https://github.com/Microsoft/CNTK
Tip revision: 956bd9ef68ed66a1a8530179e31ad7c3dc85061d authored by Snail on 05 July 2016, 02:51:02 UTC
Update SGD.cpp
Update SGD.cpp
Tip revision: 956bd9e
LinearAlgebraNodes.h
//
// Copyright (c) Microsoft. All rights reserved.
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
//
#pragma once
#include "Basics.h"
#include "ComputationNode.h"
#include "Matrix.h"
#include "TensorView.h"
#include <unordered_set>
#include <map>
#include <string>
#include <vector>
#include <stdexcept>
#include <list>
#include <memory>
#include <algorithm>
#include <utility>
#include <assert.h>
namespace Microsoft { namespace MSR { namespace CNTK {
// -----------------------------------------------------------------------
// PlusNode (summand1, summand2)
// -----------------------------------------------------------------------
template <class ElemType>
class PlusNode : public BinaryElementWiseNode<ElemType>
{
typedef BinaryElementWiseNode<ElemType> Base; UsingBinaryElementwiseNodeBaseMembers;
static const std::wstring TypeName() { return L"Plus"; }
public:
DeclareConstructorFromConfigWithNumInputs(PlusNode);
PlusNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name)
{
}
virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
{
size_t rank = DetermineElementwiseTensorRank();
auto result = ValueTensorFor(rank, fr);
auto input0 = Input(0)->ValueTensorFor(rank, fr.AllowBroadcast());
auto input1 = Input(1)->ValueTensorFor(rank, fr.AllowBroadcast());
result.AssignSumOf(input0, input1);
}
virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
{
size_t rank = DetermineElementwiseTensorRank();
auto gradient = GradientTensorFor(rank, fr);
auto inputGradient = Input(inputIndex)->GradientTensorFor(rank, fr.AllowBroadcast());
// if reduction then mask the respective input(s) (zero out the gaps)
if (Input(inputIndex)->ReducesInTimeWrt(shared_from_this()))
MaskMissingGradientColumnsToZero(fr);
inputGradient.AddCopyOf(gradient);
}
};
template class PlusNode<float>;
template class PlusNode<double>;
// -----------------------------------------------------------------------
// LogPlusNode (summand1, summand2)
// -----------------------------------------------------------------------
template <class ElemType>
class LogPlusNode : public BinaryElementWiseNode<ElemType>
{
typedef BinaryElementWiseNode<ElemType> Base; UsingBinaryElementwiseNodeBaseMembers;
static const std::wstring TypeName() { return L"LogPlus"; }
public:
DeclareConstructorFromConfigWithNumInputs(LogPlusNode);
LogPlusNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name)
{
}
virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
{
size_t rank = DetermineElementwiseTensorRank();
auto result = ValueTensorFor(rank, fr);
auto input0 = Input(0)->ValueTensorFor(rank, fr.AllowBroadcast());
auto input1 = Input(1)->ValueTensorFor(rank, fr.AllowBroadcast());
result.AssignLogSumOf(input0, input1);
}
virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
{
size_t rank = DetermineElementwiseTensorRank();
auto gradient = GradientTensorFor(rank, fr);
auto inputGradient = Input(inputIndex)->GradientTensorFor(rank, fr.AllowBroadcast());
auto input0 = Input(0)->ValueTensorFor(rank, fr.AllowBroadcast());
auto input1 = Input(1)->ValueTensorFor(rank, fr.AllowBroadcast());
// if reduction then mask the respective input(s) (zero out the gaps)
if (Input(inputIndex)->ReducesInTimeWrt(shared_from_this()))
MaskMissingGradientColumnsToZero(fr);
if (Input(inputIndex)->ReducesInTimeWrt(Input(1 - inputIndex)))
Input(1 - inputIndex)->MaskMissingValueColumnsToZero(fr);
// TODO: would be nice to state the derivative here in a comment
inputGradient.AddElementwiseProductWithLogSumDerivativeOf(gradient, input0, input1);
}
};
template class LogPlusNode<float>;
template class LogPlusNode<double>;
// -----------------------------------------------------------------------
// MinusNode (minuend, subtrahend)
// -----------------------------------------------------------------------
template <class ElemType>
class MinusNode : public BinaryElementWiseNode<ElemType>
{
typedef BinaryElementWiseNode<ElemType> Base; UsingBinaryElementwiseNodeBaseMembers;
static const std::wstring TypeName() { return L"Minus"; }
public:
DeclareConstructorFromConfigWithNumInputs(MinusNode);
MinusNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name)
{
}
virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
{
size_t rank = DetermineElementwiseTensorRank();
auto result = ValueTensorFor(rank, fr);
auto input0 = Input(0)->ValueTensorFor(rank, fr.AllowBroadcast());
auto input1 = Input(1)->ValueTensorFor(rank, fr.AllowBroadcast());
result.AssignDifferenceOf(input0, input1);
}
virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
{
size_t rank = DetermineElementwiseTensorRank();
auto gradient = GradientTensorFor(rank, fr);
auto inputGradient = Input(inputIndex)->GradientTensorFor(rank, fr.AllowBroadcast());
// if reduction then mask the respective input(s) (zero out the gaps)
if (Input(inputIndex)->ReducesInTimeWrt(shared_from_this()))
MaskMissingGradientColumnsToZero(fr);
ElemType sign = inputIndex == 0 ? 1.0f : -1.0f;
inputGradient.AddCopyOf(gradient, sign);
}
};
template class MinusNode<float>;
template class MinusNode<double>;
// -----------------------------------------------------------------------
// ElementTimesNode (factor1, factor2)
// This allows broadcasting, and can thus also scale with a row, a column, or a scalar,
// as well as mutliplying with a diagonal matrix (if represented as a column vector).
