)); - ASSERT_EQ(loco::shape_get(tfl_node).domain(), loco::Domain::Tensor); - - auto shape = loco::shape_get(tfl_node).as(); - ASSERT_EQ(shape.rank(), 4); - ASSERT_EQ(shape.dim(0).value(), 1); - ASSERT_EQ(shape.dim(1).value(), 2); - ASSERT_EQ(shape.dim(2).value(), 2); - ASSERT_EQ(shape.dim(3).value(), 1); + ASSERT_TRUE(loco::shape_known(avg_node)); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(avg_node).domain()); + + auto shape = loco::shape_get(avg_node).as(); + ASSERT_EQ(4, shape.rank()); + ASSERT_EQ(1, shape.dim(0).value()); + ASSERT_EQ(2, shape.dim(1).value()); + ASSERT_EQ(2, shape.dim(2).value()); + ASSERT_EQ(1, shape.dim(3).value()); } } @@ -186,47 +196,51 @@ TEST(CircleShapeInferenceRuleTest, TFAdd_shapeinf_different) { auto g = loco::make_graph(); - auto x_node = g->nodes()->create(); + auto x_node = g->nodes()->create(); { x_node->rank(3); x_node->dim(0) = 2; x_node->dim(1) = 1; x_node->dim(2) = 5; } - auto y_node = g->nodes()->create(); + auto y_node = g->nodes()->create(); { y_node->rank(2); y_node->dim(0) = 3; y_node->dim(1) = 5; } - auto tfl_node = g->nodes()->create(); + auto add_node = g->nodes()->create(); { - tfl_node->x(x_node); - tfl_node->y(y_node); + add_node->x(x_node); + add_node->y(y_node); } - auto push_node = g->nodes()->create(); + auto output_node = g->nodes()->create(); { - push_node->from(tfl_node); + output_node->from(add_node); } auto x_input = g->inputs()->create(); { x_input->name("x"); - loco::link(x_input, x_node); + luci::link(x_input, x_node); } auto y_input = g->inputs()->create(); { y_input->name("y"); - loco::link(y_input, y_node); + luci::link(y_input, y_node); } auto output = g->outputs()->create(); { output->name("output"); - loco::link(output, push_node); + luci::link(output, output_node); } + luci::test::graph_input_shape(x_node); + luci::test::graph_input_shape(y_node); + luci::test::graph_output_shape(output_node); + // pre-check - ASSERT_FALSE(loco::shape_known(tfl_node)); + ASSERT_FALSE(loco::shape_known(add_node)); // shape inference while (shape_pass(g.get()) == true) @@ -234,14 +248,14 @@ TEST(CircleShapeInferenceRuleTest, TFAdd_shapeinf_different) // Verify { - ASSERT_TRUE(loco::shape_known(tfl_node)); - ASSERT_EQ(loco::shape_get(tfl_node).domain(), loco::Domain::Tensor); - - auto shape = loco::shape_get(tfl_node).as(); - ASSERT_EQ(shape.rank(), 3); - ASSERT_EQ(shape.dim(0), 2); - ASSERT_EQ(shape.dim(1), 3); - ASSERT_EQ(shape.dim(2), 5); + ASSERT_TRUE(loco::shape_known(add_node)); + ASSERT_EQ(loco::Domain::Tensor, loco::shape_get(add_node).domain()); + + auto shape = loco::shape_get(add_node).as(); + ASSERT_EQ(3, shape.rank()); + ASSERT_EQ(2, shape.dim(0)); + ASSERT_EQ(3, shape.dim(1)); + ASSERT_EQ(5, shape.dim(2)); } } @@ -249,10 +263,10 @@ TEST(CircleShapeInferenceRuleTest, CircleTranspose_simple) { luci::test::ExampleGraph g; - g.pull->rank(3); - g.pull->dim(0) = 3; - g.pull->dim(1) = 8; - g.pull->dim(2) = 1; + g.input_node->rank(3); + g.input_node->dim(0) = 3; + g.input_node->dim(1) = 8; + g.input_node->dim(2) = 1; g.const_perm->dtype(loco::DataType::S32); g.const_perm->rank(1); @@ -262,6 +276,9 @@ TEST(CircleShapeInferenceRuleTest, CircleTranspose_simple) g.const_perm->at(1) = 2; g.const_perm->at(2) = 0; + luci::test::graph_input_shape(g.input_node); + luci::test::graph_output_shape(g.output_node); + // pre-check ASSERT_FALSE(loco::shape_known(g.transpose_node)); @@ -274,9 +291,336 @@ TEST(CircleShapeInferenceRuleTest, CircleTranspose_simple) ASSERT_TRUE(loco::shape_known(g.transpose_node)); auto shape = loco::shape_get(g.transpose_node).as(); - ASSERT_EQ(shape.rank(), 3); - ASSERT_EQ(shape.dim(0), 8); - ASSERT_EQ(shape.dim(1), 1); - ASSERT_EQ(shape.dim(2), 3); + ASSERT_EQ(3, shape.rank()); + ASSERT_EQ(8, shape.dim(0)); + ASSERT_EQ(1, shape.dim(1)); + ASSERT_EQ(3, shape.dim(2)); + } +} + +TEST(CircleShapeInferenceRuleTest, CircleSqueeze) +{ + luci::test::TestGraph graph; + auto squeeze_node = graph.append(graph.input_node); + graph.complete(); + + auto input_node = graph.input_node; + { + input_node->shape({1, 4, 3, 1}); + } + auto output_node = graph.output_node; + { + output_node->shape({4, 3, 1}); + } + + luci::test::graph_input_shape(input_node); + luci::test::graph_output_shape(output_node); + + squeeze_node->squeeze_dims({0}); + + // pre-check + ASSERT_FALSE(loco::shape_known(squeeze_node)); + + // shape inference + while (shape_pass(graph.graph()) == true) + ; + + // Verify + { + ASSERT_TRUE(loco::shape_known(squeeze_node)); + + auto shape = loco::shape_get(squeeze_node).as(); + ASSERT_EQ(3, shape.rank()); + ASSERT_EQ(4, shape.dim(0)); + ASSERT_EQ(3, shape.dim(1)); + ASSERT_EQ(1, shape.dim(2)); + } +} + +TEST(CircleShapeInferenceRuleTest, CircleExpandDims) +{ + luci::test::TestGraph graph; + auto axis = graph.append(); + axis->dtype(loco::DataType::S32); + axis->rank(0); + axis->size(1); + axis->at(0) = 1; + + auto expand_dims = graph.append(graph.input_node, axis); + graph.complete(); + + auto input_node = graph.input_node; + { + input_node->shape({4, 3}); + } + + auto output_node = graph.output_node; + { + output_node->from(expand_dims); + } + + luci::test::graph_input_shape(input_node); + luci::test::graph_output_shape(output_node); + + // shape inference + while (shape_pass(graph.graph())) + ; + + // validation + { + ASSERT_TRUE(loco::shape_known(expand_dims)); + + auto shape = loco::shape_get(expand_dims).as(); + + ASSERT_EQ(3, shape.rank()); + ASSERT_EQ(4, shape.dim(0)); + ASSERT_EQ(1, shape.dim(1)); + ASSERT_EQ(3, shape.dim(2)); + } +} + +TEST(CircleShapeInferenceRuleTest, CircleSqueezeAll) +{ + luci::test::TestGraph graph; + auto squeeze_node = graph.append(graph.input_node); + graph.complete(); + + auto input_node = graph.input_node; + { + input_node->shape({1, 4, 3, 1}); + } + auto output_node = graph.output_node; + { + input_node->shape({4, 3}); + } + + luci::test::graph_input_shape(input_node); + luci::test::graph_output_shape(output_node); + + squeeze_node->squeeze_dims({}); + + // pre-check + ASSERT_FALSE(loco::shape_known(squeeze_node)); + + // shape inference + while (shape_pass(graph.graph()) == true) + ; + + // Verify + { + ASSERT_TRUE(loco::shape_known(squeeze_node)); + + auto shape = loco::shape_get(squeeze_node).as(); + ASSERT_EQ(2, shape.rank()); + ASSERT_EQ(4, shape.dim(0)); + ASSERT_EQ(3, shape.dim(1)); + } +} + +TEST(CircleShapeInferenceRuleTest, CircleGatherNd_simple) +{ + luci::test::TestGraph graph; + auto indices_const = graph.append(); + auto gather_nd_node = graph.append(graph.input_node, indices_const); + graph.complete(); + + { + auto input_node = graph.input_node; + input_node->shape({1, 4, 4, 3}); + luci::test::graph_input_shape(input_node); + } + { + auto output_node = graph.output_node; + output_node->shape({1, 2, 2, 3}); + luci::test::graph_output_shape(output_node); + } + + { + indices_const->shape({1, 2, 3}); + } + + // pre-check + ASSERT_FALSE(loco::shape_known(gather_nd_node)); + + // shape inference + while (shape_pass(graph.graph()) == true) + ; + + // Verify + { + ASSERT_TRUE(loco::shape_known(gather_nd_node)); + + auto shape = loco::shape_get(gather_nd_node).as(); + ASSERT_EQ(3, shape.rank()); + ASSERT_EQ(1, shape.dim(0)); + ASSERT_EQ(2, shape.dim(1)); + ASSERT_EQ(3, shape.dim(2)); + } +} + +TEST(CircleShapeInferenceRuleTest, CircleGatherNd_slices) +{ + luci::test::TestGraph graph; + auto indices_const = graph.append(); + auto gather_nd_node = graph.append(graph.input_node, indices_const); + graph.complete(); + + { + auto input_node = graph.input_node; + input_node->shape({1, 4, 4, 3}); + luci::test::graph_input_shape(input_node); + } + { + auto output_node = graph.output_node; + output_node->shape({1, 2, 4, 4, 3}); + luci::test::graph_output_shape(output_node); + } + + { + indices_const->shape({1, 2, 1}); + } + + // pre-check + ASSERT_FALSE(loco::shape_known(gather_nd_node)); + + // shape inference + while (shape_pass(graph.graph()) == true) + ; + + // Verify + { + ASSERT_TRUE(loco::shape_known(gather_nd_node)); + + auto shape = loco::shape_get(gather_nd_node).as(); + ASSERT_EQ(5, shape.rank()); + ASSERT_EQ(1, shape.dim(0)); + ASSERT_EQ(2, shape.dim(1)); + ASSERT_EQ(4, shape.dim(2)); + ASSERT_EQ(4, shape.dim(3)); + ASSERT_EQ(3, shape.dim(4)); + } +} + +TEST(CircleShapeInferenceRuleTest, CircleGatherNd_NEG) +{ + luci::test::TestGraph graph; + auto indices_const = graph.append(); + auto gather_nd_node = graph.append(graph.input_node, indices_const); + graph.complete(); + + { + auto input_node = graph.input_node; + input_node->shape({1, 4, 4, 3}); + luci::test::graph_input_shape(input_node); + } + { + // Does not matter, because test should fail anyway + auto output_node = graph.output_node; + output_node->shape({0, 0, 0}); + luci::test::graph_output_shape(output_node); + } + + { + indices_const->shape({1, 2, 5}); + } + + // pre-check + ASSERT_FALSE(loco::shape_known(gather_nd_node)); + + // had to pack into lambda to check throw + auto lambda = [&]() { + // shape inference + while (shape_pass(graph.graph()) == true) + ; + }; + + ASSERT_THROW(lambda(), oops::InternalExn); +} + +TEST(CircleShapeInferenceRuleTest, CircleResizeNearestNeighbor) +{ + luci::test::TestGraph graph; + auto size_const = graph.append(); + size_const->dtype(loco::DataType::S32); + size_const->rank(1); + size_const->dim(0) = 2; + size_const->size(2); + size_const->at(0) = 16; + size_const->at(1) = 16; + auto resize_node = graph.append(graph.input_node, size_const); + graph.complete(); + + { + auto input_node = graph.input_node; + input_node->shape({1, 4, 4, 3}); + luci::test::graph_input_shape(input_node); + } + { + auto output_node = graph.output_node; + output_node->from(resize_node); + luci::test::graph_output_shape(output_node); + } + + // pre-check + ASSERT_FALSE(loco::shape_known(resize_node)); + + // shape inference + while (shape_pass(graph.graph()) == true) + ; + + // Verify + { + ASSERT_TRUE(loco::shape_known(resize_node)); + + auto shape = loco::shape_get(resize_node).as(); + ASSERT_EQ(4, shape.rank()); + ASSERT_EQ(1, shape.dim(0)); + ASSERT_EQ(16, shape.dim(1)); + ASSERT_EQ(16, shape.dim(2)); + ASSERT_EQ(3, shape.dim(3)); + } +} + +TEST(CircleShapeInferenceRuleTest, CircleResizeBilinear) +{ + luci::test::TestGraph graph; + auto size_const = graph.append(); + size_const->dtype(loco::DataType::S32); + size_const->rank(1); + size_const->dim(0) = 2; + size_const->size(2); + size_const->at(0) = 16; + size_const->at(1) = 16; + auto resize_node = graph.append(graph.input_node, size_const); + graph.complete(); + + { + auto input_node = graph.input_node; + input_node->shape({1, 4, 4, 3}); + luci::test::graph_input_shape(input_node); + } + { + auto output_node = graph.output_node; + output_node->from(resize_node); + luci::test::graph_output_shape(output_node); + } + + // pre-check + ASSERT_FALSE(loco::shape_known(resize_node)); + + // shape inference + while (shape_pass(graph.graph()) == true) + ; + + // Verify + { + ASSERT_TRUE(loco::shape_known(resize_node)); + + auto shape = loco::shape_get(resize_node).as(); + ASSERT_EQ(4, shape.rank()); + ASSERT_EQ(1, shape.dim(0)); + ASSERT_EQ(16, shape.dim(1)); + ASSERT_EQ(16, shape.dim(2)); + ASSERT_EQ(3, shape.dim(3)); } } diff --git a/compiler/luci/service/src/CircleTypeInference.cpp b/compiler/luci/service/src/CircleTypeInference.cpp index 6699061..aa8524a 100644 --- a/compiler/luci/service/src/CircleTypeInference.cpp +++ b/compiler/luci/service/src/CircleTypeInference.cpp @@ -15,19 +15,13 @@ */ #include "luci/Service/CircleTypeInference.h" -#include "luci/Service/CircleTypeInferenceRule.h" -#include - -#include -#include -#include +#include #include + #include #include -#include -#include #include namespace diff --git a/compiler/luci/service/src/CircleTypeInferenceRule.cpp b/compiler/luci/service/src/CircleTypeInferenceRule.cpp index 21a28c1..de2ba3e 100644 --- a/compiler/luci/service/src/CircleTypeInferenceRule.cpp +++ b/compiler/luci/service/src/CircleTypeInferenceRule.cpp @@ -33,18 +33,45 @@ struct TypeInferenceAlgorithm final : public luci::CircleNodeVisitorx()); } + loco::DataType visit(const luci::CircleAddN *node) final + { + auto dtype = loco::dtype_get(node->inputs(0)); + + for (uint32_t idx = 1; idx < node->arity(); ++idx) + { + auto dtype_idx = loco::dtype_get(node->inputs(idx)); + if (dtype != dtype_idx) + { + INTERNAL_EXN_V("ADD_N dtype not same as the first input: ", idx); + } + } + + return loco::dtype_get(node->inputs(0)); + } + loco::DataType visit(const luci::CircleArgMax *node) final { return node->output_type(); } + loco::DataType visit(const luci::CircleArgMin *node) final { return node->output_type(); } + loco::DataType visit(const luci::CircleAveragePool2D *node) final { return loco::dtype_get(node->value()); } + loco::DataType visit(const luci::CircleBatchMatMul *node) final + { + return loco::dtype_get(node->x()); + } + loco::DataType visit(const luci::CircleBatchToSpaceND *node) final { return loco::dtype_get(node->input()); } + loco::DataType visit(const luci::CircleCast *node) final { return node->dtype(); } + + loco::DataType visit(const luci::CircleCeil *node) final { return loco::dtype_get(node->x()); } + loco::DataType visit(const luci::CircleConcatenation *node) final { // TODO Support when CircleConcatenation has 0 input @@ -65,6 +92,20 @@ struct TypeInferenceAlgorithm final : public luci::CircleNodeVisitorx()); } + loco::DataType visit(const luci::CircleCustom *node) final + { + if (node->custom_code() == "BatchMatMulV2") + { + return loco::dtype_get(node->inputs(0)); + } + return node->dtype(); + } + + loco::DataType visit(const luci::CircleDepthToSpace *node) final + { + return loco::dtype_get(node->input()); + } + loco::DataType visit(const luci::CircleDepthwiseConv2D *node) final { return loco::dtype_get(node->input()); @@ -72,15 +113,94 @@ struct TypeInferenceAlgorithm final : public luci::CircleNodeVisitorx()); } + loco::DataType visit(const luci::CircleElu *node) final + { + return loco::dtype_get(node->features()); + } + loco::DataType visit(const luci::CircleEqual *) final { return loco::DataType::BOOL; } loco::DataType visit(const luci::CircleExp *node) final { return loco::dtype_get(node->x()); } + loco::DataType visit(const luci::CircleExpandDims *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleFill *node) final + { + return loco::dtype_get(node->value()); + } + + loco::DataType visit(const luci::CircleFloor *node) final { return loco::dtype_get(node->x()); } + + loco::DataType visit(const luci::CircleFloorDiv *node) final + { + return loco::dtype_get(node->x()); + } + + loco::DataType visit(const luci::CircleFloorMod *node) final + { + return loco::dtype_get(node->x()); + } + loco::DataType visit(const luci::CircleFullyConnected *node) final { return loco::dtype_get(node->input()); } + loco::DataType visit(const luci::CircleGather *node) final + { + return loco::dtype_get(node->params()); + } + + loco::DataType visit(const luci::CircleGatherNd *node) final + { + return loco::dtype_get(node->params()); + } + + loco::DataType visit(const luci::CircleGreater *) final { return loco::DataType::BOOL; } + + loco::DataType visit(const luci::CircleGreaterEqual *) final { return loco::DataType::BOOL; } + + loco::DataType visit(const luci::CircleIf *node) final + { + // Type of If is not used. Just use input 0 + assert(node->input_count() > 0); + return loco::dtype_get(node->input(0)); + } + + loco::DataType visit(const luci::CircleL2Normalize *node) final + { + return loco::dtype_get(node->x()); + } + + loco::DataType visit(const luci::CircleL2Pool2D *node) final + { + return loco::dtype_get(node->value()); + } + + loco::DataType visit(const luci::CircleLeakyRelu *node) final + { + return loco::dtype_get(node->features()); + } + + loco::DataType visit(const luci::CircleLess *) final { return loco::DataType::BOOL; } + + loco::DataType visit(const luci::CircleLessEqual *) final { return loco::DataType::BOOL; } + + loco::DataType visit(const luci::CircleLocalResponseNormalization *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleLog *node) final { return loco::dtype_get(node->x()); } + + loco::DataType visit(const luci::CircleLogicalAnd *node) final + { + return loco::dtype_get(node->x()); + } + loco::DataType visit(const luci::CircleLogicalNot *node) final { return loco::dtype_get(node->x()); @@ -91,6 +211,26 @@ struct TypeInferenceAlgorithm final : public luci::CircleNodeVisitorx()); } + loco::DataType visit(const luci::CircleLogistic *node) final + { + return loco::dtype_get(node->x()); + } + + loco::DataType visit(const luci::CircleLogSoftmax *node) final + { + return loco::dtype_get(node->logits()); + } + + loco::DataType visit(const luci::CircleMatrixDiag *node) final + { + return loco::dtype_get(node->diagonal()); + } + + loco::DataType visit(const luci::CircleMatrixSetDiag *node) final + { + return loco::dtype_get(node->input()); + } + loco::DataType visit(const luci::CircleMaximum *node) final { return loco::dtype_get(node->x()); } loco::DataType visit(const luci::CircleMaxPool2D *node) final @@ -103,6 +243,17 @@ struct TypeInferenceAlgorithm final : public luci::CircleNodeVisitorinput()); } + loco::DataType visit(const luci::CircleMinimum *node) final { return loco::dtype_get(node->x()); } + + loco::DataType visit(const luci::CircleMirrorPad *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleNeg *node) final { return loco::dtype_get(node->x()); } + + loco::DataType visit(const luci::CircleNotEqual *) final { return loco::DataType::BOOL; } + loco::DataType visit(const luci::CirclePack *node) final { // Only support CirclePack with one or more inputs @@ -117,8 +268,63 @@ struct TypeInferenceAlgorithm final : public luci::CircleNodeVisitorinput()); } + loco::DataType visit(const luci::CirclePow *node) final + { + // TODO make sure types cannot differ + auto x_type = loco::dtype_get(node->x()); + auto y_type = loco::dtype_get(node->y()); + + if (x_type != y_type) + INTERNAL_EXN("Different datatype for x and y are not supported"); + + return x_type; + } + + loco::DataType visit(const luci::CirclePRelu *node) final + { + auto input_type = loco::dtype_get(node->input()); + auto alpha_type = loco::dtype_get(node->alpha()); + + if (input_type != alpha_type) + INTERNAL_EXN("Different datatype for input and alpha are not supported"); + + return input_type; + } + + loco::DataType visit(const luci::CircleRange *node) final + { + return loco::dtype_get(node->start()); + } + + loco::DataType visit(const luci::CircleRank *) final { return loco::DataType::S32; } + loco::DataType visit(const luci::CircleMul *node) final { return loco::dtype_get(node->x()); } + loco::DataType visit(const luci::CircleOneHot *node) final + { + return loco::dtype_get(node->on_value()); + } + + loco::DataType visit(const luci::CircleReduceAny *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleReduceMax *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleReduceMin *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleReduceProd *node) final + { + return loco::dtype_get(node->input()); + } + loco::DataType visit(const luci::CircleRelu *node) final { return loco::dtype_get(node->features()); @@ -129,28 +335,132 @@ struct TypeInferenceAlgorithm final : public luci::CircleNodeVisitorfeatures()); } + loco::DataType visit(const luci::CircleReluN1To1 *node) final + { + return loco::dtype_get(node->features()); + } + loco::DataType visit(const luci::CircleReshape *node) final { return loco::dtype_get(node->tensor()); } + loco::DataType visit(const luci::CircleResizeBilinear *) final { return loco::DataType::FLOAT32; } + + loco::DataType visit(const luci::CircleResizeNearestNeighbor *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleReverseSequence *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleReverseV2 *node) final + { + return loco::dtype_get(node->tensor()); + } + + loco::DataType visit(const luci::CircleRound *node) final { return loco::dtype_get(node->x()); } + loco::DataType visit(const luci::CircleRsqrt *node) final { return loco::dtype_get(node->x()); } + loco::DataType visit(const luci::CircleScatterNd *node) final + { + return loco::dtype_get(node->updates()); + } + + loco::DataType visit(const luci::CircleSegmentSum *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleSelect *node) final + { + assert(loco::dtype_get(node->t()) == loco::dtype_get(node->e())); + return loco::dtype_get(node->t()); + } + + loco::DataType visit(const luci::CircleSelectV2 *node) final + { + assert(loco::dtype_get(node->t()) == loco::dtype_get(node->e())); + return loco::dtype_get(node->t()); + } + + loco::DataType visit(const luci::CircleShape *node) final { return node->out_type(); } + + loco::DataType visit(const luci::CircleSin *node) final { return loco::dtype_get(node->x()); } + + loco::DataType visit(const luci::CircleSlice *node) final + { + return loco::dtype_get(node->input()); + } + loco::DataType visit(const luci::CircleSoftmax *node) final { return loco::dtype_get(node->logits()); } + loco::DataType visit(const luci::CircleSpaceToBatchND *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleSpaceToDepth *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleSparseToDense *node) final + { + return loco::dtype_get(node->values()); + } + + loco::DataType visit(const luci::CircleSplit *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleSplitV *node) final + { + return loco::dtype_get(node->input()); + } + loco::DataType visit(const luci::CircleSqrt *node) final { return loco::dtype_get(node->x()); } + loco::DataType visit(const luci::CircleSquare *node) final { return loco::dtype_get(node->x()); } + loco::DataType visit(const luci::CircleSquaredDifference *node) final { return loco::dtype_get(node->x()); } + loco::DataType visit(const luci::CircleSqueeze *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleStridedSlice *node) final + { + return loco::dtype_get(node->input()); + } + loco::DataType visit(const luci::CircleSub *node) final { return loco::dtype_get(node->x()); } - // TODO CircleTanh + loco::DataType visit(const luci::CircleSum *node) final { return loco::dtype_get(node->input()); } + + loco::DataType visit(const luci::CircleTanh *node) final { return loco::dtype_get(node->x()); } + + loco::DataType visit(const luci::CircleTile *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleTopKV2 *node) final + { + return loco::dtype_get(node->input()); + } loco::DataType visit(const luci::CircleTranspose *node) final { @@ -162,7 +472,33 @@ struct TypeInferenceAlgorithm final : public luci::CircleNodeVisitoroutBackprop()); } + loco::DataType visit(const luci::CircleUnpack *node) final + { + return loco::dtype_get(node->value()); + } + + loco::DataType visit(const luci::CircleWhere *) final { return loco::DataType::S64; } + + loco::DataType visit(const luci::CircleWhile *node) final + { + // Type of While is not used. Just use input 0 + assert(node->input_count() > 0); + return loco::dtype_get(node->input(0)); + } + + loco::DataType visit(const luci::CircleZerosLike *node) final + { + return loco::dtype_get(node->input()); + } + // Circle Only + loco::DataType visit(const luci::CircleBCQFullyConnected *) final + { + return loco::DataType::FLOAT32; + } + + loco::DataType visit(const luci::CircleBCQGather *) final { return loco::DataType::FLOAT32; } + loco::DataType visit(const luci::CircleInstanceNorm *node) final { return loco::dtype_get(node->input()); @@ -173,7 +509,116 @@ struct TypeInferenceAlgorithm final : public luci::CircleNodeVisitorfrom()); + auto graph_outputs = node->graph()->outputs(); + auto graph_output = graph_outputs->at(node->index()); + auto output_dtype = graph_output->dtype(); + + if (dynamic_cast(node->from()) == nullptr && + dynamic_cast(node->from()) == nullptr) + { + // We don't care for the type if from() is CircleOutputDummy or CircleOutputExclude + // from() type should match that of CircleOutput + assert(output_dtype == loco::dtype_get(node->from())); + } + return output_dtype; + } + + loco::DataType visit(const luci::CircleOutputDummy *node) final { return node->dtype(); } + + loco::DataType visit(const luci::CircleOutputExclude *node) final { return node->dtype(); } + + loco::DataType visit(const luci::CircleCustomOut *node) final { return node->dtype(); } + + loco::DataType visit(const luci::CircleIfOut *node) final + { + /** + * @note IF operator type and shape are that of the "then" and "else" + * Graph Outputs. + */ + auto circle_if = dynamic_cast(node->input()); + if (circle_if == nullptr) + { + INTERNAL_EXN("CircleIf IR is not configured correctly"); + } + + auto index = node->index(); + auto then_graph = circle_if->then_graph(); + auto else_graph = circle_if->else_graph(); + assert(then_graph != nullptr); + assert(else_graph != nullptr); + + // shape and type are assumed to be same + // these are checked at post_import_graph() in Import + auto then_outputs = loco::output_nodes(then_graph); + auto else_outputs = loco::output_nodes(else_graph); + assert(then_outputs.size() == else_outputs.size()); + assert(index < static_cast(then_outputs.size())); + + auto then_out = loco::must_cast(then_outputs.at(index)); + auto else_out = loco::must_cast(else_outputs.at(index)); + + auto then_graph_outputs = then_graph->outputs(); // loco::GraphOutput items + auto else_graph_outputs = else_graph->outputs(); + assert(then_graph_outputs->size() == else_graph_outputs->size()); + + auto then_graph_output = then_graph_outputs->at(then_out->index()); + auto else_graph_output = else_graph_outputs->at(else_out->index()); + (void)else_graph_output; // make compiler happy for unused variable warnings + assert(then_graph_output->dtype() == else_graph_output->dtype()); + + return then_graph_output->dtype(); + } + + loco::DataType visit(const luci::CircleSplitOut *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleSplitVOut *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleTopKV2Out *node) final + { + // First output is same as input + if (node->index() == 0) + return loco::dtype_get(node->input()); + // Second outout is always S32 + assert(node->index() == 1); + return loco::DataType::S32; + } + + loco::DataType visit(const luci::CircleUnpackOut *node) final + { + return loco::dtype_get(node->input()); + } + + loco::DataType visit(const luci::CircleWhileOut *node) final + { + /** + * @note WHILE operator's type is the same with the "cond" + * Graph Input. + */ + auto circle_while = dynamic_cast(node->input()); + if (circle_while == nullptr) + { + INTERNAL_EXN("CircleWhile IR is not configured correctly"); + } + + auto index = node->index(); + auto cond_graph = circle_while->cond_graph(); + assert(cond_graph != nullptr); + + // Assumption: the index of CircleWhileOut matches with the index of input nodes returned by + // loco::input_nodes + auto cond_inputs = loco::input_nodes(cond_graph); + auto cond_in = loco::must_cast(cond_inputs.at(index)); + + auto cond_graph_inputs = cond_graph->inputs(); + auto cond_graph_input = cond_graph_inputs->at(cond_in->index()); + + return cond_graph_input->dtype(); } }; @@ -193,7 +638,8 @@ bool CircleTypeInferenceRule::infer(const loco::Node *node, loco::DataType &dtyp TypeInferenceAlgorithm alg; - dtype = dynamic_cast(node)->accept(&alg); + auto circle_node = loco::must_cast(node); + dtype = circle_node->accept(&alg); assert(dtype != loco::DataType::Unknown); return true; diff --git a/compiler/luci/service/src/CircleTypeInferenceRule.test.cpp b/compiler/luci/service/src/CircleTypeInferenceRule.test.cpp index 29f4517..711a489 100644 --- a/compiler/luci/service/src/CircleTypeInferenceRule.test.cpp +++ b/compiler/luci/service/src/CircleTypeInferenceRule.test.cpp @@ -32,26 +32,32 @@ TEST(CircleTypeInferenceRuleTest, minimal_with_CircleRelu) { // Create a simple network luci::test::TestGraph graph; - auto tfl_node = graph.append(graph.pull); - graph.complete(tfl_node); + auto relu_node = graph.append(graph.input_node); + graph.complete(relu_node); - graph.pull->dtype(loco::DataType::S32); + // set dtype for nodes; like setting them in import + graph.input_node->dtype(loco::DataType::S32); + relu_node->dtype(loco::DataType::S32); + graph.output_node->dtype(loco::DataType::S32); + + luci::test::graph_input_dtype(graph.input_node); + luci::test::graph_output_dtype(graph.output_node); // pre-check - ASSERT_FALSE(loco::dtype_known(tfl_node)); + ASSERT_FALSE(loco::dtype_known(relu_node)); // type inference - luci::CircleTypeInferenceRule tfl_rule; + luci::CircleTypeInferenceRule circle_rule; loco::CanonicalTypeInferenceRule canon_rule; loco::MultiDialectTypeInferenceRule rules; rules.bind(loco::CanonicalDialect::get(), &canon_rule); - rules.bind(luci::CircleDialect::get(), &tfl_rule); + rules.bind(luci::CircleDialect::get(), &circle_rule); loco::apply(&rules).to(graph.g.get()); // Verify - ASSERT_TRUE(loco::dtype_known(tfl_node)); - auto type = loco::dtype_get(tfl_node); - ASSERT_EQ(type, loco::DataType::S32); + ASSERT_TRUE(loco::dtype_known(relu_node)); + auto type = loco::dtype_get(relu_node); + ASSERT_EQ(loco::DataType::S32, type); } diff --git a/compiler/luci/service/src/GraphBlock.h b/compiler/luci/service/src/GraphBlock.h deleted file mode 100644 index 2a45588..0000000 --- a/compiler/luci/service/src/GraphBlock.h +++ /dev/null @@ -1,201 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __GRAPH_BLOCK_H__ -#define __GRAPH_BLOCK_H__ - -#include -#include - -#include - -#include - -// TODO Change all Canonical nodes to Circle nodes - -namespace luci -{ - -/// @brief feature layout of TFlite/Circle file -enum class FeatureLayout -{ - NHWC, -}; - -/// @brief Creates a loco::FeatureEncode with T layout (NHWC for tflite) and add it to graph. -template loco::FeatureEncode *make_feature_encode(loco::Node *input_for_encode); - -/// @brief Creates a loco::FeatureDecode with T layout (NHWC for tflite) and add it to graph. -template loco::FeatureDecode *make_feature_decode(loco::Node *input_for_decode); - -enum class FilterLayout -{ - OHWI, // a.k.a., NHWC, Tensorflow Lite uses this layout for filter - HWIO, // a.k.a., HWCN, Tensorflow uses this layout for filter -}; - -/// @brief Create a loco::FilterEncode of given layout -template loco::FilterEncode *make_filter_encode(loco::Node *input_for_encode); - -/// @brief Create a loco::FilterDecode of given layout -template loco::FilterDecode *make_filter_decode(loco::Node *input_for_decode); - -enum class DepthwiseFilterLayout -{ - HWCM, -}; - -/// @brief Create a loco::DepthwiseFilterDecode of given layout -template -loco::DepthwiseFilterDecode *make_dw_filter_decode(loco::Node *input_for_decode); - -enum class MatrixLayout -{ - HW, - WH -}; - -/// @brief Create a loco::MatrixEncode of given layout -template loco::MatrixEncode *make_matrix_encode(loco::Node *input_for_encode); - -/// @brief Create a loco::MatrixDecode of given layout -template loco::MatrixDecode *make_matrix_decode(loco::Node *input_for_decode); - -} // luci - -// -// DomainConverter -// - -/** - * Some canonical nodes can have input of various loco::Domain, e.g., loco::Domain::Tensor, - * loco::Domain::Feature, etc. However, TFL node accepts only loco::Domain::Tensor. - * So, When converting such canonical node to TFL node and input(s) of a canonical node are not - * loco::Domain::Tensor, additional nodes need to be inserted. - * - * The following two classes helps this insertion. - * - * For example, in case of loco::Relu conversion, - * - * Before: - * - * A (output: feature) -- loco::ReLU --- B (input:feature) - * - * After: - * - * A -- loco::FeatureDecode -- locoex::TFLRelu -- loco::FeatureEncode --- B - * - * loco::ReLU (dead node) - */ - -namespace luci -{ - -/** - * @brief Handles input(s) while converting a canonical node to TFL node(s). - * This class informs DomainConverter how to handle inputs of a specific canonical node. - */ -template class InputHandler -{ -public: - /** - * @brief Assign origin's inputs to replacer's inputs. - * (This is called when origin belongs in Tensor domain.) - */ - virtual void handover(CanonicalT *origin, TFLT *replacer) = 0; - - /** - * @brief Returns the list of inputs that needs to have FeatureDecode as its input. - * (This is called when origin belongs in Feature domain.) - */ - virtual std::vector getInputsToConvert(CanonicalT *origin) = 0; - - /// @brief Set the inputs of replacer to new_inputs - virtual void set(TFLT *replacer, std::vector &new_inputs) = 0; - - /// @brief Set the inputs to nullptr - virtual void nullify(CanonicalT *origin) = 0; -}; - -/** - * @brief Class to handle domain conversion while converting a canonical node to TFL node(s) - */ -template class DomainConverter -{ -public: - template - TFLT *convert(CanonicalT *origin, InputHandler &input_handler); -}; - -/** - * @brief Performs domain conversion - * - * 1. if origin belong to loco::Domain::Tensor, and replace origin to a TFL node. - * 2. if origin belong to loco::Domain::Feature, insert loco::FeatureDecode for input(s) and - * insert loco::FeatureEncode for output. Then replace origin to a TFL node. - * - * @return new TFL node; nullptr if shape of origin cannot be known - */ -template -template -TFLT *DomainConverter::convert(CanonicalT *origin, - InputHandler &input_handler) -{ - static_assert(FeatureLayoutT == FeatureLayout::NHWC, "Feature layout should be NHWC"); - - if (!loco::shape_known(origin)) - { - return nullptr; - } - - auto tfl_node = origin->graph()->nodes()->template create(); - - // when the input is Tensor, just replace canonical node to TFL node. - if (loco::shape_get(origin).domain() == loco::Domain::Tensor) - { - input_handler.handover(origin, tfl_node); - - loco::replace(origin).with(tfl_node); - input_handler.nullify(origin); - - return tfl_node; - } - else if (loco::shape_get(origin).domain() == loco::Domain::Feature) - { - std::vector feature_decodes; - - for (auto input : input_handler.getInputsToConvert(origin)) - { - auto dec = make_feature_decode(input); - feature_decodes.emplace_back(dec); - } - - input_handler.set(tfl_node, feature_decodes); - - auto enc = make_feature_encode(tfl_node); - - loco::replace(origin).with(enc); - input_handler.nullify(origin); - - return tfl_node; - } - else - INTERNAL_EXN_V("Unsupported loco::Domain", oops::to_uint32(loco::shape_get(origin).domain())); -} - -} // namespace luci - -#endif //__GRAPH_BLOCK_H__ diff --git a/compiler/luci/service/src/GraphBlock.test.cpp b/compiler/luci/service/src/GraphBlock.test.cpp deleted file mode 100644 index 1da8c18..0000000 --- a/compiler/luci/service/src/GraphBlock.test.cpp +++ /dev/null @@ -1,246 +0,0 @@ -/* - * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "GraphBlock.h" - -#include "Check.h" - -#include - -#include - -// TODO Change all Canonical nodes to Circle nodes - -namespace -{ - -template loco::Permutation perm(); - -template <> loco::Permutation perm() -{ - // Make NHWC permutation for encoder and decoder - loco::Permutation NHWC; - - NHWC.axis(loco::FeatureAxis::Count) = 0; - NHWC.axis(loco::FeatureAxis::Height) = 1; - NHWC.axis(loco::FeatureAxis::Width) = 2; - NHWC.axis(loco::FeatureAxis::Depth) = 3; - - return NHWC; -} - -template loco::Permutation perm(); - -template <> loco::Permutation perm() -{ - loco::Permutation HWIO; // a.k.a., HWCN - - HWIO.axis(loco::FilterAxis::Height) = 0; - HWIO.axis(loco::FilterAxis::Width) = 1; - HWIO.axis(loco::FilterAxis::Depth) = 2; - HWIO.axis(loco::FilterAxis::Count) = 3; - - return HWIO; -} - -template <> loco::Permutation perm() -{ - - // Make NHWC permutation for encoder and decoder - loco::Permutation OHWI; // a.k.a., NHWC - - OHWI.axis(loco::FilterAxis::Count) = 0; - OHWI.axis(loco::FilterAxis::Height) = 1; - OHWI.axis(loco::FilterAxis::Width) = 2; - OHWI.axis(loco::FilterAxis::Depth) = 3; - - return OHWI; -} - -template loco::Permutation perm(); - -template <> -loco::Permutation perm() -{ - loco::Permutation HWCM; - - HWCM.axis(loco::DepthwiseFilterAxis::Height) = 0; - HWCM.axis(loco::DepthwiseFilterAxis::Width) = 1; - HWCM.axis(loco::DepthwiseFilterAxis::Depth) = 2; - HWCM.axis(loco::DepthwiseFilterAxis::Multiplier) = 3; - - return HWCM; -} - -template loco::Permutation perm(); - -template <> loco::Permutation perm() -{ - loco::Permutation HW; - - HW.axis(loco::MatrixAxis::Height) = 0; - HW.axis(loco::MatrixAxis::Width) = 1; - - return HW; -} - -template <> loco::Permutation perm() -{ - loco::Permutation WH; - - WH.axis(loco::MatrixAxis::Height) = 1; - WH.axis(loco::MatrixAxis::Width) = 0; - - return WH; -} - -} // namespace - -namespace luci -{ - -template loco::FeatureEncode *make_feature_encode(loco::Node *input_for_encode) -{ - LUCI_ASSERT(input_for_encode != nullptr, "input should not be nullptr"); - loco::Graph *g = input_for_encode->graph(); - - auto encoder = std::make_unique>(); - - encoder->perm(perm()); - - auto enc = g->nodes()->create(); - enc->input(input_for_encode); - enc->encoder(std::move(encoder)); - - return enc; -} - -template loco::FeatureDecode *make_feature_decode(loco::Node *input_for_decode) -{ - LUCI_ASSERT(input_for_decode != nullptr, "input should not be nullptr"); - loco::Graph *g = input_for_decode->graph(); - - auto decoder = std::make_unique>(); - - decoder->perm(perm()); - - auto dec = g->nodes()->create(); - dec->input(input_for_decode); - dec->decoder(std::move(decoder)); - - return dec; -} - -template loco::FilterEncode *make_filter_encode(loco::Node *input_for_encode) -{ - LUCI_ASSERT(input_for_encode != nullptr, "filter should not be nullptr"); - loco::Graph *g = input_for_encode->graph(); - - auto encoder = std::make_unique>(); - - encoder->perm(perm()); - - auto enc = g->nodes()->create(); - enc->input(input_for_encode); - enc->encoder(std::move(encoder)); - - return enc; -} - -template loco::FilterDecode *make_filter_decode(loco::Node *input_for_decode) -{ - LUCI_ASSERT(input_for_decode != nullptr, "filter should not be nullptr"); - loco::Graph *g = input_for_decode->graph(); - - auto decoder = std::make_unique>(); - - decoder->perm(perm()); - - auto dec = g->nodes()->create(); - dec->input(input_for_decode); - dec->decoder(std::move(decoder)); - - return dec; -} - -template -loco::DepthwiseFilterDecode *make_dw_filter_decode(loco::Node *input_for_decode) -{ - LUCI_ASSERT(input_for_decode != nullptr, "filter should not be nullptr"); - loco::Graph *g = input_for_decode->graph(); - - auto decoder = std::make_unique>(); - - decoder->perm(perm()); - - auto dec = g->nodes()->create(); - dec->input(input_for_decode); - dec->decoder(std::move(decoder)); - - return dec; -} - -template loco::MatrixEncode *make_matrix_encode(loco::Node *input_for_encode) -{ - LUCI_ASSERT(input_for_encode != nullptr, "input should not be nullptr"); - loco::Graph *g = input_for_encode->graph(); - - auto encoder = std::make_unique>(); - - encoder->perm(perm()); - - auto enc = g->nodes()->create(); - enc->input(input_for_encode); - enc->encoder(std::move(encoder)); - - return enc; -} - -template loco::MatrixDecode *make_matrix_decode(loco::Node *input_for_decode) -{ - LUCI_ASSERT(input_for_decode != nullptr, "input should not be nullptr"); - loco::Graph *g = input_for_decode->graph(); - - auto decoder = std::make_unique>(); - - decoder->perm(perm()); - - auto dec = g->nodes()->create(); - dec->input(input_for_decode); - dec->decoder(std::move(decoder)); - - return dec; -} - -// template instantiation -template loco::FeatureEncode * -make_feature_encode(loco::Node *input_for_encode); - -template loco::FeatureDecode * -make_feature_decode(loco::Node *input_for_encode); - -template loco::FilterEncode *make_filter_encode(loco::Node *input_for_encode); -template loco::FilterDecode *make_filter_decode(loco::Node *input_for_decode); - -template loco::DepthwiseFilterDecode * -make_dw_filter_decode(loco::Node *input_for_decode); - -template loco::MatrixEncode *make_matrix_encode(loco::Node *input_for_encode); -template loco::MatrixEncode *make_matrix_encode(loco::Node *input_for_encode); -template loco::MatrixDecode *make_matrix_decode(loco::Node *input_for_decode); -template loco::MatrixDecode *make_matrix_decode(loco::Node *input_for_decode); - -} // namespace luci diff --git a/compiler/luci/service/src/ShapeInfer_StridedSlice.cpp b/compiler/luci/service/src/ShapeInfer_StridedSlice.cpp new file mode 100644 index 0000000..3412011 --- /dev/null +++ b/compiler/luci/service/src/ShapeInfer_StridedSlice.cpp @@ -0,0 +1,298 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2018 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ShapeInfer_StridedSlice.h" +#include "Check.h" + +#include +#include +#include +#include +#include + +#include +#include +#include + +namespace +{ + +// This Op only supports 1-4D cases and since we use the reference 4D +// implementation, the 1-3D tensors are mapped to 4D. +const int kMaxDim = 4; + +const loco::DataType S32 = loco::DataType::S32; + +using int8 = int8_t; +using int16 = int16_t; + +struct StridedSliceParams +{ + int8 start_indices_count; + int16 start_indices[kMaxDim]; + int8 stop_indices_count; + int16 stop_indices[kMaxDim]; + int8 strides_count; + int16 strides[kMaxDim]; + + int16 begin_mask; + int16 ellipsis_mask; + int16 end_mask; + int16 new_axis_mask; + int16 shrink_axis_mask; +}; + +// Use until std::clamp() is available from C++17. +inline int Clamp(const int32_t v, const int32_t lo, const int32_t hi) +{ + LUCI_ASSERT(!(hi < lo), "Clamp hi < lo"); + if (hi < v) + return hi; + if (v < lo) + return lo; + return v; +} + +// Return the index for the first element along that axis. This index will be a +// positive integer between [0, axis_size - 1] that can be used to index +// directly into the data. +inline int StartForAxis(const StridedSliceParams ¶ms, const loco::TensorShape &input_shape, + uint32_t axis) +{ + const auto begin_mask = params.begin_mask; + const auto *start_indices = params.start_indices; + const auto *strides = params.strides; + const int32_t axis_size = static_cast(input_shape.dim(axis).value()); + if (axis_size == 0) + { + return 0; + } + // Begin with the specified index. + int32_t start = start_indices[axis]; + + // begin_mask override + if (begin_mask & (1 << axis)) + { + if (strides[axis] > 0) + { + // Forward iteration - use the first element. These values will get + // clamped below (Note: We could have set them to 0 and axis_size-1, but + // use lowest() and max() to maintain symmetry with StopForAxis()) + start = std::numeric_limits::lowest(); + } + else + { + // Backward iteration - use the last element. + start = std::numeric_limits::max(); + } + } + + // Handle negative indices + if (start < 0) + { + start += axis_size; + } + + // Clamping + start = Clamp(start, 0, axis_size - 1); + + return start; +} + +// Return the "real" index for the end of iteration along that axis. This is an +// "end" in the traditional C sense, in that it points to one past the last +// element. ie. So if you were iterating through all elements of a 1D array of +// size 4, this function would return 4 as the stop, because it is one past the +// "real" indices of 0, 1, 2 & 3. +inline int StopForAxis(const StridedSliceParams ¶ms, const loco::TensorShape &input_shape, + int axis, int start_for_axis) +{ + const auto end_mask = params.end_mask; + const auto shrink_axis_mask = params.shrink_axis_mask; + const auto *stop_indices = params.stop_indices; + const auto *strides = params.strides; + const int axis_size = static_cast(input_shape.dim(axis).value()); + if (axis_size == 0) + { + return 0; + } + + // Begin with the specified index + const bool shrink_axis = shrink_axis_mask & (1 << axis); + int32_t stop = stop_indices[axis]; + + // When shrinking an axis, the end position does not matter (and can be + // incorrect when negative indexing is used, see Issue #19260). Always use + // start_for_axis + 1 to generate a length 1 slice, since start_for_axis has + // already been adjusted for negative indices. + if (shrink_axis) + { + stop = start_for_axis + 1; + } + + // end_mask override + if (end_mask & (1 << axis)) + { + if (strides[axis] > 0) + { + // Forward iteration - use the last element. These values will get + // clamped below + stop = std::numeric_limits::max(); + } + else + { + // Backward iteration - use the first element. + stop = std::numeric_limits::lowest(); + } + } + + // Handle negative indices + if (stop < 0) + { + stop += axis_size; + } + + // Clamping + // Because the end index points one past the last element, we need slightly + // different clamping ranges depending on the direction. + if (strides[axis] > 0) + { + // Forward iteration + stop = Clamp(stop, 0, axis_size); + } + else + { + // Backward iteration + stop = Clamp(stop, -1, axis_size - 1); + } + + return stop; +} + +StridedSliceParams BuildStridedSliceParams(const luci::CircleStridedSlice *node) +{ + StridedSliceParams op_params; + + if (kMaxDim < node->rank()) + { + INTERNAL_EXN_V("Cannot support StridedSlice rank > ", kMaxDim); + } + + auto begin_node = loco::must_cast(node->begin()); + auto end_node = loco::must_cast(node->end()); + auto strides_node = loco::must_cast(node->strides()); + + uint32_t dims_count = begin_node->size(); + + op_params.start_indices_count = dims_count; + op_params.stop_indices_count = dims_count; + op_params.strides_count = dims_count; + + for (uint32_t i = 0; i < dims_count; ++i) + { + op_params.start_indices[i] = begin_node->at(i); + op_params.stop_indices[i] = end_node->at(i); + op_params.strides[i] = strides_node->at(i); + } + + op_params.begin_mask = node->begin_mask(); + op_params.ellipsis_mask = 0; + op_params.end_mask = node->end_mask(); + op_params.new_axis_mask = 0; + op_params.shrink_axis_mask = node->shrink_axis_mask(); + + return op_params; +} + +} // namespace + +namespace luci +{ + +loco::TensorShape infer_output_shape(const CircleStridedSlice *node) +{ + loco::TensorShape output_shape; + + auto input_node = loco::must_cast(node->input()); + + auto begin_node = dynamic_cast(node->begin()); + auto end_node = dynamic_cast(node->end()); + auto strides_node = dynamic_cast(node->strides()); + if (begin_node == nullptr || end_node == nullptr || strides_node == nullptr) + { + INTERNAL_EXN("StridedSlice begin/end/strides nodes are not Constant"); + } + + LUCI_ASSERT(begin_node->dtype() == S32, "Only support S32 for begin_node"); + LUCI_ASSERT(end_node->dtype() == S32, "Only support S32 for end_node"); + LUCI_ASSERT(strides_node->dtype() == S32, "Only support S32 for strides_node"); + + assert(node->ellipsis_mask() == 0); + assert(node->new_axis_mask() == 0); + + auto op_params = BuildStridedSliceParams(node); + loco::TensorShape input_shape = loco::shape_get(input_node).as(); + + uint32_t num_input_axes = input_shape.rank(); + assert(begin_node->size() <= num_input_axes); + assert(end_node->size() <= num_input_axes); + assert(strides_node->size() <= num_input_axes); + for (uint32_t i = 0; i < strides_node->size(); i++) + { + LUCI_ASSERT(strides_node->at(i) != 0, "Stride value has to be non-zero"); + } + + uint32_t shape_size = 0; + std::array output_shape_data; + + for (uint32_t idx = 0; idx < num_input_axes; ++idx) + { + int32_t begin = StartForAxis(op_params, input_shape, idx); + int32_t end = StopForAxis(op_params, input_shape, idx, begin); + if (end < 0) + end = input_shape.dim(idx).value() + end + 1; + + // This is valid for both positive and negative strides + int32_t stride = strides_node->at(idx); + int32_t dim_shape = std::ceil(static_cast(end - begin) / stride); + assert(dim_shape > 0); + + // When shrinking an axis, the end position does not matter (and can be + // incorrect when negative indexing is used, see Issue #19260). Always use + // begin + 1 to generate a length 1 slice, since begin has + // already been adjusted for negative indices by StartForAxis. + const bool shrink_axis = node->shrink_axis_mask() & (1 << idx); + if (shrink_axis) + { + assert(dim_shape == 1); + } + else + { + output_shape_data[shape_size++] = dim_shape; + } + } + + output_shape.rank(shape_size); + for (uint32_t idx = 0; idx < shape_size; ++idx) + { + output_shape.dim(idx) = output_shape_data[idx]; + } + + return output_shape; +} + +} // namespace luci diff --git a/compiler/luci/service/src/ShapeInfer_StridedSlice.h b/compiler/luci/service/src/ShapeInfer_StridedSlice.h new file mode 100644 index 0000000..fa800b7 --- /dev/null +++ b/compiler/luci/service/src/ShapeInfer_StridedSlice.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SHAPE_INFER_STRIDED_SLICE_H__ +#define __SHAPE_INFER_STRIDED_SLICE_H__ + +#include + +#include + +namespace luci +{ + +loco::TensorShape infer_output_shape(const CircleStridedSlice *node); + +} // namespace luci + +#endif // __SHAPE_INFER_STRIDED_SLICE_H__ diff --git a/compiler/luci/service/src/TestGraph.h b/compiler/luci/service/src/TestGraph.h index 7356204..2865b0f 100644 --- a/compiler/luci/service/src/TestGraph.h +++ b/compiler/luci/service/src/TestGraph.h @@ -18,7 +18,6 @@ #define __TEST_GRAPH_H__ #include -#include "GraphBlock.h" #include @@ -36,29 +35,29 @@ class TestGraph { public: std::unique_ptr g; - loco::Pull *pull; - loco::Push *push; + luci::CircleInput *input_node = nullptr; + luci::CircleOutput *output_node = nullptr; TestGraph() // creates Pull and Push { g = loco::make_graph(); - pull = g->nodes()->create(); + input_node = g->nodes()->create(); - push = g->nodes()->create(); + output_node = g->nodes()->create(); auto input = g->inputs()->create(); { input->name("input"); - loco::link(input, pull); + luci::link(input, input_node); } auto output = g->outputs()->create(); { output->name("output"); - loco::link(output, push); + luci::link(output, output_node); } - _next_input = pull; + _next_input = input_node; } loco::Graph *graph() { return g.get(); } @@ -73,7 +72,7 @@ public: } /// @brief Creates op T (arity=1) with arg1 as an input and appends it to graph - template T *append(loco::Node *arg1) + template T *append(luci::CircleNode *arg1) { auto node = g->nodes()->create(); setInput(node, arg1); @@ -83,7 +82,7 @@ public: } /// @brief Creates op T (arity=2) with arg1, arg2 as inputs and appends it to graph - template T *append(loco::Node *arg1, loco::Node *arg2) + template T *append(luci::CircleNode *arg1, luci::CircleNode *arg2) { auto node = g->nodes()->create(); setInput(node, arg1, arg2); @@ -93,7 +92,8 @@ public: } /// @brief Creates op T (arity=3) with arg1, arg2, arg3 as inputs and appends it to graph - template T *append(loco::Node *arg1, loco::Node *arg2, loco::Node *arg3) + template + T *append(luci::CircleNode *arg1, luci::CircleNode *arg2, luci::CircleNode *arg3) { auto node = g->nodes()->create(); setInput(node, arg1, arg2, arg3); @@ -102,101 +102,68 @@ public: return node; } - // push will get the last appended node - void complete() { push->from(_next_input); } + // output will get the last appended node + void complete() { output_node->from(_next_input); } - void complete(loco::Node *last_node) { push->from(last_node); } + void complete(luci::CircleNode *last_node) { output_node->from(last_node); } private: // arity 1 - void setInput(loco::Node *node, loco::Node *) { assert(false && "NYI"); }; - - void setInput(loco::AvgPool2D *node, loco::Node *input) { node->ifm(input); } - void setInput(loco::BiasDecode *node, loco::Node *input) { node->input(input); }; - void setInput(loco::BiasEncode *node, loco::Node *input) { node->input(input); }; - void setInput(loco::FeatureDecode *node, loco::Node *input) { node->input(input); }; - void setInput(loco::FeatureEncode *node, loco::Node *input) { node->input(input); }; - void setInput(loco::MaxPool2D *node, loco::Node *input) { node->ifm(input); } - void setInput(loco::Push *node, loco::Node *input) { node->from(input); }; - void setInput(loco::ReLU *node, loco::Node *input) { node->input(input); }; - void setInput(loco::ReLU6 *node, loco::Node *input) { node->input(input); }; - void setInput(loco::Tanh *node, loco::Node *input) { node->input(input); }; - void setInput(loco::TensorTranspose *node, loco::Node *input) { node->input(input); }; - - void setInput(luci::CircleAveragePool2D *node, loco::Node *input) { node->value(input); }; - void setInput(luci::CircleMaxPool2D *node, loco::Node *input) { node->value(input); }; - void setInput(luci::CircleRelu *node, loco::Node *input) { node->features(input); }; - void setInput(luci::CircleRelu6 *node, loco::Node *input) { node->features(input); }; + void setInput(luci::CircleNode *, luci::CircleNode *) { assert(false && "NYI"); }; - // arity 2 - void setInput(loco::Node *node, loco::Node *, loco::Node *) { assert(false && "NYI"); }; + void setInput(luci::CircleAveragePool2D *node, luci::CircleNode *input) { node->value(input); }; + void setInput(luci::CircleRelu *node, luci::CircleNode *input) { node->features(input); }; + void setInput(luci::CircleSqueeze *node, luci::CircleNode *input) { node->input(input); }; - void setInput(loco::Conv2D *node, loco::Node *input, loco::Node *filter) + void setInput(luci::CircleGatherNd *node, luci::CircleNode *params, luci::CircleNode *indices) { - node->ifm(input); - node->ker(filter); - } - - void setInput(loco::EltwiseAdd *node, loco::Node *arg1, loco::Node *arg2) - { - node->lhs(arg1); - node->rhs(arg2); + node->params(params); + node->indices(indices); }; - void setInput(loco::FeatureBiasAdd *node, loco::Node *arg1, loco::Node *arg2) + // arity 2 + void setInput(luci::CircleNode *, luci::CircleNode *, luci::CircleNode *) { - node->value(arg1); - node->bias(arg2); + assert(false && "NYI"); }; - void setInput(luci::CircleAdd *node, loco::Node *arg1, loco::Node *arg2) + void setInput(luci::CircleExpandDims *node, luci::CircleNode *arg1, luci::CircleNode *arg2) { - node->x(arg1); - node->y(arg2); + node->input(arg1); + node->axis(arg2); }; - void setInput(luci::CircleMul *node, loco::Node *arg1, loco::Node *arg2) + void setInput(luci::CircleTranspose *node, luci::CircleNode *arg1, luci::CircleNode *arg2) { - node->x(arg1); - node->y(arg2); + node->a(arg1); + node->perm(arg2); }; - void setInput(luci::CircleSub *node, loco::Node *arg1, loco::Node *arg2) + void setInput(luci::CircleResizeBilinear *node, luci::CircleNode *input, luci::CircleNode *size) { - node->x(arg1); - node->y(arg2); + node->input(input); + node->size(size); }; - void setInput(luci::CircleTranspose *node, loco::Node *arg1, loco::Node *arg2) + void setInput(luci::CircleResizeNearestNeighbor *node, luci::CircleNode *input, + luci::CircleNode *size) { - node->a(arg1); - node->perm(arg2); + node->input(input); + node->size(size); }; // arity 3 - void setInput(loco::Node *node, loco::Node *, loco::Node *, loco::Node *) + void setInput(luci::CircleNode *, luci::CircleNode *, luci::CircleNode *, luci::CircleNode *) { assert(false && "NYI"); }; - void setInput(luci::CircleConv2D *node, loco::Node *input, loco::Node *filter, loco::Node *bias) - { - node->input(input); - node->filter(filter); - node->bias(bias); - } - private: loco::Node *_next_input; }; enum class ExampleGraphType { - FeatureBiasAdd, - ConstGen_ReLU, - FilterEncode_FilterDecode, - Transpose, - CircleTranspose, }; @@ -205,109 +172,42 @@ template class ExampleGraph; /** * @brief Class to create the following: * - * Pull - FeatureEncoder - FeatureBiasAdd - FeatureDecode - Push - * | - * ConstGen - BiasEncode --+ + * CircleInput -- CircleTranspose -- CircleOutput */ -template <> class ExampleGraph : public TestGraph +template <> class ExampleGraph : public TestGraph { public: - loco::FeatureEncode *fea_enc = nullptr; - loco::ConstGen *constgen = nullptr; - loco::BiasEncode *bias_enc = nullptr; - loco::FeatureBiasAdd *fea_bias_add = nullptr; - loco::FeatureDecode *fea_dec = nullptr; + luci::CircleConst *const_perm = nullptr; + luci::CircleTranspose *transpose_node = nullptr; public: ExampleGraph() { - fea_enc = luci::make_feature_encode(pull); - constgen = append(); - bias_enc = append(constgen); - fea_bias_add = append(fea_enc, bias_enc); - fea_dec = luci::make_feature_decode(fea_bias_add); - complete(fea_dec); + const_perm = append(); + transpose_node = append(input_node, const_perm); + complete(transpose_node); } }; -/** - * @brief Class to creates the following: - * - * ConstGen -- ReLU -- Push - */ -template <> class ExampleGraph : public TestGraph -{ -public: - loco::ConstGen *constgen = nullptr; - loco::ReLU *relu = nullptr; - -public: - ExampleGraph() - { - constgen = append(); - relu = append(constgen); - complete(relu); - } -}; +} // namespace test +} // namespace luci -/** - * @brief Class to creates the following: - * - * Pull -- Transpose -- Push - */ -template <> class ExampleGraph : public TestGraph +namespace luci { -public: - loco::TensorTranspose *transpose = nullptr; - -public: - ExampleGraph() - { - transpose = append(pull); - complete(transpose); - } -}; - -/** - * @brief Class to creates the following: - * - * Pull -- FilterEncode -- FilterDecode -- Push - */ -template <> class ExampleGraph : public TestGraph +namespace test { -public: - loco::FilterEncode *filterEncode = nullptr; - loco::FilterDecode *filterDecode = nullptr; -public: - ExampleGraph() - { - filterEncode = luci::make_filter_encode(pull); // from Tensorflow - filterDecode = - luci::make_filter_decode(filterEncode); // to Tensorflow Lite - complete(filterDecode); - } -}; +/// @brief This will set GraphInput shape from CircleInput shape +void graph_input_shape(luci::CircleInput *input); -/** - * @brief Class to create the following: - * - * Pull -- CircleTranspose -- Push - */ -template <> class ExampleGraph : public TestGraph -{ -public: - loco::ConstGen *const_perm = nullptr; - luci::CircleTranspose *transpose_node = nullptr; +/// @brief This will set GraphOutput shape from CircleOutput shape +void graph_output_shape(luci::CircleOutput *output); -public: - ExampleGraph() - { - const_perm = append(); - transpose_node = append(pull, const_perm); - complete(transpose_node); - } -}; +/// @brief This will set GraphInput dtype from CircleInput dtype +void graph_input_dtype(luci::CircleInput *input); + +/// @brief This will set GraphOutput dtype from CircleOutput dtype +void graph_output_dtype(luci::CircleOutput *output); } // namespace test } // namespace luci diff --git a/compiler/luci/service/src/TestGraph.test.cpp b/compiler/luci/service/src/TestGraph.test.cpp new file mode 100644 index 0000000..9ede703 --- /dev/null +++ b/compiler/luci/service/src/TestGraph.test.cpp @@ -0,0 +1,101 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "TestGraph.h" + +namespace luci +{ +namespace test +{ + +void graph_input_shape(luci::CircleInput *input) +{ + auto index = input->index(); + auto inputs = input->graph()->inputs(); + + for (uint32_t idx = 0; idx < inputs->size(); ++idx) + { + auto gi = inputs->at(idx); + if (gi->index() == index) + { + auto input_shape = std::make_unique(); + + input_shape->rank(input->rank()); + for (uint32_t r = 0; r < input->rank(); ++r) + input_shape->dim(r) = loco::Dimension(input->dim(r)); + + gi->shape(std::move(input_shape)); + break; + } + } +} + +void graph_output_shape(luci::CircleOutput *output) +{ + auto index = output->index(); + auto outputs = output->graph()->outputs(); + + for (uint32_t idx = 0; idx < outputs->size(); ++idx) + { + auto go = outputs->at(idx); + if (go->index() == index) + { + auto output_shape = std::make_unique(); + + output_shape->rank(output->rank()); + for (uint32_t r = 0; r < output->rank(); ++r) + output_shape->dim(r) = loco::Dimension(output->dim(r)); + + go->shape(std::move(output_shape)); + break; + } + } +} + +void graph_input_dtype(luci::CircleInput *input) +{ + auto index = input->index(); + auto inputs = input->graph()->inputs(); + + for (uint32_t idx = 0; idx < inputs->size(); ++idx) + { + auto gi = inputs->at(idx); + if (gi->index() == index) + { + gi->dtype(input->dtype()); + break; + } + } +} + +void graph_output_dtype(luci::CircleOutput *output) +{ + auto index = output->index(); + auto outputs = output->graph()->outputs(); + + for (uint32_t idx = 0; idx < outputs->size(); ++idx) + { + auto go = outputs->at(idx); + if (go->index() == index) + { + go->dtype(output->dtype()); + break; + } + } +} + +} // namespace test +} // namespace luci diff --git a/compiler/luci/service/src/Validate.cpp b/compiler/luci/service/src/Validate.cpp index 65b82c2..282a068 100644 --- a/compiler/luci/service/src/Validate.cpp +++ b/compiler/luci/service/src/Validate.cpp @@ -29,6 +29,19 @@ namespace { +std::ostream &operator<<(std::ostream &os, const loco::TensorShape &tensor_shape) +{ + os << "["; + for (uint32_t r = 0; r < tensor_shape.rank(); ++r) + { + if (r) + os << ","; + os << tensor_shape.dim(r).value(); + } + os << "]"; + return os; +} + /** * @brief returns a node that is CircleOutput with index is out_index in nodes */ @@ -46,7 +59,7 @@ luci::CircleOutput *find_node(std::vector nodes, loco::GraphOutput return nullptr; } -bool validate_shape_type(loco::Graph *g) +bool validate_shape_dtype(loco::Graph *g) { LOGGER(l); @@ -61,18 +74,18 @@ bool validate_shape_type(loco::Graph *g) auto circle_output = find_node(output_nodes, out_index); assert(circle_output != nullptr); assert(circle_output->from() != nullptr); - auto circle_node = dynamic_cast(circle_output->from()); - assert(circle_node != nullptr); + auto circle_node = loco::must_cast(circle_output->from()); assert(loco::shape_known(circle_node)); // check if output node shape is same as graph output shape - auto co_shape = loco::shape_get(circle_node); + auto co_tensor_shape = loco::shape_get(circle_node).as(); auto go_tensor_shape = graph_out->shape(); assert(go_tensor_shape); - auto go_shape = loco::NodeShape(*go_tensor_shape); - if (!(co_shape == go_shape)) + if (!(co_tensor_shape == *go_tensor_shape)) { - INFO(l) << "Shape for #" << out_index << " not same " << std::endl; + INFO(l) << "[luci] Shape for output #" << out_index << " not same " << std::endl; + INFO(l) << "[luci] " << circle_node->name() << " " << co_tensor_shape << " vs " + << *go_tensor_shape << std::endl; return false; } @@ -80,7 +93,7 @@ bool validate_shape_type(loco::Graph *g) assert(loco::dtype_known(circle_node)); if (graph_out->dtype() != loco::dtype_get(circle_node)) { - INFO(l) << "Type for #" << out_index << " not same " << std::endl; + INFO(l) << "[luci] Type for output #" << out_index << " not same " << std::endl; return false; } } @@ -98,7 +111,7 @@ bool validate(loco::Graph *g) if (!loco::valid(g)) return false; - if (!validate_shape_type(g)) + if (!validate_shape_dtype(g)) return false; // TODO add more validation diff --git a/compiler/luci/tester/CMakeLists.txt b/compiler/luci/tester/CMakeLists.txt index bcb4718..3ac06ef 100644 --- a/compiler/luci/tester/CMakeLists.txt +++ b/compiler/luci/tester/CMakeLists.txt @@ -1,17 +1,23 @@ +nnas_include(TargetRequire) + +unset(REQUIRED_TARGETS) +list(APPEND REQUIRED_TARGETS safemain) +TargetRequire_Return(${REQUIRED_TARGETS}) + set(SRCS_READ_TESTER src/ReadTester.cpp - src/Model.cpp ) add_executable(luci_readtester "${SRCS_READ_TESTER}") target_link_libraries(luci_readtester PRIVATE luci_import) target_link_libraries(luci_readtester PRIVATE luci_service) target_link_libraries(luci_readtester PRIVATE luci_pass) +target_link_libraries(luci_readtester PRIVATE foder) target_link_libraries(luci_readtester PRIVATE oops) +target_link_libraries(luci_readtester PRIVATE safemain) set(SRCS_WRITE_TESTER src/WriteTester.cpp - src/Model.cpp ) add_executable(luci_writetester "${SRCS_WRITE_TESTER}") @@ -19,4 +25,6 @@ target_link_libraries(luci_writetester PRIVATE luci_import) target_link_libraries(luci_writetester PRIVATE luci_service) target_link_libraries(luci_writetester PRIVATE luci_pass) target_link_libraries(luci_writetester PRIVATE luci_export) +target_link_libraries(luci_writetester PRIVATE foder) target_link_libraries(luci_writetester PRIVATE oops) +target_link_libraries(luci_writetester PRIVATE safemain) diff --git a/compiler/luci/tester/src/Model.cpp b/compiler/luci/tester/src/Model.cpp deleted file mode 100644 index b02c191..0000000 --- a/compiler/luci/tester/src/Model.cpp +++ /dev/null @@ -1,62 +0,0 @@ -#include "Model.h" - -#include -#include - -#include -#include -#include - -namespace -{ - -class FileModel final : public luci::Model -{ -public: - explicit FileModel(const std::string &filename) : _filename(filename) {} - -public: - FileModel(const FileModel &) = delete; - FileModel(FileModel &&) = delete; - -public: - const ::circle::Model *model(void) override - { - std::ifstream file(_filename, std::ios::binary | std::ios::in); - if (!file.good()) - return nullptr; - - file.unsetf(std::ios::skipws); - - std::streampos fileSize; - file.seekg(0, std::ios::end); - fileSize = file.tellg(); - file.seekg(0, std::ios::beg); - - // reserve capacity - _data.reserve(fileSize); - - // read the data - file.read(_data.data(), fileSize); - if (file.fail()) - return nullptr; - - return ::circle::GetModel(_data.data()); - } - -private: - const std::string _filename; - std::vector _data; -}; - -} // namespace - -namespace luci -{ - -std::unique_ptr load_model(const std::string &path) -{ - return std::make_unique(path); -} - -} // namespace luci diff --git a/compiler/luci/tester/src/Model.h b/compiler/luci/tester/src/Model.h deleted file mode 100644 index e40faf3..0000000 --- a/compiler/luci/tester/src/Model.h +++ /dev/null @@ -1,27 +0,0 @@ -#ifndef __TESTER_MODEL_H__ -#define __TESTER_MODEL_H__ - -#include - -#include - -namespace luci -{ - -struct Model -{ - virtual ~Model() = default; - - virtual const ::circle::Model *model(void) = 0; -}; - -/** - * @brief Load Circle model (as a raw Model) from a given path - * - * @note May return a nullptr - */ -std::unique_ptr load_model(const std::string &path); - -} // namespace luci - -#endif // __TESTER_MODEL_H__ diff --git a/compiler/luci/tester/src/ReadTester.cpp b/compiler/luci/tester/src/ReadTester.cpp index c105d6c..a1aead1 100644 --- a/compiler/luci/tester/src/ReadTester.cpp +++ b/compiler/luci/tester/src/ReadTester.cpp @@ -1,4 +1,20 @@ -#include "Model.h" +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include #include #include @@ -37,7 +53,7 @@ void show_error_message(const char *progname, std::ostream &os, const std::strin * dump graph to console if set. * i.e. "LUCI_LOG=1 luci_readtester mymodel.circle" */ -int main(int argc, char **argv) +int entry(int argc, char **argv) { if (argc != 2) { @@ -50,22 +66,17 @@ int main(int argc, char **argv) std::cout << "[INFO] Circle is '" << input_path << "'" << std::endl; // Load model from the file - std::unique_ptr model = luci::load_model(input_path); - if (model == nullptr) + foder::FileLoader file_loader{input_path}; + std::vector model_data = file_loader.load(); + const circle::Model *circle_model = circle::GetModel(model_data.data()); + if (circle_model == nullptr) { - std::cerr << "ERROR: Failed to load '" << input_path << "'" << std::endl; - return 255; - } - - const circle::Model *input_model = model->model(); - if (input_model == nullptr) - { - std::cerr << "ERROR: Failed to read '" << input_path << "'" << std::endl; - return 255; + std::cerr << "ERROR: Failed to load circle '" << input_path << "'" << std::endl; + return EXIT_FAILURE; } luci::Importer importer; - auto module = importer.importModule(input_model); + auto module = importer.importModule(circle_model); assert(module->size() > 0); for (size_t g = 0; g < module->size(); ++g) diff --git a/compiler/luci/tester/src/WriteTester.cpp b/compiler/luci/tester/src/WriteTester.cpp index 80019d1..aa7085c 100644 --- a/compiler/luci/tester/src/WriteTester.cpp +++ b/compiler/luci/tester/src/WriteTester.cpp @@ -1,4 +1,20 @@ -#include "Model.h" +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include #include #include @@ -52,8 +68,8 @@ public: bool store(const char *ptr, const size_t size) const final; private: - loco::Graph *_graph; - luci::Module *_module; + loco::Graph *_graph{nullptr}; + luci::Module *_module{nullptr}; const std::string _filepath; }; @@ -79,7 +95,7 @@ bool CircleExpContract::store(const char *ptr, const size_t size) const * With the graph, this will use luci_export to write to the second file * Like ReadTester, LUCI_LOG=1 environment variable is available to dump the graph */ -int main(int argc, char **argv) +int entry(int argc, char **argv) { if (argc != 3) { @@ -93,23 +109,18 @@ int main(int argc, char **argv) std::cout << "[INFO] Circle from '" << input_path << "' to '" << output_path << "'" << std::endl; // Load model from the file - std::unique_ptr model = luci::load_model(input_path); - if (model == nullptr) + foder::FileLoader file_loader{input_path}; + std::vector model_data = file_loader.load(); + const circle::Model *circle_model = circle::GetModel(model_data.data()); + if (circle_model == nullptr) { - std::cerr << "ERROR: Failed to load '" << input_path << "'" << std::endl; - return 255; - } - - const circle::Model *input_model = model->model(); - if (input_model == nullptr) - { - std::cerr << "ERROR: Failed to read '" << input_path << "'" << std::endl; - return 255; + std::cerr << "ERROR: Failed to load circle '" << input_path << "'" << std::endl; + return EXIT_FAILURE; } // Import from input Circle file luci::Importer importer; - auto module = importer.importModule(input_model); + auto module = importer.importModule(circle_model); assert(module->size() > 0); for (size_t g = 0; g < module->size(); ++g) diff --git a/compiler/luci/tests/CMakeLists.txt b/compiler/luci/tests/CMakeLists.txt index 4e56390..c038358 100644 --- a/compiler/luci/tests/CMakeLists.txt +++ b/compiler/luci/tests/CMakeLists.txt @@ -65,6 +65,35 @@ foreach(RECIPE IN ITEMS ${RECIPES}) list(APPEND TESTFILES "${CIRCLE_OUTPUT_FILE}") endforeach(RECIPE) +# Generate from res/CircleRecipes +# NOTE duplicate names should not exist or test may be incorrect +nncc_find_resource(CircleRecipes) +set(CIRCLERECIPES_DIR "${CircleRecipes_DIR}") + +file(GLOB RECIPES2 RELATIVE ${CIRCLERECIPES_DIR} "${CIRCLERECIPES_DIR}/*/test.recipe") + +foreach(RECIPE IN ITEMS ${RECIPES2}) + get_filename_component(RECIPE_PREFIX ${RECIPE} DIRECTORY) + + set(RECIPE_SOURCE_FILE "${RECIPE_PREFIX}.recipe") + set(CIRCLE_OUTPUT_FILE "${RECIPE_PREFIX}.circle") + + # Copy .recipe + add_custom_command(OUTPUT "${RECIPE_SOURCE_FILE}" + COMMAND ${CMAKE_COMMAND} -E copy_if_different + "${CIRCLERECIPES_DIR}/${RECIPE}" "${RECIPE_SOURCE_FILE}" + DEPENDS "${CIRCLERECIPES_DIR}/${RECIPE}" + COMMENT "Generating ${RECIPE_SOURCE_FILE}") + + # Generate .circle + add_custom_command(OUTPUT "${CIRCLE_OUTPUT_FILE}" + COMMAND circlechef-file "${RECIPE_SOURCE_FILE}" "${CIRCLE_OUTPUT_FILE}" + DEPENDS circlechef-file "${RECIPE_SOURCE_FILE}" + COMMENT "Generating ${CIRCLE_OUTPUT_FILE}") + + list(APPEND TESTFILES "${CIRCLE_OUTPUT_FILE}") +endforeach(RECIPE) + # Add a dummy target to create a target-level dependency. # TODO Find a way to create dependency between CTest tests (added below) and generated testfiles. add_custom_target(luci_testfiles ALL DEPENDS ${TESTFILES}) diff --git a/compiler/luci/tests/readverify.sh b/compiler/luci/tests/readverify.sh index 3403e9c..6d6753d 100755 --- a/compiler/luci/tests/readverify.sh +++ b/compiler/luci/tests/readverify.sh @@ -7,6 +7,9 @@ # ./readverify.sh ... VERIFY_SOURCE_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +# set LOG enable to execute/test luci/logex codes +export LUCI_LOG=100 + WORKDIR="$1"; shift VERIFY_BINARY_PATH="$1"; shift diff --git a/compiler/luci/tests/test.lst b/compiler/luci/tests/test.lst index 08cbd6b..188e298 100644 --- a/compiler/luci/tests/test.lst +++ b/compiler/luci/tests/test.lst @@ -1,5 +1,8 @@ +addread(Abs_000) addread(Add_000) +addread(Add_001) addread(Add_U8_000) +addread(AddN_000) addread(ArgMax_000) addread(ArgMax_001) addread(ArgMax_002) @@ -8,44 +11,190 @@ addread(ArgMax_U8_000) addread(ArgMax_U8_001) addread(ArgMax_U8_002) addread(ArgMax_U8_003) +addread(ArgMin_000) +addread(ArgMin_001) +addread(ArgMin_002) +addread(ArgMin_003) +addread(ArgMin_U8_000) +addread(ArgMin_U8_001) +addread(ArgMin_U8_002) +addread(ArgMin_U8_003) +addread(AveragePool2D_000) +addread(BatchMatMul_000) +addread(BatchMatMulV2_000) +addread(BatchMatMulV2_001) addread(BatchToSpaceND_000) +addread(Cast_000) +addread(Cast_001) +addread(Ceil_000) addread(Concatenation_000) addread(Concatenation_U8_000) addread(Conv2D_000) -addread(Conv2D_U8_000) addread(Conv2D_002) +addread(Conv2D_003) +addread(Conv2D_U8_000) addread(Cos_000) +addread(DepthToSpace_000) addread(DepthwiseConv2D_000) addread(DepthwiseConv2D_U8_000) +addread(DepthwiseConv2D_001) addread(Div_000) +addread(ELU_000) addread(Equal_000) addread(Exp_000) +addread(ExpandDims_000) +addread(ExpandDims_001) +addread(ExpandDims_002) +addread(ExpandDims_003) +addread(Fill_000) +addread(Fill_001) +addread(Floor_000) +addread(FloorDiv_000) +addread(FloorDiv_001) +addread(FloorMod_000) +addread(FloorMod_001) addread(FullyConnected_000) addread(FullyConnected_001) +addread(FullyConnected_002) addread(FullyConnected_U8_000) +addread(Gather_000) +addread(GatherNd_000) +addread(Greater_000) +addread(GreaterEqual_000) +addread(If_000) +addread(If_001) +addread(L2Normalize_000) +addread(L2Pool2D_000) +addread(L2Pool2D_U8_000) +addread(LeakyRelu_000) +addread(Less_000) +addread(LessEqual_000) +addread(LocalResponseNormalization_000) +addread(Log_000) +addread(LogicalAnd_000) addread(LogicalNot_000) addread(LogicalOr_000) +addread(Logistic_000) +addread(LogSoftmax_000) +addread(MatMul_000) +addread(MatrixDiag_000) +addread(MatrixSetDiag_000) +addread(Maximum_000) addread(MaxPool2D_000) addread(MaxPool2D_U8_000) addread(Mean_000) +addread(Mean_001) +addread(Minimum_000) +addread(MirrorPad_000) addread(Mul_000) addread(Mul_U8_000) +addread(Neg_000) +addread(NotEqual_000) +addread(OneHot_000) +addread(OneHot_001) +addread(OneHot_002) +addread(OneHot_003) addread(Pack_000) addread(Pack_U8_000) addread(Pad_000) +addread(Pow_000) +addread(PRelu_000) +addread(Range_000) +addread(Rank_000) +addread(ReduceAny_000) +addread(ReduceAny_001) +addread(ReduceAny_002) +addread(ReduceAny_003) +addread(ReduceMax_000) +addread(ReduceMin_000) +addread(ReduceProd_000) +addread(ReduceProd_001) +addread(ReduceProd_002) +addread(ReduceProd_003) addread(ReLU_000) +addread(ReLU6_000) +addread(ReLUN1To1_000) addread(Reshape_000) addread(Reshape_001) +addread(Reshape_002) +addread(Reshape_003) addread(Reshape_U8_000) +addread(ResizeBilinear_000) +addread(ResizeNearestNeighbor_000) +addread(ReverseSequence_000) +addread(ReverseV2_000) +addread(Round_000) addread(Rsqrt_000) +addread(ScatterNd_000) +addread(SegmentSum_000) +addread(Select_000) +addread(Select_001) +addread(Select_002) +addread(SelectV2_000) +addread(SelectV2_001) +addread(SelectV2_002) +addread(Shape_000) +addread(Sin_000) +addread(Slice_000) addread(Softmax_000) addread(Softmax_U8_000) +addread(SpaceToBatchND_000) +addread(SpaceToBatchND_001) +addread(SpaceToBatchND_002) +addread(SpaceToBatchND_003) +addread(SpaceToDepth_000) +addread(SparseToDense_000) +addread(Split_000) +addread(SplitV_000) +addread(Sqrt_000) +addread(Square_000) +addread(SquaredDifference_000) +addread(Squeeze_000) +addread(StridedSlice_000) +addread(StridedSlice_001) +addread(StridedSlice_002) addread(Sub_000) addread(Sub_U8_000) +addread(Sum_000) +addread(Sum_001) +addread(Tanh_000) +addread(Tile_000) +addread(Tile_U8_000) +addread(TopKV2_000) +addread(TopKV2_001) addread(Transpose_000) +addread(TransposeConv_000) +addread(Unpack_000) +addread(Unpack_001) +addread(Unpack_002) +addread(Unpack_003) +addread(Where_000) +addread(Where_001) +addread(While_000) +addread(While_001) +addread(While_002) +addread(While_003) +addread(YUV_TO_RGB_U8_000) +addread(ZerosLike_000) + +addread(Net_Dangle_001) +addread(Net_InstanceNorm_001) +addread(Net_InstanceNorm_002) +addread(Net_UnpackAdd_001) +addread(Net_ZeroDim_001) + +# from res/CircleRecipes +addread(BCQFullyConnected_000) +addread(BCQFullyConnected_001) +addread(BCQGather_000) +addread(CircleBatchMatMul_000) +addread(InstanceNorm_000) +addwrite(Abs_000) addwrite(Add_000) +addwrite(Add_001) addwrite(Add_U8_000) +addwrite(AddN_000) addwrite(ArgMax_000) addwrite(ArgMax_001) addwrite(ArgMax_002) @@ -54,38 +203,181 @@ addwrite(ArgMax_U8_000) addwrite(ArgMax_U8_001) addwrite(ArgMax_U8_002) addwrite(ArgMax_U8_003) +addwrite(ArgMin_000) +addwrite(ArgMin_001) +addwrite(ArgMin_002) +addwrite(ArgMin_003) +addwrite(ArgMin_U8_000) +addwrite(ArgMin_U8_001) +addwrite(ArgMin_U8_002) +addwrite(ArgMin_U8_003) +addwrite(AveragePool2D_000) +addwrite(BatchMatMul_000) +addwrite(BatchMatMulV2_000) +addwrite(BatchMatMulV2_001) addwrite(BatchToSpaceND_000) +addwrite(Cast_000) +addwrite(Cast_001) +addwrite(Ceil_000) addwrite(Concatenation_000) addwrite(Concatenation_U8_000) addwrite(Conv2D_000) -addwrite(Conv2D_U8_000) addwrite(Conv2D_002) +addwrite(Conv2D_003) +addwrite(Conv2D_U8_000) addwrite(Cos_000) +addwrite(DepthToSpace_000) addwrite(DepthwiseConv2D_000) addwrite(DepthwiseConv2D_U8_000) +addwrite(DepthwiseConv2D_001) addwrite(Div_000) +addwrite(ELU_000) addwrite(Equal_000) addwrite(Exp_000) +addwrite(ExpandDims_000) +addwrite(ExpandDims_001) +addwrite(ExpandDims_002) +addwrite(ExpandDims_003) +addwrite(Fill_000) +addwrite(Fill_001) +addwrite(Floor_000) +addwrite(FloorDiv_000) +addwrite(FloorDiv_001) +addwrite(FloorMod_000) +addwrite(FloorMod_001) addwrite(FullyConnected_000) addwrite(FullyConnected_001) +addwrite(FullyConnected_002) addwrite(FullyConnected_U8_000) +addwrite(Gather_000) +addwrite(GatherNd_000) +addwrite(Greater_000) +addwrite(GreaterEqual_000) +addwrite(If_000) +addwrite(If_001) +addwrite(L2Normalize_000) +addwrite(L2Pool2D_000) +addwrite(L2Pool2D_U8_000) +addwrite(LeakyRelu_000) +addwrite(Less_000) +addwrite(LessEqual_000) +addwrite(LocalResponseNormalization_000) +addwrite(Log_000) +addwrite(LogicalAnd_000) addwrite(LogicalNot_000) addwrite(LogicalOr_000) +addwrite(Logistic_000) +addwrite(LogSoftmax_000) +addwrite(MatMul_000) +addwrite(MatrixDiag_000) +addwrite(MatrixSetDiag_000) +addwrite(Maximum_000) addwrite(MaxPool2D_000) addwrite(MaxPool2D_U8_000) addwrite(Mean_000) +addwrite(Mean_001) +addwrite(Minimum_000) +addwrite(MirrorPad_000) addwrite(Mul_000) addwrite(Mul_U8_000) +addwrite(Neg_000) +addwrite(NotEqual_000) +addwrite(OneHot_000) +addwrite(OneHot_001) +addwrite(OneHot_002) +addwrite(OneHot_003) addwrite(Pack_000) addwrite(Pack_U8_000) addwrite(Pad_000) +addwrite(Pow_000) +addwrite(PRelu_000) +addwrite(Range_000) +addwrite(Rank_000) +addwrite(ReduceAny_000) +addwrite(ReduceAny_001) +addwrite(ReduceAny_002) +addwrite(ReduceAny_003) +addwrite(ReduceMax_000) +addwrite(ReduceMin_000) +addwrite(ReduceProd_000) +addwrite(ReduceProd_001) +addwrite(ReduceProd_002) +addwrite(ReduceProd_003) addwrite(ReLU_000) +addwrite(ReLU6_000) +addwrite(ReLUN1To1_000) addwrite(Reshape_000) addwrite(Reshape_001) +addwrite(Reshape_002) +addwrite(Reshape_003) addwrite(Reshape_U8_000) +addwrite(ResizeBilinear_000) +addwrite(ResizeNearestNeighbor_000) +addwrite(ReverseSequence_000) +addwrite(ReverseV2_000) +addwrite(Round_000) addwrite(Rsqrt_000) +addwrite(ScatterNd_000) +addwrite(SegmentSum_000) +addwrite(Select_000) +addwrite(Select_001) +addwrite(Select_002) +addwrite(SelectV2_000) +addwrite(SelectV2_001) +addwrite(SelectV2_002) +addwrite(Shape_000) +addwrite(Sin_000) +addwrite(Slice_000) addwrite(Softmax_000) addwrite(Softmax_U8_000) +addwrite(SpaceToBatchND_000) +addwrite(SpaceToBatchND_001) +addwrite(SpaceToBatchND_002) +addwrite(SpaceToBatchND_003) +addwrite(SpaceToDepth_000) +addwrite(SparseToDense_000) +addwrite(Split_000) +addwrite(SplitV_000) +addwrite(Sqrt_000) +addwrite(Square_000) +addwrite(SquaredDifference_000) +addwrite(Squeeze_000) +addwrite(StridedSlice_000) +addwrite(StridedSlice_001) +addwrite(StridedSlice_002) addwrite(Sub_000) addwrite(Sub_U8_000) +addwrite(Sum_000) +addwrite(Sum_001) +addwrite(Tanh_000) +addwrite(Tile_000) +addwrite(Tile_U8_000) +addwrite(TopKV2_000) +addwrite(TopKV2_001) addwrite(Transpose_000) +addwrite(TransposeConv_000) +addwrite(Unpack_000) +addwrite(Unpack_001) +addwrite(Unpack_002) +addwrite(Unpack_003) +addwrite(Where_000) +addwrite(Where_001) +addwrite(While_000) +addwrite(While_001) +addwrite(While_002) +addwrite(While_003) +addwrite(YUV_TO_RGB_U8_000) +addwrite(ZerosLike_000) + +addwrite(Net_Dangle_001) +addwrite(Net_InstanceNorm_001) +addwrite(Net_InstanceNorm_002) +addwrite(Net_UnpackAdd_001) +addwrite(Net_ZeroDim_001) + +# from res/CircleRecipes +addwrite(BCQFullyConnected_000) +addwrite(BCQFullyConnected_001) +addwrite(BCQGather_000) +addwrite(CircleBatchMatMul_000) +addwrite(InstanceNorm_000) diff --git a/compiler/mio-circle/CMakeLists.txt b/compiler/mio-circle/CMakeLists.txt index f97ec2b..9c1126d 100644 --- a/compiler/mio-circle/CMakeLists.txt +++ b/compiler/mio-circle/CMakeLists.txt @@ -13,7 +13,7 @@ set(SCHEMA_FILE "${NNAS_PROJECT_SOURCE_DIR}/nnpackage/schema/circle_schema.fbs") add_custom_command(OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/schema.fbs" COMMAND ${CMAKE_COMMAND} -E copy "${SCHEMA_FILE}" schema.fbs WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}" - DEPENDS "${NNAS_PROJECT_SOURCE_DIR}/nnpackage/schema/circle_schema.fbs" + DEPENDS "${SCHEMA_FILE}" ) FlatBuffers_Target(mio_circle diff --git a/compiler/mio-tflite/CMakeLists.txt b/compiler/mio-tflite/CMakeLists.txt index cb0795a..2cfed14 100644 --- a/compiler/mio-tflite/CMakeLists.txt +++ b/compiler/mio-tflite/CMakeLists.txt @@ -1,10 +1,15 @@ nnas_find_package(FlatBuffers QUIET) if(NOT FlatBuffers_FOUND) + message(STATUS "Build mio-tflite: FAILED (missing Flatbuffers)") return() endif(NOT FlatBuffers_FOUND) -nnas_find_package(TensorFlowSource EXACT 2.1.0 QUIET) +# TODO recover official release version +# NOTE we cannot use version number like "2.3.0-rc0" for find_package() +# use TensorFlowSource-2.3.0-rc0 as config itself +# nnas_find_package(TensorFlowSource EXACT 2.3.0 QUIET) +nnas_find_package(TensorFlowSource-2.3.0-rc0 QUIET) if(NOT TensorFlowSource_FOUND) return() diff --git a/compiler/mir-caffe-importer/CMakeLists.txt b/compiler/mir-caffe-importer/CMakeLists.txt deleted file mode 100644 index 8317651..0000000 --- a/compiler/mir-caffe-importer/CMakeLists.txt +++ /dev/null @@ -1,17 +0,0 @@ -nnas_find_package(CaffeProto QUIET) - -if (NOT CaffeProto_FOUND) - return() -endif () - -set(MIR_CAFFE_IMPORTER_SOURCES - caffe_importer.cpp - caffe_importer.h - caffe_op_creator.cpp - caffe_op_creator.h - caffe_op_types.h) - -add_library(mir_caffe_importer STATIC ${MIR_CAFFE_IMPORTER_SOURCES}) -set_target_properties(mir_caffe_importer PROPERTIES POSITION_INDEPENDENT_CODE ON) -target_include_directories(mir_caffe_importer PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}) -target_link_libraries(mir_caffe_importer PUBLIC mir caffeproto PRIVATE stdex) diff --git a/compiler/mir-caffe-importer/caffe_importer.cpp b/compiler/mir-caffe-importer/caffe_importer.cpp deleted file mode 100644 index 8e5ebda..0000000 --- a/compiler/mir-caffe-importer/caffe_importer.cpp +++ /dev/null @@ -1,439 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "caffe_importer.h" -#include "caffe/proto/caffe.pb.h" -#include "caffe_op_creator.h" -#include "caffe_op_types.h" - -#include "mir/ops/OutputOp.h" - -#include -#include -#include - -#include - -#include -#include -#include -#include -#include -#include -#include -#include - -namespace mir_caffe -{ - -namespace -{ - -class CaffeImporter -{ -public: - /// @brief Load the model and convert it into a MIR Graph. - std::unique_ptr importModelFromBinaryFile(const std::string &filename); - std::unique_ptr importModelFromTextFile(const std::string &filename); - -private: - std::unique_ptr importModel(); - - std::unique_ptr _net; - std::unique_ptr _opCreator; - - // Maps Caffe blob names to corresponding MIR operation outputs. - std::map _blobNameToOpOutput; - - static const std::map _operatorTypes; - - /** - * @brief Mark output MIR nodes - */ - void setGraphOutputs(mir::Graph *graph); - - /** - * @brief Pass through caffe graph and collect unsupported by NNC layers - * @throw PassException with message, containing detected problems - */ - void collectUnsupportedLayers(); - - /** - * @brief Create MIR node from single caffe layer - */ - void createMIRNodesFromLayer(const caffe::LayerParameter &layer); - - mir::Operation::Output *getOutputForBlob(const std::string &blob_name) const; - void setOutputForBlob(const std::string &blob_name, mir::Operation::Output *output); - - /** - * @brief Collect unsupported parts of caffe layer - */ - void collectUnsupportedOp(const caffe::LayerParameter &layer, std::set &problems); - - /** - * @brief Returns MIR operation outputs corresponding to the inputs of the given layer. - */ - std::vector getMIRInputsForLayer(const caffe::LayerParameter &layer); - - void processDeprecatedInput(); -}; - -void loadModelFromBinaryFile(const std::string &filename, caffe::NetParameter *net) -{ - GOOGLE_PROTOBUF_VERIFY_VERSION; - - int file_handle = open(filename.c_str(), O_RDONLY); - - if (file_handle == -1) - throw std::runtime_error("Couldn't open file \"" + filename + "\": " + std::strerror(errno) + - "."); - - google::protobuf::io::FileInputStream file_stream(file_handle); - file_stream.SetCloseOnDelete(true); - - google::protobuf::io::CodedInputStream coded_stream(&file_stream); - coded_stream.SetTotalBytesLimit(INT_MAX, INT_MAX); - - if (!net->ParseFromCodedStream(&coded_stream)) - throw std::runtime_error("Couldn't parse file \"" + filename + "\"."); - - // If the file has not been consumed entirely, assume that the file is in the wrong format. - if (!coded_stream.ConsumedEntireMessage()) - throw std::runtime_error("File \"" + filename + "\" has not been consumed entirely."); -} - -void loadModelFromTextFile(const std::string &filename, caffe::NetParameter *net) -{ - GOOGLE_PROTOBUF_VERIFY_VERSION; - - int file_handle = open(filename.c_str(), O_RDONLY); - - if (file_handle == -1) - throw std::runtime_error("Couldn't open file \"" + filename + "\": " + std::strerror(errno) + - "."); - - google::protobuf::io::FileInputStream file_stream(file_handle); - file_stream.SetCloseOnDelete(true); - - if (!google::protobuf::TextFormat::Parse(&file_stream, net)) - throw std::runtime_error("Couldn't parse file \"" + filename + "\"."); -} - -std::unique_ptr CaffeImporter::importModel() -{ - auto graph = stdex::make_unique(); - _opCreator = stdex::make_unique(graph.get()); - - collectUnsupportedLayers(); - - for (int i = 0; i < _net->layer_size(); ++i) - createMIRNodesFromLayer(_net->layer(i)); - - setGraphOutputs(graph.get()); - - return std::move(graph); -} - -std::unique_ptr CaffeImporter::importModelFromBinaryFile(const std::string &filename) -{ - _net = stdex::make_unique(); - loadModelFromBinaryFile(filename, _net.get()); - - return importModel(); -} - -std::unique_ptr CaffeImporter::importModelFromTextFile(const std::string &filename) -{ - _net = stdex::make_unique(); - loadModelFromTextFile(filename, _net.get()); - - return importModel(); -} - -void CaffeImporter::collectUnsupportedLayers() -{ - processDeprecatedInput(); - - std::set problems; - - for (const caffe::LayerParameter &layer : _net->layer()) - collectUnsupportedOp(layer, problems); - - if (!problems.empty()) - { - std::string msg("NNC can't load model. Detected problems:"); - for (const auto &problemStr : problems) - msg.append("\n * " + problemStr); - throw std::runtime_error(msg); - } -} - -void CaffeImporter::createMIRNodesFromLayer(const caffe::LayerParameter &layer) -{ - std::vector inputs = getMIRInputsForLayer(layer); - std::vector outputs; - - switch (_operatorTypes.at(layer.type())) - { - case CaffeOpType::input: - outputs = _opCreator->convertInput(layer); - break; - case CaffeOpType::convolution: - outputs = _opCreator->convertConvolution(layer, inputs); - break; - case CaffeOpType::innerProduct: - outputs = _opCreator->convertInnerProduct(layer, inputs); - break; - case CaffeOpType::pooling: - outputs = _opCreator->convertPooling(layer, inputs); - break; - case CaffeOpType::concat: - outputs = _opCreator->convertConcat(layer, inputs); - break; - case CaffeOpType::reshape: - outputs = _opCreator->convertReshape(layer, inputs); - break; - case CaffeOpType::ReLU: - outputs = _opCreator->convertReLU(layer, inputs); - break; - case CaffeOpType::softmax: - outputs = _opCreator->convertSoftmax(layer, inputs); - break; - case CaffeOpType::scale: - outputs = _opCreator->convertScale(layer, inputs); - break; - case CaffeOpType::batchNorm: - outputs = _opCreator->convertBatchNorm(layer, inputs); - break; - case CaffeOpType::dropout: - outputs = _opCreator->convertDropout(layer, inputs); - break; - case CaffeOpType::tanh: - outputs = _opCreator->convertTanH(layer, inputs); - break; - case CaffeOpType::ELU: - outputs = _opCreator->convertELU(layer, inputs); - break; - case CaffeOpType::eltwise: - outputs = _opCreator->convertEltwise(layer, inputs); - break; - case CaffeOpType::embed: - outputs = _opCreator->convertEmbed(layer, inputs); - break; - case CaffeOpType::deconvolution: - outputs = _opCreator->convertDeconvolution(layer, inputs); - break; - case CaffeOpType::split: - outputs = _opCreator->convertSplit(layer, inputs); - break; - case CaffeOpType::sigmoid: - outputs = _opCreator->convertSigmoid(layer, inputs); - break; - case CaffeOpType::LSTM: - outputs = _opCreator->convertLSTM(layer, inputs); - break; - default: - assert(false && "All unsupported types should have been found before this pass."); - } - - assert(static_cast(outputs.size()) == layer.top_size() && "Number of outputs differs."); - for (int i = 0; i < layer.top_size(); ++i) - setOutputForBlob(layer.top(i), outputs[i]); -} - -void CaffeImporter::collectUnsupportedOp(const caffe::LayerParameter &layer, - std::set &problems) -{ - auto it = _operatorTypes.find(layer.type()); - if (it == _operatorTypes.end()) - { - problems.insert(layer.type() + ": unknown layer"); - return; - } - - CaffeOpType op_type = it->second; - - switch (op_type) - { - case CaffeOpType::concat: - case CaffeOpType::input: - case CaffeOpType::softmax: - case CaffeOpType::scale: - case CaffeOpType::dropout: - case CaffeOpType::split: - case CaffeOpType::eltwise: - case CaffeOpType::ELU: - case CaffeOpType::ReLU: - case CaffeOpType::embed: - case CaffeOpType::sigmoid: - case CaffeOpType::tanh: - case CaffeOpType::innerProduct: - // No checks - break; - case CaffeOpType::deconvolution: - case CaffeOpType::convolution: - _opCreator->checkConvolution(layer, problems); - break; - case CaffeOpType::pooling: - _opCreator->checkPooling(layer, problems); - break; - case CaffeOpType::reshape: - _opCreator->checkReshape(layer, problems); - break; - case CaffeOpType::batchNorm: - _opCreator->checkBatchNorm(layer, problems); - break; - case CaffeOpType::LSTM: - _opCreator->checkLSTM(layer, problems); - break; - default: - problems.insert(layer.type() + ": unsupported layer"); - break; - } -} - -void CaffeImporter::processDeprecatedInput() -{ - if (_net->input_dim_size() != 0 || _net->input_shape_size() != 0) - throw std::runtime_error("Deprecated Caffe input types are not supported"); -} - -std::vector -CaffeImporter::getMIRInputsForLayer(const caffe::LayerParameter &layer) -{ - std::vector inputs; - - for (const auto &input_name : layer.bottom()) - inputs.push_back(getOutputForBlob(input_name)); - - return inputs; -} - -mir::Operation::Output *CaffeImporter::getOutputForBlob(const std::string &blob_name) const -{ - return _blobNameToOpOutput.at(blob_name); -} - -void CaffeImporter::setOutputForBlob(const std::string &blob_name, mir::Operation::Output *output) -{ - const auto it = _blobNameToOpOutput.find(blob_name); - if (it != _blobNameToOpOutput.cend()) - { - // caffe input blob name could be same as output blob name, and next line will overwrite - // '_blobNameToOpOutput' element, but in all networks that I saw it was not a problem - it->second->setName(""); - } - - // Do not overwrite the name in case of fall-through layers (ex. Dropout, Split). - // TODO Find a way to handle it properly. - if (output->getName().empty()) - output->setName(blob_name); - - _blobNameToOpOutput[blob_name] = output; -} - -void CaffeImporter::setGraphOutputs(mir::Graph *graph) -{ - // TODO For now, we assume that: - // - there is exactly one output; - // - the output is from the last layer. - const auto &last_layer = *_net->layer().rbegin(); - auto output = getOutputForBlob(last_layer.top(0)); - graph->create(output); -} - -const std::map CaffeImporter::_operatorTypes = { - {"AbsVal", CaffeOpType::absVal}, - {"Accuracy", CaffeOpType::accuracy}, - {"ArgMax", CaffeOpType::argMax}, - {"BatchNorm", CaffeOpType::batchNorm}, - {"BatchReindex", CaffeOpType::batchReindex}, - {"Bias", CaffeOpType::bias}, - {"BNLL", CaffeOpType::BNLL}, - {"Clip", CaffeOpType::clip}, - {"Concat", CaffeOpType::concat}, - {"ContrastiveLoss", CaffeOpType::contrastiveLoss}, - {"Convolution", CaffeOpType::convolution}, - {"Crop", CaffeOpType::crop}, - {"Data", CaffeOpType::data}, - {"Deconvolution", CaffeOpType::deconvolution}, - {"Dropout", CaffeOpType::dropout}, - {"DummyData", CaffeOpType::dummyData}, - {"Eltwise", CaffeOpType::eltwise}, - {"ELU", CaffeOpType::ELU}, - {"Embed", CaffeOpType::embed}, - {"EuclidianLoss", CaffeOpType::euclidianLoss}, - {"Exp", CaffeOpType::exp}, - {"Filter", CaffeOpType::filter}, - {"Flatten", CaffeOpType::flatten}, - {"HDF5Data", CaffeOpType::HDF5Data}, - {"HDF5Output", CaffeOpType::HDF5Output}, - {"HingeLoss", CaffeOpType::hingeLoss}, - {"Im2Col", CaffeOpType::im2Col}, - {"ImageData", CaffeOpType::imageData}, - {"InfogainLoss", CaffeOpType::infogainLoss}, - {"InnerProduct", CaffeOpType::innerProduct}, - {"Input", CaffeOpType::input}, - {"Log", CaffeOpType::log}, - {"LRN", CaffeOpType::LRN}, - {"LSTM", CaffeOpType::LSTM}, - {"MemoryData", CaffeOpType::memoryData}, - {"MultinomialLogisticLoss", CaffeOpType::multinomialLogisticLoss}, - {"MVN", CaffeOpType::MVN}, - {"Parameter", CaffeOpType::parameter}, - {"Pooling", CaffeOpType::pooling}, - {"Power", CaffeOpType::power}, - {"PReLU", CaffeOpType::PReLU}, - {"Python", CaffeOpType::python}, - {"Recurrent", CaffeOpType::recurrent}, - {"Reduction", CaffeOpType::reduction}, - {"ReLU", CaffeOpType::ReLU}, - {"Reshape", CaffeOpType::reshape}, - {"RNN", CaffeOpType::RNN}, - {"Scale", CaffeOpType::scale}, - {"SigmoidCrossEntropyLoss", CaffeOpType::sigmoidCrossEntropyLoss}, - {"Sigmoid", CaffeOpType::sigmoid}, - {"Silence", CaffeOpType::silence}, - {"Softmax", CaffeOpType::softmax}, - {"SoftmaxWithLoss", CaffeOpType::softmaxWithLoss}, - {"SPP", CaffeOpType::SPP}, - {"Split", CaffeOpType::split}, - {"Slice", CaffeOpType::slice}, - {"TanH", CaffeOpType::tanh}, - {"Threshold", CaffeOpType::threshold}, - {"Tile", CaffeOpType::tile}, - {"WindowData", CaffeOpType::windowData}}; -} // namespace - -std::unique_ptr importModelFromBinaryFile(const std::string &filename) -{ - CaffeImporter importer; - return importer.importModelFromBinaryFile(filename); -} - -std::unique_ptr importModelFromTextFile(const std::string &filename) -{ - CaffeImporter importer; - return importer.importModelFromTextFile(filename); -} - -std::unique_ptr loadModel(const std::string &filename) -{ - return importModelFromBinaryFile(filename); -} - -} // namespace mir_caffe diff --git a/compiler/mir-caffe-importer/caffe_op_creator.cpp b/compiler/mir-caffe-importer/caffe_op_creator.cpp deleted file mode 100644 index 5d43d24..0000000 --- a/compiler/mir-caffe-importer/caffe_op_creator.cpp +++ /dev/null @@ -1,834 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "caffe_op_creator.h" - -#include "mir/ops/AddOp.h" -#include "mir/ops/AvgPool2DOp.h" -#include "mir/ops/ConcatOp.h" -#include "mir/ops/ConstantOp.h" -#include "mir/ops/Conv2DOp.h" -#include "mir/ops/Deconv2DOp.h" -#include "mir/ops/EluOp.h" -#include "mir/ops/FullyConnectedOp.h" -#include "mir/ops/GatherOp.h" -#include "mir/ops/LeakyReluOp.h" -#include "mir/ops/MaxOp.h" -#include "mir/ops/MaxPool2DOp.h" -#include "mir/ops/MulOp.h" -#include "mir/ops/ReluOp.h" -#include "mir/ops/ReshapeOp.h" -#include "mir/ops/SigmoidOp.h" -#include "mir/ops/SliceOp.h" -#include "mir/ops/SoftmaxOp.h" -#include "mir/ops/TanhOp.h" -#include "mir/ops/TransposeOp.h" -#include "mir/Index.h" -#include "mir/ShapeRange.h" -#include "mir/Tensor.h" - -#include -#include -#include - -namespace mir_caffe -{ - -static mir::Shape convertBlobShape(const caffe::BlobShape &shape) -{ - mir::Shape mir_shape(shape.dim_size()); - - for (int i = 0; i < shape.dim_size(); ++i) - { - mir_shape.dim(i) = shape.dim(i); - } - - return mir_shape; -} - -using namespace mir; - -/// @brief Split arg into @p num_parts equal parts along @p axis axis. -std::vector CaffeOpCreator::createSplit(mir::Operation::Output *arg, - int32_t num_parts, int32_t axis) -{ - const auto &arg_shape = arg->getShape(); - - assert(axis >= 0 && axis < arg_shape.rank()); - int32_t part_size = arg_shape.dim(axis) / num_parts; - assert(part_size * num_parts == arg_shape.dim(axis)); - - Shape starts(arg_shape.rank()); - Shape sizes(arg_shape); - sizes.dim(axis) = part_size; - - std::vector outputs(num_parts); - for (int32_t i = 0; i < num_parts; ++i) - { - outputs[i] = createOp(arg, starts, sizes)->getOutput(0); - starts.dim(axis) += part_size; - } - - return outputs; -} - -/// @brief Helper function for creating FullyConnected operation with non-square input. -mir::Operation::Output *CaffeOpCreator::createFullyConnected(mir::Operation::Output *input, - mir::Operation::Output *weights, - int32_t axis) -{ - const auto &input_shape = input->getShape(); - const auto &weights_shape = weights->getShape(); - - assert(axis >= 0 && axis < input_shape.rank()); - assert(weights_shape.rank() == 2); - - // Result shape is: input.shape[0:axis] + weights.shape[1]. - Shape result_shape = input_shape; - result_shape.resize(axis + 1); - result_shape.dim(axis) = weights_shape.dim(1); - - // Flatten input to 2-D shape. - int32_t outer_size = 1; - for (int32_t i = 0; i < axis; ++i) - outer_size *= input_shape.dim(i); - int32_t inner_size = 1; - for (int32_t i = axis; i < input_shape.rank(); ++i) - inner_size *= input_shape.dim(i); - - auto flatten = createOp(input, Shape{outer_size, inner_size})->getOutput(0); - auto fc = createOp(flatten, weights)->getOutput(0); - return createOp(fc, result_shape)->getOutput(0); -} - -TensorVariant CaffeOpCreator::convertBlob(const caffe::BlobProto &blob) -{ - const void *src_data; - - mir::DataType dtype; - if (blob.data_size() != 0) - { - assert(blob.double_data_size() == 0); - dtype = mir::DataType::FLOAT32; - src_data = blob.data().data(); - } - else if (blob.double_data_size() != 0) - { - dtype = mir::DataType::FLOAT64; - src_data = blob.double_data().data(); - } - else - { - throw std::runtime_error("No data in Caffe BlobProto, investigate"); - } - - const mir::Shape shape = convertBlobShape(blob.shape()); - return TensorVariant({dtype, shape}, src_data); -} - -std::vector -CaffeOpCreator::convertInput(const caffe::LayerParameter &layer) -{ - const auto ¶ms = layer.input_param(); - const auto num_inputs = layer.top_size(); - const auto num_shapes = params.shape_size(); - std::vector outputs; - - assert((num_shapes == 1 || num_shapes == num_inputs) && "Unsupported number of shapes."); - - for (int i = 0; i < num_inputs; ++i) - { - const auto &blob_shape = params.shape(num_shapes == 1 ? 0 : i); - mir::TensorType input_type(DataType::FLOAT32, convertBlobShape(blob_shape)); - auto input = createOp(input_type)->getOutput(0); - outputs.push_back(input); - } - - return outputs; -} - -template -static void convertConvolutionParam(const caffe::ConvolutionParameter &conv_param, - OperationAttributes &attributes) -{ - std::int32_t stride_h, stride_w; - if (conv_param.has_stride_h() || conv_param.has_stride_w()) - { - // If stride_h or stride_w are set, they take precedence. - stride_h = conv_param.stride_h(); - stride_w = conv_param.stride_w(); - } - else if (conv_param.stride_size() == 0) - { - // If no strides specified, they defaults to 1. - stride_h = stride_w = 1; - } - else if (conv_param.stride_size() == 1) - { - // If only one stride specified, all strides take the same value. - stride_h = stride_w = conv_param.stride(0); - } - else - { - // Otherwise, there must be a stride for each dimension. - assert(conv_param.stride_size() == 2); - stride_h = conv_param.stride(0); - stride_w = conv_param.stride(1); - } - attributes.strides = {stride_h, stride_w}; - - std::int32_t pad_h, pad_w; - if (conv_param.has_pad_h() || conv_param.has_pad_w()) - { - // If pad_h or pad_w are set, they take precedence. - pad_h = conv_param.pad_h(); - pad_w = conv_param.pad_w(); - } - else if (conv_param.pad_size() == 0) - { - // If no pads specified, they defaults to 0. - pad_h = pad_w = 0; - } - else if (conv_param.pad_size() == 1) - { - // If only one pad specified, all pads take the same value. - pad_h = pad_w = conv_param.pad(0); - } - else - { - // Otherwise, there must be a pad for each dimension. - assert(conv_param.pad_size() == 2); - pad_h = conv_param.pad(0); - pad_w = conv_param.pad(1); - } - attributes.padding_after = attributes.padding_before = {pad_h, pad_w}; -} - -void CaffeOpCreator::checkConvolution(const caffe::LayerParameter &layer, - std::set &problems_ops_set) -{ - const caffe::ConvolutionParameter ¶ms = layer.convolution_param(); - - assert(params.stride_size() <= 2); - - if (params.axis() != 1) - problems_ops_set.insert("Conv2D: Unsupported axis"); - - if (params.pad_size() != 0 && (params.has_pad_h() || params.has_pad_w())) - problems_ops_set.insert("Conv2D: Conflicting padding properties"); - - if (params.pad_size() > 2) - problems_ops_set.insert("Conv2D: Unsupported number of pads"); -} - -std::vector -CaffeOpCreator::convertConvolution(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - const auto ¶ms = layer.convolution_param(); - Conv2DOpAttributes attributes; - - convertConvolutionParam(params, attributes); - attributes.num_groups = params.group(); - attributes.data_format = DataFormat::NCHW; - - assert(layer.blobs(0).shape().dim_size() == 4); - auto kernel = createOp(convertBlob(layer.blobs(0)))->getOutput(0); - std::vector perm{0, 2, 3, 1}; // OIHW -> OHWI - kernel = createOp(kernel, perm)->getOutput(0); - auto result = createOp(inputs[0], kernel, attributes)->getOutput(0); - - // Add the bias, if any. - if (params.bias_term()) - { - auto bias = createOp(convertBlob(layer.blobs(1)))->getOutput(0); - bias = createOp(bias, Shape{1, bias->getShape().dim(0), 1, 1})->getOutput(0); - result = createOp(result, bias)->getOutput(0); - } - - return {result}; -} - -std::vector -CaffeOpCreator::convertDeconvolution(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - const caffe::ConvolutionParameter ¶ms = layer.convolution_param(); - Deconv2DOpAttributes attributes; - - convertConvolutionParam(params, attributes); - attributes.data_format = DataFormat::NCHW; - - if (params.group() != 1) - { - throw std::runtime_error("Deconvolution: 'group' != 1 is not supported."); - } - - auto kernel = createOp(convertBlob(layer.blobs(0)))->getOutput(0); - std::vector perm{2, 3, 1, 0}; // IOHW -> HWOI - kernel = createOp(kernel, perm)->getOutput(0); - auto result = createOp(inputs[0], kernel, attributes)->getOutput(0); - - // bias_term is optional (so might not be present) and defaults to true - if (params.bias_term()) - { - auto bias = createOp(convertBlob(layer.blobs(1)))->getOutput(0); - bias = createOp(bias, Shape{1, bias->getShape().dim(0), 1, 1})->getOutput(0); - result = createOp(result, bias)->getOutput(0); - } - - return {result}; -} - -std::vector -CaffeOpCreator::convertInnerProduct(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - const auto ¶ms = layer.inner_product_param(); - auto weights = createOp(convertBlob(layer.blobs(0)))->getOutput(0); - - if (!params.transpose()) - weights = createOp(weights, std::vector{1, 0})->getOutput(0); - - auto result = createFullyConnected(inputs[0], weights, params.axis()); - - // Add the bias, if any. - if (params.bias_term()) - { - auto bias = createOp(convertBlob(layer.blobs(1)))->getOutput(0); - result = createOp(result, bias)->getOutput(0); - } - - return {result}; -} - -std::vector -CaffeOpCreator::convertConcat(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - const auto ¶ms = layer.concat_param(); - auto concat = createOp(inputs, params.axis()); - return {concat->getOutput(0)}; -} - -template -static void convertPoolingParam(const caffe::PoolingParameter ¶ms, - const mir::Shape &input_shape, PoolingAttributes &attributes) -{ - std::int32_t kernel_h, kernel_w; - assert(!params.global_pooling()); - if (params.has_kernel_size()) - { - kernel_h = kernel_w = params.kernel_size(); - } - else - { - kernel_h = params.kernel_h(); - kernel_w = params.kernel_w(); - } - attributes.window = {kernel_h, kernel_w}; - - std::int32_t stride_h, stride_w; - if (params.has_stride_h() || params.has_stride_w()) - { - stride_h = params.stride_h(); - stride_w = params.stride_w(); - } - else - { - stride_h = stride_w = params.stride(); - } - attributes.strides = {stride_h, stride_w}; - - std::int32_t pad_h, pad_w; - if (params.has_pad_h() || params.has_pad_w()) - { - pad_h = params.pad_h(); - pad_w = params.pad_w(); - } - else - { - pad_h = pad_w = params.pad(); - } - - attributes.padding_before = attributes.padding_after = {pad_h, pad_w}; - - // Caffe uses different formula for computing output shape than MIR. Adjust padding so that - // the output shape stays the same. - constexpr int num_spatial_dims = 2; - for (int i = 0; i < num_spatial_dims; ++i) - { - // Assuming NCHW format. - const std::int32_t padded_input = - input_shape.dim(2 + i) + attributes.padding_before[i] + attributes.padding_after[i]; - if ((padded_input - attributes.window[i]) % attributes.strides[i] != 0) - ++attributes.padding_after[i]; - } -} - -void CaffeOpCreator::checkPooling(const caffe::LayerParameter &layer, - std::set &problems_ops_set) -{ - const caffe::PoolingParameter ¶ms = layer.pooling_param(); - - if (params.has_global_pooling() && params.global_pooling()) - problems_ops_set.insert("Pooling: pooling layer global_pooling param is not supported yet"); - - if (params.pool() != caffe::PoolingParameter::AVE && - params.pool() != caffe::PoolingParameter::MAX) - problems_ops_set.insert("Pooling: unsupported pooling type"); - - if (params.has_pad() && (params.has_pad_h() || params.has_pad_w())) - problems_ops_set.insert("Pooling: conflicting padding properties in pooling"); -} - -std::vector -CaffeOpCreator::convertPooling(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - const auto ¶ms = layer.pooling_param(); - - assert(inputs.size() == 1); - auto input = inputs[0]; - - mir::Operation::Output *result; - - switch (params.pool()) - { - case caffe::PoolingParameter::AVE: - { - AvgPool2DOpAttributes attributes_avg; - attributes_avg.data_format = DataFormat::NCHW; - convertPoolingParam(params, input->getShape(), attributes_avg); - result = createOp(input, attributes_avg)->getOutput(0); - break; - } - case caffe::PoolingParameter::MAX: - { - MaxPool2DOpAttributes attributes_max; - attributes_max.data_format = DataFormat::NCHW; - convertPoolingParam(params, input->getShape(), attributes_max); - result = createOp(input, attributes_max)->getOutput(0); - break; - } - default: - assert(false); - } - - return {result}; -} - -std::vector -CaffeOpCreator::convertSoftmax(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - const auto ¶ms = layer.softmax_param(); - - // CPP and ACL backends are able to perform Softmax only along the last axis. - // FIXME Do it in backends. - if (inputs[0]->getShape().rank() == 4) - { - // For now, we only account for the most common case. - if (params.axis() != 1) - throw std::runtime_error("Softmax: unsupported axis"); - int32_t axis = 3; - auto input = createOp(inputs[0], std::vector{0, 2, 3, 1}); - auto softmax = createOp(input->getOutput(0), axis); - auto result = - createOp(softmax->getOutput(0), std::vector{0, 3, 1, 2}); - return {result->getOutput(0)}; - } - - auto softmax = createOp(inputs[0], params.axis()); - return {softmax->getOutput(0)}; -} - -void CaffeOpCreator::checkReshape(const caffe::LayerParameter &layer, - std::set &problems_ops_set) -{ - const caffe::ReshapeParameter ¶ms = layer.reshape_param(); - - if (params.has_axis() || params.has_num_axes()) - problems_ops_set.insert("Reshape layer axis and num_axes params are not supported yet"); - - if (!params.has_shape()) - problems_ops_set.insert("Reshape layer doesn't have shape parameter"); - - const mir::Shape newShape = convertBlobShape(params.shape()); - - for (int32_t i = 0; i < newShape.rank(); ++i) - if (newShape.dim(i) == 0) - problems_ops_set.insert("Reshape layer zero shape values are not supported yet"); -} - -/** - * @brief Converts Caffe Reshape layer to Model IR Reshape operation. - * @todo Support "axis" and "num_axes" parameters as needed. - * @todo Decide how to react to the absence of "shape" parameter. - * @todo Support zero values in "shape" parameter. - */ -std::vector -CaffeOpCreator::convertReshape(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - const caffe::ReshapeParameter ¶ms = layer.reshape_param(); - - const mir::Shape new_shape = convertBlobShape(params.shape()); - auto reshape = createOp(inputs[0], new_shape); - return {reshape->getOutput(0)}; -} - -std::vector -CaffeOpCreator::convertReLU(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - mir::Operation *relu; - if (layer.relu_param().has_negative_slope()) - { - float alpha = layer.relu_param().negative_slope(); - relu = createOp(inputs[0], alpha); - } - else - { - relu = createOp(inputs[0]); - } - - return {relu->getOutput(0)}; -} - -std::vector -CaffeOpCreator::convertScale(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - const auto ¶ms = layer.scale_param(); - auto scale = createOp(convertBlob(layer.blobs(0)))->getOutput(0); - scale = createOp(scale, Shape{1, scale->getShape().dim(0), 1, 1})->getOutput(0); - auto result = createOp(inputs[0], scale)->getOutput(0); - - // Add the bias, if any. - if (params.bias_term()) - { - auto bias = createOp(convertBlob(layer.blobs(1)))->getOutput(0); - bias = createOp(bias, Shape{1, bias->getShape().dim(0), 1, 1})->getOutput(0); - result = createOp(result, bias)->getOutput(0); - } - - return {result}; -} - -void CaffeOpCreator::checkBatchNorm(const caffe::LayerParameter &layer, - std::set &problems_ops_set) -{ - const auto &scale_shape = layer.blobs(2).shape(); - - // Check that last blob(with scaleFactor) containing only one number - if (scale_shape.dim_size() != 1 || scale_shape.dim(0) != 1) - problems_ops_set.insert("Unexpected shape of scale parameter in batch norm"); -} - -std::vector -CaffeOpCreator::convertBatchNorm(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - const caffe::BatchNormParameter ¶ms = layer.batch_norm_param(); - - auto input = inputs[0]; - auto mean_tensor = convertBlob(layer.blobs(0)); - auto var_tensor = convertBlob(layer.blobs(1)); - auto scale_tensor = convertBlob(layer.blobs(2)); - const float eps = params.eps(); - - float scale_factor = *reinterpret_cast(scale_tensor.at(mir::Index{0})); - - // See https://github.com/BVLC/caffe/blob/master/src/caffe/layers/batch_norm_layer.cpp#L100 - // Y = (X - mean / scale_factor) / sqrt(var / scale_factor + epsilon) = - // = (X + C1) * C2 - if (scale_factor != 0.0f) - scale_factor = 1.0f / scale_factor; - - // C1 = -mean / scale_factor - Tensor mean_accessor(mean_tensor); - for (const auto &idx : ShapeRange(mean_accessor.getShape())) - mean_accessor.at(idx) *= -scale_factor; - auto c1 = createOp(mean_tensor)->getOutput(0); - - // C2 = 1 / sqrt(var / scale_factor + epsilon) - Tensor var_accessor(var_tensor); - for (const auto &idx : ShapeRange(var_accessor.getShape())) - var_accessor.at(idx) = 1.0f / std::sqrt(var_accessor.at(idx) * scale_factor + eps); - auto c2 = createOp(var_tensor)->getOutput(0); - - c1 = createOp(c1, Shape{1, c1->getShape().dim(0), 1, 1})->getOutput(0); - c2 = createOp(c2, Shape{1, c2->getShape().dim(0), 1, 1})->getOutput(0); - - // Y = (X + C1) * C2 - auto result = createOp(input, c1)->getOutput(0); - result = createOp(result, c2)->getOutput(0); - - return {result}; -} - -std::vector -CaffeOpCreator::convertDropout(const caffe::LayerParameter &, - const std::vector &inputs) -{ - // This is a no-op in inference mode. - return {inputs[0]}; -} - -std::vector -CaffeOpCreator::convertELU(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - const caffe::ELUParameter ¶ms = layer.elu_param(); - - auto elu = createOp(inputs[0], params.alpha()); - return {elu->getOutput(0)}; -} - -std::vector -CaffeOpCreator::convertEmbed(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - const auto ¶ms = layer.embed_param(); - auto data = createOp(convertBlob(layer.blobs(0))); - auto result = createOp(data->getOutput(0), inputs[0], 0)->getOutput(0); - - // Add the bias, if any. - if (params.bias_term()) - { - auto bias = createOp(convertBlob(layer.blobs(1)))->getOutput(0); - result = createOp(result, bias)->getOutput(0); - } - - return {result}; -} - -std::vector -CaffeOpCreator::convertSigmoid(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - auto result = createOp(inputs[0]); - return {result->getOutput(0)}; -} - -std::vector -CaffeOpCreator::convertTanH(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - auto tanh = createOp(inputs[0]); - return {tanh->getOutput(0)}; -} - -std::vector -CaffeOpCreator::convertEltwise(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - auto ¶ms = layer.eltwise_param(); - - mir::Operation::Output *result; - switch (params.operation()) - { - case caffe::EltwiseParameter::PROD: - { - result = createOp(inputs[0], inputs[1])->getOutput(0); - for (int i = 2; i < layer.bottom_size(); ++i) - { - result = createOp(result, inputs[i])->getOutput(0); - } - break; - } - case caffe::EltwiseParameter::SUM: - { - std::vector scaled_inputs = inputs; - if (params.coeff_size() > 0) - { - assert(params.coeff_size() == layer.bottom_size()); - for (int i = 0; i < layer.bottom_size(); i++) - { - if (params.coeff(i) != 1.0f) - { - const float coeff_val = params.coeff(i); - TensorVariant coeff_tensor({DataType::FLOAT32, {}}, &coeff_val); - auto coeff_const = createOp(coeff_tensor)->getOutput(0); - scaled_inputs[i] = createOp(coeff_const, inputs[i])->getOutput(0); - } - } - } - result = createOp(scaled_inputs[0], scaled_inputs[1])->getOutput(0); - for (int i = 2; i < layer.bottom_size(); ++i) - { - result = createOp(result, scaled_inputs[i])->getOutput(0); - } - break; - } - case caffe::EltwiseParameter::MAX: - { - result = createOp(inputs[0], inputs[1])->getOutput(0); - for (int i = 2; i < layer.bottom_size(); ++i) - { - result = createOp(result, inputs[i])->getOutput(0); - } - break; - } - default: - throw std::runtime_error("Unknown element-wise operation."); - } - return {result}; -} - -std::vector -CaffeOpCreator::convertSplit(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - std::vector outputs(layer.top_size(), inputs.at(0)); - return outputs; -} - -void CaffeOpCreator::checkLSTM(const caffe::LayerParameter &layer, - std::set &problems_ops_set) -{ - const auto ¶ms = layer.recurrent_param(); - if (params.expose_hidden()) - problems_ops_set.insert("LSTM: parameter 'expose_hidden' has unsupported value: " + - std::to_string(params.expose_hidden())); -} - -static TensorVariant createZeroedTensor(const mir::Shape &shape) -{ - // TODO For now it is hardcoded float32. - auto elem_type = mir::DataType::FLOAT32; - std::vector zeros(static_cast(shape.numElements()), 0.0f); - return TensorVariant({elem_type, shape}, zeros.data()); -} - -/* See the following links for details on implementation: - * https://github.com/BVLC/caffe/blob/master/src/caffe/layers/recurrent_layer.cpp - * https://github.com/BVLC/caffe/blob/master/src/caffe/layers/lstm_layer.cpp - * https://github.com/BVLC/caffe/blob/master/src/caffe/layers/lstm_unit_layer.cpp - * - * Inputs: - * x -- The time-varying input. Shape: [T, N, d0, d1, ..., dn]. - * cont -- The sequence continuation indicators. Shape: [T, N]. - * x_static -- The static (non-time-varying) input. Shape: [N, ...]. - * This parameter is optional and not currently supported. - * - * Additional inputs when parameter "expose_hidden" is true (not currently supported): - * h_0 -- The initial value of the hidden state. Shape: [1, N, D]. - * c_0 -- The initial value of the cell state. Shape: [1, N, D]. - * - * Learned parameters: - * xw -- x weights for input, output, forget and cell gates concatenated. - * Shape: [4 * D, d0 * d1 * ... * dn]. - * xb -- x biases for input, output, forget and cell gates concatenated. Shape: [4 * D]. - * hw -- h weights for input, output, forget and cell gates concatenated. Shape: [4 * D, D]. - * - * Outputs: - * h -- The time-varying output. Shape: [T, N, D]. - * - * Additional outputs when parameter "expose_hidden" is true (not currently supported): - * h_T -- The value of the hidden state at the last timestep. Shape: [1, N, D]. - * c_T -- The value of the cell state at the last timestep. Shape: [1, N, D]. - * - * Here: - * T - the number of timesteps, - * N - the number of independent streams. - * D - the number of hidden parameters. - * - * Formulas: - * c_cont = c[t-1] * cont[t] - * h_cont = h[t-1] * cont[t] - * i[t] = Sigmoid(x[t] . xw_i + xb_i + h_cont . hw_i) - * f[t] = Sigmoid(x[t] . xw_f + xb_f + h_cont . hw_f) - * o[t] = Sigmoid(x[t] . xw_o + xb_o + h_cont . hw_o) - * g[t] = Tanh(x[t] . xw_g + xb_g + h_cont . hw_g) - * c[t] = c_cont * f[t] + i[t] * g[t] - * h[t] = o[t] * Tanh(c[t]) - * - * Here: - * t -- the timestep (ranges from 1 to T), - * * -- the inner product, - * . -- the Hadamard product (elementwise product). - * - * In this implementation the inner products for all gates are performed as single inner product for - * efficiency. - */ -std::vector -CaffeOpCreator::convertLSTM(const caffe::LayerParameter &layer, - const std::vector &inputs) -{ - const auto ¶ms = layer.recurrent_param(); - - // Inputs to the layer. - auto x = inputs[0]; - auto cont = inputs[1]; - assert(inputs.size() == 2); - - const auto &x_shape = x->getShape(); - const int32_t seq_length = x_shape.dim(0); - const int32_t batch_size = x_shape.dim(1); - const int32_t hidden_size = params.num_output(); - - // Learned parameters of the layer. Tensors are transposed to match the ModelIR. - auto xw = createOp(convertBlob(layer.blobs(0)))->getOutput(0); - auto xb = createOp(convertBlob(layer.blobs(1)))->getOutput(0); - auto hw = createOp(convertBlob(layer.blobs(2)))->getOutput(0); - xw = createOp(xw, std::vector{1, 0})->getOutput(0); - hw = createOp(hw, std::vector{1, 0})->getOutput(0); - - // Add a dummy dimension so that element-wise operations perform properly. - cont = createOp(cont, Shape{seq_length, batch_size, 1})->getOutput(0); - - // Initialize cell and hidden states with zeros. - auto zero_tensor = createZeroedTensor(Shape{1, batch_size, hidden_size}); - auto c_t = createOp(zero_tensor)->getOutput(0); - auto h_t = createOp(zero_tensor)->getOutput(0); - - auto x_xw = createFullyConnected(x, xw, 2); - auto x_xw_b = createOp(x_xw, xb)->getOutput(0); - - // Split input and continuation tensors into seq_length slices. - std::vector x_xw_b_slices = createSplit(x_xw_b, seq_length, 0); - std::vector cont_slices = createSplit(cont, seq_length, 0); - std::vector h_slices(seq_length); - - for (int32_t t = 0; t < seq_length; t++) - { - auto c_cont_t = createOp(c_t, cont_slices[t])->getOutput(0); - auto h_cont_t = createOp(h_t, cont_slices[t])->getOutput(0); - - auto x_xw_b_t = x_xw_b_slices[t]; - auto h_hw_t = createFullyConnected(h_cont_t, hw, 2); - auto activation_inputs_concat = createOp(x_xw_b_t, h_hw_t)->getOutput(0); - auto activation_inputs = createSplit(activation_inputs_concat, 4, 2); - - auto i_t = createOp(activation_inputs[0])->getOutput(0); - auto f_t = createOp(activation_inputs[1])->getOutput(0); - auto o_t = createOp(activation_inputs[2])->getOutput(0); - auto g_t = createOp(activation_inputs[3])->getOutput(0); - - c_t = createOp(createOp(c_cont_t, f_t)->getOutput(0), - createOp(i_t, g_t)->getOutput(0)) - ->getOutput(0); - h_t = createOp(createOp(c_t)->getOutput(0), o_t)->getOutput(0); - - h_slices[t] = h_t; - } - - return {createOp(h_slices, 0)->getOutput(0)}; -} - -} // namespace mir_caffe diff --git a/compiler/mir-caffe-importer/requires.cmake b/compiler/mir-caffe-importer/requires.cmake deleted file mode 100644 index 1059c50..0000000 --- a/compiler/mir-caffe-importer/requires.cmake +++ /dev/null @@ -1 +0,0 @@ -require("mir") diff --git a/compiler/mir-caffe2-importer/CMakeLists.txt b/compiler/mir-caffe2-importer/CMakeLists.txt deleted file mode 100644 index da55839..0000000 --- a/compiler/mir-caffe2-importer/CMakeLists.txt +++ /dev/null @@ -1,29 +0,0 @@ -nnas_find_package(PytorchSource QUIET) -nnas_find_package(Protobuf QUIET) - -if (NOT PytorchSource_FOUND OR NOT Protobuf_FOUND) - return() -endif() - -Protobuf_Generate(CAFFE2_PROTO "${CMAKE_CURRENT_BINARY_DIR}/generated/caffe2" - "${PytorchSource_DIR}" "caffe2/proto/caffe2.proto") - -add_library(caffe2proto STATIC ${CAFFE2_PROTO_SOURCES}) -set_target_properties(caffe2proto PROPERTIES POSITION_INDEPENDENT_CODE ON) -target_include_directories(caffe2proto PUBLIC ${CAFFE2_PROTO_INCLUDE_DIRS}) -target_link_libraries(caffe2proto PUBLIC libprotobuf) - - -set(MIR_CAFFE2_IMPORTER_SOURCES - caffe2_importer.cpp - caffe2_importer.h - caffe2_op_creator.cpp - caffe2_op_creator.h - caffe2_op_types.h - caffe2_proto_helper.cpp - caffe2_proto_helper.h) - -add_library(mir_caffe2_importer STATIC ${MIR_CAFFE2_IMPORTER_SOURCES}) -set_target_properties(mir_caffe2_importer PROPERTIES POSITION_INDEPENDENT_CODE ON) -target_include_directories(mir_caffe2_importer PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}) -target_link_libraries(mir_caffe2_importer PUBLIC mir caffe2proto PRIVATE stdex) diff --git a/compiler/mir-caffe2-importer/caffe2_importer.cpp b/compiler/mir-caffe2-importer/caffe2_importer.cpp deleted file mode 100644 index 5a6eef0..0000000 --- a/compiler/mir-caffe2-importer/caffe2_importer.cpp +++ /dev/null @@ -1,343 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "caffe2_importer.h" -#include "caffe2/proto/caffe2.pb.h" -#include "caffe2_op_types.h" -#include "caffe2_op_creator.h" -#include "caffe2_proto_helper.h" - -#include "mir/ops/InputOp.h" -#include "mir/ops/OutputOp.h" - -#include -#include - -#include - -#include -#include -#include -#include -#include -#include -#include - -namespace -{ - -using namespace mir_caffe2; - -class Caffe2Importer -{ -public: - explicit Caffe2Importer(std::string predict_net, std::string init_net, - const std::vector> &input_shapes); - - /// @brief Load the model and convert it into a MIR Graph. - std::unique_ptr importModel(); - - ~Caffe2Importer(); - -private: - std::string _predictNet; - std::string _initNet; - std::unique_ptr _graph; - std::unique_ptr _predict_net; - std::unique_ptr _init_net; - std::unique_ptr _opCreator; - std::vector _inputShapes; - - static const std::map _operatorTypes; - - // Maps Caffe2 operator input names to corresponding MIR operation outputs. - std::unordered_map _blobNameToOutput; - - void import(); - std::unique_ptr createIR(); - - /** - * @brief Pass through caffe2 graph and collect ops unsupported by NNC - * @throw PassException with message, containing detected problems - */ - void collectUnsupportedOps(); - - /** - * @brief Creating MIR node from single caffe2 operator - */ - void createMIRNodesFromOp(const ::caffe2::OperatorDef &op); - - /** - * @brief Returns MIR operation outputs corresponding to the inputs of the given operator. - */ - std::vector getInputMIROps(const ::caffe2::OperatorDef &op); - - void setOutputForTensor(const std::string &tensor_name, Operation::Output *output); - mir::Operation::Output *getOutputForTensor(const std::string &name) const; - - /** - * @brief Mark output MIR nodes - */ - void setGraphOutputs(); -}; - -using namespace ::caffe2; -using mir::Shape; - -Caffe2Importer::Caffe2Importer(std::string predict_net, std::string init_net, - const std::vector> &input_shapes) - : _predictNet(std::move(predict_net)), _initNet(std::move(init_net)) -{ - for (auto &shape : input_shapes) - _inputShapes.emplace_back(shape); - - _graph = stdex::make_unique(); - _opCreator = stdex::make_unique(_graph.get()); -} - -Caffe2Importer::~Caffe2Importer() = default; - -static void loadModelFile(const std::string &filename, caffe2::NetDef *net) -{ - GOOGLE_PROTOBUF_VERIFY_VERSION; - - int file_handle = open(filename.c_str(), O_RDONLY); - - if (file_handle == -1) - throw std::runtime_error("Couldn't open file \"" + filename + "\": " + std::strerror(errno) + - "."); - - google::protobuf::io::FileInputStream file_stream(file_handle); - file_stream.SetCloseOnDelete(true); - - google::protobuf::io::CodedInputStream coded_stream(&file_stream); - coded_stream.SetTotalBytesLimit(INT_MAX, INT_MAX); - - if (!net->ParseFromCodedStream(&coded_stream)) - throw std::runtime_error("Couldn't parse file \"" + filename + "\"."); - - // If the file has not been consumed entirely, assume that the file is in the wrong format. - if (!coded_stream.ConsumedEntireMessage()) - throw std::runtime_error("File \"" + filename + "\" has not been consumed entirely."); -} - -void Caffe2Importer::import() -{ - _predict_net = stdex::make_unique(); - loadModelFile(_predictNet, _predict_net.get()); - - _init_net = stdex::make_unique(); - loadModelFile(_initNet, _init_net.get()); - - collectUnsupportedOps(); -} - -std::unique_ptr Caffe2Importer::createIR() -{ - // Load initializers. - for (const auto &op : _init_net->op()) - createMIRNodesFromOp(op); - - // Create inputs. This has to be done after processing initializers, because they may contain - // fake inputs. - // TODO Caffe2 does not provide a way to detect model inputs and outputs. For now assume that: - // - there is exactly one input; - // - the input is for the first layer; - // - the input has 'float' element type. - const auto &input_name = _predict_net->op(0).input(0); - mir::TensorType input_type(mir::DataType::FLOAT32, _inputShapes[0]); - auto input = _graph->create(input_type)->getOutput(0); - setOutputForTensor(input_name, input); - - for (const auto &op : _predict_net->op()) - createMIRNodesFromOp(op); - - setGraphOutputs(); - - return std::move(_graph); -} - -std::unique_ptr Caffe2Importer::importModel() -{ - import(); - return createIR(); -} - -void Caffe2Importer::collectUnsupportedOps() -{ - std::set unsupportedOps; - for (const auto &op : _predict_net->op()) - { - if (_operatorTypes.find(op.type()) == _operatorTypes.end()) - unsupportedOps.insert(op.type()); - } - - if (!unsupportedOps.empty()) - { - std::string exceptionMsg("Can't load model, unsupported operators:"); - for (const auto &op : unsupportedOps) - exceptionMsg.append("\n * " + op); - throw std::runtime_error(exceptionMsg); - } -} - -void Caffe2Importer::createMIRNodesFromOp(const OperatorDef &op) -{ - std::vector outputs; - - auto inputs = getInputMIROps(op); - - SupportedCaffe2OpType opType = _operatorTypes.at(op.type()); - switch (opType) - { - case SupportedCaffe2OpType::constantFill: - case SupportedCaffe2OpType::givenTensorFill: - case SupportedCaffe2OpType::givenTensorInt64Fill: - outputs = _opCreator->convertConstant(inputs, op); - break; - case SupportedCaffe2OpType::add: - outputs = _opCreator->convertAdd(inputs, op); - break; - case SupportedCaffe2OpType::averagePool: - outputs = _opCreator->convertAveragePool(inputs, op); - break; - case SupportedCaffe2OpType::conv: - outputs = _opCreator->convertConv(inputs, op); - break; - case SupportedCaffe2OpType::concat: - outputs = _opCreator->convertConcat(inputs, op); - break; - case SupportedCaffe2OpType::dropout: - outputs = _opCreator->convertDropout(inputs, op); - break; - case SupportedCaffe2OpType::FC: - outputs = _opCreator->convertFC(inputs, op); - break; - case SupportedCaffe2OpType::maxPool: - outputs = _opCreator->convertMaxPool(inputs, op); - break; - case SupportedCaffe2OpType::mul: - outputs = _opCreator->convertMul(inputs, op); - break; - case SupportedCaffe2OpType::relu: - outputs = _opCreator->convertRelu(inputs); - break; - case SupportedCaffe2OpType::resizeNearest: - outputs = _opCreator->convertResizeNearest(inputs, op); - break; - case SupportedCaffe2OpType::sigmoid: - outputs = _opCreator->convertSigmoid(inputs); - break; - case SupportedCaffe2OpType::softmax: - outputs = _opCreator->convertSoftmax(inputs, op); - break; - case SupportedCaffe2OpType::spatialBN: - outputs = _opCreator->convertSpatialBN(inputs, op); - break; - case SupportedCaffe2OpType::sum: - outputs = _opCreator->convertSum(inputs); - break; - case SupportedCaffe2OpType::clip: - outputs = _opCreator->convertClip(inputs, op); - break; - case SupportedCaffe2OpType::reshape: - outputs = _opCreator->convertReshape(inputs, op); - break; - default: - assert(false && "All unsupported types should have been found before this pass."); - } - - for (size_t i = 0; i < outputs.size(); ++i) - { - setOutputForTensor(op.output(i), outputs[i]); - } -} - -std::vector Caffe2Importer::getInputMIROps(const OperatorDef &op) -{ - std::vector inputs; - - for (const auto &input_name : op.input()) - { - inputs.push_back(getOutputForTensor(input_name)); - } - - return inputs; -} - -void Caffe2Importer::setOutputForTensor(const std::string &tensor_name, Operation::Output *output) -{ - auto it = _blobNameToOutput.find(tensor_name); - if (it != _blobNameToOutput.cend()) - { - // caffe2 input blob name could be same as output blob name, and next line will overwrite - // '_blobNameToOpOutput' element, but in all networks that I saw it was not a problem - it->second->setName(""); - } - output->setName(tensor_name); - _blobNameToOutput[tensor_name] = output; -} - -mir::Operation::Output *Caffe2Importer::getOutputForTensor(const std::string &name) const -{ - return _blobNameToOutput.at(name); -} - -void Caffe2Importer::setGraphOutputs() -{ - // Create outputs. - // TODO Caffe2 does not provide a way to detect model inputs and outputs. For now assume that: - // - there is exactly one output; - // - the output is from the last layer. - const auto &output_name = _predict_net->op().rbegin()->output(0); - auto output = getOutputForTensor(output_name); - _graph->create(output); -} - -const std::map Caffe2Importer::_operatorTypes = { - {"Add", SupportedCaffe2OpType::add}, - {"AveragePool", SupportedCaffe2OpType::averagePool}, - {"Conv", SupportedCaffe2OpType::conv}, - {"Concat", SupportedCaffe2OpType::concat}, - {"ConstantFill", SupportedCaffe2OpType::constantFill}, - {"Dropout", SupportedCaffe2OpType::dropout}, - {"FC", SupportedCaffe2OpType::FC}, - {"GivenTensorFill", SupportedCaffe2OpType::givenTensorFill}, - {"MaxPool", SupportedCaffe2OpType::maxPool}, - {"Mul", SupportedCaffe2OpType::mul}, - {"Relu", SupportedCaffe2OpType::relu}, - {"ResizeNearest", SupportedCaffe2OpType::resizeNearest}, - {"Sigmoid", SupportedCaffe2OpType::sigmoid}, - {"Softmax", SupportedCaffe2OpType::softmax}, - {"SpatialBN", SupportedCaffe2OpType::spatialBN}, - {"Sum", SupportedCaffe2OpType::sum}, - {"Clip", SupportedCaffe2OpType::clip}, - {"Reshape", SupportedCaffe2OpType::reshape}, - {"GivenTensorInt64Fill", SupportedCaffe2OpType::givenTensorInt64Fill}, -}; -} - -namespace mir_caffe2 -{ - -std::unique_ptr loadModel(std::string predict_net, std::string init_net, - const std::vector> &input_shapes) -{ - Caffe2Importer importer(std::move(predict_net), std::move(init_net), input_shapes); - return importer.importModel(); -} - -} // namespace mir_caffe2 diff --git a/compiler/mir-caffe2-importer/caffe2_op_creator.cpp b/compiler/mir-caffe2-importer/caffe2_op_creator.cpp deleted file mode 100644 index d279fb1..0000000 --- a/compiler/mir-caffe2-importer/caffe2_op_creator.cpp +++ /dev/null @@ -1,547 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "caffe2_op_creator.h" -#include "caffe2_proto_helper.h" - -#include "mir/ops/AddOp.h" -#include "mir/ops/AvgPool2DOp.h" -#include "mir/ops/CappedReluOp.h" -#include "mir/ops/ConcatOp.h" -#include "mir/ops/ConstantOp.h" -#include "mir/ops/Conv2DOp.h" -#include "mir/ops/FullyConnectedOp.h" -#include "mir/ops/MaxPool2DOp.h" -#include "mir/ops/MulOp.h" -#include "mir/ops/ReluOp.h" -#include "mir/ops/ReshapeOp.h" -#include "mir/ops/ResizeOp.h" -#include "mir/ops/SigmoidOp.h" -#include "mir/ops/SoftmaxOp.h" -#include "mir/ops/TransposeOp.h" - -#include "mir/Index.h" -#include "mir/Shape.h" -#include "mir/ShapeRange.h" -#include "mir/Tensor.h" -#include "mir/TensorUtil.h" - -#include -#include - -namespace mir_caffe2 -{ - -using namespace ::caffe2; -using namespace mir; - -// -// Helper functions -// - -static std::pair, std::vector> -getPadding(const ::caffe2::OperatorDef &op) -{ - - if (hasArgument(op.arg(), "pads")) - { - // pads order: t l b r - auto pads_arg = findArgumentByName(op.arg(), "pads"); - - std::vector paddings; - for (const auto &pad : pads_arg.ints()) - paddings.push_back(static_cast(pad)); - - assert(paddings.size() == 4); - - int32_t pad_t = paddings[0]; - int32_t pad_l = paddings[1]; - int32_t pad_b = paddings[2]; - int32_t pad_r = paddings[3]; - - std::vector padding_before{pad_t, pad_l}; - std::vector padding_after{pad_b, pad_r}; - return {padding_before, padding_after}; - } - - bool has_custom_pad = hasArgument(op.arg(), "pad_l") || hasArgument(op.arg(), "pad_r") || - hasArgument(op.arg(), "pad_t") || hasArgument(op.arg(), "pad_b"); - - if (has_custom_pad) - { - int32_t pad_l = getSingleArgument(op, "pad_l", 0); - int32_t pad_t = getSingleArgument(op, "pad_t", 0); - int32_t pad_r = getSingleArgument(op, "pad_r", 0); - int32_t pad_b = getSingleArgument(op, "pad_b", 0); - - std::vector padding_before{pad_t, pad_l}; - std::vector padding_after{pad_b, pad_r}; - return {padding_before, padding_after}; - } - - int32_t pad = getSingleArgument(op, "pad", 0); - return {{pad, pad}, {pad, pad}}; -} - -static std::vector getStrides(const ::caffe2::OperatorDef &op) -{ - std::vector strides; - - if (hasArgument(op.arg(), "stride")) - { - std::int32_t stride = getSingleArgument(op, "stride", 1); - strides = {stride, stride}; - } - - if (hasArgument(op.arg(), "strides")) - { - // strides order: h w - auto strides_arg = findArgumentByName(op.arg(), "strides"); - for (const auto &s : strides_arg.ints()) - strides.push_back(s); - } - - assert(!strides.empty() && "Strides not found"); - - return strides; -} - -static std::vector getWindowSize(const ::caffe2::OperatorDef &op, - const std::vector &inputs) -{ - int is_global_pooling = getSingleArgument(op, "global_pooling", 0); - bool has_custom_kernel_size = - hasArgument(op.arg(), "kernel_h") || hasArgument(op.arg(), "kernel_w"); - bool has_custom_kernels_size = hasArgument(op.arg(), "kernels"); - - int kernel_h(0), kernel_w(0); - if (is_global_pooling) - { - const auto &input_shape = inputs[0]->getShape(); - assert(input_shape.rank() == 4 && "getWindowSize() inputs must be of rank 4"); - kernel_h = input_shape.dim(2); - kernel_w = input_shape.dim(3); - } - else - { - if (has_custom_kernel_size) - { - kernel_h = getSingleArgument(op, "kernel_h", 0); - kernel_w = getSingleArgument(op, "kernel_w", 0); - } - else - { - if (has_custom_kernels_size) - { - // kernels order: h w - std::vector kernels; - auto kernels_arg = findArgumentByName(op.arg(), "kernels"); - for (const auto &ker : kernels_arg.ints()) - kernels.push_back(static_cast(ker)); - assert(kernels.size() == 2); - kernel_h = kernels[0]; - kernel_w = kernels[1]; - } - else - { - kernel_h = kernel_w = getSingleArgument(op, "kernel", 0); - } - } - } - return {kernel_h, kernel_w}; -} - -// -// Check functions -// - -static void checkLayout(const OperatorDef &op) -{ - if (getSingleArgument(op, "order", "NCHW") != "NCHW") - throw std::runtime_error(op.type() + ": only 'NCHW' axis order is supported"); -} - -static void checkConvLikeOp(const ::caffe2::OperatorDef &op) -{ - checkLayout(op); - - // Padding - bool has_custom_pad = hasArgument(op.arg(), "pad_l") || hasArgument(op.arg(), "pad_r") || - hasArgument(op.arg(), "pad_t") || hasArgument(op.arg(), "pad_b"); - - if (has_custom_pad && hasArgument(op.arg(), "pad")) - throw std::runtime_error("Custom pad can't be combined with overall pad"); - - if (has_custom_pad && - !(hasArgument(op.arg(), "pad_l") && hasArgument(op.arg(), "pad_r") && - hasArgument(op.arg(), "pad_t") && hasArgument(op.arg(), "pad_b"))) - throw std::runtime_error("If one custom pad specified - all custom pads must be specified"); - - // Kernel size - bool has_custom_kernel_size = - hasArgument(op.arg(), "kernel_h") || hasArgument(op.arg(), "kernel_w"); - - if (has_custom_kernel_size && hasArgument(op.arg(), "kernel")) - throw std::runtime_error("Custom kernel size can't be combined with overall kernel size"); - - if (has_custom_kernel_size && - !(hasArgument(op.arg(), "kernel_h") && hasArgument(op.arg(), "kernel_w"))) - throw std::runtime_error( - "If one custom kernel size specified - all custom kernel sizes must be specified"); -} - -static mir::TensorVariant createTensor(const OperatorDef &op) -{ - assert(hasArgument(op.arg(), "shape") && hasArgument(op.arg(), "values")); - - const auto &shape = findArgumentByName(op.arg(), "shape"); - const auto &values = findArgumentByName(op.arg(), "values"); - - mir::DataType element_type; - const void *src_data; - // if values on floats - if (!values.floats().empty()) - { - element_type = mir::DataType::FLOAT32; - src_data = values.floats().data(); - } - else - { - assert(!values.ints().empty()); - if (op.type() == "GivenTensorInt64Fill") - { - element_type = mir::DataType::INT64; - } - else - { - element_type = mir::DataType::INT32; - } - src_data = values.ints().data(); - } - - mir::Shape tensor_shape(shape.ints_size()); - - for (int i = 0; i < shape.ints_size(); ++i) - { - tensor_shape.dim(i) = shape.ints(i); - } - - return mir::TensorVariant({element_type, tensor_shape}, src_data); -} - -// -// Convert functions -// - -std::vector -Caffe2OpCreator::convertConstant(const std::vector &inputs, - const ::caffe2::OperatorDef &op) -{ - // Constant may not contain any data if it is a fake input. - if (!hasArgument(op.arg(), "values")) - return {}; - - return {createOp(createTensor(op))->getOutput(0)}; -} - -std::vector -Caffe2OpCreator::convertAdd(const std::vector &inputs, - const ::caffe2::OperatorDef &op) -{ - assert(inputs.size() == 2); - auto lhs = inputs[0]; - auto rhs = inputs[1]; - - if (getSingleArgument(op, "broadcast", 0) != 0) - { - // FIXME This only works when 'axis' == 1 and the second input is 1-D. - rhs = createOp(rhs, Shape{1, rhs->getShape().dim(0), 1, 1})->getOutput(0); - auto result = createOp(lhs, rhs)->getOutput(0); - return {result}; - } - - auto result = createOp(lhs, rhs)->getOutput(0); - return {result}; -} - -std::vector -Caffe2OpCreator::convertAveragePool(const std::vector &inputs, - const OperatorDef &op) -{ - checkConvLikeOp(op); - - assert(inputs.size() == 1); - auto input = inputs[0]; - - AvgPool2DOpAttributes attributes; - std::tie(attributes.padding_before, attributes.padding_after) = getPadding(op); - attributes.window = getWindowSize(op, inputs); - attributes.strides = getStrides(op); - attributes.include_pad = false; - attributes.data_format = DataFormat::NCHW; - auto result = createOp(input, attributes)->getOutput(0); - return {result}; -} - -std::vector -Caffe2OpCreator::convertConv(const std::vector &inputs, - const ::caffe2::OperatorDef &op) -{ - // dilation order: h w (not used) - mir::Conv2DOpAttributes attributes; - attributes.strides = getStrides(op); - std::tie(attributes.padding_before, attributes.padding_after) = getPadding(op); - attributes.num_groups = getSingleArgument(op, "group", 1); - attributes.data_format = DataFormat::NCHW; - - std::vector perm{0, 2, 3, 1}; // OIHW -> OHWI - auto kernel = createOp(inputs[1], perm)->getOutput(0); - auto result = createOp(inputs[0], kernel, attributes)->getOutput(0); - - if (op.input_size() > 2) - { - auto bias = inputs[2]; - bias = createOp(bias, Shape{1, bias->getShape().dim(0), 1, 1})->getOutput(0); - result = createOp(result, bias)->getOutput(0); - } - - return {result}; -} - -std::vector -Caffe2OpCreator::convertConcat(const std::vector &inputs, - const ::caffe2::OperatorDef &op) -{ - checkLayout(op); - - // `1` corresponds to the default (channels) axis. - int axis = getSingleArgument(op, "axis", 1); - auto result = createOp(inputs, axis); - return {result->getOutput(0)}; -} - -std::vector -Caffe2OpCreator::convertDropout(const std::vector &inputs, - const ::caffe2::OperatorDef &) -{ - // This is a no-op in inference mode. - return {inputs[0]}; -} - -std::vector -Caffe2OpCreator::convertFC(const std::vector &inputs, - const ::caffe2::OperatorDef &op) -{ - for (auto &s : {"axis", "axis_w", "float16_compute"}) - if (hasArgument(op.arg(), s)) - throw std::runtime_error(std::string("FC: only default '") + s + "' value is supported"); - - const auto &input_shape = inputs[0]->getShape(); - // Transform input into 2-D tensor by flattening axes - Shape shape{input_shape.dim(0), input_shape.numElements() / input_shape.dim(0)}; - - auto reshape = createOp(inputs[0], shape)->getOutput(0); - auto weights = - createOp(inputs[1], std::vector{1, 0})->getOutput(0); - auto result = createOp(reshape, weights)->getOutput(0); - result = createOp(result, inputs[2])->getOutput(0); - - return {result}; -} - -std::vector -Caffe2OpCreator::convertMaxPool(const std::vector &inputs, - const OperatorDef &op) -{ - checkConvLikeOp(op); - - assert(inputs.size() == 1); - auto input = inputs[0]; - - MaxPool2DOpAttributes attributes; - std::tie(attributes.padding_before, attributes.padding_after) = getPadding(op); - attributes.window = getWindowSize(op, inputs); - attributes.strides = getStrides(op); - attributes.data_format = DataFormat::NCHW; - auto result = createOp(input, attributes)->getOutput(0); - return {result}; -} - -std::vector -Caffe2OpCreator::convertMul(const std::vector &inputs, - const ::caffe2::OperatorDef &op) -{ - assert(inputs.size() == 2); - auto lhs = inputs[0]; - auto rhs = inputs[1]; - - if (getSingleArgument(op, "broadcast", 0) != 0) - { - // FIXME This only works when `axis` == 1 and the second input is 1-D. - rhs = createOp(rhs, Shape{1, rhs->getShape().dim(0), 1, 1})->getOutput(0); - auto result = createOp(lhs, rhs)->getOutput(0); - return {result}; - } - - auto result = createOp(lhs, rhs)->getOutput(0); - return {result}; -} - -std::vector -Caffe2OpCreator::convertRelu(const std::vector &inputs) -{ - auto relu = createOp(inputs[0]); - return {relu->getOutput(0)}; -} - -std::vector -Caffe2OpCreator::convertResizeNearest(const std::vector &inputs, - const ::caffe2::OperatorDef &op) -{ - std::vector scales(4); - assert(inputs[0]->getShape().rank() == 4 && "only 4d tensors is supported"); - // Assuming NCHW format. - scales[0] = 1.0f; - scales[1] = 1.0f; - scales[2] = getSingleArgument(op, "height_scale", 1.0f); - scales[3] = getSingleArgument(op, "width_scale", 1.0f); - auto result = - createOp(inputs[0], ops::ResizeOp::ResizeMethod::nearestNeighbor, scales) - ->getOutput(0); - return {result}; -} - -std::vector -Caffe2OpCreator::convertSigmoid(const std::vector &inputs) -{ - auto result = createOp(inputs[0]); - return {result->getOutput(0)}; -} - -std::vector -Caffe2OpCreator::convertSoftmax(const std::vector &inputs, - const ::caffe2::OperatorDef &op) -{ - int axis = getSingleArgument(op, "axis", 1); - auto softmax = createOp(inputs[0], axis); - return {softmax->getOutput(0)}; -} - -std::vector -Caffe2OpCreator::convertSpatialBN(const std::vector &inputs, - const ::caffe2::OperatorDef &op) -{ - checkLayout(op); - - // Sanity checks - if (op.input_size() != 5) - throw std::runtime_error( - "SpatialBN must have exactly 5 inputs ('sums' and 'sumsq' are not supported yet)"); - if (getSingleArgument(op, "is_test", 1) != 1) - throw std::runtime_error("SpatialBN: only test mode supported"); - - // overall_res = (X - mean) / sqrt(var + epsilon) * scale + bias - - auto scale_op = dynamic_cast(inputs[1]->getNode()); - auto bias_op = dynamic_cast(inputs[2]->getNode()); - auto mean_op = dynamic_cast(inputs[3]->getNode()); - auto var_op = dynamic_cast(inputs[4]->getNode()); - if (scale_op == nullptr || bias_op == nullptr || mean_op == nullptr || var_op == nullptr) - throw std::runtime_error( - "SpatialBN: non-constant 'scale', 'bias', 'mean' and 'var' inputs are not supported yet."); - - const auto &scale_tensor = scale_op->getValue(); - const auto &bias_tensor = bias_op->getValue(); - const auto &mean_tensor = mean_op->getValue(); - const auto &var_tensor = var_op->getValue(); - float eps = getSingleArgument(op, "epsilon", 1e-5f); - - // res1 = X - mean - Tensor bias_data(mean_tensor); - for (auto &idx : ShapeRange(bias_data.getShape())) - bias_data.at(idx) *= -1; - - auto mean = createOp(mean_tensor)->getOutput(0); - mean = createOp(mean, Shape{1, mean->getShape().dim(0), 1, 1})->getOutput(0); - auto result = createOp(inputs[0], mean)->getOutput(0); - - // res2 = res1 * scale / (var + epsilon) - Tensor multiplier(scale_tensor); - for (auto &idx : ShapeRange(scale_tensor.getShape())) - multiplier.at(idx) /= std::sqrt(*reinterpret_cast(var_tensor.at(idx)) + eps); - auto scale = createOp(scale_tensor)->getOutput(0); - scale = createOp(scale, Shape{1, scale->getShape().dim(0), 1, 1})->getOutput(0); - result = createOp(result, scale)->getOutput(0); - - // overall_res = res2 + bias - auto bias = createOp(bias_tensor)->getOutput(0); - bias = createOp(bias, Shape{1, bias->getShape().dim(0), 1, 1})->getOutput(0); - result = createOp(result, bias)->getOutput(0); - - return {result}; -} - -std::vector -Caffe2OpCreator::convertSum(const std::vector &inputs) -{ - auto result = createOp(inputs[0], inputs[1])->getOutput(0); - for (int i = 2; i < static_cast(inputs.size()); ++i) - { - result = createOp(result, inputs[i])->getOutput(0); - } - return {result}; -} - -std::vector -Caffe2OpCreator::convertClip(const std::vector &inputs, - const ::caffe2::OperatorDef &op) -{ - - float max = getSingleArgument(op, "max", float(0)); - float min = getSingleArgument(op, "min", float(0)); - - assert(max > 0.0 && min == 0.0 && "Support only if clip is CappedRelu"); - auto cap_relu = createOp(inputs[0], max); - - return {cap_relu->getOutput(0)}; -} - -std::vector -Caffe2OpCreator::convertReshape(const std::vector &inputs, - const ::caffe2::OperatorDef &op) -{ - auto shape_op = dynamic_cast(inputs[1]->getNode()); - if (shape_op == nullptr) - throw std::runtime_error("Reshape: non-constant shape is not supported yet."); - - const auto &shape_tensor = shape_op->getValue(); - - Tensor out_shape_tensor(shape_tensor); - - ShapeRange range(out_shape_tensor.getShape()); - std::vector shape_vec; - for (const auto &index : range) - { - shape_vec.push_back(static_cast(out_shape_tensor.at(index))); - } - Shape out_shape(shape_vec); - - auto reshape = createOp(inputs[0], out_shape); - - return {reshape->getOutput(0)}; -} - -} // namespace mir_caffe2 diff --git a/compiler/mir-caffe2-importer/caffe2_proto_helper.cpp b/compiler/mir-caffe2-importer/caffe2_proto_helper.cpp deleted file mode 100644 index a7cde64..0000000 --- a/compiler/mir-caffe2-importer/caffe2_proto_helper.cpp +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "caffe2_proto_helper.h" - -namespace mir_caffe2 -{ - -const ::caffe2::Argument &findArgumentByName(RepArgument args, const std::string &name) -{ - for (auto &arg : args) - if (arg.name() == name) - return arg; - throw std::runtime_error("Can't find argument with name: " + name); -} - -const bool hasArgument(RepArgument args, const std::string &name) -{ - for (auto &arg : args) - if (arg.name() == name) - return true; - return false; -} - -int getSingleArgument(const ::caffe2::OperatorDef &op, const std::string &argument_name, - const int default_value) -{ - if (hasArgument(op.arg(), argument_name)) - return static_cast(findArgumentByName(op.arg(), argument_name).i()); - return default_value; -} - -float getSingleArgument(const ::caffe2::OperatorDef &op, const std::string &argument_name, - const float default_value) -{ - if (hasArgument(op.arg(), argument_name)) - return findArgumentByName(op.arg(), argument_name).f(); - return default_value; -} - -std::string getSingleArgument(const ::caffe2::OperatorDef &op, const std::string &argument_name, - const std::string &default_value) -{ - if (hasArgument(op.arg(), argument_name)) - return findArgumentByName(op.arg(), argument_name).s(); - return default_value; -} - -} // namespace mir_caffe2 diff --git a/compiler/mir-caffe2-importer/caffe2_proto_helper.h b/compiler/mir-caffe2-importer/caffe2_proto_helper.h deleted file mode 100644 index 4c47ede..0000000 --- a/compiler/mir-caffe2-importer/caffe2_proto_helper.h +++ /dev/null @@ -1,40 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MIR_CAFFE2_PROTO_HELPER_H -#define MIR_CAFFE2_PROTO_HELPER_H - -#include "caffe2/proto/caffe2.pb.h" - -namespace mir_caffe2 -{ - -using RepArgument = const ::google::protobuf::RepeatedPtrField<::caffe2::Argument> &; - -const ::caffe2::Argument &findArgumentByName(RepArgument args, const std::string &name); - -const bool hasArgument(RepArgument args, const std::string &name); - -int getSingleArgument(const ::caffe2::OperatorDef &op, const std::string &argument_name, - int default_value); -float getSingleArgument(const ::caffe2::OperatorDef &op, const std::string &argument_name, - float default_value); -std::string getSingleArgument(const ::caffe2::OperatorDef &op, const std::string &argument_name, - const std::string &default_value); - -} // namespace mir_caffe2 - -#endif // MIR_CAFFE2_PROTO_HELPER_H diff --git a/compiler/mir-caffe2-importer/requires.cmake b/compiler/mir-caffe2-importer/requires.cmake deleted file mode 100644 index 1059c50..0000000 --- a/compiler/mir-caffe2-importer/requires.cmake +++ /dev/null @@ -1 +0,0 @@ -require("mir") diff --git a/compiler/mir-interpreter/README.md b/compiler/mir-interpreter/README.md new file mode 100644 index 0000000..4ed0c73 --- /dev/null +++ b/compiler/mir-interpreter/README.md @@ -0,0 +1 @@ +# mir-interpreter diff --git a/compiler/mir-onnx-importer/AttributeHelpers.h b/compiler/mir-onnx-importer/AttributeHelpers.h deleted file mode 100644 index d5cc150..0000000 --- a/compiler/mir-onnx-importer/AttributeHelpers.h +++ /dev/null @@ -1,105 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MIR_ONNX_ATTRIBUTE_HELPERS_H -#define MIR_ONNX_ATTRIBUTE_HELPERS_H - -#include "onnx/onnx.pb.h" - -#include -#include -#include -#include -#include -#include -#include - -namespace mir_onnx -{ - -template T getAttributeValue(const onnx::AttributeProto &attribute) = delete; - -template <> inline float getAttributeValue(const onnx::AttributeProto &attribute) -{ - assert(attribute.type() == onnx::AttributeProto::FLOAT); - return attribute.f(); -} - -template <> inline std::int64_t getAttributeValue(const onnx::AttributeProto &attribute) -{ - assert(attribute.type() == onnx::AttributeProto::INT); - return attribute.i(); -} - -template <> inline std::string getAttributeValue(const onnx::AttributeProto &attribute) -{ - assert(attribute.type() == onnx::AttributeProto::STRING); - return attribute.s(); -} - -template <> inline onnx::TensorProto getAttributeValue(const onnx::AttributeProto &attribute) -{ - assert(attribute.type() == onnx::AttributeProto::TENSOR); - return attribute.t(); -} - -template <> -inline std::vector getAttributeValue(const onnx::AttributeProto &attribute) -{ - assert(attribute.type() == onnx::AttributeProto::INTS); - // TODO Check that values fit. - return {attribute.ints().cbegin(), attribute.ints().cend()}; -} - -template <> -inline std::vector getAttributeValue(const onnx::AttributeProto &attribute) -{ - assert(attribute.type() == onnx::AttributeProto::INTS); - return {attribute.ints().cbegin(), attribute.ints().cend()}; -} - -inline const onnx::AttributeProto *findAttribute(const onnx::NodeProto &node, - const std::string &name) -{ - const auto &attributes = node.attribute(); - const auto it = std::find_if( - attributes.cbegin(), attributes.cend(), - [&name](const onnx::AttributeProto &attribute) { return attribute.name() == name; }); - if (it == attributes.cend()) - return nullptr; - return &*it; -} - -template T getAttributeValue(const onnx::NodeProto &node, const std::string &name) -{ - const auto *attribute = findAttribute(node, name); - if (attribute == nullptr) - throw std::runtime_error("Cannot find attribute '" + name + "' in node '" + node.name() + "'."); - return getAttributeValue(*attribute); -} - -template -T getAttributeValue(const onnx::NodeProto &node, const std::string &name, T default_value) -{ - const auto *attribute = findAttribute(node, name); - if (attribute == nullptr) - return std::move(default_value); - return getAttributeValue(*attribute); -} - -} // namespace mir_onnx - -#endif // MIR_ONNX_ATTRIBUTE_HELPERS_H diff --git a/compiler/mir-onnx-importer/CMakeLists.txt b/compiler/mir-onnx-importer/CMakeLists.txt deleted file mode 100644 index 5f27bc0..0000000 --- a/compiler/mir-onnx-importer/CMakeLists.txt +++ /dev/null @@ -1,119 +0,0 @@ -nnas_find_package(ONNXSource EXACT 1.6.0 QUIET) -nnas_find_package(Protobuf QUIET) - -if (NOT ONNXSource_FOUND) - return() -endif () - -if (NOT Protobuf_FOUND) - return() -endif () - -Protobuf_Generate(MIR_ONNX_PROTO - ${CMAKE_CURRENT_BINARY_DIR}/generated - ${ONNXSource_DIR} - onnx/onnx.proto) - -add_library(mir_onnx_proto STATIC ${MIR_ONNX_PROTO_SOURCES}) -set_target_properties(mir_onnx_proto PROPERTIES POSITION_INDEPENDENT_CODE ON) -target_include_directories(mir_onnx_proto PUBLIC ${MIR_ONNX_PROTO_INCLUDE_DIRS}) -target_link_libraries(mir_onnx_proto PUBLIC libprotobuf) - -set(MIR_ONNX_IMPORTER_SOURCES - AttributeHelpers.h - ConvPoolHelpers.cpp - ConvPoolHelpers.h - ONNXHelpers.cpp - ONNXHelpers.h - ONNXImporterImpl.cpp - ONNXImporterImpl.h - ONNXNodeConverterRegistry.h - ONNXNodeConverterRegistry.cpp - ONNXOpRegistration.h - Op/Abs.cpp - Op/Abs.h - Op/Add.cpp - Op/Add.h - Op/AveragePool.cpp - Op/AveragePool.h - Op/BatchNormalization.cpp - Op/BatchNormalization.h - Op/Concat.cpp - Op/Concat.h - Op/Constant.cpp - Op/Constant.h - Op/Conv.cpp - Op/Conv.h - Op/ConvTranspose.cpp - Op/ConvTranspose.h - Op/Div.cpp - Op/Div.h - Op/Dropout.cpp - Op/Dropout.h - Op/Equal.cpp - Op/Equal.h - Op/Expand.cpp - Op/Expand.h - Op/Flatten.cpp - Op/Flatten.h - Op/Gather.cpp - Op/Gather.h - Op/Greater.cpp - Op/Greater.h - Op/Gemm.cpp - Op/Gemm.h - Op/Identity.cpp - Op/Identity.h - Op/Less.cpp - Op/Less.h - Op/MatMul.cpp - Op/MatMul.h - Op/GlobalAveragePool.cpp - Op/GlobalAveragePool.h - Op/Max.cpp - Op/Max.h - Op/MaxPool.cpp - Op/MaxPool.h - Op/Mul.cpp - Op/Mul.h - Op/Pad.cpp - Op/Pad.h - Op/Reciprocal.cpp - Op/Reciprocal.h - Op/ReduceMean.cpp - Op/ReduceMean.h - Op/Relu.cpp - Op/Relu.h - Op/Reshape.cpp - Op/Reshape.h - Op/Shape.cpp - Op/Shape.h - Op/Sigmoid.cpp - Op/Sigmoid.h - Op/Softmax.cpp - Op/Softmax.h - Op/Sqrt.cpp - Op/Sqrt.h - Op/Sub.cpp - Op/Sub.h - Op/Sum.cpp - Op/Sum.h - Op/Tanh.cpp - Op/Tanh.h - Op/Transpose.cpp - Op/Transpose.h - Op/Unsqueeze.cpp - Op/Unsqueeze.h - Op/Upsample.cpp - Op/Upsample.h) - -add_library(mir_onnx_importer STATIC ${MIR_ONNX_IMPORTER_SOURCES}) -set_target_properties(mir_onnx_importer PROPERTIES POSITION_INDEPENDENT_CODE ON) -target_include_directories(mir_onnx_importer PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}) -target_link_libraries(mir_onnx_importer PUBLIC mir_onnx_proto mir PRIVATE stdex mir_interpreter) - -nnas_find_package(GTest REQUIRED) - -file(GLOB_RECURSE TEST_SOURCES "*.test.cpp") -GTest_AddTest(mir_onnx_importer_test ${TEST_SOURCES}) -target_link_libraries(mir_onnx_importer_test mir_onnx_importer) diff --git a/compiler/mir-onnx-importer/ONNXImporterImpl.cpp b/compiler/mir-onnx-importer/ONNXImporterImpl.cpp deleted file mode 100644 index c331041..0000000 --- a/compiler/mir-onnx-importer/ONNXImporterImpl.cpp +++ /dev/null @@ -1,241 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "ONNXImporterImpl.h" -#include "ONNXHelpers.h" -#include "ONNXOpRegistration.h" -#include "onnx/onnx.pb.h" - -#include "mir/Shape.h" -#include "mir/TensorUtil.h" - -#include "mir/ops/ConstantOp.h" - -#include - -#include -#include -#include -#include -#include -#include -#include - -namespace mir_onnx -{ - -namespace -{ - -class ONNXImporterImpl final -{ -public: - ONNXImporterImpl(); - ~ONNXImporterImpl(); - /// @brief Load the model and convert it into a MIR Graph. - std::unique_ptr importModelFromBinaryFile(const std::string &filename); - std::unique_ptr importModelFromTextFile(const std::string &filename); - -private: - std::unique_ptr createIR(); - void createGraphInputs(); - void collectUnsupportedOps(); - std::unique_ptr _model; - std::unique_ptr _converterCtx; - std::unique_ptr _modelCtx; - std::unique_ptr _graph; -}; - -ONNXImporterImpl::ONNXImporterImpl() { registerSupportedOps(); } - -ONNXImporterImpl::~ONNXImporterImpl() = default; - -void loadModelFromBinaryFile(const std::string &filename, onnx::ModelProto *model) -{ - GOOGLE_PROTOBUF_VERIFY_VERSION; - - int file_handle = open(filename.c_str(), O_RDONLY); - - if (file_handle == -1) - throw std::runtime_error("Couldn't open file \"" + filename + "\": " + std::strerror(errno) + - "."); - - google::protobuf::io::FileInputStream file_stream(file_handle); - file_stream.SetCloseOnDelete(true); - - google::protobuf::io::CodedInputStream coded_stream(&file_stream); - coded_stream.SetTotalBytesLimit(INT_MAX, INT_MAX); - - if (!model->ParseFromCodedStream(&coded_stream)) - throw std::runtime_error("Couldn't parse file \"" + filename + "\"."); - - // If the file has not been consumed entirely, assume that the file is in the wrong format. - if (!coded_stream.ConsumedEntireMessage()) - throw std::runtime_error("File \"" + filename + "\" has not been consumed entirely."); -} - -void loadModelFromTextFile(const std::string &filename, onnx::ModelProto *model) -{ - GOOGLE_PROTOBUF_VERIFY_VERSION; - - int file_handle = open(filename.c_str(), O_RDONLY); - - if (file_handle == -1) - throw std::runtime_error("Couldn't open file \"" + filename + "\": " + std::strerror(errno) + - "."); - - google::protobuf::io::FileInputStream file_stream(file_handle); - file_stream.SetCloseOnDelete(true); - - if (!google::protobuf::TextFormat::Parse(&file_stream, model)) - throw std::runtime_error("Couldn't parse file \"" + filename + "\"."); -} - -std::unique_ptr ONNXImporterImpl::importModelFromBinaryFile(const std::string &filename) -{ - _model = stdex::make_unique(); - loadModelFromBinaryFile(filename, _model.get()); - _modelCtx = stdex::make_unique(_model.get()); - collectUnsupportedOps(); - return createIR(); -} - -std::unique_ptr ONNXImporterImpl::importModelFromTextFile(const std::string &filename) -{ - _model = stdex::make_unique(); - loadModelFromTextFile(filename, _model.get()); - _modelCtx = stdex::make_unique(_model.get()); - collectUnsupportedOps(); - return createIR(); -} - -void ONNXImporterImpl::collectUnsupportedOps() -{ - std::set> problems_op_set; - - for (int i = 0; i < _model->graph().node_size(); i++) - { - const auto &onnx_node = _model->graph().node(i); - assert(onnx_node.has_op_type()); - const auto &op_type = onnx_node.op_type(); - auto opset = _modelCtx->getDomainOpsetVersion(onnx_node.domain()); - - NodeConverterRegistry::ConverterFunc converter = - NodeConverterRegistry::getInstance().lookup(op_type, opset); - - if (converter == nullptr) - problems_op_set.emplace(op_type, opset); - } - if (!problems_op_set.empty()) - { - std::cerr << "The following operators are not supported:\n"; - for (const auto &op : problems_op_set) - std::cerr << op.first << " opset " << op.second << std::endl; - throw std::runtime_error("Unsupported operators found"); - } -} - -void ONNXImporterImpl::createGraphInputs() -{ - const auto &graph = _model->graph(); - const auto &initializer = graph.initializer(); - const auto &value_info = graph.value_info(); - - // Create all initializer Tensors - for (const auto &tensor : initializer) - { - const auto mir_tensor = createTensor(&tensor); - auto *op = _graph->create(mir_tensor); - _converterCtx->setOutput(tensor.name(), op->getOutput(0)); - } - - for (const auto &input : graph.input()) - { - assert(input.has_name()); - - if (_converterCtx->getOutput(input.name()) == nullptr) - { - const auto &onnx_input_shape = input.type().tensor_type().shape(); - mir::Shape shape(onnx_input_shape.dim_size()); - for (int i = 0; i < onnx_input_shape.dim_size(); i++) - { - assert(onnx_input_shape.dim(i).has_dim_value()); - shape.dim(i) = static_cast(onnx_input_shape.dim(i).dim_value()); - } - - auto elem_type = onnxDataTypeToMirDataType( - (onnx::TensorProto_DataType)input.type().tensor_type().elem_type()); - mir::TensorType type{elem_type, shape}; - auto *op = _graph->create(type); - _converterCtx->setOutput(input.name(), op->getOutput(0)); - } - } -} - -std::unique_ptr ONNXImporterImpl::createIR() -{ - _graph = stdex::make_unique(); - _converterCtx = stdex::make_unique(_graph.get()); - - createGraphInputs(); - - // Forming partially ordered computation graph - for (const auto &onnx_node : _model->graph().node()) - { - assert(onnx_node.has_op_type()); - auto &op_type = onnx_node.op_type(); - auto opset = _modelCtx->getDomainOpsetVersion(onnx_node.domain()); - // Get converter - NodeConverterRegistry::ConverterFunc converter = - NodeConverterRegistry::getInstance().lookup(op_type, opset); - assert(converter != nullptr); - converter(onnx_node, _converterCtx.get()); - } - // Set graph outputs - const auto &outputs = _model->graph().output(); - for (const auto &output : outputs) - { - assert(output.has_name()); - auto mir_output = _converterCtx->getOutput(output.name()); - if (mir_output == nullptr) - throw std::runtime_error("Bad output name!"); - - _graph->create(mir_output); - } - - return std::move(_graph); -} - -} // namespace - -std::unique_ptr importModelFromBinaryFile(const std::string &filename) -{ - ONNXImporterImpl importer; - return importer.importModelFromBinaryFile(filename); -} - -std::unique_ptr importModelFromTextFile(const std::string &filename) -{ - ONNXImporterImpl importer; - return importer.importModelFromTextFile(filename); -} - -std::unique_ptr loadModel(const std::string &filename) -{ - return importModelFromBinaryFile(filename); -} - -} // namespace mir_onnx diff --git a/compiler/mir-onnx-importer/Op/Pad.cpp b/compiler/mir-onnx-importer/Op/Pad.cpp deleted file mode 100644 index 504a32b..0000000 --- a/compiler/mir-onnx-importer/Op/Pad.cpp +++ /dev/null @@ -1,70 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Pad.h" - -#include "ONNXHelpers.h" -#include "AttributeHelpers.h" - -#include "mir/ops/PadOp.h" - -namespace mir_onnx -{ - -void convertPadAttrName(const std::string &pad_attr_name, const onnx::NodeProto &onnx_node, - ConverterContext *context) -{ - std::vector inputs = context->getNodeInputs(onnx_node); - mir::Graph *graph = context->getGraph(); - - assert(inputs.size() == 1); - auto input = inputs[0]; - - // 0.0f is the default value to be filled into padded cells. - const auto value = getAttributeValue(onnx_node, "value", 0.0f); - const auto pads = getAttributeValue>(onnx_node, pad_attr_name); - // "constant" is the default mode. - const auto mode = getAttributeValue(onnx_node, "mode", "constant"); - if (mode != "constant") - throw std::runtime_error("Not supported Pad mode attribute!"); - - const int num_dims = input->getShape().rank(); - assert(pads.size() == num_dims * 2); - mir::PadOpAttributes attributes(num_dims); - for (int i = 0; i < num_dims; i++) - { - attributes.padding_before[i] = pads[i]; - attributes.padding_after[i] = pads[num_dims + i]; - } - - attributes.padding_value = value; - - auto result = createOp(graph, input, attributes)->getOutput(0); - - context->setNodeOutputs(onnx_node, {result}); -} - -void convertPadV1(const onnx::NodeProto &onnx_node, ConverterContext *context) -{ - convertPadAttrName("paddings", onnx_node, context); -} - -void convertPadV2(const onnx::NodeProto &onnx_node, ConverterContext *context) -{ - convertPadAttrName("pads", onnx_node, context); -} - -} // namespace mir_onnx diff --git a/compiler/mir-onnx-importer/Op/Transpose.cpp b/compiler/mir-onnx-importer/Op/Transpose.cpp deleted file mode 100644 index 82bb2f1..0000000 --- a/compiler/mir-onnx-importer/Op/Transpose.cpp +++ /dev/null @@ -1,57 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Transpose.h" -#include "ONNXHelpers.h" -#include "AttributeHelpers.h" - -#include "mir/ops/TransposeOp.h" - -#include - -namespace mir_onnx -{ - -void convertTransposeV1(const onnx::NodeProto &onnx_node, ConverterContext *context) -{ - const auto inputs = context->getNodeInputs(onnx_node); - mir::Graph *graph = context->getGraph(); - - assert(inputs.size() == 1); - auto input = inputs[0]; - - const auto num_axes = input->getShape().rank(); - std::vector axis_order(num_axes); - const auto *perm_attr = findAttribute(onnx_node, "perm"); - - if (perm_attr == nullptr) - { - // Reverse the dimensions. - std::iota(axis_order.rbegin(), axis_order.rend(), 0); - } - else - { - const auto perm = getAttributeValue>(*perm_attr); - assert(perm.size() == num_axes); - std::copy(perm.cbegin(), perm.cend(), axis_order.begin()); - } - - auto result = createOp(graph, input, axis_order)->getOutput(0); - - context->setNodeOutputs(onnx_node, {result}); -} - -} // namespace mir_onnx diff --git a/compiler/mir-onnx-importer/Op/Upsample.cpp b/compiler/mir-onnx-importer/Op/Upsample.cpp deleted file mode 100644 index 49a5556..0000000 --- a/compiler/mir-onnx-importer/Op/Upsample.cpp +++ /dev/null @@ -1,124 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Upsample.h" - -#include "ONNXHelpers.h" -#include "AttributeHelpers.h" - -#include "mir/Tensor.h" - -#include "mir/ops/ConstantOp.h" -#include "mir/ops/ResizeOp.h" - -namespace mir_onnx -{ - -void convertUpsampleV1(const onnx::NodeProto &onnx_node, ConverterContext *context) -{ - std::vector inputs = context->getNodeInputs(onnx_node); - mir::Graph *graph = context->getGraph(); - - // "nearest" is the default mode. - std::string mode = getAttributeValue(onnx_node, "mode", "nearest"); - assert(mode == "nearest" && "Unsupported upscale mode!"); - - const float h_scale = getAttributeValue(onnx_node, "height_scale", 0.0f); // required - const float w_scale = getAttributeValue(onnx_node, "width_scale", 0.0f); // required - if (h_scale < 1.0f || w_scale < 1.0f) - throw std::runtime_error("Wrong scale attributes!"); - - assert(inputs[0]->getShape().rank() == 4 && "Only rank 4 is supported"); - std::vector scales_vector(4); - // NCHW - scales_vector.at(0) = 1.0f; - scales_vector.at(1) = 1.0f; - scales_vector.at(2) = h_scale; - scales_vector.at(3) = w_scale; - - auto result = - createOp(graph, inputs[0], - mir::ops::ResizeOp::ResizeMethod::nearestNeighbor, scales_vector) - ->getOutput(0); - - context->setNodeOutputs(onnx_node, {result}); -} - -void convertUpsampleV7(const onnx::NodeProto &onnx_node, ConverterContext *context) -{ - std::vector inputs = context->getNodeInputs(onnx_node); - mir::Graph *graph = context->getGraph(); - - // "nearest" is the default mode. - std::string mode = getAttributeValue(onnx_node, "mode", "nearest"); - assert(mode == "nearest" && "Unsupported upscale mode!"); - - const auto *scales_attr = findAttribute(onnx_node, "scales"); - if (!scales_attr) - throw std::runtime_error("Not enough required scales attribute!"); - - if (scales_attr->floats_size() != inputs[0]->getShape().rank()) - throw std::runtime_error( - "Number of elements of scales should be the same as the rank of input"); - - assert(inputs[0]->getShape().rank() == 4 && "Only rank 4 is supported"); - std::vector scales_vector(4); - // NCHW - scales_vector.at(0) = scales_attr->floats(0); - scales_vector.at(1) = scales_attr->floats(1); - scales_vector.at(2) = scales_attr->floats(2); - scales_vector.at(3) = scales_attr->floats(3); - - auto result = - createOp(graph, inputs[0], - mir::ops::ResizeOp::ResizeMethod::nearestNeighbor, scales_vector) - ->getOutput(0); - - context->setNodeOutputs(onnx_node, {result}); -} - -void convertUpsampleV9(const onnx::NodeProto &onnx_node, ConverterContext *context) -{ - std::vector inputs = context->getNodeInputs(onnx_node); - mir::Graph *graph = context->getGraph(); - - // "nearest" is the default mode. - const auto mode = getAttributeValue(onnx_node, "mode", "nearest"); - assert(mode == "nearest" && "Unsupported upscale mode!"); - - // relies on attributes being lifted to constants (ONNX optimization pass) - assert(inputs.size() > 1); - auto *scales = dynamic_cast(inputs[1]->getNode()); - assert(scales && "Weights could be a constant tensor only"); - auto scales_tensor = mir::Tensor(scales->getValue()); - int rank = inputs[0]->getShape().rank(); - assert(scales_tensor.getShape().numElements() == rank && - "The number of elements of 'scales' should be the same as the rank of input 'X'"); - assert(rank == 4 && "Only rank 4 is supported"); - std::vector scales_vector(4); - assert(scales_tensor.getShape().rank() == 1 && "Scales are a 1d tensor"); - for (int i = 0; i < scales_tensor.getShape().numElements(); i++) - scales_vector[i] = scales_tensor.atOffset(i); - - auto result = - createOp(graph, inputs[0], - mir::ops::ResizeOp::ResizeMethod::nearestNeighbor, scales_vector) - ->getOutput(0); - - context->setNodeOutputs(onnx_node, {result}); -} - -} // namespace mir_onnx diff --git a/compiler/mir-onnx-importer/requires.cmake b/compiler/mir-onnx-importer/requires.cmake deleted file mode 100644 index 52a7837..0000000 --- a/compiler/mir-onnx-importer/requires.cmake +++ /dev/null @@ -1,2 +0,0 @@ -require("mir") -require("mir-interpreter") diff --git a/compiler/mir-tflite-importer/CMakeLists.txt b/compiler/mir-tflite-importer/CMakeLists.txt deleted file mode 100644 index 4a06d51..0000000 --- a/compiler/mir-tflite-importer/CMakeLists.txt +++ /dev/null @@ -1,22 +0,0 @@ -nnas_find_package(FlatBuffers REQUIRED) - -if (NOT FlatBuffers_FOUND) - return() -endif () - -FlatBuffers_Target(mir_tflite_schema - OUTPUT_DIR "${CMAKE_CURRENT_BINARY_DIR}/generated/schema" - SCHEMA_DIR "${CMAKE_CURRENT_SOURCE_DIR}/schema" - SCHEMA_FILES schema.fbs) - - -set(MIR_TFLITE_IMPORTER_SOURCES - tflite_importer.cpp - tflite_importer.h - tflite_op_creator.cpp - tflite_op_creator.h) - -add_library(mir_tflite_importer STATIC ${MIR_TFLITE_IMPORTER_SOURCES}) -set_target_properties(mir_tflite_importer PROPERTIES POSITION_INDEPENDENT_CODE ON) -target_include_directories(mir_tflite_importer PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}) -target_link_libraries(mir_tflite_importer PUBLIC mir mir_tflite_schema PRIVATE stdex) diff --git a/compiler/mir-tflite-importer/requires.cmake b/compiler/mir-tflite-importer/requires.cmake deleted file mode 100644 index 1059c50..0000000 --- a/compiler/mir-tflite-importer/requires.cmake +++ /dev/null @@ -1 +0,0 @@ -require("mir") diff --git a/compiler/mir-tflite-importer/tflite_importer.cpp b/compiler/mir-tflite-importer/tflite_importer.cpp deleted file mode 100644 index e3001d3..0000000 --- a/compiler/mir-tflite-importer/tflite_importer.cpp +++ /dev/null @@ -1,428 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "tflite_importer.h" -#include "tflite_op_creator.h" -#include "schema_generated.h" - -#include "mir/TensorVariant.h" -#include "mir/ops/ConstantOp.h" -#include "mir/ops/OutputOp.h" - -#include -#include -#include -#include -#include - -namespace mir_tflite -{ - -namespace -{ - -class TfliteImporter -{ -public: - explicit TfliteImporter(std::string filename); - - /// @brief Load the model and convert it into a MIR Graph. - std::unique_ptr importModel(); - - ~TfliteImporter(); - -private: - std::string _filename; - std::unique_ptr _model; - - std::unique_ptr _graph; - std::unique_ptr _opCreator; - - // Maps TFLite tensors indices to corresponding MIR operation outputs. - std::vector _tensorMap; - - void import(); - - void walkModel(const tflite::ModelT *model); - - void walkSubgraph(const tflite::SubGraphT *subgraph); - - void walkOperator(const tflite::SubGraphT *subgraph, const tflite::OperatorT *op); - - /** - * @brief Pass through tflite graph and collect operators unsupported by NNC - * @throw PassException with message, containing detected problems - */ - void collectUnsupportedOps(); - - /** - * @brief Returns MIR operation outputs corresponding to the inputs of the given operator. - */ - std::vector getMIRInputsForOperator(const tflite::SubGraphT *subgraph, - const tflite::OperatorT *op); -}; - -TfliteImporter::TfliteImporter(std::string filename) : _filename(std::move(filename)) -{ - _graph = stdex::make_unique(); - _opCreator = stdex::make_unique(_graph.get()); -} - -TfliteImporter::~TfliteImporter() = default; - -void TfliteImporter::import() -{ - std::ifstream stream(_filename, std::ios::in | std::ios::binary); - if (stream.fail()) - throw std::runtime_error("Couldn't open file \"" + _filename + "\"."); - - std::vector model_buffer((std::istreambuf_iterator(stream)), - std::istreambuf_iterator()); - - if (stream.fail()) - throw std::runtime_error("Couldn't read file \"" + _filename + "\"."); - - flatbuffers::Verifier verifier(reinterpret_cast(model_buffer.data()), - model_buffer.size()); - - if (!tflite::VerifyModelBuffer(verifier)) - throw std::runtime_error("Could not load model: " + _filename + "\n"); - - _model = tflite::UnPackModel(model_buffer.data()); -} - -static const std::set supportedOperators = { - tflite::BuiltinOperator_ADD, - tflite::BuiltinOperator_AVERAGE_POOL_2D, - tflite::BuiltinOperator_CONCATENATION, - tflite::BuiltinOperator_CONV_2D, - tflite::BuiltinOperator_DEPTHWISE_CONV_2D, - tflite::BuiltinOperator_DIV, - tflite::BuiltinOperator_FULLY_CONNECTED, - tflite::BuiltinOperator_HARD_SWISH, - tflite::BuiltinOperator_LEAKY_RELU, - tflite::BuiltinOperator_LOGISTIC, - tflite::BuiltinOperator_MAX_POOL_2D, - tflite::BuiltinOperator_MAXIMUM, - tflite::BuiltinOperator_MEAN, - tflite::BuiltinOperator_MUL, - tflite::BuiltinOperator_PAD, - tflite::BuiltinOperator_RELU, - tflite::BuiltinOperator_RELU6, - tflite::BuiltinOperator_RESHAPE, - tflite::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR, - tflite::BuiltinOperator_RSQRT, - tflite::BuiltinOperator_SHAPE, - tflite::BuiltinOperator_SLICE, - tflite::BuiltinOperator_SOFTMAX, - tflite::BuiltinOperator_SQRT, - tflite::BuiltinOperator_SQUARED_DIFFERENCE, - tflite::BuiltinOperator_SQUEEZE, - tflite::BuiltinOperator_STRIDED_SLICE, - tflite::BuiltinOperator_SUB, - tflite::BuiltinOperator_TANH, - tflite::BuiltinOperator_TRANSPOSE, - tflite::BuiltinOperator_TRANSPOSE_CONV, -}; - -void TfliteImporter::collectUnsupportedOps() -{ - std::set errors; - for (const auto &subgraph : _model->subgraphs) - for (const auto &op : subgraph->operators) - { - tflite::BuiltinOperator opcode = _model->operator_codes[op->opcode_index]->builtin_code; - if (supportedOperators.find(opcode) == supportedOperators.end()) - { - if (opcode <= tflite::BuiltinOperator_MAX) - errors.insert(std::string(EnumNameBuiltinOperator(opcode)) + ": unsupported operator"); - else - errors.insert(std::to_string(opcode) + ": unsuppored in tflite custom opcode"); - } - } - - if (!errors.empty()) - { - std::string msg("NNC can't load model. Detected problems:"); - for (const auto &e : errors) - msg.append("\n * " + e); - throw std::runtime_error(msg); - } -} - -std::unique_ptr TfliteImporter::importModel() -{ - import(); - collectUnsupportedOps(); - walkModel(_model.get()); - return std::move(_graph); -} - -void TfliteImporter::walkModel(const tflite::ModelT *model) -{ - for (const auto &subgraph : model->subgraphs) - walkSubgraph(subgraph.get()); -} - -mir::DataType convertElementType(tflite::TensorType type) -{ - switch (type) - { - case tflite::TensorType_INT32: - return mir::DataType::INT32; - case tflite::TensorType_FLOAT32: - return mir::DataType::FLOAT32; - case tflite::TensorType_INT64: - return mir::DataType::INT64; - case tflite::TensorType_UINT8: - return mir::DataType::UINT8; - default: - throw std::runtime_error(std::string("Unsupported tensor type: ") + EnumNameTensorType(type)); - } -} - -mir::TensorType getMirTensorType(const tflite::TensorT &tensor) -{ - mir::DataType element_type = convertElementType(tensor.type); - - mir::Shape shape(tensor.shape.size()); - for (std::size_t i = 0; i < tensor.shape.size(); ++i) - { - shape.dim(i) = tensor.shape[i]; - } - - if (tensor.quantization != nullptr) - { - const tflite::QuantizationParametersT ¶ms = *tensor.quantization; - - if (params.details.type != tflite::QuantizationDetails_NONE) - throw std::runtime_error("Custom quantization is not supported."); - - // Empty parameters mean no quantization at all. - if (params.scale.empty() && params.zero_point.empty()) - return mir::TensorType{element_type, shape}; - - if (params.scale.size() != 1 || params.zero_point.size() != 1) - throw std::runtime_error("Non-scalar quantization is not supported."); - - mir::AffineQuantization quantization{params.scale[0], static_cast(params.zero_point[0])}; - - return mir::TensorType{element_type, shape, quantization}; - } - else - { - return mir::TensorType{element_type, shape}; - } -} - -void TfliteImporter::walkSubgraph(const tflite::SubGraphT *subgraph) -{ - _tensorMap.assign(subgraph->tensors.size(), nullptr); - - for (const auto input_tensor_index : subgraph->inputs) - { - const tflite::TensorT &tensor = *subgraph->tensors[input_tensor_index]; - - mir::TensorType input_type = getMirTensorType(tensor); - auto input = _graph->create(input_type)->getOutput(0); - input->setName(tensor.name); - - assert(_tensorMap[input_tensor_index] == nullptr); - _tensorMap[input_tensor_index] = input; - } - - for (const auto &op : subgraph->operators) - { - walkOperator(subgraph, op.get()); - } - - for (const auto output_tensor_index : subgraph->outputs) - { - auto output = _tensorMap[output_tensor_index]; - _graph->create(output); - } -} - -void TfliteImporter::walkOperator(const tflite::SubGraphT *subgraph, const tflite::OperatorT *op) -{ - std::vector inputs = getMIRInputsForOperator(subgraph, op); - std::vector outputs; - - tflite::BuiltinOperator opcode = _model->operator_codes[op->opcode_index]->builtin_code; - switch (opcode) - { - case tflite::BuiltinOperator_CONV_2D: - outputs = _opCreator->convertConv2D(op->builtin_options.AsConv2DOptions(), inputs); - break; - case tflite::BuiltinOperator_DEPTHWISE_CONV_2D: - outputs = _opCreator->convertDepthwiseConv2D(op->builtin_options.AsDepthwiseConv2DOptions(), - inputs); - break; - case tflite::BuiltinOperator_MAX_POOL_2D: - outputs = _opCreator->convertMaxPool2D(op->builtin_options.AsPool2DOptions(), inputs); - break; - case tflite::BuiltinOperator_AVERAGE_POOL_2D: - outputs = _opCreator->convertAveragePool2D(op->builtin_options.AsPool2DOptions(), inputs); - break; - case tflite::BuiltinOperator_CONCATENATION: - outputs = - _opCreator->convertConcatenation(op->builtin_options.AsConcatenationOptions(), inputs); - break; - case tflite::BuiltinOperator_RESHAPE: - outputs = _opCreator->convertReshape(op->builtin_options.AsReshapeOptions(), inputs); - break; - case tflite::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR: - outputs = _opCreator->convertResizeNearestNeighbor( - op->builtin_options.AsResizeNearestNeighborOptions(), inputs); - break; - case tflite::BuiltinOperator_MEAN: - outputs = _opCreator->convertMean(op->builtin_options.AsReducerOptions(), inputs); - break; - case tflite::BuiltinOperator_FULLY_CONNECTED: - outputs = - _opCreator->convertFullyConnected(op->builtin_options.AsFullyConnectedOptions(), inputs); - break; - case tflite::BuiltinOperator_SOFTMAX: - outputs = _opCreator->convertSoftmax(op->builtin_options.AsSoftmaxOptions(), inputs); - break; - case tflite::BuiltinOperator_SLICE: - outputs = _opCreator->convertSlice(op->builtin_options.AsSliceOptions(), inputs); - break; - case tflite::BuiltinOperator_SQUEEZE: - outputs = _opCreator->convertSqueeze(op->builtin_options.AsSqueezeOptions(), inputs); - break; - case tflite::BuiltinOperator_LOGISTIC: - outputs = _opCreator->convertLogistic(inputs); - break; - case tflite::BuiltinOperator_RSQRT: - outputs = _opCreator->convertRsqrt(inputs); - break; - case tflite::BuiltinOperator_SQRT: - outputs = _opCreator->convertSqrt(inputs); - break; - case tflite::BuiltinOperator_ADD: - outputs = _opCreator->convertAdd(op->builtin_options.AsAddOptions(), inputs); - break; - case tflite::BuiltinOperator_SUB: - outputs = _opCreator->convertSub(op->builtin_options.AsSubOptions(), inputs); - break; - case tflite::BuiltinOperator_MUL: - outputs = _opCreator->convertMul(op->builtin_options.AsMulOptions(), inputs); - break; - case tflite::BuiltinOperator_DIV: - outputs = _opCreator->convertDiv(op->builtin_options.AsDivOptions(), inputs); - break; - case tflite::BuiltinOperator_MAXIMUM: - outputs = _opCreator->convertMax(inputs); - break; - case tflite::BuiltinOperator_SQUARED_DIFFERENCE: - outputs = _opCreator->convertSquaredDifference(inputs); - break; - case tflite::BuiltinOperator_TRANSPOSE_CONV: - outputs = - _opCreator->convertTransposeConv(op->builtin_options.AsTransposeConvOptions(), inputs); - break; - case tflite::BuiltinOperator_PAD: - outputs = _opCreator->convertPad(op->builtin_options.AsPadOptions(), inputs); - break; - case tflite::BuiltinOperator_TANH: - outputs = _opCreator->convertTanh(inputs); - break; - case tflite::BuiltinOperator_RELU: - outputs = _opCreator->convertReLU(inputs); - break; - case tflite::BuiltinOperator_RELU6: - outputs = _opCreator->convertReLU6(inputs); - break; - case tflite::BuiltinOperator_TRANSPOSE: - outputs = _opCreator->convertTranspose(op->builtin_options.AsTransposeOptions(), inputs); - break; - case tflite::BuiltinOperator_STRIDED_SLICE: - outputs = - _opCreator->convertStridedSlice(op->builtin_options.AsStridedSliceOptions(), inputs); - break; - case tflite::BuiltinOperator_LEAKY_RELU: - outputs = _opCreator->convertLeakyReLU(op->builtin_options.AsLeakyReluOptions(), inputs); - break; - case tflite::BuiltinOperator_SHAPE: - outputs = _opCreator->convertShape(op->builtin_options.AsShapeOptions(), inputs); - break; - case tflite::BuiltinOperator_HARD_SWISH: - outputs = _opCreator->convertHardSwish(op->builtin_options.AsHardSwishOptions(), inputs); - break; - default: - assert(false && "All unsupported types should have been found before this pass."); - } - - assert(outputs.size() == op->outputs.size()); - for (std::size_t i = 0; i < op->outputs.size(); ++i) - { - const auto tensor_index = op->outputs[i]; - const tflite::TensorT &tensor = *subgraph->tensors[tensor_index]; - - mir::TensorType output_type = getMirTensorType(tensor); - - // The type should have been inferred correctly, except for quantization information. - assert(outputs[i]->getType().getElementType() == output_type.getElementType() && - outputs[i]->getType().getShape() == output_type.getShape()); - - outputs[i]->setName(tensor.name); - outputs[i]->setType(output_type); - - assert(_tensorMap[tensor_index] == nullptr); - _tensorMap[tensor_index] = outputs[i]; - } -} - -std::vector -TfliteImporter::getMIRInputsForOperator(const tflite::SubGraphT *subgraph, - const tflite::OperatorT *op) -{ - std::vector inputs; - - for (const auto tensor_index : op->inputs) - { - const tflite::TensorT &tensor = *subgraph->tensors[tensor_index]; - const tflite::BufferT &buffer = *_model->buffers[tensor.buffer]; - if (!buffer.data.empty()) - { - assert(_tensorMap[tensor_index] == nullptr); - mir::TensorType type = getMirTensorType(tensor); - mir::TensorVariant mir_tensor{type, buffer.data.data()}; - inputs.emplace_back(_graph->create(mir_tensor)->getOutput(0)); - } - else - { - assert(_tensorMap[tensor_index] != nullptr); - // By this point every input for the operation "op" should have corresponding - // Model IR operations that output its inputs. This assumption is provided by the fact - // that TFLite format specifies all operations in the execution order. - inputs.emplace_back(_tensorMap[tensor_index]); - } - } - - return inputs; -} - -} // namespace - -std::unique_ptr loadModel(std::string filename) -{ - TfliteImporter importer(std::move(filename)); - return importer.importModel(); -} - -} // namespace mir_tflite diff --git a/compiler/mir-tflite-importer/tflite_op_creator.cpp b/compiler/mir-tflite-importer/tflite_op_creator.cpp deleted file mode 100644 index 5f4279f..0000000 --- a/compiler/mir-tflite-importer/tflite_op_creator.cpp +++ /dev/null @@ -1,649 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "tflite_op_creator.h" -#include "schema_generated.h" - -#include "mir/ops/AddOp.h" -#include "mir/ops/AvgPool2DOp.h" -#include "mir/ops/CappedReluOp.h" -#include "mir/ops/ConcatOp.h" -#include "mir/ops/ConstantOp.h" -#include "mir/ops/Conv2DOp.h" -#include "mir/ops/Deconv2DOp.h" -#include "mir/ops/DepthwiseConv2DOp.h" -#include "mir/ops/DivOp.h" -#include "mir/ops/FullyConnectedOp.h" -#include "mir/ops/HardSwishOp.h" -#include "mir/ops/LeakyReluOp.h" -#include "mir/ops/MaxOp.h" -#include "mir/ops/MaxPool2DOp.h" -#include "mir/ops/MulOp.h" -#include "mir/ops/PadOp.h" -#include "mir/ops/ReduceMeanOp.h" -#include "mir/ops/ReluOp.h" -#include "mir/ops/ReshapeOp.h" -#include "mir/ops/ResizeOp.h" -#include "mir/ops/SigmoidOp.h" -#include "mir/ops/SliceOp.h" -#include "mir/ops/SoftmaxOp.h" -#include "mir/ops/SqrtOp.h" -#include "mir/ops/SqueezeOp.h" -#include "mir/ops/SubOp.h" -#include "mir/ops/TanhOp.h" -#include "mir/ops/TransposeOp.h" - -#include "mir/Shape.h" -#include "mir/ShapeRange.h" -#include "mir/Tensor.h" - -namespace mir_tflite -{ - -namespace ops = mir::ops; -using mir::Shape; - -static mir::ops::PaddingType convertPadding(tflite::Padding padding) -{ - switch (padding) - { - case tflite::Padding_VALID: - return mir::ops::PaddingType::Valid; - case tflite::Padding_SAME: - return mir::ops::PaddingType::SameUpper; - default: - assert(false); - } -} - -// TODO Move this to MIR? -static void calculatePadding(mir::ops::PaddingType padding_type, const mir::Shape &input_shape, - const std::vector &window_size, - const std::vector &strides, - std::vector &padding_before, - std::vector &padding_after) -{ - constexpr int num_spatial_dims = 2; - assert(window_size.size() == num_spatial_dims); - assert(strides.size() == num_spatial_dims); - assert(padding_before.size() == num_spatial_dims); - assert(padding_after.size() == num_spatial_dims); - - switch (padding_type) - { - case mir::ops::PaddingType::SameUpper: - for (int i = 0; i < num_spatial_dims; ++i) - { - // Assuming NHWC format. - const std::int32_t total_padding = - (input_shape.dim(1 + i) % strides[i] == 0) - ? std::max(0, window_size[i] - strides[i]) - : std::max(0, window_size[i] - input_shape.dim(1 + i) % strides[i]); - padding_before[i] = total_padding / 2; - padding_after[i] = total_padding - padding_before[i]; - } - break; - case mir::ops::PaddingType::Valid: - for (int i = 0; i < num_spatial_dims; ++i) - { - padding_before[i] = 0; - padding_after[i] = 0; - } - break; - default: - assert(false); - } -} - -template -static std::vector convertIntTensorToVector(const mir::Tensor &tensor) -{ - std::vector v; - for (const auto &i : mir::ShapeRange(tensor.getShape())) - v.emplace_back(static_cast(tensor.at(i))); - return v; -} - -static const mir::TensorVariant &extractTensor(const mir::Operation::Output *output) -{ - auto constant_op = dynamic_cast(output->getNode()); - if (constant_op == nullptr) - throw std::runtime_error("Non-constant input is not supported."); - return constant_op->getValue(); -} - -std::vector -TFLiteOpCreator::convertConv2D(const tflite::Conv2DOptionsT *opts, - const std::vector &inputs) -{ - auto input = inputs.at(0); - auto kernel = inputs.at(1); - auto bias = inputs.at(2); - - mir::Conv2DOpAttributes attributes; - attributes.strides = {opts->stride_h, opts->stride_w}; - - const auto padding_type = convertPadding(opts->padding); - const auto &input_shape = input->getShape(); - const auto &kernel_shape = kernel->getShape(); - const auto &strides = attributes.strides; - auto &pad_before = attributes.padding_before; - auto &pad_after = attributes.padding_after; - std::vector kernel_size{kernel_shape.dim(1), kernel_shape.dim(2)}; - calculatePadding(padding_type, input_shape, kernel_size, strides, pad_before, pad_after); - - mir::Operation::Output *result; - if (input->getType().isQuantized()) - { - result = createOp(input, kernel, bias, attributes)->getOutput(0); - } - else // TODO Fuse bias to other backends - { - result = createOp(input, kernel, attributes)->getOutput(0); - result = createOp(result, bias)->getOutput(0); - } - return {addFusedActivation(result, opts->fused_activation_function)}; -} - -std::vector -TFLiteOpCreator::convertDepthwiseConv2D(const tflite::DepthwiseConv2DOptionsT *opts, - const std::vector &inputs) -{ - auto input = inputs.at(0); - auto kernel = inputs.at(1); - auto bias = inputs.at(2); - - // OHWI -> HWIO - const std::vector axis_order{1, 2, 3, 0}; - kernel = createOp(kernel, axis_order)->getOutput(0); - - mir::Conv2DOpAttributes attributes; - attributes.strides = {opts->stride_h, opts->stride_w}; - - const auto padding_type = convertPadding(opts->padding); - const auto &input_shape = input->getShape(); - const auto &kernel_shape = kernel->getShape(); - std::vector kernel_size{kernel_shape.dim(0), kernel_shape.dim(1)}; - const auto &strides = attributes.strides; - auto &pad_before = attributes.padding_before; - auto &pad_after = attributes.padding_after; - calculatePadding(padding_type, input_shape, kernel_size, strides, pad_before, pad_after); - - mir::Operation::Output *result; - if (input->getType().isQuantized()) - { - result = createOp(input, kernel, bias, attributes)->getOutput(0); - } - else // TODO Fuse bias to other backends - { - result = createOp(input, kernel, attributes)->getOutput(0); - result = createOp(result, bias)->getOutput(0); - } - return {addFusedActivation(result, opts->fused_activation_function)}; -} - -std::vector -TFLiteOpCreator::convertConcatenation(const tflite::ConcatenationOptionsT *opts, - const std::vector &inputs) -{ - auto result = createOp(inputs, opts->axis); - return {addFusedActivation(result->getOutput(0), opts->fused_activation_function)}; -} - -std::vector -TFLiteOpCreator::convertMaxPool2D(const tflite::Pool2DOptionsT *opts, - const std::vector &inputs) -{ - auto input = inputs.at(0); - - const auto &input_shape = input->getShape(); - - mir::MaxPool2DOpAttributes attributes; - attributes.window = {opts->filter_height, opts->filter_width}; - attributes.strides = {opts->stride_h, opts->stride_w}; - - const auto padding_type = convertPadding(opts->padding); - const auto &window_size = attributes.window; - const auto &strides = attributes.strides; - auto &pad_before = attributes.padding_before; - auto &pad_after = attributes.padding_after; - calculatePadding(padding_type, input_shape, window_size, strides, pad_before, pad_after); - - auto result = createOp(input, attributes); - return {addFusedActivation(result->getOutput(0), opts->fused_activation_function)}; -} - -std::vector -TFLiteOpCreator::convertAveragePool2D(const tflite::Pool2DOptionsT *opts, - const std::vector &inputs) -{ - auto input = inputs.at(0); - - const auto &input_shape = input->getShape(); - - mir::AvgPool2DOpAttributes attributes; - attributes.window = {opts->filter_height, opts->filter_width}; - attributes.strides = {opts->stride_h, opts->stride_w}; - attributes.include_pad = false; - - const auto padding_type = convertPadding(opts->padding); - const auto &window_size = attributes.window; - const auto &strides = attributes.strides; - auto &pad_before = attributes.padding_before; - auto &pad_after = attributes.padding_after; - calculatePadding(padding_type, input_shape, window_size, strides, pad_before, pad_after); - - auto result = createOp(input, attributes); - return {addFusedActivation(result->getOutput(0), opts->fused_activation_function)}; -} - -std::vector -TFLiteOpCreator::convertSoftmax(const tflite::SoftmaxOptionsT * /*opts*/, - const std::vector &inputs) -{ - auto input = inputs.at(0); - - // Softmax in TFLite is always 2-D. - assert(input->getShape().rank() == 2); - const int32_t axis = 1; - auto result = createOp(input, axis); - return {result->getOutput(0)}; -} - -std::vector -TFLiteOpCreator::convertSlice(const tflite::SliceOptionsT * /*opts*/, - const std::vector &inputs) -{ - auto input = inputs.at(0); - mir::Tensor begin_tensor(extractTensor(inputs.at(1))); - mir::Tensor size_tensor(extractTensor(inputs.at(2))); - - Shape starts(convertIntTensorToVector(begin_tensor)); - Shape sizes(convertIntTensorToVector(size_tensor)); - auto result = createOp(input, starts, sizes); - return {result->getOutput(0)}; -} - -std::vector -TFLiteOpCreator::convertReshape(const tflite::ReshapeOptionsT *opts, - const std::vector &inputs) -{ - auto input = inputs.at(0); - - // TODO: we should also support "-1" values in new_shape, which means that correct - // shape values must be calculated. Better do it in the shape inference module. - Shape new_shape(opts->new_shape.size()); - for (int i = 0; i < opts->new_shape.size(); ++i) - { - new_shape.dim(i) = opts->new_shape[i]; - } - auto result = createOp(input, new_shape); - return {result->getOutput(0)}; -} - -std::vector -TFLiteOpCreator::convertTransposeConv(const tflite::TransposeConvOptionsT *opts, - const std::vector &inputs) -{ - mir::Tensor output_shape_tensor(extractTensor(inputs.at(0))); - auto kernel = inputs.at(1); - auto input = inputs.at(2); - - mir::Deconv2DOpAttributes attributes; - attributes.strides = {opts->stride_h, opts->stride_w}; - Shape output_shape(convertIntTensorToVector(output_shape_tensor)); - - // OHWI -> HWOI - const std::vector axis_order{1, 2, 0, 3}; - kernel = createOp(kernel, axis_order)->getOutput(0); - - attributes.padding_type = convertPadding(opts->padding); - auto result = createOp(input, kernel, attributes, output_shape)->getOutput(0); - return {result}; -} - -std::vector -TFLiteOpCreator::convertResizeNearestNeighbor(const tflite::ResizeNearestNeighborOptionsT *opts, - const std::vector &inputs) -{ - if (opts->align_corners) - throw std::runtime_error("'align_corners' is not currently supported"); - - auto input = inputs.at(0); - mir::Tensor size_tensor(extractTensor(inputs.at(1))); - - const auto &input_shape = input->getShape(); - Shape res_shape{input_shape.dim(0), size_tensor.at(mir::Index{0}), size_tensor.at(mir::Index{1}), - input_shape.dim(3)}; - auto result = - createOp(input, ops::ResizeOp::ResizeMethod::nearestNeighbor, res_shape); - return {result->getOutput(0)}; -} - -std::vector -TFLiteOpCreator::convertAdd(const tflite::AddOptionsT *opts, - const std::vector &inputs) -{ - assert(inputs.size() == 2); - auto result = createOp(inputs[0], inputs[1])->getOutput(0); - return {addFusedActivation(result, opts->fused_activation_function)}; -} - -std::vector -TFLiteOpCreator::convertSub(const tflite::SubOptionsT *opts, - const std::vector &inputs) -{ - assert(inputs.size() == 2); - auto result = createOp(inputs[0], inputs[1])->getOutput(0); - return {addFusedActivation(result, opts->fused_activation_function)}; -} - -std::vector -TFLiteOpCreator::convertMul(const tflite::MulOptionsT *opts, - const std::vector &inputs) -{ - assert(inputs.size() == 2); - auto result = createOp(inputs[0], inputs[1])->getOutput(0); - return {addFusedActivation(result, opts->fused_activation_function)}; -} - -std::vector -TFLiteOpCreator::convertDiv(const tflite::DivOptionsT *opts, - const std::vector &inputs) -{ - assert(inputs.size() == 2); - auto result = createOp(inputs[0], inputs[1])->getOutput(0); - return {addFusedActivation(result, opts->fused_activation_function)}; -} - -std::vector -TFLiteOpCreator::convertMax(const std::vector &inputs) -{ - assert(inputs.size() == 2); - auto result = createOp(inputs[0], inputs[1])->getOutput(0); - return {result}; -} - -std::vector -TFLiteOpCreator::convertSquaredDifference(const std::vector &inputs) -{ - assert(inputs.size() == 2); - auto result = createOp(inputs[0], inputs[1])->getOutput(0); - result = createOp(result, result)->getOutput(0); - return {result}; -} - -std::vector -TFLiteOpCreator::convertMean(const tflite::ReducerOptionsT *opts, - const std::vector &inputs) -{ - auto input = inputs.at(0); - mir::Tensor axes_tensor(extractTensor(inputs.at(1))); - - std::vector axes = convertIntTensorToVector(axes_tensor); - auto result = createOp(input, axes, opts->keep_dims); - return {result->getOutput(0)}; -} - -std::vector -TFLiteOpCreator::convertFullyConnected(const tflite::FullyConnectedOptionsT *opts, - const std::vector &inputs) -{ - auto input = inputs.at(0); - auto weights = inputs.at(1); - auto bias = inputs.at(2); - - // Flatten input to 2-D shape. - const auto &input_shape = input->getShape(); - int32_t outer_size = input_shape.dim(0); - int32_t inner_size = input_shape.numElements() / outer_size; - auto flatten = createOp(input, Shape{outer_size, inner_size})->getOutput(0); - - // Transpose the weights. - const std::vector axis_order{1, 0}; - weights = createOp(weights, axis_order)->getOutput(0); - - mir::Operation::Output *result; - if (input->getType().isQuantized()) - { - result = createOp(flatten, weights, bias)->getOutput(0); - } - else // TODO Fuse bias to other backends - { - result = createOp(flatten, weights)->getOutput(0); - result = createOp(result, bias)->getOutput(0); - } - return {addFusedActivation(result, opts->fused_activation_function)}; -} - -mir::Operation::Output * -TFLiteOpCreator::addFusedActivation(mir::Operation::Output *input, - tflite::ActivationFunctionType activation_type) -{ - switch (activation_type) - { - case tflite::ActivationFunctionType_NONE: - return input; - case tflite::ActivationFunctionType_RELU: - return createOp(input)->getOutput(0); - case tflite::ActivationFunctionType_RELU6: - return createOp(input, 6)->getOutput(0); - case tflite::ActivationFunctionType_TANH: - return createOp(input)->getOutput(0); - default: - throw std::runtime_error(std::string("Unsupported activation type: ") + - tflite::EnumNameActivationFunctionType(activation_type)); - } -} - -std::vector -TFLiteOpCreator::convertSqueeze(const tflite::SqueezeOptionsT *opts, - const std::vector &inputs) -{ - auto input = inputs.at(0); - - std::vector squeeze_dims(opts->squeeze_dims.begin(), opts->squeeze_dims.end()); - auto result = createOp(input, squeeze_dims); - return {result->getOutput(0)}; -} - -std::vector -TFLiteOpCreator::convertPad(const tflite::PadOptionsT * /*opts*/, - const std::vector &inputs) -{ - auto input = inputs.at(0); - mir::Tensor paddings_tensor(extractTensor(inputs.at(1))); - - const auto &input_shape = input->getShape(); - const int num_dims = input_shape.rank(); - - mir::PadOpAttributes attributes(num_dims); - for (int i = 0; i < num_dims; i++) - { - attributes.padding_before[i] = paddings_tensor.at(mir::Index({i, 0})); - attributes.padding_after[i] = paddings_tensor.at(mir::Index({i, 1})); - } - - auto result = createOp(input, attributes)->getOutput(0); - return {result}; -} - -std::vector -TFLiteOpCreator::convertTanh(const std::vector &inputs) -{ - auto input = inputs.at(0); - - auto result = createOp(input); - return {result->getOutput(0)}; -} - -std::vector -TFLiteOpCreator::convertReLU(const std::vector &inputs) -{ - auto input = inputs.at(0); - - auto result = createOp(input); - return {result->getOutput(0)}; -} - -std::vector -TFLiteOpCreator::convertReLU6(const std::vector &inputs) -{ - auto input = inputs.at(0); - - auto result = createOp(input, 6); - return {result->getOutput(0)}; -} - -std::vector -TFLiteOpCreator::convertRsqrt(const std::vector &inputs) -{ - auto input = inputs.at(0); - - const float one_value = 1.0f; - mir::TensorVariant one_tensor({mir::DataType::FLOAT32, {}}, &one_value); - auto one = createOp(one_tensor)->getOutput(0); - auto sqrt = createOp(input)->getOutput(0); - auto result = createOp(one, sqrt)->getOutput(0); - return {result}; -} - -std::vector -TFLiteOpCreator::convertSqrt(const std::vector &inputs) -{ - auto input = inputs.at(0); - - auto result = createOp(input)->getOutput(0); - return {result}; -} - -std::vector -TFLiteOpCreator::convertLogistic(const std::vector &inputs) -{ - auto input = inputs.at(0); - - auto result = createOp(input); - return {result->getOutput(0)}; -} - -std::vector -TFLiteOpCreator::convertTranspose(const tflite::TransposeOptionsT * /*opts*/, - const std::vector &inputs) -{ - auto input = inputs.at(0); - mir::Tensor perm_tensor(extractTensor(inputs.at(1))); - - std::vector axis_order = convertIntTensorToVector(perm_tensor); - auto result = createOp(input, axis_order); - return {result->getOutput(0)}; -} - -std::vector -TFLiteOpCreator::convertStridedSlice(const tflite::StridedSliceOptionsT *opts, - const std::vector &inputs) -{ - if (opts->ellipsis_mask != 0) - throw std::runtime_error("StridedSlice: parameter 'ellipsis_mask' is not supported."); - - if (opts->new_axis_mask != 0) - throw std::runtime_error("StridedSlice: parameter 'new_axis_mask' is not supported."); - - auto input = inputs.at(0); - mir::Tensor begin_tensor(extractTensor(inputs.at(1))); - mir::Tensor end_tensor(extractTensor(inputs.at(2))); - mir::Tensor strides_tensor(extractTensor(inputs.at(3))); - - std::vector begin = convertIntTensorToVector(begin_tensor); - std::vector end = convertIntTensorToVector(end_tensor); - std::vector strides = convertIntTensorToVector(strides_tensor); - - int32_t begin_mask = opts->begin_mask; - int32_t end_mask = opts->end_mask; - int32_t shrink_axis_mask = opts->shrink_axis_mask; - - const auto &input_shape = input->getShape(); - int32_t num_dims = input_shape.rank(); - - for (int32_t stride : strides) - { - if (stride != 1) - throw std::runtime_error("StridedSlice: parameter 'strides' is not supported"); - } - - Shape start(num_dims); - Shape size(num_dims); - std::vector squeeze_dims; - for (int axis = 0; axis < num_dims; axis++) - { - if (static_cast(begin_mask) & (1u << static_cast(axis))) - start.dim(axis) = 0; - else - start.dim(axis) = begin.at(static_cast(axis)); - - if (static_cast(end_mask) & (1u << static_cast(axis))) - size.dim(axis) = input_shape.dim(axis) - start.dim(axis); - else - size.dim(axis) = end.at(static_cast(axis)) - start.dim(axis); - - if (static_cast(shrink_axis_mask) & (1u << static_cast(axis))) - squeeze_dims.push_back(axis); - } - - auto result = createOp(input, start, size); - result = createOp(result->getOutput(0), squeeze_dims); - return {result->getOutput(0)}; -} - -std::vector -TFLiteOpCreator::convertLeakyReLU(const tflite::LeakyReluOptionsT *opts, - const std::vector &inputs) -{ - auto input = inputs.at(0); - - auto result = createOp(input, opts->alpha); - return {result->getOutput(0)}; -} - -std::vector -TFLiteOpCreator::convertShape(const tflite::ShapeOptionsT *opts, - const std::vector &inputs) -{ - if (opts->out_type != tflite::TensorType_INT32) - { - throw std::runtime_error(std::string("SHAPE: Unsupported tensor type: ") + - EnumNameTensorType(opts->out_type)); - } - - const auto &input_shape = inputs[0]->getShape(); - int32_t rank = input_shape.rank(); - std::vector data; - data.reserve(static_cast(rank)); - for (int32_t i = 0; i < rank; i++) - data.emplace_back(input_shape.dim(i)); - mir::TensorVariant tensor({mir::DataType::INT32, {rank}}, data.data()); - auto result = createOp(tensor); - return {result->getOutput(0)}; -} - -std::vector -TFLiteOpCreator::convertHardSwish(const tflite::HardSwishOptionsT *opts, - const std::vector &inputs) -{ - auto result = createOp(inputs[0])->getOutput(0); - return {result}; -} - -} // namespace mir_tflite diff --git a/compiler/mir/CMakeLists.txt b/compiler/mir/CMakeLists.txt index ecb1de2..4c89893 100644 --- a/compiler/mir/CMakeLists.txt +++ b/compiler/mir/CMakeLists.txt @@ -36,3 +36,8 @@ set_target_properties(mir PROPERTIES POSITION_INDEPENDENT_CODE ON) set_target_properties(mir PROPERTIES LINKER_LANGUAGE CXX) add_subdirectory(unittests) + +add_subdirectory(src/mir_caffe_importer) +add_subdirectory(src/mir_caffe2_importer) +add_subdirectory(src/mir_tflite_importer) +add_subdirectory(src/mir_onnx_importer) diff --git a/compiler/mir-caffe2-importer/caffe2_importer.h b/compiler/mir/include/mir_caffe2_importer/caffe2_importer.h similarity index 100% rename from compiler/mir-caffe2-importer/caffe2_importer.h rename to compiler/mir/include/mir_caffe2_importer/caffe2_importer.h diff --git a/compiler/mir-caffe-importer/caffe_importer.h b/compiler/mir/include/mir_caffe_importer/caffe_importer.h similarity index 100% rename from compiler/mir-caffe-importer/caffe_importer.h rename to compiler/mir/include/mir_caffe_importer/caffe_importer.h diff --git a/compiler/mir-onnx-importer/ONNXImporterImpl.h b/compiler/mir/include/mir_onnx_importer/ONNXImporterImpl.h similarity index 100% rename from compiler/mir-onnx-importer/ONNXImporterImpl.h rename to compiler/mir/include/mir_onnx_importer/ONNXImporterImpl.h diff --git a/compiler/mir-tflite-importer/tflite_importer.h b/compiler/mir/include/mir_tflite_importer/tflite_importer.h similarity index 100% rename from compiler/mir-tflite-importer/tflite_importer.h rename to compiler/mir/include/mir_tflite_importer/tflite_importer.h diff --git a/compiler/mir/src/mir_caffe2_importer/CMakeLists.txt b/compiler/mir/src/mir_caffe2_importer/CMakeLists.txt new file mode 100644 index 0000000..a553781 --- /dev/null +++ b/compiler/mir/src/mir_caffe2_importer/CMakeLists.txt @@ -0,0 +1,28 @@ +nnas_find_package(PytorchSource QUIET) +nnas_find_package(Protobuf QUIET) + +if (NOT PytorchSource_FOUND OR NOT Protobuf_FOUND) + return() +endif() + +Protobuf_Generate(CAFFE2_PROTO "${CMAKE_CURRENT_BINARY_DIR}/generated/caffe2" + "${PytorchSource_DIR}" "caffe2/proto/caffe2.proto") + +add_library(caffe2proto STATIC ${CAFFE2_PROTO_SOURCES}) +set_target_properties(caffe2proto PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_include_directories(caffe2proto PUBLIC ${CAFFE2_PROTO_INCLUDE_DIRS}) +target_link_libraries(caffe2proto PUBLIC libprotobuf) + + +set(MIR_CAFFE2_IMPORTER_SOURCES + caffe2_importer.cpp + caffe2_op_creator.cpp + caffe2_op_creator.h + caffe2_op_types.h + caffe2_proto_helper.cpp + caffe2_proto_helper.h) + +add_library(mir_caffe2_importer STATIC ${MIR_CAFFE2_IMPORTER_SOURCES}) +set_target_properties(mir_caffe2_importer PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_include_directories(mir_caffe2_importer PUBLIC ../../include/mir_caffe2_importer) +target_link_libraries(mir_caffe2_importer PUBLIC mir PRIVATE caffe2proto nncc_common) diff --git a/compiler/mir/src/mir_caffe2_importer/caffe2_importer.cpp b/compiler/mir/src/mir_caffe2_importer/caffe2_importer.cpp new file mode 100644 index 0000000..812fcc5 --- /dev/null +++ b/compiler/mir/src/mir_caffe2_importer/caffe2_importer.cpp @@ -0,0 +1,343 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "caffe2_importer.h" +#include "caffe2/proto/caffe2.pb.h" +#include "caffe2_op_types.h" +#include "caffe2_op_creator.h" +#include "caffe2_proto_helper.h" + +#include "mir/ops/InputOp.h" +#include "mir/ops/OutputOp.h" + +#include +#include + +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace +{ + +using namespace mir_caffe2; + +class Caffe2Importer +{ +public: + explicit Caffe2Importer(std::string predict_net, std::string init_net, + const std::vector> &input_shapes); + + /// @brief Load the model and convert it into a MIR Graph. + std::unique_ptr importModel(); + + ~Caffe2Importer(); + +private: + std::string _predictNet; + std::string _initNet; + std::unique_ptr _graph; + std::unique_ptr _predict_net; + std::unique_ptr _init_net; + std::unique_ptr _opCreator; + std::vector _inputShapes; + + static const std::map _operatorTypes; + + // Maps Caffe2 operator input names to corresponding MIR operation outputs. + std::unordered_map _blobNameToOutput; + + void import(); + std::unique_ptr createIR(); + + /** + * @brief Pass through caffe2 graph and collect ops unsupported by NNC + * @throw PassException with message, containing detected problems + */ + void collectUnsupportedOps(); + + /** + * @brief Creating MIR node from single caffe2 operator + */ + void createMIRNodesFromOp(const ::caffe2::OperatorDef &op); + + /** + * @brief Returns MIR operation outputs corresponding to the inputs of the given operator. + */ + std::vector getInputMIROps(const ::caffe2::OperatorDef &op); + + void setOutputForTensor(const std::string &tensor_name, Operation::Output *output); + mir::Operation::Output *getOutputForTensor(const std::string &name) const; + + /** + * @brief Mark output MIR nodes + */ + void setGraphOutputs(); +}; + +using namespace ::caffe2; +using mir::Shape; + +Caffe2Importer::Caffe2Importer(std::string predict_net, std::string init_net, + const std::vector> &input_shapes) + : _predictNet(std::move(predict_net)), _initNet(std::move(init_net)) +{ + for (auto &shape : input_shapes) + _inputShapes.emplace_back(shape); + + _graph = std::make_unique(); + _opCreator = std::make_unique(_graph.get()); +} + +Caffe2Importer::~Caffe2Importer() = default; + +static void loadModelFile(const std::string &filename, caffe2::NetDef *net) +{ + GOOGLE_PROTOBUF_VERIFY_VERSION; + + int file_handle = open(filename.c_str(), O_RDONLY); + + if (file_handle == -1) + throw std::runtime_error("Couldn't open file \"" + filename + "\": " + std::strerror(errno) + + "."); + + google::protobuf::io::FileInputStream file_stream(file_handle); + file_stream.SetCloseOnDelete(true); + + google::protobuf::io::CodedInputStream coded_stream(&file_stream); + coded_stream.SetTotalBytesLimit(INT_MAX, INT_MAX); + + if (!net->ParseFromCodedStream(&coded_stream)) + throw std::runtime_error("Couldn't parse file \"" + filename + "\"."); + + // If the file has not been consumed entirely, assume that the file is in the wrong format. + if (!coded_stream.ConsumedEntireMessage()) + throw std::runtime_error("File \"" + filename + "\" has not been consumed entirely."); +} + +void Caffe2Importer::import() +{ + _predict_net = std::make_unique(); + loadModelFile(_predictNet, _predict_net.get()); + + _init_net = std::make_unique(); + loadModelFile(_initNet, _init_net.get()); + + collectUnsupportedOps(); +} + +std::unique_ptr Caffe2Importer::createIR() +{ + // Load initializers. + for (const auto &op : _init_net->op()) + createMIRNodesFromOp(op); + + // Create inputs. This has to be done after processing initializers, because they may contain + // fake inputs. + // TODO Caffe2 does not provide a way to detect model inputs and outputs. For now assume that: + // - there is exactly one input; + // - the input is for the first layer; + // - the input has 'float' element type. + const auto &input_name = _predict_net->op(0).input(0); + mir::TensorType input_type(mir::DataType::FLOAT32, _inputShapes[0]); + auto input = _graph->create(input_type)->getOutput(0); + setOutputForTensor(input_name, input); + + for (const auto &op : _predict_net->op()) + createMIRNodesFromOp(op); + + setGraphOutputs(); + + return std::move(_graph); +} + +std::unique_ptr Caffe2Importer::importModel() +{ + import(); + return createIR(); +} + +void Caffe2Importer::collectUnsupportedOps() +{ + std::set unsupportedOps; + for (const auto &op : _predict_net->op()) + { + if (_operatorTypes.find(op.type()) == _operatorTypes.end()) + unsupportedOps.insert(op.type()); + } + + if (!unsupportedOps.empty()) + { + std::string exceptionMsg("Can't load model, unsupported operators:"); + for (const auto &op : unsupportedOps) + exceptionMsg.append("\n * " + op); + throw std::runtime_error(exceptionMsg); + } +} + +void Caffe2Importer::createMIRNodesFromOp(const OperatorDef &op) +{ + std::vector outputs; + + auto inputs = getInputMIROps(op); + + SupportedCaffe2OpType opType = _operatorTypes.at(op.type()); + switch (opType) + { + case SupportedCaffe2OpType::constantFill: + case SupportedCaffe2OpType::givenTensorFill: + case SupportedCaffe2OpType::givenTensorInt64Fill: + outputs = _opCreator->convertConstant(inputs, op); + break; + case SupportedCaffe2OpType::add: + outputs = _opCreator->convertAdd(inputs, op); + break; + case SupportedCaffe2OpType::averagePool: + outputs = _opCreator->convertAveragePool(inputs, op); + break; + case SupportedCaffe2OpType::conv: + outputs = _opCreator->convertConv(inputs, op); + break; + case SupportedCaffe2OpType::concat: + outputs = _opCreator->convertConcat(inputs, op); + break; + case SupportedCaffe2OpType::dropout: + outputs = _opCreator->convertDropout(inputs, op); + break; + case SupportedCaffe2OpType::FC: + outputs = _opCreator->convertFC(inputs, op); + break; + case SupportedCaffe2OpType::maxPool: + outputs = _opCreator->convertMaxPool(inputs, op); + break; + case SupportedCaffe2OpType::mul: + outputs = _opCreator->convertMul(inputs, op); + break; + case SupportedCaffe2OpType::relu: + outputs = _opCreator->convertRelu(inputs); + break; + case SupportedCaffe2OpType::resizeNearest: + outputs = _opCreator->convertResizeNearest(inputs, op); + break; + case SupportedCaffe2OpType::sigmoid: + outputs = _opCreator->convertSigmoid(inputs); + break; + case SupportedCaffe2OpType::softmax: + outputs = _opCreator->convertSoftmax(inputs, op); + break; + case SupportedCaffe2OpType::spatialBN: + outputs = _opCreator->convertSpatialBN(inputs, op); + break; + case SupportedCaffe2OpType::sum: + outputs = _opCreator->convertSum(inputs); + break; + case SupportedCaffe2OpType::clip: + outputs = _opCreator->convertClip(inputs, op); + break; + case SupportedCaffe2OpType::reshape: + outputs = _opCreator->convertReshape(inputs, op); + break; + default: + assert(false && "All unsupported types should have been found before this pass."); + } + + for (size_t i = 0; i < outputs.size(); ++i) + { + setOutputForTensor(op.output(i), outputs[i]); + } +} + +std::vector Caffe2Importer::getInputMIROps(const OperatorDef &op) +{ + std::vector inputs; + + for (const auto &input_name : op.input()) + { + inputs.push_back(getOutputForTensor(input_name)); + } + + return inputs; +} + +void Caffe2Importer::setOutputForTensor(const std::string &tensor_name, Operation::Output *output) +{ + auto it = _blobNameToOutput.find(tensor_name); + if (it != _blobNameToOutput.cend()) + { + // caffe2 input blob name could be same as output blob name, and next line will overwrite + // '_blobNameToOpOutput' element, but in all networks that I saw it was not a problem + it->second->setName(""); + } + output->setName(tensor_name); + _blobNameToOutput[tensor_name] = output; +} + +mir::Operation::Output *Caffe2Importer::getOutputForTensor(const std::string &name) const +{ + return _blobNameToOutput.at(name); +} + +void Caffe2Importer::setGraphOutputs() +{ + // Create outputs. + // TODO Caffe2 does not provide a way to detect model inputs and outputs. For now assume that: + // - there is exactly one output; + // - the output is from the last layer. + const auto &output_name = _predict_net->op().rbegin()->output(0); + auto output = getOutputForTensor(output_name); + _graph->create(output); +} + +const std::map Caffe2Importer::_operatorTypes = { + {"Add", SupportedCaffe2OpType::add}, + {"AveragePool", SupportedCaffe2OpType::averagePool}, + {"Conv", SupportedCaffe2OpType::conv}, + {"Concat", SupportedCaffe2OpType::concat}, + {"ConstantFill", SupportedCaffe2OpType::constantFill}, + {"Dropout", SupportedCaffe2OpType::dropout}, + {"FC", SupportedCaffe2OpType::FC}, + {"GivenTensorFill", SupportedCaffe2OpType::givenTensorFill}, + {"MaxPool", SupportedCaffe2OpType::maxPool}, + {"Mul", SupportedCaffe2OpType::mul}, + {"Relu", SupportedCaffe2OpType::relu}, + {"ResizeNearest", SupportedCaffe2OpType::resizeNearest}, + {"Sigmoid", SupportedCaffe2OpType::sigmoid}, + {"Softmax", SupportedCaffe2OpType::softmax}, + {"SpatialBN", SupportedCaffe2OpType::spatialBN}, + {"Sum", SupportedCaffe2OpType::sum}, + {"Clip", SupportedCaffe2OpType::clip}, + {"Reshape", SupportedCaffe2OpType::reshape}, + {"GivenTensorInt64Fill", SupportedCaffe2OpType::givenTensorInt64Fill}, +}; +} + +namespace mir_caffe2 +{ + +std::unique_ptr loadModel(std::string predict_net, std::string init_net, + const std::vector> &input_shapes) +{ + Caffe2Importer importer(std::move(predict_net), std::move(init_net), input_shapes); + return importer.importModel(); +} + +} // namespace mir_caffe2 diff --git a/compiler/mir/src/mir_caffe2_importer/caffe2_op_creator.cpp b/compiler/mir/src/mir_caffe2_importer/caffe2_op_creator.cpp new file mode 100644 index 0000000..3390f44 --- /dev/null +++ b/compiler/mir/src/mir_caffe2_importer/caffe2_op_creator.cpp @@ -0,0 +1,551 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "caffe2_op_creator.h" +#include "caffe2_proto_helper.h" + +#include "mir/ops/AddOp.h" +#include "mir/ops/AvgPool2DOp.h" +#include "mir/ops/CappedReluOp.h" +#include "mir/ops/ConcatOp.h" +#include "mir/ops/ConstantOp.h" +#include "mir/ops/Conv2DOp.h" +#include "mir/ops/FullyConnectedOp.h" +#include "mir/ops/MaxPool2DOp.h" +#include "mir/ops/MulOp.h" +#include "mir/ops/ReluOp.h" +#include "mir/ops/ReshapeOp.h" +#include "mir/ops/ResizeOp.h" +#include "mir/ops/SigmoidOp.h" +#include "mir/ops/SoftmaxOp.h" +#include "mir/ops/TransposeOp.h" + +#include "mir/Index.h" +#include "mir/Shape.h" +#include "mir/ShapeRange.h" +#include "mir/Tensor.h" +#include "mir/TensorUtil.h" + +#include +#include +#include + +namespace mir_caffe2 +{ + +using namespace ::caffe2; +using namespace mir; + +// +// Helper functions +// + +static std::pair, std::vector> +getPadding(const ::caffe2::OperatorDef &op) +{ + + if (hasArgument(op.arg(), "pads")) + { + // pads order: t l b r + auto pads_arg = findArgumentByName(op.arg(), "pads"); + + std::vector paddings; + for (const auto &pad : pads_arg.ints()) + paddings.push_back(static_cast(pad)); + + assert(paddings.size() == 4); + + int32_t pad_t = paddings[0]; + int32_t pad_l = paddings[1]; + int32_t pad_b = paddings[2]; + int32_t pad_r = paddings[3]; + + std::vector padding_before{pad_t, pad_l}; + std::vector padding_after{pad_b, pad_r}; + return {padding_before, padding_after}; + } + + bool has_custom_pad = hasArgument(op.arg(), "pad_l") || hasArgument(op.arg(), "pad_r") || + hasArgument(op.arg(), "pad_t") || hasArgument(op.arg(), "pad_b"); + + if (has_custom_pad) + { + int32_t pad_l = getSingleArgument(op, "pad_l", 0); + int32_t pad_t = getSingleArgument(op, "pad_t", 0); + int32_t pad_r = getSingleArgument(op, "pad_r", 0); + int32_t pad_b = getSingleArgument(op, "pad_b", 0); + + std::vector padding_before{pad_t, pad_l}; + std::vector padding_after{pad_b, pad_r}; + return {padding_before, padding_after}; + } + + int32_t pad = getSingleArgument(op, "pad", 0); + return {{pad, pad}, {pad, pad}}; +} + +static std::vector getStrides(const ::caffe2::OperatorDef &op) +{ + std::vector strides; + + if (hasArgument(op.arg(), "stride")) + { + std::int32_t stride = getSingleArgument(op, "stride", 1); + strides = {stride, stride}; + } + + if (hasArgument(op.arg(), "strides")) + { + // strides order: h w + auto strides_arg = findArgumentByName(op.arg(), "strides"); + for (const auto &s : strides_arg.ints()) + strides.push_back(s); + } + + assert(!strides.empty() && "Strides not found"); + + return strides; +} + +static std::vector getWindowSize(const ::caffe2::OperatorDef &op, + const std::vector &inputs) +{ + int is_global_pooling = getSingleArgument(op, "global_pooling", 0); + bool has_custom_kernel_size = + hasArgument(op.arg(), "kernel_h") || hasArgument(op.arg(), "kernel_w"); + bool has_custom_kernels_size = hasArgument(op.arg(), "kernels"); + + int kernel_h(0), kernel_w(0); + if (is_global_pooling) + { + const auto &input_shape = inputs[0]->getShape(); + assert(input_shape.rank() == 4 && "getWindowSize() inputs must be of rank 4"); + kernel_h = input_shape.dim(2); + kernel_w = input_shape.dim(3); + } + else + { + if (has_custom_kernel_size) + { + kernel_h = getSingleArgument(op, "kernel_h", 0); + kernel_w = getSingleArgument(op, "kernel_w", 0); + } + else + { + if (has_custom_kernels_size) + { + // kernels order: h w + std::vector kernels; + auto kernels_arg = findArgumentByName(op.arg(), "kernels"); + for (const auto &ker : kernels_arg.ints()) + kernels.push_back(static_cast(ker)); + assert(kernels.size() == 2); + kernel_h = kernels[0]; + kernel_w = kernels[1]; + } + else + { + kernel_h = kernel_w = getSingleArgument(op, "kernel", 0); + } + } + } + return {kernel_h, kernel_w}; +} + +// +// Check functions +// + +static void checkLayout(const OperatorDef &op) +{ + if (getSingleArgument(op, "order", "NCHW") != "NCHW") + throw std::runtime_error(op.type() + ": only 'NCHW' axis order is supported"); +} + +static void checkConvLikeOp(const ::caffe2::OperatorDef &op) +{ + checkLayout(op); + + // Padding + bool has_custom_pad = hasArgument(op.arg(), "pad_l") || hasArgument(op.arg(), "pad_r") || + hasArgument(op.arg(), "pad_t") || hasArgument(op.arg(), "pad_b"); + + if (has_custom_pad && hasArgument(op.arg(), "pad")) + throw std::runtime_error("Custom pad can't be combined with overall pad"); + + if (has_custom_pad && + !(hasArgument(op.arg(), "pad_l") && hasArgument(op.arg(), "pad_r") && + hasArgument(op.arg(), "pad_t") && hasArgument(op.arg(), "pad_b"))) + throw std::runtime_error("If one custom pad specified - all custom pads must be specified"); + + // Kernel size + bool has_custom_kernel_size = + hasArgument(op.arg(), "kernel_h") || hasArgument(op.arg(), "kernel_w"); + + if (has_custom_kernel_size && hasArgument(op.arg(), "kernel")) + throw std::runtime_error("Custom kernel size can't be combined with overall kernel size"); + + if (has_custom_kernel_size && + !(hasArgument(op.arg(), "kernel_h") && hasArgument(op.arg(), "kernel_w"))) + throw std::runtime_error( + "If one custom kernel size specified - all custom kernel sizes must be specified"); +} + +static mir::TensorVariant createTensor(const OperatorDef &op) +{ + assert(hasArgument(op.arg(), "shape") && hasArgument(op.arg(), "values")); + + const auto &shape = findArgumentByName(op.arg(), "shape"); + const auto &values = findArgumentByName(op.arg(), "values"); + + mir::DataType element_type; + const void *src_data; + // if values on floats + if (!values.floats().empty()) + { + element_type = mir::DataType::FLOAT32; + src_data = values.floats().data(); + } + else + { + assert(!values.ints().empty()); + if (op.type() == "GivenTensorInt64Fill") + { + element_type = mir::DataType::INT64; + } + else + { + element_type = mir::DataType::INT32; + } + src_data = values.ints().data(); + } + + mir::Shape tensor_shape(shape.ints_size()); + + for (int i = 0; i < shape.ints_size(); ++i) + { + tensor_shape.dim(i) = shape.ints(i); + } + + return mir::TensorVariant({element_type, tensor_shape}, src_data); +} + +// +// Convert functions +// + +std::vector +Caffe2OpCreator::convertConstant(const std::vector &, + const ::caffe2::OperatorDef &op) +{ + // Constant may not contain any data if it is a fake input. + if (!hasArgument(op.arg(), "values")) + return {}; + + return {createOp(createTensor(op))->getOutput(0)}; +} + +std::vector +Caffe2OpCreator::convertAdd(const std::vector &inputs, + const ::caffe2::OperatorDef &op) +{ + assert(inputs.size() == 2); + auto lhs = inputs[0]; + auto rhs = inputs[1]; + + if (getSingleArgument(op, "broadcast", 0) != 0) + { + // FIXME This only works when 'axis' == 1 and the second input is 1-D. + rhs = createOp(rhs, Shape{1, rhs->getShape().dim(0), 1, 1})->getOutput(0); + auto result = createOp(lhs, rhs)->getOutput(0); + return {result}; + } + + auto result = createOp(lhs, rhs)->getOutput(0); + return {result}; +} + +std::vector +Caffe2OpCreator::convertAveragePool(const std::vector &inputs, + const OperatorDef &op) +{ + checkConvLikeOp(op); + + assert(inputs.size() == 1); + auto input = inputs[0]; + + AvgPool2DOpAttributes attributes; + std::tie(attributes.padding_before, attributes.padding_after) = getPadding(op); + attributes.window = getWindowSize(op, inputs); + attributes.strides = getStrides(op); + attributes.include_pad = false; + attributes.data_format = DataFormat::NCHW; + auto result = createOp(input, attributes)->getOutput(0); + return {result}; +} + +std::vector +Caffe2OpCreator::convertConv(const std::vector &inputs, + const ::caffe2::OperatorDef &op) +{ + // dilation order: h w (not used) + mir::Conv2DOpAttributes attributes; + attributes.strides = getStrides(op); + std::tie(attributes.padding_before, attributes.padding_after) = getPadding(op); + attributes.num_groups = getSingleArgument(op, "group", 1); + attributes.data_format = DataFormat::NCHW; + + std::vector perm{0, 2, 3, 1}; // OIHW -> OHWI + auto kernel = createOp(inputs[1], perm)->getOutput(0); + auto result = createOp(inputs[0], kernel, attributes)->getOutput(0); + + if (op.input_size() > 2) + { + auto bias = inputs[2]; + bias = createOp(bias, Shape{1, bias->getShape().dim(0), 1, 1})->getOutput(0); + result = createOp(result, bias)->getOutput(0); + } + + return {result}; +} + +std::vector +Caffe2OpCreator::convertConcat(const std::vector &inputs, + const ::caffe2::OperatorDef &op) +{ + checkLayout(op); + + // `1` corresponds to the default (channels) axis. + int axis = getSingleArgument(op, "axis", 1); + auto result = createOp(inputs, axis); + return {result->getOutput(0)}; +} + +std::vector +Caffe2OpCreator::convertDropout(const std::vector &inputs, + const ::caffe2::OperatorDef &) +{ + // This is a no-op in inference mode. + return {inputs[0]}; +} + +std::vector +Caffe2OpCreator::convertFC(const std::vector &inputs, + const ::caffe2::OperatorDef &op) +{ + for (auto &s : {"axis", "axis_w", "float16_compute"}) + if (hasArgument(op.arg(), s)) + throw std::runtime_error(std::string("FC: only default '") + s + "' value is supported"); + + const auto &input_shape = inputs[0]->getShape(); + // Transform input into 2-D tensor by flattening axes + Shape shape{input_shape.dim(0), input_shape.numElements() / input_shape.dim(0)}; + + auto reshape = createOp(inputs[0], shape)->getOutput(0); + auto weights = + createOp(inputs[1], std::vector{1, 0})->getOutput(0); + auto result = createOp(reshape, weights)->getOutput(0); + result = createOp(result, inputs[2])->getOutput(0); + + return {result}; +} + +std::vector +Caffe2OpCreator::convertMaxPool(const std::vector &inputs, + const OperatorDef &op) +{ + checkConvLikeOp(op); + + assert(inputs.size() == 1); + auto input = inputs[0]; + + MaxPool2DOpAttributes attributes; + std::tie(attributes.padding_before, attributes.padding_after) = getPadding(op); + attributes.window = getWindowSize(op, inputs); + attributes.strides = getStrides(op); + attributes.data_format = DataFormat::NCHW; + auto result = createOp(input, attributes)->getOutput(0); + return {result}; +} + +std::vector +Caffe2OpCreator::convertMul(const std::vector &inputs, + const ::caffe2::OperatorDef &op) +{ + assert(inputs.size() == 2); + auto lhs = inputs[0]; + auto rhs = inputs[1]; + + if (getSingleArgument(op, "broadcast", 0) != 0) + { + // FIXME This only works when `axis` == 1 and the second input is 1-D. + rhs = createOp(rhs, Shape{1, rhs->getShape().dim(0), 1, 1})->getOutput(0); + auto result = createOp(lhs, rhs)->getOutput(0); + return {result}; + } + + auto result = createOp(lhs, rhs)->getOutput(0); + return {result}; +} + +std::vector +Caffe2OpCreator::convertRelu(const std::vector &inputs) +{ + auto relu = createOp(inputs[0]); + return {relu->getOutput(0)}; +} + +std::vector +Caffe2OpCreator::convertResizeNearest(const std::vector &inputs, + const ::caffe2::OperatorDef &op) +{ + std::vector scales(4); + assert(inputs[0]->getShape().rank() == 4 && "only 4d tensors is supported"); + // Assuming NCHW format. + scales[0] = 1.0f; + scales[1] = 1.0f; + scales[2] = getSingleArgument(op, "height_scale", 1.0f); + scales[3] = getSingleArgument(op, "width_scale", 1.0f); + auto result = + createOp(inputs[0], ops::ResizeOp::ResizeMethod::nearestNeighbor, scales) + ->getOutput(0); + return {result}; +} + +std::vector +Caffe2OpCreator::convertSigmoid(const std::vector &inputs) +{ + auto result = createOp(inputs[0]); + return {result->getOutput(0)}; +} + +std::vector +Caffe2OpCreator::convertSoftmax(const std::vector &inputs, + const ::caffe2::OperatorDef &op) +{ + int axis = getSingleArgument(op, "axis", 1); + auto softmax = createOp(inputs[0], axis); + return {softmax->getOutput(0)}; +} + +std::vector +Caffe2OpCreator::convertSpatialBN(const std::vector &inputs, + const ::caffe2::OperatorDef &op) +{ + checkLayout(op); + + // Sanity checks + if (op.input_size() != 5) + throw std::runtime_error( + "SpatialBN must have exactly 5 inputs ('sums' and 'sumsq' are not supported yet)"); + if (getSingleArgument(op, "is_test", 1) != 1) + throw std::runtime_error("SpatialBN: only test mode supported"); + + // overall_res = (X - mean) / sqrt(var + epsilon) * scale + bias + + auto scale_op = dynamic_cast(inputs[1]->getNode()); + auto bias_op = dynamic_cast(inputs[2]->getNode()); + auto mean_op = dynamic_cast(inputs[3]->getNode()); + auto var_op = dynamic_cast(inputs[4]->getNode()); + if (scale_op == nullptr || bias_op == nullptr || mean_op == nullptr || var_op == nullptr) + throw std::runtime_error( + "SpatialBN: non-constant 'scale', 'bias', 'mean' and 'var' inputs are not supported yet."); + + const auto &scale_tensor = scale_op->getValue(); + const auto &bias_tensor = bias_op->getValue(); + const auto &mean_tensor = mean_op->getValue(); + const auto &var_tensor = var_op->getValue(); + float eps = getSingleArgument(op, "epsilon", 1e-5f); + + // res1 = X - mean + Tensor bias_data(mean_tensor); + for (auto &idx : ShapeRange(bias_data.getShape())) + bias_data.at(idx) *= -1; + + auto mean = createOp(mean_tensor)->getOutput(0); + mean = createOp(mean, Shape{1, mean->getShape().dim(0), 1, 1})->getOutput(0); + auto result = createOp(inputs[0], mean)->getOutput(0); + + // res2 = res1 * scale / (var + epsilon) + Tensor multiplier(scale_tensor); + for (auto &idx : ShapeRange(scale_tensor.getShape())) + multiplier.at(idx) /= std::sqrt(*reinterpret_cast(var_tensor.at(idx)) + eps); + auto scale = createOp(scale_tensor)->getOutput(0); + scale = createOp(scale, Shape{1, scale->getShape().dim(0), 1, 1})->getOutput(0); + result = createOp(result, scale)->getOutput(0); + + // overall_res = res2 + bias + auto bias = createOp(bias_tensor)->getOutput(0); + bias = createOp(bias, Shape{1, bias->getShape().dim(0), 1, 1})->getOutput(0); + result = createOp(result, bias)->getOutput(0); + + return {result}; +} + +std::vector +Caffe2OpCreator::convertSum(const std::vector &inputs) +{ + auto result = createOp(inputs[0], inputs[1])->getOutput(0); + for (int i = 2; i < static_cast(inputs.size()); ++i) + { + result = createOp(result, inputs[i])->getOutput(0); + } + return {result}; +} + +std::vector +Caffe2OpCreator::convertClip(const std::vector &inputs, + const ::caffe2::OperatorDef &op) +{ + + float max = getSingleArgument(op, "max", float(0)); + float min = getSingleArgument(op, "min", float(0)); + + if (min != 0.0f) + throw std::runtime_error("Clip: min != 0 is not supported."); + if (max <= min) + throw std::runtime_error("Clip: max <= min is not supported."); + auto cap_relu = createOp(inputs[0], max); + + return {cap_relu->getOutput(0)}; +} + +std::vector +Caffe2OpCreator::convertReshape(const std::vector &inputs, + const ::caffe2::OperatorDef &) +{ + auto shape_op = dynamic_cast(inputs[1]->getNode()); + if (shape_op == nullptr) + throw std::runtime_error("Reshape: non-constant shape is not supported yet."); + + const auto &shape_tensor = shape_op->getValue(); + + Tensor out_shape_tensor(shape_tensor); + + ShapeRange range(out_shape_tensor.getShape()); + std::vector shape_vec; + for (const auto &index : range) + { + shape_vec.push_back(static_cast(out_shape_tensor.at(index))); + } + Shape out_shape(shape_vec); + + auto reshape = createOp(inputs[0], out_shape); + + return {reshape->getOutput(0)}; +} + +} // namespace mir_caffe2 diff --git a/compiler/mir-caffe2-importer/caffe2_op_creator.h b/compiler/mir/src/mir_caffe2_importer/caffe2_op_creator.h similarity index 100% rename from compiler/mir-caffe2-importer/caffe2_op_creator.h rename to compiler/mir/src/mir_caffe2_importer/caffe2_op_creator.h diff --git a/compiler/mir-caffe2-importer/caffe2_op_types.h b/compiler/mir/src/mir_caffe2_importer/caffe2_op_types.h similarity index 100% rename from compiler/mir-caffe2-importer/caffe2_op_types.h rename to compiler/mir/src/mir_caffe2_importer/caffe2_op_types.h diff --git a/compiler/mir/src/mir_caffe2_importer/caffe2_proto_helper.cpp b/compiler/mir/src/mir_caffe2_importer/caffe2_proto_helper.cpp new file mode 100644 index 0000000..7bb9cf0 --- /dev/null +++ b/compiler/mir/src/mir_caffe2_importer/caffe2_proto_helper.cpp @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "caffe2_proto_helper.h" + +namespace mir_caffe2 +{ + +const ::caffe2::Argument &findArgumentByName(RepArgument args, const std::string &name) +{ + for (auto &arg : args) + if (arg.name() == name) + return arg; + throw std::runtime_error("Can't find argument with name: " + name); +} + +bool hasArgument(RepArgument args, const std::string &name) +{ + for (auto &arg : args) + if (arg.name() == name) + return true; + return false; +} + +int getSingleArgument(const ::caffe2::OperatorDef &op, const std::string &argument_name, + const int default_value) +{ + if (hasArgument(op.arg(), argument_name)) + return static_cast(findArgumentByName(op.arg(), argument_name).i()); + return default_value; +} + +float getSingleArgument(const ::caffe2::OperatorDef &op, const std::string &argument_name, + const float default_value) +{ + if (hasArgument(op.arg(), argument_name)) + return findArgumentByName(op.arg(), argument_name).f(); + return default_value; +} + +std::string getSingleArgument(const ::caffe2::OperatorDef &op, const std::string &argument_name, + const std::string &default_value) +{ + if (hasArgument(op.arg(), argument_name)) + return findArgumentByName(op.arg(), argument_name).s(); + return default_value; +} + +} // namespace mir_caffe2 diff --git a/compiler/mir/src/mir_caffe2_importer/caffe2_proto_helper.h b/compiler/mir/src/mir_caffe2_importer/caffe2_proto_helper.h new file mode 100644 index 0000000..4f5c4ef --- /dev/null +++ b/compiler/mir/src/mir_caffe2_importer/caffe2_proto_helper.h @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MIR_CAFFE2_PROTO_HELPER_H +#define MIR_CAFFE2_PROTO_HELPER_H + +#include "caffe2/proto/caffe2.pb.h" + +namespace mir_caffe2 +{ + +using RepArgument = const ::google::protobuf::RepeatedPtrField<::caffe2::Argument> &; + +const ::caffe2::Argument &findArgumentByName(RepArgument args, const std::string &name); + +bool hasArgument(RepArgument args, const std::string &name); + +int getSingleArgument(const ::caffe2::OperatorDef &op, const std::string &argument_name, + int default_value); +float getSingleArgument(const ::caffe2::OperatorDef &op, const std::string &argument_name, + float default_value); +std::string getSingleArgument(const ::caffe2::OperatorDef &op, const std::string &argument_name, + const std::string &default_value); + +} // namespace mir_caffe2 + +#endif // MIR_CAFFE2_PROTO_HELPER_H diff --git a/compiler/mir/src/mir_caffe_importer/CMakeLists.txt b/compiler/mir/src/mir_caffe_importer/CMakeLists.txt new file mode 100644 index 0000000..34be520 --- /dev/null +++ b/compiler/mir/src/mir_caffe_importer/CMakeLists.txt @@ -0,0 +1,16 @@ +nnas_find_package(CaffeProto QUIET) + +if (NOT CaffeProto_FOUND) + return() +endif () + +set(MIR_CAFFE_IMPORTER_SOURCES + caffe_importer.cpp + caffe_op_creator.cpp + caffe_op_creator.h + caffe_op_types.h) + +add_library(mir_caffe_importer STATIC ${MIR_CAFFE_IMPORTER_SOURCES}) +set_target_properties(mir_caffe_importer PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_include_directories(mir_caffe_importer PUBLIC ../../include/mir_caffe_importer) +target_link_libraries(mir_caffe_importer PUBLIC mir PRIVATE caffeproto nncc_common) diff --git a/compiler/mir/src/mir_caffe_importer/caffe_importer.cpp b/compiler/mir/src/mir_caffe_importer/caffe_importer.cpp new file mode 100644 index 0000000..49f13fb --- /dev/null +++ b/compiler/mir/src/mir_caffe_importer/caffe_importer.cpp @@ -0,0 +1,439 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "caffe_importer.h" +#include "caffe/proto/caffe.pb.h" +#include "caffe_op_creator.h" +#include "caffe_op_types.h" + +#include "mir/ops/OutputOp.h" + +#include +#include +#include + +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +namespace mir_caffe +{ + +namespace +{ + +class CaffeImporter +{ +public: + /// @brief Load the model and convert it into a MIR Graph. + std::unique_ptr importModelFromBinaryFile(const std::string &filename); + std::unique_ptr importModelFromTextFile(const std::string &filename); + +private: + std::unique_ptr importModel(); + + std::unique_ptr _net; + std::unique_ptr _opCreator; + + // Maps Caffe blob names to corresponding MIR operation outputs. + std::map _blobNameToOpOutput; + + static const std::map _operatorTypes; + + /** + * @brief Mark output MIR nodes + */ + void setGraphOutputs(mir::Graph *graph); + + /** + * @brief Pass through caffe graph and collect unsupported by NNC layers + * @throw PassException with message, containing detected problems + */ + void collectUnsupportedLayers(); + + /** + * @brief Create MIR node from single caffe layer + */ + void createMIRNodesFromLayer(const caffe::LayerParameter &layer); + + mir::Operation::Output *getOutputForBlob(const std::string &blob_name) const; + void setOutputForBlob(const std::string &blob_name, mir::Operation::Output *output); + + /** + * @brief Collect unsupported parts of caffe layer + */ + void collectUnsupportedOp(const caffe::LayerParameter &layer, std::set &problems); + + /** + * @brief Returns MIR operation outputs corresponding to the inputs of the given layer. + */ + std::vector getMIRInputsForLayer(const caffe::LayerParameter &layer); + + void processDeprecatedInput(); +}; + +void loadModelFromBinaryFile(const std::string &filename, caffe::NetParameter *net) +{ + GOOGLE_PROTOBUF_VERIFY_VERSION; + + int file_handle = open(filename.c_str(), O_RDONLY); + + if (file_handle == -1) + throw std::runtime_error("Couldn't open file \"" + filename + "\": " + std::strerror(errno) + + "."); + + google::protobuf::io::FileInputStream file_stream(file_handle); + file_stream.SetCloseOnDelete(true); + + google::protobuf::io::CodedInputStream coded_stream(&file_stream); + coded_stream.SetTotalBytesLimit(INT_MAX, INT_MAX); + + if (!net->ParseFromCodedStream(&coded_stream)) + throw std::runtime_error("Couldn't parse file \"" + filename + "\"."); + + // If the file has not been consumed entirely, assume that the file is in the wrong format. + if (!coded_stream.ConsumedEntireMessage()) + throw std::runtime_error("File \"" + filename + "\" has not been consumed entirely."); +} + +void loadModelFromTextFile(const std::string &filename, caffe::NetParameter *net) +{ + GOOGLE_PROTOBUF_VERIFY_VERSION; + + int file_handle = open(filename.c_str(), O_RDONLY); + + if (file_handle == -1) + throw std::runtime_error("Couldn't open file \"" + filename + "\": " + std::strerror(errno) + + "."); + + google::protobuf::io::FileInputStream file_stream(file_handle); + file_stream.SetCloseOnDelete(true); + + if (!google::protobuf::TextFormat::Parse(&file_stream, net)) + throw std::runtime_error("Couldn't parse file \"" + filename + "\"."); +} + +std::unique_ptr CaffeImporter::importModel() +{ + auto graph = std::make_unique(); + _opCreator = std::make_unique(graph.get()); + + collectUnsupportedLayers(); + + for (int i = 0; i < _net->layer_size(); ++i) + createMIRNodesFromLayer(_net->layer(i)); + + setGraphOutputs(graph.get()); + + return graph; +} + +std::unique_ptr CaffeImporter::importModelFromBinaryFile(const std::string &filename) +{ + _net = std::make_unique(); + loadModelFromBinaryFile(filename, _net.get()); + + return importModel(); +} + +std::unique_ptr CaffeImporter::importModelFromTextFile(const std::string &filename) +{ + _net = std::make_unique(); + loadModelFromTextFile(filename, _net.get()); + + return importModel(); +} + +void CaffeImporter::collectUnsupportedLayers() +{ + processDeprecatedInput(); + + std::set problems; + + for (const caffe::LayerParameter &layer : _net->layer()) + collectUnsupportedOp(layer, problems); + + if (!problems.empty()) + { + std::string msg("NNC can't load model. Detected problems:"); + for (const auto &problemStr : problems) + msg.append("\n * " + problemStr); + throw std::runtime_error(msg); + } +} + +void CaffeImporter::createMIRNodesFromLayer(const caffe::LayerParameter &layer) +{ + std::vector inputs = getMIRInputsForLayer(layer); + std::vector outputs; + + switch (_operatorTypes.at(layer.type())) + { + case CaffeOpType::input: + outputs = _opCreator->convertInput(layer); + break; + case CaffeOpType::convolution: + outputs = _opCreator->convertConvolution(layer, inputs); + break; + case CaffeOpType::innerProduct: + outputs = _opCreator->convertInnerProduct(layer, inputs); + break; + case CaffeOpType::pooling: + outputs = _opCreator->convertPooling(layer, inputs); + break; + case CaffeOpType::concat: + outputs = _opCreator->convertConcat(layer, inputs); + break; + case CaffeOpType::reshape: + outputs = _opCreator->convertReshape(layer, inputs); + break; + case CaffeOpType::ReLU: + outputs = _opCreator->convertReLU(layer, inputs); + break; + case CaffeOpType::softmax: + outputs = _opCreator->convertSoftmax(layer, inputs); + break; + case CaffeOpType::scale: + outputs = _opCreator->convertScale(layer, inputs); + break; + case CaffeOpType::batchNorm: + outputs = _opCreator->convertBatchNorm(layer, inputs); + break; + case CaffeOpType::dropout: + outputs = _opCreator->convertDropout(layer, inputs); + break; + case CaffeOpType::tanh: + outputs = _opCreator->convertTanH(layer, inputs); + break; + case CaffeOpType::ELU: + outputs = _opCreator->convertELU(layer, inputs); + break; + case CaffeOpType::eltwise: + outputs = _opCreator->convertEltwise(layer, inputs); + break; + case CaffeOpType::embed: + outputs = _opCreator->convertEmbed(layer, inputs); + break; + case CaffeOpType::deconvolution: + outputs = _opCreator->convertDeconvolution(layer, inputs); + break; + case CaffeOpType::split: + outputs = _opCreator->convertSplit(layer, inputs); + break; + case CaffeOpType::sigmoid: + outputs = _opCreator->convertSigmoid(layer, inputs); + break; + case CaffeOpType::LSTM: + outputs = _opCreator->convertLSTM(layer, inputs); + break; + default: + assert(false && "All unsupported types should have been found before this pass."); + } + + assert(static_cast(outputs.size()) == layer.top_size() && "Number of outputs differs."); + for (int i = 0; i < layer.top_size(); ++i) + setOutputForBlob(layer.top(i), outputs[i]); +} + +void CaffeImporter::collectUnsupportedOp(const caffe::LayerParameter &layer, + std::set &problems) +{ + auto it = _operatorTypes.find(layer.type()); + if (it == _operatorTypes.end()) + { + problems.insert(layer.type() + ": unknown layer"); + return; + } + + CaffeOpType op_type = it->second; + + switch (op_type) + { + case CaffeOpType::concat: + case CaffeOpType::input: + case CaffeOpType::softmax: + case CaffeOpType::scale: + case CaffeOpType::dropout: + case CaffeOpType::split: + case CaffeOpType::eltwise: + case CaffeOpType::ELU: + case CaffeOpType::ReLU: + case CaffeOpType::embed: + case CaffeOpType::sigmoid: + case CaffeOpType::tanh: + case CaffeOpType::innerProduct: + // No checks + break; + case CaffeOpType::deconvolution: + case CaffeOpType::convolution: + _opCreator->checkConvolution(layer, problems); + break; + case CaffeOpType::pooling: + _opCreator->checkPooling(layer, problems); + break; + case CaffeOpType::reshape: + _opCreator->checkReshape(layer, problems); + break; + case CaffeOpType::batchNorm: + _opCreator->checkBatchNorm(layer, problems); + break; + case CaffeOpType::LSTM: + _opCreator->checkLSTM(layer, problems); + break; + default: + problems.insert(layer.type() + ": unsupported layer"); + break; + } +} + +void CaffeImporter::processDeprecatedInput() +{ + if (_net->input_dim_size() != 0 || _net->input_shape_size() != 0) + throw std::runtime_error("Deprecated Caffe input types are not supported"); +} + +std::vector +CaffeImporter::getMIRInputsForLayer(const caffe::LayerParameter &layer) +{ + std::vector inputs; + + for (const auto &input_name : layer.bottom()) + inputs.push_back(getOutputForBlob(input_name)); + + return inputs; +} + +mir::Operation::Output *CaffeImporter::getOutputForBlob(const std::string &blob_name) const +{ + return _blobNameToOpOutput.at(blob_name); +} + +void CaffeImporter::setOutputForBlob(const std::string &blob_name, mir::Operation::Output *output) +{ + const auto it = _blobNameToOpOutput.find(blob_name); + if (it != _blobNameToOpOutput.cend()) + { + // caffe input blob name could be same as output blob name, and next line will overwrite + // '_blobNameToOpOutput' element, but in all networks that I saw it was not a problem + it->second->setName(""); + } + + // Do not overwrite the name in case of fall-through layers (ex. Dropout, Split). + // TODO Find a way to handle it properly. + if (output->getName().empty()) + output->setName(blob_name); + + _blobNameToOpOutput[blob_name] = output; +} + +void CaffeImporter::setGraphOutputs(mir::Graph *graph) +{ + // TODO For now, we assume that: + // - there is exactly one output; + // - the output is from the last layer. + const auto &last_layer = *_net->layer().rbegin(); + auto output = getOutputForBlob(last_layer.top(0)); + graph->create(output); +} + +const std::map CaffeImporter::_operatorTypes = { + {"AbsVal", CaffeOpType::absVal}, + {"Accuracy", CaffeOpType::accuracy}, + {"ArgMax", CaffeOpType::argMax}, + {"BatchNorm", CaffeOpType::batchNorm}, + {"BatchReindex", CaffeOpType::batchReindex}, + {"Bias", CaffeOpType::bias}, + {"BNLL", CaffeOpType::BNLL}, + {"Clip", CaffeOpType::clip}, + {"Concat", CaffeOpType::concat}, + {"ContrastiveLoss", CaffeOpType::contrastiveLoss}, + {"Convolution", CaffeOpType::convolution}, + {"Crop", CaffeOpType::crop}, + {"Data", CaffeOpType::data}, + {"Deconvolution", CaffeOpType::deconvolution}, + {"Dropout", CaffeOpType::dropout}, + {"DummyData", CaffeOpType::dummyData}, + {"Eltwise", CaffeOpType::eltwise}, + {"ELU", CaffeOpType::ELU}, + {"Embed", CaffeOpType::embed}, + {"EuclidianLoss", CaffeOpType::euclidianLoss}, + {"Exp", CaffeOpType::exp}, + {"Filter", CaffeOpType::filter}, + {"Flatten", CaffeOpType::flatten}, + {"HDF5Data", CaffeOpType::HDF5Data}, + {"HDF5Output", CaffeOpType::HDF5Output}, + {"HingeLoss", CaffeOpType::hingeLoss}, + {"Im2Col", CaffeOpType::im2Col}, + {"ImageData", CaffeOpType::imageData}, + {"InfogainLoss", CaffeOpType::infogainLoss}, + {"InnerProduct", CaffeOpType::innerProduct}, + {"Input", CaffeOpType::input}, + {"Log", CaffeOpType::log}, + {"LRN", CaffeOpType::LRN}, + {"LSTM", CaffeOpType::LSTM}, + {"MemoryData", CaffeOpType::memoryData}, + {"MultinomialLogisticLoss", CaffeOpType::multinomialLogisticLoss}, + {"MVN", CaffeOpType::MVN}, + {"Parameter", CaffeOpType::parameter}, + {"Pooling", CaffeOpType::pooling}, + {"Power", CaffeOpType::power}, + {"PReLU", CaffeOpType::PReLU}, + {"Python", CaffeOpType::python}, + {"Recurrent", CaffeOpType::recurrent}, + {"Reduction", CaffeOpType::reduction}, + {"ReLU", CaffeOpType::ReLU}, + {"Reshape", CaffeOpType::reshape}, + {"RNN", CaffeOpType::RNN}, + {"Scale", CaffeOpType::scale}, + {"SigmoidCrossEntropyLoss", CaffeOpType::sigmoidCrossEntropyLoss}, + {"Sigmoid", CaffeOpType::sigmoid}, + {"Silence", CaffeOpType::silence}, + {"Softmax", CaffeOpType::softmax}, + {"SoftmaxWithLoss", CaffeOpType::softmaxWithLoss}, + {"SPP", CaffeOpType::SPP}, + {"Split", CaffeOpType::split}, + {"Slice", CaffeOpType::slice}, + {"TanH", CaffeOpType::tanh}, + {"Threshold", CaffeOpType::threshold}, + {"Tile", CaffeOpType::tile}, + {"WindowData", CaffeOpType::windowData}}; +} // namespace + +std::unique_ptr importModelFromBinaryFile(const std::string &filename) +{ + CaffeImporter importer; + return importer.importModelFromBinaryFile(filename); +} + +std::unique_ptr importModelFromTextFile(const std::string &filename) +{ + CaffeImporter importer; + return importer.importModelFromTextFile(filename); +} + +std::unique_ptr loadModel(const std::string &filename) +{ + return importModelFromBinaryFile(filename); +} + +} // namespace mir_caffe diff --git a/compiler/mir/src/mir_caffe_importer/caffe_op_creator.cpp b/compiler/mir/src/mir_caffe_importer/caffe_op_creator.cpp new file mode 100644 index 0000000..37edc69 --- /dev/null +++ b/compiler/mir/src/mir_caffe_importer/caffe_op_creator.cpp @@ -0,0 +1,835 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "caffe_op_creator.h" + +#include "mir/ops/AddOp.h" +#include "mir/ops/AvgPool2DOp.h" +#include "mir/ops/ConcatOp.h" +#include "mir/ops/ConstantOp.h" +#include "mir/ops/Conv2DOp.h" +#include "mir/ops/Deconv2DOp.h" +#include "mir/ops/EluOp.h" +#include "mir/ops/FullyConnectedOp.h" +#include "mir/ops/GatherOp.h" +#include "mir/ops/LeakyReluOp.h" +#include "mir/ops/MaxOp.h" +#include "mir/ops/MaxPool2DOp.h" +#include "mir/ops/MulOp.h" +#include "mir/ops/ReluOp.h" +#include "mir/ops/ReshapeOp.h" +#include "mir/ops/SigmoidOp.h" +#include "mir/ops/SliceOp.h" +#include "mir/ops/SoftmaxOp.h" +#include "mir/ops/TanhOp.h" +#include "mir/ops/TransposeOp.h" +#include "mir/Index.h" +#include "mir/ShapeRange.h" +#include "mir/Tensor.h" + +#include +#include +#include +#include + +namespace mir_caffe +{ + +static mir::Shape convertBlobShape(const caffe::BlobShape &shape) +{ + mir::Shape mir_shape(shape.dim_size()); + + for (int i = 0; i < shape.dim_size(); ++i) + { + mir_shape.dim(i) = shape.dim(i); + } + + return mir_shape; +} + +using namespace mir; + +/// @brief Split arg into @p num_parts equal parts along @p axis axis. +std::vector CaffeOpCreator::createSplit(mir::Operation::Output *arg, + int32_t num_parts, int32_t axis) +{ + const auto &arg_shape = arg->getShape(); + + assert(axis >= 0 && axis < arg_shape.rank()); + int32_t part_size = arg_shape.dim(axis) / num_parts; + assert(part_size * num_parts == arg_shape.dim(axis)); + + Shape starts(arg_shape.rank()); + Shape sizes(arg_shape); + sizes.dim(axis) = part_size; + + std::vector outputs(num_parts); + for (int32_t i = 0; i < num_parts; ++i) + { + outputs[i] = createOp(arg, starts, sizes)->getOutput(0); + starts.dim(axis) += part_size; + } + + return outputs; +} + +/// @brief Helper function for creating FullyConnected operation with non-square input. +mir::Operation::Output *CaffeOpCreator::createFullyConnected(mir::Operation::Output *input, + mir::Operation::Output *weights, + int32_t axis) +{ + const auto &input_shape = input->getShape(); + const auto &weights_shape = weights->getShape(); + + assert(axis >= 0 && axis < input_shape.rank()); + assert(weights_shape.rank() == 2); + + // Result shape is: input.shape[0:axis] + weights.shape[1]. + Shape result_shape = input_shape; + result_shape.resize(axis + 1); + result_shape.dim(axis) = weights_shape.dim(1); + + // Flatten input to 2-D shape. + int32_t outer_size = 1; + for (int32_t i = 0; i < axis; ++i) + outer_size *= input_shape.dim(i); + int32_t inner_size = 1; + for (int32_t i = axis; i < input_shape.rank(); ++i) + inner_size *= input_shape.dim(i); + + auto flatten = createOp(input, Shape{outer_size, inner_size})->getOutput(0); + auto fc = createOp(flatten, weights)->getOutput(0); + return createOp(fc, result_shape)->getOutput(0); +} + +TensorVariant CaffeOpCreator::convertBlob(const caffe::BlobProto &blob) +{ + const void *src_data; + + mir::DataType dtype; + if (blob.data_size() != 0) + { + assert(blob.double_data_size() == 0); + dtype = mir::DataType::FLOAT32; + src_data = blob.data().data(); + } + else if (blob.double_data_size() != 0) + { + dtype = mir::DataType::FLOAT64; + src_data = blob.double_data().data(); + } + else + { + throw std::runtime_error("No data in Caffe BlobProto, investigate"); + } + + const mir::Shape shape = convertBlobShape(blob.shape()); + return TensorVariant({dtype, shape}, src_data); +} + +std::vector +CaffeOpCreator::convertInput(const caffe::LayerParameter &layer) +{ + const auto ¶ms = layer.input_param(); + const auto num_inputs = layer.top_size(); + const auto num_shapes = params.shape_size(); + std::vector outputs; + + assert((num_shapes == 1 || num_shapes == num_inputs) && "Unsupported number of shapes."); + + for (int i = 0; i < num_inputs; ++i) + { + const auto &blob_shape = params.shape(num_shapes == 1 ? 0 : i); + mir::TensorType input_type(DataType::FLOAT32, convertBlobShape(blob_shape)); + auto input = createOp(input_type)->getOutput(0); + outputs.push_back(input); + } + + return outputs; +} + +template +static void convertConvolutionParam(const caffe::ConvolutionParameter &conv_param, + OperationAttributes &attributes) +{ + std::int32_t stride_h, stride_w; + if (conv_param.has_stride_h() || conv_param.has_stride_w()) + { + // If stride_h or stride_w are set, they take precedence. + stride_h = conv_param.stride_h(); + stride_w = conv_param.stride_w(); + } + else if (conv_param.stride_size() == 0) + { + // If no strides specified, they defaults to 1. + stride_h = stride_w = 1; + } + else if (conv_param.stride_size() == 1) + { + // If only one stride specified, all strides take the same value. + stride_h = stride_w = conv_param.stride(0); + } + else + { + // Otherwise, there must be a stride for each dimension. + assert(conv_param.stride_size() == 2); + stride_h = conv_param.stride(0); + stride_w = conv_param.stride(1); + } + attributes.strides = {stride_h, stride_w}; + + std::int32_t pad_h, pad_w; + if (conv_param.has_pad_h() || conv_param.has_pad_w()) + { + // If pad_h or pad_w are set, they take precedence. + pad_h = conv_param.pad_h(); + pad_w = conv_param.pad_w(); + } + else if (conv_param.pad_size() == 0) + { + // If no pads specified, they defaults to 0. + pad_h = pad_w = 0; + } + else if (conv_param.pad_size() == 1) + { + // If only one pad specified, all pads take the same value. + pad_h = pad_w = conv_param.pad(0); + } + else + { + // Otherwise, there must be a pad for each dimension. + assert(conv_param.pad_size() == 2); + pad_h = conv_param.pad(0); + pad_w = conv_param.pad(1); + } + attributes.padding_after = attributes.padding_before = {pad_h, pad_w}; +} + +void CaffeOpCreator::checkConvolution(const caffe::LayerParameter &layer, + std::set &problems_ops_set) +{ + const caffe::ConvolutionParameter ¶ms = layer.convolution_param(); + + assert(params.stride_size() <= 2); + + if (params.axis() != 1) + problems_ops_set.insert("Conv2D: Unsupported axis"); + + if (params.pad_size() != 0 && (params.has_pad_h() || params.has_pad_w())) + problems_ops_set.insert("Conv2D: Conflicting padding properties"); + + if (params.pad_size() > 2) + problems_ops_set.insert("Conv2D: Unsupported number of pads"); +} + +std::vector +CaffeOpCreator::convertConvolution(const caffe::LayerParameter &layer, + const std::vector &inputs) +{ + const auto ¶ms = layer.convolution_param(); + Conv2DOpAttributes attributes; + + convertConvolutionParam(params, attributes); + attributes.num_groups = params.group(); + attributes.data_format = DataFormat::NCHW; + + assert(layer.blobs(0).shape().dim_size() == 4); + auto kernel = createOp(convertBlob(layer.blobs(0)))->getOutput(0); + std::vector perm{0, 2, 3, 1}; // OIHW -> OHWI + kernel = createOp(kernel, perm)->getOutput(0); + auto result = createOp(inputs[0], kernel, attributes)->getOutput(0); + + // Add the bias, if any. + if (params.bias_term()) + { + auto bias = createOp(convertBlob(layer.blobs(1)))->getOutput(0); + bias = createOp(bias, Shape{1, bias->getShape().dim(0), 1, 1})->getOutput(0); + result = createOp(result, bias)->getOutput(0); + } + + return {result}; +} + +std::vector +CaffeOpCreator::convertDeconvolution(const caffe::LayerParameter &layer, + const std::vector &inputs) +{ + const caffe::ConvolutionParameter ¶ms = layer.convolution_param(); + Deconv2DOpAttributes attributes; + + convertConvolutionParam(params, attributes); + attributes.data_format = DataFormat::NCHW; + + if (params.group() != 1) + { + throw std::runtime_error("Deconvolution: 'group' != 1 is not supported."); + } + + auto kernel = createOp(convertBlob(layer.blobs(0)))->getOutput(0); + std::vector perm{2, 3, 1, 0}; // IOHW -> HWOI + kernel = createOp(kernel, perm)->getOutput(0); + auto result = createOp(inputs[0], kernel, attributes)->getOutput(0); + + // bias_term is optional (so might not be present) and defaults to true + if (params.bias_term()) + { + auto bias = createOp(convertBlob(layer.blobs(1)))->getOutput(0); + bias = createOp(bias, Shape{1, bias->getShape().dim(0), 1, 1})->getOutput(0); + result = createOp(result, bias)->getOutput(0); + } + + return {result}; +} + +std::vector +CaffeOpCreator::convertInnerProduct(const caffe::LayerParameter &layer, + const std::vector &inputs) +{ + const auto ¶ms = layer.inner_product_param(); + auto weights = createOp(convertBlob(layer.blobs(0)))->getOutput(0); + + if (!params.transpose()) + weights = createOp(weights, std::vector{1, 0})->getOutput(0); + + auto result = createFullyConnected(inputs[0], weights, params.axis()); + + // Add the bias, if any. + if (params.bias_term()) + { + auto bias = createOp(convertBlob(layer.blobs(1)))->getOutput(0); + result = createOp(result, bias)->getOutput(0); + } + + return {result}; +} + +std::vector +CaffeOpCreator::convertConcat(const caffe::LayerParameter &layer, + const std::vector &inputs) +{ + const auto ¶ms = layer.concat_param(); + auto concat = createOp(inputs, params.axis()); + return {concat->getOutput(0)}; +} + +template +static void convertPoolingParam(const caffe::PoolingParameter ¶ms, + const mir::Shape &input_shape, PoolingAttributes &attributes) +{ + std::int32_t kernel_h, kernel_w; + assert(!params.global_pooling()); + if (params.has_kernel_size()) + { + kernel_h = kernel_w = params.kernel_size(); + } + else + { + kernel_h = params.kernel_h(); + kernel_w = params.kernel_w(); + } + attributes.window = {kernel_h, kernel_w}; + + std::int32_t stride_h, stride_w; + if (params.has_stride_h() || params.has_stride_w()) + { + stride_h = params.stride_h(); + stride_w = params.stride_w(); + } + else + { + stride_h = stride_w = params.stride(); + } + attributes.strides = {stride_h, stride_w}; + + std::int32_t pad_h, pad_w; + if (params.has_pad_h() || params.has_pad_w()) + { + pad_h = params.pad_h(); + pad_w = params.pad_w(); + } + else + { + pad_h = pad_w = params.pad(); + } + + attributes.padding_before = attributes.padding_after = {pad_h, pad_w}; + + // Caffe uses different formula for computing output shape than MIR. Adjust padding so that + // the output shape stays the same. + constexpr int num_spatial_dims = 2; + for (int i = 0; i < num_spatial_dims; ++i) + { + // Assuming NCHW format. + const std::int32_t padded_input = + input_shape.dim(2 + i) + attributes.padding_before[i] + attributes.padding_after[i]; + if ((padded_input - attributes.window[i]) % attributes.strides[i] != 0) + ++attributes.padding_after[i]; + } +} + +void CaffeOpCreator::checkPooling(const caffe::LayerParameter &layer, + std::set &problems_ops_set) +{ + const caffe::PoolingParameter ¶ms = layer.pooling_param(); + + if (params.has_global_pooling() && params.global_pooling()) + problems_ops_set.insert("Pooling: pooling layer global_pooling param is not supported yet"); + + if (params.pool() != caffe::PoolingParameter::AVE && + params.pool() != caffe::PoolingParameter::MAX) + problems_ops_set.insert("Pooling: unsupported pooling type"); + + if (params.has_pad() && (params.has_pad_h() || params.has_pad_w())) + problems_ops_set.insert("Pooling: conflicting padding properties in pooling"); +} + +std::vector +CaffeOpCreator::convertPooling(const caffe::LayerParameter &layer, + const std::vector &inputs) +{ + const auto ¶ms = layer.pooling_param(); + + assert(inputs.size() == 1); + auto input = inputs[0]; + + mir::Operation::Output *result; + + switch (params.pool()) + { + case caffe::PoolingParameter::AVE: + { + AvgPool2DOpAttributes attributes_avg; + attributes_avg.data_format = DataFormat::NCHW; + convertPoolingParam(params, input->getShape(), attributes_avg); + result = createOp(input, attributes_avg)->getOutput(0); + break; + } + case caffe::PoolingParameter::MAX: + { + MaxPool2DOpAttributes attributes_max; + attributes_max.data_format = DataFormat::NCHW; + convertPoolingParam(params, input->getShape(), attributes_max); + result = createOp(input, attributes_max)->getOutput(0); + break; + } + default: + throw std::runtime_error("Unsupported PoolMethod: " + std::to_string(params.pool())); + } + + return {result}; +} + +std::vector +CaffeOpCreator::convertSoftmax(const caffe::LayerParameter &layer, + const std::vector &inputs) +{ + const auto ¶ms = layer.softmax_param(); + + // CPP and ACL backends are able to perform Softmax only along the last axis. + // FIXME Do it in backends. + if (inputs[0]->getShape().rank() == 4) + { + // For now, we only account for the most common case. + if (params.axis() != 1) + throw std::runtime_error("Softmax: unsupported axis"); + int32_t axis = 3; + auto input = createOp(inputs[0], std::vector{0, 2, 3, 1}); + auto softmax = createOp(input->getOutput(0), axis); + auto result = + createOp(softmax->getOutput(0), std::vector{0, 3, 1, 2}); + return {result->getOutput(0)}; + } + + auto softmax = createOp(inputs[0], params.axis()); + return {softmax->getOutput(0)}; +} + +void CaffeOpCreator::checkReshape(const caffe::LayerParameter &layer, + std::set &problems_ops_set) +{ + const caffe::ReshapeParameter ¶ms = layer.reshape_param(); + + if (params.has_axis() || params.has_num_axes()) + problems_ops_set.insert("Reshape layer axis and num_axes params are not supported yet"); + + if (!params.has_shape()) + problems_ops_set.insert("Reshape layer doesn't have shape parameter"); + + const mir::Shape newShape = convertBlobShape(params.shape()); + + for (int32_t i = 0; i < newShape.rank(); ++i) + if (newShape.dim(i) == 0) + problems_ops_set.insert("Reshape layer zero shape values are not supported yet"); +} + +/** + * @brief Converts Caffe Reshape layer to Model IR Reshape operation. + * @todo Support "axis" and "num_axes" parameters as needed. + * @todo Decide how to react to the absence of "shape" parameter. + * @todo Support zero values in "shape" parameter. + */ +std::vector +CaffeOpCreator::convertReshape(const caffe::LayerParameter &layer, + const std::vector &inputs) +{ + const caffe::ReshapeParameter ¶ms = layer.reshape_param(); + + const mir::Shape new_shape = convertBlobShape(params.shape()); + auto reshape = createOp(inputs[0], new_shape); + return {reshape->getOutput(0)}; +} + +std::vector +CaffeOpCreator::convertReLU(const caffe::LayerParameter &layer, + const std::vector &inputs) +{ + mir::Operation *relu; + if (layer.relu_param().has_negative_slope()) + { + float alpha = layer.relu_param().negative_slope(); + relu = createOp(inputs[0], alpha); + } + else + { + relu = createOp(inputs[0]); + } + + return {relu->getOutput(0)}; +} + +std::vector +CaffeOpCreator::convertScale(const caffe::LayerParameter &layer, + const std::vector &inputs) +{ + const auto ¶ms = layer.scale_param(); + auto scale = createOp(convertBlob(layer.blobs(0)))->getOutput(0); + scale = createOp(scale, Shape{1, scale->getShape().dim(0), 1, 1})->getOutput(0); + auto result = createOp(inputs[0], scale)->getOutput(0); + + // Add the bias, if any. + if (params.bias_term()) + { + auto bias = createOp(convertBlob(layer.blobs(1)))->getOutput(0); + bias = createOp(bias, Shape{1, bias->getShape().dim(0), 1, 1})->getOutput(0); + result = createOp(result, bias)->getOutput(0); + } + + return {result}; +} + +void CaffeOpCreator::checkBatchNorm(const caffe::LayerParameter &layer, + std::set &problems_ops_set) +{ + const auto &scale_shape = layer.blobs(2).shape(); + + // Check that last blob(with scaleFactor) containing only one number + if (scale_shape.dim_size() != 1 || scale_shape.dim(0) != 1) + problems_ops_set.insert("Unexpected shape of scale parameter in batch norm"); +} + +std::vector +CaffeOpCreator::convertBatchNorm(const caffe::LayerParameter &layer, + const std::vector &inputs) +{ + const caffe::BatchNormParameter ¶ms = layer.batch_norm_param(); + + auto input = inputs[0]; + auto mean_tensor = convertBlob(layer.blobs(0)); + auto var_tensor = convertBlob(layer.blobs(1)); + auto scale_tensor = convertBlob(layer.blobs(2)); + const float eps = params.eps(); + + float scale_factor = *reinterpret_cast(scale_tensor.at(mir::Index{0})); + + // See https://github.com/BVLC/caffe/blob/master/src/caffe/layers/batch_norm_layer.cpp#L100 + // Y = (X - mean / scale_factor) / sqrt(var / scale_factor + epsilon) = + // = (X + C1) * C2 + if (scale_factor != 0.0f) + scale_factor = 1.0f / scale_factor; + + // C1 = -mean / scale_factor + Tensor mean_accessor(mean_tensor); + for (const auto &idx : ShapeRange(mean_accessor.getShape())) + mean_accessor.at(idx) *= -scale_factor; + auto c1 = createOp(mean_tensor)->getOutput(0); + + // C2 = 1 / sqrt(var / scale_factor + epsilon) + Tensor var_accessor(var_tensor); + for (const auto &idx : ShapeRange(var_accessor.getShape())) + var_accessor.at(idx) = 1.0f / std::sqrt(var_accessor.at(idx) * scale_factor + eps); + auto c2 = createOp(var_tensor)->getOutput(0); + + c1 = createOp(c1, Shape{1, c1->getShape().dim(0), 1, 1})->getOutput(0); + c2 = createOp(c2, Shape{1, c2->getShape().dim(0), 1, 1})->getOutput(0); + + // Y = (X + C1) * C2 + auto result = createOp(input, c1)->getOutput(0); + result = createOp(result, c2)->getOutput(0); + + return {result}; +} + +std::vector +CaffeOpCreator::convertDropout(const caffe::LayerParameter &, + const std::vector &inputs) +{ + // This is a no-op in inference mode. + return {inputs[0]}; +} + +std::vector +CaffeOpCreator::convertELU(const caffe::LayerParameter &layer, + const std::vector &inputs) +{ + const caffe::ELUParameter ¶ms = layer.elu_param(); + + auto elu = createOp(inputs[0], params.alpha()); + return {elu->getOutput(0)}; +} + +std::vector +CaffeOpCreator::convertEmbed(const caffe::LayerParameter &layer, + const std::vector &inputs) +{ + const auto ¶ms = layer.embed_param(); + auto data = createOp(convertBlob(layer.blobs(0))); + auto result = createOp(data->getOutput(0), inputs[0], 0)->getOutput(0); + + // Add the bias, if any. + if (params.bias_term()) + { + auto bias = createOp(convertBlob(layer.blobs(1)))->getOutput(0); + result = createOp(result, bias)->getOutput(0); + } + + return {result}; +} + +std::vector +CaffeOpCreator::convertSigmoid(const caffe::LayerParameter &, + const std::vector &inputs) +{ + auto result = createOp(inputs[0]); + return {result->getOutput(0)}; +} + +std::vector +CaffeOpCreator::convertTanH(const caffe::LayerParameter &, + const std::vector &inputs) +{ + auto tanh = createOp(inputs[0]); + return {tanh->getOutput(0)}; +} + +std::vector +CaffeOpCreator::convertEltwise(const caffe::LayerParameter &layer, + const std::vector &inputs) +{ + auto ¶ms = layer.eltwise_param(); + + mir::Operation::Output *result; + switch (params.operation()) + { + case caffe::EltwiseParameter::PROD: + { + result = createOp(inputs[0], inputs[1])->getOutput(0); + for (int i = 2; i < layer.bottom_size(); ++i) + { + result = createOp(result, inputs[i])->getOutput(0); + } + break; + } + case caffe::EltwiseParameter::SUM: + { + std::vector scaled_inputs = inputs; + if (params.coeff_size() > 0) + { + assert(params.coeff_size() == layer.bottom_size()); + for (int i = 0; i < layer.bottom_size(); i++) + { + if (params.coeff(i) != 1.0f) + { + const float coeff_val = params.coeff(i); + TensorVariant coeff_tensor({DataType::FLOAT32, {}}, &coeff_val); + auto coeff_const = createOp(coeff_tensor)->getOutput(0); + scaled_inputs[i] = createOp(coeff_const, inputs[i])->getOutput(0); + } + } + } + result = createOp(scaled_inputs[0], scaled_inputs[1])->getOutput(0); + for (int i = 2; i < layer.bottom_size(); ++i) + { + result = createOp(result, scaled_inputs[i])->getOutput(0); + } + break; + } + case caffe::EltwiseParameter::MAX: + { + result = createOp(inputs[0], inputs[1])->getOutput(0); + for (int i = 2; i < layer.bottom_size(); ++i) + { + result = createOp(result, inputs[i])->getOutput(0); + } + break; + } + default: + throw std::runtime_error("Unknown element-wise operation."); + } + return {result}; +} + +std::vector +CaffeOpCreator::convertSplit(const caffe::LayerParameter &layer, + const std::vector &inputs) +{ + std::vector outputs(layer.top_size(), inputs.at(0)); + return outputs; +} + +void CaffeOpCreator::checkLSTM(const caffe::LayerParameter &layer, + std::set &problems_ops_set) +{ + const auto ¶ms = layer.recurrent_param(); + if (params.expose_hidden()) + problems_ops_set.insert("LSTM: parameter 'expose_hidden' has unsupported value: " + + std::to_string(params.expose_hidden())); +} + +static TensorVariant createZeroedTensor(const mir::Shape &shape) +{ + // TODO For now it is hardcoded float32. + auto elem_type = mir::DataType::FLOAT32; + std::vector zeros(static_cast(shape.numElements()), 0.0f); + return TensorVariant({elem_type, shape}, zeros.data()); +} + +/* See the following links for details on implementation: + * https://github.com/BVLC/caffe/blob/master/src/caffe/layers/recurrent_layer.cpp + * https://github.com/BVLC/caffe/blob/master/src/caffe/layers/lstm_layer.cpp + * https://github.com/BVLC/caffe/blob/master/src/caffe/layers/lstm_unit_layer.cpp + * + * Inputs: + * x -- The time-varying input. Shape: [T, N, d0, d1, ..., dn]. + * cont -- The sequence continuation indicators. Shape: [T, N]. + * x_static -- The static (non-time-varying) input. Shape: [N, ...]. + * This parameter is optional and not currently supported. + * + * Additional inputs when parameter "expose_hidden" is true (not currently supported): + * h_0 -- The initial value of the hidden state. Shape: [1, N, D]. + * c_0 -- The initial value of the cell state. Shape: [1, N, D]. + * + * Learned parameters: + * xw -- x weights for input, output, forget and cell gates concatenated. + * Shape: [4 * D, d0 * d1 * ... * dn]. + * xb -- x biases for input, output, forget and cell gates concatenated. Shape: [4 * D]. + * hw -- h weights for input, output, forget and cell gates concatenated. Shape: [4 * D, D]. + * + * Outputs: + * h -- The time-varying output. Shape: [T, N, D]. + * + * Additional outputs when parameter "expose_hidden" is true (not currently supported): + * h_T -- The value of the hidden state at the last timestep. Shape: [1, N, D]. + * c_T -- The value of the cell state at the last timestep. Shape: [1, N, D]. + * + * Here: + * T - the number of timesteps, + * N - the number of independent streams. + * D - the number of hidden parameters. + * + * Formulas: + * c_cont = c[t-1] * cont[t] + * h_cont = h[t-1] * cont[t] + * i[t] = Sigmoid(x[t] . xw_i + xb_i + h_cont . hw_i) + * f[t] = Sigmoid(x[t] . xw_f + xb_f + h_cont . hw_f) + * o[t] = Sigmoid(x[t] . xw_o + xb_o + h_cont . hw_o) + * g[t] = Tanh(x[t] . xw_g + xb_g + h_cont . hw_g) + * c[t] = c_cont * f[t] + i[t] * g[t] + * h[t] = o[t] * Tanh(c[t]) + * + * Here: + * t -- the timestep (ranges from 1 to T), + * * -- the inner product, + * . -- the Hadamard product (elementwise product). + * + * In this implementation the inner products for all gates are performed as single inner product for + * efficiency. + */ +std::vector +CaffeOpCreator::convertLSTM(const caffe::LayerParameter &layer, + const std::vector &inputs) +{ + const auto ¶ms = layer.recurrent_param(); + + // Inputs to the layer. + auto x = inputs[0]; + auto cont = inputs[1]; + assert(inputs.size() == 2); + + const auto &x_shape = x->getShape(); + const int32_t seq_length = x_shape.dim(0); + const int32_t batch_size = x_shape.dim(1); + const int32_t hidden_size = params.num_output(); + + // Learned parameters of the layer. Tensors are transposed to match the ModelIR. + auto xw = createOp(convertBlob(layer.blobs(0)))->getOutput(0); + auto xb = createOp(convertBlob(layer.blobs(1)))->getOutput(0); + auto hw = createOp(convertBlob(layer.blobs(2)))->getOutput(0); + xw = createOp(xw, std::vector{1, 0})->getOutput(0); + hw = createOp(hw, std::vector{1, 0})->getOutput(0); + + // Add a dummy dimension so that element-wise operations perform properly. + cont = createOp(cont, Shape{seq_length, batch_size, 1})->getOutput(0); + + // Initialize cell and hidden states with zeros. + auto zero_tensor = createZeroedTensor(Shape{1, batch_size, hidden_size}); + auto c_t = createOp(zero_tensor)->getOutput(0); + auto h_t = createOp(zero_tensor)->getOutput(0); + + auto x_xw = createFullyConnected(x, xw, 2); + auto x_xw_b = createOp(x_xw, xb)->getOutput(0); + + // Split input and continuation tensors into seq_length slices. + std::vector x_xw_b_slices = createSplit(x_xw_b, seq_length, 0); + std::vector cont_slices = createSplit(cont, seq_length, 0); + std::vector h_slices(seq_length); + + for (int32_t t = 0; t < seq_length; t++) + { + auto c_cont_t = createOp(c_t, cont_slices[t])->getOutput(0); + auto h_cont_t = createOp(h_t, cont_slices[t])->getOutput(0); + + auto x_xw_b_t = x_xw_b_slices[t]; + auto h_hw_t = createFullyConnected(h_cont_t, hw, 2); + auto activation_inputs_concat = createOp(x_xw_b_t, h_hw_t)->getOutput(0); + auto activation_inputs = createSplit(activation_inputs_concat, 4, 2); + + auto i_t = createOp(activation_inputs[0])->getOutput(0); + auto f_t = createOp(activation_inputs[1])->getOutput(0); + auto o_t = createOp(activation_inputs[2])->getOutput(0); + auto g_t = createOp(activation_inputs[3])->getOutput(0); + + c_t = createOp(createOp(c_cont_t, f_t)->getOutput(0), + createOp(i_t, g_t)->getOutput(0)) + ->getOutput(0); + h_t = createOp(createOp(c_t)->getOutput(0), o_t)->getOutput(0); + + h_slices[t] = h_t; + } + + return {createOp(h_slices, 0)->getOutput(0)}; +} + +} // namespace mir_caffe diff --git a/compiler/mir-caffe-importer/caffe_op_creator.h b/compiler/mir/src/mir_caffe_importer/caffe_op_creator.h similarity index 100% rename from compiler/mir-caffe-importer/caffe_op_creator.h rename to compiler/mir/src/mir_caffe_importer/caffe_op_creator.h diff --git a/compiler/mir-caffe-importer/caffe_op_types.h b/compiler/mir/src/mir_caffe_importer/caffe_op_types.h similarity index 100% rename from compiler/mir-caffe-importer/caffe_op_types.h rename to compiler/mir/src/mir_caffe_importer/caffe_op_types.h diff --git a/compiler/mir/src/mir_onnx_importer/AttributeHelpers.h b/compiler/mir/src/mir_onnx_importer/AttributeHelpers.h new file mode 100644 index 0000000..9a93b5b --- /dev/null +++ b/compiler/mir/src/mir_onnx_importer/AttributeHelpers.h @@ -0,0 +1,105 @@ +/* + * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef MIR_ONNX_ATTRIBUTE_HELPERS_H +#define MIR_ONNX_ATTRIBUTE_HELPERS_H + +#include "onnx/onnx.pb.h" + +#include +#include +#include +#include +#include +#include +#include + +namespace mir_onnx +{ + +template T getAttributeValue(const onnx::AttributeProto &attribute) = delete; + +template <> inline float getAttributeValue(const onnx::AttributeProto &attribute) +{ + assert(attribute.type() == onnx::AttributeProto::FLOAT); + return attribute.f(); +} + +template <> inline std::int64_t getAttributeValue(const onnx::AttributeProto &attribute) +{ + assert(attribute.type() == onnx::AttributeProto::INT); + return attribute.i(); +} + +template <> inline std::string getAttributeValue(const onnx::AttributeProto &attribute) +{ + assert(attribute.type() == onnx::AttributeProto::STRING); + return attribute.s(); +} + +template <> inline onnx::TensorProto getAttributeValue(const onnx::AttributeProto &attribute) +{ + assert(attribute.type() == onnx::AttributeProto::TENSOR); + return attribute.t(); +} + +template <> +inline std::vector getAttributeValue(const onnx::AttributeProto &attribute) +{ + assert(attribute.type() == onnx::AttributeProto::INTS); + // TODO Check that values fit. + return {attribute.ints().cbegin(), attribute.ints().cend()}; +} + +template <> +inline std::vector getAttributeValue(const onnx::AttributeProto &attribute) +{ + assert(attribute.type() == onnx::AttributeProto::INTS); + return {attribute.ints().cbegin(), attribute.ints().cend()}; +} + +inline const onnx::AttributeProto *findAttribute(const onnx::NodeProto &node, + const std::string &name) +{ + const auto &attributes = node.attribute(); + const auto it = std::find_if( + attributes.cbegin(), attributes.cend(), + [&name](const onnx::AttributeProto &attribute) { return attribute.name() == name; }); + if (it == attributes.cend()) + return nullptr; + return &*it; +} + +template T getAttributeValue(const onnx::NodeProto &node, const std::string &name) +{ + const auto *attribute = findAttribute(node, name); + if (attribute == nullptr) + throw std::runtime_error("Cannot find attribute '" + name + "' in node '" + node.name() + "'."); + return getAttributeValue(*attribute); +} + +template +T getAttributeValue(const onnx::NodeProto &node, const std::string &name, T default_value) +{ + const auto *attribute = findAttribute(node, name); + if (attribute == nullptr) + return default_value; + return getAttributeValue(*attribute); +} + +} // namespace mir_onnx + +#endif // MIR_ONNX_ATTRIBUTE_HELPERS_H diff --git a/compiler/mir/src/mir_onnx_importer/CMakeLists.txt b/compiler/mir/src/mir_onnx_importer/CMakeLists.txt new file mode 100644 index 0000000..e6eb13b --- /dev/null +++ b/compiler/mir/src/mir_onnx_importer/CMakeLists.txt @@ -0,0 +1,119 @@ +nnas_find_package(ONNXSource EXACT 1.6.0 QUIET) +nnas_find_package(Protobuf QUIET) + +if (NOT ONNXSource_FOUND) + return() +endif () + +if (NOT Protobuf_FOUND) + return() +endif () + +Protobuf_Generate(MIR_ONNX_PROTO + ${CMAKE_CURRENT_BINARY_DIR}/generated + ${ONNXSource_DIR} + onnx/onnx.proto) + +add_library(mir_onnx_proto STATIC ${MIR_ONNX_PROTO_SOURCES}) +set_target_properties(mir_onnx_proto PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_include_directories(mir_onnx_proto PUBLIC ${MIR_ONNX_PROTO_INCLUDE_DIRS}) +target_link_libraries(mir_onnx_proto PUBLIC libprotobuf) + +set(MIR_ONNX_IMPORTER_SOURCES + AttributeHelpers.h + ConvPoolHelpers.cpp + ConvPoolHelpers.h + ONNXHelpers.cpp + ONNXHelpers.h + ONNXImporterImpl.cpp + ONNXNodeConverterRegistry.h + ONNXNodeConverterRegistry.cpp + ONNXOpRegistration.h + Op/Abs.cpp + Op/Abs.h + Op/Add.cpp + Op/Add.h + Op/AveragePool.cpp + Op/AveragePool.h + Op/BatchNormalization.cpp + Op/BatchNormalization.h + Op/Concat.cpp + Op/Concat.h + Op/Constant.cpp + Op/Constant.h + Op/Conv.cpp + Op/Conv.h + Op/ConvTranspose.cpp + Op/ConvTranspose.h + Op/Div.cpp + Op/Div.h + Op/Dropout.cpp + Op/Dropout.h + Op/Equal.cpp + Op/Equal.h + Op/Expand.cpp + Op/Expand.h + Op/Flatten.cpp + Op/Flatten.h + Op/Gather.cpp + Op/Gather.h + Op/Greater.cpp + Op/Greater.h + Op/Gemm.cpp + Op/Gemm.h + Op/Identity.cpp + Op/Identity.h + Op/Less.cpp + Op/Less.h + Op/MatMul.cpp + Op/MatMul.h + Op/GlobalAveragePool.cpp + Op/GlobalAveragePool.h + Op/Max.cpp + Op/Max.h + Op/MaxPool.cpp + Op/MaxPool.h + Op/Mul.cpp + Op/Mul.h + Op/Pad.cpp + Op/Pad.h + Op/Reciprocal.cpp + Op/Reciprocal.h + Op/ReduceMean.cpp + Op/ReduceMean.h + Op/Relu.cpp + Op/Relu.h + Op/Reshape.cpp + Op/Reshape.h + Op/Shape.cpp + Op/Shape.h + Op/Sigmoid.cpp + Op/Sigmoid.h + Op/Softmax.cpp + Op/Softmax.h + Op/Sqrt.cpp + Op/Sqrt.h + Op/Sub.cpp + Op/Sub.h + Op/Sum.cpp + Op/Sum.h + Op/Tanh.cpp + Op/Tanh.h + Op/Transpose.cpp + Op/Transpose.h + Op/Unsqueeze.cpp + Op/Unsqueeze.h + Op/Upsample.cpp + Op/Upsample.h) + +add_library(mir_onnx_importer STATIC ${MIR_ONNX_IMPORTER_SOURCES}) +set_target_properties(mir_onnx_importer PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_include_directories(mir_onnx_importer PUBLIC ../../include/mir_onnx_importer) +target_include_directories(mir_onnx_importer PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}) +target_link_libraries(mir_onnx_importer PUBLIC mir mir_onnx_proto PRIVATE mir_interpreter nncc_common) + +nnas_find_package(GTest REQUIRED) + +file(GLOB_RECURSE TEST_SOURCES "*.test.cpp") +GTest_AddTest(mir_onnx_importer_test ${TEST_SOURCES}) +target_link_libraries(mir_onnx_importer_test mir_onnx_importer) diff --git a/compiler/mir-onnx-importer/ConvPoolHelpers.cpp b/compiler/mir/src/mir_onnx_importer/ConvPoolHelpers.cpp similarity index 100% rename from compiler/mir-onnx-importer/ConvPoolHelpers.cpp rename to compiler/mir/src/mir_onnx_importer/ConvPoolHelpers.cpp diff --git a/compiler/mir-onnx-importer/ConvPoolHelpers.h b/compiler/mir/src/mir_onnx_importer/ConvPoolHelpers.h similarity index 100% rename from compiler/mir-onnx-importer/ConvPoolHelpers.h rename to compiler/mir/src/mir_onnx_importer/ConvPoolHelpers.h diff --git a/compiler/mir-onnx-importer/ONNXHelpers.cpp b/compiler/mir/src/mir_onnx_importer/ONNXHelpers.cpp similarity index 100% rename from compiler/mir-onnx-importer/ONNXHelpers.cpp rename to compiler/mir/src/mir_onnx_importer/ONNXHelpers.cpp diff --git a/compiler/mir-onnx-importer/ONNXHelpers.h b/compiler/mir/src/mir_onnx_importer/ONNXHelpers.h similarity index 100% rename from compiler/mir-onnx-importer/ONNXHelpers.h rename to compiler/mir/src/mir_onnx_importer/ONNXHelpers.h diff --git a/compiler/mir/src/mir_onnx_importer/ONNXImporterImpl.cpp b/compiler/mir/src/mir_onnx_importer/ONNXImporterImpl.cpp new file mode 100644 index 0000000..8b99624 --- /dev/null +++ b/compiler/mir/src/mir_onnx_importer/ONNXImporterImpl.cpp @@ -0,0 +1,240 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ONNXImporterImpl.h" +#include "ONNXHelpers.h" +#include "ONNXOpRegistration.h" +#include "onnx/onnx.pb.h" + +#include "mir/Shape.h" +#include "mir/TensorUtil.h" + +#include "mir/ops/ConstantOp.h" + +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace mir_onnx +{ + +namespace +{ + +class ONNXImporterImpl final +{ +public: + ONNXImporterImpl(); + ~ONNXImporterImpl(); + /// @brief Load the model and convert it into a MIR Graph. + std::unique_ptr importModelFromBinaryFile(const std::string &filename); + std::unique_ptr importModelFromTextFile(const std::string &filename); + +private: + std::unique_ptr createIR(); + void createGraphInputs(); + void collectUnsupportedOps(); + std::unique_ptr _model; + std::unique_ptr _converterCtx; + std::unique_ptr _modelCtx; + std::unique_ptr _graph; +}; + +ONNXImporterImpl::ONNXImporterImpl() { registerSupportedOps(); } + +ONNXImporterImpl::~ONNXImporterImpl() = default; + +void loadModelFromBinaryFile(const std::string &filename, onnx::ModelProto *model) +{ + GOOGLE_PROTOBUF_VERIFY_VERSION; + + int file_handle = open(filename.c_str(), O_RDONLY); + + if (file_handle == -1) + throw std::runtime_error("Couldn't open file \"" + filename + "\": " + std::strerror(errno) + + "."); + + google::protobuf::io::FileInputStream file_stream(file_handle); + file_stream.SetCloseOnDelete(true); + + google::protobuf::io::CodedInputStream coded_stream(&file_stream); + coded_stream.SetTotalBytesLimit(INT_MAX, INT_MAX); + + if (!model->ParseFromCodedStream(&coded_stream)) + throw std::runtime_error("Couldn't parse file \"" + filename + "\"."); + + // If the file has not been consumed entirely, assume that the file is in the wrong format. + if (!coded_stream.ConsumedEntireMessage()) + throw std::runtime_error("File \"" + filename + "\" has not been consumed entirely."); +} + +void loadModelFromTextFile(const std::string &filename, onnx::ModelProto *model) +{ + GOOGLE_PROTOBUF_VERIFY_VERSION; + + int file_handle = open(filename.c_str(), O_RDONLY); + + if (file_handle == -1) + throw std::runtime_error("Couldn't open file \"" + filename + "\": " + std::strerror(errno) + + "."); + + google::protobuf::io::FileInputStream file_stream(file_handle); + file_stream.SetCloseOnDelete(true); + + if (!google::protobuf::TextFormat::Parse(&file_stream, model)) + throw std::runtime_error("Couldn't parse file \"" + filename + "\"."); +} + +std::unique_ptr ONNXImporterImpl::importModelFromBinaryFile(const std::string &filename) +{ + _model = std::make_unique(); + loadModelFromBinaryFile(filename, _model.get()); + _modelCtx = std::make_unique(_model.get()); + collectUnsupportedOps(); + return createIR(); +} + +std::unique_ptr ONNXImporterImpl::importModelFromTextFile(const std::string &filename) +{ + _model = std::make_unique(); + loadModelFromTextFile(filename, _model.get()); + _modelCtx = std::make_unique(_model.get()); + collectUnsupportedOps(); + return createIR(); +} + +void ONNXImporterImpl::collectUnsupportedOps() +{ + std::set> problems_op_set; + + for (int i = 0; i < _model->graph().node_size(); i++) + { + const auto &onnx_node = _model->graph().node(i); + assert(onnx_node.has_op_type()); + const auto &op_type = onnx_node.op_type(); + auto opset = _modelCtx->getDomainOpsetVersion(onnx_node.domain()); + + NodeConverterRegistry::ConverterFunc converter = + NodeConverterRegistry::getInstance().lookup(op_type, opset); + + if (converter == nullptr) + problems_op_set.emplace(op_type, opset); + } + if (!problems_op_set.empty()) + { + std::cerr << "The following operators are not supported:\n"; + for (const auto &op : problems_op_set) + std::cerr << op.first << " opset " << op.second << std::endl; + throw std::runtime_error("Unsupported operators found"); + } +} + +void ONNXImporterImpl::createGraphInputs() +{ + const auto &graph = _model->graph(); + const auto &initializer = graph.initializer(); + + // Create all initializer Tensors + for (const auto &tensor : initializer) + { + const auto mir_tensor = createTensor(&tensor); + auto *op = _graph->create(mir_tensor); + _converterCtx->setOutput(tensor.name(), op->getOutput(0)); + } + + for (const auto &input : graph.input()) + { + assert(input.has_name()); + + if (_converterCtx->getOutput(input.name()) == nullptr) + { + const auto &onnx_input_shape = input.type().tensor_type().shape(); + mir::Shape shape(onnx_input_shape.dim_size()); + for (int i = 0; i < onnx_input_shape.dim_size(); i++) + { + assert(onnx_input_shape.dim(i).has_dim_value()); + shape.dim(i) = static_cast(onnx_input_shape.dim(i).dim_value()); + } + + auto elem_type = onnxDataTypeToMirDataType( + (onnx::TensorProto_DataType)input.type().tensor_type().elem_type()); + mir::TensorType type{elem_type, shape}; + auto *op = _graph->create(type); + _converterCtx->setOutput(input.name(), op->getOutput(0)); + } + } +} + +std::unique_ptr ONNXImporterImpl::createIR() +{ + _graph = std::make_unique(); + _converterCtx = std::make_unique(_graph.get()); + + createGraphInputs(); + + // Forming partially ordered computation graph + for (const auto &onnx_node : _model->graph().node()) + { + assert(onnx_node.has_op_type()); + auto &op_type = onnx_node.op_type(); + auto opset = _modelCtx->getDomainOpsetVersion(onnx_node.domain()); + // Get converter + NodeConverterRegistry::ConverterFunc converter = + NodeConverterRegistry::getInstance().lookup(op_type, opset); + assert(converter != nullptr); + converter(onnx_node, _converterCtx.get()); + } + // Set graph outputs + const auto &outputs = _model->graph().output(); + for (const auto &output : outputs) + { + assert(output.has_name()); + auto mir_output = _converterCtx->getOutput(output.name()); + if (mir_output == nullptr) + throw std::runtime_error("Bad output name!"); + + _graph->create(mir_output); + } + + return std::move(_graph); +} + +} // namespace + +std::unique_ptr importModelFromBinaryFile(const std::string &filename) +{ + ONNXImporterImpl importer; + return importer.importModelFromBinaryFile(filename); +} + +std::unique_ptr importModelFromTextFile(const std::string &filename) +{ + ONNXImporterImpl importer; + return importer.importModelFromTextFile(filename); +} + +std::unique_ptr loadModel(const std::string &filename) +{ + return importModelFromBinaryFile(filename); +} + +} // namespace mir_onnx diff --git a/compiler/mir-onnx-importer/ONNXNodeConverterRegistry.cpp b/compiler/mir/src/mir_onnx_importer/ONNXNodeConverterRegistry.cpp similarity index 100% rename from compiler/mir-onnx-importer/ONNXNodeConverterRegistry.cpp rename to compiler/mir/src/mir_onnx_importer/ONNXNodeConverterRegistry.cpp diff --git a/compiler/mir-onnx-importer/ONNXNodeConverterRegistry.h b/compiler/mir/src/mir_onnx_importer/ONNXNodeConverterRegistry.h similarity index 100% rename from compiler/mir-onnx-importer/ONNXNodeConverterRegistry.h rename to compiler/mir/src/mir_onnx_importer/ONNXNodeConverterRegistry.h diff --git a/compiler/mir-onnx-importer/ONNXNodeConverterRegistry.test.cpp b/compiler/mir/src/mir_onnx_importer/ONNXNodeConverterRegistry.test.cpp similarity index 100% rename from compiler/mir-onnx-importer/ONNXNodeConverterRegistry.test.cpp rename to compiler/mir/src/mir_onnx_importer/ONNXNodeConverterRegistry.test.cpp diff --git a/compiler/mir-onnx-importer/ONNXOpRegistration.h b/compiler/mir/src/mir_onnx_importer/ONNXOpRegistration.h similarity index 100% rename from compiler/mir-onnx-importer/ONNXOpRegistration.h rename to compiler/mir/src/mir_onnx_importer/ONNXOpRegistration.h diff --git a/compiler/mir-onnx-importer/Op/Abs.cpp b/compiler/mir/src/mir_onnx_importer/Op/Abs.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Abs.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Abs.cpp diff --git a/compiler/mir-onnx-importer/Op/Abs.h b/compiler/mir/src/mir_onnx_importer/Op/Abs.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Abs.h rename to compiler/mir/src/mir_onnx_importer/Op/Abs.h diff --git a/compiler/mir-onnx-importer/Op/Add.cpp b/compiler/mir/src/mir_onnx_importer/Op/Add.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Add.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Add.cpp diff --git a/compiler/mir-onnx-importer/Op/Add.h b/compiler/mir/src/mir_onnx_importer/Op/Add.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Add.h rename to compiler/mir/src/mir_onnx_importer/Op/Add.h diff --git a/compiler/mir-onnx-importer/Op/AveragePool.cpp b/compiler/mir/src/mir_onnx_importer/Op/AveragePool.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/AveragePool.cpp rename to compiler/mir/src/mir_onnx_importer/Op/AveragePool.cpp diff --git a/compiler/mir-onnx-importer/Op/AveragePool.h b/compiler/mir/src/mir_onnx_importer/Op/AveragePool.h similarity index 100% rename from compiler/mir-onnx-importer/Op/AveragePool.h rename to compiler/mir/src/mir_onnx_importer/Op/AveragePool.h diff --git a/compiler/mir-onnx-importer/Op/BatchNormalization.cpp b/compiler/mir/src/mir_onnx_importer/Op/BatchNormalization.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/BatchNormalization.cpp rename to compiler/mir/src/mir_onnx_importer/Op/BatchNormalization.cpp diff --git a/compiler/mir-onnx-importer/Op/BatchNormalization.h b/compiler/mir/src/mir_onnx_importer/Op/BatchNormalization.h similarity index 100% rename from compiler/mir-onnx-importer/Op/BatchNormalization.h rename to compiler/mir/src/mir_onnx_importer/Op/BatchNormalization.h diff --git a/compiler/mir-onnx-importer/Op/Concat.cpp b/compiler/mir/src/mir_onnx_importer/Op/Concat.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Concat.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Concat.cpp diff --git a/compiler/mir-onnx-importer/Op/Concat.h b/compiler/mir/src/mir_onnx_importer/Op/Concat.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Concat.h rename to compiler/mir/src/mir_onnx_importer/Op/Concat.h diff --git a/compiler/mir-onnx-importer/Op/Constant.cpp b/compiler/mir/src/mir_onnx_importer/Op/Constant.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Constant.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Constant.cpp diff --git a/compiler/mir-onnx-importer/Op/Constant.h b/compiler/mir/src/mir_onnx_importer/Op/Constant.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Constant.h rename to compiler/mir/src/mir_onnx_importer/Op/Constant.h diff --git a/compiler/mir-onnx-importer/Op/Conv.cpp b/compiler/mir/src/mir_onnx_importer/Op/Conv.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Conv.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Conv.cpp diff --git a/compiler/mir-onnx-importer/Op/Conv.h b/compiler/mir/src/mir_onnx_importer/Op/Conv.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Conv.h rename to compiler/mir/src/mir_onnx_importer/Op/Conv.h diff --git a/compiler/mir-onnx-importer/Op/ConvTranspose.cpp b/compiler/mir/src/mir_onnx_importer/Op/ConvTranspose.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/ConvTranspose.cpp rename to compiler/mir/src/mir_onnx_importer/Op/ConvTranspose.cpp diff --git a/compiler/mir-onnx-importer/Op/ConvTranspose.h b/compiler/mir/src/mir_onnx_importer/Op/ConvTranspose.h similarity index 100% rename from compiler/mir-onnx-importer/Op/ConvTranspose.h rename to compiler/mir/src/mir_onnx_importer/Op/ConvTranspose.h diff --git a/compiler/mir-onnx-importer/Op/Div.cpp b/compiler/mir/src/mir_onnx_importer/Op/Div.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Div.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Div.cpp diff --git a/compiler/mir-onnx-importer/Op/Div.h b/compiler/mir/src/mir_onnx_importer/Op/Div.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Div.h rename to compiler/mir/src/mir_onnx_importer/Op/Div.h diff --git a/compiler/mir-onnx-importer/Op/Dropout.cpp b/compiler/mir/src/mir_onnx_importer/Op/Dropout.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Dropout.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Dropout.cpp diff --git a/compiler/mir-onnx-importer/Op/Dropout.h b/compiler/mir/src/mir_onnx_importer/Op/Dropout.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Dropout.h rename to compiler/mir/src/mir_onnx_importer/Op/Dropout.h diff --git a/compiler/mir-onnx-importer/Op/Equal.cpp b/compiler/mir/src/mir_onnx_importer/Op/Equal.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Equal.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Equal.cpp diff --git a/compiler/mir-onnx-importer/Op/Equal.h b/compiler/mir/src/mir_onnx_importer/Op/Equal.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Equal.h rename to compiler/mir/src/mir_onnx_importer/Op/Equal.h diff --git a/compiler/mir-onnx-importer/Op/Expand.cpp b/compiler/mir/src/mir_onnx_importer/Op/Expand.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Expand.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Expand.cpp diff --git a/compiler/mir-onnx-importer/Op/Expand.h b/compiler/mir/src/mir_onnx_importer/Op/Expand.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Expand.h rename to compiler/mir/src/mir_onnx_importer/Op/Expand.h diff --git a/compiler/mir-onnx-importer/Op/Flatten.cpp b/compiler/mir/src/mir_onnx_importer/Op/Flatten.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Flatten.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Flatten.cpp diff --git a/compiler/mir-onnx-importer/Op/Flatten.h b/compiler/mir/src/mir_onnx_importer/Op/Flatten.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Flatten.h rename to compiler/mir/src/mir_onnx_importer/Op/Flatten.h diff --git a/compiler/mir-onnx-importer/Op/Gather.cpp b/compiler/mir/src/mir_onnx_importer/Op/Gather.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Gather.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Gather.cpp diff --git a/compiler/mir-onnx-importer/Op/Gather.h b/compiler/mir/src/mir_onnx_importer/Op/Gather.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Gather.h rename to compiler/mir/src/mir_onnx_importer/Op/Gather.h diff --git a/compiler/mir-onnx-importer/Op/Gemm.cpp b/compiler/mir/src/mir_onnx_importer/Op/Gemm.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Gemm.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Gemm.cpp diff --git a/compiler/mir-onnx-importer/Op/Gemm.h b/compiler/mir/src/mir_onnx_importer/Op/Gemm.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Gemm.h rename to compiler/mir/src/mir_onnx_importer/Op/Gemm.h diff --git a/compiler/mir-onnx-importer/Op/GlobalAveragePool.cpp b/compiler/mir/src/mir_onnx_importer/Op/GlobalAveragePool.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/GlobalAveragePool.cpp rename to compiler/mir/src/mir_onnx_importer/Op/GlobalAveragePool.cpp diff --git a/compiler/mir-onnx-importer/Op/GlobalAveragePool.h b/compiler/mir/src/mir_onnx_importer/Op/GlobalAveragePool.h similarity index 100% rename from compiler/mir-onnx-importer/Op/GlobalAveragePool.h rename to compiler/mir/src/mir_onnx_importer/Op/GlobalAveragePool.h diff --git a/compiler/mir-onnx-importer/Op/Greater.cpp b/compiler/mir/src/mir_onnx_importer/Op/Greater.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Greater.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Greater.cpp diff --git a/compiler/mir-onnx-importer/Op/Greater.h b/compiler/mir/src/mir_onnx_importer/Op/Greater.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Greater.h rename to compiler/mir/src/mir_onnx_importer/Op/Greater.h diff --git a/compiler/mir-onnx-importer/Op/Identity.cpp b/compiler/mir/src/mir_onnx_importer/Op/Identity.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Identity.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Identity.cpp diff --git a/compiler/mir-onnx-importer/Op/Identity.h b/compiler/mir/src/mir_onnx_importer/Op/Identity.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Identity.h rename to compiler/mir/src/mir_onnx_importer/Op/Identity.h diff --git a/compiler/mir-onnx-importer/Op/Less.cpp b/compiler/mir/src/mir_onnx_importer/Op/Less.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Less.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Less.cpp diff --git a/compiler/mir-onnx-importer/Op/Less.h b/compiler/mir/src/mir_onnx_importer/Op/Less.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Less.h rename to compiler/mir/src/mir_onnx_importer/Op/Less.h diff --git a/compiler/mir-onnx-importer/Op/MatMul.cpp b/compiler/mir/src/mir_onnx_importer/Op/MatMul.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/MatMul.cpp rename to compiler/mir/src/mir_onnx_importer/Op/MatMul.cpp diff --git a/compiler/mir-onnx-importer/Op/MatMul.h b/compiler/mir/src/mir_onnx_importer/Op/MatMul.h similarity index 100% rename from compiler/mir-onnx-importer/Op/MatMul.h rename to compiler/mir/src/mir_onnx_importer/Op/MatMul.h diff --git a/compiler/mir-onnx-importer/Op/Max.cpp b/compiler/mir/src/mir_onnx_importer/Op/Max.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Max.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Max.cpp diff --git a/compiler/mir-onnx-importer/Op/Max.h b/compiler/mir/src/mir_onnx_importer/Op/Max.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Max.h rename to compiler/mir/src/mir_onnx_importer/Op/Max.h diff --git a/compiler/mir-onnx-importer/Op/MaxPool.cpp b/compiler/mir/src/mir_onnx_importer/Op/MaxPool.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/MaxPool.cpp rename to compiler/mir/src/mir_onnx_importer/Op/MaxPool.cpp diff --git a/compiler/mir-onnx-importer/Op/MaxPool.h b/compiler/mir/src/mir_onnx_importer/Op/MaxPool.h similarity index 100% rename from compiler/mir-onnx-importer/Op/MaxPool.h rename to compiler/mir/src/mir_onnx_importer/Op/MaxPool.h diff --git a/compiler/mir-onnx-importer/Op/Mul.cpp b/compiler/mir/src/mir_onnx_importer/Op/Mul.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Mul.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Mul.cpp diff --git a/compiler/mir-onnx-importer/Op/Mul.h b/compiler/mir/src/mir_onnx_importer/Op/Mul.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Mul.h rename to compiler/mir/src/mir_onnx_importer/Op/Mul.h diff --git a/compiler/mir/src/mir_onnx_importer/Op/Pad.cpp b/compiler/mir/src/mir_onnx_importer/Op/Pad.cpp new file mode 100644 index 0000000..08d7c4a --- /dev/null +++ b/compiler/mir/src/mir_onnx_importer/Op/Pad.cpp @@ -0,0 +1,70 @@ +/* + * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Pad.h" + +#include "ONNXHelpers.h" +#include "AttributeHelpers.h" + +#include "mir/ops/PadOp.h" + +namespace mir_onnx +{ + +void convertPadAttrName(const std::string &pad_attr_name, const onnx::NodeProto &onnx_node, + ConverterContext *context) +{ + std::vector inputs = context->getNodeInputs(onnx_node); + mir::Graph *graph = context->getGraph(); + + assert(inputs.size() == 1); + auto input = inputs[0]; + + // 0.0f is the default value to be filled into padded cells. + const auto value = getAttributeValue(onnx_node, "value", 0.0f); + const auto pads = getAttributeValue>(onnx_node, pad_attr_name); + // "constant" is the default mode. + const auto mode = getAttributeValue(onnx_node, "mode", "constant"); + if (mode != "constant") + throw std::runtime_error("Not supported Pad mode attribute!"); + + const int num_dims = input->getShape().rank(); + assert(static_cast(pads.size()) == num_dims * 2); + mir::PadOpAttributes attributes(num_dims); + for (int i = 0; i < num_dims; i++) + { + attributes.padding_before[i] = pads[i]; + attributes.padding_after[i] = pads[num_dims + i]; + } + + attributes.padding_value = value; + + auto result = createOp(graph, input, attributes)->getOutput(0); + + context->setNodeOutputs(onnx_node, {result}); +} + +void convertPadV1(const onnx::NodeProto &onnx_node, ConverterContext *context) +{ + convertPadAttrName("paddings", onnx_node, context); +} + +void convertPadV2(const onnx::NodeProto &onnx_node, ConverterContext *context) +{ + convertPadAttrName("pads", onnx_node, context); +} + +} // namespace mir_onnx diff --git a/compiler/mir-onnx-importer/Op/Pad.h b/compiler/mir/src/mir_onnx_importer/Op/Pad.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Pad.h rename to compiler/mir/src/mir_onnx_importer/Op/Pad.h diff --git a/compiler/mir-onnx-importer/Op/Reciprocal.cpp b/compiler/mir/src/mir_onnx_importer/Op/Reciprocal.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Reciprocal.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Reciprocal.cpp diff --git a/compiler/mir-onnx-importer/Op/Reciprocal.h b/compiler/mir/src/mir_onnx_importer/Op/Reciprocal.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Reciprocal.h rename to compiler/mir/src/mir_onnx_importer/Op/Reciprocal.h diff --git a/compiler/mir-onnx-importer/Op/ReduceMean.cpp b/compiler/mir/src/mir_onnx_importer/Op/ReduceMean.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/ReduceMean.cpp rename to compiler/mir/src/mir_onnx_importer/Op/ReduceMean.cpp diff --git a/compiler/mir-onnx-importer/Op/ReduceMean.h b/compiler/mir/src/mir_onnx_importer/Op/ReduceMean.h similarity index 100% rename from compiler/mir-onnx-importer/Op/ReduceMean.h rename to compiler/mir/src/mir_onnx_importer/Op/ReduceMean.h diff --git a/compiler/mir-onnx-importer/Op/Relu.cpp b/compiler/mir/src/mir_onnx_importer/Op/Relu.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Relu.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Relu.cpp diff --git a/compiler/mir-onnx-importer/Op/Relu.h b/compiler/mir/src/mir_onnx_importer/Op/Relu.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Relu.h rename to compiler/mir/src/mir_onnx_importer/Op/Relu.h diff --git a/compiler/mir-onnx-importer/Op/Reshape.cpp b/compiler/mir/src/mir_onnx_importer/Op/Reshape.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Reshape.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Reshape.cpp diff --git a/compiler/mir-onnx-importer/Op/Reshape.h b/compiler/mir/src/mir_onnx_importer/Op/Reshape.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Reshape.h rename to compiler/mir/src/mir_onnx_importer/Op/Reshape.h diff --git a/compiler/mir-onnx-importer/Op/Shape.cpp b/compiler/mir/src/mir_onnx_importer/Op/Shape.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Shape.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Shape.cpp diff --git a/compiler/mir-onnx-importer/Op/Shape.h b/compiler/mir/src/mir_onnx_importer/Op/Shape.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Shape.h rename to compiler/mir/src/mir_onnx_importer/Op/Shape.h diff --git a/compiler/mir-onnx-importer/Op/Sigmoid.cpp b/compiler/mir/src/mir_onnx_importer/Op/Sigmoid.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Sigmoid.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Sigmoid.cpp diff --git a/compiler/mir-onnx-importer/Op/Sigmoid.h b/compiler/mir/src/mir_onnx_importer/Op/Sigmoid.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Sigmoid.h rename to compiler/mir/src/mir_onnx_importer/Op/Sigmoid.h diff --git a/compiler/mir-onnx-importer/Op/Softmax.cpp b/compiler/mir/src/mir_onnx_importer/Op/Softmax.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Softmax.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Softmax.cpp diff --git a/compiler/mir-onnx-importer/Op/Softmax.h b/compiler/mir/src/mir_onnx_importer/Op/Softmax.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Softmax.h rename to compiler/mir/src/mir_onnx_importer/Op/Softmax.h diff --git a/compiler/mir-onnx-importer/Op/Sqrt.cpp b/compiler/mir/src/mir_onnx_importer/Op/Sqrt.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Sqrt.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Sqrt.cpp diff --git a/compiler/mir-onnx-importer/Op/Sqrt.h b/compiler/mir/src/mir_onnx_importer/Op/Sqrt.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Sqrt.h rename to compiler/mir/src/mir_onnx_importer/Op/Sqrt.h diff --git a/compiler/mir-onnx-importer/Op/Sub.cpp b/compiler/mir/src/mir_onnx_importer/Op/Sub.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Sub.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Sub.cpp diff --git a/compiler/mir-onnx-importer/Op/Sub.h b/compiler/mir/src/mir_onnx_importer/Op/Sub.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Sub.h rename to compiler/mir/src/mir_onnx_importer/Op/Sub.h diff --git a/compiler/mir-onnx-importer/Op/Sum.cpp b/compiler/mir/src/mir_onnx_importer/Op/Sum.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Sum.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Sum.cpp diff --git a/compiler/mir-onnx-importer/Op/Sum.h b/compiler/mir/src/mir_onnx_importer/Op/Sum.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Sum.h rename to compiler/mir/src/mir_onnx_importer/Op/Sum.h diff --git a/compiler/mir-onnx-importer/Op/Tanh.cpp b/compiler/mir/src/mir_onnx_importer/Op/Tanh.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Tanh.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Tanh.cpp diff --git a/compiler/mir-onnx-importer/Op/Tanh.h b/compiler/mir/src/mir_onnx_importer/Op/Tanh.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Tanh.h rename to compiler/mir/src/mir_onnx_importer/Op/Tanh.h diff --git a/compiler/mir/src/mir_onnx_importer/Op/Transpose.cpp b/compiler/mir/src/mir_onnx_importer/Op/Transpose.cpp new file mode 100644 index 0000000..ffe0e84 --- /dev/null +++ b/compiler/mir/src/mir_onnx_importer/Op/Transpose.cpp @@ -0,0 +1,57 @@ +/* + * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Transpose.h" +#include "ONNXHelpers.h" +#include "AttributeHelpers.h" + +#include "mir/ops/TransposeOp.h" + +#include + +namespace mir_onnx +{ + +void convertTransposeV1(const onnx::NodeProto &onnx_node, ConverterContext *context) +{ + const auto inputs = context->getNodeInputs(onnx_node); + mir::Graph *graph = context->getGraph(); + + assert(inputs.size() == 1); + auto input = inputs[0]; + + const int num_axes = input->getShape().rank(); + std::vector axis_order(num_axes); + const auto *perm_attr = findAttribute(onnx_node, "perm"); + + if (perm_attr == nullptr) + { + // Reverse the dimensions. + std::iota(axis_order.rbegin(), axis_order.rend(), 0); + } + else + { + const auto perm = getAttributeValue>(*perm_attr); + assert(static_cast(perm.size()) == num_axes); + std::copy(perm.cbegin(), perm.cend(), axis_order.begin()); + } + + auto result = createOp(graph, input, axis_order)->getOutput(0); + + context->setNodeOutputs(onnx_node, {result}); +} + +} // namespace mir_onnx diff --git a/compiler/mir-onnx-importer/Op/Transpose.h b/compiler/mir/src/mir_onnx_importer/Op/Transpose.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Transpose.h rename to compiler/mir/src/mir_onnx_importer/Op/Transpose.h diff --git a/compiler/mir-onnx-importer/Op/Unsqueeze.cpp b/compiler/mir/src/mir_onnx_importer/Op/Unsqueeze.cpp similarity index 100% rename from compiler/mir-onnx-importer/Op/Unsqueeze.cpp rename to compiler/mir/src/mir_onnx_importer/Op/Unsqueeze.cpp diff --git a/compiler/mir-onnx-importer/Op/Unsqueeze.h b/compiler/mir/src/mir_onnx_importer/Op/Unsqueeze.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Unsqueeze.h rename to compiler/mir/src/mir_onnx_importer/Op/Unsqueeze.h diff --git a/compiler/mir/src/mir_onnx_importer/Op/Upsample.cpp b/compiler/mir/src/mir_onnx_importer/Op/Upsample.cpp new file mode 100644 index 0000000..346e22c --- /dev/null +++ b/compiler/mir/src/mir_onnx_importer/Op/Upsample.cpp @@ -0,0 +1,127 @@ +/* + * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Upsample.h" + +#include "ONNXHelpers.h" +#include "AttributeHelpers.h" + +#include "mir/Tensor.h" + +#include "mir/ops/ConstantOp.h" +#include "mir/ops/ResizeOp.h" + +#include + +namespace mir_onnx +{ + +void convertUpsampleV1(const onnx::NodeProto &onnx_node, ConverterContext *context) +{ + std::vector inputs = context->getNodeInputs(onnx_node); + mir::Graph *graph = context->getGraph(); + + // "nearest" is the default mode. + std::string mode = getAttributeValue(onnx_node, "mode", "nearest"); + assert(mode == "nearest" && "Unsupported upscale mode!"); + + const float h_scale = getAttributeValue(onnx_node, "height_scale", 0.0f); // required + const float w_scale = getAttributeValue(onnx_node, "width_scale", 0.0f); // required + if (h_scale < 1.0f || w_scale < 1.0f) + throw std::runtime_error("Wrong scale attributes!"); + + assert(inputs[0]->getShape().rank() == 4 && "Only rank 4 is supported"); + std::vector scales_vector(4); + // NCHW + scales_vector.at(0) = 1.0f; + scales_vector.at(1) = 1.0f; + scales_vector.at(2) = h_scale; + scales_vector.at(3) = w_scale; + + auto result = + createOp(graph, inputs[0], + mir::ops::ResizeOp::ResizeMethod::nearestNeighbor, scales_vector) + ->getOutput(0); + + context->setNodeOutputs(onnx_node, {result}); +} + +void convertUpsampleV7(const onnx::NodeProto &onnx_node, ConverterContext *context) +{ + std::vector inputs = context->getNodeInputs(onnx_node); + mir::Graph *graph = context->getGraph(); + + // "nearest" is the default mode. + std::string mode = getAttributeValue(onnx_node, "mode", "nearest"); + assert(mode == "nearest" && "Unsupported upscale mode!"); + + const auto *scales_attr = findAttribute(onnx_node, "scales"); + if (!scales_attr) + throw std::runtime_error("Not enough required scales attribute!"); + + if (scales_attr->floats_size() != inputs[0]->getShape().rank()) + throw std::runtime_error( + "Number of elements of scales should be the same as the rank of input"); + + assert(inputs[0]->getShape().rank() == 4 && "Only rank 4 is supported"); + std::vector scales_vector(4); + // NCHW + scales_vector.at(0) = scales_attr->floats(0); + scales_vector.at(1) = scales_attr->floats(1); + scales_vector.at(2) = scales_attr->floats(2); + scales_vector.at(3) = scales_attr->floats(3); + + auto result = + createOp(graph, inputs[0], + mir::ops::ResizeOp::ResizeMethod::nearestNeighbor, scales_vector) + ->getOutput(0); + + context->setNodeOutputs(onnx_node, {result}); +} + +void convertUpsampleV9(const onnx::NodeProto &onnx_node, ConverterContext *context) +{ + std::vector inputs = context->getNodeInputs(onnx_node); + mir::Graph *graph = context->getGraph(); + + // "nearest" is the default mode. + const auto mode = getAttributeValue(onnx_node, "mode", "nearest"); + if (mode != "nearest") + throw std::runtime_error("Upsample: only 'nearest' mode is supported."); + + // relies on attributes being lifted to constants (ONNX optimization pass) + assert(inputs.size() > 1); + auto *scales = dynamic_cast(inputs[1]->getNode()); + assert(scales && "Weights could be a constant tensor only"); + auto scales_tensor = mir::Tensor(scales->getValue()); + int rank = inputs[0]->getShape().rank(); + if (rank != 4) + throw std::runtime_error("Upsample: only 4-D input is supported."); + assert(scales_tensor.getShape().numElements() == rank && + "The number of elements of 'scales' should be the same as the rank of input 'X'"); + std::vector scales_vector(rank); + for (int i = 0; i < scales_tensor.getShape().numElements(); i++) + scales_vector[i] = scales_tensor.atOffset(i); + + auto result = + createOp(graph, inputs[0], + mir::ops::ResizeOp::ResizeMethod::nearestNeighbor, scales_vector) + ->getOutput(0); + + context->setNodeOutputs(onnx_node, {result}); +} + +} // namespace mir_onnx diff --git a/compiler/mir-onnx-importer/Op/Upsample.h b/compiler/mir/src/mir_onnx_importer/Op/Upsample.h similarity index 100% rename from compiler/mir-onnx-importer/Op/Upsample.h rename to compiler/mir/src/mir_onnx_importer/Op/Upsample.h diff --git a/compiler/mir/src/mir_tflite_importer/CMakeLists.txt b/compiler/mir/src/mir_tflite_importer/CMakeLists.txt new file mode 100644 index 0000000..952857c --- /dev/null +++ b/compiler/mir/src/mir_tflite_importer/CMakeLists.txt @@ -0,0 +1,21 @@ +nnas_find_package(FlatBuffers REQUIRED) + +if (NOT FlatBuffers_FOUND) + return() +endif () + +FlatBuffers_Target(mir_tflite_schema + OUTPUT_DIR "${CMAKE_CURRENT_BINARY_DIR}/generated/schema" + SCHEMA_DIR "${CMAKE_CURRENT_SOURCE_DIR}/schema" + SCHEMA_FILES schema.fbs) + + +set(MIR_TFLITE_IMPORTER_SOURCES + tflite_importer.cpp + tflite_op_creator.cpp + tflite_op_creator.h) + +add_library(mir_tflite_importer STATIC ${MIR_TFLITE_IMPORTER_SOURCES}) +set_target_properties(mir_tflite_importer PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_include_directories(mir_tflite_importer PUBLIC ../../include/mir_tflite_importer) +target_link_libraries(mir_tflite_importer PUBLIC mir PRIVATE mir_tflite_schema nncc_common) diff --git a/compiler/mir-tflite-importer/schema/schema.fbs b/compiler/mir/src/mir_tflite_importer/schema/schema.fbs similarity index 100% rename from compiler/mir-tflite-importer/schema/schema.fbs rename to compiler/mir/src/mir_tflite_importer/schema/schema.fbs diff --git a/compiler/mir-tflite-importer/schema/schema.meta b/compiler/mir/src/mir_tflite_importer/schema/schema.meta similarity index 100% rename from compiler/mir-tflite-importer/schema/schema.meta rename to compiler/mir/src/mir_tflite_importer/schema/schema.meta diff --git a/compiler/mir-tflite-importer/schema/schema_v0.fbs b/compiler/mir/src/mir_tflite_importer/schema/schema_v0.fbs similarity index 100% rename from compiler/mir-tflite-importer/schema/schema_v0.fbs rename to compiler/mir/src/mir_tflite_importer/schema/schema_v0.fbs diff --git a/compiler/mir-tflite-importer/schema/schema_v0.meta b/compiler/mir/src/mir_tflite_importer/schema/schema_v0.meta similarity index 100% rename from compiler/mir-tflite-importer/schema/schema_v0.meta rename to compiler/mir/src/mir_tflite_importer/schema/schema_v0.meta diff --git a/compiler/mir-tflite-importer/schema/schema_v1.fbs b/compiler/mir/src/mir_tflite_importer/schema/schema_v1.fbs similarity index 100% rename from compiler/mir-tflite-importer/schema/schema_v1.fbs rename to compiler/mir/src/mir_tflite_importer/schema/schema_v1.fbs diff --git a/compiler/mir-tflite-importer/schema/schema_v1.meta b/compiler/mir/src/mir_tflite_importer/schema/schema_v1.meta similarity index 100% rename from compiler/mir-tflite-importer/schema/schema_v1.meta rename to compiler/mir/src/mir_tflite_importer/schema/schema_v1.meta diff --git a/compiler/mir-tflite-importer/schema/schema_v2.fbs b/compiler/mir/src/mir_tflite_importer/schema/schema_v2.fbs similarity index 100% rename from compiler/mir-tflite-importer/schema/schema_v2.fbs rename to compiler/mir/src/mir_tflite_importer/schema/schema_v2.fbs diff --git a/compiler/mir-tflite-importer/schema/schema_v2.meta b/compiler/mir/src/mir_tflite_importer/schema/schema_v2.meta similarity index 100% rename from compiler/mir-tflite-importer/schema/schema_v2.meta rename to compiler/mir/src/mir_tflite_importer/schema/schema_v2.meta diff --git a/compiler/mir-tflite-importer/schema/schema_v3.fbs b/compiler/mir/src/mir_tflite_importer/schema/schema_v3.fbs similarity index 100% rename from compiler/mir-tflite-importer/schema/schema_v3.fbs rename to compiler/mir/src/mir_tflite_importer/schema/schema_v3.fbs diff --git a/compiler/mir-tflite-importer/schema/schema_v3.meta b/compiler/mir/src/mir_tflite_importer/schema/schema_v3.meta similarity index 100% rename from compiler/mir-tflite-importer/schema/schema_v3.meta rename to compiler/mir/src/mir_tflite_importer/schema/schema_v3.meta diff --git a/compiler/mir/src/mir_tflite_importer/tflite_importer.cpp b/compiler/mir/src/mir_tflite_importer/tflite_importer.cpp new file mode 100644 index 0000000..3f245d2 --- /dev/null +++ b/compiler/mir/src/mir_tflite_importer/tflite_importer.cpp @@ -0,0 +1,428 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite_importer.h" +#include "tflite_op_creator.h" +#include "schema_generated.h" + +#include "mir/TensorVariant.h" +#include "mir/ops/ConstantOp.h" +#include "mir/ops/OutputOp.h" + +#include +#include +#include +#include +#include + +namespace mir_tflite +{ + +namespace +{ + +class TfliteImporter +{ +public: + explicit TfliteImporter(std::string filename); + + /// @brief Load the model and convert it into a MIR Graph. + std::unique_ptr importModel(); + + ~TfliteImporter(); + +private: + std::string _filename; + std::unique_ptr _model; + + std::unique_ptr _graph; + std::unique_ptr _opCreator; + + // Maps TFLite tensors indices to corresponding MIR operation outputs. + std::vector _tensorMap; + + void import(); + + void walkModel(const tflite::ModelT *model); + + void walkSubgraph(const tflite::SubGraphT *subgraph); + + void walkOperator(const tflite::SubGraphT *subgraph, const tflite::OperatorT *op); + + /** + * @brief Pass through tflite graph and collect operators unsupported by NNC + * @throw PassException with message, containing detected problems + */ + void collectUnsupportedOps(); + + /** + * @brief Returns MIR operation outputs corresponding to the inputs of the given operator. + */ + std::vector getMIRInputsForOperator(const tflite::SubGraphT *subgraph, + const tflite::OperatorT *op); +}; + +TfliteImporter::TfliteImporter(std::string filename) : _filename(std::move(filename)) +{ + _graph = std::make_unique(); + _opCreator = std::make_unique(_graph.get()); +} + +TfliteImporter::~TfliteImporter() = default; + +void TfliteImporter::import() +{ + std::ifstream stream(_filename, std::ios::in | std::ios::binary); + if (stream.fail()) + throw std::runtime_error("Couldn't open file \"" + _filename + "\"."); + + std::vector model_buffer((std::istreambuf_iterator(stream)), + std::istreambuf_iterator()); + + if (stream.fail()) + throw std::runtime_error("Couldn't read file \"" + _filename + "\"."); + + flatbuffers::Verifier verifier(reinterpret_cast(model_buffer.data()), + model_buffer.size()); + + if (!tflite::VerifyModelBuffer(verifier)) + throw std::runtime_error("Could not load model: " + _filename + "\n"); + + _model = tflite::UnPackModel(model_buffer.data()); +} + +static const std::set supportedOperators = { + tflite::BuiltinOperator_ADD, + tflite::BuiltinOperator_AVERAGE_POOL_2D, + tflite::BuiltinOperator_CONCATENATION, + tflite::BuiltinOperator_CONV_2D, + tflite::BuiltinOperator_DEPTHWISE_CONV_2D, + tflite::BuiltinOperator_DIV, + tflite::BuiltinOperator_FULLY_CONNECTED, + tflite::BuiltinOperator_HARD_SWISH, + tflite::BuiltinOperator_LEAKY_RELU, + tflite::BuiltinOperator_LOGISTIC, + tflite::BuiltinOperator_MAX_POOL_2D, + tflite::BuiltinOperator_MAXIMUM, + tflite::BuiltinOperator_MEAN, + tflite::BuiltinOperator_MUL, + tflite::BuiltinOperator_PAD, + tflite::BuiltinOperator_RELU, + tflite::BuiltinOperator_RELU6, + tflite::BuiltinOperator_RESHAPE, + tflite::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR, + tflite::BuiltinOperator_RSQRT, + tflite::BuiltinOperator_SHAPE, + tflite::BuiltinOperator_SLICE, + tflite::BuiltinOperator_SOFTMAX, + tflite::BuiltinOperator_SQRT, + tflite::BuiltinOperator_SQUARED_DIFFERENCE, + tflite::BuiltinOperator_SQUEEZE, + tflite::BuiltinOperator_STRIDED_SLICE, + tflite::BuiltinOperator_SUB, + tflite::BuiltinOperator_TANH, + tflite::BuiltinOperator_TRANSPOSE, + tflite::BuiltinOperator_TRANSPOSE_CONV, +}; + +void TfliteImporter::collectUnsupportedOps() +{ + std::set errors; + for (const auto &subgraph : _model->subgraphs) + for (const auto &op : subgraph->operators) + { + tflite::BuiltinOperator opcode = _model->operator_codes[op->opcode_index]->builtin_code; + if (supportedOperators.find(opcode) == supportedOperators.end()) + { + if (opcode <= tflite::BuiltinOperator_MAX) + errors.insert(std::string(EnumNameBuiltinOperator(opcode)) + ": unsupported operator"); + else + errors.insert(std::to_string(opcode) + ": unsuppored in tflite custom opcode"); + } + } + + if (!errors.empty()) + { + std::string msg("NNC can't load model. Detected problems:"); + for (const auto &e : errors) + msg.append("\n * " + e); + throw std::runtime_error(msg); + } +} + +std::unique_ptr TfliteImporter::importModel() +{ + import(); + collectUnsupportedOps(); + walkModel(_model.get()); + return std::move(_graph); +} + +void TfliteImporter::walkModel(const tflite::ModelT *model) +{ + for (const auto &subgraph : model->subgraphs) + walkSubgraph(subgraph.get()); +} + +mir::DataType convertElementType(tflite::TensorType type) +{ + switch (type) + { + case tflite::TensorType_INT32: + return mir::DataType::INT32; + case tflite::TensorType_FLOAT32: + return mir::DataType::FLOAT32; + case tflite::TensorType_INT64: + return mir::DataType::INT64; + case tflite::TensorType_UINT8: + return mir::DataType::UINT8; + default: + throw std::runtime_error(std::string("Unsupported tensor type: ") + EnumNameTensorType(type)); + } +} + +mir::TensorType getMirTensorType(const tflite::TensorT &tensor) +{ + mir::DataType element_type = convertElementType(tensor.type); + + mir::Shape shape(tensor.shape.size()); + for (std::size_t i = 0; i < tensor.shape.size(); ++i) + { + shape.dim(i) = tensor.shape[i]; + } + + if (tensor.quantization != nullptr) + { + const tflite::QuantizationParametersT ¶ms = *tensor.quantization; + + if (params.details.type != tflite::QuantizationDetails_NONE) + throw std::runtime_error("Custom quantization is not supported."); + + // Empty parameters mean no quantization at all. + if (params.scale.empty() && params.zero_point.empty()) + return mir::TensorType{element_type, shape}; + + if (params.scale.size() != 1 || params.zero_point.size() != 1) + throw std::runtime_error("Non-scalar quantization is not supported."); + + mir::AffineQuantization quantization{params.scale[0], static_cast(params.zero_point[0])}; + + return mir::TensorType{element_type, shape, quantization}; + } + else + { + return mir::TensorType{element_type, shape}; + } +} + +void TfliteImporter::walkSubgraph(const tflite::SubGraphT *subgraph) +{ + _tensorMap.assign(subgraph->tensors.size(), nullptr); + + for (const auto input_tensor_index : subgraph->inputs) + { + const tflite::TensorT &tensor = *subgraph->tensors[input_tensor_index]; + + mir::TensorType input_type = getMirTensorType(tensor); + auto input = _graph->create(input_type)->getOutput(0); + input->setName(tensor.name); + + assert(_tensorMap[input_tensor_index] == nullptr); + _tensorMap[input_tensor_index] = input; + } + + for (const auto &op : subgraph->operators) + { + walkOperator(subgraph, op.get()); + } + + for (const auto output_tensor_index : subgraph->outputs) + { + auto output = _tensorMap[output_tensor_index]; + _graph->create(output); + } +} + +void TfliteImporter::walkOperator(const tflite::SubGraphT *subgraph, const tflite::OperatorT *op) +{ + std::vector inputs = getMIRInputsForOperator(subgraph, op); + std::vector outputs; + + tflite::BuiltinOperator opcode = _model->operator_codes[op->opcode_index]->builtin_code; + switch (opcode) + { + case tflite::BuiltinOperator_CONV_2D: + outputs = _opCreator->convertConv2D(op->builtin_options.AsConv2DOptions(), inputs); + break; + case tflite::BuiltinOperator_DEPTHWISE_CONV_2D: + outputs = _opCreator->convertDepthwiseConv2D(op->builtin_options.AsDepthwiseConv2DOptions(), + inputs); + break; + case tflite::BuiltinOperator_MAX_POOL_2D: + outputs = _opCreator->convertMaxPool2D(op->builtin_options.AsPool2DOptions(), inputs); + break; + case tflite::BuiltinOperator_AVERAGE_POOL_2D: + outputs = _opCreator->convertAveragePool2D(op->builtin_options.AsPool2DOptions(), inputs); + break; + case tflite::BuiltinOperator_CONCATENATION: + outputs = + _opCreator->convertConcatenation(op->builtin_options.AsConcatenationOptions(), inputs); + break; + case tflite::BuiltinOperator_RESHAPE: + outputs = _opCreator->convertReshape(op->builtin_options.AsReshapeOptions(), inputs); + break; + case tflite::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR: + outputs = _opCreator->convertResizeNearestNeighbor( + op->builtin_options.AsResizeNearestNeighborOptions(), inputs); + break; + case tflite::BuiltinOperator_MEAN: + outputs = _opCreator->convertMean(op->builtin_options.AsReducerOptions(), inputs); + break; + case tflite::BuiltinOperator_FULLY_CONNECTED: + outputs = + _opCreator->convertFullyConnected(op->builtin_options.AsFullyConnectedOptions(), inputs); + break; + case tflite::BuiltinOperator_SOFTMAX: + outputs = _opCreator->convertSoftmax(op->builtin_options.AsSoftmaxOptions(), inputs); + break; + case tflite::BuiltinOperator_SLICE: + outputs = _opCreator->convertSlice(op->builtin_options.AsSliceOptions(), inputs); + break; + case tflite::BuiltinOperator_SQUEEZE: + outputs = _opCreator->convertSqueeze(op->builtin_options.AsSqueezeOptions(), inputs); + break; + case tflite::BuiltinOperator_LOGISTIC: + outputs = _opCreator->convertLogistic(inputs); + break; + case tflite::BuiltinOperator_RSQRT: + outputs = _opCreator->convertRsqrt(inputs); + break; + case tflite::BuiltinOperator_SQRT: + outputs = _opCreator->convertSqrt(inputs); + break; + case tflite::BuiltinOperator_ADD: + outputs = _opCreator->convertAdd(op->builtin_options.AsAddOptions(), inputs); + break; + case tflite::BuiltinOperator_SUB: + outputs = _opCreator->convertSub(op->builtin_options.AsSubOptions(), inputs); + break; + case tflite::BuiltinOperator_MUL: + outputs = _opCreator->convertMul(op->builtin_options.AsMulOptions(), inputs); + break; + case tflite::BuiltinOperator_DIV: + outputs = _opCreator->convertDiv(op->builtin_options.AsDivOptions(), inputs); + break; + case tflite::BuiltinOperator_MAXIMUM: + outputs = _opCreator->convertMax(inputs); + break; + case tflite::BuiltinOperator_SQUARED_DIFFERENCE: + outputs = _opCreator->convertSquaredDifference(inputs); + break; + case tflite::BuiltinOperator_TRANSPOSE_CONV: + outputs = + _opCreator->convertTransposeConv(op->builtin_options.AsTransposeConvOptions(), inputs); + break; + case tflite::BuiltinOperator_PAD: + outputs = _opCreator->convertPad(op->builtin_options.AsPadOptions(), inputs); + break; + case tflite::BuiltinOperator_TANH: + outputs = _opCreator->convertTanh(inputs); + break; + case tflite::BuiltinOperator_RELU: + outputs = _opCreator->convertReLU(inputs); + break; + case tflite::BuiltinOperator_RELU6: + outputs = _opCreator->convertReLU6(inputs); + break; + case tflite::BuiltinOperator_TRANSPOSE: + outputs = _opCreator->convertTranspose(op->builtin_options.AsTransposeOptions(), inputs); + break; + case tflite::BuiltinOperator_STRIDED_SLICE: + outputs = + _opCreator->convertStridedSlice(op->builtin_options.AsStridedSliceOptions(), inputs); + break; + case tflite::BuiltinOperator_LEAKY_RELU: + outputs = _opCreator->convertLeakyReLU(op->builtin_options.AsLeakyReluOptions(), inputs); + break; + case tflite::BuiltinOperator_SHAPE: + outputs = _opCreator->convertShape(op->builtin_options.AsShapeOptions(), inputs); + break; + case tflite::BuiltinOperator_HARD_SWISH: + outputs = _opCreator->convertHardSwish(op->builtin_options.AsHardSwishOptions(), inputs); + break; + default: + assert(false && "All unsupported types should have been found before this pass."); + } + + assert(outputs.size() == op->outputs.size()); + for (std::size_t i = 0; i < op->outputs.size(); ++i) + { + const auto tensor_index = op->outputs[i]; + const tflite::TensorT &tensor = *subgraph->tensors[tensor_index]; + + mir::TensorType output_type = getMirTensorType(tensor); + + // The type should have been inferred correctly, except for quantization information. + assert(outputs[i]->getType().getElementType() == output_type.getElementType() && + outputs[i]->getType().getShape() == output_type.getShape()); + + outputs[i]->setName(tensor.name); + outputs[i]->setType(output_type); + + assert(_tensorMap[tensor_index] == nullptr); + _tensorMap[tensor_index] = outputs[i]; + } +} + +std::vector +TfliteImporter::getMIRInputsForOperator(const tflite::SubGraphT *subgraph, + const tflite::OperatorT *op) +{ + std::vector inputs; + + for (const auto tensor_index : op->inputs) + { + const tflite::TensorT &tensor = *subgraph->tensors[tensor_index]; + const tflite::BufferT &buffer = *_model->buffers[tensor.buffer]; + if (!buffer.data.empty()) + { + assert(_tensorMap[tensor_index] == nullptr); + mir::TensorType type = getMirTensorType(tensor); + mir::TensorVariant mir_tensor{type, buffer.data.data()}; + inputs.emplace_back(_graph->create(mir_tensor)->getOutput(0)); + } + else + { + assert(_tensorMap[tensor_index] != nullptr); + // By this point every input for the operation "op" should have corresponding + // Model IR operations that output its inputs. This assumption is provided by the fact + // that TFLite format specifies all operations in the execution order. + inputs.emplace_back(_tensorMap[tensor_index]); + } + } + + return inputs; +} + +} // namespace + +std::unique_ptr loadModel(std::string filename) +{ + TfliteImporter importer(std::move(filename)); + return importer.importModel(); +} + +} // namespace mir_tflite diff --git a/compiler/mir/src/mir_tflite_importer/tflite_op_creator.cpp b/compiler/mir/src/mir_tflite_importer/tflite_op_creator.cpp new file mode 100644 index 0000000..d9f98da --- /dev/null +++ b/compiler/mir/src/mir_tflite_importer/tflite_op_creator.cpp @@ -0,0 +1,652 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "tflite_op_creator.h" +#include "schema_generated.h" + +#include "mir/ops/AddOp.h" +#include "mir/ops/AvgPool2DOp.h" +#include "mir/ops/CappedReluOp.h" +#include "mir/ops/ConcatOp.h" +#include "mir/ops/ConstantOp.h" +#include "mir/ops/Conv2DOp.h" +#include "mir/ops/Deconv2DOp.h" +#include "mir/ops/DepthwiseConv2DOp.h" +#include "mir/ops/DivOp.h" +#include "mir/ops/FullyConnectedOp.h" +#include "mir/ops/HardSwishOp.h" +#include "mir/ops/LeakyReluOp.h" +#include "mir/ops/MaxOp.h" +#include "mir/ops/MaxPool2DOp.h" +#include "mir/ops/MulOp.h" +#include "mir/ops/PadOp.h" +#include "mir/ops/ReduceMeanOp.h" +#include "mir/ops/ReluOp.h" +#include "mir/ops/ReshapeOp.h" +#include "mir/ops/ResizeOp.h" +#include "mir/ops/SigmoidOp.h" +#include "mir/ops/SliceOp.h" +#include "mir/ops/SoftmaxOp.h" +#include "mir/ops/SqrtOp.h" +#include "mir/ops/SqueezeOp.h" +#include "mir/ops/SubOp.h" +#include "mir/ops/TanhOp.h" +#include "mir/ops/TransposeOp.h" + +#include "mir/Shape.h" +#include "mir/ShapeRange.h" +#include "mir/Tensor.h" + +#include + +namespace mir_tflite +{ + +namespace ops = mir::ops; +using mir::Shape; + +static mir::ops::PaddingType convertPadding(tflite::Padding padding) +{ + switch (padding) + { + case tflite::Padding_VALID: + return mir::ops::PaddingType::Valid; + case tflite::Padding_SAME: + return mir::ops::PaddingType::SameUpper; + default: + throw std::runtime_error(std::string("Unsupported Padding: ") + + tflite::EnumNamePadding(padding)); + } +} + +// TODO Move this to MIR? +static void calculatePadding(mir::ops::PaddingType padding_type, const mir::Shape &input_shape, + const std::vector &window_size, + const std::vector &strides, + std::vector &padding_before, + std::vector &padding_after) +{ + constexpr int num_spatial_dims = 2; + assert(window_size.size() == num_spatial_dims); + assert(strides.size() == num_spatial_dims); + assert(padding_before.size() == num_spatial_dims); + assert(padding_after.size() == num_spatial_dims); + + switch (padding_type) + { + case mir::ops::PaddingType::SameUpper: + for (int i = 0; i < num_spatial_dims; ++i) + { + // Assuming NHWC format. + const std::int32_t total_padding = + (input_shape.dim(1 + i) % strides[i] == 0) + ? std::max(0, window_size[i] - strides[i]) + : std::max(0, window_size[i] - input_shape.dim(1 + i) % strides[i]); + padding_before[i] = total_padding / 2; + padding_after[i] = total_padding - padding_before[i]; + } + break; + case mir::ops::PaddingType::Valid: + for (int i = 0; i < num_spatial_dims; ++i) + { + padding_before[i] = 0; + padding_after[i] = 0; + } + break; + default: + assert(false); + } +} + +template +static std::vector convertIntTensorToVector(const mir::Tensor &tensor) +{ + std::vector v; + for (const auto &i : mir::ShapeRange(tensor.getShape())) + v.emplace_back(static_cast(tensor.at(i))); + return v; +} + +static const mir::TensorVariant &extractTensor(const mir::Operation::Output *output) +{ + auto constant_op = dynamic_cast(output->getNode()); + if (constant_op == nullptr) + throw std::runtime_error("Non-constant input is not supported."); + return constant_op->getValue(); +} + +std::vector +TFLiteOpCreator::convertConv2D(const tflite::Conv2DOptionsT *opts, + const std::vector &inputs) +{ + auto input = inputs.at(0); + auto kernel = inputs.at(1); + auto bias = inputs.at(2); + + mir::Conv2DOpAttributes attributes; + attributes.strides = {opts->stride_h, opts->stride_w}; + + const auto padding_type = convertPadding(opts->padding); + const auto &input_shape = input->getShape(); + const auto &kernel_shape = kernel->getShape(); + const auto &strides = attributes.strides; + auto &pad_before = attributes.padding_before; + auto &pad_after = attributes.padding_after; + std::vector kernel_size{kernel_shape.dim(1), kernel_shape.dim(2)}; + calculatePadding(padding_type, input_shape, kernel_size, strides, pad_before, pad_after); + + mir::Operation::Output *result; + if (input->getType().isQuantized()) + { + result = createOp(input, kernel, bias, attributes)->getOutput(0); + } + else // TODO Fuse bias to other backends + { + result = createOp(input, kernel, attributes)->getOutput(0); + result = createOp(result, bias)->getOutput(0); + } + return {addFusedActivation(result, opts->fused_activation_function)}; +} + +std::vector +TFLiteOpCreator::convertDepthwiseConv2D(const tflite::DepthwiseConv2DOptionsT *opts, + const std::vector &inputs) +{ + auto input = inputs.at(0); + auto kernel = inputs.at(1); + auto bias = inputs.at(2); + + // OHWI -> HWIO + const std::vector axis_order{1, 2, 3, 0}; + kernel = createOp(kernel, axis_order)->getOutput(0); + + mir::Conv2DOpAttributes attributes; + attributes.strides = {opts->stride_h, opts->stride_w}; + + const auto padding_type = convertPadding(opts->padding); + const auto &input_shape = input->getShape(); + const auto &kernel_shape = kernel->getShape(); + std::vector kernel_size{kernel_shape.dim(0), kernel_shape.dim(1)}; + const auto &strides = attributes.strides; + auto &pad_before = attributes.padding_before; + auto &pad_after = attributes.padding_after; + calculatePadding(padding_type, input_shape, kernel_size, strides, pad_before, pad_after); + + mir::Operation::Output *result; + if (input->getType().isQuantized()) + { + result = createOp(input, kernel, bias, attributes)->getOutput(0); + } + else // TODO Fuse bias to other backends + { + result = createOp(input, kernel, attributes)->getOutput(0); + result = createOp(result, bias)->getOutput(0); + } + return {addFusedActivation(result, opts->fused_activation_function)}; +} + +std::vector +TFLiteOpCreator::convertConcatenation(const tflite::ConcatenationOptionsT *opts, + const std::vector &inputs) +{ + auto result = createOp(inputs, opts->axis); + return {addFusedActivation(result->getOutput(0), opts->fused_activation_function)}; +} + +std::vector +TFLiteOpCreator::convertMaxPool2D(const tflite::Pool2DOptionsT *opts, + const std::vector &inputs) +{ + auto input = inputs.at(0); + + const auto &input_shape = input->getShape(); + + mir::MaxPool2DOpAttributes attributes; + attributes.window = {opts->filter_height, opts->filter_width}; + attributes.strides = {opts->stride_h, opts->stride_w}; + + const auto padding_type = convertPadding(opts->padding); + const auto &window_size = attributes.window; + const auto &strides = attributes.strides; + auto &pad_before = attributes.padding_before; + auto &pad_after = attributes.padding_after; + calculatePadding(padding_type, input_shape, window_size, strides, pad_before, pad_after); + + auto result = createOp(input, attributes); + return {addFusedActivation(result->getOutput(0), opts->fused_activation_function)}; +} + +std::vector +TFLiteOpCreator::convertAveragePool2D(const tflite::Pool2DOptionsT *opts, + const std::vector &inputs) +{ + auto input = inputs.at(0); + + const auto &input_shape = input->getShape(); + + mir::AvgPool2DOpAttributes attributes; + attributes.window = {opts->filter_height, opts->filter_width}; + attributes.strides = {opts->stride_h, opts->stride_w}; + attributes.include_pad = false; + + const auto padding_type = convertPadding(opts->padding); + const auto &window_size = attributes.window; + const auto &strides = attributes.strides; + auto &pad_before = attributes.padding_before; + auto &pad_after = attributes.padding_after; + calculatePadding(padding_type, input_shape, window_size, strides, pad_before, pad_after); + + auto result = createOp(input, attributes); + return {addFusedActivation(result->getOutput(0), opts->fused_activation_function)}; +} + +std::vector +TFLiteOpCreator::convertSoftmax(const tflite::SoftmaxOptionsT * /*opts*/, + const std::vector &inputs) +{ + auto input = inputs.at(0); + + // Softmax in TFLite is always 2-D. + assert(input->getShape().rank() == 2); + const int32_t axis = 1; + auto result = createOp(input, axis); + return {result->getOutput(0)}; +} + +std::vector +TFLiteOpCreator::convertSlice(const tflite::SliceOptionsT * /*opts*/, + const std::vector &inputs) +{ + auto input = inputs.at(0); + mir::Tensor begin_tensor(extractTensor(inputs.at(1))); + mir::Tensor size_tensor(extractTensor(inputs.at(2))); + + Shape starts(convertIntTensorToVector(begin_tensor)); + Shape sizes(convertIntTensorToVector(size_tensor)); + auto result = createOp(input, starts, sizes); + return {result->getOutput(0)}; +} + +std::vector +TFLiteOpCreator::convertReshape(const tflite::ReshapeOptionsT *opts, + const std::vector &inputs) +{ + auto input = inputs.at(0); + + // TODO: we should also support "-1" values in new_shape, which means that correct + // shape values must be calculated. Better do it in the shape inference module. + Shape new_shape(opts->new_shape.size()); + for (int i = 0; i < static_cast(opts->new_shape.size()); ++i) + { + new_shape.dim(i) = opts->new_shape[i]; + } + auto result = createOp(input, new_shape); + return {result->getOutput(0)}; +} + +std::vector +TFLiteOpCreator::convertTransposeConv(const tflite::TransposeConvOptionsT *opts, + const std::vector &inputs) +{ + mir::Tensor output_shape_tensor(extractTensor(inputs.at(0))); + auto kernel = inputs.at(1); + auto input = inputs.at(2); + + mir::Deconv2DOpAttributes attributes; + attributes.strides = {opts->stride_h, opts->stride_w}; + Shape output_shape(convertIntTensorToVector(output_shape_tensor)); + + // OHWI -> HWOI + const std::vector axis_order{1, 2, 0, 3}; + kernel = createOp(kernel, axis_order)->getOutput(0); + + attributes.padding_type = convertPadding(opts->padding); + auto result = createOp(input, kernel, attributes, output_shape)->getOutput(0); + return {result}; +} + +std::vector +TFLiteOpCreator::convertResizeNearestNeighbor(const tflite::ResizeNearestNeighborOptionsT *opts, + const std::vector &inputs) +{ + if (opts->align_corners) + throw std::runtime_error("'align_corners' is not currently supported"); + + auto input = inputs.at(0); + mir::Tensor size_tensor(extractTensor(inputs.at(1))); + + const auto &input_shape = input->getShape(); + Shape res_shape{input_shape.dim(0), size_tensor.at(mir::Index{0}), size_tensor.at(mir::Index{1}), + input_shape.dim(3)}; + auto result = + createOp(input, ops::ResizeOp::ResizeMethod::nearestNeighbor, res_shape); + return {result->getOutput(0)}; +} + +std::vector +TFLiteOpCreator::convertAdd(const tflite::AddOptionsT *opts, + const std::vector &inputs) +{ + assert(inputs.size() == 2); + auto result = createOp(inputs[0], inputs[1])->getOutput(0); + return {addFusedActivation(result, opts->fused_activation_function)}; +} + +std::vector +TFLiteOpCreator::convertSub(const tflite::SubOptionsT *opts, + const std::vector &inputs) +{ + assert(inputs.size() == 2); + auto result = createOp(inputs[0], inputs[1])->getOutput(0); + return {addFusedActivation(result, opts->fused_activation_function)}; +} + +std::vector +TFLiteOpCreator::convertMul(const tflite::MulOptionsT *opts, + const std::vector &inputs) +{ + assert(inputs.size() == 2); + auto result = createOp(inputs[0], inputs[1])->getOutput(0); + return {addFusedActivation(result, opts->fused_activation_function)}; +} + +std::vector +TFLiteOpCreator::convertDiv(const tflite::DivOptionsT *opts, + const std::vector &inputs) +{ + assert(inputs.size() == 2); + auto result = createOp(inputs[0], inputs[1])->getOutput(0); + return {addFusedActivation(result, opts->fused_activation_function)}; +} + +std::vector +TFLiteOpCreator::convertMax(const std::vector &inputs) +{ + assert(inputs.size() == 2); + auto result = createOp(inputs[0], inputs[1])->getOutput(0); + return {result}; +} + +std::vector +TFLiteOpCreator::convertSquaredDifference(const std::vector &inputs) +{ + assert(inputs.size() == 2); + auto result = createOp(inputs[0], inputs[1])->getOutput(0); + result = createOp(result, result)->getOutput(0); + return {result}; +} + +std::vector +TFLiteOpCreator::convertMean(const tflite::ReducerOptionsT *opts, + const std::vector &inputs) +{ + auto input = inputs.at(0); + mir::Tensor axes_tensor(extractTensor(inputs.at(1))); + + std::vector axes = convertIntTensorToVector(axes_tensor); + auto result = createOp(input, axes, opts->keep_dims); + return {result->getOutput(0)}; +} + +std::vector +TFLiteOpCreator::convertFullyConnected(const tflite::FullyConnectedOptionsT *opts, + const std::vector &inputs) +{ + auto input = inputs.at(0); + auto weights = inputs.at(1); + auto bias = inputs.at(2); + + // Flatten input to 2-D shape. + const auto &input_shape = input->getShape(); + int32_t outer_size = input_shape.dim(0); + int32_t inner_size = input_shape.numElements() / outer_size; + auto flatten = createOp(input, Shape{outer_size, inner_size})->getOutput(0); + + // Transpose the weights. + const std::vector axis_order{1, 0}; + weights = createOp(weights, axis_order)->getOutput(0); + + mir::Operation::Output *result; + if (input->getType().isQuantized()) + { + result = createOp(flatten, weights, bias)->getOutput(0); + } + else // TODO Fuse bias to other backends + { + result = createOp(flatten, weights)->getOutput(0); + result = createOp(result, bias)->getOutput(0); + } + return {addFusedActivation(result, opts->fused_activation_function)}; +} + +mir::Operation::Output * +TFLiteOpCreator::addFusedActivation(mir::Operation::Output *input, + tflite::ActivationFunctionType activation_type) +{ + switch (activation_type) + { + case tflite::ActivationFunctionType_NONE: + return input; + case tflite::ActivationFunctionType_RELU: + return createOp(input)->getOutput(0); + case tflite::ActivationFunctionType_RELU6: + return createOp(input, 6)->getOutput(0); + case tflite::ActivationFunctionType_TANH: + return createOp(input)->getOutput(0); + default: + throw std::runtime_error(std::string("Unsupported activation type: ") + + tflite::EnumNameActivationFunctionType(activation_type)); + } +} + +std::vector +TFLiteOpCreator::convertSqueeze(const tflite::SqueezeOptionsT *opts, + const std::vector &inputs) +{ + auto input = inputs.at(0); + + std::vector squeeze_dims(opts->squeeze_dims.begin(), opts->squeeze_dims.end()); + auto result = createOp(input, squeeze_dims); + return {result->getOutput(0)}; +} + +std::vector +TFLiteOpCreator::convertPad(const tflite::PadOptionsT * /*opts*/, + const std::vector &inputs) +{ + auto input = inputs.at(0); + mir::Tensor paddings_tensor(extractTensor(inputs.at(1))); + + const auto &input_shape = input->getShape(); + const int num_dims = input_shape.rank(); + + mir::PadOpAttributes attributes(num_dims); + for (int i = 0; i < num_dims; i++) + { + attributes.padding_before[i] = paddings_tensor.at(mir::Index({i, 0})); + attributes.padding_after[i] = paddings_tensor.at(mir::Index({i, 1})); + } + + auto result = createOp(input, attributes)->getOutput(0); + return {result}; +} + +std::vector +TFLiteOpCreator::convertTanh(const std::vector &inputs) +{ + auto input = inputs.at(0); + + auto result = createOp(input); + return {result->getOutput(0)}; +} + +std::vector +TFLiteOpCreator::convertReLU(const std::vector &inputs) +{ + auto input = inputs.at(0); + + auto result = createOp(input); + return {result->getOutput(0)}; +} + +std::vector +TFLiteOpCreator::convertReLU6(const std::vector &inputs) +{ + auto input = inputs.at(0); + + auto result = createOp(input, 6); + return {result->getOutput(0)}; +} + +std::vector +TFLiteOpCreator::convertRsqrt(const std::vector &inputs) +{ + auto input = inputs.at(0); + + const float one_value = 1.0f; + mir::TensorVariant one_tensor({mir::DataType::FLOAT32, {}}, &one_value); + auto one = createOp(one_tensor)->getOutput(0); + auto sqrt = createOp(input)->getOutput(0); + auto result = createOp(one, sqrt)->getOutput(0); + return {result}; +} + +std::vector +TFLiteOpCreator::convertSqrt(const std::vector &inputs) +{ + auto input = inputs.at(0); + + auto result = createOp(input)->getOutput(0); + return {result}; +} + +std::vector +TFLiteOpCreator::convertLogistic(const std::vector &inputs) +{ + auto input = inputs.at(0); + + auto result = createOp(input); + return {result->getOutput(0)}; +} + +std::vector +TFLiteOpCreator::convertTranspose(const tflite::TransposeOptionsT * /*opts*/, + const std::vector &inputs) +{ + auto input = inputs.at(0); + mir::Tensor perm_tensor(extractTensor(inputs.at(1))); + + std::vector axis_order = convertIntTensorToVector(perm_tensor); + auto result = createOp(input, axis_order); + return {result->getOutput(0)}; +} + +std::vector +TFLiteOpCreator::convertStridedSlice(const tflite::StridedSliceOptionsT *opts, + const std::vector &inputs) +{ + if (opts->ellipsis_mask != 0) + throw std::runtime_error("StridedSlice: parameter 'ellipsis_mask' is not supported."); + + if (opts->new_axis_mask != 0) + throw std::runtime_error("StridedSlice: parameter 'new_axis_mask' is not supported."); + + auto input = inputs.at(0); + mir::Tensor begin_tensor(extractTensor(inputs.at(1))); + mir::Tensor end_tensor(extractTensor(inputs.at(2))); + mir::Tensor strides_tensor(extractTensor(inputs.at(3))); + + std::vector begin = convertIntTensorToVector(begin_tensor); + std::vector end = convertIntTensorToVector(end_tensor); + std::vector strides = convertIntTensorToVector(strides_tensor); + + int32_t begin_mask = opts->begin_mask; + int32_t end_mask = opts->end_mask; + int32_t shrink_axis_mask = opts->shrink_axis_mask; + + const auto &input_shape = input->getShape(); + int32_t num_dims = input_shape.rank(); + + for (int32_t stride : strides) + { + if (stride != 1) + throw std::runtime_error("StridedSlice: parameter 'strides' is not supported"); + } + + Shape start(num_dims); + Shape size(num_dims); + std::vector squeeze_dims; + for (int axis = 0; axis < num_dims; axis++) + { + if (static_cast(begin_mask) & (1u << static_cast(axis))) + start.dim(axis) = 0; + else + start.dim(axis) = begin.at(static_cast(axis)); + + if (static_cast(end_mask) & (1u << static_cast(axis))) + size.dim(axis) = input_shape.dim(axis) - start.dim(axis); + else + size.dim(axis) = end.at(static_cast(axis)) - start.dim(axis); + + if (static_cast(shrink_axis_mask) & (1u << static_cast(axis))) + squeeze_dims.push_back(axis); + } + + auto result = createOp(input, start, size); + result = createOp(result->getOutput(0), squeeze_dims); + return {result->getOutput(0)}; +} + +std::vector +TFLiteOpCreator::convertLeakyReLU(const tflite::LeakyReluOptionsT *opts, + const std::vector &inputs) +{ + auto input = inputs.at(0); + + auto result = createOp(input, opts->alpha); + return {result->getOutput(0)}; +} + +std::vector +TFLiteOpCreator::convertShape(const tflite::ShapeOptionsT *opts, + const std::vector &inputs) +{ + if (opts->out_type != tflite::TensorType_INT32) + { + throw std::runtime_error(std::string("SHAPE: Unsupported tensor type: ") + + EnumNameTensorType(opts->out_type)); + } + + const auto &input_shape = inputs[0]->getShape(); + int32_t rank = input_shape.rank(); + std::vector data; + data.reserve(static_cast(rank)); + for (int32_t i = 0; i < rank; i++) + data.emplace_back(input_shape.dim(i)); + mir::TensorVariant tensor({mir::DataType::INT32, {rank}}, data.data()); + auto result = createOp(tensor); + return {result->getOutput(0)}; +} + +std::vector +TFLiteOpCreator::convertHardSwish(const tflite::HardSwishOptionsT *, + const std::vector &inputs) +{ + auto result = createOp(inputs[0])->getOutput(0); + return {result}; +} + +} // namespace mir_tflite diff --git a/compiler/mir-tflite-importer/tflite_op_creator.h b/compiler/mir/src/mir_tflite_importer/tflite_op_creator.h similarity index 100% rename from compiler/mir-tflite-importer/tflite_op_creator.h rename to compiler/mir/src/mir_tflite_importer/tflite_op_creator.h diff --git a/compiler/mir2loco/CMakeLists.txt b/compiler/mir2loco/CMakeLists.txt index 49bf3db..a8a096e 100644 --- a/compiler/mir2loco/CMakeLists.txt +++ b/compiler/mir2loco/CMakeLists.txt @@ -7,7 +7,6 @@ target_include_directories(mir2loco PRIVATE src) target_include_directories(mir2loco PUBLIC include) target_link_libraries(mir2loco PUBLIC mir) target_link_libraries(mir2loco PUBLIC loco) -target_link_libraries(mir2loco PRIVATE stdex) nnas_find_package(GTest QUIET) diff --git a/compiler/mir2loco/README.md b/compiler/mir2loco/README.md new file mode 100644 index 0000000..a11e104 --- /dev/null +++ b/compiler/mir2loco/README.md @@ -0,0 +1 @@ +# mir2loco diff --git a/compiler/mir2loco/src/mir2loco.cpp b/compiler/mir2loco/src/mir2loco.cpp index fc1f693..e1370fe 100644 --- a/compiler/mir2loco/src/mir2loco.cpp +++ b/compiler/mir2loco/src/mir2loco.cpp @@ -37,7 +37,7 @@ #include #include -#include +#include namespace mir2loco { @@ -54,7 +54,7 @@ template void setupShape(const mir::Shape &shape, NodeType *nod std::unique_ptr make_tensor_shape(const mir::Shape &shape) { - auto res = stdex::make_unique(); + auto res = std::make_unique(); setupShape(shape, res.get()); return std::move(res); } @@ -107,13 +107,13 @@ loco::Permutation createFeaturePermutation(mir::DataForma std::unique_ptr createFeatureEncoder(mir::DataFormat data_format) { auto perm = createFeaturePermutation(data_format); - return stdex::make_unique>(perm); + return std::make_unique>(perm); } std::unique_ptr createFeatureDecoder(mir::DataFormat data_format) { auto perm = createFeaturePermutation(data_format); - return stdex::make_unique>(perm); + return std::make_unique>(perm); } std::unique_ptr createOHWIFilterEncoder() @@ -123,7 +123,7 @@ std::unique_ptr createOHWIFilterEncoder() perm.axis(loco::FilterAxis::Height) = 1; perm.axis(loco::FilterAxis::Width) = 2; perm.axis(loco::FilterAxis::Depth) = 3; - return stdex::make_unique>(perm); + return std::make_unique>(perm); } std::unique_ptr createHWOIFilterEncoder() @@ -133,7 +133,7 @@ std::unique_ptr createHWOIFilterEncoder() perm.axis(loco::FilterAxis::Width) = 1; perm.axis(loco::FilterAxis::Count) = 2; perm.axis(loco::FilterAxis::Depth) = 3; - return stdex::make_unique>(perm); + return std::make_unique>(perm); } std::unique_ptr createHWIMDepthwiseFilterEncoder() @@ -143,7 +143,7 @@ std::unique_ptr createHWIMDepthwiseFilterEncoder() perm.axis(loco::DepthwiseFilterAxis::Width) = 1; perm.axis(loco::DepthwiseFilterAxis::Depth) = 2; perm.axis(loco::DepthwiseFilterAxis::Multiplier) = 3; - return stdex::make_unique>(perm); + return std::make_unique>(perm); } std::unique_ptr createIHWMDepthwiseFilterEncoder() @@ -153,7 +153,7 @@ std::unique_ptr createIHWMDepthwiseFilterEncoder() perm.axis(loco::DepthwiseFilterAxis::Height) = 1; perm.axis(loco::DepthwiseFilterAxis::Width) = 2; perm.axis(loco::DepthwiseFilterAxis::Multiplier) = 3; - return stdex::make_unique>(perm); + return std::make_unique>(perm); } std::unique_ptr createHWMatrixEncoder() @@ -161,7 +161,7 @@ std::unique_ptr createHWMatrixEncoder() loco::Permutation perm; perm.axis(loco::MatrixAxis::Height) = 0; perm.axis(loco::MatrixAxis::Width) = 1; - return stdex::make_unique>(perm); + return std::make_unique>(perm); } std::unique_ptr createHWMatrixDecoder() @@ -169,7 +169,7 @@ std::unique_ptr createHWMatrixDecoder() loco::Permutation perm; perm.axis(loco::MatrixAxis::Height) = 0; perm.axis(loco::MatrixAxis::Width) = 1; - return stdex::make_unique>(perm); + return std::make_unique>(perm); } loco::DataType convertDataType(mir::DataType data_type) diff --git a/compiler/moco-tf/requires.cmake b/compiler/moco-tf/requires.cmake index 751192f..3e0fabe 100644 --- a/compiler/moco-tf/requires.cmake +++ b/compiler/moco-tf/requires.cmake @@ -11,3 +11,4 @@ require("plier-tf") require("locoex-customop") require("logo") require("oops") +require("bino") diff --git a/compiler/moco-tf/src/Canonicalization/PadCanonicalizer.cpp b/compiler/moco-tf/src/Canonicalization/PadCanonicalizer.cpp index 10816f4..36136ae 100644 --- a/compiler/moco-tf/src/Canonicalization/PadCanonicalizer.cpp +++ b/compiler/moco-tf/src/Canonicalization/PadCanonicalizer.cpp @@ -58,7 +58,7 @@ bool canonicalize_pad(loco::Graph *graph, moco::TFPad *node) constant_node->size(1); constant_node->at(0) = 0.0f; - auto const_paddings_node = dynamic_cast(node->paddings()); + auto const_paddings_node = loco::must_cast(node->paddings()); // TODO: support S64 type. assert(const_paddings_node->dtype() == loco::DataType::S32); assert(const_paddings_node->rank() == 2); diff --git a/compiler/moco/import/src/Importer.cpp b/compiler/moco/import/src/Importer.cpp index 8d3ca6c..3813aff 100644 --- a/compiler/moco/import/src/Importer.cpp +++ b/compiler/moco/import/src/Importer.cpp @@ -126,8 +126,7 @@ void convert_graph(const moco::GraphBuilderSource &source, const moco::ModelSign auto graph_input = graph->inputs()->create(); - auto placeholder_node = dynamic_cast(node); - assert(placeholder_node != nullptr); + auto placeholder_node = loco::must_cast(node); graph_input->name(input.nodeName()); diff --git a/compiler/moco/import/src/Nodes/BiasAdd.test.cpp b/compiler/moco/import/src/Nodes/BiasAdd.test.cpp index 626456d..207045a 100644 --- a/compiler/moco/import/src/Nodes/BiasAdd.test.cpp +++ b/compiler/moco/import/src/Nodes/BiasAdd.test.cpp @@ -61,7 +61,7 @@ TEST(TensorFlowImport, bias_add_01) tester.output("out"); tester.run(nodedef, graphbuilder); - auto test_node = dynamic_cast(tester.output()); + auto test_node = loco::must_cast(tester.output()); ASSERT_NE(test_node, nullptr); ASSERT_TRUE(test_node->data_layout() == "NHWC"); } @@ -106,7 +106,7 @@ TEST(TensorFlowImport, bias_add_NCHW_axis) tester.output("out"); tester.run(nodedef, graphbuilder); - auto test_node = dynamic_cast(tester.output()); + auto test_node = loco::must_cast(tester.output()); ASSERT_NE(test_node, nullptr); ASSERT_TRUE(test_node->data_layout() == "NCHW"); } diff --git a/compiler/moco/import/src/Nodes/Concat.test.cpp b/compiler/moco/import/src/Nodes/Concat.test.cpp index c098657..30a7db7 100644 --- a/compiler/moco/import/src/Nodes/Concat.test.cpp +++ b/compiler/moco/import/src/Nodes/Concat.test.cpp @@ -72,7 +72,7 @@ TEST(TensorFlowImport, concat_01) tester.output("Concat"); tester.run(nodedef, graphbuilder); - auto test_node = dynamic_cast(tester.output()); + auto test_node = loco::must_cast(tester.output()); ASSERT_NE(test_node, nullptr); ASSERT_EQ(test_node->num_values(), 2); } @@ -128,7 +128,7 @@ TEST(TensorFlowImport, concat_02) tester.output("Concat"); tester.run(nodedef, graphbuilder); - auto test_node = dynamic_cast(tester.output()); + auto test_node = loco::must_cast(tester.output()); ASSERT_NE(test_node, nullptr); ASSERT_EQ(test_node->num_values(), 3); } diff --git a/compiler/moco/import/src/Nodes/Const.test.cpp b/compiler/moco/import/src/Nodes/Const.test.cpp index 854499f..5a9390b 100644 --- a/compiler/moco/import/src/Nodes/Const.test.cpp +++ b/compiler/moco/import/src/Nodes/Const.test.cpp @@ -79,7 +79,7 @@ TEST(TensorFlowImport, const_float_01) tester.output("const/float"); tester.run(nodedef, graphbuilder); - auto test_node = dynamic_cast(tester.output()); + auto test_node = loco::must_cast(tester.output()); ASSERT_NE(test_node, nullptr); ASSERT_EQ(test_node->size(), 6); ASSERT_EQ(test_node->at(0), 1.1f); @@ -142,7 +142,7 @@ TEST(TensorFlowImport, const_float_02) tester.output("const/float"); tester.run(nodedef, graphbuilder); - auto test_node = dynamic_cast(tester.output()); + auto test_node = loco::must_cast(tester.output()); ASSERT_NE(test_node, nullptr); ASSERT_EQ(test_node->size(), 6); ASSERT_EQ(test_node->at(0), 1.1f); @@ -206,7 +206,7 @@ TEST(TensorFlowImport, const_float_03) tester.output("const/float"); tester.run(nodedef, graphbuilder); - auto test_node = dynamic_cast(tester.output()); + auto test_node = loco::must_cast(tester.output()); ASSERT_NE(test_node, nullptr); ASSERT_EQ(test_node->size(), 6); ASSERT_EQ(test_node->at(0), 1.1f); @@ -270,7 +270,7 @@ TEST(TensorFlowImport, const_float_04) tester.output("const/float"); tester.run(nodedef, graphbuilder); - auto test_node = dynamic_cast(tester.output()); + auto test_node = loco::must_cast(tester.output()); ASSERT_NE(test_node, nullptr); ASSERT_EQ(test_node->size(), 6); ASSERT_EQ(test_node->at(0), 1.1f); @@ -334,7 +334,7 @@ TEST(TensorFlowImport, const_int32_04) tester.output("const/int"); tester.run(nodedef, graphbuilder); - auto test_node = dynamic_cast(tester.output()); + auto test_node = loco::must_cast(tester.output()); ASSERT_NE(test_node, nullptr); ASSERT_EQ(test_node->size(), 6); ASSERT_EQ(test_node->at(0), 1); @@ -391,7 +391,7 @@ TEST(TensorFlowImport, const_int32_scalar) tester.output("const/int"); tester.run(nodedef, graphbuilder); - auto test_node = dynamic_cast(tester.output()); + auto test_node = loco::must_cast(tester.output()); ASSERT_NE(test_node, nullptr); ASSERT_EQ(test_node->size(), 1); ASSERT_EQ(test_node->at(0), 3); @@ -453,7 +453,7 @@ TEST(TensorFlowImport, const_int8_01) tester.output("const/int8"); tester.run(nodedef, graphbuilder); - auto test_node = dynamic_cast(tester.output()); + auto test_node = loco::must_cast(tester.output()); ASSERT_NE(test_node, nullptr); ASSERT_EQ(test_node->size(), 6); ASSERT_EQ(test_node->at(0), 0); diff --git a/compiler/moco/import/src/Nodes/Mean.test.cpp b/compiler/moco/import/src/Nodes/Mean.test.cpp index 6321fad..4666c32 100644 --- a/compiler/moco/import/src/Nodes/Mean.test.cpp +++ b/compiler/moco/import/src/Nodes/Mean.test.cpp @@ -65,7 +65,7 @@ TEST(TensorFlowImport, mean_true) tester.output("Mean"); tester.run(nodedef, graphbuilder); - auto test_node = dynamic_cast(tester.output()); + auto test_node = loco::must_cast(tester.output()); ASSERT_NE(test_node, nullptr); ASSERT_EQ(test_node->keep_dims(), true); } @@ -114,7 +114,7 @@ TEST(TensorFlowImport, mean_false) tester.output("Mean"); tester.run(nodedef, graphbuilder); - auto test_node = dynamic_cast(tester.output()); + auto test_node = loco::must_cast(tester.output()); ASSERT_NE(test_node, nullptr); ASSERT_EQ(test_node->keep_dims(), false); } diff --git a/compiler/moco/import/src/Nodes/Pack.test.cpp b/compiler/moco/import/src/Nodes/Pack.test.cpp index 01774a9..3ee2745 100644 --- a/compiler/moco/import/src/Nodes/Pack.test.cpp +++ b/compiler/moco/import/src/Nodes/Pack.test.cpp @@ -73,7 +73,7 @@ TEST(TensorFlowImport, tf_pack_basic) tester.output("Pack"); tester.run(nodedef, graphbuilder); - auto test_node = dynamic_cast(tester.output()); + auto test_node = loco::must_cast(tester.output()); ASSERT_NE(test_node, nullptr); ASSERT_EQ(test_node->N(), 4); ASSERT_NE(test_node->values(0), nullptr); diff --git a/compiler/moco/import/src/Nodes/StridedSlice.test.cpp b/compiler/moco/import/src/Nodes/StridedSlice.test.cpp index b6959d7..dd3c133 100644 --- a/compiler/moco/import/src/Nodes/StridedSlice.test.cpp +++ b/compiler/moco/import/src/Nodes/StridedSlice.test.cpp @@ -95,7 +95,7 @@ TEST(TensorFlowImport, tf_stridedslice_basic) tester.output("StridedSlice"); tester.run(nodedef, graphbuilder); - auto test_node = dynamic_cast(tester.output()); + auto test_node = loco::must_cast(tester.output()); ASSERT_NE(test_node, nullptr); ASSERT_EQ(test_node->begin_mask(), 0); ASSERT_EQ(test_node->end_mask(), 0); diff --git a/compiler/moco/pass/src/Passes/ConstantFoldPack.cpp b/compiler/moco/pass/src/Passes/ConstantFoldPack.cpp index cc8a23d..105c96c 100644 --- a/compiler/moco/pass/src/Passes/ConstantFoldPack.cpp +++ b/compiler/moco/pass/src/Passes/ConstantFoldPack.cpp @@ -24,6 +24,8 @@ #include +#include + #include #include @@ -32,19 +34,6 @@ namespace { -// TODO move to loco -bool operator==(const loco::TensorShape &lhs, const loco::TensorShape &rhs) -{ - if (lhs.rank() != rhs.rank()) - return false; - for (uint32_t axis = 0; axis < lhs.rank(); ++axis) - { - if (!(lhs.dim(axis) == rhs.dim(axis))) - return false; - } - return true; -} - bool valid_axis_range(int32_t output_rank, int32_t pack_axis) { // check axis range in [-r-1, r+1) diff --git a/compiler/moco/pass/src/Passes/ConstantFoldStridedSlice.cpp b/compiler/moco/pass/src/Passes/ConstantFoldStridedSlice.cpp index 8be4764..3542e60 100644 --- a/compiler/moco/pass/src/Passes/ConstantFoldStridedSlice.cpp +++ b/compiler/moco/pass/src/Passes/ConstantFoldStridedSlice.cpp @@ -35,10 +35,9 @@ namespace loco::TensorShape calc_output_shape(moco::TFStridedSlice *node) { - auto const_input = dynamic_cast(node->input()); - auto const_begin = dynamic_cast(node->begin()); - auto const_end = dynamic_cast(node->end()); - auto const_strides = dynamic_cast(node->strides()); + auto const_input = loco::must_cast(node->input()); + auto const_begin = loco::must_cast(node->begin()); + auto const_end = loco::must_cast(node->end()); auto input_rank = const_input->rank(); auto output_rank = input_rank; loco::TensorShape output_shape_range; diff --git a/compiler/moco/pass/src/Passes/FuseBinaryIntoPreceding.cpp b/compiler/moco/pass/src/Passes/FuseBinaryIntoPreceding.cpp index 4a9631e..f97546a 100644 --- a/compiler/moco/pass/src/Passes/FuseBinaryIntoPreceding.cpp +++ b/compiler/moco/pass/src/Passes/FuseBinaryIntoPreceding.cpp @@ -471,8 +471,7 @@ bool fuse_to_preceding(loco::Graph *graph, moco::TFAdd *node) } // Let's fuse addparam into biasadd bias - auto biasadd_bias = dynamic_cast(biasadd->bias()); - assert(biasadd_bias != nullptr); + auto biasadd_bias = loco::must_cast(biasadd->bias()); if (!shape_match(biasadd_bias, addparam)) { // INFO(l) << "TFBiasAdd bias and TFAdd input shape mismatch"; diff --git a/compiler/moco/service/src/Service/TFShapeInferenceRule.cpp b/compiler/moco/service/src/Service/TFShapeInferenceRule.cpp index 6d122c8..9843415 100644 --- a/compiler/moco/service/src/Service/TFShapeInferenceRule.cpp +++ b/compiler/moco/service/src/Service/TFShapeInferenceRule.cpp @@ -473,8 +473,7 @@ public: auto input_shape = node_shape(node->input()); assert(input_shape.domain() == loco::Domain::Tensor); - auto const_paddings = dynamic_cast(node->paddings()); - assert(const_paddings); + auto const_paddings = loco::must_cast(node->paddings()); assert(const_paddings->dtype() == loco::DataType::S32); assert(const_paddings->rank() == 2); @@ -707,9 +706,9 @@ public: assert(node->ellipsis_mask() == 0); assert(node->shrink_axis_mask() == 1); - auto const_begin = dynamic_cast(node->begin()); - auto const_end = dynamic_cast(node->end()); - auto const_strides = dynamic_cast(node->strides()); + auto const_begin = loco::must_cast(node->begin()); + auto const_end = loco::must_cast(node->end()); + auto const_strides = loco::must_cast(node->strides()); assert(dynamic_cast(node->input()) != nullptr); assert(const_begin != nullptr); @@ -880,7 +879,7 @@ void TFShapeInferenceRule::infer(const Context *ctx, const loco::Node *node, Sin assert(dynamic_cast(node) != nullptr); ShapeInferenceAlgorithm alg{ctx}; - auto shape = dynamic_cast(node)->accept(&alg); + auto shape = loco::must_cast(node)->accept(&alg); if (shape.domain() == loco::Domain::Unknown) sink->fail(); diff --git a/compiler/moco/service/src/Service/TFTypeInferenceRule.cpp b/compiler/moco/service/src/Service/TFTypeInferenceRule.cpp index 112ab95..f168c80 100644 --- a/compiler/moco/service/src/Service/TFTypeInferenceRule.cpp +++ b/compiler/moco/service/src/Service/TFTypeInferenceRule.cpp @@ -98,7 +98,7 @@ bool TFTypeInferenceRule::infer(const loco::Node *node, loco::DataType &dtype) c #define TENSORFLOW_NODE(OPCODE,CLASS) \ if (dynamic_cast(node)) \ { \ - auto tfnode = dynamic_cast(node); \ + auto tfnode = loco::must_cast(node); \ dtype = tfnode->accept(&alg); \ assert(dtype != loco::DataType::Unknown); \ return true; \ diff --git a/compiler/nest/core/src/Block.test.cpp b/compiler/nest/core/src/Block.test.cpp index b40fbea..d8faa0b 100644 --- a/compiler/nest/core/src/Block.test.cpp +++ b/compiler/nest/core/src/Block.test.cpp @@ -30,12 +30,12 @@ TEST(BLOCK, use_case_1) { nest::Block block; - ASSERT_EQ(block.size(), 0); + ASSERT_EQ(0, block.size()); auto stmt = std::make_shared(); block.append(stmt); - ASSERT_EQ(block.size(), 1); - ASSERT_EQ(block.at(0), stmt); + ASSERT_EQ(1, block.size()); + ASSERT_EQ(stmt, block.at(0)); } diff --git a/compiler/nest/core/src/Bound.test.cpp b/compiler/nest/core/src/Bound.test.cpp index 7b2f0b6..a4c3d4d 100644 --- a/compiler/nest/core/src/Bound.test.cpp +++ b/compiler/nest/core/src/Bound.test.cpp @@ -22,6 +22,6 @@ TEST(BOUND, ctor) { const nest::Bound b{-10, 20}; - ASSERT_EQ(b.min(), -10); - ASSERT_EQ(b.max(), 20); + ASSERT_EQ(-10, b.min()); + ASSERT_EQ(20, b.max()); } diff --git a/compiler/nest/core/src/Closure.test.cpp b/compiler/nest/core/src/Closure.test.cpp index 1dae849..495e218 100644 --- a/compiler/nest/core/src/Closure.test.cpp +++ b/compiler/nest/core/src/Closure.test.cpp @@ -30,8 +30,8 @@ TEST(Closure, ctor) nest::DomainID dom_id{0}; nest::Closure closure{dom_id, std::make_shared()}; - ASSERT_EQ(closure.id().value(), 0); - ASSERT_EQ(closure.sub().rank(), 1); + ASSERT_EQ(0, closure.id().value()); + ASSERT_EQ(1, closure.sub().rank()); } TEST(Closure, cast) diff --git a/compiler/nest/core/src/Domain.test.cpp b/compiler/nest/core/src/Domain.test.cpp index 5f973ec..8d18459 100644 --- a/compiler/nest/core/src/Domain.test.cpp +++ b/compiler/nest/core/src/Domain.test.cpp @@ -36,6 +36,6 @@ TEST(_DOMAIN, base_usecase) nest::Closure clo = dom(std::make_shared<::expr::DummyNode>()); - ASSERT_EQ(clo.id(), dom_id); - ASSERT_EQ(clo.sub().rank(), 1); + ASSERT_EQ(dom_id, clo.id()); + ASSERT_EQ(1, clo.sub().rank()); } diff --git a/compiler/nest/core/src/DomainContext.test.cpp b/compiler/nest/core/src/DomainContext.test.cpp index 10882df..cc553ea 100644 --- a/compiler/nest/core/src/DomainContext.test.cpp +++ b/compiler/nest/core/src/DomainContext.test.cpp @@ -24,30 +24,30 @@ TEST(DOMAIN_CONTEXT, usecase) auto dom_0 = ctx.make({1, 3, 4}); - ASSERT_EQ(ctx.count(), 1); + ASSERT_EQ(1, ctx.count()); auto check_dom_0 = [&](void) { - ASSERT_EQ(ctx.info(dom_0).rank(), 3); - ASSERT_EQ(ctx.info(dom_0).dim(0), 1); - ASSERT_EQ(ctx.info(dom_0).dim(1), 3); - ASSERT_EQ(ctx.info(dom_0).dim(2), 4); + ASSERT_EQ(3, ctx.info(dom_0).rank()); + ASSERT_EQ(1, ctx.info(dom_0).dim(0)); + ASSERT_EQ(3, ctx.info(dom_0).dim(1)); + ASSERT_EQ(4, ctx.info(dom_0).dim(2)); }; check_dom_0(); auto dom_1 = ctx.make({7, 6, 2, 1}); - ASSERT_EQ(ctx.count(), 2); + ASSERT_EQ(2, ctx.count()); // Domain ID should be unique for each domain ASSERT_FALSE(dom_0.id() == dom_1.id()); auto check_dom_1 = [&](void) { - ASSERT_EQ(ctx.info(dom_1).rank(), 4); - ASSERT_EQ(ctx.info(dom_1).dim(0), 7); - ASSERT_EQ(ctx.info(dom_1).dim(1), 6); - ASSERT_EQ(ctx.info(dom_1).dim(2), 2); - ASSERT_EQ(ctx.info(dom_1).dim(3), 1); + ASSERT_EQ(4, ctx.info(dom_1).rank()); + ASSERT_EQ(7, ctx.info(dom_1).dim(0)); + ASSERT_EQ(6, ctx.info(dom_1).dim(1)); + ASSERT_EQ(2, ctx.info(dom_1).dim(2)); + ASSERT_EQ(1, ctx.info(dom_1).dim(3)); }; // make() SHOULD NOT affect the existing domain information diff --git a/compiler/nest/core/src/DomainID.test.cpp b/compiler/nest/core/src/DomainID.test.cpp index 6b1ce83..3a77050 100644 --- a/compiler/nest/core/src/DomainID.test.cpp +++ b/compiler/nest/core/src/DomainID.test.cpp @@ -22,7 +22,7 @@ TEST(DOMAIN_ID, ctor) { nest::DomainID id{0}; - ASSERT_EQ(id.value(), 0); + ASSERT_EQ(0, id.value()); } TEST(DOMAIN_ID, operator_eq) diff --git a/compiler/nest/core/src/DomainInfo.test.cpp b/compiler/nest/core/src/DomainInfo.test.cpp index 7a5d811..ddee683 100644 --- a/compiler/nest/core/src/DomainInfo.test.cpp +++ b/compiler/nest/core/src/DomainInfo.test.cpp @@ -22,9 +22,9 @@ TEST(DOMAIN_INFO, ctor) { nest::DomainInfo info{1, 2, 3, 4}; - ASSERT_EQ(info.rank(), 4); - ASSERT_EQ(info.dim(0), 1); - ASSERT_EQ(info.dim(1), 2); - ASSERT_EQ(info.dim(2), 3); - ASSERT_EQ(info.dim(3), 4); + ASSERT_EQ(4, info.rank()); + ASSERT_EQ(1, info.dim(0)); + ASSERT_EQ(2, info.dim(1)); + ASSERT_EQ(3, info.dim(2)); + ASSERT_EQ(4, info.dim(3)); } diff --git a/compiler/nest/core/src/Expr.test.cpp b/compiler/nest/core/src/Expr.test.cpp index 0c96f77..2e26c23 100644 --- a/compiler/nest/core/src/Expr.test.cpp +++ b/compiler/nest/core/src/Expr.test.cpp @@ -38,8 +38,8 @@ TEST(EXPR, operator_sum) auto add = expr->asAdd(); - ASSERT_EQ(add->lhs().get(), left.get()); - ASSERT_EQ(add->rhs().get(), right.get()); + ASSERT_EQ(left.get(), add->lhs().get()); + ASSERT_EQ(right.get(), add->rhs().get()); } TEST(EXPR, operator_mul) @@ -53,6 +53,6 @@ TEST(EXPR, operator_mul) auto add = expr->asMul(); - ASSERT_EQ(add->lhs().get(), left.get()); - ASSERT_EQ(add->rhs().get(), right.get()); + ASSERT_EQ(left.get(), add->lhs().get()); + ASSERT_EQ(right.get(), add->rhs().get()); } diff --git a/compiler/nest/core/src/FV.test.cpp b/compiler/nest/core/src/FV.test.cpp index 55f5f58..8bb061c 100644 --- a/compiler/nest/core/src/FV.test.cpp +++ b/compiler/nest/core/src/FV.test.cpp @@ -27,7 +27,7 @@ TEST(FV, var_expr) auto fvs = nest::FV::in(var); - ASSERT_EQ(fvs.size(), 1); + ASSERT_EQ(1, fvs.size()); ASSERT_NE(fvs.find(var.id()), fvs.end()); } @@ -40,7 +40,7 @@ TEST(FV, deref_expr) auto fvs = nest::FV::in(dom(var)); - ASSERT_EQ(fvs.size(), 1); + ASSERT_EQ(1, fvs.size()); ASSERT_NE(fvs.find(var.id()), fvs.end()); } @@ -53,7 +53,7 @@ TEST(FV, add_expr) auto fvs = nest::FV::in(v_0 + v_1); - ASSERT_EQ(fvs.size(), 2); + ASSERT_EQ(2, fvs.size()); ASSERT_NE(fvs.find(v_0.id()), fvs.end()); ASSERT_NE(fvs.find(v_1.id()), fvs.end()); } @@ -69,7 +69,7 @@ TEST(FV, mul_expr) auto fvs = nest::FV::in(v_0 * v_1); - ASSERT_EQ(fvs.size(), 2); + ASSERT_EQ(2, fvs.size()); ASSERT_NE(fvs.find(v_0.id()), fvs.end()); ASSERT_NE(fvs.find(v_1.id()), fvs.end()); } diff --git a/compiler/nest/core/src/Level.test.cpp b/compiler/nest/core/src/Level.test.cpp index b9e203d..cc447e8 100644 --- a/compiler/nest/core/src/Level.test.cpp +++ b/compiler/nest/core/src/Level.test.cpp @@ -22,7 +22,7 @@ TEST(LEVEL, constructor) { nest::Level lv{3}; - ASSERT_EQ(lv.value(), 3); + ASSERT_EQ(3, lv.value()); } TEST(LEVEL, operator_eq) diff --git a/compiler/nest/core/src/Module.test.cpp b/compiler/nest/core/src/Module.test.cpp index 01e414d..70a6c47 100644 --- a/compiler/nest/core/src/Module.test.cpp +++ b/compiler/nest/core/src/Module.test.cpp @@ -29,7 +29,7 @@ TEST(MODULE, create_var) auto check = [](const nest::Module &m) { // This code will invoke 'const VarContext &var(void) const' method - ASSERT_EQ(m.var().count(), 1); + ASSERT_EQ(1, m.var().count()); }; create(m); @@ -47,7 +47,7 @@ TEST(MODULE, create_domain) auto check = [](const nest::Module &m) { // This code will invoke 'const DomainContext &domain(void) const' method - ASSERT_EQ(m.domain().count(), 1); + ASSERT_EQ(1, m.domain().count()); }; create(m, {1, 3, 3}); @@ -66,7 +66,7 @@ TEST(MODULE, push) m.push(ifm(var_ch, var_row, var_col)); - ASSERT_EQ(m.block().size(), 1); + ASSERT_EQ(1, m.block().size()); ASSERT_NE(m.block().at(0)->asPush(), nullptr); } @@ -82,8 +82,8 @@ TEST(MODULE, ret) m.push(ifm(ind)); m.ret(ofm(ind)); - ASSERT_EQ(m.ret().id(), ofm.id()); - ASSERT_EQ(m.ret().sub().rank(), 1); + ASSERT_EQ(ofm.id(), m.ret().id()); + ASSERT_EQ(1, m.ret().sub().rank()); } TEST(MODULE, copy) @@ -95,5 +95,5 @@ TEST(MODULE, copy) orig.var().make(); - ASSERT_EQ(copy.var().count(), 0); + ASSERT_EQ(0, copy.var().count()); } diff --git a/compiler/nest/core/src/Ret.test.cpp b/compiler/nest/core/src/Ret.test.cpp index 703f049..a852235 100644 --- a/compiler/nest/core/src/Ret.test.cpp +++ b/compiler/nest/core/src/Ret.test.cpp @@ -32,8 +32,8 @@ TEST(RET, ctor) nest::Ret ret{dom_id, sub}; - ASSERT_EQ(ret.id().value(), 0); - ASSERT_EQ(ret.sub().rank(), 1); + ASSERT_EQ(0, ret.id().value()); + ASSERT_EQ(1, ret.sub().rank()); } TEST(RET, copy) @@ -48,11 +48,11 @@ TEST(RET, copy) nest::Ret dst{dst_id, dst_sub}; - ASSERT_EQ(dst.id().value(), 1); - ASSERT_EQ(dst.sub().rank(), 2); + ASSERT_EQ(1, dst.id().value()); + ASSERT_EQ(2, dst.sub().rank()); dst = src; - ASSERT_EQ(dst.id().value(), 0); - ASSERT_EQ(dst.sub().rank(), 1); + ASSERT_EQ(0, dst.id().value()); + ASSERT_EQ(1, dst.sub().rank()); } diff --git a/compiler/nest/core/src/Schedule.test.cpp b/compiler/nest/core/src/Schedule.test.cpp index 8f0ddb2..7994d7a 100644 --- a/compiler/nest/core/src/Schedule.test.cpp +++ b/compiler/nest/core/src/Schedule.test.cpp @@ -26,7 +26,7 @@ TEST(SCHEDULE, module) nest::Schedule sch{m}; - ASSERT_EQ(sch.level(var_1).value(), 0); + ASSERT_EQ(0, sch.level(var_1).value()); } TEST(SCHEDULE, module_copy) @@ -40,5 +40,5 @@ TEST(SCHEDULE, module_copy) // Update on 'm' does not affect the schedule m.var().make(); - ASSERT_EQ(sch.var().count(), 1); + ASSERT_EQ(1, sch.var().count()); } diff --git a/compiler/nest/core/src/Var.test.cpp b/compiler/nest/core/src/Var.test.cpp index 29f8795..aea8c5d 100644 --- a/compiler/nest/core/src/Var.test.cpp +++ b/compiler/nest/core/src/Var.test.cpp @@ -23,7 +23,7 @@ TEST(VAR, ctor) nest::VarID id{0}; nest::Var var{id}; - ASSERT_EQ(var.id(), id); + ASSERT_EQ(id, var.id()); } TEST(VAR, cast) @@ -34,5 +34,5 @@ TEST(VAR, cast) nest::Expr expr = var; ASSERT_NE(expr->asVar(), nullptr); - ASSERT_EQ(expr->asVar()->id(), id); + ASSERT_EQ(id, expr->asVar()->id()); } diff --git a/compiler/nest/core/src/VarContext.test.cpp b/compiler/nest/core/src/VarContext.test.cpp index 169bd61..d953f2f 100644 --- a/compiler/nest/core/src/VarContext.test.cpp +++ b/compiler/nest/core/src/VarContext.test.cpp @@ -32,15 +32,15 @@ TEST(VAR_CONTEXT, count) { nest::VarContext ctx; - ASSERT_EQ(ctx.count(), 0); + ASSERT_EQ(0, ctx.count()); auto var_0 = ctx.make(); - ASSERT_EQ(ctx.count(), 1); + ASSERT_EQ(1, ctx.count()); auto var_1 = ctx.make(); - ASSERT_EQ(ctx.count(), 2); + ASSERT_EQ(2, ctx.count()); } TEST(VAR_CONTEXT, bound_one) @@ -49,13 +49,13 @@ TEST(VAR_CONTEXT, bound_one) auto var_0 = ctx.make(); - ASSERT_EQ(ctx.bound(var_0).min(), 0); - ASSERT_EQ(ctx.bound(var_0).max(), 0); + ASSERT_EQ(0, ctx.bound(var_0).min()); + ASSERT_EQ(0, ctx.bound(var_0).max()); ctx.bound(var_0) = nest::Bound{-3, 5}; - ASSERT_EQ(ctx.bound(var_0).min(), -3); - ASSERT_EQ(ctx.bound(var_0).max(), 5); + ASSERT_EQ(-3, ctx.bound(var_0).min()); + ASSERT_EQ(5, ctx.bound(var_0).max()); } TEST(VAR_CONTEXT, bound_independent) @@ -64,19 +64,19 @@ TEST(VAR_CONTEXT, bound_independent) auto var_0 = ctx.make(); - ASSERT_EQ(ctx.bound(var_0).min(), 0); - ASSERT_EQ(ctx.bound(var_0).max(), 0); + ASSERT_EQ(0, ctx.bound(var_0).min()); + ASSERT_EQ(0, ctx.bound(var_0).max()); auto var_1 = ctx.make(); - ASSERT_EQ(ctx.bound(var_1).min(), 0); - ASSERT_EQ(ctx.bound(var_1).max(), 0); + ASSERT_EQ(0, ctx.bound(var_1).min()); + ASSERT_EQ(0, ctx.bound(var_1).max()); ctx.bound(var_0) = nest::Bound{-3, 5}; - ASSERT_EQ(ctx.bound(var_0).min(), -3); - ASSERT_EQ(ctx.bound(var_0).max(), 5); + ASSERT_EQ(-3, ctx.bound(var_0).min()); + ASSERT_EQ(5, ctx.bound(var_0).max()); - ASSERT_EQ(ctx.bound(var_1).min(), 0); - ASSERT_EQ(ctx.bound(var_1).max(), 0); + ASSERT_EQ(0, ctx.bound(var_1).min()); + ASSERT_EQ(0, ctx.bound(var_1).max()); } diff --git a/compiler/nest/core/src/VarID.test.cpp b/compiler/nest/core/src/VarID.test.cpp index e4a17a5..05e4b9a 100644 --- a/compiler/nest/core/src/VarID.test.cpp +++ b/compiler/nest/core/src/VarID.test.cpp @@ -22,7 +22,7 @@ TEST(VAR_ID, ctor) { nest::VarID id{0}; - ASSERT_EQ(id.value(), 0); + ASSERT_EQ(0, id.value()); } TEST(VAR_ID, operator_eq) diff --git a/compiler/nest/core/src/expr/AddNode.test.cpp b/compiler/nest/core/src/expr/AddNode.test.cpp index 5c44c47..dba6cc8 100644 --- a/compiler/nest/core/src/expr/AddNode.test.cpp +++ b/compiler/nest/core/src/expr/AddNode.test.cpp @@ -36,8 +36,8 @@ TEST(ADD_NODE, cast) std::shared_ptr base = derived; ASSERT_NE(derived.get(), nullptr); - ASSERT_EQ(base->asAdd(), derived.get()); + ASSERT_EQ(derived.get(), base->asAdd()); - ASSERT_EQ(derived->lhs().get(), left.get()); - ASSERT_EQ(derived->rhs().get(), right.get()); + ASSERT_EQ(left.get(), derived->lhs().get()); + ASSERT_EQ(right.get(), derived->rhs().get()); } diff --git a/compiler/nest/core/src/expr/DerefNode.test.cpp b/compiler/nest/core/src/expr/DerefNode.test.cpp index e02a7de..125d8bf 100644 --- a/compiler/nest/core/src/expr/DerefNode.test.cpp +++ b/compiler/nest/core/src/expr/DerefNode.test.cpp @@ -35,5 +35,5 @@ TEST(DEREF_NODE, cast) std::shared_ptr base = derived; ASSERT_NE(derived.get(), nullptr); - ASSERT_EQ(base->asDeref(), derived.get()); + ASSERT_EQ(derived.get(), base->asDeref()); } diff --git a/compiler/nest/core/src/expr/MulNode.test.cpp b/compiler/nest/core/src/expr/MulNode.test.cpp index b2d2947..85cb5a5 100644 --- a/compiler/nest/core/src/expr/MulNode.test.cpp +++ b/compiler/nest/core/src/expr/MulNode.test.cpp @@ -36,8 +36,8 @@ TEST(MUL_NODE, cast) std::shared_ptr base = derived; ASSERT_NE(derived.get(), nullptr); - ASSERT_EQ(base->asMul(), derived.get()); + ASSERT_EQ(derived.get(), base->asMul()); - ASSERT_EQ(derived->lhs().get(), left.get()); - ASSERT_EQ(derived->rhs().get(), right.get()); + ASSERT_EQ(left.get(), derived->lhs().get()); + ASSERT_EQ(right.get(), derived->rhs().get()); } diff --git a/compiler/nest/core/src/expr/Subscript.test.cpp b/compiler/nest/core/src/expr/Subscript.test.cpp index 2f187b8..cfcdd44 100644 --- a/compiler/nest/core/src/expr/Subscript.test.cpp +++ b/compiler/nest/core/src/expr/Subscript.test.cpp @@ -31,7 +31,7 @@ TEST(SUBSCRIPT, ctor) nest::expr::Subscript sub{expr_0, expr_1}; - ASSERT_EQ(sub.rank(), 2); - ASSERT_EQ(sub.at(0), expr_0); - ASSERT_EQ(sub.at(1), expr_1); + ASSERT_EQ(2, sub.rank()); + ASSERT_EQ(expr_0, sub.at(0)); + ASSERT_EQ(expr_1, sub.at(1)); } diff --git a/compiler/nest/core/src/expr/VarNode.test.cpp b/compiler/nest/core/src/expr/VarNode.test.cpp index e8b2764..9400551 100644 --- a/compiler/nest/core/src/expr/VarNode.test.cpp +++ b/compiler/nest/core/src/expr/VarNode.test.cpp @@ -31,7 +31,7 @@ TEST(VAR_NODE, ctor) auto node = make(4); // NOTE 'id' should be copied - ASSERT_EQ(node->id().value(), 4); + ASSERT_EQ(4, node->id().value()); } TEST(VAR_NODE, cast) @@ -43,5 +43,5 @@ TEST(VAR_NODE, cast) // NOTE Cast method should be overrided ASSERT_NE(derived.get(), nullptr); - ASSERT_EQ(base->asVar(), derived.get()); + ASSERT_EQ(derived.get(), base->asVar()); } diff --git a/compiler/nest/core/src/stmt/PushNode.test.cpp b/compiler/nest/core/src/stmt/PushNode.test.cpp index a54efbb..c02c692 100644 --- a/compiler/nest/core/src/stmt/PushNode.test.cpp +++ b/compiler/nest/core/src/stmt/PushNode.test.cpp @@ -33,5 +33,5 @@ TEST(STMT_PUSH_NODE, cast) std::shared_ptr base = derived; ASSERT_NE(derived.get(), nullptr); - ASSERT_EQ(base->asPush(), derived.get()); + ASSERT_EQ(derived.get(), base->asPush()); } diff --git a/compiler/nnc/CMakeLists.txt b/compiler/nnc/CMakeLists.txt index f899ffb..ab91cd4 100644 --- a/compiler/nnc/CMakeLists.txt +++ b/compiler/nnc/CMakeLists.txt @@ -14,7 +14,6 @@ configure_file(${NNC_ROOT_SRC_DIR}/include/Definitions.h.in # target for compiler executable add_executable(${NNC_TARGET_EXECUTABLE} ${NNC_DRIVER_DIR}/main.cpp ${NNC_DRIVER_DIR}/Driver.cpp ${NNC_DRIVER_DIR}/Options.cpp) -target_link_libraries(${NNC_TARGET_EXECUTABLE} PRIVATE stdex) # install compiler nnc_install_executable(${NNC_TARGET_EXECUTABLE}) diff --git a/compiler/nnc/cmake/config.cmake b/compiler/nnc/cmake/config.cmake index d9a1288..8623c8c 100644 --- a/compiler/nnc/cmake/config.cmake +++ b/compiler/nnc/cmake/config.cmake @@ -21,7 +21,8 @@ set(NNC_INSTALL_LIB_PATH ${NNC_INSTALL_PATH}/lib) # directory that contains othe # # find necessary packages # -find_package(HDF5 COMPONENTS CXX QUIET) +nnas_find_package(HDF5 QUIET) + # defines if hdf5 package was found if(HDF5_FOUND) set(NNC_HDF5_SUPPORTED ON) diff --git a/compiler/nnc/driver/Driver.cpp b/compiler/nnc/driver/Driver.cpp index 5b36962..995fa9b 100644 --- a/compiler/nnc/driver/Driver.cpp +++ b/compiler/nnc/driver/Driver.cpp @@ -49,7 +49,7 @@ #include #endif // NNC_FRONTEND_ONNX_ENABLED -#include +#include namespace nnc { @@ -169,19 +169,19 @@ void Driver::registerBackendSpecificPasses() if (cli::target == NNC_TARGET_ARM_CPP || cli::target == NNC_TARGET_X86_CPP) { - _passManager.registerPass(stdex::make_unique()); - _passManager.registerPass(stdex::make_unique(mir::DataFormat::NHWC)); + _passManager.registerPass(std::make_unique()); + _passManager.registerPass(std::make_unique(mir::DataFormat::NHWC)); } else if (cli::target == NNC_TARGET_ARM_GPU_CPP) { - _passManager.registerPass(stdex::make_unique()); - _passManager.registerPass(stdex::make_unique()); + _passManager.registerPass(std::make_unique()); + _passManager.registerPass(std::make_unique()); // TODO Change to DataFormat::NCHW when fix it in ACL - _passManager.registerPass(stdex::make_unique(mir::DataFormat::NHWC)); + _passManager.registerPass(std::make_unique(mir::DataFormat::NHWC)); } else if (cli::target == NNC_TARGET_INTERPRETER) { - _passManager.registerPass(stdex::make_unique(mir::DataFormat::NHWC)); + _passManager.registerPass(std::make_unique(mir::DataFormat::NHWC)); } else { diff --git a/compiler/nnc/requires.cmake b/compiler/nnc/requires.cmake index 8b460d9..2fcaea4 100644 --- a/compiler/nnc/requires.cmake +++ b/compiler/nnc/requires.cmake @@ -1,7 +1,3 @@ require("adtidas") -require("mir-caffe2-importer") -require("mir-caffe-importer") -require("mir-onnx-importer") +require("mir") require("mir-interpreter") -require("mir-tflite-importer") -require("stdex") diff --git a/compiler/nnc/tests/acl_soft_backend/CMakeLists.txt b/compiler/nnc/tests/acl_soft_backend/CMakeLists.txt index b33c1e6..69a3be8 100644 --- a/compiler/nnc/tests/acl_soft_backend/CMakeLists.txt +++ b/compiler/nnc/tests/acl_soft_backend/CMakeLists.txt @@ -29,7 +29,8 @@ if(NOT DEFINED ENV{ODROID_H5_DIR}) return() endif() -find_package(HDF5 COMPONENTS CXX REQUIRED) +nnas_find_package(HDF5 QUIET) + nnas_find_package(GTest REQUIRED) # Provide the test suite with the information where to locate executalbes to run etc. diff --git a/compiler/nnc/tests/acl_soft_backend/artifact_cmake/CMakeLists.txt b/compiler/nnc/tests/acl_soft_backend/artifact_cmake/CMakeLists.txt index ceecded..b0a805a 100644 --- a/compiler/nnc/tests/acl_soft_backend/artifact_cmake/CMakeLists.txt +++ b/compiler/nnc/tests/acl_soft_backend/artifact_cmake/CMakeLists.txt @@ -11,7 +11,8 @@ set(ODROID_ACL_BUILD_DIR ${ODROID_ACL_DIR}/build) find_library(OPEN_CL OpenCL /usr/lib/arm-linux-gnueabihf) find_library(ARM_COMPUTE arm_compute PATHS ${ODROID_ACL_BUILD_DIR}) find_library(ARM_COMPUTE_CORE arm_compute_core PATHS ${ODROID_ACL_BUILD_DIR}) -find_package(HDF5 COMPONENTS CXX REQUIRED) +nnas_find_package(HDF5 QUIET) + add_executable(nnc_test main.cpp AclArtifact.cpp) diff --git a/compiler/nnc/utils/caffe_model_maker/AllFill.sh b/compiler/nnc/utils/caffe_model_maker/AllFill.sh deleted file mode 100755 index 93e38d1..0000000 --- a/compiler/nnc/utils/caffe_model_maker/AllFill.sh +++ /dev/null @@ -1,48 +0,0 @@ -#!