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体验新版 GitCode,发现更多精彩内容 >>
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53bdee64
编写于
5月 13, 2020
作者:
W
wangchaochaohu
提交者:
GitHub
5月 13, 2020
浏览文件
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电子邮件补丁
差异文件
add tensor support for gaussian_random_op test=develop (#24389)
上级
da4a1db7
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
354 addition
and
133 deletion
+354
-133
paddle/fluid/operators/fill_constant_op.h
paddle/fluid/operators/fill_constant_op.h
+7
-28
paddle/fluid/operators/gaussian_random_op.cc
paddle/fluid/operators/gaussian_random_op.cc
+74
-10
paddle/fluid/operators/gaussian_random_op.cu
paddle/fluid/operators/gaussian_random_op.cu
+30
-4
paddle/fluid/operators/mkldnn/gaussian_random_mkldnn_op.cc
paddle/fluid/operators/mkldnn/gaussian_random_mkldnn_op.cc
+6
-1
python/paddle/fluid/layers/distributions.py
python/paddle/fluid/layers/distributions.py
+3
-2
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+55
-22
python/paddle/fluid/tests/unittests/mkldnn/test_gaussian_random_mkldnn_op.py
.../tests/unittests/mkldnn/test_gaussian_random_mkldnn_op.py
+5
-7
python/paddle/fluid/tests/unittests/test_gaussian_random_op.py
...n/paddle/fluid/tests/unittests/test_gaussian_random_op.py
+174
-59
未找到文件。
paddle/fluid/operators/fill_constant_op.h
浏览文件 @
53bdee64
...
@@ -20,48 +20,26 @@ limitations under the License. */
...
@@ -20,48 +20,26 @@ limitations under the License. */
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/utils.h"
namespace
paddle
{
namespace
paddle
{
namespace
operators
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
Tensor
=
framework
::
Tensor
;
inline
framework
::
DDim
GetShape
(
const
framework
::
ExecutionContext
&
ctx
)
{
inline
framework
::
DDim
GetShape
(
const
framework
::
ExecutionContext
&
ctx
,
std
::
string
op_type
)
{
// 1. shape is a Tensor
// 1. shape is a Tensor
if
(
ctx
.
HasInput
(
"ShapeTensor"
))
{
if
(
ctx
.
HasInput
(
"ShapeTensor"
))
{
auto
*
shape_tensor
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"ShapeTensor"
);
auto
*
shape_tensor
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"ShapeTensor"
);
auto
*
shape_data
=
shape_tensor
->
data
<
int
>
();
auto
vec_shape
=
GetDataFromTensor
<
int
>
(
shape_tensor
);
framework
::
Tensor
cpu_shape_tensor
;
if
(
platform
::
is_gpu_place
(
shape_tensor
->
place
()))
{
TensorCopySync
(
*
shape_tensor
,
platform
::
CPUPlace
(),
&
cpu_shape_tensor
);
shape_data
=
cpu_shape_tensor
.
data
<
int
>
();
}
auto
vec_shape
=
std
::
vector
<
int
>
(
shape_data
,
shape_data
+
shape_tensor
->
numel
());
return
framework
::
make_ddim
(
vec_shape
);
return
framework
::
make_ddim
(
vec_shape
);
}
}
// 2. shape is a list/tuple containing Tensor
// 2. shape is a list/tuple containing Tensor
auto
shape_tensor_list
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"ShapeTensorList"
);
auto
shape_tensor_list
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"ShapeTensorList"
);
if
(
shape_tensor_list
.
size
()
>
0
)
{
if
(
shape_tensor_list
.
size
()
>
0
)
{
std
::
vector
<
int
>
vec_shape
;
auto
vec_shape
=
GetDataFromTensorList
(
shape_tensor_list
);
for
(
size_t
i
=
0
;
i
<
shape_tensor_list
.
size
();
++
i
)
{
auto
tensor
=
shape_tensor_list
[
i
];
PADDLE_ENFORCE_EQ
(
tensor
->
dims
(),
framework
::
make_ddim
({
1
}),
platform
::
errors
::
InvalidArgument
(
"If the element type of 'shape'(tensor_list type) in "
"FillConstantOp is Tensor, the shape of this Tensor element must "
"be [1]. But received the Tensor element's shape is [%s]"
,
tensor
->
dims
()));
if
(
platform
::
is_gpu_place
(
tensor
->
place
()))
{
framework
::
Tensor
temp
;
TensorCopySync
(
*
tensor
,
platform
::
CPUPlace
(),
&
temp
);
vec_shape
.
push_back
(
*
temp
.
data
<
int
>
());
}
else
{
vec_shape
.
push_back
(
*
tensor
->
data
<
int
>
());
}
}
return
framework
::
make_ddim
(
vec_shape
);
return
framework
::
make_ddim
(
vec_shape
);
}
}
...
@@ -115,7 +93,8 @@ class FillConstantKernel : public framework::OpKernel<T> {
...
