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749945b3
编写于
9月 10, 2021
作者:
B
baoachun
提交者:
GitHub
9月 10, 2021
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电子邮件补丁
差异文件
add prelu trt converter test case (#35512)
上级
922e23bf
变更
4
显示空白变更内容
内联
并排
Showing
4 changed file
with
218 addition
and
11 deletion
+218
-11
paddle/fluid/inference/tensorrt/convert/prelu_op.cc
paddle/fluid/inference/tensorrt/convert/prelu_op.cc
+0
-10
paddle/fluid/inference/tensorrt/op_teller.cc
paddle/fluid/inference/tensorrt/op_teller.cc
+22
-0
python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt
.../paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt
+1
-1
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_prelu.py
...id/tests/unittests/ir/inference/test_trt_convert_prelu.py
+195
-0
未找到文件。
paddle/fluid/inference/tensorrt/convert/prelu_op.cc
浏览文件 @
749945b3
...
...
@@ -34,11 +34,7 @@ class PReluOpConverter : public OpConverter {
auto
*
input
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"X"
)[
0
]);
// Get attrs
std
::
string
mode
=
BOOST_GET_CONST
(
std
::
string
,
op_desc
.
GetAttr
(
"mode"
));
//
auto
*
alpha_var
=
scope
.
FindVar
(
op_desc
.
Input
(
"Alpha"
)[
0
]);
PADDLE_ENFORCE_NOT_NULL
(
alpha_var
,
platform
::
errors
::
NotFound
(
"Variable Alpha of prelu TRT converter is not found."
));
auto
*
alpha_tensor
=
alpha_var
->
GetMutable
<
framework
::
LoDTensor
>
();
platform
::
CPUPlace
cpu_place
;
...
...
@@ -50,15 +46,9 @@ class PReluOpConverter : public OpConverter {
nvinfer1
::
ILayer
*
layer
=
nullptr
;
if
(
engine_
->
with_dynamic_shape
())
{
#if IS_TRT_VERSION_GE(6000)
plugin
::
PReluPluginDynamic
*
plugin
=
new
plugin
::
PReluPluginDynamic
(
alpha_data
,
alpha_tensor_temp
->
numel
(),
mode
);
layer
=
engine_
->
AddDynamicPlugin
(
&
input
,
input_num
,
plugin
);
#else
PADDLE_THROW
(
platform
::
errors
::
Fatal
(
"You are running the TRT Dynamic Shape mode, need to confirm that "
"your TRT version is no less than 6.0"
));
#endif
}
else
{
#if IS_TRT_VERSION_GE(7000)
float
*
alpha_weight_data
=
engine_
->
GetWeightCPUData
(
...
...
paddle/fluid/inference/tensorrt/op_teller.cc
浏览文件 @
749945b3
...
...
@@ -661,6 +661,28 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
<<
desc
.
Output
(
"Out"
).
size
()
<<
"."
;
return
false
;
}
auto
*
block
=
desc
.
Block
();
auto
*
var_desc
=
block
->
FindVar
(
desc
.
Input
(
"Alpha"
)[
0
]);
if
(
!
var_desc
)
{
VLOG
(
3
)
<<
"Variable Alpha of prelu TRT converter not found."
;
return
false
;
}
auto
x_var_name
=
desc
.
Input
(
"X"
)[
0
];
auto
*
x_var_desc
=
block
->
FindVar
(
x_var_name
);
const
auto
x_shape
=
x_var_desc
->
GetShape
();
if
(
x_shape
.
size
()
==
1
)
{
VLOG
(
3
)
<<
"prelu op does not support input's dim is 1 in tensorrt."
;
return
false
;
}
if
(
!
with_dynamic_shape
)
{
if
(
x_shape
.
size
()
==
2
)
{
VLOG
(
3
)
<<
"prelu op does not support input's dim is 2 in tensorrt."
;
return
false
;
}
}
}
if
(
op_type
==
"roi_align"
)
{
...
...
python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt
浏览文件 @
749945b3
...
...
@@ -31,7 +31,7 @@ if(WITH_GPU AND TENSORRT_FOUND)
foreach
(
target
${
TEST_TRT_CONVERTER
}
)
py_test_modules
(
${
target
}
MODULES
${
target
}
)
set_tests_properties
(
${
target
}
PROPERTIES TIMEOUT
1
00
)
set_tests_properties
(
${
target
}
PROPERTIES TIMEOUT
3
00
)
endforeach
()
endif
()
...
...
