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体验新版 GitCode,发现更多精彩内容 >>
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6d649d9e
编写于
11月 01, 2017
作者:
Z
Zhuoyuan
提交者:
GitHub
11月 01, 2017
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差异文件
Merge pull request #5235 from zchen0211/develop
deconv cudnn forward passed
上级
e0c3a668
0efac253
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
321 addition
and
36 deletion
+321
-36
paddle/operators/conv2d_transpose_cudnn_op.cc
paddle/operators/conv2d_transpose_cudnn_op.cc
+50
-0
paddle/operators/conv2d_transpose_cudnn_op.cu
paddle/operators/conv2d_transpose_cudnn_op.cu
+240
-0
paddle/operators/conv2d_transpose_op.cc
paddle/operators/conv2d_transpose_op.cc
+5
-5
paddle/operators/conv2d_transpose_op.cu
paddle/operators/conv2d_transpose_op.cu
+3
-3
paddle/operators/conv2d_transpose_op.h
paddle/operators/conv2d_transpose_op.h
+1
-1
python/paddle/v2/framework/tests/test_conv2d_transpose_op.py
python/paddle/v2/framework/tests/test_conv2d_transpose_op.py
+22
-27
未找到文件。
paddle/operators/conv2d_transpose_cudnn_op.cc
0 → 100644
浏览文件 @
6d649d9e
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
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/operators/conv2d_transpose_op.h"
namespace
paddle
{
namespace
operators
{
class
CudnnConv2DTransposeOpMaker
:
public
Conv2DTransposeOpMaker
{
public:
CudnnConv2DTransposeOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
Conv2DTransposeOpMaker
(
proto
,
op_checker
)
{
AddAttr
<
std
::
vector
<
int
>>
(
"dilations"
,
"dilations of convolution operator."
)
.
SetDefault
(
std
::
vector
<
int
>
{
1
,
1
});
AddAttr
<
int
>
(
"workspace_size_MB"
,
"workspace size for cudnn, in MB, "
"workspace is a section of GPU memory which will be "
"allocated/freed each time the operator runs, larger "
"workspace size can increase performance but also requires "
"better hardward. This size should be carefully setted."
)
.
SetDefault
(
4096
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
conv2d_transpose_cudnn
,
ops
::
Conv2DTransposeOp
,
ops
::
CudnnConv2DTransposeOpMaker
,
conv2d_transpose_cudnn_grad
,
ops
::
Conv2DTransposeOpGrad
);
REGISTER_OP_CPU_KERNEL
(
conv2d_transpose_cudnn
,
ops
::
GemmConv2DTransposeKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
conv2d_transpose_cudnn_grad
,
ops
::
GemmConv2DTransposeGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/conv2d_transpose_cudnn_op.cu
0 → 100644
浏览文件 @
6d649d9e
/* Copyright (c) 2016 PaddlePaddle Authors All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
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/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/memory/memory.h"
#include "paddle/operators/conv2d_transpose_op.h"
#include "paddle/platform/assert.h"
#include "paddle/platform/cudnn_helper.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
ScopedTensorDescriptor
=
platform
::
ScopedTensorDescriptor
;
using
ScopedFilterDescriptor
=
platform
::
ScopedFilterDescriptor
;
using
ScopedConvolutionDescriptor
=
platform
::
ScopedConvolutionDescriptor
;
using
DataLayout
=
platform
::
DataLayout
;
using
CUDADeviceContext
=
platform
::
CUDADeviceContext
;
static
constexpr
size_t
kConvCudnnWorkspaceLimitBytes
=
1024
*
1024
*
1024
;
template
<
typename
T
>
class
CudnnConvTransposeOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()),
"It must use GPUPlace."
);
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"Input"
);
auto
*
filter
=
ctx
.
Input
<
Tensor
>
(
"Filter"
);
auto
*
output
=
ctx
.
Output
<
Tensor
>
(
"Output"
);
std
::
vector
<
int
>
strides
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
// cudnn v5 does not support dilations
std
::
vector
<
int
>
dilations
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
int
user_workspace_size
=
ctx
.
