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Tensor.LowPrecision
Commit 3ad0e638 by linye
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int8 matrix bug fixed

parent b5c4aa4e
正在显示 包含 27 行增加 和 169 行删除
source/tensor/core/arithmetic/XTensorBLAS.cu
......@@ -17,7 +17,7 @@
/*
* $Created by: XIAO Tong (email: xiaotong@mail.neu.edu.cn) 2018-04-24
* $Update by: Lin Ye (email: linye2015@outlook.com) 2019-07-06 float16/int8 added
* $Update by: Lin Ye (email: linye2015@outlook.com) 2019-07-16 float16/int8 added
*/
#include "../../XUtility.h"
......@@ -94,25 +94,23 @@ void _CudaBLASMatrixMUL(cublasHandle_t * handle,
cublasSetMathMode(*handle, CUBLAS_DEFAULT_MATH);
}
else if (dataTypeA == X_INT8 && dataTypeB == X_INT8 && dataTypeC == X_FLOAT) {
int alpha2 = (int)alpha;
int beta2 = (int)beta;
/*
CUDA requires that the dimension of two tensor( lda, ldb ) should be multiples of 4.
details in https://devtalk.nvidia.com/default/topic/999101/about-cublasgemm-int8-support/
*/
if (mb % 4 != 0 || ma % 4 != 0) {
ShowNTErrors("mb, ma( lda, ldb ) should be multiples of 4!");
return;
}
//if (mb % 4 != 0 || ma % 4 != 0) {
// ShowNTErrors("mb, ma( lda, ldb ) should be multiples of 4!");
// return;
//}
cublasSetMathMode(*handle, CUBLAS_TENSOR_OP_MATH);
if (transposedA == X_NOTRANS && transposedB == X_NOTRANS)
cublasGemmEx(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, (__int8*)&beta2, c, CUDA_R_32F, mc, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasGemmEx(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, &alpha, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, &beta, c, CUDA_R_32F, mc, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
else if (transposedA == X_TRANS && transposedB == X_NOTRANS)
cublasGemmEx(*handle, CUBLAS_OP_N, CUBLAS_OP_T, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, (__int8*)&beta2, c, CUDA_R_32F, mc, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasGemmEx(*handle, CUBLAS_OP_N, CUBLAS_OP_T, mc, nc, ma, &alpha, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, &beta, c, CUDA_R_32F, mc, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
else if (transposedA == X_NOTRANS && transposedB == X_TRANS)
cublasGemmEx(*handle, CUBLAS_OP_T, CUBLAS_OP_N, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, (__int8*)&beta2, c, CUDA_R_32F, mc, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasGemmEx(*handle, CUBLAS_OP_T, CUBLAS_OP_N, mc, nc, ma, &alpha, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, &beta, c, CUDA_R_32F, mc, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
else if (transposedA == X_TRANS && transposedB == X_TRANS)
cublasGemmEx(*handle, CUBLAS_OP_T, CUBLAS_OP_T, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, (__int8*)&beta2, c, CUDA_R_32F, mc, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasGemmEx(*handle, CUBLAS_OP_T, CUBLAS_OP_T, mc, nc, ma, &alpha, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, &beta, c, CUDA_R_32F, mc, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasSetMathMode(*handle, CUBLAS_DEFAULT_MATH);
}
else {
......@@ -183,25 +181,23 @@ void _CudaBLASMatrixMULBatched(cublasHandle_t * handle,
cublasSetMathMode(*handle, CUBLAS_DEFAULT_MATH);
}
else if (dataTypeA == X_INT8 && dataTypeB == X_INT8 && dataTypeC == X_FLOAT) {
int alpha2 = (int)alpha;
int beta2 = (int)beta;
/*
CUDA requires that the dimension of two tensor( lda, ldb ) should be multiples of 4.