@@ -115,7 +93,8 @@ class FillConstantKernel : public framework::OpKernel<T> {
}
}
value
=
tensor_data
[
0
];
value
=
tensor_data
[
0
];
}
}
auto
shape
=
GetShape
(
ctx
);
const
std
::
string
op_type
=
"fill_constant"
;
auto
shape
=
GetShape
(
ctx
,
op_type
);
if
(
out_var
->
IsType
<
framework
::
LoDTensor
>
())
{
if
(
out_var
->
IsType
<
framework
::
LoDTensor
>
())
{
tensor
=
out_var
->
GetMutable
<
framework
::
LoDTensor
>
();
tensor
=
out_var
->
GetMutable
<
framework
::
LoDTensor
>
();
...
...
paddle/fluid/operators/gaussian_random_op.cc
浏览文件 @
53bdee64
...
@@ -14,7 +14,7 @@ limitations under the License. */
...
@@ -14,7 +14,7 @@ limitations under the License. */
#include <random>
#include <random>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/fill_constant_op.h"
#ifdef PADDLE_WITH_MKLDNN
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
#endif
...
@@ -22,8 +22,37 @@ limitations under the License. */
...
@@ -22,8 +22,37 @@ limitations under the License. */
namespace
paddle
{
namespace
paddle
{
namespace
operators
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
template
<
typename
T
>
class
CPUGaussianRandomKernel
:
public
framework
::
OpKernel
<
T
>
{
class
CPUGaussianRandomKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
float
mean
=
context
.
Attr
<
float
>
(
"mean"
);
float
std
=
context
.
Attr
<
float
>
(
"std"
);
auto
*
tensor
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
unsigned
int
seed
=
static_cast
<
unsigned
int
>
(
context
.
Attr
<
int
>
(
"seed"
));
std
::
minstd_rand
engine
;
if
(
seed
==
0
)
{
seed
=
std
::
random_device
()();
}
engine
.
seed
(
seed
);
std
::
normal_distribution
<
T
>
dist
(
mean
,
std
);
const
std
::
string
op_type
=
"gaussian_random"
;
auto
shape
=
GetShape
(
context
,
op_type
);
tensor
->
Resize
(
shape
);
int64_t
size
=
tensor
->
numel
();
T
*
data
=
tensor
->
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int64_t
i
=
0
;
i
<
size
;
++
i
)
{
data
[
i
]
=
dist
(
engine
);
}
}
};
template
<
typename
T
>
class
CPUGaussianRandomBatchSizeLikeKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
float
mean
=
context
.
Attr
<
float
>
(
"mean"
);
float
mean
=
context
.
Attr
<
float
>
(
"mean"
);
...
@@ -58,12 +87,26 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
...
@@ -58,12 +87,26 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
for
(
auto
dim
:
shape
)
{
for
(
auto
dim
:
shape
)
{
temp
.
push_back
(
static_cast
<
int64_t
>
(
dim
));
temp
.
push_back
(
static_cast
<
int64_t
>
(
dim
));
}
}
PADDLE_ENFORCE_GT
(
if
(
shape
.
empty
()
&&
ctx
->
HasInput
(
"ShapeTensor"
))
{
shape
.
size
(),
0UL
,
auto
shape_dims
=
ctx
->
GetInputDim
(
"ShapeTensor"
);
platform
::
errors
::
InvalidArgument
(
int
num_ele
=
1
;
"Attribute(shape) of GaussianRandomOp must be set "
for
(
int
i
=
0
;
i
<
shape_dims
.
size
();
++
i
)
{
"and shape.size() > 0, but reveived shape.size() is %d"
,
num_ele
*=
shape_dims
[
i
];
shape
.
size
()));
}
auto
vec_dims
=
std
::
vector
<
int
>
(
num_ele
,
-
1
);
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
vec_dims
));
return
;
}
if
(
!
(
ctx
->
HasInput
(
"ShapeTensor"
)
&&
!
ctx
->
HasInputs
(
"ShapeTensorList"
)))
{
PADDLE_ENFORCE_GT
(
shape
.
size
(),
0UL
,
platform
::
errors
::
InvalidArgument
(
"Attribute(shape) of GaussianRandomOp must be set "
"and shape.size() > 0, but reveived shape.size() is %d"
,
shape
.
size
()));
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
temp
));
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
temp
));
}
}
...
@@ -85,6 +128,16 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
...
@@ -85,6 +128,16 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
static_cast
<
framework
::
proto
::
VarType
::
Type
>
(
ctx
.
Attr
<
int
>
(
"dtype"
)),
static_cast
<
framework
::
proto
::
VarType
::
Type
>
(
ctx
.
Attr
<
int
>
(
"dtype"
)),
ctx
.
device_context
(),
layout
,
library
);
ctx
.
device_context
(),
layout
,
library
);
}
}
framework
::
OpKernelType
GetKernelTypeForVar
(
const
std
::
string
&
var_name
,
const
Tensor
&
tensor
,
const
framework
::
OpKernelType
&
expected_kernel_type
)
const
override
{
if
(
var_name
==
"ShapeTensor"
||
var_name
==
"ShapeTensorList"
)
{
return
expected_kernel_type
;
}
return
framework
::
OpKernelType
(
expected_kernel_type
.
data_type_
,
tensor
.
place
(),
tensor
.
layout
());
}
};
};
class
GaussianRandomOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
class
GaussianRandomOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
...
