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56faf513
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56faf513
编写于
8月 09, 2017
作者:
D
dzhwinter
提交者:
GitHub
8月 09, 2017
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差异文件
Merge pull request #3060 from dzhwinter/random_op
Random op
上级
1ea91ca3
f702e797
变更
7
显示空白变更内容
内联
并排
Showing
7 changed file
with
177 addition
and
0 deletion
+177
-0
paddle/framework/CMakeLists.txt
paddle/framework/CMakeLists.txt
+1
-0
paddle/framework/pybind.cc
paddle/framework/pybind.cc
+2
-0
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+1
-0
paddle/operators/gaussian_random_op.cc
paddle/operators/gaussian_random_op.cc
+82
-0
paddle/operators/gaussian_random_op.cu
paddle/operators/gaussian_random_op.cu
+52
-0
python/paddle/v2/framework/tests/CMakeLists.txt
python/paddle/v2/framework/tests/CMakeLists.txt
+3
-0
python/paddle/v2/framework/tests/test_gaussian_random_op.py
python/paddle/v2/framework/tests/test_gaussian_random_op.py
+36
-0
未找到文件。
paddle/framework/CMakeLists.txt
浏览文件 @
56faf513
...
...
@@ -50,5 +50,6 @@ cc_library(paddle_pybind SHARED
cross_entropy_op
recurrent_op
uniform_random_op
gaussian_random_op
fill_zeros_like_op
)
endif
(
WITH_PYTHON
)
paddle/framework/pybind.cc
浏览文件 @
56faf513
...
...
@@ -40,7 +40,9 @@ USE_OP(softmax);
USE_OP
(
rowwise_add
);
USE_OP
(
fill_zeros_like
);
USE_OP_WITHOUT_KERNEL
(
recurrent_op
);
USE_OP
(
gaussian_random
);
USE_OP
(
uniform_random
);
namespace
paddle
{
namespace
framework
{
...
...
paddle/operators/CMakeLists.txt
浏览文件 @
56faf513
...
...
@@ -53,6 +53,7 @@ op_library(rowwise_add_op SRCS rowwise_add_op.cu rowwise_add_op.cc)
op_library
(
sigmoid_op SRCS sigmoid_op.cc sigmoid_op.cu
)
op_library
(
softmax_op SRCS softmax_op.cc softmax_op.cu
)
op_library
(
gaussian_random_op SRCS gaussian_random_op.cc gaussian_random_op.cu
)
op_library
(
cross_entropy_op SRCS cross_entropy_op.cc cross_entropy_op.cu
)
op_library
(
fill_zeros_like_op SRCS fill_zeros_like_op.cc fill_zeros_like_op.cu
)
...
...
paddle/operators/gaussian_random_op.cc
0 → 100644
浏览文件 @
56faf513
/* 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 <random>
#include "paddle/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
class
GaussianRandomKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
float
mean
=
context
.
op_
.
GetAttr
<
float
>
(
"mean"
);
float
std
=
context
.
op_
.
GetAttr
<
float
>
(
"std"
);
auto
*
tensor
=
context
.
Output
<
framework
::
Tensor
>
(
0
);
T
*
data
=
tensor
->
mutable_data
<
T
>
(
context
.
GetPlace
());
// TODO(dzh): attribute does not support unsigned int.
// And we need a global random seed configuration.
int
seed
=
context
.
op_
.
GetAttr
<
int
>
(
"seed"
);
if
(
seed
==
0
)
{
seed
=
std
::
random_device
()();
}
std
::
mt19937
g
(
seed
);
std
::
normal_distribution
<
T
>
distribution
(
mean
,
std
);
ssize_t
size
=
framework
::
product
(
tensor
->
dims
());
for
(
int
i
=
0
;
i
<
size
;
++
i
)
{
data
[
i
]
=
distribution
(
g
);
}
}
};
class
GaussianRandomOp
:
public
framework
::
OperatorWithKernel
{
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
context
)
const
override
{
auto
*
tensor
=
context
.
Output
<
framework
::
Tensor
>
(
0
);
auto
dims
=
GetAttr
<
std
::
vector
<
int
>>
(
"dims"
);
PADDLE_ENFORCE
(
dims
.
size
()
>
0UL
,
"dims can be one int or array. dims must be set."
