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1bb0e294
编写于
10月 11, 2017
作者:
C
chengduoZH
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add pool2d cudnn
上级
e9325ea8
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
390 addition
and
0 deletion
+390
-0
paddle/framework/operator.h
paddle/framework/operator.h
+9
-0
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+7
-0
paddle/operators/pool_cudnn_op.cc
paddle/operators/pool_cudnn_op.cc
+34
-0
paddle/operators/pool_cudnn_op.cu
paddle/operators/pool_cudnn_op.cu
+174
-0
paddle/operators/pool_cudnn_op.h
paddle/operators/pool_cudnn_op.h
+22
-0
python/paddle/v2/framework/tests/test_pool2d_cudnn_op.py
python/paddle/v2/framework/tests/test_pool2d_cudnn_op.py
+144
-0
未找到文件。
paddle/framework/operator.h
浏览文件 @
1bb0e294
...
...
@@ -289,6 +289,15 @@ class ExecutionContext {
return
device_context_
;
}
#ifdef PADDLE_WITH_CUDA
const
platform
::
CUDADeviceContext
&
cuda_device_context
()
const
{
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
device_context_
.
GetPlace
()));
auto
cuda_ctx
=
reinterpret_cast
<
const
platform
::
CUDADeviceContext
*>
(
&
device_context_
);
return
*
cuda_ctx
;
}
#endif // PADDLE_WITH_CUDA
private:
const
OperatorBase
&
op_
;
const
Scope
&
scope_
;
...
...
paddle/operators/CMakeLists.txt
浏览文件 @
1bb0e294
...
...
@@ -69,6 +69,13 @@ function(op_library TARGET)
file
(
APPEND
${
pybind_file
}
"USE_OP(max_pool2d_with_index);
\n
"
)
endif
()
# pool_cudnn_op contains several operators
if
(
"
${
TARGET
}
"
STREQUAL
"pool_cudnn_op"
)
set
(
pybind_flag 1
)
# It's enough to just adding one operator to pybind
file
(
APPEND
${
pybind_file
}
"USE_OP(pool2d_cudnn);
\n
"
)
endif
()
# activation_op contains several operators
if
(
"
${
TARGET
}
"
STREQUAL
"activation_op"
)
set
(
pybind_flag 1
)
...
...
paddle/operators/pool_cudnn_op.cc
0 → 100644
浏览文件 @
1bb0e294
/* 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/pool_cudnn_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
pool2d_cudnn
,
ops
::
PoolOp
,
ops
::
Pool2dOpMaker
,
pool2d_cudnn_grad
,
ops
::
PoolOpGrad
);
REGISTER_OP_CPU_KERNEL
(
pool2d_cudnn
,
ops
::
PoolKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
pool2d_cudnn_grad
,
ops
::
PoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
)
// REGISTER_OP(pool3d_cudnn, ops::PoolOp, ops::Pool3dOpMaker, pool3d_cudnn_grad,
// ops::PoolOpGrad);
//
// REGISTER_OP_CPU_KERNEL(pool3d_cudnn,
// ops::PoolKernel<paddle::platform::CPUPlace, float>);
// REGISTER_OP_CPU_KERNEL(pool3d_cudnn_grad,
// ops::PoolGradKernel<paddle::platform::CPUPlace,
// float>);
paddle/operators/pool_cudnn_op.cu
0 → 100644
浏览文件 @
1bb0e294
/* 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/pool_cudnn_op.h"
#include "paddle/platform/cudnn_helper.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
ScopedTensorDescriptor
=
platform
::
ScopedTensorDescriptor
;
using
ScopedPoolingDescriptor
=
platform
::
ScopedPoolingDescriptor
;
using
DataLayout
=
platform
::
DataLayout
;
using
PoolingMode
=
platform
::
PoolingMode
;
// NOTE: copy from conv_cudnn
std
::
vector
<
int
>
Dims2Vector
(
const
framework
::
DDim
&
dims
)
{
std
::
vector
<
int
>
ret
;
for
(
int
i
=
0
;
i
<
dims
.
size
();
i
++
)
{
ret
.
push_back
(
dims
[
i
]);
}
return
ret
;
}
template
<
typename
T
>
class
PoolCudnnOpKernel
:
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."
);
const
Tensor
*
input
=
ctx
.
Input
<
Tensor
>
(
"X"
);
Tensor
*
output
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
const
T
*
input_data
=
input
->
data
<
T
>
();
T
*
output_data
=
output
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
std
::
string
pooling_type
=
ctx
.
Attr
<
std
::
string
>
(
"poolingType"
);
std
::
vector
<
int
>
ksize
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"ksize"
);
std
::
vector
<
int
>
strides
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
if
(
ctx
.