Attr
<
int
>
(
"workspace_size_MB"
);
const
T
*
input_data
=
input
->
data
<
T
>
();
const
T
*
filter_data
=
filter
->
data
<
T
>
();
T
*
output_data
=
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
// ------------------- cudnn descriptors ---------------------
ScopedTensorDescriptor
input_desc
;
ScopedTensorDescriptor
output_desc
;
ScopedFilterDescriptor
filter_desc
;
ScopedConvolutionDescriptor
conv_desc
;
DataLayout
layout
=
DataLayout
::
kNCHW
;
// N, M, H, W
cudnnTensorDescriptor_t
cudnn_input_desc
=
input_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize2int
(
input
->
dims
()));
// N, C, O_h, O_w
cudnnTensorDescriptor_t
cudnn_output_desc
=
output_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize2int
(
output
->
dims
()));
// M, C, K_h, K_w
cudnnFilterDescriptor_t
cudnn_filter_desc
=
filter_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize2int
(
filter
->
dims
()));
cudnnConvolutionDescriptor_t
cudnn_conv_desc
=
conv_desc
.
descriptor
<
T
>
(
paddings
,
strides
,
dilations
);
// ------------------- cudnn conv workspace ---------------------
void
*
cudnn_workspace
=
nullptr
;
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
;
}
// ------------------- cudnn conv algorithm ---------------------
cudnnConvolutionBwdDataAlgo_t
algo
;
auto
handle
=
ctx
.
cuda_device_context
().
cudnn_handle
();
// Get the algorithm
PADDLE_ENFORCE
(
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
));
// get workspace size able to allocate
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnGetConvolutionBackwardDataWorkspaceSize
(
handle
,
cudnn_filter_desc
,
cudnn_input_desc
,
cudnn_conv_desc
,
cudnn_output_desc
,
algo
,
&
workspace_size_in_bytes
));
// Allocate on GPU memory
platform
::
GPUPlace
gpu
=
boost
::
get
<
platform
::
GPUPlace
>
(
ctx
.
GetPlace
());
cudnn_workspace
=
paddle
::
memory
::
Alloc
(
gpu
,
workspace_size_in_bytes
);
// ------------------- cudnn conv transpose forward ---------------------
T
alpha
=
1.0
f
,
beta
=
0.0
f
;
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnConvolutionBackwardData
(
handle
,
&
alpha
,
cudnn_filter_desc
,
filter_data
,
cudnn_input_desc
,
input_data
,
cudnn_conv_desc
,
algo
,
cudnn_workspace
,
workspace_size_in_bytes
,
&
beta
,
cudnn_output_desc
,
output_data
));
// Release the cudnn workspace
paddle
::
memory
::
Free
(
gpu
,
cudnn_workspace
);
}
};
template
<
typename
T
>
class
CudnnConvTransposeGradOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()),
"It must use GPUPlace."
);
auto
input
=
ctx
.
Input
<
Tensor
>
(
"Input"
);
auto
filter
=
ctx
.
Input
<
Tensor
>
(
"Filter"
);
auto
output_grad
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Output"
));
auto
input_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
auto
filter_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Filter"
));
const
T
*
input_data
=
input
->
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
->
data
<
T
>
();
const
T
*
filter_data
=
filter
->
data
<
T
>
();
std
::
vector
<
int
>
strides
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
// cudnn v5 does not support dilations
std
::
vector
<
int
>
dilations
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
int
user_workspace_size
=
ctx
.
