https://github.com/Microsoft/CNTK
Tip revision: 16a41cef30894ca92667bd93079cd6fa11b3e92d authored by Sayan Pathak on 02 November 2017, 16:10:10 UTC
Added super resolution tutorial contributed by Borna with added code to minimize test downloads, fix tests, added documentation and small editorial changes to LSGAN tutorial
Added super resolution tutorial contributed by Borna with added code to minimize test downloads, fix tests, added documentation and small editorial changes to LSGAN tutorial
Tip revision: 16a41ce
SpecialPurposeNodes.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 "gammacalculation.h"
#include "NonlinearityNodes.h"
#include <map>
#include <string>
#include <vector>
#include <stdexcept>
#include <list>
#include <memory>
namespace Microsoft { namespace MSR { namespace CNTK {
// This header collects special-purpose nodes.
// -----------------------------------------------------------------------
// TraceNode (input, say='', enabled=true, gradient=false, showFrequency=10, showFirst=10, format=[]) -- trace a node's value
// Traces a node's value using WriteMinibatchWithFormatting().
// -----------------------------------------------------------------------
template <class ElemType>
class TraceNode : public ComputationNode<ElemType>, public NumInputs<1>
{
typedef ComputationNode<ElemType> Base; UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName() { return L"Trace"; }
public:
TraceNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name)
{
}
TraceNode(const ScriptableObjects::IConfigRecordPtr configp);
virtual void Save(File& fstream) const override;
virtual void Load(File& fstream, size_t modelVersion) override;
virtual void /*IComputationNode::*/ BeginForwardProp() override;
virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override;
virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override;
virtual void /*ComputationNode::*/ Validate(bool isFinalValidationPass) override;
virtual bool OutputUsedInComputingInputNodesGradients() const override { return false; }
virtual bool InputUsedInComputingInputNodesGradients(size_t /*childIndex*/) const override { return false; }
private:
void Log(const FrameRange& fr, bool logGradientInstead) const;
private:
// configuration
std::wstring m_message;
size_t m_logFrequency = 0; // Note: This can be changed in the debugger on the fly.
size_t m_logFirst = 0;
bool m_logGradientToo = false;
WriteFormattingOptions m_formattingOptions;
size_t m_onlyUpToRow = SIZE_MAX;
size_t m_onlyUpToT = SIZE_MAX;
// cached stuff (not persisted)
size_t m_numMBsRun = 0;
std::vector<std::string> m_labelMapping;
};
#ifdef COMING_SOON
// -----------------------------------------------------------------------
// GMMLogLikelihoodNode (unnormedPrior, means, logStdDevs, features) -- GMM log LL over input vector(s)
// calculates the log likelihood of a feature given parameters of a Gaussian mixture model (GMM) with shared diagonal variance
// - unnormedPrior: mix weights, #rows = #mixture components
// - means: means, all mix means concatenated (i.e. dim = feature dim x prior dim)
// - logStdDevs: std deviations, pooled across mix (i.e. same dim as features)
// UnnormedPrior, means, and logStdDevs can be either a single column or one per sample, e.g.
// when parameters are computed by other nodes.
// -----------------------------------------------------------------------
template <class ElemType>
class GMMLogLikelihoodNode : public ComputationNode<ElemType>, public NumInputs<4>
{
typedef ComputationNode<ElemType> Base; UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName() { return L"GMMLogLikelihood"; }
public:
DeclareConstructorFromConfigWithNumInputs(GMMLogLikelihoodNode);
GMMLogLikelihoodNode(DEVICEID_TYPE deviceId, const wstring& name)
: ComputationNode<ElemType>(deviceId, name)
{
}
virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
{
// get the right slice
const size_t colsPrior = Input(0)->GetSampleMatrixNumCols();
Matrix<ElemType> sliceGradientValue = DataFor(*m_gradient, fr);
Matrix<ElemType> slicePosterior = DataFor(*m_posterior, fr);
switch (inputIndex)
{
case 0:
{
if (colsPrior == 1)
BackpropToUnnormedPrior(Input(0)->Gradient(), sliceGradientValue, *m_prior, slicePosterior, *m_temp);
else
{
Matrix<ElemType> sliceUnnormedPriorGradient = Input(0)->GradientFor(fr);
Matrix<ElemType> slicePrior = DataFor(*m_prior, fr); // TODO: use the right MBLayout, then we won't need the special case
BackpropToUnnormedPrior(sliceUnnormedPriorGradient, sliceGradientValue, slicePrior, slicePosterior, *m_temp);
}
}
break;
case 1:
{
Matrix<ElemType> sliceNormedDeviationVectors = DataFor(*m_normedDeviationVectors, fr);
if (colsPrior == 1)
BackpropToMean(Input(1)->Gradient(), sliceGradientValue, sliceNormedDeviationVectors, slicePosterior, *m_temp);
else
{
Matrix<ElemType> sliceMeanGradient = Input(1)->GradientFor(fr);
BackpropToMean(sliceMeanGradient, sliceGradientValue, sliceNormedDeviationVectors, slicePosterior, *m_temp);
}
}
break;
case 2:
{
Matrix<ElemType> sliceNormedDeviation = DataFor(*m_normedDeviation, fr);
if (colsPrior == 1)
BackpropToLogStddev(Input(2)->Gradient(), sliceGradientValue, sliceNormedDeviation, slicePosterior, *m_temp);
else
{
Matrix<ElemType> sliceLotStddevGradient = Input(2)->GradientFor(fr);
BackpropToLogStddev(sliceLotStddevGradient, sliceGradientValue, sliceNormedDeviation, slicePosterior, *m_temp);
}
}
break;
case 3:
{
Matrix<ElemType> sliceNormedDeviationVectors = DataFor(*m_normedDeviationVectors, fr);
Matrix<ElemType> sliceFeatureGradient = Input(3)->GradientFor(fr);
BackpropToFeature(sliceFeatureGradient, sliceGradientValue, sliceNormedDeviationVectors, slicePosterior, *m_temp);