Attr
<
bool
>
(
"globalPooling"
))
{
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
{
ksize
[
i
]
=
static_cast
<
int
>
(
input
->
dims
()[
i
+
2
]);
}
}
// ------------------- cudnn descriptors ---------------------
ScopedTensorDescriptor
input_desc
;
ScopedTensorDescriptor
output_desc
;
ScopedPoolingDescriptor
pool_desc
;
DataLayout
layout
=
DataLayout
::
kNCHW
;
cudnnTensorDescriptor_t
cudnn_input_desc
=
input_desc
.
descriptor
<
T
>
(
layout
,
Dims2Vector
(
input
->
dims
()));
cudnnTensorDescriptor_t
cudnn_output_desc
=
output_desc
.
descriptor
<
T
>
(
layout
,
Dims2Vector
(
output
->
dims
()));
PoolingMode
pooling_mode
;
if
(
pooling_type
==
"max"
)
{
pooling_mode
=
PoolingMode
::
kMaximum
;
}
else
{
pooling_mode
=
PoolingMode
::
kAverage
;
}
cudnnPoolingDescriptor_t
cudnn_pool_desc
=
pool_desc
.
descriptor
(
pooling_mode
,
ksize
,
paddings
,
strides
);
// ------------------- cudnn pool algorithm ---------------------
auto
handle
=
ctx
.
cuda_device_context
().
cudnn_handle
();
T
alpha
=
1.0
f
,
beta
=
0.0
f
;
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnPoolingForward
(
handle
,
cudnn_pool_desc
,
&
alpha
,
cudnn_input_desc
,
input_data
,
&
beta
,
cudnn_output_desc
,
output_data
));
}
};
template
<
typename
T
>
class
PoolCudnnGradOpKernel
:
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."
);
const
Tensor
*
input
=
ctx
.
Input
<
Tensor
>
(
"X"
);
const
Tensor
*
output
=
ctx
.
Input
<
Tensor
>
(
"Out"
);
const
Tensor
*
output_grad
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
Tensor
*
input_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
std
::
string
pooling_type
=
ctx
.
Attr
<
std
::
string
>
(
"poolingType"
);
std
::
vector
<
int
>
ksize
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"ksize"
);
std
::
vector
<
int
>
strides
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
if
(
ctx
.
Attr
<
bool
>
(
"globalPooling"
))
{
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
ksize
[
i
]
=
static_cast
<
int
>
(
input
->
dims
()[
i
+
2
]);
}
const
T
*
input_data
=
input
->
data
<
T
>
();
const
T
*
output_data
=
output
->
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
->
data
<
T
>
();
// ------------------- cudnn descriptors ---------------------
ScopedTensorDescriptor
input_desc
;
ScopedTensorDescriptor
output_desc
;
ScopedTensorDescriptor
input_grad_desc
;
ScopedTensorDescriptor
output_grad_desc
;
ScopedPoolingDescriptor
pool_desc
;
DataLayout
layout
=
DataLayout
::
kNCHW
;
cudnnTensorDescriptor_t
cudnn_input_desc
=
input_desc
.
descriptor
<
T
>
(
layout
,
Dims2Vector
(
input
->
dims
()));
cudnnTensorDescriptor_t
cudnn_output_desc
=
output_desc
.
descriptor
<
T
>
(
layout
,
Dims2Vector
(
output
->
dims
()));
cudnnTensorDescriptor_t
cudnn_output_grad_desc
=
output_grad_desc
.
descriptor
<
T
>
(
layout
,
Dims2Vector
(
output_grad
->
dims
()));
PoolingMode
pooling_mode
;
if
(
pooling_type
==
"max"
)
{
pooling_mode
=
PoolingMode
::
kMaximum
;
}
else
{
pooling_mode
=
PoolingMode
::
kAverage
;
}
cudnnPoolingDescriptor_t
cudnn_pool_desc
=
pool_desc
.
descriptor
(
pooling_mode
,
ksize
,
paddings
,
strides
);
// ------------------- cudnn pool algorithm ---------------------
auto
handle
=
ctx
.
cuda_device_context
().
cudnn_handle
();
T
alpha
=
1.0
f
,
beta
=
0.0
f
;
if
(
input_grad
)
{
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
temp
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
input_grad
);
temp
.
device
(
ctx
.
