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体验新版 GitCode,发现更多精彩内容 >>
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840d54de
编写于
10月 12, 2020
作者:
M
mapingshuo
提交者:
GitHub
10月 12, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add XPU support for shape op and reshape op (#27804)
上级
0a1862d1
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
354 addition
and
5 deletion
+354
-5
paddle/fluid/operators/reshape_op.cc
paddle/fluid/operators/reshape_op.cc
+32
-5
paddle/fluid/operators/shape_op_xpu.cc
paddle/fluid/operators/shape_op_xpu.cc
+21
-0
python/paddle/fluid/tests/unittests/xpu/test_reshape2_op_xpu.py
.../paddle/fluid/tests/unittests/xpu/test_reshape2_op_xpu.py
+207
-0
python/paddle/fluid/tests/unittests/xpu/test_shape_op_xpu.py
python/paddle/fluid/tests/unittests/xpu/test_shape_op_xpu.py
+94
-0
未找到文件。
paddle/fluid/operators/reshape_op.cc
浏览文件 @
840d54de
...
...
@@ -49,7 +49,8 @@ inline std::vector<int> get_new_shape(
"the element's shape must be [1]. But received the element's shape "
"is [%s]"
,
tensor
->
dims
()));
if
(
platform
::
is_gpu_place
(
tensor
->
place
()))
{
if
(
platform
::
is_gpu_place
(
tensor
->
place
())
||
platform
::
is_xpu_place
(
tensor
->
place
()))
{
framework
::
Tensor
temp
;
TensorCopySync
(
*
tensor
,
platform
::
CPUPlace
(),
&
temp
);
...
...
@@ -362,7 +363,8 @@ class ReshapeKernel {
if
(
shape_tensor
)
{
auto
*
shape_data
=
shape_tensor
->
data
<
int
>
();
framework
::
Tensor
cpu_shape_tensor
;
if
(
platform
::
is_gpu_place
(
shape_tensor
->
place
()))
{
if
(
platform
::
is_gpu_place
(
shape_tensor
->
place
())
||
platform
::
is_xpu_place
(
shape_tensor
->
place
()))
{
TensorCopySync
(
*
shape_tensor
,
platform
::
CPUPlace
(),
&
cpu_shape_tensor
);
shape_data
=
cpu_shape_tensor
.
data
<
int
>
();
...
...
@@ -375,9 +377,22 @@ class ReshapeKernel {
out
->
Resize
(
out_dims
);
out
->
mutable_data
(
ctx
.
GetPlace
(),
in
->
type
());
framework
::
TensorCopy
(
*
in
,
ctx
.
GetPlace
(),
ctx
.
template
device_context
<
platform
::
DeviceContext
>(),
out
);
#ifdef PADDLE_WITH_XPU
if
(
platform
::
is_xpu_place
(
ctx
.
GetPlace
()))
{
auto
&
dev_ctx
=
ctx
.
template
device_context
<
paddle
::
platform
::
XPUDeviceContext
>();
xpu
::
memcpy_device
(
dev_ctx
.
x_context
(),
out
->
data
<
void
>
(),
in
->
data
<
void
>
(),
in
->
numel
()
*
paddle
::
framework
::
SizeOfType
(
in
->
type
()));
}
else
{
#endif
framework
::
TensorCopy
(
*
in
,
ctx
.
GetPlace
(),
ctx
.
template
device_context
<
platform
::
DeviceContext
>(),
out
);
#ifdef PADDLE_WITH_XPU
}
#endif
out
->
Resize
(
out_dims
);
}
};
...
...
@@ -644,3 +659,15 @@ REGISTER_OP_CUDA_KERNEL_FUNCTOR(reshape2_grad_grad, float,
ops
::
ReshapeDoubleGradKernel
,
plat
::
float16
,
ops
::
ReshapeDoubleGradKernel
);
#endif
#ifdef PADDLE_WITH_XPU
REGISTER_OP_XPU_KERNEL_FUNCTOR
(
reshape2
,
float
,
ops
::
ReshapeKernel
,
double
,
ops
::
ReshapeKernel
,
int
,
ops
::
ReshapeKernel
,
int64_t
,
ops
::
ReshapeKernel
,
plat
::
float16
,
ops
::
ReshapeKernel
);
REGISTER_OP_XPU_KERNEL_FUNCTOR
(
reshape2_grad
,
float
,
ops
::
ReshapeGradKernel
,
double
,
ops
::
ReshapeGradKernel
,
int
,
ops
::
ReshapeGradKernel
,
int64_t
,
ops
::
ReshapeGradKernel
,
plat
::
float16
,
ops
::
ReshapeGradKernel
);
#endif
paddle/fluid/operators/shape_op_xpu.cc
0 → 100644
浏览文件 @
840d54de
/* Copyright (c) 2020 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. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/operators/shape_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_XPU_KERNEL
(
shape
,
ops
::
ShapeKernel
<
bool
>
,
ops
::
ShapeKernel
<
int
>
,
ops
::
ShapeKernel
<
int64_t
>
,
ops
::
ShapeKernel
<
float
>
,
ops
::
ShapeKernel
<
double
>
);
#endif
python/paddle/fluid/tests/unittests/xpu/test_reshape2_op_xpu.py
0 → 100644
浏览文件 @
840d54de
# Copyright (c) 2018 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
as
np
import
sys
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid
import
compiler
,
Program
,
program_guard
# situation 1: have shape( list, no tensor), no actual shape(Tensor)
class
TestReshapeOp
(
OpTest
):
def
setUp
(
self
):
self
.