);
tensor
->
Resize
(
framework
::
make_ddim
(
dims
));
}
};
class
GaussianRandomOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
GaussianRandomOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddOutput
(
"Out"
,
"output matrix of random op"
);
AddComment
(
R"DOC(
GaussianRandom operator.
Use to initialize tensor with gaussian random generator.
)DOC"
);
AddAttr
<
std
::
vector
<
int
>>
(
"dims"
,
"The dimension of random tensor."
);
AddAttr
<
float
>
(
"mean"
,
"mean value of random."
).
SetDefault
(
.0
f
);
AddAttr
<
float
>
(
"std"
,
"minimum value of random value."
).
SetDefault
(
1.0
f
);
AddAttr
<
int
>
(
"seed"
,
"Random seed of generator."
"0 means use system wide seed"
)
.
SetDefault
(
0
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
gaussian_random
,
ops
::
GaussianRandomOp
,
ops
::
GaussianRandomOpMaker
);
REGISTER_OP_CPU_KERNEL
(
gaussian_random
,
ops
::
GaussianRandomKernel
<
float
>
);
paddle/operators/gaussian_random_op.cu
0 → 100644
浏览文件 @
56faf513
/* 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 <memory>
#include <random>
#include "paddle/platform/dynload/curand.h"
#include "paddle/platform/gpu_info.h"
#include "paddle/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
class
GaussianRandomKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
float
mean
=
context
.
op_
.
GetAttr
<
float
>
(
"mean"
);
float
std
=
context
.
op_
.
GetAttr
<
float
>
(
"std"
);
auto
*
tensor
=
context
.
Output
<
framework
::
Tensor
>
(
0
);
T
*
data
=
tensor
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
seed
=
context
.
op_
.
GetAttr
<
int
>
(
"seed"
);
if
(
seed
==
0
)
{
seed
=
std
::
random_device
()();
}
curandGenerator_t
g
;
PADDLE_ENFORCE
(
platform
::
dynload
::
curandCreateGenerator
(
&
g
,
CURAND_RNG_PSEUDO_DEFAULT
));
PADDLE_ENFORCE
(
platform
::
dynload
::
curandSetPseudoRandomGeneratorSeed
(
g
,
seed
));
curandGenerateNormal
(
g
,
data
,
framework
::
product
(
tensor
->
dims
()),
mean
,
std
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
gaussian_random
,
ops
::
GaussianRandomKernel
<
float
>
);
\ No newline at end of file
python/paddle/v2/framework/tests/CMakeLists.txt
浏览文件 @
56faf513
...
...
@@ -21,5 +21,8 @@ py_test(gradient_checker SRCS gradient_checker.py)
py_test
(
test_rowwise_add_op SRCS test_rowwise_add_op.py
)
py_test
(
test_default_scope_funcs SRCS test_default_scope_funcs.py
)
py_test
(
test_operator SRCS test_operator.py
)
py_test
(
test_gaussian_random_op SRCS test_gaussian_random_op.py
)
py_test
(
test_uniform_random_op SRCS test_uniform_random_op.py
)
python/paddle/v2/framework/tests/test_gaussian_random_op.py
0 → 100644
浏览文件 @
56faf513
import
unittest
import
paddle.v2.framework.core
as
core
from
paddle.v2.framework.op
import
Operator
import
numpy
class
GaussianRandomTest
(
unittest
.
TestCase
):
def
test_cpu
(
self
):
self
.
gaussian_random_test
(
place
=
core
.
CPUPlace
())
def
test_gpu
(
self
):
if
core
.
is_compile_gpu
():
self
.
gaussian_random_test
(
place
=
core
.
GPUPlace
(
0
))
def
gaussian_random_test
(
self
,
place
):
scope
=
core
.
Scope
()
scope
.
new_var
(
"Out"
).
get_tensor
()
op
=
Operator
(
"gaussian_random"
,
Out
=
"Out"
,
dims
=
[
1000
,
784
],
mean
=
.
0
,
std
=
1.
,
seed
=
10
)
op
.
infer_shape
(
scope
)
context
=
core
.
DeviceContext
.
create
(
place
)
op
.
run
(
scope
,
context
)
tensor
=
numpy
.
array
(
scope
.
find_var
(
"Out"
).
get_tensor
())
self
.
assertAlmostEqual
(
numpy
.
mean
(
tensor
),
.
0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
numpy
.
std
(
tensor
),
1.
,
delta
=
0.1
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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