// -----------------------------------------------------------------------
template <class ElemType>
class ElementTimesNode : public BinaryElementWiseNode<ElemType>
{
typedef BinaryElementWiseNode<ElemType> Base;
UsingBinaryElementwiseNodeBaseMembers;
static const std::wstring TypeName()
{
return L"ElementTimes";
}
public:
DeclareConstructorFromConfigWithNumInputs(ElementTimesNode);
ElementTimesNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name)
{
}
virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
{
size_t rank = DetermineElementwiseTensorRank();
auto result = ValueTensorFor(rank, fr);
auto input0 = Input(0)->ValueTensorFor(rank, fr.AllowBroadcast());
auto input1 = Input(1)->ValueTensorFor(rank, fr.AllowBroadcast());
result.AssignElementwiseProductOf(input0, input1);
}
virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
{
size_t rank = DetermineElementwiseTensorRank();
auto gradient = GradientTensorFor(rank, fr);
auto inputGradient = Input(inputIndex)->GradientTensorFor(rank, fr.AllowBroadcast());
auto otherInputValue = Input(1 - inputIndex)->ValueTensorFor(rank, fr.AllowBroadcast());
// if reduction then mask the respective input(s) (zero out the gaps)
if (Input(inputIndex)->ReducesInTimeWrt(shared_from_this()))
MaskMissingGradientColumnsToZero(fr);
if (Input(inputIndex)->ReducesInTimeWrt(Input(1 - inputIndex)))
Input(1 - inputIndex)->MaskMissingValueColumnsToZero(fr);
inputGradient.AddElementwiseProductOf(gradient, otherInputValue);
}
virtual bool InputUsedInComputingInputNodesGradients(size_t /*childIndex*/) const override { return true; }
};
template class ElementTimesNode<float>;
template class ElementTimesNode<double>;
// -----------------------------------------------------------------------
// TimesNodeBase (A, B, outputRank=1)
// shared code of TimesNode and TransposeTimesNode (which transposes A)
// The common case, W * v with weights W and minibatch data v is efficiently
// implemented as a per-minibatch BLAS GEMM call.
// If A is minibatch data, then this operation is currently not efficient.
// TODO: Implement this with TensorView::DoElementwiseProductOf() and stride magic
// TODO: Transpose flags for all matrices, inputs and outputs?
// TODO: allow outputRank < 0 meaning to denote "all but", from right
// -----------------------------------------------------------------------
template <class ElemType, bool m_transpose>
class TimesNodeBase : public ComputationNode<ElemType>, public NumInputs<2>
{
typedef ComputationNode<ElemType> Base; UsingComputationNodeMembers; using Base::OperationName; \
public:
TimesNodeBase(DEVICEID_TYPE deviceId, const wstring& name, size_t outputRank = 1)
: Base(deviceId, name), m_outputRank(outputRank)
{
}
virtual void CopyTo(ComputationNodeBasePtr nodeP, const std::wstring& newName, const CopyNodeFlags flags) const override
{
Base::CopyTo(nodeP, newName, flags);
if (flags & CopyNodeFlags::copyNodeValue)
{
auto node = dynamic_pointer_cast<TimesNodeBase<ElemType, m_transpose>>(nodeP);
node->m_outputRank = m_outputRank;
}
}
void Save(File& fstream) const
{
Base::Save(fstream);
fstream << m_outputRank;
}
virtual void Load(File& fstream, size_t modelVersion) override
{
Base::Load(fstream, modelVersion);
if (modelVersion >= CNTK_MODEL_VERSION_3)
fstream >> m_outputRank;
else
m_outputRank = 1;
}
private:
// if the left argument of the matrix product (A) has a time axis, it can only be applied sample by sample
// where each sample is treated as a separate matrix object (as a consequence, it then also applies to B and the result as well)
TensorView<ElemType> OneSampleTensorFor(int inputIndex/*-1 for output*/, bool gradient/*instead of value*/, const FrameRange& fr)
{
auto input = inputIndex < 0 ? this : Input(inputIndex).get();
auto data = gradient ? input->GradientPtr() : input->ValuePtr();
size_t rank = input->GetSampleLayout().GetRank();
if (inputIndex == 0 && m_transpose && rank == 1) // transposing a 1D tensor implies it is really a 2D tensor. Note that m_transpose applies to left operand only.
rank = 2;
if (!Input(0)->HasMBLayout()) // left input is no MB data: run normally
return input->DataTensorFor(data, rank, fr);
auto tensorShape = input->GetOneSampleTensorSliceFor(rank, fr);
return TensorView<ElemType>(data, tensorShape);
}
public:
virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
{
// If argument A is minibatch data, then this must be performed frame-by-frame, sequence-by-sequence, one GEMM call each.
// This will be inefficient. We hope this will be the baseline of a future, more efficient TensorView-based implementation.
if (!fr.IsOneColumnWrt(Input(0)->GetMBLayout()))
{
// recursively call ourselves for each individual time and sequence
auto timeRange = fr.GetTimeRange();
auto sequenceRange = fr.GetSequenceRange();
for (auto t = timeRange.first; t < timeRange.second; t++)
for (auto s = sequenceRange.first; s < sequenceRange.second; s++)
ForwardProp(fr.WithTimeStep(t).Sequence(s));
return;
}
// TensorView::DoMatrixProductOf() will reduce each tensor object into a 2D tensor (or fail if it cannot)
// and recreate actual Matrix objects (in case of sparse, they must be identical to the original tensor storage object).
// Transposition is applied after flattening into 2D, but only allowed if the input sample is 2D anyway.
auto input0 = OneSampleTensorFor(0, /*gradient=*/false, fr.AllowBroadcast());
auto input1 = OneSampleTensorFor(1, /*gradient=*/false, fr.AllowBroadcast());
auto output = OneSampleTensorFor(-1, /*gradient=*/false, fr);
output.AssignMatrixProductOf(false/*transC*/, input0, m_transpose/*transA*/, input1, false/*transB*/);
}
virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
{
// special treatment if A is minibatch data; see Forward() for comment
if (!fr.IsOneColumnWrt(Input(0)->GetMBLayout()))
{
auto timeRange = fr.GetTimeRange();
auto sequenceRange = fr.GetSequenceRange();
for (auto t = timeRange.first; t < timeRange.second; t++) // step left to right to allow to build a sparse matrix
for (auto s = sequenceRange.first; s < sequenceRange.second; s++)
BackpropTo(inputIndex, fr.WithTimeStep(t).Sequence(s));
return;
}
// this potentially computes inner products over time, so we must mask gaps to 0
if (Input(inputIndex)->ReducesInTimeWrt(shared_from_this()))
MaskMissingGradientColumnsToZero(fr);
if (Input(inputIndex)->ReducesInTimeWrt(Input(1 - inputIndex)))
Input(1 - inputIndex)->MaskMissingValueColumnsToZero(fr);
if (inputIndex == 0) // left derivative
{
// currently we only support one combination when the input is sparse
// If input data is sparse, then gradient is block sparse.
