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6da637e8
编写于
4月 19, 2022
作者:
Q
qipengh
提交者:
GitHub
4月 19, 2022
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差异文件
[MLU]add op: cumsum, fill_any_like, unsqueeze (#41791)
上级
8f77f8bc
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
799 addition
and
0 deletion
+799
-0
paddle/fluid/operators/cumsum_op_mlu.cc
paddle/fluid/operators/cumsum_op_mlu.cc
+67
-0
paddle/fluid/operators/fill_any_like_op_mlu.cc
paddle/fluid/operators/fill_any_like_op_mlu.cc
+72
-0
paddle/fluid/operators/unsqueeze_op_mlu.cc
paddle/fluid/operators/unsqueeze_op_mlu.cc
+57
-0
python/paddle/fluid/tests/unittests/mlu/test_cumsum_op_mlu.py
...on/paddle/fluid/tests/unittests/mlu/test_cumsum_op_mlu.py
+183
-0
python/paddle/fluid/tests/unittests/mlu/test_fill_any_like_op_mlu.py
...le/fluid/tests/unittests/mlu/test_fill_any_like_op_mlu.py
+104
-0
python/paddle/fluid/tests/unittests/mlu/test_unsqueeze2_op_mlu.py
...addle/fluid/tests/unittests/mlu/test_unsqueeze2_op_mlu.py
+226
-0
python/paddle/fluid/tests/unittests/mlu/test_unsqueeze_op_mlu.py
...paddle/fluid/tests/unittests/mlu/test_unsqueeze_op_mlu.py
+90
-0
未找到文件。
paddle/fluid/operators/cumsum_op_mlu.cc
0 → 100644
浏览文件 @
6da637e8
/* 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/framework/op_registry.h"
#include "paddle/fluid/operators/mlu/mlu_baseop.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
class
CumSumMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
out
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
bool
exclusive
=
ctx
.
Attr
<
bool
>
(
"exclusive"
);
bool
reverse
=
ctx
.
Attr
<
bool
>
(
"reverse"
);
bool
flatten
=
ctx
.
Attr
<
bool
>
(
"flatten"
);
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
Tensor
*
input_ptr
=
const_cast
<
Tensor
*>
(
x
);
Tensor
flat_x
(
x
->
type
());
if
(
flatten
)
{
PADDLE_ENFORCE_EQ
(
axis
,
-
1
,
platform
::
errors
::
InvalidArgument
(
"when flatten is true, attr axis must be default %d, but got %d"
,
-
1
,
axis
));
flat_x
.
ShareDataWith
(
*
x
);
flat_x
.
Resize
(
phi
::
make_ddim
({
x
->
numel
()}));
input_ptr
=
&
flat_x
;
}
const
int
true_axis
=
(
axis
<
0
)
?
input_ptr
->
dims
().
size
()
+
axis
:
axis
;
MLUCnnlTensorDesc
input_desc
(
*
input_ptr
);
MLUCnnlTensorDesc
out_desc
(
*
out
);
MLUCnnl
::
Cumsum
(
ctx
,
true_axis
,
exclusive
,
reverse
,
input_desc
.
get
(),
GetBasePtr
(
input_ptr
),
out_desc
.
get
(),
GetBasePtr
(
out
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_MLU_KERNEL
(
cumsum
,
ops
::
CumSumMLUKernel
<
int
>
,
ops
::
CumSumMLUKernel
<
float
>
,
ops
::
CumSumMLUKernel
<
plat
::
float16
>
);
paddle/fluid/operators/fill_any_like_op_mlu.cc
0 → 100644
浏览文件 @
6da637e8
/* 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/framework/op_registry.h"
#include "paddle/fluid/operators/mlu/mlu_baseop.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
class
FillAnyLikeMLUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
using
CommonType
=
typename
std
::
common_type
<
float
,
typename
std
::
conditional
<
std
::
is_same
<
T
,
platform
::
float16
>::
value
,
float
,
T
>::
type
>::
type
;
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
float
value
=
ctx
.