}
break;
default:
InvalidArgument("GMMLogLikelihoodNode criterion only takes four inputs.");
}
}
virtual bool OutputUsedInComputingInputNodesGradients() const override { return false; }
virtual bool InputUsedInComputingInputNodesGradients(size_t /*childIndex*/) const override { return false; }
void BackpropToUnnormedPrior(Matrix<ElemType>& unnormedPriorGradientValues, const Matrix<ElemType>& gradientValues,
const Matrix<ElemType>& prior, const Matrix<ElemType>& posterior, Matrix<ElemType>& temp)
{
temp.AssignDifferenceOf(posterior, prior);
temp.RowElementMultiplyWith(gradientValues);
if (prior.GetNumCols() == posterior.GetNumCols())
unnormedPriorGradientValues += temp;
else if (prior.GetNumCols() == 1)
Matrix<ElemType>::MultiplyAndAdd(temp, false, ConstOnes(posterior.GetNumCols(), 1, unnormedPriorGradientValues.GetDeviceId()), false, unnormedPriorGradientValues);
else
RuntimeError("GMMLogLikelihoodNode: UnnormedPrior should either have same number of columns as the features or have only one column.");
}
void BackpropToMean(Matrix<ElemType>& meanGradientValues, const Matrix<ElemType>& gradientValues, const Matrix<ElemType>& normedDeviationVectors,
Matrix<ElemType>& posterior, Matrix<ElemType>& temp)
{
size_t numComponent = posterior.GetNumRows();
size_t numSamples = posterior.GetNumCols();
size_t featureSize = normedDeviationVectors.GetNumRows() / numComponent;
temp.SetValue(normedDeviationVectors); // recall normedDeviationVectors <-- (x-u_c)/(stddev^2)
temp.Reshape(featureSize, numSamples * numComponent);
posterior.Reshape(1, numSamples * numComponent);
temp.RowElementMultiplyWith(posterior); // temp <-- posterior * (x-u_c)/(stddev^2)
posterior.Reshape(numComponent, numSamples); // reshape back
temp.Reshape(featureSize * numComponent, numSamples); // reshape back
temp.RowElementMultiplyWith(gradientValues);
if (numSamples == meanGradientValues.GetNumCols())
meanGradientValues += temp;
else if (meanGradientValues.GetNumCols() == 1)
Matrix<ElemType>::MultiplyAndAdd(temp, false, ConstOnes(numSamples, 1, meanGradientValues.GetDeviceId()), false, meanGradientValues);
else
RuntimeError("GMMLogLikelihoodNode: stddev should either have same number of columns as the features or have only one column.");
}
void BackpropToLogStddev(Matrix<ElemType>& logStddevGradientValues, const Matrix<ElemType>& gradientValues, const Matrix<ElemType>& normedDeviation,
const Matrix<ElemType>& posterior, Matrix<ElemType>& temp)
{
size_t numComponent = posterior.GetNumRows();
size_t numSamples = posterior.GetNumCols();
temp.AssignDifferenceOf(normedDeviation, (ElemType) numComponent);
temp.ElementMultiplyWith(posterior);
temp.RowElementMultiplyWith(gradientValues);
if (logStddevGradientValues.GetNumCols() == numSamples)
logStddevGradientValues += temp;
else if (logStddevGradientValues.GetNumCols() == 1)
Matrix<ElemType>::MultiplyAndAdd(temp, false, ConstOnes(numSamples, 1, logStddevGradientValues.GetDeviceId()), false, logStddevGradientValues);
else
RuntimeError("GMMLogLikelihoodNode: stddev should either have same number of columns as the features or have only one column.");
}
void BackpropToFeature(Matrix<ElemType>& featureGradientValues, const Matrix<ElemType>& gradientValues, const Matrix<ElemType>& normedDeviationVectors,
Matrix<ElemType>& posterior, Matrix<ElemType>& temp)
{
size_t numComponent = posterior.GetNumRows();
size_t numSamples = posterior.GetNumCols();
size_t featureSize = normedDeviationVectors.GetNumRows() / numComponent;
temp.SetValue(normedDeviationVectors);
temp *= -1;
temp.Reshape(featureSize, numSamples * numComponent);
posterior.Reshape(1, numSamples * numComponent);
temp.RowElementMultiplyWith(posterior);
posterior.Reshape(numComponent, numSamples);
temp.Reshape(featureSize * numComponent, numSamples);
temp.RowElementMultiplyWith(gradientValues);
for (int i = 0; i < numComponent; i++)
featureGradientValues.AddWithRowSliceValuesOf(temp, i * featureSize, featureSize);
}
virtual void UpdateFunctionMBSize() override
{
Base::UpdateFunctionMBSize();
size_t numCols = Input(3)->GetSampleMatrixNumCols();
size_t numComponents = Input(0)->GetSampleMatrixNumRows();
size_t colsPrior = Input(0)->GetSampleMatrixNumCols(); // may be 1
size_t featureSize = Input(3)->GetSampleMatrixNumRows();
m_prior->Resize(numComponents, colsPrior);
m_stddev->Resize(numComponents, colsPrior);
m_normedDeviation->Resize(numComponents, numCols);
m_normedDeviationVectors->Resize(numComponents * featureSize, numCols);
m_posterior->Resize(numComponents, numCols);
}
// input0=unnormedPrior, input1=mean, input2=logstddev, input3=feature
virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
{
size_t colsPrior = Input(0)->GetSampleMatrixNumCols();
size_t numSamples = Input(3)->GetSampleMatrixNumCols();
// get the right slice
Matrix<ElemType> sliceOutputValue = ValueFor(fr);
Matrix<ElemType> sliceFeature = Input(3)->ValueFor(fr);
Matrix<ElemType> sliceNormedDeviation = DataFor(*m_normedDeviation, fr);
Matrix<ElemType> sliceNormedDeviationVectors = DataFor(*m_normedDeviationVectors, fr);
Matrix<ElemType> slicePosterior = DataFor(*m_posterior, fr);
if (colsPrior == 1)
{
ForwardPropS(sliceOutputValue, Input(0)->Value(), Input(1)->Value(), Input(2)->Value(), sliceFeature,
*m_prior, *m_stddev, sliceNormedDeviationVectors, sliceNormedDeviation, slicePosterior, *m_temp);
}
else if (colsPrior == numSamples)
{
Matrix<ElemType> sliceUnnormedPrior = Input(0)->ValueFor(fr);
Matrix<ElemType> sliceMean = Input(1)->ValueFor(fr);
Matrix<ElemType> sliceLogstddev = Input(2)->ValueFor(fr);
Matrix<ElemType> slicePrior = DataFor(*m_prior, fr);
Matrix<ElemType> sliceStddev = DataFor(*m_stddev, fr);
