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Xtensor.cu3
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/* NiuTrans - an open-source MT toolkit
* Copyright (C) 2017, Natural Language Processing Lab. All rights reserved.
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU General Public
* License as published by the Free Software Foundation; either
* version 2 of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* General Public License for more details.
*
* You should have received a copy of the GNU General Public
* License along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA
*/
/*
* $Id:
* XTensor; XTensor.h
* implementation of tensors used in this work. It it is the basis of XMatrix and
* XVector
*
* $Version:
* 0.1.0
*
* $Created by:
* XIAO Tong (email: [email protected]) 2017-07-31
* $Update by:
* LI Yinqiao (email: [email protected]) 2017-11-18 bug fixes
*
*/
#define WORKERSNUM 64
#include "XTensor.h"
#include "XTensor.cuh"
#include "XDevice.h"
#include "XHeap.h"
#ifdef USE_CUDA
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <cuda_fp16.h>
/************************************************************
* basic kernels
*/
/*
matrix multiplication via cuda version BLAS
*/
void CudaBLASMatrixMUL(cublasHandle_t * handle,
void * a, MATRIX_TRANS_TYPE transposedA, MATRIX_DATA_TYPE dataTypeA,
void * b, MATRIX_TRANS_TYPE transposedB, MATRIX_DATA_TYPE dataTypeB,
void * c, MATRIX_DATA_TYPE dataTypeC,
int na, int ma, int nb, int mb, int nc, int mc,
DTYPE alpha, DTYPE beta)
{
/*
matrxi-matrix multiplication
For row-major matrices (as in c/c++), the trick used here is (AB)^T = B^T * A^T
*/
if (dataTypeA == X_DOUBLE && dataTypeB == X_DOUBLE && dataTypeC == X_DOUBLE) {
double alpha2 = (double)alpha;
double beta2 = (double)beta;
if (transposedA == X_NOTRANS && transposedB == X_NOTRANS)
cublasDgemm(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, &alpha2, (const double*)b, mb, (const double*)a, ma, &beta2, (double*)c, mc);
else if (transposedA == X_TRANS && transposedB == X_NOTRANS)
cublasDgemm(*handle, CUBLAS_OP_N, CUBLAS_OP_T, mc, nc, na, &alpha2, (const double*)b, mb, (const double*)a, ma, &beta2, (double*)c, mc);
else if (transposedA == X_NOTRANS && transposedB == X_TRANS)
cublasDgemm(*handle, CUBLAS_OP_T, CUBLAS_OP_N, mc, nc, ma, &alpha2, (const double*)b, mb, (const double*)a, ma, &beta2, (double*)c, mc);
else if (transposedA == X_TRANS && transposedB == X_TRANS)
cublasDgemm(*handle, CUBLAS_OP_T, CUBLAS_OP_T, mc, nc, na, &alpha2, (const double*)b, nb, (const double*)a, ma, &beta2, (double*)c, mc);
}
else if (dataTypeA == X_FLOAT && dataTypeB == X_FLOAT && dataTypeC == X_FLOAT) {
float alpha2 = (float)alpha;
float beta2 = (float)beta;
if (transposedA == X_NOTRANS && transposedB == X_NOTRANS)
cublasSgemm(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, &alpha2, (const float*)b, mb, (const float*)a, ma, &beta2, (float*)c, mc);
else if (transposedA == X_TRANS && transposedB == X_NOTRANS)
cublasSgemm(*handle, CUBLAS_OP_N, CUBLAS_OP_T, mc, nc, na, &alpha2, (const float*)b, mb, (const float*)a, ma, &beta2, (float*)c, mc);
else if (transposedA == X_NOTRANS && transposedB == X_TRANS)
cublasSgemm(*handle, CUBLAS_OP_T, CUBLAS_OP_N, mc, nc, ma, &alpha2, (const float*)b, mb, (const float*)a, ma, &beta2, (float*)c, mc);
else if (transposedA == X_TRANS && transposedB == X_TRANS)
cublasSgemm(*handle, CUBLAS_OP_T, CUBLAS_OP_T, mc, nc, na, &alpha2, (const float*)b, mb, (const float*)a, ma, &beta2, (float*)c, mc);
}
else if (dataTypeA == X_FLOAT16 && dataTypeB == X_FLOAT16 && dataTypeC == X_FLOAT16) {
unsigned short alpha2 = FloatToFloat16(alpha);
unsigned short beta2 = FloatToFloat16(beta);
__half * alpha3 = (__half*)&alpha2;
__half * beta3 = (__half*)&beta2;
if (transposedA == X_NOTRANS && transposedB == X_NOTRANS)
cublasHgemm(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, alpha3, (const __half*)b, mb, (const __half*)a, ma, beta3, (__half*)c, mc);
else if (transposedA == X_TRANS && transposedB == X_NOTRANS)
cublasHgemm(*handle, CUBLAS_OP_N, CUBLAS_OP_T, mc, nc, na, alpha3, (const __half*)b, mb, (const __half*)a, ma, beta3, (__half*)c, mc);
