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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn/backends/ITensorHandle.hpp>
#include <armnn/backends/TensorHandle.hpp>
#include <armnn/Tensor.hpp>
#include <armnn/utility/PolymorphicDowncast.hpp>
#include <armnnUtils/Permute.hpp>
#include <Half.hpp>
#include <Profiling.hpp>
namespace armnn
{
namespace
{
template <typename ArrayType, typename Arg>
void AssignValues(unsigned int num, unsigned int& idx, const ArrayType& array, Arg& arg)
{
if (idx >= num)
{
return;
}
arg = array[(num - 1) - idx];
idx++;
}
template <typename T, typename ArrayType, typename... Args>
void AssignValues(unsigned int num, unsigned int idx, const ArrayType& array, T& assignee, Args&... args)
{
AssignValues(num, idx, array, assignee);
AssignValues(num, idx, array, args...);
}
} // anonymous namespace
template <typename CopyFunc>
void CopyTensorContentsGeneric(const ITensorHandle* srcTensor, ITensorHandle* dstTensor, CopyFunc copy)
{
// For ease of understanding, names are assigned to the dimensions
// of the tensor as if NHWC, however this routine works with any 5D tensor
static_assert(MaxNumOfTensorDimensions == 5, "Please update CopyTensorContents");
TensorShape srcStrides = srcTensor->GetStrides();
const TensorShape& srcShape = srcTensor->GetShape();
const auto srcSize = srcTensor->GetStrides()[0] * srcShape[0];
IgnoreUnused(srcSize); // Only used for asserts
TensorShape dstStrides = dstTensor->GetStrides();
const TensorShape& dstShape = dstTensor->GetShape();
const auto dstSize = dstTensor->GetStrides()[0] * dstShape[0];
IgnoreUnused(dstSize); // Only used for asserts
size_t srcDepth = 1;
size_t srcBatches = 1;
size_t srcHeight = 1;
size_t srcWidth = 1;
size_t srcChannels = 1;
AssignValues(srcShape.GetNumDimensions(),
0,
srcShape,
srcChannels,
srcWidth,
srcHeight,
srcBatches,
srcDepth);
size_t srcDepthStride = 0;
size_t srcBatchStride = 0;
size_t srcHeightStride = 0;
size_t srcWidthStride = 0;
size_t srcChannelStride = 0;
AssignValues(srcStrides.GetNumDimensions(),
0,
srcStrides,
srcChannelStride,
srcWidthStride,
srcHeightStride,
srcBatchStride,
srcDepthStride);
size_t dstDepth = 1;
size_t dstBatches = 1;
size_t dstHeight = 1;
size_t dstWidth = 1;
size_t dstChannels = 1;
AssignValues(dstShape.GetNumDimensions(),
0,
dstShape,
dstChannels,
dstWidth,
dstHeight,
dstBatches,
dstDepth);
size_t dstDepthStride = 0;
size_t dstBatchStride = 0;
size_t dstHeightStride = 0;
size_t dstWidthStride = 0;
size_t dstChannelStride = 0;
AssignValues(dstStrides.GetNumDimensions(),
0,
dstStrides,
dstChannelStride,
dstWidthStride,
dstHeightStride,
dstBatchStride,
dstDepthStride);
const unsigned char* srcDataStart;
unsigned char* dstDataStart;
{
ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "Synchronize buffers");
srcDataStart = static_cast<const uint8_t*>(srcTensor->Map());
dstDataStart = static_cast<uint8_t*>(dstTensor->Map());
}
size_t copyLength = std::min(srcChannels * srcChannelStride, dstChannels * dstChannelStride);
size_t copyWidth = std::min(srcWidth, dstWidth);
size_t copyHeight = std::min(srcHeight, dstHeight);
size_t copyBatches = std::min(srcBatches, dstBatches);
size_t copyDepth = std::min(srcDepth, dstDepth);
// Coalesce inner dimensions where possible
// to reduce overheard calling copy() and to
// allow for memory bandwidth optimisations
if (copyLength == srcWidthStride &&
copyLength == dstWidthStride)
{
// There is no special padding between rows,
// and sizes are compatible, so copy whole rows
copyLength *= copyWidth;
copyWidth = 1;
if (copyLength == srcHeightStride &&
copyLength == dstHeightStride)
{
// There is no special padding between batches
// and sizes are compatible so copy whole batches
copyLength *= copyHeight;
copyHeight = 1;
}
}
const unsigned char* srcData = srcDataStart;
unsigned char* dstData = dstDataStart;
for (unsigned int d = 0; d < copyDepth; ++d)
{
auto srcPtrDepth = srcData;
auto dstPtrDepth = dstData;
for (unsigned int b = 0; b < copyBatches; ++b)
{
auto srcPtrBatch = srcData;
auto dstPtrBatch = dstData;
for (unsigned int h = 0; h < copyHeight; ++h)
{
