diff options
Diffstat (limited to 'src/backends/tosaCommon')
8 files changed, 324 insertions, 7 deletions
diff --git a/src/backends/tosaCommon/TosaMappings.cpp b/src/backends/tosaCommon/TosaMappings.cpp index 15629ffab0..7ecf7266ac 100644 --- a/src/backends/tosaCommon/TosaMappings.cpp +++ b/src/backends/tosaCommon/TosaMappings.cpp @@ -67,6 +67,11 @@ TosaSerializationBasicBlock* GetTosaMapping(const Layer* layer, auto sliceDesc = PolymorphicDowncast<const SliceDescriptor*>(&descriptor); return ConvertSliceToTosaOperator(layer, inputs, outputs, sliceDesc); } + case LayerType::TransposeConvolution2d: + { + auto transposeConv2dDesc = PolymorphicDowncast<const TransposeConvolution2dDescriptor*>(&descriptor); + return ConvertTransposeConv2dToTosaOperator(layer, inputs, outputs, transposeConv2dDesc); + } default: { return CreateEmptyTosaSerializationBasicBlock(); diff --git a/src/backends/tosaCommon/operatorMappings/CMakeLists.txt b/src/backends/tosaCommon/operatorMappings/CMakeLists.txt index cb1d68e625..90c1a4f958 100644 --- a/src/backends/tosaCommon/operatorMappings/CMakeLists.txt +++ b/src/backends/tosaCommon/operatorMappings/CMakeLists.txt @@ -19,6 +19,8 @@ list(APPEND armnnTosaBackendOperators_sources SliceOperator.hpp SliceOperator.cpp TosaOperatorUtils.hpp + TransposeConv2dOperator.hpp + TransposeConv2dOperator.cpp ) add_library(armnnTosaBackendOperators OBJECT ${armnnTosaBackendOperators_sources}) diff --git a/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp b/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp index a3597f0461..1a9d6be9c0 100644 --- a/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp +++ b/src/backends/tosaCommon/operatorMappings/TosaCommonOperators.hpp @@ -11,4 +11,5 @@ #include "AvgPool2DIgnoreValueOperator.hpp" #include "Pooling2DOperator.hpp" #include "ReshapeOperator.hpp" -#include "SliceOperator.hpp"
\ No newline at end of file +#include "SliceOperator.hpp" +#include "TransposeConv2dOperator.hpp"
\ No newline at end of file diff --git a/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp b/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp index 288966badd..be2f53e413 100644 --- a/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp +++ b/src/backends/tosaCommon/operatorMappings/TosaOperatorUtils.hpp @@ -75,6 +75,23 @@ inline std::string GenerateUniqueName(const Layer& layer, uint32_t layerSlot) } } +// Function that generates unique output name using the layer type, input slot and layer guid. +inline std::string GenerateUniqueOutputName(const Layer& layer, uint32_t layerSlot) +{ + Layer& connectedLayer = layer.GetOutputSlot().GetConnection(0)->GetOwningLayer(); + + // Get the layer connected to the output slot, if output use that layer and id, + // otherwise use current layer and id. + if(connectedLayer.GetType() == LayerType::Output) + { + return GenerateUniqueName(connectedLayer, layerSlot); + } + else + { + return GenerateUniqueName(layer, layerSlot); + } +} + // 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() diff --git a/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp b/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp new file mode 100644 index 0000000000..a0d58e2fa8 --- /dev/null +++ b/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp @@ -0,0 +1,161 @@ +// +// Copyright © 2022 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "TransposeConv2dOperator.hpp" + +#include "layers/TransposeConvolution2dLayer.hpp" + +TosaSerializationBasicBlock* ConvertTransposeConv2dToTosaOperator(const Layer* layer, + const std::vector<const TensorInfo*>& inputs, + const std::vector<const TensorInfo*>& outputs, + const TransposeConvolution2dDescriptor* descriptor) +{ + std::string input0Name = std::string("input0_"); + std::string input1Name = std::string("constant_") + GetUniqueTosaMappingID(); + std::string input2Name = std::string("constant_") + GetUniqueTosaMappingID(); + std::string outputName = std::string("output0_"); + std::string blockName = std::string("Op_TRANSPOSE_CONV2D_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) + { + Layer& connectedInputLayer = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer(); + input0Name = GenerateUniqueName(connectedInputLayer, 0); + + outputName = GenerateUniqueOutputName(*layer, 0); + } + + std::vector<TosaSerializationTensor*> tensors; + std::vector<TosaSerializationOperator*> operators; + + // Setup input tensor + // Only add tensor if connected layer is an input layer. + // As intermediate or constant tensors will be created separately. + // There also can't be duplicate tensors. + if(input0Name.find("input0_") != std::string::npos) + { + std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape()); + DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType()); + + tensors.push_back(new TosaSerializationTensor(input0Name, inputShape0, inputDType0, {})); + } + + // Setup weights tensor, constant data will get copied during SetConstantTensorData + operators.push_back(new TosaSerializationOperator(Op_CONST, Attribute_NONE, nullptr, {}, {input1Name})); + + // During validation the TensorInfo can be retrieved from the inputs. + // During execution, it is only available through the layer so use m_Weight. + if(layer == nullptr) + { + std::vector<int32_t> inputShape1 = GetTosaTensorShape(inputs[1]->GetShape()); + DType inputDType1 = ArmNNToDType(inputs[1]->GetDataType()); + + tensors.push_back(new TosaSerializationTensor(input1Name, inputShape1, inputDType1, {})); + } + else + { + auto transposeConv2dLayer = PolymorphicDowncast<const TransposeConvolution2dLayer*>(layer); + + std::vector<int32_t> inputShape1 = GetTosaTensorShape( + transposeConv2dLayer->m_Weight->GetTensorInfo().GetShape()); + DType inputDType1 = ArmNNToDType(transposeConv2dLayer->m_Weight->GetTensorInfo().GetDataType()); + + std::vector<uint8_t> uint8Data = ConvertConstantTensorDataToBuffer(transposeConv2dLayer->m_Weight); + tensors.push_back(new TosaSerializationTensor(input1Name, inputShape1, inputDType1, uint8Data)); + } + + // Setup bias operator and tensor, constant data will get copied during SetConstantTensorData + operators.push_back(new TosaSerializationOperator(Op_CONST, Attribute_NONE, nullptr, {}, {input2Name})); + + // During validation the TensorInfo can be retrieved from the inputs. + // During execution, it is only available through the layer so use m_Bias. + if(layer == nullptr && descriptor->m_BiasEnabled) + { + std::vector<int32_t> inputShape2 = GetTosaTensorShape(inputs[2]->GetShape()); + DType inputDType2 = ArmNNToDType(inputs[2]->GetDataType()); + + tensors.push_back(new TosaSerializationTensor(input2Name, inputShape2, inputDType2, {})); + } + else if(descriptor->m_BiasEnabled) + { + auto transposeConv2dLayer = PolymorphicDowncast<const TransposeConvolution2dLayer*>(layer); + + std::vector<int32_t> inputShape2 = GetTosaTensorShape( + transposeConv2dLayer->m_Bias->GetTensorInfo().GetShape()); + DType inputDType2 = ArmNNToDType(transposeConv2dLayer->m_Bias->GetTensorInfo().GetDataType()); + + std::vector<uint8_t> uint8Data = ConvertConstantTensorDataToBuffer(transposeConv2dLayer->m_Bias); + tensors.push_back(new TosaSerializationTensor(input2Name, inputShape2, inputDType2, uint8Data)); + } + else + { + // If bias is disabled, create a constant bias tensor of 0's as three inputs are required. + // The size of the bias must match the channels dimension, so get the correct index. + unsigned int index = (descriptor->m_DataLayout == DataLayout::NHWC) ? + outputs[0]->GetShape()[3] : outputs[0]->GetShape()[1]; + + std::vector<uint8_t> uint8Data; + std::vector<float> data(outputs[0]->GetShape()[index], 0.0f); + + TosaSerializationHandler::ConvertF32toU8(data, uint8Data); + + tensors.push_back(new