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//
// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn_delegate.hpp>
#include <armnn/ArmNN.hpp>
#include <armnn/BackendHelper.hpp>
#include <armnn/utility/Assert.hpp>
#include <armnn/utility/NumericCast.hpp>
#include <armnnUtils/Permute.hpp>
#include <armnnUtils/TensorUtils.hpp>
#include <tensorflow/lite/builtin_ops.h>
#include <tensorflow/lite/c/builtin_op_data.h>
#include <tensorflow/lite/c/common.h>
#include <tensorflow/lite/minimal_logging.h>
#include <tensorflow/lite/kernels/kernel_util.h>
#include <numeric>
namespace
{
uint32_t NonNegative(int32_t value, int nodeIndex)
{
if (value < 0)
{
throw armnn::Exception(
"TfLiteArmnnDelegate: Non-negative value in node " + std::to_string(static_cast<int>(nodeIndex)));
}
else
{
return static_cast<uint32_t>(value);
}
}
void ExpandTensorRankToEqual(armnn::TensorInfo& inputInfo0,
armnn::TensorInfo& inputInfo1)
{
unsigned int inputDimensions0 = inputInfo0.GetNumDimensions();
unsigned int inputDimensions1 = inputInfo1.GetNumDimensions();
if (inputDimensions0 == inputDimensions1)
{
return;
}
unsigned int biggerInputDimensions = std::max(inputDimensions0, inputDimensions1);
bool input0IsSmaller = inputDimensions0 < inputDimensions1;
armnn::TensorInfo& smallInfo = input0IsSmaller ? inputInfo0 : inputInfo1;
const armnn::TensorShape& newShape = armnnUtils::ExpandDimsToRank(smallInfo.GetShape(), biggerInputDimensions);
smallInfo.SetShape(newShape);
}
void CalcPadding(uint32_t inputSize,
uint32_t filterSize,
uint32_t stride,
uint32_t dilation,
uint32_t& paddingFront,
uint32_t& paddingBack,
TfLitePadding padding)
{
paddingFront = 0;
paddingBack = 0;
if (padding == kTfLitePaddingSame)
{
uint32_t outputSize = (inputSize + stride - 1) / stride;
uint32_t dilatedSize = filterSize + (dilation - 1) * (filterSize - 1);
uint32_t temp = (outputSize - 1) * stride + dilatedSize;
if (temp > inputSize)
{
paddingFront = (temp - inputSize) / 2;
paddingBack = (temp - inputSize) - paddingFront;
}
}
}
// Function that calculates explicit padding when the output shape is known.
// At the moment the output is only given as an input parameter in Transpose Convolution,
// not in Convolution and Depthwise Convolution
void CalcPadding(uint32_t inputSize,
uint32_t filterSize,
uint32_t stride,
uint32_t dilation,
uint32_t& paddingFront,
uint32_t& paddingBack,
TfLitePadding padding,
uint32_t outputSize)
{
armnn::IgnoreUnused(dilation);
paddingFront = 0;
paddingBack = 0;
if (padding == kTfLitePaddingSame)
{
uint32_t totalPadding = (inputSize - 1) * stride + filterSize - outputSize;
paddingFront = totalPadding / 2;
paddingBack = totalPadding - paddingFront;
}
}
unsigned int ComputeWrappedIndex(int index, unsigned int numDimensions)
{
int numDims = armnn::numeric_cast<int>(numDimensions);
int wrappedIndex = index < 0 ? numDims + index : index;
if (wrappedIndex < 0 || wrappedIndex >= numDims)
{
throw armnn::ParseException("Unable to compute wrapped index");
}
return static_cast<unsigned int>(wrappedIndex);
};
bool AreAllSigned32(const armnn::TensorInfo& inputInfo1,
const armnn::TensorInfo& inputInfo2,
const armnn::TensorInfo& outputInfo)
{
return (armnn::DataType::Signed32 == inputInfo1.GetDataType()) &&
(armnn::DataType::Signed32 == inputInfo2.GetDataType()) &&
(armnn::DataType::Signed32 == outputInfo.GetDataType());
}
void UpdateConstantTensorOutputs(const armnn::TensorInfo& inputInfo, armnn::TensorInfo& outputInfo)
{
// If input tensor info is constant and output tensor info shape is not specified
// set the output shape from input shape
