From ffd065f1802a9e4ec6f85013f0cee0627bee4d68 Mon Sep 17 00:00:00 2001 From: =?utf8?q?=EC=B1=84=EC=84=B1=EC=9A=B0/On-Device=20Lab=28SR=29/Enginee?= =?utf8?q?r/=EC=82=BC=EC=84=B1=EC=A0=84=EC=9E=90?= Date: Thu, 17 Oct 2019 19:04:16 +0900 Subject: [PATCH] [locomotiv] Introduce TensorConstantPad (#8027) * [locomotiv] Introduce TensorConstantPad This commit introduces TensorConstantPad to locomotiv. Signed-off-by: seongwoo * change algorithm of pad interpretation. * make constant value more general. * change assert to validate. * modify test. * add assert for pad_data. * constant input can be all operator. * fix some wrong valiate and message. --- compiler/locomotiv/src/Node.lst | 1 + compiler/locomotiv/src/Node/TensorConstantPad.cpp | 113 +++++++++++ .../locomotiv/src/Node/TensorConstantPad.test.cpp | 218 +++++++++++++++++++++ 3 files changed, 332 insertions(+) create mode 100644 compiler/locomotiv/src/Node/TensorConstantPad.cpp create mode 100644 compiler/locomotiv/src/Node/TensorConstantPad.test.cpp diff --git a/compiler/locomotiv/src/Node.lst b/compiler/locomotiv/src/Node.lst index 35aef1c..238337f 100644 --- a/compiler/locomotiv/src/Node.lst +++ b/compiler/locomotiv/src/Node.lst @@ -33,6 +33,7 @@ NODE(Reshape) NODE(Tanh) NODE(TensorBroadcast) NODE(TensorConcat) +NODE(TensorConstantPad) NODE(TensorReduce) NODE(TensorSoftmax) NODE(TransposedConv2D) diff --git a/compiler/locomotiv/src/Node/TensorConstantPad.cpp b/compiler/locomotiv/src/Node/TensorConstantPad.cpp new file mode 100644 index 0000000..989afaf --- /dev/null +++ b/compiler/locomotiv/src/Node/TensorConstantPad.cpp @@ -0,0 +1,113 @@ +/* + * 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 "NodeExecution.h" + +#include "NodeDataImpl.h" +#include "NodeDomain.h" +#include "Validation.h" + +#include +#include + +#include + +using nncc::core::ADT::tensor::Shape; +using nncc::core::ADT::tensor::Index; +using nncc::core::ADT::tensor::IndexEnumerator; +using nncc::core::ADT::tensor::LexicalLayout; +using nncc::core::ADT::tensor::make_buffer; + +namespace locomotiv +{ + +void NodeExecution::execute(loco::TensorConstantPad *pad) +{ + auto input_data = annot_data(pad->input()); + auto input_domain = annot_domain(pad->input()); + validate(input_data, "Input not ready"); + validate(input_domain == loco::Domain::Tensor, "Input domain of TensorConstantPad is not Tensor"); + + auto input_shape = input_data->shape(); + const uint32_t input_rank = input_shape->rank(); + + auto padding = pad->padding(); + validate(input_rank == padding->rank(), "input and padding should have same rank"); + + auto constant_node = pad->constant(); + auto constant_data = annot_data(constant_node); + validate(constant_data->dtype() == input_data->dtype(), "constant and input have same data type"); + validate(constant_data->shape()->rank() == 1 && constant_data->shape()->dim(0) == 1, + "constant should have one rank with one dimension at zero axis"); + + std::unique_ptr pad_data = nullptr; + Index base_index; + base_index.resize(input_rank); + + // Tensor is padded by relocating its base. + // padded output index = input index + base index + for (uint32_t axis = 0; axis < padding->rank(); axis++) + { + base_index.at(axis) = padding->front(axis); + } + + // calculate output shape + Shape output_shape; + output_shape.resize(input_rank); + for (uint32_t i = 0; i < input_rank; i++) + { + output_shape.dim(i) = input_shape->dim(i) + padding->front(i) + padding->back(i); + } + + switch (input_data->dtype()) + { + case loco::DataType::FLOAT32: + { + auto input_buf = input_data->as_f32_bufptr(); + auto constant_data_buf = constant_data->as_f32_bufptr(); + const auto constant_value = constant_data_buf->at(Index{0}); + + auto output_buf = make_buffer(output_shape); + + for (IndexEnumerator ie{*input_shape}, oe{output_shape}; oe.valid(); oe.advance()) + { + auto input_index = ie.current(); + auto output_index = oe.current(); + + if ((input_index + base_index) == output_index) + { + output_buf.at(output_index) = input_buf->at(input_index); + ie.advance(); + } + else + { + output_buf.at(output_index) = constant_value; + } + } + + pad_data = make_data(output_buf); + break; + } + default: + throw std::runtime_error("NYI for this DataType"); + } + + assert(pad_data != nullptr); + annot_data(pad, std::move(pad_data)); + annot_domain(pad, annot_domain(pad->input())); +} + +} // namespace locomotiv diff --git a/compiler/locomotiv/src/Node/TensorConstantPad.test.cpp b/compiler/locomotiv/src/Node/TensorConstantPad.test.cpp new file mode 100644 index 0000000..0f60c5f --- /dev/null +++ b/compiler/locomotiv/src/Node/TensorConstantPad.test.cpp @@ -0,0 +1,218 @@ +/* + * 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 "NodeExecution.h" + +#include "locomotiv/NodeData.h" +#include "NodeDataImpl.h" +#include "NodeDomain.h" + +#include +#include + +#include + +using