else if (transposedA == X_NOTRANS && transposedB == X_TRANS)
cublasHgemm(*handle, CUBLAS_OP_T, CUBLAS_OP_N, mc, nc, ma, alpha3, (const __half*)b, mb, (const __half*)a, ma, beta3, (__half*)c, mc);
else if (transposedA == X_TRANS && transposedB == X_TRANS)
cublasHgemm(*handle, CUBLAS_OP_T, CUBLAS_OP_T, mc, nc, na, alpha3, (const __half*)b, mb, (const __half*)a, ma, beta3, (__half*)c, mc);
}
else {
ShowNiuTransErrors("Unsupported data type!");
}
}
/*
matrix multiplication via cuda version BLAS
*/
void CudaBLASMatrixMULBatched(cublasHandle_t * handle,
const void ** a, MATRIX_TRANS_TYPE transposedA, MATRIX_DATA_TYPE dataTypeA,
const void ** b, MATRIX_TRANS_TYPE transposedB, MATRIX_DATA_TYPE dataTypeB,
void ** c, MATRIX_DATA_TYPE dataTypeC,
int count, int na, int ma, int nb, int mb, int nc, int mc,
DTYPE alpha, DTYPE beta)
{
/*
matrxi-matrix multiplication
For row-major matrices (as in c/c++), the trick used here is (AB)^T = B^T * A^T
*/
if (dataTypeA == X_DOUBLE && dataTypeB == X_DOUBLE && dataTypeC == X_DOUBLE) {
double alpha2 = (double)alpha;
double beta2 = (double)beta;
if (transposedA == X_NOTRANS && transposedB == X_NOTRANS)
cublasDgemmBatched(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, &alpha2, (const double**)b, mb, (const double**)a, ma, &beta2, (double**)c, mc, count);
else if (transposedA == X_TRANS && transposedB == X_NOTRANS)
cublasDgemmBatched(*handle, CUBLAS_OP_N, CUBLAS_OP_T, mc, nc, na, &alpha2, (const double**)b, mb, (const double**)a, ma, &beta2, (double**)c, mc, count);
else if (transposedA == X_NOTRANS && transposedB == X_TRANS)
cublasDgemmBatched(*handle, CUBLAS_OP_T, CUBLAS_OP_N, mc, nc, ma, &alpha2, (const double**)b, mb, (const double**)a, ma, &beta2, (double**)c, mc, count);
else if (transposedA == X_TRANS && transposedB == X_TRANS)
cublasDgemmBatched(*handle, CUBLAS_OP_T, CUBLAS_OP_T, mc, nc, na, &alpha2, (const double**)b, nb, (const double**)a, ma, &beta2, (double**)c, mc, count);
}
else if (dataTypeA == X_FLOAT && dataTypeB == X_FLOAT && dataTypeC == X_FLOAT) {
float alpha2 = (float)alpha;
float beta2 = (float)beta;
if (transposedA == X_NOTRANS && transposedB == X_NOTRANS)
cublasSgemmBatched(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, &alpha2, (const float**)b, mb, (const float**)a, ma, &beta2, (float**)c, mc, count);
else if (transposedA == X_TRANS && transposedB == X_NOTRANS)
cublasSgemmBatched(*handle, CUBLAS_OP_N, CUBLAS_OP_T, mc, nc, na, &alpha2, (const float**)b, mb, (const float**)a, ma, &beta2, (float**)c, mc, count);
else if (transposedA == X_NOTRANS && transposedB == X_TRANS)
cublasSgemmBatched(*handle, CUBLAS_OP_T, CUBLAS_OP_N, mc, nc, ma, &alpha2, (const float**)b, mb, (const float**)a, ma, &beta2, (float**)c, mc, count);
else if (transposedA == X_TRANS && transposedB == X_TRANS)
cublasSgemmBatched(*handle, CUBLAS_OP_T, CUBLAS_OP_T, mc, nc, na, &alpha2, (const float**)b, mb, (const float**)a, ma, &beta2, (float**)c, mc, count);
}
else if (dataTypeA == X_FLOAT16 && dataTypeB == X_FLOAT16 && dataTypeC == X_FLOAT16) {
unsigned short alpha2 = FloatToFloat16(alpha);
unsigned short beta2 = FloatToFloat16(beta);
__half * alpha3 = (__half*)&alpha2;
__half * beta3 = (__half*)&beta2;
if (transposedA == X_NOTRANS && transposedB == X_NOTRANS)
cublasHgemmBatched(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, alpha3, (const __half**)b, mb, (const __half**)a, ma, beta3, (__half**)c, mc, count);
else if (transposedA == X_TRANS && transposedB == X_NOTRANS)
cublasHgemmBatched(*handle, CUBLAS_OP_N, CUBLAS_OP_T, mc, nc, na, alpha3, (const __half**)b, mb, (const __half**)a, ma, beta3, (__half**)c, mc, count);
else if (transposedA == X_NOTRANS && transposedB == X_TRANS)
cublasHgemmBatched(*handle, CUBLAS_OP_T, CUBLAS_OP_N, mc, nc, ma, alpha3, (const __half**)b, mb, (const __half**)a, ma, beta3, (__half**)c, mc, count);
else if (transposedA == X_TRANS && transposedB == X_TRANS)
cublasHgemmBatched(*handle, CUBLAS_OP_T, CUBLAS_OP_T, mc, nc, na, alpha3, (const __half**)b, mb, (const __half**)a, ma, beta3, (__half**)c, mc, count);
}
else {
