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CNTK/Source/Math/GPUMatrix.cu

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//
// Copyright (c) Microsoft. All rights reserved.
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
//
#include "stdafx.h"
#include "Basics.h"
#include "BestGpu.h"
#ifndef CPUONLY
#include "GPUMatrix.h"
#include "GPUMatrixCUDAKernels.cuh"
//#include "GPUSparseMatrix.h"
#include "GPUTensor.h"
#include "CommonMatrix.h"
#define TENSOR_OPS_DECL __device__ __host__
#include "TensorOps.h"
#include "device_launch_parameters.h"
#include <cuda.h>
#include <cuda_runtime.h>
#include <curand.h>
#include <curand_kernel.h>
#include "cublas_v2.h"
#include <assert.h>
#include <memory>
#include "CntkBatchNormalization.cuh"
#include "Convolution.cuh"
#include "CuDnnRNN.h"
#pragma comment(lib, "cudart.lib") // instruct linker to reference these libs
#pragma comment(lib, "cublas.lib")
#pragma comment(lib, "cusparse.lib")
#pragma comment(lib, "curand.lib")
#pragma warning(disable : 4267) // conversion from 'size_t' to 'unsigned int'; happens in CUDA <<<a,b>>> syntax if a and b are size_t
#pragma warning(disable : 4127) // conditional expression is constant; "if (sizeof(ElemType)==sizeof(float))" triggers this
#pragma warning(disable : 4702) // unreachable code; triggered for unknown reasons
#define DEFAULT_THREAD_PER_DIM 16
#define UNCONST(t, c, uc) GPUMatrix<t>& uc = const_cast<GPUMatrix<t>&>(c);
#ifdef _WIN32
// thread local storage to access the current stream, initialize to default stream
__declspec(thread)
#endif
cudaStream_t t_stream = cudaStreamDefault;
#define DEFAULT_THREAD_PER_DIM 16
extern int _ConvertSMVer2Cores(int major, int minor); // forward declaration
// SetStream - set the stream that will be used by the GPU routines
void MATH_API SetStream(cudaStream_t stream)
{
t_stream = stream;
}
// GetStream - get the stream that will be used by the GPU routines
cudaStream_t MATH_API GetStream()
{
return t_stream;
}
// Helper macro patterns for elementwise methods
#define DEF_ELEMWISE_INPLACE_FUNC(f) \
template <class ElemType> \
GPUMatrix<ElemType>& GPUMatrix<ElemType>::Inplace##f() \
{ \
performElementWiseFunction(ElementWiseOperator::op##f, Data()); \
return *this; \
}
#define DEF_ELEMWISE_ASSIGN_FUNC(f) \
template <class ElemType> \
GPUMatrix<ElemType>& GPUMatrix<ElemType>::Assign##f##Of(const GPUMatrix<ElemType>& a) \
{ \
if (a.IsEmpty()) \
LogicError("Assign##f##Of: Matrix a is empty."); \
if (this != &a) \
RequireSize(a.GetNumRows(), a.GetNumCols()); \
performElementWiseFunction(ElementWiseOperator::op##f, a.Data()); \
return *this; \
}
template <>
const char* CudaErrString<cudaError_t>(cudaError_t x)
{
cudaDeviceSynchronize();
return cudaGetErrorString(x);
}
template <>
const char* CudaErrString<cublasStatus_t>(cublasStatus_t e)
{
cudaDeviceSynchronize();
switch (e)
{
case CUBLAS_STATUS_SUCCESS: return "CUBLAS_STATUS_SUCCESS";
case CUBLAS_STATUS_NOT_INITIALIZED: return "CUBLAS_STATUS_NOT_INITIALIZED";
case CUBLAS_STATUS_ALLOC_FAILED: return "CUBLAS_STATUS_ALLOC_FAILED";
case CUBLAS_STATUS_INVALID_VALUE: return "CUBLAS_STATUS_INVALID_VALUE";
case CUBLAS_STATUS_ARCH_MISMATCH: return "CUBLAS_STATUS_ARCH_MISMATCH";
case CUBLAS_STATUS_MAPPING_ERROR: return "CUBLAS_STATUS_MAPPING_ERROR";
case CUBLAS_STATUS_EXECUTION_FAILED: return "CUBLAS_STATUS_EXECUTION_FAILED";
case CUBLAS_STATUS_INTERNAL_ERROR: return "CUBLAS_STATUS_INTERNAL_ERROR";
case CUBLAS_STATUS_NOT_SUPPORTED: return "CUBLAS_STATUS_NOT_SUPPORTED";
case CUBLAS_STATUS_LICENSE_ERROR: return "CUBLAS_STATUS_LICENSE_ERROR";
default: return "(look for CUBLAS_STATUS_xxx in cublas_api.h)";
}
}
template <>
const char* CudaErrString<curandStatus>(curandStatus)
{
cudaDeviceSynchronize();
return "(see curand.h & look for curandStatus or CURAND_STATUS_xxx)";
}
namespace Microsoft { namespace MSR { namespace CNTK {
/*static*/ std::vector<cudaDeviceProp> GridDim::s_cachedDeviceProps;
/*static*/ std::once_flag GridDim::s_cachedDevicePropsInitFlag;
/*static*/ bool SyncGuard::s_isSyncEnabled = false;
/*static*/ void SyncGuard::EnableSync()
{
s_isSyncEnabled = true;
}
SyncGuard::SyncGuard(bool forceSync /*= false*/)
: m_forceSync(forceSync)
{
m_done = nullptr;
if (m_forceSync || s_isSyncEnabled)
{
CUDA_CALL(cudaGetLastError());
CUDA_CALL(cudaEventCreate(&m_done));
}
}
SyncGuard::~SyncGuard()
{
if (m_forceSync || s_isSyncEnabled)
{
// The regular use of this destructor is to synchronize the GPU, but also
// to check for errors. So this destructor is where CUDA errors would be thrown.
// If this destructor runs during stack unwinding, then a different error has
// already happened that should be reported; so we only clean up the resource.
if (std::uncaught_exception())
cudaEventDestroy(m_done);
else
{
// failures in a prior launch might be reported here
CUDA_CALL(cudaEventRecord(m_done));
CUDA_CALL(cudaEventSynchronize(m_done));
CUDA_CALL(cudaEventDestroy(m_done));
}
}
}
template <typename AllocatedElemType>
AllocatedElemType* TracingGPUMemoryAllocator::Allocate(int deviceId, size_t numRows, size_t numCols)
{
if (IsTraceEnabled())
{
auto freeAndTotalMemory = GetFreeAndTotalMemoryInMBs(deviceId);
fprintf(stderr, "Allocating Matrix<%s> (Rows = %d, Cols = %d) buffer on DeviceId = %d; GPU Memory Free = %d MB of %d MB\n", typeid(AllocatedElemType).name(), (int)numRows, (int)numCols, (int)deviceId, (int)freeAndTotalMemory.first, (int)freeAndTotalMemory.second);
Microsoft::MSR::CNTK::DebugUtil::PrintCallStack();
}
AllocatedElemType* deviceBufferPtr = AllocateNoTrace<AllocatedElemType>(deviceId, numRows * numCols);
if (IsTraceEnabled())
{
fprintf(stderr, "Allocated DeviceData = %p\n", (void*) deviceBufferPtr);
}
return deviceBufferPtr;
}
template <typename AllocatedElemType>
AllocatedElemType* TracingGPUMemoryAllocator::Allocate(int deviceId, size_t numElements)
{
if (IsTraceEnabled())
{
auto freeAndTotalMemory = GetFreeAndTotalMemoryInMBs(deviceId);
fprintf(stderr, "Allocating array<%s> (NumElements = %d) on DeviceId = %d; GPU Memory Free = %d MB of %d MB\n", typeid(AllocatedElemType).name(), (int)numElements, (int)deviceId, (int)freeAndTotalMemory.first, (int)freeAndTotalMemory.second);
Microsoft::MSR::CNTK::DebugUtil::PrintCallStack();
}
AllocatedElemType* deviceBufferPtr = AllocateNoTrace<AllocatedElemType>(deviceId, numElements);
if (IsTraceEnabled())
{
fprintf(stderr, "Allocated DeviceData = %p\n", (void*)deviceBufferPtr);
}
return deviceBufferPtr;
}
template <typename AllocatedElemType>
void TracingGPUMemoryAllocator::Free(int deviceId, AllocatedElemType* bufferPtr, bool ignoreCUDARetCode /*= false*/)
{
PrepareDevice(deviceId);
if (ignoreCUDARetCode)
cudaFree((void*) bufferPtr);
else
CUDA_CALL(cudaFree((void*) bufferPtr));
if (IsTraceEnabled())
{
auto freeAndTotalMemory = GetFreeAndTotalMemoryInMBs(deviceId);
fprintf(stderr, "Freed buffer<%s> DeviceData = %p on DeviceId = %d; GPU Memory Free = %d MB of %d MB\n", typeid(AllocatedElemType).name(), (void*) bufferPtr, (int) deviceId, (int) freeAndTotalMemory.first, (int) freeAndTotalMemory.second);
Microsoft::MSR::CNTK::DebugUtil::PrintCallStack();
}
}
template <typename AllocatedElemType>
AllocatedElemType* TracingGPUMemoryAllocator::AllocateNoTrace(int deviceId, size_t numElements)
{
AllocatedElemType* deviceBufferPtr;
PrepareDevice(deviceId);
CUDA_CALL(cudaMalloc((void**) &deviceBufferPtr, sizeof(AllocatedElemType) * numElements));
return deviceBufferPtr;
}
std::pair<size_t, size_t> TracingGPUMemoryAllocator::GetFreeAndTotalMemoryInMBs(int deviceId)
{
PrepareDevice(deviceId);
size_t free, total;
CUDA_CALL(cudaMemGetInfo(&free, &total));
size_t numBytesPerMB = 1 << 20;
return {free / numBytesPerMB, total / numBytesPerMB};
}
// PrepareDevice - Setup the correct cuda context for an operation
// deviceId - the device on which the operation will take place
void PrepareDevice(DEVICEID_TYPE deviceId)
{
THREAD_LOCAL static DEVICEID_TYPE currentDevice = DEVICEID_NOTYETDETERMINED;
// and if we last set the device to be this device we are good
if (deviceId == currentDevice)
return;
CUDA_CALL(cudaSetDevice(deviceId));
currentDevice = deviceId;
}
#pragma region DeviceBoundNumber class
template <class ElemType>
DeviceBoundNumber<ElemType>::DeviceBoundNumber(const DeviceBoundNumber<ElemType>& /*deepCopy*/)
{
NOT_IMPLEMENTED;
}
template <class ElemType>
DeviceBoundNumber<ElemType>::DeviceBoundNumber(DeviceBoundNumber<ElemType>&& shallowCopy)
{
ShallowCopyFrom(shallowCopy.m_data, shallowCopy.m_computeDevice);
shallowCopy.m_data = NULL;
}
template <class ElemType>
void DeviceBoundNumber<ElemType>::ShallowCopyFrom(ElemType* newVal, int newValsDevceId)
{
m_computeDevice = newValsDevceId;
m_data = newVal;
}
template <class ElemType>
DeviceBoundNumber<ElemType>::~DeviceBoundNumber()
{
if (m_data != NULL)
{
if (m_computeDevice < 0)
{
delete m_data;
m_data = NULL;
}
else
{
TracingGPUMemoryAllocator::Free<ElemType>(m_computeDevice, m_data);
}
}
}
#pragma endregion DeviceBoundNumber class
#pragma region Helper functions
template <class ElemType>
cublasHandle_t _initCUBLAS(int devId)
{
PrepareDevice((DEVICEID_TYPE) devId);
cublasHandle_t cuHandle;
CUBLAS_CALL(cublasCreate(&cuHandle));
return cuHandle;
}
template <class ElemType>
void GPUMatrix<ElemType>::SetDevice(DEVICEID_TYPE deviceId)
{
assert(deviceId >= 0);
CUDA_CALL(cudaSetDevice(deviceId));
}
// PrepareDevice - Setup the correct cuda context for an operation
// deviceId - the device on which the operation will take place
// defaults to -1, which means use matrices current device
template <class ElemType>
DEVICEID_TYPE GPUMatrix<ElemType>::PrepareDevice(DEVICEID_TYPE deviceId /*=-1*/) const
{
// if default value use current compute device
DEVICEID_TYPE newId = deviceId >= 0 ? deviceId : GetComputeDeviceId();
Microsoft::MSR::CNTK::PrepareDevice(newId);
return newId;
}
template <class ElemType>
ElemType* GPUMatrix<ElemType>::CopyToArray() const
{
size_t numElements = GetNumElements();
if (numElements != 0)
{
PrepareDevice();
ElemType* pArray = new ElemType[numElements];
CUDA_CALL(cudaMemcpy(pArray, Data(), sizeof(ElemType) * m_numRows * m_numCols, cudaMemcpyDeviceToHost));
return pArray;
}
else
{
return NULL;
}
}
//memory will be allocated by the callee if not enough but need to be deleted by the caller after it's done
//return number of elements copied
template <class ElemType>
size_t GPUMatrix<ElemType>::CopyToArray(ElemType*& arrayCopyTo, size_t& currentArraySize) const
{
size_t numElements = GetNumElements();
if (numElements > currentArraySize)
{
delete arrayCopyTo;
arrayCopyTo = new ElemType[numElements];
currentArraySize = numElements;
}
if (numElements != 0)
{
PrepareDevice();
CUDA_CALL(cudaMemcpy(arrayCopyTo, Data(), sizeof(ElemType) * numElements, cudaMemcpyDeviceToHost));
}
return numElements;
}
template <typename ElemType>
void GPUMatrix<ElemType>::CopySection(size_t numRows, size_t numCols, ElemType* dst, size_t colStride) const
{
CUBLAS_CALL(cublasGetMatrix((int) numRows, (int) numCols, sizeof(ElemType),
Data(), (int) GetNumRows(), dst, (int) colStride));
}
template <class ElemType>
void GPUMatrix<ElemType>::ChangeDeviceTo(DEVICEID_TYPE to_id)
{
if (to_id == CPUDEVICE)
LogicError("to_id must be valid GPU");
if (GetComputeDeviceId() == to_id)
return;
ElemType* d_dst = TracingGPUMemoryAllocator::Allocate<ElemType>(to_id, m_numRows, m_numCols);
SetSizeAllocated(m_numRows * m_numCols);
// check to make sure we have something to copy (on init we often have zero sized allocations)
if (GetSizeAllocated() > 0)
{
// first try peer access
int canAccessPeer = false;
CUDA_CALL(cudaDeviceCanAccessPeer(&canAccessPeer, to_id, GetComputeDeviceId()));
if (canAccessPeer)
{
cudaError_t cudaStatus = cudaDeviceEnablePeerAccess(GetComputeDeviceId(), 0);
if (cudaStatus != cudaErrorPeerAccessAlreadyEnabled)
{
CUDA_CALL(cudaStatus);
}
CUDA_CALL(cudaMemcpyPeer(d_dst, to_id, Data(), GetComputeDeviceId(), sizeof(ElemType) * m_numRows * m_numCols));
}
else
{
// peer access didn't work, just copy normal
// make this more efficient by keeping some buffers available for each copy
ElemType* h_dst = NULL;
PrepareDevice();
CUDA_CALL(cudaMallocHost((void**) &h_dst, sizeof(ElemType) * m_numRows * m_numCols));
CUDA_CALL(cudaMemcpy(h_dst, Data(), sizeof(ElemType) * m_numRows * m_numCols, cudaMemcpyDeviceToHost));
PrepareDevice((DEVICEID_TYPE) to_id);
CUDA_CALL(cudaMemcpy(d_dst, h_dst, sizeof(ElemType) * m_numRows * m_numCols, cudaMemcpyHostToDevice));
CUDA_CALL(cudaFreeHost(h_dst));
}
}
TracingGPUMemoryAllocator::Free<ElemType>(GetComputeDeviceId(), Buffer());
SetBuffer(d_dst, m_numRows * m_numCols * sizeof(ElemType));
PrepareDevice((DEVICEID_TYPE) to_id);
SetComputeDeviceId(to_id);
}
template <class ElemType>
void GPUMatrix<ElemType>::performElementWiseFunction(ElementWiseOperator kind, const ElemType* src)
{
PrepareDevice();
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
SyncGuard syncGuard;
switch (kind)
{
case ElementWiseOperator::opSigmoid:
return _elementWiseSigmoidOnCuda<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(src, Data(), N);
case ElementWiseOperator::opTanh:
return _elementWiseTanhOnCuda<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(src, Data(), N);
case ElementWiseOperator::opSqrt:
return _elementWiseSqrtOnCuda<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(src, Data(), N);
case ElementWiseOperator::opExp:
return _elementWiseExpOnCuda<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(src, Data(), N);
case ElementWiseOperator::opLog:
return _elementWiseLogOnCuda<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(src, Data(), N);
case ElementWiseOperator::opAbs:
return _elementWiseAbsOnCuda<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(src, Data(), N);
case ElementWiseOperator::opLinearRectifierDerivative:
return _elementWiseLinRectDerivativeOnCuda<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(src, Data(), N);
case ElementWiseOperator::opCosine:
return _elementWiseCosineOnCuda<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(src, Data(), N);
case ElementWiseOperator::opNegativeSine:
return _elementWiseNegativeSineOnCuda<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(src, Data(), N);
case ElementWiseOperator::opSigmoidDerivative:
return _elementWiseSigmoidDerivativeOnCuda<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(src, Data(), N);
default: LogicError("performElementWiseFunction: unexpected op code %d", (int)kind);
}
}
#pragma endregion Helper functions
#pragma region Constructors and Destructor
// should only be used by constructors
template <class ElemType>
void GPUMatrix<ElemType>::ZeroInit(int deviceId)
{
BaseMatrix<ElemType>::ZeroInit();
SetComputeDeviceId(deviceId);
}
template <class ElemType>
GPUMatrix<ElemType>::GPUMatrix(int deviceId)
{
ZeroInit(deviceId);
};
template <class ElemType>
GPUMatrix<ElemType>::GPUMatrix(const size_t numRows, const size_t numCols, int deviceId)
{
ZeroInit(deviceId);
m_numRows = numRows;
m_numCols = numCols;
SetSizeAllocated(GetNumElements());
if (GetNumElements() != 0)
{
SetBuffer(TracingGPUMemoryAllocator::Allocate<ElemType>(GetComputeDeviceId(), m_numRows, m_numCols), GetNumElements() * sizeof(ElemType));
CUDA_CALL(cudaMemset(Buffer(), 0, sizeof(ElemType) * GetSizeAllocated()));
}
};
template <class ElemType>
GPUMatrix<ElemType>::GPUMatrix(const size_t numRows, const size_t numCols, int deviceId, ElemType* pArray, const size_t matrixFlags)
{
ZeroInit(deviceId);
SetValue(numRows, numCols, deviceId, pArray, matrixFlags);
};
template <class ElemType>
GPUMatrix<ElemType>::GPUMatrix(const GPUMatrix<ElemType>& deepCopyFrom)
{
ZeroInit();
SetValue(deepCopyFrom);
}
template <class ElemType>
GPUMatrix<ElemType>::GPUMatrix(GPUMatrix<ElemType>&& moveFrom)
{
ShallowCopyFrom(moveFrom);
moveFrom.ZeroValues();
}
//assignment operator, deep copy
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::operator=(const GPUMatrix<ElemType>& deepCopyFrom)
{
if (this != &deepCopyFrom)
{
SetValue(deepCopyFrom);
}
return *this;
}
//move assignment operator, shallow copy
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::operator=(GPUMatrix<ElemType>&& moveFrom)
{
if (this != &moveFrom)
{
ShallowCopyFrom(moveFrom);
moveFrom.ZeroValues();
}
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>::~GPUMatrix(void)
{
}
// TODO: This should be in the storage object.
// Clear will clear your storage, zeroinit just drops it on the ground.
template <class ElemType>
void GPUMatrix<ElemType>::Clear()
{
VerifyWritable(__FUNCTION__);
//if (OwnBuffer() && m_pArray != NULL)
if (m_sob != nullptr)
{
if (GetComputeDeviceId()>= 0)
{
// BUG: We do not check the CUDA return code for cudaFree here since this may get called
// during processExit when cudaFree will fail. The destruction of CUDA objects during
// process exit must be avoided
ReleaseStorageMemory();
}
}
ZeroInit(GetComputeDeviceId());
}
#pragma endregion Constructors and Destructor
template <class ElemType>
std::unique_ptr<GPUMatrix<ElemType>> GPUMatrix<ElemType>::GetOrCreateWorkspace() const
{
// REVIEW alexeyk: not thread-safe, fine for now.
if (m_workspace == nullptr)
m_workspace = std::make_unique<conc_stack<std::unique_ptr<GPUMatrix<ElemType>>>>();
assert(m_workspace != nullptr);
auto deviceId = GetComputeDeviceId();
return m_workspace->pop_or_create([deviceId]()
{
return std::make_unique<GPUMatrix<ElemType>>(deviceId);
});
}
template <class ElemType>
void GPUMatrix<ElemType>::ReleaseWorkspace(std::unique_ptr<GPUMatrix<ElemType>> src) const
{
assert(m_workspace != nullptr);
m_workspace->push(std::move(src));
}
#pragma region Basic Operators
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::ColumnSlice(size_t startColumn, size_t numCols) const
{
if (startColumn + numCols > GetNumCols())
InvalidArgument("The slice (%d+%d) is out of range of the source matrix (%d).", (int) startColumn, (int) numCols, (int) GetNumCols());
GPUMatrix<ElemType> slice(GetComputeDeviceId());
slice.ShallowCopyFrom(*this);
slice.m_numCols = numCols;
slice.m_sliceViewOffset = m_sliceViewOffset + startColumn * GetNumRows();
return slice;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignColumnSlice(const GPUMatrix<ElemType>& fromMatrix, size_t startColumn, size_t numCols)
{
if (numCols == 0)
LogicError("The slice cannot have 0 columns.");
if (startColumn + numCols > fromMatrix.GetNumCols())
InvalidArgument("The slice (%d+%d) is out of range of the source matrix (%d).", (int) startColumn, (int) numCols, (int) fromMatrix.GetNumCols());
Clear();
ShallowCopyFrom(fromMatrix);
m_numCols = numCols;
m_sliceViewOffset = fromMatrix.m_sliceViewOffset + startColumn * GetNumRows();
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::SetColumnSlice(const GPUMatrix<ElemType>& fromMatrix, size_t startColumn, size_t numCols)
{
if (startColumn + numCols > GetNumCols())
LogicError("The slice is out of range of the destination matrix.");
if (numCols > fromMatrix.GetNumCols())
InvalidArgument("The slice (%d) is out of range of the source matrix (%d).", (int) numCols, (int) fromMatrix.GetNumCols());
if (m_numRows != fromMatrix.m_numRows)
LogicError("The number of rows in source and destination matrices do not match");
if (m_numRows * numCols > 0) // TODO: remove if unnecessary
CUDA_CALL(cudaMemcpy(Data() + m_sliceViewOffset + LocateColumn(startColumn), fromMatrix.Data(), sizeof(ElemType) * m_numRows * numCols, cudaMemcpyDeviceToDevice));
return *this;
}
template <class ElemType>
void GPUMatrix<ElemType>::CopyColumnsStrided(const GPUMatrix<ElemType>& fromMatrix, size_t numCols, size_t srcNumColsStride, size_t destNumColsStride)
{
if ((((numCols - 1) * srcNumColsStride) + 1) > fromMatrix.m_numCols)
LogicError("The numCols to copy and srcNumColsStride specified is out of range of the source matrix.");
if ((((numCols - 1) * destNumColsStride) + 1) > m_numCols)
LogicError("The numCols to copy and srcNumColsStride specified is out of range of the destination matrix.");
if (m_numRows != fromMatrix.m_numRows)
LogicError("The number of rows in source and destination matrices do not match");
if ((m_numRows * numCols) > 0)
{
// Launch a kernel to do the strided copy
CUDA_LONG N = (CUDA_LONG)(m_numRows * numCols);
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_copyColumnsStrided<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), fromMatrix.Data(), N, (CUDA_LONG) m_numRows, (CUDA_LONG) destNumColsStride, (CUDA_LONG) srcNumColsStride);
}
}
//for each column of a, we assign all rows of a to this starting from startIndex
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignToRowSliceValuesOf(const GPUMatrix<ElemType>& a, const size_t startIndex, const size_t numRows)
{
if (a.IsEmpty())
LogicError("AddToRowSliceValuesOf: input matrix a is empty.");
if (a.GetNumRows() != numRows)
LogicError("AddToRowSliceValuesOf: a.GetNumRows() != numRows.");
if (startIndex + numRows > GetNumRows())
LogicError("AddToRowSliceValuesOf: startIndex + numRows exceeds GetNumRows().");
if (a.GetNumCols() != GetNumCols())
LogicError("AddToRowSliceValuesOf: columns does not match.");
CUDA_LONG N = (CUDA_LONG) a.GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_assignToRowSliceValuesOf<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), N, (CUDA_LONG) startIndex, (CUDA_LONG) GetNumRows(), (CUDA_LONG) a.GetNumRows());
return *this;
}
//for each column of a, we assign numRows starting from startIndex to this
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignRowSliceValuesOf(const GPUMatrix<ElemType>& a, const size_t startIndex, const size_t numRows)
{
if (a.IsEmpty())
LogicError("AssignRowSliceValuesOf: input matrix a is empty.");
if (startIndex + numRows > a.GetNumRows())
LogicError("AssignRowSliceValuesOf: startIndex + numRows exceeds a.GetNumRows().");
RequireSize(numRows, a.GetNumCols());
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_assignRowSliceValuesOf<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), N, (CUDA_LONG) startIndex, (CUDA_LONG) numRows, (CUDA_LONG) a.GetNumRows());
return *this;
}
//for the row slice of this starting from startIndex we add a to it.