@@ -94,7 +147,18 @@ class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker {
...
@@ -94,7 +147,18 @@ class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr
<
std
::
vector
<
int64_t
>>
(
"shape"
,
AddAttr
<
std
::
vector
<
int64_t
>>
(
"shape"
,
"(vector<int64_t>) "
"(vector<int64_t>) "
"The dimension of random tensor."
);
"The dimension of random tensor."
)
.
SetDefault
({});
AddInput
(
"ShapeTensor"
,
"(Tensor<int>), optional). The shape of the output."
"It has a higher priority than Attr(shape)."
)
.
AsDispensable
();
AddInput
(
"ShapeTensorList"
,
"(vector<Tensor<int>>, optional). The shape of the output. "
"It has a higher priority than Attr(shape)."
"The shape of the element in vector must be [1]."
)
.
AsDuplicable
()
.
AsDispensable
();
AddAttr
<
float
>
(
"mean"
,
AddAttr
<
float
>
(
"mean"
,
"(float, default 0.0) "
"(float, default 0.0) "
"mean of random tensor."
)
"mean of random tensor."
)
...
@@ -135,5 +199,5 @@ REGISTER_OP_WITHOUT_GRADIENT(gaussian_random, ops::GaussianRandomOp,
...
@@ -135,5 +199,5 @@ REGISTER_OP_WITHOUT_GRADIENT(gaussian_random, ops::GaussianRandomOp,
REGISTER_OP_CPU_KERNEL
(
gaussian_random
,
ops
::
CPUGaussianRandomKernel
<
float
>
,
REGISTER_OP_CPU_KERNEL
(
gaussian_random
,
ops
::
CPUGaussianRandomKernel
<
float
>
,
ops
::
CPUGaussianRandomKernel
<
double
>
);
ops
::
CPUGaussianRandomKernel
<
double
>
);
REGISTER_OP_CPU_KERNEL
(
gaussian_random_batch_size_like
,
REGISTER_OP_CPU_KERNEL
(
gaussian_random_batch_size_like
,
ops
::
CPUGaussianRandomKernel
<
float
>
,
ops
::
CPUGaussianRandom
BatchSizeLike
Kernel
<
float
>
,
ops
::
CPUGaussianRandomKernel
<
double
>
);
ops
::
CPUGaussianRandom
BatchSizeLike
Kernel
<
double
>
);
paddle/fluid/operators/gaussian_random_op.cu
浏览文件 @
53bdee64
...
@@ -15,6 +15,7 @@ limitations under the License. */
...
@@ -15,6 +15,7 @@ limitations under the License. */
#include <thrust/transform.h>
#include <thrust/transform.h>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/fill_constant_op.h"
namespace
paddle
{
namespace
paddle
{
namespace
operators
{
namespace
operators
{
...
@@ -41,7 +42,6 @@ class GPUGaussianRandomKernel : public framework::OpKernel<T> {
...
@@ -41,7 +42,6 @@ class GPUGaussianRandomKernel : public framework::OpKernel<T> {
public:
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
tensor
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
*
tensor
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
T
*
data
=
tensor
->
mutable_data
<
T
>
(
context
.
GetPlace
());
unsigned
int
seed
=
static_cast
<
unsigned
int
>
(
context
.
Attr
<
int
>
(
"seed"
));
unsigned
int
seed
=
static_cast
<
unsigned
int
>
(
context
.
Attr
<
int
>
(
"seed"
));
if
(
seed
==
0
)
{
if
(
seed
==
0
)
{
std
::
random_device
rd
;
std
::
random_device
rd
;
...
@@ -50,6 +50,11 @@ class GPUGaussianRandomKernel : public framework::OpKernel<T> {
...
@@ -50,6 +50,11 @@ class GPUGaussianRandomKernel : public framework::OpKernel<T> {
T
mean
=
static_cast
<
T
>
(
context
.
Attr
<
float
>
(
"mean"
));
T
mean
=
static_cast
<
T
>
(
context
.
Attr
<
float
>
(
"mean"
));
T
std
=
static_cast
<
T
>
(
context
.
Attr
<
float
>
(
"std"
));
T
std
=
static_cast
<
T
>
(
context
.
Attr
<
float
>
(
"std"
));
thrust
::
counting_iterator
<
unsigned
int
>
index_sequence_begin
(
0
);
thrust
::
counting_iterator
<
unsigned
int
>
index_sequence_begin
(
0
);
const
std
::
string
op_type
=
"gaussian_random"
;
auto
shape
=
GetShape
(
context
,
op_type
);
tensor
->
Resize
(
shape
);
T
*
data
=
tensor
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int64_t
size
=
tensor
->
numel
();
int64_t
size
=
tensor
->
numel
();
thrust
::
transform
(
index_sequence_begin
,
index_sequence_begin
+
size
,
thrust
::
transform
(
index_sequence_begin
,
index_sequence_begin
+
size
,
thrust
::
device_ptr
<
T
>
(
data
),
thrust
::
device_ptr
<
T
>
(
data
),
...
