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体验新版 GitCode,发现更多精彩内容 >>
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a5f2e1f7
编写于
1月 31, 2023
作者:
W
wangshengxiang
提交者:
GitHub
1月 31, 2023
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差异文件
bind pixel_shuffle & pixel_shuffle_grad op for xpu (#50090)
上级
0d9185b9
变更
4
显示空白变更内容
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并排
Showing
4 changed file
with
253 addition
and
0 deletion
+253
-0
paddle/phi/backends/xpu/xpu2_op_list.cc
paddle/phi/backends/xpu/xpu2_op_list.cc
+2
-0
paddle/phi/kernels/xpu/pixel_shuffle_grad_kernel.cc
paddle/phi/kernels/xpu/pixel_shuffle_grad_kernel.cc
+54
-0
paddle/phi/kernels/xpu/pixel_shuffle_kernel.cc
paddle/phi/kernels/xpu/pixel_shuffle_kernel.cc
+54
-0
python/paddle/fluid/tests/unittests/xpu/test_pixel_shuffle_op_xpu.py
...le/fluid/tests/unittests/xpu/test_pixel_shuffle_op_xpu.py
+143
-0
未找到文件。
paddle/phi/backends/xpu/xpu2_op_list.cc
浏览文件 @
a5f2e1f7
...
...
@@ -415,6 +415,8 @@ XPUOpMap& get_kl2_ops() {
{
"p_norm_grad"
,
XPUKernelSet
({
phi
::
DataType
::
FLOAT32
})},
{
"pad3d_grad"
,
XPUKernelSet
({
phi
::
DataType
::
FLOAT32
})},
{
"pad3d"
,
XPUKernelSet
({
phi
::
DataType
::
FLOAT32
})},
{
"pixel_shuffle"
,
XPUKernelSet
({
phi
::
DataType
::
FLOAT32
})},
{
"pixel_shuffle_grad"
,
XPUKernelSet
({
phi
::
DataType
::
FLOAT32
})},
{
"pool2d_grad"
,
XPUKernelSet
({
phi
::
DataType
::
FLOAT32
,
phi
::
DataType
::
FLOAT16
})},
{
"pool2d"
,
...
...
paddle/phi/kernels/xpu/pixel_shuffle_grad_kernel.cc
0 → 100644
浏览文件 @
a5f2e1f7
// Copyright (c) 2023 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/pixel_shuffle_grad_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
PixelShuffleGradKernel
(
const
Context
&
ctx
,
const
DenseTensor
&
out_grad
,
int
upscale_factor
,
const
std
::
string
&
data_format
,
DenseTensor
*
x_grad
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
const
T
*
x_ptr
=
out_grad
.
data
<
T
>
();
T
*
y_ptr
=
ctx
.
template
Alloc
<
T
>(
x_grad
);
bool
is_nchw
=
data_format
==
"NCHW"
;
int64_t
n
=
out_grad
.
dims
()[
0
];
int64_t
xc
=
out_grad
.
dims
()[
is_nchw
?
1
:
3
];
int64_t
xh
=
out_grad
.
dims
()[
is_nchw
?
2
:
1
];
int64_t
xw
=
out_grad
.
dims
()[
is_nchw
?
3
:
2
];
int
r
=
pixel_unshuffle
(
ctx
.
x_context
(),
reinterpret_cast
<
const
XPUType
*>
(
x_ptr
),
reinterpret_cast
<
XPUType
*>
(
y_ptr
),
n
,
xc
,
xh
,
xw
,
upscale_factor
,
is_nchw
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"pixel_unshuffle"
);
}
}
// namespace phi
PD_REGISTER_KERNEL
(
pixel_shuffle_grad
,
XPU
,
ALL_LAYOUT
,
phi
::
PixelShuffleGradKernel
,
float
)
{}
paddle/phi/kernels/xpu/pixel_shuffle_kernel.cc
0 → 100644
浏览文件 @
a5f2e1f7
// Copyright (c) 2023 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/pixel_shuffle_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
PixelShuffleKernel
(
const
Context
&
ctx
,
const
DenseTensor
&
x
,
int
upscale_factor
,
const
std
::
string
&
data_format
,
DenseTensor
*
out
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
const
T
*
x_ptr
=
x
.
data
<
T
>
();
T
*
y_ptr
=
ctx
.
template
Alloc
<
T
>(
out
);
bool
is_nchw
=
data_format
==
"NCHW"
;
int64_t
n
=
x
.
dims
()[
0
];
int64_t
xc
=
x
.
dims
()[
is_nchw
?
1
:
3
];
int64_t
xh
=
x
.
dims
()[
is_nchw
?
2
:
1
];
int64_t
xw
=
x
.
dims
()[
is_nchw
?
