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b721e23b
编写于
3月 24, 2020
作者:
W
wangchaochaohu
提交者:
GitHub
3月 24, 2020
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
transpose cudnn using cudnn v7 api (#19738)
* refine the transopose conv using v7 to choose algorithm
上级
11f94cdc
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
91 addition
and
129 deletion
+91
-129
paddle/fluid/operators/conv_transpose_cudnn_op.cu
paddle/fluid/operators/conv_transpose_cudnn_op.cu
+91
-129
未找到文件。
paddle/fluid/operators/conv_transpose_cudnn_op.cu
浏览文件 @
b721e23b
...
...
@@ -15,6 +15,7 @@ limitations under the License. */
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/operators/conv_cudnn_helper.h"
#include "paddle/fluid/operators/conv_transpose_op.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/padding.h"
...
...
@@ -24,13 +25,8 @@ namespace paddle {
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
ScopedTensorDescriptor
=
platform
::
ScopedTensorDescriptor
;
using
ScopedFilterDescriptor
=
platform
::
ScopedFilterDescriptor
;
using
ScopedConvolutionDescriptor
=
platform
::
ScopedConvolutionDescriptor
;
using
DataLayout
=
platform
::
DataLayout
;
static
constexpr
size_t
kConvCUDNNWorkspaceLimitBytes
=
1024
*
1024
*
1024
;
template
<
typename
T
,
int
D
>
static
void
DataTranspose
(
const
framework
::
ExecutionContext
&
ctx
,
const
Tensor
*
input
,
Tensor
*
output
,
...
...
@@ -68,7 +64,6 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
// cudnn v5 does not support dilations
std
::
vector
<
int
>
dilations
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
int
groups
=
ctx
.
Attr
<
int
>
(
"groups"
);
int
user_workspace_size
=
ctx
.
Attr
<
int
>
(
"workspace_size_MB"
);
const
T
*
filter_data
=
filter
->
data
<
T
>
();
const
std
::
string
data_layout_str
=
ctx
.
Attr
<
std
::
string
>
(
"data_format"
);
const
paddle
::
operators
::
DataLayout
data_layout
=
...
...
@@ -200,60 +195,44 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
}
T
*
transformed_output_data
=
transformed_output
.
data
<
T
>
();
// ------------------- cudnn descriptors ---------------------
ScopedTensorDescriptor
input_desc
;
ScopedTensorDescriptor
output_desc
;
ScopedFilterDescriptor
filter_desc
;
ScopedConvolutionDescriptor
conv_desc
;
DataLayout
layout
;
int
iwo_groups
=
groups
;
int
c_groups
=
1
;
#if CUDNN_VERSION_MIN(7, 0, 1)
iwo_groups
=
1
;
c_groups
=
groups
;
groups
=
1
;
#endif
if
(
strides
.
size
()
==
2U
)
{
layout
=
DataLayout
::
kNCHW
;
}
else
{
layout
=
DataLayout
::
kNCDHW
;
}
// (N, M, H, W) or (N, M, D, H, W)
cudnnTensorDescriptor_t
cudnn_input_desc
=
input_desc
.
descriptor
<
T
>
(
layout
,
input_vec
,
groups
);
// (N, C, O_h, O_w) or (N, C, O_d, O_h, O_w)
cudnnTensorDescriptor_t
cudnn_output_desc
=
output_desc
.
descriptor
<
T
>
(
layout
,
transformed_output_vec
,
groups
);
// (M, C, K_h, K_w) or (M, C, K_d, K_h, K_w)
cudnnFilterDescriptor_t
cudnn_filter_desc
=
filter_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize
<
int
>
(
filter
->
dims
()),
groups
);
cudnnConvolutionDescriptor_t
cudnn_conv_desc
=
conv_desc
.
descriptor
<
T
>
(
padding_common
,
strides
,
dilations
);
// ------------------- cudnn conv workspace ---------------------
size_t
workspace_size_in_bytes
;
// final workspace to allocate.
size_t
workspace_size_limit
=
kConvCUDNNWorkspaceLimitBytes
;
if
(
user_workspace_size
>
0
)
{
workspace_size_limit
=
user_workspace_size
*
1024
*
1024
;
}
size_t
workspace_size
=
0
;
cudnnConvolutionBwdDataAlgo_t
algo
{};
// ------------------- cudnn conv algorithm ---------------------
cudnnConvolutionBwdDataAlgo_t
algo
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
CUDADeviceContext
>();
auto
handle
=
dev_ctx
.
cudnn_handle
();
// Get the algorithm
PADDLE_ENFORCE_CUDA_SUCCESS
(
platform
::
dynload
::
cudnnGetConvolutionBackwardDataAlgorithm
(
handle
,
cudnn_filter_desc
,
cudnn_input_desc
,
cudnn_conv_desc
,
// dxDesc: Handle to the previously initialized output tensor
// descriptor.
