diff options
Diffstat (limited to 'src/backends/tosaCommon')
20 files changed, 637 insertions, 140 deletions
diff --git a/src/backends/tosaCommon/TosaLayerSupportRules.hpp b/src/backends/tosaCommon/TosaLayerSupportRules.hpp index 792908c619..8855dd612d 100644 --- a/src/backends/tosaCommon/TosaLayerSupportRules.hpp +++ b/src/backends/tosaCommon/TosaLayerSupportRules.hpp @@ -48,14 +48,34 @@ struct TosaAssertSize : public Rule } }; -struct TosaContainerContains : public Rule +struct TosaContainerContainsTwoTypes : public Rule { - explicit TosaContainerContains(std::tuple<DType, DType>& check, const std::vector<std::tuple<DType, DType>>& c) + explicit TosaContainerContainsTwoTypes(std::tuple<DType, DType>& check, + const std::vector<std::tuple<DType, DType>>& c) { for (auto item: c) { - if (std::get<0>(check) == std::get<0>(item) - && std::get<1>(check) == std::get<1>(item)) + if (std::get<0>(check) == std::get<0>(item) && + std::get<1>(check) == std::get<1>(item)) + { + m_Res = true; + return; + } + } + m_Res = false; + } +}; + +struct TosaContainerContainsThreeTypes : public Rule +{ + explicit TosaContainerContainsThreeTypes(std::tuple<DType, DType, DType>& check, + const std::vector<std::tuple<DType, DType, DType>>& c) + { + for (auto item: c) + { + if (std::get<0>(check) == std::get<0>(item) && + std::get<1>(check) == std::get<1>(item) && + std::get<2>(check) == std::get<2>(item)) { m_Res = true; return; diff --git a/src/backends/tosaCommon/TosaMappings.cpp b/src/backends/tosaCommon/TosaMappings.cpp index a37eaf29b3..00ba429555 100644 --- a/src/backends/tosaCommon/TosaMappings.cpp +++ b/src/backends/tosaCommon/TosaMappings.cpp @@ -8,40 +8,33 @@ using namespace armnn; using namespace tosa; -void SetBasicBlockConstantTensorData(Layer* layer, TosaSerializationBasicBlock* /*basicBlock*/) -{ - switch (layer->GetType()) - { - case LayerType::Convolution2d: - { - // ToDo: using Convolution2d as an example as it has constant tensors for weights and bias. - // ToDo: manually set TosaOperator data of basicBlock where constant tensors exist. - } - default: - // If no switch statement for layer, no constant tensors exist in that layer, return - return; - } -} - TosaSerializationBasicBlock* CreateEmptyTosaSerializationBasicBlock() { - // empty basic block when no tosa mapping implemented/exists - TosaSerializationOperator* op = - new TosaSerializationOperator(Op_UNKNOWN, Attribute_NONE, nullptr, {}, {}); + // Empty basic block when no TOSA mapping implemented/exists + auto* op = new TosaSerializationOperator(Op_UNKNOWN, Attribute_NONE, nullptr, {}, {}); return new TosaSerializationBasicBlock("", {op}, {}, {}, {}); } -TosaSerializationBasicBlock* GetTosaMapping(const LayerType type, +TosaSerializationBasicBlock* GetTosaMapping(const Layer* layer, + const LayerType type, const std::vector<const TensorInfo*>& inputs, const std::vector<const TensorInfo*>& outputs, - const BaseDescriptor& descriptor, - bool isMain = false) + const BaseDescriptor& descriptor) { switch (type) { case LayerType::Addition: { - return ConvertAdditionToTosaOperator(inputs, outputs, isMain); + return ConvertAdditionToTosaOperator(layer, inputs, outputs); + } + case LayerType::Constant: + { + return ConvertConstantToTosaOperator(layer, outputs); + } + case LayerType::Convolution2d: + { + auto conv2dDesc = PolymorphicDowncast<const Convolution2dDescriptor*>(&descriptor); + return ConvertConv2dToTosaOperator(layer, inputs, outputs, conv2dDesc); } case LayerType::Pooling2d: { @@ -57,11 +50,11 @@ TosaSerializationBasicBlock* GetTosaMapping(const LayerType type, } else if (avgPoolIgnoreValue) { - return ConvertAvgPool2DIgnoreValueToTosaOperator(inputs, outputs, isMain, poolDesc); + return ConvertAvgPool2DIgnoreValueToTosaOperator(layer, inputs, outputs, poolDesc); } else { - return ConvertPooling2DToTosaOperator(inputs, outputs, isMain, poolDesc); + return ConvertPooling2DToTosaOperator(layer, inputs, outputs, poolDesc); } } default: @@ -71,7 +64,7 @@ TosaSerializationBasicBlock* GetTosaMapping(const LayerType type, } } -TosaSerializationBasicBlock* GetTosaMappingFromLayer(Layer* layer, bool isMain = false) +TosaSerializationBasicBlock* GetTosaMappingFromLayer(Layer* layer) { std::vector<const TensorInfo*> inputs; for (auto inputSlot : layer->GetInputSlots()) @@ -85,11 +78,10 @@ TosaSerializationBasicBlock* GetTosaMappingFromLayer(Layer* layer, bool isMain = outputs.push_back(&outputSlot.GetTensorInfo()); } - TosaSerializationBasicBlock* basicBlock = GetTosaMapping(layer->GetType(), + TosaSerializationBasicBlock* basicBlock = GetTosaMapping(layer, + layer->GetType(), inputs, outputs, - layer->GetParameters(), - isMain); - SetBasicBlockConstantTensorData(layer, basicBlock); + layer->GetParameters()); return basicBlock; } diff --git a/src/backends/tosaCommon/TosaMappings.hpp b/src/backends/tosaCommon/TosaMappings.hpp index 8df2493d6e..cc41f1b7c8 100644 --- a/src/backends/tosaCommon/TosaMappings.hpp +++ b/src/backends/tosaCommon/TosaMappings.hpp @@ -13,22 +13,18 @@ using namespace armnn; using namespace tosa; -// From the input armnn::Layer, set the corresponding data field in the -// tosa::TosaSerializationTensor where constant tensor data exists in the armnn::Layer. -void SetBasicBlockConstantTensorData(Layer* layer, TosaSerializationBasicBlock* /*basicBlock*/); - // Populates a tosa::TosaSerializationBasicBlock from constructing // tosa::TosaSerializationOperator(s) and tosa::TosaSerializationTensor(s) // based on the input armnn::LayerType and associated armnn::TensorInfos and armnn::Descriptor. // -// If an armnn::LayerType does not have a tosa mapping or the mapping is not implemented in ArmNN, +// If an armnn::LayerType does not have a TOSA mapping or the mapping is not implemented in ArmNN, // an empty tosa::TosaSerializationBasicBlock() is returned with operator tosa::Op_UNKNOWN. -TosaSerializationBasicBlock* GetTosaMapping(const LayerType type, +TosaSerializationBasicBlock* GetTosaMapping(const Layer* layer, + const LayerType type, const std::vector<const TensorInfo*>& inputs, const std::vector<const TensorInfo*>& outputs, - const BaseDescriptor& /*descriptor*/, - bool isMain); + const BaseDescriptor& /*descriptor*/); // Function called in armnn::OptimizeSubgraphView() when access to armnn::Layer is available -// and there is an option to set tosa basic block data from constant layer tenors available from the input layer. -TosaSerializationBasicBlock* GetTosaMappingFromLayer(Layer* layer, bool