ForwardPropS(sliceOutputValue, sliceUnnormedPrior, sliceMean, sliceLogstddev, sliceFeature,
slicePrior, sliceStddev, sliceNormedDeviationVectors, sliceNormedDeviation, slicePosterior, *m_temp);
}
else // should not reach the code since validation should fail already
RuntimeError("GMMLogLikelihoodNode: UnnormedPrior should either have same number of columns as the features or have only one column.");
}
// input0=unnormedPrior, input1=mean, input2=logstddev, input3=feature
// If we want to speed up we need to replace following code with a several specialized GPU functions
/*TODO: merge with call site*/ void ForwardPropS(Matrix<ElemType>& functionValues, const Matrix<ElemType>& unnormedPrior, const Matrix<ElemType>& mean, Matrix<ElemType>& logstddev,
const Matrix<ElemType>& feature, Matrix<ElemType>& prior, Matrix<ElemType>& stddev, Matrix<ElemType>& normedDeviationVectors,
Matrix<ElemType>& normedDeviation, Matrix<ElemType>& posterior, Matrix<ElemType>& temp)
{
int numComponent = unnormedPrior.GetNumRows();
size_t numSamples = feature.GetNumCols();
size_t featureDim = feature.GetNumRows();
// compute prior which is softmax of unnormedPrior
prior.AssignLogSoftmaxOf(unnormedPrior, true); // log prior
prior.InplaceExp();
// compute stddev
stddev.AssignExpOf(logstddev);
#if DUMPOUTPUT
unnormedPrior.Print("unnormedPrior", 0, min(5, unnormedPrior.GetNumRows() - 1), 0, min(10, unnormedPrior.GetNumCols() - 1));
mean.Print("mean", 0, min(5, mean.GetNumRows() - 1), 0, min(10, mean.GetNumCols() - 1));
logstddev.Print("logstddev", 0, min(5, logstddev.GetNumRows() - 1), 0, min(10, logstddev.GetNumCols() - 1));
prior.Print("prior", 0, min(5, prior.GetNumRows() - 1), 0, min(10, prior.GetNumCols() - 1));
stddev.Print("stddev", 0, min(5, stddev.GetNumRows() - 1), 0, min(10, stddev.GetNumCols() - 1));
#endif
// compute normedDeviation <-- ||x-u_c||^2/(stddev^2)
normedDeviationVectors.AssignRepeatOf(feature, numComponent, 1);
normedDeviationVectors -= mean; // each column of the mean has multiple mean components
normedDeviationVectors.Reshape(featureDim, numSamples * numComponent); // now each column is feature-mean_i
normedDeviation.AssignVectorNorm2Of(normedDeviationVectors, true);
normedDeviation ^= 2;
temp.AssignRepeatOf(stddev, 1, numSamples / stddev.GetNumCols()); // stddev.GetNumCols() is either 1 or =numSamples
temp.Reshape(1, temp.GetNumElements()); // one stddev value for each component for each sample
temp ^= 2;
normedDeviation.ElementDivideBy(temp); // normedDeviation and temp have same dim (1, numSamples* numComponent)
// compute normedDeviationVectors <-- (x-u_c)/(stddev^2)
normedDeviationVectors.RowElementDivideBy(temp); // divide twice
normedDeviationVectors.Reshape(featureDim * numComponent, numSamples); // reshape back
// compute per-component likelihood
posterior.AssignProductOf(-0.5f, normedDeviation); // posterior <-- -||x-u_c||^2/(stddev^2)/2 and in (1, numSamples* numComponent) dim
temp.InplaceLog();
temp *= ((ElemType) numComponent / 2.0f); // temp <-- stddev^c and in (1, numSamples* numComponent) dim
posterior -= temp; // posterior <-- exp[-||x-u_c||^2/(stddev^2)/2]/(stddev^c)
posterior -= (ElemType)(numComponent / 2.0f * log(TWO_PI)); // likelihood for each component and sample is now computed and stored in posterior
posterior.InplaceExp(); // posterior <-- exp(-||x-u_c||^2/(stddev^2)/2)
normedDeviation.Reshape(numComponent, numSamples); // reshape back
posterior.Reshape(numComponent, numSamples); // reshape back
// compute posterior <-- prior_i * likelihood_i
if (unnormedPrior.GetNumCols() == numSamples) // each sample has different prior
posterior.ElementMultiplyWith(prior);
else // all samples share the same prior
posterior.ColumnElementMultiplyWith(prior);
// compute GMM log-likelihood
Matrix<ElemType>::Multiply(ConstOnes(1, numComponent, posterior.GetDeviceId()), false, posterior, false, functionValues); // functionValues <-- total likelihood
posterior.RowElementDivideBy(functionValues); // posterior <-- per-comp likelihood / total likelihood
functionValues.InplaceLog(); // log likelihood
#if DUMPOUTPUT
temp.Print("temp", 0, min(5, temp.GetNumRows() - 1), 0, min(10, temp.GetNumCols() - 1));
normedDeviation.Print("normedDeviation", 0, min(5, normedDeviation.GetNumRows() - 1), 0, min(10, normedDeviation.GetNumCols() - 1));
posterior.Print("posterior", 0, min(5, posterior.GetNumRows() - 1), 0, min(10, posterior.GetNumCols() - 1));
functionValues.Print("functionValues", 0, min(5, functionValues.GetNumRows() - 1), 0, min(10, functionValues.GetNumCols() - 1));
functionValues.Print("GMMLogLikelihoodNode");
#endif
}
virtual void /*ComputationNodeBase::*/ Validate(bool isFinalValidationPass) override
{
Base::Validate(isFinalValidationPass);
InferMBLayoutFromInputsForStandardCase(isFinalValidationPass);
size_t rows[4];
for (int i = 0; i < 4; i++)
rows[i] = Input(i)->GetSampleMatrixNumRows();
if (isFinalValidationPass)
{
if (!Input(3)->HasMBLayout())
InvalidArgument("GMMLogLikelihoodNode: Features must be a minibatch.");
if (Input(0)->GetMBLayout() != Input(1)->GetMBLayout() || Input(0)->GetMBLayout() != Input(2)->GetMBLayout())
InvalidArgument("GMMLogLikelihoodNode: First three arguments must have the same MBLayout (which may be none).");
if (rows[0] != rows[2])
LogicError("GMMLogLikelihoodNode: UnnormedPrior (first input) should have same dimension as logStddev (third input), i.e., all dimensions in each Gaussian component share the same stddev.");
if (rows[1] != rows[0] * rows[3])
LogicError("GMMLogLikelihoodNode: the number of rows in mean (second input) should equal rows(unnormedPrior(first input) * rows(feature(fourth input)).");
}
SetDims(TensorShape(1), true);