ShowNiuTransErrors("Unsupported data type!");
}
}
/* matrix multiplication in batch and strided mode via cuda version BLAS */
extern "C"
void CudaBLASMatrixMULBatchedStrided(cublasHandle_t * handle,
const void * a, MATRIX_TRANS_TYPE transposedA, MATRIX_DATA_TYPE dataTypeA, long long int strideA,
const void * b, MATRIX_TRANS_TYPE transposedB, MATRIX_DATA_TYPE dataTypeB, long long int strideB,
void * c, MATRIX_DATA_TYPE dataTypeC, long long int strideC,
int count, int na, int ma, int nb, int mb, int nc, int mc,
DTYPE alpha, DTYPE beta)
{
/*
matrxi-matrix multiplication
For row-major matrices (as in c/c++), the trick used here is (AB)^T = B^T * A^T
*/
if (dataTypeA == X_DOUBLE && dataTypeB == X_DOUBLE && dataTypeC == X_DOUBLE) {
double alpha2 = (double)alpha;
double beta2 = (double)beta;
if (transposedA == X_NOTRANS && transposedB == X_NOTRANS)
cublasDgemmStridedBatched(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, &alpha2, (const double*)b, mb, strideB, (const double*)a, ma, strideA, &beta2, (double*)c, mc, strideC, count);
else if (transposedA == X_TRANS && transposedB == X_NOTRANS)
cublasDgemmStridedBatched(*handle, CUBLAS_OP_N, CUBLAS_OP_T, mc, nc, na, &alpha2, (const double*)b, mb, strideB, (const double*)a, ma, strideA, &beta2, (double*)c, mc, strideC, count);
else if (transposedA == X_NOTRANS && transposedB == X_TRANS)
cublasDgemmStridedBatched(*handle, CUBLAS_OP_T, CUBLAS_OP_N, mc, nc, ma, &alpha2, (const double*)b, mb, strideB, (const double*)a, ma, strideA, &beta2, (double*)c, mc, strideC, count);
else if (transposedA == X_TRANS && transposedB == X_TRANS)
cublasDgemmStridedBatched(*handle, CUBLAS_OP_T, CUBLAS_OP_T, mc, nc, na, &alpha2, (const double*)b, nb, strideB, (const double*)a, ma, strideA, &beta2, (double*)c, mc, strideC, count);
}
else if (dataTypeA == X_FLOAT && dataTypeB == X_FLOAT && dataTypeC == X_FLOAT) {
float alpha2 = (float)alpha;
float beta2 = (float)beta;
if (transposedA == X_NOTRANS && transposedB == X_NOTRANS)
cublasSgemmStridedBatched(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, &alpha2, (const float*)b, mb, strideB, (const float*)a, ma, strideA, &beta2, (float*)c, mc, strideC, count);
else if (transposedA == X_TRANS && transposedB == X_NOTRANS)
cublasSgemmStridedBatched(*handle, CUBLAS_OP_N, CUBLAS_OP_T, mc, nc, na, &alpha2, (const float*)b, mb, strideB, (const float*)a, ma, strideA, &beta2, (float*)c, mc, strideC, count);
else if (transposedA == X_NOTRANS && transposedB == X_TRANS)
cublasSgemmStridedBatched(*handle, CUBLAS_OP_T, CUBLAS_OP_N, mc, nc, ma, &alpha2, (const float*)b, mb, strideB, (const float*)a, ma, strideA, &beta2, (float*)c, mc, strideC, count);
else if (transposedA == X_TRANS && transposedB == X_TRANS)
cublasSgemmStridedBatched(*handle, CUBLAS_OP_T, CUBLAS_OP_T, mc, nc, na, &alpha2, (const float*)b, mb, strideB, (const float*)a, ma, strideA, &beta2, (float*)c, mc, strideC, count);
}
else if (dataTypeA == X_FLOAT16 && dataTypeB == X_FLOAT16 && dataTypeC == X_FLOAT16) {
unsigned short alpha2 = FloatToFloat16(alpha);
unsigned short beta2 = FloatToFloat16(beta);
__half * alpha3 = (__half*)&alpha2;
__half * beta3 = (__half*)&beta2;
if (transposedA == X_NOTRANS && transposedB == X_NOTRANS)
cublasHgemmStridedBatched(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, (const __half*)alpha3, (const __half*)b, mb, strideB, (const __half*)a, ma, strideA, (const __half*)beta3, (__half*)c, mc, strideC, count);
else if (transposedA == X_TRANS && transposedB == X_NOTRANS)
cublasHgemmStridedBatched(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, (const __half*)alpha3, (const __half*)b, mb, strideB, (const __half*)a, ma, strideA, (const __half*)beta3, (__half*)c, mc, strideC, count);
else if (transposedA == X_NOTRANS && transposedB == X_TRANS)
cublasHgemmStridedBatched(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, (const __half*)alpha3, (const __half*)b, mb, strideB, (const __half*)a, ma, strideA, (const __half*)beta3, (__half*)c, mc, strideC, count);
else if (transposedA == X_TRANS && transposedB == X_TRANS)