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_prelu.py
0 → 100644
浏览文件 @
749945b3
# 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
trt_layer_auto_scan_test
import
TrtLayerAutoScanTest
,
SkipReasons
from
program_config
import
TensorConfig
,
ProgramConfig
import
numpy
as
np
import
paddle.inference
as
paddle_infer
from
functools
import
partial
from
typing
import
Optional
,
List
,
Callable
,
Dict
,
Any
,
Set
class
TrtConvertPreluTest
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
def
sample_program_configs
(
self
):
def
generate_input
(
batch
,
dim1
,
dim2
,
dim3
):
shape
=
[
batch
]
if
dim1
!=
0
:
shape
.
append
(
dim1
)
if
dim2
!=
0
:
shape
.
append
(
dim2
)
if
dim3
!=
0
:
shape
.
append
(
dim3
)
return
np
.
random
.
random
(
shape
).
astype
(
np
.
float32
)
def
generate_alpha
(
attrs
:
List
[
Dict
[
str
,
Any
]],
dim1
,
dim2
,
dim3
):
if
attrs
[
0
][
"mode"
]
==
"all"
:
return
np
.
random
.
random
(
size
=
(
1
)).
astype
(
np
.
float32
)
elif
attrs
[
0
][
"mode"
]
==
"channel"
:
shape
=
[
1
]
if
dim1
!=
0
:
shape
.
append
(
dim1
)
if
dim2
!=
0
:
shape
.
append
(
1
)
if
dim3
!=
0
:
shape
.
append
(
1
)
return
np
.
random
.
random
(
size
=
shape
).
astype
(
np
.
float32
)
elif
attrs
[
0
][
"mode"
]
==
"element"
:
shape
=
[
1
]
if
dim1
!=
0
:
shape
.
append
(
dim1
)
if
dim2
!=
0
:
shape
.
append
(
dim2
)
if
dim3
!=
0
:
shape
.
append
(
dim3
)
return
np
.
random
.
random
(
size
=
shape
).
astype
(
np
.
float32
)
for
batch
in
[
1
,
4
]:
for
dim1
in
[
0
,
3
]:
for
dim2
in
[
0
,
16
]:
for
dim3
in
[
0
,
32
]:
self
.
dim1
=
dim1
self
.
dim2
=
dim2
self
.
dim3
=
dim3
if
dim1
==
0
and
dim2
!=
0
:
continue
if
dim1
==
0
and
dim2
==
0
and
dim3
!=
0
:
continue
for
mode
in
[
"all"
,
"channel"
,
"element"
]:
if
mode
==
"channel"
and
dim1
==
0
:
continue
dics
=
[{
"mode"
:
mode
}]
ops_config
=
[{
"op_type"
:
"prelu"
,
"op_inputs"
:
{
"X"
:
[
"input_data"
],
"Alpha"
:
[
"alpha_weight"
]
},
"op_outputs"
:
{
"Out"
:
[
"output_data"
]
},
"op_attrs"
:
dics
[
0
]
}]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{
"alpha_weight"
:
TensorConfig
(
data_gen
=
partial
(
generate_alpha
,
dics
,
dim1
,
dim2
,
dim3
))
},
inputs
=
{
"input_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input
,
batch
,
dim1
,
dim2
,
dim3
)),
},
outputs
=
[
"output_data"
])
yield
program_config
def
sample_predictor_configs
(
self
,
program_config
)
->
(
paddle_infer
.
Config
,
List
[
int
],
float
):
def
generate_dynamic_shape
(
attrs
):
if
self
.
dim1
==
0
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
],
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
],
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
2
],
}
else
:
if
self
.
dim2
==
0
and
self
.
dim3
==
0
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
1
],
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
64
],
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
2
,
3
],
}
elif
self
.
dim2
!=
0
and
self
.
dim3
!=
0
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
1
,
1
,
1
],
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
64
,
128
,
128
],
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
2
,
3
,
16
,
32
],
}
elif
self
.
dim3
==
0
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
1
,
1
],
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
64
,
256
],
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
2
,
3
,
128
],
}
def
clear_dynamic_shape
():
self
.
dynamic_shape
.
max_input_shape
=
{}
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
]
# for static_shape
clear_dynamic_shape
()
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
1
,
2
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
1
,
2
),
1e-5
# for dynamic_shape
generate_dynamic_shape
(
attrs
)
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
1
,
2
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
1
,
2
),
1e-5
def
add_skip_trt_case
(
self
):
def
teller1
(
program_config
,
predictor_config
):
if
self
.
dim1
==
0
and
self
.
dim2
==
0
and
self
.
dim3
==
0
:
return
True
return
False
self
.
add_skip_case
(
teller1
,
SkipReasons
.
TRT_NOT_SUPPORT
,
"Trt does not support 1-dimensional input."
)
def
teller2
(
program_config
,
predictor_config
):
if
(
len
(
self
.
dynamic_shape
.
min_input_shape
)
==
0
):
if
self
.
dim1
!=
0
and
self
.
dim2
==
0
and
self
.
dim3
==
0
:
return
True
return
False
self
.
add_skip_case
(
teller2
,
SkipReasons
.
TRT_NOT_SUPPORT
,
"Need to repair the case: the output of GPU and tensorrt has diff when the input dimension is 2 in static shape mode."
)
def
test
(
self
):
self
.
add_skip_trt_case
()
self
.
run_test
()
if
__name__
==
"__main__"
:
unittest
.
main
()
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