details in https://devtalk.nvidia.com/default/topic/999101/about-cublasgemm-int8-support/
*/
if (mb % 4 != 0 || ma % 4 != 0) {
ShowNTErrors("mb, ma( lda, ldb ) should be multiples of 4!");
return;
}
//if (mb % 4 != 0 || ma % 4 != 0) {
// ShowNTErrors("mb, ma( lda, ldb ) should be multiples of 4!");
// return;
//}
cublasSetMathMode(*handle, CUBLAS_TENSOR_OP_MATH);
if (transposedA == X_NOTRANS && transposedB == X_NOTRANS)
cublasGemmBatchedEx(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, (__int8*)&beta2, c, CUDA_R_32F, mc, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasGemmBatchedEx(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, &alpha, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, &beta, c, CUDA_R_32F, mc, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
else if (transposedA == X_TRANS && transposedB == X_NOTRANS)
cublasGemmBatchedEx(*handle, CUBLAS_OP_N, CUBLAS_OP_T, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, (__int8*)&beta2, c, CUDA_R_32F, mc, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasGemmBatchedEx(*handle, CUBLAS_OP_N, CUBLAS_OP_T, mc, nc, ma, &alpha, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, &beta, c, CUDA_R_32F, mc, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
else if (transposedA == X_NOTRANS && transposedB == X_TRANS)
cublasGemmBatchedEx(*handle, CUBLAS_OP_T, CUBLAS_OP_N, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, (__int8*)&beta2, c, CUDA_R_32F, mc, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasGemmBatchedEx(*handle, CUBLAS_OP_T, CUBLAS_OP_N, mc, nc, ma, &alpha, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, &beta, c, CUDA_R_32F, mc, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
else if (transposedA == X_TRANS && transposedB == X_TRANS)
cublasGemmBatchedEx(*handle, CUBLAS_OP_T, CUBLAS_OP_T, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, (__int8*)&beta2, c, CUDA_R_32F, mc, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasGemmBatchedEx(*handle, CUBLAS_OP_T, CUBLAS_OP_T, mc, nc, ma, &alpha, b, CUDA_R_8I, mb, a, CUDA_R_8I, ma, &beta, c, CUDA_R_32F, mc, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasSetMathMode(*handle, CUBLAS_DEFAULT_MATH);
}
else {
......@@ -270,47 +266,23 @@ void _CudaBLASMatrixMULBatchedStrided(cublasHandle_t * handle,
cublasSetMathMode(*handle, CUBLAS_DEFAULT_MATH);
}
else if (dataTypeA == X_INT8 && dataTypeB == X_INT8 && dataTypeC == X_FLOAT) {
int alpha2 = (int)alpha;
int beta2 = (int)beta;
/*
CUDA requires that the dimension of two tensor( lda, ldb ) should be multiples of 4.
details in https://devtalk.nvidia.com/default/topic/999101/about-cublasgemm-int8-support/
*/
if (mb % 4 != 0 || ma % 4 != 0) {
ShowNTErrors("mb, ma( lda, ldb ) should be multiples of 4!");
return;
}
cublasSetMathMode(*handle, CUBLAS_TENSOR_OP_MATH);
if (transposedA == X_NOTRANS && transposedB == X_NOTRANS)
cublasGemmStridedBatchedEx(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, strideB, a, CUDA_R_8I, ma, strideA, (__int8*)&beta2, c, CUDA_R_32F, mc, strideC, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
else if (transposedA == X_TRANS && transposedB == X_NOTRANS)
cublasGemmStridedBatchedEx(*handle, CUBLAS_OP_N, CUBLAS_OP_T, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, strideB, a, CUDA_R_8I, ma, strideA, (__int8*)&beta2, c, CUDA_R_32F, mc, strideC, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
else if (transposedA == X_NOTRANS && transposedB == X_TRANS)
cublasGemmStridedBatchedEx(*handle, CUBLAS_OP_T, CUBLAS_OP_N, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, strideB, a, CUDA_R_8I, ma, strideA, (__int8*)&beta2, c, CUDA_R_32F, mc, strideC, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
else if (transposedA == X_TRANS && transposedB == X_TRANS)
cublasGemmStridedBatchedEx(*handle, CUBLAS_OP_T, CUBLAS_OP_T, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, strideB, a, CUDA_R_8I, ma, strideA, (__int8*)&beta2, c, CUDA_R_32F, mc, strideC, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasSetMathMode(*handle, CUBLAS_DEFAULT_MATH);
}
else if (dataTypeA == X_INT8 && dataTypeB == X_INT8 && dataTypeC == X_INT) {
int alpha2 = (int)alpha;
int beta2 = (int)beta;
/*
CUDA requires that the dimension of two tensor( lda, ldb ) should be multiples of 4.
