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提交 877f4210 authored 作者: carriepl's avatar carriepl 提交者： Frederic
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Avoid using method cudnnGetConvolution2dDescriptor() method

上级 ebd97785
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正在显示 包含 52 行增加 和 40 行删除
theano/sandbox/cuda/dnn_fwd.c
浏览文件 @ 877f4210
......@@ -176,12 +176,14 @@ APPLY_SPECIFIC(conv_fwd)(CudaNdarray *input, CudaNdarray *kerns,
{
// Extract the properties of the convolution descriptor
int pad_h, pad_w, stride_v, stride_h, upscale_x, upscale_y;
int nd;
int pad[2];
int stride[2];
int upscale[2];
cudnnConvolutionMode_t mode;
err = cudnnGetConvolution2dDescriptor(desc, &pad_h, &pad_w,
&stride_v, &stride_h,
&upscale_x, &upscale_y,
&mode);
cudnnDataType_t data_type;
err = cudnnGetConvolutionNdDescriptor_v3(desc, 2, &nd, pad, stride,
upscale, &mode, &data_type);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
......@@ -202,7 +204,7 @@ APPLY_SPECIFIC(conv_fwd)(CudaNdarray *input, CudaNdarray *kerns,
// convolution. Fall back to a safe implementation otherwise.
if (chosen_algo == CUDNN_CONVOLUTION_FWD_ALGO_FFT)
{
if (stride_v != 1 || stride_h != 1 || input_h > 1024 ||
if (stride[0] != 1 || stride[1] != 1 || input_h > 1024 ||
input_w > 1024 || (filter_h == 1 && filter_w == 1))
{
chosen_algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
......@@ -211,7 +213,7 @@ APPLY_SPECIFIC(conv_fwd)(CudaNdarray *input, CudaNdarray *kerns,
else
{
// chosen_algo == CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING
if (stride_v != 1 || stride_h != 1)
if (stride[0] != 1 || stride[1] != 1)
{
chosen_algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
}
......
theano/sandbox/cuda/dnn_gi.c
浏览文件 @ 877f4210
......@@ -173,12 +173,14 @@ APPLY_SPECIFIC(conv_gi)(CudaNdarray *kerns, CudaNdarray *output,
{
// Extract the properties of the convolution descriptor
int pad_h, pad_w, stride_v, stride_h, upscale_x, upscale_y;
int nd;
int pad[2];
int stride[2];
int upscale[2];
cudnnConvolutionMode_t mode;
err = cudnnGetConvolution2dDescriptor(desc, &pad_h, &pad_w,
&stride_v, &stride_h,
&upscale_x, &upscale_y,
&mode);
cudnnDataType_t data_type;
err = cudnnGetConvolutionNdDescriptor_v3(desc, 2, &nd, pad, stride,
upscale, &mode, &data_type);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
......@@ -199,7 +201,7 @@ APPLY_SPECIFIC(conv_gi)(CudaNdarray *kerns, CudaNdarray *output,
// convolution. Fall back to a safe implementation otherwise.
if (chosen_algo == CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT)
{
if (stride_v != 1 || stride_h != 1 || input_h > 1024 ||
if (stride[0] != 1 || stride[1] != 1 || input_h > 1024 ||
input_w > 1024 || (filter_h == 1 && filter_w == 1))
{
chosen_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0;
......@@ -208,7 +210,7 @@ APPLY_SPECIFIC(conv_gi)(CudaNdarray *kerns, CudaNdarray *output,
else
{
// chosen_algo == CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING
if (stride_v != 1 || stride_h != 1)
if (stride[0] != 1 || stride[1] != 1)
{
chosen_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0;
}
......
theano/sandbox/cuda/dnn_gw.c
浏览文件 @ 877f4210
......@@ -168,12 +168,14 @@ APPLY_SPECIFIC(conv_gw)(CudaNdarray *input, CudaNdarray *output,
{
// Extract the properties of the convolution descriptor
int pad_h, pad_w, stride_v, stride_h, upscale_x, upscale_y;
int nd;
int pad[2];
int stride[2];
int upscale[2];
cudnnConvolutionMode_t mode;
err = cudnnGetConvolution2dDescriptor(desc, &pad_h, &pad_w,
&stride_v, &stride_h,
&upscale_x, &upscale_y,
&mode);
cudnnDataType_t data_type;
err = cudnnGetConvolutionNdDescriptor_v3(desc, 2, &nd, pad, stride,
upscale, &mode, &data_type);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
......@@ -192,7 +194,7 @@ APPLY_SPECIFIC(conv_gw)(CudaNdarray *input, CudaNdarray *output,
// Ensure that the selected implementation supports the requested
// convolution. Fall back to a safe implementation otherwise.
if (stride_v != 1 || stride_h != 1 || input_h > 1024 ||
if (stride[0] != 1 || stride[1] != 1 || input_h > 1024 ||
input_w > 1024 || (filter_h == 1 && filter_w == 1))
{
chosen_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0;
......
