https://github.com/Microsoft/CNTK
Tip revision: 2528557bd1e538a0b35f5335a5ba124b78dc47a9 authored by Jian Jiao on 04 October 2017, 18:20:45 UTC
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Tip revision: 2528557
NDArrayView.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 "CNTKLibrary.h"
#include "Utils.h"
#include "TensorView.h"
#include "Matrix.h"
#include "CPUSparseMatrix.h"
#include "GPUSparseMatrix.h"
#include <algorithm>
#include "TensorShape.h"
using namespace Microsoft::MSR::CNTK;
namespace CNTK
{
template <typename ElementType>
static TensorView<ElementType>* AllocateTensorView(const NDShape& viewShape,
const DeviceDescriptor& device,
void* dataBuffer,
size_t bufferSizeInBytes)
{
if (dataBuffer == nullptr)
InvalidArgument("Cannot create a NDArrayView over a null data buffer.");
if (bufferSizeInBytes < (viewShape.TotalSize() * sizeof(ElementType)))
InvalidArgument("Size (%d) of the specified buffer for creating the NDArrayView is smaller than the specified view shape '%S'.",
(int)bufferSizeInBytes, viewShape.AsString().c_str());
auto matrixDims = GetMatrixDimensions(viewShape);
std::shared_ptr<Matrix<ElementType>> matrix = std::make_shared<Matrix<ElementType>>(matrixDims.first, matrixDims.second, (ElementType*)dataBuffer, AsCNTKImplDeviceId(device), matrixFlagDontOwnBuffer);
return new TensorView<ElementType>(matrix, AsTensorViewShape(viewShape));
}
static void* AllocateTensorView(CNTK::DataType dataType,
const NDShape& viewShape,
const DeviceDescriptor& device,
void* dataBuffer,
size_t bufferSizeInBytes)
{
switch (dataType)
{
case DataType::Float:
return AllocateTensorView<float>(viewShape, device, dataBuffer, bufferSizeInBytes);
case DataType::Double:
return AllocateTensorView<double>(viewShape, device, dataBuffer, bufferSizeInBytes);
default:
LogicError("Unsupported DataType %s", DataTypeName(dataType));
break;
}
}
template <typename ElementType>
static TensorView<ElementType>* AllocateTensorView(const NDShape& viewShape,
CNTK::StorageFormat storageType,
const DeviceDescriptor& device,
size_t numNonZeroValues = 0)
{
auto matrixDims = GetMatrixDimensions(viewShape);
std::shared_ptr<Matrix<ElementType>> matrix = std::make_shared<Matrix<ElementType>>(matrixDims.first,
matrixDims.second,
AsCNTKImplDeviceId(device),
IsSparseStorageFormat(storageType) ? MatrixType::SPARSE : MatrixType::DENSE,
AsCNTKImplMatrixFormat(storageType),
numNonZeroValues);
return new TensorView<ElementType>(matrix, AsTensorViewShape(viewShape));
}
static void* AllocateTensorView(CNTK::DataType dataType,
CNTK::StorageFormat storageType,
const NDShape& viewShape,
const DeviceDescriptor& device,
size_t numNonZeroValues = 0)
{
switch (dataType)
{
case DataType::Float:
return AllocateTensorView<float>(viewShape, storageType, device, numNonZeroValues);
case DataType::Double:
return AllocateTensorView<double>(viewShape, storageType, device, numNonZeroValues);
default:
LogicError("Unsupported DataType %s", DataTypeName(dataType));
break;
}
}
NDArrayView::NDArrayView(CNTK::DataType dataType, const NDShape& viewShape, void* dataBuffer, size_t bufferSizeInBytes, const DeviceDescriptor& device, bool readOnly/* = false*/)
: NDArrayView(dataType, device, StorageFormat::Dense, viewShape, readOnly, AllocateTensorView(dataType, viewShape, device, dataBuffer, bufferSizeInBytes))
{
}
template <typename ElementType>
NDArrayView::NDArrayView(const NDShape& viewShape, const SparseIndexType* colStarts, const SparseIndexType* rowIndices, const ElementType* nonZeroValues, size_t numNonZeroValues, const DeviceDescriptor& device, bool readOnly/* = false*/)
: NDArrayView(AsDataType<ElementType>(), device, StorageFormat::SparseCSC, viewShape, false, AllocateTensorView<ElementType>(viewShape, StorageFormat::SparseCSC, device, numNonZeroValues))
{