// BUGBUG: This does not accumulate into the Input(0)->Gradient, which might cause problems elsewhere.
if (Input(1)->Value().GetMatrixType() == SPARSE && Input(0)->Gradient().GetMatrixType() == DENSE && Gradient().GetMatrixType() == DENSE)
Input(0)->Gradient().SwitchToMatrixType(SPARSE, MatrixFormat::matrixFormatSparseBlockCol, false);
auto input0Gradient = OneSampleTensorFor(0, /*gradient=*/true, fr.AllowBroadcast());
auto input1 = OneSampleTensorFor(1, /*gradient=*/false, fr.AllowBroadcast());
auto outputGradient = OneSampleTensorFor(-1, /*gradient=*/true, fr);
input0Gradient.AddMatrixProductOf(m_transpose/*transC*/, outputGradient, false/*transA*/, input1, true/*transB*/);
}
else if (inputIndex == 1) // right derivative
{
auto input0 = OneSampleTensorFor(0, /*gradient=*/false, fr.AllowBroadcast());
auto input1Gradient = OneSampleTensorFor(1, /*gradient=*/true, fr.AllowBroadcast());
auto outputGradient = OneSampleTensorFor(-1, /*gradient=*/true, fr);
input1Gradient.AddMatrixProductOf(false/*transC*/, input0, !m_transpose/*transA*/, outputGradient, false/*transB*/);
}
}
virtual bool OutputUsedInComputingInputNodesGradients() const override { return false; }
// but both *inputs* are used, so we don't overload the InputUsed-() function which defaults to 'true'
virtual void /*ComputationNodeBase::*/ Validate(bool isFinalValidationPass) override
{
Base::Validate(isFinalValidationPass);
InferMBLayoutFromInputsForStandardCase(isFinalValidationPass);
bool transpose = m_transpose; // (assigning to a non-const variable avoids a compiler warning C4127: conditional expression is constant)
// get tensor shapes
auto dimsA = Input(0)->GetSampleLayout().GetDims();
auto dimsB = Input(1)->GetSampleLayout().GetDims();
string dimsAstring = string(Input(0)->GetSampleLayout()); // for error messages
string dimsBstring = string(Input(1)->GetSampleLayout());
// validate and infer
if (isFinalValidationPass || (dimsA.size() > 0 && dimsB.size() > 0)) // only if we got at least some input dimensions to work with or need to wrap up
{
// if transposing then only support actual matrices or column vectors
if (transpose)
{
if (dimsA.size() == 1) // column vector transposed becomes a 2D tensor
dimsA.push_back(1);
else if (dimsA.size() != 2)
InvalidArgument("%ls %ls operation: Transposition requires a 2D tensor (matrix) or a 1D tensor (column vector), instead of a [%s].", NodeName().c_str(), OperationName().c_str(), dimsAstring.c_str());
else if (m_outputRank != 1)
InvalidArgument("%ls %ls operation: The outputRank (%d) must be 1 when transposing.", NodeName().c_str(), OperationName().c_str(), (int)m_outputRank);
// swap them temporarily, to get transposition out of the way for validation
std::swap(dimsA[0], dimsA[1]);
}
if (m_outputRank > dimsA.size()) // note: it may be equal in case of dyadic product uv'
InvalidArgument("%ls %ls operation: outputRank %d exceeds left argument's shape [%s].", NodeName().c_str(), OperationName().c_str(), (int)m_outputRank, dimsAstring.c_str());
auto numReductionDims = dimsA.size() - m_outputRank; // we reduce over the remaining dims; this is their number. Can be 0 in case of dyadic product uv'
if (numReductionDims > dimsB.size())
InvalidArgument("%ls %ls operation: right argument shape [%s] has too few dimensions for outputRank %d.", NodeName().c_str(), OperationName().c_str(), dimsBstring.c_str(), (int)m_outputRank);
#if 1 // support for legacy models when only the matrix dimensions had to match
// Note: This is non-ambiguous w.r.t. valid new configurations because this condition would otherwise just be considered an error.
// But it will fail to discover trailing reduction dimensions that are 1. We assume that no such legacy models exist.
// Note: This is very ugly [Wayne Xiong]. I agree [fseide].
if (dimsA.size() == 2 && !transpose && m_outputRank == 1 && dimsA[1] != dimsB[0])
{
// search whether we can interpret dimsA[1] as the flattening of the first dimensions
size_t dim = 1;
for (size_t k = 0; k < dimsB.size(); k++)
{
dim *= dimsB[k];
if (dim == dimsA[1])
{
// OK, we have an explanation: Patch dimsA and back-patch the sample layout of Input(0)
numReductionDims = k + 1;
dimsA.resize(m_outputRank + numReductionDims);
for (size_t kk = 0; kk < numReductionDims; kk++)
dimsA[m_outputRank + kk] = dimsB[kk];
Input(0)->SetDims(TensorShape(dimsA), false);
fprintf(stderr, "\n%ls %ls operation: For legacy compatibility, the sample layout of left input (%ls %ls operation) was patched to [%s] (from [%s])\n",
NodeName().c_str(), OperationName().c_str(), Input(0)->NodeName().c_str(), Input(0)->OperationName().c_str(), string(Input(0)->GetSampleLayout()).c_str(), dimsAstring.c_str());
dimsAstring = string(Input(0)->GetSampleLayout()); // for error messages
break; // we will continue with this patched up model from here on
}
}
}
#endif
// validate or automatically infer dimension inference for learnable parameters
for (size_t k = 0; k < m_outputRank; k++) // outputRank dimensions cannot be inferred
if (dimsA[k] == 0)
InvalidArgument("%ls %ls operation: The outputRank (%d) dimensions in left argument's shape [%s] must not be 0.", NodeName().c_str(), OperationName().c_str(), (int)m_outputRank, dimsAstring.c_str());
// fill in the missing ones
// We fill in dimensions given as 0. The tensor rank is not inferred.
for (size_t k = m_outputRank; k < dimsA.size(); k++)
{
auto& dimA = dimsA[k];
auto& dimB = dimsB[k - m_outputRank];
if (isFinalValidationPass && dimB == 0)
InvalidArgument("%ls %ls operation: Right [%s] operand must not have zero dimensions.", NodeName().c_str(), OperationName().c_str(), dimsBstring.c_str());
else if (dimA == 0)
dimA = dimB; // infer dimension
else if (isFinalValidationPass && dimA != dimB)
InvalidArgument("%ls %ls operation: Left [%s] and right [%s] operands' shapes are not compatible.", NodeName().c_str(), OperationName().c_str(), dimsAstring.c_str(), dimsBstring.c_str());
}
// now determine result dimensions
auto dimsC = dimsA;
dimsC.resize(m_outputRank); // output dims
for (size_t k = numReductionDims; k < dimsB.size(); k++)
dimsC.push_back(dimsB[k]); // input dims
SetDims(TensorShape(dimsC), HasMBLayout());
// update dimensions of A
if (transpose)
std::swap(dimsA[0], dimsA[1]);
// update if LearnableParameter
Input(0)->ValidateInferInputDimsFrom(TensorShape(dimsA));
}
}
virtual void AllocateGradientMatricesForInputs(MatrixPool& matrixPool) override
{
// this is a special handling case. We need to allocate sparse matrix directly instead of from pool.
if (Input(0)->NeedsGradient() && Input(1)->Value().GetMatrixType() == SPARSE)
{
Input(0)->CreateGradientMatrixIfNull();
Input(0)->Gradient().SwitchToMatrixType(SPARSE, MatrixFormat::matrixFormatSparseBlockCol, false);
}
// we need to call base allocation at end since we will need to allocate special ones first
// so that the default allocator will not allocate it again.
