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NiuTrans.Tensor
Commit 1f1413ca by liyinqiao
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Support fp16 data type for more operations and fix the minor errors. (Don't use… 

Support fp16 data type for more operations and fix the minor errors.  (Don't use this! It's an incomplete version)
parent 1bf5cc90
正在显示 包含 251 行增加 和 138 行删除
source/tensor/core/CHeader.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/getandset/ConvertDataType.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/math/ScaleAndShift.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/math/ScaleAndShift.cu
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/math/ScaleAndShift.cuh
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/movement/CopyBlocksSelected.cu
...@@ -97,7 +97,7 @@ void _CudaCopyBlocksSelected(void * source, int unitSize, int blockSize, int * s ...@@ -97,7 +97,7 @@ void _CudaCopyBlocksSelected(void * source, int unitSize, int blockSize, int * s
GDevs.GetCudaThread2D(devID, bSize, blockNum, MAX_INT, cudaGrids, cudaBlocks); GDevs.GetCudaThread2D(devID, bSize, blockNum, MAX_INT, cudaGrids, cudaBlocks);
if (unitSize == 4) if (unitSize == 4)
KernelCopyBlocksSelected <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>> KernelCopyBlocksSelected <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>>
((DTYPE*)source, bSize, sourceBlocksTMP, blockNum, (DTYPE*)target, targetBlocksTMP); ((float*)source, bSize, sourceBlocksTMP, blockNum, (float*)target, targetBlocksTMP);
else if (unitSize == 2) else if (unitSize == 2)
KernelCopyBlocksSelected <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>> KernelCopyBlocksSelected <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1])>>>
((half*)source, bSize, sourceBlocksTMP, blockNum, (half*)target, targetBlocksTMP); ((half*)source, bSize, sourceBlocksTMP, blockNum, (half*)target, targetBlocksTMP);
......
source/tensor/core/movement/CopyIndexed.cu
...@@ -128,9 +128,9 @@ void _CudaCopyIndexed(const XTensor * s, XTensor * t, int dim, ...@@ -128,9 +128,9 @@ void _CudaCopyIndexed(const XTensor * s, XTensor * t, int dim,
dim3 blocks(cudaGrids[0], cudaGrids[1]); dim3 blocks(cudaGrids[0], cudaGrids[1]);
dim3 threads(cudaBlocks[0], cudaBlocks[1]); dim3 threads(cudaBlocks[0], cudaBlocks[1]);
if (s->dataType == X_FLOAT && t->dataType == X_FLOAT) { if (s->dataType == DEFAULT_DTYPE && t->dataType == DEFAULT_DTYPE) {
DTYPE * sData = (float*)s->data; DTYPE * sData = (DTYPE*)s->data;
DTYPE * tData = (float*)t->data; DTYPE * tData = (DTYPE*)t->data;
int * sIndex = (int*)srcIndex->data; int * sIndex = (int*)srcIndex->data;
int * tIndex = (int*)tgtIndex->data; int * tIndex = (int*)tgtIndex->data;
......
source/tensor/core/movement/Spread.cu
...@@ -401,11 +401,11 @@ void _CudaSpreadForGather(XTensor * source, XTensor * collection, XTensor * srcI ...@@ -401,11 +401,11 @@ void _CudaSpreadForGather(XTensor * source, XTensor * collection, XTensor * srcI
else else
sIndex = (int *)srcIndex->data; sIndex = (int *)srcIndex->data;
if (source->dataType == X_FLOAT && collection->dataType == X_FLOAT) if (source->dataType == DEFAULT_DTYPE && collection->dataType == DEFAULT_DTYPE)
{ {
DTYPE * sData = (float*)source->data; DTYPE * sData = (DTYPE*)source->data;
DTYPE * cData = (float*)collection->data; DTYPE * cData = (DTYPE*)collection->data;
KernelSpreadForGather<float> << <blocks, threads >> >(sData, cData, sIndex, indexSize, stride); KernelSpreadForGather<DTYPE> << <blocks, threads >> >(sData, cData, sIndex, indexSize, stride);
} }
else if (source->dataType == X_FLOAT16 && collection->dataType == X_FLOAT16) else if (source->dataType == X_FLOAT16 && collection->dataType == X_FLOAT16)
{ {
......
source/tensor/core/reduce/ReduceMax.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/reduce/ReduceMax.cu
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
...@@ -33,7 +33,7 @@ namespace nts{ // namespace nts(NiuTrans.Tensor) ...@@ -33,7 +33,7 @@ namespace nts{ // namespace nts(NiuTrans.Tensor)
/* /*
use PTX code to reduce float data use PTX code to reduce float data
*/ */
#define SHLFUNCFLOAT(funcName, reducePTXOp) \ #define SHLFUNCFLOAT(funcName, reducePTXOp) \
__device__ __forceinline__ \ __device__ __forceinline__ \
float funcName(float input) \ float funcName(float input) \
{ \ { \
......
source/tensor/core/reduce/ReduceMax.cuh
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/reduce/ReduceMean.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/reduce/ReduceSum.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/reduce/ReduceSum.cu
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
...@@ -96,7 +96,7 @@ crossing of the i-th columne and the j-th row. ...@@ -96,7 +96,7 @@ crossing of the i-th columne and the j-th row.