Attr
<
float
>
(
"value"
);
auto
common_type_value
=
static_cast
<
CommonType
>
(
value
);
PADDLE_ENFORCE_EQ
(
(
common_type_value
>=
static_cast
<
CommonType
>
(
std
::
numeric_limits
<
T
>::
lowest
()))
&&
(
common_type_value
<=
static_cast
<
CommonType
>
(
std
::
numeric_limits
<
T
>::
max
())),
true
,
platform
::
errors
::
InvalidArgument
(
"The filled value is out of range for target type, "
"current kernel type is %s, the range should between %f "
"and %f, but now value is %f."
,
typeid
(
T
).
name
(),
static_cast
<
CommonType
>
(
std
::
numeric_limits
<
T
>::
lowest
()),
static_cast
<
CommonType
>
(
std
::
numeric_limits
<
T
>::
max
()),
value
));
PADDLE_ENFORCE_EQ
(
std
::
isnan
(
value
),
false
,
platform
::
errors
::
InvalidArgument
(
"The filled value is NaN."
));
auto
value_t
=
static_cast
<
T
>
(
value
);
MLUCnnlTensorDesc
out_desc
(
*
out
,
CNNL_LAYOUT_ARRAY
,
ToCnnlDataType
<
T
>
());
MLUCnnl
::
Fill
(
ctx
,
CNNL_POINTER_MODE_HOST
,
&
value_t
,
out_desc
.
get
(),
GetBasePtr
(
out
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_MLU_KERNEL
(
fill_any_like
,
ops
::
FillAnyLikeMLUKernel
<
int
>
,
ops
::
FillAnyLikeMLUKernel
<
int64_t
>
,
ops
::
FillAnyLikeMLUKernel
<
float
>
,
ops
::
FillAnyLikeMLUKernel
<
plat
::
float16
>
);
paddle/fluid/operators/unsqueeze_op_mlu.cc
0 → 100644
浏览文件 @
6da637e8
/* 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. */
#ifdef PADDLE_WITH_MLU
#include <memory>
#include <string>
#include "paddle/fluid/operators/unsqueeze_op.h"
#include "paddle/fluid/platform/device/mlu/device_context.h"
namespace
ops
=
paddle
::
operators
;
namespace
plat
=
paddle
::
platform
;
REGISTER_OP_MLU_KERNEL
(
unsqueeze
,
ops
::
UnsqueezeKernel
<
plat
::
MLUDeviceContext
,
float
>
,
ops
::
UnsqueezeKernel
<
plat
::
MLUDeviceContext
,
double
>
,
ops
::
UnsqueezeKernel
<
plat
::
MLUDeviceContext
,
plat
::
float16
>
,
ops
::
UnsqueezeKernel
<
plat
::
MLUDeviceContext
,
bool
>
,
ops
::
UnsqueezeKernel
<
plat
::
MLUDeviceContext
,
int
>
,
ops
::
UnsqueezeKernel
<
plat
::
MLUDeviceContext
,
int8_t
>
,
ops
::
UnsqueezeKernel
<
plat
::
MLUDeviceContext
,
int64_t
>
);
REGISTER_OP_MLU_KERNEL
(
unsqueeze2
,
ops
::
UnsqueezeKernel
<
plat
::
MLUDeviceContext
,
float
>
,
ops
::
UnsqueezeKernel
<
plat
::
MLUDeviceContext
,
double
>
,
ops
::
UnsqueezeKernel
<
plat
::
MLUDeviceContext
,
plat
::
float16
>
,
ops
::
UnsqueezeKernel
<
plat
::
MLUDeviceContext
,
bool
>
,
ops
::
UnsqueezeKernel
<
plat
::
MLUDeviceContext
,
int
>
,
ops
::
UnsqueezeKernel
<
plat
::
MLUDeviceContext
,
int8_t
>
,
ops
::
UnsqueezeKernel
<
plat
::
MLUDeviceContext
,
int64_t
>
);
REGISTER_OP_MLU_KERNEL
(
unsqueeze_grad
,
ops
::
UnsqueezeGradKernel
<
plat
::
MLUDeviceContext
,
float
>
,
ops
::
UnsqueezeGradKernel
<
plat
::
MLUDeviceContext
,
double
>
,
ops
::
UnsqueezeGradKernel