@@ -57,12 +62,33 @@ class GPUGaussianRandomKernel : public framework::OpKernel<T> {
...
@@ -57,12 +62,33 @@ class GPUGaussianRandomKernel : public framework::OpKernel<T> {
}
}
};
};
template
<
typename
T
>
class
GPUGaussianRandomBatchSizeLikeKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
tensor
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
T
*
data
=
tensor
->
mutable_data
<
T
>
(
context
.
GetPlace
());
unsigned
int
seed
=
static_cast
<
unsigned
int
>
(
context
.
Attr
<
int
>
(
"seed"
));
if
(
seed
==
0
)
{
std
::
random_device
rd
;
seed
=
rd
();
}
T
mean
=
static_cast
<
T
>
(
context
.
Attr
<
float
>
(
"mean"
));
T
std
=
static_cast
<
T
>
(
context
.
Attr
<
float
>
(
"std"
));
thrust
::
counting_iterator
<
unsigned
int
>
index_sequence_begin
(
0
);
int64_t
size
=
tensor
->
numel
();
thrust
::
transform
(
index_sequence_begin
,
index_sequence_begin
+
size
,
thrust
::
device_ptr
<
T
>
(
data
),
GaussianGenerator
<
T
>
(
mean
,
std
,
seed
));
}
};
}
// namespace operators
}
// namespace operators
}
// namespace paddle
}
// namespace paddle
REGISTER_OP_CUDA_KERNEL
(
gaussian_random
,
REGISTER_OP_CUDA_KERNEL
(
gaussian_random
,
paddle
::
operators
::
GPUGaussianRandomKernel
<
float
>
,
paddle
::
operators
::
GPUGaussianRandomKernel
<
float
>
,
paddle
::
operators
::
GPUGaussianRandomKernel
<
double
>
);
paddle
::
operators
::
GPUGaussianRandomKernel
<
double
>
);
REGISTER_OP_CUDA_KERNEL
(
gaussian_random_batch_size_like
,
REGISTER_OP_CUDA_KERNEL
(
paddle
::
operators
::
GPUGaussianRandomKernel
<
float
>
,
gaussian_random_batch_size_like
,
paddle
::
operators
::
GPUGaussianRandomKernel
<
double
>
);
paddle
::
operators
::
GPUGaussianRandomBatchSizeLikeKernel
<
float
>
,
paddle
::
operators
::
GPUGaussianRandomBatchSizeLikeKernel
<
double
>
);
paddle/fluid/operators/mkldnn/gaussian_random_mkldnn_op.cc
浏览文件 @
53bdee64
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
limitations under the License. */
#include <string>
#include <string>
#include "paddle/fluid/operators/fill_constant_op.h"
#include "paddle/fluid/operators/mean_op.h"
#include "paddle/fluid/operators/mean_op.h"
namespace
paddle
{
namespace
paddle
{
...
@@ -26,7 +27,6 @@ class GaussianMKLDNNKernel : public paddle::framework::OpKernel<T> {
...
@@ -26,7 +27,6 @@ class GaussianMKLDNNKernel : public paddle::framework::OpKernel<T> {
float
mean
=
context
.
Attr
<
float
>
(
"mean"
);
float
mean
=
context
.
Attr
<
float
>
(
"mean"
);
float
std
=
context
.
Attr
<
float
>
(
"std"
);
float
std
=
context
.
Attr
<
float
>
(
"std"
);
auto
*
tensor
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
*
tensor
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
T
*
data
=
tensor
->
mutable_data
<
T
>
(
context
.
GetPlace
());
unsigned
int
seed
=
static_cast
<
unsigned
int
>
(
context
.
Attr
<
int
>
(
"seed"
));
unsigned
int
seed
=
static_cast
<
unsigned
int
>
(
context
.
Attr
<
int
>
(
"seed"
));
std
::
minstd_rand
engine
;
std
::
minstd_rand
engine
;
...
@@ -35,6 +35,11 @@ class GaussianMKLDNNKernel : public paddle::framework::OpKernel<T> {
...
@@ -35,6 +35,11 @@ class GaussianMKLDNNKernel : public paddle::framework::OpKernel<T> {
}
}
engine
.
seed
(
seed
);
engine
.
seed
(
seed
);
std
::
normal_distribution
<
T
>
dist
(
mean
,
std
);
std
::
normal_distribution
<
T
>
dist
(
mean
,
std
);
const
std
::
string
op_type
=
"gaussian_random"
;
auto
shape
=
GetShape
(
context
,
op_type
);
tensor
->
Resize
(
shape
);
T
*
data
=
tensor
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int64_t
size
=
tensor
->
numel
();
int64_t
size
=
tensor
->
numel
();
for
(
int64_t
i
=
0
;
i
<
size
;
++
i
)
{
for
(
int64_t
i
=
0
;
i
<
size
;
++
i
)
{
data
[
i
]
=
dist
(
engine
);
data
[
i
]
=
dist
(
engine
);
...