/bin/sh -: ' -Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -' - - -#Fills all models and writes errors -usage () { - echo "Filler.sh should be in the working directory\nusage: - no args - assumes current directory - -d= fills models in - Example: - $(basename $0) -d='./foobar/'" -} - -DIR="./" -for i in "$@" -do - case $i in - -h|--help|help) - usage - exit 1 - ;; - -d=*) - DIR=${i#*=} - ;; - esac - shift -done -echo $DIR -if [ $# -eq 0 ]; then - echo "Assume working directory" -fi -for a in `ls $DIR*.prototxt`; do - ./Filler.sh $a -done 2>error.log diff --git a/compiler/nnc/utils/caffe_model_maker/Filler.sh b/compiler/nnc/utils/caffe_model_maker/Filler.sh deleted file mode 100755 index 963edbf..0000000 --- a/compiler/nnc/utils/caffe_model_maker/Filler.sh +++ /dev/null @@ -1,28 +0,0 @@ -#!/bin/sh -: ' -Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -' - -#Fills $1 with random weights -if [ $# -eq 0 ] - then - echo "usage:\n $(basename $0) foo.prototxt" - exit 1 -fi -FN=$1 -NOEXT=${FN%%.*} # filename without the extension -mkdir $NOEXT -caffegen init < $FN > $NOEXT/filled.prototxt -caffegen encode < $NOEXT/filled.prototxt > $NOEXT/model.caffemodel diff --git a/compiler/nnc/utils/caffe_model_maker/GenerateCaffeModels.py b/compiler/nnc/utils/caffe_model_maker/GenerateCaffeModels.py deleted file mode 100755 index ca8b377..0000000 --- a/compiler/nnc/utils/caffe_model_maker/GenerateCaffeModels.py +++ /dev/null @@ -1,722 +0,0 @@ -#!/usr/bin/python3 -""" -Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -""" - -import caffe -import numpy as np -import sys -import h5py -from itertools import chain -from caffe import layers as L -import random -import lmdb -from collections import Counter, OrderedDict - -if (len(sys.argv) < 2): - dest_folder = '' - print('Using current directory as destination folder') -else: - dest_folder = sys.argv[1] + '/' - - -class PH: - """ - PlaceHolder value - """ - - def __init__(self, type, param): - self.type = type - self.param = param - - -# Bookkeeping -LS = 224 -# bynaryProto file for Infogain -H = np.eye(3, dtype='f4') -blob = caffe.io.array_to_blobproto(H.reshape((1, 1, 3, 3))) -with open(dest_folder + 'infogainH.binaryproto', 'wb+') as f: - f.write(blob.SerializeToString()) - -# List of hdf5 files -with open(dest_folder + "in", 'w+') as f: - f.write('in.hdf5') - -#Window File -with open(dest_folder + "in_winds", 'w+') as f: - f.write("""# 1 -in.jpg -3 -224 -224 -2 -1 0.1 50 50 60 70 -1 0.9 30 30 50 50 -# 2 -in.jpg -3 -224 -224 -2 -1 0.1 50 50 70 70 -1 0.9 30 30 50 50 -""") - -# HDF5 file for HDF5DataSet -h5f = h5py.File(dest_folder + "in.hdf5", "w") -h5f.create_dataset("data", data=np.random.rand(1, 3, LS, LS)) -h5f.close() - -# LMDB file -env = lmdb.open(dest_folder + 'test-lmdb') -with env.begin(write=True) as txn: - img_data = np.random.rand(3, LS, LS) - datum = caffe.io.array_to_datum(img_data, label=1) - txn.put('{:0>10d}'.format(1).encode('ascii'), datum.SerializeToString()) -env.close() - -# recurring parameters -losspara = {'ignore_label': True, 'normalization': 1, 'normalize': True} -softmaxpara = {'engine': 0, 'axis': 1} -gdfil = {'type': 'gaussian', 'std': 0.001} -cofil = {'type': 'constant', 'value': 0} -rp = { - 'num_output': 1, - 'weight_filler': gdfil, - 'bias_filler': cofil, - 'expose_hidden': True -} - -filler_par = { - 'type': 'constant', - 'value': 0, - 'min': 0, - 'max': 1, - 'mean': 0, - 'std': 1, - 'sparse': -1, # -1 means no sparsification - 'variance_norm': 0 -} # 0 = FAN_IN, 1 = FAN_OUT, 2 = AVERAGE - -OPS = [ - ('Parameter', { - 'shape': { - 'dim': [1] - }, - "is_data": True - }), # ok - ( - 'Data', - { - 'source': 'test-lmdb', # FIXME: unknown DB backend - 'batch_size': 1, - 'rand_skip': 0, - 'backend': 1, # 0 = LEVELDB, 1 = LMDB - 'scale': 1.0, # deprecated in favor of TransformationParameter - 'mean_file': 'wtf.is_that', - 'crop_size': 0, - 'mirror': False, - 'force_encoded_color': False, - 'prefetch': 4, - "is_data": True - }), - ( - 'DummyData', - { - 'data_filler': cofil, # ok - #'num' : [1,1,1], # deprecated shape specification - #'channels' : [2,2,2], - #'height' : [3,3,3], - #'width' : [4,4,4]}, - 'shape': { - 'dim': [1, 3, LS, LS] - }, - "is_data": True - }), - ( - 'ImageData', - { - 'source': 'in_imgs', # file with list of imgs - 'top': 'op2', - 'batch_size': 1, - 'rand_skip': 0, - 'shuffle': False, - 'new_height': 0, - 'new_width': 0, - 'is_color': True, - 'root_folder': '', - 'scale': 1.0, # deprecated in favor of TransformationParameter - 'mirror': False, - "is_data": True - }), - ( - 'WindowData', - { - 'source': 'in_winds', - 'top': 'op2', - 'batch_size': 1, - 'mean_file': 'in.jpg', - 'transform_param': { - 'scale': 0.8, - 'crop_size': 24, - 'mirror': False, - #'fg_treshold' : 0.5, - #'bg_treshold' : 0.5, - #'fg_fraction' : 0.25, - }, - 'context_pad': 1, - 'crop_mode': 'warp', - 'cache_images': True, - 'root_folder': './', - "is_data": True - }), - ( - 'HDF5Data', - { - 'source': 'in', # This is the name of the file WITH HDF5 FILENAMES 0_0 - # Top should have the same name as the dataset in the hdf5 file - # FIXME Requires Caffegen to be built with Caffe that supports LMDB - 'batch_size': 1, - 'shuffle': False, - "is_data": True - }), - ('Input', { - 'shape': { - 'dim': [1, 2, 3, 4] - }, - "is_data": True - }), # ok - ( - 'MemoryData', - { - 'batch_size': 1, # ok - 'channels': 2, - 'height': 3, - 'width': 4, - 'top': "foo", - "is_data": True - }), - - ## Regular OPS - ( - "Convolution", - { - 'num_output': 64, # ok - 'kernel_size': 9, - 'stride': 1, - 'pad': 0, - 'weight_filler': gdfil, - 'param': [{ - 'lr_mult': 1 - }, { - 'lr_mult': 0.1 - }], - 'bias_filler': cofil - }), - - # Depthvise conv - ( - "Convolution", - { - 'num_output': 12, # ok - 'kernel_size': 9, - 'stride': 1, - 'dilation': 2, - 'group': 3, - 'pad': 0, - 'weight_filler': gdfil, - 'param': [{ - 'lr_mult': 1 - }, { - 'lr_mult': 0.1 - }], - 'bias_filler': cofil - }), - ( - "Deconvolution", - { - 'convolution_param': # ok - { - 'num_output': 4, - 'kernel_size': 9, - 'stride': 1, - 'pad': 0, - 'weight_filler': gdfil, - 'bias_filler': cofil - } - }), - # Depthvise deconv - ( - "Deconvolution", - { - 'convolution_param': # ok - { - 'num_output': 12, - 'kernel_size': 9, - 'stride': 1, - 'dilation': 2, - 'group': 3, - 'pad': 0, - 'weight_filler': gdfil, - 'bias_filler': cofil - } - }), - ( - 'BatchNorm', - { - 'eps': 1e-5, # ok - 'moving_average_fraction': 0.999 - }), - ( - 'LRN', - { - 'alpha': 1., # ok - 'beta': 0.75, - 'norm_region': 1, - 'local_size': 5, - 'k': 1, - 'engine': 0 - }), - # local_size[default 5]: the number of channels to sum over - # alpha[default 1]: the scaling paramete - # beta[default5]: the exponent - # norm_region[default ACROSS_CHANNLS]: whether to sum over adjacent channels(ACROSS_CHANNLS) or nearby - # spatial locations(WITHIN_CHANNLS) - # `input / (1 + (\alpha/n) \sum_i x_i^2)^\beta` - ( - "MVN", - { - 'normalize_variance': True, # ok - 'across_channels': False, - 'eps': 1e-9 - }), - ( - 'Im2col', - { - 'convolution_param': # ok - { - 'num_output': 64, - 'kernel_size': 9, - 'stride': 1, - 'pad': 0, - 'weight_filler': gdfil, - # 'param' : [{'lr_mult':1},{'lr_mult':0.1}], - 'bias_filler': cofil - } - }), - ('Dropout', { - 'dropout_ratio': 0.5 - }), # ok - ('Split', {}), # ok - ('Concat', { - 'axis': 1 - }), # ok - ( - 'Tile', - { - 'axis': 1, # ok - 'tiles': 2 - }), - ('Slice', { - 'axis': 1, - 'top': 'op2', - 'slice_point': 1 - }), - ( - 'Reshape', - { - 'shape': { - 'dim': [1, 0, -1] - }, # ok - 'axis': 0, - 'num_axes': -1 - }), - # reshapes only [axis, axis + num_axes] if those aren't 0 and -1; axis can be negative - # 0 in shape means retaining dim size, -1 means auto size - ( - 'Flatten', - { - 'axis': 1, # ok - 'end_axis': -1 - }), - ( - 'Pooling', - { - 'pool': 0, # ok # pool: 0 = MAX, 1 = AVE, 2 = STOCHASTIC - 'pad': 0, # can be replaced with pad_w, pad_h - 'kernel_size': 3, # can be replaced with kernel_w, kernel_h - 'stride': 1, # can be replaced with stride_w, stride_h - 'engine': 0, - 'global_pooling': False - }), - # 'round_mode' : 0}), # 0 = CELS, 1 = FLOOR - ( - 'Reduction', - { - 'operation': 1, # ok # 1 = SUM, 2 = ASUM, 3 = SUMSQ, 4 = MEAN # ok - 'axis': 0, - 'coeff': 1.0 - }), - ( - 'SPP', - { - 'pyramid_height': 1, # ok - 'pool': 0, - 'engine': 0 - }), - ( - 'InnerProduct', - { - 'num_output': 2, # ok - 'bias_term': True, - 'weight_filler': filler_par, - 'bias_filler': filler_par, - 'axis': 1, - 'transpose': False - }), - ( - 'Embed', - { - 'num_output': 2, # ok - 'input_dim': 1, - 'bias_term': True, - 'weight_filler': filler_par, - 'bias_filler': filler_par - }), - ( - 'ArgMax', - { - 'out_max_val': False, # ok # if True, outputs pairs (argmax, maxval) # ok - 'top_k': 1, - 'axis': -1 - }), - ( - 'Softmax', - { - 'engine': 0, # ok - 'axis': 1 - }), - ( - 'ReLU', - { - 'negative_slope': 0, # ok - 'engine': 0 - }), - ( - 'PReLU', - { - 'filler': filler_par, # ok - 'channel_shared': False - }), - ('ELU', { - 'alpha': 1 - }), # ok - ('Sigmoid', { - 'engine': 0 - }), # ok - ('BNLL', {}), # ok - ('TanH', { - 'engine': 0 - }), # ok - ('Threshold', { - 'threshold': 0 - }), # ok - ( - 'Bias', - { - 'axis': 0, # ok - 'num_axes': -1, - 'filler': filler_par - }), - ( - 'Scale', - { - 'axis': 0, # ok - 'num_axes': -1, - 'filler': filler_par, - 'bias_term': False, - 'bias_filler': filler_par - }), - ('AbsVal', {}), # ok - ( - 'Log', - { - 'base': -1.0, # ok - 'scale': 1.0, - 'shift': PH(float, (2.0, 10.0)), - 'how_many': 10 - }), # y = ln(shift + scale * x) (log_base() for base > 0) - ( - 'Power', - { - 'power': -1.0, # ok - 'scale': 1.0, - 'shift': 0.0 - }), # y = (shift + scale * x) ^ power - ( - 'Exp', - { - 'base': -1.0, # ok - 'scale': 1.0, - 'shift': 0.0 - }), - - ## TWO INPUTS - ( - 'Crop', - { - 'axis': 2, # ok - 'offset': [0], - "inputs": 2 - }), # if one offset - for all dims, more - specifies - ( - "Eltwise", - { - 'operation': 1, # ok - 'coeff': [3, 3], - 'stable_prod_grad': True, - "inputs": 2 - }), - ("EuclideanLoss", { - "inputs": 2 - }), # ok - ("HingeLoss", { - 'norm': 1, - "inputs": 2 - }), # L1 = 1; L2 = 2; # ok - ("SigmoidCrossEntropyLoss", { - 'loss_param': losspara, - "inputs": 2 - }), # ok - - ## TWO Inputs, special shape - ( - "Accuracy", - { - 'top_k': 1, # FIXME: different bottom shapes needed - 'axis': 0, - 'ignore_label': 0, - "inputs": 2, - "special_shape": [1, 3, 1, 1] - }), - ( - "SoftmaxWithLoss", - { - 'loss_param': losspara, # FIXME: different bottom shapes needed - 'softmax_param': softmaxpara, - "inputs": 2, - "special_shape": [1, 1, 1, 1] - }), - ("MultinomialLogisticLoss", { - 'loss_param': losspara, - "inputs": 2, - "special_shape": [1, 1, 1, 1] - }), # FIXME: different bottom shapes needed - ("Filter", { - "inputs": 2, - "special_shape": [1, 1, 1, 1] - }), # FIXME: different bottom shapes needed - ('BatchReindex', { - "inputs": 2, - "special_shape": [2] - }), # takes indices as second blob - ("InfogainLoss", { - 'source': 'infogainH.binaryproto', - 'axis': 1, - "inputs": 2, - "special_shape": [1, 1, 1, 1] - }), - ( - 'Python', - { - 'python_param': # Custom Loss layer - { - 'module': 'Pyloss', # the module name -- usually the filename -- that needs to be in $PYTHONPATH - 'layer': 'EuclideanLossLayer', # the layer name -- the class name in the module - 'share_in_parallel': False - }, - # set loss weight so Caffe knows this is a loss layer. - # since PythonLayer inherits directly from Layer, this isn't automatically - # known to Caffe - 'loss_weight': 1, - "inputs": 2, - "special_shape": [1, 3, 1, 1] - }, - ), - - ## NOTOP OPS - ('HDF5Output', { - 'file_name': 'out.hdf5', - "inputs": 2, - "is_notop": True - }), # ok - ('Silence', { - "inputs": 2, - "is_notop": True - }), # ok, need to remove tops - - ## THREE INPUTS - ("RNN", { - 'recurrent_param': rp, - 'top': "out2", - "inputs": 3 - }), # ok - ("Recurrent", { - 'recurrent_param': rp, - 'top': "out2", - "inputs": 3 - }), # ok - - ## FOUR INPUTS - ("LSTM", { - 'recurrent_param': rp, - 'top': ["out2", "out3"], - "inputs": 4 - }), # ok - - ## Handled explicitly (special case) - ("ContrastiveLoss", { - 'margin': 1.0, - 'legacy_version': False - }), -] - -#Helper functions - - -def traverse(obj, callback=None): - """ - walks a nested dict/list recursively - :param obj: - :param callback: - :return: - """ - if isinstance(obj, dict): - value = {k: traverse(v, callback) for k, v in obj.items()} - elif isinstance(obj, list): - value = [traverse(elem, callback) for elem in obj] - else: - value = obj - - if callback is None: - return value - else: - return callback(value) - - -def mock(inp): - if not (isinstance(inp, PH)): return inp - if inp.type == int: - return random.randint(*inp.param) - if inp.type == float: - return random.uniform(*inp.param) - - -EXTRA_SHAPES = \ - [(), # alredy defined - [1, 3], - [1, 3, 1], - [1, 3, 1]] - - -class Layer: - """ - Represents a caffe layer - """ - - def __init__(self, name, params): - self.name = name - self.args = params - if self.args == None: self.args = dict() - self.num_inp = self.args.pop("inputs", 1) - self.num_out = self.args.pop("outputs", 1) - self.special_shape = self.args.pop("special_shape", - False) # 2nd input has special shape - self.is_data = self.args.pop("is_data", False) - self.is_notop = self.args.pop("is_notop", False) - - def make_net(self): - """ - Creates a protobuf network - :return: - """ - net = caffe.NetSpec() - - if self.is_data: - net.data = getattr(L, self.name)(**self.args) - - # Very special, - elif self.name == "ContrastiveLoss": - net.data = L.Input(shape={'dim': [1, 4]}) - net.data1 = L.DummyData(data_filler=cofil, shape={'dim': [1, 4]}) - net.data2 = L.DummyData(data_filler=cofil, shape={'dim': [1, 1]}) - - net.op = getattr(L, self.name)(net.data, net.data1, net.data2, **self.args) - - # this covers most cases - else: - net.data = L.Input(shape={'dim': [1, 3, LS, LS]}) - if self.num_inp == 2: - net.data1 = L.DummyData(data_filler=cofil, shape={'dim': [1, 3, LS, LS]}) - elif self.num_inp > 2: - for i in range(1, self.num_inp): - setattr( - net, "data" + str(i), - L.DummyData(data_filler=cofil, shape={'dim': EXTRA_SHAPES[i]})) - if self.special_shape: - net.data = L.Input(shape={'dim': [1, 3, 1, 1]}) - net.data1 = L.DummyData( - data_filler=cofil, shape={'dim': self.special_shape}) - - net.op = getattr(L, self.name)( - net.data, - *[getattr(net, "data" + str(i)) - for i in range(1, self.num_inp)], **self.args) - - if self.is_notop: - net.op.fn.tops = OrderedDict() - net.op.fn.ntop = 0 # the messing about in question - - return net - - -class LayerMaker: - """ - Factory class for Layer - """ - - def __init__(self, params): - self.name, self.args = params - self.how_many = self.args.pop("how_many", 1) - - def make(self): - return [Layer(self.name, traverse(self.args, mock)) for i in range(self.how_many)] - - -layer_gen = chain(*map(lambda para: LayerMaker(para).make(), OPS)) - -filename = dest_folder + '{}_{}.prototxt' - -counter = Counter() -for layer in layer_gen: - n = layer.make_net() - counter[layer.name] += 1 - - with open(filename.format(layer.name, counter[layer.name] - 1), 'w+') as ptxt_file: - print(n.to_proto(), file=ptxt_file) - - if layer.name == "Python": # Special case for python layer - with open("Python_0.caffemodel", 'wb+') as caffemodelFile: - caffemodelFile.write(n.to_proto().SerializeToString()) diff --git a/compiler/nnc/utils/caffe_model_maker/Pyloss.py b/compiler/nnc/utils/caffe_model_maker/Pyloss.py deleted file mode 100755 index e3f7817..0000000 --- a/compiler/nnc/utils/caffe_model_maker/Pyloss.py +++ /dev/null @@ -1,83 +0,0 @@ -""" -COPYRIGHT - -All contributions by the University of California: -Copyright (c) 2014-2017 The Regents of the University of California (Regents) -All rights reserved. - -All other contributions: -Copyright (c) 2014-2017, the respective contributors -All rights reserved. - -Caffe uses a shared copyright model: each contributor holds copyright over -their contributions to Caffe. The project versioning records all such -contribution and copyright details. If a contributor wants to further mark -their specific copyright on a particular contribution, they should indicate -their copyright solely in the commit message of the change when it is -committed. - -LICENSE - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. -2. Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR -ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -CONTRIBUTION AGREEMENT - -By contributing to the BVLC/caffe repository through pull-request, comment, -or otherwise, the contributor releases their content to the -license and copyright terms herein. -""" -import caffe -import numpy as np - - -class EuclideanLossLayer(caffe.Layer): - """ - Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer - to demonstrate the class interface for developing layers in Python. - """ - - def setup(self, bottom, top): - # check input pair - if len(bottom) != 2: - raise Exception("Need two inputs to compute distance.") - - def reshape(self, bottom, top): - # check input dimensions match - if bottom[0].count != bottom[1].count: - raise Exception("Inputs must have the same dimension.") - # difference is shape of inputs - self.diff = np.zeros_like(bottom[0].data, dtype=np.float32) - # loss output is scalar - top[0].reshape(1) - - def forward(self, bottom, top): - self.diff[...] = bottom[0].data - bottom[1].data - top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2. - - def backward(self, top, propagate_down, bottom): - for i in range(2): - if not propagate_down[i]: - continue - if i == 0: - sign = 1 - else: - sign = -1 - bottom[i].diff[...] = sign * self.diff / bottom[i].num diff --git a/compiler/nnc/utils/caffe_model_maker/README.md b/compiler/nnc/utils/caffe_model_maker/README.md deleted file mode 100644 index e34a769..0000000 --- a/compiler/nnc/utils/caffe_model_maker/README.md +++ /dev/null @@ -1,22 +0,0 @@ -# Utils -Caffe model generation helpers - -REQUIRES: - -* caffe -* h5py -* lmdb -* numpy -* caffegen in `$PATH` - -`GenerateCaffeModels.py` creates `*.prototxt` files for 1 and 2 layer caffe models -The generator can create multiple examples of any layer, assuming you add a -`how_many` field into the layer's dict. You will also need to replace the constants in said dict with `PH(type, param)` values, where `type` is the type of the placeholder variable -and `params` is a list (or tuple) of paramenters for generating the mock. - -For an example of generating multiple instances of a layer see the `Log` layer. - -`Filler.sh` fills a single model with random weights by using `caffegen` and creates a dir with a filled `prototxt` and a `caffemodel` binary file. The result directory is located in the same directory as the `prototxt` file - -`AllFill.sh` fills all `*.prototxt` files in the current directory or in provided directory -(-d) diff --git a/compiler/nnkit-caffe/README.md b/compiler/nnkit-caffe/README.md new file mode 100644 index 0000000..c5d8bb2 --- /dev/null +++ b/compiler/nnkit-caffe/README.md @@ -0,0 +1 @@ +# nnkit-caffe diff --git a/compiler/nnkit-mocotf/README.md b/compiler/nnkit-mocotf/README.md new file mode 100644 index 0000000..e81692c --- /dev/null +++ b/compiler/nnkit-mocotf/README.md @@ -0,0 +1 @@ +# nnkit-mocotf diff --git a/compiler/nnkit-onnxrt/README.md b/compiler/nnkit-onnxrt/README.md new file mode 100644 index 0000000..ccc948c --- /dev/null +++ b/compiler/nnkit-onnxrt/README.md @@ -0,0 +1 @@ +# nnkit-onnxrt diff --git a/compiler/nnkit-tf/README.md b/compiler/nnkit-tf/README.md new file mode 100644 index 0000000..82d279b --- /dev/null +++ b/compiler/nnkit-tf/README.md @@ -0,0 +1 @@ +# nnkit-tf diff --git a/compiler/nnkit-tflite/CMakeLists.txt b/compiler/nnkit-tflite/CMakeLists.txt index d1dbbd7..2ca9a13 100644 --- a/compiler/nnkit-tflite/CMakeLists.txt +++ b/compiler/nnkit-tflite/CMakeLists.txt @@ -1,4 +1,4 @@ -nnas_find_package(TensorFlowLite QUIET EXACT 1.12) +nnas_find_package(TensorFlowLite QUIET EXACT 1.13.1) if(NOT TensorFlowLite_FOUND) return() diff --git a/compiler/nnkit-tflite/README.md b/compiler/nnkit-tflite/README.md new file mode 100644 index 0000000..6d294f6 --- /dev/null +++ b/compiler/nnkit-tflite/README.md @@ -0,0 +1 @@ +# nnkit-tflite diff --git a/compiler/nnkit-tflite/backend/Backend.cpp b/compiler/nnkit-tflite/backend/Backend.cpp index 7d76606..08ba338 100644 --- a/compiler/nnkit-tflite/backend/Backend.cpp +++ b/compiler/nnkit-tflite/backend/Backend.cpp @@ -16,8 +16,8 @@ #include "nnkit/support/tflite/AbstractBackend.h" -#include -#include +#include +#include #include diff --git a/compiler/nnkit-tflite/support/CMakeLists.txt b/compiler/nnkit-tflite/support/CMakeLists.txt index 0a3e2fb..90d6948 100644 --- a/compiler/nnkit-tflite/support/CMakeLists.txt +++ b/compiler/nnkit-tflite/support/CMakeLists.txt @@ -1,10 +1,10 @@ file(GLOB_RECURSE SOURCES "src/*.cpp") -# TODO Rename nnkit_support_tflite-1.12 as nnkit_tflite_support-1.12 -add_library(nnkit_support_tflite-1.12 STATIC ${SOURCES}) -set_target_properties(nnkit_support_tflite-1.12 PROPERTIES POSITION_INDEPENDENT_CODE ON) -target_include_directories(nnkit_support_tflite-1.12 PUBLIC include) -target_link_libraries(nnkit_support_tflite-1.12 nnkit_intf_backend) -target_link_libraries(nnkit_support_tflite-1.12 tensorflowlite-1.12) +# TODO Rename nnkit_support_tflite-1.13.1 as nnkit_tflite_support-1.13.1 +add_library(nnkit_support_tflite-1.13.1 STATIC ${SOURCES}) +set_target_properties(nnkit_support_tflite-1.13.1 PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_include_directories(nnkit_support_tflite-1.13.1 PUBLIC include) +target_link_libraries(nnkit_support_tflite-1.13.1 nnkit_intf_backend) +target_link_libraries(nnkit_support_tflite-1.13.1 tensorflowlite-1.13.1) -add_library(nnkit_support_tflite ALIAS nnkit_support_tflite-1.12) +add_library(nnkit_support_tflite ALIAS nnkit_support_tflite-1.13.1) diff --git a/compiler/nnkit-tflite/support/include/nnkit/support/tflite/AbstractBackend.h b/compiler/nnkit-tflite/support/include/nnkit/support/tflite/AbstractBackend.h index d2f6aa9..7b89c7f 100644 --- a/compiler/nnkit-tflite/support/include/nnkit/support/tflite/AbstractBackend.h +++ b/compiler/nnkit-tflite/support/include/nnkit/support/tflite/AbstractBackend.h @@ -18,7 +18,7 @@ #define __NNKIT_SUPPORT_TFLITE_ABSTRACT_BACKEND_H__ #include -#include +#include namespace nnkit { diff --git a/compiler/nnkit-tflite/support/include/nnkit/support/tflite/TensorSet.h b/compiler/nnkit-tflite/support/include/nnkit/support/tflite/TensorSet.h index d28ab6e..4226a3a 100644 --- a/compiler/nnkit-tflite/support/include/nnkit/support/tflite/TensorSet.h +++ b/compiler/nnkit-tflite/support/include/nnkit/support/tflite/TensorSet.h @@ -17,7 +17,7 @@ #ifndef __NNKIT_SUPPORT_TFLITE_TENSOR_SET_H__ #define __NNKIT_SUPPORT_TFLITE_TENSOR_SET_H__ -#include +#include #include diff --git a/compiler/nnkit-tflite/support/include/nnkit/support/tflite/TensorSets.h b/compiler/nnkit-tflite/support/include/nnkit/support/tflite/TensorSets.h index 5708031..13dea98 100644 --- a/compiler/nnkit-tflite/support/include/nnkit/support/tflite/TensorSets.h +++ b/compiler/nnkit-tflite/support/include/nnkit/support/tflite/TensorSets.h @@ -19,7 +19,7 @@ #include "nnkit/support/tflite/TensorSet.h" -#include +#include namespace nnkit { diff --git a/compiler/nnkit-tflite/support/include/nnkit/support/tflite/TensorUtils.h b/compiler/nnkit-tflite/support/include/nnkit/support/tflite/TensorUtils.h index 05fb7d5..20b34a3 100644 --- a/compiler/nnkit-tflite/support/include/nnkit/support/tflite/TensorUtils.h +++ b/compiler/nnkit-tflite/support/include/nnkit/support/tflite/TensorUtils.h @@ -17,7 +17,7 @@ #ifndef __NNKIT_SUPPORT_TENSOR_UTILS_H__ #define __NNKIT_SUPPORT_TENSOR_UTILS_H__ -#include +#include #include namespace nnkit diff --git a/compiler/nnkit/actions/HDF5/CMakeLists.txt b/compiler/nnkit/actions/HDF5/CMakeLists.txt index b799f6d..63d3320 100644 --- a/compiler/nnkit/actions/HDF5/CMakeLists.txt +++ b/compiler/nnkit/actions/HDF5/CMakeLists.txt @@ -1,4 +1,4 @@ -find_package(HDF5 COMPONENTS CXX QUIET) +nnas_find_package(HDF5 QUIET) if(NOT HDF5_FOUND) return() diff --git a/compiler/nnop/README.md b/compiler/nnop/README.md new file mode 100644 index 0000000..89edf81 --- /dev/null +++ b/compiler/nnop/README.md @@ -0,0 +1 @@ +# nnop diff --git a/compiler/nnsuite/README.md b/compiler/nnsuite/README.md new file mode 100644 index 0000000..d3b2828 --- /dev/null +++ b/compiler/nnsuite/README.md @@ -0,0 +1 @@ +# nnsuite diff --git a/compiler/one-cmds/CMakeLists.txt b/compiler/one-cmds/CMakeLists.txt new file mode 100644 index 0000000..7d73d9b --- /dev/null +++ b/compiler/one-cmds/CMakeLists.txt @@ -0,0 +1,44 @@ +set(ONE_COMMAND_FILES + one-import + one-import-tf + one-import-tflite + one-optimize + one-quantize + one-pack + one-codegen + one-prepare-venv +) + +foreach(ONE_COMMAND IN ITEMS ${ONE_COMMAND_FILES}) + + set(ONE_COMMAND_FILE ${ONE_COMMAND}) + set(ONE_COMMAND_SRC "${CMAKE_CURRENT_SOURCE_DIR}/${ONE_COMMAND_FILE}") + set(ONE_COMMAND_BIN "${CMAKE_CURRENT_BINARY_DIR}/${ONE_COMMAND_FILE}") + set(ONE_COMMAND_TARGET "${ONE_COMMAND}_target") + + add_custom_command(OUTPUT ${ONE_COMMAND_BIN} + COMMAND ${CMAKE_COMMAND} -E copy "${ONE_COMMAND_SRC}" "${ONE_COMMAND_BIN}" + DEPENDS ${ONE_COMMAND_SRC} + COMMENT "Generate ${ONE_COMMAND_BIN}" + ) + + add_custom_target(${ONE_COMMAND_TARGET} ALL DEPENDS ${ONE_COMMAND_BIN}) + + install(FILES ${ONE_COMMAND} + PERMISSIONS OWNER_WRITE OWNER_READ OWNER_EXECUTE + GROUP_READ GROUP_WRITE GROUP_EXECUTE + WORLD_READ WORLD_EXECUTE + DESTINATION bin) + +endforeach(ONE_COMMAND) + +set(ONE_DOCUMENT_FILES + how-to-use-one-commands.txt + how-to-prepare-virtualenv.txt +) + +foreach(ONE_DOCUMENT IN ITEMS ${ONE_DOCUMENT_FILES}) + + install(FILES ${ONE_DOCUMENT} DESTINATION doc) + +endforeach(ONE_DOCUMENT) diff --git a/compiler/one-cmds/README.md b/compiler/one-cmds/README.md new file mode 100644 index 0000000..2ce6eb4 --- /dev/null +++ b/compiler/one-cmds/README.md @@ -0,0 +1,3 @@ +# one-cmds + +_one-cmds_ provides user commands driver diff --git a/compiler/one-cmds/how-to-prepare-virtualenv.txt b/compiler/one-cmds/how-to-prepare-virtualenv.txt new file mode 100644 index 0000000..41fff3a --- /dev/null +++ b/compiler/one-cmds/how-to-prepare-virtualenv.txt @@ -0,0 +1,37 @@ +About +----- + +Last update: 2020-07-14 + +This document explains about 'one-prepare-venv' command. + +'one-prepare-venv' will prepare python3 virtual environment with tensorflow-cpu +version 2.3.0rc0, recommanded 2.x version as of now, so that 'one-import-tf' +command can execute properly. + + +Prerequisite +------------ + +Please install these required packages before venv preparation. + +$ sudo apt-get update +$ sudo apt-get upgrade +$ sudo apt-get install python3-pip python3-venv + + +How to run +---------- + +Just run 'one-prepare-venv' command + +$ one-prepare-venv + +There will be venv folder as of result. + + +Trouble shooting +---------------- + +If you have any problems, please click 'New issue' at +https://github.com/Samsung/ONE/issues page. diff --git a/compiler/one-cmds/how-to-use-one-commands.txt b/compiler/one-cmds/how-to-use-one-commands.txt new file mode 100644 index 0000000..6c2176a --- /dev/null +++ b/compiler/one-cmds/how-to-use-one-commands.txt @@ -0,0 +1,114 @@ +About +----- + +Last update: 2020-07-14 + +This document briefly explains how to use one-* commands. +Detailed options are not explained here. Run the command to see options. + +Compilation flow for running with onert; +1) one-import will import model files generated from famous frameworks +2) one-optimize will optimize models. This step is optional. +3) one-quantize will quantize models. This step is also optional. +4) one-pack will pack to nnpkg so that we can run the model with our onert + runtime + +Compilation flow for NPU +1) one-import will import model files generated from famous frameworks +2) one-optimize will optimize models. This step is optional. +3) one-quantize will quantize models. Depending on the NPUs. +4) one-codegen will compile to binary codes. + + +one-import +----------- + +one-import will invokes one-import-* commands. + +Syntax: one-import [framework] [options] + +Currently supported frameworks are 'tf', 'tflite' for TensorFlow and TensorFlow +lite. + + +one-import-tf +------------- + +This will convert TensorFlow model (.pb) file to our circle model. You can also +directly call this command. one-import-tf invokes tf2tfliteV2.py script that +will internally use TensorFlow lite converter and then invoke tflite2circle +converter to convert tflite model to circle model. + +As tf2tfliteV2.py runs TensorFlow lite converter, you need to have TensorFlow +installed in your system. We recommand to use 2.3.0rc0 for now. + +We provide python virtual environment and one-import-tf will enter and leave +this environment so that you don't need to explictly 'activate' virtual +environment. + + +one-import-tflite +----------------- + +You can use one-import-tflite to convert TensorFlow lite model (.tflite) file to +our circle model. Internally this will invoke tflite2circle. + + +one-optimize +------------ + +one-optimize provides network or operator transformation shown below. + +Current transformation options are +- fuse_bcq: This enables Binary-Coded-bases Quantized DNNs + - read https://arxiv.org/abs/2005.09904 for detailed information +- fuse_instnorm: This will convert instance normalization related operators to + one InstanceNormalization operator that our onert provides for faster + execution. +- resolve_customop_add: This will convert Custom(Add) to normal Add operator +- resolve_customop_batchmatmul: This will convert Custom(BatchMatMul) to + normal BatchMatMul operator +- resolve_customop_matmul: This will convert Custom(MatMul) to normal MatMul + operator + + +one-quantize +------------ + +one-quantize will quantize float32 model to uint8 so that the model can benefit +for speed that our onert runtime and NPU provides. For convolution type +operators we currently support layer-wise quantization. Later we will support +int16 and channel-wise quantization. + +Internally this calls circle-quantizer and record-minmax tools. + + +one-pack +-------- + +one-pack will generate a package from circle model to nnpackage for our onert +runtime. + +Output is a folder with the model(s) and meta information. + +ex) if you have a model named '20200719.circle' and want to pack to 'testnnpack' + +$ one-pack -i 20200709.circle -o testnnpack + +$ tree testnnpack +testnnpack +└── 20200709 + ├── 20200709.circle + └── metadata + └── MANIFEST + + +one-codegen +----------- + +one-codegen, like one-import will invoke backend code generation commands. +As of now, our ONE repo does not provide any code generation commands yet. + +Syntax: one-codegen [target-backend] [options] + +This will invoke [target-backend]-compile command if available. diff --git a/compiler/one-cmds/one-codegen b/compiler/one-cmds/one-codegen new file mode 100644 index 0000000..2c80664 --- /dev/null +++ b/compiler/one-cmds/one-codegen @@ -0,0 +1,55 @@ +#!/bin/bash + +# Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +DRIVER_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +function Usage() +{ + echo "Usage: $0 [BACKEND] ..." + echo "Available BACKEND drivers:" + backend_exist=0 + for file in `find $DRIVER_PATH -name *-compile -type f`; + do + backend_driver=$(basename $file) + sub_length=8 + driver_length=$(expr ${#backend_driver} - ${sub_length}) + backend=${backend_driver:0:${driver_length}} # 8 is length of "-compile" + echo " $backend" + backend_exist=1 + done + if [ $backend_exist == 0 ]; then + echo " (There is no available backend drivers)" + fi +} + +# Get command from command-line +BACKEND=$1; shift +BACKEND_DRIVER="$BACKEND-compile" + +if [[ -z "${BACKEND_DRIVER}" ]]; then + Usage + exit 255 +fi + +BACKEND_DRIVER_CMD="${DRIVER_PATH}/${BACKEND_DRIVER}" + +if [[ ! -f "${BACKEND_DRIVER_CMD}" ]]; then + echo "ERROR: '${BACKEND_DRIVER}' is not supported" + Usage + exit 255 +fi + +"${BACKEND_DRIVER_CMD}" "$@" diff --git a/compiler/one-cmds/one-import b/compiler/one-cmds/one-import new file mode 100644 index 0000000..dbf4af5 --- /dev/null +++ b/compiler/one-cmds/one-import @@ -0,0 +1,53 @@ +#!/bin/bash + +# Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +DRIVER_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +function Usage() +{ + echo "Usage: $0 [FRAMEWORK] ..." + echo "Available FRAMEWORK drivers:" + framework_exist=0 + for file in "$DRIVER_PATH"/one-import-*; + do + framework_driver=$(basename $file) + framework=${framework_driver:11} # 11 is length of "one-import-" + echo " $framework" + framework_exist=1 + done + if [ $framework_exist == 0 ]; then + echo " (There is no available import drivers)" + fi +} + +# Get command from command-line +FRAMEWORK=$1; shift +FRAMEWORK_DRIVER="one-import-$FRAMEWORK" + +if [[ -z "${FRAMEWORK_DRIVER}" ]]; then + Usage + exit 255 +fi + +FRAMEWORK_DRIVER_CMD="${DRIVER_PATH}/${FRAMEWORK_DRIVER}" + +if [[ ! -f "${FRAMEWORK_DRIVER_CMD}" ]]; then + echo "ERROR: '${FRAMEWORK_DRIVER}' is not supported" + Usage + exit 255 +fi + +"${FRAMEWORK_DRIVER_CMD}" "$@" diff --git a/compiler/one-cmds/one-import-tf b/compiler/one-cmds/one-import-tf new file mode 100644 index 0000000..c048a4e --- /dev/null +++ b/compiler/one-cmds/one-import-tf @@ -0,0 +1,114 @@ +#!/bin/bash + +# Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +set -e + +DRIVER_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +usage() +{ + echo "Convert TensorFlow model to circle." + echo "Usage: one-import-tf" + echo " --input_path " + echo " --output_path " + echo " --input_arrays " + echo " --input_shapes " + echo " --output_arrays " + exit 0 +} + +# Parse command-line arguments +# +while [ "$#" -ne 0 ]; do + CUR="$1" + + case $CUR in + '--help') + usage + ;; + '--input_path') + export INPUT_PATH="$2" + shift 2 + ;; + '--output_path') + export OUTPUT_PATH="$2" + shift 2 + ;; + '--input_arrays') + export INPUT_ARRAYS="$2" + shift 2 + ;; + '--input_shapes') + export INPUT_SHAPES="$2" + shift 2 + ;; + '--output_arrays') + export OUTPUT_ARRAYS="$2" + shift 2 + ;; + *) + echo "Unknown parameter: ${CUR}" + shift + ;; + esac +done + +if [ -z ${INPUT_PATH} ] || [ ! -e ${INPUT_PATH} ]; then + echo "Error: input model not found" + echo "" + usage + exit 2 +fi + +FILE_BASE=$(basename ${OUTPUT_PATH}) +MODEL_NAME="${FILE_BASE%.*}" + +TMPDIR=$(mktemp -d) +trap "{ rm -rf $TMPDIR; }" EXIT + +# activate python virtual environment +VIRTUALENV_LINUX="${DRIVER_PATH}/venv/bin/activate" +VIRTUALENV_WINDOWS="${DRIVER_PATH}/venv/Scripts/activate" + +if [ -e ${VIRTUALENV_LINUX} ]; then + source ${VIRTUALENV_LINUX} +elif [ -e ${VIRTUALENV_WINDOWS} ]; then + source ${VIRTUALENV_WINDOWS} +fi + +# remove previous log +rm -rf "${OUTPUT_PATH}.log" + +# generate temporary tflite file +echo "python" "${DRIVER_PATH}/tf2tfliteV2.py" --v2 --input_path ${INPUT_PATH} \ +--input_arrays ${INPUT_ARRAYS} --input_shapes ${INPUT_SHAPES} \ +--output_path "${TMPDIR}/${MODEL_NAME}.tflite" \ +--output_arrays ${OUTPUT_ARRAYS} > "${OUTPUT_PATH}.log" +echo " " >> "${OUTPUT_PATH}.log" + +python "${DRIVER_PATH}/tf2tfliteV2.py" --v2 --input_path ${INPUT_PATH} \ +--input_arrays ${INPUT_ARRAYS} --input_shapes ${INPUT_SHAPES} \ +--output_path "${TMPDIR}/${MODEL_NAME}.tflite" \ +--output_arrays ${OUTPUT_ARRAYS} >> "${OUTPUT_PATH}.log" 2>&1 + +# convert .tflite to .circle +echo " " >> "${OUTPUT_PATH}.log" +echo "${DRIVER_PATH}/tflite2circle" "${TMPDIR}/${MODEL_NAME}.tflite" \ +"${OUTPUT_PATH}" >> "${OUTPUT_PATH}.log" +echo " " >> "${OUTPUT_PATH}.log" + +"${DRIVER_PATH}/tflite2circle" "${TMPDIR}/${MODEL_NAME}.tflite" \ +"${OUTPUT_PATH}" >> "${OUTPUT_PATH}.log" 2>&1 diff --git a/compiler/one-cmds/one-import-tflite b/compiler/one-cmds/one-import-tflite new file mode 100644 index 0000000..31ed5af --- /dev/null +++ b/compiler/one-cmds/one-import-tflite @@ -0,0 +1,67 @@ +#!/bin/bash + +# Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +set -e + +DRIVER_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +usage() +{ + echo "Convert TensorFlow lite model to circle." + echo "Usage: one-import-tflite" + echo " --input_path " + echo " --output_path " + exit 0 +} + +# Parse command-line arguments +# +while [ "$#" -ne 0 ]; do + CUR="$1" + + case $CUR in + '--help') + usage + ;; + '--input_path') + export INPUT_PATH="$2" + shift 2 + ;; + '--output_path') + export OUTPUT_PATH="$2" + shift 2 + ;; + *) + echo "Unknown parameter: ${CUR}" + shift + ;; + esac +done + +if [ -z ${INPUT_PATH} ] || [ ! -e ${INPUT_PATH} ]; then + echo "Error: input model not found" + echo "" + usage + exit 2 +fi + +# remove previous log +rm -rf "${OUTPUT_PATH}.log" + +# convert .tflite to .circle +echo "${DRIVER_PATH}/tflite2circle" "${INPUT_PATH}" "${OUTPUT_PATH}" > "${OUTPUT_PATH}.log" + +"${DRIVER_PATH}/tflite2circle" "${INPUT_PATH}" "${OUTPUT_PATH}" >> "${OUTPUT_PATH}.log" 2>&1 diff --git a/compiler/one-cmds/one-optimize b/compiler/one-cmds/one-optimize new file mode 100644 index 0000000..95384c1 --- /dev/null +++ b/compiler/one-cmds/one-optimize @@ -0,0 +1,132 @@ +#!/bin/bash + +# Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +set -e + +DRIVER_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +usage() +{ + echo "Optimize circle model." + echo "Usage: one-optimize" + echo " --all Enable all optimization algorithms" + echo " --fuse_bcq Enable FuseBCQ Pass" + echo " --fuse_instnorm Enable FuseInstanceNormalization Pass" + echo " --resolve_customop_add" + echo " Enable ResolveCustomOpAddPass Pass" + echo " --resolve_customop_batchmatmul" + echo " Enable ResolveCustomOpBatchMatMulPass Pass" + echo " --resolve_customop_matmul" + echo " Enable ResolveCustomOpMatMulPass Pass" + echo " --input_path " + echo " --output_path " + exit 0 +} + +OPTIMIZE_all=0 +OPTIMIZE_fuse_bcq=0 +OPTIMIZE_fuse_instnorm=0 +OPTIMIZE_resolve_customop_add=0 +OPTIMIZE_resolve_customop_batchmatmul=0 +OPTIMIZE_resolve_customop_matmul=0 + +# Parse command-line arguments +# +while [ "$#" -ne 0 ]; do + CUR="$1" + + case $CUR in + '--help') + usage + ;; + '--all') + OPTIMIZE_all=1 + shift + ;; + '--fuse_bcq') + OPTIMIZE_fuse_bcq=1 + shift + ;; + '--fuse_instnorm') + OPTIMIZE_fuse_instnorm=1 + shift + ;; + '--resolve_customop_add') + OPTIMIZE_resolve_customop_add=1 + shift + ;; + '--resolve_customop_batchmatmul') + OPTIMIZE_resolve_customop_batchmatmul=1 + shift + ;; + '--resolve_customop_matmul') + OPTIMIZE_resolve_customop_matmul=1 + shift + ;; + + '--input_path') + export INPUT_PATH="$2" + shift 2 + ;; + '--output_path') + export OUTPUT_PATH="$2" + shift 2 + ;; + *) + echo "Unknown parameter: ${CUR}" + shift + ;; + esac +done + +if [ -z ${INPUT_PATH} ] || [ ! -e ${INPUT_PATH} ]; then + echo "Error: input model not found" + echo "" + usage + exit 2 +fi + +OPTIMIZE_OPTIONS="" + +if [ $OPTIMIZE_all == 1 ]; then + OPTIMIZE_OPTIONS+="--all " +fi +if [ $OPTIMIZE_fuse_bcq == 1 ]; then + OPTIMIZE_OPTIONS+="--fuse_bcq " +fi +if [ $OPTIMIZE_fuse_instnorm == 1 ]; then + OPTIMIZE_OPTIONS+="--fuse_instnorm " +fi +if [ $OPTIMIZE_resolve_customop_add == 1 ]; then + OPTIMIZE_OPTIONS+="--resolve_customop_add " +fi +if [ $OPTIMIZE_resolve_customop_batchmatmul == 1 ]; then + OPTIMIZE_OPTIONS+="--resolve_customop_batchmatmul " +fi +if [ $OPTIMIZE_resolve_customop_matmul == 1 ]; then + OPTIMIZE_OPTIONS+="--resolve_customop_matmul " +fi + +# remove previous log +rm -rf "${OUTPUT_PATH}.log" + +# NOTE do not wrap ${OPTIMIZE_OPTIONS} with "" +# optimize circle +echo "${DRIVER_PATH}/circle2circle" ${OPTIMIZE_OPTIONS} \ +"${INPUT_PATH}" "${OUTPUT_PATH}" > "${OUTPUT_PATH}.log" + +"${DRIVER_PATH}/circle2circle" ${OPTIMIZE_OPTIONS} \ +"${INPUT_PATH}" "${OUTPUT_PATH}" >> "${OUTPUT_PATH}.log" 2>&1 diff --git a/compiler/one-cmds/one-pack b/compiler/one-cmds/one-pack new file mode 100644 index 0000000..2bc4c60 --- /dev/null +++ b/compiler/one-cmds/one-pack @@ -0,0 +1,67 @@ +#!/bin/bash + +# Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +set -e + +DRIVER_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +usage() +{ + echo "Package circle to nnpkg" + echo "Usage: one-pack" + echo " -i " + echo " -o " + exit 0 +} + +# Parse command-line arguments +# +while [ "$#" -ne 0 ]; do + CUR="$1" + + case $CUR in + '--help') + usage + ;; + '-i') + export INPUT_PATH="$2" + shift 2 + ;; + '-o') + export OUTPUT_PATH="$2" + shift 2 + ;; + *) + echo "Unknown parameter: ${CUR}" + shift + ;; + esac +done + +if [ -z ${INPUT_PATH} ] || [ ! -e ${INPUT_PATH} ]; then + echo "Error: input model not found" + echo "" + usage + exit 2 +fi + +# remove previous log +rm -rf "${OUTPUT_PATH}.log" + +# Package circle model file to nnpkg +echo "${DRIVER_PATH}/model2nnpkg.sh" -o "${OUTPUT_PATH}" "${INPUT_PATH}" > "${OUTPUT_PATH}.log" + +"${DRIVER_PATH}/model2nnpkg.sh" -o "${OUTPUT_PATH}" "${INPUT_PATH}" >> "${OUTPUT_PATH}.log" 2>&1 diff --git a/compiler/one-cmds/one-prepare-venv b/compiler/one-cmds/one-prepare-venv new file mode 100644 index 0000000..fce838d --- /dev/null +++ b/compiler/one-cmds/one-prepare-venv @@ -0,0 +1,40 @@ +#!/bin/bash + +# Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +set -e + +DRIVER_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +VENV_ACTIVATE=${DRIVER_PATH}/venv/bin/activate + +if [ -f ${VENV_ACTIVATE} ]; then + echo "Virtual environment is already prepared." + exit 0 +fi + +# Install prerequisites +python3 -m pip install -U virtualenv + +# Create python virtual enviornment +python3 -m venv "${DRIVER_PATH}/venv" + +# Install tensorflow +source "${VENV_ACTIVATE}" + +python -m pip --default-timeout=1000 --trusted-host pypi.org --trusted-host files.pythonhost.org \ + install -U pip setuptools +python -m pip --default-timeout=1000 --trusted-host pypi.org --trusted-host files.pythonhost.org \ + install tensorflow-cpu==2.3.0rc0 diff --git a/compiler/one-cmds/one-quantize b/compiler/one-cmds/one-quantize new file mode 100644 index 0000000..ff9e266 --- /dev/null +++ b/compiler/one-cmds/one-quantize @@ -0,0 +1,155 @@ +#!/bin/bash + +# Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +set -e + +DRIVER_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +usage() +{ + echo "Quantize circle model." + echo "Usage: one-quantize" + echo " --input_dtype Input data type (supported: float32, default=float32)" + echo " --quantized_dtype Output quantized data type (supported: uint8, default=uint8)" + echo " --granularity Quantize granularity (supported: layer, default=layer)" + echo " --min_percentile Minimum percentile (0.0~100.0, default=1.0)" + echo " --max_percentile Maximum percentile (0.0~100.0, default=99.0)" + echo " --mode Record mode (supported: percentile/moving_average, default=percentile)" + echo " --input_path " + echo " --input_data " + echo " --output_path " + exit 0 +} + +INPUT_DTYPE=float32 +QUANTIZED_DTYPE=uint8 +GRANULARITY=layer +MIN_PERCENTILE=1 +MAX_PERCENTILE=99 +MODE=percentile + +# Parse command-line arguments +# +while [ "$#" -ne 0 ]; do + CUR="$1" + + case $CUR in + '--help') + usage + ;; + + '--input_dtype') + INPUT_DTYPE="$2" + shift 2 + ;; + '--quantized_dtype') + QUANTIZED_DTYPE="$2" + shift 2 + ;; + '--granularity') + GRANULARITY="$2" + shift 2 + ;; + '--min_percentile') + MIN_PERCENTILE="$2" + shift 2 + ;; + '--max_percentile') + MAX_PERCENTILE="$2" + shift 2 + ;; + '--mode') + MODE="$2" + shift 2 + ;; + + '--input_path') + INPUT_PATH="$2" + shift 2 + ;; + '--input_data') + INPUT_DATA="$2" + shift 2 + ;; + '--output_path') + OUTPUT_PATH="$2" + shift 2 + ;; + + *) + echo "Unknown parameter: ${CUR}" + shift + ;; + esac +done + +if [ -z ${INPUT_PATH} ] || [ ! -e ${INPUT_PATH} ]; then + echo "Error: input model not found" + echo "" + usage + exit 2 +fi +if [ -z ${INPUT_DATA} ] || [ ! -e ${INPUT_DATA} ]; then + echo "Error: input data not found" + echo "" + usage + exit 2 +fi + +FILE_BASE=$(basename ${OUTPUT_PATH}) +MODEL_NAME="${FILE_BASE%.*}" + +TMPDIR=$(mktemp -d) +trap "{ rm -rf $TMPDIR; }" EXIT + +# remove previous log +rm -rf "${OUTPUT_PATH}.log" + +# quantize circle +echo "${DRIVER_PATH}/circle-quantizer" \ +--quantize_dequantize_weights ${INPUT_DTYPE} ${QUANTIZED_DTYPE} ${GRANULARITY} \ +"${INPUT_PATH}" "${TMPDIR}/${MODEL_NAME}.1.circle" > "${OUTPUT_PATH}.log" +echo " " >> "${OUTPUT_PATH}.log" + +"${DRIVER_PATH}/circle-quantizer" \ +--quantize_dequantize_weights ${INPUT_DTYPE} ${QUANTIZED_DTYPE} ${GRANULARITY} \ +"${INPUT_PATH}" "${TMPDIR}/${MODEL_NAME}.1.circle" >> "${OUTPUT_PATH}.log" 2>&1 + +echo " " >> "${OUTPUT_PATH}.log" +echo "${DRIVER_PATH}/record-minmax" \ +--input_model "${TMPDIR}/${MODEL_NAME}.1.circle" \ +--input_data "${INPUT_DATA}" \ +--min_percentile ${MIN_PERCENTILE} --max_percentile ${MAX_PERCENTILE} \ +--mode "${MODE}" \ +--output_model "${TMPDIR}/${MODEL_NAME}.2.circle" >> "${OUTPUT_PATH}.log" 2>&1 +echo " " >> "${OUTPUT_PATH}.log" + +"${DRIVER_PATH}/record-minmax" \ +--input_model "${TMPDIR}/${MODEL_NAME}.1.circle" \ +--input_data "${INPUT_DATA}" \ +--min_percentile ${MIN_PERCENTILE} --max_percentile ${MAX_PERCENTILE} \ +--mode "${MODE}" \ +--output_model "${TMPDIR}/${MODEL_NAME}.2.circle" >> "${OUTPUT_PATH}.log" 2>&1 + +echo " " >> "${OUTPUT_PATH}.log" +echo "${DRIVER_PATH}/circle-quantizer" \ +--quantize_with_minmax ${INPUT_DTYPE} ${QUANTIZED_DTYPE} ${GRANULARITY} \ +"${TMPDIR}/${MODEL_NAME}.2.circle" "${OUTPUT_PATH}" >> "${OUTPUT_PATH}.log" 2>&1 +echo " " >> "${OUTPUT_PATH}.log" + +"${DRIVER_PATH}/circle-quantizer" \ +--quantize_with_minmax ${INPUT_DTYPE} ${QUANTIZED_DTYPE} ${GRANULARITY} \ +"${TMPDIR}/${MODEL_NAME}.2.circle" "${OUTPUT_PATH}" >> "${OUTPUT_PATH}.log" 2>&1 diff --git a/compiler/one-cmds/requires.cmake b/compiler/one-cmds/requires.cmake new file mode 100644 index 0000000..9b858ad --- /dev/null +++ b/compiler/one-cmds/requires.cmake @@ -0,0 +1,5 @@ +require("tf2tfliteV2") +require("tflite2circle") +require("circle2circle") +require("circle-quantizer") +require("record-minmax") diff --git a/compiler/oneco-value-pbtxt-test/README.md b/compiler/oneco-value-pbtxt-test/README.md new file mode 100644 index 0000000..6ae4519 --- /dev/null +++ b/compiler/oneco-value-pbtxt-test/README.md @@ -0,0 +1 @@ +# oneco-value-pbtxt-test diff --git a/compiler/oneco/README.md b/compiler/oneco/README.md new file mode 100644 index 0000000..9687faf --- /dev/null +++ b/compiler/oneco/README.md @@ -0,0 +1 @@ +# oneco diff --git a/compiler/onnx2circle/requires.cmake b/compiler/onnx2circle/requires.cmake index 418148e..f52e404 100644 --- a/compiler/onnx2circle/requires.cmake +++ b/compiler/onnx2circle/requires.cmake @@ -1,7 +1,7 @@ require("stdex") require("hermes-std") require("mir2loco") -require("mir-onnx-importer") +require("mir") require("exo") require("locop") require("loco") diff --git a/compiler/onnx2tflite-integration-test/README.md b/compiler/onnx2tflite-integration-test/README.md new file mode 100644 index 0000000..cf160fe --- /dev/null +++ b/compiler/onnx2tflite-integration-test/README.md @@ -0,0 +1 @@ +# onnx2tflite-integration-test diff --git a/compiler/onnx2tflite/requires.cmake b/compiler/onnx2tflite/requires.cmake index cc05edd..b16a511 100644 --- a/compiler/onnx2tflite/requires.cmake +++ b/compiler/onnx2tflite/requires.cmake @@ -1,3 +1,3 @@ -require("mir-onnx-importer") +require("mir") require("mir2loco") require("exo") diff --git a/compiler/oops/README.md b/compiler/oops/README.md new file mode 100644 index 0000000..9794cf8 --- /dev/null +++ b/compiler/oops/README.md @@ -0,0 +1 @@ +# oops diff --git a/compiler/pepper-assert/README.md b/compiler/pepper-assert/README.md new file mode 100644 index 0000000..df41371 --- /dev/null +++ b/compiler/pepper-assert/README.md @@ -0,0 +1 @@ +# pepper-assert diff --git a/compiler/pota-quantization-value-test/CMakeLists.txt b/compiler/pota-quantization-value-test/CMakeLists.txt new file mode 100644 index 0000000..d97ffc1 --- /dev/null +++ b/compiler/pota-quantization-value-test/CMakeLists.txt @@ -0,0 +1,69 @@ +unset(QUANTIZATION_VALUE_TEST) +unset(QUANTIZATION_VALUE_TEST_WITH_PARAM) + +macro(addTest NAME GRANULARITY DTYPE) + list(APPEND QUANTIZATION_VALUE_TEST ${NAME}) + list(APPEND QUANTIZATION_VALUE_TEST_WITH_PARAM ${NAME} ${GRANULARITY} ${DTYPE}) +endmacro(addTest) + +# Read "test.lst" +include("test.lst") +# Read "test.local.lst" if exists +include("test.local.lst" OPTIONAL) + +unset(TEST_DEPS) + +get_target_property(ARTIFACTS_BIN_PATH testDataGenerator BINARY_DIR) + +set(VIRTUALENV "${NNCC_OVERLAY_DIR}/venv_1_13_2") + +### +### Generate test.config +### +set(TEST_CONFIG "${CMAKE_CURRENT_BINARY_DIR}/test.config") + +add_custom_command( + OUTPUT ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E remove -f ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E echo 'RECORD_MINMAX_PATH=\"$\"' >> ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E echo 'CIRCLE_QUANTIZER_PATH=\"$\"' >> ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E echo 'CIRCLE_TENSORDUMP_PATH=\"$\"' >> ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E echo 'VIRTUALENV=\"${VIRTUALENV}\"' >> ${TEST_CONFIG} + DEPENDS record-minmax + DEPENDS circle-quantizer + DEPENDS circle-tensordump + COMMENT "Generate test configuration" +) + +list(APPEND TEST_DEPS "${TEST_CONFIG}") + +# This enforces CMake to generate all the dependencies during "build" phase +add_custom_target(pota_quantization_value_test_deps ALL DEPENDS ${TEST_DEPS}) + +# Run tests +add_test( + NAME pota_fake_wquant_test + COMMAND "${CMAKE_CURRENT_SOURCE_DIR}/test_fake_wquant.sh" + "${TEST_CONFIG}" + "${ARTIFACTS_BIN_PATH}" + ${QUANTIZATION_VALUE_TEST_WITH_PARAM} +) + +#add_test( +# NAME pota_record_minmax_test +# COMMAND "${CMAKE_CURRENT_SOURCE_DIR}/test_record_minmax.sh" +# "${TEST_CONFIG}" +# "${ARTIFACTS_BIN_PATH}" +# ${QUANTIZATION_VALUE_TEST_WITH_PARAM} +#) + +#add_test( +# NAME pota_quantization_test +# COMMAND "${CMAKE_CURRENT_SOURCE_DIR}/test_quantization.sh" +# "${TEST_CONFIG}" +# "${ARTIFACTS_BIN_PATH}" +# ${QUANTIZATION_VALUE_TEST_WITH_PARAM} +#) + +#set_tests_properties(pota_record_minmax_test PROPERTIES DEPENDS pota_fake_wquant_test) +#set_tests_properties(pota_quantization_test PROPERTIES DEPENDS pota_record_minmax_test) diff --git a/compiler/pota-quantization-value-test/README.md b/compiler/pota-quantization-value-test/README.md new file mode 100644 index 0000000..e3359ae --- /dev/null +++ b/compiler/pota-quantization-value-test/README.md @@ -0,0 +1,41 @@ +# pota-quantization-value-test + +`pota-quantization-value-test` checks whether a Circle model listed in `test.lst` is correctly quantized (`pota` denotes post-training affine). The circle models are generated from the recipes saved in `res/TensorFlowLiteRecipes`. + +Write `test.local.lst` for local test. + +### Test process + +#### Step 1. Fake quantization + +Run `circle-quantizer` with `--quantize_dequantize_weights` option. + +Dump the fake-quantized model with `circle-tensordump`. + +Compare the dumped model with the expected output in "expected_outputs//\//fake_quantization/.json" + +The expected output should include + (1) values of weights (only for conv, transposed_conv, depthwise_conv, and fc layers) + +#### Step 2. Record moving avg of min and moving avg of max for activations + +Run `record-minmax` with the fake-quantized model (input data is saved in "test_inputs//\//.txt") + +Dump the minmax-recorded model with `circle-tensordump`. +Compare the dumped model with the expected output in "expected_outputs//\//record_minmax/.json" + +The expected output should include + (1) min/max of activations + +#### Step 3. Quantization + +Run `circle-quantizer` with `--quantize_with_minmax` option. + +Dump the quantized model with `circle-tensordump`. + +Compare the dumped model with the expected output in "expected_outputs//\//quantization/.json" + +The expected output should include + (1) scale, zero point of activations + (2) scale, zero point, values of weights + (3) scale, values (weights) of bias diff --git a/compiler/pota-quantization-value-test/compare_tensors.py b/compiler/pota-quantization-value-test/compare_tensors.py new file mode 100755 index 0000000..258d46d --- /dev/null +++ b/compiler/pota-quantization-value-test/compare_tensors.py @@ -0,0 +1,111 @@ +#!/usr/bin/env python3 +import h5py as h5 +import numpy as np +import argparse +import os.path +import json +import sys + +# +# This script checks if the min/max values recorded in the circle model are the same with the expected values +# +# Basic usage: +# compare_tensors.py --input_h5 --expect_dir --mode +# ex: compare_minmax.py --input_h5 Add_000.h5 --expect_dir expected_outputs/Add_000 --mode fake_quantization + +parser = argparse.ArgumentParser() +parser.add_argument('--input_h5', type=str, required=True) +parser.add_argument('--expect_dir', type=str, required=True) +parser.add_argument('--mode', type=str, required=True) +args = parser.parse_args() + +supported_modes = ["fake_quantization", "record_minmax", "quantization"] + +model = args.input_h5 +expect_dir = args.expect_dir +mode = args.mode + +failed_cases = 0 + +if mode not in supported_modes: + raise SystemExit("Unsupported mode. --mode should be one of " + str(supported_modes)) + + +def compare_fake_quantization(tensor, tensor_name, expect_dir): + global failed_cases + with open(expect_dir + "/" + tensor_name + ".json", "r") as expect_file: + json_load = json.load(expect_file) + expected_weights = np.array(json_load["weights"]) + input_weights = tensor["weights"][:] + if np.allclose(input_weights, expected_weights, rtol=1.e-5, atol=1.e-5) == False: + print("Fake-quantized weights of " + tensor_name + " (" + str(input_weights) + + ") do not match with expected value (" + str(expected_weights) + ").") + failed_cases += 1 + + +def compare_record_minmax(tensor, tensor_name, expect_dir): + global failed_cases + with open(expect_dir + "/" + tensor_name + ".json", "r") as expect_file: + json_load = json.load(expect_file) + expected_min = np.array(json_load["min"]) + expected_max = np.array(json_load["max"]) + input_min = tensor["min"][:] + input_max = tensor["max"][:] + if np.allclose(input_min, expected_min, rtol=1.e-5, atol=1.e-5) == False: + print("Recorded min of " + tensor_name + " (" + str(input_min) + + ") does not match with expected value (" + str(expected_min) + ").") + failed_cases += 1 + if np.allclose(input_max, expected_max, rtol=1.e-5, atol=1.e-5) == False: + print("Recorded max of " + tensor_name + " (" + str(input_max) + + ") does not match with expected value (" + str(expected_max) + ").") + failed_cases += 1 + + +def compare_quantization(tensor, tensor_name, expect_dir): + global failed_cases + with open(expect_dir + "/" + tensor_name + ".json", "r") as expect_file: + json_load = json.load(expect_file) + for key in json_load: + if key == "weights": + expected_weights = np.array(json_load["weights"]) + input_weights = tensor["weights"][:] + if np.allclose(input_weights, expected_weights, rtol=0, atol=0) == False: + print("Quantized weights of " + tensor_name + " (" + str(input_weights) + + ") do not match with expected value (" + str(expected_weights) + + ").") + failed_cases += 1 + + if key == "scale": + expected_scale = np.array(json_load["scale"]) + input_scale = tensor["scale"][:] + if np.allclose(input_scale, expected_scale, rtol=1.e-5, atol=1.e-5) == False: + print("Quantized scale of " + tensor_name + " (" + str(input_scale) + + ") do not match with expected value (" + str(expected_scale) + ").") + failed_cases += 1 + + if key == "zero_point": + expected_zero_point = np.array(json_load["zero_point"]) + input_zero_point = tensor["zero_point"][:] + if np.allclose( + input_zero_point, expected_zero_point, rtol=0, atol=0) == False: + print("Quantized zero_point of " + tensor_name + " (" + + str(input_zero_point) + ") do not match with expected value (" + + str(expected_zero_point) + ").") + failed_cases += 1 + + +with h5.File(model, "r") as input: + for tensor_name in input.keys(): + # We only check the given golden data + if os.path.isfile(expect_dir + "/" + tensor_name + ".json"): + print("Compare " + tensor_name) + if mode == "fake_quantization": + compare_fake_quantization(input[tensor_name], tensor_name, expect_dir) + elif mode == "record_minmax": + compare_record_minmax(input[tensor_name], tensor_name, expect_dir) + elif mode == "quantization": + compare_quantization(input[tensor_name], tensor_name, expect_dir) + else: + raise SystemExit("Unsupproted mode.") + +sys.exit(failed_cases) diff --git a/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/fake_quantization/ker.json b/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/fake_quantization/ker.json new file mode 100644 index 0000000..21b8eca --- /dev/null +++ b/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/fake_quantization/ker.json @@ -0,0 +1,48 @@ +{ + "weights": [ + [ + [ + [ + 1.003921627998352, + 2.007843255996704 + ], + [ + -3.0117647647857666, + -4.015686511993408 + ] + ], + [ + [ + -5.019608020782471, + 6.023529529571533 + ], + [ + -7.027451038360596, + 7.968627452850342 + ] + ] + ], + [ + [ + [ + 4.015686511993408, + -2.007843255996704 + ], + [ + 3.0117647647857666, + -1.003921627998352 + ] + ], + [ + [ + -7.968627452850342, + -6.023529529571533 + ], + [ + 7.027451038360596, + 5.019608020782471 + ] + ] + ] + ] +} diff --git a/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/quantization/bias.json b/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/quantization/bias.json new file mode 100644 index 0000000..462d0d3 --- /dev/null +++ b/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/quantization/bias.json @@ -0,0 +1,7 @@ + { + "scale": 0.0059054209919261825, + "weights": [ + 169.0, + 339.0 + ] + } diff --git a/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/quantization/ifm.json b/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/quantization/ifm.json new file mode 100644 index 0000000..107117b --- /dev/null +++ b/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/quantization/ifm.json @@ -0,0 +1,4 @@ +{ + "scale": 0.09411764705882353, + "zero_point": 0.0 +} diff --git a/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/quantization/ker.json b/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/quantization/ker.json new file mode 100644 index 0000000..3a6e171 --- /dev/null +++ b/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/quantization/ker.json @@ -0,0 +1,52 @@ +{ + "max": 7.968627450980392, + "scale": 0.06274509803921569, + "weights": [ + [ + [ + [ + 144, + 160 + ], + [ + 80, + 64 + ] + ], + [ + [ + 48, + 224 + ], + [ + 16, + 255 + ] + ] + ], + [ + [ + [ + 192, + 96 + ], + [ + 176, + 112 + ] + ], + [ + [ + 1, + 32 + ], + [ + 240, + 208 + ] + ] + ] + ], + "min": -8.031372549019608, + "zero_point": 128.0 +} diff --git a/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/quantization/ofm.json b/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/quantization/ofm.json new file mode 100644 index 0000000..2374639 --- /dev/null +++ b/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/quantization/ofm.json @@ -0,0 +1,4 @@ +{ + "scale": 0.17836222929113052, + "zero_point": 0.0 +} diff --git a/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/record_minmax/ifm.json b/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/record_minmax/ifm.json new file mode 100644 index 0000000..563c042 --- /dev/null +++ b/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/record_minmax/ifm.json @@ -0,0 +1,4 @@ +{ + "max": 24.0, + "min": 1.0 +} diff --git a/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/record_minmax/ofm.json b/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/record_minmax/ofm.json new file mode 100644 index 0000000..fd0c6dc --- /dev/null +++ b/compiler/pota-quantization-value-test/expected_outputs/Conv2D_004/layer/uint8/record_minmax/ofm.json @@ -0,0 +1,4 @@ +{ + "max": 45.48236846923828, + "min": 0.0 +} diff --git a/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/fake_quantization/ker.json b/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/fake_quantization/ker.json new file mode 100644 index 0000000..11e91ca --- /dev/null +++ b/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/fake_quantization/ker.json @@ -0,0 +1,34 @@ +{ + "weights": [ + [ + [ + [ + 0.9725490212440491, + 1.9450980424880981, + 3.0392158031463623, + 4.0117645263671875 + ], + [ + -8.996078491210938, + 9.968626976013184, + -10.941176414489746, + 12.035294532775879 + ] + ], + [ + [ + 4.984313488006592, + 5.956862926483154, + 7.050980567932129, + 8.023529052734375 + ], + [ + 13.007843017578125, + -13.980392456054688, + 14.952940940856934, + -16.04705810546875 + ] + ] + ] + ] +} diff --git a/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/quantization/bias.json b/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/quantization/bias.json new file mode 100644 index 0000000..df7cb14 --- /dev/null +++ b/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/quantization/bias.json @@ -0,0 +1,9 @@ +{ + "scale": 0.007627835447904652, + "weights": [ + 131.0, + 262.0, + 393.0, + 524.0 + ] +} diff --git a/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/quantization/ifm.json b/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/quantization/ifm.json new file mode 100644 index 0000000..254ce89 --- /dev/null +++ b/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/quantization/ifm.json @@ -0,0 +1,4 @@ +{ + "scale": 0.06274509803921569, + "zero_point": 0.0 +} diff --git a/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/quantization/ker.json b/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/quantization/ker.json new file mode 100644 index 0000000..3d14da1 --- /dev/null +++ b/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/quantization/ker.json @@ -0,0 +1,38 @@ +{ + "max": 14.952941176470588, + "scale": 0.12156862745098039, + "weights": [ + [ + [ + [ + 140, + 148, + 157, + 165 + ], + [ + 58, + 214, + 42, + 231 + ] + ], + [ + [ + 173, + 181, + 190, + 198 + ], + [ + 239, + 17, + 255, + 0 + ] + ] + ] + ], + "min": -16.04705882352941, + "zero_point": 132.0 +} diff --git a/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/quantization/ofm.json b/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/quantization/ofm.json new file mode 100644 index 0000000..85dd4d9 --- /dev/null +++ b/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/quantization/ofm.json @@ -0,0 +1,4 @@ +{ + "scale": 0.893733185412837, + "zero_point": 0.0 +} diff --git a/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/record_minmax/ifm.json b/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/record_minmax/ifm.json new file mode 100644 index 0000000..9aee7bc --- /dev/null +++ b/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/record_minmax/ifm.json @@ -0,0 +1,4 @@ +{ + "max": 16.0, + "min": 1.0 +} diff --git a/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/record_minmax/ofm.json b/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/record_minmax/ofm.json new file mode 100644 index 0000000..aa42a66 --- /dev/null +++ b/compiler/pota-quantization-value-test/expected_outputs/DepthwiseConv2D_002/layer/uint8/record_minmax/ofm.json @@ -0,0 +1,4 @@ +{ + "max": 227.90196228027344, + "min": 0.0 +} diff --git a/compiler/pota-quantization-value-test/gen_h5_explicit_inputs.py b/compiler/pota-quantization-value-test/gen_h5_explicit_inputs.py new file mode 100755 index 0000000..9863c80 --- /dev/null +++ b/compiler/pota-quantization-value-test/gen_h5_explicit_inputs.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python3 +import h5py as h5 +import numpy as np +import tensorflow as tf +import argparse +import glob + +# +# This script generates a pack of random input data (.h5) expected by the input tflite model +# +# Basic usage: +# gen_h5_explicit_inputs.py --model --input --output +# ex: gen_h5_explicit_inputs.py --model Add_000.tflite --input Add_000 --output Add_000.input.h5 +# (This will create Add_000.input.h5) +# +# The input directory should be organized as follows +# / +# -> .txt +# ... +# Each txt file has the explicit values of inputs +# Example. if the model has two inputs whose shapes are both (1, 3), +# the first record file name is 0.txt, and its contents is something like below +# 1, 2, 3 +# 4, 5, 6 +# +parser = argparse.ArgumentParser() +parser.add_argument('--model', type=str, required=True) +parser.add_argument('--input', type=str, required=True) +parser.add_argument('--output', type=str, required=True) +args = parser.parse_args() + +model = args.model +input = args.input +output = args.output + +# Build TFLite interpreter. (to get the information of model input) +interpreter = tf.lite.Interpreter(model) +input_details = interpreter.get_input_details() + +# Create h5 file +h5_file = h5.File(output, 'w') +group = h5_file.create_group("value") +group.attrs['desc'] = "Input data for " + model + +# Input files +records = sorted(glob.glob(input + "/*.txt")) +for i, record in enumerate(records): + sample = group.create_group(str(i)) + sample.attrs['desc'] = "Input data " + str(i) + with open(record, 'r') as f: + lines = f.readlines() + for j, line in enumerate(lines): + data = np.array(line.split(',')) + input_detail = input_details[j] + input_data = np.array( + data.reshape(input_detail["shape"]), input_detail["dtype"]) + sample.create_dataset(str(j), data=input_data) + +h5_file.close() diff --git a/compiler/pota-quantization-value-test/requires.cmake b/compiler/pota-quantization-value-test/requires.cmake new file mode 100644 index 0000000..883a925 --- /dev/null +++ b/compiler/pota-quantization-value-test/requires.cmake @@ -0,0 +1,4 @@ +require("record-minmax") +require("circle-quantizer") +require("circle-tensordump") +require("common-artifacts") diff --git a/compiler/pota-quantization-value-test/test.lst b/compiler/pota-quantization-value-test/test.lst new file mode 100644 index 0000000..65613ff --- /dev/null +++ b/compiler/pota-quantization-value-test/test.lst @@ -0,0 +1,2 @@ +addTest(Conv2D_004 layer uint8) +addTest(DepthwiseConv2D_002 layer uint8) diff --git a/compiler/pota-quantization-value-test/test_fake_wquant.sh b/compiler/pota-quantization-value-test/test_fake_wquant.sh new file mode 100755 index 0000000..1331703 --- /dev/null +++ b/compiler/pota-quantization-value-test/test_fake_wquant.sh @@ -0,0 +1,87 @@ +#!/bin/bash + +# This script tests the basic behavior of record-minmax +# +# HOW TO USE +# +# ./test_fake_quantization.sh ... +# test.config : set ${RECORD_MINMAX_PATH} and ${CIRCLE_QUANTIZER_PATH} +# work_dir : build directory of quantization-value-test (ex: build/compiler/quantization-value-test) + +SOURCE_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +COMPARE_SCRIPT_PATH="${SOURCE_PATH}/compare_tensors.py" +CONFIG_PATH="$1"; shift +BIN_PATH=$(dirname "${CONFIG_PATH}") +TEST_INPUT_PATH="${SOURCE_PATH}/test_inputs" +WORKDIR="$1"; shift + +source "${CONFIG_PATH}" + +echo "-- Found CIRCLE_QUANTIZER: ${CIRCLE_QUANTIZER_PATH}" +echo "-- Found CIRCLE_TENSORDUMP: ${CIRCLE_TENSORDUMP_PATH}" +echo "-- Found workdir: ${WORKDIR}" + +TESTED=() +PASSED=() +FAILED=() + +pushd "${WORKDIR}" +while [ "$1" != "" ]; do + MODELNAME=$1; shift + GRANULARITY=$1; shift + DTYPE=$1; shift + TESTCASE="${MODELNAME}.${GRANULARITY}.${DTYPE}" + + TESTED+=("${TESTCASE}") + + TESTCASE_FILE="${WORKDIR}/${TESTCASE}" + TEST_RESULT_FILE="${BIN_PATH}/${TESTCASE}" + + PASSED_TAG="${TEST_RESULT_FILE}.fake_quantized.passed" + rm -f "${PASSED_TAG}" + + cat > "${TEST_RESULT_FILE}_fake_quantization.log" <( + exec 2>&1 + set -ex + + # Run circle-quantizer with --quantize_dequantize_weights + "${CIRCLE_QUANTIZER_PATH}" \ + --quantize_dequantize_weights float32 "${DTYPE}" "${GRANULARITY}" \ + "${WORKDIR}/${MODELNAME}.circle" \ + "${TEST_RESULT_FILE}.fake_quantized.circle" + + # Dump weights values (circle-tensordump) + "${CIRCLE_TENSORDUMP_PATH}" \ + "${TEST_RESULT_FILE}.fake_quantized.circle" \ + --tensors_to_hdf5 "${TEST_RESULT_FILE}.fake_quantized.circle.h5" + + # Compare result + "${VIRTUALENV}/bin/python" "${COMPARE_SCRIPT_PATH}" \ + --input_h5 "${TEST_RESULT_FILE}.fake_quantized.circle.h5" \ + --expect_dir "${SOURCE_PATH}/expected_outputs/${MODELNAME}/${GRANULARITY}/${DTYPE}/fake_quantization" \ + --mode fake_quantization + + if [[ $? -eq 0 ]]; then + touch "${PASSED_TAG}" + fi + ) + + if [[ -f "${PASSED_TAG}" ]]; then + PASSED+=("$TESTCASE") + else + FAILED+=("$TESTCASE") + fi +done +popd + +if [[ ${#TESTED[@]} -ne ${#PASSED[@]} ]]; then + echo "FAILED" + for TEST in "${FAILED[@]}" + do + echo "- ${TEST}" + done + exit 255 +fi + +echo "PASSED" +exit 0 diff --git a/compiler/pota-quantization-value-test/test_inputs/Conv2D_004/layer/uint8/0.txt b/compiler/pota-quantization-value-test/test_inputs/Conv2D_004/layer/uint8/0.txt new file mode 100644 index 0000000..8803cb1 --- /dev/null +++ b/compiler/pota-quantization-value-test/test_inputs/Conv2D_004/layer/uint8/0.txt @@ -0,0 +1 @@ +1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 diff --git a/compiler/pota-quantization-value-test/test_inputs/DepthwiseConv2D_002/layer/uint8/0.txt b/compiler/pota-quantization-value-test/test_inputs/DepthwiseConv2D_002/layer/uint8/0.txt new file mode 100644 index 0000000..c210774 --- /dev/null +++ b/compiler/pota-quantization-value-test/test_inputs/DepthwiseConv2D_002/layer/uint8/0.txt @@ -0,0 +1 @@ +1, 2, 7, 8, 3, 4, 9, 10, 5, 6, 11, 12, 13, 14, 15, 16 diff --git a/compiler/pota-quantization-value-test/test_quantization.sh b/compiler/pota-quantization-value-test/test_quantization.sh new file mode 100755 index 0000000..5ebd726 --- /dev/null +++ b/compiler/pota-quantization-value-test/test_quantization.sh @@ -0,0 +1,87 @@ +#!/bin/bash + +# This script tests the basic behavior of record-minmax +# +# HOW TO USE +# +# ./test_quantization.sh ... +# test.config : set ${RECORD_MINMAX_PATH} and ${CIRCLE_QUANTIZER_PATH} +# work_dir : build directory of quantization-value-test (ex: build/compiler/quantization-value-test) + +SOURCE_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +COMPARE_SCRIPT_PATH="${SOURCE_PATH}/compare_tensors.py" +CONFIG_PATH="$1"; shift +BIN_PATH=$(dirname "${CONFIG_PATH}") +TEST_INPUT_PATH="${SOURCE_PATH}/test_inputs" +WORKDIR="$1"; shift + +source "${CONFIG_PATH}" + +echo "-- Found CIRCLE_QUANTIZER: ${CIRCLE_QUANTIZER_PATH}" +echo "-- Found CIRCLE_TENSORDUMP: ${CIRCLE_TENSORDUMP_PATH}" +echo "-- Found workdir: ${WORKDIR}" + +TESTED=() +PASSED=() +FAILED=() + +pushd "${WORKDIR}" +while [ "$1" != "" ]; do + MODELNAME=$1; shift + GRANULARITY=$1; shift + DTYPE=$1; shift + TESTCASE="${MODELNAME}.${GRANULARITY}.${DTYPE}" + + TESTED+=("${TESTCASE}") + + TESTCASE_FILE="${WORKDIR}/${TESTCASE}" + TEST_RESULT_FILE="${BIN_PATH}/${TESTCASE}" + + PASSED_TAG="${TEST_RESULT_FILE}.quantization.passed" + rm -f "${PASSED_TAG}" + + cat > "${TEST_RESULT_FILE}_quantization.log" <( + exec 2>&1 + set -ex + + # Run circle-quantizer with --quantize_with_minmax + "${CIRCLE_QUANTIZER_PATH}" \ + --quantize_with_minmax float32 "${DTYPE}" "${GRANULARITY}" \ + "${TEST_RESULT_FILE}.minmax_recorded.circle" \ + "${TEST_RESULT_FILE}.quantized.circle" + + # Dump scale, zp, weights values (circle-tensordump) + "${CIRCLE_TENSORDUMP_PATH}" \ + "${TEST_RESULT_FILE}.quantized.circle" \ + --tensors_to_hdf5 "${TEST_RESULT_FILE}.quantized.circle.h5" + + # Compare result + "${VIRTUALENV}/bin/python" "${COMPARE_SCRIPT_PATH}" \ + --input_h5 "${TEST_RESULT_FILE}.quantized.circle.h5" \ + --expect_dir "${SOURCE_PATH}/expected_outputs/${MODELNAME}/${GRANULARITY}/${DTYPE}/quantization" \ + --mode quantization + + if [[ $? -eq 0 ]]; then + touch "${PASSED_TAG}" + fi + ) + + if [[ -f "${PASSED_TAG}" ]]; then + PASSED+=("$TESTCASE") + else + FAILED+=("$TESTCASE") + fi +done +popd + +if [[ ${#TESTED[@]} -ne ${#PASSED[@]} ]]; then + echo "FAILED" + for TEST in "${FAILED[@]}" + do + echo "- ${TEST}" + done + exit 255 +fi + +echo "PASSED" +exit 0 diff --git a/compiler/pota-quantization-value-test/test_record_minmax.sh b/compiler/pota-quantization-value-test/test_record_minmax.sh new file mode 100755 index 0000000..eaa462d --- /dev/null +++ b/compiler/pota-quantization-value-test/test_record_minmax.sh @@ -0,0 +1,100 @@ +#!/bin/bash + +# This script tests the basic behavior of record-minmax +# +# HOW TO USE +# +# ./test_record_minmax.sh ... +# test.config : set ${RECORD_MINMAX_PATH} and ${CIRCLE2CIRCLE_PATH} +# work_dir : build directory of quantization-value-test (ex: build/compiler/quantization-value-test) + +SOURCE_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +GEN_SCRIPT_PATH="${SOURCE_PATH}/gen_h5_explicit_inputs.py" +COMPARE_SCRIPT_PATH="${SOURCE_PATH}/compare_tensors.py" +CONFIG_PATH="$1"; shift +BIN_PATH=$(dirname "${CONFIG_PATH}") +TEST_INPUT_PATH="${SOURCE_PATH}/test_inputs" +WORKDIR="$1"; shift + +source "${CONFIG_PATH}" + +echo "-- Found RECORD-MINMAX: ${RECORD_MINMAX_PATH}" +echo "-- Found CIRCLE_TENSORDUMP: ${CIRCLE_TENSORDUMP_PATH}" +echo "-- Found workdir: ${WORKDIR}" + +TESTED=() +PASSED=() +FAILED=() + +pushd "${WORKDIR}" +while [ "$1" != "" ]; do + MODELNAME=$1; shift + GRANULARITY=$1; shift + DTYPE=$1; shift + TESTCASE="${MODELNAME}.${GRANULARITY}.${DTYPE}" + + TESTED+=("${TESTCASE}") + + TESTCASE_FILE="${WORKDIR}/${TESTCASE}" + TEST_RESULT_FILE="${BIN_PATH}/${TESTCASE}" + + PASSED_TAG="${TEST_RESULT_FILE}.record_minmax.passed" + rm -f "${PASSED_TAG}" + + cat > "${TEST_RESULT_FILE}_record_minmax.log" <( + exec 2>&1 + set -ex + + # Generate h5 input data + source "${VIRTUALENV}/bin/activate" + "${VIRTUALENV}/bin/python" "${GEN_SCRIPT_PATH}" \ + --model "${WORKDIR}/${MODELNAME}.tflite" \ + --input "${TEST_INPUT_PATH}/${MODELNAME}/${GRANULARITY}/${DTYPE}" \ + --output "${TESTCASE_FILE}.input.h5" + + if [[ $? -ne 0 ]]; then + echo "FAILED TO GENERATE INPUT" + continue + fi + + # Run record-minmax + "${RECORD_MINMAX_PATH}" \ + "${TEST_RESULT_FILE}.fake_quantized.circle" \ + "${TEST_RESULT_FILE}.input.h5" \ + "${TEST_RESULT_FILE}.minmax_recorded.circle" + + # Dump min/max values (circle-tensordump) + "${CIRCLE_TENSORDUMP_PATH}" \ + "${TEST_RESULT_FILE}.minmax_recorded.circle" \ + --tensors_to_hdf5 "${TEST_RESULT_FILE}.minmax_recorded.circle.h5" + + # Compare result + "${VIRTUALENV}/bin/python" "${COMPARE_SCRIPT_PATH}" \ + --input_h5 "${TEST_RESULT_FILE}.minmax_recorded.circle.h5" \ + --expect_dir "${SOURCE_PATH}/expected_outputs/${MODELNAME}/${GRANULARITY}/${DTYPE}/record_minmax" \ + --mode record_minmax + + if [[ $? -eq 0 ]]; then + touch "${PASSED_TAG}" + fi + ) + + if [[ -f "${PASSED_TAG}" ]]; then + PASSED+=("$TESTCASE") + else + FAILED+=("$TESTCASE") + fi +done +popd + +if [[ ${#TESTED[@]} -ne ${#PASSED[@]} ]]; then + echo "FAILED" + for TEST in "${FAILED[@]}" + do + echo "- ${TEST}" + done + exit 255 +fi + +echo "PASSED" +exit 0 diff --git a/compiler/pp/src/LinearDocument.cpp b/compiler/pp/src/LinearDocument.cpp index 2bc5f26..2cd35ef 100644 --- a/compiler/pp/src/LinearDocument.cpp +++ b/compiler/pp/src/LinearDocument.cpp @@ -63,9 +63,9 @@ const std::string &LinearDocument::line(uint32_t n) const { return _lines.at(lines() - n - 1); } + default: + throw std::runtime_error{"Not supported Direction"}; } - - throw std::runtime_error{"unreachable"}; } } // namespace pp diff --git a/compiler/record-minmax-conversion-test/CMakeLists.txt b/compiler/record-minmax-conversion-test/CMakeLists.txt new file mode 100644 index 0000000..2221e17 --- /dev/null +++ b/compiler/record-minmax-conversion-test/CMakeLists.txt @@ -0,0 +1,42 @@ +unset(RECORD_MINMAX_CONVERSION_TEST) + +macro(addTest NAME) + list(APPEND RECORD_MINMAX_CONVERSION_TEST ${NAME}) +endmacro(addTest) + +# Read "test.lst" +include("test.lst") +# Read "test.local.lst" if exists +include("test.local.lst" OPTIONAL) + +unset(TEST_DEPS) + +get_target_property(ARTIFACTS_BIN_PATH testDataGenerator BINARY_DIR) + +### +### Generate test.config +### +set(TEST_CONFIG "${CMAKE_CURRENT_BINARY_DIR}/test.config") + +add_custom_command( + OUTPUT ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E remove -f ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E echo 'RECORD_MINMAX_PATH=\"$\"' >> ${TEST_CONFIG} + DEPENDS record-minmax + COMMENT "Generate test configuration" +) + +list(APPEND TEST_DEPS "${TEST_CONFIG}") + +# This enforces CMake to generate all the dependencies during "build" phase +add_custom_target(record_minmax_conversion_test_deps ALL DEPENDS ${TEST_DEPS}) + +# Run tests +add_test( + NAME record_minmax_conversion_test + COMMAND "${CMAKE_CURRENT_SOURCE_DIR}/testall.sh" + "${TEST_CONFIG}" + "${ARTIFACTS_BIN_PATH}" + "${NNCC_OVERLAY_DIR}/venv_1_13_2" + ${RECORD_MINMAX_CONVERSION_TEST} +) diff --git a/compiler/record-minmax-conversion-test/README.md b/compiler/record-minmax-conversion-test/README.md new file mode 100644 index 0000000..cc06c15 --- /dev/null +++ b/compiler/record-minmax-conversion-test/README.md @@ -0,0 +1,5 @@ +# record-minmax-conversion-test + +Run `record-minmax` with random input data and Circle models listed in `test.lst`. This test checks whether a given Circle model can be converted to a Circle model embedded with min/max values without failure. + +Write `test.local.lst` for local test. diff --git a/compiler/record-minmax-conversion-test/gen_h5_random_inputs.py b/compiler/record-minmax-conversion-test/gen_h5_random_inputs.py new file mode 100755 index 0000000..b770981 --- /dev/null +++ b/compiler/record-minmax-conversion-test/gen_h5_random_inputs.py @@ -0,0 +1,46 @@ +#!/usr/bin/env python3 +import h5py as h5 +import numpy as np +import tensorflow as tf +import argparse + +# +# This script generates a pack of random input data (.h5) expected by the input tflite model +# +# Basic usage: +# gen_h5_inputs.py --model --num_data --output +# ex: gen_h5_inputs.py --model add.tflite --num_data 3 --output add.tflite.input.h5 +# (This will create add.tflite.input.h5 composed of three random inputs in the same directory as the model) +parser = argparse.ArgumentParser() +parser.add_argument('--model', type=str, required=True) +parser.add_argument('--num_data', type=int, required=True) +parser.add_argument('--output', type=str, required=True) +args = parser.parse_args() + +model = args.model + +num_data = args.num_data + +output_path = args.output + +# Build TFLite interpreter. (to get the information of model input) +interpreter = tf.lite.Interpreter(model) +input_details = interpreter.get_input_details() + +# Create h5 file +h5_file = h5.File(output_path, 'w') +group = h5_file.create_group("value") +group.attrs['desc'] = "Input data for " + model + +# Generate random data +for i in range(num_data): + sample = group.create_group(str(i)) + sample.attrs['desc'] = "Input data " + str(i) + + for j in range(len(input_details)): + input_detail = input_details[j] + input_data = np.array( + np.random.random_sample(input_detail["shape"]), input_detail["dtype"]) + sample.create_dataset(str(j), data=input_data) + +h5_file.close() diff --git a/compiler/record-minmax-conversion-test/requires.cmake b/compiler/record-minmax-conversion-test/requires.cmake new file mode 100644 index 0000000..9105c3e --- /dev/null +++ b/compiler/record-minmax-conversion-test/requires.cmake @@ -0,0 +1,2 @@ +require("common-artifacts") +require("record-minmax") diff --git a/compiler/record-minmax-conversion-test/test.lst b/compiler/record-minmax-conversion-test/test.lst new file mode 100644 index 0000000..771c3bd --- /dev/null +++ b/compiler/record-minmax-conversion-test/test.lst @@ -0,0 +1,16 @@ +addTest(Add_000) +addTest(AveragePool2D_000) +addTest(Concatenation_000) +addTest(Conv2D_000) +addTest(Conv2D_001) +addTest(Conv2D_002) +addTest(DepthwiseConv2D_000) +addTest(FullyConnected_000) +addTest(FullyConnected_001) +addTest(MaxPool2D_000) +addTest(Mul_000) +addTest(Pad_000) +addTest(Reshape_000) +addTest(Reshape_001) +addTest(Reshape_002) +addTest(Softmax_000) diff --git a/compiler/record-minmax-conversion-test/testall.sh b/compiler/record-minmax-conversion-test/testall.sh new file mode 100755 index 0000000..29c9ed3 --- /dev/null +++ b/compiler/record-minmax-conversion-test/testall.sh @@ -0,0 +1,81 @@ +#!/bin/bash + +# This script tests the basic behavior of record-minmax +# +# HOW TO USE +# +# ./testall.sh ... +# test.config : set ${RECORD_MINMAX_PATH} +# work_dir : build directory of record-minmax-conversion-test (ex: build/compiler/record-minmax-conversion-test) + +GEN_SOURCE_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +GEN_SCRIPT_PATH="${GEN_SOURCE_PATH}/gen_h5_random_inputs.py" +CONFIG_PATH="$1"; shift +BIN_PATH=$(dirname "$CONFIG_PATH") +WORKDIR="$1"; shift +VIRTUALENV="$1"; shift + +source "${CONFIG_PATH}" + +echo "-- Found RECORD-MINMAX: ${RECORD_MINMAX_PATH}" +echo "-- Found workdir: ${WORKDIR}" + +TESTED=() +PASSED=() +FAILED=() + +pushd "${WORKDIR}" +for TESTCASE in "$@"; do + TESTED+=("${TESTCASE}") + + TESTCASE_FILE="${WORKDIR}/${TESTCASE}" + + PASSED_TAG="${BIN_PATH}/${TESTCASE}.passed" + rm -f "${PASSED_TAG}" + + cat > "${BIN_PATH}/${TESTCASE}.log" <( + exec 2>&1 + set -ex + + # Generate h5 input data + source "${VIRTUALENV}/bin/activate" + "${VIRTUALENV}/bin/python" "${GEN_SCRIPT_PATH}" \ + --model "${TESTCASE_FILE}.tflite" \ + --num_data 3 \ + --output "${BIN_PATH}/${TESTCASE}.tflite.input.h5" + + if [[ $? -ne 0 ]]; then + echo "FAILED TO GENERATE INPUT" + continue + fi + + # Run record-minmax + "${RECORD_MINMAX_PATH}" \ + --input_model "${TESTCASE_FILE}.circle" \ + --input_data "${BIN_PATH}/${TESTCASE}.tflite.input.h5" \ + --output_model "${BIN_PATH}/${TESTCASE}.out.circle" + + if [[ $? -eq 0 ]]; then + touch "${PASSED_TAG}" + fi + ) + + if [[ -f "${PASSED_TAG}" ]]; then + PASSED+=("$TESTCASE") + else + FAILED+=("$TESTCASE") + fi +done +popd + +if [[ ${#TESTED[@]} -ne ${#PASSED[@]} ]]; then + echo "FAILED" + for TEST in "${FAILED[@]}" + do + echo "- ${TEST}" + done + exit 255 +fi + +echo "PASSED" +exit 0 diff --git a/compiler/record-minmax/CMakeLists.txt b/compiler/record-minmax/CMakeLists.txt new file mode 100644 index 0000000..862660e --- /dev/null +++ b/compiler/record-minmax/CMakeLists.txt @@ -0,0 +1,27 @@ +nnas_find_package(HDF5 COMPONENTS STATIC QUIET) + +if(NOT HDF5_FOUND) + message(STATUS "Build record-minmax: FAILED (missing HDF5)") + return() +endif(NOT HDF5_FOUND) + +set(DRIVER "driver/Driver.cpp") + +file(GLOB_RECURSE SOURCES "src/*.cpp") + +add_executable(record-minmax ${DRIVER} ${SOURCES}) +target_include_directories(record-minmax PRIVATE include) +target_include_directories(record-minmax PRIVATE ${HDF5_INCLUDE_DIRS}) + +target_link_libraries(record-minmax ${HDF5_CXX_LIBRARIES}) +target_link_libraries(record-minmax arser) +target_link_libraries(record-minmax safemain) +target_link_libraries(record-minmax luci_import) +target_link_libraries(record-minmax luci_export) +target_link_libraries(record-minmax luci_interpreter) + +install(TARGETS record-minmax DESTINATION bin) + +nnas_find_package(GTest REQUIRED) +GTest_AddTest(record_minmax_function_test "${CMAKE_CURRENT_SOURCE_DIR}/tests/RecordFunction.test.cpp") +target_include_directories(record_minmax_function_test PRIVATE include) diff --git a/compiler/record-minmax/README.md b/compiler/record-minmax/README.md new file mode 100644 index 0000000..6a491f2 --- /dev/null +++ b/compiler/record-minmax/README.md @@ -0,0 +1,18 @@ +# record-minmax + +_record-minmax_ is a tool to embed min/max values of activations to the circle model for post-training quantization. + +## Usage + +This will run with the path to the input model (.circle), a pack of input data (.h5), and the output model (.circle). + +``` +$ ./record-minmax +``` + +For example, +``` +$ ./record-minmax input.circle input.h5 out.circle +``` + +Output is a circle model where min/max values of activation tensors are saved in QuantizationParameters. diff --git a/compiler/record-minmax/driver/Driver.cpp b/compiler/record-minmax/driver/Driver.cpp new file mode 100644 index 0000000..ae4fcb7 --- /dev/null +++ b/compiler/record-minmax/driver/Driver.cpp @@ -0,0 +1,105 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "RecordMinMax.h" + +#include + +int entry(const int argc, char **argv) +{ + using namespace record_minmax; + + arser::Arser arser( + "Embedding min/max values of activations to the circle model for post-training quantization"); + + arser.add_argument("--input_model") + .nargs(1) + .type(arser::DataType::STR) + .required(true) + .help("Input model filepath"); + + arser.add_argument("--input_data") + .nargs(1) + .type(arser::DataType::STR) + .required(true) + .help("Input data filepath"); + + arser.add_argument("--output_model") + .nargs(1) + .type(arser::DataType::STR) + .required(true) + .help("Output model filepath"); + + arser.add_argument("--min_percentile") + .nargs(1) + .type(arser::DataType::FLOAT) + .help("Record n'th percentile of min"); + + arser.add_argument("--max_percentile") + .nargs(1) + .type(arser::DataType::FLOAT) + .help("Record n'th percentile of max"); + + arser.add_argument("--mode") + .nargs(1) + .type(arser::DataType::STR) + .help("Record mode. percentile (default) or moving_average"); + + try + { + arser.parse(argc, argv); + } + catch (const std::runtime_error &err) + { + std::cout << err.what() << std::endl; + std::cout << arser; + return 0; + } + + auto input_model_path = arser.get("--input_model"); + auto input_data_path = arser.get("--input_data"); + auto output_model_path = arser.get("--output_model"); + + // Default values + std::string mode("percentile"); + float min_percentile = 1.0; + float max_percentile = 99.0; + + if (arser["--min_percentile"]) + min_percentile = arser.get("--min_percentile"); + + if (arser["--max_percentile"]) + max_percentile = arser.get("--max_percentile"); + + if (arser["--mode"]) + mode = arser.get("--mode"); + + if (mode != "percentile" && mode != "moving_average") + throw std::runtime_error("Unsupported mode"); + + RecordMinMax rmm; + + // Initialize interpreter and observer + rmm.initialize(input_model_path); + + // Profile min/max while executing the given input data + rmm.profileData(mode, input_data_path, min_percentile, max_percentile); + + // Save profiled values to the model + rmm.saveModel(output_model_path); + + return EXIT_SUCCESS; +} diff --git a/compiler/record-minmax/include/MinMaxObserver.h b/compiler/record-minmax/include/MinMaxObserver.h new file mode 100644 index 0000000..ce63438 --- /dev/null +++ b/compiler/record-minmax/include/MinMaxObserver.h @@ -0,0 +1,74 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __RECORD_MINMAX_MINMAXOBSERVER_H__ +#define __RECORD_MINMAX_MINMAXOBSERVER_H__ + +#include +#include + +#include +#include + +namespace record_minmax +{ + +struct MinMaxVectors +{ + std::vector min_vector; + std::vector max_vector; +}; + +class MinMaxMap +{ +public: + // Record min/max of node + void recordMinMax(const luci::CircleNode *node, float min, float max) + { + MinMaxVectors &vectors = _minmax_map[node]; + vectors.min_vector.push_back(min); + vectors.max_vector.push_back(max); + } + + const std::unordered_map *getMap() const + { + return &_minmax_map; + } + +private: + std::unordered_map _minmax_map; +}; + +class MinMaxObserver : public luci_interpreter::ExecutionObserver +{ +public: + MinMaxObserver() + { + // Do nothing + } + + void postTensorWrite(const luci::CircleNode *node, + const luci_interpreter::Tensor *tensor) override; + + const MinMaxMap *minMaxData() { return &_minmax_data; } + +private: + MinMaxMap _minmax_data; +}; + +} // namespace record_minmax + +#endif // __RECORD_MINMAX_MINMAXOBSERVER_H__ diff --git a/compiler/record-minmax/include/RecordFunction.h b/compiler/record-minmax/include/RecordFunction.h new file mode 100644 index 0000000..b570c6a --- /dev/null +++ b/compiler/record-minmax/include/RecordFunction.h @@ -0,0 +1,102 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include +#include +#include +#include +#include + +namespace record_minmax +{ + +/** + * @brief getNthPercentile calculates the n-th percentile of input vector (0.0 <= n <= 100.0) + * linear interpolation is used when the desired percentile lies between two data points + */ +float getNthPercentile(std::vector &vector, float percentile) +{ + if (percentile < 0 || percentile > 100) + throw std::runtime_error("Percentile must be ranged from 0 to 100"); + + if (percentile == 0.0) + return vector.front(); + + if (percentile == 100.0) + return vector.back(); + + if (vector.empty()) + throw std::runtime_error("Percentile must take a non-empty vector as an argument"); + + if (vector.size() == 1) + return vector[0]; + + std::vector copy; + copy.assign(vector.begin(), vector.end()); + std::sort(copy.begin(), copy.end()); + + int index = static_cast(std::floor((copy.size() - 1) * percentile / 100.0)); + + float percent_i = static_cast(index) / static_cast(copy.size() - 1); + float fraction = + (percentile / 100.0 - percent_i) / ((index + 1.0) / (copy.size() - 1.0) - percent_i); + float res = copy[index] + fraction * (copy[index + 1] - copy[index]); + return res; +} + +/** + * @brief getMovingAverage calculates the weighted moving average of input vector + * The initial value is the minimum (or maximum) value of the first batch of the vector + */ +float getMovingAverage(const std::vector &vector, const float alpha, + const uint8_t batch_size, bool is_min) +{ + assert(!vector.empty()); + assert(alpha >= 0.0 && alpha <= 1.0); + assert(batch_size > 0); + + auto getBatchMinOrMax = [&](int start_index) { + assert(start_index >= 0 && start_index < vector.size()); + + float res = is_min ? std::numeric_limits::max() : std::numeric_limits::lowest(); + for (int offset = 0; offset < batch_size; offset++) + { + int index = start_index + offset; + if (index >= vector.size()) + break; + + if (is_min) + { + res = vector[index] < res ? vector[index] : res; + } + else + { + res = vector[index] > res ? vector[index] : res; + } + } + return res; + }; + + float curr_avg = getBatchMinOrMax(0); + for (size_t i = batch_size; i < vector.size(); i += batch_size) + { + curr_avg = curr_avg * alpha + getBatchMinOrMax(i) * (1.0 - alpha); + } + return curr_avg; +} + +} // namespace record_minmax diff --git a/compiler/record-minmax/include/RecordMinMax.h b/compiler/record-minmax/include/RecordMinMax.h new file mode 100644 index 0000000..ffdb17a --- /dev/null +++ b/compiler/record-minmax/include/RecordMinMax.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __RECORD_MINMAX_H__ +#define __RECORD_MINMAX_H__ + +#include +#include + +#include "MinMaxObserver.h" + +#include + +namespace record_minmax +{ + +class RecordMinMax +{ +public: + explicit RecordMinMax() = default; + + ~RecordMinMax() = default; + + void initialize(const std::string &input_model_path); + + void profileData(const std::string &mode, const std::string &input_data_path, + float min_percentile, float max_percentile); + + void saveModel(const std::string &output_model_path); + +private: + std::unique_ptr _module; + std::unique_ptr _interpreter; + std::unique_ptr _observer; +}; + +} // namespace record_minmax + +#endif // __RECORD_MINMAX_H__ diff --git a/compiler/record-minmax/requires.cmake b/compiler/record-minmax/requires.cmake new file mode 100644 index 0000000..0545035 --- /dev/null +++ b/compiler/record-minmax/requires.cmake @@ -0,0 +1,3 @@ +require("luci") +require("safemain") +require("arser") diff --git a/compiler/record-minmax/src/CircleExpContract.cpp b/compiler/record-minmax/src/CircleExpContract.cpp new file mode 100644 index 0000000..b703250 --- /dev/null +++ b/compiler/record-minmax/src/CircleExpContract.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "CircleExpContract.h" + +#include + +#include +#include + +namespace record_minmax +{ + +bool CircleExpContract::store(const char *ptr, const size_t size) const +{ + if (!ptr) + INTERNAL_EXN("Graph was not serialized by FlatBuffer for some reason"); + + std::ofstream fs(_filepath, std::ofstream::binary); + fs.write(ptr, size); + + return fs.good(); +} + +} // namespace record_minmax diff --git a/compiler/record-minmax/src/CircleExpContract.h b/compiler/record-minmax/src/CircleExpContract.h new file mode 100644 index 0000000..ab00fa8 --- /dev/null +++ b/compiler/record-minmax/src/CircleExpContract.h @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __RECORD_MINMAX_CIRCLEXPCONTRACT_H__ +#define __RECORD_MINMAX_CIRCLEXPCONTRACT_H__ + +#include +#include +#include + +#include + +namespace record_minmax +{ + +struct CircleExpContract : public luci::CircleExporter::Contract +{ +public: + CircleExpContract(luci::Module *module, const std::string &filename) + : _module(module), _filepath(filename) + { + // NOTHING TO DO + } + virtual ~CircleExpContract() = default; + +public: + loco::Graph *graph(void) const final { return nullptr; } + luci::Module *module(void) const final { return _module; }; + +public: + bool store(const char *ptr, const size_t size) const final; + +private: + luci::Module *_module; + const std::string _filepath; +}; + +} // namespace record_minmax + +#endif // __RECORD_MINMAX_CIRCLEXPCONTRACT_H__ diff --git a/compiler/record-minmax/src/HDF5Importer.cpp b/compiler/record-minmax/src/HDF5Importer.cpp new file mode 100644 index 0000000..cf30cd8 --- /dev/null +++ b/compiler/record-minmax/src/HDF5Importer.cpp @@ -0,0 +1,132 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "HDF5Importer.h" + +#include + +#include +#include + +using Shape = luci_interpreter::Shape; +using DataType = luci_interpreter::DataType; + +namespace +{ + +Shape toInternalShape(const H5::DataSpace &dataspace) +{ + int rank = dataspace.getSimpleExtentNdims(); + + std::vector dims; + dims.resize(rank, 0); + dataspace.getSimpleExtentDims(dims.data()); + + Shape res(rank); + for (int axis = 0; axis < rank; ++axis) + { + res.dim(axis) = dims[axis]; + } + + return res; +} + +DataType toInternalDtype(const H5::DataType &h5_type) +{ + if (h5_type == H5::PredType::IEEE_F32BE || h5_type == H5::PredType::IEEE_F32LE) + { + return DataType::FLOAT32; + } + if (h5_type == H5::PredType::STD_I32BE || h5_type == H5::PredType::STD_I32LE) + { + return DataType::S32; + } + if (h5_type == H5::PredType::STD_I64BE || h5_type == H5::PredType::STD_I64LE) + { + return DataType::S64; + } + // Only support three datatypes for now + return DataType::Unknown; +} + +void readTensorData(H5::DataSet &tensor, uint8_t *buffer) +{ + tensor.read(buffer, H5::PredType::NATIVE_UINT8); +} + +void readTensorData(H5::DataSet &tensor, float *buffer) +{ + tensor.read(buffer, H5::PredType::NATIVE_FLOAT); +} + +void readTensorData(H5::DataSet &tensor, int32_t *buffer) +{ + tensor.read(buffer, H5::PredType::NATIVE_INT); +} + +void readTensorData(H5::DataSet &tensor, int64_t *buffer) +{ + tensor.read(buffer, H5::PredType::NATIVE_LONG); +} + +} // namespace + +namespace record_minmax +{ + +int32_t HDF5Importer::numInputs(int32_t record_idx) +{ + auto records = _value_grp.openGroup(std::to_string(record_idx)); + return records.getNumObjs(); +} + +void HDF5Importer::readTensor(int32_t record_idx, int32_t input_idx, void *buffer) +{ + auto record = _value_grp.openGroup(std::to_string(record_idx)); + auto tensor = record.openDataSet(std::to_string(input_idx)); + + readTensorData(tensor, static_cast(buffer)); +} + +void HDF5Importer::readTensor(int32_t record_idx, int32_t input_idx, DataType *dtype, Shape *shape, + void *buffer) +{ + auto record = _value_grp.openGroup(std::to_string(record_idx)); + auto tensor = record.openDataSet(std::to_string(input_idx)); + + auto tensor_dtype = tensor.getDataType(); + *dtype = toInternalDtype(tensor_dtype); + + auto tensor_shape = tensor.getSpace(); + *shape = toInternalShape(tensor_shape); + + switch (*dtype) + { + case DataType::FLOAT32: + readTensorData(tensor, static_cast(buffer)); + break; + case DataType::S32: + readTensorData(tensor, static_cast(buffer)); + break; + case DataType::S64: + readTensorData(tensor, static_cast(buffer)); + break; + default: + throw std::runtime_error{"Unsupported data type for input data (.h5)"}; + } +} + +} // namespace record_minmax diff --git a/compiler/record-minmax/src/HDF5Importer.h b/compiler/record-minmax/src/HDF5Importer.h new file mode 100644 index 0000000..cf65266 --- /dev/null +++ b/compiler/record-minmax/src/HDF5Importer.h @@ -0,0 +1,82 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __RECORD_MINMAX_HDF5IMPORTER_H__ +#define __RECORD_MINMAX_HDF5IMPORTER_H__ + +#include + +#include + +using Shape = luci_interpreter::Shape; +using DataType = luci_interpreter::DataType; + +namespace record_minmax +{ + +// HDF5Importer reads an input data saved in the hdf5 file in the given path +// The hierarchy of the hdf5 file is as follows. +// Group "/" +// > Group "value" +// > Group +// > Dataset +// record_idx : index of the record (dataset file can contain multiple records) +// input_idx : index of the input (DNN model can have multiple inputs) +// Ex: the j'th input of the i'th record can be accessed by "/value/i/j" +class HDF5Importer +{ +public: + explicit HDF5Importer(const std::string &path) : _file{path, H5F_ACC_RDONLY} + { + // Do nothing + } + +public: + /** + * @brief importGroup has to be called before readTensor is called + * Otherwise, readTensor will throw an exception + */ + void importGroup() { _value_grp = _file.openGroup("value"); } + + /** + * @brief Read tensor data from file and store it into buffer + * @details A tensor in the file can be retrieved with (record_idx, input_idx) + * @param record_idx : index of the record + * @param input_idx : index of the input + * @param dtype : pointer to write the tensor's data type + * @param shape : pointer to write the tensor's shape + * @param buffer : pointer to write the tensor's data + */ + void readTensor(int32_t record_idx, int32_t input_idx, DataType *dtype, Shape *shape, + void *buffer); + + // Read a raw tensor (no type/shape is specified) + void readTensor(int32_t record_idx, int32_t input_idx, void *buffer); + + bool isRawData() { return _value_grp.attrExists("rawData"); } + + int32_t numRecords() { return _value_grp.getNumObjs(); } + + int32_t numInputs(int32_t record_idx); + +private: + H5::H5File _file; + H5::Group _value_grp; +}; + +} // namespace record_minmax + +#endif // __RECORD_MINMAX_HDF5IMPORTER_H__ diff --git a/compiler/record-minmax/src/MinMaxObserver.cpp b/compiler/record-minmax/src/MinMaxObserver.cpp new file mode 100644 index 0000000..45f0197 --- /dev/null +++ b/compiler/record-minmax/src/MinMaxObserver.cpp @@ -0,0 +1,69 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "MinMaxObserver.h" + +#include + +using DataType = luci_interpreter::DataType; + +namespace record_minmax +{ + +// postTensorWrite is only called for a node producing a tensor +void MinMaxObserver::postTensorWrite(const luci::CircleNode *node, + const luci_interpreter::Tensor *tensor) +{ + // CircleOutput does not produce a tensor + assert(node->opcode() != luci::CircleOpcode::CIRCLEOUTPUT); + + // Operators with multiple outputs + assert(node->opcode() != luci::CircleOpcode::IF); + assert(node->opcode() != luci::CircleOpcode::SPLIT); + assert(node->opcode() != luci::CircleOpcode::SPLIT_V); + assert(node->opcode() != luci::CircleOpcode::TOPK_V2); + assert(node->opcode() != luci::CircleOpcode::UNPACK); + assert(node->opcode() != luci::CircleOpcode::WHILE); + + if (node->opcode() == luci::CircleOpcode::CONST) + { + // node is not activation. Do nothing. + return; + } + + if (node->opcode() == luci::CircleOpcode::ARG_MAX) + { + // Output of arg_max is the index of the largest value across axes of a tensor + // this should not be quantized + return; + } + + // Only support recording of float32 values + if (tensor->element_type() != DataType::FLOAT32) + throw std::runtime_error("Tensor's data type is not float"); + + const auto data = tensor->data(); + const auto num_elements = tensor->shape().num_elements(); + + std::vector buf(data, data + num_elements); + auto minmax = std::minmax_element(buf.begin(), buf.end()); + float min = *minmax.first; + float max = *minmax.second; + + _minmax_data.recordMinMax(node, min, max); +} + +} // namespace record_minmax diff --git a/compiler/record-minmax/src/RecordMinMax.cpp b/compiler/record-minmax/src/RecordMinMax.cpp new file mode 100644 index 0000000..d12a0d3 --- /dev/null +++ b/compiler/record-minmax/src/RecordMinMax.cpp @@ -0,0 +1,196 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "RecordMinMax.h" +#include "RecordFunction.h" +#include "CircleExpContract.h" +#include "MinMaxObserver.h" +#include "HDF5Importer.h" + +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +using Shape = luci_interpreter::Shape; +using DataType = luci_interpreter::DataType; + +namespace +{ + +/** + * @brief getTensorSize will return size in bytes + */ +template size_t getTensorSize(const NodeT *node) +{ + uint32_t tensor_size = loco::size(node->dtype()); + for (uint32_t i = 0; i < node->rank(); ++i) + tensor_size *= node->dim(i).value(); + return tensor_size; +} + +/** + * @brief verifyTypeShape checks the type and the shape of CircleInput + * This throws an exception if type or shape does not match + */ +void verifyTypeShape(const luci::CircleInput *input_node, const DataType &dtype, const Shape &shape) +{ + // Type check + if (dtype != input_node->dtype()) + throw std::runtime_error("Wrong input type."); + + if (shape.num_dims() != input_node->rank()) + throw std::runtime_error("Input rank mismatch."); + + for (uint32_t i = 0; i < shape.num_dims(); i++) + { + if (shape.dim(i) != input_node->dim(i).value()) + throw std::runtime_error("Input shape mismatch."); + } +} + +} // namespace + +namespace record_minmax +{ + +void RecordMinMax::initialize(const std::string &input_model_path) +{ + // Load model from the file + std::ifstream fs(input_model_path, std::ifstream::binary); + if (fs.fail()) + { + throw std::runtime_error("Cannot open model file \"" + input_model_path + "\".\n"); + } + std::vector model_data((std::istreambuf_iterator(fs)), + std::istreambuf_iterator()); + _module = luci::Importer().importModule(circle::GetModel(model_data.data())); + + if (_module == nullptr) + { + throw std::runtime_error("ERROR: Failed to load '" + input_model_path + "'"); + } + + // Initialize interpreter + _interpreter = std::make_unique(_module.get()); + + _observer = std::make_unique(); + + _interpreter->attachObserver(_observer.get()); +} + +void RecordMinMax::profileData(const std::string &mode, const std::string &input_data_path, + float min_percentile, float max_percentile) +{ + HDF5Importer importer(input_data_path); + importer.importGroup(); + + bool is_raw_data = importer.isRawData(); + + const auto num_records = importer.numRecords(); + if (num_records == 0) + throw std::runtime_error("The input data file does not contain any record."); + + const auto input_nodes = loco::input_nodes(_module->graph()); + const auto num_inputs = input_nodes.size(); + + for (int32_t record_idx = 0; record_idx < num_records; record_idx++) + { + if (num_inputs != importer.numInputs(record_idx)) + throw std::runtime_error("Wrong number of inputs."); + + if (record_idx % 100 == 0) + std::cout << "Recording " << record_idx << "'th data" << std::endl; + + for (int32_t input_idx = 0; input_idx < num_inputs; input_idx++) + { + const auto *input_node = loco::must_cast(input_nodes[input_idx]); + assert(input_node->index() == input_idx); + std::vector input_data(getTensorSize(input_node)); + + if (!is_raw_data) + { + DataType dtype; + Shape shape(input_node->rank()); + importer.readTensor(record_idx, input_idx, &dtype, &shape, input_data.data()); + + // Check the type and the shape of the input data is valid + verifyTypeShape(input_node, dtype, shape); + } + else + { + // Skip type/shape check for raw data + importer.readTensor(record_idx, input_idx, input_data.data()); + } + + // TODO: Input data is copied twice (file -> buffer (input_data) -> interpreter inputs) + // We can redcue the copy by directly writing data from file to interpreter inputs + _interpreter->writeInputTensor(input_node, input_data.data(), input_data.size()); + } + + _interpreter->interpret(); + } + + std::cout << "Recording finished. Number of recorded data: " << num_records << std::endl; + + auto minmax_map = _observer->minMaxData()->getMap(); + for (auto iter = minmax_map->begin(); iter != minmax_map->end(); ++iter) + { + auto node = iter->first; + auto minmax = iter->second; + + float min, max; + if (mode == "percentile") + { + min = getNthPercentile(minmax.min_vector, min_percentile); + max = getNthPercentile(minmax.max_vector, max_percentile); + } + else if (mode == "moving_average") + { + min = getMovingAverage(minmax.min_vector, 0.9, 16, true); + max = getMovingAverage(minmax.max_vector, 0.9, 16, false); + } + assert(mode == "percentile" || mode == "moving_average"); + auto quantparam = std::make_unique(); + quantparam->min.push_back(min); + quantparam->max.push_back(max); + + assert(node->quantparam() == nullptr); + + auto mutable_node = const_cast(node); + mutable_node->quantparam(std::move(quantparam)); + } +} + +void RecordMinMax::saveModel(const std::string &output_model_path) +{ + // Export to output Circle file + luci::CircleExporter exporter; + CircleExpContract contract(_module.get(), output_model_path); + + if (!exporter.invoke(&contract)) + { + throw std::runtime_error("ERROR: Failed to export '" + output_model_path + "'"); + } +} + +} // namespace record_minmax diff --git a/compiler/record-minmax/tests/RecordFunction.test.cpp b/compiler/record-minmax/tests/RecordFunction.test.cpp new file mode 100644 index 0000000..13b464d --- /dev/null +++ b/compiler/record-minmax/tests/RecordFunction.test.cpp @@ -0,0 +1,104 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "RecordFunction.h" + +#include +#include + +#include + +namespace record_minmax +{ + +#define EXPECT_FLOAT_NEAR(exp, val) EXPECT_NEAR(exp, val, 1e-5 + 1e-5 * std::abs(exp)) + +TEST(GetNthPercentileTest, Edge) +{ + std::vector input{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}; + + EXPECT_FLOAT_NEAR(0, getNthPercentile(input, 0)); + EXPECT_FLOAT_NEAR(9, getNthPercentile(input, 100)); +} + +TEST(GetNthPercentileTest, Simple) +{ + std::vector input{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}; + + for (float i = 1; i <= 99; i++) + { + EXPECT_FLOAT_NEAR(0.09 * i, getNthPercentile(input, i)); + } + + for (float i = 0.5; i <= 99.5; i++) + { + EXPECT_FLOAT_NEAR(0.09 * std::floor(i) + 0.045, getNthPercentile(input, i)); + } +} + +TEST(GetNthPercentileTest, Float) +{ + std::vector input{8.48424583, 89.39998456, 65.83323245, 87.85243858, 68.85414866, + 98.40591775, 16.74266565, 25.09415131, 74.54084952, 29.70536481, + 49.26803928, 79.49602425, 53.69395631, 73.73140271, 99.81245733, + 46.76997646, 78.37688474, 10.43076744, 30.39480496, 14.30875609, + 86.72073486, 17.97364969, 14.66724564, 0.47818459, 17.77138025, + 85.68981239, 22.18322696, 78.81541331, 93.04085581, 40.2147895}; + + EXPECT_FLOAT_NEAR(2.799942346802177, getNthPercentile(input, 1)); + EXPECT_FLOAT_NEAR(7.768503955476342, getNthPercentile(input, 3.14)); + EXPECT_FLOAT_NEAR(99.40456084968194, getNthPercentile(input, 99)); +} + +TEST(GetNthPercentileTest, FloatWithNegative) +{ + std::vector input{-41.51575417, 39.39998456, 15.83323245, 37.85243858, 18.85414866, + 48.40591775, -33.25733435, -24.90584869, 24.54084952, -20.29463519, + -0.73196072, 29.49602425, 3.69395631, 23.73140271, 49.81245733, + -3.23002354, 28.37688474, -39.56923256, -19.60519504, -35.69124391, + 36.72073486, -32.02635031, -35.33275436, -49.52181541, -32.22861975, + 35.68981239, -27.81677304, 28.81541331, 43.04085581, -9.7852105}; + + EXPECT_FLOAT_NEAR(-47.20005765319782, getNthPercentile(input, 1)); + EXPECT_FLOAT_NEAR(-42.23149604452366, getNthPercentile(input, 3.14)); + EXPECT_FLOAT_NEAR(49.40456084968194, getNthPercentile(input, 99)); +} + +TEST(GetNthPercentileTest, SigleElement) +{ + std::vector input{33}; + + EXPECT_FLOAT_NEAR(33, getNthPercentile(input, 0)); + EXPECT_FLOAT_NEAR(33, getNthPercentile(input, 50)); + EXPECT_FLOAT_NEAR(33, getNthPercentile(input, 100)); +} + +TEST(GetNthPercentileTest, OutOfBoundary_NEG) +{ + std::vector input{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}; + + EXPECT_THROW(getNthPercentile(input, -1), std::runtime_error); + EXPECT_THROW(getNthPercentile(input, 101), std::runtime_error); +} + +TEST(GetNthPercentileTest, EmptyVector_NEG) +{ + std::vector input; + + EXPECT_THROW(getNthPercentile(input, 10), std::runtime_error); +} + +} // namespace record_minmax diff --git a/compiler/safemain/README.md b/compiler/safemain/README.md new file mode 100644 index 0000000..1844704 --- /dev/null +++ b/compiler/safemain/README.md @@ -0,0 +1 @@ +# safemain diff --git a/compiler/souschef/CMakeLists.txt b/compiler/souschef/CMakeLists.txt new file mode 100644 index 0000000..5a307be --- /dev/null +++ b/compiler/souschef/CMakeLists.txt @@ -0,0 +1,5 @@ +file(GLOB_RECURSE SOURCES "src/*.cpp") + +add_library(souschef STATIC ${SOURCES}) +set_target_properties(souschef PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_include_directories(souschef PUBLIC include) diff --git a/compiler/souschef/README.md b/compiler/souschef/README.md new file mode 100644 index 0000000..a475ce4 --- /dev/null +++ b/compiler/souschef/README.md @@ -0,0 +1,3 @@ +# souschef + +_souschef_ is common library for _tflchef_ and _circlechef_ diff --git a/compiler/souschef/include/souschef/Arguments.h b/compiler/souschef/include/souschef/Arguments.h new file mode 100644 index 0000000..4556ce7 --- /dev/null +++ b/compiler/souschef/include/souschef/Arguments.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SOUSCHEF_ARGUMENTS_H__ +#define __SOUSCHEF_ARGUMENTS_H__ + +#include +#include + +namespace souschef +{ + +/** + * @brief Read-only string sequence view + */ +struct Arguments +{ + virtual ~Arguments() = default; + + virtual uint32_t count(void) const = 0; + virtual const std::string &value(uint32_t n) const = 0; +}; + +} // namespace souschef + +#endif // __SOUSCHEF_ARGUMENTS_H__ diff --git a/compiler/souschef/include/souschef/Data/Constant.h b/compiler/souschef/include/souschef/Data/Constant.h new file mode 100644 index 0000000..6bb7bba --- /dev/null +++ b/compiler/souschef/include/souschef/Data/Constant.h @@ -0,0 +1,67 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SOUSCHEF_DATA_CONSTANT_H__ +#define __SOUSCHEF_DATA_CONSTANT_H__ + +#include "souschef/DataChef.h" +#include "souschef/LexicalCast.h" + +namespace souschef +{ + +template class ConstantDataChef final : public DataChef +{ +public: + ConstantDataChef(const T &value) : _value{value} + { + // DO NOTHING + } + +public: + std::vector generate(int32_t count) const override + { + std::vector res; + + for (uint32_t n = 0; n < count; ++n) + { + const uint8_t *arr = reinterpret_cast(&_value); + + for (uint32_t b = 0; b < sizeof(T); ++b) + { + res.emplace_back(arr[b]); + } + } + + return res; + } + +private: + T _value; +}; + +template struct ConstantDataChefFactory : public DataChefFactory +{ + std::unique_ptr create(const Arguments &args) const + { + auto const value = to_number(args.value(0)); + return std::unique_ptr{new ConstantDataChef{value}}; + } +}; + +} // namespace souschef + +#endif // __SOUSCHEF_DATA_CONSTANT_H__ diff --git a/compiler/souschef/include/souschef/Data/Explicit.h b/compiler/souschef/include/souschef/Data/Explicit.h new file mode 100644 index 0000000..6e5ee81 --- /dev/null +++ b/compiler/souschef/include/souschef/Data/Explicit.h @@ -0,0 +1,80 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SOUSCHEF_DATA_EXPLICIT_H__ +#define __SOUSCHEF_DATA_EXPLICIT_H__ + +#include "souschef/DataChef.h" +#include "souschef/LexicalCast.h" + +#include + +namespace souschef +{ + +template class ExplicitDataChef final : public DataChef +{ +public: + ExplicitDataChef() + { + // DO NOTHING + } + +public: + std::vector generate(int32_t count) const override + { + std::vector res; + + for (uint32_t n = 0; n < count; ++n) + { + T const value = (n < _values.size()) ? _values.at(n) : T{}; + const uint8_t *arr = reinterpret_cast(&value); + + for (uint32_t b = 0; b < sizeof(T); ++b) + { + res.emplace_back(arr[b]); + } + } + + return res; + } + +public: + void insert(const T &value) { _values.emplace_back(value); } + +private: + std::vector _values; +}; + +template struct ExplicitDataChefFactory : public DataChefFactory +{ + std::unique_ptr create(const Arguments &args) const + { + std::unique_ptr> res{new ExplicitDataChef}; + + for (uint32_t n = 0; n < args.count(); ++n) + { + auto const value = to_number(args.value(n)); + res->insert(value); + } + + return std::move(res); + } +}; + +} // namespace souschef + +#endif // __SOUSCHEF_DATA_EXPLICIT_H__ diff --git a/compiler/souschef/include/souschef/Data/Gaussian.h b/compiler/souschef/include/souschef/Data/Gaussian.h new file mode 100644 index 0000000..75570e0 --- /dev/null +++ b/compiler/souschef/include/souschef/Data/Gaussian.h @@ -0,0 +1,93 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SOUSCHEF_DATA_GAUSSIAN_H__ +#define __SOUSCHEF_DATA_GAUSSIAN_H__ + +#include "souschef/DataChef.h" + +namespace souschef +{ + +/** + * @brief Generate a sequence of random values according to the gaussian(=normal) distribution + */ +class GaussianFloat32DataChef final : public DataChef +{ +public: + GaussianFloat32DataChef(float mean, float stddev) : _mean{mean}, _stddev{stddev} + { + // DO NOTHING + } + +public: + std::vector generate(int32_t count) const override; + +private: + float _mean; + float _stddev; +}; + +class GaussianInt32DataChef final : public DataChef +{ +public: + GaussianInt32DataChef(float mean, float stddev) : _mean{mean}, _stddev{stddev} + { + // DO NOTHING + } + +public: + std::vector generate(int32_t count) const override; + +private: + float _mean; + float _stddev; +}; + +class GaussianUint8DataChef final : public DataChef +{ +public: + GaussianUint8DataChef(float mean, float stddev) : _mean{mean}, _stddev{stddev} + { + // DO NOTHING + } + +public: + std::vector generate(int32_t count) const override; + +private: + float _mean; + float _stddev; +}; + +struct GaussianFloat32DataChefFactory : public DataChefFactory +{ + std::unique_ptr create(const Arguments &args) const; +}; + +struct GaussianInt32DataChefFactory : public DataChefFactory +{ + std::unique_ptr create(const Arguments &args) const; +}; + +struct GaussianUint8DataChefFactory : public DataChefFactory +{ + std::unique_ptr create(const Arguments &args) const; +}; + +} // namespace souschef + +#endif // __SOUSCHEF_DATA_GAUSSIAN_H__ diff --git a/compiler/souschef/include/souschef/DataChef.def b/compiler/souschef/include/souschef/DataChef.def new file mode 100644 index 0000000..28901db --- /dev/null +++ b/compiler/souschef/include/souschef/DataChef.def @@ -0,0 +1,19 @@ +#ifndef DATA_CHEF +#error "Define DATA_CHEF first" +#endif // DATA_CHEF + +// DATA_CHEF(TYPE, NAME, FACTORY_CLASS) +// "TYPE" SHOULD BE an enum tag of tflchef::TensorType +DATA_CHEF(FLOAT32, constant, ConstantDataChefFactory) +DATA_CHEF(BOOL, constant, ConstantDataChefFactory) +DATA_CHEF(UINT8, constant, ConstantDataChefFactory) +DATA_CHEF(INT32, constant, ConstantDataChefFactory) +DATA_CHEF(INT64, constant, ConstantDataChefFactory) +DATA_CHEF(INT64, explicit, ExplicitDataChefFactory) +DATA_CHEF(INT32, explicit, ExplicitDataChefFactory) +DATA_CHEF(UINT8, explicit, ExplicitDataChefFactory) +DATA_CHEF(BOOL, explicit, ExplicitDataChefFactory) +DATA_CHEF(FLOAT32, explicit, ExplicitDataChefFactory) +DATA_CHEF(FLOAT32, gaussian, GaussianFloat32DataChefFactory) +DATA_CHEF(INT32, gaussian, GaussianInt32DataChefFactory) +DATA_CHEF(UINT8, gaussian, GaussianUint8DataChefFactory) diff --git a/compiler/souschef/include/souschef/DataChef.h b/compiler/souschef/include/souschef/DataChef.h new file mode 100644 index 0000000..4a65dfc --- /dev/null +++ b/compiler/souschef/include/souschef/DataChef.h @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SOUSCHEF_DATA_CHEF_H__ +#define __SOUSCHEF_DATA_CHEF_H__ + +#include "Arguments.h" + +#include +#include +#include + +namespace souschef +{ + +using Data = std::vector; + +/** + * @brief Data Generator + */ +struct DataChef +{ + virtual ~DataChef() = default; + + // TODO Allow users to query the type of elements that this DataChef generates + + /** + * @brief Generate a sequence of 'count' elements as a byte sequence + * + * Let D be the return value of generate(N). + * Then, D.size() == N * sizeof(T) where T is the element type. + */ + virtual Data generate(int32_t count) const = 0; +}; + +/** + * @brief Data Generator Factory + */ +struct DataChefFactory +{ + virtual ~DataChefFactory() = default; + + virtual std::unique_ptr create(const Arguments &args) const = 0; +}; + +} // namespace souschef + +#endif // __SOUSCHEF_DATA_CHEF_H__ diff --git a/compiler/souschef/include/souschef/DataChefs.h b/compiler/souschef/include/souschef/DataChefs.h new file mode 100644 index 0000000..7a86a2c --- /dev/null +++ b/compiler/souschef/include/souschef/DataChefs.h @@ -0,0 +1,24 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SOUSCHEF_DATA_CHEFS_H__ +#define __SOUSCHEF_DATA_CHEFS_H__ + +#include "Data/Constant.h" +#include "Data/Explicit.h" +#include "Data/Gaussian.h" + +#endif // __SOUSCHEF_DATA_CHEFS_H__ diff --git a/compiler/souschef/include/souschef/Dataset.h b/compiler/souschef/include/souschef/Dataset.h new file mode 100644 index 0000000..46a12e4 --- /dev/null +++ b/compiler/souschef/include/souschef/Dataset.h @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SOUSCHEF_DATASET_H__ +#define __SOUSCHEF_DATASET_H__ + +#include + +namespace souschef +{ + +template class Dataset +{ +public: + Dataset(const std::vector &vec) : _vec{vec} + { + // DO NOTHING + } + +public: + Dataset(std::vector &&vec) : _vec{std::move(vec)} + { + // DO NOTHING + } + +public: + template auto map(Func f) const -> Dataset()))> + { + using U = decltype(f(std::declval())); + std::vector res; + + for (const auto &elem : _vec) + { + res.emplace_back(f(elem)); + } + + return Dataset(std::move(res)); + } + +public: + const std::vector &vectorize(void) const { return _vec; } + +private: + std::vector _vec; +}; + +} // namespace souschef + +#endif // __SOUSCHEF_DATASET_H__ diff --git a/compiler/souschef/include/souschef/LexicalCast.h b/compiler/souschef/include/souschef/LexicalCast.h new file mode 100644 index 0000000..d83cb2a --- /dev/null +++ b/compiler/souschef/include/souschef/LexicalCast.h @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +/** + * @brief This file provides string <-> number cast helpers + */ +#ifndef __SOUSCHEF_LEXICAL_CAST_H__ +#define __SOUSCHEF_LEXICAL_CAST_H__ + +#include + +namespace souschef +{ + +/** + * @brief Return a numeric value that corresponds to a given string + * + * @note This function will throw an exception on casting failure + */ +template Number to_number(const std::string &s); + +} // namespace souschef + +#endif // __SOUSCHEF_LEXICAL_CAST_H__ diff --git a/compiler/souschef/include/souschef/RangedArguments.h b/compiler/souschef/include/souschef/RangedArguments.h new file mode 100644 index 0000000..dd50f59 --- /dev/null +++ b/compiler/souschef/include/souschef/RangedArguments.h @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SOUSCHEF_RANGED_ARGUMENTS_H__ +#define __SOUSCHEF_RANGED_ARGUMENTS_H__ + +#include "Arguments.h" + +#include + +namespace souschef +{ + +template class RangedArguments : public Arguments +{ +public: + RangedArguments(InputIt beg, InputIt end) : _beg{beg}, _end{end} + { + // DO NOTHING + } + +public: + uint32_t count(void) const override { return _end - _beg; } + +public: + const std::string &value(uint32_t n) const override { return *(_beg + n); } + +private: + InputIt _beg; + InputIt _end; +}; + +template RangedArguments ranged_arguments(InputIt beg, InputIt end) +{ + return RangedArguments{beg, end}; +} + +} // namespace souschef + +#endif // __SOUSCHEF_RANGED_ARGUMENTS_H__ diff --git a/compiler/souschef/include/souschef/Registry.h b/compiler/souschef/include/souschef/Registry.h new file mode 100644 index 0000000..9457b6a --- /dev/null +++ b/compiler/souschef/include/souschef/Registry.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __SOUSCHEF_REGISTRY_H__ +#define __SOUSCHEF_REGISTRY_H__ + +#include +#include +#include + +namespace souschef +{ + +template class Registry +{ +public: + void add(const std::string &name, std::unique_ptr &&entry) + { + _content[name] = std::move(entry); + } + + const T &lookup(const std::string &name) const { return *(_content.at(name)); } + +private: + std::map> _content; +}; + +} // namespace souschef + +#endif // __SOUSCHEF_REGISTRY_H__ diff --git a/compiler/souschef/src/Gaussian.cpp b/compiler/souschef/src/Gaussian.cpp new file mode 100644 index 0000000..4a5083d --- /dev/null +++ b/compiler/souschef/src/Gaussian.cpp @@ -0,0 +1,140 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "souschef/Data/Gaussian.h" +#include "souschef/LexicalCast.h" + +#include +#include + +#include +#include + +namespace souschef +{ + +std::vector GaussianFloat32DataChef::generate(int32_t count) const +{ + // TODO Support seed value override + auto seed = std::chrono::system_clock::now().time_since_epoch().count(); + + std::minstd_rand rand{static_cast(seed)}; + std::normal_distribution dist{_mean, _stddev}; + + std::vector res; + + for (uint32_t n = 0; n < count; ++n) + { + auto const value = dist(rand); + auto const arr = reinterpret_cast(&value); + + for (uint32_t b = 0; b < sizeof(float); ++b) + { + res.emplace_back(arr[b]); + } + } + + return res; +} + +std::vector GaussianInt32DataChef::generate(int32_t count) const +{ + // TODO Support seed value override + auto seed = std::chrono::system_clock::now().time_since_epoch().count(); + + std::minstd_rand rand{static_cast(seed)}; + std::normal_distribution dist{_mean, _stddev}; + + std::vector res; + + for (uint32_t n = 0; n < count; ++n) + { + auto const value = static_cast(dist(rand)); + auto const arr = reinterpret_cast(&value); + + for (uint32_t b = 0; b < sizeof(int32_t); ++b) + { + res.emplace_back(arr[b]); + } + } + + return res; +} + +std::vector GaussianUint8DataChef::generate(int32_t count) const +{ + // TODO Support seed value override + auto seed = std::chrono::system_clock::now().time_since_epoch().count(); + + std::minstd_rand rand{static_cast(seed)}; + std::normal_distribution dist{_mean, _stddev}; + + std::vector res; + + for (uint32_t n = 0; n < count; ++n) + { + auto const value = static_cast(dist(rand)); // uint8_t for data type + auto const arr = reinterpret_cast(&value); // uint8_t for byte streaming + + for (uint32_t b = 0; b < sizeof(uint8_t); ++b) + { + res.emplace_back(arr[b]); + } + } + + return res; +} + +std::unique_ptr GaussianFloat32DataChefFactory::create(const Arguments &args) const +{ + if (args.count() != 2) + { + throw std::runtime_error{"invalid argument count: two arguments (mean/stddev) are expected"}; + } + + auto const mean = to_number(args.value(0)); + auto const stddev = to_number(args.value(1)); + + return std::unique_ptr{new GaussianFloat32DataChef{mean, stddev}}; +} + +std::unique_ptr GaussianInt32DataChefFactory::create(const Arguments &args) const +{ + if (args.count() != 2) + { + throw std::runtime_error{"invalid argument count: two arguments (mean/stddev) are expected"}; + } + + auto const mean = to_number(args.value(0)); + auto const stddev = to_number(args.value(1)); + + return std::unique_ptr{new GaussianInt32DataChef{mean, stddev}}; +} + +std::unique_ptr GaussianUint8DataChefFactory::create(const Arguments &args) const +{ + if (args.count() != 2) + { + throw std::runtime_error{"invalid argument count: two arguments (mean/stddev) are expected"}; + } + + auto const mean = to_number(args.value(0)); + auto const stddev = to_number(args.value(1)); + + return std::unique_ptr{new GaussianUint8DataChef{mean, stddev}}; +} + +} // namespace souschef diff --git a/compiler/souschef/src/LexicalCast.cpp b/compiler/souschef/src/LexicalCast.cpp new file mode 100644 index 0000000..8e3d4cb --- /dev/null +++ b/compiler/souschef/src/LexicalCast.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "souschef/LexicalCast.h" + +#include +#include + +namespace souschef +{ + +template <> float to_number(const std::string &s) { return std::stof(s); } +template <> int to_number(const std::string &s) { return std::stoi(s); } +template <> int64_t to_number(const std::string &s) { return std::stoll(s); } +template <> uint8_t to_number(const std::string &s) +{ + int temp = std::stoi(s); + assert(temp >= 0); + assert(temp <= std::numeric_limits::max()); + return static_cast(temp); +} +template <> bool to_number(const std::string &s) +{ + if (std::stoi(s) || s == "T" || s == "t" || s == "TRUE" || s == "true") + return true; + return false; +} + +} // namespace souschef diff --git a/compiler/tf2circle-value-pbtxt-remote-test/CMakeLists.txt b/compiler/tf2circle-value-pbtxt-remote-test/CMakeLists.txt index f5292a0..64dcc28 100644 --- a/compiler/tf2circle-value-pbtxt-remote-test/CMakeLists.txt +++ b/compiler/tf2circle-value-pbtxt-remote-test/CMakeLists.txt @@ -141,7 +141,7 @@ add_custom_command( COMMAND ${CMAKE_COMMAND} -E echo 'HDF5_EXPORT_ACTION_PATH=\"$\"' >> ${TEST_CONFIG} COMMAND ${CMAKE_COMMAND} -E echo 'HDF5_IMPORT_ACTION_PATH=\"$\"' >> ${TEST_CONFIG} COMMAND ${CMAKE_COMMAND} -E echo 'MODEL2NNPKG_PATH=\"${NNAS_PROJECT_SOURCE_DIR}/tools/nnpackage_tool/model2nnpkg/model2nnpkg.sh\"' >> ${TEST_CONFIG} - COMMAND ${CMAKE_COMMAND} -E echo 'NNPKG_TEST_PATH=\"${NNAS_PROJECT_SOURCE_DIR}/tools/nnpackage_tool/nnpkg_test/nnpkg_test.sh\"' >> ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E echo 'NNPKG_TEST_PATH=\"${NNAS_PROJECT_SOURCE_DIR}/tests/scripts/nnpkg_test.sh\"' >> ${TEST_CONFIG} COMMAND ${CMAKE_COMMAND} -E echo 'RUNTIME_LIBRARY_PATH=\"${NNAS_PROJECT_SOURCE_DIR}/Product/out/\"' >> ${TEST_CONFIG} DEPENDS nnkit-run diff --git a/compiler/tf2nnpackage-value-remote-test/CMakeLists.txt b/compiler/tf2nnpackage-value-remote-test/CMakeLists.txt new file mode 100644 index 0000000..4a59e88 --- /dev/null +++ b/compiler/tf2nnpackage-value-remote-test/CMakeLists.txt @@ -0,0 +1,94 @@ +include("test.lst") + +# Do not make test if there are no remote machine information +if(NOT REMOTE_IP) + message(STATUS "tf2nnpackage-value-remote-test: need to set REMOTE IP") + return() +endif(NOT REMOTE_IP) + +if(NOT REMOTE_USER) + message(STATUS "tf2nnpackage-value-remote-test: need to set REMOTE_USER") + return() +endif(NOT REMOTE_USER) + +nnas_include(TargetRequire) + +unset(REQUIRED_TARGETS) +list(APPEND REQUIRED_TARGETS testDataGenerator) +TargetRequire_Return(${REQUIRED_TARGETS}) + +message(STATUS "tf2nnpackage-value-remote-test: run tests") + +unset(TEST_NAMES) + +nncc_find_resource(TensorFlowLiteRecipes) +set(TFLITE_RECIPE_REPO "${TensorFlowLiteRecipes_DIR}") + +file(GLOB SUBDIR RELATIVE ${TFLITE_RECIPE_REPO} ${TFLITE_RECIPE_REPO}/*) +foreach(DIR IN ITEMS ${SUBDIR}) + if(IS_DIRECTORY ${TFLITE_RECIPE_REPO}/${DIR}) + list(APPEND TEST_NAMES ${DIR}) + endif() +endforeach() + +get_target_property(ARTIFACTS_SRC_PATH testDataGenerator SOURCE_DIR) + +# In this test, only the runtime test is performed because the test from tf to +# nnpackage is done in common-artifacts, and for this runtime test, generation of +# test data is required. And, tcgenerate in ${ARTIFACTS_SRC_PATH}/exclude.lst +# means it won't generate test data, which is why below "tcgenerate" macro excludes +# specific opearators from runtime test. +# Also, since circlize and optimize macro included in `exclude.lst` file is only +# needed in common-artifacts, it has no function here. +macro(circlize) +endmacro() +macro(optimize) +endmacro() + +macro(tcgenerate NAME) + list(REMOVE_ITEM TEST_NAMES ${NAME}) +endmacro() + +include("${ARTIFACTS_SRC_PATH}/exclude.lst") + +# Copy testall +set(TEST_RUNNER_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/testall.sh") +set(TEST_RUNNER "${CMAKE_CURRENT_BINARY_DIR}/testall.sh") + +add_custom_command( + OUTPUT ${TEST_RUNNER} + COMMAND ${CMAKE_COMMAND} -E copy "${TEST_RUNNER_SOURCE}" "${TEST_RUNNER}" + DEPENDS ${TEST_RUNNER_SOURCE} + COMMENT "Generate test runner" +) + +list(APPEND TEST_DEPS "${TEST_RUNNER}") + +get_target_property(ARTIFACTS_BIN_PATH testDataGenerator BINARY_DIR) + +# Generate test.config +set(TEST_CONFIG "${CMAKE_CURRENT_BINARY_DIR}/test.config") + +add_custom_command( + OUTPUT ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E remove -f ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E echo 'NNPKG_TEST_PATH=\"${NNAS_PROJECT_SOURCE_DIR}/tests/scripts/nnpkg_test.sh\"' >> ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E echo 'RUNTIME_LIBRARY_PATH=\"${NNAS_PROJECT_SOURCE_DIR}/Product/out/\"' >> ${TEST_CONFIG} + COMMENT "Generate test configuration" +) + +list(APPEND TEST_DEPS "${TEST_CONFIG}") + +# This "tf2nnpackage_value_remote_test_deps" target enforces CMake to generate all the dependencies during "build" phase +add_custom_target(tf2nnpackage_value_remote_test_deps ALL DEPENDS ${TEST_DEPS}) + +# Run tests +add_test( + NAME tf2nnpackage_value_remote_test + COMMAND "${TEST_RUNNER}" + "${TEST_CONFIG}" + "${ARTIFACTS_BIN_PATH}" + "${REMOTE_IP}" + "${REMOTE_USER}" + ${TEST_NAMES} +) diff --git a/compiler/tf2nnpackage-value-remote-test/README.md b/compiler/tf2nnpackage-value-remote-test/README.md new file mode 100644 index 0000000..36436fc --- /dev/null +++ b/compiler/tf2nnpackage-value-remote-test/README.md @@ -0,0 +1,60 @@ +# tf2nnpackage-value-remote-test + +`tf2nnpackage-value-remote-test` does random value test for nnpackage file using remote machine, normally Odroid, which `onert` runs on. + +### Prerequisites + +1. Runtime Library and Binary files + - Detailed information is located in [here](../../docs/howto/how-to-cross-build-runtime-for-arm.md) + - If you build runtime, related files will be produced in `Product/out`. Do not rename or move it. +1. Remote machine information and test list + - You should create `test.lst` file first as shown below. + - Set IP address and username of remote machine using `set` command. + ```cmake + #--------------- Remote Machine Setting ---------------# + set(REMOTE_IP "xxx.xxx.xxx.xxx") + set(REMOTE_USER "remote_username") + ``` + - If any recipe is added, or if `REMOTE_IP` and `REMOTE_USER` is not given, `tf2nnpackage-value-remote-test` will not be created. +1. (Optional) ssh authentication + - This test uses `ssh` and `scp` commands, and those commands require a password of remote machine whenever they are called. This means that you should enter the password everytime when `ssh` and `scp` require. + - This test resolves the problem by using `ssh-copy-id`, which copies the public key of host machine to `authorized_keys` of remote machine. Because of that, this test will ask the password of remote machine only once, at the first time. This is the only user interaction while running this test. + - If you do not want to interact with system, just do `ssh-copy-id ${REMOTE_USER}@${REMOTE_IP}` in advance, before running this test. Once `ssh-copy-id` is done, there will be no user-interaction action while running the test. + +### Running + +- If you finished prerequisites properly, configuring -> building -> testing steps create cmake test automatically. +- All the related materials will be sent to `REMOTE_WORKDIR` in remote machine. Default value of `REMOTE_WORKDIR` is `CVT_YYMMDD_hhmmss`, which means Circle Value Test done on YY/MM/DD at hh:mm:ss. +- `REMOTE_WORKDIR` will not be removed automatically after this test finish. + ```sh + $ ./nncc configure && ./nncc build + + # Default REMOTE_WORKDIR is CVT_YYMMDD_hhmmss folder + $ ./nncc test -R tf2nnpackage_value_remote_test + + # You can set REMOTE_WORKDIR where you have write privilege + $ REMOTE_WORKDIR=/path/you/want/ ./nncc test -R tf2circle_value_pbtxt_remote_test + ``` + +### Generated Files While Running + +- All related files(`pb`, `circle`, `h5` ... etc.) are taken from `build/compiler/common-artifacts` folder. +- `nnpkg_test.sh`, runtime products and each nnpackage are sent to `REMOTE_WORKDIR` in remote machine. +- Each test result is generated in `build/compiler/common-artifacts` with the name `${RECIPE}.log` + +### Check Test Result + +- Summary of test result will be created as csv file in host. + ```sh + # Result_latest is symbolic link to the latest csv result file + # Print the latest test result + $ cat build/compiler/tf2circle-value-pbtxt-remote-test/Result_latest + TEST_NAME, TF2CIRCLE, CIRCLE_VALUE_TEST + UNIT_Add_000, TRUE, TRUE + ... + + # List all result csv files + $ ls build/compiler/tf2circle-value-pbtxt-remote-test/Result_*.csv + Result_20191119_212521.csv + ... + ``` diff --git a/compiler/tf2nnpackage-value-remote-test/requires.cmake b/compiler/tf2nnpackage-value-remote-test/requires.cmake new file mode 100644 index 0000000..06a4a8a --- /dev/null +++ b/compiler/tf2nnpackage-value-remote-test/requires.cmake @@ -0,0 +1 @@ +require("common-artifacts") diff --git a/compiler/tf2nnpackage-value-remote-test/test.lst b/compiler/tf2nnpackage-value-remote-test/test.lst new file mode 100644 index 0000000..10eb52d --- /dev/null +++ b/compiler/tf2nnpackage-value-remote-test/test.lst @@ -0,0 +1,3 @@ +#--------------- Remote Machine Setting ---------------# +# set(REMOTE_IP "xxx.xxx.xxx.xxx") +# set(REMOTE_USER "remote_username") diff --git a/compiler/tf2nnpackage-value-remote-test/testall.sh b/compiler/tf2nnpackage-value-remote-test/testall.sh new file mode 100755 index 0000000..f1c9789 --- /dev/null +++ b/compiler/tf2nnpackage-value-remote-test/testall.sh @@ -0,0 +1,119 @@ +#!/bin/bash + +# Need at least 4 arguments +if [[ $# -lt 4 ]]; then + echo "USAGE: $0 ..." + echo + echo "ARGUMENTS:" + echo " [test.config path]" + echo " [WORKDIR]" + echo " [REMOTE_IP]" + echo " [REMOTE_USER]" + echo " [Prefix1]" + echo " [Prefix2]" + echo " ..." + exit 255 +fi + +CONFIG_PATH="$1"; shift +WORKDIR="$1"; shift +REMOTE_IP="$1"; shift +REMOTE_USER="$1"; shift + +BINDIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +CURRENT_DATETIME=$(date +'%Y%m%d_%H%M%S') +REMOTE_WORKDIR=${REMOTE_WORKDIR:-"CVT_${CURRENT_DATETIME}"} +RESULT_CSV="${BINDIR}/Result_${CURRENT_DATETIME}.csv" + +source "${CONFIG_PATH}" + +echo "-- Found nnpkg_test: ${NNPKG_TEST_PATH}" +echo "-- Found Runtime library: ${RUNTIME_LIBRARY_PATH}" +echo "-- Found workdir: ${WORKDIR}" + +if [ -z ${NNPKG_TEST_PATH} ] || [ ! -f ${NNPKG_TEST_PATH} ]; then + echo "nnpkg_test is not found" + exit 4 +fi + +# Register remote machine ssh information +cat /dev/zero | ssh-keygen -q -N "" +ssh-copy-id -o ConnectTimeout=5 "${REMOTE_USER}@${REMOTE_IP}" + +# Odroid IP address validation +if [[ $? -ne 0 ]]; then + echo "Cannot reach to given remote machine. Check IP address or username." + exit 5 +fi + +# Send runtime library files +ssh "${REMOTE_USER}@${REMOTE_IP}" "mkdir -p ${REMOTE_WORKDIR}/Product/" +scp -r "${RUNTIME_LIBRARY_PATH}" "${REMOTE_USER}@${REMOTE_IP}:${REMOTE_WORKDIR}/Product/" + +# Send nnpkg_test.sh +scp "${NNPKG_TEST_PATH}" "${REMOTE_USER}@${REMOTE_IP}:${REMOTE_WORKDIR}/" + +TESTED=() +PASSED=() +FAILED=() +echo "TEST_NAME, CIRCLE_VALUE_TEST" >> ${RESULT_CSV} + +pushd "${WORKDIR}" +while [[ $# -ne 0 ]]; do + PREFIX="$1"; shift + + TESTED+=("${PREFIX}") + + PASSED_TAG="${PREFIX}.passed" + + rm -f "${BINDIR}/${PASSED_TAG}" + + # Information to be recorded + CIRCLE_VALUE_PASSED=FALSE + + cat > "${BINDIR}/${PREFIX}.log" <( + exec 2>&1 + + # Exit immediately if any command fails + set -e + # Show commands + set -x + + # Run nnpkg_test in remote machine + if [ ! -d "${PREFIX}" ] ; then + PREFIX=${PREFIX}.opt ; + fi + scp -r "${PREFIX}/" "${REMOTE_USER}@${REMOTE_IP}:${REMOTE_WORKDIR}/${PREFIX}/" + ssh "${REMOTE_USER}@${REMOTE_IP}" "cd ${REMOTE_WORKDIR}; ./nnpkg_test.sh ${PREFIX}" + + if [[ $? -eq 0 ]]; then + touch "${BINDIR}/${PASSED_TAG}" + fi + ) + + if [[ -f "${BINDIR}/${PASSED_TAG}" ]]; then + PASSED+=("$PREFIX") + CIRCLE_VALUE_PASSED=TRUE + else + FAILED+=("$PREFIX") + CIRCLE_VALUE_PASSED=FALSE + fi + + echo "${PREFIX}, ${CIRCLE_VALUE_PASSED}" >> ${RESULT_CSV} +done +popd + +rm -f Result_latest +ln -s ${RESULT_CSV} Result_latest + +if [[ ${#TESTED[@]} -ne ${#PASSED[@]} ]]; then + echo "FAILED" + for TEST in "${FAILED[@]}" + do + echo "- ${TEST}" + done + exit 255 +fi + +echo "PASSED" +exit 0 diff --git a/compiler/tf2nnpkg/README.md b/compiler/tf2nnpkg/README.md new file mode 100644 index 0000000..f8ec2c9 --- /dev/null +++ b/compiler/tf2nnpkg/README.md @@ -0,0 +1 @@ +# tf2nnpkg diff --git a/compiler/tf2tflite-dredd-pbtxt-test/README.md b/compiler/tf2tflite-dredd-pbtxt-test/README.md new file mode 100644 index 0000000..0918679 --- /dev/null +++ b/compiler/tf2tflite-dredd-pbtxt-test/README.md @@ -0,0 +1 @@ +# tf2tflite-dredd-pbtxt-test diff --git a/compiler/tf2tfliteV2-conversion-test/CMakeLists.txt b/compiler/tf2tfliteV2-conversion-test/CMakeLists.txt new file mode 100644 index 0000000..3e7e577 --- /dev/null +++ b/compiler/tf2tfliteV2-conversion-test/CMakeLists.txt @@ -0,0 +1,109 @@ +nncc_find_resource(TensorFlowTests) + +# +# Copy [PREFIX]/test.pbtxt to PREFIX.pbtxt in binary folder +# Copy [PREFIX]/test.info to PREFIX.info in binary folder +# Encode PREFIX.pbtxt to PREFIX.pb +# +set(TEST_REPO "${TensorFlowTests_DIR}") +set(TEST_PBTXT_FILENAME "test.pbtxt") +set(TEST_INFO_FILENAME "test.info") + +unset(TESTCASES) + +macro(add NAME) + list(APPEND TESTCASES ${NAME}) +endmacro(add) + +# Read "test.lst" +include("test.lst") +# Read "test.local.lst" if exists +include("test.local.lst" OPTIONAL) + +unset(TEST_DEPS) +unset(TEST_NAMES) + +foreach(PREFIX IN ITEMS ${TESTCASES}) + if(NOT IS_DIRECTORY "${TEST_REPO}/${PREFIX}") + message(FATAL_ERROR "Missing '${PREFIX}' test") + endif() + + set(PBTXT_SOURCE_PATH "${TEST_REPO}/${PREFIX}/${TEST_PBTXT_FILENAME}") + set(INFO_SOURCE_PATH "${TEST_REPO}/${PREFIX}/${TEST_INFO_FILENAME}") + + set(PBTXT_FILE "${PREFIX}.pbtxt") + set(PBTXT_PATH "${CMAKE_CURRENT_BINARY_DIR}/${PBTXT_FILE}") + + set(INFO_FILE "${PREFIX}.info") + set(INFO_PATH "${CMAKE_CURRENT_BINARY_DIR}/${INFO_FILE}") + + # Copy .pbtxt + add_custom_command(OUTPUT ${PBTXT_PATH} + COMMAND ${CMAKE_COMMAND} -E copy "${PBTXT_SOURCE_PATH}" "${PBTXT_PATH}" + DEPENDS ${PBTXT_SOURCE_PATH} + COMMENT "Generate ${PBTXT_FILE}" + ) + + # Copy .info + add_custom_command(OUTPUT ${INFO_PATH} + COMMAND ${CMAKE_COMMAND} -E copy "${INFO_SOURCE_PATH}" "${INFO_PATH}" + DEPENDS ${INFO_SOURCE_PATH} + COMMENT "Generate ${INFO_FILE}" + ) + + list(APPEND TEST_DEPS ${INFO_PATH} ${PBTXT_PATH}) + list(APPEND TEST_NAMES ${PREFIX}) +endforeach(PREFIX) + +## +## Copy testall +## +set(TEST_RUNNER "${CMAKE_CURRENT_BINARY_DIR}/testall.sh") +set(TEST_RUNNER_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/testall.sh") + +add_custom_command( + OUTPUT ${TEST_RUNNER} + COMMAND ${CMAKE_COMMAND} -E copy "${TEST_RUNNER_SOURCE}" "${TEST_RUNNER}" + DEPENDS ${TEST_RUNNER_SOURCE} + COMMENT "Generate test runner" +) + +list(APPEND TEST_DEPS "${TEST_RUNNER}") + +get_target_property(ARTIFACTS_BIN_PATH testDataGenerator BINARY_DIR) + +set(VIRTUALENV "${NNCC_OVERLAY_DIR}/venv_1_13_2") + +### +### Generate test.config +### +set(TEST_CONFIG "${CMAKE_CURRENT_BINARY_DIR}/test.config") + +# Get tf2tfliteV2 binary path +get_target_property(TF2TFLITEV2_BIN_DIR tf2tfliteV2 BINARY_DIR) +set(TF2TFLITEV2_PATH "${TF2TFLITEV2_BIN_DIR}/tf2tfliteV2.py") + +add_custom_command( + OUTPUT ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E remove -f ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E echo 'TF2TFLITEV2_PATH=\"${TF2TFLITEV2_PATH}\"' >> ${TEST_CONFIG} + COMMAND ${CMAKE_COMMAND} -E echo 'VIRTUALENV=\"${VIRTUALENV}\"' >> ${TEST_CONFIG} + DEPENDS + tf2tfliteV2 + COMMENT "Generate test configuration" +) + +list(APPEND TEST_DEPS "${TEST_CONFIG}") + +# This "tf2tfliteV2_conversion_test_deps" target enforces CMake to generate all the dependencies during "build" phase +add_custom_target(tf2tfliteV2_conversion_test_deps ALL DEPENDS ${TEST_DEPS}) + +# TODO This test takes a long time and will only be tested once a day. +# Run tests +# add_test( +# NAME tf2tfliteV2_conversion_test +# COMMAND "${TEST_RUNNER}" +# "${TEST_CONFIG}" +# "${CMAKE_CURRENT_BINARY_DIR}" +# ${TEST_NAMES} +# ) diff --git a/compiler/tf2tfliteV2-conversion-test/README.md b/compiler/tf2tfliteV2-conversion-test/README.md new file mode 100644 index 0000000..d4d2a97 --- /dev/null +++ b/compiler/tf2tfliteV2-conversion-test/README.md @@ -0,0 +1,2 @@ +# tf2tfliteV2-conversion-test + diff --git a/compiler/tf2tfliteV2-conversion-test/requires.cmake b/compiler/tf2tfliteV2-conversion-test/requires.cmake new file mode 100644 index 0000000..31b3354 --- /dev/null +++ b/compiler/tf2tfliteV2-conversion-test/requires.cmake @@ -0,0 +1,2 @@ +require("common-artifacts") +require("tf2tfliteV2") diff --git a/compiler/tf2tfliteV2-conversion-test/test.lst b/compiler/tf2tfliteV2-conversion-test/test.lst new file mode 100644 index 0000000..2f771ba --- /dev/null +++ b/compiler/tf2tfliteV2-conversion-test/test.lst @@ -0,0 +1,124 @@ +# TODO Enable skipped tests + +add(NET_0000) +add(NET_0001) +add(NET_0002) +add(NET_0003) +add(NET_0004) +add(NET_0005) +add(NET_0006) +add(NET_0007) +add(NET_0008) +add(NET_0009) +add(NET_0010) +add(NET_0011) +add(NET_0012) +add(NET_0013) +add(NET_0014) +add(NET_0015) +add(NET_0016) +add(NET_0017) +add(NET_0018) +add(NET_0019) +add(NET_0020) +add(NET_0021) +add(NET_0022) +#add(NET_0023) +add(NET_0024) +add(NET_0025) +add(NET_0026) +add(NET_0027) +add(NET_0028) +add(NET_0029) +add(NET_0030) +add(NET_0031) +add(NET_0032) +add(NET_0033) +add(NET_0034) +add(NET_0035) +add(NET_0036) +add(NET_0037) +add(NET_0038) +add(NET_0039) +add(NET_0040) +add(NET_0041) +#add(NET_0042) +add(REGRESSION_0000) +add(REGRESSION_0001) +add(REGRESSION_0002) +add(UNIT_Add_000) +add(UNIT_Add_001) +add(UNIT_Add_002) +#add(UNIT_Add_003) +add(UNIT_Add_004) +add(UNIT_Add_005) +add(UNIT_AvgPool_000) +add(UNIT_AvgPool_001) +#add(UNIT_BiasAdd_000) +#add(UNIT_BiasAdd_001) +add(UNIT_BiasAdd_002) +#add(UNIT_ConcatV2_000) +#add(UNIT_ConcatV2_001) +add(UNIT_ConcatV2_002) +#add(UNIT_Const_000) +#add(UNIT_Const_001) +add(UNIT_Conv2D_000) +add(UNIT_Conv2D_001) +add(UNIT_Conv2D_002) +add(UNIT_Conv2DBackpropInput_000) +add(UNIT_Conv2DBackpropInput_001) +add(UNIT_Conv2DBackpropInput_002) +add(UNIT_CustomOp_000) +add(UNIT_CustomOp_001) +add(UNIT_DepthwiseConv2dNative_000) +add(UNIT_DepthwiseConv2dNative_001) +add(UNIT_FusedBatchNorm_000) +add(UNIT_FusedBatchNorm_001) +add(UNIT_Maximum_000) +add(UNIT_Maximum_001) +add(UNIT_Maximum_002) +add(UNIT_MaxPool_000) +add(UNIT_MaxPool_001) +add(UNIT_Mean_000) +add(UNIT_Mean_001) +add(UNIT_Mean_002) +add(UNIT_Mean_003) +add(UNIT_MirrorPad_000) +add(UNIT_Mul_000) +add(UNIT_Mul_001) +add(UNIT_Mul_002) +#add(UNIT_Pack_000) +#add(UNIT_Pack_001) +#add(UNIT_Pack_002) +#add(UNIT_Pack_003) +#add(UNIT_Pack_004) +add(UNIT_Pad_000) +add(UNIT_PadV2_000) +#add(UNIT_Placeholder_000) +#add(UNIT_Placeholder_001) +#add(UNIT_Placeholder_002) +#add(UNIT_Placeholder_003) +add(UNIT_RealDiv_000) +add(UNIT_RealDiv_001) +add(UNIT_Relu_000) +add(UNIT_Relu6_000) +add(UNIT_Reshape_000) +add(UNIT_Rsqrt_000) +#add(UNIT_Shape_000) +add(UNIT_Softmax_000) +add(UNIT_Softmax_001) +add(UNIT_Softmax_002) +add(UNIT_Softmax_003) +add(UNIT_Sqrt_000) +add(UNIT_SquaredDifference_000) +add(UNIT_SquaredDifference_001) +add(UNIT_Squeeze_000) +add(UNIT_Squeeze_001) +add(UNIT_Squeeze_002) +add(UNIT_Squeeze_003) +#add(UNIT_StopGradient_000) +#add(UNIT_StopGradient_001) +#add(UNIT_StridedSlice_000) +add(UNIT_Sub_000) +add(UNIT_Sub_001) +add(UNIT_Tanh_000) diff --git a/compiler/tf2tfliteV2-conversion-test/testall.sh b/compiler/tf2tfliteV2-conversion-test/testall.sh new file mode 100755 index 0000000..2d9a423 --- /dev/null +++ b/compiler/tf2tfliteV2-conversion-test/testall.sh @@ -0,0 +1,81 @@ +#!/bin/bash + +# Need at least 2 arguments +if [[ $# -lt 2 ]]; then + echo "USAGE: $0 ..." + echo + echo "ARGUMENTS:" + echo " [test.config path]" + echo " [WORKDIR]" + echo " [Prefix1]" + echo " [Prefix2]" + echo " ..." + exit 255 +fi + +CONFIG_PATH="$1"; shift +WORKDIR="$1"; shift + +source "${CONFIG_PATH}" + +echo "-- Found TF2TFLITEV2: ${TF2TFLITEV2_PATH}" +echo "-- Found python virtualenv: ${VIRTUALENV}" + +TESTED=() +PASSED=() +FAILED=() + +pushd "${WORKDIR}" +while [[ $# -ne 0 ]]; do + PREFIX="$1"; shift + + TESTED+=("${PREFIX}") + + PASSED_TAG="${PREFIX}.passed" + + rm -f "${PASSED_TAG}" + + cat > "${PREFIX}.log" <( + exec 2>&1 + + echo "-- Found pbtxt: ${PREFIX}.pbtxt" + + # Exit immediately if any command fails + set -e + # Show commands + set -x + + # Generate tflite + source "${VIRTUALENV}/bin/activate" + "${VIRTUALENV}/bin/python" "${TF2TFLITEV2_PATH}" \ + --v1 \ + --input_path "${WORKDIR}/${PREFIX}.pbtxt" \ + --input_arrays "$(awk -F, '/^input/ { print $2 }' ${PREFIX}.info | cut -d: -f1 | tr -d ' ' | paste -d, -s)" \ + --input_shapes "$(cat ${PREFIX}.info | grep '^input' | cut -d '[' -f2 | cut -d ']' -f1 | tr -d ' ' | xargs | tr ' ' ':')" \ + --output_path "${WORKDIR}/${PREFIX}.tflite" \ + --output_arrays "$(awk -F, '/^output/ { print $2 }' ${PREFIX}.info | cut -d: -f1 | tr -d ' ' | paste -d, -s)" + + if [[ $? -eq 0 ]]; then + touch "${PASSED_TAG}" + fi + ) + + if [[ -f "${PASSED_TAG}" ]]; then + PASSED+=("$PREFIX") + else + FAILED+=("$PREFIX") + fi +done +popd + +if [[ ${#TESTED[@]} -ne ${#PASSED[@]} ]]; then + echo "FAILED" + for TEST in "${FAILED[@]}" + do + echo "- ${TEST}" + done + exit 255 +fi + +echo "PASSED" +exit 0 diff --git a/compiler/tf2tfliteV2-value-pbtxt-test/CMakeLists.txt b/compiler/tf2tfliteV2-value-pbtxt-test/CMakeLists.txt deleted file mode 100644 index 2526db5..0000000 --- a/compiler/tf2tfliteV2-value-pbtxt-test/CMakeLists.txt +++ /dev/null @@ -1,183 +0,0 @@ -find_package(PythonInterp 3 QUIET) -find_package(PythonLibs 3 QUIET) - -if(NOT ${PYTHONINTERP_FOUND}) - message("Build tf2tfliteV2-value-pbtxt-test: FALSE (Python3 is missing)") - return() -endif() - -if(${PYTHON_VERSION_MINOR} LESS 3) - message("Build tf2tfliteV2-value-pbtxt-test: FALSE (You need to install Python version higher than 3.3)") - return() -endif() - -nnas_include(TargetRequire) - -unset(REQUIRED_TARGETS) -list(APPEND REQUIRED_TARGETS tfkit) -list(APPEND REQUIRED_TARGETS tf2tfliteV2) -list(APPEND REQUIRED_TARGETS nnkit-run) -list(APPEND REQUIRED_TARGETS nnkit_tf_backend) -list(APPEND REQUIRED_TARGETS nnkit_tflite_backend) -list(APPEND REQUIRED_TARGETS nnkit_randomize_action) -list(APPEND REQUIRED_TARGETS nnkit_HDF5_export_action) -list(APPEND REQUIRED_TARGETS nnkit_HDF5_import_action) -list(APPEND REQUIRED_TARGETS i5diff) -TargetRequire_Return(${REQUIRED_TARGETS}) - -message(STATUS "tf2tfliteV2-value-pbtxt-test: run tests") - -# Create python virtual environment -set(VIRTUALENV "${CMAKE_CURRENT_BINARY_DIR}/venv") - -add_custom_command( - OUTPUT ${VIRTUALENV} - COMMAND ${PYTHON_EXECUTABLE} -m venv ${VIRTUALENV} -) - -# Copy requirements.txt and install required pip packages -set(REQUIREMENTS_FILE "requirements.txt") -set(REQUIREMENTS_SRC_PATH "${CMAKE_CURRENT_SOURCE_DIR}/${REQUIREMENTS_FILE}") -set(REQUIREMENTS_BIN_PATH "${CMAKE_CURRENT_BINARY_DIR}/${REQUIREMENTS_FILE}") - -add_custom_command( - OUTPUT ${REQUIREMENTS_BIN_PATH} - COMMAND ${CMAKE_COMMAND} -E copy ${REQUIREMENTS_SRC_PATH} ${REQUIREMENTS_BIN_PATH} - COMMAND ${VIRTUALENV}/bin/python -m pip install --upgrade pip setuptools --timeout 100 - COMMAND ${VIRTUALENV}/bin/python -m pip install -r requirements.txt --upgrade --timeout 100 - DEPENDS ${VIRTUALENV} ${REQUIREMENTS_SRC_PATH} -) - -add_custom_target(tf2tfliteV2_value_pbtxt_python_deps ALL - DEPENDS ${VIRTUALENV} ${REQUIREMENTS_BIN_PATH} #${TF2TFLITEV2_BIN_PATH} -) - -nncc_find_resource(TensorFlowTests) - -# -# Copy [PREFIX]/test.pbtxt to PREFIX.pbtxt in binary folder -# Copy [PREFIX]/test.info to PREFIX.info in binary folder -# Encode PREFIX.pbtxt to PREFIX.pb -# -set(TEST_REPO "${TensorFlowTests_DIR}") -set(TEST_PBTXT_FILENAME "test.pbtxt") -set(TEST_INFO_FILENAME "test.info") - -unset(TESTCASES) - -macro(add NAME) - list(APPEND TESTCASES ${NAME}) -endmacro(add) - -# Read "test.lst" -include("test.lst") -# Read "test.local.lst" if exists -include("test.local.lst" OPTIONAL) - -unset(TEST_DEPS) -unset(TEST_NAMES) - -foreach(PREFIX IN ITEMS ${TESTCASES}) - if(NOT IS_DIRECTORY "${TEST_REPO}/${PREFIX}") - message(FATAL_ERROR "Missing '${PREFIX}' test") - endif() - - set(PBTXT_SOURCE_PATH "${TEST_REPO}/${PREFIX}/${TEST_PBTXT_FILENAME}") - set(INFO_SOURCE_PATH "${TEST_REPO}/${PREFIX}/${TEST_INFO_FILENAME}") - - set(PBTXT_FILE "${PREFIX}.pbtxt") - set(PBTXT_PATH "${CMAKE_CURRENT_BINARY_DIR}/${PBTXT_FILE}") - - set(INFO_FILE "${PREFIX}.info") - set(INFO_PATH "${CMAKE_CURRENT_BINARY_DIR}/${INFO_FILE}") - - set(PB_FILE "${PREFIX}.pb") - set(PB_PATH "${CMAKE_CURRENT_BINARY_DIR}/${PB_FILE}") - - # Copy .pbtxt - add_custom_command(OUTPUT ${PBTXT_PATH} - COMMAND ${CMAKE_COMMAND} -E copy "${PBTXT_SOURCE_PATH}" "${PBTXT_PATH}" - DEPENDS ${PBTXT_SOURCE_PATH} - COMMENT "Generate ${PBTXT_FILE}" - ) - - # Copy .info - add_custom_command(OUTPUT ${INFO_PATH} - COMMAND ${CMAKE_COMMAND} -E copy "${INFO_SOURCE_PATH}" "${INFO_PATH}" - DEPENDS ${INFO_SOURCE_PATH} - COMMENT "Generate ${INFO_FILE}" - ) - - # Generate .pb from .pbtxt - add_custom_command(OUTPUT ${PB_PATH} - COMMAND $ encode ${PBTXT_PATH} ${PB_PATH} - DEPENDS ${PBTXT_PATH} - COMMENT "Generate ${PB_FILE}" - ) - - list(APPEND TEST_DEPS ${INFO_PATH} ${PB_PATH}) - list(APPEND TEST_NAMES ${PREFIX}) -endforeach(PREFIX) - -## -## Copy testall -## -set(TEST_RUNNER "${CMAKE_CURRENT_BINARY_DIR}/testall.sh") -set(TEST_RUNNER_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/testall.sh") - -add_custom_command( - OUTPUT ${TEST_RUNNER} - COMMAND ${CMAKE_COMMAND} -E copy "${TEST_RUNNER_SOURCE}" "${TEST_RUNNER}" - DEPENDS ${TEST_RUNNER_SOURCE} - COMMENT "Generate test runner" -) - -list(APPEND TEST_DEPS "${TEST_RUNNER}") - -### -### Generate test.config -### -set(TEST_CONFIG "${CMAKE_CURRENT_BINARY_DIR}/test.config") - -# Get tf2tfliteV2 binary path -get_target_property(TF2TFLITEV2_BIN_DIR tf2tfliteV2 BINARY_DIR) -set(TF2TFLITEV2_PATH "${TF2TFLITEV2_BIN_DIR}/tf2tfliteV2.py") - -add_custom_command( - OUTPUT ${TEST_CONFIG} - COMMAND ${CMAKE_COMMAND} -E remove -f ${TEST_CONFIG} - COMMAND ${CMAKE_COMMAND} -E echo 'NNKIT_RUN_PATH=\"$\"' >> ${TEST_CONFIG} - COMMAND ${CMAKE_COMMAND} -E echo 'TF_BACKEND_PATH=\"$\"' >> ${TEST_CONFIG} - COMMAND ${CMAKE_COMMAND} -E echo 'TFLITE_BACKEND_PATH=\"$\"' >> ${TEST_CONFIG} - COMMAND ${CMAKE_COMMAND} -E echo 'RANDOMIZE_ACTION_PATH=\"$\"' >> ${TEST_CONFIG} - COMMAND ${CMAKE_COMMAND} -E echo 'HDF5_EXPORT_ACTION_PATH=\"$\"' >> ${TEST_CONFIG} - COMMAND ${CMAKE_COMMAND} -E echo 'HDF5_IMPORT_ACTION_PATH=\"$\"' >> ${TEST_CONFIG} - COMMAND ${CMAKE_COMMAND} -E echo 'I5DIFF_PATH=\"$\"' >> ${TEST_CONFIG} - COMMAND ${CMAKE_COMMAND} -E echo 'TF2TFLITEV2_PATH=\"${TF2TFLITEV2_PATH}\"' >> ${TEST_CONFIG} - COMMAND ${CMAKE_COMMAND} -E echo 'VIRTUALENV=\"${VIRTUALENV}\"' >> ${TEST_CONFIG} - DEPENDS - i5diff - nnkit-run - nnkit_tf_backend - nnkit_tflite_backend - nnkit_randomize_action - nnkit_HDF5_export_action - nnkit_HDF5_import_action - tf2tfliteV2 - tf2tfliteV2_value_pbtxt_python_deps - COMMENT "Generate test configuration" -) - -list(APPEND TEST_DEPS "${TEST_CONFIG}") - -# This "tf2tfliteV2_value_pbtxt_test_deps" target enforces CMake to generate all the dependencies during "build" phase -add_custom_target(tf2tfliteV2_value_pbtxt_test_deps ALL DEPENDS ${TEST_DEPS}) - -# Run tests -add_test( - NAME tf2tfliteV2_value_pbtxt_test - COMMAND "${TEST_RUNNER}" - "${TEST_CONFIG}" - "${CMAKE_CURRENT_BINARY_DIR}" - ${TEST_NAMES} -) diff --git a/compiler/tf2tfliteV2-value-pbtxt-test/requirements.txt b/compiler/tf2tfliteV2-value-pbtxt-test/requirements.txt deleted file mode 100644 index 2eef5db..0000000 --- a/compiler/tf2tfliteV2-value-pbtxt-test/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -# TODO : Handling TF v2 -tensorflow==1.13.1 diff --git a/compiler/tf2tfliteV2-value-pbtxt-test/requires.cmake b/compiler/tf2tfliteV2-value-pbtxt-test/requires.cmake deleted file mode 100644 index a512361..0000000 --- a/compiler/tf2tfliteV2-value-pbtxt-test/requires.cmake +++ /dev/null @@ -1,4 +0,0 @@ -require("nnkit") -require("tfkit") -require("i5diff") -require("tf2tfliteV2") diff --git a/compiler/tf2tfliteV2-value-pbtxt-test/test.lst b/compiler/tf2tfliteV2-value-pbtxt-test/test.lst deleted file mode 100644 index 328366b..0000000 --- a/compiler/tf2tfliteV2-value-pbtxt-test/test.lst +++ /dev/null @@ -1,101 +0,0 @@ -# TODO Enable skipped tests - -add(NET_0000) -add(NET_0001) -add(NET_0002) -add(NET_0003) -add(NET_0004) -add(NET_0005) -add(NET_0006) -add(NET_0007) -add(NET_0008) -add(NET_0009) -add(NET_0010) -add(NET_0011) -add(NET_0012) -add(NET_0013) -add(NET_0014) -add(NET_0015) -add(NET_0016) -add(NET_0017) -add(NET_0018) -add(NET_0019) -add(NET_0020) -add(NET_0021) -add(NET_0022) -#add(NET_0023) -add(NET_0024) -add(NET_0025) -#add(NET_0028) -add(NET_0029) -add(NET_0030) -add(NET_0031) -add(NET_0032) -add(NET_0033) -add(NET_0034) -add(NET_0035) -add(NET_0036) -add(NET_0037) -add(NET_0038) -add(NET_0039) -add(NET_0040) -add(NET_0041) -add(REGRESSION_0000) -add(REGRESSION_0001) -add(REGRESSION_0002) -add(UNIT_Add_000) -add(UNIT_Add_001) -add(UNIT_Add_002) -add(UNIT_Add_004) -add(UNIT_Add_005) -add(UNIT_AvgPool_000) -add(UNIT_AvgPool_001) -#add(UNIT_BiasAdd_000) -#add(UNIT_BiasAdd_001) -add(UNIT_BiasAdd_002) -#add(UNIT_ConcatV2_000) -#add(UNIT_ConcatV2_001) -add(UNIT_ConcatV2_002) -#add(UNIT_Const_000) -#add(UNIT_Const_001) -add(UNIT_Conv2D_000) -add(UNIT_Conv2DBackpropInput_000) -add(UNIT_Conv2DBackpropInput_001) -add(UNIT_DepthwiseConv2dNative_000) -add(UNIT_DepthwiseConv2dNative_001) -add(UNIT_Maximum_000) -add(UNIT_Maximum_001) -add(UNIT_Maximum_002) -add(UNIT_MaxPool_000) -add(UNIT_MaxPool_001) -add(UNIT_Mean_000) -add(UNIT_Mean_001) -add(UNIT_Mean_002) -add(UNIT_Mean_003) -add(UNIT_Mul_000) -add(UNIT_Mul_001) -add(UNIT_Mul_002) -add(UNIT_Pad_000) -#add(UNIT_Placeholder_000) -#add(UNIT_Placeholder_001) -#add(UNIT_Placeholder_002) -#add(UNIT_Placeholder_003) -add(UNIT_RealDiv_000) -add(UNIT_RealDiv_001) -add(UNIT_Relu_000) -add(UNIT_Relu6_000) -add(UNIT_Reshape_000) -add(UNIT_Rsqrt_000) -add(UNIT_Softmax_001) -add(UNIT_Sqrt_000) -#add(UNIT_SquaredDifference_000) -#add(UNIT_SquaredDifference_001) -add(UNIT_Squeeze_000) -add(UNIT_Squeeze_001) -#add(UNIT_Squeeze_002) -#add(UNIT_Squeeze_003) -#add(UNIT_StopGradient_000) -#add(UNIT_StopGradient_001) -add(UNIT_Sub_000) -add(UNIT_Sub_001) -add(UNIT_Tanh_000) diff --git a/compiler/tf2tfliteV2-value-pbtxt-test/testall.sh b/compiler/tf2tfliteV2-value-pbtxt-test/testall.sh deleted file mode 100755 index 9dde41b..0000000 --- a/compiler/tf2tfliteV2-value-pbtxt-test/testall.sh +++ /dev/null @@ -1,110 +0,0 @@ -#!/bin/bash - -# Need at least 2 arguments -if [[ $# -lt 2 ]]; then - echo "USAGE: $0 ..." - echo - echo "ARGUMENTS:" - echo " [test.config path]" - echo " [WORKDIR]" - echo " [Prefix1]" - echo " [Prefix2]" - echo " ..." - exit 255 -fi - -CONFIG_PATH="$1"; shift -WORKDIR="$1"; shift - -source "${CONFIG_PATH}" - -echo "-- Found nnkit-run: ${NNKIT_RUN_PATH}" -echo "-- Found TF backend: ${TF_BACKEND_PATH}" -echo "-- Found TFLITE backend: ${TFLITE_BACKEND_PATH}" -echo "-- Found TF2TFLITEV2: ${TF2TFLITEV2_PATH}" -echo "-- Found randomize action: ${RANDOMIZE_ACTION_PATH}" -echo "-- Found HDF5 export action: ${HDF5_EXPORT_ACTION_PATH}" -echo "-- Found HDF5 import action: ${HDF5_IMPORT_ACTION_PATH}" -echo "-- Found i5diff: ${I5DIFF_PATH}" -echo "-- Found workdir: ${WORKDIR}" - -TESTED=() -PASSED=() -FAILED=() - -pushd "${WORKDIR}" -while [[ $# -ne 0 ]]; do - PREFIX="$1"; shift - - TESTED+=("${PREFIX}") - - PASSED_TAG="${PREFIX}.passed" - - rm -f "${PASSED_TAG}" - - cat > "${PREFIX}.log" <( - exec 2>&1 - - echo "-- Found pb: ${PREFIX}.pb" - - # Exit immediately if any command fails - set -e - # Show commands - set -x - - # Generate tflite - source "${VIRTUALENV}/bin/activate" - "${VIRTUALENV}/bin/python" "${TF2TFLITEV2_PATH}" \ - --v1 \ - --input_path "${WORKDIR}/${PREFIX}.pb" \ - --input_arrays "$(awk -F, '/^input/ { print $2 }' ${PREFIX}.info | cut -d: -f1 | tr -d ' ' | paste -d, -s)" \ - --input_shapes "$(cat ${PREFIX}.info | grep '^input' | cut -d '[' -f2 | cut -d ']' -f1 | tr -d ' ' | xargs | tr ' ' ':')" \ - --output_path "${WORKDIR}/${PREFIX}.tflite" \ - --output_arrays "$(awk -F, '/^output/ { print $2 }' ${PREFIX}.info | cut -d: -f1 | tr -d ' ' | paste -d, -s)" - - # Run TensorFlow - "${NNKIT_RUN_PATH}" \ - --backend "${TF_BACKEND_PATH}" \ - --backend-arg "${WORKDIR}/${PREFIX}.pb" \ - --backend-arg "${WORKDIR}/${PREFIX}.info" \ - --pre "${RANDOMIZE_ACTION_PATH}" \ - --pre "${HDF5_EXPORT_ACTION_PATH}" \ - --pre-arg "${WORKDIR}/${PREFIX}.input.h5" \ - --post "${HDF5_EXPORT_ACTION_PATH}" \ - --post-arg "${WORKDIR}/${PREFIX}.expected.h5" - - # Run TensorFlow Lite - "${NNKIT_RUN_PATH}" \ - --backend "${TFLITE_BACKEND_PATH}" \ - --backend-arg "${WORKDIR}/${PREFIX}.tflite" \ - --pre "${HDF5_IMPORT_ACTION_PATH}" \ - --pre-arg "${WORKDIR}/${PREFIX}.input.h5" \ - --post "${HDF5_EXPORT_ACTION_PATH}" \ - --post-arg "${WORKDIR}/${PREFIX}.obtained.h5" - - "${I5DIFF_PATH}" -d 0.001 "${PREFIX}.expected.h5" "${PREFIX}.obtained.h5" - - if [[ $? -eq 0 ]]; then - touch "${PASSED_TAG}" - fi - ) - - if [[ -f "${PASSED_TAG}" ]]; then - PASSED+=("$PREFIX") - else - FAILED+=("$PREFIX") - fi -done -popd - -if [[ ${#TESTED[@]} -ne ${#PASSED[@]} ]]; then - echo "FAILED" - for TEST in "${FAILED[@]}" - do - echo "- ${TEST}" - done - exit 255 -fi - -echo "PASSED" -exit 0 diff --git a/compiler/tf2tfliteV2/CMakeLists.txt b/compiler/tf2tfliteV2/CMakeLists.txt index 8a5c2dc..5591ac4 100644 --- a/compiler/tf2tfliteV2/CMakeLists.txt +++ b/compiler/tf2tfliteV2/CMakeLists.txt @@ -9,3 +9,5 @@ add_custom_command(OUTPUT ${tf2tfliteV2_BIN} ) add_custom_target(tf2tfliteV2 ALL DEPENDS ${tf2tfliteV2_BIN}) + +install(FILES ${tf2tfliteV2_BIN} DESTINATION bin) diff --git a/compiler/tf2tfliteV2/README.md b/compiler/tf2tfliteV2/README.md index 836740a..13359aa 100644 --- a/compiler/tf2tfliteV2/README.md +++ b/compiler/tf2tfliteV2/README.md @@ -6,13 +6,19 @@ _tf2tfliteV2_ is a TensorFlow to TensorFlow Lite model Converter. Even though we alreay have _tf2tflite_, we cannot cover all opeartors in TensorFlow. To expand coverage, we introduce _tf2tfliteV2_ which uses `TensorFlow Lite Converter`(by Google) internally. ## Prerequisite -- Frozen graph from TensorFlow 1.13.1 +- Frozen graph from TensorFlow 1.13.1 in binary(`*.pb`) or text(`*.pbtxt`) format - Desired version of TensorFlow(You can use python virtualenv, docker, etc.) ## Example ``` python tf2tfliteV2.py \ > --v1 \ +> -i frozen_graph.pb -o converted.tflite +> -I model_inputs -O model_outputs +``` +``` +python tf2tfliteV2.py \ +> --v1 \ > --input_path=frozen_graph.pb \ > --output_path=converted.tflite \ > --input_arrays=model_inputs \ @@ -22,26 +28,34 @@ python tf2tfliteV2.py \ ``` python tf2tfliteV2.py \ > --v2 \ -> --input_path=frozen_graph.pb \ +> --input_path=frozen_graph.pbtxt \ > --output_path=converted.tflite \ > --input_arrays=model_inputs \ > --output_arrays=model_outputs ``` +``` +python tf2tfliteV2.py \ +> --v2 \ +> --input_path=multiple_output_graph.pb \ +> --output_path=converted.tflite \ +> --input_arrays=model_inputs \ +> --output_arrays=output,output:1,output:2 +``` ## optional argument ``` -h, --help show this help message and exit --v1 Use TensorFlow Lite Converter 1.x --v2 Use TensorFlow Lite Converter 2.x - --input_path INPUT_PATH + -i INPUT_PATH, --input_path INPUT_PATH Full filepath of the input file. - --output_path OUTPUT_PATH + -o OUTPUT_PATH, --output_path OUTPUT_PATH Full filepath of the output file. - --input_arrays INPUT_ARRAYS + -I INPUT_ARRAYS, --input_arrays INPUT_ARRAYS Names of the input arrays, comma-separated. - --input_shapes INPUT_SHAPES + -s INPUT_SHAPES, --input_shapes INPUT_SHAPES Shapes corresponding to --input_arrays, colon- separated. - --output_arrays OUTPUT_ARRAYS + -O OUTPUT_ARRAYS, --output_arrays OUTPUT_ARRAYS Names of the output arrays, comma-separated. ``` diff --git a/compiler/tf2tfliteV2/tf2tfliteV2.py b/compiler/tf2tfliteV2/tf2tfliteV2.py index 8b6ba0d..82d6ee2 100755 --- a/compiler/tf2tfliteV2/tf2tfliteV2.py +++ b/compiler/tf2tfliteV2/tf2tfliteV2.py @@ -1,3 +1,5 @@ +#!/usr/bin/env python + # Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved # Copyright (C) 2018 The TensorFlow Authors # @@ -48,8 +50,13 @@ def _get_parser(): # Input and output path. parser.add_argument( - "--input_path", type=str, help="Full filepath of the input file.", required=True) + "-i", + "--input_path", + type=str, + help="Full filepath of the input file.", + required=True) parser.add_argument( + "-o", "--output_path", type=str, help="Full filepath of the output file.", @@ -57,15 +64,20 @@ def _get_parser(): # Input and output arrays. parser.add_argument( + "-I", "--input_arrays", type=str, help="Names of the input arrays, comma-separated.", required=True) parser.add_argument( + "-s", "--input_shapes", type=str, - help="Shapes corresponding to --input_arrays, colon-separated.") + help= + "Shapes corresponding to --input_arrays, colon-separated.(ex:\"1,4,4,3:1,20,20,3\")" + ) parser.add_argument( + "-O", "--output_arrays", type=str, help="Names of the output arrays, comma-separated.", @@ -141,9 +153,14 @@ def _v2_convert(flags): wrap_func = wrap_frozen_graph( graph_def, - inputs=[_str + ":0" for _str in _parse_array(flags.input_arrays)], - # TODO What if multiple outputs come in? - outputs=[_str + ":0" for _str in _parse_array(flags.output_arrays)]) + inputs=[ + _str + ":0" if len(_str.split(":")) == 1 else _str + for _str in _parse_array(flags.input_arrays) + ], + outputs=[ + _str + ":0" if len(_str.split(":")) == 1 else _str + for _str in _parse_array(flags.output_arrays) + ]) converter = tf.lite.TFLiteConverter.from_concrete_functions([wrap_func]) converter.allow_custom_ops = True diff --git a/compiler/tfinfo-v2/README.md b/compiler/tfinfo-v2/README.md new file mode 100644 index 0000000..72fbdb0 --- /dev/null +++ b/compiler/tfinfo-v2/README.md @@ -0,0 +1 @@ +# tfinfo-v2 diff --git a/compiler/tfkit/src/PackCommand.cpp b/compiler/tfkit/src/PackCommand.cpp index 36bf5a7..a1c4a6f 100644 --- a/compiler/tfkit/src/PackCommand.cpp +++ b/compiler/tfkit/src/PackCommand.cpp @@ -51,21 +51,18 @@ template <> void pack(tensorflow::TensorProto *input_tensor) } else if (input_tensor->float_val().size() == input_flat_size) { - // clang-format off - // TODO fix indentation - input_tensor->clear_tensor_content(); + input_tensor->clear_tensor_content(); - std::vector tensor_content; - for (int i = 0; i < input_flat_size; ++i) - { - tensor_content.push_back(input_tensor->float_val(i)); - } + std::vector tensor_content; + for (int i = 0; i < input_flat_size; ++i) + { + tensor_content.push_back(input_tensor->float_val(i)); + } - input_tensor->set_tensor_content(std::string( - reinterpret_cast(tensor_content.data()), sizeof(float) * input_flat_size)); + input_tensor->set_tensor_content(std::string( + reinterpret_cast(tensor_content.data()), sizeof(float) * input_flat_size)); - input_tensor->clear_float_val(); - // clang-format on + input_tensor->clear_float_val(); } else { diff --git a/compiler/tfkit/src/UnpackCommand.cpp b/compiler/tfkit/src/UnpackCommand.cpp index 77ec1ed..a6711f1 100644 --- a/compiler/tfkit/src/UnpackCommand.cpp +++ b/compiler/tfkit/src/UnpackCommand.cpp @@ -49,18 +49,15 @@ template <> void unpack(tensorflow::TensorProto *input_tensor) } else if (input_tensor->tensor_content().size() == input_flat_size * sizeof(float)) { - // clang-format off - // TODO fix indentation - input_tensor->clear_float_val(); + input_tensor->clear_float_val(); - const float *tensor_content = - reinterpret_cast(input_tensor->tensor_content().data()); - for (int i = 0; i < input_flat_size; i++) - { - input_tensor->add_float_val(tensor_content[i]); - } - input_tensor->clear_tensor_content(); - // clang-format on + const float *tensor_content = + reinterpret_cast(input_tensor->tensor_content().data()); + for (int i = 0; i < input_flat_size; i++) + { + input_tensor->add_float_val(tensor_content[i]); + } + input_tensor->clear_tensor_content(); } else { @@ -139,6 +136,41 @@ template <> void unpack(tensorflow::TensorProto *input_tensor) } } +template <> void unpack(tensorflow::TensorProto *input_tensor) +{ + const auto &input_shape = input_tensor->tensor_shape(); + assert(input_shape.dim_size() <= 6); + int input_flat_size = tfkit::tf::GetElementCount(input_shape); + + // Adjust where shape is not set but actual value exist + if (input_tensor->tensor_content().size() > 0 && input_flat_size == -1) + { + input_flat_size = input_tensor->tensor_content().size() / sizeof(bool); + } + + if (input_tensor->tensor_content().size() == 0) + { + // Do nothing as there is no tensor content to unpack + } + else if (input_tensor->tensor_content().size() == input_flat_size * sizeof(bool)) + { + input_tensor->clear_bool_val(); + + const bool *tensor_content = + reinterpret_cast(input_tensor->tensor_content().data()); + for (int i = 0; i < input_flat_size; i++) + { + input_tensor->add_bool_val(tensor_content[i]); + } + input_tensor->clear_tensor_content(); + } + else + { + throw std::runtime_error{"Number of elements mismatch in unpack."}; + // TODO: support for these + } +} + void unpack(tensorflow::GraphDef &graph_def) { auto nodes = graph_def.mutable_node(); @@ -162,6 +194,9 @@ void unpack(tensorflow::GraphDef &graph_def) case tensorflow::DT_INT8: unpack(tensor); break; + case tensorflow::DT_BOOL: + unpack(tensor); + break; default: throw std::runtime_error{"Unsupported dtype"}; } diff --git a/compiler/tfl-inspect/CMakeLists.txt b/compiler/tfl-inspect/CMakeLists.txt index c2c7dfe..ba01986 100644 --- a/compiler/tfl-inspect/CMakeLists.txt +++ b/compiler/tfl-inspect/CMakeLists.txt @@ -8,6 +8,7 @@ file(GLOB_RECURSE SOURCES "src/*.cpp") add_executable(tfl-inspect ${DRIVER} ${SOURCES}) target_include_directories(tfl-inspect PRIVATE src) +target_link_libraries(tfl-inspect arser) +target_link_libraries(tfl-inspect foder) target_link_libraries(tfl-inspect mio_tflite) target_link_libraries(tfl-inspect safemain) -target_link_libraries(tfl-inspect stdex) diff --git a/compiler/tfl-inspect/driver/Driver.cpp b/compiler/tfl-inspect/driver/Driver.cpp index 5cad63c..a480011 100644 --- a/compiler/tfl-inspect/driver/Driver.cpp +++ b/compiler/tfl-inspect/driver/Driver.cpp @@ -14,74 +14,63 @@ * limitations under the License. */ -#include "Model.h" #include "Dump.h" -#include +#include +#include #include #include #include +#include #include #include -using OptionHook = std::function(void)>; - int entry(int argc, char **argv) { - if (argc < 3) + arser::Arser arser{"tfl-inspect allows users to retrieve various information from a TensorFlow " + "Lite model files"}; + arser.add_argument("--operators").nargs(0).help("Dump operators in tflite file"); + arser.add_argument("--conv2d_weight") + .nargs(0) + .help("Dump Conv2D series weight operators in tflite file"); + arser.add_argument("--op_version").nargs(0).help("Dump versions of the operators in tflite file"); + arser.add_argument("tflite").type(arser::DataType::STR).help("TFLite file to inspect"); + + try + { + arser.parse(argc, argv); + } + catch (const std::runtime_error &err) { - std::cerr << "ERROR: Failed to parse arguments" << std::endl; - std::cerr << std::endl; - std::cerr << "USAGE: " << argv[0] << " [options] [tflite]" << std::endl; - std::cerr << " --operators : dump operators in tflite file" << std::endl; - std::cerr << " --conv2d_weight : dump Conv2D series weight operators in tflite file" - << std::endl; + std::cout << err.what() << std::endl; + std::cout << arser; return 255; } - // Simple argument parser (based on map) - std::map argparse; - - argparse["--operators"] = [&](void) { - // dump all operators - return std::move(stdex::make_unique()); - }; - - argparse["--conv2d_weight"] = [&](void) { - // dump Conv2D, DepthwiseConv2D weight operators - return std::move(stdex::make_unique()); - }; + if (!arser["--operators"] && !arser["--conv2d_weight"] && !arser["--op_version"]) + { + std::cout << "At least one option must be specified" << std::endl; + std::cout << arser; + return 255; + } std::vector> dumps; - for (int n = 1; n < argc - 1; ++n) - { - const std::string tag{argv[n]}; - - auto it = argparse.find(tag); - if (it == argparse.end()) - { - std::cerr << "Option '" << tag << "' is not supported" << std::endl; - return 255; - } - auto dump = it->second(); - assert(dump != nullptr); - dumps.push_back(std::move(dump)); - } + if (arser["--operators"]) + dumps.push_back(std::make_unique()); + if (arser["--conv2d_weight"]) + dumps.push_back(std::make_unique()); + if (arser["--op_version"]) + dumps.push_back(std::make_unique()); - std::string model_file = argv[argc - 1]; + std::string model_file = arser.get("tflite"); // Load TF lite model from a tflite file - auto model = tflinspect::load_tflite(model_file); - if (model == nullptr) - { - std::cerr << "ERROR: Failed to load tflite '" << model_file << "'" << std::endl; - return 255; - } - - const tflite::Model *tflmodel = model->model(); - if (tflmodel == nullptr) + foder::FileLoader fileLoader{model_file}; + std::vector modelData = fileLoader.load(); + const tflite::Model *tfliteModel = tflite::GetModel(modelData.data()); + if (tfliteModel == nullptr) { std::cerr << "ERROR: Failed to load tflite '" << model_file << "'" << std::endl; return 255; @@ -89,7 +78,7 @@ int entry(int argc, char **argv) for (auto &dump : dumps) { - dump->run(std::cout, tflmodel); + dump->run(std::cout, tfliteModel); } return 0; diff --git a/compiler/tfl-inspect/requires.cmake b/compiler/tfl-inspect/requires.cmake index 2aa101e..25857ad 100644 --- a/compiler/tfl-inspect/requires.cmake +++ b/compiler/tfl-inspect/requires.cmake @@ -1,3 +1,4 @@ +require("arser") +require("foder") require("mio-tflite") require("safemain") -require("stdex") diff --git a/compiler/tfl-inspect/src/Dump.cpp b/compiler/tfl-inspect/src/Dump.cpp index 8d879a8..78e7700 100644 --- a/compiler/tfl-inspect/src/Dump.cpp +++ b/compiler/tfl-inspect/src/Dump.cpp @@ -28,19 +28,22 @@ void DumpOperators::run(std::ostream &os, const tflite::Model *model) { tflinspect::Reader reader(model); - assert(reader.num_subgraph() == 1); - reader.select_subgraph(0); + const uint32_t subgraph_size = reader.num_subgraph(); - auto ops = reader.operators(); - - // dump operators - for (uint32_t i = 0; i < ops->Length(); ++i) + for (uint32_t g = 0; g < subgraph_size; g++) { - const auto op = ops->Get(i); + reader.select_subgraph(g); + auto ops = reader.operators(); - auto op_name = reader.opcode_name(op); + // dump operators + for (uint32_t i = 0; i < ops->Length(); ++i) + { + const auto op = ops->Get(i); - os << op_name << std::endl; + auto op_name = reader.opcode_name(op); + + os << op_name << std::endl; + } } } @@ -94,44 +97,85 @@ void DumpConv2DWeight::run(std::ostream &os, const tflite::Model *model) { tflinspect::Reader reader(model); - assert(reader.num_subgraph() == 1); - reader.select_subgraph(0); - - auto ops = reader.operators(); + const uint32_t subgraph_size = reader.num_subgraph(); - // dump Conv2D, DepthwiseConv2D and its weight input operator - for (uint32_t i = 0; i < ops->Length(); ++i) + for (uint32_t g = 0; g < subgraph_size; g++) { - const auto op = ops->Get(i); - auto bc = reader.builtin_code(op); + reader.select_subgraph(g); + auto ops = reader.operators(); - if (bc == tflite::BuiltinOperator_CONV_2D || bc == tflite::BuiltinOperator_DEPTHWISE_CONV_2D) + // dump Conv2D, DepthwiseConv2D and its weight input operator + for (uint32_t i = 0; i < ops->Length(); ++i) { - const std::vector &inputs = tflinspect::as_index_vector(op->inputs()); - if (inputs.size() < 2) + const auto op = ops->Get(i); + auto bc = reader.builtin_code(op); + + if (bc == tflite::BuiltinOperator_CONV_2D || bc == tflite::BuiltinOperator_DEPTHWISE_CONV_2D) { - throw std::runtime_error("Operator has invalid input"); + const std::vector &inputs = tflinspect::as_index_vector(op->inputs()); + if (inputs.size() < 2) + { + throw std::runtime_error("Operator has invalid input"); + } + auto weight_input = inputs[1]; // Tensor ID of weight input + + const auto op_weight = operator_match_output(reader, weight_input); + const auto buffer_size = tensor_buffer_size(reader, weight_input); + + std::string weight_op_name = "?"; + + if (op_weight == nullptr && buffer_size > 0) + { + weight_op_name = "CONST"; + } + else if (op_weight != nullptr) + { + weight_op_name = reader.opcode_name(op_weight); + } + + auto op_name = reader.opcode_name(op); + os << op_name << "," << weight_op_name << std::endl; } - auto weight_input = inputs[1]; // Tensor ID of weight input + } + } +} - const auto op_weight = operator_match_output(reader, weight_input); - const auto buffer_size = tensor_buffer_size(reader, weight_input); +} // namespace tflinspect - std::string weight_op_name = "?"; +namespace tflinspect +{ - if (op_weight == nullptr && buffer_size > 0) - { - weight_op_name = "CONST"; - } - else if (op_weight != nullptr) - { - weight_op_name = reader.opcode_name(op_weight); - } +void DumpOperatorVersion::run(std::ostream &os, const tflite::Model *model) +{ + std::map op_version_map; + + tflinspect::Reader reader(model); + + const uint32_t subgraph_size = reader.num_subgraph(); + + for (uint32_t g = 0; g < subgraph_size; g++) + { + reader.select_subgraph(g); + auto ops = reader.operators(); + + // dump Conv2D, DepthwiseConv2D and its weight input operator + for (uint32_t i = 0; i < ops->Length(); ++i) + { + const auto op = ops->Get(i); auto op_name = reader.opcode_name(op); - os << op_name << "," << weight_op_name << std::endl; + auto op_version = reader.opcodes().at(op->opcode_index())->version(); + + if (op_version_map.find(op_name) == op_version_map.end() || + op_version_map[op_name] < op_version) + op_version_map[op_name] = op_version; } } + + for (auto op : op_version_map) + { + os << op.first << "," << op.second << std::endl; + } } } // namespace tflinspect diff --git a/compiler/tfl-inspect/src/Dump.h b/compiler/tfl-inspect/src/Dump.h index 798c1db..83397a2 100644 --- a/compiler/tfl-inspect/src/Dump.h +++ b/compiler/tfl-inspect/src/Dump.h @@ -51,6 +51,15 @@ public: void run(std::ostream &os, const tflite::Model *model); }; +class DumpOperatorVersion final : public DumpInterface +{ +public: + DumpOperatorVersion() = default; + +public: + void run(std::ostream &os, const tflite::Model *model); +}; + } // namespace tflinspect #endif // __DUMP_H__ diff --git a/compiler/tfl-inspect/src/Model.cpp b/compiler/tfl-inspect/src/Model.cpp deleted file mode 100644 index 8c3bf37..0000000 --- a/compiler/tfl-inspect/src/Model.cpp +++ /dev/null @@ -1,143 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Model.h" - -#include -#include -#include -#include - -namespace -{ - -class MemoryMappedModel final : public tflinspect::Model -{ -public: - /** - * @require fd and data SHOULD be valid - */ - explicit MemoryMappedModel(int fd, void *data, size_t size) : _fd{fd}, _data{data}, _size{size} - { - // DO NOTHING - } - -public: - ~MemoryMappedModel() - { - munmap(_data, _size); - close(_fd); - } - -public: - MemoryMappedModel(const MemoryMappedModel &) = delete; - MemoryMappedModel(MemoryMappedModel &&) = delete; - -public: - const ::tflite::Model *model(void) const override { return ::tflite::GetModel(_data); } - -private: - int _fd = -1; - void *_data = nullptr; - size_t _size = 0; -}; - -class FileDescriptor final -{ -public: - FileDescriptor(int value) : _value{value} - { - // DO NOTHING - } - -public: - // NOTE Copy is not allowed - FileDescriptor(const FileDescriptor &) = delete; - -public: - // NOTE Move is allowed - FileDescriptor(FileDescriptor &&fd) { _value = fd.release(); } - -public: - ~FileDescriptor() - { - if (_value != -1) - { - // Close on descturction - close(_value); - } - } - -public: - int value(void) const { return _value; } - -public: - int release(void) - { - auto res = _value; - _value = -1; - return res; - } - -private: - int _value = -1; -}; - -} // namespace - -namespace tflinspect -{ - -std::unique_ptr load_tflite(const std::string &path) -{ - FileDescriptor fd = open(path.c_str(), O_RDONLY); - - if (fd.value() == -1) - { - // Return nullptr on open failure - return nullptr; - } - - struct stat st; - if (fstat(fd.value(), &st) == -1) - { - // Return nullptr on fstat failure - return nullptr; - } - - auto size = st.st_size; - auto data = mmap(nullptr, size, PROT_READ, MAP_SHARED, fd.value(), 0); - - if (data == MAP_FAILED) - { - // Return nullptr on mmap failure - return nullptr; - } - - // Check if file is a valid Flatbuffer file - const uint8_t *u8data = reinterpret_cast(data); - flatbuffers::Verifier verifier{u8data, static_cast(size)}; - if (!tflite::VerifyModelBuffer(verifier)) - { - munmap(data, size); - close(fd.release()); - return nullptr; - } - - return std::unique_ptr{new MemoryMappedModel(fd.release(), data, size)}; -} - -} // namespace tflinspect diff --git a/compiler/tfl-inspect/src/Model.h b/compiler/tfl-inspect/src/Model.h deleted file mode 100644 index a69fb8b..0000000 --- a/compiler/tfl-inspect/src/Model.h +++ /dev/null @@ -1,43 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __MODEL_H__ -#define __MODEL_H__ - -#include - -#include - -namespace tflinspect -{ - -struct Model -{ - virtual ~Model() = default; - - virtual const ::tflite::Model *model(void) const = 0; -}; - -/** - * @brief Load TensorFlow Lite model (as a raw Model) from a given path - * - * @note May return a nullptr - */ -std::unique_ptr load_tflite(const std::string &path); - -} // namespace tflinspeat - -#endif // __MODEL_H__ diff --git a/compiler/tfl-inspect/src/Reader.cpp b/compiler/tfl-inspect/src/Reader.cpp index 7bd2fe2..5be2894 100644 --- a/compiler/tfl-inspect/src/Reader.cpp +++ b/compiler/tfl-inspect/src/Reader.cpp @@ -50,7 +50,10 @@ std::string opcode_name(const tflite::OperatorCode *opcode) if (!opcode->custom_code()) return "(invalid custom)"; - return opcode->custom_code()->c_str(); + std::string custom_op = "CUSTOM("; + custom_op += opcode->custom_code()->c_str(); + custom_op += ")"; + return custom_op; } tflite::BuiltinOperator code = opcode->builtin_code(); diff --git a/compiler/tfl-verify/CMakeLists.txt b/compiler/tfl-verify/CMakeLists.txt index a368b29..d33059f 100644 --- a/compiler/tfl-verify/CMakeLists.txt +++ b/compiler/tfl-verify/CMakeLists.txt @@ -6,7 +6,7 @@ file(GLOB_RECURSE SOURCES "src/*.cpp") add_executable(tfl-verify ${SOURCES}) target_include_directories(tfl-verify PRIVATE src) +target_link_libraries(tfl-verify foder) target_link_libraries(tfl-verify mio_tflite) target_link_libraries(tfl-verify safemain) target_link_libraries(tfl-verify cwrap) -target_link_libraries(tfl-verify stdex) diff --git a/compiler/tfl-verify/requires.cmake b/compiler/tfl-verify/requires.cmake index e479a83..ed6b84d 100644 --- a/compiler/tfl-verify/requires.cmake +++ b/compiler/tfl-verify/requires.cmake @@ -1,4 +1,4 @@ +require("foder") require("mio-tflite") require("safemain") require("cwrap") -require("stdex") diff --git a/compiler/tfl-verify/src/Driver.cpp b/compiler/tfl-verify/src/Driver.cpp index 367c731..81f6d54 100644 --- a/compiler/tfl-verify/src/Driver.cpp +++ b/compiler/tfl-verify/src/Driver.cpp @@ -16,9 +16,8 @@ #include "VerifyFlatBuffers.h" -#include - #include +#include #include int entry(int argc, char **argv) @@ -30,7 +29,7 @@ int entry(int argc, char **argv) std::cerr << "USAGE: " << argv[0] << " [tflite]" << std::endl; return 255; } - auto verifier = stdex::make_unique(); + auto verifier = std::make_unique(); std::string model_file = argv[argc - 1]; diff --git a/compiler/tfl-verify/src/Model.cpp b/compiler/tfl-verify/src/Model.cpp deleted file mode 100644 index efac121..0000000 --- a/compiler/tfl-verify/src/Model.cpp +++ /dev/null @@ -1,90 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Model.h" - -#include - -#include -#include -#include -#include - -namespace -{ - -class MemoryMappedModel final : public ModelData -{ -public: - /** - * @require fd and data SHOULD be valid - */ - explicit MemoryMappedModel(int fd, void *data, size_t size) : _fd{fd}, _data{data}, _size{size} - { - // DO NOTHING - } - -public: - ~MemoryMappedModel() - { - munmap(_data, _size); - close(_fd); - } - -public: - MemoryMappedModel(const MemoryMappedModel &) = delete; - MemoryMappedModel(MemoryMappedModel &&) = delete; - -public: - const void *data(void) const override { return _data; }; - const size_t size(void) const override { return _size; }; - -private: - int _fd = -1; - void *_data = nullptr; - size_t _size = 0; -}; - -} // namespace - -std::unique_ptr load_modeldata(const std::string &path) -{ - cwrap::Fildes fd(open(path.c_str(), O_RDONLY)); - - if (fd.get() == -1) - { - // Return nullptr on open failure - return nullptr; - } - - struct stat st; - if (fstat(fd.get(), &st) == -1) - { - // Return nullptr on fstat failure - return nullptr; - } - - auto size = st.st_size; - auto data = mmap(nullptr, size, PROT_READ, MAP_SHARED, fd.get(), 0); - - if (data == MAP_FAILED) - { - // Return nullptr on mmap failure - return nullptr; - } - - return std::unique_ptr{new MemoryMappedModel(fd.release(), data, size)}; -} diff --git a/compiler/tfl-verify/src/Model.h b/compiler/tfl-verify/src/Model.h deleted file mode 100644 index 44f40e2..0000000 --- a/compiler/tfl-verify/src/Model.h +++ /dev/null @@ -1,38 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __MODEL_H__ -#define __MODEL_H__ - -#include -#include - -struct ModelData -{ - virtual ~ModelData() = default; - - virtual const void *data(void) const = 0; - virtual const size_t size(void) const = 0; -}; - -/** - * @brief Load TF Lite model (as a raw data) from a given path - * - * @note May return a nullptr - */ -std::unique_ptr load_modeldata(const std::string &path); - -#endif // __MODEL_H__ diff --git a/compiler/tfl-verify/src/VerifyFlatBuffers.cpp b/compiler/tfl-verify/src/VerifyFlatBuffers.cpp index 253fcb2..7fb48a7 100644 --- a/compiler/tfl-verify/src/VerifyFlatBuffers.cpp +++ b/compiler/tfl-verify/src/VerifyFlatBuffers.cpp @@ -16,16 +16,16 @@ #include "VerifyFlatBuffers.h" -#include "Model.h" - +#include #include int VerifyFlatbuffers::run(const std::string &model_file) { - auto modeldata = load_modeldata(model_file); + foder::FileLoader fileLoader{model_file}; + std::vector modeldata = fileLoader.load(); - const uint8_t *data = reinterpret_cast(modeldata->data()); - flatbuffers::Verifier verifier{data, modeldata->size()}; + const uint8_t *data = reinterpret_cast(modeldata.data()); + flatbuffers::Verifier verifier{data, modeldata.size()}; if (!tflite::VerifyModelBuffer(verifier)) { diff --git a/compiler/tflchef/CMakeLists.txt b/compiler/tflchef/CMakeLists.txt index 71c5e2a..ebc8733 100644 --- a/compiler/tflchef/CMakeLists.txt +++ b/compiler/tflchef/CMakeLists.txt @@ -1,15 +1,19 @@ nnas_find_package(Protobuf QUIET) if(NOT Protobuf_FOUND) + message(STATUS "Build tflchef: FAILED (missing Protobuf)") return() endif(NOT Protobuf_FOUND) if(NOT TARGET mio_tflite) + message(STATUS "Build tflchef: FAILED (missing mio_tflite)") return() endif(NOT TARGET mio_tflite) # Recipe Parser add_subdirectory(proto) +# Log +add_subdirectory(log) # Core Library add_subdirectory(core) # TFlite Library diff --git a/compiler/tflchef/core/CMakeLists.txt b/compiler/tflchef/core/CMakeLists.txt index 6a62820..43f6b8b 100644 --- a/compiler/tflchef/core/CMakeLists.txt +++ b/compiler/tflchef/core/CMakeLists.txt @@ -4,4 +4,6 @@ add_library(tflchef_core STATIC ${SOURCES}) target_include_directories(tflchef_core PUBLIC include) target_include_directories(tflchef_core PRIVATE src) target_link_libraries(tflchef_core tflchef_proto) +target_link_libraries(tflchef_core tflchef_log) target_link_libraries(tflchef_core mio_tflite) +target_link_libraries(tflchef_core souschef) diff --git a/compiler/tflchef/core/src/Arguments.h b/compiler/tflchef/core/src/Arguments.h deleted file mode 100644 index 341aea6..0000000 --- a/compiler/tflchef/core/src/Arguments.h +++ /dev/null @@ -1,34 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __ARGUMENTS_H__ -#define __ARGUMENTS_H__ - -#include -#include - -/** - * @brief Read-only string sequence view - */ -struct Arguments -{ - virtual ~Arguments() = default; - - virtual uint32_t count(void) const = 0; - virtual const std::string &value(uint32_t n) const = 0; -}; - -#endif // __ARGUMENTS_H__ diff --git a/compiler/tflchef/core/src/Convert.cpp b/compiler/tflchef/core/src/Convert.cpp index 86a31d9..dc8e31d 100644 --- a/compiler/tflchef/core/src/Convert.cpp +++ b/compiler/tflchef/core/src/Convert.cpp @@ -41,6 +41,8 @@ tflite::ActivationFunctionType as_tflite_activation(const tflchef::Activation &v return tflite::ActivationFunctionType_NONE; case tflchef::RELU: return tflite::ActivationFunctionType_RELU; + case tflchef::RELU_N1_TO_1: + return tflite::ActivationFunctionType_RELU_N1_TO_1; case tflchef::RELU6: return tflite::ActivationFunctionType_RELU6; default: @@ -70,3 +72,18 @@ tflite::TensorType as_tflite_tensortype(const tflchef::TensorType &value) throw std::runtime_error{"Unknown tensor type"}; } + +tflite::MirrorPadMode as_tflite_mirrorpadmode(const tflchef::MirrorPadMode &value) +{ + switch (value) + { + case tflchef::REFLECT: + return tflite::MirrorPadMode_REFLECT; + case tflchef::SYMMETRIC: + return tflite::MirrorPadMode_SYMMETRIC; + default: + break; + } + + throw std::runtime_error{"Unknown mirrorpad mode"}; +} diff --git a/compiler/tflchef/core/src/Convert.h b/compiler/tflchef/core/src/Convert.h index ed15a55..b56e6ef 100644 --- a/compiler/tflchef/core/src/Convert.h +++ b/compiler/tflchef/core/src/Convert.h @@ -27,5 +27,6 @@ tflite::Padding as_tflite_padding(const tflchef::Padding &value); tflite::ActivationFunctionType as_tflite_activation(const tflchef::Activation &value); tflite::TensorType as_tflite_tensortype(const tflchef::TensorType &value); +tflite::MirrorPadMode as_tflite_mirrorpadmode(const tflchef::MirrorPadMode &value); #endif // __CONVERT_H__ diff --git a/compiler/tflchef/core/src/CustomOp/AddV2.cpp b/compiler/tflchef/core/src/CustomOp/AddV2.cpp new file mode 100644 index 0000000..dffd336 --- /dev/null +++ b/compiler/tflchef/core/src/CustomOp/AddV2.cpp @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2015 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "AddV2.h" + +#include "flatbuffers/flexbuffers.h" + +flatbuffers::Offset AddV2Chef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + return flatbuffers::Offset(); +} + +flatbuffers::Offset> +AddV2Chef::custom_value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.type() == "AddV2"); + + /** + * REGISTER_OP("AddV2") + .Input("x: T") + .Input("y: T") + .Output("z: T") + .Attr( + "T: {bfloat16, half, float, double, uint8, int8, int16, int32, int64, " + "complex64, complex128}") + .SetShapeFn(shape_inference::BroadcastBinaryOpShapeFn) + .SetIsAggregate() + .SetIsCommutative(); + */ + + auto flex_buffers = std::make_unique(); + size_t map_start = flex_buffers->StartMap(); + + // TODO Support more data types + flex_buffers->Int("T", tflite::TensorType_FLOAT32); + + flex_buffers->EndMap(map_start); + flex_buffers->Finish(); + + auto circle_custom_options = fbb.CreateVector(flex_buffers->GetBuffer()); + return circle_custom_options; +} + +std::unique_ptr AddV2ChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new AddV2Chef{operation}}; +} diff --git a/compiler/tflchef/core/src/CustomOp/AddV2.h b/compiler/tflchef/core/src/CustomOp/AddV2.h new file mode 100644 index 0000000..dbbaf5a --- /dev/null +++ b/compiler/tflchef/core/src/CustomOp/AddV2.