blob: 796797728eef05cdc257ddbffab31316e69b0743 [file] [log] [blame]
//
// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "AdditionOperator.hpp"
TosaSerializationBasicBlock* ConvertAdditionToTosaOperator(const std::vector<const TensorInfo*>& inputs,
const std::vector<const TensorInfo*>& outputs,
bool isMain)
{
// A helper function with static global variables ensures uniqueness
// for dynamically generating input, output and block names
std::string input0Name = std::string("Op_ADD_input0_") + GetUniqueTosaMappingID();
std::string input1Name = std::string("Op_ADD_input1_") + GetUniqueTosaMappingID();
std::string outputName = std::string("Op_ADD_output0_") + GetUniqueTosaMappingID();
std::string blockName = std::string("Op_ADD_block_") + GetUniqueTosaMappingID();
// If it's the first block, overwrite block name with main.
if (isMain)
{
blockName = std::string("main");
}
TosaSerializationOperator* op = new TosaSerializationOperator(Op_ADD,
Attribute_NONE,
nullptr,
{input0Name, input1Name},
{outputName});
std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape());
DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType());
std::vector<int32_t> inputShape1 = GetTosaTensorShape(inputs[1]->GetShape());
DType inputDType1 = ArmNNToDType(inputs[1]->GetDataType());
std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape());
DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType());
TosaSerializationTensor* inputTensor0 = new TosaSerializationTensor(input0Name, inputShape0, inputDType0, {});
TosaSerializationTensor* inputTensor1 = new TosaSerializationTensor(input1Name, inputShape1, inputDType1, {});
TosaSerializationTensor* outputTensor0 = 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
{op}, // operators
{inputTensor0, inputTensor1, outputTensor0}, // tensors
{input0Name, input1Name}, // inputs
{outputName}); // outputs
}