...
python/paddle/fluid/layers/distributions.py
浏览文件 @
53bdee64
...
@@ -357,8 +357,9 @@ class Normal(Distribution):
...
@@ -357,8 +357,9 @@ class Normal(Distribution):
output_shape
=
shape
+
batch_shape
output_shape
=
shape
+
batch_shape
zero_tmp
=
tensor
.
fill_constant_batch_size_like
(
zero_tmp
=
tensor
.
fill_constant_batch_size_like
(
self
.
loc
+
self
.
scale
,
batch_shape
+
shape
,
self
.
loc
.
dtype
,
0.
)
self
.
loc
+
self
.
scale
,
batch_shape
+
shape
,
self
.
loc
.
dtype
,
0.
)
normal_random_tmp
=
nn
.
gaussian_random_batch_size_like
(
zero_tmp_shape
=
nn
.
shape
(
zero_tmp
)
zero_tmp
,
zero_tmp
.
shape
,
mean
=
0.
,
std
=
1.
,
seed
=
seed
)
normal_random_tmp
=
nn
.
gaussian_random
(
zero_tmp_shape
,
mean
=
0.
,
std
=
1.
,
seed
=
seed
)
output
=
normal_random_tmp
*
(
zero_tmp
+
self
.
scale
)
+
self
.
loc
output
=
normal_random_tmp
*
(
zero_tmp
+
self
.
scale
)
+
self
.
loc
return
nn
.
reshape
(
output
,
output_shape
)
return
nn
.
reshape
(
output
,
output_shape
)
else
:
else
:
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
53bdee64
...
@@ -10169,33 +10169,55 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
...
@@ -10169,33 +10169,55 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
Generate a random tensor whose data is drawn from a Gaussian distribution.
Generate a random tensor whose data is drawn from a Gaussian distribution.
Args:
Args:
shape (
Tuple[int] | List[int
]): Shape of the generated random tensor.
shape (
tuple[int] | list[int] | Variable | list[Variable
]): Shape of the generated random tensor.
mean (float): Mean of the random tensor, defaults to 0.0.
mean (float): Mean of the random tensor, defaults to 0.0.
std (float): Standard deviation of the random tensor, defaults to 1.0.
std (float): Standard deviation of the random tensor, defaults to 1.0.
seed (int): ${seed_comment}
seed (int): ${seed_comment}
dtype(np.dtype | core.VarDesc.VarType | str): Output data type, float32 or float64.
dtype(np.dtype | core.VarDesc.VarType | str): Output data type, float32 or float64.
Returns:
Returns:
Variable: Random tensor whose data is drawn from a Gaussian distribution, dtype: flaot32 or float64 as specified.
Variable: Random tensor whose data is drawn from a Gaussian distribution, dtype: flaot32 or float64 as specified.
Examples:
Examples:
.. code-block:: python
.. code-block:: python
# declarative mode
import paddle.fluid as fluid
# example 1:
# attr shape is a list which doesn't contain tensor Variable.
result_1 = fluid.layers.gaussian_random(shape=[3, 4])
# example 2:
# attr shape is a list which contains tensor Variable.
dim_1 = fluid.layers.fill_constant([1],"int64",3)
dim_2 = fluid.layers.fill_constant([1],"int32",5)
result_2 = fluid.layers.gaussian_random(shape=[dim_1, dim_2])
# example 3:
# attr shape is a Variable, the data type must be int64 or int32.
var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
result_3 = fluid.layers.gaussian_random(var_shape)
var_shape_int32 = fluid.data(name='var_shape_int32', shape=[2], dtype="int32")
result_4 = fluid.layers.gaussian_random(var_shape_int32)
.. code-block:: python
# declarative mode
import numpy as np
import numpy as np
from paddle import fluid
from paddle import fluid
x = fluid.layers.gaussian_random((2, 3), std=2., seed=10)
x = fluid.layers.gaussian_random((2, 3), std=2., seed=10)
place = fluid.CPUPlace()
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe = fluid.Executor(place)
start = fluid.default_startup_program()
start = fluid.default_startup_program()
main = fluid.default_main_program()
main = fluid.default_main_program()
exe.run(start)
exe.run(start)
x_np, = exe.run(main, feed={}, fetch_list=[x])
x_np, = exe.run(main, feed={}, fetch_list=[x])
...
@@ -10209,33 +10231,44 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
...