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_ADDV2_H__ +#define __OP_ADDV2_H__ + +#include "OpChef.h" + +class AddV2Chef final : public OpChef +{ +public: + explicit AddV2Chef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_CUSTOM; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NONE; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + + flatbuffers::Offset> + custom_value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct AddV2ChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_ADDV2_H__ diff --git a/compiler/tflchef/core/src/CustomOp/All.cpp b/compiler/tflchef/core/src/CustomOp/All.cpp new file mode 100644 index 0000000..b3ae821 --- /dev/null +++ b/compiler/tflchef/core/src/CustomOp/All.cpp @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2015 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "All.h" + +#include "flatbuffers/flexbuffers.h" + +flatbuffers::Offset AllChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + return flatbuffers::Offset(); +} + +flatbuffers::Offset> +AllChef::custom_value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.type() == "All"); + + /** + * REGISTER_OP("All") + .Input("input: bool") + .Input("reduction_indices: Tidx") + .Output("output: bool") + .Attr("keep_dims: bool = false") + .Attr("Tidx: {int32, int64} = DT_INT32") + .SetShapeFn(shape_inference::ReductionShape); + */ + + auto flex_buffers = std::make_unique(); + size_t map_start = flex_buffers->StartMap(); + + // TODO Support more data types + flex_buffers->Int("Tidx", tflite::TensorType_INT32); + flex_buffers->Bool("keep_dims", operation.all_options().keep_dims()); + + flex_buffers->EndMap(map_start); + flex_buffers->Finish(); + + auto circle_custom_options = fbb.CreateVector(flex_buffers->GetBuffer()); + return circle_custom_options; +} + +std::unique_ptr AllChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new AllChef{operation}}; +} diff --git a/compiler/tflchef/core/src/CustomOp/All.h b/compiler/tflchef/core/src/CustomOp/All.h new file mode 100644 index 0000000..f7949f3 --- /dev/null +++ b/compiler/tflchef/core/src/CustomOp/All.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_ALL_H__ +#define __OP_ALL_H__ + +#include "OpChef.h" + +class AllChef final : public OpChef +{ +public: + explicit AllChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_CUSTOM; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NONE; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + + flatbuffers::Offset> + custom_value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct AllChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_ALL_H__ diff --git a/compiler/tflchef/core/src/CustomOp/BatchMatMulV2.cpp b/compiler/tflchef/core/src/CustomOp/BatchMatMulV2.cpp new file mode 100644 index 0000000..595f3b9 --- /dev/null +++ b/compiler/tflchef/core/src/CustomOp/BatchMatMulV2.cpp @@ -0,0 +1,65 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2015 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "BatchMatMulV2.h" + +#include "flatbuffers/flexbuffers.h" + +flatbuffers::Offset BatchMatMulV2Chef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + return flatbuffers::Offset(); +} + +flatbuffers::Offset> +BatchMatMulV2Chef::custom_value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.type() == "BatchMatMulV2"); + + /** + * REGISTER_OP("BatchMatMulV2") + .Input("x: T") + .Input("y: T") + .Output("output: T") + .Attr( + "T: {bfloat16, half, float, double, int32, int64, complex64, " + "complex128}") + .Attr("adj_x: bool = false") + .Attr("adj_y: bool = false") + .SetShapeFn(shape_inference::BatchMatMulV2Shape); + */ + + auto flex_buffers = std::make_unique(); + size_t map_start = flex_buffers->StartMap(); + + flex_buffers->Bool("adj_x", operation.batch_matmul_options().adj_x()); + flex_buffers->Bool("adj_y", operation.batch_matmul_options().adj_y()); + // TODO Support more data types + flex_buffers->Int("T", tflite::TensorType_FLOAT32); + + flex_buffers->EndMap(map_start); + flex_buffers->Finish(); + + auto circle_custom_options = fbb.CreateVector(flex_buffers->GetBuffer()); + return circle_custom_options; +} + +std::unique_ptr BatchMatMulV2ChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new BatchMatMulV2Chef{operation}}; +} diff --git a/compiler/tflchef/core/src/CustomOp/BatchMatMulV2.h b/compiler/tflchef/core/src/CustomOp/BatchMatMulV2.h new file mode 100644 index 0000000..d20f4d2 --- /dev/null +++ b/compiler/tflchef/core/src/CustomOp/BatchMatMulV2.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_BATCH_MATMUL_V2_H__ +#define __OP_BATCH_MATMUL_V2_H__ + +#include "OpChef.h" + +class BatchMatMulV2Chef final : public OpChef +{ +public: + explicit BatchMatMulV2Chef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_CUSTOM; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NONE; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + + flatbuffers::Offset> + custom_value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct BatchMatMulV2ChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_BATCH_MATMUL_V2_H__ diff --git a/compiler/tflchef/core/src/CustomOp/MatMul.cpp b/compiler/tflchef/core/src/CustomOp/MatMul.cpp new file mode 100644 index 0000000..ba34aa8 --- /dev/null +++ b/compiler/tflchef/core/src/CustomOp/MatMul.cpp @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2015 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "MatMul.h" + +#include "flatbuffers/flexbuffers.h" + +flatbuffers::Offset MatMulChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + return flatbuffers::Offset(); +} + +flatbuffers::Offset> +MatMulChef::custom_value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.type() == "MatMul"); + + /** + * REGISTER_OP("MatMul") + .Input("a: T") + .Input("b: T") + .Output("product: T") + .Attr("transpose_a: bool = false") + .Attr("transpose_b: bool = false") + .Attr("T: {half, float, double, int32, complex64, complex128}") + .SetShapeFn(shape_inference::MatMulShape) + */ + + auto flex_buffers = std::make_unique(); + size_t map_start = flex_buffers->StartMap(); + + flex_buffers->Bool("transpose_a", operation.matmul_options().transpose_a()); + flex_buffers->Bool("transpose_b", operation.matmul_options().transpose_b()); + // TODO how do we support other types? + flex_buffers->Int("T", tflite::TensorType_FLOAT32); + + flex_buffers->EndMap(map_start); + flex_buffers->Finish(); + + auto circle_custom_options = fbb.CreateVector(flex_buffers->GetBuffer()); + return circle_custom_options; +} + +std::unique_ptr MatMulChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new MatMulChef{operation}}; +} diff --git a/compiler/tflchef/core/src/CustomOp/MatMul.h b/compiler/tflchef/core/src/CustomOp/MatMul.h new file mode 100644 index 0000000..b0307f9 --- /dev/null +++ b/compiler/tflchef/core/src/CustomOp/MatMul.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_MATMUL_H__ +#define __OP_MATMUL_H__ + +#include "OpChef.h" + +class MatMulChef final : public OpChef +{ +public: + explicit MatMulChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_CUSTOM; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NONE; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + + flatbuffers::Offset> + custom_value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct MatMulChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_MATMUL_H__ diff --git a/compiler/tflchef/core/src/CustomOp/MatrixBandPart.cpp b/compiler/tflchef/core/src/CustomOp/MatrixBandPart.cpp new file mode 100644 index 0000000..d12597e --- /dev/null +++ b/compiler/tflchef/core/src/CustomOp/MatrixBandPart.cpp @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2015 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "MatrixBandPart.h" + +#include "flatbuffers/flexbuffers.h" + +flatbuffers::Offset MatrixBandPartChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + return flatbuffers::Offset(); +} + +flatbuffers::Offset> +MatrixBandPartChef::custom_value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.type() == "MatrixBandPart"); + + /** + * REGISTER_OP("MatrixBandPart") + .Input("input: T") + .Input("num_lower: Tindex") + .Input("num_upper: Tindex") + .Output("band: T") + .Attr("T: type") + .Attr("Tindex: {int32, int64} = DT_INT64") + .SetShapeFn(shape_inference::UnchangedShape); + */ + + auto flex_buffers = std::make_unique(); + size_t map_start = flex_buffers->StartMap(); + + // TODO Support more data types + flex_buffers->Int("T", tflite::TensorType_FLOAT32); + flex_buffers->Int("Tindex", tflite::TensorType_INT64); + + flex_buffers->EndMap(map_start); + flex_buffers->Finish(); + + auto circle_custom_options = fbb.CreateVector(flex_buffers->GetBuffer()); + return circle_custom_options; +} + +std::unique_ptr MatrixBandPartChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new MatrixBandPartChef{operation}}; +} diff --git a/compiler/tflchef/core/src/CustomOp/MatrixBandPart.h b/compiler/tflchef/core/src/CustomOp/MatrixBandPart.h new file mode 100644 index 0000000..54a8a3a --- /dev/null +++ b/compiler/tflchef/core/src/CustomOp/MatrixBandPart.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_MATRIXBANDPART_H__ +#define __OP_MATRIXBANDPART_H__ + +#include "OpChef.h" + +class MatrixBandPartChef final : public OpChef +{ +public: + explicit MatrixBandPartChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_CUSTOM; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NONE; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + + flatbuffers::Offset> + custom_value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct MatrixBandPartChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_MATRIXBANDPART_H__ diff --git a/compiler/tflchef/core/src/Data/Constant.h b/compiler/tflchef/core/src/Data/Constant.h deleted file mode 100644 index ebe1f3d..0000000 --- a/compiler/tflchef/core/src/Data/Constant.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __CONSTANT_FILLER_H__ -#define __CONSTANT_FILLER_H__ - -#include "DataChef.h" -#include "LexicalCast.h" - -template class ConstantDataChef final : public DataChef -{ -public: - ConstantDataChef(const T &value) : _value{value} - { - // DO NOTHING - } - -public: - std::vector generate(int32_t count) const override - { - std::vector res; - - for (uint32_t n = 0; n < count; ++n) - { - const uint8_t *arr = reinterpret_cast(&_value); - - for (uint32_t b = 0; b < sizeof(T); ++b) - { - res.emplace_back(arr[b]); - } - } - - return res; - } - -private: - T _value; -}; - -template struct ConstantDataChefFactory : public DataChefFactory -{ - std::unique_ptr create(const Arguments &args) const - { - auto const value = to_number(args.value(0)); - return std::unique_ptr{new ConstantDataChef{value}}; - } -}; - -#endif // __CONSTANT_FILLER_H__ diff --git a/compiler/tflchef/core/src/Data/Explicit.h b/compiler/tflchef/core/src/Data/Explicit.h deleted file mode 100644 index 088e791..0000000 --- a/compiler/tflchef/core/src/Data/Explicit.h +++ /dev/null @@ -1,75 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __EXPLICIT_FILLER_H__ -#define __EXPLICIT_FILLER_H__ - -#include "DataChef.h" -#include "LexicalCast.h" - -#include - -template class ExplicitDataChef final : public DataChef -{ -public: - ExplicitDataChef() - { - // DO NOTHING - } - -public: - std::vector generate(int32_t count) const override - { - std::vector res; - - for (uint32_t n = 0; n < count; ++n) - { - T const value = (n < _values.size()) ? _values.at(n) : T{}; - const uint8_t *arr = reinterpret_cast(&value); - - for (uint32_t b = 0; b < sizeof(T); ++b) - { - res.emplace_back(arr[b]); - } - } - - return res; - } - -public: - void insert(const T &value) { _values.emplace_back(value); } - -private: - std::vector _values; -}; - -template struct ExplicitDataChefFactory : public DataChefFactory -{ - std::unique_ptr create(const Arguments &args) const - { - std::unique_ptr> res{new ExplicitDataChef}; - - for (uint32_t n = 0; n < args.count(); ++n) - { - auto const value = to_number(args.value(n)); - res->insert(value); - } - - return std::move(res); - } -}; - -#endif // __EXPLICIT_FILLER_H__ diff --git a/compiler/tflchef/core/src/Data/Gaussian.cpp b/compiler/tflchef/core/src/Data/Gaussian.cpp deleted file mode 100644 index c515d11..0000000 --- a/compiler/tflchef/core/src/Data/Gaussian.cpp +++ /dev/null @@ -1,135 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "Gaussian.h" -#include "LexicalCast.h" - -#include -#include - -#include -#include - -std::vector GaussianFloat32DataChef::generate(int32_t count) const -{ - // TODO Support seed value override - int seed = std::chrono::system_clock::now().time_since_epoch().count(); - - std::minstd_rand rand{seed}; - std::normal_distribution dist{_mean, _stddev}; - - std::vector res; - - for (uint32_t n = 0; n < count; ++n) - { - auto const value = dist(rand); - auto const arr = reinterpret_cast(&value); - - for (uint32_t b = 0; b < sizeof(float); ++b) - { - res.emplace_back(arr[b]); - } - } - - return res; -} - -std::vector GaussianInt32DataChef::generate(int32_t count) const -{ - // TODO Support seed value override - int seed = std::chrono::system_clock::now().time_since_epoch().count(); - - std::minstd_rand rand{seed}; - std::normal_distribution dist{_mean, _stddev}; - - std::vector res; - - for (uint32_t n = 0; n < count; ++n) - { - auto const value = static_cast(dist(rand)); - auto const arr = reinterpret_cast(&value); - - for (uint32_t b = 0; b < sizeof(int32_t); ++b) - { - res.emplace_back(arr[b]); - } - } - - return res; -} - -std::vector GaussianUint8DataChef::generate(int32_t count) const -{ - // TODO Support seed value override - int seed = std::chrono::system_clock::now().time_since_epoch().count(); - - std::minstd_rand rand{seed}; - std::normal_distribution dist{_mean, _stddev}; - - std::vector res; - - for (uint32_t n = 0; n < count; ++n) - { - auto const value = static_cast(dist(rand)); // uint8_t for data type - auto const arr = reinterpret_cast(&value); // uint8_t for byte streaming - - for (uint32_t b = 0; b < sizeof(uint8_t); ++b) - { - res.emplace_back(arr[b]); - } - } - - return res; -} - -std::unique_ptr GaussianFloat32DataChefFactory::create(const Arguments &args) const -{ - if (args.count() != 2) - { - throw std::runtime_error{"invalid argument count: two arguments (mean/stddev) are expected"}; - } - - auto const mean = to_number(args.value(0)); - auto const stddev = to_number(args.value(1)); - - return std::unique_ptr{new GaussianFloat32DataChef{mean, stddev}}; -} - -std::unique_ptr GaussianInt32DataChefFactory::create(const Arguments &args) const -{ - if (args.count() != 2) - { - throw std::runtime_error{"invalid argument count: two arguments (mean/stddev) are expected"}; - } - - auto const mean = to_number(args.value(0)); - auto const stddev = to_number(args.value(1)); - - return std::unique_ptr{new GaussianInt32DataChef{mean, stddev}}; -} - -std::unique_ptr GaussianUint8DataChefFactory::create(const Arguments &args) const -{ - if (args.count() != 2) - { - throw std::runtime_error{"invalid argument count: two arguments (mean/stddev) are expected"}; - } - - auto const mean = to_number(args.value(0)); - auto const stddev = to_number(args.value(1)); - - return std::unique_ptr{new GaussianUint8DataChef{mean, stddev}}; -} diff --git a/compiler/tflchef/core/src/Data/Gaussian.h b/compiler/tflchef/core/src/Data/Gaussian.h deleted file mode 100644 index 81a28d2..0000000 --- a/compiler/tflchef/core/src/Data/Gaussian.h +++ /dev/null @@ -1,88 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __GAUSSIAN_FILLER_H__ -#define __GAUSSIAN_FILLER_H__ - -#include "DataChef.h" - -/** - * @brief Generate a sequence of random values according to the gaussian(=normal) distribution - */ -class GaussianFloat32DataChef final : public DataChef -{ -public: - GaussianFloat32DataChef(float mean, float stddev) : _mean{mean}, _stddev{stddev} - { - // DO NOTHING - } - -public: - std::vector generate(int32_t count) const override; - -private: - float _mean; - float _stddev; -}; - -class GaussianInt32DataChef final : public DataChef -{ -public: - GaussianInt32DataChef(float mean, float stddev) : _mean{mean}, _stddev{stddev} - { - // DO NOTHING - } - -public: - std::vector generate(int32_t count) const override; - -private: - float _mean; - float _stddev; -}; - -class GaussianUint8DataChef final : public DataChef -{ -public: - GaussianUint8DataChef(float mean, float stddev) : _mean{mean}, _stddev{stddev} - { - // DO NOTHING - } - -public: - std::vector generate(int32_t count) const override; - -private: - float _mean; - float _stddev; -}; - -struct GaussianFloat32DataChefFactory : public DataChefFactory -{ - std::unique_ptr create(const Arguments &args) const; -}; - -struct GaussianInt32DataChefFactory : public DataChefFactory -{ - std::unique_ptr create(const Arguments &args) const; -}; - -struct GaussianUint8DataChefFactory : public DataChefFactory -{ - std::unique_ptr create(const Arguments &args) const; -}; - -#endif // __GAUSSIAN_FILLER_H__ diff --git a/compiler/tflchef/core/src/DataChef.def b/compiler/tflchef/core/src/DataChef.def deleted file mode 100644 index 89d34a2..0000000 --- a/compiler/tflchef/core/src/DataChef.def +++ /dev/null @@ -1,15 +0,0 @@ -#ifndef DATA_CHEF -#error "Define DATA_CHEF first" -#endif // DATA_CHEF - -// DATA_CHEF(TYPE, NAME, FACTORY_CLASS) -// "TYPE" SHOULD BE an enum tag of tflchef::TensorType -DATA_CHEF(FLOAT32, constant, ConstantDataChefFactory) -DATA_CHEF(BOOL, constant, ConstantDataChefFactory) -DATA_CHEF(INT32, explicit, ExplicitDataChefFactory) -DATA_CHEF(UINT8, explicit, ExplicitDataChefFactory) -DATA_CHEF(BOOL, explicit, ExplicitDataChefFactory) -DATA_CHEF(FLOAT32, explicit, ExplicitDataChefFactory) -DATA_CHEF(FLOAT32, gaussian, GaussianFloat32DataChefFactory) -DATA_CHEF(INT32, gaussian, GaussianInt32DataChefFactory) -DATA_CHEF(UINT8, gaussian, GaussianUint8DataChefFactory) diff --git a/compiler/tflchef/core/src/DataChef.h b/compiler/tflchef/core/src/DataChef.h deleted file mode 100644 index d057102..0000000 --- a/compiler/tflchef/core/src/DataChef.h +++ /dev/null @@ -1,56 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __DATA_CHEF_H__ -#define __DATA_CHEF_H__ - -#include "Arguments.h" - -#include -#include -#include - -using Data = std::vector; - -/** - * @brief Data Generator - */ -struct DataChef -{ - virtual ~DataChef() = default; - - // TODO Allow users to query the type of elements that this DataChef generates - - /** - * @brief Generate a sequence of 'count' elements as a byte sequence - * - * Let D be the return value of generate(N). - * Then, D.size() == N * sizeof(T) where T is the element type. - */ - virtual Data generate(int32_t count) const = 0; -}; - -/** - * @brief Data Generator Factory - */ -struct DataChefFactory -{ - virtual ~DataChefFactory() = default; - - virtual std::unique_ptr create(const Arguments &args) const = 0; -}; - -#endif // __DATA_CHEF_H__ diff --git a/compiler/tflchef/core/src/DataChefs.h b/compiler/tflchef/core/src/DataChefs.h deleted file mode 100644 index 2310ae8..0000000 --- a/compiler/tflchef/core/src/DataChefs.h +++ /dev/null @@ -1,24 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __DATA_CHEFS_H__ -#define __DATA_CHEFS_H__ - -#include "Data/Constant.h" -#include "Data/Explicit.h" -#include "Data/Gaussian.h" - -#endif // __DATA_CHEFS_H__ diff --git a/compiler/tflchef/core/src/Dataset.h b/compiler/tflchef/core/src/Dataset.h deleted file mode 100644 index 9d5c7a4..0000000 --- a/compiler/tflchef/core/src/Dataset.h +++ /dev/null @@ -1,57 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __DATASET_H__ -#define __DATASET_H__ - -#include - -template class Dataset -{ -public: - Dataset(const std::vector &vec) : _vec{vec} - { - // DO NOTHING - } - -public: - Dataset(std::vector &&vec) : _vec{std::move(vec)} - { - // DO NOTHING - } - -public: - template auto map(Func f) const -> Dataset()))> - { - using U = decltype(f(std::declval())); - std::vector res; - - for (const auto &elem : _vec) - { - res.emplace_back(f(elem)); - } - - return Dataset(std::move(res)); - } - -public: - const std::vector &vectorize(void) const { return _vec; } - -private: - std::vector _vec; -}; - -#endif // __DATASET_H__ diff --git a/compiler/tflchef/core/src/LexicalCast.cpp b/compiler/tflchef/core/src/LexicalCast.cpp deleted file mode 100644 index 38a5f92..0000000 --- a/compiler/tflchef/core/src/LexicalCast.cpp +++ /dev/null @@ -1,36 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "LexicalCast.h" - -#include -#include - -template <> float to_number(const std::string &s) { return std::stof(s); } -template <> int to_number(const std::string &s) { return std::stoi(s); } -template <> uint8_t to_number(const std::string &s) -{ - int temp = std::stoi(s); - assert(temp >= 0); - assert(temp <= std::numeric_limits::max()); - return static_cast(temp); -} -template <> bool to_number(const std::string &s) -{ - if (std::stoi(s) || s == "T" || s == "t" || s == "TRUE" || s == "true") - return true; - return false; -} diff --git a/compiler/tflchef/core/src/LexicalCast.h b/compiler/tflchef/core/src/LexicalCast.h deleted file mode 100644 index 4aeccb4..0000000 --- a/compiler/tflchef/core/src/LexicalCast.h +++ /dev/null @@ -1,32 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @brief This file provides string <-> number cast helpers - */ -#ifndef __LEXICAL_CAST_H__ -#define __LEXICAL_CAST_H__ - -#include - -/** - * @brief Return a numeric value that corresponds to a given string - * - * @note This function will throw an exception on casting failure - */ -template Number to_number(const std::string &s); - -#endif // __LEXICAL_CAST_H__ diff --git a/compiler/tflchef/core/src/ModelChef.cpp b/compiler/tflchef/core/src/ModelChef.cpp index 2c69efd..932a649 100644 --- a/compiler/tflchef/core/src/ModelChef.cpp +++ b/compiler/tflchef/core/src/ModelChef.cpp @@ -15,17 +15,19 @@ */ #include "tflchef/ModelChef.h" -#include "Arguments.h" +#include +#include #include "Convert.h" -#include "DataChef.h" -#include "DataChefs.h" +#include #include "OpChef.h" #include "OpChefs.h" -#include "Dataset.h" +#include + +#include "Log.h" #include #include @@ -42,34 +44,7 @@ namespace { -template class RangedArguments : public Arguments -{ -public: - RangedArguments(InputIt beg, InputIt end) : _beg{beg}, _end{end} - { - // DO NOTHING - } - -public: - uint32_t count(void) const override { return _end - _beg; } - -public: - const std::string &value(uint32_t n) const override { return *(_beg + n); } - -private: - InputIt _beg; - InputIt _end; -}; - -template RangedArguments ranged_arguments(InputIt beg, InputIt end) -{ - return RangedArguments{beg, end}; -} - -} // namespace - -namespace -{ +using namespace souschef; template std::vector as_vector(const ::google::protobuf::RepeatedPtrField &field) { @@ -147,20 +122,6 @@ private: namespace { -template class Registry -{ -public: - void add(const std::string &name, std::unique_ptr &&entry) - { - _content[name] = std::move(entry); - } - - const T &lookup(const std::string &name) const { return *(_content.at(name)); } - -private: - std::map> _content; -}; - struct DataChefRegistry final : public Registry { }; @@ -168,6 +129,7 @@ struct DataChefRegistry final : public Registry DataChefRegistry &data_chef_registry(const tflchef::TensorType &type) { static DataChefRegistry s32; + static DataChefRegistry s64; static DataChefRegistry fp32; static DataChefRegistry u8; static DataChefRegistry boolean; @@ -176,6 +138,8 @@ DataChefRegistry &data_chef_registry(const tflchef::TensorType &type) { case tflchef::INT32: return s32; + case tflchef::INT64: + return s64; case tflchef::FLOAT32: return fp32; case tflchef::UINT8: @@ -199,14 +163,54 @@ OpChefRegistry &op_chef_registry(void) return registry; } -// @brief This will prepare a set of unique operator codes in the mode recipe -std::set gather_opcode_set(const ::tflchef::ModelRecipe &model_recipe) +/// @brief This will prepare a map of unique builtin codes in the model recipe +std::map +gather_builtincode_map(const ::tflchef::ModelRecipe &model_recipe) +{ + // Key and value of the map are BuiltinOperator and operator version + std::map builtin_map; + + for (const auto &operation : model_recipe.operation()) + { + auto op_chef = op_chef_registry().lookup(operation.type()).create(&operation); + if (op_chef->code() == tflite::BuiltinOperator_CUSTOM) + continue; + + // Various operation version is unified as the highest version among them + if (builtin_map.find(op_chef->code()) == builtin_map.end() || + builtin_map[op_chef->code()] < operation.version()) + builtin_map[op_chef->code()] = operation.version(); + } + + // Add ops used in Graphs(subgraphs) + for (int g = 0; g < model_recipe.graph_size(); ++g) + { + const auto &graph = model_recipe.graph(g); + for (const auto &operation : graph.operation()) + { + auto op_chef = op_chef_registry().lookup(operation.type()).create(&operation); + if (op_chef->code() == tflite::BuiltinOperator_CUSTOM) + continue; + + // Various operation version is unified as the highest version among them + if (builtin_map.find(op_chef->code()) == builtin_map.end() || + builtin_map[op_chef->code()] < operation.version()) + builtin_map[op_chef->code()] = operation.version(); + } + } + + return builtin_map; +} + +/// @brief This will prepare a set of unique custom codes in the mode recipe +std::set gather_customcode_set(const ::tflchef::ModelRecipe &model_recipe) { - std::set opcode_set; + std::set customcode_set; for (const auto &operation : model_recipe.operation()) { auto op_chef = op_chef_registry().lookup(operation.type()).create(&operation); - opcode_set.insert(op_chef->code()); + if (op_chef->code() == tflite::BuiltinOperator_CUSTOM) + customcode_set.insert(operation.type()); } // Add ops used in Graphs(subgraphs) @@ -216,525 +220,386 @@ std::set gather_opcode_set(const ::tflchef::ModelRecipe for (const auto &operation : graph.operation()) { auto op_chef = op_chef_registry().lookup(operation.type()).create(&operation); - opcode_set.insert(op_chef->code()); + if (op_chef->code() == tflite::BuiltinOperator_CUSTOM) + customcode_set.insert(operation.type()); } } - return opcode_set; + return customcode_set; } } // namespace -namespace tflchef +namespace { -/** - * @brief Generate a (in-memory) TensorFlow Lite model from a given model recipe - */ -GeneratedModel cook(const ::tflchef::ModelRecipe &model_recipe) +struct CookParams { -// Initialize Op Chef Registry -#define OP_CHEF(NAME, FACTORY_CLASS) \ - op_chef_registry().add(#NAME, std::unique_ptr(new FACTORY_CLASS())); -#include "OpChef.def" -#undef OP_CHEF + std::vector> &buffer_vec; + std::vector> &code_vec; + std::vector> &subgraph_vec; + std::unique_ptr &flatbuffer_builder; + std::map &builtin_code_map; + std::string noname; +}; -// Initialize Data Chef Registry -#define DATA_CHEF(TYPE, NAME, FACTORY_CLASS) \ - data_chef_registry(::tflchef::TYPE) \ - .add(#NAME, std::unique_ptr(new FACTORY_CLASS())); -#include "DataChef.def" -#undef DATA_CHEF +template void cook_graph(const T &graph, CookParams &cp) +{ + LOGGER(l); - // - // Create FlatBufferBuilder - // - auto flatbuffer_builder = - std::unique_ptr(new flatbuffers::FlatBufferBuilder(1024)); + std::vector> &buffer_vec = cp.buffer_vec; + std::vector> &code_vec = cp.code_vec; + std::vector> &subgraph_vec = cp.subgraph_vec; + std::unique_ptr &flatbuffer_builder = cp.flatbuffer_builder; + std::map &builtin_code_map = cp.builtin_code_map; // Operand-related - std::vector> buffer_vec; + std::vector> tensor_vec; // Operation-related - std::vector> code_vec; + std::vector> operator_vec; + + // default name for graph + std::string graph_name = cp.noname; + if (graph.has_name()) + graph_name = graph.name(); + + // Tensor Name -> Tensor ID mapping (per Graph) + std::map symbol_table; + + auto lookup = [&symbol_table, &graph_name](const std::string &name) { + if (symbol_table.find(name) != symbol_table.end()) + return symbol_table.at(name); + else if (name == "") + return -1; // -1 in TFLite means that optional input tensor is empty. + else + { + std::string msg = "tflchef : input not found in " + graph_name + " graph"; + throw std::runtime_error(msg.c_str()); + } + }; - // Graphs-related - std::vector> subgraph_vec; + int32_t buffer_start = buffer_vec.size(); + int32_t buffer_index = 0; - // Create OperatorCode - std::set opcode_set = gather_opcode_set(model_recipe); - for (auto opcode : opcode_set) + // Create buffer(s) 1~n(I) for input(s) + const auto size_input = graph.input_size(); + for (int ci = 0; ci < size_input; ++ci) { - tflite::OperatorCodeBuilder code_builder{*flatbuffer_builder}; - code_builder.add_builtin_code(opcode); - auto code = code_builder.Finish(); - // Update OperatorCode vector - code_vec.emplace_back(code); + tflite::BufferBuilder buffer_builder{*flatbuffer_builder}; + buffer_vec.emplace_back(buffer_builder.Finish()); } - - // Create an Empty Buffer - // - // Buffer 0 SHOULD be an empty buffer in TensorFlow Lite model file - // (Please refer to the comment for Tensor.buffer field in schema) + // Create buffer(s) n(I)+1~n(I)+n(O) for output(s) + const auto size_output = graph.output_size(); + for (int co = 0; co < size_output; ++co) { tflite::BufferBuilder buffer_builder{*flatbuffer_builder}; buffer_vec.emplace_back(buffer_builder.Finish()); } - // - // Create Main graph - // - { - // Operand-related - std::vector> tensor_vec; + auto input_names = as_dataset(graph.input()).vectorize(); + auto output_names = as_dataset(graph.output()).vectorize(); - // Operation-related - std::vector> operator_vec; - - // Tensor Name -> Tensor ID mapping (per Graph) - std::map symbol_table; - - auto lookup = [&symbol_table](const std::string &name) { return symbol_table.at(name); }; + for (const auto &operand : graph.operand()) + { + assert(operand.has_name()); - int32_t buffer_start = buffer_vec.size(); - int32_t buffer_index = 0; + assert(operand.has_type()); - // Create buffer(s) 1~n(I) for input(s) - const auto size_input = model_recipe.input_size(); - for (int ci = 0; ci < size_input; ++ci) + flatbuffers::Offset> shape; + std::vector dims; + if (operand.has_shape()) { - tflite::BufferBuilder buffer_builder{*flatbuffer_builder}; - buffer_vec.emplace_back(buffer_builder.Finish()); - } - // Create buffer(s) n(I)+1~n(I)+n(O) for output(s) - const auto size_output = model_recipe.output_size(); - for (int co = 0; co < size_output; ++co) - { - tflite::BufferBuilder buffer_builder{*flatbuffer_builder}; - buffer_vec.emplace_back(buffer_builder.Finish()); + dims = as_dims(operand.shape()); + shape = flatbuffer_builder->CreateVector(dims); } - // default name for main graph - std::string graph_name = "main"; - if (model_recipe.has_name()) - graph_name = model_recipe.name(); + auto name = flatbuffer_builder->CreateString(operand.name()); - auto input_names = as_dataset(model_recipe.input()).vectorize(); - auto output_names = as_dataset(model_recipe.output()).vectorize(); + buffer_index = 0; - for (const auto &operand : model_recipe.operand()) + // Create Buffer if filler is specified + if (operand.has_filler()) { - assert(operand.has_name()); - - assert(operand.has_type()); - assert(operand.has_shape()); - - std::vector dims = as_dims(operand.shape()); - - auto shape = flatbuffer_builder->CreateVector(dims); - auto name = flatbuffer_builder->CreateString(operand.name()); - - buffer_index = 0; - - // Create Buffer if filler is specified - if (operand.has_filler()) - { - const auto &filler = operand.filler(); + const auto &filler = operand.filler(); - assert(filler.has_tag()); + assert(filler.has_tag()); - auto args = ranged_arguments(filler.arg().begin(), filler.arg().end()); - auto chef = data_chef_registry(operand.type()).lookup(filler.tag()).create(args); + auto args = ranged_arguments(filler.arg().begin(), filler.arg().end()); + auto chef = data_chef_registry(operand.type()).lookup(filler.tag()).create(args); - assert(chef != nullptr); + assert(chef != nullptr); - // Create Data - auto data_vec = chef->generate(element_count(dims)); - auto data = flatbuffer_builder->CreateVector(data_vec); + // Create Data + int32_t count = (element_count(dims) > 0) ? element_count(dims) : filler.arg_size(); + auto data_vec = chef->generate(count); + auto data = flatbuffer_builder->CreateVector(data_vec); - // Create Buffer - tflite::BufferBuilder buffer_builder{*flatbuffer_builder}; - buffer_builder.add_data(data); - auto buffer = buffer_builder.Finish(); + // Create Buffer + tflite::BufferBuilder buffer_builder{*flatbuffer_builder}; + buffer_builder.add_data(data); + auto buffer = buffer_builder.Finish(); - // Update Buffer Index & Vector - buffer_index = buffer_vec.size(); - buffer_vec.emplace_back(buffer); + // Update Buffer Index & Vector + buffer_index = buffer_vec.size(); + buffer_vec.emplace_back(buffer); + } + else + { + // if this is input or output, assign to that buffer_index + int idx = 0; + for (auto it = input_names.begin(); it != input_names.end(); ++it, ++idx) + { + if (*it == operand.name()) + { + buffer_index = buffer_start + idx; + break; + } } - else + if (buffer_index == 0) { - // if this is input or output, assign to that buffer_index - int idx = 0; - for (auto it = input_names.begin(); it != input_names.end(); ++it, ++idx) + idx = 0; + for (auto it = output_names.begin(); it != output_names.end(); ++it, ++idx) { if (*it == operand.name()) { - buffer_index = buffer_start + idx; + buffer_index = buffer_start + size_input + idx; break; } } - if (buffer_index == 0) - { - idx = 0; - for (auto it = output_names.begin(); it != output_names.end(); ++it, ++idx) - { - if (*it == operand.name()) - { - buffer_index = buffer_start + size_input + idx; - break; - } - } - } } - - flatbuffers::Offset quant_index; - - // Create QuantizationParameters if quant is specified - if (operand.has_quant()) + if (buffer_index == 0) { - const auto &quant = operand.quant(); - - // Create each parameters - // NOTE if some parameters are not given, those will be set to default value - std::vector quant_max_vec(quant.max_size()); - std::vector quant_min_vec(quant.min_size()); - std::vector quant_scale_vec(quant.scale_size()); - std::vector quant_zero_point_vec(quant.zero_point_size()); - - for (uint32_t i = 0; i < quant.max_size(); ++i) - quant_max_vec.at(i) = quant.max(i); - for (uint32_t i = 0; i < quant.min_size(); ++i) - quant_min_vec.at(i) = quant.min(i); - for (uint32_t i = 0; i < quant.scale_size(); ++i) - quant_scale_vec.at(i) = quant.scale(i); - for (uint32_t i = 0; i < quant.zero_point_size(); ++i) - quant_zero_point_vec.at(i) = quant.zero_point(i); - - auto quant_max = flatbuffer_builder->CreateVector(quant_max_vec); - auto quant_min = flatbuffer_builder->CreateVector(quant_min_vec); - auto quant_scale = flatbuffer_builder->CreateVector(quant_scale_vec); - auto quant_zero_point = flatbuffer_builder->CreateVector(quant_zero_point_vec); - - // Create QuantizationParameters - tflite::QuantizationParametersBuilder quant_builder{*flatbuffer_builder}; - quant_builder.add_max(quant_max); - quant_builder.add_min(quant_min); - quant_builder.add_scale(quant_scale); - quant_builder.add_zero_point(quant_zero_point); - - // Update QuantizationParameters Index - quant_index = quant_builder.Finish(); - } - - // Create Tensor - tflite::TensorBuilder tensor_builder{*flatbuffer_builder}; - - tensor_builder.add_shape(shape); - tensor_builder.add_type(as_tflite_tensortype(operand.type())); - tensor_builder.add_buffer(buffer_index); - tensor_builder.add_name(name); - if (operand.has_quant()) - tensor_builder.add_quantization(quant_index); - - // Append! - tensor_vec.emplace_back(tensor_builder.Finish()); - - // Update Tensor Name -> Tensor Index Map - int32_t tensor_index = symbol_table.size(); - const auto &tensor_name = operand.name(); + // we couldn't find the buffer; create an empty buffer for this tensor + buffer_index = buffer_vec.size(); - symbol_table[tensor_name] = tensor_index; + tflite::BufferBuilder buffer_builder{*flatbuffer_builder}; + buffer_vec.emplace_back(buffer_builder.Finish()); + } } + assert(buffer_index != 0); - // Create Operator - for (const auto &operation : model_recipe.operation()) - { - assert(operation.has_type()); - - auto op_chef = op_chef_registry().lookup(operation.type()).create(&operation); - - // Create 'inputs' - std::vector input_vec = as_dataset(operation.input()).map(lookup).vectorize(); - auto inputs = flatbuffer_builder->CreateVector(input_vec); - - // Create 'outputs' - std::vector output_vec = as_dataset(operation.output()).map(lookup).vectorize(); - auto outputs = flatbuffer_builder->CreateVector(output_vec); - - // Create Option - auto options = op_chef->value(*flatbuffer_builder); - - // Create Operator - tflite::OperatorBuilder op_builder{*flatbuffer_builder}; - - // Get operator code index from opcode_set with assumption, order of - // opcode_set is same as that of code_vec - auto op_it = opcode_set.find(op_chef->code()); - assert(op_it != opcode_set.end()); - uint32_t opcode_index = std::distance(opcode_set.begin(), op_it); - - op_builder.add_opcode_index(opcode_index); - op_builder.add_inputs(inputs); - op_builder.add_outputs(outputs); - op_builder.add_builtin_options_type(op_chef->type()); - op_builder.add_builtin_options(options); + flatbuffers::Offset quant_index; - // Append Operator - operator_vec.emplace_back(op_builder.Finish()); + // Create QuantizationParameters if quant is specified + if (operand.has_quant()) + { + const auto &quant = operand.quant(); + + // Create each parameters + // NOTE if some parameters are not given, those will be set to default value + std::vector quant_max_vec(quant.max_size()); + std::vector quant_min_vec(quant.min_size()); + std::vector quant_scale_vec(quant.scale_size()); + std::vector quant_zero_point_vec(quant.zero_point_size()); + + for (uint32_t i = 0; i < quant.max_size(); ++i) + quant_max_vec.at(i) = quant.max(i); + for (uint32_t i = 0; i < quant.min_size(); ++i) + quant_min_vec.at(i) = quant.min(i); + for (uint32_t i = 0; i < quant.scale_size(); ++i) + quant_scale_vec.at(i) = quant.scale(i); + for (uint32_t i = 0; i < quant.zero_point_size(); ++i) + quant_zero_point_vec.at(i) = quant.zero_point(i); + + auto quant_max = flatbuffer_builder->CreateVector(quant_max_vec); + auto quant_min = flatbuffer_builder->CreateVector(quant_min_vec); + auto quant_scale = flatbuffer_builder->CreateVector(quant_scale_vec); + auto quant_zero_point = flatbuffer_builder->CreateVector(quant_zero_point_vec); + + // Create QuantizationParameters + tflite::QuantizationParametersBuilder quant_builder{*flatbuffer_builder}; + quant_builder.add_max(quant_max); + quant_builder.add_min(quant_min); + quant_builder.add_scale(quant_scale); + quant_builder.add_zero_point(quant_zero_point); + + // Update QuantizationParameters Index + quant_index = quant_builder.Finish(); } - // Create network input/output vector - std::vector input_vec = as_dataset(model_recipe.input()).map(lookup).vectorize(); - std::vector output_vec = as_dataset(model_recipe.output()).map(lookup).vectorize(); + // Create Tensor + tflite::TensorBuilder tensor_builder{*flatbuffer_builder}; - // Create "SubGraph" arguments - auto tensors = flatbuffer_builder->CreateVector(tensor_vec); - auto inputs = flatbuffer_builder->CreateVector(input_vec); - auto outputs = flatbuffer_builder->CreateVector(output_vec); - auto operators = flatbuffer_builder->CreateVector(operator_vec); - auto name = flatbuffer_builder->CreateString(graph_name); + tensor_builder.add_shape(shape); + tensor_builder.add_type(as_tflite_tensortype(operand.type())); + tensor_builder.add_buffer(buffer_index); + tensor_builder.add_name(name); + if (operand.has_quant()) + tensor_builder.add_quantization(quant_index); + + // Append! + tensor_vec.emplace_back(tensor_builder.Finish()); - tflite::SubGraphBuilder subgraph_builder{*flatbuffer_builder}; + // Update Tensor Name -> Tensor Index Map + int32_t tensor_index = symbol_table.size(); + const auto &tensor_name = operand.name(); - subgraph_builder.add_tensors(tensors); - subgraph_builder.add_inputs(inputs); - subgraph_builder.add_outputs(outputs); - subgraph_builder.add_operators(operators); - subgraph_builder.add_name(name); + INFO(l) << "Symbol [" << tensor_name << "] = Tensor " << tensor_index << std::endl; - subgraph_vec.emplace_back(subgraph_builder.Finish()); + symbol_table[tensor_name] = tensor_index; } - // - // Create subgraphs if exist - // TODO refactor main graph and subgraphs generation to reduce duplicate codes - // - for (int g = 0; g < model_recipe.graph_size(); ++g) + // Create Operator + for (const auto &operation : graph.operation()) { - // Operand-related - std::vector> tensor_vec; - - // Operation-related - std::vector> operator_vec; - - // Tensor Name -> Tensor ID mapping (per Graph) - std::map symbol_table; - - auto lookup = [&symbol_table](const std::string &name) { return symbol_table.at(name); }; - - const auto &graph = model_recipe.graph(g); - - int32_t buffer_start = buffer_vec.size(); - int32_t buffer_index = 0; - - // Create buffer(s) for input(s) - const auto size_input = graph.input_size(); - for (int ci = 0; ci < size_input; ++ci) - { - tflite::BufferBuilder buffer_builder{*flatbuffer_builder}; - buffer_vec.emplace_back(buffer_builder.Finish()); - } - // Create buffer(s) for output(s) - const auto size_output = graph.output_size(); - for (int co = 0; co < size_output; ++co) - { - tflite::BufferBuilder buffer_builder{*flatbuffer_builder}; - buffer_vec.emplace_back(buffer_builder.Finish()); - } - - // default name for sub graph - // TODO naming rule here may have conflit if recipe file provides it. - // fix this when this happens. - std::ostringstream stringStream; - stringStream << "sub_" << (g + 1); - std::string graph_name = stringStream.str(); - if (graph.has_name()) - graph_name = graph.name(); + assert(operation.has_type()); - auto input_names = as_dataset(graph.input()).vectorize(); - auto output_names = as_dataset(graph.output()).vectorize(); - - for (const auto &operand : graph.operand()) - { - assert(operand.has_name()); - - assert(operand.has_type()); - assert(operand.has_shape()); - - std::vector dims = as_dims(operand.shape()); - - auto shape = flatbuffer_builder->CreateVector(dims); - auto name = flatbuffer_builder->CreateString(operand.name()); + auto op_chef = op_chef_registry().lookup(operation.type()).create(&operation); - // Create Buffer if filler is specified - if (operand.has_filler()) - { - const auto &filler = operand.filler(); + // Create 'inputs' + std::vector input_vec = as_dataset(operation.input()).map(lookup).vectorize(); + auto inputs = flatbuffer_builder->CreateVector(input_vec); - assert(filler.has_tag()); + // Create 'outputs' + std::vector output_vec = as_dataset(operation.output()).map(lookup).vectorize(); + auto outputs = flatbuffer_builder->CreateVector(output_vec); - auto args = ranged_arguments(filler.arg().begin(), filler.arg().end()); - auto chef = data_chef_registry(operand.type()).lookup(filler.tag()).create(args); + // Create Option + auto options = op_chef->value(*flatbuffer_builder); - assert(chef != nullptr); + // Create Custom option + auto circle_custom_options = op_chef->custom_value(*flatbuffer_builder); - // Create Data - auto data_vec = chef->generate(element_count(dims)); - auto data = flatbuffer_builder->CreateVector(data_vec); + // Create Operator + tflite::OperatorBuilder op_builder{*flatbuffer_builder}; + + // Get operator code index from builtin_code_set with assumption, order of + // builtin_code_set is same as that of code_vec + auto op_it = builtin_code_map.find(op_chef->code()); + assert(op_it != builtin_code_map.end()); + uint32_t opcode_index = std::distance(builtin_code_map.begin(), op_it); + + op_builder.add_opcode_index(opcode_index); + op_builder.add_inputs(inputs); + op_builder.add_outputs(outputs); + op_builder.add_builtin_options_type(op_chef->type()); + op_builder.add_builtin_options(options); + op_builder.add_custom_options(circle_custom_options); + op_builder.add_custom_options_format(tflite::CustomOptionsFormat_FLEXBUFFERS); + // Append Operator + operator_vec.emplace_back(op_builder.Finish()); + } - // Create Buffer - tflite::BufferBuilder buffer_builder{*flatbuffer_builder}; - buffer_builder.add_data(data); - auto buffer = buffer_builder.Finish(); + // Create network input/output vector + std::vector input_vec = as_dataset(graph.input()).map(lookup).vectorize(); + std::vector output_vec = as_dataset(graph.output()).map(lookup).vectorize(); - // Update Buffer Index & Vector - buffer_index = buffer_vec.size(); - buffer_vec.emplace_back(buffer); - } - else - { - // if this is input or output, assign to that buffer_index - int idx = 0; - buffer_index = 0; - for (auto it = input_names.begin(); it != input_names.end(); ++it, ++idx) - { - if (*it == operand.name()) - { - buffer_index = buffer_start + idx; - break; - } - } - if (buffer_index == 0) - { - idx = 0; - for (auto it = output_names.begin(); it != output_names.end(); ++it, ++idx) - { - if (*it == operand.name()) - { - buffer_index = buffer_start + size_input + idx; - break; - } - } - } - } - // NOTE buffer_index can be 0 when this operand does not have a filler or not I/O + // Create "SubGraph" arguments + auto tensors = flatbuffer_builder->CreateVector(tensor_vec); + auto inputs = flatbuffer_builder->CreateVector(input_vec); + auto outputs = flatbuffer_builder->CreateVector(output_vec); + auto operators = flatbuffer_builder->CreateVector(operator_vec); + auto name = flatbuffer_builder->CreateString(graph_name); - flatbuffers::Offset quant_index; + tflite::SubGraphBuilder subgraph_builder{*flatbuffer_builder}; - // Create QuantizationParameters if quant is specified - if (operand.has_quant()) - { - const auto &quant = operand.quant(); - - // Create each parameters - // NOTE if some parameters are not given, those will be set to default value - std::vector quant_max_vec(quant.max_size()); - std::vector quant_min_vec(quant.min_size()); - std::vector quant_scale_vec(quant.scale_size()); - std::vector quant_zero_point_vec(quant.zero_point_size()); - - for (uint32_t i = 0; i < quant.max_size(); ++i) - quant_max_vec.at(i) = quant.max(i); - for (uint32_t i = 0; i < quant.min_size(); ++i) - quant_min_vec.at(i) = quant.min(i); - for (uint32_t i = 0; i < quant.scale_size(); ++i) - quant_scale_vec.at(i) = quant.scale(i); - for (uint32_t i = 0; i < quant.zero_point_size(); ++i) - quant_zero_point_vec.at(i) = quant.zero_point(i); - - auto quant_max = flatbuffer_builder->CreateVector(quant_max_vec); - auto quant_min = flatbuffer_builder->CreateVector(quant_min_vec); - auto quant_scale = flatbuffer_builder->CreateVector(quant_scale_vec); - auto quant_zero_point = flatbuffer_builder->CreateVector(quant_zero_point_vec); - - // Create QuantizationParameters - tflite::QuantizationParametersBuilder quant_builder{*flatbuffer_builder}; - quant_builder.add_max(quant_max); - quant_builder.add_min(quant_min); - quant_builder.add_scale(quant_scale); - quant_builder.add_zero_point(quant_zero_point); - - // Update QuantizationParameters Index - quant_index = quant_builder.Finish(); - } + subgraph_builder.add_tensors(tensors); + subgraph_builder.add_inputs(inputs); + subgraph_builder.add_outputs(outputs); + subgraph_builder.add_operators(operators); + subgraph_builder.add_name(name); - // Create Tensor - tflite::TensorBuilder tensor_builder{*flatbuffer_builder}; + subgraph_vec.emplace_back(subgraph_builder.Finish()); +} - tensor_builder.add_shape(shape); - tensor_builder.add_type(as_tflite_tensortype(operand.type())); - tensor_builder.add_buffer(buffer_index); - tensor_builder.add_name(name); - if (operand.has_quant()) - tensor_builder.add_quantization(quant_index); +} // namespace - // Append! - tensor_vec.emplace_back(tensor_builder.Finish()); +namespace tflchef +{ - // Update Tensor Name -> Tensor Index Map - int32_t tensor_index = symbol_table.size(); - const auto &tensor_name = operand.name(); +/** + * @brief Generate a (in-memory) TensorFlow Lite model from a given model recipe + */ +GeneratedModel cook(const ::tflchef::ModelRecipe &model_recipe) +{ +// Initialize Op Chef Registry +#define OP_CHEF(NAME, FACTORY_CLASS) \ + op_chef_registry().add(#NAME, std::unique_ptr(new FACTORY_CLASS())); +#include "OpChef.def" +#undef OP_CHEF - symbol_table[tensor_name] = tensor_index; - } +// Initialize Data Chef Registry +#define DATA_CHEF(TYPE, NAME, FACTORY_CLASS) \ + data_chef_registry(::tflchef::TYPE) \ + .add(#NAME, std::unique_ptr(new FACTORY_CLASS())); +#include +#undef DATA_CHEF - // Create Operator - for (const auto &operation : graph.operation()) - { - assert(operation.has_type()); + // + // Create FlatBufferBuilder + // + auto flatbuffer_builder = + std::unique_ptr(new flatbuffers::FlatBufferBuilder(1024)); - auto op_chef = op_chef_registry().lookup(operation.type()).create(&operation); + // Operand-related + std::vector> buffer_vec; - // Create 'inputs' - std::vector input_vec = as_dataset(operation.input()).map(lookup).vectorize(); - auto inputs = flatbuffer_builder->CreateVector(input_vec); + // Operation-related + std::vector> code_vec; - // Create 'outputs' - std::vector output_vec = as_dataset(operation.output()).map(lookup).vectorize(); - auto outputs = flatbuffer_builder->CreateVector(output_vec); + // Graphs-related + std::vector> subgraph_vec; - // Create Option - auto options = op_chef->value(*flatbuffer_builder); + // Create OperatorCode with Builtin Operator + auto builtin_code_map = gather_builtincode_map(model_recipe); + for (auto const &opcode : builtin_code_map) + { + tflite::OperatorCodeBuilder code_builder{*flatbuffer_builder}; + code_builder.add_builtin_code(opcode.first); + code_builder.add_version(opcode.second); + auto code = code_builder.Finish(); + // Update OperatorCode vector + code_vec.emplace_back(code); + } - // Create Operator - tflite::OperatorBuilder op_builder{*flatbuffer_builder}; + // Create OperatorCode with Custom Operator + std::set custom_code_set = gather_customcode_set(model_recipe); + if (custom_code_set.size() && + builtin_code_map.find(tflite::BuiltinOperator_CUSTOM) == builtin_code_map.end()) + builtin_code_map[tflite::BuiltinOperator_CUSTOM] = 1; - // Get operator code index from opcode_set with assumption, order of - // opcode_set is same as that of code_vec - auto op_it = opcode_set.find(op_chef->code()); - assert(op_it != opcode_set.end()); - uint32_t opcode_index = std::distance(opcode_set.begin(), op_it); + for (auto opcode : custom_code_set) + { + auto custom_code = flatbuffer_builder->CreateString(opcode); + tflite::OperatorCodeBuilder code_builder{*flatbuffer_builder}; + code_builder.add_builtin_code(tflite::BuiltinOperator_CUSTOM); + code_builder.add_custom_code(custom_code); + auto code = code_builder.Finish(); + // Update OperatorCode vector + code_vec.emplace_back(code); + } - op_builder.add_opcode_index(opcode_index); - op_builder.add_inputs(inputs); - op_builder.add_outputs(outputs); - op_builder.add_builtin_options_type(op_chef->type()); - op_builder.add_builtin_options(options); + // Create an Empty Buffer + // + // Buffer 0 SHOULD be an empty buffer in TensorFlow Lite model file + // (Please refer to the comment for Tensor.buffer field in schema) + { + tflite::BufferBuilder buffer_builder{*flatbuffer_builder}; + buffer_vec.emplace_back(buffer_builder.Finish()); + } - // Append Operator - operator_vec.emplace_back(op_builder.Finish()); - } + // + // Create Main graph + // + CookParams cp{buffer_vec, code_vec, subgraph_vec, flatbuffer_builder, builtin_code_map, "main"}; - // Create network input/output vector - std::vector input_vec = as_dataset(graph.input()).map(lookup).vectorize(); - std::vector output_vec = as_dataset(graph.output()).map(lookup).vectorize(); + cook_graph<::tflchef::ModelRecipe>(model_recipe, cp); - // Create "SubGraph" arguments - auto tensors = flatbuffer_builder->CreateVector(tensor_vec); - auto inputs = flatbuffer_builder->CreateVector(input_vec); - auto outputs = flatbuffer_builder->CreateVector(output_vec); - auto operators = flatbuffer_builder->CreateVector(operator_vec); - auto name = flatbuffer_builder->CreateString(graph_name); + // + // Create subgraphs if exist + // + for (int g = 0; g < model_recipe.graph_size(); ++g) + { + const auto &graph = model_recipe.graph(g); - tflite::SubGraphBuilder subgraph_builder{*flatbuffer_builder}; + std::ostringstream stringStream; + stringStream << "sub_" << (g + 1); - subgraph_builder.add_tensors(tensors); - subgraph_builder.add_inputs(inputs); - subgraph_builder.add_outputs(outputs); - subgraph_builder.add_operators(operators); - subgraph_builder.add_name(name); + CookParams cp{buffer_vec, code_vec, subgraph_vec, + flatbuffer_builder, builtin_code_map, stringStream.str()}; - subgraph_vec.emplace_back(subgraph_builder.Finish()); + cook_graph<::tflchef::Graph>(graph, cp); } // Create "Model" arguments diff --git a/compiler/tflchef/core/src/Op/AddN.cpp b/compiler/tflchef/core/src/Op/AddN.cpp new file mode 100644 index 0000000..2ac02d2 --- /dev/null +++ b/compiler/tflchef/core/src/Op/AddN.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "AddN.h" +#include "Convert.h" + +#include + +flatbuffers::Offset AddNChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::AddNOptionsBuilder add_n_options_builder{fbb}; + + return add_n_options_builder.Finish().Union(); +} + +std::unique_ptr AddNChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new AddNChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/AddN.h b/compiler/tflchef/core/src/Op/AddN.h new file mode 100644 index 0000000..44dcc63 --- /dev/null +++ b/compiler/tflchef/core/src/Op/AddN.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_ADD_N_H__ +#define __OP_ADD_N_H__ + +#include "OpChef.h" + +class AddNChef final : public OpChef +{ +public: + explicit AddNChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_ADD_N; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_AddNOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct AddNChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_ADD_N_H__ diff --git a/compiler/tflchef/core/src/Op/ArgMin.cpp b/compiler/tflchef/core/src/Op/ArgMin.cpp new file mode 100644 index 0000000..b599270 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ArgMin.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ArgMin.h" +#include "Convert.h" + +#include + +flatbuffers::Offset ArgMinChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_argmin_options()); + + auto tflite_output_type = as_tflite_tensortype(operation.argmin_options().output_type()); + + tflite::ArgMinOptionsBuilder argmin_options_builder{fbb}; + argmin_options_builder.add_output_type(tflite_output_type); + + return argmin_options_builder.Finish().Union(); +} + +std::unique_ptr ArgMinChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new ArgMinChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/ArgMin.h b/compiler/tflchef/core/src/Op/ArgMin.h new file mode 100644 index 0000000..222039f --- /dev/null +++ b/compiler/tflchef/core/src/Op/ArgMin.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_ARGMIN_H__ +#define __OP_ARGMIN_H__ + +#include "OpChef.h" + +class ArgMinChef final : public OpChef +{ +public: + explicit ArgMinChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_ARG_MIN; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_ArgMinOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct ArgMinChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_ARGMIN_H__ diff --git a/compiler/tflchef/core/src/Op/BatchMatMul.cpp b/compiler/tflchef/core/src/Op/BatchMatMul.cpp new file mode 100644 index 0000000..7722bcc --- /dev/null +++ b/compiler/tflchef/core/src/Op/BatchMatMul.cpp @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "BatchMatMul.h" + +flatbuffers::Offset BatchMatMulChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_batch_matmul_options()); + + tflite::BatchMatMulOptionsBuilder batch_matmul_options_options_builder{fbb}; + batch_matmul_options_options_builder.add_adj_x(operation.batch_matmul_options().adj_x()); + batch_matmul_options_options_builder.add_adj_y(operation.batch_matmul_options().adj_y()); + + return batch_matmul_options_options_builder.Finish().Union(); +} + +std::unique_ptr BatchMatMulChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new BatchMatMulChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/BatchMatMul.h b/compiler/tflchef/core/src/Op/BatchMatMul.h new file mode 100644 index 0000000..eaf943c --- /dev/null +++ b/compiler/tflchef/core/src/Op/BatchMatMul.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_BATCH_MATMUL_H__ +#define __OP_BATCH_MATMUL_H__ + +#include "OpChef.h" + +class BatchMatMulChef final : public OpChef +{ +public: + explicit BatchMatMulChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_BATCH_MATMUL; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_BatchMatMulOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct BatchMatMulChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_BATCH_MATMUL_H__ diff --git a/compiler/tflchef/core/src/Op/Cast.cpp b/compiler/tflchef/core/src/Op/Cast.cpp new file mode 100644 index 0000000..1a29f9a --- /dev/null +++ b/compiler/tflchef/core/src/Op/Cast.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Cast.h" +#include "Convert.h" + +#include + +flatbuffers::Offset CastChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + if (!operation.has_cast_options()) + return 0; + + auto tflite_in_data_type = as_tflite_tensortype(operation.cast_options().in_data_type()); + auto tflite_out_data_type = as_tflite_tensortype(operation.cast_options().out_data_type()); + + tflite::CastOptionsBuilder cast_options_builder{fbb}; + cast_options_builder.add_in_data_type(tflite_in_data_type); + cast_options_builder.add_out_data_type(tflite_out_data_type); + + return cast_options_builder.Finish().Union(); +} + +std::unique_ptr CastChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new CastChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Cast.h b/compiler/tflchef/core/src/Op/Cast.h new file mode 100644 index 0000000..84c8e29 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Cast.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_CAST_H__ +#define __OP_CAST_H__ + +#include "OpChef.h" + +class CastChef final : public OpChef +{ +public: + explicit CastChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_CAST; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_CastOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct CastChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_CAST_H__ diff --git a/compiler/tflchef/core/src/Op/Ceil.cpp b/compiler/tflchef/core/src/Op/Ceil.cpp new file mode 100644 index 0000000..3da0477 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Ceil.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Ceil.h" + +flatbuffers::Offset CeilChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + // No tflite option for Ceil. Use void. + return flatbuffers::Offset(); +} + +std::unique_ptr CeilChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new CeilChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Ceil.h b/compiler/tflchef/core/src/Op/Ceil.h new file mode 100644 index 0000000..5a42b7f --- /dev/null +++ b/compiler/tflchef/core/src/Op/Ceil.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_CEIL_H__ +#define __OP_CEIL_H__ + +#include "OpChef.h" + +class CeilChef final : public OpChef +{ +public: + explicit CeilChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_CEIL; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NONE; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct CeilChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_CEIL_H__ diff --git a/compiler/tflchef/core/src/Op/Conv2D.cpp b/compiler/tflchef/core/src/Op/Conv2D.cpp index d99c533..0b3ef8a 100644 --- a/compiler/tflchef/core/src/Op/Conv2D.cpp +++ b/compiler/tflchef/core/src/Op/Conv2D.cpp @@ -25,14 +25,18 @@ flatbuffers::Offset Conv2DChef::value(flatbuffers::FlatBufferBuilder &fbb) assert(operation.has_conv2d_options()); - auto tflite_padding = as_tflite_padding(operation.conv2d_options().padding()); - auto tflite_activation = as_tflite_activation(operation.conv2d_options().activation()); + const auto &conv2d_options = operation.conv2d_options(); + + auto tflite_padding = as_tflite_padding(conv2d_options.padding()); + auto tflite_activation = as_tflite_activation(conv2d_options.activation()); tflite::Conv2DOptionsBuilder conv2d_options_builder{fbb}; conv2d_options_builder.add_padding(tflite_padding); - conv2d_options_builder.add_stride_h(operation.conv2d_options().stride_h()); - conv2d_options_builder.add_stride_w(operation.conv2d_options().stride_w()); + conv2d_options_builder.add_stride_h(conv2d_options.stride_h()); + conv2d_options_builder.add_stride_w(conv2d_options.stride_w()); conv2d_options_builder.add_fused_activation_function(tflite_activation); + conv2d_options_builder.add_dilation_w_factor(conv2d_options.dilation_w_factor()); + conv2d_options_builder.add_dilation_h_factor(conv2d_options.dilation_h_factor()); return conv2d_options_builder.Finish().Union(); } diff --git a/compiler/tflchef/core/src/Op/DepthToSpace.cpp b/compiler/tflchef/core/src/Op/DepthToSpace.cpp new file mode 100644 index 0000000..f0531d9 --- /dev/null +++ b/compiler/tflchef/core/src/Op/DepthToSpace.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "DepthToSpace.h" +#include "Convert.h" + +#include + +flatbuffers::Offset DepthToSpaceChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_depth_to_space_options()); + + auto options = operation.depth_to_space_options(); + + auto tflite_block_size = options.block_size(); + + tflite::DepthToSpaceOptionsBuilder options_builder{fbb}; + + options_builder.add_block_size(tflite_block_size); + + return options_builder.Finish().Union(); +} + +std::unique_ptr DepthToSpaceChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new DepthToSpaceChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/DepthToSpace.h b/compiler/tflchef/core/src/Op/DepthToSpace.h new file mode 100644 index 0000000..32cb242 --- /dev/null +++ b/compiler/tflchef/core/src/Op/DepthToSpace.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_DEPTHTOSPACE_H__ +#define __OP_DEPTHTOSPACE_H__ + +#include "OpChef.h" + +class DepthToSpaceChef final : public OpChef +{ +public: + explicit DepthToSpaceChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override + { + return tflite::BuiltinOperator_DEPTH_TO_SPACE; + } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_DepthToSpaceOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct DepthToSpaceChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_DEPTHTOSPACE_H__ diff --git a/compiler/tflchef/core/src/Op/DepthwiseConv2D.cpp b/compiler/tflchef/core/src/Op/DepthwiseConv2D.cpp index e04cf50..5da5b63 100644 --- a/compiler/tflchef/core/src/Op/DepthwiseConv2D.cpp +++ b/compiler/tflchef/core/src/Op/DepthwiseConv2D.cpp @@ -36,6 +36,8 @@ flatbuffers::Offset DepthwiseConv2DChef::value(flatbuffers::FlatBufferBuil options_builder.add_stride_h(options.stride_h()); options_builder.add_depth_multiplier(options.depth_multiplier()); options_builder.add_fused_activation_function(tflite_activation); + options_builder.add_dilation_w_factor(options.dilation_w_factor()); + options_builder.add_dilation_h_factor(options.dilation_h_factor()); return options_builder.Finish().Union(); } diff --git a/compiler/tflchef/core/src/Op/ELU.cpp b/compiler/tflchef/core/src/Op/ELU.cpp new file mode 100644 index 0000000..d9dae16 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ELU.cpp @@ -0,0 +1,27 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ELU.h" + +flatbuffers::Offset ELUChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + return flatbuffers::Offset(); +} + +std::unique_ptr ELUChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new ELUChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/ELU.h b/compiler/tflchef/core/src/Op/ELU.h new file mode 100644 index 0000000..e164c00 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ELU.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_ELU_H__ +#define __OP_ELU_H__ + +#include "OpChef.h" + +class ELUChef final : public OpChef +{ +public: + explicit ELUChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_ELU; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NONE; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct ELUChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_ELU_H__ diff --git a/compiler/tflchef/core/src/Op/ExpandDims.cpp b/compiler/tflchef/core/src/Op/ExpandDims.cpp new file mode 100644 index 0000000..c608281 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ExpandDims.cpp @@ -0,0 +1,30 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ExpandDims.h" +#include "Convert.h" + +flatbuffers::Offset ExpandDimsChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::ExpandDimsOptionsBuilder expand_dims_options_builder{fbb}; + + return expand_dims_options_builder.Finish().Union(); +} + +std::unique_ptr ExpandDimsChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new ExpandDimsChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/ExpandDims.h b/compiler/tflchef/core/src/Op/ExpandDims.h new file mode 100644 index 0000000..1f4c34a --- /dev/null +++ b/compiler/tflchef/core/src/Op/ExpandDims.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_EXPAND_DIMS_H__ +#define __OP_EXPAND_DIMS_H__ + +#include "OpChef.h" + +class ExpandDimsChef final : public OpChef +{ +public: + explicit ExpandDimsChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_EXPAND_DIMS; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_ExpandDimsOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct ExpandDimsChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_EXPAND_DIMS_H__ diff --git a/compiler/tflchef/core/src/Op/Fill.cpp b/compiler/tflchef/core/src/Op/Fill.cpp new file mode 100644 index 0000000..4a68294 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Fill.cpp @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Fill.h" + +#include +#include + +flatbuffers::Offset FillChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::FillOptionsBuilder options_builder{fbb}; + return options_builder.Finish().Union(); +} + +std::unique_ptr FillChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new FillChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Fill.h b/compiler/tflchef/core/src/Op/Fill.h new file mode 100644 index 0000000..60f9084 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Fill.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_FILL_H__ +#define __OP_FILL_H__ + +#include "OpChef.h" + +class FillChef final : public OpChef +{ +public: + explicit FillChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_FILL; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_FillOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct FillChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_FILL_H__ diff --git a/compiler/tflchef/core/src/Op/Floor.cpp b/compiler/tflchef/core/src/Op/Floor.cpp new file mode 100644 index 0000000..8f68201 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Floor.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Floor.h" + +flatbuffers::Offset FloorChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + // No tflite option for Floor. Use void. + return flatbuffers::Offset(); +} + +std::unique_ptr FloorChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new FloorChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Floor.h b/compiler/tflchef/core/src/Op/Floor.h new file mode 100644 index 0000000..23385d7 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Floor.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_FLOOR_H__ +#define __OP_FLOOR_H__ + +#include "OpChef.h" + +class FloorChef final : public OpChef +{ +public: + explicit FloorChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_FLOOR; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NONE; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct FloorChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_FLOOR_H__ diff --git a/compiler/tflchef/core/src/Op/FloorMod.cpp b/compiler/tflchef/core/src/Op/FloorMod.cpp new file mode 100644 index 0000000..d17795a --- /dev/null +++ b/compiler/tflchef/core/src/Op/FloorMod.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "FloorMod.h" + +flatbuffers::Offset FloorModChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::FloorModOptionsBuilder options_builder{fbb}; + return options_builder.Finish().Union(); +} + +std::unique_ptr FloorModChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new FloorModChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/FloorMod.h b/compiler/tflchef/core/src/Op/FloorMod.h new file mode 100644 index 0000000..b501f61 --- /dev/null +++ b/compiler/tflchef/core/src/Op/FloorMod.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_FLOOR_MOD_H__ +#define __OP_FLOOR_MOD_H__ + +#include "OpChef.h" + +class FloorModChef final : public OpChef +{ +public: + explicit FloorModChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_FLOOR_MOD; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_FloorModOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct FloorModChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_FLOOR_MOD_H__ diff --git a/compiler/tflchef/core/src/Op/Gather.cpp b/compiler/tflchef/core/src/Op/Gather.cpp new file mode 100644 index 0000000..2b62c7b --- /dev/null +++ b/compiler/tflchef/core/src/Op/Gather.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Gather.h" +#include "Convert.h" + +#include + +flatbuffers::Offset GatherChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_gather_options()); + + auto options = operation.gather_options(); + + tflite::GatherOptionsBuilder options_builder{fbb}; + + options_builder.add_axis(options.axis()); + return options_builder.Finish().Union(); +} + +std::unique_ptr GatherChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new GatherChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Gather.h b/compiler/tflchef/core/src/Op/Gather.h new file mode 100644 index 0000000..d937178 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Gather.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_GATHER_H__ +#define __OP_GATHER_H__ + +#include "OpChef.h" + +class GatherChef final : public OpChef +{ +public: + explicit GatherChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_GATHER; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_GatherOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct GatherChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_GATHER_H__ diff --git a/compiler/tflchef/core/src/Op/GatherNd.cpp b/compiler/tflchef/core/src/Op/GatherNd.cpp new file mode 100644 index 0000000..c04db53 --- /dev/null +++ b/compiler/tflchef/core/src/Op/GatherNd.cpp @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "GatherNd.h" +#include "Convert.h" + +#include + +flatbuffers::Offset GatherNdChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::GatherNdOptionsBuilder gather_nd_options_builder{fbb}; + return gather_nd_options_builder.Finish().Union(); +} + +std::unique_ptr GatherNdChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new GatherNdChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/GatherNd.h b/compiler/tflchef/core/src/Op/GatherNd.h new file mode 100644 index 0000000..8865e77 --- /dev/null +++ b/compiler/tflchef/core/src/Op/GatherNd.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_GATHER_ND_H__ +#define __OP_GATHER_ND_H__ + +#include "OpChef.h" + +class GatherNdChef final : public OpChef +{ +public: + explicit GatherNdChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_GATHER_ND; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_GatherNdOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct GatherNdChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_GATHER_ND_H__ diff --git a/compiler/tflchef/core/src/Op/Greater.cpp b/compiler/tflchef/core/src/Op/Greater.cpp new file mode 100644 index 0000000..81765ae --- /dev/null +++ b/compiler/tflchef/core/src/Op/Greater.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Greater.h" + +flatbuffers::Offset GreaterChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::GreaterOptionsBuilder options_builder{fbb}; + + return options_builder.Finish().Union(); +} + +std::unique_ptr GreaterChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new GreaterChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Greater.h b/compiler/tflchef/core/src/Op/Greater.h new file mode 100644 index 0000000..c54eaa6 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Greater.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_GREATER_H__ +#define __OP_GREATER_H__ + +#include "OpChef.h" + +class GreaterChef final : public OpChef +{ +public: + explicit GreaterChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_GREATER; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_GreaterOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct GreaterChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_GREATER_H__ diff --git a/compiler/tflchef/core/src/Op/GreaterEqual.cpp b/compiler/tflchef/core/src/Op/GreaterEqual.cpp new file mode 100644 index 0000000..80045f6 --- /dev/null +++ b/compiler/tflchef/core/src/Op/GreaterEqual.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "GreaterEqual.h" + +flatbuffers::Offset GreaterEqualChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::GreaterEqualOptionsBuilder options_builder{fbb}; + + return options_builder.Finish().Union(); +} + +std::unique_ptr GreaterEqualChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new GreaterEqualChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/GreaterEqual.h b/compiler/tflchef/core/src/Op/GreaterEqual.h new file mode 100644 index 0000000..105bac8 --- /dev/null +++ b/compiler/tflchef/core/src/Op/GreaterEqual.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_GREATEREQUAL_H__ +#define __OP_GREATEREQUAL_H__ + +#include "OpChef.h" + +class GreaterEqualChef final : public OpChef +{ +public: + explicit GreaterEqualChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override + { + return tflite::BuiltinOperator_GREATER_EQUAL; + } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_GreaterEqualOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct GreaterEqualChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_GREATEREQUAL_H__ diff --git a/compiler/tflchef/core/src/Op/If.cpp b/compiler/tflchef/core/src/Op/If.cpp new file mode 100644 index 0000000..b0e575e --- /dev/null +++ b/compiler/tflchef/core/src/Op/If.cpp @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "If.h" + +flatbuffers::Offset IfChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_if_options()); + + tflite::IfOptionsBuilder if_options_builder{fbb}; + if_options_builder.add_then_subgraph_index(operation.if_options().then_subgraph_index()); + if_options_builder.add_else_subgraph_index(operation.if_options().else_subgraph_index()); + + return if_options_builder.Finish().Union(); +} + +std::unique_ptr IfChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new IfChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/If.h b/compiler/tflchef/core/src/Op/If.h new file mode 100644 index 0000000..7e18c56 --- /dev/null +++ b/compiler/tflchef/core/src/Op/If.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_IF_H__ +#define __OP_IF_H__ + +#include "OpChef.h" + +class IfChef final : public OpChef +{ +public: + explicit IfChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_IF; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_IfOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct IfChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_IF_H__ diff --git a/compiler/tflchef/core/src/Op/L2Normalize.cpp b/compiler/tflchef/core/src/Op/L2Normalize.cpp new file mode 100644 index 0000000..62d15e5 --- /dev/null +++ b/compiler/tflchef/core/src/Op/L2Normalize.cpp @@ -0,0 +1,33 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "L2Normalize.h" +#include "Convert.h" + +flatbuffers::Offset L2Normalize::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + tflite::L2NormOptionsBuilder l2norm_options_builder{fbb}; + auto tflite_activation = as_tflite_activation(operation.l2norm_options().activation()); + l2norm_options_builder.add_fused_activation_function(tflite_activation); + return l2norm_options_builder.Finish().Union(); +} + +std::unique_ptr L2NormalizeChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new L2Normalize{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/L2Normalize.h b/compiler/tflchef/core/src/Op/L2Normalize.h new file mode 100644 index 0000000..dd5f21c --- /dev/null +++ b/compiler/tflchef/core/src/Op/L2Normalize.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_L2NORMALIZE_H__ +#define __OP_L2NORMALIZE_H__ + +#include "OpChef.h" + +class L2Normalize final : public OpChef +{ +public: + explicit L2Normalize(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override + { + return tflite::BuiltinOperator_L2_NORMALIZATION; + } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_L2NormOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct L2NormalizeChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_L2NORMALIZE_H__ diff --git a/compiler/tflchef/core/src/Op/L2Pool2D.cpp b/compiler/tflchef/core/src/Op/L2Pool2D.cpp new file mode 100644 index 0000000..f22bb96 --- /dev/null +++ b/compiler/tflchef/core/src/Op/L2Pool2D.cpp @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "L2Pool2D.h" +#include "Convert.h" + +#include + +flatbuffers::Offset L2Pool2DChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_l2pool2d_options()); + + auto options = operation.l2pool2d_options(); + + auto tflite_padding = as_tflite_padding(options.padding()); + auto tflite_activation = as_tflite_activation(options.activation()); + + tflite::Pool2DOptionsBuilder options_builder{fbb}; + options_builder.add_padding(tflite_padding); + options_builder.add_stride_h(options.stride_h()); + options_builder.add_stride_w(options.stride_w()); + options_builder.add_filter_width(options.filter_width()); + options_builder.add_filter_height(options.filter_height()); + options_builder.add_fused_activation_function(tflite_activation); + + return options_builder.Finish().Union(); +} + +std::unique_ptr L2Pool2DChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new L2Pool2DChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/L2Pool2D.h b/compiler/tflchef/core/src/Op/L2Pool2D.h new file mode 100644 index 0000000..6bd8bdb --- /dev/null +++ b/compiler/tflchef/core/src/Op/L2Pool2D.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_L2_POOL_2D_H__ +#define __OP_L2_POOL_2D_H__ + +#include "OpChef.h" + +class L2Pool2DChef final : public OpChef +{ +public: + explicit L2Pool2DChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_L2_POOL_2D; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_Pool2DOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct L2Pool2DChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_L2_POOL_2D_H__ diff --git a/compiler/tflchef/core/src/Op/LeakyRelu.cpp b/compiler/tflchef/core/src/Op/LeakyRelu.cpp new file mode 100644 index 0000000..247739a --- /dev/null +++ b/compiler/tflchef/core/src/Op/LeakyRelu.cpp @@ -0,0 +1,34 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LeakyRelu.h" + +flatbuffers::Offset LeakyReluChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + assert(_operation->has_leaky_relu_options()); + + const auto &options = _operation->leaky_relu_options(); + + tflite::LeakyReluOptionsBuilder options_builder{fbb}; + options_builder.add_alpha(options.alpha()); + + return options_builder.Finish().Union(); +} + +std::unique_ptr LeakyReluChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new LeakyReluChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/LeakyRelu.h b/compiler/tflchef/core/src/Op/LeakyRelu.h new file mode 100644 index 0000000..5449e59 --- /dev/null +++ b/compiler/tflchef/core/src/Op/LeakyRelu.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_LEAKY_RELU_H__ +#define __OP_LEAKY_RELU_H__ + +#include "OpChef.h" + +class LeakyReluChef final : public OpChef +{ +public: + explicit LeakyReluChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_LEAKY_RELU; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_LeakyReluOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct LeakyReluChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_LEAKY_RELU_H__ diff --git a/compiler/tflchef/core/src/Op/Less.cpp b/compiler/tflchef/core/src/Op/Less.cpp new file mode 100644 index 0000000..c143d83 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Less.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Less.h" + +flatbuffers::Offset LessChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::LessOptionsBuilder options_builder{fbb}; + + return options_builder.Finish().Union(); +} + +std::unique_ptr LessChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new LessChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Less.h b/compiler/tflchef/core/src/Op/Less.h new file mode 100644 index 0000000..280036c --- /dev/null +++ b/compiler/tflchef/core/src/Op/Less.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_LESS_H__ +#define __OP_LESS_H__ + +#include "OpChef.h" + +class LessChef final : public OpChef +{ +public: + explicit LessChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_LESS; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_LessOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct LessChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_LESS_H__ diff --git a/compiler/tflchef/core/src/Op/LessEqual.cpp b/compiler/tflchef/core/src/Op/LessEqual.cpp new file mode 100644 index 0000000..dc383e7 --- /dev/null +++ b/compiler/tflchef/core/src/Op/LessEqual.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LessEqual.h" + +flatbuffers::Offset LessEqualChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::LessEqualOptionsBuilder options_builder{fbb}; + + return options_builder.Finish().Union(); +} + +std::unique_ptr LessEqualChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new LessEqualChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/LessEqual.h b/compiler/tflchef/core/src/Op/LessEqual.h new file mode 100644 index 0000000..1315b9c --- /dev/null +++ b/compiler/tflchef/core/src/Op/LessEqual.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_LESSEQUAL_H__ +#define __OP_LESSEQUAL_H__ + +#include "OpChef.h" + +class LessEqualChef final : public OpChef +{ +public: + explicit LessEqualChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_LESS_EQUAL; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_LessEqualOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct LessEqualChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_LESSEQUAL_H__ diff --git a/compiler/tflchef/core/src/Op/LocalResponseNormalization.cpp b/compiler/tflchef/core/src/Op/LocalResponseNormalization.cpp new file mode 100644 index 0000000..f5430d4 --- /dev/null +++ b/compiler/tflchef/core/src/Op/LocalResponseNormalization.cpp @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LocalResponseNormalization.h" +#include "Convert.h" + +#include + +flatbuffers::Offset +LocalResponseNormalizationChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_local_response_normalization_options()); + + auto &lrn_options = operation.local_response_normalization_options(); + + auto tflite_radius = lrn_options.radius(); + auto tflite_bias = lrn_options.bias(); + auto tflite_alpha = lrn_options.alpha(); + auto tflite_beta = lrn_options.beta(); + + tflite::LocalResponseNormalizationOptionsBuilder lrn_options_builder{fbb}; + + lrn_options_builder.add_radius(tflite_radius); + lrn_options_builder.add_bias(tflite_bias); + lrn_options_builder.add_alpha(tflite_alpha); + lrn_options_builder.add_beta(tflite_beta); + + return lrn_options_builder.Finish().Union(); +} + +std::unique_ptr +LocalResponseNormalizationChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new LocalResponseNormalizationChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/LocalResponseNormalization.h b/compiler/tflchef/core/src/Op/LocalResponseNormalization.h new file mode 100644 index 0000000..62a2355 --- /dev/null +++ b/compiler/tflchef/core/src/Op/LocalResponseNormalization.h @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_LOCAL_RESPONSE_NORMALIZATION_H__ +#define __OP_LOCAL_RESPONSE_NORMALIZATION_H__ + +#include "OpChef.h" + +class LocalResponseNormalizationChef final : public OpChef +{ +public: + explicit LocalResponseNormalizationChef(const tflchef::Operation *operation) + : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override + { + return tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION; + } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_LocalResponseNormalizationOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct LocalResponseNormalizationChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_LOCAL_RESPONSE_NORMALIZATION_H__ diff --git a/compiler/tflchef/core/src/Op/Log.cpp b/compiler/tflchef/core/src/Op/Log.cpp new file mode 100644 index 0000000..c4e65ad --- /dev/null +++ b/compiler/tflchef/core/src/Op/Log.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Log.h" + +flatbuffers::Offset LogChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + // No tflite option for Log. Use void. + return flatbuffers::Offset(); +} + +std::unique_ptr LogChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new LogChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Log.h b/compiler/tflchef/core/src/Op/Log.h new file mode 100644 index 0000000..2cc8a66 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Log.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_LOG_H__ +#define __OP_LOG_H__ + +#include "OpChef.h" + +class LogChef final : public OpChef +{ +public: + explicit LogChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_LOG; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NONE; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct LogChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_LOG_H__ diff --git a/compiler/tflchef/core/src/Op/LogSoftmax.cpp b/compiler/tflchef/core/src/Op/LogSoftmax.cpp new file mode 100644 index 0000000..eb2f132 --- /dev/null +++ b/compiler/tflchef/core/src/Op/LogSoftmax.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LogSoftmax.h" +#include "Convert.h" + +#include + +flatbuffers::Offset LogSoftmaxChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::LogSoftmaxOptionsBuilder soft_options_builder{fbb}; + + return soft_options_builder.Finish().Union(); +} + +std::unique_ptr LogSoftmaxChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new LogSoftmaxChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/LogSoftmax.h b/compiler/tflchef/core/src/Op/LogSoftmax.h new file mode 100644 index 0000000..3ce08b7 --- /dev/null +++ b/compiler/tflchef/core/src/Op/LogSoftmax.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_LOG_SOFTMAX_H__ +#define __OP_LOG_SOFTMAX_H__ + +#include "OpChef.h" + +class LogSoftmaxChef final : public OpChef +{ +public: + explicit LogSoftmaxChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_LOG_SOFTMAX; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_LogSoftmaxOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct LogSoftmaxChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_LOG_SOFTMAX_H__ diff --git a/compiler/tflchef/core/src/Op/LogicalAnd.cpp b/compiler/tflchef/core/src/Op/LogicalAnd.cpp new file mode 100644 index 0000000..64a6113 --- /dev/null +++ b/compiler/tflchef/core/src/Op/LogicalAnd.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LogicalAnd.h" + +flatbuffers::Offset LogicalAndChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::LogicalAndOptionsBuilder options_builder{fbb}; + + return options_builder.Finish().Union(); +} + +std::unique_ptr LogicalAndChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new LogicalAndChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/LogicalAnd.h b/compiler/tflchef/core/src/Op/LogicalAnd.h new file mode 100644 index 0000000..1f27227 --- /dev/null +++ b/compiler/tflchef/core/src/Op/LogicalAnd.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_LOGICALAND_H__ +#define __OP_LOGICALAND_H__ + +#include "OpChef.h" + +class LogicalAndChef final : public OpChef +{ +public: + explicit LogicalAndChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_LOGICAL_AND; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_LogicalAndOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct LogicalAndChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_LOGICALAND_H__ diff --git a/compiler/tflchef/core/src/Op/Logistic.cpp b/compiler/tflchef/core/src/Op/Logistic.cpp new file mode 100644 index 0000000..4a58082 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Logistic.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Logistic.h" + +flatbuffers::Offset LogisticChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + // No tflite option for Logistic. Use void. + return flatbuffers::Offset(); +} + +std::unique_ptr LogisticChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new LogisticChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Logistic.h b/compiler/tflchef/core/src/Op/Logistic.h new file mode 100644 index 0000000..c158af3 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Logistic.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_LOGISTIC_H__ +#define __OP_LOGISTIC_H__ + +#include "OpChef.h" + +class LogisticChef final : public OpChef +{ +public: + explicit LogisticChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_LOGISTIC; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NONE; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct LogisticChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_LOGISTIC_H__ diff --git a/compiler/tflchef/core/src/Op/MatrixDiag.cpp b/compiler/tflchef/core/src/Op/MatrixDiag.cpp new file mode 100644 index 0000000..de505c0 --- /dev/null +++ b/compiler/tflchef/core/src/Op/MatrixDiag.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "MatrixDiag.h" + +flatbuffers::Offset MatrixDiagChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::MatrixDiagOptionsBuilder options_builder{fbb}; + return options_builder.Finish().Union(); +} + +std::unique_ptr MatrixDiagChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new MatrixDiagChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/MatrixDiag.h b/compiler/tflchef/core/src/Op/MatrixDiag.h new file mode 100644 index 0000000..cbadf6b --- /dev/null +++ b/compiler/tflchef/core/src/Op/MatrixDiag.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_MATRIX_DIAG_H__ +#define __OP_MATRIX_DIAG_H__ + +#include "OpChef.h" + +class MatrixDiagChef final : public OpChef +{ +public: + explicit MatrixDiagChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_MATRIX_DIAG; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_MatrixDiagOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct MatrixDiagChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_MATRIX_DIAG_H__ diff --git a/compiler/tflchef/core/src/Op/MatrixSetDiag.cpp b/compiler/tflchef/core/src/Op/MatrixSetDiag.cpp new file mode 100644 index 0000000..0a4ee71 --- /dev/null +++ b/compiler/tflchef/core/src/Op/MatrixSetDiag.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "MatrixSetDiag.h" + +flatbuffers::Offset MatrixSetDiagChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::MatrixSetDiagOptionsBuilder options_builder{fbb}; + return options_builder.Finish().Union(); +} + +std::unique_ptr MatrixSetDiagChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new MatrixSetDiagChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/MatrixSetDiag.h b/compiler/tflchef/core/src/Op/MatrixSetDiag.h new file mode 100644 index 0000000..8114d32 --- /dev/null +++ b/compiler/tflchef/core/src/Op/MatrixSetDiag.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_MATRIX_SET_DIAG_H__ +#define __OP_MATRIX_SET_DIAG_H__ + +#include "OpChef.h" + +class MatrixSetDiagChef final : public OpChef +{ +public: + explicit MatrixSetDiagChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override + { + return tflite::BuiltinOperator_MATRIX_SET_DIAG; + } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_MatrixSetDiagOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct MatrixSetDiagChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_MATRIX_SET_DIAG_H__ diff --git a/compiler/tflchef/core/src/Op/Maximum.cpp b/compiler/tflchef/core/src/Op/Maximum.cpp new file mode 100644 index 0000000..8f415e2 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Maximum.cpp @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Maximum.h" +#include "Convert.h" + +#include + +flatbuffers::Offset MaximumChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::MaximumMinimumOptionsBuilder maximum_options_builder{fbb}; + return maximum_options_builder.Finish().Union(); +} + +std::unique_ptr MaximumChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new MaximumChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Maximum.h b/compiler/tflchef/core/src/Op/Maximum.h new file mode 100644 index 0000000..53e9524 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Maximum.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_MAXIMUM_H__ +#define __OP_MAXIMUM_H__ + +#include "OpChef.h" + +class MaximumChef final : public OpChef +{ +public: + explicit MaximumChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_MAXIMUM; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_MaximumMinimumOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct MaximumChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_MAXIMUM_H__ diff --git a/compiler/tflchef/core/src/Op/Minimum.cpp b/compiler/tflchef/core/src/Op/Minimum.cpp new file mode 100644 index 0000000..cc0c919 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Minimum.cpp @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Minimum.h" +#include "Convert.h" + +#include + +flatbuffers::Offset MinimumChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::MaximumMinimumOptionsBuilder minimum_options_builder{fbb}; + return minimum_options_builder.Finish().Union(); +} + +std::unique_ptr MinimumChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new MinimumChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Minimum.h b/compiler/tflchef/core/src/Op/Minimum.h new file mode 100644 index 0000000..3990e1e --- /dev/null +++ b/compiler/tflchef/core/src/Op/Minimum.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_MINIMUM_H__ +#define __OP_MINIMUM_H__ + +#include "OpChef.h" + +class MinimumChef final : public OpChef +{ +public: + explicit MinimumChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_MINIMUM; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_MaximumMinimumOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct MinimumChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_MINIMUM_H__ diff --git a/compiler/tflchef/core/src/Op/MirrorPad.cpp b/compiler/tflchef/core/src/Op/MirrorPad.cpp new file mode 100644 index 0000000..2d68b69 --- /dev/null +++ b/compiler/tflchef/core/src/Op/MirrorPad.cpp @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "MirrorPad.h" +#include "Convert.h" + +#include + +flatbuffers::Offset MirrorPadChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_mirrorpad_options()); + + auto options = operation.mirrorpad_options(); + + auto tflite_mode = as_tflite_mirrorpadmode(options.mode()); + + tflite::MirrorPadOptionsBuilder options_builder{fbb}; + options_builder.add_mode(tflite_mode); + + return options_builder.Finish().Union(); +} + +std::unique_ptr MirrorPadChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new MirrorPadChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/MirrorPad.h b/compiler/tflchef/core/src/Op/MirrorPad.h new file mode 100644 index 0000000..49461df --- /dev/null +++ b/compiler/tflchef/core/src/Op/MirrorPad.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_MIRRORPAD_H__ +#define __OP_MIRRORPAD_H__ + +#include "OpChef.h" + +class MirrorPadChef final : public OpChef +{ +public: + explicit MirrorPadChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_MIRROR_PAD; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_MirrorPadOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct MirrorPadChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_MIRRORPAD_H__ diff --git a/compiler/tflchef/core/src/Op/Neg.cpp b/compiler/tflchef/core/src/Op/Neg.cpp new file mode 100644 index 0000000..0e9fb93 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Neg.cpp @@ -0,0 +1,30 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Neg.h" +#include "Convert.h" + +flatbuffers::Offset NegChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::NegOptionsBuilder neg_options_builder{fbb}; + + return neg_options_builder.Finish().Union(); +} + +std::unique_ptr NegChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new NegChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Neg.h b/compiler/tflchef/core/src/Op/Neg.h new file mode 100644 index 0000000..f7a2692 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Neg.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_NEG_H__ +#define __OP_NEG_H__ + +#include "OpChef.h" + +class NegChef final : public OpChef +{ +public: + explicit NegChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_NEG; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NegOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct NegChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_NEG_H__ diff --git a/compiler/tflchef/core/src/Op/NotEqual.cpp b/compiler/tflchef/core/src/Op/NotEqual.cpp new file mode 100644 index 0000000..a408266 --- /dev/null +++ b/compiler/tflchef/core/src/Op/NotEqual.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "NotEqual.h" + +flatbuffers::Offset NotEqualChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::NotEqualOptionsBuilder options_builder{fbb}; + + return options_builder.Finish().Union(); +} + +std::unique_ptr NotEqualChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new NotEqualChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/NotEqual.h b/compiler/tflchef/core/src/Op/NotEqual.h new file mode 100644 index 0000000..3fd2547 --- /dev/null +++ b/compiler/tflchef/core/src/Op/NotEqual.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_NOT_EQUAL_H__ +#define __OP_NOT_EQUAL_H__ + +#include "OpChef.h" + +class NotEqualChef final : public OpChef +{ +public: + explicit NotEqualChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_NOT_EQUAL; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_NotEqualOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct NotEqualChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_NOT_EQUAL_H__ diff --git a/compiler/tflchef/core/src/Op/OneHot.cpp b/compiler/tflchef/core/src/Op/OneHot.cpp new file mode 100644 index 0000000..421e50c --- /dev/null +++ b/compiler/tflchef/core/src/Op/OneHot.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "OneHot.h" +#include "Convert.h" + +#include + +flatbuffers::Offset OneHotChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_onehot_options()); + + auto options = operation.onehot_options(); + + tflite::OneHotOptionsBuilder options_builder{fbb}; + + options_builder.add_axis(options.axis()); + return options_builder.Finish().Union(); +} + +std::unique_ptr OneHotChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new OneHotChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/OneHot.h b/compiler/tflchef/core/src/Op/OneHot.h new file mode 100644 index 0000000..b29cb79 --- /dev/null +++ b/compiler/tflchef/core/src/Op/OneHot.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_ONEHOT_H__ +#define __OP_ONEHOT_H__ + +#include "OpChef.h" + +class OneHotChef final : public OpChef +{ +public: + explicit OneHotChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_ONE_HOT; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_OneHotOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct OneHotChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_ONEHOT_H__ diff --git a/compiler/tflchef/core/src/Op/PRelu.cpp b/compiler/tflchef/core/src/Op/PRelu.cpp new file mode 100644 index 0000000..30e8b8e --- /dev/null +++ b/compiler/tflchef/core/src/Op/PRelu.cpp @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "PRelu.h" +#include "Convert.h" + +#include + +flatbuffers::Offset PReluChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + // No option for PRelu + return flatbuffers::Offset(); +} + +std::unique_ptr PReluChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new PReluChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/PRelu.h b/compiler/tflchef/core/src/Op/PRelu.h new file mode 100644 index 0000000..4a5a935 --- /dev/null +++ b/compiler/tflchef/core/src/Op/PRelu.h @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_PRELU_H__ +#define __OP_PRELU_H__ + +#include "OpChef.h" + +class PReluChef final : public OpChef +{ +public: + explicit PReluChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_PRELU; } + + // no builtin options for PRelu + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NONE; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct PReluChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_PRELU_H__ diff --git a/compiler/tflchef/core/src/Op/Pow.cpp b/compiler/tflchef/core/src/Op/Pow.cpp new file mode 100644 index 0000000..25e1802 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Pow.cpp @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Pow.h" +#include "Convert.h" + +#include + +flatbuffers::Offset PowChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::PowOptionsBuilder options_builder{fbb}; + return options_builder.Finish().Union(); +} + +std::unique_ptr PowChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new PowChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Pow.h b/compiler/tflchef/core/src/Op/Pow.h new file mode 100644 index 0000000..f2d809e --- /dev/null +++ b/compiler/tflchef/core/src/Op/Pow.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_POW_H__ +#define __OP_POW_H__ + +#include "OpChef.h" + +class PowChef final : public OpChef +{ +public: + explicit PowChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_POW; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_PowOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct PowChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_POW_H__ diff --git a/compiler/tflchef/core/src/Op/Range.cpp b/compiler/tflchef/core/src/Op/Range.cpp new file mode 100644 index 0000000..189c465 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Range.cpp @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Range.h" + +#include +#include + +flatbuffers::Offset RangeChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::RangeOptionsBuilder options_builder{fbb}; + return options_builder.Finish().Union(); +} + +std::unique_ptr RangeChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new RangeChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Range.h b/compiler/tflchef/core/src/Op/Range.h new file mode 100644 index 0000000..f294d15 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Range.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_RANGE_H__ +#define __OP_RANGE_H__ + +#include "OpChef.h" + +class RangeChef final : public OpChef +{ +public: + explicit RangeChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_RANGE; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_RangeOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct RangeChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_RANGE_H__ diff --git a/compiler/tflchef/core/src/Op/Rank.cpp b/compiler/tflchef/core/src/Op/Rank.cpp new file mode 100644 index 0000000..4eb2aa7 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Rank.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Rank.h" + +flatbuffers::Offset RankChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::RankOptionsBuilder options_builder{fbb}; + + return options_builder.Finish().Union(); +} + +std::unique_ptr RankChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new RankChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Rank.h b/compiler/tflchef/core/src/Op/Rank.h new file mode 100644 index 0000000..0bce380 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Rank.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_RANK_H__ +#define __OP_RANK_H__ + +#include "OpChef.h" + +class RankChef final : public OpChef +{ +public: + explicit RankChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_RANK; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_RankOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct RankChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_RANK_H__ diff --git a/compiler/tflchef/core/src/Op/ReLUN1To1.cpp b/compiler/tflchef/core/src/Op/ReLUN1To1.cpp new file mode 100644 index 0000000..d57e823 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ReLUN1To1.cpp @@ -0,0 +1,27 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReLUN1To1.h" + +flatbuffers::Offset ReLUN1To1Chef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + return flatbuffers::Offset(); +} + +std::unique_ptr ReLUN1To1ChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new ReLUN1To1Chef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/ReLUN1To1.h b/compiler/tflchef/core/src/Op/ReLUN1To1.h new file mode 100644 index 0000000..e034c79 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ReLUN1To1.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_RELU_N1_TO_1_H__ +#define __OP_RELU_N1_TO_1_H__ + +#include "OpChef.h" + +class ReLUN1To1Chef final : public OpChef +{ +public: + explicit ReLUN1To1Chef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_RELU_N1_TO_1; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NONE; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct ReLUN1To1ChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_RELU_N1_TO_1_H__ diff --git a/compiler/tflchef/core/src/Op/ReduceAny.cpp b/compiler/tflchef/core/src/Op/ReduceAny.cpp new file mode 100644 index 0000000..c94c8a3 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ReduceAny.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReduceAny.h" +#include "Convert.h" + +#include + +flatbuffers::Offset ReduceAnyChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_reduce_any_options()); + + auto keep_dims = operation.reduce_any_options().keep_dims(); + + tflite::ReducerOptionsBuilder reducer_options_builder{fbb}; + reducer_options_builder.add_keep_dims(keep_dims); + + return reducer_options_builder.Finish().Union(); +} + +std::unique_ptr ReduceAnyChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new ReduceAnyChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/ReduceAny.h b/compiler/tflchef/core/src/Op/ReduceAny.h new file mode 100644 index 0000000..cf65317 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ReduceAny.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_REDUCE_ANY_H__ +#define __OP_REDUCE_ANY_H__ + +#include "OpChef.h" + +class ReduceAnyChef final : public OpChef +{ +public: + explicit ReduceAnyChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_REDUCE_ANY; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_ReducerOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct ReduceAnyChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_REDUCE_ANY_H__ diff --git a/compiler/tflchef/core/src/Op/ReduceMax.cpp b/compiler/tflchef/core/src/Op/ReduceMax.cpp new file mode 100644 index 0000000..31543cd --- /dev/null +++ b/compiler/tflchef/core/src/Op/ReduceMax.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReduceMax.h" +#include "Convert.h" + +#include + +flatbuffers::Offset ReduceMaxChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_reduce_max_options()); + + auto keep_dims = operation.reduce_max_options().keep_dims(); + + tflite::ReducerOptionsBuilder reduce_max_options_builder{fbb}; + reduce_max_options_builder.add_keep_dims(keep_dims); + + return reduce_max_options_builder.Finish().Union(); +} + +std::unique_ptr ReduceMaxChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new ReduceMaxChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/ReduceMax.h b/compiler/tflchef/core/src/Op/ReduceMax.h new file mode 100644 index 0000000..854c5b8 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ReduceMax.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_REDUCEMAX_H__ +#define __OP_REDUCEMAX_H__ + +#include "OpChef.h" + +class ReduceMaxChef final : public OpChef +{ +public: + explicit ReduceMaxChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_REDUCE_MAX; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_ReducerOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct ReduceMaxChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_REDUCEMAX_H__ diff --git a/compiler/tflchef/core/src/Op/ReduceMin.cpp b/compiler/tflchef/core/src/Op/ReduceMin.cpp new file mode 100644 index 0000000..e194a18 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ReduceMin.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReduceMin.h" +#include "Convert.h" + +#include + +flatbuffers::Offset ReduceMinChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_reduce_min_options()); + + auto keep_dims = operation.reduce_min_options().keep_dims(); + + tflite::ReducerOptionsBuilder reduce_min_options_builder{fbb}; + reduce_min_options_builder.add_keep_dims(keep_dims); + + return reduce_min_options_builder.Finish().Union(); +} + +std::unique_ptr ReduceMinChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new ReduceMinChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/ReduceMin.h b/compiler/tflchef/core/src/Op/ReduceMin.h new file mode 100644 index 0000000..f29d273 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ReduceMin.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_REDUCEMIN_H__ +#define __OP_REDUCEMIN_H__ + +#include "OpChef.h" + +class ReduceMinChef final : public OpChef +{ +public: + explicit ReduceMinChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_REDUCE_MIN; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_ReducerOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct ReduceMinChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_REDUCEMIN_H__ diff --git a/compiler/tflchef/core/src/Op/ReduceProd.cpp b/compiler/tflchef/core/src/Op/ReduceProd.cpp new file mode 100644 index 0000000..c89aca2 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ReduceProd.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReduceProd.h" +#include "Convert.h" + +#include + +flatbuffers::Offset ReduceProdChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_reduce_prod_options()); + + auto keep_dims = operation.reduce_prod_options().keep_dims(); + + tflite::ReducerOptionsBuilder reducer_options_builder{fbb}; + reducer_options_builder.add_keep_dims(keep_dims); + + return reducer_options_builder.Finish().Union(); +} + +std::unique_ptr ReduceProdChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new ReduceProdChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/ReduceProd.h b/compiler/tflchef/core/src/Op/ReduceProd.h new file mode 100644 index 0000000..d5a11fd --- /dev/null +++ b/compiler/tflchef/core/src/Op/ReduceProd.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_REDUCE_PROD_H__ +#define __OP_REDUCE_PROD_H__ + +#include "OpChef.h" + +class ReduceProdChef final : public OpChef +{ +public: + explicit ReduceProdChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_REDUCE_PROD; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_ReducerOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct ReduceProdChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_REDUCE_PROD_H__ diff --git a/compiler/tflchef/core/src/Op/Reshape.cpp b/compiler/tflchef/core/src/Op/Reshape.cpp index 99555e8..01e5219 100644 --- a/compiler/tflchef/core/src/Op/Reshape.cpp +++ b/compiler/tflchef/core/src/Op/Reshape.cpp @@ -41,7 +41,8 @@ flatbuffers::Offset ReshapeChef::value(flatbuffers::FlatBufferBuilder &fbb { auto &operation = (*_operation); - assert(operation.has_reshape_options()); + if (!operation.has_reshape_options()) + return 0; auto options = operation.reshape_options(); auto shapes = vector_new_shape(options); diff --git a/compiler/tflchef/core/src/Op/ResizeBilinear.cpp b/compiler/tflchef/core/src/Op/ResizeBilinear.cpp new file mode 100644 index 0000000..3d9299c --- /dev/null +++ b/compiler/tflchef/core/src/Op/ResizeBilinear.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ResizeBilinear.h" +#include "Convert.h" + +#include +#include + +flatbuffers::Offset ResizeBilinearChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_resize_bilinear_options()); + + auto options = operation.resize_bilinear_options(); + + tflite::ResizeBilinearOptionsBuilder options_builder{fbb}; + + options_builder.add_align_corners(options.align_corners()); + options_builder.add_half_pixel_centers(options.half_pixel_centers()); + + return options_builder.Finish().Union(); +} + +std::unique_ptr ResizeBilinearChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new ResizeBilinearChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/ResizeBilinear.h b/compiler/tflchef/core/src/Op/ResizeBilinear.h new file mode 100644 index 0000000..9bd6185 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ResizeBilinear.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_RESIZE_BILINEAR_H__ +#define __OP_RESIZE_BILINEAR_H__ + +#include "OpChef.h" + +class ResizeBilinearChef final : public OpChef +{ +public: + explicit ResizeBilinearChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override + { + return tflite::BuiltinOperator_RESIZE_BILINEAR; + } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_ResizeBilinearOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct ResizeBilinearChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_RESIZE_BILINEAR_H__ diff --git a/compiler/tflchef/core/src/Op/ResizeNearestNeighbor.cpp b/compiler/tflchef/core/src/Op/ResizeNearestNeighbor.cpp new file mode 100644 index 0000000..1f93040 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ResizeNearestNeighbor.cpp @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ResizeNearestNeighbor.h" +#include "Convert.h" + +#include +#include + +flatbuffers::Offset +ResizeNearestNeighborChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_resize_nearest_neighbor_options()); + + auto options = operation.resize_nearest_neighbor_options(); + + tflite::ResizeNearestNeighborOptionsBuilder options_builder{fbb}; + + options_builder.add_align_corners(options.align_corners()); + + return options_builder.Finish().Union(); +} + +std::unique_ptr +ResizeNearestNeighborChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new ResizeNearestNeighborChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/ResizeNearestNeighbor.h b/compiler/tflchef/core/src/Op/ResizeNearestNeighbor.h new file mode 100644 index 0000000..e6ee832 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ResizeNearestNeighbor.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_RESIZE_NEAREST_NEIGHBOR_H__ +#define __OP_RESIZE_NEAREST_NEIGHBOR_H__ + +#include "OpChef.h" + +class ResizeNearestNeighborChef final : public OpChef +{ +public: + explicit ResizeNearestNeighborChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override + { + return tflite::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR; + } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_ResizeNearestNeighborOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct ResizeNearestNeighborChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_RESIZE_NEAREST_NEIGHBOR_H__ diff --git a/compiler/tflchef/core/src/Op/ReverseSequence.cpp b/compiler/tflchef/core/src/Op/ReverseSequence.cpp new file mode 100644 index 0000000..9354117 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ReverseSequence.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReverseSequence.h" + +flatbuffers::Offset ReverseSequenceChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_reverse_sequence_options()); + + auto options = operation.reverse_sequence_options(); + + auto tflite_seq_dim = options.seq_dim(); + auto tflite_batch_dim = options.batch_dim(); + + tflite::ReverseSequenceOptionsBuilder options_builder{fbb}; + + options_builder.add_seq_dim(tflite_seq_dim); + options_builder.add_batch_dim(tflite_batch_dim); + + return options_builder.Finish().Union(); +} + +std::unique_ptr +ReverseSequenceChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new ReverseSequenceChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/ReverseSequence.h b/compiler/tflchef/core/src/Op/ReverseSequence.h new file mode 100644 index 0000000..329505c --- /dev/null +++ b/compiler/tflchef/core/src/Op/ReverseSequence.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_REVERSE_SEQUENCE_H__ +#define __OP_REVERSE_SEQUENCE_H__ + +#include "OpChef.h" + +class ReverseSequenceChef final : public OpChef +{ +public: + explicit ReverseSequenceChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override + { + return tflite::BuiltinOperator_REVERSE_SEQUENCE; + } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_ReverseSequenceOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct ReverseSequenceChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_REVERSE_SEQUENCE_H__ diff --git a/compiler/tflchef/core/src/Op/ReverseV2.cpp b/compiler/tflchef/core/src/Op/ReverseV2.cpp new file mode 100644 index 0000000..58ace1d --- /dev/null +++ b/compiler/tflchef/core/src/Op/ReverseV2.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReverseV2.h" + +flatbuffers::Offset ReverseV2Chef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::ReverseV2OptionsBuilder options_builder{fbb}; + + return options_builder.Finish().Union(); +} + +std::unique_ptr ReverseV2ChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new ReverseV2Chef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/ReverseV2.h b/compiler/tflchef/core/src/Op/ReverseV2.h new file mode 100644 index 0000000..a48a2d9 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ReverseV2.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_REVERSEV2_H__ +#define __OP_REVERSEV2_H__ + +#include "OpChef.h" + +class ReverseV2Chef final : public OpChef +{ +public: + explicit ReverseV2Chef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_REVERSE_V2; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_ReverseV2Options; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct ReverseV2ChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_REVERSEV2_H__ diff --git a/compiler/tflchef/core/src/Op/Round.cpp b/compiler/tflchef/core/src/Op/Round.cpp new file mode 100644 index 0000000..e16c865 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Round.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Round.h" + +flatbuffers::Offset RoundChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + // No tflite option for Round. Use void. + return flatbuffers::Offset(); +} + +std::unique_ptr RoundChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new RoundChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Round.h b/compiler/tflchef/core/src/Op/Round.h new file mode 100644 index 0000000..7f0fbe3 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Round.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_ROUND_H__ +#define __OP_ROUND_H__ + +#include "OpChef.h" + +class RoundChef final : public OpChef +{ +public: + explicit RoundChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_ROUND; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NONE; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct RoundChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_ROUND_H__ diff --git a/compiler/tflchef/core/src/Op/ScatterNd.cpp b/compiler/tflchef/core/src/Op/ScatterNd.cpp new file mode 100644 index 0000000..7114dda --- /dev/null +++ b/compiler/tflchef/core/src/Op/ScatterNd.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ScatterNd.h" +#include "Convert.h" + +#include + +flatbuffers::Offset ScatterNdChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::ScatterNdOptionsBuilder scatter_nd_options_builder{fbb}; + + return scatter_nd_options_builder.Finish().Union(); +} + +std::unique_ptr ScatterNdChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new ScatterNdChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/ScatterNd.h b/compiler/tflchef/core/src/Op/ScatterNd.h new file mode 100644 index 0000000..2c89cf6 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ScatterNd.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_SCATTER_ND_H__ +#define __OP_SCATTER_ND_H__ + +#include "OpChef.h" + +class ScatterNdChef final : public OpChef +{ +public: + explicit ScatterNdChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_SCATTER_ND; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_ScatterNdOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct ScatterNdChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_SCATTER_ND_H__ diff --git a/compiler/tflchef/core/src/Op/SegmentSum.cpp b/compiler/tflchef/core/src/Op/SegmentSum.cpp new file mode 100644 index 0000000..934bcb0 --- /dev/null +++ b/compiler/tflchef/core/src/Op/SegmentSum.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SegmentSum.h" + +flatbuffers::Offset SegmentSumChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::SegmentSumOptionsBuilder options_builder{fbb}; + + return options_builder.Finish().Union(); +} + +std::unique_ptr SegmentSumChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new SegmentSumChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/SegmentSum.h b/compiler/tflchef/core/src/Op/SegmentSum.h new file mode 100644 index 0000000..c0ebfba --- /dev/null +++ b/compiler/tflchef/core/src/Op/SegmentSum.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_SEGMENT_SUM_H__ +#define __OP_SEGMENT_SUM_H__ + +#include "OpChef.h" + +class SegmentSumChef final : public OpChef +{ +public: + explicit SegmentSumChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_SEGMENT_SUM; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_SegmentSumOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct SegmentSumChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_SEGMENT_SUM_H__ diff --git a/compiler/tflchef/core/src/Op/Select.cpp b/compiler/tflchef/core/src/Op/Select.cpp new file mode 100644 index 0000000..31be736 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Select.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Select.h" + +flatbuffers::Offset SelectChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::SelectOptionsBuilder options_builder{fbb}; + + return options_builder.Finish().Union(); +} + +std::unique_ptr SelectChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new SelectChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Select.h b/compiler/tflchef/core/src/Op/Select.h new file mode 100644 index 0000000..91ace16 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Select.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_SELECT_H__ +#define __OP_SELECT_H__ + +#include "OpChef.h" + +class SelectChef final : public OpChef +{ +public: + explicit SelectChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_SELECT; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_SelectOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct SelectChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_SELECT_H__ diff --git a/compiler/tflchef/core/src/Op/SelectV2.cpp b/compiler/tflchef/core/src/Op/SelectV2.cpp new file mode 100644 index 0000000..f6c0bfc --- /dev/null +++ b/compiler/tflchef/core/src/Op/SelectV2.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SelectV2.h" + +flatbuffers::Offset SelectV2Chef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::SelectV2OptionsBuilder options_builder{fbb}; + + return options_builder.Finish().Union(); +} + +std::unique_ptr SelectV2ChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new SelectV2Chef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/SelectV2.h b/compiler/tflchef/core/src/Op/SelectV2.h new file mode 100644 index 0000000..36d74c3 --- /dev/null +++ b/compiler/tflchef/core/src/Op/SelectV2.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_SELECT_V2_H__ +#define __OP_SELECT_V2_H__ + +#include "OpChef.h" + +class SelectV2Chef final : public OpChef +{ +public: + explicit SelectV2Chef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_SELECT_V2; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_SelectV2Options; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct SelectV2ChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_SELECT_V2_H__ diff --git a/compiler/tflchef/core/src/Op/Sin.cpp b/compiler/tflchef/core/src/Op/Sin.cpp new file mode 100644 index 0000000..1752ce7 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Sin.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Sin.h" + +flatbuffers::Offset SinChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + // No tflite option for Sin. Use void. + return flatbuffers::Offset(); +} + +std::unique_ptr SinChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new SinChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Sin.h b/compiler/tflchef/core/src/Op/Sin.h new file mode 100644 index 0000000..121b73b --- /dev/null +++ b/compiler/tflchef/core/src/Op/Sin.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_SIN_H__ +#define __OP_SIN_H__ + +#include "OpChef.h" + +class SinChef final : public OpChef +{ +public: + explicit SinChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_SIN; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_NONE; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct SinChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_SIN_H__ diff --git a/compiler/tflchef/core/src/Op/Slice.cpp b/compiler/tflchef/core/src/Op/Slice.cpp new file mode 100644 index 0000000..27ae80a --- /dev/null +++ b/compiler/tflchef/core/src/Op/Slice.cpp @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Slice.h" +#include "Convert.h" + +#include + +flatbuffers::Offset SliceChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::SliceOptionsBuilder slice_options_builder{fbb}; + return slice_options_builder.Finish().Union(); +} + +std::unique_ptr SliceChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new SliceChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Slice.h b/compiler/tflchef/core/src/Op/Slice.h new file mode 100644 index 0000000..06fd634 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Slice.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_SLICE_H__ +#define __OP_SLICE_H__ + +#include "OpChef.h" + +class SliceChef final : public OpChef +{ +public: + explicit SliceChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_SLICE; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_SliceOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct SliceChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_SLICE_H__ diff --git a/compiler/tflchef/core/src/Op/SpaceToBatchND.cpp b/compiler/tflchef/core/src/Op/SpaceToBatchND.cpp new file mode 100644 index 0000000..74e0528 --- /dev/null +++ b/compiler/tflchef/core/src/Op/SpaceToBatchND.cpp @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SpaceToBatchND.h" + +#include + +flatbuffers::Offset SpaceToBatchNDChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::SpaceToBatchNDOptionsBuilder space_to_batch_nd_options_builder{fbb}; + + return space_to_batch_nd_options_builder.Finish().Union(); +} + +std::unique_ptr SpaceToBatchNDChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new SpaceToBatchNDChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/SpaceToBatchND.h b/compiler/tflchef/core/src/Op/SpaceToBatchND.h new file mode 100644 index 0000000..e263bdc --- /dev/null +++ b/compiler/tflchef/core/src/Op/SpaceToBatchND.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_SPACETOBATCHND_H__ +#define __OP_SPACETOBATCHND_H__ + +#include "OpChef.h" + +class SpaceToBatchNDChef final : public OpChef +{ +public: + explicit SpaceToBatchNDChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override + { + return tflite::BuiltinOperator_SPACE_TO_BATCH_ND; + } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_SpaceToBatchNDOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct SpaceToBatchNDChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_SPACETOBATCHND_H__ diff --git a/compiler/tflchef/core/src/Op/SpaceToDepth.cpp b/compiler/tflchef/core/src/Op/SpaceToDepth.cpp new file mode 100644 index 0000000..98eed8c --- /dev/null +++ b/compiler/tflchef/core/src/Op/SpaceToDepth.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SpaceToDepth.h" + +#include + +flatbuffers::Offset SpaceToDepthChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_space_to_depth_options()); + + auto tflite_block_size = operation.space_to_depth_options().block_size(); + + tflite::SpaceToDepthOptionsBuilder std_options_builder{fbb}; + std_options_builder.add_block_size(tflite_block_size); + + return std_options_builder.Finish().Union(); +} + +std::unique_ptr SpaceToDepthChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new SpaceToDepthChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/SpaceToDepth.h b/compiler/tflchef/core/src/Op/SpaceToDepth.h new file mode 100644 index 0000000..db852fe --- /dev/null +++ b/compiler/tflchef/core/src/Op/SpaceToDepth.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_SPACETODEPTH_H__ +#define __OP_SPACETODEPTH_H__ + +#include "OpChef.h" + +class SpaceToDepthChef final : public OpChef +{ +public: + explicit SpaceToDepthChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override + { + return tflite::BuiltinOperator_SPACE_TO_DEPTH; + } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_SpaceToDepthOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct SpaceToDepthChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_SPACETODEPTH_H__ diff --git a/compiler/tflchef/core/src/Op/SparseToDense.cpp b/compiler/tflchef/core/src/Op/SparseToDense.cpp new file mode 100644 index 0000000..f1f8a71 --- /dev/null +++ b/compiler/tflchef/core/src/Op/SparseToDense.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SparseToDense.h" +#include "Convert.h" + +#include + +flatbuffers::Offset SparseToDenseChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_sparse_to_dense_options()); + + auto tflite_validate_indices = operation.sparse_to_dense_options().validate_indices(); + + tflite::SparseToDenseOptionsBuilder sparse_to_dense_options_builder(fbb); + sparse_to_dense_options_builder.add_validate_indices(tflite_validate_indices); + + return sparse_to_dense_options_builder.Finish().Union(); +} + +std::unique_ptr SparseToDenseChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new SparseToDenseChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/SparseToDense.h b/compiler/tflchef/core/src/Op/SparseToDense.h new file mode 100644 index 0000000..02cbd6a --- /dev/null +++ b/compiler/tflchef/core/src/Op/SparseToDense.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_SPARSETODENSE_H__ +#define __OP_SPARSETODENSE_H__ + +#include "OpChef.h" + +class SparseToDenseChef final : public OpChef +{ +public: + explicit SparseToDenseChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override + { + return tflite::BuiltinOperator_SPARSE_TO_DENSE; + } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_SparseToDenseOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct SparseToDenseChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_SPARSETODENSE_H__ diff --git a/compiler/tflchef/core/src/Op/Split.cpp b/compiler/tflchef/core/src/Op/Split.cpp new file mode 100644 index 0000000..f4704e5 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Split.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Split.h" +#include "Convert.h" + +#include + +flatbuffers::Offset SplitChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_split_options()); + + auto num_splits = operation.split_options().num_splits(); + + tflite::SplitOptionsBuilder split_options_builder{fbb}; + split_options_builder.add_num_splits(num_splits); + + return split_options_builder.Finish().Union(); +} + +std::unique_ptr SplitChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new SplitChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Split.h b/compiler/tflchef/core/src/Op/Split.h new file mode 100644 index 0000000..db61580 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Split.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_SPLIT_H__ +#define __OP_SPLIT_H__ + +#include "OpChef.h" + +class SplitChef final : public OpChef +{ +public: + explicit SplitChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_SPLIT; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_SplitOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct SplitChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_SPLIT_H__ diff --git a/compiler/tflchef/core/src/Op/SplitV.cpp b/compiler/tflchef/core/src/Op/SplitV.cpp new file mode 100644 index 0000000..fa93db6 --- /dev/null +++ b/compiler/tflchef/core/src/Op/SplitV.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SplitV.h" +#include "Convert.h" + +#include + +flatbuffers::Offset SplitVChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_split_v_options()); + + auto num_splits = operation.split_v_options().num_splits(); + + tflite::SplitVOptionsBuilder split_v_options_builder{fbb}; + split_v_options_builder.add_num_splits(num_splits); + + return split_v_options_builder.Finish().Union(); +} + +std::unique_ptr SplitVChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new SplitVChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/SplitV.h b/compiler/tflchef/core/src/Op/SplitV.h new file mode 100644 index 0000000..c37736e --- /dev/null +++ b/compiler/tflchef/core/src/Op/SplitV.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_SPLIT_V_H__ +#define __OP_SPLIT_V_H__ + +#include "OpChef.h" + +class SplitVChef final : public OpChef +{ +public: + explicit SplitVChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_SPLIT_V; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_SplitVOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct SplitVChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_SPLIT_V_H__ diff --git a/compiler/tflchef/core/src/Op/Square.cpp b/compiler/tflchef/core/src/Op/Square.cpp new file mode 100644 index 0000000..fd35380 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Square.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Square.h" + +flatbuffers::Offset SquareChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::SquareOptionsBuilder options_builder{fbb}; + + return options_builder.Finish().Union(); +} + +std::unique_ptr SquareChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new SquareChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Square.h b/compiler/tflchef/core/src/Op/Square.h new file mode 100644 index 0000000..5b76e63 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Square.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_SQUARE_H__ +#define __OP_SQUARE_H__ + +#include "OpChef.h" + +class SquareChef final : public OpChef +{ +public: + explicit SquareChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_SQUARE; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_SquareOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct SquareChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_SQUARE_H__ diff --git a/compiler/tflchef/core/src/Op/SquaredDifference.cpp b/compiler/tflchef/core/src/Op/SquaredDifference.cpp new file mode 100644 index 0000000..757c148 --- /dev/null +++ b/compiler/tflchef/core/src/Op/SquaredDifference.cpp @@ -0,0 +1,30 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SquaredDifference.h" + +flatbuffers::Offset SquaredDifferenceChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::SquaredDifferenceOptionsBuilder options_builder{fbb}; + + return options_builder.Finish().Union(); +} + +std::unique_ptr +SquaredDifferenceChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new SquaredDifferenceChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/SquaredDifference.h b/compiler/tflchef/core/src/Op/SquaredDifference.h new file mode 100644 index 0000000..f919975 --- /dev/null +++ b/compiler/tflchef/core/src/Op/SquaredDifference.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_SQUAREDDIFFERENCE_H__ +#define __OP_SQUAREDDIFFERENCE_H__ + +#include "OpChef.h" + +class SquaredDifferenceChef final : public OpChef +{ +public: + explicit SquaredDifferenceChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override + { + return tflite::BuiltinOperator_SQUARED_DIFFERENCE; + } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_SquaredDifferenceOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct SquaredDifferenceChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_SQUAREDDIFFERENCE_H__ diff --git a/compiler/tflchef/core/src/Op/Squeeze.cpp b/compiler/tflchef/core/src/Op/Squeeze.cpp new file mode 100644 index 0000000..8d6ef42 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Squeeze.cpp @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Squeeze.h" +#include "Convert.h" + +#include +#include + +flatbuffers::Offset SqueezeChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_squeeze_options()); + + const auto &options = operation.squeeze_options(); + // Note: 'CreateVector' should be placed before 'CreateOptions' + // Read flatbuffers.h 'void NotNested()' for more information + auto fb_squeeze_dims = + fbb.CreateVector(options.squeeze_dim().data(), options.squeeze_dim().size()); + + return tflite::CreateSqueezeOptions(fbb, fb_squeeze_dims).Union(); +} + +std::unique_ptr SqueezeChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new SqueezeChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Squeeze.h b/compiler/tflchef/core/src/Op/Squeeze.h new file mode 100644 index 0000000..2787231 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Squeeze.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_SQUEEZE_H__ +#define __OP_SQUEEZE_H__ + +#include "OpChef.h" + +class SqueezeChef final : public OpChef +{ +public: + explicit SqueezeChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_SQUEEZE; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_SqueezeOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct SqueezeChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_SQUEEZE_H__ diff --git a/compiler/tflchef/core/src/Op/StridedSlice.cpp b/compiler/tflchef/core/src/Op/StridedSlice.cpp new file mode 100644 index 0000000..587a95c --- /dev/null +++ b/compiler/tflchef/core/src/Op/StridedSlice.cpp @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "StridedSlice.h" +#include "Convert.h" + +#include + +flatbuffers::Offset StridedSliceChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_strided_slice_options()); + + tflite::StridedSliceOptionsBuilder strided_slice_options_builder{fbb}; + strided_slice_options_builder.add_begin_mask(operation.strided_slice_options().begin_mask()); + strided_slice_options_builder.add_end_mask(operation.strided_slice_options().end_mask()); + strided_slice_options_builder.add_ellipsis_mask( + operation.strided_slice_options().ellipsis_mask()); + strided_slice_options_builder.add_new_axis_mask( + operation.strided_slice_options().new_axis_mask()); + strided_slice_options_builder.add_shrink_axis_mask( + operation.strided_slice_options().shrink_axis_mask()); + + return strided_slice_options_builder.Finish().Union(); +} + +std::unique_ptr StridedSliceChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new StridedSliceChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/StridedSlice.h b/compiler/tflchef/core/src/Op/StridedSlice.h new file mode 100644 index 0000000..49da44f --- /dev/null +++ b/compiler/tflchef/core/src/Op/StridedSlice.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_STRIDEDSLICE_H__ +#define __OP_STRIDEDSLICE_H__ + +#include "OpChef.h" + +class StridedSliceChef final : public OpChef +{ +public: + explicit StridedSliceChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override + { + return tflite::BuiltinOperator_STRIDED_SLICE; + } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_StridedSliceOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct StridedSliceChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_STRIDEDSLICE_H__ diff --git a/compiler/tflchef/core/src/Op/Sum.cpp b/compiler/tflchef/core/src/Op/Sum.cpp new file mode 100644 index 0000000..6b79d3e --- /dev/null +++ b/compiler/tflchef/core/src/Op/Sum.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Sum.h" +#include "Convert.h" + +#include + +flatbuffers::Offset SumChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_sum_options()); + + auto keep_dims = operation.sum_options().keep_dims(); + + tflite::ReducerOptionsBuilder sum_options_builder{fbb}; + sum_options_builder.add_keep_dims(keep_dims); + + return sum_options_builder.Finish().Union(); +} + +std::unique_ptr SumChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new SumChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Sum.h b/compiler/tflchef/core/src/Op/Sum.h new file mode 100644 index 0000000..d3cc8c1 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Sum.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_SUM_H__ +#define __OP_SUM_H__ + +#include "OpChef.h" + +class SumChef final : public OpChef +{ +public: + explicit SumChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_SUM; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_ReducerOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct SumChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_SUM_H__ diff --git a/compiler/tflchef/core/src/Op/Tile.cpp b/compiler/tflchef/core/src/Op/Tile.cpp new file mode 100644 index 0000000..18710b4 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Tile.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Tile.h" + +flatbuffers::Offset TileChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::TileOptionsBuilder options_builder{fbb}; + + return options_builder.Finish().Union(); +} + +std::unique_ptr TileChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new TileChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Tile.h b/compiler/tflchef/core/src/Op/Tile.h new file mode 100644 index 0000000..2870ff1 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Tile.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_TILE_H__ +#define __OP_TILE_H__ + +#include "OpChef.h" + +class TileChef final : public OpChef +{ +public: + explicit TileChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_TILE; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_TileOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct TileChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_TILE_H__ diff --git a/compiler/tflchef/core/src/Op/TopKV2.cpp b/compiler/tflchef/core/src/Op/TopKV2.cpp new file mode 100644 index 0000000..08c4de6 --- /dev/null +++ b/compiler/tflchef/core/src/Op/TopKV2.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "TopKV2.h" + +flatbuffers::Offset TopKV2Chef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::TopKV2OptionsBuilder options_builder{fbb}; + + return options_builder.Finish().Union(); +} + +std::unique_ptr TopKV2ChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new TopKV2Chef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/TopKV2.h b/compiler/tflchef/core/src/Op/TopKV2.h new file mode 100644 index 0000000..5548223 --- /dev/null +++ b/compiler/tflchef/core/src/Op/TopKV2.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_TOPK_V2_H__ +#define __OP_TOPK_V2_H__ + +#include "OpChef.h" + +class TopKV2Chef final : public OpChef +{ +public: + explicit TopKV2Chef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_TOPK_V2; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_TopKV2Options; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct TopKV2ChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_TOPK_V2_H__ diff --git a/compiler/tflchef/core/src/Op/TransposeConv.cpp b/compiler/tflchef/core/src/Op/TransposeConv.cpp new file mode 100644 index 0000000..c9e4527 --- /dev/null +++ b/compiler/tflchef/core/src/Op/TransposeConv.cpp @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "TransposeConv.h" +#include "Convert.h" + +#include + +flatbuffers::Offset TransposeConvChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + tflite::TransposeConvOptionsBuilder options_builder{fbb}; + + assert(operation.has_transpose_conv_options()); + + auto tflite_padding = as_tflite_padding(operation.transpose_conv_options().padding()); + + options_builder.add_padding(tflite_padding); + + options_builder.add_stride_h(operation.transpose_conv_options().stride_h()); + options_builder.add_stride_w(operation.transpose_conv_options().stride_w()); + + return options_builder.Finish().Union(); +} + +std::unique_ptr TransposeConvChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new TransposeConvChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/TransposeConv.h b/compiler/tflchef/core/src/Op/TransposeConv.h new file mode 100644 index 0000000..e664bff --- /dev/null +++ b/compiler/tflchef/core/src/Op/TransposeConv.h @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_TRANSPOSE_CONV_H__ +#define __OP_TRANSPOSE_CONV_H__ + +#include "OpChef.h" + +class TransposeConvChef final : public OpChef +{ +public: + explicit TransposeConvChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override + { + return tflite::BuiltinOperator_TRANSPOSE_CONV; + } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_TransposeConvOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct TransposeConvChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_TRANSPOSE_CONV_H__ diff --git a/compiler/tflchef/core/src/Op/Unique.cpp b/compiler/tflchef/core/src/Op/Unique.cpp new file mode 100644 index 0000000..d9a7293 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Unique.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Unique.h" +#include "Convert.h" + +#include + +flatbuffers::Offset UniqueChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_unique_options()); + + auto tflite_out_idx = as_tflite_tensortype(operation.unique_options().idx_out_type()); + + tflite::UniqueOptionsBuilder unique_options_builder{fbb}; + unique_options_builder.add_idx_out_type(tflite_out_idx); + + return unique_options_builder.Finish().Union(); +} + +std::unique_ptr UniqueChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new UniqueChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Unique.h b/compiler/tflchef/core/src/Op/Unique.h new file mode 100644 index 0000000..58aa7bf --- /dev/null +++ b/compiler/tflchef/core/src/Op/Unique.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_UNIQUE_H__ +#define __OP_UNIQUE_H__ + +#include "OpChef.h" + +class UniqueChef final : public OpChef +{ +public: + explicit UniqueChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_UNIQUE; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_UniqueOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct UniqueChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_UNIQUE_H__ diff --git a/compiler/tflchef/core/src/Op/Unpack.cpp b/compiler/tflchef/core/src/Op/Unpack.cpp new file mode 100644 index 0000000..504da5a --- /dev/null +++ b/compiler/tflchef/core/src/Op/Unpack.cpp @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Unpack.h" + +flatbuffers::Offset UnpackChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_unpack_options()); + + tflite::UnpackOptionsBuilder unpack_options_builder{fbb}; + unpack_options_builder.add_num(operation.unpack_options().num()); + unpack_options_builder.add_axis(operation.unpack_options().axis()); + + return unpack_options_builder.Finish().Union(); +} + +std::unique_ptr UnpackChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new UnpackChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Unpack.h b/compiler/tflchef/core/src/Op/Unpack.h new file mode 100644 index 0000000..3a425b1 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Unpack.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_UNPACK_H__ +#define __OP_UNPACK_H__ + +#include "OpChef.h" + +class UnpackChef final : public OpChef +{ +public: + explicit UnpackChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_UNPACK; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_UnpackOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct UnpackChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_ABS_H__ diff --git a/compiler/tflchef/core/src/Op/Where.cpp b/compiler/tflchef/core/src/Op/Where.cpp new file mode 100644 index 0000000..0ce9102 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Where.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specwhileic language governing permissions and + * limitations under the License. + */ + +#include "Where.h" + +flatbuffers::Offset WhereChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::WhereOptionsBuilder where_options_builder{fbb}; + return where_options_builder.Finish().Union(); +} + +std::unique_ptr WhereChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new WhereChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/Where.h b/compiler/tflchef/core/src/Op/Where.h new file mode 100644 index 0000000..7991c64 --- /dev/null +++ b/compiler/tflchef/core/src/Op/Where.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_WHERE_H__ +#define __OP_WHERE_H__ + +#include "OpChef.h" + +class WhereChef final : public OpChef +{ +public: + explicit WhereChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_WHERE; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_WhereOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct WhereChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_WHERE_H__ diff --git a/compiler/tflchef/core/src/Op/While.cpp b/compiler/tflchef/core/src/Op/While.cpp new file mode 100644 index 0000000..1253d0f --- /dev/null +++ b/compiler/tflchef/core/src/Op/While.cpp @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specwhileic language governing permissions and + * limitations under the License. + */ + +#include "While.h" + +flatbuffers::Offset WhileChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + auto &operation = (*_operation); + + assert(operation.has_while_options()); + + tflite::WhileOptionsBuilder while_options_builder{fbb}; + while_options_builder.add_cond_subgraph_index(operation.while_options().cond_subgraph_index()); + while_options_builder.add_body_subgraph_index(operation.while_options().body_subgraph_index()); + + return while_options_builder.Finish().Union(); +} + +std::unique_ptr WhileChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new WhileChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/While.h b/compiler/tflchef/core/src/Op/While.h new file mode 100644 index 0000000..150a14b --- /dev/null +++ b/compiler/tflchef/core/src/Op/While.h @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_WHILE_H__ +#define __OP_WHILE_H__ + +#include "OpChef.h" + +class WhileChef final : public OpChef +{ +public: + explicit WhileChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_WHILE; } + + tflite::BuiltinOptions type(void) const override { return tflite::BuiltinOptions_WhileOptions; } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct WhileChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_WHILE_H__ diff --git a/compiler/tflchef/core/src/Op/ZerosLike.cpp b/compiler/tflchef/core/src/Op/ZerosLike.cpp new file mode 100644 index 0000000..e47e2ab --- /dev/null +++ b/compiler/tflchef/core/src/Op/ZerosLike.cpp @@ -0,0 +1,28 @@ +/* + * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ZerosLike.h" + +flatbuffers::Offset ZerosLikeChef::value(flatbuffers::FlatBufferBuilder &fbb) const +{ + tflite::ZerosLikeOptionsBuilder builder{fbb}; + return builder.Finish().Union(); +} + +std::unique_ptr ZerosLikeChefFactory::create(const tflchef::Operation *operation) const +{ + return std::unique_ptr{new ZerosLikeChef{operation}}; +} diff --git a/compiler/tflchef/core/src/Op/ZerosLike.h b/compiler/tflchef/core/src/Op/ZerosLike.h new file mode 100644 index 0000000..0af5b93 --- /dev/null +++ b/compiler/tflchef/core/src/Op/ZerosLike.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __OP_ZEROS_LIKE_H__ +#define __OP_ZEROS_LIKE_H__ + +#include "OpChef.h" + +class ZerosLikeChef final : public OpChef +{ +public: + explicit ZerosLikeChef(const tflchef::Operation *operation) : _operation{operation} + { + // DO NOTHING + } + +public: + tflite::BuiltinOperator code(void) const override { return tflite::BuiltinOperator_ZEROS_LIKE; } + + tflite::BuiltinOptions type(void) const override + { + return tflite::BuiltinOptions_ZerosLikeOptions; + } + + flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const override; + +private: + const tflchef::Operation *_operation; +}; + +struct ZerosLikeChefFactory final : public OpChefFactory +{ + std::unique_ptr create(const tflchef::Operation *operation) const override; +}; + +#endif // __OP_ZEROS_LIKE_H__ diff --git a/compiler/tflchef/core/src/OpChef.def b/compiler/tflchef/core/src/OpChef.def index a25250c..263725a 100644 --- a/compiler/tflchef/core/src/OpChef.def +++ b/compiler/tflchef/core/src/OpChef.def @@ -6,32 +6,111 @@ // OP_CHEF(NAME, FACTORY_CLASS) OP_CHEF(Abs, AbsChefFactory) OP_CHEF(Add, AddChefFactory) +OP_CHEF(AddN, AddNChefFactory) OP_CHEF(ArgMax, ArgMaxChefFactory) -OP_CHEF(BatchToSpaceND, BatchToSpaceNDChefFactory) +OP_CHEF(ArgMin, ArgMinChefFactory) OP_CHEF(AveragePool2D, AveragePool2DChefFactory) +OP_CHEF(BatchMatMul, BatchMatMulChefFactory) +OP_CHEF(BatchToSpaceND, BatchToSpaceNDChefFactory) +OP_CHEF(Cast, CastChefFactory) +OP_CHEF(Ceil, CeilChefFactory) OP_CHEF(Concatenation, ConcatenationChefFactory) OP_CHEF(Conv2D, Conv2DChefFactory) OP_CHEF(Cos, CosChefFactory) +OP_CHEF(DepthToSpace, DepthToSpaceChefFactory) OP_CHEF(DepthwiseConv2D, DepthwiseConv2DChefFactory) OP_CHEF(Div, DivChefFactory) +OP_CHEF(ELU, ELUChefFactory) OP_CHEF(Equal, EqualChefFactory) OP_CHEF(Exp, ExpChefFactory) +OP_CHEF(ExpandDims, ExpandDimsChefFactory) +OP_CHEF(Fill, FillChefFactory) +OP_CHEF(Floor, FloorChefFactory) OP_CHEF(FloorDiv, FloorDivChefFactory) +OP_CHEF(FloorMod, FloorModChefFactory) OP_CHEF(FullyConnected, FullyConnectedChefFactory) +OP_CHEF(Gather, GatherChefFactory) +OP_CHEF(GatherNd, GatherNdChefFactory) +OP_CHEF(Greater, GreaterChefFactory) +OP_CHEF(GreaterEqual, GreaterEqualChefFactory) +OP_CHEF(If, IfChefFactory) +OP_CHEF(L2Normalize, L2NormalizeChefFactory) +OP_CHEF(L2Pool2D, L2Pool2DChefFactory) +OP_CHEF(LeakyRelu, LeakyReluChefFactory) +OP_CHEF(Less, LessChefFactory) +OP_CHEF(LessEqual, LessEqualChefFactory) +OP_CHEF(LocalResponseNormalization, LocalResponseNormalizationChefFactory) +OP_CHEF(Log, LogChefFactory) +OP_CHEF(LogicalAnd, LogicalAndChefFactory) OP_CHEF(LogicalNot, LogicalNotChefFactory) OP_CHEF(LogicalOr, LogicalOrChefFactory) +OP_CHEF(Logistic, LogisticChefFactory) +OP_CHEF(LogSoftmax, LogSoftmaxChefFactory) +OP_CHEF(MatrixDiag, MatrixDiagChefFactory) +OP_CHEF(MatrixSetDiag, MatrixSetDiagChefFactory) +OP_CHEF(Maximum, MaximumChefFactory) OP_CHEF(MaxPool2D, MaxPool2DChefFactory) OP_CHEF(Mean, MeanChefFactory) +OP_CHEF(Minimum, MinimumChefFactory) +OP_CHEF(MirrorPad, MirrorPadChefFactory) OP_CHEF(Mul, MulChefFactory) +OP_CHEF(Neg, NegChefFactory) +OP_CHEF(NotEqual, NotEqualChefFactory) +OP_CHEF(OneHot, OneHotChefFactory) OP_CHEF(Pack, PackChefFactory) OP_CHEF(Pad, PadChefFactory) +OP_CHEF(Pow, PowChefFactory) +OP_CHEF(PRelu, PReluChefFactory) +OP_CHEF(Range, RangeChefFactory) +OP_CHEF(Rank, RankChefFactory) +OP_CHEF(ReduceAny, ReduceAnyChefFactory) +OP_CHEF(ReduceMax, ReduceMaxChefFactory) +OP_CHEF(ReduceMin, ReduceMinChefFactory) +OP_CHEF(ReduceProd, ReduceProdChefFactory) OP_CHEF(ReLU, ReLUChefFactory) OP_CHEF(ReLU6, ReLU6ChefFactory) +OP_CHEF(ReLUN1To1, ReLUN1To1ChefFactory) OP_CHEF(Reshape, ReshapeChefFactory) +OP_CHEF(ResizeBilinear, ResizeBilinearChefFactory) +OP_CHEF(ResizeNearestNeighbor, ResizeNearestNeighborChefFactory) +OP_CHEF(ReverseSequence, ReverseSequenceChefFactory) +OP_CHEF(ReverseV2, ReverseV2ChefFactory) +OP_CHEF(Round, RoundChefFactory) OP_CHEF(Rsqrt, RsqrtChefFactory) +OP_CHEF(ScatterNd, ScatterNdChefFactory) +OP_CHEF(SegmentSum,SegmentSumChefFactory) +OP_CHEF(Select, SelectChefFactory) +OP_CHEF(SelectV2, SelectV2ChefFactory) OP_CHEF(Shape, ShapeChefFactory) +OP_CHEF(Sin, SinChefFactory) +OP_CHEF(Slice, SliceChefFactory) OP_CHEF(Softmax, SoftmaxChefFactory) +OP_CHEF(SpaceToBatchND, SpaceToBatchNDChefFactory) +OP_CHEF(SpaceToDepth, SpaceToDepthChefFactory) +OP_CHEF(SparseToDense, SparseToDenseChefFactory) +OP_CHEF(Split, SplitChefFactory) +OP_CHEF(SplitV, SplitVChefFactory) OP_CHEF(Sqrt, SqrtChefFactory) +OP_CHEF(Square, SquareChefFactory) +OP_CHEF(SquaredDifference, SquaredDifferenceChefFactory) +OP_CHEF(Squeeze, SqueezeChefFactory) +OP_CHEF(StridedSlice, StridedSliceChefFactory) OP_CHEF(Sub, SubChefFactory) +OP_CHEF(Sum, SumChefFactory) OP_CHEF(Tanh, TanhChefFactory) +OP_CHEF(Tile, TileChefFactory) +OP_CHEF(TopKV2, TopKV2ChefFactory) OP_CHEF(Transpose, TransposeChefFactory) +OP_CHEF(TransposeConv, TransposeConvChefFactory) +OP_CHEF(Unique, UniqueChefFactory) +OP_CHEF(Unpack, UnpackChefFactory) +OP_CHEF(Where, WhereChefFactory) +OP_CHEF(While, WhileChefFactory) +OP_CHEF(ZerosLike, ZerosLikeChefFactory) + +// Custom Op +OP_CHEF(AddV2, AddV2ChefFactory) +OP_CHEF(All, AllChefFactory) +OP_CHEF(BatchMatMulV2, BatchMatMulV2ChefFactory) +OP_CHEF(MatMul, MatMulChefFactory) +OP_CHEF(MatrixBandPart, MatrixBandPartChefFactory) diff --git a/compiler/tflchef/core/src/OpChef.h b/compiler/tflchef/core/src/OpChef.h index 0b7d9cf..7efa096 100644 --- a/compiler/tflchef/core/src/OpChef.h +++ b/compiler/tflchef/core/src/OpChef.h @@ -29,6 +29,13 @@ struct OpChef virtual tflite::BuiltinOperator code(void) const = 0; virtual tflite::BuiltinOptions type(void) const = 0; virtual flatbuffers::Offset value(flatbuffers::FlatBufferBuilder &fbb) const = 0; + + // TODO Find a way to place this method in a better place + virtual flatbuffers::Offset> + custom_value(flatbuffers::FlatBufferBuilder &fbb) const + { + return flatbuffers::Offset>(); + } }; struct OpChefFactory diff --git a/compiler/tflchef/core/src/OpChefs.h b/compiler/tflchef/core/src/OpChefs.h index 65dbd4b..55c37eb 100644 --- a/compiler/tflchef/core/src/OpChefs.h +++ b/compiler/tflchef/core/src/OpChefs.h @@ -19,34 +19,112 @@ #include "Op/Abs.h" #include "Op/Add.h" +#include "Op/AddN.h" #include "Op/ArgMax.h" +#include "Op/ArgMin.h" #include "Op/AveragePool2D.h" +#include "Op/BatchMatMul.h" #include "Op/BatchToSpaceND.h" +#include "Op/Cast.h" +#include "Op/Ceil.h" #include "Op/Concatenation.h" #include "Op/Conv2D.h" #include "Op/Cos.h" +#include "Op/DepthToSpace.h" #include "Op/DepthwiseConv2D.h" #include "Op/Div.h" +#include "Op/ELU.h" #include "Op/Equal.h" #include "Op/Exp.h" +#include "Op/ExpandDims.h" +#include "Op/Fill.h" +#include "Op/Floor.h" #include "Op/FloorDiv.h" +#include "Op/FloorMod.h" #include "Op/FullyConnected.h" -#include "Op/LogicalOr.h" +#include "Op/Gather.h" +#include "Op/GatherNd.h" +#include "Op/Greater.h" +#include "Op/GreaterEqual.h" +#include "Op/If.h" +#include "Op/L2Normalize.h" +#include "Op/L2Pool2D.h" +#include "Op/LeakyRelu.h" +#include "Op/Less.h" +#include "Op/LessEqual.h" +#include "Op/LocalResponseNormalization.h" +#include "Op/Log.h" +#include "Op/LogicalAnd.h" #include "Op/LogicalNot.h" +#include "Op/LogicalOr.h" +#include "Op/Logistic.h" +#include "Op/LogSoftmax.h" +#include "Op/MatrixDiag.h" +#include "Op/MatrixSetDiag.h" +#include "Op/Maximum.h" #include "Op/MaxPool2D.h" #include "Op/Mean.h" +#include "Op/Minimum.h" +#include "Op/MirrorPad.h" #include "Op/Mul.h" +#include "Op/Neg.h" +#include "Op/NotEqual.h" +#include "Op/OneHot.h" #include "Op/Pack.h" #include "Op/Pad.h" +#include "Op/Pow.h" +#include "Op/PRelu.h" +#include "Op/Range.h" +#include "Op/Rank.h" +#include "Op/ReduceAny.h" +#include "Op/ReduceMax.h" +#include "Op/ReduceMin.h" +#include "Op/ReduceProd.h" #include "Op/ReLU.h" #include "Op/ReLU6.h" +#include "Op/ReLUN1To1.h" #include "Op/Reshape.h" +#include "Op/ResizeBilinear.h" +#include "Op/ResizeNearestNeighbor.h" +#include "Op/ReverseSequence.h" +#include "Op/ReverseV2.h" +#include "Op/Round.h" #include "Op/Rsqrt.h" +#include "Op/ScatterNd.h" +#include "Op/SegmentSum.h" +#include "Op/Select.h" +#include "Op/SelectV2.h" #include "Op/Shape.h" +#include "Op/Sin.h" +#include "Op/Slice.h" #include "Op/Softmax.h" +#include "Op/SpaceToBatchND.h" +#include "Op/SpaceToDepth.h" +#include "Op/SparseToDense.h" +#include "Op/Split.h" +#include "Op/SplitV.h" #include "Op/Sqrt.h" +#include "Op/Square.h" +#include "Op/SquaredDifference.h" +#include "Op/Squeeze.h" +#include "Op/StridedSlice.h" #include "Op/Sub.h" +#include "Op/Sum.h" #include "Op/Tanh.h" +#include "Op/Tile.h" +#include "Op/TopKV2.h" #include "Op/Transpose.h" +#include "Op/TransposeConv.h" +#include "Op/Unique.h" +#include "Op/Unpack.h" +#include "Op/Where.h" +#include "Op/While.h" +#include "Op/ZerosLike.h" + +#include "CustomOp/AddV2.h" +#include "CustomOp/All.h" +#include "CustomOp/BatchMatMulV2.h" +#include "CustomOp/MatMul.h" +#include "CustomOp/MatrixBandPart.h" #endif // __OP_CHEFS_H__ diff --git a/compiler/tflchef/log/CMakeLists.txt b/compiler/tflchef/log/CMakeLists.txt new file mode 100644 index 0000000..330459e --- /dev/null +++ b/compiler/tflchef/log/CMakeLists.txt @@ -0,0 +1,7 @@ +# TODO Find how to test logging framework +file(GLOB_RECURSE SOURCES "src/*.cpp") + +add_library(tflchef_log STATIC ${SOURCES}) +target_include_directories(tflchef_log PUBLIC include) +target_link_libraries(tflchef_log PUBLIC hermes) +target_link_libraries(tflchef_log PRIVATE hermes_std) diff --git a/compiler/tflchef/log/include/Log.h b/compiler/tflchef/log/include/Log.h new file mode 100644 index 0000000..178fe31 --- /dev/null +++ b/compiler/tflchef/log/include/Log.h @@ -0,0 +1,75 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLCHEF_LOG_H__ +#define __TFLCHEF_LOG_H__ + +#include + +namespace tflchef +{ + +/** + * @brief Logger Implementation + */ +class Logger final : public hermes::Source +{ +public: + Logger(hermes::Context *ctx); + ~Logger(); +}; + +/** + * @brief Logger Configuration + * + * Users are able to turn logging on/off via TFLCHEF_LOG environment variable. + */ +class LoggerConfig final : public hermes::Config +{ +public: + LoggerConfig(); + +public: + void configure(const hermes::Source *, hermes::Source::Setting &) const final; + void configure(const Logger *, hermes::Source::Setting &) const; + +private: + bool _enabled; +}; + +} // namespace tflchef + +#include "LoggingContext.h" + +/** + * HOW TO USE: + * + * LOGGER(l); + * + * INFO(l) << "Hello, World" << std::endl; + * + */ +#define LOGGER(name) ::tflchef::Logger name{::tflchef::LoggingContext::get()}; + +// TODO Support FATAL, ERROR, WARN, and VERBOSE +#define INFO(name) HERMES_INFO(name) + +// WARNING! +// +// THE CURRENT IMPLEMENTATION IS NOT THREAD SAFE. +// + +#endif // __TFLCHEF_LOG_H__ diff --git a/compiler/tflchef/log/include/LoggingContext.h b/compiler/tflchef/log/include/LoggingContext.h new file mode 100644 index 0000000..8600994 --- /dev/null +++ b/compiler/tflchef/log/include/LoggingContext.h @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLCHEF_LOGGING_CONTEXT_H__ +#define __TFLCHEF_LOGGING_CONTEXT_H__ + +#include + +namespace tflchef +{ + +/** + * @brief Global logging context + */ +struct LoggingContext +{ + static hermes::Context *get(void); +}; + +} // namespace tflchef + +#endif // __TFLCHEF_LOGGING_CONTEXT_H__ diff --git a/compiler/tflchef/log/src/Log.cpp b/compiler/tflchef/log/src/Log.cpp new file mode 100644 index 0000000..62c3777 --- /dev/null +++ b/compiler/tflchef/log/src/Log.cpp @@ -0,0 +1,87 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Log.h" + +#include +#include +#include + +// TODO Extract these lexical conversion routines as a library +namespace +{ + +/** + * @brief Convert C-string as a value of type T + * + * safecast(s, v) returns v if s is nullptr. + */ +template T safecast(const char *, const T &); + +template <> bool safecast(const char *s, const bool &value) +{ + return (s == nullptr) ? value : (std::stoi(s) != 0); +} + +} // namespace + +// +// Logger +// +namespace tflchef +{ + +Logger::Logger(hermes::Context *ctx) { activate(ctx->sources(), ctx->bus()); } +Logger::~Logger() { deactivate(); } + +} // namespace tflchef + +// +// LoggerConfig +// +namespace tflchef +{ + +LoggerConfig::LoggerConfig() +{ + // Turn on logging if TFLCHEF_LOG is set as non-zero value + _enabled = safecast(std::getenv("TFLCHEF_LOG"), false); +} + +void LoggerConfig::configure(const hermes::Source *source, hermes::Source::Setting &setting) const +{ + // Let's ignore hermes::Sources if that is not a moco logger + if (auto logger = dynamic_cast(source)) + { + configure(logger, setting); + } +} + +void LoggerConfig::configure(const Logger *, hermes::Source::Setting &setting) const +{ + if (_enabled) + { + // Enable all catagories + setting.accept_all(); + } + else + { + // Disable all catagories + setting.reject_all(); + } +} + +} // namespace tflchef diff --git a/compiler/tflchef/log/src/LoggingContext.cpp b/compiler/tflchef/log/src/LoggingContext.cpp new file mode 100644 index 0000000..0514dc3 --- /dev/null +++ b/compiler/tflchef/log/src/LoggingContext.cpp @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LoggingContext.h" +#include "Log.h" + +#include + +#include + +namespace tflchef +{ + +hermes::Context *LoggingContext::get(void) +{ + static hermes::Context *ctx = nullptr; + + if (ctx == nullptr) + { + ctx = new hermes::Context; + ctx->sinks()->append(std::make_unique()); + ctx->config(std::make_unique()); + } + + return ctx; +} + +} // namespace tflchef diff --git a/compiler/tflchef/proto/tflchef.proto b/compiler/tflchef/proto/tflchef.proto index 486aa8a..792503b 100644 --- a/compiler/tflchef/proto/tflchef.proto +++ b/compiler/tflchef/proto/tflchef.proto @@ -55,15 +55,24 @@ enum Padding { enum Activation { NONE = 0; RELU = 1; + RELU_N1_TO_1 = 2; RELU6 = 3; } +// This enum value corresponds to MirrorPadMode in TensorFlow Lite schema +enum MirrorPadMode { + REFLECT = 0; + SYMMETRIC = 1; +} + message Conv2DOptions { optional Padding padding = 1 [default = VALID]; optional int32 stride_w = 2 [default = 1]; optional int32 stride_h = 3 [default = 1]; optional Activation activation = 4 [default = NONE]; + optional int32 dilation_w_factor = 5 [default = 1]; + optional int32 dilation_h_factor = 6 [default = 1]; } message Pool2DOptions { @@ -91,6 +100,12 @@ message DepthwiseConv2DOptions optional int32 stride_h = 3 [default = 1]; optional int32 depth_multiplier = 4 [default = 1]; optional Activation activation = 5 [default = NONE]; + optional int32 dilation_w_factor = 6 [default = 1]; + optional int32 dilation_h_factor = 7 [default = 1]; +} + +message ScatterNdOptions { + // None } message SubOptions { @@ -105,6 +120,10 @@ message FloorDivOptions { // None } +message FloorModOptions { + // None +} + message FullyConnectedOptions { optional Activation activation = 1 [default = NONE]; } @@ -113,10 +132,18 @@ message AddOptions { optional Activation activation = 1 [default = NONE]; } +message AddNOptions { + // None +} + message ArgMaxOptions { optional TensorType output_type = 1 [default = INT64]; } +message ArgMinOptions { + optional TensorType output_type = 1 [default = INT64]; +} + message PackOptions { optional int32 values_count = 1; optional int32 axis = 2 [default = 0]; @@ -126,6 +153,10 @@ message PadOptions { // None } +message MirrorPadOptions { + optional MirrorPadMode mode = 1 [default = REFLECT]; +} + message SoftmaxOptions { optional float beta = 1 [default = 0.0]; } @@ -134,10 +165,22 @@ message MulOptions { optional Activation activation = 1 [default = NONE]; } +message NegOptions { + // None +} + +message RangeOptions { + // None +} + message ReducerOptions { optional bool keep_dims = 1 [ default = false ]; } +message SpaceToDepthOptions { + optional int32 block_size = 1; +} + message LogicalOrOptions { // None } @@ -174,14 +217,224 @@ message BatchToSpaceNDOptions { // None } +message SpaceToBatchNDOptions { + // None +} + +message StridedSliceOptions { + optional int32 begin_mask = 1; + optional int32 end_mask = 2; + optional int32 ellipsis_mask = 3; + optional int32 new_axis_mask = 4; + optional int32 shrink_axis_mask = 5; +} + +message SliceOptions { + // None +} + message ExpOptions { // None } +message ExpandDimsOptions { + // None +} + +message UnpackOptions { + optional int32 num = 1; + optional int32 axis = 2 [default = 0]; +} + +message GatherOptions { + optional int32 axis = 1 [default = 0]; +} + +message TileOptions { + // None +} + +message BatchMatMulOptions { + optional bool adj_x = 1 [default = false]; + optional bool adj_y = 2 [default = false]; +} + +message IfOptions { + optional int32 then_subgraph_index = 1; + optional int32 else_subgraph_index = 2; +} + +message WhileOptions { + optional int32 cond_subgraph_index = 1; + optional int32 body_subgraph_index = 2; +} + +message CastOptions { + optional TensorType in_data_type = 1 [default = FLOAT32]; + optional TensorType out_data_type = 2 [default = FLOAT32]; +} + +message SquareOptions { + // None +} + +message MaximumMinimumOptions { + //None +} + +message GreaterEqualOptions { + // None +} + +message SelectOptions { + // None +} + +message SelectV2Options { + // None +} + +message SplitOptions { + optional int32 num_splits = 1; +} + +message SplitVOptions { + optional int32 num_splits = 1; +} + +message SquaredDifferenceOptions { + // None +} + +message FillOptions { + // None +} + +message GreaterOptions { + // None +} + +message L2NormOptions { + optional Activation activation = 1 [default = NONE]; +} + +message LessOptions { + // None +} + +message LessEqualOptions { + // None +} + +message LocalResponseNormalizationOptions { + optional int32 radius = 1 [default = 5]; + optional float bias = 2 [default = 1.0]; + optional float alpha = 3 [default = 1.0]; + optional float beta = 4 [default = 0.5]; +} + +message MatMulOptions { + optional bool transpose_a = 1 [default = false]; + optional bool transpose_b = 2 [default = false]; +} + +message SqueezeOptions { + repeated int32 squeeze_dim = 1; +} + +message OneHotOptions { + optional int32 axis = 1 [default = -1]; +} + +message TopKV2Options { + // None +} + +message LogSoftmaxOptions { + // None +} + +message ZerosLikeOptions { + // None +} + +message GatherNdOptions { + // None +} + +message NotEqualOptions { + // None +} + +message PowOptions { + // None +} + +message LeakyReluOptions { + optional float alpha = 1 [default = 0.2]; +} + +message ResizeNearestNeighborOptions { + optional bool align_corners = 1 [default = false]; +} + +message ResizeBilinearOptions { + optional bool align_corners = 1 [default = false]; + optional bool half_pixel_centers = 2 [default = false]; +} + +message DepthToSpaceOptions { + optional int32 block_size = 1; +} + +message TransposeConvOptions { + optional Padding padding = 1 [default = VALID]; + optional int32 stride_w = 2 [default = 1]; + optional int32 stride_h = 3 [default = 1]; +} + +message ReverseSequenceOptions { + optional int32 seq_dim = 1 [default = 0]; + optional int32 batch_dim = 2 [default = 0]; +} + +message RankOptions { + // NONE +} + +message SegmentSumOptions { + // NONE +} + +message UniqueOptions { + optional TensorType idx_out_type = 1 [default = INT32]; +} + +message WhereOptions { + // None +} + +message SparseToDenseOptions { + optional bool validate_indices = 1 [default = true]; +} + +message ReverseV2Options { + // None +} + +message MatrixDiagOptions { + // NONE +} + +message MatrixSetDiagOptions { + // NONE +} + message Operation { optional string type = 1; repeated string input = 2; repeated string output = 3; + optional int32 version = 4 [default = 1]; optional Conv2DOptions conv2d_options = 100; optional Pool2DOptions averagepool2d_options = 101; @@ -210,6 +463,91 @@ message Operation { optional FloorDivOptions floordiv_options = 124; optional BatchToSpaceNDOptions batch_to_space_options = 125; optional ExpOptions exp_options = 126; + optional UnpackOptions unpack_options = 127; + optional GatherOptions gather_options = 128; + optional BatchMatMulOptions batch_matmul_options = 129; + optional TileOptions tile_options = 130; + optional IfOptions if_options = 131; + optional WhileOptions while_options = 132; + optional SpaceToBatchNDOptions space_to_batch_nd_options = 133; + optional CastOptions cast_options = 134; + optional GreaterEqualOptions greaterequal_options = 135; + optional MaximumMinimumOptions maximum_options = 136; + optional StridedSliceOptions strided_slice_options = 137; + optional SquaredDifferenceOptions squared_difference_options = 138; + optional FillOptions fill_options = 139; + optional SelectOptions select_options = 140; + optional ReducerOptions reduce_prod_options = 141; + optional SplitOptions split_options = 142; + optional SplitVOptions split_v_options = 143; + optional ReducerOptions sum_options = 144; + optional GreaterOptions greater_options = 145; + optional SqueezeOptions squeeze_options = 146; + optional FloorModOptions floormod_options = 147; + optional OneHotOptions onehot_options = 148; + optional LessOptions less_options = 149; + optional ReducerOptions reduce_max_options = 150; + optional MaximumMinimumOptions minimum_options = 151; + optional ReducerOptions reduce_any_options = 152; + optional ZerosLikeOptions zeros_like_options = 153; + // ConcatEmbeddingsOptions 154 + // LSHProjectionOptions 155 + // SVDFOptions 156 + // RNNOptions 157 + optional L2NormOptions l2norm_options = 158; + optional LocalResponseNormalizationOptions local_response_normalization_options = 159; + // LSTMOptions 160 + optional ResizeBilinearOptions resize_bilinear_options = 161; + // CallOptions 162 + // SkipGramOptions 163 + optional SpaceToDepthOptions space_to_depth_options = 164; + // EmbeddingLookupSparseOptions 165 + // SequenceRNNOptions 166 + optional TopKV2Options topk_v2_options = 167; + optional LogSoftmaxOptions log_softmax_options = 168; + // DequantizeOptions 169 + optional NegOptions neg_options = 170; + // PadV2Options 171 + optional LessEqualOptions lessequal_options = 172; + optional SliceOptions slice_options = 173; + optional TransposeConvOptions transpose_conv_options = 174; + optional SparseToDenseOptions sparse_to_dense_options = 175; + optional PowOptions pow_options = 176; + optional ArgMinOptions argmin_options = 177; + // FakeQuantOptions 178 + // BidirectionalSequenceLSTMOptions 179 + // BidirectionalSequenceRNNOptions 180 + // UnidirectionalSequenceLSTMOptions 181 + optional RangeOptions range_options = 182; + optional ResizeNearestNeighborOptions resize_nearest_neighbor_options = 183; + optional LeakyReluOptions leaky_relu_options = 184; + optional MirrorPadOptions mirrorpad_options = 185; + optional UniqueOptions unique_options = 186; + optional ReverseV2Options reversev2_options = 187; + // AddNOptions 188 + optional GatherNdOptions gather_nd_options = 189; + optional WhereOptions where_options = 190; + optional RankOptions rank_options = 191; + optional ReverseSequenceOptions reverse_sequence_options = 192; + optional MatrixDiagOptions matrix_diag_options = 193; + // QuantizeOptions 194 + optional MatrixSetDiagOptions matrix_set_diag_options = 195; + // HardSwishOptions 196 + optional DepthToSpaceOptions depth_to_space_options = 197; + // NonMaxSuppressionV4Options 198 + // NonMaxSuppressionV5Options 199 + optional ScatterNdOptions scatter_nd_options = 200; + optional NotEqualOptions notequal_options = 201; + optional ExpandDimsOptions expand_dims_options = 202; + optional Pool2DOptions l2pool2d_options = 203; + optional ReducerOptions all_options = 204; + optional ReducerOptions reduce_min_options = 205; + optional SegmentSumOptions segment_sum_options = 206; + optional AddNOptions add_n_options = 207; + optional MatMulOptions matmul_options = 208; + + // NOTE if there are more than two options with same type of Options + // use the number not listed in the above reserve list } // For additional subgraphs diff --git a/compiler/tflchef/requires.cmake b/compiler/tflchef/requires.cmake index 3c5bb19..4c02174 100644 --- a/compiler/tflchef/requires.cmake +++ b/compiler/tflchef/requires.cmake @@ -1,4 +1,9 @@ +require("arser") require("nnkit") require("cwrap") require("mio-tflite") require("safemain") +require("hermes") +require("hermes-std") +require("foder") +require("souschef") diff --git a/compiler/tflchef/tests/no_shape/test.recipe b/compiler/tflchef/tests/no_shape/test.recipe new file mode 100644 index 0000000..38efef9 --- /dev/null +++ b/compiler/tflchef/tests/no_shape/test.recipe @@ -0,0 +1,43 @@ +operand { + name: "indices" + type: INT32 + shape { dim: 4 } +} +operand { + name: "depth" + type: INT32 + # shape is intentionally omitted here + filler { tag: "explicit" arg: "1" } +} +operand { + name: "on_value" + type: INT32 + # shape is intentionally omitted here + filler { tag: "explicit" arg: "1" } +} +operand { + name: "off_value" + type: INT32 + # shape is intentionally omitted here + filler { tag: "explicit" arg: "0" } +} +operand { + name: "ofm" + type: INT32 + shape { dim: 4 dim: 1 } +} +operation { + type: "OneHot" + onehot_options { + axis: -1 + } + input: "indices" + input: "depth" + input: "on_value" + input: "off_value" + output: "ofm" +} +input: "indices" +input: "on_value" +input: "off_value" +output: "ofm" diff --git a/tools/lcov-to-cobertura-xml/.FORMATDENY b/compiler/tflchef/tests/no_shape/test.reverse similarity index 100% rename from tools/lcov-to-cobertura-xml/.FORMATDENY rename to compiler/tflchef/tests/no_shape/test.reverse diff --git a/compiler/tflchef/tests/runvalidate.sh b/compiler/tflchef/tests/runvalidate.sh index a1453b3..0dd9d16 100755 --- a/compiler/tflchef/tests/runvalidate.sh +++ b/compiler/tflchef/tests/runvalidate.sh @@ -1,13 +1,13 @@ #!/bin/bash -if [[ $# -le 3 ]]; then +if [[ $# -le 2 ]]; then echo "USAGE: $0 [mio_tflite_validate path] [prefix 0] " exit 255 fi MIO_TFLITE_VALIDATE_PATH="$1"; shift -echo "-- Found mio_tflite_validate: ${NNKIT_RUN_PATH}" +echo "-- Found mio_tflite_validate: ${MIO_TFLITE_VALIDATE_PATH}" TESTED=() PASSED=() diff --git a/compiler/tflchef/tflite/include/tflchef/RawModel.h b/compiler/tflchef/tflite/include/tflchef/RawModel.h deleted file mode 100644 index a8c8fef..0000000 --- a/compiler/tflchef/tflite/include/tflchef/RawModel.h +++ /dev/null @@ -1,41 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef __RAW_MODEL_H__ -#define __RAW_MODEL_H__ - -#include - -namespace tflchef -{ - -struct RawModel -{ - virtual ~RawModel() = default; - - virtual const ::tflite::Model *model(void) const = 0; -}; - -/** - * @brief Load TensorFlow Lite model (as a RawModel) from a given path - * - * @note May return a nullptr - */ -std::unique_ptr load_tflite(const std::string &path); - -} // namespace tflchef - -#endif // __RAW_MODEL_H__ diff --git a/compiler/tflchef/tflite/src/Convert.cpp b/compiler/tflchef/tflite/src/Convert.cpp index dc60e00..3cc1c92 100644 --- a/compiler/tflchef/tflite/src/Convert.cpp +++ b/compiler/tflchef/tflite/src/Convert.cpp @@ -51,10 +51,11 @@ tflchef::Activation as_tflchef_activation(const tflite::ActivationFunctionType t return tflchef::NONE; case tflite::ActivationFunctionType_RELU: return tflchef::RELU; + case tflite::ActivationFunctionType_RELU_N1_TO_1: + return tflchef::RELU_N1_TO_1; case tflite::ActivationFunctionType_RELU6: return tflchef::RELU6; // TODO handle other types - // ActivationFunctionType_RELU_N1_TO_1 // ActivationFunctionType_TANH // ActivationFunctionType_SIGN_BIT default: @@ -75,4 +76,17 @@ tflchef::Padding as_tflchef_padding(const tflite::Padding padding) } } +tflchef::MirrorPadMode as_tflchef_mirrorpadmode(const tflite::MirrorPadMode mode) +{ + switch (mode) + { + case tflite::MirrorPadMode_REFLECT: + return tflchef::REFLECT; + case tflite::MirrorPadMode_SYMMETRIC: + return tflchef::SYMMETRIC; + default: + throw std::runtime_error{"Unknown mirrorpad mode"}; + } +} + } // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Convert.h b/compiler/tflchef/tflite/src/Convert.h index 8623e7b..770bffa 100644 --- a/compiler/tflchef/tflite/src/Convert.h +++ b/compiler/tflchef/tflite/src/Convert.h @@ -27,12 +27,14 @@ namespace tflchef tflchef::TensorType as_tflchef_type(const tflite::TensorType type); tflchef::Activation as_tflchef_activation(const tflite::ActivationFunctionType type); tflchef::Padding as_tflchef_padding(const tflite::Padding padding); +tflchef::MirrorPadMode as_tflchef_mirrorpadmode(const tflite::MirrorPadMode mode); /** * @brief extract buffer data to std::vector