isMain); +// and there is an option to set TOSA basic block data from constant layer tensors available from the input layer. +TosaSerializationBasicBlock* GetTosaMappingFromLayer(Layer* layer); diff --git a/src/backends/tosaCommon/operatorMappings/AdditionOperator.cpp b/src/backends/tosaCommon/operatorMappings/AdditionOperator.cpp index 796797728e..66ca869ac4 100644 --- a/src/backends/tosaCommon/operatorMappings/AdditionOperator.cpp +++ b/src/backends/tosaCommon/operatorMappings/AdditionOperator.cpp @@ -5,28 +5,36 @@ #include "AdditionOperator.hpp" -TosaSerializationBasicBlock* ConvertAdditionToTosaOperator(const std::vector<const TensorInfo*>& inputs, - const std::vector<const TensorInfo*>& outputs, - bool isMain) +TosaSerializationBasicBlock* ConvertAdditionToTosaOperator(const Layer* layer, + const std::vector<const TensorInfo*>& inputs, + const std::vector<const TensorInfo*>& outputs) { - // A helper function with static global variables ensures uniqueness - // for dynamically generating input, output and block names - std::string input0Name = std::string("Op_ADD_input0_") + GetUniqueTosaMappingID(); - std::string input1Name = std::string("Op_ADD_input1_") + GetUniqueTosaMappingID(); - std::string outputName = std::string("Op_ADD_output0_") + GetUniqueTosaMappingID(); - std::string blockName = std::string("Op_ADD_block_") + GetUniqueTosaMappingID(); - - // If it's the first block, overwrite block name with main. - if (isMain) + std::string input0Name = std::string("input0_"); + std::string input1Name = std::string("input1_"); + std::string outputName = std::string("output0_"); + std::string blockName = std::string("Op_ADD_block_") + GetUniqueTosaMappingID(); + + // If a layer is present then the block will be used for execution, so input and output names need to be determined + // using the previous and following layers so the graph is connected correctly. For validation this doesn't matter. + if(layer != nullptr) { - blockName = std::string("main"); + // Get the layers connected to the input slots and determine unique layer names. + Layer& connectedLayer0 = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer(); + input0Name = GenerateUniqueName(connectedLayer0, 0); + + Layer& connectedLayer1 = layer->GetInputSlot(1).GetConnectedOutputSlot()->GetOwningLayer(); + input1Name = GenerateUniqueName(connectedLayer1, 1); + + // Get the layer connected to the output slot and determine unique layer name. + Layer& connectedOutputLayer = layer->GetOutputSlot().GetConnection(0)->GetOwningLayer(); + outputName = GenerateUniqueName(connectedOutputLayer, 0); } - TosaSerializationOperator* op = new TosaSerializationOperator(Op_ADD, - Attribute_NONE, - nullptr, - {input0Name, input1Name}, - {outputName}); + auto* op = new TosaSerializationOperator(Op_ADD, + Attribute_NONE, + nullptr, + {input0Name, input1Name}, + {outputName}); std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape()); DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType()); @@ -37,9 +45,9 @@ TosaSerializationBasicBlock* ConvertAdditionToTosaOperator(const std::vector<con std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape()); DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType()); - TosaSerializationTensor* inputTensor0 = new TosaSerializationTensor(input0Name, inputShape0, inputDType0, {}); - TosaSerializationTensor* inputTensor1 = new TosaSerializationTensor(input1Name, inputShape1, inputDType1, {}); - TosaSerializationTensor* outputTensor0 = new TosaSerializationTensor(outputName, outputShape0, outputDType0, {}); + auto* inputTensor0 = new TosaSerializationTensor(input0Name, inputShape0, inputDType0, {}); + auto* inputTensor1 = new TosaSerializationTensor(input1Name, inputShape1, inputDType1, {}); + auto* outputTensor0 = new TosaSerializationTensor(outputName, outputShape0, outputDType0, {}); // operatorInputNames/operatorOutputNames ends up being the same as // blockInputNames/blockOutputNames for one-to-one ArmNN to Tosa mappings diff --git a/src/backends/tosaCommon/operatorMappings/AdditionOperator.hpp b/src/backends/tosaCommon/operatorMappings/AdditionOperator.hpp index f467bb6d10..5eb7441531 100644 --- a/src/backends/tosaCommon/operatorMappings/AdditionOperator.hpp +++ b/src/backends/tosaCommon/operatorMappings/AdditionOperator.hpp @@ -5,15 +5,16 @@ #pragma once +#include "TosaOperatorUtils.hpp" + #include <Layer.hpp> #include <tosa_serialization_handler.h> -#include "TosaOperatorUtils.hpp" using namespace armnn; using namespace tosa; -TosaSerializationBasicBlock* ConvertAdditionToTosaOperator(const std::vector<const TensorInfo*>& inputs, - const std::vector<const TensorInfo*>& outputs, - bool isMain); +TosaSerializationBasicBlock* ConvertAdditionToTosaOperator(const Layer* layer, + const std::vector<const TensorInfo*>& inputs, + const std::vector<const TensorInfo*>& outputs); diff --git a/src/backends/tosaCommon/operatorMappings/AvgPool2DIgnoreValueOperator.cpp b/src/backends/tosaCommon/operatorMappings/AvgPool2DIgnoreValueOperator.cpp index b3d2687c30..2601a6243d 100644 --- a/src/backends/tosaCommon/operatorMappings/AvgPool2DIgnoreValueOperator.cpp +++ b/src/backends/tosaCommon/operatorMappings/AvgPool2DIgnoreValueOperator.cpp @@ -5,23 +5,27 @@ #include "Pooling2DOperator.hpp" -TosaSerializationBasicBlock* ConvertAvgPool2DIgnoreValueToTosaOperator(const std::vector<const TensorInfo*>& inputs, +TosaSerializationBasicBlock* ConvertAvgPool2DIgnoreValueToTosaOperator(const Layer* layer, + const std::vector<const TensorInfo*>& inputs, const std::vector<const TensorInfo*>& outputs, - bool isMain, const Pooling2dDescriptor* poolDescriptor) { + std::string padInputName = std::string("input0_"); + std::string padOutputName = std::string("intermediate0_") + GetUniqueTosaMappingID(); + std::string poolOutputName = std::string("output0_"); + std::string blockName = std::string("Op_AVG_POOL2D_block_") + GetUniqueTosaMappingID(); - // A helper function with static global variables ensures uniqueness - // for dynamically generating input, output and block names - std::string padInputName = std::string("Op_PAD_input0_") + GetUniqueTosaMappingID(); - std::string padOutputName = std::string("Op_PAD_intermediate0_") + GetUniqueTosaMappingID(); - std::string poolOutputName = std::string("Op_AVG_POOL2D_output0_") + GetUniqueTosaMappingID(); - std::string blockName = std::string("Op_AVG_POOL2D_block_") + GetUniqueTosaMappingID(); - - // If it's the first