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d45d3112
编写于
8月 11, 2021
作者:
R
Roc
提交者:
GitHub
8月 11, 2021
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差异文件
split_op for npu (#34699)
上级
9e3e08f0
变更
2
隐藏空白更改
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并排
Showing
2 changed file
with
241 addition
and
0 deletion
+241
-0
paddle/fluid/operators/split_op_npu.cc
paddle/fluid/operators/split_op_npu.cc
+83
-0
python/paddle/fluid/tests/unittests/npu/test_split_op_npu.py
python/paddle/fluid/tests/unittests/npu/test_split_op_npu.py
+158
-0
未找到文件。
paddle/fluid/operators/split_op_npu.cc
0 → 100644
浏览文件 @
d45d3112
/* Copyright (c) 2021 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 <memory>
#include <string>
#include "paddle/fluid/operators/npu_op_runner.h"
#include "paddle/fluid/operators/split_op.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
class
SplitNPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
in
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
outs
=
ctx
.
MultiOutput
<
framework
::
Tensor
>
(
"Out"
);
int
num
=
ctx
.
Attr
<
int
>
(
"num"
);
std
::
vector
<
int
>
sections
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"sections"
);
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
if
(
ctx
.
HasInput
(
"AxisTensor"
))
{
// TODO(liupeng51):
PADDLE_THROW
(
platform
::
errors
::
Unimplemented
(
"The AxisTensor is not supported on NPU now."
));
}
if
(
ctx
.
HasInput
(
"SectionsTensorList"
))
{
// TODO(liupeng51):
PADDLE_THROW
(
platform
::
errors
::
Unimplemented
(
"The SectionsTensorList is not supported on NPU now."
));
}
std
::
vector
<
Tensor
>
outputs
;
auto
place
=
ctx
.
GetPlace
();
for
(
size_t
j
=
0
;
j
<
outs
.
size
();
++
j
)
{
outs
[
j
]
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
outputs
.
push_back
(
*
outs
[
j
]);
}
auto
stream
=
ctx
.
template
device_context
<
paddle
::
platform
::
NPUDeviceContext
>()
.
stream
();
NpuOpRunner
runner
;
if
(
sections
.
size
()
==
0
)
{
framework
::
NPUAttributeMap
attr_input
=
{{
"num_split"
,
num
},
{
"split_dim"
,
axis
}};
runner
.
SetType
(
"SplitD"
).
AddInputs
({
*
in
}).
AddOutputs
(
outputs
).
AddAttrs
(
attr_input
);
}
else
{
framework
::
NPUAttributeMap
attr_input
=
{
{
"size_splits"
,
sections
},
{
"split_dim"
,
axis
},
{
"num_split"
,
static_cast
<
int32_t
>
(
sections
.
size
())}};
runner
.
SetType
(
"SplitVD"
).
AddInput
(
*
in
).
AddOutputs
(
outputs
).
AddAttrs
(
attr_input
);
}
runner
.
Run
(
stream
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_NPU_KERNEL
(
split
,
ops
::
SplitNPUKernel
<
float
>
,
ops
::
SplitNPUKernel
<
int
>
,
ops
::
SplitNPUKernel
<
plat
::
float16
>
);
python/paddle/fluid/tests/unittests/npu/test_split_op_npu.py
0 → 100644
浏览文件 @
d45d3112
# Copyright (c) 2021 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
numpy
as
np
import
unittest
import
sys
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
paddle
.
enable_static
()
SEED
=
2021
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_npu
(),
"core is not compiled with NPU"
)
class
TestCase1
(
OpTest
):
def
setUp
(
self
):
self
.
set_npu
()
self
.
set_example
()
self
.
op_type
=
"split"
self
.
place
=
paddle
.
NPUPlace
(
0
)
ipt
=
self
.
x
.
astype
(
self
.
dtype
)
axis
=
self
.
axis
if
isinstance
(
self
.
axis
,
int
)
else
int
(
self
.
axis
[
0
])
tmp_outs
=
np
.
split
(
ipt
,
axis
=
axis
,
indices_or_sections
=
self
.
num_or_sections
)
tmp_outs
=
[
o
.
astype
(
self
.
dtype
)
for
o
in
tmp_outs
]
self
.
outputs
=
{
'Out'
:
[]}
self
.
outs
=
[]
for
i
,
o
in
enumerate
(
tmp_outs
):
self
.
outputs
[
"Out"
].
append
((
str
(
i
),
o
))
self
.
outs
.
append
(
str
(
i
))
self
.
attrs
=
{
"axis"
:
self
.
axis
,
"num"
:
self
.
num_or_sections
}
self
.
inputs
=
{}
self
.
inputs
.
update
({
'X'
:
ipt
.
astype
(
self
.
dtype
)})
def
set_npu
(
self
):
self
.
__class__
.
use_npu
=
True
self
.