<
plat
::
MLUDeviceContext
,
plat
::
float16
>
,
ops
::
UnsqueezeGradKernel
<
plat
::
MLUDeviceContext
,
bool
>
,
ops
::
UnsqueezeGradKernel
<
plat
::
MLUDeviceContext
,
int
>
,
ops
::
UnsqueezeGradKernel
<
plat
::
MLUDeviceContext
,
int8_t
>
,
ops
::
UnsqueezeGradKernel
<
plat
::
MLUDeviceContext
,
int64_t
>
);
REGISTER_OP_MLU_KERNEL
(
unsqueeze2_grad
,
ops
::
Unsqueeze2GradKernel
<
plat
::
MLUDeviceContext
,
float
>
,
ops
::
Unsqueeze2GradKernel
<
plat
::
MLUDeviceContext
,
double
>
,
ops
::
Unsqueeze2GradKernel
<
plat
::
MLUDeviceContext
,
plat
::
float16
>
,
ops
::
Unsqueeze2GradKernel
<
plat
::
MLUDeviceContext
,
bool
>
,
ops
::
Unsqueeze2GradKernel
<
plat
::
MLUDeviceContext
,
int
>
,
ops
::
Unsqueeze2GradKernel
<
plat
::
MLUDeviceContext
,
int8_t
>
,
ops
::
Unsqueeze2GradKernel
<
plat
::
MLUDeviceContext
,
int64_t
>
);
#endif
python/paddle/fluid/tests/unittests/mlu/test_cumsum_op_mlu.py
0 → 100644
浏览文件 @
6da637e8
# 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
unittest
import
numpy
as
np
from
paddle.fluid.tests.unittests.op_test
import
OpTest
import
paddle
import
paddle.fluid.core
as
core
import
paddle.fluid
as
fluid
from
paddle.fluid
import
compiler
,
Program
,
program_guard
paddle
.
enable_static
()
class
TestMLUCumSumOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"cumsum"
self
.
set_mlu
()
self
.
init_dtype
()
self
.
init_testcase
()
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
set_mlu
(
self
):
self
.
__class__
.
use_mlu
=
True
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float32
def
init_testcase
(
self
):
self
.
attrs
=
{
'axis'
:
2
}
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
5
,
6
,
10
)).
astype
(
self
.
dtype
)}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
].
cumsum
(
axis
=
2
)}
class
TestMLUCumSumOp2
(
TestMLUCumSumOp
):
def
init_testcase
(
self
):
self
.
attrs
=
{
'axis'
:
-
1
,
'reverse'
:
True
}
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
5
,
6
,
10
)).
astype
(
self
.
dtype
)}
self
.
outputs
=
{
'Out'
:
np
.
flip
(
np
.
flip
(
self
.
inputs
[
'X'
],
axis
=
2
).
cumsum
(
axis
=
2
),
axis
=
2
)
}
class
TestMLUCumSumOp3
(
TestMLUCumSumOp
):
def
init_testcase
(
self
):
self
.
attrs
=
{
'axis'
:
1
}
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
5
,
6
,
10
)).
astype
(
self
.
dtype
)}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
].
cumsum
(
axis
=
1
)}
class
TestMLUCumSumOp4
(
TestMLUCumSumOp
):
def
init_testcase
(
self
):
self
.
attrs
=
{
'axis'
:
0
}
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
5
,
6
,
10
)).
astype
(
self
.
dtype
)}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
].
cumsum
(
axis
=
0
)}
class
TestMLUCumSumOp5
(
TestMLUCumSumOp
):
def
init_testcase
(
self
):
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
5
,
20
)).
