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体验新版 GitCode,发现更多精彩内容 >>
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63d9a175
编写于
8月 25, 2022
作者:
H
haosicheng
提交者:
GitHub
8月 25, 2022
浏览文件
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电子邮件补丁
差异文件
add temporal shift and grad *test=kunlun (#45300)
上级
0bf40070
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
327 addition
and
0 deletion
+327
-0
paddle/fluid/platform/device/xpu/xpu2_op_list.h
paddle/fluid/platform/device/xpu/xpu2_op_list.h
+4
-0
paddle/phi/kernels/xpu/temporal_shift_grad_kernel.cc
paddle/phi/kernels/xpu/temporal_shift_grad_kernel.cc
+84
-0
paddle/phi/kernels/xpu/temporal_shift_kernel.cc
paddle/phi/kernels/xpu/temporal_shift_kernel.cc
+83
-0
python/paddle/fluid/tests/unittests/xpu/test_temporal_shift_op_xpu.py
...e/fluid/tests/unittests/xpu/test_temporal_shift_op_xpu.py
+156
-0
未找到文件。
paddle/fluid/platform/device/xpu/xpu2_op_list.h
浏览文件 @
63d9a175
...
@@ -552,6 +552,10 @@ XPUOpMap& get_kl2_ops() {
...
@@ -552,6 +552,10 @@ XPUOpMap& get_kl2_ops() {
{
"tanh"
,
{
"tanh"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
pOpKernelType
(
vartype
::
FP16
,
XPUPlace
())})},
pOpKernelType
(
vartype
::
FP16
,
XPUPlace
())})},
{
"temporal_shift"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"temporal_shift_grad"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"tril_triu"
,
{
"tril_triu"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
pOpKernelType
(
vartype
::
INT32
,
XPUPlace
())})},
pOpKernelType
(
vartype
::
INT32
,
XPUPlace
())})},
...
...
paddle/phi/kernels/xpu/temporal_shift_grad_kernel.cc
0 → 100644
浏览文件 @
63d9a175
// 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/phi/kernels/temporal_shift_grad_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/common/layout.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/axis_utils.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
TemporalShiftGradKernel
(
const
Context
&
dev_ctx
,
const
DenseTensor
&
out_grad
,
int
seg_num
,
float
shift_ratio
,
const
std
::
string
&
data_format_str
,
DenseTensor
*
x_grad
)
{
auto
*
input_grad
=
x_grad
;
auto
*
output_grad
=
&
out_grad
;
int
t
=
seg_num
;
const
DataLayout
data_layout
=
paddle
::
framework
::
StringToDataLayout
(
data_format_str
);
const
int
nt
=
output_grad
->
dims
()[
0
];
const
int
n
=
nt
/
t
;
const
int
c
=
(
data_layout
==
DataLayout
::
kNCHW
?
output_grad
->
dims
()[
1
]
:
output_grad
->
dims
()[
3
]);
const
int
h
=
(
data_layout
==
DataLayout
::
kNCHW
?
output_grad
->
dims
()[
2
]
:
output_grad
->
dims
()[
1
]);
const
int
w
=
(
data_layout
==
DataLayout
::
kNCHW
?
output_grad
->
dims
()[
3
]
:
output_grad
->
dims
()[
2
]);
DDim
in_grad_dims
=
(
data_layout
==
DataLayout
::
kNCHW
?
phi
::
make_ddim
({
nt
,
c
,
h
,
w
})
:
phi
::
make_ddim
({
nt
,
h
,
w
,
c
}));
const
T
*
output_grad_data
=
output_grad
->
data
<
T
>
();
input_grad
->
Resize
(
in_grad_dims
);
T
*
input_grad_data
=
dev_ctx
.
template
Alloc
<
T
>(
input_grad
);
if
(
data_layout
==
DataLayout
::
kNCHW
)
{
int
r
=
xpu
::
temporal_shift_grad
(
dev_ctx
.
x_context
(),
output_grad_data
,
input_grad_data
,
n
,
c
,
h
,
w
,
t
,
shift_ratio
,
false
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"temporal_shift_grad"
);
}
else
{
int
r
=
xpu
::
temporal_shift_grad
(
dev_ctx
.
