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//
// Copyright © 2022-2024 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("input_");
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)
{
input0Name = GenerateUniqueInputName(layer->GetInputSlot(0));
outputName = GenerateUniqueOutputName(*layer);
}
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("input_") != 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) ? 3 : 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, false); // input_zp, weight_zp, local_bound
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
mainName, // region name
operators, // operators
tensors, // tensors
{input0Name, input1Name, input2Name}, // inputs
{outputName}); // outputs
}
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