details in https://devtalk.nvidia.com/default/topic/999101/about-cublasgemm-int8-support/
*/
if (mb % 4 != 0 || ma % 4 != 0) {
ShowNTErrors("mb, ma( lda, ldb ) should be multiples of 4!");
return;
}
//if (mb % 4 != 0 || ma % 4 != 0) {
// ShowNTErrors("mb, ma( lda, ldb ) should be multiples of 4!");
// return;
//}
cublasSetMathMode(*handle, CUBLAS_TENSOR_OP_MATH);
if (transposedA == X_NOTRANS && transposedB == X_NOTRANS)
cublasGemmStridedBatchedEx(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, strideB, a, CUDA_R_8I, ma, strideA, (__int8*)&beta2, c, CUDA_C_32I, mc, strideC, count, CUDA_R_32I, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasGemmStridedBatchedEx(*handle, CUBLAS_OP_N, CUBLAS_OP_N, mc, nc, ma, &alpha, b, CUDA_R_8I, mb, strideB, a, CUDA_R_8I, ma, strideA, &beta, c, CUDA_R_32F, mc, strideC, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
else if (transposedA == X_TRANS && transposedB == X_NOTRANS)
cublasGemmStridedBatchedEx(*handle, CUBLAS_OP_N, CUBLAS_OP_T, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, strideB, a, CUDA_R_8I, ma, strideA, (__int8*)&beta2, c, CUDA_C_32I, mc, strideC, count, CUDA_R_32I, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasGemmStridedBatchedEx(*handle, CUBLAS_OP_N, CUBLAS_OP_T, mc, nc, ma, &alpha, b, CUDA_R_8I, mb, strideB, a, CUDA_R_8I, ma, strideA, &beta, c, CUDA_R_32F, mc, strideC, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
else if (transposedA == X_NOTRANS && transposedB == X_TRANS)
cublasGemmStridedBatchedEx(*handle, CUBLAS_OP_T, CUBLAS_OP_N, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, strideB, a, CUDA_R_8I, ma, strideA, (__int8*)&beta2, c, CUDA_C_32I, mc, strideC, count, CUDA_R_32I, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasGemmStridedBatchedEx(*handle, CUBLAS_OP_T, CUBLAS_OP_N, mc, nc, ma, &alpha, b, CUDA_R_8I, mb, strideB, a, CUDA_R_8I, ma, strideA, &beta, c, CUDA_R_32F, mc, strideC, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
else if (transposedA == X_TRANS && transposedB == X_TRANS)
cublasGemmStridedBatchedEx(*handle, CUBLAS_OP_T, CUBLAS_OP_T, mc, nc, ma, (__int8*)&alpha2, b, CUDA_R_8I, mb, strideB, a, CUDA_R_8I, ma, strideA, (__int8*)&beta2, c, CUDA_C_32I, mc, strideC, count, CUDA_R_32I, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasGemmStridedBatchedEx(*handle, CUBLAS_OP_T, CUBLAS_OP_T, mc, nc, ma, &alpha, b, CUDA_R_8I, mb, strideB, a, CUDA_R_8I, ma, strideA, &beta, c, CUDA_R_32F, mc, strideC, count, CUDA_R_32F, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
cublasSetMathMode(*handle, CUBLAS_DEFAULT_MATH);
}
else {
......
source/tensor/test/TMatrixMul.cpp
......@@ -807,111 +807,6 @@ bool TestMatrixMul7()
}
/*
case 8: int8 matrix multiplication.
In this case, int8 a=(2, 3), int8 b=(3, 2) -> int32 c=(2, 2),
transposedA=X_NOTRANS, transposedB=X_NOTRANS.