}
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<GMMLogLikelihoodNode<ElemType>>(nodeP);
*node->m_prior = *m_prior;
*node->m_normedDeviation = *m_normedDeviation;
*node->m_normedDeviationVectors = *m_normedDeviationVectors;
*node->m_stddev = *m_stddev;
*node->m_posterior = *m_posterior;
}
}
// request matrices needed to do node function value evaluation
virtual void RequestMatricesBeforeForwardProp(MatrixPool& matrixPool)
{
Base::RequestMatricesBeforeForwardProp(matrixPool);
RequestMatrixFromPool(m_prior, matrixPool);
RequestMatrixFromPool(m_normedDeviation, matrixPool);
RequestMatrixFromPool(m_normedDeviationVectors, matrixPool);
RequestMatrixFromPool(m_stddev, matrixPool);
RequestMatrixFromPool(m_posterior, 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_prior, matrixPool);
ReleaseMatrixToPool(m_normedDeviation, matrixPool);
ReleaseMatrixToPool(m_normedDeviationVectors, matrixPool);
ReleaseMatrixToPool(m_stddev, matrixPool);
ReleaseMatrixToPool(m_posterior, matrixPool);
ReleaseMatrixToPool(m_temp, matrixPool);
}
protected:
shared_ptr<Matrix<ElemType>> m_prior;
shared_ptr<Matrix<ElemType>> m_normedDeviation;
shared_ptr<Matrix<ElemType>> m_normedDeviationVectors;
shared_ptr<Matrix<ElemType>> m_stddev;
shared_ptr<Matrix<ElemType>> m_posterior;
shared_ptr<Matrix<ElemType>> m_temp;
};
template class GMMLogLikelihoodNode<float>;
template class GMMLogLikelihoodNode<double>;
#endif
// -----------------------------------------------------------------------
// SequenceWithSoftmaxNode (label, prediction, loglikelihood)
// word-lattice based sequence training criterion, using a Microsoft-proprietary lattice format
//
// This node is likely not very useful for external use since it uses an MS-proprietary lattice-archive format
// that requires Frank's DBN.exe tool to create. The inner C++ code for converting HTK lattices
// into this format is in this repo (latticearchive.h), but not the outer main program.
// -----------------------------------------------------------------------
template <class ElemType>
class SequenceWithSoftmaxNode : public ComputationNodeNonLooping<ElemType>, public NumInputs<3>
{
typedef ComputationNodeNonLooping<ElemType> Base;
UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName()
{
return L"SequenceWithSoftmax";
}
public:
DeclareConstructorFromConfigWithNumInputs(SequenceWithSoftmaxNode);
SequenceWithSoftmaxNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name), m_gammaCalcInitialized(false)
{
}
// compute gradients to input observations, the weights to the observations, and the class log posterior probabilities
virtual void BackpropToNonLooping(size_t inputIndex) override
{
// auto t_start_time = Timer::MilliSecondElapsed();
// left Node must be a scalar
if (inputIndex == 0) // left derivative
{
BackpropToLeft(*m_logSoftmaxOfRight, Input(inputIndex)->Gradient(), Gradient());
}
else if (inputIndex == 1)
{
FrameRange fr(Input(0)->GetMBLayout());
BackpropToRight(*m_softmaxOfRight, Input(0)->Value(), Input(inputIndex)->Gradient(),
Gradient(), *m_gammaFromLattice, m_fsSmoothingWeight, m_frameDropThreshold);
MaskMissingColumnsToZero(Input(inputIndex)->Gradient(), Input(0)->GetMBLayout(), fr);
#ifdef _DEBUG
Input(inputIndex)->InvalidateMissingGradientColumns(FrameRange(Input(inputIndex)->GetMBLayout()));
#endif
}
else if (inputIndex == 2)
{
#if 1 // no gradient flows to log LLs (but otherwise we leave it to user if, e.g., another node propagates a gradient into there)
; // gradient does not flow here
#else
Input(inputIndex)->SetLearningRateMultiplier(0);
Input(inputIndex)->Gradient().SetValue(0.0); // BUGBUG: Gradients must always be added, since nodes may have multiple parents.
#endif
}
else
RuntimeError("SequenceWithSoftmaxNode criterion only takes with respect to label, DNN output and log likelihood.");
}
static void WINAPI BackpropToLeft(const Matrix<ElemType>& logSoftmaxOfRight, Matrix<ElemType>& inputGradientValues, const Matrix<ElemType>& gradientValues)
{
#if DUMPOUTPUT
logSoftmaxOfRight.Print("SequenceWithSoftmaxNode Partial-logSoftmaxOfRight");
gradientValues.Print("SequenceWithSoftmaxNode Partial-gradientValues");
inputGradientValues.Print("SequenceWithSoftmaxNode Partial-Left-in");
#endif
Matrix<ElemType>::Multiply1x1AndWeightedAdd(-1.0f, gradientValues /*1x1*/, logSoftmaxOfRight, 1.0f, inputGradientValues);
#if DUMPOUTPUT
inputGradientValues.Print("SequenceWithSoftmaxNode Partial-Left-out");
#endif
}
static void WINAPI BackpropToRight(const Matrix<ElemType>& softmaxOfRight, const Matrix<ElemType>& inputFunctionValues,
Matrix<ElemType>& inputGradientValues, const Matrix<ElemType>& gradientValues,
const Matrix<ElemType>& gammaFromLattice, double hsmoothingWeight, double frameDropThresh)
{
#if DUMPOUTPUT
softmaxOfRight.Print("SequenceWithSoftmaxNode Partial-softmaxOfRight");
inputFunctionValues.Print("SequenceWithSoftmaxNode Partial-inputFunctionValues");
gradientValues.Print("SequenceWithSoftmaxNode Partial-gradientValues");
inputGradientValues.Print("SequenceWithSoftmaxNode Partial-Right-in");
#endif
inputGradientValues.AssignSequenceError((ElemType) hsmoothingWeight, inputFunctionValues, softmaxOfRight, gammaFromLattice, gradientValues.Get00Element());
inputGradientValues.DropFrame(inputFunctionValues, gammaFromLattice, (ElemType) frameDropThresh);
#if DUMPOUTPUT
inputGradientValues.Print("SequenceWithSoftmaxNode Partial-Right");
#endif
}
virtual bool OutputUsedInComputingInputNodesGradients() const override
{
return false;
}
// -sum(left_i * log(softmax_i(right)))
virtual void ForwardPropNonLooping()
{
// Initialize m_gammaCalculator
// TODO: Would this lend itself to a unique_ptr instead of the init flag?
