https://github.com/Microsoft/CNTK
Tip revision: e28e66abad9b51b4b56f7e0da730e215e549dacd authored by Emad Barsoum on 12 January 2018, 02:09:28 UTC
Merge branch 'swish' of https://github.com/lakshayg/CNTK into lakshayg-swish
Merge branch 'swish' of https://github.com/lakshayg/CNTK into lakshayg-swish
Tip revision: e28e66a
ConvolutionEngine.cpp
//
// Copyright (c) Microsoft. All rights reserved.
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
//
#include "stdafx.h"
#include "ConvolutionEngine.h"
#include "CuDnnFactories.h"
#include "Mkl2017DnnCommon.h"
namespace Microsoft { namespace MSR { namespace CNTK {
template <class ElemType>
void ConvolutionEngine<ElemType>::Forward(const Mat& in, const Mat& kernel, Mat& out, Mat& workspace)
{
const auto& g = *m_geometry;
assert(g.InputShape().GetNumElements() == in.GetNumRows());
assert(g.OutputShape().GetNumElements() == out.GetNumRows());
size_t batchSize = in.GetNumCols();
assert(batchSize == out.GetNumCols());
// REVIEW alexeyk: add shape-aware asserts?
assert(g.KernelShape().GetNumElements() * g.KernelCount() == kernel.GetNumElements());
#ifdef NDEBUG
UNUSED(g);
UNUSED(batchSize);
#endif
EnsureCompatible();
EnsureConvolutionInitialized();
ForwardCore(in, kernel, out, workspace);
}
template <class ElemType>
void ConvolutionEngine<ElemType>::BackwardData(const Mat& srcGrad, const Mat& kernel, Mat& grad, bool accumulateGradient, Mat& workspace)
{
const auto& g = *m_geometry;
assert(g.InputShape().GetNumElements() == grad.GetNumRows());
assert(g.OutputShape().GetNumElements() == srcGrad.GetNumRows());
size_t batchSize = srcGrad.GetNumCols();
assert(batchSize == grad.GetNumCols());
assert(g.KernelShape().GetNumElements() * g.KernelCount() == kernel.GetNumElements());
#ifdef NDEBUG
UNUSED(g);
UNUSED(batchSize);
#endif
EnsureCompatible();
EnsureConvolutionInitialized();
BackwardDataCore(srcGrad, kernel, grad, accumulateGradient, workspace);
}
template <class ElemType>
void ConvolutionEngine<ElemType>::BackwardKernel(const Mat& srcGrad, const Mat& in, Mat& kernel, bool accumulateGradient, bool allowReuse, Mat& workspace)
{
const auto& g = *m_geometry;
assert(g.InputShape().GetNumElements() == in.GetNumRows());
assert(g.OutputShape().GetNumElements() == srcGrad.GetNumRows());
size_t batchSize = in.GetNumCols();
assert(batchSize == srcGrad.GetNumCols());
assert(g.KernelShape().GetNumElements() * g.KernelCount() == kernel.GetNumElements());
#ifdef NDEBUG
UNUSED(g);
UNUSED(batchSize);
#endif
EnsureCompatible();
EnsureConvolutionInitialized();
BackwardKernelCore(srcGrad, in, kernel, accumulateGradient, allowReuse, workspace);
}
template <class ElemType>
void ConvolutionEngine<ElemType>::ForwardPooling(const Mat& in, Mat& out)
{
const auto& g = *m_geometry;
assert(g.InputShape().GetNumElements() == in.GetNumRows());
assert(g.OutputShape().GetNumElements() == out.GetNumRows());
size_t batchSize = in.GetNumCols();
assert(batchSize == out.GetNumCols());
#ifdef NDEBUG
UNUSED(g);
UNUSED(batchSize);
#endif
EnsureCompatible();
EnsurePoolingInitialized();
ForwardPoolingCore(in, out);
}
template <class ElemType>
void ConvolutionEngine<ElemType>::BackwardPooling(const Mat& out, const Mat& srcGrad, const Mat& in, Mat& grad, bool accumulateGradient)
{
const auto& g = *m_geometry;
assert(g.InputShape().GetNumElements() == grad.GetNumRows());
assert(g.InputShape().GetNumElements() == in.GetNumRows());
assert(g.OutputShape().GetNumElements() == srcGrad.GetNumRows());
assert(g.OutputShape().GetNumElements() == out.GetNumRows());
size_t batchSize = out.GetNumCols();
assert(batchSize == srcGrad.GetNumCols());
assert(batchSize == in.GetNumCols());
assert(batchSize == grad.GetNumCols());
#ifdef NDEBUG
UNUSED(g);
UNUSED(batchSize);
#endif
EnsureCompatible();
EnsurePoolingInitialized();
BackwardPoolingCore(out, srcGrad, in, grad, accumulateGradient);
}
template <class ElemType>
void ConvolutionEngine<ElemType>::MaxUnpooling(const Mat& out, const Mat& poolIn, Mat& in)
{
const auto& g = *m_geometry;
assert(g.InputShape().GetNumElements() == in.GetNumRows());
assert(g.InputShape().GetNumElements() == poolIn.GetNumRows());
assert(g.OutputShape().GetNumElements() == out.GetNumRows());
size_t batchSize = in.GetNumCols();
assert(batchSize == out.GetNumCols());
assert(batchSize == poolIn.GetNumCols());
#ifdef NDEBUG
UNUSED(g);
UNUSED(batchSize);
#endif
EnsureCompatible();
EnsurePoolingInitialized();
MaxUnpoolingCore(out, poolIn, in);
}
//------------------------------------------------------------------
// Reference convolution engine implementation.
// This engine supports arbitrary convolution geometry but does not provide efficient implementation.
// Its main purpose is to serve as a baseline for optmized engines (e.g. cuDNN) that
// usually implement only a subset of a general convolution geometry.
//------------------------------------------------------------------
template <class ElemType>
class ReferenceConvolutionEngine : public ConvolutionEngine<ElemType>
{
public:
using Base = ConvolutionEngine<ElemType>;
using typename Base::Mat;
public:
ReferenceConvolutionEngine(ConvolveGeometryPtr geometry, DEVICEID_TYPE deviceId, ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples, PoolKind poolKind, bool poolIncludePad)
: Base(geometry, deviceId, imageLayout, maxTempMemSizeInSamples, poolKind, poolIncludePad),
m_isConvGeometryComputed(geometry->ComputeConvGeometryExplicit()), // IMP NOTE: m_isConvGeometryComputed MUST be initialized before m_mpRowCol here in this list.