cublasHgemmStridedBatched(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, (const __half*)alpha3, (const __half*)b, mb, strideB, (const __half*)a, ma, strideA, (const __half*)beta3, (__half*)c, mc, strideC, count);
}
else {
ShowNiuTransErrors("Unsupported data type!");
}
}
/*
matrix multiplication via cuda version BLAS
*/
void CudaBLASMatrixMULList(cublasHandle_t * handle,
List * a, MATRIX_TRANS_TYPE transposedA,
List * b, MATRIX_TRANS_TYPE transposedB,
List * c,
int count, DTYPE alpha, DTYPE beta)
{
CheckNiuTransErrors((a && b && c), "Empty input lists!");
CheckNiuTransErrors((a->count == b->count && a->count == c->count), "Input lists must be of the same size!");
if (a->count == 0)
return;
bool isUniform = true;
bool isStrided = true;
MTYPEINT strideA = MAX_INT;
MTYPEINT strideB = MAX_INT;
MTYPEINT strideC = MAX_INT;
for (int i = 1; i < a->count; i++) {
XTensor * aim = (XTensor*)a->GetItem(i - 1);
XTensor * bim = (XTensor*)b->GetItem(i - 1);
XTensor * cim = (XTensor*)c->GetItem(i - 1);
XTensor * ai = (XTensor*)a->GetItem(i);
XTensor * bi = (XTensor*)b->GetItem(i);
XTensor * ci = (XTensor*)c->GetItem(i);
if (!XTensor::IsIdentical(aim, ai) ||
!XTensor::IsIdentical(bim, bi) ||
!XTensor::IsIdentical(cim, ci))
{
isUniform = false;
break;
}
if (isStrided) {
MTYPEINT gapA = MTYPEINT(ai->data) - MTYPEINT(aim->data);
MTYPEINT gapB = MTYPEINT(bi->data) - MTYPEINT(bim->data);
MTYPEINT gapC = MTYPEINT(ci->data) - MTYPEINT(cim->data);
if (strideA == MAX_INT)
strideA = gapA;
if (strideB == MAX_INT)
strideB = gapB;
if (strideC == MAX_INT)
strideC = gapC;
if (strideA != gapA || strideB != gapB || strideC != gapC)
isStrided = false;
}
}
XTensor * a0 = (XTensor*)a->GetItem(0);
XTensor * b0 = (XTensor*)b->GetItem(0);
XTensor * c0 = (XTensor*)c->GetItem(0);
if (isUniform) {
XMem * mem = a0->mem;
if (isStrided) {
CudaBLASMatrixMULBatchedStrided(handle,
a0->data, transposedA, a0->dataType, strideA / a0->unitSize,
b0->data, transposedB, b0->dataType, strideB / b0->unitSize,
c0->data, c0->dataType, strideC / c0->unitSize, a->count,
a0->dimSize[1], a0->dimSize[0],
b0->dimSize[1], b0->dimSize[0],
c0->dimSize[1], c0->dimSize[0], alpha, beta);
}
else {
DTYPE ** ap = new DTYPE*[a->count];
DTYPE ** bp = new DTYPE*[b->count];
DTYPE ** cp = new DTYPE*[c->count];
for (int i = 0; i < a->count; i++) {
XTensor * ai = (XTensor*)a->GetItem(i);
XTensor * bi = (XTensor*)b->GetItem(i);
XTensor * ci = (XTensor*)c->GetItem(i);
ap[i] = (DTYPE*)ai->data;
bp[i] = (DTYPE*)bi->data;
cp[i] = (DTYPE*)ci->data;
}
mem->SetPinBuf();
DTYPE ** apGPU = (DTYPE**)mem->AllocBuf(mem->devID, sizeof(DTYPE*) * a->count, 256);
DTYPE ** bpGPU = (DTYPE**)mem->AllocBuf(mem->devID, sizeof(DTYPE*) * a->count, 256);
DTYPE ** cpGPU = (DTYPE**)mem->AllocBuf(mem->devID, sizeof(DTYPE*) * a->count, 256);
cudaMemcpy(apGPU, ap, sizeof(DTYPE*) * a->count, cudaMemcpyHostToDevice);
cudaMemcpy(bpGPU, bp, sizeof(DTYPE*) * b->count, cudaMemcpyHostToDevice);
cudaMemcpy(cpGPU, cp, sizeof(DTYPE*) * c->count, cudaMemcpyHostToDevice);
CudaBLASMatrixMULBatched(handle,
(const void**)apGPU, transposedA, a0->dataType,
(const void**)bpGPU, transposedB, b0->dataType,
(void**)cpGPU, c0->dataType, a->count,
a0->dimSize[1], a0->dimSize[0],
b0->dimSize[1], b0->dimSize[0],
c0->dimSize[1], c0->dimSize[0], alpha, beta);
delete[] ap;
delete[] bp;
delete[] cp;
mem->BackToPinBuf();
}
}
else {
for (int i = 0; i < a->count; i++) {
XTensor * ai = (XTensor*)a->GetItem(i);
XTensor * bi = (XTensor*)b->GetItem(i);
XTensor * ci = (XTensor*)c->GetItem(i);
CudaBLASMatrixMUL(handle,
ai->data, transposedA, ai->dataType,
bi->data, transposedB, bi->dataType,
ci->data, ci->dataType,
ai->dimSize[1], ai->dimSize[0],
bi->dimSize[1], bi->dimSize[0],
ci->dimSize[1], ci->dimSize[0], alpha, beta);
}
}
}
__global__
void KernelFloatToFloat16(float * s, __half * t, int size)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < size) {
t[i] = __float2half(s[i]);
}
}
__global__
void KernelFloat16ToFloat(__half * s, float * t, int size)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < size) {
t[i] = __half2float(s[i]);
}
}
/*
data conversion (cuda code)
>> devID - device id
>> s - source data array
