https://github.com/Microsoft/CNTK
Tip revision: 1946ba77a6acdbaef3a660e9e1fec2e5802f6a11 authored by Spandan Tiwari on 06 June 2018, 23:08:04 UTC
Check groups attribute existence in convolution op ONNX export.
Check groups attribute existence in convolution op ONNX export.
Tip revision: 1946ba7
CuDnnRNN.h
#pragma once
#include "Matrix.h"
#include "GPUMatrix.h"
#include "TensorShape.h"
#include <typeinfo>
#include <typeindex>
#include "CuDnnCommon.h"
#include "RNNCommon.h"
namespace Microsoft { namespace MSR { namespace CNTK {
// The CUDNN RNN API requires a dropout descriptor for every RNN. It currently isn't used,
// so this wrapper creates a default descriptor and makes sure the lifetime of the object
// is managed properly.
class CuDnnDropout
{
CuDnn::ptr_t m_cudnn;
unsigned long long m_seed = 1;
public:
CuDnnDropout(float dropout = 0.0f, unsigned long long seed = 1)
: m_dropoutDesc(nullptr), m_cudnn(CuDnn::Instance())
{
CUDNN_CALL(cudnnCreateDropoutDescriptor(&m_dropoutDesc));
size_t stateSize;
void *states;
CUDNN_CALL(cudnnDropoutGetStatesSize(*m_cudnn, &stateSize));
// bugbug: possible leak. Does CuDnn release this for us?
CUDA_CALL(cudaMalloc(&states, stateSize));
CUDNN_CALL(cudnnSetDropoutDescriptor(m_dropoutDesc,
*m_cudnn,
dropout,
states,
stateSize,
seed));
}
~CuDnnDropout()
{
if (m_dropoutDesc != nullptr)
{
cudnnDestroyDropoutDescriptor(m_dropoutDesc);
}
}
operator cudnnDropoutDescriptor_t() const
{
return m_dropoutDesc;
}
DISABLE_COPY_AND_MOVE(CuDnnDropout);
private:
cudnnDropoutDescriptor_t m_dropoutDesc;
};
template <class ElemType>
class CuDnnRNN
{
private:
cudnnDataType_t m_dataType;
cudnnRNNDescriptor_t m_rnnDesc;
CuDnnDropout m_dropout;
RnnAttributes m_rnnAttributes;
cudnnRNNMode_t GetMode()
{
if (m_rnnAttributes.m_recurrentOp == wstring(L"lstm")) return cudnnRNNMode_t::CUDNN_LSTM;
else if (m_rnnAttributes.m_recurrentOp == wstring(L"gru")) return cudnnRNNMode_t::CUDNN_GRU;
else if (m_rnnAttributes.m_recurrentOp == wstring(L"rnnReLU")) return cudnnRNNMode_t::CUDNN_RNN_RELU;
else if (m_rnnAttributes.m_recurrentOp == wstring(L"rnnTanh")) return cudnnRNNMode_t::CUDNN_RNN_TANH;
else InvalidArgument("Unknown cell type '%ls'. Supported values are 'lstm', 'gru', 'rnnReLU', 'rnnTanh'.", m_rnnAttributes.m_recurrentOp.c_str());
}
public:
CuDnnRNN(const RnnAttributes& rnnAttributes)
: m_rnnDesc(nullptr), m_dropout(0.0f), m_rnnAttributes(rnnAttributes),
m_dataType(CuDnnTensor::GetDataType<ElemType>())
{
CUDNN_CALL(cudnnCreateRNNDescriptor(&m_rnnDesc));
#if CUDNN_VERSION >= 7000
CUDNN_CALL(cudnnSetRNNDescriptor_v5(m_rnnDesc,
(int)m_rnnAttributes.m_hiddenSize,
(int)m_rnnAttributes.m_numLayers,
m_dropout,
CUDNN_LINEAR_INPUT, // We can also skip the input matrix transformation
m_rnnAttributes.m_bidirectional ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL,
GetMode(),
m_dataType));
#else
CUDNN_CALL(cudnnSetRNNDescriptor(m_rnnDesc,
(int)m_rnnAttributes.m_hiddenSize,
(int)m_rnnAttributes.m_numLayers,
m_dropout,
CUDNN_LINEAR_INPUT, // We can also skip the input matrix transformation
m_rnnAttributes.m_bidirectional ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL,
GetMode(),
m_dataType));
#endif
}
~CuDnnRNN()
{
if (m_rnnDesc != nullptr)
{
cudnnDestroyRNNDescriptor(m_rnnDesc);
m_rnnDesc = nullptr;
}
}
bool IsCompatible(const RnnAttributes& rnnAttributes) const
{
return this->m_rnnAttributes == rnnAttributes;
}
operator cudnnRNNDescriptor_t() const
{
return m_rnnDesc;
}
bool isBidirectional() const { return m_rnnAttributes.m_bidirectional; }
size_t GetNumLayers() { return m_rnnAttributes.m_numLayers; }
size_t GetNumHidden() { return m_rnnAttributes.m_hiddenSize; }
DISABLE_COPY_AND_MOVE(CuDnnRNN);
};
// The CUDNN RNN API describes the monolithic block of RNN parameters as a filter. This class
// wraps the concept of this filter, including the ability to calculate the necessary size
// based on the rnn descriptor.
