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体验新版 GitCode,发现更多精彩内容 >>
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75d5e3bf
编写于
9月 13, 2021
作者:
B
baoachun
提交者:
GitHub
9月 13, 2021
浏览文件
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电子邮件补丁
差异文件
add gather trt converter test case (#35523)
上级
42559f72
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
224 addition
and
0 deletion
+224
-0
paddle/fluid/inference/tensorrt/op_teller.cc
paddle/fluid/inference/tensorrt/op_teller.cc
+11
-0
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather.py
...d/tests/unittests/ir/inference/test_trt_convert_gather.py
+213
-0
未找到文件。
paddle/fluid/inference/tensorrt/op_teller.cc
浏览文件 @
75d5e3bf
...
...
@@ -319,6 +319,17 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
if
(
op_type
==
"gather"
)
{
if
(
!
with_dynamic_shape
)
return
false
;
if
(
with_dynamic_shape
)
{
auto
*
block
=
desc
.
Block
();
auto
*
x_var_desc
=
block
->
FindVar
(
desc
.
Input
(
"X"
)[
0
]);
const
auto
x_shape
=
x_var_desc
->
GetShape
();
if
(
x_shape
.
size
()
==
1
)
{
VLOG
(
3
)
<<
"Gather does not support 1-dimensional input in tensorrt"
;
return
false
;
}
}
auto
inputs
=
desc
.
InputArgumentNames
();
for
(
auto
&
input
:
inputs
)
{
if
(
input
==
"Axis"
&&
desc
.
Input
(
"Axis"
).
size
()
>
0
)
return
false
;
...
...
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather.py
0 → 100644
浏览文件 @
75d5e3bf
# 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
import
logging
class
TrtConvertGatherTest
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
inputs
=
program_config
.
inputs
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
]
if
len
(
inputs
[
'input_data'
].
shape
)
<=
attrs
[
0
][
'axis'
]:
return
False
return
True
def
sample_program_configs
(
self
):
def
generate_input1
(
shape
):
return
np
.
random
.
random
(
shape
).
astype
(
np
.
float32
)
def
generate_input2
(
index
):
return
np
.
array
(
index
).
astype
(
np
.
int32
)
def
generate_input3
(
axis
):
return
np
.
array
([
axis
]).
astype
(
np
.
int32
)
for
shape
in
[[
32
],
[
16
,
64
],
[
32
,
16
,
16
],
[
32
,
64
,
16
,
32
]]:
for
index
in
[[
1
,
4
],
[
4
,
8
]]:
for
axis
in
[
0
,
1
,
2
,
3
]:
for
overwrite
in
[
True
,
False
]:
for
input
in
[{
"X"
:
[
"input_data"
],
"Index"
:
[
"index_data"
]
},
{
"X"
:
[
"input_data"
],
"Index"
:
[
"index_data"
],
"Axis"
:
[
"axis_data"
]
}]:
self
.
shape
=
shape
self
.
axis
=
axis
self
.
input_num
=
len
(
input
)
dics
=
[{
"overwrite"
:
overwrite
,
"axis"
:
axis
}]
ops_config
=
[{
"op_type"
:
"gather"
,
"op_inputs"
:
input
,
"op_outputs"
:
{
"Out"
:
[
"output_data"
]
},
"op_attrs"
:
dics
[
0
]
}]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"input_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
,
shape
)),
"index_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input2
,
index
)),
}
if
len
(
input
)
==
2
else
{
"input_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
,
shape
)),
"index_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input2
,
index
)),
"axis_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input3
,
axis
)),
},
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
len
(
self
.
shape
)
==
1
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
4
],
"index_data"
:
[
1
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
128
],
"index_data"
:
[
4
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
16
],
"index_data"
:
[
2
]
}
elif
len
(
self
.
shape
)
==
2
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
2
,
4
],
"index_data"
:
[
1
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
256
,
256
],
"index_data"
:
[
4
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
64
,
32
],
"index_data"
:
[
2
]
}
elif
len
(
self
.
shape
)
==
3
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
2
,
4
,
4
],
"index_data"
:
[
1
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
128
,
256
,
256
],
"index_data"
:
[
4
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
16
,
64
,
32
],
"index_data"
:
[
2
]
}
elif
len
(
self
.
shape
)
==
4
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
2
,
4
,
4
,
2
],
"index_data"
:
[
1
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
128
,
256
,
128
,
256
],
"index_data"
:
[
4
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
16
,
64
,
16
,
32
],
"index_data"
:
[
2
]
}
def
clear_dynamic_shape
():
self
.
dynamic_shape
.
max_input_shape
=
{}
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
def
generate_trt_nodes_num
(
dynamic_shape
):
if
self
.
input_num
==
3
:
return
0
,
5
else
:
if
dynamic_shape
and
self
.
axis
==
0
:
return
1
,
3
else
:
return
0
,
4
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
(),
generate_trt_nodes_num
(
False
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
False
),
1e-5
# for dynamic_shape
generate_dynamic_shape
(
attrs
)
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
True
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
True
),
1e-5
def
add_skip_trt_case
(
self
):
def
teller1
(
program_config
,
predictor_config
):
if
len
(
self
.
dynamic_shape
.
min_input_shape
)
!=
0
:
inputs
=
program_config
.
inputs
if
len
(
inputs
[
'input_data'
].
shape
)
==
1
or
len
(
inputs
[
'index_data'
].
shape
)
==
1
:
return
True
return
False
self
.
add_skip_case
(
teller1
,
SkipReasons
.
TRT_NOT_SUPPORT
,
"Need to repair the case: trt reshape out failed for dynamic shape mode when inputs' dims==1."
)
def
teller2
(
program_config
,
predictor_config
):
inputs
=
program_config
.
inputs
if
"axis_data"
in
inputs
.
keys
():
return
True
return
False
self
.
add_skip_case
(
teller2
,
SkipReasons
.
TRT_NOT_SUPPORT
,
"Need to repair the case: trt do not support axis tensor input."
)
def
test
(
self
):
self
.
add_skip_trt_case
()
self
.
run_test
()
if
__name__
==
"__main__"
:
unittest
.
main
()
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