>> typeS - source data type
>> t - target data array
>> typeT - target data type
>> size - number of the items in s (and t)
*/
void CudaConvertDataType(int devID, void * s, MATRIX_DATA_TYPE typeS, void * t, MATRIX_DATA_TYPE typeT, int size)
{
CheckNiuTransErrors((devID >= 0), "This code must be run on GPUs!");
if (typeS == typeT)
return;
int gridSize[3];
int blockSize[3];
GDevs->GetGridAndBlockSize(devID, size, gridSize, blockSize);
dim3 blocks(gridSize[0]);
dim3 threads(blockSize[0]);
if (typeS == X_FLOAT && typeT == X_FLOAT16)
KernelFloatToFloat16 << <blocks, threads >> >((float*)s, (__half*)t, size);
else if (typeS == X_FLOAT16 && typeT == X_FLOAT)
KernelFloat16ToFloat << <blocks, threads >> >((__half*)s, (float*)t, size);
else {
ShowNiuTransErrors("Unsupported data types for conversion!");
}
}
/************************************************************
* cuda means
*/
/*
copy a range of elements from a source vector to a target vector
>> s - source matrix
>> t - target matrix
>> stream - the stream for creating the job pipeline
<< return - succeed or not
*/
bool CudaCopyValues(XTensor * s, XTensor * t, XStream * stream)
{
if (s == NULL || t == NULL)
return false;
CheckNiuTransErrors(s->dataType == t->dataType, "Unmatched data type!");
CheckNiuTransErrors((s->unitSize == t->unitSize), "Incompatible vectors in value copy.");
CheckNiuTransErrors((s->denseRatio <= s->denseRatio), "Incompatible vectors in value copy.");
/* dense -> dense */
if (!s->isSparse && !t->isSparse) {
if (stream == NULL)
XMemCopy(t->data, t->devID, s->data, s->devID, s->unitSize * s->unitNum);
else
XMemCopyAsync(t->data, t->devID, s->data, s->devID, s->unitSize * s->unitNum, stream->stream, stream->devID);
}
/* dense -> sparse */
else if (!s->isSparse && t->isSparse &&
s->dataType == DTYPE_IN_MATRIX &&
t->dataType == DTYPE_IN_MATRIX)
{
ShowNiuTransErrors("TODO!");
}
/* sparse -> dense */
else if (s->isSparse && !t->isSparse &&
s->dataType == DTYPE_IN_MATRIX &&
t->dataType == DTYPE_IN_MATRIX)
{
ShowNiuTransErrors("TODO!");
}
/* sparse -> sparse */
else if (s->isSparse && t->isSparse &&
s->dataType == DTYPE_IN_MATRIX &&
t->dataType == DTYPE_IN_MATRIX)
{
int num = s->GetNonzeroSize();
int size = sizeof(int) + num * (s->unitSize + sizeof(int));
if (stream == NULL)
XMemCopy(t->data, t->devID, s->data, s->devID, size);
else
XMemCopyAsync(t->data, t->devID, s->data, s->devID, size, stream->stream, stream->devID);
t->unitNumNonZero = num;
}
else {
ShowNiuTransErrors("TODO!");
}
return true;
}
/*
flush a list of XTensor to GPU memory
>> mList - list of the tensors
>> GPUMem - memory pool for the GPU
*/
void CudaCPUToGPUFlush(List * mList, XMem * GPUMem)
{
if (mList == NULL || mList->count == 0)
return;
#ifdef USE_CUDA
int size = 0, p = 0;
int reqiredSize = 0;
/* compute the requried memory size */
for (int i = 0; i < mList->count; i++) {
XTensor * m = (XTensor*)mList->GetItem(i);
CheckNiuTransErrors((m->devID < 0), "Cannot do gpu-flush on matrices that are already on GPUs.");
if (m->isSparse)
reqiredSize = sizeof(int) + (sizeof(int) + m->unitSize) * m->unitNumNonZero;
else
reqiredSize = m->unitSize * m->unitNum;
//reqiredSize = (int)GPUMem->GetPitch(GPUMem->devID, (MTYPE)GPUMem->GetAddress() + size, reqiredSize);
size += reqiredSize;
}
char * data = new char[size];
char * GPUData = (char*)GPUMem->Alloc(GPUMem->devID, size);
int pSize = 0;
/* place the data in a memory block */
for (int i = 0; i < mList->count; i++) {
XTensor * m = (XTensor*)mList->GetItem(i);
if (m->isSparse)
pSize = sizeof(int) + (sizeof(int) + m->unitSize) * m->unitNumNonZero;
else
pSize = m->unitSize * m->unitNum;
//reqiredSize = (int)GPUMem->GetPitch(GPUMem->devID, (MTYPE)GPUMem->GetAddress() + p, pSize);
reqiredSize = pSize;
memcpy(data + p, m->data, pSize);
if (m->dataHost != NULL)
delete[](char*)m->dataHost;
m->dataHost = NULL;
m->data = GPUData + p;
m->devID = GPUMem->devID;
m->mem = GPUMem;
p += reqiredSize;
}
/* copy from CPU memory to GPU memory */
cudaMemcpy(GPUData, data, size, cudaMemcpyHostToDevice);
delete[] data;
#endif
}
/* copy the data from GPU memory to CPU memory */
void CudaGPUToCPUFlush(XTensor * tensor)
