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a21a2b5b
编写于
10月 18, 2022
作者:
X
xiaoxiaohehe001
提交者:
GitHub
10月 18, 2022
浏览文件
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差异文件
[Paddle Inference] Add_expand_v2_trt_layer (#47002)
上级
ad4c773b
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
541 addition
and
3 deletion
+541
-3
paddle/fluid/inference/api/analysis_predictor.cc
paddle/fluid/inference/api/analysis_predictor.cc
+1
-0
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
+2
-1
paddle/fluid/inference/tensorrt/convert/expand_v2_op.cc
paddle/fluid/inference/tensorrt/convert/expand_v2_op.cc
+97
-0
paddle/fluid/inference/tensorrt/op_teller.cc
paddle/fluid/inference/tensorrt/op_teller.cc
+25
-2
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_expand_v2.py
...ests/unittests/ir/inference/test_trt_convert_expand_v2.py
+416
-0
未找到文件。
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @
a21a2b5b
...
...
@@ -2263,6 +2263,7 @@ USE_TRT_CONVERTER(layernorm_shift_partition)
USE_TRT_CONVERTER
(
generic_plugin_creater
)
USE_TRT_CONVERTER
(
custom_plugin_creater
)
USE_TRT_CONVERTER
(
lookup_table
)
USE_TRT_CONVERTER
(
expand_v2
)
#if PADDLE_WITH_CUSPARSELT && IS_TRT_VERSION_GE(8000)
USE_TRT_CONVERTER
(
sparse_fc
)
USE_TRT_CONVERTER
(
sparse_multihead_matmul
)
...
...
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
浏览文件 @
a21a2b5b
...
...
@@ -78,7 +78,8 @@ list(
fused_token_prune_op.cc
layernorm_shift_partition_op.cc
generic_and_custom_plugin_creater.cc
fused_lookup_tables_op.cc
)
fused_lookup_tables_op.cc
expand_v2_op.cc
)
if
(
${
TENSORRT_MAJOR_VERSION
}
GREATER_EQUAL 7 AND NOT WIN32
)
list
(
APPEND CONVERT_FILES emb_eltwise_layernorm.cc
...
...
paddle/fluid/inference/tensorrt/convert/expand_v2_op.cc
0 → 100644
浏览文件 @
a21a2b5b
/* 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/inference/tensorrt/convert/op_converter.h"
namespace
paddle
{
namespace
framework
{
class
Scope
;
namespace
proto
{
class
OpDesc
;
}
// namespace proto
}
// namespace framework
}
// namespace paddle
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
class
ExpandV2OpConverter
:
public
OpConverter
{
public:
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
const
framework
::
Scope
&
scope
,
bool
test_mode
)
override
{
framework
::
OpDesc
op_desc
(
op
,
nullptr
);
auto
*
input
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"X"
)[
0
]);
auto
input_dims
=
input
->
getDimensions
();
auto
output_name
=
op_desc
.
Output
(
"Out"
)[
0
];
auto
rank
=
input_dims
.
nbDims
;
std
::
vector
<
int32_t
>
shape
=
PADDLE_GET_CONST
(
std
::
vector
<
int32_t
>
,
op_desc
.
GetAttr
(
"shape"
));
int32_t
nbDims_num
=
shape
.
size
();
auto
*
shape_tensor
=
Add1DConstantLayer
(
shape
,
output_name
+
"_shape_tensor_"
);
nvinfer1
::
ITensor
*
input_shape_tensor
;
if
(
rank
<
nbDims_num
)
{
auto
*
one_rank_tensor
=
Add1DConstantLayer
(
std
::
vector
<
int32_t
>
(
nbDims_num
-
rank
,
1
),
output_name
+
"_one_rank_tensor_"
);
auto
in_shape_tensor
=
Shape
(
input
);
std
::
vector
<
nvinfer1
::
ITensor
*>
itensors
;
itensors
.
push_back
(
one_rank_tensor
);
itensors
.
push_back
(
in_shape_tensor
);
input_shape_tensor
=
Concat
(
itensors
);
}
else
{
input_shape_tensor
=
Shape
(
input
);
}
auto
*
shuffle
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Shuffle
,
*
input
);
shuffle
->
setInput
(
1
,
*
input_shape_tensor
);
std
::
vector
<
int32_t
>
start_vec
(
nbDims_num
,
0
);
nvinfer1
::
Dims
start
;
start
.
nbDims
=
nbDims_num
;
for
(
int32_t
i
=
0
;
i
<
nbDims_num
;
++
i
)
{
start
.
d
[
i
]
=
start_vec
[
i
];
}
nvinfer1
::
Dims
size
;
size
.
nbDims
=
nbDims_num
;
nvinfer1
::
Dims
stride
;
stride
.