Base::AllocateGradientMatricesForInputs(matrixPool);
}
private:
size_t m_outputRank;
};
// -----------------------------------------------------------------------
// TimesNode (A, B, outputRank=1) -- matrix product
// Right operand and output can have MB layout, while left operand cannot.
// This is generalized to tensors, in that B's leading dimension(s) must match
// the trailing dimension(s) of A, and those dimensions get flattened into a single
// dimension, after which the regular matrix product is applied.
// Leading dimension(s) of A and trailing ones of B remain unchanged.
// For example:
// [I x J x K] * [J x K x L x *] = [I x L x *]
// How many dimensions must match is controlled by an optional parameter
// 'outputRank', where all but the first 'outputRank' dimensions of A must match.
// 'outputRank' defaults to 1, which is used in the above example.
// Example for outputRank = 2:
// [I x J x K] * [K x L x M x *] = [I x J x L x M x *]
// -----------------------------------------------------------------------
template <class ElemType>
class TimesNode : public TimesNodeBase<ElemType, false>
{
typedef TimesNodeBase<ElemType, false> Base;
UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName() { return L"Times"; }
public:
TimesNode(DEVICEID_TYPE deviceId, const wstring& name, size_t outputRank = 1)
: Base(deviceId, name, outputRank)
{
}
TimesNode(const ScriptableObjects::IConfigRecordPtr configp)
: TimesNode(configp->Get(L"deviceId"), L"<placeholder>", configp->Get(L"outputRank"))
{
AttachInputsFromConfig(configp, this->GetExpectedNumInputs());
}
};
template class TimesNode<float>;
template class TimesNode<double>;
// -----------------------------------------------------------------------
// TransposeTimesNode (A', B)
// Right operand and output can have MB layout, while left operand cannot.
// This differs from TimesNode in that A is transposed, where A must be a
// rank-1 or rank-2 tensor.
// A common use of transposition is trace(X'X) where X is a matrix of samples.
// This can be more efficiently implemented as ReduceSum (ElementTimes (X, X))
// -----------------------------------------------------------------------
template <class ElemType>
class TransposeTimesNode : public TimesNodeBase<ElemType, true>
{
typedef TimesNodeBase<ElemType, true> Base;
UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName() { return L"TransposeTimes"; }
public:
DeclareConstructorFromConfigWithNumInputs(TransposeTimesNode);
TransposeTimesNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name, /*outputRank=*/1)
{
}
};
template class TransposeTimesNode<float>;
template class TransposeTimesNode<double>;
// -----------------------------------------------------------------------
// DiagTimesNode (vector representing the diagonal of a square matrix, data)
// TODO: This is redundant with ElementTimes and should be removed (with a compat stub).
// -----------------------------------------------------------------------
template <class ElemType>
class DiagTimesNode : public ComputationNode<ElemType>, public NumInputs<2>
{
typedef ComputationNode<ElemType> Base;
UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName()
{
return L"DiagTimes";
}
public:
DeclareConstructorFromConfigWithNumInputs(DiagTimesNode);
DiagTimesNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name)
{
}
virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
{
if (inputIndex == 0) // left derivative
{
Matrix<ElemType> sliceOutputGrad = MaskedGradientFor(fr); // use Masked- version since this is reducing over frames
Matrix<ElemType> sliceInput1Value = Input(1)->MaskedValueFor(fr);
m_innerproduct->AssignInnerProductOf(sliceOutputGrad, sliceInput1Value, false);
Input(0)->GradientAsMatrix() += *m_innerproduct;
}
else // right derivative
{
Matrix<ElemType> sliceOutputGrad = GradientFor(fr);
Matrix<ElemType> sliceInput1Grad = Input(1)->GradientFor(fr);
m_rightGradient->SetValue(sliceOutputGrad);
m_rightGradient->ColumnElementMultiplyWith(Input(0)->ValueAsMatrix());
sliceInput1Grad += *m_rightGradient;
}
}
virtual bool OutputUsedInComputingInputNodesGradients() const override
{
// The DiagTimesNode does not require its output value for computing
// the gradients of its input nodes
return false;
}
virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
{
Matrix<ElemType> sliceInput1Value = Input(1)->ValueFor(fr);
Matrix<ElemType> sliceOutputValue = ValueFor(fr);
sliceOutputValue.AssignValuesOf(sliceInput1Value);
sliceOutputValue.ColumnElementMultiplyWith(Input(0)->ValueAsMatrix());
}
virtual void /*ComputationNodeBase::*/ Validate(bool isFinalValidationPass) override
{
Base::Validate(isFinalValidationPass);
InferMBLayoutFromInputsForStandardCase(isFinalValidationPass);
size_t rows0 = Input(0)->GetAsMatrixNumRows();
size_t rows1 = Input(1)->HasMBLayout() ? Input(1)->GetSampleMatrixNumRows() : Input(1)->GetAsMatrixNumRows();
// if dimension not specified we assume two operands' dimensions should match
Input(0)->ValidateInferInputDimsFrom(TensorShape(rows1));
if (Input(1)->HasMBLayout())
{
// infer rows1 as rows0
Input(1)->ValidateInferInputDimsFrom(TensorShape(rows0));
SetDims(TensorShape(rows0), true);
}
else // multiplying two straight matrices
{
size_t cols1 = Input(1)->GetAsMatrixNumCols();
// infer rows1 as rows0
Input(1)->ValidateInferInputDimsFrom(TensorShape(rows0, cols1));
SetDims(TensorShape(rows0, cols1), false);
}
// update after inference
rows0 = Input(0)->GetAsMatrixNumRows();
rows1 = Input(1)->HasMBLayout() ? Input(1)->GetSampleMatrixNumRows() : Input(1)->GetAsMatrixNumRows();
if (isFinalValidationPass && rows0 != rows1)
InvalidArgument("The inner matrix dimension in the %ls %ls operation does not match (%d vs. %d).", NodeName().c_str(), OperationName().c_str(), (int) rows1, (int) rows0);
size_t cols0 = Input(0)->GetAsMatrixNumCols();
if (isFinalValidationPass && cols0 != 1)
InvalidArgument("The first matrix should be a column vector representing the diagonal of a square matrix in the DiagTimes operation.");
SetDims(Input(1));
}
virtual void CopyTo(ComputationNodeBasePtr nodeP, const std::wstring& newName, const CopyNodeFlags flags) const override
{
Base::CopyTo(nodeP, newName, flags);
if (flags & CopyNodeFlags::copyNodeValue)
{
auto node = dynamic_pointer_cast<DiagTimesNode<ElemType>>(nodeP);
node->m_innerproduct->SetValue(*m_innerproduct);
node->m_rightGradient->SetValue(*m_rightGradient);
}
}
// request matrices that are needed for gradient computation
virtual void RequestMatricesBeforeBackprop(MatrixPool& matrixPool)
{
Base::RequestMatricesBeforeBackprop(matrixPool);
RequestMatrixFromPool(m_innerproduct, matrixPool);
RequestMatrixFromPool(m_rightGradient, matrixPool);
}
// release gradient and temp matrices that no longer needed after all the children's gradients are computed.
