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//
// Copyright © 2022-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "ConcatOperator.hpp"
TosaSerializationBasicBlock* ConvertConcatToTosaOperator(const Layer* layer,
const std::vector<const TensorInfo*>& inputs,
const std::vector<const TensorInfo*>& outputs,
const OriginsDescriptor* concatDescriptor)
{
auto numInputs = inputs.size();
std::vector<std::string> inputNames;
inputNames.reserve(numInputs);
std::string outputName = std::string("output0_");
std::string blockName = std::string("Op_CONCAT_block_") + GetUniqueTosaMappingID();
// Set input names for validation purposes only.
if (layer == nullptr)
{
for (uint32_t i = 0; i < numInputs; ++i)
{
inputNames.push_back("input_"+ std::to_string(i));
}
}
// 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 layers connected to the input slots and determine unique tensor names.
for (uint32_t i = 0; i < numInputs; ++i)
{
std::string inputName = GenerateUniqueInputName(layer->GetInputSlot(i));
inputNames.push_back(inputName);
}
// Determine unique output tensor name.
outputName = GenerateUniqueOutputName(*layer);
}
auto axis = static_cast<int32_t>(concatDescriptor->GetConcatAxis());
TosaAxisAttribute attribute(axis);
TosaSerializationOperator* op = new TosaSerializationOperator(Op_CONCAT,
Attribute_AxisAttribute,
&attribute,
inputNames,
{outputName});
std::vector<TosaSerializationTensor*> tensors;
tensors.reserve(numInputs + 1);
for (uint32_t i = 0; i < numInputs; ++i)
{
// Only add input tensors for validation or when the connected layer is an input layer.
// As there can't be duplicate tensors and intermediate or constant tensors are created separately.
if(inputNames[i].find("input") != std::string::npos)
{
std::vector<int32_t> inputShape = GetTosaTensorShape(inputs[i]->GetShape());
DType inputDType = ArmNNToDType(inputs[i]->GetDataType());
tensors.push_back(new TosaSerializationTensor(inputNames[i], inputShape, inputDType, {}));
}
}
std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape());
DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType());
TosaSerializationTensor* outputTensor0 = new TosaSerializationTensor(outputName, outputShape0, outputDType0, {});
tensors.push_back(outputTensor0);
// 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
{op}, // operators
tensors, // tensors
inputNames, // inputs
{outputName}); // outputs
}
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