m_mpRowCol(geometry->MpRowCol().size(), 1, const_cast<int*>(geometry->MpRowCol().data()), deviceId, IsGpu(deviceId) ? matrixFlagNormal : matrixFlagDontOwnBuffer)
{
assert(m_isConvGeometryComputed);
}
protected:
using Base::m_geometry;
using Base::m_deviceId;
using Base::m_imageLayout;
using Base::m_maxTempMemSizeInSamples;
using Base::m_poolKind;
using Base::m_poolIncludePad;
void EnsureCompatible() override
{
if (m_imageLayout != ImageLayoutKind::CHW)
RuntimeError("Reference convolution engine supports only CHW/cudnn layout.");
}
void EnsureConvolutionInitialized() override
{
if (m_mpRowIwht == nullptr)
{
auto flags = IsGpu(m_deviceId) ? matrixFlagNormal : matrixFlagDontOwnBuffer;
m_mpRowIwht = std::make_unique<Matrix<int>>(m_geometry->MpRowIwht().size(), 1,
const_cast<int*>(m_geometry->MpRowIwht().data()), m_deviceId, flags);
m_mpRowRun = std::make_unique<Matrix<int>>(m_geometry->MpRowRun().size(), 1,
const_cast<int*>(m_geometry->MpRowRun().data()), m_deviceId, flags);
m_runs = std::make_unique<Matrix<int>>(m_geometry->Runs().size(), 1,
const_cast<int*>(m_geometry->Runs().data()), m_deviceId, flags);
}
}
void ForwardCore(const Mat& in, const Mat& kernel, Mat& out, Mat& /*workspace*/) override
{
in.ConvolutionForward(kernel, m_mpRowCol, *m_mpRowIwht, *m_mpRowRun, *m_runs, out);
}
void BackwardDataCore(const Mat& srcGrad, const Mat& kernel, Mat& grad, bool /*accumulateGradient*/, Mat& /*workspace*/) override
{
srcGrad.ConvolutionBackwardData(kernel, m_mpRowCol, *m_mpRowIwht, *m_mpRowRun, *m_runs, grad);
}
void BackwardKernelCore(const Mat& srcGrad, const Mat& in, Mat& kernelGrad, bool /*accumulateGradient*/, bool /*allowReuse*/, Mat& /*workspace*/) override
{
srcGrad.ConvolutionBackwardKernel(in, m_mpRowCol, *m_mpRowIwht, *m_mpRowRun, *m_runs, kernelGrad);
}
void EnsurePoolingInitialized() override
{
if (m_indices == nullptr)
{
auto flags = IsGpu(m_deviceId) ? matrixFlagNormal : matrixFlagDontOwnBuffer;
m_mpRowIndices = std::make_unique<Matrix<int>>(m_geometry->MpRowIndices().size(), 1,
const_cast<int*>(m_geometry->MpRowIndices().data()), m_deviceId, flags);
m_indices = std::make_unique<Matrix<int>>(m_geometry->Indices().size(), 1,
const_cast<int*>(m_geometry->Indices().data()), m_deviceId, flags);
}
}
void ForwardPoolingCore(const Mat& in, Mat& out) override
{
if (m_poolKind == PoolKind::Max)
{
in.MaxPoolingForward(m_mpRowCol, *m_mpRowIndices, *m_indices, out);
}
else if (m_poolKind == PoolKind::Average)
{
in.AveragePoolingForward(m_mpRowCol, *m_mpRowIndices, *m_indices, out, m_poolIncludePad);
}
else
InvalidArgument("Pooling type %d is not supported.", (int)m_poolKind);
}
void BackwardPoolingCore(const Mat& out, const Mat& srcGrad, const Mat& in, Mat& grad, bool accumulateGradient) override
{
if (m_poolKind == PoolKind::Max)
{
srcGrad.MaxPoolingBackward(out, in, m_mpRowCol, *m_mpRowIndices, *m_indices, grad, accumulateGradient);
}
else if (m_poolKind == PoolKind::Average)
{
srcGrad.AveragePoolingBackward(m_mpRowCol, *m_mpRowIndices, *m_indices, grad, m_poolIncludePad, accumulateGradient);
}
else
InvalidArgument("Pooling type %d is not supported.", (int)m_poolKind);
}
void MaxUnpoolingCore(const Mat& out, const Mat& poolIn, Mat& in) override
{
out.MaxUnpooling(m_mpRowCol, *m_mpRowIndices, *m_indices, poolIn, in);
}
protected:
static bool IsGpu(DEVICEID_TYPE deviceId)
{
return deviceId >= 0;
}
protected:
using IntMatPtr = std::unique_ptr<Matrix<int>>;
// IMP NOTE: Make sure that in the declaration below m_isConvGeometryComputed is declared
// before m_mpRowCol. This ordering is required to ensure the right order of initialization
// in the initializer list in the ctor (above) of this class.
bool m_isConvGeometryComputed;
Matrix<int> m_mpRowCol;
// Convolution-specific maps.
IntMatPtr m_mpRowIwht;
IntMatPtr m_mpRowRun;
IntMatPtr m_runs;
// Pooling-specific maps.
IntMatPtr m_mpRowIndices;
IntMatPtr m_indices;
};
//------------------------------------------------------------------
// Legacy convolution engine implementation.
//------------------------------------------------------------------
template <class ElemType>
class LegacyConvolutionEngine : public ConvolutionEngine<ElemType>
{
public:
using Base = ConvolutionEngine<ElemType>;
using typename Base::Mat;
public:
LegacyConvolutionEngine(ConvolveGeometryPtr geometry, DEVICEID_TYPE deviceId, ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples, PoolKind poolKind, bool poolIncludePad)
: Base(geometry, deviceId, imageLayout, maxTempMemSizeInSamples, poolKind, poolIncludePad),
m_inT(m_geometry->InputShape(), ImageLayoutKind::CHW), m_outT(m_geometry->OutputShape(), ImageLayoutKind::CHW),
m_kernelT(m_geometry->KernelShape(), ImageLayoutKind::CHW), m_strideT(m_geometry->Stride(), ImageLayoutKind::CHW)
{
m_padding = m_geometry->AutoPad()[0];
}
protected:
using Base::m_geometry;
using Base::m_deviceId;
using Base::m_imageLayout;
using Base::m_maxTempMemSizeInSamples;
using Base::m_poolKind;
using Base::m_poolIncludePad;
void EnsureCompatible() override
{
if (m_imageLayout != ImageLayoutKind::HWC)
RuntimeError("Legacy convolution engine supports only HWC/legacy layout.");
}
void EnsureConvolutionInitialized() override
{
}
void ForwardCore(const Mat& in, const Mat& kernel, Mat& out, Mat& workspace) override
{
size_t batchSize = in.GetNumCols();
size_t packedInputRows = m_kernelT.w() * m_kernelT.h() * m_kernelT.c();
size_t packedInputColsPerSample = m_outT.w() * m_outT.h();
size_t outputSizePerChannel = packedInputColsPerSample;
// size_t packedInputDim = packedInputRows * packedInputColsPerSample; // size of each packed input sample
// size_t inputDim = inT.w() * inT.h() * inT.c(); // size of each input sample
size_t maxTempMemSizeInSamples = (m_maxTempMemSizeInSamples == 0 ? batchSize : m_maxTempMemSizeInSamples);
assert(kernel.GetNumCols() == packedInputRows && kernel.GetNumRows() == m_outT.c());
UNUSED(packedInputRows);
// GPU and 1-dimensional image
m_gpuSparseOpt = (m_kernelT.h() == 1 &&
in.GetCurrentMatrixLocation() == CurrentDataLocation::GPU &&
m_strideT.w() == 1 &&
!m_padding &&
in.GetMatrixType() == MatrixType::SPARSE);
m_gpuSparse1D = (m_gpuSparseOpt && m_inT.h() == 1);
out.SwitchToMatrixType(MatrixType::DENSE, MatrixFormat::matrixFormatDense, false);
// Reshaping is only necessary if we are going to use the unpacking trick
if (m_gpuSparseOpt)
out.Reshape(m_outT.c() * m_outT.w(), m_outT.h() * batchSize);
else
out.Reshape(m_outT.c(), outputSizePerChannel * batchSize);
size_t subBatchSize = min(batchSize, maxTempMemSizeInSamples);
size_t numSubBatches = (batchSize + subBatchSize - 1) / subBatchSize;
for (size_t i = 0; i < numSubBatches; i++)
{
size_t startSampleId = i * subBatchSize;
size_t endSampleId = min(batchSize, startSampleId + subBatchSize);
size_t smallBatchSize = endSampleId - startSampleId;
Mat inputSubBatch(in.GetDeviceId());
// We optimize for three different scenarios here by handling them slightly differently.
