https://github.com/Microsoft/CNTK
Revision 9fc6fa4b112f3225b0035b3e65fae8b88e041465 authored by Zhou Wang on 19 May 2017, 06:30:25 UTC, committed by Zhou Wang on 19 May 2017, 06:30:25 UTC
1 parent 8b5035c
Tip revision: 9fc6fa4b112f3225b0035b3e65fae8b88e041465 authored by Zhou Wang on 19 May 2017, 06:30:25 UTC
make FindAllWithName private for all
make FindAllWithName private for all
Tip revision: 9fc6fa4
UserDefinedV2FunctionNode.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 "CNTKLibrary.h"
#include "Utils.h"
namespace Microsoft { namespace MSR { namespace CNTK {
template <typename ElemType>
class OutputMultiplexerNode;
// -----------------------------------------------------------------------
// UserDefinedV2Function
// Proxy ComputationNode type for a V2 user-defined custom Function, instances
// of which can be part of a CNTK computation network.
// The actual implementation of the operation itself is external to the CNTK engine.
// -----------------------------------------------------------------------
// TODO: We currently only support external nodes that cannot be part of CNTK recurrent loops
template <class ElemType>
class UserDefinedV2FunctionNode final : public ComputationNodeNonLooping<ElemType>, public MultiOutputNode<ElemType>
{
typedef ComputationNodeNonLooping<ElemType> Base; UsingComputationNodeMembersBoilerplate;
static const std::wstring TypeName() { return L"UserDefinedV2Function"; }
friend class OutputMultiplexerNode<ElemType>;
public:
UserDefinedV2FunctionNode(DEVICEID_TYPE deviceId, const wstring& name, const ::CNTK::FunctionPtr& externalFunction = nullptr)
: Base(deviceId, name), m_externalFunction(externalFunction), MultiOutputNode<ElemType>(externalFunction ? externalFunction->Outputs().size() : 0)
{
if (!m_externalFunction)
LogicError("UserDefinedV2FunctionNode ctor should never be called with externalFunction == nullptr");
}
virtual void ForwardPropNonLooping() override
{
this->m_outputsValue[0] = m_value;
// Get the arguments of the external function
auto arguments = m_externalFunction->Arguments();
std::unordered_map<::CNTK::Variable, ::CNTK::ValuePtr> argumentValues;
auto numInputs = GetNumInputs();
size_t j = 0;
for (size_t i = 0; i < numInputs; ++i)
{
auto& input = InputRef(i);
if (input.template Is<LearnableParameter<ElemType>>())
continue;
auto argumentVar = arguments[j++];
auto argumentShape = ::CNTK::AsNDShape(input.GetSampleLayout());
auto argumentValue = ::CNTK::Utils::GetValueObjectFromCNTKImplMatrixAndMBLayout(argumentShape, argumentVar.DynamicAxes(), input.Value(), input.GetMBLayout());
argumentValues.insert(std::make_pair(argumentVar, argumentValue));
}
assert(j == arguments.size());
auto outputs = m_externalFunction->Outputs();
// TODO: Instead of passing null for output values, we should have the forward call directly produce the outputs in the output Value() of this node
std::unordered_map<::CNTK::Variable, ::CNTK::ValuePtr> outputValues;
for (auto output : outputs)
outputValues.insert({output, nullptr});
std::unordered_set<::CNTK::Variable> outputsToRetainBackwardStateFor;
if (Environment().IsTraining())