>> power - power of the item in the array >> power - power of the item in the array
>> isExp - specify if we perform exp() on the input >> isExp - specify if we perform exp() on the input
*/ */
__global__ __global__
void KernelReduceSum(DTYPE * input, DTYPE * output, void KernelReduceSum(DTYPE * input, DTYPE * output,
int stride, int strideNum, int reducedStrideNum, int stride, int strideNum, int reducedStrideNum,
int blockSize, int blockNum, int blockSize, int blockNum,
...@@ -152,7 +152,7 @@ void KernelReduceSum(DTYPE * input, DTYPE * output, ...@@ -152,7 +152,7 @@ void KernelReduceSum(DTYPE * input, DTYPE * output,
output[(k * reducedStrideNum + blockIdx.x) * stride + iOffset] = iData[threadIdx.y * blockDim.x]; output[(k * reducedStrideNum + blockIdx.x) * stride + iOffset] = iData[threadIdx.y * blockDim.x];
} }
/* /*
reduce a tensor to another that keeps the sum along a dimension - slow version reduce a tensor to another that keeps the sum along a dimension - slow version
This is for float16 reduction. This is for float16 reduction.
Given a block of data, we go over each dimension i in the stride and we have Given a block of data, we go over each dimension i in the stride and we have
...@@ -171,7 +171,7 @@ crossing of the i-th columne and the j-th row. ...@@ -171,7 +171,7 @@ crossing of the i-th columne and the j-th row.
>> power - power of the item in the array >> power - power of the item in the array
>> isExp - specify if we perform exp() on the input >> isExp - specify if we perform exp() on the input
*/ */
__global__ __global__
void KernelReduceSum(__half * input, __half * output, void KernelReduceSum(__half * input, __half * output,
int stride, int strideNum, int reducedStrideNum, int stride, int strideNum, int reducedStrideNum,
int blockSize, int blockNum, int blockSize, int blockNum,
...@@ -726,7 +726,7 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen ...@@ -726,7 +726,7 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen
DTYPE * sp = shift != NULL ? (DTYPE*)shift->data : NULL; DTYPE * sp = shift != NULL ? (DTYPE*)shift->data : NULL;
if (stride == 1 && blockNum >= 10) { if (stride == 1 && blockNum >= 10 && input->dataType == DEFAULT_DTYPE) {
dim3 grids; dim3 grids;
dim3 blocks; dim3 blocks;
continuousStorageThreadAllocation(grids, blocks, (long long)blockNum, strideNum); continuousStorageThreadAllocation(grids, blocks, (long long)blockNum, strideNum);
...@@ -742,7 +742,7 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen ...@@ -742,7 +742,7 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen
strideNum, blockNum, sp, power, isExp); strideNum, blockNum, sp, power, isExp);
} }
} }
else if (stride != 1 && stride * blockNum > 4096) { else if (stride != 1 && stride * blockNum > 4096 && input->dataType == DEFAULT_DTYPE){
//GDevs->GetGridAndBlockSize2D(devID, stride * blockNum, strideNum,MAX_INT, cudaGridSize, cudaBlockSize); //GDevs->GetGridAndBlockSize2D(devID, stride * blockNum, strideNum,MAX_INT, cudaGridSize, cudaBlockSize);
//unsigned int* goutput = (unsigned int *)input->data; //unsigned int* goutput = (unsigned int *)input->data;
//convert2uintV2 << <dim3(cudaGridSize[0], cudaGridSize[1]), dim3(cudaBlockSize[0], cudaBlockSize[1]) >> > ((float*)input->data, goutput, stride, strideNum, blockNum, strideNum*blockNum*stride); //convert2uintV2 << <dim3(cudaGridSize[0], cudaGridSize[1]), dim3(cudaBlockSize[0], cudaBlockSize[1]) >> > ((float*)input->data, goutput, stride, strideNum, blockNum, strideNum*blockNum*stride);
...@@ -766,15 +766,15 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen ...@@ -766,15 +766,15 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen
DTYPE * oData = NULL; DTYPE * oData = NULL;
if (iter == 0) { if (iter == 0) {
iData = (DTYPE*)input->data; iData = (DTYPE*)input->data;
oData = buf1; oData = (DTYPE*)buf1;
} }
else if (iter % 2 == 1) { else if (iter % 2 == 1) {
iData = buf1; iData = (DTYPE*)buf1;
oData = buf2; oData = (DTYPE*)buf2;
} }
else { else {
iData = buf2; iData = (DTYPE*)buf2;
oData = buf1; oData = (DTYPE*)buf1;
} }
/* unroll the reduction procedure. The code is messy but it is faster. */ /* unroll the reduction procedure. The code is messy but it is faster. */
if (strideNum <= 32) { if (strideNum <= 32) {
...@@ -830,8 +830,7 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen ...@@ -830,8 +830,7 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen
__half * buf1ft16 = (__half *)buf1; __half * buf1ft16 = (__half *)buf1;
__half * buf2ft16 = (__half *)buf2; __half * buf2ft16 = (__half *)buf2;
__half * spft16 = (__half *)sp; __half * spft16 = (__half *)sp;
unsigned short power2 = FloatToFloat16(power); __half powerft16p = __float2half(power);
