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体验新版 GitCode,发现更多精彩内容 >>
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b045fdfb
编写于
11月 02, 2022
作者:
丁
丁一
提交者:
GitHub
11月 02, 2022
浏览文件
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电子邮件补丁
差异文件
Logsigmoid and Tanhshrink ops convert to trt (#47322)
上级
dac1087e
变更
12
显示空白变更内容
内联
并排
Showing
12 changed file
with
783 addition
and
49 deletion
+783
-49
paddle/fluid/framework/ir/is_test_pass.cc
paddle/fluid/framework/ir/is_test_pass.cc
+13
-12
paddle/fluid/inference/api/analysis_predictor.cc
paddle/fluid/inference/api/analysis_predictor.cc
+3
-0
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
+3
-0
paddle/fluid/inference/tensorrt/convert/celu_op.cc
paddle/fluid/inference/tensorrt/convert/celu_op.cc
+102
-0
paddle/fluid/inference/tensorrt/convert/logsigmoid_op.cc
paddle/fluid/inference/tensorrt/convert/logsigmoid_op.cc
+78
-0
paddle/fluid/inference/tensorrt/convert/tanhshrink_op.cc
paddle/fluid/inference/tensorrt/convert/tanhshrink_op.cc
+79
-0
paddle/fluid/inference/tensorrt/convert/test_celu_op.cc
paddle/fluid/inference/tensorrt/convert/test_celu_op.cc
+48
-0
paddle/fluid/inference/tensorrt/op_teller.cc
paddle/fluid/inference/tensorrt/op_teller.cc
+17
-10
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_celu.py
...uid/tests/unittests/ir/inference/test_trt_convert_celu.py
+140
-0
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_logsigmoid.py
...sts/unittests/ir/inference/test_trt_convert_logsigmoid.py
+137
-0
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_silu.py
...uid/tests/unittests/ir/inference/test_trt_convert_silu.py
+26
-27
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_tanhshrink.py
...sts/unittests/ir/inference/test_trt_convert_tanhshrink.py
+137
-0
未找到文件。
paddle/fluid/framework/ir/is_test_pass.cc
浏览文件 @
b045fdfb
...
...
@@ -35,8 +35,9 @@ void IsTestPass::ApplyImpl(ir::Graph* graph) const {
"hard_shrink"
,
"hard_sigmoid"
,
"relu6"
,
"soft_relu"
,
"swish"
,
"thresholded_relu"
,
"log"
,
"square"
,
"softplus"
,
"softsign"
,
"silu"
,
"mish"
,
"gumbel_softmax"
};
"softsign"
,
"silu"
,
"gumbel_softmax"
,
"mish"
,
"celu"
,
"tanhshrink"
,
"logsigmoid"
};
for
(
const
Node
*
n
:
graph
->
Nodes
())
{
if
(
n
->
IsOp
())
{
auto
*
op
=
n
->
Op
();
...
...
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @
b045fdfb
...
...
@@ -2318,11 +2318,14 @@ USE_TRT_CONVERTER(sum)
USE_TRT_CONVERTER
(
shape
)
USE_TRT_CONVERTER
(
fill_constant
)
USE_TRT_CONVERTER
(
fused_token_prune
)
USE_TRT_CONVERTER
(
celu
)
USE_TRT_CONVERTER
(
layernorm_shift_partition
)
USE_TRT_CONVERTER
(
preln_layernorm_shift_partition
)
USE_TRT_CONVERTER
(
merge_layernorm
)
USE_TRT_CONVERTER
(
generic_plugin_creater
)
USE_TRT_CONVERTER
(
custom_plugin_creater
)
USE_TRT_CONVERTER
(
tanh_shrink
)
USE_TRT_CONVERTER
(
logsigmoid
)
USE_TRT_CONVERTER
(
lookup_table
)
USE_TRT_CONVERTER
(
expand_v2
)
#if PADDLE_WITH_CUSPARSELT && IS_TRT_VERSION_GE(8000)
...
...
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
浏览文件 @
b045fdfb
...
...
@@ -76,7 +76,10 @@ list(
shape_op.cc
fill_constant_op.cc
fused_token_prune_op.cc
celu_op.cc
layernorm_shift_partition_op.cc
tanhshrink_op.cc
logsigmoid_op.cc
preln_layernorm_shift_partition_op.cc
merge_layernorm_op.cc
generic_and_custom_plugin_creater.cc
...
...
paddle/fluid/inference/tensorrt/convert/celu_op.cc
0 → 100644
浏览文件 @
b045fdfb
/* 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. */
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
namespace
nvinfer1
{
class
ILayer
;
}
// namespace nvinfer1
namespace
paddle
{
namespace
framework
{
class
Scope
;
namespace
proto
{
class
OpDesc
;
}
// namespace proto
}
// namespace framework
}
// namespace paddle
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
class
CeluOpConverter
:
public
OpConverter
{
public:
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
const
framework
::
Scope
&
scope
,
bool
test_mode
)
override
{
VLOG
(
4
)
<<
"convert fluid celu op to tensorrt layer"
;
framework
::
OpDesc
op_desc
(
op
,
nullptr
);
// Declare inputs
int
input_num
=
op_desc
.