Attr
<
int
>
(
"workspace_size_MB"
);
// ------------------- cudnn descriptors ---------------------
ScopedTensorDescriptor
input_desc
;
ScopedTensorDescriptor
output_desc
;
ScopedFilterDescriptor
filter_desc
;
ScopedConvolutionDescriptor
conv_desc
;
DataLayout
layout
=
DataLayout
::
kNCHW
;
// Input: (N, M, H, W)
cudnnTensorDescriptor_t
cudnn_input_desc
=
input_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize2int
(
input
->
dims
()));
// Output: (N, C, O_H, O_W)
cudnnTensorDescriptor_t
cudnn_output_desc
=
output_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize2int
(
output_grad
->
dims
()));
// Filter (M, C, K_H, K_W)
cudnnFilterDescriptor_t
cudnn_filter_desc
=
filter_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize2int
(
filter
->
dims
()));
cudnnConvolutionDescriptor_t
cudnn_conv_desc
=
conv_desc
.
descriptor
<
T
>
(
paddings
,
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
;
}
auto
handle
=
ctx
.
cuda_device_context
().
cudnn_handle
();
if
(
input_grad
)
{
// choose backward algorithm for data
PADDLE_ENFORCE
(
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
));
PADDLE_ENFORCE
(
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
);
}
if
(
filter_grad
)
{
// choose backward algorithm for filter
PADDLE_ENFORCE
(
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
));
// get workspace for backwards filter algorithm
PADDLE_ENFORCE
(
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
);
}
// ------------------- cudnn conv workspace ---------------------
// Already on GPU
void
*
cudnn_workspace
=
nullptr
;
platform
::
GPUPlace
gpu
=
boost
::
get
<
platform
::
GPUPlace
>
(
ctx
.
GetPlace
());
cudnn_workspace
=
paddle
::
memory
::
Alloc
(
gpu
,
workspace_size_in_bytes
);
// ------------------- cudnn conv backward data ---------------------
// FIXME(typhoonzero): template type T may not be the same as cudnn call.
T
alpha
=
1.0
f
,
beta
=
0.0
f
;
if
(
input_grad
)
{
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
input_grad
);
t
.
device
(
ctx
.
GetEigenDevice
<
platform
::
GPUPlace
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnConvolutionForward
(
handle
,
&
alpha
,
cudnn_output_desc
,
output_grad_data
,
cudnn_filter_desc
,
filter_data
,
cudnn_conv_desc
,
data_algo
,
cudnn_workspace
,
workspace_size_in_bytes
,
&
beta
,
cudnn_input_desc
,
input_grad_data
));
}
// ------------------- cudnn conv backward filter ---------------------
if
(
filter_grad
)
{
T
*
filter_grad_data
=
filter_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
filter_grad
);
t
.
device
(
ctx
.
GetEigenDevice
<
platform
::
GPUPlace
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
// Gradient with respect to the filter
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnConvolutionBackwardFilter
(
handle
,
&
alpha
,
cudnn_output_desc
,
output_grad_data
,
cudnn_input_desc
,
input_data
,
cudnn_conv_desc
,
filter_algo
,
cudnn_workspace
,
workspace_size_in_bytes
,
&
beta
,
cudnn_filter_desc
,
filter_grad_data
));
}
// Release the cudnn workspace
paddle
::
memory
::
Free
(
gpu
,
cudnn_workspace
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
conv2d_transpose_cudnn
,
ops
::
CudnnConvTransposeOpKernel
<
float
>
);
REGISTER_OP_GPU_KERNEL
(
conv2d_transpose_cudnn_grad
,
ops
::
CudnnConvTransposeGradOpKernel
<
float
>
);
paddle/operators/conv2dtranspose_op.cc
→
paddle/operators/conv2d
_
transpose_op.cc
浏览文件 @
6d649d9e
...
...
@@ -12,7 +12,7 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/conv2dtranspose_op.h"
#include "paddle/operators/conv2d
_
transpose_op.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -95,13 +95,13 @@ void Conv2DTransposeOpGrad::InferShape(
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
conv2dtranspose
,
ops
::
Conv2DTransposeOp
,
ops
::
Conv2DTransposeOpMaker
,
conv2dtranspose_grad
,
REGISTER_OP
(
conv2d
_
transpose
,
ops
::
Conv2DTransposeOp
,
ops
::
Conv2DTransposeOpMaker
,
conv2d
_
transpose_grad
,
ops
::
Conv2DTransposeOpGrad
);
REGISTER_OP_CPU_KERNEL
(
conv2dtranspose
,
conv2d
_
transpose
,
ops
::
GemmConv2DTransposeKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
conv2dtranspose_grad
,
conv2d
_
transpose_grad
,
ops
::
GemmConv2DTransposeGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/conv2dtranspose_op.cu
→
paddle/operators/conv2d
_
transpose_op.cu
浏览文件 @
6d649d9e
...