astype
(
self
.
dtype
)}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
].
cumsum
(
axis
=
1
)}
class
TestMLUCumSumOp7
(
TestMLUCumSumOp
):
def
init_testcase
(
self
):
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
100
)).
astype
(
self
.
dtype
)}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
].
cumsum
(
axis
=
0
)}
class
TestNPUCumSumExclusive1
(
TestMLUCumSumOp
):
def
init_testcase
(
self
):
self
.
attrs
=
{
'axis'
:
2
,
"exclusive"
:
True
}
a
=
np
.
random
.
random
((
4
,
5
,
65
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
a
}
self
.
outputs
=
{
'Out'
:
np
.
concatenate
(
(
np
.
zeros
(
(
4
,
5
,
1
),
dtype
=
self
.
dtype
),
a
[:,
:,
:
-
1
].
cumsum
(
axis
=
2
)),
axis
=
2
)
}
class
TestNPUCumSumExclusive2
(
TestMLUCumSumOp
):
def
init_testcase
(
self
):
self
.
attrs
=
{
'axis'
:
2
,
"exclusive"
:
True
}
a
=
np
.
random
.
random
((
1
,
1
,
888
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
a
}
self
.
outputs
=
{
'Out'
:
np
.
concatenate
(
(
np
.
zeros
(
(
1
,
1
,
1
),
dtype
=
self
.
dtype
),
a
[:,
:,
:
-
1
].
cumsum
(
axis
=
2
)),
axis
=
2
)
}
class
TestNPUCumSumExclusive3
(
TestMLUCumSumOp
):
def
init_testcase
(
self
):
self
.
attrs
=
{
'axis'
:
2
,
"exclusive"
:
True
}
a
=
np
.
random
.
random
((
4
,
5
,
888
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
a
}
self
.
outputs
=
{
'Out'
:
np
.
concatenate
(
(
np
.
zeros
(
(
4
,
5
,
1
),
dtype
=
self
.
dtype
),
a
[:,
:,
:
-
1
].
cumsum
(
axis
=
2
)),
axis
=
2
)
}
class
TestNPUCumSumExclusive4
(
TestMLUCumSumOp
):
def
init_testcase
(
self
):
self
.
attrs
=
{
'axis'
:
2
,
"exclusive"
:
True
}
a
=
np
.
random
.
random
((
1
,
1
,
3049
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
a
}
self
.
outputs
=
{
'Out'
:
np
.
concatenate
(
(
np
.
zeros
(
(
1
,
1
,
1
),
dtype
=
self
.
dtype
),
a
[:,
:,
:
-
1
].
cumsum
(
axis
=
2
)),
axis
=
2
)
}
class
TestNPUCumSumExclusive5
(
TestMLUCumSumOp
):
def
init_testcase
(
self
):
self
.
attrs
=
{
'axis'
:
2
,
"exclusive"
:
True
}
a
=
np
.
random
.
random
((
4
,
5
,
3096
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
a
}
self
.
outputs
=
{
'Out'
:
np
.
concatenate
(
(
np
.
zeros
(
(
4
,
5
,
1
),
dtype
=
self
.
dtype
),
a
[:,
:,
:
-
1
].
cumsum
(
axis
=
2
)),
axis
=
2
)
}
class
TestNPUCumSumReverseExclusive
(
TestMLUCumSumOp
):
def
init_testcase
(
self
):
self
.
attrs
=
{
'axis'
:
2
,
'reverse'
:
True
,
"exclusive"
:
True
}
a
=
np
.
random
.
random
((
4
,
5
,
6
)).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
a
}
a
=
np
.
flip
(
a
,
axis
=
2
)
self
.
outputs
=
{
'Out'
:
np
.
concatenate
(
(
np
.
flip
(
a
[:,
:,
:
-
1
].
cumsum
(
axis
=
2
),
axis
=
2
),
np
.
zeros
(
(
4
,
5
,
1
),
dtype
=
self
.