auto srcPtrChannel = srcData;
auto dstPtrChannel = dstData;
for (unsigned int w = 0; w < copyWidth; ++w)
{
ARMNN_ASSERT(srcData >= srcDataStart && srcData + copyLength <= srcDataStart + srcSize);
ARMNN_ASSERT(dstData >= dstDataStart && dstData + copyLength <= dstDataStart + dstSize);
copy(dstData, srcData, copyLength);
dstData += dstWidthStride;
srcData += srcWidthStride;
}
dstData += (static_cast<long>(dstHeightStride) - (dstData - dstPtrChannel));
srcData += (static_cast<long>(srcHeightStride) - (srcData - srcPtrChannel));
}
dstData += (static_cast<long>(dstBatchStride) - (dstData - dstPtrBatch));
srcData += (static_cast<long>(srcBatchStride) - (srcData - srcPtrBatch));
}
dstData += (static_cast<long>(dstDepthStride) - (dstData - dstPtrDepth));
srcData += (static_cast<long>(srcDepthStride) - (srcData - srcPtrDepth));
}
srcTensor->Unmap();
dstTensor->Unmap();
}
template <typename SrcTensorHandleType, typename DstTensorHandleType, typename DescriptorType>
void GatherTensorHandlePairs(const DescriptorType& descriptor,
std::vector<std::pair<SrcTensorHandleType*, DstTensorHandleType*>>& tensorHandlePairs)
{
const unsigned int numInputs = static_cast<unsigned int>(descriptor.m_Inputs.size());
tensorHandlePairs.reserve(numInputs);
for (unsigned int i = 0; i < numInputs; ++i)
{
SrcTensorHandleType* const srcTensorHandle =
PolymorphicDowncast<SrcTensorHandleType*>(descriptor.m_Inputs[i]);
DstTensorHandleType* const dstTensorHandle =
PolymorphicDowncast<DstTensorHandleType*>(descriptor.m_Outputs[i]);
tensorHandlePairs.emplace_back(srcTensorHandle, dstTensorHandle);
}
}
int32_t ConvertMaskToACLFormat(int32_t mask, int32_t numDim);
armnn::ConstTensor PermuteTensor(const ConstTensorHandle* tensor,
const PermutationVector& permutationVector,
void* permuteBuffer);
void ReshapeWeightsForAcl(TensorInfo& weightInfo, DataLayout dataLayout);
TensorInfo ConvertWeightTensorInfoFromArmnnToAcl(const TensorInfo& weightInfo, DataLayout dataLayout);
/// Weights for depthwise have a datalayout of [1,H,W,O] = [1,H,W,I*M]
/// This function coverts a TensorInfo from [1,H,W,I*M] to [1,I*M,H,W] (if NCHW) or keeps it at [1,H,W,I*M] (if NHWC)
/// as required by the compute library
/// Returns a tuple of converted weights tensor info and depth multiplier
std::tuple<TensorInfo, unsigned int> Convert1HWOTensorInfoToAcl(const TensorInfo& weightInfo,
const TensorInfo& inputInfo,
const DataLayout dataLayout);
armnn::ConstTensor ConvertWeightTensorFromArmnnToAcl(const ConstTensorHandle* weightTensor,
DataLayout dataLayout,
void* permuteBuffer);
/// Weights for depthwise have a datalayout of [1,H,W,O] = [1,H,W,I*M]
/// This function coverts a ConstCpuTensorHandle from [1,H,W,I*M] to [1,I*M,H,W] (if NCHW) or
/// keeps it at [1,H,W,I*M] (if NHWC) as required by the compute library
///
/// \param weightTensor - ConstTensorHandle of weights tensor
/// \param inputInfo - TensorInfo of input tensor
/// \param dataLayout - DataLayout of the input tensor
/// \param permuteBuffer - Pointer to memory with the size of tensor. Used for the permutation
/// \return tuple of transformed weights-ConstTensor and depthwise multiplier
std::tuple<ConstTensor, unsigned int> Convert1HWOTensorToAcl(const ConstTensorHandle* weightTensor,
const TensorInfo& inputInfo,
const DataLayout dataLayout,
void* permuteBuffer);
/// Converts a (weights) tensor from [1, H, W, I*M] = [1, H, W, O] to [M, I, H, W]
///
/// \param weightTensor - ConstTensorHandle of the weight tensor that should be converted
/// \param inputInfo - TensorInfo of the corresponding input tensor
/// \param dataLayout - DataLayout of the input tensor e.g. NHWC or NCHW
/// \param permuteBuffer - Memory location with the same size as the weight tensor to write converted data to
/// \return - A tuple of ConstTensor and unsigned int which is the converted weightTensor and the depthMultiplier
std::tuple<ConstTensor, unsigned int> Convert1HWOtoMIHW(const ConstTensorHandle* weightTensor,
const TensorInfo& inputInfo,
const DataLayout& dataLayout,
void* permuteBuffer);
} //namespace armnn
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