init_data
()
self
.
op_type
=
"reshape2"
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)}
self
.
attrs
=
{
"shape"
:
self
.
new_shape
,
"use_xpu"
:
True
}
self
.
outputs
=
{
"Out"
:
self
.
inputs
[
"X"
].
reshape
(
self
.
infered_shape
),
'XShape'
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)
}
def
init_data
(
self
):
self
.
ori_shape
=
(
2
,
60
)
self
.
new_shape
=
(
12
,
10
)
self
.
infered_shape
=
(
12
,
10
)
def
test_check_output
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
,
no_check_set
=
[
'XShape'
])
def
test_check_grad
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_grad_with_place
(
place
,
[
"X"
],
"Out"
)
class
TestReshapeOpDimInfer1
(
TestReshapeOp
):
def
init_data
(
self
):
self
.
ori_shape
=
(
5
,
25
)
self
.
new_shape
=
(
5
,
-
1
,
5
)
self
.
infered_shape
=
(
5
,
-
1
,
5
)
class
TestReshapeOpDimInfer2
(
TestReshapeOp
):
def
init_data
(
self
):
self
.
ori_shape
=
(
10
,
2
,
6
)
self
.
new_shape
=
(
10
,
0
,
3
,
-
1
)
self
.
infered_shape
=
(
10
,
2
,
3
,
-
1
)
# situation 2: have shape(list, no tensor), have actual shape(Tensor)
class
TestReshapeOpWithInputShape
(
OpTest
):
def
setUp
(
self
):
self
.
init_data
()
self
.
op_type
=
"reshape2"
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
),
"Shape"
:
np
.
array
(
self
.
actual_shape
,
dtype
=
"int32"
)
}
self
.
attrs
=
{
"shape"
:
self
.
new_shape
,
"use_xpu"
:
True
}
self
.
outputs
=
{
"Out"
:
self
.
inputs
[
"X"
].
reshape
(
self
.
actual_shape
),
'XShape'
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)
}
def
init_data
(
self
):
self
.
ori_shape
=
(
6
,
20
)
self
.
new_shape
=
(
0
,
-
1
,
20
)
self
.
actual_shape
=
(
2
,
3
,
20
)
def
test_check_output
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
,
no_check_set
=
[
'XShape'
])
def
test_check_grad
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_grad_with_place
(
place
,
[
"X"
],
"Out"
)
# Situation 3: have shape(list, have tensor), no actual shape(Tensor)
class
TestReshapeOp_attr_ShapeTensor
(
OpTest
):
def
setUp
(
self
):
self
.
init_data
()
self
.
op_type
=
"reshape2"
shape_tensor
=
[]
for
index
,
ele
in
enumerate
(
self
.
new_shape
):
shape_tensor
.
append
((
"x"
+
str
(
index
),
np
.
ones
(
(
1
)).
astype
(
'int32'
)
*
ele
))
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
),
'ShapeTensor'
:
shape_tensor
}
self
.
attrs
=
{
'shape'
:
self
.
shape
,
"use_xpu"
:
True
}
self
.
outputs
=
{
"Out"
:
self
.
inputs
[
"X"
].
reshape
(
self
.
infered_shape
),
'XShape'
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)
}
def
init_data
(
self
):
self
.
ori_shape
=
(
4
,
25
)
self
.
new_shape
=
(
10
,
10
)
self
.
infered_shape
=
(
10
,
10
)
self
.
shape
=
(
-
1
,
-
1
)
def
test_check_output
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
,
no_check_set
=
[
'XShape'
])
def
test_check_grad
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_grad_with_place
(
place
,
[
"X"
],
"Out"
)
class
TestReshapeOpDimInfer1_attr_ShapeTensor
(
TestReshapeOp_attr_ShapeTensor
):
def
init_data
(
self
):
self
.
ori_shape
=
(
5
,
20
)
self
.
new_shape
=
(
5
,
-
1
,
20
)
self
.
infered_shape
=
(
5
,
-
1
,
20
)
self
.