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AddToRowSliceValuesOf(const GPUMatrix<ElemType>& a, const size_t startIndex, const size_t numRows)
{
if (a.IsEmpty())
LogicError("AddToRowSliceValuesOf: input matrix a is empty.");
if (a.GetNumRows() != numRows)
LogicError("AddToRowSliceValuesOf: a.GetNumRows() != numRows.");
if (startIndex + numRows > GetNumRows())
LogicError("AddToRowSliceValuesOf: startIndex + numRows exceeds GetNumRows().");
if (a.GetNumCols() != GetNumCols())
LogicError("AddToRowSliceValuesOf: columns does not match.");
CUDA_LONG N = (CUDA_LONG) a.GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_addToRowSliceValuesOf<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), N, (CUDA_LONG) startIndex, (CUDA_LONG) GetNumRows(), (CUDA_LONG) a.GetNumRows());
return *this;
}
//for each column of this, we add row slice of a starting from startIndex
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AddWithRowSliceValuesOf(const GPUMatrix<ElemType>& a, const size_t startIndex, const size_t numRows)
{
if (a.IsEmpty())
LogicError("AddWithRowSliceValuesOf: input matrix a is empty.");
if (GetNumRows() != numRows)
LogicError("AddWithRowSliceValuesOf: GetNumRows() != numRows.");
if (startIndex + numRows > a.GetNumRows())
LogicError("AddWithRowSliceValuesOf: startIndex + numRows exceeds a.GetNumRows().");
if (a.GetNumCols() != GetNumCols())
LogicError("AddWithRowSliceValuesOf: columns does not match.");
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_addWithRowSliceValuesOf<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), N, (CUDA_LONG) startIndex, (CUDA_LONG) GetNumRows(), (CUDA_LONG) a.GetNumRows());
return *this;
}
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::Diagonal() const
{
size_t m = GetNumRows();
size_t n = GetNumCols();
if (m != n)
LogicError("Diagonal can be called only for square matrix. (rows=%d, cols=%d)", (int) m, (int) n);
GPUMatrix<ElemType> diag(1, n, GetComputeDeviceId());
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_assignToDiagonalValuesOf<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(diag.Data(), Data(), N, (CUDA_LONG) n);
return diag;
}
// c = c - 1.0 for a specific position
template <class ElemType>
void GPUMatrix<ElemType>::MinusOneAt(GPUMatrix<ElemType>& c, const size_t position)
{
assert(position < c.GetNumElements());
CUDA_LONG n = (CUDA_LONG) c.GetNumElements();
CUDA_LONG p = (CUDA_LONG) position;
int blocksPerGrid = (int) ceil(1.0 * n / GridDim::maxThreadsPerBlock);
// BUGBUG: PrepareDevice() missing?
SyncGuard syncGuard;
_minusOneAt<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(c.Data(), p, n);
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignRepeatOf(const GPUMatrix<ElemType>& a, const size_t numRowRepeats, const size_t numColRepeats)
{
if (this == &a)
LogicError("AssignRepeatOf: a is the same as [this]. Does not support inplace repeat.");
if (a.IsEmpty())
LogicError("AssignRepeatOf: Matrix a is empty.");
RequireSize(a.GetNumRows() * numRowRepeats, a.GetNumCols() * numColRepeats);
CUDA_LONG N = (CUDA_LONG) GetNumElements();
CUDA_LONG n = (CUDA_LONG) a.GetNumCols(), m = (CUDA_LONG) a.GetNumRows();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_assignRepeatOf<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), N, m, n, (CUDA_LONG) GetNumRows());
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AddToRowRepeatValuesOf(const GPUMatrix<ElemType>& a, const size_t numRepeats)
{
if (a.IsEmpty())
LogicError("AddToRowRepeatValuesOf: input matrix a is empty.");
if (a.GetNumRows() != GetNumRows() * numRepeats)
LogicError("AddToRowSliceValuesOf: a.GetNumRows() != GetNumRows() * numRepeats.");
RequireSize(a.GetNumRows() / numRepeats, a.GetNumCols());
CUDA_LONG N = (CUDA_LONG) a.GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_addToRowRepeatValuesOf<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), N, (CUDA_LONG) a.GetNumRows(), (CUDA_LONG) a.GetNumCols(), (CUDA_LONG) GetNumRows());
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignPositiveAndShiftedNegSample(const GPUMatrix<ElemType>& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber)
{
if (this == &a)
LogicError("AssignPositiveAndShiftedNegSample: a is the same as [this]. Does not support inplace assignment.");
if (a.IsEmpty())
LogicError("AssignPositiveAndShiftedNegSample: Matrix a is empty.");
RequireSize(a.GetNumRows() * (posNumber + negNumber), a.GetNumCols());
CUDA_LONG N = (CUDA_LONG) GetNumElements();
CUDA_LONG n = (CUDA_LONG) a.GetNumCols(), m = (CUDA_LONG) a.GetNumRows();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_assignPositiveAndShiftedNegSample<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), N, m, n, (CUDA_LONG) GetNumRows(), posNumber, shiftNumber);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AddFoldedPositiveAndShiftedNegSample(const GPUMatrix<ElemType>& a, const size_t posNumber, const size_t negNumber, const size_t shiftNumber)
{
if (this == &a)
LogicError("AddFoldedPositiveAndShiftedNegSample: a is the same as [this]. Does not support inplace assignment.");
if (a.IsEmpty())
LogicError("AddFoldedPositiveAndShiftedNegSample: Matrix a is empty.");
if (a.GetNumRows() != GetNumRows() * (posNumber + negNumber) || a.GetNumCols() != GetNumCols())
LogicError("AddFoldedPositiveAndShiftedNegSample: dimensions mismatch.");
CUDA_LONG N = (CUDA_LONG) a.GetNumElements();
CUDA_LONG n = (CUDA_LONG) a.GetNumCols(), m = (CUDA_LONG) a.GetNumRows();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_addFoldedPositiveAndShiftedNegSample<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), N, m, n, (CUDA_LONG) GetNumRows(), posNumber, shiftNumber);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::Transpose() const
{
if (IsEmpty())
LogicError("Transpose: Matrix is empty.");
GPUMatrix<ElemType> c(GetComputeDeviceId());
c.AssignTransposeOf(*this);
return c;
}
// GetCublasHandle - get a cublas handle for the given GPU, should only need one per GPU
// computeDevice - The compute device for which the cublas handle is desired
// returns: cublas handle
// NOTE: we currently don't bother to ever free the CUBLAS handle, it will be freed automatically by CUDA when the process ends
template <class ElemType>
cublasHandle_t GPUMatrix<ElemType>::GetCublasHandle(int computeDevice /*=-1*/)
{
// if the compute device is not passed, get the current device from CUDA
if (computeDevice < 0)
cudaGetDevice(&computeDevice);
if (computeDevice < 0 || computeDevice >= MaxGpus)
LogicError("GetCublasHandle: Maximum GPU exceeded");
cublasHandle_t cuHandle = s_cuHandle[computeDevice];
if (cuHandle == NULL)
{
s_cuHandle[computeDevice] = cuHandle = _initCUBLAS<ElemType>(computeDevice);
}
CUBLAS_CALL(cublasSetStream(cuHandle, t_stream));
return cuHandle;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignTransposeOf(const GPUMatrix<ElemType>& a)
{
if (this == &a)
LogicError("AssignTransposeOf: a is the same as [this]. Does not support inplace transpose.");
if (a.IsEmpty())
LogicError("AssignTransposeOf: Matrix a is empty.");
if (GetNumRows() != a.GetNumCols() || GetNumCols() != a.GetNumRows())
RequireSize(a.GetNumCols(), a.GetNumRows());
cublasHandle_t cuHandle = GetCublasHandle(a.GetComputeDeviceId());
cublasOperation_t transA = CUBLAS_OP_T;
cublasOperation_t transB = CUBLAS_OP_T;
int m = (int) a.m_numCols;
int n = (int) a.m_numRows;
ElemType alpha = 1;
ElemType beta = 0;
cublasStatus_t st;
if (sizeof(ElemType) == sizeof(float))
st = cublasSgeam(cuHandle, transA, transB, m, n, reinterpret_cast<float*>(&alpha), reinterpret_cast<float*>(a.Data()), (int) a.m_numRows, reinterpret_cast<float*>(&beta), reinterpret_cast<float*>(a.Data()), (int) a.m_numRows, reinterpret_cast<float*>(Data()), (int) m_numRows);
else if (sizeof(ElemType) == sizeof(double))
st = cublasDgeam(cuHandle, transA, transB, m, n, reinterpret_cast<double*>(&alpha), reinterpret_cast<double*>(a.Data()), (int) a.m_numRows, reinterpret_cast<double*>(&beta), reinterpret_cast<double*>(a.Data()), (int) a.m_numRows, reinterpret_cast<double*>(Data()), (int) m_numRows);
else
RuntimeError("Unsupported template argument in GPUMatrix");
if (st != CUBLAS_STATUS_SUCCESS)
RuntimeError("AssignTransposeOf failed");
m_numRows = a.m_numCols;
m_numCols = a.m_numRows;
return *this;
}
template <class ElemType>
__global__ void _doGatherColumnsOf(ElemType* us, size_t usStride, const ElemType beta, const ElemType* idx, size_t idxStride, const ElemType* a, size_t aStride, size_t aCols, const ElemType alpha, CUDA_LONG numElements)
{
CUDA_LONG id = GridDim::GetLinearThreadId();
if (id >= numElements) // note: there are no __syncthread() calls inside
return;
// id = i + jOut * usStride;
// Each thread processes one element of the output matrix.
CUDA_LONG i = id % usStride; // row index into 'us' and 'a'
CUDA_LONG jOut = id / usStride; // col index into 'us' and 'idx'
auto jInF = idx[jOut * idxStride]; // this is the column we need to get
if (::isnan(jInF) || jInF < 0) // negative index means gap
return;
size_t jIn = (size_t)jInF;
//if (jIn >= aCols)
// return; // actually a failure
const ElemType& ra = a[ i + jIn * aStride ];
ElemType& rus = us[id/*i + jOut * usStride*/];
ElemType res = ra * alpha;
if (beta != 0)
res += rus * beta;
rus = res;
}
// *this[:,j] = a[:,idx[j]] * alpha + *this[:,j] * beta
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::DoGatherColumnsOf(ElemType beta, const GPUMatrix<ElemType>& idx, const GPUMatrix<ElemType>& a, ElemType alpha)
{
if (idx.GetNumRows() != 1) // index is 1-dimensional only
InvalidArgument("DoGatherColumnsOf: Map must be a row vector.");
if (beta == 0)
RequireSize(a.GetNumRows(), idx.GetNumCols()); // output has same column format as a, but number of columns comes from idx
else
VerifySize(a.GetNumRows(), idx.GetNumCols());
if (idx.GetComputeDeviceId() != a.GetComputeDeviceId() || GetComputeDeviceId() != a.GetComputeDeviceId())
InvalidArgument("All matrices must be on the same GPU");
a.PrepareDevice();
// launch the kernel
CUDA_LONG NN = (CUDA_LONG)GetNumElements(); // linear space identifying each individual input element
SyncGuard syncGuard;
GridDim grid(NN);
_doGatherColumnsOf<ElemType><<<grid.m_blocksPerGrid, grid.m_threadsPerBlock, 0, t_stream>>>(Data(), GetNumRows(), beta, idx.Data(), idx.GetNumRows(), a.Data(), a.GetNumRows(), a.GetNumCols(), alpha, grid.m_N);
// Note: The following fails silently (no error, immediate or delayed) for numcols = 10000 under CUDA 7.0.
//_doGatherColumnsOf<ElemType><<<GetNumCols(), GetNumRows(), 0, t_stream>>>(Data(), GetNumRows(), beta, idx.Data(), idx.GetNumRows(), a.Data(), a.GetNumRows(), a.GetNumCols(), alpha);
return *this;
}
// little helper for debugging
template <class ElemType>
static void Peek(const GPUMatrix<ElemType>& m, const char* which)
{
size_t rows = m.GetNumRows();
size_t cols = m.GetNumCols();
ElemType buf[10000] = { 0 };
size_t n = min(rows * cols, _countof(buf));
CUDA_CALL(cudaMemcpy(buf, m.Data(), sizeof(ElemType) * n, cudaMemcpyDeviceToHost));
UNUSED(which); UNUSED(rows); UNUSED(cols); sin(1.0f); // set breakpoint here
//CUDA_CALL(cudaMemcpy(const_cast<ElemType*>(m.Data()), buf, sizeof(ElemType) * n, cudaMemcpyHostToDevice));
}
#define ALLOW_ATOMIC_SCATTER // allow to disable this, until we know atomicAdd() works properly here
template <class ElemType>
__global__ void _doScatterColumnsOf(ElemType* us, size_t usStride, size_t usCols, const ElemType* idx, size_t idxStride, const ElemType* a, size_t aStride, const ElemType alpha, CUDA_LONG numElements)
{
CUDA_LONG id = GridDim::GetLinearThreadId();
if (id >= numElements) // note: there are no __syncthread() calls inside
return;
// id = i + jIn * aStride
// Each thread processes one element of a
CUDA_LONG i = id % aStride; // row index into 'a' and 'us'
CUDA_LONG jIn = id / aStride; // col index into 'a' and 'idx'
auto jOutF = idx[jIn * idxStride]; // this is the column we copy/add into
if (::isnan(jOutF) || jOutF < 0) // negative index means gap
return;
size_t jOut = (size_t)jOutF;
//if (jOut >= usCols)
// return; // actually a failure --TODO: This should not be necessary. Why is it?
const ElemType& ra = a[id/*i + jIn * aStride*/];
ElemType& rus = us[ i + jOut * usStride ];
ElemType res = ra * alpha;
if (res != 0) // avoid memory conflict if e.g. an entire column has no gradient
#ifdef ALLOW_ATOMIC_SCATTER
atomicAdd(&rus, res); // rus += res;
#else
rus += res;
#endif
// Note: atomicAdd() is supposed to be fast in case of no conflict (the simple case of Scatter())
}
// *this[:,idx[j]] = a[:,j] * alpha + *this[:,idx[j]] * beta
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::DoScatterColumnsOf(ElemType beta, const GPUMatrix<ElemType>& idx, const GPUMatrix<ElemType>& a, ElemType alpha)
{
if (idx.GetNumRows() != 1) // index is 1-dimensional only
InvalidArgument("DoScatterColumnsOf: Map must be a row vector.");
if (idx.GetNumCols() != a.GetNumCols())
InvalidArgument("DoScatterColumnsOf: Map must have width of input vector.");
if (a.GetNumRows() != GetNumRows())
InvalidArgument("DoScatterColumnsOf: Output must have same height as input vector.");
if (idx.GetComputeDeviceId() != a.GetComputeDeviceId() || GetComputeDeviceId() != a.GetComputeDeviceId())
InvalidArgument("All matrices must be on the same GPU");
a.PrepareDevice();
auto& us = *this;
#ifndef ALLOW_ATOMIC_SCATTER // verify that atomicAdd is not needed --this is not efficient
{
vector<ElemType> buf(idx.GetNumRows() * idx.GetNumCols()); // idx(,)are the column(s) we copy/add into
CUDA_CALL(cudaMemcpy(buf.data(), idx.Data(), sizeof(ElemType) * buf.size(), cudaMemcpyDeviceToHost));
vector<bool> writtenTo(GetNumCols(), false); // remember whether an output column is in fact a target
for (size_t i = 0; i < buf.size(); i++)
{
auto colF = buf[i];
if (std::isnan(colF) || colF < 0)
continue;
size_t col = (size_t)colF;
if (col >= GetNumCols())
LogicError("DoScatterColumnsOf: Index value out of bounds.");
if (writtenTo[col])
LogicError("DoScatterColumnsOf: #ifndef ALLOW_ATOMIC_SCATTER then columns must be unique. Column idx(%d,%d)=%d is used twice.", (int)(i % idx.GetNumCols()), (int)(i / idx.GetNumCols()), (int)col);
else
writtenTo[col] = true;
}
}
#endif
// pre-scale with beta upfront
// Scatter may add more than one source column to the same target, so we must pre-scale with beta, and then just keep adding.
Scale(beta, us); // if beta is 0, then this will be a memset()
// launch the kernel
CUDA_LONG NN = (CUDA_LONG)(a.GetNumElements()); // linear space identifying each individual input element
SyncGuard syncGuard;
GridDim grid(NN);
_doScatterColumnsOf<ElemType><<<grid.m_blocksPerGrid, grid.m_threadsPerBlock, 0, t_stream>>>(Data(), GetNumRows(), GetNumCols(), idx.Data(), idx.GetNumRows(), a.Data(), a.GetNumRows(), alpha, NN);
//SyncGuard syncGuard;
//_doScatterColumnsOf<ElemType><<<a.GetNumCols(), a.GetNumRows(), 0, t_stream>>>(Data(), GetNumRows(), GetNumCols(), idx.Data(), idx.GetNumRows(), a.Data(), a.GetNumRows(), alpha, NN);
return *this;
}
template <class ElemType>
void GPUMatrix<ElemType>::SetValue(const ElemType v)
{
if (IsEmpty())
return;
CUDA_LONG N = (CUDA_LONG) GetNumElements();
// Check if value is zero, which can be set using cudaMemset
bool isZero = true;
const char* valArray = reinterpret_cast<const char*>(&v);
for (int i = 0; i < sizeof(ElemType); i++)
{
if (valArray[i] != 0)
{
isZero = false;
break;
}
}
if (isZero)
{
CUDA_CALL(cudaMemset(Data(), 0, N * sizeof(ElemType)));
}
else
{
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_setValue<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), v, N);
}
}
template <class ElemType>
void GPUMatrix<ElemType>::SetValue(const ElemType* d_v) // d_v is pointer to the the value in GPU memory
{
if (IsEmpty())
LogicError("SetValue: Matrix is empty.");
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_setValue<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), d_v, N);
}
template <class ElemType>
void GPUMatrix<ElemType>::MaskColumnsValue(const GPUMatrix<char>& columnsMask, ElemType val)
{
if (GetNumCols() != columnsMask.GetNumCols())
RuntimeError("Matrix and column mask must have equal number of columns");
if (GetComputeDeviceId() != columnsMask.GetComputeDeviceId())
RuntimeError("Matrix and column mask must be on the same device");
int blocksPerGrid = (int) GetNumCols();
PrepareDevice();
SyncGuard syncGuard;
_maskColumnsValue<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), columnsMask.Data(), (CUDA_LONG) GetNumCols(), (CUDA_LONG) GetNumRows(), val);
}
template <class ElemType>
void GPUMatrix<ElemType>::SetColumn(const ElemType* colPointer, size_t colInd)
{
if (IsEmpty())
LogicError("SetValue: Matrix is empty.");
if (colPointer == NULL)
return;
CUDA_CALL(cudaMemcpy(Data() + LocateColumn(colInd), colPointer, sizeof(ElemType) * m_numRows, cudaMemcpyHostToDevice));
}
template <class ElemType>
void GPUMatrix<ElemType>::SetColumn(const GPUMatrix<ElemType>& valMat, size_t colInd)
{
if (IsEmpty())
LogicError("SetColumn: Matrix is empty.");
if (valMat.GetNumCols() != 1)
LogicError("SetColumn: only support one column matrix now.");
CUDA_CALL(cudaMemcpy(Data() + LocateColumn(colInd), valMat.Data(), sizeof(ElemType) * m_numRows, cudaMemcpyDeviceToDevice));
}
template <class ElemType>
void GPUMatrix<ElemType>::SetValue(const GPUMatrix<ElemType>& deepCopyFrom)
{
if (this == &deepCopyFrom)
return;
SetValue(deepCopyFrom.GetNumRows(), deepCopyFrom.GetNumCols(), deepCopyFrom.GetComputeDeviceId(), deepCopyFrom.Data(), matrixFlagSetValueOnDevice);
}
#if 0
template <class ElemType>
void GPUMatrix<ElemType>::SetValue(const CPUMatrix<ElemType>& /*deepCopyFrom*/)
{
NOT_IMPLEMENTED;
}
template <class ElemType>
void GPUMatrix<ElemType>::SetValue(const CPUSparseMatrix<ElemType>& /*deepCopyFrom*/)
{
NOT_IMPLEMENTED;
}
template <class ElemType>
void GPUMatrix<ElemType>::SetValue(const GPUSparseMatrix<ElemType>& deepCopyFrom)
{
deepCopyFrom.CopyToDenseMatrix(*this);
}
#endif
template <class ElemType>
void GPUMatrix<ElemType>::SetValue(const size_t numRows, const size_t numCols, int deviceId, ElemType* pArray, size_t matrixFlags, DataTransferer* transferer)
{
// handle externally managed case
// BUGBUG: This is super super ugly, and needs to be fixed, but if matrixFlags has the right value, then we can't free anything,
// and everything gets wonky. This should be fixed, and would go away if it is made a shared_ptr.
if (matrixFlags & matrixFlagDontOwnBuffer)
{
// free the existing array if it used to be an owned array
if ( Buffer() != NULL)
{
TracingGPUMemoryAllocator::Free<ElemType>(GetComputeDeviceId(), Buffer());
}
m_numRows = numRows;
m_numCols = numCols;
SetBuffer(pArray, GetNumElements() * sizeof(ElemType), true);
SetSizeAllocated(GetNumElements());
SetFormat(matrixFormatDense);
SetComputeDeviceId(deviceId);
}
else
{
if (transferer && (matrixFlags & matrixFlagSetValueOnDevice))
RuntimeError("Asynchronous data copy from device to device is currently not supported.");
// if the devices are different move it now
if (GetComputeDeviceId() != deviceId && deviceId >= 0)
{
Clear();
ZeroInit(deviceId);
}
// now RequireSize/allocate as necessary
RequireSize(numRows, numCols);
// copy over the content to the buffer
PrepareDevice();
if (pArray != NULL)
{
if (!(matrixFlags & matrixFormatRowMajor))
{
if (transferer)
transferer->CopyCPUToGPUAsync(pArray, GetNumElements(), sizeof(ElemType), Data());
else
CUDA_CALL(cudaMemcpy(Data(), pArray, sizeof(ElemType) * GetNumElements(), (matrixFlags & matrixFlagSetValueOnDevice) ? cudaMemcpyDeviceToDevice : cudaMemcpyHostToDevice));
}
else // row major: must transpose (this is not meant to be efficient, but very useful for defining inline matrices for test code)
{
vector<ElemType> transposed(GetNumElements());
for (size_t i = 0; i < numRows; i++)
for (size_t j = 0; j < numCols; j++)
transposed[i + numRows * j] = pArray[j + numCols * i];
if (transferer)
transferer->CopyCPUToGPUAsync(transposed.data(), GetNumElements(), sizeof(ElemType), Data());
else
CUDA_CALL(cudaMemcpy(Data(), transposed.data(), sizeof(ElemType) * GetNumElements(), (matrixFlags & matrixFlagSetValueOnDevice) ? cudaMemcpyDeviceToDevice : cudaMemcpyHostToDevice));
}
}
}
SetFormat(matrixFormatDense);
}
template <class ElemType>
void GPUMatrix<ElemType>::SetDiagonalValue(const ElemType v)
{
CUDA_LONG N = (CUDA_LONG) GetNumRows();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_setDiagonalValue<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), v, N, (CUDA_LONG) GetNumRows());
}
template <class ElemType>
void GPUMatrix<ElemType>::SetDiagonalValue(const GPUMatrix<ElemType>& vector)
{
if (IsEmpty() || vector.IsEmpty())
LogicError("SetDiagonalValue: Matrix is empty.");
if (GetNumRows() != GetNumCols())
LogicError("SetDiagonalValue: NumRows and NumCols do not agree.");
if (vector.GetNumRows() != 1 && vector.GetNumCols() != 1)
LogicError("SetDiagonalValue: input vector must be a vector.");
if (vector.GetNumElements() == 1) // reduce to simple form
SetDiagonalValue(vector.Data()[0]);
else if (vector.GetNumRows() != GetNumRows())
LogicError("SetDiagonalValue: input vector's dimension does not agree with [this].");
else
{
CUDA_LONG N = (CUDA_LONG) GetNumRows();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_setDiagonalValueFromVector<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), vector.Data(), N);
}
}
template <class ElemType>
void GPUMatrix<ElemType>::SetUniformRandomValue(const ElemType low, const ElemType high, unsigned long seed)
{
PrepareDevice();
CreateCurandObject(seed, __FUNCTION__); // TODO call ResetCurandObject() instead?
cudaEvent_t done = nullptr;
CUDA_CALL(cudaEventCreate(&done)); // TODO: why not condition on do_sync, so that we can use SyncGuard?
if (sizeof(ElemType) == sizeof(float))
CURAND_CALL(curandGenerateUniform(((curandGenerator_t*) s_curandGenerator)[0], reinterpret_cast<float*>(Data()), GetNumElements()));
else
CURAND_CALL(curandGenerateUniformDouble(((curandGenerator_t*) s_curandGenerator)[0], reinterpret_cast<double*>(Data()), GetNumElements()));
CUDA_CALL(cudaEventRecord(done));
CUDA_CALL(cudaEventSynchronize(done));
// CURAND_CALL(curandDestroyGenerator(gen));
CUDA_CALL(cudaEventDestroy(done));
size_t N = GetNumElements();
size_t blocksPerGrid = (size_t) ceil(N / (double) GridDim::maxThreadsPerBlock);
SyncGuard syncGuard;
_rescaleToRange<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), N, low, high);
}
template <class ElemType>
void GPUMatrix<ElemType>::SetGaussianRandomValue(const ElemType mean, const ElemType sigma, unsigned long seed)
{
PrepareDevice();
CreateCurandObject(seed, __FUNCTION__); // TODO call ResetCurandObject() instead?