virtual void ReleaseMatricesAfterBackprop(MatrixPool& matrixPool)
{
Base::ReleaseMatricesAfterBackprop(matrixPool);
ReleaseMatrixToPool(m_innerproduct, matrixPool);
ReleaseMatrixToPool(m_rightGradient, matrixPool);
}
private:
shared_ptr<Matrix<ElemType>> m_innerproduct;
shared_ptr<Matrix<ElemType>> m_rightGradient;
};
template class DiagTimesNode<float>;
template class DiagTimesNode<double>;
// -----------------------------------------------------------------------
// SumElementsNode (input)
// Sums up all elements in the input across all samples into a single scalar.
// When applied to minibatch data, this will sum across all sequences in the
// minibatch, like a training-criterion node. This is one of the few operations
// that cross the boundary between input sequences.
// Note that SGD itself aggregates over samples in a criterion node.
// So the only proper use of this node is for multi-task learning, where
// different nodes have different numbers of samples (sequence lenth).
// -----------------------------------------------------------------------
template <class ElemType>
class SumElementsNode : public ComputationNodeNonLooping /*ComputationNode*/<ElemType>, public NumInputs<1>
{
typedef ComputationNodeNonLooping<ElemType> Base;
UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName()
{
return L"SumElements";
}
public:
DeclareConstructorFromConfigWithNumInputs(SumElementsNode);
SumElementsNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name)
{
}
virtual void /*ComputationNodeNonLooping::*/ ForwardPropNonLooping() override
{
FrameRange fr(Input(0)->GetMBLayout());
// TODO: change to TensorView and AssignCopyOf() with reduction
Value().AssignSumOfElements(Input(0)->MaskedValueFor(fr)); // since we are reducing over frames, we must first mask gaps in input to zero
}
virtual void /*ComputationNodeNonLooping::*/ BackpropToNonLooping(size_t inputIndex) override
{
FrameRange fr(Input(0)->GetMBLayout());
Input(0)->GradientFor(fr) += Gradient(); // here the assumption is that gradientValues are 1x1 matrix
}
virtual bool OutputUsedInComputingInputNodesGradients() const override { return false; }
virtual bool InputUsedInComputingInputNodesGradients(size_t /*childIndex*/) const override { return false; }
virtual void /*ComputationNodeBase::*/ Validate(bool isFinalValidationPass) override
{
ValidateUnaryReduce(isFinalValidationPass);
}
};
template class SumElementsNode<float>;
template class SumElementsNode<double>;
// -----------------------------------------------------------------------
// TransposeDimensions (input, axis1, axis2)
// - swaps index dimensions axis1 and axis2. The values are 1-based; 1 stands for the leading dimension.
// - new dimensions can be created; e.g. a column vector can be transposed into a row vector, which is a [1 x N] tensor
// - transposing into the time dimension is currently not supported
// - internally implemented with tensor lib by shuffling dimensions with their strides
// - input may be minibatch data or not
// Transpose (input) = TransposeDimensions (input, 1, 2)
// -----------------------------------------------------------------------
template <class ElemType>
class TransposeDimensionsNode : public ComputationNode /*ComputationNode*/<ElemType>, public NumInputs<1>
{
typedef ComputationNode<ElemType> Base; UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName() { return L"TransposeDimensions"; }
public:
TransposeDimensionsNode(DEVICEID_TYPE deviceId, const wstring& name, int axis1 = 1, int axis2 = 2)
: Base(deviceId, name), m_axis1(axis1), m_axis2(axis2)
{
}
TransposeDimensionsNode(const ScriptableObjects::IConfigRecordPtr configp)
: TransposeDimensionsNode(configp->Get(L"deviceId"), L"<placeholder>", configp->Get(L"axis1"), configp->Get(L"axis2"))
{
AttachInputsFromConfig(configp, this->GetExpectedNumInputs());
}
void Save(File& fstream) const
{
Base::Save(fstream);
fstream << m_axis1 << m_axis2;
}
virtual void Load(File& fstream, size_t modelVersion) override
{
Base::Load(fstream, modelVersion);
if (modelVersion >= CNTK_MODEL_VERSION_3)
fstream >> m_axis1 >> m_axis2;
else
m_axis1 = 1, m_axis2 = 2; // default
}
private:
// compute the transposed tensor shape (in-place)
void TransposeShape(TensorShape& shape) const
{
assert(m_axis1 > 0 && m_axis2 > 0);
size_t i = m_axis1 - 1;
size_t j = m_axis2 - 1;
shape.SwapDimsInPlace(i, j);
}
// get the transposed input shape
// Using this shape yields the same tensor but index positions are swapped.
TensorShape GetTransposedTensorSliceFor(size_t rank, const FrameRange& fr)
{
auto shape = Input(0)->GetTensorSliceFor(rank, fr);
TransposeShape(shape);
return shape;
}
public:
virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
{
size_t rank = DetermineElementwiseTensorRank();
auto output = ValueTensorFor( rank, fr);
auto input = TensorView<ElemType>(Input(0)->ValuePtr(), GetTransposedTensorSliceFor(rank, fr));
output.AssignCopyOf(input);
}
virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
{
size_t rank = DetermineElementwiseTensorRank();
auto outputGradient = GradientTensorFor( rank, fr);
auto inputGradient = TensorView<ElemType>(Input(0)->GradientPtr(), GetTransposedTensorSliceFor(rank, fr));
inputGradient.AddCopyOf(outputGradient);
}
virtual bool OutputUsedInComputingInputNodesGradients() const override { return false; }
virtual bool InputUsedInComputingInputNodesGradients(size_t /*childIndex*/) const override { return false; }
virtual void /*ComputationNodeBase::*/ Validate(bool isFinalValidationPass) override
{
assert(m_inputs.size() == 1);
ComputationNodeBase::Validate(isFinalValidationPass);
InferMBLayoutFromInputsForStandardCase(isFinalValidationPass);
// input shape
auto shape = Input(0)->GetSampleLayout();
// validate indices
if (m_axis1 < 1 || m_axis2 < 1)
InvalidArgument("%ls %ls operation: Indices to transpose must be >= 1.", NodeName().c_str(), OperationName().c_str());
size_t i = m_axis1 - 1;
size_t j = m_axis2 - 1;
if (i >= shape.GetRank() && j >= shape.GetRank())
InvalidArgument("%ls %ls operation: At least one index must refer to an existing index.", NodeName().c_str(), OperationName().c_str());
// pad
// Permutation is allowed to create new dimensions, specifically to be able to transpose a [N] column vector into a [1 x N] row vector.