@@ -10209,33 +10231,44 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
import numpy as np
import numpy as np
from paddle import fluid
from paddle import fluid
import paddle.fluid.dygraph as dg
import paddle.fluid.dygraph as dg
place = fluid.CPUPlace()
place = fluid.CPUPlace()
with dg.guard(place) as g:
with dg.guard(place) as g:
x = fluid.layers.gaussian_random((2, 4), mean=2., dtype="float32", seed=10)
x = fluid.layers.gaussian_random((2, 4), mean=2., dtype="float32", seed=10)
x_np = x.numpy()
x_np = x.numpy()
x_np
x_np
# array([[2.3060477 , 2.676496 , 3.9911983 , 0.9990833 ],
# array([[2.3060477 , 2.676496 , 3.9911983 , 0.9990833 ],
# [2.8675377 , 2.2279181 , 0.79029655, 2.8447366 ]], dtype=float32)
# [2.8675377 , 2.2279181 , 0.79029655, 2.8447366 ]], dtype=float32)
"""
"""
helper = LayerHelper('gaussian_random', **locals())
helper = LayerHelper('gaussian_random', **locals())
check_type(shape, 'shape', (list, tuple), 'fluid.layers.gaussian_random')
check_dtype(dtype, 'dtype', ['float32', 'float64'],
'fluid.layers.gaussian_random')
out = helper.create_variable_for_type_inference(dtype)
out = helper.create_variable_for_type_inference(dtype)
if not isinstance(shape, (list, tuple, Variable)):
raise TypeError(
"The type of 'shape' in fill_constant must be Variable, list or tuple, but "
"received %s." % (type(shape)))
c_dtype = convert_np_dtype_to_dtype_(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
attrs = {
'mean': mean,
'std': std,
'seed': seed,
'dtype': c_dtype,
'use_mkldnn': False
}
inputs = {}
utils._get_shape_tensor_inputs(
inputs=inputs,
helper=helper,
attrs=attrs,
shape=shape,
op_type='gaussian_random')
helper.append_op(
helper.append_op(
type='gaussian_random',
type='gaussian_random',
inputs=inputs,
outputs={'Out': out},
outputs={'Out': out},
attrs={
attrs=attrs)
'shape': shape,
'mean': mean,
'std': std,
'seed': seed,
'dtype': c_dtype,
'use_mkldnn': False
})
return out
return out
...
...
python/paddle/fluid/tests/unittests/mkldnn/test_gaussian_random_mkldnn_op.py
浏览文件 @
53bdee64
...
@@ -27,17 +27,15 @@ class TestMKLDNNGaussianRandomOpSeed10(TestGaussianRandomOp):
...
@@ -27,17 +27,15 @@ class TestMKLDNNGaussianRandomOpSeed10(TestGaussianRandomOp):
class
TestMKLDNNGaussianRandomOpSeed0
(
TestGaussianRandomOp
):
class
TestMKLDNNGaussianRandomOpSeed0
(
TestGaussianRandomOp
):
def
setUp
(
self
):
def
setUp
(
self
):
TestGaussianRandomOp
.
setUp
(
self
)
TestGaussianRandomOp
.
setUp
(
self
)
self
.
use_mkldnn
=
True
self
.
attrs
=
{
self
.
attrs
=
{
"shape"
:
[
1
000
,
784
],
"shape"
:
[
1
23
,
92
],
"mean"
:
.
0
,
"mean"
:
1
.0
,
"std"
:
1.
,
"std"
:
2.0
,
"seed"
:
0
,
"seed"
:
1
0
,
"use_mkldnn"
:
self
.
use_mkldnn
"use_mkldnn"
:
self
.
use_mkldnn
}
}
def
init_kernel_type
(
self
):
self
.
use_mkldnn
=
True
if
__name__
==
'__main__'
:
if
__name__
==
'__main__'
:
unittest
.
main
()
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_gaussian_random_op.py
浏览文件 @
53bdee64
...
@@ -15,98 +15,213 @@
...
@@ -15,98 +15,213 @@
from
__future__
import
print_function
from
__future__
import
print_function
import
unittest
import
unittest
import
numpy
import
numpy
as
np
import
paddle.fluid
as
fluid
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
paddle.fluid.core
as
core
from
paddle.fluid.op
import
Operator
from
paddle.fluid.op
import
Operator
from
paddle.fluid.executor
import
Executor
from
paddle.fluid.executor
import
Executor
from
op_test
import
OpTest
class
TestGaussianRandomOp
(
unittest
.
TestCase
):
class
TestGaussianRandomOp
(
OpTest
):
def
setUp
(
self
):
def
setUp
(
self
):
self
.
op_type
=
"gaussian_random"
self
.
op_type
=
"gaussian_random"
self
.
inputs
=
{}
self
.
inputs
=
{}
self
.
use_mkldnn
=
False
self
.
use_mkldnn
=
False
self
.
init_kernel_type
()
self
.
attrs
=
{
self
.
attrs
=
{
"shape"
:
[
1
000
,
784
],
"shape"
:
[
1
23
,
92
],
"mean"
:
.
0
,
"mean"
:
1
.0
,
"std"
:
1
.
,
"std"
:
2
.
,
"seed"
:
10
,
"seed"
:
10
,
"use_mkldnn"
:
self
.
use_mkldnn
"use_mkldnn"
:
self
.
use_mkldnn
}
}
self
.
outputs
=
[
"Out"
]
self
.
outputs
=
{
'Out'
:
np
.
zeros
((
123
,
92
),
dtype
=
'float32'
)}
def
test_c
pu
(
self
):
def
test_c
heck_output
(
self
):
self
.
gaussian_random_test
(
place
=
fluid
.