nncc::core::ADT::tensor::Index; +using nncc::core::ADT::tensor::LexicalLayout; +using nncc::core::ADT::tensor::make_buffer; +using nncc::core::ADT::tensor::Shape; + +TEST(NodeExecution_Pad, tensor_constant_pad_4_dim) +{ + auto g = loco::make_graph(); + + auto inputTensor = g->nodes()->create(); + inputTensor->dtype(loco::DataType::FLOAT32); + inputTensor->shape({1, 2, 2, 1}); + auto inputTensor_buf = make_buffer(Shape{1, 2, 2, 1}); + inputTensor_buf.at(Index{0, 0, 0, 0}) = 1.0f; + inputTensor_buf.at(Index{0, 0, 1, 0}) = 2.0f; + inputTensor_buf.at(Index{0, 1, 0, 0}) = 3.0f; + inputTensor_buf.at(Index{0, 1, 1, 0}) = 4.0f; + auto inputTensor_data = locomotiv::make_data(inputTensor_buf); + locomotiv::annot_data(inputTensor, std::move(inputTensor_data)); + locomotiv::annot_domain(inputTensor, loco::Domain::Tensor); + + auto constant = g->nodes()->create(); + constant->dtype(loco::DataType::FLOAT32); + constant->shape({1}); + auto constant_buf = make_buffer(Shape{1}); + constant_buf.at(Index{0}) = 0.0f; + auto constant_data = locomotiv::make_data(constant_buf); + locomotiv::annot_data(constant, std::move(constant_data)); + locomotiv::annot_domain(constant, loco::Domain::Tensor); + + auto pad = g->nodes()->create(); + pad->input(inputTensor); + pad->constant(constant); + + auto padding = pad->padding(); + padding->rank(4); + padding->front(0) = 0; + padding->back(0) = 0; + padding->front(1) = 3; + padding->back(1) = 1; + padding->front(2) = 1; + padding->back(2) = 1; + padding->front(3) = 0; + padding->back(3) = 0; + + locomotiv::NodeExecution::get().run(pad); + + 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_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_EQ(locomotiv::annot_domain(pad), loco::Domain::Tensor); +} + +TEST(NodeExecution_Pad, tensor_constant_pad_1_dim) +{ + auto g = loco::make_graph(); + + auto inputTensor = g->nodes()->create(); + inputTensor->dtype(loco::DataType::FLOAT32); + inputTensor->shape({3}); + auto inputTensor_buf = make_buffer(Shape{3}); + inputTensor_buf.at(Index{0}) = 1.0f; + inputTensor_buf.at(Index{1}) = 5.0f; + inputTensor_buf.at(Index{2}) = 3.0f; + auto inputTensor_data = locomotiv::make_data(inputTensor_buf); + locomotiv::annot_data(inputTensor, std::move(inputTensor_data)); + locomotiv::annot_domain(inputTensor, loco::Domain::Tensor); + + auto constant = g->nodes()->create(); + constant->dtype(loco::DataType::FLOAT32); + constant->shape({1}); + auto constant_buf = make_buffer(Shape{1}); + constant_buf.at(Index{0}) = 0.0f; + auto constant_data = locomotiv::make_data(constant_buf); + locomotiv::annot_data(constant, std::move(constant_data)); + locomotiv::annot_domain(constant, loco::Domain::Tensor); + + auto pad = g->nodes()->create(); + pad->input(inputTensor); + pad->constant(constant); + auto padding = pad->padding(); + padding->rank(1); + padding->front(0) = 2; + padding->back(0) = 1; + + locomotiv::NodeExecution::get().run(pad); + + 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_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_EQ(locomotiv::annot_domain(pad), loco::Domain::Tensor); +} + +TEST(NodeExecution_Pad, tensor_constant_pad_6_dim) +{ + auto g = loco::make_graph(); + + auto inputTensor = g->nodes()->create(); + inputTensor->dtype(loco::DataType::FLOAT32); + inputTensor->shape({2, 1, 3, 2, 1, 2}); + auto inputTensor_buf = make_buffer(Shape{2, 1, 3, 2, 1, 2}); + int a, b, c, d, e, f; + float dummy = 1.0f; + for (uint32_t a = 0; a < 2; a++) + { + for (uint32_t b = 0; b < 1; b++) + { + for (uint32_t c = 0; c < 3; c++) + { + for (uint32_t d = 0; d < 2; d++) + { + for (uint32_t e = 0; e < 1; e++) + { + for (uint32_t f = 0; f < 2; f++) + { + inputTensor_buf.at(Index{a, b, c, d, e, f}) = dummy++; + } + } + } + } + } + } + auto inputTensor_data = locomotiv::make_data(inputTensor_buf); + locomotiv::annot_data(inputTensor, std::move(inputTensor_data)); + locomotiv::annot_domain(inputTensor, loco::Domain::Tensor); + + auto constant = g->nodes()->create(); + constant->dtype(loco::DataType::FLOAT32); + constant->shape({1}); + auto constant_buf = make_buffer(Shape{1}); + constant_buf.at(Index{0}) = 0.0f; + auto constant_data = locomotiv::make_data(constant_buf); + locomotiv::annot_data(constant, std::move(constant_data)); + locomotiv::annot_domain(constant, loco::Domain::Tensor); + + auto pad = g->nodes()->create(); + pad->input(inputTensor); + pad->constant(constant); + auto padding = pad->padding(); + + padding->rank(6); + padding->front(0) = 1; + padding->back(0) = 1; + padding->front(1) = 0; + padding->back(1) = 0; + padding->front(2) = 1; + padding->back(2) = 2; + padding->front(3) = 2; + padding->back(3) = 1; + padding->front(4) = 0; + padding->back(4) = 0; + padding->front(5) = 1; + padding->back(5) = 2; + + locomotiv::NodeExecution::get().run(pad); + + 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); +} -- 2.7.4