nbDims
=
nbDims_num
;
auto
starts_tensor
=
Add1DConstantLayer
(
start_vec
,
output_name
+
"_start_tensor_"
);
auto
one_tensor
=
Add1DConstantLayer
(
1
,
output_name
+
"_one_tensor_"
);
auto
sizes_tensor
=
Max
(
input_shape_tensor
,
shape_tensor
);
auto
input_sub_tensor
=
Sub
(
input_shape_tensor
,
one_tensor
);
auto
strides_tensor
=
Min
(
one_tensor
,
input_sub_tensor
);
auto
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Slice
,
*
shuffle
->
getOutput
(
0
),
start
,
size
,
stride
);
layer
->
setInput
(
1
,
*
starts_tensor
);
layer
->
setInput
(
2
,
*
sizes_tensor
);
layer
->
setInput
(
3
,
*
strides_tensor
);
RreplenishLayerAndOutput
(
layer
,
"expand_v2"
,
{
output_name
},
test_mode
);
}
};
}
// namespace tensorrt
}
// namespace inference
}
// namespace paddle
REGISTER_TRT_OP_CONVERTER
(
expand_v2
,
ExpandV2OpConverter
);
paddle/fluid/inference/tensorrt/op_teller.cc
浏览文件 @
a21a2b5b
...
...
@@ -2109,6 +2109,27 @@ struct SimpleOpTypeSetTeller : public Teller {
}
}
if
(
op_type
==
"expand_v2"
)
{
if
(
!
with_dynamic_shape
)
{
return
false
;
}
if
(
!
desc
.
HasAttr
(
"shape"
))
{
return
false
;
}
auto
expand_v2_inputs
=
desc
.
Inputs
();
if
(
expand_v2_inputs
.
find
(
"Shape"
)
!=
expand_v2_inputs
.
end
())
{
if
(
desc
.
Input
(
"Shape"
).
size
()
>=
1
)
{
return
false
;
}
}
if
(
expand_v2_inputs
.
find
(
"expand_shapes_tensor"
)
!=
expand_v2_inputs
.
end
())
{
if
(
desc
.
Input
(
"expand_shapes_tensor"
).
size
()
>=
1
)
{
return
false
;
}
}
}
if
(
use_no_calib_int8
)
{
return
int8_teller_set
.
count
(
op_type
);
}
else
{
...
...
@@ -2232,7 +2253,8 @@ struct SimpleOpTypeSetTeller : public Teller {
"unsqueeze2"
,
"layernorm_shift_partition"
,
"lookup_table"
,
"lookup_table_v2"
};
"lookup_table_v2"
,
"expand_v2"
};
std
::
unordered_set
<
std
::
string
>
teller_set
{
"mul"
,
"matmul"
,
...
...
@@ -2348,7 +2370,8 @@ struct SimpleOpTypeSetTeller : public Teller {
"fused_token_prune"
,
"layernorm_shift_partition"
,
"lookup_table"
,
"lookup_table_v2"
};
"lookup_table_v2"
,
"expand_v2"
};
};
struct
GenericPluginTeller
:
public
Teller
{
...
...
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_expand_v2.py
0 → 100644
浏览文件 @
a21a2b5b
# 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
trt_layer_auto_scan_test
import
TrtLayerAutoScanTest
from
program_config
import
TensorConfig
,
ProgramConfig
import
numpy
as
np
import
paddle.inference
as
paddle_infer
from
functools
import
partial
from
typing
import
List
,
Dict
,
Any
import
unittest
class
TrtConvertExpandV2Test
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
]
if
len
(
attrs
[
0
][
'shape'
])
<
self
.
dims
:
return
False
if
self
.
dims
==
1
:
if
len
(
attrs
[
0
][
'shape'
])
==
4
:
return
False
return
True
def
sample_program_configs
(
self
):
def
generate_input1
(
attrs
:
List
[
Dict
[
str
,
Any
]]):
if
self
.
dims
==
4
:
self
.
input_shape
=
[
1
,
1
,
4
,
6
]
return
np
.
random
.
random
([
1
,
1
,
4
,
6
]).
astype
(
np
.
float32
)
elif
self
.
dims
==
3
:
self
.
input_shape
=
[
1
,
8
,
6
]
return
np
.
random
.
random
([
1
,
8
,
6
]).
astype
(
np
.
float32
)
elif
self
.
dims
==
2
:
self
.
input_shape
=
[
1
,
48
]
return
np
.
random
.
random
([
1
,
48
]).
astype
(
np
.
float32
)
elif
self
.
dims
==
1
:
self
.
input_shape
=
[
48
]
return
np
.
random
.
random
([
48
]).
astype
(
np
.
float32
)
def
generate_weight1
(
attrs
:
List
[
Dict
[
str
,
Any
]]):
return
np
.