cudnn_output_desc
,
CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT
,
workspace_size_limit
,
&
algo
));
if
(
FLAGS_cudnn_deterministic
)
{
algo
=
static_cast
<
cudnnConvolutionBwdDataAlgo_t
>
(
1
);
}
auto
layout_tensor
=
GetCudnnTensorFormat
(
layout
);
bool
deterministic
=
FLAGS_cudnn_deterministic
;
// get workspace size able to allocate
PADDLE_ENFORCE_CUDA_SUCCESS
(
platform
::
dynload
::
cudnnGetConvolutionBackwardDataWorkspaceSize
(
handle
,
cudnn_filter_desc
,
cudnn_input_desc
,
cudnn_conv_desc
,
cudnn_output_desc
,
algo
,
&
workspace_size_in_bytes
));
auto
dtype
=
platform
::
CudnnDataType
<
T
>::
type
;
// ------------------- cudnn descriptors ---------------------
ConvArgs
args
{
&
transformed_output
,
filter
,
&
transformed_input
,
strides
,
padding_common
,
dilations
};
args
.
handle
=
handle
;
args
.
idesc
.
set
(
transformed_output
,
iwo_groups
);
args
.
wdesc
.
set
(
*
filter
,
layout_tensor
,
iwo_groups
);
args
.
odesc
.
set
(
transformed_input
,
iwo_groups
);
args
.
cdesc
.
set
(
dtype
,
padding_common
,
strides
,
dilations
,
c_groups
);
using
search
=
SearchAlgorithm
<
cudnnConvolutionBwdDataAlgoPerf_t
>
;
algo
=
search
::
Find
<
T
>
(
args
,
false
,
deterministic
,
2
,
ctx
);
workspace_size
=
std
::
max
(
workspace_size
,
search
::
GetWorkspaceSize
(
args
,
algo
));
// ------------------- cudnn conv transpose forward ---------------------
int
input_offset
=
...
...
@@ -267,16 +246,14 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
auto
cudnn_func
=
[
&
](
void
*
cudnn_workspace
)
{
PADDLE_ENFORCE_CUDA_SUCCESS
(
platform
::
dynload
::
cudnnConvolutionBackwardData
(
handle
,
&
alpha
,
cudnn_filter_desc
,
filter_data
+
filter_offset
*
g
,
cudnn_input_desc
,
input_data
+
input_offset
*
g
,
cudnn_conv_desc
,
algo
,
cudnn_workspace
,
workspace_size_in_bytes
,
&
beta
,
cudnn_output_desc
,
handle
,
&
alpha
,
args
.
wdesc
.
desc
(),
filter_data
+
filter_offset
*
g
,
args
.
odesc
.
desc
(),
input_data
+
input_offset
*
g
,
args
.
cdesc
.
desc
(),
algo
,
cudnn_workspace
,
workspace_size
,
&
beta
,
args
.
idesc
.
desc
(),
transformed_output_data
+
output_offset
*
g
));
};
workspace_handle
.
RunFunc
(
cudnn_func
,
workspace_size
_in_bytes
);
workspace_handle
.
RunFunc
(
cudnn_func
,
workspace_size
);
}
if
(
!
is_sys_pad
&&
strides
.
size
()
==
2U
)
{
Slice
<
paddle
::
platform
::
CUDADeviceContext
,
T
,
4
>
(
ctx
,
&
transformed_output
,
output
,
starts
,
ends
,
axes
);
...
...
@@ -432,10 +409,6 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
output_vec
=
framework
::
vectorize
<
int
>
(
transformed_output_grad
.
dims
());
// ------------------- cudnn descriptors ---------------------
ScopedTensorDescriptor
input_desc
;
ScopedTensorDescriptor
output_desc
;
ScopedFilterDescriptor
filter_desc
;
ScopedConvolutionDescriptor
conv_desc
;
DataLayout
layout
;
if
(
strides
.
size
()
==
2U
)
{
...
...
