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体验新版 GitCode,发现更多精彩内容 >>
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02f06708
编写于
2月 11, 2022
作者:
J
joeqiao12
提交者:
GitHub
2月 11, 2022
浏览文件
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电子邮件补丁
差异文件
uniform_random op for mlu (#39450)
上级
1e6047f1
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
187 addition
and
3 deletion
+187
-3
paddle/fluid/operators/mlu/mlu_baseop.cc
paddle/fluid/operators/mlu/mlu_baseop.cc
+3
-2
paddle/fluid/operators/mlu/mlu_baseop.h
paddle/fluid/operators/mlu/mlu_baseop.h
+1
-1
paddle/fluid/operators/uniform_random_op_mlu.cc
paddle/fluid/operators/uniform_random_op_mlu.cc
+75
-0
python/paddle/fluid/tests/unittests/mlu/test_uniform_random_op_mlu.py
...e/fluid/tests/unittests/mlu/test_uniform_random_op_mlu.py
+108
-0
未找到文件。
paddle/fluid/operators/mlu/mlu_baseop.cc
浏览文件 @
02f06708
...
...
@@ -923,11 +923,12 @@ MLUCnnlTrigonDesc::~MLUCnnlTrigonDesc() {
/* static */
void
MLUCnnl
::
RandomUniform
(
const
ExecutionContext
&
ctx
,
const
int
num
,
const
cnnlDataType_t
data_type
,
const
cnnlRandGenerator_t
mlu_generator
,
void
*
output
)
{
const
cnnlRandGenerator_t
mlu_generator
,
const
float
min
,
const
float
max
,
void
*
output
)
{
cnnlHandle_t
handle
=
GetHandleFromCTX
(
ctx
);
PADDLE_ENFORCE_MLU_SUCCESS
(
cnnlRandGenerateUniform
(
handle
,
mlu_generator
,
data_type
,
nullptr
,
num
,
0
,
1
,
output
));
handle
,
mlu_generator
,
data_type
,
nullptr
,
num
,
min
,
max
,
output
));
}
/* static */
void
MLUCnnl
::
TopK
(
...
...
paddle/fluid/operators/mlu/mlu_baseop.h
浏览文件 @
02f06708
...
...
@@ -513,7 +513,7 @@ class MLUCnnl {
static
void
RandomUniform
(
const
ExecutionContext
&
ctx
,
const
int
num
,
const
cnnlDataType_t
data_type
,
const
cnnlRandGenerator_t
mlu_generator
,
void
*
output
);
const
float
min
,
const
float
max
,
void
*
output
);
static
void
Cumsum
(
const
ExecutionContext
&
ctx
,
const
int
axis
,
const
bool
exclusive
,
const
bool
reverse
,
...
...
paddle/fluid/operators/uniform_random_op_mlu.cc
0 → 100644
浏览文件 @
02f06708
/* 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 "paddle/fluid/operators/uniform_random_op.h"
#include "paddle/fluid/operators/mlu/mlu_baseop.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
class
MLUUniformRandomKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
framework
::
Tensor
*
tensor
=
nullptr
;
auto
out_var
=
ctx
.
OutputVar
(
"Out"
);
std
::
vector
<
int64_t
>
new_shape
;
auto
list_new_shape_tensor
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"ShapeTensorList"
);
if
(
list_new_shape_tensor
.
size
()
>
0
||
ctx
.
HasInput
(
"ShapeTensor"
))
{
if
(
ctx
.
HasInput
(
"ShapeTensor"
))
{
auto
*
shape_tensor
=
ctx
.
Input
<
framework
::
Tensor
>
(
"ShapeTensor"
);
new_shape
=
GetNewDataFromShapeTensor
(
shape_tensor
);
}
else
if
(
list_new_shape_tensor
.
size
()
>
0
)
{
new_shape
=
GetNewDataFromShapeTensorList
(
list_new_shape_tensor
);
}
}
if
(
out_var
->
IsType
<
pten
::
SelectedRows
>
())
{
auto
*
selected_rows
=
out_var
->
GetMutable
<
pten
::
SelectedRows
>
();
tensor
=
selected_rows
->
mutable_value
();
auto
shape
=
ctx
.
Attr
<
std
::
vector
<
int64_t
>>
(
"shape"
);
if
(
!
new_shape
.
empty
())
shape
=
new_shape
;
tensor
->
Resize
(
framework
::
make_ddim
(
shape
));
selected_rows
->
mutable_rows
()
->
reserve
(
shape
[
0
]);
}
else
if
(
out_var
->
IsType
<
framework
::
LoDTensor
>
())
{
tensor
=
out_var
->
GetMutable
<
framework
::
LoDTensor
>
();
if
(
!
new_shape
.
empty
())
tensor
->
Resize
(
framework
::
make_ddim
(
new_shape
));
}
else
{
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
"Expected type of Output(out) in uniform_random_op must be Tensor, "
"SelectedRows. But got "
"unsupport type: %s."