// TODO: Why not use SyncGuard?
if (sizeof(ElemType) == sizeof(float))
CURAND_CALL(curandGenerateNormal(((curandGenerator_t*) s_curandGenerator)[0], reinterpret_cast<float*>(Data()), GetNumElements(), (float) mean, (float) sigma));
else
CURAND_CALL(curandGenerateNormalDouble(((curandGenerator_t*) s_curandGenerator)[0], reinterpret_cast<double*>(Data()), GetNumElements(), (double) mean, (double) sigma));
// CURAND_CALL(curandDestroyGenerator(gen));
}
//maskRate: percentage of values masked out (similar to dropout rate)
//scaleValue: which scale value to set to the left ones (unmasked items).
template <class ElemType>
void GPUMatrix<ElemType>::SetUniformRandomMask(const ElemType maskRate, const ElemType scaleValue, RNGHandle& rngHandle)
{
PrepareDevice();
GPURNGHandle* gpuRNGHandle = dynamic_cast<GPURNGHandle*>(&rngHandle);
assert(gpuRNGHandle != nullptr);
cudaEvent_t done = nullptr;
CUDA_CALL(cudaEventCreate(&done)); // TODO: why not condition on do_sync, so that we can use SyncGuard?
if (sizeof(ElemType) == sizeof(float))
CURAND_CALL(curandGenerateUniform(gpuRNGHandle->Generator(), reinterpret_cast<float*>(Data()), GetNumElements()));
else
CURAND_CALL(curandGenerateUniformDouble(gpuRNGHandle->Generator(), reinterpret_cast<double*>(Data()), GetNumElements()));
CUDA_CALL(cudaEventRecord(done));
CUDA_CALL(cudaEventSynchronize(done));
CUDA_CALL(cudaEventDestroy(done));
size_t N = GetNumElements();
size_t blocksPerGrid = (size_t) ceil(N / (double) GridDim::maxThreadsPerBlock);
SyncGuard syncGuard;
_setMaskAndScale<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), N, maskRate, scaleValue);
}
template <class ElemType>
ElemType GPUMatrix<ElemType>::Adagrad(GPUMatrix<ElemType>& gradients, const bool needAveMultiplier)
{
size_t numColsNeeded = gradients.GetNumCols();
if (needAveMultiplier)
numColsNeeded += gradients.GetNumCols();
if (IsEmpty() || GetNumCols() < numColsNeeded)
{
RequireSize(gradients.GetNumRows(), numColsNeeded);
SetValue(0.0);
}
assert(GetNumRows() == gradients.GetNumRows() && GetNumCols() == numColsNeeded);
size_t n = gradients.GetNumElements();
ElemType* multipliers = nullptr;
if (needAveMultiplier)
multipliers = Data() + n; // temp memory used to store multipliers,
int blocksPerGrid = (n + GridDim::maxThreadsPerBlock - 1) / GridDim::maxThreadsPerBlock;
_adagrad<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock>>>(Data(), gradients.Data(), n, multipliers);
if (!needAveMultiplier)
return 1;
cublasHandle_t cuHandle = GetCublasHandle(GetComputeDeviceId());
if (sizeof(ElemType) == sizeof(float))
{
float aveMultiplier = 0;
CUBLAS_CALL(cublasSasum(cuHandle, (CUDA_LONG) n, reinterpret_cast<float*>(multipliers), 1, &aveMultiplier));
return (ElemType) aveMultiplier / n;
}
else
{
double aveMultiplier = 0;
CUBLAS_CALL(cublasDasum(cuHandle, (CUDA_LONG) n, reinterpret_cast<double*>(multipliers), 1, &aveMultiplier));
return (ElemType) aveMultiplier / n;
}
}
template <class ElemType>
void GPUMatrix<ElemType>::FSAdagrad(GPUMatrix<ElemType>& gradients,
GPUMatrix<ElemType>& functionValues,
ElemType learnRatePerSample,
ElemType momentum,
ElemType adaWeight,
ElemType adaMul)
{
size_t numColsNeeded = 2 * gradients.GetNumCols();
if (IsEmpty() || (GetNumCols() < numColsNeeded))
{
RequireSize(gradients.GetNumRows(), numColsNeeded);
SetValue(0.0);
}
assert((GetNumRows() == gradients.GetNumRows()) && (GetNumCols() == numColsNeeded));
size_t n = gradients.GetNumElements();
int blocksPerGrid = (n + GridDim::maxThreadsPerBlock - 1) / GridDim::maxThreadsPerBlock;
_fsadagrad<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock>>>(n, gradients.Data(), Data(), Data()+ n, functionValues.Data(),
learnRatePerSample, momentum, adaWeight, adaMul);
}
template <class ElemType>
ElemType GPUMatrix<ElemType>::RmsProp(GPUMatrix<ElemType>& gradients,
ElemType RMS_GAMMA,
ElemType RMS_WGT_INC,
ElemType RMS_WGT_MAX,
ElemType RMS_WGT_DEC,
ElemType RMS_WGT_MIN,
const bool needAveMultiplier)
{
const ElemType floor = 1e-6f;
static ElemType* upd_gpu = (ElemType*) 0;
size_t n = gradients.GetNumElements();
int blocksPerGrid = (GetNumElements() + GridDim::maxThreadsPerBlock - 1) / GridDim::maxThreadsPerBlock;
size_t numColsNeeded = gradients.GetNumCols() * 3;
if (needAveMultiplier)
numColsNeeded += gradients.GetNumCols();
if (IsEmpty() || GetNumCols() < numColsNeeded)
{
RequireSize(gradients.GetNumRows(), numColsNeeded);
SetValue(0.0);
ElemType* avars = Data(); // accumulated variances for RMS scaling
ElemType* signs = Data() + n; // sign of previous gradient
ElemType* steps = Data() + 2 * n; // current step size
// Data()+3*n is temp memory used to store multipliers, no need to initialize
_rmsprop_init<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock>>>(avars, signs, steps, gradients.Data(), n);
}
assert(GetNumRows() == gradients.GetNumRows() && GetNumCols() == numColsNeeded);
ElemType* avars = Data(); // accumulated variances for RMS scaling
ElemType* signs = Data() + n; // sign of previous gradient
ElemType* steps = Data() + 2 * n; // current step size
ElemType* multipliers = nullptr;
if (needAveMultiplier)
multipliers = Data() + 3 * n; // temp memory used to store multipliers,
if (!upd_gpu)
{
const ElemType upd[] = {
2, 2, 0,
2, 2, 0,
1, 1, 1,
2, 2, 0,
1, 2, 1,
0, 2, 2,
1, 1, 1,
0, 2, 2,
0, 2, 2,
};
upd_gpu = TracingGPUMemoryAllocator::Allocate<ElemType>(GetComputeDeviceId(), 27);
CUDA_CALL(cudaMemcpy(upd_gpu, upd, sizeof(ElemType) * _countof(upd), cudaMemcpyHostToDevice));
}
_rmsprop<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock>>>(avars, signs, steps, gradients.Data(), n,
RMS_GAMMA, RMS_WGT_INC, RMS_WGT_MAX, RMS_WGT_DEC, RMS_WGT_MIN,
floor, upd_gpu, multipliers);
if (!needAveMultiplier)
return 1;
cublasHandle_t cuHandle = GetCublasHandle(GetComputeDeviceId());
if (sizeof(ElemType) == sizeof(float))
{
float aveMultiplier = 0;
CUBLAS_CALL(cublasSasum(cuHandle, (CUDA_LONG) n, reinterpret_cast<float*>(multipliers), 1, &aveMultiplier));
return aveMultiplier / n;
}
else
{
double aveMultiplier = 0;
CUBLAS_CALL(cublasDasum(cuHandle, (CUDA_LONG) n, reinterpret_cast<double*>(multipliers), 1, &aveMultiplier));
return (ElemType) aveMultiplier / n;
}
}
template <class ElemType>
void GPUMatrix<ElemType>::Reshape(const size_t numRows, const size_t numCols)
{
assert(numRows * numCols == GetNumElements());
if (numRows * numCols != GetNumElements())
InvalidArgument("Reshape: total number of elements does not match.");
m_numRows = numRows;
m_numCols = numCols;
}
template <class ElemType>
void GPUMatrix<ElemType>::RequireSize(const size_t numRows, const size_t numCols, bool growOnly, bool cachedResize)
{
if (GetNumRows() != numRows || GetNumCols() != numCols)
Resize(numRows, numCols, growOnly, cachedResize);
}
template <class ElemType>
void GPUMatrix<ElemType>::Resize(const size_t numRows, const size_t numCols, bool growOnly, bool cachedResize)
{
if (GetNumRows() == numRows && GetNumCols() == numCols)
return;
VerifyResizable(__FUNCTION__);
bool isForceResize = (!growOnly) || cachedResize;
size_t numElements = numRows * numCols;
if (numElements > GetSizeAllocated() || // grow allocation
(isForceResize && numElements != GetSizeAllocated())) // shrink allocation if not growOnly
{
// reallocate buffer if numElements > 0
ElemType* pArray = nullptr;
if (numElements > 0)
{
if (cachedResize)
pArray = BufferManagement::GetManagerInstance(GetComputeDeviceId()).RequestBuffer<ElemType>(numElements);
else
pArray = TracingGPUMemoryAllocator::Allocate<ElemType>(GetComputeDeviceId(), numRows, numCols);
}
// If the buffer exists, free it
if (Buffer())
{
if(cachedResize)
BufferManagement::GetManagerInstance(GetComputeDeviceId()).LogicalReleaseBuffer<ElemType>(Buffer(), GetSizeAllocated());
else
TracingGPUMemoryAllocator::Free<ElemType>(GetComputeDeviceId(), Buffer());
}
SetBuffer(pArray, numElements * sizeof(ElemType));
SetSizeAllocated(numElements);
}
// success
m_sliceViewOffset = 0;
m_numRows = numRows;
m_numCols = numCols;
}
template <class ElemType>
size_t GPUMatrix<ElemType>::LocateElement(const size_t row, const size_t col) const
{
assert(row < m_numRows && col < m_numCols);
return LocateColumn(col) + row; // matrix in column-wise storage
}
template <class ElemType>
size_t GPUMatrix<ElemType>::LocateColumn(const size_t col) const
{
assert(col < GetNumCols());
return col * m_numRows; // matrix in column-wise storage
}
template <class ElemType>
ElemType GPUMatrix<ElemType>::Get00Element() const
{
ElemType res = 0;
CUDA_CALL(cudaMemcpy(&res, Data(), sizeof(ElemType), cudaMemcpyDeviceToHost));
return res;
}
#pragma endregion Basic Operators
#pragma region Member BLAS Functions
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::operator+=(ElemType alpha)
{
if (IsEmpty())
LogicError("operator+=: Matrix is empty.");
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
SyncGuard syncGuard;
_addValue<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), alpha, N);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::operator+(ElemType alpha) const
{
if (IsEmpty())
LogicError("operator+: Matrix is empty.");
GPUMatrix<ElemType> c(*this);
c += alpha;
return c;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignSumOf(const ElemType alpha, const GPUMatrix<ElemType>& a)
{
SetValue(a);
(*this) += alpha;
return (*this);
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::operator+=(const GPUMatrix<ElemType>& a)
{
ScaleAndAdd(1, a, *this);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::operator+(const GPUMatrix<ElemType>& a) const
{
if (GetNumElements() == 1)
{
GPUMatrix<ElemType> c(a);
c += Get00Element();
return c;
}
else if (a.GetNumElements() == 1)
{
GPUMatrix<ElemType> c(*this);
c += a.Get00Element();
return c;
}
else
{
GPUMatrix<ElemType> c(*this); // this implementation will introduce a copy overhead. but make resue of the code
c += a;
return c;
}
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignSumOf(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b)
{
SetValue(a);
(*this) += b;
return (*this);
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::operator-=(ElemType alpha)
{
if (IsEmpty())
LogicError("operato-=: Matrix is empty.");
return operator+=(-1 * alpha);
}
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::operator-(ElemType alpha) const
{
if (IsEmpty())
LogicError("operator-: Matrix is empty.");
return operator+(-1 * alpha);
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignDifferenceOf(const ElemType alpha, const GPUMatrix<ElemType>& a)
{
RequireSize(a.m_numRows, a.m_numCols);
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
a.PrepareDevice();
SyncGuard syncGuard;
_assignDifferenceOf1<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), alpha, a.Data(), N);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignDifferenceOf(const GPUMatrix<ElemType>& a, const ElemType alpha)
{
RequireSize(a.m_numRows, a.m_numCols);
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
a.PrepareDevice();
SyncGuard syncGuard;
_assignDifferenceOf2<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), alpha, a.Data(), N);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::operator-=(const GPUMatrix<ElemType>& a)
{
ScaleAndAdd(-1, a, *this);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::operator-(const GPUMatrix<ElemType>& a) const
{
GPUMatrix<ElemType> c(*this); // this implementation will introduce a copy overhead. but make resue of the code
c -= a;
return c;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignDifferenceOf(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b)
{
if (this != &a)
{
RequireSize(a.GetNumRows(), a.GetNumCols());
SetValue(a);
}
(*this) -= b;
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::operator*=(ElemType alpha)
{
Scale(alpha, *this);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::operator*(ElemType alpha) const
{
GPUMatrix<ElemType> c(GetNumRows(), GetNumCols(), GetComputeDeviceId());
Scale(alpha, *this, c);
return c;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignProductOf(const ElemType alpha, const GPUMatrix<ElemType>& a)
{
Scale(alpha, a, *this);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignProductOf(const GPUMatrix<ElemType>& a, const bool transposeA, const GPUMatrix<ElemType>& b, const bool transposeB)
{
if (a.GetNumElements() == 1)
{
if (transposeB)
AssignTransposeOf(b);
(*this) *= a.Get00Element();
}
else if (b.GetNumElements() == 1)
{
if (transposeA)
AssignTransposeOf(a);
(*this) *= b.Get00Element();
}
else
Multiply(a, transposeA, b, transposeB, *this);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::operator*(const GPUMatrix<ElemType>& a) const
{
const GPUMatrix<ElemType>& us = *this;
if (GetNumElements() == 1)
{
GPUMatrix<ElemType> c(GetComputeDeviceId());
c.AssignProductOf(Get00Element(), a);
return c;
}
else if (a.GetNumElements() == 1)
{
GPUMatrix<ElemType> c(GetComputeDeviceId());
c.AssignProductOf(a.Get00Element(), us);
return c;
}
else
{
GPUMatrix<ElemType> c(GetNumRows(), a.GetNumCols(), GetComputeDeviceId());
Multiply(*this, a, c);
return c;
}
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::operator/=(ElemType alpha)
{
(*this) *= 1 / alpha;
return (*this);
}
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::operator/(ElemType alpha) const
{
return ((*this) * (1 / alpha));
}
//element-wise power
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::operator^=(ElemType alpha)
{
GPUMatrix<ElemType>& us = *this;
ElementWisePower(alpha, us, us);
return us;
}
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::operator^(ElemType alpha) const
{
GPUMatrix<ElemType> c(GetNumRows(), GetNumCols(), GetComputeDeviceId());
ElementWisePower(alpha, *this, c);
return c;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignElementPowerOf(const GPUMatrix<ElemType>& a, const ElemType power)
{
ElementWisePower(power, a, *this);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AddElementProductOf(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b)
{
if (a.IsEmpty() || b.IsEmpty())
LogicError("AddElementProductOf: Matrix is empty.");
assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols());
if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()))
InvalidArgument("The input matrix dimensions do not match.");
if (!(a.GetNumRows() == GetNumRows() && a.GetNumCols() == GetNumCols()))
InvalidArgument("The input matrix dimensions do not match [this].");
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
a.PrepareDevice();
SyncGuard syncGuard;
_addElementProductOf<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), b.Data(), N);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::ColumnElementMultiplyWith(const GPUMatrix<ElemType>& a)
{
if (a.IsEmpty() || IsEmpty())
LogicError("ColumnElementMultiplyWith: Matrix is empty.");
if (!(a.GetNumRows() == GetNumRows() && a.GetNumCols() == 1))
InvalidArgument("ColumnElementMultiplyWith: The input matrix should be a col vector and match [this]'s rows.");
CUDA_LONG N = (CUDA_LONG) a.GetNumRows();
CUDA_LONG M = (CUDA_LONG) GetNumCols();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
a.PrepareDevice();
SyncGuard syncGuard;
_columnElementMultiplyWith<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), N, M);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::RowElementMultiplyWith(const GPUMatrix<ElemType>& a)
{
if (a.IsEmpty() || IsEmpty())
LogicError("RowElementMultiplyWith: Matrix is empty.");
if (!(a.GetNumRows() == 1 && a.GetNumCols() == GetNumCols()))
InvalidArgument("RowElementMultiplyWith: The input matrix should be a row vector and match [this]'s columns.");
CUDA_LONG N = (CUDA_LONG) GetNumRows();
CUDA_LONG M = (CUDA_LONG) a.GetNumCols();
int blocksPerGrid = (int) ceil(1.0 * M / GridDim::maxThreadsPerBlock);
a.PrepareDevice();
SyncGuard syncGuard;
_rowElementMultiplyWith<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock>>>(Data(), a.Data(), N, M);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::RowElementDivideBy(const GPUMatrix<ElemType>& a)
{
if (a.IsEmpty() || IsEmpty())
LogicError("RowElementDivideBy: Matrix is empty.");
if (!(a.GetNumRows() == 1 && a.GetNumCols() == GetNumCols()))
InvalidArgument("RowElementDivideBy: The input matrix should be a row vector and match [this]'s columns.");
CUDA_LONG N = (CUDA_LONG) GetNumRows();
CUDA_LONG M = (CUDA_LONG) a.GetNumCols();
int blocksPerGrid = (int) ceil(1.0 * M / GridDim::maxThreadsPerBlock);
a.PrepareDevice();
SyncGuard syncGuard;
_rowElementDivideBy<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock>>>(Data(), a.Data(), N, M);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::ColumnElementDivideBy(const GPUMatrix<ElemType>& a)
{
if (a.IsEmpty() || IsEmpty())
LogicError("ColumnElementDivideBy: Matrix is empty.");
if (!(a.GetNumRows() == GetNumRows() && a.GetNumCols() == 1))
InvalidArgument("ColumnElementDivideBy: The input matrix should be a col vector and match [this]'s rows.");
CUDA_LONG N = (CUDA_LONG) a.GetNumRows();
CUDA_LONG M = (CUDA_LONG) GetNumCols();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
a.PrepareDevice();
SyncGuard syncGuard;
_ColumnElementDivideBy<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), N, M);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::ElementInverse()
{
if (IsEmpty())
LogicError("ElementInverse: Matrix is empty.");
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_elemInverse<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), N);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignElementInverseOf(const GPUMatrix<ElemType>& a)
{
SetValue(a);
return ElementInverse();
}
DEF_ELEMWISE_INPLACE_FUNC(Sigmoid)
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignSigmoidOf(const GPUMatrix<ElemType>& a)
{
RequireSize(a.GetNumRows(), a.GetNumCols());
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
// _elementWIseSigmoidOnCuda has an implementation that avoids possible overflow errors, but has a slight accuracy regression.
