Conv3dOperator.cpp

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//
// Copyright © 2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
//
// Copyright © 2020 The TensorFlow Authors. All Rights Reserved.
// SPDX-License-Identifier: Apache-2.0
//
 
#include "Conv3dOperator.hpp"
#include "TosaRescaleOperatorUtils.hpp"
#include <ResolveType.hpp>
 
// This function is paraphrased from:
// tensorflow/compiler/mlir/tosa/transforms/legalize_tfl.cc from function ConvertTFLConv3DOp
TosaSerializationBasicBlock* ConvertConv3dToTosaOperator(const Layer* layer,
                                                         const std::vector<const TensorInfo*>& inputs,
                                                         const std::vector<const TensorInfo*>& outputs,
                                                         const Convolution3dDescriptor* conv3dDescriptor)
{
    // TOSA currently only supports NDHWC input
    if (conv3dDescriptor->m_DataLayout != DataLayout::NDHWC)
    {
        throw InvalidArgumentException("Only NDHWC input is supported for Conv3D");
    }
 
    std::vector<std::string> inputNames;
    std::string outputName = std::string("output0_");
    std::string blockName  = std::string("Op_CONV3D_block_") + GetUniqueTosaMappingID();
 
    DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType());
    DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType());
 
    // Set input names for validation purposes only.
    if(layer == nullptr)
    {
        inputNames.emplace_back("input_0");
        inputNames.emplace_back("input_1");
        if(conv3dDescriptor->m_BiasEnabled)
        {
            inputNames.emplace_back("input_2");
        }
    }
    // 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.
    else
    {
        // Get the layer connected to the input slot and determine unique tensor names.
        for (uint32_t i = 0; i < inputs.size(); ++i)
        {
            std::string inputName = GenerateUniqueInputName(layer->GetInputSlot(i));
            inputNames.push_back(inputName);
        }
 
        // Determine unique output tensor name.
        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(inputNames[0].find("input_") != std::string::npos)
    {
        std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape());
        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(conv3dDescriptor->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}));
 
        // The size of the bias must match the channels dimension, so get the correct index for NDHWC input.
        unsigned int index = 4;
 
        const DType dType = (inputDType0 == DType_INT8) ? DType_INT32 : outputDType0;
        std::vector<float> data(outputs[0]->GetShape()[index], 0);
 
        std::vector<uint8_t> uint8Data;
        TosaSerializationHandler::ConvertF32toU8(data, uint8Data);
 
        tensors.push_back(new TosaSerializationTensor(constantName,
                                                      {static_cast<int32_t>(outputs[0]->GetShape()[index])},
                                                      dType,
                                                      uint8Data));
        inputNames.emplace_back(constantName);
    }
 
    // Setup Output Tensor
    std::vector<int32_t> outputShape0 = {GetTosaTensorShape(outputs[0]->GetShape())};
    std::string outputConv3dName;
    bool isInputInt8 = (inputDType0 == DType_INT8);
    if (isInputInt8)
    {
        outputConv3dName = std::string("intermediate0_") + GetUniqueTosaMappingID();
        tensors.push_back(new TosaSerializationTensor(outputConv3dName, outputShape0, DType_INT32, {}));
    }
    else
    {
        tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {}));
    }
 
    // Setup Transpose Output Tensor
    auto transposeInputShape = GetTosaTensorShape(inputs[1]->GetShape());
    std::vector<int32_t> transposeOutputShape = {transposeInputShape[4],
                                                 transposeInputShape[0],
                                                 transposeInputShape[1],
                                                 transposeInputShape[2],
                                                 transposeInputShape[3]};
 
    std::string transposeOutputName = std::string("intermediate1_") + GetUniqueTosaMappingID();
    tensors.push_back(new TosaSerializationTensor(transposeOutputName, transposeOutputShape, inputDType0, {}));
 
    // Connect the layer input to Transpose
    std::string transposeInputName = inputNames[1];
    std::string conv3dInput = inputNames[0];
    std::string conv3dWeight = transposeOutputName;
    std::string conv3dBias = inputNames[2];
 
    // Set up TRANSPOSE operator for weight
    // The weight shape of tflite conv3d is not NDHWC but DHWCN, so will need to transpose DHWCN to NDHWC
    std::vector<int> perm = {4, 0, 1, 2, 3};
    TosaTransposeAttribute transposeAttribute(perm);
    auto transpose_op = new TosaSerializationOperator(Op_TRANSPOSE,
                                                      Attribute_TransposeAttribute,
                                                      &transposeAttribute,
                                                      {transposeInputName},
                                                      {transposeOutputName});
    operators.push_back(transpose_op);
 
    // Set up CONV3D operator
    std::vector<int> pad = {static_cast<int>(conv3dDescriptor->m_PadFront),
                            static_cast<int>(conv3dDescriptor->m_PadBack),
                            static_cast<int>(conv3dDescriptor->m_PadTop),
                            static_cast<int>(conv3dDescriptor->m_PadBottom),
                            static_cast<int>(conv3dDescriptor->m_PadLeft),
                            static_cast<int>(conv3dDescriptor->m_PadRight)};
    std::vector<int> stride = {static_cast<int>(conv3dDescriptor->m_StrideZ),
                               static_cast<int>(conv3dDescriptor->m_StrideY),
                               static_cast<int>(conv3dDescriptor->m_StrideX)};
    std::vector<int> dilation = {static_cast<int>(conv3dDescriptor->m_DilationZ),
                                 static_cast<int>(conv3dDescriptor->m_DilationY),
                                 static_cast<int>(conv3dDescriptor->m_DilationX)};
 
    TosaConvAttribute attribute(pad, stride, dilation,
                                inputs[0]->GetQuantizationOffset(), // input_zp
                                inputs[1]->GetQuantizationOffset(), // weight_zp
                                false); // local_bound
 
    std::string& convOutStr = isInputInt8 ? outputConv3dName : outputName;
    auto* conv3d_op = new TosaSerializationOperator(Op_CONV3D,
                                                    Attribute_ConvAttribute,
                                                    &attribute,
                                                    {conv3dInput, conv3dWeight, conv3dBias},
                                                    {convOutStr});
    operators.push_back(conv3d_op);
 
    if (isInputInt8)
    {
        int32_t output_zp = outputs[0]->GetQuantizationOffset();
        double output_scale = outputs[0]->GetQuantizationScales()[0];
        double input_scale = inputs[0]->GetQuantizationScales()[0];
        const std::vector<float>& weight_scales = inputs[1]->GetQuantizationScales();
 
        TosaSerializationOperator* rescaleOp = nullptr;
        CreateRescaleTosaOperatorForWeights(outputConv3dName,
                                            outputName,
                                            0,
                                            output_zp,
                                            false,
                                            false,
                                            true,
                                            true,
                                            input_scale,
                                            output_scale,
                                            weight_scales,
                                            &rescaleOp);
        operators.push_back(rescaleOp);
        tensors.push_back(new TosaSerializationTensor(outputName,
                                                      outputShape0,
                                                      DType_INT8, {}));
    }
 
    return new TosaSerializationBasicBlock(blockName,     // name
                                           mainName,      // region name
                                           operators,     // operators
                                           tensors,       // tensors
                                           inputNames,    // inputs
                                           {outputName});
}