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PaddleDetection
提交 75185d82 编写于 作者: G gangliao 提交者： GitHub
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Merge pull request #3228 from gangliao/clang-format 

ClangFormat for proto and cuda
上级 fa839c52 d1e75433
隐藏空白更改
内联 并排
Showing with 3660 addition and 2920 deletion
.pre-commit-config.yaml
浏览文件 @ 75185d82
......@@ -24,7 +24,7 @@
description: Format files with ClangFormat.
entry: clang-format -i
language: system
files: \.(c|cc|cxx|cpp|h|hpp|hxx)$
files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto)$
- repo: https://github.com/PaddlePaddle/pre-commit-golang
sha: 8337620115c25ff8333f1b1a493bd031049bd7c0
hooks:
......
paddle/cuda/src/hl_batch_transpose.cu
浏览文件 @ 75185d82
......@@ -12,17 +12,15 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "hl_batch_transpose.h"
#include "hl_base.h"
#include "hl_batch_transpose.h"
const int TILE_DIM = 64;
const int BLOCK_ROWS = 16;
// No bank-conflict transpose for a batch of data.
__global__ void batchTransposeNoBankConflicts(real* odata,
const real* idata,
int numSamples, int width,
int height) {
__global__ void batchTransposeNoBankConflicts(
real* odata, const real* idata, int numSamples, int width, int height) {
__shared__ float tile[TILE_DIM][TILE_DIM + 1];
const int x = blockIdx.x * TILE_DIM + threadIdx.x;
......@@ -50,12 +48,12 @@ __global__ void batchTransposeNoBankConflicts(real* odata,
newX] = tile[threadIdx.x][j];
}
void batchTranspose(const real* input, real* output, int width, int height,
int batchSize) {
void batchTranspose(
const real* input, real* output, int width, int height, int batchSize) {
dim3 dimBlock(TILE_DIM, BLOCK_ROWS, 1);
dim3 dimGrid(DIVUP(width, TILE_DIM), DIVUP(height, TILE_DIM), batchSize);
batchTransposeNoBankConflicts<<<dimGrid, dimBlock, 0, STREAM_DEFAULT>>>
(output, input, batchSize, width, height);
batchTransposeNoBankConflicts<<<dimGrid, dimBlock, 0, STREAM_DEFAULT>>>(
output, input, batchSize, width, height);
CHECK_SYNC("batchTranspose failed!");
}
paddle/cuda/src/hl_cuda_aggregate.cu
浏览文件 @ 75185d82
......@@ -12,27 +12,23 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "hl_aggregate.h"
#include "hl_base.h"
#include "hl_cuda.h"
#include "hl_cuda.ph"
#include "hl_aggregate.h"
#include "hl_thread.ph"
#include "hl_matrix_base.cuh"
#include "hl_thread.ph"
#include "paddle/utils/Logging.h"
/**
* @brief matrix row operator.
*/
template<class Agg, int blockSize>
__global__ void KeMatrixRowOp(Agg agg,
real *E,
real *Sum,
int dimN) {
template <class Agg, int blockSize>
__global__ void KeMatrixRowOp(Agg agg, real *E, real *Sum, int dimN) {
__shared__ real sum_s[blockSize];
int cnt = (dimN + blockSize -1) / blockSize;
int rowId = blockIdx.x + blockIdx.y*gridDim.x;
int index = rowId*dimN;
int cnt = (dimN + blockSize - 1) / blockSize;
int rowId = blockIdx.x + blockIdx.y * gridDim.x;
int index = rowId * dimN;
int tid = threadIdx.x;
int lmt = tid;
......@@ -44,7 +40,7 @@ __global__ void KeMatrixRowOp(Agg agg,
sum_s[tid] = tmp;
__syncthreads();
for (int stride = blockSize/2; stride > 0; stride = stride/2) {
for (int stride = blockSize / 2; stride > 0; stride = stride / 2) {
if (tid < stride) {
sum_s[tid] = agg(sum_s[tid], sum_s[tid + stride]);
}
......@@ -58,29 +54,21 @@ __global__ void KeMatrixRowOp(Agg agg,
}
template <class Agg>
void hl_matrix_row_op(Agg agg,
real *A_d,
real *C_d,
int dimM,
int dimN) {
void hl_matrix_row_op(Agg agg, real *A_d, real *C_d, int dimM, int dimN) {
int blocksX = dimM;
int blocksY = 1;
dim3 threads(128, 1);
dim3 grid(blocksX, blocksY);
KeMatrixRowOp<Agg, 128><<< grid, threads, 0, STREAM_DEFAULT >>>
(agg, A_d, C_d, dimN);
KeMatrixRowOp<Agg, 128><<<grid, threads, 0, STREAM_DEFAULT>>>(
agg, A_d, C_d, dimN);
}
void hl_matrix_row_sum(real *A_d, real *C_d, int dimM, int dimN) {
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(C_d);
hl_matrix_row_op(aggregate::sum(),
A_d,
C_d,
dimM,
dimN);
hl_matrix_row_op(aggregate::sum(), A_d, C_d, dimM, dimN);
CHECK_SYNC("hl_matrix_row_sum failed");
}
......@@ -88,11 +76,7 @@ void hl_matrix_row_max(real *A_d, real *C_d, int dimM, int dimN) {
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(C_d);
hl_matrix_row_op(aggregate::max(),
A_d,
C_d,
dimM,
dimN);
hl_matrix_row_op(aggregate::max(), A_d, C_d, dimM, dimN);
CHECK_SYNC("hl_matrix_row_max failed");
}
......@@ -100,23 +84,16 @@ void hl_matrix_row_min(real *A_d, real *C_d, int dimM, int dimN) {
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(C_d);
hl_matrix_row_op(aggregate::min(),
A_d,
C_d,
dimM,
dimN);
hl_matrix_row_op(aggregate::min(), A_d, C_d, dimM, dimN);
CHECK_SYNC("hl_matrix_row_min failed");
}
/**
* @brief matrix column operator.
*/
template<class Agg>
__global__ void KeMatrixColumnOp(Agg agg,
real *E,
real *Sum,
int dimM,
int dimN) {
template <class Agg>
__global__ void KeMatrixColumnOp(
Agg agg, real *E, real *Sum, int dimM, int dimN) {
int rowIdx = blockIdx.x * blockDim.x + threadIdx.x;
real tmp = agg.init();
if (rowIdx < dimN) {
......@@ -127,15 +104,12 @@ __global__ void KeMatrixColumnOp(Agg agg,
}
}
template<class Agg, int blockDimX, int blockDimY>
__global__ void KeMatrixColumnOp_S(Agg agg,
real *E,
real *Sum,
int dimM,
int dimN) {
__shared__ real _sum[blockDimX*blockDimY];
int rowIdx = blockIdx.x * blockDim.x + threadIdx.x;
int index = threadIdx.y;
template <class Agg, int blockDimX, int blockDimY>
__global__ void KeMatrixColumnOp_S(
Agg agg, real *E, real *Sum, int dimM, int dimN) {
__shared__ real _sum[blockDimX * blockDimY];
int rowIdx = blockIdx.x * blockDim.x + threadIdx.x;
int index = threadIdx.y;
real tmp = agg.init();
if (rowIdx < dimN) {
......@@ -144,14 +118,14 @@ __global__ void KeMatrixColumnOp_S(Agg agg,
index += blockDimY;
}
}
_sum[threadIdx.x + threadIdx.y*blockDimX] = tmp;
_sum[threadIdx.x + threadIdx.y * blockDimX] = tmp;
__syncthreads();
if (rowIdx < dimN) {
if (threadIdx.y ==0) {
if (threadIdx.y == 0) {
real tmp = agg.init();
for (int i=0; i < blockDimY; i++) {
tmp = agg(tmp, _sum[threadIdx.x + i*blockDimX]);
for (int i = 0; i < blockDimY; i++) {
tmp = agg(tmp, _sum[threadIdx.x + i * blockDimX]);
}
Sum[rowIdx] = tmp;
}
......@@ -159,25 +133,21 @@ __global__ void KeMatrixColumnOp_S(Agg agg,
}
template <class Agg>
void hl_matrix_column_op(Agg agg,
real *A_d,
real *C_d,
int dimM,
int dimN) {
void hl_matrix_column_op(Agg agg, real *A_d, real *C_d, int dimM, int dimN) {
if (dimN >= 8192) {
int blocksX = (dimN + 128 -1) / 128;
int blocksX = (dimN + 128 - 1) / 128;
int blocksY = 1;
dim3 threads(128, 1);
dim3 grid(blocksX, blocksY);
KeMatrixColumnOp<Agg><<< grid, threads, 0, STREAM_DEFAULT >>>
(agg, A_d, C_d, dimM, dimN);
KeMatrixColumnOp<Agg><<<grid, threads, 0, STREAM_DEFAULT>>>(
agg, A_d, C_d, dimM, dimN);
} else {
int blocksX = (dimN + 32 -1) / 32;
int blocksX = (dimN + 32 - 1) / 32;
int blocksY = 1;
dim3 threads(32, 32);
dim3 grid(blocksX, blocksY);
KeMatrixColumnOp_S<Agg, 32, 32><<< grid, threads, 0, STREAM_DEFAULT>>>
(agg, A_d, C_d, dimM, dimN);
KeMatrixColumnOp_S<Agg, 32, 32><<<grid, threads, 0, STREAM_DEFAULT>>>(
agg, A_d, C_d, dimM, dimN);
}
return;
......@@ -187,11 +157,7 @@ void hl_matrix_column_sum(real *A_d, real *C_d, int dimM, int dimN) {
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(C_d);
hl_matrix_column_op(aggregate::sum(),
A_d,
C_d,
dimM,
dimN);
hl_matrix_column_op(aggregate::sum(), A_d, C_d, dimM, dimN);
CHECK_SYNC("hl_matrix_column_sum failed");
}
......@@ -200,11 +166,7 @@ void hl_matrix_column_max(real *A_d, real *C_d, int dimM, int dimN) {
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(C_d);
hl_matrix_column_op(aggregate::max(),
A_d,
C_d,
dimM,
dimN);
hl_matrix_column_op(aggregate::max(), A_d, C_d, dimM, dimN);
CHECK_SYNC("hl_matrix_column_max failed");
}
......@@ -213,11 +175,7 @@ void hl_matrix_column_min(real *A_d, real *C_d, int dimM, int dimN) {
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(C_d);
hl_matrix_column_op(aggregate::min(),
A_d,
C_d,
dimM,
dimN);
hl_matrix_column_op(aggregate::min(), A_d, C_d, dimM, dimN);
CHECK_SYNC("hl_matrix_column_min failed");
}
......@@ -226,16 +184,16 @@ template <int blockSize>
__global__ void KeVectorSum(real *E, real *Sum, int dimM) {
__shared__ double sum_s[blockSize];
int tid = threadIdx.x;
int index = blockIdx.y*blockDim.x+threadIdx.x;
int index = blockIdx.y * blockDim.x + threadIdx.x;
sum_s[tid] = 0.0f;
while (index < dimM) {
sum_s[tid] += E[index];
index += blockDim.x*gridDim.y;
index += blockDim.x * gridDim.y;
}
__syncthreads();
for (int stride = blockSize/2; stride > 0; stride = stride/2) {
for (int stride = blockSize / 2; stride > 0; stride = stride / 2) {
if (tid < stride) {
sum_s[tid] += sum_s[tid + stride];
}
......@@ -259,38 +217,39 @@ void hl_vector_sum(real *A_d, real *C_h, int dimM) {
dim3 threads(blockSize, 1);
dim3 grid(blocksX, blocksY);
struct _hl_event_st hl_event_st = {.cu_event = t_resource.event};
struct _hl_event_st hl_event_st = {.cu_event = t_resource.event};
hl_event_t hl_event = &hl_event_st;
while (!hl_cuda_event_is_ready(hl_event)) {}
while (!hl_cuda_event_is_ready(hl_event)) {
}
KeVectorSum<128><<< grid, threads, 0, STREAM_DEFAULT >>>
(A_d, t_resource.gpu_mem, dimM);
KeVectorSum<128><<< 1, threads, 0, STREAM_DEFAULT >>>
(t_resource.gpu_mem, t_resource.cpu_mem, 128);
KeVectorSum<128><<<grid, threads, 0, STREAM_DEFAULT>>>(
A_d, t_resource.gpu_mem, dimM);
KeVectorSum<128><<<1, threads, 0, STREAM_DEFAULT>>>(
t_resource.gpu_mem, t_resource.cpu_mem, 128);
hl_memcpy_async(C_h, t_resource.cpu_mem, sizeof(real), HPPL_STREAM_DEFAULT);
hl_stream_record_event(HPPL_STREAM_DEFAULT, hl_event);
hl_stream_synchronize(HPPL_STREAM_DEFAULT);
cudaError_t err = (cudaError_t)hl_get_device_last_error();
CHECK_EQ(cudaSuccess, err)
<< "CUDA error: " << hl_get_device_error_string((size_t)err);
CHECK_EQ(cudaSuccess, err) << "CUDA error: "
<< hl_get_device_error_string((size_t)err);
}
template <int blockSize>
__global__ void KeVectorAbsSum(real *E, real *Sum, int dimM) {
__shared__ double sum_s[blockSize];
int tid = threadIdx.x;
int index = blockIdx.y*blockDim.x+threadIdx.x;
int index = blockIdx.y * blockDim.x + threadIdx.x;
sum_s[tid] = 0.0f;
while (index < dimM) {
sum_s[tid] += abs(E[index]);
index += blockDim.x*gridDim.y;
index += blockDim.x * gridDim.y;
}
__syncthreads();
for (int stride = blockSize/2; stride > 0; stride = stride/2) {
for (int stride = blockSize / 2; stride > 0; stride = stride / 2) {
if (tid < stride) {
sum_s[tid] += sum_s[tid + stride];
}
......@@ -314,20 +273,21 @@ void hl_vector_abs_sum(real *A_d, real *C_h, int dimM) {
dim3 threads(blockSize, 1);
dim3 grid(blocksX, blocksY);
struct _hl_event_st hl_event_st = {.cu_event = t_resource.event};
struct _hl_event_st hl_event_st = {.cu_event = t_resource.event};
hl_event_t hl_event = &hl_event_st;
while (!hl_cuda_event_is_ready(hl_event)) {}
while (!hl_cuda_event_is_ready(hl_event)) {
}
KeVectorAbsSum<128><<< grid, threads, 0, STREAM_DEFAULT >>>
(A_d, t_resource.gpu_mem, dimM);
KeVectorAbsSum<128><<< 1, threads, 0, STREAM_DEFAULT >>>
(t_resource.gpu_mem, t_resource.cpu_mem, 128);
KeVectorAbsSum<128><<<grid, threads, 0, STREAM_DEFAULT>>>(
A_d, t_resource.gpu_mem, dimM);
KeVectorAbsSum<128><<<1, threads, 0, STREAM_DEFAULT>>>(
t_resource.gpu_mem, t_resource.cpu_mem, 128);
hl_memcpy_async(C_h, t_resource.cpu_mem, sizeof(real), HPPL_STREAM_DEFAULT);
hl_stream_record_event(HPPL_STREAM_DEFAULT, hl_event);
hl_stream_synchronize(HPPL_STREAM_DEFAULT);
cudaError_t err = (cudaError_t)hl_get_device_last_error();
CHECK_EQ(cudaSuccess, err)
<< "CUDA error: " << hl_get_device_error_string((size_t)err);
CHECK_EQ(cudaSuccess, err) << "CUDA error: "
<< hl_get_device_error_string((size_t)err);
}
paddle/cuda/src/hl_cuda_cnn.cu
浏览文件 @ 75185d82
......@@ -12,21 +12,27 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <float.h>
#include "hl_base.h"
#include "hl_cnn.h"
#include "hl_device_functions.cuh"
__global__ void KeMaxPoolForward(const int nthreads, const real* inputData,
const int channels, const int height,
__global__ void KeMaxPoolForward(const int nthreads,
const real* inputData,
const int channels,
const int height,
const int width,
const int pooledH, const int pooledW,
const int ksizeW, const int ksizeH,
const int strideH, const int strideW,
const int offsetH, const int offsetW,
real* tgtData, const int tgtStride) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
const int pooledH,
const int pooledW,
const int ksizeW,
const int ksizeH,
const int strideH,
const int strideW,
const int offsetH,
const int offsetW,
real* tgtData,
const int tgtStride) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < nthreads) {
int pw = index % pooledW;
int ph = (index / pooledW) % pooledH;
......@@ -46,44 +52,70 @@ __global__ void KeMaxPoolForward(const int nthreads, const real* inputData,
maxval = inputData[h * width + w];
}
}
int tgtIndex = index % (pooledW * pooledH * channels) +
frameNum * tgtStride;
int tgtIndex =
index % (pooledW * pooledH * channels) + frameNum * tgtStride;
tgtData[tgtIndex] = maxval;
}
}
void hl_maxpool_forward(const int frameCnt, const real* inputData,
void hl_maxpool_forward(const int frameCnt,
const real* inputData,
const int channels,
const int height, const int width,
const int pooledH, const int pooledW,
const int sizeX, const int sizeY,
const int strideH, const int strideW,
const int paddingH, const int paddingW,
real* tgtData, const int tgtStride) {
const int height,
const int width,
const int pooledH,
const int pooledW,
const int sizeX,
const int sizeY,
const int strideH,
const int strideW,
const int paddingH,
const int paddingW,
real* tgtData,
const int tgtStride) {
int num_kernels = pooledH * pooledW * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
dim3 threads(1024, 1);
dim3 grid(blocks, 1);
KeMaxPoolForward<<< grid, threads, 0, STREAM_DEFAULT >>>
(num_kernels, inputData, channels, height, width,
pooledH, pooledW, sizeX, sizeY, strideH, strideW,
paddingH, paddingW, tgtData, tgtStride);
KeMaxPoolForward<<<grid, threads, 0, STREAM_DEFAULT>>>(num_kernels,
inputData,
channels,
height,
width,
pooledH,
pooledW,
sizeX,
sizeY,
strideH,
strideW,
paddingH,
paddingW,
tgtData,
tgtStride);
CHECK_SYNC("hl_maxpool_forward failed");
}
__global__ void KeMaxPoolBackward(const int nthreads, const real* inputData,
const real* outData, const real* outGrad,
const int channels, const int height,
__global__ void KeMaxPoolBackward(const int nthreads,
const real* inputData,
const real* outData,
const real* outGrad,
const int channels,
const int height,
const int width,
const int pooledH, const int pooledW,
const int sizeX, const int sizeY,
const int strideH, const int strideW,
const int padH, const int padW,
real scaleA, real scaleB,
real* targetGrad, const int outStride) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
const int pooledH,
const int pooledW,
const int sizeX,
const int sizeY,
const int strideH,
const int strideW,
const int padH,
const int padW,
real scaleA,
real scaleB,
real* targetGrad,
const int outStride) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < nthreads) {
// find out the local index
// find out the local offset
......@@ -107,43 +139,69 @@ __global__ void KeMaxPoolBackward(const int nthreads, const real* inputData,
}
}
}
targetGrad[index] =
scaleB * targetGrad[index] + scaleA * gradient;
targetGrad[index] = scaleB * targetGrad[index] + scaleA * gradient;
}
}
void hl_maxpool_backward(const int frameCnt, const real* inputData,
const real* outData, const real* outGrad,
const int channels, const int height,
const int width,
const int pooledH, const int pooledW,
const int sizeX, const int sizeY,
const int strideH, const int strideW,
const int paddingH, const int paddingW,
real scaleA, real scaleB,
real* targetGrad, const int outStride) {
void hl_maxpool_backward(const int frameCnt,
const real* inputData,
const real* outData,
const real* outGrad,
const int channels,
const int height,
const int width,
const int pooledH,
const int pooledW,
const int sizeX,
const int sizeY,
const int strideH,
const int strideW,
const int paddingH,
const int paddingW,
real scaleA,
real scaleB,
real* targetGrad,
const int outStride) {
int num_kernels = height * width * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
KeMaxPoolBackward<<< blocks, 1024, 0, STREAM_DEFAULT >>>
(num_kernels, inputData, outData, outGrad, channels,
height, width, pooledH, pooledW, sizeX, sizeY,
strideH, strideW,
paddingH, paddingW,
scaleA, scaleB,
targetGrad, outStride);
KeMaxPoolBackward<<<blocks, 1024, 0, STREAM_DEFAULT>>>(num_kernels,
inputData,
outData,
outGrad,
channels,
height,
width,
pooledH,
pooledW,
sizeX,
sizeY,
strideH,
strideW,
paddingH,
paddingW,
scaleA,
scaleB,
targetGrad,
outStride);
CHECK_SYNC("hl_maxpool_backward");
}
__global__ void KeAvgPoolForward(const int nthreads, const real* inputData,
__global__ void KeAvgPoolForward(const int nthreads,
const real* inputData,
const int channels,
const int height, const int width,
const int pooledH, const int pooledW,
const int sizeX, const int sizeY,
const int strideH, const int strideW,
const int padH, const int padW,
real* tgtData, const int tgtStride) {
const int height,
const int width,
const int pooledH,
const int pooledW,
const int sizeX,
const int sizeY,
const int strideH,
const int strideW,
const int padH,
const int padW,
real* tgtData,
const int tgtStride) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < nthreads) {
int pw = index % pooledW;
......@@ -168,39 +226,64 @@ __global__ void KeAvgPoolForward(const int nthreads, const real* inputData,
aveval += inputData[h * width + w];
}
}
int tgtIndex = index % (pooledW * pooledH * channels) +
frameNum * tgtStride;
int tgtIndex =
index % (pooledW * pooledH * channels) + frameNum * tgtStride;
tgtData[tgtIndex] = aveval / pool_size;
}
}
void hl_avgpool_forward(const int frameCnt, const real* inputData,
void hl_avgpool_forward(const int frameCnt,
const real* inputData,
const int channels,
const int height, const int width,
const int pooledH, const int pooledW,
const int sizeX, const int sizeY,
const int strideH, const int strideW,
const int paddingH, const int paddingW,
real* tgtData, const int tgtStride) {
const int height,
const int width,
const int pooledH,
const int pooledW,
const int sizeX,
const int sizeY,
const int strideH,
const int strideW,
const int paddingH,
const int paddingW,
real* tgtData,
const int tgtStride) {
int num_kernels = pooledH * pooledW * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
KeAvgPoolForward<<< blocks, 1024, 0, STREAM_DEFAULT >>>
(num_kernels, inputData, channels,
height, width, pooledH, pooledW,
sizeX, sizeY, strideH, strideW,
paddingH, paddingW, tgtData, tgtStride);
KeAvgPoolForward<<<blocks, 1024, 0, STREAM_DEFAULT>>>(num_kernels,
inputData,
channels,
height,
width,
pooledH,
pooledW,
sizeX,
sizeY,
strideH,
strideW,
paddingH,
paddingW,
tgtData,
tgtStride);
CHECK_SYNC("hl_avgpool_forward failed");
}
__global__ void KeAvgPoolBackward(const int nthreads, const real* outGrad,
const int channels, const int height,
__global__ void KeAvgPoolBackward(const int nthreads,
const real* outGrad,
const int channels,
const int height,
const int width,
const int pooledH, const int pooledW,
const int sizeX, const int sizeY,
const int strideH, const int strideW,
const int padH, const int padW,
real scaleA, real scaleB,
real* tgtGrad, const int outStride) {
const int pooledH,
const int pooledW,
const int sizeX,
const int sizeY,
const int strideH,
const int strideW,
const int padH,
const int padW,
real scaleA,
real scaleB,
real* tgtGrad,
const int outStride) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < nthreads) {
int offsetW = index % width + padW;
......@@ -215,7 +298,6 @@ __global__ void KeAvgPoolBackward(const int nthreads, const real* outGrad,
real gradient = 0;
outGrad += (frameNum * outStride + offsetC * pooledH * pooledW);
for (int ph = phstart; ph < phend; ++ph) {
for (int pw = pwstart; pw < pwend; ++pw) {
// figure out the pooling size
......@@ -224,32 +306,50 @@ __global__ void KeAvgPoolBackward(const int nthreads, const real* outGrad,
int hend = min(hstart + sizeY, height + padH);
int wend = min(wstart + sizeX, width + padW);
int poolsize = (hend - hstart) * (wend - wstart);
gradient += outGrad[ph * pooledW + pw]/poolsize;
gradient += outGrad[ph * pooledW + pw] / poolsize;
}
}
tgtGrad[index] = scaleB * tgtGrad[index] + scaleA * gradient;
}
}
void hl_avgpool_backward(const int frameCnt, const real* outGrad,
void hl_avgpool_backward(const int frameCnt,
const real* outGrad,
const int channels,
const int height, const int width,
const int pooledH, const int pooledW,
const int sizeX, const int sizeY,
const int strideH, const int strideW,
const int paddingH, const int paddingW,
real scaleA, real scaleB,
real* backGrad, const int outStride) {
const int height,
const int width,
const int pooledH,
const int pooledW,
const int sizeX,
const int sizeY,
const int strideH,
const int strideW,
const int paddingH,
const int paddingW,
real scaleA,
real scaleB,
real* backGrad,
const int outStride) {
int num_kernels = height * width * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
KeAvgPoolBackward <<< blocks, 1024, 0, STREAM_DEFAULT >>>
(num_kernels, outGrad, channels, height, width,
pooledH, pooledW, sizeX, sizeY,
strideH, strideW,
paddingH, paddingW,
scaleA, scaleB,
backGrad, outStride);
KeAvgPoolBackward<<<blocks, 1024, 0, STREAM_DEFAULT>>>(num_kernels,
outGrad,
channels,
height,
width,
pooledH,
pooledW,
sizeX,
sizeY,
strideH,
strideW,
paddingH,
paddingW,
scaleA,
scaleB,
backGrad,
outStride);
CHECK_SYNC("hl_avgpool_backward failed");
}
......@@ -266,7 +366,7 @@ __global__ void KeBilinearInterpFw(const real* in,
const size_t numChannels,
const real ratioH,
const real ratioW) {
int nthreads = outputH * outputW;
int nthreads = outputH * outputW;
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < nthreads) {
int outIdH = tid / outputW;
......@@ -287,13 +387,14 @@ __global__ void KeBilinearInterpFw(const real* in,
real w1lambda = ratioW * outImgIdx - inImgIdx;
real w2lambda = 1.f - w1lambda;
const real* inPos =
&in[outIdH * inputW + channelId * inImgSize + inImgIdy * inImgW + inImgIdx];
const real* inPos = &in[outIdH * inputW + channelId * inImgSize +
inImgIdy * inImgW + inImgIdx];
// bilinear interpolation
out[outIdH * outputW + outIdW] =
h2lambda * (w2lambda * inPos[0] + w1lambda * inPos[wId]) +
h1lambda * (w2lambda * inPos[hId * inImgW] + w1lambda * inPos[hId * inImgW + wId]);
h2lambda * (w2lambda * inPos[0] + w1lambda * inPos[wId]) +
h1lambda * (w2lambda * inPos[hId * inImgW] +
w1lambda * inPos[hId * inImgW + wId]);
}
}
......@@ -313,9 +414,19 @@ void hl_bilinear_forward(const real* inData,
int threadNum = outputH * outputW;
int blocks = (threadNum + 1024 - 1) / 1024;
KeBilinearInterpFw<<< blocks, 1024, 0, STREAM_DEFAULT>>>(
inData, inImgH, inImgW, inputH, inputW, outData, outImgH,
outImgW, outputH, outputW, numChannels, ratioH, ratioW);
KeBilinearInterpFw<<<blocks, 1024, 0, STREAM_DEFAULT>>>(inData,
inImgH,
inImgW,
inputH,
inputW,
outData,
outImgH,
outImgW,
outputH,
outputW,
numChannels,
ratioH,
ratioW);
CHECK_SYNC("hl_bilinear_forward failed");
}
......@@ -353,13 +464,15 @@ __global__ void KeBilinearInterpBw(real* in,
real w1lambda = ratioW * outImgIdx - inImgIdx;
real w2lambda = 1.f - w1lambda;
real* inPos =
&in[outIdH * inputW + channelId * inImgSize + inImgIdy * inImgW + inImgIdx];
real* inPos = &in[outIdH * inputW + channelId * inImgSize +
inImgIdy * inImgW + inImgIdx];
const real* outPos = &out[outIdH * outputW + outIdW];
paddle::paddleAtomicAdd(&inPos[0], h2lambda * w2lambda * outPos[0]);
paddle::paddleAtomicAdd(&inPos[wId], h2lambda * w1lambda * outPos[0]);
paddle::paddleAtomicAdd(&inPos[hId * inImgW], h1lambda * w2lambda * outPos[0]);
paddle::paddleAtomicAdd(&inPos[hId * inImgW + wId], h1lambda * w1lambda * outPos[0]);
paddle::paddleAtomicAdd(&inPos[hId * inImgW],
h1lambda * w2lambda * outPos[0]);
paddle::paddleAtomicAdd(&inPos[hId * inImgW + wId],
h1lambda * w1lambda * outPos[0]);
}
}
......@@ -379,22 +492,37 @@ void hl_bilinear_backward(real* inGrad,
int threadNum = outputH * outputW;
int blocks = (threadNum + 1024 - 1) / 1024;
KeBilinearInterpBw<<< blocks, 1024, 0, STREAM_DEFAULT>>>(
inGrad, inImgH, inImgW, inputH, inputW, outGrad, outImgH,
outImgW, outputH, outputW, numChannels, ratioH, ratioW);
KeBilinearInterpBw<<<blocks, 1024, 0, STREAM_DEFAULT>>>(inGrad,
inImgH,
inImgW,
inputH,
inputW,
outGrad,
outImgH,
outImgW,
outputH,
outputW,
numChannels,
ratioH,
ratioW);
CHECK_SYNC("hl_bilinear_backward failed");
}
__global__ void maxoutFpCompute(size_t nthreads, const real * inData,
real * outData, int* idData,
size_t size, size_t featLen, size_t groups) {
__global__ void maxoutFpCompute(size_t nthreads,
const real* inData,
real* outData,
int* idData,
size_t size,
size_t featLen,
size_t groups) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if(index < nthreads) {
if (index < nthreads) {
size_t batch_idx = index / size;
size_t i = index % size;
size_t channel_idx = i / featLen;
size_t feat_idx = i % featLen;
size_t data_idx = (batch_idx * size + channel_idx * featLen) * groups + feat_idx;
size_t data_idx =
(batch_idx * size + channel_idx * featLen) * groups + feat_idx;
real max = inData[data_idx];
int maxId = 0;
for (size_t g = 1; g < groups; ++g) {
......@@ -409,37 +537,50 @@ __global__ void maxoutFpCompute(size_t nthreads, const real * inData,
}
}
void hl_maxout_forward(const real* inData, real* outData,
int* idData, size_t batchSize, size_t size,
size_t featLen, size_t groups) {
void hl_maxout_forward(const real* inData,
real* outData,
int* idData,
size_t batchSize,
size_t size,
size_t featLen,
size_t groups) {
int num_kernels = size * batchSize;
int blocks = (num_kernels + 1024 - 1) / 1024;
maxoutFpCompute<<< blocks, 1024, 0, STREAM_DEFAULT>>>(
num_kernels, inData, outData, idData, size, featLen, groups);
maxoutFpCompute<<<blocks, 1024, 0, STREAM_DEFAULT>>>(
num_kernels, inData, outData, idData, size, featLen, groups);
CHECK_SYNC("hl_maxout_forward failed");
}
__global__ void maxoutBpCompute(size_t nthreads, real* inGrad,
const real* outGrad, const int* idData,
size_t size, size_t featLen, size_t groups) {
__global__ void maxoutBpCompute(size_t nthreads,
real* inGrad,
const real* outGrad,
const int* idData,
size_t size,
size_t featLen,
size_t groups) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if(index < nthreads) {
if (index < nthreads) {
size_t batch_idx = index / size;
size_t i = index % size;
size_t channel_idx = i / featLen;
size_t feat_idx = i % featLen;
size_t newIndex = batch_idx * size;
size_t gradIdx = (channel_idx * groups + (idData + newIndex)[i]) * featLen + feat_idx;
size_t gradIdx =
(channel_idx * groups + (idData + newIndex)[i]) * featLen + feat_idx;
(inGrad + newIndex * groups)[gradIdx] += (outGrad + newIndex)[i];
}
}
void hl_maxout_backward(real* inGrad, const real* outGrad,
const int* idData, size_t batchSize, size_t size,
size_t featLen, size_t groups) {
void hl_maxout_backward(real* inGrad,
const real* outGrad,
const int* idData,
size_t batchSize,
size_t size,
size_t featLen,
size_t groups) {
int num_kernels = size * batchSize;
int blocks = (num_kernels + 1024 - 1) / 1024;
maxoutBpCompute<<< blocks, 1024, 0, STREAM_DEFAULT >>>(
num_kernels, inGrad, outGrad, idData, size, featLen, groups);
maxoutBpCompute<<<blocks, 1024, 0, STREAM_DEFAULT>>>(
num_kernels, inGrad, outGrad, idData, size, featLen, groups);
CHECK_SYNC("hl_maxout_backward failed");
}
paddle/cuda/src/hl_cuda_lstm.cu
浏览文件 @ 75185d82
......@@ -12,14 +12,13 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "hl_activation_functions.h"
#include "hl_base.h"
#include "hl_cuda_cublas.h"
#include "hl_device_functions.cuh"
#include "hl_activation_functions.h"
#include "paddle/utils/Logging.h"
typedef hppl::Active<real>::forward t_forward;
typedef hppl::Active<real>::forward t_forward;
typedef hppl::Active<real>::backward t_backward;
bool hl_lstm_sequence_parallel(int frameSize) {
......@@ -42,9 +41,9 @@ public:
value_ += (start + length - 1) * frameSize + idx;
}
}
__device__ inline real *getPtr() const {return value_;}
__device__ inline real getValue() {return *value_;}
__device__ inline void setValue(real value) {*value_ = value;}
__device__ inline real *getPtr() const { return value_; }
__device__ inline real getValue() { return *value_; }
__device__ inline void setValue(real value) { *value_ = value; }
template <int reversed, int frameSize>
__device__ inline void nextFrame() {
if (reversed == 0) {
......@@ -55,28 +54,25 @@ public:
}
};
__device__ __forceinline__
void ptx_sync(const int id, const int barriers) {
__device__ __forceinline__ void ptx_sync(const int id, const int barriers) {
asm volatile("bar.sync %0, %1;" : : "r"(id), "r"(barriers) : "memory");
}
__device__ __forceinline__
void ptx_arrive(const int id, const int barriers) {
__device__ __forceinline__ void ptx_arrive(const int id, const int barriers) {
asm volatile("bar.arrive %0, %1;" : : "r"(id), "r"(barriers) : "memory");
}
template<int valueSize, int frameSize>
__device__ __forceinline__ real
forward_sequence(real value,
real *shValue,
real *state,
real *preOutput,
real *output,
real check,
int index,
t_forward activeNode,
t_forward activeGate,
t_forward activeState) {
template <int valueSize, int frameSize>
__device__ __forceinline__ real forward_sequence(real value,
real *shValue,
real *state,
real *preOutput,
real *output,
real check,
int index,
t_forward activeNode,
t_forward activeGate,
t_forward activeState) {
real out;
real prevOut;
real state_r;
......@@ -112,17 +108,20 @@ forward_sequence(real value,
if (idy == 0) {
ptx_sync(2, frameSize * 2);
prevOut = state[idx];
prevOut = activeState(prevOut);
prevOut = activeState(prevOut);
preOutput[idx] = prevOut;
ptx_arrive(3, frameSize * 2);
}
return value;
}
#define OUTPUT_BARRIER_ID 10
#define OUTPUT_BARRIER_ID2 11
template<int valueSize, int frameSize, int reversed,
int computeThreads, int blockSize>
#define OUTPUT_BARRIER_ID 10
#define OUTPUT_BARRIER_ID2 11
template <int valueSize,
int frameSize,
int reversed,
int computeThreads,
int blockSize>
__global__ void KeLstmForward(real *gateValue,
real *state,
real *output,
......@@ -184,10 +183,16 @@ __global__ void KeLstmForward(real *gateValue,
}
}
value = forward_sequence<valueSize, frameSize>(
value, shValue, shState, shPrevOutput, shOutput, check, index,
hppl::gpu::forward[active_node],
hppl::gpu::forward[active_gate],
hppl::gpu::forward[active_state]);
value,
shValue,
shState,
shPrevOutput,
shOutput,
check,
index,
hppl::gpu::forward[active_node],
hppl::gpu::forward[active_gate],
hppl::gpu::forward[active_state]);
const int idx = index % frameSize;
const int idy = index / frameSize;
if (valueSize == 128) {
......@@ -218,7 +223,7 @@ __global__ void KeLstmForward(real *gateValue,
real B_r[frameSize];
const int computeIdx = index - valueSize;
if (i == 0) {
#pragma unroll
#pragma unroll
for (int n = 0; n < frameSize; n++) {
B_r[n] = weight[n * valueSize + computeIdx];
}
......@@ -230,7 +235,7 @@ __global__ void KeLstmForward(real *gateValue,
}
real sum = 0.0f;
for (int n = 0; n < frameSize; n++) {
sum += A_r[n]*B_r[n];
sum += A_r[n] * B_r[n];
}
shValue[computeIdx] = sum;
ptx_arrive(OUTPUT_BARRIER_ID2, blockSize);
......@@ -239,14 +244,14 @@ __global__ void KeLstmForward(real *gateValue,
if (valueSize == 256) {
real B_r[frameSize];
if (i == 0) {
#pragma unroll
#pragma unroll
for (int n = 0; n < frameSize; n++) {
B_r[n] = weight[n * valueSize + index];
}
}
real sum = 0.0f;
for (int n = 0; n < frameSize; n++) {
sum += shOutput[n]*B_r[n];
sum += shOutput[n] * B_r[n];
}
value += sum;
}
......@@ -273,50 +278,81 @@ void hl_lstm_parallel_forward(real *gateValue,
dim3 grid(numSequences, 1);
if (!reversed) {
if (frameSize == 32) {
KeLstmForward<128, 32, 0, 128, 256>
<<<grid, 256, 0, STREAM_DEFAULT>>>
(gateValue, stateValue, outputValue, preOutputValue,
checkIg, checkFg, checkOg, weight, sequence,
active_node, active_gate, active_state);
KeLstmForward<128, 32, 0, 128, 256><<<grid, 256, 0, STREAM_DEFAULT>>>(
gateValue,
stateValue,
outputValue,
preOutputValue,
checkIg,
checkFg,
checkOg,
weight,
sequence,
active_node,
active_gate,
active_state);
} else if (frameSize == 64) {
KeLstmForward<256, 64, 0, 256, 256>
<<<grid, 256, 0, STREAM_DEFAULT>>>
(gateValue, stateValue, outputValue, preOutputValue,
checkIg, checkFg, checkOg, weight, sequence,
active_node, active_gate, active_state);
KeLstmForward<256, 64, 0, 256, 256><<<grid, 256, 0, STREAM_DEFAULT>>>(
gateValue,
stateValue,
outputValue,
preOutputValue,
checkIg,
checkFg,
checkOg,
weight,
sequence,
active_node,
active_gate,
active_state);
}
} else {
if (frameSize == 32) {
KeLstmForward<128, 32, 1, 128, 256>
<<<grid, 256, 0, STREAM_DEFAULT>>>
(gateValue, stateValue, outputValue, preOutputValue,
checkIg, checkFg, checkOg, weight, sequence,
active_node, active_gate, active_state);
KeLstmForward<128, 32, 1, 128, 256><<<grid, 256, 0, STREAM_DEFAULT>>>(
gateValue,
stateValue,
outputValue,
preOutputValue,
checkIg,
checkFg,
checkOg,
weight,
sequence,
active_node,
active_gate,
active_state);
} else if (frameSize == 64) {
KeLstmForward<256, 64, 1, 256, 256>
<<<grid, 256, 0, STREAM_DEFAULT>>>
(gateValue, stateValue, outputValue, preOutputValue,
checkIg, checkFg, checkOg, weight, sequence,
active_node, active_gate, active_state);
KeLstmForward<256, 64, 1, 256, 256><<<grid, 256, 0, STREAM_DEFAULT>>>(
gateValue,
stateValue,
outputValue,
preOutputValue,
checkIg,
checkFg,
checkOg,
weight,
sequence,
active_node,
active_gate,
active_state);
}
}
CHECK_SYNC("hl_lstm_parallel_forward failed");
}
__device__ __forceinline__
void transpose_32x32(real a[], const int idx) {
__device__ __forceinline__ void transpose_32x32(real a[], const int idx) {
int addr = idx % 32;
#pragma unroll
#pragma unroll
for (int k = 1; k < 32; k++) {
// rSrc[k] = __shfl(rSrc[k], (threadIdx.x + k) % 32, 32);
addr = __shfl(addr, (idx + 1) % 32, 32);
a[k] = __shfl(a[k], addr, 32);
}
#pragma unroll
#pragma unroll
for (int tid = 0; tid < 31; tid++) {
real tmp = (idx > tid) ? a[0] : a[1];
#pragma unroll
#pragma unroll
for (int k = 31; k > 0; k--) {
a[(k + 1) % 32] = (idx > tid) ? a[k] : a[(k + 1) % 32];
}
......@@ -324,29 +360,28 @@ void transpose_32x32(real a[], const int idx) {
}
addr = (32 - idx) % 32;
#pragma unroll
#pragma unroll
for (int k = 0; k < 32; k++) {
a[k] = __shfl(a[k], addr, 32);
addr = __shfl(addr, (idx + 31) % 32, 32);
}
}
template<int valueSize, int frameSize>
__device__ void
backward_sequence(real rGateValue,
real rOutputGrad,
real rPreOutputValue,
real &rGateGrad,
real &rStateGrad,
real *shStateGrad,
real *shStateValue,
real *shGateValue,
real rCheck,
real &rGateValuePrev,
int index,
t_backward activeNode,
t_backward activeGate,
t_backward activeState) {
template <int valueSize, int frameSize>
__device__ void backward_sequence(real rGateValue,
real rOutputGrad,
real rPreOutputValue,
real &rGateGrad,
real &rStateGrad,
real *shStateGrad,
real *shStateValue,
real *shGateValue,
real rCheck,
real &rGateValuePrev,
int index,
t_backward activeNode,
t_backward activeGate,
t_backward activeState) {
const int frameIdx = index % frameSize;
const int frameIdy = index / frameSize;
if (frameIdy == 3) {
......@@ -363,8 +398,8 @@ backward_sequence(real rGateValue,
rStateGrad = rGateGrad * rCheck;
shStateGrad[index] = rStateGrad;
ptx_sync(3, valueSize);
rStateGrad += shStateGrad[frameIdx + frameSize *2];
rStateGrad += shStateGrad[frameIdx + frameSize *3];
rStateGrad += shStateGrad[frameIdx + frameSize * 2];
rStateGrad += shStateGrad[frameIdx + frameSize * 3];
rGateGrad = rStateGrad * shGateValue[frameIdx];
rGateGrad = activeGate(rGateGrad, rGateValue);
} else if (frameIdy == 2) {
......@@ -373,7 +408,7 @@ backward_sequence(real rGateValue,
shStateGrad[index] = rStateGrad;
ptx_sync(3, valueSize);
rStateGrad += shStateGrad[frameIdx + frameSize];
rStateGrad += shStateGrad[frameIdx + frameSize *3];
rStateGrad += shStateGrad[frameIdx + frameSize * 3];
rGateValuePrev = rGateValue;
rGateGrad = rStateGrad * shStateValue[frameIdx];
rGateGrad = activeGate(rGateGrad, rGateValue);
......@@ -381,43 +416,43 @@ backward_sequence(real rGateValue,
shGateValue[frameIdx] = rGateValue;
ptx_sync(3, valueSize);
rStateGrad = shStateGrad[frameIdx + frameSize];
rStateGrad += shStateGrad[frameIdx + frameSize *2];
rStateGrad += shStateGrad[frameIdx + frameSize *3];
rStateGrad += shStateGrad[frameIdx + frameSize * 2];
rStateGrad += shStateGrad[frameIdx + frameSize * 3];
rGateGrad = rStateGrad * shGateValue[frameIdx + frameSize];
rGateGrad = activeNode(rGateGrad, rGateValue);
}
}
template<int valueSize, int frameSize>
template <int valueSize, int frameSize>
__device__ void load_weight(real rWeight[], real *weight, const int index) {
if (valueSize == 128) {
weight += index;
#pragma unroll
#pragma unroll
for (int n = 0; n < frameSize; n++) {
rWeight[n] = weight[n*valueSize];
rWeight[n] = weight[n * valueSize];
}
transpose_32x32(rWeight, index % 32);
}
if (valueSize == 256) {
int id = (index / 32) % 2;
weight += index - id * 32 + id * 32 * valueSize;
#pragma unroll
#pragma unroll
for (int n = 0; n < 32; n++) {
rWeight[n] = weight[n*valueSize];
rWeight[n + 32] = weight[n*valueSize + 32];
rWeight[n] = weight[n * valueSize];
rWeight[n + 32] = weight[n * valueSize + 32];
}
transpose_32x32(rWeight, index % 32);
transpose_32x32(&rWeight[32], index % 32);
}
}
template<int valueSize, int frameSize, int reversed>
template <int valueSize, int frameSize, int reversed>
__global__ void KeLstmBackward(real *gateValue,
real *gateGrad,
real *stateValue,
real *stateGrad, /* do not need save */
real *stateGrad, /* do not need save */
real *preOutputValue,
real *preOutputGrad, /* do not need save */
real *preOutputGrad, /* do not need save */
real *checkIg,
real *checkIgGrad,
real *checkFg,
......@@ -484,20 +519,27 @@ __global__ void KeLstmBackward(real *gateValue,
for (int i = 0; i < length; ++i) {
if (frameIdy == 3) {
if (i != length -1) {
if (i != length - 1) {
frameStateValue.nextFrame<!reversed, frameSize>();
shStateValue[frameIdx] = frameStateValue.getValue();
} else {
shStateValue[frameIdx] = 0.0;
}
}
backward_sequence<valueSize, frameSize>(
rGateValue, rOutputGrad, rPreOutputValue, rGateGrad,
rStateGrad, shStateGrad, shStateValue, shGateValue,
rCheck, rGateValuePrev, index,
hppl::gpu::backward[active_node],
hppl::gpu::backward[active_gate],
hppl::gpu::backward[active_state]);
backward_sequence<valueSize, frameSize>(rGateValue,
rOutputGrad,
rPreOutputValue,
rGateGrad,
rStateGrad,
shStateGrad,
shStateValue,
shGateValue,
rCheck,
rGateValuePrev,
index,
hppl::gpu::backward[active_node],
hppl::gpu::backward[active_gate],
hppl::gpu::backward[active_state]);
if (frameIdy == 3) {
rCheckGrad += rGateGrad * rStateValue;
rStateValue = shStateValue[frameIdx];
......@@ -523,9 +565,9 @@ __global__ void KeLstmBackward(real *gateValue,
shGateGrad[frameIdy][frameIdx] = rGateGrad;
