LogOperator.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 "LogOperator.hpp"
#include "TosaTableUtils.hpp"
 
TosaSerializationBasicBlock* ConvertLogOperator(const Layer* layer,
                                                const std::vector<const TensorInfo*>& inputs,
                                                const std::vector<const TensorInfo*>& outputs,
                                                const ElementwiseUnaryDescriptor* unaryDescriptor)
{
    if (unaryDescriptor->m_Operation != UnaryOperation::Log)
    {
        throw armnn::Exception("ConvertLogOperator: Unsupported elementwise unary operation in descriptor.");
    }
 
    std::string inputName = std::string("input_");
    std::string outputName = std::string("output0_");
    std::string blockName  = std::string("Op_LOG_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)
    {
        inputName = GenerateUniqueInputName(layer->GetInputSlot(0));
        outputName = GenerateUniqueOutputName(*layer);
    }
 
    std::vector<TosaSerializationTensor*> tensors;
    std::vector<TosaSerializationOperator*> operators;
 
    float input_scale = inputs[0]->GetQuantizationScale();
    float output_scale = outputs[0]->GetQuantizationScale();
    int32_t input_zp = inputs[0]->GetQuantizationOffset();
    int32_t output_zp = outputs[0]->GetQuantizationOffset();
    DataType inputDType = inputs[0]->GetDataType();
 
    if (inputDType == DataType::QAsymmS8 ||
        inputDType == DataType::QSymmS8)
    {
        const float output_min = static_cast<float>(-128 - output_zp) * output_scale;
 
        auto log_func = [&](float x) -> float
        {
            if (x <= 0.0f) 
            {
                return output_min;
            }
            return std::log(x);
        };
 
        TosaTableAttribute attribute(
            getTosaConst8bitTable(input_scale, input_zp, output_scale, output_zp, log_func));
        operators.push_back(new TosaSerializationOperator(tosa::Op_TABLE,
                                                          Attribute_TableAttribute,
                                                          &attribute,
                                                          {inputName},
                                                          {outputName}));
    }
    else if (inputDType == DataType::QSymmS16)
    {
        throw Exception("ConvertLogOperator() unsupported int 16 not implemented yet.");
        // The following generates the table, tosa attribute and operator for int16 log.
        // However, running the int16 LOG EndToEnd test causes incorrect output values.
        // At the time of writing the LOG operator there is no requirment for int16 support.
        // Points to enable int16 in the future:
        //     - TOSA specifies LOG int16 input must have int32 output
        //     - We potentially need a rescale after the int32 LOG output to convert back to int16.
        /*
        const float output_min = (-32768 - output_zp) * static_cast<float>(output_scale);
 
        auto log_func = [&](float x) -> float {
        if (x <= 0.0f) {
            return output_min;
        }
        return std::log(x);
        };
 
        TosaTableAttribute attribute(
            getTosaConst16bitTable<float>(input_scale, input_zp, output_scale, output_zp, log_func));
        operators.push_back(new TosaSerializationOperator(tosa::Op_TABLE,
                                                          Attribute_TableAttribute,
                                                          &attribute,
                                                          {inputName},
                                                          {outputName}));
        */
    }
    else if (inputDType == DataType::Signed32 ||
             inputDType == DataType::Signed64)
    {
        throw Exception(
            "ConvertLogOperator() unsupported int 32. Only int 8 and int 16 quantized types are supported.");
    }
    // Floating point LOG operator
    else
    {
        operators.push_back(new TosaSerializationOperator(tosa::Op_LOG,
                                                          Attribute_NONE,
                                                          nullptr,
                                                          {inputName},
                                                          {outputName}));
    }
 
    // Only add input tensor if connected layer is an input layer.
    // As intermediate or constant tensors will be created separately.
    // There also can't be duplicate tensor.
    if(inputName.find("input_") != std::string::npos)
    {
        std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape());
        DType inputDType0 = ArmNNToDType(inputDType);
        tensors.push_back(new TosaSerializationTensor(inputName, inputShape0, inputDType0, {}));
    }
 
    std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape());
 
    // Re-enable below line for int16 LOG support which requires int32 output in TOSA and remove second line.
    // DType outputDType0 =
    //     (inputDType == DataType::QSymmS16) ? DType::DType_INT32 : ArmNNToDType(outputs[0]->GetDataType());
    DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType());
 
    tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {}));
 
    // 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
                                           {inputName}, // inputs
                                           {outputName}); // outputs
}