block, overwrite block name with main. - if (isMain) + // If a layer is present then the block will be used for execution, so input and output names need to be determined + // using the previous and following layers so the graph is connected correctly. For validation this doesn't matter. + if(layer != nullptr) { - blockName = std::string("main"); + // Get the layers connected to the input slots and determine unique layer names. + Layer& connectedInputLayer = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer(); + padInputName = GenerateUniqueName(connectedInputLayer, 0); + + // Get the layer connected to the output slot and determine unique layer name. + Layer& connectedOutputLayer = layer->GetOutputSlot().GetConnection(0)->GetOwningLayer(); + poolOutputName = GenerateUniqueName(connectedOutputLayer, 0); } std::vector<int> paddings; @@ -51,11 +55,11 @@ TosaSerializationBasicBlock* ConvertAvgPool2DIgnoreValueToTosaOperator(const std } TosaPadAttribute padAttribute(paddings, 0, 0.0f); - TosaSerializationOperator* opPad = new TosaSerializationOperator(Op_PAD, - Attribute_PadAttribute, - &padAttribute, - {padInputName}, - {padOutputName}); + auto* opPad = new TosaSerializationOperator(Op_PAD, + Attribute_PadAttribute, + &padAttribute, + {padInputName}, + {padOutputName}); std::vector<int> pad = {0, 0, 0, 0}; std::vector<int> kernel = {static_cast<int>(poolDescriptor->m_PoolHeight), @@ -64,11 +68,11 @@ TosaSerializationBasicBlock* ConvertAvgPool2DIgnoreValueToTosaOperator(const std static_cast<int>(poolDescriptor->m_StrideX)}; TosaPoolAttribute poolAttribute(pad, kernel, stride, 0, 0, ArmNNToDType(inputs[0]->GetDataType())); - TosaSerializationOperator* opPool = new TosaSerializationOperator(Op_AVG_POOL2D, - Attribute_PoolAttribute, - &poolAttribute, - {padOutputName}, - {poolOutputName}); + auto* opPool = new TosaSerializationOperator(Op_AVG_POOL2D, + Attribute_PoolAttribute, + &poolAttribute, + {padOutputName}, + {poolOutputName}); std::vector<int32_t> inputShape = GetTosaTensorShape(inputs[0]->GetShape()); DType inputDType = ArmNNToDType(inputs[0]->GetDataType()); @@ -92,10 +96,9 @@ TosaSerializationBasicBlock* ConvertAvgPool2DIgnoreValueToTosaOperator(const std inputShape[3] + paddings[6] + paddings[7]}; } - TosaSerializationTensor* inputTensor = new TosaSerializationTensor(padInputName, inputShape, inputDType, {}); - TosaSerializationTensor* intermediateTensor = new TosaSerializationTensor( - padOutputName, intermediateShape, inputDType, {}); - TosaSerializationTensor* outputTensor = new TosaSerializationTensor(poolOutputName, outputShape, outputDType, {}); + auto* inputTensor = new TosaSerializationTensor(padInputName, inputShape, inputDType, {}); + auto* intermediateTensor = new TosaSerializationTensor(padOutputName, intermediateShape, inputDType, {}); + auto* outputTensor = new TosaSerializationTensor(poolOutputName, outputShape, outputDType, {}); // operatorInputNames/operatorOutputNames ends up being the same as // blockInputNames/blockOutputNames for one-to-one ArmNN to Tosa mappings diff --git a/src/backends/tosaCommon/operatorMappings/AvgPool2DIgnoreValueOperator.hpp b/src/backends/tosaCommon/operatorMappings/AvgPool2DIgnoreValueOperator.hpp index 63ae190cc9..f9d09754b0 100644 --- a/src/backends/tosaCommon/operatorMappings/AvgPool2DIgnoreValueOperator.hpp +++ b/src/backends/tosaCommon/operatorMappings/AvgPool2DIgnoreValueOperator.hpp @@ -5,15 +5,16 @@ #pragma once +#include "TosaOperatorUtils.hpp" + #include <Layer.hpp> #include <tosa_serialization_handler.h> -#include "TosaOperatorUtils.hpp" using namespace armnn; using namespace tosa; -TosaSerializationBasicBlock* ConvertAvgPool2DIgnoreValueToTosaOperator(const std::vector<const TensorInfo*>& inputs, +TosaSerializationBasicBlock* ConvertAvgPool2DIgnoreValueToTosaOperator(const Layer* layer, + const std::vector<const TensorInfo*>& inputs, const std::vector<const TensorInfo*>& outputs, - bool isMain, const Pooling2dDescriptor* poolDescriptor); diff --git a/src/backends/tosaCommon/operatorMappings/CMakeLists.txt b/src/backends/tosaCommon/operatorMappings/CMakeLists.txt index 7fdc9226af..b256eddda1 100644 --- a/src/backends/tosaCommon/operatorMappings/CMakeLists.txt +++ b/src/backends/tosaCommon/operatorMappings/CMakeLists.txt @@ -8,6 +8,10 @@ list(APPEND armnnTosaBackendOperators_sources AdditionOperator.cpp AvgPool2DIgnoreValueOperator.hpp AvgPool2DIgnoreValueOperator.cpp + ConstantOperator.hpp + ConstantOperator.cpp + Conv2dOperator.hpp + Conv2dOperator.cpp Pooling2DOperator.hpp Pooling2DOperator.cpp TosaOperatorUtils.hpp diff --git a/src/backends/tosaCommon/operatorMappings/ConstantOperator.cpp b/src/backends/tosaCommon/operatorMappings/ConstantOperator.cpp new file mode 100644 index 0000000000..5e3973f8ec --- /dev/null +++ b/src/backends/tosaCommon/operatorMappings/ConstantOperator.cpp @@ -0,0 +1,44 @@ +// +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "ConstantOperator.hpp" + +#include <layers/ConstantLayer.hpp> + +TosaSerializationBasicBlock* ConvertConstantToTosaOperator(const Layer* layer, + const std::vector<const TensorInfo*>& outputs) +{ + std::string outputName = std::string("constant_"); + std::string blockName = std::string("Op_CONST_block_") + GetUniqueTosaMappingID(); + + std::vector<uint8_t> uint8Data; + + // If a layer is present then the block will be used for execution, so names need to be unique. + // Also, set constant tensor data. + if(layer != nullptr) + { + outputName.append(std::to_string(layer->GetGuid())); + blockName.append(std::to_string(layer->GetGuid())); + + auto constantLayer = PolymorphicDowncast<const armnn::ConstantLayer*>(layer); + auto tensorInfo = constantLayer->GetOutputSlot().GetTensorInfo(); + + uint8Data = ConvertConstantTensorDataToBuffer(constantLayer->m_LayerOutput); + } + + auto* op = new TosaSerializationOperator(Op_CONST, Attribute_NONE, nullptr, {}, {outputName}); + + std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape()); + DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType()); + + // Setup output tensor with constant tensor data if available. + auto* outputTensor0 = new TosaSerializationTensor(outputName, outputShape0, outputDType0, uint8Data); + + return new TosaSerializationBasicBlock(blockName, // name + {op}, // operators + {outputTensor0}, // tensors + {}, // inputs + {outputName}); // outputs +}