Input
(
"X"
).
size
();
PADDLE_ENFORCE_EQ
(
input_num
,
1
,
platform
::
errors
::
InvalidArgument
(
"The input X's size must equal to 1 in TRT celu op."
" But received X's size %d."
,
input_num
));
auto
*
input
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"X"
)[
0
]);
// Get output
size_t
output_num
=
op_desc
.
Output
(
"Out"
).
size
();
PADDLE_ENFORCE_EQ
(
output_num
,
1UL
,
platform
::
errors
::
InvalidArgument
(
"The output Out's size must equal to 1 in TRT celu op. "
"But received Out's size %u."
,
output_num
));
// Get attrs
float
alpha
=
PADDLE_GET_CONST
(
float
,
op_desc
.
GetAttr
(
"alpha"
));
nvinfer1
::
ILayer
*
layer
=
nullptr
;
int32_t
rank
=
input
->
getDimensions
().
nbDims
;
nvinfer1
::
Dims
constant_shape
;
constant_shape
.
nbDims
=
rank
;
std
::
fill
(
constant_shape
.
d
,
constant_shape
.
d
+
rank
,
1
);
std
::
vector
<
float
>
weight_alpha_data
{
alpha
};
std
::
vector
<
float
>
weight_zero_data
{
0.
f
};
std
::
vector
<
float
>
weight_one_data
{
1.
f
};
auto
*
alpha_data
=
AddConstantLayer
(
weight_alpha_data
.
data
(),
constant_shape
);
auto
*
constant_zero_data
=
AddConstantLayer
(
weight_zero_data
.
data
(),
constant_shape
);
auto
*
constant_one_data
=
AddConstantLayer
(
weight_one_data
.
data
(),
constant_shape
);
auto
*
input_div_with_alpha
=
Div
(
input
,
alpha_data
);
auto
*
input_exp
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Unary
,
*
input_div_with_alpha
,
nvinfer1
::
UnaryOperation
::
kEXP
);
auto
*
input_sub_with_one
=
Sub
(
input_exp
->
getOutput
(
0
),
constant_one_data
);
auto
*
input_prod_with_alpha
=
Prod
(
input_sub_with_one
,
alpha_data
);
auto
*
min_input
=
Min
(
input_prod_with_alpha
,
constant_zero_data
);
auto
*
relu
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Activation
,
*
input
,
nvinfer1
::
ActivationType
::
kRELU
);
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
ElementWise
,
*
relu
->
getOutput
(
0
),
*
min_input
,
nvinfer1
::
ElementWiseOperation
::
kSUM
);
auto
output_name
=
op_desc
.
Output
(
"Out"
)[
0
];
RreplenishLayerAndOutput
(
layer
,
"celu"
,
{
output_name
},
test_mode
);
}
};
}
// namespace tensorrt
}
// namespace inference
}
// namespace paddle
REGISTER_TRT_OP_CONVERTER
(
celu
,
CeluOpConverter
);
paddle/fluid/inference/tensorrt/convert/logsigmoid_op.cc
0 → 100644
浏览文件 @
b045fdfb
/* 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
nvinfer1
{
class
ILayer
;
}
// namespace nvinfer1
namespace
paddle
{
namespace
framework
{
class
Scope
;
namespace
proto
{
class
OpDesc
;
}
// namespace proto
}
// namespace framework
}
// namespace paddle
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
class
LogSigmoidOpConverter
:
public
OpConverter
{
public:
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
const
framework
::
Scope
&
scope
,
bool
test_mode
)
override
{
VLOG
(
4
)
<<
"convert fluid LogSigmoid op to tensorrt layer"
;
framework
::
OpDesc
op_desc
(
op
,
nullptr
);
// Declare inputs
int
input_num
=
op_desc
.
Input
(
"X"
).
size
();
PADDLE_ENFORCE_EQ
(
input_num
,
1
,
platform
::
errors
::
InvalidArgument
(
"The input X's size must equal to 1 in TRT LogSigmoid op."
" But received X's size %d."
,
input_num
));
auto
*
input
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"X"
)[
0
]);
// Get output
size_t
output_num
=
op_desc
.
Output
(
"Out"
).
size
();
PADDLE_ENFORCE_EQ
(
output_num
,
1UL
,
platform
::
errors
::
InvalidArgument
(
"The output Out's size must equal to 1 in TRT LogSigmoid op. "
"But received Out's size %u."
,
output_num
));
nvinfer1
::
ILayer
*
layer
=
nullptr
;
auto
*
sigmoid
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Activation
,
*
input
,
nvinfer1
::
ActivationType
::
kSIGMOID
);
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Unary
,
*
(
sigmoid
->
getOutput
(
0
)),
nvinfer1
::
UnaryOperation
::
kLOG
);
auto
output_name
=
op_desc
.