if (valueSize == 128) {
real sum = 0.0f;
#pragma unroll
#pragma unroll
for (int n = 0; n < frameSize; n++) {
sum += shGateGrad[frameIdy][n]*B_r[n];
sum += shGateGrad[frameIdy][n] * B_r[n];
}
if (frameIdy == 3) {
rOutputGrad += sum;
......@@ -541,7 +583,7 @@ __global__ void KeLstmBackward(real *gateValue,
}
real sum = 0.0f;
for (int n = 0; n < frameSize; n++) {
sum += A_r[n]*B_r[n];
sum += A_r[n] * B_r[n];
}
if (frameIdy == 3) {
rOutputGrad += sum;
......@@ -552,8 +594,8 @@ __global__ void KeLstmBackward(real *gateValue,
if (frameIdy == 3) {
ptx_sync(6, valueSize);
#pragma unroll
for (int i = 0; i < 3; i ++) {
#pragma unroll
for (int i = 0; i < 3; i++) {
rOutputGrad += shOutputGrad[i][frameIdx];
}
} else {
......@@ -564,11 +606,14 @@ __global__ void KeLstmBackward(real *gateValue,
/* TODO: Temporary save & merger in another kernel */
if (frameIdy == 1) {
if (checkIgGrad) paddle::paddleAtomicAdd(checkIgGrad+frameIdx, rCheckGrad);
if (checkIgGrad)
paddle::paddleAtomicAdd(checkIgGrad + frameIdx, rCheckGrad);
} else if (frameIdy == 2) {
if (checkFgGrad) paddle::paddleAtomicAdd(checkFgGrad+frameIdx, rCheckGrad);
if (checkFgGrad)
paddle::paddleAtomicAdd(checkFgGrad + frameIdx, rCheckGrad);
} else if (frameIdy == 3) {
if (checkOgGrad) paddle::paddleAtomicAdd(checkOgGrad+frameIdx, rCheckGrad);
if (checkOgGrad)
paddle::paddleAtomicAdd(checkOgGrad + frameIdx, rCheckGrad);
}
}
......@@ -593,68 +638,183 @@ void hl_lstm_parallel_backward_data(real *gateValue,
hl_activation_mode_t active_node,
hl_activation_mode_t active_gate,
hl_activation_mode_t active_state) {
CHECK(frameSize == 32 || frameSize == 64 ||
frameSize == 128 || frameSize == 256);
CHECK(frameSize == 32 || frameSize == 64 || frameSize == 128 ||
frameSize == 256);
dim3 grid(numSequences, 1);
if (!reversed) {
if (frameSize == 32) {
KeLstmBackward<128, 32, 0><<<grid, 128, 0, STREAM_DEFAULT>>>
(gateValue, gateGrad, stateValue, stateGrad, preOutputValue,
preOutputGrad, checkIg, checkIgGrad, checkFg, checkFgGrad, checkOg,
checkOgGrad, outputGrad, weight, sequence,
active_node, active_gate, active_state);
KeLstmBackward<128, 32, 0><<<grid, 128, 0, STREAM_DEFAULT>>>(
gateValue,
gateGrad,
stateValue,
stateGrad,
preOutputValue,
preOutputGrad,
checkIg,
checkIgGrad,
checkFg,
checkFgGrad,
checkOg,
checkOgGrad,
outputGrad,
weight,
sequence,
active_node,
active_gate,
active_state);
} else if (frameSize == 64) {
KeLstmBackward<256, 64, 0><<<grid, 256, 0, STREAM_DEFAULT>>>
(gateValue, gateGrad, stateValue, stateGrad, preOutputValue,
preOutputGrad, checkIg, checkIgGrad, checkFg, checkFgGrad, checkOg,
checkOgGrad, outputGrad, weight, sequence,
active_node, active_gate, active_state);
KeLstmBackward<256, 64, 0><<<grid, 256, 0, STREAM_DEFAULT>>>(
gateValue,
gateGrad,
stateValue,
stateGrad,
preOutputValue,
preOutputGrad,
checkIg,
checkIgGrad,
checkFg,
checkFgGrad,
checkOg,
checkOgGrad,
outputGrad,
weight,
sequence,
active_node,
active_gate,
active_state);
} else if (frameSize == 128) {
KeLstmBackward<512, 128, 0><<<grid, 512, 0, STREAM_DEFAULT>>>
(gateValue, gateGrad, stateValue, stateGrad, preOutputValue,
preOutputGrad, checkIg, checkIgGrad, checkFg, checkFgGrad, checkOg,
checkOgGrad, outputGrad, weight, sequence,
active_node, active_gate, active_state);
KeLstmBackward<512, 128, 0><<<grid, 512, 0, STREAM_DEFAULT>>>(
gateValue,
gateGrad,
stateValue,
stateGrad,
preOutputValue,
preOutputGrad,
checkIg,
checkIgGrad,
checkFg,
checkFgGrad,
checkOg,
checkOgGrad,
outputGrad,
weight,
sequence,
active_node,
active_gate,
active_state);
} else if (frameSize == 256) {
KeLstmBackward<1024, 256, 0><<<grid, 1024, 0, STREAM_DEFAULT>>>
(gateValue, gateGrad, stateValue, stateGrad, preOutputValue,
preOutputGrad, checkIg, checkIgGrad, checkFg, checkFgGrad, checkOg,
checkOgGrad, outputGrad, weight, sequence,
active_node, active_gate, active_state);
KeLstmBackward<1024, 256, 0><<<grid, 1024, 0, STREAM_DEFAULT>>>(
gateValue,
gateGrad,
stateValue,
stateGrad,
preOutputValue,
preOutputGrad,
checkIg,
checkIgGrad,
checkFg,
checkFgGrad,
checkOg,
checkOgGrad,
outputGrad,
weight,
sequence,
active_node,
active_gate,
active_state);
}
} else {
if (frameSize == 32) {
KeLstmBackward<128, 32, 1><<<grid, 128, 0, STREAM_DEFAULT>>>
(gateValue, gateGrad, stateValue, stateGrad, preOutputValue,
preOutputGrad, checkIg, checkIgGrad, checkFg, checkFgGrad, checkOg,
checkOgGrad, outputGrad, weight, sequence,
active_node, active_gate, active_state);
KeLstmBackward<128, 32, 1><<<grid, 128, 0, STREAM_DEFAULT>>>(
gateValue,
gateGrad,
stateValue,
stateGrad,
preOutputValue,
preOutputGrad,
checkIg,
checkIgGrad,
checkFg,
checkFgGrad,
checkOg,
checkOgGrad,
outputGrad,
weight,
sequence,
active_node,
active_gate,
active_state);
} else if (frameSize == 64) {
KeLstmBackward<256, 64, 1><<<grid, 256, 0, STREAM_DEFAULT>>>
(gateValue, gateGrad, stateValue, stateGrad, preOutputValue,
preOutputGrad, checkIg, checkIgGrad, checkFg, checkFgGrad, checkOg,
checkOgGrad, outputGrad, weight, sequence,
active_node, active_gate, active_state);
KeLstmBackward<256, 64, 1><<<grid, 256, 0, STREAM_DEFAULT>>>(
gateValue,
gateGrad,
stateValue,
stateGrad,
preOutputValue,
preOutputGrad,
checkIg,
checkIgGrad,
checkFg,
checkFgGrad,
checkOg,
checkOgGrad,
outputGrad,
weight,
sequence,
active_node,
active_gate,
active_state);
} else if (frameSize == 128) {
KeLstmBackward<512, 128, 1><<<grid, 512, 0, STREAM_DEFAULT>>>
(gateValue, gateGrad, stateValue, stateGrad, preOutputValue,
preOutputGrad, checkIg, checkIgGrad, checkFg, checkFgGrad, checkOg,
checkOgGrad, outputGrad, weight, sequence,
active_node, active_gate, active_state);
KeLstmBackward<512, 128, 1><<<grid, 512, 0, STREAM_DEFAULT>>>(
gateValue,
gateGrad,
stateValue,
stateGrad,
preOutputValue,
preOutputGrad,
checkIg,
checkIgGrad,
checkFg,
checkFgGrad,
checkOg,
checkOgGrad,
outputGrad,
weight,
sequence,
active_node,
active_gate,
active_state);
} else if (frameSize == 256) {
KeLstmBackward<1024, 256, 1><<<grid, 1024, 0, STREAM_DEFAULT>>>
(gateValue, gateGrad, stateValue, stateGrad, preOutputValue,
preOutputGrad, checkIg, checkIgGrad, checkFg, checkFgGrad, checkOg,
checkOgGrad, outputGrad, weight, sequence,
active_node, active_gate, active_state);
KeLstmBackward<1024, 256, 1><<<grid, 1024, 0, STREAM_DEFAULT>>>(
gateValue,
gateGrad,
stateValue,
stateGrad,
preOutputValue,
preOutputGrad,
checkIg,
checkIgGrad,
checkFg,
checkFgGrad,
checkOg,
checkOgGrad,
outputGrad,
weight,
sequence,
active_node,
active_gate,
active_state);
}
}
CHECK_SYNC("hl_lstm_parallel_backward_data");
}
template<int B_X, int B_Y>
template <int B_X, int B_Y>
__global__ void KeSetGradZero(real *gateGrad,
const int *starts, int valueSize, int numSequences, bool reversed) {
const int *starts,
int valueSize,
int numSequences,
bool reversed) {
// const int tid = threadIdx.x;
const int frameIdx = blockIdx.x * B_X + threadIdx.x;
......@@ -682,19 +842,31 @@ void hl_lstm_parallel_backward_weight(real *weightGrad,
int valueSize = 4 * frameSize;
dim3 threads(32, 32);
dim3 grid((valueSize + 32 - 1) / 32, (numSequences + 32 - 1) / 32);
KeSetGradZero<32, 32><<<grid, threads, 0, STREAM_DEFAULT>>>
(gateGrad, sequence, valueSize, numSequences, reversed);
KeSetGradZero<32, 32><<<grid, threads, 0, STREAM_DEFAULT>>>(
gateGrad, sequence, valueSize, numSequences, reversed);
if (!reversed) {
hl_matrix_mul(outputValue,
HPPL_OP_T, gateGrad + valueSize, HPPL_OP_N, weightGrad,
frameSize, valueSize, batchSize - 1,
1.0, 1.0);
HPPL_OP_T,
gateGrad + valueSize,
HPPL_OP_N,
weightGrad,
frameSize,
valueSize,
batchSize - 1,
1.0,
1.0);
} else {
hl_matrix_mul(outputValue + frameSize,
HPPL_OP_T, gateGrad, HPPL_OP_N, weightGrad,
frameSize, valueSize, batchSize - 1,
1.0, 1.0);
HPPL_OP_T,
gateGrad,
HPPL_OP_N,
weightGrad,
frameSize,
valueSize,
batchSize - 1,
1.0,
1.0);
}
CHECK_SYNC("hl_lstm_parallel_backward_weight");
}
paddle/cuda/src/hl_cuda_matrix.cu
浏览文件 @ 75185d82
......@@ -12,22 +12,21 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "hl_base.h"
#include "hl_device_functions.cuh"
#include "hl_gpu_matrix_kernel.cuh"
#include "hl_matrix.h"
#include "hl_matrix_ops.cuh"
#include "hl_matrix_apply.cuh"
#include "hl_matrix_ops.cuh"
#include "hl_sequence.h"
#include "hl_sparse.ph"
#include "paddle/utils/Logging.h"
#include "hl_device_functions.cuh"
#include "hl_gpu_matrix_kernel.cuh"
DEFINE_MATRIX_UNARY_OP(Zero, a = 0);
DEFINE_MATRIX_TERNARY_PARAMETER_OP(_add, TWO_PARAMETER, c = p1*a + p2*b);
void hl_matrix_add(real *A_d,
real *B_d,
real *C_d,
DEFINE_MATRIX_TERNARY_PARAMETER_OP(_add, TWO_PARAMETER, c = p1 * a + p2 * b);
void hl_matrix_add(real* A_d,
real* B_d,
real* C_d,
int dimM,
int dimN,
real alpha,
......@@ -36,33 +35,32 @@ void hl_matrix_add(real *A_d,
CHECK_NOTNULL(B_d);
CHECK_NOTNULL(C_d);
hl_gpu_apply_ternary_op
<real, ternary::_add<real>, 0, 0>(ternary::_add<real>(alpha, beta),
A_d,
B_d,
C_d,
dimM,
dimN,
dimN,
dimN,
dimN);
hl_gpu_apply_ternary_op<real, ternary::_add<real>, 0, 0>(
ternary::_add<real>(alpha, beta),
A_d,
B_d,
C_d,
dimM,
dimN,
dimN,
dimN,
dimN);
CHECK_SYNC("hl_matrix_add failed");
}
#ifdef PADDLE_TYPE_DOUBLE
#define THRESHOLD 128
#define THRESHOLD 128
#else
#define THRESHOLD 64
#define THRESHOLD 64
#endif
__device__ __forceinline__
void findMax(real* I,
real* dfMax_s,
int blockSize,
int base,
int curIdx,
int nextIdx,
int dimN,
real* max) {
__device__ __forceinline__ void findMax(real* I,
real* dfMax_s,
int blockSize,
int base,
int curIdx,
int nextIdx,
int dimN,
real* max) {
dfMax_s[base] = -1.0e20;
while (curIdx < dimN) {
if (dfMax_s[base] < I[nextIdx]) {
......@@ -78,25 +76,24 @@ void findMax(real* I,
if (base < stride) {
nextIdx = base + stride;
if (dfMax_s[base] < dfMax_s[nextIdx]) {
dfMax_s[base] = dfMax_s[nextIdx];
dfMax_s[base] = dfMax_s[nextIdx];
}
}
}
if (0 == base) {
if (0 == base) {
max[0] = dfMax_s[0];
}
__syncthreads();
}
__device__ __forceinline__
void subMaxAndExp(real* I,
real* O,
int curIdx,
int nextIdx,
int blockSize,
int dimN,
real max) {
__device__ __forceinline__ void subMaxAndExp(real* I,
real* O,
int curIdx,
int nextIdx,
int blockSize,
int dimN,
real max) {
real val;
while (curIdx < dimN) {
val = I[nextIdx] - max;
......@@ -115,14 +112,13 @@ void subMaxAndExp(real* I,
__syncthreads();
}
__device__ __forceinline__
void valueSum(real* O,
real* dfMax_s,
int blockSize,
int base,
int curIdx,
int nextIdx,
int dimN) {
__device__ __forceinline__ void valueSum(real* O,
real* dfMax_s,
int blockSize,
int base,
int curIdx,
int nextIdx,
int dimN) {
dfMax_s[base] = 0;
while (curIdx < dimN) {
dfMax_s[base] += O[nextIdx];
......@@ -141,13 +137,8 @@ void valueSum(real* O,
__syncthreads();
}
__device__ __forceinline__
void divSum(real* O,
real sum,
int curIdx,
int nextIdx,
int blockSize,
int dimN) {
__device__ __forceinline__ void divSum(
real* O, real sum, int curIdx, int nextIdx, int blockSize, int dimN) {
while (curIdx < dimN) {
O[nextIdx] /= sum;
nextIdx += blockSize;
......@@ -155,20 +146,18 @@ void divSum(real* O,
}
}
__device__ __forceinline__
void softmax(real* I,
real* O,
real* dfMax_s,
int blockSize,
int base,
int curIdx,
int nextIdx,
int dimN) {
__device__ __forceinline__ void softmax(real* I,
real* O,
real* dfMax_s,
int blockSize,
int base,
int curIdx,
int nextIdx,
int dimN) {
__shared__ real max;
// find the max number
findMax(I, dfMax_s, blockSize, base, curIdx,
nextIdx, dimN, &max);
findMax(I, dfMax_s, blockSize, base, curIdx, nextIdx, dimN, &max);
// sub max Value and do Exp operation
subMaxAndExp(I, O, base, nextIdx, blockSize, dimN, max);
......@@ -181,8 +170,8 @@ void softmax(real* I,
divSum(O, dfMax_s[0], curIdx, nextIdx, blockSize, dimN);
}
template<int blockSize>
__global__ void KeMatrixSoftMax(real *O, real *I, int dimN) {
template <int blockSize>
__global__ void KeMatrixSoftMax(real* O, real* I, int dimN) {
int base = threadIdx.x;
__shared__ real dfMax_s[blockSize];
int nextIdx = blockIdx.x * dimN + base;
......@@ -191,19 +180,18 @@ __global__ void KeMatrixSoftMax(real *O, real *I, int dimN) {
softmax(I, O, dfMax_s, blockSize, base, curIdx, nextIdx, dimN);
}
void hl_matrix_softmax(real *A_d, real *C_d, int dimM, int dimN) {
void hl_matrix_softmax(real* A_d, real* C_d, int dimM, int dimN) {
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(C_d);
dim3 block(512, 1);
dim3 grid(dimM, 1);
KeMatrixSoftMax<512>
<<<grid, block, 0, STREAM_DEFAULT>>>(C_d, A_d, dimN);
KeMatrixSoftMax<512><<<grid, block, 0, STREAM_DEFAULT>>>(C_d, A_d, dimN);
CHECK_SYNC("hl_matrix_softmax failed");
}
template<int blockSize>
__global__ void KeSequenceSoftMax(real *O, real *I, const int* index) {
template <int blockSize>
__global__ void KeSequenceSoftMax(real* O, real* I, const int* index) {
int base = threadIdx.x;
int bid = blockIdx.x;
__shared__ real dfMax_s[blockSize];
......@@ -217,8 +205,8 @@ __global__ void KeSequenceSoftMax(real *O, real *I, const int* index) {
softmax(I, O, dfMax_s, blockSize, base, curIdx, nextIdx, dimN);
}
void hl_sequence_softmax_forward(real *A_d,
real *C_d,
void hl_sequence_softmax_forward(real* A_d,
real* C_d,
const int* index,
int numSequence) {
CHECK_NOTNULL(A_d);
......@@ -226,59 +214,48 @@ void hl_sequence_softmax_forward(real *A_d,
dim3 block(512, 1);
dim3 grid(numSequence, 1);
KeSequenceSoftMax<512>
<<<grid, block, 0, STREAM_DEFAULT>>>(C_d, A_d, index);
KeSequenceSoftMax<512><<<grid, block, 0, STREAM_DEFAULT>>>(C_d, A_d, index);
CHECK_SYNC("hl_sequence_softmax_forward failed");
}
__global__ void KeMatrixDerivative(real *grad_d,
real *output_d,
real *sftmaxSum_d,
int dimM,
int dimN) {
int rowIdx = blockIdx.x*blockDim.x + threadIdx.x;
int colIdx = blockIdx.y*blockDim.y + threadIdx.y;
__global__ void KeMatrixDerivative(
real* grad_d, real* output_d, real* sftmaxSum_d, int dimM, int dimN) {
int rowIdx = blockIdx.x * blockDim.x + threadIdx.x;
int colIdx = blockIdx.y * blockDim.y + threadIdx.y;
int index;
if (rowIdx < dimM && colIdx < dimN) {
index = rowIdx*dimN + colIdx;
index = rowIdx * dimN + colIdx;
grad_d[index] = output_d[index] * (grad_d[index] - sftmaxSum_d[rowIdx]);
}
}
void hl_matrix_softmax_derivative(real *grad_d,
real *output_d,
real *sftmaxSum_d,
int dimM,
int dimN) {
void hl_matrix_softmax_derivative(
real* grad_d, real* output_d, real* sftmaxSum_d, int dimM, int dimN) {
CHECK_NOTNULL(grad_d);
CHECK_NOTNULL(output_d);
CHECK_NOTNULL(sftmaxSum_d);
int blocksX = (dimM + 0) / 1;
int blocksY = (dimN + 1024 -1) / 1024;
int blocksY = (dimN + 1024 - 1) / 1024;
dim3 threads(1, 1024);
dim3 grid(blocksX, blocksY);
KeMatrixDerivative<<< grid, threads, 0, STREAM_DEFAULT >>>
(grad_d, output_d, sftmaxSum_d, dimM, dimN);
KeMatrixDerivative<<<grid, threads, 0, STREAM_DEFAULT>>>(
grad_d, output_d, sftmaxSum_d, dimM, dimN);
CHECK_SYNC("hl_matrix_softmax_derivative failed");
}
__global__ void KeMatrixMultiBinaryCrossEntropy(real* output,
real* entropy,
int* row,
int* col,
int dimM,
int dimN) {
__global__ void KeMatrixMultiBinaryCrossEntropy(
real* output, real* entropy, int* row, int* col, int dimM, int dimN) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < dimM) {
for (int i = 0; i < dimN; i ++) {
for (int i = 0; i < dimN; i++) {
entropy[index] -= log(1 - output[index * dimN + i]);
}
int *row_col = col + row[index];
int* row_col = col + row[index];
int col_num = row[index + 1] - row[index];
for (int i = 0; i < col_num; i ++) {
for (int i = 0; i < col_num; i++) {
real o = output[index * dimN + row_col[i]];
entropy[index] -= log(o / (1 - o));
}
......@@ -299,37 +276,30 @@ void hl_matrix_multi_binary_cross_entropy(real* output,
dim3 threads(n_threads);
dim3 grid(blocks);
hl_csr_matrix mat = (hl_csr_matrix)(csr_mat->matrix);
KeMatrixMultiBinaryCrossEntropy<<< grid, threads, 0, STREAM_DEFAULT >>>
(output, entropy, mat->csr_row, mat->csr_col, dimM, dimN);
KeMatrixMultiBinaryCrossEntropy<<<grid, threads, 0, STREAM_DEFAULT>>>(
output, entropy, mat->csr_row, mat->csr_col, dimM, dimN);
CHECK_SYNC("hl_matrix_multi_binary_cross_entropy failed");
}
__global__ void KeMatrixMultiBinaryCrossEntropyBp(real* output,
real* grad,
int* row,
int* col,
int dimM,
int dimN) {
__global__ void KeMatrixMultiBinaryCrossEntropyBp(
real* output, real* grad, int* row, int* col, int dimM, int dimN) {
int row_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (row_idx < dimM) {
for (int i = 0; i < dimN; i ++) {
for (int i = 0; i < dimN; i++) {
int index = row_idx * dimN + i;
grad[index] += 1.0 / (1 - output[index]);
}
int col_num = row[row_idx + 1] - row[row_idx];
int *row_col = col + row[row_idx];
for (int i = 0; i < col_num; i ++) {
int* row_col = col + row[row_idx];
for (int i = 0; i < col_num; i++) {
int index = row_idx * dimN + row_col[i];
grad[index] -= 1.0 / (output[index] * (1 - output[index]));
}
}
}
void hl_matrix_multi_binary_cross_entropy_bp(real* output,
real* grad,
hl_sparse_matrix_s csr_mat,
int dimM,
int dimN) {
void hl_matrix_multi_binary_cross_entropy_bp(
real* output, real* grad, hl_sparse_matrix_s csr_mat, int dimM, int dimN) {
CHECK_NOTNULL(output);
CHECK_NOTNULL(grad);
CHECK_NOTNULL(csr_mat);
......@@ -339,16 +309,13 @@ void hl_matrix_multi_binary_cross_entropy_bp(real* output,
dim3 threads(n_threads);
dim3 grid(blocks);
hl_csr_matrix mat = (hl_csr_matrix)(csr_mat->matrix);
KeMatrixMultiBinaryCrossEntropyBp<<< grid, threads, 0, STREAM_DEFAULT >>>
(output, grad, mat->csr_row, mat->csr_col, dimM, dimN);
KeMatrixMultiBinaryCrossEntropyBp<<<grid, threads, 0, STREAM_DEFAULT>>>(
output, grad, mat->csr_row, mat->csr_col, dimM, dimN);
CHECK_SYNC("hl_matrix_multi_binary_cross_entropy_bp failed");
}
__global__ void KeMatrixCrossEntropy(real* O,
real* E,
int* label,
int dimM,
int dimN) {
__global__ void KeMatrixCrossEntropy(
real* O, real* E, int* label, int dimM, int dimN) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
int newBase;
if (index < dimM) {
......@@ -358,59 +325,49 @@ __global__ void KeMatrixCrossEntropy(real* O,
}
}
void hl_matrix_cross_entropy(real* A_d,
real* C_d,
int* label_d,
int dimM,
int dimN) {
void hl_matrix_cross_entropy(
real* A_d, real* C_d, int* label_d, int dimM, int dimN) {
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(C_d);
int blocks = (dimM + 1024 - 1) / 1024;
dim3 threads(1024, 1);
dim3 grid(blocks, 1);
KeMatrixCrossEntropy<<< grid, threads, 0, STREAM_DEFAULT >>>
(A_d, C_d, label_d, dimM, dimN);
KeMatrixCrossEntropy<<<grid, threads, 0, STREAM_DEFAULT>>>(
A_d, C_d, label_d, dimM, dimN);
CHECK_SYNC("hl_matrix_cross_entropy failed");
}
__global__ void KeMatrixCrossEntropyBp(real* grad_d,
real* output_d,
int* label_d,
int dimM,
int dimN) {
int rowIdx = blockIdx.x*blockDim.x + threadIdx.x;
int colIdx = blockIdx.y*blockDim.y + threadIdx.y;
__global__ void KeMatrixCrossEntropyBp(
real* grad_d, real* output_d, int* label_d, int dimM, int dimN) {
int rowIdx = blockIdx.x * blockDim.x + threadIdx.x;
int colIdx = blockIdx.y * blockDim.y + threadIdx.y;
int index;
if (rowIdx < dimM && colIdx < dimN) {
index = rowIdx*dimN + colIdx;
index = rowIdx * dimN + colIdx;
if (label_d[rowIdx] == colIdx) {
grad_d[index] -= 1.0f / output_d[index];
}
}
}
void hl_matrix_cross_entropy_bp(real* grad_d,
real* output_d,
int* label_d,
int dimM,
int dimN) {
void hl_matrix_cross_entropy_bp(
real* grad_d, real* output_d, int* label_d, int dimM, int dimN) {
CHECK_NOTNULL(grad_d);
CHECK_NOTNULL(output_d);
CHECK_NOTNULL(label_d);
int blocksX = (dimM + 0)/1;
int blocksY = (dimN + 1024 -1) / 1024;
int blocksX = (dimM + 0) / 1;
int blocksY = (dimN + 1024 - 1) / 1024;
dim3 threads(1, 1024);
dim3 grid(blocksX, blocksY);
KeMatrixCrossEntropyBp<<< grid, threads, 0, STREAM_DEFAULT >>>
(grad_d, output_d, label_d, dimM, dimN);
KeMatrixCrossEntropyBp<<<grid, threads, 0, STREAM_DEFAULT>>>(
grad_d, output_d, label_d, dimM, dimN);
CHECK_SYNC("hl_matrix_cross_entropy_bp failed");
}
void hl_matrix_zero_mem(real* data, int num) {
hl_gpu_apply_unary_op(
unary::Zero<real>(), data, 1, num, num);
hl_gpu_apply_unary_op(unary::Zero<real>(), data, 1, num, num);
}
__global__ void KeParamReluForward(real* output,
......@@ -423,8 +380,8 @@ __global__ void KeParamReluForward(real* output,
int ty = blockIdx.y * blockDim.y + threadIdx.y;
if (tx < width && ty < height) {
int index = ty * width + tx;
output[index] = input[index] > 0 ? input[index] :
input[index] * w[tx / partial_sum];
output[index] =
input[index] > 0 ? input[index] : input[index] * w[tx / partial_sum];
}
}
......@@ -439,14 +396,14 @@ void hl_param_relu_forward(real* output,
CHECK_NOTNULL(w);
dim3 threads(16, 16);
int blockX = (width + 16 - 1) / 16;
int blockY = (height + 16 -1) / 16;
int blockY = (height + 16 - 1) / 16;
dim3 grid(blockX, blockY);
KeParamReluForward<<<grid, threads, 0, STREAM_DEFAULT>>>
(output, input, w, width, height, partial_sum);
KeParamReluForward<<<grid, threads, 0, STREAM_DEFAULT>>>(
output, input, w, width, height, partial_sum);
CHECK_SYNC("hl_param_relu_forward failed");
}
template<int blockSize>
template <int blockSize>
__global__ void KeParamReluBackWardW(real* grad_w,
real* grad_o,
real* input,
......@@ -491,8 +448,8 @@ void hl_param_relu_backward_w(real* grad_w,
int grid_num = width / partial_sum;
dim3 threads(blockSize, 1);
dim3 grid(grid_num, 1);
KeParamReluBackWardW<blockSize><<<grid, threads, 0, STREAM_DEFAULT>>>
(grad_w, grad_o, input, width, height, partial_sum);
KeParamReluBackWardW<blockSize><<<grid, threads, 0, STREAM_DEFAULT>>>(
grad_w, grad_o, input, width, height, partial_sum);
CHECK_SYNC("hl_param_relu_backward_w failed");
}
......@@ -524,19 +481,15 @@ void hl_param_relu_backward_diff(real* grad_o,
CHECK_NOTNULL(diff);
dim3 threads(16, 16);
int blockX = (width + 16 - 1) / 16;
int blockY = (height + 16 -1) / 16;
int blockY = (height + 16 - 1) / 16;
dim3 grid(blockX, blockY);
KeParamReluBackwardDiff<<<grid, threads, 0, STREAM_DEFAULT>>>
(grad_o, data, w, diff, width, height, partial_sum);
KeParamReluBackwardDiff<<<grid, threads, 0, STREAM_DEFAULT>>>(
grad_o, data, w, diff, width, height, partial_sum);
CHECK_SYNC("hl_param_relu_backward_diff failed");
}
__global__ void KeMatrixAddSharedBias(real* A,
real* B,
const int channel,
const int M,
const int N,
real scale) {
__global__ void KeMatrixAddSharedBias(
real* A, real* B, const int channel, const int M, const int N, real scale) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
int dim = N / channel;
if (index < M * N) {
......@@ -554,15 +507,14 @@ void hl_matrix_add_shared_bias(real* A_d,
real scale) {
const int blocks = 512;
const int grids = DIVUP(dimM * dimN, blocks);
KeMatrixAddSharedBias<<<grids, blocks, 0, STREAM_DEFAULT>>>
(A_d, B_d, channel, dimM, dimN, scale);
KeMatrixAddSharedBias<<<grids, blocks, 0, STREAM_DEFAULT>>>(
A_d, B_d, channel, dimM, dimN, scale);
CHECK_SYNC("hl_matrix_add_shared_bias failed");
}
template <int blockSize>
__global__ void KeMatrixCollectSharedBias(real *B,
real *A,
__global__ void KeMatrixCollectSharedBias(real* B,
real* A,
const int channel,
const int M,
const int N,
......@@ -589,7 +541,7 @@ __global__ void KeMatrixCollectSharedBias(real *B,
int n = j * blockSize + tid;
int m = n / dim;
int w = n % dim;
smem[tid] = (m < M && w < dim) ? A[m * N + bid * dim + w] : 0.0;
smem[tid] = (m < M && w < dim) ? A[m * N + bid * dim + w] : 0.0;
__syncthreads();
simpleReduce(smem, tid, blockSize);
sum += smem[0];
......@@ -611,33 +563,32 @@ void hl_matrix_collect_shared_bias(real* B_d,
const int limit = 64;
int grids = (dimM * dim) < limit ? DIVUP(channel, blocks) : channel;
KeMatrixCollectSharedBias<blocks>
<<< grids, blocks, 0, STREAM_DEFAULT>>>
(B_d, A_d, channel, dimM, dimN, dim, limit, scale);
KeMatrixCollectSharedBias<blocks><<<grids, blocks, 0, STREAM_DEFAULT>>>(
B_d, A_d, channel, dimM, dimN, dim, limit, scale);
CHECK_SYNC("hl_matrix_collect_shared_bias failed");
}
__global__ void keMatrixRotate(real* mat, real* matRot,
int dimM, int dimN, bool clockWise) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < dimM * dimN) {
int i = idx / dimN;
int j = idx % dimN;
if (clockWise) {
matRot[j * dimM + i] = mat[(dimM - i - 1) * dimN + j];
} else {
matRot[j * dimM + i] = mat[i * dimN + (dimN - j - 1)];
}
__global__ void keMatrixRotate(
real* mat, real* matRot, int dimM, int dimN, bool clockWise) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < dimM * dimN) {
int i = idx / dimN;
int j = idx % dimN;
if (clockWise) {
matRot[j * dimM + i] = mat[(dimM - i - 1) * dimN + j];
} else {
matRot[j * dimM + i] = mat[i * dimN + (dimN - j - 1)];
}
}
}
void hl_matrix_rotate(real *mat, real* matRot,
int dimM, int dimN, bool clockWise) {
CHECK_NOTNULL(mat);
CHECK_NOTNULL(matRot);
const int threads = 512;
const int blocks = DIVUP(dimM * dimN, threads);
keMatrixRotate<<< blocks, threads, 0, STREAM_DEFAULT >>>
(mat, matRot, dimM, dimN, clockWise);
CHECK_SYNC("hl_matrix_rotate failed");
void hl_matrix_rotate(
real* mat, real* matRot, int dimM, int dimN, bool clockWise) {
CHECK_NOTNULL(mat);
CHECK_NOTNULL(matRot);
const int threads = 512;
const int blocks = DIVUP(dimM * dimN, threads);
keMatrixRotate<<<blocks, threads, 0, STREAM_DEFAULT>>>(
mat, matRot, dimM, dimN, clockWise);
CHECK_SYNC("hl_matrix_rotate failed");
}
paddle/cuda/src/hl_cuda_sequence.cu
浏览文件 @ 75185d82
......@@ -16,36 +16,36 @@ limitations under the License. */
#include "hl_device_functions.cuh"
#include "paddle/utils/Logging.h"
__global__ void KeMaxSequenceForward(real *input,
const int *sequence,
__global__ void KeMaxSequenceForward(real* input,
const int* sequence,
real* output,
int *index,
int* index,
int numSequences,
int dim) {
int dimIdx = threadIdx.x;
int sequenceId = blockIdx.x;
if (sequenceId >= numSequences) return;
int start = sequence[sequenceId];
int end = sequence[sequenceId+1];
int end = sequence[sequenceId + 1];
for (int i = dimIdx; i < dim; i += blockDim.x) {
real tmp = -HL_FLOAT_MAX;
int tmpId = -1;
for (int insId = start; insId < end; insId++) {
if (tmp < input[insId*dim + i]) {
tmp = input[insId*dim + i];
if (tmp < input[insId * dim + i]) {
tmp = input[insId * dim + i];
tmpId = insId;
}
}
output[sequenceId*dim + i] = tmp;
index[sequenceId*dim + i] = tmpId;
output[sequenceId * dim + i] = tmp;
index[sequenceId * dim + i] = tmpId;
}
}
void hl_max_sequence_forward(real* input,
const int* sequence,
real* output,
int *index,
int* index,
int numSequences,
int dim) {
CHECK_NOTNULL(input);
......@@ -55,29 +55,23 @@ void hl_max_sequence_forward(real* input,
dim3 threads(256, 1);
dim3 grid(numSequences, 1);
KeMaxSequenceForward<<< grid, threads, 0, STREAM_DEFAULT >>>
(input, sequence, output, index, numSequences, dim);
KeMaxSequenceForward<<<grid, threads, 0, STREAM_DEFAULT>>>(
input, sequence, output, index, numSequences, dim);
CHECK_SYNC("hl_max_sequence_forward failed");
}
__global__ void KeMaxSequenceBackward(real *outputGrad,
int *index,
real* inputGrad,
int numSequences,
int dim) {
__global__ void KeMaxSequenceBackward(
real* outputGrad, int* index, real* inputGrad, int numSequences, int dim) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
int colIdx = idx % dim;
if (idx < numSequences*dim) {
if (idx < numSequences * dim) {
int insId = index[idx];
inputGrad[insId * dim + colIdx] += outputGrad[idx];
}
}
void hl_max_sequence_backward(real* outputGrad,
int *index,
real* inputGrad,
int numSequences,
int dim) {
void hl_max_sequence_backward(
real* outputGrad, int* index, real* inputGrad, int numSequences, int dim) {
CHECK_NOTNULL(outputGrad);
CHECK_NOTNULL(index);
CHECK_NOTNULL(inputGrad);
......@@ -85,12 +79,12 @@ void hl_max_sequence_backward(real* outputGrad,
unsigned int blocks = (numSequences * dim + 128 - 1) / 128;
dim3 threads(128, 1);
dim3 grid(blocks, 1);
KeMaxSequenceBackward<<< grid, threads, 0, STREAM_DEFAULT >>>
(outputGrad, index, inputGrad, numSequences, dim);
KeMaxSequenceBackward<<<grid, threads, 0, STREAM_DEFAULT>>>(
outputGrad, index, inputGrad, numSequences, dim);
CHECK_SYNC("hl_max_sequence_backward failed");
}
template<int blockDimX, int blockDimY, int gridDimX, bool AddRow>
template <int blockDimX, int blockDimY, int gridDimX, bool AddRow>
__global__ void KeMatrixAddRows(real* output,
real* table,
int* ids,
......@@ -104,8 +98,8 @@ __global__ void KeMatrixAddRows(real* output,
while (sampleId < numSamples) {
int tableId = ids[sampleId];
if ((0 <= tableId) && (tableId < tableSize)) {
real *outputData = output + sampleId * dim;
real *tableData = table + tableId * dim;
real* outputData = output + sampleId * dim;
real* tableData = table + tableId * dim;
for (int i = idx; i < dim; i += blockDimX) {
if (AddRow == 0) {
outputData[i] += tableData[i];
......@@ -114,24 +108,27 @@ __global__ void KeMatrixAddRows(real* output,
}
}
}
sampleId += blockDimY*gridDimX;
sampleId += blockDimY * gridDimX;
}
}
template<int blockDimX, int blockDimY, int gridDimX, bool seq2batch, bool isAdd>
__global__
void KeSequence2Batch(real *batch,
real *sequence,
const int *batchIndex,
int seqWidth,
int batchCount) {
template <int blockDimX,
int blockDimY,
int gridDimX,
bool seq2batch,
bool isAdd>
__global__ void KeSequence2Batch(real* batch,
real* sequence,
const int* batchIndex,
int seqWidth,
int batchCount) {
int idx = threadIdx.x;
int idy = threadIdx.y;
int id = blockIdx.x + idy * gridDimX;
while (id < batchCount) {
int seqId = batchIndex[id];
real* batchData = batch + id*seqWidth;
real* seqData = sequence + seqId*seqWidth;
real* batchData = batch + id * seqWidth;
real* seqData = sequence + seqId * seqWidth;
for (int i = idx; i < seqWidth; i += blockDimX) {
if (seq2batch) {
if (isAdd) {
......@@ -147,13 +144,13 @@ void KeSequence2Batch(real *batch,
}
}
}
id += blockDimY*gridDimX;
id += blockDimY * gridDimX;
}
}
void hl_sequence2batch_copy(real *batch,
real *sequence,
const int *batchIndex,
void hl_sequence2batch_copy(real* batch,
real* sequence,
const int* batchIndex,
int seqWidth,
int batchCount,
bool seq2batch) {
......@@ -164,18 +161,18 @@ void hl_sequence2batch_copy(real *batch,
dim3 threads(128, 8);
dim3 grid(8, 1);
if (seq2batch) {
KeSequence2Batch<128, 8, 8, 1, 0><<< grid, threads, 0, STREAM_DEFAULT >>>
(batch, sequence, batchIndex, seqWidth, batchCount);
KeSequence2Batch<128, 8, 8, 1, 0><<<grid, threads, 0, STREAM_DEFAULT>>>(
batch, sequence, batchIndex, seqWidth, batchCount);
} else {
KeSequence2Batch<128, 8, 8, 0, 0><<< grid, threads, 0, STREAM_DEFAULT >>>
(batch, sequence, batchIndex, seqWidth, batchCount);
KeSequence2Batch<128, 8, 8, 0, 0><<<grid, threads, 0, STREAM_DEFAULT>>>(
batch, sequence, batchIndex, seqWidth, batchCount);
}
CHECK_SYNC("hl_sequence2batch_copy failed");
}
void hl_sequence2batch_add(real *batch,
real *sequence,
int *batchIndex,
void hl_sequence2batch_add(real* batch,
real* sequence,
int* batchIndex,
int seqWidth,
int batchCount,
bool seq2batch) {
......@@ -186,23 +183,22 @@ void hl_sequence2batch_add(real *batch,
dim3 threads(128, 8);
dim3 grid(8, 1);
if (seq2batch) {
KeSequence2Batch<128, 8, 8, 1, 1><<< grid, threads, 0, STREAM_DEFAULT >>>
(batch, sequence, batchIndex, seqWidth, batchCount);
KeSequence2Batch<128, 8, 8, 1, 1><<<grid, threads, 0, STREAM_DEFAULT>>>(
batch, sequence, batchIndex, seqWidth, batchCount);
} else {
KeSequence2Batch<128, 8, 8, 0, 1><<< grid, threads, 0, STREAM_DEFAULT >>>
(batch, sequence, batchIndex, seqWidth, batchCount);
KeSequence2Batch<128, 8, 8, 0, 1><<<grid, threads, 0, STREAM_DEFAULT>>>(
batch, sequence, batchIndex, seqWidth, batchCount);
}
CHECK_SYNC("hl_sequence2batch_add failed");
}
template<bool normByTimes, bool seq2batch>
__global__
void KeSequence2BatchPadding(real* batch,
real* sequence,
const int* sequenceStartPositions,
const size_t sequenceWidth,
const size_t maxSequenceLength,
const size_t numSequences) {
template <bool normByTimes, bool seq2batch>
__global__ void KeSequence2BatchPadding(real* batch,
real* sequence,
const int* sequenceStartPositions,
const size_t sequenceWidth,
const size_t maxSequenceLength,
const size_t numSequences) {
int batchIdx = blockIdx.y;
int sequenceStart = sequenceStartPositions[batchIdx];
int sequenceLength = sequenceStartPositions[batchIdx + 1] - sequenceStart;
......@@ -276,37 +272,49 @@ void hl_sequence2batch_copy_padding(real* batch,
if (seq2batch) {
/* sequence -> batch */
if (normByTimes) {
KeSequence2BatchPadding<1, 1><<< grid, threads, 0, STREAM_DEFAULT >>>(
batch, sequence, sequenceStartPositions,
sequenceWidth, maxSequenceLength, numSequences);
KeSequence2BatchPadding<1, 1><<<grid, threads, 0, STREAM_DEFAULT>>>(
batch,
sequence,
sequenceStartPositions,
sequenceWidth,
maxSequenceLength,
numSequences);
} else {
KeSequence2BatchPadding<0, 1><<< grid, threads, 0, STREAM_DEFAULT >>>(
batch, sequence, sequenceStartPositions,
sequenceWidth, maxSequenceLength, numSequences);
KeSequence2BatchPadding<0, 1><<<grid, threads, 0, STREAM_DEFAULT>>>(
batch,
sequence,
sequenceStartPositions,
sequenceWidth,
maxSequenceLength,
numSequences);
}
} else {
/* batch -> sequence */
if (normByTimes) {
KeSequence2BatchPadding<1, 0><<< grid, threads, 0, STREAM_DEFAULT >>>(
batch, sequence, sequenceStartPositions,
sequenceWidth, maxSequenceLength, numSequences);
KeSequence2BatchPadding<1, 0><<<grid, threads, 0, STREAM_DEFAULT>>>(
batch,
sequence,
sequenceStartPositions,
sequenceWidth,
maxSequenceLength,
numSequences);
} else {
KeSequence2BatchPadding<0, 0><<< grid, threads, 0, STREAM_DEFAULT >>>(
batch, sequence, sequenceStartPositions,
sequenceWidth, maxSequenceLength, numSequences);
KeSequence2BatchPadding<0, 0><<<grid, threads, 0, STREAM_DEFAULT>>>(
batch,
sequence,
sequenceStartPositions,
sequenceWidth,
maxSequenceLength,
numSequences);
}
}
CHECK_SYNC("hl_sequence2batch_copy_padding failed");
}
__device__ inline float my_rsqrt(float x) {
return rsqrtf(x);
}
__device__ inline float my_rsqrt(float x) { return rsqrtf(x); }
__device__ inline double my_rsqrt(double x) {
return rsqrt(x);
}
__device__ inline double my_rsqrt(double x) { return rsqrt(x); }
__global__ void KeSequenceAvgForward(real* dst,
real* src,
......@@ -327,8 +335,8 @@ __global__ void KeSequenceAvgForward(real* dst,
for (int i = start; i < end; i++) {
sum += src[i * width + col];
}
sum = mode == 1 ? sum :
(mode == 0 ? sum / seqLength : sum * my_rsqrt((real)seqLength));
sum = mode == 1 ? sum : (mode == 0 ? sum / seqLength
: sum * my_rsqrt((real)seqLength));
dst[gid] += sum;
}
}
......@@ -347,10 +355,10 @@ void hl_sequence_avg_forward(real* dst,
int grid = DIVUP(width * height, 512);
CHECK(mode == 0 || mode == 1 || mode == 2)
<< "mode error in hl_sequence_avg_forward!";
<< "mode error in hl_sequence_avg_forward!";
KeSequenceAvgForward<<< grid, block, 0, STREAM_DEFAULT >>>
(dst, src, starts, height, width, mode);
KeSequenceAvgForward<<<grid, block, 0, STREAM_DEFAULT>>>(
dst, src, starts, height, width, mode);
CHECK_SYNC("hl_sequence_avg_forward failed");
}
......@@ -370,8 +378,8 @@ __global__ void KeSequenceAvgBackward(real* dst,
int seqLength = end - start;
if (seqLength == 0) return;
real grad = src[gid];
grad = mode == 1 ? grad :
(mode == 0 ? grad / seqLength : grad * my_rsqrt((real)seqLength));
grad = mode == 1 ? grad : (mode == 0 ? grad / seqLength
: grad * my_rsqrt((real)seqLength));
for (int i = start; i < end; i++) {
dst[i * width + col] += grad;
}
......@@ -392,9 +400,9 @@ void hl_sequence_avg_backward(real* dst,
int grid = DIVUP(width * height, 512);
CHECK(mode == 0 || mode == 1 || mode == 2)
<< "mode error in hl_sequence_avg_backward!";
<< "mode error in hl_sequence_avg_backward!";
KeSequenceAvgBackward<<< grid, block, 0, STREAM_DEFAULT >>>
(dst, src, starts, height, width, mode);
KeSequenceAvgBackward<<<grid, block, 0, STREAM_DEFAULT>>>(
dst, src, starts, height, width, mode);
CHECK_SYNC("hl_sequence_avg_backward failed");
}
paddle/cuda/src/hl_cuda_sparse.cu
浏览文件 @ 75185d82
......@@ -12,13 +12,12 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "hl_cuda.h"
#include "hl_cuda_sparse.cuh"
#include "hl_matrix_apply.cuh"
#include "hl_matrix_ops.cuh"
#include "hl_sparse.h"
#include "hl_sparse.ph"
#include "hl_matrix_ops.cuh"
#include "hl_matrix_apply.cuh"
#include "hl_cuda_sparse.cuh"
#include "paddle/utils/Logging.h"
DEFINE_MATRIX_UNARY_PARAMETER_OP(mul_scalar, ONE_PARAMETER, a = a * p);
......@@ -34,15 +33,15 @@ void hl_matrix_csr2dense(hl_sparse_matrix_s A_d,
CHECK(A_d->format == HL_SPARSE_CSR) << "matrix format error!";
if (A_d->nnz == 0) {
hl_gpu_apply_unary_op(
unary::Zero<real>(), C_d, dimM, dimN, dimN);
hl_gpu_apply_unary_op(unary::Zero<real>(), C_d, dimM, dimN, dimN);
return;
}
/* nnz != 0 */
hl_csr_matrix A_d2 = (hl_csr_matrix)(A_d->matrix);
CHECK((A_d2->csr_val || A_d->type == HL_NO_VALUE) &&
A_d2->csr_row && A_d2->csr_col) << "parameter transa error!";
CHECK((A_d2->csr_val || A_d->type == HL_NO_VALUE) && A_d2->csr_row &&
A_d2->csr_col)
<< "parameter transa error!";
int blocksX = (dimN + CU_CSR2DENSE_THREAD_X - 1) / CU_CSR2DENSE_THREAD_X;
int blocksY = (dimM + CU_CSR2DENSE_THREAD_X - 1) / CU_CSR2DENSE_THREAD_X;
......@@ -50,21 +49,11 @@ void hl_matrix_csr2dense(hl_sparse_matrix_s A_d,
dim3 grid(blocksX, blocksY);
if (A_d->type == HL_NO_VALUE) {
KeSMatrixCsr2Dense<0>
<<<grid, threads, 0, STREAM_DEFAULT>>>(A_d2->csr_val,
A_d2->csr_row,
A_d2->csr_col,
C_d,
dimM,
dimN);
KeSMatrixCsr2Dense<0><<<grid, threads, 0, STREAM_DEFAULT>>>(
A_d2->csr_val, A_d2->csr_row, A_d2->csr_col, C_d, dimM, dimN);
} else if (A_d->type == HL_FLOAT_VALUE) {
KeSMatrixCsr2Dense<1>
<<<grid, threads, 0, STREAM_DEFAULT>>>(A_d2->csr_val,
A_d2->csr_row,
A_d2->csr_col,
C_d,
dimM,
dimN);
KeSMatrixCsr2Dense<1><<<grid, threads, 0, STREAM_DEFAULT>>>(
A_d2->csr_val, A_d2->csr_row, A_d2->csr_col, C_d, dimM, dimN);
} else {
}
CHECK_SYNC("hl_matrix_csr2dense failed");
......@@ -80,15 +69,15 @@ void hl_matrix_csc2dense(hl_sparse_matrix_s A_d,
CHECK(A_d->format == HL_SPARSE_CSC) << "matrix format error!";
if (A_d->nnz == 0) {
hl_gpu_apply_unary_op(
unary::Zero<real>(), C_d, dimM, dimN, dimN);
hl_gpu_apply_unary_op(unary::Zero<real>(), C_d, dimM, dimN, dimN);
return;
}
/* nnz != 0 */
hl_csc_matrix A_d2 = (hl_csc_matrix)(A_d->matrix);
CHECK((A_d2->csc_val || A_d->type == HL_NO_VALUE) &&
A_d2->csc_row && A_d2->csc_col) << "parameter transa error!";
CHECK((A_d2->csc_val || A_d->type == HL_NO_VALUE) && A_d2->csc_row &&
A_d2->csc_col)
<< "parameter transa error!";
int blocksX = (dimN + CU_CSR2DENSE_THREAD_X - 1) / CU_CSR2DENSE_THREAD_X;
int blocksY = (dimM + CU_CSR2DENSE_THREAD_X - 1) / CU_CSR2DENSE_THREAD_X;
......@@ -96,21 +85,11 @@ void hl_matrix_csc2dense(hl_sparse_matrix_s A_d,
dim3 grid(blocksX, blocksY);
if (A_d->type == HL_NO_VALUE) {
KeSMatrixCsc2Dense<0>
<<<grid, threads, 0, STREAM_DEFAULT>>>(A_d2->csc_val,
A_d2->csc_row,
A_d2->csc_col,
C_d,
dimM,
dimN);
KeSMatrixCsc2Dense<0><<<grid, threads, 0, STREAM_DEFAULT>>>(
A_d2->csc_val, A_d2->csc_row, A_d2->csc_col, C_d, dimM, dimN);
} else if (A_d->type == HL_FLOAT_VALUE) {
KeSMatrixCsc2Dense<1>
<<<grid, threads, 0, STREAM_DEFAULT>>>(A_d2->csc_val,
A_d2->csc_row,
A_d2->csc_col,
C_d,
dimM,
dimN);
KeSMatrixCsc2Dense<1><<<grid, threads, 0, STREAM_DEFAULT>>>(
A_d2->csc_val, A_d2->csc_row, A_d2->csc_col, C_d, dimM, dimN);
} else {
}
CHECK_SYNC("hl_matrix_csc2dense failed");