__half * powerft16p = (__half*)&power2;
__half * iData = NULL; __half * iData = NULL;
__half * oData = NULL; __half * oData = NULL;
if (iter == 0) { if (iter == 0) {
...@@ -854,7 +853,7 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen ...@@ -854,7 +853,7 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen
if (cudaGridSize[0] == 1) if (cudaGridSize[0] == 1)
oData = (__half*)output->data; oData = (__half*)output->data;
KernelReduceSum <<<blocks, threads>>> (iData, oData, stride, strideNum, blocks.y, KernelReduceSum <<<blocks, threads>>> (iData, oData, stride, strideNum, blocks.y,
blockSize, blockNum, spft16, *powerft16p, isExp); blockSize, blockNum, spft16, powerft16p, isExp);
} }
else if (strideNum < 128) { else if (strideNum < 128) {
GDevs.GetCudaThread2D(devID, MAX(strideNum / 2 + 1, 64), stride * blockNum, MAX_INT, cudaGridSize, cudaBlockSize); GDevs.GetCudaThread2D(devID, MAX(strideNum / 2 + 1, 64), stride * blockNum, MAX_INT, cudaGridSize, cudaBlockSize);
...@@ -863,7 +862,7 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen ...@@ -863,7 +862,7 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen
oData = (__half*)output->data; oData = (__half*)output->data;
CheckNTErrors((cudaBlockSize[0] >= 64), "Incorrect thread number when calling the cuda kernel!"); CheckNTErrors((cudaBlockSize[0] >= 64), "Incorrect thread number when calling the cuda kernel!");
KernelReduceSumFast<64> <<<blocks, threads>>> (iData, oData, stride, strideNum, blocks.y, KernelReduceSumFast<64> <<<blocks, threads>>> (iData, oData, stride, strideNum, blocks.y,
blockSize, blockNum, spft16, *powerft16p, isExp); blockSize, blockNum, spft16, powerft16p, isExp);
} }
else if (strideNum < 256) { else if (strideNum < 256) {
GDevs.GetCudaThread2D(devID, MAX(strideNum / 2 + 1, 128), stride * blockNum, MAX_INT, cudaGridSize, cudaBlockSize); GDevs.GetCudaThread2D(devID, MAX(strideNum / 2 + 1, 128), stride * blockNum, MAX_INT, cudaGridSize, cudaBlockSize);
...@@ -872,7 +871,7 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen ...@@ -872,7 +871,7 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen
oData = (__half*)output->data; oData = (__half*)output->data;
CheckNTErrors((cudaBlockSize[0] >= 128), "Incorrect thread number when calling the cuda kernel!"); CheckNTErrors((cudaBlockSize[0] >= 128), "Incorrect thread number when calling the cuda kernel!");
KernelReduceSumFast<128> <<<blocks, threads>>> (iData, oData, stride, strideNum, blocks.y, KernelReduceSumFast<128> <<<blocks, threads>>> (iData, oData, stride, strideNum, blocks.y,
blockSize, blockNum, spft16, *powerft16p, isExp); blockSize, blockNum, spft16, powerft16p, isExp);
} }
else if (strideNum < 512) { else if (strideNum < 512) {
GDevs.GetCudaThread2D(devID, MAX(strideNum / 2 + 1, 256), stride * blockNum, MAX_INT, cudaGridSize, cudaBlockSize); GDevs.GetCudaThread2D(devID, MAX(strideNum / 2 + 1, 256), stride * blockNum, MAX_INT, cudaGridSize, cudaBlockSize);
...@@ -881,7 +880,7 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen ...@@ -881,7 +880,7 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen
oData = (__half*)output->data; oData = (__half*)output->data;
CheckNTErrors((cudaBlockSize[0] >= 256), "Incorrect thread number when calling the cuda kernel!"); CheckNTErrors((cudaBlockSize[0] >= 256), "Incorrect thread number when calling the cuda kernel!");
KernelReduceSumFast<256> <<<blocks, threads>>> (iData, oData, stride, strideNum, blocks.y, KernelReduceSumFast<256> <<<blocks, threads>>> (iData, oData, stride, strideNum, blocks.y,
blockSize, blockNum, spft16, *powerft16p, isExp); blockSize, blockNum, spft16, powerft16p, isExp);
} }
else { else {
GDevs.GetCudaThread2D(devID, MAX(strideNum / 2 + 1, 512), stride * blockNum, MAX_INT, cudaGridSize, cudaBlockSize); GDevs.GetCudaThread2D(devID, MAX(strideNum / 2 + 1, 512), stride * blockNum, MAX_INT, cudaGridSize, cudaBlockSize);
...@@ -890,9 +889,12 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen ...@@ -890,9 +889,12 @@ void _CudaReduceSum(const XTensor * input, XTensor * output, int dim, const XTen
oData = (__half*)output->data; oData = (__half*)output->data;
CheckNTErrors((cudaBlockSize[0] >= 512), "Incorrect thread number when calling the cuda kernel!"); CheckNTErrors((cudaBlockSize[0] >= 512), "Incorrect thread number when calling the cuda kernel!");
KernelReduceSumFast<512> <<<blocks, threads>>> (iData, oData, stride, strideNum, blocks.y, KernelReduceSumFast<512> <<<blocks, threads>>> (iData, oData, stride, strideNum, blocks.y,
blockSize, blockNum, spft16, *powerft16p, isExp); blockSize, blockNum, spft16, powerft16p, isExp);
} }
} }
else {
ShowNTErrors("Unsupported dataType!");
}
strideNum = cudaGridSize[0]; strideNum = cudaGridSize[0];
blockSize = cudaGridSize[0]; blockSize = cudaGridSize[0];
......