// [Scenario 1] Dense: Unroll using AssignPackedConvolutionInput and multiply.
// [Scenario 2] Sparse 1-D convolution on GPU: for text scenarios we have a specific kernel.
// [Scenario 3] Sparse all others: convert to dense. Temporary work-around - allocating/de-allocating memory is costly!
if (in.GetMatrixType() == MatrixType::DENSE || m_gpuSparse1D)
inputSubBatch = in.ColumnSlice(startSampleId, smallBatchSize);
else
inputSubBatch.SetValue(in.ColumnSlice(startSampleId, smallBatchSize));
if (m_gpuSparseOpt)
{
if (m_kernelT.w() * m_inT.c() != kernel.GetNumCols())
LogicError("Kernel width and weight matrix dimensions don't match.");
inputSubBatch.Reshape(m_inT.c() * m_inT.w(), m_inT.h() * smallBatchSize);
Mat outputSubBatch = out.ColumnSlice(startSampleId, m_outT.h() * smallBatchSize);
Mat::ConvolveAndWeightedAdd(1, kernel, false, inputSubBatch, false, 0, outputSubBatch,
static_cast<int>(m_inT.c()), m_strideT.w(), m_padding, true);
}
else
{
inputSubBatch.SwitchToMatrixType(MatrixType::DENSE, MatrixFormat::matrixFormatDense, true);
workspace.AssignPackedConvolutionInput(inputSubBatch,
m_inT.w(), m_inT.h(), m_inT.c(),
m_outT.w(), m_outT.h(), m_outT.c(),
m_kernelT.w(), m_kernelT.h(), m_strideT.w(), m_strideT.h(),
m_padding);
Mat outputSubBatch = out.ColumnSlice(outputSizePerChannel * startSampleId, outputSizePerChannel * smallBatchSize);
// workspace.Resize(packedInputRows, packedInputColsPerSample * smallBatchSize);
// BUGBUG: This ^^ destroys the content of the matrix. Also it seems not to change the size. Does it? Should this be a Reshape()?
Mat::Multiply(kernel, false, workspace, false, outputSubBatch);
}
}
out.Reshape(m_outT.c() * outputSizePerChannel, batchSize); // each sample becomes a column
assert(m_outT.w() * m_outT.h() * m_outT.c() == out.GetNumRows());
assert(batchSize == out.GetNumCols());
}
void BackwardDataCore(const Mat& srcGrad, const Mat& kernel, Mat& grad, bool /*accumulateGradient*/, Mat& workspace) override
{
size_t batchSize = srcGrad.GetNumCols();
size_t packedInputRows = m_kernelT.w() * m_kernelT.h() * m_kernelT.c();
size_t packedInputColsPerSample = m_outT.w() * m_outT.h();
size_t outputSizePerChannel = packedInputColsPerSample;
// size_t packedInputDim = packedInputRows * packedInputColsPerSample; // size of each packed input sample
// size_t inputDim = m_inT.w() * m_inT.h() * m_inT.c(); // size of each input sample
size_t maxTempMemSizeInSamples = (m_maxTempMemSizeInSamples == 0 ? batchSize : m_maxTempMemSizeInSamples);
// Create slice which is the same as full matrix so we can reshape it.
Matrix<ElemType> srcGradTmp = srcGrad.ColumnSlice(0, srcGrad.GetNumCols());
srcGradTmp.Reshape(m_outT.c(), outputSizePerChannel * batchSize); // reshape to match the longernal operation
size_t subBatchSize = min(batchSize, maxTempMemSizeInSamples);
size_t numSubBatches = (batchSize + subBatchSize - 1) / subBatchSize;
for (size_t i = 0; i < numSubBatches; i++)
{
size_t startSampleId = i * subBatchSize;
size_t endSampleId = min(batchSize, startSampleId + subBatchSize);
size_t smallBatchSize = endSampleId - startSampleId;
workspace.Resize(packedInputRows, packedInputColsPerSample * smallBatchSize);
Matrix<ElemType> outputGradientSubBatch = srcGradTmp.ColumnSlice(startSampleId * outputSizePerChannel, smallBatchSize * outputSizePerChannel);
Matrix<ElemType>::Multiply(kernel, true, outputGradientSubBatch, false, workspace);
Matrix<ElemType> inputGradientSubBatch = grad.ColumnSlice(startSampleId, smallBatchSize);
workspace.UnpackConvolutionInput(inputGradientSubBatch,
m_inT.w(), m_inT.h(), m_inT.c(),
m_outT.w(), m_outT.h(), m_outT.c(),
m_kernelT.w(), m_kernelT.h(), m_strideT.w(), m_strideT.h(),
m_padding);
}
assert(m_outT.w() * m_outT.h() * m_outT.c() == srcGrad.GetNumRows());
assert(batchSize == srcGrad.GetNumCols());
}
void BackwardKernelCore(const Mat& srcGrad, const Mat& in, Mat& kernelGrad, bool /*accumulateGradient*/, bool allowReuse, Mat& workspace) override
{
size_t batchSize = in.GetNumCols();
size_t packedInputRows = m_kernelT.w() * m_kernelT.h() * m_kernelT.c();
size_t packedInputColsPerSample = m_outT.w() * m_outT.h();
size_t outputSizePerChannel = packedInputColsPerSample;
// size_t packedInputDim = packedInputRows * packedInputColsPerSample; // size of each packed input sample
// size_t inputDim = m_inputImageLayout.width * m_inputImageLayout.height * m_inputImageLayout.channels; // size of each input sample
size_t maxTempMemSizeInSamples = (m_maxTempMemSizeInSamples == 0 ? batchSize : m_maxTempMemSizeInSamples);
// const Matrix<ElemType> & weightMatrix = input0;
// inputGradientValues.Resize(weightMatrix.GetNumRows(), weightMatrix.GetNumCols()); // should have been resized when preparing gradient computation
// Create slice which is the same as full matrix so we can reshape it.
Matrix<ElemType> srcGradTmp = srcGrad.ColumnSlice(0, srcGrad.GetNumCols());
srcGradTmp.Reshape(m_outT.c(), outputSizePerChannel * batchSize); // reshape to match the longernal operation
size_t subBatchSize = min(batchSize, maxTempMemSizeInSamples);
size_t numSubBatches = (batchSize + subBatchSize - 1) / subBatchSize;
if (numSubBatches == 1 && allowReuse && !m_gpuSparseOpt) // reuse packed input from evaluation step if it's not changed by either subbatch or recurrent steps.
// REVIEW alexeyk: the following makes an assumption that data in workspace was filled by Forward call and remained unchanged. Find way to enforce/verify that.
Matrix<ElemType>::MultiplyAndAdd(srcGradTmp, false, workspace, true, kernelGrad);
else
{
for (size_t i = 0; i < numSubBatches; i++)
{
size_t startSampleID = i * subBatchSize;
size_t endSampleID = min(batchSize, startSampleID + subBatchSize);
size_t smallBatchSize = endSampleID - startSampleID;
Matrix<ElemType> outputGradientSubBatch = srcGradTmp.ColumnSlice(startSampleID * outputSizePerChannel, smallBatchSize * outputSizePerChannel);
// We optimize for three different scenarios here by handling them slightly differently.