......@@ -118,43 +97,43 @@ void hl_matrix_csc2dense(hl_sparse_matrix_s A_d,
void hl_malloc_sparse_matrix(hl_sparse_matrix_s *A_d,
hl_matrix_format_t format,
hl_matrix_value_t value_type,
hl_matrix_value_t value_type,
int dimM,
int dimN,
int nnz) {
CHECK_NOTNULL(A_d);
CHECK(format == HL_SPARSE_CSR || format == HL_SPARSE_CSC)
<< "sparse matrix format error!";
<< "sparse matrix format error!";
CHECK(value_type == HL_FLOAT_VALUE || value_type == HL_NO_VALUE)
<< "sparse matrix value type error!";
<< "sparse matrix value type error!";
/* avoid malloc 0 bytes */
int nnz_s = (nnz == 0 ? 1 : nnz);
if (format == HL_SPARSE_CSR) {
CHECK(dimM > 0 && nnz >= 0) << "sparse matrix size error!";
char* tmp = (char*)malloc(sizeof(_hl_sparse_matrix_s)
+ sizeof(_hl_csr_matrix));
char *tmp =
(char *)malloc(sizeof(_hl_sparse_matrix_s) + sizeof(_hl_csr_matrix));
CHECK_NOTNULL(tmp);
hl_csr_matrix csr = (hl_csr_matrix)(tmp+sizeof(_hl_sparse_matrix_s));
hl_csr_matrix csr = (hl_csr_matrix)(tmp + sizeof(_hl_sparse_matrix_s));
csr->sparsity = -1.0;
if (value_type == HL_NO_VALUE) {
csr->csr_val = NULL;
csr->nnz_s = nnz_s;
csr->row_s = dimM+1;
csr->csr_row = (int*)hl_malloc_device((dimM+1)*sizeof(int));
csr->csr_col = (int*)hl_malloc_device((nnz_s)*sizeof(int));
csr->row_s = dimM + 1;
csr->csr_row = (int *)hl_malloc_device((dimM + 1) * sizeof(int));
csr->csr_col = (int *)hl_malloc_device((nnz_s) * sizeof(int));
*A_d = (hl_sparse_matrix_s)tmp;
(*A_d)->matrix = (hl_matrix_s)csr;
} else if (value_type == HL_FLOAT_VALUE) {
csr->nnz_s = nnz_s;
csr->row_s = dimM+1;
csr->csr_val = (real*)hl_malloc_device((nnz_s)*sizeof(real));
csr->csr_row = (int*)hl_malloc_device((dimM+1)*sizeof(int));
csr->csr_col = (int*)hl_malloc_device((nnz_s)*sizeof(int));
csr->row_s = dimM + 1;
csr->csr_val = (real *)hl_malloc_device((nnz_s) * sizeof(real));
csr->csr_row = (int *)hl_malloc_device((dimM + 1) * sizeof(int));
csr->csr_col = (int *)hl_malloc_device((nnz_s) * sizeof(int));
*A_d = (hl_sparse_matrix_s)tmp;
(*A_d)->matrix = (hl_matrix_s)csr;
......@@ -162,28 +141,28 @@ void hl_malloc_sparse_matrix(hl_sparse_matrix_s *A_d,
} else if (format == HL_SPARSE_CSC) {
CHECK(dimM > 0 && nnz >= 0) << "sparse matrix size error!";
char* tmp = (char*)malloc(sizeof(_hl_sparse_matrix_s)
+ sizeof(_hl_csc_matrix));
char *tmp =
(char *)malloc(sizeof(_hl_sparse_matrix_s) + sizeof(_hl_csc_matrix));
CHECK_NOTNULL(tmp);
hl_csc_matrix csc = (hl_csc_matrix)(tmp+sizeof(_hl_sparse_matrix_s));
hl_csc_matrix csc = (hl_csc_matrix)(tmp + sizeof(_hl_sparse_matrix_s));
csc->sparsity = -1.0f;
if (value_type == HL_NO_VALUE) {
csc->csc_val = NULL;
csc->nnz_s = nnz_s;
csc->col_s = dimN+1;
csc->csc_row = (int*)hl_malloc_device((nnz_s)*sizeof(int));
csc->csc_col = (int*)hl_malloc_device((dimN+1)*sizeof(int));
csc->col_s = dimN + 1;
csc->csc_row = (int *)hl_malloc_device((nnz_s) * sizeof(int));
csc->csc_col = (int *)hl_malloc_device((dimN + 1) * sizeof(int));
*A_d = (hl_sparse_matrix_s)tmp;
(*A_d)->matrix = (hl_matrix_s)csc;
} else if (value_type == HL_FLOAT_VALUE) {
csc->nnz_s = nnz_s;
csc->col_s = dimN+1;
csc->csc_val = (real*)hl_malloc_device((nnz_s)*sizeof(real));
csc->csc_row = (int*)hl_malloc_device((nnz_s)*sizeof(int));
csc->csc_col = (int*)hl_malloc_device((dimN+1)*sizeof(int));
csc->col_s = dimN + 1;
csc->csc_val = (real *)hl_malloc_device((nnz_s) * sizeof(real));
csc->csc_row = (int *)hl_malloc_device((nnz_s) * sizeof(int));
csc->csc_col = (int *)hl_malloc_device((dimN + 1) * sizeof(int));
*A_d = (hl_sparse_matrix_s)tmp;
(*A_d)->matrix = (hl_matrix_s)csc;
......@@ -200,7 +179,7 @@ void hl_malloc_sparse_matrix(hl_sparse_matrix_s *A_d,
void hl_free_sparse_matrix(hl_sparse_matrix_s A_d) {
CHECK_NOTNULL(A_d);
CHECK(A_d->format == HL_SPARSE_CSR || A_d->format == HL_SPARSE_CSC)
<< "sparse matrix format error!";
<< "sparse matrix format error!";
if (A_d->matrix == NULL) {
free(A_d);
......@@ -249,77 +228,77 @@ void hl_free_sparse_matrix(hl_sparse_matrix_s A_d) {
}
void hl_construct_sparse_matrix(hl_sparse_matrix_s *A_d,
void * dest_d,
void *dest_d,
size_t size,
hl_matrix_format_t format,
hl_matrix_value_t value_type,
hl_matrix_value_t value_type,
int dimM,
int dimN,
int nnz) {
CHECK_NOTNULL(A_d);
CHECK(format == HL_SPARSE_CSR || format == HL_SPARSE_CSC)
<< "sparse matrix format error!";
<< "sparse matrix format error!";
if (format == HL_SPARSE_CSR) {
CHECK(dimM > 0 && nnz >= 0) << "sparse matrix size error!";
size_t size_ = (dimM+1)*sizeof(int) + nnz*sizeof(int);
size_t size_ = (dimM + 1) * sizeof(int) + nnz * sizeof(int);
if (value_type != HL_NO_VALUE) {
size_ += nnz*sizeof(real);
size_ += nnz * sizeof(real);
}
CHECK_LE(size_, size) << "dest_d size(" << size
<< ") too small, should bigger than(" << size_ << ")!";
<< ") too small, should bigger than(" << size_
<< ")!";
char* tmp = (char*)malloc(sizeof(_hl_sparse_matrix_s)
+ sizeof(_hl_csr_matrix));
char *tmp =
(char *)malloc(sizeof(_hl_sparse_matrix_s) + sizeof(_hl_csr_matrix));
CHECK_NOTNULL(tmp);
hl_csr_matrix csr = (hl_csr_matrix)(tmp+sizeof(_hl_sparse_matrix_s));
hl_csr_matrix csr = (hl_csr_matrix)(tmp + sizeof(_hl_sparse_matrix_s));
if (value_type == HL_NO_VALUE) {
csr->csr_val = NULL;
csr->csr_row = (int*)dest_d;
csr->csr_col = (int*)((char*)dest_d + (dimM+1)*sizeof(int));
csr->csr_row = (int *)dest_d;
csr->csr_col = (int *)((char *)dest_d + (dimM + 1) * sizeof(int));
} else {
csr->csr_val = (real*)dest_d;
csr->csr_row = (int*)((char*)dest_d + nnz*sizeof(real));
csr->csr_col = (int*)((char*)dest_d +
nnz*sizeof(real) +
(dimM+1)*sizeof(int));
csr->csr_val = (real *)dest_d;
csr->csr_row = (int *)((char *)dest_d + nnz * sizeof(real));
csr->csr_col = (int *)((char *)dest_d + nnz * sizeof(real) +
(dimM + 1) * sizeof(int));
}
csr->nnz_s = nnz;
csr->row_s = dimM+1;
csr->row_s = dimM + 1;
csr->sparsity = -1.0;
*A_d = (hl_sparse_matrix_s)tmp;
(*A_d)->matrix = (hl_matrix_s)csr;
} else if (format == HL_SPARSE_CSC) {
CHECK(dimM > 0 && nnz >= 0) << "sparse matrix size error!";
size_t size_ = (dimN+1)*sizeof(int) + nnz*sizeof(int);
size_t size_ = (dimN + 1) * sizeof(int) + nnz * sizeof(int);
if (value_type != HL_NO_VALUE) {
size_ += nnz*sizeof(real);
size_ += nnz * sizeof(real);
}
CHECK_LE(size_, size) << "dest_d size(" << size
<< ") too small, should bigger than(" << size_ << ")!";
<< ") too small, should bigger than(" << size_
<< ")!";
char* tmp = (char*)malloc(sizeof(_hl_sparse_matrix_s)
+ sizeof(_hl_csc_matrix));
char *tmp =
(char *)malloc(sizeof(_hl_sparse_matrix_s) + sizeof(_hl_csc_matrix));
CHECK_NOTNULL(tmp);
hl_csc_matrix csc = (hl_csc_matrix)(tmp+sizeof(_hl_sparse_matrix_s));
hl_csc_matrix csc = (hl_csc_matrix)(tmp + sizeof(_hl_sparse_matrix_s));
if (value_type == HL_NO_VALUE) {
csc->csc_val = NULL;
csc->csc_col = (int*)dest_d;
csc->csc_row = (int*)((char*)dest_d + (dimN+1)*sizeof(int));
csc->csc_col = (int *)dest_d;
csc->csc_row = (int *)((char *)dest_d + (dimN + 1) * sizeof(int));
} else {
csc->csc_val = (real*)dest_d;
csc->csc_col = (int*)((char*)dest_d + nnz*sizeof(real));
csc->csc_row = (int*)((char*)dest_d +
nnz*sizeof(real) +
(dimN+1)*sizeof(int));
csc->csc_val = (real *)dest_d;
csc->csc_col = (int *)((char *)dest_d + nnz * sizeof(real));
csc->csc_row = (int *)((char *)dest_d + nnz * sizeof(real) +
(dimN + 1) * sizeof(int));
}
csc->nnz_s = nnz;
csc->col_s = dimN+1;
csc->col_s = dimN + 1;
csc->sparsity = -1.0f;
*A_d = (hl_sparse_matrix_s)tmp;
(*A_d)->matrix = (hl_matrix_s)csc;
......@@ -333,11 +312,11 @@ void hl_construct_sparse_matrix(hl_sparse_matrix_s *A_d,
}
void hl_construct_sparse_matrix(hl_sparse_matrix_s *A_d,
real* value_d,
int* rows_d,
int* cols_d,
real *value_d,
int *rows_d,
int *cols_d,
hl_matrix_format_t format,
hl_matrix_value_t value_type,
hl_matrix_value_t value_type,
int dimM,
int dimN,
int nnz) {
......@@ -345,11 +324,11 @@ void hl_construct_sparse_matrix(hl_sparse_matrix_s *A_d,
CHECK(dimM > 0 && nnz >= 0) << "sparse matrix size error!";
CHECK(format == HL_SPARSE_CSR || format == HL_SPARSE_CSC)
<< "sparse matrix format error!";
<< "sparse matrix format error!";
if (format == HL_SPARSE_CSR) {
char* tmp = (char*)malloc(sizeof(_hl_sparse_matrix_s)
+ sizeof(_hl_csr_matrix));
char *tmp =
(char *)malloc(sizeof(_hl_sparse_matrix_s) + sizeof(_hl_csr_matrix));
CHECK_NOTNULL(tmp);
hl_csr_matrix csr = (hl_csr_matrix)(tmp + sizeof(_hl_sparse_matrix_s));
......@@ -362,8 +341,8 @@ void hl_construct_sparse_matrix(hl_sparse_matrix_s *A_d,
*A_d = (hl_sparse_matrix_s)tmp;
(*A_d)->matrix = (hl_matrix_s)csr;
} else if (format == HL_SPARSE_CSC) {
char* tmp = (char*)malloc(sizeof(_hl_sparse_matrix_s)
+ sizeof(_hl_csc_matrix));
char *tmp =
(char *)malloc(sizeof(_hl_sparse_matrix_s) + sizeof(_hl_csc_matrix));
CHECK_NOTNULL(tmp);
hl_csc_matrix csc = (hl_csc_matrix)(tmp + sizeof(_hl_sparse_matrix_s));
......@@ -396,35 +375,30 @@ void hl_memcpy_csr_matrix(hl_sparse_matrix_s csr_matrix,
hl_stream_t stream) {
CHECK_NOTNULL(csr_matrix);
CHECK_EQ(csr_matrix->format, HL_SPARSE_CSR)
<< "csr_matrix is not csr format!";
<< "csr_matrix is not csr format!";
CHECK_NOTNULL(csr_matrix->matrix);
hl_csr_matrix csr = (hl_csr_matrix)(csr_matrix->matrix);
CHECK_LE(csr_matrix->nnz, csr->nnz_s)
<< "copy size " << csr_matrix->nnz
<< " is big than alloc size " << csr->nnz_s;
CHECK_LE(csr_matrix->nnz, csr->nnz_s) << "copy size " << csr_matrix->nnz
<< " is big than alloc size "
<< csr->nnz_s;
CHECK_LE((csr_matrix->rows+1), csr->row_s)
<< "copy size " << (csr_matrix->rows + 1)
<< " is big than alloc size " << csr->row_s;
CHECK_LE((csr_matrix->rows + 1), csr->row_s)
<< "copy size " << (csr_matrix->rows + 1) << " is big than alloc size "
<< csr->row_s;
CHECK(csr_matrix->type == HL_FLOAT_VALUE ||
csr_matrix->type == HL_NO_VALUE)
<< "sparse matrix value type error!";
CHECK(csr_matrix->type == HL_FLOAT_VALUE || csr_matrix->type == HL_NO_VALUE)
<< "sparse matrix value type error!";
if (csr_matrix->type == HL_NO_VALUE) {
if (csr_row == NULL && csr_col == NULL) {
return;
} else if (csr_row != NULL && csr_col != NULL) {
hl_memcpy_async(csr->csr_row,
csr_row,
(csr_matrix->rows+1)*sizeof(int),
stream);
hl_memcpy_async(
csr->csr_row, csr_row, (csr_matrix->rows + 1) * sizeof(int), stream);
hl_memcpy_async(csr->csr_col,
csr_col,
(csr_matrix->nnz)*sizeof(int),
stream);
hl_memcpy_async(
csr->csr_col, csr_col, (csr_matrix->nnz) * sizeof(int), stream);
} else {
LOG(FATAL) << "parameter csr_row or csr_col is null pointer!";
}
......@@ -432,30 +406,21 @@ void hl_memcpy_csr_matrix(hl_sparse_matrix_s csr_matrix,
if (csr_val == NULL && csr_row == NULL && csr_col == NULL) {
return;
} else if (csr_val != NULL && csr_row == NULL && csr_col == NULL) {
hl_memcpy_async(csr->csr_val,
csr_val,
(csr_matrix->nnz)*sizeof(real),
stream);
hl_memcpy_async(
csr->csr_val, csr_val, (csr_matrix->nnz) * sizeof(real), stream);
} else if (csr_val != NULL && csr_row != NULL && csr_col != NULL) {
hl_memcpy_async(csr->csr_val,
csr_val,
(csr_matrix->nnz)*sizeof(real),
stream);
hl_memcpy_async(csr->csr_row,
csr_row,
(csr_matrix->rows+1)*sizeof(int),
stream);
hl_memcpy_async(csr->csr_col,
csr_col,
(csr_matrix->nnz)*sizeof(int),
stream);
hl_memcpy_async(
csr->csr_val, csr_val, (csr_matrix->nnz) * sizeof(real), stream);
hl_memcpy_async(
csr->csr_row, csr_row, (csr_matrix->rows + 1) * sizeof(int), stream);
hl_memcpy_async(
csr->csr_col, csr_col, (csr_matrix->nnz) * sizeof(int), stream);
} else {
LOG(FATAL) << "parameter csr_row or csr_col is null pointer!";
}
}
csr->sparsity = ((float)csr_matrix->nnz) /
((float)csr_matrix->rows) /
csr->sparsity = ((float)csr_matrix->nnz) / ((float)csr_matrix->rows) /
((float)csr_matrix->cols);
}
......@@ -466,33 +431,28 @@ void hl_memcpy_csc_matrix(hl_sparse_matrix_s csc_matrix,
hl_stream_t stream) {
CHECK_NOTNULL(csc_matrix);
CHECK_EQ(csc_matrix->format, HL_SPARSE_CSC)
<< "csc_matrix is not csc format error!";
<< "csc_matrix is not csc format error!";
hl_csc_matrix csc = (hl_csc_matrix)(csc_matrix->matrix);
CHECK_LE(csc_matrix->nnz, csc->nnz_s)
<< "copy size " << csc_matrix->nnz
<< " is big than alloc size " << csc->nnz_s;
CHECK_LE(csc_matrix->nnz, csc->nnz_s) << "copy size " << csc_matrix->nnz
<< " is big than alloc size "
<< csc->nnz_s;
CHECK_LE((csc_matrix->cols+1), csc->col_s)
<< "copy size " <<(csc_matrix->cols + 1)
<< " is big than alloc size " << csc->col_s;
CHECK_LE((csc_matrix->cols + 1), csc->col_s)
<< "copy size " << (csc_matrix->cols + 1) << " is big than alloc size "
<< csc->col_s;
CHECK(csc_matrix->type == HL_FLOAT_VALUE ||
csc_matrix->type == HL_NO_VALUE)
<< "sparse matrix value type error!";
CHECK(csc_matrix->type == HL_FLOAT_VALUE || csc_matrix->type == HL_NO_VALUE)
<< "sparse matrix value type error!";
if (csc_matrix->type == HL_NO_VALUE) {
if (csc_row == NULL && csc_col == NULL) {
return;
} else if (csc_row != NULL && csc_col != NULL) {
hl_memcpy_async(csc->csc_row,
csc_row,
(csc_matrix->nnz)*sizeof(int),
stream);
hl_memcpy_async(csc->csc_col,
csc_col,
(csc_matrix->cols+1)*sizeof(int),
stream);
hl_memcpy_async(
csc->csc_row, csc_row, (csc_matrix->nnz) * sizeof(int), stream);
hl_memcpy_async(
csc->csc_col, csc_col, (csc_matrix->cols + 1) * sizeof(int), stream);
} else {
LOG(FATAL) << "parameter csc_row or csc_col is null pointer!";
}
......@@ -500,30 +460,21 @@ void hl_memcpy_csc_matrix(hl_sparse_matrix_s csc_matrix,
if (csc_val == NULL && csc_row == NULL && csc_col == NULL) {
return;
} else if (csc_val != NULL && csc_row == NULL && csc_col == NULL) {
hl_memcpy_async(csc->csc_val,
csc_val,
(csc_matrix->nnz)*sizeof(real),
stream);
hl_memcpy_async(
csc->csc_val, csc_val, (csc_matrix->nnz) * sizeof(real), stream);
} else if (csc_val != NULL && csc_row != NULL && csc_col != NULL) {
hl_memcpy_async(csc->csc_val,
csc_val,
(csc_matrix->nnz)*sizeof(real),
stream);
hl_memcpy_async(csc->csc_row,
csc_row,
(csc_matrix->nnz)*sizeof(int),
stream);
hl_memcpy_async(csc->csc_col,
csc_col,
(csc_matrix->cols+1)*sizeof(int),
stream);
hl_memcpy_async(
csc->csc_val, csc_val, (csc_matrix->nnz) * sizeof(real), stream);
hl_memcpy_async(
csc->csc_row, csc_row, (csc_matrix->nnz) * sizeof(int), stream);
hl_memcpy_async(
csc->csc_col, csc_col, (csc_matrix->cols + 1) * sizeof(int), stream);
} else {
LOG(FATAL) << "parameter csc_row or csc_col is null pointer!";
}
}
csc->sparsity = ((float)csc_matrix->nnz) /
((float)csc_matrix->rows) /
csc->sparsity = ((float)csc_matrix->nnz) / ((float)csc_matrix->rows) /
((float)csc_matrix->cols);
}
......@@ -531,32 +482,23 @@ void hl_memcpy_sparse_matrix(hl_sparse_matrix_s dst,
hl_sparse_matrix_s src,
hl_stream_t stream) {
CHECK(dst && src && dst->matrix && src->matrix)
<< "parameter dst or src is null pointer!";
CHECK_EQ(dst->format, src->format)
<< "sparse matrix format does not match!";
<< "parameter dst or src is null pointer!";
CHECK_EQ(dst->format, src->format) << "sparse matrix format does not match!";
CHECK(dst->type != HL_FLOAT_VALUE || src->type != HL_NO_VALUE)
<< "src sparse matrix is no value, dst sparse matrix has value!";
<< "src sparse matrix is no value, dst sparse matrix has value!";
if (dst->format == HL_SPARSE_CSR) {
dst->rows = src->rows;
dst->cols = src->cols;
dst->nnz = src->nnz;
dst->nnz = src->nnz;
hl_csr_matrix csr = (hl_csr_matrix)src->matrix;
hl_memcpy_csr_matrix(dst,
csr->csr_val,
csr->csr_row,
csr->csr_col,
stream);
hl_memcpy_csr_matrix(dst, csr->csr_val, csr->csr_row, csr->csr_col, stream);
} else if (dst->format == HL_SPARSE_CSC) {
dst->rows = src->rows;
dst->cols = src->cols;
dst->nnz = src->nnz;
dst->nnz = src->nnz;
hl_csc_matrix csc = (hl_csc_matrix)src->matrix;
hl_memcpy_csc_matrix(dst,
csc->csc_val,
csc->csc_row,
csc->csc_col,
stream);
hl_memcpy_csc_matrix(dst, csc->csc_val, csc->csc_row, csc->csc_col, stream);
} else {
LOG(FATAL) << "sparse matrix format error!";
}
......@@ -569,20 +511,24 @@ static void _beta_mul_c(real *c, int dimM, int dimN, real beta) {
if (beta == 0.0) {
hl_gpu_apply_unary_op(unary::Zero<real>(), c, dimM, dimN, dimN);
} else {
if (beta != 1.0){
hl_gpu_apply_unary_op(
unary::mul_scalar<real>(beta), c, dimM, dimN, dimN);
if (beta != 1.0) {
hl_gpu_apply_unary_op(unary::mul_scalar<real>(beta), c, dimM, dimN, dimN);
}
}
return;
}
void hl_matrix_csr_mul_dense(hl_sparse_matrix_s A_d, hl_trans_op_t transa,
real *B_d, hl_trans_op_t transb,
void hl_matrix_csr_mul_dense(hl_sparse_matrix_s A_d,
hl_trans_op_t transa,
real *B_d,
hl_trans_op_t transb,
real *C_d,
int dimM, int dimN, int dimK,
real alpha, real beta) {
int dimM,
int dimN,
int dimK,
real alpha,
real beta) {
CHECK_EQ(transb, HPPL_OP_N);
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(B_d);
......@@ -592,7 +538,7 @@ void hl_matrix_csr_mul_dense(hl_sparse_matrix_s A_d, hl_trans_op_t transa,
if ((HPPL_OP_N == transa && (A_d->rows != dimM || A_d->cols != dimK)) ||
(HPPL_OP_T == transa && (A_d->rows != dimK || A_d->cols != dimM))) {
LOG(FATAL) << "parameter error!";
LOG(FATAL) << "parameter error!";
}
if (A_d->nnz == 0) {
......@@ -603,8 +549,7 @@ void hl_matrix_csr_mul_dense(hl_sparse_matrix_s A_d, hl_trans_op_t transa,
/* nnz != 0 */
hl_csr_matrix A_d2 = (hl_csr_matrix)(A_d->matrix);
if ((A_d2->csr_val == NULL && A_d->type != HL_NO_VALUE) ||
A_d2->csr_row == NULL ||
A_d2->csr_col == NULL) {
A_d2->csr_row == NULL || A_d2->csr_col == NULL) {
LOG(FATAL) << "parameter error!";
}
......@@ -617,63 +562,63 @@ void hl_matrix_csr_mul_dense(hl_sparse_matrix_s A_d, hl_trans_op_t transa,
/* sparsity pattern */
// A_d->sparsity;
if (A_d->type == HL_NO_VALUE) {
KeSMatrixCsrMulDense<0>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d2->csr_val,
A_d2->csr_col,
A_d2->csr_row,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixCsrMulDense<0><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d2->csr_val,
A_d2->csr_col,
A_d2->csr_row,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
} else {
KeSMatrixCsrMulDense<1>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d2->csr_val,
A_d2->csr_col,
A_d2->csr_row,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixCsrMulDense<1><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d2->csr_val,
A_d2->csr_col,
A_d2->csr_row,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
}
} else if (HPPL_OP_T == transa) {
_beta_mul_c(C_d, dimM, dimN, beta);
int blocksX = (dimN + CU_CSC_MUL_DENSE_BLOCK_N - 1) /
CU_CSC_MUL_DENSE_BLOCK_N;
int blocksY = (dimK + CU_CSC_MUL_DENSE_BLOCK_K - 1) /
CU_CSC_MUL_DENSE_BLOCK_K;
int blocksX =
(dimN + CU_CSC_MUL_DENSE_BLOCK_N - 1) / CU_CSC_MUL_DENSE_BLOCK_N;
int blocksY =
(dimK + CU_CSC_MUL_DENSE_BLOCK_K - 1) / CU_CSC_MUL_DENSE_BLOCK_K;
dim3 threads(CU_CSC_MUL_DENSE_THREAD_X, CU_CSC_MUL_DENSE_THREAD_Y);
dim3 grid(blocksX, blocksY);
if (A_d->type == HL_NO_VALUE) {
KeSMatrixCscMulDense<0>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d2->csr_val,
A_d2->csr_col,
A_d2->csr_row,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixCscMulDense<0><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d2->csr_val,
A_d2->csr_col,
A_d2->csr_row,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
} else {
KeSMatrixCscMulDense<1>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d2->csr_val,
A_d2->csr_col,
A_d2->csr_row,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixCscMulDense<1><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d2->csr_val,
A_d2->csr_col,
A_d2->csr_row,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
}
} else {
LOG(FATAL) << "parameter transa error!";
......@@ -682,11 +627,16 @@ void hl_matrix_csr_mul_dense(hl_sparse_matrix_s A_d, hl_trans_op_t transa,
CHECK_SYNC("hl_matrix_csr_mul_dense failed");
}
void hl_matrix_dense_mul_csc(real *A_d, hl_trans_op_t transa,
hl_sparse_matrix_s B_d, hl_trans_op_t transb,
void hl_matrix_dense_mul_csc(real *A_d,
hl_trans_op_t transa,
hl_sparse_matrix_s B_d,
hl_trans_op_t transb,
real *C_d,
int dimM, int dimN, int dimK,
real alpha, real beta) {
int dimM,
int dimN,
int dimK,
real alpha,
real beta) {
CHECK_EQ(transa, HPPL_OP_N);
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(B_d);
......@@ -698,8 +648,7 @@ void hl_matrix_dense_mul_csc(real *A_d, hl_trans_op_t transa,
LOG(FATAL) << "parameter dims error!";
}
CHECK_EQ(B_d->format, HL_SPARSE_CSC)
<< "matrix format error!";
CHECK_EQ(B_d->format, HL_SPARSE_CSC) << "matrix format error!";
if (B_d->nnz == 0) {
_beta_mul_c(C_d, dimM, dimN, beta);
......@@ -709,8 +658,7 @@ void hl_matrix_dense_mul_csc(real *A_d, hl_trans_op_t transa,
/* nnz != 0 */
hl_csc_matrix B_d2 = (hl_csc_matrix)(B_d->matrix);
if ((B_d2->csc_val == NULL && B_d->type != HL_NO_VALUE) ||
B_d2->csc_row == NULL ||
B_d2->csc_col == NULL) {
B_d2->csc_row == NULL || B_d2->csc_col == NULL) {
LOG(FATAL) << "parameter B is null!";
}
......@@ -721,60 +669,60 @@ void hl_matrix_dense_mul_csc(real *A_d, hl_trans_op_t transa,
dim3 grid(blocksX, blocksY);
if (B_d->type == HL_NO_VALUE) {
KeSMatrixDenseMulCsc<0>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d,
B_d2->csc_val,
B_d2->csc_row,
B_d2->csc_col,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixDenseMulCsc<0><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d,
B_d2->csc_val,
B_d2->csc_row,
B_d2->csc_col,
dimM,
dimN,
dimK,
alpha,
beta);
} else {
KeSMatrixDenseMulCsc<1>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d,
B_d2->csc_val,
B_d2->csc_row,
B_d2->csc_col,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixDenseMulCsc<1><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d,
B_d2->csc_val,
B_d2->csc_row,
B_d2->csc_col,
dimM,
dimN,
dimK,
alpha,
beta);
}
} else if (transb == HPPL_OP_T) {
_beta_mul_c(C_d, dimM, dimN, beta);
int blocksX = 1 + (dimK-1)/CU_DM_CSR_THREAD_X;
int blocksY = 1 + (dimM-1)/CU_DM_CSR_BLOCK_M;
int blocksX = 1 + (dimK - 1) / CU_DM_CSR_THREAD_X;
int blocksY = 1 + (dimM - 1) / CU_DM_CSR_BLOCK_M;
dim3 threads(CU_DM_CSR_THREAD_X, CU_DM_CSR_THREAD_Y);
dim3 grid(blocksX, blocksY);
if (B_d->type == HL_NO_VALUE) {
KeSMatrixDenseMulCsr<0>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d,
B_d2->csc_val,
B_d2->csc_col,
B_d2->csc_row,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixDenseMulCsr<0><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d,
B_d2->csc_val,
B_d2->csc_col,
B_d2->csc_row,
dimM,
dimN,
dimK,
alpha,
beta);
} else {
KeSMatrixDenseMulCsr<1>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d,
B_d2->csc_val,
B_d2->csc_col,
B_d2->csc_row,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixDenseMulCsr<1><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d,
B_d2->csc_val,
B_d2->csc_col,
B_d2->csc_row,
dimM,
dimN,
dimK,
alpha,
beta);
}
} else {
LOG(FATAL) << "parameter transb error!";
......@@ -783,24 +731,28 @@ void hl_matrix_dense_mul_csc(real *A_d, hl_trans_op_t transa,
CHECK_SYNC("hl_matrix_dense_mul_csc failed");
}
void hl_matrix_dense_mul_csr(real *A_d, hl_trans_op_t transa,
hl_sparse_matrix_s B_d, hl_trans_op_t transb,
void hl_matrix_dense_mul_csr(real *A_d,
hl_trans_op_t transa,
hl_sparse_matrix_s B_d,
hl_trans_op_t transb,
real *C_d,
int dimM, int dimN, int dimK,
real alpha, real beta) {
int dimM,
int dimN,
int dimK,
real alpha,
real beta) {
CHECK_EQ(transa, HPPL_OP_N);
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(B_d);
CHECK_NOTNULL(C_d);
if (dimM <= 0 || dimN <= 0 || dimK <= 0
|| (transb == HPPL_OP_N && (B_d->rows != dimK || B_d->cols != dimN))
|| (transb == HPPL_OP_T && (B_d->rows != dimN || B_d->cols != dimK))) {
if (dimM <= 0 || dimN <= 0 || dimK <= 0 ||
(transb == HPPL_OP_N && (B_d->rows != dimK || B_d->cols != dimN)) ||
(transb == HPPL_OP_T && (B_d->rows != dimN || B_d->cols != dimK))) {
LOG(FATAL) << "parameter dims error!";
}
CHECK_EQ(B_d->format, HL_SPARSE_CSR)
<< "matrix format error!";
CHECK_EQ(B_d->format, HL_SPARSE_CSR) << "matrix format error!";
if (B_d->nnz == 0) {
_beta_mul_c(C_d, dimM, dimN, beta);
......@@ -810,41 +762,40 @@ void hl_matrix_dense_mul_csr(real *A_d, hl_trans_op_t transa,
/* nnz != 0 */
hl_csr_matrix B_d2 = (hl_csr_matrix)(B_d->matrix);
if ((B_d2->csr_val == NULL && B_d->type != HL_NO_VALUE) ||
B_d2->csr_row == NULL ||
B_d2->csr_col == NULL) {
B_d2->csr_row == NULL || B_d2->csr_col == NULL) {
LOG(FATAL) << "parameter transa error!";
}
if (transb == HPPL_OP_N) {
_beta_mul_c(C_d, dimM, dimN, beta);
int blocksX = 1 + (dimK-1)/CU_DM_CSR_THREAD_X;
int blocksY = 1 + (dimM-1)/CU_DM_CSR_BLOCK_M;
int blocksX = 1 + (dimK - 1) / CU_DM_CSR_THREAD_X;
int blocksY = 1 + (dimM - 1) / CU_DM_CSR_BLOCK_M;
dim3 threads(CU_DM_CSR_THREAD_X, CU_DM_CSR_THREAD_Y);
dim3 grid(blocksX, blocksY);
if (B_d->type == HL_NO_VALUE) {
KeSMatrixDenseMulCsr<0>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d,
B_d2->csr_val,
B_d2->csr_row,
B_d2->csr_col,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixDenseMulCsr<0><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d,
B_d2->csr_val,
B_d2->csr_row,
B_d2->csr_col,
dimM,
dimN,
dimK,
alpha,
beta);
} else {
KeSMatrixDenseMulCsr<1>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d,
B_d2->csr_val,
B_d2->csr_row,
B_d2->csr_col,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixDenseMulCsr<1><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d,
B_d2->csr_val,
B_d2->csr_row,
B_d2->csr_col,
dimM,
dimN,
dimK,
alpha,
beta);
}
} else if (transb == HPPL_OP_T) {
int blocksX = (dimM + CU_CSCMM_BLOCK_M_BEST - 1) / CU_CSCMM_BLOCK_M_BEST;
......@@ -852,29 +803,29 @@ void hl_matrix_dense_mul_csr(real *A_d, hl_trans_op_t transa,
dim3 threads(CU_CSCMM_THREAD_X_BEST, CU_CSCMM_THREAD_Y_BEST);
dim3 grid(blocksX, blocksY);
if (B_d->type == HL_NO_VALUE) {
KeSMatrixDenseMulCsc<0>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d,
B_d2->csr_val,
B_d2->csr_col,
B_d2->csr_row,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixDenseMulCsc<0><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d,
B_d2->csr_val,
B_d2->csr_col,
B_d2->csr_row,
dimM,
dimN,
dimK,
alpha,
beta);
} else {
KeSMatrixDenseMulCsc<1>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d,
B_d2->csr_val,
B_d2->csr_col,
B_d2->csr_row,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixDenseMulCsc<1><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d,
B_d2->csr_val,
B_d2->csr_col,
B_d2->csr_row,
dimM,
dimN,
dimK,
alpha,
beta);
}
} else {
LOG(FATAL) << "parameter transb error!";
......@@ -883,11 +834,16 @@ void hl_matrix_dense_mul_csr(real *A_d, hl_trans_op_t transa,
CHECK_SYNC("hl_matrix_dense_mul_csr failed");
}
void hl_matrix_csc_mul_dense(hl_sparse_matrix_s A_d, hl_trans_op_t transa,
real *B_d, hl_trans_op_t transb,
void hl_matrix_csc_mul_dense(hl_sparse_matrix_s A_d,
hl_trans_op_t transa,
real *B_d,
hl_trans_op_t transb,
real *C_d,
int dimM, int dimN, int dimK,
real alpha, real beta) {
int dimM,
int dimN,
int dimK,
real alpha,
real beta) {
CHECK_EQ(transb, HPPL_OP_N);
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(B_d);
......@@ -908,42 +864,43 @@ void hl_matrix_csc_mul_dense(hl_sparse_matrix_s A_d, hl_trans_op_t transa,
/* nnz != 0 */
hl_csc_matrix A_d2 = (hl_csc_matrix)(A_d->matrix);
if ((A_d2->csc_val == NULL && A_d->type != HL_NO_VALUE) ||
A_d2->csc_row == NULL ||
A_d2->csc_col == NULL) {
A_d2->csc_row == NULL || A_d2->csc_col == NULL) {
LOG(FATAL) << "parameter error!";
}
if (HPPL_OP_N == transa) {
_beta_mul_c(C_d, dimM, dimN, beta);
int blocksX = (dimN + CU_CSC_MUL_DENSE_BLOCK_N -1)/CU_CSC_MUL_DENSE_BLOCK_N;
int blocksY = (dimK + CU_CSC_MUL_DENSE_BLOCK_K -1)/CU_CSC_MUL_DENSE_BLOCK_K;
int blocksX =
(dimN + CU_CSC_MUL_DENSE_BLOCK_N - 1) / CU_CSC_MUL_DENSE_BLOCK_N;
int blocksY =
(dimK + CU_CSC_MUL_DENSE_BLOCK_K - 1) / CU_CSC_MUL_DENSE_BLOCK_K;
dim3 threads(CU_CSC_MUL_DENSE_THREAD_X, CU_CSC_MUL_DENSE_THREAD_Y);
dim3 grid(blocksX, blocksY);
if (A_d->type == HL_NO_VALUE) {
KeSMatrixCscMulDense<0>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d2->csc_val,
A_d2->csc_row,
A_d2->csc_col,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixCscMulDense<0><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d2->csc_val,
A_d2->csc_row,
A_d2->csc_col,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
} else {
KeSMatrixCscMulDense<1>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d2->csc_val,
A_d2->csc_row,
A_d2->csc_col,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixCscMulDense<1><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d2->csc_val,
A_d2->csc_row,
A_d2->csc_col,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
}
} else if (HPPL_OP_T == transa) {
int blocksX = (dimN + CU_CSRMM_BLOCK_N - 1) / CU_CSRMM_BLOCK_N;
......@@ -954,29 +911,29 @@ void hl_matrix_csc_mul_dense(hl_sparse_matrix_s A_d, hl_trans_op_t transa,
/* sparsity pattern */
// A_d->sparsity;
if (A_d->type == HL_NO_VALUE) {
KeSMatrixCsrMulDense<0>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d2->csc_val,
A_d2->csc_row,
A_d2->csc_col,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixCsrMulDense<0><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d2->csc_val,
A_d2->csc_row,
A_d2->csc_col,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
} else {
KeSMatrixCsrMulDense<1>
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d,
A_d2->csc_val,
A_d2->csc_row,
A_d2->csc_col,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixCsrMulDense<1><<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d,
A_d2->csc_val,
A_d2->csc_row,
A_d2->csc_col,
B_d,
dimM,
dimN,
dimK,
alpha,
beta);
}
} else {
LOG(FATAL) << "parameter transa error!";
......@@ -985,11 +942,16 @@ void hl_matrix_csc_mul_dense(hl_sparse_matrix_s A_d, hl_trans_op_t transa,
CHECK_SYNC("hl_matrix_csc_mul_dense failed");
}
void hl_sparse_matrix_mul(real *A_d, hl_trans_op_t transa,
real *B_d, hl_trans_op_t transb,
hl_sparse_matrix_s C_d,
int dimM, int dimN, int dimK,
real alpha, real beta) {
void hl_sparse_matrix_mul(real *A_d,
hl_trans_op_t transa,
real *B_d,
hl_trans_op_t transb,
hl_sparse_matrix_s C_d,
int dimM,
int dimN,
int dimK,
real alpha,
real beta) {
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(B_d);
CHECK_NOTNULL(C_d);
......@@ -1000,18 +962,14 @@ void hl_sparse_matrix_mul(real *A_d, hl_trans_op_t transa,
if (C_d->format == HL_SPARSE_CSC) {
hl_csc_matrix C_d2 = (hl_csc_matrix)(C_d->matrix);
if (C_d2->csc_val == NULL ||
C_d2->csc_row == NULL ||
if (C_d2->csc_val == NULL || C_d2->csc_row == NULL ||
C_d2->csc_col == NULL) {
LOG(FATAL) << "parameter error!";
}
if (beta != 1.0) {
hl_gpu_apply_unary_op(unary::mul_scalar<real>(beta),
C_d2->csc_val,
1,
C_d->nnz,
C_d->nnz);
hl_gpu_apply_unary_op(
unary::mul_scalar<real>(beta), C_d2->csc_val, 1, C_d->nnz, C_d->nnz);
}
int blocksX = dimN;
......@@ -1020,34 +978,30 @@ void hl_sparse_matrix_mul(real *A_d, hl_trans_op_t transa,
dim3 grid(blocksX, blocksY);
bool transA = transa == HPPL_OP_T ? 1 : 0;
bool transB = transb == HPPL_OP_T ? 1 : 0;
KeSMatrixDenseMulDense2CSC
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d2->csc_val,
C_d2->csc_row,
C_d2->csc_col,
A_d,
B_d,
transA,
transB,
dimM,
dimN,
dimK,
alpha,
beta);
KeSMatrixDenseMulDense2CSC<<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d2->csc_val,
C_d2->csc_row,
C_d2->csc_col,
A_d,
B_d,
transA,
transB,
dimM,
dimN,
dimK,
alpha,
beta);
CHECK_SYNC("hl_sparse_matrix_mul failed");
} else {
hl_csr_matrix C_d2 = (hl_csr_matrix)(C_d->matrix);
if ((C_d2->csr_val == NULL && C_d->type != HL_NO_VALUE) ||
C_d2->csr_row == NULL ||
C_d2->csr_col == NULL) {
C_d2->csr_row == NULL || C_d2->csr_col == NULL) {
LOG(FATAL) << "parameter error!";
}
if (beta != 1.0) {
hl_gpu_apply_unary_op(unary::mul_scalar<real>(beta),
C_d2->csr_val,
1,
C_d->nnz,
C_d->nnz);
hl_gpu_apply_unary_op(
unary::mul_scalar<real>(beta), C_d2->csr_val, 1, C_d->nnz, C_d->nnz);
}
bool transA = transa == HPPL_OP_T ? 1 : 0;
......@@ -1058,20 +1012,20 @@ void hl_sparse_matrix_mul(real *A_d, hl_trans_op_t transa,
dim3 threads(CU_CSCMM_DMD2CSR_THREAD_X, 1);
dim3 grid(blocksX, blocksY);
KeSMatrixDenseMulDense2CSR
<<<grid, threads, 0, STREAM_DEFAULT>>>(C_d2->csr_val,
C_d2->csr_row,
C_d2->csr_col,
A_d,
B_d,
transA,
transB,
dimM,
dimN,
dimK,
alpha,
beta);
CHECK_SYNC("hl_sparse_matrix_mul failed");
KeSMatrixDenseMulDense2CSR<<<grid, threads, 0, STREAM_DEFAULT>>>(
C_d2->csr_val,
C_d2->csr_row,
C_d2->csr_col,
A_d,
B_d,
transA,
transB,
dimM,
dimN,
dimK,
alpha,
beta);
CHECK_SYNC("hl_sparse_matrix_mul failed");
} else {
CHECK(!transA) << "Not supported A is trans and B is not trans!";
......@@ -1080,21 +1034,21 @@ void hl_sparse_matrix_mul(real *A_d, hl_trans_op_t transa,
avgNnzPerRow = avgNnzPerRow > 0 ? avgNnzPerRow : 1;
int gridx = DIVUP(avgNnzPerRow, CU_BLOCK_SIZE);
dim3 grid(gridx, dimM);
KeSMatrixDenseMulDenseTrans2CSR
<<<grid, block, 0, STREAM_DEFAULT>>>(C_d2->csr_val,
C_d2->csr_row,
C_d2->csr_col,
A_d,
B_d,
transA,
transB,
dimM,
dimN,
dimK,
alpha,
beta);
CHECK_SYNC("hl_sparse_matrix_mul failed");
}
KeSMatrixDenseMulDenseTrans2CSR<<<grid, block, 0, STREAM_DEFAULT>>>(
C_d2->csr_val,
C_d2->csr_row,
C_d2->csr_col,
A_d,
B_d,
transA,
transB,
dimM,
dimN,
dimK,
alpha,
beta);
CHECK_SYNC("hl_sparse_matrix_mul failed");
}
}
}
......@@ -1111,7 +1065,7 @@ void hl_memcpy_from_csc_matrix(real *csc_val,
CHECK_NOTNULL(csc_col);
CHECK_EQ(csc_matrix->format, HL_SPARSE_CSC)
<< "csc_matrix is not csc format error!";
<< "csc_matrix is not csc format error!";
if (csc_matrix->nnz > row_size ||
csc_matrix->cols + 1 > static_cast<int>(col_size)) {
......@@ -1119,20 +1073,20 @@ void hl_memcpy_from_csc_matrix(real *csc_val,
}
hl_csc_matrix csc = (hl_csc_matrix)(csc_matrix->matrix);
hl_memcpy_async((void*)csc_row,
(void*)csc->csc_row,
hl_memcpy_async((void *)csc_row,
(void *)csc->csc_row,
(csc_matrix->nnz) * sizeof(int),
stream);
hl_memcpy_async((void*)csc_col,
(void*)csc->csc_col,
hl_memcpy_async((void *)csc_col,
(void *)csc->csc_col,
(csc_matrix->cols + 1) * sizeof(int),
stream);
if (csc_matrix->type == HL_FLOAT_VALUE) {
if (csc_val != NULL) {
CHECK_LE(csc_matrix->nnz, val_size) << "size not match!";
hl_memcpy_async((void*)csc_val,
(void*)csc->csc_val,
(csc_matrix->nnz)*sizeof(real),
hl_memcpy_async((void *)csc_val,
(void *)csc->csc_val,
(csc_matrix->nnz) * sizeof(real),
stream);
} else {
LOG(FATAL) << "parameter csr_val is null pointer!";
......@@ -1152,7 +1106,7 @@ void hl_memcpy_from_csr_matrix(real *csr_val,
CHECK_NOTNULL(csr_row);
CHECK_NOTNULL(csr_col);
CHECK_EQ(csr_matrix->format, HL_SPARSE_CSR)
<< "csr_matrix is not csr format error!";
<< "csr_matrix is not csr format error!";
if (csr_matrix->nnz > col_size ||
csr_matrix->rows + 1 > static_cast<int>(row_size)) {
......@@ -1160,20 +1114,20 @@ void hl_memcpy_from_csr_matrix(real *csr_val,
}
hl_csr_matrix csr = (hl_csr_matrix)(csr_matrix->matrix);
hl_memcpy_async((void*)csr_row,
(void*)csr->csr_row,
(csr_matrix->rows+1)*sizeof(int),
hl_memcpy_async((void *)csr_row,
(void *)csr->csr_row,
(csr_matrix->rows + 1) * sizeof(int),
stream);
hl_memcpy_async((void*)csr_col,
(void*)csr->csr_col,
(csr_matrix->nnz)*sizeof(int),
hl_memcpy_async((void *)csr_col,
(void *)csr->csr_col,
(csr_matrix->nnz) * sizeof(int),
stream);
if (csr_matrix->type == HL_FLOAT_VALUE) {
if (csr_val != NULL) {
CHECK_LE(csr_matrix->nnz, val_size) << "size not match!";
hl_memcpy_async((void*)csr_val,
(void*)csr->csr_val,
(csr_matrix->nnz)*sizeof(real),
hl_memcpy_async((void *)csr_val,
(void *)csr->csr_val,
(csr_matrix->nnz) * sizeof(real),
stream);
} else {
LOG(FATAL) << "parameter csr_val is null pointer!";
......@@ -1181,8 +1135,8 @@ void hl_memcpy_from_csr_matrix(real *csr_val,
}
}
void hl_sparse_matrix_column_sum(real* A_d, hl_sparse_matrix_s B_d, int dimM,
int dimN, real scale) {
void hl_sparse_matrix_column_sum(
real *A_d, hl_sparse_matrix_s B_d, int dimM, int dimN, real scale) {
if (B_d->format == HL_SPARSE_CSR) {
hl_matrix_csr_column_sum(A_d, B_d, dimM, dimN, scale);
} else {
......@@ -1190,8 +1144,8 @@ void hl_sparse_matrix_column_sum(real* A_d, hl_sparse_matrix_s B_d, int dimM,
}
}
void hl_matrix_csr_column_sum(real* A_d, hl_sparse_matrix_s B_d,
int dimM, int dimN, real scale) {
void hl_matrix_csr_column_sum(
real *A_d, hl_sparse_matrix_s B_d, int dimM, int dimN, real scale) {
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(B_d);
......@@ -1216,8 +1170,7 @@ void hl_matrix_csr_column_sum(real* A_d, hl_sparse_matrix_s B_d,
CHECK_SYNC("hl_matrix_csr_column_sum failed");
}
void hl_sparse_matrix_add_bias(hl_sparse_matrix_s A_d,
real* B_d, real scale) {
void hl_sparse_matrix_add_bias(hl_sparse_matrix_s A_d, real *B_d, real scale) {
if (A_d->format == HL_SPARSE_CSR) {
hl_matrix_csr_add_bias(A_d, B_d, scale);
} else {
......@@ -1225,8 +1178,7 @@ void hl_sparse_matrix_add_bias(hl_sparse_matrix_s A_d,
}
}
void hl_matrix_csr_add_bias(hl_sparse_matrix_s A_d, real* B_d,
real scale) {
void hl_matrix_csr_add_bias(hl_sparse_matrix_s A_d, real *B_d, real scale) {
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(B_d);
......@@ -1247,8 +1199,12 @@ void hl_matrix_csr_add_bias(hl_sparse_matrix_s A_d, real* B_d,
CHECK_SYNC("hl_sparse_matrix_add_bias failed");
}
void hl_sparse_matrix_add_dense(hl_sparse_matrix_s A_d, real *B_d, int dimM,
int dimN, real alpha, real beta) {
void hl_sparse_matrix_add_dense(hl_sparse_matrix_s A_d,
real *B_d,
int dimM,
int dimN,
real alpha,
real beta) {
if (A_d->format == HL_SPARSE_CSR) {
hl_matrix_csr_add_dense(A_d, B_d, dimM, dimN, alpha, beta);
} else {
......@@ -1256,8 +1212,12 @@ void hl_sparse_matrix_add_dense(hl_sparse_matrix_s A_d, real *B_d, int dimM,
}
}
void hl_matrix_csr_add_dense(hl_sparse_matrix_s A_d, real* B_d, int dimM,
int dimN, real alpha, real beta) {
void hl_matrix_csr_add_dense(hl_sparse_matrix_s A_d,
real *B_d,
int dimM,
int dimN,
real alpha,
real beta) {
CHECK_NOTNULL(A_d);
CHECK_NOTNULL(B_d);
......@@ -1277,20 +1237,26 @@ void hl_matrix_csr_add_dense(hl_sparse_matrix_s A_d, real* B_d, int dimM,
gridX = gridX > 0 ? gridX : 1;
dim3 block(512, 1);
dim3 grid(gridX, dimM);
KeSMatrixCsrAddDense<<<grid, block, 0, STREAM_DEFAULT>>>(
A_d2->csr_val, A_d2->csr_row, A_d2->csr_col, B_d, alpha, beta, dimM, dimN);
KeSMatrixCsrAddDense<<<grid, block, 0, STREAM_DEFAULT>>>(A_d2->csr_val,
A_d2->csr_row,
A_d2->csr_col,
B_d,
alpha,
beta,
dimM,
dimN);
CHECK_SYNC("hl_sparse_matrix_add_dense failed");
}
int* hl_sparse_matrix_get_rows(hl_sparse_matrix_s sMat) {
int *hl_sparse_matrix_get_rows(hl_sparse_matrix_s sMat) {
__sparse_get_return__(sMat, row);
}
int* hl_sparse_matrix_get_cols(hl_sparse_matrix_s sMat) {
int *hl_sparse_matrix_get_cols(hl_sparse_matrix_s sMat) {
__sparse_get_return__(sMat, col);
}
real* hl_sparse_matrix_get_value(hl_sparse_matrix_s sMat) {
real *hl_sparse_matrix_get_value(hl_sparse_matrix_s sMat) {
__sparse_get_return__(sMat, val);
}
paddle/cuda/src/hl_perturbation_util.cu
浏览文件 @ 75185d82
......@@ -12,13 +12,12 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <cmath>
#include <stdlib.h>
#include "hl_cuda.h"
#include "hl_time.h"
#include <cmath>
#include "hl_base.h"
#include "hl_cuda.h"
#include "hl_perturbation_util.cuh"
#include "hl_time.h"
#define _USE_MATH_DEFINES
......@@ -30,10 +29,16 @@ limitations under the License. */
* centerX, centerY: translation.
* sourceX, sourceY: output coordinates in the original image.