#if 0
_elementWiseSigmoidOnCuda<<<blocksPerGrid, threadsPerBlock, 0, t_stream>>>(a.Data(), Data(), N);
#else
_assignSigmoidOf<<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(a.Data(), Data(), N);
#endif
return *this;
}
DEF_ELEMWISE_INPLACE_FUNC(SigmoidDerivative)
DEF_ELEMWISE_ASSIGN_FUNC(SigmoidDerivative)
template <class ElemType>
void GPUMatrix<ElemType>::AssignNoiseContrastiveEstimation(const GPUMatrix<ElemType>& a,
const GPUMatrix<ElemType>& b, const GPUMatrix<ElemType>& bias, size_t sampleCount, GPUMatrix<ElemType>& tmp, GPUMatrix<ElemType>& c)
//this: samples+probs
// a : hidden
// b : embedding
// tmp: softmax
// c : loglikelihood
{
UNCONST(ElemType, a, my_a);
UNCONST(ElemType, b, my_b);
UNCONST(ElemType, bias, my_bias);
SyncGuard syncGuard;
// a: dim * minibatch
// b: dim * |vocab|
int p = 512;
int width = a.GetNumRows(); // dimension of hidden vector
while (p / 2 > width)
p = p / 2;
// note: kernel has hard-coded dimension of 512
_computeNceOutputMax512Threads<ElemType> << <GetNumElements() / 2, p >> >(
Data(),
sampleCount,
m_numRows / 2,
my_a.Data(), // a
a.GetNumRows(),
my_b.Data(), // b
my_bias.Data(),
tmp.Data()); // tmp
p = 512;
while (p / 2 > GetNumElements() / 2)
p = p / 2;
// summing up objective must be done in one block
// note: kernel has hard-coded dimension of 512
_assignNoiseContrastiveEstimationMax512Threads<ElemType> << <1, p >> >(
Data(),
sampleCount,
m_numRows / 2,
my_a.Data(),
a.GetNumCols(),
my_b.Data(),
tmp.Data(),
c.Data());
}
template <class ElemType>
void GPUMatrix<ElemType>::AssignNCEDerivative(GPUMatrix<ElemType>& tmp, const GPUMatrix<ElemType>& a,
const GPUMatrix<ElemType>& b, size_t inputIndex, GPUMatrix<ElemType>& c)
{
UNCONST(ElemType, a, my_a);
UNCONST(ElemType, b, my_b);
SyncGuard syncGuard;
int p = 512;
int width = a.GetNumRows();
while (p / 2 > width)
p = p / 2;
_assignNceDerivativeNew<ElemType><<<(tmp.GetNumElements() + p - 1) / p, p>>>(
Data(),
tmp.GetNumCols(),
m_numRows / 2,
my_a.Data(),
a.GetNumRows(),
my_b.Data(),
tmp.Data(),
c.Data(),
inputIndex);
}
template <class ElemType>
void GPUMatrix<ElemType>::AssignSoftmaxSum(const GPUMatrix<ElemType>& a, GPUMatrix<ElemType>& c)
{
UNCONST(ElemType, a, my_a);
SyncGuard syncGuard;
int p = 512;
int width = a.GetNumRows();
while (p / 2 > width)
p = p / 2;
// note: kernel has hard-coded dimension of 512
_assignSoftmaxSumMax512Threads<ElemType> << <1, p >> >(
my_a.Data(),
width,
Data(),
c.Data());
}
template <class ElemType>
void GPUMatrix<ElemType>::AssignNCEUnnormalizedEval(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, GPUMatrix<ElemType>& c)
{
assert(a.GetComputeDeviceId() == b.GetComputeDeviceId());
assert(GetNumRows() == a.GetNumRows());
assert(GetNumCols() == b.GetNumRows());
assert(a.GetNumCols() == b.GetNumRows());
UNUSED(a);
UNUSED(b);
UNUSED(c); // TODO: this function seems like a stub
/*
EnsureAuxMemory();
int p = 512;
int width = a.GetNumCols();
while (p / 2 > width) p = p / 2;
// this kernel need be launched in nnz blocks
_sparseInnerProductDenseTimesDense<ElemType> << <m_nz, p >> >(
m_dVal,
m_buf,
m_dCol,
m_nz,
GetNumRows(),
a.Buffer(),
b.Buffer(),
b.GetNumRows(),
m_res);
// sum up the results
_reductionSum32<ElemType> << <1, 32 >> >(m_res, c.Buffer(), m_nz);*/
}
DEF_ELEMWISE_INPLACE_FUNC(Tanh)
DEF_ELEMWISE_ASSIGN_FUNC(Tanh)
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::InplaceLogSoftmax(const bool isColWise)
{
if (IsEmpty())
LogicError("InplaceLogSoftmax: Matrix is empty.");
PrepareDevice();
if (isColWise)
{
CUDA_LONG N = (CUDA_LONG) GetNumCols(); // one kernel per column
int blocksPerGrid = (int) ceil(N * 1.0 / GridDim::maxThreadsPerBlock);
SyncGuard syncGuard;
_logSoftMaxColWise<<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), (CUDA_LONG) m_numCols, (CUDA_LONG) m_numRows);
}
else
{
CUDA_LONG N = (CUDA_LONG) GetNumRows(); // one kernel per column
int blocksPerGrid = (int) ceil(N * 1.0 / GridDim::maxThreadsPerBlock);
SyncGuard syncGuard;
_logSoftMaxRowWise<<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), (CUDA_LONG) m_numCols, (CUDA_LONG) m_numRows);
}
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignLogSoftmaxOf(const GPUMatrix<ElemType>& a, const bool isColWise)
{
RequireSize(a.GetNumRows(), a.GetNumCols());
if (isColWise)
{
PrepareDevice();
CUDA_LONG N = (CUDA_LONG) GetNumCols();
CUDA_LONG M = (CUDA_LONG) GetNumRows();
SyncGuard syncGuard;
// note: kernel uses hard-coded thread dimension
_assignColumnwiseLogSoftmaxOf512Threads<<<N, 512, 0, t_stream>>>(a.Data(), Data(), N, M);
}
else
{
NOT_IMPLEMENTED;
}
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::InplaceHardmax(const bool isColWise)
{
return AssignHardmaxOf(*this, isColWise);
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignHardmaxOf(const GPUMatrix<ElemType>& a, const bool isColWise)
{
RequireSize(a.GetNumRows(), a.GetNumCols());
if (isColWise)
{
PrepareDevice();
CUDA_LONG N = (CUDA_LONG) GetNumCols();
CUDA_LONG M = (CUDA_LONG) GetNumRows();
SyncGuard syncGuard;
// note: kernel uses hard-coded thread dimension
_assignColumnwiseHardmaxOf512Threads << <N, 512, 0, t_stream >> >(a.Data(), Data(), N, M);
}
else
{
NOT_IMPLEMENTED;
}
return *this;
}
DEF_ELEMWISE_INPLACE_FUNC(Sqrt)
DEF_ELEMWISE_ASSIGN_FUNC(Sqrt)
DEF_ELEMWISE_INPLACE_FUNC(Exp)
DEF_ELEMWISE_ASSIGN_FUNC(Exp)
DEF_ELEMWISE_INPLACE_FUNC(Log)
DEF_ELEMWISE_ASSIGN_FUNC(Log)
DEF_ELEMWISE_INPLACE_FUNC(Abs)
DEF_ELEMWISE_ASSIGN_FUNC(Abs)
DEF_ELEMWISE_INPLACE_FUNC(LinearRectifierDerivative)
DEF_ELEMWISE_ASSIGN_FUNC(LinearRectifierDerivative)
DEF_ELEMWISE_INPLACE_FUNC(Cosine)
DEF_ELEMWISE_ASSIGN_FUNC(Cosine)
DEF_ELEMWISE_INPLACE_FUNC(NegativeSine)
DEF_ELEMWISE_ASSIGN_FUNC(NegativeSine)
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::InplaceTruncateBottom(const ElemType threshold)
{
return AssignTruncateBottomOf(*this, threshold);
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignTruncateBottomOf(const GPUMatrix<ElemType>& a, const ElemType threshold)
{
if (a.IsEmpty())
LogicError("AssignTruncateBottomOf: Matrix a is empty.");
if (this != &a)
{
RequireSize(a.GetNumRows(), a.GetNumCols());
}
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(N * 1.0 / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_assignTruncateBottom<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), threshold, N);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::InplaceTruncateTop(const ElemType threshold)
{
return AssignTruncateTopOf(*this, threshold);
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignTruncateTopOf(const GPUMatrix<ElemType>& a, const ElemType threshold)
{
if (a.IsEmpty())
LogicError("AssignTruncateTopOf: Matrix a is empty.");
if (this != &a)
{
RequireSize(a.GetNumRows(), a.GetNumCols());
}
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(N * 1.0 / GridDim::maxThreadsPerBlock);
a.PrepareDevice();
SyncGuard syncGuard;
_assignTruncateTop<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), threshold, N);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::InplaceTruncate(const ElemType threshold)
{
if (IsEmpty())
LogicError("InplaceTruncate: Matrix is empty.");
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(N * 1.0 / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_inplaceTruncate<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), threshold, N);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::InplaceSoftThreshold(const ElemType threshold)
{
if (IsEmpty())
LogicError("InplaceSoftThreshold: Matrix is empty.");
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(N * 1.0 / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_inplaceSoftThreshold<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), threshold, N);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::SetToZeroIfAbsLessThan(const ElemType threshold)
{
if (IsEmpty())
LogicError("SetToZeroIfAbsLessThan: Matrix is empty.");
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(N * 1.0 / GridDim::maxThreadsPerBlock);
PrepareDevice();
SyncGuard syncGuard;
_setToZeroIfAbsLessThan<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), threshold, N);
return *this;
}
template <class ElemType>
ElemType GPUMatrix<ElemType>::SumOfAbsElements() const
{
if (IsEmpty())
LogicError("SumOfAbsElements: Matrix is empty");
cublasHandle_t cuHandle = GetCublasHandle(GetComputeDeviceId());
if (sizeof(ElemType) == sizeof(float))
{
float res = 0;
CUBLAS_CALL(cublasSasum(cuHandle, (CUDA_LONG) GetNumElements(), reinterpret_cast<float*>(Data()), 1, &res));
return res;
}
else
{
double res = 0;
CUBLAS_CALL(cublasDasum(cuHandle, (CUDA_LONG) GetNumElements(), reinterpret_cast<double*>(Data()), 1, &res));
return ElemType(res);
}
}
template <class ElemType>
ElemType GPUMatrix<ElemType>::SumOfElements() const
{
if (IsEmpty())
LogicError("SumOfElements: Matrix is empty");
ElemType* d_sum = TracingGPUMemoryAllocator::Allocate<ElemType>(GetComputeDeviceId(), 1);
ElemType h_sum;
// WARNING: THIS kernel is not the most efficient way!
// note: kernel has hard-coded dimension of 1024
_reductionSum1024Threads<ElemType> << <1, 1024, 0, t_stream >> >(Data(), d_sum, (CUDA_LONG)GetNumElements());
CUDA_CALL(cudaMemcpy(&h_sum, d_sum, sizeof(ElemType), cudaMemcpyDeviceToHost));
TracingGPUMemoryAllocator::Free<ElemType>(GetComputeDeviceId(), d_sum);
return h_sum;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignSumOfElements(const GPUMatrix<ElemType>& a)
{
if (a.IsEmpty())
LogicError("AssignSumOfElements: Matrix a is empty");
RequireSize(1, 1);
PrepareDevice();
SyncGuard syncGuard;
// WARNING: THIS kernel is not the most efficient way!
// note: kernel has hard-coded dimension of 1024
_reductionSumAndAssign1024Threads<ElemType> << <1, 1024 >> >(Data(), a.Data(), (CUDA_LONG)a.GetNumElements(), (CUDA_LONG)GetNumElements());
return (*this);
}
template <class ElemType>
DeviceBoundNumber<ElemType> GPUMatrix<ElemType>::Sum_AsDeviceBoundNum() const
{
if (IsEmpty())
LogicError("Matrix is empty");
ElemType* d_sum = TracingGPUMemoryAllocator::Allocate<ElemType>(GetComputeDeviceId(), 1);
// WARNING: THIS kernel is not the most efficient way!
// note: kernel has hard-coded dimension of 1024
_reductionSum1024Threads<ElemType> << <1, 1024, 0, t_stream >> >(Data(), d_sum, (CUDA_LONG)GetNumElements());
DeviceBoundNumber<ElemType> result;
result.ShallowCopyFrom(d_sum, GetComputeDeviceId());
return result;
}
template <class ElemType>
ElemType GPUMatrix<ElemType>::Max() const
{
cublasHandle_t cuHandle = GetCublasHandle(GetComputeDeviceId());
ElemType res;
if (sizeof(ElemType) == sizeof(float))
{
int resInd = 0;
cublasIsamax(cuHandle, (CUDA_LONG) GetNumElements(), reinterpret_cast<float*>(Data()), 1, &resInd);
resInd--;
CUDA_CALL(cudaMemcpy(reinterpret_cast<float*>(&res), reinterpret_cast<float*>(Data()+ resInd), sizeof(float), cudaMemcpyDeviceToHost));
return res;
}
else
{
int resInd = 0;
cublasIdamax(cuHandle, (CUDA_LONG) GetNumElements(), reinterpret_cast<double*>(Data()), 1, &resInd);
resInd--;
CUDA_CALL(cudaMemcpy(reinterpret_cast<double*>(&res), Data()+ resInd, sizeof(float), cudaMemcpyDeviceToHost));
return res;
}
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::ElementMultiplyWith(const GPUMatrix<ElemType>& a)
{
if (IsEmpty() || a.IsEmpty())
LogicError("ElementMultiplyWith: Matrix is empty.");
GPUMatrix<ElemType>& us = *this;
assert(us.GetNumRows() == a.GetNumRows() && us.GetNumCols() == a.GetNumCols());
if (us.GetNumRows() != a.GetNumRows() || us.GetNumCols() != a.GetNumCols())
InvalidArgument("The matrix dimensions do not match.");
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(((double) N) / GridDim::maxThreadsPerBlock);
a.PrepareDevice();
SyncGuard syncGuard;
_elemMul<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), N);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignElementProductOf(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b)
{
if (a.IsEmpty() || b.IsEmpty())
LogicError("AssignElementProductOf: Matrix is empty.");
assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols());
if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()))
InvalidArgument("The input matrix dimensions do not match.");
RequireSize(a.GetNumRows(), a.GetNumCols());
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(((double) N) / GridDim::maxThreadsPerBlock);
a.PrepareDevice();
SyncGuard syncGuard;
_assignElementProductOf<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), b.Data(), N);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::ElementDivideBy(const GPUMatrix<ElemType>& a)
{
return AssignElementDivisionOf(*this, a);
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignElementDivisionOf(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b)
{
if (a.IsEmpty() || b.IsEmpty())
LogicError("AssignElementDivisionOf: Matrix is empty.");
assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols());
if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()))
InvalidArgument("The input matrix dimensions do not match.");
RequireSize(a.GetNumRows(), a.GetNumCols());
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(((double) N) / GridDim::maxThreadsPerBlock);
a.PrepareDevice();
SyncGuard syncGuard;
_assignElementDivisionOf<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), b.Data(), N);
return *this;
}
template <class ElemType>
bool GPUMatrix<ElemType>::IsEqualTo(const GPUMatrix<ElemType>& a, const ElemType threshold /*= 1e-8*/) const
{
return AreEqual(*this, a, threshold);
}
template <class ElemType>
void GPUMatrix<ElemType>::VectorSum(const GPUMatrix<ElemType>& a, GPUMatrix<ElemType>& c, const bool isColWise)
{
if (a.GetComputeDeviceId() != c.GetComputeDeviceId())
{
InvalidArgument("All matrices must be on the same GPU");
}
a.PrepareDevice();
if (a.IsEmpty())
LogicError("VectorSum: Input matrix is empty.");
const CUDA_LONG n = (CUDA_LONG) a.GetNumRows();
const CUDA_LONG m = (CUDA_LONG) a.GetNumCols();
assert(m > 0 && n > 0); // converting from size_t to int may cause overflow
int blocksPerGrid = 0;
if (isColWise) // col-wise
{
c.RequireSize(1, m);
blocksPerGrid = (int) ceil(1.0 * m / GridDim::maxThreadsPerBlock);
}
else
{
c.RequireSize(n, 1);
blocksPerGrid = (int) ceil(1.0 * n / GridDim::maxThreadsPerBlock);
}
SyncGuard syncGuard;
_vectorSum<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(c.Data(), a.Data(), n, m, isColWise);
}
template <class ElemType>
void GPUMatrix<ElemType>::VectorNorm1(GPUMatrix<ElemType>& c, const bool isColWise) const
{
if (IsEmpty())
LogicError("VectorNorm1: Matrix is empty.");
const CUDA_LONG n = (CUDA_LONG) GetNumRows();
const CUDA_LONG m = (CUDA_LONG) GetNumCols();
assert(m > 0 && n > 0); // converting from size_t to int may cause overflow
PrepareDevice();
c.ChangeDeviceTo(GetComputeDeviceId());
int blocksPerGrid = 0;
if (isColWise) // col-wise
{
c.RequireSize(1, m);
blocksPerGrid = (int) ceil(1.0 * m / GridDim::maxThreadsPerBlock);
}
else
{
c.RequireSize(n, 1);
blocksPerGrid = (int) ceil(1.0 * n / GridDim::maxThreadsPerBlock);
}
SyncGuard syncGuard;
_vectorNorm1<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(c.Data(), Data(), n, m, isColWise);
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignVectorNorm1Of(GPUMatrix<ElemType>& a, const bool isColWise)
{
a.VectorNorm1(*this, isColWise);
return *this;
}
template <class ElemType>
void GPUMatrix<ElemType>::VectorNorm2(GPUMatrix<ElemType>& c, const bool isColWise) const
{
if (IsEmpty())
LogicError("VectorNorm2: Matrix is empty.");
const CUDA_LONG n = (CUDA_LONG) GetNumRows();
const CUDA_LONG m = (CUDA_LONG) GetNumCols();
assert(m > 0 && n > 0); // converting from size_t to int may cause overflow
PrepareDevice();
c.ChangeDeviceTo(GetComputeDeviceId());
int blocksPerGrid = 0;
if (isColWise) // col-wise
{
c.RequireSize(1, m);
blocksPerGrid = (int) ceil(1.0 * m / GridDim::maxThreadsPerBlock);
}
else
{
c.RequireSize(n, 1);
c.ChangeDeviceTo(GetComputeDeviceId());
blocksPerGrid = (int) ceil(1.0 * n / GridDim::maxThreadsPerBlock);
}
SyncGuard syncGuard;
_vectorNorm2<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(c.Data(), Data(), n, m, isColWise);
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignVectorNorm2Of(GPUMatrix<ElemType>& a, const bool isColWise)
{
a.VectorNorm2(*this, isColWise);
return *this;
}
template <class ElemType>
void GPUMatrix<ElemType>::VectorNormInf(GPUMatrix<ElemType>& c, const bool isColWise) const
{
if (IsEmpty())
LogicError("VectorMax: Matrix is empty.");
// this implementation is not efficient
GPUMatrix<ElemType> tmp(GetComputeDeviceId());
GPUMatrix<ElemType> tmp1(GetComputeDeviceId());
tmp.AssignAbsOf((*this));
tmp.VectorMax(tmp1, c, isColWise);
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignVectorNormInfOf(GPUMatrix<ElemType>& a, const bool isColWise)
{
a.VectorNormInf(*this, isColWise);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignInnerProductOf(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, const bool isColWise)
{
InnerProduct(a, b, *this, isColWise);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignKhatriRaoProductOf(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b)
{
if (a.IsEmpty() || b.IsEmpty())
LogicError("AssignKhatriRaoProductOf: Matrix is empty.");
CUDA_LONG cols = a.GetNumCols();
assert(cols == b.GetNumCols());
if (!(cols == b.GetNumCols()))
InvalidArgument("AssignKhatriRaoProductOf: The input matrix dimensions do not match.");
CUDA_LONG rowsA = (CUDA_LONG) a.GetNumRows();
CUDA_LONG rowsB = (CUDA_LONG) b.GetNumRows();
RequireSize(rowsA * rowsB, cols);
float N = (float) GetNumElements();
int blocksPerGrid = (int) ceil(N / GridDim::maxThreadsPerBlock);
a.PrepareDevice();
SyncGuard syncGuard;
_assignKhatriRaoProductOf<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), b.Data(), rowsA, rowsB, cols);
return *this;
}
//column-wise reshaped product. Used to compute KhatriRaoProduct Gradient
// this = reshape each column of a from (K1xK2,1) to (K1, K2)
// if each column of a is not transposed, each (K1, K2) times each column of b (K2, frames).
// the output is a (K1, frames) matrix
// if each column of a is tranposed, each (K1, K2)^T times each column of b(K1, frames) and output is (K2, frames)
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AddColumnReshapeProductOf(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, const bool transposeAColumn)
{
if (a.IsEmpty() || b.IsEmpty())
LogicError("AddColumnReshapeProductOf: Matrix is empty.");
CUDA_LONG cols = a.GetNumCols();
assert(cols == b.GetNumCols());
if (!(cols == b.GetNumCols()))
InvalidArgument("AddColumnReshapeProductOf: The input matrix dimensions do not match.");
CUDA_LONG rowsA = (CUDA_LONG) a.GetNumRows();
CUDA_LONG rowsB = (CUDA_LONG) b.GetNumRows();
if (rowsA % rowsB != 0)
InvalidArgument("AddColumnReshapeProductOf: number of rows in a should be multiples of that in b.");
CUDA_LONG rowsC = rowsA / rowsB;
if (rowsC != GetNumRows() || cols != GetNumCols())
InvalidArgument("AddColumnReshapeProductOf: This matrix does not have the right size.");
float N = (float) GetNumElements();
int blocksPerGrid = (int) ceil(N / GridDim::maxThreadsPerBlock);
a.PrepareDevice();
SyncGuard syncGuard;
_addColumnReshapeProductOf<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), b.Data(), rowsB, rowsC, cols, transposeAColumn);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AddWithScaleOf(ElemType alpha, const GPUMatrix<ElemType>& a)
{
ScaleAndAdd(alpha, a, *this);
return *this;
}
template <class ElemType>
ElemType GPUMatrix<ElemType>::FrobeniusNorm() const
{
if (IsEmpty())
LogicError("FrobeniusNorm: Matrix is empty.");
ElemType* d_sum = TracingGPUMemoryAllocator::Allocate<ElemType>(GetComputeDeviceId(), 1);
ElemType h_sum = 0;
// WARNING: THIS kernel is not the most efficient way!
// note: kernel has hard-coded dimension of 1024
_reductionSum21024Threads<ElemType> << <1, 1024, 0, t_stream >> >(Data(), d_sum, (CUDA_LONG)GetNumElements(), true);
CUDA_CALL(cudaMemcpy(&h_sum, d_sum, sizeof(ElemType), cudaMemcpyDeviceToHost));
TracingGPUMemoryAllocator::Free<ElemType>(GetComputeDeviceId(), d_sum);
return (h_sum);
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignFrobeniusNormOf(const GPUMatrix<ElemType>& a)
{
if (a.IsEmpty())
LogicError("AssignFrobeniusNormOf: Matrix a is empty.");
RequireSize(1, 1);
PrepareDevice();
// WARNING: THIS kernel is not the most efficient way!
// note: kernel has hard-coded dimension of 1024
_reductionSum21024Threads<ElemType> << <1, 1024, 0, t_stream >> >(a.Data(), Data(), (CUDA_LONG)a.GetNumElements(), true);
return *this;
}
template <class ElemType>
ElemType GPUMatrix<ElemType>::MatrixNormInf() const
{
if (IsEmpty())
LogicError("MatrixNormInf: Matrix is empty.");
ElemType* d_maxAbs = TracingGPUMemoryAllocator::Allocate<ElemType>(GetComputeDeviceId(), 1);
ElemType h_maxAbs = 0;
// WARNING: THIS kernel is not the most efficient way!
// note: kernel has hard-coded dimension of 1024
_reductionMatrixNormInf1024Threads<ElemType> << <1, 1024, 0, t_stream >> >(Data(), d_maxAbs, (CUDA_LONG)GetNumElements());
CUDA_CALL(cudaMemcpy(&h_maxAbs, d_maxAbs, sizeof(ElemType), cudaMemcpyDeviceToHost));
TracingGPUMemoryAllocator::Free<ElemType>(GetComputeDeviceId(), d_maxAbs);
return h_maxAbs;
}
template <class ElemType>
ElemType GPUMatrix<ElemType>::MatrixNorm1() const
{
if (IsEmpty())
LogicError("MatrixNorm1: Matrix is empty.");
return SumOfAbsElements();
}
template <class ElemType>
ElemType GPUMatrix<ElemType>::MatrixNorm0() const
{
if (IsEmpty())
LogicError("MatrixNorm0: Matrix is empty.");
ElemType* d_nz = TracingGPUMemoryAllocator::Allocate<ElemType>(GetComputeDeviceId(), 1);
ElemType h_nz = 0;
// WARNING: THIS kernel is not the most efficient way!