// [Scenario 1] Dense: Unroll using AssignPackedConvolutionInput and multiply.
// [Scenario 2] Sparse 1-D convolution on GPU: for text scenarios we have a specific kernel.
// [Scenario 3] Sparse all others: convert to dense. Temporary work-around - allocating/de-allocating memory is costly!
if (m_gpuSparseOpt)
{
Matrix<ElemType> inputSubBatch(in.GetDeviceId());
inputSubBatch.SetValue(in.ColumnSlice(startSampleID, smallBatchSize));
inputSubBatch.Reshape(m_inT.c(), smallBatchSize * m_inT.w() * m_inT.h());
Matrix<ElemType> inputSubBatchSparseReordered(inputSubBatch.GetNumCols(), inputSubBatch.GetNumRows(), inputSubBatch.GetDeviceId(), MatrixType::SPARSE, MatrixFormat::matrixFormatSparseCSC);
Matrix<ElemType>::TensorShuffleScaleAndAdd(0.0f, inputSubBatch.Transpose(), 1, m_inT.w(), 1, smallBatchSize * m_inT.h(), m_inT.c(), 1.0f, inputSubBatchSparseReordered, inputSubBatchSparseReordered);
Matrix<ElemType> outputGradientSubBatchReordered = Matrix<ElemType>::Zeros(smallBatchSize * m_outT.h() * m_outT.w(), m_outT.c(), outputGradientSubBatch.GetDeviceId());
Matrix<ElemType>::TensorShuffleScaleAndAdd(0.0f, outputGradientSubBatch.Transpose(), 1, m_outT.w(), 1, smallBatchSize * m_outT.h(), m_outT.c(), 1.0f, outputGradientSubBatchReordered, outputGradientSubBatchReordered);
kernelGrad.Reshape(m_outT.c() * m_kernelT.w(), m_inT.c());
Matrix<ElemType>::ConvolveAndWeightedAdd(1, outputGradientSubBatchReordered, true, inputSubBatchSparseReordered, false, 1, kernelGrad, smallBatchSize * m_inT.h(), m_strideT.w(), m_padding, false);
kernelGrad.Reshape(m_outT.c(), m_inT.c() * m_kernelT.w());
}
else
{
workspace.Resize(packedInputRows, packedInputColsPerSample * smallBatchSize);
Matrix<ElemType> inputSubBatch = in.ColumnSlice(startSampleID, smallBatchSize);
inputSubBatch.SwitchToMatrixType(MatrixType::DENSE, inputSubBatch.GetFormat(), true);
workspace.AssignPackedConvolutionInput(inputSubBatch,
m_inT.w(), m_inT.h(), m_inT.c(),
m_outT.w(), m_outT.h(), m_outT.c(),
m_kernelT.w(), m_kernelT.h(), m_strideT.w(), m_strideT.h(),
m_padding);
Matrix<ElemType>::MultiplyAndAdd(outputGradientSubBatch, false, workspace, true, kernelGrad);
}
}
}
assert(m_outT.w() * m_outT.h() * m_outT.c() == srcGrad.GetNumRows());
assert(batchSize == srcGrad.GetNumCols());
}
void EnsurePoolingInitialized() override
{
}
void ForwardPoolingCore(const Mat& in, Mat& out) override
{
if (m_poolKind == PoolKind::Max)
{
out.AssignMaxPoolingResult(in, m_inT.c(), m_inT.w(), m_inT.h(), m_inT.w() * m_inT.h() * m_inT.c(),
m_outT.w(), m_outT.h(), m_outT.w() * m_outT.h() * m_outT.c(),
m_kernelT.w(), m_kernelT.h(), m_strideT.w(), m_strideT.h());
}
else if (m_poolKind == PoolKind::Average)
{
out.AssignAveragePoolingResult(in, m_inT.c(), m_inT.w(), m_inT.h(), m_inT.w() * m_inT.h() * m_inT.c(),
m_outT.w(), m_outT.h(), m_outT.w() * m_outT.h() * m_outT.c(),
m_kernelT.w(), m_kernelT.h(), m_strideT.w(), m_strideT.h());
}
else
InvalidArgument("Pooling type %d is not supported.", (int)m_poolKind);
}
void BackwardPoolingCore(const Mat& out, const Mat& srcGrad, const Mat& in, Mat& grad, bool accumulateGradient) override
{
if (!accumulateGradient)
grad.SetValue(0);
if (m_poolKind == PoolKind::Max)
{
grad.AddMaxPoolingGradient(srcGrad, in, out,
m_inT.c(), m_inT.w(), m_inT.h(), m_inT.w() * m_inT.h() * m_inT.c(),
m_outT.w(), m_outT.h(), m_outT.w() * m_outT.h() * m_outT.c(),
m_kernelT.w(), m_kernelT.h(), m_strideT.w(), m_strideT.h());
}
else if (m_poolKind == PoolKind::Average)
{
grad.AddAveragePoolingGradient(srcGrad, m_inT.c(), m_inT.w(), m_inT.h(), m_inT.w() * m_inT.h() * m_inT.c(),
m_outT.w(), m_outT.h(), m_outT.w() * m_outT.h() * m_outT.c(),
m_kernelT.w(), m_kernelT.h(), m_strideT.w(), m_strideT.h());
}
else
InvalidArgument("Pooling type %d is not supported.", (int)m_poolKind);
}
void MaxUnpoolingCore(const Mat& out, const Mat& poolIn, Mat& in) override
{
UNUSED(out);
UNUSED(poolIn);
UNUSED(in);
// Not implemented but potentially can make a fallback to reference engine.
LogicError("MaxUnpooling is not implemented for legacy engine.");
}
private:
ImageDimensions m_inT;
ImageDimensions m_outT;
ImageDimensions m_kernelT;
ImageDimensions m_strideT;
bool m_padding;
bool m_gpuSparseOpt;
bool m_gpuSparse1D;
};
//------------------------------------------------------------------
// GEMM convolution engine implementation.
// This engine supports arbitrary convolution configuration with full
// sharing and implemented using unroll + GEMM technique
// (High performance convolutional neural networks for document processing; Chellapilla, Puri, Simard)
// Uses reference engine for pooling operations.
//------------------------------------------------------------------
template <class ElemType>
class GemmConvolutionEngine : public ReferenceConvolutionEngine<ElemType>
{
public:
using Base = ReferenceConvolutionEngine<ElemType>;
using typename Base::Mat;
public:
GemmConvolutionEngine(ConvolveGeometryPtr geometry, DEVICEID_TYPE deviceId, ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples, PoolKind poolKind, bool poolIncludePad)
: Base(geometry, deviceId, imageLayout, maxTempMemSizeInSamples, poolKind, poolIncludePad)
{
}
protected:
using typename Base::IntMatPtr;
using Base::IsGpu;
using Base::m_geometry;
using Base::m_deviceId;
using Base::m_imageLayout;
using Base::m_maxTempMemSizeInSamples;
using Base::m_poolIncludePad;
using Base::m_mpRowCol;
using Base::m_mpRowIwht;
using Base::m_mpRowRun;
using Base::m_runs;
void EnsureCompatible() override
{
if (m_imageLayout != ImageLayoutKind::CHW)
LogicError("GEMM convolution engine supports only CHW/cudnn layout.");
if (IsGpu(m_deviceId))
LogicError("GEMM convolution engine currently supports only CPU device.");
}
// A note on notation used in the documentation for the next 3 functions:
// for simplicity we use cuDNN-style notation for 2D convolutions (though this engine supports arbitrary convolution configuration)
// where N - is the number of samples in a batch, C, H, W are number of channels, height and width of the input respectively.