@@ -444,68 +417,59 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
layout
=
DataLayout
::
kNCDHW
;
}
// Input: (N, M, H, W) or (N, M, D, H, W)
cudnnTensorDescriptor_t
cudnn_input_desc
=
input_desc
.
descriptor
<
T
>
(
layout
,
input_vec
,
groups
);
// Output: (N, C, O_h, O_w) or (N, C, O_d, O_h, O_w)
cudnnTensorDescriptor_t
cudnn_output_desc
=
output_desc
.
descriptor
<
T
>
(
layout
,
output_vec
,
groups
);
// Filter (M, C, K_h, K_w) or (M, C, K_d K_h, K_w)
cudnnFilterDescriptor_t
cudnn_filter_desc
=
filter_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize
<
int
>
(
filter
->
dims
()),
groups
);
cudnnConvolutionDescriptor_t
cudnn_conv_desc
=
conv_desc
.
descriptor
<
T
>
(
padding_common
,
strides
,
dilations
);
// ------------------- cudnn backward algorithm ---------------------
cudnnConvolutionFwdAlgo_t
data_algo
;
cudnnConvolutionBwdFilterAlgo_t
filter_algo
;
size_t
bwd_filter_ws_size
,
fwd_ws_size
;
size_t
workspace_size_in_bytes
=
0
;
size_t
workspace_size_limit
=
kConvCUDNNWorkspaceLimitBytes
;
if
(
user_workspace_size
>
0
)
{
workspace_size_limit
=
user_workspace_size
*
1024
*
1024
;
}
int
iwo_groups
=
groups
;
int
c_groups
=
1
;
#if CUDNN_VERSION_MIN(7, 0, 1)
iwo_groups
=
1
;
c_groups
=
groups
;
groups
=
1
;
#endif
ConvArgs
args1
{
&
transformed_output_grad
,
filter
,
&
input_transpose
,
strides
,
padding_common
,
dilations
};
ConvArgs
args2
{
&
transformed_output_grad
,
filter
,
&
input_transpose
,
strides
,
padding_common
,
dilations
};
cudnnConvolutionFwdAlgo_t
data_algo
{};
cudnnConvolutionBwdFilterAlgo_t
filter_algo
{};
auto
layout_tensor
=
GetCudnnTensorFormat
(
layout
);
size_t
workspace_size
=
0
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
platform
::
CUDADeviceContext
>();
auto
handle
=
dev_ctx
.
cudnn_handle
();
auto
dtype
=
platform
::
CudnnDataType
<
T
>::
type
;
bool
deterministic
=
FLAGS_cudnn_deterministic
;
T
*
input_grad_data
=
nullptr
;
T
*
filter_grad_data
=
nullptr
;
if
(
input_grad
)
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
if
(
filter_grad
)
filter_grad_data
=
filter_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
if
(
input_grad
)
{
// choose backward algorithm for data
PADDLE_ENFORCE_CUDA_SUCCESS
(
platform
::
dynload
::
cudnnGetConvolutionForwardAlgorithm
(
handle
,
cudnn_output_desc
,
cudnn_filter_desc
,
cudnn_conv_desc
,
cudnn_input_desc
,
CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT
,
workspace_size_limit
,
&
data_algo
));
if
(
FLAGS_cudnn_deterministic
)
{
data_algo
=
static_cast
<
cudnnConvolutionFwdAlgo_t
>
(
1
);
}
PADDLE_ENFORCE_CUDA_SUCCESS
(
platform
::
dynload
::
cudnnGetConvolutionForwardWorkspaceSize
(
handle
,
cudnn_output_desc
,
cudnn_filter_desc
,
cudnn_conv_desc
,
cudnn_input_desc
,
data_algo
,
&
fwd_ws_size
));
workspace_size_in_bytes
=
std
::
max
(
workspace_size_in_bytes
,
fwd_ws_size
);
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
args1
.
handle
=
handle
;
args1
.
idesc
.
set
(
transformed_output_grad
,
iwo_groups
);
args1
.
wdesc
.
set
(
*
filter
,
layout_tensor
,
iwo_groups
);
args1
.
odesc
.
set
(
input_transpose
,
iwo_groups
);
args1
.
cdesc
.
set
(
dtype
,
padding_common
,
strides
,
dilations
,
c_groups
);
using
search1
=
SearchAlgorithm
<
cudnnConvolutionFwdAlgoPerf_t
>
;
data_algo
=
search1
::
Find
<
T
>
(
args1
,
false
,
deterministic
,
0
,
ctx
);
workspace_size
=
std
::
max
(
workspace_size
,
search1
::
GetWorkspaceSize
(
args1
,
data_algo
));
}
if
(
filter_grad
)
{
// choose backward algorithm for filter
PADDLE_ENFORCE_CUDA_SUCCESS
(
platform
::
dynload
::
cudnnGetConvolutionBackwardFilterAlgorithm
(
handle
,
cudnn_output_desc
,
cudnn_input_desc
,
cudnn_conv_desc
,
cudnn_filter_desc
,
CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT
,
workspace_size_limit
,
&
filter_algo
));
if
(
FLAGS_cudnn_deterministic
)
{
filter_algo
=
static_cast
<
cudnnConvolutionBwdFilterAlgo_t
>
(
1
);
}
// get workspace for backwards filter algorithm
PADDLE_ENFORCE_CUDA_SUCCESS
(
platform
::
dynload
::
cudnnGetConvolutionBackwardFilterWorkspaceSize
(
handle
,
cudnn_output_desc
,
cudnn_input_desc
,
cudnn_conv_desc
,
cudnn_filter_desc
,
filter_algo
,
&
bwd_filter_ws_size
));
workspace_size_in_bytes
=
std
::
max
(
workspace_size_in_bytes
,
bwd_filter_ws_size
);
filter_grad_data
=
filter_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
args2
.