if ((colStarts == nullptr) || (rowIndices == nullptr) || (nonZeroValues == nullptr) || (numNonZeroValues == 0) || (numNonZeroValues > viewShape.TotalSize()))
InvalidArgument("Invalid sparse CSC format data specified for construction of NDArrayView with shape '%S'; "
"either one of the specified buffers is null or the count (%d) of non-zero values is invalid.",
viewShape.AsString().c_str(), (int)numNonZeroValues);
auto sparseMatrix = GetWritableMatrix<ElementType>(1);
sparseMatrix->SetMatrixFromCSCFormat(colStarts, rowIndices, nonZeroValues, numNonZeroValues, sparseMatrix->GetNumRows(), sparseMatrix->GetNumCols());
m_isReadOnly = readOnly;
}
NDArrayView::NDArrayView(CNTK::DataType dataType, const DeviceDescriptor& device, CNTK::StorageFormat storageType, const NDShape& viewShape, bool readOnly, void* tensorView)
: m_dataType(dataType), m_device(device), m_storageFormat(storageType), m_viewShape(viewShape), m_isReadOnly(readOnly)
{
m_tensorView = std::shared_ptr<void>(tensorView, [this](void*) {
switch (m_dataType)
{
case DataType::Float:
delete GetTensorView<float>();
break;
case DataType::Double:
delete GetTensorView<double>();
break;
default:
LogicError("Unsupported DataType %s", DataTypeName(m_dataType));
break;
}
});
}
NDArrayView::NDArrayView(CNTK::DataType dataType, CNTK::StorageFormat storageType, const NDShape& viewShape, const DeviceDescriptor& device)
: NDArrayView(dataType, device, storageType, viewShape, false, AllocateTensorView(dataType, storageType, viewShape, device))
{}
NDArrayView::~NDArrayView()
{}
void NDArrayView::SetValue(float value)
{
if (GetDataType() == DataType::Double)
SetValue((double)value);
else
{
if (IsSparse())
LogicError("NDArrayView::SetValue: Setting a NDArrayView contents to a scalar is only allowed for objects with dense storage format.");
GetWritableMatrix<float>()->SetValue(value);
}
}
void NDArrayView::SetValue(double value)
{
if (IsSparse())
LogicError("NDArrayView::SetValue: Setting a NDArrayView contents to a scalar is only allowed for objects with dense storage format.");
GetWritableMatrix<double>()->SetValue(value);
}
template <typename ElementType>
/*static*/ std::shared_ptr<Matrix<ElementType>> NDArrayView::GetMatrixImpl(const TensorView<ElementType>* tensorView, size_t rowColSplitPoint)
{
auto tensorShape = tensorView->GetShape();
// we should always reshape for rank-0, so that batch and sequence axis goes to columns
if (tensorShape.GetRank() <= 1 && rowColSplitPoint != 0)
return tensorView->AsMatrix();
size_t splitPoint = rowColSplitPoint;
if (splitPoint == NDArrayView::AutoSelectRowColSplitPoint)
{
// Determine the split point by determining which of the axes can be
// folded and selecting the non-foldable axis as the split point
std::vector<bool> dimsToDrop(tensorShape.GetRank(), false);
for (size_t k = 1; k < tensorShape.GetRank(); ++k)
if (tensorShape.CanFlatten(k))
dimsToDrop[k - 1] = true;
// There should be at most 2 dims we cannot drop
auto numDimsThatCannotBeDropped = std::count_if(dimsToDrop.begin(), dimsToDrop.end(), [](const bool& val) {
return !val;
});
if (numDimsThatCannotBeDropped > 2)
LogicError("The TensorView (shape = %s) underlying this NDArrayView cannot be flattened to a Matrix.", ((std::string)tensorShape).c_str());
// If we can fold the entire tensor down to a vector so any of the axes can be a valid split point,
// let's pick the split point to be 1
splitPoint = 1;
if (numDimsThatCannotBeDropped > 1)
{
while (dimsToDrop[splitPoint - 1])
splitPoint++;
}
}
tensorShape.FlattenTo2DInPlace(splitPoint, "NDArrayView::GetMatrix");
return tensorView->Reshaped(tensorShape).AsMatrix();
}
template <typename ElementType>
std::shared_ptr<const Matrix<ElementType>> NDArrayView::GetMatrix(size_t rowColSplitPoint/* = AutoSelectRowColSplitPoint*/) const
{
return GetMatrixImpl<ElementType>(GetTensorView<ElementType>(), rowColSplitPoint);