dtype
)),
axis
=
2
)
}
class
TestNPUCumSumWithFlatten1
(
TestMLUCumSumOp
):
def
init_testcase
(
self
):
self
.
attrs
=
{
'flatten'
:
True
}
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
5
,
6
)).
astype
(
self
.
dtype
)}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
].
cumsum
()}
class
TestNPUCumSumWithFlatten2
(
TestMLUCumSumOp
):
def
init_testcase
(
self
):
self
.
attrs
=
{
'flatten'
:
True
}
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
5
,
6
,
10
)).
astype
(
self
.
dtype
)}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
].
cumsum
()}
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/mlu/test_fill_any_like_op_mlu.py
0 → 100644
浏览文件 @
6da637e8
# 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
sys
.
path
.
append
(
".."
)
import
paddle
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
paddle
.
enable_static
()
class
TestFillAnyLikeOp
(
OpTest
):
def
setUp
(
self
):
self
.
init_dtype
()
self
.
set_mlu
()
self
.
op_type
=
"fill_any_like"
self
.
set_value
()
self
.
set_input
()
self
.
attrs
=
{
'value'
:
self
.
value
}
self
.
outputs
=
{
'Out'
:
self
.
value
*
np
.
ones_like
(
self
.
inputs
[
"X"
])}
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float32
def
set_mlu
(
self
):
self
.
__class__
.
use_mlu
=
True
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
no_need_check_grad
=
True
def
set_input
(
self
):
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
219
,
232
)).
astype
(
self
.
dtype
)}
def
set_value
(
self
):
self
.
value
=
0.0
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
class
TestFillAnyLikeOp2
(
TestFillAnyLikeOp
):
def
set_value
(
self
):
self
.
value
=
-
0.0
class
TestFillAnyLikeOp3
(
TestFillAnyLikeOp
):
def
set_value
(
self
):
self
.
value
=
1.0
class
TestFillAnyLikeOp4
(
TestFillAnyLikeOp
):
def
set_value
(
self
):
self
.
value
=
1e-9
class
TestFillAnyLikeOp5
(
TestFillAnyLikeOp
):
def
set_value
(
self
):
if
self
.
dtype
==
"float16"
:
self
.
value
=
0.05
else
:
self
.
value
=
5.0
class
TestFillAnyLikeOpInt32
(
TestFillAnyLikeOp
):
def
init_dtype
(
self
):
self
.
dtype
=
np
.
int32
def
set_value
(
self
):
self
.
value
=
-
1
class
TestFillAnyLikeOpInt64
(
TestFillAnyLikeOp
):
def
init_dtype
(
self
):
self
.
dtype
=
np
.
int64
def
set_value
(
self
):
self
.
value
=
-
1
class
TestFillAnyLikeOpFloat32
(
TestFillAnyLikeOp
):
def
init_dtype
(
self
):
self
.
dtype
=
np
.
float32
def
set_value
(
self
):
self
.
value
=
0.09
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/mlu/test_unsqueeze2_op_mlu.py
0 → 100644
浏览文件 @
6da637e8
# 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
sys
sys
.
path
.
append
(
".."
)
import
numpy
as
np
import
paddle
from
op_test
import
OpTest
paddle
.
enable_static
()
# Correct: General.
class
TestUnsqueezeOp
(
OpTest
):
def
setUp
(
self
):
self
.
init_test_case
()
self
.
set_mlu
()
self
.
op_type
=
"unsqueeze2"
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)}
self
.
init_attrs
()
self
.
outputs
=
{
"Out"
:
self
.
inputs
[
"X"
].
reshape
(
self
.
new_shape
),
"XShape"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)
}
def
set_mlu
(
self
):
self
.