*/
__device__ void getTranformCoord(int x, int y, real theta, real scale,
real tgtCenter, real imgCenter,
real centerR, real centerC,
int* sourceX, int* sourceY) {
__device__ void getTranformCoord(int x,
int y,
real theta,
real scale,
real tgtCenter,
real imgCenter,
real centerR,
real centerC,
int* sourceX,
int* sourceY) {
real H[4] = {cosf(-theta), -sinf(-theta), sinf(-theta), cosf(-theta)};
// compute coornidates in the rotated and scaled image
......@@ -57,11 +62,17 @@ __device__ void getTranformCoord(int x, int y, real theta, real scale,
* created by Wei Xu (genome), converted by Jiang Wang
*/
__global__ void kSamplingPatches(const real* imgs, real* targets,
int imgSize, int tgtSize, const int channels,
int samplingRate, const real* thetas,
const real* scales, const int* centerRs,
const int* centerCs, const real padValue,
__global__ void kSamplingPatches(const real* imgs,
real* targets,
int imgSize,
int tgtSize,
const int channels,
int samplingRate,
const real* thetas,
const real* scales,
const int* centerRs,
const int* centerCs,
const real padValue,
const int numImages) {
const int caseIdx = blockIdx.x * 4 + threadIdx.x;
const int pxIdx = blockIdx.y * 128 + threadIdx.y;
......@@ -80,8 +91,15 @@ __global__ void kSamplingPatches(const real* imgs, real* targets,
const int pxY = pxIdx / tgtSize;
int srcPxX, srcPxY;
getTranformCoord(pxX, pxY, thetas[imgIdx], scales[imgIdx], tgtCenter,
imgCenter, centerCs[caseIdx], centerRs[caseIdx], &srcPxX,
getTranformCoord(pxX,
pxY,
thetas[imgIdx],
scales[imgIdx],
tgtCenter,
imgCenter,
centerCs[caseIdx],
centerRs[caseIdx],
&srcPxX,
&srcPxY);
imgs += (imgIdx * imgPixels + srcPxY * imgSize + srcPxX) * channels;
......@@ -100,10 +118,15 @@ __global__ void kSamplingPatches(const real* imgs, real* targets,
*
* created by Wei Xu
*/
void hl_generate_disturb_params(real*& gpuAngle, real*& gpuScaleRatio,
int*& gpuCenterR, int*& gpuCenterC,
int numImages, int imgSize, real rotateAngle,
real scaleRatio, int samplingRate,
void hl_generate_disturb_params(real*& gpuAngle,
real*& gpuScaleRatio,
int*& gpuCenterR,
int*& gpuCenterC,
int numImages,
int imgSize,
real rotateAngle,
real scaleRatio,
int samplingRate,
bool isTrain) {
// The number of output samples.
int numPatches = numImages * samplingRate;
......@@ -123,7 +146,8 @@ void hl_generate_disturb_params(real*& gpuAngle, real*& gpuScaleRatio,
for (int i = 0; i < numImages; i++) {
r_angle[i] =
(rotateAngle * M_PI / 180.0) * (rand() / (RAND_MAX + 1.0) // NOLINT
- 0.5);
-
0.5);
s_ratio[i] =
1 + (rand() / (RAND_MAX + 1.0) - 0.5) * scaleRatio; // NOLINT
}
......@@ -140,8 +164,10 @@ void hl_generate_disturb_params(real*& gpuAngle, real*& gpuScaleRatio,
int pxY =
(int)(real(imgSize - 1) * rand() / (RAND_MAX + 1.0)); // NOLINT
const real H[4] = {cos(-r_angle[i]), -sin(-r_angle[i]),
sin(-r_angle[i]), cos(-r_angle[i])};
const real H[4] = {cos(-r_angle[i]),
-sin(-r_angle[i]),
sin(-r_angle[i]),
cos(-r_angle[i])};
real x = pxX - imgCenter;
real y = pxY - imgCenter;
real xx = H[0] * x + H[1] * y;
......@@ -185,9 +211,12 @@ void hl_generate_disturb_params(real*& gpuAngle, real*& gpuScaleRatio,
delete[] center_c;
}
void hl_conv_random_disturb_with_params(const real* images, int imgSize,
int tgtSize, int channels,
int numImages, int samplingRate,
void hl_conv_random_disturb_with_params(const real* images,
int imgSize,
int tgtSize,
int channels,
int numImages,
int samplingRate,
const real* gpuRotationAngle,
const real* gpuScaleRatio,
const int* gpuCenterR,
......@@ -202,29 +231,59 @@ void hl_conv_random_disturb_with_params(const real* images, int imgSize,
dim3 threadsPerBlock(4, 128);
dim3 numBlocks(DIVUP(numPatches, 4), DIVUP(targetSize, 128));
kSamplingPatches <<<numBlocks, threadsPerBlock>>>
(images, target, imgSize, tgtSize, channels, samplingRate,
gpuRotationAngle, gpuScaleRatio, gpuCenterR, gpuCenterC,
paddingValue, numImages);
kSamplingPatches<<<numBlocks, threadsPerBlock>>>(images,
target,
imgSize,
tgtSize,
channels,
samplingRate,
gpuRotationAngle,
gpuScaleRatio,
gpuCenterR,
gpuCenterC,
paddingValue,
numImages);
hl_device_synchronize();
}
void hl_conv_random_disturb(const real* images, int imgSize,
int tgtSize, int channels, int numImages,
real scaleRatio, real rotateAngle,
int samplingRate, real* gpu_r_angle,
real* gpu_s_ratio, int* gpu_center_r,
int* gpu_center_c, int paddingValue,
bool isTrain, real* targets) {
void hl_conv_random_disturb(const real* images,
int imgSize,
int tgtSize,
int channels,
int numImages,
real scaleRatio,
real rotateAngle,
int samplingRate,
real* gpu_r_angle,
real* gpu_s_ratio,
int* gpu_center_r,
int* gpu_center_c,
int paddingValue,
bool isTrain,
real* targets) {
// generate the random disturbance sequence and the sampling locations
hl_generate_disturb_params(gpu_r_angle, gpu_s_ratio, gpu_center_r,
gpu_center_c, numImages, imgSize, rotateAngle,
scaleRatio, samplingRate, isTrain);
hl_conv_random_disturb_with_params(
images, imgSize, tgtSize, channels, numImages,
samplingRate, gpu_r_angle, gpu_s_ratio,
gpu_center_r, gpu_center_r, paddingValue,
targets);
hl_generate_disturb_params(gpu_r_angle,
gpu_s_ratio,
gpu_center_r,
gpu_center_c,
numImages,
imgSize,
rotateAngle,
scaleRatio,
samplingRate,
isTrain);
hl_conv_random_disturb_with_params(images,
imgSize,
tgtSize,
channels,
numImages,
samplingRate,
gpu_r_angle,
gpu_s_ratio,
gpu_center_r,
gpu_center_r,
paddingValue,
targets);
}
paddle/cuda/src/hl_table_apply.cu
浏览文件 @ 75185d82
......@@ -12,15 +12,16 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "hl_base.h"
#include "hl_device_functions.cuh"
#include "hl_cuda.h"
#include "hl_device_functions.cuh"
#include "paddle/utils/Logging.h"
template<int blockDimX, int blockDimY, int gridDimX, bool AddRow>
__global__ void KeMatrixAddRows(real* output, int ldo,
real* table, int ldt,
template <int blockDimX, int blockDimY, int gridDimX, bool AddRow>
__global__ void KeMatrixAddRows(real* output,
int ldo,
real* table,
int ldt,
int* ids,
int numSamples,
int tableSize,
......@@ -31,8 +32,8 @@ __global__ void KeMatrixAddRows(real* output, int ldo,
while (idy < numSamples) {
int tableId = ids[idy];
if ((0 <= tableId) && (tableId < tableSize)) {
real *out = output + idy * ldo;
real *tab = table + tableId * ldt;
real* out = output + idy * ldo;
real* tab = table + tableId * ldt;
for (int i = idx; i < dim; i += blockDimX) {
if (AddRow) {
paddle::paddleAtomicAdd(&tab[i], out[i]);
......@@ -45,8 +46,10 @@ __global__ void KeMatrixAddRows(real* output, int ldo,
}
}
void hl_matrix_select_rows(real* output, int ldo,
real* table, int ldt,
void hl_matrix_select_rows(real* output,
int ldo,
real* table,
int ldt,
int* ids,
int numSamples,
int tableSize,
......@@ -57,14 +60,16 @@ void hl_matrix_select_rows(real* output, int ldo,
dim3 threads(128, 8);
dim3 grid(8, 1);
KeMatrixAddRows<128, 8, 8, 0><<< grid, threads, 0, STREAM_DEFAULT >>>
(output, ldo, table, ldt, ids, numSamples, tableSize, dim);
KeMatrixAddRows<128, 8, 8, 0><<<grid, threads, 0, STREAM_DEFAULT>>>(
output, ldo, table, ldt, ids, numSamples, tableSize, dim);
CHECK_SYNC("hl_matrix_select_rows failed");
}
void hl_matrix_add_to_rows(real* table, int ldt,
real* input, int ldi,
void hl_matrix_add_to_rows(real* table,
int ldt,
real* input,
int ldi,
int* ids,
int numSamples,
int tableSize,
......@@ -75,16 +80,15 @@ void hl_matrix_add_to_rows(real* table, int ldt,
dim3 threads(128, 8);
dim3 grid(8, 1);
KeMatrixAddRows<128, 8, 8, 1><<< grid, threads, 0, STREAM_DEFAULT >>>
(input, ldi, table, ldt, ids, numSamples, tableSize, dim);
KeMatrixAddRows<128, 8, 8, 1><<<grid, threads, 0, STREAM_DEFAULT>>>(
input, ldi, table, ldt, ids, numSamples, tableSize, dim);
CHECK_SYNC("hl_matrix_add_to_rows failed");
}
template<class T, int blockDimX, int gridDimX>
__global__ void KeVectorSelect(T* dst, int sized,
const T* src, int sizes,
const int* ids, int sizei) {
template <class T, int blockDimX, int gridDimX>
__global__ void KeVectorSelect(
T* dst, int sized, const T* src, int sizes, const int* ids, int sizei) {
int idx = threadIdx.x + blockDimX * blockIdx.x;
while (idx < sizei) {
int index = ids[idx];
......@@ -95,9 +99,8 @@ __global__ void KeVectorSelect(T* dst, int sized,
}
template <class T>
void hl_vector_select_from(T* dst, int sized,
const T* src, int sizes,
const int* ids, int sizei) {
void hl_vector_select_from(
T* dst, int sized, const T* src, int sizes, const int* ids, int sizei) {
CHECK_NOTNULL(dst);
CHECK_NOTNULL(src);
CHECK_NOTNULL(ids);
......@@ -105,18 +108,17 @@ void hl_vector_select_from(T* dst, int sized,
dim3 threads(512, 1);
dim3 grid(8, 1);
KeVectorSelect<T, 512, 8><<< grid, threads, 0, STREAM_DEFAULT >>>
(dst, sized, src, sizes, ids, sizei);
KeVectorSelect<T, 512, 8><<<grid, threads, 0, STREAM_DEFAULT>>>(
dst, sized, src, sizes, ids, sizei);
CHECK_SYNC("hl_vector_select_from failed");
}
template
void hl_vector_select_from(real* dst, int sized,
const real* src, int sizes,
const int* ids, int sizei);
template
void hl_vector_select_from(int* dst, int sized,
const int* src, int sizes,
const int* ids, int sizei);
template void hl_vector_select_from(real* dst,
int sized,
const real* src,
int sizes,
const int* ids,
int sizei);
template void hl_vector_select_from(
int* dst, int sized, const int* src, int sizes, const int* ids, int sizei);
paddle/cuda/src/hl_top_k.cu
浏览文件 @ 75185d82
......@@ -12,45 +12,37 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "hl_base.h"
#include "hl_top_k.h"
#include "hl_sparse.ph"
#include "hl_top_k.h"
#include "paddle/utils/Logging.h"
// using namespace hppl;
struct Pair {
__device__ __forceinline__
Pair() {}
__device__ __forceinline__ Pair() {}
__device__ __forceinline__
Pair(real value, int id) : v_(value), id_(id) {}
__device__ __forceinline__ Pair(real value, int id) : v_(value), id_(id) {}
__device__ __forceinline__
void set(real value, int id) {
__device__ __forceinline__ void set(real value, int id) {
v_ = value;
id_ = id;
}
__device__ __forceinline__
void operator=(const Pair& in) {
__device__ __forceinline__ void operator=(const Pair& in) {
v_ = in.v_;
id_ = in.id_;
}
__device__ __forceinline__
bool operator<(const real value) const {
__device__ __forceinline__ bool operator<(const real value) const {
return (v_ < value);
}
__device__ __forceinline__
bool operator<(const Pair& in) const {
__device__ __forceinline__ bool operator<(const Pair& in) const {
return (v_ < in.v_) || ((v_ == in.v_) && (id_ > in.id_));
}
__device__ __forceinline__
bool operator>(const Pair& in) const {
__device__ __forceinline__ bool operator>(const Pair& in) const {
return (v_ > in.v_) || ((v_ == in.v_) && (id_ < in.id_));
}
......@@ -58,8 +50,9 @@ struct Pair {
int id_;
};
__device__ __forceinline__
void addTo(Pair topK[], const Pair &p, int beamSize) {
__device__ __forceinline__ void addTo(Pair topK[],
const Pair& p,
int beamSize) {
for (int k = beamSize - 2; k >= 0; k--) {
if (topK[k] < p) {
topK[k + 1] = topK[k];
......@@ -71,9 +64,8 @@ void addTo(Pair topK[], const Pair &p, int beamSize) {
topK[0] = p;
}
template<int beamSize>
__device__ __forceinline__
void addTo(Pair topK[], const Pair &p) {
template <int beamSize>
__device__ __forceinline__ void addTo(Pair topK[], const Pair& p) {
for (int k = beamSize - 2; k >= 0; k--) {
if (topK[k] < p) {
topK[k + 1] = topK[k];
......@@ -85,9 +77,9 @@ void addTo(Pair topK[], const Pair &p) {
topK[0] = p;
}
template<int blockSize>
__device__ __forceinline__
void getTopK(Pair topK[], real *src, int idx, int dim, int beamSize) {
template <int blockSize>
__device__ __forceinline__ void getTopK(
Pair topK[], real* src, int idx, int dim, int beamSize) {
while (idx < dim) {
if (topK[beamSize - 1] < src[idx]) {
Pair tmp(src[idx], idx);
......@@ -97,10 +89,9 @@ void getTopK(Pair topK[], real *src, int idx, int dim, int beamSize) {
}
}
template<int blockSize>
__device__ __forceinline__
void getTopK(Pair topK[], real *src, int idx, int dim,
const Pair& max, int beamSize) {
template <int blockSize>
__device__ __forceinline__ void getTopK(
Pair topK[], real* src, int idx, int dim, const Pair& max, int beamSize) {
while (idx < dim) {
if (topK[beamSize - 1] < src[idx]) {
Pair tmp(src[idx], idx);
......@@ -112,10 +103,9 @@ void getTopK(Pair topK[], real *src, int idx, int dim,
}
}
template<int blockSize>
__device__ __forceinline__
void getTopK(Pair topK[], real *val, int *col,
int idx, int dim, int beamSize) {
template <int blockSize>
__device__ __forceinline__ void getTopK(
Pair topK[], real* val, int* col, int idx, int dim, int beamSize) {
while (idx < dim) {
if (topK[beamSize - 1] < val[idx]) {
Pair tmp(val[idx], col[idx]);
......@@ -125,10 +115,14 @@ void getTopK(Pair topK[], real *val, int *col,
}
}
template<int blockSize>
__device__ __forceinline__
void getTopK(Pair topK[], real *val, int *col, int idx, int dim,
const Pair& max, int beamSize) {
template <int blockSize>
__device__ __forceinline__ void getTopK(Pair topK[],
real* val,
int* col,
int idx,
int dim,
const Pair& max,
int beamSize) {
while (idx < dim) {
if (topK[beamSize - 1] < val[idx]) {
Pair tmp(val[idx], col[idx]);
......@@ -140,12 +134,16 @@ void getTopK(Pair topK[], real *val, int *col, int idx, int dim,
}
}
template<int maxLength, int blockSize>
__device__ __forceinline__
void threadGetTopK(Pair topK[], int& beam, int beamSize,
real* src,
bool& firstStep, bool& isEmpty, Pair& max,
int dim, const int tid) {
template <int maxLength, int blockSize>
__device__ __forceinline__ void threadGetTopK(Pair topK[],
int& beam,
int beamSize,
real* src,
bool& firstStep,
bool& isEmpty,
Pair& max,
int dim,
const int tid) {
if (beam > 0) {
int length = beam < beamSize ? beam : beamSize;
if (firstStep) {
......@@ -160,8 +158,7 @@ void threadGetTopK(Pair topK[], int& beam, int beamSize,
}
}
if (!isEmpty) {
getTopK<blockSize>(topK + maxLength - beam, src, tid, dim,
max, length);
getTopK<blockSize>(topK + maxLength - beam, src, tid, dim, max, length);
}
}
......@@ -171,12 +168,17 @@ void threadGetTopK(Pair topK[], int& beam, int beamSize,
}
}
template<int maxLength, int blockSize>
__device__ __forceinline__
void threadGetTopK(Pair topK[], int& beam, int beamSize,
real* val, int* col,
bool& firstStep, bool& isEmpty, Pair& max,
int dim, const int tid) {
template <int maxLength, int blockSize>
__device__ __forceinline__ void threadGetTopK(Pair topK[],
int& beam,
int beamSize,
real* val,
int* col,
bool& firstStep,
bool& isEmpty,
Pair& max,
int dim,
const int tid) {
if (beam > 0) {
int length = beam < beamSize ? beam : beamSize;
if (firstStep) {
......@@ -191,8 +193,8 @@ void threadGetTopK(Pair topK[], int& beam, int beamSize,
}
}
if (!isEmpty) {
getTopK<blockSize>(topK + maxLength - beam, val, col, tid, dim,
max, length);
getTopK<blockSize>(
topK + maxLength - beam, val, col, tid, dim, max, length);
}
}
......@@ -202,12 +204,16 @@ void threadGetTopK(Pair topK[], int& beam, int beamSize,
}
}
template<int maxLength, int blockSize>
__device__ __forceinline__
void blockReduce(Pair* shTopK, int* maxId, Pair topK[],
real** topVal, int** topIds,
int& beam, int& beamSize,
const int tid, const int warp) {
template <int maxLength, int blockSize>
__device__ __forceinline__ void blockReduce(Pair* shTopK,
int* maxId,
Pair topK[],
real** topVal,
int** topIds,
int& beam,
int& beamSize,
const int tid,
const int warp) {
while (true) {
__syncthreads();
if (tid < blockSize / 2) {
......@@ -218,7 +224,7 @@ void blockReduce(Pair* shTopK, int* maxId, Pair topK[],
}
}
__syncthreads();
for (int stride = blockSize / 4; stride > 0; stride = stride/2) {
for (int stride = blockSize / 4; stride > 0; stride = stride / 2) {
if (tid < stride) {
if (shTopK[maxId[tid]] < shTopK[maxId[tid + stride]]) {
maxId[tid] = maxId[tid + stride];
......@@ -257,10 +263,12 @@ void blockReduce(Pair* shTopK, int* maxId, Pair topK[],
* 3. go to the second setp, until one thread's topK value is null;
* 4. go to the first setp, until get the topK value.
*/
template<int maxLength, int blockSize>
__global__ void KeMatrixTopK(real* topVal, int ldv,
int * topIds,
real* src, int lds,
template <int maxLength, int blockSize>
__global__ void KeMatrixTopK(real* topVal,
int ldv,
int* topIds,
real* src,
int lds,
int dim,
int beamSize) {
__shared__ Pair shTopK[blockSize];
......@@ -271,7 +279,7 @@ __global__ void KeMatrixTopK(real* topVal, int ldv,
topVal += blockIdx.x * ldv;
topIds += blockIdx.x * beamSize;
Pair topK[maxLength]; // NOLINT
Pair topK[maxLength]; // NOLINT
int beam = maxLength;
Pair max;
bool isEmpty = false;
......@@ -281,18 +289,19 @@ __global__ void KeMatrixTopK(real* topVal, int ldv,
topK[k].set(-HL_FLOAT_MAX, -1);
}
while (beamSize) {
threadGetTopK<maxLength, blockSize>
(topK, beam, beamSize, src, firstStep, isEmpty, max, dim, tid);
threadGetTopK<maxLength, blockSize>(
topK, beam, beamSize, src, firstStep, isEmpty, max, dim, tid);
shTopK[tid] = topK[0];
blockReduce<maxLength, blockSize>
(shTopK, maxId, topK, &topVal, &topIds, beam, beamSize, tid, warp);
blockReduce<maxLength, blockSize>(
shTopK, maxId, topK, &topVal, &topIds, beam, beamSize, tid, warp);
}
}
template<int maxLength, int blockSize>
__global__ void KeSMatrixTopK(real* topVal, int ldv,
int * topIds,
template <int maxLength, int blockSize>
__global__ void KeSMatrixTopK(real* topVal,
int ldv,
int* topIds,
real* val,
int* row,
int* col,
......@@ -304,7 +313,7 @@ __global__ void KeSMatrixTopK(real* topVal, int ldv,
topVal += blockIdx.x * ldv;
topIds += blockIdx.x * beamSize;
Pair topK[maxLength]; // NOLINT
Pair topK[maxLength]; // NOLINT
int beam = maxLength;
Pair max;
bool isEmpty = false;
......@@ -330,18 +339,20 @@ __global__ void KeSMatrixTopK(real* topVal, int ldv,
topK[k].set(-HL_FLOAT_MAX, -1);
}
while (beamSize) {
threadGetTopK<maxLength, blockSize>
(topK, beam, beamSize, val, col, firstStep, isEmpty, max, dim, tid);
threadGetTopK<maxLength, blockSize>(
topK, beam, beamSize, val, col, firstStep, isEmpty, max, dim, tid);
shTopK[tid] = topK[0];
blockReduce<maxLength, blockSize>
(shTopK, maxId, topK, &topVal, &topIds, beam, beamSize, tid, warp);
blockReduce<maxLength, blockSize>(
shTopK, maxId, topK, &topVal, &topIds, beam, beamSize, tid, warp);
}
}
void hl_matrix_top_k(real* topVal, int ldv,
int * topIds,
real* src, int lds,
void hl_matrix_top_k(real* topVal,
int ldv,
int* topIds,
real* src,
int lds,
int dim,
int beamSize,
int numSamples) {
......@@ -353,33 +364,32 @@ void hl_matrix_top_k(real* topVal, int ldv,
dim3 threads(256, 1);
dim3 grid(numSamples, 1);
KeMatrixTopK<5, 256><<< grid, threads, 0, STREAM_DEFAULT >>>
(topVal, ldv, topIds, src, lds, dim, beamSize);
KeMatrixTopK<5, 256><<<grid, threads, 0, STREAM_DEFAULT>>>(
topVal, ldv, topIds, src, lds, dim, beamSize);
CHECK_SYNC("hl_matrix_top_k failed");
}
void hl_sparse_matrix_top_k(real* topVal, int ldv,
int * topIds,
void hl_sparse_matrix_top_k(real* topVal,
int ldv,
int* topIds,
hl_sparse_matrix_s src,
int beamSize,
int numSamples) {
CHECK_NOTNULL(topVal);
CHECK_NOTNULL(topIds);
CHECK_NOTNULL(src);
CHECK_EQ(src->format, HL_SPARSE_CSR)
<<"sparse matrix format error!";
CHECK_EQ(src->format, HL_SPARSE_CSR) << "sparse matrix format error!";
hl_csr_matrix csr = (hl_csr_matrix)src->matrix;
if (csr->csr_val == NULL || csr->csr_row == NULL ||
csr->csr_col == NULL) {
if (csr->csr_val == NULL || csr->csr_row == NULL || csr->csr_col == NULL) {
LOG(FATAL) << "parameter src is null!";
}
dim3 threads(256, 1);
dim3 grid(numSamples, 1);
KeSMatrixTopK<5, 256><<< grid, threads, 0, STREAM_DEFAULT >>>
(topVal, ldv, topIds, csr->csr_val, csr->csr_row, csr->csr_col, beamSize);
KeSMatrixTopK<5, 256><<<grid, threads, 0, STREAM_DEFAULT>>>(
topVal, ldv, topIds, csr->csr_val, csr->csr_row, csr->csr_col, beamSize);
CHECK_SYNC("hl_sparse_matrix_top_k failed");
}
......@@ -392,10 +402,12 @@ void hl_sparse_matrix_top_k(real* topVal, int ldv,
* 3. go to the second setp, until one thread's topK value is null;
* 4. go to the first setp, until get the topK value.
*/
template<int maxLength, int blockSize>
__global__ void KeMatrixTopKClassificationError(real* topVal, int ldv,
int * topIds,
real* src, int lds,
template <int maxLength, int blockSize>
__global__ void KeMatrixTopKClassificationError(real* topVal,
int ldv,
int* topIds,
real* src,
int lds,
int dim,
int beamSize,
int* label,
......@@ -408,7 +420,7 @@ __global__ void KeMatrixTopKClassificationError(real* topVal, int ldv,
topVal += blockIdx.x * ldv;
topIds += blockIdx.x * beamSize;
Pair topK[maxLength]; // NOLINT
Pair topK[maxLength]; // NOLINT
int beam = maxLength;
Pair max;
bool isEmpty = false;
......@@ -420,34 +432,36 @@ __global__ void KeMatrixTopKClassificationError(real* topVal, int ldv,
}
while (beamSize) {
threadGetTopK<maxLength, blockSize>
(topK, beam, beamSize, src, firstStep, isEmpty, max, dim, tid);
threadGetTopK<maxLength, blockSize>(
topK, beam, beamSize, src, firstStep, isEmpty, max, dim, tid);
shTopK[tid] = topK[0];
blockReduce<maxLength, blockSize>
(shTopK, maxId, topK, &topVal, &topIds, beam, beamSize, tid, warp);
blockReduce<maxLength, blockSize>(
shTopK, maxId, topK, &topVal, &topIds, beam, beamSize, tid, warp);
}
__syncthreads();
if (tid == 0) {
for (int i = 0; i < topkSize; i++) {
if (*--topIds == label[blockIdx.x]) {
recResult[blockIdx.x] = 0;
break;
}
recResult[blockIdx.x] = 1.0f;
if (*--topIds == label[blockIdx.x]) {
recResult[blockIdx.x] = 0;
break;
}
recResult[blockIdx.x] = 1.0f;
}
}
}
void hl_matrix_classification_error(real* topVal, int ldv,
int* topIds,
real* src, int lds,
int dim,
int topkSize,
int numSamples,
int* label,
real* recResult) {
void hl_matrix_classification_error(real* topVal,
int ldv,
int* topIds,
real* src,
int lds,
int dim,
int topkSize,
int numSamples,
int* label,
real* recResult) {
CHECK_NOTNULL(topVal);
CHECK_NOTNULL(topIds);
CHECK_NOTNULL(src);
......@@ -456,9 +470,8 @@ void hl_matrix_classification_error(real* topVal, int ldv,
dim3 threads(256, 1);
dim3 grid(numSamples, 1);
KeMatrixTopKClassificationError<5, 256>
<<< grid, threads, 0, STREAM_DEFAULT >>>
(topVal, ldv, topIds, src, lds, dim, topkSize, label, recResult);
KeMatrixTopKClassificationError<5, 256><<<grid, threads, 0, STREAM_DEFAULT>>>(
topVal, ldv, topIds, src, lds, dim, topkSize, label, recResult);
CHECK_SYNC("hl_matrix_top_k classification error failed");
}
paddle/framework/attribute.proto
浏览文件 @ 75185d82
......@@ -12,17 +12,17 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
syntax="proto2";
syntax = "proto2";
package paddle.framework;
// Attribute Type for paddle's Op.
// Op contains many attributes. Each type of attributes could be different.
// The AttrType will be shared between AttrDesc and AttrProto.
enum AttrType {
INT = 0;
FLOAT = 1;
STRING = 2;
INTS = 3;
FLOATS = 4;
STRINGS = 5;
INT = 0;
FLOAT = 1;
STRING = 2;
INTS = 3;
FLOATS = 4;
STRINGS = 5;
}
\ No newline at end of file
paddle/framework/op_desc.proto
浏览文件 @ 75185d82
......@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
syntax="proto2";
syntax = "proto2";
package paddle.framework;
import "attribute.proto";
......@@ -22,14 +22,14 @@ import "attribute.proto";
//
// e.g, for scale=3.0: name=scala, type=AttrType.FLOAT, value=3.0
message AttrDesc {
required string name = 1;
required AttrType type = 2;
optional int32 i = 3;
optional float f = 4;
optional string s = 5;
repeated int32 ints = 6;
repeated float floats = 7;
repeated string strings = 8;
required string name = 1;
required AttrType type = 2;
optional int32 i = 3;
optional float f = 4;
optional string s = 5;
repeated int32 ints = 6;
repeated float floats = 7;
repeated string strings = 8;
};
// Protocol Message to describe an Operator.
......@@ -42,15 +42,15 @@ message AttrDesc {
// 3rd-party language can build this proto message and call
// AddOp(const OpDesc& op_desc) of Paddle core to create an Operator.
message OpDesc {
// input names of this Operator.
repeated string inputs = 1;
// input names of this Operator.
repeated string inputs = 1;
// output names of this Operator.
repeated string outputs = 2;
// output names of this Operator.
repeated string outputs = 2;
// type of this Operator, such as "add", "sub", "fc".
required string type = 3;
// type of this Operator, such as "add", "sub", "fc".
required string type = 3;
// Attributes of this Operator. e.g., scale=3.0 in cosine op.
repeated AttrDesc attrs = 4;
// Attributes of this Operator. e.g., scale=3.0 in cosine op.
repeated AttrDesc attrs = 4;
};
\ No newline at end of file
paddle/framework/op_proto.proto
浏览文件 @ 75185d82
......@@ -15,10 +15,11 @@ limitations under the License. */
// Protocol Message for 3rd-party language binding.
//
// Paddle Python package will use `OpProto` to generate op creation methods.
// The op creation methods take user's input and generate `OpDesc` proto message,
// The op creation methods take user's input and generate `OpDesc` proto
// message,
// then pass `OpDesc` to C++ side and create Op pointer.
//
syntax="proto2";
syntax = "proto2";
package paddle.framework;
import "attribute.proto";
......@@ -26,89 +27,90 @@ import "attribute.proto";
// Attribute protocol message for 3rd-party language binding.
// It will store the Op support what attribute and what type.
message AttrProto {
// Supported attribute name. e.g. `scale` for cosine op.
required string name = 1;
// Supported attribute name. e.g. `scale` for cosine op.
required string name = 1;
// Supported attribute type.
required AttrType type = 2;
// Supported attribute type.
required AttrType type = 2;
// Supported attribute comments. It helps 3rd-party language generate doc-string.
required string comment = 3;
// Supported attribute comments. It helps 3rd-party language generate
// doc-string.
required string comment = 3;
// If that attribute is generated, it means the Paddle third language
// binding has responsibility to fill that attribute. End-User should
// not set that attribute.
optional bool generated = 4 [default=false];
// If that attribute is generated, it means the Paddle third language
// binding has responsibility to fill that attribute. End-User should
// not set that attribute.
optional bool generated = 4 [ default = false ];
}
// Input or output message for 3rd-party language binding.
// It contains parameter name and its comments.
message VarProto {
// Input or output name in that op creation function.
// e.g. `cos(a, b, output, ...)`, "a", "b", "output" are names.
required string name = 1;
// The comment for that input. It helps 3rd-party language generate doc-string.
required string comment = 2;
// Is that input/output could be a list or not.
// If so, that Op should write a attributed named `input_format` or
// `output_format`.
//
// e.g.
// If the op is a fc op, the inputs are `X`, `W`, `b`. The `X` and `W`
// could be multiple, so the multiple of `X` and `W` is True, and OpDesc
// will hold a attribute of them.
//
// The Op desc of same fc could be
// {
// "type": "fc",
// "input": ["X1", "X2", "W1", "W2", "b"],
// "output": "fc.out",
// "attrs" : {
// "input_format": [0, 2, 4, 5]
// }
// }
//
optional bool multiple = 3 [default=false];
// It marks that output is a temporary output. That output is not used by
// user, but used by other op internally as input. If other op is not use
// that output, it could be optimized early.
//
// Attribute temporary_index will be set in OpDesc if there is some
// outputs are temporary.
//
// output = [ "xxx.out1", "xxx.tmp", "xxx.out2"],
// attrs = {
// "temporary_index": [1]
// }
optional bool temporary = 4 [default=false];
// The gradient of operator can be ignored immediately
// e.g. operator AddOp, y = x1 + x2, the gradient of dy/dx1, dy/dx2
// can be ignored for the future optimized on graph.
optional bool ignore_gradient = 6;
// Input or output name in that op creation function.
// e.g. `cos(a, b, output, ...)`, "a", "b", "output" are names.
required string name = 1;
// The comment for that input. It helps 3rd-party language generate
// doc-string.
required string comment = 2;
// Is that input/output could be a list or not.
// If so, that Op should write a attributed named `input_format` or
// `output_format`.
//
// e.g.
// If the op is a fc op, the inputs are `X`, `W`, `b`. The `X` and `W`
// could be multiple, so the multiple of `X` and `W` is True, and OpDesc
// will hold a attribute of them.
//
// The Op desc of same fc could be
// {
// "type": "fc",
// "input": ["X1", "X2", "W1", "W2", "b"],
// "output": "fc.out",
// "attrs" : {
// "input_format": [0, 2, 4, 5]
// }
// }
//
optional bool multiple = 3 [ default = false ];
// It marks that output is a temporary output. That output is not used by
// user, but used by other op internally as input. If other op is not use
// that output, it could be optimized early.
//
// Attribute temporary_index will be set in OpDesc if there is some
// outputs are temporary.
//
// output = [ "xxx.out1", "xxx.tmp", "xxx.out2"],
// attrs = {
// "temporary_index": [1]
// }
optional bool temporary = 4 [ default = false ];
// The gradient of operator can be ignored immediately
// e.g. operator AddOp, y = x1 + x2, the gradient of dy/dx1, dy/dx2
// can be ignored for the future optimized on graph.
optional bool ignore_gradient = 6;
}
// Op protocol message for 3rd-party language binding.
// It contains all information for generating op creation method.
message OpProto {
// The input information to generate op creation method.
repeated VarProto inputs = 1;
// The input information to generate op creation method.
repeated VarProto inputs = 1;
// The output information to generate op creation method.
repeated VarProto outputs = 2;
// The output information to generate op creation method.
repeated VarProto outputs = 2;
// The attribute information to generate op creation method.
repeated AttrProto attrs = 3;
// The attribute information to generate op creation method.
repeated AttrProto attrs = 3;
// The comments for that Op. It helps 3rd-party language generate
// doc-string. The whole documentation of that Op is generated by comment,
// inputs, outputs, attrs together.
required string comment = 4;
// The type of that Op.
required string type = 5;
// The comments for that Op. It helps 3rd-party language generate
// doc-string. The whole documentation of that Op is generated by comment,
// inputs, outputs, attrs together.
required string comment = 4;
// The type of that Op.
required string type = 5;
}
paddle/function/ContextProjectionOpGpu.cu
浏览文件 @ 75185d82
......@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "hl_base.h"
#include "ContextProjectionOp.h"
#include "hl_base.h"
namespace paddle {
......@@ -30,7 +30,7 @@ __global__ void KeContextProjectionForward(const real* input,
int block_size = blockDim.x;
int sequenceId = blockIdx.x;
int seq_start = sequence[sequenceId];
int seq_end = sequence[sequenceId+1];
int seq_end = sequence[sequenceId + 1];
real value = 0;
int instances = seq_end - seq_start + context_length - 1;
......@@ -49,8 +49,9 @@ __global__ void KeContextProjectionForward(const real* input,
} else if ((i + context_start) >= (seq_end - seq_start)) {
if (padding) {
value =
weight[(begin_pad + i + context_start - (seq_end - seq_start)) *
input_dim + idx];
weight[(begin_pad + i + context_start - (seq_end - seq_start)) *
input_dim +
idx];
} else {
continue;
}
......@@ -61,7 +62,7 @@ __global__ void KeContextProjectionForward(const real* input,
int outx = (i - context_length) < 0 ? i : (context_length - 1);
int outy = (i - context_length) < 0 ? 0 : (i - (context_length - 1));
real* output_r =
output + outy * input_dim * context_length + outx * input_dim;
output + outy * input_dim * context_length + outx * input_dim;
for (int j = outy; j < seq_end - seq_start; j++) {
output_r[idx] += value;
if (j - outy == outx) break;
......@@ -108,13 +109,25 @@ void hl_context_projection_forward(const real* input,
dim3 grid(blocks_x, blocks_y);
if (weight) {
KeContextProjectionForward<true><<< grid, threads, 0, STREAM_DEFAULT >>>
(input, sequence, weight, output, input_dim,
context_length, context_start, begin_pad);
} else {
KeContextProjectionForward<false><<< grid, threads, 0, STREAM_DEFAULT >>>
(input, sequence, weight, output, input_dim,
context_length, context_start, begin_pad);
KeContextProjectionForward<true><<<grid, threads, 0, STREAM_DEFAULT>>>(
input,
sequence,
weight,
output,
input_dim,
context_length,
context_start,
begin_pad);
} else {
KeContextProjectionForward<false><<<grid, threads, 0, STREAM_DEFAULT>>>(
input,
sequence,
weight,
output,
input_dim,
context_length,
context_start,
begin_pad);
}
CHECK_SYNC("hl_context_projection_forward failed");
}
......@@ -148,7 +161,7 @@ __global__ void KeContextProjectionBackwardData(const real* out_grad,
int block_size = blockDim.x;
int sequenceId = blockIdx.x;
int seq_start = sequence[sequenceId];
int seq_end = sequence[sequenceId+1];
int seq_end = sequence[sequenceId + 1];
real value = 0;
int instances = seq_end - seq_start + context_length - 1;
......@@ -170,7 +183,7 @@ __global__ void KeContextProjectionBackwardData(const real* out_grad,
int outx = (i - context_length) < 0 ? i : (context_length - 1);
int outy = (i - context_length) < 0 ? 0 : (i - (context_length - 1));
real* output_r =
out + outy * input_dim * context_length + outx * input_dim;
out + outy * input_dim * context_length + outx * input_dim;
for (int j = outy; j < seq_end - seq_start; j++) {
value += output_r[idx];
if (j - outy == outx) break;
......@@ -211,8 +224,8 @@ void hl_context_projection_backward_data(const real* out_grad,
int blocks_y = 1;
dim3 threads(block_size, 1);
dim3 grid(blocks_x, blocks_y);
KeContextProjectionBackwardData<<< grid, threads, 0, STREAM_DEFAULT >>>
(out_grad, sequence, input_grad, input_dim, context_length, context_start);
KeContextProjectionBackwardData<<<grid, threads, 0, STREAM_DEFAULT>>>(
out_grad, sequence, input_grad, input_dim, context_length, context_start);
CHECK_SYNC("hl_context_projection_backward_data failed");
}
......@@ -231,7 +244,7 @@ void ContextProjectionBackwardData<DEVICE_TYPE_GPU>(const GpuMatrix& out_grad,
context_start);
}
template<int THREADS_X, int THREADS_Y>
template <int THREADS_X, int THREADS_Y>
__global__ void KeContextProjectionBackwardWeight(const real* out_grad,
const int* sequence,
real* w_grad,
......@@ -254,17 +267,17 @@ __global__ void KeContextProjectionBackwardWeight(const real* out_grad,
if (weight_idx < w_dim) {
for (int seqId = idy; seqId < num_sequences; seqId += THREADS_Y) {
int seq_start = sequence[seqId];
int seq_end = sequence[seqId+1];
output_r = const_cast<real*>(out_grad)
+ seq_start * w_dim * context_length;
int seq_end = sequence[seqId + 1];
output_r =
const_cast<real*>(out_grad) + seq_start * w_dim * context_length;
if (context_start < 0) {
if (padId + context_start < 0) {
instanceId = padId;
} else {
// begin_pad > 0;
instanceId = (padId - begin_pad) +
(seq_end - seq_start) - context_start;
instanceId =
(padId - begin_pad) + (seq_end - seq_start) - context_start;
}
} else {
if (padId + (seq_end - seq_start) < context_start) {
......@@ -275,10 +288,11 @@ __global__ void KeContextProjectionBackwardWeight(const real* out_grad,
}
}
int outx = (instanceId - context_length) < 0 ?
instanceId : (context_length - 1);
int outy = (instanceId - context_length) < 0 ?
0 : (instanceId - (context_length - 1));
int outx =
(instanceId - context_length) < 0 ? instanceId : (context_length - 1);
int outy = (instanceId - context_length) < 0
? 0
: (instanceId - (context_length - 1));
output_r += outy * w_dim * context_length + outx * w_dim;
for (int j = outy; j < seq_end - seq_start; j++) {
value += output_r[weight_idx];
......@@ -290,7 +304,7 @@ __global__ void KeContextProjectionBackwardWeight(const real* out_grad,
}
__syncthreads();
for (int stride = THREADS_Y/2; stride > 0; stride = stride/2) {
for (int stride = THREADS_Y / 2; stride > 0; stride = stride / 2) {
if (idy < stride) {
sum_s[idy][idx] += sum_s[idy + stride][idx];
}
......@@ -339,22 +353,27 @@ void hl_context_projection_backward_weight(const real* out_grad,
dim3 threads(threads_x, threads_y);
dim3 grid(blocks_x, 1);
KeContextProjectionBackwardWeight<32, 32>
<<< grid, threads, 0, STREAM_DEFAULT >>>
(out_grad, sequence, w_grad, num_sequences, w_dim,
context_length, context_start, begin_pad);
KeContextProjectionBackwardWeight<32,
32><<<grid, threads, 0, STREAM_DEFAULT>>>(
out_grad,
sequence,
w_grad,
num_sequences,
w_dim,
context_length,
context_start,
begin_pad);
CHECK_SYNC("hl_context_projection_backward_weight failed");
}
template <>
void ContextProjectionBackwardWeight<DEVICE_TYPE_GPU>(
const GpuMatrix& out_grad,
GpuMatrix& w_grad,
const GpuIVector& seq_vec,
size_t context_length,
int context_start,
size_t total_pad,
size_t begin_pad) {
void ContextProjectionBackwardWeight<DEVICE_TYPE_GPU>(const GpuMatrix& out_grad,
GpuMatrix& w_grad,
const GpuIVector& seq_vec,
size_t context_length,
int context_start,
size_t total_pad,
size_t begin_pad) {
hl_context_projection_backward_weight(out_grad.getData(),
seq_vec.getData(),
w_grad.getData(),
......@@ -376,23 +395,18 @@ void ContextProjectionBackward<DEVICE_TYPE_GPU>(const GpuMatrix& out_grad,
size_t begin_pad,
bool is_padding,
size_t total_pad) {
if (in_grad) {
ContextProjectionBackwardData<DEVICE_TYPE_GPU>(
out_grad,
in_grad,
sequence,
context_length,
context_start);
}
if (is_padding && w_grad) {
ContextProjectionBackwardWeight<DEVICE_TYPE_GPU>(
out_grad,
w_grad,
sequence,
context_length,
context_start,
total_pad,
begin_pad);
if (in_grad) {
ContextProjectionBackwardData<DEVICE_TYPE_GPU>(
out_grad, in_grad, sequence, context_length, context_start);
}
if (is_padding && w_grad) {
ContextProjectionBackwardWeight<DEVICE_TYPE_GPU>(out_grad,
w_grad,
sequence,
context_length,
context_start,
total_pad,
begin_pad);
}
}
......
paddle/function/CosSimOpGpu.cu
浏览文件 @ 75185d82
......@@ -12,13 +12,13 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "CosSimOp.h"
#include "hl_base.h"
#include "hl_device_functions.cuh"
#include "CosSimOp.h"
namespace paddle {
template<int block_size>
template <int block_size>
__global__ void KeCosSim(real* output,
const real* input1,
const real* input2,
......@@ -78,8 +78,8 @@ void hlCossim(real* output,
dim3 threads(block_size, 1);
dim3 grid(1, input1_height);
KeCosSim<block_size><<<grid, threads, 0, STREAM_DEFAULT>>>
(output, input1, input2, width, input1_height, input2_height, scale);
KeCosSim<block_size><<<grid, threads, 0, STREAM_DEFAULT>>>(
output, input1, input2, width, input1_height, input2_height, scale);
CHECK_SYNC("hlCossim failed");
}
......@@ -99,7 +99,7 @@ void CosSimForward<DEVICE_TYPE_GPU>(GpuMatrix& out_mat,
hlCossim(out, x, y, dim, in1_mat.getHeight(), in2_mat.getHeight(), scale);
}
template<int block_size>
template <int block_size>
__global__ void KeCosSimDerivative(const real* grad,
const real* output,
const real* prev_out_x,
......@@ -148,14 +148,13 @@ __global__ void KeCosSimDerivative(const real* grad,
if (xy[0] == 0) {
real reciprocal = 1.0 / (sqrt(xx[0]) * sqrt(yy[0]));
for (int index = tid; index < width; index += block_size) {
prev_grad_x[index] +=
scale * grad[ty] * prev_out_y[index] * reciprocal;
prev_grad_x[index] += scale * grad[ty] * prev_out_y[index] * reciprocal;
if (input2_height > 1) {
prev_grad_y[index] +=
scale * grad[ty] * prev_out_x[index] * reciprocal;
prev_grad_y[index] += scale * grad[ty] * prev_out_x[index] * reciprocal;
} else {
paddle::paddleAtomicAdd(prev_grad_y + index,
scale * grad[ty] * prev_out_x[index] * reciprocal);
paddle::paddleAtomicAdd(
prev_grad_y + index,
scale * grad[ty] * prev_out_x[index] * reciprocal);
}
}
} else {
......@@ -163,17 +162,18 @@ __global__ void KeCosSimDerivative(const real* grad,
real reciprocalSquareSumX = 1.0 / xx[0];
real reciprocalSquareSumY = 1.0 / yy[0];
for (int index = tid; index < width; index += block_size) {
prev_grad_x[index] += output[ty] * grad[ty] *
(prev_out_y[index] * reciprocalXY -
prev_out_x[index] * reciprocalSquareSumX);
prev_grad_x[index] +=
output[ty] * grad[ty] * (prev_out_y[index] * reciprocalXY -
prev_out_x[index] * reciprocalSquareSumX);
if (input2_height > 1) {
prev_grad_y[index] += output[ty] * grad[ty] *
(prev_out_x[index] * reciprocalXY -
prev_out_y[index] * reciprocalSquareSumY);
prev_grad_y[index] +=
output[ty] * grad[ty] * (prev_out_x[index] * reciprocalXY -
prev_out_y[index] * reciprocalSquareSumY);
} else {
paddle::paddleAtomicAdd(prev_grad_y + index, output[ty] * grad[ty] *
(prev_out_x[index] * reciprocalXY -
prev_out_y[index] * reciprocalSquareSumY));
paddle::paddleAtomicAdd(
prev_grad_y + index,
output[ty] * grad[ty] * (prev_out_x[index] * reciprocalXY -
prev_out_y[index] * reciprocalSquareSumY));
}
}
}
......@@ -198,9 +198,17 @@ void hlCossimDerivative(const real* grad,
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid(1, input1_height);
KeCosSimDerivative<block_size><<<grid, threads, 0, STREAM_DEFAULT>>>
(grad, output, prev_out_x, prev_out_y, prev_grad_x, prev_grad_y, width,
input1_height, input2_height, scale);
KeCosSimDerivative<block_size><<<grid, threads, 0, STREAM_DEFAULT>>>(
grad,
output,
prev_out_x,
prev_out_y,
prev_grad_x,
prev_grad_y,
width,
input1_height,
input2_height,
scale);
CHECK_SYNC("hlCossimDerivate failed");
}
......@@ -214,9 +222,9 @@ void CosSimBackward<DEVICE_TYPE_GPU>(const GpuMatrix& out_grad,
real scale) {
CHECK(out_grad.getData() && out_val.getData() && in1_val.getData() &&
in2_val.getData() && in1_grad.getData() && in2_grad.getData());
CHECK(out_grad.useGpu_ && out_val.useGpu_ && in1_val.useGpu_
&& in2_val.useGpu_ && in1_grad.useGpu_ && in2_grad.useGpu_)
<< "Matrix types are not equally GPU";
CHECK(out_grad.useGpu_ && out_val.useGpu_ && in1_val.useGpu_ &&
in2_val.useGpu_ && in1_grad.useGpu_ && in2_grad.useGpu_)
<< "Matrix types are not equally GPU";
size_t dim = in1_val.getWidth();
const real* grad = out_grad.getData();
......
paddle/function/CropOpGpu.cu
浏览文件 @ 75185d82
......@@ -12,15 +12,23 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "hl_base.h"
#include "CropOp.h"
#include "hl_base.h"
namespace paddle {
__global__ void KeCrop(real* outputs, const real* inputs,
int inC, int inH, int inW,
int cropC, int cropH, int cropW,
int outC, int outH, int outW, int nthreads) {
__global__ void KeCrop(real* outputs,
const real* inputs,
int inC,
int inH,
int inW,
int cropC,
int cropH,
int cropW,
int outC,
int outH,
int outW,
int nthreads) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < nthreads) {
const int w = idx % outW;
......@@ -35,12 +43,12 @@ __global__ void KeCrop(real* outputs, const real* inputs,
template <>
void Crop<DEVICE_TYPE_GPU>(real* outputs,
const real* inputs,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf) {
const real* inputs,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf) {
std::vector<uint32_t> crop_corner =
conf.get<std::vector<uint32_t>>("crop_corner");
conf.get<std::vector<uint32_t>>("crop_corner");
int cropC = crop_corner[1];
int cropH = crop_corner[2];
int cropW = crop_corner[3];
......@@ -58,16 +66,33 @@ void Crop<DEVICE_TYPE_GPU>(real* outputs,
int blockSize = 1024;
int gridSize = (nth + blockSize - 1) / blockSize;
KeCrop<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
(outputs, inputs, inC, inH, inW, cropC, cropH, cropW,
outC, outH, outW, nth);
KeCrop<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(outputs,
inputs,
inC,
inH,
inW,
cropC,
cropH,
cropW,
outC,
outH,
outW,
nth);
CHECK_SYNC("Crop");
}
__global__ void KeCropDiff(const real* inGrad, real* outGrad,
int inC, int inH, int inW,
int cropC, int cropH, int cropW,
int outC, int outH, int outW, int nthreads) {
__global__ void KeCropDiff(const real* inGrad,
real* outGrad,
int inC,
int inH,
int inW,
int cropC,
int cropH,
int cropW,
int outC,
int outH,
int outW,
int nthreads) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < nthreads) {
const int w = idx % inW;
......@@ -84,12 +109,12 @@ __global__ void KeCropDiff(const real* inGrad, real* outGrad,
template <>
void CropGrad<DEVICE_TYPE_GPU>(const real* inGrad,
real* outGrad,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf) {
real* outGrad,
const TensorShape inShape,
const TensorShape outShape,
const FuncConfig& conf) {
std::vector<uint32_t> crop_corner =
conf.get<std::vector<uint32_t>>("crop_corner");
conf.get<std::vector<uint32_t>>("crop_corner");
int cropC = crop_corner[1];
int cropH = crop_corner[2];
int cropW = crop_corner[3];
......@@ -107,9 +132,18 @@ void CropGrad<DEVICE_TYPE_GPU>(const real* inGrad,
int blockSize = 1024;
int gridSize = (nth + blockSize - 1) / blockSize;
KeCropDiff <<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
(inGrad, outGrad, inC, inH, inW, cropC, cropH, cropW,
outC, outH, outW, nth);
KeCropDiff<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(inGrad,
outGrad,
inC,
inH,
inW,
cropC,
cropH,
cropW,
outC,
outH,
outW,
nth);
CHECK_SYNC("CropGrad");
}
......
paddle/function/CrossMapNormalOpGpu.cu
浏览文件 @ 75185d82
......@@ -12,14 +12,18 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "hl_base.h"
#include "CrossMapNormalOp.h"
#include "hl_base.h"
namespace paddle {
__global__ void KeCMRNormFillScale(size_t imageSize, const real* in,
real* scale, size_t channels,
size_t height, size_t width, size_t size,
__global__ void KeCMRNormFillScale(size_t imageSize,
const real* in,
real* scale,
size_t channels,
size_t height,
size_t width,
size_t size,
real alpha) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < imageSize) {
......@@ -51,8 +55,10 @@ __global__ void KeCMRNormFillScale(size_t imageSize, const real* in,
}
}
__global__ void KeCMRNormOutput(size_t inputSize, const real* in,
const real* scale, real negative_beta,
__global__ void KeCMRNormOutput(size_t inputSize,
const real* in,
const real* scale,
real negative_beta,
real* out) {
const int index = threadIdx.x + blockIdx.x * blockDim.x;
if (index < inputSize) {
......@@ -74,24 +80,30 @@ void CrossMapNormal<DEVICE_TYPE_GPU>(real* outputs,
size_t imageSize = numSamples * height * width;
int blockSize = 1024;
int gridSize = (imageSize + 1024 - 1) / 1024;
KeCMRNormFillScale<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
(imageSize, inputs, denoms, channels, height, width, size, scale);
KeCMRNormFillScale<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(
imageSize, inputs, denoms, channels, height, width, size, scale);
size_t inputSize = numSamples * height * width *channels;
size_t inputSize = numSamples * height * width * channels;
blockSize = 1024;
gridSize = (inputSize + 1024 - 1) / 1024;
KeCMRNormOutput<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
(inputSize, inputs, denoms, -pow, outputs);
KeCMRNormOutput<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(
inputSize, inputs, denoms, -pow, outputs);
CHECK_SYNC("CrossMapNormal");
}
__global__ void KeCMRNormDiff(size_t imageSize, const real* bottom_data,
const real* top_data, const real* scale,
const real* top_diff, size_t channels,
size_t height, size_t width, size_t size,
real negative_beta, real cache_ratio,
real* bottom_diff ) {
__global__ void KeCMRNormDiff(size_t imageSize,
const real* bottom_data,
const real* top_data,
const real* scale,
const real* top_diff,
size_t channels,
size_t height,
size_t width,
size_t size,
real negative_beta,
real cache_ratio,
real* bottom_diff) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < imageSize) {
const int w = idx % width;
......@@ -113,17 +125,17 @@ __global__ void KeCMRNormDiff(size_t imageSize, const real* bottom_data,
while (index < channels + post_pad) {
if (index < channels) {
accum += top_diff[index * step] * top_data[index * step] /
scale[index * step];
scale[index * step];
}
if (index >= size) {
accum -= top_diff[(index - size) * step] *
top_data[(index - size) * step] / scale[(index - size) * step];
top_data[(index - size) * step] / scale[(index - size) * step];
}
if (index >= post_pad) {
bottom_diff[(index - post_pad) * step] +=
top_diff[(index - post_pad) * step] *
pow(scale[(index - post_pad) * step], negative_beta) - cache_ratio *
bottom_data[(index - post_pad) * step] * accum;
top_diff[(index - post_pad) * step] *
pow(scale[(index - post_pad) * step], negative_beta) -
cache_ratio * bottom_data[(index - post_pad) * step] * accum;
}
++index;
}
......@@ -147,9 +159,18 @@ void CrossMapNormalGrad<DEVICE_TYPE_GPU>(real* inputsGrad,
int blockSize = 1024;
int gridSize = (imageSize + 1024 - 1) / 1024;
KeCMRNormDiff <<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
(imageSize, inputsValue, outputsValue, denoms, outputsGrad, channels,
height, width, size, -pow, 2.0f * pow * scale, inputsGrad);
KeCMRNormDiff<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(imageSize,
inputsValue,
outputsValue,
denoms,
outputsGrad,
channels,
height,
width,
size,
-pow,
2.0f * pow * scale,
inputsGrad);
CHECK_SYNC("CrossMapNormalGrad");
}
......