*/ template std::vector
extract_buffer(const tflite::Buffer *buffer) { + assert(buffer->data() != nullptr); auto buffer_length = buffer->data()->size(); auto num_elements = buffer_length / sizeof(DT); std::vector
result(num_elements); diff --git a/compiler/tflchef/tflite/src/FillerHelper.cpp b/compiler/tflchef/tflite/src/FillerHelper.cpp new file mode 100644 index 0000000..cf96d2e --- /dev/null +++ b/compiler/tflchef/tflite/src/FillerHelper.cpp @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "FillerHelper.h" + +#include "Convert.h" + +namespace tflchef +{ + +void fill_tensor_to_import(int32_t idx, TFliteImport *import) +{ + const tflite::Tensor *tensor = import->tensors()->Get(idx); + if (tensor != nullptr) + { + if (tensor->type() == tflite::TensorType::TensorType_INT32) + { + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + if (buffer && buffer->data()) + { + auto vec = extract_buffer(buffer); + import->set_tensor_filler(idx, vec); + } + } + else if (tensor->type() == tflite::TensorType::TensorType_FLOAT32) + { + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + if (buffer && buffer->data()) + { + auto vec = extract_buffer(buffer); + import->set_tensor_filler(idx, vec); + } + } + } +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/FillerHelper.h b/compiler/tflchef/tflite/src/FillerHelper.h new file mode 100644 index 0000000..053a5c1 --- /dev/null +++ b/compiler/tflchef/tflite/src/FillerHelper.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __FILLER_HELPER_H__ +#define __FILLER_HELPER_H__ + +#include "TFliteImport.h" + +#include + +namespace tflchef +{ + +void fill_tensor_to_import(int32_t idx, TFliteImport *import); + +} // namespace tflchef + +#endif // __FILLER_HELPER_H__ diff --git a/compiler/tflchef/tflite/src/Op/Add.cpp b/compiler/tflchef/tflite/src/Op/Add.cpp index 7e669ec..3e880a6 100644 --- a/compiler/tflchef/tflite/src/Op/Add.cpp +++ b/compiler/tflchef/tflite/src/Op/Add.cpp @@ -17,6 +17,7 @@ #include "Add.h" #include "Convert.h" +#include "FillerHelper.h" namespace tflchef { @@ -24,7 +25,13 @@ namespace tflchef void TFliteOpAdd::filler(const tflite::Operator *op, TFliteImport *import, tflchef::ModelRecipe *model_recipe) const { - // Nothing to do with filler + // Add may have constant input + + const std::vector &inputs = as_index_vector(op->inputs()); + assert(inputs.size() == 2); + + fill_tensor_to_import(inputs[0], import); + fill_tensor_to_import(inputs[1], import); } tflchef::Operation *TFliteOpAdd::build(const tflite::Operator *op, TFliteImport *import, diff --git a/compiler/tflchef/tflite/src/Op/AddN.cpp b/compiler/tflchef/tflite/src/Op/AddN.cpp new file mode 100644 index 0000000..aeb3803 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/AddN.cpp @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "AddN.h" + +#include "Convert.h" +#include "FillerHelper.h" + +namespace tflchef +{ + +void TFliteOpAddN::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // AddN may have constant input + + const std::vector &inputs = as_index_vector(op->inputs()); + + for (uint32_t idx = 0; idx < inputs.size(); ++idx) + fill_tensor_to_import(inputs[idx], import); +} + +tflchef::Operation *TFliteOpAddN::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("AddN"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/AddN.h b/compiler/tflchef/tflite/src/Op/AddN.h new file mode 100644 index 0000000..4387aa0 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/AddN.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_ADD_N_H__ +#define __TFLITE_OP_ADD_N_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for AddN + */ +class TFliteOpAddN : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_ADD_N_H__ diff --git a/compiler/tflchef/tflite/src/Op/ArgMin.cpp b/compiler/tflchef/tflite/src/Op/ArgMin.cpp new file mode 100644 index 0000000..faab0b8 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ArgMin.cpp @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ArgMin.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpArgMin::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // filler for second input, argmin/dim + const auto &inputs = *op->inputs(); + + const tflite::Tensor *dim_tensor = import->tensors()->Get(inputs[1]); + assert(dim_tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(dim_tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); +} + +tflchef::Operation *TFliteOpArgMin::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_ArgMinOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("ArgMin"); + + auto op_options = operation->mutable_argmin_options(); + + op_options->set_output_type(as_tflchef_type(op_params->output_type())); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/ArgMin.h b/compiler/tflchef/tflite/src/Op/ArgMin.h new file mode 100644 index 0000000..83c643c --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ArgMin.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_ARGMIN_H__ +#define __TFLITE_OP_ARGMIN_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for ArgMin + */ +class TFliteOpArgMin : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_ARGMIN_H__ diff --git a/compiler/tflchef/tflite/src/Op/BatchMatMul.cpp b/compiler/tflchef/tflite/src/Op/BatchMatMul.cpp new file mode 100644 index 0000000..598e58c --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/BatchMatMul.cpp @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "BatchMatMul.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpBatchMatMul::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpBatchMatMul::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("BatchMatMul"); + + auto op_options = operation->mutable_batch_matmul_options(); + + auto op_params = op->builtin_options_as_BatchMatMulOptions(); + assert(op_params != nullptr); + + op_options->set_adj_x(op_params->adj_x()); + op_options->set_adj_y(op_params->adj_y()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/BatchMatMul.h b/compiler/tflchef/tflite/src/Op/BatchMatMul.h new file mode 100644 index 0000000..6eb4c6e --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/BatchMatMul.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_BATCHMATMUL_H__ +#define __TFLITE_OP_BATCHMATMUL_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for BATCH_MATMUL + */ +class TFliteOpBatchMatMul : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_BATCHMATMUL_H__ diff --git a/compiler/tflchef/tflite/src/Op/Cast.cpp b/compiler/tflchef/tflite/src/Op/Cast.cpp new file mode 100644 index 0000000..393bb4b --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Cast.cpp @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Cast.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpCast::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpCast::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_CastOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("Cast"); + + auto op_options = operation->mutable_cast_options(); + + op_options->set_in_data_type(as_tflchef_type(op_params->in_data_type())); + op_options->set_out_data_type(as_tflchef_type(op_params->out_data_type())); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Cast.h b/compiler/tflchef/tflite/src/Op/Cast.h new file mode 100644 index 0000000..29c126c --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Cast.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_CAST_H__ +#define __TFLITE_OP_CAST_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for CAST + */ +class TFliteOpCast : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_CAST_H__ diff --git a/compiler/tflchef/tflite/src/Op/Ceil.cpp b/compiler/tflchef/tflite/src/Op/Ceil.cpp new file mode 100644 index 0000000..d3ef3ad --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Ceil.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Ceil.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpCeil::filler(const tflite::Operator *, TFliteImport *, tflchef::ModelRecipe *) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpCeil::build(const tflite::Operator *, TFliteImport *, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Ceil"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Ceil.h b/compiler/tflchef/tflite/src/Op/Ceil.h new file mode 100644 index 0000000..44df207 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Ceil.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_CEIL_H__ +#define __TFLITE_OP_CEIL_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for CEIL + */ +class TFliteOpCeil : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_CEIL_H__ diff --git a/compiler/tflchef/tflite/src/Op/Conv2D.cpp b/compiler/tflchef/tflite/src/Op/Conv2D.cpp index 5d48ee2..57abd46 100644 --- a/compiler/tflchef/tflite/src/Op/Conv2D.cpp +++ b/compiler/tflchef/tflite/src/Op/Conv2D.cpp @@ -50,7 +50,8 @@ tflchef::Operation *TFliteOpConv2D::build(const tflite::Operator *op, TFliteImpo op_options->set_stride_h(op_params->stride_h()); op_options->set_stride_w(op_params->stride_w()); op_options->set_padding(as_tflchef_padding(op_params->padding())); - // TODO support dilation + op_options->set_dilation_w_factor(op_params->dilation_w_factor()); + op_options->set_dilation_h_factor(op_params->dilation_h_factor()); return operation; } diff --git a/compiler/tflchef/tflite/src/Op/DepthToSpace.cpp b/compiler/tflchef/tflite/src/Op/DepthToSpace.cpp new file mode 100644 index 0000000..1a0917e --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/DepthToSpace.cpp @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "DepthToSpace.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpDepthToSpace::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpDepthToSpace::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("DepthToSpace"); + + auto op_params = op->builtin_options_as_DepthToSpaceOptions(); + assert(op_params != nullptr); + + auto op_options = operation->mutable_depth_to_space_options(); + + op_options->set_block_size(op_params->block_size()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/DepthToSpace.h b/compiler/tflchef/tflite/src/Op/DepthToSpace.h new file mode 100644 index 0000000..b5852ac --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/DepthToSpace.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_DEPTHTOSPACE_H__ +#define __TFLITE_OP_DEPTHTOSPACE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for DepthToSpace + */ +class TFliteOpDepthToSpace : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_DEPTHTOSPACE_H__ diff --git a/compiler/tflchef/tflite/src/Op/DepthwiseConv2D.cpp b/compiler/tflchef/tflite/src/Op/DepthwiseConv2D.cpp index b19f933..5fed335 100644 --- a/compiler/tflchef/tflite/src/Op/DepthwiseConv2D.cpp +++ b/compiler/tflchef/tflite/src/Op/DepthwiseConv2D.cpp @@ -50,9 +50,8 @@ tflchef::Operation *TFliteOpDepthwiseConv2D::build(const tflite::Operator *op, T op_options->set_stride_h(op_params->stride_h()); op_options->set_stride_w(op_params->stride_w()); op_options->set_depth_multiplier(op_params->depth_multiplier()); - // TODO support dilation - // op_params->dilation_w_factor() - // op_params->dilation_h_factor() + op_options->set_dilation_w_factor(op_params->dilation_w_factor()); + op_options->set_dilation_h_factor(op_params->dilation_h_factor()); op_options->set_padding(as_tflchef_padding(op_params->padding())); return operation; diff --git a/compiler/tflchef/tflite/src/Op/ELU.cpp b/compiler/tflchef/tflite/src/Op/ELU.cpp new file mode 100644 index 0000000..cb4b61d --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ELU.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ELU.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpELU::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpELU::build(const tflite::Operator *, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("ELU"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/ELU.h b/compiler/tflchef/tflite/src/Op/ELU.h new file mode 100644 index 0000000..490c9fd --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ELU.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_ELU_H__ +#define __TFLITE_OP_ELU_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for ELU + */ +class TFliteOpELU : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_ELU_H__ diff --git a/compiler/tflchef/tflite/src/Op/ExpandDims.cpp b/compiler/tflchef/tflite/src/Op/ExpandDims.cpp new file mode 100644 index 0000000..e30e8db --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ExpandDims.cpp @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ExpandDims.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpExpandDims::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Fill for axis input + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[1]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); +} + +tflchef::Operation *TFliteOpExpandDims::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("ExpandDims"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/ExpandDims.h b/compiler/tflchef/tflite/src/Op/ExpandDims.h new file mode 100644 index 0000000..e2f3e4e --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ExpandDims.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_EXPAND_DIMS_H__ +#define __TFLITE_OP_EXPAND_DIMS_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for ExpandDims + */ +class TFliteOpExpandDims : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_EXPAND_DIMS_H__ diff --git a/compiler/tflchef/tflite/src/Op/Fill.cpp b/compiler/tflchef/tflite/src/Op/Fill.cpp new file mode 100644 index 0000000..08b695f --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Fill.cpp @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Fill.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpFill::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + const auto &inputs = *op->inputs(); + + const tflite::Tensor *dims_tensor = import->tensors()->Get(inputs[0]); + assert(dims_tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(dims_tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[0], vec); +} + +tflchef::Operation *TFliteOpFill::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + operation->set_type("Fill"); + + // FillOptions are empty + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Fill.h b/compiler/tflchef/tflite/src/Op/Fill.h new file mode 100644 index 0000000..4f46f62 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Fill.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_FILL_H__ +#define __TFLITE_OP_FILL_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Fill + */ +class TFliteOpFill : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_FILL_H__ diff --git a/compiler/tflchef/tflite/src/Op/Floor.cpp b/compiler/tflchef/tflite/src/Op/Floor.cpp new file mode 100644 index 0000000..373c69f --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Floor.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Floor.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpFloor::filler(const tflite::Operator *, TFliteImport *, tflchef::ModelRecipe *) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpFloor::build(const tflite::Operator *, TFliteImport *, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Floor"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Floor.h b/compiler/tflchef/tflite/src/Op/Floor.h new file mode 100644 index 0000000..f0f8ef3 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Floor.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_FLOOR_H__ +#define __TFLITE_OP_FLOOR_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for FLOOR + */ +class TFliteOpFloor : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_FLOOR_H__ diff --git a/compiler/tflchef/tflite/src/Op/FloorMod.cpp b/compiler/tflchef/tflite/src/Op/FloorMod.cpp new file mode 100644 index 0000000..997d826 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/FloorMod.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "FloorMod.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpFloorMod::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpFloorMod::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("FloorMod"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/FloorMod.h b/compiler/tflchef/tflite/src/Op/FloorMod.h new file mode 100644 index 0000000..f36dfe8 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/FloorMod.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_FLOOR_MOD_H__ +#define __TFLITE_OP_FLOOR_MOD_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for FLOOR_MOD + */ +class TFliteOpFloorMod : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_FLOOR_MOD_H__ diff --git a/compiler/tflchef/tflite/src/Op/Gather.cpp b/compiler/tflchef/tflite/src/Op/Gather.cpp new file mode 100644 index 0000000..98da3ec --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Gather.cpp @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Gather.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpGather::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler + // But second input has filler for constant inputs + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[1]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + + if (buffer && buffer->data()) + { + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); + } +} + +tflchef::Operation *TFliteOpGather::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_GatherOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("Gather"); + + auto op_options = operation->mutable_gather_options(); + + op_options->set_axis(op_params->axis()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Gather.h b/compiler/tflchef/tflite/src/Op/Gather.h new file mode 100644 index 0000000..e01276b --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Gather.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_GATHER_H__ +#define __TFLITE_OP_GATHER_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Gather + */ +class TFliteOpGather : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_GATHER_H__ diff --git a/compiler/tflchef/tflite/src/Op/GatherNd.cpp b/compiler/tflchef/tflite/src/Op/GatherNd.cpp new file mode 100644 index 0000000..0ff5a0b --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/GatherNd.cpp @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "GatherNd.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpGatherNd::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // indices buffer has filler + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[1]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + + if (buffer && buffer->data()) + { + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); + } +} + +tflchef::Operation *TFliteOpGatherNd::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + operation->set_type("GatherNd"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/GatherNd.h b/compiler/tflchef/tflite/src/Op/GatherNd.h new file mode 100644 index 0000000..112f23d --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/GatherNd.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_GATHER_ND_H__ +#define __TFLITE_OP_GATHER_ND_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for GatherNd + */ +class TFliteOpGatherNd : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_GATHER_ND_H__ diff --git a/compiler/tflchef/tflite/src/Op/Greater.cpp b/compiler/tflchef/tflite/src/Op/Greater.cpp new file mode 100644 index 0000000..4e41efb --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Greater.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Greater.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpGreater::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpGreater::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Greater"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Greater.h b/compiler/tflchef/tflite/src/Op/Greater.h new file mode 100644 index 0000000..3ab2d1a --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Greater.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_GREATER_H__ +#define __TFLITE_OP_GREATER_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Greater + */ +class TFliteOpGreater : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_GREATER_H__ diff --git a/compiler/tflchef/tflite/src/Op/GreaterEqual.cpp b/compiler/tflchef/tflite/src/Op/GreaterEqual.cpp new file mode 100644 index 0000000..aead30e --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/GreaterEqual.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "GreaterEqual.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpGreaterEqual::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpGreaterEqual::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("GreaterEqual"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/GreaterEqual.h b/compiler/tflchef/tflite/src/Op/GreaterEqual.h new file mode 100644 index 0000000..96b0af7 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/GreaterEqual.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_GREATEREQUAL_H__ +#define __TFLITE_OP_GREATEREQUAL_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Greater Equal + */ +class TFliteOpGreaterEqual : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_GREATEREQUAL_H__ diff --git a/compiler/tflchef/tflite/src/Op/L2Normalize.cpp b/compiler/tflchef/tflite/src/Op/L2Normalize.cpp new file mode 100644 index 0000000..0a89084 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/L2Normalize.cpp @@ -0,0 +1,45 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "L2Normalize.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpL2Normalize::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpL2Normalize::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_L2NormOptions(); + auto operation = model_recipe->add_operation(); + + operation->set_type("L2Normalize"); + + auto op_options = operation->mutable_l2norm_options(); + + op_options->set_activation(as_tflchef_activation(op_params->fused_activation_function())); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/L2Normalize.h b/compiler/tflchef/tflite/src/Op/L2Normalize.h new file mode 100644 index 0000000..a73eae6 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/L2Normalize.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_L2NORMALIZE_H__ +#define __TFLITE_OP_L2NORMALIZE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for L2Normalize + */ +class TFliteOpL2Normalize : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_L2NORMALIZE_H__ diff --git a/compiler/tflchef/tflite/src/Op/L2Pool2D.cpp b/compiler/tflchef/tflite/src/Op/L2Pool2D.cpp new file mode 100644 index 0000000..8db4b02 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/L2Pool2D.cpp @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "L2Pool2D.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpL2Pool2D::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpL2Pool2D::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_Pool2DOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("L2Pool2D"); + + auto op_options = operation->mutable_l2pool2d_options(); + + op_options->set_padding(as_tflchef_padding(op_params->padding())); + op_options->set_stride_h(op_params->stride_h()); + op_options->set_stride_w(op_params->stride_w()); + op_options->set_filter_height(op_params->filter_height()); + op_options->set_filter_width(op_params->filter_width()); + op_options->set_activation(as_tflchef_activation(op_params->fused_activation_function())); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/L2Pool2D.h b/compiler/tflchef/tflite/src/Op/L2Pool2D.h new file mode 100644 index 0000000..0463534 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/L2Pool2D.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_L2_POOL2D_H__ +#define __TFLITE_OP_L2_POOL2D_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for L2_POOL_2D + */ +class TFliteOpL2Pool2D : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_L2_POOL2D_H__ diff --git a/compiler/tflchef/tflite/src/Op/LeakyRelu.cpp b/compiler/tflchef/tflite/src/Op/LeakyRelu.cpp new file mode 100644 index 0000000..bf9cb2f --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/LeakyRelu.cpp @@ -0,0 +1,46 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LeakyRelu.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpLeakyRelu::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpLeakyRelu::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_LeakyReluOptions(); + + auto operation = model_recipe->add_operation(); + + operation->set_type("LeakyRelu"); + + auto *op_options = operation->mutable_leaky_relu_options(); + + op_options->set_alpha(op_params->alpha()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/LeakyRelu.h b/compiler/tflchef/tflite/src/Op/LeakyRelu.h new file mode 100644 index 0000000..28e63e0 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/LeakyRelu.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_LEAKY_RELU_H__ +#define __TFLITE_OP_LEAKY_RELU_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for LeakyReLU + */ +class TFliteOpLeakyRelu : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_LEAKY_RELU_H__ diff --git a/compiler/tflchef/tflite/src/Op/Less.cpp b/compiler/tflchef/tflite/src/Op/Less.cpp new file mode 100644 index 0000000..0360317 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Less.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Less.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpLess::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpLess::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Less"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Less.h b/compiler/tflchef/tflite/src/Op/Less.h new file mode 100644 index 0000000..1316cb6 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Less.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_LESS_H__ +#define __TFLITE_OP_LESS_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Less + */ +class TFliteOpLess : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_LESS_H__ diff --git a/compiler/tflchef/tflite/src/Op/LessEqual.cpp b/compiler/tflchef/tflite/src/Op/LessEqual.cpp new file mode 100644 index 0000000..b8c42e8 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/LessEqual.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LessEqual.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpLessEqual::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpLessEqual::build(const tflite::Operator *, TFliteImport *, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("LessEqual"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/LessEqual.h b/compiler/tflchef/tflite/src/Op/LessEqual.h new file mode 100644 index 0000000..81c710f --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/LessEqual.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_LESSEQUAL_H__ +#define __TFLITE_OP_LESSEQUAL_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for LessEqual + */ +class TFliteOpLessEqual : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_LESSEQUAL_H__ diff --git a/compiler/tflchef/tflite/src/Op/LocalResponseNormalization.cpp b/compiler/tflchef/tflite/src/Op/LocalResponseNormalization.cpp new file mode 100644 index 0000000..8bebd9e --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/LocalResponseNormalization.cpp @@ -0,0 +1,51 @@ +/* + * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LocalResponseNormalization.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpLocalResponseNormalization::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation * +TFliteOpLocalResponseNormalization::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_LocalResponseNormalizationOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("LocalResponseNormalization"); + + auto op_options = operation->mutable_local_response_normalization_options(); + + op_options->set_radius(op_params->radius()); + op_options->set_bias(op_params->bias()); + op_options->set_alpha(op_params->alpha()); + op_options->set_beta(op_params->beta()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/LocalResponseNormalization.h b/compiler/tflchef/tflite/src/Op/LocalResponseNormalization.h new file mode 100644 index 0000000..c0eb3f2 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/LocalResponseNormalization.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_LOCAL_RESPONSE_NORMALIZATION_H__ +#define __TFLITE_OP_LOCAL_RESPONSE_NORMALIZATION_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for LocalResponseNormalization + */ +class TFliteOpLocalResponseNormalization : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_LOCAL_RESPONSE_NORMALIZATION_H__ diff --git a/compiler/tflchef/tflite/src/Op/Log.cpp b/compiler/tflchef/tflite/src/Op/Log.cpp new file mode 100644 index 0000000..a68dc9a --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Log.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Log.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpLog::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpLog::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Log"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Log.h b/compiler/tflchef/tflite/src/Op/Log.h new file mode 100644 index 0000000..9d17e2f --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Log.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_LOG_H__ +#define __TFLITE_OP_LOG_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Log + */ +class TFliteOpLog : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_LOG_H__ diff --git a/compiler/tflchef/tflite/src/Op/LogSoftmax.cpp b/compiler/tflchef/tflite/src/Op/LogSoftmax.cpp new file mode 100644 index 0000000..8f0e1a9 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/LogSoftmax.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LogSoftmax.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpLogSoftmax::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpLogSoftmax::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("LogSoftmax"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/LogSoftmax.h b/compiler/tflchef/tflite/src/Op/LogSoftmax.h new file mode 100644 index 0000000..efd81f3 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/LogSoftmax.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_LOG_SOFTMAX_H__ +#define __TFLITE_OP_LOG_SOFTMAX_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for LogSoftmax + */ +class TFliteOpLogSoftmax : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_LOG_SOFTMAX_H__ diff --git a/compiler/tflchef/tflite/src/Op/LogicalAnd.cpp b/compiler/tflchef/tflite/src/Op/LogicalAnd.cpp new file mode 100644 index 0000000..2cc4864 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/LogicalAnd.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LogicalAnd.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpLogicalAnd::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpLogicalAnd::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("LogicalAnd"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/LogicalAnd.h b/compiler/tflchef/tflite/src/Op/LogicalAnd.h new file mode 100644 index 0000000..1f7a964 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/LogicalAnd.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_LOGICALAND_H__ +#define __TFLITE_OP_LOGICALAND_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for LogicalAnd + */ +class TFliteOpLogicalAnd : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_LOGICALAND_H__ diff --git a/compiler/tflchef/tflite/src/Op/Logistic.cpp b/compiler/tflchef/tflite/src/Op/Logistic.cpp new file mode 100644 index 0000000..18b3b5c --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Logistic.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Logistic.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpLogistic::filler(const tflite::Operator *, TFliteImport *, + tflchef::ModelRecipe *) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpLogistic::build(const tflite::Operator *, TFliteImport *, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Logistic"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Logistic.h b/compiler/tflchef/tflite/src/Op/Logistic.h new file mode 100644 index 0000000..a75bf49 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Logistic.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_LOGISTIC_H__ +#define __TFLITE_OP_LOGISTIC_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for LOGISTIC + */ +class TFliteOpLogistic : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_LOGISTIC_H__ diff --git a/compiler/tflchef/tflite/src/Op/MatrixDiag.cpp b/compiler/tflchef/tflite/src/Op/MatrixDiag.cpp new file mode 100644 index 0000000..ca84c49 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/MatrixDiag.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "MatrixDiag.h" + +namespace tflchef +{ + +void TFliteOpMatrixDiag::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpMatrixDiag::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("MatrixDiag"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/MatrixDiag.h b/compiler/tflchef/tflite/src/Op/MatrixDiag.h new file mode 100644 index 0000000..4074f2c --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/MatrixDiag.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_MATRIX_DIAG_H__ +#define __TFLITE_OP_MATRIX_DIAG_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for MatrixDiag + */ +class TFliteOpMatrixDiag : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_MATRIX_DIAG_H__ diff --git a/compiler/tflchef/tflite/src/Op/MatrixSetDiag.cpp b/compiler/tflchef/tflite/src/Op/MatrixSetDiag.cpp new file mode 100644 index 0000000..97c7de4 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/MatrixSetDiag.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "MatrixSetDiag.h" + +namespace tflchef +{ + +void TFliteOpMatrixSetDiag::filler(const tflite::Operator *, TFliteImport *, + tflchef::ModelRecipe *) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpMatrixSetDiag::build(const tflite::Operator *, TFliteImport *, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("MatrixSetDiag"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/MatrixSetDiag.h b/compiler/tflchef/tflite/src/Op/MatrixSetDiag.h new file mode 100644 index 0000000..0e7ec7f --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/MatrixSetDiag.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_MATRIX_SET_DIAG_H__ +#define __TFLITE_OP_MATRIX_SET_DIAG_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for MatrixSetDiag + */ +class TFliteOpMatrixSetDiag : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_MATRIX_SET_DIAG_H__ diff --git a/compiler/tflchef/tflite/src/Op/Maximum.cpp b/compiler/tflchef/tflite/src/Op/Maximum.cpp new file mode 100644 index 0000000..fb977b6 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Maximum.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Maximum.h" + +namespace tflchef +{ + +void TFliteOpMaximum::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpMaximum::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Maximum"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Maximum.h b/compiler/tflchef/tflite/src/Op/Maximum.h new file mode 100644 index 0000000..acafec3 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Maximum.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_MAXIMUM_H__ +#define __TFLITE_OP_MAXIMUM_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for maximum + */ +class TFliteOpMaximum : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_MAXIMUM_H__ diff --git a/compiler/tflchef/tflite/src/Op/Minimum.cpp b/compiler/tflchef/tflite/src/Op/Minimum.cpp new file mode 100644 index 0000000..2bb50cb --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Minimum.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Minimum.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpMinimum::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpMinimum::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Minimum"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Minimum.h b/compiler/tflchef/tflite/src/Op/Minimum.h new file mode 100644 index 0000000..5db5b79 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Minimum.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_MINIMUM_H__ +#define __TFLITE_OP_MINIMUM_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for minimum + */ +class TFliteOpMinimum : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_MINIMUM_H__ diff --git a/compiler/tflchef/tflite/src/Op/MirrorPad.cpp b/compiler/tflchef/tflite/src/Op/MirrorPad.cpp new file mode 100644 index 0000000..c688552 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/MirrorPad.cpp @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "MirrorPad.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpMirrorPad::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // filler for second input + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[1]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); +} + +tflchef::Operation *TFliteOpMirrorPad::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + operation->set_type("MirrorPad"); + + auto op_options = operation->mutable_mirrorpad_options(); + + auto op_params = op->builtin_options_as_MirrorPadOptions(); + assert(op_params != nullptr); + + op_options->set_mode(as_tflchef_mirrorpadmode(op_params->mode())); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/MirrorPad.h b/compiler/tflchef/tflite/src/Op/MirrorPad.h new file mode 100644 index 0000000..c9acdd4 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/MirrorPad.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_MIRROR_PAD_H__ +#define __TFLITE_OP_MIRROR_PAD_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for MIRROR_PAD + */ +class TFliteOpMirrorPad : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_MIRROR_PAD_H__ diff --git a/compiler/tflchef/tflite/src/Op/Mul.cpp b/compiler/tflchef/tflite/src/Op/Mul.cpp new file mode 100644 index 0000000..9faa4ac --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Mul.cpp @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Mul.h" + +#include "Convert.h" +#include "FillerHelper.h" + +namespace tflchef +{ + +void TFliteOpMul::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Mul may have constant input + + const std::vector &inputs = as_index_vector(op->inputs()); + assert(inputs.size() == 2); + + fill_tensor_to_import(inputs[0], import); + fill_tensor_to_import(inputs[1], import); +} + +tflchef::Operation *TFliteOpMul::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Mul"); + + auto op_params = op->builtin_options_as_MulOptions(); + assert(op_params != nullptr); + + auto op_options = operation->mutable_mul_options(); + op_options->set_activation(as_tflchef_activation(op_params->fused_activation_function())); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Mul.h b/compiler/tflchef/tflite/src/Op/Mul.h new file mode 100644 index 0000000..fd009d2 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Mul.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_MUL_H__ +#define __TFLITE_OP_MUL_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for MUL + */ +class TFliteOpMul : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_MUL_H__ diff --git a/compiler/tflchef/tflite/src/Op/Neg.cpp b/compiler/tflchef/tflite/src/Op/Neg.cpp new file mode 100644 index 0000000..c691390 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Neg.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Neg.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpNeg::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpNeg::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Neg"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Neg.h b/compiler/tflchef/tflite/src/Op/Neg.h new file mode 100644 index 0000000..c77ab7e --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Neg.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_NEG_H__ +#define __TFLITE_OP_NEG_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for abs + */ +class TFliteOpNeg : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_NEG_H__ diff --git a/compiler/tflchef/tflite/src/Op/NotEqual.cpp b/compiler/tflchef/tflite/src/Op/NotEqual.cpp new file mode 100644 index 0000000..c2275db --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/NotEqual.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "NotEqual.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpNotEqual::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpNotEqual::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("NotEqual"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/NotEqual.h b/compiler/tflchef/tflite/src/Op/NotEqual.h new file mode 100644 index 0000000..b1febdc --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/NotEqual.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_NOTEQUAL_H__ +#define __TFLITE_OP_NOTEQUAL_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Not Equal + */ +class TFliteOpNotEqual : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_NOTEQUAL_H__ diff --git a/compiler/tflchef/tflite/src/Op/OneHot.cpp b/compiler/tflchef/tflite/src/Op/OneHot.cpp new file mode 100644 index 0000000..f26ed3e --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/OneHot.cpp @@ -0,0 +1,87 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "OneHot.h" +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpOneHot::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // only depth(second input) has constant on recipe cause depth value is used in shape inference. + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[1]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + + if (buffer && buffer->data()) + { + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); + } + + // on/off can be dtype of input/output. let's support INT32/FLOAT32 for now + for (int32_t index = 2; index <= 3; ++index) + { + const tflite::Tensor *tensor = import->tensors()->Get(inputs[index]); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + if (buffer && buffer->data()) + { + switch (tensor->type()) + { + case tflite::TensorType::TensorType_INT32: + { + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[index], vec); + break; + } + + case tflite::TensorType::TensorType_FLOAT32: + { + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[index], vec); + break; + } + + default: + assert(false); + break; + } + } + } +} + +tflchef::Operation *TFliteOpOneHot::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_OneHotOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("OneHot"); + + auto op_options = operation->mutable_onehot_options(); + + op_options->set_axis(op_params->axis()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/OneHot.h b/compiler/tflchef/tflite/src/Op/OneHot.h new file mode 100644 index 0000000..50bbed0 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/OneHot.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_ONEHOT_H__ +#define __TFLITE_OP_ONEHOT_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for OneHot + */ +class TFliteOpOneHot : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_ONEHOT_H__ diff --git a/compiler/tflchef/tflite/src/Op/PRelu.cpp b/compiler/tflchef/tflite/src/Op/PRelu.cpp new file mode 100644 index 0000000..8a5e83a --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/PRelu.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "PRelu.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpPRelu::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ +} + +tflchef::Operation *TFliteOpPRelu::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + operation->set_type("PRelu"); + + // PReluOptions are empty + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/PRelu.h b/compiler/tflchef/tflite/src/Op/PRelu.h new file mode 100644 index 0000000..b35c6e7 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/PRelu.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_PRELU_H__ +#define __TFLITE_OP_PRELU_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for PRelu + */ +class TFliteOpPRelu : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_PRELU_H__ diff --git a/compiler/tflchef/tflite/src/Op/Pow.cpp b/compiler/tflchef/tflite/src/Op/Pow.cpp new file mode 100644 index 0000000..fe8e8ac --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Pow.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Pow.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpPow::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ +} + +tflchef::Operation *TFliteOpPow::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + operation->set_type("Pow"); + + // PowOptions are empty + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Pow.h b/compiler/tflchef/tflite/src/Op/Pow.h new file mode 100644 index 0000000..20e8473 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Pow.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_POW_H__ +#define __TFLITE_OP_POW_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Pow + */ +class TFliteOpPow : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_POW_H__ diff --git a/compiler/tflchef/tflite/src/Op/Range.cpp b/compiler/tflchef/tflite/src/Op/Range.cpp new file mode 100644 index 0000000..2958b9c --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Range.cpp @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Range.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpRange::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // filler for all inputs + const auto &inputs = *op->inputs(); + + for (int index = 0; index < 3; ++index) + { + const tflite::Tensor *tensor = import->tensors()->Get(inputs[index]); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + if (tensor->type() == tflite::TensorType::TensorType_INT32) + { + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[index], vec); + } + else if (tensor->type() == tflite::TensorType::TensorType_FLOAT32) + { + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[index], vec); + } + else + { + assert(false && "Invalid tensor type"); + } + } +} + +tflchef::Operation *TFliteOpRange::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Range"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Range.h b/compiler/tflchef/tflite/src/Op/Range.h new file mode 100644 index 0000000..ad10dc5 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Range.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_RANGE_H__ +#define __TFLITE_OP_RANGE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for abs + */ +class TFliteOpRange : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_RANGE_H__ diff --git a/compiler/tflchef/tflite/src/Op/Rank.cpp b/compiler/tflchef/tflite/src/Op/Rank.cpp new file mode 100644 index 0000000..184c8e4 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Rank.cpp @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Rank.h" + +namespace tflchef +{ + +void TFliteOpRank::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpRank::build(const tflite::Operator *, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Rank"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Rank.h b/compiler/tflchef/tflite/src/Op/Rank.h new file mode 100644 index 0000000..003d9d3 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Rank.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_RANK_H__ +#define __TFLITE_OP_RANK_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for rank + */ +class TFliteOpRank : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_RANK_H__ diff --git a/compiler/tflchef/tflite/src/Op/ReLUN1To1.cpp b/compiler/tflchef/tflite/src/Op/ReLUN1To1.cpp new file mode 100644 index 0000000..4cc8dbd --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ReLUN1To1.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReLUN1To1.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpReLUN1To1::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpReLUN1To1::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("ReLUN1To1"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/ReLUN1To1.h b/compiler/tflchef/tflite/src/Op/ReLUN1To1.h new file mode 100644 index 0000000..0767006 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ReLUN1To1.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_RELU_N1_TO_1_H__ +#define __TFLITE_OP_RELU_N1_TO_1_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for RELU_N1_TO_1 + */ +class TFliteOpReLUN1To1 : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_RELU_N1_TO_1_H__ diff --git a/compiler/tflchef/tflite/src/Op/ReduceAny.cpp b/compiler/tflchef/tflite/src/Op/ReduceAny.cpp new file mode 100644 index 0000000..e0cc503 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ReduceAny.cpp @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReduceAny.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpReduceAny::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // filler for second input + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[1]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); +} + +tflchef::Operation *TFliteOpReduceAny::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + operation->set_type("ReduceAny"); + + auto op_params = op->builtin_options_as_ReducerOptions(); + assert(op_params != nullptr); + + auto op_options = operation->mutable_reduce_any_options(); + op_options->set_keep_dims(op_params->keep_dims()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/ReduceAny.h b/compiler/tflchef/tflite/src/Op/ReduceAny.h new file mode 100644 index 0000000..dd5e361 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ReduceAny.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_REDUCE_ANY_H__ +#define __TFLITE_OP_REDUCE_ANY_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for REDUCE_ANY + */ +class TFliteOpReduceAny : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_REDUCE_ANY_H__ diff --git a/compiler/tflchef/tflite/src/Op/ReduceMax.cpp b/compiler/tflchef/tflite/src/Op/ReduceMax.cpp new file mode 100644 index 0000000..499f585 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ReduceMax.cpp @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReduceMax.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpReduceMax::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // filler for second input + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[1]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); +} + +tflchef::Operation *TFliteOpReduceMax::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_ReducerOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("ReduceMax"); + + auto op_options = operation->mutable_reduce_max_options(); + + op_options->set_keep_dims(op_params->keep_dims()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/ReduceMax.h b/compiler/tflchef/tflite/src/Op/ReduceMax.h new file mode 100644 index 0000000..8e65cf4 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ReduceMax.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_REDUCEMAX_H__ +#define __TFLITE_OP_REDUCEMAX_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Reduce Max + */ +class TFliteOpReduceMax : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_REDUCEMAX_H__ diff --git a/compiler/tflchef/tflite/src/Op/ReduceMin.cpp b/compiler/tflchef/tflite/src/Op/ReduceMin.cpp new file mode 100644 index 0000000..09e2e13 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ReduceMin.cpp @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReduceMin.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpReduceMin::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // filler for second input + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[1]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); +} + +tflchef::Operation *TFliteOpReduceMin::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_ReducerOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("ReduceMin"); + + auto op_options = operation->mutable_reduce_min_options(); + + op_options->set_keep_dims(op_params->keep_dims()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/ReduceMin.h b/compiler/tflchef/tflite/src/Op/ReduceMin.h new file mode 100644 index 0000000..88cba6f --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ReduceMin.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_REDUCEMIN_H__ +#define __TFLITE_OP_REDUCEMIN_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Reduce Min + */ +class TFliteOpReduceMin : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_REDUCEMIN_H__ diff --git a/compiler/tflchef/tflite/src/Op/ReduceProd.cpp b/compiler/tflchef/tflite/src/Op/ReduceProd.cpp new file mode 100644 index 0000000..e2d9897 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ReduceProd.cpp @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReduceProd.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpReduceProd::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // filler for second input + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[1]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); +} + +tflchef::Operation *TFliteOpReduceProd::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + operation->set_type("ReduceProd"); + + auto op_params = op->builtin_options_as_ReducerOptions(); + assert(op_params != nullptr); + + auto op_options = operation->mutable_reduce_prod_options(); + op_options->set_keep_dims(op_params->keep_dims()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/ReduceProd.h b/compiler/tflchef/tflite/src/Op/ReduceProd.h new file mode 100644 index 0000000..e776684 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ReduceProd.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_REDUCE_PROD_H__ +#define __TFLITE_OP_REDUCE_PROD_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for REDUCE_PROD + */ +class TFliteOpReduceProd : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_REDUCE_PROD_H__ diff --git a/compiler/tflchef/tflite/src/Op/Reshape.cpp b/compiler/tflchef/tflite/src/Op/Reshape.cpp index 663ab3e..0094d5d 100644 --- a/compiler/tflchef/tflite/src/Op/Reshape.cpp +++ b/compiler/tflchef/tflite/src/Op/Reshape.cpp @@ -17,6 +17,7 @@ #include "Reshape.h" #include "Convert.h" +#include "FillerHelper.h" namespace tflchef { @@ -27,33 +28,29 @@ void TFliteOpReshape::filler(const tflite::Operator *op, TFliteImport *import, const std::vector &inputs = as_index_vector(op->inputs()); bool hasShape = (inputs.size() == 2); - assert(inputs.size() == 1 || hasShape); - if (hasShape) { - auto op_params = op->builtin_options_as_ReshapeOptions(); - std::vector new_shape = as_index_vector(op_params->new_shape()); - import->set_tensor_filler(inputs.at(1), new_shape); + fill_tensor_to_import(inputs[1], import); } } tflchef::Operation *TFliteOpReshape::build(const tflite::Operator *op, TFliteImport *import, tflchef::ModelRecipe *model_recipe) const { - auto op_params = op->builtin_options_as_ReshapeOptions(); - assert(op_params != nullptr); - auto operation = model_recipe->add_operation(); operation->set_type("Reshape"); - auto op_options = operation->mutable_reshape_options(); - - std::vector new_shape = as_index_vector(op_params->new_shape()); - - for (auto shape : new_shape) + auto op_params = op->builtin_options_as_ReshapeOptions(); + if (op_params != nullptr) { - op_options->add_new_shape(shape); + auto op_options = operation->mutable_reshape_options(); + + std::vector new_shape = as_index_vector(op_params->new_shape()); + for (auto shape : new_shape) + { + op_options->add_new_shape(shape); + } } return operation; diff --git a/compiler/tflchef/tflite/src/Op/ResizeBilinear.cpp b/compiler/tflchef/tflite/src/Op/ResizeBilinear.cpp new file mode 100644 index 0000000..0f6db1f --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ResizeBilinear.cpp @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ResizeBilinear.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpResizeBilinear::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // size buffer has filler + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[1]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + + if (buffer && buffer->data()) + { + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); + } +} + +tflchef::Operation *TFliteOpResizeBilinear::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_ResizeBilinearOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("ResizeBilinear"); + + auto op_options = operation->mutable_resize_bilinear_options(); + + op_options->set_align_corners(op_params->align_corners()); + op_options->set_half_pixel_centers(op_params->half_pixel_centers()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/ResizeBilinear.h b/compiler/tflchef/tflite/src/Op/ResizeBilinear.h new file mode 100644 index 0000000..98c49c5 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ResizeBilinear.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_RESIZE_BILINEAR_H__ +#define __TFLITE_OP_RESIZE_BILINEAR_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for ResizeBilinear + */ +class TFliteOpResizeBilinear : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_RESIZE_BILINEAR_H__ diff --git a/compiler/tflchef/tflite/src/Op/ResizeNearestNeighbor.cpp b/compiler/tflchef/tflite/src/Op/ResizeNearestNeighbor.cpp new file mode 100644 index 0000000..f3dd8fe --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ResizeNearestNeighbor.cpp @@ -0,0 +1,59 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ResizeNearestNeighbor.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpResizeNearestNeighbor::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // size buffer has filler + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[1]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + + if (buffer && buffer->data()) + { + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); + } +} + +tflchef::Operation *TFliteOpResizeNearestNeighbor::build(const tflite::Operator *op, + TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_ResizeNearestNeighborOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("ResizeNearestNeighbor"); + + auto op_options = operation->mutable_resize_nearest_neighbor_options(); + + op_options->set_align_corners(op_params->align_corners()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/ResizeNearestNeighbor.h b/compiler/tflchef/tflite/src/Op/ResizeNearestNeighbor.h new file mode 100644 index 0000000..5090bb9 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ResizeNearestNeighbor.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_RESIZE_NEAREST_NEIGHBOR_H__ +#define __TFLITE_OP_RESIZE_NEAREST_NEIGHBOR_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for ResizeNearestNeighbor + */ +class TFliteOpResizeNearestNeighbor : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_RESIZE_NEAREST_NEIGHBOR_H__ diff --git a/compiler/tflchef/tflite/src/Op/ReverseSequence.cpp b/compiler/tflchef/tflite/src/Op/ReverseSequence.cpp new file mode 100644 index 0000000..6ef6c23 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ReverseSequence.cpp @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReverseSequence.h" + +#include "Convert.h" +#include "FillerHelper.h" + +namespace tflchef +{ + +void TFliteOpReverseSequence::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *) const +{ + const std::vector &inputs = as_index_vector(op->inputs()); + assert(inputs.size() == 2); + + fill_tensor_to_import(inputs[1], import); +} + +tflchef::Operation *TFliteOpReverseSequence::build(const tflite::Operator *op, TFliteImport *, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("ReverseSequence"); + + auto op_params = op->builtin_options_as_ReverseSequenceOptions(); + + assert(op_params != nullptr); + + auto op_options = operation->mutable_reverse_sequence_options(); + + op_options->set_seq_dim(op_params->seq_dim()); + op_options->set_batch_dim(op_params->batch_dim()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/ReverseSequence.h b/compiler/tflchef/tflite/src/Op/ReverseSequence.h new file mode 100644 index 0000000..8c8c811 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ReverseSequence.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_REVERSE_SEQUENCE_H__ +#define __TFLITE_OP_REVERSE_SEQUENCE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for ReverseSequence + */ +class TFliteOpReverseSequence : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_REVERSE_SEQUENCE_H__ diff --git a/compiler/tflchef/tflite/src/Op/ReverseV2.cpp b/compiler/tflchef/tflite/src/Op/ReverseV2.cpp new file mode 100644 index 0000000..c59d975 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ReverseV2.cpp @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReverseV2.h" + +#include "Convert.h" +#include "FillerHelper.h" + +namespace tflchef +{ + +void TFliteOpReverseV2::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *) const +{ + const std::vector &inputs = as_index_vector(op->inputs()); + assert(inputs.size() == 2); + + fill_tensor_to_import(inputs[1], import); +} + +tflchef::Operation *TFliteOpReverseV2::build(const tflite::Operator *, TFliteImport *, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("ReverseV2"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/ReverseV2.h b/compiler/tflchef/tflite/src/Op/ReverseV2.h new file mode 100644 index 0000000..6a8a75e --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ReverseV2.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_REVERSEV2_H__ +#define __TFLITE_OP_REVERSEV2_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for ReverseV2 + */ +class TFliteOpReverseV2 : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_REVERSEV2_H__ diff --git a/compiler/tflchef/tflite/src/Op/Round.cpp b/compiler/tflchef/tflite/src/Op/Round.cpp new file mode 100644 index 0000000..c3f6bf6 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Round.cpp @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Round.h" + +namespace tflchef +{ + +void TFliteOpRound::filler(const tflite::Operator *, TFliteImport *, tflchef::ModelRecipe *) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpRound::build(const tflite::Operator *, TFliteImport *, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Round"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Round.h b/compiler/tflchef/tflite/src/Op/Round.h new file mode 100644 index 0000000..df0da3f --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Round.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_ROUND_H__ +#define __TFLITE_OP_ROUND_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Round + */ +class TFliteOpRound : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_ROUND_H__ diff --git a/compiler/tflchef/tflite/src/Op/ScatterNd.cpp b/compiler/tflchef/tflite/src/Op/ScatterNd.cpp new file mode 100644 index 0000000..548a09a --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ScatterNd.cpp @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ScatterNd.h" + +#include "Convert.h" +#include "FillerHelper.h" + +namespace tflchef +{ + +void TFliteOpScatterNd::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Filler for indices and shape + fill_tensor_to_import(0, import); + fill_tensor_to_import(2, import); +} + +tflchef::Operation *TFliteOpScatterNd::build(const tflite::Operator *, TFliteImport *, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("ScatterNd"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/ScatterNd.h b/compiler/tflchef/tflite/src/Op/ScatterNd.h new file mode 100644 index 0000000..76362d7 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ScatterNd.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SCATTER_ND_H__ +#define __TFLITE_OP_SCATTER_ND_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for ScatterNd + */ +class TFliteOpScatterNd : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SCATTER_ND_H__ diff --git a/compiler/tflchef/tflite/src/Op/SegmentSum.cpp b/compiler/tflchef/tflite/src/Op/SegmentSum.cpp new file mode 100644 index 0000000..a975ca4 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/SegmentSum.cpp @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SegmentSum.h" + +#include "FillerHelper.h" + +namespace tflchef +{ + +void TFliteOpSegmentSum::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Filler for indices and shape + fill_tensor_to_import(1, import); +} + +tflchef::Operation *TFliteOpSegmentSum::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("SegmentSum"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/SegmentSum.h b/compiler/tflchef/tflite/src/Op/SegmentSum.h new file mode 100644 index 0000000..d20e63b --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/SegmentSum.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SEGMENT_SUM_H__ +#define __TFLITE_OP_SEGMENT_SUM_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for SEGMENT_SUM + */ +class TFliteOpSegmentSum : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SEGMENT_SUM_H__ diff --git a/compiler/tflchef/tflite/src/Op/Select.cpp b/compiler/tflchef/tflite/src/Op/Select.cpp new file mode 100644 index 0000000..741ffb8 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Select.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Select.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpSelect::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpSelect::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Select"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Select.h b/compiler/tflchef/tflite/src/Op/Select.h new file mode 100644 index 0000000..bf8e57d --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Select.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SELECT_H__ +#define __TFLITE_OP_SELECT_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for SELECT + */ +class TFliteOpSelect : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SELECT_H__ diff --git a/compiler/tflchef/tflite/src/Op/SelectV2.cpp b/compiler/tflchef/tflite/src/Op/SelectV2.cpp new file mode 100644 index 0000000..0ddabb4 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/SelectV2.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SelectV2.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpSelectV2::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpSelectV2::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("SelectV2"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/SelectV2.h b/compiler/tflchef/tflite/src/Op/SelectV2.h new file mode 100644 index 0000000..ff03341 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/SelectV2.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SELECT_V2_H__ +#define __TFLITE_OP_SELECT_V2_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for SELECT + */ +class TFliteOpSelectV2 : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SELECT_V2_H__ diff --git a/compiler/tflchef/tflite/src/Op/Shape.cpp b/compiler/tflchef/tflite/src/Op/Shape.cpp new file mode 100644 index 0000000..d6e490d --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Shape.cpp @@ -0,0 +1,45 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Shape.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpShape::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpShape::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + operation->set_type("Shape"); + + auto op_params = op->builtin_options_as_ShapeOptions(); + assert(op_params != nullptr); + + auto op_options = operation->mutable_shape_options(); + op_options->set_out_type(as_tflchef_type(op_params->out_type())); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Shape.h b/compiler/tflchef/tflite/src/Op/Shape.h new file mode 100644 index 0000000..ebe1bef --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Shape.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SHAPE_H__ +#define __TFLITE_OP_SHAPE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for SHAPE + */ +class TFliteOpShape : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SHAPE_H__ diff --git a/compiler/tflchef/tflite/src/Op/Sin.cpp b/compiler/tflchef/tflite/src/Op/Sin.cpp new file mode 100644 index 0000000..8c063f4 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Sin.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Sin.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpSin::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpSin::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Sin"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Sin.h b/compiler/tflchef/tflite/src/Op/Sin.h new file mode 100644 index 0000000..51eabce --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Sin.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SIN_H__ +#define __TFLITE_OP_SIN_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Sin + */ +class TFliteOpSin : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SIN_H__ diff --git a/compiler/tflchef/tflite/src/Op/Slice.cpp b/compiler/tflchef/tflite/src/Op/Slice.cpp new file mode 100644 index 0000000..f0c44da --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Slice.cpp @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Slice.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpSlice::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + const std::vector &inputs = as_index_vector(op->inputs()); + + // for begin and size + for (int32_t index = 1; index <= 2; ++index) + { + const tflite::Tensor *tensor = import->tensors()->Get(inputs[index]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[index], vec); + } +} + +tflchef::Operation *TFliteOpSlice::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Slice"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Slice.h b/compiler/tflchef/tflite/src/Op/Slice.h new file mode 100644 index 0000000..6ca6724 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Slice.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SLICE_H__ +#define __TFLITE_OP_SLICE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for SLICE + */ +class TFliteOpSlice : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SLICE_H__ diff --git a/compiler/tflchef/tflite/src/Op/SpaceToBatchND.cpp b/compiler/tflchef/tflite/src/Op/SpaceToBatchND.cpp new file mode 100644 index 0000000..9de0775 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/SpaceToBatchND.cpp @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SpaceToBatchND.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpSpaceToBatchND::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // filler for second, third input + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[1]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); + + tensor = import->tensors()->Get(inputs[2]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + buffer = import->buffers()->Get(tensor->buffer()); + vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[2], vec); +} + +tflchef::Operation *TFliteOpSpaceToBatchND::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("SpaceToBatchND"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/SpaceToBatchND.h b/compiler/tflchef/tflite/src/Op/SpaceToBatchND.h new file mode 100644 index 0000000..9d7bc44 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/SpaceToBatchND.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SPACETOBATCHND_H__ +#define __TFLITE_OP_SPACETOBATCHND_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for SpaceToBatchND + */ +class TFliteOpSpaceToBatchND : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SPACETOBATCHND_H__ diff --git a/compiler/tflchef/tflite/src/Op/SpaceToDepth.cpp b/compiler/tflchef/tflite/src/Op/SpaceToDepth.cpp new file mode 100644 index 0000000..e5718b5 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/SpaceToDepth.cpp @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SpaceToDepth.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpSpaceToDepth::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpSpaceToDepth::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("SpaceToDepth"); + + auto op_params = op->builtin_options_as_SpaceToDepthOptions(); + assert(op_params != nullptr); + + auto op_options = operation->mutable_space_to_depth_options(); + + op_options->set_block_size(op_params->block_size()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/SpaceToDepth.h b/compiler/tflchef/tflite/src/Op/SpaceToDepth.h new file mode 100644 index 0000000..784ad94 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/SpaceToDepth.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SPACETODEPTH_H__ +#define __TFLITE_OP_SPACETODEPTH_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for SpaceToDepth + */ +class TFliteOpSpaceToDepth : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SPACETODEPTH_H__ diff --git a/compiler/tflchef/tflite/src/Op/SparseToDense.cpp b/compiler/tflchef/tflite/src/Op/SparseToDense.cpp new file mode 100644 index 0000000..9e4f0a0 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/SparseToDense.cpp @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SparseToDense.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpSparseToDense::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // filler for Shape + const auto &inputs = *op->inputs(); + + const tflite::Tensor *output_shape_tensor = import->tensors()->Get(inputs[1]); + assert(output_shape_tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(output_shape_tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); +} + +tflchef::Operation *TFliteOpSparseToDense::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_SparseToDenseOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("SparseToDense"); + + auto op_options = operation->mutable_sparse_to_dense_options(); + + op_options->set_validate_indices(op_params->validate_indices()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/SparseToDense.h b/compiler/tflchef/tflite/src/Op/SparseToDense.h new file mode 100644 index 0000000..5ffe478 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/SparseToDense.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SPARSETODENSE_H__ +#define __TFLITE_OP_SPARSETODENSE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for SparseToDense + */ +class TFliteOpSparseToDense : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SPARSETODENSE_H__ diff --git a/compiler/tflchef/tflite/src/Op/Split.cpp b/compiler/tflchef/tflite/src/Op/Split.cpp new file mode 100644 index 0000000..49f9aa2 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Split.cpp @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Split.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpSplit::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + const auto &inputs = *op->inputs(); + + // for input split_dim + // NOTE unlike other Ops, Split input 0 is split_dim + const tflite::Tensor *tensor = import->tensors()->Get(inputs[0]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[0], vec); +} + +tflchef::Operation *TFliteOpSplit::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Split"); + + auto op_options = operation->mutable_split_options(); + + auto op_params = op->builtin_options_as_SplitOptions(); + assert(op_params != nullptr); + + op_options->set_num_splits(op_params->num_splits()); + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Split.h b/compiler/tflchef/tflite/src/Op/Split.h new file mode 100644 index 0000000..af247a1 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Split.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SPLIT_H__ +#define __TFLITE_OP_SPLIT_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for SPLIT + */ +class TFliteOpSplit : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SPLIT_H__ diff --git a/compiler/tflchef/tflite/src/Op/SplitV.cpp b/compiler/tflchef/tflite/src/Op/SplitV.cpp new file mode 100644 index 0000000..18035e6 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/SplitV.cpp @@ -0,0 +1,56 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SplitV.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpSplitV::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + const auto &inputs = *op->inputs(); + + // for input "size_splits" and "split_dim" + for (int32_t idx = 1; idx <= 2; idx++) + { + const tflite::Tensor *tensor = import->tensors()->Get(inputs[idx]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[idx], vec); + } +} + +tflchef::Operation *TFliteOpSplitV::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("SplitV"); + + auto op_options = operation->mutable_split_v_options(); + + auto op_params = op->builtin_options_as_SplitVOptions(); + assert(op_params != nullptr); + + op_options->set_num_splits(op_params->num_splits()); + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/SplitV.h b/compiler/tflchef/tflite/src/Op/SplitV.h new file mode 100644 index 0000000..3f715b5 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/SplitV.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SPLIT_V_H__ +#define __TFLITE_OP_SPLIT_V_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for SPLIT_V + */ +class TFliteOpSplitV : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SPLIT_V_H__ diff --git a/compiler/tflchef/tflite/src/Op/Square.cpp b/compiler/tflchef/tflite/src/Op/Square.cpp new file mode 100644 index 0000000..d380328 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Square.cpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Square.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpSquare::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpSquare::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Square"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Square.h b/compiler/tflchef/tflite/src/Op/Square.h new file mode 100644 index 0000000..9c008fe --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Square.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SQUARE_H__ +#define __TFLITE_OP_SQUARE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Square + */ +class TFliteOpSquare : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SQUARE_H__ diff --git a/compiler/tflchef/tflite/src/Op/SquaredDifference.cpp b/compiler/tflchef/tflite/src/Op/SquaredDifference.cpp new file mode 100644 index 0000000..1ee536e --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/SquaredDifference.cpp @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SquaredDifference.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpSquaredDifference::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpSquaredDifference::build(const tflite::Operator *op, + TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("SquaredDifference"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/SquaredDifference.h b/compiler/tflchef/tflite/src/Op/SquaredDifference.h new file mode 100644 index 0000000..58c2ed4 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/SquaredDifference.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SQUAREDDIFFERENCE_H__ +#define __TFLITE_OP_SQUAREDDIFFERENCE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for SquaredDifference + */ +class TFliteOpSquaredDifference : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SQUAREDDIFFERENCE_H__ diff --git a/compiler/tflchef/tflite/src/Op/Squeeze.cpp b/compiler/tflchef/tflite/src/Op/Squeeze.cpp new file mode 100644 index 0000000..7983fc6 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Squeeze.cpp @@ -0,0 +1,52 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Squeeze.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpSqueeze::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpSqueeze::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_SqueezeOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("Squeeze"); + + auto op_options = operation->mutable_squeeze_options(); + + std::vector squeeze_dims = as_index_vector(op_params->squeeze_dims()); + + for (auto dim : squeeze_dims) + { + op_options->add_squeeze_dim(dim); + } + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Squeeze.h b/compiler/tflchef/tflite/src/Op/Squeeze.h new file mode 100644 index 0000000..b6c89f7 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Squeeze.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SQUEEZE_H__ +#define __TFLITE_OP_SQUEEZE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Squeeze + */ +class TFliteOpSqueeze : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SQUEEZE_H__ diff --git a/compiler/tflchef/tflite/src/Op/StridedSlice.cpp b/compiler/tflchef/tflite/src/Op/StridedSlice.cpp new file mode 100644 index 0000000..c770236 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/StridedSlice.cpp @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "StridedSlice.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpStridedSlice::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + const std::vector &inputs = as_index_vector(op->inputs()); + + // for begin, end and strides + for (int32_t index = 1; index <= 3; ++index) + { + const tflite::Tensor *tensor = import->tensors()->Get(inputs[index]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[index], vec); + } +} + +tflchef::Operation *TFliteOpStridedSlice::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_StridedSliceOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("StridedSlice"); + + auto op_options = operation->mutable_strided_slice_options(); + + op_options->set_begin_mask(op_params->begin_mask()); + op_options->set_end_mask(op_params->end_mask()); + op_options->set_ellipsis_mask(op_params->ellipsis_mask()); + op_options->set_new_axis_mask(op_params->new_axis_mask()); + op_options->set_shrink_axis_mask(op_params->shrink_axis_mask()); + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/StridedSlice.h b/compiler/tflchef/tflite/src/Op/StridedSlice.h new file mode 100644 index 0000000..98054b9 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/StridedSlice.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_STRIDEDSLICE_H__ +#define __TFLITE_OP_STRIDEDSLICE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for StridedSlice + */ +class TFliteOpStridedSlice : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_STRIDEDSLICE_H__ diff --git a/compiler/tflchef/tflite/src/Op/Sub.cpp b/compiler/tflchef/tflite/src/Op/Sub.cpp index db77fdd..0a08bbf 100644 --- a/compiler/tflchef/tflite/src/Op/Sub.cpp +++ b/compiler/tflchef/tflite/src/Op/Sub.cpp @@ -17,6 +17,7 @@ #include "Sub.h" #include "Convert.h" +#include "FillerHelper.h" namespace tflchef { @@ -24,7 +25,13 @@ namespace tflchef void TFliteOpSub::filler(const tflite::Operator *op, TFliteImport *import, tflchef::ModelRecipe *model_recipe) const { - // Nothing to do with filler + // Sub may have constant input + + const std::vector &inputs = as_index_vector(op->inputs()); + assert(inputs.size() == 2); + + fill_tensor_to_import(inputs[0], import); + fill_tensor_to_import(inputs[1], import); } tflchef::Operation *TFliteOpSub::build(const tflite::Operator *op, TFliteImport *import, diff --git a/compiler/tflchef/tflite/src/Op/Sum.cpp b/compiler/tflchef/tflite/src/Op/Sum.cpp new file mode 100644 index 0000000..9f3133e --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Sum.cpp @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Sum.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpSum::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // filler for second input + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[1]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); +} + +tflchef::Operation *TFliteOpSum::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_ReducerOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("Sum"); + + auto op_options = operation->mutable_sum_options(); + + op_options->set_keep_dims(op_params->keep_dims()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Sum.h b/compiler/tflchef/tflite/src/Op/Sum.h new file mode 100644 index 0000000..38eeb08 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Sum.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_SUM_H__ +#define __TFLITE_OP_SUM_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for sum + */ +class TFliteOpSum : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_SUM_H__ diff --git a/compiler/tflchef/tflite/src/Op/Tile.cpp b/compiler/tflchef/tflite/src/Op/Tile.cpp new file mode 100644 index 0000000..14e6513 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Tile.cpp @@ -0,0 +1,48 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Tile.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpTile::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // filler for second input + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[1]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32 || + tensor->type() == tflite::TensorType::TensorType_INT64); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); +} + +tflchef::Operation *TFliteOpTile::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Tile"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Tile.h b/compiler/tflchef/tflite/src/Op/Tile.h new file mode 100644 index 0000000..640f52a --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Tile.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_TILE_H__ +#define __TFLITE_OP_TILE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Tile + */ +class TFliteOpTile : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_TILE_H__ diff --git a/compiler/tflchef/tflite/src/Op/TopKV2.cpp b/compiler/tflchef/tflite/src/Op/TopKV2.cpp new file mode 100644 index 0000000..461456a --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/TopKV2.cpp @@ -0,0 +1,50 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "TopKV2.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpTopKV2::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // filler for second input + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[1]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32 || + tensor->type() == tflite::TensorType::TensorType_INT64); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[1], vec); +} + +tflchef::Operation *TFliteOpTopKV2::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("TopKV2"); + + // NOTE there is `sorted` attribute in TF but it's always true for TFlite + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/TopKV2.h b/compiler/tflchef/tflite/src/Op/TopKV2.h new file mode 100644 index 0000000..b2b74cc --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/TopKV2.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_TOPK_V2_H__ +#define __TFLITE_OP_TOPK_V2_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for TOPK_V2 + */ +class TFliteOpTopKV2 : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_TOPK_V2_H__ diff --git a/compiler/tflchef/tflite/src/Op/Transpose.cpp b/compiler/tflchef/tflite/src/Op/Transpose.cpp index ae97a19..a997bb0 100644 --- a/compiler/tflchef/tflite/src/Op/Transpose.cpp +++ b/compiler/tflchef/tflite/src/Op/Transpose.cpp @@ -36,16 +36,11 @@ void TFliteOpTranspose::filler(const tflite::Operator *op, TFliteImport *import, tflchef::Operation *TFliteOpTranspose::build(const tflite::Operator *op, TFliteImport *import, tflchef::ModelRecipe *model_recipe) const { - auto op_params = op->builtin_options_as(); - assert(op_params != nullptr); - (void)op_params; - auto operation = model_recipe->add_operation(); operation->set_type("Transpose"); - auto op_options = operation->mutable_transpose_options(); - (void)op_options; + // No options for Transpose return operation; } diff --git a/compiler/tflchef/tflite/src/Op/TransposeConv.cpp b/compiler/tflchef/tflite/src/Op/TransposeConv.cpp new file mode 100644 index 0000000..7e772b9 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/TransposeConv.cpp @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "TransposeConv.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpTransposeConv::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + const auto &inputs = *op->inputs(); + + const tflite::Tensor *tensor = import->tensors()->Get(inputs[0]); + assert(tensor->type() == tflite::TensorType::TensorType_INT32); + const tflite::Buffer *buffer = import->buffers()->Get(tensor->buffer()); + + if (buffer && buffer->data()) + { + auto vec = extract_buffer(buffer); + import->set_tensor_filler(inputs[0], vec); + } +} + +tflchef::Operation *TFliteOpTransposeConv::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_TransposeConvOptions(); + + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("TransposeConv"); + + auto op_options = operation->mutable_transpose_conv_options(); + + op_options->set_stride_h(op_params->stride_h()); + op_options->set_stride_w(op_params->stride_w()); + op_options->set_padding(as_tflchef_padding(op_params->padding())); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/TransposeConv.h b/compiler/tflchef/tflite/src/Op/TransposeConv.h new file mode 100644 index 0000000..c79cdab --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/TransposeConv.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_TRANSPOSE_CONV_H__ +#define __TFLITE_OP_TRANSPOSE_CONV_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for TransposeConv + */ +class TFliteOpTransposeConv : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_TRANSPOSE_CONV_H__ diff --git a/compiler/tflchef/tflite/src/Op/Unique.cpp b/compiler/tflchef/tflite/src/Op/Unique.cpp new file mode 100644 index 0000000..e3f77f4 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Unique.cpp @@ -0,0 +1,47 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Unique.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpUnique::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpUnique::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto op_params = op->builtin_options_as_UniqueOptions(); + assert(op_params != nullptr); + + auto operation = model_recipe->add_operation(); + + operation->set_type("Unique"); + + auto op_options = operation->mutable_unique_options(); + + op_options->set_idx_out_type(as_tflchef_type(op_params->idx_out_type())); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Unique.h b/compiler/tflchef/tflite/src/Op/Unique.h new file mode 100644 index 0000000..fae037c --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Unique.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_UNIQUE_H__ +#define __TFLITE_OP_UNIQUE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Unique + */ +class TFliteOpUnique : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_UNIQUE_H__ diff --git a/compiler/tflchef/tflite/src/Op/Unpack.cpp b/compiler/tflchef/tflite/src/Op/Unpack.cpp new file mode 100644 index 0000000..a51ef84 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Unpack.cpp @@ -0,0 +1,45 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Unpack.h" + +namespace tflchef +{ + +void TFliteOpUnpack::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with filler +} + +tflchef::Operation *TFliteOpUnpack::build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + + operation->set_type("Unpack"); + + auto op_params = op->builtin_options_as_UnpackOptions(); + assert(op_params != nullptr); + + auto op_options = operation->mutable_unpack_options(); + op_options->set_num(op_params->num()); + op_options->set_axis(op_params->axis()); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Unpack.h b/compiler/tflchef/tflite/src/Op/Unpack.h new file mode 100644 index 0000000..1036bdc --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Unpack.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_UNPACK_H__ +#define __TFLITE_OP_UNPACK_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Unpack + */ +class TFliteOpUnpack : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_UNPACK_H__ diff --git a/compiler/tflchef/tflite/src/Op/Where.cpp b/compiler/tflchef/tflite/src/Op/Where.cpp new file mode 100644 index 0000000..e42de37 --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Where.cpp @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "Where.h" + +namespace tflchef +{ + +void TFliteOpWhere::filler(const tflite::Operator *, TFliteImport *, tflchef::ModelRecipe *) const +{ + // Nothing to do with fillers here +} + +tflchef::Operation *TFliteOpWhere::build(const tflite::Operator *, TFliteImport *, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + operation->set_type("Where"); + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/Where.h b/compiler/tflchef/tflite/src/Op/Where.h new file mode 100644 index 0000000..00cdc4b --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/Where.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_WHERE_H__ +#define __TFLITE_OP_WHERE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for Where + */ +class TFliteOpWhere : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_WHERE_H__ diff --git a/compiler/tflchef/tflite/src/Op/ZerosLike.cpp b/compiler/tflchef/tflite/src/Op/ZerosLike.cpp new file mode 100644 index 0000000..a56b6bd --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ZerosLike.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ZerosLike.h" + +#include "Convert.h" + +namespace tflchef +{ + +void TFliteOpZerosLike::filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const +{ + // Nothing to do with fillers here +} + +tflchef::Operation *TFliteOpZerosLike::build(const tflite::Operator *op, TFliteImport *, + tflchef::ModelRecipe *model_recipe) const +{ + auto operation = model_recipe->add_operation(); + operation->set_type("ZerosLike"); + + auto op_options = operation->mutable_zeros_like_options(); + (void)op_options; + + return operation; +} + +} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/Op/ZerosLike.h b/compiler/tflchef/tflite/src/Op/ZerosLike.h new file mode 100644 index 0000000..163c1fa --- /dev/null +++ b/compiler/tflchef/tflite/src/Op/ZerosLike.h @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __TFLITE_OP_ZEROS_LIKE_H__ +#define __TFLITE_OP_ZEROS_LIKE_H__ + +#include "TFliteOpChef.h" + +namespace tflchef +{ + +/** + * @brief tflchef operator builder for ZerosLike + */ +class TFliteOpZerosLike : public TFliteOpChef +{ +public: + void filler(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; + tflchef::Operation *build(const tflite::Operator *op, TFliteImport *import, + tflchef::ModelRecipe *model_recipe) const override; +}; + +} // namespace tflchef + +#endif // __TFLITE_OP_ZEROS_LIKE_H__ diff --git a/compiler/tflchef/tflite/src/RawModelLoader.cpp b/compiler/tflchef/tflite/src/RawModelLoader.cpp deleted file mode 100644 index e9ef8ec..0000000 --- a/compiler/tflchef/tflite/src/RawModelLoader.cpp +++ /dev/null @@ -1,94 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include - -#include - -#include -#include -#include -#include - -namespace -{ - -class MemoryMappedRawModel final : public tflchef::RawModel -{ -public: - /** - * @require fd and data SHOULD be valid - */ - explicit MemoryMappedRawModel(int fd, void *data, size_t size) : _fd{fd}, _data{data}, _size{size} - { - // DO NOTHING - } - -public: - ~MemoryMappedRawModel() - { - munmap(_data, _size); - close(_fd); - } - -public: - MemoryMappedRawModel(const MemoryMappedRawModel &) = delete; - MemoryMappedRawModel(MemoryMappedRawModel &&) = delete; - -public: - const ::tflite::Model *model(void) const override { return ::tflite::GetModel(_data); } - -private: - int _fd = -1; - void *_data = nullptr; - size_t _size = 0; -}; - -} // namespace - -namespace tflchef -{ - -std::unique_ptr load_tflite(const std::string &path) -{ - cwrap::Fildes fildes{open(path.c_str(), O_RDONLY)}; - - if (fildes.get() == -1) - { - // Return nullptr on open failure - return nullptr; - } - - struct stat st; - if (fstat(fildes.get(), &st) == -1) - { - // Return nullptr on fstat failure - return nullptr; - } - - auto size = st.st_size; - auto data = mmap(nullptr, size, PROT_READ, MAP_SHARED, fildes.get(), 0); - - if (data == MAP_FAILED) - { - // Return nullptr on mmap failure - return nullptr; - } - - return std::unique_ptr{new MemoryMappedRawModel(fildes.release(), data, size)}; -} - -} // namespace tflchef diff --git a/compiler/tflchef/tflite/src/RecipeChef.cpp b/compiler/tflchef/tflite/src/RecipeChef.cpp index 407006b..db62d0e 100644 --- a/compiler/tflchef/tflite/src/RecipeChef.cpp +++ b/compiler/tflchef/tflite/src/RecipeChef.cpp @@ -35,9 +35,16 @@ void set_inputs(TFliteImport *import, tflchef::Operation *operation, const tflit for (auto input : inputs) { - auto tensor = tensors->Get(input); - std::string name = tensor_name(tensor); - operation->add_input(name); + if (input == -1) + { + operation->add_input(""); + } + else + { + auto tensor = tensors->Get(input); + std::string name = tensor_name(tensor); + operation->add_input(name); + } } } @@ -104,11 +111,14 @@ std::unique_ptr generate_recipe(const tflite::Model *model) operand->set_name(tensor_name(tensor)); operand->set_type(as_tflchef_type(tensor->type())); - std::vector dims = as_index_vector(tensor->shape()); - ::tflchef::TensorShape *shape = operand->mutable_shape(); - for (auto dim : dims) + if (tensor->shape()) { - shape->add_dim(dim); + std::vector dims = as_index_vector(tensor->shape()); + ::tflchef::TensorShape *shape = operand->mutable_shape(); + for (auto dim : dims) + { + shape->add_dim(dim); + } } // filler for weights, bias and so on diff --git a/compiler/tflchef/tflite/src/TFliteImport.h b/compiler/tflchef/tflite/src/TFliteImport.h index fa81964..5b46f45 100644 --- a/compiler/tflchef/tflite/src/TFliteImport.h +++ b/compiler/tflchef/tflite/src/TFliteImport.h @@ -121,18 +121,18 @@ public: } private: - const TFliteSubGraphs_t *_subgraphs; - const TFliteBuffers_t *_buffers; - const TFliteTensors_t *_tensors; - const TFliteOperators_t *_operators; - - std::vector _op_codes; - std::vector _inputs; - std::vector _outputs; - - std::map _tensor_filler; - std::map> _tensor_filler_vint32; - std::map> _tensor_filler_vfloat; + const TFliteSubGraphs_t *_subgraphs{nullptr}; + const TFliteBuffers_t *_buffers{nullptr}; + const TFliteTensors_t *_tensors{nullptr}; + const TFliteOperators_t *_operators{nullptr}; + + std::vector _op_codes{}; + std::vector _inputs{}; + std::vector _outputs{}; + + std::map _tensor_filler{}; + std::map> _tensor_filler_vint32{}; + std::map> _tensor_filler_vfloat{}; }; } // namespace tflchef diff --git a/compiler/tflchef/tflite/src/TFliteOpChefs.h b/compiler/tflchef/tflite/src/TFliteOpChefs.h index 685d686..ad52af1 100644 --- a/compiler/tflchef/tflite/src/TFliteOpChefs.h +++ b/compiler/tflchef/tflite/src/TFliteOpChefs.h @@ -20,32 +20,104 @@ // In alphabet order #include "Op/Abs.h" #include "Op/Add.h" +#include "Op/AddN.h" #include "Op/ArgMax.h" +#include "Op/ArgMin.h" #include "Op/AveragePool2D.h" +#include "Op/BatchMatMul.h" #include "Op/BatchToSpaceND.h" +#include "Op/Cast.h" +#include "Op/Ceil.h" #include "Op/Concatenation.h" #include "Op/Conv2D.h" #include "Op/Cos.h" +#include "Op/DepthToSpace.h" #include "Op/DepthwiseConv2D.h" #include "Op/Div.h" +#include "Op/ELU.h" #include "Op/Equal.h" #include "Op/Exp.h" +#include "Op/ExpandDims.h" +#include "Op/Fill.h" +#include "Op/Floor.h" #include "Op/FloorDiv.h" +#include "Op/FloorMod.h" #include "Op/FullyConnected.h" +#include "Op/Gather.h" +#include "Op/GatherNd.h" +#include "Op/Greater.h" +#include "Op/GreaterEqual.h" +#include "Op/L2Normalize.h" +#include "Op/L2Pool2D.h" +#include "Op/LeakyRelu.h" +#include "Op/Less.h" +#include "Op/LessEqual.h" +#include "Op/LocalResponseNormalization.h" +#include "Op/Log.h" +#include "Op/LogicalAnd.h" #include "Op/LogicalNot.h" #include "Op/LogicalOr.h" +#include "Op/Logistic.h" +#include "Op/LogSoftmax.h" +#include "Op/MatrixDiag.h" +#include "Op/MatrixSetDiag.h" +#include "Op/Maximum.h" #include "Op/MaxPool2D.h" #include "Op/Mean.h" +#include "Op/Minimum.h" +#include "Op/MirrorPad.h" +#include "Op/Mul.h" +#include "Op/Neg.h" +#include "Op/NotEqual.h" +#include "Op/OneHot.h" #include "Op/Pack.h" #include "Op/Pad.h" +#include "Op/Pow.h" +#include "Op/PRelu.h" +#include "Op/Range.h" +#include "Op/Rank.h" +#include "Op/ReduceAny.h" +#include "Op/ReduceMax.h" +#include "Op/ReduceMin.h" +#include "Op/ReduceProd.h" #include "Op/ReLU.h" #include "Op/ReLU6.h" +#include "Op/ReLUN1To1.h" #include "Op/Reshape.h" +#include "Op/ResizeBilinear.h" +#include "Op/ResizeNearestNeighbor.h" +#include "Op/ReverseSequence.h" +#include "Op/ReverseV2.h" +#include "Op/Round.h" #include "Op/Rsqrt.h" +#include "Op/ScatterNd.h" +#include "Op/SegmentSum.h" +#include "Op/Select.h" +#include "Op/SelectV2.h" +#include "Op/Shape.h" +#include "Op/Sin.h" +#include "Op/Slice.h" #include "Op/Softmax.h" +#include "Op/SpaceToBatchND.h" +#include "Op/SpaceToDepth.h" +#include "Op/SparseToDense.h" +#include "Op/Split.h" +#include "Op/SplitV.h" #include "Op/Sqrt.h" +#include "Op/Square.h" +#include "Op/SquaredDifference.h" +#include "Op/Squeeze.h" +#include "Op/StridedSlice.h" #include "Op/Sub.h" +#include "Op/Sum.h" #include "Op/Tanh.h" +#include "Op/Tile.h" +#include "Op/TopKV2.h" #include "Op/Transpose.h" +#include "Op/TransposeConv.h" +#include "Op/Unique.h" +#include "Op/Unpack.h" +#include "Op/Where.h" +#include "Op/ZerosLike.h" #endif // __TFLITE_OP_CHEFS_H__ diff --git a/compiler/tflchef/tflite/src/TFliteOpRegistry.h b/compiler/tflchef/tflite/src/TFliteOpRegistry.h index f0aed21..0a44b3f 100644 --- a/compiler/tflchef/tflite/src/TFliteOpRegistry.h +++ b/compiler/tflchef/tflite/src/TFliteOpRegistry.h @@ -57,33 +57,105 @@ private: REG_TFL_OP(ABS, TFliteOpAbs); REG_TFL_OP(ADD, TFliteOpAdd); + REG_TFL_OP(ADD_N, TFliteOpAddN); REG_TFL_OP(ARG_MAX, TFliteOpArgMax); + REG_TFL_OP(ARG_MIN, TFliteOpArgMin); REG_TFL_OP(AVERAGE_POOL_2D, TFliteOpAveragePool2D); + REG_TFL_OP(BATCH_MATMUL, TFliteOpBatchMatMul); REG_TFL_OP(BATCH_TO_SPACE_ND, TFliteOpBatchToSpaceND); + REG_TFL_OP(CAST, TFliteOpCast); + REG_TFL_OP(CEIL, TFliteOpCeil); REG_TFL_OP(CONCATENATION, TFliteOpConcatenation); REG_TFL_OP(CONV_2D, TFliteOpConv2D); REG_TFL_OP(COS, TFliteOpCos); + REG_TFL_OP(DEPTH_TO_SPACE, TFliteOpDepthToSpace); REG_TFL_OP(DEPTHWISE_CONV_2D, TFliteOpDepthwiseConv2D); REG_TFL_OP(DIV, TFliteOpDiv); + REG_TFL_OP(ELU, TFliteOpELU); REG_TFL_OP(EQUAL, TFliteOpEqual); REG_TFL_OP(EXP, TFliteOpExp); + REG_TFL_OP(EXPAND_DIMS, TFliteOpExpandDims); + REG_TFL_OP(FILL, TFliteOpFill); + REG_TFL_OP(FLOOR, TFliteOpFloor); REG_TFL_OP(FLOOR_DIV, TFliteOpFloorDiv); + REG_TFL_OP(FLOOR_MOD, TFliteOpFloorMod); REG_TFL_OP(FULLY_CONNECTED, TFliteOpFullyConnected); + REG_TFL_OP(GATHER, TFliteOpGather); + REG_TFL_OP(GATHER_ND, TFliteOpGatherNd); + REG_TFL_OP(GREATER, TFliteOpGreater); + REG_TFL_OP(GREATER_EQUAL, TFliteOpGreaterEqual); + REG_TFL_OP(L2_NORMALIZATION, TFliteOpL2Normalize); + REG_TFL_OP(L2_POOL_2D, TFliteOpL2Pool2D); + REG_TFL_OP(LEAKY_RELU, TFliteOpLeakyRelu); + REG_TFL_OP(LESS, TFliteOpLess); + REG_TFL_OP(LESS_EQUAL, TFliteOpLessEqual); + REG_TFL_OP(LOCAL_RESPONSE_NORMALIZATION, TFliteOpLocalResponseNormalization); + REG_TFL_OP(LOG, TFliteOpLog); + REG_TFL_OP(LOGICAL_AND, TFliteOpLogicalAnd); REG_TFL_OP(LOGICAL_NOT, TFliteOpLogicalNot); REG_TFL_OP(LOGICAL_OR, TFliteOpLogicalOr); + REG_TFL_OP(LOGISTIC, TFliteOpLogistic); + REG_TFL_OP(LOG_SOFTMAX, TFliteOpLogSoftmax); + REG_TFL_OP(MATRIX_DIAG, TFliteOpMatrixDiag); REG_TFL_OP(MAX_POOL_2D, TFliteOpMaxPool2D); + REG_TFL_OP(MATRIX_SET_DIAG, TFliteOpMatrixSetDiag); + REG_TFL_OP(MAXIMUM, TFliteOpMaximum); REG_TFL_OP(MEAN, TFliteOpMean); + REG_TFL_OP(MINIMUM, TFliteOpMinimum); + REG_TFL_OP(MIRROR_PAD, TFliteOpMirrorPad); + REG_TFL_OP(MUL, TFliteOpMul); + REG_TFL_OP(NEG, TFliteOpNeg); + REG_TFL_OP(NOT_EQUAL, TFliteOpNotEqual); + REG_TFL_OP(ONE_HOT, TFliteOpOneHot); REG_TFL_OP(PACK, TFliteOpPack); REG_TFL_OP(PAD, TFliteOpPad); + REG_TFL_OP(POW, TFliteOpPow); + REG_TFL_OP(PRELU, TFliteOpPRelu); + REG_TFL_OP(RANGE, TFliteOpRange); + REG_TFL_OP(RANK, TFliteOpRank); + REG_TFL_OP(REDUCE_ANY, TFliteOpReduceAny); + REG_TFL_OP(REDUCE_MAX, TFliteOpReduceMax); + REG_TFL_OP(REDUCE_MIN, TFliteOpReduceMin); + REG_TFL_OP(REDUCE_PROD, TFliteOpReduceProd); REG_TFL_OP(RELU, TFliteOpReLU); REG_TFL_OP(RELU6, TFliteOpReLU6); + REG_TFL_OP(RELU_N1_TO_1, TFliteOpReLUN1To1); REG_TFL_OP(RESHAPE, TFliteOpReshape); + REG_TFL_OP(RESIZE_BILINEAR, TFliteOpResizeBilinear); + REG_TFL_OP(RESIZE_NEAREST_NEIGHBOR, TFliteOpResizeNearestNeighbor); + REG_TFL_OP(REVERSE_SEQUENCE, TFliteOpReverseSequence); + REG_TFL_OP(REVERSE_V2, TFliteOpReverseV2); + REG_TFL_OP(ROUND, TFliteOpRound); REG_TFL_OP(RSQRT, TFliteOpRsqrt); + REG_TFL_OP(SCATTER_ND, TFliteOpScatterNd); + REG_TFL_OP(SEGMENT_SUM, TFliteOpSegmentSum); + REG_TFL_OP(SELECT, TFliteOpSelect); + REG_TFL_OP(SELECT_V2, TFliteOpSelectV2); + REG_TFL_OP(SHAPE, TFliteOpShape); + REG_TFL_OP(SIN, TFliteOpSin); + REG_TFL_OP(SLICE, TFliteOpSlice); REG_TFL_OP(SOFTMAX, TFliteOpSoftmax); + REG_TFL_OP(SPACE_TO_BATCH_ND, TFliteOpSpaceToBatchND); + REG_TFL_OP(SPACE_TO_DEPTH, TFliteOpSpaceToDepth); + REG_TFL_OP(SPARSE_TO_DENSE, TFliteOpSparseToDense); + REG_TFL_OP(SPLIT, TFliteOpSplit); + REG_TFL_OP(SPLIT_V, TFliteOpSplitV); REG_TFL_OP(SQRT, TFliteOpSqrt); + REG_TFL_OP(SQUARE, TFliteOpSquare); + REG_TFL_OP(SQUARED_DIFFERENCE, TFliteOpSquaredDifference); + REG_TFL_OP(SQUEEZE, TFliteOpSqueeze); + REG_TFL_OP(STRIDED_SLICE, TFliteOpStridedSlice); REG_TFL_OP(SUB, TFliteOpSub); + REG_TFL_OP(SUM, TFliteOpSum); REG_TFL_OP(TANH, TFliteOpTanh); + REG_TFL_OP(TILE, TFliteOpTile); + REG_TFL_OP(TOPK_V2, TFliteOpTopKV2); REG_TFL_OP(TRANSPOSE, TFliteOpTranspose); + REG_TFL_OP(TRANSPOSE_CONV, TFliteOpTransposeConv); + REG_TFL_OP(UNIQUE, TFliteOpUnique); + REG_TFL_OP(UNPACK, TFliteOpUnpack); + REG_TFL_OP(WHERE, TFliteOpWhere); + REG_TFL_OP(ZEROS_LIKE, TFliteOpZerosLike); #undef REG_TFL_OP } diff --git a/compiler/tflchef/tools/file/CMakeLists.txt b/compiler/tflchef/tools/file/CMakeLists.txt index 477b7d9..f411d60 100644 --- a/compiler/tflchef/tools/file/CMakeLists.txt +++ b/compiler/tflchef/tools/file/CMakeLists.txt @@ -1,3 +1,4 @@ add_executable(tflchef-file Driver.cpp) +target_link_libraries(tflchef-file arser) target_link_libraries(tflchef-file tflchef_core) target_link_libraries(tflchef-file safemain) diff --git a/compiler/tflchef/tools/file/Driver.cpp b/compiler/tflchef/tools/file/Driver.cpp index 3ef7019..cecfeeb 100644 --- a/compiler/tflchef/tools/file/Driver.cpp +++ b/compiler/tflchef/tools/file/Driver.cpp @@ -20,30 +20,42 @@ #include #include +#include + #include #include int entry(int argc, char **argv) { - if (argc != 3) + arser::Arser arser; + arser.add_argument("recipe") + .type(arser::DataType::STR) + .help("Source recipe file path to convert"); + arser.add_argument("tflite").type(arser::DataType::STR).help("Target tflite file path"); + + try { - std::cerr << "ERROR: Failed to parse arguments" << std::endl; - std::cerr << std::endl; - std::cerr << "USAGE: " << argv[0] << " [recipe] [output]" << std::endl; - return 255; + arser.parse(argc, argv); + } + catch (const std::runtime_error &err) + { + std::cout << err.what() << std::endl; + std::cout << arser; + return 0; } int32_t model_version = 1; ::tflchef::ModelRecipe model_recipe; + std::string recipe_path = arser.get("recipe"); // Load model recipe from a file { - std::ifstream is{argv[1]}; + std::ifstream is{recipe_path}; google::protobuf::io::IstreamInputStream iis{&is}; if (!google::protobuf::TextFormat::Parse(&iis, &model_recipe)) { - std::cerr << "ERROR: Failed to parse recipe '" << argv[1] << "'" << std::endl; + std::cerr << "ERROR: Failed to parse recipe '" << recipe_path << "'" << std::endl; return 255; } @@ -62,9 +74,10 @@ int entry(int argc, char **argv) auto generated_model = tflchef::cook(model_recipe); + std::string tflite_path = arser.get("tflite"); // Dump generated model into a file { - std::ofstream os{argv[2], std::ios::binary}; + std::ofstream os{tflite_path, std::ios::binary}; os.write(generated_model.base(), generated_model.size()); } diff --git a/compiler/tflchef/tools/reverse/CMakeLists.txt b/compiler/tflchef/tools/reverse/CMakeLists.txt index 63cb36c..a5c0f5b 100644 --- a/compiler/tflchef/tools/reverse/CMakeLists.txt +++ b/compiler/tflchef/tools/reverse/CMakeLists.txt @@ -1,3 +1,5 @@ add_executable(tflchef-reverse Driver.cpp) +target_link_libraries(tflchef-reverse arser) target_link_libraries(tflchef-reverse tflchef_tflite) target_link_libraries(tflchef-reverse safemain) +target_link_libraries(tflchef-reverse foder) diff --git a/compiler/tflchef/tools/reverse/Driver.cpp b/compiler/tflchef/tools/reverse/Driver.cpp index 5497564..1116dec 100644 --- a/compiler/tflchef/tools/reverse/Driver.cpp +++ b/compiler/tflchef/tools/reverse/Driver.cpp @@ -14,34 +14,41 @@ * limitations under the License. */ -#include #include +#include +#include + #include #include int entry(int argc, char **argv) { - if (argc != 3) + arser::Arser arser; + arser.add_argument("tflite") + .type(arser::DataType::STR) + .help("Source tflite file path to convert"); + arser.add_argument("recipe").type(arser::DataType::STR).help("Target recipe file path"); + + try { - std::cerr << "ERROR: Failed to parse arguments" << std::endl; - std::cerr << std::endl; - std::cerr << "USAGE: " << argv[0] << " [tflite] [output]" << std::endl; - return 255; + arser.parse(argc, argv); } - - // Load TF lite model from a tflite file - std::unique_ptr rawmodel = tflchef::load_tflite(argv[1]); - if (rawmodel == nullptr) + catch (const std::runtime_error &err) { - std::cerr << "ERROR: Failed to load tflite '" << argv[1] << "'" << std::endl; - return 255; + std::cout << err.what() << std::endl; + std::cout << arser; + return 0; } - const tflite::Model *tflmodel = rawmodel->model(); + std::string tflite_path = arser.get("tflite"); + // Load TF lite model from a tflite file + const foder::FileLoader fileLoader{tflite_path}; + std::vector modelData = fileLoader.load(); + const tflite::Model *tflmodel = tflite::GetModel(modelData.data()); if (tflmodel == nullptr) { - std::cerr << "ERROR: Failed to load tflite '" << argv[1] << "'" << std::endl; + std::cerr << "ERROR: Failed to load tflite '" << tflite_path << "'" << std::endl; return 255; } @@ -53,11 +60,12 @@ int entry(int argc, char **argv) return 255; } + std::string recipe_path = arser.get("recipe"); // Save to a file - bool result = tflchef::write_recipe(argv[2], recipe); + bool result = tflchef::write_recipe(recipe_path, recipe); if (!result) { - std::cerr << "ERROR: Failed to write to recipe '" << argv[2] << "'" << std::endl; + std::cerr << "ERROR: Failed to write to recipe '" << recipe_path << "'" << std::endl; return 255; } return 0; diff --git a/compiler/tfldump/CMakeLists.txt b/compiler/tfldump/CMakeLists.txt index 99b6365..e6afcb6 100644 --- a/compiler/tfldump/CMakeLists.txt +++ b/compiler/tfldump/CMakeLists.txt @@ -1,4 +1,5 @@ if(NOT TARGET mio_tflite) + message(STATUS "Build tfldump: FAILED (missing mio_tflite)") return() endif(NOT TARGET mio_tflite) @@ -8,7 +9,7 @@ file(GLOB_RECURSE SOURCES "src/*.cpp") add_executable(tfldump ${DRIVER} ${SOURCES}) target_include_directories(tfldump PRIVATE include) +target_link_libraries(tfldump arser) target_link_libraries(tfldump mio_tflite) target_link_libraries(tfldump safemain) -target_link_libraries(tfldump stdex) target_link_libraries(tfldump flatbuffers) diff --git a/compiler/tfldump/driver/Driver.cpp b/compiler/tfldump/driver/Driver.cpp index 2ede0fd..3961d2f 100644 --- a/compiler/tfldump/driver/Driver.cpp +++ b/compiler/tfldump/driver/Driver.cpp @@ -14,6 +14,7 @@ * limitations under the License. */ +#include #include #include @@ -21,30 +22,37 @@ int entry(int argc, char **argv) { - if (argc != 2) + arser::Arser arser; + arser.add_argument("tflite").type(arser::DataType::STR).help("TFLite file to dump"); + + try { - std::cerr << "ERROR: Failed to parse arguments" << std::endl; - std::cerr << std::endl; - std::cerr << "USAGE: " << argv[0] << " [tflite]" << std::endl; - return 255; + arser.parse(argc, argv); + } + catch (const std::runtime_error &err) + { + std::cout << err.what() << '\n'; + std::cout << arser; + return 0; } + std::string tflite_path = arser.get("tflite"); // Load TF lite model from a tflite file - std::unique_ptr model = tflread::load_tflite(argv[1]); + std::unique_ptr model = tflread::load_tflite(tflite_path); if (model == nullptr) { - std::cerr << "ERROR: Failed to load tflite '" << argv[1] << "'" << std::endl; + std::cerr << "ERROR: Failed to load tflite '" << tflite_path << "'" << std::endl; return 255; } const tflite::Model *tflmodel = model->model(); if (tflmodel == nullptr) { - std::cerr << "ERROR: Failed to load tflite '" << argv[1] << "'" << std::endl; + std::cerr << "ERROR: Failed to load tflite '" << tflite_path << "'" << std::endl; return 255; } - std::cout << "Dump: " << argv[1] << std::endl << std::endl; + std::cout << "Dump: " << tflite_path << std::endl << std::endl; std::cout << tflmodel << std::endl; diff --git a/compiler/tfldump/requires.cmake b/compiler/tfldump/requires.cmake index adcae7c..2cdd3a3 100644 --- a/compiler/tfldump/requires.cmake +++ b/compiler/tfldump/requires.cmake @@ -1 +1,3 @@ +require("arser") require("mio-tflite") +require("safemain") diff --git a/compiler/tfldump/src/Dump.cpp b/compiler/tfldump/src/Dump.cpp index e6b8425..e1562d4 100644 --- a/compiler/tfldump/src/Dump.cpp +++ b/compiler/tfldump/src/Dump.cpp @@ -110,8 +110,8 @@ void dump_sub_graph(std::ostream &os, tflread::Reader &reader) auto operators = reader.operators(); // dump operands(tensors) - os << "Operands: T(subgraph index : tensor index) TYPE (shape) B(buffer index) OperandName" - << std::endl; + os << "Operands: T(subgraph index : tensor index) TYPE (shape) (shape_signature) " + << "B(buffer index) OperandName" << std::endl; for (uint32_t i = 0; i < tensors->Length(); ++i) { // TODO refactor to some better structure @@ -124,6 +124,11 @@ void dump_sub_graph(std::ostream &os, tflread::Reader &reader) os << "T(" << reader.subgraph_index() << ":" << i << ") " << tflread::tensor_type(tensor) << " "; os << "(" << dims << ") "; + if (tensor->shape_signature()) + { + std::vector dims_sig = tflread::as_index_vector(tensor->shape_signature()); + os << "(" << dims_sig << ") "; + } os << "B(" << tensor->buffer() << ") "; os << tflread::tensor_name(tensor) << std::endl; @@ -154,7 +159,12 @@ void dump_sub_graph(std::ostream &os, tflread::Reader &reader) os << std::endl << strqindent; } if (q_params->zero_point()) + { os << "zeropt(" << q_params->zero_point() << ") "; + if (q_params->zero_point()->size() > 1) + os << std::endl << strqindent; + } + os << "quantized_dimension(" << q_params->quantized_dimension() << ")"; os << std::endl; } @@ -186,7 +196,7 @@ void dump_sub_graph(std::ostream &os, tflread::Reader &reader) for (auto input : inputs) { - os << " I T(" << input << ") "; + os << " I T(" << reader.subgraph_index() << ":" << input << ") "; if (input >= 0) { auto tensor = tensors->Get(input); @@ -196,7 +206,7 @@ void dump_sub_graph(std::ostream &os, tflread::Reader &reader) } for (auto output : outputs) { - os << " O T(" << output << ") "; + os << " O T(" << reader.subgraph_index() << ":" << output << ") "; if (output >= 0) { auto tensor = tensors->Get(output); @@ -214,14 +224,14 @@ void dump_sub_graph(std::ostream &os, tflread::Reader &reader) { auto tensor = tensors->Get(input); std::string name = tflread::tensor_name(tensor); - os << "I T(" << input << ") " << name << std::endl; + os << "I T(" << reader.subgraph_index() << ":" << input << ") " << name << std::endl; } for (const auto output : reader.outputs()) { auto tensor = tensors->Get(output); std::string name = tflread::tensor_name(tensor); - os << "O T(" << output << ") " << name << std::endl; + os << "O T(" << reader.subgraph_index() << ":" << output << ") " << name << std::endl; } os << std::endl; diff --git a/compiler/tfldump/src/OpPrinter.cpp b/compiler/tfldump/src/OpPrinter.cpp index 5120f42..9fc1a64 100644 --- a/compiler/tfldump/src/OpPrinter.cpp +++ b/compiler/tfldump/src/OpPrinter.cpp @@ -17,11 +17,11 @@ #include "OpPrinter.h" #include "Read.h" -#include +#include #include -using stdex::make_unique; +using std::make_unique; namespace tfldump { @@ -60,6 +60,35 @@ public: } }; +class ArgMinPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_ArgMinOptions()) + { + os << " "; + os << "OutputType(" << EnumNameTensorType(params->output_type()) << ") "; + os << std::endl; + } + } +}; + +class CastPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto cast_params = op->builtin_options_as_CastOptions()) + { + os << " "; + os << "in_data_type(" << tflite::EnumNameTensorType(cast_params->in_data_type()) << ") "; + os << "out_data_type(" << tflite::EnumNameTensorType(cast_params->out_data_type()) << ") "; + os << std::endl; + } + } +}; + class Conv2DPrinter : public OpPrinter { public: @@ -71,6 +100,8 @@ public: os << "Padding(" << conv_params->padding() << ") "; os << "Stride.W(" << conv_params->stride_w() << ") "; os << "Stride.H(" << conv_params->stride_h() << ") "; + os << "Dilation.W(" << conv_params->dilation_w_factor() << ") "; + os << "Dilation.H(" << conv_params->dilation_h_factor() << ") "; os << "Activation(" << EnumNameActivationFunctionType(conv_params->fused_activation_function()) << ")"; os << std::endl; @@ -130,6 +161,20 @@ public: } }; +class ReducerPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto reducer_params = op->builtin_options_as_ReducerOptions()) + { + os << " "; + os << "keep_dims(" << reducer_params->keep_dims() << ") "; + os << std::endl; + } + } +}; + class ReshapePrinter : public OpPrinter { public: @@ -145,6 +190,80 @@ public: } }; +class ResizeBilinearPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *resize_params = op->builtin_options_as_ResizeBilinearOptions()) + { + os << " "; + os << std::boolalpha; + os << "align_corners(" << resize_params->align_corners() << ")"; + os << "half_pixel_centers(" << resize_params->half_pixel_centers() << ")"; + os << std::endl; + } + } +}; + +class ResizeNearestNeighborPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *resize_params = op->builtin_options_as_ResizeNearestNeighborOptions()) + { + os << " "; + os << std::boolalpha; + os << "align_corners(" << resize_params->align_corners() << ")"; + os << std::endl; + } + } +}; + +class ReverseSequencePrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *std_params = op->builtin_options_as_ReverseSequenceOptions()) + { + os << " "; + os << "seq_dim(" << std_params->seq_dim() << ") "; + os << "batch_dim(" << std_params->batch_dim() << ") "; + os << std::endl; + } + } +}; + +class DepthToSpacePrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *std_params = op->builtin_options_as_DepthToSpaceOptions()) + { + os << " "; + os << "BlockSize(" << std_params->block_size() << ")"; + os << std::endl; + } + } +}; + +class SparseToDensePrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *std_params = op->builtin_options_as_SparseToDenseOptions()) + { + os << " "; + os << "ValidateIndices(" << std_params->validate_indices() << ")"; + os << std::endl; + } + } +}; + class DepthwiseConv2DPrinter : public OpPrinter { public: @@ -184,6 +303,95 @@ public: } }; +class GatherPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_GatherOptions()) + { + os << " "; + os << "Axis(" << params->axis() << ") "; + + os << std::endl; + } + } +}; + +class IfPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_IfOptions()) + { + os << " "; + os << "then_subgraph_index(" << params->then_subgraph_index() << ") "; + os << "else_subgraph_index(" << params->else_subgraph_index() << ") "; + os << std::endl; + } + } +}; + +class L2NormPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_L2NormOptions()) + { + os << " "; + os << "Activation(" << EnumNameActivationFunctionType(params->fused_activation_function()) + << ") "; + os << std::endl; + } + } +}; + +class LeakyReluPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_LeakyReluOptions()) + { + os << " "; + os << "alpha(" << params->alpha() << ") "; + } + } +}; + +class LocalResponseNormalizationPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_LocalResponseNormalizationOptions()) + { + os << " "; + os << "radius(" << params->radius() << ") "; + os << "bias(" << params->bias() << ") "; + os << "alpha(" << params->alpha() << ") "; + os << "beta(" << params->beta() << ") "; + os << std::endl; + } + } +}; + +class MirrorPadPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_MirrorPadOptions()) + { + os << " "; + os << "mode(" << EnumNameMirrorPadMode(params->mode()) << ") "; + os << std::endl; + } + } +}; + class MulPrinter : public OpPrinter { public: @@ -214,6 +422,35 @@ public: } }; +class OneHotPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_OneHotOptions()) + { + os << " "; + os << "Axis(" << params->axis() << ") "; + + os << std::endl; + } + } +}; + +class ShapePrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_ShapeOptions()) + { + os << " "; + os << "out_type(" << EnumNameTensorType(params->out_type()) << ") "; + os << std::endl; + } + } +}; + class SoftmaxPrinter : public OpPrinter { public: @@ -228,6 +465,87 @@ public: } }; +class SpaceToDepthPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *std_params = op->builtin_options_as_SpaceToDepthOptions()) + { + os << " "; + os << "BlockSize(" << std_params->block_size() << ")"; + os << std::endl; + } + } +}; + +class SqueezePrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_SqueezeOptions()) + { + os << " "; + os << "SqueezeDims("; + for (int i = 0; i < params->squeeze_dims()->size(); ++i) + { + if (i != 0) + os << ", "; + os << params->squeeze_dims()->Get(i); + } + os << ")"; + os << std::endl; + } + } +}; + +class StridedSlicePrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *strided_slice_params = op->builtin_options_as_StridedSliceOptions()) + { + os << " "; + os << "begin_mask(" << strided_slice_params->begin_mask() << ") "; + os << "end_mask(" << strided_slice_params->end_mask() << ") "; + os << "ellipsis_mask(" << strided_slice_params->ellipsis_mask() << ") "; + os << "new_axis_mask(" << strided_slice_params->new_axis_mask() << ") "; + os << "shrink_axis_mask(" << strided_slice_params->shrink_axis_mask() << ") "; + os << std::endl; + } + } +}; + +class SplitPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_SplitOptions()) + { + os << " "; + os << "num_splits(" << params->num_splits() << ") "; + os << std::endl; + } + } +}; + +class SplitVPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_SplitVOptions()) + { + os << " "; + os << "num_splits(" << params->num_splits() << ") "; + os << std::endl; + } + } +}; + class SubPrinter : public OpPrinter { public: @@ -243,6 +561,51 @@ public: } }; +class TransposeConvPrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_TransposeConvOptions()) + { + os << " "; + os << "Padding(" << params->padding() << ") "; + os << "Stride.W(" << params->stride_w() << ") "; + os << "Stride.H(" << params->stride_h() << ") "; + os << std::endl; + } + } +}; + +class WhilePrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_WhileOptions()) + { + os << " "; + os << "cond_subgraph_index(" << params->cond_subgraph_index() << ") "; + os << "body_subgraph_index(" << params->body_subgraph_index() << ") "; + os << std::endl; + } + } +}; + +class UniquePrinter : public OpPrinter +{ +public: + void options(const tflite::Operator *op, std::ostream &os) const override + { + if (auto *params = op->builtin_options_as_UniqueOptions()) + { + os << " "; + os << "idx_out_type(" << EnumNameTensorType(params->idx_out_type()) << ") "; + os << std::endl; + } + } +}; + class CustomOpPrinter : public OpPrinter { public: @@ -286,20 +649,69 @@ public: OpPrinterRegistry::OpPrinterRegistry() { _op_map[tflite::BuiltinOperator_ADD] = make_unique(); + // There is no Option for ADD_N _op_map[tflite::BuiltinOperator_ARG_MAX] = make_unique(); + _op_map[tflite::BuiltinOperator_ARG_MIN] = make_unique(); _op_map[tflite::BuiltinOperator_AVERAGE_POOL_2D] = make_unique(); + _op_map[tflite::BuiltinOperator_CAST] = make_unique(); + // There is no Option for CEIL _op_map[tflite::BuiltinOperator_CONCATENATION] = make_unique(); _op_map[tflite::BuiltinOperator_CONV_2D] = make_unique(); + _op_map[tflite::BuiltinOperator_DEPTH_TO_SPACE] = make_unique(); _op_map[tflite::BuiltinOperator_DEPTHWISE_CONV_2D] = make_unique(); _op_map[tflite::BuiltinOperator_DIV] = make_unique(); + // There is no Option for FLOOR + // There is no Option for FLOOR_MOD _op_map[tflite::BuiltinOperator_FULLY_CONNECTED] = make_unique(); + _op_map[tflite::BuiltinOperator_GATHER] = make_unique(); + _op_map[tflite::BuiltinOperator_IF] = make_unique(); + _op_map[tflite::BuiltinOperator_L2_POOL_2D] = make_unique(); + _op_map[tflite::BuiltinOperator_L2_NORMALIZATION] = make_unique(); + _op_map[tflite::BuiltinOperator_LEAKY_RELU] = make_unique(); + _op_map[tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION] = + make_unique(); + // There is no Option for LOG + // There is no Option for LOGISTIC + // There is no Option for LOG_SOFTMAX _op_map[tflite::BuiltinOperator_MAX_POOL_2D] = make_unique(); + _op_map[tflite::BuiltinOperator_MIRROR_PAD] = make_unique(); _op_map[tflite::BuiltinOperator_MUL] = make_unique(); + _op_map[tflite::BuiltinOperator_ONE_HOT] = make_unique(); _op_map[tflite::BuiltinOperator_PACK] = make_unique(); - // There is no Option for ReLU and ReLU6 + // There is no Option for PAD + // There is no Option for PRELU + // There is no Option for RELU + // There is no Option for RELU6 + // There is no Option for RELU_N1_TO_1 + _op_map[tflite::BuiltinOperator_REDUCE_ANY] = make_unique(); + _op_map[tflite::BuiltinOperator_REDUCE_MAX] = make_unique(); + _op_map[tflite::BuiltinOperator_REDUCE_MIN] = make_unique(); + _op_map[tflite::BuiltinOperator_REDUCE_PROD] = make_unique(); _op_map[tflite::BuiltinOperator_RESHAPE] = make_unique(); + _op_map[tflite::BuiltinOperator_RESIZE_BILINEAR] = make_unique(); + _op_map[tflite::BuiltinOperator_RESIZE_NEAREST_NEIGHBOR] = + make_unique(); + _op_map[tflite::BuiltinOperator_REVERSE_SEQUENCE] = make_unique(); + // There is no Option for ROUND + // There is no Option for SELECT + // There is no Option for SELECT_V2 + _op_map[tflite::BuiltinOperator_SHAPE] = make_unique(); + // There is no Option for SIN + // There is no Option for SLICE _op_map[tflite::BuiltinOperator_SOFTMAX] = make_unique(); + _op_map[tflite::BuiltinOperator_SPACE_TO_DEPTH] = make_unique(); + // There is no Option for SPACE_TO_BATCH_ND + _op_map[tflite::BuiltinOperator_SPARSE_TO_DENSE] = make_unique(); + _op_map[tflite::BuiltinOperator_SPLIT] = make_unique(); + _op_map[tflite::BuiltinOperator_SPLIT_V] = make_unique(); + _op_map[tflite::BuiltinOperator_SQUEEZE] = make_unique(); + _op_map[tflite::BuiltinOperator_STRIDED_SLICE] = make_unique(); _op_map[tflite::BuiltinOperator_SUB] = make_unique(); + _op_map[tflite::BuiltinOperator_SUM] = make_unique(); + _op_map[tflite::BuiltinOperator_TRANSPOSE_CONV] = make_unique(); + // There is no Option for TOPK_V2 + _op_map[tflite::BuiltinOperator_UNIQUE] = make_unique(); + _op_map[tflite::BuiltinOperator_WHILE] = make_unique(); _op_map[tflite::BuiltinOperator_CUSTOM] = make_unique(); } diff --git a/compiler/tflite2circle-conversion-test/CMakeLists.txt b/compiler/tflite2circle-conversion-test/CMakeLists.txt index d7b6442..83fe23a 100644 --- a/compiler/tflite2circle-conversion-test/CMakeLists.txt +++ b/compiler/tflite2circle-conversion-test/CMakeLists.txt @@ -2,7 +2,6 @@ nnas_include(TargetRequire) unset(REQUIRED_TARGETS) list(APPEND REQUIRED_TARGETS tflite2circle) -list(APPEND REQUIRED_TARGETS tflchef) TargetRequire_Return(${REQUIRED_TARGETS}) nncc_find_resource(TensorFlowLiteRecipes) @@ -10,46 +9,19 @@ nncc_find_resource(TensorFlowLiteRecipes) set(TEST_REPO "${TensorFlowLiteRecipes_DIR}") set(TEST_RECIPE_FILENAME "test.recipe") -unset(TESTCASES) -macro(add NAME) - list(APPEND TESTCASES ${NAME}) -endmacro(add) - -# Read "test.lst" -include("test.lst") +file(GLOB RECIPES RELATIVE ${TEST_REPO} "${TEST_REPO}/*/${TEST_RECIPE_FILENAME}") unset(TEST_DEPS) unset(TEST_NAMES) -foreach(PREFIX IN ITEMS ${TESTCASES}) - if(NOT IS_DIRECTORY "${TEST_REPO}/${PREFIX}") - message(FATAL_ERROR "Missing '${PREFIX}' test") +foreach(RECIPE IN ITEMS ${RECIPES}) + get_filename_component(PREFIX ${RECIPE} DIRECTORY) + if(NOT IS_DIRECTORY "${TEST_REPO}/${RECIPE_PREFIX}") + message(FATAL_ERROR "Missing '${RECIPE_PREFIX}' test") endif() - set(RECIPE_SOURCE_PATH "${TEST_REPO}/${PREFIX}/${TEST_RECIPE_FILENAME}") - set(RECIPE_FILE "${PREFIX}.recipe") - set(RECIPE_BINARY_PATH "${CMAKE_CURRENT_BINARY_DIR}/${RECIPE_FILE}") - - set(TFLITE_FILE "${PREFIX}.tflite") - set(TFLITE_OUTPUT_PATH "${CMAKE_CURRENT_BINARY_DIR}/${TFLITE_FILE}") - - # Copy .recipe - add_custom_command(OUTPUT ${RECIPE_BINARY_PATH} - COMMAND ${CMAKE_COMMAND} -E copy "${RECIPE_SOURCE_PATH}" "${RECIPE_BINARY_PATH}" - DEPENDS ${RECIPE_SOURCE_PATH} - COMMENT "Generate ${RECIPE_FILE}" - ) - - # Generate .tflite - add_custom_command(OUTPUT ${TFLITE_OUTPUT_PATH} - COMMAND $ ${RECIPE_BINARY_PATH} ${TFLITE_OUTPUT_PATH} - DEPENDS ${RECIPE_BINARY_PATH} - COMMENT "Generate ${PREFIX}.tflite" - ) - - list(APPEND TEST_DEPS ${RECIPE_BINARY_PATH} ${TFLITE_OUTPUT_PATH}) list(APPEND TEST_NAMES ${PREFIX}) -endforeach(PREFIX IN ITEMS ${TESTCASES}) +endforeach(RECIPE IN ITEMS ${RECIPES}) ## ## Copy testall @@ -66,6 +38,8 @@ add_custom_command( list(APPEND TEST_DEPS "${TEST_RUNNER}") +get_target_property(ARTIFACTS_BIN_PATH testDataGenerator BINARY_DIR) + ### ### Generate test.config ### @@ -89,6 +63,6 @@ add_test( NAME tflite2circle_conversion_test COMMAND "${TEST_RUNNER}" "${TEST_CONFIG}" - "${CMAKE_CURRENT_BINARY_DIR}" + "${ARTIFACTS_BIN_PATH}" ${TEST_NAMES} ) diff --git a/compiler/tflite2circle-conversion-test/requires.cmake b/compiler/tflite2circle-conversion-test/requires.cmake index 730711a..87b0099 100644 --- a/compiler/tflite2circle-conversion-test/requires.cmake +++ b/compiler/tflite2circle-conversion-test/requires.cmake @@ -1,2 +1,2 @@ +require("common-artifacts") require("tflite2circle") -require("tflchef") diff --git a/compiler/tflite2circle-conversion-test/test.lst b/compiler/tflite2circle-conversion-test/test.lst deleted file mode 100644 index 8b8f0aa..0000000 --- a/compiler/tflite2circle-conversion-test/test.lst +++ /dev/null @@ -1,20 +0,0 @@ -add(Add_000) -add(AveragePool2D_000) -add(Concatenation_000) -add(Conv2D_000) -add(Conv2D_001) -add(Conv2D_U8_000) -add(DepthwiseConv2D_000) -add(Div_000) -add(FullyConnected_000) -add(FullyConnected_001) -add(MaxPool2D_000) -add(Quantization_000) -add(ReLU_000) -add(ReLU6_000) -add(Reshape_000) -add(Reshape_001) -add(Reshape_U8_000) -add(Sqrt_000) -add(Sub_000) -add(Sub_001) diff --git a/compiler/tflite2circle-conversion-test/testall.sh b/compiler/tflite2circle-conversion-test/testall.sh index 6645437..2290ee6 100755 --- a/compiler/tflite2circle-conversion-test/testall.sh +++ b/compiler/tflite2circle-conversion-test/testall.sh @@ -13,6 +13,7 @@ if [[ $# -lt 2 ]]; then exit 255 fi +BINDIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" CONFIG_PATH="$1"; shift WORKDIR="$1"; shift @@ -31,11 +32,11 @@ while [[ $# -ne 0 ]]; do TESTED+=("${PREFIX}") - PASSED_TAG="${PREFIX}.passed" + PASSED_TAG="${BINDIR}/${PREFIX}.passed" rm -f "${PASSED_TAG}" - cat > "${PREFIX}.log" <( + cat > "${BINDIR}/${PREFIX}.log" <( exec 2>&1 echo "-- Found tflite: ${PREFIX}.tflite" @@ -48,7 +49,7 @@ while [[ $# -ne 0 ]]; do # Generate circle "${TFLITE2CIRCLE_PATH}" \ "${WORKDIR}/${PREFIX}.tflite" \ - "${WORKDIR}/${PREFIX}.circle" + "${BINDIR}/${PREFIX}.circle" if [[ $? -eq 0 ]]; then touch "${PASSED_TAG}" diff --git a/compiler/tflite2circle/CMakeLists.txt b/compiler/tflite2circle/CMakeLists.txt index f846a2b..a0a2e02 100644 --- a/compiler/tflite2circle/CMakeLists.txt +++ b/compiler/tflite2circle/CMakeLists.txt @@ -10,7 +10,9 @@ file(GLOB_RECURSE SOURCES "src/*.cpp") add_executable(tflite2circle ${DRIVER} ${SOURCES}) target_include_directories(tflite2circle PRIVATE include) target_include_directories(tflite2circle PRIVATE src) +target_link_libraries(tflite2circle arser) target_link_libraries(tflite2circle safemain) -target_link_libraries(tflite2circle stdex) target_link_libraries(tflite2circle mio_tflite) target_link_libraries(tflite2circle mio_circle) + +install(TARGETS tflite2circle DESTINATION bin) diff --git a/compiler/tflite2circle/driver/Driver.cpp b/compiler/tflite2circle/driver/Driver.cpp index 826f9de..67b8e33 100644 --- a/compiler/tflite2circle/driver/Driver.cpp +++ b/compiler/tflite2circle/driver/Driver.cpp @@ -15,43 +15,60 @@ */ #include +#include +#include #include +#include + #include "CircleModel.h" #include "TFLModel.h" int entry(int argc, char **argv) { - if (argc != 3) + arser::Arser arser{"tflite2circle is a Tensorflow lite to circle model converter"}; + + arser.add_argument("tflite") + .nargs(1) + .type(arser::DataType::STR) + .help("Source tflite file path to convert"); + arser.add_argument("circle").nargs(1).type(arser::DataType::STR).help("Target circle file path"); + + try { - std::cerr << "ERROR: Failed to parse arguments" << std::endl; - std::cerr << std::endl; - std::cerr << "USAGE: " << argv[0] << " [tflite] [circle]" << std::endl; - return 255; + arser.parse(argc, argv); + } + catch (const std::runtime_error &err) + { + std::cout << err.what() << std::endl; + std::cout << arser; + return 0; } + std::string tfl_path = arser.get("tflite"); + std::string circle_path = arser.get("circle"); // read tflite file - tflite2circle::TFLModel tfl_model(argv[1]); + tflite2circle::TFLModel tfl_model(tfl_path); if (!tfl_model.is_valid()) { - std::cerr << "ERROR: Failed to load tflite '" << argv[1] << "'" << std::endl; + std::cerr << "ERROR: Failed to load tflite '" << tfl_path << "'" << std::endl; return 255; } // create flatbuffer builder - auto flatbuffer_builder = stdex::make_unique(1024); + auto flatbuffer_builder = std::make_unique(1024); // convert tflite to circle tflite2circle::CircleModel circle_model{flatbuffer_builder, tfl_model}; - std::ofstream outfile{argv[2], std::ios::binary}; + std::ofstream outfile{circle_path, std::ios::binary}; outfile.write(circle_model.base(), circle_model.size()); outfile.close(); // TODO find a better way of error handling if (outfile.fail()) { - std::cerr << "ERROR: Failed to write circle '" << argv[1] << "'" << std::endl; + std::cerr << "ERROR: Failed to write circle '" << circle_path << "'" << std::endl; return 255; } diff --git a/compiler/tflite2circle/include/CircleModel.h b/compiler/tflite2circle/include/CircleModel.h index ee1a8fe..e1e35d8 100644 --- a/compiler/tflite2circle/include/CircleModel.h +++ b/compiler/tflite2circle/include/CircleModel.h @@ -23,7 +23,6 @@ #include #include #include -#include #include "TFLModel.h" diff --git a/compiler/tflite2circle/requires.cmake b/compiler/tflite2circle/requires.cmake index dd67319..ff19b74 100644 --- a/compiler/tflite2circle/requires.cmake +++ b/compiler/tflite2circle/requires.cmake @@ -1,4 +1,4 @@ +require("arser") require("mio-tflite") require("mio-circle") require("safemain") -require("stdex") diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions.h index 0167da2..159a8af 100644 --- a/compiler/tflite2circle/src/BuildBuiltinOptions.h +++ b/compiler/tflite2circle/src/BuildBuiltinOptions.h @@ -21,34 +21,90 @@ #include "BuildBuiltinOptions/AbsOptions.h" #include "BuildBuiltinOptions/AddOptions.h" +#include "BuildBuiltinOptions/AddNOptions.h" #include "BuildBuiltinOptions/ArgMaxOptions.h" +#include "BuildBuiltinOptions/ArgMinOptions.h" +#include "BuildBuiltinOptions/BatchMatMulOptions.h" #include "BuildBuiltinOptions/BatchToSpaceNDOptions.h" #include "BuildBuiltinOptions/CastOptions.h" #include "BuildBuiltinOptions/ConcatenationOptions.h" #include "BuildBuiltinOptions/Conv2DOptions.h" #include "BuildBuiltinOptions/CosOptions.h" +#include "BuildBuiltinOptions/DepthToSpaceOptions.h" #include "BuildBuiltinOptions/DepthwiseConv2DOptions.h" #include "BuildBuiltinOptions/DivOptions.h" #include "BuildBuiltinOptions/EqualOptions.h" #include "BuildBuiltinOptions/ExpandDimsOptions.h" #include "BuildBuiltinOptions/ExpOptions.h" #include "BuildBuiltinOptions/FillOptions.h" +#include "BuildBuiltinOptions/FloorDivOptions.h" +#include "BuildBuiltinOptions/FloorModOptions.h" #include "BuildBuiltinOptions/FullyConnectedOptions.h" +#include "BuildBuiltinOptions/GatherOptions.h" +#include "BuildBuiltinOptions/GatherNdOptions.h" +#include "BuildBuiltinOptions/GreaterOptions.h" #include "BuildBuiltinOptions/GreaterEqualOptions.h" +#include "BuildBuiltinOptions/IfOptions.h" +#include "BuildBuiltinOptions/L2NormalizeOptions.h" +// L2Pool2D uses Pool2DOptions +#include "BuildBuiltinOptions/LeakyReluOptions.h" +#include "BuildBuiltinOptions/LessOptions.h" +#include "BuildBuiltinOptions/LessEqualOptions.h" +#include "BuildBuiltinOptions/LocalResponseNormalizationOptions.h" +#include "BuildBuiltinOptions/LogicalAndOptions.h" #include "BuildBuiltinOptions/LogicalNotOptions.h" #include "BuildBuiltinOptions/LogicalOrOptions.h" +// There is no LogisticOptions +#include "BuildBuiltinOptions/LogSoftmaxOptions.h" +#include "BuildBuiltinOptions/MatrixDiagOptions.h" +#include "BuildBuiltinOptions/MatrixSetDiagOptions.h" +#include "BuildBuiltinOptions/MaximumMinimumOptions.h" +#include "BuildBuiltinOptions/MirrorPadOptions.h" #include "BuildBuiltinOptions/MulOptions.h" +#include "BuildBuiltinOptions/NegOptions.h" #include "BuildBuiltinOptions/NotEqualOptions.h" +#include "BuildBuiltinOptions/OneHotOptions.h" #include "BuildBuiltinOptions/PackOptions.h" #include "BuildBuiltinOptions/PadOptions.h" +#include "BuildBuiltinOptions/RangeOptions.h" #include "BuildBuiltinOptions/Pool2DOptions.h" +#include "BuildBuiltinOptions/PowOptions.h" +#include "BuildBuiltinOptions/RankOptions.h" +// There is no PReluOptions #include "BuildBuiltinOptions/ReducerOptions.h" #include "BuildBuiltinOptions/ReshapeOptions.h" +#include "BuildBuiltinOptions/ResizeBilinearOptions.h" +#include "BuildBuiltinOptions/ResizeNearestNeighborOptions.h" +#include "BuildBuiltinOptions/ReverseSequenceOptions.h" +#include "BuildBuiltinOptions/ReverseV2Options.h" +// There is no RoundOptions +// There is no RsqrtOptions +#include "BuildBuiltinOptions/ScatterNdOptions.h" +#include "BuildBuiltinOptions/SegmentSumOptions.h" +#include "BuildBuiltinOptions/SelectOptions.h" +#include "BuildBuiltinOptions/SelectV2Options.h" #include "BuildBuiltinOptions/ShapeOptions.h" +// There is no SinOptions +#include "BuildBuiltinOptions/SliceOptions.h" #include "BuildBuiltinOptions/SoftmaxOptions.h" +#include "BuildBuiltinOptions/SpaceToBatchNDOptions.h" +#include "BuildBuiltinOptions/SpaceToDepthOptions.h" +#include "BuildBuiltinOptions/SparseToDenseOptions.h" #include "BuildBuiltinOptions/SplitOptions.h" +#include "BuildBuiltinOptions/SplitVOptions.h" +#include "BuildBuiltinOptions/SquaredDifferenceOptions.h" +#include "BuildBuiltinOptions/SquareOptions.h" #include "BuildBuiltinOptions/SqueezeOptions.h" +#include "BuildBuiltinOptions/StridedSliceOptions.h" #include "BuildBuiltinOptions/SubOptions.h" +#include "BuildBuiltinOptions/TileOptions.h" +#include "BuildBuiltinOptions/TopKV2Options.h" #include "BuildBuiltinOptions/TransposeOptions.h" +#include "BuildBuiltinOptions/TransposeConvOptions.h" +#include "BuildBuiltinOptions/UniqueOptions.h" +#include "BuildBuiltinOptions/UnpackOptions.h" +#include "BuildBuiltinOptions/WhereOptions.h" +#include "BuildBuiltinOptions/WhileOptions.h" +#include "BuildBuiltinOptions/ZerosLikeOptions.h" #endif // __BUILD_BUITIN_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/AddNOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/AddNOptions.cpp new file mode 100644 index 0000000..df8b083 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/AddNOptions.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "AddNOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_AddNOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::AddNOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/AddNOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/AddNOptions.h new file mode 100644 index 0000000..9e18e8a --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/AddNOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_ADD_N_OPTIONS_H__ +#define __BBO_ADD_N_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_AddNOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_ADD_N_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/ArgMinOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/ArgMinOptions.cpp new file mode 100644 index 0000000..204558d --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/ArgMinOptions.cpp @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ArgMinOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_ArgMinOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_ArgMinOptions(); + assert(tflite_builtin_options); + circle::ArgMinOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_output_type( + get_circle_tensortype(tflite_builtin_options->output_type())); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/ArgMinOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/ArgMinOptions.h new file mode 100644 index 0000000..76cbc39 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/ArgMinOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_ARGMIN_OPTIONS_H__ +#define __BBO_ARGMIN_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_ArgMinOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_ARGMIN_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/BatchMatMulOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/BatchMatMulOptions.cpp new file mode 100644 index 0000000..ac0a094 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/BatchMatMulOptions.cpp @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "BatchMatMulOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_BatchMatMulOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_BatchMatMulOptions(); + assert(tflite_builtin_options); + circle::BatchMatMulOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_adjoint_lhs(tflite_builtin_options->adj_x()); + builtin_options_builder.add_adjoint_rhs(tflite_builtin_options->adj_y()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/BatchMatMulOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/BatchMatMulOptions.h new file mode 100644 index 0000000..b1e90aa --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/BatchMatMulOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_BATCH_MATMUL_OPTIONS_H__ +#define __BBO_BATCH_MATMUL_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_BatchMatMulOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_BATCH_MATMUL_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/CastOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/CastOptions.cpp index f07fb3b..bc14452 100644 --- a/compiler/tflite2circle/src/BuildBuiltinOptions/CastOptions.cpp +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/CastOptions.cpp @@ -26,7 +26,9 @@ flatbuffers::Offset build_circle_CastOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) { auto tflite_builtin_options = op->builtin_options_as_CastOptions(); - assert(tflite_builtin_options); + if (tflite_builtin_options == nullptr) + return 0; + circle::CastOptionsBuilder builtin_options_builder{fb}; builtin_options_builder.add_in_data_type( get_circle_tensortype(tflite_builtin_options->in_data_type())); diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/DepthToSpaceOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/DepthToSpaceOptions.cpp new file mode 100644 index 0000000..669aadb --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/DepthToSpaceOptions.cpp @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "DepthToSpaceOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_DepthToSpaceOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_DepthToSpaceOptions(); + assert(tflite_builtin_options); + circle::DepthToSpaceOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_block_size(tflite_builtin_options->block_size()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/DepthToSpaceOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/DepthToSpaceOptions.h new file mode 100644 index 0000000..47c09a8 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/DepthToSpaceOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_DEPTH_TO_SPACE_OPTIONS_H__ +#define __BBO_DEPTH_TO_SPACE_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_DepthToSpaceOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_DEPTH_TO_SPACE_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/FloorDivOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/FloorDivOptions.cpp new file mode 100644 index 0000000..aa08cfd --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/FloorDivOptions.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "FloorDivOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_FloorDivOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *) +{ + circle::FloorDivOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/FloorDivOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/FloorDivOptions.h new file mode 100644 index 0000000..0f65591 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/FloorDivOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_FLOOR_DIV_OPTIONS_H__ +#define __BBO_FLOOR_DIV_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_FloorDivOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_FLOOR_DIV_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/FloorModOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/FloorModOptions.cpp new file mode 100644 index 0000000..770351e --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/FloorModOptions.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "FloorModOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_FloorModOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::FloorModOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/FloorModOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/FloorModOptions.h new file mode 100644 index 0000000..e53d5f7 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/FloorModOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_FLOOR_MOD_OPTIONS_H__ +#define __BBO_FLOOR_MOD_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_FloorModOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_FLOOR_MOD_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/GatherNdOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/GatherNdOptions.cpp new file mode 100644 index 0000000..487cce1 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/GatherNdOptions.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "GatherNdOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_GatherNdOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *) +{ + circle::GatherNdOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/GatherNdOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/GatherNdOptions.h new file mode 100644 index 0000000..55e4f7b --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/GatherNdOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_GATHER_ND_OPTIONS_H__ +#define __BBO_GATHER_ND_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_GatherNdOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_GATHER_ND_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/GatherOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/GatherOptions.cpp new file mode 100644 index 0000000..ecd5dc1 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/GatherOptions.cpp @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "GatherOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_GatherOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_GatherOptions(); + assert(tflite_builtin_options); + circle::GatherOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_axis(tflite_builtin_options->axis()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/GatherOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/GatherOptions.h new file mode 100644 index 0000000..300a7f7 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/GatherOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_GATHER_OPTIONS_H__ +#define __BBO_GATHER_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_GatherOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_GATHER_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/GreaterOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/GreaterOptions.cpp new file mode 100644 index 0000000..14ca0cd --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/GreaterOptions.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "GreaterOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_GreaterOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::GreaterOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/GreaterOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/GreaterOptions.h new file mode 100644 index 0000000..46e4d99 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/GreaterOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_GREATER_OPTIONS_H__ +#define __BBO_GREATER_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_GreaterOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_GREATER_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/IfOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/IfOptions.cpp new file mode 100644 index 0000000..cc5be49 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/IfOptions.cpp @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "IfOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset build_circle_IfOptions(flatbuffers::FlatBufferBuilder &fb, + const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_IfOptions(); + assert(tflite_builtin_options); + circle::IfOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_then_subgraph_index(tflite_builtin_options->then_subgraph_index()); + builtin_options_builder.add_else_subgraph_index(tflite_builtin_options->else_subgraph_index()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/IfOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/IfOptions.h new file mode 100644 index 0000000..6483d7b --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/IfOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_IF_OPTIONS_H__ +#define __BBO_IF_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset build_circle_IfOptions(flatbuffers::FlatBufferBuilder &fb, + const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_IF_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/L2NormalizeOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/L2NormalizeOptions.cpp new file mode 100644 index 0000000..d58aed8 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/L2NormalizeOptions.cpp @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "L2NormalizeOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_L2NormOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_L2NormOptions(); + assert(tflite_builtin_options); + circle::L2NormOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_fused_activation_function( + get_circle_activation_function_type(tflite_builtin_options->fused_activation_function())); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/L2NormalizeOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/L2NormalizeOptions.h new file mode 100644 index 0000000..5568c4e --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/L2NormalizeOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_L2NORMALIZE_OPTIONS_H__ +#define __BBO_L2NORMALIZE_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_L2NormOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_L2NORMALIZE_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/LeakyReluOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/LeakyReluOptions.cpp new file mode 100644 index 0000000..793b1d7 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/LeakyReluOptions.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LeakyReluOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_LeakyReluOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto *tflite_builtin_options = op->builtin_options_as_LeakyReluOptions(); + assert(tflite_builtin_options); + circle::LeakyReluOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_alpha(tflite_builtin_options->alpha()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/LeakyReluOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/LeakyReluOptions.h new file mode 100644 index 0000000..a2168f2 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/LeakyReluOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_LEAKY_RELU_OPTIONS_H__ +#define __BBO_LEAKY_RELU_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_LeakyReluOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_LEAKY_RELU_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/LessEqualOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/LessEqualOptions.cpp new file mode 100644 index 0000000..09a7753 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/LessEqualOptions.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LessEqualOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_LessEqualOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::LessEqualOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/LessEqualOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/LessEqualOptions.h new file mode 100644 index 0000000..3477c02 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/LessEqualOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_LESSEQUAL_OPTIONS_H__ +#define __BBO_LESSEQUAL_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_LessEqualOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_LESSEQUAL_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/LessOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/LessOptions.cpp new file mode 100644 index 0000000..0009dd6 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/LessOptions.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LessOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_LessOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::LessOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/LessOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/LessOptions.h new file mode 100644 index 0000000..932809a --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/LessOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_LESS_OPTIONS_H__ +#define __BBO_LESS_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_LessOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_LESS_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/LocalResponseNormalizationOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/LocalResponseNormalizationOptions.cpp new file mode 100644 index 0000000..342f6a8 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/LocalResponseNormalizationOptions.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LocalResponseNormalizationOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_LocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &fb, + const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_LocalResponseNormalizationOptions(); + assert(tflite_builtin_options); + circle::LocalResponseNormalizationOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_radius(tflite_builtin_options->radius()); + builtin_options_builder.add_bias(tflite_builtin_options->bias()); + builtin_options_builder.add_alpha(tflite_builtin_options->alpha()); + builtin_options_builder.add_beta(tflite_builtin_options->beta()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/LocalResponseNormalizationOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/LocalResponseNormalizationOptions.h new file mode 100644 index 0000000..0b43fee --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/LocalResponseNormalizationOptions.h @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_LOCAL_RESPONSE_NORMALIZATION_OPTIONS_H__ +#define __BBO_LOCAL_RESPONSE_NORMALIZATION_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_LocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &fb, + const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_LOCAL_RESPONSE_NORMALIZATION_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/LogSoftmaxOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/LogSoftmaxOptions.cpp new file mode 100644 index 0000000..2710288 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/LogSoftmaxOptions.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LogSoftmaxOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_LogSoftmaxOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::LogSoftmaxOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/LogSoftmaxOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/LogSoftmaxOptions.h new file mode 100644 index 0000000..920d1bd --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/LogSoftmaxOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_LOG_SOFTMAX_OPTIONS_H__ +#define __BBO_LOG_SOFTMAX_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_LogSoftmaxOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_LOG_SOFTMAX_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/LogicalAndOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/LogicalAndOptions.cpp new file mode 100644 index 0000000..52bfe0b --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/LogicalAndOptions.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "LogicalAndOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_LogicalAndOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::LogicalAndOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/LogicalAndOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/LogicalAndOptions.h new file mode 100644 index 0000000..90243d8 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/LogicalAndOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_LOGICALAND_OPTIONS_H__ +#define __BBO_LOGICALAND_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_LogicalAndOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_LOGICALAND_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/MatrixDiagOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/MatrixDiagOptions.cpp new file mode 100644 index 0000000..87a5504 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/MatrixDiagOptions.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "MatrixDiagOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_MatrixDiagOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::MatrixDiagOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/MatrixDiagOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/MatrixDiagOptions.h new file mode 100644 index 0000000..0fddaee --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/MatrixDiagOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_MATRIX_DIAG_OPTIONS_H__ +#define __BBO_MATRIX_DIAG_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_MatrixDiagOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_MATRIX_DIAG_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/MatrixSetDiagOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/MatrixSetDiagOptions.cpp new file mode 100644 index 0000000..c2e6890 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/MatrixSetDiagOptions.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "MatrixSetDiagOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_MatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::MatrixSetDiagOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/MatrixSetDiagOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/MatrixSetDiagOptions.h new file mode 100644 index 0000000..2da7426 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/MatrixSetDiagOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_MATRIX_SET_DIAG_OPTIONS_H__ +#define __BBO_MATRIX_SET_DIAG_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_MatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_MATRIX_SET_DIAG_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/MaximumMinimumOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/MaximumMinimumOptions.cpp new file mode 100644 index 0000000..d2d2888 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/MaximumMinimumOptions.cpp @@ -0,0 +1,34 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "MaximumMinimumOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_MaximumMinimumOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_MaximumMinimumOptions(); + assert(tflite_builtin_options); + circle::MaximumMinimumOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/MaximumMinimumOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/MaximumMinimumOptions.h new file mode 100644 index 0000000..fdaa7b8 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/MaximumMinimumOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_MAXIMUMMINIMUM_OPTIONS_H__ +#define __BBO_MAXIMUMMINIMUM_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_MaximumMinimumOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_MAXIMUMMINIMUM_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/MirrorPadOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/MirrorPadOptions.cpp new file mode 100644 index 0000000..5a0f7aa --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/MirrorPadOptions.cpp @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "PadOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_MirrorPadOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_MirrorPadOptions(); + assert(tflite_builtin_options); + circle::MirrorPadOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_mode(get_circle_mirrorpad_mode(tflite_builtin_options->mode())); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/MirrorPadOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/MirrorPadOptions.h new file mode 100644 index 0000000..6459dc3 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/MirrorPadOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_MIRROR_PAD_OPTIONS_H__ +#define __BBO_MIRROR_PAD_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_MirrorPadOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_MIRROR_PAD_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/NegOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/NegOptions.cpp new file mode 100644 index 0000000..9bc62bb --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/NegOptions.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "NegOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset build_circle_NegOptions(flatbuffers::FlatBufferBuilder &fb, + const tflite::Operator *) +{ + circle::NegOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/NegOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/NegOptions.h new file mode 100644 index 0000000..8cdba0d --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/NegOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_NEG_OPTIONS_H__ +#define __BBO_NEG_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset build_circle_NegOptions(flatbuffers::FlatBufferBuilder &fb, + const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_NEG_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/OneHotOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/OneHotOptions.cpp new file mode 100644 index 0000000..d4ca0b8 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/OneHotOptions.cpp @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "OneHotOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_OneHotOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_OneHotOptions(); + assert(tflite_builtin_options); + circle::OneHotOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_axis(tflite_builtin_options->axis()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/OneHotOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/OneHotOptions.h new file mode 100644 index 0000000..fa24be9 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/OneHotOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_ONEHOT_OPTIONS_H__ +#define __BBO_ONEHOT_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_OneHotOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_ONEHOT_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/PowOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/PowOptions.cpp new file mode 100644 index 0000000..1be39d7 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/PowOptions.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "PowOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset build_circle_PowOptions(flatbuffers::FlatBufferBuilder &fb, + const tflite::Operator *op) +{ + circle::PowOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/PowOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/PowOptions.h new file mode 100644 index 0000000..9bacd46 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/PowOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_POW_OPTIONS_H__ +#define __BBO_POW_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset build_circle_PowOptions(flatbuffers::FlatBufferBuilder &fb, + const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_POW_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/RangeOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/RangeOptions.cpp new file mode 100644 index 0000000..4937613 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/RangeOptions.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "RangeOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_RangeOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *) +{ + circle::RangeOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/RangeOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/RangeOptions.h new file mode 100644 index 0000000..3ee4004 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/RangeOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_RANGE_OPTIONS_H__ +#define __BBO_RANGE_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_RangeOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_RANGE_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/RankOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/RankOptions.cpp new file mode 100644 index 0000000..a5cbed5 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/RankOptions.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "RankOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_RankOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *) +{ + circle::RankOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/RankOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/RankOptions.h new file mode 100644 index 0000000..4f70bb3 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/RankOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_RANK_OPTIONS_H__ +#define __BBO_RANK_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_RankOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_RANK_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/ReshapeOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/ReshapeOptions.cpp index dc8e736..265f20d 100644 --- a/compiler/tflite2circle/src/BuildBuiltinOptions/ReshapeOptions.cpp +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/ReshapeOptions.cpp @@ -25,7 +25,9 @@ flatbuffers::Offset build_circle_ReshapeOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) { auto tflite_builtin_options = op->builtin_options_as_ReshapeOptions(); - assert(tflite_builtin_options); + if (tflite_builtin_options == nullptr) + return 0; + std::vector new_shape_vec{tflite_builtin_options->new_shape()->begin(), tflite_builtin_options->new_shape()->end()}; auto new_shape = fb.CreateVector(new_shape_vec); diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/ResizeBilinearOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/ResizeBilinearOptions.cpp new file mode 100644 index 0000000..e02ca83 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/ResizeBilinearOptions.cpp @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ResizeBilinearOptions.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_ResizeBilinearOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_ResizeBilinearOptions(); + assert(tflite_builtin_options); + + circle::ResizeBilinearOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_align_corners(tflite_builtin_options->align_corners()); + builtin_options_builder.add_half_pixel_centers(tflite_builtin_options->half_pixel_centers()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/ResizeBilinearOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/ResizeBilinearOptions.h new file mode 100644 index 0000000..d645eff --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/ResizeBilinearOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_RESIZE_BILINEAR_OPTIONS_H__ +#define __BBO_RESIZE_BILINEAR_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_ResizeBilinearOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_RESIZE_BILINEAR_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/ResizeNearestNeighborOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/ResizeNearestNeighborOptions.cpp new file mode 100644 index 0000000..572bd3d --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/ResizeNearestNeighborOptions.cpp @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ResizeNearestNeighborOptions.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_ResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &fb, + const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_ResizeNearestNeighborOptions(); + assert(tflite_builtin_options); + + circle::ResizeNearestNeighborOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_align_corners(tflite_builtin_options->align_corners()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/ResizeNearestNeighborOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/ResizeNearestNeighborOptions.h new file mode 100644 index 0000000..f87a433 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/ResizeNearestNeighborOptions.h @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_RESIZE_NEAREST_NEIGHBOR_OPTIONS_H__ +#define __BBO_RESIZE_NEAREST_NEIGHBOR_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_ResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &fb, + const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_RESIZE_NEAREST_NEIGHBOR_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/ReverseSequenceOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/ReverseSequenceOptions.cpp new file mode 100644 index 0000000..57f7d68 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/ReverseSequenceOptions.cpp @@ -0,0 +1,33 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReverseSequenceOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_ReverseSequenceOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_ReverseSequenceOptions(); + assert(tflite_builtin_options); + circle::ReverseSequenceOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_seq_dim(tflite_builtin_options->seq_dim()); + builtin_options_builder.add_batch_dim(tflite_builtin_options->batch_dim()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/ReverseSequenceOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/ReverseSequenceOptions.h new file mode 100644 index 0000000..b056790 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/ReverseSequenceOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_REVERSE_SEQUENCE_OPTIONS_H__ +#define __BBO_REVERSE_SEQUENCE_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_ReverseSequenceOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_REVERSE_SEQUENCE_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/ReverseV2Options.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/ReverseV2Options.cpp new file mode 100644 index 0000000..b6771d3 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/ReverseV2Options.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ReverseV2Options.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_ReverseV2Options(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *) +{ + circle::ReverseV2OptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/ReverseV2Options.h b/compiler/tflite2circle/src/BuildBuiltinOptions/ReverseV2Options.h new file mode 100644 index 0000000..a92dd9e --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/ReverseV2Options.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_REVERSE_V2_OPTIONS_H__ +#define __BBO_REVERSE_V2_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_ReverseV2Options(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_REVERSE_V2_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/ScatterNdOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/ScatterNdOptions.cpp new file mode 100644 index 0000000..e794805 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/ScatterNdOptions.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ScatterNdOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_ScatterNdOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::ScatterNdOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/ScatterNdOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/ScatterNdOptions.h new file mode 100644 index 0000000..de17ebc --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/ScatterNdOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_SCATTER_ND_OPTIONS_H__ +#define __BBO_SCATTER_ND_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_ScatterNdOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_SCATTER_ND_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SegmentSumOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/SegmentSumOptions.cpp new file mode 100644 index 0000000..efd80c1 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SegmentSumOptions.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SegmentSumOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SegmentSumOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::SegmentSumOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SegmentSumOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/SegmentSumOptions.h new file mode 100644 index 0000000..e616384 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SegmentSumOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_SEGMENT_SUM_OPTIONS_H__ +#define __BBO_SEGMENT_SUM_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SegmentSumOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_SEGMENT_SUM_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SelectOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/SelectOptions.cpp new file mode 100644 index 0000000..c584fbf --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SelectOptions.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SelectOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SelectOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::SelectOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SelectOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/SelectOptions.h new file mode 100644 index 0000000..4d4e6fe --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SelectOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_SELECT_OPTIONS_H__ +#define __BBO_SELECT_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SelectOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_SELECT_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SelectV2Options.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/SelectV2Options.cpp new file mode 100644 index 0000000..9032d5e --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SelectV2Options.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SelectV2Options.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SelectV2Options(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::SelectV2OptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SelectV2Options.h b/compiler/tflite2circle/src/BuildBuiltinOptions/SelectV2Options.h new file mode 100644 index 0000000..14e91d2 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SelectV2Options.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_SELECT_V2_OPTIONS_H__ +#define __BBO_SELECT_V2_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SelectV2Options(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_SELECT_V2_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SliceOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/SliceOptions.cpp new file mode 100644 index 0000000..d1dba1b --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SliceOptions.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SliceOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SliceOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::SliceOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SliceOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/SliceOptions.h new file mode 100644 index 0000000..0d7ca60 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SliceOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_SLICE_OPTIONS_H__ +#define __BBO_SLICE_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SliceOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_SLICE_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SpaceToBatchNDOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/SpaceToBatchNDOptions.cpp new file mode 100644 index 0000000..0cb4dba --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SpaceToBatchNDOptions.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SpaceToBatchNDOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *) +{ + circle::SpaceToBatchNDOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SpaceToBatchNDOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/SpaceToBatchNDOptions.h new file mode 100644 index 0000000..dd3d983 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SpaceToBatchNDOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_SPACE_TO_BATCH_ND_OPTIONS_H__ +#define __BBO_SPACE_TO_BATCH_ND_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_SPACE_TO_BATCH_ND_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SpaceToDepthOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/SpaceToDepthOptions.cpp new file mode 100644 index 0000000..d9d6a8f --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SpaceToDepthOptions.cpp @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SpaceToDepthOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SpaceToDepthOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_SpaceToDepthOptions(); + assert(tflite_builtin_options); + circle::SpaceToDepthOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_block_size(tflite_builtin_options->block_size()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SpaceToDepthOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/SpaceToDepthOptions.h new file mode 100644 index 0000000..e53875b --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SpaceToDepthOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_SPACE_TO_DEPTH_OPTIONS_H__ +#define __BBO_SPACE_TO_DEPTH_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SpaceToDepthOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_SPACE_TO_DEPTH_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SparseToDenseOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/SparseToDenseOptions.cpp new file mode 100644 index 0000000..48abaac --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SparseToDenseOptions.cpp @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SparseToDenseOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SparseToDenseOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_SparseToDenseOptions(); + assert(tflite_builtin_options); + circle::SparseToDenseOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_validate_indices(tflite_builtin_options->validate_indices()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SparseToDenseOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/SparseToDenseOptions.h new file mode 100644 index 0000000..c2058f0 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SparseToDenseOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_SPARSETODENSE_OPTIONS_H__ +#define __BBO_SPARSETODENSE_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SparseToDenseOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_SPARSETODENSE_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SplitVOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/SplitVOptions.cpp new file mode 100644 index 0000000..da309f3 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SplitVOptions.cpp @@ -0,0 +1,34 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SplitVOptions.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SplitVOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_SplitVOptions(); + assert(tflite_builtin_options); + circle::SplitVOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_num_splits(tflite_builtin_options->num_splits()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SplitVOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/SplitVOptions.h new file mode 100644 index 0000000..0550c00 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SplitVOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_SPLIT_V_OPTIONS_H__ +#define __BBO_SPLIT_V_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SplitVOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_SPLIT_V_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SquareOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/SquareOptions.cpp new file mode 100644 index 0000000..4656f91 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SquareOptions.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SquareOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SquareOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *) +{ + circle::SquareOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SquareOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/SquareOptions.h new file mode 100644 index 0000000..647686e --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SquareOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_SQUARE_OPTIONS_H__ +#define __BBO_SQUARE_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SquareOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_SQUARE_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SquaredDifferenceOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/SquaredDifferenceOptions.cpp new file mode 100644 index 0000000..0c2dbde --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SquaredDifferenceOptions.cpp @@ -0,0 +1,30 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "SquaredDifferenceOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &fb, + const tflite::Operator *op) +{ + circle::SquaredDifferenceOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/SquaredDifferenceOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/SquaredDifferenceOptions.h new file mode 100644 index 0000000..76b45c0 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/SquaredDifferenceOptions.h @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_SQUAREDDIFFERENCE_OPTIONS_H__ +#define __BBO_SQUAREDDIFFERENCE_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_SquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &fb, + const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_SQUAREDDIFFERENCE_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/StridedSliceOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/StridedSliceOptions.cpp new file mode 100644 index 0000000..68cf897 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/StridedSliceOptions.cpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "StridedSliceOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_StridedSliceOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_StridedSliceOptions(); + assert(tflite_builtin_options); + circle::StridedSliceOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_begin_mask(tflite_builtin_options->begin_mask()); + builtin_options_builder.add_end_mask(tflite_builtin_options->end_mask()); + builtin_options_builder.add_ellipsis_mask(tflite_builtin_options->ellipsis_mask()); + builtin_options_builder.add_new_axis_mask(tflite_builtin_options->new_axis_mask()); + builtin_options_builder.add_shrink_axis_mask(tflite_builtin_options->shrink_axis_mask()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/StridedSliceOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/StridedSliceOptions.h new file mode 100644 index 0000000..e6782a0 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/StridedSliceOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_STRIDEDSLICE_OPTIONS_H__ +#define __BBO_STRIDEDSLICE_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_StridedSliceOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_STRIDEDSLICE_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/TileOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/TileOptions.cpp new file mode 100644 index 0000000..47316bf --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/TileOptions.cpp @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "TileOptions.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_TileOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::TileOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/TileOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/TileOptions.h new file mode 100644 index 0000000..e3e9a71 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/TileOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_TILE_OPTIONS_H__ +#define __BBO_TILE_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_TileOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_TILE_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/TopKV2Options.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/TopKV2Options.cpp new file mode 100644 index 0000000..a2bc76f --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/TopKV2Options.cpp @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "TopKV2Options.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_TopKV2Options(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + circle::TopKV2OptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/TopKV2Options.h b/compiler/tflite2circle/src/BuildBuiltinOptions/TopKV2Options.h new file mode 100644 index 0000000..89b5b99 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/TopKV2Options.