array
([
1
,
48
]).
astype
(
np
.
int32
)
def
generate_shapeT1_data
(
attrs
:
List
[
Dict
[
str
,
Any
]]):
return
np
.
array
([
2
]).
astype
(
np
.
int32
)
def
generate_shapeT2_data
(
attrs
:
List
[
Dict
[
str
,
Any
]]):
return
np
.
array
([
24
]).
astype
(
np
.
int32
)
for
dims
in
[
4
,
3
,
2
,
1
]:
for
shape
in
[[
10
,
12
,
-
1
,
-
1
],
[
8
,
64
,
-
1
,
-
1
],
[
6
,
8
,
-
1
]]:
dics
=
[
{
"shape"
:
shape
,
},
]
self
.
dims
=
dims
dics_intput
=
[{
"X"
:
[
"expand_v2_input"
]}]
ops_config
=
[{
"op_type"
:
"expand_v2"
,
"op_inputs"
:
dics_intput
[
0
],
"op_outputs"
:
{
"Out"
:
[
"expand_v2_out"
]
},
"op_attrs"
:
dics
[
0
]
}]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"expand_v2_input"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
,
dics
))
},
outputs
=
[
"expand_v2_out"
])
yield
program_config
def
sample_predictor_configs
(
self
,
program_config
)
->
(
paddle_infer
.
Config
,
List
[
int
],
float
):
def
generate_dynamic_shape
(
attrs
):
if
self
.
dims
==
4
:
self
.
dynamic_shape
.
min_input_shape
=
{
"expand_v2_input"
:
[
1
,
1
,
4
,
6
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"expand_v2_input"
:
[
10
,
1
,
4
,
6
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"expand_v2_input"
:
[
1
,
1
,
4
,
6
]
}
elif
self
.
dims
==
3
:
self
.
dynamic_shape
.
min_input_shape
=
{
"expand_v2_input"
:
[
1
,
8
,
6
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"expand_v2_input"
:
[
4
,
8
,
6
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"expand_v2_input"
:
[
1
,
8
,
6
]
}
elif
self
.
dims
==
2
:
self
.
dynamic_shape
.
min_input_shape
=
{
"expand_v2_input"
:
[
1
,
48
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"expand_v2_input"
:
[
4
,
48
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"expand_v2_input"
:
[
1
,
48
]
}
elif
self
.
dims
==
1
:
self
.
dynamic_shape
.
min_input_shape
=
{
"expand_v2_input"
:
[
48
]}
self
.
dynamic_shape
.
max_input_shape
=
{
"expand_v2_input"
:
[
48
]}
self
.
dynamic_shape
.
opt_input_shape
=
{
"expand_v2_input"
:
[
48
]}
def
clear_dynamic_shape
():
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
max_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
def
generate_trt_nodes_num
(
attrs
,
dynamic_shape
):
if
dynamic_shape
:
return
1
,
2
else
:
return
0
,
3
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
]
clear_dynamic_shape
()
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
attrs
,
False
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
attrs
,
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
(
attrs
,
True
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
attrs
,
True
),
1e-5
def
add_skip_trt_case
(
self
):
pass
def
test
(
self
):
self
.
add_skip_trt_case
()
self
.
run_test
()
class
TrtConvertExpandV2Test2
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
]
return
True
def
sample_program_configs
(
self
):
def
generate_input1
(
attrs
:
List
[
Dict
[
str
,
Any
]]):
if
self
.
dims
==
1
:
self
.
input_shape
=
[
1
]
return
np
.
random
.
random
([
1
]).
astype
(
np
.
float32
)
for
dims
in
[
1
]:
for
shape
in
[[
10
,
12
,
-
1
,
-
1
],
[
8
,
64
,
-
1
,
-
1
]]:
dics
=
[
{
"shape"
:
shape
,
},
]
self
.
dims
=
dims
dics_intput
=
[
{
"X"
:
[
"expand_v2_input"
],
"Shape"
:
[
"shapeT1_data"
]
},
]
ops_config
=
[
{
"op_type"
:
"fill_constant"
,
"op_inputs"
:
{},
"op_outputs"
:
{
"Out"
:
[
"shapeT1_data"
]
},
"op_attrs"
:
{
"dtype"
:
2
,
"str_value"
:
"10"
,
"shape"
:
[
1
],
},
},
{
"op_type"
:
"expand_v2"
,
"op_inputs"
:
dics_intput
[
0
],
"op_outputs"
:
{
"Out"
:
[
"expand_v2_out"
]
},
"op_attrs"
:
dics
[
0
]
},
]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"expand_v2_input"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
,
dics
))
},
outputs
=
[
"expand_v2_out"
])
yield
program_config
def
sample_predictor_configs
(
self
,
program_config
)
->
(
paddle_infer
.