paddle/function/DepthwiseConvOpGpu.cu
浏览文件 @ 75185d82
......@@ -20,17 +20,25 @@ namespace paddle {
// CUDA kernel to compute the depthwise convolution forward pass
template <class T>
__global__
void ConvolutionDepthwiseForward(const int nthreads,
const T* const inputData, const T* const filterData,
const int batchSize, const int outputChannels, const int outputHeight,
const int outputWidth, const int inputChannels, const int inputHeight,
const int inputWidth, const int filterMultiplier, const int filterHeight,
const int filterWidth, const int strideH, const int strideW,
const int paddingH, const int paddingW, T* const outputData) {
int index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
__global__ void ConvolutionDepthwiseForward(const int nthreads,
const T* const inputData,
const T* const filterData,
const int batchSize,
const int outputChannels,
const int outputHeight,
const int outputWidth,
const int inputChannels,
const int inputHeight,
const int inputWidth,
const int filterMultiplier,
const int filterHeight,
const int filterWidth,
const int strideH,
const int strideW,
const int paddingH,
const int paddingW,
T* const outputData) {
int index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if (index < nthreads) {
const int batch = index / outputChannels / outputHeight / outputWidth;
......@@ -45,32 +53,36 @@ void ConvolutionDepthwiseForward(const int nthreads,
const int w_in_start = -paddingW + w_out * strideW;
const int h_in_end = -paddingH + h_out * strideH + filterHeight - 1;
const int w_in_end = -paddingW + w_out * strideW + filterWidth - 1;
if ((h_in_start >= 0) && (h_in_end < inputHeight)
&& (w_in_start >= 0) && (w_in_end < inputWidth)) {
for (int kh = 0; kh < filterHeight; ++kh) {
for (int kw = 0; kw < filterWidth; ++kw) {
const int h_in = -paddingH + h_out * strideH + kh;
const int w_in = -paddingW + w_out * strideW + kw;
const int offset = ((batch * inputChannels + c_in)
* inputHeight + h_in) * inputWidth + w_in;
value += (*weight) * inputData[offset];
++weight;
}
if ((h_in_start >= 0) && (h_in_end < inputHeight) && (w_in_start >= 0) &&
(w_in_end < inputWidth)) {
for (int kh = 0; kh < filterHeight; ++kh) {
for (int kw = 0; kw < filterWidth; ++kw) {
const int h_in = -paddingH + h_out * strideH + kh;
const int w_in = -paddingW + w_out * strideW + kw;
const int offset =
((batch * inputChannels + c_in) * inputHeight + h_in) *
inputWidth +
w_in;
value += (*weight) * inputData[offset];
++weight;
}
}
} else {
for (int kh = 0; kh < filterHeight; ++kh) {
for (int kw = 0; kw < filterWidth; ++kw) {
const int h_in = -paddingH + h_out * strideH + kh;
const int w_in = -paddingW + w_out * strideW + kw;
if ((h_in >= 0) && (h_in < inputHeight)
&& (w_in >= 0) && (w_in < inputWidth)) {
const int offset = ((batch * inputChannels + c_in)
* inputHeight + h_in) * inputWidth + w_in;
value += (*weight) * inputData[offset];
}
++weight;
}
}
for (int kh = 0; kh < filterHeight; ++kh) {
for (int kw = 0; kw < filterWidth; ++kw) {
const int h_in = -paddingH + h_out * strideH + kh;
const int w_in = -paddingW + w_out * strideW + kw;
if ((h_in >= 0) && (h_in < inputHeight) && (w_in >= 0) &&
(w_in < inputWidth)) {
const int offset =
((batch * inputChannels + c_in) * inputHeight + h_in) *
inputWidth +
w_in;
value += (*weight) * inputData[offset];
}
++weight;
}
}
}
outputData[index] = value;
}
......@@ -78,16 +90,25 @@ void ConvolutionDepthwiseForward(const int nthreads,
// CUDA kernel to compute the depthwise convolution backprop w.r.t input.
template <class T>
__global__
void ConvolutionDepthwiseInputBackward(const int nthreads,
const T* const top_diff, const T* const weight_data,
const int num, const int outputChannels, const int outputHeight,
const int outputWidth, const int inputChannels, const int inputHeight,
const int inputWidth, const int filterMultiplier, const int filterHeight,
const int filterWidth, const int strideH, const int strideW,
const int paddingH, const int paddingW, T* const bottom_diff) {
int index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
__global__ void ConvolutionDepthwiseInputBackward(const int nthreads,
const T* const top_diff,
const T* const weight_data,
const int num,
const int outputChannels,
const int outputHeight,
const int outputWidth,
const int inputChannels,
const int inputHeight,
const int inputWidth,
const int filterMultiplier,
const int filterHeight,
const int filterWidth,
const int strideH,
const int strideW,
const int paddingH,
const int paddingW,
T* const bottom_diff) {
int index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if (index < nthreads) {
const int batch = index / inputChannels / inputHeight / inputWidth;
const int c_in = (index / inputHeight / inputWidth) % inputChannels;
......@@ -96,65 +117,80 @@ void ConvolutionDepthwiseInputBackward(const int nthreads,
const int c_out_start = c_in * filterMultiplier;
int h_out_start = (h_in - filterHeight + paddingH + strideH)/strideH;
int h_out_start = (h_in - filterHeight + paddingH + strideH) / strideH;
h_out_start = 0 > h_out_start ? 0 : h_out_start;
int h_out_end = (h_in + paddingH)/strideH;
h_out_end = outputHeight - 1 < h_out_end? outputHeight - 1 : h_out_end;
int w_out_start = (w_in - filterWidth + paddingW + strideW)/strideW;
int h_out_end = (h_in + paddingH) / strideH;
h_out_end = outputHeight - 1 < h_out_end ? outputHeight - 1 : h_out_end;
int w_out_start = (w_in - filterWidth + paddingW + strideW) / strideW;
w_out_start = 0 > w_out_start ? 0 : w_out_start;
int w_out_end = (w_in + paddingW)/strideW;
w_out_end = outputWidth - 1 < w_out_end? outputWidth - 1 : w_out_end;
int w_out_end = (w_in + paddingW) / strideW;
w_out_end = outputWidth - 1 < w_out_end ? outputWidth - 1 : w_out_end;
T value = 0;
for (int c_out = c_out_start;
c_out < c_out_start + filterMultiplier; c_out ++) {
for (int h_out = h_out_start; h_out <= h_out_end; ++h_out) {
const int filter_h = h_in + paddingH - h_out * strideH;
for (int w_out = w_out_start; w_out <= w_out_end; ++w_out) {
const int filter_w = w_in + paddingW - w_out * strideW;
const int filter_offset = c_out * filterHeight * filterWidth
+ filter_h * filterWidth + filter_w;
const int top_diff_offset = ((batch * outputChannels + c_out) *
outputHeight + h_out)* outputWidth + w_out;
value += top_diff[top_diff_offset] * weight_data[filter_offset];
}
for (int c_out = c_out_start; c_out < c_out_start + filterMultiplier;
c_out++) {
for (int h_out = h_out_start; h_out <= h_out_end; ++h_out) {
const int filter_h = h_in + paddingH - h_out * strideH;
for (int w_out = w_out_start; w_out <= w_out_end; ++w_out) {
const int filter_w = w_in + paddingW - w_out * strideW;
const int filter_offset = c_out * filterHeight * filterWidth +
filter_h * filterWidth + filter_w;
const int top_diff_offset =
((batch * outputChannels + c_out) * outputHeight + h_out) *
outputWidth +
w_out;
value += top_diff[top_diff_offset] * weight_data[filter_offset];
}
}
}
bottom_diff[index] += value;
}
}
}
// CUDA kernel to compute the depthwise convolution backprop w.r.t filter.
template <class T>
__global__
void ConvolutionDepthwiseFilterBackward(const int num_i, const int nthreads,
const T* const top_diff, const T* const inputData,
const int num, const int outputChannels, const int outputHeight,
const int outputWidth, const int inputChannels, const int inputHeight,
const int inputWidth, const int filterMultiplier, const int filterHeight,
const int filterWidth, const int strideH, const int strideW,
const int paddingH, const int paddingW, T* const buffer_data) {
int index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
__global__ void ConvolutionDepthwiseFilterBackward(const int num_i,
const int nthreads,
const T* const top_diff,
const T* const inputData,
const int num,
const int outputChannels,
const int outputHeight,
const int outputWidth,
const int inputChannels,
const int inputHeight,
const int inputWidth,
const int filterMultiplier,
const int filterHeight,
const int filterWidth,
const int strideH,
const int strideW,
const int paddingH,
const int paddingW,
T* const buffer_data) {
int index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if (index < nthreads) {
const int h_out = (index / outputWidth) % outputHeight;
const int w_out = index % outputWidth;
const int kh = (index / filterWidth / outputHeight / outputWidth)
% filterHeight;
const int kh =
(index / filterWidth / outputHeight / outputWidth) % filterHeight;
const int kw = (index / outputHeight / outputWidth) % filterWidth;
const int h_in = -paddingH + h_out * strideH + kh;
const int w_in = -paddingW + w_out * strideW + kw;
if ((h_in >= 0) && (h_in < inputHeight)
&& (w_in >= 0) && (w_in < inputWidth)) {
const int c_out = index /
(filterHeight * filterWidth * outputHeight * outputWidth);
if ((h_in >= 0) && (h_in < inputHeight) && (w_in >= 0) &&
(w_in < inputWidth)) {
const int c_out =
index / (filterHeight * filterWidth * outputHeight * outputWidth);
const int c_in = c_out / filterMultiplier;
const int batch = num_i;
const int top_offset = ((batch * outputChannels + c_out) *
outputHeight + h_out) * outputWidth + w_out;
const int bottom_offset = ((batch * inputChannels + c_in)
* inputHeight + h_in) * inputWidth + w_in;
const int top_offset =
((batch * outputChannels + c_out) * outputHeight + h_out) *
outputWidth +
w_out;
const int bottom_offset =
((batch * inputChannels + c_in) * inputHeight + h_in) * inputWidth +
w_in;
buffer_data[index] = top_diff[top_offset] * inputData[bottom_offset];
} else {
buffer_data[index] = 0;
......@@ -163,170 +199,169 @@ void ConvolutionDepthwiseFilterBackward(const int num_i, const int nthreads,
}
template <class T>
class DepthwiseConvFunctor<DEVICE_TYPE_GPU, T>{
class DepthwiseConvFunctor<DEVICE_TYPE_GPU, T> {
public:
void operator()(const T* inputData,
const T* filterData,
int batchSize,
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
int strideW,
int paddingH,
int paddingW,
T* outputData){
const T* filterData,
int batchSize,
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
int strideW,
int paddingH,
int paddingW,
T* outputData) {
int outputSize = batchSize * outputChannels * outputHeight * outputWidth;
size_t blocks = (outputSize + 1024 -1) / 1024;
size_t blocks = (outputSize + 1024 - 1) / 1024;
size_t blockX = 512;
size_t blockY = (blocks+512-1)/512;
size_t blockY = (blocks + 512 - 1) / 512;
dim3 threads(1024, 1);
dim3 grid(blockX, blockY);
ConvolutionDepthwiseForward<T>
<<< grid, threads, 0, STREAM_DEFAULT >>>(
outputSize,
inputData,
filterData,
batchSize,
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH,
strideW,
paddingH,
paddingW,
outputData);
}
ConvolutionDepthwiseForward<T><<<grid, threads, 0, STREAM_DEFAULT>>>(
outputSize,
inputData,
filterData,
batchSize,
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH,
strideW,
paddingH,
paddingW,
outputData);
}
};
template <class T>
class DepthwiseConvGradInputFunctor<DEVICE_TYPE_GPU, T>{
class DepthwiseConvGradInputFunctor<DEVICE_TYPE_GPU, T> {
public:
void operator()(const T* outputGrad,
const T* filterData,
int batchSize,
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
int strideW,
int paddingH,
int paddingW,
T* inputGrad){
const T* filterData,
int batchSize,
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
int strideW,
int paddingH,
int paddingW,
T* inputGrad) {
int inputSize = batchSize * inputChannels * inputHeight * inputWidth;
size_t blocks = (inputSize + 1024 -1) / 1024;
size_t blocks = (inputSize + 1024 - 1) / 1024;
size_t blockX = 512;
size_t blockY = (blocks+512-1)/512;
size_t blockY = (blocks + 512 - 1) / 512;
dim3 threads(1024, 1);
dim3 grid(blockX, blockY);
ConvolutionDepthwiseInputBackward<T>
// NOLINT_NEXT_LINE(whitespace/operators)
<<< grid, threads, 0, STREAM_DEFAULT >>>(
inputSize,
outputGrad,
filterData,
batchSize,
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH,
strideW,
paddingH,
paddingW,
inputGrad);
}
// NOLINT_NEXT_LINE(whitespace/operators)
<<<grid, threads, 0, STREAM_DEFAULT>>>(inputSize,
outputGrad,
filterData,
batchSize,
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH,
strideW,
paddingH,
paddingW,
inputGrad);
}
};
template <class T>
class DepthwiseConvGradFilterFunctor<DEVICE_TYPE_GPU, T> {
public:
void operator()(const T* outputGrad,
const T* inputData,
int batchSize,
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
int strideW,
int paddingH,
int paddingW,
T* colData,
T* filterGrad){
int colDataSize = outputChannels * filterHeight * filterWidth
* outputHeight * outputWidth;
const T* inputData,
int batchSize,
int outputChannels,
int outputHeight,
int outputWidth,
int inputChannels,
int inputHeight,
int inputWidth,
int filterMultiplier,
int filterHeight,
int filterWidth,
int strideH,
int strideW,
int paddingH,
int paddingW,
T* colData,
T* filterGrad) {
int colDataSize = outputChannels * filterHeight * filterWidth *
outputHeight * outputWidth;
size_t blocks = (colDataSize + 1024 -1) / 1024;
size_t blockX = 512;
size_t blockY = (blocks+512-1)/512;
dim3 threads(1024, 1);
dim3 grid(blockX, blockY);
BaseMatrix filterGradMatrix(outputChannels * filterHeight * filterWidth,
1, filterGrad, false, true);
size_t blocks = (colDataSize + 1024 - 1) / 1024;
size_t blockX = 512;
size_t blockY = (blocks + 512 - 1) / 512;
dim3 threads(1024, 1);
dim3 grid(blockX, blockY);
BaseMatrix filterGradMatrix(outputChannels * filterHeight * filterWidth,
1,
filterGrad,
false,
true);
for (int i = 0; i < batchSize; i++) {
ConvolutionDepthwiseFilterBackward<T>
<<< grid, threads, 0, STREAM_DEFAULT >>>(
i,
colDataSize,
outputGrad,
inputData,
batchSize,
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH,
strideW,
paddingH,
paddingW,
colData);
int K = outputHeight * outputWidth;
int M = colDataSize / K;
for (int i = 0; i < batchSize; i++) {
ConvolutionDepthwiseFilterBackward<
T><<<grid, threads, 0, STREAM_DEFAULT>>>(i,
colDataSize,
outputGrad,
inputData,
batchSize,
outputChannels,
outputHeight,
outputWidth,
inputChannels,
inputHeight,
inputWidth,
filterMultiplier,
filterHeight,
filterWidth,
strideH,
strideW,
paddingH,
paddingW,
colData);
int K = outputHeight * outputWidth;
int M = colDataSize / K;
BaseMatrix colMatrix(M, K, colData, false, true);
filterGradMatrix.sumRows(colMatrix, (T)1.0, (T)1.0);
}
BaseMatrix colMatrix(M, K, colData, false, true);
filterGradMatrix.sumRows(colMatrix, (T)1.0, (T)1.0);
}
}
};
#ifdef PADDLE_TYPE_DOUBLE
......
paddle/function/Im2ColOpGpu.cu
浏览文件 @ 75185d82
......@@ -17,16 +17,21 @@ limitations under the License. */
namespace paddle {
template<class T>
__global__
void im2col(const T* data_im, int numOuts, int height, int width,
int blockH, int blockW,
int strideH, int strideW,
int paddingH, int paddingW,
int height_col, int width_col,
T* data_col) {
int index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
template <class T>
__global__ void im2col(const T* data_im,
int numOuts,
int height,
int width,
int blockH,
int blockW,
int strideH,
int strideW,
int paddingH,
int paddingW,
int height_col,
int width_col,
T* data_col) {
int index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if (index < numOuts) {
int w_out = index % width_col;
index /= width_col;
......@@ -39,17 +44,17 @@ void im2col(const T* data_im, int numOuts, int height, int width,
data_col += (channel_out * height_col + h_out) * width_col + w_out;
for (int i = 0; i < blockH; ++i) {
for (int j = 0; j < blockW; ++j) {
int rIdx = int(h_in+i);
int cIdx = int(w_in+j);
if ((rIdx-(int)paddingH) >= (int)height ||
(rIdx-(int)paddingH) < 0 ||
(cIdx-(int)paddingW) >= (int)width ||
(cIdx-(int)paddingW) < 0) {
int rIdx = int(h_in + i);
int cIdx = int(w_in + j);
if ((rIdx - (int)paddingH) >= (int)height ||
(rIdx - (int)paddingH) < 0 ||
(cIdx - (int)paddingW) >= (int)width ||
(cIdx - (int)paddingW) < 0) {
*data_col = 0;
} else {
rIdx = rIdx + channel_in*height - paddingH;
rIdx = rIdx + channel_in * height - paddingH;
cIdx = cIdx - paddingW;
*data_col = data_im[rIdx* width + cIdx];
*data_col = data_im[rIdx * width + cIdx];
}
data_col += height_col * width_col;
}
......@@ -82,60 +87,73 @@ public:
int outputWidth = colShape[4];
int numKernels = inputChannels * outputHeight * outputWidth;
int blocks = (numKernels + 1024 -1) / 1024;
int blocks = (numKernels + 1024 - 1) / 1024;
int blockX = 512;
int blockY = (blocks + 512 - 1) / 512;
dim3 threads(1024, 1);
dim3 grid(blockX, blockY);
im2col<T><<< grid, threads, 0, STREAM_DEFAULT >>>
(imData, numKernels, inputHeight, inputWidth, filterHeight, filterWidth,
strideHeight, strideWidth, paddingHeight, paddingWidth,
outputHeight, outputWidth, colData);
im2col<T><<<grid, threads, 0, STREAM_DEFAULT>>>(imData,
numKernels,
inputHeight,
inputWidth,
filterHeight,
filterWidth,
strideHeight,
strideWidth,
paddingHeight,
paddingWidth,
outputHeight,
outputWidth,
colData);
CHECK_SYNC("Im2ColFunctor GPU failed");
}
};
template<class T>
__global__
void col2im(size_t n, const T* data_col, size_t height,
size_t width, size_t channels,
size_t blockH, size_t blockW,
size_t strideH, size_t strideW,
size_t paddingH, size_t paddingW,
size_t height_col, size_t width_col,
T* data_im) {
template <class T>
__global__ void col2im(size_t n,
const T* data_col,
size_t height,
size_t width,
size_t channels,
size_t blockH,
size_t blockW,
size_t strideH,
size_t strideW,
size_t paddingH,
size_t paddingW,
size_t height_col,
size_t width_col,
T* data_im) {
size_t index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if (index < n) {
T val = 0;
int w = int(index % width);
int h = int((index / width) % height);
int c = int(index / (width * height));
if ((w - (int)paddingW) >= 0 &&
(w - (int)paddingW) < (width-2 * paddingW) &&
(h - (int)paddingH) >= 0 &&
(h - paddingH) < (height - 2 * paddingH)) {
(w - (int)paddingW) < (width - 2 * paddingW) &&
(h - (int)paddingH) >= 0 && (h - paddingH) < (height - 2 * paddingH)) {
// compute the start and end of the output
int w_col_start =
(w < (int)blockW) ? 0 : (w - int(blockW)) / (int)strideW + 1;
int w_col_end =
min((int)(w / (int)strideW + 1), (int)(width_col));
(w < (int)blockW) ? 0 : (w - int(blockW)) / (int)strideW + 1;
int w_col_end = min((int)(w / (int)strideW + 1), (int)(width_col));
int h_col_start =
(h < (int)blockH) ? 0 : (h - (int)blockH) / (int)strideH + 1;
(h < (int)blockH) ? 0 : (h - (int)blockH) / (int)strideH + 1;
int h_col_end = min(int(h / strideH + 1), int(height_col));
for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
// the col location: [c * width * height + h_out, w_out]
int c_col = int(c * blockH* blockW) + \
(h - h_col * (int)strideH) * (int)blockW +
(w - w_col * (int)strideW);
int c_col = int(c * blockH * blockW) +
(h - h_col * (int)strideH) * (int)blockW +
(w - w_col * (int)strideW);
val += data_col[(c_col * height_col + h_col) * width_col + w_col];
}
}
h -= paddingH;
w -= paddingW;
data_im[c*((width-2*paddingW) * (height-2*paddingH)) +
h*(width-2*paddingW) + w] += val;
data_im[c * ((width - 2 * paddingW) * (height - 2 * paddingH)) +
h * (width - 2 * paddingW) + w] += val;
}
}
}
......@@ -164,32 +182,32 @@ public:
int outputHeight = colShape[3];
int outputWidth = colShape[4];
size_t numKernels = inputChannels * (inputHeight + 2*paddingHeight)
* (inputWidth + 2*paddingWidth);
size_t numKernels = inputChannels * (inputHeight + 2 * paddingHeight) *
(inputWidth + 2 * paddingWidth);
size_t blocks = (numKernels + 1024 -1) / 1024;
size_t blocks = (numKernels + 1024 - 1) / 1024;
size_t blockX = 512;
size_t blockY = (blocks+512-1)/512;
size_t blockY = (blocks + 512 - 1) / 512;
dim3 threads(1024, 1);
dim3 grid(blockX, blockY);
// To avoid involving atomic operations, we will launch one kernel per
// bottom dimension, and then in the kernel add up the top dimensions.
col2im<T><<< grid, threads, 0, STREAM_DEFAULT >>>
(numKernels,
colData,
inputHeight + 2*paddingHeight,
inputWidth + 2*paddingWidth,
inputChannels,
filterHeight,
filterWidth,
strideHeight,
strideWidth,
paddingHeight,
paddingWidth,
outputHeight,
outputWidth,
imData);
col2im<T><<<grid, threads, 0, STREAM_DEFAULT>>>(
numKernels,
colData,
inputHeight + 2 * paddingHeight,
inputWidth + 2 * paddingWidth,
inputChannels,
filterHeight,
filterWidth,
strideHeight,
strideWidth,
paddingHeight,
paddingWidth,
outputHeight,
outputWidth,
imData);
CHECK_SYNC("Col2ImFunctor GPU failed");
}
};
......@@ -199,31 +217,35 @@ template class Im2ColFunctor<kCFO, DEVICE_TYPE_GPU, double>;
template class Col2ImFunctor<kCFO, DEVICE_TYPE_GPU, float>;
template class Col2ImFunctor<kCFO, DEVICE_TYPE_GPU, double>;
template<class T>
__global__
void im2colOCF(const T* imData, T* colData,
int inputChannels,
int inputHeight, int inputWidth,
int filterHeight, int filterWidth,
int strideHeight, int strideWidth,
int paddingHeight, int paddingWidth,
int outputHeight, int outputWidth) {
template <class T>
__global__ void im2colOCF(const T* imData,
T* colData,
int inputChannels,
int inputHeight,
int inputWidth,
int filterHeight,
int filterWidth,
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth,
int outputHeight,
int outputWidth) {
int swId = blockIdx.x;
int shId = blockIdx.y;
for (int channelId = threadIdx.z;
channelId < inputChannels;
for (int channelId = threadIdx.z; channelId < inputChannels;
channelId += blockDim.z) {
for (int idy = threadIdx.y; idy < filterHeight; idy += blockDim.y) {
for (int idx = threadIdx.x; idx < filterWidth; idx += blockDim.x) {
int widthOffset = idx + swId * strideWidth - paddingWidth;
int heightOffset = idy + shId * strideHeight - paddingHeight;
int imOffset = widthOffset + heightOffset * inputWidth
+ channelId * inputHeight * inputWidth;
int imOffset = widthOffset + heightOffset * inputWidth +
channelId * inputHeight * inputWidth;
int colOffset = idx + idy * filterWidth
+ channelId * filterHeight * filterWidth
+ (shId * outputWidth + swId)
* (inputChannels * filterHeight * filterWidth);
int colOffset = idx + idy * filterWidth +
channelId * filterHeight * filterWidth +
(shId * outputWidth + swId) *
(inputChannels * filterHeight * filterWidth);
if (heightOffset >= inputHeight || heightOffset < 0 ||
widthOffset >= inputWidth || widthOffset < 0) {
......@@ -279,39 +301,52 @@ public:
int blockDimZ = 1024 / blockDimX / blockDimY;
dim3 threads(blockDimX, blockDimY, std::min(blockDimZ, inputChannels));
dim3 grid(outputWidth, outputHeight);
im2colOCF<T><<< grid, threads, 0, STREAM_DEFAULT >>>
(imData, colData, inputChannels, inputHeight, inputWidth,
filterHeight, filterWidth, strideHeight, strideWidth,
paddingHeight, paddingWidth, outputHeight, outputWidth);
im2colOCF<T><<<grid, threads, 0, STREAM_DEFAULT>>>(imData,
colData,
inputChannels,
inputHeight,
inputWidth,
filterHeight,
filterWidth,
strideHeight,
strideWidth,
paddingHeight,
paddingWidth,
outputHeight,
outputWidth);
CHECK_SYNC("Im2ColFunctor GPU failed");
}
};
template<class T>
__global__
void col2imOCF(T* imData, const T* colData,
int inputChannels,
int inputHeight, int inputWidth,
int filterHeight, int filterWidth,
int strideHeight, int strideWidth,
int paddingHeight, int paddingWidth,
int outputHeight, int outputWidth) {
template <class T>
__global__ void col2imOCF(T* imData,
const T* colData,
int inputChannels,
int inputHeight,
int inputWidth,
int filterHeight,
int filterWidth,
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth,
int outputHeight,
int outputWidth) {
int swId = blockIdx.x;
int shId = blockIdx.y;
for (int channelId = threadIdx.z;
channelId < inputChannels;
for (int channelId = threadIdx.z; channelId < inputChannels;
channelId += blockDim.z) {
for (int idy = threadIdx.y; idy < filterHeight; idy += blockDim.y) {
for (int idx = threadIdx.x; idx < filterWidth; idx += blockDim.x) {
int widthOffset = idx + swId * strideWidth - paddingWidth;
int heightOffset = idy + shId * strideHeight - paddingHeight;
int imOffset = widthOffset + heightOffset * inputWidth
+ channelId * inputHeight * inputWidth;
int imOffset = widthOffset + heightOffset * inputWidth +
channelId * inputHeight * inputWidth;
int colOffset = idx + idy * filterWidth
+ channelId * filterHeight * filterWidth
+ (shId * outputWidth + swId)
* (inputChannels * filterHeight * filterWidth);
int colOffset = idx + idy * filterWidth +
channelId * filterHeight * filterWidth +
(shId * outputWidth + swId) *
(inputChannels * filterHeight * filterWidth);
if (heightOffset >= 0 && heightOffset < inputHeight &&
widthOffset >= 0 && widthOffset < inputWidth) {
......@@ -365,10 +400,19 @@ public:
int blockDimZ = 1024 / blockDimX / blockDimY;
dim3 threads(blockDimX, blockDimY, std::min(blockDimZ, inputChannels));
dim3 grid(outputWidth, outputHeight);
col2imOCF<T><<< grid, threads, 0, STREAM_DEFAULT >>>
(imData, colData, inputChannels, inputHeight, inputWidth,
filterHeight, filterWidth, strideHeight, strideWidth,
paddingHeight, paddingWidth, outputHeight, outputWidth);
col2imOCF<T><<<grid, threads, 0, STREAM_DEFAULT>>>(imData,
colData,
inputChannels,
inputHeight,
inputWidth,
filterHeight,
filterWidth,
strideHeight,
strideWidth,
paddingHeight,
paddingWidth,
outputHeight,
outputWidth);
CHECK_SYNC("Col2ImFunctor GPU failed");
}
};
......
paddle/function/MulOpGpu.cu
浏览文件 @ 75185d82
......@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "hl_base.h"
#include "MulOp.h"
#include "hl_base.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/SparseMatrix.h"
......
paddle/function/PadOpGpu.cu
浏览文件 @ 75185d82
......@@ -12,15 +12,23 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "hl_base.h"
#include "PadOp.h"
#include "hl_base.h"
namespace paddle {
__global__ void KePad(real* outputs, const real* inputs,
int inC, int inH, int inW,
int padc, int padh, int padw,
int outC, int outH, int outW, int nthreads) {
__global__ void KePad(real* outputs,
const real* inputs,
int inC,
int inH,
int inW,
int padc,
int padh,
int padw,
int outC,
int outH,
int outW,
int nthreads) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < nthreads) {
const int w = idx % inW;
......@@ -50,16 +58,33 @@ void Pad<DEVICE_TYPE_GPU>(real* outputs,
int outC = inC + cstart + cend;
int outH = inH + hstart + hend;
int outW = inW + wstart + wend;
KePad<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
(outputs, inputs, inC, inH, inW, cstart, hstart, wstart,
outC, outH, outW, nth);
KePad<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(outputs,
inputs,
inC,
inH,
inW,
cstart,
hstart,
wstart,
outC,
outH,
outW,
nth);
CHECK_SYNC("Pad");
}
__global__ void KePadDiff(real* inGrad, const real* outGrad,
int inC, int inH, int inW,
int padc, int padh, int padw,
int outC, int outH, int outW, int nthreads) {
__global__ void KePadDiff(real* inGrad,
const real* outGrad,
int inC,
int inH,
int inW,
int padc,
int padh,
int padw,
int outC,
int outH,
int outW,
int nthreads) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < nthreads) {
const int w = idx % inW;
......@@ -89,9 +114,18 @@ void PadGrad<DEVICE_TYPE_GPU>(real* inGrad,
int outC = inC + cstart + cend;
int outH = inH + hstart + hend;
int outW = inW + wstart + wend;
KePadDiff <<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
(inGrad, outGrad, inC, inH, inW, cstart, hstart, wstart,
outC, outH, outW, nth);
KePadDiff<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(inGrad,
outGrad,
inC,
inH,
inW,
cstart,
hstart,
wstart,
outC,
outH,
outW,
nth);
CHECK_SYNC("PadGrad");
}
......
paddle/function/RowConvOpGpu.cu
浏览文件 @ 75185d82
......@@ -12,16 +12,20 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "hl_base.h"
#include "RowConvOp.h"
#include "hl_base.h"
namespace paddle {
template<int BLOCK_H, int BLOCK_W>
__global__ void KeRowConv(real* y, const real* x, const real* w,
const int* starts, const int height, const int width,
const int numSeq, const int context) {
template <int BLOCK_H, int BLOCK_W>
__global__ void KeRowConv(real* y,
const real* x,
const real* w,
const int* starts,
const int height,
const int width,
const int numSeq,
const int context) {
const int tidx = threadIdx.x;
const int tidy = threadIdx.y;
const int blky = blockDim.y;
......@@ -30,7 +34,7 @@ __global__ void KeRowConv(real* y, const real* x, const real* w,
__shared__ real sw[BLOCK_H][BLOCK_W];
for (int i = tidy; i < context; i += blky) {
sw[i][tidx] = gidx + tidx < width ? w[i*width + gidx + tidx] : 0.0;
sw[i][tidx] = gidx + tidx < width ? w[i * width + gidx + tidx] : 0.0;
}
__syncthreads();
......@@ -56,9 +60,14 @@ __global__ void KeRowConv(real* y, const real* x, const real* w,
}
}
__global__ void KeRowConv2(real* y, const real* x, const real* w,
const int* starts, const int height, const int width,
const int numSeq, const int context) {
__global__ void KeRowConv2(real* y,
const real* x,
const real* w,
const int* starts,
const int height,
const int width,
const int numSeq,
const int context) {
const int tidx = threadIdx.x;
const int tidy = threadIdx.y;
const int blky = blockDim.y;
......@@ -84,8 +93,6 @@ __global__ void KeRowConv2(real* y, const real* x, const real* w,
}
}
template <>
void RowConv<DEVICE_TYPE_GPU>(GpuMatrix& out,
const GpuMatrix& in,
......@@ -105,21 +112,24 @@ void RowConv<DEVICE_TYPE_GPU>(GpuMatrix& out,
dim3 dimGrid(DIVUP(width, dimBlock.x), 1);
if (contextLength <= 32) {
KeRowConv<32, 32><<<dimGrid, dimBlock, 0, STREAM_DEFAULT>>>
(y, x, w, starts, height, width, numSeq, contextLength);
KeRowConv<32, 32><<<dimGrid, dimBlock, 0, STREAM_DEFAULT>>>(
y, x, w, starts, height, width, numSeq, contextLength);
} else {
KeRowConv2<<<dimGrid, dimBlock, 0, STREAM_DEFAULT>>>
(y, x, w, starts, height, width, numSeq, contextLength);
KeRowConv2<<<dimGrid, dimBlock, 0, STREAM_DEFAULT>>>(
y, x, w, starts, height, width, numSeq, contextLength);
}
CHECK_SYNC("RowConv");
}
template<int BLOCK_H, int BLOCK_W, int CONTEXT>
__global__ void KeRowConvBwWeight(real* dw, const real* x, const real* dy,
const int* starts, const int height, const int width, const int numSeq,
const int context) {
template <int BLOCK_H, int BLOCK_W, int CONTEXT>
__global__ void KeRowConvBwWeight(real* dw,
const real* x,
const real* dy,
const int* starts,
const int height,
const int width,
const int numSeq,
const int context) {
const int tidx = threadIdx.x;
const int tidy = threadIdx.y;
const int blky = blockDim.y;
......@@ -138,21 +148,21 @@ __global__ void KeRowConvBwWeight(real* dw, const real* x, const real* dy,
const int start = starts[i];
const int end = starts[i + 1];
const int steps = end - start;
const int size = ((steps + BLOCK_H - 1)/BLOCK_H) * BLOCK_H;
const int size = ((steps + BLOCK_H - 1) / BLOCK_H) * BLOCK_H;
for (int j = tidy; j < size; j += BLOCK_H) {
int xoff = gidx + tidx;
int yoff = start + j;
// transpose
sh_x[tidx][tidy] = (xoff < width && yoff < end) ?
x[yoff * width + xoff] : 0.0;
sh_dy[tidx][tidy + context - 1] = (xoff < width && yoff < end) ?
dy[yoff * width + xoff] : 0.0;
sh_x[tidx][tidy] =
(xoff < width && yoff < end) ? x[yoff * width + xoff] : 0.0;
sh_dy[tidx][tidy + context - 1] =
(xoff < width && yoff < end) ? dy[yoff * width + xoff] : 0.0;
__syncthreads();
if (tidy < (context - 1)) {
yoff = yoff - context + 1;
sh_dy[tidx][tidy] = (xoff < width && yoff >= start) ?
dy[yoff * width + xoff] : 0.0;
sh_dy[tidx][tidy] =
(xoff < width && yoff >= start) ? dy[yoff * width + xoff] : 0.0;
}
__syncthreads();
......@@ -179,11 +189,15 @@ __global__ void KeRowConvBwWeight(real* dw, const real* x, const real* dy,
}
}
template<int BLOCK_H, int BLOCK_W>
__global__ void KeRowConvBwWeight2(real* dw, const real* x, const real* dy,
const int* starts, const int height, const int width, const int numSeq,
const int context) {
template <int BLOCK_H, int BLOCK_W>
__global__ void KeRowConvBwWeight2(real* dw,
const real* x,
const real* dy,
const int* starts,
const int height,
const int width,
const int numSeq,
const int context) {
const int tidx = threadIdx.x;
const int tidy = threadIdx.y;
const int gidx = blockIdx.x * blockDim.x;
......@@ -196,19 +210,21 @@ __global__ void KeRowConvBwWeight2(real* dw, const real* x, const real* dy,
const int end = starts[i + 1];
const int steps = end - start;
const int size = ((steps + BLOCK_H - 1)/BLOCK_H) * BLOCK_H;
const int size = ((steps + BLOCK_H - 1) / BLOCK_H) * BLOCK_H;
for (int j = tidy; j < size; j += BLOCK_H) {
int xoff = gidx + tidx;
int yoff = start + j;
// transpose
sh_x[tidx][tidy] = (xoff < width && yoff < end) ?
x[yoff * width + xoff] : 0.0;
sh_x[tidx][tidy] =
(xoff < width && yoff < end) ? x[yoff * width + xoff] : 0.0;
__syncthreads();
for (int t = 0; t < context; t++) {
sh_dy[tidx][tidy] = (xoff < width && (yoff - t) >= start &&
yoff - t < end) ? dy[(yoff - t) * width + xoff] : 0.0;
sh_dy[tidx][tidy] =
(xoff < width && (yoff - t) >= start && yoff - t < end)
? dy[(yoff - t) * width + xoff]
: 0.0;
__syncthreads();
real val = sh_x[tidy][tidx] * sh_dy[tidy][tidx];
......@@ -222,18 +238,22 @@ __global__ void KeRowConvBwWeight2(real* dw, const real* x, const real* dy,
__syncthreads();
if (tidx == 0 && (gidx + tidy) < width) {
dw[t*width + gidx + tidy] += val;
dw[t * width + gidx + tidy] += val;
}
}
}
}
}
template<int BLOCK_H, int BLOCK_W>
__global__ void KeRowConvBwData(real* dx, const real* w, const real* dy,
const int* starts, const int height, const int width, const int numSeq,
const int context) {
template <int BLOCK_H, int BLOCK_W>
__global__ void KeRowConvBwData(real* dx,
const real* w,
const real* dy,
const int* starts,
const int height,
const int width,
const int numSeq,
const int context) {
const int tidx = threadIdx.x;
const int tidy = threadIdx.y;
const int blky = blockDim.y;
......@@ -242,7 +262,7 @@ __global__ void KeRowConvBwData(real* dx, const real* w, const real* dy,
__shared__ real sw[BLOCK_H][BLOCK_W];
for (int i = tidy; i < context; i += blky) {
sw[i][tidx] = gidx + tidx < width ? w[i*width + gidx + tidx] : 0.0;
sw[i][tidx] = gidx + tidx < width ? w[i * width + gidx + tidx] : 0.0;
}
__syncthreads();
......@@ -266,10 +286,14 @@ __global__ void KeRowConvBwData(real* dx, const real* w, const real* dy,
}
}
__global__ void KeRowConvBwData2(real* dx, const real* w, const real* dy,
const int* starts, const int height, const int width, const int numSeq,
const int context) {
__global__ void KeRowConvBwData2(real* dx,
const real* w,
const real* dy,
const int* starts,
const int height,
const int width,
const int numSeq,
const int context) {
const int tidx = threadIdx.x;
const int tidy = threadIdx.y;
const int blky = blockDim.y;
......@@ -295,14 +319,13 @@ __global__ void KeRowConvBwData2(real* dx, const real* w, const real* dy,
}
}
template <>
void RowConvGrad<DEVICE_TYPE_GPU>(const GpuMatrix& outG,
const GpuMatrix& in,
const GpuMatrix& filter,
GpuMatrix& inG,
GpuMatrix& filterG,
const GpuIVector& seq) {
const GpuMatrix& in,
const GpuMatrix& filter,
GpuMatrix& inG,
GpuMatrix& filterG,
const GpuIVector& seq) {
const size_t numSeq = seq.getSize() - 1;
const size_t contextLength = filter.getHeight();
const size_t height = in.getHeight();
......@@ -318,13 +341,11 @@ void RowConvGrad<DEVICE_TYPE_GPU>(const GpuMatrix& outG,
dim3 dimGrid(DIVUP(width, dimBlock.x), 1);
real* dw = filterG.getData();
if (contextLength <= 32) {
KeRowConvBwWeight<32, 32, 32>
<<<dimGrid, dimBlock, 0, STREAM_DEFAULT>>>
(dw, x, dy, starts, height, width, numSeq, contextLength);
KeRowConvBwWeight<32, 32, 32><<<dimGrid, dimBlock, 0, STREAM_DEFAULT>>>(
dw, x, dy, starts, height, width, numSeq, contextLength);
} else {
KeRowConvBwWeight2<32, 32>
<<<dimGrid, dimBlock, 0, STREAM_DEFAULT>>>
(dw, x, dy, starts, height, width, numSeq, contextLength);
KeRowConvBwWeight2<32, 32><<<dimGrid, dimBlock, 0, STREAM_DEFAULT>>>(
dw, x, dy, starts, height, width, numSeq, contextLength);
}
}
......@@ -333,13 +354,11 @@ void RowConvGrad<DEVICE_TYPE_GPU>(const GpuMatrix& outG,
dim3 dimBlock2(32, 32);
dim3 dimGrid2(DIVUP(width, dimBlock2.x), 1);
if (contextLength <= 64) {
KeRowConvBwData<32, 64>
<<<dimGrid2, dimBlock2, 0, STREAM_DEFAULT>>>
(dx, w, dy, starts, height, width, numSeq, contextLength);
KeRowConvBwData<32, 64><<<dimGrid2, dimBlock2, 0, STREAM_DEFAULT>>>(
dx, w, dy, starts, height, width, numSeq, contextLength);
} else {
KeRowConvBwData2
<<<dimGrid2, dimBlock2, 0, STREAM_DEFAULT>>>
(dx, w, dy, starts, height, width, numSeq, contextLength);
KeRowConvBwData2<<<dimGrid2, dimBlock2, 0, STREAM_DEFAULT>>>(
dx, w, dy, starts, height, width, numSeq, contextLength);
}
}
......
paddle/gserver/layers/GruCompute.cu
浏览文件 @ 75185d82
......@@ -12,7 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "GruCompute.h"
#include "hl_recurrent_apply.cuh"
......@@ -31,8 +30,10 @@ void GruCompute::forward<1>(hl_gru_value value, int frameSize, int batchSize) {
}
template <>
void GruCompute::backward<1>(hl_gru_value value, hl_gru_grad grad,
int frameSize, int batchSize) {
void GruCompute::backward<1>(hl_gru_value value,
hl_gru_grad grad,
int frameSize,
int batchSize) {
hl_gpu_gru_backward(hppl::backward::gru_stateGrad(),
hppl::backward::gru_resetGrad(),
value,
......