{
CheckNiuTransErrors((sizeof(DTYPE) == tensor->unitSize), "Unsupported data type.");
if (tensor->dataHost != NULL)
delete[](char*)tensor->dataHost;
if (tensor->isSparse) {
int num = int(tensor->unitNum * tensor->denseRatio + 1);
cudaMemcpy(&num, (DTYPE*)tensor->data, sizeof(int), cudaMemcpyDeviceToHost);
int tupleSize = sizeof(int) + sizeof(DTYPE);
int size = sizeof(int) + tupleSize*(num);
CheckNiuTransErrors((size >= 0), "Illegal data size in the sparse matrix!");
tensor->dataHost = new char[size];
cudaMemcpy(tensor->dataHost, tensor->data, size, cudaMemcpyDeviceToHost);
}
else {
tensor->dataHost = new char[tensor->unitNum * tensor->unitSize];
if (tensor->data != NULL)
cudaMemcpy(tensor->dataHost, tensor->data, tensor->unitNum * tensor->unitSize, cudaMemcpyDeviceToHost);
else
memset(tensor->dataHost, 0, tensor->unitNum * tensor->unitSize);
}
}
/*
set the cell to the ascending order along a given dimension (kernel code)
>> data - the data array
>> stride - how many items we go ove when move to the next item along the dimension
>> strideNum - size of the given dimension
>> blockNum - block number
*/
__global__
void KernelSetAscendingOrder(int * data, int stride, int strideNum, int blockNum)
{
__shared__ int iBlock[MAX_CUDA_THREAD_NUM_PER_BLOCK];
__shared__ int iOffset[MAX_CUDA_THREAD_NUM_PER_BLOCK];
/* index along the "stride" dimension */
int i = blockDim.x * blockIdx.x + threadIdx.x;
/* index along the leading dimension */
int j = blockDim.y * blockIdx.y + threadIdx.y;
if (i >= stride * blockNum || j >= strideNum)
return;
if (threadIdx.y == 0) {
iBlock[threadIdx.x] = i / stride;
iOffset[threadIdx.x] = i % stride;
}
__syncthreads();
int * d = (int*)data + (iBlock[threadIdx.x] * strideNum + j) * stride + iOffset[threadIdx.x];
*d = j;
}
/*
set the cell to the ascending order along a given dimension
>> a - the tensor
>> dim - the dimension
*/
extern "C"
void CudaSetAscendingOrder(XTensor * a, int dim)
{
CheckNiuTransErrors((a->dataType == X_INT), "TODO!");
int stride = 1;
int strideNum = a->dimSize[dim];
for (int i = 0; i < dim; i++)
stride *= a->dimSize[i];
int blockNum = 1;
for (int i = dim + 1; i < a->order; i++)
blockNum *= a->dimSize[i];
int gridSize[3];
int blockSize[3];
GDevs->GetGridAndBlockSize2D(a->devID, strideNum, stride * blockNum, MAX_INT, gridSize, blockSize);
KernelSetAscendingOrder << <dim3(gridSize[1], gridSize[0]), dim3(blockSize[1], blockSize[0]) >> >
((int*)a->data, stride, strideNum, blockNum);
}
/*
set each entry to its negtive value (CUDA Kernel)
>> d - pointer to the data array
>> size - size of the data array
*/
__global__
void KernelNegate(DTYPE * d, int size)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < size)
d[i] = -d[i];
}
/*
set each entry to its negtive value (CUDA Kernel)
This is for float16 computation
>> d - pointer to the data array
>> size - size of the data array
*/
__global__
void KernelNegate(__half * d, int size)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__)
if (i < size)
d[i] = __hsub(__float2half(0), d[i]);
#else
if (i < size)
d[i] = __float2half(-__half2float(d[i]));
#endif
}
/*
set each entry to its negtive value
>> a - the tensor
*/
extern "C"
void CudaNegate(XTensor * a)
{
CheckNiuTransErrors((a->isSparse == false), "TODO!");
int gridSize[3];
int blockSize[3];
GDevs->GetGridAndBlockSize(a->devID, a->unitNum, gridSize, blockSize);
dim3 blocks(gridSize[0]);
dim3 threads(blockSize[0]);
if (a->dataType == DTYPE_IN_MATRIX) {
KernelNegate << <blocks, threads >> >((DTYPE*)a->data, a->unitNum);
}
else if (a->dataType == X_FLOAT16) {
KernelNegate << <blocks, threads >> >((__half*)a->data, a->unitNum);
}
else {
ShowNiuTransErrors("TODO!");
}
}
/*
set all entries to its root (CUDA Kernel)
>> d - data array
>> size - size of the data array
*/
__global__
void KernelSqrtV2(DTYPE * d, int size)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < size)
d[i] = sqrt(d[i]);
}
/*
set all entries to its root (CUDA Kernel)
>> d - data array
>> size - size of the data array
*/
__global__
void KernelSqrtV2(__half * d, int size)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__)