// For the output we use K as the number of output feature maps and H', W' as height and width of the output.
// We also use column-major notation everywhere (as opposed to cuDNN which uses row-major) to follow CNTK rules.
// For kernels we use X, Y, Z to represent width, height and depth. This engine requires Z == C which is
// not a significant restriction as tensors of higher dimensions (+1) can be used to describe the same convolution configuration.
// Example: [WHC x N] - is a matrix of WHC rows by N columns and represents a convolution input
// where each column is a sample that has layout of WHC, so W dimension stride is 1.
//
// The forward method consists of 3 parts:
// 1. Unrolling convolution input (in) into a matrix: [WHC x N] -> [XYC x NW'H']
// Using this format allows to perform convolution for the whole minibatch as a single GEMM operation
// which is not possible with WHCN format. Alternatively, CWHN format (used in legacy engine) could be used
// but this would require both unrolling the input and transforming the weight matrix.
// 2. Performing matrix multiplication of unrolled input with weight matrix:
// [XYC x NW'H']^T * [XYC x K] -> [NW'H' x K]
// 3. Reshape and transpose result: [NW'H' x K] -> [N x W'H'K]^T -> [W'H'K x N]
// In case minibatch size == 1 this step is not required and step 2 writes results directly to output (out).
void ForwardCore(const Mat& in, const Mat& kernel, Mat& out, Mat& workspace) override
{
#ifdef USE_MKL2017DNN
if (ForwardCoreMKL(in, kernel, out)) return;
#endif
size_t batchSize = in.GetNumCols();
size_t subBatchSize = m_maxTempMemSizeInSamples == 0 ? batchSize : min(batchSize, m_maxTempMemSizeInSamples);
size_t mapCount = m_geometry->GetMapCount(m_geometry->InputShape().GetRank() - 1);
size_t mapOutSize = m_geometry->OutputShape().GetNumElements() / mapCount;
size_t unrollRows = mapOutSize * subBatchSize;
size_t unrollCols = m_geometry->KernelShape().GetNumElements();
// Reserve space for unrolled inputs and, if needed, intermediate outputs.
// Intermediate outputs will be transposed to final outputs after GEMM operation.
// Transpose is not required if subBatchSize == 1.
workspace.Resize(unrollRows, unrollCols + (subBatchSize > 1 ? mapCount : 0));
for (size_t start = 0; start < batchSize; start += subBatchSize)
{
size_t curBatchSize = min(subBatchSize, batchSize - start);
auto inputSlice = in.ColumnSlice(start, curBatchSize);
auto unrolledInput = workspace.ColumnSlice(0, unrollCols);
if (curBatchSize != subBatchSize)
{
unrolledInput.Reshape(mapOutSize, subBatchSize * unrollCols);
unrolledInput = unrolledInput.ColumnSlice(0, curBatchSize * unrollCols);
}
// Need to reshape (soft transpose) as matrices are column-major.
unrolledInput.Reshape(unrollCols, mapOutSize * curBatchSize);
// Unroll inputs.
unrolledInput.SetValue(0);
inputSlice.UnrollConvolutionInput(unrollCols, mapOutSize, m_mpRowCol, *m_mpRowRun, *m_runs, unrolledInput);
// cudnn layout uses row-major kernel weight matrix.
auto kern = kernel.ColumnSlice(0, kernel.GetNumCols());
kern.Reshape(unrollCols, kernel.GetNumElements()/unrollCols);
// Perform matrix multiplication of unrolled inputs with weights.
// If there is just one sample in the sub-batch then compute result directly to the output matrix.
if (curBatchSize == 1)
{
auto outSlice = out.ColumnSlice(start, 1);
outSlice.Reshape(mapOutSize, mapCount);
Mat::Multiply(unrolledInput, true, kern, false, outSlice);
}
else
{
auto outTempSlice = workspace.ColumnSlice(unrollCols, mapCount);
if (curBatchSize != subBatchSize)
{
outTempSlice.Reshape(mapOutSize, subBatchSize * mapCount);
outTempSlice = outTempSlice.ColumnSlice(0, curBatchSize * mapCount);
outTempSlice.Reshape(mapOutSize * curBatchSize, mapCount);
}
Mat::Multiply(unrolledInput, true, kern, false, outTempSlice);
outTempSlice.Reshape(curBatchSize, mapOutSize * mapCount);
auto outSlice = out.ColumnSlice(start, curBatchSize);
outSlice.AssignTransposeOf(outTempSlice);
}
}
}
// The backward data method works by representing this operation as a "reverse" convolution
// in case kernel's last dimension is equal to input dimension. Gradients matrix (grad) becomes
// an output of such reverse convolution.
// There are 4 steps:
// 1. Transpose and reshape kernel weights: [XYC x K]^T -> [K x XYC] -> [KXY x C]
// 2. Unroll convolution output (here source gradients, srcGrad):
// [W'H'K' x N] -> [KXY x NWH]
// 3. Performing matrix multiplication of unrolled scrGrad with transposed weights:
// [KXY x NWH]^T * [KXY x C] -> [NWH x C]
// 4. Reshape and transpose outputs (grad): [NWH x C] -> [N x WHC]^T -> [WHC x N]
// In case minibatch size == 1 this step is not required and step 3 writes results directly to output (grad).
void BackwardDataCore(const Mat& srcGrad, const Mat& kernel, Mat& grad, bool accumulateGradient, Mat& workspace) override
{
#ifdef USE_MKL2017DNN
if (BackwardDataMKL(srcGrad, kernel, grad, accumulateGradient, workspace)) return;
#else
UNUSED(accumulateGradient);
#endif
size_t batchSize = srcGrad.GetNumCols();
size_t subBatchSize = m_maxTempMemSizeInSamples == 0 ? batchSize : min(batchSize, m_maxTempMemSizeInSamples);
const auto& inT = m_geometry->InputShape();
const auto& kernT = m_geometry->KernelShape();
size_t dimCount = inT.GetRank();
assert(kernT[dimCount - 1] == inT[dimCount - 1]);
if (kernT[dimCount - 1] != inT[dimCount - 1])
{
RuntimeError("GEMM convolution engine does not support this convolution configuration. "
"It is possible to make GEMM engine work with this configuration by defining "
"input/output/kernel using tensors of higher(+1) dimension. Geometry: %s", ((string)*m_geometry).c_str());
}
size_t mapInCount = kernT[dimCount - 1];
size_t mapOutCount = m_geometry->GetMapCount(dimCount - 1);
size_t mapInSize = inT.GetNumElements() / mapInCount;
size_t unrollRows = mapInSize * subBatchSize;
size_t unrollCols = kernel.GetNumElements() / mapInCount;
// Reserve space for:
// 1. Transposed kernel weights.
// 2. Unrolled source gradients.
// 3. Intermediate gradients (optional).
// Intermediate outputs will be transposed to final outputs after GEMM operation.