outputsToRetainBackwardStateFor.insert(outputs.begin(), outputs.end());
auto computeDevice = ::CNTK::AsDeviceDescriptor(InputRef(0).Value().GetDeviceId());
m_currentBackpropStatePtr = m_externalFunction->Forward(argumentValues, outputValues, computeDevice, outputsToRetainBackwardStateFor);
// Copy the computed output
for (size_t i = 0; i < outputs.size(); ++i)
{
auto output = outputs[i];
::CNTK::NDShape inferredVarShape;
auto outputMatrixAndLayout = ::CNTK::Utils::GetCNTKImplMatrixAndMBLayoutFromValueObject<ElemType>(output, outputValues[output], &inferredVarShape);
if (inferredVarShape.IsUnknown() || inferredVarShape.HasUnboundDimension())
LogicError("The output shape '%S' of an external user defined Function '%S' must be fully defined.", inferredVarShape.AsString().c_str(), m_externalFunction->AsString().c_str());
if (output.Shape().HasFreeDimension())
{
this->m_outputsShape[i] = ::CNTK::AsTensorShape(inferredVarShape);
if (i == 0)
SetDims(this->m_outputsShape[i], HasMBLayout());
}
this->m_outputsValue[i]->SetValue(*outputMatrixAndLayout.first);
if ((this->m_outputsMBLayout[i] != nullptr) && (outputMatrixAndLayout.second == nullptr))
LogicError("The UserDefinedFunction node has a non-null output MBLayout but none found from the '%S' user Function::Forward output Value", m_externalFunction->Name().c_str());
else if ((this->m_outputsMBLayout[i] == nullptr) && (outputMatrixAndLayout.second != nullptr))
LogicError("The UserDefinedFunction node does not have an output MBLayout but the '%S' user Function::Forward output Value has a non-null layout", m_externalFunction->Name().c_str());
else if ((this->m_outputsMBLayout[i] == nullptr) && (outputMatrixAndLayout.second == nullptr))
;
else
{
if (this->m_outputsHasNewMBLayout[i])
this->m_outputsMBLayout[i]->CopyFrom(outputMatrixAndLayout.second);
else
{
if (*this->m_outputsMBLayout[i] != *outputMatrixAndLayout.second)
LogicError("The MBLayout 'NumSequences=%zu, NumTimeSteps=%zu' of the output computed by the external function '%S' does not match the expected MBLayout 'NumSequences=%zu, NumTimeSteps=%zu'.",
outputMatrixAndLayout.second->GetNumSequences(), outputMatrixAndLayout.second->GetNumTimeSteps(),
m_externalFunction->Name().c_str(),
this->m_outputsMBLayout[i]->GetNumSequences(), this->m_outputsMBLayout[i]->GetNumTimeSteps());
}
}
}
}
virtual void BackpropToNonLooping(size_t /*inputIndex*/) override
{
if (m_currentBackpropStatePtr == nullptr)
return;
this->m_outputsGradient[0] = m_gradient;
std::unordered_map<::CNTK::Variable, ::CNTK::ValuePtr> outputGradientValues;
auto outputs = m_externalFunction->Outputs();
bool noOutputNeedsGradient = std::all_of(outputs.begin(), outputs.end(), [](const ::CNTK::Variable& outVar) { return !outVar.NeedsGradient(); });
if (noOutputNeedsGradient)
return;
for (size_t i = 0; i < outputs.size(); ++i)
{
auto output = outputs[i];
// TODO: We unpack the same output gradients each time this method is called for a different input.
// We should be able to cache the unpacked values during backpropagation of gradients to the first
// input, and reuse them for subsequence inputs.