Config
,
List
[
int
],
float
):
def
generate_dynamic_shape
():
if
self
.
dims
==
1
:
self
.
dynamic_shape
.
min_input_shape
=
{
"expand_v2_input"
:
[
1
]}
self
.
dynamic_shape
.
max_input_shape
=
{
"expand_v2_input"
:
[
1
]}
self
.
dynamic_shape
.
opt_input_shape
=
{
"expand_v2_input"
:
[
1
]}
def
clear_dynamic_shape
():
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
max_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
clear_dynamic_shape
()
# for dynamic_shape
generate_dynamic_shape
()
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
1
,
3
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
1
,
3
),
1e-5
def
add_skip_trt_case
(
self
):
pass
def
test
(
self
):
self
.
add_skip_trt_case
()
self
.
run_test
()
class
TrtConvertExpandV2Test3
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
]
return
True
def
sample_program_configs
(
self
):
def
generate_input1
(
attrs
:
List
[
Dict
[
str
,
Any
]]):
if
self
.
dims
==
4
:
self
.
input_shape
=
[
1
,
1
,
4
,
6
]
return
np
.
random
.
random
([
1
,
1
,
4
,
6
]).
astype
(
np
.
float32
)
elif
self
.
dims
==
3
:
self
.
input_shape
=
[
1
,
4
,
6
]
return
np
.
random
.
random
([
1
,
4
,
6
]).
astype
(
np
.
float32
)
for
dims
in
[
4
,
3
]:
for
shape
in
[[
10
,
12
,
-
1
,
-
1
],
[
8
,
64
,
-
1
,
-
1
]]:
dics
=
[
{
"shape"
:
shape
,
},
]
self
.
dims
=
dims
dics_intput
=
[
{
"X"
:
[
"expand_v2_input"
],
"expand_shapes_tensor"
:
[
"shapeT1_data"
,
"shapeT2_data"
,
"shapeT3_data"
,
"shapeT4_data"
]
},
]
ops_config
=
[
{
"op_type"
:
"fill_constant"
,
"op_inputs"
:
{},
"op_outputs"
:
{
"Out"
:
[
"shapeT1_data"
]
},
"op_attrs"
:
{
"dtype"
:
2
,
"str_value"
:
"10"
,
"shape"
:
[
1
],
},
},
{
"op_type"
:
"fill_constant"
,
"op_inputs"
:
{},
"op_outputs"
:
{
"Out"
:
[
"shapeT2_data"
]
},
"op_attrs"
:
{
"dtype"
:
2
,
"str_value"
:
"12"
,
"shape"
:
[
1
],
},
},
{
"op_type"
:
"fill_constant"
,
"op_inputs"
:
{},
"op_outputs"
:
{
"Out"
:
[
"shapeT3_data"
]
},
"op_attrs"
:
{
"dtype"
:
2
,
"str_value"
:
"4"
,
"shape"
:
[
1
],
},
},
{
"op_type"
:
"fill_constant"
,
"op_inputs"
:
{},
"op_outputs"
:
{
"Out"
:
[
"shapeT4_data"
]
},
"op_attrs"
:
{
"dtype"
:
2
,
"str_value"
:
"6"
,
"shape"
:
[
1
],
},
},
{
"op_type"
:
"expand_v2"
,
"op_inputs"
:
dics_intput
[
0
],
"op_outputs"
:
{
"Out"
:
[
"expand_v2_out"
]
},
"op_attrs"
:
dics
[
0
]
},
]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"expand_v2_input"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
,
dics
))
},
outputs
=
[
"expand_v2_out"
])
yield
program_config
def
sample_predictor_configs
(
self
,
program_config
)
->
(
paddle_infer
.
Config
,
List
[
int
],
float
):
def
generate_dynamic_shape
():
if
self
.
dims
==
4
:
self
.
dynamic_shape
.
min_input_shape
=
{
"expand_v2_input"
:
[
1
,
1
,
4
,
6
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"expand_v2_input"
:
[
10
,
1
,
4
,
6
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"expand_v2_input"
:
[
1
,
1
,
4
,
6
]
}
elif
self
.
dims
==
3
:
self
.
dynamic_shape
.
min_input_shape
=
{
"expand_v2_input"
:
[
1
,
4
,
6
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"expand_v2_input"
:
[
4
,
4
,
6
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"expand_v2_input"
:
[
1
,
4
,
6
]
}
def
clear_dynamic_shape
():
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
max_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
clear_dynamic_shape
()
# for dynamic_shape
generate_dynamic_shape
()
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
4
,
3
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
4
,
3
),
1e-5
def
add_skip_trt_case
(
self
):
pass
def
test
(
self
):
self
.
add_skip_trt_case
()
self
.
run_test
()
if
__name__
==
"__main__"
:
unittest
.
main
()
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