source/tensor/core/reduce/ReduceSum.cuh
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/reduce/ReduceSumAll.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/reduce/ReduceSumAll.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/reduce/ReduceSumSquared.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/reduce/ReduceVariance.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/shape/Reshape.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/shape/Reshape.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/shape/Squeeze.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/shape/Squeeze.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/shape/Unsqueeze.cu
...@@ -268,6 +268,14 @@ void _CudaUnsqueeze(const XTensor * a, XTensor * b, int dim, int dSize) ...@@ -268,6 +268,14 @@ void _CudaUnsqueeze(const XTensor * a, XTensor * b, int dim, int dSize)
KernelUnsqueezeByCol<int> << <dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1]) >> > KernelUnsqueezeByCol<int> << <dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1]) >> >
(a->data, blockNumA, dSize, b->data); (a->data, blockNumA, dSize, b->data);
} }
else if (a->dataType == X_FLOAT16 && b->dataType == X_FLOAT16) {
if (cudaBlocks[1] == 1)
KernelUnsqueezeByColBigRow<__half> << <dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1]) >> >
(a->data, blockNumA, dSize, b->data);
else
KernelUnsqueezeByCol<__half> << <dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1]) >> >
(a->data, blockNumA, dSize, b->data);
}
else { else {
ShowNTErrors("TODO!"); ShowNTErrors("TODO!");
} }
...@@ -285,6 +293,10 @@ void _CudaUnsqueeze(const XTensor * a, XTensor * b, int dim, int dSize) ...@@ -285,6 +293,10 @@ void _CudaUnsqueeze(const XTensor * a, XTensor * b, int dim, int dSize)
KernelUnsqueeze<int> << <dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1]) >> > KernelUnsqueeze<int> << <dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1]) >> >
(a->data, blockSize, blockNumA, blockSize * dSize, b->data, dSize); (a->data, blockSize, blockNumA, blockSize * dSize, b->data, dSize);
} }
else if (a->dataType == X_FLOAT16 && b->dataType == X_FLOAT16) {
KernelUnsqueeze<half> << <dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1]) >> >
(a->data, blockSize, blockNumA, blockSize * dSize, b->data, dSize);
}
else { else {
ShowNTErrors("TODO!"); ShowNTErrors("TODO!");
} }
...@@ -300,6 +312,10 @@ void _CudaUnsqueeze(const XTensor * a, XTensor * b, int dim, int dSize) ...@@ -300,6 +312,10 @@ void _CudaUnsqueeze(const XTensor * a, XTensor * b, int dim, int dSize)
KernelUnsqueezeFlat2D<int> << <dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1]) >> > KernelUnsqueezeFlat2D<int> << <dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1]) >> >
(a->data, blockSize, blockSize * dSize, b->data, dSize); (a->data, blockSize, blockSize * dSize, b->data, dSize);
} }
else if (a->dataType == X_FLOAT16 && b->dataType == X_FLOAT16) {
KernelUnsqueezeFlat2D<half> << <dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1]) >> >
(a->data, blockSize, blockSize * dSize, b->data, dSize);
}
else { else {
ShowNTErrors("TODO!"); ShowNTErrors("TODO!");
} }
...@@ -315,6 +331,10 @@ void _CudaUnsqueeze(const XTensor * a, XTensor * b, int dim, int dSize) ...@@ -315,6 +331,10 @@ void _CudaUnsqueeze(const XTensor * a, XTensor * b, int dim, int dSize)
KernelUnsqueezeFlatBigram<int> << <dim3(cudaGrids[0]), dim3(cudaBlocks[0]) >> > KernelUnsqueezeFlatBigram<int> << <dim3(cudaGrids[0]), dim3(cudaBlocks[0]) >> >
(a->data, blockSize, blockSize * dSize, b->data, dSize); (a->data, blockSize, blockSize * dSize, b->data, dSize);
} }
else if (a->dataType == X_FLOAT16 && b->dataType == X_FLOAT16) {
KernelUnsqueezeFlatBigram<half> << <dim3(cudaGrids[0]), dim3(cudaBlocks[0]) >> >
(a->data, blockSize, blockSize * dSize, b->data, dSize);
}
else { else {
ShowNTErrors("TODO!"); ShowNTErrors("TODO!");
} }
...@@ -330,6 +350,10 @@ void _CudaUnsqueeze(const XTensor * a, XTensor * b, int dim, int dSize) ...@@ -330,6 +350,10 @@ void _CudaUnsqueeze(const XTensor * a, XTensor * b, int dim, int dSize)
KernelUnsqueezeFlat<int> << <dim3(cudaGrids[0]), dim3(cudaBlocks[0]) >> > KernelUnsqueezeFlat<int> << <dim3(cudaGrids[0]), dim3(cudaBlocks[0]) >> >
(a->data, blockSize, blockSize * dSize, b->data, dSize); (a->data, blockSize, blockSize * dSize, b->data, dSize);
} }
else if (a->dataType == X_FLOAT16 && b->dataType == X_FLOAT16) {
KernelUnsqueezeFlat<half> << <dim3(cudaGrids[0]), dim3(cudaBlocks[0]) >> >
(a->data, blockSize, blockSize * dSize, b->data, dSize);
}
else { else {
ShowNTErrors("TODO!"); ShowNTErrors("TODO!");
} }
......
source/tensor/core/shape/Unsqueeze.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/core/sort/TopK.cu
...@@ -863,6 +863,11 @@ void _CudaTopK(const XTensor * a, XTensor * b, XTensor * index, int dim, int k, ...@@ -863,6 +863,11 @@ void _CudaTopK(const XTensor * a, XTensor * b, XTensor * index, int dim, int k,
((DTYPE*)a->data, stride, strideNumA, blockNum, k, DTYPE_MIN, ((DTYPE*)a->data, stride, strideNumA, blockNum, k, DTYPE_MIN,
(DTYPE*)b->data, (int*)index->data, isSorted); (DTYPE*)b->data, (int*)index->data, isSorted);
} }
else if (a->dataType == X_FLOAT16) {
KernelTopK3<__half> <<<dim3(cudaGrids[0], cudaGrids[1]), dim3(cudaBlocks[0], cudaBlocks[1]) >>>
((__half*)a->data, stride, strideNumA, blockNum, k, DTYPE_MIN,
(__half*)b->data, (int*)index->data, isSorted);
}
else { else {
ShowNTErrors("TODO!"); ShowNTErrors("TODO!");
} }
......