}
template <typename ElementType>
std::shared_ptr<Matrix<ElementType>> NDArrayView::GetWritableMatrix(size_t rowColSplitPoint/* = AutoSelectRowColSplitPoint*/)
{
return GetMatrixImpl<ElementType>(GetWritableTensorView<ElementType>(), rowColSplitPoint);
}
template <typename ElementType>
const TensorView<ElementType>* NDArrayView::GetTensorView() const
{
if (AsDataType<ElementType>() != m_dataType)
LogicError("NDArrayView::GetTensorView: The specified ElementType %s does not match the DataType %s", typeid(ElementType).name(), DataTypeName(m_dataType));
return (const TensorView<ElementType>*)(m_tensorView.get());
}
template <typename ElementType>
TensorView<ElementType>* NDArrayView::GetWritableTensorView()
{
if (IsReadOnly())
InvalidArgument("NDArrayView::GetWritableTensorView: Cannot get a writable TensorView from a read-only NDArrayView.");
return const_cast<TensorView<ElementType>*>(GetTensorView<ElementType>());
}
NDArrayViewPtr NDArrayView::DeepClone(const DeviceDescriptor& device, bool readOnly/* = false*/) const
{
NDArrayViewPtr newView = MakeSharedObject<NDArrayView>(this->GetDataType(), this->GetStorageFormat(), this->Shape(), device);
switch (m_dataType)
{
case DataType::Float:
{
auto newMatrix = newView->GetWritableMatrix<float>();
auto thisMatrix = GetMatrix<float>();
newMatrix->AssignValuesOf(*thisMatrix);
break;
}
case DataType::Double:
{
auto newMatrix = newView->GetWritableMatrix<double>();
auto thisMatrix = GetMatrix<double>();
newMatrix->AssignValuesOf(*thisMatrix);
break;
}
default:
LogicError("NDArrayView::DeepClone: Unsupported DataType %s", DataTypeName(m_dataType));
break;
}
newView->m_isReadOnly = readOnly;
return newView;
}
void NDArrayView::CopyFrom(const NDArrayView& source)
{
if ((source.Shape() != Shape()) && (AsTensorShape(source.Shape()) != AsTensorShape(Shape())))
InvalidArgument("NDArrayView::CopyFrom: The source view shape '%S' is not same as the shape '%S' of this NDArrayView.",
source.Shape().AsString().c_str(), Shape().AsString().c_str());
if (IsReadOnly())
RuntimeError("NDArrayView::CopyFrom: Cannot modify contents of a readonly NDArrayView.");
switch (m_dataType)
{
case DataType::Float:
{
auto sourceMatrix = source.GetMatrix<float>();
auto destMatrix = GetWritableMatrix<float>();
destMatrix->AssignValuesOf(*sourceMatrix);
break;
}
case DataType::Double:
{
auto sourceMatrix = source.GetMatrix<double>();
auto destMatrix = GetWritableMatrix<double>();
destMatrix->AssignValuesOf(*sourceMatrix);
break;
}
default:
LogicError("NDArrayView::CopyFrom: Unsupported DataType %s", DataTypeName(m_dataType));
break;
}
}
NDArrayViewPtr NDArrayView::Alias(bool readOnly/* = false*/) const
{
void* tensorView = nullptr;
switch (m_dataType)
{
case DataType::Float:
tensorView = new TensorView<float>(*(GetTensorView<float>()));
break;
case DataType::Double:
tensorView = new TensorView<double>(*(GetTensorView<double>()));
break;
default:
LogicError("NDArrayView::Alias: Unsupported DataType %s", DataTypeName(m_dataType));
break;
}
return MakeSharedObject<NDArrayView>(GetDataType(), Device(), GetStorageFormat(), Shape(), IsReadOnly() || readOnly, tensorView);
}
NDArrayViewPtr NDArrayView::SliceView(const std::vector<size_t>& startOffset, const std::vector<size_t>& extent, bool readOnly) const
{
auto rank = Shape().Rank();
if (startOffset.size() != rank)
InvalidArgument("NDArrayView::SliceView: Rank (%d) of the NDArrayView does not match the dimensionality (%d) of the specified slice offset.", (int)rank, (int)startOffset.size());
if (extent.size() > rank)
InvalidArgument("NDArrayView::SliceView: Dimensionality (%d) of the specified slice extent exceeds the rank (%d) of this NDArrayView.", (int)extent.size(), (int)rank);
if (std::find(extent.begin(), extent.end(), 0) != extent.end())