// One can also use SplitDimensions() for this, but this seems a natural thing to do.
size_t maxij = std::max(i, j);
if (maxij >= shape.GetRank())
shape.PadRankInPlace(maxij + 1);
// apply the permutation
TransposeShape(shape);
// drop the strides, since output is dense (swapped strides will be used with the input in ForwardProp())
SetDims(TensorShape(shape.GetDims()), HasMBLayout());
}
private:
int m_axis1, m_axis2; // the two dimensions (axes, 1-based) to swap
};
template class TransposeDimensionsNode<float>;
template class TransposeDimensionsNode<double>;
// -----------------------------------------------------------------------
// CosDistanceNode (left, right)
// column-wise cos distance
// TODO: Would it be useful to allow one of the two to be a single column?
// TODO: Allow to reduce only over a single dimension, or a subset.
// -----------------------------------------------------------------------
template <class ElemType>
class CosDistanceNode : public ComputationNode<ElemType>, public NumInputs<2>
{
typedef ComputationNode<ElemType> Base;
UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName()
{
return L"CosDistance";
}
public:
DeclareConstructorFromConfigWithNumInputs(CosDistanceNode);
CosDistanceNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name)
{
}
virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
{
// functionValues, invNorm0, invNorm1 - output from the EvaluateNode() method
// temp, rightTerm, leftTerm - temporary matrices
if (inputIndex == 0) // left derivative
m_temp->AssignElementProductOf(*m_invNorm0, *m_invNorm0);
else // right derivative
m_temp->AssignElementProductOf(*m_invNorm1, *m_invNorm1);
m_temp->ElementMultiplyWith(ValueFor(fr));
m_rightTerm->SetValue(Input(inputIndex)->ValueFor(fr));
m_rightTerm->RowElementMultiplyWith(*m_temp);
m_temp->AssignElementProductOf(*m_invNorm0, *m_invNorm1);
m_leftTerm->SetValue(Input(1 - inputIndex)->ValueFor(fr));
m_leftTerm->RowElementMultiplyWith(*m_temp);
*m_leftTerm -= *m_rightTerm;
m_leftTerm->RowElementMultiplyWith(GradientFor(fr));
Input(inputIndex)->GradientFor(fr) += *m_leftTerm;
}
virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
{
Matrix<ElemType> sliceInput0Value = Input(0)->ValueFor(fr);
Matrix<ElemType> sliceInput1Value = Input(1)->ValueFor(fr);
Matrix<ElemType> sliceOutputValue = ValueFor(fr);
m_invNorm0->AssignVectorNorm2Of(sliceInput0Value, true);
m_invNorm0->AssignElementInverseOf(*m_invNorm0);
m_invNorm1->AssignVectorNorm2Of(sliceInput1Value, true);
m_invNorm1->AssignElementInverseOf(*m_invNorm1);
sliceOutputValue.AssignInnerProductOf(sliceInput0Value, sliceInput1Value, true);
sliceOutputValue.ElementMultiplyWith(*m_invNorm0);
sliceOutputValue.ElementMultiplyWith(*m_invNorm1);
// TODO: This formulation above would allow to use the TensorView lib for this, with automatic broadcasting.
}
virtual void /*ComputationNodeBase::*/ Validate(bool isFinalValidationPass) override
{
Base::Validate(isFinalValidationPass);
InferMBLayoutFromInputsForStandardCase(isFinalValidationPass);
ValidateInferBinaryInputDims();
// TODO: We could do something more interesting with tensors.
// E.g. apply a cos distance of a whole set of data with a single reference.
SetDims(TensorShape(1), Input(1)->HasMBLayout());
}
virtual void CopyTo(ComputationNodeBasePtr nodeP, const std::wstring& newName, const CopyNodeFlags flags) const override
{
Base::CopyTo(nodeP, newName, flags);
if (flags & CopyNodeFlags::copyNodeValue)
{
auto node = dynamic_pointer_cast<CosDistanceNode<ElemType>>(nodeP);
node->m_invNorm0->SetValue(*m_invNorm0);
node->m_invNorm1->SetValue(*m_invNorm1);
node->m_leftTerm->SetValue(*m_leftTerm);
node->m_rightTerm->SetValue(*m_rightTerm);
node->m_temp->SetValue(*m_temp);
}
}
// request matrices needed to do node function value evaluation
virtual void RequestMatricesBeforeForwardProp(MatrixPool& matrixPool)
{
Base::RequestMatricesBeforeForwardProp(matrixPool);
RequestMatrixFromPool(m_invNorm0, matrixPool);
RequestMatrixFromPool(m_invNorm1, matrixPool);
}
// request matrices that are needed for gradient computation
virtual void RequestMatricesBeforeBackprop(MatrixPool& matrixPool)
{
Base::RequestMatricesBeforeBackprop(matrixPool);
RequestMatrixFromPool(m_leftTerm, matrixPool);
RequestMatrixFromPool(m_rightTerm, matrixPool);
RequestMatrixFromPool(m_temp, matrixPool);
}
// release gradient and temp matrices that no longer needed after all the children's gradients are computed.
virtual void ReleaseMatricesAfterBackprop(MatrixPool& matrixPool)
{
Base::ReleaseMatricesAfterBackprop(matrixPool);
ReleaseMatrixToPool(m_invNorm0, matrixPool);
ReleaseMatrixToPool(m_invNorm1, matrixPool);
ReleaseMatrixToPool(m_leftTerm, matrixPool);
ReleaseMatrixToPool(m_rightTerm, matrixPool);
ReleaseMatrixToPool(m_temp, matrixPool);
}
private:
// invNorm nodes tranfer data between ForwardProp and BackpropTo
shared_ptr<Matrix<ElemType>> m_invNorm0;
shared_ptr<Matrix<ElemType>> m_invNorm1;
// the rest are temporaries, values don't need to be maintained
shared_ptr<Matrix<ElemType>> m_leftTerm;
shared_ptr<Matrix<ElemType>> m_rightTerm;
shared_ptr<Matrix<ElemType>> m_temp;
};
template class CosDistanceNode<float>;
template class CosDistanceNode<double>;
// -----------------------------------------------------------------------
// KhatriRaoProductNode (left, right)
// Compute an outer product of column vectors (for each sample).
// TODO: This is a special kind of tensor product, and calls for a tensor representation.