source/tensor/core/utilities/FlushToMem.cu
...@@ -128,16 +128,14 @@ void CudaGPUToCPUFlush(XTensor * tensor, int devID, XMem * CPUMem) ...@@ -128,16 +128,14 @@ void CudaGPUToCPUFlush(XTensor * tensor, int devID, XMem * CPUMem)
/* copy the data from GPU memory to CPU memory ((dataHost)) and do not delete the data */ /* copy the data from GPU memory to CPU memory ((dataHost)) and do not delete the data */
void CudaGPUToCPUFlush(XTensor * tensor) void CudaGPUToCPUFlush(XTensor * tensor)
{ {
CheckNTErrors((sizeof(DTYPE) == tensor->unitSize), "Unsupported data type.");
if (tensor->dataHost != NULL) if (tensor->dataHost != NULL)
delete[](char*)tensor->dataHost; delete[](char*)tensor->dataHost;
if (tensor->isSparse) { if (tensor->isSparse) {
int num = int(tensor->unitNum * tensor->denseRatio + 1); int num = int(tensor->unitNum * tensor->denseRatio + 1);
cudaMemcpy(&num, (DTYPE*)tensor->data, sizeof(int), cudaMemcpyDeviceToHost); cudaMemcpy(&num, tensor->data, sizeof(int), cudaMemcpyDeviceToHost);
int tupleSize = sizeof(int) + sizeof(DTYPE); int tupleSize = sizeof(int) + tensor->unitSize;
int size = sizeof(int) + tupleSize*(num); int size = sizeof(int) + tupleSize*(num);
CheckNTErrors((size >= 0), "Illegal data size in the sparse matrix!"); CheckNTErrors((size >= 0), "Illegal data size in the sparse matrix!");
......
source/tensor/function/Dropout.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Dropout.cu
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Dropout.cuh
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Dropout.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/FHeader.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/HardTanH.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/HardTanH.cu
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
...@@ -36,17 +36,18 @@ y = 1 if x > 1 ...@@ -36,17 +36,18 @@ y = 1 if x > 1
>> y - output data array >> y - output data array
>> size - size of input/output >> size - size of input/output
*/ */
template <class T>
__global__ __global__
void KernelHardtanhCompute(DTYPE * x, DTYPE * y, int size) void KernelHardtanhCompute(T * x, T * y, int size)
{ {
int i = blockDim.x * blockIdx.x + threadIdx.x; int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < size){ if (i < size){
DTYPE p = x[i]; T p = x[i];
if(p > (DTYPE)1.0) if (p >(T)1.0)
p = (DTYPE)1.0; p = (T)1.0;
else if(p < (DTYPE)-1.0) else if (p < (T)-1.0)
p = (DTYPE)-1.0; p = (T)-1.0;
y[i] = p; y[i] = p;
} }
} }
...@@ -71,7 +72,16 @@ void _CudaHardTanH(const XTensor * x, XTensor * y) ...@@ -71,7 +72,16 @@ void _CudaHardTanH(const XTensor * x, XTensor * y)
int devIDBackup; int devIDBackup;
ProtectCudaDev(x->devID, devIDBackup); ProtectCudaDev(x->devID, devIDBackup);
KernelHardtanhCompute<<<dim3(gridSize[0]), dim3(blockSize[0])>>>((DTYPE*)x->data, (DTYPE*)y->data, x->unitNum); if(x->dataType == DEFAULT_DTYPE && y->dataType == DEFAULT_DTYPE){
KernelHardtanhCompute<<<dim3(gridSize[0]), dim3(blockSize[0])>>>((DTYPE*)x->data, (DTYPE*)y->data, x->unitNum);
}
else if (x->dataType == X_FLOAT16 && y->dataType == X_FLOAT16) {
KernelHardtanhCompute<<<dim3(gridSize[0]), dim3(blockSize[0])>>>((__half *)x->data, (__half *)y->data, x->unitNum);
}
else {
//TODO!
ShowNTErrors("TODO!");
}
BacktoCudaDev(x->devID, devIDBackup); BacktoCudaDev(x->devID, devIDBackup);
} }
...@@ -84,18 +94,18 @@ dy/dx = 1 if -1 <= x <= 1 ...@@ -84,18 +94,18 @@ dy/dx = 1 if -1 <= x <= 1
>> dedy - dE/dy >> dedy - dE/dy
>> dedx - dE/dx >> dedx - dE/dx
>> y - y of the function
>> x - x of the function >> x - x of the function
>> size - size of y/x >> size - size of y/x
*/ */
template <class T>
__global__ __global__
void KernelHardtanhBackward(DTYPE * dedy, DTYPE * dedx, DTYPE * x, int size) void KernelHardtanhBackward(T * dedy, T * dedx, T * x, int size)
{ {
int i = blockDim.x * blockIdx.x + threadIdx.x; int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < size){ if (i < size){
DTYPE s = x[i]; T s = x[i];
if(s > (DTYPE)1.0 || s < (DTYPE)-1.0) if(s > (T)1.0 || s < (T)-1.0)
dedx[i] = 0; dedx[i] = 0;
else else
dedx[i] = dedy[i]; dedx[i] = dedy[i];
...@@ -129,12 +139,25 @@ void _CudaHardTanHBackward(XTensor * y, XTensor * x, ...@@ -129,12 +139,25 @@ void _CudaHardTanHBackward(XTensor * y, XTensor * x,
int devIDBackup; int devIDBackup;
ProtectCudaDev(x->devID, devIDBackup); ProtectCudaDev(x->devID, devIDBackup);
/* dE/dx = dE/dy * dy/dx */ if(x->dataType == DEFAULT_DTYPE && y->dataType == DEFAULT_DTYPE){
KernelHardtanhBackward<<<dim3(gridSize[0]),dim3(blockSize[0])>>> /* dE/dx = dE/dy * dy/dx */
((DTYPE*)dedy->data, KernelHardtanhBackward<<<dim3(gridSize[0]),dim3(blockSize[0])>>>
(DTYPE*)dedx->data, ((DTYPE*)dedy->data,
(DTYPE*)x->data, (DTYPE*)dedx->data,
x->unitNum); (DTYPE*)x->data,
x->unitNum);
}
else if (x->dataType == X_FLOAT16 && y->dataType == X_FLOAT16) {
/* dE/dx = dE/dy * dy/dx */
KernelHardtanhBackward<<<dim3(gridSize[0]), dim3(blockSize[0])>>>
((half*)dedy->data,
(half*)dedx->data,
(half*)x->data,
x->unitNum);
}
else {
ShowNTErrors("Unsupported dataType!");
}
BacktoCudaDev(x->devID, devIDBackup); BacktoCudaDev(x->devID, devIDBackup);
} }
......