// Transpose is not required if subBatchSize == 1.
size_t kernCols = kernel.GetNumElements();
workspace.Resize(1, kernCols + unrollRows * (unrollCols + (subBatchSize > 1 ? mapInCount : 0)));
auto kern = kernel.ColumnSlice(0, kernel.GetNumCols());
size_t kernTCols = kernT.GetNumElements();
// cudnn layout uses row-major kernel weight matrix.
kern.Reshape(kernTCols, kernCols/kernTCols);
// Now transpose and reshape to [KXY x C].
auto kernTran = workspace.ColumnSlice(0, kernCols);
// Reshape to transpose shape, AssignTransposeOf requires that.
kernTran.Reshape(kern.GetNumCols(), kern.GetNumRows());
kernTran.AssignTransposeOf(kern);
kern = kernTran.ColumnSlice(0, kernTran.GetNumCols());
// Reshape to final shape.
kern.Reshape(unrollCols, mapInCount);
for (size_t start = 0; start < batchSize; start += subBatchSize)
{
size_t curBatchSize = min(subBatchSize, batchSize - start);
auto srcGradSlice = srcGrad.ColumnSlice(start, curBatchSize);
auto unrolledSrcGrad = workspace.ColumnSlice(kernCols, unrollRows * unrollCols);
if (curBatchSize != subBatchSize)
unrolledSrcGrad = unrolledSrcGrad.ColumnSlice(0, mapInSize * curBatchSize * unrollCols);
// Need to reshape (soft transpose) as matrices are column-major.
unrolledSrcGrad.Reshape(unrollCols, mapInSize * curBatchSize);
// Unroll outputs (source gradients).
unrolledSrcGrad.SetValue(0);
srcGradSlice.UnrollConvolutionOutput(unrollCols, mapInCount, mapOutCount, m_mpRowCol, *m_mpRowRun, *m_runs, unrolledSrcGrad);
// Perform matrix multiplication of unrolled outputs with weights.
// If there is just one sample in the sub-batch then compute result directly to the output matrix.
if (curBatchSize == 1)
{
auto gradSlice = grad.ColumnSlice(start, 1);
gradSlice.Reshape(mapInSize, mapInCount);
Mat::MultiplyAndAdd(unrolledSrcGrad, true, kern, false, gradSlice);
}
else
{
// Need to transpose existing destination gradients first so we can add new values to them.
auto gradTempSlice = workspace.ColumnSlice(kernCols + unrollRows * unrollCols, unrollRows * mapInCount);
if (curBatchSize != subBatchSize)
gradTempSlice = gradTempSlice.ColumnSlice(0, mapInSize * curBatchSize * mapInCount);
gradTempSlice.Reshape(curBatchSize, mapInSize * mapInCount);
auto gradSlice = grad.ColumnSlice(start, curBatchSize);
gradTempSlice.AssignTransposeOf(gradSlice);
gradTempSlice.Reshape(mapInSize * curBatchSize, mapInCount);
// Multiply unrolled srcGrad with weights and add to grad.
Mat::MultiplyAndAdd(unrolledSrcGrad, true, kern, false, gradTempSlice);
// Reshape and transpose grads back to original form.
gradTempSlice.Reshape(curBatchSize, mapInSize * mapInCount);
gradSlice.AssignTransposeOf(gradTempSlice);
}
}
}
// The backward kernel method consists of 3 parts:
// 1. Transpose and reshape convolution output matrix (srcGrad) into [NW'H' x K] layout.
// This step is not needed if current minibatch size == 1 and srcGrad are used instead.
// 2. Unrolling convolution input (in) into a matrix of [NW'H' x WHC] layout.
// 3. Performing matrix multiplication of unrolled input with transposed output:
// [NW'H' x WHC]^T * [NW'H' x K] -> [WHC x K] - kernel gradients.
void BackwardKernelCore(const Mat& srcGrad, const Mat& in, Mat& kernelGrad, bool accumulateGradient, bool /*allowReuse*/, Mat& workspace) override
{
#ifdef USE_MKL2017DNN
if (BackwardKernelMKL(srcGrad, in, kernelGrad, accumulateGradient, workspace)) return;
#else
UNUSED(accumulateGradient);
#endif
size_t batchSize = srcGrad.GetNumCols();
size_t subBatchSize = m_maxTempMemSizeInSamples == 0 ? batchSize : min(batchSize, m_maxTempMemSizeInSamples);
const auto& inT = m_geometry->InputShape();
const auto& kernT = m_geometry->KernelShape();
const auto& outT = m_geometry->OutputShape();
size_t dimCount = inT.GetRank();
size_t mapOutCount = m_geometry->GetMapCount(dimCount - 1);
size_t mapOutSize = outT.GetNumElements() / mapOutCount;
assert(kernT[dimCount - 1] == inT[dimCount - 1]);
if (kernT[dimCount - 1] != inT[dimCount - 1])
{
RuntimeError("GEMM convolution engine does not support this convolution configuration. "
"It is possible to make GEMM engine work with this configuration by defining "
"input/output/kernel using tensors of higher(+1) dimension. Geometry: %s", ((string)*m_geometry).c_str());
}
size_t unrollRows = kernT.GetNumElements();
size_t unrollCols = mapOutSize * subBatchSize;
// Reserve space for:
// 1. Unrolled inputs.
// 2. Transposed source gradients (optional).
workspace.Resize(unrollCols, unrollRows + (subBatchSize > 1 ? mapOutCount : 0));
for (size_t start = 0; start < batchSize; start += subBatchSize)
{
size_t curBatchSize = min(subBatchSize, batchSize - start);
// 1. Transpose and reshape srcGrad.
auto srcGradSlice = srcGrad.ColumnSlice(start, curBatchSize);
if (curBatchSize > 1)
{
auto srcGradTranSlice = workspace.ColumnSlice(unrollRows, mapOutCount);
if (curBatchSize != subBatchSize)
{
srcGradTranSlice.Reshape(mapOutCount * mapOutSize, subBatchSize);
srcGradTranSlice = srcGradTranSlice.ColumnSlice(0, curBatchSize);
}
// Reshape to transposed shape - required by AssignTransposeOf.
srcGradTranSlice.Reshape(srcGradSlice.GetNumCols(), srcGradSlice.GetNumRows());
srcGradTranSlice.AssignTransposeOf(srcGradSlice);
srcGradSlice = srcGradTranSlice.ColumnSlice(0, srcGradTranSlice.GetNumCols());
}
srcGradSlice.Reshape(mapOutSize * curBatchSize, mapOutCount);
// 2. Unroll inputs.
auto inputSlice = in.ColumnSlice(start, curBatchSize);
auto unrolledInputSlice = workspace.ColumnSlice(0, unrollRows);
if (curBatchSize != subBatchSize)
{
unrolledInputSlice.Reshape(mapOutSize * unrollRows, subBatchSize);
unrolledInputSlice = unrolledInputSlice.ColumnSlice(0, curBatchSize);
}
unrolledInputSlice.Reshape(mapOutSize * curBatchSize, unrollRows);
unrolledInputSlice.SetValue(0);
inputSlice.UnrollConvolutionInputForKernelBackprop(mapOutSize, m_mpRowCol, *m_mpRowRun, *m_runs, unrolledInputSlice);
// cudnn layout uses row-major kernel weight matrix.
auto kernGrad = kernelGrad.ColumnSlice(0, kernelGrad.GetNumCols());
kernGrad.Reshape(unrollRows, kernGrad.GetNumElements() / unrollRows);
// 3. Multiply.