InvalidArgument("NDArrayView::SliceView: Specified slice extent is zero along at least one of the axes.");
bool anyPrevAxisSliced = false;
NDShape sliceViewShape(extent);
std::vector<size_t> endOffset(rank);
for (size_t i = 0; i < rank; ++i)
{
if ((i < sliceViewShape.Rank()) && (sliceViewShape[i] == NDShape::InferredDimension))
sliceViewShape[i] = Shape()[i] - startOffset[i];
endOffset[i] = startOffset[i] + ((i < sliceViewShape.Rank()) ? sliceViewShape[i] : 1);
if (anyPrevAxisSliced && ((endOffset[i] - startOffset[i]) != 1))
InvalidArgument("NDArrayView::SliceView: Cannot create a slice which is not contiguous in memory. "
"This NDArrayView shape = %S, slice offset = %S, slice extent = %S.",
Shape().AsString().c_str(), NDShape(startOffset).AsString().c_str(), NDShape(extent).AsString().c_str());
bool isCurrentAxisSliced = (startOffset[i] != 0) || (endOffset[i] != Shape()[i]);
anyPrevAxisSliced = anyPrevAxisSliced || isCurrentAxisSliced;
}
auto flatBufferOffset = AsTensorShape(Shape()).Locate(startOffset);
auto sliceViewMatrixDims = GetMatrixDimensions(sliceViewShape);
assert((flatBufferOffset % sliceViewMatrixDims.first) == 0);
auto sliceMatrixColumnOffset = flatBufferOffset / sliceViewMatrixDims.first;
void* tensorView = nullptr;
switch (m_dataType)
{
case DataType::Float:
{
auto currentMatrix = GetMatrix<float>();
std::pair<size_t, size_t> currentMatrixDims = { currentMatrix->GetNumRows(), currentMatrix->GetNumCols() };
std::shared_ptr<Matrix<float>> slicedMatrixView;
if (sliceViewMatrixDims.first != currentMatrixDims.first)
slicedMatrixView = make_shared<Matrix<float>>(currentMatrix->Reshaped(1, currentMatrix->GetNumElements()).ColumnSlice(flatBufferOffset, sliceViewShape.TotalSize()));
else
slicedMatrixView = make_shared<Matrix<float>>(currentMatrix->ColumnSlice(sliceMatrixColumnOffset, sliceViewMatrixDims.second));
tensorView = new TensorView<float>(slicedMatrixView, AsTensorViewShape(sliceViewShape));
break;
}
case DataType::Double:
{
auto currentMatrix = GetMatrix<double>();
std::pair<size_t, size_t> currentMatrixDims = { currentMatrix->GetNumRows(), currentMatrix->GetNumCols() };
std::shared_ptr<Matrix<double>> slicedMatrixView;
if (sliceViewMatrixDims.first != currentMatrixDims.first)
slicedMatrixView = make_shared<Matrix<double>>(currentMatrix->Reshaped(1, currentMatrix->GetNumElements()).ColumnSlice(flatBufferOffset, sliceViewShape.TotalSize()));
else
slicedMatrixView = make_shared<Matrix<double>>(currentMatrix->ColumnSlice(sliceMatrixColumnOffset, sliceViewMatrixDims.second));
tensorView = new TensorView<double>(slicedMatrixView, AsTensorViewShape(sliceViewShape));
break;
}
default:
LogicError("NDArrayView::SliceView: Unsupported DataType %s", DataTypeName(m_dataType));
break;
}
return MakeSharedObject<NDArrayView>(GetDataType(), Device(), GetStorageFormat(), sliceViewShape, IsReadOnly() || readOnly, tensorView);
}
NDArrayViewPtr NDArrayView::AsShape(const NDShape& newShape) const
{
if (newShape.TotalSize() != Shape().TotalSize())
{
InvalidArgument("NDArrayView::AsShape: The total size (%d) of this view's shape '%S' must be same as the size (%d) of the newShape '%S'.",
(int)Shape().TotalSize(), Shape().AsString().c_str(),
(int)newShape.TotalSize(), newShape.AsString().c_str());
}
auto newTensorShape = AsTensorViewShape(newShape);
void* tensorView = nullptr;
switch (m_dataType)
{
case DataType::Float:
tensorView = new TensorView<float>(*(GetTensorView<float>()), newTensorShape);
break;
case DataType::Double:
tensorView = new TensorView<double>(*(GetTensorView<double>()), newTensorShape);
break;
default:
LogicError("NDArrayView::AsShape: Unsupported DataType %s", DataTypeName(m_dataType));
break;
}
return MakeSharedObject<NDArrayView>(GetDataType(), Device(), GetStorageFormat(), newShape, IsReadOnly(), tensorView);
}
// TODO: This could actually be strided?