__class__
.
op_type
=
"split"
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
"X"
],
self
.
outs
)
def
set_example
(
self
):
self
.
dtype
=
"float32"
self
.
x
=
np
.
random
.
random
((
2
,
4
,
6
))
self
.
axis
=
1
self
.
num_or_sections
=
2
class
TestCase2
(
TestCase1
):
def
set_example
(
self
):
self
.
dtype
=
"float32"
self
.
x
=
np
.
random
.
random
((
20
,
4
,
50
))
self
.
axis
=
0
self
.
num_or_sections
=
4
class
TestCase4
(
TestCase1
):
def
set_example
(
self
):
self
.
dtype
=
"float16"
self
.
x
=
np
.
random
.
random
((
4
,
50
,
20
))
self
.
axis
=
2
self
.
num_or_sections
=
4
# Test Sections
class
TestCase5
(
TestCase1
):
def
set_example
(
self
):
super
().
set_example
()
self
.
x
=
np
.
random
.
random
((
2
,
10
,
4
))
self
.
axis
=
1
self
.
num_or_sections
=
[
2
,
4
,
8
]
def
setUp
(
self
):
super
().
setUp
()
self
.
attrs
.
update
({
"sections"
:
[
2
,
2
,
4
,
2
],
"num"
:
0
})
class
API_TestSplit
(
unittest
.
TestCase
):
def
test_out
(
self
):
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
data
=
fluid
.
layers
.
data
(
'data'
,
shape
=
[
-
1
,
10
],
dtype
=
'float32'
)
x0
,
x1
=
paddle
.
split
(
data
,
num_or_sections
=
(
3
,
7
),
axis
=
1
)
place
=
fluid
.
NPUPlace
(
0
)
exe
=
fluid
.
Executor
(
place
)
input1
=
np
.
random
.
random
([
1
,
10
]).
astype
(
'float32'
)
r0
,
r1
=
exe
.
run
(
feed
=
{
"data"
:
input1
},
fetch_list
=
[
x0
,
x1
])
ex_x0
,
ex_x1
=
np
.
split
(
input1
,
(
3
,
),
axis
=
1
)
self
.
assertTrue
(
np
.
allclose
(
ex_x0
,
r0
))
self
.
assertTrue
(
np
.
allclose
(
ex_x1
,
r1
))
class
API_TestSplit2
(
unittest
.
TestCase
):
def
test_out
(
self
):
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
data
=
fluid
.
layers
.
data
(
'data'
,
shape
=
[
-
1
,
10
],
dtype
=
'float32'
)
x0
,
x1
=
paddle
.
split
(
data
,
num_or_sections
=
2
,
axis
=
1
)
place
=
fluid
.
NPUPlace
(
0
)
exe
=
fluid
.
Executor
(
place
)
input1
=
np
.
random
.
random
([
1
,
10
]).
astype
(
'float32'
)
r0
,
r1
=
exe
.
run
(
feed
=
{
"data"
:
input1
},
fetch_list
=
[
x0
,
x1
])
ex_x0
,
ex_x1
=
np
.
split
(
input1
,
2
,
axis
=
1
)
self
.
assertTrue
(
np
.
allclose
(
ex_x0
,
r0
))
self
.
assertTrue
(
np
.
allclose
(
ex_x1
,
r1
))
class
API_TestDygraphSplit
(
unittest
.
TestCase
):
def
test_out1
(
self
):
with
fluid
.
dygraph
.
guard
(
paddle
.
NPUPlace
(
0
)):
input_1
=
np
.
random
.
random
([
4
,
6
,
6
]).
astype
(
"int32"
)
# input is a variable which shape is [4, 6, 6]
input
=
fluid
.
dygraph
.
to_variable
(
input_1
)
x0
,
x1
,
x2
=
paddle
.
split
(
input
,
num_or_sections
=
3
,
axis
=
1
)
x0_out
=
x0
.
numpy
()
x1_out
=
x1
.
numpy
()
x2_out
=
x2
.
numpy
()
ex_x0
,
ex_x1
,
ex_x2
=
np
.
split
(
input_1
,
3
,
axis
=
1
)
self
.
assertTrue
(
np
.
allclose
(
ex_x0
,
x0_out
))
self
.
assertTrue
(
np
.
allclose
(
ex_x1
,
x1_out
))
self
.
assertTrue
(
np
.
allclose
(
ex_x2
,
x2_out
))
def
test_out2
(
self
):
with
fluid
.
dygraph
.
guard
(
paddle
.
NPUPlace
(
0
)):
input_1
=
np
.
random
.
random
([
4
,
6
,
6
]).
astype
(
"int32"
)
# input is a variable which shape is [4, 6, 6]
input
=
fluid
.
dygraph
.
to_variable
(
input_1
)
x0
,
x1
,
x2
=
paddle
.
split
(
input
,
num_or_sections
=
[
1
,
2
,
3
],
axis
=
1
)
x0_out
=
x0
.
numpy
()
x1_out
=
x1
.
numpy
()
x2_out
=
x2
.
numpy
()
ex_x0
,
ex_x1
,
ex_x2
=
np
.
split
(
input_1
,
(
1
,
3
),
axis
=
1
)
self
.
assertTrue
(
np
.
allclose
(
ex_x0
,
x0_out
))
self
.
assertTrue
(
np
.
allclose
(
ex_x1
,
x1_out
))
self
.
assertTrue
(
np
.
allclose
(
ex_x2
,
x2_out
))
if
__name__
==
'__main__'
:
unittest
.
main
()
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