TosaSerializationTensor(input2Name, + {static_cast<int32_t>(outputs[0]->GetShape()[index])}, + DType_FP32, + uint8Data)); + } + + // 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 TRANSPOSE_CONV2D operator + // The TOSA Reference Model pads the output shape, so it is added to output shape. + // In Arm NN we pad the input shape, so it is taken away. + // To offset this the negative padding value can be used. + std::vector<int> pad = {-static_cast<int>(descriptor->m_PadTop), + -static_cast<int>(descriptor->m_PadBottom), + -static_cast<int>(descriptor->m_PadLeft), + -static_cast<int>(descriptor->m_PadRight)}; + std::vector<int> stride = {static_cast<int>(descriptor->m_StrideY), + static_cast<int>(descriptor->m_StrideX)}; + + std::vector<int> outputShape; + // If available use shape in descriptor otherwise use output shape. + if (descriptor->m_OutputShape.size() == 4) + { + for (uint32_t i = 0; i < descriptor->m_OutputShape.size(); ++i) + { + outputShape.push_back(static_cast<int>(descriptor->m_OutputShape[i])); + } + } + else + { + for (uint32_t i = 0; i < outputs[0]->GetNumDimensions(); ++i) + { + outputShape.push_back(static_cast<int>(outputs[0]->GetShape()[i])); + } + } + + TosaTransposeConvAttribute attribute(pad, stride, outputShape, 0, 0, ArmNNToDType(inputs[0]->GetDataType())); + + auto* op = new TosaSerializationOperator(Op_TRANSPOSE_CONV2D, + Attribute_TransposeConvAttribute, + &attribute, + {input0Name, input1Name, input2Name}, + {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 + {input0Name, input1Name, input2Name}, // inputs + {outputName}); // outputs +}
\ No newline at end of file diff --git a/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.hpp b/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.hpp new file mode 100644 index 0000000000..eb911a1195 --- /dev/null +++ b/src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.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* ConvertTransposeConv2dToTosaOperator(const Layer* layer, + const std::vector<const TensorInfo*>& inputs, + const std::vector<const TensorInfo*>& outputs, + const TransposeConvolution2dDescriptor* descriptor); diff --git a/src/backends/tosaCommon/test/OneToOneMappingTests.cpp b/src/backends/tosaCommon/test/OneToOneMappingTests.cpp index 0d19a328d6..2b0c1e55c7 100644 --- a/src/backends/tosaCommon/test/OneToOneMappingTests.cpp +++ b/src/backends/tosaCommon/test/OneToOneMappingTests.cpp @@ -438,6 +438,108 @@ TEST_CASE("GetTosaMappingFromLayer_SliceLayer") } +TEST_CASE("GetTosaMapping_TransposeConv2dLayer") +{ + const TensorInfo inputInfo ({ 1, 7, 7, 1 }, DataType::Float32); + const TensorInfo outputInfo({ 1, 9, 9, 1 }, DataType::Float32); + const TensorInfo weightsInfo({ 1, 3, 3, 1 }, DataType::Float32, 0.0f, 0, true); + const TensorInfo biasesInfo ({ 1 }, DataType::Float32, 0.0f, 0, true); + + TransposeConvolution2dDescriptor descriptor; + descriptor.m_PadLeft = 1; + descriptor.m_PadRight = 1; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 1; + descriptor.m_StrideX = 1; + descriptor.m_StrideY = 1; + descriptor.m_BiasEnabled = true; + descriptor.m_DataLayout = DataLayout::NHWC; + + TosaSerializationBasicBlock* basicBlock = GetTosaMapping(nullptr, + LayerType::TransposeConvolution2d, + {&inputInfo, &weightsInfo, &biasesInfo}, + {&outputInfo}, + descriptor); + + CHECK(basicBlock->GetInputs().size() == 3); + CHECK(basicBlock->GetOutputs().size() == 1); + CHECK(basicBlock->GetOperators().size() == 3); + CHECK(basicBlock->GetTensors().size() == 4); + + CHECK(basicBlock->GetInputs()[0].find("input0_") != std::string::npos); + CHECK(basicBlock->GetInputs()[1].find("constant_") != std::string::npos); + CHECK(basicBlock->GetInputs()[2].find("constant_") != std::string::npos); + CHECK(basicBlock->GetOutputs()[0].find("output0_") != std::string::npos); + + VerifyTosaAttribute(descriptor, + basicBlock->GetOperators().at(2)->GetAttribute(), + {}, + {}, + LayerType::TransposeConvolution2d); +} + +TEST_CASE("GetTosaMappingFromLayer_TransposeConv2dLayer") +{ + IRuntime::CreationOptions options; + IRuntimePtr runtime(IRuntime::Create(options)); + + // Builds up the structure of the network. + INetworkPtr net(INetwork::Create()); + + const TensorInfo inputInfo ({ 1, 7, 7, 1 }, DataType::Float32); + const TensorInfo outputInfo({ 1, 9, 9, 1 }, DataType::Float32); + const TensorInfo weightsInfo({ 1, 3, 3, 1 }, DataType::Float32, 0.0f, 0, true); + const TensorInfo biasesInfo ({ 1 }, DataType::Float32, 0.0f, 0, true); + + std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements()); + ConstTensor weights(weightsInfo, weightsData); + + std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements()); + ConstTensor biases(biasesInfo, biasesData); + + TransposeConvolution2dDescriptor descriptor; + descriptor.m_PadLeft = 1; + descriptor.m_PadRight = 1; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 1; + descriptor.m_StrideX = 1; + descriptor.m_StrideY = 1; + descriptor.m_BiasEnabled = true; + descriptor.m_DataLayout = DataLayout::NHWC; + + IConnectableLayer* const inputLayer = net->AddInputLayer(0); + IConnectableLayer* const convLayer = + net->AddTransposeConvolution2dLayer(descriptor, + weights, + Optional<ConstTensor>(biases), + "transposeConvolution2d"); + IConnectableLayer* const outputLayer = net->AddOutputLayer(0); + + inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0)); + convLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0)); + + inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo); + convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo); + + TosaSerializationBasicBlock* basicBlock = GetTosaMappingFromLayer(PolymorphicDowncast<Layer*>(convLayer)); + + CHECK(basicBlock->GetInputs().size() == 3); + CHECK(basicBlock->GetOutputs().size() == 1); + CHECK(basicBlock->GetOperators().size() == 3); + CHECK(basicBlock->GetTensors().size() == 4); + + CHECK(basicBlock->GetInputs()[0].find("input0_") != std::string::npos); + CHECK(basicBlock->GetInputs()[1].find("constant_") != std::string::npos); + CHECK(basicBlock->GetInputs()[2].find("constant_") != std::string::npos); + CHECK(basicBlock->GetOutputs()[0].find("output0_") != std::string::npos); + + VerifyTosaAttribute(descriptor, + basicBlock->GetOperators().at(2)->GetAttribute(), + {}, + {}, + LayerType::TransposeConvolution2d); +} + TEST_CASE("GetTosaMapping_Unimplemented") { TosaSerializationBasicBlock* basicBlock = diff --git a/src/backends/tosaCommon/test/TosaTestUtils.hpp b/src/backends/tosaCommon/test/TosaTestUtils.hpp index 93b9e7d36f..140cb83983 100644 --- a/src/backends/tosaCommon/test/TosaTestUtils.hpp +++ b/src/backends/tosaCommon/test/TosaTestUtils.hpp @@ -144,6 +144,20 @@ inline void VerifyTosaAttribute(const BaseDescriptor& descriptor, break; } + case LayerType::TransposeConvolution2d: + { + auto transposeConv2dDesc = PolymorphicDowncast<const TransposeConvolution2dDescriptor*>(&descriptor); + std::vector<int> outPad = {-static_cast<int>(transposeConv2dDesc->m_PadTop), + -static_cast<int>(transposeConv2dDesc->m_PadBottom), + -static_cast<int>(transposeConv2dDesc->m_PadLeft), + -static_cast<int>(transposeConv2dDesc->m_PadRight)}; + std::vector<int> stride = {static_cast<int>(transposeConv2dDesc->m_StrideY), + static_cast<int>(transposeConv2dDesc->m_StrideX)}; + TosaTransposeConvAttribute transposeConvAttribute(attribute); + CHECK(outPad == transposeConvAttribute.out_pad()); + CHECK(stride == transposeConvAttribute.stride()); + break; + } default: break; } @@ -167,12 +181,7 @@ inline void AssertTosaOneToOneMappingBasicBlock(TosaSerializationBasicBlock* bas // 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; - } + numInputTensors = PolymorphicDowncast<const Convolution2dDescriptor*>(&descriptor)->m_BiasEnabled ? 3 : 2; } std::string blockStr = operatorString + "_block_"; |