source/tensor/function/HardTanH.cuh
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/HardTanH.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Identity.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Identity.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/LogSoftmax.cu
...@@ -17,6 +17,7 @@ ...@@ -17,6 +17,7 @@
/* /*
* $Created by: XIAO Tong (email: xiaotong@mail.neu.edu.cn) 2018-04-26 * $Created by: XIAO Tong (email: xiaotong@mail.neu.edu.cn) 2018-04-26
* $Update by: Lin Ye (email: linye2015@outlook.com) 2019-07-01 float16 added
*/ */
#include "LogSoftmax.h" #include "LogSoftmax.h"
...@@ -27,6 +28,7 @@ ...@@ -27,6 +28,7 @@
#include "../core/reduce/ReduceMax.cuh" #include "../core/reduce/ReduceMax.cuh"
#include "../core/shape/IsSameShaped.h" #include "../core/shape/IsSameShaped.h"
#include "../XDevice.h" #include "../XDevice.h"
#include <device_launch_parameters.h>
namespace nts { // namespace nts(NiuTrans.Tensor) namespace nts { // namespace nts(NiuTrans.Tensor)
...@@ -58,11 +60,12 @@ y_{i,j} = log(e^x_{i,j} / \sum_{i} e^{x_{i,j}) ...@@ -58,11 +60,12 @@ y_{i,j} = log(e^x_{i,j} / \sum_{i} e^{x_{i,j})
>> rowNum - row number of the matrix >> rowNum - row number of the matrix
>> colNum - column number of the matrix >> colNum - column number of the matrix
*/ */
template <class T ,TENSOR_DATA_TYPE dataType>
__global__ __global__
void KernelLogSoftmaxComputeByRow(DTYPE * x, DTYPE * max, DTYPE * sum, DTYPE * y, int rowNum, int colNum) void KernelLogSoftmaxComputeByRow(T * x, T * max, T * sum, T * y, int rowNum, int colNum)
{ {
__shared__ DTYPE inputSum[MAX_CUDA_THREAD_NUM_PER_BLOCK]; __shared__ T inputSum[MAX_CUDA_THREAD_NUM_PER_BLOCK];
__shared__ DTYPE inputMax[MAX_CUDA_THREAD_NUM_PER_BLOCK]; __shared__ T inputMax[MAX_CUDA_THREAD_NUM_PER_BLOCK];
int i = blockDim.y * blockIdx.y + threadIdx.y; int i = blockDim.y * blockIdx.y + threadIdx.y;
int j = blockDim.x * blockIdx.x + threadIdx.x; int j = blockDim.x * blockIdx.x + threadIdx.x;
...@@ -79,14 +82,20 @@ void KernelLogSoftmaxComputeByRow(DTYPE * x, DTYPE * max, DTYPE * sum, DTYPE * y ...@@ -79,14 +82,20 @@ void KernelLogSoftmaxComputeByRow(DTYPE * x, DTYPE * max, DTYPE * sum, DTYPE * y
/* y_{i,j} = log(e^(s_{i,j} - max_{j}) / \sum_{k} e^{s_{k,j} - max_{j}}) */ /* y_{i,j} = log(e^(s_{i,j} - max_{j}) / \sum_{k} e^{s_{k,j} - max_{j}}) */
if (i < rowNum && j < colNum) { if (i < rowNum && j < colNum) {
int key = i * colNum + j; int key = i * colNum + j;
DTYPE r = log(exp(x[key] - inputMax[threadIdx.x]) / inputSum[threadIdx.x]); if (dataType == DEFAULT_DTYPE) {
DTYPE r = log((DTYPE)exp((DTYPE)(x[key] - inputMax[threadIdx.x])) / (DTYPE)inputSum[threadIdx.x]);
if (isnan(r)) if (isnan(r))
r = LOGPROB_MIN; r = LOGPROB_MIN;
if (isinf(r)) if (isinf(r))
r = LOGPROB_MIN; r = LOGPROB_MIN;
y[key] = MAX(r, LOGPROB_MIN); y[key] = MAX(r, LOGPROB_MIN);
}
else if (dataType == X_FLOAT16) {
half r = hlog((half)hexp(x[key] - inputMax[threadIdx.y]) / (half)inputSum[threadIdx.y]);
y[key] = r;
}
} }
} }
...@@ -105,11 +114,12 @@ y_{i,j} = log(e^x_{i,j} / \sum_{j} e^{x_{i,j}) ...@@ -105,11 +114,12 @@ y_{i,j} = log(e^x_{i,j} / \sum_{j} e^{x_{i,j})
>> rowNum - row number of the matrix >> rowNum - row number of the matrix
>> colNum - column number of the matrix >> colNum - column number of the matrix
*/ */
template <class T ,TENSOR_DATA_TYPE dataType>
__global__ __global__
void KernelLogSoftmaxComputeByCol(DTYPE * x, DTYPE * max, DTYPE * sum, DTYPE * y, int rowNum, int colNum) void KernelLogSoftmaxComputeByCol(T * x, T * max, T * sum, T * y, int rowNum, int colNum)
{ {
__shared__ DTYPE inputSum[MAX_CUDA_THREAD_NUM_PER_BLOCK]; __shared__ T inputSum[MAX_CUDA_THREAD_NUM_PER_BLOCK];
__shared__ DTYPE inputMax[MAX_CUDA_THREAD_NUM_PER_BLOCK]; __shared__ T inputMax[MAX_CUDA_THREAD_NUM_PER_BLOCK];
int i = blockDim.y * blockIdx.y + threadIdx.y; int i = blockDim.y * blockIdx.y + threadIdx.y;
int j = blockDim.x * blockIdx.x + threadIdx.x; int j = blockDim.x * blockIdx.x + threadIdx.x;