handle
=
handle
;
args2
.
idesc
.
set
(
transformed_output_grad
,
iwo_groups
);
args2
.
wdesc
.
set
(
*
filter_grad
,
layout_tensor
,
iwo_groups
);
args2
.
odesc
.
set
(
input_transpose
,
iwo_groups
);
args2
.
cdesc
.
set
(
dtype
,
padding_common
,
strides
,
dilations
,
c_groups
);
using
search2
=
SearchAlgorithm
<
cudnnConvolutionBwdFilterAlgoPerf_t
>
;
filter_algo
=
search2
::
Find
<
T
>
(
args2
,
false
,
deterministic
,
1
,
ctx
);
workspace_size
=
std
::
max
(
workspace_size
,
search2
::
GetWorkspaceSize
(
args2
,
filter_algo
));
}
// ------------------- cudnn conv backward data ---------------------
...
...
@@ -517,19 +481,18 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
T
alpha
=
static_cast
<
T
>
(
1.0
),
beta
=
static_cast
<
T
>
(
0.0
);
auto
workspace_handle
=
dev_ctx
.
cudnn_workspace_handle
();
if
(
input_grad
)
{
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
// Because beta is zero, it is unnecessary to reset input_grad.
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
auto
cudnn_func
=
[
&
](
void
*
cudnn_workspace
)
{
PADDLE_ENFORCE_CUDA_SUCCESS
(
platform
::
dynload
::
cudnnConvolutionForward
(
handle
,
&
alpha
,
cudnn_output_desc
,
output_grad_data
+
output_grad_offset
*
g
,
cudnn_filter_desc
,
filter_data
+
filter_offset
*
g
,
cudnn_conv_desc
,
data_algo
,
cudnn_workspace
,
workspace_size_in_bytes
,
&
beta
,
cudnn_input_desc
,
input_grad_data
+
input_offset
*
g
));
handle
,
&
alpha
,
args1
.
idesc
.
desc
()
,
output_grad_data
+
output_grad_offset
*
g
,
args1
.
wdesc
.
desc
()
,
filter_data
+
filter_offset
*
g
,
args1
.
cdesc
.
desc
()
,
data_algo
,
cudnn_workspace
,
workspace_size
,
&
beta
,
args1
.
odesc
.
desc
()
,
input_grad_data
+
input_offset
*
g
));
};
workspace_handle
.
RunFunc
(
cudnn_func
,
workspace_size
_in_bytes
);
workspace_handle
.
RunFunc
(
cudnn_func
,
workspace_size
);
}
if
(
data_layout
==
DataLayout
::
kNHWC
)
{
...
...
@@ -553,20 +516,19 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn conv backward filter ---------------------
if
(
filter_grad
)
{
T
*
filter_grad_data
=
filter_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
// Because beta is zero, it is unnecessary to reset filter_grad.
// Gradient with respect to the filter
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
auto
cudnn_func
=
[
&
](
void
*
cudnn_workspace
)
{
PADDLE_ENFORCE_CUDA_SUCCESS
(
platform
::
dynload
::
cudnnConvolutionBackwardFilter
(
handle
,
&
alpha
,
cudnn_output_desc
,
output_grad_data
+
output_grad_offset
*
g
,
cudnn_input_desc
,
input_data
+
input_offset
*
g
,
cudnn_conv_desc
,
filter_algo
,
cudnn_workspace
,
workspace_size_in_bytes
,
&
beta
,
cudnn_filter_desc
,
filter_grad_data
+
filter_offset
*
g
));
handle
,
&
alpha
,
args2
.
idesc
.
desc
()
,
output_grad_data
+
output_grad_offset
*
g
,
args2
.
odesc
.
desc
()
,
input_data
+
input_offset
*
g
,
args2
.
cdesc
.
desc
()
,
filter_algo
,
cudnn_workspace
,
workspace_size
,
&
beta
,
args2
.
wdesc
.
desc
()
,
filter_grad_data
+
filter_offset
*
g
));
};
workspace_handle
.
RunFunc
(
cudnn_func
,
workspace_size
_in_bytes
);
workspace_handle
.
RunFunc
(
cudnn_func
,
workspace_size
);
}
}
}
...
...
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