if (i < size)
d[i] = hsqrt(d[i]);
#else
if (i < size)
d[i] = __float2half(sqrt(__half2float(d[i])));
#endif
}
/*
get power(d[i], p)
>> d - data array
>> p - power
>> size - size of the data array
*/
__global__
void KernelPower(DTYPE * d, DTYPE p, int size)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < size)
d[i] = pow(d[i], p);
}
/*
get power(d[i], p)
>> d - data array
>> p - power
>> size - size of the data array
*/
__global__
void KernelPower(__half * d, __half p, int size)
{
#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__)
//int i = blockDim.x * blockIdx.x + threadIdx.x;
//if (i < size)
// d[i] = hpow(d[i], p);
#else
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < size)
d[i] = __float2half(pow(__half2float(d[i]), __half2float(p)));
#endif
}
/* get the power of the entries */
extern "C"
void CudaPower(XTensor * a, DTYPE p)
{
int gridSize[3];
int blockSize[3];
GDevs->GetGridAndBlockSize(a->devID, a->unitNum, gridSize, blockSize);
dim3 blocks(gridSize[0]);
dim3 threads(blockSize[0]);
if (a->dataType == DTYPE_IN_MATRIX) {
if (p == (DTYPE)0.5) {
KernelSqrtV2 << <blocks, threads >> >((DTYPE*)a->data, a->unitNum);
}
else if (p != (DTYPE)1.0) {
KernelPower << <blocks, threads >> >((DTYPE*)a->data, p, a->unitNum);
}
}
else if (a->dataType == X_FLOAT16) {
if (p == (DTYPE)0.5) {
KernelSqrtV2 << <blocks, threads >> >((__half*)a->data, a->unitNum);
}
else if (p != (DTYPE)1.0) {
ShowNiuTransErrors("TODO!");
//unsigned short p2 = FloatToFloat16(p);
//__half * pp = (__half*)&p2;
//KernelPower<<<blocks, threads>>>((__half*)a->data, *pp, a->unitNum);
}
}
else {
ShowNiuTransErrors("TODO!");
}
}
/*
scale and shift all matrix entires p = p * scale + shift (CUDA Kernel)
>> d - the data array
>> size - the size of d
>> scale - how much we want to scale it
>> shift - how much we want to shift it
*/
__global__
void KernelScaleAndShift(DTYPE * d, int size, DTYPE scale, DTYPE shift)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < size)
d[i] = d[i] * scale + shift;
}
/*
scale and shift all matrix entires p = p * scale + shift (CUDA Kernel)
This is for float16 computation
>> d - the data array
>> size - the size of d
>> scale - how much we want to scale it
>> shift - how much we want to shift it
*/
__global__
void KernelScaleAndShift(__half * d, int size, __half scale, __half shift)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
#if __CUDA_ARCH__ >= 530 || !defined(__CUDA_ARCH__)
if (i < size)
d[i] = __hadd(__hmul(d[i], scale), shift);
#else
if (i < size)
d[i] = __float2half(__half2float(d[i]) * __half2float(scale) + __half2float(shift));
#endif
}
/*
scale and shift all matrix entires
p = p * scale + shift
>> a - the matrix
>> scale - the scaler factor
>> shift - the shift factor
*/
void CudaScaleAndShift(XTensor * a, DTYPE scale, DTYPE shift)
{
/* sparse matrix */
if (a->isSparse) {
// TODO
}
/* dense matrix */
else {
int gridSize[3];
int blockSize[3];
GDevs->GetGridAndBlockSize(a->devID, a->unitNum, gridSize, blockSize);
dim3 blocks(gridSize[0]);
dim3 threads(blockSize[0]);
if (a->dataType == DTYPE_IN_MATRIX) {
KernelScaleAndShift << <blocks, threads >> >((DTYPE*)a->data, a->unitNum, scale, shift);
}
else if (a->dataType == X_FLOAT16) {
unsigned short scale2 = FloatToFloat16(scale);
unsigned short shift2 = FloatToFloat16(shift);
__half * scaleft16p = (__half*)&scale2;
__half * shiftft16p = (__half*)&shift2;
KernelScaleAndShift << <blocks, threads >> >((__half*)a->data, a->unitNum, *scaleft16p, *shiftft16p);
}
else {
ShowNiuTransErrors("TODO!");
}
}
}
/*
copy a number of blocks to target positions
NOTE that this version makes more use of the 2d threads in cuda
>> source - data array (head of the blocks) to copy from
>> blockSize - size of block
>> blockNum - number of blocks
>> target - target data array
>> targetBlocks - target positions of the copy
*/
template<int miniBlockSize>
__global__
void KernelCopyBlocks(DTYPE * source, int blockSize, int blockNum, DTYPE * target, int * targetBlocks)
{
/* entry index in the block */
int i = (blockDim.x * blockIdx.x + threadIdx.x) * miniBlockSize;
/* block index */
