diff options
Diffstat (limited to 'src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp')
| -rw-r--r-- | src/backends/tosaCommon/operatorMappings/TransposeConv2dOperator.cpp | 161 |
1 files changed, 161 insertions, 0 deletions
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 |