paddle/gserver/layers/LstmCompute.cu
浏览文件 @ 75185d82
......@@ -12,41 +12,62 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "LstmCompute.h"
#include "hl_recurrent_apply.cuh"
namespace paddle {
template <>
void LstmCompute::forwardBatch<1>(hl_lstm_value value, int frameSize,
int batchSize) {
hl_gpu_lstm_forward(hppl::forward::lstm(), value, frameSize,
batchSize, activeNode_, activeGate_,
void LstmCompute::forwardBatch<1>(hl_lstm_value value,
int frameSize,
int batchSize) {
hl_gpu_lstm_forward(hppl::forward::lstm(),
value,
frameSize,
batchSize,
activeNode_,
activeGate_,
activeState_);
}
template <>
void LstmCompute::backwardBatch<1>(hl_lstm_value value, hl_lstm_grad grad,
int frameSize, int batchSize) {
hl_gpu_lstm_backward(hppl::backward::lstm(), value, grad,
frameSize, batchSize, activeNode_,
activeGate_, activeState_);
void LstmCompute::backwardBatch<1>(hl_lstm_value value,
hl_lstm_grad grad,
int frameSize,
int batchSize) {
hl_gpu_lstm_backward(hppl::backward::lstm(),
value,
grad,
frameSize,
batchSize,
activeNode_,
activeGate_,
activeState_);
}
template <>
void LstmCompute::forwardOneSequence<1>(hl_lstm_value value, int frameSize) {
hl_gpu_lstm_forward(hppl::forward::lstm(), value,
frameSize, /* batchSize */ 1,
activeNode_, activeGate_, activeState_);
hl_gpu_lstm_forward(hppl::forward::lstm(),
value,
frameSize,
/* batchSize */ 1,
activeNode_,
activeGate_,
activeState_);
}
template <>
void LstmCompute::backwardOneSequence<1>(hl_lstm_value value, hl_lstm_grad grad,
void LstmCompute::backwardOneSequence<1>(hl_lstm_value value,
hl_lstm_grad grad,
int frameSize) {
hl_gpu_lstm_backward(hppl::backward::lstm(), value, grad,
frameSize, /* batchSize */ 1,
activeNode_, activeGate_, activeState_);
hl_gpu_lstm_backward(hppl::backward::lstm(),
value,
grad,
frameSize,
/* batchSize */ 1,
activeNode_,
activeGate_,
activeState_);
}
} // namespace paddle
paddle/math/BaseMatrix.cu
浏览文件 @ 75185d82
......@@ -12,21 +12,21 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <cmath>
#include <string.h>
#include <paddle/utils/Logging.h>
#include <string.h>
#include <cmath>
#include "BaseMatrix.h"
#include "hl_matrix_ops.cuh"
#include "hl_matrix_base.cuh"
#include "hl_matrix_apply.cuh"
#include "SIMDFunctions.h"
#include "MathFunctions.h"
#include "SIMDFunctions.h"
#include "hl_matrix_apply.cuh"
#include "hl_matrix_base.cuh"
#include "hl_matrix_ops.cuh"
namespace paddle {
const char* SPARSE_SUPPORT_ERROR = "Sparse Matrix/Vector is not supported.";
template<class T>
template <class T>
template <class Op>
int BaseMatrixT<T>::applyUnary(Op op) {
MatrixOffset offset(0, 0);
......@@ -34,9 +34,11 @@ int BaseMatrixT<T>::applyUnary(Op op) {
return 0;
}
template<class T>
template <class T>
template <class Op>
int BaseMatrixT<T>::applyUnary(Op op, int numRows, int numCols,
int BaseMatrixT<T>::applyUnary(Op op,
int numRows,
int numCols,
MatrixOffset& offset) {
CHECK(!this->isSparse()) << SPARSE_SUPPORT_ERROR;
int dimM = numRows;
......@@ -56,7 +58,7 @@ int BaseMatrixT<T>::applyUnary(Op op, int numRows, int numCols,
return 0;
}
template<class T>
template <class T>
template <class Op>
int BaseMatrixT<T>::applyBinary(Op op, BaseMatrixT& b) {
CHECK(height_ == b.height_ && width_ == b.width_)
......@@ -67,18 +69,23 @@ int BaseMatrixT<T>::applyBinary(Op op, BaseMatrixT& b) {
return 0;
}
template<class T>
template <class T>
template <class Op>
int BaseMatrixT<T>::applyBinary(Op op, BaseMatrixT& b, int numRows, int numCols,
MatrixOffset& offset) {
int BaseMatrixT<T>::applyBinary(
Op op, BaseMatrixT& b, int numRows, int numCols, MatrixOffset& offset) {
applyBinary(op, b, numRows, numCols, offset, false_type(), false_type());
return 0;
}
template<class T>
template <class T>
template <class Op, class bAsRowVector, class bAsColVector>
int BaseMatrixT<T>::applyBinary(Op op, BaseMatrixT& b, int numRows, int numCols,
MatrixOffset& offset, bAsRowVector, bAsColVector) {
int BaseMatrixT<T>::applyBinary(Op op,
BaseMatrixT& b,
int numRows,
int numCols,
MatrixOffset& offset,
bAsRowVector,
bAsColVector) {
CHECK(!this->isSparse()) << SPARSE_SUPPORT_ERROR;
CHECK(!b.isSparse()) << SPARSE_SUPPORT_ERROR;
CHECK(useGpu_ == b.useGpu_) << "Matrix type mismatch";
......@@ -91,8 +98,8 @@ int BaseMatrixT<T>::applyBinary(Op op, BaseMatrixT& b, int numRows, int numCols,
T* A = data_;
T* B = b.data_;
CAL_MATRIX_START_ADDRESS(A, height_, width_, lda, offset.aCol_, offset.aRow_);
CAL_MATRIX_START_ADDRESS(B, b.height_, b.width_, ldb, offset.bCol_,
offset.bRow_);
CAL_MATRIX_START_ADDRESS(
B, b.height_, b.width_, ldb, offset.bCol_, offset.bRow_);
CHECK_LE(dimM + offset.aRow_, this->height_);
CHECK_LE(dimN + offset.aCol_, this->width_);
if (!bAsRowVector::value && !bAsColVector::value) {
......@@ -115,7 +122,7 @@ int BaseMatrixT<T>::applyBinary(Op op, BaseMatrixT& b, int numRows, int numCols,
return 0;
}
template<class T>
template <class T>
template <class Op>
int BaseMatrixT<T>::applyTernary(Op op, BaseMatrixT& b, BaseMatrixT& c) {
CHECK_EQ(height_, b.height_);
......@@ -129,21 +136,29 @@ int BaseMatrixT<T>::applyTernary(Op op, BaseMatrixT& b, BaseMatrixT& c) {
return 0;
}
template<class T>
template <class T>
template <class Op>
int BaseMatrixT<T>::applyTernary(Op op, BaseMatrixT& b, BaseMatrixT& c,
int numRows, int numCols,
int BaseMatrixT<T>::applyTernary(Op op,
BaseMatrixT& b,
BaseMatrixT& c,
int numRows,
int numCols,
MatrixOffset& offset) {
applyTernary(op, b, c, numRows, numCols, offset, false_type(), false_type());
return 0;
}
template<class T>
template <class T>
template <class Op, class cAsRowVector, class cAsColVector>
int BaseMatrixT<T>::applyTernary(Op op, BaseMatrixT& b, BaseMatrixT& c,
int numRows, int numCols, MatrixOffset& offset,
cAsRowVector, cAsColVector) {
int BaseMatrixT<T>::applyTernary(Op op,
BaseMatrixT& b,
BaseMatrixT& c,
int numRows,
int numCols,
MatrixOffset& offset,
cAsRowVector,
cAsColVector) {
CHECK(!this->isSparse()) << SPARSE_SUPPORT_ERROR;
CHECK(!b.isSparse()) << SPARSE_SUPPORT_ERROR;
CHECK(!c.isSparse()) << SPARSE_SUPPORT_ERROR;
......@@ -160,10 +175,10 @@ int BaseMatrixT<T>::applyTernary(Op op, BaseMatrixT& b, BaseMatrixT& c,
T* B = b.data_;
T* C = c.data_;
CAL_MATRIX_START_ADDRESS(A, height_, width_, lda, offset.aCol_, offset.aRow_);
CAL_MATRIX_START_ADDRESS(B, b.height_, b.width_, ldb, offset.bCol_,
offset.bRow_);
CAL_MATRIX_START_ADDRESS(C, c.height_, c.width_, ldc, offset.cCol_,
offset.cRow_);
CAL_MATRIX_START_ADDRESS(
B, b.height_, b.width_, ldb, offset.bCol_, offset.bRow_);
CAL_MATRIX_START_ADDRESS(
C, c.height_, c.width_, ldc, offset.cCol_, offset.cRow_);
CHECK_LE(dimM + offset.aRow_, this->height_);
CHECK_LE(dimN + offset.aCol_, this->width_);
......@@ -180,21 +195,21 @@ int BaseMatrixT<T>::applyTernary(Op op, BaseMatrixT& b, BaseMatrixT& c,
}
if (true == useGpu_) {
hl_gpu_apply_ternary_op
<T, Op, cAsRowVector::value, cAsColVector::value>(
hl_gpu_apply_ternary_op<T, Op, cAsRowVector::value, cAsColVector::value>(
op, A, B, C, dimM, dimN, lda, ldb, ldc);
} else {
hl_cpu_apply_ternary_op
<T, Op, cAsRowVector::value, cAsColVector::value>(
hl_cpu_apply_ternary_op<T, Op, cAsRowVector::value, cAsColVector::value>(
op, A, B, C, dimM, dimN, lda, ldb, ldc);
}
return 0;
}
template<class T>
template <class T>
template <class Op>
int BaseMatrixT<T>::applyQuaternary(Op op, BaseMatrixT& b, BaseMatrixT& c,
int BaseMatrixT<T>::applyQuaternary(Op op,
BaseMatrixT& b,
BaseMatrixT& c,
BaseMatrixT& d) {
CHECK_EQ(height_, b.height_);
CHECK_EQ(width_, b.width_);
......@@ -209,10 +224,14 @@ int BaseMatrixT<T>::applyQuaternary(Op op, BaseMatrixT& b, BaseMatrixT& c,
return 0;
}
template<class T>
template <class T>
template <class Op>
int BaseMatrixT<T>::applyQuaternary(Op op, BaseMatrixT& b, BaseMatrixT& c,
BaseMatrixT& d, int numRows, int numCols,
int BaseMatrixT<T>::applyQuaternary(Op op,
BaseMatrixT& b,
BaseMatrixT& c,
BaseMatrixT& d,
int numRows,
int numCols,
MatrixOffset& offset) {
CHECK(!this->isSparse()) << SPARSE_SUPPORT_ERROR;
CHECK(!b.isSparse()) << SPARSE_SUPPORT_ERROR;
......@@ -234,12 +253,12 @@ int BaseMatrixT<T>::applyQuaternary(Op op, BaseMatrixT& b, BaseMatrixT& c,
T* C = c.data_;
T* D = d.data_;
CAL_MATRIX_START_ADDRESS(A, height_, width_, lda, offset.aCol_, offset.aRow_);
CAL_MATRIX_START_ADDRESS(B, b.height_, b.width_, ldb, offset.bCol_,
offset.bRow_);
CAL_MATRIX_START_ADDRESS(C, c.height_, c.width_, ldc, offset.cCol_,
offset.cRow_);
CAL_MATRIX_START_ADDRESS(D, d.height_, d.width_, ldd, offset.dCol_,
offset.dRow_);
CAL_MATRIX_START_ADDRESS(
B, b.height_, b.width_, ldb, offset.bCol_, offset.bRow_);
CAL_MATRIX_START_ADDRESS(
C, c.height_, c.width_, ldc, offset.cCol_, offset.cRow_);
CAL_MATRIX_START_ADDRESS(
D, d.height_, d.width_, ldd, offset.dCol_, offset.dRow_);
CHECK_LE(dimM + offset.aRow_, this->height_);
CHECK_LE(dimN + offset.aCol_, this->width_);
......@@ -250,22 +269,29 @@ int BaseMatrixT<T>::applyQuaternary(Op op, BaseMatrixT& b, BaseMatrixT& c,
CHECK_LE(dimM + offset.dRow_, d.height_);
CHECK_LE(dimN + offset.dCol_, d.width_);
if (true == useGpu_) {
hl_gpu_apply_quaternary_op(op, A, B, C, D, dimM, dimN, lda, ldb,
ldc, ldd);
hl_gpu_apply_quaternary_op(op, A, B, C, D, dimM, dimN, lda, ldb, ldc, ldd);
} else {
hl_cpu_apply_quaternary_op(op, A, B, C, D, dimM, dimN, lda, ldb,
ldc, ldd);
hl_cpu_apply_quaternary_op(op, A, B, C, D, dimM, dimN, lda, ldb, ldc, ldd);
}
return 0;
}
template<class T>
template <class Agg, class Op, class Saver, class aAsRowVector,
template <class T>
template <class Agg,
class Op,
class Saver,
class aAsRowVector,
class aAsColVector>
int BaseMatrixT<T>::aggregate(Agg agg, Op op, Saver sv, BaseMatrixT& b,
int numRows, int numCols, MatrixOffset& offset,
aAsRowVector, aAsColVector) {
int BaseMatrixT<T>::aggregate(Agg agg,
Op op,
Saver sv,
BaseMatrixT& b,
int numRows,
int numCols,
MatrixOffset& offset,
aAsRowVector,
aAsColVector) {
CHECK_EQ(useGpu_, b.useGpu_);
int ld = stride_;
......@@ -273,10 +299,10 @@ int BaseMatrixT<T>::aggregate(Agg agg, Op op, Saver sv, BaseMatrixT& b,
T* dst = data_;
T* B = b.data_;
CAL_MATRIX_START_ADDRESS(dst, height_, width_, ld, offset.aCol_,
offset.aRow_);
CAL_MATRIX_START_ADDRESS(B, b.height_, b.width_, ldb, offset.bCol_,
offset.bRow_);
CAL_MATRIX_START_ADDRESS(
dst, height_, width_, ld, offset.aCol_, offset.aRow_);
CAL_MATRIX_START_ADDRESS(
B, b.height_, b.width_, ldb, offset.bCol_, offset.bRow_);
if (aAsRowVector::value && !aAsColVector::value) {
if (useGpu_) {
......@@ -297,12 +323,21 @@ int BaseMatrixT<T>::aggregate(Agg agg, Op op, Saver sv, BaseMatrixT& b,
return 0;
}
template<class T>
template <class Agg, class Op, class Saver, class aAsRowVector,
template <class T>
template <class Agg,
class Op,
class Saver,
class aAsRowVector,
class aAsColVector>
int BaseMatrixT<T>::aggregate(Agg agg, Op op, Saver sv, BaseMatrixT& b,
BaseMatrixT& c, int numRows, int numCols,
MatrixOffset& offset, aAsRowVector,
int BaseMatrixT<T>::aggregate(Agg agg,
Op op,
Saver sv,
BaseMatrixT& b,
BaseMatrixT& c,
int numRows,
int numCols,
MatrixOffset& offset,
aAsRowVector,
aAsColVector) {
CHECK_EQ(useGpu_, b.useGpu_);
CHECK_EQ(useGpu_, c.useGpu_);
......@@ -314,28 +349,28 @@ int BaseMatrixT<T>::aggregate(Agg agg, Op op, Saver sv, BaseMatrixT& b,
T* dst = data_;
T* B = b.data_;
T* C = c.data_;
CAL_MATRIX_START_ADDRESS(dst, height_, width_, ld, offset.aCol_,
offset.aRow_);
CAL_MATRIX_START_ADDRESS(B, b.height_, b.width_, ldb, offset.bCol_,
offset.bRow_);
CAL_MATRIX_START_ADDRESS(C, c.height_, c.width_, ldc, offset.cCol_,
offset.cRow_);
CAL_MATRIX_START_ADDRESS(
dst, height_, width_, ld, offset.aCol_, offset.aRow_);
CAL_MATRIX_START_ADDRESS(
B, b.height_, b.width_, ldb, offset.bCol_, offset.bRow_);
CAL_MATRIX_START_ADDRESS(
C, c.height_, c.width_, ldc, offset.cCol_, offset.cRow_);
if (aAsRowVector::value && !aAsColVector::value) {
if (useGpu_) {
hl_gpu_matrix_column_op(agg, op, sv, numRows, numCols, dst, B,
ldb, C, ldc);
hl_gpu_matrix_column_op(
agg, op, sv, numRows, numCols, dst, B, ldb, C, ldc);
} else {
hl_cpu_matrix_column_op(agg, op, sv, numRows, numCols, dst, B,
ldb, C, ldc);
hl_cpu_matrix_column_op(
agg, op, sv, numRows, numCols, dst, B, ldb, C, ldc);
}
} else if (!aAsRowVector::value && aAsColVector::value) {
if (useGpu_) {
hl_gpu_matrix_row_op(agg, op, sv, numRows, numCols, dst, ld, B,
ldb, C, ldc);
hl_gpu_matrix_row_op(
agg, op, sv, numRows, numCols, dst, ld, B, ldb, C, ldc);
} else {
hl_cpu_matrix_row_op(agg, op, sv, numRows, numCols, dst, ld, B,
ldb, C, ldc);
hl_cpu_matrix_row_op(
agg, op, sv, numRows, numCols, dst, ld, B, ldb, C, ldc);
}
} else {
LOG(FATAL) << "not supported";
......@@ -350,15 +385,19 @@ int BaseMatrixT<T>::aggregate(Agg agg, Op op, Saver sv, BaseMatrixT& b,
*/
DEFINE_MATRIX_UNARY_OP(Neg, a = -a);
template<class T>
void BaseMatrixT<T>::neg() { applyUnary(unary::Neg<T>()); }
template <class T>
void BaseMatrixT<T>::neg() {
applyUnary(unary::Neg<T>());
}
DEFINE_MATRIX_UNARY_OP(Exp, a = exp(a));
template<>
void BaseMatrixT<real>::exp2() { applyUnary(unary::Exp<real>()); }
template <>
void BaseMatrixT<real>::exp2() {
applyUnary(unary::Exp<real>());
}
DEFINE_MATRIX_UNARY_OP(Log, a = log(a));
template<>
template <>
void BaseMatrixT<real>::log2() {
if (useGpu_) {
applyUnary(unary::Log<real>());
......@@ -368,30 +407,42 @@ void BaseMatrixT<real>::log2() {
}
DEFINE_MATRIX_UNARY_OP(Sqrt, a = sqrt(a));
template<>
void BaseMatrixT<real>::sqrt2() { applyUnary(unary::Sqrt<real>()); }
template <>
void BaseMatrixT<real>::sqrt2() {
applyUnary(unary::Sqrt<real>());
}
DEFINE_MATRIX_UNARY_OP(Square, a = a * a);
template<class T>
void BaseMatrixT<T>::square2() { applyUnary(unary::Square<T>()); }
template <class T>
void BaseMatrixT<T>::square2() {
applyUnary(unary::Square<T>());
}
DEFINE_MATRIX_UNARY_OP(Reciprocal, a = 1.0f / a);
template<class T>
void BaseMatrixT<T>::reciprocal2() { applyUnary(unary::Reciprocal<T>()); }
template <class T>
void BaseMatrixT<T>::reciprocal2() {
applyUnary(unary::Reciprocal<T>());
}
DEFINE_MATRIX_UNARY_OP(Abs, a = a > 0 ? a : -a);
template<class T>
void BaseMatrixT<T>::abs2() { applyUnary(unary::Abs<T>()); }
template <class T>
void BaseMatrixT<T>::abs2() {
applyUnary(unary::Abs<T>());
}
DEFINE_MATRIX_UNARY_OP(Sign, a = (a > 0) - (a < 0));
template<class T>
void BaseMatrixT<T>::sign2() { applyUnary(unary::Sign<T>()); }
template <class T>
void BaseMatrixT<T>::sign2() {
applyUnary(unary::Sign<T>());
}
DEFINE_MATRIX_UNARY_OP(Zero, a = 0);
template<class T>
void BaseMatrixT<T>::zero() { applyUnary(unary::Zero<T>()); }
template <class T>
void BaseMatrixT<T>::zero() {
applyUnary(unary::Zero<T>());
}
template<class T>
template <class T>
void BaseMatrixT<T>::zeroAtOffset(int64_t columnOffset, int64_t numColumns) {
int numRows = height_;
int numCols = numColumns;
......@@ -400,11 +451,13 @@ void BaseMatrixT<T>::zeroAtOffset(int64_t columnOffset, int64_t numColumns) {
}
DEFINE_MATRIX_UNARY_OP(One, a = 1);
template<class T>
void BaseMatrixT<T>::one() { applyUnary(unary::One<T>()); }
template <class T>
void BaseMatrixT<T>::one() {
applyUnary(unary::One<T>());
}
DEFINE_MATRIX_UNARY_PARAMETER_OP(Pow, ONE_PARAMETER, a = pow(a, p));
template<>
template <>
void BaseMatrixT<real>::pow2(real p) {
if (useGpu_) {
applyUnary(unary::Pow<real>(p));
......@@ -414,51 +467,67 @@ void BaseMatrixT<real>::pow2(real p) {
}
DEFINE_MATRIX_UNARY_PARAMETER_OP(SubScalar, ONE_PARAMETER, a -= p);
template<class T>
void BaseMatrixT<T>::subScalar(T p) { applyUnary(unary::SubScalar<T>(p)); }
template <class T>
void BaseMatrixT<T>::subScalar(T p) {
applyUnary(unary::SubScalar<T>(p));
}
DEFINE_MATRIX_UNARY_PARAMETER_OP(MulScalar, ONE_PARAMETER, a *= p);
template<class T>
void BaseMatrixT<T>::mulScalar(T p) { applyUnary(unary::MulScalar<T>(p)); }
template <class T>
void BaseMatrixT<T>::mulScalar(T p) {
applyUnary(unary::MulScalar<T>(p));
}
DEFINE_MATRIX_UNARY_PARAMETER_OP(DivScalar, ONE_PARAMETER, a /= p);
template<class T>
void BaseMatrixT<T>::divScalar(T p) { applyUnary(unary::DivScalar<T>(p)); }
template <class T>
void BaseMatrixT<T>::divScalar(T p) {
applyUnary(unary::DivScalar<T>(p));
}
DEFINE_MATRIX_UNARY_PARAMETER_OP(Assign, ONE_PARAMETER, a = p);
template<class T>
void BaseMatrixT<T>::assign(T p) { applyUnary(unary::Assign<T>(p)); }
template <class T>
void BaseMatrixT<T>::assign(T p) {
applyUnary(unary::Assign<T>(p));
}
DEFINE_MATRIX_UNARY_PARAMETER_OP(Add, ONE_PARAMETER, a += p);
template<class T>
void BaseMatrixT<T>::add(T p) { applyUnary(unary::Add<T>(p)); }
template <class T>
void BaseMatrixT<T>::add(T p) {
applyUnary(unary::Add<T>(p));
}
DEFINE_MATRIX_UNARY_PARAMETER_OP(Add2, TWO_PARAMETER, a = a * p1 + p2);
template<class T>
void BaseMatrixT<T>::add(T p1, T p2) { applyUnary(unary::Add2<T>(p1, p2)); }
template <class T>
void BaseMatrixT<T>::add(T p1, T p2) {
applyUnary(unary::Add2<T>(p1, p2));
}
DEFINE_MATRIX_UNARY_PARAMETER_OP(Clip, TWO_PARAMETER,
DEFINE_MATRIX_UNARY_PARAMETER_OP(Clip,
TWO_PARAMETER,
a = a < p1 ? p1 : (a > p2 ? p2 : a));
template<class T>
void BaseMatrixT<T>::clip(T p1, T p2) { applyUnary(unary::Clip<T>(p1, p2)); }
template <class T>
void BaseMatrixT<T>::clip(T p1, T p2) {
applyUnary(unary::Clip<T>(p1, p2));
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(ClipDerivative, TWO_PARAMETER,
a = b < p1 ? 0 : (b > p2 ? 0 : 1));
template<class T>
DEFINE_MATRIX_BINARY_PARAMETER_OP(ClipDerivative,
TWO_PARAMETER,
a = b < p1 ? 0 : (b > p2 ? 0 : 1));
template <class T>
void BaseMatrixT<T>::clipDerivative(BaseMatrixT& b, T p1, T p2) {
applyBinary(binary::ClipDerivative<T>(p1, p2), b);
}
DEFINE_MATRIX_UNARY_PARAMETER_OP(BiggerThanScalar, ONE_PARAMETER,
DEFINE_MATRIX_UNARY_PARAMETER_OP(BiggerThanScalar,
ONE_PARAMETER,
a = a > p ? 1.0f : 0.0f);
template<class T>
template <class T>
void BaseMatrixT<T>::biggerThanScalar(T p) {
applyUnary(unary::BiggerThanScalar<T>(p));
}
DEFINE_MATRIX_UNARY_PARAMETER_OP(DownClip, ONE_PARAMETER,
a = a > p ? a : p);
template<class T>
DEFINE_MATRIX_UNARY_PARAMETER_OP(DownClip, ONE_PARAMETER, a = a > p ? a : p);
template <class T>
void BaseMatrixT<T>::downClip(T p) {
applyUnary(unary::DownClip<T>(p));
}
......@@ -469,12 +538,12 @@ void BaseMatrixT<T>::downClip(T p) {
*/
DEFINE_MATRIX_BINARY_OP(Add, a += b);
template<class T>
template <class T>
void BaseMatrixT<T>::add(BaseMatrixT& b) {
applyBinary(binary::Add<T>(), b);
}
template<>
template <>
void BaseMatrixT<real>::add(BaseMatrixT& b) {
if (useGpu_) {
applyBinary(binary::Add<real>(), b);
......@@ -485,7 +554,7 @@ void BaseMatrixT<real>::add(BaseMatrixT& b) {
}
}
template<class T>
template <class T>
void BaseMatrixT<T>::addAtOffset(BaseMatrixT& b, int64_t columnOffset) {
if (columnOffset + b.width_ <= width_) {
int numRows = height_;
......@@ -504,43 +573,53 @@ void BaseMatrixT<T>::addAtOffset(BaseMatrixT& b, int64_t columnOffset) {
}
}
template<class T>
template <class T>
void BaseMatrixT<T>::addP2P(BaseMatrixT& b) {
T* A = data_;
T* B = b.data_;
int dimM = height_;
int dimN = width_;
hl_gpu_apply_binary_op<T, binary::Add<T>, 0, 0>
(binary::Add<T>(), A, B, dimM, dimN, dimN, dimN);
hl_gpu_apply_binary_op<T, binary::Add<T>, 0, 0>(
binary::Add<T>(), A, B, dimM, dimN, dimN, dimN);
}
template<class T>
template <class T>
void BaseMatrixT<T>::addColVector(BaseMatrixT& b) {
MatrixOffset offset(0, 0, 0, 0);
int numRows = height_;
int numCols = width_;
applyBinary(binary::Add<T>(), b, numRows, numCols, offset, false_type(),
applyBinary(binary::Add<T>(),
b,
numRows,
numCols,
offset,
false_type(),
true_type() /* bAsColVector */);
}
template<class T>
template <class T>
void BaseMatrixT<T>::addRowVector(BaseMatrixT& b) {
MatrixOffset offset(0, 0, 0, 0);
int numRows = height_;
int numCols = width_;
applyBinary(binary::Add<T>(), b, numRows, numCols, offset,
true_type() /* bAsRowVector */, false_type());
applyBinary(binary::Add<T>(),
b,
numRows,
numCols,
offset,
true_type() /* bAsRowVector */,
false_type());
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(Add1, ONE_PARAMETER, a += b * p);
template<class T>
template <class T>
void BaseMatrixT<T>::add(BaseMatrixT& b, T p) {
applyBinary(binary::Add1<T>(p), b);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(Pow, ONE_PARAMETER, a = pow(b, p));
template<>
template <>
void BaseMatrixT<real>::pow2(BaseMatrixT& b, real p) {
if (useGpu_) {
applyBinary(binary::Pow<real>(p), b);
......@@ -550,36 +629,45 @@ void BaseMatrixT<real>::pow2(BaseMatrixT& b, real p) {
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(Add2, TWO_PARAMETER, a = p1 * a + p2 * b);
template<class T>
template <class T>
void BaseMatrixT<T>::add(BaseMatrixT& b, T p1, T p2) {
applyBinary(binary::Add2<T>(p1, p2), b);
}
template<class T>
template <class T>
void BaseMatrixT<T>::addBias(BaseMatrixT& b, T scale) {
MatrixOffset offset(0, 0, 0, 0);
int numRows = height_;
int numCols = width_;
applyBinary(binary::Add1<T>(scale), b, numRows, numCols, offset,
true_type() /* bAsRowVector */, false_type());
applyBinary(binary::Add1<T>(scale),
b,
numRows,
numCols,
offset,
true_type() /* bAsRowVector */,
false_type());
}
DEFINE_MATRIX_BINARY_OP(Sub, a -= b);
template<class T>
void BaseMatrixT<T>::sub(BaseMatrixT& b) { applyBinary(binary::Sub<T>(), b); }
template <class T>
void BaseMatrixT<T>::sub(BaseMatrixT& b) {
applyBinary(binary::Sub<T>(), b);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(Sub1, ONE_PARAMETER, a -= b * p);
template<class T>
template <class T>
void BaseMatrixT<T>::sub(BaseMatrixT& b, T p) {
applyBinary(binary::Sub1<T>(p), b);
}
DEFINE_MATRIX_BINARY_OP(Relu, b = a > 0.0f ? a : 0.0f);
template<class T>
void BaseMatrixT<T>::relu(BaseMatrixT& b) { applyBinary(binary::Relu<T>(), b); }
template <class T>
void BaseMatrixT<T>::relu(BaseMatrixT& b) {
applyBinary(binary::Relu<T>(), b);
}
DEFINE_MATRIX_BINARY_OP(ReluDerivative, a *= (b > 0.0f ? 1.0f : 0.0f));
template<class T>
template <class T>
void BaseMatrixT<T>::reluDerivative(BaseMatrixT& b) {
applyBinary(binary::ReluDerivative<T>(), b);
}
......@@ -589,7 +677,7 @@ DEFINE_MATRIX_BINARY_OP(Softrelu, const T THRESHOLD = 40.0;
? THRESHOLD
: ((a < -THRESHOLD) ? (-THRESHOLD)
: a))));
template<>
template <>
void BaseMatrixT<real>::softrelu(BaseMatrixT& b) {
applyBinary(binary::Softrelu<real>(), b);
}
......@@ -599,97 +687,100 @@ DEFINE_MATRIX_BINARY_OP(
a *= (1.0 - exp(-1.0 * ((b > THRESHOLD)
? THRESHOLD
: ((b < -THRESHOLD) ? (-THRESHOLD) : b)))));
template<>
template <>
void BaseMatrixT<real>::softreluDerivative(BaseMatrixT& b) {
applyBinary(binary::SoftreluDerivative<real>(), b);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(Brelu, TWO_PARAMETER, b = a > p1 ? a : p1;
b = b < p2 ? b : p2);
template<class T>
template <class T>
void BaseMatrixT<T>::brelu(BaseMatrixT& b) {
int p1 = 0, p2 = 24; //! TODO(yuyang18): Make p1,p2 configuable.
int p1 = 0, p2 = 24; //! TODO(yuyang18): Make p1,p2 configuable.
applyBinary(binary::Brelu<T>(p1, p2), b);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(BreluDerivative, TWO_PARAMETER,
DEFINE_MATRIX_BINARY_PARAMETER_OP(BreluDerivative,
TWO_PARAMETER,
a *= (b > p1 && b < p2) ? 1.0 : 0.0);
template<class T>
template <class T>
void BaseMatrixT<T>::breluDerivative(BaseMatrixT& b) {
int p1 = 0, p2 = 24;
applyBinary(binary::BreluDerivative<T>(p1, p2), b);
}
DEFINE_MATRIX_BINARY_OP(Square, b = a * a);
template<class T>
template <class T>
void BaseMatrixT<T>::square2(BaseMatrixT& b) {
applyBinary(binary::Square<T>(), b);
}
DEFINE_MATRIX_BINARY_OP(SquareDerivative, a *= 2.0 * b);
template<class T>
template <class T>
void BaseMatrixT<T>::squareDerivative(BaseMatrixT& b) {
applyBinary(binary::SquareDerivative<T>(), b);
}
DEFINE_MATRIX_BINARY_OP(Tanh,
T tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
b = 2.0 / (1.0 + std::exp(tmp)) - 1.0);
template<>
DEFINE_MATRIX_BINARY_OP(Tanh, T tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
b = 2.0 / (1.0 + std::exp(tmp)) - 1.0);
template <>
void BaseMatrixT<real>::tanh(BaseMatrixT& b) {
applyBinary(binary::Tanh<real>(), b);
}
DEFINE_MATRIX_BINARY_OP(TanhDerivative, a *= 1 - b * b);
template<class T>
template <class T>
void BaseMatrixT<T>::tanhDerivative(BaseMatrixT& b) {
applyBinary(binary::TanhDerivative<T>(), b);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(ScaledTanh, TWO_PARAMETER,
b = p1 *
(2.0 / (1.0 + exp(-2 * p2 * a)) - 1.0));
template<>
DEFINE_MATRIX_BINARY_PARAMETER_OP(
ScaledTanh, TWO_PARAMETER, b = p1 * (2.0 / (1.0 + exp(-2 * p2 * a)) - 1.0));
template <>
void BaseMatrixT<real>::scaledTanh(BaseMatrixT& b, real p1, real p2) {
applyBinary(binary::ScaledTanh<real>(p1, p2), b);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(ScaledTanhDerivative, TWO_PARAMETER,
DEFINE_MATRIX_BINARY_PARAMETER_OP(ScaledTanhDerivative,
TWO_PARAMETER,
a *= p2 * (p1 - b * b));
template<class T>
template <class T>
void BaseMatrixT<T>::scaledTanhDerivative(BaseMatrixT& b, T p1, T p2) {
applyBinary(binary::ScaledTanhDerivative<T>(p1 * p1, p2 / p1), b);
}
DEFINE_MATRIX_BINARY_OP(Reciprocal, b = 1.0f / a);
template<class T>
template <class T>
void BaseMatrixT<T>::reciprocal2(BaseMatrixT& b) {
applyBinary(binary::Reciprocal<T>(), b);
}
DEFINE_MATRIX_BINARY_OP(ReciprocalDerivative, a *= -b * b);
template<class T>
template <class T>
void BaseMatrixT<T>::reciprocalDerivative(BaseMatrixT& b) {
applyBinary(binary::ReciprocalDerivative<T>(), b);
}
DEFINE_MATRIX_BINARY_OP(Abs, b = a > 0.0f ? a : -a);
template<class T>
void BaseMatrixT<T>::abs2(BaseMatrixT& b) { applyBinary(binary::Abs<T>(), b); }
template <class T>
void BaseMatrixT<T>::abs2(BaseMatrixT& b) {
applyBinary(binary::Abs<T>(), b);
}
DEFINE_MATRIX_BINARY_OP(AbsDerivative, a = (b > 0) ? a : (b < 0) ? -a : 0);
template<class T>
template <class T>
void BaseMatrixT<T>::absDerivative(BaseMatrixT& b) {
applyBinary(binary::AbsDerivative<T>(), b);
}
DEFINE_MATRIX_BINARY_OP(
Sigmoid, const T THRESHOLD_MIN = -40.0; const T THRESHOLD_MAX = 13.0;
T tmp = (a < THRESHOLD_MIN) ? THRESHOLD_MIN
: ((a > THRESHOLD_MAX) ? THRESHOLD_MAX : a);
b = 1.0f / (1.0f + exp(-tmp)));
template<>
DEFINE_MATRIX_BINARY_OP(Sigmoid, const T THRESHOLD_MIN = -40.0;
const T THRESHOLD_MAX = 13.0;
T tmp = (a < THRESHOLD_MIN)
? THRESHOLD_MIN
: ((a > THRESHOLD_MAX) ? THRESHOLD_MAX : a);
b = 1.0f / (1.0f + exp(-tmp)));
template <>
void BaseMatrixT<real>::sigmoid(BaseMatrixT& b) {
if (useGpu_) {
applyBinary(binary::Sigmoid<real>(), b);
......@@ -723,31 +814,31 @@ void BaseMatrixT<real>::sigmoid(BaseMatrixT& b) {
}
DEFINE_MATRIX_BINARY_OP(SigmoidDerivative, a *= b * (1 - b));
template<class T>
template <class T>
void BaseMatrixT<T>::sigmoidDerivative(BaseMatrixT& b) {
applyBinary(binary::SigmoidDerivative<T>(), b);
}
DEFINE_MATRIX_BINARY_OP(ExpDerivative, a *= b);
template<class T>
template <class T>
void BaseMatrixT<T>::expDerivative(BaseMatrixT& b) {
applyBinary(binary::ExpDerivative<T>(), b);
}
DEFINE_MATRIX_BINARY_OP(Sign, b = a > 0.0f ? 1.0f : -1.0f);
template<class T>
template <class T>
void BaseMatrixT<T>::sign2(BaseMatrixT& b) {
applyBinary(binary::Sign<T>(), b);
}
DEFINE_MATRIX_BINARY_OP(Exp, a = exp(b));
template<>
template <>
void BaseMatrixT<real>::exp2(BaseMatrixT& b) {
applyBinary(binary::Exp<real>(), b);
}
DEFINE_MATRIX_BINARY_OP(Log, a = log(b));
template<>
template <>
void BaseMatrixT<real>::log2(BaseMatrixT& b) {
if (useGpu_) {
applyBinary(binary::Log<real>(), b);
......@@ -757,13 +848,13 @@ void BaseMatrixT<real>::log2(BaseMatrixT& b) {
}
DEFINE_MATRIX_BINARY_OP(Sqrt, a = sqrt(b));
template<>
template <>
void BaseMatrixT<real>::sqrt2(BaseMatrixT& b) {
applyBinary(binary::Sqrt<real>(), b);
}
DEFINE_MATRIX_BINARY_OP(InvSqrt, a = 1.0f / sqrt(b));
template<>
template <>
void BaseMatrixT<real>::invSqrt(BaseMatrixT& b) {
if (useGpu_) {
applyBinary(binary::InvSqrt<real>(), b);
......@@ -775,37 +866,37 @@ void BaseMatrixT<real>::invSqrt(BaseMatrixT& b) {
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(IsEqual, ONE_PARAMETER, a = (b == p));
template<class T>
template <class T>
void BaseMatrixT<T>::isEqualTo(BaseMatrixT& b, T value) {
applyBinary(binary::IsEqual<T>(value), b);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(AddScalar, ONE_PARAMETER, a = b + p);
template<class T>
template <class T>
void BaseMatrixT<T>::addScalar(BaseMatrixT& b, T p) {
applyBinary(binary::AddScalar<T>(p), b);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(SubScalar, ONE_PARAMETER, a = b - p);
template<class T>
template <class T>
void BaseMatrixT<T>::subScalar(BaseMatrixT& b, T p) {
applyBinary(binary::SubScalar<T>(p), b);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(MulScalar, ONE_PARAMETER, a = b * p);
template<class T>
template <class T>
void BaseMatrixT<T>::mulScalar(BaseMatrixT& b, T p) {
applyBinary(binary::MulScalar<T>(p), b);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(DivScalar, ONE_PARAMETER, a = b / p);
template<class T>
template <class T>
void BaseMatrixT<T>::divScalar(BaseMatrixT& b, T p) {
applyBinary(binary::DivScalar<T>(p), b);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(ScalarDiv, ONE_PARAMETER, a = p / b);
template<class T>
template <class T>
void BaseMatrixT<T>::scalarDiv(BaseMatrixT& b, T p) {
applyBinary(binary::ScalarDiv<T>(p), b);
}
......@@ -817,20 +908,20 @@ void BaseMatrixT<T>::scalarDiv(BaseMatrixT& b, T p) {
DEFINE_MATRIX_TERNARY_OP(SoftCrossEntropy,
a = -c * log(b) - (1 - c) * log(1 - b));
template<>
template <>
void BaseMatrixT<real>::softCrossEntropy(BaseMatrixT& b, BaseMatrixT& c) {
applyTernary(ternary::SoftCrossEntropy<real>(), b, c);
}
DEFINE_MATRIX_TERNARY_OP(SoftCrossEntropyBp, a += (b - c) / (b * (1 - b)));
template<class T>
template <class T>
void BaseMatrixT<T>::softCrossEntropyBp(BaseMatrixT& b, BaseMatrixT& c) {
applyTernary(ternary::SoftCrossEntropyBp<T>(), b, c);
}
DEFINE_MATRIX_TERNARY_OP(BinaryCrossEntropy,
a = c > 0.5 ? -log(b) : -log(1.0 - b));
template<>
template <>
void BaseMatrixT<real>::binaryLabelCrossEntropy(BaseMatrixT& b,
BaseMatrixT& c) {
if (useGpu_) {
......@@ -858,70 +949,73 @@ void BaseMatrixT<real>::binaryLabelCrossEntropy(BaseMatrixT& b,
DEFINE_MATRIX_TERNARY_OP(BinaryCrossEntropyBp,
a += c > 0.5 ? -1.0 / b : 1.0 / (1.0 - b));
template<class T>
template <class T>
void BaseMatrixT<T>::binaryLabelCrossEntropyBp(BaseMatrixT& b, BaseMatrixT& c) {
applyTernary(ternary::BinaryCrossEntropyBp<T>(), b, c);
}
DEFINE_MATRIX_TERNARY_OP(Add, a = b + c);
template<class T>
template <class T>
void BaseMatrixT<T>::add(BaseMatrixT& b, BaseMatrixT& c) {
applyTernary(ternary::Add<T>(), b, c);
}
DEFINE_MATRIX_TERNARY_PARAMETER_OP(Add1, TWO_PARAMETER, a = p1 * b + p2 * c);
template<class T>
template <class T>
void BaseMatrixT<T>::add(BaseMatrixT& b, T p1, BaseMatrixT& c, T p2) {
applyTernary(ternary::Add1<T>(p1, p2), b, c);
}
DEFINE_MATRIX_TERNARY_OP(Sub, a = b - c);
template<class T>
template <class T>
void BaseMatrixT<T>::sub(BaseMatrixT& b, BaseMatrixT& c) {
applyTernary(ternary::Sub<T>(), b, c);
}
DEFINE_MATRIX_TERNARY_PARAMETER_OP(Sub1, TWO_PARAMETER, a = p1 * b - p2 * c);
template<class T>
template <class T>
void BaseMatrixT<T>::sub(BaseMatrixT& b, T p1, BaseMatrixT& c, T p2) {
applyTernary(ternary::Sub1<T>(p1, p2), b, c);
}
DEFINE_MATRIX_TERNARY_OP(Add2, a = a + b + c);
template<class T>
template <class T>
void BaseMatrixT<T>::add2(BaseMatrixT& b, BaseMatrixT& c) {
applyTernary(ternary::Add2<T>(), b, c);
}
DEFINE_MATRIX_TERNARY_PARAMETER_OP(Add3, THREE_PARAMETER,
DEFINE_MATRIX_TERNARY_PARAMETER_OP(Add3,
THREE_PARAMETER,
a = p1 * a + p2 * b + p3 * c);
template<class T>
template <class T>
void BaseMatrixT<T>::add2(BaseMatrixT& b, BaseMatrixT& c, T p1, T p2, T p3) {
applyTernary(ternary::Add3<T>(p1, p2, p3), b, c);
}
DEFINE_MATRIX_TERNARY_PARAMETER_OP(SgdUpdate, THREE_PARAMETER,
DEFINE_MATRIX_TERNARY_PARAMETER_OP(SgdUpdate,
THREE_PARAMETER,
c = p2 * c - p1 * (b + p3 * a);
a = a + c);
template<class T>
template <class T>
void BaseMatrixT<T>::sgdUpdate(BaseMatrixT& b, // grad
BaseMatrixT& c, // mom
T p1, // learningRate,
T p2, // momentum,
T p3) { // decayRate
T p1, // learningRate,
T p2, // momentum,
T p3) { // decayRate
applyTernary(ternary::SgdUpdate<T>(p1, p2, p3), b, c);
}
DEFINE_MATRIX_QUATERNARY_PARAMETER_OP(SgdUpdate, THREE_PARAMETER,
DEFINE_MATRIX_QUATERNARY_PARAMETER_OP(SgdUpdate,
THREE_PARAMETER,
c = p2 * c - p1 * d * (b + p3 * a);
a += c);
template<class T>
template <class T>
void BaseMatrixT<T>::sgdUpdate(BaseMatrixT& b, // grad,
BaseMatrixT& c, // mom,
BaseMatrixT& d, // lr,
T p1, // learningRate,
T p2, // momentum,
T p3) { // decayRate
T p1, // learningRate,
T p2, // momentum,
T p3) { // decayRate
applyQuaternary(quaternary::SgdUpdate<T>(p1, p2, p3), b, c, d);
}
......@@ -929,19 +1023,22 @@ DEFINE_MATRIX_BINARY_PARAMETER_OP(ApplyL1, ONE_PARAMETER, T lambda = p * b;
a = (a > lambda)
? (a - lambda)
: (a < -lambda) ? (a + lambda) : 0);
template<class T>
template <class T>
void BaseMatrixT<T>::applyL1(BaseMatrixT& lr, T learningRate, T decayRate) {
applyBinary(binary::ApplyL1<T>(learningRate * decayRate), lr);
}
template<>
template <>
void BaseMatrixT<real>::applyL1(BaseMatrixT& lr,
real learningRate,
real decayRate) {
if (useGpu_) {
applyBinary(binary::ApplyL1<real>(learningRate * decayRate), lr);
} else {
simd::decayL1(this->data_, this->data_, lr.data_, learningRate * decayRate,
simd::decayL1(this->data_,
this->data_,
lr.data_,
learningRate * decayRate,
height_ * width_);
}
}
......@@ -950,24 +1047,25 @@ DEFINE_MATRIX_UNARY_PARAMETER_OP(ApplyL1, ONE_PARAMETER, T lambda = p;
a = (a > lambda)
? (a - lambda)
: (a < -lambda) ? (a + lambda) : 0);
template<class T>
template <class T>
void BaseMatrixT<T>::applyL1(T learningRate, T decayRate) {
applyUnary(unary::ApplyL1<T>(learningRate * decayRate));
}
template<>
template <>
void BaseMatrixT<real>::applyL1(real learningRate, real decayRate) {
if (useGpu_) {
applyUnary(unary::ApplyL1<real>(learningRate * decayRate));
} else {
simd::decayL1(this->data_, this->data_, learningRate * decayRate,
height_ * width_);
simd::decayL1(
this->data_, this->data_, learningRate * decayRate, height_ * width_);
}
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(ApplyL2, ONE_PARAMETER,
DEFINE_MATRIX_BINARY_PARAMETER_OP(ApplyL2,
ONE_PARAMETER,
a *= (1.0f / (1.0f + p * b)));
template<class T>
template <class T>
void BaseMatrixT<T>::applyL2(BaseMatrixT& lr, T learningRate, T decayRate) {
if (useGpu_) {
applyBinary(binary::ApplyL2<T>(learningRate * decayRate), lr);
......@@ -980,32 +1078,33 @@ void BaseMatrixT<T>::applyL2(BaseMatrixT& lr, T learningRate, T decayRate) {
}
}
template<class T>
template <class T>
void BaseMatrixT<T>::applyL2(T learningRate, T decayRate) {
BaseMatrixT<T>::mulScalar(1.0f / (1.0f + learningRate * decayRate));
}
DEFINE_MATRIX_BINARY_OP(DotMul, a *= b);
template<class T>
template <class T>
void BaseMatrixT<T>::dotMul(BaseMatrixT& b) {
applyBinary(binary::DotMul<T>(), b);
}
DEFINE_MATRIX_TERNARY_OP(DotMul, a = b * c);
template<class T>
template <class T>
void BaseMatrixT<T>::dotMul(BaseMatrixT& b, BaseMatrixT& c) {
applyTernary(ternary::DotMul<T>(), b, c);
}
DEFINE_MATRIX_TERNARY_OP(DotDiv, a = (b == 0.0) ? 0.0 : b / c);
template<class T>
template <class T>
void BaseMatrixT<T>::dotDiv(BaseMatrixT& b, BaseMatrixT& c) {
applyTernary(ternary::DotDiv<T>(), b, c);
}
DEFINE_MATRIX_TERNARY_PARAMETER_OP(DotDiv2P, TWO_PARAMETER,
DEFINE_MATRIX_TERNARY_PARAMETER_OP(DotDiv2P,
TWO_PARAMETER,
a = (b + p1) / (c + p2));
template<class T>
template <class T>
void BaseMatrixT<T>::dotDiv(BaseMatrixT& b, BaseMatrixT& c, T p1, T p2) {
applyTernary(ternary::DotDiv2P<T>(p1, p2), b, c);
}
......@@ -1015,7 +1114,7 @@ DEFINE_MATRIX_QUATERNARY_OP(RankLoss, const T THRESHOLD = 40.0; a = b - c;
? THRESHOLD
: ((a < -THRESHOLD) ? (-THRESHOLD) : a);
a = log(1 + exp(a)) - a * d);
template<>
template <>
void BaseMatrixT<real>::rankLoss(BaseMatrixT& b,
BaseMatrixT& c,
BaseMatrixT& d) {
......@@ -1026,8 +1125,9 @@ DEFINE_MATRIX_QUATERNARY_OP(RankLossBp, const T THRESHOLD = 40.0; a = b - c;
a = (a > THRESHOLD)
? THRESHOLD
: ((a < -THRESHOLD) ? (-THRESHOLD) : a);
a = exp(a); a = (a / (1 + a) - d));
template<>
a = exp(a);
a = (a / (1 + a) - d));
template <>
void BaseMatrixT<real>::rankLossBp(BaseMatrixT& b,
BaseMatrixT& c,
BaseMatrixT& d) {
......@@ -1040,7 +1140,7 @@ DEFINE_MATRIX_TERNARY_OP(LogisticRegressionLoss, const T THRESHOLD = 40.0;
? -THRESHOLD
: b;
a = log(1 + exp(x)) - c * x);
template<>
template <>
void BaseMatrixT<real>::logisticRegressionLoss(BaseMatrixT& b, BaseMatrixT& c) {
applyTernary(ternary::LogisticRegressionLoss<real>(), b, c);
}
......@@ -1050,22 +1150,23 @@ DEFINE_MATRIX_TERNARY_OP(LogisticRegressionLossBp, const T THRESHOLD = 40.0;
T x = (b > THRESHOLD) ? THRESHOLD : (b < -THRESHOLD)
? -THRESHOLD
: b;
x = exp(x); a = x / (1 + x) - c);
template<>
x = exp(x);
a = x / (1 + x) - c);
template <>
void BaseMatrixT<real>::logisticRegressionLossBp(BaseMatrixT& b,
BaseMatrixT& c) {
applyTernary(ternary::LogisticRegressionLossBp<real>(), b, c);
}
DEFINE_MATRIX_TERNARY_OP(BiggerThan, a = (b > c) ? 1.0f : 0.0f);
template<class T>
template <class T>
void BaseMatrixT<T>::biggerThan(BaseMatrixT& b, BaseMatrixT& c) {
applyTernary(ternary::BiggerThan<T>(), b, c);
}
DEFINE_MATRIX_QUATERNARY_OP(
BiggerThan, a = ((b > c && d > 0.5f) || (b < c && d < 0.5f)) ? 1.0f : 0.0f);
template<class T>
template <class T>
void BaseMatrixT<T>::biggerThan(BaseMatrixT& b,
BaseMatrixT& c,
BaseMatrixT& d) {
......@@ -1073,25 +1174,34 @@ void BaseMatrixT<T>::biggerThan(BaseMatrixT& b,
}
DEFINE_MATRIX_TERNARY_OP(Max, a = (b > c) ? b : c);
template<class T>
template <class T>
void BaseMatrixT<T>::max2(BaseMatrixT& b, BaseMatrixT& c) {
applyTernary(ternary::Max<T>(), b, c);
}
DEFINE_MATRIX_TERNARY_PARAMETER_OP(BinaryClassificationError, ONE_PARAMETER,
DEFINE_MATRIX_TERNARY_PARAMETER_OP(BinaryClassificationError,
ONE_PARAMETER,
c += ((a > p) == (b > p)) ? 0.0f : 1.0f);
template<class T>
void BaseMatrixT<T>::binaryClassificationError2(size_t destCol, BaseMatrixT& b,
BaseMatrixT& c, T p) {
template <class T>
void BaseMatrixT<T>::binaryClassificationError2(size_t destCol,
BaseMatrixT& b,
BaseMatrixT& c,
T p) {
CHECK(!useGpu_) << "do not support gpu";
MatrixOffset offset(0, 0, 0, 0, destCol, 0);
int numRows = b.height_;
int numCols = b.width_;
b.applyTernary(ternary::BinaryClassificationError<T>(p), c, *this, numRows,
numCols, offset, false_type(), true_type() /*cAsColVector*/);
b.applyTernary(ternary::BinaryClassificationError<T>(p),
c,
*this,
numRows,
numCols,
offset,
false_type(),
true_type() /*cAsColVector*/);
}
template<>
template <>
void BaseMatrixT<real>::binaryClassificationError(size_t destCol,
BaseMatrixT& b,
BaseMatrixT& c,
......@@ -1099,127 +1209,148 @@ void BaseMatrixT<real>::binaryClassificationError(size_t destCol,
MatrixOffset offset(destCol, 0, 0, 0, 0, 0);
int numRows = b.height_;
int numCols = b.width_;
aggregate(aggregate::sum(), base::binary::classificationError(p),
base::binary::add(), b, c, numRows, numCols, offset, false_type(),
aggregate(aggregate::sum(),
base::binary::classificationError(p),
base::binary::add(),
b,
c,
numRows,
numCols,
offset,
false_type(),
true_type() /*aAsColVector*/);
}
DEFINE_MATRIX_QUATERNARY_PARAMETER_OP(Add3, THREE_PARAMETER,
DEFINE_MATRIX_QUATERNARY_PARAMETER_OP(Add3,
THREE_PARAMETER,
a = p1 * b + p2 * c + p3 * d);
template<class T>
void BaseMatrixT<T>::add3(BaseMatrixT& b, BaseMatrixT& c, BaseMatrixT& d, T p1,
T p2, T p3) {
template <class T>
void BaseMatrixT<T>::add3(
BaseMatrixT& b, BaseMatrixT& c, BaseMatrixT& d, T p1, T p2, T p3) {
applyQuaternary(quaternary::Add3<T>(p1, p2, p3), b, c, d);
}
DEFINE_MATRIX_TERNARY_OP(DotMulSquare, a = b * c * c);
template<class T>
template <class T>
void BaseMatrixT<T>::dotMulSquare(BaseMatrixT& b, BaseMatrixT& c) {
applyTernary(ternary::DotMulSquare<T>(), b, c);
}
DEFINE_MATRIX_TERNARY_OP(DotSquareSquare, a = b * b * c * c);
template<class T>
template <class T>
void BaseMatrixT<T>::dotSquareSquare(BaseMatrixT& b, BaseMatrixT& c) {
applyTernary(ternary::DotSquareSquare<T>(), b, c);
}
DEFINE_MATRIX_BINARY_OP(DotMulSquare, a *= b * b);
template<class T>
template <class T>
void BaseMatrixT<T>::dotMulSquare(BaseMatrixT& b) {
applyBinary(binary::DotMulSquare<T>(), b);
}
DEFINE_MATRIX_BINARY_OP(DotSquareMul, a = a * a * b);
template<class T>
template <class T>
void BaseMatrixT<T>::dotSquareMul(BaseMatrixT& b) {
applyBinary(binary::DotSquareMul<T>(), b);
}
DEFINE_MATRIX_QUATERNARY_PARAMETER_OP(AddSquareSum, THREE_PARAMETER,
DEFINE_MATRIX_QUATERNARY_PARAMETER_OP(AddSquareSum,
THREE_PARAMETER,
T tmp = p1 * b + p2 * c + p3 * d;
a += tmp * tmp);
template<class T>
void BaseMatrixT<T>::addSquareSum(BaseMatrixT& b, BaseMatrixT& c, BaseMatrixT d,
T p1, T p2, T p3) {
template <class T>
void BaseMatrixT<T>::addSquareSum(
BaseMatrixT& b, BaseMatrixT& c, BaseMatrixT d, T p1, T p2, T p3) {
applyQuaternary(quaternary::AddSquareSum<T>(p1, p2, p3), b, c, d);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(AddSquare, ONE_PARAMETER, a += p * b * b);
template<class T>
template <class T>
void BaseMatrixT<T>::addSquare(BaseMatrixT& b, T p) {
applyBinary(binary::AddSquare<T>(p), b);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(DecayAddSquare, TWO_PARAMETER,
DEFINE_MATRIX_BINARY_PARAMETER_OP(DecayAddSquare,
TWO_PARAMETER,
a = p1 * a + p2 * b * b);
template<class T>
template <class T>
void BaseMatrixT<T>::decayAddSquare(BaseMatrixT& b, T p1, T p2) {
applyBinary(binary::DecayAddSquare<T>(p1, p2), b);
}
DEFINE_MATRIX_TERNARY_PARAMETER_OP(DecayAddSquareMul, TWO_PARAMETER,
DEFINE_MATRIX_TERNARY_PARAMETER_OP(DecayAddSquareMul,
TWO_PARAMETER,
a = p1 * a + p2 * b * b * c * c);
template<class T>
void BaseMatrixT<T>::decayAddSquareMul(BaseMatrixT& b, BaseMatrixT& c, T p1,
template <class T>
void BaseMatrixT<T>::decayAddSquareMul(BaseMatrixT& b,
BaseMatrixT& c,
T p1,
T p2) {
applyTernary(ternary::DecayAddSquareMul<T>(p1, p2), b, c);
}
DEFINE_MATRIX_TERNARY_PARAMETER_OP(ReciprocalSum, THREE_PARAMETER,
DEFINE_MATRIX_TERNARY_PARAMETER_OP(ReciprocalSum,
THREE_PARAMETER,
a = 1 / (p1 * b + p2 * c + p3));
template<class T>
void BaseMatrixT<T>::reciprocalSum(BaseMatrixT& b, BaseMatrixT& c, T p1, T p2,
T p3) {
template <class T>
void BaseMatrixT<T>::reciprocalSum(
BaseMatrixT& b, BaseMatrixT& c, T p1, T p2, T p3) {
applyTernary(ternary::ReciprocalSum<T>(p1, p2, p3), b, c);
}
DEFINE_MATRIX_BINARY_PARAMETER_OP(Reciprocal2, TWO_PARAMETER,
DEFINE_MATRIX_BINARY_PARAMETER_OP(Reciprocal2,
TWO_PARAMETER,
a = 1 / (p1 * b + p2));
template<class T>
template <class T>
void BaseMatrixT<T>::reciprocal2(BaseMatrixT& b, T p1, T p2) {
applyBinary(binary::Reciprocal2<T>(p1, p2), b);
}
DEFINE_MATRIX_TERNARY_PARAMETER_OP(DotMulSquareSum, TWO_PARAMETER,
DEFINE_MATRIX_TERNARY_PARAMETER_OP(DotMulSquareSum,
TWO_PARAMETER,
T tmp = p1 * b + p2 * c;
a *= tmp * tmp);
template<class T>
void BaseMatrixT<T>::dotMulSquareSum(BaseMatrixT& b, BaseMatrixT& c, T p1,
template <class T>
void BaseMatrixT<T>::dotMulSquareSum(BaseMatrixT& b,
BaseMatrixT& c,
T p1,
T p2) {
applyTernary(ternary::DotMulSquareSum<T>(p1, p2), b, c);
}
DEFINE_MATRIX_TERNARY_PARAMETER_OP(DotSquareSum, TWO_PARAMETER,
DEFINE_MATRIX_TERNARY_PARAMETER_OP(DotSquareSum,
TWO_PARAMETER,
T tmp = p1 * b + p2 * c;
a = tmp * tmp);
template<class T>
template <class T>
void BaseMatrixT<T>::dotSquareSum(BaseMatrixT& b, BaseMatrixT& c, T p1, T p2) {
applyTernary(ternary::DotSquareSum<T>(p1, p2), b, c);
}
DEFINE_MATRIX_TERNARY_PARAMETER_OP(DotMulSum, TWO_PARAMETER,
DEFINE_MATRIX_TERNARY_PARAMETER_OP(DotMulSum,
TWO_PARAMETER,
a *= p1 * b + p2 * c);
template<class T>
template <class T>
void BaseMatrixT<T>::dotMulSum(BaseMatrixT& b, BaseMatrixT& c, T p1, T p2) {
applyTernary(ternary::DotMulSum<T>(p1, p2), b, c);
}
DEFINE_MATRIX_BINARY_OP(CopyAndClear, b = a; a = 0);
template<class T>