\ No newline at end of file diff --git a/src/backends/tosaCommon/operatorMappings/ConstantOperator.hpp b/src/backends/tosaCommon/operatorMappings/ConstantOperator.hpp new file mode 100644 index 0000000000..df158aca3d --- /dev/null +++ b/src/backends/tosaCommon/operatorMappings/ConstantOperator.hpp @@ -0,0 +1,19 @@ +// +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "TosaOperatorUtils.hpp" + +#include <Layer.hpp> + +#include <tosa_serialization_handler.h> + +using namespace armnn; +using namespace tosa; + +TosaSerializationBasicBlock* ConvertConstantToTosaOperator(const Layer* layer, + const std::vector<const TensorInfo*>& outputs); + diff --git a/src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp b/src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp new file mode 100644 index 0000000000..9c095d627f --- /dev/null +++ b/src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp @@ -0,0 +1,123 @@ +// +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "Conv2dOperator.hpp" + +TosaSerializationBasicBlock* ConvertConv2dToTosaOperator(const Layer* layer, + const std::vector<const TensorInfo*>& inputs, + const std::vector<const TensorInfo*>& outputs, + const Convolution2dDescriptor* conv2dDescriptor) +{ + std::vector<std::string> inputNames; + std::string outputName = std::string("output0_"); + std::string blockName = std::string("Op_CONV2D_block_") + GetUniqueTosaMappingID(); + + // Set input names for validation purposes only. + if(layer == nullptr) + { + inputNames.emplace_back("input0_"); + inputNames.emplace_back("input1_"); + if(conv2dDescriptor->m_BiasEnabled) + { + inputNames.emplace_back("input2_"); + } + } + else + { + // If a layer is present then the block will be used for execution, so input and output names need to be + // determined using the previous and following layers so the graph is connected correctly. + // For validation this doesn't matter. + for (uint32_t i = 0; i < inputs.size(); ++i) + { + // Get the layer connected to the input slot and determine unique layer name. + Layer& connectedLayer = layer->GetInputSlot(i).GetConnectedOutputSlot()->GetOwningLayer(); + + std::string inputName = GenerateUniqueName(connectedLayer, i); + inputNames.push_back(inputName); + } + + // Get the layer connected to the output slot and determine unique layer name. + Layer& connectedLayer = layer->GetOutputSlot().GetConnection(0)->GetOwningLayer(); + + outputName = GenerateUniqueName(connectedLayer, 0); + } + + std::vector<TosaSerializationTensor*> tensors; + std::vector<TosaSerializationOperator*> operators; + + // Setup input Tensor + std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape()); + DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType()); + + tensors.push_back(new TosaSerializationTensor(inputNames[0], inputShape0, inputDType0, {})); + + // Only add input tensors if weights and bias are not constant or if running validation. + // Constant tensors will be created in the ConvertConstantToTosaOperator function. + if(!inputs[1]->IsConstant() || layer == nullptr) + { + std::vector<int32_t> inputShape1 = GetTosaTensorShape(inputs[1]->GetShape()); + DType inputDType1 = ArmNNToDType(inputs[1]->GetDataType()); + + tensors.push_back(new TosaSerializationTensor(inputNames[1], inputShape1, inputDType1, {})); + } + + if(conv2dDescriptor->m_BiasEnabled) + { + if(!inputs[2]->IsConstant() || layer == nullptr) + { + std::vector<int32_t> inputShape2 = GetTosaTensorShape(inputs[2]->GetShape()); + DType inputDType2 = ArmNNToDType(inputs[2]->GetDataType()); + + tensors.push_back(new TosaSerializationTensor(inputNames[2], inputShape2, inputDType2, {})); + } + } + else + { + // If bias is disabled, create a constant bias of 0 as three inputs are required. + std::string constantName = std::string("constant_") + GetUniqueTosaMappingID(); + + operators.push_back(new TosaSerializationOperator(Op_CONST, Attribute_NONE, nullptr, {}, {constantName})); + + std::vector<uint8_t> uint8Data; + std::vector<float> data = { 0.0 }; + + TosaSerializationHandler::ConvertF32toU8(data, uint8Data); + + tensors.push_back(new TosaSerializationTensor(constantName, {1}, DType_FP32, uint8Data)); + inputNames.emplace_back(constantName); + } + + // Setup Output Tensor + std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape()); + DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType()); + + tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {})); + + // Set up CONV2D operator + std::vector<int> pad = {static_cast<int>(conv2dDescriptor->m_PadTop), + static_cast<int>(conv2dDescriptor->m_PadBottom), + static_cast<int>(conv2dDescriptor->m_PadLeft), + static_cast<int>(conv2dDescriptor->m_PadRight)}; + std::vector<int> stride = {static_cast<int>(conv2dDescriptor->m_StrideY), + static_cast<int>(conv2dDescriptor->m_StrideX)}; + std::vector<int> dilation = {static_cast<int>(conv2dDescriptor->m_DilationY), + static_cast<int>(conv2dDescriptor->m_DilationX)}; + TosaConvAttribute attribute(pad, dilation, stride, 0, 0, ArmNNToDType(inputs[0]->GetDataType())); + + auto* op = new TosaSerializationOperator(Op_CONV2D, + Attribute_ConvAttribute, + &attribute, + inputNames, + {outputName}); + operators.push_back(op); + + // operatorInputNames/operatorOutputNames ends up being the same as + // blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings + return new TosaSerializationBasicBlock(blockName, // name + operators, // operators + tensors, // tensors + inputNames, // inputs + {outputName}); // outputs +}
\ No newline at end of file diff --git a/src/backends/tosaCommon/operatorMappings/Conv2dOperator.hpp b/src/backends/tosaCommon/operatorMappings/Conv2dOperator.hpp new file mode 100644 index 0000000000..909151b9ac --- /dev/null +++ b/src/backends/tosaCommon/operatorMappings/Conv2dOperator.hpp @@ -0,0 +1,20 @@ +// +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "TosaOperatorUtils.hpp" + +#include <Layer.hpp> + +#include <tosa_serialization_handler.h> + +using namespace armnn; +using namespace tosa; + +TosaSerializationBasicBlock* ConvertConv2dToTosaOperator(const Layer* layer, + const std::vector<const TensorInfo*>& inputs, + const std::vector<const TensorInfo*>& outputs, + const Convolution2dDescriptor* conv2dDescriptor); diff --git a/src/backends/tosaCommon/operatorMappings/Pooling2DOperator.cpp b/src/backends/tosaCommon/operatorMappings/Pooling2DOperator.cpp index cd707edb3a..eaeb8a4cde 100644 --- a/src/backends/tosaCommon/operatorMappings/Pooling2DOperator.cpp +++ b/src/backends/tosaCommon/operatorMappings/Pooling2DOperator.cpp @@ -5,24 +5,29 @@ #include "Pooling2DOperator.hpp" -TosaSerializationBasicBlock* ConvertPooling2DToTosaOperator(const std::vector<const TensorInfo*>& inputs, +TosaSerializationBasicBlock* ConvertPooling2DToTosaOperator(const Layer* layer, + const std::vector<const TensorInfo*>& inputs, const std::vector<const TensorInfo*>& outputs, - bool isMain, const Pooling2dDescriptor* poolDescriptor) { std::string poolType = (poolDescriptor->m_PoolType == PoolingAlgorithm::Max) ? "Op_MAX" : "Op_AVG"; Op opcode = (poolDescriptor->m_PoolType == PoolingAlgorithm::Max) ? Op_MAX_POOL2D : Op_AVG_POOL2D; - // A helper function with static global variables ensures uniqueness - // for dynamically generating input, output and block names - std::string input0Name = poolType + std::string("_POOL2D_input0_") + GetUniqueTosaMappingID(); - std::string outputName = poolType + std::string("_POOL2D_output0_") + GetUniqueTosaMappingID(); - std::string blockName = poolType + std::string("_POOL2D_block_") + GetUniqueTosaMappingID(); + std::string input0Name = std::string("input0_"); + std::string outputName = std::string("output0_"); + std::string blockName = std::string("Op_") + poolType + std::string("_POOL2D_block_") + GetUniqueTosaMappingID(); - // If it's the first block, overwrite block name with main. - if (isMain) + // If a layer is present then the block will be used for execution, so input and output names need to be determined + // using the previous and following layers so the graph is connected correctly. For validation this doesn't matter. + if(layer != nullptr) { - blockName = std::string("main"); + // Get the layers connected to the input slots and determine unique layer names. + Layer& connectedInputLayer = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer(); + input0Name = GenerateUniqueName(connectedInputLayer, 0); + + // Get the layer connected to the output slot and determine unique layer name. + Layer& connectedOutputLayer = layer->GetOutputSlot().GetConnection(0)->GetOwningLayer(); + outputName = GenerateUniqueName(connectedOutputLayer, 0); } std::vector<int> pad = {static_cast<int>(poolDescriptor->m_PadTop), @@ -35,11 +40,11 @@ TosaSerializationBasicBlock* ConvertPooling2DToTosaOperator(const std::vector<co static_cast<int>(poolDescriptor->m_StrideX)}; TosaPoolAttribute attribute(pad, kernel, stride, 0, 0, ArmNNToDType(inputs[0]->GetDataType())); - TosaSerializationOperator* op = new TosaSerializationOperator(opcode, - Attribute_PoolAttribute, - &attribute, - {input0Name}, - {outputName}); + auto* op = new TosaSerializationOperator(opcode, + Attribute_PoolAttribute, + &attribute, + {input0Name}, + {outputName}); std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape()); DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType()); @@ -47,8 +52,8 @@ TosaSerializationBasicBlock* ConvertPooling2DToTosaOperator(const std::vector<co std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape()); DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType()); - TosaSerializationTensor* inputTensor0 = new TosaSerializationTensor(input0Name, inputShape0, inputDType0, {}); - TosaSerializationTensor* outputTensor0 = new TosaSerializationTensor(outputName, outputShape0, outputDType0, {}); + auto* inputTensor0 = new TosaSerializationTensor(input0Name, inputShape0, inputDType0, {}); + auto* outputTensor0 = new TosaSerializationTensor(outputName, outputShape0, outputDType0, {}); // operatorInputNames/operatorOutputNames ends up being the same as // blockInputNames/blockOutputNames for one-to-one ArmNN to Tosa mappings diff --git a/src/backends/tosaCommon/operatorMappings/Pooling2DOperator.hpp b/src/backends/tosaCommon/operatorMappings/Pooling2DOperator.hpp index 22d2a3ae29..cc9ec097f9 100644 --- a/src/backends/tosaCommon/operatorMappings/Pooling2DOperator.hpp +++ b/src/backends/tosaCommon/operatorMappings/Pooling2DOperator.hpp @@ -5,15 +5,16 @@ #pragma once +#include "TosaOperatorUtils.hpp" + #include <Layer.hpp> #include <tosa_serialization_handler.h> -#include "TosaOperatorUtils.hpp" using namespace armnn; using namespace tosa; -TosaSerializationBasicBlock* ConvertPooling2DToTosaOperator(const std::vector<const TensorInfo*>& inputs, +TosaSerializationBasicBlock* ConvertPooling2DToTosaOperator(const Layer* layer, + const std::vector<const TensorInfo*>& inputs, const std::vector<const TensorInfo*>& outputs, - bool isMain, const Pooling2dDescriptor* poolDescriptor); diff --git a/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp b/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp index 00b5f0fa68..513db0c039 100644 --- a/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp +++ b/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp @@ -6,5 +6,7 @@ #pragma once #include "AdditionOperator.hpp" +#include "ConstantOperator.hpp" +#include "Conv2dOperator.hpp" #include "AvgPool2DIgnoreValueOperator.hpp" #include "Pooling2DOperator.hpp"
\ No newline at end of file diff --git a/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp b/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp index f51b2109b4..176e4e1cfb 100644 --- a/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp +++ b/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp @@ -5,10 +5,13 @@ #pragma once +#include <Layer.hpp> #include <armnn/Tensor.hpp> #include <armnn/Types.hpp> -#include <tosa_generated.h> +#include "common/include/ProfilingGuid.hpp" + +#include <tosa_serialization_handler.h> using namespace armnn; using namespace tosa; @@ -53,6 +56,33 @@ inline std::vector<int32_t> GetTosaTensorShape(const TensorShape& shape) return returnShape; } +// Function that generates unique name using the layer type, input slot and layer guid. +inline std::string GenerateUniqueName(const Layer& layer, uint32_t layerSlot) +{ + std::string name; + std::string guid = std::to_string(layer.GetGuid()); + std::string slotAndGuid = std::to_string(layerSlot) + "_" + guid; + LayerType layerType = layer.GetType(); + + if (layerType == LayerType::Input) + { + name = "input" + slotAndGuid; + } + else if (layerType == LayerType::Output) + { + name = "output" + slotAndGuid; + } + else if (layerType == LayerType::Constant) + { + name = "constant_" + guid; + } + else + { + name = "intermediate" + slotAndGuid; + } + return name; +} + // Function to return unique int as a string to ensure uniqueness between all input, output and block names. static int uniqueTosaMappingID = 0; inline std::string GetUniqueTosaMappingID() @@ -206,3 +236,72 @@ inline std::string TosaOpToString(Op tosaOp) } return ""; } + +inline std::vector<uint8_t> ConvertConstantTensorDataToBuffer(const std::shared_ptr<ConstTensorHandle>& tensorHandle) +{ + tosa_err_t error; + std::vector<uint8_t> uint8Data; + auto tensorInfo = tensorHandle->GetTensorInfo(); + + switch (tensorInfo.GetDataType()) + { + case DataType::Float32: + { + std::vector<float> data(tensorInfo.GetNumElements()); + memcpy(data.data(), tensorHandle->Map(true), tensorInfo.GetNumBytes()); + + error = TosaSerializationHandler::ConvertF32toU8(data, uint8Data); + break; + } + case DataType::Float16: + { + std::vector<float> data(tensorInfo.GetNumElements()); + memcpy(data.data(), tensorHandle->Map(true), tensorInfo.GetNumBytes()); + + error = TosaSerializationHandler::ConvertF16toU8(data, uint8Data); + break; + } + case DataType::QSymmS8: + case DataType::QAsymmS8: + { + std::vector<int8_t> data(tensorInfo.GetNumElements()); + memcpy(data.data(), tensorHandle->Map(true), tensorInfo.GetNumBytes()); + + error = TosaSerializationHandler::ConvertI8toU8(data, uint8Data); + break; + } + case DataType::QAsymmU8: + { + memcpy(uint8Data.data(), tensorHandle->Map(true), tensorInfo.GetNumBytes()); + break; + } + case DataType::QSymmS16: + { + std::vector<int16_t> data(tensorInfo.GetNumElements()); + memcpy(data.data(), tensorHandle->Map(true), tensorInfo.GetNumBytes()); + + error = TosaSerializationHandler::ConvertI16toU8(data, uint8Data); + break; + } + case DataType::Signed32: + { + std::vector<int32_t> data(tensorInfo.GetNumElements()); + memcpy(data.data(), tensorHandle->Map(true), tensorInfo.GetNumBytes()); + + error = TosaSerializationHandler::ConvertI32toU8(data, uint8Data); + break; + } + default: + { + throw armnn::Exception("SetConstantTensorData: An unsupported data type was encountered."); + } + } + + if(error != tosa_err_t::TOSA_OK) + { + throw armnn::Exception("SetConstantTensorData: An error occurred when converting constant data"); + } + + tensorHandle->Unmap(); + return uint8Data; +} diff --git a/src/backends/tosaCommon/test/AvgPool2DIgnoreValueChecker.hpp b/src/backends/tosaCommon/test/AvgPool2DIgnoreValueChecker.hpp index 8869b3a8ff..a38f66b466 100644 --- a/src/backends/tosaCommon/test/AvgPool2DIgnoreValueChecker.hpp +++ b/src/backends/tosaCommon/test/AvgPool2DIgnoreValueChecker.hpp @@ -17,10 +17,8 @@ void VerifyAvgPool2DIgnoreValue(TosaSerializationBasicBlock* basicBlock, { uint32_t numInputs = static_cast<uint32_t>(inputShape.size()); uint32_t numOutputs = static_cast<uint32_t>(outputShape.size()); - std::string operatorString0 = TosaOpToString(Op_PAD); - std::string operatorString1 = TosaOpToString(Op_AVG_POOL2D); - std::string blockStr = operatorString1 + "_block_"; + std::string blockStr = TosaOpToString(Op_AVG_POOL2D) + "_block_"; CHECK(basicBlock->GetName().find(blockStr) != std::string::npos); CHECK(basicBlock->GetInputs().size() == numInputs); CHECK(basicBlock->GetOutputs().size() == numOutputs); @@ -41,7 +39,7 @@ void VerifyAvgPool2DIgnoreValue(TosaSerializationBasicBlock* basicBlock, std::basic_string<char> blockInputName = basicBlock->GetInputs()[i]; std::basic_string<char> operatorInputName = padOp->GetInputTensorNames()[i]; - std::string opStr = operatorString0 + "_input" + std::to_string(i) + "_"; + std::string opStr = "input" + std::to_string(i) + "_"; CHECK(blockInputName == operatorInputName); CHECK(basicBlock->GetTensorByName(blockInputName)); @@ -56,7 +54,7 @@ void VerifyAvgPool2DIgnoreValue(TosaSerializationBasicBlock* basicBlock, for (uint32_t i = 0; i < padOpOutputs; i++) { std::basic_string<char> operatorOutputName = padOp->GetOutputTensorNames()[i]; - std::string opStr = operatorString0 + "_intermediate" + std::to_string(i) + "_"; + std::string opStr = "intermediate" + std::to_string(i) + "_"; CHECK(basicBlock->GetTensorByName(operatorOutputName)); CHECK(operatorOutputName.find(opStr) != std::string::npos); @@ -86,7 +84,7 @@ void VerifyAvgPool2DIgnoreValue(TosaSerializationBasicBlock* basicBlock, for (uint32_t i = 0; i < poolOpInputs; i++) { std::basic_string<char> operatorInputName = poolOp->GetInputTensorNames()[i]; - std::string opStr = operatorString0 + "_intermediate" + std::to_string(i) + "_"; + std::string opStr = "intermediate" + std::to_string(i) + "_"; CHECK(basicBlock->GetTensorByName(operatorInputName)); CHECK(operatorInputName.find(opStr) != std::string::npos); @@ -102,7 +100,7 @@ void VerifyAvgPool2DIgnoreValue(TosaSerializationBasicBlock* basicBlock, std::basic_string<char> blockOutputName = basicBlock->GetOutputs()[i]; std::basic_string<char> operatorOutputName = poolOp->GetOutputTensorNames()[i]; - std::string opStr = operatorString1 + "_output" + std::to_string(i) + "_"; + std::string opStr = "output" + std::to_string(i) + "_"; CHECK(blockOutputName == operatorOutputName); CHECK(basicBlock->GetTensorByName(blockOutputName)); diff --git a/src/backends/tosaCommon/test/OneToManyMappingTests.cpp b/src/backends/tosaCommon/test/OneToManyMappingTests.cpp index 98fd563da1..dd61ba8191 100644 --- a/src/backends/tosaCommon/test/OneToManyMappingTests.cpp +++ b/src/backends/tosaCommon/test/OneToManyMappingTests.cpp @@ -30,7 +30,7 @@ TEST_CASE("GetTosaMapping_AvgPool2DIgnoreValueLayer") std::vector<std::vector<int32_t>> outputShape = {{ 1, 1, 3, 3 }}; TosaSerializationBasicBlock* basicBlock = - GetTosaMapping(LayerType::Pooling2d, {&inputTensorInfo}, {&outputTensorInfo}, descriptor, false); + GetTosaMapping(nullptr, LayerType::Pooling2d, {&inputTensorInfo}, {&outputTensorInfo}, descriptor); VerifyAvgPool2DIgnoreValue(basicBlock, inputShape, outputShape, @@ -74,7 +74,7 @@ TEST_CASE("GetTosaMappingFromLayer_AvgPool2DIgnoreValueLayer") pool->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); TosaSerializationBasicBlock* basicBlock = - GetTosaMappingFromLayer(PolymorphicDowncast<Layer*>(pool), false); + GetTosaMappingFromLayer(PolymorphicDowncast<Layer*>(pool)); VerifyAvgPool2DIgnoreValue(basicBlock, inputShape, outputShape, diff --git a/src/backends/tosaCommon/test/OneToOneMappingTests.cpp b/src/backends/tosaCommon/test/OneToOneMappingTests.cpp index 04d1eb46aa..af9f9e26df 100644 --- a/src/backends/tosaCommon/test/OneToOneMappingTests.cpp +++ b/src/backends/tosaCommon/test/OneToOneMappingTests.cpp @@ -4,6 +4,7 @@ // #include "TosaTestUtils.hpp" +#include "CommonTestUtils.hpp" using namespace armnn; using namespace tosa; @@ -18,7 +19,7 @@ TEST_CASE("GetTosaMapping_AdditionLayer") std::vector<std::vector<int32_t>> outputShape = {{ 1, 2, 4, 2 }}; TosaSerializationBasicBlock* basicBlock = - GetTosaMapping(LayerType::Addition, {&info, &info}, {&info}, BaseDescriptor(), false); + GetTosaMapping(nullptr, LayerType::Addition, {&info, &info}, {&info}, BaseDescriptor()); AssertTosaOneToOneMappingBasicBlock( basicBlock, inputShape, outputShape, Op_ADD, Attribute_NONE, BaseDescriptor(), LayerType::Addition); } @@ -50,11 +51,132 @@ TEST_CASE("GetTosaMappingFromLayer_AdditionLayer") std::vector<std::vector<int32_t>> outputShape = {{ 1, 2, 4, 2 }}; TosaSerializationBasicBlock* basicBlock = - GetTosaMappingFromLayer(PolymorphicDowncast<Layer*>(add), false); + GetTosaMappingFromLayer(PolymorphicDowncast<Layer*>(add)); AssertTosaOneToOneMappingBasicBlock( basicBlock, inputShape, outputShape, Op_ADD, Attribute_NONE, BaseDescriptor(), LayerType::Addition); } +TEST_CASE("GetTosaMapping_ConstantLayer") +{ + TensorInfo outputInfo = TensorInfo({ 1, 2, 4, 2 }, DataType::Float32, 0.0f, 0, true); + std::vector<std::vector<int32_t>> outputShape = {{ 1, 2, 4, 2 }}; + + TosaSerializationBasicBlock* basicBlock = + GetTosaMapping(nullptr, LayerType::Constant, {}, {&outputInfo}, BaseDescriptor()); + AssertTosaOneToOneMappingBasicBlock( + basicBlock, {}, outputShape, Op_CONST, Attribute_NONE, BaseDescriptor(), LayerType::Constant); +} + +TEST_CASE("GetTosaMappingFromLayer_ConstantLayer") +{ + IRuntime::CreationOptions options; + IRuntimePtr runtime(IRuntime::Create(options)); + + // Builds up the structure of the network. + INetworkPtr net(INetwork::Create()); + + TensorInfo info = TensorInfo({ 1, 2, 4, 2 }, DataType::Float32, 0.0f, 0, true); + std::vector<std::vector<int32_t>> outputShape = {{ 1, 2, 4, 2 }}; + + std::vector<float> data = GenerateRandomData<float>(info.GetNumElements()); + armnn::ConstTensor constTensor(info, data); + + IConnectableLayer* constant = net->AddConstantLayer(constTensor, "constant"); + IConnectableLayer* output = net->AddOutputLayer(0, "output"); + + constant->GetOutputSlot(0).Connect(output->GetInputSlot(0)); + constant->GetOutputSlot(0).SetTensorInfo(info); + + TosaSerializationBasicBlock* basicBlock = + GetTosaMappingFromLayer(PolymorphicDowncast<Layer*>(constant)); + AssertTosaOneToOneMappingBasicBlock( + basicBlock, {}, outputShape, Op_CONST, Attribute_NONE, BaseDescriptor(), LayerType::Constant); +} + +TEST_CASE("GetTosaMapping_Conv2dLayer") +{ + armnn::Convolution2dDescriptor descriptor; + descriptor.m_PadLeft = 1; + descriptor.m_PadRight = 1; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 1; + descriptor.m_StrideX = 2; + descriptor.m_StrideY = 2; + descriptor.m_DilationX = 2; + descriptor.m_DilationY = 2; + descriptor.m_BiasEnabled = true; + + const armnn::TensorInfo inputInfo ({ 1, 5, 5, 1 }, armnn::DataType::Float32); + const armnn::TensorInfo outputInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32); + const armnn::TensorInfo weightsInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32, 0.0f, 0, true); + const armnn::TensorInfo biasesInfo ({ 1 }, armnn::DataType::Float32, 0.0f, 0, true); + + std::vector<std::vector<int32_t>> inputShape = {{ 1, 5, 5, 1 }, { 1, 3, 3, 1 }, { 1 }}; + std::vector<std::vector<int32_t>> outputShape = {{ 1, 3, 3, 1 }}; + + TosaSerializationBasicBlock* basicBlock = GetTosaMapping(nullptr, + LayerType::Convolution2d, + {&inputInfo, &weightsInfo, &biasesInfo}, + {&outputInfo}, + descriptor); + AssertTosaOneToOneMappingBasicBlock( + basicBlock, inputShape, outputShape, Op_CONV2D, Attribute_ConvAttribute, descriptor, LayerType::Convolution2d); +} + +TEST_CASE("GetTosaMappingFromLayer_Conv2dLayer") +{ + IRuntime::CreationOptions options; + IRuntimePtr runtime(IRuntime::Create(options)); + + // Builds up the structure of the network. + INetworkPtr net(INetwork::Create()); + + armnn::Convolution2dDescriptor descriptor; + descriptor.m_PadLeft = 1; + descriptor.m_PadRight = 1; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 1; + descriptor.m_StrideX = 2; + descriptor.m_StrideY = 2; + descriptor.m_DilationX = 2; + descriptor.m_DilationY = 2; + descriptor.m_BiasEnabled = true; + + const armnn::TensorInfo inputInfo ({ 1, 5, 5, 1 }, armnn::DataType::Float32); + const armnn::TensorInfo outputInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32); + const armnn::TensorInfo weightsInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32, 0.0f, 0, true); + const armnn::TensorInfo biasesInfo ({ 1 }, armnn::DataType::Float32, 0.0f, 0, true); + + std::vector<std::vector<int32_t>> inputShape = {{ 1, 5, 5, 1 }}; + std::vector<std::vector<int32_t>> outputShape = {{ 1, 3, 3, 1 }}; + + std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements()); + armnn::ConstTensor weights(weightsInfo, weightsData); + + std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements()); + armnn::ConstTensor biases(biasesInfo, biasesData); + + armnn::IConnectableLayer* const inputLayer = net->AddInputLayer(0, "input0"); + armnn::IConnectableLayer* const weightsLayer = net->AddConstantLayer(weights, "weights"); + armnn::IConnectableLayer* const biasesLayer = net->AddConstantLayer(biases, "biases"); + armnn::IConnectableLayer* const convLayer = net->AddConvolution2dLayer(descriptor, "conv2d"); + armnn::IConnectableLayer* const outputLayer = net->AddOutputLayer(0); + + inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0)); + weightsLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(1)); + biasesLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(2)); + convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); + + inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); + weightsLayer->GetOutputSlot(0).SetTensorInfo(weightsInfo); + biasesLayer->GetOutputSlot(0).SetTensorInfo(biasesInfo); + convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); + + TosaSerializationBasicBlock* basicBlock = GetTosaMappingFromLayer(PolymorphicDowncast<Layer*>(convLayer)); + AssertTosaOneToOneMappingBasicBlock( + basicBlock, inputShape, outputShape, Op_CONV2D, Attribute_ConvAttribute, descriptor, LayerType::Convolution2d); +} + TEST_CASE("GetTosaMapping_MaxPool2DLayer") { armnn::Pooling2dDescriptor descriptor; @@ -74,7 +196,7 @@ TEST_CASE("GetTosaMapping_MaxPool2DLayer") std::vector<std::vector<int32_t>> outputShape = {{ 1, 1, 3, 3 }}; TosaSerializationBasicBlock* basicBlock = - GetTosaMapping(LayerType::Pooling2d, {&inputTensorInfo}, {&outputTensorInfo}, descriptor, false); + GetTosaMapping(nullptr, LayerType::Pooling2d, {&inputTensorInfo}, {&outputTensorInfo}, descriptor); AssertTosaOneToOneMappingBasicBlock( basicBlock, inputShape, outputShape, Op_MAX_POOL2D, Attribute_PoolAttribute, descriptor, LayerType::Pooling2d); } @@ -114,7 +236,7 @@ TEST_CASE("GetTosaMappingFromLayer_MaxPool2DLayer") pool->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); TosaSerializationBasicBlock* basicBlock = - GetTosaMappingFromLayer(PolymorphicDowncast<Layer*>(pool), false); + GetTosaMappingFromLayer(PolymorphicDowncast<Layer*>(pool)); AssertTosaOneToOneMappingBasicBlock( basicBlock, inputShape, outputShape, Op_MAX_POOL2D, Attribute_PoolAttribute, descriptor, LayerType::Pooling2d); } @@ -138,7 +260,7 @@ TEST_CASE("GetTosaMapping_AvgPool2DLayer") std::vector<std::vector<int32_t>> outputShape = {{ 1, 1, 3, 3 }}; TosaSerializationBasicBlock* basicBlock = - GetTosaMapping(LayerType::Pooling2d, {&inputTensorInfo}, {&outputTensorInfo}, descriptor, false); + GetTosaMapping(nullptr, LayerType::Pooling2d, {&inputTensorInfo}, {&outputTensorInfo}, descriptor); AssertTosaOneToOneMappingBasicBlock(basicBlock, inputShape, outputShape, @@ -183,7 +305,7 @@ TEST_CASE("GetTosaMappingFromLayer_AvgPool2DLayer") pool->GetOutputSlot(0).SetTensorInfo(outputTensorInfo); TosaSerializationBasicBlock* basicBlock = - GetTosaMappingFromLayer(PolymorphicDowncast<Layer*>(pool), false); + GetTosaMappingFromLayer(PolymorphicDowncast<Layer*>(pool)); AssertTosaOneToOneMappingBasicBlock(basicBlock, inputShape, outputShape, @@ -196,7 +318,7 @@ TEST_CASE("GetTosaMappingFromLayer_AvgPool2DLayer") TEST_CASE("GetTosaMapping_Unimplemented") { TosaSerializationBasicBlock* basicBlock = - GetTosaMapping(LayerType::UnidirectionalSequenceLstm, {}, {}, BaseDescriptor(), false); + GetTosaMapping(nullptr, LayerType::UnidirectionalSequenceLstm, {}, {}, BaseDescriptor()); CHECK(basicBlock->GetName() == ""); CHECK(basicBlock->GetTensors().size() == 0); diff --git a/src/backends/tosaCommon/test/TosaTestUtils.hpp b/src/backends/tosaCommon/test/TosaTestUtils.hpp index a362bde10d..dd63c0efdf 100644 --- a/src/backends/tosaCommon/test/TosaTestUtils.hpp +++ b/src/backends/tosaCommon/test/TosaTestUtils.hpp @@ -21,6 +21,24 @@ inline void VerifyTosaAttributeFromDescriptor(const BaseDescriptor& descriptor, { switch (type) { + case LayerType::Convolution2d: + { + auto conv2dDesc = PolymorphicDowncast<const Convolution2dDescriptor*>(&descriptor); + std::vector<int> pad = {static_cast<int>(conv2dDesc->m_PadTop), + static_cast<int>(conv2dDesc->m_PadBottom), + static_cast<int>(conv2dDesc->m_PadLeft), + static_cast<int>(conv2dDesc->m_PadRight)}; + + std::vector<int> dilation = {static_cast<int>(conv2dDesc->m_DilationY), + static_cast<int>(conv2dDesc->m_DilationX)}; + std::vector<int> stride = {static_cast<int>(conv2dDesc->m_StrideY), + static_cast<int>(conv2dDesc->m_StrideX)}; + TosaConvAttribute convAttribute(attribute); + CHECK(pad == convAttribute.pad()); + CHECK(dilation == convAttribute.dilation()); + CHECK(stride == convAttribute.stride()); + break; + } case LayerType::Pooling2d: { auto poolDesc = PolymorphicDowncast<const Pooling2dDescriptor*>(&descriptor); @@ -80,6 +98,7 @@ inline void VerifyTosaAttributeFromDescriptor(const BaseDescriptor& descriptor, CHECK(pad == poolAttribute.pad()); CHECK(kernel == poolAttribute.kernel()); CHECK(stride == poolAttribute.stride()); + break; } default: break; @@ -97,18 +116,30 @@ inline void AssertTosaOneToOneMappingBasicBlock(TosaSerializationBasicBlock* bas DType dataType = DType_FP32) { uint32_t numInputs = static_cast<uint32_t>(inputShape.size()); + uint32_t numInputTensors = static_cast<uint32_t>(inputShape.size()); uint32_t numOutputs = static_cast<uint32_t>(outputShape.size()); std::string operatorString = TosaOpToString(tosaOp); + // The number of tensors in the block can be different if there are constant layers, as they are created separately. + if(type == LayerType::Convolution2d) + { + numInputTensors = 2; + auto conv2dDesc = PolymorphicDowncast<const Convolution2dDescriptor*>(&descriptor); + if(conv2dDesc->m_BiasEnabled) + { + numInputTensors = 3; + } + } + std::string blockStr = operatorString + "_block_"; CHECK(basicBlock->GetName().find(blockStr) != std::string::npos); - CHECK(basicBlock->GetInputs().size() == numInputs); + CHECK(basicBlock->GetInputs().size() == numInputTensors); CHECK(basicBlock->GetOutputs().size() == numOutputs); CHECK(basicBlock->GetOperators().size() == 1); CHECK(basicBlock->GetTensors().size() == (numInputs + numOutputs)); TosaSerializationOperator* op = basicBlock->GetOperators().at(0); - CHECK(op->GetInputTensorNames().size() == numInputs); + CHECK(op->GetInputTensorNames().size() == numInputTensors); CHECK(op->GetOutputTensorNames().size() == numOutputs); for (uint32_t i = 0; i < numInputs; i++) @@ -117,11 +148,11 @@ inline void AssertTosaOneToOneMappingBasicBlock(TosaSerializationBasicBlock* bas std::basic_string<char> operatorInputName = op->GetInputTensorNames()[i]; std::basic_string<char> tensorName = basicBlock->GetTensors()[i]->GetName(); - std::string opStr = operatorString + "_input" + std::to_string(i) + "_"; + std::string opStr = "input" + std::to_string(i) + "_"; CHECK(blockInputName == operatorInputName); CHECK(tensorName == operatorInputName); - CHECK(blockInputName.find(opStr) != std::string::npos); + CHECK(blockInputName.find(opStr) != std::string::npos); } for (uint32_t i = 0; i < numOutputs; i++) @@ -130,7 +161,11 @@ inline void AssertTosaOneToOneMappingBasicBlock(TosaSerializationBasicBlock* bas std::basic_string<char> operatorOutputName = op->GetOutputTensorNames()[i]; std::basic_string<char> tensorName = basicBlock->GetTensors()[numInputs + i]->GetName(); - std::string opStr = operatorString + "_output" + std::to_string(i) + "_"; + std::string opStr = "output" + std::to_string(i) + "_"; + if (tosaOp == Op_CONST) + { + opStr = "constant_"; + } CHECK(blockOutputName == operatorOutputName); CHECK(tensorName == operatorOutputName); @@ -152,8 +187,12 @@ inline void AssertTosaOneToOneMappingBasicBlock(TosaSerializationBasicBlock* bas { TosaSerializationTensor* tensor = basicBlock->GetTensors()[i + inputShape.size()]; CHECK(tensor->GetDtype() == dataType); - CHECK(tensor->GetData().size() == 0); CHECK(tensor->GetShape() == outputShape[static_cast<unsigned long int>(i)]); + if (tosaOp != Op_CONST) + { + // Const tensors contain data. + CHECK(tensor->GetData().size() == 0); + } } VerifyTosaAttributeFromDescriptor(descriptor, |