https://github.com/Microsoft/CNTK
Tip revision: 8b776e6058882f4ad805f09e1f879950d4266992 authored by Jiamei Shuai on 05 July 2016, 06:33:35 UTC
Fix string format bug
Fix string format bug
Tip revision: 8b776e6
TensorView.h
//
// Copyright (c) Microsoft. All rights reserved.
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
//
// This implements the TensorView class, which is a layer around Matrix that reinterprets its content as a generic tensor. [fseide]
//
#pragma once
#include "Basics.h"
#include "Matrix.h"
#include "TensorShape.h"
#pragma warning(push)
#pragma warning(disable : 4251) // needs to have dll-interface to be used by clients of... caused by TensorView::m_shape which is only private. We use the same compiler everywhere.
// This class is exported from the Math.dll.
namespace Microsoft { namespace MSR { namespace CNTK {
template <class ElemType>
class MATH_API TensorView
{
public:
// -------------------------------------------------------------------
// construction
// -------------------------------------------------------------------
// reinterpret a matrix storage object (SOB) as a TensorView with a given TensorShape --this is the main constructor
TensorView(const MatrixBasePtr& sob, const TensorShape& shape);
#if 0
// cast a Matrix as a 2D TensorView (without shape change)
TensorView(const MatrixBasePtr& sob)
: m_sob(sob), m_shape(TensorShape(array<size_t, 2>{sob->GetNumRows(), sob->GetNumCols()}))
{
}
#endif
// reshape a TensorView
TensorView(const TensorView<ElemType>& other, const TensorShape& shape)
: m_sob(other.m_sob), m_shape(shape)
{
}
// copy constructor
TensorView(const TensorView<ElemType>& other)
: m_sob(other.m_sob), m_shape(other.m_shape)
{
}
// reshaped view
TensorView<ElemType> Reshaped(const TensorShape& shape) const
{
return TensorView(*this, shape);
}
// -------------------------------------------------------------------
// elementwise operations
// Result goes into 'this', and can optionally be added to the existing value.
// E.g. c.DoSumOf(beta,a,b,alpha) means c := beta * c + alpha * (a + b),
// c.AssignDiffOf(c,a) means c -= a,
// and c.AddElementwiseProductOf(a, b, 1) means c += a .* b.
// All operators support elementwise in-place operations, i.e. a, b, and c
// may all reference the same underlying SOB, with one exception:
// The output cannot be in-place and inverse-broadcasting at the same time.
// E.g. with c=[10] and a=[10 x 20], c.AssignDiffOf(c,a) will fail.
// In that case, you can use c.AddCopyOf(a,-1).
// Aliasing is not detected, so don't pass distinct TensorView objects that
// reference overlapping but not identical slices.
// If beta == 0, c is not read out, i.e. it can be uninitialized or contain NaNs.
// -------------------------------------------------------------------
#pragma push_macro("DeclareUnaryTensorOp")
#define DeclareUnaryTensorOp(oper) \
void Do##oper##Of(ElemType beta, const TensorView& a, ElemType alpha) \
{ \
DoUnaryOpOf(beta, a, alpha, ElementWiseOperator::op##oper, ElementWiseOperator::opSum); \
} \
void Assign##oper##Of(const TensorView& a, ElemType alpha = 1.0f) \
{ \
DoUnaryOpOf(0, a, alpha, ElementWiseOperator::op##oper, ElementWiseOperator::opSum); \
} \
void Add##oper##Of(const TensorView& a, ElemType alpha = 1.0f) \
{ \
DoUnaryOpOf(1.0f, a, alpha, ElementWiseOperator::op##oper, ElementWiseOperator::opSum); \
}
ForAllUnaryOps(DeclareUnaryTensorOp);
#pragma pop_macro("DeclareUnaryTensorOp")
#pragma push_macro("DeclareBinaryTensorOp")
#define DeclareBinaryTensorOp(oper) \
void Do##oper##Of(ElemType beta, const TensorView& a, const TensorView& b, ElemType alpha) \
{ \
DoBinaryOpOf(beta, a, b, alpha, ElementWiseOperator::op##oper, ElementWiseOperator::opSum); \