__class__
.
use_mlu
=
True
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
,
no_check_set
=
[
'XShape'
])
def
test_check_grad
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'X'
],
'Out'
)
def
init_test_case
(
self
):
self
.
ori_shape
=
(
3
,
40
)
self
.
axes
=
(
1
,
2
)
self
.
new_shape
=
(
3
,
1
,
1
,
40
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
}
# Correct: Single input index.
class
TestUnsqueezeOp1
(
TestUnsqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
20
,
5
)
self
.
axes
=
(
-
1
,
)
self
.
new_shape
=
(
20
,
5
,
1
)
# Correct: Mixed input axis.
class
TestUnsqueezeOp2
(
TestUnsqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
20
,
5
)
self
.
axes
=
(
0
,
-
1
)
self
.
new_shape
=
(
1
,
20
,
5
,
1
)
# Correct: There is duplicated axis.
class
TestUnsqueezeOp3
(
TestUnsqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
10
,
2
,
5
)
self
.
axes
=
(
0
,
3
,
3
)
self
.
new_shape
=
(
1
,
10
,
2
,
1
,
1
,
5
)
# Correct: Reversed axes.
class
TestUnsqueezeOp4
(
TestUnsqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
10
,
2
,
5
)
self
.
axes
=
(
3
,
1
,
1
)
self
.
new_shape
=
(
10
,
1
,
1
,
2
,
5
,
1
)
# axes is a list(with tensor)
class
TestUnsqueezeOp_AxesTensorList
(
OpTest
):
def
setUp
(
self
):
self
.
init_test_case
()
self
.
set_mlu
()
self
.
op_type
=
"unsqueeze2"
axes_tensor_list
=
[]
for
index
,
ele
in
enumerate
(
self
.
axes
):
axes_tensor_list
.
append
((
"axes"
+
str
(
index
),
np
.
ones
(
(
1
)).
astype
(
'int32'
)
*
ele
))
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
),
"AxesTensorList"
:
axes_tensor_list
}
self
.
init_attrs
()
self
.
outputs
=
{
"Out"
:
self
.
inputs
[
"X"
].
reshape
(
self
.
new_shape
),
"XShape"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)
}
def
set_mlu
(
self
):
self
.
__class__
.
use_mlu
=
True
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
,
no_check_set
=
[
'XShape'
])
def
test_check_grad
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'X'
],
'Out'
)
def
init_test_case
(
self
):
self
.
ori_shape
=
(
20
,
5
)
self
.
axes
=
(
1
,
2
)
self
.
new_shape
=
(
20
,
1
,
1
,
5
)
def
init_attrs
(
self
):
self
.
attrs
=
{}
class
TestUnsqueezeOp1_AxesTensorList
(
TestUnsqueezeOp_AxesTensorList
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
20
,
5
)
self
.
axes
=
(
-
1
,
)
self
.
new_shape
=
(
20
,
5
,
1
)
class
TestUnsqueezeOp2_AxesTensorList
(
TestUnsqueezeOp_AxesTensorList
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
20
,
5
)
self
.
axes
=
(
0
,
-
1
)
self
.
new_shape
=
(
1
,
20
,
5
,
1
)
class
TestUnsqueezeOp3_AxesTensorList
(
TestUnsqueezeOp_AxesTensorList
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
10
,
2
,
5
)
self
.
axes
=
(
0
,
3
,
3
)
self
.
new_shape
=
(
1
,
10
,
2
,
1
,
1
,
5
)
class
TestUnsqueezeOp4_AxesTensorList
(
TestUnsqueezeOp_AxesTensorList
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
10
,
2
,
5
)
self
.
axes
=
(
3
,
1
,
1
)
self
.
new_shape
=
(
10
,
1
,
1
,
2
,
5
,
1
)
# axes is a Tensor
class
TestUnsqueezeOp_AxesTensor
(
OpTest
):
def
setUp
(
self
):
self
.
init_test_case
()
self
.
set_mlu
()
self
.
op_type
=
"unsqueeze2"
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
),
"AxesTensor"
:
np
.
array
(
self
.
axes
).
astype
(
"int32"
)
}
self
.
init_attrs
()
self
.
outputs
=
{
"Out"
:
self
.
inputs
[
"X"
].
reshape
(
self
.
new_shape
),
"XShape"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)
}
def
set_mlu
(
self
):
self
.