x_context
(),
output_grad_data
,
input_grad_data
,
n
,
c
,
h
,
w
,
t
,
shift_ratio
,
true
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"temporal_shift_grad"
);
}
}
}
// namespace phi
PD_REGISTER_KERNEL
(
temporal_shift_grad
,
XPU
,
ALL_LAYOUT
,
phi
::
TemporalShiftGradKernel
,
float
)
{
}
paddle/phi/kernels/xpu/temporal_shift_kernel.cc
0 → 100644
浏览文件 @
63d9a175
// 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/phi/kernels/temporal_shift_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/common/layout.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/axis_utils.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
TemporalShiftKernel
(
const
Context
&
dev_ctx
,
const
DenseTensor
&
x
,
int
seg_num
,
float
shift_ratio
,
const
std
::
string
&
data_format_str
,
DenseTensor
*
out
)
{
auto
*
input
=
&
x
;
auto
*
output
=
out
;
int
t
=
seg_num
;
const
DataLayout
data_layout
=
paddle
::
framework
::
StringToDataLayout
(
data_format_str
);
const
int
nt
=
input
->
dims
()[
0
];
const
int
n
=
nt
/
t
;
const
int
c
=
(
data_layout
==
DataLayout
::
kNCHW
?
input
->
dims
()[
1
]
:
input
->
dims
()[
3
]);
const
int
h
=
(
data_layout
==
DataLayout
::
kNCHW
?
input
->
dims
()[
2
]
:
input
->
dims
()[
1
]);
const
int
w
=
(
data_layout
==
DataLayout
::
kNCHW
?
input
->
dims
()[
3
]
:
input
->
dims
()[
2
]);
DDim
out_dims
=
(
data_layout
==
DataLayout
::
kNCHW
?
phi
::
make_ddim
({
nt
,
c
,
h
,
w
})
:
phi
::
make_ddim
({
nt
,
h
,
w
,
c
}));
const
T
*
input_data
=
input
->
data
<
T
>
();
output
->
Resize
(
out_dims
);
T
*
output_data
=
dev_ctx
.
template
Alloc
<
T
>(
output
);
if
(
data_layout
==
DataLayout
::
kNCHW
)
{
int
r
=
xpu
::
temporal_shift
(
dev_ctx
.
x_context
(),
input_data
,
output_data
,
n
,
c
,
h
,
w
,
t
,
shift_ratio
,
false
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"temporal_shift"
);
}
else
{
int
r
=
xpu
::
temporal_shift
(
dev_ctx
.
x_context
(),
input_data
,
output_data
,
n
,
c
,
h
,
w
,
t
,
shift_ratio
,
true
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"temporal_shift"
);
}
}
}
// namespace phi
PD_REGISTER_KERNEL
(
temporal_shift
,
XPU
,
ALL_LAYOUT
,
phi
::
TemporalShiftKernel
,
float
)
{}
python/paddle/fluid/tests/unittests/xpu/test_temporal_shift_op_xpu.py
0 → 100644
浏览文件 @
63d9a175
# 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
division
import
unittest
import
numpy
as
np
import
sys
sys
.
path
.
append
(
".."
)
import
paddle
import
paddle.nn.functional
as
F
from
op_test_xpu
import
XPUOpTest
from
xpu.get_test_cover_info
import
create_test_class
,
get_xpu_op_support_types
,
XPUOpTestWrapper
paddle
.
enable_static
()
np
.
random
.
seed
(
10
)
def
temporal_shift
(
x
,
seg_num
,
shift_ratio
,
data_format
):
if
data_format
==
"NHWC"
:
x
=
np
.
transpose
(
x
,
(
0
,
3
,
1
,
2
))
shape
=
x
.
shape
reshape_x
=
x
.
reshape
((
-
1
,
seg_num
,
shape
[
1
],
shape
[
2
],
shape
[
3
]))
pad_x
=
np
.
pad
(
reshape_x
,
((
0
,
0
),
(
1
,
1
),
(
0
,
0
),
(
0
,
0
),
(
0
,
0
)),
'constant'
)
c1
=
int
(
shape
[
1
]
*
shift_ratio
)
c2
=
int
(
shape
[
1
]
*
2
*
shift_ratio
)
slice1
=
pad_x
[:,
:
seg_num
,
:
c1
,
:,
:]
slice2
=
pad_x
[:,
2
:
seg_num
+
2
,
c1
:
c2
,
:,
:]
slice3
=
pad_x
[:,
1
:
seg_num
+
1
,
c2
:,
:,
:]
concat_x
=
np
.