if (!m_gammaCalcInitialized)
{
if (m_hmm.hmms.size() == 0)
{
LogicError("SequenceWithSoftmaxNode criterion evaluation requires HMM states to be set.");
}
m_gammaCalculator.init(m_hmm, m_deviceId);
m_gammaCalcInitialized = true;
}
// softmax
m_logSoftmaxOfRight->AssignLogSoftmaxOf(Input(1)->Value() /*prediction*/, true);
m_softmaxOfRight->SetValue(*m_logSoftmaxOfRight);
m_softmaxOfRight->InplaceExp();
m_gammaFromLattice->SwitchToMatrixType(m_softmaxOfRight->GetMatrixType(), m_softmaxOfRight->GetFormat(), false);
m_gammaFromLattice->Resize(*m_softmaxOfRight);
m_gammaCalculator.calgammaformb(Value(), m_lattices, Input(2)->Value() /*log LLs*/,
Input(0)->Value() /*labels*/, *m_gammaFromLattice,
m_uids, m_boundaries, Input(1)->GetNumParallelSequences(),
Input(0)->GetMBLayout(), m_extraUttMap, m_doReferenceAlignment);
#if NANCHECK
Value().HasNan("SequenceWithSoftmaxNode");
#endif
#if DUMPOUTPUT
Value().Print("SequenceWithSoftmaxNode");
#endif
}
virtual void /*ComputationNodeBase::*/ Validate(bool isFinalValidationPass) override
{
Base::Validate(isFinalValidationPass);
m_pMBLayout = nullptr; // no layout
if (Input(0)->OperationName() != L"InputValue" && Input(0)->OperationName() != L"SparseInputValue")
LogicError("SequenceWithSoftmaxNode criterion requires the first input to be the label.");
if (isFinalValidationPass)
if (!(Input(0)->GetSampleMatrixNumRows() == Input(1)->GetSampleMatrixNumRows() && // match size
Input(1)->GetSampleMatrixNumRows() == Input(2)->GetSampleMatrixNumRows() &&
Input(0)->HasMBLayout() &&
Input(0)->GetMBLayout() == Input(1)->GetMBLayout() &&
Input(0)->GetMBLayout() == Input(2)->GetMBLayout()))
{
LogicError("The Matrix dimension in the SequenceWithSoftmaxNode operation does not match.");
}
SetDims(TensorShape(1), false);
m_gammatime = 0;
m_partialtime = 0;
}
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<SequenceWithSoftmaxNode<ElemType>>(nodeP);
node->m_logSoftmaxOfRight->SetValue(*m_logSoftmaxOfRight);
node->m_softmaxOfRight->SetValue(*m_softmaxOfRight);
node->m_gammaFromLattice->SetValue(*m_gammaFromLattice);
node->m_fsSmoothingWeight = m_fsSmoothingWeight;
node->m_frameDropThreshold = m_frameDropThreshold;
node->m_doReferenceAlignment = m_doReferenceAlignment;
}
}
// request matrices needed to do node function value evaluation
virtual void RequestMatricesBeforeForwardProp(MatrixPool& matrixPool)
{
Base::RequestMatricesBeforeForwardProp(matrixPool);
RequestMatrixFromPool(m_logSoftmaxOfRight, matrixPool);
RequestMatrixFromPool(m_softmaxOfRight, matrixPool);
RequestMatrixFromPool(m_gammaFromLattice, 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_logSoftmaxOfRight, matrixPool);
ReleaseMatrixToPool(m_softmaxOfRight, matrixPool);
ReleaseMatrixToPool(m_gammaFromLattice, matrixPool);
}
// TODO: method names should be CamelCase
std::vector<shared_ptr<const msra::dbn::latticepair>>* getLatticePtr() { return &m_lattices; }
std::vector<size_t>* getuidprt() { return &m_uids; }
std::vector<size_t>* getboundaryprt() { return &m_boundaries; }
std::vector<size_t>* getextrauttmap() { return &m_extraUttMap; }
msra::asr::simplesenonehmm* gethmm() { return &m_hmm; }
void SetSmoothWeight(double fsSmoothingWeight) { m_fsSmoothingWeight = fsSmoothingWeight; }
void SetFrameDropThresh(double frameDropThresh) { m_frameDropThreshold = frameDropThresh; }
void SetReferenceAlign(const bool doreferencealign) { m_doReferenceAlignment = doreferencealign; }
void SetGammarCalculationParam(const double& amf, const double& lmf, const double& wp, const double& bMMIfactor, const bool& sMBR)
{
msra::lattices::SeqGammarCalParam param;
param.amf = amf;
param.lmf = lmf;
param.wp = wp;
param.bMMIfactor = bMMIfactor;
param.sMBRmode = sMBR;
m_gammaCalculator.SetGammarCalculationParams(param);
}
void gettime(unsigned long long& gammatime, unsigned long long& partialtime)
{
gammatime = m_gammatime;
partialtime = m_partialtime;
}
protected:
shared_ptr<Matrix<ElemType>> m_logSoftmaxOfRight;
shared_ptr<Matrix<ElemType>> m_softmaxOfRight;
shared_ptr<Matrix<ElemType>> m_gammaFromLattice;
double m_frameDropThreshold;
double m_fsSmoothingWeight; // frame-sequence criterion interpolation weight --TODO: can this be done outside?
double m_seqGammarAMF;
double m_seqGammarLMF;
double m_seqGammarWP;
double m_seqGammarbMMIFactor;
double m_seqGammarUsesMBR;
bool m_doReferenceAlignment;
std::vector<shared_ptr<const msra::dbn::latticepair>> m_lattices;
msra::asr::simplesenonehmm m_hmm;
msra::lattices::GammaCalculation<ElemType> m_gammaCalculator;
bool m_gammaCalcInitialized;
std::vector<size_t> m_uids;
std::vector<size_t> m_boundaries;
std::vector<size_t> m_extraUttMap;
unsigned long long m_gammatime; // TODO: what are these? Not even the context can be guessed from these names.
unsigned long long m_partialtime;
};
template class SequenceWithSoftmaxNode<float>;
template class SequenceWithSoftmaxNode<double>;
// -----------------------------------------------------------------------
// DummyCriterionNode (objectiveValues, userSuppliedGradient, prediction)
// TODO: Rename to CustomCriterionNode?
//
// Apply user-supplied gradient, computed as Forward(), as the gradient into 'prediction'.