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_TOPK_V2_OPTIONS_H__ +#define __BBO_TOPK_V2_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_TopKV2Options(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_TOPK_V2_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/TransposeConvOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/TransposeConvOptions.cpp new file mode 100644 index 0000000..301f2c4 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/TransposeConvOptions.cpp @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "TransposeConvOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_TransposeConvOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_TransposeConvOptions(); + assert(tflite_builtin_options); + circle::TransposeConvOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_padding(get_circle_padding(tflite_builtin_options->padding())); + builtin_options_builder.add_stride_w(tflite_builtin_options->stride_w()); + builtin_options_builder.add_stride_h(tflite_builtin_options->stride_h()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/TransposeConvOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/TransposeConvOptions.h new file mode 100644 index 0000000..dd0bec2 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/TransposeConvOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_TRANSPOSE_CONV_OPTIONS_H__ +#define __BBO_TRANSPOSE_CONV_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_TransposeConvOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_TRANSPOSE_CONV_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/UniqueOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/UniqueOptions.cpp new file mode 100644 index 0000000..96ddc15 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/UniqueOptions.cpp @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "UniqueOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_UniqueOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_UniqueOptions(); + assert(tflite_builtin_options); + circle::UniqueOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_idx_out_type( + get_circle_tensortype(tflite_builtin_options->idx_out_type())); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/UniqueOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/UniqueOptions.h new file mode 100644 index 0000000..35736e9 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/UniqueOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_UNIQUE_OPTIONS_H__ +#define __BBO_UNIQUE_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_UniqueOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_UNIQUE_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/UnpackOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/UnpackOptions.cpp new file mode 100644 index 0000000..c9a332a --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/UnpackOptions.cpp @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "UnpackOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_UnpackOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_UnpackOptions(); + assert(tflite_builtin_options); + circle::UnpackOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_num(tflite_builtin_options->num()); + builtin_options_builder.add_axis(tflite_builtin_options->axis()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/UnpackOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/UnpackOptions.h new file mode 100644 index 0000000..6dfed87 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/UnpackOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_UNPACK_OPTIONS_H__ +#define __BBO_UNPACK_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_UnpackOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_UNPACK_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/WhereOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/WhereOptions.cpp new file mode 100644 index 0000000..d591a54 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/WhereOptions.cpp @@ -0,0 +1,29 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "WhereOptions.h" + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_WhereOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *) +{ + circle::WhereOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/WhereOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/WhereOptions.h new file mode 100644 index 0000000..a113f69 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/WhereOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_WHERE_OPTIONS_H__ +#define __BBO_WHERE_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_WhereOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_WHERE_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/WhileOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/WhileOptions.cpp new file mode 100644 index 0000000..1ad1af7 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/WhileOptions.cpp @@ -0,0 +1,36 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "WhileOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_WhileOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op) +{ + auto tflite_builtin_options = op->builtin_options_as_WhileOptions(); + assert(tflite_builtin_options); + circle::WhileOptionsBuilder builtin_options_builder{fb}; + builtin_options_builder.add_cond_subgraph_index(tflite_builtin_options->cond_subgraph_index()); + builtin_options_builder.add_body_subgraph_index(tflite_builtin_options->body_subgraph_index()); + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/WhileOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/WhileOptions.h new file mode 100644 index 0000000..000e1a2 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/WhileOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_WHILE_OPTIONS_H__ +#define __BBO_WHILE_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_WhileOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_WHILE_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/ZerosLikeOptions.cpp b/compiler/tflite2circle/src/BuildBuiltinOptions/ZerosLikeOptions.cpp new file mode 100644 index 0000000..e64e421 --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/ZerosLikeOptions.cpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "ZerosLikeOptions.h" +#include "DataLookup.h" + +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_ZerosLikeOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *) +{ + circle::ZerosLikeOptionsBuilder builtin_options_builder{fb}; + return builtin_options_builder.Finish(); +} + +} // namespace tflite2circle diff --git a/compiler/tflite2circle/src/BuildBuiltinOptions/ZerosLikeOptions.h b/compiler/tflite2circle/src/BuildBuiltinOptions/ZerosLikeOptions.h new file mode 100644 index 0000000..5113ecc --- /dev/null +++ b/compiler/tflite2circle/src/BuildBuiltinOptions/ZerosLikeOptions.h @@ -0,0 +1,31 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __BBO_ZEROS_LIKE_OPTIONS_H__ +#define __BBO_ZEROS_LIKE_OPTIONS_H__ + +#include +#include + +namespace tflite2circle +{ + +flatbuffers::Offset +build_circle_ZerosLikeOptions(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +} // namespace tflite2circle + +#endif // __BBO_ZEROS_LIKE_OPTIONS_H__ diff --git a/compiler/tflite2circle/src/CircleModel.cpp b/compiler/tflite2circle/src/CircleModel.cpp index 3a56932..cb4437a 100644 --- a/compiler/tflite2circle/src/CircleModel.cpp +++ b/compiler/tflite2circle/src/CircleModel.cpp @@ -15,6 +15,7 @@ */ #include +#include #include "CircleModel.h" #include "DataLookup.h" @@ -67,8 +68,12 @@ Offset::Offset(FlatBufBuilder &fb, const TFLFlatBufVec *tflite_fla for (auto it : *tflite_tensors) { // shape - std::vector shape_vec{it->shape()->begin(), it->shape()->end()}; - auto shape = fb->CreateVector(shape_vec); + flatbuffers::Offset> shape; + if (it->shape()) + { + auto shape_vec = std::vector({it->shape()->begin(), it->shape()->end()}); + shape = fb->CreateVector(shape_vec); + } // name flatbuffers::Offset name; if (it->name()) @@ -85,6 +90,7 @@ Offset::Offset(FlatBufBuilder &fb, const TFLFlatBufVec *tflite_fla flatbuffers::Offset> max; flatbuffers::Offset> scale; flatbuffers::Offset> zero_point; + int32_t quantized_dimension = it->quantization()->quantized_dimension(); if (it->quantization()->min() && it->quantization()->max()) { @@ -106,7 +112,9 @@ Offset::Offset(FlatBufBuilder &fb, const TFLFlatBufVec *tflite_fla zero_point = fb->CreateVector(tfzerop); } - quantization = circle::CreateQuantizationParameters(*fb, min, max, scale, zero_point); + quantization = circle::CreateQuantizationParameters(*fb, min, max, scale, zero_point, + circle::QuantizationDetails_NONE, 0, + quantized_dimension); } // is_variable bool is_variable = it->is_variable(); @@ -150,6 +158,18 @@ Offset::Offset(FlatBufBuilder &fb, const TFLFlatBufVec *tflite_fla // builtin options auto circle_builtin_options = get_circle_builtin_options(*fb, it); auto circle_builtin_options_type = get_circle_builtin_options_type(it); + // custom options + flatbuffers::Offset> circle_custom_options; + if (it->custom_options()) + { + std::vector custom_options_vec{it->custom_options()->begin(), + it->custom_options()->end()}; + circle_custom_options = fb->CreateVector(custom_options_vec); + } + // custom options format + // TODO Make get_circle_custom_options_format + assert(it->custom_options_format() == tflite::CustomOptionsFormat_FLEXBUFFERS); + auto circle_custom_options_format = circle::CustomOptionsFormat_FLEXBUFFERS; circle::OperatorBuilder operator_builder{*fb}; operator_builder.add_opcode_index(it->opcode_index()); @@ -157,7 +177,9 @@ Offset::Offset(FlatBufBuilder &fb, const TFLFlatBufVec *tflite_fla operator_builder.add_outputs(circle_outputs); operator_builder.add_builtin_options(circle_builtin_options); operator_builder.add_builtin_options_type(circle_builtin_options_type); - // TODO custom_options, mutating_variable_inputs + operator_builder.add_custom_options(circle_custom_options); + operator_builder.add_custom_options_format(circle_custom_options_format); + // TODO mutating_variable_inputs auto opeartor = operator_builder.Finish(); operator_vec.emplace_back(opeartor); } @@ -205,11 +227,11 @@ CircleModel::CircleModel(FlatBufBuilder &fb, TFLModel &model) { const tflite::Model *tfl_model = model.load_model(); _operator_codes_offset = - stdex::make_unique>(fb, tfl_model->operator_codes()); - _subGraphs_offset = stdex::make_unique>(fb, tfl_model->subgraphs()); - _buffers_offset = stdex::make_unique>(fb, tfl_model->buffers()); + std::make_unique>(fb, tfl_model->operator_codes()); + _subGraphs_offset = std::make_unique>(fb, tfl_model->subgraphs()); + _buffers_offset = std::make_unique>(fb, tfl_model->buffers()); _metadata_buffer_offset = - stdex::make_unique>(fb, tfl_model->metadata_buffer()); + std::make_unique>(fb, tfl_model->metadata_buffer()); model_build(); } diff --git a/compiler/tflite2circle/src/DataLookup.cpp b/compiler/tflite2circle/src/DataLookup.cpp index a1b7806..b0d35d1 100644 --- a/compiler/tflite2circle/src/DataLookup.cpp +++ b/compiler/tflite2circle/src/DataLookup.cpp @@ -110,4 +110,17 @@ circle::BuiltinOptions get_circle_builtin_options_type(const tflite::Operator *o } } +circle::MirrorPadMode get_circle_mirrorpad_mode(tflite::MirrorPadMode tfl_mode) +{ + switch (tfl_mode) + { + case tflite::MirrorPadMode_REFLECT: + return circle::MirrorPadMode_REFLECT; + case tflite::MirrorPadMode_SYMMETRIC: + return circle::MirrorPadMode_SYMMETRIC; + default: + throw std::runtime_error("tflite2circle: wrong mirrorpad mode."); + } +} + } // namespace tflite2circle diff --git a/compiler/tflite2circle/src/DataLookup.h b/compiler/tflite2circle/src/DataLookup.h index 3f141ec..7ea01b9 100644 --- a/compiler/tflite2circle/src/DataLookup.h +++ b/compiler/tflite2circle/src/DataLookup.h @@ -23,15 +23,59 @@ namespace tflite2circle { +/** + * @brief Returns circle builtin_code according to tflite. + * + * @note You can see a list of currently supported BuiltinOperator in TFLOperator.lst file. +*/ circle::BuiltinOperator get_circle_builtin_code(tflite::BuiltinOperator tfl_bop); + +/** + * @brief Returns circle TensorType according to tflite. + * + * @note You can see a list of currently supported TensorType in TFLTensorType.lst file. +*/ circle::TensorType get_circle_tensortype(tflite::TensorType tfl_tt); + +/** + * @brief Returns circle Padding enum according to tflite. +*/ circle::Padding get_circle_padding(tflite::Padding tfl_p); + +/** + * @brief Returns circle ActivationFunctionType according to tflite. + * + * @note You can see a list of currently supported ActivationFunctionType in + * TFLActivationFunctionType.lst file. +*/ circle::ActivationFunctionType get_circle_activation_function_type(tflite::ActivationFunctionType tfl_aft); + +/** + * @brief Returns circle builtin_options according to tflite. + * + * @note You can see a list of currently supported BuiltinOptions in + * TFLBuiltinOptions.lst file. + * + * This function calls the build_circle_##BuiltinOptions internally(e.g. + * build_circle_AbsOptions, build_circle_AddOptions, etc.), so refer to it for a more + * detailed implementation. +*/ flatbuffers::Offset get_circle_builtin_options(flatbuffers::FlatBufferBuilder &fb, const tflite::Operator *op); + +/** + * @brief Returns circle builtin_options_type according to tflite. + * + * @note You can see a list of currently supported BuiltinOptions in TFLBuiltinOptions.lst file. +*/ circle::BuiltinOptions get_circle_builtin_options_type(const tflite::Operator *op); +/** + * @brief Returns circle MirrorPadMode according to tflite. +*/ +circle::MirrorPadMode get_circle_mirrorpad_mode(tflite::MirrorPadMode tfl_mode); + } // namespace tflite2circle #endif // __DATA_LOOKUP_H__ diff --git a/compiler/tflite2circle/src/TFLBuiltinOptions.lst b/compiler/tflite2circle/src/TFLBuiltinOptions.lst index 65c60b8..3ef9f15 100644 --- a/compiler/tflite2circle/src/TFLBuiltinOptions.lst +++ b/compiler/tflite2circle/src/TFLBuiltinOptions.lst @@ -15,89 +15,93 @@ TFL_BUILTIN_OPTIONS(FullyConnectedOptions) TFL_BUILTIN_OPTIONS(SoftmaxOptions) TFL_BUILTIN_OPTIONS(ConcatenationOptions) TFL_BUILTIN_OPTIONS(AddOptions) -//TFL_BUILTIN_OPTIONS(L2NormOptions) -//TFL_BUILTIN_OPTIONS(LocalResponseNormalizationOptions) +TFL_BUILTIN_OPTIONS(L2NormOptions) +TFL_BUILTIN_OPTIONS(LocalResponseNormalizationOptions) //TFL_BUILTIN_OPTIONS(LSTMOptions) -//TFL_BUILTIN_OPTIONS(ResizeBilinearOptions) +TFL_BUILTIN_OPTIONS(ResizeBilinearOptions) //TFL_BUILTIN_OPTIONS(CallOptions) TFL_BUILTIN_OPTIONS(ReshapeOptions) //TFL_BUILTIN_OPTIONS(SkipGramOptions) -//TFL_BUILTIN_OPTIONS(SpaceToDepthOptions) +TFL_BUILTIN_OPTIONS(SpaceToDepthOptions) //TFL_BUILTIN_OPTIONS(EmbeddingLookupSparseOptions) TFL_BUILTIN_OPTIONS(MulOptions) TFL_BUILTIN_OPTIONS(PadOptions) -//TFL_BUILTIN_OPTIONS(GatherOptions) +TFL_BUILTIN_OPTIONS(GatherOptions) TFL_BUILTIN_OPTIONS(BatchToSpaceNDOptions) -//TFL_BUILTIN_OPTIONS(SpaceToBatchNDOptions) +TFL_BUILTIN_OPTIONS(SpaceToBatchNDOptions) TFL_BUILTIN_OPTIONS(TransposeOptions) TFL_BUILTIN_OPTIONS(ReducerOptions) TFL_BUILTIN_OPTIONS(SubOptions) TFL_BUILTIN_OPTIONS(DivOptions) TFL_BUILTIN_OPTIONS(SqueezeOptions) //TFL_BUILTIN_OPTIONS(SequenceRNNOptions) -//TFL_BUILTIN_OPTIONS(StridedSliceOptions) +TFL_BUILTIN_OPTIONS(StridedSliceOptions) TFL_BUILTIN_OPTIONS(ExpOptions) -//TFL_BUILTIN_OPTIONS(TopKV2Options) +TFL_BUILTIN_OPTIONS(TopKV2Options) TFL_BUILTIN_OPTIONS(SplitOptions) -//TFL_BUILTIN_OPTIONS(LogSoftmaxOptions) +TFL_BUILTIN_OPTIONS(LogSoftmaxOptions) TFL_BUILTIN_OPTIONS(CastOptions) //TFL_BUILTIN_OPTIONS(DequantizeOptions) -//TFL_BUILTIN_OPTIONS(MaximumMinimumOptions) +TFL_BUILTIN_OPTIONS(MaximumMinimumOptions) TFL_BUILTIN_OPTIONS(ArgMaxOptions) -//TFL_BUILTIN_OPTIONS(LessOptions) -//TFL_BUILTIN_OPTIONS(NegOptions) +TFL_BUILTIN_OPTIONS(LessOptions) +TFL_BUILTIN_OPTIONS(NegOptions) //TFL_BUILTIN_OPTIONS(PadV2Options) -//TFL_BUILTIN_OPTIONS(GreaterOptions) +TFL_BUILTIN_OPTIONS(GreaterOptions) TFL_BUILTIN_OPTIONS(GreaterEqualOptions) -//TFL_BUILTIN_OPTIONS(LessEqualOptions) -//TFL_BUILTIN_OPTIONS(SelectOptions) -//TFL_BUILTIN_OPTIONS(SliceOptions) -//TFL_BUILTIN_OPTIONS(TransposeConvOptions) -//TFL_BUILTIN_OPTIONS(SparseToDenseOptions) -//TFL_BUILTIN_OPTIONS(TileOptions) +TFL_BUILTIN_OPTIONS(LessEqualOptions) +TFL_BUILTIN_OPTIONS(SelectOptions) +TFL_BUILTIN_OPTIONS(SelectV2Options) +TFL_BUILTIN_OPTIONS(SliceOptions) +TFL_BUILTIN_OPTIONS(TransposeConvOptions) +TFL_BUILTIN_OPTIONS(SparseToDenseOptions) +TFL_BUILTIN_OPTIONS(TileOptions) TFL_BUILTIN_OPTIONS(ExpandDimsOptions) TFL_BUILTIN_OPTIONS(EqualOptions) TFL_BUILTIN_OPTIONS(NotEqualOptions) TFL_BUILTIN_OPTIONS(ShapeOptions) -//TFL_BUILTIN_OPTIONS(PowOptions) -//TFL_BUILTIN_OPTIONS(ArgMinOptions) +TFL_BUILTIN_OPTIONS(PowOptions) +TFL_BUILTIN_OPTIONS(ArgMinOptions) //TFL_BUILTIN_OPTIONS(FakeQuantOptions) TFL_BUILTIN_OPTIONS(PackOptions) TFL_BUILTIN_OPTIONS(LogicalOrOptions) -//TFL_BUILTIN_OPTIONS(OneHotOptions) -//TFL_BUILTIN_OPTIONS(LogicalAndOptions) +TFL_BUILTIN_OPTIONS(OneHotOptions) +TFL_BUILTIN_OPTIONS(LogicalAndOptions) TFL_BUILTIN_OPTIONS(LogicalNotOptions) -//TFL_BUILTIN_OPTIONS(UnpackOptions) -//TFL_BUILTIN_OPTIONS(FloorDivOptions) -//TFL_BUILTIN_OPTIONS(SquareOptions) -//TFL_BUILTIN_OPTIONS(ZerosLikeOptions) +TFL_BUILTIN_OPTIONS(UnpackOptions) +TFL_BUILTIN_OPTIONS(FloorDivOptions) +TFL_BUILTIN_OPTIONS(SquareOptions) +TFL_BUILTIN_OPTIONS(ZerosLikeOptions) TFL_BUILTIN_OPTIONS(FillOptions) //TFL_BUILTIN_OPTIONS(BidirectionalSequenceLSTMOptions) //TFL_BUILTIN_OPTIONS(BidirectionalSequenceRNNOptions) //TFL_BUILTIN_OPTIONS(UnidirectionalSequenceLSTMOptions) -//TFL_BUILTIN_OPTIONS(FloorModOptions) -//TFL_BUILTIN_OPTIONS(RangeOptions) -//TFL_BUILTIN_OPTIONS(ResizeNearestNeighborOptions) -//TFL_BUILTIN_OPTIONS(LeakyReluOptions) -//TFL_BUILTIN_OPTIONS(SquaredDifferenceOptions) -//TFL_BUILTIN_OPTIONS(MirrorPadOptions) +TFL_BUILTIN_OPTIONS(FloorModOptions) +TFL_BUILTIN_OPTIONS(RangeOptions) +TFL_BUILTIN_OPTIONS(ResizeNearestNeighborOptions) +TFL_BUILTIN_OPTIONS(LeakyReluOptions) +TFL_BUILTIN_OPTIONS(SquaredDifferenceOptions) +TFL_BUILTIN_OPTIONS(MirrorPadOptions) TFL_BUILTIN_OPTIONS(AbsOptions) -//TFL_BUILTIN_OPTIONS(SplitVOptions) -//TFL_BUILTIN_OPTIONS(UniqueOptions) -//TFL_BUILTIN_OPTIONS(ReverseV2Options) -//TFL_BUILTIN_OPTIONS(AddNOptions) -//TFL_BUILTIN_OPTIONS(GatherNdOptions) +TFL_BUILTIN_OPTIONS(SplitVOptions) +TFL_BUILTIN_OPTIONS(UniqueOptions) +TFL_BUILTIN_OPTIONS(ReverseV2Options) +TFL_BUILTIN_OPTIONS(AddNOptions) +TFL_BUILTIN_OPTIONS(GatherNdOptions) TFL_BUILTIN_OPTIONS(CosOptions) -//TFL_BUILTIN_OPTIONS(WhereOptions) +TFL_BUILTIN_OPTIONS(WhereOptions) //TFL_BUILTIN_OPTIONS(RankOptions) -//TFL_BUILTIN_OPTIONS(ReverseSequenceOptions) -//TFL_BUILTIN_OPTIONS(MatrixDiagOptions) +TFL_BUILTIN_OPTIONS(ReverseSequenceOptions) +TFL_BUILTIN_OPTIONS(MatrixDiagOptions) //TFL_BUILTIN_OPTIONS(QuantizeOptions) -//TFL_BUILTIN_OPTIONS(MatrixSetDiagOptions) +TFL_BUILTIN_OPTIONS(MatrixSetDiagOptions) //TFL_BUILTIN_OPTIONS(HardSwishOptions) -//TFL_BUILTIN_OPTIONS(IfOptions) -//TFL_BUILTIN_OPTIONS(WhileOptions) -//TFL_BUILTIN_OPTIONS(DepthToSpaceOptions) +TFL_BUILTIN_OPTIONS(IfOptions) +TFL_BUILTIN_OPTIONS(WhileOptions) +TFL_BUILTIN_OPTIONS(DepthToSpaceOptions) //TFL_BUILTIN_OPTIONS(NonMaxSuppressionV4Options) //TFL_BUILTIN_OPTIONS(NonMaxSuppressionV5Options) -//TFL_BUILTIN_OPTIONS(ScatterNdOptions) +TFL_BUILTIN_OPTIONS(RankOptions) +TFL_BUILTIN_OPTIONS(ScatterNdOptions) +TFL_BUILTIN_OPTIONS(SegmentSumOptions) +TFL_BUILTIN_OPTIONS(BatchMatMulOptions) diff --git a/compiler/tflite2circle/src/TFLOperator.lst b/compiler/tflite2circle/src/TFLOperator.lst index ac2f9da..942c846 100644 --- a/compiler/tflite2circle/src/TFLOperator.lst +++ b/compiler/tflite2circle/src/TFLOperator.lst @@ -8,6 +8,7 @@ TFL_OPERATOR(ADD) TFL_OPERATOR(AVERAGE_POOL_2D) TFL_OPERATOR(CONCATENATION) TFL_OPERATOR(CONV_2D) +TFL_OPERATOR(DEPTH_TO_SPACE) TFL_OPERATOR(DEPTHWISE_CONV_2D) TFL_OPERATOR(DEQUANTIZE) TFL_OPERATOR(EMBEDDING_LOOKUP) @@ -126,3 +127,7 @@ TFL_OPERATOR(WHILE) TFL_OPERATOR(NON_MAX_SUPPRESSION_V4) TFL_OPERATOR(NON_MAX_SUPPRESSION_V5) TFL_OPERATOR(SCATTER_ND) +TFL_OPERATOR(SELECT_V2) +TFL_OPERATOR(DENSIFY) +TFL_OPERATOR(SEGMENT_SUM) +TFL_OPERATOR(BATCH_MATMUL) diff --git a/compute/ARMComputeEx/CMakeLists.txt b/compute/ARMComputeEx/CMakeLists.txt index 8dfe0c4..58f558d 100644 --- a/compute/ARMComputeEx/CMakeLists.txt +++ b/compute/ARMComputeEx/CMakeLists.txt @@ -1,4 +1,4 @@ -nnas_find_package(ARMCompute QUIET) +nnfw_find_package(ARMCompute QUIET) if(NOT ARMCompute_FOUND) message(STATUS "Check ARM Compute library extension build: need ARM Compute library") diff --git a/compute/ARMComputeEx/arm_compute/core/CL/kernels/CLSpaceToBatchNDKernel.h b/compute/ARMComputeEx/arm_compute/core/CL/kernels/CLSpaceToBatchNDKernel.h deleted file mode 100644 index 75ba0c0..0000000 --- a/compute/ARMComputeEx/arm_compute/core/CL/kernels/CLSpaceToBatchNDKernel.h +++ /dev/null @@ -1,93 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* - * Copyright (c) 2016-2018 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ - -#ifndef __ARM_COMPUTE_CLSPACE_TO_BATCH_ND_KERNEL_H__ -#define __ARM_COMPUTE_CLSPACE_TO_BATCH_ND_KERNEL_H__ - -#include "arm_compute/core/CL/ICLKernel.h" - -namespace arm_compute -{ -class ICLTensor; - -/** OpenCL kernel to perform SPACE_TO_BATCH_ND operation */ -class CLSpaceToBatchNDKernel final : public ICLKernel -{ -public: - /** Default constructor */ - CLSpaceToBatchNDKernel(); - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLSpaceToBatchNDKernel(const CLSpaceToBatchNDKernel &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - CLSpaceToBatchNDKernel &operator=(const CLSpaceToBatchNDKernel &) = delete; - /** Allow instances of this class to be moved */ - CLSpaceToBatchNDKernel(CLSpaceToBatchNDKernel &&) = default; - /** Allow instances of this class to be moved */ - CLSpaceToBatchNDKernel &operator=(CLSpaceToBatchNDKernel &&) = default; - /** Default destructor */ - ~CLSpaceToBatchNDKernel() = default; - /** Initialise the kernel's input and output. - * - * @note The data layout of input and output must be the same. - * @note The number of dimensions of input and output must be 4, and `spatial` dimensions - * are height and width. - * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S16/F16/S32/F32. - * Data layout supported: NCHW/NHWC - * @param[in] block_size Block size tensor. Data types supported: S32. - * @param[in] padding_size Padding size tensor. Data types supported: S32. - * @param[out] output Output tensor. Data types supported: U8/QASYMM8/S16/F16/S32/F32. - * Data layout supported: NCHW/NHWC - */ - void configure(const ICLTensor *input, const ICLTensor *block_size, const ICLTensor *padding_size, - ICLTensor *output); - - // Inherited methods overridden: - void run(const Window &window, cl::CommandQueue &queue) override; - -private: - const ICLTensor *_input{nullptr}; /**< Source tensor */ - const ICLTensor *_block_size{nullptr}; /**< Block size tensor */ - const ICLTensor *_padding_size{nullptr}; /**< Padding size tensor */ - ICLTensor *_output{nullptr}; /**< Destination tensor */ -}; - -} // namespace arm_compute - -#endif /* __ARM_COMPUTE_CLSPACE_TO_BATCH_ND_KERNEL_H__ */ diff --git a/compute/ARMComputeEx/arm_compute/core/CPP/kernels/CPPOneHotKernelEx.h b/compute/ARMComputeEx/arm_compute/core/CPP/kernels/CPPOneHotKernelEx.h index 59c337a..6e8bdc1 100644 --- a/compute/ARMComputeEx/arm_compute/core/CPP/kernels/CPPOneHotKernelEx.h +++ b/compute/ARMComputeEx/arm_compute/core/CPP/kernels/CPPOneHotKernelEx.h @@ -68,28 +68,28 @@ public: /** Set the input and output of the kernel. * * @param[in] indices A tensor for indices. Data types supported: S32 + * @param[in] depth A tensor for depth. Data types supported: S32 + * @param[in] on_value A tensor for on_value. Data types supported: F32 + * @param[in] off_value A tensor for off_value. Data types supported: F32* * @param[out] output A tensor for computed value of one hot operator - * @param[in] depth An int value for depth - * @param[in] on_value A float value for on_value - * @param[in] off_value A float value for off_value * @param[in] axis An int value for axis */ - void configure(const ITensor *indices, ITensor *output, const int depth, const float on_value, - const float off_value, const int axis); + void configure(const ITensor *indices, const ITensor *depth, const ITensor *on_value, + const ITensor *off_value, ITensor *output, const int axis); /** Static function to check if given info will lead to a valid configuration of @ref * CPPOneHotKernelEx * * @param[in] indices A tensor for indices. Data types supported: S32 - * @param[in] depth An int value for depth - * @param[in] on_value A float value for on_value - * @param[in] off_value A float value for off_value + * @param[in] depth A tensor for depth. Data types supported: S32 + * @param[in] on_value A tensor for on_value. Data types supported: F32 + * @param[in] off_value A tensor for off_value. Data types supported: F32* * @param[in] axis An int value for axis * * @return a status */ - static Status validate(const ITensor *indices, const int depth, const float on_value, - const float off_value, const int axis); + static Status validate(const ITensor *indices, const ITensor *depth, const ITensor *on_value, + const ITensor *off_value, const int axis); // Inherited methods overridden: void run(const Window &window, const ThreadInfo &info) override; @@ -100,10 +100,10 @@ private: template void run_one_hot(); const ITensor *_indices; + const ITensor *_depth; + const ITensor *_on_value; + const ITensor *_off_value; ITensor *_output; - int _depth; - float _on_value; - float _off_value; int _axis; }; } // namespace arm_compute diff --git a/compute/ARMComputeEx/arm_compute/runtime/CL/CLFunctionsEx.h b/compute/ARMComputeEx/arm_compute/runtime/CL/CLFunctionsEx.h index 831bb54..97bc4ce 100644 --- a/compute/ARMComputeEx/arm_compute/runtime/CL/CLFunctionsEx.h +++ b/compute/ARMComputeEx/arm_compute/runtime/CL/CLFunctionsEx.h @@ -32,7 +32,6 @@ #include #include #include -#include #include #include #include diff --git a/compute/ARMComputeEx/arm_compute/runtime/CL/functions/CLSpaceToBatchND.h b/compute/ARMComputeEx/arm_compute/runtime/CL/functions/CLSpaceToBatchND.h deleted file mode 100644 index 09eb69a..0000000 --- a/compute/ARMComputeEx/arm_compute/runtime/CL/functions/CLSpaceToBatchND.h +++ /dev/null @@ -1,80 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* - * Copyright (c) 2016-2018 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ - -#ifndef __ARM_COMPUTE_CLSPACE_TO_BATCH_ND_H__ -#define __ARM_COMPUTE_CLSPACE_TO_BATCH_ND_H__ - -#include "arm_compute/runtime/CL/ICLSimpleFunction.h" - -namespace arm_compute -{ -class ICLTensor; - -/** Basic function to run @ref CLSpaceToBatchNDKernel - * - * @note The tensor data type for the inputs must be U8/QASYMM8/S16/F16/S32/F32. - * @note The function divides "spatial" dimensions of the input into a grid of blocks of shape - * block_shape, and interleaves these blocks with the "batch" dimension such that in the output. - */ -class CLSpaceToBatchND : public ICLSimpleFunction -{ -public: - /** Initialise the kernel's input and output. - * - * @note The data layout of input and output must be the same. - * @note The number of dimensions of input and output must be 4, and `spatial` dimensions - * are height and width. - * @param[in] input Input tensor. Data types supported: U8/QASYMM8/S16/F16/S32/F32. - * Data layout supported: NCHW/NHWC - * @param[in] block_size Tensor of integer values specifying block sizes for spatial - * dimension. - * Data types supported: S32 - * @param[in] padding_size Tensor of integer values specifying padding sizes for spatial - * dimension. - * Data types supported: S32 - * @param[out] output Output tensor. Data types supported: same as @p input. - * Data layout supported: NCHW/NHWC - */ - void configure(const ICLTensor *input, const ICLTensor *block_size, const ICLTensor *padding_size, - ICLTensor *output); -}; - -} // namespace arm_compute -#endif /* __ARM_COMPUTE_CLSPACE_TO_BATCH_ND_H__ */ diff --git a/compute/ARMComputeEx/arm_compute/runtime/CPP/functions/CPPOneHotEx.h b/compute/ARMComputeEx/arm_compute/runtime/CPP/functions/CPPOneHotEx.h index f132dfc..7930e4e 100644 --- a/compute/ARMComputeEx/arm_compute/runtime/CPP/functions/CPPOneHotEx.h +++ b/compute/ARMComputeEx/arm_compute/runtime/CPP/functions/CPPOneHotEx.h @@ -55,14 +55,14 @@ public: /** Configure the one_hot function * * @param[in] indices A tensor for indices. Data types supported: S32 + * @param[in] depth A tensor for depth. Data types supported: S32 + * @param[in] on_value A tensor for on_value. Data types supported: F32 + * @param[in] off_value A tensor for off_value. Data types supported: F32 * @param[out] output A tensor for computed value of one hot operator - * @param[in] depth An int value for depth - * @param[in] on_value A float value for on_value - * @param[in] off_value A float value for off_value * @param[in] axis An int value for axis */ - void configure(const ITensor *indices, ITensor *output, const int depth, const float on_value, - const float off_value, const int axis); + void configure(const ITensor *indices, const ITensor *depth, const ITensor *on_value, + const ITensor *off_value, ITensor *output, const int axis); }; } #endif /* __ARM_COMPUTE_CPPONEHOT_EX_H__ */ diff --git a/compute/ARMComputeEx/arm_compute/runtime/misc/functions/GenericGather.h b/compute/ARMComputeEx/arm_compute/runtime/misc/functions/GenericGather.h deleted file mode 100644 index 3db0c7e..0000000 --- a/compute/ARMComputeEx/arm_compute/runtime/misc/functions/GenericGather.h +++ /dev/null @@ -1,84 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file        GenericGather.h - * @brief       This file contains GenericGather class - * @ingroup     COM_AI_RUNTIME - */ - -#ifndef __ARM_COMPUTE_MISC_GENERIC_GATHER_H__ -#define __ARM_COMPUTE_MISC_GENERIC_GATHER_H__ - -#include -#include - -#include -#include - -#include "Utils.h" - -namespace arm_compute -{ -namespace misc -{ - -/** - * @brief Class to run Gather with both CPU and GPU - */ -class GenericGather : public arm_compute::IFunction -{ -public: - GenericGather(void) - { - // DO NOTHING - } - -public: - /** - * @brief Configure the layer - * @param[in] input The source tensor - * @param[in] indices The indices tensor - * @param[in] output The destination tensor - * @param[in] axis (Optional) The axis in input to gather indices from - * @return N/A - */ - void configure(arm_compute::ITensor *input, arm_compute::ITensor *indices, - arm_compute::ITensor *output, int axis = 0); - -public: - /** - * @brief Run the operation. Must be called after configure(). - * @return N/A - */ - void run(void) override; - -private: - arm_compute::ITensor *_input{nullptr}; - arm_compute::ITensor *_indices{nullptr}; - arm_compute::ITensor *_output{nullptr}; - int _axis{0}; - arm_compute::CLTensor _cl_permuted; - -private: - arm_compute::CLPermute _cl_permute; - arm_compute::CLGatherEx _cl_gather; -}; - -} // namespace misc -} // namespace arm_compute - -#endif // __ARM_COMPUTE_MISC_GENERIC_GATHER_H__ diff --git a/compute/ARMComputeEx/arm_compute/runtime/misc/functions/GenericReshapeLayer.h b/compute/ARMComputeEx/arm_compute/runtime/misc/functions/GenericReshapeLayer.h deleted file mode 100644 index ab2fdc7..0000000 --- a/compute/ARMComputeEx/arm_compute/runtime/misc/functions/GenericReshapeLayer.h +++ /dev/null @@ -1,87 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file        GenericReshapeLayer.h - * @brief       This file contains GenericReshapeLayer class - * @ingroup     COM_AI_RUNTIME - */ - -#ifndef __ARM_COMPUTE_MISC_GENERIC_RESHAPE_LAYER_H__ -#define __ARM_COMPUTE_MISC_GENERIC_RESHAPE_LAYER_H__ - -#include -#include - -#include -#include -#include -#include - -#include "Utils.h" - -namespace arm_compute -{ -namespace misc -{ - -/** - * @brief Class to run Reshape Layer with both CPU and GPU - */ -class GenericReshapeLayer : public arm_compute::IFunction -{ -public: - GenericReshapeLayer(void) - : _input(nullptr), _output(nullptr), _cl_permuted{}, _neon_permuted{}, _cl_permute{}, - _cl_reshape{}, _neon_permute{}, _neon_reshape{} - { - // DO NOTHING - } - -public: - /** - * @brief Configure the layer - * @param[in] input The source tensor - * @param[in] output The destination tensor - * @return N/A - */ - void configure(const arm_compute::ITensor *input, arm_compute::ITensor *output); - -public: - /** - * @brief Run the operation. Must be called after configure(). - * @return N/A - */ - void run(void) override; - -private: - const arm_compute::ITensor *_input; - arm_compute::ITensor *_output; - arm_compute::CLTensor _cl_permuted; - arm_compute::Tensor _neon_permuted; - -private: - arm_compute::CLPermute _cl_permute; - arm_compute::CLReshapeLayer _cl_reshape; - - arm_compute::NEPermute _neon_permute; - arm_compute::NEReshapeLayer _neon_reshape; -}; - -} // namespace misc -} // namespace arm_compute - -#endif // __ARM_COMPUTE_MISC_GENERIC_RESHAPE_LAYER_H__ diff --git a/compute/ARMComputeEx/arm_compute/runtime/misc/functions/Utils.h b/compute/ARMComputeEx/arm_compute/runtime/misc/functions/Utils.h deleted file mode 100644 index 84ee4ce..0000000 --- a/compute/ARMComputeEx/arm_compute/runtime/misc/functions/Utils.h +++ /dev/null @@ -1,112 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/** - * @file utils.h - * @ingroup COM_AI_RUNTIME - * @brief This file contains utils for arm compute library - */ -#ifndef __ARM_COMPUTE_MISC_UTILS_H__ -#define __ARM_COMPUTE_MISC_UTILS_H__ - -#include -#include -#include - -#include -#include -#include -#include - -// TODO : It should be extracted to independent module. - -namespace arm_compute -{ -namespace misc -{ -namespace utils -{ - -/** - * @brief Check if this runtime runs on GPU or NEON - * @return @c true if GPU mode, otherwise @c false - */ -bool isGpuMode(); - -#ifndef CAST_CL -#define CAST_CL(tensor) static_cast<::arm_compute::CLTensor *>(tensor) -#endif - -#ifndef CAST_NE -#define CAST_NE(tensor) static_cast<::arm_compute::Tensor *>(tensor) -#endif - -/** -* @brief Generate arm compute permutation vector from runtime permutation vector -* @param[in] rank Rank number supported upto 4 -* @param[in] runtime_pv Integer array for runtime permutation vector -* @return Permutation vector of arm compute -*/ -arm_compute::PermutationVector getARMComputePermutationVector(uint32_t rank, - const int32_t *runtime_pv); - -/** - * @brief Set value to arm compute tensor with casting - * @param[in] value Value to set - * @param[out] to Target tensor of arm compute - * @param[in] id Position of element - * @return N/A - */ -template -void copyCast(const FromT value, arm_compute::ITensor *to, const arm_compute::Coordinates &id) -{ - switch (to->info()->data_type()) - { - case arm_compute::DataType::F32: - { - *reinterpret_cast(to->ptr_to_element(id)) = static_cast(value); - break; - } - case arm_compute::DataType::S32: - { - *reinterpret_cast(to->ptr_to_element(id)) = static_cast(value); - break; - } - case arm_compute::DataType::U32: - { - *reinterpret_cast(to->ptr_to_element(id)) = static_cast(value); - break; - } - case arm_compute::DataType::QASYMM8: - { - float realValue = static_cast(value); - // NOTE We haven't known the policy of rounding for quantization. - // So this is set to a temporary value. - *(to->ptr_to_element(id)) = quantize_qasymm8(realValue, to->info()->quantization_info(), - arm_compute::RoundingPolicy::TO_ZERO); - break; - } - default: - throw std::runtime_error("Not supported, yet"); - break; - } -} - -} // namespace utils -} // namespace misc -} // namespace arm_compute - -#endif // __ARM_COMPUTE_MISC_UTILS_H__ diff --git a/compute/ARMComputeEx/src/core/CL/CLKernelLibrary.cpp b/compute/ARMComputeEx/src/core/CL/CLKernelLibrary.cpp index 191a5bc..7b6b974 100644 --- a/compute/ARMComputeEx/src/core/CL/CLKernelLibrary.cpp +++ b/compute/ARMComputeEx/src/core/CL/CLKernelLibrary.cpp @@ -91,8 +91,6 @@ const std::map CLKernelLibraryEx::_kernel_program_map {"radixsort_reorder", "topkv2_radixsort.cl"}, {"topkv2_quicksort", "topkv2_quicksort.cl"}, {"scale_factor_symm8", "scale_factor.cl"}, - {"space_to_batch_4d_nchw", "space_to_batch.cl"}, - {"space_to_batch_4d_nhwc", "space_to_batch.cl"}, {"space_to_depth_nchw", "space_to_depth.cl"}, {"space_to_depth_nhwc", "space_to_depth.cl"}, }; @@ -172,10 +170,6 @@ const std::map CLKernelLibraryEx::_program_source_map #include "./cl_kernels/scale_factor.clembed" }, { - "space_to_batch.cl", -#include "./cl_kernels/space_to_batch.clembed" - }, - { "space_to_depth.cl", #include "./cl_kernels/space_to_depth.clembed" }, diff --git a/compute/ARMComputeEx/src/core/CL/cl_kernels/space_to_batch.cl b/compute/ARMComputeEx/src/core/CL/cl_kernels/space_to_batch.cl deleted file mode 100644 index b161104..0000000 --- a/compute/ARMComputeEx/src/core/CL/cl_kernels/space_to_batch.cl +++ /dev/null @@ -1,274 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* - * Copyright (c) 2016, 2017 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ - -#include "helpers.h" - -#if defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(BATCH_IN) && defined(HEIGHT_IN) && \ - defined(WIDTH_IN) && defined(ZERO_VALUE) -/** Perform space to batch with input of 4D and NCHW format - * - * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float - * @attention Output tensor depth should be given as a preprocessor argument using -DDEPTH_OUT=size. - * e.g. -DDEPTH_OUT=16 - * @attention Input tensor batch should be given as a preprocessor argument using -DBATCH_IN=size. - * e.g. -DBATCH_IN=16 - * @attention Input tensor height should be given as a preprocessor argument using -DHEIGHT_IN=size. - * e.g. -DHEIGHT_IN=16 - * @attention Input tensor width should be given as a preprocessor argument using -DHEIGHT_IN=size. - * e.g. -DWIDTH_IN=16 - * @attention The value to be set by pad value using -DZERO_VALUE=value. e.g. -DZERO_VALUE=0 - * - * @param[in] input_ptr Pointer to the source tensor. Supported - * data types: U8/S8/U16/S16/F16/U32/S32/F32 - * @param[in] input_stride_x Stride of the source tensor in X - * dimension (in bytes) - * @param[in] input_step_x input_stride_x * number of elements along - * X processed per workitem(in bytes) - * @param[in] input_stride_y Stride of the source tensor in Y - * dimension (in bytes) - * @param[in] input_step_y input_stride_y * number of elements along - * Y processed per workitem(in bytes) - * @param[in] input_stride_z Stride of the source tensor in Z - * dimension (in bytes) - * @param[in] input_step_z input_stride_z * number of elements along - * Z processed per workitem(in bytes) - * @param[in] input_stride_w Stride of the destination tensor in W - * dimension (in bytes) - * @param[in] input_step_w input_stride_w * number of elements along - * W processed per workitem(in bytes) - * @param[in] input_offset_first_element_in_bytes The offset of the first element in the - * source tensor - * @param[out] output_ptr Pointer to the destination tensor. - * Supported data types: same as @p - * input_ptr - * @param[in] output_stride_x Stride of the destination tensor in X - * dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements - * along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the destination tensor in Y - * dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements - * along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the destination tensor in Z - * dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements - * along Z processed per workitem(in bytes) - * @param[in] output_stride_w Stride of the destination tensor in W - * dimension (in bytes) - * @param[in] output_step_w output_stride_w * number of elements - * along W processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the - * destination tensor - * @param[in] block_size_ptr Pointer to the source tensor. Supported - * data types: S32 - * @param[in] block_size_stride_x Stride of the source tensor in X - * dimension (in bytes) - * @param[in] block_size_step_x block_size_stride_x * number of elements - * along X processed per workitem(in bytes) - * @param[in] block_size_offset_first_element_in_bytes The offset of the first element in the - * destination tensor - * @param[in] padding_size_ptr Pointer to the source tensor. Supported - * data types: S32 - * @param[in] padding_size_stride_x Stride of the source tensor in X - * dimension (in bytes) - * @param[in] padding_size_step_x padding_size_stride_x * number of - * elements along X processed per workitem - * (in bytes) - * @param[in] padding_size_stride_y Stride of the source tensor in Y - * dimension (in bytes) - * @param[in] padding_size_step_y padding_size_stride_y * number of - * elements along Y processed per workitem - * (in bytes) - * @param[in] padding_size_offset_first_element_in_bytes The offset of the first element in the - * destination tensor - */ -__kernel void space_to_batch_4d_nchw(TENSOR4D_DECLARATION(input), TENSOR4D_DECLARATION(output), - VECTOR_DECLARATION(block_size), - IMAGE_DECLARATION(padding_size)) -{ - Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT(input, 0); - Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT(output, DEPTH_OUT); - - int block_size_x = *((__global int *)(block_size_ptr)); - int block_size_y = *((__global int *)(block_size_ptr + block_size_stride_x)); - int shift_x = (get_global_id(2) / DEPTH_OUT / BATCH_IN) % block_size_x; - int shift_y = (get_global_id(2) / DEPTH_OUT / BATCH_IN) / block_size_x; - - int in_index[4] = { - 0, - }; - in_index[0] = get_global_id(0) * block_size_x + shift_x - *((__global int *)(padding_size_ptr)); - in_index[1] = get_global_id(1) * block_size_y + shift_y - - *((__global int *)(padding_size_ptr + padding_size_stride_y)); - in_index[2] = get_global_id(2) % DEPTH_OUT; - in_index[3] = (get_global_id(2) / DEPTH_OUT) % BATCH_IN; - - if (in_index[0] < 0 || in_index[0] >= WIDTH_IN || in_index[1] < 0 || in_index[1] >= HEIGHT_IN) - { - *((__global DATA_TYPE *)out.ptr) = (DATA_TYPE)ZERO_VALUE; - } - else - { - *((__global DATA_TYPE *)out.ptr) = *((__global DATA_TYPE *)tensor4D_offset( - &in, in_index[0], in_index[1], in_index[2], in_index[3])); - } -} -#endif // defined(DATA_TYPE) && defined(DEPTH_OUT) && defined(BATCH_IN) && defined(HEIGHT_IN) && - // defined(WIDTH_IN) && defined(ZERO_VALUE) - -#if defined(DATA_TYPE) && defined(HEIGHT_OUT) && defined(BATCH_IN) && defined(HEIGHT_IN) && \ - defined(WIDTH_IN) && defined(ZERO_VALUE) && defined(VEC_SIZE) -/** Perform space to batch with input of 4D and NHWC format - * - * @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float - * @attention Output tensor depth should be given as a preprocessor argument using - * -DHEIGHT_OUT=size. e.g. -DHEIGHT_OUT=16 - * @attention Input tensor batch should be given as a preprocessor argument using -DBATCH_IN=size. - * e.g. -DBATCH_IN=16 - * @attention Input tensor height should be given as a preprocessor argument using -DHEIGHT_IN=size. - * e.g. -DHEIGHT_IN=16 - * @attention Input tensor width should be given as a preprocessor argument using -DHEIGHT_IN=size. - * e.g. -DWIDTH_IN=16 - * @attention The value to be set by pad value using -DZERO_VALUE=value. e.g. -DZERO_VALUE=0 - * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. - * -DVEC_SIZE=16 - * - * @param[in] input_ptr Pointer to the source tensor. Supported - * data types: U8/S8/U16/S16/F16/U32/S32/F32 - * @param[in] input_stride_x Stride of the source tensor in X - * dimension (in bytes) - * @param[in] input_step_x input_stride_x * number of elements along - * X processed per workitem(in bytes) - * @param[in] input_stride_y Stride of the source tensor in Y - * dimension (in bytes) - * @param[in] input_step_y input_stride_y * number of elements along - * Y processed per workitem(in bytes) - * @param[in] input_stride_z Stride of the source tensor in Z - * dimension (in bytes) - * @param[in] input_step_z input_stride_z * number of elements along - * Z processed per workitem(in bytes) - * @param[in] input_stride_w Stride of the destination tensor in W - * dimension (in bytes) - * @param[in] input_step_w input_stride_w * number of elements along - * W processed per workitem(in bytes) - * @param[in] input_offset_first_element_in_bytes The offset of the first element in the - * source tensor - * @param[out] output_ptr Pointer to the destination tensor. - * Supported data types: same as @p - * input_ptr - * @param[in] output_stride_x Stride of the destination tensor in X - * dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements - * along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the destination tensor in Y - * dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements - * along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the destination tensor in Z - * dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements - * along Z processed per workitem(in bytes) - * @param[in] output_stride_w Stride of the destination tensor in W - * dimension (in bytes) - * @param[in] output_step_w output_stride_w * number of elements - * along W processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the - * destination tensor - * @param[in] block_size_ptr Pointer to the source tensor. Supported - * data types: S32 - * @param[in] block_size_stride_x Stride of the source tensor in X - * dimension (in bytes) - * @param[in] block_size_step_x block_size_stride_x * number of elements - * along X processed per workitem(in bytes) - * @param[in] block_size_offset_first_element_in_bytes The offset of the first element in the - * destination tensor - * @param[in] padding_size_ptr Pointer to the source tensor. Supported - * data types: S32 - * @param[in] padding_size_stride_x Stride of the source tensor in X - * dimension (in bytes) - * @param[in] padding_size_step_x padding_size_stride_x * number of - * elements along X processed per workitem - * (in bytes) - * @param[in] padding_size_stride_y Stride of the source tensor in Y - * dimension (in bytes) - * @param[in] padding_size_step_y padding_size_stride_y * number of - * elements along Y processed per workitem - * (in bytes) - * @param[in] padding_size_offset_first_element_in_bytes The offset of the first element in the - * destination tensor - */ -__kernel void space_to_batch_4d_nhwc(TENSOR4D_DECLARATION(input), TENSOR4D_DECLARATION(output), - VECTOR_DECLARATION(block_size), - IMAGE_DECLARATION(padding_size)) -{ - Tensor4D in = CONVERT_TO_TENSOR4D_STRUCT(input, 0); - Tensor4D out = CONVERT_TO_TENSOR4D_STRUCT(output, HEIGHT_OUT); - - int block_size_x = *((__global int *)(block_size_ptr)); - int block_size_y = *((__global int *)(block_size_ptr + block_size_stride_x)); - int shift_x = (get_global_id(2) / HEIGHT_OUT / BATCH_IN) % block_size_x; - int shift_y = (get_global_id(2) / HEIGHT_OUT / BATCH_IN) / block_size_x; - - int in_index[4] = { - 0, - }; - in_index[0] = get_global_id(0) * VEC_SIZE; - in_index[1] = get_global_id(1) * block_size_x + shift_x - *((__global int *)(padding_size_ptr)); - in_index[2] = get_global_id(2) % HEIGHT_OUT * block_size_y + shift_y - - *((__global int *)(padding_size_ptr + padding_size_stride_y)); - in_index[3] = (get_global_id(2) / HEIGHT_OUT) % BATCH_IN; - - if (in_index[1] < 0 || in_index[1] >= WIDTH_IN || in_index[2] < 0 || in_index[2] >= HEIGHT_IN) - { - VSTORE(VEC_SIZE) - ((VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))ZERO_VALUE, 0, (__global DATA_TYPE *)out.ptr); - } - else - { - VSTORE(VEC_SIZE) - (CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)tensor4D_offset(&in, in_index[0], in_index[1], - in_index[2], in_index[3])), - VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)), - 0, (__global DATA_TYPE *)out.ptr); - } -} - -#endif // defined(DATA_TYPE) && defined(HEIGHT_OUT) && defined(BATCH_IN) && defined(HEIGHT_IN) && - // defined(WIDTH_IN) && defined(ZERO_VALUE) && defined(VEC_SIZE) diff --git a/compute/ARMComputeEx/src/core/CL/kernels/CLSpaceToBatchNDKernel.cpp b/compute/ARMComputeEx/src/core/CL/kernels/CLSpaceToBatchNDKernel.cpp deleted file mode 100644 index 8b9b57f..0000000 --- a/compute/ARMComputeEx/src/core/CL/kernels/CLSpaceToBatchNDKernel.cpp +++ /dev/null @@ -1,265 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* - * Copyright (c) 2016-2018 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ - -#include "arm_compute/core/CL/kernels/CLSpaceToBatchNDKernel.h" - -#include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLKernelLibraryEx.h" -#include "arm_compute/core/CL/ICLTensor.h" - -using namespace arm_compute; - -namespace -{ -constexpr unsigned int num_elems_processed_per_iteration = 16; - -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *block_size, - const ITensorInfo *padding_size, const ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, - DataType::S16, DataType::F16, DataType::S32, - DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(block_size, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(padding_size, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QASYMM8, - DataType::S16, DataType::F16, DataType::S32, - DataType::F32); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() != output->num_dimensions(), - "The number of dimensions of input should be equal to output"); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_layout() != output->data_layout(), - "The input and output layouts are different!"); - - // TODO Support other cases - if (input->num_dimensions() == 4 && input->data_layout() == DataLayout::NCHW) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->dimension(2) != output->dimension(2), - "Input Depth should be equal to Output Depth"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(block_size->dimension(0) != 2 || - padding_size->dimension(1) != 2, - "Only 2-dimensional spatial block's size was wrong"); - } - else if (input->num_dimensions() == 4 && input->data_layout() == DataLayout::NHWC) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->dimension(0) != output->dimension(0), - "Input Depth should be equal to Output Depth"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(block_size->dimension(0) != 2 || - padding_size->dimension(1) != 2, - "Only 2-dimensional spatial block's size was wrong"); - } - else - { - ARM_COMPUTE_RETURN_ERROR_MSG("CLSpaceToBatchNDKernel supports only 4-dimensional input"); - } - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() < 2 && input->num_dimensions() > 4, - "CLSpaceToBatchNDKernel supports dimensions up to 4"); - - if (input->data_type() == DataType::QASYMM8) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->quantization_info() != output->quantization_info(), - "The input and output quantization info are different!"); - } - - return Status{}; -} - -} // namespace - -CLSpaceToBatchNDKernel::CLSpaceToBatchNDKernel() -{ - // DO NOTHING -} - -void CLSpaceToBatchNDKernel::configure(const ICLTensor *input, const ICLTensor *block_size, - const ICLTensor *padding_size, ICLTensor *output) -{ - - ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_ERROR_THROW_ON( - validate_arguments(input->info(), block_size->info(), padding_size->info(), output->info())); - - _input = input; - _block_size = block_size; - _padding_size = padding_size; - _output = output; - - // Set kernel build options - // TODO Support other cases - std::string kernel_name = "space_to_batch_4d"; - std::set build_opts; - Window win; - - if (input->info()->data_layout() == DataLayout::NCHW) - { - kernel_name += "_nchw"; - build_opts.emplace("-DDEPTH_OUT=" + support::cpp11::to_string(output->info()->dimension(2))); - build_opts.emplace("-DHEIGHT_IN=" + support::cpp11::to_string(input->info()->dimension(1))); - build_opts.emplace("-DWIDTH_IN=" + support::cpp11::to_string(input->info()->dimension(0))); - - win = calculate_max_window(*output->info(), Steps()); - - Coordinates coord; - coord.set_num_dimensions(output->info()->num_dimensions()); - output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape())); - } - else if (input->info()->data_layout() == DataLayout::NHWC) - { - kernel_name += "_nhwc"; - build_opts.emplace("-DHEIGHT_OUT=" + support::cpp11::to_string(output->info()->dimension(2))); - build_opts.emplace("-DHEIGHT_IN=" + support::cpp11::to_string(input->info()->dimension(2))); - build_opts.emplace("-DWIDTH_IN=" + support::cpp11::to_string(input->info()->dimension(1))); - build_opts.emplace("-DVEC_SIZE=" + - support::cpp11::to_string(num_elems_processed_per_iteration)); - - win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration)); - AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration); - - bool window_changed = update_window_and_padding(win, input_access, output_access); - input_access.set_valid_region(win, output->info()->valid_region()); - - if (window_changed) - { - ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!"); - } - } - else - { - ARM_COMPUTE_ERROR("Unsupported layout"); - } - - build_opts.emplace("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); - build_opts.emplace("-DBATCH_IN=" + support::cpp11::to_string(input->info()->dimension(3))); - if (input->info()->data_type() == DataType::QASYMM8) - { - build_opts.emplace("-DZERO_VALUE=" + support::cpp11::to_string( - input->info()->quantization_info().uniform().offset)); - } - else - { - build_opts.emplace("-DZERO_VALUE=" + support::cpp11::to_string(0)); - } - - // Create kernel - _kernel = - static_cast(CLKernelLibraryEx::get().create_kernel(kernel_name, build_opts)); - - // Configure kernel window - ICLKernel::configure_internal(win); -} - -void CLSpaceToBatchNDKernel::run(const Window &window, cl::CommandQueue &queue) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window); - -#if defined(ARM_COMPUTE_DEBUG_ENABLED) - const_cast(_block_size)->map(queue); - const_cast(_padding_size)->map(queue); - - const size_t num_dimensions = _input->info()->num_dimensions(); - const size_t num_spacial_dimensions = _block_size->info()->dimension(0); - uint32_t batch_size = _input->info()->dimension(num_dimensions - 1); - for (size_t i = 0; i < num_spacial_dimensions; ++i) - { - const int32_t block_size = *reinterpret_cast(_block_size->ptr_to_element({i})); - const int32_t padding_size_pre = - *reinterpret_cast(_padding_size->ptr_to_element({0, i})); - const int32_t padding_size_post = - *reinterpret_cast(_padding_size->ptr_to_element({1, i})); - - ARM_COMPUTE_ERROR_ON_MSG(block_size < 1, "Block size should be greater than or equal to 1"); - ARM_COMPUTE_ERROR_ON_MSG(padding_size_pre < 0 && padding_size_post < 0, - "Padding size should be greater than or equal to 0"); - - if (num_dimensions == 4 && _input->info()->data_layout() == DataLayout::NCHW) - { - ARM_COMPUTE_ERROR_ON_MSG( - _output->info()->dimension(i) != - (_input->info()->dimension(i) + padding_size_pre + padding_size_post) / block_size, - "Dimension value of spatial block does not match output's dimension value"); - } - else - { - ARM_COMPUTE_ERROR_ON_MSG( - _output->info()->dimension(num_dimensions - num_spacial_dimensions - 1 + i) != - (_input->info()->dimension(num_dimensions - num_spacial_dimensions - 1 + i) + - padding_size_pre + padding_size_post) / - block_size, - "Dimension value of spatial block does not match output's dimension value"); - } - - batch_size *= block_size; - } - ARM_COMPUTE_ERROR_ON_MSG( - _output->info()->dimension(num_dimensions - 1) != batch_size, - "Output batch size should be equal to input batch size * (multiplication of all block size)"); - - const_cast(_block_size)->unmap(queue); - const_cast(_padding_size)->unmap(queue); -#endif // defined(ARM_COMPUTE_DEBUG_ENABLED) - - Window slice_out = window.first_slice_window_4D().collapse(ICLKernel::window(), 2, 4); - - // Setup output slice - Window slice_in(slice_out); - slice_in.set(Window::DimX, Window::Dimension(0, 0, 0)); - slice_in.set(Window::DimY, Window::Dimension(0, 0, 0)); - slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); - slice_in.set(3, Window::Dimension(0, 0, 0)); - - // Set block size window - Window win_block = calculate_max_window(*_block_size->info(), Steps()); - - // Set padding size window - Window win_padding = calculate_max_window(*_padding_size->info(), Steps()); - - do - { - unsigned int idx = 0; - add_4D_tensor_argument(idx, _input, slice_in); - add_4D_tensor_argument(idx, _output, slice_out); - add_1D_tensor_argument(idx, _block_size, win_block); - add_2D_tensor_argument(idx, _padding_size, win_padding); - enqueue(queue, *this, slice_out); - } while (window.slide_window_slice_4D(slice_out) && window.slide_window_slice_4D(slice_in)); -} diff --git a/compute/ARMComputeEx/src/core/CPP/kernels/CPPOneHotKernelEx.cpp b/compute/ARMComputeEx/src/core/CPP/kernels/CPPOneHotKernelEx.cpp index d6c49b2..4805323 100644 --- a/compute/ARMComputeEx/src/core/CPP/kernels/CPPOneHotKernelEx.cpp +++ b/compute/ARMComputeEx/src/core/CPP/kernels/CPPOneHotKernelEx.cpp @@ -52,34 +52,37 @@ namespace arm_compute { CPPOneHotKernelEx::CPPOneHotKernelEx() - : _indices(nullptr), _output(nullptr), _depth(0), _on_value(0), _off_value(0), _axis(-1) + : _indices(nullptr), _depth(nullptr), _on_value(nullptr), _off_value(nullptr), _output(nullptr), + _axis(-1) { } -void CPPOneHotKernelEx::configure(const ITensor *indices, ITensor *output, const int depth, - const float on_value, const float off_value, const int axis) +void CPPOneHotKernelEx::configure(const ITensor *indices, const ITensor *depth, + const ITensor *on_value, const ITensor *off_value, + ITensor *output, const int axis) { ARM_COMPUTE_ERROR_ON_NULLPTR(indices, output); ARM_COMPUTE_ERROR_THROW_ON(validate(indices, depth, on_value, off_value, axis)); _indices = indices; - _output = output; _depth = depth; _on_value = on_value; _off_value = off_value; + _output = output; _axis = axis; ICPPKernel::configure(Window()); // Default 1 iteration window } -Status CPPOneHotKernelEx::validate(const ITensor *indices, const int depth, const float on_value, - const float off_value, const int axis) +Status CPPOneHotKernelEx::validate(const ITensor *indices, const ITensor *depth, + const ITensor *on_value, const ITensor *off_value, + const int axis) { ARM_COMPUTE_UNUSED(on_value, off_value); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(indices, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(depth, DataType::S32); ARM_COMPUTE_RETURN_ERROR_ON_MSG(indices->info()->num_dimensions() != 1, "Only 1D indices are supported."); - ARM_COMPUTE_RETURN_ERROR_ON(depth <= 0); ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis != -1, "Only axis = -1 is supported."); return Status{}; } @@ -92,12 +95,25 @@ void CPPOneHotKernelEx::run(const Window &window, const ThreadInfo &info) ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(IKernel::window(), window); const auto num_indices = _indices->info()->dimension(0); - for (size_t i = 0; i < num_indices; ++i) + const auto depth = *reinterpret_cast(_depth->ptr_to_element(Coordinates{0})); + const auto dtype = _output->info()->data_type(); + switch (dtype) { - const auto index = *reinterpret_cast(_indices->ptr_to_element(Coordinates{i})); - for (int d = 0; d < _depth; ++d) - *reinterpret_cast(_output->ptr_to_element(Coordinates(d, i))) = - (d == index) ? _on_value : _off_value; + case DataType::F32: + { + const auto on_value = *reinterpret_cast(_on_value->ptr_to_element(Coordinates{0})); + const auto off_value = *reinterpret_cast(_off_value->ptr_to_element(Coordinates{0})); + for (size_t i = 0; i < num_indices; ++i) + { + const auto index = *reinterpret_cast(_indices->ptr_to_element(Coordinates{i})); + for (int d = 0; d < depth; ++d) + *reinterpret_cast(_output->ptr_to_element(Coordinates(d, i))) = + (d == index) ? on_value : off_value; + } + break; + } + default: + ARM_COMPUTE_ERROR("Unsupported data type."); } } } // namespace arm_compute diff --git a/compute/ARMComputeEx/src/runtime/CL/functions/CLSpaceToBatchND.cpp b/compute/ARMComputeEx/src/runtime/CL/functions/CLSpaceToBatchND.cpp deleted file mode 100644 index 1f946d3..0000000 --- a/compute/ARMComputeEx/src/runtime/CL/functions/CLSpaceToBatchND.cpp +++ /dev/null @@ -1,53 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -/* - * Copyright (c) 2016-2018 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ - -#include "arm_compute/runtime/CL/functions/CLSpaceToBatchND.h" - -#include "arm_compute/core/CL/kernels/CLSpaceToBatchNDKernel.h" - -using namespace arm_compute; - -void CLSpaceToBatchND::configure(const ICLTensor *input, const ICLTensor *block_size, - const ICLTensor *padding_size, ICLTensor *output) -{ - auto k = arm_compute::support::cpp14::make_unique(); - k->configure(input, block_size, padding_size, output); - _kernel = std::move(k); -} diff --git a/compute/ARMComputeEx/src/runtime/CPP/functions/CPPOneHotEx.cpp b/compute/ARMComputeEx/src/runtime/CPP/functions/CPPOneHotEx.cpp index 5405934..114e1a7 100644 --- a/compute/ARMComputeEx/src/runtime/CPP/functions/CPPOneHotEx.cpp +++ b/compute/ARMComputeEx/src/runtime/CPP/functions/CPPOneHotEx.cpp @@ -45,10 +45,10 @@ using namespace arm_compute; -void CPPOneHotEx::configure(const ITensor *indices, ITensor *output, const int depth, - const float on_value, const float off_value, const int axis) +void CPPOneHotEx::configure(const ITensor *indices, const ITensor *depth, const ITensor *on_value, + const ITensor *off_value, ITensor *output, const int axis) { auto k = arm_compute::support::cpp14::make_unique(); - k->configure(indices, output, depth, on_value, off_value, axis); + k->configure(indices, depth, on_value, off_value, output, axis); _kernel = std::move(k); } diff --git a/compute/ARMComputeEx/src/runtime/misc/functions/GenericGather.cpp b/compute/ARMComputeEx/src/runtime/misc/functions/GenericGather.cpp deleted file mode 100644 index 67e1bfb..0000000 --- a/compute/ARMComputeEx/src/runtime/misc/functions/GenericGather.cpp +++ /dev/null @@ -1,92 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "arm_compute/runtime/misc/functions/GenericGather.h" - -namespace arm_compute -{ -namespace misc -{ - -bool shouldPermute(arm_compute::ITensorInfo *input, arm_compute::ITensorInfo *output) -{ - return (input->num_dimensions() != 4 && output->num_dimensions() == 4 && - input->data_layout() == DataLayout::NCHW); -} - -void GenericGather::configure(arm_compute::ITensor *input, arm_compute::ITensor *indices, - arm_compute::ITensor *output, int axis) -{ - _input = input; - _indices = indices; - _output = output; - _axis = axis; - - arm_compute::PermutationVector pv; - if (shouldPermute(input->info(), output->info())) - { - // NOTE This vector comes from CLPermuteKernel implementation - // - // This implementation permutes a tensor of shape C / W / H into another tensor of shape W / H / - // C - // - // Original | Permuted - // 0 | C | W (from 1) - // 1 | W | H (from 2) - // 2 | H | C (from 0) - // - pv = arm_compute::PermutationVector{1, 2, 0}; - } - - if (utils::isGpuMode()) - { - if (shouldPermute(input->info(), output->info())) - { - _cl_gather.configure(CAST_CL(input), CAST_CL(indices), &_cl_permuted, axis); - _cl_permute.configure(&_cl_permuted, CAST_CL(output), pv); - - // NOTE _permuted is inaccessible from outside, and thus it is safe to invoke allocate here. - _cl_permuted.allocator()->allocate(); - } - else - { - _cl_gather.configure(CAST_CL(input), CAST_CL(indices), CAST_CL(output), axis); - } - } - else - { - throw std::runtime_error("Not supported, yet"); - } -} - -void GenericGather::run(void) -{ - if (utils::isGpuMode()) - { - _cl_gather.run(); - if (shouldPermute(_input->info(), _output->info())) - { - _cl_permute.run(); - } - } - else - { - throw std::runtime_error("Not supported, yet"); - } -} - -} // namespace misc -} // namespace arm_compute diff --git a/compute/ARMComputeEx/src/runtime/misc/functions/GenericReshapeLayer.cpp b/compute/ARMComputeEx/src/runtime/misc/functions/GenericReshapeLayer.cpp deleted file mode 100644 index 8025ae2..0000000 --- a/compute/ARMComputeEx/src/runtime/misc/functions/GenericReshapeLayer.cpp +++ /dev/null @@ -1,128 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "arm_compute/core/ITensorInfo.h" -#include "arm_compute/runtime/misc/functions/GenericReshapeLayer.h" - -namespace arm_compute -{ -namespace misc -{ - -namespace -{ - -bool shouldPermute(const arm_compute::ITensorInfo *input, arm_compute::ITensorInfo *output) -{ - return (input->num_dimensions() == 4 || output->num_dimensions() == 4) && - (input->num_dimensions() != output->num_dimensions() && - input->data_layout() == DataLayout::NCHW); -} - -} // namespace - -void GenericReshapeLayer::configure(const arm_compute::ITensor *input, arm_compute::ITensor *output) -{ - _input = input; - _output = output; - - arm_compute::PermutationVector pv; - if (input->info()->data_layout() == DataLayout::NCHW && input->info()->num_dimensions() == 4 && - output->info()->num_dimensions() != 4) - { - // NOTE This vector comes from CLPermuteKernel implementation - // - // This implementation permutes a tensor of shape W / H / C into another tensor of shape - // C / W / H - // - // Original | Permuted - // 0 | W | C (from 2) - // 1 | H | W (from 0) - // 2 | C | H (from 1) - // - pv = arm_compute::PermutationVector{2, 0, 1}; - } - else if (input->info()->data_layout() == DataLayout::NCHW && - input->info()->num_dimensions() != 4 && output->info()->num_dimensions() == 4) - { - // NOTE This vector comes from CLPermuteKernel implementation - // - // This implementation permutes a tensor of shape C / W / H into another tensor of shape - // W / H / C - // - // Original | Permuted - // 0 | C | W (from 1) - // 1 | W | H (from 2) - // 2 | H | C (from 0) - // - pv = arm_compute::PermutationVector{1, 2, 0}; - } - - if (utils::isGpuMode()) - { - const auto const_input = CAST_CL(const_cast(input)); - if (shouldPermute(input->info(), output->info())) - { - _cl_permute.configure(const_input, &_cl_permuted, pv); - _cl_reshape.configure(&_cl_permuted, CAST_CL(output)); - - // NOTE _permuted is inaccessible from outside, and thus it is safe to invoke allocate here. - _cl_permuted.allocator()->allocate(); - } - else - { - _cl_reshape.configure(const_input, CAST_CL(output)); - } - } - else - { - if (shouldPermute(input->info(), output->info())) - { - _neon_permute.configure(input, &_neon_permuted, pv); - _neon_reshape.configure(&_neon_permuted, output); - - // NOTE _permuted is inaccessible from outside, and thus it is safe to invoke allocate here. - _neon_permuted.allocator()->allocate(); - } - else - { - _neon_reshape.configure(input, output); - } - } -} - -void GenericReshapeLayer::run(void) -{ - if (utils::isGpuMode()) - { - if (shouldPermute(_input->info(), _output->info())) - { - _cl_permute.run(); - } - _cl_reshape.run(); - } - else - { - if (shouldPermute(_input->info(), _output->info())) - { - _neon_permute.run(); - } - _neon_reshape.run(); - } -} - -} // namespace misc -} // namespace arm_compute diff --git a/compute/ARMComputeEx/src/runtime/misc/functions/Utils.cpp b/compute/ARMComputeEx/src/runtime/misc/functions/Utils.cpp deleted file mode 100644 index 44a4bb9..0000000 --- a/compute/ARMComputeEx/src/runtime/misc/functions/Utils.cpp +++ /dev/null @@ -1,38 +0,0 @@ -/* - * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "arm_compute/runtime/misc/functions/Utils.h" - -namespace arm_compute -{ -namespace misc -{ -namespace utils -{ - -bool isGpuMode() -{ - char *neon = std::getenv("NEON"); - if (neon == nullptr) - return true; - else if (neon[0] == '1') - return false; - return true; -} - -} // namespace utils -} // namespace misc -} // namespace arm_compute diff --git a/compute/cker/CMakeLists.txt b/compute/cker/CMakeLists.txt index b8ceba5..09f6725 100644 --- a/compute/cker/CMakeLists.txt +++ b/compute/cker/CMakeLists.txt @@ -1,7 +1,19 @@ nnfw_find_package(Eigen REQUIRED) nnfw_find_package(GEMMLowp REQUIRED) +nnfw_find_package(Ruy REQUIRED) add_library(nnfw_lib_cker INTERFACE) target_link_libraries(nnfw_lib_cker INTERFACE eigen) target_link_libraries(nnfw_lib_cker INTERFACE gemmlowp) +target_link_libraries(nnfw_lib_cker INTERFACE ruy) +target_link_libraries(nnfw_lib_cker INTERFACE ruy_instrumentation) +target_compile_definitions(nnfw_lib_cker INTERFACE USE_RUY_GEMV) +if(PROFILE_RUY) + target_link_libraries(nnfw_lib_cker INTERFACE ruy_profiler) +endif(PROFILE_RUY) + target_include_directories(nnfw_lib_cker INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/include) + +# Workaround to avoid warning +# TODO Resolve warning +target_compile_options(nnfw_lib_cker INTERFACE -Wno-attributes) diff --git a/compute/cker/include/cker/NeonTensorUtils.h b/compute/cker/include/cker/NeonTensorUtils.h index 4d97dd1..5c38bc6 100644 --- a/compute/cker/include/cker/NeonTensorUtils.h +++ b/compute/cker/include/cker/NeonTensorUtils.h @@ -18,7 +18,13 @@ #ifndef __NNFW_CKER_NEON_TENSOR_UTILS_H__ #define __NNFW_CKER_NEON_TENSOR_UTILS_H__ +#include +#include +#include +#include "cker/Types.h" #include "cker/neon/neon_check.h" +#include "cker/ruy/RuySupport.h" +#include "util/logging.h" #include #include @@ -35,6 +41,14 @@ namespace cker namespace { +// TODO(ahentz): Clean up. +using int8 = std::int8_t; +using uint8 = std::uint8_t; +using int16 = std::int16_t; +using uint16 = std::uint16_t; +using int32 = std::int32_t; +using uint32 = std::uint32_t; + // Allocates, at least, size bytes of uninitialized storage whose alignment is // specified by alignment. The size parameter must be an integral multiple of // alignment. @@ -47,8 +61,458 @@ void *aligned_alloc(size_t alignment, size_t size, void **freeing_buffer) return offset == 0 ? *freeing_buffer : ((char *)*freeing_buffer + (alignment - offset)); // NOLINT } +inline int32_t AccumulateNeonLane(const int32x4_t lane) +{ +#ifdef __aarch64__ + return vaddvq_s32(lane); +#else + int64x2_t pairwiseAdded = vpaddlq_s32(lane); + return vgetq_lane_s64(pairwiseAdded, 0) + vgetq_lane_s64(pairwiseAdded, 1); +#endif +} + } // namespace +#ifdef __aarch64__ + +bool HasSdotInstruction() +{ + static const bool has_dotprod = ruy::DetectDotprod(); + return has_dotprod; +} + +// We interleave vector data to make the dot product logic more efficient. +// Suppose that vectors is: +// a0 a1 a2 a3 a4 a5 ... +// b0 b1 b2 b3 b4 b5 ... +// c0 c1 c2 c3 c4 c5 ... +// d0 d1 d2 d3 d4 d5 ... +// e0 e1 e2 e3 e4 e5 ... +// This code interleaves them like this: +// a0 a1 a2 a3 b0 b1 b2 b3 c0 c1 c2 c3 d0 d1 d2 d3 a4 a5 a6 a7 b4 ... +// e0 e1 e2 e3 f0 f1 f2 f3 ... +// Once the data is interleaved, each 16-byte read from the vectors pointer +// contains 4 bytes from each of 4 vectors. +const int8_t *ShuffleVectors(const int8_t *vectors, const int n_batch, const int m_cols, + void **shuffled_vectors_free) +{ + const int kWeightsPerUint32 = 4; + + int8 *shuffled_vectors = reinterpret_cast( + aligned_alloc(kWeightsPerUint32, n_batch * m_cols, shuffled_vectors_free)); + + for (int i = 0; i < n_batch; i += 4) + { + int8 *shuffled_vectors_ptr = shuffled_vectors + (i * m_cols); + const int8 *unshuffled_vec0_ptr = reinterpret_cast(vectors) + (i * m_cols); + const int8 *unshuffled_vec1_ptr = reinterpret_cast(vectors) + ((i + 1) * m_cols); + const int8 *unshuffled_vec2_ptr = reinterpret_cast(vectors) + ((i + 2) * m_cols); + const int8 *unshuffled_vec3_ptr = reinterpret_cast(vectors) + ((i + 3) * m_cols); + const int8 *const end_vec0_ptr = unshuffled_vec1_ptr; + + while (unshuffled_vec0_ptr != end_vec0_ptr) + { + asm volatile( + // This code path requires that (n_cols % 16) == 0 so we can safely + // read in 16-byte chunks from each row. + "ld1 {v0.16b}, [%[unshuffled_vec0_ptr]], #16\n" + "ld1 {v1.16b}, [%[unshuffled_vec1_ptr]], #16\n" + "ld1 {v2.16b}, [%[unshuffled_vec2_ptr]], #16\n" + "ld1 {v3.16b}, [%[unshuffled_vec3_ptr]], #16\n" + + "st4 {v0.s, v1.s, v2.s, v3.s}[0], [%[shuffled_vectors_ptr]], #16\n" + "st4 {v0.s, v1.s, v2.s, v3.s}[1], [%[shuffled_vectors_ptr]], #16\n" + "st4 {v0.s, v1.s, v2.s, v3.s}[2], [%[shuffled_vectors_ptr]], #16\n" + "st4 {v0.s, v1.s, v2.s, v3.s}[3], [%[shuffled_vectors_ptr]], #16\n" + + : [unshuffled_vec0_ptr] "+r"(unshuffled_vec0_ptr), + [unshuffled_vec1_ptr] "+r"(unshuffled_vec1_ptr), + [unshuffled_vec2_ptr] "+r"(unshuffled_vec2_ptr), + [unshuffled_vec3_ptr] "+r"(unshuffled_vec3_ptr), + [shuffled_vectors_ptr] "+r"(shuffled_vectors_ptr) + : + : "v0", "v1", "v2", "v3", "cc", "memory"); + } + } + + return reinterpret_cast(shuffled_vectors); +} + +// Notes about the speed of this version vs. the baseline (from memory): +// - With 256K of L1, we can keep a lot of vectors in cache. +// I recall a reasonable speedup just by rearranging the loop to have +// row on the outside and batch on the inside. +// - I also recall getting a nice speedup from sdot. +// - I tried many times to do better than the current implementation, using +// loop unrolling and instruction reordering to avoid stalls, etc. +// but I was not able to do significantly better. This code is, however, +// much worse than what the processor spec sheet suggests is possible. +static void DotprodMatrixBatchFourVectorMultiplyAccumulate(const int8_t *__restrict__ matrix, + const int m_rows, const int m_cols, + const int8_t *vectors, + const float *scaling_factors, + int n_batch, float *__restrict__ result) +{ + void *shuffled_vectors_free; + + const int8_t *shuffled_vectors = ShuffleVectors(vectors, n_batch, m_cols, &shuffled_vectors_free); + + for (int row = 0; row < m_rows; row += 2) + { + for (int batch = 0; batch < n_batch; batch += 4) + { + float *result_ptr = result + (batch * m_rows) + row; + const int8 *mat_ptr0 = matrix + (row * m_cols); + const int8 *mat_ptr1 = matrix + ((row + 1) * m_cols); + const int8 *mat_ptr0_end = mat_ptr1; + const int8 *vec_ptr = shuffled_vectors + (batch * m_cols); + const float *scaling_factors_ptr = scaling_factors + batch; + const uint64_t wide_rows = m_rows * sizeof(float); + const int8 *mat_ptr2 = matrix + ((row + 2) * m_cols); + const int8 *mat_ptr3 = matrix + ((row + 3) * m_cols); + + asm volatile( + // Zero out the accumulator registers. + "dup v0.4s, wzr\n" + "dup v1.4s, wzr\n" + "dup v2.4s, wzr\n" + "dup v3.4s, wzr\n" + + "1:\n" // batch_cols_loop + + // Read 16 more bytes from a pair of matrix rows. + "ld1 {v12.16b}, [%[mat_ptr0]], #16\n" + + // Prefetch two rows ahead. + "prfm pldl1strm, [%[mat_ptr2]]\n" + "prfm pldl1strm, [%[mat_ptr3]]\n" + + // Read from input vectors 4 times; 64 bytes total. + // Each 16-byte register contains parts of 4 vectors; see the + // shuffle logic above. + + // From Benoit, places to look in the future: + // - Move load instructions further from sdot + // - Switch loop use-then-reload + // - Do partial unrolling to use register space better + "ld1 {v8.16b}, [%[vec_ptr]], #16\n" + ".word 0x4f8ce100 // sdot v0.4s, v8.16b, v12.4b[0]\n" + "ld1 {v9.16b}, [%[vec_ptr]], #16\n" + ".word 0x4face121 // sdot v1.4s, v9.16b, v12.4b[1]\n" + "ld1 {v10.16b}, [%[vec_ptr]], #16\n" + ".word 0x4f8ce940 // sdot v0.4s, v10.16b, v12.4b[2]\n" + "ld1 {v11.16b}, [%[vec_ptr]], #16\n" + ".word 0x4face961 // sdot v1.4s, v11.16b, v12.4b[3]\n" + + // Update prefetch pointers. + "add %[mat_ptr2], %[mat_ptr2], #16\n" + "add %[mat_ptr3], %[mat_ptr3], #16\n" + + // Re-use those vectors for the next row as well. + "ld1 {v13.16b}, [%[mat_ptr1]], #16\n" + ".word 0x4f8de102 // sdot v2.4s, v8.16b, v13.4b[0]\n" + ".word 0x4fade123 // sdot v3.4s, v9.16b, v13.4b[1]\n" + ".word 0x4f8de942 // sdot v2.4s, v10.16b, v13.4b[2]\n" + ".word 0x4fade963 // sdot v3.4s, v11.16b, v13.4b[3]\n" + + // If we're not done with these rows, continue. + "cmp %[mat_ptr0], %[mat_ptr0_end]\n" + "bne 1b\n" // batch_cols_loop + + // Done with the rows, sum the results. + "add v0.4s, v0.4s, v1.4s\n" + "add v2.4s, v2.4s, v3.4s\n" + + // Convert the per-vector sums to floating point. + "scvtf v0.4s, v0.4s\n" + "scvtf v1.4s, v2.4s\n" + + // Fetch scale factors. + "ld1 {v4.4s}, [%[scaling_factors_ptr]]\n" + + // Multiply scale factors times sums. + "fmul v0.4s, v4.4s, v0.4s\n" + "fmul v1.4s, v4.4s, v1.4s\n" + + // Load previous result values. + // The result position is: + // result[batch * m_rows + row] + // Here that is factored into: + // result_ptr = result + row + // *result_ptr = res[0] + // (uint8*)result_ptr += (m_rows * sizeof(float)) + // *result_ptr = res[1] + // ... + // Since we're reading two rows at a time, though, we read both + // result[batch * m_rows + row] + // and + // result[batch * m_rows + row + 1] + "ld2 {v9.s, v10.s}[0], [%[result_ptr]], %[wide_rows]\n" + "ld2 {v9.s, v10.s}[1], [%[result_ptr]], %[wide_rows]\n" + "ld2 {v9.s, v10.s}[2], [%[result_ptr]], %[wide_rows]\n" + "ld2 {v9.s, v10.s}[3], [%[result_ptr]], %[wide_rows]\n" + + // Go back to the starting position (subtract wide_rows * 4). + "sub %[result_ptr], %[result_ptr], %[wide_rows], lsl #2\n" + + // Add previous result values. + "fadd v9.4s, v9.4s, v0.4s\n" + "fadd v10.4s, v10.4s, v1.4s\n" + + // Store results. + "st2 {v9.s, v10.s}[0], [%[result_ptr]], %[wide_rows]\n" + "st2 {v9.s, v10.s}[1], [%[result_ptr]], %[wide_rows]\n" + "st2 {v9.s, v10.s}[2], [%[result_ptr]], %[wide_rows]\n" + "st2 {v9.s, v10.s}[3], [%[result_ptr]], %[wide_rows]\n" + : [mat_ptr0] "+r"(mat_ptr0), [mat_ptr1] "+r"(mat_ptr1), [vec_ptr] "+r"(vec_ptr), + [result_ptr] "+r"(result_ptr), [mat_ptr2] "+r"(mat_ptr2), [mat_ptr3] "+r"(mat_ptr3) + : [mat_ptr0_end] "r"(mat_ptr0_end), [scaling_factors_ptr] "r"(scaling_factors_ptr), + [wide_rows] "r"(wide_rows) + : "x0", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", + "v13", "cc", "memory"); + } + } + + free(shuffled_vectors_free); +} + +static void DotprodMatrixBatchFourVectorMultiplyAccumulate( + const int8_t *__restrict__ matrix, const int m_rows, const int m_cols, const int8_t *vectors, + const float *scaling_factors, int n_batch, float *__restrict__ result, + const float *per_channel_scale, const int32_t *input_offset, int32_t *row_sums) +{ + void *shuffled_vectors_free; + const int8_t *shuffled_vectors = ShuffleVectors(vectors, n_batch, m_cols, &shuffled_vectors_free); + + for (int row = 0; row < m_rows; row += 2) + { + const float *channel_scales_ptr = per_channel_scale + row; + int32_t *row_sums_ptr = row_sums ? row_sums + row : nullptr; + for (int batch = 0; batch < n_batch; batch += 4) + { + float *result_ptr = result + (batch * m_rows) + row; + const int8 *mat_ptr0 = matrix + (row * m_cols); + const int8 *mat_ptr1 = matrix + ((row + 1) * m_cols); + const int8 *mat_ptr0_end = mat_ptr1; + const int8 *vec_ptr = shuffled_vectors + (batch * m_cols); + const float *scaling_factors_ptr = scaling_factors + batch; + const uint64_t wide_rows = m_rows * sizeof(float); + const int32_t *batch_offsets_ptr = input_offset + batch; + const int32_t is_channel_scale_nullptr = per_channel_scale == nullptr; + const int32_t is_row_sums_nullptr = row_sums_ptr == nullptr; + asm volatile("dup v0.4s, wzr\n" + "dup v1.4s, wzr\n" + "dup v2.4s, wzr\n" + "dup v3.4s, wzr\n" + // Load zero points. + "ld1 {v7.4s}, [%[batch_offsets_ptr]]\n" + "ld1 {v4.4s}, [%[scaling_factors_ptr]]\n" + // Zero out zero point accumulators. + "dup v14.4s, wzr\n" + "dup v15.4s, wzr\n" + + // Load per channel scales if not null. + "cmp %w[is_channel_scale_nullptr], #0\n" + "bne 1f\n" + "ld1r {v16.4s}, [%[channel_scales_ptr]], #4\n" + "ld1r {v17.4s}, [%[channel_scales_ptr]]\n" + "fmul v16.4s, v16.4s, v4.4s\n" + "fmul v17.4s, v17.4s, v4.4s\n" + "b 2f\n" + "1:\n" + "mov v16.16b, v4.16b\n" + "mov v17.16b, v4.16b\n" + "2:\n" + "ld1 {v12.16b}, [%[mat_ptr0]], #16\n" + "ld1 {v8.16b}, [%[vec_ptr]], #16\n" + ".word 0x4f8ce100 // sdot v0.4s, v8.16b, v12.4b[0]\n" + "ld1 {v9.16b}, [%[vec_ptr]], #16\n" + ".word 0x4face121 // sdot v1.4s, v9.16b, v12.4b[1]\n" + "ld1 {v10.16b}, [%[vec_ptr]], #16\n" + ".word 0x4f8ce940 // sdot v0.4s, v10.16b, v12.4b[2]\n" + "ld1 {v11.16b}, [%[vec_ptr]], #16\n" + ".word 0x4face961 // sdot v1.4s, v11.16b, v12.4b[3]\n" + "ld1 {v13.16b}, [%[mat_ptr1]], #16\n" + ".word 0x4f8de102 // sdot v2.4s, v8.16b, v13.4b[0]\n" + ".word 0x4fade123 // sdot v3.4s, v9.16b, v13.4b[1]\n" + ".word 0x4f8de942 // sdot v2.4s, v10.16b, v13.4b[2]\n" + ".word 0x4fade963 // sdot v3.4s, v11.16b, v13.4b[3]\n" + "cmp %w[is_row_sums_nullptr], #1\n" + "bne 3f\n" + // Accumulate row_sums for zero point calculations. + "saddlp v12.8h, v12.16b\n" + "saddlp v13.8h, v13.16b\n" + "sadalp v14.4s, v12.8h\n" + "sadalp v15.4s, v13.8h\n" + "3:\n" + "cmp %[mat_ptr0], %[mat_ptr0_end]\n" + "bne 2b\n" + "add v0.4s, v0.4s, v1.4s\n" + "add v2.4s, v2.4s, v3.4s\n" + + "cmp %w[is_row_sums_nullptr], #1\n" + "bne 4f\n" + // Calculate zero point offsets. + "addv s14, v14.4s\n" + "addv s15, v15.4s\n" + "dup v14.4s, v14.s[0]\n" + "dup v15.4s, v15.s[0]\n" + "b 5f\n" + "4:\n" + "ld1r {v14.4s}, [%[row_sums_ptr]], #4\n" + "ld1r {v15.4s}, [%[row_sums_ptr]]\n" + "5:\n" + + "mul v14.4s, v14.4s, v7.4s\n" + "mul v15.4s, v15.4s, v7.4s\n" + "sub v0.4s, v0.4s, v14.4s\n" + "sub v2.4s, v2.4s, v15.4s\n" + + "scvtf v0.4s, v0.4s\n" + "scvtf v1.4s, v2.4s\n" + + // Multiply scale. + "fmul v0.4s, v16.4s, v0.4s\n" + "fmul v1.4s, v17.4s, v1.4s\n" + + "ld2 {v9.s, v10.s}[0], [%[result_ptr]], %[wide_rows]\n" + "ld2 {v9.s, v10.s}[1], [%[result_ptr]], %[wide_rows]\n" + "ld2 {v9.s, v10.s}[2], [%[result_ptr]], %[wide_rows]\n" + "ld2 {v9.s, v10.s}[3], [%[result_ptr]], %[wide_rows]\n" + "sub %[result_ptr], %[result_ptr], %[wide_rows], lsl #2\n" + "fadd v9.4s, v9.4s, v0.4s\n" + "fadd v10.4s, v10.4s, v1.4s\n" + "st2 {v9.s, v10.s}[0], [%[result_ptr]], %[wide_rows]\n" + "st2 {v9.s, v10.s}[1], [%[result_ptr]], %[wide_rows]\n" + "st2 {v9.s, v10.s}[2], [%[result_ptr]], %[wide_rows]\n" + "st2 {v9.s, v10.s}[3], [%[result_ptr]], %[wide_rows]\n" + : [mat_ptr0] "+r"(mat_ptr0), [mat_ptr1] "+r"(mat_ptr1), [vec_ptr] "+r"(vec_ptr), + [result_ptr] "+r"(result_ptr), [row_sums_ptr] "+r"(row_sums_ptr) + : [mat_ptr0_end] "r"(mat_ptr0_end), + [scaling_factors_ptr] "r"(scaling_factors_ptr), [wide_rows] "r"(wide_rows), + [channel_scales_ptr] "r"(channel_scales_ptr), + [batch_offsets_ptr] "r"(batch_offsets_ptr), + [is_channel_scale_nullptr] "r"(is_channel_scale_nullptr), + [is_row_sums_nullptr] "r"(is_row_sums_nullptr) + : "x0", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", + "v12", "v13", "v14", "v15", "v16", "v17", "w0", "w1", "cc", "memory"); + } + } + + free(shuffled_vectors_free); +} + +// The DotprodMatrixBatchFourVectorMultiplyAccumulate kernel processes 4 +// vectors in the same time as the baseline processes 1 vector. However, it +// requires 4 vectors of input. +// +// To take advantage of this speed difference, we add some zero-valued +// vectors to the batch so that n_batch is a multiple of 4. Then we execute +// DotprodMatrixBatchPaddedFourVectorMultiplyAccumulate on that padded batch, +// then extract just the results we want at the end (ignoring the extra padding +// outputs). +// +// The relative cost of the padding is large when the matrix is smaller than +// 128x128, so we don't use this code path on small matrices. On larger +// matrices, the computation cost dwarfs the padding cost, making this code +// viable. +// +// If we ignore the cost of padding, this kernel is: +// 1x the speed of NeonMatrixBatchVectorMultiplyImpl for n_batch = 1 +// 2x the speed of NeonMatrixBatchVectorMultiplyImpl for n_batch = 2 +// 3x the speed of NeonMatrixBatchVectorMultiplyImpl for n_batch = 3 +// ... +// +// We don't use this kernel when n_batch = 1 because the baseline kernel +// is fine for that case. +void DotprodMatrixBatchPaddedFourVectorMultiplyAccumulate( + const int8_t *__restrict__ matrix, const int m_rows, const int m_cols, const int8_t *vectors, + const float *scaling_factors, int n_batch, float *__restrict__ result, + const float *per_channel_scale, const int32_t *input_offset, int32_t *row_sums) +{ + const int kWeightsPerUint32 = 4; + + // Round to the nearest multiple of 4. + int batch_round_up = n_batch; + if (n_batch % 4 != 0) + { + batch_round_up += (4 - n_batch % 4); + } + assert(n_batch <= batch_round_up); + + void *padded_vectors_free; + const int padded_vectors_size = batch_round_up * m_cols; + int8_t *padded_vectors = reinterpret_cast( + aligned_alloc(kWeightsPerUint32, padded_vectors_size, &padded_vectors_free)); + memset(padded_vectors, 0, padded_vectors_size); + + void *padded_result_free; + const int result_size = n_batch * m_rows * sizeof(float); + const int padded_result_size = batch_round_up * m_rows * sizeof(float); + float *padded_result = reinterpret_cast( + aligned_alloc(kWeightsPerUint32, padded_result_size, &padded_result_free)); + memcpy(padded_result, result, result_size); + memset(reinterpret_cast(padded_result) + result_size, 0, + padded_result_size - result_size); + + // Copy the input into the padded data structure. + assert(n_batch * m_cols <= padded_vectors_size); + memcpy(padded_vectors, vectors, n_batch * m_cols); + + void *padded_scaling_factors_free; + const int padded_scaling_factors_size = batch_round_up * sizeof(float); + float *padded_scaling_factors = reinterpret_cast( + aligned_alloc(kWeightsPerUint32, padded_scaling_factors_size, &padded_scaling_factors_free)); + assert(static_cast(n_batch * sizeof(float)) <= padded_scaling_factors_size); + assert(static_cast(batch_round_up * sizeof(float)) <= padded_scaling_factors_size); + memset(padded_scaling_factors, 0, batch_round_up * sizeof(float)); + memcpy(padded_scaling_factors, scaling_factors, n_batch * sizeof(float)); + + if (input_offset != nullptr) + { + void *padded_input_offset_free; + const int padded_input_offset_size = batch_round_up * sizeof(int32_t); + int32_t *padded_input_offset = reinterpret_cast( + aligned_alloc(kWeightsPerUint32, padded_input_offset_size, &padded_input_offset_free)); + assert(static_cast(n_batch * sizeof(int32_t)) <= padded_input_offset_size); + assert(static_cast(batch_round_up * sizeof(int32_t)) <= padded_input_offset_size); + memset(padded_input_offset, 0, batch_round_up * sizeof(int32_t)); + memcpy(padded_input_offset, input_offset, n_batch * sizeof(int32_t)); + + // Call the main kernel. + DotprodMatrixBatchFourVectorMultiplyAccumulate( + matrix, m_rows, m_cols, padded_vectors, padded_scaling_factors, batch_round_up, + padded_result, per_channel_scale, padded_input_offset, row_sums); + + free(padded_input_offset_free); + } + else + { + // Call the main kernel. + DotprodMatrixBatchFourVectorMultiplyAccumulate(matrix, m_rows, m_cols, padded_vectors, + padded_scaling_factors, batch_round_up, + padded_result); + } + memcpy(result, padded_result, result_size); + + free(padded_result_free); + free(padded_vectors_free); + free(padded_scaling_factors_free); +} + +void DotprodMatrixBatchPaddedFourVectorMultiplyAccumulate(const int8_t *__restrict__ matrix, + const int m_rows, const int m_cols, + const int8_t *vectors, + const float *scaling_factors, int n_batch, + float *__restrict__ result) +{ + DotprodMatrixBatchPaddedFourVectorMultiplyAccumulate( + matrix, m_rows, m_cols, vectors, scaling_factors, n_batch, result, + /*per_channel_scale=*/nullptr, /*input_offset=*/nullptr, + /*row_sums=*/nullptr); +} +#endif // __aarch64__ + bool NeonIsZeroVector(const float *vector, int v_size) { // If v_size is not divisible by kFloatWeightsPerNeonLane, we cannot @@ -80,6 +544,49 @@ bool NeonIsZeroVector(const float *vector, int v_size) return true; } +void NeonCpuBackendGemm(const int8_t *input, const int32_t *bias, + const int8_t *input_to_gate_weights, int32_t n_batch, int32_t n_input, + int32_t n_output, int32_t, int32_t *scratch) +{ + MatrixParams lhs_params; + lhs_params.order = Order::kRowMajor; + lhs_params.rows = n_output; + lhs_params.cols = n_input; + lhs_params.cacheable = true; + + MatrixParams rhs_params; + rhs_params.order = Order::kColMajor; + rhs_params.rows = n_input; + rhs_params.cols = n_batch; + + MatrixParams dst_params; + dst_params.order = Order::kColMajor; + dst_params.rows = n_output; + dst_params.cols = n_batch; + + GemmParams gemm_params; + if (bias) + { + gemm_params.bias = bias; + } + + // Below code is from tflite::cpu_backend_gemm::detail::GemmImplUsingRuy + ruy::Context *ruy_context = ruy_support::GetRuyContext(); + + ruy::Matrix ruy_lhs; + ruy::Matrix ruy_rhs; + ruy::Matrix ruy_dst; + ruy_support::MakeRuyMatrix(lhs_params, input_to_gate_weights, &ruy_lhs); + ruy_support::MakeRuyMatrix(rhs_params, input, &ruy_rhs); + ruy_support::MakeRuyMatrix(dst_params, scratch, &ruy_dst); + + ruy::BasicSpec ruy_spec; + ruy_support::MakeRuySpec(gemm_params, &ruy_spec); + + constexpr ruy::Path kRuyPath = ruy::kAllPaths; + ruy::Mul(ruy_lhs, ruy_rhs, ruy_spec, ruy_context, &ruy_dst); +} + void NeonSymmetricQuantizeFloats(const float *values, const int size, int8_t *quantized_values, float *min, float *max, float *scaling_factor) { @@ -163,11 +670,38 @@ void NeonMatrixBatchVectorMultiplyAccumulate(const int8_t *__restrict__ matrix, const float *scaling_factors, int n_batch, float *__restrict__ result, int result_stride) { - const int kWeightsPerUint32 = 4; - const int kWeightsPerNeonLane = 16; - // If the number of rows is not divisible by kWeightsPerUint32, we set a - // flag and allocate an aligned memory block. The flag is used to use the - // aligned memory block later in the kernel loop. +#ifdef __aarch64__ + if (HasSdotInstruction() && m_cols % 16 == 0 && m_rows % 2 == 0 && m_rows >= n_batch) + { + if (n_batch % 4 == 0 && result_stride == 1) + { + // Benchmarks suggest that it's always better to use the batch code + // when we can, even on small matrices. + DotprodMatrixBatchFourVectorMultiplyAccumulate(matrix, m_rows, m_cols, vectors, + scaling_factors, n_batch, result); + return; + } + else if (result_stride == 1 && n_batch >= 2 && m_rows * m_cols >= 128 * 128) + { + DotprodMatrixBatchPaddedFourVectorMultiplyAccumulate(matrix, m_rows, m_cols, vectors, + scaling_factors, n_batch, result); + return; + } + } +#endif // __aarch64__ + + static const int kWeightsPerUint32 = 4; + static const int kWeightsPerNeonLane = 16; + // Assuming *matrix is kWeightsPerUint32-byte aligned, + // every row of the matrix is also + // kWeightsPerUint32-byte aligned as long as cols is + // a multiple of kWeightsPerUint32. The assumption + // is currently satisfied by TFLite's 16-byte memory + // alignment scheme. + // + // Otherwise, we allocate an aligned memory block and set + // a flag to later copy rows from matrix to the block + // for aligned multiplication. bool unaligned = false; int8_t *aligned_row = nullptr; void *aligned_row_free = nullptr; @@ -182,18 +716,20 @@ void NeonMatrixBatchVectorMultiplyAccumulate(const int8_t *__restrict__ matrix, &aligned_vec_free); // If m_cols is not at least kWeightsPerNeonLane, we cannot use the main - // vectorized loop, and we need to process sequentially. postamble_start shows - // the start index where this should happen. - const int postamble_start = m_cols - (m_cols & (kWeightsPerNeonLane - 1)); - - int batch, row, col; - for (batch = 0; batch < n_batch; ++batch) + // vectorized loop, and we need to process sequentially. postamble_half_start + // shows the start index where this should happen. Between postamble_start and + // postamble_half_start we can still process kWeightsPerNeonLane >> 1 in a + // vectorized form. + const int postamble_half_start = m_cols & ~(kWeightsPerNeonLane - 1); + const int postamble_start = m_cols & ~((kWeightsPerNeonLane >> 1) - 1); + + for (int batch = 0; batch < n_batch; ++batch) { const float batch_scaling_factor = scaling_factors[batch]; // Copy the vector data to an aligned vector. memcpy(aligned_vec, vectors + batch * m_cols, sizeof(int8_t) * m_cols); // Compute dot-product for every column. - for (row = 0; row < m_rows; ++row, result += result_stride) + for (int row = 0; row < m_rows; ++row, result += result_stride) { // Get the address of the first element of the row. int8_t *row_ptr = (int8_t *)matrix + row * m_cols; // NOLINT @@ -204,24 +740,26 @@ void NeonMatrixBatchVectorMultiplyAccumulate(const int8_t *__restrict__ matrix, } // Initialize the dot product sum for the row to 0. - int32x4_t dotprod = vmovq_n_s32(0); + int32x4_t dotprod_32x4 = vmovq_n_s32(0); // Prefetch the row to cache. __builtin_prefetch(row_ptr, 0 /* prefetch for read */, 3 /* temporal locality */); // For every block of 16 8-bit elements. - col = 0; - for (; col < postamble_start; col += kWeightsPerNeonLane) + int col = 0; + for (; col < postamble_half_start; col += kWeightsPerNeonLane) { // Load 16 8-bit values from the row and vector, each, to operate on. - // Here the assumption is that each buffer is 4-byte aligned. - assert(((uintptr_t)(&row_ptr[col]) & (kWeightsPerUint32 - 1)) == 0); + // Here the assumption is that each buffer is 4-byte aligned. Otherwise, + // performance may suffer significantly. + assert( // NOLINT + ((uintptr_t)(&row_ptr[col]) & (kWeightsPerUint32 - 1)) == 0); const int8x16_t s1_8x16 = vld1q_s8((const int8_t *)(aligned_vec + col)); const int8x16_t s2_8x16 = vld1q_s8((const int8_t *)(row_ptr + col)); // Multiply the low bits (i.e. the lower 8 8bit numbers in the // registers). int16x8_t prod_16x8 = vmull_s8(vget_low_s8(s1_8x16), vget_low_s8(s2_8x16)); - // Multiply the high bits (i.e. the lower 8 8bit numbers in the + // Multiply the high bits (i.e. the higher 8 8bit numbers in the // registers), and accumulate with the result of the low bits product. // The assumption here is that overflow will not happen as we quantize // our values to be in the range [-127, 127]. As such the sum of the 2 @@ -229,37 +767,35 @@ void NeonMatrixBatchVectorMultiplyAccumulate(const int8_t *__restrict__ matrix, // value). prod_16x8 = vmlal_s8(prod_16x8, vget_high_s8(s1_8x16), vget_high_s8(s2_8x16)); - dotprod = vpadalq_s16(dotprod, prod_16x8); + dotprod_32x4 = vpadalq_s16(dotprod_32x4, prod_16x8); } // for col - int32_t postable_sum = 0; - // Postamble loop. - // TODO(raziel): if (ABSL_PREDICT_FALSE(postamble_start < m_rows)) - if (postamble_start < m_cols) + // Half iteration dealing only 8 elements + // TODO(raziel): if (ABSL_PREDICT_FALSE(col < postamble_start)) + if (col < postamble_start) { - col = postamble_start; - if ((m_cols - postamble_start) >= (kWeightsPerNeonLane >> 1)) - { - // Load 8 8-bit values from the row and column each to operate on. - // Here the assumption is that each buffer is 4-bytes aligned. - assert(((uintptr_t)(&row_ptr[col]) & (kWeightsPerUint32 - 1)) == 0); - const int8x8_t s1_8x8 = vld1_s8((const int8_t *)(aligned_vec + col)); - const int8x8_t s2_8x8 = vld1_s8((const int8_t *)(row_ptr + col)); - const int16x8_t prod_16x8 = vmull_s8(s1_8x8, s2_8x8); - dotprod = vpadalq_s16(dotprod, prod_16x8); - col += (kWeightsPerNeonLane >> 1); - } - for (; col < m_cols; ++col) - { - postable_sum += row_ptr[col] * aligned_vec[col]; - } // for col + // Load 8 8-bit values from the row and column each to operate on. + // Here the assumption is that each buffer is 4-bytes aligned. + // Otherwise, performance may suffer significantly. + assert( // NOLINT + ((uintptr_t)(&row_ptr[col]) & (kWeightsPerUint32 - 1)) == 0); + const int8x8_t s1_8x8 = vld1_s8((const int8_t *)(aligned_vec + col)); + const int8x8_t s2_8x8 = vld1_s8((const int8_t *)(row_ptr + col)); + const int16x8_t prod_16x8 = vmull_s8(s1_8x8, s2_8x8); + dotprod_32x4 = vpadalq_s16(dotprod_32x4, prod_16x8); + col += (kWeightsPerNeonLane >> 1); } // Add the 4 intermediate sum values to get the final dot-prod value for // this row. - int64x2_t pairwiseAdded = vpaddlq_s32(dotprod); - int32_t neon_sum = vgetq_lane_s64(pairwiseAdded, 0) + vgetq_lane_s64(pairwiseAdded, 1); + int32_t dotprod = AccumulateNeonLane(dotprod_32x4); + // Postamble loop. + // TODO(raziel): if (ABSL_PREDICT_FALSE(col < m_cols)) + for (; col < m_cols; ++col) + { + dotprod += row_ptr[col] * aligned_vec[col]; + } // for col - *result += ((neon_sum + postable_sum) * batch_scaling_factor); + *result += dotprod * batch_scaling_factor; } // for row } // for batch @@ -311,6 +847,50 @@ void NeonMatrixBatchVectorMultiplyAccumulate(const float *matrix, int m_rows, in } } +void NeonMatrixBatchVectorMultiplyAccumulate(const int8_t *__restrict__ matrix, const int m_rows, + const int m_cols, const int8_t *__restrict__ vectors, + const float *scaling_factors, int n_batch, + int32_t *scratch, float *__restrict__ result, + int result_stride) +{ + if (m_rows % 4 == 0 && result_stride == 1) + { + const int32_t *bias = static_cast(nullptr); + NeonCpuBackendGemm(vectors, bias, matrix, n_batch, m_cols, m_rows, + /*output_zp =*/0, scratch); + + // Multiply by float scaling factors and write to result + const int total_size = n_batch * m_rows; + int i = 0; + for (; i <= total_size - 8; i += 8, result += 8 * result_stride) + { + const float batch_scaling_factor0 = scaling_factors[i / m_rows]; + const float batch_scaling_factor1 = scaling_factors[(i + 4) / m_rows]; + const float32x4_t scaling_factor0 = vdupq_n_f32(batch_scaling_factor0); + const float32x4_t scaling_factor1 = vdupq_n_f32(batch_scaling_factor1); + const int32x4_t scratch_val0 = vld1q_s32(scratch + i); + const int32x4_t scratch_val1 = vld1q_s32(scratch + i + 4); + const float32x4_t float_val0 = vcvtq_f32_s32(scratch_val0); + const float32x4_t float_val1 = vcvtq_f32_s32(scratch_val1); + const float32x4_t result0 = vmlaq_f32(vld1q_f32(result), float_val0, scaling_factor0); + const float32x4_t result1 = + vmlaq_f32(vld1q_f32(result + 4 * result_stride), float_val1, scaling_factor1); + vst1q_f32(result, result0); + vst1q_f32(result + 4 * result_stride, result1); + } + scratch += i; + for (; i < total_size; i++, result += result_stride) + { + const float batch_scaling_factor = scaling_factors[i / m_rows]; + int32_t x = *(scratch++); + *result += x * batch_scaling_factor; + } + return; + } + NeonMatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vectors, scaling_factors, n_batch, + result, result_stride); +} + } // namespace cker } // namespace nnfw diff --git a/compute/cker/include/cker/PortableTensorUtils.h b/compute/cker/include/cker/PortableTensorUtils.h index 87b792e..9769d4b 100644 --- a/compute/cker/include/cker/PortableTensorUtils.h +++ b/compute/cker/include/cker/PortableTensorUtils.h @@ -137,6 +137,17 @@ void PortableMatrixBatchVectorMultiplyAccumulate(const int8_t *__restrict__ matr } // for batch } +void PortableMatrixBatchVectorMultiplyAccumulate(const int8_t *__restrict__ matrix, + const int m_rows, const int m_cols, + const int8_t *__restrict__ vector, + const float *scaling_factors, int n_batch, + int32_t *, float *__restrict__ result, + int result_stride) +{ + PortableMatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vector, scaling_factors, + n_batch, result, result_stride); +} + void PortableMatrixBatchVectorMultiplyAccumulate(const float *matrix, int m_rows, int m_cols, const float *vector, int n_batch, float *result, int result_stride) diff --git a/compute/cker/include/cker/Shape.h b/compute/cker/include/cker/Shape.h index 43b511d..2486f01 100644 --- a/compute/cker/include/cker/Shape.h +++ b/compute/cker/include/cker/Shape.h @@ -33,9 +33,9 @@ namespace cker class Shape { public: - // Shapes with dimensions up to 4 are stored directly in the structure, while + // Shapes with dimensions up to 5 are stored directly in the structure, while // larger shapes are separately allocated. - static constexpr int kMaxSmallSize = 4; + static constexpr int kMaxSmallSize = 5; Shape &operator=(Shape const &) = delete; @@ -201,7 +201,7 @@ private: int32_t _size; union { int32_t _dims[kMaxSmallSize]; - int32_t *_dims_pointer; + int32_t *_dims_pointer{nullptr}; }; }; @@ -213,6 +213,15 @@ inline int MatchingDim(const Shape &shape1, int index1, const Shape &shape2, int return shape1.Dims(index1); } +template +int MatchingDim(const Shape &shape1, int index1, const Shape &shape2, int index2, Args... args) +{ + assert(shape1.Dims(index1) == shape2.Dims(index2)); + UNUSED_RELEASE(shape2); + UNUSED_RELEASE(index2); + return MatchingDim(shape1, index1, args...); +} + inline Shape GetShape(const std::vector &data) { return Shape(data.size(), data.data()); } inline int Offset(const Shape &shape, int i0, int i1, int i2, int i3) @@ -251,16 +260,21 @@ template inline bool checkMatching(const Shape &shape, Ts... ch const Shape check_shapes_array[sizeof...(Ts)] = {std::forward(check_shapes)...}; for (const auto &check_shape : check_shapes_array) { - if (shape.DimensionsCount() != check_shape.DimensionsCount()) + // Check matching of shapes except the case of that two shapes can be scalar + if (shape.DimensionsCount() > 1 || check_shape.DimensionsCount() > 1 || shape.FlatSize() != 1 || + check_shape.FlatSize() != 1) { - return false; - } - for (int i = 0; i < shape.DimensionsCount(); ++i) - { - if (shape.Dims(i) != check_shape.Dims(i)) + if (shape.DimensionsCount() != check_shape.DimensionsCount()) { return false; } + for (int i = 0; i < shape.DimensionsCount(); ++i) + { + if (shape.Dims(i) != check_shape.Dims(i)) + { + return false; + } + } } } return true; @@ -291,6 +305,34 @@ inline int MatchingFlatSizeSkipDim(const Shape &shape, int skip_dim, const Shape return FlatSizeSkipDim(shape, skip_dim); } +inline int MatchingFlatSizeSkipDim(const Shape &shape, int skip_dim, const Shape &check_shape_0, + const Shape &check_shape_1) +{ + UNUSED_RELEASE(check_shape_0); + const int dims_count = shape.DimensionsCount(); + for (int i = 0; i < dims_count; ++i) + { + if (i != skip_dim) + { + assert(shape.Dims(i) == check_shape_0.Dims(i)); + } + } + return MatchingFlatSizeSkipDim(shape, skip_dim, check_shape_1); +} + +inline int MatchingElementsSize(const Shape &shape, const Shape &check_shape_0, + const Shape &check_shape_1) +{ + const int size_1 = shape.FlatSize(); + const int size_2 = check_shape_0.FlatSize(); + const int size_3 = check_shape_1.FlatSize(); + assert(size_1 == size_2); + assert(size_2 == size_3); + UNUSED_RELEASE(size_2); + UNUSED_RELEASE(size_3); + return size_1; +} + } // namespace cker } // namespace nnfw diff --git a/compute/cker/include/cker/TensorUtils.h b/compute/cker/include/cker/TensorUtils.h index 6c5bacb..6b23c0b 100644 --- a/compute/cker/include/cker/TensorUtils.h +++ b/compute/cker/include/cker/TensorUtils.h @@ -70,6 +70,15 @@ void MatrixBatchVectorMultiplyAccumulate(const float *matrix, int m_rows, int m_ result, result_stride); } +void MatrixBatchVectorMultiplyAccumulate(const int8_t *matrix, const int m_rows, const int m_cols, + const int8_t *vectors, const float *scaling_factors, + int n_batch, int32_t *scratch, float *result, + int result_stride) +{ + NEON_OR_PORTABLE(MatrixBatchVectorMultiplyAccumulate, matrix, m_rows, m_cols, vectors, + scaling_factors, n_batch, scratch, result, result_stride); +} + void ZeroVector(float *vector, int v_size) { PortableZeroVector(vector, v_size); } } // namespace cker diff --git a/compute/cker/include/cker/Types.h b/compute/cker/include/cker/Types.h index 3c3396a..41b1916 100644 --- a/compute/cker/include/cker/Types.h +++ b/compute/cker/include/cker/Types.h @@ -19,6 +19,9 @@ #define __NNFW_CKER_TYPES_H__ #include +#include +#include +#include namespace nnfw { @@ -45,6 +48,7 @@ enum class BinaryArithmeticOpType SUB = 1, MUL = 2, DIV = 3, + POW = 4, }; enum class ComparisonOpType @@ -63,6 +67,15 @@ struct PaddingValues int16_t height; }; +enum class BroadcastableOpCategory : uint8_t +{ + kNone, + kNonBroadcast, // Matching input shapes. + kFirstInputBroadcastsFast, // Fivefold nested loops. + kSecondInputBroadcastsFast, // Fivefold nested loops. + kGenericBroadcast, // Fall-back. +}; + struct PoolParams { FusedActivationFunctionType activation; @@ -85,6 +98,7 @@ struct SoftmaxParams // beta is not really used (not a Tensorflow parameter) and not implemented // for LogSoftmax. double beta; + int axis; // uint8 inference params. Used even when beta defaults to 1.0. int32_t input_multiplier; int32_t input_left_shift; @@ -140,14 +154,20 @@ struct ConvParams struct ComparisonParams { ComparisonOpType type; + int left_shift; + int input1_shift; + int input2_shift; + int32_t input1_offset; + int32_t input1_multiplier; + int32_t input2_offset; + int32_t input2_multiplier; bool is_broadcast; }; struct BinaryArithmeticOpParam { - BinaryArithmeticOpType type; // Shape dependent / common to data / op types. - // BroadcastableOpCategory broadcast_category; + BroadcastableOpCategory broadcast_category; // uint8 inference params. int32_t input1_offset; int32_t input2_offset; @@ -176,7 +196,7 @@ struct BinaryArithmeticOpParam // broadcast_shape[2] = b2 = a2. // broadcast_shape[1] = a3; b3 = 1. // broadcast_shape[0] = b4 = a4. - // int broadcast_shape[5]; + int broadcast_shape[5] = {}; }; struct TransposeParams @@ -305,6 +325,172 @@ struct SplitParams int16_t axis; }; +struct FusedBatchNormParams +{ + bool is_training; + std::string data_format; // UNKNOWN(0), NHWC(1), NCHW(2) + float epsilon; +}; + +struct SpaceToBatchParams +{ + // "Zero" padding for uint8 means padding with the output offset. + int32_t output_offset; +}; + +enum class Order +{ + kColMajor, + kRowMajor +}; + +// MatrixParams encapsulates the parameters that Gemm needs about each +// matrix, besides the buffer data pointer. +// Compare to ruy::Matrix, which also encapsulates the data pointer. +// Rationale for leaving the data pointer out of here: doing so +// requires complicated const-correctness mechanics. See +// ruy::ConstCheckingPtr. +template struct MatrixParams +{ + // Storage layout order. For now we only do plain linear non-strided + // layout. It would be easy to support a stride if needed. + Order order = Order::kColMajor; + // Number of rows of the matrix. + int rows = 0; + // Number of columns of the matrix. + int cols = 0; + // The zero_point, i.e. which Scalar value is to be interpreted as zero. + // When Scalar is floating-point, this must be 0. + Scalar zero_point = 0; + // Indicate whether the underlying data will remain unchanged for + // some period of time. Defaults to false, but should be set to true + // for unchanging data (e.g. weights buffers in many cases) + bool cacheable = false; +}; + +// Enumeration of broad categories of Gemm. +// +// The primary reason for this to exist is to allow Gemm to compile +// only uniform-quantized or only per-channel-quantized code paths. +// This is unneeded with ruy as the back-end, as this is only a runtime +// difference in ruy, but with gemmlowp these really are separate code +// paths and templatizing in a QuantizationFlavor is necessary to avoid +// compiling unused gemmlowp code. Indeed, TFLite currently uses +// uint8 with uniform quantization and int8 with per-channel quantization, +// and does not use uint8 with per-channel. We want to avoid compiling +// the gemmlowp uint8 per-channel path when gemmlowp is the back-end. +// +// It's possible to drop this in the future if gemmlowp goes away and no +// other then-relevant backend library handles quantized paths in a way that +// requires knowing this at compile-time. +enum class QuantizationFlavor +{ + // Floating-point Gemm: the accumulators are not multiplied by any + // 'multiplier'. + kFloatingPoint, + // Quantized Gemm using a single multiplier for all accumulators. + kIntegerWithUniformMultiplier, + // Quantized Gemm using a separate multipliers for accumulators of each + // row of the destination matrix. This is what is called 'per-channel' + // in GemmParams. Here we use the more specific 'per-row' terminology + // to allow for the possibility of 'per-column' in the future, and to + // allow for that to be a separate code path in some back-end such as + // gemmlowp. + kIntegerWithPerRowMultiplier +}; + +// Additional parameters that Gemm needs, beyond what falls into +// the MatrixParams that it takes. Compare to ruy::Spec. +// +// Decoupling AccumScalar from DstScalar (rather than deducing it from that) +// is useful future-proofing. Think of a float16 path using float32 accum. +// +// QuantizationFlavor is passed here even though it's technically not used +// in this class. This is so that we retain the ability in the future to +// specialize this class for quantization flavor, and this allows for +// Gemm to be templatized in quantization_flavor via the GemmParams that it +// takes, allowing for automatic template parameter deduction to take place, +// so that most call sites don't need to specify a QuantizationFlavor +// (only those that need perchannel quantization do). +template ::value + ? QuantizationFlavor::kFloatingPoint + : QuantizationFlavor::kIntegerWithUniformMultiplier> +struct GemmParams +{ + // Only for non-floating-point cases. The fixed-point part (i.e. the mantissa) + // of the multiplier by which accumulators are multiplied before being casted + // to the destination type. + AccumScalar multiplier_fixedpoint = 0; + // Only for non-floating-point cases. The exponent part of the aforementioned + // multiplier. + int multiplier_exponent = 0; + // Per-channel variant of multiplier_fixedpoint. If not nullptr, this must + // point to a buffer of as many values as there are rows in the destination + // matrix. Each row of the destination matrix will use the corresponding + // buffer element instead of multiplier_fixedpoint. + const AccumScalar *multiplier_fixedpoint_perchannel = nullptr; + // Per-channel variant of multiplier_exponent. If not nullptr, this must + // point to a buffer of as many values as there are rows in the destination + // matrix. Each row of the destination matrix will use the corresponding + // buffer element instead of multiplier_exponent. + // + // Either none or both of multiplier_exponent_perchannel and + // multiplier_fixedpoint_perchannel must be nullptr. + const int *multiplier_exponent_perchannel = nullptr; + // The bias vector data, if not null. + const AccumScalar *bias = nullptr; + // min clamp bound of destination values. + DstScalar clamp_min = std::is_floating_point::value + ? -std::numeric_limits::infinity() + : std::numeric_limits::lowest(); + // max clamp bound of destination values. + DstScalar clamp_max = std::is_floating_point::value + ? std::numeric_limits::infinity() + : std::numeric_limits::max(); +}; + +// Validates self-consistency of GemmParams. +template +void ValidateGemmParams(const GemmParams ¶ms) +{ + // Guard consistency of the quantized multiplier fields. + if (quantization_flavor == QuantizationFlavor::kFloatingPoint) + { + assert(!params.multiplier_fixedpoint); + assert(!params.multiplier_exponent); + assert(!params.multiplier_fixedpoint_perchannel); + assert(!params.multiplier_exponent_perchannel); + } + else if (quantization_flavor == QuantizationFlavor::kIntegerWithUniformMultiplier && + !std::is_same::value) + { + assert(params.multiplier_fixedpoint); + // Nothing to check about multiplier_exponent + assert(!params.multiplier_fixedpoint_perchannel); + assert(!params.multiplier_exponent_perchannel); + } + else if (quantization_flavor == QuantizationFlavor::kIntegerWithPerRowMultiplier && + !std::is_same::value) + { + assert(!params.multiplier_fixedpoint); + assert(!params.multiplier_exponent); + assert(params.multiplier_fixedpoint_perchannel); + assert(params.multiplier_exponent_perchannel); + } + else + { + // For the get raw accumulator case, we should make sure none of the + // quantization params are set. + assert(!params.multiplier_fixedpoint); + assert(!params.multiplier_exponent); + assert(!params.multiplier_fixedpoint_perchannel); + assert(!params.multiplier_exponent_perchannel); + } + UNUSED_RELEASE(params); +} + } // namespace cker } // namespace nnfw diff --git a/compute/cker/include/cker/Utils.h b/compute/cker/include/cker/Utils.h index 17ef3c3..b69d55c 100644 --- a/compute/cker/include/cker/Utils.h +++ b/compute/cker/include/cker/Utils.h @@ -35,6 +35,54 @@ inline T ActivationFunctionWithMinMax(T x, T output_activation_min, T output_act return std::min(std::max(x, output_activation_min), output_activation_max); } +inline void QuantizeMultiplier(double double_multiplier, int32_t *quantized_multiplier, int *shift) +{ + if (double_multiplier == 0.) + { + *quantized_multiplier = 0; + *shift = 0; + return; + } + + const double q = std::frexp(double_multiplier, shift); + auto q_fixed = static_cast(round(q * (1ll << 31))); + + assert(q_fixed <= (1ll << 31)); + if (q_fixed == (1ll << 31)) + { + q_fixed /= 2; + ++*shift; + } + assert(q_fixed <= std::numeric_limits::max()); + // A shift amount smaller than -31 would cause all bits to be shifted out + // and thus all results would be zero. We implement that instead with + // q_fixed==0, so as to avoid hitting issues with right-shift + // operations with shift amounts greater than 31. Note that this happens + // roughly when abs(double_multiplier) < 2^-31 and the present handling means + // that we're effectively flushing tiny double_multiplier's to zero. + // We could conceivably handle values in the range (roughly) [32, 63] + // as 'denormals' i.e. (shift==0, q_fixed < 2^30). In that point of view + // the present handling is just doing 'flush denormals to zero'. We could + // reconsider and actually generate nonzero denormals if a need arises. + if (*shift < -31) + { + *shift = 0; + q_fixed = 0; + } + *quantized_multiplier = static_cast(q_fixed); +} + +inline void QuantizeMultiplierSmallerThanOneExp(double double_multiplier, + int32_t *quantized_multiplier, int *left_shift) +{ + assert(double_multiplier < 1.0); + assert(double_multiplier > 0.0); + int shift; + QuantizeMultiplier(double_multiplier, quantized_multiplier, &shift); + assert(shift <= 0); + *left_shift = shift; +} + inline int32_t MultiplyByQuantizedMultiplier(int32_t x, int32_t quantized_multiplier, int shift) { int left_shift = shift > 0 ? shift : 0; @@ -50,6 +98,14 @@ inline int32_t MultiplyByQuantizedMultiplierGreaterThanOne(int32_t x, int32_t qu return gemmlowp::SaturatingRoundingDoublingHighMul(x * (1 << left_shift), quantized_multiplier); } +inline int32_t MultiplyByQuantizedMultiplierSmallerThanOneExp(int32_t x, + int32_t quantized_multiplier, + int left_shift) +{ + return gemmlowp::RoundingDivideByPOT( + gemmlowp::SaturatingRoundingDoublingHighMul(x, quantized_multiplier), -left_shift); +} + inline int NodeOffset(int b, int h, int w, int height, int width) { return (b * height + h) * width + w; @@ -105,6 +161,30 @@ inline int SubscriptToIndex(const NdArrayDesc<4> &desc, int i0, int i1, int i2, return i0 * desc.strides[0] + i1 * desc.strides[1] + i2 * desc.strides[2] + i3 * desc.strides[3]; } +template inline int SubscriptToIndexGeneric(const NdArrayDesc *desc, int *iter) +{ + int ret_indx = 0; + for (size_t idx = 0; idx < static_cast(N); idx++) + { + assert(iter[idx] >= 0 && iter[idx] < desc->extents[idx]); + ret_indx += iter[idx] * desc->strides[idx]; + } + + return ret_indx; +} + +// Copies dims to desc, calculating strides. +template inline void CopyDimsToDesc(const Shape &input_shape, NdArrayDesc *desc_out) +{ + int desc_stride = 1; + for (int i = N - 1; i >= 0; --i) + { + desc_out->extents[i] = input_shape.Dims(i); + desc_out->strides[i] = desc_stride; + desc_stride *= input_shape.Dims(i); + } +} + template inline void NdArrayDescsForElementwiseBroadcast(const Shape &input0_shape, const Shape &input1_shape, @@ -117,17 +197,8 @@ NdArrayDescsForElementwiseBroadcast(const Shape &input0_shape, const Shape &inpu auto extended_input1_shape = Shape::ExtendedShape(N, input1_shape); // Copy dims to desc, calculating strides. - int desc0_stride = 1; - int desc1_stride = 1; - for (int i = N - 1; i >= 0; --i) - { - desc0_out->extents[i] = extended_input0_shape.Dims(i); - desc0_out->strides[i] = desc0_stride; - desc0_stride *= extended_input0_shape.Dims(i); - desc1_out->extents[i] = extended_input1_shape.Dims(i); - desc1_out->strides[i] = desc1_stride; - desc1_stride *= extended_input1_shape.Dims(i); - } + CopyDimsToDesc(extended_input0_shape, desc0_out); + CopyDimsToDesc(extended_input1_shape, desc1_out); // Walk over each dimension. If the extents are equal do nothing. // Otherwise, set the desc with extent 1 to have extent equal to the other and @@ -153,6 +224,65 @@ NdArrayDescsForElementwiseBroadcast(const Shape &input0_shape, const Shape &inpu } } +template +inline void +NdArrayDescsForElementwiseBroadcast(const Shape &input0_shape, const Shape &input1_shape, + const Shape &input2_shape, NdArrayDesc *desc0_out, + NdArrayDesc *desc1_out, NdArrayDesc *desc2_out) +{ + assert(desc0_out != nullptr); + assert(desc1_out != nullptr); + assert(desc2_out != nullptr); + + auto extended_input0_shape = Shape::ExtendedShape(N, input0_shape); + auto extended_input1_shape = Shape::ExtendedShape(N, input1_shape); + auto extended_input2_shape = Shape::ExtendedShape(N, input2_shape); + + // Copy dims to desc, calculating strides. + CopyDimsToDesc(extended_input0_shape, desc0_out); + CopyDimsToDesc(extended_input1_shape, desc1_out); + CopyDimsToDesc(extended_input2_shape, desc2_out); + + // Walk over each dimension. If the extents are equal do nothing. + // Otherwise, set the desc with extent 1 to have extent equal to the other and + // stride 0. + for (int i = 0; i < N; ++i) + { + const int extent0 = extended_input0_shape.Dims(i); + const int extent1 = extended_input1_shape.Dims(i); + const int extent2 = extended_input2_shape.Dims(i); + + int extent = extent0; + if (extent1 != 1) + extent = extent1; + if (extent2 != 1) + extent = extent2; + + assert(extent0 == 1 || extent0 == extent); + assert(extent1 == 1 || extent1 == extent); + assert(extent2 == 1 || extent2 == extent); + + if (!(extent0 == extent1 && extent1 == extent2)) + { + if (extent0 == 1) + { + desc0_out->strides[i] = 0; + desc0_out->extents[i] = extent; + } + if (extent1 == 1) + { + desc1_out->strides[i] = 0; + desc1_out->extents[i] = extent; + } + if (extent2 == 1) + { + desc2_out->strides[i] = 0; + desc2_out->extents[i] = extent; + } + } + } +} + // Gets next index to iterate through a multidimensional array. inline bool NextIndex(const int num_dims, const int *dims, int *current) { diff --git a/compute/cker/include/cker/eigen/eigen_convolution_helpers.h b/compute/cker/include/cker/eigen/eigen_convolution_helpers.h index 52bd122..a27871e 100644 --- a/compute/cker/include/cker/eigen/eigen_convolution_helpers.h +++ b/compute/cker/include/cker/eigen/eigen_convolution_helpers.h @@ -62,7 +62,7 @@ public: typedef decltype( functionExistsSfinae(nullptr)) status; - static const bool value = status::value; + static constexpr bool value = status::value; }; // Compute a mask for loading/storing coefficients in/from a packet in a diff --git a/compute/cker/include/cker/eigen/eigen_spatial_convolutions-inl.h b/compute/cker/include/cker/eigen/eigen_spatial_convolutions-inl.h index 6536c6f..92e1614 100644 --- a/compute/cker/include/cker/eigen/eigen_spatial_convolutions-inl.h +++ b/compute/cker/include/cker/eigen/eigen_spatial_convolutions-inl.h @@ -1,17 +1,19 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ #ifndef __NNFW_CKER_EIGEN_EIGEN_SPATIAL_CONVOLUTIONS_INL_H__ #define __NNFW_CKER_EIGEN_EIGEN_SPATIAL_CONVOLUTIONS_INL_H__ @@ -127,6 +129,7 @@ public: m_colStride = patch_rows; m_outputRows = tensor.impl().outputRows(); + m_outputCols = tensor.impl().outputCols(); m_row_strides = tensor.impl().userRowStride(); m_col_strides = tensor.impl().userColStride(); @@ -185,6 +188,7 @@ public: m_inputCols = base_mapper.m_inputCols; m_outputRows = base_mapper.m_outputRows; + m_outputCols = base_mapper.m_outputCols; m_row_strides = base_mapper.m_row_strides; m_col_strides = base_mapper.m_col_strides; @@ -361,7 +365,7 @@ private: // the elements corresponding to the column (specified through colOffset) are // loaded and the rest of the elements are zero-filled into the 'partial' // packet. This function is called from loadPacketStandardFromTwoColumns(). - // This code path is exercied only when the packet type supports masked load + // This code path is exercised only when the packet type supports masked load // and when the partial packet load is available in the TensorEvaluator. EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPartialPacketStandard(Index rowIndex, Index colIndex, @@ -482,7 +486,7 @@ private: // Load standard packet from a patch specified by the "within patch offset" // (patchId) and the precomputed indices of the first element of the patch. // This function will be called if partial packet loading is not available - // for the TesnorEvaluator or if the packet type does not support masked + // for the TensorEvaluator or if the packet type does not support masked // load. template EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE typename std::enable_if< @@ -524,7 +528,7 @@ private: // Load standard packet from a patch specified by the "within patch offset" // (patchId) and the precomputed indices of the first element of the patch. // This function will be called if partial packet loading is available for - // the TesnorEvaluator and if the packet type supports masked load. + // the TensorEvaluator and if the packet type supports masked load. // The only difference between this and the other case is that if the packet // to load is split across two columns, then in this case instead of going to // the slow (element-by-element) load, we load two packets - each containing @@ -663,7 +667,8 @@ private: Index m_inputRows; // Number of rows in the input tensor Index m_inputCols; // Number of cols in the input tensor - Index m_outputRows; // Number of patch rows + Index m_outputRows; // Number of convolution output rows + Index m_outputCols; // Number of convolution output column Index m_row_strides; // User specified row stride Index m_col_strides; // User specified col stride @@ -865,6 +870,32 @@ public: mask(0, num_coeffs)); } EIGEN_DEVICE_FUNC + EIGEN_ALWAYS_INLINE bool hasPadding() const + { + // TODO(ezhulenev): It does seems that for inflated filter it's still + // possible to guarantee "no padding or skipping" for non-standard packing. + if (nonStandardPatches()) + return true; + + // Non zero padding before. + if (m_base_mapper.m_rowPaddingTop > 0) + return true; + if (m_base_mapper.m_colPaddingLeft > 0) + return true; + + // Non zero padding after in rows. + const Index last_row = (m_base_mapper.m_outputRows - 1) * m_base_mapper.m_row_strides; + if (last_row + (patchRows() - 1) >= m_base_mapper.m_inputRows) + return true; + + // Non zero padding after in cols. + const Index last_col = (m_base_mapper.m_outputCols - 1) * m_base_mapper.m_col_strides; + if (last_col + (patchCols() - 1) >= m_base_mapper.m_inputCols) + return true; + + return false; + } + EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool padRow(const Index row) const { const Index r = m_rowIndex + row; @@ -1619,17 +1650,15 @@ SpatialConvolution(const Input &input, const Kernel &kernel, const Index row_str { const TensorIndex InputRowsEff = InputRows + padding_top + padding_bottom; const TensorIndex InputColsEff = InputCols + padding_left + padding_right; - out_height = - numext::ceil((InputRowsEff - kernelRowsEff + 1.f) / static_cast(row_stride)); - out_width = - numext::ceil((InputColsEff - kernelColsEff + 1.f) / static_cast(col_stride)); + out_height = divup(InputRowsEff - kernelRowsEff + 1, row_stride); + out_width = divup(InputColsEff - kernelColsEff + 1, col_stride); break; } case PADDING_SAME: { eigen_assert(!padding_explicit); - out_height = numext::ceil(InputRows / static_cast(row_stride)); - out_width = numext::ceil(InputCols / static_cast(col_stride)); + out_height = divup(InputRows, row_stride); + out_width = divup(InputCols, col_stride); break; } default: diff --git a/compute/cker/include/cker/eigen/eigen_tensor_reduced_instantiations_oss.h b/compute/cker/include/cker/eigen/eigen_tensor_reduced_instantiations_oss.h index 4ca3219..87697e2 100644 --- a/compute/cker/include/cker/eigen/eigen_tensor_reduced_instantiations_oss.h +++ b/compute/cker/include/cker/eigen/eigen_tensor_reduced_instantiations_oss.h @@ -72,10 +72,6 @@ typedef unsigned __int64 uint64_t; #include #endif -// #if defined(EIGEN_USE_LIBXSMM) -// #include "libxsmm.h" -// #endif - #ifdef EIGEN_USE_THREADS #include "unsupported/Eigen/CXX11/ThreadPool" #endif @@ -97,7 +93,6 @@ typedef unsigned __int64 uint64_t; #include "unsupported/Eigen/CXX11/src/Tensor/TensorDeviceDefault.h" #include "unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h" #include "unsupported/Eigen/CXX11/src/Tensor/TensorDeviceGpu.h" -#include "unsupported/Eigen/CXX11/src/Tensor/TensorDeviceSycl.h" #include "unsupported/Eigen/CXX11/src/Tensor/TensorIndexList.h" #include "unsupported/Eigen/CXX11/src/Tensor/TensorDimensionList.h" #include "unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h" @@ -154,7 +149,6 @@ typedef unsigned __int64 uint64_t; #include "unsupported/Eigen/CXX11/src/Tensor/TensorAssign.h" #include "unsupported/Eigen/CXX11/src/Tensor/TensorScan.h" #include "unsupported/Eigen/CXX11/src/Tensor/TensorTrace.h" -#include "unsupported/Eigen/CXX11/src/Tensor/TensorSycl.h" #include "unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h" #include "unsupported/Eigen/CXX11/src/Tensor/TensorDevice.h" #include "unsupported/Eigen/CXX11/src/Tensor/TensorStorage.h" diff --git a/compute/cker/include/cker/operation/ArgMinMax.h b/compute/cker/include/cker/operation/ArgMinMax.h new file mode 100644 index 0000000..f7a06d7 --- /dev/null +++ b/compute/cker/include/cker/operation/ArgMinMax.h @@ -0,0 +1,78 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_ARGMINMAX_H__ +#define __NNFW_CKER_ARGMINMAX_H__ + +#include "cker/Shape.h" + +namespace nnfw +{ +namespace cker +{ + +template +void ArgMinMax(const Shape &input1_shape, const T1 *input1_data, const Shape &output_shape, + T2 *output_data, int32_t axis, const Cmp &cmp) +{ + UNUSED_RELEASE(output_shape); + assert(input1_shape.DimensionsCount() > 0); + assert(input1_shape.DimensionsCount() - 1 == output_shape.DimensionsCount()); + if (axis < 0) + { + axis += input1_shape.DimensionsCount(); + } + const int axis_size = input1_shape.Dims(axis); + + int outer_size = 1; + for (int i = 0; i < axis; ++i) + { + assert(input1_shape.Dims(i) == output_shape.Dims(i)); + outer_size *= input1_shape.Dims(i); + } + + int inner_size = 1; + const int dims_count = input1_shape.DimensionsCount(); + for (int i = axis + 1; i < dims_count; ++i) + { + assert(input1_shape.Dims(i) == output_shape.Dims(i - 1)); + inner_size *= input1_shape.Dims(i); + } + for (int outer = 0; outer < outer_size; ++outer) + { + for (int inner = 0; inner < inner_size; ++inner) + { + auto min_max_value = input1_data[outer * axis_size * inner_size + inner]; + T2 min_max_index = 0; + for (int i = 1; i < axis_size; ++i) + { + const auto &curr_value = input1_data[(outer * axis_size + i) * inner_size + inner]; + if (cmp(curr_value, min_max_value)) + { + min_max_value = curr_value; + min_max_index = static_cast(i); + } + } + output_data[outer * inner_size + inner] = min_max_index; + } + } +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_ARGMINMAX_H__ diff --git a/compute/cker/include/cker/operation/BatchMatMul.h b/compute/cker/include/cker/operation/BatchMatMul.h new file mode 100644 index 0000000..1807098 --- /dev/null +++ b/compute/cker/include/cker/operation/BatchMatMul.h @@ -0,0 +1,146 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_BATCH_MATMUL_H__ +#define __NNFW_CKER_BATCH_MATMUL_H__ + +#include "Transpose.h" + +#include "cker/Types.h" +#include "cker/Shape.h" +#include "cker/Utils.h" +#include "cker/operation/reference/BatchMatMul.h" + +#include + +namespace nnfw +{ +namespace cker +{ + +class BatchMatMul +{ +public: + BatchMatMul() + { + // DO NOTHING + } + + /** + * @brief Prepare temporary area for calculation + */ + void prepare(const Shape &lhs_shape, const Shape &rhs_shape, bool adj_x, bool adj_y) + { + if (adj_x) + { + int32_t rank = lhs_shape.DimensionsCount(); + _temp_lhs_shape.Resize(rank); + + for (int32_t i = 0; i < rank - 2; i++) + { + _temp_lhs_shape.SetDim(i, lhs_shape.Dims(i)); + } + _temp_lhs_shape.SetDim(rank - 2, lhs_shape.Dims(rank - 1)); + _temp_lhs_shape.SetDim(rank - 1, lhs_shape.Dims(rank - 2)); + + _temp_lhs.resize(_temp_lhs_shape.FlatSize()); + } + + if (!adj_y) + { + int32_t rank = rhs_shape.DimensionsCount(); + _temp_rhs_shape.Resize(rank); + + for (int32_t i = 0; i < rank - 2; i++) + { + _temp_rhs_shape.SetDim(i, rhs_shape.Dims(i)); + } + _temp_rhs_shape.SetDim(rank - 2, rhs_shape.Dims(rank - 1)); + _temp_rhs_shape.SetDim(rank - 1, rhs_shape.Dims(rank - 2)); + + _temp_rhs.resize(_temp_rhs_shape.FlatSize()); + } + } + + void operator()(const Shape &lhs_shape, const float *lhs_data, const Shape &rhs_shape, + const float *rhs_data, bool adj_x, bool adj_y, const Shape &output_shape, + float *output_data) + { + // Assume lhs and rhs is not constant + // TODO Handle constant input + + if (!adj_y) + { + transposeRowsCols(rhs_shape, rhs_data, _temp_rhs_shape, _temp_rhs.data()); + } + + if (adj_x) + { + transposeRowsCols(lhs_shape, lhs_data, _temp_lhs_shape, _temp_lhs.data()); + } + + Shape new_lhs_shape = adj_x ? lhs_shape : swapRowColDims(lhs_shape); + Shape new_rhs_shape = adj_y ? rhs_shape : swapRowColDims(rhs_shape); + const float *new_lhs_data = adj_x ? _temp_lhs.data() : lhs_data; + const float *new_rhs_data = adj_y ? rhs_data : _temp_rhs.data(); + + // Note we pass RHS args first, LHS args second + // Check accumulative dimensions of lhs and rhs of are equal + assert(Shape::ExtendedShape(5, new_rhs_shape).Dims(4) == + Shape::ExtendedShape(5, new_lhs_shape).Dims(3)); + reference::BatchMatMul(new_rhs_shape, new_rhs_data, new_lhs_shape, new_lhs_data, output_shape, + output_data); + } + +private: + Shape swapRowColDims(const Shape &shape) + { + Shape swapped_shape(shape); + const uint32_t dims = shape.DimensionsCount(); + swapped_shape.SetDim(dims - 2, shape.Dims(dims - 1)); + swapped_shape.SetDim(dims - 1, shape.Dims(dims - 2)); + + return swapped_shape; + } + + void transposeRowsCols(const Shape &input_shape, const float *input_data, + const Shape &output_shape, float *output_data) + { + TransposeParams params; + int rank = input_shape.DimensionsCount(); + params.perm_count = rank; + for (int i = 0; i < 2; i++) + { + params.perm[i] = i; + } + params.perm[rank - 2] = rank - 1; + params.perm[rank - 1] = rank - 2; + + Transpose(params, input_shape, input_data, output_shape, output_data); + } + +private: + std::vector _temp_lhs; + Shape _temp_lhs_shape; + std::vector _temp_rhs; + Shape _temp_rhs_shape; +}; + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_BATCH_MATMUL_H__ diff --git a/compute/cker/include/cker/operation/BinaryArithmeticOps.h b/compute/cker/include/cker/operation/BinaryArithmeticOps.h index 27b3fa4..8aef1f8 100644 --- a/compute/cker/include/cker/operation/BinaryArithmeticOps.h +++ b/compute/cker/include/cker/operation/BinaryArithmeticOps.h @@ -32,10 +32,10 @@ namespace cker namespace { -template -const std::function GetBinaryArtithmeticFn(BinaryArithmeticOpType type) +template +const std::function GetBinaryArtithmeticFn() { - switch (type) + switch (op_type) { case BinaryArithmeticOpType::ADD: { @@ -51,13 +51,18 @@ const std::function GetBinaryArtithmeticFn(BinaryArithm } case BinaryArithmeticOpType::DIV: { - return [](const T &a, const T &b) -> T { - if (b == 0) - { - throw std::runtime_error("Divide by zero"); - } - return a / b; - }; + if (std::is_floating_point::value) + return [](const T &a, const T &b) -> T { return a / b; }; + else + return [](const T &a, const T &b) -> T { + if (b == 0) + throw std::runtime_error("Divide by zero"); + return a / b; + }; + } + case BinaryArithmeticOpType::POW: + { + return [](const T &a, const T &b) -> T { return std::pow(a, b); }; } default: { @@ -68,23 +73,165 @@ const std::function GetBinaryArtithmeticFn(BinaryArithm } } // namespace -template +// Consolidates dimensions in broadcast inputs, checks for five-fold pattern. +// +// For example, if sequence of dimensions of one input is +// ..., 1, 3, 1, 7, 9, 5,... and the other is ..., 2, 3, 1, 7, 1, 1, ... +// we can consolidate these as +// ..., 1, 3*7, 9*5, ... and 2, 3*7, 1. +// +// The category is updated in the less-frequent case of shapes that are +// not suited to a fivefold-loop broadcast. +// +// Falls back to generic pattern when it does not know how to process properly. +// +// Returns true iff there is some sort of broadcast, which includes five-fold +// patterns and falling back to generic broadcast. +inline bool ProcessBroadcastShapes(const Shape &shape0, const Shape &shape1, + BinaryArithmeticOpParam *params) +{ + const int dims_count = std::max(shape0.DimensionsCount(), shape1.DimensionsCount()); + + params->broadcast_category = BroadcastableOpCategory::kGenericBroadcast; + Shape scalar_shape(dims_count, 1); + + auto extended_shape0 = Shape::ExtendedShape(dims_count, shape0); + auto extended_shape1 = Shape::ExtendedShape(dims_count, shape1); + + // Check for "exact" match, implicitly accepting any scalar shapes. + if (extended_shape0 == extended_shape1) + { + params->broadcast_category = BroadcastableOpCategory::kNonBroadcast; + return false; + } + + for (int i = dims_count - 1; i >= 0; --i) + { + if (extended_shape0.Dims(i) == extended_shape1.Dims(i)) + { + continue; + } + else if (extended_shape0.Dims(i) == 1) + { + params->broadcast_category = BroadcastableOpCategory::kFirstInputBroadcastsFast; + break; + } + else if (extended_shape1.Dims(i) == 1) + { + params->broadcast_category = BroadcastableOpCategory::kSecondInputBroadcastsFast; + break; + } + else + { + // This case is erroneous: there is a dimension that does not match and + // is not a broadcast from one shape to the other. + params->broadcast_category = BroadcastableOpCategory::kGenericBroadcast; + return true; + } + } + + if (params->broadcast_category != BroadcastableOpCategory::kFirstInputBroadcastsFast && + params->broadcast_category != BroadcastableOpCategory::kSecondInputBroadcastsFast) + { + return false; + } + + // From this point it is assumed contractually that corresponding dimensions + // in shape0 and shape1 are either (a) equal or (b) one or other equals 1. + const bool swap_inputs = + params->broadcast_category == BroadcastableOpCategory::kSecondInputBroadcastsFast; + const Shape *shape_a = swap_inputs ? &extended_shape1 : &extended_shape0; + const Shape *shape_b = swap_inputs ? &extended_shape0 : &extended_shape1; + + int i = dims_count - 1; + params->broadcast_shape[0] = 1; + params->broadcast_shape[1] = 1; + params->broadcast_shape[2] = 1; + params->broadcast_shape[3] = 1; + params->broadcast_shape[4] = 1; + // y_0 is greedy: include dims if both or neither equal 1: in other words, + // test for equality rather than (shape_a->Dims(i) != 1). + while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) + { + params->broadcast_shape[4] *= shape_b->Dims(i); + --i; + } + // Here either input_a or input_b has dim of 1 (if i >= 0). If it is input_b + // that has the unit dimension, the next two loops are not entered. + while (i >= 0 && shape_a->Dims(i) == 1) + { + params->broadcast_shape[3] *= shape_b->Dims(i); + --i; + } + while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) + { + params->broadcast_shape[2] *= shape_a->Dims(i); + --i; + } + // Here either input_a or input_b has dim of 1 (if i >= 0). + while (i >= 0 && shape_b->Dims(i) == 1) + { + params->broadcast_shape[1] *= shape_a->Dims(i); + --i; + } + while (i >= 0 && shape_a->Dims(i) == shape_b->Dims(i)) + { + params->broadcast_shape[0] *= shape_b->Dims(i); + --i; + } + + // Rarer case is when the broadcast dimensions cannot be handled by a fivefold + // loop. + if (i >= 0) + { + params->broadcast_category = BroadcastableOpCategory::kGenericBroadcast; + } + return true; +} + +template inline void BinaryArithmeticOp(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, const T *input1_data, const Shape &input2_shape, const T *input2_data, const Shape &output_shape, T *output_data) { reference::BinaryArithmeticOp(params, input1_shape, input1_data, input2_shape, input2_data, - output_shape, output_data, GetBinaryArtithmeticFn(params.type)); + output_shape, output_data, GetBinaryArtithmeticFn()); +} + +template +inline void BinaryArithmeticOp(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, + const uint8_t *input1_data, const Shape &input2_shape, + const uint8_t *input2_data, const Shape &output_shape, + uint8_t *output_data) +{ + switch (op_type) + { + case nnfw::cker::BinaryArithmeticOpType::ADD: + case nnfw::cker::BinaryArithmeticOpType::SUB: + optimized::AddQuant8(params, input1_shape, input1_data, input2_shape, input2_data, + output_shape, output_data); + break; + case nnfw::cker::BinaryArithmeticOpType::MUL: + optimized::MulQuant8(params, input1_shape, const_cast(input1_data), input2_shape, + const_cast(input2_data), output_shape, output_data); + break; + case nnfw::cker::BinaryArithmeticOpType::DIV: + throw std::runtime_error{"Quant8 Asymm NYI"}; + + default: + assert(false); + break; + } } -template <> +template inline void BinaryArithmeticOp(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, const float *input1_data, const Shape &input2_shape, const float *input2_data, const Shape &output_shape, float *output_data) { // Supported type is only float now - switch (params.type) + switch (op_type) { case nnfw::cker::BinaryArithmeticOpType::ADD: optimized::Add(params, input1_shape, input1_data, input2_shape, input2_data, output_shape, @@ -99,9 +246,9 @@ inline void BinaryArithmeticOp(const BinaryArithmeticOpParam ¶ms, const Shap output_data); break; case nnfw::cker::BinaryArithmeticOpType::DIV: - reference::BinaryArithmeticOp(params, input1_shape, input1_data, input2_shape, input2_data, - output_shape, output_data, - GetBinaryArtithmeticFn(params.type)); + reference::BinaryArithmeticOp(params, input1_shape, input1_data, input2_shape, + input2_data, output_shape, output_data, + GetBinaryArtithmeticFn()); break; default: assert(false); @@ -109,78 +256,71 @@ inline void BinaryArithmeticOp(const BinaryArithmeticOpParam ¶ms, const Shap } } -template -inline void BroadcastBinaryArithmeticOpSlow(const BinaryArithmeticOpParam ¶ms, - const Shape &input1_shape, const T *input1_data, - const Shape &input2_shape, const T *input2_data, - const Shape &output_shape, T *output_data) +template +inline void BroadcastBinaryArithmeticOp(BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, + const T *input1_data, const Shape &input2_shape, + const T *input2_data, const Shape &output_shape, + T *output_data) +{ + reference::BroadcastBinaryArithmeticOpSlow(params, input1_shape, input1_data, input2_shape, + input2_data, output_shape, output_data, + GetBinaryArtithmeticFn()); +} + +template +inline void BroadcastBinaryArithmeticOp(BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, + const uint8_t *input1_data, const Shape &input2_shape, + const uint8_t *input2_data, const Shape &output_shape, + uint8_t *output_data) { - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, &desc2); - const Shape extended_output_shape = Shape::ExtendedShape(4, output_shape); - - const auto fn = GetBinaryArtithmeticFn(params.type); - - // Comment from tensorflow lite: - // - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < extended_output_shape.Dims(0); ++b) + switch (op_type) { - for (int y = 0; y < extended_output_shape.Dims(1); ++y) - { - for (int x = 0; x < extended_output_shape.Dims(2); ++x) - { - for (int c = 0; c < extended_output_shape.Dims(3); ++c) - { - output_data[Offset(extended_output_shape, b, y, x, c)] = ActivationFunctionWithMinMax( - fn(input1_data[SubscriptToIndex(desc1, b, y, x, c)], - input2_data[SubscriptToIndex(desc2, b, y, x, c)]), - params.quantized_activation_min, params.quantized_activation_max); - } - } - } + case nnfw::cker::BinaryArithmeticOpType::ADD: + case nnfw::cker::BinaryArithmeticOpType::SUB: + optimized::BroadcastAddDispatchQuant8(params, input1_shape, input1_data, input2_shape, + input2_data, output_shape, output_data); + break; + case nnfw::cker::BinaryArithmeticOpType::MUL: + optimized::BroadcastMulDispatchQuant8( + params, input1_shape, const_cast(input1_data), input2_shape, + const_cast(input2_data), output_shape, output_data); + break; + case nnfw::cker::BinaryArithmeticOpType::DIV: + case nnfw::cker::BinaryArithmeticOpType::POW: + throw std::runtime_error{"Quant8 Asymm NYI"}; + default: + assert(false); + break; } } -template <> -inline void BroadcastBinaryArithmeticOpSlow(const BinaryArithmeticOpParam ¶ms, - const Shape &input1_shape, const float *input1_data, - const Shape &input2_shape, const float *input2_data, - const Shape &output_shape, float *output_data) +template +inline void BroadcastBinaryArithmeticOp(BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, + const float *input1_data, const Shape &input2_shape, + const float *input2_data, const Shape &output_shape, + float *output_data) { - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, &desc2); - const Shape extended_output_shape = Shape::ExtendedShape(4, output_shape); - - const auto fn = GetBinaryArtithmeticFn(params.type); - - for (int b = 0; b < extended_output_shape.Dims(0); ++b) + // Supported type is only float now + switch (op_type) { - for (int y = 0; y < extended_output_shape.Dims(1); ++y) - { - for (int x = 0; x < extended_output_shape.Dims(2); ++x) - { - for (int c = 0; c < extended_output_shape.Dims(3); ++c) - { - output_data[Offset(extended_output_shape, b, y, x, c)] = ActivationFunctionWithMinMax( - fn(input1_data[SubscriptToIndex(desc1, b, y, x, c)], - input2_data[SubscriptToIndex(desc2, b, y, x, c)]), - params.float_activation_min, params.float_activation_max); - } - } - } + case nnfw::cker::BinaryArithmeticOpType::ADD: + optimized::BroadcastAddDispatch(params, input1_shape, input1_data, input2_shape, input2_data, + output_shape, output_data); + break; + case nnfw::cker::BinaryArithmeticOpType::MUL: + optimized::BroadcastMulDispatch(params, input1_shape, input1_data, input2_shape, input2_data, + output_shape, output_data); + break; + case nnfw::cker::BinaryArithmeticOpType::SUB: + case nnfw::cker::BinaryArithmeticOpType::DIV: + case nnfw::cker::BinaryArithmeticOpType::POW: + reference::BroadcastBinaryArithmeticOpSlow( + params, input1_shape, input1_data, input2_shape, input2_data, output_shape, output_data, + GetBinaryArtithmeticFn()); + break; + default: + assert(false); + break; } } diff --git a/compute/cker/include/cker/operation/BroadcastTo.h b/compute/cker/include/cker/operation/BroadcastTo.h new file mode 100644 index 0000000..5068eca --- /dev/null +++ b/compute/cker/include/cker/operation/BroadcastTo.h @@ -0,0 +1,182 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2018 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_BROADCAST_TO_H__ +#define __NNFW_CKER_BROADCAST_TO_H__ + +#include "cker/Types.h" +#include "cker/Shape.h" +#include "cker/Utils.h" + +#include "cker/eigen/EigenSupport.h" + +#include "cker/operation/Helper/Tensor.h" +#include "cker/operation/Helper/BCast.h" + +#include + +#define UNUSED(x) (void)(x) + +namespace nnfw +{ +namespace cker +{ +namespace functor +{ +static const int32_t kint32max = ((int32_t)0x7FFFFFFF); + +template struct FillFunctor +{ + // Computes on device "d": out = out.constant(in(0)), + void operator()(const Device &d, typename TTypes::Flat out, + typename TTypes::ConstScalar in); +}; + +template struct FillFunctor +{ + void operator()(const Eigen::ThreadPoolDevice &d, typename TTypes::Flat out, + typename TTypes::ConstScalar in) + { + out.device(d) = out.constant(in()); + } +}; + +template struct BroadcastTo +{ + template + void DoBCast32Bit(const Device &device, typename TTypes::Tensor out, + typename TTypes::ConstTensor in, + const typename Eigen::array &bcast) const + { + To32Bit(out).device(device) = To32Bit(in).broadcast(bcast); + } + + template + void DoBCast(const Device &device, typename TTypes::Tensor out, + typename TTypes::ConstTensor in, + const typename Eigen::array &bcast) const + { + out.device(device) = in.broadcast(bcast); + } + + template + void ReshapeAndBCast(const Device &device, Tensor &output_tensor, const Tensor &input_tensor, + const BCast &bcast) const + { + const bool can_use_32bit = std::is_same::value && + output_tensor.shape.FlatSize() < kint32max && + input_tensor.shape.FlatSize() < kint32max; + if (can_use_32bit) + { + DoBCast32Bit(device, output_tensor.template shaped(bcast.result_shape()), + input_tensor.template shaped(bcast.x_reshape()), + BCast::ToIndexArrayType(bcast.x_bcast())); + } + else + { + DoBCast(device, output_tensor.template shaped(bcast.result_shape()), + input_tensor.template shaped(bcast.x_reshape()), + BCast::ToIndexArrayType(bcast.x_bcast())); + } + } + + // PRECONDITION: rank(input_shape) > 0 && + // rank(input_shape) <= rank(output_shape) && + // output_shape.num_elements() > 0. + void operator()(const Device &device, Tensor &output_tensor, const Shape &output_shape, + const Tensor &input_tensor, const Shape &input_shape, const BCast &bcast) const + { + const int ndims = bcast.y_reshape().size(); + switch (ndims) + { + case 1: + ReshapeAndBCast<1>(device, output_tensor, input_tensor, bcast); + break; + case 2: + ReshapeAndBCast<2>(device, output_tensor, input_tensor, bcast); + break; + case 3: + ReshapeAndBCast<3>(device, output_tensor, input_tensor, bcast); + break; + case 4: + ReshapeAndBCast<4>(device, output_tensor, input_tensor, bcast); + break; + case 5: + ReshapeAndBCast<5>(device, output_tensor, input_tensor, bcast); + break; + default: + // NOTE : UNUSED leaves for maintenance purposes. + UNUSED(output_shape); + UNUSED(input_shape); + break; + } + } +}; +} // functor + +template +inline void BroadcastTo(const Shape &input_shape, T *input_data, const Shape &output_shape, + T *output_data) +{ + const int input_flatsize = input_shape.FlatSize(); + + if (input_shape == output_shape) + { + memcpy(output_data, input_data, input_flatsize * sizeof(T)); + return; + } + + // Input shape's rank must be no greater than rank of output shape. + assert(input_shape.DimensionsCount() <= output_shape.DimensionsCount()); + + // It shouldn't be 0. + assert(output_shape.DimensionsCount()); + + Tensor output_tensor; + Tensor input_tensor; + + input_tensor.shape.ReplaceWith(input_shape.DimensionsCount(), input_shape.DimsData()); + input_tensor.buffer = input_data; + + output_tensor.shape.ReplaceWith(output_shape.DimensionsCount(), output_shape.DimsData()); + output_tensor.buffer = output_data; + + const Eigen::ThreadPoolDevice &device = *eigen_support::GetThreadPoolDevice(); + + // Handle broadcast from Scalar. + if (input_flatsize == 0) + { + functor::FillFunctor()(device, output_tensor.flat(), + input_tensor.scalar()); + } + + BCast bcast(BCast::FromShape(input_shape), BCast::FromShape(output_shape), + /*fewer_dims_optimization=*/true); + + // Predict TRUE. + assert(bcast.IsValid()); + // should be same. + assert(BCast::ToShape(bcast.output_shape()) == output_shape); + + functor::BroadcastTo()(device, output_tensor, output_shape, + input_tensor, input_shape, bcast); +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_BROADCAST_TO_H__ diff --git a/compute/cker/include/cker/operation/Comparison.h b/compute/cker/include/cker/operation/Comparison.h index 4516358..47eb603 100644 --- a/compute/cker/include/cker/operation/Comparison.h +++ b/compute/cker/include/cker/operation/Comparison.h @@ -58,6 +58,34 @@ inline void Comparison(const Shape &input1_shape, const float *input1_data, output_data); } +template F> +inline void ComparisonWithScaling(ComparisonParams ¶ms, const Shape &input1_shape, + const T *input1_data, const Shape &input2_shape, + const T *input2_data, const Shape &output_shape, + bool *output_data) +{ + int left_shift = params.left_shift; + int32_t input1_offset = params.input1_offset; + int32_t input1_multiplier = params.input1_multiplier; + int input1_shift = params.input1_shift; + int32_t input2_offset = params.input2_offset; + int32_t input2_multiplier = params.input2_multiplier; + int input2_shift = params.input2_shift; + const int64_t flatsize = MatchingFlatSize(input1_shape, input2_shape, output_shape); + for (int64_t i = 0; i < flatsize; ++i) + { + const int32_t input1_val = input1_offset + input1_data[i]; + const int32_t input2_val = input2_offset + input2_data[i]; + const int32_t shifted_input1_val = input1_val * (1 << left_shift); + const int32_t shifted_input2_val = input2_val * (1 << left_shift); + const int32_t scaled_input1_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input1_val, input1_multiplier, input1_shift); + const int32_t scaled_input2_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input2_val, input2_multiplier, input2_shift); + output_data[i] = F(scaled_input1_val, scaled_input2_val); + } +} + template F> inline void BroadcastComparison4DSlowImpl(const Shape &unextended_input1_shape, const T *input1_data, @@ -100,36 +128,102 @@ inline void BroadcastComparison4DSlow(const Shape &input1_shape, const T *input1 output_shape, output_data); } -#define TFLITE_COMPARISON_OP(name) \ - inline void name(const Shape &input1_shape, const float *input1_data, const Shape &input2_shape, \ - const float *input2_data, const Shape &output_shape, bool *output_data) \ - { \ - Comparison(input1_shape, input1_data, input2_shape, input2_data, output_shape, \ - output_data); \ - } \ - template \ - inline void name##NoScaling(const Shape &input1_shape, const T *input1_data, \ - const Shape &input2_shape, const T *input2_data, \ - const Shape &output_shape, bool *output_data) \ - { \ - ComparisonImpl(input1_shape, input1_data, input2_shape, input2_data, \ - output_shape, output_data); \ - } \ - template \ - inline void Broadcast4DSlow##name##NoScaling(const Shape &input1_shape, const T *input1_data, \ - const Shape &input2_shape, const T *input2_data, \ - const Shape &output_shape, bool *output_data) \ - { \ - BroadcastComparison4DSlowImpl(input1_shape, input1_data, input2_shape, \ - input2_data, output_shape, output_data); \ - } \ - template \ - inline void Broadcast4DSlow##name(const Shape &input1_shape, const T *input1_data, \ - const Shape &input2_shape, const T *input2_data, \ - const Shape &output_shape, bool *output_data) \ - { \ - BroadcastComparison4DSlow(input1_shape, input1_data, input2_shape, input2_data, \ - output_shape, output_data); \ +template F> +inline void BroadcastComparison4DSlowWithScaling(ComparisonParams ¶ms, + const Shape &input1_shape, const T *input1_data, + const Shape &input2_shape, const T *input2_data, + const Shape &output_shape, bool *output_data) +{ + assert(input1_shape.DimensionsCount() <= 4); + assert(input2_shape.DimensionsCount() <= 4); + assert(output_shape.DimensionsCount() <= 4); + + int left_shift = params.left_shift; + int32_t input1_offset = params.input1_offset; + int32_t input1_multiplier = params.input1_multiplier; + int input1_shift = params.input1_shift; + int32_t input2_offset = params.input2_offset; + int32_t input2_multiplier = params.input2_multiplier; + int input2_shift = params.input2_shift; + + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, &desc2); + + for (int b = 0; b < output_shape.Dims(0); ++b) + { + for (int y = 0; y < output_shape.Dims(1); ++y) + { + for (int x = 0; x < output_shape.Dims(2); ++x) + { + for (int c = 0; c < output_shape.Dims(3); ++c) + { + const int32_t input1_val = + input1_offset + input1_data[SubscriptToIndex(desc1, b, y, x, c)]; + const int32_t input2_val = + input2_offset + input2_data[SubscriptToIndex(desc2, b, y, x, c)]; + const int32_t shifted_input1_val = input1_val * (1 << left_shift); + const int32_t shifted_input2_val = input2_val * (1 << left_shift); + const int32_t scaled_input1_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input1_val, input1_multiplier, input1_shift); + const int32_t scaled_input2_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input2_val, input2_multiplier, input2_shift); + output_data[Offset(output_shape, b, y, x, c)] = F(scaled_input1_val, scaled_input2_val); + } + } + } + } +} + +#define TFLITE_COMPARISON_OP(name) \ + template \ + inline void name(const Shape &input1_shape, const T *input1_data, const Shape &input2_shape, \ + const T *input2_data, const Shape &output_shape, bool *output_data) \ + { \ + Comparison(input1_shape, input1_data, input2_shape, input2_data, output_shape, \ + output_data); \ + } \ + template \ + inline void name##NoScaling(const Shape &input1_shape, const T *input1_data, \ + const Shape &input2_shape, const T *input2_data, \ + const Shape &output_shape, bool *output_data) \ + { \ + ComparisonImpl(input1_shape, input1_data, input2_shape, input2_data, \ + output_shape, output_data); \ + } \ + template \ + inline void name##WithScaling(ComparisonParams ¶ms, const Shape &input1_shape, \ + const T *input1_data, const Shape &input2_shape, \ + const T *input2_data, const Shape &output_shape, \ + bool *output_data) \ + { \ + ComparisonWithScaling(params, input1_shape, input1_data, input2_shape, \ + input2_data, output_shape, output_data); \ + } \ + template \ + inline void Broadcast4DSlow##name##NoScaling(const Shape &input1_shape, const T *input1_data, \ + const Shape &input2_shape, const T *input2_data, \ + const Shape &output_shape, bool *output_data) \ + { \ + BroadcastComparison4DSlowImpl(input1_shape, input1_data, input2_shape, \ + input2_data, output_shape, output_data); \ + } \ + template \ + inline void Broadcast4DSlow##name(const Shape &input1_shape, const T *input1_data, \ + const Shape &input2_shape, const T *input2_data, \ + const Shape &output_shape, bool *output_data) \ + { \ + BroadcastComparison4DSlow(input1_shape, input1_data, input2_shape, input2_data, \ + output_shape, output_data); \ + } \ + template \ + inline void Broadcast4DSlow##name##WithScaling(ComparisonParams ¶ms, \ + const Shape &input1_shape, const T *input1_data, \ + const Shape &input2_shape, const T *input2_data, \ + const Shape &output_shape, bool *output_data) \ + { \ + BroadcastComparison4DSlowWithScaling( \ + params, input1_shape, input1_data, input2_shape, input2_data, output_shape, output_data); \ } TFLITE_COMPARISON_OP(Equal); diff --git a/compute/cker/include/cker/operation/Conv.h b/compute/cker/include/cker/operation/Conv.h index 191cdb3..1bf191b 100644 --- a/compute/cker/include/cker/operation/Conv.h +++ b/compute/cker/include/cker/operation/Conv.h @@ -63,19 +63,11 @@ public: void prepare(const Shape &filter_shape, const float *filter_data, PaddingType padding_type, bool &is_replaced_weights) { - (void)filter_shape; - (void)filter_data; - (void)padding_type; - (void)is_replaced_weights; if (!_prepared) { - if (padding_type != PaddingType::kNone && std::thread::hardware_concurrency() > 1) + if (usableMultiThreaded(padding_type)) { - const auto output_depth = filter_shape.Dims(0); - const Shape hwcn_filter_shape{filter_shape.FlatSize() / output_depth, output_depth}; - _modified_filter_data.resize(hwcn_filter_shape.FlatSize()); - TransposeFloatTensor(filter_data, hwcn_filter_shape, &_modified_filter_data[0]); - is_replaced_weights = true; + transposeFilter(filter_shape, filter_data, is_replaced_weights); } _prepared = true; } @@ -84,30 +76,26 @@ public: void prepareQuant(const Shape &input_shape, const Shape &kernel_shape, const Shape &output_shape, uint32_t stride_width, uint32_t stride_height) { - _need_im2col = stride_width != 1 || stride_height != 1 || kernel_shape.Dims(1) != 1 || - kernel_shape.Dims(2) != 1; - if (!_prepared && _need_im2col) + if (!_prepared) { - _im2col_shape.SetDim(0, output_shape.Dims(0)); - _im2col_shape.SetDim(1, output_shape.Dims(1)); - _im2col_shape.SetDim(2, output_shape.Dims(2)); - _im2col_shape.SetDim(3, input_shape.Dims(3) * kernel_shape.Dims(1) * kernel_shape.Dims(2)); - _im2col_data.resize(_im2col_shape.FlatSize()); + IsRequiredIm2col(input_shape, kernel_shape, output_shape, stride_width, stride_height); + _prepared = true; } - _prepared = true; } void operator()(const ConvParams ¶ms, const Shape &input_shape, const float *input_data, const Shape &filter_shape, const float *filter_data, const Shape &bias_shape, const float *bias_data, const Shape &output_shape, float *output_data) { - if (params.padding_type != PaddingType::kNone && std::thread::hardware_concurrency() > 1) + if (usableMultiThreaded(params.padding_type)) { + bool transposed_in_execution = false; if (!_prepared) { - bool not_used_condition = false; - prepare(filter_shape, filter_data, params.padding_type, not_used_condition); - _prepared = true; + // This means that filter is not constant + // TODO Apply optimized kernel if multithreaded kernel is slower than optimized kernel by + // transposing filter data + transposeFilter(filter_shape, filter_data, transposed_in_execution); } multithreaded::Conv(params, input_shape, input_data, filter_shape, &_modified_filter_data[0], bias_shape, bias_data, output_shape, output_data); @@ -124,16 +112,46 @@ public: const Shape &filter_shape, const uint8_t *filter_data, const Shape &bias_shape, const int32_t *bias_data, const Shape &output_shape, uint8_t *output_data) { - if (_prepared) + if (!_prepared) { - uint8_t *im2col_raw_data = _im2col_data.data(); - optimized::Conv(params, input_shape, input_data, filter_shape, filter_data, bias_shape, - bias_data, output_shape, output_data, _im2col_shape, im2col_raw_data); + // This means that input or output are dynamic or filter is not constant + IsRequiredIm2col(input_shape, filter_shape, output_shape, params.stride_width, + params.stride_height); } - else + + uint8_t *im2col_raw_data = _im2col_data.data(); + optimized::Conv(params, input_shape, input_data, filter_shape, filter_data, bias_shape, + bias_data, output_shape, output_data, _im2col_shape, im2col_raw_data); + } + +private: + bool usableMultiThreaded(PaddingType padding_type) + { + return padding_type != PaddingType::kNone && std::thread::hardware_concurrency() > 1; + } + + void transposeFilter(const Shape &filter_shape, const float *filter_data, + bool &is_replaced_weights) + { + const auto output_depth = filter_shape.Dims(0); + const Shape hwcn_filter_shape{filter_shape.FlatSize() / output_depth, output_depth}; + _modified_filter_data.resize(hwcn_filter_shape.FlatSize()); + TransposeFloatTensor(filter_data, hwcn_filter_shape, &_modified_filter_data[0]); + is_replaced_weights = true; + } + + void IsRequiredIm2col(const Shape &input_shape, const Shape &kernel_shape, + const Shape &output_shape, uint32_t stride_width, uint32_t stride_height) + { + _need_im2col = stride_width != 1 || stride_height != 1 || kernel_shape.Dims(1) != 1 || + kernel_shape.Dims(2) != 1; + if (_need_im2col) { - reference::Conv(params, input_shape, input_data, filter_shape, filter_data, bias_shape, - bias_data, output_shape, output_data); + _im2col_shape.SetDim(0, output_shape.Dims(0)); + _im2col_shape.SetDim(1, output_shape.Dims(1)); + _im2col_shape.SetDim(2, output_shape.Dims(2)); + _im2col_shape.SetDim(3, input_shape.Dims(3) * kernel_shape.Dims(1) * kernel_shape.Dims(2)); + _im2col_data.resize(_im2col_shape.FlatSize()); } } diff --git a/compute/cker/include/cker/operation/Einsum.h b/compute/cker/include/cker/operation/Einsum.h new file mode 100644 index 0000000..3d1837f --- /dev/null +++ b/compute/cker/include/cker/operation/Einsum.h @@ -0,0 +1,934 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_EINSUM_H__ +#define __NNFW_CKER_EINSUM_H__ + +#include "cker/Types.h" +#include "cker/Shape.h" +#include "cker/Utils.h" + +#include "cker/operation/Helper/Tensor.h" +#include "cker/operation/Helper/MatmulBCast.h" + +#include "Transpose.h" +#include "BatchMatMul.h" + +#include +#include +#include +#include +#include + +namespace nnfw +{ +namespace cker +{ + +namespace functor +{ + +template struct StrideFunctor +{ + void operator()(const Device &d, typename TTypes::ConstTensor input, + const std::vector &strides, typename TTypes::Tensor output) + { + + Eigen::DSizes dsizes; + for (size_t d = 0; d < strides.size(); d++) + { + dsizes[d] = static_cast(strides[d]); + } + for (size_t d = strides.size(); d < N; d++) + { + dsizes[d] = 1; + } + + output.device(d) = input.stride(dsizes); + } +}; + +template struct InflateFunctor +{ + void operator()(const Device &d, typename TTypes::ConstTensor input, + const std::vector &strides, typename TTypes::Tensor output) + { + + Eigen::DSizes dsizes; + for (size_t d = 0; d < strides.size(); d++) + { + dsizes[d] = static_cast(strides[d]); + } + for (size_t d = strides.size(); d < N; d++) + { + dsizes[d] = 1; + } + + output.device(d) = input.inflate(dsizes); + } +}; + +template struct ReduceFunctor +{ + template + static void Reduce(const Device &d, OUT_T out, IN_T in, const ReductionAxes &reduction_axes, + const Reducer &reducer) + { + out.device(d) = in.reduce(reduction_axes, reducer); + } +}; + +template struct SetZeroFunctor +{ + // Computes on device "d": out = out.setZero(), + void operator()(const Device &d, typename TTypes::Flat out) + { + out.device(d) = out.constant(T(0)); + } +}; + +} // namespace functor + +using ShapeVec = std::vector; +using Labels = std::vector; +using OperandLabels = std::vector; +using LabelCounts = std::vector; +using OperandLabelCounts = std::vector; +using LabelToDimSizes = std::vector; + +// Each dimension is categorized into exactly one of five types based on +// whether its corresponding label is present in the input and/or the output +// subscripts. +enum DimensionType +{ + // Batch dimensions are those present in two inputs as well as the output. + // They are part of the batch dimensions during Tensor contraction. + // Such dimensions may be broadcasting dimensions (those mapping to + // ellipsis) + // or explicit batch dimensions corresponding to named axis labels. + kBroadcasting = 0, + kBatch = 1, + // Free dimensions are present in exactly one of the inputs, and also the + // output. These are non-contracted axes in the Tensor contraction. + kFree = 2, + // Contract dimensions are present in two inputs, but not the output. These + // dimensions are contracted in Tensor contraction. + kContract = 3, + // Reduce dimensions are present in exactly one input; and not in the output + // and are summed over prior to Tensor contraction. + kReduce = 4, +}; + +namespace +{ + +constexpr int kEllipsisLabel = -1; + +std::vector strSplit(const std::string &text, const std::string delimiter) +{ + std::vector result; + + size_t start = 0; + size_t pos = 0; + + do + { + pos = text.find(delimiter, start); + if (pos == std::string::npos) + { + result.push_back(text.substr(start, text.size() - start)); + break; + } + + result.push_back(text.substr(start, pos - start)); + start = pos + delimiter.size(); + } while (pos != std::string::npos); + + return result; +} + +inline DimensionType getDimensionType(bool is_removed, bool is_unique) +{ + if (!is_removed && !is_unique) + return kBatch; + else if (!is_removed && is_unique) + return kFree; + else if (is_removed && !is_unique) + return kContract; + else // is_removed && is_unique + return kReduce; +} + +inline Shape copyShape(const Shape &shape) +{ + return Shape::ExtendedShape(shape.DimensionsCount(), shape); +} +} + +class Einsum +{ +public: + Einsum() : _prepared(false) + { + // DO NOTHING + } + + void prepare(std::string &equation) + { + if (_prepared) + { + return; + } + + // Parse equation + parseEquation(equation); + _prepared = true; + } + + void operator()(std::string &equation, const std::vector &input_shapes, + const std::vector &input_data, const Shape &output_shape, + float *output_data) + { + if (!_prepared) + { + prepare(equation); + } + + const int num_inputs = input_shapes.size(); + std::vector> inputs(num_inputs); + for (int i = 0; i < num_inputs; i++) + { + inputs[i].shape.ReplaceWith(input_shapes[i].DimensionsCount(), input_shapes[i].DimsData()); + inputs[i].buffer = input_data[i]; + } + + OperandLabels input_labels(_input_labels); + Labels output_labels(_output_labels); + std::vector label_types(_label_types); + OperandLabelCounts input_label_counts(_input_label_counts); + LabelCounts output_label_counts(_output_label_counts); + LabelToDimSizes label_to_dim_sizes; + + processDimensions(inputs, &input_labels, &output_labels, &label_types, &input_label_counts, + &output_label_counts, &label_to_dim_sizes); + + // The reduction phase (a) sums across reduction dimensions, (b) takes + // generalized diagonals, and (c) reshapes it into shape + // [(broadcasting) batch shape] + [F,C] + // where F and C denote the total (compacted) size of free and contract + // dimensions, respectively. + + OperandLabels free_labels(num_inputs); + std::vector inputs_reduced(num_inputs); + std::vector swap_free_and_contract(num_inputs); + for (int i = 0; i < num_inputs; ++i) + { + bool temp_swap_free_and_contract = false; + reduceOperand(inputs[i], label_types, input_label_counts[i], &input_labels[i], + &free_labels[i], &temp_swap_free_and_contract, &inputs_reduced[i]); + swap_free_and_contract[i] = temp_swap_free_and_contract; + } + + // After reduction, the inputs should be reshaped to Tensors suitable for + // contraction. If num_inputs is 1, the reduced input is simply forwarded to + // the output. + Tensor contraction_output_reshaped; + contractOperands(inputs_reduced, swap_free_and_contract, &contraction_output_reshaped); + + // Copy the batch labels from the contraction output. Recover the batch + // shape, which may have been broadcasted. + std::vector result_shape_dims(contraction_output_reshaped.shape.DimensionsCount() - 2); + + for (size_t i = 0; i < result_shape_dims.size(); i++) + { + result_shape_dims[i] = contraction_output_reshaped.shape.Dims(i); + } + + int num_labels = label_types.size(); + Labels result_labels; + // All batch dimensions should be present in the contracted result. First + // the broadcasting dimensions, then the named batch dimensions. + for (int label = 0; label < num_labels; ++label) + { + if (label_types[label] == kBroadcasting) + result_labels.push_back(label); + } + for (int label = 0; label < num_labels; ++label) + { + if (label_types[label] == kBatch) + result_labels.push_back(label); + } + for (int i = 0; i < num_inputs; ++i) + { + for (int label : free_labels[i]) + { + result_labels.push_back(label); + result_shape_dims.push_back(label_to_dim_sizes[label]); + } + } + + Shape result_shape(result_shape_dims.size(), result_shape_dims.data()); + + // Reshape the contraction (or reduction) result to its expanded shape: + // [(broadcasted) batch shape] + [free shape 0] + [free shape 1]. + Tensor contraction_output; + copyFrom(contraction_output_reshaped, result_shape, &contraction_output); + + // Inflate the output if necessary. (E.g. for the equation 'i->iii' which + // may arise while computing gradient of a regular Einsum). + // TODO(anudhyan): It's possible that Eigen's contract and inflate can be + // chained here to avoid materializing an intermediate. + Tensor output_inflated; + strideOrInflate(contraction_output, result_labels, output_label_counts, + true /* should_inflate */, &output_inflated); + + if (output_inflated.shape.DimensionsCount() > contraction_output.shape.DimensionsCount()) + { + // We inflated the output. Modify result labels accordingly. + Labels inflated_labels; + for (int label : result_labels) + { + inflated_labels.insert(inflated_labels.end(), output_label_counts[label], label); + } + result_labels.swap(inflated_labels); + } + + // Find the permutation to map the result labels to the output labels. Note + // that both the result and the final output may have the repeated labels, + // in which case the permutation preserves the left-to-right ordering. + // E.g. if result labels are [0, 0, 1] and output is [0, l, 0] then the + // permutation should be [0, 2, 1]. We also use the fact that repeated + // labels in the result are adjacent to each other. + std::vector output_permutation(output_labels.size()); + std::vector label_to_position(num_labels, -1); + for (size_t i = 0; i < result_labels.size(); ++i) + { + // Remember the position of only the leftmost result label. + if (label_to_position[result_labels[i]] == -1) + { + label_to_position[result_labels[i]] = i; + } + } + for (size_t i = 0; i < output_labels.size(); ++i) + { + output_permutation[i] = label_to_position[output_labels[i]]; + // We have found the leftmost occurrence. The next one would be adjacent. + label_to_position[output_labels[i]] += 1; + } + + InputTensor temp_inflated; + temp_inflated.shape.ReplaceWith(output_inflated.shape.DimensionsCount(), + output_inflated.shape.DimsData()); + temp_inflated.buffer = (reinterpret_cast(output_inflated.buffer)); + ; + + Tensor output; + transposeOperand(temp_inflated, output_permutation, &output); + + memcpy(output_data, output.buffer, output_shape.FlatSize() * sizeof(float)); + + temp_operand.clear(); + } + +private: + void parseEquation(std::string &equation) + { + std::vector input_str; + std::string output_str; + + parseEinsumEquation(equation, input_str, output_str); + + // Temporary map from single character labels to (consecutive) integer + // labels. + std::map label_mapping; + int num_inputs = input_str.size(); + _input_labels.resize(num_inputs); + + // Map from single characters to integer labels. + for (int i = 0; i < num_inputs; ++i) + { + mapToLabels(input_str[i], _input_labels.at(i), label_mapping); + } + mapToLabels(output_str, _output_labels, label_mapping); + + // Compute counts for input and output labels. + int num_labels = label_mapping.size(); + _input_label_counts.resize(num_inputs); + _input_has_ellipsis.resize(num_inputs); + for (int i = 0; i < num_inputs; ++i) + { + _input_label_counts.at(i).resize(num_labels); + for (const int label : _input_labels.at(i)) + { + if (label != kEllipsisLabel) + _input_label_counts.at(i)[label] += 1; + else + _input_has_ellipsis.at(i) = true; + } + } + _output_label_counts.resize(num_labels); + for (const int label : _output_labels) + { + if (label != kEllipsisLabel) + _output_label_counts.at(label) += 1; + else + _output_has_ellipsis = true; + } + + // Map each label to a unique DimensionType. + _label_types.resize(num_labels); + for (int label = 0; label < num_labels; ++label) + { + bool removed = (_output_label_counts[label] == 0); + bool unique = num_inputs == 1 || _input_label_counts[0][label] == 0 || + _input_label_counts[1][label] == 0; + _label_types[label] = getDimensionType(removed, unique); + } + } + + void parseEinsumEquation(const std::string &equation, std::vector &input_subscripts, + std::string &output_subscript) + { + std::vector inputs_and_output_subscripts = strSplit(equation, "->"); + if (inputs_and_output_subscripts.size() != 2) + { + throw std::runtime_error{"Einsum: Expecting exactly one '->' in einsum equation: " + + equation}; + } + + output_subscript = inputs_and_output_subscripts[1]; + input_subscripts = strSplit(inputs_and_output_subscripts[0], ","); + if (input_subscripts.size() != 1 && input_subscripts.size() != 2) + { + throw std::runtime_error{"Einsum: Expecting 1 or 2 input subscripts in equation '" + + equation + "' but got: " + std::to_string(input_subscripts.size())}; + } + } + + // Maps the character labels to consecutive integers. + void mapToLabels(const std::string &subscript, Labels &labels, std::map &label_mapping) + { + for (size_t i = 0; i < subscript.size(); ++i) + { + const char label_char = subscript[i]; + if (label_char == '.') + { + labels.push_back(kEllipsisLabel); + i += 2; // Skip next 2 characters as well. + continue; + } + if (label_mapping.find(label_char) == label_mapping.end()) + { + const int next_label = label_mapping.size(); + label_mapping[label_char] = next_label; + } + const int mapped_label = label_mapping[label_char]; + labels.push_back(mapped_label); + } + } + + template + void processDimensions(const std::vector> &inputs, OperandLabels *input_labels, + Labels *output_labels, std::vector *label_types, + OperandLabelCounts *input_label_counts, LabelCounts *output_label_counts, + LabelToDimSizes *label_to_dim_sizes) + { + if (inputs.size() != input_labels->size()) + { + throw std::runtime_error{"Expected " + std::to_string(input_labels->size()) + + " inputs but got: " + std::to_string(inputs.size())}; + } + const int num_inputs = inputs.size(); + + // We infer the number of broadcasting dimensions by taking the maximum rank + // among the broadcasting subshapes of the input. + int max_bcast_dims = 0; + const int num_named_labels = label_types->size(); + label_to_dim_sizes->resize(num_named_labels); + for (int i = 0; i < num_inputs; ++i) + { + Labels *labels = &(*input_labels)[i]; + + if (!_input_has_ellipsis[i]) + { + if (inputs[i].shape.DimensionsCount() != ((int32_t)labels->size())) + { + throw std::runtime_error{"Expected input " + std::to_string(i) + " to have rank " + + std::to_string(labels->size()) + " but got: " + + std::to_string(inputs[i].shape.DimensionsCount())}; + } + for (size_t label_idx = 0; label_idx < labels->size(); ++label_idx) + { + const int label = (*labels)[label_idx]; + recordLabelToDimension(label, label_idx, inputs[i].shape, label_to_dim_sizes); + } + continue; + } + + // Input has an ellipsis. + if (inputs[i].shape.DimensionsCount() + 1 < (int32_t)labels->size()) + { + throw std::runtime_error{"Expected input " + std::to_string(i) + " to have rank at least " + + std::to_string(labels->size() - 1) + " but got: " + + std::to_string(inputs[i].shape.DimensionsCount())}; + } + int ellipsis_axis = -1; + const int num_bcast_dims = inputs[i].shape.DimensionsCount() - labels->size() + 1; + for (size_t label_idx = 0; label_idx < labels->size(); ++label_idx) + { + const int label = (*labels)[label_idx]; + if (label == kEllipsisLabel) + { + ellipsis_axis = label_idx; + continue; + } + // Current label is not an ellipsis. + const int axis = label_idx + (ellipsis_axis == -1 ? 0 : num_bcast_dims - 1); + recordLabelToDimension(label, axis, inputs[i].shape, label_to_dim_sizes); + } + // Found an ellipsis. Replace 'kEllipsisLabel' with broadcasting + // dimensions. + if (ellipsis_axis != -1) + { + insertBroadcastLabels(num_bcast_dims, num_named_labels, ellipsis_axis, labels, + &input_label_counts->at(i)); + max_bcast_dims = std::max(max_bcast_dims, num_bcast_dims); + } + } + + std::vector::iterator it_input = + std::find(_input_has_ellipsis.begin(), _input_has_ellipsis.end(), true); + if (it_input == _input_has_ellipsis.end() && !_output_has_ellipsis) + { + return; + } + // Insert broadcasting dimensions in the output labels. + auto it = std::find(output_labels->begin(), output_labels->end(), kEllipsisLabel); + if (it != output_labels->end()) + { + const int ellipsis_axis = it - output_labels->begin(); + insertBroadcastLabels(max_bcast_dims, num_named_labels, ellipsis_axis, output_labels, + output_label_counts); + } + else if (max_bcast_dims > 0) + { + std::runtime_error{"Output contains " + std::to_string(max_bcast_dims) + + " broadcasting dimension(s) but no ellipsis " + + "(...) was found in the output subscripts."}; + } + // Populate DimensionType for the new broadcasting labels. + label_types->resize(num_named_labels + max_bcast_dims, kBroadcasting); + } + + void recordLabelToDimension(const int32_t label, const int axis, const Shape &input_shape, + LabelToDimSizes *label_to_dim_sizes) + { + const int32_t input_dim = input_shape.Dims(axis); + // We know that label_to_dim_sizes has the size to accommodate named labels. + if (label_to_dim_sizes->at(label) != 0 && label_to_dim_sizes->at(label) != input_dim) + { + std::runtime_error{"Expected dimension " + std::to_string(label_to_dim_sizes->at(label)) + + " at axis " + std::to_string(axis) + + " of the input shaped but got dimension " + std::to_string(input_dim)}; + } + (*label_to_dim_sizes)[label] = input_dim; + } + + void insertBroadcastLabels(int num_bcast_dims, int num_named_labels, int ellipsis_axis, + Labels *labels, LabelCounts *label_counts) + { + labels->erase(labels->begin() + ellipsis_axis); + labels->insert(labels->begin() + ellipsis_axis, num_bcast_dims, 0); + std::iota(labels->begin() + ellipsis_axis, labels->begin() + ellipsis_axis + num_bcast_dims, + num_named_labels); + // Increment label counts. Since these are new labels, the count is set + // to 1. + label_counts->resize(num_named_labels + num_bcast_dims, 1); + } + + template + void reduceOperand(const InputTensor &input, const std::vector &label_types, + const LabelCounts &label_counts, Labels *labels, Labels *free_labels, + bool *swap_free_and_contract, Tensor *output) + { + // Find the permutation to transpose the input dimensions in the order of + // DimensionType; i.e. batch, free, contract and reduce dimensions. This + // makes it more convenient to invoke Reduce/Contract operations. + std::vector permutation(input.shape.DimensionsCount()); + std::iota(permutation.begin(), permutation.end(), 0); + Tensor input_transposed; + + // Check if we can avoid the transpose. We need to flip the adj_x (or adj_y) + // flag during BatchMatMul. This is an extra optimization not necessary for + // correctness. + if (shouldSwapFreeAndContract(*labels, label_types)) + { + *swap_free_and_contract = true; + } + else + { + std::sort(permutation.begin(), permutation.end(), [&](int i, int j) { + int label_i = (*labels)[i]; + int label_j = (*labels)[j]; + return std::tie(label_types[label_i], label_i) < std::tie(label_types[label_j], label_j); + }); + } + // Transpose the input so that DimensionTypes are in order. + transposeOperand(input, permutation, &input_transposed); + + permuteLabels(permutation, labels); + + // Take the generalized diagonal for dimensions with repeated axis labels. + Tensor input_deduped; + labels->erase(std::unique(labels->begin(), labels->end()), labels->end()); + strideOrInflate(input_transposed, *labels, label_counts, false /* should_inflate */, + &input_deduped); + + // Reshape denotes the rank-5 shape [broadcast, batch, free, contract, + // reduce] where we've compacted the dimensions of each DimensionType. + std::vector reshape(5, 1); + + // The output shape is [batch shape] + [free size, contract size] + // That is, the batch shape is preserved (for broadcasting while + // contracting) while the free dims and contract dims are compressed to one + // dimension each. + Shape output_shape; + std::vector output_shape_dims; + for (size_t label_idx = 0; label_idx < labels->size(); ++label_idx) + { + const int label = labels->at(label_idx); + int32_t dim = input_deduped.shape.Dims(label_idx); + if (label_types[label] == kBroadcasting || label_types[label] == kBatch) + { + output_shape_dims.push_back(dim); + } + else if (label_types[label] == kFree) + { + free_labels->push_back(label); + } + reshape[label_types[label]] *= dim; + } + + if (*swap_free_and_contract) + std::swap(reshape[kFree], reshape[kContract]); + + output_shape_dims.push_back(reshape[kFree]); + output_shape_dims.push_back(reshape[kContract]); + + output_shape.ReplaceWith(output_shape_dims.size(), output_shape_dims.data()); + + if (reshape[kReduce] == 1) + { // No need to actually reduce. + return copyFrom(input_deduped, output_shape, output); + } + + allocateTemp(output_shape, output); + + using Reducer = Eigen::internal::SumReducer; + using Index = typename TTypes::Tensor::Index; + + const Eigen::ThreadPoolDevice &device = *eigen_support::GetThreadPoolDevice(); + + // Reduce along the last axis (i.e axis 1) of the rank-2 Tensor. + const int32_t output_size = + reshape[kBroadcasting] * reshape[kBatch] * reshape[kFree] * reshape[kContract]; + functor::ReduceFunctor::Reduce( + device, output->shaped({output_size}), + input_deduped.shaped({output_size, reshape[kReduce]}), Eigen::array({1}), + Reducer()); + } + + bool shouldSwapFreeAndContract(const Labels &labels, + const std::vector &label_types) + { + // Check that ordering is according to dimension type, with the role of + // free and contract dimensions swapped. + std::vector remap = {0, 1, 3, 2, 4}; + for (size_t i = 0; i + 1 < labels.size(); ++i) + { + const int dimtype_a = remap[label_types[labels[i]]]; + const int dimtype_b = remap[label_types[labels[i + 1]]]; + if (dimtype_a > dimtype_b || (dimtype_a == dimtype_b && labels[i] > labels[i + 1])) + { + return false; + } + } + return true; + } + + template + void transposeOperand(const InputTensor &input, const std::vector &permutation, + Tensor *output) + { + if (!shouldTranspose(input.shape, permutation)) + { + copyFrom(input, input.shape, output); + return; + } + Shape transposed_shape(input.shape.DimensionsCount()); + for (int i = 0; i < input.shape.DimensionsCount(); ++i) + { + transposed_shape.SetDim(i, input.shape.Dims(permutation[i])); + } + // For empty Tensors, just change the shape. E.g. we may need to transpose + // from shape [1, 0, 5] to [5, 1, 0]. + if (input.shape.FlatSize() == 0) + { + copyFrom(input, transposed_shape, output); + return; + } + + temp_operand.emplace_back(std::make_unique(transposed_shape.FlatSize())); + T *new_buffer = temp_operand.back().get(); + + TransposeParams transpose_params; + transpose_params.perm_count = permutation.size(); + for (size_t i = 0; i < permutation.size(); i++) + { + transpose_params.perm[i] = permutation[i]; + } + + Transpose(transpose_params, input.shape, input.buffer, transposed_shape, new_buffer); + + output->shape.ReplaceWith(transposed_shape.DimensionsCount(), transposed_shape.DimsData()); + output->buffer = new_buffer; + } + + bool shouldTranspose(const Shape &input_shape, const std::vector &permutation) + { + if (input_shape.DimensionsCount() < 2) + return false; + for (size_t i = 0; i < permutation.size(); ++i) + { + if (permutation[i] != (int32_t)i) + return true; + } + return false; + } + + template + void copyFrom(const InputTensor &input, const Shape &shape, Tensor *output) + { + Tensor temp_tensor; + temp_tensor.shape.ReplaceWith(input.shape.DimensionsCount(), input.shape.DimsData()); + temp_operand.emplace_back(std::make_unique(input.shape.FlatSize())); + temp_tensor.buffer = temp_operand.back().get(); + memcpy(temp_tensor.buffer, input.buffer, input.shape.FlatSize() * sizeof(float)); + + copyFrom(temp_tensor, shape, output); + } + + void copyFrom(const Tensor &input, const Shape &shape, Tensor *output) + { + if (output->copyFrom(input, shape)) + return; + + throw std::runtime_error{"Einsum: Encountered error while reshaping a Tensor"}; + } + + // Permutes the labels according to the given permutation. + void permuteLabels(const std::vector &permutation, Labels *labels) + { + Labels permuted_labels(labels->size()); + for (size_t i = 0; i < labels->size(); ++i) + { + permuted_labels[i] = (*labels)[permutation[i]]; + } + labels->swap(permuted_labels); + } + + // If there are repeated labels in either the input or output, then this + // strides the input (e.g. iii->i) or inflates it (e.g. i->iii), respectively. + template + void strideOrInflate(const Tensor &input, const Labels &labels, const LabelCounts &label_counts, + const bool should_inflate, Tensor *output) + { + // Return early if there are no repeated indices. + if (std::all_of(label_counts.begin(), label_counts.end(), [](int c) { return c <= 1; })) + { + return copyFrom(input, input.shape, output); + } + // We reshape so that each repeated label is compressed to one dimension. + // E.g. For iiij -> ij, The shape [3, 3, 3, 5] would be compressed to [27, + // 5]. Striding appropriately (in this case with strides 14 (=1+3+9) and 1) + // recovers the generalized diagonal of shape [3, 5]. + std::vector reshape; + std::vector strides; + // Strided and inflated shapes correspond to input and output shapes, + // respectively, should_inflate is true (vice-versa if should_inflate is + // false). E.g. they are [3, 5] and [3, 3, 3, 5] in the above example. + Shape strided_shape; + Shape inflated_shape; + std::vector strided_shape_dims; + std::vector inflated_shape_dims; + for (int label : labels) + { + const int32_t count = label_counts[label]; + const int current_axis = + should_inflate ? strided_shape_dims.size() : inflated_shape_dims.size(); + const int32_t dim = input.shape.Dims(current_axis); + strided_shape_dims.push_back(dim); + inflated_shape_dims.insert(inflated_shape_dims.end(), count, dim); + const int32_t reshape_dim = std::pow(dim, count); + reshape.push_back(reshape_dim); + // While taking the d-diagonal in a rank k Tensor, we take d + // equally-spaced elements including the first and last element. Then, (k + // - 1) * stride = d^k - 1, or, stride = (d^k - 1)/(d - 1). + const int32_t stride = (dim > 1 && count > 1) ? (reshape_dim - 1) / (dim - 1) : 1; + strides.push_back(stride); + } + + strided_shape.ReplaceWith(strided_shape_dims.size(), strided_shape_dims.data()); + inflated_shape.ReplaceWith(inflated_shape_dims.size(), inflated_shape_dims.data()); + + Shape output_shape = Shape(should_inflate ? inflated_shape : strided_shape); + + output->shape.ReplaceWith(output_shape.DimensionsCount(), output_shape.DimsData()); + temp_operand.emplace_back(std::make_unique(output_shape.FlatSize())); + output->buffer = temp_operand.back().get(); + + const Eigen::ThreadPoolDevice &device = *eigen_support::GetThreadPoolDevice(); + + switch (reshape.size()) + { +#define NDIMS_CASE(N) \ + case N: \ + { \ + if (should_inflate) \ + { \ + auto output_map = output->shaped(reshape); \ + auto input_map = input.shaped(strided_shape_dims); \ + functor::InflateFunctor()(device, input_map, strides, \ + output_map); \ + } \ + else \ + { \ + auto input_map = input.shaped(reshape); \ + auto output_map = output->shaped(strided_shape_dims); \ + functor::StrideFunctor()(device, input_map, strides, \ + output_map); \ + } \ + } \ + break; + NDIMS_CASE(1); + NDIMS_CASE(2); + NDIMS_CASE(3); + NDIMS_CASE(4); + NDIMS_CASE(5); + NDIMS_CASE(6); + default: + throw std::runtime_error{"Unsupported rank: " + std::to_string(reshape.size()) + + " while handling repeated indices. Up to rank 6 is supported."}; +#undef NDIMS_CASE + } + } + + void allocateTemp(const Shape &shape, Tensor *output) + { + output->shape.ReplaceWith(shape.DimensionsCount(), shape.DimsData()); + temp_operand.emplace_back(std::make_unique(shape.FlatSize())); + output->buffer = temp_operand.back().get(); + } + + // Contracts the inputs along the last axis. (or the second last if the + // corresponding value of swap_free_and_contract is true). The batch + // dimensions are broadcast to the output shape. + // TODO(anudhyan): Factor this function into a BatchMatMul functor and support + // transpose_x and transpose_y attributes (in addition to adj_x and adj_y). + // Also, the BatchMatMul might devolve into a component-wise multiplication + // when the matrix shape is [1,1]; in this case BatchMatMul functor would be + // very inefficient. The functor should detect if this is the case and perform + // componentwise multiplication functor instead. + void contractOperands(std::vector &inputs, std::vector &swap_free_and_contract, + Tensor *output) + { + if (inputs.size() == 1) + return copyFrom(inputs[0], inputs[0].shape, output); + + MatMulBCast bcast(inputs[0].shape, inputs[1].shape); + if (!bcast.IsValid()) + { + throw std::runtime_error{"Einsum: Invalid broadcasting dimensions"}; + } + + Tensor lhs; + reshapeToRank3(inputs[0], bcast.x_batch_size(), &lhs); + Tensor rhs; + reshapeToRank3(inputs[1], bcast.y_batch_size(), &rhs); + Shape old_output_shape = bcast.output_batch_shape(); + Shape output_shape(old_output_shape.DimensionsCount() + inputs.size()); + for (int i = 0; i < old_output_shape.DimensionsCount(); i++) + { + output_shape.SetDim(i, old_output_shape.Dims(i)); + } + + for (size_t i = 0; i < inputs.size(); ++i) + { + const int32_t free_axis = + inputs[i].shape.DimensionsCount() - (swap_free_and_contract[i] ? 