template <class T>
void BaseMatrixT<T>::copyAndClear(BaseMatrixT& b) {
applyBinary(binary::CopyAndClear<T>(), b);
}
DEFINE_MATRIX_TERNARY_PARAMETER_OP(AddDotMul, TWO_PARAMETER,
DEFINE_MATRIX_TERNARY_PARAMETER_OP(AddDotMul,
TWO_PARAMETER,
a = p1 * a + p2 * b * c);
template<class T>
template <class T>
void BaseMatrixT<T>::addDotMul(BaseMatrixT& b, BaseMatrixT& c, T p1, T p2) {
applyTernary(ternary::AddDotMul<T>(p1, p2), b, c);
}
DEFINE_MATRIX_BINARY_OP(Assign, a = b;);
template<class T>
template <class T>
void BaseMatrixT<T>::assign(BaseMatrixT& b) {
if (useGpu_) {
applyBinary(binary::Assign<T>(), b);
......@@ -1230,7 +1361,7 @@ void BaseMatrixT<T>::assign(BaseMatrixT& b) {
}
}
template<class T>
template <class T>
void BaseMatrixT<T>::assignAtOffset(BaseMatrixT& b, int64_t columnOffset) {
if (columnOffset + b.width_ <= width_) {
int numRows = height_;
......@@ -1250,24 +1381,31 @@ void BaseMatrixT<T>::assignAtOffset(BaseMatrixT& b, int64_t columnOffset) {
}
DEFINE_MATRIX_BINARY_OP(DeepSwap, T tmp = a; a = b; b = tmp);
template<class T>
template <class T>
void BaseMatrixT<T>::deepSwap(BaseMatrixT& b) {
applyBinary(binary::DeepSwap<T>(), b);
applyBinary(binary::DeepSwap<T>(), b);
}
template<>
template <>
void BaseMatrixT<real>::rowDotMul(size_t destCol,
BaseMatrixT& b,
BaseMatrixT& c) {
int numRows = b.height_;
int numCols = b.width_;
MatrixOffset offset(destCol, 0, 0, 0, 0, 0);
aggregate(aggregate::sum(), base::binary::mul(), base::binary::add(), b, c,
numRows, numCols, offset, false_type(),
aggregate(aggregate::sum(),
base::binary::mul(),
base::binary::add(),
b,
c,
numRows,
numCols,
offset,
false_type(),
true_type() /*aAsColVector*/);
}
template<class T>
template <class T>
void BaseMatrixT<T>::rowDotMul2(size_t destCol,
BaseMatrixT& b,
BaseMatrixT& c) {
......@@ -1290,17 +1428,24 @@ void BaseMatrixT<T>::rowDotMul2(size_t destCol,
}
}
template<>
template <>
void BaseMatrixT<real>::addDotMulVMM(BaseMatrixT& b, BaseMatrixT& c) {
MatrixOffset offset(0, 0, 0, 0, 0, 0);
int numRows = b.height_;
int numCols = b.width_;
aggregate(aggregate::sum(), base::binary::mul(), base::binary::add(), b, c,
numRows, numCols, offset, true_type() /*aAsRowVector*/,
aggregate(aggregate::sum(),
base::binary::mul(),
base::binary::add(),
b,
c,
numRows,
numCols,
offset,
true_type() /*aAsRowVector*/,
false_type());
}
template<class T>
template <class T>
void BaseMatrixT<T>::addDotMulVMM2(BaseMatrixT& b, BaseMatrixT& c) {
CHECK(!useGpu_) << "do not support gpu";
......@@ -1321,16 +1466,22 @@ void BaseMatrixT<T>::addDotMulVMM2(BaseMatrixT& b, BaseMatrixT& c) {
}
DEFINE_MATRIX_TERNARY_OP(addDotMulMMV, a += b * c);
template<class T>
template <class T>
void BaseMatrixT<T>::addDotMulMMV(BaseMatrixT& b, BaseMatrixT& c) {
MatrixOffset offset(0, 0, 0, 0, 0, 0);
int numRows = height_;
int numCols = width_;
applyTernary(ternary::addDotMulMMV<T>(), b, c, numRows, numCols, offset,
true_type() /*cAsRowVector*/, false_type());
applyTernary(ternary::addDotMulMMV<T>(),
b,
c,
numRows,
numCols,
offset,
true_type() /*cAsRowVector*/,
false_type());
}
template<class T>
template <class T>
void BaseMatrixT<T>::addDotMulMMV2(BaseMatrixT& b, BaseMatrixT& c) {
CHECK(!useGpu_) << "do not support gpu";
......@@ -1350,16 +1501,22 @@ void BaseMatrixT<T>::addDotMulMMV2(BaseMatrixT& b, BaseMatrixT& c) {
}
}
template<class T>
template <class T>
void BaseMatrixT<T>::rowScale(size_t cCol, BaseMatrixT& b, BaseMatrixT& c) {
MatrixOffset offset(0, 0, 0, 0, cCol, 0);
int numRows = height_;
int numCols = width_;
applyTernary(ternary::DotMul<T>(), b, c, numRows, numCols, offset,
false_type(), true_type() /*cAsColVector*/);
applyTernary(ternary::DotMul<T>(),
b,
c,
numRows,
numCols,
offset,
false_type(),
true_type() /*cAsColVector*/);
}
template<class T>
template <class T>
void BaseMatrixT<T>::rowScale2(size_t cCol, BaseMatrixT& b, BaseMatrixT& c) {
CHECK(!useGpu_) << "do not support gpu";
......@@ -1379,52 +1536,82 @@ void BaseMatrixT<T>::rowScale2(size_t cCol, BaseMatrixT& b, BaseMatrixT& c) {
}
}
template<class T>
template <class T>
void BaseMatrixT<T>::colScale(size_t cRow, BaseMatrixT& b, BaseMatrixT& c) {
MatrixOffset offset(0, 0, 0, 0, 0, cRow);
int numRows = height_;
int numCols = width_;
applyTernary(ternary::DotMul<T>(), b, c, numRows, numCols, offset,
true_type() /* cAsRowVector */, false_type() /* cAsColVector */);
applyTernary(ternary::DotMul<T>(),
b,
c,
numRows,
numCols,
offset,
true_type() /* cAsRowVector */,
false_type() /* cAsColVector */);
}
template<class T>
template <class T>
void BaseMatrixT<T>::addColScale(size_t cRow, BaseMatrixT& b, BaseMatrixT& c) {
MatrixOffset offset(0, 0, 0, 0, 0, cRow);
int numRows = height_;
int numCols = width_;
applyTernary(ternary::addDotMulMMV<T>(), b, c, numRows, numCols, offset,
true_type() /* cAsRowVector */, false_type() /* cAsColVector */);
applyTernary(ternary::addDotMulMMV<T>(),
b,
c,
numRows,
numCols,
offset,
true_type() /* cAsRowVector */,
false_type() /* cAsColVector */);
}
template<class T>
template <class T>
void BaseMatrixT<T>::addRowScale(size_t cCol, BaseMatrixT& b, BaseMatrixT& c) {
MatrixOffset offset(0, 0, 0, 0, cCol, 0);
int numRows = height_;
int numCols = width_;
applyTernary(ternary::addDotMulMMV<T>(), b, c, numRows, numCols, offset,
false_type(), true_type() /*cAsColVector*/);
applyTernary(ternary::addDotMulMMV<T>(),
b,
c,
numRows,
numCols,
offset,
false_type(),
true_type() /*cAsColVector*/);
}
DEFINE_MATRIX_TERNARY_PARAMETER_OP(RowAdd, ONE_PARAMETER, a = b + p * c);
template<class T>
template <class T>
void BaseMatrixT<T>::rowAdd(size_t cCol, BaseMatrixT& b, BaseMatrixT& c, T p) {
MatrixOffset offset(0, 0, 0, 0, cCol, 0);
int numRows = height_;
int numCols = width_;
applyTernary(ternary::RowAdd<T>(p), b, c, numRows, numCols, offset,
false_type(), true_type() /*cAsColVector*/);
applyTernary(ternary::RowAdd<T>(p),
b,
c,
numRows,
numCols,
offset,
false_type(),
true_type() /*cAsColVector*/);
}
DEFINE_MATRIX_TERNARY_OP(RowPow, a = pow(b, c));
template<>
template <>
void BaseMatrixT<real>::rowPow(size_t cCol, BaseMatrixT& b, BaseMatrixT& c) {
if (useGpu_) {
MatrixOffset offset(0, 0, 0, 0, cCol, 0);
int numRows = height_;
int numCols = width_;
applyTernary(ternary::RowPow<real>(), b, c, numRows, numCols, offset,
false_type(), true_type() /*cAsColVector*/);
applyTernary(ternary::RowPow<real>(),
b,
c,
numRows,
numCols,
offset,
false_type(),
true_type() /*cAsColVector*/);
} else {
size_t height = this->height_;
size_t width = this->width_;
......@@ -1441,44 +1628,64 @@ void BaseMatrixT<real>::rowPow(size_t cCol, BaseMatrixT& b, BaseMatrixT& c) {
}
}
template<class T>
template <class T>
void BaseMatrixT<T>::mulRowVector(BaseMatrixT& b) {
MatrixOffset offset(0, 0, 0, 0);
int numRows = height_;
int numCols = width_;
applyBinary(binary::DotMul<T>(), b, numRows, numCols, offset,
true_type() /* bAsRowVector */, false_type());
applyBinary(binary::DotMul<T>(),
b,
numRows,
numCols,
offset,
true_type() /* bAsRowVector */,
false_type());
}
DEFINE_MATRIX_BINARY_OP(DotDiv, a /= b);
template<class T>
template <class T>
void BaseMatrixT<T>::divRowVector(BaseMatrixT& b) {
MatrixOffset offset(0, 0, 0, 0);
int numRows = height_;
int numCols = width_;
applyBinary(binary::DotDiv<T>(), b, numRows, numCols, offset,
true_type() /* bAsRowVector */, false_type());
applyBinary(binary::DotDiv<T>(),
b,
numRows,
numCols,
offset,
true_type() /* bAsRowVector */,
false_type());
}
template<class T>
template <class T>
void BaseMatrixT<T>::mulColVector(BaseMatrixT& b) {
MatrixOffset offset(0, 0, 0, 0);
int numRows = height_;
int numCols = width_;
applyBinary(binary::DotMul<T>(), b, numRows, numCols, offset,
false_type(), true_type() /* bAsColVector */);
applyBinary(binary::DotMul<T>(),
b,
numRows,
numCols,
offset,
false_type(),
true_type() /* bAsColVector */);
}
template<class T>
template <class T>
void BaseMatrixT<T>::divColVector(BaseMatrixT& b) {
MatrixOffset offset(0, 0, 0, 0);
int numRows = height_;
int numCols = width_;
applyBinary(binary::DotDiv<T>(), b, numRows, numCols, offset,
false_type(), true_type() /* bAsColVector */);
applyBinary(binary::DotDiv<T>(),
b,
numRows,
numCols,
offset,
false_type(),
true_type() /* bAsColVector */);
}
template<>
template <>
template <class Agg>
int BaseMatrixT<real>::applyRow(Agg agg, BaseMatrixT& b) {
MatrixOffset offset(0, 0, 0, 0, 0, 0);
......@@ -1486,13 +1693,20 @@ int BaseMatrixT<real>::applyRow(Agg agg, BaseMatrixT& b) {
size_t numCols = b.width_;
CHECK_EQ(height_, numRows);
CHECK_EQ(width_, 1UL);
aggregate(agg, base::unary::identity(), base::binary::second(), b, numRows,
numCols, offset, false_type(), true_type() /*aAsColVector*/);
aggregate(agg,
base::unary::identity(),
base::binary::second(),
b,
numRows,
numCols,
offset,
false_type(),
true_type() /*aAsColVector*/);
return 0;
}
template<>
template <>
template <class Agg, class Saver>
int BaseMatrixT<real>::applyRow(Agg agg, Saver sv, BaseMatrixT& b) {
MatrixOffset offset(0, 0, 0, 0, 0, 0);
......@@ -1500,16 +1714,25 @@ int BaseMatrixT<real>::applyRow(Agg agg, Saver sv, BaseMatrixT& b) {
size_t numCols = b.width_;
CHECK_EQ(height_, numRows);
CHECK_EQ(width_, 1UL);
aggregate(agg, base::unary::identity(), sv, b, numRows, numCols, offset,
false_type(), true_type() /*aAsColVector*/);
aggregate(agg,
base::unary::identity(),
sv,
b,
numRows,
numCols,
offset,
false_type(),
true_type() /*aAsColVector*/);
return 0;
}
template<>
template <>
template <class Agg>
int BaseMatrixT<real>::applyRow(
Agg agg, real scaleDest, real scaleAgg, BaseMatrixT& b) {
int BaseMatrixT<real>::applyRow(Agg agg,
real scaleDest,
real scaleAgg,
BaseMatrixT& b) {
if (scaleDest != 0) {
applyRow(agg, base::binary::add2(scaleDest, scaleAgg), b);
} else {
......@@ -1521,10 +1744,10 @@ int BaseMatrixT<real>::applyRow(
return 0;
}
template<>
template <>
template <class Agg, class Op, class Saver>
int BaseMatrixT<real>::applyRow(Agg agg, Op op, Saver sv,
BaseMatrixT& b, BaseMatrixT& c) {
int BaseMatrixT<real>::applyRow(
Agg agg, Op op, Saver sv, BaseMatrixT& b, BaseMatrixT& c) {
MatrixOffset offset(0, 0, 0, 0, 0, 0);
size_t numRows = b.height_;
size_t numCols = b.width_;
......@@ -1532,16 +1755,27 @@ int BaseMatrixT<real>::applyRow(Agg agg, Op op, Saver sv,
CHECK_EQ(width_, 1UL);
CHECK_EQ(c.height_, numRows);
CHECK_EQ(c.width_, numCols);
aggregate(agg, op, sv,
b, c, numRows, numCols, offset,
false_type(), true_type() /*aAsColVector*/);
aggregate(agg,
op,
sv,
b,
c,
numRows,
numCols,
offset,
false_type(),
true_type() /*aAsColVector*/);
return 0;
}
template<>
template <>
template <class Agg, class Op>
int BaseMatrixT<real>::applyRow(Agg agg, Op op, real scaleDest, real scaleAgg,
BaseMatrixT& b, BaseMatrixT& c) {
int BaseMatrixT<real>::applyRow(Agg agg,
Op op,
real scaleDest,
real scaleAgg,
BaseMatrixT& b,
BaseMatrixT& c) {
if (scaleDest != 0) {
applyRow(agg, op, base::binary::add2(scaleDest, scaleAgg), b, c);
} else {
......@@ -1553,7 +1787,7 @@ int BaseMatrixT<real>::applyRow(Agg agg, Op op, real scaleDest, real scaleAgg,
return 0;
}
template<>
template <>
template <class Agg>
int BaseMatrixT<real>::applyCol(Agg agg, BaseMatrixT& b) {
MatrixOffset offset(0, 0, 0, 0, 0, 0);
......@@ -1561,13 +1795,20 @@ int BaseMatrixT<real>::applyCol(Agg agg, BaseMatrixT& b) {
size_t numCols = b.width_;
CHECK_EQ(width_, numCols);
CHECK_EQ(height_, 1UL);
aggregate(agg, base::unary::identity(), base::binary::second(), b, numRows,
numCols, offset, true_type() /*aAsRowVector*/, false_type());
aggregate(agg,
base::unary::identity(),
base::binary::second(),
b,
numRows,
numCols,
offset,
true_type() /*aAsRowVector*/,
false_type());
return 0;
}
template<>
template <>
template <class Agg, class Saver>
int BaseMatrixT<real>::applyCol(Agg agg, Saver sv, BaseMatrixT& b) {
MatrixOffset offset(0, 0, 0, 0, 0, 0);
......@@ -1575,16 +1816,25 @@ int BaseMatrixT<real>::applyCol(Agg agg, Saver sv, BaseMatrixT& b) {
size_t numCols = b.width_;
CHECK_EQ(width_, numCols);
CHECK_EQ(height_, 1UL);
aggregate(agg, base::unary::identity(), sv, b, numRows, numCols, offset,
true_type() /*aAsRowVector*/, false_type());
aggregate(agg,
base::unary::identity(),
sv,
b,
numRows,
numCols,
offset,
true_type() /*aAsRowVector*/,
false_type());
return 0;
}
template<>
template <>
template <class Agg>
int BaseMatrixT<real>::applyCol(
Agg agg, real scaleDest, real scaleAgg, BaseMatrixT& b) {
int BaseMatrixT<real>::applyCol(Agg agg,
real scaleDest,
real scaleAgg,
BaseMatrixT& b) {
if (scaleDest != 0) {
applyCol(agg, base::binary::add2(scaleDest, scaleAgg), b);
} else {
......@@ -1596,48 +1846,51 @@ int BaseMatrixT<real>::applyCol(
return 0;
}
template<>
template <>
void BaseMatrixT<real>::sumRows(BaseMatrixT& b, real scaleSum, real scaleDest) {
applyRow(aggregate::sum(), scaleDest, scaleSum, b);
}
template<>
template <>
void BaseMatrixT<real>::maxRows(BaseMatrixT& b) {
applyRow(aggregate::max(), b);
}
template<>
template <>
void BaseMatrixT<real>::minRows(BaseMatrixT& b) {
applyRow(aggregate::min(), b);
}
template<>
template <>
void BaseMatrixT<real>::maxCols(BaseMatrixT& b) {
applyCol(aggregate::max(), b);
}
template<>
template <>
void BaseMatrixT<real>::minCols(BaseMatrixT& b) {
applyCol(aggregate::min(), b);
}
template<>
template <>
void BaseMatrixT<real>::sumCols(BaseMatrixT& b, real scaleSum, real scaleDest) {
applyCol(aggregate::sum(), scaleDest, scaleSum, b);
}
template<>
void BaseMatrixT<real>::sumOfSquaredDiffs(
BaseMatrixT& b, BaseMatrixT& c, real scaleSum, real scaleDest) {
applyRow(aggregate::sum(), base::binary::squaredDiff(),
scaleDest, scaleSum, b, c);
template <>
void BaseMatrixT<real>::sumOfSquaredDiffs(BaseMatrixT& b,
BaseMatrixT& c,
real scaleSum,
real scaleDest) {
applyRow(
aggregate::sum(), base::binary::squaredDiff(), scaleDest, scaleSum, b, c);
}
template<>
void BaseMatrixT<real>::sumOfProducts(
BaseMatrixT& b, BaseMatrixT& c, real scaleSum, real scaleDest) {
applyRow(aggregate::sum(), base::binary::mul(),
scaleDest, scaleSum, b, c);
template <>
void BaseMatrixT<real>::sumOfProducts(BaseMatrixT& b,
BaseMatrixT& c,
real scaleSum,
real scaleDest) {
applyRow(aggregate::sum(), base::binary::mul(), scaleDest, scaleSum, b, c);
}
template class BaseMatrixT<real>;
......
paddle/math/TrainingAlgorithmOp.cu
浏览文件 @ 75185d82
......@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/utils/Logging.h"
#include "BaseMatrix.h"
#include "TrainingAlgorithmOp.h"
#include "paddle/utils/Logging.h"
#if __cplusplus > 199711L
......@@ -32,10 +32,10 @@ void sparseMomentumApply(BaseMatrix& value,
real tau,
real learningRate) {
auto expr1 = momU.lazyAssign(momU - (alpha * gamma * learningRate) * grad);
auto expr2 = momV.lazyAssign(
momV + (tau * alpha * gamma * learningRate) * grad);
auto expr3 = value.lazyAssign(
(tau / beta + (real)1 / alpha) * momU + ((real)1 / beta) * momV);
auto expr2 =
momV.lazyAssign(momV + (tau * alpha * gamma * learningRate) * grad);
auto expr3 = value.lazyAssign((tau / beta + (real)1 / alpha) * momU +
((real)1 / beta) * momV);
AssignEvaluate(expr1, expr2, expr3);
}
......@@ -52,12 +52,12 @@ void adadeltaApply(BaseMatrix& value,
real momentum,
real decayRate) {
auto expr1 = accum.lazyAssign(rou * accum + ((real)1 - rou) * grad.square());
auto expr2 = lr.lazyAssign(
((accum_update + epsilon) / (accum + epsilon)).sqrt());
auto expr3 = accum_update.lazyAssign(
rou * accum_update + ((real)1 - rou) * (grad * lr).square());
auto expr4 = mom.lazyAssign(
mom * momentum - learningRate * lr * (grad + value * decayRate));
auto expr2 =
lr.lazyAssign(((accum_update + epsilon) / (accum + epsilon)).sqrt());
auto expr3 = accum_update.lazyAssign(rou * accum_update +
((real)1 - rou) * (grad * lr).square());
auto expr4 = mom.lazyAssign(mom * momentum -
learningRate * lr * (grad + value * decayRate));
auto expr5 = value.lazyAssign(value + mom);
AssignEvaluate(expr1, expr2, expr3, expr4, expr5);
......@@ -74,10 +74,10 @@ void adagradApply(BaseMatrix& value,
real momentum,
real decayRate) {
auto expr1 = accum.lazyAssign(accum + grad.square());
auto expr2 = lr.lazyAssign(
(accum_buffer + accum + epsilon).sqrt().reciprocal());
auto expr3 = mom.lazyAssign(
mom * momentum - learningRate * lr * (grad + value * decayRate));
auto expr2 =
lr.lazyAssign((accum_buffer + accum + epsilon).sqrt().reciprocal());
auto expr3 = mom.lazyAssign(mom * momentum -
learningRate * lr * (grad + value * decayRate));
auto expr4 = value.lazyAssign(value + mom);
AssignEvaluate(expr1, expr2, expr3, expr4);
......@@ -98,8 +98,8 @@ void rmspropApply(BaseMatrix& value,
bool firstTime) {
auto expr2 = f.lazyAssign(accumulatedRou * f + ((real)1 - rou) * grad);
auto expr3 = lr.lazyAssign((g - f.square() + epsilon).sqrt().reciprocal());
auto expr4 = mom.lazyAssign(
mom * momentum - learningRate * lr * (grad + value * decayRate));
auto expr4 = mom.lazyAssign(mom * momentum -
learningRate * lr * (grad + value * decayRate));
auto expr5 = value.lazyAssign(value + mom);
if (firstTime) {
......@@ -107,8 +107,8 @@ void rmspropApply(BaseMatrix& value,
AssignEvaluate(expr1, expr2, expr3, expr4, expr5);
} else {
auto expr1 = g.lazyAssign(
accumulatedRou * g + ((real)1 - rou) * grad.square());
auto expr1 =
g.lazyAssign(accumulatedRou * g + ((real)1 - rou) * grad.square());
AssignEvaluate(expr1, expr2, expr3, expr4, expr5);
}
......@@ -127,8 +127,8 @@ void decayedAdagradApply(BaseMatrix& value,
real decayRate,
bool firstTime) {
auto expr2 = lr.lazyAssign((accum + epsilon).sqrt().reciprocal());
auto expr3 = mom.lazyAssign(
mom * momentum - learningRate * lr * (grad + value * decayRate));
auto expr3 = mom.lazyAssign(mom * momentum -
learningRate * lr * (grad + value * decayRate));
auto expr4 = value.lazyAssign(value + mom);
if (firstTime) {
......@@ -136,8 +136,8 @@ void decayedAdagradApply(BaseMatrix& value,
AssignEvaluate(expr1, expr2, expr3, expr4);
} else {
auto expr1 = accum.lazyAssign(
accumulatedRou * accum + ((real)1 - rou) * grad.square());
auto expr1 = accum.lazyAssign(accumulatedRou * accum +
((real)1 - rou) * grad.square());
AssignEvaluate(expr1, expr2, expr3, expr4);
}
......@@ -153,13 +153,12 @@ void adamApply(BaseMatrix& value,
real beta2_power,
real epsilon,
real learningRate) {
real alpha = learningRate *
std::sqrt((real)1 - beta2_power) / ((real)1 - beta1_power);
real alpha =
learningRate * std::sqrt((real)1 - beta2_power) / ((real)1 - beta1_power);
auto expr1 = mom.lazyAssign(beta1 * mom + ((real)1 - beta1) * grad);
auto expr2 = v.lazyAssign(beta2 * v + ((real)1 - beta2) * grad.square());
auto expr3 = value.lazyAssign(
value - (mom * alpha) / (v.sqrt() + epsilon));
auto expr3 = value.lazyAssign(value - (mom * alpha) / (v.sqrt() + epsilon));
AssignEvaluate(expr1, expr2, expr3);
}
......@@ -173,10 +172,10 @@ void adamaxApply(BaseMatrix& value,
int64_t step,
real alpha) {
auto expr1 = mom.lazyAssign(beta1 * mom + ((real)1 - beta1) * grad);
auto expr2 = u.lazyAssign(
(beta2 * u > grad.abs()).condition(beta2 * u, grad.abs()));
auto expr2 =
u.lazyAssign((beta2 * u > grad.abs()).condition(beta2 * u, grad.abs()));
auto expr3 = value.lazyAssign(
value - (alpha / ((real)1 - (real)std::pow(beta1, step))) * (mom / u));
value - (alpha / ((real)1 - (real)std::pow(beta1, step))) * (mom / u));
AssignEvaluate(expr1, expr2, expr3);
}
......@@ -322,8 +321,8 @@ void adamApply(BaseMatrix& value,
real beta2_power,
real epsilon,
real learningRate) {
real alpha = learningRate *
std::sqrt((real)1 - beta2_power) / ((real)1 - beta1_power);
real alpha =
learningRate * std::sqrt((real)1 - beta2_power) / ((real)1 - beta1_power);
// m_t = \beta_1 * m_{t-1} + (1-\beta_1)* g_t;
mom = beta1 * mom + ((real)1 - beta1) * grad;
......@@ -331,7 +330,7 @@ void adamApply(BaseMatrix& value,
// v_t = \beta_2 * v_{t-1} + (1-\beta_2)* g_{t-1}^2
v = beta2 * v + ((real)1 - beta2) * grad.square();
value -= (mom * alpha) / (v.sqrt() + epsilon);
value -= (mom * alpha) / (v.sqrt() + epsilon);
}
void adamaxApply(BaseMatrix& value,
......
paddle/math/tests/test_Tensor.cu
浏览文件 @ 75185d82
......@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "paddle/math/Matrix.h"
#include "TensorCheck.h"
#include "paddle/math/Matrix.h"
using paddle::Matrix;
using paddle::CpuMatrix;
......@@ -26,25 +26,25 @@ using paddle::GpuIVector;
using autotest::TensorCheckEqual;
using autotest::TensorCheckErr;
#define INIT_UNARY(A1, A2) \
Tensor A1(height, width); \
Tensor A2(height, width); \
A1.randomizeUniform(); \
A2.copyFrom(A1)
#define INIT_BINARY(A1, A2, B) \
INIT_UNARY(A1, A2); \
Tensor B(height, width); \
B.randomizeUniform()
#define INIT_TERNARY(A1, A2, B, C) \
INIT_BINARY(A1, A2, B); \
Tensor C(height, width); \
C.randomizeUniform()
#define INIT_QUATERNARY(A1, A2, B, C, D) \
INIT_TERNARY(A1, A2, B, C); \
Tensor D(height, width); \
D.randomizeUniform()
template<typename Tensor>
#define INIT_UNARY(A1, A2) \
Tensor A1(height, width); \
Tensor A2(height, width); \
A1.randomizeUniform(); \
A2.copyFrom(A1)
#define INIT_BINARY(A1, A2, B) \
INIT_UNARY(A1, A2); \
Tensor B(height, width); \
B.randomizeUniform()
#define INIT_TERNARY(A1, A2, B, C) \
INIT_BINARY(A1, A2, B); \
Tensor C(height, width); \
C.randomizeUniform()
#define INIT_QUATERNARY(A1, A2, B, C, D) \
INIT_TERNARY(A1, A2, B, C); \
Tensor D(height, width); \
D.randomizeUniform()
template <typename Tensor>
struct TestUnaryMatrix {
typedef std::function<void(Tensor& A1, Tensor& A2)> UnaryFunc;
......@@ -59,7 +59,7 @@ struct TestUnaryMatrix {
}
};
template<typename Tensor>
template <typename Tensor>
struct TestBinaryMatrix {
typedef std::function<void(Tensor& A1, Tensor& A2, Tensor& B)> BinaryFunc;
......@@ -74,10 +74,10 @@ struct TestBinaryMatrix {
}
};
template<typename Tensor>
template <typename Tensor>
struct TestTernaryMatrix {
typedef std::function<void(
Tensor& A1, Tensor& A2, Tensor& B, Tensor& C)> TernaryFunc;
typedef std::function<void(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C)>
TernaryFunc;
explicit TestTernaryMatrix(TernaryFunc testTernaryFunc) {
for (auto height : {1, 11, 73, 128, 200, 330}) {
......@@ -90,10 +90,11 @@ struct TestTernaryMatrix {
}
};
template<typename Tensor>
template <typename Tensor>
struct TestQuaternaryMatrix {
typedef std::function<void(
Tensor& A1, Tensor& A2, Tensor& B, Tensor& C, Tensor& D)> QuaternaryFunc;
Tensor& A1, Tensor& A2, Tensor& B, Tensor& C, Tensor& D)>
QuaternaryFunc;
explicit TestQuaternaryMatrix(QuaternaryFunc testQuaternaryFunc) {
for (auto height : {1, 11, 73, 128, 200, 330}) {
......@@ -106,7 +107,7 @@ struct TestQuaternaryMatrix {
}
};
template<typename Tensor, class T>
template <typename Tensor, class T>
struct TestUnaryVectorT {
typedef std::function<void(Tensor& A1, Tensor& A2)> UnaryFunc;
......@@ -142,11 +143,11 @@ void SetTensorValue(Matrix& matrix, real value) {
}
}
template<typename Tensor>
template <typename Tensor>
void testTensorAddScalar(Tensor& A1, Tensor& A2) {
real p1 = 2.5;
real p2 = 3.0;
A1.add(p1); // a += p
A1.add(p1); // a += p
A2 += p1;
TensorCheckEqual(A1, A2);
......@@ -155,7 +156,7 @@ void testTensorAddScalar(Tensor& A1, Tensor& A2) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSubScalar(Tensor& A1, Tensor& A2) {
real p = 2.5;
A1.subScalar(p); // a -= p
......@@ -163,7 +164,7 @@ void testTensorSubScalar(Tensor& A1, Tensor& A2) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorMulScalar(Tensor& A1, Tensor& A2) {
real p = 2.5;
A1.mulScalar(p); // a *= p
......@@ -177,7 +178,7 @@ void testTensorMulScalar(Tensor& A1, Tensor& A2) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorDivScalar(Tensor& A1, Tensor& A2) {
real p = 2.5;
A1.divScalar(p); // a /= p
......@@ -185,44 +186,44 @@ void testTensorDivScalar(Tensor& A1, Tensor& A2) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorNeg(Tensor& A1, Tensor& A2) {
A1.neg(); // a = -a
A2 = -A2;
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorAbs(Tensor& A1, Tensor& A2) {
A1.abs2(); // a = a > 0 ? a : -a
A2 = A2.abs();
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSquare(Tensor& A1, Tensor& A2) {
A1.square2(); // a = a * a
A2 = A2.square();
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorReciprocal(Tensor& A1, Tensor& A2) {
A1.reciprocal2(); // a = 1.0f / a
A2 = A2.reciprocal();
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSign(Tensor& A1, Tensor& A2) {
A1.sign2(); // a = (a > 0) - (a < 0)
A2 = A2.sign();
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorAssign(Tensor& A1, Tensor& A2) {
A1.assign(1.5); // a = p
A1.assign(1.5); // a = p
A2 = A2.constant(1.5);
TensorCheckEqual(A1, A2);
......@@ -235,7 +236,7 @@ void testTensorAssign(Tensor& A1, Tensor& A2) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testUnaryBaseOp(Tensor& A1, Tensor& A2) {
testTensorAddScalar(A1, A2);
testTensorSubScalar(A1, A2);
......@@ -249,9 +250,9 @@ void testUnaryBaseOp(Tensor& A1, Tensor& A2) {
testTensorAssign(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testUnaryBaseOpInt(Tensor& A1, Tensor& A2) {
A1.add(2); // a += p
A1.add(2); // a += p
A2 += 2;
TensorCheckEqual(A1, A2);
......@@ -266,46 +267,46 @@ void testUnaryBaseOpInt(Tensor& A1, Tensor& A2) {
TEST(Unary, BaseOp) {
TestUnaryMatrix<CpuMatrix> testCpuMatrix(testUnaryBaseOp<CpuMatrix>);
TestUnaryVectorT<CpuVector, real> testCpuVector(testUnaryBaseOp<CpuVector>);
TestUnaryVectorT<CpuIVector, int>
testCpuIVector(testUnaryBaseOpInt<CpuIVector>);
TestUnaryVectorT<CpuIVector, int> testCpuIVector(
testUnaryBaseOpInt<CpuIVector>);
#ifndef PADDLE_ONLY_CPU
TestUnaryMatrix<GpuMatrix> testGpuMatrix(testUnaryBaseOp<GpuMatrix>);
TestUnaryVectorT<GpuVector, real> testGpuVector(testUnaryBaseOp<GpuVector>);
TestUnaryVectorT<GpuIVector, int>
testGpuIVector(testUnaryBaseOpInt<GpuIVector>);
TestUnaryVectorT<GpuIVector, int> testGpuIVector(
testUnaryBaseOpInt<GpuIVector>);
#endif
}
template<typename Tensor>
template <typename Tensor>
void testTensorExp(Tensor& A1, Tensor& A2) {
A1.exp2(); // a = exp(a)
A2 = A2.exp();
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorLog(Tensor& A1, Tensor& A2) {
A1.log2(); // a = log(a)
A2 = A2.log();
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSqrt(Tensor& A1, Tensor& A2) {
A1.sqrt2(); // a = sqrt(a)
A2 = A2.sqrt();
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorPow(Tensor& A1, Tensor& A2) {
A1.pow2(3.2); // a = pow(a, p)
A2 = A2.pow(3.2);
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testUnayrMathOp(Tensor& A1, Tensor& A2) {
testTensorExp(A1, A2);
testTensorLog(A1, A2);
......@@ -321,7 +322,7 @@ TEST(Unary, MathOp) {
#endif
}
template<typename Tensor>
template <typename Tensor>
void testTensorClip(Tensor& A1, Tensor& A2) {
real p1 = 0.003f;
real p2 = 0.877f;
......@@ -331,7 +332,7 @@ void testTensorClip(Tensor& A1, Tensor& A2) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorBiggerThanScalar(Tensor& A1, Tensor& A2) {
real p = 0.5f;
A1.biggerThanScalar(p); // a = a > p ? 1.0f : 0.0f
......@@ -339,7 +340,7 @@ void testTensorBiggerThanScalar(Tensor& A1, Tensor& A2) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorapplyL1(Tensor& A1, Tensor& A2) {
/**
* T lambda = p;
......@@ -351,14 +352,15 @@ void testTensorapplyL1(Tensor& A1, Tensor& A2) {
real learningRate = 0.7f;
real decayRate = 0.6f;
A1.applyL1(learningRate, decayRate);
A2 = (A2 > (learningRate * decayRate)).condition(
(A2 - (learningRate * decayRate)),
(A2 < -(learningRate * decayRate)).condition(
(A2 + (learningRate * decayRate)), (real)0.0));
A2 = (A2 > (learningRate * decayRate))
.condition(
(A2 - (learningRate * decayRate)),
(A2 < -(learningRate * decayRate))
.condition((A2 + (learningRate * decayRate)), (real)0.0));
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testUnayrCompareOp(Tensor& A1, Tensor& A2) {
testTensorClip(A1, A2);
testTensorBiggerThanScalar(A1, A2);
......@@ -377,7 +379,7 @@ TEST(Unary, CompareOp) {
#endif
}
template<typename Tensor>
template <typename Tensor>
void testTensorAdd(Tensor& A1, Tensor& A2, Tensor& B) {
real p1 = 2.5;
real p2 = 3.2;
......@@ -406,7 +408,7 @@ void testTensorAdd(Tensor& A1, Tensor& A2, Tensor& B) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSub(Tensor& A1, Tensor& A2, Tensor& B) {
real p = 2.5;
A1.sub(B); // a -= b
......@@ -422,7 +424,7 @@ void testTensorSub(Tensor& A1, Tensor& A2, Tensor& B) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorMul(Tensor& A1, Tensor& A2, Tensor& B) {
real p = 2.5;
A1.mulScalar(B, p); // a = b * p
......@@ -442,7 +444,7 @@ void testTensorMul(Tensor& A1, Tensor& A2, Tensor& B) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorDiv(Tensor& A1, Tensor& A2, Tensor& B) {
real p = 2.5;
A1.divScalar(B, p); // a = b / p
......@@ -454,28 +456,28 @@ void testTensorDiv(Tensor& A1, Tensor& A2, Tensor& B) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorAssign(Tensor& A1, Tensor& A2, Tensor& B) {
A1.assign(B); // a = b
A2 = B;
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSquare(Tensor& A1, Tensor& A2, Tensor& B) {
B.square2(A1); // b = a * a
B.square2(A1); // b = a * a
A2 = B.square();
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSquareDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
A1.squareDerivative(B); // a *= 2.0 * b
A2 = A2 * (real)2.0 * B;
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorReciprocal(Tensor& A1, Tensor& A2, Tensor& B) {
B.reciprocal2(A1); // b = 1.0f / a
A2 = B.reciprocal();
......@@ -490,33 +492,33 @@ void testTensorReciprocal(Tensor& A1, Tensor& A2, Tensor& B) {
real learningRate = 0.7f;
real decayRate = 1.2f;
A1.applyL2(B, learningRate, decayRate); // a *= (1.0f / (1.0f + p * b))
A2 *= (B.constant(1.0f) +
B.constant(learningRate * decayRate) * B).reciprocal();
A2 *= (B.constant(1.0f) + B.constant(learningRate * decayRate) * B)
.reciprocal();
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorReciprocalDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
A1.reciprocalDerivative(B); // a *= -b * b
A2 *= (-B) * B;
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSign(Tensor& A1, Tensor& A2, Tensor& B) {
B.sign2(A1); // b = a > 0.0f ? 1.0f : -1.0f
A2 = B.sign();
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorAbs(Tensor& A1, Tensor& A2, Tensor& B) {
B.abs2(A1); // b = a > 0.0f ? a : -a
A2 = B.abs();
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testBinaryBaseOp(Tensor& A1, Tensor& A2, Tensor& B) {
testTensorAdd(A1, A2, B);
testTensorSub(A1, A2, B);
......@@ -539,7 +541,7 @@ TEST(Binary, BaseOp) {
#endif
}
template<typename Tensor>
template <typename Tensor>
void testTensorExp(Tensor& A1, Tensor& A2, Tensor& B) {
// a = exp(b)
A1.exp2(B);
......@@ -547,14 +549,14 @@ void testTensorExp(Tensor& A1, Tensor& A2, Tensor& B) {
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorExpDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
A1.expDerivative(B); // a *= b
A2 *= B;
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorLog(Tensor& A1, Tensor& A2, Tensor& B) {
// a = log(b)
A1.log2(B);
......@@ -562,7 +564,7 @@ void testTensorLog(Tensor& A1, Tensor& A2, Tensor& B) {
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSqrt(Tensor& A1, Tensor& A2, Tensor& B) {
// a = sqrt(b)
A1.sqrt2(B);
......@@ -570,7 +572,7 @@ void testTensorSqrt(Tensor& A1, Tensor& A2, Tensor& B) {
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorInvSqrt(Tensor& A1, Tensor& A2, Tensor& B) {
// a = 1.0f / sqrt(b)
A1.invSqrt(B);
......@@ -578,14 +580,14 @@ void testTensorInvSqrt(Tensor& A1, Tensor& A2, Tensor& B) {
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorPow(Tensor& A1, Tensor& A2, Tensor& B) {
A1.pow2(B, 2.5f); // a = pow(b, p)
A2 = B.pow(2.5f);
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSoftrelu(Tensor& A1, Tensor& A2, Tensor& B) {
/*
* const T THRESHOLD = 40.0;
......@@ -597,12 +599,14 @@ void testTensorSoftrelu(Tensor& A1, Tensor& A2, Tensor& B) {
real THRESHOLD = 40.0;
A2 = (B.constant(1.0f) +
(B > THRESHOLD).condition(
THRESHOLD, (B < -THRESHOLD).condition(-THRESHOLD, B)).exp()).log();
(B > THRESHOLD)
.condition(THRESHOLD, (B < -THRESHOLD).condition(-THRESHOLD, B))
.exp())
.log();
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSoftreluDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
/*
* const T THRESHOLD = 40.0;
......@@ -612,14 +616,16 @@ void testTensorSoftreluDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
*/
A1.softreluDerivative(B);
real THRESHOLD = 40.0;
A2 = A2 * (B.constant(1.0f) -
(B.constant(-1.0f) *
(B > THRESHOLD).condition(
THRESHOLD, (B < -THRESHOLD).condition(-THRESHOLD, B))).exp());
A2 = A2 *
(B.constant(1.0f) -
(B.constant(-1.0f) *
(B > THRESHOLD)
.condition(THRESHOLD, (B < -THRESHOLD).condition(-THRESHOLD, B)))
.exp());
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSigmoid(Tensor& A1, Tensor& A2, Tensor& B) {
/*
const T THRESHOLD_MIN = -40.0;
......@@ -632,46 +638,47 @@ void testTensorSigmoid(Tensor& A1, Tensor& A2, Tensor& B) {
const real THRESHOLD_MIN = -40.0;
const real THRESHOLD_MAX = 13.0;
auto tmp = (B < THRESHOLD_MIN).condition(
THRESHOLD_MIN, (B > THRESHOLD_MAX).condition(THRESHOLD_MAX, B));
auto tmp = (B < THRESHOLD_MIN)
.condition(THRESHOLD_MIN,
(B > THRESHOLD_MAX).condition(THRESHOLD_MAX, B));
A2 = (B.constant(1.0f) + (-tmp).exp()).reciprocal();
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSigmoidDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
A1.sigmoidDerivative(B); // a *= b * (1 - b)
A2 *= B * (B.constant(1.0f) - B);
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorTanh(Tensor& A1, Tensor& A2, Tensor& B) {
B.tanh(A1); // b = 2.0 / (1.0 + exp(-2 * a)) - 1.0
A2 = B.constant(2.0f) / ((B * ((real)-2.0f)).exp() + (real)1.0f) - (real)1.0f;
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorTanhDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
A1.tanhDerivative(B); // a *= 1 - b * b
A2 *= B.constant(1.0f) - B * B;
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorScaledTanh(Tensor& A1, Tensor& A2, Tensor& B) {
real p1 = 2.5;
real p2 = 3.1;
// b = p1 * (2.0 / (1.0 + exp(-2 * p2 * a)) - 1.0)
B.scaledTanh(A1, p1, p2);
A2 = B.constant(p1) *
(B.constant(2.0f) / ((B.constant(-2.0f) * p2 * B).exp() + (real)1.0)
- (real)1.0);
(B.constant(2.0f) / ((B.constant(-2.0f) * p2 * B).exp() + (real)1.0) -
(real)1.0);
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorScaledTanhDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
real p1 = 2.5;
real p2 = 3.1;
......@@ -681,7 +688,7 @@ void testTensorScaledTanhDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testBinaryMathOp(Tensor& A1, Tensor& A2, Tensor& B) {
testTensorTanhDerivative(A1, A2, B);
testTensorScaledTanhDerivative(A1, A2, B);
......@@ -708,21 +715,21 @@ TEST(Binary, MathOp) {
#endif
}
template<typename Tensor>
template <typename Tensor>
void testTensorRelu(Tensor& A1, Tensor& A2, Tensor& B) {
B.relu(A1); // b = a > 0.0f ? a : 0.0f
A2 = (B > (real)0.0f).condition(B, (real)0.0f);
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorReluDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
A1.reluDerivative(B); // a *= (b > 0.0f ? 1.0f : 0.0f)
A2 *= (B > (real)0.0).condition((real)1.0, (real)0.0);
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorBrelu(Tensor& A1, Tensor& A2, Tensor& B) {
/*
* b = a > p1 ? a : p1
......@@ -736,7 +743,7 @@ void testTensorBrelu(Tensor& A1, Tensor& A2, Tensor& B) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorBreluDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
SetTensorValue(B, 32.0f);
/*
......@@ -748,15 +755,15 @@ void testTensorBreluDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorAbsDerivative(Tensor& A1, Tensor& A2, Tensor& B) {
A1.absDerivative(B); // a = (b > 0) ? a : (b < 0) ? -a : 0
A2 = (B > (real)0.0f).condition(A2,
(B < (real)0.0f).condition(-A2, (real)0.0f));
A2 = (B > (real)0.0f)
.condition(A2, (B < (real)0.0f).condition(-A2, (real)0.0f));
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorIsEqualTo(Tensor& A1, Tensor& A2, Tensor& B) {
real p = 0.613;
SetTensorValue(B, p);
......@@ -765,7 +772,7 @@ void testTensorIsEqualTo(Tensor& A1, Tensor& A2, Tensor& B) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorapplyL1(Tensor& A1, Tensor& A2, Tensor& B) {
/**
* T lambda = p * b;
......@@ -778,12 +785,13 @@ void testTensorapplyL1(Tensor& A1, Tensor& A2, Tensor& B) {
real decayRate = 0.6f;
A1.applyL1(B, learningRate, decayRate);
auto lambda = B.constant(learningRate * decayRate) * B;
A2 = (A2 > lambda).condition(
(A2 - lambda), (A2 < -lambda).condition((A2 + lambda), (real)0.0f));
A2 = (A2 > lambda)
.condition((A2 - lambda),
(A2 < -lambda).condition((A2 + lambda), (real)0.0f));
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testBinaryCompareOp(Tensor& A1, Tensor& A2, Tensor& B) {
B.subScalar(0.5f);
SetTensorValue(B, 0.0f);
......@@ -807,7 +815,7 @@ TEST(Binary, CompareOp) {
#endif
}
template<typename Tensor>
template <typename Tensor>
void testTensorAdd(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
A1.add(B, C); // a = b + c
A2 = B + C;
......@@ -833,7 +841,7 @@ void testTensorAdd(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSub(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
A1.sub(B, C); // a = b - c
A2 = B - C;
......@@ -846,7 +854,7 @@ void testTensorSub(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorMul(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
A1.dotMul(B, C); // a = b * c
A2 = B * C;
......@@ -892,7 +900,7 @@ void testTensorMul(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorDiv(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
A1.dotDiv(B, C); // a = (b == 0.0) ? 0.0 : b / c
A2 = (B == (real)0.0).condition((real)0.0, B / C);
......@@ -905,7 +913,7 @@ void testTensorDiv(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorReciprocal(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
real p1 = 1.5;
real p2 = 2.5;
......@@ -915,14 +923,14 @@ void testTensorReciprocal(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSoftCrossEntropy(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
A1.softCrossEntropy(B, C); // a = -c * log(b) - (1 - c) * log(1 - b)
A2 = -C * B.log() - (C.constant(1.0f) - C) * (B.constant(1.0f) - B).log();
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorSoftCrossEntropyBp(Tensor& A1,
Tensor& A2,
Tensor& B,
......@@ -932,7 +940,7 @@ void testTensorSoftCrossEntropyBp(Tensor& A1,
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTernaryBaseOp(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
testTensorAdd(A1, A2, B, C);
testTensorSub(A1, A2, B, C);
......@@ -952,30 +960,30 @@ TEST(Ternary, BaseOp) {
#endif
}
template<typename Tensor>
template <typename Tensor>
void testTensorBinaryLabelCrossEntropy(Tensor& A1,
Tensor& A2,
Tensor& B,
Tensor& C) {
A1.binaryLabelCrossEntropy(B, C); // a = c > 0.5 ? -log(b) : -log(1.0 - b)
A2 = (C > (real)0.5).condition(
-(B.log()), -((B.constant(1.0f) - B).log()));
A2 = (C > (real)0.5).condition(-(B.log()), -((B.constant(1.0f) - B).log()));
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorBinaryLabelCrossEntropyBp(Tensor& A1,
Tensor& A2,
Tensor& B,
Tensor& C) {
// a += c > 0.5 ? -1.0 / b : 1.0 / (1.0 - b)
A1.binaryLabelCrossEntropyBp(B, C);
A2 += (C > (real)0.5).condition(
(B.constant(-1.0f) / B), (B.constant(1.0f) - B).reciprocal());
A2 += (C > (real)0.5)
.condition((B.constant(-1.0f) / B),
(B.constant(1.0f) - B).reciprocal());
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorLogisticRegressionLoss(Tensor& A1,
Tensor& A2,
Tensor& B,
......@@ -991,13 +999,14 @@ void testTensorLogisticRegressionLoss(Tensor& A1,
*/
A1.logisticRegressionLoss(B, C);
real THRESHOLD = 40.0;
auto tmp = (B > THRESHOLD).condition(
THRESHOLD, (B < -THRESHOLD).condition(-THRESHOLD, B));
auto tmp =
(B > THRESHOLD)
.condition(THRESHOLD, (B < -THRESHOLD).condition(-THRESHOLD, B));
A2 = (C.constant(1.0f) + tmp.exp()).log() - C * tmp;
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorLogisticRegressionLossBp(Tensor& A1,
Tensor& A2,
Tensor& B,
......@@ -1013,28 +1022,29 @@ void testTensorLogisticRegressionLossBp(Tensor& A1,
*/
A1.logisticRegressionLossBp(B, C);
real THRESHOLD = 40.0;
auto tmp = (B > THRESHOLD).condition(
THRESHOLD, (B < -THRESHOLD).condition(-THRESHOLD, B));
auto tmp =
(B > THRESHOLD)
.condition(THRESHOLD, (B < -THRESHOLD).condition(-THRESHOLD, B));
auto tmp2 = tmp.exp();
A2 = tmp2 / (C.constant(1.0) + tmp2) - C;
TensorCheckErr(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorBiggerThan(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
A1.biggerThan(B, C); // a = (b > c) ? 1.0f : 0.0f
A2 = (B > C).condition((real)1.0f, (real)0.0f);
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTensorMax(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
A1.max2(B, C); // a = (b > c) ? b : c
A2 = (B > C).condition(B, C);
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
template <typename Tensor>
void testTernaryCompareOp(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) {
testTensorBinaryLabelCrossEntropyBp(A1, A2, B, C);
testTensorBinaryLabelCrossEntropy(A1, A2, B, C);
......@@ -1053,12 +1063,9 @@ TEST(Ternary, CompareOp) {
#endif
}
template<typename Tensor>
void testQuaternaryAdd(Tensor& A1,
Tensor& A2,
Tensor& B,
Tensor& C,
Tensor& D) {
template <typename Tensor>
void testQuaternaryAdd(
Tensor& A1, Tensor& A2, Tensor& B, Tensor& C, Tensor& D) {
// A1.add3(B, C, D, 1.5f, 2.5f, 3.5f); // a = p1 * b + p2 * c + p3 * d
// A2 = B * 1.5f + C * 2.5f + D * 3.5f;
// TensorCheckEqual(A1, A2);
......@@ -1084,25 +1091,19 @@ TEST(Quaternary, BaseOp) {
#endif
}
template<typename Tensor>
void testTensorBiggerThan(Tensor& A1,
Tensor& A2,
Tensor& B,
Tensor& C,
Tensor& D) {
template <typename Tensor>
void testTensorBiggerThan(
Tensor& A1, Tensor& A2, Tensor& B, Tensor& C, Tensor& D) {
// a = ((b > c && d > 0.5f) || (b < c && d < 0.5f)) ? 1.0f : 0.0f);
A1.biggerThan(B, C, D);
A2 = ((B > C && D > (real)0.5)
|| (B < C && D < (real)0.5)).condition((real)1.0, (real)0.0);
A2 = ((B > C && D > (real)0.5) || (B < C && D < (real)0.5))
.condition((real)1.0, (real)0.0);
TensorCheckEqual(A1, A2);
}
template<typename Tensor>
void testTensorRankLoss(Tensor& A1,
Tensor& A2,
Tensor& B,
Tensor& C,
Tensor& D) {
template <typename Tensor>
void testTensorRankLoss(
Tensor& A1, Tensor& A2, Tensor& B, Tensor& C, Tensor& D) {
/**
* const T THRESHOLD = 40.0; a = b - c;
* a = (a > THRESHOLD)
......@@ -1114,19 +1115,17 @@ void testTensorRankLoss(Tensor& A1,
real THRESHOLD = 40.0;
auto tmp = B - C;
auto tmp2 = (tmp > THRESHOLD).condition(
THRESHOLD, (tmp < -THRESHOLD).condition(-THRESHOLD, tmp));
auto tmp2 =
(tmp > THRESHOLD)
.condition(THRESHOLD, (tmp < -THRESHOLD).condition(-THRESHOLD, tmp));
A2 = (D.constant(1.0f) + tmp2.exp()).log() - tmp2 * D;
TensorCheckErr(A1, A2);
}
template<typename Tensor>
void testTensorRankLossBp(Tensor& A1,
Tensor& A2,
Tensor& B,
Tensor& C,
Tensor& D) {
template <typename Tensor>
void testTensorRankLossBp(
Tensor& A1, Tensor& A2, Tensor& B, Tensor& C, Tensor& D) {
/**
* const T THRESHOLD = 40.0; a = b - c;
* a = (a > THRESHOLD)
......@@ -1137,20 +1136,18 @@ void testTensorRankLossBp(Tensor& A1,
A1.rankLossBp(B, C, D);
real THRESHOLD = 40.0;
auto tmp = B - C;
auto tmp2 = (tmp > THRESHOLD).condition(
THRESHOLD, (tmp < -THRESHOLD).condition(-THRESHOLD, tmp));
auto tmp2 =
(tmp > THRESHOLD)
.condition(THRESHOLD, (tmp < -THRESHOLD).condition(-THRESHOLD, tmp));
auto tmp3 = tmp2.exp();
A2 = tmp3 / (D.constant(1.0f) + tmp3) - D;
TensorCheckErr(A1, A2);
}
template<typename Tensor>
void testQuaternaryCompareOp(Tensor& A1,
Tensor& A2,
Tensor& B,
Tensor& C,
Tensor& D) {
template <typename Tensor>
void testQuaternaryCompareOp(
Tensor& A1, Tensor& A2, Tensor& B, Tensor& C, Tensor& D) {
testTensorBiggerThan(A1, A2, B, C, D);
testTensorRankLoss(A1, A2, B, C, D);
testTensorRankLossBp(A1, A2, B, C, D);
......