CPUPlace
()
)
self
.
check_output_customized
(
self
.
verify_output
)
def
test_gpu
(
self
):
def
verify_output
(
self
,
outs
):
if
core
.
is_compiled_with_cuda
():
self
.
assertEqual
(
outs
[
0
].
shape
,
(
123
,
92
))
self
.
gaussian_random_test
(
place
=
fluid
.
CUDAPlace
(
0
))
hist
,
_
=
np
.
histogram
(
outs
[
0
],
range
=
(
-
3
,
5
))
hist
=
hist
.
astype
(
"float32"
)
hist
/=
float
(
outs
[
0
].
size
)
data
=
np
.
random
.
normal
(
size
=
(
123
,
92
),
loc
=
1
,
scale
=
2
)
hist2
,
_
=
np
.
histogram
(
data
,
range
=
(
-
3
,
5
))
hist2
=
hist2
.
astype
(
"float32"
)
hist2
/=
float
(
outs
[
0
].
size
)
self
.
assertTrue
(
np
.
allclose
(
hist
,
hist2
,
rtol
=
0
,
atol
=
0.01
),
"hist: "
+
str
(
hist
)
+
" hist2: "
+
str
(
hist2
))
def
gaussian_random_test
(
self
,
place
):
program
=
fluid
.
Program
()
# Situation 2: Attr(shape) is a list(with tensor)
block
=
program
.
global_block
()
class
TestGaussianRandomOp_ShapeTensorList
(
TestGaussianRandomOp
):
vout
=
block
.
create_var
(
name
=
"Out"
)
def
setUp
(
self
):
op
=
block
.
append_op
(
'''Test gaussian_random op with specified value
type
=
self
.
op_type
,
outputs
=
{
"Out"
:
vout
},
attrs
=
self
.
attrs
)
'''
self
.
op_type
=
"gaussian_random"
self
.
init_data
()
shape_tensor_list
=
[]
for
index
,
ele
in
enumerate
(
self
.
shape
):
shape_tensor_list
.
append
((
"x"
+
str
(
index
),
np
.
ones
(
(
1
)).
astype
(
'int32'
)
*
ele
))
op
.
desc
.
infer_var_type
(
block
.
desc
)
self
.
attrs
=
{
op
.
desc
.
infer_shape
(
block
.
desc
)
'shape'
:
self
.
infer_shape
,
'mean'
:
self
.
mean
,
'std'
:
self
.
std
,
'seed'
:
self
.
seed
,
'use_mkldnn'
:
self
.
use_mkldnn
}
fetch_list
=
[]
self
.
inputs
=
{
"ShapeTensorList"
:
shape_tensor_list
}
for
var_name
in
self
.
outputs
:
self
.
outputs
=
{
'Out'
:
np
.
zeros
((
123
,
92
),
dtype
=
'float32'
)}
fetch_list
.
append
(
block
.
var
(
var_name
))
exe
=
Executor
(
place
)
def
init_data
(
self
):
outs
=
exe
.
run
(
program
,
fetch_list
=
fetch_list
)
self
.
shape
=
[
123
,
92
]
tensor
=
outs
[
0
]
self
.
infer_shape
=
[
-
1
,
92
]
self
.
use_mkldnn
=
False
self
.
mean
=
1.0
self
.
std
=
2.0
self
.
seed
=
10
self
.
assertAlmostEqual
(
numpy
.
mean
(
tensor
),
.
0
,
delta
=
0.1
)
def
test_check_output
(
self
):
self
.
assertAlmostEqual
(
numpy
.
std
(
tensor
),
1.
,
delta
=
0.1
)
self
.
check_output_customized
(
self
.
verify_output
)
def
init_kernel_type
(
self
):
pass
class
TestGaussianRandomOp2_ShapeTensorList
(
TestGaussianRandomOp_ShapeTensorList
):
def
init_data
(
self
):
self
.
shape
=
[
123
,
92
]
self
.
infer_shape
=
[
-
1
,
-
1
]
self
.
use_mkldnn
=
False
self
.
mean
=
1.0
self
.
std
=
2.0
self
.
seed
=
10
class
TestGaussianRandomOp3_ShapeTensorList
(
TestGaussianRandomOp_ShapeTensorList
):
def
init_data
(
self
):
self
.
shape
=
[
123
,
92
]
self
.
infer_shape
=
[
123
,
-
1
]
self
.
use_mkldnn
=
True
self
.
mean
=
1.0
self
.
std
=
2.0
self
.
seed
=
10
class
TestGaussianRandomOp4_ShapeTensorList
(
TestGaussianRandomOp_ShapeTensorList
):
def
init_data
(
self
):
self
.
shape
=
[
123
,
92
]
self
.
infer_shape
=
[
123
,
-
1
]
self
.
use_mkldnn
=
False
self
.
mean
=
1.0
self
.
std
=
2.0
self
.
seed
=
10
class
TestGaussianRandomOpError
(
unittest
.