if (inputInfo.IsConstant() && outputInfo.GetShape().GetDimensionality() == armnn::Dimensionality::NotSpecified)
{
outputInfo.SetShape(inputInfo.GetShape());
}
}
void SetupConcatViewOrigin(const armnn::TensorInfo& inputTensorInfo,
armnn::OriginsDescriptor& concatDescriptor,
const unsigned int concatAxis,
unsigned int inputIndex,
unsigned int& mergeDimOrigin)
{
const uint32_t inputRank = concatDescriptor.GetNumDimensions();
// double check dimensions of the tensors
if (inputTensorInfo.GetNumDimensions() != inputRank)
{
throw armnn::ParseException("The number of dimensions for input tensors "
"of the concatenation operator should be: " + std::to_string(inputRank));
}
for (unsigned int j = 0; j < concatAxis; ++j)
{
concatDescriptor.SetViewOriginCoord(inputIndex, j, 0);
}
concatDescriptor.SetViewOriginCoord(inputIndex, concatAxis, mergeDimOrigin);
mergeDimOrigin += inputTensorInfo.GetShape()[concatAxis];
for (unsigned int j = concatAxis + 1; j < inputRank; ++j)
{
concatDescriptor.SetViewOriginCoord(inputIndex, j, 0);
}
}
TfLiteStatus CreateOutputTensorShape(const armnn::TensorInfo& inputTensorInfo,
const std::vector<int32_t>& targetShape,
armnn::ReshapeDescriptor& reshapeDesc)
{
std::vector<unsigned int> outputDims(targetShape.begin(), targetShape.end());
const auto stretchDim = std::find(targetShape.begin(), targetShape.end(), -1);
if (stretchDim != targetShape.end())
{
if (std::find(std::next(stretchDim), targetShape.end(), -1) != targetShape.end())
{
// Return kTfLiteError and log the error after returning
return kTfLiteError;
}
auto targetNumElements =
armnn::numeric_cast<unsigned int>(
std::accumulate(targetShape.begin(), targetShape.end(), -1, std::multiplies<int32_t>()));
auto stretchIndex = static_cast<size_t>(std::distance(targetShape.begin(), stretchDim));
outputDims[stretchIndex] = inputTensorInfo.GetNumElements() / targetNumElements;
}
armnn::TensorShape outputShape = armnn::TensorShape(static_cast<unsigned int>(outputDims.size()),
outputDims.data());
reshapeDesc.m_TargetShape = outputShape;
return kTfLiteOk;
}
armnn::TensorInfo OutputShapeOfSqueeze(std::vector<uint32_t> squeezeDims,
const armnn::TensorInfo& inputTensorInfo)
{
static const uint32_t dimensionSequence[] = { 0, 1, 2, 3 };
if (inputTensorInfo.GetNumDimensions() > 4)
{
std::stringstream ss;
ss << "Input tensor has unexpected number of dimensions:"
<< inputTensorInfo.GetNumDimensions()
<< " shape:" << inputTensorInfo.GetShape()
<< " "
<< CHECK_LOCATION().AsString();
throw armnn::ParseException(ss.str());
}
if (squeezeDims.empty())
{
squeezeDims.assign(dimensionSequence, dimensionSequence + inputTensorInfo.GetNumDimensions());
}
std::vector<uint32_t> outputDims;
for(unsigned int i = 0; i < inputTensorInfo.GetNumDimensions(); i++)
{
bool skipSqueeze = (std::find(squeezeDims.begin(), squeezeDims.end(), i) == squeezeDims.end());
auto currentDimension = inputTensorInfo.GetShape()[i];
if (skipSqueeze || currentDimension != 1)
{
outputDims.push_back(currentDimension);
}
}
if (outputDims.size() > 4)
{
std::stringstream ss;
ss << "Output tensor has unexpected number of dimensions:"
<< inputTensorInfo.GetNumDimensions()
<< " shape:" << inputTensorInfo.GetShape()
<< " "
<< CHECK_LOCATION().AsString();
throw armnn::ParseException(ss.str());
}
armnn::TensorShape outShape = armnn::TensorShape(static_cast<unsigned int>(outputDims.size()), outputDims.data());
// We need to preserve the tensor type and the quantization data as well
armnn::TensorInfo outTensorInfo = inputTensorInfo;
outTensorInfo.SetShape(outShape);
return outTensorInfo;
}
} // namespace anonymous
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