template <typename ElementType>
ElementType* NDArrayView::WritableDataBuffer()
{
if (IsReadOnly())
InvalidArgument("NDArrayView::WritableDataBuffer: Cannot get writable data buffer from a read-only NDArrayView.");
return const_cast<ElementType*>(DataBuffer<ElementType>());
}
// TODO: This could actually be strided?
template <typename ElementType>
const ElementType* NDArrayView::DataBuffer() const
{
if (AsDataType<ElementType>() != m_dataType)
InvalidArgument("NDArrayView::DataBuffer: The specified ElementType '%s' does not match this NDArrayView's DataType '%s'.", typeid(ElementType).name(), DataTypeName(m_dataType));
if (IsSparse())
InvalidArgument("The stroage format of 'this' NDArrayView is sparse. Please use SparseDataBuffers().");
// First make sure that the underlying matrix is on the right device
auto matrix = GetMatrix<ElementType>();
matrix->TransferToDeviceIfNotThere(AsCNTKImplDeviceId(m_device), true);
return matrix->Data();
}
template <typename ElementType>
std::tuple<const ElementType *, const SparseIndexType*, const SparseIndexType*, size_t> NDArrayView::SparseCSCDataBuffers() const
{
if (AsDataType<ElementType>() != m_dataType)
InvalidArgument("NDArrayView::SparseDataBuffers: The specified ElementType '%s' does not match this NDArrayView's DataType '%s'.", typeid(ElementType).name(), DataTypeName(m_dataType));
if (!IsSparse())
RuntimeError("The storage format of 'this' NDArrayView is dense. Please use another DataBuffer().");
if(GetStorageFormat() != StorageFormat::SparseCSC)
RuntimeError("The SparseCSCDataBuffers() method only supports CSC sparse format.");
std::shared_ptr<const Matrix<ElementType>> matrix = GetMatrix<ElementType>();
auto matrixDims = GetMatrixDimensions(Shape());
if (matrix->GetNumRows() != matrixDims.first)
LogicError("The number of rows of the underlying matrix does not match the shape.");
if (matrix->GetNumCols() != matrixDims.second)
LogicError("The number of columns of the underlying matrix does not match the shape.");
matrix->TransferToDeviceIfNotThere(AsCNTKImplDeviceId(m_device), true);
if ((matrix->GetMatrixType() != Microsoft::MSR::CNTK::MatrixType::SPARSE) || (matrix->GetFormat() != Microsoft::MSR::CNTK::MatrixFormat::matrixFormatSparseCSC))
RuntimeError("NDArrayView::SparseDataBuffers: The underlying matrix of 'this' NDArrayView is not in the CSC sparse format.");
size_t numNonZeroValues;
ElementType* nonZeroValues;
SparseIndexType* colStarts;
SparseIndexType* rowIndices;
if (m_device.Type() == DeviceKind::CPU)
{
if (sizeof(CPUSPARSE_INDEX_TYPE) != sizeof(SparseIndexType))
LogicError("Inconsistent data type for sparse index in 'this' Value and the underlying matrix on CPU.");
std::shared_ptr<Microsoft::MSR::CNTK::CPUSparseMatrix<ElementType>> sparseMatrix = matrix->m_CPUSparseMatrix;
numNonZeroValues = sparseMatrix->NzCount();
nonZeroValues = static_cast<ElementType *>(sparseMatrix->NzValues());
colStarts = static_cast<SparseIndexType *>(sparseMatrix->ColLocation());
rowIndices = static_cast<SparseIndexType *>(sparseMatrix->RowLocation());
}
else if (m_device.Type() == DeviceKind::GPU)
{
if (sizeof(GPUSPARSE_INDEX_TYPE) != sizeof(SparseIndexType))
LogicError("Inconsistent data type for sparse index in 'this' Value and the underlying matrix on GPU.");