TestCase
):
# Situation 3: shape is a tensor
class
TestGaussianRandomOp1_ShapeTensor
(
TestGaussianRandomOp
):
def
setUp
(
self
):
def
setUp
(
self
):
'''Test gaussian_random op with specified value
'''
self
.
op_type
=
"gaussian_random"
self
.
op_type
=
"gaussian_random"
self
.
in
puts
=
{}
self
.
in
it_data
()
self
.
use_mkldnn
=
False
self
.
use_mkldnn
=
False
self
.
inputs
=
{
"ShapeTensor"
:
np
.
array
(
self
.
shape
).
astype
(
"int32"
)}
self
.
attrs
=
{
self
.
attrs
=
{
"shape"
:
[
1000
,
784
],
'mean'
:
self
.
mean
,
"mean"
:
.
0
,
'std'
:
self
.
std
,
"std"
:
1.
,
'seed'
:
self
.
seed
,
"seed"
:
10
,
'use_mkldnn'
:
self
.
use_mkldnn
"use_mkldnn"
:
self
.
use_mkldnn
}
}
self
.
outputs
=
{
'Out'
:
np
.
zeros
((
123
,
92
),
dtype
=
'float32'
)}
self
.
outputs
=
[
"Out"
]
def
init_data
(
self
):
self
.
shape
=
[
123
,
92
]
def
test_errors
(
self
):
self
.
use_mkldnn
=
False
program
=
fluid
.
Program
()
self
.
mean
=
1.0
with
fluid
.
program_guard
(
fluid
.
Program
(),
program
):
self
.
std
=
2.0
input_data
=
numpy
.
random
.
random
((
2
,
4
)).
astype
(
"float32"
)
self
.
seed
=
10
block
=
program
.
global_block
()
vout
=
block
.
create_var
(
name
=
"Out"
,
dtype
=
'int32'
)
normal_initializer
=
fluid
.
initializer
.
NormalInitializer
(
# Test python API
loc
=
0.0
,
scale
=
1.0
,
seed
=
0
)
class
TestGaussianRandomAPI
(
unittest
.
TestCase
):
def
test_api
(
self
):
def
test_Variable
():
positive_2_int32
=
fluid
.
layers
.
fill_constant
([
1
],
"int32"
,
2000
)
# the input type must be Variable
normal_initializer
(
input_data
)
positive_2_int64
=
fluid
.
layers
.
fill_constant
([
1
],
"int64"
,
500
)
shape_tensor_int32
=
fluid
.
data
(
self
.
assertRaises
(
TypeError
,
test_Variable
)
name
=
"shape_tensor_int32"
,
shape
=
[
2
],
dtype
=
"int32"
)
def
test_type
():
shape_tensor_int64
=
fluid
.
data
(
# dtype must be float32 or float64
name
=
"shape_tensor_int64"
,
shape
=
[
2
],
dtype
=
"int64"
)
normal_initializer
(
vout
)
out_1
=
fluid
.
layers
.
gaussian_random
(
self
.
assertRaises
(
TypeError
,
test_type
)
shape
=
[
2000
,
500
],
dtype
=
"float32"
,
mean
=
0.0
,
std
=
1.0
,
seed
=
10
)
out_2
=
fluid
.
layers
.
gaussian_random
(
shape
=
[
2000
,
positive_2_int32
],
dtype
=
"float32"
,
mean
=
0.
,
std
=
1.0
,
seed
=
10
)
out_3
=
fluid
.
layers
.
gaussian_random
(
shape
=
[
2000
,
positive_2_int64
],
dtype
=
"float32"
,
mean
=
0.
,
std
=
1.0
,
seed
=
10
)
out_4
=
fluid
.
layers
.
gaussian_random
(
shape
=
shape_tensor_int32
,
dtype
=
"float32"
,
mean
=
0.
,
std
=
1.0
,
seed
=
10
)
out_5
=
fluid
.
layers
.
gaussian_random
(
shape
=
shape_tensor_int64
,
dtype
=
"float32"
,
mean
=
0.
,
std
=
1.0
,
seed
=
10
)
out_6
=
fluid
.
layers
.
gaussian_random
(
shape
=
shape_tensor_int64
,
dtype
=
np
.
float32
,
mean
=
0.
,
std
=
1.0
,
seed
=
10
)
exe
=
fluid
.
Executor
(
place
=
fluid
.
CPUPlace
())
res_1
,
res_2
,
res_3
,
res_4
,
res_5
,
res_6
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"shape_tensor_int32"
:
np
.
array
([
2000
,
500
]).
astype
(
"int32"
),
"shape_tensor_int64"
:
np
.
array
([
2000
,
500
]).
astype
(
"int64"
),
},
fetch_list
=
[
out_1
,
out_2
,
out_3
,
out_4
,
out_5
,
out_6
])
self
.
assertAlmostEqual
(
np
.
mean
(
res_1
),
0.0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res_1
),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res_2
),
0.0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res_2
),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res_3
),
0.0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res_3
),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res_4
),
0.0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res_5
),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res_5
),
0.0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res_5
),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res_6
),
0.0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res_6
),
1.
,
delta
=
0.1
)
if
__name__
==
"__main__"
:
if
__name__
==
"__main__"
:
...
...
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