paddle/math/tests/test_lazyAssign.cu
浏览文件 @ 75185d82
......@@ -13,10 +13,10 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "PerfUtils.h"
#include "TensorCheck.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/TensorAssign.h"
#include "TensorCheck.h"
#include "PerfUtils.h"
using paddle::BaseMatrix;
using paddle::CpuMatrix;
......@@ -27,14 +27,28 @@ using autotest::TensorCheckErr;
typedef std::function<void(int height, int width)> testMatrixFunc;
void testMatrixCase(testMatrixFunc matrixFunc) {
for (auto height : {1}) {
for (auto width : {1, 32, 64, 128, 512, 1024, 4096, 32768, 65536, 131072,
262144, 524288, 1048576, 2097152, 4194304, 8388608}) {
for (auto width : {1,
32,
64,
128,
512,
1024,
4096,
32768,
65536,
131072,
262144,
524288,
1048576,
2097152,
4194304,
8388608}) {
matrixFunc(height, width);
}
}
}
template<typename Tensor>
template <typename Tensor>
void testLazyAssign(int height, int width) {
Tensor A1(height, width);
Tensor A2(height, width);
......@@ -49,40 +63,39 @@ void testLazyAssign(int height, int width) {
EXPRESSION_PERFORMANCE(A1 = B + C; A1 = A1 * D;);
EXPRESSION_PERFORMANCE(
auto expr1 = A2.lazyAssign(B + C);
auto expr2 = A2.lazyAssign(A2 * D);
AssignEvaluate(expr1, expr2););
EXPRESSION_PERFORMANCE(auto expr1 = A2.lazyAssign(B + C);
auto expr2 = A2.lazyAssign(A2 * D);
AssignEvaluate(expr1, expr2););
TensorCheckErr(A1, A2);
}
TEST(lazyAssign, CPU) {
testMatrixCase(testLazyAssign<CpuMatrix>);
}
TEST(lazyAssign, CPU) { testMatrixCase(testLazyAssign<CpuMatrix>); }
#ifndef PADDLE_ONLY_CPU
TEST(lazyAssign, GPU) {
testMatrixCase(testLazyAssign<GpuMatrix>);
}
TEST(lazyAssign, GPU) { testMatrixCase(testLazyAssign<GpuMatrix>); }
#endif
template<typename Tensor>
void sgdUpdateTensor(Tensor& A, Tensor& B, Tensor& C, Tensor& D,
real p1, real p2, real p3) {
template <typename Tensor>
void sgdUpdateTensor(
Tensor& A, Tensor& B, Tensor& C, Tensor& D, real p1, real p2, real p3) {
C = C * p2 - D * (B + A * p3) * p1;
A += C;
}
void sgdUpdateLazyAssign(BaseMatrix& A, BaseMatrix& B,
BaseMatrix& C, BaseMatrix& D,
real p1, real p2, real p3) {
void sgdUpdateLazyAssign(BaseMatrix& A,
BaseMatrix& B,
BaseMatrix& C,
BaseMatrix& D,
real p1,
real p2,
real p3) {
auto expr1 = C.lazyAssign(C * p2 - D * (B + A * p3) * p1);
auto expr2 = A.lazyAssign(A + C);
AssignEvaluate(expr1, expr2);
}
template<typename Tensor>
template <typename Tensor>
void testSgdUpdate(int height, int width) {
Tensor A1(height, width);
Tensor A2(height, width);
......@@ -113,16 +126,13 @@ void testSgdUpdate(int height, int width) {
* a = a + c;
*/
// BaseMatrix API
EXPRESSION_PERFORMANCE(
A1.sgdUpdate(B, C1, D, p1, p2, p3););
EXPRESSION_PERFORMANCE(A1.sgdUpdate(B, C1, D, p1, p2, p3););
// Tensor expression
EXPRESSION_PERFORMANCE(
sgdUpdateTensor(A2, B, C2, D, p1, p2, p3));
EXPRESSION_PERFORMANCE(sgdUpdateTensor(A2, B, C2, D, p1, p2, p3));
// lazyAssign
EXPRESSION_PERFORMANCE(
sgdUpdateLazyAssign(A3, B, C3, D, p1, p2, p3));
EXPRESSION_PERFORMANCE(sgdUpdateLazyAssign(A3, B, C3, D, p1, p2, p3));
TensorCheckErr(A1, A2);
TensorCheckErr(A1, A3);
......@@ -130,12 +140,8 @@ void testSgdUpdate(int height, int width) {
TensorCheckErr(C1, C3);
}
TEST(sgdUpdate, CPU) {
testMatrixCase(testSgdUpdate<CpuMatrix>);
}
TEST(sgdUpdate, CPU) { testMatrixCase(testSgdUpdate<CpuMatrix>); }
#ifndef PADDLE_ONLY_CPU
TEST(sgdUpdate, GPU) {
testMatrixCase(testSgdUpdate<GpuMatrix>);
}
TEST(sgdUpdate, GPU) { testMatrixCase(testSgdUpdate<GpuMatrix>); }
#endif
paddle/trainer/tests/pydata_provider_wrapper_dir/test_pydata_provider_wrapper.protolist
浏览文件 @ 75185d82
./trainer/tests/pydata_provider_wrapper_dir/test_pydata_provider_wrapper.proto
./trainer/tests/pydata_provider_wrapper_dir/test_pydata_provider_wrapper.proto_data
proto/DataConfig.proto
浏览文件 @ 75185d82
......@@ -15,14 +15,13 @@ syntax = "proto2";
package paddle;
message FileGroupConf {
optional uint32 queue_capacity = 1 [default = 1];
optional uint32 queue_capacity = 1 [ default = 1 ];
// how many files to load for a load file thread
optional int32 load_file_count = 2 [default = 1];
optional int32 load_file_count = 2 [ default = 1 ];
// how many threads to load files
// Setting to be 5~10 is appropriate when loading files by hadoop vfs
optional int32 load_thread_num = 3 [default = 1];
optional int32 load_thread_num = 3 [ default = 1 ];
};
message DataConfig {
......@@ -32,26 +31,28 @@ message DataConfig {
// name of a text file which contains a list of file names at each line
optional string files = 3;
optional int32 feat_dim = 4;//feature dimension of one frame
repeated int32 slot_dims = 5;//feature slot dims
optional int32 context_len = 6;//max neibour frame numbers
optional uint64 buffer_capacity = 7;//the number of samples
optional int32 feat_dim = 4; // feature dimension of one frame
repeated int32 slot_dims = 5; // feature slot dims
optional int32 context_len = 6; // max neibour frame numbers
optional uint64 buffer_capacity = 7; // the number of samples
//part of data used in training
//if not -1, part of train data is used in training
optional int64 train_sample_num = 8 [default = -1];
// part of data used in training
// if not -1, part of train data is used in training
optional int64 train_sample_num = 8 [ default = -1 ];
//The number of documents processed once
optional int32 file_load_num = 9 [default = -1];
optional bool async_load_data = 12 [default = false];
// The number of documents processed once
optional int32 file_load_num = 9 [ default = -1 ];
optional bool async_load_data = 12 [ default = false ];
/// Note the field number 10, 11 and 13 have been deprecated.
optional bool for_test = 14 [default = false]; // whether this data is for test
optional bool for_test = 14
[ default = false ]; // whether this data is for test
optional FileGroupConf file_group_conf = 15;
repeated int32 float_slot_dims = 16;
/// Note the field number 17, 18 and 19 have been deprecated.
// a list of values which will be used to create additional one dimensional float
// a list of values which will be used to create additional one dimensional
// float
// values slots. These one dimensional slots can be used as the weight input
// for cost layers.
// Currently this is only supported by ProtoDataProvider.
......@@ -65,21 +66,21 @@ message DataConfig {
// for MultiDataProvider
repeated DataConfig sub_data_configs = 24; // sub dataproviders
/*
* the ratio of each sub dataproviders:
* e.g. sub dataprovider A's ratio is 1, B's ratio is 9, batch_size is 100,
* then each mini-batch is combined by 10 instance from A and 90 instances
* from B.
*/
/*
* the ratio of each sub dataproviders:
* e.g. sub dataprovider A's ratio is 1, B's ratio is 9, batch_size is 100,
* then each mini-batch is combined by 10 instance from A and 90 instances
* from B.
*/
optional int32 data_ratio = 25;
/*
* if one of the sub dataproviders is running out of data, then
* (1) it is "main data", then finish current pass.
* (2) it is not "main data", then reset it, and try getNextBatch again.
*/
optional bool is_main_data = 26 [default = true];
optional bool is_main_data = 26 [ default = true ];
// the usage ratio of instances. Setting to 1.0 means the use of all instances.
optional double usage_ratio = 27 [default = 1.0];
// the usage ratio of instances. Setting to 1.0 means the use of all
// instances.
optional double usage_ratio = 27 [ default = 1.0 ];
};
proto/DataFormat.proto
浏览文件 @ 75185d82
......@@ -17,27 +17,32 @@ package paddle;
/*
If values is not empty and ids is empty, this is a dense vector.
If values is not empty and ids is not empty, this is a sparse vector. The position of each value
If values is not empty and ids is not empty, this is a sparse vector. The
position of each value
is specified by ids.
If values is empty and ids is not empty, this is a sparse vector whose non-zero values are 1.
If values is empty and ids is not empty, this is a sparse vector whose non-zero
values are 1.
The position of each 1 is specified by ids.
*/
message VectorSlot {
repeated float values = 1 [packed = true];
repeated uint32 ids = 2 [packed = true];
repeated float values = 1 [ packed = true ];
repeated uint32 ids = 2 [ packed = true ];
/* For multidimensional data, for example "image width height depth" */
repeated uint32 dims = 3 [packed = true];
repeated string strs = 4;
repeated uint32 dims = 3 [ packed = true ];
repeated string strs = 4;
};
/*
SubseqSlot use to record whether VectorSlot or any other slot in future has subseq.
If not all VectorSlot have subseq, we only store the one who has subseq, and use *slot_id* to record it.
One vector_slots has one sequence, and it may have N subseq, thus the number of *lens* will be N too.
SubseqSlot use to record whether VectorSlot or any other slot in future has
subseq.
If not all VectorSlot have subseq, we only store the one who has subseq, and
use *slot_id* to record it.
One vector_slots has one sequence, and it may have N subseq, thus the number of
*lens* will be N too.
*/
message SubseqSlot {
required uint32 slot_id = 1; //the id of slot who has subseq
repeated uint32 lens = 2; // lengths of sub-sequence in the slot
required uint32 slot_id = 1; // the id of slot who has subseq
repeated uint32 lens = 2; // lengths of sub-sequence in the slot
};
message SlotDef {
......@@ -45,13 +50,14 @@ message SlotDef {
VECTOR_DENSE = 0;
VECTOR_SPARSE_NON_VALUE = 1;
VECTOR_SPARSE_VALUE = 2;
INDEX = 3; // This can be used as label, or word id, etc.
INDEX = 3; // This can be used as label, or word id, etc.
VAR_MDIM_DENSE = 4;
VAR_MDIM_INDEX = 5;
STRING = 6;
}
required SlotType type = 1;
required uint32 dim = 2; // For INDEX slots, this means the maximal index plus 1.
required uint32 dim =
2; // For INDEX slots, this means the maximal index plus 1.
};
message DataHeader {
......@@ -60,11 +66,11 @@ message DataHeader {
};
message DataSample {
optional bool is_beginning = 1 [default = true]; // is the beginning of a sequence
optional bool is_beginning = 1
[ default = true ]; // is the beginning of a sequence
repeated VectorSlot vector_slots = 2;
repeated uint32 id_slots = 3 [packed = true];
repeated uint32 id_slots = 3 [ packed = true ];
/* use ids of VectorSlot */
repeated VectorSlot var_id_slots = 4;
repeated SubseqSlot subseq_slots = 5;
};
proto/ModelConfig.proto
浏览文件 @ 75185d82
......@@ -21,7 +21,6 @@ package paddle;
* Various structs for the configuration of a neural network
*/
message ExternalConfig {
repeated string layer_names = 1;
repeated string input_layer_names = 2;
......@@ -68,7 +67,7 @@ message ConvConfig {
required uint32 img_size = 8;
// caffe mode for output size coherence
required bool caffe_mode = 9 [default = true];
required bool caffe_mode = 9 [ default = true ];
// if filter_size_y is set , this convolutional layer will use
// filters of size filter_size * filter_size_y pixels.
......@@ -99,7 +98,7 @@ message PoolConfig {
optional uint32 start = 4;
// Defines the stride size between successive pooling squares.
required uint32 stride = 5 [default = 1];
required uint32 stride = 5 [ default = 1 ];
// The size of output feature map.
required uint32 output_x = 6;
......@@ -109,7 +108,7 @@ message PoolConfig {
// padding = 4, instructs the net to implicitly
// pad the images with a 4-pixel border of zeros.
optional uint32 padding = 8 [default = 0];
optional uint32 padding = 8 [ default = 0 ];
// if not set, use size_x
optional uint32 size_y = 9;
......@@ -194,9 +193,7 @@ message MaxOutConfig {
required uint32 groups = 2;
}
message RowConvConfig {
required uint32 context_length = 1;
}
message RowConvConfig { required uint32 context_length = 1; }
message SliceConfig {
required uint32 start = 1;
......@@ -212,14 +209,14 @@ message ProjectionConfig {
// For ShiftProjection
optional int32 context_start = 5;
optional int32 context_length = 6;
optional bool trainable_padding = 7 [default = false];
optional bool trainable_padding = 7 [ default = false ];
// For convolution
optional ConvConfig conv_conf = 8;
optional int32 num_filters = 9;
// For IdentityOffsetProjection
optional uint64 offset = 11 [default = 0];
optional uint64 offset = 11 [ default = 0 ];
// For pool
optional PoolConfig pool_conf = 12;
......@@ -236,7 +233,7 @@ message OperatorConfig {
required uint64 output_size = 4;
// For DotMulOperator
optional double dotmul_scale = 5 [default = 1.0];
optional double dotmul_scale = 5 [ default = 1.0 ];
// For ConvOperator
optional ConvConfig conv_conf = 6;
......@@ -282,8 +279,8 @@ message MultiBoxLossConfig {
required float neg_overlap = 4;
required uint32 background_id = 5;
required uint32 input_num = 6;
optional uint32 height = 7 [default = 1];
optional uint32 width = 8 [default = 1];
optional uint32 height = 7 [ default = 1 ];
optional uint32 width = 8 [ default = 1 ];
}
message DetectionOutputConfig {
......@@ -294,8 +291,8 @@ message DetectionOutputConfig {
required uint32 input_num = 5;
required uint32 keep_top_k = 6;
required float confidence_threshold = 7;
optional uint32 height = 8 [default = 1];
optional uint32 width = 9 [default = 1];
optional uint32 height = 8 [ default = 1 ];
optional uint32 width = 9 [ default = 1 ];
}
message ClipConfig {
......@@ -331,7 +328,7 @@ message LayerConfig {
required string name = 1;
required string type = 2;
optional uint64 size = 3;
//optional ActivationConfig activation = 4;
// optional ActivationConfig activation = 4;
optional string active_type = 4;
repeated LayerInputConfig inputs = 5;
optional string bias_parameter_name = 6;
......@@ -344,7 +341,7 @@ message LayerConfig {
// (which is how convnets are usually trained). Setting this to
// false will untie the biases, yielding a separate bias for
// every location at which the filter is applied.
optional bool shared_biases = 8 [default = false];
optional bool shared_biases = 8 [ default = false ];
// Valid values are ones that divide the area of the output
// grid in this convolutional layer. For example if this layer
......@@ -362,33 +359,35 @@ message LayerConfig {
// the gpu device which the Layer's data in.
// Only used by ParallelNeuralNetork. Ignored otherwise.
optional int32 device = 12 [default = -1];
optional int32 device = 12 [ default = -1 ];
// for recurrent layer. If true, the recurrence runs from the end to the beginning.
optional bool reversed = 13 [default = false];
// for recurrent layer. If true, the recurrence runs from the end to the
// beginning.
optional bool reversed = 13 [ default = false ];
// for lstmemory layer. Different types of nodes have different activation type.
optional string active_gate_type = 14;
// for lstmemory layer. Different types of nodes have different activation
// type.
optional string active_gate_type = 14;
optional string active_state_type = 15;
// For NCELayer
// The number of random negative labels for each sample
optional int32 num_neg_samples = 16 [default = 10];
optional int32 num_neg_samples = 16 [ default = 10 ];
// For NCELayer
// The distribution for generating the random negative labels.
// A uniform distribution will be used if not provided
repeated double neg_sampling_dist = 17 [packed = true];
repeated double neg_sampling_dist = 17 [ packed = true ];
// For MaxLayer
// default: output VALUE of MaxLayer. set this flag to true for output INDEX
// INDEX will be put in Argument::value as double values.
optional bool output_max_index = 19 [default = false];
optional bool output_max_index = 19 [ default = false ];
/// The filed number 20 have been deprecated.
// For self-normalized estimation
optional double softmax_selfnorm_alpha = 21 [default = 0.1];
optional double softmax_selfnorm_alpha = 21 [ default = 0.1 ];
/// The filed numbers 22 and 23 have been deprecated.
......@@ -399,14 +398,14 @@ message LayerConfig {
optional bool norm_by_times = 25;
// for CostLayers
optional double coeff = 26 [default = 1.0];
optional double coeff = 26 [ default = 1.0 ];
// for AverageLayer
// can be set to: 'average', 'sum' or 'squarerootn'
optional string average_strategy = 27;
// for error clipping
optional double error_clipping_threshold = 28 [default = 0.0];
optional double error_clipping_threshold = 28 [ default = 0.0 ];
// for operators used by mixed layer
repeated OperatorConfig operator_confs = 29;
......@@ -434,43 +433,44 @@ message LayerConfig {
optional uint32 beam_size = 39;
// for seqlastins layer, whether select first instead last
optional bool select_first = 40 [default = false];
optional bool select_first = 40 [ default = false ];
// for seqlastins layer, AverageLayer, MaxLayer and ExpandLayer
// can be set to: 'non-seq','seq'
optional string trans_type = 41 [default = 'non-seq'];
optional string trans_type = 41 [ default = 'non-seq' ];
// to indicate whether selective_fc layer
// is used in sequence generation or not
optional bool selective_fc_pass_generation = 42 [default = false];
optional bool selective_fc_pass_generation = 42 [ default = false ];
// to indicate whether selective_fc layer take its last input to
// selected several columns and only compute the multiplications
// between the input matrices and the selected columns of
// the parameter matrices of this layer.
// if set false, selective_fc degrades into fc.
optional bool has_selected_colums = 43 [default = true];
optional bool has_selected_colums = 43 [ default = true ];
// this parameter is for speed consideration.
// if number of the selected columns is less than
// sample number * selective_fc output size * selective_fc_mull_mull_ratio
// sparse multiplication is used, otherwise, using full multiplication.
optional double selective_fc_full_mul_ratio = 44 [default = 0.02];
optional double selective_fc_full_mul_ratio = 44 [ default = 0.02 ];
// to indicate how many threads selective_fc use to to accelate
// the plain_mul period
// leave empty or set to 0 to disable multi-thread accleleration
optional uint32 selective_fc_parallel_plain_mul_thread_num = 45 [default = 0];
optional uint32 selective_fc_parallel_plain_mul_thread_num = 45
[ default = 0 ];
// for batch normalization layer
// if set use_global_stats true, will use the loaded mean and variance.
optional bool use_global_stats = 46;
// use to compute moving mean and variance.
optional double moving_average_fraction = 47 [default = 0.9];
optional double moving_average_fraction = 47 [ default = 0.9 ];
// bias size
optional uint32 bias_size = 48 [default = 0];
optional uint32 bias_size = 48 [ default = 0 ];
// this parameter can be used as a user-defined parameter when necessary,
// without changing the proto file.
......@@ -485,18 +485,17 @@ message LayerConfig {
optional uint64 width = 51;
// blank label used in ctc loss
optional uint32 blank = 52 [default = 0];
optional uint32 blank = 52 [ default = 0 ];
// stride parameter for seqlastins layer, AverageLayer, MaxLayer, which
// controls the scope of pooling operation. can be set > 0.
// leave empty or set to -1 to disable this stride pooling.
optional int32 seq_pool_stride = 53 [default = -1];
optional int32 seq_pool_stride = 53 [ default = -1 ];
// for crop layer
optional int32 axis = 54 [default = 2];
optional int32 axis = 54 [ default = 2 ];
repeated uint32 offset = 55;
repeated uint32 shape = 56;
}
message EvaluatorConfig {
......@@ -512,9 +511,9 @@ message EvaluatorConfig {
// Used by PrecisionRecallEvaluator and ClassificationErrorEvaluator
// For multi binary labels: true if output > classification_threshold
optional double classification_threshold = 6 [default = 0.5];
optional double classification_threshold = 6 [ default = 0.5 ];
// The positive label. -1 means average precision and recall
optional int32 positive_label = 7 [default = -1];
optional int32 positive_label = 7 [ default = -1 ];
// load dict from this file
optional string dict_file = 8;
......@@ -523,10 +522,10 @@ message EvaluatorConfig {
optional string result_file = 9;
// top # results for max id printer
optional int32 num_results = 10 [default = 1];
optional int32 num_results = 10 [ default = 1 ];
// whether to delimit the sequence in the seq_text_printer
optional bool delimited = 11 [default = true];
optional bool delimited = 11 [ default = true ];
// Used by ChunkEvaluator
// chunk of these types are not counted
......@@ -534,23 +533,23 @@ message EvaluatorConfig {
// Used by ClassificationErrorEvaluator
// top # classification error
optional int32 top_k = 13 [default = 1];
optional int32 top_k = 13 [ default = 1 ];
// Used by DetectionMAPEvaluator
optional double overlap_threshold = 14 [default = 0.5];
optional double overlap_threshold = 14 [ default = 0.5 ];
optional int32 background_id = 15 [default = 0];
optional int32 background_id = 15 [ default = 0 ];
optional bool evaluate_difficult = 16 [default = false];
optional bool evaluate_difficult = 16 [ default = false ];
optional string ap_type = 17 [default = "11point"];
optional string ap_type = 17 [ default = "11point" ];
}
message LinkConfig {
required string layer_name = 1;
required string link_name = 2;
// If true, this link has sub-sequence
optional bool has_subseq = 3 [default = false];
optional bool has_subseq = 3 [ default = false ];
}
message MemoryConfig {
......@@ -563,18 +562,18 @@ message MemoryConfig {
optional uint32 boot_with_const_id = 7;
// memory is a sequence, initailized by a sequence boot layer
optional bool is_sequence = 6 [default = false];
optional bool is_sequence = 6 [ default = false ];
}
message GeneratorConfig {
required uint32 max_num_frames = 1;
required string eos_layer_name = 2;
optional int32 num_results_per_sample = 3 [default = 1];
optional int32 num_results_per_sample = 3 [ default = 1 ];
// for beam search
optional int32 beam_size = 4 [default = 1];
optional int32 beam_size = 4 [ default = 1 ];
optional bool log_prob = 5 [default = true];
optional bool log_prob = 5 [ default = true ];
}
message SubModelConfig {
......@@ -584,10 +583,10 @@ message SubModelConfig {
repeated string output_layer_names = 4;
repeated string evaluator_names = 5;
optional bool is_recurrent_layer_group = 6 [default = false];
optional bool is_recurrent_layer_group = 6 [ default = false ];
// If true, the recurrence runs from the end to the beginning.
optional bool reversed = 7 [default = false];
optional bool reversed = 7 [ default = false ];
// name and link name of memory
repeated MemoryConfig memories = 8;
......@@ -601,14 +600,15 @@ message SubModelConfig {
optional GeneratorConfig generator = 11;
// the id of inlink which share info with outlinks, used in recurrent layer group
// the id of inlink which share info with outlinks, used in recurrent layer
// group
optional int32 target_inlinkid = 12;
}
message ModelConfig {
// type of the model.
// Currently, "nn", "recurrent_nn" and "recursive_nn" are supported
required string type = 1 [default = "nn"];
required string type = 1 [ default = "nn" ];
// layers should be ordered in such a way that the forward propagation
// can be correctly executed by going from the first layer to the last layer
......
proto/OptimizerConfig.proto
浏览文件 @ 75185d82
syntax = "proto2";
option optimize_for = LITE_RUNTIME;
package paddle;
......@@ -9,13 +9,11 @@ message SGDConfig {
// momentum: float >= 0. Parameter updates momentum.
// decay: float >= 0. Learning rate decay over each update.
// nesterov: boolean. Whether to apply Nesterov momentum.
optional double momentum = 21 [default = 0.0];
optional double decay = 23 [default = 0.0];
optional bool nesterov =24 [default = false];
optional double momentum = 21 [ default = 0.0 ];
optional double decay = 23 [ default = 0.0 ];
optional bool nesterov = 24 [ default = false ];
}
message AdadeltaConfig {
// Adadelta
// It is recommended to leave it at the default value.
......@@ -23,21 +21,23 @@ message AdadeltaConfig {
// epsilon: float >= 0. Fuzz factor.
// decay: float >= 0. Learning rate decay over each update.
// reference : [Adadelta - an adaptive learning rate method](http://arxiv.org/abs/1212.5701)
optional double rho = 33 [default = 0.90];
optional double epsilon = 31 [default = 1e-5];
optional double decay = 32 [default = 0.0];
// reference : [Adadelta - an adaptive learning rate
// method](http://arxiv.org/abs/1212.5701)
optional double rho = 33 [ default = 0.90 ];
optional double epsilon = 31 [ default = 1e-5 ];
optional double decay = 32 [ default = 0.0 ];
}
message AdagradConfig {
// Adagrad
// epsilon: float >= 0.
// decay: float >= 0. Learning rate decay over each update.
// Adagrad
// epsilon: float >= 0.
// decay: float >= 0. Learning rate decay over each update.
// reference : [Adaptive Subgradient Methods for Online Learning and Stochastic Optimization](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
optional double epsilon = 41 [default = 1e-5];
optional double decay = 42 [default = 0.0];
// reference : [Adaptive Subgradient Methods for Online Learning and
// Stochastic
// Optimization](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
optional double epsilon = 41 [ default = 1e-5 ];
optional double decay = 42 [ default = 0.0 ];
}
message AdamConfig {
......@@ -46,7 +46,8 @@ message AdamConfig {
// beta_2: float, 0 < beta < 1. Generally close to 1.
// epsilon: float >= 0. Fuzz factor.
// decay: float >= 0. Learning rate decay over each update.
// reference : [Adam - A Method for Stochastic Optimization](http://arxiv.org/abs/1412.6980v8)
// reference : [Adam - A Method for Stochastic
// Optimization](http://arxiv.org/abs/1412.6980v8)
optional double beta_1 = 41;
optional double beta_2 = 42;
optional double epsilon = 43;
......@@ -55,32 +56,32 @@ message AdamConfig {
message ConstLrConfig {
// learninRate Policy
optional double learning_rate = 1 [default = 1.0];
optional double learning_rate = 1 [ default = 1.0 ];
}
message LinearLrConfig {
// learninRate Policy
optional double learning_rate = 1 [default = 1.0];
optional double learning_rate = 1 [ default = 1.0 ];
optional double lr_decay_a = 2;
optional double lr_decay_b = 3;
}
message TensorProto {
enum DataType {
PADDLE_ELEMENT_TYPE_INT32 = 0;
PADDLE_ELEMENT_TYPE_UINT32 = 1;
PADDLE_ELEMENT_TYPE_INT64 = 2;
PADDLE_ELEMENT_TYPE_UINT64 = 3;
PADDLE_ELEMENT_TYPE_FLOAT32 = 4;
PADDLE_ELEMENT_TYPE_FLOAT64 = 5;
}
enum DataType {
PADDLE_ELEMENT_TYPE_INT32 = 0;
PADDLE_ELEMENT_TYPE_UINT32 = 1;
PADDLE_ELEMENT_TYPE_INT64 = 2;
PADDLE_ELEMENT_TYPE_UINT64 = 3;
PADDLE_ELEMENT_TYPE_FLOAT32 = 4;
PADDLE_ELEMENT_TYPE_FLOAT64 = 5;
}
optional DataType data_type = 1;
repeated bytes content = 2;
}
message LrPolicyState {
// learninRate Policy
optional double learning_rate = 1 [default = 1.0];
optional double learning_rate = 1 [ default = 1.0 ];
optional double lr_decay_a = 2;
optional double lr_decay_b = 3;
}
......@@ -104,7 +105,6 @@ message AdadeltaOptimizerState {
optional TensorProto update_delta = 4;
}
message AdagradOptimizerState {
optional LrPolicyState lr_state = 101;
optional double num_sample_passed = 104;
......@@ -124,10 +124,10 @@ message AdamOptimizerState {
message OptimizerConfig {
enum Optimizer {
SGD = 1;
Adadelta = 2;
Adagrad = 3;
Adam = 4;
SGD = 1;
Adadelta = 2;
Adagrad = 3;
Adam = 4;
}
optional Optimizer optimizer = 1;
optional SGDConfig sgd = 3;
......@@ -136,8 +136,8 @@ message OptimizerConfig {
optional AdamConfig adam = 6;
enum LrPolicy {
Const = 0;
Linear = 1;
Const = 0;
Linear = 1;
}
optional LrPolicy lr_policy = 11;
optional ConstLrConfig const_lr = 12;
......
proto/ParameterConfig.proto
浏览文件 @ 75185d82
......@@ -27,56 +27,57 @@ enum ParameterInitStrategy {
message ParameterUpdaterHookConfig {
// hook type such as 'pruning'
required string type = 1;
// this represents the ratio of zero element to be set by the Parameter
optional double sparsity_ratio = 2 [default = 0.6];
// this represents the ratio of zero element to be set by the Parameter
optional double sparsity_ratio = 2 [ default = 0.6 ];
}
message ParameterConfig {
required string name = 1;
required uint64 size = 2;
optional double learning_rate = 3 [default = 1.0];
optional double momentum = 4 [default = 0.0];
optional double initial_mean = 5 [default = 0.0];
optional double initial_std = 6 [default = 0.01];
optional double learning_rate = 3 [ default = 1.0 ];
optional double momentum = 4 [ default = 0.0 ];
optional double initial_mean = 5 [ default = 0.0 ];
optional double initial_std = 6 [ default = 0.01 ];
// use L2-regularization if decay_rate set and decay_rate_l1 not set
optional double decay_rate = 7 [default = 0.0];
optional double decay_rate = 7 [ default = 0.0 ];
// use L1-regularization if decay_rate_l1 set
optional double decay_rate_l1 = 8 [default = 0.0];
optional double decay_rate_l1 = 8 [ default = 0.0 ];
// dims of Parameter, e.g. dims[0] as height, dims[1] as width..
repeated uint64 dims = 9;
// the gpu device which the parameter in.
// Only used by ParallelNeuralNetork. Ignored otherwise.
optional int32 device = 10 [default = -1];
optional int32 device = 10 [ default = -1 ];
// how to init the parameter: 0 -> normal, 1 -> uniform
// 0: treat initial_mean as mean, intial_std as standard deviation
// 1: range is (initial_mean - initial_std) to (initial_mean + initial_std)
optional int32 initial_strategy = 11 [default = 0];
optional int32 initial_strategy = 11 [ default = 0 ];
// define the variance when init the parameter, by height of the Matrix
optional bool initial_smart = 12 [default = false];
optional bool initial_smart = 12 [ default = false ];
// apply regularization every # batches
optional int32 num_batches_regularization = 13 [default = 1];
optional int32 num_batches_regularization = 13 [ default = 1 ];
// if is_sparse is true, para is sparse, else para is dense
optional bool is_sparse = 14[default = false];
// if para is sparse, format should be "csc" or "csr", empty means is not sparse
optional string format = 15 [default = ""];
optional bool is_sparse = 14 [ default = false ];
// if para is sparse, format should be "csc" or "csr", empty means is not
// sparse
optional string format = 15 [ default = "" ];
// sparse remote update or not
optional bool sparse_remote_update = 16 [default = false];
optional bool sparse_remote_update = 16 [ default = false ];
// gradient clipping threshold, no clipping by default
optional double gradient_clipping_threshold = 17 [default = 0.0];
optional double gradient_clipping_threshold = 17 [ default = 0.0 ];
// static parameters are fixed when training
optional bool is_static = 18 [default = false];
optional bool is_static = 18 [ default = false ];
// para_id should NOT be set by config_parser. It is for
// internal use.
optional uint64 para_id = 19;
repeated ParameterUpdaterHookConfig update_hooks = 20;
// setup load mat -> csr
optional bool need_compact = 21 [default = false];
optional bool need_compact = 21 [ default = false ];
// whether to do sparse update for this parameter
optional bool sparse_update = 22 [default = false];
optional bool sparse_update = 22 [ default = false ];
// whether this parameter is shared or not.
optional bool is_shared = 23 [default = false];
optional bool is_shared = 23 [ default = false ];
// parameter block size
optional uint64 parameter_block_size = 24 [default = 0];
optional uint64 parameter_block_size = 24 [ default = 0 ];
}
proto/ParameterServerConfig.proto
浏览文件 @ 75185d82
......@@ -15,13 +15,10 @@ syntax = "proto2";
package paddle;
/**
* Configuration structure for ParameterClient2.
*/
message ParameterClientConfig {
required int32 trainer_id = 1;
}
message ParameterClientConfig { required int32 trainer_id = 1; }
/**
* Configuration structure for ParameterServer2.
......@@ -30,24 +27,24 @@ message ParameterServerConfig {
// Number of ports for sending dense parameter,
// following ports on parameter server will be visited
// for sending dense parameter: [port, port+ports_num-1]
required int32 ports_num = 1 [default = 1];
required int32 ports_num = 1 [ default = 1 ];
// Number of ports for sending sparse parameter,
// following ports on parameter server will be visited
// for sending sparse parameter:
// [port+ports_num, port+ports_num+ports_num_for_sparse-1]
required int32 ports_num_for_sparse = 2 [default = 0];
required int32 ports_num_for_sparse = 2 [ default = 0 ];
// network device name for pservers
required string nics = 3 [default = "xgbe0,xgbe1"];
required string rdma_tcp = 4 [default = "tcp"];
required string nics = 3 [ default = "xgbe0,xgbe1" ];
required string rdma_tcp = 4 [ default = "tcp" ];
// Listening port for pserver
required int32 port = 5 [default = 20134];
required int32 port = 5 [ default = 20134 ];
// number of gradient servers
required int32 num_gradient_servers = 6 [default = 1];
required int32 num_gradient_servers = 6 [ default = 1 ];
// number of threads for sync op exec
required int32 pserver_num_threads = 7 [default = 1];
required int32 pserver_num_threads = 7 [ default = 1 ];
// control config_.async_lagged_grad_discard_ratio() min value
required double async_lagged_ratio_min = 8 [default = 1.0];
required double async_lagged_ratio_min = 8 [ default = 1.0 ];
// if async_lagged_grad_discard_ratio is not set in trainer_config.conf
// use it as defalut value
required double async_lagged_ratio_default = 9 [default = 1.5];
required double async_lagged_ratio_default = 9 [ default = 1.5 ];
}
\ No newline at end of file
proto/ParameterService.proto
浏览文件 @ 75185d82
......@@ -23,8 +23,8 @@ package paddle;
*/
enum ParameterUpdateMode {
// Set parameter
PSERVER_UPDATE_MODE_SET_PARAM = 0;//use local param
PSERVER_UPDATE_MODE_SET_PARAM_ZERO = 1;//set zero param
PSERVER_UPDATE_MODE_SET_PARAM = 0; // use local param
PSERVER_UPDATE_MODE_SET_PARAM_ZERO = 1; // set zero param
// Update parameter once a gradient is received
PSERVER_UPDATE_MODE_ASYNC_SGD = 2;
......@@ -37,7 +37,7 @@ enum ParameterUpdateMode {
// No update. Only get parameters back.
PSERVER_UPDATE_MODE_GET_PARAM = 5;
PSERVER_UPDATE_MODE_GET_PARAM_SPARSE = 6;//only get sparse rows
PSERVER_UPDATE_MODE_GET_PARAM_SPARSE = 6; // only get sparse rows
};
message ParameterBlock {
......@@ -80,42 +80,34 @@ message SendParameterRequest {
optional int32 trainer_id = 7;
// send back parameter type on pserver, PARAMETER_VALUE by default
optional int32 send_back_parameter_type = 8 [default = 0];
optional int32 send_back_parameter_type = 8 [ default = 0 ];
// forwardbackward time in usec
optional uint64 forwardbackward_time = 9;
}
message WaitPassStartRequest {
}
message WaitPassStartRequest {}
message WaitPassStartResponse {
}
message WaitPassStartResponse {}
message WaitPassFinishRequest {
}
message WaitPassFinishRequest {}
message WaitPassFinishResponse {
}
message WaitPassFinishResponse {}
enum SyncObject {
SYNC_DEFAULT = 0; // wait for the synchronizeBarrier_
SYNC_DATA = 1; // wait for the synchronizeDataBarrier_
SYNC_DATA = 1; // wait for the synchronizeDataBarrier_
}
message SynchronizeRequest {
required SyncObject sync_object_id = 1 [default = SYNC_DEFAULT];
required SyncObject sync_object_id = 1 [ default = SYNC_DEFAULT ];
optional int32 trainer_id = 2;
}
message SynchronizeResponse {
}
message SynchronizeResponse {}
message SendParameterResponse {
repeated ParameterBlock blocks = 1;
}
message SendParameterResponse { repeated ParameterBlock blocks = 1; }
message SetConfigRequest {
repeated ParameterConfig param_configs = 1;
......@@ -125,26 +117,18 @@ message SetConfigRequest {
required bool is_sparse_server = 6;
}
message SetConfigResponse{
}
message SetConfigResponse {}
message GetStatusRequest {
}
message GetStatusRequest {}
message GetStatusResponse {
required PServerStatus status = 1;
}
message GetStatusResponse { required PServerStatus status = 1; }
message SetStatusRequest {
required PServerStatus status = 1;
}
message SetStatusRequest { required PServerStatus status = 1; }
message SetStatusResponse {
}
message SetStatusResponse {}
// create a column vector. The size is the dimension of parameter
message CreateVectorRequest {
}
message CreateVectorRequest {}
message CreateVectorResponse {
// error message. Empty if success
......@@ -153,9 +137,7 @@ message CreateVectorResponse {
required int64 handle = 2;
}
message ReleaseVectorRequest {
required int64 handle = 1;
}
message ReleaseVectorRequest { required int64 handle = 1; }
message ReleaseVectorResponse {
// error message. Empty if success
......@@ -164,9 +146,7 @@ message ReleaseVectorResponse {
// Create a column major matrix. The number of rows is the dimension
// of parameter. The number of columns is specifed by num_cols
message CreateMatrixRequest {
required int32 num_cols = 1;
}
message CreateMatrixRequest { required int32 num_cols = 1; }
message CreateMatrixResponse {
// error message. Empty if success
......@@ -175,16 +155,13 @@ message CreateMatrixResponse {
required int64 handle = 2;
}
message ReleaseMatrixRequest {
required int64 handle = 1;
}
message ReleaseMatrixRequest { required int64 handle = 1; }
message ReleaseMatrixResponse {
// error message. Empty if success
optional string return_message = 1;
}
/**
* The operations are defined using the variables commented at Operation
* and OperationResult
......@@ -245,36 +222,36 @@ enum MatrixVectorOperation {
message ProtoVector {
required int64 dim = 1;
repeated double values = 2 [packed = true];
repeated double values = 2 [ packed = true ];
}
message ProtoMatrix {
required int64 num_rows = 1;
required int64 num_cols = 2;
repeated double values = 3 [packed = true];
repeated double values = 3 [ packed = true ];
}
message Operation {
required MatrixVectorOperation operation = 1;
// vector handles created on the pserver
repeated int64 pvectors = 2; // u, v, w
repeated int64 pvectors = 2; // u, v, w
// matrix handles created on the pserver
repeated int64 pmatrices = 3; // A, B, C
repeated int64 pmatrices = 3; // A, B, C
repeated double scalars = 4; // a, b, c
repeated ProtoVector vectors = 5; // x, y, z
repeated ProtoMatrix matrices = 6; // X, Y, Z
repeated double scalars = 4; // a, b, c
repeated ProtoVector vectors = 5; // x, y, z
repeated ProtoMatrix matrices = 6; // X, Y, Z
}
message OperationResult {
// error message. Empty if success
optional string return_message = 1;
//
repeated double scalars = 2; // d, e, f
//
repeated double scalars = 2; // d, e, f
repeated ProtoVector vectors = 3; // p, q, r
repeated ProtoMatrix matrices = 4; // P, Q, R
repeated ProtoMatrix matrices = 4; // P, Q, R
}
message DoOperationRequest {
......@@ -301,18 +278,14 @@ message DoOperationResponse {
required bool pass_finish = 3;
}
message LoadValueRequest {
required string dir_name = 1;
}
message LoadValueRequest { required string dir_name = 1; }
message LoadValueResponse {
// error message. Empty if success
optional string return_message = 1;
}
message SaveValueRequest {
required string dir_name = 1;
}
message SaveValueRequest { required string dir_name = 1; }
message SaveValueResponse {
// error message. Empty if success
......@@ -331,11 +304,11 @@ enum DataUpdateMode {
// Client send it's own ref label to pserver
DATA_UPDATE_MODE_SET_REF_LABEL = 4;
// Client get all ref labels from all pservers
DATA_UPDATE_MODE_GET_REF_LABEL =5;
DATA_UPDATE_MODE_GET_REF_LABEL = 5;
// Client send it's own ref grad to pserver
DATA_UPDATE_MODE_SET_REF_GRAD =6;
DATA_UPDATE_MODE_SET_REF_GRAD = 6;
// Client get all ref grad from all pservers
DATA_UPDATE_MODE_GET_REF_GRAD =7;
DATA_UPDATE_MODE_GET_REF_GRAD = 7;
}
enum SendDataType {
......@@ -360,7 +333,7 @@ message DataBlock {
// byte size of one data type
required int32 data_size = 2;
// data_type
optional TransDataType data_type = 3 [default = TRANS_DOUBLE];
optional TransDataType data_type = 3 [ default = TRANS_DOUBLE ];
}
message SendDataRequest {
......
proto/TrainerConfig.proto
浏览文件 @ 75185d82
......@@ -20,14 +20,14 @@ package paddle;
message OptimizationConfig {
required int32 batch_size = 3;
required string algorithm = 4 [default = "async_sgd"];
optional int32 num_batches_per_send_parameter = 5 [default = 1];
optional int32 num_batches_per_get_parameter = 6 [default = 1];
required string algorithm = 4 [ default = "async_sgd" ];
optional int32 num_batches_per_send_parameter = 5 [ default = 1 ];
optional int32 num_batches_per_get_parameter = 6 [ default = 1 ];
required double learning_rate = 7;
optional double learning_rate_decay_a = 8 [default = 0];
optional double learning_rate_decay_b = 9 [default = 0];
optional string learning_rate_schedule = 27 [default = "constant"];
optional double learning_rate_decay_a = 8 [ default = 0 ];
optional double learning_rate_decay_b = 9 [ default = 0 ];
optional string learning_rate_schedule = 27 [ default = "constant" ];
// learning rate will be scaled according to learning_rate_schedule
// 1), constant:
// lr = learning_rate
......@@ -49,88 +49,92 @@ message OptimizationConfig {
// owlqn related
// L1-regularization
optional double l1weight = 10 [default = 0.1];
optional double l1weight = 10 [ default = 0.1 ];
// L2-regularization
optional double l2weight = 11 [default = 0];
optional double l2weight = 11 [ default = 0 ];
// "c1" in wolfe condition: if (newobj <= oldobj + c1 * origDirDeriv * step)
// then accept the step
optional double c1 = 12 [default = 0.0001];
optional double c1 = 12 [ default = 0.0001 ];
// multiply the step with "backoff", when wolfe condition doesn't satisfy
optional double backoff = 13 [default = 0.5];
optional double backoff = 13 [ default = 0.5 ];
// how many "s"s and "y"s are kept in owlqn
optional int32 owlqn_steps = 14 [default = 10];
optional int32 owlqn_steps = 14 [ default = 10 ];
// accept the step if encountered "max_backoff" times of "reduce the step"
optional int32 max_backoff = 15 [default = 5];
optional int32 max_backoff = 15 [ default = 5 ];
// L2-regularization coefficient is reduced linearly from iteration 0 to
// "l2weight_zero_iter", and set to 0 after "l2weight_zero_iter"
// iterations. set "l2weight_zero_iter" to 0 to disable this strategy.
optional int32 l2weight_zero_iter = 17 [default = 0];
optional int32 l2weight_zero_iter = 17 [ default = 0 ];
// averaged sgd
// About average_window * numBatchProcessed parameter are used
// for average. To be accurate, between average_window * numBatchProcessed
// and 2 * average_window * numBatchProcessed parameters are used for
// average.
optional double average_window = 18 [default = 0];
optional int64 max_average_window = 19 [default = 0x7fffffffffffffff];
optional double average_window = 18 [ default = 0 ];
optional int64 max_average_window = 19 [ default = 0x7fffffffffffffff ];
//////////////////////////
// Options Adaptive SGD //
//////////////////////////
// learning method for sgd/asgd, such as "momentum", "adagrad", "adadelta", "rmsprop"
// default learning method("momentum") use global decayed learning rate with momentum.
// learning method for sgd/asgd, such as "momentum", "adagrad", "adadelta",
// "rmsprop"
// default learning method("momentum") use global decayed learning rate with
// momentum.
// "adagrad", "adadelta" and "rmsprop" can set momentum too.
optional string learning_method = 23 [default = "momentum"];
optional double ada_epsilon = 24 [default = 1e-6];
optional double ada_rou = 26 [default = 0.95];
optional string learning_method = 23 [ default = "momentum" ];
optional double ada_epsilon = 24 [ default = 1e-6 ];
optional double ada_rou = 26 [ default = 0.95 ];
// Force to do average in cpu in order to save gpu memory usage
optional bool do_average_in_cpu = 25 [default = false];
optional bool do_average_in_cpu = 25 [ default = false ];
// delta add rate in pserver, used while num_batches_per_send_parameter>1
// will be divided by #machines automatically.
optional double delta_add_rate = 28 [default = 1.0];
optional double delta_add_rate = 28 [ default = 1.0 ];
// We split a large size into smaller mini-batches, whose sizes are
// determined by mini_batch_size. It only takes effect when there is
// an ExternalMachine.
optional int32 mini_batch_size = 29 [default = 128];
optional int32 mini_batch_size = 29 [ default = 128 ];
// automatically set if any one of parameters set sparse remote update flag
optional bool use_sparse_remote_updater = 30 [default = false];
optional bool use_sparse_remote_updater = 30 [ default = false ];
// how to update center parameter and feedback to local parameter,
// how to update center parameter and feedback to local parameter,
// when use local sgd update in cluster training.
// A option is elastic_average, proposed by the paper: Deep learning with elastic averaging SGD.
// If use elastic_average method, every trainer node should sample from whole data sets.
optional string center_parameter_update_method = 31 [default = "average"];
// A option is elastic_average, proposed by the paper: Deep learning with
// elastic averaging SGD.
// If use elastic_average method, every trainer node should sample from whole
// data sets.
optional string center_parameter_update_method = 31 [ default = "average" ];
// shrink sparse parameter value
// only works if parameter is remote sparse update and has L1 decay rate
optional double shrink_parameter_value = 32 [default = 0];
optional double shrink_parameter_value = 32 [ default = 0 ];
////////////////////////////
// Options Adam Optimizer //
////////////////////////////
optional double adam_beta1 = 33 [default = 0.9];
optional double adam_beta2 = 34 [default = 0.999];
optional double adam_epsilon = 35 [default = 1e-8];
optional double adam_beta1 = 33 [ default = 0.9 ];
optional double adam_beta2 = 34 [ default = 0.999 ];
optional double adam_epsilon = 35 [ default = 1e-8 ];
// arguments for learning rate scheduler
// Format: num1:rate1,num2:rate2,...,numK:rateK
// For learning_rate_schedule="manual", num is the number of samples,
// For learning_rate_schedule="pass_manual",
// num is the number of passes (starting from 0)
optional string learning_rate_args = 36 [default = ""];
optional string learning_rate_args = 36 [ default = "" ];
// for async sgd gradient commit control.
// when async_lagged_grad_discard_ratio * num_gradient_servers commit passed,
// current async gradient will be discard silently.
optional double async_lagged_grad_discard_ratio = 37 [default = 1.5];
optional double async_lagged_grad_discard_ratio = 37 [ default = 1.5 ];
// global threshold for gradient clipping
optional double gradient_clipping_threshold = 38 [default = 0.0];
// global threshold for gradient clipping
optional double gradient_clipping_threshold = 38 [ default = 0.0 ];
};
message TrainerConfig {
......@@ -141,7 +145,7 @@ message TrainerConfig {
repeated string config_files = 5;
// the directory to save/load model files for each training path
optional string save_dir = 6 [default = "./output/model"];
optional string save_dir = 6 [ default = "./output/model" ];
// Path of the initial model parameters.
// If it was set, start_pass will be ignored.
......@@ -149,7 +153,7 @@ message TrainerConfig {
// Start training from this pass.
// Will load parameter from the previous pass.
optional int32 start_pass = 8 [default = 0];
optional int32 start_pass = 8 [ default = 0 ];
// file path to the trainer config file
optional string config_file = 9;
......
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