theano/sandbox/gpuarray/dnn_fwd.c
浏览文件 @ 877f4210
......@@ -149,12 +149,14 @@ APPLY_SPECIFIC(conv_fwd)(PyGpuArrayObject *input, PyGpuArrayObject *kerns,
algo == CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING) && PyGpuArray_NDIM(input) == 4) {
// Extract the properties of the convolution descriptor
int pad_h, pad_w, stride_v, stride_h, upscale_x, upscale_y;
int nd;
int pad[2];
int stride[2];
int upscale[2];
cudnnConvolutionMode_t mode;
err = cudnnGetConvolution2dDescriptor(desc, &pad_h, &pad_w,
&stride_v, &stride_h,
&upscale_x, &upscale_y,
&mode);
cudnnDataType_t data_type;
err = cudnnGetConvolutionNdDescriptor_v3(desc, 2, &nd, pad, stride,
upscale, &mode, &data_type);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"error getting convolution properties: %s",
......@@ -165,7 +167,7 @@ APPLY_SPECIFIC(conv_fwd)(PyGpuArrayObject *input, PyGpuArrayObject *kerns,
if (algo == CUDNN_CONVOLUTION_FWD_ALGO_FFT)
{
if (stride_v != 1 || stride_h != 1 ||
if (stride[0] != 1 || stride[1] != 1 ||
PyGpuArray_DIM(input, 2) > 1024 || PyGpuArray_DIM(input, 3) > 1024 ||
(PyGpuArray_DIM(kerns, 2) == 1 && PyGpuArray_DIM(kerns, 3) == 1))
{
......@@ -175,7 +177,7 @@ APPLY_SPECIFIC(conv_fwd)(PyGpuArrayObject *input, PyGpuArrayObject *kerns,
else
{
// algo == CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING
if (stride_v != 1 || stride_h != 1)
if (stride[0] != 1 || stride[1] != 1)
{
algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
}
......
theano/sandbox/gpuarray/dnn_gi.c
浏览文件 @ 877f4210
......@@ -141,12 +141,14 @@ APPLY_SPECIFIC(conv_gi)(PyGpuArrayObject *kerns, PyGpuArrayObject *output,
algo == CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT) && PyGpuArray_NDIM(kerns) == 4) {
// Extract the properties of the convolution descriptor
int pad_h, pad_w, stride_v, stride_h, upscale_x, upscale_y;
int nd;
int pad[2];
int stride[2];
int upscale[2];
cudnnConvolutionMode_t mode;
err = cudnnGetConvolution2dDescriptor(desc, &pad_h, &pad_w,
&stride_v, &stride_h,
&upscale_x, &upscale_y,
&mode);
cudnnDataType_t data_type;
err = cudnnGetConvolutionNdDescriptor_v3(desc, 2, &nd, pad, stride,
upscale, &mode, &data_type);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"error getting convolution properties: %s",
......@@ -157,7 +159,7 @@ APPLY_SPECIFIC(conv_gi)(PyGpuArrayObject *kerns, PyGpuArrayObject *output,
if (algo == CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT)
{
if (stride_v != 1 || stride_h != 1 ||
if (stride[0] != 1 || stride[1] != 1 ||
PyGpuArray_DIM(*input, 2) > 1024 || PyGpuArray_DIM(*input, 3) > 1024 ||
(PyGpuArray_DIM(kerns, 2) == 1 && PyGpuArray_DIM(kerns, 3) == 1))
{
......@@ -167,7 +169,7 @@ APPLY_SPECIFIC(conv_gi)(PyGpuArrayObject *kerns, PyGpuArrayObject *output,
else
{
// algo == CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING
if (stride_v != 1 || stride_h != 1)
if (stride[0] != 1 || stride[1] != 1)
{
algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0;
}
......
theano/sandbox/gpuarray/dnn_gw.c
浏览文件 @ 877f4210
......@@ -141,12 +141,14 @@ APPLY_SPECIFIC(conv_gw)(PyGpuArrayObject *input, PyGpuArrayObject *output,
if (algo == CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT &&
PyGpuArray_NDIM(input) == 4) {
// Extract the properties of the convolution descriptor
int pad_h, pad_w, stride_v, stride_h, upscale_x, upscale_y;
int nd;
int pad[2];
int stride[2];
int upscale[2];
cudnnConvolutionMode_t mode;
err = cudnnGetConvolution2dDescriptor(desc, &pad_h, &pad_w,
&stride_v, &stride_h,
&upscale_x, &upscale_y,
&mode);
cudnnDataType_t data_type;
err = cudnnGetConvolutionNdDescriptor_v3(desc, 2, &nd, pad, stride,
upscale, &mode, &data_type);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"error getting convolution properties: %s",
......@@ -155,7 +157,7 @@ APPLY_SPECIFIC(conv_gw)(PyGpuArrayObject *input, PyGpuArrayObject *output,
return 1;
}
if (stride_v != 1 || stride_h != 1 ||
if (stride[0] != 1 || stride[1] != 1 ||
PyGpuArray_DIM(input, 2) > 1024 || PyGpuArray_DIM(input, 3) > 1024 ||
(PyGpuArray_DIM(*kerns, 2) == 1 && PyGpuArray_DIM(*kerns, 3) == 1)) {
algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0;
......
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