...@@ -126,19 +136,25 @@ void KernelLogSoftmaxComputeByCol(DTYPE * x, DTYPE * max, DTYPE * sum, DTYPE * y ...@@ -126,19 +136,25 @@ void KernelLogSoftmaxComputeByCol(DTYPE * x, DTYPE * max, DTYPE * sum, DTYPE * y
/* y_{i,j} = log(e^(s_{i,j} - max_{i}) / \sum_{k} e^{s_{i,k} - max_{i}}) */ /* y_{i,j} = log(e^(s_{i,j} - max_{i}) / \sum_{k} e^{s_{i,k} - max_{i}}) */
if (i < rowNum && j < colNum) { if (i < rowNum && j < colNum) {
int key = i * colNum + j; int key = i * colNum + j;
DTYPE r = log(exp(x[key] - inputMax[threadIdx.y]) / inputSum[threadIdx.y]); if (dataType == DEFAULT_DTYPE) {
DTYPE r = log((DTYPE)exp((DTYPE)(x[key] - inputMax[threadIdx.y])) / (DTYPE)inputSum[threadIdx.y]);
/*if (r < LOGPROB_MIN)
{ /*if (r < LOGPROB_MIN)
printf("min %e %e, %e %e, %e %e\n", r, x[key] - inputMax[threadIdx.y], x[key], inputMax[threadIdx.y], exp(x[key] - inputMax[threadIdx.y]), inputSum[threadIdx.y]); {
}*/ printf("min %e %e, %e %e, %e %e\n", r, x[key] - inputMax[threadIdx.y], x[key], inputMax[threadIdx.y], exp(x[key] - inputMax[threadIdx.y]), inputSum[threadIdx.y]);
}*/
if (isnan(r))
r = LOGPROB_MIN; if (isnan(r))
if (isinf(r)) r = LOGPROB_MIN;
r = LOGPROB_MIN; if (isinf(r))
r = LOGPROB_MIN;
y[key] = MAX(r, LOGPROB_MIN); y[key] = MAX(r, LOGPROB_MIN);
}
else if (dataType == X_FLOAT16) {
half r = hlog((half)hexp(x[key] - inputMax[threadIdx.y]) / (half)inputSum[threadIdx.y]);
y[key] = r;
}
} }
} }
...@@ -174,17 +190,37 @@ void _CudaLogSoftmaxSumMax(XTensor * x, XTensor * y, int leadDim, XTensor * sum, ...@@ -174,17 +190,37 @@ void _CudaLogSoftmaxSumMax(XTensor * x, XTensor * y, int leadDim, XTensor * sum,
GDevs.GetCudaThread2D(x->devID, n, m, MAX_INT, gridSize, blockSize); GDevs.GetCudaThread2D(x->devID, n, m, MAX_INT, gridSize, blockSize);
/* y_{i,j} = log(e^(s_{i,j} - max_{j}) / \sum_{k} e^{s_{k,j} - max_{j}}) */ /* y_{i,j} = log(e^(s_{i,j} - max_{j}) / \sum_{k} e^{s_{k,j} - max_{j}}) */
KernelLogSoftmaxComputeByRow << <dim3(gridSize[1], gridSize[0]), dim3(blockSize[1], blockSize[0]) >> > KernelLogSoftmaxComputeByRow<DTYPE, DEFAULT_DTYPE> <<<dim3(gridSize[1], gridSize[0]), dim3(blockSize[1], blockSize[0])>>>
((DTYPE*)x->data, maxData, sumData, (DTYPE*)y->data, n, m); ((DTYPE*)x->data, maxData, sumData, (DTYPE*)y->data, n, m);
} }
else { else {
GDevs.GetCudaThread2D(x->devID, m, n, MAX_INT, gridSize, blockSize); GDevs.GetCudaThread2D(x->devID, m, n, MAX_INT, gridSize, blockSize);
/* y_{i,j} = log(e^(s_{i,j} - max_{i}) / \sum_{k} e^{s_{i,k} - max_{i}}) */ /* y_{i,j} = log(e^(s_{i,j} - max_{i}) / \sum_{k} e^{s_{i,k} - max_{i}}) */
KernelLogSoftmaxComputeByCol << <dim3(gridSize[0], gridSize[1]), dim3(blockSize[0], blockSize[1]) >> > KernelLogSoftmaxComputeByCol<DTYPE, DEFAULT_DTYPE> <<<dim3(gridSize[0], gridSize[1]), dim3(blockSize[0], blockSize[1])>>>
((DTYPE*)x->data, maxData, sumData, (DTYPE*)y->data, n, m); ((DTYPE*)x->data, maxData, sumData, (DTYPE*)y->data, n, m);
} }
} }
else if (x->dataType == X_FLOAT16 && y->dataType == X_FLOAT16) {
int gridSize[3], blockSize[3];
int n = x->dimSize[0];
int m = x->dimSize[1];
/* allocate the buffer */
__half * maxData = (half*)max->data;
__half * sumData = (half*)sum->data;
if (leadDim == 0) {
GDevs.GetCudaThread2D(x->devID, n, m, MAX_INT, gridSize, blockSize);
/* y_{i,j} = log(e^(s_{i,j} - max_{j}) / \sum_{k} e^{s_{k,j} - max_{j}}) */
KernelLogSoftmaxComputeByRow<half, X_FLOAT16> <<<dim3(gridSize[1], gridSize[0]), dim3(blockSize[1], blockSize[0])>>>
((half*)x->data, maxData, sumData, (half *)y->data, n, m);
}
else {
GDevs.GetCudaThread2D(x->devID, m, n, MAX_INT, gridSize, blockSize);
/* y_{i,j} = log(e^(s_{i,j} - max_{i}) / \sum_{k} e^{s_{i,k} - max_{i}}) */
KernelLogSoftmaxComputeByCol<half, X_FLOAT16> <<<dim3(gridSize[0], gridSize[1]), dim3(blockSize[0], blockSize[1])>>>
((half*)x->data, maxData, sumData, (half*)y->data, n, m);
}
}
else { else {
ShowNTErrors("TODO!"); ShowNTErrors("TODO!");
} }
......