1 : 2); + output_shape.SetDim(i + old_output_shape.DimensionsCount(), inputs[i].shape.Dims(free_axis)); + } + bool adj_x = swap_free_and_contract[0]; + bool adj_y = !swap_free_and_contract[1]; + + allocateTemp(output_shape, output); + + const Eigen::ThreadPoolDevice &device = *eigen_support::GetThreadPoolDevice(); + + if (lhs.shape.FlatSize() == 0 || rhs.shape.FlatSize() == 0) + { + functor::SetZeroFunctor set_zero; + set_zero(device, + typename TTypes::Tensor(output->base(), output->shape.FlatSize())); + return; + } + + Tensor output_reshaped; + reshapeToRank3(*output, bcast.output_batch_size(), &output_reshaped); + + // LaunchBatchMatMul::Launch(lhs, rhs, adj_x, adj_y, bcast, &output_reshaped); + BatchMatMul batchMatMul; + batchMatMul.prepare(lhs.shape, rhs.shape, adj_x, adj_y); + batchMatMul(lhs.shape, lhs.base(), rhs.shape, rhs.base(), adj_x, adj_y, + output_reshaped.shape, output_reshaped.base()); + } + + void reshapeToRank3(const Tensor &input, int batch_size, Tensor *output) + { + const int rank = input.shape.DimensionsCount(); + Shape output_shape({batch_size, input.shape.Dims(rank - 2), input.shape.Dims(rank - 1)}); + copyFrom(input, output_shape, output); + } + +private: + bool _prepared; + + OperandLabels _input_labels; + Labels _output_labels; + std::vector _label_types; + OperandLabelCounts _input_label_counts; + LabelCounts _output_label_counts; + std::vector _input_has_ellipsis; + bool _output_has_ellipsis = false; + + std::vector> temp_operand; +}; + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_EINSUM_H__ diff --git a/compute/cker/include/cker/operation/Elementwise.h b/compute/cker/include/cker/operation/Elementwise.h index 83581e8..598a032 100644 --- a/compute/cker/include/cker/operation/Elementwise.h +++ b/compute/cker/include/cker/operation/Elementwise.h @@ -38,6 +38,16 @@ inline void Sin(const Shape &input_shape, const float *input_data, const Shape & } } +inline void Cos(const Shape &input_shape, const float *input_data, const Shape &output_shape, + float *output_data) +{ + const int size = MatchingFlatSize(input_shape, output_shape); + for (int i = 0; i < size; i++) + { + output_data[i] = std::cos(input_data[i]); + } +} + inline void Abs(const Shape &input_shape, const float *input_data, const Shape &output_shape, float *output_data) { @@ -56,6 +66,26 @@ inline void Rsqrt(const Shape &input_shape, const float *input_data, const Shape } } +inline void Neg(const Shape &input_shape, const float *input_data, const Shape &output_shape, + float *output_data) +{ + const int size = MatchingFlatSize(input_shape, output_shape); + for (int i = 0; i < size; i++) + { + output_data[i] = -input_data[i]; + } +} + +inline void Log(const Shape &input_shape, const float *input_data, const Shape &output_shape, + float *output_data) +{ + const int size = MatchingFlatSize(input_shape, output_shape); + for (int i = 0; i < size; i++) + { + output_data[i] = std::log(input_data[i]); + } +} + } // namespace cker } // namespace nnfw diff --git a/compute/cker/include/cker/operation/Fill.h b/compute/cker/include/cker/operation/Fill.h new file mode 100644 index 0000000..14daf98 --- /dev/null +++ b/compute/cker/include/cker/operation/Fill.h @@ -0,0 +1,54 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2018 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_FILL_H__ +#define __NNFW_CKER_FILL_H__ + +#include "cker/Shape.h" + +namespace nnfw +{ +namespace cker +{ +template +inline void Fill(const Shape &input_shape, int *input_data, const T value_data, + const Shape &output_shape, T output_data) +{ + int input_size = input_shape.FlatSize(); + int output_size = 1; + for (int i = 0; i < input_size; i++) + { + output_size *= input_data[i]; + } + + if (output_size == output_shape.FlatSize()) + { + for (int i = 0; i < output_size; i++) + { + output_data[i] = *value_data; + } + } + else + { + throw std::runtime_error("Cker Fill.h: output's size is not matched inferred size of output"); + } +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_FILL_H__ diff --git a/compute/cker/include/cker/operation/FullyConnected.h b/compute/cker/include/cker/operation/FullyConnected.h index 01b925e..9bcf3fd 100644 --- a/compute/cker/include/cker/operation/FullyConnected.h +++ b/compute/cker/include/cker/operation/FullyConnected.h @@ -31,7 +31,7 @@ namespace cker class FCTempArena { public: - FCTempArena(void) : prepared(false), input_quantized(), scaling_factors() + FCTempArena(void) : prepared(false), input_quantized(), scaling_factors(), accum_scratch() { // DO NOTHING } @@ -51,6 +51,7 @@ public: bool prepared; std::vector input_quantized; std::vector scaling_factors; + std::vector accum_scratch; }; inline void FullyConnected(const FullyConnectedParams ¶ms, const Shape &input_shape, @@ -138,7 +139,8 @@ inline void FullyConnected(const FullyConnectedParams ¶ms, const Shape &inpu inline void FullyConnectedHybrid(const FullyConnectedParams ¶ms, const Shape &input_shape, const float *input_data, const Shape &filter_shape, const int8_t *filter_data, const Shape &, const float *bias_data, - const Shape &, float *output_data, FCTempArena &temp_arena) + const Shape &output_shape, float *output_data, + FCTempArena &temp_arena) { int total_input_size = input_shape.FlatSize(); const int input_size = filter_shape.Dims(1); @@ -146,7 +148,14 @@ inline void FullyConnectedHybrid(const FullyConnectedParams ¶ms, const Shape const int num_units = filter_shape.Dims(0); // Output = bias if bias tensor exists. - VectorBatchVectorAssign(bias_data, num_units, batch_size, output_data); + if (bias_data) + { + VectorBatchVectorAssign(bias_data, num_units, batch_size, output_data); + } + else + { + ZeroVector(output_data, batch_size * num_units); + } // Save matrix multiplication computation for all zero input. if (IsZeroVector(input_data, total_input_size)) @@ -170,10 +179,20 @@ inline void FullyConnectedHybrid(const FullyConnectedParams ¶ms, const Shape scaling_factors_ptr[b] *= params.weights_scale; } - // Compute output += weight * quantized_input +// Compute output += weight * quantized_input +#ifdef USE_RUY_GEMV + auto output_size = output_shape.FlatSize(); + temp_arena.accum_scratch.resize(output_size); + int32_t *scratch = temp_arena.accum_scratch.data(); + MatrixBatchVectorMultiplyAccumulate(filter_data, num_units, input_size, quant_data, + scaling_factors_ptr, batch_size, scratch, output_data, + /*result_stride=*/1); +#else MatrixBatchVectorMultiplyAccumulate(filter_data, num_units, input_size, quant_data, scaling_factors_ptr, batch_size, output_data, /*result_stride=*/1); + UNUSED_RELEASE(output_shape); +#endif // Apply activation function to floats. ApplyActivationToVector(output_data, batch_size * num_units, params.activation, output_data); diff --git a/compute/cker/include/cker/operation/FusedBatchNorm.h b/compute/cker/include/cker/operation/FusedBatchNorm.h new file mode 100644 index 0000000..d17a579 --- /dev/null +++ b/compute/cker/include/cker/operation/FusedBatchNorm.h @@ -0,0 +1,162 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_FUSEDBATCHNORM_H__ +#define __NNFW_CKER_FUSEDBATCHNORM_H__ + +#include "cker/Types.h" +#include "cker/Shape.h" +#include "cker/Utils.h" + +#include "cker/operation/Helper/Tensor.h" +#include "cker/operation/Helper/MatmulBCast.h" + +#include "Transpose.h" +#include "BatchMatMul.h" + +#include +#include +#include +#include +#include + +namespace nnfw +{ +namespace cker +{ + +class FusedBatchNorm +{ +public: + FusedBatchNorm() : _prepared(false) + { + // DO NOTHING + } + + void prepare() { _prepared = true; } + + void operator()(const std::vector &input_shapes, + const std::vector &input_data, const Shape &output_shape, + float *output_data, FusedBatchNormParams param) + { + // TODO: support fused_batch_norm if is_traninig is false + assert(param.is_training == true); + + // TODO: support case where dim[1] != 1 or dim[3] !=1. + // Here we only support input tensor of [B, 1, X, 1] shape + assert(input_shapes[0].Dims(1) == 1 && input_shapes[0].Dims(3) == 1); + + if (!_prepared) + + { + prepare(); + } + + Tensor transformed_input[5]; + Tensor transformed_output; + + const int num_inputs = input_shapes.size(); + std::vector> inputs(num_inputs); + for (int i = 0; i < num_inputs; i++) + { + inputs[i].shape.ReplaceWith(input_shapes[i].DimensionsCount(), input_shapes[i].DimsData()); + inputs[i].buffer = input_data[i]; + copyFrom(inputs[i], inputs[i].shape, &transformed_input[i]); + } + + InputTensor output; + output.shape.ReplaceWith(output_shape.DimensionsCount(), output_shape.DimsData()); + output.buffer = output_data; + copyFrom(output, output.shape, &transformed_output); + + // TODO: support transpose if data_format is NCHW + // Here, Eigen use RowMajor kernel(NHWC) + + typename TTypes::Tensor x(transformed_input[0].shaped()); + typename TTypes::Tensor y(transformed_output.shaped()); + typename TTypes::Tensor scale(transformed_input[1].shaped()); + typename TTypes::Tensor offset(transformed_input[2].shaped()); + + const int depth = x.dimension(3); + const int size = x.size(); + const int rest_size = size / depth; + Eigen::DSizes rest_by_depth(rest_size, depth); + + Eigen::DSizes one_by_depth(1, depth); + Eigen::array reduce_dims({0}); + Eigen::array bcast_spec({rest_size, 1}); + + auto x_rest_by_depth = x.reshape(rest_by_depth).template cast(); + const int rest_size_minus_one = (rest_size > 1) ? (rest_size - 1) : 1; + float rest_size_inv = static_cast(1.0f / static_cast(rest_size)); + // This adjustment is for Bessel's correction + float rest_size_adjust = + static_cast(rest_size) / static_cast(rest_size_minus_one); + + Eigen::Tensor batch_mean(depth); + Eigen::Tensor batch_variance(depth); + + const Eigen::ThreadPoolDevice &d = *eigen_support::GetThreadPoolDevice(); + + batch_mean.device(d) = (x_rest_by_depth.sum(reduce_dims) * rest_size_inv); + auto x_centered = x_rest_by_depth - batch_mean.reshape(one_by_depth).broadcast(bcast_spec); + + batch_variance.device(d) = x_centered.square().sum(reduce_dims) * rest_size_inv; + auto scaling_factor = ((batch_variance + param.epsilon).rsqrt() * scale) + .eval() + .reshape(one_by_depth) + .broadcast(bcast_spec); + auto x_scaled = x_centered * scaling_factor; + auto x_shifted = + (x_scaled + offset.reshape(one_by_depth).broadcast(bcast_spec)).template cast(); + + UNUSED_RELEASE(rest_size_adjust); + + y.reshape(rest_by_depth).device(d) = x_shifted; + + memcpy(output_data, y.data(), output_shape.FlatSize() * sizeof(float)); + } + + template + void copyFrom(const InputTensor &input, const Shape &shape, Tensor *output) + { + Tensor temp_tensor; + temp_tensor.shape.ReplaceWith(input.shape.DimensionsCount(), input.shape.DimsData()); + temp_operand.emplace_back(std::make_unique(input.shape.FlatSize())); + temp_tensor.buffer = temp_operand.back().get(); + memcpy(temp_tensor.buffer, input.buffer, input.shape.FlatSize() * sizeof(float)); + + copyFrom(temp_tensor, shape, output); + } + + void copyFrom(const Tensor &input, const Shape &shape, Tensor *output) + { + if (output->copyFrom(input, shape)) + return; + + throw std::runtime_error{"Einsum: Encountered error while reshaping a Tensor"}; + } + +private: + bool _prepared; + std::vector> temp_operand; +}; + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_FUSEDBATCHNORM_H__ diff --git a/compute/cker/include/cker/operation/Helper/BCast.h b/compute/cker/include/cker/operation/Helper/BCast.h new file mode 100644 index 0000000..a0abf29 --- /dev/null +++ b/compute/cker/include/cker/operation/Helper/BCast.h @@ -0,0 +1,471 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2015 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_HELPER_BCAST_H__ +#define __NNFW_CKER_HELPER_BCAST_H__ + +/** + * ToDo : This file will be moved into upper folder when integrate with other + * custom operations. + * And It should merged with EinsumHelper's BCast. +**/ + +#include "cker/Shape.h" +#include "cker/eigen/EigenSupport.h" + +namespace nnfw +{ +namespace cker +{ +// Returns the mapping from the output batch indices to the corresponding +// input's batch indices, given the input's "reshape" and "bcast" shapes as +// returned by the BCastList helper class. The i'th element denotes the +// (flattened) batch index of the input that must be used to compute the i'th +// batch output. +// +inline void ComputeBatchIndices(const int32_t output_batch_size, + const std::vector &reshape, + const std::vector &bcast, + std::vector *out_indices) +{ + // Populates the mapping in out_indices. This algorithm is identical to + // the following steps: + // - Reshape {0, 1, ..., input_batch_size - 1} to the input shape. + // - Broadcast to the output shape. + // - Reshape back to a flat 1D vector. + out_indices->resize(output_batch_size); + int32_t num_output_elements = 1; + int32_t num_input_elements = 1; + for (int32_t i = reshape.size() - 1; i >= 0; --i) + { + // Replicate the already populated mapping an additional (dim - 1) times. + // If we are broadcasting, just copy the existing mapping. + // Otherwise, add another dimension from the input shape. + const int32_t dim = std::max(reshape[i], bcast[i]); + const int32_t incr = bcast[i] > 1 ? 0 : num_input_elements; + for (int32_t k = 0; k < (dim - 1) * num_output_elements; ++k) + { + (*out_indices)[num_output_elements + k] = (*out_indices)[k] + incr; + } + num_output_elements *= dim; + num_input_elements *= reshape[i]; + } +} + +template class BCastList +{ +public: + // A vector of int32_t representing the shape of tensor. The 0-th + // element is the outer-most dimension and the last element is the + // inner-most dimension. Note that we do not use Shape since + // it's more convenient to manipulate Vec directly for this module. + typedef std::vector Vec; + + // Constructs all helper shapes, following the aforementioned rules. + // + // If "fewer_dims_optimization" is set to true (the default), the + // implementation tries to reduce intermediate dimensions needed to be more + // efficient. This is transparent to the caller. + // + // If false, all intermediate shapes (except for grad_{x,y}_reduce_idx()) have + // the same number of dimensions as the larger of the two inputs. + // + // If return_flattened_batch_indices is true, the implementation will compute + // for each output member of the flattened output, which batch indices of + // each input correspond to it. This is disabled by default. + explicit BCastList(const Vec (&x)[N], const bool fewer_dims_optimization = true, + const bool return_flattened_batch_indices = false); + ~BCastList() {} + + // Returns true iff two operands are compatible according to the + // broadcasting rule. + bool IsValid() const { return valid_; } + bool IsBroadcastingRequired() const { return broadcasting_required_; } + + // If and only if IsValid(), the following fields can be used in + // implementing a broadcasted binary tensor operation according to + // the broadcasting rule. + const Vec &reshape(int i) const { return reshape_[i]; } + const Vec &bcast(int i) const { return bcast_[i]; } + const Vec &result_shape() const { return result_; } + const Vec &output_shape() const { return output_; } + const Vec &grad_reduce_idx(int i) const { return grad_reduce_idx_[i]; } + int32_t output_batch_size() const { return output_batch_size_; } + + // Returns the mapping from the flattened output batch indices to x's + // flattened batch indices. The result is a vector of length + // output_batch_size(). To compute the i'th batch output, a binary matmul-like + // operation should use the `x_batch_indices()[i]`th batch index of `x`. + // Note: Returns an empty vector if broadcasting is not required. Callers + // should only use this when IsBroadcastingRequired() returns true. + const std::vector &batch_indices(int i) const { return batch_indices_[i]; } + +protected: + bool valid_ = true; + bool broadcasting_required_ = true; + Vec reshape_[N]; + Vec bcast_[N]; + Vec result_; + Vec output_; + Vec grad_reduce_idx_[N]; + + int32_t output_batch_size_; + std::vector batch_indices_[N]; + + static void Reverse(Vec *shape) { std::reverse(shape->begin(), shape->end()); } +}; // BCastList + +template +BCastList::BCastList(const BCastList::Vec (&x)[N], const bool fewer_dims_optimization, + const bool return_flattened_batch_indices) +{ + typedef BCastList::Vec Vec; + bool all_equal = true; + size_t largest_rank = 0; + output_batch_size_ = 1; + for (int i = 0; i < N; ++i) + { + if (x[i] != x[0]) + { + all_equal = false; + } + if (x[i].size() > largest_rank) + { + largest_rank = x[i].size(); + } + } + if (all_equal) + { + broadcasting_required_ = false; + } + if (all_equal && fewer_dims_optimization) + { + // Fast path for common case of identical shapes. + int32_t elements = 1; + const int rank = x[0].size(); + output_.resize(rank); + for (int i = 0; i < rank; i++) + { + const int32_t dim = x[0][i]; + elements *= dim; + output_[i] = dim; + } + result_.push_back(elements); + output_batch_size_ = elements; + for (int i = 0; i < N; ++i) + { + reshape_[i].push_back(elements); + bcast_[i].push_back(1); + } + // grad_reduce_ is left as empty + return; + } + + // Reverse all the shapes for convenience + // After the reverse, 0-th is the inner-most dimension. + Vec copy[N]; + for (int i = 0; i < N; ++i) + { + copy[i] = x[i]; + Reverse(©[i]); + } + + // 1-extend and align all vectors. + for (int i = 0; i < N; ++i) + { + if (copy[i].size() < largest_rank) + { + copy[i].resize(largest_rank, 1); + } + } + // Going through each dimension starting from the inner-most + // dimension, compares dimension of x and y. They are compatible if + // they are equal or either is 1. + + // indices of j-th component of each input. + bool prev_is_one[N]; + bool current_is_one[N]; + for (int i = 0; i < N; ++i) + { + prev_is_one[i] = false; + current_is_one[i] = false; + } + Vec output; + bool output_dim_set = false; + int output_dim = -1; + bool none_is_one = true; + bool set_one = false; + for (size_t j = 0; j < largest_rank; ++j) + { + output_dim = -1; + output_dim_set = false; + none_is_one = true; + // Find which indices are 1. + for (int i = 0; i < N; ++i) + { + // Keep track of which indices are 1. + if (copy[i][j] == 1) + { + current_is_one[i] = true; + none_is_one = false; + } + else + { + current_is_one[i] = false; + if (!output_dim_set || copy[i][j] == output_dim) + { + output_dim = copy[i][j]; + output_dim_set = true; + } + else + { + valid_ = false; + return; + } + } + } + output_.push_back(output_dim_set ? output_dim : 1); + output_batch_size_ *= output_.back(); + // All dimensions are 1. + if (!output_dim_set) + { + if (!fewer_dims_optimization) + { + for (int i = 0; i < N; ++i) + { + bcast_[i].push_back(1); + reshape_[i].push_back(1); + } + result_.push_back(1); + } + for (int i = 0; i < N; ++i) + { + grad_reduce_idx_[i].push_back(largest_rank - 1 - j); + } + // This will skip updating the previous state to the current one. We'll + // explain why this is safe below. + // Consider the previous state P, current state C and the next state N. + // In the case where N also is all ones (N == C), we'll do the same + // optimization here (push back one dimensions if we need to), which is + // safe and is expected. + // + // When N != C, we'll continue as usual. However, we might trigger the + // next block if N == P (because we didn't update the previous state). + // We trigger the next block if `fewer_dims_optimization` is true. + // This means that we did not modify and broadcast / reshapes in this + // block (we skipped updating, since the one dimensions can be ignored). + // In essence, we only need to check whether the previous non-one state is + // equal to the current non-one state. + + continue; + } + else if ((fewer_dims_optimization) && + std::equal(current_is_one, current_is_one + N, prev_is_one) && set_one) + { + // It is a run of the same broadcasting case as last time. + // We can reshape the input so that fewer dimensions + // are involved in the intermediate computation. + result_.back() *= output_dim; + for (int i = 0; i < N; ++i) + { + reshape_[i].back() *= copy[i][j]; + bcast_[i].back() *= current_is_one[i] ? output_dim : 1; + if (current_is_one[i] && !none_is_one) + { + grad_reduce_idx_[i].push_back(largest_rank - 1 - j); + } + } + } + else + { + result_.push_back(output_dim); + for (int i = 0; i < N; ++i) + { + reshape_[i].push_back(copy[i][j]); + bcast_[i].push_back(current_is_one[i] ? output_dim : 1); + if (current_is_one[i] && !none_is_one) + { + grad_reduce_idx_[i].push_back(largest_rank - 1 - j); + } + } + } + set_one = true; + for (int i = 0; i < N; ++i) + { + prev_is_one[i] = current_is_one[i]; + } + } + if (result_.empty()) + { + result_.push_back(1); + for (int i = 0; i < N; ++i) + { + reshape_[i].push_back(1); + bcast_[i].push_back(1); + } + } + // Do something about batches. + for (int i = 0; i < N; ++i) + { + Reverse(&reshape_[i]); + Reverse(&bcast_[i]); + Reverse(&grad_reduce_idx_[i]); + } + Reverse(&result_); + Reverse(&output_); + // Only compute batch indices when we need broadcasting, and we aren't doing + // needless work (when the output size is 0 or the + // return_flattened_batch_indices isn't enabled). + if (return_flattened_batch_indices && broadcasting_required_ && output_batch_size_ > 0) + { + for (int i = 0; i < N; ++i) + { + ComputeBatchIndices(output_batch_size_, reshape_[i], bcast_[i], &batch_indices_[i]); + } + } +} + +// BCast is a helper for broadcasting binary tensor operation. +// TensorFlow's broadcasting rule follows that of numpy (See +// http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html). +// +// The rule has the following properties: +// +// 1. suffix matching: the rule starts with the right-most +// dimension, and works towards the left-most dimension. Since +// TensorFlow is row-major, the right-most dimension (the last +// element in the shape of a tensor) is the inner-most, a.k.a. +// the fastest changing, dimension. +// +// 2. Two dimensions are compatible for broadcasting if both are the +// same or either is 1. +// +// BCast takes the shape of two tensors and computes a few vectors of +// int32 that are useful for the caller to reshape the tensors, apply +// the right broadcasts to them, compute the broadcasted operation, +// and possibly the gradients. In a nutshell, the caller is expected +// to compute the broadcasted operation as following: +// +// BCast b(x.shape(), y.shape()); +// output = x.reshape(b.x_reshape()).broadcast(b.x_bcast()) +// _op_ +// y.reshape(b.y_reshape()).broadcast(b.y_bcast()) +// +// For the gradient computation, +// grad_x = sum(grad * backprop_x(x, y), grad_x_reduce_idx) +// .reshape(x.shape()) +// grad_y = sum(grad * backprop_y(x, y), grad_y_reduce_idx) +// .reshape(y.shape()) +// backprop_x and backprop_y are functionals of the binary function "op", +// e.g., +// for +, backprop_x(x, y) = backprop_y(x, y) = 1; +// for *, backprop_x(x, y) = y, backprop_y(x, y) = x; +// for /, backprop_x(x, y) = 1/y, backprop_y(x, y) = -x/y^2; +// +// The multiplication in the grad * backprop_x itself is also +// broadcasting following the same rule. +class BCast : public BCastList<2> +{ +public: + // Constructs all helper shapes, following the aforementioned rules. + // + // If "fewer_dims_optimization" is set to true (the default), the + // implementation tries to reduce intermediate dimensions needed to be more + // efficient. This is transparent to the caller. + // + // If false, all intermediate shapes (except for grad_{x,y}_reduce_idx()) have + // the same number of dimensions as the larger of the two inputs. + typedef std::vector Vec; + + BCast(const Vec &x, const Vec &y, const bool fewer_dims_optimization = true, + const bool return_flattened_batch_indices = false) + : BCastList<2>({x, y}, fewer_dims_optimization, return_flattened_batch_indices) + { + } + + ~BCast() {} + + // If and only if IsValid(), the following fields can be used in + // implementing a broadcasted binary tensor operation according to + // the broadcasting rule. + const Vec &x_reshape() const { return reshape_[0]; } + const Vec &x_bcast() const { return bcast_[0]; } + const Vec &y_reshape() const { return reshape_[1]; } + const Vec &y_bcast() const { return bcast_[1]; } + const Vec &result_shape() const { return result_; } + const Vec &output_shape() const { return output_; } + const Vec &grad_x_reduce_idx() const { return grad_reduce_idx_[0]; } + const Vec &grad_y_reduce_idx() const { return grad_reduce_idx_[1]; } + + // Returns the mapping from the flattened output batch indices to x's + // flattened batch indices. The result is a vector of length + // output_batch_size(). To compute the i'th batch output, a binary matmul-like + // operation should use the `x_batch_indices()[i]`th batch index of `x`. + // Note: Returns an empty vector if broadcasting is not required. Callers + // should only use this when IsBroadcastingRequired() returns true. + const std::vector &x_batch_indices() const { return batch_indices_[0]; } + // Returns the mapping from the flattened output batch indices to y's + // flattened batch indices. Similar to x_batch_indices(). + // Note: Returns an empty vector if broadcasting is not required. Callers + // should only use this when IsBroadcastingRequired() returns true. + const std::vector &y_batch_indices() const { return batch_indices_[1]; } + + template + static Eigen::array ToIndexArrayType(const BCast::Vec &vec) + { + assert(vec.size() == NDIMS); + Eigen::array ret; + for (int i = 0; i < NDIMS; ++i) + ret[i] = vec[i]; + return ret; + } + + template + static Eigen::array ToIndexArray(const BCast::Vec &vec) + { + return ToIndexArrayType(vec); + } + + // Static helpers. + static Vec FromShape(const Shape &shape) + { + const int N = shape.DimensionsCount(); + BCastList::Vec ret(N); + for (int i = 0; i < N; ++i) + { + ret[i] = shape.Dims(i); + } + return ret; + } + + static Shape ToShape(const BCastList::Vec &vec) + { + const int N = vec.size(); + Shape shape(N); + + for (int i = 0; i < N; ++i) + { + shape.SetDim(i, vec[i]); + } + return shape; + } + +}; // BCast +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_HELPER_BCAST_H__ diff --git a/compute/cker/include/cker/operation/Helper/MatmulBCast.h b/compute/cker/include/cker/operation/Helper/MatmulBCast.h new file mode 100644 index 0000000..b80ccc0 --- /dev/null +++ b/compute/cker/include/cker/operation/Helper/MatmulBCast.h @@ -0,0 +1,95 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2019 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_EINSUM_HELPER_MATMUL_BCAST_H__ +#define __NNFW_CKER_EINSUM_HELPER_MATMUL_BCAST_H__ + +#include +#include +#include + +#include "BCast.h" +#include "cker/Shape.h" + +namespace nnfw +{ +namespace cker +{ + +// Simple wrapper over BCast specialized for MatMul. +// Provides utilities for broadcasting across batch dimensions for binary +// MatMul-like operations. + +// Fix: Use Shape directly instead of Vec +class MatMulBCast +{ +public: + MatMulBCast(Shape &shape_x, Shape &shape_y) + { + if (shape_x.DimensionsCount() < 2 || shape_y.DimensionsCount() < 2) + return; + + std::vector x; + std::vector y; + + x.resize(shape_x.DimensionsCount() - 2); + y.resize(shape_y.DimensionsCount() - 2); + + for (size_t i = 0; i < x.size(); i++) + { + x[i] = shape_x.Dims(i); + } + for (size_t i = 0; i < y.size(); i++) + { + y[i] = shape_y.Dims(i); + } + + _batch_bcast = std::make_unique(std::move(x), std::move(y)); + if (!_batch_bcast->IsValid()) + return; + + auto x_reshaped = _batch_bcast->x_reshape(); + auto y_reshaped = _batch_bcast->y_reshape(); + auto output_shape = _batch_bcast->output_shape(); + + _x_batch_size = std::accumulate(x_reshaped.cbegin(), x_reshaped.cend(), INT32_C(1), + std::multiplies()); + _y_batch_size = std::accumulate(x_reshaped.cbegin(), x_reshaped.cend(), INT32_C(1), + std::multiplies()); + _output_shape.ReplaceWith(output_shape.size(), output_shape.data()); + _output_batch_size = _output_shape.FlatSize(); + } + + bool IsValid() const { return (_batch_bcast != nullptr) && _batch_bcast->IsValid(); } + int32_t x_batch_size() const { return _x_batch_size; } + int32_t y_batch_size() const { return _y_batch_size; } + int32_t output_batch_size() const { return _output_batch_size; } + const Shape &output_batch_shape() const { return _output_shape; } + +private: + std::unique_ptr _batch_bcast; + + int32_t _x_batch_size; + int32_t _y_batch_size; + Shape _output_shape; + int32_t _output_batch_size; +}; + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_EINSUM_HELPER_MATMUL_BCAST_H__ diff --git a/compute/cker/include/cker/operation/Helper/Tensor.h b/compute/cker/include/cker/operation/Helper/Tensor.h new file mode 100644 index 0000000..e6ac008 --- /dev/null +++ b/compute/cker/include/cker/operation/Helper/Tensor.h @@ -0,0 +1,183 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2015 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_HELPER_TENSOR_H__ +#define __NNFW_CKER_HELPER_TENSOR_H__ + +#include "cker/Shape.h" +#include "cker/eigen/EigenSupport.h" + +namespace nnfw +{ +namespace cker +{ +template struct TTypes +{ + // Rank- tensor of scalar type T. + typedef Eigen::TensorMap, Eigen::Aligned> + Tensor; + typedef Eigen::TensorMap, + Eigen::Aligned> + ConstTensor; + + // Unaligned Rank- tensor of scalar type T. + typedef Eigen::TensorMap> UnalignedTensor; + typedef Eigen::TensorMap> + UnalignedConstTensor; + + typedef Eigen::TensorMap, Eigen::Aligned> + Tensor32Bit; + + // Scalar tensor (implemented as a rank-0 tensor) of scalar type T. + typedef Eigen::TensorMap, Eigen::RowMajor, IndexType>, + Eigen::Aligned> + Scalar; + typedef Eigen::TensorMap< + Eigen::TensorFixedSize, Eigen::RowMajor, IndexType>, Eigen::Aligned> + ConstScalar; + + // Unaligned Scalar tensor of scalar type T. + typedef Eigen::TensorMap, Eigen::RowMajor, IndexType>> + UnalignedScalar; + typedef Eigen::TensorMap< + Eigen::TensorFixedSize, Eigen::RowMajor, IndexType>> + UnalignedConstScalar; + + // Rank-1 tensor (vector) of scalar type T. + typedef Eigen::TensorMap, Eigen::Aligned> Flat; + typedef Eigen::TensorMap, Eigen::Aligned> + ConstFlat; + typedef Eigen::TensorMap, Eigen::Aligned> Vec; + typedef Eigen::TensorMap, Eigen::Aligned> + ConstVec; + + // Unaligned Rank-1 tensor (vector) of scalar type T. + typedef Eigen::TensorMap> UnalignedFlat; + typedef Eigen::TensorMap> + UnalignedConstFlat; + typedef Eigen::TensorMap> UnalignedVec; + typedef Eigen::TensorMap> UnalignedConstVec; + + // Rank-2 tensor (matrix) of scalar type T. + typedef Eigen::TensorMap, Eigen::Aligned> Matrix; + typedef Eigen::TensorMap, Eigen::Aligned> + ConstMatrix; + + // Unaligned Rank-2 tensor (matrix) of scalar type T. + typedef Eigen::TensorMap> UnalignedMatrix; + typedef Eigen::TensorMap> + UnalignedConstMatrix; +}; + +typedef typename TTypes::Tensor32Bit::Index Index32; + +template struct InputTensor +{ + Shape shape; + const T *buffer; +}; + +struct Tensor +{ + Shape shape; + void *buffer; + +public: + bool copyFrom(const Tensor &other, const Shape &new_shape) + { + if (other.shape.FlatSize() != new_shape.FlatSize()) + return false; + + this->shape.ReplaceWith(new_shape.DimensionsCount(), new_shape.DimsData()); + this->buffer = other.buffer; + + return true; + } + + template T *base() const + { + return buffer == nullptr ? nullptr : reinterpret_cast(buffer); + } + + template + typename TTypes::Tensor shaped(const std::vector &new_sizes) + { + Eigen::array dims; + for (size_t d = 0; d < NDIMS; d++) + { + dims[d] = new_sizes[d]; + } + return typename TTypes::Tensor(base(), dims); + } + + template typename TTypes::Flat flat() { return shaped({shape.FlatSize()}); } + + template + typename TTypes::ConstTensor shaped(const std::vector new_sizes) const + { + Eigen::array dims; + for (size_t d = 0; d < NDIMS; d++) + { + dims[d] = new_sizes[d]; + } + return typename TTypes::ConstTensor(base(), dims); + } + + // Create Eigen Tensor with current shape + template typename TTypes::Tensor shaped() const + { + Eigen::array dims; + for (size_t d = 0; d < NDIMS; d++) + { + dims[d] = shape.Dims(d); + } + return typename TTypes::Tensor(base(), dims); + } + + template typename TTypes::ConstFlat flat() const + { + return shaped({shape.FlatSize()}); + } + + template typename TTypes::ConstScalar scalar() const + { + return typename TTypes::ConstScalar(base()); + } +}; // Tensor + +template Eigen::DSizes To32BitDims(const DSizes &in) +{ + Eigen::DSizes out; + for (int i = 0; i < DSizes::count; ++i) + { + out[i] = in[i]; + } + return out; +} + +template +typename TTypes::Tensor32Bit +To32Bit(TensorType in) +{ + typedef typename TTypes::Tensor32Bit RetType; + return RetType(in.data(), To32BitDims(in.dimensions())); +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_HELPER_TENSOR_H__ diff --git a/compute/cker/include/cker/operation/LogSoftMax.h b/compute/cker/include/cker/operation/LogSoftMax.h new file mode 100644 index 0000000..326a3ee --- /dev/null +++ b/compute/cker/include/cker/operation/LogSoftMax.h @@ -0,0 +1,83 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_LOGSOFTMAX_H__ +#define __NNFW_CKER_LOGSOFTMAX_H__ + +#include "cker/Shape.h" +#include "cker/Utils.h" +#include "cker/Types.h" +#include "cker/eigen/Utils.h" + +#include +#include +#include + +namespace nnfw +{ +namespace cker +{ + +inline void LogSoftmax(const SoftmaxParams ¶ms, const Shape &input_shape, + const float *input_data, const Shape &output_shape, float *output_data) +{ + const int rank = input_shape.DimensionsCount(); + const int axis = (params.axis < 0) ? params.axis + rank : params.axis; + const double beta = params.beta; + const int depth = MatchingDim(input_shape, axis, output_shape, axis); + + int outer_size = 1; + for (int i = 0; i < axis; ++i) + { + outer_size *= input_shape.Dims(i); + } + + int inner_size = 1; + for (int i = axis + 1; i < rank; ++i) + { + inner_size *= input_shape.Dims(i); + } + + for (int i = 0; i < outer_size; ++i) + { + for (int j = 0; j < inner_size; ++j) + { + float max = std::numeric_limits::lowest(); + for (int c = 0; c < depth; ++c) + { + max = std::max(max, input_data[(i * depth + c) * inner_size]); + } + + float sum = 0.f; + for (int c = 0; c < depth; ++c) + { + sum += std::exp((input_data[(i * depth + c) * inner_size + j] - max) * beta); + } + + const float log_sum = std::log(sum); + for (int c = 0; c < depth; ++c) + { + output_data[(i * depth + c) * inner_size + j] = + (input_data[(i * depth + c) * inner_size + j] - max) * beta - log_sum; + } + } + } +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_LOGSOFTMAX_H__ diff --git a/compute/cker/include/cker/operation/LogicalNot.h b/compute/cker/include/cker/operation/LogicalNot.h new file mode 100644 index 0000000..5e8d38b --- /dev/null +++ b/compute/cker/include/cker/operation/LogicalNot.h @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_LOGICALNOT_H__ +#define __NNFW_CKER_LOGICALNOT_H__ + +#include "cker/Shape.h" + +namespace nnfw +{ +namespace cker +{ + +inline void LogicalNot(const Shape &input_shape, const bool *input_data, const Shape &output_shape, + bool *output_data) +{ + const int size = MatchingFlatSize(input_shape, output_shape); + for (int i = 0; i < size; i++) + { + output_data[i] = !input_data[i]; + } +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_LOGICALNOT_H__ diff --git a/compute/cker/include/cker/operation/LogicalOr.h b/compute/cker/include/cker/operation/LogicalOr.h new file mode 100644 index 0000000..ec07c23 --- /dev/null +++ b/compute/cker/include/cker/operation/LogicalOr.h @@ -0,0 +1,80 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_LOGICAL_OR_H__ +#define __NNFW_CKER_LOGICAL_OR_H__ + +#include "cker/Shape.h" +#include "cker/Utils.h" + +namespace nnfw +{ +namespace cker +{ + +template +inline void LogicalOrBroadcast(const Shape &unextended_input1_shape, const T *input1_data, + const Shape &unextended_input2_shape, const T *input2_data, + const Shape &unextended_output_shape, T *output_data) +{ + assert(unextended_input1_shape.DimensionsCount() <= 4); + assert(unextended_input2_shape.DimensionsCount() <= 4); + assert(unextended_output_shape.DimensionsCount() <= 4); + const Shape output_shape = Shape::ExtendedShape(4, unextended_output_shape); + + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(unextended_input1_shape, unextended_input2_shape, &desc1, + &desc2); + + for (int b = 0; b < output_shape.Dims(0); ++b) + { + for (int y = 0; y < output_shape.Dims(1); ++y) + { + for (int x = 0; x < output_shape.Dims(2); ++x) + { + for (int c = 0; c < output_shape.Dims(3); ++c) + { + auto out_idx = Offset(output_shape, b, y, x, c); + auto in1_idx = SubscriptToIndex(desc1, b, y, x, c); + auto in2_idx = SubscriptToIndex(desc2, b, y, x, c); + auto in1_val = input1_data[in1_idx]; + auto in2_val = input2_data[in2_idx]; + output_data[out_idx] = in1_val || in2_val; + } + } + } + } +} + +template +inline void LogicalOrElementwise(const Shape &shape, const T *input1_data, const T *input2_data, + T *output_data) +{ + + int num_elements = shape.FlatSize(); + + for (int t = 0; t < num_elements; t++) + { + output_data[t] = input1_data[t] || input2_data[t]; + } +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_LOGICAL_OR_H___ diff --git a/compute/cker/include/cker/operation/Logistic.h b/compute/cker/include/cker/operation/Logistic.h index 8720955..7477858 100644 --- a/compute/cker/include/cker/operation/Logistic.h +++ b/compute/cker/include/cker/operation/Logistic.h @@ -19,8 +19,10 @@ #define __NNFW_CKER_LOGISTIC_H__ #include "cker/Shape.h" +#include "cker/eigen/Utils.h" #include +#include namespace nnfw { @@ -30,12 +32,18 @@ namespace cker inline void Logistic(const Shape &input_shape, const float *input_data, const Shape &output_shape, float *output_data) { +#ifdef __aarch64__ + auto input_map = MapAsVector(input_data, input_shape); + auto output_map = MapAsVector(output_data, output_shape); + output_map.array() = input_map.array().unaryExpr(Eigen::internal::scalar_logistic_op()); +#else // Note, this can be done using TANH: (1/2) + (1/2) * TANH(x/2) const int size = MatchingFlatSize(input_shape, output_shape); for (int i = 0; i < size; i++) { output_data[i] = 1.f / (1.f + std::exp(-input_data[i])); } +#endif } } // namespace cker diff --git a/compute/cker/include/cker/operation/MatrixBandPart.h b/compute/cker/include/cker/operation/MatrixBandPart.h new file mode 100644 index 0000000..9f49c8f --- /dev/null +++ b/compute/cker/include/cker/operation/MatrixBandPart.h @@ -0,0 +1,77 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_MATRIX_BAND_PART_H__ +#define __NNFW_CKER_MATRIX_BAND_PART_H__ + +#include "cker/Shape.h" + +#include + +namespace nnfw +{ +namespace cker +{ +template +void MatrixBandPart(const T num_lower_diags, const T num_upper_diags, const Shape &input_shape, + const float *input_data, const Shape &output_shape, float *output_data) +{ + auto last_dim = input_shape.DimensionsCount() - 1; + + T batch_num = 0; + for (int dim = 0; dim < last_dim - 2; dim++) + { + batch_num += input_shape.Dims(dim); + } + + const T row_num = input_shape.Dims(last_dim - 1); + const T col_num = input_shape.Dims(last_dim); + + if (!(num_lower_diags <= row_num)) + throw std::runtime_error( + "MatrixBandPart : num_lower must be negative or less or equal to number of rows"); + + if (!(num_upper_diags <= col_num)) + throw std::runtime_error( + "MatrixBandPart : num_upper must be negative or less or equal to number of columns"); + + std::fill(output_data, output_data + output_shape.FlatSize(), 0); // output matrix init + + // reference code, without multithreading + for (T batch = 0; batch < batch_num; ++batch) + { + for (T row = 0; row < row_num; ++row) + { + auto output = output_data + (batch * row_num * col_num + row * col_num); + auto input = input_data + (batch * row_num * col_num + row * col_num); + + const T band_start = + num_lower_diags < 0 ? 0 : std::min(col_num, std::max(T{0}, row - num_lower_diags)); + const T band_end = num_upper_diags < 0 ? col_num : std::min(static_cast(col_num), + row + num_upper_diags + 1); + + for (T band_idx = band_start; band_idx < band_end; band_idx++) + { + output[band_idx] = input[band_idx]; + } + } + } +} +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_MATRIX_BAND_PART_H__ diff --git a/compute/cker/include/cker/operation/OneHot.h b/compute/cker/include/cker/operation/OneHot.h index 2df82d1..c0dbc6d 100644 --- a/compute/cker/include/cker/operation/OneHot.h +++ b/compute/cker/include/cker/operation/OneHot.h @@ -29,6 +29,9 @@ template void OneHot(const int32_t depth, const T on_value, const T off_value, int32_t axis, const Shape &indices_shape, const TI *indices_data, const Shape &, T *output_data) { + if (axis == -1) + axis = indices_shape.DimensionsCount(); + // prefix_dim_size == # of elements before the axis // depth == # of elements per axis // suffix_dim_size == # of elements after the axis diff --git a/compute/cker/include/cker/operation/Pow.h b/compute/cker/include/cker/operation/Pow.h new file mode 100644 index 0000000..1214e09 --- /dev/null +++ b/compute/cker/include/cker/operation/Pow.h @@ -0,0 +1,44 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_POW_H__ +#define __NNFW_CKER_POW_H__ + +#include "cker/Shape.h" + +#include + +namespace nnfw +{ +namespace cker +{ + +template +inline void powImpl(const Shape &input1_shape, const T *input1_data, const Shape &input2_shape, + const T *input2_data, const Shape &output_shape, T *output_data) +{ + const int flat_size = MatchingFlatSize(input1_shape, input2_shape, output_shape); + for (int i = 0; i < flat_size; ++i) + { + output_data[i] = std::pow(input1_data[i], input2_data[i]); + } +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_POW_H__ diff --git a/compute/cker/include/cker/operation/Range.h b/compute/cker/include/cker/operation/Range.h new file mode 100644 index 0000000..5c3a773 --- /dev/null +++ b/compute/cker/include/cker/operation/Range.h @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2018 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_RANGE_H__ +#define __NNFW_CKER_RANGE_H__ + +#include "cker/Shape.h" + +#include +#include + +namespace nnfw +{ +namespace cker +{ +template inline int GetSize(T start, T limit, T delta) +{ + if (!((start > limit && delta < 0) || (start < limit && delta > 0))) + { + throw std::runtime_error("Range: invalid input values"); + } + + int size = (std::is_integral::value + ? ((std::abs(limit - start) + std::abs(delta) - 1) / std::abs(delta)) + : std::ceil(std::abs((limit - start) / delta))); + return size; +} + +template +inline void Range(const T *start_data, const T *limit_data, const T *delta_data, T *output_data) +{ + const T start_value = *start_data; + const T delta_value = *delta_data; + const T limit_value = *limit_data; + + const int num_elements = GetSize(start_value, limit_value, delta_value); + T value = start_value; + + for (int i = 0; i < num_elements; ++i) + { + output_data[i] = value; + value += delta_value; + } +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_RANGE_H__ diff --git a/compute/cker/include/cker/operation/ReLU.h b/compute/cker/include/cker/operation/ReLU.h new file mode 100644 index 0000000..2a6cc4a --- /dev/null +++ b/compute/cker/include/cker/operation/ReLU.h @@ -0,0 +1,43 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2018 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_RELU_H__ +#define __NNFW_CKER_RELU_H__ + +#include "cker/Shape.h" +#include "cker/eigen/Utils.h" + +#include +#include + +namespace nnfw +{ +namespace cker +{ + +inline void ReLU(const Shape &input_shape, const float *input_data, const Shape &output_shape, + float *output_data) +{ + const auto input_map = MapAsVector(input_data, input_shape); + auto output_map = MapAsVector(output_data, output_shape); + output_map = input_map.cwiseMax(0.0f); +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_RELU_H__ diff --git a/compute/cker/include/cker/operation/Reduce.h b/compute/cker/include/cker/operation/Reduce.h index 52e7395..4ba3652 100644 --- a/compute/cker/include/cker/operation/Reduce.h +++ b/compute/cker/include/cker/operation/Reduce.h @@ -128,8 +128,10 @@ public: return; // prepare space for temp_index and resolved_axis - _temp_index.resize(temp_index_size); - _resolved_axis.resize(resolved_axis_size); + if (temp_index_size > kMaxSmallSize) + _temp_index.resize(temp_index_size); + if (resolved_axis_size > kMaxSmallSize) + _resolved_axis.resize(resolved_axis_size); _prepared = true; } @@ -148,20 +150,31 @@ public: // Resolve axis. int num_resolved_axis = 0; - if (!ResolveAxis(input_shape.DimensionsCount(), axes, _resolved_axis.data(), - &num_resolved_axis)) + if (!ResolveAxis(input_shape.DimensionsCount(), axes, resolved_axis_data(), &num_resolved_axis)) { return false; } - return ReduceImpl(input_data, input_shape, output_shape, _resolved_axis.data(), - num_resolved_axis, _temp_index.data(), reducer, output_data); + return ReduceImpl(input_data, input_shape, output_shape, resolved_axis_data(), + num_resolved_axis, temp_index_data(), reducer, output_data); + } + + inline int32_t *resolved_axis_data(void) + { + return _resolved_axis.size() ? _resolved_axis.data() : _resolved_axis_small; + } + inline int32_t *temp_index_data(void) + { + return _temp_index.size() ? _temp_index.data() : _temp_index_small; } private: std::vector _temp_index; std::vector _resolved_axis; bool _prepared; + static constexpr int kMaxSmallSize = 4; + int _temp_index_small[kMaxSmallSize]; + int _resolved_axis_small[kMaxSmallSize]; }; } // namespace cker diff --git a/compute/cker/include/cker/operation/ReduceMean.h b/compute/cker/include/cker/operation/ReduceMean.h new file mode 100644 index 0000000..2e4fc62 --- /dev/null +++ b/compute/cker/include/cker/operation/ReduceMean.h @@ -0,0 +1,237 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_REDUCEMEAN_H__ +#define __NNFW_CKER_REDUCEMEAN_H__ + +#include "cker/Shape.h" +#include "cker/operation/Reduce.h" + +namespace nnfw +{ +namespace cker +{ + +float round_nearest(float value) +{ + if (value < 0) + { + return static_cast(static_cast(value - 0.5f)); + } + else + { + return static_cast(static_cast(value + 0.5f)); + } +} +template +Out mean_reducer(const Out data1, const In data2, int normalizer) +{ + return data1 + static_cast(data2) / normalizer; +} + +template int sum_reducer(const int data1, const In data2) +{ + return data1 + static_cast(data2); +} + +template +inline bool ReduceMeanImpl(const In *input_data, const Shape &input_shape, const int *axis, + const int num_axis, int *input_iter, + Out reducer(const Out current, const In in, int normalizer), + Out *output_data) +{ + const auto input_dims = input_shape.DimsData(); + const auto input_num_dims = input_shape.DimensionsCount(); + int normalizer = 1; + // Reset input iterator. + for (int idx = 0; idx < input_num_dims; ++idx) + { + input_iter[idx] = 0; + } + // Compute number of output elements + for (int idx = 0; idx < num_axis; ++idx) + { + normalizer *= input_dims[axis[idx]]; + } + // Iterate through input_data. + do + { + size_t input_offset = ReducedOutputOffset(input_num_dims, input_dims, input_iter, 0, nullptr); + size_t output_offset = + ReducedOutputOffset(input_num_dims, input_dims, input_iter, num_axis, axis); + output_data[output_offset] = + reducer(output_data[output_offset], input_data[input_offset], normalizer); + } while (NextIndex(input_num_dims, input_dims, input_iter)); + return true; +} + +template +inline size_t ReduceSumQuantImpl(const In *input_data, const Shape &input_shape, const int *axis, + const int num_axis, int *input_iter, + int reducer(const int current, const In in), int *temp_sum) +{ + const auto input_dims = input_shape.DimsData(); + const auto input_num_dims = input_shape.DimensionsCount(); + size_t normalizer = 1; + // Reset input iterator. + for (int idx = 0; idx < input_num_dims; ++idx) + { + input_iter[idx] = 0; + } + // Compute number of output elements + for (int idx = 0; idx < num_axis; ++idx) + { + normalizer *= input_dims[axis[idx]]; + } + // Iterate through input_data. + do + { + size_t input_offset = ReducedOutputOffset(input_num_dims, input_dims, input_iter, 0, nullptr); + size_t output_offset = + ReducedOutputOffset(input_num_dims, input_dims, input_iter, num_axis, axis); + temp_sum[output_offset] = reducer(temp_sum[output_offset], input_data[input_offset]); + } while (NextIndex(input_num_dims, input_dims, input_iter)); + return normalizer; +} + +class ReduceMean : public Reduce +{ +public: + ReduceMean() : Reduce(){}; + + template + int PrepareforReduce(const Shape &input_shape, const Shape &output_shape, + const std::vector &axes, T *output_data, T init_value) + { + // Reset output data. + if (!InitTensorDataForReduce(output_shape, init_value, output_data)) + { + return -1; + } + const auto input_dims = input_shape.DimsData(); + const int num_dims = input_shape.DimensionsCount(); + int resolved_axis_size = 1; + const auto num_axes = axes.size(); + + for (size_t idx = 0; idx < num_axes; idx++) + { + int current = axes[idx] < 0 ? (axes[idx] + num_dims) : axes[idx]; + assert(current >= 0 && current < num_dims); + resolved_axis_size *= input_dims[current]; + } + + prepare(num_dims, resolved_axis_size); + + // Resolve axis. + int num_resolved_axis = 0; + if (!ResolveAxis(input_shape.DimensionsCount(), axes, resolved_axis_data(), &num_resolved_axis)) + { + return -1; + } + + return num_resolved_axis; + } + + // Computes the generic value (i.e., sum/max/min/prod) of elements across + // dimensions given in axis. It needs to pass in init_value and reducer. + template + inline bool ReduceOp(const Shape &input_shape, const In *input_data, const Shape &output_shape, + Out *output_data, const std::vector &axes, bool, Out init_value, + Out reducer(const Out current, const Out in, int normalizer)) + { + int num_resolved_axis; + num_resolved_axis = PrepareforReduce(input_shape, output_shape, axes, output_data, init_value); + if (num_resolved_axis == -1) + { + return false; + } + return ReduceMeanImpl(input_data, input_shape, resolved_axis_data(), num_resolved_axis, + temp_index_data(), reducer, output_data); + } + + template + inline bool ReduceOp(const Shape &input_shape, const In *input_data, float input_scale, + int32_t input_offset, const Shape &output_shape, Out *output_data, + float output_scale, int32_t output_offset, const std::vector &axes, + bool, Out init_value, int reducer(const int current, const In in)) + { + size_t num_outputs = 1; + auto output_dims = output_shape.DimsData(); + + for (size_t idx = 0; idx < static_cast(output_shape.DimensionsCount()); idx++) + { + num_outputs *= output_dims[idx]; + } + _temp_sum.resize(num_outputs, 0); + int num_resolved_axis; + num_resolved_axis = PrepareforReduce(input_shape, output_shape, axes, output_data, init_value); + if (num_resolved_axis == -1) + { + return false; + } + + size_t normalizer = + ReduceSumQuantImpl(input_data, input_shape, resolved_axis_data(), num_resolved_axis, + temp_index_data(), reducer, _temp_sum.data()); + if (num_outputs > 0) + { + float scale = input_scale / output_scale; + float bias = -input_offset * scale; + for (size_t idx = 0; idx < num_outputs; idx++) + { + float float_mean = static_cast(_temp_sum[idx]) / normalizer; + float result = std::min(round_nearest(float_mean * scale + bias + output_offset), + static_cast(std::numeric_limits::max())); + result = std::max(result, static_cast(std::numeric_limits::min())); + output_data[idx] = static_cast(result); + } + } + return false; + } + +private: + std::vector _temp_sum; +}; + +template +void Mean(const Shape &input_shape, const In *input_data, const Shape &output_shape, + Out *output_data, const std::vector &axes) +{ + UNUSED_RELEASE(output_shape); + assert(input_shape.DimensionsCount() > 0); + ReduceMean m_obj; + m_obj.ReduceOp(input_shape, input_data, output_shape, output_data, axes, true, (Out)0, + mean_reducer); +} + +template +void MeanQ8Asymm(const Shape &input_shape, const In *input_data, float input_scale, + int32_t input_offset, const Shape &output_shape, Out *output_data, + float output_scale, int32_t output_offset, const std::vector &axes) +{ + UNUSED_RELEASE(output_shape); + assert(input_shape.DimensionsCount() > 0); + ReduceMean m_obj; + m_obj.ReduceOp(input_shape, input_data, input_scale, input_offset, output_shape, + output_data, output_scale, output_offset, axes, true, (Out)0, + sum_reducer); +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_REDUCEMEAN_H__ diff --git a/compute/cker/include/cker/operation/Reverse.h b/compute/cker/include/cker/operation/Reverse.h new file mode 100644 index 0000000..ef4673f --- /dev/null +++ b/compute/cker/include/cker/operation/Reverse.h @@ -0,0 +1,62 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_REVERSE_H__ +#define __NNFW_CKER_REVERSE_H__ + +#include "cker/Shape.h" +#include "cker/Types.h" +#include "cker/Utils.h" + +namespace nnfw +{ +namespace cker +{ + +template +void Reverse(int axis, const Shape &input_shape, const Scalar *input_data, const Shape &, + Scalar *output_data) +{ + int outer_size = 1; + for (int i = 0; i < axis; ++i) + { + outer_size *= input_shape.Dims(i); + } + + int copy_size = 1; + for (int i = axis + 1; i < input_shape.DimensionsCount(); ++i) + { + copy_size *= input_shape.Dims(i); + } + + const int dims_at_axis = input_shape.Dims(axis); + for (int i = 0; i < outer_size; ++i) + { + for (int j = 0; j < dims_at_axis; ++j) + { + const int start_pos = (i * dims_at_axis + j) * copy_size; + Scalar *output_ptr = output_data + start_pos; + int loc = (i * dims_at_axis + dims_at_axis - j - 1) * copy_size; + memcpy(output_ptr, input_data + loc, copy_size * sizeof(Scalar)); + } + } +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_REVERSE_H__ diff --git a/compute/cker/include/cker/operation/Round.h b/compute/cker/include/cker/operation/Round.h new file mode 100644 index 0000000..a04a741 --- /dev/null +++ b/compute/cker/include/cker/operation/Round.h @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2018 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_ROUND_H__ +#define __NNFW_CKER_ROUND_H__ + +#include "cker/Shape.h" + +#include + +namespace nnfw +{ +namespace cker +{ + +inline float RoundToNearest(float value) +{ + auto floor_val = std::floor(value); + auto diff = value - floor_val; + if ((diff < 0.5f) || ((diff == 0.5f) && (static_cast(floor_val) % 2 == 0))) + { + return floor_val; + } + else + { + return floor_val = floor_val + 1.0f; + } +} + +inline void Round(const Shape &input_shape, const float *input_data, const Shape &output_shape, + float *output_data) +{ + const int flat_size = MatchingFlatSize(input_shape, output_shape); + for (int i = 0; i < flat_size; ++i) + { + // Note that this implementation matches that of tensorFlow tf.round + // and corresponds to the bankers rounding method. + // cfenv (for fesetround) is not yet supported universally on Android, so + // using a work around. + output_data[i] = RoundToNearest(input_data[i]); + } +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_ROUND_H__ diff --git a/compute/cker/include/cker/operation/Select.h b/compute/cker/include/cker/operation/Select.h new file mode 100644 index 0000000..ab2de94 --- /dev/null +++ b/compute/cker/include/cker/operation/Select.h @@ -0,0 +1,114 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2018 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_SELECT_H__ +#define __NNFW_CKER_SELECT_H__ + +#include "cker/Shape.h" +#include "cker/Utils.h" + +#include + +namespace nnfw +{ +namespace cker +{ + +template +void Select(const Shape &input_condition_shape, const D *input_condition_data, + const Shape &input_x_shape, const T *input_x_data, const Shape &input_y_shape, + const T *input_y_data, const Shape &output_shape, T *output_data) +{ + const int64_t flatsize = + MatchingFlatSize(input_condition_shape, input_x_shape, input_y_shape, output_shape); + for (int64_t i = 0; i < flatsize; ++i) + { + output_data[i] = (input_condition_data[i] != 0) ? input_x_data[i] : input_y_data[i]; + } +} + +template +void RankOneSelect(const Shape &input_condition_shape, const D *input_condition_data, + const Shape &input_x_shape, const T *input_x_data, const Shape &input_y_shape, + const T *input_y_data, const Shape &output_shape, T *output_data) +{ + const int64_t outer_size = input_condition_shape.FlatSize(); + assert(MatchingDim(input_x_shape, 0, input_y_shape, 0, output_shape, 0) == outer_size); + const int64_t inner_size = MatchingFlatSizeSkipDim(input_x_shape, 0, input_y_shape, output_shape); + + int64_t offset = 0; + for (int64_t i = 0; i < outer_size; i++) + { + const T *input_data = (input_condition_data[i] != 0) ? input_x_data : input_y_data; + memcpy(output_data + offset, input_data + offset, inner_size * sizeof(T)); + offset += inner_size; + } +} + +template +void BroadcastSelect4DSlow(const Shape &input_condition_shape, const D *input_condition_data, + const Shape &input_x_shape, const T *input_x_data, + const Shape &input_y_shape, const T *input_y_data, + const Shape &output_shape, T *output_data) +{ + assert(input_condition_shape.DimensionsCount() <= 4); + assert(input_x_shape.DimensionsCount() <= 4); + assert(input_y_shape.DimensionsCount() <= 4); + assert(output_shape.DimensionsCount() <= 4); + + const Shape extended_output_shape = Shape::ExtendedShape(4, output_shape); + + NdArrayDesc<4> desc_condition; + NdArrayDesc<4> desc_x; + NdArrayDesc<4> desc_y; + NdArrayDescsForElementwiseBroadcast(input_condition_shape, input_x_shape, input_y_shape, + &desc_condition, &desc_x, &desc_y); + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest + // stride, typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for + // the best cache behavior. + for (int b = 0; b < extended_output_shape.Dims(0); ++b) + { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) + { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) + { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) + { + const int condition_index = SubscriptToIndex(desc_condition, b, y, x, c); + const int x_index = SubscriptToIndex(desc_x, b, y, x, c); + const int y_index = SubscriptToIndex(desc_y, b, y, x, c); + output_data[Offset(extended_output_shape, b, y, x, c)] = + input_condition_data[condition_index] ? input_x_data[x_index] : input_y_data[y_index]; + } + } + } + } +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_SELECT_H__ diff --git a/compute/cker/include/cker/operation/SpaceToBatchND.h b/compute/cker/include/cker/operation/SpaceToBatchND.h new file mode 100644 index 0000000..feeb358 --- /dev/null +++ b/compute/cker/include/cker/operation/SpaceToBatchND.h @@ -0,0 +1,95 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_SPACE_TO_BATCH_ND_H__ +#define __NNFW_CKER_SPACE_TO_BATCH_ND_H__ + +#include "cker/Shape.h" +#include "cker/Types.h" + +namespace nnfw +{ +namespace cker +{ + +template +inline void SpaceToBatchND(const SpaceToBatchParams ¶ms, const Shape &unextended_input_shape, + const T *input_data, const Shape &unextended_block_shape_shape, + const int32_t *block_shape_data, const Shape &unextended_padding_shape, + const int32_t *paddings_data, const Shape &unextended_output_shape, + T *output_data) +{ + UNUSED_RELEASE(unextended_block_shape_shape); + UNUSED_RELEASE(unextended_padding_shape); + + assert(unextended_input_shape.DimensionsCount() <= 4); + assert(unextended_output_shape.DimensionsCount() <= 4); + const Shape input_shape = Shape::ExtendedShape(4, unextended_input_shape); + const Shape output_shape = Shape::ExtendedShape(4, unextended_output_shape); + + const int depth = input_shape.Dims(3); + const int input_width = input_shape.Dims(2); + const int input_height = input_shape.Dims(1); + const int input_batch_size = input_shape.Dims(0); + + const int output_width = output_shape.Dims(2); + const int output_height = output_shape.Dims(1); + const int output_batch_size = output_shape.Dims(0); + + const int block_shape_height = block_shape_data[0]; + const int block_shape_width = block_shape_data[1]; + const int padding_top = paddings_data[0]; + const int padding_left = paddings_data[2]; + + // For uint8 quantized, the correct padding "zero value" is the output offset. + const int32_t pad_value = params.output_offset; + + for (int out_b = 0; out_b < output_batch_size; ++out_b) + { + int input_batch = out_b % input_batch_size; + int shift_w = (out_b / input_batch_size) % block_shape_width; + int shift_h = (out_b / input_batch_size) / block_shape_width; + for (int out_h = 0; out_h < output_height; ++out_h) + { + for (int out_w = 0; out_w < output_width; ++out_w) + { + T *out = output_data + Offset(output_shape, out_b, out_h, out_w, 0); + if (out_h * block_shape_height + shift_h < padding_top || + out_h * block_shape_height + shift_h >= padding_top + input_height || + out_w * block_shape_width + shift_w < padding_left || + out_w * block_shape_width + shift_w >= padding_left + input_width) + { + // This may not execute correctly when pad_value != 0 and T != uint8. + memset(out, pad_value, depth * sizeof(T)); + } + else + { + const T *in = + input_data + Offset(input_shape, input_batch, + (out_h * block_shape_height + shift_h) - padding_top, + (out_w * block_shape_width + shift_w) - padding_left, 0); + memcpy(out, in, depth * sizeof(T)); + } + } + } + } +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_SPACE_TO_BATCH_ND_H__ diff --git a/compute/cker/include/cker/operation/SqDiff.h b/compute/cker/include/cker/operation/SqDiff.h new file mode 100644 index 0000000..93428d5 --- /dev/null +++ b/compute/cker/include/cker/operation/SqDiff.h @@ -0,0 +1,97 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_REDUCESQDIFF_H__ +#define __NNFW_CKER_REDUCESQDIFF_H__ + +#include "cker/Shape.h" +#include "cker/Utils.h" + +namespace nnfw +{ +namespace cker +{ + +#define SQDIFF(N) \ + do \ + { \ + NdArrayDesc input1_desc; \ + NdArrayDesc input2_desc; \ + NdArrayDesc output_desc; \ + SqDiffImpl(input1_shape, input1_data, input2_shape, input2_data, output_shape, \ + output_data, &input1_desc, &input2_desc, &output_desc); \ + } while (0); + +template +void SqDiffImpl(const Shape &input1_shape, const T *input1_data, const Shape &input2_shape, + const T *input2_data, const Shape &output_shape, T *output_data, + NdArrayDesc *desc1_in, NdArrayDesc *desc2_in, NdArrayDesc *desc_out) +{ + std::vector input_iter; + input_iter.resize(N); + const auto output_dims = output_shape.DimsData(); + + // Copy dims to desc, calculating strides. + CopyDimsToDesc(output_shape, desc_out); + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, desc1_in, desc2_in); + + do + { + int input1_indx = SubscriptToIndexGeneric(desc1_in, input_iter.data()); + int input2_indx = SubscriptToIndexGeneric(desc2_in, input_iter.data()); + int output_indx = SubscriptToIndexGeneric(desc_out, input_iter.data()); + output_data[output_indx] = (input1_data[input1_indx] - input2_data[input2_indx]) * + (input1_data[input1_indx] - input2_data[input2_indx]); + } while (NextIndex(N, output_dims, input_iter.data())); +} + +template +void SqDiff(const Shape &input1_shape, const T *input1_data, const Shape &input2_shape, + const T *input2_data, const Shape &output_shape, T *output_data) +{ + UNUSED_RELEASE(output_shape); + assert(input1_shape.DimensionsCount() > 0 && input2_shape.DimensionsCount() > 0 && + output_shape.DimensionsCount() > 0); + int outRank = output_shape.DimensionsCount(); + + switch (outRank) + { + case 4: + SQDIFF(4); + break; + + case 3: + SQDIFF(3); + break; + + case 2: + SQDIFF(2); + break; + + case 1: + SQDIFF(1); + break; + + default: + throw std::runtime_error("Support up to 4-D tensors at present"); + break; + } +} +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_REDUCESQDIFF_H__ diff --git a/compute/cker/include/cker/operation/Tile.h b/compute/cker/include/cker/operation/Tile.h new file mode 100644 index 0000000..1dcdd9b --- /dev/null +++ b/compute/cker/include/cker/operation/Tile.h @@ -0,0 +1,73 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_TILE_H__ +#define __NNFW_CKER_TILE_H__ + +#include "cker/Shape.h" + +namespace nnfw +{ +namespace cker +{ + +template +void CopyMultipleTimes(const T *in_data, int32_t in_size, M multiplier, T *out_data) +{ + for (M i = 0; i < multiplier; ++i) + { + const T *in_end = in_data + in_size; + T *new_out_data = std::copy(in_data, in_end, out_data); + in_data = out_data; + out_data = new_out_data; + } +} + +template +std::pair TileOneDimension(const Shape &in_dimensions, const T *in_data, + const M *multipliers, T *out_data, int dimension) +{ + const int dimension_size = in_dimensions.Dims(dimension); + if (dimension == in_dimensions.DimensionsCount() - 1) + { + CopyMultipleTimes(in_data, dimension_size, multipliers[dimension], out_data); + return std::make_pair(dimension_size, + dimension_size * static_cast(multipliers[dimension])); + } + int total_stride_size = 0, total_tiled_stride_size = 0; + const T *copy_from_data = in_data; + T *copy_to_data = out_data; + for (int i = 0; i < dimension_size; ++i) + { + int stride_size = 0, tiled_stride_size = 0; + std::tie(stride_size, tiled_stride_size) = + TileOneDimension(in_dimensions, copy_from_data, multipliers, copy_to_data, dimension + 1); + copy_from_data += stride_size; + copy_to_data += tiled_stride_size; + total_stride_size += stride_size; + total_tiled_stride_size += tiled_stride_size; + } + CopyMultipleTimes(out_data, total_tiled_stride_size, multipliers[dimension] - 1, + out_data + total_tiled_stride_size); + return std::make_pair(total_stride_size, + static_cast(total_tiled_stride_size * multipliers[dimension])); +} + +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_TILE_H__ diff --git a/compute/cker/include/cker/operation/optimized/BinaryArithmeticOps.h b/compute/cker/include/cker/operation/optimized/BinaryArithmeticOps.h index c887065..2b2ea8f 100644 --- a/compute/cker/include/cker/operation/optimized/BinaryArithmeticOps.h +++ b/compute/cker/include/cker/operation/optimized/BinaryArithmeticOps.h @@ -18,10 +18,13 @@ #ifndef __NNFW_CKER_OPTIMIZED_BINARYARITHMETICOPS_H__ #define __NNFW_CKER_OPTIMIZED_BINARYARITHMETICOPS_H__ +#include #include "cker/neon/neon_check.h" +#include "cker/operation/reference/BinaryArithmeticOps.h" #include "cker/Shape.h" #include "cker/Types.h" #include "cker/Utils.h" +#include "fixedpoint/fixedpoint.h" namespace nnfw { @@ -30,12 +33,133 @@ namespace cker namespace optimized { -inline void Add(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, - const float *input1_data, const Shape &input2_shape, const float *input2_data, - const Shape &output_shape, float *output_data) +template +inline void BinaryBroadcastFiveFold(const BinaryArithmeticOpParam ¶ms, + const Shape & /* unswitched_input1_shape */, + const T *unswitched_input1_data, + const Shape & /* unswitched_input2_shape */, + const T *unswitched_input2_data, + const Shape & /* output_shape */, T *output_data, + ElementwiseF elementwise_f, ScalarBroadcastF scalar_broadcast_f) +{ + const bool use_unswitched = + params.broadcast_category == BroadcastableOpCategory::kFirstInputBroadcastsFast; + + const T *input1_data = use_unswitched ? unswitched_input1_data : unswitched_input2_data; + const T *input2_data = use_unswitched ? unswitched_input2_data : unswitched_input1_data; + + // Fivefold nested loops. The second input resets its position for each + // iteration of the second loop. The first input resets its position at the + // beginning of the fourth loop. The innermost loop is an elementwise add of + // sections of the arrays. + T *output_data_ptr = output_data; + const T *input1_data_ptr = input1_data; + const T *input2_data_reset = input2_data; + // In the fivefold pattern, y0, y2 and y4 are not broadcast, and so shared + // between input shapes. y3 for input 1 is always broadcast, and so the + // dimension there is 1, whereas optionally y1 might be broadcast for input 2. + // Put another way, + // input1.shape.FlatSize = y0 * y1 * y2 * y4, + // input2.shape.FlatSize = y0 * y2 * y3 * y4. + int y0 = params.broadcast_shape[0]; + int y1 = params.broadcast_shape[1]; + int y2 = params.broadcast_shape[2]; + int y3 = params.broadcast_shape[3]; + int y4 = params.broadcast_shape[4]; + if (y4 > 1) + { + // General fivefold pattern, with y4 > 1 so there is a non-broadcast inner + // dimension. + for (int i0 = 0; i0 < y0; ++i0) + { + const T *input2_data_ptr = nullptr; + for (int i1 = 0; i1 < y1; ++i1) + { + input2_data_ptr = input2_data_reset; + for (int i2 = 0; i2 < y2; ++i2) + { + for (int i3 = 0; i3 < y3; ++i3) + { + elementwise_f(y4, params, input1_data_ptr, input2_data_ptr, output_data_ptr); + input2_data_ptr += y4; + output_data_ptr += y4; + } + // We have broadcast y4 of input1 data y3 times, and now move on. + input1_data_ptr += y4; + } + } + // We have broadcast y2*y3*y4 of input2 data y1 times, and now move on. + input2_data_reset = input2_data_ptr; + } + } + else + { + // Special case of y4 == 1, in which the innermost loop is a single element + // and can be combined with the next (y3) as an inner broadcast. + // + // Note that this handles the case of pure scalar broadcast when + // y0 == y1 == y2 == 1. With low overhead it handles cases such as scalar + // broadcast with batch (as y2 > 1). + // + // NOTE The process is the same as the above general case except simplified + // for y4 == 1 and the loop over y3 is contained within the + // AddScalarBroadcast function. + for (int i0 = 0; i0 < y0; ++i0) + { + const T *input2_data_ptr = nullptr; + for (int i1 = 0; i1 < y1; ++i1) + { + input2_data_ptr = input2_data_reset; + for (int i2 = 0; i2 < y2; ++i2) + { + scalar_broadcast_f(y3, params, *input1_data_ptr, input2_data_ptr, output_data_ptr); + input2_data_ptr += y3; + output_data_ptr += y3; + input1_data_ptr += 1; + } + } + input2_data_reset = input2_data_ptr; + } + } +} + +inline int32_t quant8_sum(const BinaryArithmeticOpParam ¶ms, const uint8_t input1_data, + const uint8_t input2_data) +{ + const int32_t input1_val = params.input1_offset + input1_data; + const int32_t input2_val = params.input2_offset + input2_data; + const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); + const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); + const int32_t scaled_input1_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input1_val, params.input1_multiplier, params.input1_shift); + const int32_t scaled_input2_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input2_val, params.input2_multiplier, params.input2_shift); + const int32_t raw_sum = scaled_input1_val + scaled_input2_val; + const int32_t raw_output = MultiplyByQuantizedMultiplierSmallerThanOneExp( + raw_sum, params.output_multiplier, params.output_shift) + + params.output_offset; + const int32_t clamped_output = std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + return clamped_output; +} + +inline void AddElementwiseQuant8(int size, const BinaryArithmeticOpParam ¶ms, + const uint8_t *input1_data, const uint8_t *input2_data, + uint8_t *output_data) { int i = 0; - const int size = MatchingFlatSize(input1_shape, input2_shape, output_shape); + for (; i < size; ++i) + { + int32_t clamped_output = quant8_sum(params, input1_data[i], input2_data[i]); + output_data[i] = static_cast(clamped_output); + } +} + +inline void AddElementwise(int size, const BinaryArithmeticOpParam ¶ms, + const float *input1_data, const float *input2_data, float *output_data) +{ + int i = 0; + #ifdef USE_NEON const auto activation_min = vdupq_n_f32(params.float_activation_min); const auto activation_max = vdupq_n_f32(params.float_activation_max); @@ -76,12 +200,120 @@ inline void Add(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape vst1q_f32(output_data + i, x); } #endif // NEON - for (; i < size; i++) { auto x = input1_data[i] + input2_data[i]; - output_data[i] = - ActivationFunctionWithMinMax(x, params.float_activation_min, params.float_activation_max); + output_data[i] = ActivationFunctionWithMinMax(x, params.float_activation_min, + params.float_activation_max); + } +} + +inline void AddQuant8(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, + const uint8_t *input1_data, const Shape &input2_shape, + const uint8_t *input2_data, const Shape &output_shape, uint8_t *output_data) +{ + const int flat_size = MatchingElementsSize(input1_shape, input2_shape, output_shape); + AddElementwiseQuant8(flat_size, params, input1_data, input2_data, output_data); +} + +inline void Add(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, + const float *input1_data, const Shape &input2_shape, const float *input2_data, + const Shape &output_shape, float *output_data) +{ + const int flat_size = MatchingElementsSize(input1_shape, input2_shape, output_shape); + AddElementwise(flat_size, params, input1_data, input2_data, output_data); +} + +// Scalar-broadcast add that can be used for inner loop of more general +// broadcast add, so that, for example, scalar-broadcast with batch will still +// be fast. +inline void AddScalarBroadcastQuant8(int size, const BinaryArithmeticOpParam ¶ms, + uint8_t broadcast_value, const uint8_t *input2_data, + uint8_t *output_data) +{ + int i = 0; + int32_t clamped_output; + for (; i < size; ++i) + { + clamped_output = quant8_sum(params, broadcast_value, input2_data[i]); + output_data[i] = static_cast(clamped_output); + } +} + +inline void AddScalarBroadcast(int size, const BinaryArithmeticOpParam ¶ms, + float broadcast_value, const float *input2_data, float *output_data) +{ + int i = 0; +#ifdef USE_NEON + const float32x4_t output_activation_min_vector = vdupq_n_f32(params.float_activation_min); + const float32x4_t output_activation_max_vector = vdupq_n_f32(params.float_activation_max); + const float32x4_t broadcast_value_dup = vdupq_n_f32(broadcast_value); + for (; i <= size - 4; i += 4) + { + const float32x4_t input2_val_original = vld1q_f32(input2_data + i); + + const float32x4_t output = vaddq_f32(input2_val_original, broadcast_value_dup); + + const float32x4_t clamped = + vmaxq_f32(output_activation_min_vector, vminq_f32(output_activation_max_vector, output)); + vst1q_f32(output_data + i, clamped); + } +#endif // NEON + for (; i < size; ++i) + { + auto x = broadcast_value + input2_data[i]; + output_data[i] = ActivationFunctionWithMinMax(x, params.float_activation_min, + params.float_activation_max); + } +} + +inline void BroadcastAddDispatchQuant8(const BinaryArithmeticOpParam ¶ms, + const Shape &input1_shape, const uint8_t *input1_data, + const Shape &input2_shape, const uint8_t *input2_data, + const Shape &output_shape, uint8_t *output_data) +{ + if (params.broadcast_category == BroadcastableOpCategory::kGenericBroadcast) + { + const std::function + fn = [](const BinaryArithmeticOpParam ¶ms, const uint8_t &a, + const uint8_t &b) -> uint8_t { + return static_cast(quant8_sum(params, a, b)); + }; + reference::BroadcastBinaryArithmeticOpSlowQuant8(params, input1_shape, input1_data, + input2_shape, input2_data, output_shape, + output_data, fn); + } + else + { + BinaryBroadcastFiveFold( + params, input1_shape, input1_data, input2_shape, input2_data, output_shape, output_data, + static_cast(AddElementwiseQuant8), + static_cast(AddScalarBroadcastQuant8)); + } +} + +inline void BroadcastAddDispatch(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, + const float *input1_data, const Shape &input2_shape, + const float *input2_data, const Shape &output_shape, + float *output_data) +{ + if (params.broadcast_category == BroadcastableOpCategory::kGenericBroadcast) + { + const std::function fn = + [](const float &a, const float &b) -> float { return a + b; }; + reference::BroadcastBinaryArithmeticOpSlow(params, input1_shape, input1_data, input2_shape, + input2_data, output_shape, output_data, fn); + } + else + { + BinaryBroadcastFiveFold( + params, input1_shape, input1_data, input2_shape, input2_data, output_shape, output_data, + static_cast(AddElementwise), + static_cast( + AddScalarBroadcast)); } } @@ -90,7 +322,7 @@ inline void Sub(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape const Shape &output_shape, float *output_data) { int i = 0; - const int size = MatchingFlatSize(input1_shape, input2_shape, output_shape); + const int size = MatchingElementsSize(input1_shape, input2_shape, output_shape); #ifdef USE_NEON const auto activation_min = vdupq_n_f32(params.float_activation_min); const auto activation_max = vdupq_n_f32(params.float_activation_max); @@ -140,12 +372,39 @@ inline void Sub(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape } } -inline void Mul(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, - const float *input1_data, const Shape &input2_shape, const float *input2_data, - const Shape &output_shape, float *output_data) +inline int32_t quant8_mul(const BinaryArithmeticOpParam ¶ms, const uint8_t input1_data, + const uint8_t input2_data) +{ + const int32_t input1_val = params.input1_offset + input1_data; + const int32_t input2_val = params.input2_offset + input2_data; + const int32_t unclamped_result = + params.output_offset + MultiplyByQuantizedMultiplier(input1_val * input2_val, + params.output_multiplier, + params.output_shift); + const int32_t clamped_output = std::min( + params.quantized_activation_max, std::max(params.quantized_activation_min, unclamped_result)); + + return clamped_output; +} + +inline void MulElementwiseQuant8(int size, const BinaryArithmeticOpParam ¶ms, + const uint8_t *input1_data, const uint8_t *input2_data, + uint8_t *output_data) { int i = 0; - const int size = MatchingFlatSize(input1_shape, input2_shape, output_shape); + int32_t clamped_output; + for (; i < size; i++) + { + clamped_output = quant8_mul(params, input1_data[i], input2_data[i]); + output_data[i] = static_cast(clamped_output); + } +} + +inline void MulElementwise(int size, const BinaryArithmeticOpParam ¶ms, + const float *input1_data, const float *input2_data, float *output_data) +{ + int i = 0; + #ifdef USE_NEON const auto activation_min = vdupq_n_f32(params.float_activation_min); const auto activation_max = vdupq_n_f32(params.float_activation_max); @@ -195,6 +454,114 @@ inline void Mul(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape } } +inline void MulQuant8(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, + const uint8_t *input1_data, const Shape &input2_shape, + const uint8_t *input2_data, const Shape &output_shape, uint8_t *output_data) +{ + const int flat_size = MatchingElementsSize(input1_shape, input2_shape, output_shape); + MulElementwiseQuant8(flat_size, params, input1_data, input2_data, output_data); +} + +inline void Mul(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, + const float *input1_data, const Shape &input2_shape, const float *input2_data, + const Shape &output_shape, float *output_data) +{ + const int flat_size = MatchingElementsSize(input1_shape, input2_shape, output_shape); + MulElementwise(flat_size, params, input1_data, input2_data, output_data); +} + +inline void MulSimpleBroadcastQuant8(int size, const BinaryArithmeticOpParam ¶ms, + const uint8_t broadcast_value, const uint8_t *input2_data, + uint8_t *output_data) +{ + int i = 0; + int32_t clamped_output; + for (; i < size; ++i) + { + clamped_output = quant8_mul(params, broadcast_value, input2_data[i]); + output_data[i] = static_cast(clamped_output); + } +} + +// Broadcast mul that can often be used for inner loop of broadcast Mul. +// This function will handle scalar_value (LHS) * vector_values (RHS). +// Since it's a float function, input params does not matter here. +inline void MulSimpleBroadcast(int size, const BinaryArithmeticOpParam ¶ms, + const float broadcast_value, const float *input2_data, + float *output_data) +{ + int i = 0; +#ifdef USE_NEON + const float32x4_t output_activation_min_vector = vdupq_n_f32(params.float_activation_min); + const float32x4_t output_activation_max_vector = vdupq_n_f32(params.float_activation_max); + const float32x4_t broadcast_value_dup = vdupq_n_f32(broadcast_value); + for (; i <= size - 4; i += 4) + { + const float32x4_t input2_val_original = vld1q_f32(input2_data + i); + + const float32x4_t output = vmulq_f32(input2_val_original, broadcast_value_dup); + + const float32x4_t clamped = + vmaxq_f32(output_activation_min_vector, vminq_f32(output_activation_max_vector, output)); + vst1q_f32(output_data + i, clamped); + } +#endif // NEON + + for (; i < size; ++i) + { + float x = broadcast_value * input2_data[i]; + output_data[i] = + ActivationFunctionWithMinMax(x, params.float_activation_min, params.float_activation_max); + } +} + +inline void BroadcastMulDispatchQuant8(const BinaryArithmeticOpParam ¶ms, + const Shape &input1_shape, const uint8_t *input1_data, + const Shape &input2_shape, const uint8_t *input2_data, + const Shape &output_shape, uint8_t *output_data) +{ + if (params.broadcast_category == BroadcastableOpCategory::kGenericBroadcast) + { + const std::function + fn = [](const BinaryArithmeticOpParam ¶ms, const uint8_t &a, + const uint8_t &b) -> uint8_t { + return static_cast(quant8_mul(params, a, b)); + }; + reference::BroadcastBinaryArithmeticOpSlowQuant8(params, input1_shape, input1_data, + input2_shape, input2_data, output_shape, + output_data, fn); + return; + } + BinaryBroadcastFiveFold( + params, input1_shape, input1_data, input2_shape, input2_data, output_shape, output_data, + static_cast(MulElementwiseQuant8), + static_cast(MulSimpleBroadcastQuant8)); +} + +inline void BroadcastMulDispatch(const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, + const float *input1_data, const Shape &input2_shape, + const float *input2_data, const Shape &output_shape, + float *output_data) +{ + if (params.broadcast_category == BroadcastableOpCategory::kGenericBroadcast) + { + // TODO: Use GetBinaryArithmeticFn + const std::function fn = + [](const float &a, const float &b) -> float { return a * b; }; + reference::BroadcastBinaryArithmeticOpSlow(params, input1_shape, input1_data, input2_shape, + input2_data, output_shape, output_data, fn); + return; + } + BinaryBroadcastFiveFold( + params, input1_shape, input1_data, input2_shape, input2_data, output_shape, output_data, + static_cast(MulElementwise), + static_cast( + MulSimpleBroadcast)); +} + } // namespace optimized } // namespace cker } // namespace nnfw diff --git a/compute/cker/include/cker/operation/reference/BatchMatMul.h b/compute/cker/include/cker/operation/reference/BatchMatMul.h new file mode 100644 index 0000000..e8ffd40 --- /dev/null +++ b/compute/cker/include/cker/operation/reference/BatchMatMul.h @@ -0,0 +1,115 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2020 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_REFERENCE_BATCH_MATMUL_H__ +#define __NNFW_CKER_REFERENCE_BATCH_MATMUL_H__ + +#include "cker/Types.h" +#include "cker/Shape.h" + +namespace nnfw +{ +namespace cker +{ +namespace reference +{ + +inline void BatchMatMul(const Shape &lhs_shape, const float *lhs_data, const Shape &rhs_shape, + const float *rhs_data, const Shape &, float *output_data) +{ + const Shape extended_lhs_shape = Shape::ExtendedShape(5, lhs_shape); + const Shape extended_rhs_shape = Shape::ExtendedShape(5, rhs_shape); + + // Determine which dimension is the broadcast dimension. + auto broadcast_dim = [](int lhs_dim, int rhs_dim) { + if (lhs_dim == rhs_dim) + return lhs_dim; + if (lhs_dim == 1) + return rhs_dim; + assert(rhs_dim == 1); + return lhs_dim; + }; + + // Compute the "extent" for iterating on this dimension. + // If we are broadcasting, then don't advance (i.e return 0). + auto extent = [](const Shape &shape, int x) { + if (shape.Dims(x) == 1) + { + return 0; + } + int prod = 1; + for (int i = x + 1; i < shape.DimensionsCount(); ++i) + { + prod *= shape.Dims(i); + } + return prod; + }; + + const int batch_dim0 = broadcast_dim(extended_lhs_shape.Dims(0), extended_rhs_shape.Dims(0)); + const int batch_dim1 = broadcast_dim(extended_lhs_shape.Dims(1), extended_rhs_shape.Dims(1)); + const int batch_dim2 = broadcast_dim(extended_lhs_shape.Dims(2), extended_rhs_shape.Dims(2)); + + const int lhs_ext0 = extent(extended_lhs_shape, 0); + const int lhs_ext1 = extent(extended_lhs_shape, 1); + const int lhs_ext2 = extent(extended_lhs_shape, 2); + const int rhs_ext0 = extent(extended_rhs_shape, 0); + const int rhs_ext1 = extent(extended_rhs_shape, 1); + const int rhs_ext2 = extent(extended_rhs_shape, 2); + + // Set params for each matrix multiply. + const int lhs_rows = extended_lhs_shape.Dims(3); + const int rhs_cols = extended_rhs_shape.Dims(4); + const int accum_depth = extended_lhs_shape.Dims(4); + + for (int b0 = 0; b0 < batch_dim0; ++b0) + { + const float *lhs_ptr0 = lhs_data + (b0 * lhs_ext0); + const float *rhs_ptr0 = rhs_data + (b0 * rhs_ext0); + for (int b1 = 0; b1 < batch_dim1; ++b1) + { + const float *lhs_ptr1 = lhs_ptr0 + b1 * lhs_ext1; + const float *rhs_ptr1 = rhs_ptr0 + b1 * rhs_ext1; + for (int b2 = 0; b2 < batch_dim2; ++b2) + { + const float *lhs_ptr2 = lhs_ptr1 + b2 * lhs_ext2; + const float *rhs_ptr2 = rhs_ptr1 + b2 * rhs_ext2; + float *out_ptr = + output_data + + ((b0 * batch_dim1 * batch_dim2) + b1 * batch_dim2 + b2) * lhs_rows * rhs_cols; + for (int j = 0; j < rhs_cols; ++j) + { + for (int i = 0; i < lhs_rows; ++i) + { + float total = 0.f; + for (int k = 0; k < accum_depth; ++k) + { + total += lhs_ptr2[accum_depth * i + k] * rhs_ptr2[j * accum_depth + k]; + } + int idx = lhs_rows * j + i; + out_ptr[idx] = total; + } + } + } + } + } +} + +} // namespace reference +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_REFERENCE_BATCH_MATMUL_H__ diff --git a/compute/cker/include/cker/operation/reference/BinaryArithmeticOps.h b/compute/cker/include/cker/operation/reference/BinaryArithmeticOps.h index 438a671..7a2b896 100644 --- a/compute/cker/include/cker/operation/reference/BinaryArithmeticOps.h +++ b/compute/cker/include/cker/operation/reference/BinaryArithmeticOps.h @@ -19,6 +19,7 @@ #define __NNFW_CKER_REFERENCE_BINARYARITHMETICOPS_H__ #include "cker/Shape.h" +#include "cker/Types.h" #include "cker/Utils.h" #include @@ -61,6 +62,125 @@ inline void BinaryArithmeticOp(const BinaryArithmeticOpParam ¶ms, const Shap } } +template +inline void BroadcastBinaryArithmeticOpSlowQuant8( + const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, const T *input1_data, + const Shape &input2_shape, const T *input2_data, const Shape &output_shape, T *output_data, + const std::function &fn) +{ + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, &desc2); + const Shape extended_output_shape = Shape::ExtendedShape(4, output_shape); + + if ((params.quantized_activation_min < 0) && (params.quantized_activation_max > 255)) + { + throw std::runtime_error{"Support only for Quant8."}; + } + + // Comment from tensorflow lite: + // + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + for (int b = 0; b < extended_output_shape.Dims(0); ++b) + { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) + { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) + { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) + { + output_data[Offset(extended_output_shape, b, y, x, c)] = + ActivationFunctionWithMinMax( + fn(params, input1_data[SubscriptToIndex(desc1, b, y, x, c)], + input2_data[SubscriptToIndex(desc2, b, y, x, c)]), + params.quantized_activation_min, params.quantized_activation_max); + } + } + } + } +} +template +inline void BroadcastBinaryArithmeticOpSlow(const BinaryArithmeticOpParam ¶ms, + const Shape &input1_shape, const T *input1_data, + const Shape &input2_shape, const T *input2_data, + const Shape &output_shape, T *output_data, + const std::function &fn) +{ + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, &desc2); + const Shape extended_output_shape = Shape::ExtendedShape(4, output_shape); + + // Comment from tensorflow lite: + // + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + for (int b = 0; b < extended_output_shape.Dims(0); ++b) + { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) + { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) + { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) + { + output_data[Offset(extended_output_shape, b, y, x, c)] = ActivationFunctionWithMinMax( + fn(input1_data[SubscriptToIndex(desc1, b, y, x, c)], + input2_data[SubscriptToIndex(desc2, b, y, x, c)]), + params.quantized_activation_min, params.quantized_activation_max); + } + } + } + } +} + +template <> +inline void BroadcastBinaryArithmeticOpSlow( + const BinaryArithmeticOpParam ¶ms, const Shape &input1_shape, const float *input1_data, + const Shape &input2_shape, const float *input2_data, const Shape &output_shape, + float *output_data, const std::function &fn) +{ + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, &desc2); + const Shape extended_output_shape = Shape::ExtendedShape(4, output_shape); + + for (int b = 0; b < extended_output_shape.Dims(0); ++b) + { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) + { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) + { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) + { + output_data[Offset(extended_output_shape, b, y, x, c)] = ActivationFunctionWithMinMax( + fn(input1_data[SubscriptToIndex(desc1, b, y, x, c)], + input2_data[SubscriptToIndex(desc2, b, y, x, c)]), + params.float_activation_min, params.float_activation_max); + } + } + } + } +} + } // namespace reference } // namespace cker } // namespace nnfw diff --git a/compute/cker/include/cker/ruy/RuySupport.h b/compute/cker/include/cker/ruy/RuySupport.h new file mode 100644 index 0000000..432b181 --- /dev/null +++ b/compute/cker/include/cker/ruy/RuySupport.h @@ -0,0 +1,117 @@ +/* + * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved + * Copyright 2017 The TensorFlow Authors. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef __NNFW_CKER_RUY_RUY_SUPPORT_H__ +#define __NNFW_CKER_RUY_RUY_SUPPORT_H__ + +#include +#include +#include "cker/Types.h" + +namespace +{ +const int kDefaultNumThreadpoolThreads = 4; +} + +namespace nnfw +{ +namespace cker +{ +namespace ruy_support +{ + +struct RuyContext +{ +public: + RuyContext() : ruy_context_(new ruy::Context) + { + SetMaxNumThreads(onert::util::getConfigInt(onert::util::config::RUY_THREADS)); +#ifdef USE_RUY_GEMV + ruy_context_->cache_policy = ruy::kCacheLHSOnNarrowMul; +#endif + }; + + ruy::Context *ruy_context() const { return ruy_context_.get(); } + + static inline RuyContext &GetRuyContext() + { + static thread_local RuyContext instance; + return instance; + } + + void SetMaxNumThreads(int max_num_threads) + { + const int target_num_threads = + max_num_threads > -1 ? max_num_threads : kDefaultNumThreadpoolThreads; + ruy_context_->max_num_threads = target_num_threads; + } + +private: + const std::unique_ptr ruy_context_; +}; + +inline ruy::Context *GetRuyContext() +{ + auto &ctx = RuyContext::GetRuyContext(); + return ctx.ruy_context(); +} + +template +void MakeRuyMatrix(const MatrixParams ¶ms, DataPointer data_ptr, + ruy::Matrix *dst) +{ + dst->layout.rows = params.rows; + dst->layout.cols = params.cols; + if (params.order == Order::kColMajor) + { + dst->layout.order = ruy::Order::kColMajor; + dst->layout.stride = params.rows; + } + else + { + dst->layout.order = ruy::Order::kRowMajor; + dst->layout.stride = params.cols; + } + // Note that ruy::Matrix::data is a ConstCheckingPtr, not a plain pointer. + // It does care whether we assign to it a Scalar* or a const Scalar*. + dst->data = data_ptr; + dst->zero_point = params.zero_point; + dst->cacheable = params.cacheable; +} + +template +void MakeRuySpec(const GemmParamsType ¶ms, RuySpecType *ruy_spec) +{ + // This validation has already been performed by the Gemm API entry point, + // but it doesn't hurt to test specifically this again here, where it's + // being used. + ValidateGemmParams(params); + + ruy_spec->multiplier_fixedpoint = params.multiplier_fixedpoint; + ruy_spec->multiplier_exponent = params.multiplier_exponent; + ruy_spec->multiplier_fixedpoint_perchannel = params.multiplier_fixedpoint_perchannel; + ruy_spec->multiplier_exponent_perchannel = params.multiplier_exponent_perchannel; + ruy_spec->bias = params.bias; + ruy_spec->clamp_min = params.clamp_min; + ruy_spec->clamp_max = params.clamp_max; +} + +} // namespace ruy_support +} // namespace cker +} // namespace nnfw + +#endif // __NNFW_CKER_RUY_RUY_SUPPORT_H__ diff --git a/compute/test/CMakeLists.txt b/compute/test/CMakeLists.txt new file mode 100644 index 0000000..c016d2b --- /dev/null +++ b/compute/test/CMakeLists.txt @@ -0,0 +1,13 @@ +set(TEST_COMPUTE test_compute) + +file(GLOB_RECURSE TESTS "*.cc") + +add_executable(${TEST_COMPUTE} ${TESTS}) + +target_link_libraries(${TEST_COMPUTE} nnfw_lib_cker) +target_link_libraries(${TEST_COMPUTE} gtest) +target_link_libraries(${TEST_COMPUTE} gtest_main) +target_link_libraries(${TEST_COMPUTE} ${LIB_PTHREAD} dl) +add_test(${TEST_COMPUTE} ${TEST_COMPUTE}) + +install(TARGETS ${TEST_COMPUTE} DESTINATION unittest_standalone) diff --git a/compute/test/cker/Range.cc b/compute/test/cker/Range.cc new file mode 100644 index 0000000..55f4fcf --- /dev/null +++ b/compute/test/cker/Range.cc @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include + +#include +#include + +TEST(CKer_Operation, Range) +{ + { + const int start = 0; + const int limit = 10; + const int delta = 1; + std::vector actual(10); + nnfw::cker::Range(&start, &limit, &delta, actual.data()); + + for (int i = 0; i < actual.size(); i++) + ASSERT_EQ(actual[i], i); + } + + { + const int start = 3; + const int limit = 18; + const int delta = 3; + std::vector expected = {3, 6, 9, 12, 15}; + std::vector actual(expected.size()); + nnfw::cker::Range(&start, &limit, &delta, actual.data()); + + for (int i = 0; i < actual.size(); i++) + ASSERT_EQ(actual[i], expected[i]); + } + + { + const float start = 3; + const float limit = 1; + const float delta = -0.5; + std::vector expected = { + 3, 2.5, 2, 1.5, + }; + std::vector actual(expected.size()); + nnfw::cker::Range(&start, &limit, &delta, actual.data()); + + for (int i = 0; i < actual.size(); i++) + ASSERT_FLOAT_EQ(actual[i], expected[i]); + } +} + +TEST(CKer_Operation, neg_Range) +{ + { + const int start = 212; + const int limit = 10; + const int delta = 1; + std::vector actual(10); + + EXPECT_ANY_THROW(nnfw::cker::Range(&start, &limit, &delta, actual.data())); + } +} diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d4bb2cb --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/common-ir/index.rst b/docs/common-ir/index.rst new file mode 100644 index 0000000..0f8c1ef --- /dev/null +++ b/docs/common-ir/index.rst @@ -0,0 +1,14 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Common IR +========= + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./introduction-to-circle.md + ./what-is-common-ir.md diff --git a/docs/common-ir/introduction-to-circle.md b/docs/common-ir/introduction-to-circle.md new file mode 100644 index 0000000..30b4517 --- /dev/null +++ b/docs/common-ir/introduction-to-circle.md @@ -0,0 +1 @@ +# Introduction to circle diff --git a/docs/common-ir/what-is-common-ir.md b/docs/common-ir/what-is-common-ir.md new file mode 100644 index 0000000..33afdc7 --- /dev/null +++ b/docs/common-ir/what-is-common-ir.md @@ -0,0 +1 @@ +# What is Common IR diff --git a/docs/compiler/backend.rst b/docs/compiler/backend.rst new file mode 100644 index 0000000..2e14d66 --- /dev/null +++ b/docs/compiler/backend.rst @@ -0,0 +1,12 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Backend +======= + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + diff --git a/docs/compiler/compiler b/docs/compiler/compiler new file mode 120000 index 0000000..9b713b9 --- /dev/null +++ b/docs/compiler/compiler @@ -0,0 +1 @@ +../../compiler/ \ No newline at end of file diff --git a/docs/compiler/frontend.rst b/docs/compiler/frontend.rst new file mode 100644 index 0000000..3385b74 --- /dev/null +++ b/docs/compiler/frontend.rst @@ -0,0 +1,22 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Frontend +======== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./compiler/caffe2circle/README.md + ./compiler/circle2circle/README.md + ./compiler/enco/README.md + ./compiler/nnc/README.md + ./compiler/onnx2circle/README.md + ./compiler/tf2circle/README.md + ./compiler/tf2nnpkg/README.md + ./compiler/tf2tflite/README.md + ./compiler/tf2tfliteV2/README.md + ./compiler/tflite2circle/README.md diff --git a/docs/compiler/index.rst b/docs/compiler/index.rst new file mode 100644 index 0000000..8db9543 --- /dev/null +++ b/docs/compiler/index.rst @@ -0,0 +1,20 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Compiler +======== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./frontend + ./middleend + ./backend + ./ir + ./interpreters + ./libraries + ./tools + diff --git a/docs/compiler/interpreters.rst b/docs/compiler/interpreters.rst new file mode 100644 index 0000000..e1e1dfd --- /dev/null +++ b/docs/compiler/interpreters.rst @@ -0,0 +1,14 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Interpreter +=========== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./compiler/locomotiv/README.md + ./compiler/mir-interpreter/README.md diff --git a/docs/compiler/ir.rst b/docs/compiler/ir.rst new file mode 100644 index 0000000..957f34e --- /dev/null +++ b/docs/compiler/ir.rst @@ -0,0 +1,36 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +IR +== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./compiler/coco/README.md + + ./compiler/loco/README.md + ./compiler/loco/doc/LEP_000_Dialect_Service.md + + ./compiler/luci/README.md + ./compiler/luci/export/README.md + ./compiler/luci/import/README.md + ./compiler/luci/lang/README.md + ./compiler/luci/logex/README.md + ./compiler/luci/log/README.md + ./compiler/luci/pass/README.md + ./compiler/luci/service/README.md + + ./compiler/mir/Readme.md + + ./compiler/moco/README.md + ./compiler/moco/import/README.md + ./compiler/moco/lang/README.md + ./compiler/moco/pass/README.md + ./compiler/moco/service/README.md + ./compiler/moco/support/README.md + + ./compiler/oneco/README.md diff --git a/docs/compiler/libraries.rst b/docs/compiler/libraries.rst new file mode 100644 index 0000000..9a7d7c1 --- /dev/null +++ b/docs/compiler/libraries.rst @@ -0,0 +1,60 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Libraries +========= + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./compiler/adtidas/README.md + ./compiler/angkor/README.md + ./compiler/bino/README.md + ./compiler/cli/README.md + ./compiler/cwrap/README.md + ./compiler/enco-intf/README.md + ./compiler/fipe/README.md + ./compiler/hermes/README.md + ./compiler/hermes-std/README.md + ./compiler/kuma/README.md + ./compiler/locop/README.md + ./compiler/mio-circle/README.md + ./compiler/mio-tf/README.md + ./compiler/mio-tflite/README.md + ./compiler/moco-log/README.md + ./compiler/morph/README.md + ./compiler/nest/README.md + ./compiler/nike/README.md + ./compiler/nnop/README.md + ./compiler/nnsuite/README.md + ./compiler/oops/README.md + ./compiler/pepper-assert/README.md + ./compiler/pepper-env/README.md + ./compiler/pepper-str/README.md + ./compiler/pepper-strcast/README.md + ./compiler/plier-tf/README.md + ./compiler/pp/README.md + ./compiler/safemain/README.md + ./compiler/stdex/README.md + ./compiler/v4tf/README.md + + For Validataion + --------------- + + ./compiler/ann-api/README.md + ./compiler/ann-ref/README.md + ./compiler/dredd-rule-lib/README.md + ./compiler/gen-core/README.md + + ./compiler/nnkit-caffe/README.md + ./compiler/nnkit-intf/README.md + ./compiler/nnkit-misc/README.md + ./compiler/nnkit-mocotf/README.md + ./compiler/nnkit-onnxrt/README.md + ./compiler/nnkit-tf/README.md + ./compiler/nnkit-tflite/README.md + ./compiler/tfinfo/README.md + ./compiler/tfinfo-v2/README.md diff --git a/docs/compiler/middleend.rst b/docs/compiler/middleend.rst new file mode 100644 index 0000000..12b39ec --- /dev/null +++ b/docs/compiler/middleend.rst @@ -0,0 +1,21 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Middleend +========= + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./compiler/exo/README.md + ./compiler/locoex-customop/README.md + ./compiler/logo/README.md + ./compiler/logo-core/README.md + ./compiler/mir2loco/README.md + + ./compiler/moco-tf/README.md + ./compiler/moco-tf/doc/Conversion.md + diff --git a/docs/compiler/tools.rst b/docs/compiler/tools.rst new file mode 100644 index 0000000..b30870d --- /dev/null +++ b/docs/compiler/tools.rst @@ -0,0 +1,66 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Tools +===== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + For nnc + ------- + + ./compiler/nnc/utils/caffe_model_maker/README.md + ./compiler/nnc/utils/infer_tests/README.md + ./compiler/nnc/utils/model_runner/readme.md + ./compiler/nnc/utils/prepare_inputs/README.md + + For Validation + -------------- + + ./compiler/caffegen/README.md + ./compiler/circle-inspect/README.md + ./compiler/circle-verify/README.md + ./compiler/circlechef/README.md + ./compiler/circledump/README.md + ./compiler/encodump/README.md + ./compiler/gen-tf-input/README.md + ./compiler/gen-tf-output/README.md + ./compiler/gen-tflite-output/README.md + ./compiler/i5diff/README.md + ./compiler/nnkit/README.md + ./compiler/onnxkit/README.md + ./compiler/tfgraph-xform/README.md + ./compiler/tfkit/README.md + ./compiler/tfl-inspect/README.md + ./compiler/tfl-verify/README.md + ./compiler/tflchef/README.md + ./compiler/tfldump/README.md + + Test Driver + ----------- + + ./compiler/tfts/README.md + + For CI + ------ + + ./compiler/circle2circle-dredd-pbtxt-test/README.md + ./compiler/moco-value-pbtxt-test/README.md + ./compiler/oneco-value-pbtxt-test/README.md + ./compiler/onnx2tflite-integration-test/README.md + ./compiler/tf2circle-conversion-test/README.md + ./compiler/tf2circle-dredd-pb-test/README.md + ./compiler/tf2circle-dredd-pbtxt-test/README.md + ./compiler/tf2circle-model-test/README.md + ./compiler/tf2circle-ui-check/README.md + ./compiler/tf2circle-value-pbtxt-remote-test/README.md + ./compiler/tf2tflite-dredd-pb-test/README.md + ./compiler/tf2tflite-dredd-pbtxt-test/README.md + ./compiler/tf2tflite-value-pb-test/README.md + ./compiler/tf2tflite-value-pbtxt-test/README.md + ./compiler/tf2tfliteV2-conversion-test/README.md + ./compiler/tflite2circle-conversion-test/README.md diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..3abe4f4 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,51 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +# import os +# import sys +# sys.path.insert(0, os.path.abspath('.')) + +# -- Project information ----------------------------------------------------- + +project = 'ONE(On-device Neural Engine)' +copyright = '2020, Samsung Research & contributors' +author = 'Samsung Research & contributors' + +# The full version, including alpha/beta/rc tags +release = '1.7.0' + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = ['recommonmark', 'sphinx_markdown_tables'] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_rtd_theme' + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] diff --git a/docs/contents.rst b/docs/contents.rst new file mode 100644 index 0000000..4e8492b --- /dev/null +++ b/docs/contents.rst @@ -0,0 +1,29 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to ONE's documentation! +=============================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./overview/index + ./howto/index + ./runtime/index + ./compiler/index + ./common-ir/index + ./package/index + ./platform/index + ./device/index + ./test/index + ./release/index + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/device/index.rst b/docs/device/index.rst new file mode 100644 index 0000000..9990043 --- /dev/null +++ b/docs/device/index.rst @@ -0,0 +1,15 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Devices +======= + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./odroid-xu3.md + ./odroid-xu4.md + ./raspberry-pi-3.md diff --git a/docs/device/odroid-xu3.md b/docs/device/odroid-xu3.md new file mode 100644 index 0000000..57825b7 --- /dev/null +++ b/docs/device/odroid-xu3.md @@ -0,0 +1 @@ +# ODROID-XU3 diff --git a/docs/device/odroid-xu4.md b/docs/device/odroid-xu4.md new file mode 100644 index 0000000..363e2cd --- /dev/null +++ b/docs/device/odroid-xu4.md @@ -0,0 +1 @@ +# ODROID-XU4 diff --git a/docs/device/raspberry-pi-3.md b/docs/device/raspberry-pi-3.md new file mode 100644 index 0000000..51152f3 --- /dev/null +++ b/docs/device/raspberry-pi-3.md @@ -0,0 +1 @@ +# Raspberry Pi 3 diff --git a/docs/howto/how-to-add-a-new-operation.md b/docs/howto/how-to-add-a-new-operation.md new file mode 100644 index 0000000..8ea7014 --- /dev/null +++ b/docs/howto/how-to-add-a-new-operation.md @@ -0,0 +1,9 @@ +# How to Add a New Operation + +## Compiler + +- [How to introduce a new operation into compiler](how-to-introduce-a-new-operation-into-compiler.md) + +## Runtime + +- [How to introduce a new operatoin into runtime](how-to-introduce-a-new-operation-into-runtime.md) diff --git a/docs/howto/how-to-build-compiler.md b/docs/howto/how-to-build-compiler.md new file mode 100644 index 0000000..e9dcb26 --- /dev/null +++ b/docs/howto/how-to-build-compiler.md @@ -0,0 +1,7 @@ +# How to Build Compiler + +## Build Requires + +## Build for Ubuntu + +## Build for windows diff --git a/docs/howto/how-to-build-package.md b/docs/howto/how-to-build-package.md new file mode 100644 index 0000000..65cc3c7 --- /dev/null +++ b/docs/howto/how-to-build-package.md @@ -0,0 +1 @@ +# How to Build Package diff --git a/docs/howto/how-to-build-runtime-using-prebuilt-docker-image.md b/docs/howto/how-to-build-runtime-using-prebuilt-docker-image.md new file mode 100644 index 0000000..b716d38 --- /dev/null +++ b/docs/howto/how-to-build-runtime-using-prebuilt-docker-image.md @@ -0,0 +1,2 @@ +# How to Build Using Prebuilt Docker Image + diff --git a/docs/howto/how-to-build-runtime.md b/docs/howto/how-to-build-runtime.md new file mode 100644 index 0000000..2bfd14c --- /dev/null +++ b/docs/howto/how-to-build-runtime.md @@ -0,0 +1,228 @@ +# How to Build Runtime + +This document is based on the system where Ubuntu Desktop Linux 18.04 LTS is installed with default settings, and can be applied in other environments without much difference. For reference, the development of our project started in the Ubuntu Desktop Linux 16.04 LTS environment. + +## Build requirements + +If you are going to build this project, the following modules must be installed on your system: + +- CMake +- Boost C++ libraries + +In the Ubuntu, you can easily install it with the following command. + +``` +$ sudo apt-get install cmake libboost-all-dev +``` + +If your linux system does not have the basic development configuration, you will need to install more packages. A list of all packages needed to configure the development environment can be found in the https://github.com/Samsung/ONE/blob/master/infra/docker/Dockerfile.1804 file. + +Here is a summary of it + +``` +$ sudo apt install \ +build-essential \ +clang-format-3.9 \ +cmake \ +doxygen \ +git \ +graphviz \ +hdf5-tools \ +lcov \ +libatlas-base-dev \ +libboost-all-dev \ +libgflags-dev \ +libgoogle-glog-dev \ +libgtest-dev \ +libhdf5-dev \ +libprotobuf-dev \ +protobuf-compiler \ +pylint \ +python3 \ +python3-pip \ +python3-venv \ +scons \ +software-properties-common \ +unzip \ +wget + +$ mkdir /tmp/gtest +$ cd /tmp/gtest +$ cmake /usr/src/gtest +$ make +$ sudo mv *.a /usr/lib + +$ pip install yapf==0.22.0 numpy + +``` + +## Build from source code, for Ubuntu + +In a typical linux development environment, including Ubuntu, you can build the runtime with a simple command like this: + +``` +$ git clone https://github.com/Samsung/ONE.git one +$ cd one +$ cp -n Makefile.template Makefile; make install +``` + +Unfortunately, the debug build on the x86_64 architecture currently has an error. To solve the problem, you must use gcc version 9 or higher. Another workaround is to do a release build rather than a debug build. This is not a suitable method for debugging during development, but it is enough to check the function of the runtime. To release build the runtime, add the environment variable `BUILD_TYPE=release` to the build command as follows. + +``` +$ export BUILD_TYPE=release +$ make install +``` + +Or you can simply do something like this: + +``` +$ BUILD_TYPE=release make install +``` + +The build method described here is a `native build` in which the build environment and execution environment are same. So, this command creates a runtime binary targeting the current build architecture, probably x86_64, as the execution environment. You can find the build output in the ./Product folder as follows: + +``` +$ tree -L 2 ./Product +./Product +├── obj -> /home/sjlee/star/one/Product/x86_64-linux.debug/obj +├── out -> /home/sjlee/star/one/Product/x86_64-linux.debug/out +└── x86_64-linux.debug + ├── BUILD + ├── CONFIGURE + ├── INSTALL + ├── obj + └── out + +5 directories, 3 files + +$ tree -L 3 ./Product/out +./Product/out +├── bin +│   ├── nnapi_test +│   ├── nnpackage_run +│   ├── tflite_loader_test_tool +│   └── tflite_run +├── include +│   ├── nnfw +│   │   ├── NeuralNetworksEx.h +│   │   ├── NeuralNetworksExtensions.h +│   │   ├── NeuralNetworks.h +│   │   ├── nnfw_dev.h +│   │   └── nnfw.h +│   └── onert +│   ├── backend +│   ├── compiler +│   ├── exec +│   ├── ir +│   ├── misc +│   └── util +├── lib +│   ├── libbackend_cpu.so +│   ├── libcircle_loader.so +│   ├── libneuralnetworks.so +│   ├── libnnfw-dev.so +│   ├── libnnfw_lib_benchmark.so +│   ├── libnnfw_lib_misc.a +│   ├── libonert_core.so +│   └── libtflite_loader.so +├── tests +│   ├── FillFrom_runner +│   ├── nnpkgs +│   │   └── FillFrom +│   └── scripts +│   ├── benchmark_nnapi.sh +│   ├── benchmark_nnpkg.sh +│   ├── common.sh +│   ├── framework +│   ├── list +│   ├── print_to_json.sh +│   ├── test-driver.sh +│   ├── test_framework.sh +│   ├── test_scheduler_with_profiling.sh +│   └── unittest.sh +├── unittest +│   ├── nnapi_gtest +│   ├── nnapi_gtest.skip +│   ├── nnapi_gtest.skip.noarch.interp +│   ├── nnapi_gtest.skip.x86_64-linux.cpu +│   ├── test_compute +│   ├── test_onert +│   ├── test_onert_backend_cpu_common +│   ├── test_onert_frontend_nnapi +│   └── tflite_test +└── unittest_standalone + └── nnfw_api_gtest + +19 directories, 36 files + +``` + +Here, let's recall that the main target of our project is the arm architecture. If you have a development environment running Linux for arm on a device made of an arm CPU, such as Odroid-XU4, you will get a runtime binary that can be run on the arm architecture with the same command above. This is the simplest way to get a binary for an arm device. However, in most cases, native builds on arm devices are too impractical as they require too long. Therefore, we will create an executable binary of an architecture other than x86_64 through a `cross build`. For cross-build method for each architecture, please refer to the corresponding document in the following section, [How to cross-build runtime for different architecture](#how-to-cross-build-runtime-for-different-architecture). + +### Run test + +The simple way to check whether the build was successful is to perform inference of the NN model using the runtime. The model to be used for the test can be obtained as follows. + +``` +$ wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz +$ tar zxvf inception_v3_2018_04_27.tgz ./inception_v3.tflite +$ ls *.tflite +inception_v3.tflite +``` + +The result of running the inception_v3 model using runtime is as follows. Please consider that this is a test that simply checks execution latency without considering the accuracy of the model. + +``` +$ USE_NNAPI=1 LD_LIBRARY_PATH="./Product/out/lib/:$LD_LIBRARY_PATH" ./Product/out +/bin/tflite_run ./inception_v3.tflite +nnapi function 'ANeuralNetworksModel_create' is loaded from './Product/out/lib/libneuralnetworks.so' +nnapi function 'ANeuralNetworksModel_addOperand' is loaded from './Product/out/lib/libneuralnetworks.so' +nnapi function 'ANeuralNetworksModel_setOperandValue' is loaded from './Product/out/lib/libneuralnetworks.so' +nnapi function 'ANeuralNetworksModel_addOperation' is loaded from './Product/out/lib/libneuralnetworks.so' +nnapi function 'ANeuralNetworksModel_identifyInputsAndOutputs' is loaded from './Product/out/lib/libneuralnetworks.so' +nnapi function 'ANeuralNetworksModel_finish' is loaded from './Product/out/lib/libneuralnetworks.so' +nnapi function 'ANeuralNetworksCompilation_create' is loaded from './Product/out/lib/libneuralnetworks.so' +nnapi function 'ANeuralNetworksCompilation_finish' is loaded from './Product/out/lib/libneuralnetworks.so' +input tensor indices = [317,] +nnapi function 'ANeuralNetworksExecution_create' is loaded from './Product/out/lib/libneuralnetworks.so' +nnapi function 'ANeuralNetworksExecution_setInput' is loaded from './Product/out/lib/libneuralnetworks.so' +nnapi function 'ANeuralNetworksExecution_setOutput' is loaded from './Product/out/lib/libneuralnetworks.so' +nnapi function 'ANeuralNetworksExecution_startCompute' is loaded from './Product/out/lib/libneuralnetworks.so' +nnapi function 'ANeuralNetworksEvent_wait' is loaded from './Product/out/lib/libneuralnetworks.so' +nnapi function 'ANeuralNetworksEvent_free' is loaded from './Product/out/lib/libneuralnetworks.so' +nnapi function 'ANeuralNetworksExecution_free' is loaded from './Product/out/lib/libneuralnetworks.so' +... run 1 takes 183.895 ms +output tensor indices = [316(max:905),] +=================================== +MODEL_LOAD takes 1.108 ms +PREPARE takes 0.190 ms +EXECUTE takes 183.895 ms +- MEAN : 183.895 ms +- MAX : 183.895 ms +- MIN : 183.895 ms +- GEOMEAN : 183.895 ms +=================================== +nnapi function 'ANeuralNetworksCompilation_free' is loaded from './Product/out/lib/libneuralnetworks.so' +nnapi function 'ANeuralNetworksModel_free' is loaded from './Product/out/lib/libneuralnetworks.so' +``` +Here, `USE_NNAPI=1` means that **ONE** runtime is used for model inference. If omitted, the model will be executed using Tensorflow lite, the basic framework for verification. `LD_LIBRARY_PATH="./Product/out/lib/:$LD_LIBRARY_PATH"` specifies the location of the runtime library to be used for testing. From the previous build result, you can see that it is the path to the directory where `libneuralnetworks.so` and `libonert_core.so` are located. + +If you come here without any problems, you have all of the basic environments for runtime development. + +## Build for Tizen + +(Will be written) + +## Build using docker image + +If your development system is not a linux environment like Ubuntu, but you can use docker on your system, you can build a runtime using a pre-configured docker image. Of course, you can also build a runtime using a docker image in a ubuntu environment, without setting up a complicated development environment. For more information, please refer to the following document. + +- [Build using prebuilt docker image](how-to-build-runtime-using-prebuilt-docker-image.md) + +## How to cross-build runtime for different architecture + +Please refer to the following document for the build method for architecture other than x86_64, which is the basic development environment. + +- [Cross building for ARM](how-to-cross-build-runtime-for-arm.md) +- [Cross building for AARCH64](how-to-cross-build-runtime-for-aarch64.md) +- [Cross building for Android](how-to-cross-build-runtime-for-android.md) diff --git a/docs/howto/how-to-contribute.md b/docs/howto/how-to-contribute.md new file mode 100644 index 0000000..fe7b140 --- /dev/null +++ b/docs/howto/how-to-contribute.md @@ -0,0 +1,72 @@ +# How to Contribute + +**ONE** always welcomes your contribution, but there are basic guidelines that you should follow in +order to make your contribution be accepted. + +This document explains such guidelines for beginners. + +## General contribution guidelines + +If you are not familiar with git or github, please visit +[here](https://guides.github.com/activities/hello-world/) for basic understanding of git and github. + +## How to create a Pull Request + +This section explains the steps to create a pull request (PR). + +1. Create an issue + + Maintainers will accept your contribution only when it is well aligned with the + [roadmap](../overview/roadmap.md) and design + principles of **ONE**. So, it is optional, but recommended for contributors to create an issue + and have a discussion with maintainers before writing code. + +1. Create a draft PR + + Maintainers will accept your pull request only when it is **reasonably small** and **focused**. + Sometimes, your contribution may require huge and loosely-coupled changes. You **should** split + your contribution into multiple small, but focused pull requests in this case. Unfortunately, it + is possible that maintainers reject your pull request as it is hard for them to understand the + intuition behind these changes. So, it is optional, but recommended for contributors to present + the full [draft](https://github.com/Samsung/ONE/pulls?q=is%3Apr+label%3ADRAFT+) of your + contribution and have a discussion with maintainers before creating PR(s). + +1. Create a commit + + It is time to create a commit for submission once you are convinced that your contribution is + ready to go. Please include signed-off message at the end of commit message. If not, your pull + request will be **rejected** by CI. + +1. Check code format locally + + **ONE** has its code formatting rules, and any pull request that violates these rules will be + **rejected** by CI. So, it is optional, but recommended for contributor to check code format + locally before submission. + +1. Create a PR + + It is time to send a pull request. Please explain your intention via description. Maintainers + will review your pull request based on that description. Each pull request needs approval from at + least two reviewers to be accepted. Note that **description should include at least four words**. + If not, your pull request will be **rejected** by CI. + +1. Request review + + It is recommended to assign reviewers yourself. Maintainers will honor your review request, + and accept your pull request only when + + - Approved by 1+ reviewers + - 0 rejection(Request Changes) + - 0 pending review request + - All the reviewers in the list must approve your pull request + + You can add/remove pending review requests in the middle of the review process. Maintainers + (or reviewers) could review your pull request even without explicit review request. + +1. Update per feedback + + Sometimes, maintainers (or reviewers) will request changes on your pull request. Please update + your pull request upon such feedbacks. These update commits will be squashed into the first + commit of your pull request later. Please do **NOT** include a sign-off message or write a full + description for update commits. + diff --git a/docs/howto/how-to-cross-build-runtime-for-aarch64.md b/docs/howto/how-to-cross-build-runtime-for-aarch64.md new file mode 100644 index 0000000..b30ce34 --- /dev/null +++ b/docs/howto/how-to-cross-build-runtime-for-aarch64.md @@ -0,0 +1,100 @@ +# How to Cross-build Runtime for AARCH64 + +In **ONE**, we use `AARCH64` on build files such as Makefile, CMakeLists.txt and so on. + +## Prepare AARCH64 Ubuntu RootFS + +Install required packages + +``` +$ sudo apt-get install qemu qemu-user-static binfmt-support debootstrap +``` + +Use `install_rootfs.sh` script to prepare Root File System. You should have `sudo` + +``` +$ sudo ./tools/cross/install_rootfs.sh aarch64 +``` +- supports `arm`(default) and `aarch64` architecutre for now +- supports `xenial`(default), `trusty` and `bionic` release + +To see the options, +``` +$ ./tools/cross/install_rootfs.sh -h +``` + +RootFS will be prepared at `tools/cross/rootfs/aarch64` folder. + +***\* CAUTION: The OS version of rootfs must match the OS version of execution target device. On the other hand, you need to match the Ubuntu version of the development PC with the Ubuntu version of rootfs to be used for cross-build. Otherwise, unexpected build errors may occur.*** + +If you are using Ubuntu 16.04 LTS, select `xenial`, if you are using Ubuntu 18.04 LTS, select `bionic`. You can check your Ubuntu code name in the following way. + +``` +$ cat /etc/lsb-release +DISTRIB_ID=Ubuntu +DISTRIB_RELEASE=18.04 +DISTRIB_CODENAME=bionic +DISTRIB_DESCRIPTION="Ubuntu 18.04.4 LTS" +``` + +If a build error occurs because the version of the development system and the target system do not match, and if you can't replace your development system for any reason, you can consider [cross-build using the docker image](how-to-build-runtime-using-prebuilt-docker-image.md). + +### Prepare RootFS at alternative folder + +Use `ROOTFS_DIR` to a full path to prepare at alternative path. + +``` +$ ROOTFS_DIR=/home/user/rootfs/aarch64-xenial sudo -E ./tools/cross/install_rootfs.sh aarch64 +``` + +### Using proxy + +If you need to use proxy server while building the rootfs, use `--setproxy` option. + +``` +# for example, +$ sudo ./tools/cross/install_rootfs.sh aarch64 --setproxy="1.2.3.4:8080" +# or +$ sudo ./tools/cross/install_rootfs.sh aarch64 --setproxy="proxy.server.com:8888" +``` + +This will put `apt` proxy settings in `rootfs/etc/apt/apt.conf.d/90proxy` file +for `http`, `https` and `ftp` protocol. + +## Cross build for AARCH64 + +Install cross compilers +``` +$ sudo apt-get install gcc-aarch64-linux-gnu g++-aarch64-linux-gnu +``` + +Give `TARGET_ARCH` variable to set the target architecture +``` +$ CROSS_BUILD=1 TARGET_ARCH=aarch64 make +$ CROSS_BUILD=1 TARGET_ARCH=aarch64 make install +``` +- supports `armv7l` and `aarch64` for now + +If you used `ROOTFS_DIR` to prepare in alternative folder, +you should also give this to makefile. +``` +$ CROSS_BUILD=1 ROOTFS_DIR=/home/user/rootfs/aarch64-xenial TARGET_ARCH=aarch64 make +$ CROSS_BUILD=1 ROOTFS_DIR=/home/user/rootfs/aarch64-xenial TARGET_ARCH=aarch64 make install +``` +You can also omit the `CROSS_BUILD=1` option if you explicitly pass `ROOTFS_DIR`. In that case, if +the `TARGET_ARCH` are differs from the hostarchitecture, the make script automatically applies +`CROSS_BUILD=1`. So, if you set `ROOTFS_DIR` as an environment variable, you can simply perform +normal build and cross build as follows. + +``` +$ export ROOTFS_DIR=xxx +... +$ make all install # do normal build +$ TARGET_ARCH=aarch64 make all install # do cross build +``` + +### Run test + +To run and test the cross-compiled runtime, you need to copy the compiled output to the target device of the architecture in which it is executable. Please refer to the following document for details on the test procedure. In the guide, `armv7l-linux.debug` in path should be replaced by referring to your build result. + +- [Testing neural network model inference with a cross-build runtime](./how-to-cross-build-runtime-for-arm.md#run-test) diff --git a/docs/howto/how-to-cross-build-runtime-for-android.md b/docs/howto/how-to-cross-build-runtime-for-android.md new file mode 100644 index 0000000..13984a0 --- /dev/null +++ b/docs/howto/how-to-cross-build-runtime-for-android.md @@ -0,0 +1,2 @@ +# How to Cross-build Runtime for Android + diff --git a/docs/howto/how-to-cross-build-runtime-for-arm.md b/docs/howto/how-to-cross-build-runtime-for-arm.md new file mode 100644 index 0000000..c17c465 --- /dev/null +++ b/docs/howto/how-to-cross-build-runtime-for-arm.md @@ -0,0 +1,164 @@ +# How to Cross-build Runtime for ARM + +## Prepare ARM Ubuntu RootFS + +Install required packages + +``` +$ sudo apt-get install qemu qemu-user-static binfmt-support debootstrap +``` + +Use `install_rootfs.sh` script to prepare Root File System. You should have `sudo` + +``` +$ sudo ./tools/cross/install_rootfs.sh arm +``` +- supports `arm`(default) and `aarch` architecutre for now +- supports `xenial`(default) `trusty`, and `bionic` release + +To see the options, +``` +$ ./tools/cross/install_rootfs.sh -h +``` + +RootFS will be prepared at `tools/cross/rootfs/arm` folder. + +***\* CAUTION: The OS version of rootfs must match the OS version of execution target device. On the other hand, you need to match the Ubuntu version of the development PC with the Ubuntu version of rootfs to be used for cross-build. Otherwise, unexpected build errors may occur.*** + +If you are using Ubuntu 16.04 LTS, select `xenial`, if you are using Ubuntu 18.04 LTS, select `bionic`. You can check your Ubuntu code name in the following way. + +``` +$ cat /etc/lsb-release +DISTRIB_ID=Ubuntu +DISTRIB_RELEASE=18.04 +DISTRIB_CODENAME=bionic +DISTRIB_DESCRIPTION="Ubuntu 18.04.4 LTS" +``` + +If a build error occurs because the version of the development system and the target system do not match, and if you can't replace your development system for any reason, you can consider [cross-build using the docker image](how-to-build-runtime-using-prebuilt-docker-image.md). + +### Prepare RootFS at alternative folder + +Use `ROOTFS_DIR` to a full path to prepare at alternative path. + +``` +$ ROOTFS_DIR=/home/user/rootfs/arm-xenial sudo -E ./tools/cross/install_rootfs.sh arm +``` + +### Using proxy + +If you need to use proxy server while building the rootfs, use `--setproxy` option. + +``` +# for example, +$ sudo ./tools/cross/install_rootfs.sh arm --setproxy="1.2.3.4:8080" +# or +$ sudo ./tools/cross/install_rootfs.sh arm --setproxy="proxy.server.com:8888" +``` + +This will put `apt` proxy settings in `rootfs/etc/apt/apt.conf.d/90proxy` file +for `http`, `https` and `ftp` protocol. + +## Install ARM Cross Toolchain + +We recommend you have g++ >= 6 installed on your system because NN generated tests require it. + +- On Ubuntu 16.04 or older, follow the next steps: + +``` +$ cd ~/your/path +$ wget https://releases.linaro.org/components/toolchain/binaries/7.2-2017.11/arm-linux-gnueabihf/gcc-linaro-7.2.1-2017.11-x86_64_arm-linux-gnueabihf.tar.xz +$ tar xvf gcc-linaro-7.2.1-2017.11-x86_64_arm-linux-gnueabihf.tar.xz +$ echo 'export PATH=~/your/path/gcc-linaro-7.2.1-2017.11-x86_64_arm-linux-gnueabihf/bin:$PATH' >> ~/.bashrc +``` + +- On Ubuntu 18.04 LTS, you can install using `apt-get`. +Choose g++ version whatever you prefer: 6, 7 or 8. + +``` +$ sudo apt-get install g++-{6,7,8}-arm-linux-gnueabihf +``` + +Make sure you get `libstdc++.so` updated on your target with your new toolchain's corresponding one. + +For example, if you installed gcc-linaro-7.2.1-2017.11 above, do + +``` +$ wget https://releases.linaro.org/components/toolchain/binaries/7.2-2017.11/arm-linux-gnueabihf/runtime-gcc-linaro-7.2.1-2017.11-arm-linux-gnueabihf.tar.xz +$ tar xvf runtime-gcc-linaro-7.2.1-2017.11-arm-linux-gnueabihf.tar.xz +``` + +Then, copy `libstdc++.so.6.0.24` into `/usr/lib/arm-linux-gnueabihf`, and update symbolic links on your device. + +## Build and install ARM Compute Library + +Mostly you only need once of ACL build. + +ACL will be automatically installed in `externals/acl` when you build runtime without any changes. + +You can check ACL source information in `cmake/packages/ARMComputeSourceConfig.cmake` + +## Cross build for ARM + +Give `TARGET_ARCH` variable to set the target architecture. + +If you used `ROOTFS_DIR` to prepare in alternative folder, you should also give this to makefile. + +``` +$ CROSS_BUILD=1 TARGET_ARCH=armv7l make all install + +# If ROOTFS_DIR is in alternative folder +$ ROOTFS_DIR=/path/to/your/rootfs/arm \ +CROSS_BUILD=1 TARGET_ARCH=armv7l make all install +``` + +You can also omit the `CROSS_BUILD=1` option if you explicitly pass `ROOTFS_DIR`. In that case, if +the `TARGET_ARCH` are differs from the hostarchitecture, the make script automatically applies +`CROSS_BUILD=1`. So, if you set `ROOTFS_DIR` as an environment variable, you can simply perform +normal build and cross build as follows. + +``` +$ export ROOTFS_DIR=xxx +... +$ make all install # do normal build +$ TARGET_ARCH=armv7l make all install # do cross build +``` + +### Run test + +To run and test the cross-compiled runtime, you need to copy the compiled output to the target device of the architecture in which it is executable. + +1. Copy all artifacts under the `./Product` folder to the target device, Odroid-XU4 for example, as a whole. + +``` +$ ssh odroid mkdir -p one/Product +sjlee@odroid's password: +$ scp -rp ./Product/armv7l-linux.debug odroid:one/Product +sjlee@odroid's password: +FillFrom_runner 100% 224KB 223.6KB/s 00:00 +benchmark_nnapi.sh 100% 7464 7.3KB/s 00:00 +common.sh 100% 2084 2.0KB/s 00:00 +test_framework.sh 100% 3154 3.1KB/s 00:00 +test-driver.sh +... +``` + +2. Log in to the target device, go to the copied path, and reestore the symbolic link settings of the `Product` directory. + +``` +$ ssh odroid +sjlee@odroid's password: +... +$ cd ~/one/Product +$ ln ${PWD}/armv7l-linux.debug/obj obj +$ ln ${PWD}/armv7l-linux.debug/out out +$ cd .. +$ ls -la Product +drwxrwxr-x 5 sjlee sjlee 4096 Jun 4 20:55 armv7l-linux.debug +lrwxrwxrwx 1 sjlee sjlee 51 Jun 4 20:54 obj -> /home/sjlee/one/Product/armv7l-linux.debug/obj +lrwxrwxrwx 1 sjlee sjlee 51 Jun 4 20:55 out -> /home/sjlee/one/Product/armv7l-linux.debug/out +``` + +Now you can test the compilation result in the same way as the native build. Please refer to the following document for details on the test procedure. + +- [Testing neural network model inference using the runtime](./how-to-build-runtime.md#run-test) diff --git a/docs/howto/how-to-introduce-a-new-operation-into-compiler.md b/docs/howto/how-to-introduce-a-new-operation-into-compiler.md new file mode 100644 index 0000000..804f530 --- /dev/null +++ b/docs/howto/how-to-introduce-a-new-operation-into-compiler.md @@ -0,0 +1,2 @@ +# How To Introduce a New Operation Into Compiler + diff --git a/docs/howto/how-to-introduce-a-new-operation-into-runtime.md b/docs/howto/how-to-introduce-a-new-operation-into-runtime.md new file mode 100644 index 0000000..4e295ba --- /dev/null +++ b/docs/howto/how-to-introduce-a-new-operation-into-runtime.md @@ -0,0 +1,440 @@ +# How To Introduce a New Operation Into Runtime + +**ONE**'s runtime has three main modules: **core**, **frontend** and **backend**. This document +provides some lightweight guidance about how to introduce a new operation into these modules to make +onert support the operation. + +## Index + +- [How To Introduce a New Operation Into Runtime](#how-to-introduce-a-new-operation-into-runtime) + - [Index](#index) + - [Core](#core) + - [Frontend](#frontend) + - [Loaders](#loaders) + - [Base Loader](#base-loader) + - [TFLite Loader](#tflite-loader) + - [Circle Loader](#circle-loader) + - [NNAPI](#nnapi) + - [Backend](#backend) + - [ShapeFixer](#shapefixer) + - [acl_cl](#acl_cl) + - [acl_neon](#acl_neon) + - [cpu](#cpu) + - [KernelGenerator](#kernelgenerator) + - [acl_cl](#acl_cl-1) + - [acl_neon](#acl_neon-1) + - [cpu](#cpu-1) + - [TensorRegister (in some cases)](#tensorregister-in-some-cases) + - [ConstantInitializer (in some cases)](#constantinitializer-in-some-cases) + - [cpu](#cpu-2) + - [Samples (to be updated)](#samples-to-be-updated) + +## Core + +This module has graph-based IR(intermediate representation). You have to add IR for the new +operation. + +1. Add name of new operation at [Operations.lst](/runtime/onert/core/include/ir/Operations.lst) + +```cpp +OP(Select) +``` + +2. Create a class for node of new operation in [here](/runtime/onert/core/include/ir/operation/) + +```cpp +#include "ir/Operation.h" + +namespace onert +{ +namespace ir +{ +namespace operation +{ + +class Select : public Operation +{ +public: + enum Input + { + COND = 0, + INPUT1 = 1, + INPUT2 = 2 + }; + + enum Output + { + OUTPUT = 0, + }; + +public: + Select(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs); + +public: + void accept(OperationVisitor &v) const override; + OpCode opcode() const final { return OpCode::Select; } +}; + +} // namespace operation +} // namespace ir +} // namespace onert +``` + +You can also define the class in other source file like below + +```cpp +#include "ir/operation/Select.h" + +#include "ir/OperationVisitor.h" + +namespace onert +{ +namespace ir +{ +namespace operation +{ + +void Select::accept(OperationVisitor &v) const { v.visit(*this); } + +Select::Select(const OperandIndexSequence &inputs, const OperandIndexSequence &outputs) + : Operation{OperandConstraint::createExact(3u), inputs, outputs} +{ +} +``` + - [Operations.Include.h](/runtime/onert/core/include/ir/Operations.Include.h) + +```cpp +#include "ir/operation/Select.h" +``` + +3. Add to the OperationValidator to check if the node is valid. + - [OperationValidator.h](/runtime/onert/core/src/compiler/OperationValidator.h) + +```cpp +void visit(const operation::Select &node) override; +``` + + - [OperationValidator.cc](/runtime/onert/core/src/compiler/OperationValidator.cc) + +```cpp +void OperationValidator::visit(const ir::operation::Select &node) +{ + const auto output_index{node.getOutputs().at(ir::operation::Select::Output::OUTPUT)}; + const auto cond_index{node.getInputs().at(ir::operation::Select::Input::COND)}; + const auto input1_index{node.getInputs().at(ir::operation::Select::Input::INPUT1)}; + const auto input2_index{node.getInputs().at(ir::operation::Select::Input::INPUT2)}; + + UNUSED_RELEASE(output_index); + UNUSED_RELEASE(cond_index); + UNUSED_RELEASE(input1_index); + UNUSED_RELEASE(input2_index); + + const auto output_type = _ctx.at(output_index).typeInfo(); + const auto cond_type = _ctx.at(cond_index).typeInfo(); + const auto input1_type = _ctx.at(input1_index).typeInfo(); + const auto input2_type = _ctx.at(input2_index).typeInfo(); + + UNUSED_RELEASE(output_type); + UNUSED_RELEASE(cond_type); + UNUSED_RELEASE(input1_type); + UNUSED_RELEASE(input2_type); + + assert(cond_type.type() == ir::DataType::BOOL8); + assert(output_type.type() == ir::DataType::FLOAT32 || output_type.type() == ir::DataType::INT32 || + output_type.type() == ir::DataType::QUANT8_ASYMM); + assert(output_type.type() == input1_type.type()); + assert(output_type.type() == input2_type.type()); + + const auto output_shape = _ctx.at(output_index).shape(); + const auto cond_shape = _ctx.at(cond_index).shape(); + const auto input1_shape = _ctx.at(input1_index).shape(); + const auto input2_shape = _ctx.at(input2_index).shape(); + + UNUSED_RELEASE(output_shape); + UNUSED_RELEASE(cond_shape); + UNUSED_RELEASE(input1_shape); + UNUSED_RELEASE(input2_shape); + + assert(output_shape == input1_shape); + assert(cond_shape == input1_shape); + assert(input2_shape == input1_shape); +} +``` + +4. Add to the Dumper to dump IR information of new operation. +- [Dumper.cc](/runtime/onert/core/src/ir/dumper/Dumper.cc) + +```cpp +void Dumper::visit(const Select &node) +{ + VERBOSE(LIR) << "* Select" << std::endl; + VERBOSE(LIR) << " - Inputs : Cond(" << node.getInputs().at(Select::Input::COND).value() + << ") Input1" << node.getInputs().at(Select::Input::INPUT1).value() << ") Input2" + << node.getInputs().at(Select::Input::INPUT2).value() << ")" << std::endl; + VERBOSE(LIR) << " - Output : Output(" << node.getOutputs().at(Select::Output::OUTPUT).value() + << ")" << std::endl; +} +``` + +## Frontend + +This module generates IR from a model. There are two kinds of frontend: Loader and NNAPI. First, Loader loads a model file and generates IR from it. Second, NNAPI generates IR from a model set via [Neural Networks API of android](https://developer.android.com/ndk/guides/neuralnetworks) + +### Loaders + +#### Base Loader + +This is where the common parts of loaders are implemented. + +1. Add to base_loader to load new operation and to generate IR from it +- [base_loader](/runtime/onert/frontend/base_loader/include/base_loader.h) + +```cpp + case BuiltinOperator::BuiltinOperator_SELECT: + loadSelect(op); + return; +``` + +```cpp +template +void BaseLoader::loadSelect(const Operator *op) +{ + ir::OperandIndexSequence inputs; + ir::OperandIndexSequence outputs; + + loadOperationIO(op, inputs, outputs); + + std::unique_ptr new_op{new ir::operation::Select{inputs, outputs}}; + _graph.addOperation(std::move(new_op)); +} +``` + +#### TFLite Loader + +This loads a tflite file. +If you want new operation to be loaded on only TFLite Loader, you only need to implement loading the operation here. + +#### Circle Loader + +This loads a circle file generated by the compiler. +If you want new operation to be loaded on only Circle Loader, you only need to implement loading the operation here. + +### NNAPI + +1. Add to the OperationFactory to generate IR of new operation +- [OperationFactory](/runtime/onert/frontend/nnapi/wrapper/OperationFactory.cc) + +```cpp + _map[ANEURALNETWORKS_SELECT] = [](const OperationFactory::Param &init_param, Operands &) { + assert(init_param.input_count == 3 && init_param.output_count == 1); + + OperandIndexSequence outputs{init_param.outputs[0]}; + + // Each input should be interpreted as follows: + // + // 0 -> Cond Tensor Index + // 1 -> Input1 Tensor Index + // 2 -> Input2 Tensor Index + OperandIndexSequence inputs; + for (uint32_t n = 0; n < init_param.input_count; ++n) + { + inputs.append(OperandIndex{init_param.inputs[n]}); + } + + return new operation::Select{inputs, outputs}; + }; +``` + +2. If you want that NNAPI supports new operation of TFLite's model, you need to update the things related to the operation in [nnapi_delegate](/runtime/libs/tflite/port/1.13.1/src/nnapi_delegate.cpp) like below + +```cpp + case tflite::BuiltinOperator_SELECT: + nnapi_version = 12; // require NNAPI 1.2 + nn_op_type = ANEURALNETWORKS_SELECT; + break; +``` + +## Backend + +This module generates kernels and tensors of backend such as [ComputeLibrary](https://github.com/ARM-software/ComputeLibrary/) from generated graph-based IR. For this, the runtime fairly works on it internally. But this is not enough because of dependence on backend. So, there are several components that require additional implementation on each backend. + +### ShapeFixer + +Even for tensors of the same operation, the shape required for each backend can be different. Therefore, this component modifies and fixes shape of tensors of the backend. + +#### acl_cl + +The kernel of the ACL for the Add operation needs to match the same rank to support the broadcast. +- [ShapeFixer.h](/runtime/onert/backend/acl_cl/ShapeFixer.h) + +```cpp +void visit(const ir::operation::Add &) override; +``` + +- [ShapeFixer.cc](/runtime/onert/backend/acl_cl/ShapeFixer.cc) + +```cpp +void ShapeFixer::visit(const ir::operation::Add &node) +{ + const auto lhs_index{node.getInputs().at(ir::operation::Add::Input::LHS)}; + const auto rhs_index{node.getInputs().at(ir::operation::Add::Input::RHS)}; + + if (!(_ctx.at(lhs_index).shape() == _ctx.at(rhs_index).shape())) + { + const auto broadcast_rank = + std::max(_ctx.at(lhs_index).shape().rank(), _ctx.at(rhs_index).shape().rank()); + const_cast(_ctx.at(lhs_index).shape()).extendRank(broadcast_rank); + const_cast(_ctx.at(rhs_index).shape()).extendRank(broadcast_rank); + } +} +``` + +#### acl_neon + +Same implementation as acl_cl is required. + +#### cpu + +This backend doesn't usually require a change of shape. +- [ShapeFixer.h](/runtime/onert/backend/cpu/ShapeFixer.h) + +```cpp +void visit(const ir::operation::Select &) override; +``` + +- [ShapeFixer.cc](/runtime/onert/backend/cpu/ShapeFixer.cc) + +```cpp +void ShapeFixer::visit(const ir::operation::Select &) { /* DO NOTHING */} +``` + +### KernelGenerator + +This component generates kernels of backend. You have to generate kernel of new operation. And then append it to execution builder. You can obtain information of the node from IR and necessary tensors from tensor builder. + +#### acl_cl + +- [KernelGenerator.h](/runtime/onert/backend/acl_cl/KernelGenerator.h) + +```cpp +void visit(const ir::operation::Select &) override; +``` + +- [KernelGenerator.cc](/runtime/onert/backend/acl_cl/KernelGenerator.cc) + +```cpp +void KernelGenerator::visit(const ir::operation::Select &node) +{ + const auto output_index{node.getOutputs().at(ir::operation::Select::Output::OUTPUT)}; + const auto cond_index{node.getInputs().at(ir::operation::Select::Input::COND)}; + const auto input1_index{node.getInputs().at(ir::operation::Select::Input::INPUT1)}; + const auto input2_index{node.getInputs().at(ir::operation::Select::Input::INPUT2)}; + + auto output_alloc = _tensor_builder->at(output_index).get(); + auto cond_alloc = _tensor_builder->at(cond_index).get(); + auto input1_alloc = _tensor_builder->at(input1_index).get(); + auto input2_alloc = _tensor_builder->at(input2_index).get(); + + auto fn = std::make_unique<::arm_compute::CLSelect>(); + + fn->configure(cond_alloc->handle(), input1_alloc->handle(), input2_alloc->handle(), + output_alloc->handle()); + + auto acl_fn = asAclFunction(std::move(fn)); + + _execution_builder->append(std::move(acl_fn)); +} +``` + +#### acl_neon + +Similar implementation as acl_cl is required. + +#### cpu + +- [KernelGenerator.h](/runtime/onert/backend/cpu/KernelGenerator.h) + +```cpp +void visit(const ir::operation::Select &) override; +``` + +- [KernelGenerator.cc](/runtime/onert/backend/cpu/KernelGenerator.cc) + +```cpp +void KernelGenerator::visit(const ir::operation::Select &node) +{ + const auto output_index{node.getOutputs().at(ir::operation::Select::Output::OUTPUT)}; + const auto cond_index{node.getInputs().at(ir::operation::Select::Input::COND)}; + const auto input1_index{node.getInputs().at(ir::operation::Select::Input::INPUT1)}; + const auto input2_index{node.getInputs().at(ir::operation::Select::Input::INPUT2)}; + + const auto output_backend_descr = ::onert::backend::cpu::kernel::getTensorDescriptor( + _ctx.at(output_index), _current_op_seq_layout); + const auto cond_backend_descr = ::onert::backend::cpu::kernel::getTensorDescriptor( + _ctx.at(cond_index), _current_op_seq_layout); + const auto input1_backend_descr = ::onert::backend::cpu::kernel::getTensorDescriptor( + _ctx.at(input1_index), _current_op_seq_layout); + const auto input2_backend_descr = ::onert::backend::cpu::kernel::getTensorDescriptor( + _ctx.at(input2_index), _current_op_seq_layout); + + auto output_alloc = _tensor_builder->at(output_index).get(); + auto cond_alloc = _tensor_builder->at(cond_index).get(); + auto input1_alloc = _tensor_builder->at(input1_index).get(); + auto input2_alloc = _tensor_builder->at(input2_index).get(); + + auto fn = std::make_unique<::onert::backend::cpu::kernel::SelectLayer>(); + + fn->configure(cond_alloc->buffer(), cond_backend_descr, input1_alloc->buffer(), + input1_backend_descr, input2_alloc->buffer(), input2_backend_descr, + output_alloc->buffer(), output_backend_descr); + + _execution_builder->append(std::move(fn)); +} +``` + +### TensorRegister (in some cases) + +This component registers tensors. Most tensors will be automatically registered internally. There +are some exceptions, however, where additional implementations are required. It is the case when a +tensor is treated unusually in its backend. + +The kernel of some operation has weights in `HWIO` as layout(data format) in case of that input's +layout is `NHWC`. And, for `NCHW`, weights is `OIHW`. But TFLite model has weigths, `OHWI` for +`NHWC` and `OIHW` for `NCHW`. Therefore, to register the appropriate tensor on the backend, you have +to implement it additionally. + +### ConstantInitializer (in some cases) + +This component registers function initializing constant tensors and initialize constant tensor +layer. This is similar to TensorRegister. Most tensors will be automatically registered internally. +And there are some exceptions. + +#### cpu + +- [ConstantInitializer.h](/runtime/onert/backend/cpu/ConstantInitializer.h) + +```cpp +void visit(const ir::operation::Conv2D &) override; +``` + +- [ConstantInitializer.cc](/runtime/onert/backend/cpu/ConstantInitializer.cc) + +```cpp +void ConstantInitializer::visit(const ir::operation::Conv2D &node) +{ + const auto &kernel_index = node.getInputs().at(ir::operation::Conv2D::KERNEL); + const auto &kernel_obj = _operands.at(kernel_index); + registerCopyInitializer(kernel_index, kernel_obj); + + const auto &bias_index = node.getInputs().at(ir::operation::Conv2D::BIAS); + const auto &bias_obj = _operands.at(bias_index); + registerCopyInitializer(bias_index, bias_obj); +} +``` + +## Samples (to be updated) + +- `Select` operation + - Simple explanation : `Output[i] = Condition[i] ? input1[i] : input2[i]` + - PR : https://github.com/Samsung/ONE/pull/XXX + diff --git a/docs/howto/how-to-make-an-application-with-runtime.md b/docs/howto/how-to-make-an-application-with-runtime.md new file mode 100644 index 0000000..4d9580e --- /dev/null +++ b/docs/howto/how-to-make-an-application-with-runtime.md @@ -0,0 +1 @@ +# How to Make an Application with Runtime diff --git a/docs/howto/how-to-remote-debugging-with-visual-studio-code.md b/docs/howto/how-to-remote-debugging-with-visual-studio-code.md new file mode 100644 index 0000000..6be9e3b --- /dev/null +++ b/docs/howto/how-to-remote-debugging-with-visual-studio-code.md @@ -0,0 +1,150 @@ +# How to Remote Debugging with Visual Studio Code + +This document describes how to debug ONE runtime on arm devices using visual studio code. + +## Setup build host + +### Install gdb-multiarch + +1. Install `gdb-multiarch` + +```bash +$ sudo apt install gdb-multiarch +``` + +### Configure VS code + +1. Install [Native Debug](https://marketplace.visualstudio.com/items?itemName=webfreak.debug) extension on VS code + +2. Setup GDB environment on VS code + +- Debug -> Add configuration -> GDB: Connect to gdbserver +- Change configuration as below + - Change `` to IP of your target + - The default port number for gdbserver is 2345. You can change this number. + - You can change `executable` configuration from `tflite_run` to other binaries you want to debug. + +```json +{ + "version": "0.2.0", + "configurations": [ + { + "type": "gdb", + "request": "attach", + "name": "Attach to gdbserver", + "gdbpath": "/usr/bin/gdb-multiarch", + "executable": "./Product/armv7l-linux.debug/out/bin/tflite_run", + "target": ":2345", + "remote": true, + "printCalls": true, + "cwd": "${workspaceRoot}", + "valuesFormatting": "parseText" + } + ] +} +``` + +## Setup target device + +### Install gdbserver and debugging symbols + +You need to setup a target device for remote debugging. + +1. Install `gdbserver` +```bash +$ sudo apt install gdbserver +``` + +2. Install `libc6-dbg` and copy debugging symbols +```bash +$ sudo apt install libc6-dbg +$ sudo mkdir -p /lib/.debug +$ sudo ln -s /usr/lib/debug/lib/arm-linux-gnueabihf/ld-2.27.so /lib/.debug +``` + +## Run remote debugging + +1. Start gdbserver on target + +```bash +gdbserver --multi : +``` + +Example +```bash +gdbserver --multi :2345 Product/armv7l-linux.debug/out/bin/tflite_run ../models/slice_test.tflite +``` + +2. Connect to gdbserver using VS code + - Setup breakpoints on any code you want. + - Click F5 to start remote debugging. + - Program will execute and exit if no breakpoint exists. + +## Optional: Setup rootfs on build host + +When debugging starts, `gdb` downloads shared libraries that one runtime uses from the target device. +This process makes `gdb` to wait for shared library download to finish for every debugging start. + +To reduce shared library loading, you can setup an arm root file system on your build host and use it. + +1. Create arm root file system + +Following [CrossBuildForArm](how-to-cross-build-runtime-for-arm.md) to create an arm root file system. + +You can use an arm root file system created for arm cross-compile. + +2. Install `libc6-dbg` on arm root file system + +`` should point ARM root file system. + +Default path is `tools/cross/rootfs/arm` folder. + +```bash +$ sudo chroot +$ apt install libc6-dbg +$ exit +``` + +3. Create symbolic link of one runtime on arm rootfs + +`gdb` will use source code folder at sysroot. + +```bash +$ ln -s / +``` +Example +```bash +$ ln -s /home/user/one /home/user/one/tools/cross/rootfs/arm/home/user/one/ +``` + +4. Setup `.gdbinit` file on one folder + +`gdb` will use `` to find arm related symbols. + +```bash +set sysroot +set debug-file-directory /usr/lib/debug +``` + +## Troubleshooting + +### Unable to open 'unordered_map.h' + +If you are using docker to build one runtime, you should download and decompress gcc-linaro at `/opt` folder + +```bash +wget https://releases.linaro.org/components/toolchain/binaries/6.3-2017.02/arm-linux-gnueabihf/gcc-linaro-6.3.1-2017.02-x86_64_arm-linux-gnueabihf.tar.xz -O gcc-hardfp.tar.xz +sudo tar -xf gcc-hardfp.tar.xz -C /opt/ && sudo rm -rf gcc-hardfp.tar.xz +``` + +### Skip STL files + +Step into (F11) will debug STL files such as `unordered_map` or `vector`. + +To skip those files from debugging, you can add below line to `.gdbinit` file. + +This function is supported on gdb versions >= 7.12. + +```bash +skip -gfile /opt/gcc-linaro-6.3.1-2017.02-x86_64_arm-linux-gnueabihf/arm-linux-gnueabihf/include/c++/6.3.1/bits/* +``` diff --git a/docs/howto/how-to-run-package.md b/docs/howto/how-to-run-package.md new file mode 100644 index 0000000..2a0e025 --- /dev/null +++ b/docs/howto/how-to-run-package.md @@ -0,0 +1 @@ +# How to Run Package diff --git a/docs/howto/how-to-use-api.md b/docs/howto/how-to-use-api.md new file mode 100644 index 0000000..4b34e69 --- /dev/null +++ b/docs/howto/how-to-use-api.md @@ -0,0 +1 @@ +# How to Use API diff --git a/docs/howto/how-to-use-nnapi-binding.md b/docs/howto/how-to-use-nnapi-binding.md new file mode 100644 index 0000000..ade4e7b --- /dev/null +++ b/docs/howto/how-to-use-nnapi-binding.md @@ -0,0 +1,5 @@ +# How to Use NNAPI Binding + +Runtime supports [Android Neural Networks API](https://developer.android.com/ndk/guides/neuralnetworks) as a frontend. We provide the whole `NeuralNetworks.h` implementation. Its source code is located at `runtime/onert/frontend/nnapi`. + +So users can just use NN API in the same way as [the official guide](https://developer.android.com/ndk/guides/neuralnetworks), but should watch the version of `NeuralNetworks.h`. It may not be the latest. There is a copy of the file is located at `runtime/nnapi-header/include/NeuralNetworks.h`. diff --git a/docs/howto/how-to-use-nnfw-api.md b/docs/howto/how-to-use-nnfw-api.md new file mode 100644 index 0000000..6c0fb49 --- /dev/null +++ b/docs/howto/how-to-use-nnfw-api.md @@ -0,0 +1,66 @@ +# How to Use NNFW API + +## Prepare nnpackage + +### Convert tensorflow pb file to nnpackage +Follow the [compiler guide](https://github.com/Samsung/ONE/blob/master/docs/nncc/v1.0.0/tutorial.md) to generate nnpackge from tensorflow pb file + +### Convert tflite file to nnpackage +Please see [model2nnpkg](https://github.com/Samsung/ONE/tree/master/tools/nnpackage_tool/model2nnpkg) for converting from tflite model file. + +## Build app with NNFW API + +Here are basic steps to build app with [NNFW C API](https://github.com/Samsung/ONE/blob/master/runtime/onert/api/include/nnfw.h) + +1) Initialize nnfw_session +``` c +nnfw_session *session = nullptr; +nnfw_create_session(&session); +``` +2) Load nnpackage +``` c +nnfw_load_model_from_file(session, nnpackage_path); +``` +3) (Optional) Assign a specific backend to operations +``` c + // Use acl_neon backend for CONV_2D and acl_cl for otherwise. + // Note that defalut backend is acl_cl + nnfw_set_op_backend(session, "CONV_2D", "acl_neon"); +``` + +4) Compilation +``` c + // Compile model + nnfw_prepare(session); +``` + +5) Prepare Input/Output +``` c + // Prepare input. Here we just allocate dummy input arrays. + std::vector input; + nnfw_tensorinfo ti; + nnfw_input_tensorinfo(session, 0, &ti); // get first input's info + uint32_t input_elements = num_elems(&ti); + input.resize(input_elements); + // TODO: Please add initialization for your input. + nnfw_set_input(session, 0, ti.dtype, input.data(), sizeof(float) * input_elements); + + // Prepare output + std::vector output; + nnfw_output_tensorinfo(session, 0, &ti); // get first output's info + uint32_t output_elements = num_elems(&ti); + output.resize(output_elements); + nnfw_set_output(session, 0, ti.dtype, output.data(), sizeof(float) * output_elements); +``` +6) Inference +``` c + // Do inference + nnfw_run(session); +``` + +## Run Inference with app on the target devices +reference app : [minimal app](https://github.com/Samsung/ONE/blob/master/runtime/onert/sample/minimal) + +``` +$ ./minimal path_to_nnpackage_directory +``` diff --git a/docs/howto/index.rst b/docs/howto/index.rst new file mode 100644 index 0000000..d04224e --- /dev/null +++ b/docs/howto/index.rst @@ -0,0 +1,27 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +How To +====== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./how-to-add-a-new-operation.md + ./how-to-build-compiler.md + ./how-to-build-package.md + ./how-to-build-runtime.md + ./how-to-build-runtime-using-prebuilt-docker-image.md + ./how-to-cross-build-runtime-for-arm.md + ./how-to-cross-build-runtime-for-aarch64.md + ./how-to-cross-build-runtime-for-android.md + ./how-to-contribute.md + ./how-to-make-an-application-with-runtime.md + ./how-to-remote-debugging-with-visual-studio-code.md + ./how-to-run-package.md + ./how-to-use-api.md + ./how-to-use-nnfw-api.md + ./how-to-use-nnapi-binding.md diff --git a/docs/images/logo_original_samsungblue_cropped.png b/docs/images/logo_original_samsungblue_cropped.png new file mode 100644 index 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zW;}5FT-Uy-C*?P=Mz1*XkfWkS;&y(gWitT%#x{$Ab1w47>e11JH>8s%>U4xC>wnWB zvIpcPRvm`efo-weCN(bZPyV((GT>;unE35ga=`goPzNZXwQ1vD#yADslz6r^gYeBXRjp_=+?}r=v#+3r`WJKuK7sg2Hbc z2rvzxtKA9*D3_@Pzw1>m3DBgKcsQbw=;9}GG_`v3p{ literal 0 HcmV?d00001 diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 0000000..4e8492b --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,29 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to ONE's documentation! +=============================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./overview/index + ./howto/index + ./runtime/index + ./compiler/index + ./common-ir/index + ./package/index + ./platform/index + ./device/index + ./test/index + ./release/index + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..922152e --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/nncc/v1.0.0/tutorial.md b/docs/nncc/v1.0.0/tutorial.md new file mode 100644 index 0000000..9d1f97e --- /dev/null +++ b/docs/nncc/v1.0.0/tutorial.md @@ -0,0 +1,49 @@ +# Tutorial + +Let's compile Inception_v3 model and make a nnpackage! + +## Prepare inception_v3 files + +1. Download pre-trained `inception_v3.pb` model file. + ```sh + $ wget https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz + $ tar -xvf inception_v3_2018_04_27.tgz + ``` +1. Create model information file as `inception_v3.info`. + ``` + $ cat > inception_v3.info << "END" + input, input:0, TF_FLOAT, [1, 299, 299, 3] + output, InceptionV3/Predictions/Reshape_1:0, TF_FLOAT, [1, 1001] + END + ``` + +## Let's compile inception_v3 + +1. Generate `nnpkg`. In this tutorial, let's generate to current directory. + ```sh + tf2nnpkg --use-tf2circle \ + --graphdef inception_v3.pb \ + --info inception_v3.info \ + -o . + ``` + +## Check whether compilation is well done + +- Check if all files are generated correctly. + ``` + inception_v3 + ├ inception_v3.circle + └ metadata + └ MANIFEST + ``` +- Check if `MANIFEST` contents are correct. + ```sh + $ cat inception_v3/metadata/MANIFEST + { + "major-version" : "1", + "minor-version" : "0", + "patch-version" : "0", + "models" : [ "inception_v3.circle" ], + "model-types" : [ "circle" ] + } + ``` diff --git a/docs/nnfw/howto/CrossBuildForAndroid.md b/docs/nnfw/howto/CrossBuildForAndroid.md new file mode 100644 index 0000000..d7e48c8 --- /dev/null +++ b/docs/nnfw/howto/CrossBuildForAndroid.md @@ -0,0 +1,54 @@ +# Cross building for Android + +**Note: To set up a basic build environment on your development PC, please read the [howto.md](../howto.md) document first. The cross build covered in this document assumes that you have an environment in which the native build operates normally without problems.** + +Supported Architecture : AARCH64 only (ARM32 is not supported yet) + +## Prepare Android NDK + +Use `tools/cross/install_android_ndk.sh` script to prepare Android NDK. This is recommended way to build Android NDK. +You may download it yourself from the offical Android NDK website, but the script does a little more than just downloading and unzipping. + +## Build + +### Host Environment Requirements + +With Ubuntu 16.04, everything is fine except one. CMake 3.6.0 or later is required for Android NDK CMake support. +So if you want to use Docker, please use `infra/docker/Dockerfile.1804` which is based on Ubuntu 18.04. It has CMake 3.10.2. + +```bash +docker build --network host -t nnas1804 -f infra/docker/Dockerfile.1804 infra/docker +``` + +### Get prebuilt ARM Compute Library + +Download prebuilt binary from [github](https://github.com/ARM-software/ComputeLibrary/releases). Check the version we support and platform(Android). + +Then extract the tarball to the folder indicated as EXT_ACL_FOLDER in the example below. We will use the following file in `lib/android-arm64-v8a-neon-cl`. + +``` +libarm_compute_core.so +libarm_compute_graph.so +libarm_compute.so +``` + +### Build and install the runtime + +Some tools/libs are still not supported and those are not built by default - mostly due to dependency on Boost library. +Please refer to `infra/nnfw/cmake/options/options_aarch64-android.cmake` for details. + +Different from cross build for linux, + +- `NDK_DIR` is required + +Here is an example of using Makefile. + +```bash +cp -n Makefile.template Makefile + +TARGET_OS=android \ +CROSS_BUILD=1 \ +NDK_DIR=/path/android-tools/r20/ndk \ +EXT_ACL_FOLDER=/path/arm_compute-v19.11.1-bin-android/lib/android-arm64-v8a-neon-cl \ +make install +``` diff --git a/docs/overview/background.md b/docs/overview/background.md new file mode 100644 index 0000000..c4b21d4 --- /dev/null +++ b/docs/overview/background.md @@ -0,0 +1,28 @@ +# Background + +Artificial intelligence (AI) techniques are getting popular and utilized in various products and +services. While the cloud-based AI techniques have been used to perform compute/memory intensive +inferences because of the powerful servers on cloud, on-device AI technologies are recently drawing +attention from the mobile industry for response time reduction, privacy protection, and +connection-less AI service. Big mobile players are investing their research effort on the on-device +AI technologies and already announced hardware and software on-device AI solutions. We are not +leading this trend currently, but since on-device AI area is just started and still in the initial +state, there are still opportunities and possibilities to reduce the gap between pioneers and us. We +believe on-device AI will become a key differentiator for mobile phone, TV, and other home +appliances, and thus developing on-device AI software stack is of paramount importance in order to +take leadership in the on-device AI technology. + +Although the vision of on-device AI is promising, enabling on-device AI involves unique technical +challenges compared to traditional cloud-based approach. This is because on-device AI tries to +conduct inference tasks solely on device without connecting to cloud resources. Specifically, +hardware resources on device, such as processor performance, memory capacity, and power budget, are +very scarce and limit the compute capability, which is typically required to execute complicated +neural network (NN) models. For example, in one product requirement, a mobile device should consume +less than 1.2W and could use at most 2W only for 10 minutes due to thermal issue. Next, on-device AI +software stack needs to support diverse device environments, since embedded platforms may consist of +heterogeneous compute devices, such as CPU, GPU, DSP, or neural processing unit (NPU), and use +different OS platforms, such as Tizen, Android, or various Linux. + +To tackle the challenges above and to have the leadership on on-device AI technology, this project, +as the first step, aims at developing a neural network inference framework specialized and optimized +for on-device AI. diff --git a/docs/overview/index.rst b/docs/overview/index.rst new file mode 100644 index 0000000..8e8b79d --- /dev/null +++ b/docs/overview/index.rst @@ -0,0 +1,18 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Overview +======== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./background.md + ./roadmap.md + ./overall-architecture.md + ./supported-operations.md + ./workgroup.md + diff --git a/docs/overview/overall-architecture.md b/docs/overview/overall-architecture.md new file mode 100644 index 0000000..4376019 --- /dev/null +++ b/docs/overview/overall-architecture.md @@ -0,0 +1,7 @@ +# Overall Architecture + +## Compiler + +## Package + +## Runtime diff --git a/docs/overview/roadmap.md b/docs/overview/roadmap.md new file mode 100644 index 0000000..06833df --- /dev/null +++ b/docs/overview/roadmap.md @@ -0,0 +1,58 @@ +# Roadmap + +This document describes roadmap of **ONE** project. + +This project **ONE** aims at providing a high-performance, on-device neural network (NN) inference +framework that performs inference of a given NN model on processors, such as CPU, GPU, DSP, or NPU, +in the target platform, such as Tizen, Android, and Ubuntu. + +## Progress + +Until last year, we already saw significant gains in accelerating with a single CPU or GPU backend. +We have seen better performance improvements, not only when using a single backend, but even when +mixing CPUs or GPUs considering the characteristics of individual operations. It could give us an +opportunity to have a high degree of freedom in terms of operator coverage, and possibly provide +better performance compared to single backend acceleration. + +On the other hand, we introduced the compiler as a front-end. This will support a variety of deep +learning frameworks in relatively spacious host PC environments, while the runtime running on the +target device is intended to take a smaller burden. In this approach, the compiler and the runtime +will effectively share information among themselves by the Common IR, named _circle_, and a +container format which is referred to as the _NN Package_. + +## Goal + +In the meantime, we have been working on improving the acceleration performance based on the vision +model. From this year, now we start working on the voice model. The runtime requirements for the +voice model will be different from those of the vision model. There will be new requirements that +we do not recognize yet, along with some already recognized elements such as control flow and +dynamic tensor. In addition, recent studies on voice models require efficient support for specific +architectures such as attention, transformer, and BERT. Also, depending on the characteristics of +most voice models with large memory bandwidth, we will have to put more effort into optimizing the +memory bandwidth at runtime. + +## Deliverables + +- Runtime + + Control Flow support (IF/WHILE) + + Dynamic Tensor support + + High quality kernel development for UINT8 quantized model + + End-to-end performance optimization for voice models +- Compiler + + More than 100 operations support + + Standalone _circle_ interpreter + + Completion and application of _circle2circle_ pass + - _circle-quantizer_ for UINT8 and INT16 + - _circle-optimizer_ + + Grphical _circle_ model viewer + +## Milestones + +- [2020 Project Milestones](https://github.com/Samsung/ONE/projects/1) + +## Workgroups (WGs) + +- We organize WGs for major topics, and each WG will be working on its own major topic by breaking + it into small tasks/issues, performing them inside WG, and collaborating between WGs. +- The WG information can be found [here](workgroup.md). + diff --git a/docs/overview/supported-operations.md b/docs/overview/supported-operations.md new file mode 100644 index 0000000..6120e24 --- /dev/null +++ b/docs/overview/supported-operations.md @@ -0,0 +1,145 @@ +# Supported Operations + +As of 2020-06-26 +- TensorFlow commit e5023a1738cce7efcdf9d87863b85c80ab2f8c9e +- This commit is Tensorflow nightly build after v2.2.0 release + +TensorFlow lite operators | circle IR | Compiler | Runtime +-- | -- | -- | -- +ABS | O | O | O +ADD | O | O | O +ADD_N | O | O |   +ARG_MAX | O | O | O +ARG_MIN | O | O |   +AVERAGE_POOL_2D | O | O | O +BATCH_MATMUL | O | O | O +BATCH_TO_SPACE_ND | O | O |   +BIDIRECTIONAL_SEQUENCE_LSTM | O |   |   +BIDIRECTIONAL_SEQUENCE_RNN | O |   |   +CALL | O |   |   +CAST | O | O | O +CEIL | O | O |   +CONCAT_EMBEDDINGS | O |   |   +CONCATENATION | O | O | O +CONV_2D | O | O | O +COS | O | O | O +CUSTOM | O | O | O +DELEGATE | O |   |   +DENSIFY | O |   |   +DEPTH_TO_SPACE | O | O | O +DEPTHWISE_CONV_2D | O | O | O +DEQUANTIZE | O |   | O +DIV | O | O | O +ELU | O | O |   +EMBEDDING_LOOKUP | O |   | O +EMBEDDING_LOOKUP_SPARSE | O |   |   +EQUAL | O | O | O +EXP | O | O | O +EXPAND_DIMS | O | O | O +FAKE_QUANT | O |   |   +FILL | O | O | O +FLOOR | O | O | O +FLOOR_DIV | O | O |   +FLOOR_MOD | O | O |   +FULLY_CONNECTED | O | O | O +GATHER | O | O | O +GATHER_ND | O | O |   +GREATER | O | O | O +GREATER_EQUAL | O | O | O +HARD_SWISH | O |   |   +HASHTABLE_LOOKUP | O |   | O +IF | O | O | O +L2_NORMALIZATION | O | O | O +L2_POOL_2D | O | O | O +LEAKY_RELU | O | O |   +LESS | O | O | O +LESS_EQUAL | O | O | O +LOCAL_RESPONSE_NORMALIZATION | O | O | O +LOG | O | O | O +LOG_SOFTMAX | O | O |   +LOGICAL_AND | O | O | O +LOGICAL_NOT | O | O | O +LOGICAL_OR | O | O | O +LOGISTIC | O | O | O +LSH_PROJECTION | O |   |   +LSTM | O |   | O +MATRIX_DIAG | O |   |   +MATRIX_SET_DIAG | O |   |   +MAX_POOL_2D | O | O | O +MAXIMUM | O | O | O +MEAN | O | O | O +MINIMUM | O | O | O +MIRROR_PAD | O | O |   +MUL | O | O | O +NEG | O | O | O +NON_MAX_SUPPRESSION_V4 | O |   |   +NON_MAX_SUPPRESSION_V5 | O |   |   +NOT_EQUAL | O | O | O +ONE_HOT | O | O | O +PACK | O | O | O +PAD | O | O | O +PADV2 | O |   |   +POW | O | O | O +PRELU | O | O | O +QUANTIZE | O |   |   +RANGE | O | O | O +RANK | O | O |   +REDUCE_ANY | O | O | O +REDUCE_MAX | O | O | O +REDUCE_MIN | O | O | O +REDUCE_PROD | O | O | O +RELU | O | O | O +RELU_N1_TO_1 | O | O |   +RELU6 | O |   | O +RESHAPE | O | O | O +RESIZE_BILINEAR | O | O | O +RESIZE_NEAREST_NEIGHBOR | O | O |   +REVERSE_SEQUENCE | O | O |   +REVERSE_V2 | O |   | O +RNN | O |   | O +ROUND | O | O | O +RSQRT | O | O | O +SCATTER_ND | O | O |   +SEGMENT_SUM | O | O |   +SELECT | O | O | O +SELECT_V2 | O |   |   +SHAPE | O | O | O +SIN | O | O | O +SKIP_GRAM | O |   |   +SLICE | O | O |   +SOFTMAX | O | O | O +SPACE_TO_BATCH_ND | O | O | O +SPACE_TO_DEPTH | O | O | O +SPARSE_TO_DENSE | O | O |   +SPLIT | O | O | O +SPLIT_V | O | O |   +SQRT | O | O | O +SQUARE | O | O |   +SQUARED_DIFFERENCE | O | O | O +SQUEEZE | O | O | O +STRIDED_SLICE | O | O | O +SUB | O | O | O +SUM | O | O | O +SVDF | O |   |   +TANH | O | O | O +TILE | O | O | O +TOPK_V2 | O | O | O +TRANSPOSE | O | O | O +TRANSPOSE_CONV | O | O | O +UNIDIRECTIONAL_SEQUENCE_LSTM | O |   |   +UNIDIRECTIONAL_SEQUENCE_RNN | O |   |   +UNIQUE | O |   |   +UNPACK | O | O | O +WHERE | O |   |   +WHILE | O | O |   +ZEROS_LIKE | O | O |   +Count | 127 | 97 | 81 + + +### circle additional operators + +Operator | compiler | runtime +-- | -- | -- +BCQ_FULLY_CONNECTED | | +BCQ_GATHER | | +INSTANCE_NORM | | diff --git a/docs/overview/workgroup.md b/docs/overview/workgroup.md new file mode 100644 index 0000000..f3a7e08 --- /dev/null +++ b/docs/overview/workgroup.md @@ -0,0 +1,13 @@ +# Workgroup + +## Runtime WG1 + +## Runtime WG2 + +## Runtime WG3 + +## Compiler Frontend WG + +## Compiler Backend WG + +## How to Join? diff --git a/docs/package/design-of-package.md b/docs/package/design-of-package.md new file mode 100644 index 0000000..4f72084 --- /dev/null +++ b/docs/package/design-of-package.md @@ -0,0 +1,7 @@ +# Design of Package + +## Manifest + +## Supported Models + +## User Defined Operation diff --git a/docs/package/index.rst b/docs/package/index.rst new file mode 100644 index 0000000..6e5e9e1 --- /dev/null +++ b/docs/package/index.rst @@ -0,0 +1,14 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Package +======= + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./introduction-to-package.md + ./design-of-package.md diff --git a/docs/package/introduction-to-package.md b/docs/package/introduction-to-package.md new file mode 100644 index 0000000..fca9774 --- /dev/null +++ b/docs/package/introduction-to-package.md @@ -0,0 +1 @@ +# Introduction to Package diff --git a/docs/platform/android.md b/docs/platform/android.md new file mode 100644 index 0000000..f5aec8a --- /dev/null +++ b/docs/platform/android.md @@ -0,0 +1 @@ +# Android diff --git a/docs/platform/index.rst b/docs/platform/index.rst new file mode 100644 index 0000000..1a29e81 --- /dev/null +++ b/docs/platform/index.rst @@ -0,0 +1,15 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Platform +======== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./ubuntu.md + ./tizen.md + ./android.md diff --git a/docs/platform/tizen.md b/docs/platform/tizen.md new file mode 100644 index 0000000..b41a641 --- /dev/null +++ b/docs/platform/tizen.md @@ -0,0 +1 @@ +# Tizen diff --git a/docs/platform/ubuntu.md b/docs/platform/ubuntu.md new file mode 100644 index 0000000..e6f19d1 --- /dev/null +++ b/docs/platform/ubuntu.md @@ -0,0 +1 @@ +# Ubuntu diff --git a/docs/release/1.0/index.rst b/docs/release/1.0/index.rst new file mode 100644 index 0000000..25dd157 --- /dev/null +++ b/docs/release/1.0/index.rst @@ -0,0 +1,13 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +1.0 +=== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./release-note-1.0.0.md diff --git a/docs/release/1.0/release-note-1.0.0.md b/docs/release/1.0/release-note-1.0.0.md new file mode 100644 index 0000000..1013f45 --- /dev/null +++ b/docs/release/1.0/release-note-1.0.0.md @@ -0,0 +1 @@ +# Release Note 1.0.0 diff --git a/docs/release/1.1/index.rst b/docs/release/1.1/index.rst new file mode 100644 index 0000000..5ccaf1e --- /dev/null +++ b/docs/release/1.1/index.rst @@ -0,0 +1,13 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +1.1 +=== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./release-note-1.1.0.md diff --git a/docs/release/1.1/release-note-1.1.0.md b/docs/release/1.1/release-note-1.1.0.md new file mode 100644 index 0000000..b75fcfe --- /dev/null +++ b/docs/release/1.1/release-note-1.1.0.md @@ -0,0 +1 @@ +# Release Note 1.1.0 diff --git a/docs/release/1.2/index.rst b/docs/release/1.2/index.rst new file mode 100644 index 0000000..dc78cb0 --- /dev/null +++ b/docs/release/1.2/index.rst @@ -0,0 +1,13 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +1.2 +=== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./release-note-1.2.0.md diff --git a/docs/release/1.2/release-note-1.2.0.md b/docs/release/1.2/release-note-1.2.0.md new file mode 100644 index 0000000..9b0e0a7 --- /dev/null +++ b/docs/release/1.2/release-note-1.2.0.md @@ -0,0 +1 @@ +# Release Note 1.2.0 diff --git a/docs/release/1.3/index.rst b/docs/release/1.3/index.rst new file mode 100644 index 0000000..f89e7ee --- /dev/null +++ b/docs/release/1.3/index.rst @@ -0,0 +1,13 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +1.3 +=== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./release-note-1.3.0.md diff --git a/docs/release/1.3/release-note-1.3.0.md b/docs/release/1.3/release-note-1.3.0.md new file mode 100644 index 0000000..efabc36 --- /dev/null +++ b/docs/release/1.3/release-note-1.3.0.md @@ -0,0 +1 @@ +# Release Note 1.3.0 diff --git a/docs/release/1.4/index.rst b/docs/release/1.4/index.rst new file mode 100644 index 0000000..bdf64f3 --- /dev/null +++ b/docs/release/1.4/index.rst @@ -0,0 +1,13 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +1.4 +=== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./release-note-1.4.0.md diff --git a/docs/release/1.4/release-note-1.4.0.md b/docs/release/1.4/release-note-1.4.0.md new file mode 100644 index 0000000..22571f4 --- /dev/null +++ b/docs/release/1.4/release-note-1.4.0.md @@ -0,0 +1 @@ +# Release Note 1.4.0 diff --git a/docs/release/1.5/release-note-1.5.0.md b/docs/release/1.5/release-note-1.5.0.md new file mode 100644 index 0000000..dbf70b3 --- /dev/null +++ b/docs/release/1.5/release-note-1.5.0.md @@ -0,0 +1,36 @@ +# Release Note 1.5.0 + +## Feature Highlights + +- **ONE** Compiler + - Compiler supports more operations + +- **ONE** Runtime + - CPU backend supports more operations + +## ONE Compiler + +### Compiler supports more operations + +The following operations are supported : + +- Abs, Add, ArgMax, AvgPool2D, BatchToSpaceND, Cast, Concat, Const, Conv2D, Cos, +Custom, DepthwiseConv2D, Div, Elu, Equal, Exp, ExpandDims, Fill, FloorDiv, +FloorMod, FullyConnected, Gather, GatherNd, Greater, GreaterEqual, If, +LeakyRelu, Less, LocalResponseNormalize, LogicalAnd, LogicalNot, LogicalOr, +Logistic, Maximum, MaxPool2D, Mean, Minimum, MirrorPad, Mul, Neg, NotEqual, +OneHot, Pack, Pad, Pow, Range, ReduceAny(Any), ReduceMax(Max), ReduceProd, +ReduceSum(Sum), ReLU, RELU_N1_TO_1, Reshape, ResizeNearestneighbor, Rsqrt, +Select, Shape, Sin, Slice, Softmax, SpaceToBatchND, SpaceToDepth, Split, SplitV, +Square, SquaredDifference, Squeeze, StridedSlice, Sub, Tanh, Tile, TopKV2, +Transpose, Unpack(Unstack), While, ZerosLike + +## ONE Runtime + +### CPU backend supports more operations + +The following operations are supported on CPU backend : + +- ArgMax, Cos, ExpandDims, Fill, Log, LogicalNot, LogicalOr, Mean, Neg, Pow, + ReLU, ReduceAny, ReduceProd, Reverse, Round, Select, SquaredDifference, Tile, + ZerosLike diff --git a/docs/release/1.6/release-note-1.6.0.md b/docs/release/1.6/release-note-1.6.0.md new file mode 100644 index 0000000..236e317 --- /dev/null +++ b/docs/release/1.6/release-note-1.6.0.md @@ -0,0 +1,44 @@ +# Release Note 1.6.0 + +## Feature Highlights + +- **ONE** Compiler + - Compiler supports 22 more operations including 1 custom operation. + +- **ONE** Runtime + - CPU backend supports 8 more operations + - Support dynamically shaped tensors + - Support Control Flow operations + - API updates + +- Add **ONE** Runtime package for `x86_64` linux (Experimental) + +## ONE Compiler + +### Compiler supports more operations + +- AddN, ArgMin, Custom(BatchMatmul), Ceil, DepthToSpace, Floor, InstanceNormalize, +L2Normalization, L2Pool, LessEqual, Log, LogSoftmax, PReLU, Rank, ReduceMin(Min), +ResizeBilinear, Round, ScatterND, Sqrt, TransposeConv, BCQGather, +BCQFullyConnected + +## ONE Runtime + +### CPU backend supports more operations + +- BatchMatMul, BroadcastTo, Einsum, FusedBatchNorm, MatrixBandPart, Range, ReduceAll, Add(quant8) + +### Support dynamically shaped tensors + +- Support static shape inference (input resizing) +- Support dynamic shape inference (general resizing) + +### Support Control Flow operations + +- IF and WHILE +- Fully support static and dynamic tensors + +### API updates + +- Introduce `nnfw_set_input_tensorinfo` for input resizing +- `nnfw_input_tensorinfo` and `nnfw_output_tensorinfo` behavior have changed to return tensorinfo according to the session state diff --git a/docs/release/1.7/release-note-1.7.0.md b/docs/release/1.7/release-note-1.7.0.md new file mode 100644 index 0000000..c1a4f50 --- /dev/null +++ b/docs/release/1.7/release-note-1.7.0.md @@ -0,0 +1,46 @@ +## Feature Highlights + +- **ONE** Compiler + - Compiler supports more operations + - New command line interface for user interface consistancy +- **ONE** Runtime + - Runtime CPU backend supports more operations + - Runtime CPU backend supports more quant8 operations + - API changes + - New optimization + +## ONE Compiler + +### Compiler supports more operations + +- MatrixDiag, MatrixSetDiag, ReverseSequence, ReverseV2, SegmentSum, SelectV2, SparseToDense, Where + +### New command line interface for user interface consistancy + +- one-import: imports conventional model files to circle + - one-import-tf: imports TensorFlow model to circle + - one-import-tflite: imports TensorFlow lite model to circle +- one-optimize: circle optimize command +- one-quantize: circle quantize command + - supports float32 to uint8, layer wise (for Conv series) +- one-pack: package command +- one-prepare-venv: prepares python virtual environment for importing TensorFlow model +- one-codegen: backend(if available) code generator + +## ONE Runtime + +### Runtime CPU backend supports more operations + +- LogSoftmax, SpaceToBatchND + +### Runtime CPU backend supports more quant8 operations + +- Logistic, Mul, Tanh, SpaceToBatchND, Transpose, Sub, Max, Min, Less, Greater, GreaterEqual, LessEqual, Equal, NotEqual + +### API changes + +- Introduce basic asynchronous execution API + +### New optimization + +- Remove dynamic tensor overhead from static models diff --git a/docs/release/index.rst b/docs/release/index.rst new file mode 100644 index 0000000..bb542bc --- /dev/null +++ b/docs/release/index.rst @@ -0,0 +1,17 @@ +.. ONE documentation master file, created by + sphinx-quickstart on Thu May 14 18:13:12 2020. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Release +======= + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ./1.0/index + ./1.1/index + ./1.2/index + ./1.3/index + ./1.4/index diff --git a/docs/release/release_note_1.4.0.md b/docs/release/release_note_1.4.0.md deleted file mode 100644 index b061e17..0000000 --- a/docs/release/release_note_1.4.0.md +++ /dev/null @@ -1,23 +0,0 @@ -# ONE 1.4.0 Release Note - -## Feature Highlights - -- ONE runtime(a.k.a `onert`) - - CPU backend supports more operations - - (Experimental) Android package is released - -## ONE Runtime - -#### CPU backend supports more operations - -The following operations are supported on CPU backend : -- Rsqrt -- Abs -- Sin -- StrideSlice -- Shape - -#### (Experimental) Android package is released - -- For building onert from scratch, read [CrossBuildForAndroid.md](https://github.com/Samsung/ONE/blob/master/docs/nnfw/howto/CrossBuildForAndroid.md). -- For developing application with onert, please read [HowToUseONEAPI.md](https://github.com/Samsung/ONE/blob/master/docs/nnfw/howto/CrossBuildForAndroid.md). diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..83259c5 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,3 @@ +recommonmark +sphinx-markdown-tables +sphinx_rtd_theme diff --git a/docs/runtime/api.md b/docs/runtime/api.md new file mode 100644 index 0000000..5932792 --- /dev/null +++ b/docs/runtime/api.md @@ -0,0 +1 @@ +# API diff --git a/docs/runtime/backend-api.md b/docs/runtime/backend-api.md new file mode 100644 index 0000000..b291bad --- /dev/null +++ b/docs/runtime/backend-api.md @@ -0,0 +1 @@ +# Backend API diff --git a/docs/runtime/compute.md b/docs/runtime/compute.md new file mode 100644 index 0000000..3768cf0 --- /dev/null +++ b/docs/runtime/compute.md @@ -0,0 +1 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Core is a compilation/execution engine for neural network models. + +## Modules + +Runtime Core has four modules. These are namespaces as well as directory names in `/runtime/onert/core/src/`. + +- `ir` stands for Intermediate Representation which contains Neural Network Graph data structures +- `compiler` converts IR to an executable format +- `exec` is an execution module which is the result of a compilation +- `backend` is an interface for memory management for operands and actual calculation of operations + +### Module `ir` + +This module contains data structures of pure Neural Network models. The models from NN Packages or NN API are converted to these structures. + +- `Subgraphs` is the entire neural network model which is a set of subgraphs +- `Subgraph` consists of operands and operations +- `Operand` (a.k.a. Tensor) has a shape, data type, data and references to operations +- `Operation` (a.k.a. Operator) has operation type, params, and references to operands + +`Operand` and `Operation` are nodes of the graph, and the reference relationship between them is the edges of the graph. + +`Subgraphs` represents the whole model. A model can have more than one `Subgraph` to support control flow operations. Those operations make calls to another subgraph and when the execution on another subgraph is done it gets back to previous subgraph execution with returned operands. + +All graphs are a [DAG](https://en.wikipedia.org/wiki/Directed_acyclic_graph) so once model inputs are given we can run it in topological order. + +Here's a figure of how those data structures are organized. + +![Core](core-figure-ir.png) + +### Module `compiler` + +`Compiler` is the main class of this module. Everything starts from it. + +What it does is making models executable. It schedules execution order and assigns a backend for each operation. Here are major phases of compilation. + +#### 1. Lowering + +In Lowering, `Compiler` assigns a [backend](#module-`backend`) for each operation. It means that the operation will be run with the assigned backend's kernel. + +There is a scheduler that allows the user to manually specify backends via compiler options. There is another scheduler that automatically assigns backends based on profile information measured in advance and saved. + +#### 2. Tensor Registration + +Each backend manages its tensors. In this phase, operand informations get registered to the corresponding backend. This will be used in generating tensor objects. + +##### Q. What are the differences between 'operand' and 'tensor'? + +In **ONE** runtime, 'operand' refers to an operand in a neural network model. While 'tensor' includes all 'operand' info plus actual execution info like actual buffer pointer. In short, 'operand' is for `ir`, 'tensor' is for `backend`. + +#### 3. Linearization (Linear Executor Only) + +Linearization means sorting operations in topological order. It saves execution time since it is not needed to resolve the next available operations after every operation at execution time. + +It also makes plans for tensor memory. It can save some memory space by reusing other operands' space that does not overlap lifetime. All allocations are done at compile time (after [4. Kernel Generation](#4.-kernel-generation)) which saves execution time too. + +#### 4. Kernel Generation + +'kernel' here means an implementation of the actual calculation of an operation. + +A backend is assigned for each operation. In this phase, a kernel for each operation is generated. + +Let's say we have some functions written in a certain programming language. Then its compiler compiles each function into a chunk of assembly. Here 'function' is like 'operation' and 'assembly' is like 'kernel'. + +#### 5. Create Executor + +With generated tensors and kernels, the compiler creates executor objects. There are 3 types of executors are supported - Linear, Dataflow, and Parallel. Linear executor is the default executor and Dataflow Executor and Parallel Executor are experimental. + +For more about executors, please refer to [Executors](./executors.md) document. + +### Module `exec` + +`exec` stands for 'execution'. As a result of the compilation, `Execution` class is created. This class manages the actual execution of the model inference. Here is a typical usage of using this class. + +1. Resize input size if needed +2. Provide input and output buffers +3. Run the inference in either synchronous/asynchronous mode +4. Check out the results which are stored in output buffers provided earlier + +### Module `backend` + +Backends are plugins and they are loaded dynamically(via `dlopen`). So this module is a set of interface classes for backend implementation. `compiler` can compile with a variety of backends without knowing specific backend implementation. + +Backend interface classes are mostly about memory management and kernel generation. For more, please refer to [Backend API](./backend-api.md) document. diff --git a/docs/runtime/executors.md b/docs/runtime/executors.md new file mode 100644 index 0000000..c777e0c --- /dev/null +++ b/docs/runtime/executors.md @@ -0,0 +1,34 @@ +# Executors + +Executor(`IExecutor`) is a execution engine of a subgraph that can execute inference for the subgraph. It is the result of `Subgraph` compilation. If we would compare it with most of common programming language tools, it is just like an interpreter with code to execute. + +## Understanding models + +We can think of a NNPackage model as a set of tasks with dependencies. In other words, it is a form of [DAG](https://en.wikipedia.org/wiki/Directed_acyclic_graph)(more precisely, it is a set of DAGs as we need multiple subgraphs to support control flow operations). And that is exactly the same concept with [Dataflow programming](https://en.wikipedia.org/wiki/Dataflow_programming). + +That is, there are some input tensors that must be ready to run a operation. And the execution must be done in topological order. Here's the workflow for execution. + +1. User gives model input tensors +2. Start execution +3. Perform the operations that are ready +4. Mark the tensors as ready that are made from the operations that was just performed +5. Check if there are some operations ready + 1. If yes, Go to 3 + 2. Otherwise, Finish execution +6. User uses data of model output tensors + +We have 3 different types of executors in our codebase and they all are based on the explation above. However only `LinearExecutor` is official and the other two are experimental. + +## Linear Executor + +`LinearExecutor` is the main executor. As we know the model to run and the model graph does not change at runtime, we do not have to do step 3-5 above at runtime. During the compilation for Linear Executor, it sorts operations in topological order so we can just execute in that fixed order which means that it cannot perform the operations in parallel. + +If the tensors are static, it also can analyze the lifetimes of the tensors and pre-allocate tensor memory with reusing memory between the tensors whose lifetimes do not overlap. + +## Dataflow Executor (experimental) + +Unlike `LinearExecutor`, `DataflowExecutor` does step 3-5 at runtime. By doing it we can know which operations are available at a specific point. However this executor still executes the operations one at a time. Just choose any operation that is ready then execute. And wait for it to finish then repeat that. So there may be no advantage compared to `LinearExecutor` but `DataflowExecutor` is the parent class of `ParallelExecutor`. And `DataflowExecutor` can be used for profiling executions for the heterogeneous scheduler. + +## Parallel Executor (experimental) + +Just like `DataflowExecutor`, `ParallelExecutor` does step 3-5 at runtime. One big difference is that it creates a `ThreadPool` for each backend for parallel execution(`ThreadPool` is supposed to have multiple threads, however for now, it can have only one thread). 