::CNTK::ValuePtr gradientValue;
if (output.NeedsGradient())
gradientValue = ::CNTK::Utils::GetValueObjectFromCNTKImplMatrixAndMBLayout(::CNTK::AsNDShape(this->m_outputsShape[i]), output.DynamicAxes(), *this->m_outputsGradient[i], this->m_outputsMBLayout[i]);
outputGradientValues.insert({ output, gradientValue });
}
std::vector<::CNTK::Variable> externalFunctionUniqueInputs;
auto externalFunctionInputs = m_externalFunction->Inputs();
for (auto input : externalFunctionInputs)
{
if (std::find(externalFunctionUniqueInputs.begin(), externalFunctionUniqueInputs.end(), input) == externalFunctionUniqueInputs.end())
externalFunctionUniqueInputs.push_back(input);
}
std::unordered_map<::CNTK::Variable, ::CNTK::ValuePtr> inputGradientValues;
for (size_t i = 0; i < externalFunctionUniqueInputs.size(); ++i)
{
if (InputRef(i).NeedsGradient())
inputGradientValues.insert({ externalFunctionUniqueInputs[i], nullptr });
}
m_externalFunction->Backward(m_currentBackpropStatePtr, outputGradientValues, inputGradientValues);
// Accumulate the computed input gradient value into the existing input gradient value
// TODO: We should directly pass the actual input gradient tensor to the Backward method
// instead of allocating a new value and accumulating it ourselves
for (size_t i = 0; i < externalFunctionUniqueInputs.size(); ++i)
{
if (!InputRef(i).NeedsGradient())
continue;
InputRef(i).LazyZeroGradient(); // set gradient to 0 if this is the first time
auto input = externalFunctionUniqueInputs[i];
auto inputGradientValue = inputGradientValues[input];
if (!inputGradientValue)
continue;
auto newInputGradientMatrixAndLayout = ::CNTK::Utils::GetCNTKImplMatrixAndMBLayoutFromValueObject<ElemType>(input, inputGradientValue);
InputRef(i).Gradient() += *newInputGradientMatrixAndLayout.first;
if (*InputRef(i).GetMBLayout() != *newInputGradientMatrixAndLayout.second)
LogicError("The MBLayout 'NumSequences=%zu, NumTimeSteps=%zu' of the Input(%zu) gradient computed by the external function '%S' does not match the expected MBLayout 'NumSequences=%zu, NumTimeSteps=%zu'.",
newInputGradientMatrixAndLayout.second->GetNumSequences(), newInputGradientMatrixAndLayout.second->GetNumTimeSteps(),
i, this->GetName().c_str(),
InputRef(i).GetMBLayout()->GetNumSequences(), InputRef(i).GetMBLayout()->GetNumTimeSteps());
}
m_currentBackpropStatePtr = nullptr;
}
virtual void Validate(bool isFinalValidationPass) override
{
Base::Validate(isFinalValidationPass);
auto outputs = m_externalFunction->Outputs();
bool layoutNotInitialized = (m_pMBLayout == nullptr);
for (size_t i = 0; i < outputs.size(); ++i)
{
auto output = outputs[i];
if (output.GetDataType() != ::CNTK::AsDataType<ElemType>())
{
LogicError("The DataType '%s' of the external user defined Function's output does not match the internal ComputationNode's ElemType '%s'.",
DataTypeName(output.GetDataType()),
DataTypeName(::CNTK::AsDataType<ElemType>()));
}
auto outputNDShape = output.Shape();
if (layoutNotInitialized)
{
auto outputDynamicAxes = output.DynamicAxes();
if (outputDynamicAxes.empty())
{
this->m_outputsHasNewMBLayout[i] = true;
this->m_outputsMBLayout[i] = nullptr;
}
else
{
this->m_outputsMBLayout[i] = make_shared<MBLayout>(); // this generates a new layout
this->m_outputsMBLayout[i]->SetUniqueAxisName(InternalDynamicAxisNameFromDynamicAxes(output.DynamicAxes()));
this->m_outputsHasNewMBLayout[i] = true;
}
}
for (size_t k = 0; k < outputNDShape.Rank(); ++k)
{
if ((outputNDShape[k] == ::CNTK::NDShape::FreeDimension) || (outputNDShape[k] == ::CNTK::NDShape::InferredDimension))
outputNDShape[k] = 1;
}
this->m_outputsShape[i] = ::CNTK::AsTensorShape(outputNDShape);
if (i == 0)
{
if (layoutNotInitialized)
m_pMBLayout = this->m_outputsMBLayout[i];
SetDims(this->m_outputsShape[i], HasMBLayout());
}
}
}
private:
::CNTK::FunctionPtr m_externalFunction;
::CNTK::BackPropStatePtr m_currentBackpropStatePtr;
};
template class UserDefinedV2FunctionNode<float>;
template class UserDefinedV2FunctionNode<double>;
}}}
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