Publishing 2019 R1 content

[platform/upstream/dldt.git] / inference-engine / thirdparty / clDNN / kernel_selector / core / kernel_selector_params.h

diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h
index d4351f2..d8c5199 100644 (file)
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h
@@ -1,5 +1,5 @@
 /*
-// Copyright (c) 2016 Intel Corporation
+// Copyright (c) 2016-2018 Intel Corporation
 //
 // Licensed under the Apache License, Version 2.0 (the "License");
 // you may not use this file except in compliance with the License.
@@ -20,8 +20,8 @@
 #include <memory>
 #include <cstddef>
 #include "common_types.h"
-#include "common_tools.h"
 #include "tensor_type.h"
+#include "document.h"
 
 namespace kernel_selector
 {
@@ -58,6 +58,7 @@ namespace kernel_selector
                 struct val_t
                 {
                     uint32_t different_types : 1;
+                    uint32_t different_input_weights_types : 1;
                     uint32_t offset : 1;
                     uint32_t pitches : 1;
                     uint32_t batching : 1;
@@ -120,15 +121,18 @@ namespace kernel_selector
                             uint32_t fixedKenrelDivider : 1;
                             uint32_t dynamicKenrelDivider : 1;
                             uint32_t dynamicKenrelDividerWithPadding : 1;
+                            uint32_t position_sensitive : 1;
                         } pooling;
                         struct conv_t
                         {
                             uint32_t split : 1;
                             uint32_t dilation : 1;
-                            uint32_t depthwiseSeparableOpt : 1;
+                            uint32_t depthwise_separable_opt : 1;
                             uint32_t transposed : 1;
                             uint32_t quantization : 1;
                             uint32_t calibration : 1;
+                            uint32_t local : 1;
+                            uint32_t grouped : 1;
                         } conv;
                         struct fc_t {} fc;
                         struct softmax_t
@@ -171,6 +175,11 @@ namespace kernel_selector
                         {
                             uint32_t winograd : 1;
                         } reorder;
+                        struct eltwise_t
+                        {
+                            uint32_t stride : 1;
+                            uint32_t broadcast : 1;
+                        } eltwise;
                         struct lstm_gemm_t {
                             uint32_t bias : 1;
                             uint32_t hidden : 1;
@@ -178,6 +187,21 @@ namespace kernel_selector
                         struct lstm_elt_t {
                             uint32_t cell : 1;
                         } lstm_elt;
+                        struct fused_conv_eltw_t {
+                            // conv
+                            uint32_t split : 1;
+                            uint32_t dilation : 1;
+                            uint32_t depthwise_separable_opt : 1;
+                            uint32_t transposed : 1;
+                            uint32_t quantization : 1;
+                            uint32_t calibration : 1;
+                            uint32_t local : 1;
+                            uint32_t grouped : 1;
+                            // eltw
+                            uint32_t stride : 1;
+                            // fused conv eltw
+                            uint32_t rw_out_opt : 1;
+                        } fused_conv_eltw;
                     } dedicated;
                 } val;
                 uint64_t raw;
@@ -233,6 +257,8 @@ namespace kernel_selector
         void EnableAllOutputWeightsType();
         void EnableFP16Emulation() { key.restrict.val.FP16Emulation = 1; }
         void EnableDifferentTypes() { key.restrict.val.different_types = 1; }
+        void EnableDifferentInputWeightsTypes() {
+            key.restrict.val.different_input_weights_types = 1; }
         void EnableInputLayout(DataLayout l) { key.inputLayout |= (1 << l); }
         void EnableAllInputLayout() { key.inputLayout = 0xffffffff; }
         void EnableOutputLayout(DataLayout l) { key.outputLayout |= (1 << l); }
@@ -261,16 +287,32 @@ namespace kernel_selector
         void EnablePoolKernelDividerMode(KernelDividerMode m);
         void EnablePoolType(PoolType t);
         void EnablePoolRemainder(PoolRemainder r);
+        void EnablePositionSensitivePooling() { key.restrict.val.dedicated.pooling.position_sensitive = 1; }
         void EnableSplitSupport() { key.restrict.val.dedicated.conv.split = 1; }
         void EnableDilation() { key.restrict.val.dedicated.conv.dilation = 1; }
-        void EnableDepthwiseSeparableOpt() { key.restrict.val.dedicated.conv.depthwiseSeparableOpt = 1; }
+        void EnableDepthwiseSeparableOpt() { key.restrict.val.dedicated.conv.depthwise_separable_opt = 1; }
+        void EnableLocalConvolution() { key.restrict.val.dedicated.conv.local = 1; }
+        void EnableGroupedConvolution() { key.restrict.val.dedicated.conv.grouped = 1; }
         void EnableTranspose() { key.restrict.val.dedicated.conv.transposed = 1; }
         void EnableInt8Quantization() { key.restrict.val.dedicated.conv.quantization = 1; }
         void EnableOutputCalibration() { key.restrict.val.dedicated.conv.calibration = 1; }
+
+        void EnableFusedConvEltwSplitSupport() { key.restrict.val.dedicated.fused_conv_eltw.split = 1; }
+        void EnableFusedConvEltwDilation() { key.restrict.val.dedicated.fused_conv_eltw.dilation = 1; }
+        void EnableFusedConvEltwDepthwiseSeparableOpt() { key.restrict.val.dedicated.fused_conv_eltw.depthwise_separable_opt = 1; }
+        void EnableFusedConvEltwLocalConvolution() { key.restrict.val.dedicated.fused_conv_eltw.local = 1; }
+        void EnableFusedConvEltwGroupedConvolution() { key.restrict.val.dedicated.fused_conv_eltw.grouped = 1; }
+        void EnableFusedConvEltwTranspose() { key.restrict.val.dedicated.fused_conv_eltw.transposed = 1; }
+        void EnableFusedConvEltwInt8Quantization() { key.restrict.val.dedicated.fused_conv_eltw.quantization = 1; }
+        void EnableFusedConvEltwOutputCalibration() { key.restrict.val.dedicated.fused_conv_eltw.calibration = 1; }
+        void EnableFusedConvEltwEltwiseStride();
+
         void EnableWinogradReorder() { key.restrict.val.dedicated.reorder.winograd = 1; }
         void EnableSoftmaxDim(SoftmaxDim d);
         void EnableConcatAxis(ConcatAxis a);
         void EnableUpSamplingSampleType(SampleType a);
+        void EnableEltwiseStride();
+        void EnableEltwiseBroadcast() { key.restrict.val.dedicated.eltwise.broadcast = 1; }
         void EnableLSTMGEMMBias() { key.restrict.val.dedicated.lstm_gemm.bias = 1; }
         void EnableLSTMGEMMHidden() { key.restrict.val.dedicated.lstm_gemm.hidden = 1; }
         void EnableLSTMEltCell() { key.restrict.val.dedicated.lstm_elt.cell = 1; }
@@ -280,6 +322,7 @@ namespace kernel_selector
         void EnableArgMaxMinAxis(ArgMaxMinAxis a);
         void EnableLookUpTableIndicesFormat(Datatype a);
         void EnableIndexSelectAxis(IndexSelectAxis a);
+        void EnableFusedConvEltwiseRWOutOpt();
         bool Support(const ParamsKey& k) const;
         bool TuningSupport() const
         {
@@ -287,6 +330,9 @@ namespace kernel_selector
                 return true;
             return false;
         }
+        bool isEnabledDifferentInputWeightsTypes() const {
+            return key.restrict.val.different_input_weights_types ? true : false;
+        }
         ParamsKey Merge(const ParamsKey& k) const;
 
