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体验新版 GitCode,发现更多精彩内容 >>
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5a5649c2
编写于
6月 17, 2022
作者:
C
Chenxiao Niu
提交者:
GitHub
6月 17, 2022
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差异文件
[MLU] add truncated_gaussian_random kernel. (#43575)
上级
19e866f9
变更
2
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2 changed file
with
153 addition
and
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+153
-0
paddle/fluid/operators/truncated_gaussian_random_op_mlu.cc
paddle/fluid/operators/truncated_gaussian_random_op_mlu.cc
+62
-0
python/paddle/fluid/tests/unittests/mlu/test_truncated_gaussian_random_op_mlu.py
...ts/unittests/mlu/test_truncated_gaussian_random_op_mlu.py
+91
-0
未找到文件。
paddle/fluid/operators/truncated_gaussian_random_op_mlu.cc
0 → 100644
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5a5649c2
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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 <limits>
#include <random>
#include "paddle/fluid/framework/generator.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/truncated_gaussian_random_op.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
class
TruncatedGaussianRandomMLUKernel
:
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"
);
tensor
->
mutable_data
<
T
>
(
context
.
GetPlace
());
framework
::
Tensor
cpu_tensor
(
tensor
->
dtype
());
cpu_tensor
.
Resize
(
tensor
->
dims
());
T
*
data_cpu
=
cpu_tensor
.
mutable_data
<
T
>
(
platform
::
CPUPlace
());
std
::
uniform_real_distribution
<
T
>
dist
(
std
::
numeric_limits
<
float
>::
min
(),
1.0
);
TruncatedNormal
<
T
>
truncated_normal
(
mean
,
std
);
int64_t
size
=
tensor
->
numel
();
unsigned
int
seed
=
static_cast
<
unsigned
int
>
(
context
.
Attr
<
int
>
(
"seed"
));
auto
engine
=
framework
::
GetCPURandomEngine
(
seed
);
for
(
int64_t
i
=
0
;
i
<
size
;
++
i
)
{
data_cpu
[
i
]
=
truncated_normal
(
dist
(
*
engine
));
}
auto
&
dev_ctx
=
context
.
template
device_context
<
platform
::
MLUDeviceContext
>();
framework
::
TensorCopy
(
cpu_tensor
,
context
.
GetPlace
(),
dev_ctx
,
tensor
);
dev_ctx
.
Wait
();
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_MLU_KERNEL
(
truncated_gaussian_random
,
ops
::
TruncatedGaussianRandomMLUKernel
<
float
>
);
python/paddle/fluid/tests/unittests/mlu/test_truncated_gaussian_random_op_mlu.py
0 → 100644
浏览文件 @
5a5649c2
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from
__future__
import
print_function
import
unittest
import
numpy
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
sys
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
from
paddle.fluid.op
import
Operator
from
paddle.fluid.executor
import
Executor
from
paddle.fluid.framework
import
_test_eager_guard
paddle
.
enable_static
()
class
TestTrunctedGaussianRandomOp
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
op_type
=
"truncated_gaussian_random"
self
.
inputs
=
{}
self
.
attrs
=
{
"shape"
:
[
10000
],
"mean"
:
.
0
,
"std"
:
1.
,
"seed"
:
10
,
}
self
.
outputs
=
[
"Out"
]
def
test_cpu
(
self
):
self
.
gaussian_random_test
(
place
=
fluid
.
CPUPlace
())
self
.
gaussian_random_test_eager
(
place
=
fluid
.
CPUPlace
())
def
test_mlu
(
self
):
if
core
.
is_compiled_with_mlu
():
self
.
gaussian_random_test
(
place
=
fluid
.
MLUPlace
(
0
))
# self.gaussian_random_test_eager(place=fluid.MLUPlace(0))
def
gaussian_random_test
(
self
,
place
):
program
=
fluid
.
Program
()
block
=
program
.
global_block
()
vout
=
block
.
create_var
(
name
=
"Out"
)
op
=
block
.
append_op
(
type
=
self
.
op_type
,
outputs
=
{
"Out"
:
vout
},
attrs
=
self
.
attrs
)
op
.
desc
.
infer_var_type
(
block
.
desc
)
op
.
desc
.
infer_shape
(
block
.
desc
)
fetch_list
=
[]
for
var_name
in
self
.
outputs
:
fetch_list
.
append
(
block
.
var
(
var_name
))
exe
=
Executor
(
place
)
outs
=
exe
.
run
(
program
,
fetch_list
=
fetch_list
)
tensor
=
outs
[
0
]
self
.
assertAlmostEqual
(
numpy
.
mean
(
tensor
),
.
0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
numpy
.
var
(
tensor
),
0.773
,
delta
=
0.1
)
# TruncatedNormal.__call__ has no return value, so here call _C_ops api
# directly
def
gaussian_random_test_eager
(
self
,
place
):
with
fluid
.
dygraph
.
guard
(
place
):
with
_test_eager_guard
():
out
=
paddle
.
_C_ops
.
final_state_truncated_gaussian_random
(
self
.
attrs
[
"shape"
],
self
.
attrs
[
"mean"
],
self
.
attrs
[
"std"
],
self
.
attrs
[
"seed"
],
core
.
VarDesc
.
VarType
.
FP32
,
place
)
self
.
assertAlmostEqual
(
numpy
.
mean
(
out
.
numpy
()),
.
0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
numpy
.
var
(
out
.
numpy
()),
0.773
,
delta
=
0.1
)
if
__name__
==
"__main__"
:
unittest
.
main
()
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