//
// predictionsGradient += userSuppliedGradient * scalarGradientFromTop
//
// This training criterion node allows to compute objectives and gradient
// with custom CNTK expressions (as Forward() computations). It has 3 inputs:
// 1. custom objective values to be summed up and passed up
// 2. custom gradient values to be passed down as the gradient into 'prediction'
// 3. prediction: the node to pass the custom gradient into
// -----------------------------------------------------------------------
template <class ElemType>
class DummyCriterionNode : public ComputationNodeNonLooping /*ComputationNode*/<ElemType>, public NumInputs<3>
{
typedef ComputationNodeNonLooping<ElemType> Base;
UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName()
{
return L"DummyCriterion";
}
public:
DeclareConstructorFromConfigWithNumInputs(DummyCriterionNode);
DummyCriterionNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name)
{
}
virtual void BackpropToNonLooping(size_t inputIndex) override
{
FrameRange fr(Input(0)->GetMBLayout());
if (inputIndex == 0)
LogicError("DummyCriterionNode: Gradients with respect to objective features are not necessary, not implemented.\n");
else if (inputIndex == 1)
LogicError("DummyCriterionNode: Gradients with respect to derivative features are not necessary, not implemented.\n");
else if (inputIndex == 2)
{
// predictionsGradient += userSuppliedGradient * scalarGradientFromTop
auto gradient = Input(2)->GradientFor(fr);
Matrix<ElemType>::Multiply1x1AndWeightedAdd(+1.0f, /*gradient from top:*/Gradient() /*1x1*/, /*user-supplied gradient:*/Input(1)->ValueFor(fr), 1.0f, /*add to:*/gradient);
}
}
virtual bool OutputUsedInComputingInputNodesGradients() const override { return false; }
virtual void /*ComputationNodeNonLooping::*/ ForwardPropNonLooping() override
{
Value().VerifySize(1, 1);
assert(Input(0)->Value().GetNumRows() == 1);
Value().SetValue(Input(0)->Value().SumOfElements());
#if NANCHECK
Value().HasNan("DummyCriterionNode");
#endif
}
virtual void /*ComputationNodeBase::*/ Validate(bool isFinalValidationPass) override
{
Base::Validate(isFinalValidationPass);
m_pMBLayout = nullptr; // this node does not hold mini-batch data
if (Input(0)->OperationName() != L"InputValue")
LogicError("DummyCriterionNode criterion requires the first input to be computed objectives.");
if (Input(1)->OperationName() != L"InputValue")
LogicError("DummyCriterionNode criterion requires the second input to be computed derivatives.");
if (isFinalValidationPass)
{
if (Input(0)->GetSampleMatrixNumRows() == 0
|| Input(1)->GetSampleMatrixNumRows() == 0
|| Input(2)->GetSampleMatrixNumRows() == 0)
LogicError("DummyCriterionNode operation: one of the operands has 0 elements.");
if (Input(1)->GetSampleMatrixNumRows() != Input(2)->GetSampleMatrixNumRows()
|| Input(0)->GetSampleMatrixNumCols() != Input(2)->GetSampleMatrixNumCols()
|| Input(1)->GetSampleMatrixNumCols() != Input(2)->GetSampleMatrixNumCols())
LogicError("The Matrix dimension in the DummyCriterionNode operation does not match.");
}
SetDims(TensorShape(1), false);
}
};
template class DummyCriterionNode<float>;
template class DummyCriterionNode<double>;
// -----------------------------------------------------------------------
// ForwardBackwardNode (graph, prediction, delayConstraint)
// CTC training criterion, primarily based on the paper "Connectionist Temporal Classification: Labelling Unsegmented
// Sequence Data with Recurrent Neural Networks", ftp://ftp.idsia.ch/pub/juergen/icml2006.pdf
// blankTokenId (input): id of the blank token. If specified as SIZE_MAX, will be replaced with (numberOfLabels - 1)
// delayConstraint -- label output delay constraint introduced during training that allows to have shorter delay during inference.
// This using the original time information to enforce that CTC tokens only get aligned within a time margin.
// Setting this parameter smaller will result in shorter delay between label output during decoding, yet may hurt accuracy.
// delayConstraint=-1 means no constraint
// -----------------------------------------------------------------------
template<class ElemType>
class ForwardBackwardNode : public ComputationNodeNonLooping<ElemType>, public NumInputs<2>
{
typedef ComputationNodeNonLooping<ElemType> Base;
UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName()
{
return L"ForwardBackward";
}
public:
ForwardBackwardNode(DEVICEID_TYPE deviceId, const wstring & name, size_t blankTokenId=SIZE_MAX, int delayConstraint=-1) :
Base(deviceId, name), m_blankTokenId(blankTokenId), m_delayConstraint(delayConstraint)
{
}
ForwardBackwardNode(const ScriptableObjects::IConfigRecordPtr configp)
: ForwardBackwardNode(configp->Get(L"deviceId"), L"<placeholder>", configp->Get(L"blankTokenId"), configp->Get(L"delayConstraint"))
{
AttachInputsFromConfig(configp, this->GetExpectedNumInputs());
}
// Compute gradients to input observations, the weights to the observations, and the class log posterior probabilities
virtual void BackpropToNonLooping(size_t inputIndex) override
{
// Left node must be a scalar
if (inputIndex == 0) //left derivative
{
BackpropToLeft(*m_logSoftmaxOfRight, InputRef(inputIndex).Gradient(), Gradient());
}
else if (inputIndex == 1)
{
FrameRange frameRange(InputRef(0).GetMBLayout());
BackpropToRight(*m_softmaxOfRight, InputRef(inputIndex).Gradient(), Gradient(), *m_CTCposterior);
InputRef(inputIndex).MaskMissingGradientColumnsToZero(frameRange);
}
else
RuntimeError("ForwardBackwardNode criterion expects only two inputs: labels and network output.");
}