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zSOh@A)EZ9T4(XZ;Fq;yn0Iqx|&~&YQpXKM=f|+Gd>g7G?erCOi`o4xMz|0Zy1(*ex yIg&t*l2T}90cMUQkfWp&npuFEBMIavDgO`8fJ>K{<8&zi0000 int(required[i]): - return True - - return True - -vars = Variables("scons") -vars.AddVariables( - BoolVariable("debug", "Debug", False), - BoolVariable("asserts", "Enable asserts (this flag is forced to 1 for debug=1)", False), - BoolVariable("logging", "Logging (this flag is forced to 1 for debug=1)", False), - EnumVariable("arch", "Target Architecture", "armv7a", allowed_values=("armv7a", "arm64-v8a", "arm64-v8.2-a", "arm64-v8.2-a-sve", "x86_32", "x86_64")), - EnumVariable("os", "Target OS", "linux", allowed_values=("linux", "android", "tizen", "bare_metal")), - EnumVariable("build", "Build type", "cross_compile", allowed_values=("native", "cross_compile", "embed_only")), - BoolVariable("examples", "Build example programs", True), - BoolVariable("Werror", "Enable/disable the -Werror compilation flag", True), - BoolVariable("standalone", "Builds the tests as standalone executables, links statically with libgcc, libstdc++ and libarm_compute", False), - BoolVariable("opencl", "Enable OpenCL support", True), - BoolVariable("neon", "Enable Neon support", False), - BoolVariable("gles_compute", "Enable OpenGL ES Compute Shader support", False), - BoolVariable("embed_kernels", "Embed OpenCL kernels and OpenGL ES compute shaders in library binary", True), - BoolVariable("set_soname", "Set the library's soname and shlibversion (requires SCons 2.4 or above)", False), - BoolVariable("openmp", "Enable OpenMP backend", False), - BoolVariable("cppthreads", "Enable C++11 threads backend", True), - PathVariable("build_dir", "Specify sub-folder for the build", ".", PathVariable.PathAccept), - PathVariable("install_dir", "Specify sub-folder for the install", "", PathVariable.PathAccept), - ("extra_cxx_flags", "Extra CXX flags to be appended to the build command", ""), - ("extra_link_flags", "Extra LD flags to be appended to the build command", ""), - ("compiler_cache", "Command to prefix to the C and C++ compiler (e.g ccache)", "") -) - -env = Environment(platform="posix", variables=vars, ENV = os.environ) -build_path = env['build_dir'] -# If build_dir is a relative path then add a #build/ prefix: -if not env['build_dir'].startswith('/'): - SConsignFile('build/%s/.scons' % build_path) - build_path = "#build/%s" % build_path -else: - SConsignFile('%s/.scons' % build_path) - -install_path = env['install_dir'] -#If the install_dir is a relative path then assume it's from inside build_dir -if not env['install_dir'].startswith('/') and install_path != "": - install_path = "%s/%s" % (build_path, install_path) - -env.Append(LIBPATH = [build_path]) -Export('env') -Export('vars') - -def install_lib( lib ): - # If there is no install folder, then there is nothing to do: - if install_path == "": - return lib - return env.Install( "%s/lib/" % install_path, lib) -def install_bin( bin ): - # If there is no install folder, then there is nothing to do: - if install_path == "": - return bin - return env.Install( "%s/bin/" % install_path, bin) -def install_include( inc ): - if install_path == "": - return inc - return env.Install( "%s/include/" % install_path, inc) - -Export('install_lib') -Export('install_bin') - -Help(vars.GenerateHelpText(env)) - -if env['build'] == "embed_only": - SConscript('./SConscript', variant_dir=build_path, duplicate=0) - Return() - -if env['neon'] and 'x86' in env['arch']: - print("Cannot compile NEON for x86") - Exit(1) - -if env['set_soname'] and not version_at_least(SCons.__version__, "2.4"): - print("Setting the library's SONAME / SHLIBVERSION requires SCons 2.4 or above") - print("Update your version of SCons or use set_soname=0") - Exit(1) - -if env['os'] == 'bare_metal': - if env['cppthreads'] or env['openmp']: - print("ERROR: OpenMP and C++11 threads not supported in bare_metal. Use cppthreads=0 openmp=0") - Exit(1) - -env.Append(CXXFLAGS = ['-Wno-deprecated-declarations','-Wall','-DARCH_ARM', - '-Wextra','-Wno-unused-parameter','-pedantic','-Wdisabled-optimization','-Wformat=2', - '-Winit-self','-Wstrict-overflow=2','-Wswitch-default', - '-fpermissive','-std=gnu++11','-Wno-vla','-Woverloaded-virtual', - '-Wctor-dtor-privacy','-Wsign-promo','-Weffc++','-Wno-format-nonliteral','-Wno-overlength-strings','-Wno-strict-overflow']) - -env.Append(CPPDEFINES = ['_GLIBCXX_USE_NANOSLEEP']) - -default_cpp_compiler = 'g++' if env['os'] != 'android' else 'clang++' -default_c_compiler = 'gcc' if env['os'] != 'android' else 'clang' -cpp_compiler = os.environ.get('CXX', default_cpp_compiler) -c_compiler = os.environ.get('CC', default_c_compiler) - -if env['os'] == 'android' and ( 'clang++' not in cpp_compiler or 'clang' not in c_compiler ): - print( "WARNING: Only clang is officially supported to build the Compute Library for Android") - -if 'clang++' in cpp_compiler: - env.Append(CXXFLAGS = ['-Wno-format-nonliteral','-Wno-deprecated-increment-bool','-Wno-vla-extension','-Wno-mismatched-tags']) -else: - env.Append(CXXFLAGS = ['-Wlogical-op','-Wnoexcept','-Wstrict-null-sentinel','-Wno-implicit-fallthrough']) - -if env['cppthreads']: - env.Append(CPPDEFINES = [('ARM_COMPUTE_CPP_SCHEDULER', 1)]) - -if env['openmp']: - if 'clang++' in cpp_compiler: - print( "Clang does not support OpenMP. Use scheduler=cpp.") - Exit(1) - - env.Append(CPPDEFINES = [('ARM_COMPUTE_OPENMP_SCHEDULER', 1)]) - env.Append(CXXFLAGS = ['-fopenmp']) - env.Append(LINKFLAGS = ['-fopenmp']) - -prefix = "" -if env['arch'] == 'armv7a': - env.Append(CXXFLAGS = ['-march=armv7-a', '-mthumb', '-mfpu=neon']) - - if env['os'] == 'linux': - prefix = "arm-linux-gnueabihf-" - env.Append(CXXFLAGS = ['-mfloat-abi=hard']) - elif env['os'] == 'bare_metal': - prefix = "arm-eabi-" - env.Append(CXXFLAGS = ['-mfloat-abi=hard']) - elif env['os'] == 'android': - prefix = "arm-linux-androideabi-" - env.Append(CXXFLAGS = ['-mfloat-abi=softfp']) - elif env['os'] == 'tizen': - prefix = "arm-linux-gnueabi-" - env.Append(CXXFLAGS = ['-mfloat-abi=softfp']) -elif env['arch'] == 'arm64-v8a': - env.Append(CXXFLAGS = ['-march=armv8-a']) - env.Append(CPPDEFINES = ['ARM_COMPUTE_AARCH64_V8A','NO_DOT_IN_TOOLCHAIN']) - if env['os'] == 'linux': - prefix = "aarch64-linux-gnu-" - elif env['os'] == 'bare_metal': - prefix = "aarch64-elf-" - elif env['os'] == 'android': - prefix = "aarch64-linux-android-" - elif env['os'] == 'tizen': - prefix = "aarch64-linux-gnu-" - if 'clang++' in cpp_compiler: - env.Append(CXXFLAGS = ['-no-integrated-as']) -elif 'arm64-v8.2-a' in env['arch']: - if env['arch'] == 'arm64-v8.2-a-sve': - if env['os'] != 'bare_metal': - print("Only bare metal SVE is supported at the moment") - Exit(1) - env.Append(CXXFLAGS = ['-march=armv8.2-a+sve+fp16+dotprod']) - else: - env.Append(CXXFLAGS = ['-march=armv8.2-a+fp16']) # explicitly enable fp16 extension otherwise __ARM_FEATURE_FP16_VECTOR_ARITHMETIC is undefined - if env['os'] == 'linux': - prefix = "aarch64-linux-gnu-" - elif env['os'] == 'bare_metal': - prefix = "aarch64-elf-" - elif env['os'] == 'android': - prefix = "aarch64-linux-android-" - elif env['os'] == 'tizen': - prefix = "aarch64-linux-gnu-" - env.Append(CPPDEFINES = ['ARM_COMPUTE_AARCH64_V8_2','NO_DOT_IN_TOOLCHAIN']) - if 'clang++' in cpp_compiler: - env.Append(CXXFLAGS = ['-no-integrated-as']) -elif env['arch'] == 'x86_32': - env.Append(CCFLAGS = ['-m32']) - env.Append(LINKFLAGS = ['-m32']) -elif env['arch'] == 'x86_64': - env.Append(CCFLAGS = ['-m64']) - env.Append(LINKFLAGS = ['-m64']) - -if env['build'] == 'native': - prefix = "" - -env['CC'] = env['compiler_cache']+" "+prefix + c_compiler -env['CXX'] = env['compiler_cache']+" "+prefix + cpp_compiler -env['LD'] = prefix + "ld" -env['AS'] = prefix + "as" -env['AR'] = prefix + "ar" -env['RANLIB'] = prefix + "ranlib" - -if not GetOption("help"): - try: - compiler_ver = subprocess.check_output(env['CXX'].split() + ["-dumpversion"]).strip() - except OSError: - print("ERROR: Compiler '%s' not found" % env['CXX']) - Exit(1) - - if 'clang++' not in cpp_compiler: - if env['arch'] == 'arm64-v8.2-a' and not version_at_least(compiler_ver, '6.2.1'): - print("GCC 6.2.1 or newer is required to compile armv8.2-a code") - Exit(1) - elif env['arch'] == 'arm64-v8a' and not version_at_least(compiler_ver, '4.9'): - print("GCC 4.9 or newer is required to compile NEON code for AArch64") - Exit(1) - - if version_at_least(compiler_ver, '6.1'): - env.Append(CXXFLAGS = ['-Wno-ignored-attributes']) - - if compiler_ver == '4.8.3': - env.Append(CXXFLAGS = ['-Wno-array-bounds']) - -if env['standalone']: - env.Append(CXXFLAGS = ['-fPIC']) - env.Append(LINKFLAGS = ['-static-libgcc','-static-libstdc++']) - -if env['Werror']: - env.Append(CXXFLAGS = ['-Werror']) - -if env['os'] == 'android': - env.Append(CPPDEFINES = ['ANDROID']) - env.Append(LINKFLAGS = ['-pie', '-static-libstdc++']) -elif env['os'] == 'bare_metal': - env.Append(LINKFLAGS = ['-static']) - env.Append(LINKFLAGS = ['-specs=rdimon.specs']) - env.Append(CXXFLAGS = ['-fPIC']) - env.Append(CPPDEFINES = ['NO_MULTI_THREADING']) - env.Append(CPPDEFINES = ['BARE_METAL']) - -if env['opencl']: - if env['os'] in ['bare_metal'] or env['standalone']: - print("Cannot link OpenCL statically, which is required for bare metal / standalone builds") - Exit(1) - -if env['gles_compute']: - if env['os'] in ['bare_metal'] or env['standalone']: - print("Cannot link OpenGLES statically, which is required for bare metal / standalone builds") - Exit(1) - -if env["os"] not in ["android", "bare_metal"] and (env['opencl'] or env['cppthreads']): - env.Append(LIBS = ['pthread']) - -if env['opencl'] or env['gles_compute']: - if env['embed_kernels']: - env.Append(CPPDEFINES = ['EMBEDDED_KERNELS']) - -if env['debug']: - env['asserts'] = True - env['logging'] = True - env.Append(CXXFLAGS = ['-O0','-g','-gdwarf-2']) - env.Append(CPPDEFINES = ['ARM_COMPUTE_DEBUG_ENABLED']) -else: - env.Append(CXXFLAGS = ['-O3','-ftree-vectorize']) - -if env['asserts']: - env.Append(CPPDEFINES = ['ARM_COMPUTE_ASSERTS_ENABLED']) - env.Append(CXXFLAGS = ['-fstack-protector-strong']) - -if env['logging']: - env.Append(CPPDEFINES = ['ARM_COMPUTE_LOGGING_ENABLED']) - -env.Append(CPPPATH = ['#/include', "#"]) -env.Append(CXXFLAGS = env['extra_cxx_flags']) -env.Append(LINKFLAGS = env['extra_link_flags']) - -Default( install_include("arm_compute")) -Default( install_include("support")) - -Export('version_at_least') - -if env['opencl']: - SConscript("./opencl-1.2-stubs/SConscript", variant_dir="%s/opencl-1.2-stubs" % build_path, duplicate=0) - -if env['gles_compute'] and env['os'] != 'android': - env.Append(CPPPATH = ['#/include/linux']) - SConscript("./opengles-3.1-stubs/SConscript", variant_dir="%s/opengles-3.1-stubs" % build_path, duplicate=0) - -SConscript('./SConscript', variant_dir=build_path, duplicate=0) - -if env['examples'] and env['os'] != 'bare_metal': - SConscript('./examples/SConscript', variant_dir='%s/examples' % build_path, duplicate=0) - -if env['os'] != 'bare_metal': - SConscript('./tests/SConscript', variant_dir='%s/tests' % build_path, duplicate=0) diff --git a/infra/cmake/packages/ARMComputeConfig.cmake b/infra/cmake/packages/ARMComputeConfig.cmake deleted file mode 100644 index 83c8d20..0000000 --- a/infra/cmake/packages/ARMComputeConfig.cmake +++ /dev/null @@ -1,192 +0,0 @@ -function(_ARMCompute_Import) - include(FindPackageHandleStandardArgs) - - list(APPEND ARMCompute_LIB_SEARCH_PATHS ${ARMCompute_PREFIX}) - - find_path(INCLUDE_DIR NAMES arm_compute/core/ITensor.h PATHS ${ARMCompute_INCLUDE_SEARCH_PATHS}) - - find_library(CORE_LIBRARY NAMES arm_compute_core PATHS ${ARMCompute_LIB_SEARCH_PATHS} CMAKE_FIND_ROOT_PATH_BOTH) - find_library(RUNTIME_LIBRARY NAMES arm_compute PATHS ${ARMCompute_LIB_SEARCH_PATHS} CMAKE_FIND_ROOT_PATH_BOTH) - find_library(GRAPH_LIBRARY NAMES arm_compute_graph PATHS ${ARMCompute_LIB_SEARCH_PATHS} CMAKE_FIND_ROOT_PATH_BOTH) - - message(STATUS "Search acl in ${ARMCompute_LIB_SEARCH_PATHS}") - - if(NOT INCLUDE_DIR) - nnas_find_package(ARMComputeSource QUIET) - if (NOT ARMComputeSource_FOUND) - set(ARMCompute_FOUND FALSE PARENT_SCOPE) - return() - endif() - set(INCLUDE_DIR ${ARMComputeSource_DIR} ${ARMComputeSource_DIR}/include) - endif(NOT INCLUDE_DIR) - - if(NOT CORE_LIBRARY) - set(ARMCompute_FOUND FALSE PARENT_SCOPE) - message(STATUS "Cannot find libarm_compute_core.so") - return() - endif() - - if(NOT RUNTIME_LIBRARY) - message(STATUS "Cannot find libarm_compute.so") - set(ARMCompute_FOUND FALSE PARENT_SCOPE) - return() - endif() - - if(NOT GRAPH_LIBRARY) - message(STATUS "Cannot find libarm_compute_graph.so") - set(ARMCompute_FOUND FALSE PARENT_SCOPE) - return() - endif() - - if(NOT TARGET arm_compute_core) - add_library(arm_compute_core INTERFACE) - target_include_directories(arm_compute_core SYSTEM INTERFACE ${INCLUDE_DIR}) - target_link_libraries(arm_compute_core INTERFACE dl ${LIB_PTHREAD}) - target_link_libraries(arm_compute_core INTERFACE ${CORE_LIBRARY}) - endif(NOT TARGET arm_compute_core) - - if(NOT TARGET arm_compute) - add_library(arm_compute INTERFACE) - target_include_directories(arm_compute SYSTEM INTERFACE ${INCLUDE_DIR}) - target_link_libraries(arm_compute INTERFACE ${RUNTIME_LIBRARY}) - target_link_libraries(arm_compute INTERFACE arm_compute_core) - endif(NOT TARGET arm_compute) - - if(NOT TARGET arm_compute_graph) - add_library(arm_compute_graph INTERFACE) - target_include_directories(arm_compute_graph SYSTEM INTERFACE ${INCLUDE_DIR}) - target_link_libraries(arm_compute_graph INTERFACE ${GRAPH_LIBRARY}) - target_link_libraries(arm_compute_graph INTERFACE arm_compute) - endif(NOT TARGET arm_compute_graph) - - set(ARMCompute_FOUND TRUE PARENT_SCOPE) -endfunction(_ARMCompute_Import) - -### Check whether library exists -function(_ARMCompute_Check VAR LIBDIR) - set(FOUND TRUE) - - if(NOT EXISTS "${LIBDIR}/libarm_compute_core.so") - set(FOUND FALSE) - endif() - - if(NOT EXISTS "${LIBDIR}/libarm_compute.so") - set(FOUND FALSE) - endif() - - if(NOT EXISTS "${LIBDIR}/libarm_compute_graph.so") - set(FOUND FALSE) - endif() - - set(${VAR} ${FOUND} PARENT_SCOPE) -endfunction(_ARMCompute_Check) - -# Let's build and install ARMCompute libraries -# NOTE This function silently returns on error -function(_ARMCompute_Build ARMCompute_INSTALL_PREFIX) - ### Check whether library exists - _ARMCompute_Check(ARMCompute_FOUND ${ARMCompute_INSTALL_PREFIX}) - - if(ARMCompute_FOUND) - return() - endif(ARMCompute_FOUND) - - ### Let's build with SCONS - nnas_find_package(ARMComputeSource QUIET) - - if(NOT ARMComputeSource_FOUND) - return() - endif(NOT ARMComputeSource_FOUND) - - find_program(SCONS_PATH scons) - - if(NOT SCONS_PATH) - message(WARNING "SCONS NOT FOUND. Please install SCONS to build ARMCompute.") - return() - endif(NOT SCONS_PATH) - - if(CMAKE_BUILD_TYPE) - string(TOLOWER "${CMAKE_BUILD_TYPE}" SCON_BUILD_TYPE) - else(CMAKE_BUILD_TYPE) - set(SCON_BUILD_TYPE "release") - endif(CMAKE_BUILD_TYPE) - - #### Architecture-specific configurations - if(TARGET_ARCH STREQUAL "armv7l") - set(BUILD_ARCH "armv7a") - set(BUILD_DIR "${BUILD_ARCH}-${TARGET_OS}.${SCON_BUILD_TYPE}") - endif() - - if(TARGET_ARCH STREQUAL "aarch64") - set(BUILD_ARCH "arm64-v8a") - set(BUILD_DIR "${BUILD_ARCH}-${TARGET_OS}.${SCON_BUILD_TYPE}") - endif() - - #### Platform-specific configurations - #### TODO Support android - - #### Mode-specific configurations - if(SCON_BUILD_TYPE STREQUAL "debug") - list(APPEND SCONS_OPTIONS "debug=1") - endif() - - #### Generic configurations - list(APPEND SCONS_OPTIONS "neon=1") - list(APPEND SCONS_OPTIONS "opencl=1") - list(APPEND SCONS_OPTIONS "examples=0") - list(APPEND SCONS_OPTIONS "Werror=0") - list(APPEND SCONS_OPTIONS "os=${TARGET_OS}") - include(ProcessorCount) - ProcessorCount(N) - if((NOT N EQUAL 0) AND BUILD_EXT_MULTITHREAD) - list(APPEND SCONS_OPTIONS -j${N}) - endif() - if(DEFINED BUILD_ARCH) - list(APPEND SCONS_OPTIONS "arch=${BUILD_ARCH}") - endif(DEFINED BUILD_ARCH) - - if(DEFINED BUILD_DIR) - list(APPEND SCONS_OPTIONS "build_dir=${BUILD_DIR}") - endif(DEFINED BUILD_DIR) - - message(STATUS "Build ARMCompute with ${SCONS_PATH} ('${SCONS_OPTIONS}'") - - # Copy externals/SConstruct to externals/acl/ for Tizen build support. - # TODO The change of externals/SConstruct should be upstreamed to ARM Compute Library community layer. - # execute_process(COMMAND ${CMAKE_COMMAND} -E copy "${CMAKE_CURRENT_LIST_DIR}/ARMCompute/SConstruct" "${ARMComputeSource_DIR}") - - # Build ARMCompute libraries with SCONS - # NOTE ARMCompute SConstruct unconditioanlly appends "arm-linux-gnueabihf-" prefix for linux - execute_process(COMMAND /usr/bin/env CC=gcc CXX=g++ "${SCONS_PATH}" ${SCONS_OPTIONS} - WORKING_DIRECTORY ${ARMComputeSource_DIR} - RESULT_VARIABLE ARMCompute_BUILD) - - # Install ARMCompute libraries - # Ps. CI server will copy below installed libraries to target device to test. - execute_process(COMMAND ${CMAKE_COMMAND} -E make_directory "${ARMCompute_INSTALL_PREFIX}" - WORKING_DIRECTORY ${ARMComputeSource_DIR} - RESULT_VARIABLE ARMCompute_BUILD) - execute_process(COMMAND ${CMAKE_COMMAND} -E copy "build/${BUILD_DIR}/libarm_compute_core.so" "${ARMCompute_INSTALL_PREFIX}" - COMMAND ${CMAKE_COMMAND} -E copy "build/${BUILD_DIR}/libarm_compute.so" "${ARMCompute_INSTALL_PREFIX}" - COMMAND ${CMAKE_COMMAND} -E copy "build/${BUILD_DIR}/libarm_compute_graph.so" "${ARMCompute_INSTALL_PREFIX}" - WORKING_DIRECTORY ${ARMComputeSource_DIR} - RESULT_VARIABLE ARMCompute_BUILD) -endfunction(_ARMCompute_Build) - - -set(ARMCompute_PREFIX ${EXT_OVERLAY_DIR}/lib) - -# This is a workaround for CI issues -# Ps. CI server will copy below installed libraries to target device to test. -# TODO Remove this workaround -if(DEFINED ARMCompute_EXTDIR) - execute_process(COMMAND ${CMAKE_COMMAND} -E make_directory "${ARMCompute_PREFIX}") - execute_process(COMMAND ${CMAKE_COMMAND} -E copy "${ARMCompute_EXTDIR}/libarm_compute_core.so" "${ARMCompute_PREFIX}" - COMMAND ${CMAKE_COMMAND} -E copy "${ARMCompute_EXTDIR}/libarm_compute.so" "${ARMCompute_PREFIX}" - COMMAND ${CMAKE_COMMAND} -E copy "${ARMCompute_EXTDIR}/libarm_compute_graph.so" "${ARMCompute_PREFIX}") -endif(DEFINED ARMCompute_EXTDIR) - -if(BUILD_ARMCOMPUTE) - _ARMCompute_Build("${ARMCompute_PREFIX}") -endif(BUILD_ARMCOMPUTE) -_ARMCompute_Import() diff --git a/infra/cmake/packages/ARMComputeSourceConfig.cmake b/infra/cmake/packages/ARMComputeSourceConfig.cmake index 45d8727..51a235a 100644 --- a/infra/cmake/packages/ARMComputeSourceConfig.cmake +++ b/infra/cmake/packages/ARMComputeSourceConfig.cmake @@ -1,10 +1,15 @@ function(_ARMComputeSource_import) + if(NOT ${DOWNLOAD_ARMCOMPUTE}) + set(ARMComputeSource_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT ${DOWNLOAD_ARMCOMPUTE}) + nnas_include(ExternalSourceTools) nnas_include(OptionTools) envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") set(ARMCOMPUTE_URL ${EXTERNAL_DOWNLOAD_SERVER}/ARM-software/ComputeLibrary/archive/v19.11.1.tar.gz) - ExternalSource_Get(ARMCOMPUTE ${DOWNLOAD_ARMCOMPUTE} ${ARMCOMPUTE_URL}) + ExternalSource_Download(ARMCOMPUTE ${ARMCOMPUTE_URL}) set(ARMComputeSource_DIR ${ARMCOMPUTE_SOURCE_DIR} PARENT_SCOPE) set(ARMComputeSource_FOUND ${ARMCOMPUTE_SOURCE_GET} PARENT_SCOPE) diff --git a/infra/cmake/packages/AbseilConfig.cmake b/infra/cmake/packages/AbseilConfig.cmake index 06d36b9..e16dd94 100644 --- a/infra/cmake/packages/AbseilConfig.cmake +++ b/infra/cmake/packages/AbseilConfig.cmake @@ -17,13 +17,15 @@ function(_Abseil_import) add_library(abseil INTERFACE) target_link_libraries(abseil INTERFACE # From "Available Abseil CMake Public Targets" in CMake/README.md - absl::base absl::algorithm - absl::container + absl::base absl::debugging + absl::flat_hash_map + absl::flags absl::memory absl::meta absl::numeric + absl::random_random absl::strings absl::synchronization absl::time diff --git a/infra/cmake/packages/AbseilSourceConfig.cmake b/infra/cmake/packages/AbseilSourceConfig.cmake index 7c5d4bd..8be7326 100644 --- a/infra/cmake/packages/AbseilSourceConfig.cmake +++ b/infra/cmake/packages/AbseilSourceConfig.cmake @@ -12,10 +12,14 @@ function(_AbseilSource_import) # # The last change of "48cd2c3f351" was commited on 2018.09.27 # - # Let's use the latest released version (2018-12 release) - envoption(ABSEIL_URL https://github.com/abseil/abseil-cpp/archive/20181200.tar.gz) + # Let's use the latest released version (2020-02 release patch 2) + envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") + envoption(ABSEIL_URL ${EXTERNAL_DOWNLOAD_SERVER}/abseil/abseil-cpp/archive/20200225.2.tar.gz) - ExternalSource_Download(ABSEIL ${ABSEIL_URL}) + ExternalSource_Download(ABSEIL + DIRNAME ABSEIL + URL ${ABSEIL_URL} + CHECKSUM MD5=73f2b6e72f1599a9139170c29482ddc4) set(AbseilSource_DIR ${ABSEIL_SOURCE_DIR} PARENT_SCOPE) set(AbseilSource_FOUND TRUE PARENT_SCOPE) diff --git a/infra/cmake/packages/BoostSourceConfig.cmake b/infra/cmake/packages/BoostSourceConfig.cmake index 07d1800..ea2a6ca 100644 --- a/infra/cmake/packages/BoostSourceConfig.cmake +++ b/infra/cmake/packages/BoostSourceConfig.cmake @@ -1,11 +1,16 @@ function(_BoostSource_import) + if(NOT ${DOWNLOAD_BOOST}) + set(BoostSource_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT ${DOWNLOAD_BOOST}) + nnas_include(ExternalSourceTools) nnas_include(OptionTools) # EXTERNAL_DOWNLOAD_SERVER will be overwritten by CI server to use mirror server. envoption(EXTERNAL_DOWNLOAD_SERVER "http://sourceforge.net") set(BOOST_URL ${EXTERNAL_DOWNLOAD_SERVER}/projects/boost/files/boost/1.58.0/boost_1_58_0.tar.gz) - ExternalSource_Get(BOOST ${DOWNLOAD_BOOST} ${BOOST_URL}) + ExternalSource_Download(BOOST ${BOOST_URL}) set(BoostSource_DIR ${BOOST_SOURCE_DIR} PARENT_SCOPE) set(BoostSource_FOUND ${BOOST_SOURCE_GET} PARENT_SCOPE) diff --git a/infra/cmake/packages/CaffeConfig.cmake b/infra/cmake/packages/CaffeConfig.cmake index 0d27ef4..3ae5440 100644 --- a/infra/cmake/packages/CaffeConfig.cmake +++ b/infra/cmake/packages/CaffeConfig.cmake @@ -20,7 +20,7 @@ function(_Caffe_import) return() endif() - find_package(HDF5 COMPONENTS HL QUIET) + nnas_find_package(HDF5 QUIET) if(NOT HDF5_FOUND) set(Caffe_FOUND FALSE PARENT_SCOPE) diff --git a/infra/cmake/packages/CpuinfoSourceConfig.cmake b/infra/cmake/packages/CpuinfoSourceConfig.cmake deleted file mode 100644 index 1f3b477..0000000 --- a/infra/cmake/packages/CpuinfoSourceConfig.cmake +++ /dev/null @@ -1,14 +0,0 @@ -function(_cpuinfoSource_import) - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") - set(CPUINFO_URL ${EXTERNAL_DOWNLOAD_SERVER}/pytorch/cpuinfo/archive/d5e37adf1406cf899d7d9ec1d317c47506ccb970.tar.gz) - - ExternalSource_Get("CPUINFO" ${DOWNLOAD_NNPACK} ${CPUINFO_URL}) - - set(CPUINFO_SOURCE_DIR ${CPUINFO_SOURCE_DIR} PARENT_SCOPE) - set(CPUINFO_SOURCE_FOUND ${CPUINFO_SOURCE_GET} PARENT_SCOPE) -endfunction(_cpuinfoSource_import) - -_cpuinfoSource_import() diff --git a/infra/cmake/packages/Eigen-fd6845384b86Config.cmake b/infra/cmake/packages/Eigen-fd6845384b86Config.cmake new file mode 100644 index 0000000..e98856a --- /dev/null +++ b/infra/cmake/packages/Eigen-fd6845384b86Config.cmake @@ -0,0 +1,25 @@ +# NOTE TensorFlow 1.12 uses eigen commit ID fd6845384b86 + +# find_package rejects version with commit number. Commit ID is appended to the package name +# as a workaround. +# +# TODO Find a better way +function(_Eigen_import) + nnas_find_package(EigenSource-fd6845384b86 QUIET) + + if(NOT EigenSource-fd6845384b86_FOUND) + set(Eigen-fd6845384b86_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT EigenSource-fd6845384b86_FOUND) + + if(NOT TARGET eigen-fd6845384b86) + add_library(eigen-fd6845384b86 INTERFACE) + target_include_directories(eigen-fd6845384b86 INTERFACE "${EigenSource_DIR}") + # Add EIGEN_MPL2_ONLY to remove license issue posibility + target_compile_definitions(eigen-fd6845384b86 INTERFACE EIGEN_MPL2_ONLY) + endif(NOT TARGET eigen-fd6845384b86) + + set(Eigen-fd6845384b86_FOUND TRUE PARENT_SCOPE) +endfunction(_Eigen_import) + +_Eigen_import() diff --git a/infra/cmake/packages/EigenSource-fd6845384b86Config.cmake b/infra/cmake/packages/EigenSource-fd6845384b86Config.cmake index dc322cf..4a854e7 100644 --- a/infra/cmake/packages/EigenSource-fd6845384b86Config.cmake +++ b/infra/cmake/packages/EigenSource-fd6845384b86Config.cmake @@ -11,6 +11,7 @@ function(_import) nnas_include(ExternalSourceTools) nnas_include(ThirdPartyTools) + # NOTE TensorFlow 1.12 downloads eigen from the following URL ThirdParty_URL(EIGEN_URL PACKAGE Eigen VERSION fd6845384b86) ExternalSource_Download(EIGEN diff --git a/infra/cmake/packages/EigenSourceConfig.cmake b/infra/cmake/packages/EigenSourceConfig.cmake index 14ed5e7..a0cbf82 100644 --- a/infra/cmake/packages/EigenSourceConfig.cmake +++ b/infra/cmake/packages/EigenSourceConfig.cmake @@ -7,10 +7,15 @@ function(_EigenSource_import) nnas_include(ExternalSourceTools) nnas_include(OptionTools) - # NOTE The following URL comes from TensorFlow 1.7 - envoption(EIGEN_URL https://bitbucket.org/eigen/eigen/get/2355b229ea4c.tar.gz) + # NOTE TensorFlow 1.13.1 uses https://bitbucket.org/eigen/eigen/get/9f48e814419e.tar.gz + # but it has a issue https://eigen.tuxfamily.org/bz/show_bug.cgi?id=1643 + # The following URL resolves above issue + envoption(EXTERNAL_DOWNLOAD_SERVER "https://bitbucket.org") + envoption(EIGEN_1_13_1_URL ${EXTERNAL_DOWNLOAD_SERVER}/eigen/eigen/get/88fc23324517.tar.gz) - ExternalSource_Download(EIGEN ${EIGEN_URL}) + ExternalSource_Download(EIGEN + DIRNAME EIGEN + URL ${EIGEN_1_13_1_URL}) set(EigenSource_DIR ${EIGEN_SOURCE_DIR} PARENT_SCOPE) set(EigenSource_FOUND TRUE PARENT_SCOPE) diff --git a/infra/cmake/packages/Enum34SourceConfig.cmake b/infra/cmake/packages/Enum34SourceConfig.cmake deleted file mode 100644 index 04d22eb..0000000 --- a/infra/cmake/packages/Enum34SourceConfig.cmake +++ /dev/null @@ -1,14 +0,0 @@ -function(_enum34Source_import) - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - envoption(EXTERNAL_DOWNLOAD_SERVER "https://bitbucket.org") - set(ENUM34_URL ${EXTERNAL_DOWNLOAD_SERVER}/stoneleaf/enum34/get/1.1.6.tar.gz) - - ExternalSource_Get("PYTHON_ENUM" ${DOWNLOAD_NNPACK} ${ENUM34_URL}) - - set(PYTHON_ENUM_SOURCE_DIR ${PYTHON_ENUM_SOURCE_DIR} PARENT_SCOPE) - set(PYTHON_ENUM_SOURCE_FOUND ${PYTHON_ENUM_SOURCE_GET} PARENT_SCOPE) -endfunction(_enum34Source_import) - -_enum34Source_import() diff --git a/infra/cmake/packages/FP16SourceConfig.cmake b/infra/cmake/packages/FP16SourceConfig.cmake deleted file mode 100644 index c7e9db5..0000000 --- a/infra/cmake/packages/FP16SourceConfig.cmake +++ /dev/null @@ -1,14 +0,0 @@ -function(_FP16Source_import) - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") - set(FP16_URL ${EXTERNAL_DOWNLOAD_SERVER}/Maratyszcza/FP16/archive/febbb1c163726b5db24bed55cc9dc42529068997.tar.gz) - - ExternalSource_Get("FP16" ${DOWNLOAD_NNPACK} ${FP16_URL}) - - set(FP16_SOURCE_DIR ${FP16_SOURCE_DIR} PARENT_SCOPE) - set(FP16_SOURCE_FOUND ${FP16_SOURCE_GET} PARENT_SCOPE) -endfunction(_FP16Source_import) - -_FP16Source_import() diff --git a/infra/cmake/packages/FXdivSourceConfig.cmake b/infra/cmake/packages/FXdivSourceConfig.cmake deleted file mode 100644 index fa41adb..0000000 --- a/infra/cmake/packages/FXdivSourceConfig.cmake +++ /dev/null @@ -1,14 +0,0 @@ -function(_FXdivSource_import) - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") - set(FXDIV_URL ${EXTERNAL_DOWNLOAD_SERVER}/Maratyszcza/FXdiv/archive/f8c5354679ec2597792bc70a9e06eff50c508b9a.tar.gz) - - ExternalSource_Get("FXDIV" ${DOWNLOAD_NNPACK} ${FXDIV_URL}) - - set(FXDIV_SOURCE_DIR ${FXDIV_SOURCE_DIR} PARENT_SCOPE) - set(FXDIV_SOURCE_FOUND ${FXDIV_SOURCE_GET} PARENT_SCOPE) -endfunction(_FXdivSource_import) - -_FXdivSource_import() diff --git a/infra/cmake/packages/FarmhashSourceConfig.cmake b/infra/cmake/packages/FarmhashSourceConfig.cmake index 66cadab..8a9a384 100644 --- a/infra/cmake/packages/FarmhashSourceConfig.cmake +++ b/infra/cmake/packages/FarmhashSourceConfig.cmake @@ -7,10 +7,12 @@ function(_FarmhashSource_import) nnas_include(ExternalSourceTools) nnas_include(OptionTools) - # NOTE TensorFlow 1.7 downloads farmhash from the following URL - envoption(FARMHASH_URL https://github.com/google/farmhash/archive/816a4ae622e964763ca0862d9dbd19324a1eaf45.tar.gz) + # NOTE TensorFlow 1.12 downloads farmhash from the following URL + # TensorFlow 1.13.1 downloads farmhash from the following URL + # TensorFlow 2.3-rc0 downloads farmhash from the following URL + envoption(FARMHASH_1_12_URL https://github.com/google/farmhash/archive/816a4ae622e964763ca0862d9dbd19324a1eaf45.tar.gz) - ExternalSource_Download(FARMHASH ${FARMHASH_URL}) + ExternalSource_Download(FARMHASH ${FARMHASH_1_12_URL}) set(FarmhashSource_DIR ${FARMHASH_SOURCE_DIR} PARENT_SCOPE) set(FarmhashSource_FOUND TRUE PARENT_SCOPE) diff --git a/infra/cmake/packages/FlatBuffersSource-1.10/FlatBuffersSourceConfig.cmake b/infra/cmake/packages/FlatBuffersSource-1.10/FlatBuffersSourceConfig.cmake index 5a7aa3a..09a922b 100644 --- a/infra/cmake/packages/FlatBuffersSource-1.10/FlatBuffersSourceConfig.cmake +++ b/infra/cmake/packages/FlatBuffersSource-1.10/FlatBuffersSourceConfig.cmake @@ -8,8 +8,11 @@ function(_FlatBuffersSource_import) nnas_include(OptionTools) envoption(FLATBUFFERS_1_10_URL https://github.com/google/flatbuffers/archive/v1.10.0.tar.gz) - - ExternalSource_Download(FLATBUFFERS DIRNAME FLATBUFFERS-1.10 ${FLATBUFFERS_1_10_URL}) + ExternalSource_Download(FLATBUFFERS + DIRNAME FLATBUFFERS-1.10 + CHECKSUM MD5=f7d19a3f021d93422b0bc287d7148cd2 + URL ${FLATBUFFERS_1_10_URL} + ) set(FlatBuffersSource_DIR ${FLATBUFFERS_SOURCE_DIR} PARENT_SCOPE) set(FlatBuffersSource_FOUND TRUE PARENT_SCOPE) diff --git a/infra/cmake/packages/FlatBuffersSource-1.11/FlatBuffersSourceConfig.cmake b/infra/cmake/packages/FlatBuffersSource-1.11/FlatBuffersSourceConfig.cmake new file mode 100644 index 0000000..92efbf9 --- /dev/null +++ b/infra/cmake/packages/FlatBuffersSource-1.11/FlatBuffersSourceConfig.cmake @@ -0,0 +1,21 @@ +function(_FlatBuffersSource_import) + if(NOT DOWNLOAD_FLATBUFFERS) + set(FlatBuffersSource_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT DOWNLOAD_FLATBUFFERS) + + nnas_include(ExternalSourceTools) + nnas_include(OptionTools) + + envoption(FLATBUFFERS_1_11_URL https://github.com/google/flatbuffers/archive/v1.11.0.tar.gz) + ExternalSource_Download(FLATBUFFERS + DIRNAME FLATBUFFERS-1.11 + CHECKSUM MD5=02c64880acb89dbd57eebacfd67200d8 + URL ${FLATBUFFERS_1_11_URL} + ) + + set(FlatBuffersSource_DIR ${FLATBUFFERS_SOURCE_DIR} PARENT_SCOPE) + set(FlatBuffersSource_FOUND TRUE PARENT_SCOPE) +endfunction(_FlatBuffersSource_import) + +_FlatBuffersSource_import() diff --git a/infra/cmake/packages/FlatBuffersSource-1.11/FlatBuffersSourceConfigVersion.cmake b/infra/cmake/packages/FlatBuffersSource-1.11/FlatBuffersSourceConfigVersion.cmake new file mode 100644 index 0000000..f008e05 --- /dev/null +++ b/infra/cmake/packages/FlatBuffersSource-1.11/FlatBuffersSourceConfigVersion.cmake @@ -0,0 +1,10 @@ +set(PACKAGE_VERSION "1.11") +set(PACKAGE_VERSION_EXACT FALSE) +set(PACKAGE_VERSION_COMPATIBLE FALSE) +set(PACKAGE_VERSION_UNSUITABLE TRUE) + +if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) + set(PACKAGE_VERSION_EXACT TRUE) + set(PACKAGE_VERSION_COMPATIBLE TRUE) + set(PACKAGE_VERSION_UNSUITABLE FALSE) +endif(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) diff --git a/infra/cmake/packages/FlatBuffersSource-1.8/FlatBuffersSourceConfig.cmake b/infra/cmake/packages/FlatBuffersSource-1.8/FlatBuffersSourceConfig.cmake deleted file mode 100644 index a44f325..0000000 --- a/infra/cmake/packages/FlatBuffersSource-1.8/FlatBuffersSourceConfig.cmake +++ /dev/null @@ -1,18 +0,0 @@ -function(_FlatBuffersSource_import) - if(NOT DOWNLOAD_FLATBUFFERS) - set(FlatBuffersSource_FOUND FALSE PARENT_SCOPE) - return() - endif(NOT DOWNLOAD_FLATBUFFERS) - - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - envoption(FLATBUFFERS_1_8_URL https://github.com/google/flatbuffers/archive/v1.8.0.tar.gz) - - ExternalSource_Download(FLATBUFFERS DIRNAME FLATBUFFERS-1.8 ${FLATBUFFERS_1_8_URL}) - - set(FlatBuffersSource_DIR ${FLATBUFFERS_SOURCE_DIR} PARENT_SCOPE) - set(FlatBuffersSource_FOUND TRUE PARENT_SCOPE) -endfunction(_FlatBuffersSource_import) - -_FlatBuffersSource_import() diff --git a/infra/cmake/packages/FlatBuffersSource-1.8/FlatBuffersSourceConfigVersion.cmake b/infra/cmake/packages/FlatBuffersSource-1.8/FlatBuffersSourceConfigVersion.cmake deleted file mode 100644 index ac5e9b2..0000000 --- a/infra/cmake/packages/FlatBuffersSource-1.8/FlatBuffersSourceConfigVersion.cmake +++ /dev/null @@ -1,10 +0,0 @@ -set(PACKAGE_VERSION "1.8") -set(PACKAGE_VERSION_EXACT FALSE) -set(PACKAGE_VERSION_COMPATIBLE FALSE) -set(PACKAGE_VERSION_UNSUITABLE TRUE) - -if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) - set(PACKAGE_VERSION_EXACT TRUE) - set(PACKAGE_VERSION_COMPATIBLE TRUE) - set(PACKAGE_VERSION_UNSUITABLE FALSE) -endif(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) diff --git a/infra/cmake/packages/FlatBuffersSourceConfig.cmake b/infra/cmake/packages/FlatBuffersSourceConfig.cmake index a35bc37..52bce6d 100644 --- a/infra/cmake/packages/FlatBuffersSourceConfig.cmake +++ b/infra/cmake/packages/FlatBuffersSourceConfig.cmake @@ -15,8 +15,11 @@ function(_FlatBuffersSource_import) # # TODO Manage multiple versions envoption(FLATBUFFERS_URL https://github.com/google/flatbuffers/archive/v1.10.0.tar.gz) - - ExternalSource_Download(FLATBUFFERS ${FLATBUFFERS_URL}) + ExternalSource_Download(FLATBUFFERS + DIRNAME FLATBUFFERS + CHECKSUM MD5=f7d19a3f021d93422b0bc287d7148cd2 + URL ${FLATBUFFERS_URL} + ) set(FlatBuffersSource_DIR ${FLATBUFFERS_SOURCE_DIR} PARENT_SCOPE) set(FlatBuffersSource_FOUND TRUE PARENT_SCOPE) diff --git a/infra/cmake/packages/GEMMLowpSourceConfig.cmake b/infra/cmake/packages/GEMMLowpSourceConfig.cmake index f5fe9d7..6e1cfa9 100644 --- a/infra/cmake/packages/GEMMLowpSourceConfig.cmake +++ b/infra/cmake/packages/GEMMLowpSourceConfig.cmake @@ -7,8 +7,9 @@ function(_GEMMLowpSource_import) nnas_include(ExternalSourceTools) nnas_include(OptionTools) - # NOTE TensorFlow 1.7 uses the following URL - envoption(GEMMLOWP_URL https://github.com/google/gemmlowp/archive/7c7c744640ddc3d0af18fb245b4d23228813a71b.zip) + # NOTE TensorFlow 1.12 uses the following URL + # TensorFlow 1.13.1 uses the following URL + envoption(GEMMLOWP_URL https://github.com/google/gemmlowp/archive/38ebac7b059e84692f53e5938f97a9943c120d98.tar.gz) ExternalSource_Download(GEMMLOWP ${GEMMLOWP_URL}) diff --git a/infra/cmake/packages/HDF5Config.cmake b/infra/cmake/packages/HDF5Config.cmake new file mode 100644 index 0000000..e282e0b --- /dev/null +++ b/infra/cmake/packages/HDF5Config.cmake @@ -0,0 +1,70 @@ +function(_HDF5_build) + if(NOT BUILD_HDF5) + return() + endif(NOT BUILD_HDF5) + + nnas_find_package(HDF5Source QUIET) + + if(NOT HDF5Source_FOUND) + return() + endif(NOT HDF5Source_FOUND) + + nnas_include(ExternalBuildTools) + ExternalBuild_CMake(CMAKE_DIR ${HDF5Source_DIR} + BUILD_DIR ${CMAKE_BINARY_DIR}/externals/HDF5/build + INSTALL_DIR ${EXT_OVERLAY_DIR} + IDENTIFIER "1.8.16" + PKG_NAME "HDF5" + EXTRA_OPTS "-DBUILD_SHARED_LIBS:BOOL=ON" + "-DHDF5_BUILD_TOOLS:BOOL=ON" + "-DHDF5_ENABLE_SZIP_SUPPORT:BOOL=OFF" + "-DHDF5_ENABLE_Z_LIB_SUPPORT:BOOL=OFF") + +endfunction(_HDF5_build) + +_HDF5_build() + +find_path(HDF5_CONFIG_DIR "hdf5-config.cmake" + PATHS ${EXT_OVERLAY_DIR} + PATH_SUFFIXES + share/cmake + share/cmake/hdf5 + cmake/hdf5 + lib/cmake/hdf5) + +include(${HDF5_CONFIG_DIR}/hdf5-config.cmake) + +unset(HDF5_INCLUDE_DIRS) +unset(HDF5_C_INCLUDE_DIRS) +unset(HDF5_CXX_INCLUDE_DIRS) +unset(HDF5_HL_INCLUDE_DIRS) + +unset(HDF5_LIBRARIES) +unset(HDF5_HL_LIBRARIES) +unset(HDF5_C_LIBRARIES) +unset(HDF5_CXX_LIBRARIES) +unset(HDF5_C_HL_LIBRARIES) +unset(HDF5_CXX_HL_LIBRARIES) + +# If user doesn't specify static or shared, set it to shared by default +list(FIND HDF5_FIND_COMPONENTS "STATIC" _index) +if(${_index} GREATER -1) + # static + set(_SUFFIX "-static") +else() + # shared + set(_SUFFIX "-shared") +endif() + +list(REMOVE_ITEM HDF5_FIND_COMPONENTS "static;shared") +set(HDF5_INCLUDE_DIRS ${HDF5_INCLUDE_DIR}) +foreach(COMP HDF5_FIND_COMPONENTS) + set(HDF5_${COMP}_INCLUDE_DIRS ${HDF5_INCLUDE_DIR}) +endforeach() + +set(HDF5_LIBRARIES "hdf5${_SUFFIX}") +set(HDF5_C_LIBRARIES "hdf5${_SUFFIX}") +set(HDF5_CXX_LIBRARIES "hdf5_cpp${_SUFFIX}") +set(HDF5_HL_LIBRARIES "hdf5_hl${_SUFFIX}") +set(HDF5_C_HL_LIBRARIES "hdf5_hl${_SUFFIX}") +set(HDF5_CXX_HL_LIBRARIES "hdf5_hl_cpp${_SUFFIX}") diff --git a/infra/cmake/packages/HDF5SourceConfig.cmake b/infra/cmake/packages/HDF5SourceConfig.cmake new file mode 100644 index 0000000..134efa6 --- /dev/null +++ b/infra/cmake/packages/HDF5SourceConfig.cmake @@ -0,0 +1,18 @@ +function(_HDF5Source_import) + if(NOT DOWNLOAD_HDF5) + set(HDF5Source_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT DOWNLOAD_HDF5) + + nnas_include(ExternalSourceTools) + nnas_include(OptionTools) + + envoption(HDF5_URL https://github.com/HDFGroup/hdf5/archive/hdf5-1_8_16.tar.gz) + + ExternalSource_Download(HDF5 ${HDF5_URL}) + + set(HDF5Source_DIR ${HDF5_SOURCE_DIR} PARENT_SCOPE) + set(HDF5Source_FOUND TRUE PARENT_SCOPE) +endfunction(_HDF5Source_import) + +_HDF5Source_import() diff --git a/infra/cmake/packages/NEON2SSESourceConfig.cmake b/infra/cmake/packages/NEON2SSESourceConfig.cmake index c6585c0..5970ec7 100644 --- a/infra/cmake/packages/NEON2SSESourceConfig.cmake +++ b/infra/cmake/packages/NEON2SSESourceConfig.cmake @@ -7,10 +7,12 @@ function(_NEON2SSESource_import) nnas_include(ExternalSourceTools) nnas_include(OptionTools) - # NOTE TensorFlow 1.7 downloads NEON2SSE from the following URL - envoption(NEON2SSE_URL https://github.com/intel/ARM_NEON_2_x86_SSE/archive/0f77d9d182265259b135dad949230ecbf1a2633d.tar.gz) + # NOTE TensorFlow 1.12 downloads NEON2SSE from the following URL + # NOTE TensorFlow 1.13.1 downloads NEON2SSE from the following URL + # NOTE TensorFlow 2.2 downloads NEON2SSE from the following URL + envoption(NEON2SSE_1_12_URL https://github.com/intel/ARM_NEON_2_x86_SSE/archive/1200fe90bb174a6224a525ee60148671a786a71f.tar.gz) - ExternalSource_Download(NEON2SSE ${NEON2SSE_URL}) + ExternalSource_Download(NEON2SSE ${NEON2SSE_1_12_URL}) set(NEON2SSESource_DIR ${NEON2SSE_SOURCE_DIR} PARENT_SCOPE) set(NEON2SSESource_FOUND TRUE PARENT_SCOPE) diff --git a/infra/cmake/packages/NNPACKConfig.cmake b/infra/cmake/packages/NNPACKConfig.cmake deleted file mode 100644 index 31a02a6..0000000 --- a/infra/cmake/packages/NNPACKConfig.cmake +++ /dev/null @@ -1,51 +0,0 @@ -function(_NNPACK_Import) - nnas_find_package(NNPACKSource QUIET) - - if(NOT NNPACK_SOURCE_FOUND) - set(NNPACK_FOUND FALSE PARENT_SCOPE) - message(STATUS "NNPACK not found") - return() - endif(NOT NNPACK_SOURCE_FOUND) - - nnas_find_package(CpuinfoSource REQUIRED) - nnas_find_package(FP16Source REQUIRED) - nnas_find_package(FXdivSource REQUIRED) - nnas_find_package(PSIMDSource REQUIRED) - nnas_find_package(PthreadpoolSource REQUIRED) - nnas_find_package(SixSource REQUIRED) - nnas_find_package(Enum34Source REQUIRED) - nnas_find_package(OpcodesSource REQUIRED) - nnas_find_package(PeachpySource QUIET) - - if(NOT PYTHON_PEACHPY_SOURCE_FOUND) - set(NNPACK_FOUND FALSE PARENT_SCOPE) - return() - endif(NOT PYTHON_PEACHPY_SOURCE_FOUND) - - # workaround for CI - set(THREADS_PTHREAD_ARG "2" CACHE STRING "Forcibly set by CMakeLists.txt." FORCE) - if(NOT TARGET nnpack) - # Allows us to build nnpack at build time - set(NNPACK_BUILD_TESTS OFF CACHE BOOL "") - set(NNPACK_BUILD_BENCHMARKS OFF CACHE BOOL "") - set(NNPACK_LIBRARY_TYPE "static" CACHE STRING "") - set(PTHREADPOOL_LIBRARY_TYPE "static" CACHE STRING "") - set(CPUINFO_LIBRARY_TYPE "static" CACHE STRING "") - nnas_include(ExternalProjectTools) - add_extdirectory("${NNPACK_SOURCE_DIR}" nnpack EXCLUDE_FROM_ALL) - # We build static versions of nnpack and pthreadpool but link - # them into a shared library (high-perf-backend), so they need PIC. - set_property(TARGET nnpack PROPERTY POSITION_INDEPENDENT_CODE ON) - set_property(TARGET pthreadpool PROPERTY POSITION_INDEPENDENT_CODE ON) - set_property(TARGET cpuinfo PROPERTY POSITION_INDEPENDENT_CODE ON) - endif() - - set(NNPACK_FOUND TRUE PARENT_SCOPE) - set(NNPACK_INCLUDE_DIRS - $ - $ PARENT_SCOPE) - set(NNPACK_LIBRARIES $ $ PARENT_SCOPE) - -endfunction(_NNPACK_Import) - -_NNPACK_Import() diff --git a/infra/cmake/packages/NNPACKSourceConfig.cmake b/infra/cmake/packages/NNPACKSourceConfig.cmake deleted file mode 100644 index 10a08ea..0000000 --- a/infra/cmake/packages/NNPACKSourceConfig.cmake +++ /dev/null @@ -1,20 +0,0 @@ -function(_NNPACKSource_import) - if(NOT DOWNLOAD_NNPACK) - set(NNPACKSource_FOUND FALSE PARENT_SCOPE) - message(WARNING "NNPACK not downloaded") - return() - endif(NOT DOWNLOAD_NNPACK) - - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") - set(NNPACK_URL ${EXTERNAL_DOWNLOAD_SERVER}/Maratyszcza/NNPACK/archive/c039579abe21f5756e0f0e45e8e767adccc11852.tar.gz) - ExternalSource_Get("NNPACK" ${DOWNLOAD_NNPACK} ${NNPACK_URL}) - - set(NNPACK_SOURCE_DIR ${NNPACK_SOURCE_DIR} PARENT_SCOPE) - set(NNPACK_INCLUDE_DIR ${NNPACK_SOURCE_DIR}/include PARENT_SCOPE) - set(NNPACK_SOURCE_FOUND ${NNPACK_SOURCE_GET} PARENT_SCOPE) -endfunction(_NNPACKSource_import) - -_NNPACKSource_import() diff --git a/infra/cmake/packages/NoniusConfig.cmake b/infra/cmake/packages/NoniusConfig.cmake deleted file mode 100644 index fa18ebb..0000000 --- a/infra/cmake/packages/NoniusConfig.cmake +++ /dev/null @@ -1,26 +0,0 @@ -function(_Nonius_import) - nnas_find_package(NoniusSource QUIET) - - if(NOT NoniusSource_FOUND) - set(Nonius_FOUND FALSE PARENT_SCOPE) - return() - endif(NOT NoniusSource_FOUND) - - if(NOT TARGET nonius) - message(STATUS "Found nonius: TRUE") - add_library(nonius INTERFACE) - target_include_directories(nonius INTERFACE "${NoniusSource_DIR}/include") - endif(NOT TARGET nonius) - - if(BUILD_KBENCHMARK) - # Copy html_report_template.g.h++ file to externals/nonius. - # This header file is modified to show the html summary view according to the layer in kbenchmark. - execute_process(COMMAND ${CMAKE_COMMAND} -E copy - "${CMAKE_CURRENT_LIST_DIR}/Nonius/html_report_template.g.h++" - "${NoniusSource_DIR}/include/nonius/detail") - endif(BUILD_KBENCHMARK) - - set(Nonius_FOUND TRUE PARENT_SCOPE) -endfunction(_Nonius_import) - -_Nonius_import() diff --git a/infra/cmake/packages/NoniusSourceConfig.cmake b/infra/cmake/packages/NoniusSourceConfig.cmake index 16de639..0af23ef 100644 --- a/infra/cmake/packages/NoniusSourceConfig.cmake +++ b/infra/cmake/packages/NoniusSourceConfig.cmake @@ -1,10 +1,23 @@ function(_NoniusSource_import) + if(NOT ${DOWNLOAD_NONIUS}) + set(NoniusSource_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT ${DOWNLOAD_NONIUS}) + nnas_include(ExternalSourceTools) nnas_include(OptionTools) envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") set(NONIUS_URL ${EXTERNAL_DOWNLOAD_SERVER}/libnonius/nonius/archive/v1.2.0-beta.1.tar.gz) - ExternalSource_Get("NONIUS" ${DOWNLOAD_NONIUS} ${NONIUS_URL}) + ExternalSource_Download("NONIUS" ${NONIUS_URL}) + + if(BUILD_KBENCHMARK) + # Copy html_report_template.g.h++ file to externals/nonius. + # This header file is modified to show the html summary view according to the layer in kbenchmark. + execute_process(COMMAND ${CMAKE_COMMAND} -E copy + "${CMAKE_CURRENT_LIST_DIR}/Nonius/html_report_template.g.h++" + "${NoniusSource_DIR}/include/nonius/detail") + endif(BUILD_KBENCHMARK) set(NoniusSource_DIR ${NONIUS_SOURCE_DIR} PARENT_SCOPE) set(NoniusSource_FOUND ${NONIUS_SOURCE_GET} PARENT_SCOPE) diff --git a/infra/cmake/packages/OpcodesSourceConfig.cmake b/infra/cmake/packages/OpcodesSourceConfig.cmake deleted file mode 100644 index 8b324ea..0000000 --- a/infra/cmake/packages/OpcodesSourceConfig.cmake +++ /dev/null @@ -1,13 +0,0 @@ -function(_PeachpySource_import) - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") - set(PEACHPY_URL ${EXTERNAL_DOWNLOAD_SERVER}/Maratyszcza/Opcodes/archive/6e2b0cd9f1403ecaf164dea7019dd54db5aea252.tar.gz) - ExternalSource_Get("PYTHON_OPCODES" ${DOWNLOAD_NNPACK} ${PEACHPY_URL}) - - set(PYTHON_OPCODES_SOURCE_DIR ${PYTHON_OPCODES_SOURCE_DIR} PARENT_SCOPE) - set(PYTHON_OPCODES_SOURCE_FOUND ${PYTHON_OPCODES_SOURCE_GET} PARENT_SCOPE) -endfunction(_PeachpySource_import) - -_PeachpySource_import() diff --git a/infra/cmake/packages/PSIMDSourceConfig.cmake b/infra/cmake/packages/PSIMDSourceConfig.cmake deleted file mode 100644 index c14ad73..0000000 --- a/infra/cmake/packages/PSIMDSourceConfig.cmake +++ /dev/null @@ -1,14 +0,0 @@ -function(_PSIMDSource_import) - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") - set(PSIMD_URL ${EXTERNAL_DOWNLOAD_SERVER}/Maratyszcza/psimd/archive/90a938f30ba414ada2f4b00674ee9631d7d85e19.tar.gz) - - ExternalSource_Get("PSIMD" ${DOWNLOAD_NNPACK} ${PSIMD_URL}) - - set(PSIMD_SOURCE_DIR ${PSIMD_SOURCE_DIR} PARENT_SCOPE) - set(PSIMD_SOURCE_FOUND ${PSIMD_SOURCE_GET} PARENT_SCOPE) -endfunction(_PSIMDSource_import) - -_PSIMDSource_import() diff --git a/infra/cmake/packages/PeachpySourceConfig.cmake b/infra/cmake/packages/PeachpySourceConfig.cmake deleted file mode 100644 index fea8d14..0000000 --- a/infra/cmake/packages/PeachpySourceConfig.cmake +++ /dev/null @@ -1,31 +0,0 @@ -function(_PeachpySource_import) - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") - set(PEACHPY_URL ${EXTERNAL_DOWNLOAD_SERVER}/Maratyszcza/PeachPy/archive/01d15157a973a4ae16b8046313ddab371ea582db.tar.gz) - - ExternalSource_Get("PYTHON_PEACHPY" ${DOWNLOAD_NNPACK} ${PEACHPY_URL}) - FIND_PACKAGE(PythonInterp) - - nnfw_find_package(SixSource REQUIRED) - nnfw_find_package(Enum34Source REQUIRED) - nnfw_find_package(OpcodesSource REQUIRED) - - # Generate opcodes: - SET(ENV{PYTHONPATH} ${PYTHON_PEACHPY_SOURCE_DIR}:${PYTHON_SIX_SOURCE_DIR}:${PYTHON_ENUM_SOURCE_DIR}:${PYTHON_OPCODES_SOURCE_DIR}) - EXECUTE_PROCESS(COMMAND ${PYTHON_EXECUTABLE} ./codegen/x86_64.py - WORKING_DIRECTORY ${PYTHON_PEACHPY_SOURCE_DIR} - RESULT_VARIABLE BUILT_PP) - - if(NOT BUILT_PP EQUAL 0) - # Mark PYTHON_PEACHPY_SOURCE_FOUND as FALSE if source generation fails - set(PYTHON_PEACHPY_SOURCE_FOUND FALSE PARENT_SCOPE) - return() - endif(NOT BUILT_PP EQUAL 0) - - set(PYTHON_PEACHPY_SOURCE_DIR ${PYTHON_PEACHPY_SOURCE_DIR} PARENT_SCOPE) - set(PYTHON_PEACHPY_SOURCE_FOUND ${PYTHON_PEACHPY_SOURCE_GET} PARENT_SCOPE) -endfunction(_PeachpySource_import) - -_PeachpySource_import() diff --git a/infra/cmake/packages/PthreadpoolSourceConfig.cmake b/infra/cmake/packages/PthreadpoolSourceConfig.cmake deleted file mode 100644 index a570a99..0000000 --- a/infra/cmake/packages/PthreadpoolSourceConfig.cmake +++ /dev/null @@ -1,14 +0,0 @@ -function(_pthreadpoolSource_import) - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") - set(PTHREADPOOL_URL ${EXTERNAL_DOWNLOAD_SERVER}/Maratyszcza/pthreadpool/archive/6673a4c71fe35e077c6843a74017d9c25610c537.tar.gz) - - ExternalSource_Get("PTHREADPOOL" ${DOWNLOAD_NNPACK} ${PTHREADPOOL_URL}) - - set(PTHREADPOOL_SOURCE_DIR ${PTHREADPOOL_SOURCE_DIR} PARENT_SCOPE) - set(PTHREADPOOL_SOURCE_FOUND ${PTHREADPOOL_SOURCE_GET} PARENT_SCOPE) -endfunction(_pthreadpoolSource_import) - -_pthreadpoolSource_import() diff --git a/infra/cmake/packages/SixSourceConfig.cmake b/infra/cmake/packages/SixSourceConfig.cmake deleted file mode 100644 index f6f85ca..0000000 --- a/infra/cmake/packages/SixSourceConfig.cmake +++ /dev/null @@ -1,14 +0,0 @@ -function(_SIXSource_import) - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") - set(six_URL ${EXTERNAL_DOWNLOAD_SERVER}/benjaminp/six/archive/1.11.0.tar.gz) - - ExternalSource_Get("PYTHON_SIX" ${DOWNLOAD_NNPACK} ${six_URL}) - - set(PYTHON_SIX_SOURCE_DIR ${PYTHON_SIX_SOURCE_DIR} PARENT_SCOPE) - set(PYTHON_SIX_SOURCE_FOUND ${PYTHON_SIX_SOURCE_GET} PARENT_SCOPE) -endfunction(_SIXSource_import) - -_SIXSource_import() diff --git a/infra/cmake/packages/TensorFlowEigenSource-2.1.0/TensorFlowEigenSourceConfig.cmake b/infra/cmake/packages/TensorFlowEigenSource-2.1.0/TensorFlowEigenSourceConfig.cmake new file mode 100644 index 0000000..f846755 --- /dev/null +++ b/infra/cmake/packages/TensorFlowEigenSource-2.1.0/TensorFlowEigenSourceConfig.cmake @@ -0,0 +1,20 @@ +function(_TensorFlowEigenSource_import) + if(NOT DOWNLOAD_EIGEN) + set(TensorFlowEigenSource_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT DOWNLOAD_EIGEN) + + nnas_include(ExternalSourceTools) + nnas_include(OptionTools) + + # Exact version used by TensorFlow v2.1.0. + # See tensorflow/tensorflow/workspace.bzl. + envoption(TENSORFLOW_2_1_0_EIGEN_URL https://gitlab.com/libeigen/eigen/-/archive/4e696901f873a2347f76d931cf2f701e31e15d05/eigen-4e696901f873a2347f76d931cf2f701e31e15d05.tar.gz) + + ExternalSource_Download(EIGEN DIRNAME TENSORFLOW-2.1.0-EIGEN ${TENSORFLOW_2_1_0_EIGEN_URL}) + + set(TensorFlowEigenSource_DIR ${EIGEN_SOURCE_DIR} PARENT_SCOPE) + set(TensorFlowEigenSource_FOUND TRUE PARENT_SCOPE) +endfunction(_TensorFlowEigenSource_import) + +_TensorFlowEigenSource_import() diff --git a/infra/cmake/packages/TensorFlowEigenSource-2.1.0/TensorFlowEigenSourceConfigVersion.cmake b/infra/cmake/packages/TensorFlowEigenSource-2.1.0/TensorFlowEigenSourceConfigVersion.cmake new file mode 100644 index 0000000..80f43dd --- /dev/null +++ b/infra/cmake/packages/TensorFlowEigenSource-2.1.0/TensorFlowEigenSourceConfigVersion.cmake @@ -0,0 +1,10 @@ +set(PACKAGE_VERSION "2.1.0") +set(PACKAGE_VERSION_EXACT FALSE) +set(PACKAGE_VERSION_COMPATIBLE FALSE) +set(PACKAGE_VERSION_UNSUITABLE TRUE) + +if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) + set(PACKAGE_VERSION_EXACT TRUE) + set(PACKAGE_VERSION_COMPATIBLE TRUE) + set(PACKAGE_VERSION_UNSUITABLE FALSE) +endif(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) diff --git a/infra/cmake/packages/TensorFlowEigenSource-2.3.0-rc0Config.cmake b/infra/cmake/packages/TensorFlowEigenSource-2.3.0-rc0Config.cmake new file mode 100644 index 0000000..207f7b5 --- /dev/null +++ b/infra/cmake/packages/TensorFlowEigenSource-2.3.0-rc0Config.cmake @@ -0,0 +1,23 @@ +function(_TensorFlowEigenSource_import) + if(NOT DOWNLOAD_EIGEN) + set(TensorFlowEigenSource_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT DOWNLOAD_EIGEN) + + nnas_include(ExternalSourceTools) + nnas_include(OptionTools) + + # NOTE TensorFlow 2.3.0-rc0 uses the following URL + envoption(EXTERNAL_DOWNLOAD_SERVER "https://gitlab.com") + envoption(TENSORFLOW_2_3_0_EIGEN_URL ${EXTERNAL_DOWNLOAD_SERVER}/libeigen/eigen/-/archive/386d809bde475c65b7940f290efe80e6a05878c4/eigen-386d809bde475c65b7940f290efe80e6a05878c4.tar.gz) + + ExternalSource_Download(EIGEN + DIRNAME TENSORFLOW-2.3.0-EIGEN + URL ${TENSORFLOW_2_3_0_EIGEN_URL} +) + + set(TensorFlowEigenSource_DIR ${EIGEN_SOURCE_DIR} PARENT_SCOPE) + set(TensorFlowEigenSource_FOUND TRUE PARENT_SCOPE) +endfunction(_TensorFlowEigenSource_import) + +_TensorFlowEigenSource_import() diff --git a/infra/cmake/packages/TensorFlowGEMMLowpSource-2.1.0/TensorFlowGEMMLowpSourceConfig.cmake b/infra/cmake/packages/TensorFlowGEMMLowpSource-2.1.0/TensorFlowGEMMLowpSourceConfig.cmake new file mode 100644 index 0000000..035264f --- /dev/null +++ b/infra/cmake/packages/TensorFlowGEMMLowpSource-2.1.0/TensorFlowGEMMLowpSourceConfig.cmake @@ -0,0 +1,20 @@ +function(_TensorFlowGEMMLowpSource_import) + if(NOT DOWNLOAD_GEMMLOWP) + set(TensorFlowGEMMLowpSource_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT DOWNLOAD_GEMMLOWP) + + nnas_include(ExternalSourceTools) + nnas_include(OptionTools) + + # Exact version used by TensorFlow v2.1.0. + # See tensorflow/tensorflow/workspace.bzl. + envoption(TENSORFLOW_2_1_0_GEMMLOWP_URL https://github.com/google/gemmlowp/archive/12fed0cd7cfcd9e169bf1925bc3a7a58725fdcc3.zip) + + ExternalSource_Download(GEMMLOWP DIRNAME TENSORFLOW-2.1.0-GEMMLOWP ${TENSORFLOW_2_1_0_GEMMLOWP_URL}) + + set(TensorFlowGEMMLowpSource_DIR ${GEMMLOWP_SOURCE_DIR} PARENT_SCOPE) + set(TensorFlowGEMMLowpSource_FOUND TRUE PARENT_SCOPE) +endfunction(_TensorFlowGEMMLowpSource_import) + +_TensorFlowGEMMLowpSource_import() diff --git a/infra/cmake/packages/TensorFlowGEMMLowpSource-2.1.0/TensorFlowGEMMLowpSourceConfigVersion.cmake b/infra/cmake/packages/TensorFlowGEMMLowpSource-2.1.0/TensorFlowGEMMLowpSourceConfigVersion.cmake new file mode 100644 index 0000000..80f43dd --- /dev/null +++ b/infra/cmake/packages/TensorFlowGEMMLowpSource-2.1.0/TensorFlowGEMMLowpSourceConfigVersion.cmake @@ -0,0 +1,10 @@ +set(PACKAGE_VERSION "2.1.0") +set(PACKAGE_VERSION_EXACT FALSE) +set(PACKAGE_VERSION_COMPATIBLE FALSE) +set(PACKAGE_VERSION_UNSUITABLE TRUE) + +if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) + set(PACKAGE_VERSION_EXACT TRUE) + set(PACKAGE_VERSION_COMPATIBLE TRUE) + set(PACKAGE_VERSION_UNSUITABLE FALSE) +endif(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) diff --git a/infra/cmake/packages/TensorFlowLite-1.12/Lite/CMakeLists.txt b/infra/cmake/packages/TensorFlowLite-1.12/Lite/CMakeLists.txt index 068022f..337d6b2 100644 --- a/infra/cmake/packages/TensorFlowLite-1.12/Lite/CMakeLists.txt +++ b/infra/cmake/packages/TensorFlowLite-1.12/Lite/CMakeLists.txt @@ -38,4 +38,4 @@ if(COMPILER_SUPPORT_EXTERN_C_COMPAT_WARNING) target_compile_options(tensorflowlite-1.12 PUBLIC -Wno-extern-c-compat) endif(COMPILER_SUPPORT_EXTERN_C_COMPAT_WARNING) target_compile_definitions(tensorflowlite-1.12 PUBLIC "GEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK") -target_link_libraries(tensorflowlite-1.12 eigen gemmlowp neon2sse farmhash abseil dl) +target_link_libraries(tensorflowlite-1.12 eigen-fd6845384b86 gemmlowp neon2sse farmhash abseil dl) diff --git a/infra/cmake/packages/TensorFlowLite-1.12/TensorFlowLiteConfig.cmake b/infra/cmake/packages/TensorFlowLite-1.12/TensorFlowLiteConfig.cmake index 750c613..ff15d85 100644 --- a/infra/cmake/packages/TensorFlowLite-1.12/TensorFlowLiteConfig.cmake +++ b/infra/cmake/packages/TensorFlowLite-1.12/TensorFlowLiteConfig.cmake @@ -23,12 +23,12 @@ function(_TensorFlowLite_import) return() endif(NOT Farmhash_FOUND) - nnas_find_package(Eigen QUIET) + nnas_find_package(Eigen-fd6845384b86 QUIET) - if(NOT Eigen_FOUND) + if(NOT Eigen-fd6845384b86_FOUND) set(TensorFlowLite_FOUND FALSE PARENT_SCOPE) return() - endif(NOT Eigen_FOUND) + endif(NOT Eigen-fd6845384b86_FOUND) nnas_find_package(GEMMLowp QUIET) diff --git a/infra/cmake/packages/TensorFlowLite-1.13.1/Lite/CMakeLists.txt b/infra/cmake/packages/TensorFlowLite-1.13.1/Lite/CMakeLists.txt new file mode 100644 index 0000000..c356174 --- /dev/null +++ b/infra/cmake/packages/TensorFlowLite-1.13.1/Lite/CMakeLists.txt @@ -0,0 +1,46 @@ +# NOTE The followings SHOULD be defined before using this CMakeLists.txt +# +# 'TensorFlowSource_DIR' variable +# 'FlatBuffersSource_DIR' variable +# 'eigen' target +# 'gemmlowp' target +# 'neon2sse' target +# 'farmhash' target +# 'abseil' target +# +message(STATUS "Build TensorFlow Lite from ${TensorFlowSource_DIR}") + +set(TensorFlowLiteSource_DIR ${TensorFlowSource_DIR}/tensorflow/lite) + +file(GLOB CORE_SRCS + "${TensorFlowLiteSource_DIR}/*.c" + "${TensorFlowLiteSource_DIR}/*.cc" + "${TensorFlowLiteSource_DIR}/c/*.c" + "${TensorFlowLiteSource_DIR}/core/*.cc" + "${TensorFlowLiteSource_DIR}/core/api/*.cc") +file(GLOB_RECURSE CORE_TESTS "${TensorFlowLiteSource_DIR}/*test*.cc") +list(REMOVE_ITEM CORE_SRCS ${CORE_TESTS}) + +file(GLOB_RECURSE KERNEL_SRCS "${TensorFlowLiteSource_DIR}/kernels/*.cc") +file(GLOB_RECURSE KERNEL_TESTS "${TensorFlowLiteSource_DIR}/kernels/*test*.cc") +list(REMOVE_ITEM KERNEL_SRCS ${KERNEL_TESTS}) +# Exclude buggy kernel(s) from the build +#list(REMOVE_ITEM KERNEL_SRCS "${TensorFlowLiteSource_DIR}/kernels/internal/spectrogram.cc") + +list(APPEND SRCS ${CORE_SRCS}) +list(APPEND SRCS ${KERNEL_SRCS}) + +include(CheckCXXCompilerFlag) + +CHECK_CXX_COMPILER_FLAG(-Wno-extern-c-compat COMPILER_SUPPORT_EXTERN_C_COMPAT_WARNING) + +add_library(tensorflowlite-1.13.1 ${SRCS}) +set_target_properties(tensorflowlite-1.13.1 PROPERTIES POSITION_INDEPENDENT_CODE ON) +target_include_directories(tensorflowlite-1.13.1 PUBLIC ${TensorFlowSource_DIR}) +target_include_directories(tensorflowlite-1.13.1 PUBLIC ${FlatBuffersSource_DIR}/include) +target_compile_options(tensorflowlite-1.13.1 PUBLIC -Wno-ignored-attributes) +if(COMPILER_SUPPORT_EXTERN_C_COMPAT_WARNING) + target_compile_options(tensorflowlite-1.13.1 PUBLIC -Wno-extern-c-compat) +endif(COMPILER_SUPPORT_EXTERN_C_COMPAT_WARNING) +target_compile_definitions(tensorflowlite-1.13.1 PUBLIC "GEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK") +target_link_libraries(tensorflowlite-1.13.1 eigen gemmlowp neon2sse farmhash abseil dl) diff --git a/infra/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfig.cmake b/infra/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfig.cmake new file mode 100644 index 0000000..2c6bd9f --- /dev/null +++ b/infra/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfig.cmake @@ -0,0 +1,59 @@ +function(_TensorFlowLite_import) + nnas_find_package(TensorFlowSource EXACT 1.13.1 QUIET) + + if(NOT TensorFlowSource_FOUND) + set(TensorFlowLite_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT TensorFlowSource_FOUND) + + nnas_find_package(FlatBuffersSource EXACT 1.10 QUIET) + + if(NOT FlatBuffersSource_FOUND) + set(TensorFlowLite_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT FlatBuffersSource_FOUND) + + nnas_find_package(Farmhash QUIET) + + if(NOT Farmhash_FOUND) + set(TensorFlowLite_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT Farmhash_FOUND) + + nnas_find_package(Eigen QUIET) + + if(NOT Eigen_FOUND) + set(TensorFlowLite_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT Eigen_FOUND) + + nnas_find_package(GEMMLowp QUIET) + + if(NOT GEMMLowp_FOUND) + set(TensorFlowLite_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT GEMMLowp_FOUND) + + nnas_find_package(NEON2SSE QUIET) + + if(NOT NEON2SSE_FOUND) + set(TensorFlowLite_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT NEON2SSE_FOUND) + + nnas_find_package(Abseil QUIET) + + if(NOT Abseil_FOUND) + set(TensorFlowLite_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT Abseil_FOUND) + + if(NOT TARGET tensorflowlite-1.13.1) + nnas_include(ExternalProjectTools) + add_extdirectory("${CMAKE_CURRENT_LIST_DIR}/Lite" tflite-1.13.1) + endif(NOT TARGET tensorflowlite-1.13.1) + + set(TensorFlowLite_FOUND TRUE PARENT_SCOPE) +endfunction(_TensorFlowLite_import) + +_TensorFlowLite_import() diff --git a/infra/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfigVersion.cmake b/infra/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfigVersion.cmake new file mode 100644 index 0000000..ed79ecd --- /dev/null +++ b/infra/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfigVersion.cmake @@ -0,0 +1,9 @@ +set(PACKAGE_VERSION "1.13.1") +set(PACKAGE_VERSION_EXACT FALSE) +set(PACKAGE_VERSION_COMPATIBLE FALSE) +set(PACKAGE_VERSION_UNSUITABLE TRUE) + +if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) + set(PACKAGE_VERSION_EXACT TRUE) + set(PACKAGE_VERSION_UNSUITABLE FALSE) +endif(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) diff --git a/infra/cmake/packages/TensorFlowLite-1.7/Lite/CMakeLists.txt b/infra/cmake/packages/TensorFlowLite-1.7/Lite/CMakeLists.txt deleted file mode 100644 index c5e89ee..0000000 --- a/infra/cmake/packages/TensorFlowLite-1.7/Lite/CMakeLists.txt +++ /dev/null @@ -1,38 +0,0 @@ -# NOTE The followings SHOULD be defined before using this CMakeLists.txt -# -# 'TensorFlowSource_DIR' variable -# 'FlatBuffersSource_DIR' variable -# 'eigen' target -# 'gemmlowp' target -# 'neon2sse' target -# 'farmhash' target -# -set(TensorFlowLiteSource_DIR ${TensorFlowSource_DIR}/tensorflow/contrib/lite) - -file(GLOB CORE_SRCS "${TensorFlowLiteSource_DIR}/*.c" "${TensorFlowLiteSource_DIR}/*.cc") -file(GLOB CORE_TESTS "${TensorFlowLiteSource_DIR}/*test*.cc") -list(REMOVE_ITEM CORE_SRCS ${CORE_TESTS}) - -file(GLOB_RECURSE KERNEL_SRCS "${TensorFlowLiteSource_DIR}/kernels/*.cc") -file(GLOB_RECURSE KERNEL_TESTS "${TensorFlowLiteSource_DIR}/kernels/*test*.cc") -list(REMOVE_ITEM KERNEL_SRCS ${KERNEL_TESTS}) -# Exclude buggy kernel(s) from the build -list(REMOVE_ITEM KERNEL_SRCS "${TensorFlowLiteSource_DIR}/kernels/internal/spectrogram.cc") - -list(APPEND SRCS ${CORE_SRCS}) -list(APPEND SRCS ${KERNEL_SRCS}) - -include(CheckCXXCompilerFlag) - -CHECK_CXX_COMPILER_FLAG(-Wno-extern-c-compat COMPILER_SUPPORT_EXTERN_C_COMPAT_WARNING) - -add_library(tensorflowlite-1.7 ${SRCS}) -set_target_properties(tensorflowlite-1.7 PROPERTIES POSITION_INDEPENDENT_CODE ON) -target_include_directories(tensorflowlite-1.7 PUBLIC ${TensorFlowSource_DIR}) -target_include_directories(tensorflowlite-1.7 PUBLIC ${FlatBuffersSource_DIR}/include) -target_compile_options(tensorflowlite-1.7 PUBLIC -Wno-ignored-attributes) -if(COMPILER_SUPPORT_EXTERN_C_COMPAT_WARNING) - target_compile_options(tensorflowlite-1.7 PUBLIC -Wno-extern-c-compat) -endif(COMPILER_SUPPORT_EXTERN_C_COMPAT_WARNING) -target_compile_definitions(tensorflowlite-1.7 PUBLIC "GEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK") -target_link_libraries(tensorflowlite-1.7 eigen gemmlowp neon2sse farmhash dl) diff --git a/infra/cmake/packages/TensorFlowLite-1.7/TensorFlowLiteConfig.cmake b/infra/cmake/packages/TensorFlowLite-1.7/TensorFlowLiteConfig.cmake deleted file mode 100644 index 79b58e8..0000000 --- a/infra/cmake/packages/TensorFlowLite-1.7/TensorFlowLiteConfig.cmake +++ /dev/null @@ -1,55 +0,0 @@ -function(_TensorFlowLite_import) - nnas_find_package(TensorFlowSource EXACT 1.7 QUIET) - - if(NOT TensorFlowSource_FOUND) - set(TensorFlowLite_FOUND FALSE PARENT_SCOPE) - return() - endif(NOT TensorFlowSource_FOUND) - - # TensorFlow 1.7 downloads FlatBuffers from https://github.com/google/flatbuffers/archive/971a68110e4.tar.gz - # - # FlatBuffers 1.8 is compatible with 971a68110e4. - nnas_find_package(FlatBuffersSource EXACT 1.8 QUIET) - - if(NOT FlatBuffersSource_FOUND) - set(TensorFlowLite_FOUND FALSE PARENT_SCOPE) - return() - endif(NOT FlatBuffersSource_FOUND) - - nnas_find_package(Farmhash QUIET) - - if(NOT Farmhash_FOUND) - set(TensorFlowLite_FOUND FALSE PARENT_SCOPE) - return() - endif(NOT Farmhash_FOUND) - - nnas_find_package(Eigen QUIET) - - if(NOT Eigen_FOUND) - set(TensorFlowLite_FOUND FALSE PARENT_SCOPE) - return() - endif(NOT Eigen_FOUND) - - nnas_find_package(GEMMLowp QUIET) - - if(NOT GEMMLowp_FOUND) - set(TensorFlowLite_FOUND FALSE PARENT_SCOPE) - return() - endif(NOT GEMMLowp_FOUND) - - nnas_find_package(NEON2SSE QUIET) - - if(NOT NEON2SSE_FOUND) - set(TensorFlowLite_FOUND FALSE PARENT_SCOPE) - return() - endif(NOT NEON2SSE_FOUND) - - if(NOT TARGET tensorflowlite-1.7) - nnas_include(ExternalProjectTools) - add_extdirectory("${CMAKE_CURRENT_LIST_DIR}/Lite" tflite-1.7) - endif(NOT TARGET tensorflowlite-1.7) - - set(TensorFlowLite_FOUND TRUE PARENT_SCOPE) -endfunction(_TensorFlowLite_import) - -_TensorFlowLite_import() diff --git a/infra/cmake/packages/TensorFlowLite-1.7/TensorFlowLiteConfigVersion.cmake b/infra/cmake/packages/TensorFlowLite-1.7/TensorFlowLiteConfigVersion.cmake deleted file mode 100644 index 46609dc..0000000 --- a/infra/cmake/packages/TensorFlowLite-1.7/TensorFlowLiteConfigVersion.cmake +++ /dev/null @@ -1,9 +0,0 @@ -set(PACKAGE_VERSION "1.7") -set(PACKAGE_VERSION_EXACT FALSE) -set(PACKAGE_VERSION_COMPATIBLE FALSE) -set(PACKAGE_VERSION_UNSUITABLE TRUE) - -if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) - set(PACKAGE_VERSION_EXACT TRUE) - set(PACKAGE_VERSION_UNSUITABLE FALSE) -endif(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) diff --git a/infra/cmake/packages/TensorFlowProtoText-1.12/TensorFlowProtoTextConfig.cmake b/infra/cmake/packages/TensorFlowProtoText-1.12/TensorFlowProtoTextConfig.cmake index 01d74a5..9c7c796 100644 --- a/infra/cmake/packages/TensorFlowProtoText-1.12/TensorFlowProtoTextConfig.cmake +++ b/infra/cmake/packages/TensorFlowProtoText-1.12/TensorFlowProtoTextConfig.cmake @@ -10,7 +10,7 @@ function(_TensorFlowProtoText_import) require_package(TensorFlowSource EXACT 1.12) require_package(Abseil) - require_package(Eigen) + require_package(Eigen-fd6845384b86) require_package(Protobuf) require_package(GoogleDoubleConversion) require_package(GoogleNSync) diff --git a/infra/cmake/packages/TensorFlowProtoText-1.12/build/CMakeLists.txt b/infra/cmake/packages/TensorFlowProtoText-1.12/build/CMakeLists.txt index 86d6e6f..ac8e43b 100644 --- a/infra/cmake/packages/TensorFlowProtoText-1.12/build/CMakeLists.txt +++ b/infra/cmake/packages/TensorFlowProtoText-1.12/build/CMakeLists.txt @@ -71,7 +71,7 @@ target_include_directories(tensorflow-prototext-1.12 PRIVATE ${TensorFlowSource_ target_include_directories(tensorflow-prototext-1.12 PRIVATE ${PROTO_TEXT_PROTO_INCLUDE_DIRS}) target_link_libraries(tensorflow-prototext-1.12 PRIVATE abseil) -target_link_libraries(tensorflow-prototext-1.12 PRIVATE eigen) +target_link_libraries(tensorflow-prototext-1.12 PRIVATE eigen-fd6845384b86) target_link_libraries(tensorflow-prototext-1.12 PRIVATE ${PROTO_TEXT_PROTO_LIBRARIES}) target_link_libraries(tensorflow-prototext-1.12 PRIVATE Google::DoubleConversion) target_link_libraries(tensorflow-prototext-1.12 PRIVATE Google::NSync) diff --git a/infra/cmake/packages/TensorFlowProtoText-1.13.1/TensorFlowProtoTextConfig.cmake b/infra/cmake/packages/TensorFlowProtoText-1.13.1/TensorFlowProtoTextConfig.cmake new file mode 100644 index 0000000..738b282 --- /dev/null +++ b/infra/cmake/packages/TensorFlowProtoText-1.13.1/TensorFlowProtoTextConfig.cmake @@ -0,0 +1,104 @@ +function(_TensorFlowProtoText_import) + macro(require_package PKGNAME) + nnas_find_package(${PKGNAME} ${ARGN} QUIET) + if(NOT ${PKGNAME}_FOUND) + message(STATUS "Found TensorFlowProtoText: FALSE (${PKGNAME} is missing)") + set(TensorFlowProtoText_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT ${PKGNAME}_FOUND) + endmacro(require_package) + + require_package(TensorFlowSource EXACT 1.13.1) + require_package(Abseil) + require_package(Eigen) + require_package(Protobuf) + require_package(GoogleDoubleConversion) + require_package(GoogleNSync) + + if(NOT TARGET tensorflow-prototext-1.13.1) + nnas_include(ExternalProjectTools) + add_extdirectory("${CMAKE_CURRENT_LIST_DIR}/build" TensorFlowProtoText-1.13.1) + endif(NOT TARGET tensorflow-prototext-1.13.1) + + set(TensorFlowProtoText_FOUND TRUE PARENT_SCOPE) +endfunction(_TensorFlowProtoText_import) + +_TensorFlowProtoText_import() + +if(TensorFlowProtoText_FOUND) + # CMAKE_CURRENT_LIST_DIR + # + # ... The value has dynamic scope. ... Therefore the value of the variable inside a macro + # or function is the directory of the file invoking the bottom-most entry on the call stack, + # not the directory of the file containing the macro or function definition. + # + # Reference: https://cmake.org/cmake/help/v3.1/variable/CMAKE_CURRENT_LIST_DIR.html + set(TENSORLFLOW_PROTO_TEXT_1_13_1_CMAKE_DIR + "${CMAKE_CURRENT_LIST_DIR}" CACHE INTERNAL + "Where to find make_directories" + ) + + # Comments from "gen_proto_text_functions.cc" + # > + # > Main program to take input protos and write output pb_text source files that + # > contain generated proto text input and output functions. + # > + # > Main expects: + # > - First argument is output path + # > - Second argument is the relative path of the protos to the root. E.g., + # > for protos built by a rule in tensorflow/core, this will be + # > tensorflow/core. + # > - Then any number of source proto file names, plus one source name must be + # > placeholder.txt from this gen tool's package. placeholder.txt is + # > ignored for proto resolution, but is used to determine the root at which + # > the build tool has placed the source proto files. + # > + function(ProtoText_Generate PREFIX OUTPUT_DIR) + # THIS SHOULD SUCCEED! + nnas_find_package(TensorFlowSource EXACT 1.13.1 REQUIRED) + + set(OUTPUT_REL "tensorflow") + set(PROTO_DIR "${TensorFlowSource_DIR}") + + set(PROTO_INPUTS ${ARGN}) + list(APPEND PROTO_INPUTS "tensorflow/tools/proto_text/placeholder.txt") + + get_filename_component(abs_output_dir ${OUTPUT_DIR} ABSOLUTE) + get_filename_component(abs_proto_dir ${TensorFlowSource_DIR} ABSOLUTE) + + # Let's reset variables before using them + # NOTE This DOES NOT AFFECT variables in the parent scope + unset(PROTO_FILES) + unset(OUTPUT_DIRS) + unset(OUTPUT_FILES) + + foreach(proto ${PROTO_INPUTS}) + get_filename_component(fil "${proto}" NAME) + get_filename_component(dir "${proto}" DIRECTORY) + + get_filename_component(fil_we "${fil}" NAME_WE) + + get_filename_component(abs_fil "${abs_proto_base}/${proto}" ABSOLUTE) + get_filename_component(abs_dir "${abs_fil}" DIRECTORY) + + list(APPEND PROTO_FILES "${abs_proto_dir}/${proto}") + + if(NOT ${fil} STREQUAL "placeholder.txt") + list(APPEND OUTPUT_DIRS "${abs_output_dir}/${dir}") + list(APPEND OUTPUT_FILES "${abs_output_dir}/${dir}/${fil_we}.pb_text.h") + list(APPEND OUTPUT_FILES "${abs_output_dir}/${dir}/${fil_we}.pb_text-impl.h") + list(APPEND OUTPUT_FILES "${abs_output_dir}/${dir}/${fil_we}.pb_text.cc") + endif(NOT ${fil} STREQUAL "placeholder.txt") + endforeach() + + add_custom_command(OUTPUT ${OUTPUT_FILES} + # "make_directory" in CMake 3.1 cannot create multiple directories at once. + # COMMAND ${CMAKE_COMMAND} -E make_directory ${OUTPUT_DIRS} + COMMAND "${TENSORLFLOW_PROTO_TEXT_1_13_1_CMAKE_DIR}/make_directories.sh" ${OUTPUT_DIRS} + COMMAND "$" "${abs_output_dir}/${OUTPUT_REL}" "${OUTPUT_REL}" ${PROTO_FILES} + DEPENDS ${PROTO_FILES}) + + set(${PREFIX}_SOURCES ${OUTPUT_FILES} PARENT_SCOPE) + set(${PREFIX}_INCLUDE_DIRS ${abs_output_dir} PARENT_SCOPE) + endfunction(ProtoText_Generate) +endif(TensorFlowProtoText_FOUND) diff --git a/infra/cmake/packages/TensorFlowProtoText-1.13.1/TensorFlowProtoTextConfigVersion.cmake b/infra/cmake/packages/TensorFlowProtoText-1.13.1/TensorFlowProtoTextConfigVersion.cmake new file mode 100644 index 0000000..ed79ecd --- /dev/null +++ b/infra/cmake/packages/TensorFlowProtoText-1.13.1/TensorFlowProtoTextConfigVersion.cmake @@ -0,0 +1,9 @@ +set(PACKAGE_VERSION "1.13.1") +set(PACKAGE_VERSION_EXACT FALSE) +set(PACKAGE_VERSION_COMPATIBLE FALSE) +set(PACKAGE_VERSION_UNSUITABLE TRUE) + +if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) + set(PACKAGE_VERSION_EXACT TRUE) + set(PACKAGE_VERSION_UNSUITABLE FALSE) +endif(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) diff --git a/infra/cmake/packages/TensorFlowProtoText-1.13.1/build/CMakeLists.txt b/infra/cmake/packages/TensorFlowProtoText-1.13.1/build/CMakeLists.txt new file mode 100644 index 0000000..7079aea --- /dev/null +++ b/infra/cmake/packages/TensorFlowProtoText-1.13.1/build/CMakeLists.txt @@ -0,0 +1,78 @@ +message(STATUS "Build TensorFlowProtoText from '${TensorFlowSource_DIR}'") + +# +# Build "proto_text" tool +# +unset(SOURCE_FILES) + +macro(Source_Add RPATH) + list(APPEND SOURCE_FILES "${TensorFlowSource_DIR}/${RPATH}") +endmacro(Source_Add) + +# This list comes from "tensorflow/contrib/makefile/proto_text_cc_files.txt" +Source_Add(tensorflow/core/lib/core/status.cc) +Source_Add(tensorflow/core/lib/core/threadpool.cc) +Source_Add(tensorflow/core/lib/hash/hash.cc) +Source_Add(tensorflow/core/lib/io/inputstream_interface.cc) +Source_Add(tensorflow/core/lib/io/random_inputstream.cc) +Source_Add(tensorflow/core/lib/io/buffered_inputstream.cc) +Source_Add(tensorflow/core/lib/io/inputbuffer.cc) +Source_Add(tensorflow/core/lib/io/iterator.cc) +Source_Add(tensorflow/core/lib/io/path.cc) +Source_Add(tensorflow/core/lib/strings/numbers.cc) +Source_Add(tensorflow/core/lib/strings/scanner.cc) +Source_Add(tensorflow/core/lib/strings/str_util.cc) +Source_Add(tensorflow/core/lib/strings/strcat.cc) +Source_Add(tensorflow/core/lib/strings/stringprintf.cc) +Source_Add(tensorflow/core/lib/strings/proto_text_util.cc) +Source_Add(tensorflow/core/platform/cpu_info.cc) +Source_Add(tensorflow/core/platform/denormal.cc) +Source_Add(tensorflow/core/platform/env.cc) +Source_Add(tensorflow/core/platform/env_time.cc) +Source_Add(tensorflow/core/platform/file_system.cc) +Source_Add(tensorflow/core/platform/file_system_helper.cc) +Source_Add(tensorflow/core/platform/protobuf_util.cc) +Source_Add(tensorflow/core/platform/setround.cc) +Source_Add(tensorflow/core/platform/tracing.cc) +Source_Add(tensorflow/core/platform/posix/env.cc) +Source_Add(tensorflow/core/platform/posix/env_time.cc) +Source_Add(tensorflow/core/platform/posix/error.cc) +Source_Add(tensorflow/core/platform/posix/load_library.cc) +Source_Add(tensorflow/core/platform/posix/port.cc) +Source_Add(tensorflow/core/platform/posix/posix_file_system.cc) +Source_Add(tensorflow/core/platform/default/logging.cc) +Source_Add(tensorflow/core/platform/default/mutex.cc) +Source_Add(tensorflow/core/platform/default/protobuf.cc) + +Source_Add(tensorflow/tools/proto_text/gen_proto_text_functions_lib.cc) +Source_Add(tensorflow/tools/proto_text/gen_proto_text_functions.cc) + +unset(PROTO_FILES) + +macro(Proto_Add RPATH) + list(APPEND PROTO_FILES "${RPATH}") +endmacro(Proto_Add) + +Proto_Add(tensorflow/core/lib/core/error_codes.proto) +Proto_Add(tensorflow/core/framework/types.proto) +Proto_Add(tensorflow/core/framework/tensor.proto) +Proto_Add(tensorflow/core/framework/tensor_shape.proto) +Proto_Add(tensorflow/core/framework/summary.proto) +Proto_Add(tensorflow/core/framework/resource_handle.proto) + +Protobuf_Generate(PROTO_TEXT_PROTO + "${CMAKE_CURRENT_BINARY_DIR}/generated/proto_text" + "${TensorFlowSource_DIR}" + ${PROTO_FILES} +) + +add_executable(tensorflow-prototext-1.13.1 ${SOURCE_FILES} ${PROTO_TEXT_PROTO_SOURCES}) +target_include_directories(tensorflow-prototext-1.13.1 PRIVATE ${TensorFlowSource_DIR}) +target_include_directories(tensorflow-prototext-1.13.1 PRIVATE ${PROTO_TEXT_PROTO_INCLUDE_DIRS}) + +target_link_libraries(tensorflow-prototext-1.13.1 PRIVATE abseil) +target_link_libraries(tensorflow-prototext-1.13.1 PRIVATE eigen) +target_link_libraries(tensorflow-prototext-1.13.1 PRIVATE ${PROTO_TEXT_PROTO_LIBRARIES}) +target_link_libraries(tensorflow-prototext-1.13.1 PRIVATE Google::DoubleConversion) +target_link_libraries(tensorflow-prototext-1.13.1 PRIVATE Google::NSync) +target_link_libraries(tensorflow-prototext-1.13.1 PRIVATE dl) diff --git a/infra/cmake/packages/TensorFlowProtoText-1.13.1/make_directories.sh b/infra/cmake/packages/TensorFlowProtoText-1.13.1/make_directories.sh new file mode 100755 index 0000000..1fb2ab6 --- /dev/null +++ b/infra/cmake/packages/TensorFlowProtoText-1.13.1/make_directories.sh @@ -0,0 +1,6 @@ +#!/bin/bash + +while [[ $# -ne 0 ]]; do + DIR=$1; shift + mkdir -p "${DIR}" +done diff --git a/infra/cmake/packages/TensorFlowSource-1.13.1/TensorFlowSourceConfig.cmake b/infra/cmake/packages/TensorFlowSource-1.13.1/TensorFlowSourceConfig.cmake index 2049ab9..d837af7 100644 --- a/infra/cmake/packages/TensorFlowSource-1.13.1/TensorFlowSourceConfig.cmake +++ b/infra/cmake/packages/TensorFlowSource-1.13.1/TensorFlowSourceConfig.cmake @@ -7,7 +7,8 @@ function(_TensorFlowSource_import) nnas_include(ExternalSourceTools) nnas_include(OptionTools) - envoption(TENSORFLOW_1_13_1_URL https://github.com/tensorflow/tensorflow/archive/v1.13.1.tar.gz) + envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") + envoption(TENSORFLOW_1_13_1_URL ${EXTERNAL_DOWNLOAD_SERVER}/tensorflow/tensorflow/archive/v1.13.1.tar.gz) ExternalSource_Download(TENSORFLOW DIRNAME TENSORFLOW-1.13.1 ${TENSORFLOW_1_13_1_URL}) diff --git a/infra/cmake/packages/TensorFlowSource-1.7/TensorFlowSourceConfig.cmake b/infra/cmake/packages/TensorFlowSource-1.7/TensorFlowSourceConfig.cmake deleted file mode 100644 index 8188ee0..0000000 --- a/infra/cmake/packages/TensorFlowSource-1.7/TensorFlowSourceConfig.cmake +++ /dev/null @@ -1,18 +0,0 @@ -function(_TensorFlowSource_import) - if(NOT DOWNLOAD_TENSORFLOW) - set(TensorFlowSource_FOUND FALSE PARENT_SCOPE) - return() - endif(NOT DOWNLOAD_TENSORFLOW) - - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - envoption(TENSORFLOW_URL https://github.com/tensorflow/tensorflow/archive/v1.7.0.tar.gz) - - ExternalSource_Download(TENSORFLOW ${TENSORFLOW_URL}) - - set(TensorFlowSource_DIR ${TENSORFLOW_SOURCE_DIR} PARENT_SCOPE) - set(TensorFlowSource_FOUND TRUE PARENT_SCOPE) -endfunction(_TensorFlowSource_import) - -_TensorFlowSource_import() diff --git a/infra/cmake/packages/TensorFlowSource-1.7/TensorFlowSourceConfigVersion.cmake b/infra/cmake/packages/TensorFlowSource-1.7/TensorFlowSourceConfigVersion.cmake deleted file mode 100644 index b1d5282..0000000 --- a/infra/cmake/packages/TensorFlowSource-1.7/TensorFlowSourceConfigVersion.cmake +++ /dev/null @@ -1,10 +0,0 @@ -set(PACKAGE_VERSION "1.7") -set(PACKAGE_VERSION_EXACT FALSE) -set(PACKAGE_VERSION_COMPATIBLE FALSE) -set(PACKAGE_VERSION_UNSUITABLE TRUE) - -if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) - set(PACKAGE_VERSION_EXACT TRUE) - set(PACKAGE_VERSION_COMPATIBLE TRUE) - set(PACKAGE_VERSION_UNSUITABLE FALSE) -endif(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) diff --git a/infra/cmake/packages/TensorFlowSource-2.2.0/TensorFlowSourceConfig.cmake b/infra/cmake/packages/TensorFlowSource-2.2.0/TensorFlowSourceConfig.cmake new file mode 100644 index 0000000..71220d7 --- /dev/null +++ b/infra/cmake/packages/TensorFlowSource-2.2.0/TensorFlowSourceConfig.cmake @@ -0,0 +1,18 @@ +function(_TensorFlowSource_import) + if(NOT DOWNLOAD_TENSORFLOW) + set(TensorFlowSource_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT DOWNLOAD_TENSORFLOW) + + nnas_include(ExternalSourceTools) + nnas_include(OptionTools) + + envoption(TENSORFLOW_2_2_0_URL https://github.com/tensorflow/tensorflow/archive/v2.2.0.tar.gz) + + ExternalSource_Download(TENSORFLOW DIRNAME TENSORFLOW-2.2.0 ${TENSORFLOW_2_2_0_URL}) + + set(TensorFlowSource_DIR ${TENSORFLOW_SOURCE_DIR} PARENT_SCOPE) + set(TensorFlowSource_FOUND TRUE PARENT_SCOPE) +endfunction(_TensorFlowSource_import) + +_TensorFlowSource_import() diff --git a/infra/cmake/packages/TensorFlowSource-2.2.0/TensorFlowSourceConfigVersion.cmake b/infra/cmake/packages/TensorFlowSource-2.2.0/TensorFlowSourceConfigVersion.cmake new file mode 100644 index 0000000..8269dc5 --- /dev/null +++ b/infra/cmake/packages/TensorFlowSource-2.2.0/TensorFlowSourceConfigVersion.cmake @@ -0,0 +1,10 @@ +set(PACKAGE_VERSION "2.2.0") +set(PACKAGE_VERSION_EXACT FALSE) +set(PACKAGE_VERSION_COMPATIBLE FALSE) +set(PACKAGE_VERSION_UNSUITABLE TRUE) + +if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) + set(PACKAGE_VERSION_EXACT TRUE) + set(PACKAGE_VERSION_COMPATIBLE TRUE) + set(PACKAGE_VERSION_UNSUITABLE FALSE) +endif(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) diff --git a/infra/cmake/packages/TensorFlowSource-2.3.0-rc0Config.cmake b/infra/cmake/packages/TensorFlowSource-2.3.0-rc0Config.cmake new file mode 100644 index 0000000..82df579 --- /dev/null +++ b/infra/cmake/packages/TensorFlowSource-2.3.0-rc0Config.cmake @@ -0,0 +1,21 @@ +# find_package rejects version with extra string like "2.3.0-rc0" +# +# TODO Find a better way +function(_import) + if(NOT DOWNLOAD_TENSORFLOW) + set(TensorFlowSource_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT DOWNLOAD_TENSORFLOW) + + nnas_include(ExternalSourceTools) + nnas_include(OptionTools) + + envoption(TENSORFLOW_2_3_0_RC0_URL https://github.com/tensorflow/tensorflow/archive/v2.3.0-rc0.tar.gz) + + ExternalSource_Download(TENSORFLOW DIRNAME TENSORFLOW-2.3.0-RC0 ${TENSORFLOW_2_3_0_RC0_URL}) + + set(TensorFlowSource_DIR ${TENSORFLOW_SOURCE_DIR} PARENT_SCOPE) + set(TensorFlowSource_FOUND TRUE PARENT_SCOPE) +endfunction(_import) + +_import() diff --git a/infra/command/format b/infra/command/format index 9fe4753..e34c915 100644 --- a/infra/command/format +++ b/infra/command/format @@ -65,10 +65,28 @@ function command_exists() { command -v $1 > /dev/null 2>&1 } -function check_newline() { +function exclude_symbolic_links() { # Check all files (CMakeLists.txt, *.cl, ... not only for C++, Python) if [[ ${#FILES_TO_CHECK} -ne 0 ]]; then - CRCHECK=$(file ${FILES_TO_CHECK} | grep 'with CR') + FILES_EXCLUDE_SYMLINKS=$(file ${FILES_TO_CHECK} | grep -v "symbolic link" | cut -d':' -f1) + FILES_TO_CHECK=${FILES_EXCLUDE_SYMLINKS} + fi +} + +function check_newline() { + FILES_TO_CHECK_CR=() + for f in ${FILES_TO_CHECK[@]}; do + # Manually ignore style checking + if [[ ${f} == !(*.svg|*.pdf|*.png) ]]; then + FILES_TO_CHECK_CR+=("${f}") + fi + done + + # Check all files (CMakeLists.txt, *.cl, ... not only for C++, Python) + if [[ ${#FILES_TO_CHECK_CR} -ne 0 ]]; then + CRCHECK=$(file ${FILES_TO_CHECK_CR} | grep 'with CR') + else + return fi FILES_TO_FIX=($(echo "$CRCHECK" | grep "with CRLF line" | cut -d':' -f1)) for f in ${FILES_TO_FIX[@]}; do @@ -79,7 +97,7 @@ function check_newline() { tr '\r' '\n' < $f > $f.fixed && cat $f.fixed > $f && rm $f.fixed done # Check no new line at end of file - for f in ${FILES_TO_CHECK[@]}; do + for f in ${FILES_TO_CHECK_CR[@]}; do if diff /dev/null "$f" | tail -1 | grep '^\\ No newline' > /dev/null; then echo >> "$f" fi @@ -91,7 +109,7 @@ function check_permission() { FILES_TO_CHECK_PERMISSION=() for f in ${FILES_TO_CHECK[@]}; do # Manually ignore permission checking - if [[ ${f} == !(nnas|nnfw|nncc|*.sh|*.py) ]]; then + if [[ ${f} == !(nnas|nnfw|nncc|*.sh|*.py|*/gradlew) ]] || [[ ${f} == tests/nnapi/specs/**/*.py ]]; then FILES_TO_CHECK_PERMISSION+=("${f}") fi done @@ -224,6 +242,7 @@ for DIR_NOT_TO_BE_TESTED in $(git ls-files -co --exclude-standard '*/.FORMATDENY DIRECTORIES_NOT_TO_BE_TESTED+=($(dirname "${DIR_NOT_TO_BE_TESTED}")) done +exclude_symbolic_links check_newline check_permission check_cpp_files diff --git a/infra/nncc/CMakeLists.txt b/infra/nncc/CMakeLists.txt index 4bcb616..3ac6680 100644 --- a/infra/nncc/CMakeLists.txt +++ b/infra/nncc/CMakeLists.txt @@ -101,6 +101,8 @@ option(DOWNLOAD_ABSEIL "Download Abseil-cpp source" ON) option(DOWNLOAD_GTEST "Download Google Test source" ON) option(BUILD_GTEST "Build Google Test from the downloaded source" ON) +option(DOWNLOAD_HDF5 "Download HDF5 source" ON) +option(BUILD_HDF5 "Build HDF5 from the downloaded source" ON) nnas_find_package(GTest QUIET) diff --git a/infra/nncc/command/utcount b/infra/nncc/command/utcount index 3b3baa0..d4610e3 100644 --- a/infra/nncc/command/utcount +++ b/infra/nncc/command/utcount @@ -13,20 +13,24 @@ BUILD_ITEMS="angkor cwrap pepper-str pepper-strcast pp stdex \ oops pepper-assert \ hermes hermes-std \ loco locop locomotiv logo-core logo \ +foder souschef arser \ safemain mio-circle mio-tflite \ tflite2circle \ luci \ -circle2circle" +luci-interpreter \ +luci-value-test \ +record-minmax \ +circle2circle circle-quantizer" function count_all() { - local result=`ctest --verbose | grep -c '\[ RUN \]'` + local result=`(ctest --verbose | grep -c '\[ RUN \]') 2>/dev/null` echo $result } function count_neg() { - local result=`ctest --verbose | grep '\[ RUN \]' | grep -c '_NEG'` + local result=`(ctest --verbose | grep '\[ RUN \]' | grep -c '_NEG') 2>/dev/null` echo $result } diff --git a/infra/nnfw/CMakeLists.txt b/infra/nnfw/CMakeLists.txt index 4375735..5caf37f 100644 --- a/infra/nnfw/CMakeLists.txt +++ b/infra/nnfw/CMakeLists.txt @@ -30,7 +30,7 @@ macro(nnas_include PREFIX) include("${NNAS_PROJECT_SOURCE_DIR}/infra/cmake/modules/${PREFIX}.cmake") endmacro(nnas_include) -# 'find_package()' wrapper to find in cmake/packages folder +# Runtime 'find_package()' wrapper to find in cmake/packages folder # # Example: # nnfw_find_package(Boost): Load settings from 'BoostConfig.cmake' file @@ -38,9 +38,13 @@ endmacro(nnas_include) # nnfw_find_package(Boost QUIET): Load settings silently, without warnings # nnfw_find_package(Boost REQUIRED): Load settings but stop with error when failed macro(nnfw_find_package PREFIX) - find_package(${PREFIX} CONFIG NO_DEFAULT_PATH PATHS ${CMAKE_SOURCE_DIR}/cmake/packages ${ARGN}) + find_package(${PREFIX} CONFIG NO_DEFAULT_PATH + PATHS ${CMAKE_SOURCE_DIR}/cmake/packages + ${ARGN} + ) endmacro(nnfw_find_package) -# TODO Remove nnfw_find_pacakge + +# Common 'find_package()' wrapper to find in infra/cmake/packages folder macro(nnas_find_package PREFIX) find_package(${PREFIX} CONFIG NO_DEFAULT_PATH PATHS ${NNAS_PROJECT_SOURCE_DIR}/infra/cmake/packages diff --git a/infra/nnfw/cmake/ApplyCompileFlags.cmake b/infra/nnfw/cmake/ApplyCompileFlags.cmake index 0ee7e8e..b042b0c 100644 --- a/infra/nnfw/cmake/ApplyCompileFlags.cmake +++ b/infra/nnfw/cmake/ApplyCompileFlags.cmake @@ -2,16 +2,10 @@ # Platform independent compile flag setting # # flags for build type: debug, release -if(${ENABLE_COVERAGE}) - # test-coverage build flag for tizen - set(CMAKE_C_FLAGS_DEBUG "-O -g -DDEBUG") - set(CMAKE_CXX_FLAGS_DEBUG "-O -g -DDEBUG") -else(${ENABLE_COVERAGE}) - set(CMAKE_C_FLAGS_DEBUG "-O0 -g -DDEBUG") - set(CMAKE_CXX_FLAGS_DEBUG "-O0 -g -DDEBUG") -endif(${ENABLE_COVERAGE}) -set(CMAKE_C_FLAGS_RELEASE "-O2 -DNDEBUG") -set(CMAKE_CXX_FLAGS_RELEASE "-O2 -DNDEBUG") +set(CMAKE_C_FLAGS_DEBUG "-O0 -g -DDEBUG") +set(CMAKE_CXX_FLAGS_DEBUG "-O0 -g -DDEBUG") +set(CMAKE_C_FLAGS_RELEASE "-O3 -DNDEBUG") +set(CMAKE_CXX_FLAGS_RELEASE "-O3 -DNDEBUG") # # Platform specific compile flag setting diff --git a/infra/nnfw/cmake/CfgOptionFlags.cmake b/infra/nnfw/cmake/CfgOptionFlags.cmake index 794fcc3..d1395f8 100644 --- a/infra/nnfw/cmake/CfgOptionFlags.cmake +++ b/infra/nnfw/cmake/CfgOptionFlags.cmake @@ -15,7 +15,6 @@ option(ENABLE_COVERAGE "Build for coverage test" OFF) option(BUILD_EXT_MULTITHREAD "Build external build using multi thread" ON) option(BUILD_ONERT "Build onert" ON) option(BUILD_LOGGING "Build logging runtime" ON) -option(BUILD_PURE_ARM_COMPUTE "Build pure_arm_compute runtime" OFF) CMAKE_DEPENDENT_OPTION(BUILD_RUNTIME_NNAPI_TEST "Build Runtime NN API Generated Test" # Set BUILD_RUNTIME_NNAPI_TEST as ON # if CMAKE_COMPILER_IS_GNUCC AND NOT CMAKE_CXX_COMPILER_VERSION VERSION_LESS 6.2 @@ -24,10 +23,10 @@ CMAKE_DEPENDENT_OPTION(BUILD_RUNTIME_NNAPI_TEST "Build Runtime NN API Generated OFF) option(BUILD_RUNTIME_NNFW_API_TEST "Build Runtime NNFW API Tests" ON) option(BUILD_TFLITE_RUN "Build tflite-run" ON) -option(BUILD_TFLITE_BENCHMARK "Build tflite benchmark" ON) +option(BUILD_TFLITE_RUN_2_2_0 "Build tflite-run 2.2.0" OFF) option(BUILD_TFLITE_BENCHMARK_MODEL "Build tflite benchmark model" OFF) +option(BUILD_NNAPI_TEST "Build nnapi_test" ON) option(BUILD_NNPACKAGE_RUN "Build nnpackge_run" ON) -option(BUILD_SRCN_KERNEL "Build srcn kernel" OFF) option(BUILD_TFLITE_LOADER "Build TensorFlow Lite loader" ON) option(BUILD_CIRCLE_LOADER "Build circle loader" ON) option(BUILD_TFLITE_LOADER_TEST_TOOL "Build tflite loader testing tool" ON) @@ -59,20 +58,23 @@ option(BUILD_TFLITE_ACCURACY "Build tflite accuracy tool" OFF) # Default external libraries source download and build configuration # option(DOWNLOAD_TENSORFLOW "Download Tensorflow source" ON) -option(DOWNLOAD_ABSL "Download Absl source" ON) +option(DOWNLOAD_ABSEIL "Download Abseil source" ON) option(DOWNLOAD_EIGEN "Download Eigen source" ON) option(DOWNLOAD_FARMHASH "Download farmhash source" ON) option(DOWNLOAD_GEMMLOWP "Download GEMM low precesion library source" ON) option(DOWNLOAD_NEON2SSE "Download NEON2SSE library source" ON) -option(DOWNLOAD_NNPACK "Download NNPACK source" OFF) option(DOWNLOAD_FLATBUFFERS "Download FlatBuffers source" ON) option(DOWNLOAD_ARMCOMPUTE "Download ARM Compute source" ON) option(DOWNLOAD_NONIUS "Download nonius source" ON) option(DOWNLOAD_BOOST "Download boost source" OFF) +option(DOWNLOAD_RUY "Download ruy source" ON) option(BUILD_BOOST "Build boost source" OFF) option(BUILD_TENSORFLOW_LITE "Build TensorFlow Lite from the downloaded source" ON) +option(BUILD_TENSORFLOW_LITE_2_2_0 "Build TensorFlow Lite from the downloaded source" OFF) option(BUILD_GTEST "Download and build Google Test" ON) option(BUILD_ARMCOMPUTE "Build ARM Compute from the downloaded source" ON) +option(BUILD_RUY "Build ruy library from the downloaded source" ON) +option(PROFILE_RUY "Enable ruy library profiling" OFF) # ## Default sample build configuration diff --git a/infra/nnfw/cmake/buildtool/config/config_aarch64-tizen.cmake b/infra/nnfw/cmake/buildtool/config/config_aarch64-tizen.cmake index fa1e8b0..0f304ec 100644 --- a/infra/nnfw/cmake/buildtool/config/config_aarch64-tizen.cmake +++ b/infra/nnfw/cmake/buildtool/config/config_aarch64-tizen.cmake @@ -4,6 +4,10 @@ message(STATUS "Building for AARCH64 Tizen") +# Build flag for tizen +set(CMAKE_C_FLAGS_DEBUG "-O -g -DDEBUG") +set(CMAKE_CXX_FLAGS_DEBUG "-O -g -DDEBUG") + # TODO : add and use option_tizen if something uncommon comes up # include linux common include("cmake/buildtool/config/config_linux.cmake") diff --git a/infra/nnfw/cmake/buildtool/config/config_armv7l-linux.cmake b/infra/nnfw/cmake/buildtool/config/config_armv7l-linux.cmake index 9a0b5e5..8963f30 100644 --- a/infra/nnfw/cmake/buildtool/config/config_armv7l-linux.cmake +++ b/infra/nnfw/cmake/buildtool/config/config_armv7l-linux.cmake @@ -12,7 +12,6 @@ set(FLAGS_COMMON ${FLAGS_COMMON} "-mcpu=cortex-a7" "-mfloat-abi=hard" "-mfpu=neon-vfpv4" - "-funsafe-math-optimizations" "-ftree-vectorize" "-mfp16-format=ieee" ) diff --git a/infra/nnfw/cmake/buildtool/config/config_armv7l-tizen.cmake b/infra/nnfw/cmake/buildtool/config/config_armv7l-tizen.cmake index a5dae20..fa12ecb 100644 --- a/infra/nnfw/cmake/buildtool/config/config_armv7l-tizen.cmake +++ b/infra/nnfw/cmake/buildtool/config/config_armv7l-tizen.cmake @@ -4,6 +4,10 @@ message(STATUS "Building for ARMv7l(softfp) Tizen") +# Build flag for tizen +set(CMAKE_C_FLAGS_DEBUG "-O -g -DDEBUG") +set(CMAKE_CXX_FLAGS_DEBUG "-O -g -DDEBUG") + # TODO : add and use option_tizen if something uncommon comes up # include linux common include("cmake/buildtool/config/config_linux.cmake") diff --git a/infra/nnfw/cmake/buildtool/config/config_x86_64-darwin.cmake b/infra/nnfw/cmake/buildtool/config/config_x86_64-darwin.cmake new file mode 100644 index 0000000..dbd45fc --- /dev/null +++ b/infra/nnfw/cmake/buildtool/config/config_x86_64-darwin.cmake @@ -0,0 +1,12 @@ +# +# x86_64 darwin(macOS) compile options +# +message(STATUS "Building for x86-64 Darwin") + +# SIMD for x86 +set(FLAGS_COMMON ${FLAGS_COMMON} + "-msse4" + ) + +# lib pthread as a variable (pthread must be disabled on android) +set(LIB_PTHREAD pthread) diff --git a/infra/nnfw/cmake/buildtool/cross/toolchain_aarch64-android.cmake b/infra/nnfw/cmake/buildtool/cross/toolchain_aarch64-android.cmake index 4da2618..99cc32a 100644 --- a/infra/nnfw/cmake/buildtool/cross/toolchain_aarch64-android.cmake +++ b/infra/nnfw/cmake/buildtool/cross/toolchain_aarch64-android.cmake @@ -13,6 +13,8 @@ endif(NOT DEFINED NDK_DIR) set(ANDROID_ABI arm64-v8a) set(ANDROID_API_LEVEL 29) set(ANDROID_PLATFORM android-${ANDROID_API_LEVEL}) +set(ANDROID_STL c++_shared) +set(ANDROID_STL_LIB "${NDK_DIR}/sources/cxx-stl/llvm-libc++/libs/arm64-v8a/libc++_shared.so") # Find package in the host. `nnfw_find_package` won't work without this set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE NEVER) @@ -22,5 +24,15 @@ set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY NEVER) # Use the toolchain file that NDK provides include(${NDK_DIR}/build/cmake/android.toolchain.cmake) +# Install libc++_shared.so to lib folder +install(FILES ${ANDROID_STL_LIB} DESTINATION lib) + +# ndk always enable debug flag -g, but we don't want debug info in release build +# https://github.com/android/ndk/issues/243 +string(REPLACE "-g" "" CMAKE_C_FLAGS "${CMAKE_C_FLAGS}") +string(REPLACE "-g" "" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}") +set(CMAKE_C_FLAGS_DEBUG "-g ${CMAKE_C_FLAGS_DEBUG}") +set(CMAKE_CXX_FLAGS_DEBUG "-g ${CMAKE_CXX_FLAGS_DEBUG}") + set(TARGET_OS "android") set(TARGET_ARCH "aarch64") diff --git a/infra/nnfw/cmake/options/options_aarch64-android.cmake b/infra/nnfw/cmake/options/options_aarch64-android.cmake index 903267b..97371f6 100644 --- a/infra/nnfw/cmake/options/options_aarch64-android.cmake +++ b/infra/nnfw/cmake/options/options_aarch64-android.cmake @@ -8,7 +8,10 @@ option(BUILD_ANDROID_TFLITE "Enable android support for TensorFlow Lite" ON) option(BUILD_ANDROID_BENCHMARK_APP "Enable Android Benchmark App" ON) option(DOWNLOAD_NEON2SSE "Download NEON2SSE library source" OFF) # Need boost library +option(DOWNLOAD_BOOST "Download boost source" OFF) +option(BUILD_BOOST "Build boost source" OFF) option(BUILD_RUNTIME_NNAPI_TEST "Build Runtime NN API Generated Test" OFF) +option(BUILD_NNAPI_TEST "Build nnapi_test" OFF) option(BUILD_NNPACKAGE_RUN "Build nnpackge_run" OFF) option(BUILD_TFLITE_RUN "Build tflite-run" OFF) option(BUILD_TFLITE_LOADER_TEST_TOOL "Build tflite loader testing tool" OFF) diff --git a/infra/nnfw/cmake/options/options_aarch64-linux.cmake b/infra/nnfw/cmake/options/options_aarch64-linux.cmake index 179030e..829711f 100644 --- a/infra/nnfw/cmake/options/options_aarch64-linux.cmake +++ b/infra/nnfw/cmake/options/options_aarch64-linux.cmake @@ -2,4 +2,3 @@ # aarch64 linux cmake options # option(DOWNLOAD_NEON2SSE "Download NEON2SSE library source" OFF) -option(BUILD_SRCN_KERNEL "Build srcn kernel" ON) diff --git a/infra/nnfw/cmake/options/options_aarch64-tizen.cmake b/infra/nnfw/cmake/options/options_aarch64-tizen.cmake index 4499fcd..590d128 100644 --- a/infra/nnfw/cmake/options/options_aarch64-tizen.cmake +++ b/infra/nnfw/cmake/options/options_aarch64-tizen.cmake @@ -3,13 +3,9 @@ # option(BUILD_ARMCOMPUTE "Build ARM Compute from the downloaded source" OFF) option(BUILD_TENSORFLOW_LITE "Build TensorFlow Lite from the downloaded source" OFF) -option(DOWNLOAD_EIGEN "Download Eigen source" OFF) option(DOWNLOAD_NEON2SSE "Download NEON2SSE library source" OFF) -option(DOWNLOAD_NNPACK "Download NNPACK source" OFF) option(BUILD_LOGGING "Build logging runtime" OFF) option(BUILD_TFLITE_RUN "Build tflite-run" OFF) -option(BUILD_TFLITE_LOADER_TEST_TOOL "Build tflite loader testing tool" OFF) -option(BUILD_SRCN_KERNEL "Build srcn kernel" ON) option(GENERATE_RUNTIME_NNAPI_TESTS "Generate NNAPI operation gtest" OFF) option(ENVVAR_ONERT_CONFIG "Use environment variable for onert configuration" OFF) diff --git a/infra/nnfw/cmake/options/options_armv7l-linux.cmake b/infra/nnfw/cmake/options/options_armv7l-linux.cmake index ec2f8d6..e10e573 100644 --- a/infra/nnfw/cmake/options/options_armv7l-linux.cmake +++ b/infra/nnfw/cmake/options/options_armv7l-linux.cmake @@ -2,5 +2,4 @@ # armv7l linux cmake options # option(DOWNLOAD_NEON2SSE "Download NEON2SSE library source" OFF) -option(BUILD_SRCN_KERNEL "Build srcn kernel" ON) option(BUILD_OPENCL_TOOL "Build OpenCL tool" ON) diff --git a/infra/nnfw/cmake/options/options_armv7l-tizen.cmake b/infra/nnfw/cmake/options/options_armv7l-tizen.cmake index e1e4e68..25c848f 100644 --- a/infra/nnfw/cmake/options/options_armv7l-tizen.cmake +++ b/infra/nnfw/cmake/options/options_armv7l-tizen.cmake @@ -4,10 +4,8 @@ option(BUILD_ARMCOMPUTE "Build ARM Compute from the downloaded source" OFF) option(BUILD_TENSORFLOW_LITE "Build TensorFlow Lite from the downloaded source" OFF) option(DOWNLOAD_NEON2SSE "Download NEON2SSE library source" OFF) -option(DOWNLOAD_NNPACK "Download NNPACK source" OFF) option(BUILD_LOGGING "Build logging runtime" OFF) option(BUILD_TFLITE_RUN "Build tflite-run" OFF) -option(BUILD_SRCN_KERNEL "Build srcn kernel" ON) option(GENERATE_RUNTIME_NNAPI_TESTS "Generate NNAPI operation gtest" OFF) option(ENVVAR_ONERT_CONFIG "Use environment variable for onert configuration" OFF) diff --git a/infra/nnfw/cmake/options/options_x86_64-darwin.cmake b/infra/nnfw/cmake/options/options_x86_64-darwin.cmake new file mode 100644 index 0000000..97642e6 --- /dev/null +++ b/infra/nnfw/cmake/options/options_x86_64-darwin.cmake @@ -0,0 +1,5 @@ +# +# x86_64 linux cmake options +# +option(BUILD_ARMCOMPUTE "Build ARM Compute from the downloaded source" OFF) +option(DOWNLOAD_ARMCOMPUTE "Download ARM Compute source" OFF) diff --git a/infra/nnfw/cmake/packages/ARMComputeConfig.cmake b/infra/nnfw/cmake/packages/ARMComputeConfig.cmake new file mode 100644 index 0000000..67f6ef8 --- /dev/null +++ b/infra/nnfw/cmake/packages/ARMComputeConfig.cmake @@ -0,0 +1,181 @@ +function(_ARMCompute_Import) + include(FindPackageHandleStandardArgs) + + list(APPEND ARMCompute_LIB_SEARCH_PATHS ${ARMCompute_PREFIX}) + + find_path(INCLUDE_DIR NAMES arm_compute/core/ITensor.h PATHS ${ARMCompute_INCLUDE_SEARCH_PATHS}) + + find_library(CORE_LIBRARY NAMES arm_compute_core PATHS ${ARMCompute_LIB_SEARCH_PATHS} CMAKE_FIND_ROOT_PATH_BOTH) + find_library(RUNTIME_LIBRARY NAMES arm_compute PATHS ${ARMCompute_LIB_SEARCH_PATHS} CMAKE_FIND_ROOT_PATH_BOTH) + find_library(GRAPH_LIBRARY NAMES arm_compute_graph PATHS ${ARMCompute_LIB_SEARCH_PATHS} CMAKE_FIND_ROOT_PATH_BOTH) + + message(STATUS "Search acl in ${ARMCompute_LIB_SEARCH_PATHS}") + + if(NOT INCLUDE_DIR) + nnas_find_package(ARMComputeSource QUIET) + if (NOT ARMComputeSource_FOUND) + set(ARMCompute_FOUND FALSE PARENT_SCOPE) + return() + endif() + set(INCLUDE_DIR ${ARMComputeSource_DIR} ${ARMComputeSource_DIR}/include) + endif(NOT INCLUDE_DIR) + + if(NOT CORE_LIBRARY) + set(ARMCompute_FOUND FALSE PARENT_SCOPE) + message(STATUS "Cannot find libarm_compute_core.so") + return() + endif() + + if(NOT RUNTIME_LIBRARY) + message(STATUS "Cannot find libarm_compute.so") + set(ARMCompute_FOUND FALSE PARENT_SCOPE) + return() + endif() + + if(NOT GRAPH_LIBRARY) + message(STATUS "Cannot find libarm_compute_graph.so") + set(ARMCompute_FOUND FALSE PARENT_SCOPE) + return() + endif() + + if(NOT TARGET arm_compute_core) + add_library(arm_compute_core INTERFACE) + target_include_directories(arm_compute_core SYSTEM INTERFACE ${INCLUDE_DIR}) + target_link_libraries(arm_compute_core INTERFACE dl ${LIB_PTHREAD}) + target_link_libraries(arm_compute_core INTERFACE ${CORE_LIBRARY}) + endif(NOT TARGET arm_compute_core) + + if(NOT TARGET arm_compute) + add_library(arm_compute INTERFACE) + target_include_directories(arm_compute SYSTEM INTERFACE ${INCLUDE_DIR}) + target_link_libraries(arm_compute INTERFACE ${RUNTIME_LIBRARY}) + target_link_libraries(arm_compute INTERFACE arm_compute_core) + endif(NOT TARGET arm_compute) + + if(NOT TARGET arm_compute_graph) + add_library(arm_compute_graph INTERFACE) + target_include_directories(arm_compute_graph SYSTEM INTERFACE ${INCLUDE_DIR}) + target_link_libraries(arm_compute_graph INTERFACE ${GRAPH_LIBRARY}) + target_link_libraries(arm_compute_graph INTERFACE arm_compute) + endif(NOT TARGET arm_compute_graph) + + set(ARMCompute_FOUND TRUE PARENT_SCOPE) +endfunction(_ARMCompute_Import) + +### Check whether library exists +function(_ARMCompute_Check VAR LIBDIR) + set(FOUND TRUE) + + if(NOT EXISTS "${LIBDIR}/libarm_compute_core.so") + set(FOUND FALSE) + endif() + + if(NOT EXISTS "${LIBDIR}/libarm_compute.so") + set(FOUND FALSE) + endif() + + if(NOT EXISTS "${LIBDIR}/libarm_compute_graph.so") + set(FOUND FALSE) + endif() + + set(${VAR} ${FOUND} PARENT_SCOPE) +endfunction(_ARMCompute_Check) + +# Let's build and install ARMCompute libraries +# NOTE This function silently returns on error +function(_ARMCompute_Build ARMCompute_INSTALL_PREFIX) + ### Check whether library exists + _ARMCompute_Check(ARMCompute_FOUND ${ARMCompute_INSTALL_PREFIX}) + + if(ARMCompute_FOUND) + return() + endif(ARMCompute_FOUND) + + ### Let's build with SCONS + nnas_find_package(ARMComputeSource QUIET) + + if(NOT ARMComputeSource_FOUND) + return() + endif(NOT ARMComputeSource_FOUND) + + find_program(SCONS_PATH scons) + + if(NOT SCONS_PATH) + message(WARNING "SCONS NOT FOUND. Please install SCONS to build ARMCompute.") + return() + endif(NOT SCONS_PATH) + + if(CMAKE_BUILD_TYPE) + string(TOLOWER "${CMAKE_BUILD_TYPE}" SCON_BUILD_TYPE) + else(CMAKE_BUILD_TYPE) + set(SCON_BUILD_TYPE "release") + endif(CMAKE_BUILD_TYPE) + + #### Architecture-specific configurations + if(TARGET_ARCH STREQUAL "armv7l") + set(BUILD_ARCH "armv7a") + set(BUILD_DIR "${BUILD_ARCH}-${TARGET_OS}.${SCON_BUILD_TYPE}") + endif() + + if(TARGET_ARCH STREQUAL "aarch64") + set(BUILD_ARCH "arm64-v8a") + set(BUILD_DIR "${BUILD_ARCH}-${TARGET_OS}.${SCON_BUILD_TYPE}") + endif() + + #### Platform-specific configurations + #### TODO Support android + + #### Mode-specific configurations + if(SCON_BUILD_TYPE STREQUAL "debug") + list(APPEND SCONS_OPTIONS "debug=1") + endif() + + #### Generic configurations + list(APPEND SCONS_OPTIONS "neon=1") + list(APPEND SCONS_OPTIONS "opencl=1") + list(APPEND SCONS_OPTIONS "examples=0") + list(APPEND SCONS_OPTIONS "Werror=0") + list(APPEND SCONS_OPTIONS "os=${TARGET_OS}") + + if(DEFINED ACL_BUILD_THREADS) + set(N ${ACL_BUILD_THREADS}) + else(DEFINED ACL_BUILD_THREADS) + include(ProcessorCount) + ProcessorCount(N) + endif(DEFINED ACL_BUILD_THREADS) + + if((NOT N EQUAL 0) AND BUILD_EXT_MULTITHREAD) + list(APPEND SCONS_OPTIONS -j${N}) + endif() + if(DEFINED BUILD_ARCH) + list(APPEND SCONS_OPTIONS "arch=${BUILD_ARCH}") + endif(DEFINED BUILD_ARCH) + + if(DEFINED BUILD_DIR) + list(APPEND SCONS_OPTIONS "build_dir=${BUILD_DIR}") + endif(DEFINED BUILD_DIR) + + message(STATUS "Build ARMCompute with ${SCONS_PATH} ('${SCONS_OPTIONS}'") + + # Build ARMCompute libraries with SCONS + # NOTE ARMCompute SConstruct unconditioanlly appends "arm-linux-gnueabihf-" prefix for linux + execute_process(COMMAND /usr/bin/env CC=gcc CXX=g++ "${SCONS_PATH}" ${SCONS_OPTIONS} + WORKING_DIRECTORY ${ARMComputeSource_DIR} + RESULT_VARIABLE ARMCompute_BUILD) + + # Install ARMCompute libraries to overlay + execute_process(COMMAND ${CMAKE_COMMAND} -E make_directory "${ARMCompute_INSTALL_PREFIX}" + WORKING_DIRECTORY ${ARMComputeSource_DIR} + RESULT_VARIABLE ARMCompute_BUILD) + execute_process(COMMAND ${CMAKE_COMMAND} -E copy "build/${BUILD_DIR}/libarm_compute_core.so" "${ARMCompute_INSTALL_PREFIX}" + COMMAND ${CMAKE_COMMAND} -E copy "build/${BUILD_DIR}/libarm_compute.so" "${ARMCompute_INSTALL_PREFIX}" + COMMAND ${CMAKE_COMMAND} -E copy "build/${BUILD_DIR}/libarm_compute_graph.so" "${ARMCompute_INSTALL_PREFIX}" + WORKING_DIRECTORY ${ARMComputeSource_DIR} + RESULT_VARIABLE ARMCompute_BUILD) +endfunction(_ARMCompute_Build) + +set(ARMCompute_PREFIX ${EXT_OVERLAY_DIR}/lib) +if(BUILD_ARMCOMPUTE) + _ARMCompute_Build("${ARMCompute_PREFIX}") +endif(BUILD_ARMCOMPUTE) +_ARMCompute_Import() diff --git a/infra/nnfw/cmake/packages/AbslSourceConfig.cmake b/infra/nnfw/cmake/packages/AbslSourceConfig.cmake deleted file mode 100644 index c3bab13..0000000 --- a/infra/nnfw/cmake/packages/AbslSourceConfig.cmake +++ /dev/null @@ -1,14 +0,0 @@ -function(_AbslSource_import) - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - # NOTE The following URL comes from TensorFlow 1.12 - envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") - set(ABSL_URL ${EXTERNAL_DOWNLOAD_SERVER}/abseil/abseil-cpp/archive/389ec3f906f018661a5308458d623d01f96d7b23.tar.gz) - ExternalSource_Get("absl" ${DOWNLOAD_ABSL} ${ABSL_URL}) - - set(AbslSource_DIR ${absl_SOURCE_DIR} PARENT_SCOPE) - set(AbslSource_FOUND ${absl_SOURCE_GET} PARENT_SCOPE) -endfunction(_AbslSource_import) - -_AbslSource_import() diff --git a/infra/nnfw/cmake/packages/BoostConfig.cmake b/infra/nnfw/cmake/packages/BoostConfig.cmake new file mode 100644 index 0000000..c4d7d58 --- /dev/null +++ b/infra/nnfw/cmake/packages/BoostConfig.cmake @@ -0,0 +1,77 @@ +# Let's build and install Boost libraries +function(_Boost_Build Boost_PREFIX) + nnas_find_package(BoostSource QUIET) + + if(NOT BoostSource_FOUND) + return() + endif(NOT BoostSource_FOUND) + + #### Generic configurations + if(NOT EXISTS ${BoostSource_DIR}/b2) + execute_process(COMMAND "${BoostSource_DIR}/bootstrap.sh" + WORKING_DIRECTORY ${BoostSource_DIR} + RESULT_VARIABLE Boost_BUILD) + endif() + + set(BoostBuild_DIR ${BoostSource_DIR}) + set(BoostInstall_DIR ${Boost_PREFIX}) + + unset(Boost_Options) + + list(APPEND Boost_Options --build-dir=${BoostBuild_DIR}) + list(APPEND Boost_Options --prefix=${BoostInstall_DIR}) + list(APPEND Boost_Options --with-log) + list(APPEND Boost_Options --with-program_options) + list(APPEND Boost_Options --with-system) + list(APPEND Boost_Options --with-filesystem) + + set(JAM_FILENAME ${BoostBuild_DIR}/user-config.jam) + + if(ANDROID) + set(NDK_CXX ${NDK_DIR}/toolchains/llvm/prebuilt/linux-x86_64/bin/${TARGET_ARCH}-linux-android${ANDROID_API_LEVEL}-clang++) + file(WRITE ${JAM_FILENAME} "using clang : arm64v8a : ${NDK_CXX} ;") + list(APPEND Boost_Options toolset=clang-arm64v8a) + # without target-os=android, it complains it cannot find -lrt. + list(APPEND Boost_Options target-os=android) + else() + file(WRITE ${JAM_FILENAME} "using gcc : local : ${CMAKE_CXX_COMPILER} ;\n") + list(APPEND Boost_Options toolset=gcc-local) + endif(ANDROID) + + # Install Boost libraries + execute_process(COMMAND ${CMAKE_COMMAND} -E make_directory "${BoostInstall_DIR}") + execute_process(COMMAND /usr/bin/env BOOST_BUILD_PATH="${BoostBuild_DIR}" ${BoostSource_DIR}/b2 install ${Boost_Options} + WORKING_DIRECTORY ${BoostSource_DIR}) + +endfunction(_Boost_Build) + +# Find pre-installed boost library and update Boost variables. +if (NOT BUILD_BOOST) + # BoostConfig.cmake does not honor QUIET argument at least till cmake 1.70.0. + # Thus, don't try to find_package if you're not entirely sure you have boost. + find_package(Boost 1.58.0 QUIET COMPONENTS log program_options filesystem system) + if(Boost_FOUND) + return() + endif() +endif() + +set(Boost_PREFIX ${CMAKE_INSTALL_PREFIX}) + +if(BUILD_BOOST) + _Boost_Build("${Boost_PREFIX}") + + # Let's use locally built boost to system-wide one so sub modules + # needing Boost library and header files can search for them + # in ${Boost_PREFIX} directory + list(APPEND CMAKE_PREFIX_PATH "${Boost_PREFIX}") + + # Without Boost_INCLUDE_DIR, it complains the variable is missing during find_package. + set(Boost_INCLUDE_DIR ${CMAKE_INSTALL_PREFIX}/include) + + # 1) without static build, it will complain it cannot find libc++_shared.so. + # 2) We uses static libraries for other libraries. + set(Boost_USE_STATIC_LIBS ON) + + # We built boost library so update Boost variables. + find_package(Boost 1.58.0 QUIET COMPONENTS log program_options filesystem system) +endif(BUILD_BOOST) diff --git a/infra/nnfw/cmake/packages/EigenConfig.cmake b/infra/nnfw/cmake/packages/EigenConfig.cmake index 23e38cd..f37d653 100644 --- a/infra/nnfw/cmake/packages/EigenConfig.cmake +++ b/infra/nnfw/cmake/packages/EigenConfig.cmake @@ -1,14 +1,14 @@ function(_Eigen_import) - nnfw_find_package(EigenSource QUIET) + nnas_find_package(TensorFlowEigenSource-2.3.0-rc0 QUIET) - if(NOT EigenSource_FOUND) + if(NOT TensorFlowEigenSource_FOUND) set(Eigen_FOUND FALSE PARENT_SCOPE) return() - endif(NOT EigenSource_FOUND) + endif(NOT TensorFlowEigenSource_FOUND) if(NOT TARGET eigen) add_library(eigen INTERFACE) - target_include_directories(eigen SYSTEM INTERFACE "${EigenSource_DIR}") + target_include_directories(eigen SYSTEM INTERFACE "${TensorFlowEigenSource_DIR}") # Add EIGEN_MPL2_ONLY to remove license issue posibility target_compile_definitions(eigen INTERFACE EIGEN_MPL2_ONLY) endif(NOT TARGET eigen) diff --git a/infra/nnfw/cmake/packages/EigenSourceConfig.cmake b/infra/nnfw/cmake/packages/EigenSourceConfig.cmake deleted file mode 100644 index 1fbcf13..0000000 --- a/infra/nnfw/cmake/packages/EigenSourceConfig.cmake +++ /dev/null @@ -1,14 +0,0 @@ -function(_EigenSource_import) - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - # NOTE TensorFlow 1.12 downloads Eign from the following URL - envoption(EXTERNAL_DOWNLOAD_SERVER "https://bitbucket.org") - set(EIGEN_URL ${EXTERNAL_DOWNLOAD_SERVER}/eigen/eigen/get/88fc23324517.tar.gz) - ExternalSource_Get("eigen" ${DOWNLOAD_EIGEN} ${EIGEN_URL}) - - set(EigenSource_DIR ${eigen_SOURCE_DIR} PARENT_SCOPE) - set(EigenSource_FOUND ${eigen_SOURCE_GET} PARENT_SCOPE) -endfunction(_EigenSource_import) - -_EigenSource_import() diff --git a/infra/nnfw/cmake/packages/FarmhashSourceConfig.cmake b/infra/nnfw/cmake/packages/FarmhashSourceConfig.cmake index cba3c3c..ab53f97 100644 --- a/infra/nnfw/cmake/packages/FarmhashSourceConfig.cmake +++ b/infra/nnfw/cmake/packages/FarmhashSourceConfig.cmake @@ -1,11 +1,16 @@ function(_FarmhashSource_import) + if(NOT ${DOWNLOAD_FARMHASH}) + set(FarmhashSource_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT ${DOWNLOAD_FARMHASH}) + nnas_include(ExternalSourceTools) nnas_include(OptionTools) # NOTE TensorFlow 1.12 downloads farmhash from the following URL envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") set(FARMHASH_URL ${EXTERNAL_DOWNLOAD_SERVER}/google/farmhash/archive/816a4ae622e964763ca0862d9dbd19324a1eaf45.tar.gz) - ExternalSource_Get("farmhash" ${DOWNLOAD_FARMHASH} ${FARMHASH_URL}) + ExternalSource_Download("farmhash" ${FARMHASH_URL}) set(FarmhashSource_DIR ${farmhash_SOURCE_DIR} PARENT_SCOPE) set(FarmhashSource_FOUND ${farmhash_SOURCE_GET} PARENT_SCOPE) diff --git a/infra/nnfw/cmake/packages/FlatBuffersConfig.cmake b/infra/nnfw/cmake/packages/FlatBuffersConfig.cmake index 0646731..13ad111 100644 --- a/infra/nnfw/cmake/packages/FlatBuffersConfig.cmake +++ b/infra/nnfw/cmake/packages/FlatBuffersConfig.cmake @@ -1,5 +1,13 @@ function(_FlatBuffers_import) - nnfw_find_package(FlatBuffersSource QUIET) + + find_package(Flatbuffers QUIET) + if(Flatbuffers_FOUND) + set(FlatBuffers_FOUND TRUE PARENT_SCOPE) + return() + endif(Flatbuffers_FOUND) + + # NOTE Tizen uses 1.11 + nnas_find_package(FlatBuffersSource EXACT 1.11 QUIET) if(NOT FlatBuffersSource_FOUND) set(FlatBuffers_FOUND FALSE PARENT_SCOPE) @@ -13,65 +21,15 @@ function(_FlatBuffers_import) list(APPEND FlatBuffers_Library_SRCS "${FlatBuffersSource_DIR}/src/reflection.cpp") list(APPEND FlatBuffers_Library_SRCS "${FlatBuffersSource_DIR}/src/util.cpp") - # From FlatBuffers's CMakeLists.txt - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_cpp.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_dart.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_fbs.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_general.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_go.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_grpc.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_js.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_json_schema.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_lobster.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_lua.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_php.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/idl_gen_python.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/flatc.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/src/flatc_main.cpp") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/grpc/src/compiler/cpp_generator.cc") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/grpc/src/compiler/go_generator.cc") - list(APPEND FlatBuffers_Compiler_SRCS "${FlatBuffersSource_DIR}/grpc/src/compiler/java_generator.cc") - - if(NOT TARGET flatbuffers) + if(NOT TARGET flatbuffers::flatbuffers) add_library(flatbuffers ${FlatBuffers_Library_SRCS}) target_include_directories(flatbuffers PUBLIC "${FlatBuffersSource_DIR}/include") - endif(NOT TARGET flatbuffers) + set_property(TARGET flatbuffers PROPERTY POSITION_INDEPENDENT_CODE ON) - if(NOT TARGET flatc) - add_executable(flatc ${FlatBuffers_Compiler_SRCS}) - target_include_directories(flatc PRIVATE "${FlatBuffersSource_DIR}/grpc") - target_link_libraries(flatc flatbuffers) - endif(NOT TARGET flatc) + add_library(flatbuffers::flatbuffers ALIAS flatbuffers) + endif(NOT TARGET flatbuffers::flatbuffers) set(FlatBuffers_FOUND TRUE PARENT_SCOPE) endfunction(_FlatBuffers_import) _FlatBuffers_import() - -if(FlatBuffers_FOUND) - function(FlatBuffers_Generate PREFIX OUTPUT_DIR SCHEMA_DIR) - get_filename_component(abs_output_dir ${OUTPUT_DIR} ABSOLUTE) - get_filename_component(abs_schema_dir ${SCHEMA_DIR} ABSOLUTE) - - foreach(schema ${ARGN}) - get_filename_component(schema_fn "${schema}" NAME) - get_filename_component(dir "${schema}" DIRECTORY) - - get_filename_component(schema_fn_we "${schema_fn}" NAME_WE) - - list(APPEND SCHEMA_FILES "${abs_schema_dir}/${schema}") - list(APPEND OUTPUT_FILES "${abs_output_dir}/${schema_fn_we}_generated.h") - endforeach() - - add_custom_command(OUTPUT ${OUTPUT_FILES} - COMMAND ${CMAKE_COMMAND} -E make_directory "${abs_output_dir}" - COMMAND "$" -c --no-includes - --no-union-value-namespacing - --gen-object-api -o "${abs_output_dir}" - ${SCHEMA_FILES} - DEPENDS flatc) - - set(${PREFIX}_SOURCES ${OUTPUT_FILES} PARENT_SCOPE) - set(${PREFIX}_INCLUDE_DIRS ${abs_output_dir} PARENT_SCOPE) - endfunction(FlatBuffers_Generate) -endif(FlatBuffers_FOUND) diff --git a/infra/nnfw/cmake/packages/FlatBuffersSourceConfig.cmake b/infra/nnfw/cmake/packages/FlatBuffersSourceConfig.cmake deleted file mode 100644 index 5a59ef3..0000000 --- a/infra/nnfw/cmake/packages/FlatBuffersSourceConfig.cmake +++ /dev/null @@ -1,14 +0,0 @@ -function(_FlatBuffersSource_import) - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - # NOTE TensorFlow 1.12 downloads FlatBuffers from the following URL - envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") - set(FLATBUFFERS_URL ${EXTERNAL_DOWNLOAD_SERVER}/google/flatbuffers/archive/1f5eae5d6a135ff6811724f6c57f911d1f46bb15.tar.gz) - ExternalSource_Get("flatbuffers" ${DOWNLOAD_FLATBUFFERS} ${FLATBUFFERS_URL}) - - set(FlatBuffersSource_DIR ${flatbuffers_SOURCE_DIR} PARENT_SCOPE) - set(FlatBuffersSource_FOUND ${flatbuffers_SOURCE_GET} PARENT_SCOPE) -endfunction(_FlatBuffersSource_import) - -_FlatBuffersSource_import() diff --git a/infra/nnfw/cmake/packages/GEMMLowpSourceConfig.cmake b/infra/nnfw/cmake/packages/GEMMLowpSourceConfig.cmake index d800cdc..97c8e05 100644 --- a/infra/nnfw/cmake/packages/GEMMLowpSourceConfig.cmake +++ b/infra/nnfw/cmake/packages/GEMMLowpSourceConfig.cmake @@ -1,11 +1,16 @@ function(_GEMMLowpSource_import) + if(NOT ${DOWNLOAD_GEMMLOWP}) + set(GEMMLowpSource_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT ${DOWNLOAD_GEMMLOWP}) + nnas_include(ExternalSourceTools) nnas_include(OptionTools) # NOTE TensorFlow 1.12 uses the following URL envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") set(GEMMLOWP_URL ${EXTERNAL_DOWNLOAD_SERVER}/google/gemmlowp/archive/38ebac7b059e84692f53e5938f97a9943c120d98.tar.gz) - ExternalSource_Get("gemmlowp" ${DOWNLOAD_GEMMLOWP} ${GEMMLOWP_URL}) + ExternalSource_Download("gemmlowp" ${GEMMLOWP_URL}) set(GEMMLowpSource_DIR ${gemmlowp_SOURCE_DIR} PARENT_SCOPE) set(GEMMLowpSource_FOUND ${gemmlowp_SOURCE_GET} PARENT_SCOPE) diff --git a/infra/nnfw/cmake/packages/GTestConfig.cmake b/infra/nnfw/cmake/packages/GTestConfig.cmake index b4dd05f..f3aadf9 100644 --- a/infra/nnfw/cmake/packages/GTestConfig.cmake +++ b/infra/nnfw/cmake/packages/GTestConfig.cmake @@ -5,7 +5,7 @@ if(${BUILD_GTEST}) envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") set(GTEST_URL ${EXTERNAL_DOWNLOAD_SERVER}/google/googletest/archive/release-1.8.0.tar.gz) - ExternalSource_Get("gtest" TRUE ${GTEST_URL}) + ExternalSource_Download("gtest" ${GTEST_URL}) # gtest_SOURCE_DIR is used in gtest subdirectorty's cmake set(sourcedir_gtest ${gtest_SOURCE_DIR}) diff --git a/infra/nnfw/cmake/packages/HDF5Config.cmake b/infra/nnfw/cmake/packages/HDF5Config.cmake index 1f90dea..b965ffc 100644 --- a/infra/nnfw/cmake/packages/HDF5Config.cmake +++ b/infra/nnfw/cmake/packages/HDF5Config.cmake @@ -1,31 +1,55 @@ +# Don't cache HDF5_*. Otherwise it will use the cached value without searching. unset(HDF5_DIR CACHE) -find_package(HDF5 QUIET) +unset(HDF5_INCLUDE_DIRS CACHE) +unset(HDF5_CXX_LIBRARY_hdf5 CACHE) +unset(HDF5_CXX_LIBRARY_hdf5_cpp CACHE) -if (NOT HDF5_FOUND) - # Give second chance for some systems where sytem find_package config mode fails - unset(HDF5_FOUND) +# Case 1. external hdf5 +if(DEFINED EXT_HDF5_DIR) + find_path(HDF5_INCLUDE_DIRS NAMES H5Cpp.h NO_CMAKE_FIND_ROOT_PATH PATHS "${EXT_HDF5_DIR}/include") + find_library(HDF5_CXX_LIBRARY_hdf5 NAMES libhdf5.a PATHS "${EXT_HDF5_DIR}/lib") + find_library(HDF5_CXX_LIBRARY_hdf5_cpp NAMES libhdf5_cpp.a PATHS "${EXT_HDF5_DIR}/lib") + if (NOT (HDF5_INCLUDE_DIRS AND HDF5_CXX_LIBRARY_hdf5 AND HDF5_CXX_LIBRARY_hdf5_cpp)) + message(WARNING "Failed to find H5Cpp.h or libhdf5.a or libhdf5_cpp.a") + set(HDF5_FOUND FALSE) + return() + else() + # message(FATAL_ERROR "0=${HDF5_INCLUDE_DIRS},1=${HDF5_CXX_LIBRARIES}") + set(HDF5_FOUND TRUE) + list(APPEND HDF5_CXX_LIBRARIES ${HDF5_CXX_LIBRARY_hdf5_cpp} ${HDF5_CXX_LIBRARY_hdf5}) + return() + endif() +endif() - find_path(HDF5_INCLUDE_DIRS NAMES hdf5.h PATH_SUFFIXES include/hdf5/serial) +# Case 2. search pre-installed locations (by apt, brew, ...) +if(NOT CMAKE_CROSSCOMPILING) + find_package(HDF5 COMPONENTS CXX QUIET) +else() + find_path(HDF5_INCLUDE_DIRS NAMES hdf5.h ONLY_CMAKE_FIND_ROOT_PATH PATH_SUFFIXES include/hdf5/serial) if (NOT HDF5_INCLUDE_DIRS) set(HDF5_FOUND FALSE) return() endif() - if (HDF5_USE_STATIC_LIBRARIES) - find_library(HDF5_LIBRARIES libhdf5.a) - else (HDF5_USE_STATIC_LIBRARIES) - find_library(HDF5_LIBRARIES libhdf5.so) + if(HDF5_USE_STATIC_LIBRARIES) + find_library(HDF5_CXX_LIBRARY_hdf5 libhdf5.a) + find_library(HDF5_CXX_LIBRARY_hdf5_cpp libhdf5_cpp.a) + else(HDF5_USE_STATIC_LIBRARIES) + find_library(HDF5_CXX_LIBRARY_hdf5 libhdf5.so) + find_library(HDF5_CXX_LIBRARY_hdf5_cpp libhdf5_cpp.so) endif(HDF5_USE_STATIC_LIBRARIES) - if (NOT HDF5_LIBRARIES) + if (NOT (HDF5_CXX_LIBRARY_hdf5 AND HDF5_CXX_LIBRARY_hdf5_cpp)) set(HDF5_FOUND FALSE) return() endif() - list(APPEND HDF5_LIBRARIES "sz" "z" "dl" "m") + + # We can use "hdf5" and "hdf5_cpp" to use the same file founded with above. + list(APPEND HDF5_CXX_LIBRARIES "hdf5" "hdf5_cpp" "sz" "z" "dl" "m") + + # Append missing libaec which is required by libsz, which is required by libhdf5 + list(APPEND HDF5_CXX_LIBRARIES "aec") set(HDF5_FOUND TRUE) endif() - -# Append missing libaec which is required by libsz, which is required by libhdf5 -list(APPEND HDF5_LIBRARIES "aec") diff --git a/infra/nnfw/cmake/packages/NEON2SSESourceConfig.cmake b/infra/nnfw/cmake/packages/NEON2SSESourceConfig.cmake index b1bf6cf..7bae616 100644 --- a/infra/nnfw/cmake/packages/NEON2SSESourceConfig.cmake +++ b/infra/nnfw/cmake/packages/NEON2SSESourceConfig.cmake @@ -1,9 +1,8 @@ function(_NEON2SSESource_import) - # TODO Remove this workaround once target preset is ready - if(NOT (TARGET_ARCH_BASE STREQUAL "x86_64")) + if(NOT ${DOWNLOAD_NEON2SSE}) set(NEON2SSESource_FOUND FALSE PARENT_SCOPE) return() - endif(NOT (TARGET_ARCH_BASE STREQUAL "x86_64")) + endif(NOT ${DOWNLOAD_NEON2SSE}) nnas_include(ExternalSourceTools) nnas_include(OptionTools) @@ -11,7 +10,7 @@ function(_NEON2SSESource_import) # NOTE TensorFlow 1.12 downloads NEON2SSE from the following URL envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") set(NEON2SSE_URL ${EXTERNAL_DOWNLOAD_SERVER}/intel/ARM_NEON_2_x86_SSE/archive/0f77d9d182265259b135dad949230ecbf1a2633d.tar.gz) - ExternalSource_Get("neon_2_sse" ${DOWNLOAD_NEON2SSE} ${NEON2SSE_URL}) + ExternalSource_Download("neon_2_sse" ${NEON2SSE_URL}) set(NEON2SSESource_DIR ${neon_2_sse_SOURCE_DIR} PARENT_SCOPE) set(NEON2SSESource_FOUND ${neon_2_sse_SOURCE_GET} PARENT_SCOPE) diff --git a/infra/nnfw/cmake/packages/NoniusConfig.cmake b/infra/nnfw/cmake/packages/NoniusConfig.cmake new file mode 100644 index 0000000..049d655 --- /dev/null +++ b/infra/nnfw/cmake/packages/NoniusConfig.cmake @@ -0,0 +1,18 @@ +function(_Nonius_import) + nnas_find_package(NoniusSource QUIET) + + if(NOT NoniusSource_FOUND) + set(Nonius_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT NoniusSource_FOUND) + + if(NOT TARGET nonius) + message(STATUS "Found nonius: TRUE") + add_library(nonius INTERFACE) + target_include_directories(nonius INTERFACE "${NoniusSource_DIR}/include") + endif(NOT TARGET nonius) + + set(Nonius_FOUND TRUE PARENT_SCOPE) +endfunction(_Nonius_import) + +_Nonius_import() diff --git a/infra/nnfw/cmake/packages/Ruy/CMakeLists.txt b/infra/nnfw/cmake/packages/Ruy/CMakeLists.txt new file mode 100644 index 0000000..f4d9f88 --- /dev/null +++ b/infra/nnfw/cmake/packages/Ruy/CMakeLists.txt @@ -0,0 +1,45 @@ +set(RUY_BASE ${RuySource_DIR}/ruy) + +# +# Ruy library +# +file(GLOB RUY_CORE_SRCS "${RUY_BASE}/*.cc") +file(GLOB RUY_CORE_TESTS "${RUY_BASE}/*test*.cc") +list(REMOVE_ITEM RUY_CORE_SRCS ${RUY_CORE_TESTS}) + +list(APPEND RUY_SRCS ${RUY_CORE_SRCS}) +list(REMOVE_ITEM RUY_SRCS "${RUY_BASE}/benchmark.cc") +list(REMOVE_ITEM RUY_SRCS "${RUY_BASE}/example.cc") +list(REMOVE_ITEM RUY_SRCS "${RUY_BASE}/example_advanced.cc") +list(REMOVE_ITEM RUY_SRCS "${RUY_BASE}/tune_tool.cc") +list(REMOVE_ITEM RUY_SRCS "${RUY_BASE}/pmu.cc") + +list(APPEND RUY_INSTRUMENTATION_SRCS "${RUY_BASE}/profiler/instrumentation.cc") + +if(PROFILE_RUY) + list(APPEND RUY_PROFILER_SRCS "${RUY_BASE}/profiler/profiler.cc") + list(APPEND RUY_PROFILER_SRCS "${RUY_BASE}/profiler/treeview.cc") +endif(PROFILE_RUY) + +list(APPEND RUY_INCLUDES "${RuySource_DIR}") + +add_library(ruy STATIC ${RUY_SRCS}) +target_include_directories(ruy SYSTEM PUBLIC ${RUY_INCLUDES}) +target_compile_options(ruy PRIVATE -O3) + +add_library(ruy_instrumentation ${RUY_INSTRUMENTATION_SRCS}) +target_include_directories(ruy_instrumentation SYSTEM PUBLIC ${RUY_INCLUDES}) +target_compile_options(ruy_instrumentation PRIVATE -O3) + +set_target_properties(ruy ruy_instrumentation PROPERTIES POSITION_INDEPENDENT_CODE ON) + +if(PROFILE_RUY) + add_library(ruy_profiler STATIC ${RUY_PROFILER_SRCS}) + target_include_directories(ruy_profiler SYSTEM PUBLIC ${RUY_INCLUDES}) + target_compile_options(ruy_profiler PRIVATE -O3) + set_target_properties(ruy_profiler PROPERTIES POSITION_INDEPENDENT_CODE ON) + + target_compile_definitions(ruy PUBLIC RUY_PROFILER) + target_compile_definitions(ruy_instrumentation PUBLIC RUY_PROFILER) + target_compile_definitions(ruy_profiler PUBLIC RUY_PROFILER) +endif(PROFILE_RUY) diff --git a/infra/nnfw/cmake/packages/RuyConfig.cmake b/infra/nnfw/cmake/packages/RuyConfig.cmake new file mode 100644 index 0000000..278e33c --- /dev/null +++ b/infra/nnfw/cmake/packages/RuyConfig.cmake @@ -0,0 +1,22 @@ +function(_Ruy_import) + # NOTE This line prevents multiple definitions of ruy target + if(TARGET ruy) + set(Ruy_FOUND TRUE) + return() + endif(TARGET ruy) + + nnfw_find_package(RuySource QUIET) + + if(NOT RuySource_FOUND) + set(Ruy_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT RuySource_FOUND) + + if(BUILD_RUY) + add_extdirectory("${CMAKE_CURRENT_LIST_DIR}/Ruy" ruy) + endif(BUILD_RUY) + + set(Ruy_FOUND TRUE PARENT_SCOPE) +endfunction(_Ruy_import) + +_Ruy_import() diff --git a/infra/nnfw/cmake/packages/RuySourceConfig.cmake b/infra/nnfw/cmake/packages/RuySourceConfig.cmake new file mode 100644 index 0000000..08170fb --- /dev/null +++ b/infra/nnfw/cmake/packages/RuySourceConfig.cmake @@ -0,0 +1,19 @@ +function(_RuySource_import) + if(NOT ${DOWNLOAD_RUY}) + set(RuySource_DIR FALSE PARENT_SCOPE) + return() + endif(NOT ${DOWNLOAD_RUY}) + + nnas_include(ExternalSourceTools) + nnas_include(OptionTools) + + # NOTE Downloads source from latest ruy library (2020-04-10) + envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") + set(RUY_URL ${EXTERNAL_DOWNLOAD_SERVER}/google/ruy/archive/2e2658f964638ab7aa562d4b48b76007d44e38f0.tar.gz) + ExternalSource_Download("ruy" ${RUY_URL}) + + set(RuySource_DIR ${ruy_SOURCE_DIR} PARENT_SCOPE) + set(RuySource_FOUND ${ruy_SOURCE_GET} PARENT_SCOPE) +endfunction(_RuySource_import) + +_RuySource_import() diff --git a/infra/nnfw/cmake/packages/TensorFlowEigen-1.13.1/TensorFlowEigenConfig.cmake b/infra/nnfw/cmake/packages/TensorFlowEigen-1.13.1/TensorFlowEigenConfig.cmake new file mode 100644 index 0000000..253b290 --- /dev/null +++ b/infra/nnfw/cmake/packages/TensorFlowEigen-1.13.1/TensorFlowEigenConfig.cmake @@ -0,0 +1,19 @@ +function(_Eigen_import) + nnas_find_package(EigenSource QUIET) + + if(NOT EigenSource_FOUND) + set(TensorFlowEigen_1_13_1_FOUND FALSE PARENT_SCOPE) + return() + endif(NOT EigenSource_FOUND) + + if(NOT TARGET eigen-tf-1.13.1) + add_library(eigen-tf-1.13.1 INTERFACE) + target_include_directories(eigen-tf-1.13.1 SYSTEM INTERFACE "${EigenSource_DIR}") + # Add EIGEN_MPL2_ONLY to remove license issue posibility + target_compile_definitions(eigen-tf-1.13.1 INTERFACE EIGEN_MPL2_ONLY) + endif(NOT TARGET eigen-tf-1.13.1) + + set(TensorFlowEigen_1_13_1_FOUND TRUE PARENT_SCOPE) +endfunction(_Eigen_import) + +_Eigen_import() diff --git a/infra/nnfw/cmake/packages/TensorFlowEigen-1.13.1/TensorFlowEigenConfigVersion.cmake b/infra/nnfw/cmake/packages/TensorFlowEigen-1.13.1/TensorFlowEigenConfigVersion.cmake new file mode 100644 index 0000000..ed79ecd --- /dev/null +++ b/infra/nnfw/cmake/packages/TensorFlowEigen-1.13.1/TensorFlowEigenConfigVersion.cmake @@ -0,0 +1,9 @@ +set(PACKAGE_VERSION "1.13.1") +set(PACKAGE_VERSION_EXACT FALSE) +set(PACKAGE_VERSION_COMPATIBLE FALSE) +set(PACKAGE_VERSION_UNSUITABLE TRUE) + +if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) + set(PACKAGE_VERSION_EXACT TRUE) + set(PACKAGE_VERSION_UNSUITABLE FALSE) +endif(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) diff --git a/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLite/CMakeLists.txt b/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLite/CMakeLists.txt new file mode 100644 index 0000000..2c9618d --- /dev/null +++ b/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLite/CMakeLists.txt @@ -0,0 +1,62 @@ +set(TENSORFLOW_LITE_BASE ${TensorFlowSource_DIR}/tensorflow/lite) + +# +# Tensorflow Lite library +# +file(GLOB TFLITE_CORE_SRCS "${TENSORFLOW_LITE_BASE}/*.c" "${TENSORFLOW_LITE_BASE}/*.cc" "${TENSORFLOW_LITE_BASE}/core/*.cc") +file(GLOB TFLITE_CORE_TESTS "${TENSORFLOW_LITE_BASE}/*test*.cc") +list(REMOVE_ITEM TFLITE_CORE_SRCS ${TFLITE_CORE_TESTS}) + +file(GLOB_RECURSE TFLITE_KERNEL_SRCS "${TENSORFLOW_LITE_BASE}/kernels/*.cc") +file(GLOB_RECURSE TFLITE_KERNEL_TESTS "${TENSORFLOW_LITE_BASE}/kernels/*test*.cc") +list(REMOVE_ITEM TFLITE_KERNEL_SRCS ${TFLITE_KERNEL_TESTS}) + +file(GLOB TFLITE_LIB_SRCS "${TENSORFLOW_LITE_BASE}/c/*.c" "${TENSORFLOW_LITE_BASE}/c/*.cc") +file(GLOB TFLITE_LIB_TESTS "${TENSORFLOW_LITE_BASE}/c/*test*.cc") +list(REMOVE_ITEM TFLITE_LIB_SRCS ${TFLITE_LIB_TESTS}) + +file(GLOB TFLITE_API_SRCS "${TENSORFLOW_LITE_BASE}/core/api/*.c" "${TENSORFLOW_LITE_BASE}/core/api/*.cc") +file(GLOB TFLITE_API_TESTS "${TENSORFLOW_LITE_BASE}/core/api/*test*.cc") +list(REMOVE_ITEM TFLITE_API_SRCS ${TFLITE_API_TESTS}) + +file(GLOB TFLITE_PROFILING_SRCS "${TENSORFLOW_LITE_BASE}/profiling/*.cc") +file(GLOB TFLITE_PROFILING_TESTS "${TENSORFLOW_LITE_BASE}/profiling/*test*.cc") +list(REMOVE_ITEM TFLITE_PROFILING_SRCS ${TFLITE_PROFILING_TESTS}) + +# We will use our own BuiltinOpResolver +list(REMOVE_ITEM TFLITE_KERNEL_SRCS "${TENSORFLOW_LITE_BASE}/kernels/register.cc") +# We will use our own summarizer +list(REMOVE_ITEM TFLITE_PROFILING_SRCS "${TENSORFLOW_LITE_BASE}/profiling/profile_summarizer.cc") +list(APPEND TFLITE_SRCS ${TFLITE_CORE_SRCS}) +list(APPEND TFLITE_SRCS ${TFLITE_KERNEL_SRCS}) +list(APPEND TFLITE_SRCS ${TFLITE_LIB_SRCS}) +list(APPEND TFLITE_SRCS ${TFLITE_API_SRCS}) +list(APPEND TFLITE_SRCS ${TFLITE_PROFILING_SRCS}) + +list(APPEND TFLITE_SRCS "${FarmhashSource_DIR}/src/farmhash.cc") + +list(APPEND TFLITE_INCLUDES "${TensorFlowSource_DIR}") +list(APPEND TFLITE_INCLUDES "${AbseilSource_DIR}") +list(APPEND TFLITE_INCLUDES "${GEMMLowpSource_DIR}") +list(APPEND TFLITE_INCLUDES "${FarmhashSource_DIR}/src") + +if(NEON2SSESource_FOUND) + list(APPEND TFLITE_INCLUDES "${NEON2SSESource_DIR}") +endif(NEON2SSESource_FOUND) + +# This kernels are not used on nnfw +## spectrogram +list(REMOVE_ITEM TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/kernels/audio_spectrogram.cc") +list(REMOVE_ITEM TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/kernels/audio_spectrogram_test.cc") +list(REMOVE_ITEM TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/kernels/internal/spectrogram.cc") + +add_library(tensorflow-lite STATIC ${TFLITE_SRCS}) +target_include_directories(tensorflow-lite SYSTEM PUBLIC ${TFLITE_INCLUDES}) +target_compile_definitions(tensorflow-lite PUBLIC "GEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK") +set_property(TARGET tensorflow-lite PROPERTY POSITION_INDEPENDENT_CODE ON) +target_link_libraries(tensorflow-lite eigen-tf-1.13.1 flatbuffers::flatbuffers ${LIB_PTHREAD} dl) + +if(ANDROID) + target_link_libraries(tensorflow-lite log) + target_include_directories(tensorflow-lite PUBLIC "${NDK_DIR}/..") +endif() diff --git a/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfig.cmake b/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfig.cmake new file mode 100644 index 0000000..4cd7610 --- /dev/null +++ b/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfig.cmake @@ -0,0 +1,73 @@ +# NOTE This line prevents multiple definitions of tensorflow-lite target +if(TARGET tensorflow-lite) + set(TensorFlowLite_FOUND TRUE) + return() +endif(TARGET tensorflow-lite) + +if(BUILD_TENSORFLOW_LITE) + macro(return_unless VAR) + if(NOT ${VAR}) + set(TensorFlowLite_FOUND PARENT_SCOPE) + return() + endif(NOT ${VAR}) + endmacro(return_unless) + + # Required packages + nnas_find_package(AbseilSource QUIET) + return_unless(AbseilSource_FOUND) + nnfw_find_package(TensorFlowEigen EXACT 1.13.1 QUIET) + return_unless(TensorFlowEigen_1_13_1_FOUND) + nnfw_find_package(FarmhashSource QUIET) + return_unless(FarmhashSource_FOUND) + nnfw_find_package(FlatBuffers QUIET) + return_unless(FlatBuffers_FOUND) + nnfw_find_package(GEMMLowpSource QUIET) + return_unless(GEMMLowpSource_FOUND) + nnas_find_package(TensorFlowSource EXACT 1.13.1 QUIET) + return_unless(TensorFlowSource_FOUND) + + # Optional packages + nnfw_find_package(NEON2SSESource QUIET) + + nnas_include(ExternalProjectTools) + add_extdirectory("${CMAKE_CURRENT_LIST_DIR}/TensorFlowLite" tflite) + + set(TensorFlowLite_FOUND TRUE) + return() +endif(BUILD_TENSORFLOW_LITE) + +# Use pre-built TensorFlow Lite +find_path(TFLITE_INCLUDE_DIR NAMES tensorflow/lite/interpreter.h) +find_library(TFLITE_LIB NAMES tensorflow-lite) + +if(NOT TFLITE_INCLUDE_DIR) + set(TensorFlowLite_FOUND FALSE) + return() +endif(NOT TFLITE_INCLUDE_DIR) + +if(NOT TFLITE_LIB) + set(TensorFlowLite_FOUND FALSE) + return() +endif(NOT TFLITE_LIB) + +message(STATUS "Found TensorFlow Lite: TRUE (include: ${TFLITE_INCLUDE_DIR}, lib: ${TFLITE_LIB}") + +# TODO Use IMPORTED target +add_library(tensorflow-lite INTERFACE) +target_include_directories(tensorflow-lite SYSTEM INTERFACE ${TFLITE_INCLUDE_DIR}) +target_link_libraries(tensorflow-lite INTERFACE ${TFLITE_LIB}) +find_package(Flatbuffers) +if(Flatbuffers_FOUND) + target_link_libraries(tensorflow-lite INTERFACE flatbuffers::flatbuffers) +endif(Flatbuffers_FOUND) + +# Prefer -pthread to -lpthread +set(THREADS_PREFER_PTHREAD_FLAG TRUE) +set(CMAKE_THREAD_PREFER_PTHREAD TRUE) +find_package(Threads QUIET) + +if(Threads_FOUND) + target_link_libraries(tensorflow-lite INTERFACE ${CMAKE_THREAD_LIBS_INIT}) +endif(Threads_FOUND) + +set(TensorFlowLite_FOUND TRUE) diff --git a/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfigVersion.cmake b/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfigVersion.cmake new file mode 100644 index 0000000..ed79ecd --- /dev/null +++ b/infra/nnfw/cmake/packages/TensorFlowLite-1.13.1/TensorFlowLiteConfigVersion.cmake @@ -0,0 +1,9 @@ +set(PACKAGE_VERSION "1.13.1") +set(PACKAGE_VERSION_EXACT FALSE) +set(PACKAGE_VERSION_COMPATIBLE FALSE) +set(PACKAGE_VERSION_UNSUITABLE TRUE) + +if(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) + set(PACKAGE_VERSION_EXACT TRUE) + set(PACKAGE_VERSION_UNSUITABLE FALSE) +endif(PACKAGE_FIND_VERSION VERSION_EQUAL PACKAGE_VERSION) diff --git a/infra/nnfw/cmake/packages/TensorFlowLite-2.2.0/CMakeLists.txt b/infra/nnfw/cmake/packages/TensorFlowLite-2.2.0/CMakeLists.txt new file mode 100644 index 0000000..8e7f78e --- /dev/null +++ b/infra/nnfw/cmake/packages/TensorFlowLite-2.2.0/CMakeLists.txt @@ -0,0 +1,110 @@ +# Reference: https://github.com/tensorflow/tensorflow/blob/v2.2.0/tensorflow/lite/tools/make/Makefile +# +# Tensorflow Lite library 2.2.0 +# +set(TENSORFLOW_LITE_BASE ${TFLite220TensorFlowSource_DIR}/tensorflow/lite) + +file(GLOB TFLITE_CORE_SRCS "${TENSORFLOW_LITE_BASE}/*.c" + "${TENSORFLOW_LITE_BASE}/*.cc" + "${TENSORFLOW_LITE_BASE}/core/*.cc") + +file(GLOB_RECURSE TFLITE_KERNEL_SRCS "${TENSORFLOW_LITE_BASE}/kernels/*.cc") + +file(GLOB TFLITE_LIB_SRCS "${TENSORFLOW_LITE_BASE}/c/*.c" "${TENSORFLOW_LITE_BASE}/c/*.cc") + +file(GLOB TFLITE_API_SRCS "${TENSORFLOW_LITE_BASE}/core/api/*.c" + "${TENSORFLOW_LITE_BASE}/core/api/*.cc") + +list(APPEND TFLITE_PROFILING_SRCS "${TENSORFLOW_LITE_BASE}/profiling/memory_info.cc") +list(APPEND TFLITE_PROFILING_SRCS "${TENSORFLOW_LITE_BASE}/profiling/time.cc") + +file(GLOB TFLITE_EXPERIMENTAL_SRCS "${TENSORFLOW_LITE_BASE}/experimental/resource/*.cc" + "${TENSORFLOW_LITE_BASE}/experimental/ruy/*.cc") + +file(GLOB TFLITE_SPARSITY_SRCS "${TENSORFLOW_LITE_BASE}/tools/optimize/sparsity/*.cc") + +list(APPEND TFLITE_SRCS ${TFLITE_CORE_SRCS}) +list(APPEND TFLITE_SRCS ${TFLITE_KERNEL_SRCS}) +list(APPEND TFLITE_SRCS ${TFLITE_LIB_SRCS}) +list(APPEND TFLITE_SRCS ${TFLITE_API_SRCS}) +list(APPEND TFLITE_SRCS ${TFLITE_PROFILING_SRCS}) +list(APPEND TFLITE_SRCS ${TFLITE_EXPERIMENTAL_SRCS}) +list(APPEND TFLITE_SRCS ${TFLITE_SPARSITY_SRCS}) + +# externals +list(APPEND TFLITE_SRCS "${TFLite220FarmhashSource_DIR}/src/farmhash.cc") +list(APPEND TFLITE_SRCS "${TFLite220FFT2DSource_DIR}/fftsg.c") +list(APPEND TFLITE_SRCS "${TFLite220FFT2DSource_DIR}/fftsg2d.c") +list(APPEND TFLITE_SRCS "${TFLite220FlatBuffersSource_DIR}/src/util.cpp") + +# externals - absl +file(GLOB_RECURSE ABSL_SRCS "${TFLite220AbslSource_DIR}/absl/*.cc") +file(GLOB_RECURSE ABSL_EXCLS "${TFLite220AbslSource_DIR}/absl/*test*.cc" + "${TFLite220AbslSource_DIR}/absl/*benchmark*.cc" + "${TFLite220AbslSource_DIR}/absl/synchronization/*.cc" + "${TFLite220AbslSource_DIR}/absl/debugging/*.cc" + "${TFLite220AbslSource_DIR}/absl/hash/*.cc" + "${TFLite220AbslSource_DIR}/absl/flags/*.cc") +list(REMOVE_ITEM ABSL_SRCS ${ABSL_EXCLS}) +list(APPEND TFLITE_SRCS ${ABSL_SRCS}) + +# Build with mmap? true +# caution: v2.2.0's Makefile has wrong code on this part. This is fixed on master branch. +set(BUILD_WITH_MMAP TRUE) +if(${BUILD_WITH_MMAP}) + list(REMOVE_ITEM TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/mmap_allocation_disabled.cc") +else() + list(REMOVE_ITEM TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/mmap_allocation.cc") +endif() + +# Build with nnapi? true +# caution: this nnapi delegate comes from tflite, not ours. +set(BUILD_WITH_NNAPI TRUE) +if(${BUILD_WITH_NNAPI}) + list(APPEND TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/delegates/nnapi/nnapi_delegate.cc") + list(APPEND TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/delegates/nnapi/quant_lstm_sup.cc") + list(APPEND TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/nnapi/nnapi_implementation.cc") + list(APPEND TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/nnapi/nnapi_util.cc") +else() + list(APPEND TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/delegates/nnapi/nnapi_delegate_disabled.cc") + list(APPEND TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/nnapi/nnapi_implementation_disabled.cc") +endif() + +# ios: we don't support ios +list(REMOVE_ITEM TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/minimal_logging_ios.cc") + +# android +if(NOT ANDROID) + list(REMOVE_ITEM TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/minimal_logging_android.cc") +endif() + +# exclude some source files +file(GLOB_RECURSE TFLITE_EXCLS "${TENSORFLOW_LITE_BASE}/*test*.cc" + "${TENSORFLOW_LITE_BASE}/*benchmark*.cc" + "${TENSORFLOW_LITE_BASE}/*example*.cc" + "${TENSORFLOW_LITE_BASE}/*tool*.cc") +list(REMOVE_ITEM TFLITE_SRCS ${TFLITE_EXCLS}) + +# include headers +list(APPEND TFLITE_INCLUDES "${TFLite220TensorFlowSource_DIR}") +list(APPEND TFLITE_INCLUDES "${TFLite220EigenSource_DIR}") +list(APPEND TFLITE_INCLUDES "${TFLite220AbslSource_DIR}") +list(APPEND TFLITE_INCLUDES "${TFLite220GEMMLowpSource_DIR}") +list(APPEND TFLITE_INCLUDES "${TFLite220NEON2SSESource_DIR}") +list(APPEND TFLITE_INCLUDES "${TFLite220FarmhashSource_DIR}/src") +list(APPEND TFLITE_INCLUDES "${TFLite220FlatBuffersSource_DIR}/include") +list(APPEND TFLITE_INCLUDES "${TFLite220FP16Source_DIR}/include") + +add_library(tensorflow-lite-2.2.0 STATIC ${TFLITE_SRCS}) +target_include_directories(tensorflow-lite-2.2.0 SYSTEM PUBLIC ${TFLITE_INCLUDES}) +target_compile_definitions(tensorflow-lite-2.2.0 PUBLIC "GEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK -DTFLITE_WITH_RUY -DTFLITE_WITH_RUY_GEMV") +set_property(TARGET tensorflow-lite-2.2.0 PROPERTY POSITION_INDEPENDENT_CODE ON) +target_link_libraries(tensorflow-lite-2.2.0 eigen ${LIB_PTHREAD} dl) +if(${BUILD_WITH_NNAPI}) + target_link_libraries(tensorflow-lite-2.2.0 rt) +endif() + +if(ANDROID) + target_link_libraries(tensorflow-lite-2.2.0 log) + target_include_directories(tensorflow-lite-2.2.0 PUBLIC "${NDK_DIR}/..") +endif() diff --git a/infra/nnfw/cmake/packages/TensorFlowLite-2.2.0Config.cmake b/infra/nnfw/cmake/packages/TensorFlowLite-2.2.0Config.cmake new file mode 100644 index 0000000..e698235 --- /dev/null +++ b/infra/nnfw/cmake/packages/TensorFlowLite-2.2.0Config.cmake @@ -0,0 +1,92 @@ +if(BUILD_TENSORFLOW_LITE_2_2_0) + macro(return_unless VAR) + if(NOT ${VAR}) + message("${VAR} NOT TRUE") + set(TensorFlowLite_2_2_0_FOUND PARENT_SCOPE) + return() + endif(NOT ${VAR}) + endmacro(return_unless) + + nnas_include(ExternalSourceTools) + nnas_include(OptionTools) + + # Below urls come from https://github.com/tensorflow/tensorflow/blob/v2.2.0/tensorflow/lite/tools/make/Makefile + + set(absl_url "https://github.com/abseil/abseil-cpp/archive/43ef2148c0936ebf7cb4be6b19927a9d9d145b8f.tar.gz") + ExternalSource_Download("tflite220_Absl" ${absl_url}) + set(TFLite220AbslSource_DIR "${tflite220_Absl_SOURCE_DIR}") + if (NOT TFLite220AbslSource_DIR STREQUAL "") + set(TFLite220AbslSource_FOUND TRUE) + endif() + return_unless(TFLite220AbslSource_FOUND) + + set(eigen_url "https://gitlab.com/libeigen/eigen/-/archive/52a2fbbb008a47c5e3fb8ac1c65c2feecb0c511c/eigen-52a2fbbb008a47c5e3fb8ac1c65c2feecb0c511c.tar.gz") + ExternalSource_Download("tflite220_Eigen" ${eigen_url}) + set(TFLite220EigenSource_DIR "${tflite220_Eigen_SOURCE_DIR}") + if (NOT TFLite220EigenSource_DIR STREQUAL "") + set(TFLite220EigenSource_FOUND TRUE) + endif() + return_unless(TFLite220EigenSource_FOUND) + + set(farmhash_url "https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/farmhash/archive/816a4ae622e964763ca0862d9dbd19324a1eaf45.tar.gz") + ExternalSource_Download("tflite220_Farmhash" ${farmhash_url}) + set(TFLite220FarmhashSource_DIR "${tflite220_Farmhash_SOURCE_DIR}") + if (NOT TFLite220FarmhashSource_DIR STREQUAL "") + set(TFLite220FarmhashSource_FOUND TRUE) + endif() + return_unless(TFLite220FarmhashSource_FOUND) + + set(fft2d_url "https://storage.googleapis.com/mirror.tensorflow.org/www.kurims.kyoto-u.ac.jp/~ooura/fft2d.tgz") + ExternalSource_Download("tflite220_FFT2D" ${fft2d_url}) + set(TFLite220FFT2DSource_DIR "${tflite220_FFT2D_SOURCE_DIR}") + if (NOT TFLite220FFT2DSource_DIR STREQUAL "") + set(TFLite220FFT2DSource_FOUND TRUE) + endif() + return_unless(TFLite220FFT2DSource_FOUND) + + set(flatbuffers_url "https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/flatbuffers/archive/v1.11.0.tar.gz") + ExternalSource_Download("tflite220_FlatBuffers" ${flatbuffers_url}) + set(TFLite220FlatBuffersSource_DIR "${tflite220_FlatBuffers_SOURCE_DIR}") + if (NOT TFLite220FlatBuffersSource_DIR STREQUAL "") + set(TFLite220FlatBuffersSource_FOUND TRUE) + endif() + return_unless(TFLite220FlatBuffersSource_FOUND) + + set(fp16_url "https://github.com/Maratyszcza/FP16/archive/febbb1c163726b5db24bed55cc9dc42529068997.zip") + ExternalSource_Download("tflite220_FP16" ${fp16_url}) + set(TFLite220FP16Source_DIR "${tflite220_FP16_SOURCE_DIR}") + if (NOT TFLite220FP16Source_DIR STREQUAL "") + set(TFLite220FP16Source_FOUND TRUE) + endif() + return_unless(TFLite220FP16Source_FOUND) + + set(gemmlowp_url "https://storage.googleapis.com/mirror.tensorflow.org/github.com/google/gemmlowp/archive/12fed0cd7cfcd9e169bf1925bc3a7a58725fdcc3.zip") + ExternalSource_Download("tflite220_GEMMLowp" ${gemmlowp_url}) + set(TFLite220GEMMLowpSource_DIR "${tflite220_GEMMLowp_SOURCE_DIR}") + if (NOT TFLite220GEMMLowpSource_DIR STREQUAL "") + set(TFLite220GEMMLowpSource_FOUND TRUE) + endif() + return_unless(TFLite220GEMMLowpSource_FOUND) + + set(neon2sse_url "https://github.com/intel/ARM_NEON_2_x86_SSE/archive/master.zip") + ExternalSource_Download("tflite220_NEON2SSE" ${neon2sse_url}) + set(TFLite220NEON2SSESource_DIR "${tflite220_NEON2SSE_SOURCE_DIR}") + if (NOT TFLite220NEON2SSESource_DIR STREQUAL "") + set(TFLite220NEON2SSESource_FOUND TRUE) + endif() + return_unless(TFLite220NEON2SSESource_FOUND) + + set(tensorflow_url "https://github.com/tensorflow/tensorflow/archive/v2.2.0.tar.gz") + ExternalSource_Download("tflite220_TensorFlow" ${tensorflow_url}) + set(TFLite220TensorFlowSource_DIR "${tflite220_TensorFlow_SOURCE_DIR}") + if (NOT TFLite220TensorFlowSource_DIR STREQUAL "") + set(TFLite220TensorFlowSource_FOUND TRUE) + endif() + return_unless(TFLite220TensorFlowSource_FOUND) + + nnas_include(ExternalProjectTools) + add_extdirectory("${CMAKE_CURRENT_LIST_DIR}/TensorFlowLite-2.2.0" tflite-2.2.0) + + set(TensorFlowLite_2_2_0_FOUND TRUE) + return() +endif() diff --git a/infra/nnfw/cmake/packages/TensorFlowLite/CMakeLists.txt b/infra/nnfw/cmake/packages/TensorFlowLite/CMakeLists.txt deleted file mode 100644 index 00ec4b5..0000000 --- a/infra/nnfw/cmake/packages/TensorFlowLite/CMakeLists.txt +++ /dev/null @@ -1,63 +0,0 @@ -set(TENSORFLOW_LITE_BASE ${TensorFlowSource_DIR}/tensorflow/lite) - -# -# Tensorflow Lite library -# -file(GLOB TFLITE_CORE_SRCS "${TENSORFLOW_LITE_BASE}/*.c" "${TENSORFLOW_LITE_BASE}/*.cc" "${TENSORFLOW_LITE_BASE}/core/*.cc") -file(GLOB TFLITE_CORE_TESTS "${TENSORFLOW_LITE_BASE}/*test*.cc") -list(REMOVE_ITEM TFLITE_CORE_SRCS ${TFLITE_CORE_TESTS}) - -file(GLOB_RECURSE TFLITE_KERNEL_SRCS "${TENSORFLOW_LITE_BASE}/kernels/*.cc") -file(GLOB_RECURSE TFLITE_KERNEL_TESTS "${TENSORFLOW_LITE_BASE}/kernels/*test*.cc") -list(REMOVE_ITEM TFLITE_KERNEL_SRCS ${TFLITE_KERNEL_TESTS}) - -file(GLOB TFLITE_LIB_SRCS "${TENSORFLOW_LITE_BASE}/c/*.c" "${TENSORFLOW_LITE_BASE}/c/*.cc") -file(GLOB TFLITE_LIB_TESTS "${TENSORFLOW_LITE_BASE}/c/*test*.cc") -list(REMOVE_ITEM TFLITE_LIB_SRCS ${TFLITE_LIB_TESTS}) - -file(GLOB TFLITE_API_SRCS "${TENSORFLOW_LITE_BASE}/core/api/*.c" "${TENSORFLOW_LITE_BASE}/core/api/*.cc") -file(GLOB TFLITE_API_TESTS "${TENSORFLOW_LITE_BASE}/core/api/*test*.cc") -list(REMOVE_ITEM TFLITE_API_SRCS ${TFLITE_API_TESTS}) - -file(GLOB TFLITE_PROFILING_SRCS "${TENSORFLOW_LITE_BASE}/profiling/*.cc") -file(GLOB TFLITE_PROFILING_TESTS "${TENSORFLOW_LITE_BASE}/profiling/*test*.cc") -list(REMOVE_ITEM TFLITE_PROFILING_SRCS ${TFLITE_PROFILING_TESTS}) - -# We will use our own BuiltinOpResolver -list(REMOVE_ITEM TFLITE_KERNEL_SRCS "${TENSORFLOW_LITE_BASE}/kernels/register.cc") -# We will use our own summarizer -list(REMOVE_ITEM TFLITE_PROFILING_SRCS "${TENSORFLOW_LITE_BASE}/profiling/profile_summarizer.cc") -list(APPEND TFLITE_SRCS ${TFLITE_CORE_SRCS}) -list(APPEND TFLITE_SRCS ${TFLITE_KERNEL_SRCS}) -list(APPEND TFLITE_SRCS ${TFLITE_LIB_SRCS}) -list(APPEND TFLITE_SRCS ${TFLITE_API_SRCS}) -list(APPEND TFLITE_SRCS ${TFLITE_PROFILING_SRCS}) - -list(APPEND TFLITE_SRCS "${FarmhashSource_DIR}/src/farmhash.cc") - -list(APPEND TFLITE_INCLUDES "${TensorFlowSource_DIR}") -list(APPEND TFLITE_INCLUDES "${AbslSource_DIR}") -list(APPEND TFLITE_INCLUDES "${GEMMLowpSource_DIR}") -list(APPEND TFLITE_INCLUDES "${FarmhashSource_DIR}/src") -list(APPEND TFLITE_INCLUDES "${FlatBuffersSource_DIR}/include") - -if(NEON2SSESource_FOUND) - list(APPEND TFLITE_INCLUDES "${NEON2SSESource_DIR}") -endif(NEON2SSESource_FOUND) - -# This kernels are not used on nnfw -## spectrogram -list(REMOVE_ITEM TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/kernels/audio_spectrogram.cc") -list(REMOVE_ITEM TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/kernels/audio_spectrogram_test.cc") -list(REMOVE_ITEM TFLITE_SRCS "${TENSORFLOW_LITE_BASE}/kernels/internal/spectrogram.cc") - -add_library(tensorflow-lite STATIC ${TFLITE_SRCS}) -target_include_directories(tensorflow-lite SYSTEM PUBLIC ${TFLITE_INCLUDES}) -target_compile_definitions(tensorflow-lite PUBLIC "GEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK") -set_property(TARGET tensorflow-lite PROPERTY POSITION_INDEPENDENT_CODE ON) -target_link_libraries(tensorflow-lite eigen ${LIB_PTHREAD} dl) - -if(ANDROID) - target_link_libraries(tensorflow-lite log) - target_include_directories(tensorflow-lite PUBLIC "${NDK_DIR}/..") -endif() diff --git a/infra/nnfw/cmake/packages/TensorFlowLiteConfig.cmake b/infra/nnfw/cmake/packages/TensorFlowLiteConfig.cmake deleted file mode 100644 index 2b1c0ec..0000000 --- a/infra/nnfw/cmake/packages/TensorFlowLiteConfig.cmake +++ /dev/null @@ -1,69 +0,0 @@ -# NOTE This line prevents multiple definitions of tensorflow-lite target -if(TARGET tensorflow-lite) - set(TensorFlowLite_FOUND TRUE) - return() -endif(TARGET tensorflow-lite) - -if(BUILD_TENSORFLOW_LITE) - macro(return_unless VAR) - if(NOT ${VAR}) - set(TensorFlowLite_FOUND PARENT_SCOPE) - return() - endif(NOT ${VAR}) - endmacro(return_unless) - - # Required packages - nnfw_find_package(AbslSource QUIET) - return_unless(AbslSource_FOUND) - nnfw_find_package(Eigen QUIET) - return_unless(Eigen_FOUND) - nnfw_find_package(FarmhashSource QUIET) - return_unless(FarmhashSource_FOUND) - nnfw_find_package(FlatBuffersSource QUIET) - return_unless(FlatBuffersSource_FOUND) - nnfw_find_package(GEMMLowpSource QUIET) - return_unless(GEMMLowpSource_FOUND) - nnfw_find_package(TensorFlowSource QUIET) - return_unless(TensorFlowSource_FOUND) - - # Optional packages - nnfw_find_package(NEON2SSESource QUIET) - - nnas_include(ExternalProjectTools) - add_extdirectory("${CMAKE_CURRENT_LIST_DIR}/TensorFlowLite" tflite) - - set(TensorFlowLite_FOUND TRUE) - return() -endif(BUILD_TENSORFLOW_LITE) - -# Use pre-built TensorFlow Lite -find_path(TFLITE_INCLUDE_DIR NAMES tensorflow/lite/interpreter.h) -find_library(TFLITE_LIB NAMES tensorflow-lite) - -if(NOT TFLITE_INCLUDE_DIR) - set(TensorFlowLite_FOUND FALSE) - return() -endif(NOT TFLITE_INCLUDE_DIR) - -if(NOT TFLITE_LIB) - set(TensorFlowLite_FOUND FALSE) - return() -endif(NOT TFLITE_LIB) - -message(STATUS "Found TensorFlow Lite: TRUE (include: ${TFLITE_INCLUDE_DIR}, lib: ${TFLITE_LIB}") - -# TODO Use IMPORTED target -add_library(tensorflow-lite INTERFACE) -target_include_directories(tensorflow-lite SYSTEM INTERFACE ${TFLITE_INCLUDE_DIR}) -target_link_libraries(tensorflow-lite INTERFACE ${TFLITE_LIB}) - -# Prefer -pthread to -lpthread -set(THREADS_PREFER_PTHREAD_FLAG TRUE) -set(CMAKE_THREAD_PREFER_PTHREAD TRUE) -find_package(Threads QUIET) - -if(Threads_FOUND) - target_link_libraries(tensorflow-lite INTERFACE ${CMAKE_THREAD_LIBS_INIT}) -endif(Threads_FOUND) - -set(TensorFlowLite_FOUND TRUE) diff --git a/infra/nnfw/cmake/packages/TensorFlowSourceConfig.cmake b/infra/nnfw/cmake/packages/TensorFlowSourceConfig.cmake deleted file mode 100644 index 1896239..0000000 --- a/infra/nnfw/cmake/packages/TensorFlowSourceConfig.cmake +++ /dev/null @@ -1,18 +0,0 @@ -function(_TensorFlowSource_import) - if(NOT DOWNLOAD_TENSORFLOW) - set(TensorFlowSource_FOUND FALSE PARENT_SCOPE) - return() - endif(NOT DOWNLOAD_TENSORFLOW) - - nnas_include(ExternalSourceTools) - nnas_include(OptionTools) - - envoption(EXTERNAL_DOWNLOAD_SERVER "https://github.com") - set(TENSORFLOW_URL ${EXTERNAL_DOWNLOAD_SERVER}/tensorflow/tensorflow/archive/v1.13.1.tar.gz) - ExternalSource_Get("tensorflow" ${DOWNLOAD_TENSORFLOW} ${TENSORFLOW_URL}) - - set(TensorFlowSource_DIR ${tensorflow_SOURCE_DIR} PARENT_SCOPE) - set(TensorFlowSource_FOUND ${tensorflow_SOURCE_GET} PARENT_SCOPE) -endfunction(_TensorFlowSource_import) - -_TensorFlowSource_import() diff --git a/infra/nnfw/command/build b/infra/nnfw/command/build index 8e59f66..b0301d2 100644 --- a/infra/nnfw/command/build +++ b/infra/nnfw/command/build @@ -7,18 +7,5 @@ if [[ ! -d "${BUILD_PATH}" ]]; then exit 255 fi -# Set parallel build -# TODO Use argument instead of environment variable -HOST_OS=${HOST_OS:-linux} -NPROCS=${NPROCS:-1} -PARALLEL_BUILD=${PARALLEL_BUILD:-1} - -if [ "${PARALLEL_BUILD}" == "1" ]; then - # Get number of processors (linux only for now) - if [ "${HOST_OS}" == "linux" ]; then - NPROCS="$(grep -c ^processor /proc/cpuinfo)" - fi -fi - cd ${BUILD_PATH} -make -j ${NPROCS} "$@" +make "$@" diff --git a/infra/nnfw/command/count-unittest b/infra/nnfw/command/count-unittest new file mode 100644 index 0000000..7957f36 --- /dev/null +++ b/infra/nnfw/command/count-unittest @@ -0,0 +1,74 @@ +#!/bin/bash +# +# Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +Usage() +{ + echo "Usage: ./$0 [--install-path=Product/out]" +} + +INSTALL_PATH=Product/out + +for i in "$@" +do + case $i in + -h|--help|help) + Usage + exit 1 + ;; + --install-path=*) + INSTALL_PATH=${i#*=} + ;; + *) + Usage + exit 1 + ;; + esac + shift +done + +# List of gtest binaries +GTEST_BINS=( + unittest_standalone/nnfw_api_gtest + unittest_standalone/test_compute + unittest_standalone/test_onert + unittest_standalone/test_onert_backend_cpu_common + unittest_standalone/test_onert_frontend_nnapi + unittest_standalone/tflite_test +) + +# Collect test cases +echo "Install Path : '$INSTALL_PATH'" +TEST_LIST= +for GTEST_BIN in ${GTEST_BINS[@]}; do + GTEST_PATH=$INSTALL_PATH/$GTEST_BIN + echo "Collecting test cases from '$GTEST_PATH'" + TESTS=$($GTEST_PATH --gtest_list_tests | grep '^ ') + if [ "$?" -ne 0 ]; then + echo "Error collecting test cases from '$GTEST_PATH'" + exit 1; + fi + TEST_LIST=$TEST_LIST$TESTS +done + +# Count stats +TOTAL_TCS=$(echo "$TEST_LIST" | wc -l) +TOTAL_NEG_TCS=$(echo "$TEST_LIST" | grep '^ neg_' | wc -l) +TOTAL_POS_TCS=$(echo "$TEST_LIST" | grep '^ neg_' -v | wc -l) + +# Report stats +echo "TOTAL NUMBER OF TEST CASES : $TOTAL_TCS" +echo "TOTAL NUMBER OF POSTIVE TEST CASES : $TOTAL_NEG_TCS" +echo "TOTAL NUMBER OF NEGATIVE TEST CASES : $TOTAL_POS_TCS" diff --git a/infra/packaging/build b/infra/packaging/build index 81ab2ec..036c2d5 100644 --- a/infra/packaging/build +++ b/infra/packaging/build @@ -8,7 +8,7 @@ if [[ -z "${NNAS_PROJECT_PATH}" ]]; then fi # The default preset -PRESET="20191215" +PRESET="20200630" EXTRA_OPTIONS=() while [ "$#" -ne 0 ]; do diff --git a/infra/packaging/preset/20200508 b/infra/packaging/preset/20200508 new file mode 100644 index 0000000..15ec345 --- /dev/null +++ b/infra/packaging/preset/20200508 @@ -0,0 +1,54 @@ +#!/bin/bash + +# NOTE purpose of this file is static analysis only +# new official preset will be added when new programs are ready + +PRESET="20200508" + +function preset_configure() +{ + REQUIRED_UNITS=() + # Common Libraries + REQUIRED_UNITS+=("angkor" "cwrap" "pepper-str" "pepper-strcast" "pp" "stdex") + REQUIRED_UNITS+=("oops" "pepper-assert" "foder") + REQUIRED_UNITS+=("safemain") + # Hermes Logging Framework + REQUIRED_UNITS+=("hermes" "hermes-std") + # loco IR and related utilities + REQUIRED_UNITS+=("loco" "locop" "locomotiv" "logo-core" "logo") + # Flatbuffer I/O + REQUIRED_UNITS+=("mio-tflite" "mio-circle") + # Circle compiler library (.circle -> .circle) + REQUIRED_UNITS+=("luci") + # Tools + REQUIRED_UNITS+=("tflite2circle" "circle2circle" "tflchef" "circlechef") + REQUIRED_UNITS+=("tf2tfliteV2" "luci-interpreter" "circle-verify") + REQUIRED_UNITS+=("record-minmax") + + # TODO Use "nncc configure" and "nncc build" + cmake \ + -DCMAKE_INSTALL_PREFIX="${NNCC_INSTALL_PREFIX}" \ + -DCMAKE_BUILD_TYPE=release \ + -DBUILD_WHITELIST=$(join_by ";" "${REQUIRED_UNITS[@]}") \ + ${EXTRA_OPTIONS[@]} \ + "${NNAS_PROJECT_PATH}/infra/nncc" +} + +function preset_install() +{ + install -t "${NNPKG_INSTALL_PREFIX}/bin" -D \ + "${NNAS_PROJECT_PATH}/tools/nnpackage_tool/model2nnpkg/model2nnpkg.sh" + + # Install tf2nnpkg + install -T -m 755 -D "${SCRIPT_PATH}/res/tf2nnpkg.${PRESET}" "${NNAS_INSTALL_PREFIX}/bin/tf2nnpkg" + + # Create python virtual enviornment + python3 -m venv "${NNAS_INSTALL_PREFIX}/bin/venv" + + # Install tensorflow + source "${NNAS_INSTALL_PREFIX}/bin/venv/bin/activate" + python -m pip --default-timeout=1000 --trusted-host pypi.org --trusted-host files.pythonhost.org \ + install -U pip setuptools + python -m pip --default-timeout=1000 --trusted-host pypi.org --trusted-host files.pythonhost.org \ + install tensorflow==2.2.0 +} diff --git a/infra/packaging/preset/20200616_windows b/infra/packaging/preset/20200616_windows new file mode 100644 index 0000000..9bf533e --- /dev/null +++ b/infra/packaging/preset/20200616_windows @@ -0,0 +1,60 @@ +#!/bin/bash + +function preset_configure() +{ + REQUIRED_UNITS=() + # Common Libraries + REQUIRED_UNITS+=("angkor" "cwrap" "foder" "oops" "pepper-assert") + REQUIRED_UNITS+=("pepper-str" "pepper-strcast" "pp" "safemain" "stdex") + # Hermes Logging Framework + REQUIRED_UNITS+=("hermes" "hermes-std") + # loco IR and related utilities + REQUIRED_UNITS+=("loco" "locop" "locomotiv" "logo-core" "logo") + # TensorFlow GraphDef I/O + REQUIRED_UNITS+=("mio-tflite" "mio-circle") + # Circle compiler library (.circle -> .circle) + REQUIRED_UNITS+=("luci") + # Tools + REQUIRED_UNITS+=("circlechef" "circle2circle" "circle-verify") + REQUIRED_UNITS+=("luci-interpreter" "record-minmax" "tflchef") + REQUIRED_UNITS+=("tflite2circle" "tf2tfliteV2") + + NPROC=$(cat /proc/cpuinfo | grep -c processor) + + # TODO Use "nncc configure" and "nncc build" + cmake \ + -G "MSYS Makefiles" \ + -DTF2NNPKG_FOR_WINDOWS=ON \ + -DUSE_PROTOBUF_LEGACY_IMPORT=ON \ + -DCMAKE_EXE_LINKER_FLAGS="-Wl,--allow-multiple-definition" \ + -DCMAKE_SHARED_LINKER_FLAGS="-Wl,--allow-multiple-definition" \ + -DENABLE_TEST=OFF \ + -DDOWNLOAD_GTEST=OFF \ + -DBUILD_GTEST=OFF \ + -DCMAKE_C_COMPILER=gcc \ + -DCMAKE_CXX_COMPILER=g++ \ + -DCMAKE_INSTALL_PREFIX="${NNCC_INSTALL_PREFIX}" \ + -DCMAKE_BUILD_TYPE=release \ + -DBUILD_WHITELIST=$(join_by ";" "${REQUIRED_UNITS[@]}") \ + -DEXTERNALS_BUILD_THREADS=$((NPROC/2)) \ + ${EXTRA_OPTIONS[@]} \ + "${NNAS_PROJECT_PATH}/infra/nncc" +} + +function preset_install() +{ + # Install libraries to bin/ for Windows release + mv ${NNCC_INSTALL_PREFIX}/lib/*.dll ${NNCC_INSTALL_PREFIX}/bin + rm -rf ${NNCC_INSTALL_PREFIX}/lib + + install -t "${NNPKG_INSTALL_PREFIX}/bin" -D \ + "${NNAS_PROJECT_PATH}/tools/nnpackage_tool/model2nnpkg/model2nnpkg.sh" + + # Install tf2nnpkg + install -T -m 755 -D "${SCRIPT_PATH}/res/tf2nnpkg.${PRESET}" "${NNAS_INSTALL_PREFIX}/bin/tf2nnpkg" + + # Though you have to install tensorflow to run 'tf2tfliteV2', + # tensorflow can't be installed in mingw. First, You can install tensorflow + # from Window native CMD(run as administrator) with python virtual environment. + # And, you must copy it to "${NNAS_INSTALL_PREFIX}/bin/venv" +} diff --git a/infra/packaging/preset/20200630 b/infra/packaging/preset/20200630 new file mode 100644 index 0000000..e159935 --- /dev/null +++ b/infra/packaging/preset/20200630 @@ -0,0 +1,57 @@ +#!/bin/bash + +# NOTE purpose of this file is static analysis only +# new official preset will be added when new programs are ready + +PRESET="20200630" + +function preset_configure() +{ + REQUIRED_UNITS=() + # Common Libraries + REQUIRED_UNITS+=("angkor" "cwrap" "pepper-str" "pepper-strcast" "pp" "stdex") + REQUIRED_UNITS+=("oops" "pepper-assert" "foder") + REQUIRED_UNITS+=("souschef") + REQUIRED_UNITS+=("safemain") + REQUIRED_UNITS+=("arser") + # Hermes Logging Framework + REQUIRED_UNITS+=("hermes" "hermes-std") + # loco IR and related utilities + REQUIRED_UNITS+=("loco" "locop" "locomotiv" "logo-core" "logo") + # Flatbuffer I/O + REQUIRED_UNITS+=("mio-tflite" "mio-circle") + # Circle compiler library (.circle -> .circle) + REQUIRED_UNITS+=("luci") + # Tools + REQUIRED_UNITS+=("tflite2circle" "circle2circle" "tflchef" "circlechef") + REQUIRED_UNITS+=("tf2tfliteV2" "luci-interpreter" "circle-verify") + REQUIRED_UNITS+=("record-minmax" "circle-quantizer") + REQUIRED_UNITS+=("one-cmds") + + # TODO Use "nncc configure" and "nncc build" + cmake \ + -DCMAKE_INSTALL_PREFIX="${NNCC_INSTALL_PREFIX}" \ + -DCMAKE_BUILD_TYPE=release \ + -DBUILD_WHITELIST=$(join_by ";" "${REQUIRED_UNITS[@]}") \ + ${EXTRA_OPTIONS[@]} \ + "${NNAS_PROJECT_PATH}/infra/nncc" +} + +function preset_install() +{ + install -t "${NNPKG_INSTALL_PREFIX}/bin" -D \ + "${NNAS_PROJECT_PATH}/tools/nnpackage_tool/model2nnpkg/model2nnpkg.sh" + + # Install tf2nnpkg + install -T -m 755 -D "${SCRIPT_PATH}/res/tf2nnpkg.${PRESET}" "${NNAS_INSTALL_PREFIX}/bin/tf2nnpkg" + + # Create python virtual enviornment + python3 -m venv "${NNAS_INSTALL_PREFIX}/bin/venv" + + # Install tensorflow + source "${NNAS_INSTALL_PREFIX}/bin/venv/bin/activate" + python -m pip --default-timeout=1000 --trusted-host pypi.org --trusted-host files.pythonhost.org \ + install -U pip setuptools + python -m pip --default-timeout=1000 --trusted-host pypi.org --trusted-host files.pythonhost.org \ + install tensorflow-cpu==2.3.0rc0 +} diff --git a/infra/packaging/res/tf2nnpkg.20200508 b/infra/packaging/res/tf2nnpkg.20200508 new file mode 100644 index 0000000..a9923b4 --- /dev/null +++ b/infra/packaging/res/tf2nnpkg.20200508 @@ -0,0 +1,97 @@ +#!/bin/bash + +set -e + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" + +command_exists() { + if [ "$#" -le 0 ]; then + return 1 + fi + command -v "$@" > /dev/null 2>&1 +} + +usage() +{ + echo "Convert TensorFlow model to nnpackage." + echo "Usage: tf2nnpkg --info --graphdef [OPTION] -o " + echo "option:" + echo " --customop " + exit 0 +} + +# Parse command-line arguments +# +while [ "$#" -ne 0 ]; do + CUR="$1" + + case $CUR in + '--help') + usage + ;; + '--info') + export INFO_FILE="$2" + shift 2 + ;; + '--graphdef') + export GRAPHDEF_FILE="$2" + shift 2 + ;; + '--customop') + export CUSTOMOP_CONF_FILE="$2" + shift 2 + ;; + '-o') + export OUTPUT_DIR="$2" + shift 2 + ;; + *) + echo "${CUR}" + shift + ;; + esac +done + +if [ -z ${GRAPHDEF_FILE} ] || [ ! -e ${GRAPHDEF_FILE} ]; then + echo "pb is not found. Please check --graphdef is correct." + exit 2 +fi + +if [ -z ${INFO_FILE} ] || [ ! -e ${INFO_FILE} ]; then + echo "info is not found. Please check --info is correct." + exit 2 +fi + +# optional param +if [ ${CUSTOMOP_CONF_FILE} ]; then + if [ ! -e ${CUSTOMOP_CONF_FILE} ]; then + echo "customop.conf is not found. Please check --customop is correct." + exit 2 + fi +fi + +FILE_BASE=$(basename ${GRAPHDEF_FILE}) +MODEL_NAME="${FILE_BASE%.*}" +TMPDIR=$(mktemp -d) +trap "{ rm -rf $TMPDIR; }" EXIT + +if [ ${CUSTOMOP_CONF_FILE} ]; then + echo "This option is supported by previous version." + exit 2 +fi + +# activate python virtual environment +source "${ROOT}/bin/venv/bin/activate" + +# parse inputs, outputs from info file +INPUT=$(awk -F, '/^input/ { print $2 }' ${INFO_FILE} | cut -d: -f1 | tr -d ' ' | paste -d, -s) +OUTPUT=$(awk -F, '/^output/ { print $2 }' ${INFO_FILE} | cut -d: -f1 | tr -d ' ' | paste -d, -s) + +# generate tflite file +python "${ROOT}/bin/tf2tfliteV2.py" --v1 --input_path ${GRAPHDEF_FILE} \ +--output_path "${TMPDIR}/${MODEL_NAME}.tflite" \ +--input_arrays ${INPUT} --output_arrays ${OUTPUT} + +"${ROOT}/bin/tflite2circle" "${TMPDIR}/${MODEL_NAME}.tflite" "${TMPDIR}/${MODEL_NAME}.circle" + +"${ROOT}/bin/model2nnpkg.sh" -o "${OUTPUT_DIR}" "${TMPDIR}/${MODEL_NAME}.circle" diff --git a/infra/packaging/res/tf2nnpkg.20200616 b/infra/packaging/res/tf2nnpkg.20200616 new file mode 100644 index 0000000..9f09571 --- /dev/null +++ b/infra/packaging/res/tf2nnpkg.20200616 @@ -0,0 +1,89 @@ +#!/bin/bash + +set -e + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" + +command_exists() { + if [ "$#" -le 0 ]; then + return 1 + fi + command -v "$@" > /dev/null 2>&1 +} + +usage() +{ + echo "Convert TensorFlow model to nnpackage." + echo "Usage: tf2nnpkg --info --graphdef [OPTION] -o " + exit 0 +} + +# Parse command-line arguments +# +while [ "$#" -ne 0 ]; do + CUR="$1" + + case $CUR in + '--help') + usage + ;; + '--info') + export INFO_FILE="$2" + shift 2 + ;; + '--graphdef') + export GRAPHDEF_FILE="$2" + shift 2 + ;; + '-o') + export OUTPUT_DIR="$2" + shift 2 + ;; + *) + echo "${CUR}" + shift + ;; + esac +done + +if [ -z ${GRAPHDEF_FILE} ] || [ ! -e ${GRAPHDEF_FILE} ]; then + echo "pb is not found. Please check --graphdef is correct." + exit 2 +fi + +if [ -z ${INFO_FILE} ] || [ ! -e ${INFO_FILE} ]; then + echo "info is not found. Please check --info is correct." + exit 2 +fi + +FILE_BASE=$(basename ${GRAPHDEF_FILE}) +MODEL_NAME="${FILE_BASE%.*}" +TMPDIR=$(mktemp -d) +trap "{ rm -rf $TMPDIR; }" EXIT + +# activate python virtual environment +VIRTUALENV_LINUX="${ROOT}/bin/venv/bin/activate" +VIRTUALENV_WINDOWS="${ROOT}/bin/venv/Scripts/activate" + +if [ -e ${VIRTUALENV_LINUX} ]; then + source ${VIRTUALENV_LINUX} +elif [ -e ${VIRTUALENV_WINDOWS} ]; then + source ${VIRTUALENV_WINDOWS} +fi + +# parse inputs, outputs from info file +INPUT=$(awk -F, '/^input/ { print $2 }' ${INFO_FILE} | cut -d: -f1 | tr -d ' ' | paste -d, -s) +OUTPUT=$(awk -F, '/^output/ { print $2 }' ${INFO_FILE} | cut -d: -f1 | tr -d ' ' | paste -d, -s) + +# generate tflite file +python "${ROOT}/bin/tf2tfliteV2.py" --v1 --input_path ${GRAPHDEF_FILE} \ +--output_path "${TMPDIR}/${MODEL_NAME}.tflite" \ +--input_arrays ${INPUT} --output_arrays ${OUTPUT} + +# convert .tflite to .circle +"${ROOT}/bin/tflite2circle" "${TMPDIR}/${MODEL_NAME}.tflite" "${TMPDIR}/${MODEL_NAME}.circle" + +# optimize +"${ROOT}/bin/circle2circle" "${TMPDIR}/${MODEL_NAME}.circle" "${TMPDIR}/${MODEL_NAME}.circle" + +"${ROOT}/bin/model2nnpkg.sh" -o "${OUTPUT_DIR}" "${TMPDIR}/${MODEL_NAME}.circle" diff --git a/infra/packaging/res/tf2nnpkg.20200630 b/infra/packaging/res/tf2nnpkg.20200630 new file mode 100644 index 0000000..9101f82 --- /dev/null +++ b/infra/packaging/res/tf2nnpkg.20200630 @@ -0,0 +1,100 @@ +#!/bin/bash + +set -e + +ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" + +command_exists() { + if [ "$#" -le 0 ]; then + return 1 + fi + command -v "$@" > /dev/null 2>&1 +} + +usage() +{ + echo "Convert TensorFlow model to nnpackage." + echo "Usage: tf2nnpkg --info --graphdef [OPTION] -o " + exit 0 +} + +# Parse command-line arguments +# +while [ "$#" -ne 0 ]; do + CUR="$1" + + case $CUR in + '--help') + usage + ;; + '--info') + export INFO_FILE="$2" + shift 2 + ;; + '--graphdef') + export GRAPHDEF_FILE="$2" + shift 2 + ;; + '-o') + export OUTPUT_DIR="$2" + shift 2 + ;; + *) + echo "${CUR}" + shift + ;; + esac +done + +if [ -z ${GRAPHDEF_FILE} ] || [ ! -e ${GRAPHDEF_FILE} ]; then + echo "pb is not found. Please check --graphdef is correct." + exit 2 +fi + +if [ -z ${INFO_FILE} ] || [ ! -e ${INFO_FILE} ]; then + echo "info is not found. Please check --info is correct." + exit 2 +fi + +if [ -z ${OUTPUT_DIR} ]; then + echo "output directory is not specifed. Please check -o is correct.." + exit 2 +fi + +FILE_BASE=$(basename ${GRAPHDEF_FILE}) +MODEL_NAME="${FILE_BASE%.*}" +TMPDIR=$(mktemp -d) +trap "{ rm -rf $TMPDIR; }" EXIT + +# activate python virtual environment +VIRTUALENV_LINUX="${ROOT}/bin/venv/bin/activate" +VIRTUALENV_WINDOWS="${ROOT}/bin/venv/Scripts/activate" + +if [ -e ${VIRTUALENV_LINUX} ]; then + source ${VIRTUALENV_LINUX} +elif [ -e ${VIRTUALENV_WINDOWS} ]; then + source ${VIRTUALENV_WINDOWS} +fi + +# parse inputs, outputs from info file +INPUT=$(awk -F, '/^input/ { print $2 }' ${INFO_FILE} | cut -d: -f1 | tr -d ' ' | paste -d, -s) +OUTPUT=$(awk -F, '/^output/ { print $2 }' ${INFO_FILE} | cut -d: -f1 | tr -d ' ' | paste -d, -s) + +INPUT_SHAPES=$(grep ^input ${INFO_FILE} | cut -d "[" -f2 | cut -d "]" -f1 | tr -d ' ' | xargs | tr ' ' ':') + +# generate tflite file +python "${ROOT}/bin/tf2tfliteV2.py" --v2 --input_path ${GRAPHDEF_FILE} \ +--output_path "${TMPDIR}/${MODEL_NAME}.tflite" \ +--input_arrays ${INPUT} --output_arrays ${OUTPUT} || \ +python "${ROOT}/bin/tf2tfliteV2.py" --v1 --input_path ${GRAPHDEF_FILE} \ +--output_path "${TMPDIR}/${MODEL_NAME}.tflite" \ +--input_arrays ${INPUT} --input_shapes ${INPUT_SHAPES} \ +--output_arrays ${OUTPUT} + +# convert .tflite to .circle +"${ROOT}/bin/tflite2circle" "${TMPDIR}/${MODEL_NAME}.tflite" "${TMPDIR}/${MODEL_NAME}.tmp.circle" + +# optimize +"${ROOT}/bin/circle2circle" --all "${TMPDIR}/${MODEL_NAME}.tmp.circle" "${TMPDIR}/${MODEL_NAME}.circle" + +"${ROOT}/bin/model2nnpkg.sh" -o "${OUTPUT_DIR}" "${TMPDIR}/${MODEL_NAME}.circle" diff --git a/infra/scripts/common.sh b/infra/scripts/common.sh index edd2116..28aa213 100755 --- a/infra/scripts/common.sh +++ b/infra/scripts/common.sh @@ -19,7 +19,7 @@ # Run ./tests/scripts/test-driver.sh script unittest # # NNPackageTest $1 $2 -# Run ./tools/nnpackage_tool/nnpkg_test/nnpkg_test.sh script nnpackage test +# Run ./tests/scripts/nnpkg_test.sh script nnpackage test CURRENT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" ROOT_PATH="$(cd ${CURRENT_PATH}/../../ && pwd)" @@ -115,7 +115,7 @@ function NNPackageTest() do for entry in "nnpkg-tcs"/$f; do if [ -e $entry ]; then - BACKENDS="$1" tools/nnpackage_tool/nnpkg_test/nnpkg_test.sh -d -i nnpkg-tcs $(basename "$entry") + BACKENDS="$1" tests/scripts/nnpkg_test.sh -d -i nnpkg-tcs $(basename "$entry") fi done EXITCODE_F=$? @@ -131,3 +131,32 @@ function NNPackageTest() popd > /dev/null } + +# $1: (required) backend +# $2: (required) test list file relative path from nnfw root directory +# pass empty string if there is no skiplist +# $3: (required) relative path to report from nnfw root directory +function TFLiteLoaderTest() +{ + [[ $# -ne 3 ]] && echo "TFLiteLoaderTest: Invalid function argument setting" && exit 1 + + pushd ${ROOT_PATH} > /dev/null + + export BACKENDS=$1 + if [[ "$2" == "" ]]; then + ./tests/scripts/test-driver.sh \ + --frameworktest \ + --framework_driverbin="$ROOT_PATH/Product/out/bin/tflite_loader_test_tool" \ + --reportdir=$ROOT_PATH/$3 + . + else + ./tests/scripts/test-driver.sh \ + --frameworktest \ + --framework_driverbin="$ROOT_PATH/Product/out/bin/tflite_loader_test_tool" \ + --frameworktest_list_file=tests/scripts/list/tflite_loader_list.${TEST_ARCH}.txt \ + --reportdir=$ROOT_PATH/$3 + fi + unset BACKENDS + + popd > /dev/null +} diff --git a/infra/scripts/compiler_modules.sh b/infra/scripts/compiler_modules.sh new file mode 100644 index 0000000..d436e8a --- /dev/null +++ b/infra/scripts/compiler_modules.sh @@ -0,0 +1,23 @@ +#!/bin/bash + +# Don't run this script +[[ "${BASH_SOURCE[0]}" == "${0}" ]] && echo "Please don't execute ${BASH_SOURCE[0]}, source it" && return + +DEBUG_BUILD_ITEMS="angkor;cwrap;pepper-str;pepper-strcast;pp;stdex" +DEBUG_BUILD_ITEMS+=";oops;pepper-assert" +DEBUG_BUILD_ITEMS+=";hermes;hermes-std" +DEBUG_BUILD_ITEMS+=";loco;locop;locomotiv;logo-core;logo" +DEBUG_BUILD_ITEMS+=";foder;souschef;arser" +DEBUG_BUILD_ITEMS+=";safemain;mio-circle;mio-tflite" +DEBUG_BUILD_ITEMS+=";tflite2circle" +DEBUG_BUILD_ITEMS+=";luci" +DEBUG_BUILD_ITEMS+=";luci-interpreter" +DEBUG_BUILD_ITEMS+=";luci-value-test" +DEBUG_BUILD_ITEMS+=";circle2circle;record-minmax;circle-quantizer" +DEBUG_BUILD_ITEMS+=";circle-verify" +DEBUG_BUILD_ITEMS+=";tflchef;circlechef" +DEBUG_BUILD_ITEMS+=";common-artifacts" +DEBUG_BUILD_ITEMS+=";circle2circle-dredd-recipe-test" +DEBUG_BUILD_ITEMS+=";record-minmax-conversion-test" +DEBUG_BUILD_ITEMS+=";tf2tfliteV2;tf2tfliteV2-conversion-test" +DEBUG_BUILD_ITEMS+=";tflite2circle-conversion-test" diff --git a/infra/scripts/configure_compiler_coverage.sh b/infra/scripts/configure_compiler_coverage.sh new file mode 100755 index 0000000..e80c3e9 --- /dev/null +++ b/infra/scripts/configure_compiler_coverage.sh @@ -0,0 +1,22 @@ +#!/bin/bash + +[[ "${BASH_SOURCE[0]}" != "${0}" ]] && echo "Please don't source ${BASH_SOURCE[0]}, execute it" && return + +set -eo pipefail + +CURRENT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +ROOT_PATH="$CURRENT_PATH/../../" + +source ${CURRENT_PATH}/compiler_modules.sh + +NNCC_CFG_OPTION=" -DCMAKE_BUILD_TYPE=Debug" +NNCC_CFG_STRICT=" -DENABLE_STRICT_BUILD=ON" +NNCC_COV_DEBUG=" -DBUILD_WHITELIST=$DEBUG_BUILD_ITEMS" + +if [ $# -ne 0 ]; then + echo "Additional cmake configuration: $@" +fi + +./nncc configure \ + $NNCC_CFG_OPTION $NNCC_COV_DEBUG $NNCC_CFG_STRICT \ + -DENABLE_COVERAGE=ON "$@" diff --git a/infra/scripts/docker_build_cross_arm_benchmark_model.sh b/infra/scripts/docker_build_cross_arm_benchmark_model.sh deleted file mode 100755 index f63edf1..0000000 --- a/infra/scripts/docker_build_cross_arm_benchmark_model.sh +++ /dev/null @@ -1,51 +0,0 @@ -#!/bin/bash - -[[ "${BASH_SOURCE[0]}" != "${0}" ]] && echo "Please don't source ${BASH_SOURCE[0]}, execute it" && return - -CURRENT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" -ROOT_PATH="$CURRENT_PATH/../../" - -# prepare rootfs -if [ ! -d $ROOTFS_DIR ]; then - echo "It will use default rootfs path" -else - DOCKER_VOLUMES+=" -v $ROOTFS_DIR:/opt/rootfs" - DOCKER_ENV_VARS+=" -e ROOTFS_DIR=/opt/rootfs" -fi - -# mount volume (or directory) for externals -if [ -n "$EXTERNAL_VOLUME" ]; then - DOCKER_VOLUMES+=" -v $EXTERNAL_VOLUME:/externals" - DOCKER_ENV_VARS+=" -e EXTERNAL_VOLUME=/externals" -else - echo "It will use default external path" -fi - -# docker image name -if [ -z $DOCKER_IMAGE_NAME ]; then - echo "It will use default docker image name" -fi - -# Mirror server setting -if [ -z $EXTERNAL_DOWNLOAD_SERVER ]; then - echo "It will not use mirror server" -fi - -DOCKER_ENV_VARS+=" -e TARGET_ARCH=armv7l" -DOCKER_ENV_VARS+=" -e CROSS_BUILD=1" -DOCKER_ENV_VARS+=" -e BENCHMARK_ACL_BUILD=1" -DOCKER_ENV_VARS+=" -e BUILD_TYPE=Release" - -set -e - -pushd $ROOT_PATH > /dev/null - -# TODO use command instead of makefile -export DOCKER_ENV_VARS -export DOCKER_VOLUMES -CMD="export OPTIONS='-DBUILD_PURE_ARM_COMPUTE=ON -DBUILD_ONERT=OFF -DBUILD_TFLITE_BENCHMARK_MODEL=ON -DBUILD_TFLITE_LOADER=OFF' && \ - cp -nv Makefile.template Makefile && \ - make all install build_test_suite" -./nnfw docker-run bash -c "$CMD" - -popd > /dev/null diff --git a/infra/scripts/docker_build_test_x64.sh b/infra/scripts/docker_build_test_x64.sh index eb620df..16fcf3f 100755 --- a/infra/scripts/docker_build_test_x64.sh +++ b/infra/scripts/docker_build_test_x64.sh @@ -44,6 +44,6 @@ fi export DOCKER_ENV_VARS=" -e MODELFILE_SERVER=$MODELFILE_SERVER" ./nnfw docker-run-user ./infra/scripts/test_ubuntu_runtime.sh --backend cpu -./nnfw docker-run-user ./infra/scripts/test_ubuntu_runtime_interp.sh +./nnfw docker-run-user ./infra/scripts/test_ubuntu_runtime.sh --interp popd > /dev/null diff --git a/infra/scripts/docker_collect_nnpkg_resources.sh b/infra/scripts/docker_collect_nnpkg_resources.sh index 822c390..556c5bd 100755 --- a/infra/scripts/docker_collect_nnpkg_resources.sh +++ b/infra/scripts/docker_collect_nnpkg_resources.sh @@ -59,28 +59,21 @@ pushd $ROOT_PATH > /dev/null REQUIRED_UNITS=() # Common Libraries -REQUIRED_UNITS+=("angkor" "bino" "cwrap" "fipe" "pepper-str" "pepper-strcast" "pp" "stdex") -REQUIRED_UNITS+=("oops" "pepper-assert" "cli" "safemain") +REQUIRED_UNITS+=("angkor" "cwrap" "pepper-str" "pepper-strcast" "pp" "stdex") +REQUIRED_UNITS+=("oops" "safemain" "foder" "arser" "oops") # Hermes Logging Framework REQUIRED_UNITS+=("hermes" "hermes-std") # loco IR and related utilities REQUIRED_UNITS+=("loco" "locop" "locomotiv" "logo-core" "logo") -# loco IR extension: Custom Op Support -REQUIRED_UNITS+=("locoex-customop") -# TensorFlow Libraries -REQUIRED_UNITS+=("tfinfo" "plier-tf") -# TensorFlow GraphDef I/O -REQUIRED_UNITS+=("mio-tf") -# TensorFlow Frontend (.pb/.pbtxt -> loco.caninical) -REQUIRED_UNITS+=("moco-log" "moco" "moco-tf") -# TensorFlow Lite/Circle Backend (loco.canonical -> .tflite, loco.circle -> .circle) -REQUIRED_UNITS+=("exo") +# Circle compiler library (.circle -> .circle) +REQUIRED_UNITS+=("luci") +# Flatbuffer I/O +REQUIRED_UNITS+=("mio-tflite" "mio-circle") # Tools -REQUIRED_UNITS+=("tf2tflite" "tfkit" "i5diff") -# nnkit and related utilities -REQUIRED_UNITS+=("nnkit" "nnkit-intf" "nnkit-misc" "nnkit-tflite" "nnkit-tf") -# tf2tflite_value_pbtxt_test -REQUIRED_UNITS+=("tf2tflite-value-pbtxt-test") +REQUIRED_UNITS+=("tflite2circle" "circle2circle" "luci-interpreter") +REQUIRED_UNITS+=("souschef" "tflchef" "circlechef" "circle-verify") +# common-artifacts +REQUIRED_UNITS+=("common-artifacts") # Reset whitelist to build all ./nncc docker-run ./nncc configure -DENABLE_STRICT_BUILD=ON -DCMAKE_BUILD_TYPE=release \ @@ -88,16 +81,19 @@ REQUIRED_UNITS+=("tf2tflite-value-pbtxt-test") $CONFIG_OPTIONS ./nncc docker-run ./nncc build -j4 -# tf2tflite_value_pbtxt_test to generate graphdef, input, golden data -./nncc docker-run ./nncc test -R "tf2tflite_value_pbtxt_test" - mkdir -p ${ARCHIVE_PATH} TEMP_DIR=$(mktemp -d -t resXXXX) rm -f ${TEMP_DIR}/* -cp $NNCC_WORKSPACE/compiler/tf2tflite-value-pbtxt-test/*.h5 ${TEMP_DIR} -cp $NNCC_WORKSPACE/compiler/tf2tflite-value-pbtxt-test/*.info ${TEMP_DIR} -cp $NNCC_WORKSPACE/compiler/tf2tflite-value-pbtxt-test/*.pb ${TEMP_DIR} -tar -zcf ${ARCHIVE_PATH}/test-resources.tar.gz -C ${TEMP_DIR} ./ +mkdir -p ${TEMP_DIR}/nnpkg-tcs + +# Copy nnpakcage only if it has its test data +for nnpkg in $NNCC_WORKSPACE/compiler/common-artifacts/*; do + if [ -d $nnpkg/metadata/tc ]; then + cp -r $nnpkg ${TEMP_DIR}/nnpkg-tcs + fi +done + +tar -zcf ${ARCHIVE_PATH}/nnpkg-test-suite.tar.gz -C ${TEMP_DIR} ./ rm -rf ${TEMP_DIR} echo "resouce generation end" diff --git a/infra/scripts/docker_coverage_report.sh b/infra/scripts/docker_coverage_report.sh index dac4e3f..677462d 100755 --- a/infra/scripts/docker_coverage_report.sh +++ b/infra/scripts/docker_coverage_report.sh @@ -25,7 +25,7 @@ tar -zxf ${ARCHIVE_PATH}/coverage-data.tar.gz CMD="GCOV_PATH=arm-linux-gnueabihf-gcov NNAS_WORKSPACE=Product ./nnas gen-coverage-report runtime compute && tar -zcf coverage/coverage_report.tar.gz coverage/html && - python tools/lcov-to-cobertura-xml/lcov_cobertura.py coverage/coverage.info -o coverage/nnfw_coverage.xml" + python runtime/3rdparty/lcov-to-cobertura-xml/lcov_cobertura.py coverage/coverage.info -o coverage/nnfw_coverage.xml" ./nnfw docker-run-user bash -c "$CMD" diff --git a/infra/scripts/test_arm_nnpkg.sh b/infra/scripts/test_arm_nnpkg.sh index d6561ff..68adaf9 100755 --- a/infra/scripts/test_arm_nnpkg.sh +++ b/infra/scripts/test_arm_nnpkg.sh @@ -7,10 +7,10 @@ BACKENDS=("acl_cl" "acl_neon" "cpu") for BACKEND in "${BACKENDS[@]}"; do - NNPackageTest ${BACKEND} "tools/nnpackage_tool/nnpkg_test/list.armv7l-linux.${BACKEND}" + NNPackageTest ${BACKEND} "tests/scripts/list/nnpkg_test_list.armv7l-linux.${BACKEND}" done # Interpreter test export DISABLE_COMPILE=1 -NNPackageTest "interp" "tools/nnpackage_tool/nnpkg_test/list.noarch.interp" +NNPackageTest "interp" "tests/scripts/list/nnpkg_test_list.noarch.interp" unset DISABLE_COMPILE diff --git a/infra/scripts/test_coverage.sh b/infra/scripts/test_coverage.sh index 773122e..c3dc048 100755 --- a/infra/scripts/test_coverage.sh +++ b/infra/scripts/test_coverage.sh @@ -36,7 +36,7 @@ TFLiteModelVerification "acl_cl" "tests/scripts/list/frameworktest_list.armv7l.a unset TRACE_FILEPATH # Interpreter -./infra/scripts/test_ubuntu_runtime_interp.sh +./infra/scripts/test_ubuntu_runtime.sh --interp # nnpackage test suite if [[ -e ${ARCHIVE_PATH}/nnpkg-test-suite.tar.gz ]]; then diff --git a/infra/scripts/test_ubuntu_runtime.sh b/infra/scripts/test_ubuntu_runtime.sh index d4190bd..76e567a 100755 --- a/infra/scripts/test_ubuntu_runtime.sh +++ b/infra/scripts/test_ubuntu_runtime.sh @@ -6,8 +6,10 @@ source "$(dirname "${BASH_SOURCE[0]}")/common.sh" BACKEND="cpu" TEST_ARCH=$(uname -m | tr '[:upper:]' '[:lower:]') TEST_OS="linux" +TEST_PLATFORM="$TEST_ARCH-$TEST_OS" TFLITE_LOADER="0" LINEAR_ONLY="0" +RUN_INTERP="0" function Usage() { @@ -43,6 +45,10 @@ do LINEAR_ONLY="1" shift ;; + --interp) + RUN_INTERP="1" + shift; + ;; *) # Ignore shift @@ -51,34 +57,58 @@ do done CheckTestPrepared -echo "[[ ${TEST_ARCH}-${TEST_OS}: ${BACKEND} backend test ]]" -UNITTEST_SKIPLIST="Product/out/unittest/nnapi_gtest.skip.${TEST_ARCH}-${TEST_OS}.${BACKEND}" + +if [ $RUN_INTERP = "1" ]; then + TEST_PLATFORM="noarch" + TEST_ARCH="noarch" + BACKEND="interp" + echo "[[ Interpreter test ]]" +else + echo "[[ ${TEST_PLATFORM}: ${BACKEND} backend test ]]" +fi + +UNITTEST_SKIPLIST="Product/out/unittest/nnapi_gtest.skip.${TEST_PLATFORM}.${BACKEND}" FRAMEWORK_TESTLIST="tests/scripts/list/frameworktest_list.${TEST_ARCH}.${BACKEND}.txt" REPORT_BASE="report/${BACKEND}" EXECUTORS=("Linear" "Dataflow" "Parallel") + if [ $LINEAR_ONLY = "1" ]; then EXECUTORS=("Linear") fi +if [ $RUN_INTERP = "1" ]; then + EXECUTORS=("Interpreter") +fi for EXECUTOR in "${EXECUTORS[@]}"; do echo "[EXECUTOR]: ${EXECUTOR}" - export EXECUTOR="${EXECUTOR}" - Unittests "${BACKEND}" "${UNITTEST_SKIPLIST}" "${REPORT_BASE}/${EXECUTOR}" - TFLiteModelVerification "${BACKEND}" "${FRAMEWORK_TESTLIST}" "${REPORT_BASE}/${EXECUTOR}" - unset EXECUTOR + REPORT_PATH="${REPORT_BASE}/${EXECUTOR}" + + if [ $EXECUTOR = "Interpreter" ]; then + export DISABLE_COMPILE=1 + BACKEND="" + else + export EXECUTOR="${EXECUTOR}" + fi + + Unittests "${BACKEND}" "${UNITTEST_SKIPLIST}" "${REPORT_PATH}" + TFLiteModelVerification "${BACKEND}" "${FRAMEWORK_TESTLIST}" "${REPORT_PATH}" + + if [ $EXECUTOR = "Interpreter" ]; then + unset DISABLE_COMPILE + else + unset EXECUTOR + fi done +# Current support acl_cl backend testlist only +# TODO Support more backends +TFLITE_LOADER_TESTLIST="tests/scripts/list/tflite_loader_list.${TEST_ARCH}.txt" if [[ $TFLITE_LOADER = "1" ]]; then - # Test tflite_loader - pushd ${ROOT_PATH} > /dev/null - ./tests/scripts/test-driver.sh \ - --frameworktest \ - --framework_driverbin="$ROOT_PATH/Product/out/bin/tflite_loader_test_tool" \ - --frameworktest_list_file=tests/scripts/list/tflite_loader_list.${TEST_ARCH}.txt \ - --reportdir="$ROOT_PATH/report/tfliteloader" . + TFLiteLoaderTest "${BACKEND}" "${TFLITE_LOADER_TESTLIST}" "${REPORT_BASE}/loader/${EXECUTOR}" # Test custom op + pushd ${ROOT_PATH} > /dev/null ./Product/out/tests/FillFrom_runner popd > /dev/null fi diff --git a/infra/scripts/test_ubuntu_runtime_interp.sh b/infra/scripts/test_ubuntu_runtime_interp.sh deleted file mode 100755 index 4113b06..0000000 --- a/infra/scripts/test_ubuntu_runtime_interp.sh +++ /dev/null @@ -1,12 +0,0 @@ -#!/bin/bash - -set -eo pipefail -source "$(dirname "${BASH_SOURCE[0]}")/common.sh" - -export DISABLE_COMPILE=1 -CheckTestPrepared -echo "[[ Interpreter test ]]" -Unittests "cpu" "Product/out/unittest/nnapi_gtest.skip.noarch.interp" "report/interp" -TFLiteModelVerification "cpu" "tests/scripts/list/frameworktest_list.noarch.interp.txt" "report/interp" - -unset DISABLE_COMPILE diff --git a/infra/scripts/test_ubuntu_runtime_mixed.sh b/infra/scripts/test_ubuntu_runtime_mixed.sh index 71b33de..265a2ac 100755 --- a/infra/scripts/test_ubuntu_runtime_mixed.sh +++ b/infra/scripts/test_ubuntu_runtime_mixed.sh @@ -9,6 +9,29 @@ CheckTestPrepared TEST_ARCH=$(uname -m | tr '[:upper:]' '[:lower:]') TEST_OS="linux" +# nnfw_api_gtest +# NOTE: This test is run here as it does not depend on BACKEND or EXECUTOR + +# This test requires test model installation +pushd ${ROOT_PATH} > /dev/null +echo +echo "==== Run nnfw_api_gtest begin ====" +echo +NNFW_API_TEST_MODEL_INSTALLER=tests/scripts/nnfw_api_gtest/install_nnfw_api_gtest_nnpackages.sh +TEST_BIN=Product/out/unittest_standalone/nnfw_api_gtest +$NNFW_API_TEST_MODEL_INSTALLER --install-dir ${TEST_BIN}_models +${TEST_BIN} +echo +echo "==== Run nnfw_api_gtest end ====" +echo +popd > /dev/null + +Product/out/unittest_standalone/test_compute +Product/out/unittest_standalone/test_onert +Product/out/unittest_standalone/test_onert_backend_cpu_common +Product/out/unittest_standalone/test_onert_frontend_nnapi +Product/out/unittest_standalone/tflite_test + pushd ${ROOT_PATH} # NOTE Fixed backend assignment by type of operation @@ -28,6 +51,15 @@ for BACKEND in "${BACKENDS[@]:1}"; do done popd > /dev/null +# Fail on NCHW layout (acl_cl, acl_neon) +# TODO Fix bug +echo "GeneratedTests.*weights_as_inputs*" >> $SKIPLIST_PREFIX.union +echo "GeneratedTests.logical_or_broadcast_4D_2D_nnfw" >> $SKIPLIST_PREFIX.union +echo "GeneratedTests.mean" >> $SKIPLIST_PREFIX.union +echo "GeneratedTests.add_broadcast_4D_2D_after_nops_float_nnfw" >> $SKIPLIST_PREFIX.union +echo "GeneratedTests.argmax_*" >> $SKIPLIST_PREFIX.union +echo "GeneratedTests.squeeze_relaxed" >> $SKIPLIST_PREFIX.union + # Run the test export OP_BACKEND_Conv2D="cpu" export OP_BACKEND_MaxPool2D="acl_cl" diff --git a/infra/scripts/tizen_xu4_test.sh b/infra/scripts/tizen_xu4_test.sh index 6a479b7..5521b5f 100755 --- a/infra/scripts/tizen_xu4_test.sh +++ b/infra/scripts/tizen_xu4_test.sh @@ -8,11 +8,40 @@ fi function Usage() { - echo "Usage: ./tizen_xu4_test.sh --rpm-dir=path/to/rpm-dir --unittest --verification" - echo "Usage: ./tizen_xu4_test.sh --test-suite-path=path/to/test-suite.tar.gz --unittest --verification" - echo "--rpm-dir : directory containing nnfw.rpm and nnfw-test.rpm" - echo "--test-suite-path : filepath to test-suite.tar.gz" - echo "--gcov-dir : directory to save gcov files" + echo "Usage: ./tizen_xu4_test.sh" + echo "Usage: ./tizen_xu4_test.sh --rpm-dir=path/to/rpm-dir" + echo "Usage: ./tizen_xu4_test.sh --test-suite-path=path/to/test-suite.tar.gz" + echo "Usage: ./tizen_xu4_test.sh --skip-install-model" + echo "" + echo "--rpm-dir

: directory containing nnfw.rpm and nnfw-test.rpm" + echo "--test-suite-path : filepath to test-suite.tar.gz" + echo "--skip-install-model : skip install downloaded model" + echo "--gcov-dir : directory to save gcov files" +} + +function install_model() +{ + # download tflite model files + pushd $HOST_HOME + tests/scripts/framework/run_test.sh --download=on + # TODO Since this command removes model file(.zip), + # We must always download the file unlike model file(.tflite). + # Because caching applies only to tflite file. + find tests -name "*.zip" -exec rm {} \; + tar -zcf cache.tar.gz tests/scripts/framework/cache + $SDB_CMD push cache.tar.gz $TEST_ROOT/. + rm -rf cache.tar.gz + $SDB_CMD shell tar -zxf $TEST_ROOT/cache.tar.gz -C $TEST_ROOT + + # download api test model file for nnfw_api_gtest + MODEL_CACHE_DIR=$(mktemp -d) + tests/scripts/nnfw_api_gtest/install_nnfw_api_gtest_nnpackages.sh --install-dir $MODEL_CACHE_DIR + tar -zcf $MODEL_CACHE_DIR/api_model_test.tar.gz -C $MODEL_CACHE_DIR . + $SDB_CMD push $MODEL_CACHE_DIR/api_model_test.tar.gz $TEST_ROOT/Product/out/unittest_standalone/nnfw_api_gtest_models/ + $SDB_CMD shell tar -zxf $TEST_ROOT/Product/out/unittest_standalone/nnfw_api_gtest_models/api_model_test.tar.gz \ + -C $TEST_ROOT/Product/out/unittest_standalone/nnfw_api_gtest_models/ + rm -rf $MODEL_CACHE_DIR + popd } @@ -28,15 +57,6 @@ function prepare_rpm_test() $SDB_CMD push $file $TEST_ROOT $SDB_CMD shell rpm -Uvh $TEST_ROOT/$(basename $file) --force --nodeps done - - # download tflite model files - pushd $HOST_HOME - tests/scripts/framework/run_test.sh --download=on - find tests -name "*.zip" -exec rm {} \; - tar -zcf cache.tar.gz tests/scripts/framework/cache - $SDB_CMD push cache.tar.gz $TEST_ROOT/. - rm -rf cache.tar.gz - $SDB_CMD shell tar -zxf $TEST_ROOT/cache.tar.gz -C $TEST_ROOT } function prepare_suite_test() @@ -49,18 +69,9 @@ function prepare_suite_test() # install test-suite $SDB_CMD push $TEST_SUITE_PATH $TEST_ROOT/$(basename $TEST_SUITE_PATH) $SDB_CMD shell tar -zxf $TEST_ROOT/$(basename $TEST_SUITE_PATH) -C $TEST_ROOT - - # download tflite model files - pushd $HOST_HOME - tests/scripts/framework/run_test.sh --download=on - find tests -name "*.zip" -exec rm {} \; - tar -zcf cache.tar.gz tests/scripts/framework/cache - $SDB_CMD push cache.tar.gz $TEST_ROOT/. - rm -rf cache.tar.gz - $SDB_CMD shell tar -zxf $TEST_ROOT/cache.tar.gz -C $TEST_ROOT } - +INSTALL_MODEL="1" # Parse command argv for i in "$@" do @@ -72,9 +83,20 @@ do --rpm-dir=*) RPM_DIR=${i#*=} ;; + --rpm-dir) + RPM_DIR="$2" + shift + ;; --test-suite-path=*) TEST_SUITE_PATH=${i#*=} ;; + --test-suite-path) + RPM_DIR="$2" + shift + ;; + --skip-install-model) + INSTALL_MODEL="0" + ;; --gcov-dir=*) GCOV_DIR=${i#*=} ;; @@ -109,22 +131,27 @@ SCRIPT_ROOT="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" ROOT=$SCRIPT_ROOT/../ if [ -z "$RPM_DIR" ] && [ -z "$TEST_SUITE_PATH" ]; then - echo "Please provide --rpm-dir or --test-suite-path" - exit 255 + echo "======= Skip install runtime =======" fi if [ ! -z "$RPM_DIR" ]; then prepare_rpm_test -else +elif [ ! -z "$TEST_SUITE_PATH" ]; then prepare_suite_test fi +if [ $INSTALL_MODEL = "1" ]; then + install_model +else + echo "======= Skip install model =======" +fi + if [ -z "${GCOV_DIR}" ]; then ${SDB_CMD} shell /bin/bash -c "IGNORE_MD5=1 ${TEST_ROOT}/infra/scripts/test_ubuntu_runtime.sh --backend acl_cl --tflite-loader" ${SDB_CMD} shell /bin/bash -c "IGNORE_MD5=1 ${TEST_ROOT}/infra/scripts/test_ubuntu_runtime.sh --backend acl_neon" ${SDB_CMD} shell /bin/bash -c "IGNORE_MD5=1 ${TEST_ROOT}/infra/scripts/test_ubuntu_runtime.sh --backend cpu" ${SDB_CMD} shell /bin/bash -c "IGNORE_MD5=1 ${TEST_ROOT}/infra/scripts/test_ubuntu_runtime_mixed.sh" - ${SDB_CMD} shell /bin/bash -c "IGNORE_MD5=1 ${TEST_ROOT}/infra/scripts/test_ubuntu_runtime_interp.sh" + ${SDB_CMD} shell /bin/bash -c "IGNORE_MD5=1 ${TEST_ROOT}/infra/scripts/test_ubuntu_runtime.sh --interp" else mkdir -p ${GCOV_DIR} rm -rf ${GCOV_DIR}/* @@ -140,7 +167,7 @@ else ${SDB_CMD} shell /bin/bash -c "GCOV_PREFIX_STRIP=${GCOV_PREFIX_STRIP} IGNORE_MD5=1 ${TEST_ROOT}/infra/scripts/test_ubuntu_runtime.sh --backend acl_neon" ${SDB_CMD} shell /bin/bash -c "GCOV_PREFIX_STRIP=${GCOV_PREFIX_STRIP} IGNORE_MD5=1 ${TEST_ROOT}/infra/scripts/test_ubuntu_runtime.sh --backend cpu" ${SDB_CMD} shell /bin/bash -c "GCOV_PREFIX_STRIP=${GCOV_PREFIX_STRIP} IGNORE_MD5=1 ${TEST_ROOT}/infra/scripts/test_ubuntu_runtime_mixed.sh" - ${SDB_CMD} shell /bin/bash -c "GCOV_PREFIX_STRIP=${GCOV_PREFIX_STRIP} IGNORE_MD5=1 ${TEST_ROOT}/infra/scripts/test_ubuntu_runtime_interp.sh" + ${SDB_CMD} shell /bin/bash -c "GCOV_PREFIX_STRIP=${GCOV_PREFIX_STRIP} IGNORE_MD5=1 ${TEST_ROOT}/infra/scripts/test_ubuntu_runtime.sh --interp" # More test to check coverage ${SDB_CMD} shell "rm -rf ${GCOV_DATA_PATH} && mkdir -p ${GCOV_DATA_PATH}" diff --git a/infra/scripts/unittest_compiler_xml.sh b/infra/scripts/unittest_compiler_xml.sh new file mode 100755 index 0000000..46d3bc8 --- /dev/null +++ b/infra/scripts/unittest_compiler_xml.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +[[ "${BASH_SOURCE[0]}" != "${0}" ]] && echo "Please don't source ${BASH_SOURCE[0]}, execute it" && return + +set -eo pipefail + +CURRENT_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +ROOT_PATH="$CURRENT_PATH/../../" +NNCC_WORKSPACE=${NNCC_WORKSPACE:-${ROOT_PATH}build} +UNITTEST_REPORT_DIR=${NNCC_WORKSPACE}/unittest_compiler_xml + +for i in "$@" +do + case $i in + --reportdir=*) + UNITTEST_REPORT_DIR=${i#*=} + ;; + esac + shift +done + +if [ ! -e "$UNITTEST_REPORT_DIR" ]; then + mkdir -p $UNITTEST_REPORT_DIR +fi + +for TEST_BIN in `find ${NNCC_WORKSPACE}/compiler -type f -executable -name *_test`; do + TEST_NAME="$(basename -- $TEST_BIN)" + LUGI_LOG=999 $TEST_BIN --gtest_output="xml:$UNITTEST_REPORT_DIR/$TEST_NAME.xml" +done diff --git a/logo/ONE_logo_guideline_ENG.pdf b/logo/ONE_logo_guideline_ENG.pdf new file mode 100644 index 0000000..5d673e9 --- /dev/null +++ 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diff --git a/logo/images/logo_original_white.svg b/logo/images/logo_original_white.svg new file mode 100644 index 0000000..5f96124 --- /dev/null +++ b/logo/images/logo_original_white.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/nnpackage/schema/circle_schema.fbs b/nnpackage/schema/circle_schema.fbs index 42bce84..89e458c 100644 --- a/nnpackage/schema/circle_schema.fbs +++ b/nnpackage/schema/circle_schema.fbs @@ -26,6 +26,7 @@ // Version 0.1: Based on TF v2.2-rc2 + more (from TensorFlow `56d281c`) // `BATCH_MATMUL` operator, `FLOAT64` tensor type, // `asymmetric_quantize_inputs` for several operator options +// Version 0.2: BCQ_GATHER and BCQ_FULLY_CONNECTED are added. namespace circle; @@ -358,6 +359,8 @@ enum BuiltinOperator : ubyte { DENSIFY = 124, SEGMENT_SUM = 125, BATCH_MATMUL = 126, + BCQ_GATHER = 252, + BCQ_FULLY_CONNECTED = 253, INSTANCE_NORM = 254, } @@ -464,6 +467,8 @@ union BuiltinOptions { DensifyOptions, SegmentSumOptions, BatchMatMulOptions, + BCQGatherOptions = 252, + BCQFullyConnectedOptions = 253, InstanceNormOptions = 254, } @@ -983,6 +988,16 @@ table BatchMatMulOptions { adjoint_rhs:bool; } +table BCQGatherOptions { + input_hidden_size: int; + axis: int; +} + +table BCQFullyConnectedOptions { + weights_hidden_size: int; + fused_activation_function:ActivationFunctionType; +} + table InstanceNormOptions { epsilon:float; fused_activation_function:ActivationFunctionType; diff --git a/packaging/eigen.tar.gz b/packaging/eigen.tar.gz index 65337b4d2c467389fd511029007557babb478d8c..396d12fa764dc1c7fa321293b2d14b2fa965a4e8 100644 GIT binary patch literal 2664733 zcmV(;K-<3`iwFRUIt*U`1MEC&SKG*v^Vz?mCvy%XgKhi>FbkMn2Mpnid5m!~lVoyy zWVvlC$ZACoz{zEQ`&RWsYRR%O3732C*=QzM>PJ;~y}GKprC$5|>Y{txx%jhOuQV#v za{Kh8eZKkAQ(jfLc6PS#x3;rY)qmCXQ?0SR(|EaEt=FKu)@bbP{6w~%bQBA&CmfAIe~uBH5E9(BjgcZx?@{NLW*YWzg1PdClQ*Z*_=7tYNadu)a_ zb9W2Gp`(xtqA-Lfl3YFu&5#n)XOTN3;e-;P*_eg}GPQ?8ht75R!q_wgjcgDr^c`OL zIWn)Qf@!2-TjrGdCh=HcF31Y#l 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