...
@@ -12,13 +12,13 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/conv2dtranspose_op.h"
#include "paddle/operators/conv2d
_
transpose_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
conv2dtranspose
,
conv2d
_
transpose
,
ops
::
GemmConv2DTransposeKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
conv2dtranspose_grad
,
conv2d
_
transpose_grad
,
ops
::
GemmConv2DTransposeGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/conv2dtranspose_op.h
→
paddle/operators/conv2d
_
transpose_op.h
浏览文件 @
6d649d9e
...
...
@@ -62,7 +62,7 @@ class GemmConv2DTransposeKernel : public framework::OpKernel<T> {
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
// TODO(Zhuoyuan): Paddings can be added in future.
// groups will alway be disabled in conv2dtranspose.
// groups will alway be disabled in conv2d
_
transpose.
const
int
batch_size
=
input
->
dims
()[
0
];
const
int
m
=
input
->
dims
()[
1
];
...
...
python/paddle/v2/framework/tests/test_conv2dtranspose_op.py
→
python/paddle/v2/framework/tests/test_conv2d
_
transpose_op.py
浏览文件 @
6d649d9e
...
...
@@ -45,23 +45,36 @@ class TestConv2dTransposeOp(OpTest):
filter_
=
np
.
random
.
random
(
self
.
filter_size
).
astype
(
"float32"
)
output
=
conv2dtranspose_forward_naive
(
input_
,
filter_
,
conv2dtranspose_param
).
astype
(
'float32'
)
# print 'deconv output py', output, output.shape
self
.
inputs
=
{
'Input'
:
input_
,
'Filter'
:
filter_
}
self
.
attrs
=
{
'strides'
:
self
.
stride
,
'paddings'
:
self
.
pad
,
#
'dilations': self.dilations
'dilations'
:
self
.
dilations
}
self
.
outputs
=
{
'Output'
:
output
}
def
test_check_output
(
self
):
print
'check output here
'
print
'check output here
for'
,
self
.
op_type
self
.
check_output
()
def
test_check_grad
(
self
):
def
init_test_case
(
self
):
self
.
pad
=
[
0
,
0
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
def
init_op_type
(
self
):
self
.
op_type
=
"conv2d_transpose"
def
test_check_grad_no_input
(
self
):
self
.
check_grad
(
set
([
'Input'
,
'Filter'
]),
'Output'
,
max_relative_error
=
0.05
)
[
'Filter'
],
'Output'
,
max_relative_error
=
0.05
,
no_grad_set
=
set
([
'Input'
]))
def
test_check_grad_no_filter
(
self
):
self
.
check_grad
(
...
...
@@ -70,33 +83,15 @@ class TestConv2dTransposeOp(OpTest):
max_relative_error
=
0.05
,
no_grad_set
=
set
([
'Filter'
]))
def
test_check_grad
_no_input
(
self
):
def
test_check_grad
(
self
):
self
.
check_grad
(
[
'Filter'
],
'Output'
,
max_relative_error
=
0.05
,
no_grad_set
=
set
([
'Input'
]))
set
([
'Input'
,
'Filter'
]),
'Output'
,
max_relative_error
=
0.05
)
def
init_test_case
(
self
):
self
.
pad
=
[
0
,
0
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
class
TestCudnn
(
TestConv2dTransposeOp
):
def
init_op_type
(
self
):
self
.
op_type
=
"conv2dtranspose"
self
.
op_type
=
"conv2d_transpose_cudnn"
"""
class TestCudnn(TestConv2dOp):
def init_group(self):
self.groups = 1
def init_op_type(self):
self.op_type = "conv_cudnn"
"""
if
__name__
==
'__main__'
:
unittest
.
main
()
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