Mat::MultiplyAndAdd(unrolledInputSlice, true, srcGradSlice, false, kernGrad);
}
}
#ifdef USE_MKL2017DNN
class MKLConvolutionContext
{
public:
enum ContextIndex
{
ContextIndex_Forward = 0,
ContextIndex_BackwardData,
ContextIndex_BackwardFilter,
ContextIndex_Total
};
private:
const ConvolveGeometry* m_prevGeometry = nullptr;
size_t m_prevBatchSize = 0;
int m_contextFlags = 0;
// fixed dimension for MKL for now
static const int m_dimension = 4;
static const int NumInputs = 2;
struct PrimitiveContext
{
MKLDnnResourceAdapter<ElemType> inputs[NumInputs];
MKLDnnResourceAdapter<ElemType> output;
dnnPrimitive_t primitive = nullptr;
dnnPrimitiveAttributes_t attributes = nullptr;
void Clear()
{
if (primitive) { dnnDelete<ElemType>(primitive); primitive = nullptr; }
for (auto& i : inputs) i.Clear();
output.Clear();
if (attributes) { dnnPrimitiveAttributesDestroy<ElemType>(attributes); attributes = nullptr; }
}
~PrimitiveContext()
{
Clear();
}
} m_context[ContextIndex_Total];
static void GetSizesAndStrides(const TensorShape& shape, size_t lastDim, SmallVector<size_t>& sizes, SmallVector<size_t>& strides, size_t mapCount = 0)
{
sizes = shape.GetDims();
if (mapCount)
{
if (mapCount != shape.GetDim(shape.GetRank() - 1))
RuntimeError("Mismatching outputShape and mapCount");
// for outputShape, pad 1 before mapCount (the last dim in shape)
sizes.pop_back();
while (sizes.size() < m_dimension - 2) sizes.push_back(1);
sizes.push_back(mapCount);
}
else
{
while (sizes.size() < m_dimension - 1) sizes.push_back(1);
}
sizes.push_back(lastDim);
strides.clear();
strides.push_back(1);
for (int i = 1; i <= sizes.size(); i++)
{
strides.push_back(sizes[i - 1] * strides[i - 1]);
}
}
static void GetInputOffsets(const ConvolveGeometry* geometry, SmallVector<int>& inputOffset)
{
size_t dim_size = geometry->InputShape().GetRank();
for (size_t i = 0; i < dim_size; i++)
{
inputOffset.push_back(-geometry->GetLowerPad(i));
}
}
public:
MKLConvolutionContext() :
m_prevBatchSize(0),
m_prevGeometry(nullptr)
{}
bool Supported(const ConvolveGeometry* geometry, bool forward)
{
//MKL2017 does not support asymmetric padding yet
if (geometry->IsAsymmetricPadding()) return false;
//MKL-DNN calls does not support 4th dimention now, we will update the code once MKL release the update.
return forward ? (geometry->InputShape().GetRank() < m_dimension) : (geometry->OutputShape().GetRank() < m_dimension);
}
void Prepare(size_t batchSize, const ConvolveGeometry* geometry, ContextIndex contextIndex)
{
int flag = (1 << contextIndex);
bool sameGeometryAndBatchSize = (geometry == m_prevGeometry && batchSize == m_prevBatchSize);
if (sameGeometryAndBatchSize && !!(m_contextFlags & flag)) return;
if (!sameGeometryAndBatchSize)
m_contextFlags = 0;
if (m_contextFlags)
{
if (m_prevGeometry != geometry || m_prevBatchSize != batchSize)
RuntimeError("Inconsistent convolution geometry or batch size between forward and backward");
}
else
{
m_prevGeometry = geometry;
m_prevBatchSize = batchSize;
}
m_contextFlags |= flag;
size_t mapCount = geometry->GetMapCount(geometry->KernelShape().GetRank() - 1);
SmallVector<size_t> outputSize, outputStrides, filterSize, filterStrides, inputSize, inputStrides;
SmallVector<int> inputOffset;
GetSizesAndStrides(geometry->OutputShape(), batchSize, outputSize, outputStrides, mapCount);
GetSizesAndStrides(geometry->KernelShape(), mapCount, filterSize, filterStrides);
GetSizesAndStrides(geometry->InputShape(), batchSize, inputSize, inputStrides);
GetInputOffsets(geometry, inputOffset);
const auto& convolutionStride = geometry->Stride().GetDims();
auto& ctx = m_context[contextIndex];
ctx.Clear();
dnnLayout_t ltUserInputs[NumInputs], ltPrimInputs[NumInputs];
dnnLayout_t ltUserOutput, ltPrimOutput;
dnnResourceType_t inputTypes[NumInputs];
dnnResourceType_t outputType;
switch (contextIndex)
{
case ContextIndex_Forward:
CHECK_MKL(dnnLayoutCreate<ElemType>(<UserInputs[0], m_dimension, inputSize.begin(), inputStrides.begin()));
CHECK_MKL(dnnLayoutCreate<ElemType>(<UserInputs[1], m_dimension, filterSize.begin(), filterStrides.begin()));
CHECK_MKL(dnnLayoutCreate<ElemType>(<UserOutput, m_dimension, outputSize.begin(), outputStrides.begin()));
CHECK_MKL(dnnPrimitiveAttributesCreate<ElemType>(&ctx.attributes));
CHECK_MKL(dnnConvolutionCreateForward<ElemType>(&ctx.primitive, ctx.attributes, dnnAlgorithmConvolutionDirect, m_dimension, inputSize.begin(), outputSize.begin(), filterSize.begin(), convolutionStride.begin(), inputOffset.begin(), dnnBorderZeros));
inputTypes[0] = dnnResourceSrc;
inputTypes[1] = dnnResourceFilter;
outputType = dnnResourceDst;
break;
case ContextIndex_BackwardData:
CHECK_MKL(dnnLayoutCreate<ElemType>(<UserInputs[0], m_dimension, outputSize.begin(), outputStrides.begin()));
CHECK_MKL(dnnLayoutCreate<ElemType>(<UserInputs[1], m_dimension, filterSize.begin(), filterStrides.begin()));
CHECK_MKL(dnnLayoutCreate<ElemType>(<UserOutput, m_dimension, inputSize.begin(), inputStrides.begin()));
CHECK_MKL(dnnPrimitiveAttributesCreate<ElemType>(&ctx.attributes));
CHECK_MKL(dnnConvolutionCreateBackwardData<ElemType>(&ctx.primitive, ctx.attributes, dnnAlgorithmConvolutionDirect, m_dimension, inputSize.begin(), outputSize.begin(), filterSize.begin(), convolutionStride.begin(), inputOffset.begin(), dnnBorderZeros));
inputTypes[0] = dnnResourceDiffDst;
inputTypes[1] = dnnResourceFilter;
outputType = dnnResourceDiffSrc;
break;
case ContextIndex_BackwardFilter:
CHECK_MKL(dnnLayoutCreate<ElemType>(<UserInputs[0], m_dimension, outputSize.begin(), outputStrides.begin()));
CHECK_MKL(dnnLayoutCreate<ElemType>(<UserInputs[1], m_dimension, inputSize.begin(), inputStrides.begin()));
CHECK_MKL(dnnLayoutCreate<ElemType>(<UserOutput, m_dimension, filterSize.begin(), filterStrides.begin()));
CHECK_MKL(dnnPrimitiveAttributesCreate<ElemType>(&ctx.attributes));