// -----------------------------------------------------------------------
template <class ElemType>
class KhatriRaoProductNode : public ComputationNode<ElemType>, public NumInputs<2>
{
typedef ComputationNode<ElemType> Base;
UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName()
{
return L"KhatriRaoProduct";
}
public:
DeclareConstructorFromConfigWithNumInputs(KhatriRaoProductNode);
KhatriRaoProductNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name)
{
}
virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
{
Matrix<ElemType> sliceOutputGrad = GradientFor(fr);
if (inputIndex == 0) // left derivative
{
Matrix<ElemType> sliceInput0Grad = Input(0)->GradientFor(fr);
Matrix<ElemType> sliceInput1Value = Input(1)->ValueFor(fr);
sliceInput0Grad.AddColumnReshapeProductOf(sliceOutputGrad, sliceInput1Value, false);
}
else // right derivative
{
Matrix<ElemType> sliceInput0Value = Input(0)->ValueFor(fr);
Matrix<ElemType> sliceInput1Grad = Input(1)->GradientFor(fr);
sliceInput1Grad.AddColumnReshapeProductOf(sliceOutputGrad, sliceInput0Value, true);
}
}
virtual bool OutputUsedInComputingInputNodesGradients() const override
{
// The KhatriRaoProductNode does not require its output value for computing
// the gradients of its input nodes
return false;
}
virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
{
ValueFor(fr).AssignKhatriRaoProductOf(Input(0)->ValueFor(fr), Input(1)->ValueFor(fr));
}
virtual void /*ComputationNodeBase::*/ Validate(bool isFinalValidationPass) override
{
Base::Validate(isFinalValidationPass);
InferMBLayoutFromInputsForStandardCase(isFinalValidationPass);
size_t rows0 = Input(0)->GetSampleMatrixNumRows();
size_t rows1 = Input(1)->GetSampleMatrixNumRows();
// after KhatriRaoProduct the structure is lost
// TODO: ^^ Is that correct? Should we use a tensor here, TensorShape(rows0, rows1)?
SetDims(TensorShape(rows0 * rows1), HasMBLayout());
}
};
template class KhatriRaoProductNode<float>;
template class KhatriRaoProductNode<double>;
// -----------------------------------------------------------------------
// CosDistanceWithNegativeSamplesNode (left, right, shift, neg)
//
// Left and right forms pairs of positive samples. They are symmetric but usually
// the search key is used as the left. For example, Left is search query and right is document.
// The negative samples are formed on the fly by shifting the right side.
// The 'shift' indicates how many samples in the right node you should shift to form each negative sample pair.
// It is often choose to be one. 'Neg' indicates how many negative samples you want to generate.
// -----------------------------------------------------------------------
template <class ElemType>
class CosDistanceWithNegativeSamplesNode : public ComputationNode<ElemType>, public NumInputs<4>
{
typedef ComputationNode<ElemType> Base;
UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName()
{
return L"CosDistanceWithNegativeSamples";
}
public:
DeclareConstructorFromConfigWithNumInputs(CosDistanceWithNegativeSamplesNode);
CosDistanceWithNegativeSamplesNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name)
{
}
virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
{
Matrix<ElemType> sliceInput0Value = Input(0)->ValueFor(fr);
Matrix<ElemType> sliceInput1Value = Input(1)->ValueFor(fr);
Matrix<ElemType> sliceOutputValue = ValueFor(fr);
Matrix<ElemType> sliceInputGrad = Input(inputIndex)->GradientFor(fr);
Matrix<ElemType> sliceThisGrad = GradientFor(fr);
BackpropToS(inputIndex, *m_invNorm0, *m_invNorm1, sliceOutputValue, *m_temp, *m_rightTerm, *m_leftTerm, *m_invNormSquare, sliceInput0Value, sliceInput1Value, Input(2)->Value(), Input(3)->Value(), sliceInputGrad, sliceThisGrad);
}
// functionValues, invNorm0, invNorm1 - output from the EvaluateNode() method
// temp, rightTerm, leftTerm - temporary matrices
// in0, in1, in2, in3 - input functionValues from other nodes
// inputGradientValues(x) - gradients to update, where x matches inputIndex
/*TODO: merge with call site*/ void BackpropToS(const size_t inputIndex, const Matrix<ElemType>& invNorm0, const Matrix<ElemType>& invNorm1, const Matrix<ElemType>& functionValues,
Matrix<ElemType>& temp, Matrix<ElemType>& rightTerm, Matrix<ElemType>& leftTerm, Matrix<ElemType>& invNormSquare, // the temporary variables
const Matrix<ElemType>& in0, const Matrix<ElemType>& in1, const Matrix<ElemType>& in2, const Matrix<ElemType>& in3,
Matrix<ElemType>& inputGradientValues, Matrix<ElemType>& thisGradientValues)
{
size_t shift = (size_t) in2.Get00Element();
size_t negNumber = (size_t) in3.Get00Element();
size_t numCols = in0.GetNumCols(); // used in computing right child's graident
if (inputIndex == 0) // left derivative
{
invNormSquare.AssignElementProductOf(invNorm0, invNorm0);
for (long m = 0; m < negNumber + 1; m++)
{
temp.GetARowByIndex(functionValues, m); // set this matrx to be the m-th row in functionValues
temp.ElementMultiplyWith(invNormSquare);
Matrix<ElemType>::ConductRowElementMultiplyWithShift(temp, in0, rightTerm, 0, true);
if (m == 0)
{
temp.AssignElementProductOf(invNorm0, invNorm1);
Matrix<ElemType>::ConductRowElementMultiplyWithShift(temp, in1, leftTerm, 0, true);
}
else
{
size_t currshift = m + shift - 1; // for current line, how much should we shift
temp.AssignElementProductOfWithShift(invNorm0, invNorm1, currshift); // this is a row vector
Matrix<ElemType>::ConductRowElementMultiplyWithShift(temp, in1, leftTerm, currshift, true);
}
leftTerm -= rightTerm;
temp.GetARowByIndex(thisGradientValues, m);
Matrix<ElemType>::ConductRowElementMultiplyWithShift(temp, leftTerm, rightTerm, 0, true);
inputGradientValues += rightTerm;
}
}
else // right part
{
invNormSquare.AssignElementProductOf(invNorm1, invNorm1); // this matrix should be save and unchanged. It should not be changed
for (long m = 0; m < negNumber + 1; m++)
{