shape
=
(
5
,
-
1
,
-
1
)
class
TestReshapeOpDimInfer2_attr_ShapeTensor
(
TestReshapeOp_attr_ShapeTensor
):
def
init_data
(
self
):
self
.
ori_shape
=
(
10
,
2
,
6
)
self
.
new_shape
=
(
10
,
0
,
3
,
-
1
)
self
.
infered_shape
=
(
10
,
2
,
3
,
-
1
)
self
.
shape
=
(
10
,
0
,
3
,
-
1
)
# Situation 4: have shape(Tensor), no actual shape(Tensor)
class
TestReshapeOp_attr_OnlyShape
(
OpTest
):
def
setUp
(
self
):
self
.
init_data
()
self
.
op_type
=
"reshape2"
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
),
"Shape"
:
np
.
array
(
self
.
new_shape
,
dtype
=
"int32"
)
}
self
.
attrs
=
{
"use_xpu"
:
True
}
self
.
outputs
=
{
"Out"
:
self
.
inputs
[
"X"
].
reshape
(
self
.
infered_shape
),
'XShape'
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)
}
def
init_data
(
self
):
self
.
ori_shape
=
(
4
,
25
)
self
.
new_shape
=
(
10
,
10
)
self
.
infered_shape
=
(
10
,
10
)
def
test_check_output
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
,
no_check_set
=
[
'XShape'
])
def
test_check_grad
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_grad_with_place
(
place
,
[
"X"
],
"Out"
)
class
TestReshapeOpDimInfer1_attr_OnlyShape
(
TestReshapeOp_attr_OnlyShape
):
def
init_data
(
self
):
self
.
ori_shape
=
(
5
,
20
)
self
.
new_shape
=
(
5
,
-
1
,
10
)
self
.
infered_shape
=
(
5
,
-
1
,
10
)
self
.
shape
=
(
5
,
-
1
,
-
1
)
class
TestReshapeOpDimInfer2_attr_OnlyShape
(
TestReshapeOp_attr_OnlyShape
):
def
init_data
(
self
):
self
.
ori_shape
=
(
10
,
2
,
6
)
self
.
new_shape
=
(
10
,
0
,
3
,
-
1
)
self
.
infered_shape
=
(
10
,
2
,
3
,
-
1
)
self
.
shape
=
(
10
,
0
,
3
,
-
1
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/xpu/test_shape_op_xpu.py
0 → 100644
浏览文件 @
840d54de
# Copyright (c) 2018 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
as
np
import
sys
sys
.
path
.
append
(
".."
)
from
op_test
import
OpTest
import
paddle
from
paddle.fluid
import
core
from
paddle.fluid.op
import
Operator
class
TestShapeOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"shape"
self
.
config
()
self
.
shape
=
[
2
,
3
]
input
=
np
.
zeros
(
self
.
shape
)
self
.
inputs
=
{
'Input'
:
input
}
self
.
outputs
=
{
'Out'
:
np
.
array
(
self
.
shape
)}
def
config
(
self
):
self
.
shape
=
[
2
,
3
]
def
test_check_output
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
)
class
case1
(
TestShapeOp
):
def
config
(
self
):
self
.
shape
=
[
2
]
class
case2
(
TestShapeOp
):
def
config
(
self
):
self
.
shape
=
[
1
,
2
,
3
]
class
TestShapeWithSelectedRows
(
unittest
.
TestCase
):
def
get_places
(
self
):
places
=
[
core
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
():
places
.
append
(
core
.
CUDAPlace
(
0
))
if
core
.
is_compiled_with_xpu
():
places
.
append
(
core
.
XPUPlace
(
0
))
return
places
def
check_with_place
(
self
,
place
):
scope
=
core
.
Scope
()
x_rows
=
[
0
,
1
,
5
,
4
,
19
]
height
=
20
row_numel
=
2
np_array
=
np
.
ones
((
len
(
x_rows
),
row_numel
)).
astype
(
"float32"
)
# initialize input variable X
x
=
scope
.
var
(
'X'
).
get_selected_rows
()
x
.
set_rows
(
x_rows
)
x
.
set_height
(
height
)
x_tensor
=
x
.
get_tensor
()
x_tensor
.
set
(
np_array
,
place
)
# initialize input variable Out
out_shape
=
scope
.
var
(
"Out"
).
get_tensor
()
op
=
Operator
(
"shape"
,
Input
=
"X"
,
Out
=
"Out"
)
op
.
run
(
scope
,
place
)
out_shape
=
np
.
array
(
out_shape
).
tolist
()
self
.
assertListEqual
([
5
,
2
],
out_shape
)
def
test_check_output
(
self
):
for
place
in
self
.
get_places
():
self
.
check_with_place
(
place
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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