source/tensor/function/LogSoftmax.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Loss.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Loss.cu
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Loss.cuh
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Loss.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Rectify.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Rectify.cu
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
...@@ -34,15 +34,16 @@ rectify : y = x if x >= 0 ...@@ -34,15 +34,16 @@ rectify : y = x if x >= 0
>> output - output tensor >> output - output tensor
>> size - size of input/output >> size - size of input/output
*/ */
template<class T>
__global__ __global__
void KernelRectify(DTYPE * x, DTYPE * y, int size) void KernelRectify(T * x, T * y, int size)
{ {
int i = blockDim.x * blockIdx.x + threadIdx.x; int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < size){ if (i < size){
DTYPE p = x[i]; T p = x[i];
if(p < 0) if(p < (T)0.0)
p = 0; p = (T)0.0;
y[i] = p; y[i] = p;
} }
} }
...@@ -61,8 +62,18 @@ void _CudaRectify(const XTensor * x, XTensor * y) ...@@ -61,8 +62,18 @@ void _CudaRectify(const XTensor * x, XTensor * y)
int devIDBackup; int devIDBackup;
ProtectCudaDev(x->devID, devIDBackup); ProtectCudaDev(x->devID, devIDBackup);
KernelRectify<<<dim3(gridSize[0]), dim3(blockSize[0])>>> if (x->dataType == DEFAULT_DTYPE) {
((DTYPE*)x->data, (DTYPE*)y->data, x->unitNum); KernelRectify<<<dim3(gridSize[0]), dim3(blockSize[0])>>>
((DTYPE*)x->data, (DTYPE*)y->data, x->unitNum);
}
else if (x->dataType == X_FLOAT16) {
KernelRectify<<<dim3(gridSize[0]), dim3(blockSize[0]) >> >
((__half*)x->data, (__half*)y->data, x->unitNum);
}
else {
// TODO!!
ShowNTErrors("TODO!");
}
BacktoCudaDev(x->devID, devIDBackup); BacktoCudaDev(x->devID, devIDBackup);
} }
...@@ -78,17 +89,18 @@ dy/dx = 1 if x >= 0 ...@@ -78,17 +89,18 @@ dy/dx = 1 if x >= 0
>> x - input of the function >> x - input of the function
>> size - size of output/input >> size - size of output/input
*/ */
template<class T>
__global__ __global__
void KernelRectifyBackward(DTYPE * dedy, DTYPE * dedx, DTYPE * x, int size) void KernelRectifyBackward(T * dedy, T * dedx, T * x, int size)
{ {
int i = blockDim.x * blockIdx.x + threadIdx.x; int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < size){ if (i < size){
DTYPE s = x[i]; T s = x[i];
if(s >= 0) if(s >= (T)0.0)
dedx[i] = dedy[i]; dedx[i] = dedy[i];
else else
dedx[i] = 0; dedx[i] = (T)0.0;
} }
} }
...@@ -119,11 +131,24 @@ void _CudaRectifyBackward(XTensor * y, XTensor * x, ...@@ -119,11 +131,24 @@ void _CudaRectifyBackward(XTensor * y, XTensor * x,
ProtectCudaDev(x->devID, devIDBackup); ProtectCudaDev(x->devID, devIDBackup);
/* dE/ds = dE/dy * dy/ds */ /* dE/ds = dE/dy * dy/ds */
KernelRectifyBackward<<<dim3(gridSize[0]),dim3(blockSize[0])>>> if (x->dataType == DEFAULT_DTYPE && y->dataType == DEFAULT_DTYPE) {
((DTYPE*)dedy->data, KernelRectifyBackward<<<dim3(gridSize[0]),dim3(blockSize[0])>>>
(DTYPE*)dedx->data, ((DTYPE*)dedy->data,
(DTYPE*)x->data, (DTYPE*)dedx->data,
x->unitNum); (DTYPE*)x->data,
x->unitNum);
}
else if (x->dataType == X_FLOAT16 && y->dataType == X_FLOAT16) {
KernelRectifyBackward<<<dim3(gridSize[0]), dim3(blockSize[0]) >> >
((__half*)dedy->data,
(__half*)dedx->data,
(__half*)x->data,
x->unitNum);
}
else {
// TODO!!
ShowNTErrors("TODO!");
}
BacktoCudaDev(x->devID, devIDBackup); BacktoCudaDev(x->devID, devIDBackup);
} }
......
source/tensor/function/Rectify.cuh
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Rectify.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Sigmoid.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Sigmoid.cu
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Sigmoid.cuh
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Sigmoid.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/function/Softmax.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/loss/CrossEntropy.cpp
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/loss/CrossEntropy.cu
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/loss/CrossEntropy.cuh
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
source/tensor/loss/CrossEntropy.h
/* NiuTrans.Tensor - an open-source tensor library /* NiuTrans.Tensor - an open-source tensor library
* Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University.
* All rights reserved. * All rights reserved.
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
......
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