temp.GetARowByIndex(functionValues, m); // set this matrx to be the m-th row in functionValues
if (m == 0) // this is the first line. computation should be symmetric
{
// the following is for the right part
temp.ElementMultiplyWith(invNormSquare);
Matrix<ElemType>::ConductRowElementMultiplyWithShift(temp, in1, rightTerm, 0, true);
// the following is for the left part
temp.AssignElementProductOf(invNorm0, invNorm1);
Matrix<ElemType>::ConductRowElementMultiplyWithShift(temp, in0, leftTerm, 0, true);
leftTerm -= rightTerm;
temp.GetARowByIndex(thisGradientValues, m);
Matrix<ElemType>::ConductRowElementMultiplyWithShift(temp, leftTerm, rightTerm, 0, true);
inputGradientValues += rightTerm;
}
else // this requires shift
{
size_t currshift = (m + shift - 1) % numCols;
size_t reverseshift = numCols - currshift;
leftTerm.AssignElementProductOfWithShift(invNormSquare, temp, reverseshift); // use leftTerm as a temp variable here
Matrix<ElemType>::ConductRowElementMultiplyWithShift(leftTerm, in1, rightTerm, 0, true);
temp.AssignElementProductOfWithShift(invNorm1, invNorm0, reverseshift);
Matrix<ElemType>::ConductRowElementMultiplyWithShift(temp, in0, leftTerm, reverseshift, true);
leftTerm -= rightTerm;
temp.GetARowByIndex(thisGradientValues, m);
Matrix<ElemType>::ConductRowElementMultiplyWithShift(temp, leftTerm, rightTerm, reverseshift, false);
inputGradientValues += rightTerm;
}
}
}
}
virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
{
Matrix<ElemType> sliceInput0Value = Input(0)->ValueFor(fr);
Matrix<ElemType> sliceInput1Value = Input(1)->ValueFor(fr);
Matrix<ElemType> sliceOutputValue = ValueFor(fr);
ForwardPropS(*m_invNorm0, *m_invNorm1, sliceOutputValue, sliceInput0Value, sliceInput1Value, Input(2)->Value(), Input(3)->Value(), *m_leftTerm, *m_rightTerm);
}
/*TODO: merge with call site*/ void ForwardPropS(Matrix<ElemType>& invNorm0, Matrix<ElemType>& invNorm1, Matrix<ElemType>& functionValues, Matrix<ElemType>& in0, Matrix<ElemType>& in1, Matrix<ElemType>& in2, Matrix<ElemType>& in3, Matrix<ElemType>& leftTermTemp, Matrix<ElemType>& rightTermTemp)
{
invNorm0.AssignVectorNorm2Of(in0, true); // seems to modify input (in0)
invNorm0.AssignElementInverseOf(invNorm0);
invNorm1.AssignVectorNorm2Of(in1, true); // seems to modify the input (in1)
invNorm1.AssignElementInverseOf(invNorm1);
size_t shift = (size_t) in2.Get00Element();
size_t negNumber = (size_t) in3.Get00Element();
// mutiply invNorm0 and invNorm1 with shift and neg.
// The result is a matrix of (numberneg+1, invNorm0.Cols)
leftTermTemp.AssignElementProductOfWithShiftNeg(invNorm0, invNorm1, shift, negNumber);
// compute the right values
// Again, the output is a matrix of (negNumber+1, invNorm0.cols)
rightTermTemp.AssignInnerProductOfWithShiftNeg(in0, in1, true, shift, negNumber);
// compute the evaluation result matrix by multiply these two matrices, element by element
// we get a (negNumber+1, n) matrix
functionValues.AssignElementProductOf(leftTermTemp, rightTermTemp);
}
virtual void /*ComputationNodeBase::*/ Validate(bool isFinalValidationPass) override
{
Base::Validate(isFinalValidationPass);
InferMBLayoutFromInputsForStandardCase(isFinalValidationPass);
ValidateInferBinaryInputDims();
if (isFinalValidationPass &&
(Input(0)->GetSampleMatrixNumRows() != Input(1)->GetSampleMatrixNumRows() || Input(0)->GetMBLayout() != Input(1)->GetMBLayout()))
{
LogicError("The tensor dimension in the %ls %ls operation does not match.", NodeName().c_str(), OperationName().c_str());
}
// input(2) is shift, input(3) is the #neg
size_t negNumber = (size_t) Input(3)->Get00Element();
// TODO: This calls for a tensor representation!
SetDims(TensorShape(negNumber + 1), HasMBLayout());
}
virtual void CopyTo(ComputationNodeBasePtr nodeP, const std::wstring& newName, const CopyNodeFlags flags) const override
{
Base::CopyTo(nodeP, newName, flags);
if (flags & CopyNodeFlags::copyNodeValue)
{
auto node = dynamic_pointer_cast<CosDistanceWithNegativeSamplesNode<ElemType>>(nodeP);
node->m_invNorm0->SetValue(*m_invNorm0);
node->m_invNorm1->SetValue(*m_invNorm1);
node->m_invNormSquare->SetValue(*m_invNormSquare);
node->m_leftTerm->SetValue(*m_leftTerm);
node->m_rightTerm->SetValue(*m_rightTerm);
node->m_temp->SetValue(*m_temp);
}
}
// request matrices needed to do node function value evaluation
virtual void RequestMatricesBeforeForwardProp(MatrixPool& matrixPool)
{
Base::RequestMatricesBeforeForwardProp(matrixPool);
RequestMatrixFromPool(m_invNorm0, matrixPool);
RequestMatrixFromPool(m_invNorm1, matrixPool);
RequestMatrixFromPool(m_leftTerm, matrixPool);
RequestMatrixFromPool(m_rightTerm, matrixPool);
}
// request matrices that are needed for gradient computation
virtual void RequestMatricesBeforeBackprop(MatrixPool& matrixPool)
{
Base::RequestMatricesBeforeBackprop(matrixPool);
RequestMatrixFromPool(m_invNormSquare, matrixPool);
RequestMatrixFromPool(m_temp, matrixPool);
}
// release gradient and temp matrices that no longer needed after all the children's gradients are computed.
virtual void ReleaseMatricesAfterBackprop(MatrixPool& matrixPool)
{
Base::ReleaseMatricesAfterBackprop(matrixPool);
ReleaseMatrixToPool(m_invNorm0, matrixPool);
ReleaseMatrixToPool(m_invNorm1, matrixPool);
ReleaseMatrixToPool(m_leftTerm, matrixPool);
ReleaseMatrixToPool(m_rightTerm, matrixPool);
ReleaseMatrixToPool(m_invNormSquare, matrixPool);
ReleaseMatrixToPool(m_temp, matrixPool);
}
private:
// invNorm nodes tranfer data between ForwardProp and BackpropTo
shared_ptr<Matrix<ElemType>> m_invNorm0;
shared_ptr<Matrix<ElemType>> m_invNorm1;
shared_ptr<Matrix<ElemType>> m_leftTerm;
shared_ptr<Matrix<ElemType>> m_rightTerm;
// the rest are temporaries, values don't need to be maintained
shared_ptr<Matrix<ElemType>> m_invNormSquare;
shared_ptr<Matrix<ElemType>> m_temp;
};
template class CosDistanceWithNegativeSamplesNode<float>;
template class CosDistanceWithNegativeSamplesNode<double>;
}}}