     private:
@@ -305,6 +351,7 @@ namespace kernel_selector
         bool bImageSupport = false;
         bool bIMADSupport = false;
         bool bIMMADSupport = false;
+        uint32_t computeUnitsCount = 0;
         uint64_t maxWorkGroupSize = 0;
         uint64_t maxLocalMemSize = 0;
         uint64_t maxImage2dWidth = 0;
@@ -312,6 +359,7 @@ namespace kernel_selector
         std::string deviceId = "";
         std::string driverVersion = "";
         std::string hostVersion = "";
+        std::shared_ptr<rapidjson::Document> deviceCache;
     };
 
     ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
@@ -336,17 +384,31 @@ namespace kernel_selector
     };
 
     ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+    // base_activation_params
+    ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+    struct base_activation_params
+    {
+        ActivationFunction  function = ActivationFunction::NONE;
+        float m = 1.f;
+        float n = 0.f;
+
+        base_activation_params() = default;
+        base_activation_params(const float m, const float n) : m(m), n(n) {}
+
+        virtual std::string to_string() const;
+    };
+
+    ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     // base_params
     ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     struct base_params : public Params
     {
         virtual ~base_params() {}
 
-        ActivationFunction  activationFunc = ActivationFunction::NONE;
-        NonLinearParams     activationParams;
-        MultiDataTensor     inputs;
-        DataTensor          output;
-        bool                gradient = false;
+        base_activation_params activation;
+        MultiDataTensor        inputs;
+        DataTensor             output;
+        bool                   gradient = false;
 
         virtual std::string to_string() const;
         virtual ParamsKey GetParamsKey() const;