void BackpropToLeft(const Matrix<ElemType>& logSoftmaxOfRight, Matrix<ElemType>& inputGradientValues,
const Matrix<ElemType>& gradientValues)
{
#if DUMPOUTPUT
logSoftmaxOfRight.Print("ForwardBackwardNode Partial-logSoftmaxOfRight");
gradientValues.Print("ForwardBackwardNode Partial-gradientValues");
inputGradientValues.Print("ForwardBackwardNode Partial-Left-in");
#endif
Matrix<ElemType>::ScaleAndAdd(-gradientValues.Get00Element(), logSoftmaxOfRight, inputGradientValues);
#if DUMPOUTPUT
inputGradientValues.Print("ForwardBackwardNode Partial-Left-out");
#endif
}
void BackpropToRight(const Matrix<ElemType>& softmaxOfRight, Matrix<ElemType>& inputGradientValues, const Matrix<ElemType>& gradientValues,
const Matrix<ElemType> &CTCposterior)
{
#if DUMPOUTPUT
softmaxOfRight.Print("ForwardBackwardNode Partial-softmaxOfRight");
inputFunctionValues.Print("ForwardBackwardNode Partial-inputFunctionValues");
gradientValues.Print("ForwardBackwardNode Partial-gradientValues");
inputGradientValues.Print("ForwardBackwardNode Partial-Right-in");
#endif
// inputGradientValues+= gradientValues*(softmaxOfRight - CTCposterior)
Matrix<ElemType>::AddScaledDifference(gradientValues, softmaxOfRight, CTCposterior, inputGradientValues);
#if DUMPOUTPUT
inputGradientValues.Print("ForwardBackwardNode Partial-Right");
#endif
}
virtual bool OutputUsedInComputingInputNodesGradients() const override
{
return false;
}
virtual void ForwardPropNonLooping() override
{
m_logSoftmaxOfRight->AssignLogSoftmaxOf(InputRef(1).Value(), true);
m_softmaxOfRight->SetValue(*m_logSoftmaxOfRight);
m_softmaxOfRight->InplaceExp();
m_CTCposterior->SwitchToMatrixType(m_softmaxOfRight->GetMatrixType(), m_softmaxOfRight->GetFormat(), false);
m_CTCposterior->Resize(m_softmaxOfRight->GetNumRows(), m_softmaxOfRight->GetNumCols());
FrameRange fr(InputRef(0).GetMBLayout());
InputRef(0).ValueFor(fr).VectorMax(*m_maxIndexes, *m_maxValues, true);
// compute CTC score
m_GammaCal.doCTC(Value(), *m_logSoftmaxOfRight, *m_maxIndexes, *m_maxValues, *m_CTCposterior, InputRef(0).GetMBLayout(), m_blankTokenId, m_delayConstraint);
#if NANCHECK
functionValues.HasNan("ForwardBackwardNode");
#endif
#if DUMPOUTPUT
functionValues.Print("ForwardBackwardNode");
#endif
}
virtual void /*ComputationNodeBase::*/Validate(bool isFinalValidationPass) override
{
Base::Validate(isFinalValidationPass);
m_pMBLayout = nullptr; // no layout
if (isFinalValidationPass)
{
if (!(Input(0)->GetSampleMatrixNumRows() == Input(1)->GetSampleMatrixNumRows() && // match vector dimension
Input(0)->HasMBLayout() &&
Input(0)->GetMBLayout() == Input(1)->GetMBLayout()))
{
LogicError("The Matrix dimension in the ForwardBackwardNode operation does not match.");
}
auto leftNode = dynamic_pointer_cast<LabelsToGraphNode<ElemType>>(Input(0));
if (!leftNode)
LogicError("ForwardBackwardNode: Please pass LabelsToGraph(labels) for second argument");
}
SetDims(TensorShape::Scalar(Environment().IsV2Library()), false);
}
virtual void CopyTo(const ComputationNodePtr nodeP, const std::wstring& newName, const CopyNodeFlags flags) const
{
Base::CopyTo(nodeP, newName, flags);
if (flags & CopyNodeFlags::copyNodeValue)
{
auto node = dynamic_pointer_cast<ForwardBackwardNode<ElemType>>(nodeP);
node->m_logSoftmaxOfRight->SetValue(*m_logSoftmaxOfRight);
node->m_softmaxOfRight->SetValue(*m_softmaxOfRight);
node->m_CTCposterior->SetValue(*m_CTCposterior);
node->m_maxIndexes->SetValue(*m_maxIndexes);
node->m_maxValues->SetValue(*m_maxValues);
node->m_delayConstraint = m_delayConstraint;
}
}
// request matrices needed to do node function value evaluation
virtual void RequestMatricesBeforeForwardProp(MatrixPool& matrixPool)
{
Base::RequestMatricesBeforeForwardProp(matrixPool);
RequestMatrixFromPool(m_logSoftmaxOfRight, matrixPool);
RequestMatrixFromPool(m_softmaxOfRight, matrixPool);
RequestMatrixFromPool(m_CTCposterior, matrixPool);
RequestMatrixFromPool(m_maxIndexes, matrixPool);
RequestMatrixFromPool(m_maxValues, matrixPool);
}
virtual void ReleaseMatricesAfterBackprop(MatrixPool& matrixPool)
{
Base::ReleaseMatricesAfterBackprop(matrixPool);
ReleaseMatrixToPool(m_logSoftmaxOfRight, matrixPool);
ReleaseMatrixToPool(m_softmaxOfRight, matrixPool);
ReleaseMatrixToPool(m_CTCposterior, matrixPool);
ReleaseMatrixToPool(m_maxIndexes, matrixPool);
ReleaseMatrixToPool(m_maxValues, matrixPool);
}
virtual void UpdateFunctionMBSize() override
{
Base::UpdateFunctionMBSize();
size_t cols = Input(0)->Value().GetNumCols();
m_maxIndexes->Resize(1, cols);
m_maxValues->Resize(1, cols);
}
virtual void Save(File& fstream) const override
{
Base::Save(fstream);
fstream << m_delayConstraint;
fstream << m_blankTokenId;
}
virtual void Load(File& fstream, size_t modelVersion) override
{
Base::Load(fstream, modelVersion);
fstream >> m_delayConstraint;
fstream >> m_blankTokenId;
}
int DelayConstraint() { return m_delayConstraint; }
size_t BlankTokenId() { return m_blankTokenId; }
protected:
virtual bool NodeDoesItsOwnCustomizedMissingColumnsMasking() { return true; }
shared_ptr<Matrix<ElemType>> m_logSoftmaxOfRight;
shared_ptr<Matrix<ElemType>> m_softmaxOfRight;
shared_ptr<Matrix<ElemType>> m_CTCposterior;
shared_ptr<Matrix<ElemType>> m_maxIndexes;
shared_ptr<Matrix<ElemType>> m_maxValues;
msra::lattices::GammaCalculation<ElemType> m_GammaCal;
size_t m_blankTokenId;
int m_delayConstraint;
};
template class ForwardBackwardNode<float>;
template class ForwardBackwardNode<double>;
// -----------------------------------------------------------------------
// StopGradientNode (Input)
// Outputs its input as it and prevents any gradient contribution from its output to its input.