// note: kernel has hard-coded dimension of 1024
_reductionMatrixNorm01024Threads<ElemType> << <1, 1024, 0, t_stream >> >(Data(), d_nz, (CUDA_LONG)GetNumElements());
CUDA_CALL(cudaMemcpy(&h_nz, d_nz, sizeof(ElemType), cudaMemcpyDeviceToHost));
TracingGPUMemoryAllocator::Free<ElemType>(GetComputeDeviceId(), d_nz);
return h_nz;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignSignOf(const GPUMatrix<ElemType>& a)
{
if (a.IsEmpty())
LogicError("AssignSignOf: Matrix a is empty.");
if (this != &a)
RequireSize(a.GetNumRows(), a.GetNumCols());
PrepareDevice();
int blocksPerGrid = (int) ceil(1.0 * GetNumElements() / GridDim::maxThreadsPerBlock);
SyncGuard syncGuard;
_assignSignOf<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), (CUDA_LONG) GetNumElements());
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AddSignOf(const GPUMatrix<ElemType>& a)
{
if (a.IsEmpty())
LogicError("AddSignOf: Matrix a is empty.");
if (this != &a)
RequireSize(a.GetNumRows(), a.GetNumCols());
PrepareDevice();
int blocksPerGrid = (int) ceil(1.0 * GetNumElements() / GridDim::maxThreadsPerBlock);
SyncGuard syncGuard;
_addSignOf<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), (CUDA_LONG) GetNumElements());
return *this;
}
template <class ElemType>
void GPUMatrix<ElemType>::VectorMax(GPUMatrix<ElemType>& maxIndexes, GPUMatrix<ElemType>& maxValues, const bool isColWise) const
{
if (IsEmpty())
LogicError("VectorMax: Matrix is empty.");
const GPUMatrix<ElemType>& us = *this;
const CUDA_LONG m = (CUDA_LONG) GetNumRows();
const CUDA_LONG n = (CUDA_LONG) GetNumCols();
assert(m > 0 && n > 0); // converting from size_t to int may cause overflow
PrepareDevice();
SyncGuard syncGuard;
if (isColWise)
{
maxValues.RequireSize(1, n);
maxIndexes.RequireSize(1, n);
int blocksPerGrid = n; // we'll have 1 block processing 1 column
// note: kernel has hard-coded dimension of 512
_vectorMaxMinReduce512Threads<ElemType, true><<<blocksPerGrid, 512, 0, t_stream>>>(us.Data(), maxIndexes.Data(), maxValues.Data(), m, n);
/*int blocksPerGrid=(int)ceil(1.0*n/GridDim::maxThreadsPerBlock);
_vectorMax<ElemType><<<blocksPerGrid,GridDim::maxThreadsPerBlock,0,t_stream>>>(us.Data(),maxIndexes.Data(),maxValues.Data(),m,n,isColWise);*/
}
else
{
maxValues.RequireSize(m, 1);
maxIndexes.RequireSize(m, 1);
int blocksPerGrid = (int) ceil(1.0 * m / GridDim::maxThreadsPerBlock);
_vectorMax<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(us.Data(), maxIndexes.Data(), maxValues.Data(), m, n, isColWise);
}
}
__global__ void _initIndicesForSort(uint64_t* indexes, CUDA_LONG crow, CUDA_LONG ccol)
{
CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x;
if (id >= crow * ccol)
return;
uint32_t irow = id % crow;
uint32_t icol = id / crow;
indexes[id] = (static_cast<uint64_t>(irow) << 32) | icol;
}
template <class ElemType>
void GPUMatrix<ElemType>::VectorMax(GPUMatrix<ElemType>& maxIndexes, GPUMatrix<ElemType>& maxValues, const bool isColWise, int topK) const
{
if (IsEmpty())
LogicError("VectorMax: Matrix is empty.");
if (topK == 1)
{
VectorMax(maxIndexes, maxValues, isColWise);
return;
}
if (!isColWise)
RuntimeError("Row-wise TopK max is not supported.");
const GPUMatrix<ElemType>& us = *this;
const CUDA_LONG m = (CUDA_LONG) GetNumRows();
const CUDA_LONG n = (CUDA_LONG) GetNumCols();
assert(topK <= m);
assert(m > 0 && n > 0); // converting from size_t to int may cause overflow
PrepareDevice();
SyncGuard syncGuard;
maxValues.RequireSize(topK, n);
maxIndexes.RequireSize(topK, n);
// To sort matrix columns we use 2-pass _stable_ sort algorithm:
// 1. Sort by values (descending) with corresponding row/col indexes.
// 2. Sort by col indices (ascending) with corresponding values/row indices.
// Indices are stored as 64-bit ints where low 32 bits represent column and high 32 bits - row index.
// On the second pass only first 32 bits of the index are used in sorting, so SortPairs has
// begin_bit and end_bit set accordingly.
CUDA_LONG celt = static_cast<CUDA_LONG>(GetNumElements());
ElemType* inVal = us.Data();
ElemType* outVal1 = nullptr;
ElemType* outVal2 = nullptr;
uint64_t* inIdx = nullptr;
uint64_t* outIdx = nullptr;
// Determine temp buffer size needed for SortPairsDescending to sort values on the first pass.
size_t cbtemp = 0;
// If first param is nullptr then no actual work is done except writing result to cbtemp.
CUDA_CALL(cub::DeviceRadixSort::SortPairsDescending(nullptr, cbtemp, inVal, outVal1, inIdx, outIdx, celt, 0, sizeof(ElemType) * 8, t_stream));
size_t ctemp1 = (cbtemp + sizeof(ElemType) - 1) / sizeof(ElemType);
// Determine temp buffer size needed for SortPairs to sort indices on the second pass.
cbtemp = 0;
CUDA_CALL(cub::DeviceRadixSort::SortPairs(nullptr, cbtemp, outIdx, inIdx, outVal1, outVal2, celt, 0, 32, t_stream));
size_t ctemp2 = (cbtemp + sizeof(ElemType) - 1) / sizeof(ElemType);
size_t ctemp = std::max(ctemp1, ctemp2);
cbtemp = ctemp * sizeof(ElemType);
// ElemType count needed to store indices, accounting for natural alignment for uint64_t type.
size_t cidx = ((celt + 1) * sizeof(uint64_t) - 1 + sizeof(ElemType) - 1) / sizeof(ElemType);
// Get temp workspace.
auto workspace = GetOrCreateWorkspace();
// RequireSize to store: output values for the 1st and 2nd passes, input indices, output indices, and temp storage.
workspace->RequireSize(m, 2 * n + (2 * cidx + ctemp + m - 1) / m);
outVal1 = workspace->Data();
outVal2 = outVal1 + celt;
inIdx = reinterpret_cast<uint64_t*>(outVal2 + celt);
// Align indices pointer if needed.
size_t cbAlign = reinterpret_cast<size_t>(inIdx) % sizeof(uint64_t);
if (cbAlign != 0)
reinterpret_cast<uint8_t*&>(inIdx) += sizeof(uint64_t) - cbAlign;
outIdx = inIdx + celt;
void* ptmp = outIdx + celt;
assert(reinterpret_cast<ElemType*>(reinterpret_cast<uint8_t*>(ptmp) + cbtemp) <= workspace->Data() + workspace->GetNumElements());
// Initialize indices.
const int ThreadsPerBlock = 128;
int cblock = (celt + ThreadsPerBlock - 1) / ThreadsPerBlock;
_initIndicesForSort<<<cblock, ThreadsPerBlock, 0, t_stream>>>(inIdx, m, n);
// Sort by values.
CUDA_CALL(cub::DeviceRadixSort::SortPairsDescending(ptmp, cbtemp, inVal, outVal1, inIdx, outIdx, celt, 0, sizeof(ElemType) * 8, t_stream));
// Sort by column indices. outIdx contains indices after the first pass so it's used as an input.
CUDA_CALL(cub::DeviceRadixSort::SortPairs(ptmp, cbtemp, outIdx, inIdx, outVal1, outVal2, celt, 0, 32, t_stream));
// Copy results.
cblock = (topK * n + ThreadsPerBlock - 1) / ThreadsPerBlock;
_copyTopKResults<<<cblock, ThreadsPerBlock, 0, t_stream>>>(inIdx, outVal2, maxIndexes.Data(), maxValues.Data(), m, n, topK);
ReleaseWorkspace(std::move(workspace));
}
template <class ElemType>
void GPUMatrix<ElemType>::VectorMin(GPUMatrix<ElemType>& minIndexes, GPUMatrix<ElemType>& minValues, const bool isColWise) const
{
if (IsEmpty())
LogicError("VectorMax: Matrix is empty.");
const GPUMatrix<ElemType>& us = *this;
const int m = (int) GetNumRows();
const int n = (int) GetNumCols();
assert(m > 0 && n > 0); // converting from size_t to int may cause overflow
PrepareDevice();
SyncGuard syncGuard;
if (isColWise)
{
minValues.RequireSize(1, n);
minIndexes.RequireSize(1, n);
int blocksPerGrid = n; // we'll have 1 block processing 1 column
// note: kernel has hard-coded dimension of 512
_vectorMaxMinReduce512Threads<ElemType, false> << <blocksPerGrid, 512, 0, t_stream >> >(us.Data(), minIndexes.Data(), minValues.Data(), m, n);
/*
int blocksPerGrid=(int)ceil(1.0*n/GridDim::maxThreadsPerBlock);
_vectorMin<ElemType><<<blocksPerGrid,GridDim::maxThreadsPerBlock,0,t_stream>>>(us.Data(),minIndexes.Data(),minValues.Data(),m,n,isColWise);*/
}
else
{
minValues.RequireSize(m, 1);
minIndexes.RequireSize(m, 1);
int blocksPerGrid = (int) ceil(1.0 * m / GridDim::maxThreadsPerBlock);
_vectorMin<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(us.Data(), minIndexes.Data(), minValues.Data(), m, n, isColWise);
}
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignNumOfDiff(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, bool searchInCol)
{
if (a.GetNumCols() != b.GetNumCols())
InvalidArgument("AssignNumOfDiff: a and b must have the same number of columns.");
if (!searchInCol && a.GetNumRows() != b.GetNumRows())
InvalidArgument("AssignNumOfDiff: a and b must have the same number of rows.");
RequireSize(1, 1); // result should be one element
PrepareDevice();
SyncGuard syncGuard;
if (!searchInCol)
{
// int blocksPerGrid=(int)ceil(1.0*a.GetNumElements()/GridDim::maxThreadsPerBlock);
// _assignNumOfDiff1024Threads<ElemType><<<blocksPerGrid,GridDim::maxThreadsPerBlock,0,t_stream>>>(a.Data(), b.Data(), Data(), a.GetNumElements());
// note: kernel has hard-coded dimension of 1024
_assignNumOfDiff1024Threads<ElemType> << <1, 1024, 0, t_stream >> >(a.Data(), b.Data(), Data(), (CUDA_LONG)a.GetNumElements());
}
else
{
const int blockSize = 1024;
_assignNumOfDiffCol<blockSize><<<1, blockSize, 0, t_stream>>>(a.Data(), b.Data(), Data(),
static_cast<CUDA_LONG>(b.GetNumRows()), static_cast<CUDA_LONG>(a.GetNumCols()));
}
return *this;
}
#pragma endregion Member BLAS Functions
#pragma region Other helper functions
template <class ElemType>
void GPUMatrix<ElemType>::Print(const char* /*matrixName*/, size_t /*rowStart*/, size_t /*rowEnd*/, size_t /*colStart*/, size_t /*colEnd*/) const
{
NOT_IMPLEMENTED;
}
template <class ElemType>
void GPUMatrix<ElemType>::Print(const char* matrixName /*=nullptr*/) const
{
Print(matrixName, 0, GetNumRows() - 1, 0, GetNumCols() - 1);
}
//helpfer function used for convolution neural network
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignPackedConvolutionInput(const GPUMatrix<ElemType>& inputSubBatch,
const size_t inputWidth, const size_t inputHeight, const size_t inputChannels,
const size_t outputWidth, const size_t outputHeight, const size_t outputChannels,
const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample,
const bool zeroPadding)
{
assert(verticalSubsample <= kernelHeight && horizontalSubsample <= kernelWidth);
size_t packedInputRows = kernelWidth * kernelHeight * inputChannels;
size_t packedInputColsPerSample = outputWidth * outputHeight;
size_t smallBatchSize = inputSubBatch.GetNumCols();
RequireSize(packedInputRows, packedInputColsPerSample * smallBatchSize);
if (zeroPadding)
SetValue((ElemType) 0);
PrepareDevice();
int numThreadPerBlock = GridDim::maxThreadsPerBlock;
#if 1
int blocksPerGrid = (smallBatchSize * inputWidth * inputHeight * inputChannels + numThreadPerBlock - 1) / numThreadPerBlock;
#else
dim3 blocksPerGrid((inputWidth * inputHeight * inputChannels + numThreadPerBlock - 1) / numThreadPerBlock, smallBatchSize);
#endif
SyncGuard syncGuard;
_assignPackedConvolutionInput<<<blocksPerGrid, numThreadPerBlock, 0, t_stream>>>(Data(),
inputSubBatch.Data(),
smallBatchSize,
inputWidth, inputHeight, inputChannels,
outputWidth, outputHeight, outputChannels,
kernelWidth, kernelHeight, horizontalSubsample, verticalSubsample, zeroPadding);
return *this;
}
//helpfer function used for convolution neural network
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::UnpackConvolutionInput(GPUMatrix<ElemType>& inputSubBatch,
const size_t inputWidth, const size_t inputHeight, const size_t inputChannels,
const size_t outputWidth, const size_t outputHeight, const size_t outputChannels,
const size_t kernelWidth, const size_t kernelHeight, const size_t horizontalSubsample, const size_t verticalSubsample,
const bool zeroPadding) const
{
assert(verticalSubsample <= kernelHeight && horizontalSubsample <= kernelWidth);
size_t smallBatchSize = inputSubBatch.GetNumCols();
PrepareDevice();
int numThreadPerBlock = GridDim::maxThreadsPerBlock;
#if 1
int blocksPerGrid = (smallBatchSize * inputWidth * inputHeight * inputChannels + numThreadPerBlock - 1) / numThreadPerBlock;
#else
dim3 blocksPerGrid((inputWidth * inputHeight * inputChannels + numThreadPerBlock - 1) / numThreadPerBlock, smallBatchSize);
#endif
SyncGuard syncGuard;
_unpackConvolutionInput<<<blocksPerGrid, numThreadPerBlock, 0, t_stream>>>(Data(),
inputSubBatch.Data(),
smallBatchSize,
inputWidth, inputHeight, inputChannels,
outputWidth, outputHeight, outputChannels,
kernelWidth, kernelHeight, horizontalSubsample, verticalSubsample, zeroPadding);
return inputSubBatch;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignMaxPoolingResult(const GPUMatrix<ElemType>& inputBatch, const size_t channels,
const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample,
const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample,
const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample)
{
assert(verticalSubsample <= windowHeight && horizontalSubsample <= windowWidth);
unsigned int batchSize = inputBatch.GetNumCols();
RequireSize(outputSizePerSample, batchSize);
int numThreadPerBlock = GridDim::maxThreadsPerBlock;
int blocksPerGrid = (batchSize * outputSizePerSample + numThreadPerBlock - 1) / numThreadPerBlock;
PrepareDevice();
SyncGuard syncGuard;
_assignMaxPoolingResult<<<blocksPerGrid, numThreadPerBlock, 0, t_stream>>>(Data(), inputBatch.Data(), batchSize, channels,
inputWidth, inputHeight, inputSizePerSample,
outputWidth, outputHeight, outputSizePerSample,
windowWidth, windowHeight, horizontalSubsample, verticalSubsample);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AddMaxPoolingGradient(const GPUMatrix<ElemType>& outputGradientBatch, const GPUMatrix<ElemType>& inputBatch, const GPUMatrix<ElemType>& outputBatch,
const size_t channels,
const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample,
const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample,
const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample)
{
assert(verticalSubsample <= windowHeight && horizontalSubsample <= windowWidth);
unsigned int batchSize = outputGradientBatch.GetNumCols();
int numThreadPerBlock = GridDim::maxThreadsPerBlock;
PrepareDevice();
SyncGuard syncGuard;
int blocksPerGrid = (batchSize * inputSizePerSample + numThreadPerBlock - 1) / numThreadPerBlock;
_addMaxPoolingGradient<<<blocksPerGrid, numThreadPerBlock, 0, t_stream>>>(Data(), outputGradientBatch.Data(), inputBatch.Data(), outputBatch.Data(), batchSize, channels,
inputWidth, inputHeight, inputSizePerSample,
outputWidth, outputHeight, outputSizePerSample,
windowWidth, windowHeight, horizontalSubsample, verticalSubsample);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignAveragePoolingResult(const GPUMatrix<ElemType>& inputBatch, const size_t channels,
const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample,
const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample,
const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample)
{
assert(verticalSubsample <= windowHeight && horizontalSubsample <= windowWidth);
unsigned int batchSize = inputBatch.GetNumCols();
RequireSize(outputSizePerSample, batchSize);
int numThreadPerBlock = GridDim::maxThreadsPerBlock;
int blocksPerGrid = (batchSize * outputSizePerSample + numThreadPerBlock - 1) / numThreadPerBlock;
PrepareDevice();
SyncGuard syncGuard;
_assignAveragePoolingResult<<<blocksPerGrid, numThreadPerBlock, 0, t_stream>>>(Data(), inputBatch.Data(), batchSize, channels,
inputWidth, inputHeight, inputSizePerSample,
outputWidth, outputHeight, outputSizePerSample,
windowWidth, windowHeight, horizontalSubsample, verticalSubsample);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AddAveragePoolingGradient(const GPUMatrix<ElemType>& outputGradientBatch,
const size_t channels,
const size_t inputWidth, const size_t inputHeight, const size_t inputSizePerSample,
const size_t outputWidth, const size_t outputHeight, const size_t outputSizePerSample,
const size_t windowWidth, const size_t windowHeight, const size_t horizontalSubsample, const size_t verticalSubsample)
{
assert(verticalSubsample <= windowHeight && horizontalSubsample <= windowWidth);
size_t batchSize = outputGradientBatch.GetNumCols();
int numThreadPerBlock = GridDim::maxThreadsPerBlock;
PrepareDevice();
SyncGuard syncGuard;
size_t blocksPerGrid = (batchSize * inputSizePerSample + numThreadPerBlock - 1) / numThreadPerBlock;
_addAveragePoolingGradient<<<blocksPerGrid, numThreadPerBlock, 0, t_stream>>>(Data(), outputGradientBatch.Data(), (CUDA_LONG) batchSize, channels,
inputWidth, inputHeight, inputSizePerSample,
outputWidth, outputHeight, outputSizePerSample,
windowWidth, windowHeight, horizontalSubsample, verticalSubsample);
return *this;
}
#pragma endregion Other helper functions
template <class ElemType>
void GPUMatrix<ElemType>::ConvolutionForward(const GPUMatrix<ElemType>& kernel, const GPUMatrix<int>& mpRowCol, const GPUMatrix<int>& mpRowIwht,
const GPUMatrix<int>& mpRowRun, const GPUMatrix<int>& runs, GPUMatrix<ElemType>& output) const
{
const int BlockSize = 128;
auto gdim = dim3((output.GetNumRows() + BlockSize - 1)/ BlockSize, std::min((int)GetNumCols(), 65535));
PrepareDevice();
SyncGuard syncGuard;
kConvolutionForward<<<gdim, BlockSize, 0, t_stream>>>((int)GetNumCols(), kernel.Data(), mpRowCol.Data(), mpRowIwht.Data(), mpRowRun.Data(),
runs.Data(), Data(), (int)GetNumRows(), output.Data(), (int)output.GetNumRows());
}
template <class ElemType>
void GPUMatrix<ElemType>::ConvolutionBackwardData(const GPUMatrix<ElemType>& kernel, const GPUMatrix<int>& mpRowCol, const GPUMatrix<int>& mpRowIwht,
const GPUMatrix<int>& mpRowRun, const GPUMatrix<int>& runs, GPUMatrix<ElemType>& grad) const
{
const int BlockSize = 128;
auto gdim = dim3((GetNumRows() + BlockSize - 1)/ BlockSize, std::min((int)GetNumCols(), 65535));
PrepareDevice();
SyncGuard syncGuard;
kConvolutionBackwardData<<<gdim, BlockSize, 0, t_stream>>>((int)GetNumCols(), kernel.Data(), mpRowCol.Data(), mpRowIwht.Data(), mpRowRun.Data(),
runs.Data(), Data(), (int)GetNumRows(), grad.Data(), (int)grad.GetNumRows());
}
template <class ElemType>
void GPUMatrix<ElemType>::ConvolutionBackwardKernel(const GPUMatrix<ElemType>& in, const GPUMatrix<int>& mpRowCol, const GPUMatrix<int>& mpRowIwht,
const GPUMatrix<int>& mpRowRun, const GPUMatrix<int>& runs, GPUMatrix<ElemType>& kernelGrad) const
{
const int BlockSize = 128;
auto gdim = dim3((GetNumRows() + BlockSize - 1)/ BlockSize, std::min((int)GetNumCols(), 65535));
PrepareDevice();
SyncGuard syncGuard;
kConvolutionBackwardKernel<<<gdim, BlockSize, 0, t_stream>>>((int)GetNumCols(), (int)in.GetNumRows(), (int)GetNumRows(),
in.Data(), mpRowCol.Data(), mpRowIwht.Data(), mpRowRun.Data(),
runs.Data(), Data(), kernelGrad.Data());
}
template <class ElemType>
void GPUMatrix<ElemType>::MaxPoolingForward(const GPUMatrix<int>& mpRowCol, const GPUMatrix<int>& mpRowIndices, const GPUMatrix<int>& indices, GPUMatrix<ElemType>& output) const
{
const int BlockSize = 128;
auto gdim = dim3((output.GetNumRows() + BlockSize - 1)/ BlockSize, std::min((int)GetNumCols(), 65535));
PrepareDevice();
SyncGuard syncGuard;
kMaxPoolingForward<<<gdim, BlockSize, 0, t_stream>>>((int)GetNumCols(), mpRowCol.Data(), mpRowIndices.Data(), indices.Data(),
Data(), (int)GetNumRows(), output.Data(), (int)output.GetNumRows());
}
template <class ElemType>
void GPUMatrix<ElemType>::MaxPoolingBackward(const GPUMatrix<ElemType>& out, const GPUMatrix<ElemType>& in,
const GPUMatrix<int>& mpRowCol, const GPUMatrix<int>& mpRowIndices, const GPUMatrix<int>& indices,
GPUMatrix<ElemType>& grad) const
{
const int BlockSize = 128;
auto gdim = dim3((GetNumRows() + BlockSize - 1)/ BlockSize, std::min((int)GetNumCols(), 65535));
PrepareDevice();
SyncGuard syncGuard;
kMaxPoolingBackward<<<gdim, BlockSize, 0, t_stream>>>((int)GetNumCols(), out.Data(), in.Data(),
mpRowCol.Data(), mpRowIndices.Data(), indices.Data(),
Data(), (int)GetNumRows(), grad.Data(), (int)grad.GetNumRows());
}
template <class ElemType>
void GPUMatrix<ElemType>::ROIPoolingForward(const size_t numRois, const size_t numImg, const size_t channels, const size_t width, const size_t height,
const size_t pooledWidth, const size_t pooledHeight, const GPUMatrix<ElemType>& roiData, GPUMatrix<ElemType>& output,
GPUMatrix<ElemType>& argmax) const
{
PrepareDevice();
SyncGuard syncGuard;
int count = numRois * numImg * channels * pooledHeight * pooledWidth;
const int blockSize = GridDim::maxThreadsPerBlock;
auto numThreads = dim3((int)floor((double)(count + blockSize - 1) / blockSize));
kROIPoolingForward<<<numThreads, blockSize, 0, t_stream>>>(count, numRois, numImg, channels, width, height,
pooledWidth, pooledHeight, Data(), roiData.Data(), output.Data(), argmax.Data());
}
template <class ElemType>
void GPUMatrix<ElemType>::ROIPoolingBackward(const size_t numRois, const size_t numImg, const size_t channels, const size_t width, const size_t height,
const size_t pooledWidth, const size_t pooledHeight, const GPUMatrix<ElemType>& roiData, GPUMatrix<ElemType>& grad,
GPUMatrix<ElemType>& argmax) const
{
PrepareDevice();
SyncGuard syncGuard;
int count = numImg * channels * height * width;
const int blockSize = GridDim::maxThreadsPerBlock;
auto numThreads = dim3((int)floor((double)(count + blockSize - 1) / blockSize));
kROIPoolingBackward<<<numThreads, blockSize, 0, t_stream>>>(count, numRois, numImg, channels, width, height,
pooledWidth, pooledHeight, Data(), roiData.Data(), grad.Data(), argmax.Data());
}
template <class ElemType>
void GPUMatrix<ElemType>::MaxUnpooling(const GPUMatrix<int>& mpRowCol, const GPUMatrix<int>& mpRowIndices, const GPUMatrix<int>& indices, const GPUMatrix<ElemType>& poolInput, GPUMatrix<ElemType>& input) const
{
const int BlockSize = 128;
auto gdim = dim3((GetNumRows() + BlockSize - 1)/ BlockSize, std::min((int)GetNumCols(), 65535));
PrepareDevice();
SyncGuard syncGuard;
kMaxUnpooling<<<gdim, BlockSize, 0, t_stream>>>((int)GetNumCols(), mpRowCol.Data(), mpRowIndices.Data(), indices.Data(),
Data(), poolInput.Data(), (int)GetNumRows(), input.Data(), (int)input.GetNumRows());
}
template <class ElemType>
void GPUMatrix<ElemType>::AveragePoolingForward(const GPUMatrix<int>& mpRowCol, const GPUMatrix<int>& mpRowIndices, const GPUMatrix<int>& indices, GPUMatrix<ElemType>& output) const
{
const int BlockSize = 128;
auto gdim = dim3((output.GetNumRows() + BlockSize - 1)/ BlockSize, std::min((int)GetNumCols(), 65535));
PrepareDevice();
SyncGuard syncGuard;
kAveragePoolingForward<<<gdim, BlockSize, 0, t_stream>>>((int)GetNumCols(), mpRowCol.Data(), mpRowIndices.Data(), indices.Data(),
Data(), (int)GetNumRows(), output.Data(), (int)output.GetNumRows());
}
template <class ElemType>
void GPUMatrix<ElemType>::AveragePoolingBackward(const GPUMatrix<int>& mpRowCol, const GPUMatrix<int>& mpRowIndices, const GPUMatrix<int>& indices, GPUMatrix<ElemType>& grad) const
{
const int BlockSize = 128;
auto gdim = dim3((GetNumRows() + BlockSize - 1)/ BlockSize, std::min((int)GetNumCols(), 65535));
PrepareDevice();
SyncGuard syncGuard;
kAveragePoolingBackward<<<gdim, BlockSize, 0, t_stream>>>((int)GetNumCols(), mpRowCol.Data(), mpRowIndices.Data(), indices.Data(),
Data(), (int)GetNumRows(), grad.Data(), (int)grad.GetNumRows());
}
// returns savedMean/savedInvStdDev which are the actual values used to perform the normalization, except for blendFactor 1, in which case they are unused and set to empty
template <class ElemType>
void GPUMatrix<ElemType>::BatchNormalizationForward(const GPUMatrix<ElemType>& scale, const GPUMatrix<ElemType>& bias, bool inferenceOnly, double expAvgFactor, double blendFactor,
GPUMatrix<ElemType>& runMean, GPUMatrix<ElemType>& runVariance, GPUMatrix<ElemType>& out, double epsilon,
GPUMatrix<ElemType>& savedMean, GPUMatrix<ElemType>& savedInvStdDev) const
{
assert((GetNumRows() % scale.GetNumRows()) == 0);
bool spatial = GetNumRows() != scale.GetNumRows();
size_t vectorSize = GetNumRows();
size_t spatialSize = spatial ? (GetNumRows() / scale.GetNumRows()) : 1;
size_t batchSize = GetNumCols();
bool normalizeRunningStats;
assert(0 < vectorSize && vectorSize <= std::numeric_limits<int>::max());
assert(0 < batchSize && batchSize <= std::numeric_limits<int>::max());
SyncGuard syncGuard;
if (inferenceOnly)
{
// Pick running statistics for normalizing. No update reuqired, and
// saved statistics do not need to be produced.
assert(expAvgFactor == 0 && blendFactor == 1);
normalizeRunningStats = true;
savedMean.RequireSize(0, 0);
savedInvStdDev.RequireSize(0, 0);
}
else
{
// Compute data mean and inverse standard deviation (into savedMean and
// savedInvStdDev), and update running mean and variance.