__class__
.
use_mlu
=
True
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
,
no_check_set
=
[
'XShape'
])
def
test_check_grad
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'X'
],
'Out'
)
def
init_test_case
(
self
):
self
.
ori_shape
=
(
20
,
5
)
self
.
axes
=
(
1
,
2
)
self
.
new_shape
=
(
20
,
1
,
1
,
5
)
def
init_attrs
(
self
):
self
.
attrs
=
{}
class
TestUnsqueezeOp1_AxesTensor
(
TestUnsqueezeOp_AxesTensor
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
20
,
5
)
self
.
axes
=
(
-
1
,
)
self
.
new_shape
=
(
20
,
5
,
1
)
class
TestUnsqueezeOp2_AxesTensor
(
TestUnsqueezeOp_AxesTensor
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
20
,
5
)
self
.
axes
=
(
0
,
-
1
)
self
.
new_shape
=
(
1
,
20
,
5
,
1
)
class
TestUnsqueezeOp3_AxesTensor
(
TestUnsqueezeOp_AxesTensor
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
10
,
2
,
5
)
self
.
axes
=
(
0
,
3
,
3
)
self
.
new_shape
=
(
1
,
10
,
2
,
1
,
1
,
5
)
class
TestUnsqueezeOp4_AxesTensor
(
TestUnsqueezeOp_AxesTensor
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
10
,
2
,
5
)
self
.
axes
=
(
3
,
1
,
1
)
self
.
new_shape
=
(
10
,
1
,
1
,
2
,
5
,
1
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/mlu/test_unsqueeze_op_mlu.py
0 → 100644
浏览文件 @
6da637e8
# 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
unittest
import
sys
sys
.
path
.
append
(
".."
)
import
numpy
as
np
import
paddle
from
op_test
import
OpTest
paddle
.
enable_static
()
# Correct: General.
class
TestUnsqueezeOp
(
OpTest
):
def
setUp
(
self
):
self
.
init_test_case
()
self
.
set_mlu
()
self
.
op_type
=
"unsqueeze"
self
.
inputs
=
{
"X"
:
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
"float32"
)}
self
.
init_attrs
()
self
.
outputs
=
{
"Out"
:
self
.
inputs
[
"X"
].
reshape
(
self
.
new_shape
)}
def
set_mlu
(
self
):
self
.
__class__
.
use_mlu
=
True
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
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'
)
def
init_test_case
(
self
):
self
.
ori_shape
=
(
3
,
40
)
self
.
axes
=
(
1
,
2
)
self
.
new_shape
=
(
3
,
1
,
1
,
40
)
def
init_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
self
.
axes
}
# Correct: Single input index.
class
TestUnsqueezeOp1
(
TestUnsqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
20
,
5
)
self
.
axes
=
(
-
1
,
)
self
.
new_shape
=
(
20
,
5
,
1
)
# Correct: Mixed input axis.
class
TestUnsqueezeOp2
(
TestUnsqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
20
,
5
)
self
.
axes
=
(
0
,
-
1
)
self
.
new_shape
=
(
1
,
20
,
5
,
1
)
# Correct: There is duplicated axis.
class
TestUnsqueezeOp3
(
TestUnsqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
10
,
2
,
5
)
self
.
axes
=
(
0
,
3
,
3
)
self
.
new_shape
=
(
1
,
10
,
2
,
1
,
1
,
5
)
# Correct: Reversed axes.
class
TestUnsqueezeOp4
(
TestUnsqueezeOp
):
def
init_test_case
(
self
):
self
.
ori_shape
=
(
10
,
2
,
5
)
self
.
axes
=
(
3
,
1
,
1
)
self
.
new_shape
=
(
10
,
1
,
1
,
2
,
5
,
1
)
if
__name__
==
"__main__"
:
unittest
.
main
()
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