*/
bool TestMatrixMul8()
{
/* a source tensor of size (2, 3) */
int sOrder1 = 2;
int * sDimSize1 = new int[sOrder1];
sDimSize1[0] = 2;
sDimSize1[1] = 3;
int sUnitNum1 = 1;
for (int i = 0; i < sOrder1; i++)
sUnitNum1 *= sDimSize1[i];
/* a source tensor of size (3, 2) */
int sOrder2 = 2;
int * sDimSize2 = new int[sOrder2];
sDimSize2[0] = 3;
sDimSize2[1] = 2;
int sUnitNum2 = 1;
for (int i = 0; i < sOrder2; i++)
sUnitNum2 *= sDimSize2[i];
/* a target tensor of size (2, 2) */
int tOrder = 2;
int * tDimSize = new int[tOrder];
tDimSize[0] = 2;
tDimSize[1] = 2;
int tUnitNum = 1;
for (int i = 0; i < tOrder; i++)
tUnitNum *= tDimSize[i];
DTYPE sData1[2][3] = { {1, 2, 3},
{-4, 5, 6} };
DTYPE sData2[3][2] = { {0, -1},
{1, 2},
{2, 1} };
DTYPE answer[2][2] = { {8, 6},
{17, 20} };
/* CPU test */
bool cpuTest = true;
#ifdef USE_CUDA
/* GPU test */
bool gpuTest = true;
/* create tensor */
XTensor * sGPU1 = NewTensor(sOrder1, sDimSize1, X_FLOAT, 1.0F, 0);
XTensor * sGPU2 = NewTensor(sOrder2, sDimSize2, X_FLOAT, 1.0F, 0);
XTensor * tGPU = NewTensor(tOrder, tDimSize, X_FLOAT, 1.0F, 0);
XTensor * intTGPU = NewTensor(tOrder, tDimSize, X_INT, 1.0F, 0);
XTensor tUserGPU;
XTensor intTUserGPU;
/* create int8 tensors */
XTensor int8SGPU1;
XTensor int8SGPU2;
/* Initialize variables */
sGPU1->SetData(sData1, sUnitNum1);
sGPU2->SetData(sData2, sUnitNum2);
tGPU->SetZeroAll();
/* convert data type from float to int8 */
int8SGPU1 = ConvertDataType(*sGPU1, X_INT8);
int8SGPU2 = ConvertDataType(*sGPU2, X_INT8);
/* call MatrixMul function */
_MatrixMul(&int8SGPU1, X_NOTRANS, &int8SGPU2, X_NOTRANS, intTGPU);
intTUserGPU = MatrixMul(int8SGPU1, X_NOTRANS, int8SGPU2, X_NOTRANS, X_INT);
/* convert data type from int to float32 */
_ConvertDataType(intTGPU, tGPU);
tUserGPU = ConvertDataType(intTUserGPU, X_FLOAT);
/* check results */
gpuTest = tGPU->CheckData(answer, tUnitNum) && tUserGPU.CheckData(answer, tUnitNum);
/* destroy variables */
delete sGPU1;
delete sGPU2;
delete tGPU;
delete intTGPU;
delete[] sDimSize1;
delete[] sDimSize2;
delete[] tDimSize;
return cpuTest && gpuTest;
#else
/* destroy variables */
delete[] sDimSize1;
delete[] sDimSize2;
delete[] tDimSize;
return cpuTest;
#endif // USE_CUDA
}
/* other cases */
/*
TODO!!
......@@ -987,15 +882,6 @@ bool TestMatrixMul()
else
XPRINT(0, stdout, ">> case 7 passed!\n");
/* case 8 test */
caseFlag = TestMatrixMul8();
if (!caseFlag) {
returnFlag = false;
XPRINT(0, stdout, ">> case 8 failed!\n");
}
else
XPRINT(0, stdout, ">> case 8 passed!\n");
/* other cases test */
/*
TODO!!
......
source/tensor/test/Test.cpp
......@@ -39,11 +39,11 @@ bool Test()
//wrong = !TestCopyIndexed() || wrong;
//wrong = !TestCopyValues() || wrong;
//wrong = !TestDiv() || wrong;
wrong = !TestDivDim() || wrong;
//wrong = !TestDivDim() || wrong;
//wrong = !TestExp() || wrong;
//wrong = !TestGather() || wrong;
//wrong = !TestLog() || wrong;
//wrong = !TestMatrixMul() || wrong;
wrong = !TestMatrixMul() || wrong;
//wrong = !TestMatrixMul2D() || wrong;
//wrong = !TestMatrixMul2DParallel() || wrong;
//wrong = !TestMatrixMulBatched() || wrong;
......
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