// TODO expAvgFactor == 0 && blendFactor == 1 can be optimized (no need for update).
normalizeRunningStats = false;
savedMean.RequireSize(runMean);
savedInvStdDev.RequireSize(runMean);
if (spatial)
{
Call<ComputeSpatialBatchMeanAndInvStdDev, ElemType>(spatialSize, vectorSize, spatialSize, batchSize, Data(),
expAvgFactor, blendFactor,
runMean.Data(), runVariance.Data(), epsilon,
savedMean.Data(), savedInvStdDev.Data(), GetStream());
}
else
{
Call<ComputeBatchMeanAndInvStdDev, ElemType>(vectorSize, vectorSize, batchSize, Data(),
expAvgFactor, blendFactor,
runMean.Data(), runVariance.Data(), epsilon,
savedMean.Data(), savedInvStdDev.Data(), GetStream());
}
}
Call<NormalizeBatchTraining, ElemType>(spatial ? spatialSize : vectorSize, vectorSize, spatialSize, batchSize, spatial,
normalizeRunningStats, epsilon,
Data(), out.Data(),
scale.Data(), bias.Data(),
runMean.Data(), runVariance.Data(),
savedMean.Data(), savedInvStdDev.Data(),
GetStream());
}
// savedMean/savedInvStdDev are the interpolated mean/inverse standard deviation as used in ForwardProp().
// For blendFactor=1, they are not used and can be uninitialized or empty.
template <class ElemType>
void GPUMatrix<ElemType>::BatchNormalizationBackward(const GPUMatrix<ElemType>& in, GPUMatrix<ElemType>& grad, const GPUMatrix<ElemType>& scale, double blendFactor,
const GPUMatrix<ElemType>& savedMean, const GPUMatrix<ElemType>& savedInvStdDev,
GPUMatrix<ElemType>& scaleGrad, GPUMatrix<ElemType>& biasGrad) const
{
assert((GetNumRows() % scale.GetNumRows()) == 0);
bool spatial = GetNumRows() != scale.GetNumRows();
size_t vectorSize = GetNumRows();
size_t spatialSize = spatial ? (GetNumRows() / scale.GetNumRows()) : 1;
size_t batchSize = GetNumCols();
assert(0 < vectorSize && vectorSize <= std::numeric_limits<int>::max());
assert(0 < batchSize && batchSize <= std::numeric_limits<int>::max());
SyncGuard syncGuard;
if (spatial)
{
Call<ComputeSpatialScaleAndBiasGradients, ElemType>(spatialSize, vectorSize, spatialSize, batchSize, in.Data(), Data(), scaleGrad.Data(), biasGrad.Data(),
savedMean.Data(), savedInvStdDev.Data(), GetStream());
}
else
{
Call<ComputeScaleAndBiasGradients, ElemType>(vectorSize, vectorSize, batchSize, in.Data(), Data(), scaleGrad.Data(), biasGrad.Data(),
savedMean.Data(), savedInvStdDev.Data(), GetStream());
}
ElemType mbStatsWeight = (ElemType)(1 - blendFactor); // weight for contribution from actual MB stats (0 if none, e.g. locked BN node)
Call<BackpropagateBatchNormGradients, ElemType>(spatial ? spatialSize : vectorSize, vectorSize, spatialSize, batchSize, spatial,
in.Data(), Data(), grad.Data(), scale.Data(), mbStatsWeight, scaleGrad.Data(), biasGrad.Data(), savedMean.Data(), savedInvStdDev.Data(), GetStream());
}
#pragma region RNN Functions
template <class ElemType>
void GPUMatrix<ElemType>::RNNForward(const GPUMatrix<ElemType> &inputX, const GPUMatrix<ElemType> &paramW, size_t xDim, size_t yDim, const vector<size_t>& numSequencesForFrame, const RnnAttributes& rnnAttributes, GPUMatrix<ElemType>& reserve, GPUMatrix<ElemType>& workspace)
{
// numLayers, hiddenSize are input parameters
if (!m_rnnExecutor)
m_rnnExecutor = std::make_unique<CuDnnRNNExecutor<ElemType>>(xDim, yDim, rnnAttributes);
m_rnnExecutor->ForwardCore(paramW, inputX, *this, numSequencesForFrame, rnnAttributes, reserve, workspace);
}
template <class ElemType>
void GPUMatrix<ElemType>::RNNBackwardData(const GPUMatrix<ElemType>& outputDY, const GPUMatrix<ElemType>& paramW, GPUMatrix<ElemType>& outputDX, const RnnAttributes& rnnAttributes, GPUMatrix<ElemType>& reserve, GPUMatrix<ElemType>& workspace)
{
if (!m_rnnExecutor)
LogicError("RNNBackwardData called, but RNNWrapper object is not yet initialized");
m_rnnExecutor->BackwardDataCore(*this, outputDY, paramW, outputDX, rnnAttributes, reserve, workspace);
}
template <class ElemType>
void GPUMatrix<ElemType>::RNNBackwardWeights(const GPUMatrix<ElemType>& inputX, const GPUMatrix<ElemType>& outputY, GPUMatrix<ElemType>& dw, const RnnAttributes& rnnAttributes, GPUMatrix<ElemType>& reserve, GPUMatrix<ElemType>& workspace)
{
if (!m_rnnExecutor)
LogicError("RNNBackwardWeights called, but RNNWrapper object is not yet initialized");
m_rnnExecutor->BackwardWeightsCore(inputX, outputY, dw, rnnAttributes, reserve, workspace);
}
#pragma region Static BLAS Functions
// float/double overloads of cublasSgemm()/cublasDgemm()
static cublasStatus_t cublas_gemm(cublasHandle_t handle, cublasOperation_t transa, cublasOperation_t transb, int m, int n, int k, const float* alpha, const float* A, int lda, const float* B, int ldb, const float* beta, float* C, int ldc)
{
return cublasSgemm(handle, transa, transb, m, n, k, alpha, A, lda, B, ldb, beta, C, ldc);
}
static cublasStatus_t cublas_gemm(cublasHandle_t handle, cublasOperation_t transa, cublasOperation_t transb, int m, int n, int k, const double* alpha, const double* A, int lda, const double* B, int ldb, const double* beta, double* C, int ldc)
{
return cublasDgemm(handle, transa, transb, m, n, k, alpha, A, lda, B, ldb, beta, C, ldc);
}
static cublasStatus_t cublas_axpy(cublasHandle_t handle, int n, const float* alpha, const float* x, int incx, float* y, int incy)
{
return cublasSaxpy(handle, n, alpha, x, incx, y, incy);
}
static cublasStatus_t cublas_axpy(cublasHandle_t handle, int n, const double* alpha, const double* x, int incx, double* y, int incy)
{
return cublasDaxpy(handle, n, alpha, x, incx, y, incy);
}
template <class ElemType>
void GPUMatrix<ElemType>::MultiplyAndWeightedAdd(ElemType alpha, const GPUMatrix<ElemType>& a, const bool transposeA, const GPUMatrix<ElemType>& b, const bool transposeB,
ElemType beta, GPUMatrix<ElemType>& c)
{
a.PrepareDevice();
if ((a.GetComputeDeviceId() != b.GetComputeDeviceId()) || (b.GetComputeDeviceId() != c.GetComputeDeviceId())) // different GPUs
InvalidArgument("All matrices must be on the same GPU");
cublasHandle_t cuHandle = GetCublasHandle(b.GetComputeDeviceId());
cublasOperation_t transA = transposeA ? CUBLAS_OP_T : CUBLAS_OP_N;
cublasOperation_t transB = transposeB ? CUBLAS_OP_T : CUBLAS_OP_N;
int m = int(transposeA ? a.m_numCols : a.m_numRows);
int n = int(transposeB ? b.m_numRows : b.m_numCols);
int k = int(transposeA ? a.m_numRows : a.m_numCols);
int l = int(transposeB ? b.m_numCols : b.m_numRows);
c.RequireSize(m, n);
if (!(m > 0 && k > 0 && l > 0 && n > 0))
RuntimeError("!(m>0 && k>0 && l>0 && n>0)"); // converting from size_t to int may cause overflow
if (k != l)
RuntimeError("matrix dim mismatch in MultiplyAndWeightedAdd");
CUBLAS_CALL(cublas_gemm(cuHandle, transA, transB, m, n, k, &alpha, a.Data(), (int) a.m_numRows, b.Data(), (int) b.m_numRows, &beta, c.Data(), (int) c.m_numRows));
c.m_numRows = m;
c.m_numCols = n;
}
template <class ElemType>
void GPUMatrix<ElemType>::Multiply1x1AndWeightedAdd(ElemType alpha, const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, ElemType beta, GPUMatrix<ElemType>& c)
{
a.PrepareDevice();
if ((a.GetComputeDeviceId() != b.GetComputeDeviceId()) || (b.GetComputeDeviceId() != c.GetComputeDeviceId())) // different GPUs
InvalidArgument("All matrices must be on the same GPU");
CUDA_LONG N = (CUDA_LONG) c.GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
SyncGuard syncGuard;
_multiply1x1AndWeightedAdd<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(alpha, a.Data(), b.Data(), beta, c.Data(), N);
}
template <class ElemType>
void GPUMatrix<ElemType>::MultiplyAndAdd(const GPUMatrix<ElemType>& a, const bool transposeA, const GPUMatrix<ElemType>& b, const bool transposeB, GPUMatrix<ElemType>& c)
{
return GPUMatrix<ElemType>::MultiplyAndWeightedAdd(1, a, transposeA, b, transposeB, 1, c);
}
template <class ElemType>
void GPUMatrix<ElemType>::Multiply(const GPUMatrix<ElemType>& a, const bool transposeA, const GPUMatrix<ElemType>& b, const bool transposeB, GPUMatrix<ElemType>& c)
{
return GPUMatrix<ElemType>::MultiplyAndWeightedAdd(1, a, transposeA, b, transposeB, 0, c);
}
template <class ElemType>
void GPUMatrix<ElemType>::Multiply(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, GPUMatrix<ElemType>& c)
{
return GPUMatrix<ElemType>::MultiplyAndWeightedAdd(1, a, false, b, false, 0, c);
}
/// <summary>Matrix-scalar multiply with col-major matrices: c = alpha * a + c</summary>
/// if a is a column vector, add to all columns of c
/// if a is a row vector, add to all rows of c
/// if a is a scalar, add to all elements of c
/// <param name="alpha">Scalar</param>
/// <param name="a">Input matrix</param>
/// <param name="c">Resulting matrix, user is responsible for allocating this</param>
template <class ElemType>
/*static*/ void GPUMatrix<ElemType>::ScaleAndAdd(ElemType alpha, const GPUMatrix<ElemType>& a, GPUMatrix<ElemType>& c)
{
if (a.GetComputeDeviceId() != c.GetComputeDeviceId())
{
InvalidArgument("All matrices must be on the same GPU");
}
else
{
if (a.IsEmpty() && c.IsEmpty())
return;
a.PrepareDevice();
if (a.IsEmpty() || c.IsEmpty())
LogicError("ScaleAndAdd: one of the input matrices is empty.");
// if (a.GetNumRows() != 1 && a.GetNumCols() != 1) // a is not a col or row vector
if (a.GetNumRows() == c.GetNumRows() && a.GetNumCols() == c.GetNumCols()) // dimensions match
{
const int m = (int) a.GetNumRows();
const int n = (int) a.GetNumCols();
const int len = m * n;
const int incx = 1;
const int incy = 1;
assert(m > 0 && n > 0 && len > 0); // converting from size_t to int may cause overflow
assert((int) c.GetNumRows() == m && (int) c.GetNumCols() == n);
if ((int) c.GetNumRows() != m || (int) c.GetNumCols() != n)
InvalidArgument("dimension of matrix c does not match dimension of matrix a.");
cublasHandle_t cuHandle = GetCublasHandle(a.GetComputeDeviceId());
// TODO: Overload the call to cublas_axpy to remove these ugly if/else statements.
if (sizeof(ElemType) == sizeof(float))
{
CUBLAS_CALL(cublasSaxpy(cuHandle, len, reinterpret_cast<float*>(&alpha), reinterpret_cast<float*>(a.Data()), incx, reinterpret_cast<float*>(c.Data()), incy));
}
else if (sizeof(ElemType) == sizeof(double))
{
CUBLAS_CALL(cublasDaxpy(cuHandle, len, reinterpret_cast<double*>(&alpha), reinterpret_cast<double*>(a.Data()), incx, reinterpret_cast<double*>(c.Data()), incy));
}
else
{
RuntimeError("Unsupported template argument in GPUMatrix");
}
}
else if (a.GetNumElements() == 1)
{
CUDA_LONG N = (CUDA_LONG) c.GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
c.PrepareDevice();
SyncGuard syncGuard;
_scaleAndAddScalar<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(c.Data(), N, alpha, a.Data(), c.Data());
}
else if (a.GetNumCols() == 1) // col vector, add it to all columns
{
CUDA_LONG m = (CUDA_LONG) c.GetNumRows();
CUDA_LONG n = (CUDA_LONG) c.GetNumCols();
if (m != (CUDA_LONG) a.GetNumRows())
InvalidArgument("To add column vector, rows should match.");
int blocksPerGrid = (int) (ceil(1.0 * m * n / GridDim::maxThreadsPerBlock));
SyncGuard syncGuard;
#ifdef VALIDATION
printf(">>>> CUDA compute device is %d\n", a.GetComputeDeviceId());
printf(">>>> a.Data()= %p, c.Data()= %p, alpha = %f, m = %ld, n = %ld\n", a.Data(), c.Data(), alpha, m, n);
for (int i = 0; i < 2; i++)
{
ElemType buffer[10] = {-1.234f};
cudaError_t error = cudaMemcpy(buffer, !i ? a.Data(): c.Data(), sizeof(buffer), cudaMemcpyKind::cudaMemcpyDeviceToHost);
if (error == cudaError::cudaSuccess)
printf("buffer valid\n");
}
#endif
_matrixVectorColumnWiseAddWithThreadPerElem<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(a.Data(), c.Data(), c.Data(), alpha, m, n);
}
else if (a.GetNumRows() == 1) // row vector, add it to all rows
{
cublasHandle_t cuHandle = GetCublasHandle(a.GetComputeDeviceId());
int m = (int) c.GetNumRows();
int n = (int) c.GetNumCols();
assert(n == (int) a.GetNumCols());
if (n != (int) a.GetNumCols())
InvalidArgument("To add row vector, cols should match.");
// TODO: Overload the call to cublas_axpy to remove these ugly if/else statements.
if (sizeof(ElemType) == sizeof(double))
{
foreach_row (i, c)
{
CUBLAS_CALL(cublasDaxpy(cuHandle, n, reinterpret_cast<double*>(&alpha), reinterpret_cast<double*>(a.Data()), 1, reinterpret_cast<double*>(c.Data()+ i), m));
}
}
else
{
foreach_row (i, c)
{
CUBLAS_CALL(cublasSaxpy(cuHandle, n, reinterpret_cast<float*>(&alpha), reinterpret_cast<float*>(a.Data()), 1, reinterpret_cast<float*>(c.Data()+ i), m));
}
}
}
else
InvalidArgument("dimension of matrix c does not match dimension of matrix a.");
}
}
/// <summary>Matrix-scalar multiply with col-major matrices: c = alpha * a + b</summary>
/// if a is a column vector, add to all columns of b
/// if a is a row vector, add to all rows of b
/// if a is a scalar, add to all elements of b
/// <param name="alpha">Scalar</param>
/// <param name="a">Input matrix</param>
/// <param name="b">Input matrix</param>
/// <param name="c">Resulting matrix, user is responsible for allocating this</param>
template <class ElemType>
/*static*/ void GPUMatrix<ElemType>::ScaleAndAdd(ElemType alpha, const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, GPUMatrix<ElemType>& c)
{
if (a.GetComputeDeviceId() != c.GetComputeDeviceId() || a.GetComputeDeviceId() != b.GetComputeDeviceId())
{
InvalidArgument("All matrices must be on the same GPU");
}
else
{
if (a.IsEmpty() && b.IsEmpty())
return;
a.PrepareDevice();
if (a.IsEmpty() || b.IsEmpty())
LogicError("ScaleAndAdd: One of the input matrices is empty.");
c.RequireSize(b.GetNumRows(), b.GetNumCols());
// if (a.GetNumRows() != 1 && a.GetNumCols() != 1) // a is not a col or row vector
if (a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()) // dimensions match
{
/*
const int m = (int)a.GetNumRows();
const int n = (int)a.GetNumCols();
const int len = m * n;
const int incx = 1;
const int incy = 1;
assert (m>0 && n>0 && len>0); // converting from size_t to int may cause overflow
*/
CUDA_LONG N = (CUDA_LONG) c.GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
c.PrepareDevice();
SyncGuard syncGuard;
_matrixMatrixAddOnCuda<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(alpha, a.Data(), b.Data(), c.Data(), N);
}
else if (a.GetNumElements() == 1)
{
CUDA_LONG N = (CUDA_LONG) c.GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
c.PrepareDevice();
SyncGuard syncGuard;
_scaleAndAddScalar<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(c.Data(), N, alpha, a.Data(), b.Data());
}
else if (a.GetNumCols() == 1) // col vector, add it to all columns
{
CUDA_LONG m = (CUDA_LONG) c.GetNumRows();
CUDA_LONG n = (CUDA_LONG) c.GetNumCols();
if (m != (CUDA_LONG) a.GetNumRows())
InvalidArgument("To add column vector, rows should match.");
int blocksPerGrid = (int) (ceil(1.0 * m * n / GridDim::maxThreadsPerBlock));
SyncGuard syncGuard;
_matrixVectorColumnWiseAddWithThreadPerElem<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(a.Data(), b.Data(), c.Data(), alpha, m, n);
}
else if (a.GetNumRows() == 1) // row vector, add it to all rows
{
CUDA_LONG m = (CUDA_LONG) c.GetNumRows();
CUDA_LONG n = (CUDA_LONG) c.GetNumCols();
if (m != (CUDA_LONG) a.GetNumRows())
InvalidArgument("To add column vector, rows should match.");
int blocksPerGrid = (int) (ceil(1.0 * m * n / GridDim::maxThreadsPerBlock));
SyncGuard syncGuard;
_matrixVectorRowWiseAddWithThreadPerElem<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(a.Data(), b.Data(), c.Data(), alpha, m, n);
}
else
InvalidArgument("Dimension of matrix c does not match dimension of matrix a.");
}
}
/// <summary>c += alpha * (a-b)</summary>
/// if a, b, c must have same dim
/// <param name="alpha">Scalar</param>
/// <param name="a">Input matrix</param>
/// <param name="b">Input matrix</param>
/// <param name="c">Resulting matrix, user is responsible for allocating this</param>
template <class ElemType>
void GPUMatrix<ElemType>::AddScaledDifference(const ElemType alpha, const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, GPUMatrix<ElemType>& c)
{
if (a.GetComputeDeviceId() != c.GetComputeDeviceId())
{
InvalidArgument("All matrices must be on the same GPU");
}
else
{
a.PrepareDevice();
assert(a.GetNumRows() == b.GetNumRows() && a.GetNumRows() == c.GetNumRows() &&
a.GetNumCols() == b.GetNumCols() && a.GetNumCols() == c.GetNumCols());
if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumRows() == c.GetNumRows() &&
a.GetNumCols() == b.GetNumCols() && a.GetNumCols() == c.GetNumCols()))
{
InvalidArgument("AddScaledDifference: a, b, and c must have same dimension.");
}
if (a.IsEmpty())
LogicError("AddScaledDifference: Input matrix a is empty.");
CUDA_LONG n = (CUDA_LONG) a.GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * n / GridDim::maxThreadsPerBlock);
SyncGuard syncGuard;
_addScaledDifference<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(alpha, a.Data(), b.Data(), c.Data(), n);
}
}
/// <summary> c = alpha * (a-b)</summary>
/// if a, b, c must have same dim
/// <param name="alpha">Scalar</param>
/// <param name="a">Input matrix</param>
/// <param name="b">Input matrix</param>
/// <param name="c">Resulting matrix, user is responsible for allocating this</param>
template <class ElemType>
void GPUMatrix<ElemType>::AssignScaledDifference(const ElemType alpha, const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, GPUMatrix<ElemType>& c)
{
if (a.GetComputeDeviceId() != c.GetComputeDeviceId())
{
InvalidArgument("All matrices must be on the same GPU");
}
else
{
a.PrepareDevice();
assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols());
if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()))
InvalidArgument("AssignScaledDifference: a, b must have same dimension.");
if (a.IsEmpty())
LogicError("AssignScaledDifference: Input matrix a is empty.");
if (&c != &a && &c != &b)
c.RequireSize(a.GetNumRows(), a.GetNumCols());
CUDA_LONG n = (CUDA_LONG) a.GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * n / GridDim::maxThreadsPerBlock);
SyncGuard syncGuard;
_assignScaledDifference<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(alpha, a.Data(), b.Data(), c.Data(), n);
}
}
/// <summary>c += alpha * (a-b)</summary>
/// if a, b, c must have same dim
/// <param name="alpha">1X1 matrix</param>
/// <param name="a">Input matrix</param>
/// <param name="b">Input matrix</param>
/// <param name="c">Resulting matrix, user is responsible for allocating this</param>
template <class ElemType>
void GPUMatrix<ElemType>::AddScaledDifference(const GPUMatrix<ElemType>& alpha, const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, GPUMatrix<ElemType>& c)
{
assert(alpha.GetNumElements() == 1);
if (!(alpha.GetNumElements() == 1))
InvalidArgument("AddScaledDifference: alpha must be a 1X1 matrix.");
if (a.GetComputeDeviceId() != c.GetComputeDeviceId())
{
InvalidArgument("All matrices must be on the same GPU");
}
else
{
a.PrepareDevice();
assert(a.GetNumRows() == b.GetNumRows() && a.GetNumRows() == c.GetNumRows() &&
a.GetNumCols() == b.GetNumCols() && a.GetNumCols() == c.GetNumCols());
if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumRows() == c.GetNumRows() &&
a.GetNumCols() == b.GetNumCols() && a.GetNumCols() == c.GetNumCols()))
{
InvalidArgument("AddScaledDifference: a, b, and c must have same dimension.");
}
if (a.IsEmpty())
LogicError("AddScaledDifference: Input matrix a is empty.");
CUDA_LONG n = (CUDA_LONG) a.GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * n / GridDim::maxThreadsPerBlock);
SyncGuard syncGuard;
_addScaledDifference<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(alpha.Data(), a.Data(), b.Data(), c.Data(), n);
}
}
/// <summary> c = alpha * (a-b)</summary>
/// if a, b, c must have same dim
/// <param name="alpha">Scalar</param>
/// <param name="a">Input matrix</param>
/// <param name="b">Input matrix</param>
/// <param name="c">Resulting matrix, user is responsible for allocating this</param>
template <class ElemType>
void GPUMatrix<ElemType>::AssignScaledDifference(const GPUMatrix<ElemType>& alpha, const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, GPUMatrix<ElemType>& c)
{
assert(alpha.GetNumElements() == 1);
if (!(alpha.GetNumElements() == 1))
InvalidArgument("AddScaledDifference: alpha must be a 1X1 matrix.");
if (a.GetComputeDeviceId() != c.GetComputeDeviceId())
{
InvalidArgument("All matrices must be on the same GPU");
}
else
{
a.PrepareDevice();
assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols());
if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()))
{
InvalidArgument("AssignScaledDifference: a, b must have same dimension.");
}
if (a.IsEmpty())
LogicError("AssignScaledDifference: Input matrix a is empty.");
c.RequireSize(a.GetNumRows(), a.GetNumCols());
CUDA_LONG n = (CUDA_LONG) a.GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * n / GridDim::maxThreadsPerBlock);
SyncGuard syncGuard;
_assignScaledDifference<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(alpha.Data(), a.Data(), b.Data(), c.Data(), n);
}
}
//c[ci,cj] += a[ai,aj]
template <class ElemType>
void GPUMatrix<ElemType>::AddElementToElement(ElemType beta, const GPUMatrix<ElemType>& a, const size_t ai, const size_t aj, GPUMatrix<ElemType>& c, const size_t ci, const size_t cj)
{
if (ai >= a.GetNumRows() || aj >= a.GetNumCols() ||
ci >= c.GetNumRows() || cj >= c.GetNumCols())
InvalidArgument("AddElementToElement: Index out of range.");
a.PrepareDevice();
SyncGuard syncGuard;
_addElementToElement<ElemType><<<1, 1, 0, t_stream>>>(beta, a.Data(), (CUDA_LONG) a.LocateElement(ai, aj), c.Data(), (CUDA_LONG) c.LocateElement(ci, cj));
}
template <class ElemType>
/*static*/ void GPUMatrix<ElemType>::Scale(ElemType alpha, GPUMatrix<ElemType>& a)
{
if (alpha == 0) // if 0 then do not access the value, so that we can use this to multiply uninitialized matrices with beta=0
{
CUDA_CALL(cudaMemset(a.Data(), 0, a.m_numRows * a.m_numCols * sizeof(ElemType)));
return;
}
cublasHandle_t cuHandle = GetCublasHandle(a.GetComputeDeviceId());
// TODO: Overload the call to cublas_axpy to remove these ugly if/else statements.