GetEigenDevice
<
paddle
::
platform
::
GPUPlace
>
())
=
temp
.
constant
(
static_cast
<
T
>
(
0
));
cudnnTensorDescriptor_t
cudnn_input_grad_desc
=
input_grad_desc
.
descriptor
<
T
>
(
layout
,
Dims2Vector
(
input_grad
->
dims
()));
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnPoolingBackward
(
handle
,
cudnn_pool_desc
,
&
alpha
,
cudnn_output_desc
,
output_data
,
cudnn_output_grad_desc
,
output_grad_data
,
cudnn_input_desc
,
input_data
,
&
beta
,
cudnn_input_grad_desc
,
input_grad_data
));
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
pool2d_cudnn
,
ops
::
PoolCudnnOpKernel
<
float
>
);
REGISTER_OP_GPU_KERNEL
(
pool2d_cudnn_grad
,
ops
::
PoolCudnnGradOpKernel
<
float
>
);
//
// REGISTER_OP_GPU_KERNEL(pool3d_cudnn, ops::PoolCudnnOpKernel<float>);
// REGISTER_OP_GPU_KERNEL(pool3d_cudnn_grad, ops::PoolCudnnGradOpKernel<float>);
paddle/operators/pool_cudnn_op.h
0 → 100644
浏览文件 @
1bb0e294
/* 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. */
#pragma once
#include "paddle/framework/op_registry.h"
#include "paddle/operators/pool_op.h"
namespace
paddle
{
namespace
operators
{}
// namespace operators
}
// namespace paddle
python/paddle/v2/framework/tests/test_pool2d_cudnn_op.py
0 → 100644
浏览文件 @
1bb0e294
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
def
max_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
=
[
0
,
0
],
global_pool
=
0
):
N
,
C
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
H
,
W
]
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
out
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
for
i
in
xrange
(
H_out
):
for
j
in
xrange
(
W_out
):
r_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
r_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
c_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
c_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
x_masked
=
x
[:,
:,
r_start
:
r_end
,
c_start
:
c_end
]
out
[:,
:,
i
,
j
]
=
np
.
max
(
x_masked
,
axis
=
(
2
,
3
))
return
out
def
avg_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
=
[
0
,
0
],
global_pool
=
0
):
N
,
C
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
H
,
W
]
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
out
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
for
i
in
xrange
(
H_out
):
for
j
in
xrange
(
W_out
):
r_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
r_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
c_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
c_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
x_masked
=
x
[:,
:,
r_start
:
r_end
,
c_start
:
c_end
]
out
[:,
:,
i
,
j
]
=
np
.
sum
(
x_masked
,
axis
=
(
2
,
3
))
/
(
(
r_end
-
r_start
)
*
(
c_end
-
c_start
))
return
out
class
TestPool2d_cudnn_Op
(
OpTest
):
def
setUp
(
self
):
self
.
initTestCase
()
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
"float32"
)
output
=
self
.
pool2D_forward_naive
(
input
,
self
.
ksize
,
self
.
strides
,
self
.
paddings
,
self
.
global_pool
)
self
.
inputs
=
{
'X'
:
input
}
self
.
attrs
=
{
'strides'
:
self
.
strides
,
'paddings'
:
self
.
paddings
,
'ksize'
:
self
.
ksize
,
'poolingType'
:
self
.
pool_type
,
'globalPooling'
:
self
.
global_pool
,
}
self
.
outputs
=
{
'Out'
:
output
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
if
self
.
pool_type
!=
"max"
:
self
.
check_grad
(
set
([
'X'
]),
'Out'
,
max_relative_error
=
0.07
)
def
initTestCase
(
self
):
self
.
global_pool
=
True
self
.
op_type
=
"pool2d_cudnn"
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
5
,
5
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
class
TestCase1
(
TestPool2d_cudnn_Op
):
def
initTestCase
(
self
):
self
.
global_pool
=
False
self
.
op_type
=
"pool2d_cudnn"
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
class
TestCase2
(
TestPool2d_cudnn_Op
):
def
initTestCase
(
self
):
self
.
global_pool
=
False
self
.
op_type
=
"pool2d_cudnn"
self
.
pool_type
=
"avg"
self
.
pool2D_forward_naive
=
avg_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
1
,
1
]
class
TestCase3
(
TestPool2d_cudnn_Op
):
def
initTestCase
(
self
):
self
.
global_pool
=
True
self
.
op_type
=
"pool2d_cudnn"
self
.
pool_type
=
"max"
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
5
,
5
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
class
TestCase4
(
TestPool2d_cudnn_Op
):
def
initTestCase
(
self
):
self
.
global_pool
=
False
self
.
op_type
=
"pool2d_cudnn"
self
.
pool_type
=
"max"
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
0
,
0
]
class
TestCase5
(
TestPool2d_cudnn_Op
):
def
initTestCase
(
self
):
self
.
global_pool
=
False
self
.
op_type
=
"pool2d_cudnn"
self
.
pool_type
=
"max"
self
.
pool2D_forward_naive
=
max_pool2D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
]
self
.
ksize
=
[
3
,
3
]
self
.
strides
=
[
1
,
1
]
self
.
paddings
=
[
1
,
1
]
if
__name__
==
'__main__'
:
unittest
.
main
()
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