} \
void Assign##oper##Of(const TensorView& a, const TensorView& b, ElemType alpha = 1.0f) \
{ \
DoBinaryOpOf(0, a, b, alpha, ElementWiseOperator::op##oper, ElementWiseOperator::opSum); \
} \
void Add##oper##Of(const TensorView& a, const TensorView& b, ElemType alpha = 1.0f) \
{ \
DoBinaryOpOf(1.0f, a, b, alpha, ElementWiseOperator::op##oper, ElementWiseOperator::opSum); \
}
ForAllBinaryOps(DeclareBinaryTensorOp);
#pragma pop_macro("DeclareBinaryTensorOp")
#pragma push_macro("DeclareTernaryTensorOp")
#define DeclareTernaryTensorOp(oper) \
void Do##oper##Of(ElemType beta, const TensorView& a, const TensorView& b, const TensorView& c, ElemType alpha) \
{ \
DoTernaryOpOf(beta, a, b, c, alpha, ElementWiseOperator::op##oper, ElementWiseOperator::opSum); \
} \
void Assign##oper##Of(const TensorView& a, const TensorView& b, const TensorView& c, ElemType alpha = 1.0f) \
{ \
DoTernaryOpOf(0, a, b, c, alpha, ElementWiseOperator::op##oper, ElementWiseOperator::opSum); \
} \
void Add##oper##Of(const TensorView& a, const TensorView& b, const TensorView& c, ElemType alpha = 1.0f) \
{ \
DoTernaryOpOf(1.0f, a, b, c, alpha, ElementWiseOperator::op##oper, ElementWiseOperator::opSum); \
}
ForAllTernaryOps(DeclareTernaryTensorOp);
#pragma pop_macro("DeclareTernaryTensorOp")
void DoUnaryOpOf (ElemType beta, const TensorView& a, ElemType alpha, ElementWiseOperator op, ElementWiseOperator reductionOp);
void DoBinaryOpOf (ElemType beta, const TensorView& a, const TensorView& b, ElemType alpha, ElementWiseOperator op, ElementWiseOperator reductionOp);
void DoTernaryOpOf(ElemType beta, const TensorView& a, const TensorView& b, const TensorView& c, ElemType alpha, ElementWiseOperator op, ElementWiseOperator reductionOp);
// -------------------------------------------------------------------
// matrix product -- GEMM for flattened tensors
// Result goes into 'this', and can optionally be added to the existing value.
// [I x J x K x L] * [K x L x M x N] -> [I x J x M x N] reducing over (K,L)
// Reduction range is inferred from tensor ranks.
// [I x J], [K x L], and [M x N] must each be dense.
// Being a matrix product, the output cannot be in-place.
// If beta == 0, c is not read out, i.e. it can be uninitialized or contain NaNs.
// -------------------------------------------------------------------
void DoMatrixProductOf (ElemType beta, bool transC, const TensorView& a, bool transA, const TensorView& b, bool transB, ElemType alpha);
void AssignMatrixProductOf( bool transC, const TensorView& a, bool transA, const TensorView& b, bool transB, ElemType alpha = 1.0f) { DoMatrixProductOf(0, transC, a, transA, b, transB, alpha); }
void AddMatrixProductOf ( bool transC, const TensorView& a, bool transA, const TensorView& b, bool transB, ElemType alpha = 1.0f) { DoMatrixProductOf(1.0f, transC, a, transA, b, transB, alpha); }
shared_ptr<Matrix<ElemType>> AsMatrix() const;
private:
// -------------------------------------------------------------------
// accessors
// -------------------------------------------------------------------
const Matrix<ElemType>& GetSOB() const { return *m_sob; }
Matrix<ElemType>& GetSOB() { return *m_sob; }
const TensorShape& GetShape() const { return m_shape; }
// -------------------------------------------------------------------
// sob members
// -------------------------------------------------------------------
shared_ptr<Matrix<ElemType>> m_sob; // Storage OBject that holds the data that is being viewed with this TensorView. This is really a reference (not owing the buffer).
TensorShape m_shape; // the meta-data that describes the data's shape and/or access pattern
};
}}}
#pragma warning(pop)