if (sizeof(ElemType) == sizeof(float))
{
float alph = (float) alpha;
CUBLAS_CALL(cublasSscal(cuHandle, int(a.m_numRows * a.m_numCols), &alph, (float*) a.Data(), 1));
}
else if (sizeof(ElemType) == sizeof(double))
{
double alph = alpha;
CUBLAS_CALL(cublasDscal(cuHandle, int(a.m_numRows * a.m_numCols), &alph, (double*) a.Data(), 1));
}
else
{
RuntimeError("Unsupported template argument in GPUMatrix");
}
}
template <class ElemType>
/*static*/ void GPUMatrix<ElemType>::Scale(GPUMatrix<ElemType>& alpha, GPUMatrix<ElemType>& a)
{
if (alpha.GetNumElements() != 1)
{
RuntimeError("Matrix alpha must be 1x1");
}
cublasHandle_t cuHandle = GetCublasHandle(a.GetComputeDeviceId());
cublasSetPointerMode(cuHandle, CUBLAS_POINTER_MODE_DEVICE);
if (sizeof(ElemType) == sizeof(float))
{
CUBLAS_CALL(cublasSscal(cuHandle, int(a.m_numRows * a.m_numCols), (float*) alpha.Data(), (float*) a.Data(), 1));
}
else if (sizeof(ElemType) == sizeof(double))
{
CUBLAS_CALL(cublasDscal(cuHandle, int(a.m_numRows * a.m_numCols), (double*) alpha.Data(), (double*) a.Data(), 1));
}
else
{
cublasSetPointerMode(cuHandle, CUBLAS_POINTER_MODE_HOST);
RuntimeError("Unsupported template argument in GPUMatrix");
}
cublasSetPointerMode(cuHandle, CUBLAS_POINTER_MODE_HOST);
}
template <class ElemType> // c = alpha * a
/*static*/ void GPUMatrix<ElemType>::Scale(ElemType alpha, const GPUMatrix<ElemType>& a, GPUMatrix<ElemType>& c)
{
c = a;
Scale(alpha, c);
}
template <class ElemType>
void GPUMatrix<ElemType>::InnerProduct(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, GPUMatrix<ElemType>& c, const bool isColWise)
{
if (a.GetComputeDeviceId() != b.GetComputeDeviceId() || b.GetComputeDeviceId() != c.GetComputeDeviceId()) // different GPUs
InvalidArgument("All matrices must be on the same GPU");
if (a.IsEmpty() || b.IsEmpty())
LogicError("Scale: one of the input matrices is empty.");
const int m = (int) a.GetNumRows();
const int n = (int) a.GetNumCols();
const int k = (int) b.GetNumRows();
const int l = (int) b.GetNumCols();
assert(m > 0 && n > 0 && k > 0 && l > 0); // converting from size_t to int may cause overflow
assert(m == k && n == l); // converting from size_t to int may cause overflow
if (m != k || n != l)
InvalidArgument("Matrices a and b should have same dimension.");
if (isColWise)
c.RequireSize(1, n);
else
c.RequireSize(m, 1);
if ((isColWise && m == 1) || !isColWise && n == 1) // in this case it's equivalent to element-wise product
{
c.AssignElementProductOf(a, b);
}
else
{
c.PrepareDevice();
int blocksPerGrid = 0;
if (isColWise) // col-wise
{
c.RequireSize(1, n);
blocksPerGrid = (int) ceil(1.0 * n / GridDim::maxThreadsPerBlock);
}
else
{
c.RequireSize(m, 1);
blocksPerGrid = (int) ceil(1.0 * m / GridDim::maxThreadsPerBlock);
}
SyncGuard syncGuard;
_innerProduct<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(c.Data(), a.Data(), b.Data(), m, n, isColWise);
}
}
template <class ElemType>
ElemType GPUMatrix<ElemType>::InnerProductOfMatrices(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b)
{
if (a.IsEmpty() || b.IsEmpty())
LogicError("InnerProductOfMatrices: one of the input matrices is empty.");
const int m = (int) a.GetNumRows();
const int n = (int) a.GetNumCols();
const int k = (int) b.GetNumRows();
const int l = (int) b.GetNumCols();
assert(m > 0 && n > 0 && k > 0 && l > 0); // converting from size_t to int may cause overflow
assert(m == k && n == l); // converting from size_t to int may cause overflow
if (m != k || n != l)
InvalidArgument("InnerProductOfMatrices: Matrices a and b should have same dimension.");
cublasHandle_t cuHandle = GetCublasHandle(a.GetComputeDeviceId());
if (sizeof(ElemType) == sizeof(double))
{
double tmp = 0;
CUBLAS_CALL(cublasDdot(cuHandle, m * n, reinterpret_cast<double*>(a.Data()), 1, reinterpret_cast<double*>(b.Data()), 1, &tmp));
return ElemType(tmp);
// return (ElemType)ddot((int)a.GetNumElements(), reinterpret_cast <double*>(a.Data()), 1, reinterpret_cast <double*>(b.Data()), 1);
}
else
{
float tmp = 0;
CUBLAS_CALL(cublasSdot(cuHandle, m * n, reinterpret_cast<float*>(a.Data()), 1, reinterpret_cast<float*>(b.Data()), 1, &tmp));
return tmp;
// return (ElemType)sdot((int)a.GetNumElements(), reinterpret_cast <float*>(a.Data()), 1, reinterpret_cast <float*>(b.Data()), 1);
}
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignInnerProductOfMatrices(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b)
{
if (a.IsEmpty() || b.IsEmpty())
LogicError("InnerProductOfMatrices: one of the input matrices is empty.");
RequireSize(1, 1);
const int m = (int) a.GetNumRows();
const int n = (int) a.GetNumCols();
const int k = (int) b.GetNumRows();
const int l = (int) b.GetNumCols();
assert(m > 0 && n > 0 && k > 0 && l > 0); // converting from size_t to int may cause overflow
assert(m == k && n == l); // converting from size_t to int may cause overflow
if (m != k || n != l)
InvalidArgument("InnerProductOfMatrices: Matrices a and b should have same dimension.");
cublasHandle_t cuHandle = GetCublasHandle(a.GetComputeDeviceId());
cublasSetPointerMode(cuHandle, CUBLAS_POINTER_MODE_DEVICE);
if (sizeof(ElemType) == sizeof(double))
{
CUBLAS_CALL(cublasDdot(cuHandle, m * n, reinterpret_cast<double*>(a.Data()), 1, reinterpret_cast<double*>(b.Data()), 1, reinterpret_cast<double*>(Data())));
}
else
{
CUBLAS_CALL(cublasSdot(cuHandle, m * n, reinterpret_cast<float*>(a.Data()), 1, reinterpret_cast<float*>(b.Data()), 1, reinterpret_cast<float*>(Data())));
}
cublasSetPointerMode(cuHandle, CUBLAS_POINTER_MODE_HOST);
return *this;
}
template <class ElemType>
void GPUMatrix<ElemType>::ElementWisePower(ElemType alpha, const GPUMatrix<ElemType>& a, GPUMatrix<ElemType>& c)
{
if (a.GetComputeDeviceId() != c.GetComputeDeviceId())
{
InvalidArgument("All matrices must be on the same GPU");
}
else
{
if (a.IsEmpty())
LogicError("ElementWisePower: The input matrix a is empty.");
c.RequireSize(a.GetNumRows(), a.GetNumCols());
a.PrepareDevice();
SyncGuard syncGuard;
CUDA_LONG N = (CUDA_LONG) a.GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
_elementWisePowerOnCuda<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(alpha, a.Data(), c.Data(), N);
}
}
template <class ElemType>
bool GPUMatrix<ElemType>::AreEqual(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, const ElemType threshold /*= 1e-8*/)
{
if (a.IsEmpty() || b.IsEmpty())
LogicError("AreEqual: one of the input matrices is empty.");
if (a.GetNumRows() != b.GetNumRows() || a.GetNumCols() != b.GetNumCols())
return false;
bool bResult = false;
long* res = new long[1];
res[0] = 1;
long* d_res = TracingGPUMemoryAllocator::Allocate<long>(a.GetComputeDeviceId(), 1);
CUDA_CALL(cudaMemcpy(d_res, res, sizeof(long) * 1, cudaMemcpyHostToDevice));
CUDA_LONG N = (CUDA_LONG) a.GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
_areEqual<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(a.Data(), b.Data(), N, threshold, d_res);
CUDA_CALL(cudaMemcpy(res, d_res, sizeof(long) * 1, cudaMemcpyDeviceToHost));
TracingGPUMemoryAllocator::Free<long>(a.GetComputeDeviceId(), d_res);
if (res[0] != 0)
bResult = true;
delete[] res;
return bResult;
}
// see Matrix<ElemType>::TensorShuffleScaleAndAdd() for comments
template <class ElemType>
void GPUMatrix<ElemType>::TensorShuffleScaleAndAdd(ElemType keepWeight, const GPUMatrix<ElemType>& a, size_t D, size_t S, size_t M, size_t K, size_t T, ElemType scaleFactor, const GPUMatrix<ElemType>& b, GPUMatrix<ElemType>& c)
{
CUDA_LONG N = (CUDA_LONG) c.GetNumElements();
assert(N == (CUDA_LONG) a.GetNumElements() && N == (CUDA_LONG) b.GetNumElements());
assert(a.GetComputeDeviceId() == c.GetComputeDeviceId() && b.GetComputeDeviceId() == c.GetComputeDeviceId());
a.PrepareDevice();
SyncGuard syncGuard;
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
_tensorShuffleScaleAndAdd<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(keepWeight, a.Data(), D, S, M, K, T, scaleFactor, b.Data(), c.Data());
}
template <class ElemType>
bool GPUMatrix<ElemType>::HasElement(const GPUMatrix<ElemType>& a, const ElemType v)
{
if (a.IsEmpty())
LogicError("HasElement: the input matrix is empty.");
bool bResult = false;
ElemType* res = new ElemType[2];
res[0] = v;
res[1] = 0;
ElemType* d_res = TracingGPUMemoryAllocator::Allocate<ElemType>(a.GetComputeDeviceId(), 2);
CUDA_CALL(cudaMemcpy(d_res, res, sizeof(ElemType) * 2, cudaMemcpyHostToDevice));
CUDA_LONG N = (CUDA_LONG) a.GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
_hasElement<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(a.Data(), N, d_res);
CUDA_CALL(cudaMemcpy(res, d_res, sizeof(ElemType) * 2, cudaMemcpyDeviceToHost));
TracingGPUMemoryAllocator::Free<ElemType>(a.GetComputeDeviceId(), d_res);
if (res[1] != 0)
bResult = true;
else
bResult = false;
delete[] res;
return bResult;
}
template <class ElemType>
void GPUMatrix<ElemType>::CreateCurandObject(unsigned long seed, const char* caller)
{
assert(caller != nullptr);
if (s_curandGenerator == NULL)
{
unsigned long long cudaSeed = (seed == USE_TIME_BASED_SEED) ? time(NULL) : seed;
if (GetMathLibTraceLevel() > 0)
{
fprintf(stderr, "%s (GPU): creating curand object with seed %llu, sizeof(ElemType)==%lu\n",
caller, cudaSeed, (unsigned long)sizeof(ElemType));
}
s_curandGenerator = new curandGenerator_t;
// Create pseudo-random number generator
CURAND_CALL(curandCreateGenerator(&(((curandGenerator_t*) s_curandGenerator)[0]), CURAND_RNG_PSEUDO_XORWOW));
CURAND_CALL(curandSetPseudoRandomGeneratorSeed(((curandGenerator_t*) s_curandGenerator)[0], cudaSeed));
CURAND_CALL(curandSetGeneratorOrdering(((curandGenerator_t*) s_curandGenerator)[0], CURAND_ORDERING_PSEUDO_SEEDED));
}
}
template <class ElemType>
void GPUMatrix<ElemType>::ResetCurandObject(unsigned long seed, const char* caller)
{
assert(caller != nullptr);
if (s_curandGenerator && (seed != USE_TIME_BASED_SEED))
{
// Note: this might be slow.
CURAND_CALL(curandSetPseudoRandomGeneratorSeed(((curandGenerator_t*) s_curandGenerator)[0], seed));
CURAND_CALL(curandSetGeneratorOffset(((curandGenerator_t*) s_curandGenerator)[0], 0));
}
else
{
CreateCurandObject(seed, caller);
}
}
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::Ones(const size_t rows, const size_t cols, int deviceId)
{
GPUMatrix<ElemType> c(rows, cols, deviceId); // will initialize to 0
c.SetValue(1);
return c;
}
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::Zeros(const size_t rows, const size_t cols, int deviceId)
{
GPUMatrix<ElemType> c(rows, cols, deviceId); // will initialize to 0
// c.SetValue(0);
return c;
}
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::Eye(const size_t rows, int deviceId)
{
GPUMatrix<ElemType> c(rows, rows, deviceId); // will initialize to 0
c.SetDiagonalValue(1);
return c;
}
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::RandomUniform(const size_t rows, const size_t cols, int deviceId, const ElemType low, const ElemType high, unsigned long seed)
{
GPUMatrix<ElemType> c(rows, cols, deviceId); // will initialize to 0
c.SetUniformRandomValue(low, high, seed);
return c;
}
template <class ElemType>
GPUMatrix<ElemType> GPUMatrix<ElemType>::RandomGaussian(const size_t rows, const size_t cols, int deviceId, const ElemType mean, const ElemType sigma, unsigned long seed)
{
GPUMatrix<ElemType> c(rows, cols, deviceId); // will initialize to 0
c.SetGaussianRandomValue(mean, sigma, seed);
return c;
}
template <class ElemType>
ElemType GPUMatrix<ElemType>::GetLearnRateForBlock_Helper(const GPUMatrix<ElemType>& Gradients, const GPUMatrix<ElemType>& SmoothedGradients)
{
ElemType* d_res = TracingGPUMemoryAllocator::Allocate<ElemType>(Gradients.GetComputeDeviceId(), 1);
// Compute inner product of matrices and keep it on device
const int m = (int) Gradients.GetNumRows();
const int n = (int) Gradients.GetNumCols();
const int k = (int) SmoothedGradients.GetNumRows();
const int l = (int) SmoothedGradients.GetNumCols();
assert(m > 0 && n > 0 && k > 0 && l > 0); // converting from size_t to int may cause overflow
assert(m == k && n == l); // converting from size_t to int may cause overflow
if (m != k || n != l)
InvalidArgument("InnerProductOfMatrices: Matrices a and b should have same dimension.");
if (sizeof(ElemType) == sizeof(double))
{
cublasHandle_t cuHandle = GetCublasHandle(Gradients.GetComputeDeviceId());
cublasSetPointerMode(cuHandle, CUBLAS_POINTER_MODE_DEVICE);
CUBLAS_CALL(cublasDdot(cuHandle, m * n, reinterpret_cast<double*>(Gradients.Data()), 1, reinterpret_cast<double*>(SmoothedGradients.Data()), 1, reinterpret_cast<double*>(d_res)));
cublasSetPointerMode(cuHandle, CUBLAS_POINTER_MODE_HOST);
}
else
{
cublasHandle_t cuHandle = GetCublasHandle(Gradients.GetComputeDeviceId());
cublasSetPointerMode(cuHandle, CUBLAS_POINTER_MODE_DEVICE);
CUBLAS_CALL(cublasSdot(cuHandle, m * n, reinterpret_cast<float*>(Gradients.Data()), 1, reinterpret_cast<float*>(SmoothedGradients.Data()), 1, reinterpret_cast<float*>(d_res)));
cublasSetPointerMode(cuHandle, CUBLAS_POINTER_MODE_HOST);
}
// d_res[0] should now contain inner product of matrices
// Compute squared Frobenius norms (squared sums of elements)
// note: kernel has hard-coded dimension of 512
_lrHelper512Threads<ElemType> << <1, 512, 0, t_stream >> >(Gradients.Data(), SmoothedGradients.Data(), (CUDA_LONG)Gradients.GetNumElements(), d_res);
ElemType res;
CUDA_CALL(cudaMemcpy(&res, d_res, sizeof(ElemType), cudaMemcpyDeviceToHost));
TracingGPUMemoryAllocator::Free<ElemType>(Gradients.GetComputeDeviceId(), d_res);
return res;
}
// The inputs are two row vectors [a1 a2 a3 a4] [b1 b2 b3 b4]
// The outputs are one matrix of size (nt+1)*4
// The first row is just element multiplication
// The rest rows will be with shift
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignElementProductOfWithShiftNeg(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, const size_t shift, const size_t nt)
{
if (a.IsEmpty() || b.IsEmpty())
LogicError("AssignElementProductOf: Matrix is empty.");
assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols());
if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()))
InvalidArgument("The input matrix dimensions do not match.");
if (!(a.GetNumRows() == 1))
InvalidArgument("The input matrix must be a row vector.");
RequireSize(nt + 1, a.GetNumCols());
int BS = a.GetNumCols();
// the output matrix is of size (nt+1, BS)
dim3 thread_tail(DEFAULT_THREAD_PER_DIM, DEFAULT_THREAD_PER_DIM);
dim3 block_tail((nt + 1 + DEFAULT_THREAD_PER_DIM - 1) / DEFAULT_THREAD_PER_DIM, (BS + DEFAULT_THREAD_PER_DIM - 1) / DEFAULT_THREAD_PER_DIM);
a.PrepareDevice();
SyncGuard syncGuard;
_assignElementProductOfWithShiftNeg<ElemType><<<block_tail, thread_tail, 0, t_stream>>>(Data(), a.Data(), b.Data(), shift, nt + 1, BS);
// _assignElementProductOf<ElemType> << <block_tail, thread_tail, 0, t_stream >> >(Data(), a.Data(), b.Data(), nt);
return *this;
}
template <class ElemType>
void GPUMatrix<ElemType>::InnerProductWithShiftNeg(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, GPUMatrix<ElemType>& c, const size_t shift, const size_t nt)
{
if (a.GetComputeDeviceId() != b.GetComputeDeviceId() || b.GetComputeDeviceId() != c.GetComputeDeviceId()) // different GPUs
InvalidArgument("All matrices must be on the same GPU");
if (a.IsEmpty() || b.IsEmpty())
LogicError("Scale: one of the input matrices is empty.");
const int m = (int) a.GetNumRows();
const int n = (int) a.GetNumCols();
const int k = (int) b.GetNumRows();
const int l = (int) b.GetNumCols();
assert(m > 0 && n > 0 && k > 0 && l > 0); // converting from size_t to int may cause overflow
assert(m == k && n == l); // converting from size_t to int may cause overflow
if (m != k || n != l)
InvalidArgument("Matrices a and b should have same dimension.");
c.RequireSize(nt + 1, n);
if (true)
{
c.PrepareDevice();
dim3 thread_tail(DEFAULT_THREAD_PER_DIM, DEFAULT_THREAD_PER_DIM);
dim3 block_tail((nt + 1 + DEFAULT_THREAD_PER_DIM - 1) / DEFAULT_THREAD_PER_DIM, (n + DEFAULT_THREAD_PER_DIM - 1) / DEFAULT_THREAD_PER_DIM);
SyncGuard syncGuard;
_innerProductWithShiftNeg<ElemType><<<block_tail, thread_tail, 0, t_stream>>>(c.Data(), a.Data(), b.Data(), m, n, shift, nt + 1);
}
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::GetARowByIndex(const GPUMatrix<ElemType>& a, const size_t m)
{
if (a.IsEmpty())
LogicError("GetARowByIndex: Matrix is empty.");
RequireSize(1, a.GetNumCols());
int n = a.GetNumRows();
int P = a.GetNumCols();
if (m >= n)
LogicError("GetARowByIndex: m is out of range.");
int blocksPerGrid = (int) ceil(((double) P) / GridDim::maxThreadsPerBlock);
a.PrepareDevice();
SyncGuard syncGuard;
_getARowByIndex<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), n, P, m);
// _assignElementProductOf<ElemType> << <block_tail, thread_tail, 0, t_stream >> >(Data(), a.Data(), b.Data(), nt);
return *this;
}
template <class ElemType>
void GPUMatrix<ElemType>::ConductRowElementMultiplyWithShift(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, GPUMatrix<ElemType>& c, const size_t shift, const bool isafixed)
{
if (a.GetComputeDeviceId() != b.GetComputeDeviceId() || b.GetComputeDeviceId() != c.GetComputeDeviceId()) // different GPUs
InvalidArgument("All matrices must be on the same GPU");
if (a.IsEmpty() || b.IsEmpty())
LogicError("Scale: one of the input matrices is empty.");
const int m = (int) a.GetNumRows();
const int n = (int) a.GetNumCols();
const int O = (int) b.GetNumRows();
const int P = (int) b.GetNumCols();
assert(m > 0 && n > 0 && O > 0 && P > 0); // converting from size_t to int may cause overflow
if (m != 1 || n != P)
InvalidArgument("Matrices a and b should have same dimension.");
c.RequireSize(O, P);
if (true)
{
c.PrepareDevice();
dim3 thread_tail(DEFAULT_THREAD_PER_DIM, DEFAULT_THREAD_PER_DIM);
dim3 block_tail((O + DEFAULT_THREAD_PER_DIM - 1) / DEFAULT_THREAD_PER_DIM, (P + DEFAULT_THREAD_PER_DIM - 1) / DEFAULT_THREAD_PER_DIM);
SyncGuard syncGuard;
_conductRowElementMultiplyWithShift<ElemType><<<block_tail, thread_tail, 0, t_stream>>>(c.Data(), a.Data(), b.Data(), O, P, shift, isafixed);
}
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignElementProductOfWithShift(const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, const size_t shift)
{
if (a.IsEmpty() || b.IsEmpty())
LogicError("AssignElementProductOfWithShift: Matrix is empty.");
assert(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols());
if (!(a.GetNumRows() == b.GetNumRows() && a.GetNumCols() == b.GetNumCols()))
InvalidArgument("The input matrix dimensions do not match.");
// int O = a.GetNumRows();
int P = a.GetNumCols();
RequireSize(1, P);
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(((double) N) / GridDim::maxThreadsPerBlock);
a.PrepareDevice();
SyncGuard syncGuard;
_assignElementProductOfWithShift<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), a.Data(), b.Data(), shift, N);
return *this;
}
//sequence training
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::DropFrame(const GPUMatrix<ElemType>& label, const GPUMatrix<ElemType>& gamma, const ElemType& threshhold)
{
if (IsEmpty())
LogicError("DropFrame: Matrix is empty.");
PrepareDevice();
long N = (long) GetNumCols(); // one kernel per column
int blocksPerGrid = (int) ceil(N * 1.0 / GridDim::maxThreadsPerBlock);
SyncGuard syncGuard;
_DropFrame<<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(Data(), label.Data(), gamma.Data(), threshhold, (long) m_numCols, (long) m_numRows);
return *this;
}
template <class ElemType>
GPUMatrix<ElemType>& GPUMatrix<ElemType>::AssignSequenceError(const ElemType hsmoothingWeight, const GPUMatrix<ElemType>& label,
const GPUMatrix<ElemType>& dnnoutput, const GPUMatrix<ElemType>& gamma, ElemType alpha)
{
if (IsEmpty())
LogicError("AssignSequenceError: Matrix is empty.");
PrepareDevice();
SyncGuard syncGuard;
long N = (LONG64) label.GetNumElements();
int blocksPerGrid = (int) ceil(1.0 * N / GridDim::maxThreadsPerBlock);
_AssignSequenceError<<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream>>>(hsmoothingWeight, Data(), label.Data(), dnnoutput.Data(), gamma.Data(), alpha, N);
return *this;
}
#pragma endregion Static BLAS Functions
/// f = logadd(f, vec) to get the logadd sum of vector elments
template <class ElemType>
ElemType GPUMatrix<ElemType>::LogSumOfElements() const
{
if (IsEmpty())
LogicError("SumOfElements: Matrix is empty");
ElemType* d_sum = TracingGPUMemoryAllocator::Allocate<ElemType>(GetComputeDeviceId(), 1);
ElemType h_sum;
CUDA_LONG N = (CUDA_LONG) GetNumElements();
int blocksPerGrid = (int) ceil(((double) N) / GridDim::maxThreadsPerBlock);
_reductionLogAddSum<ElemType><<<blocksPerGrid, GridDim::maxThreadsPerBlock>>>(Data(),
d_sum, 1, N);
CUDA_CALL(cudaMemcpy(&h_sum, d_sum, sizeof(ElemType), cudaMemcpyDeviceToHost));
TracingGPUMemoryAllocator::Free<ElemType>(GetComputeDeviceId(), d_sum);
return h_sum;
}
template <class ElemType>
void GPUMatrix<ElemType>::RCRFBackwardCompute(
const GPUMatrix<ElemType>& alpha, GPUMatrix<ElemType>& beta,
const GPUMatrix<ElemType>& /*lbls*/,
const GPUMatrix<ElemType>& pos_scores, const GPUMatrix<ElemType>& pair_scores, const int shift)
{
if (alpha.IsEmpty() || pos_scores.IsEmpty() || pair_scores.IsEmpty())
LogicError("RCRFBackwardCompute: one of the input matrices is empty.");
if (alpha.GetNumRows() != pos_scores.GetNumRows() || alpha.GetNumCols() != pos_scores.GetNumCols())
LogicError("RCRFBackwardCompute: matrix dimensions mismatched.");
size_t iNumLab = alpha.GetNumRows();
size_t iNumPos = alpha.GetNumCols();
alpha.PrepareDevice();
beta.RequireSize(iNumLab, iNumPos);
ElemType* d_zeta = TracingGPUMemoryAllocator::Allocate<ElemType>(alpha.GetComputeDeviceId(), iNumLab);
CUDA_LONG N = iNumLab;
// TODO: change all three '512' to 'GridDim::maxThreadsPerBlock' (not doing this now since I cannot test it)
int blocksPerGrid = (int) ceil(1.0 * N / 512);
size_t szMemSize;
for (int t = iNumPos - 1; t >= 0; t--)
{
szMemSize = sizeof(ElemType) * iNumLab;
// This function assumes iNumLab <= 1024 and that shared memory == total (!) number of threads == iNumLab.