concatenate
([
slice1
,
slice2
,
slice3
],
axis
=
2
)
out
=
concat_x
.
reshape
(
shape
)
if
data_format
==
"NHWC"
:
out
=
np
.
transpose
(
out
,
(
0
,
2
,
3
,
1
))
return
out
class
XPUTestTemporalShiftOp
(
XPUOpTestWrapper
):
def
__init__
(
self
):
self
.
op_name
=
"temporal_shift"
self
.
use_dynamic_create_class
=
False
class
TestXPUTemporalShift
(
XPUOpTest
):
def
setUp
(
self
):
self
.
initTestCase
()
self
.
op_type
=
'temporal_shift'
self
.
python_api
=
F
.
temporal_shift
self
.
use_xpu
=
True
x
=
np
.
random
.
random
(
self
.
x_shape
).
astype
(
self
.
dtype
)
self
.
attrs
=
{
"seg_num"
:
self
.
seg_num
,
"shift_ratio"
:
self
.
shift_ratio
,
"data_format"
:
self
.
data_format
}
self
.
inputs
=
{
"X"
:
x
,
}
output
=
temporal_shift
(
x
,
self
.
seg_num
,
self
.
shift_ratio
,
self
.
data_format
)
self
.
outputs
=
{
"Out"
:
output
}
self
.
python_out_sig
=
[
"Out"
]
def
test_check_output
(
self
):
self
.
check_output
(
check_eager
=
True
)
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Out'
,
check_eager
=
True
)
def
initTestCase
(
self
):
self
.
x_shape
=
(
6
,
4
,
4
,
4
)
self
.
seg_num
=
3
self
.
shift_ratio
=
0.25
self
.
dtype
=
'float32'
self
.
data_format
=
'NCHW'
class
TestXPUTemporalShift2
(
TestXPUTemporalShift
):
def
initTestCase
(
self
):
self
.
x_shape
=
(
1
,
1
,
1
,
1
)
self
.
seg_num
=
1
self
.
shift_ratio
=
0.1
self
.
dtype
=
'float32'
self
.
data_format
=
'NCHW'
class
TestXPUTemporalShift3
(
TestXPUTemporalShift
):
def
initTestCase
(
self
):
self
.
x_shape
=
(
4
,
9
,
1
,
1
)
self
.
seg_num
=
2
self
.
shift_ratio
=
0.2
self
.
dtype
=
'float32'
self
.
data_format
=
'NCHW'
class
TestXPUTemporalShift4
(
TestXPUTemporalShift
):
def
initTestCase
(
self
):
self
.
x_shape
=
(
4
,
1
,
10
,
10
)
self
.
seg_num
=
2
self
.
shift_ratio
=
0.3
self
.
dtype
=
'float32'
self
.
data_format
=
'NCHW'
class
TestXPUTemporalShift5
(
TestXPUTemporalShift
):
def
initTestCase
(
self
):
self
.
x_shape
=
(
1
,
1
,
1
,
1
)
self
.
seg_num
=
1
self
.
shift_ratio
=
0.3
self
.
dtype
=
'float32'
self
.
data_format
=
'NHWC'
class
TestXPUTemporalShift6
(
TestXPUTemporalShift
):
def
initTestCase
(
self
):
self
.
x_shape
=
(
6
,
5
,
5
,
1
)
self
.
seg_num
=
3
self
.
shift_ratio
=
0.25
self
.
dtype
=
'float32'
self
.
data_format
=
'NHWC'
class
TestXPUTemporalShift7
(
TestXPUTemporalShift
):
def
initTestCase
(
self
):
self
.
x_shape
=
(
9
,
1
,
1
,
4
)
self
.
seg_num
=
3
self
.
shift_ratio
=
0.45
self
.
dtype
=
'float32'
self
.
data_format
=
'NHWC'
support_types
=
get_xpu_op_support_types
(
'temporal_shift'
)
for
stype
in
support_types
:
create_test_class
(
globals
(),
XPUTestTemporalShiftOp
,
stype
)
if
__name__
==
"__main__"
:
paddle
.
enable_static
()
unittest
.
main
()
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