std::shared_ptr<Microsoft::MSR::CNTK::GPUSparseMatrix<ElementType>> sparseMatrix = matrix->m_GPUSparseMatrix;
numNonZeroValues = sparseMatrix->NzCount();
nonZeroValues = static_cast<ElementType *>(sparseMatrix->NzValues());
colStarts = static_cast<SparseIndexType *>(sparseMatrix->ColLocation());
rowIndices = static_cast<SparseIndexType *>(sparseMatrix->RowLocation());
}
else
{
RuntimeError("NDArrayView::SparseDataBuffers: The device %S is currently not supported.",DeviceKindName(m_device.Type()));
}
return std::tuple<ElementType *, SparseIndexType *, SparseIndexType *, size_t>(nonZeroValues, colStarts, rowIndices, numNonZeroValues);
}
template <typename ElementType>
std::tuple<const void *, const SparseIndexType*, const SparseIndexType*, size_t, size_t, size_t> NDArrayView::SparseBlockColumnDataBuffers() const
{
if (AsDataType<ElementType>() != m_dataType)
InvalidArgument("NDArrayView::SparseBlockColumnDataBuffers: The specified ElementType '%s' does not match this NDArrayView's DataType '%s'.", typeid(ElementType).name(), DataTypeName(m_dataType));
if (!IsSparse())
RuntimeError("The storage format of 'this' NDArrayView is dense. Please use another DataBuffer().");
if (GetStorageFormat() != StorageFormat::SparseBlockCol)
RuntimeError("The SparseBlockColumnDataBuffers() method only supports sparse block column format.");
std::shared_ptr<const Matrix<ElementType>> matrix = GetMatrix<ElementType>();
size_t numBlocks;
size_t numRows;
size_t numCols;
ElementType* blockValues;
SparseIndexType* blockId2Col;
SparseIndexType* col2BlockId;
if (m_device.Type() == DeviceKind::GPU)
{
if (sizeof(GPUSPARSE_INDEX_TYPE) != sizeof(SparseIndexType))
LogicError("Inconsistent data type for sparse index in 'this' Value and the underlying matrix on GPU.");
std::shared_ptr<Microsoft::MSR::CNTK::GPUSparseMatrix<ElementType>> sparseMatrix = matrix->m_GPUSparseMatrix;
numBlocks = sparseMatrix->GetBlockSize();
numRows = sparseMatrix->GetNumRows();
numCols = sparseMatrix->GetNumCols();
blockValues = static_cast<ElementType *>(sparseMatrix->NzValues());
blockId2Col = static_cast<SparseIndexType *>(sparseMatrix->BlockId2ColOrRow());
col2BlockId = static_cast<SparseIndexType *>(sparseMatrix->ColOrRow2BlockId());
}
else
{
// CPU sparse block column is not yet supported, as the index format is different from GPU sparse block column
RuntimeError("NDArrayView::SparseBlockColumnDataBuffers: The device %S is currently not supported.", DeviceKindName(m_device.Type()));
}
return std::tuple<ElementType *, SparseIndexType *, SparseIndexType *, size_t, size_t, size_t>(blockValues, blockId2Col, col2BlockId, numBlocks, numRows, numCols);
}
void NDArrayView::AdjustSparseBlockColumn(const SparseIndexType* cpuCol2BlockId, size_t numBlocks, bool useBlockId2Col)
{
switch (m_dataType)
{
case DataType::Float:
{
auto matrix = GetWritableMatrix<float>();
matrix->AdjustSparseBlockColumn(cpuCol2BlockId, numBlocks, useBlockId2Col);
break;
}
case DataType::Double:
{
auto matrix = GetWritableMatrix<double>();
matrix->AdjustSparseBlockColumn(cpuCol2BlockId, numBlocks, useBlockId2Col);
break;
}
default:
LogicError("NDArrayView::AdjustSparseBlockColumn: Unsupported DataType %s", DataTypeName(m_dataType));
break;
}
}
void NDArrayView::ChangeDevice(const DeviceDescriptor& device)
{
if (device == m_device)
return;
switch (m_dataType)
{