assert(iNumLab <= 1024);
_rcrfBackwardComputeZetaMax1024Labels<ElemType> << <blocksPerGrid, 512, szMemSize >> >(t, iNumPos, alpha.Data(), d_zeta, pair_scores.Data(), iNumLab, shift);
szMemSize = iNumLab * 3;
szMemSize *= sizeof(ElemType);
// This function assumes iNumLab <= 1024 and that shared memory == total (!) number of threads == 3 * iNumLab.
assert(iNumLab <= 1024);
_rcrfBackwardComputeMax1024Labels<ElemType> << <blocksPerGrid, 512, szMemSize >> >(t, iNumPos, alpha.Data(), beta.Data(),
d_zeta, pair_scores.Data(), iNumLab, shift);
}
/*
error = cudaGetErrorString(cudaPeekAtLastError());
printf("%s\n", error);
error = cudaGetErrorString(cudaThreadSynchronize());
printf("%s\n", error);
*/
TracingGPUMemoryAllocator::Free<ElemType>(alpha.GetComputeDeviceId(), d_zeta);
}
/**
Compute the gradient for the first order Markov transition probabilities
It uses equations derived in R. Collobert's paper "Natural language processing (almost) from scratch"
*/
template <class ElemType>
void GPUMatrix<ElemType>::RCRFTransGrdCompute(const GPUMatrix<ElemType>& lbls,
const GPUMatrix<ElemType>& alpha,
const GPUMatrix<ElemType>& beta,
const GPUMatrix<ElemType>& pair_scores,
GPUMatrix<ElemType>& grd,
const int startLbl,
const int shift)
{
assert(shift == 1);
int iNumPos = alpha.GetNumCols();
int iNumLab = alpha.GetNumRows();
ElemType* d_zeta = TracingGPUMemoryAllocator::Allocate<ElemType>(alpha.GetComputeDeviceId(), iNumLab);
CUDA_LONG N = iNumLab;
// TODO: change all three '512' to 'GridDim::maxThreadsPerBlock' (not doing this now since I cannot test it)
int blocksPerGrid = (int)ceil(1.0 * N / 512);
size_t szMemSize;
for (int t = 0; t < iNumPos; t++)
{
szMemSize = sizeof(ElemType) * iNumLab;
// This function assumes iNumLab <= 1024 and that shared memory == total (!) number of threads == iNumLab.
assert(iNumLab <= 1024);
// BUGBUG: This is launched with 512 threads per block, but allocates shared mem as if there is only one block. Likewise for all 4 of these functions.
_rcrfTransGrdComputeZetaMax1024Labels<ElemType> << <blocksPerGrid, 512, szMemSize >> >(t - 1, iNumPos, alpha.Data(), d_zeta, pair_scores.Data(), iNumLab, startLbl, shift);
szMemSize = iNumLab * 3;
szMemSize *= sizeof(ElemType);
// This function assumes iNumLab <= 1024 and that shared memory == total (!) number of threads == iNumLab.
assert(iNumLab <= 1024);
_rcrfTransGrdComputeMax1024Labels<ElemType> << <blocksPerGrid, 512, szMemSize >> >(t, startLbl, alpha.Data(), beta.Data(),
d_zeta, pair_scores.Data(), lbls.Data(), grd.Data(), iNumPos, iNumLab, shift);
}
TracingGPUMemoryAllocator::Free<ElemType>(alpha.GetComputeDeviceId(), d_zeta);
};
// -----------------------------------------------------------------------
// TensorView entry points from Matrix.cpp
// -----------------------------------------------------------------------
// helper to provide a vector of ones of at least the given number of elements
// TODO: Use this to implement ComputationNode::ConstOnes? Or do we even need that anymore?
template <class ElemType>
static shared_ptr<GPUMatrix<ElemType>> GetOnesVector(size_t N, DEVICEID_TYPE deviceId)
{
// using a dynamically allocated array so this will never get freed, avoiding free-after-DLL-unload issues.
// and using shared_ptrs since we don't want to leak more than CacheSize elements
// when using a plain array we would have to control lifetime of the object and destructor would be called for every element in the array at the end
const int CacheSize = 32;
static shared_ptr<GPUMatrix<ElemType>> * onesCache = new shared_ptr<GPUMatrix<ElemType>>[CacheSize]; // cache of objects
if (deviceId >= CacheSize){
LogicError("GetOnesVector: onesCache[] too small (%d entries), increase (you need %d) and recompile.", CacheSize, (int)deviceId + 1);
}
auto p = onesCache[deviceId];
if (!p || p->GetNumRows() < N) // must (re-)allocate
{
p = make_shared<GPUMatrix<ElemType>>(GPUMatrix<ElemType>::Ones(N, 1, deviceId));
onesCache[deviceId] = p; // this will replace the pointer thread-safely (although weird race conditions may happen where a larger entry is overwritten by a smaller one; will still run correctly)
}
return p;
}
// perform unary operation 'op' on a giving 'this', reinterpreting the matrices as tensors as specified by the dims and strides
// This binds the N-ariness to a template parameter N, and gets the data pointers out from the matrix objects.
template <class ElemType>
void GPUMatrix<ElemType>::TensorOp(ElemType beta, const GPUMatrix<ElemType>& a, ElemType alpha, ElementWiseOperator op, ElementWiseOperator reductionOp,
const array<size_t, 2>& offsets,
const SmallVector<size_t>& regularOpDims, const array<SmallVector<ptrdiff_t>, 2>& regularStrides,
const SmallVector<size_t>& reducingOpDims, const array<SmallVector<ptrdiff_t>, 2>& reducingStrides)
{
if (reductionOp != ElementWiseOperator::opSum &&
reductionOp != ElementWiseOperator::opLogSum &&
reductionOp != ElementWiseOperator::opMin &&
reductionOp != ElementWiseOperator::opMax)
InvalidArgument("TensorOp: Unary reduction operations other than opMax, opMin, opSum, and opLogSum are not implemented.");
a.PrepareDevice();
if (a.GetComputeDeviceId() != GetComputeDeviceId())
InvalidArgument("All matrices must be on the same GPU");
// special case: linear processing
// The case statement has measurable impact for unary ops (but not for binary ops it seems, due to double mem access).
// Linear gap-free unary ops happen so regularly that we will eliminate the case statement from the CUDA kernel, and instead expand all.
if (regularOpDims.size() == 1 && regularStrides[0][0] == 1 && regularStrides[1][0] == 1 && reducingOpDims.size() == 0)
{
// special case: for copy, use cudaMemcpy() instead, or cublas_axpy()
// TODO: We should observe if these actually make a speed difference, and if not, remove these special cases.
if (op == ElementWiseOperator::opCopy && beta == 0 && alpha == 1)
return CUDA_CALL(cudaMemcpy(Data()+ offsets[1], a.Data()+ offsets[0], sizeof(ElemType) * regularOpDims[0], cudaMemcpyDeviceToDevice));
else if (op == ElementWiseOperator::opCopy && beta == 1)
return CUBLAS_CALL(cublas_axpy(GetCublasHandle(GetComputeDeviceId()), (int) regularOpDims[0], &alpha, a.Data()+ offsets[0], 1, Data()+ offsets[1], 1));
else
return LaunchUnaryTensorOp<ElemType>(beta, a.Data()+ offsets[0], Data()+ offsets[1], alpha, op, regularOpDims[0]);
}
// special case: sum-reducing a matrix onto a column vector; can be done with SGEMM
// Note: A minor risk is that with this, our own reduction function will rarely be used.
// That function was tested to give the same results with 'double', and nearly the same with 'float' (different summation order matters).
else if (op == ElementWiseOperator::opCopy && // we are just adding to target without any further operation
reductionOp == ElementWiseOperator::opSum &&
#ifdef _DEBUG
sizeof(ElemType) == sizeof(float) && // in debug don't shortcut 'double' so we have some test of our own codepath
#endif
regularOpDims.size() == 1 && regularStrides[0][0] == 1 && regularStrides[1][0] == 1 && // we are processing a column
reducingOpDims.size() == 1 && reducingStrides[0][0] >= (ptrdiff_t) regularOpDims[0]) // reducing across columns and no overlap
{
assert(reducingStrides[1][0] == 0);
auto ARows = regularOpDims[0]; // vertical steps
auto ACols = reducingOpDims[0]; // horizontal steps (reduction)
auto ALd = reducingStrides[0][0]; // horizontal step width through matrix
cublasHandle_t cuHandle = GetCublasHandle(a.GetComputeDeviceId());
CUBLAS_CALL(cublas_gemm(cuHandle, CUBLAS_OP_N, CUBLAS_OP_N, (int) /*CRows=*/ARows, /*CCols=*/1, (int) ACols, &alpha,
/*A00=*/a.Data()+ offsets[0], (int) ALd,
/*B00=*/GetOnesVector<ElemType>(ACols, a.GetComputeDeviceId())->Data(), (int) /*BRows=*/ACols, &beta,
/*C00=*/Data()+ offsets[1], (int) /*CRows=*/ARows));
return;
}
// TODO: Add a special case for tensor bias reduction. cudnn is ~7% faster on Image/QuickE2E.
// regular case
else
return TensorOpN<ElemType, 2>(beta, array<ElemType*, 2>{a.Data(), Data()}, alpha, op, reductionOp, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides);
}
// perform binary operation 'op' on a and b giving 'this', reinterpreting the matrices as tensors as specified by the dims and strides
template <class ElemType>
void GPUMatrix<ElemType>::TensorOp(ElemType beta, const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, ElemType alpha, ElementWiseOperator op, ElementWiseOperator reductionOp,
const array<size_t, 3>& offsets,
const SmallVector<size_t>& regularOpDims, const array<SmallVector<ptrdiff_t>, 3>& regularStrides,
const SmallVector<size_t>& reducingOpDims, const array<SmallVector<ptrdiff_t>, 3>& reducingStrides)
{
if (reductionOp != ElementWiseOperator::opSum)
InvalidArgument("TensorOp: The only permitted binary reduction operation is opSum.");
a.PrepareDevice();
if (a.GetComputeDeviceId() != GetComputeDeviceId() || b.GetComputeDeviceId() != GetComputeDeviceId())
InvalidArgument("All matrices must be on the same GPU");
return TensorOpN<ElemType, 3>(beta, array<ElemType*, 3>{a.Data(), b.Data(), Data()}, alpha, op, reductionOp, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides);
}
// perform ternary operation 'op' on a, and c giving 'this', reinterpreting the matrices as tensors as specified by the dims and strides
template <class ElemType>
void GPUMatrix<ElemType>::TensorOp(ElemType beta, const GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b, const GPUMatrix<ElemType>& c, ElemType alpha, ElementWiseOperator op, ElementWiseOperator reductionOp,
const array<size_t, 4>& offsets,
const SmallVector<size_t>& regularOpDims, const array<SmallVector<ptrdiff_t>, 4>& regularStrides,
const SmallVector<size_t>& reducingOpDims, const array<SmallVector<ptrdiff_t>, 4>& reducingStrides)
{
if (reductionOp != ElementWiseOperator::opSum)
InvalidArgument("TensorOp: The only permitted ternary reduction operation is opSum.");
a.PrepareDevice();
if (a.GetComputeDeviceId() != GetComputeDeviceId() || b.GetComputeDeviceId() != GetComputeDeviceId() || c.GetComputeDeviceId() != GetComputeDeviceId())
InvalidArgument("All matrices must be on the same GPU");
return TensorOpN<ElemType, 4>(beta, array<ElemType*, 4>{a.Data(), b.Data(), c.Data(), Data()}, alpha, op, reductionOp, offsets, regularOpDims, regularStrides, reducingOpDims, reducingStrides);
}
// =======================================================================
// explicit instantiations business
// =======================================================================
template class GPUMatrix<float>;
template class GPUMatrix<double>;
template class DeviceBoundNumber<float>;
template class DeviceBoundNumber<double>;
template <class ElemType>
cublasHandle_t GPUMatrix<ElemType>::s_cuHandle[GPUMatrix<ElemType>::MaxGpus] = {0};
template <class ElemType>
void* GPUMatrix<ElemType>::s_curandGenerator = NULL;
// We use Matrix<char> as the backing store for QuantizedMatrix
// Let's explicitly instantiate the methods we need for that purpose
template GPUMatrix<char>::GPUMatrix(const size_t numRows, const size_t numCols, int deviceId);
template GPUMatrix<char>::GPUMatrix(const size_t numRows, const size_t numCols, int deviceId, char* pArray, const size_t matrixFlags);
template GPUMatrix<char>::GPUMatrix(const GPUMatrix<char>&);
template GPUMatrix<char>::GPUMatrix(GPUMatrix<char>&&);
template char* GPUMatrix<char>::CopyToArray() const;
template void GPUMatrix<char>::ChangeDeviceTo(int);
template void GPUMatrix<char>::Resize(size_t, size_t, bool, bool);
template void GPUMatrix<char>::RequireSize(size_t, size_t, bool, bool);
template GPUMatrix<char>::~GPUMatrix();
template GPUMatrix<char> GPUMatrix<char>::ColumnSlice(size_t startColumn, size_t numCols) const;
template GPUMatrix<char>& GPUMatrix<char>::operator=(GPUMatrix<char>&&);
template GPUMatrix<char>::GPUMatrix(int);
template void GPUMatrix<char>::SetValue(const char);
template void GPUMatrix<char>::SetValue(const size_t numRows, const size_t numCols, int deviceId, char* pArray, size_t matrixFlags, DataTransferer* transferer);
//template void GPUMatrix<char>::SetValue(CPUMatrix<char> const&);
template void GPUMatrix<char>::SetValue(GPUMatrix<char> const&);
//template void GPUMatrix<char>::SetValue(CPUSparseMatrix<char> const&);
//template void GPUMatrix<char>::SetValue(GPUSparseMatrix<char> const&);
template void GPUMatrix<char>::CopySection(size_t numRows, size_t numCols, char* dst, size_t colStride) const;
template void GPUMatrix<char>::Reshape(const size_t, const size_t);
template GPUMatrix<char>& GPUMatrix<char>::operator*=(char);
template DEVICEID_TYPE GPUMatrix<char>::PrepareDevice(DEVICEID_TYPE deviceId) const;
// Support <short>
template GPUMatrix<short>::GPUMatrix(const size_t numRows, const size_t numCols, int deviceId);
template GPUMatrix<short>::GPUMatrix(const size_t numRows, const size_t numCols, int deviceId, short* pArray, const size_t matrixFlags);
template GPUMatrix<short>::GPUMatrix(const GPUMatrix<short>&);
template GPUMatrix<short>::GPUMatrix(GPUMatrix<short>&&);
template short* GPUMatrix<short>::CopyToArray() const;
template void GPUMatrix<short>::ChangeDeviceTo(int);
template void GPUMatrix<short>::Resize(size_t, size_t, bool, bool);
template void GPUMatrix<short>::RequireSize(size_t, size_t, bool, bool);
template GPUMatrix<short>::~GPUMatrix();
template GPUMatrix<short> GPUMatrix<short>::ColumnSlice(size_t startColumn, size_t numCols) const;
template GPUMatrix<short>& GPUMatrix<short>::operator=(GPUMatrix<short>&&);
template GPUMatrix<short>::GPUMatrix(int);
template void GPUMatrix<short>::SetValue(const short);
template void GPUMatrix<short>::SetValue(const size_t numRows, const size_t numCols, int deviceId, short* pArray, size_t matrixFlags, DataTransferer* transferer);
//template void GPUMatrix<short>::SetValue(CPUMatrix<short> const&);
template void GPUMatrix<short>::SetValue(GPUMatrix<short> const&);
//template void GPUMatrix<short>::SetValue(CPUSparseMatrix<short> const&);
//template void GPUMatrix<short>::SetValue(GPUSparseMatrix<short> const&);
template void GPUMatrix<short>::CopySection(size_t numRows, size_t numCols, short* dst, size_t colStride) const;
template void GPUMatrix<short>::Reshape(const size_t, const size_t);
template GPUMatrix<short>& GPUMatrix<short>::operator*=(short);
template DEVICEID_TYPE GPUMatrix<short>::PrepareDevice(DEVICEID_TYPE deviceId) const;
template GPUMatrix<int>::GPUMatrix(const size_t, const size_t, int, int*, const size_t);
template GPUMatrix<int>::~GPUMatrix();
template int* TracingGPUMemoryAllocator::Allocate<int>(int, size_t);
template size_t* TracingGPUMemoryAllocator::Allocate<size_t>(int, size_t);
template long* TracingGPUMemoryAllocator::Allocate<long>(int, size_t);
template short* TracingGPUMemoryAllocator::Allocate<short>(int, size_t);
template char* TracingGPUMemoryAllocator::Allocate<char>(int, size_t);
template float* TracingGPUMemoryAllocator::Allocate<float>(int, size_t);
template double* TracingGPUMemoryAllocator::Allocate<double>(int, size_t);
template void TracingGPUMemoryAllocator::Free<int>(int, int*, bool);
template void TracingGPUMemoryAllocator::Free<size_t>(int, size_t*, bool);
template void TracingGPUMemoryAllocator::Free<short>(int, short*, bool);
template void TracingGPUMemoryAllocator::Free<char>(int, char*, bool);
template void TracingGPUMemoryAllocator::Free<float>(int, float*, bool);
template void TracingGPUMemoryAllocator::Free<double>(int, double*, bool);
}}}
// !!!!This is from helper_cuda.h which comes with CUDA samples!!!! Consider if it is beneficial to just include all helper_cuda.h
// TODO: This is duplicated in BestGpu.cpp
// Beginning of GPU Architecture definitions
int _ConvertSMVer2Cores(int major, int minor)
{
// Defines for GPU Architecture types (using the SM version to determine the # of cores per SM
typedef struct
{
int SM; // 0xMm (hexidecimal notation), M = SM Major version, and m = SM minor version
int Cores;
} sSMtoCores;
sSMtoCores nGpuArchCoresPerSM[] =
{
{0x10, 8}, // Tesla Generation (SM 1.0) G80 class
{0x11, 8}, // Tesla Generation (SM 1.1) G8x class
{0x12, 8}, // Tesla Generation (SM 1.2) G9x class
{0x13, 8}, // Tesla Generation (SM 1.3) GT200 class
{0x20, 32}, // Fermi Generation (SM 2.0) GF100 class
{0x21, 48}, // Fermi Generation (SM 2.1) GF10x class
{0x30, 192}, // Kepler Generation (SM 3.0) GK10x class
{0x35, 192}, // Kepler Generation (SM 3.5) GK11x class
{-1, -1}};
int index = 0;
while (nGpuArchCoresPerSM[index].SM != -1)
{
if (nGpuArchCoresPerSM[index].SM == ((major << 4) + minor))
{
return nGpuArchCoresPerSM[index].Cores;
}
index++;
}
return nGpuArchCoresPerSM[7].Cores;
};
// end of GPU Architecture definitions
//inline CUDA_LONG _GetFreeMemoryOnCUDADevice(int devId)
//{
// CUdevice cudaDevice;
// CUresult result = cuDeviceGet(&cudaDevice, devId);
// if(result!= CUDA_SUCCESS)
// {
// return 0;
// }
//
// // create cuda context
// CUcontext cudaContext;
// result = cuCtxCreate(&cudaContext, CU_CTX_SCHED_AUTO, cudaDevice);
// if(result != CUDA_SUCCESS)
// {
// return 0;
// }
//
// // get the amount of free memory on the graphics card
// size_t free;
// size_t total;
// result = cuMemGetInfo(&free, &total);
// if (result!=CUDA_SUCCESS)
// {
// return 0;
// }
// else
// return (CUDA_LONG)free;
//}
#endif // CPUONLY
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