int j = blockDim.y * blockIdx.y + threadIdx.y;
if (j >= blockNum)
return;
/* target position */
int k = targetBlocks[j];
DTYPE * s = source + blockSize * j;
DTYPE * t = target + blockSize * k;
if (i < blockSize) {
if (miniBlockSize == 4) {
t[i] = s[i];
t[i + 1] = s[i + 1];
t[i + 2] = s[i + 2];
t[i + 3] = s[i + 3];
}
else if (miniBlockSize <= 1) {
t[i] = s[i];
}
else {
printf("something wrong!");
}
}
}
/*
copy a number of blocks to target positions (cuda version)
>> source - data array (head of the blocks) to copy from
>> blockSize - size of block
>> blockNum - number of blocks
>> target - target data array
>> targetBlocks - target positions of the copy (on the device)
>> myMem - memory pool
*/
void CudaCopyBlocks(void * source, int blockSize, int blockNum, void * target, int * targetBlocks, XMem * myMem)
{
CheckNiuTransErrors((myMem != NULL), "No memory pool!");
CheckNiuTransErrors((myMem->devID >= 0), "Wrong device to run!");
CheckNiuTransErrors((blockSize % sizeof(DTYPE) == 0), "Unsupported block size!");
int cudaGrids[3];
int cudaBlocks[3];
int bSize = blockSize / sizeof(DTYPE);
if (bSize % 4 == 0) {
GDevs->GetGridAndBlockSize2D(myMem->devID, bSize / 4, blockNum, MAX_INT, cudaGrids, cudaBlocks);
KernelCopyBlocks<4> << <dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1]) >> >
((DTYPE*)source, bSize, blockNum, (DTYPE*)target, targetBlocks);
}
else {
GDevs->GetGridAndBlockSize2D(myMem->devID, bSize, blockNum, MAX_INT, cudaGrids, cudaBlocks);
KernelCopyBlocks<1> << <dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1]) >> >
((DTYPE*)source, bSize, blockNum, (DTYPE*)target, targetBlocks);
}
}
/*
copy a number of blocks from source positions to target positions
>> source - data array (head of the blocks) to copy from
>> blockSize - size of block
>> sourceBlocks - source positions of the copy
>> blockNum - number of blocks
>> target - target data array
>> targetBlocks - target positions of the copy
*/
__global__
void KernelCopyBlocksSelected(DTYPE * source, int blockSize, int * sourceBlocks, int blockNum, DTYPE * target, int * targetBlocks)
{
/* block index */
int i = blockDim.x * blockIdx.x + threadIdx.x;
/* entry index in the block */
int j = blockDim.y * blockIdx.y + threadIdx.y;
if (j >= blockNum)
return;
/* target position */
int srcIndex = sourceBlocks[j];
int tgtIndex = targetBlocks[j];
DTYPE * s = source + blockSize * srcIndex;
DTYPE * t = target + blockSize * tgtIndex;
if (i < blockSize)
t[i] = s[i];
}
/*
copy a number of blocks from source positions to target positions (cuda version)
>> source - data array (head of the blocks) to copy from
>> blockSize - size of block
>> sourceBlocks - source positions of the copy
>> blockNum - number of blocks
>> target - target data array
>> targetBlocks - target positions of the copy
>> myMem - memory pool
*/
void CudaCopyBlocksSelected(void * source, int blockSize, int * sourceBlocks, int blockNum, void * target, int * targetBlocks, XMem * myMem)
{
CheckNiuTransErrors((myMem != NULL), "No memory pool!");
CheckNiuTransErrors((myMem->devID >= 0), "Wrong device to run!");
CheckNiuTransErrors((blockSize % sizeof(DTYPE) == 0), "Unsupported block size!");
/* copy the index to the GPU memory */
int * sourceBlocksTMP = (int*)myMem->AllocBuf(myMem->devID, blockNum * sizeof(int));
int * targetBlocksTMP = (int*)myMem->AllocBuf(myMem->devID, blockNum * sizeof(int));
XMemCopy(sourceBlocksTMP, myMem->devID, sourceBlocks, -1, blockNum * sizeof(int));
XMemCopy(targetBlocksTMP, myMem->devID, targetBlocks, -1, blockNum * sizeof(int));
int cudaGrids[3];
int cudaBlocks[3];
GDevs->GetGridAndBlockSize2D(myMem->devID, blockSize / sizeof(DTYPE), blockNum, MAX_INT, cudaGrids, cudaBlocks);
KernelCopyBlocksSelected << <dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1]) >> >
((DTYPE*)source, blockSize / sizeof(DTYPE), sourceBlocksTMP, blockNum, (DTYPE*)target, targetBlocksTMP);
myMem->ReleaseBuf(myMem->devID, blockNum * sizeof(int));
myMem->ReleaseBuf(myMem->devID, blockNum * sizeof(int));
}
/*
set target data block index for the data movement in split (device code)
>> blockIndex - block index