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//
// Copyright © 2023-2024 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 "SplitOperator.hpp"
#include <backendsCommon/WorkloadUtils.hpp>
// This function is paraphrased from:
// tensorflow/compiler/mlir/tosa/transforms/legalize_common.cc from function convertSplitOp
TosaSerializationBasicBlock* ConvertSplitToTosaOperator(const Layer* layer,
const std::vector<const TensorInfo*>& inputs,
const std::vector<const TensorInfo*>& outputs,
const SplitterDescriptor* splitDescriptor)
{
ARMNN_THROW_INVALIDARG_MSG_IF_FALSE( inputs.size() == 1,
"ConvertSplitToTosaOperator: Split must have only one input" );
ARMNN_THROW_INVALIDARG_MSG_IF_FALSE( outputs.size() >= 1,
"ConvertSplitToTosaOperator: Split must have at least one output" );
if (!inputs[0]->GetShape().AreAllDimensionsSpecified())
{
throw armnn::Exception("ConvertSplitToTosaOperator: Dynamic input dimensions are unsupported.");
}
std::string inputName = std::string("input_");
std::vector<std::string> outputNames;
std::string blockName = std::string("Op_SPLIT_block_") + GetUniqueTosaMappingID();
unsigned int numSplit = splitDescriptor->GetNumViews();
// 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));
for (unsigned int i=0; i < numSplit; ++i)
{
// Determine unique output(s) tensor name.
std::string outputName = GenerateUniqueOutputName(*layer, i);
outputNames.push_back(outputName);
}
}
else
{
for (unsigned int i=0; i < numSplit; ++i)
{
// Determine unique output(s) tensor name.
std::string outputName = "output" + std::to_string(i) + "_";
outputNames.push_back(outputName);
}
}
// Configure input and output tensors
std::set<unsigned int> splitAxis = ComputeSplitAxis(*splitDescriptor, inputs[0]->GetShape());
if (splitAxis.size() != 1)
{
throw InvalidArgumentException("Cannot derive split axis from SplitterDescriptor");
}
uint32_t axis = *splitAxis.begin();
std::vector<TosaSerializationOperator*> ops;
std::vector<int32_t> beginVals(inputs[0]->GetNumDimensions(), 0);
for (unsigned int i = 0; i < numSplit; ++i)
{
std::vector<int32_t> sizeVals = GetTosaTensorShape(outputs[i]->GetShape());
TosaSliceAttribute attribute(beginVals, sizeVals);
auto* op = new TosaSerializationOperator(Op_SLICE,
Attribute_SliceAttribute,
&attribute,
{inputName},
{outputNames[i]});
ops.push_back(op);
// Update the axis begin value for the next split operation, to be the correct size axis value.
beginVals[axis] += sizeVals[axis];
}
std::vector<TosaSerializationTensor*> tensors;
// Only add input tensors 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> inputShape = GetTosaTensorShape(inputs[0]->GetShape());
DType inputDType = ArmNNToDType(inputs[0]->GetDataType());
tensors.push_back(new TosaSerializationTensor(inputName, inputShape, inputDType, {}));
}
DType outputDType = ArmNNToDType(outputs[0]->GetDataType());
for (unsigned int i = 0; i < numSplit; ++i)
{
std::vector<int32_t> outputShape = GetTosaTensorShape(outputs[i]->GetShape());
tensors.push_back(new TosaSerializationTensor(outputNames[i], outputShape, outputDType, {}));
}
// 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
ops, // operators
tensors, // tensors
{inputName}, // inputs
outputNames); // outputs
}
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