// TODO: This could be more easily implemented as a unary operation, like PassNode.
// -----------------------------------------------------------------------
template <class ElemType>
class StopGradientNode : public UnaryElementWiseNode<ElemType>
{
typedef UnaryElementWiseNode<ElemType> Base;
UsingUnaryElementwiseNodeBaseMembers;
static const std::wstring TypeName() { return L"StopGradient"; }
public:
DeclareConstructorFromConfigWithNumInputs(StopGradientNode);
StopGradientNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name)
{
}
virtual void /*ComputationNode::*/ ForwardProp(const FrameRange& fr) override
{
auto result = ValueFor(fr);
auto inputValue = InputRef(0).ValueFor(fr);
// TODO:@Amit Due to current limitation of the network builder, we can't bypass the memory copy operation at this step.
// But idealy, we should just pass the value of input as this node's output
result.AssignValuesOf(inputValue);
}
virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
{
// Do nothing to short circuit the gradient backward propagation
}
virtual bool OutputUsedInComputingInputNodesGradients() const override { return false; }
virtual bool InputUsedInComputingInputNodesGradients(size_t /*childIndex*/) const override { return false; }
};
template class StopGradientNode<float>;
template class StopGradientNode<double>;
// -----------------------------------------------------------------------
// AssignNode (RefInput, Input)
// -----------------------------------------------------------------------
template <class ElemType>
class AssignNode : public ComputationNodeNonLooping /*ComputationNode*/<ElemType>, public NumInputs<2>
{
typedef ComputationNodeNonLooping<ElemType> Base; UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName() { return L"Assign"; }
shared_ptr<Matrix<ElemType>> m_result;
public:
DeclareConstructorFromConfigWithNumInputs(AssignNode);
AssignNode(DEVICEID_TYPE deviceId, const wstring& name)
: Base(deviceId, name)
{
}
virtual void UpdateFunctionMBSize() override
{
m_result->Resize(Input(0)->Value());
}
virtual void /*ComputationNodeNonLooping::*/ ForwardPropNonLooping() override
{
auto& result = Value();
auto& inputValue = InputRef(1).Value();
if (inputValue.GetNumElements() != result.GetNumElements())
{
InvalidArgument("%ls %ls operation: unexpected dimension mismatch", NodeName().c_str(), OperationName().c_str());
}
m_result->AssignValuesOf(inputValue);
result.AssignValuesOf(inputValue);
}
virtual void /*ComputationNodeNonLooping::*/ PostForwardAndBackProp() override
{
auto& refValue = InputRef(0).Value();
refValue.AssignValuesOf(*m_result);
// We update Input(0) so bump the timestamp for the new data.
Input(0)->BumpEvalTimeStamp();
}
virtual void BackpropToNonLooping(size_t inputIndex) override
{
if (inputIndex == 1)
Input(1)->Gradient() += Gradient();
}
virtual void /*ComputationNodeBase::*/ Validate(bool isFinalValidationPass) override
{
ValidateBinaryZip(isFinalValidationPass, false);
if (Input(0)->HasMBLayout() || Input(1)->HasMBLayout())
InvalidArgument("AssignNode: None of the inputs can have dynamic axes.");
//only check layout in final pass, as there may be free dimension axis
if (isFinalValidationPass && Input(0)->GetSampleLayout() != Input(1)->GetSampleLayout())
InvalidArgument("AssignNode: All inputs should have same sample layout.");
}
// request matrices needed to do node function value evaluation
virtual void RequestMatricesBeforeForwardProp(MatrixPool& matrixPool)
{
Base::RequestMatricesBeforeForwardProp(matrixPool);
RequestMatrixFromPool(m_result, matrixPool);
}
virtual bool OutputUsedInComputingInputNodesGradients() const override { return false; }
virtual bool InputUsedInComputingInputNodesGradients(size_t /*childIndex*/) const override { return false; }
};
template class AssignNode<float>;
template class AssignNode<double>;
// -----------------------------------------------------------------------
// OutputMultiplexerNode(userDefinedV2FunctionNode, outputIndex)
// ComputationNode for selecting one of the multiple outputs of UserDefinedV2FunctionNode
// This is needed since the CNTK computation engin natively does not support
// nodes with multiple outputs and hence, we need a separate node to multiplex
// the additional outputs.
// -----------------------------------------------------------------------
// TODO: We currently only support external nodes that cannot be part of CNTK recurrent loops
template <class ElemType>
class OutputMultiplexerNode final : public ComputationNodeNonLooping<ElemType>, public NumInputs<1>
{
typedef ComputationNodeNonLooping<ElemType> Base; UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName() { return L"OutputMultiplexer"; }
public:
OutputMultiplexerNode(DEVICEID_TYPE deviceId, const wstring& name, size_t outputIndex = 0)
: Base(deviceId, name), m_outputIndex(outputIndex)
{
if (outputIndex == 0)
LogicError("OutputMultiplexerNode ctor must not be instantiated with outputIndex == 0");
}
virtual void ForwardPropNonLooping() override
{
// TODO: We should avoid this copy but that requires carefully managing the
// lifetimes of the Value objects since to be able to directly use the
// input Value as its output, we have to make sure that the input's Value
// is not reused until all dependents of this node are finished.
auto inputNode = Input(0)->template As<MultiOutputNode<ElemType>>();
Value().AssignValuesOf(*inputNode->m_outputsValue[m_outputIndex]);
}
virtual void BackpropToNonLooping(size_t inputIndex) override
{
// TODO: We should avoid this copy but that requires carefully managing the
// lifetimes of the Gradient objects since to be able to directly use the
// Gradient as input's gradient, we have to make sure that the Gradient
// is not reused until all the inputs are finished backpropagating to their inputs.
auto inputNode = Input(0)->template As<MultiOutputNode<ElemType>>();
inputNode->m_outputsGradient[m_outputIndex]->SetValue(Gradient());
}
virtual void Validate(bool isFinalValidationPass) override
{
Base::Validate(isFinalValidationPass);
auto inputNode = Input(0)->template As<MultiOutputNode<ElemType>>();
m_pMBLayout = inputNode->m_outputsMBLayout[m_outputIndex];
SetDims(inputNode->m_outputsShape[m_outputIndex], HasMBLayout());
}
private:
size_t m_outputIndex;
};
template class OutputMultiplexerNode<float>;
template class OutputMultiplexerNode<double>;
} } }