case DataType::Float:
{
auto matrix = GetMatrix<float>();
matrix->TransferFromDeviceToDevice(matrix->GetDeviceId(), AsCNTKImplDeviceId(device), /*isBeingMoved = */ true, /*emptyTransfer =*/ false, /*updatePreferredDevice =*/ true);
matrix->CollapseDataLocation();
break;
}
case DataType::Double:
{
auto matrix = GetMatrix<double>();
matrix->TransferFromDeviceToDevice(matrix->GetDeviceId(), AsCNTKImplDeviceId(device), /*isBeingMoved = */ true, /*emptyTransfer =*/ false, /*updatePreferredDevice =*/ true);
matrix->CollapseDataLocation();
break;
}
default:
LogicError("NDArrayView::ChangeDevice: Unsupported DataType %s", DataTypeName(m_dataType));
break;
}
m_device = device;
}
template <typename ElementType>
/*static*/ NDArrayViewPtr NDArrayView::RandomNormal(const NDShape& shape, double mean, double stdDev, unsigned long seed, const DeviceDescriptor& device /*= DeviceDescriptor::UseDefaultDevice()*/)
{
auto matrixDims = GetMatrixDimensions(shape);
auto randomNormalMatrix = std::make_shared<Matrix<ElementType>>(Matrix<ElementType>::RandomGaussian(matrixDims.first, matrixDims.second, AsCNTKImplDeviceId(device), (ElementType)mean, (ElementType)stdDev, seed));
auto tensorView = new TensorView<ElementType>(randomNormalMatrix, AsTensorViewShape(shape));
return MakeSharedObject<NDArrayView>(AsDataType<ElementType>(), device, StorageFormat::Dense, shape, false, tensorView);
}
template <typename ElementType>
/*static*/ NDArrayViewPtr NDArrayView::RandomUniform(const NDShape& shape, double rangeBegin, double rangeEnd, unsigned long seed, const DeviceDescriptor& device/* = DeviceDescriptor::UseDefaultDevice()*/)
{
auto matrixDims = GetMatrixDimensions(shape);
auto randomUniformMatrix = std::make_shared<Matrix<ElementType>>(Matrix<ElementType>::RandomUniform(matrixDims.first, matrixDims.second, AsCNTKImplDeviceId(device), (ElementType)rangeBegin, (ElementType)rangeEnd, seed));
auto tensorView = new TensorView<ElementType>(randomUniformMatrix, AsTensorViewShape(shape));
return MakeSharedObject<NDArrayView>(AsDataType<ElementType>(), device, StorageFormat::Dense, shape, false, tensorView);
}
template <typename ElementType>
ElementType NDArrayView::AsScalar() const
{
auto scalarData = this->shared_from_this();
if (scalarData->Shape().TotalSize() != 1)
LogicError("NDArrayView::AsScalar: The NDArrayView shaped '%S' is not a scalar.", scalarData->Shape().AsString().c_str());
ElementType scalar = std::numeric_limits<ElementType>::quiet_NaN();
std::shared_ptr<const NDArrayView> cpuData;
if (scalarData->Device() == DeviceDescriptor::CPUDevice())
cpuData = scalarData;
else
{
auto tmpCPUData = std::make_shared<NDArrayView>(scalarData->GetDataType(), scalarData->Shape(), CNTK::DeviceDescriptor::CPUDevice());
tmpCPUData->CopyFrom(*scalarData);
cpuData = tmpCPUData;
}
if (scalarData->GetDataType() == DataType::Float)
scalar = *(cpuData->DataBuffer<float>());
else if (scalarData->GetDataType() == DataType::Double)
scalar = static_cast<ElementType>(*(cpuData->DataBuffer<double>()));
else
LogicError("NDArrayView::AsScalar: Unsupported DataType");
return scalar;
}
std::wstring NDArrayView::AsString() const
{
wstringstream wss;
std::wstring device = DeviceKindName(m_device.Type());
wss << L"NDArrayView(" << m_viewShape.AsString() << L", " << device << L")";
return wss.str();
}