,
framework
::
ToTypeName
(
out_var
->
Type
())));
}
tensor
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int64_t
size
=
tensor
->
numel
();
const
float
min
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"min"
));
const
float
max
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"max"
));
unsigned
int
seed
=
static_cast
<
unsigned
int
>
(
ctx
.
Attr
<
int
>
(
"seed"
));
// make mlu seed
MLUCnnlRandomGeneratorDesc
random_desc
(
/*is_mlu200=*/
false
,
seed
);
cnnlDataType_t
data_type
=
ToCnnlDataType
(
tensor
->
type
());
MLUCnnl
::
RandomUniform
(
ctx
,
size
,
/*data type=*/
data_type
,
random_desc
.
get
(),
min
,
max
,
GetBasePtr
(
tensor
));
}
};
}
// namespace operators
}
// namespace paddle
REGISTER_OP_MLU_KERNEL
(
uniform_random
,
paddle
::
operators
::
MLUUniformRandomKernel
<
float
>
);
python/paddle/fluid/tests/unittests/mlu/test_uniform_random_op_mlu.py
0 → 100644
浏览文件 @
02f06708
# 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
sys
import
subprocess
import
unittest
import
numpy
as
np
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
import
paddle
import
paddle.fluid.core
as
core
import
paddle
from
paddle.fluid.op
import
Operator
import
paddle.fluid
as
fluid
from
paddle.fluid
import
Program
,
program_guard
from
test_uniform_random_op
import
TestUniformRandomOp
,
TestUniformRandomOpSelectedRows
paddle
.
enable_static
()
def
output_hist
(
out
):
hist
,
_
=
np
.
histogram
(
out
,
range
=
(
-
5
,
10
))
hist
=
hist
.
astype
(
"float32"
)
hist
/=
float
(
out
.
size
)
prob
=
0.1
*
np
.
ones
((
10
))
return
hist
,
prob
class
TestMLUUniformRandomOp
(
OpTest
):
def
setUp
(
self
):
self
.
set_mlu
()
self
.
op_type
=
"uniform_random"
self
.
init_dtype
()
self
.
inputs
=
{}
self
.
init_attrs
()
self
.
outputs
=
{
"Out"
:
np
.
zeros
((
1000
,
784
)).
astype
(
self
.
dtype
)}
def
init_attrs
(
self
):
self
.
attrs
=
{
"shape"
:
[
1000
,
784
],
"min"
:
-
5.0
,
"max"
:
10.0
,
"seed"
:
10
}
self
.
output_hist
=
output_hist
def
set_mlu
(
self
):
self
.
__class__
.
use_mlu
=
True
self
.
place
=
paddle
.
MLUPlace
(
0
)
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float32
def
test_check_output
(
self
):
self
.
check_output_customized
(
self
.
verify_output
,
self
.
place
)
def
verify_output
(
self
,
outs
):
hist
,
prob
=
self
.
output_hist
(
np
.
array
(
outs
[
0
]))
self
.
assertTrue
(
np
.
allclose
(
hist
,
prob
,
rtol
=
0
,
atol
=
0.01
),
"hist: "
+
str
(
hist
))
class
TestMLUUniformRandomOpSelectedRows
(
unittest
.
TestCase
):
def
get_places
(
self
):
places
=
[
core
.
CPUPlace
()]
if
core
.
is_compiled_with_mlu
():
places
.
append
(
core
.
MLUPlace
(
0
))
return
places
def
test_check_output
(
self
):
for
place
in
self
.
get_places
():
self
.
check_with_place
(
place
)
def
check_with_place
(
self
,
place
):
scope
=
core
.
Scope
()
out
=
scope
.
var
(
"X"
).
get_selected_rows
()
paddle
.
seed
(
10
)
op
=
Operator
(
"uniform_random"
,
Out
=
"X"
,
shape
=
[
1000
,
784
],
min
=-
5.0
,
max
=
10.0
,
seed
=
10
)
op
.
run
(
scope
,
place
)
self
.
assertEqual
(
out
.
get_tensor
().
shape
(),
[
1000
,
784
])
hist
,
prob
=
output_hist
(
np
.
array
(
out
.
get_tensor
()))
self
.
assertTrue
(
np
.
allclose
(
hist
,
prob
,
rtol
=
0
,
atol
=
0.01
),
"hist: "
+
str
(
hist
))
if
__name__
==
"__main__"
:
unittest
.
main
()
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