template <class ElemType>
class CuDnnFilter
{
cudnnDataType_t m_dataType;
CuDnn::ptr_t m_cudnn;
size_t m_filterSize;
public:
CuDnnFilter(const CuDnnRNN<ElemType>& rnn, const cudnnTensorDescriptor_t& xDesc) :
m_cudnn(CuDnn::Instance()), m_dataType(CuDnnTensor::GetDataType<ElemType>())
{
CUDNN_CALL(cudnnCreateFilterDescriptor(&m_filterDesc));
try
{
size_t filterSize;
CUDNN_CALL(cudnnGetRNNParamsSize(*m_cudnn, rnn, xDesc, &filterSize, m_dataType));
size_t dataSize = 2; // CUDNN_DATA_HALF
if (m_dataType == cudnnDataType_t::CUDNN_DATA_DOUBLE)
dataSize = 8;
else if (m_dataType == cudnnDataType_t::CUDNN_DATA_FLOAT)
dataSize = 4;
// convert from bytes to items
m_filterSize = (filterSize + dataSize - 1) / dataSize;
int dimW[3] = { (int)m_filterSize, 1, 1 };
CUDNN_CALL(cudnnSetFilterNdDescriptor(m_filterDesc, m_dataType, CUDNN_TENSOR_NCHW, 3, dimW));
}
catch (exception e)
{
cudnnDestroyFilterDescriptor(m_filterDesc);
m_filterDesc = nullptr;
throw e;
}
}
~CuDnnFilter()
{
assert(m_filterDesc != nullptr);
cudnnDestroyFilterDescriptor(m_filterDesc);
}
size_t GetSize() { return m_filterSize; }
operator cudnnFilterDescriptor_t() const
{
return m_filterDesc;
}
DISABLE_COPY_AND_MOVE(CuDnnFilter);
private:
cudnnFilterDescriptor_t m_filterDesc;
};
// CuDnnRNNExecutor holds the configuration and state for an instance of an RNN for CUDNN.
// It is generally attached to a GpuMatrix() object, and all calls to the RNN need to go through
// that object.
template <class ElemType>
class CuDnnRNNExecutor
{
CuDnn::ptr_t m_cudnn;
cudnnDataType_t m_dataType;
size_t m_xDim, m_yDim;
public:
CuDnnRNNExecutor(size_t xDim, size_t yDim, const RnnAttributes& rnnAttributes ) :
m_cudnn(CuDnn::Instance()),
m_xDim(xDim), m_yDim(yDim),
m_seqLength(0),
m_dataType(CuDnnTensor::GetDataType<ElemType>()),
m_BackwardDataCalledYet(false)
{
m_rnnT = std::make_unique<CuDnnRNN<ElemType>>(rnnAttributes);
}
void ForwardCore(const GPUMatrix<ElemType>& weightsW, const GPUMatrix<ElemType>& inputX, GPUMatrix<ElemType>& outputY, const vector<size_t>& numSequencesForFrame, const RnnAttributes& rnnAttributes, GPUMatrix<ElemType>& reserve, GPUMatrix<ElemType>& workspace);
void BackwardWeightsCore(const GPUMatrix<ElemType>& inputX, const GPUMatrix<ElemType>& outputY, GPUMatrix<ElemType>& dw, const RnnAttributes& rnnAttributes, GPUMatrix<ElemType>& reserve, GPUMatrix<ElemType>& workspace);
void BackwardDataCore(const GPUMatrix<ElemType>& outputY, const GPUMatrix<ElemType>& outputDY, const GPUMatrix<ElemType>& w, GPUMatrix<ElemType>& dx, const RnnAttributes& rnnAttributes, GPUMatrix<ElemType>& reserve, GPUMatrix<ElemType>& workspace);
protected:
std::unique_ptr<CuDnnFilter<ElemType>> wDesc;
vector<cudnnTensorDescriptor_t> xDesc;
vector<cudnnTensorDescriptor_t> yDesc;
private:
static ElemType* ptr(GPUMatrix<ElemType>& src)
{
return src.Data();
}
static const ElemType* ptr(const GPUMatrix<ElemType>& src)
{
return src.Data();
}
void SetDescriptors(size_t dim, const vector<size_t>& numSequencesForFrame, vector<cudnnTensorDescriptor_t>& descriptors);
private:
std::unique_ptr<CuDnnRNN<ElemType>> m_rnnT;
bool m_BackwardDataCalledYet;
size_t m_seqLength;
};
} } }