// Explicit template instantiations
template CNTK_API NDArrayViewPtr NDArrayView::RandomUniform<float>(const NDShape& shape, double rangeBegin, double rangeEnd, unsigned long seed, const DeviceDescriptor& device/* = DeviceDescriptor::UseDefaultDevice()*/);
template CNTK_API NDArrayViewPtr NDArrayView::RandomUniform<double>(const NDShape& shape, double rangeBegin, double rangeEnd, unsigned long seed, const DeviceDescriptor& device/* = DeviceDescriptor::UseDefaultDevice()*/);
template CNTK_API NDArrayViewPtr NDArrayView::RandomNormal<float>(const NDShape& shape, double mean, double stdDev, unsigned long seed, const DeviceDescriptor& device/* = DeviceDescriptor::UseDefaultDevice()*/);
template CNTK_API NDArrayViewPtr NDArrayView::RandomNormal<double>(const NDShape& shape, double mean, double stdDev, unsigned long seed, const DeviceDescriptor& device/* = DeviceDescriptor::UseDefaultDevice()*/);
template CNTK_API const float* NDArrayView::DataBuffer<float>() const;
template CNTK_API const double* NDArrayView::DataBuffer<double>() const;
template CNTK_API const TensorView<float>* NDArrayView::GetTensorView<float>() const;
template CNTK_API const TensorView<double>* NDArrayView::GetTensorView<double>() const;
template CNTK_API std::tuple<const float*, const SparseIndexType*, const SparseIndexType*, size_t> NDArrayView::SparseCSCDataBuffers<float>() const;
template CNTK_API std::tuple<const double*, const SparseIndexType*, const SparseIndexType*, size_t> NDArrayView::SparseCSCDataBuffers<double>() const;
template CNTK_API std::tuple<const void*, const SparseIndexType*, const SparseIndexType*, size_t, size_t, size_t> NDArrayView::SparseBlockColumnDataBuffers<float>() const;
template CNTK_API std::tuple<const void*, const SparseIndexType*, const SparseIndexType*, size_t, size_t, size_t> NDArrayView::SparseBlockColumnDataBuffers<double>() const;
template CNTK_API float* NDArrayView::WritableDataBuffer<float>();
template CNTK_API double* NDArrayView::WritableDataBuffer<double>();
template std::shared_ptr<const Matrix<float>> NDArrayView::GetMatrix(size_t rowColSplitPoint/* = AutoSelectRowColSplitPoint*/) const;
template std::shared_ptr<const Matrix<double>> NDArrayView::GetMatrix(size_t rowColSplitPoint/* = AutoSelectRowColSplitPoint*/) const;
template std::shared_ptr<Matrix<float>> NDArrayView::GetWritableMatrix<float>(size_t rowColSplitPoint/* = AutoSelectRowColSplitPoint*/);
template std::shared_ptr<Matrix<double>> NDArrayView::GetWritableMatrix<double>(size_t rowColSplitPoint/* = AutoSelectRowColSplitPoint*/);
template TensorView<float>* NDArrayView::GetWritableTensorView<float>();
template TensorView<double>* NDArrayView::GetWritableTensorView<double>();
template CNTK_API NDArrayView::NDArrayView(const NDShape& viewShape, const SparseIndexType* colStarts, const SparseIndexType* rowIndices, const float* nonZeroValues, size_t numNonZeroValues, const DeviceDescriptor& device, bool readOnly/* = false*/);
template CNTK_API NDArrayView::NDArrayView(const NDShape& viewShape, const SparseIndexType* colStarts, const SparseIndexType* rowIndices, const double* nonZeroValues, size_t numNonZeroValues, const DeviceDescriptor& device, bool readOnly/* = false*/);
template float NDArrayView::AsScalar<float>() const;
template double NDArrayView::AsScalar<double>() const;
}