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体验新版 GitCode,发现更多精彩内容 >>
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89c0877e
编写于
1月 06, 2022
作者:
B
baoachun
提交者:
GitHub
1月 06, 2022
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add mkldnn matmulv2 ut (#38749)
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python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_matmulv2_op.py
...d/tests/unittests/ir/inference/test_mkldnn_matmulv2_op.py
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python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_matmulv2_op.py
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89c0877e
# 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
auto_scan_test
import
MkldnnAutoScanTest
,
SkipReasons
from
program_config
import
TensorConfig
,
ProgramConfig
,
OpConfig
import
numpy
as
np
import
paddle.inference
as
paddle_infer
from
functools
import
partial
from
typing
import
Optional
,
List
,
Callable
,
Dict
,
Any
,
Set
import
unittest
import
hypothesis
from
hypothesis
import
given
,
settings
,
seed
,
example
,
assume
import
hypothesis.strategies
as
st
class
TestMkldnnMatmulv2Op
(
MkldnnAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
if
len
(
program_config
.
inputs
[
"input_data2"
].
shape
)
==
4
:
if
program_config
.
inputs
[
"input_data1"
].
shape
[
-
4
]
!=
1
and
program_config
.
inputs
[
"input_data2"
].
shape
[
-
4
]
!=
1
:
if
program_config
.
inputs
[
"input_data1"
].
shape
[
-
4
]
!=
program_config
.
inputs
[
"input_data2"
].
shape
[
-
4
]:
return
False
if
program_config
.
inputs
[
"input_data1"
].
shape
[
-
3
]
!=
1
and
program_config
.
inputs
[
"input_data2"
].
shape
[
-
3
]
!=
1
:
if
program_config
.
inputs
[
"input_data1"
].
shape
[
-
3
]
!=
program_config
.
inputs
[
"input_data2"
].
shape
[
-
3
]:
return
False
return
True
def
sample_program_configs
(
self
,
*
args
,
**
kwargs
):
def
generate_input
(
type
,
*
args
,
**
kwargs
):
transpose_X
=
kwargs
[
"transpose_X"
]
transpose_Y
=
kwargs
[
"transpose_Y"
]
batch_size1
=
kwargs
[
"batch_size1"
]
batch_size2
=
kwargs
[
"batch_size2"
]
channel1
=
kwargs
[
"channel1"
]
channel2
=
kwargs
[
"channel2"
]
input_dim
=
kwargs
[
"input_dim"
]
y_dim_len
=
kwargs
[
"y_dim_len"
]
if
transpose_X
and
transpose_Y
:
shape_x
=
[
batch_size1
,
channel1
,
input_dim
,
32
]
if
y_dim_len
==
4
:
shape_y
=
[
batch_size2
,
channel2
,
64
,
input_dim
]
elif
y_dim_len
==
3
:
shape_y
=
[
channel2
,
64
,
input_dim
]
elif
transpose_X
:
shape_x
=
[
batch_size1
,
channel1
,
input_dim
,
32
]
if
y_dim_len
==
4
:
shape_y
=
[
batch_size2
,
channel2
,
input_dim
,
64
]
elif
y_dim_len
==
3
:
shape_y
=
[
channel2
,
input_dim
,
64
]
elif
transpose_Y
:
shape_x
=
[
batch_size1
,
channel1
,
32
,
input_dim
]
if
y_dim_len
==
4
:
shape_y
=
[
batch_size2
,
channel2
,
8
,
input_dim
]
elif
y_dim_len
==
3
:
shape_y
=
[
channel2
,
8
,
input_dim
]
else
:
shape_x
=
[
batch_size1
,
channel1
,
32
,
input_dim
]
if
y_dim_len
==
4
:
shape_y
=
[
batch_size2
,
channel2
,
input_dim
,
16
]
elif
y_dim_len
==
3
:
shape_y
=
[
channel2
,
input_dim
,
16
]
if
type
==
"x"
:
return
np
.
random
.
random
(
shape_x
).
astype
(
np
.
float32
)
else
:
return
np
.
random
.
random
(
shape_y
).
astype
(
np
.
float32
)
matmul_op
=
OpConfig
(
type
=
"matmul_v2"
,
inputs
=
{
"X"
:
[
"input_data1"
],
"Y"
:
[
"input_data2"
]},
outputs
=
{
"Out"
:
[
"matmul_output"
]},
attrs
=
{
"trans_x"
:
kwargs
[
"transpose_X"
],
"trans_y"
:
kwargs
[
"transpose_Y"
],
"fused_reshape_X"
:
[],
"fused_reshape_Y"
:
[],
"fused_transpose_X"
:
[],
"fused_transpose_Y"
:
[],
"fused_reshape_Out"
:
[],
"fused_transpose_Out"
:
[]
})
program_config
=
ProgramConfig
(
ops
=
[
matmul_op
],
weights
=
{},
inputs
=
{
"input_data1"
:
TensorConfig
(
data_gen
=
partial
(
generate_input
,
"x"
,
*
args
,
**
kwargs
)),
"input_data2"
:
TensorConfig
(
data_gen
=
partial
(
generate_input
,
"y"
,
*
args
,
**
kwargs
))
},
outputs
=
[
"matmul_output"
])
yield
program_config
def
sample_predictor_configs
(
self
,
program_config
):
config
=
self
.
create_inference_config
(
use_mkldnn
=
True
)
yield
config
,
(
1e-5
,
1e-5
)
@
given
(
transpose_X
=
st
.
booleans
(),
transpose_Y
=
st
.
booleans
(),
y_dim_len
=
st
.
sampled_from
([
3
,
4
]),
batch_size1
=
st
.
integers
(
min_value
=
1
,
max_value
=
4
),
batch_size2
=
st
.
integers
(
min_value
=
1
,
max_value
=
4
),
channel1
=
st
.
sampled_from
([
1
,
16
,
32
,
64
]),
channel2
=
st
.
sampled_from
([
1
,
16
,
32
,
64
]),
input_dim
=
st
.
sampled_from
([
16
,
32
,
64
]))
def
test
(
self
,
*
args
,
**
kwargs
):
self
.
run_test
(
*
args
,
**
kwargs
)
if
__name__
==
"__main__"
:
unittest
.
main
()
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