CHECK_MKL(dnnConvolutionCreateBackwardFilter<ElemType>(&ctx.primitive, ctx.attributes, dnnAlgorithmConvolutionDirect, m_dimension, inputSize.begin(), outputSize.begin(), filterSize.begin(), convolutionStride.begin(), inputOffset.begin(), dnnBorderZeros));
inputTypes[0] = dnnResourceDiffDst;
inputTypes[1] = dnnResourceSrc;
outputType = dnnResourceDiffFilter;
break;
default:
RuntimeError("Unexpected context type %d", (int)contextIndex);
}
for (int i = 0; i < NumInputs; i++)
{
CHECK_MKL(dnnLayoutCreateFromPrimitive<ElemType>(<PrimInputs[i], ctx.primitive, inputTypes[i]));
ctx.inputs[i].Create(ltUserInputs[i], ltPrimInputs[i], inputTypes[i], true);
}
CHECK_MKL(dnnLayoutCreateFromPrimitive<ElemType>(<PrimOutput, ctx.primitive, outputType));
ctx.output.Create(ltUserOutput, ltPrimOutput, outputType, false);
}
void Execute(void* userInput0, void* userInput1, void* userOutput, ContextIndex contextIndex)
{
auto& ctx = m_context[contextIndex];
void* userInputs[] = { userInput0, userInput1 };
void* resources[dnnResourceNumber] = { 0 };
for(int i = 0; i < NumInputs; i++)
ctx.inputs[i].PrepareForExecution(userInputs[i], resources);
ctx.output.PrepareForExecution(userOutput, resources);
CHECK_MKL(dnnExecute<ElemType>(ctx.primitive, resources));
ctx.output.ConvertOutput(userOutput);
}
};
MKLConvolutionContext m_mklContext;
// convolution implementation with MKL 2017 DNN functions
bool ForwardCoreMKL(const Mat& in, const Mat& kernel, Mat& out)
{
if (!m_mklContext.Supported(m_geometry.get(), true)) return false;
m_mklContext.Prepare(in.GetNumCols(), m_geometry.get(), MKLConvolutionContext::ContextIndex_Forward);
m_mklContext.Execute(in.Data(), kernel.Data(), out.Data(), MKLConvolutionContext::ContextIndex_Forward);
return true;
}
bool BackwardDataMKL(const Mat& srcGrad, const Mat& kernel, Mat& grad, bool accumulateGradient, Mat& workspace)
{
if (!m_mklContext.Supported(m_geometry.get(), false)) return false;
m_mklContext.Prepare(srcGrad.GetNumCols(), m_geometry.get(), MKLConvolutionContext::ContextIndex_BackwardData);
if (accumulateGradient)
workspace.AssignValuesOf(grad);
m_mklContext.Execute(srcGrad.Data(), kernel.Data(), grad.Data(), MKLConvolutionContext::ContextIndex_BackwardData);
if (accumulateGradient)
grad.AssignSumOf(grad, workspace);
return true;
}
bool BackwardKernelMKL(const Mat& srcGrad, const Mat& in, Mat& kernelGrad, bool accumulateGradient, Mat& workspace)
{
if (!m_mklContext.Supported(m_geometry.get(), false)) return false;
m_mklContext.Prepare(srcGrad.GetNumCols(), m_geometry.get(), MKLConvolutionContext::ContextIndex_BackwardFilter);
if (accumulateGradient)
workspace.AssignValuesOf(kernelGrad);
m_mklContext.Execute(srcGrad.Data(), in.Data(), kernelGrad.Data(), MKLConvolutionContext::ContextIndex_BackwardFilter);
if (accumulateGradient)
kernelGrad.AssignSumOf(kernelGrad, workspace);
return true;
}
#endif
public:
static bool IsSupported(DEVICEID_TYPE deviceId, ConvolveGeometryPtr geometry)
{
return deviceId < 0 &&
find(begin(geometry->Sharing()), end(geometry->Sharing()), false) == end(geometry->Sharing());
}
};
template <class ElemType>
std::unique_ptr<ConvolutionEngine<ElemType>> ConvolutionEngine<ElemType>::Create(ConvolveGeometryPtr geometry, DEVICEID_TYPE deviceId,
ImageLayoutKind imageLayout, size_t maxTempMemSizeInSamples, PoolKind poolKind,
ConvolutionEngineKind enabledEngines, std::wstring logPrefix,
bool forceDeterministicAlgorithms, bool poolIncludePad,
bool inputHasFreeDimension)
{
if (!logPrefix.empty())
logPrefix += L": ";
auto isEnabled = [=](ConvolutionEngineKind eng) { return ((int)enabledEngines & (int)eng) != 0; };
// Note: in some cases do not throw exception even if parameters do not match as Create
// can be called from places like MEL with default parameters and never be used.
// The check will be done later in engine's EnsureCompatible call if the egnine is actually used.
auto engStr = (std::string)(*geometry);
// Only legacy engine supports HWC layout.
if (imageLayout == ImageLayoutKind::HWC)
{
if (!isEnabled(ConvolutionEngineKind::Legacy))
RuntimeError("Trying to use Legacy convolution engine when it's disabled.");
if (GetMathLibTraceLevel() > 0)
fprintf(stderr, "%lsusing legacy convolution engine for geometry: %s.\n", logPrefix.c_str(), engStr.c_str());
return std::make_unique<LegacyConvolutionEngine<ElemType>>(geometry, deviceId, imageLayout, maxTempMemSizeInSamples, poolKind, poolIncludePad);
}
// Check if we can use cuDNN engine. Do not need to validate tensors as ConvolveGeometry has already done that.
if (isEnabled(ConvolutionEngineKind::CuDnn) &&
CuDnnConvolutionEngineFactory<ElemType>::IsSupported(deviceId, geometry, poolKind))
{
if (GetMathLibTraceLevel() > 0)
fprintf(stderr, "%lsusing cuDNN convolution engine for geometry: %s.\n", logPrefix.c_str(), engStr.c_str());
return CuDnnConvolutionEngineFactory<ElemType>::Create(geometry, deviceId, imageLayout, maxTempMemSizeInSamples, poolKind,
forceDeterministicAlgorithms, poolIncludePad, inputHasFreeDimension);
}
if (isEnabled(ConvolutionEngineKind::Gemm) && GemmConvolutionEngine<ElemType>::IsSupported(deviceId, geometry))
{
if (GetMathLibTraceLevel() > 0)
fprintf(stderr, "%lsusing GEMM convolution engine for geometry: %s.\n", logPrefix.c_str(), engStr.c_str());
return std::make_unique<GemmConvolutionEngine<ElemType>>(geometry, deviceId, imageLayout, maxTempMemSizeInSamples, poolKind, poolIncludePad);
}
if (!isEnabled(ConvolutionEngineKind::Reference))
RuntimeError("Reference convolution is disabled and no other engine supports such configuration (or disabled).");
if (GetMathLibTraceLevel() > 0)
fprintf(stderr, "%lsusing reference convolution engine for geometry, could be VERY SLOW: %s.\n", logPrefix.c_str(), engStr.c_str());
return std::make_unique<ReferenceConvolutionEngine<ElemType>>(geometry, deviceId, imageLayout, maxTempMemSizeInSamples, poolKind, poolIncludePad);
}
template class ConvolutionEngine<float>;
template class ConvolutionEngine<double>;
}}}