3
:
2
];
int
r
=
pixel_shuffle
(
ctx
.
x_context
(),
reinterpret_cast
<
const
XPUType
*>
(
x_ptr
),
reinterpret_cast
<
XPUType
*>
(
y_ptr
),
n
,
xc
,
xh
,
xw
,
upscale_factor
,
is_nchw
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"pixel_shuffle"
);
}
}
// namespace phi
PD_REGISTER_KERNEL
(
pixel_shuffle
,
XPU
,
ALL_LAYOUT
,
phi
::
PixelShuffleKernel
,
float
)
{}
python/paddle/fluid/tests/unittests/xpu/test_pixel_shuffle_op_xpu.py
0 → 100644
浏览文件 @
a5f2e1f7
# Copyright (c) 2023 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.
import
sys
import
unittest
import
numpy
as
np
sys
.
path
.
append
(
".."
)
from
op_test_xpu
import
XPUOpTest
from
xpu.get_test_cover_info
import
(
XPUOpTestWrapper
,
create_test_class
,
get_xpu_op_support_types
,
)
import
paddle
paddle
.
enable_static
()
def
pixel_shuffle_np
(
x
,
up_factor
,
data_format
=
"NCHW"
):
if
data_format
==
"NCHW"
:
n
,
c
,
h
,
w
=
x
.
shape
new_shape
=
(
n
,
c
//
(
up_factor
*
up_factor
),
up_factor
,
up_factor
,
h
,
w
,
)
# reshape to (num,output_channel,upscale_factor,upscale_factor,h,w)
npresult
=
np
.
reshape
(
x
,
new_shape
)
# transpose to (num,output_channel,h,upscale_factor,w,upscale_factor)
npresult
=
npresult
.
transpose
(
0
,
1
,
4
,
2
,
5
,
3
)
oshape
=
[
n
,
c
//
(
up_factor
*
up_factor
),
h
*
up_factor
,
w
*
up_factor
]
npresult
=
np
.
reshape
(
npresult
,
oshape
)
return
npresult
else
:
n
,
h
,
w
,
c
=
x
.
shape
new_shape
=
(
n
,
h
,
w
,
c
//
(
up_factor
*
up_factor
),
up_factor
,
up_factor
,
)
# reshape to (num,h,w,output_channel,upscale_factor,upscale_factor)
npresult
=
np
.
reshape
(
x
,
new_shape
)
# transpose to (num,h,upscale_factor,w,upscale_factor,output_channel)
npresult
=
npresult
.
transpose
(
0
,
1
,
4
,
2
,
5
,
3
)
oshape
=
[
n
,
h
*
up_factor
,
w
*
up_factor
,
c
//
(
up_factor
*
up_factor
)]
npresult
=
np
.
reshape
(
npresult
,
oshape
)
return
npresult
class
XPUTestPixelShuffleOp
(
XPUOpTestWrapper
):
def
__init__
(
self
):
self
.
op_name
=
"pixel_shuffle"
self
.
use_dynamic_create_class
=
False
class
TestPixelShuffleOp
(
XPUOpTest
):
def
setUp
(
self
):
self
.
set_xpu
()
self
.
op_type
=
"pixel_shuffle"
self
.
init_dtype
()
self
.
eager_mode
=
True
# override
self
.
init_input_shape
()
self
.
init_attr
()
self
.
x
=
np
.
random
.
random
(
self
.
x_shape
).
astype
(
self
.
dtype
)
self
.
y
=
pixel_shuffle_np
(
self
.
x
,
self
.
attrs
[
"upscale_factor"
],
self
.
attrs
[
"data_format"
]
)
self
.
inputs
=
{
'X'
:
self
.
x
}
self
.
outputs
=
{
'Out'
:
self
.
y
}
def
init_input_shape
(
self
):
self
.
x_shape
=
[
2
,
64
,
26
,
26
]
def
init_attr
(
self
):
self
.
attrs
=
{
'upscale_factor'
:
2
,
'data_format'
:
"NCHW"
}
def
set_xpu
(
self
):
self
.
__class__
.
no_need_check_grad
=
False
self
.
place
=
paddle
.
XPUPlace
(
0
)
def
init_dtype
(
self
):
self
.
dtype
=
self
.
in_type
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'
],
'Out'
,
check_eager
=
self
.
eager_mode
)
class
TestNHWC
(
TestPixelShuffleOp
):
def
init_input_shape
(
self
):
self
.
x_shape
=
[
2
,
64
,
26
,
24
]
def
init_attr
(
self
):
self
.
attrs
=
{
'upscale_factor'
:
2
,
'data_format'
:
"NHWC"
}
class
TestUpFactor3
(
TestPixelShuffleOp
):
def
init_input_shape
(
self
):
self
.
x_shape
=
[
2
,
27
,
5
,
5
]
def
init_attr
(
self
):
self
.
attrs
=
{
'upscale_factor'
:
3
,
'data_format'
:
"NCHW"
}
class
TestUpFactor3NHWC
(
TestPixelShuffleOp
):
def
init_input_shape
(
self
):
self
.
x_shape
=
[
2
,
27
,
5
,
9
]
def
init_attr
(
self
):
self
.
attrs
=
{
'upscale_factor'
:
3
,
'data_format'
:
"NHWC"
}
support_types
=
get_xpu_op_support_types
(
"pixel_shuffle"
)
for
stype
in
support_types
:
create_test_class
(
globals
(),
XPUTestPixelShuffleOp
,
stype
)
if
__name__
==
"__main__"
:
unittest
.
main
()
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