Output
(
"Out"
)[
0
];
RreplenishLayerAndOutput
(
layer
,
"logsigmoid"
,
{
output_name
},
test_mode
);
}
};
}
// namespace tensorrt
}
// namespace inference
}
// namespace paddle
REGISTER_TRT_OP_CONVERTER
(
logsigmoid
,
LogSigmoidOpConverter
);
paddle/fluid/inference/tensorrt/convert/tanhshrink_op.cc
0 → 100644
浏览文件 @
b045fdfb
/* 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
nvinfer1
{
class
ILayer
;
}
// namespace nvinfer1
namespace
paddle
{
namespace
framework
{
class
Scope
;
namespace
proto
{
class
OpDesc
;
}
// namespace proto
}
// namespace framework
}
// namespace paddle
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
class
TanhshrinkOpConverter
:
public
OpConverter
{
public:
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
const
framework
::
Scope
&
scope
,
bool
test_mode
)
override
{
VLOG
(
4
)
<<
"convert fluid Tanhshrink op to tensorrt layer"
;
framework
::
OpDesc
op_desc
(
op
,
nullptr
);
// Declare inputs
int
input_num
=
op_desc
.
Input
(
"X"
).
size
();
PADDLE_ENFORCE_EQ
(
input_num
,
1
,
platform
::
errors
::
InvalidArgument
(
"The input X's size must equal to 1 in TRT Tanhshrink op."
" But received X's size %d."
,
input_num
));
auto
*
input
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"X"
)[
0
]);
// Get output
size_t
output_num
=
op_desc
.
Output
(
"Out"
).
size
();
PADDLE_ENFORCE_EQ
(
output_num
,
1UL
,
platform
::
errors
::
InvalidArgument
(
"The output Out's size must equal to 1 in TRT Tanhshrink op. "
"But received Out's size %u."
,
output_num
));
nvinfer1
::
ILayer
*
layer
=
nullptr
;
auto
*
tanh
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Activation
,
*
input
,
nvinfer1
::
ActivationType
::
kTANH
);
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
ElementWise
,
*
input
,
*
(
tanh
->
getOutput
(
0
)),
nvinfer1
::
ElementWiseOperation
::
kSUB
);
auto
output_name
=
op_desc
.
Output
(
"Out"
)[
0
];
RreplenishLayerAndOutput
(
layer
,
"tanh_shrink"
,
{
output_name
},
test_mode
);
}
};
}
// namespace tensorrt
}
// namespace inference
}
// namespace paddle
REGISTER_TRT_OP_CONVERTER
(
tanh_shrink
,
TanhshrinkOpConverter
);
paddle/fluid/inference/tensorrt/convert/test_celu_op.cc
0 → 100644
浏览文件 @
b045fdfb
/* 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 <gtest/gtest.h>
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
namespace
paddle
{
namespace
inference
{
namespace
tensorrt
{
TEST
(
celu_op
,
test_celu
)
{
std
::
unordered_set
<
std
::
string
>
parameters
;
framework
::
Scope
scope
;
TRTConvertValidation
validator
(
10
,
parameters
,
scope
,
1000
);
validator
.
DeclInputVar
(
"celu_input"
,
nvinfer1
::
Dims3
(
3
,
2
,
2
));
validator
.
DeclOutputVar
(
"celu_out"
,
nvinfer1
::
Dims3
(
3
,
2
,
2
));
// Prepare Op description
framework
::
OpDesc
desc
;
desc
.
SetType
(
"celu"
);
desc
.
SetInput
(
"X"
,
{
"celu_input"
});
desc
.
SetOutput
(
"Out"
,
{
"celu_out"
});
desc
.
SetAttr
(
"alpha"
,
2.0
f
);
validator
.
SetOp
(
*
desc
.
Proto
());
validator
.
Execute
(
1
);
}
}
// namespace tensorrt
}
// namespace inference
}
// namespace paddle
USE_OP
(
celu
);
paddle/fluid/inference/tensorrt/op_teller.cc
浏览文件 @
b045fdfb
...
...
@@ -86,7 +86,8 @@ struct SimpleOpTypeSetTeller : public Teller {
"cosh"
,
"asin"
,
"acos"
,
"atan"
,
"asinh"
,
"atanh"
,
"ceil"
,
"floor"
,
"erf"
,
"silu"
};
"silu"
,
"celu"
,
"tanh_shrink"
,
"logsigmoid"
};
if
(
act_op_list
.
find
(
op_type
)
!=
act_op_list
.
end
())
{
auto
*
block
=
desc
.
Block
();
if
(
block
==
nullptr
)
{
...
...
@@ -2212,6 +2213,7 @@ struct SimpleOpTypeSetTeller : public Teller {
"shuffle_channel"
,
"swish"
,
"silu"
,
"celu"
,
"split"
,
"instance_norm"
,
"gelu"
,
...
...
@@ -2268,6 +2270,8 @@ struct SimpleOpTypeSetTeller : public Teller {
"squeeze2"
,
"unsqueeze2"
,
"layernorm_shift_partition"
,
"tanh_shrink"
,
"logsigmoid"
,
"preln_layernorm_shift_partition"
,
"lookup_table"
,
"lookup_table_v2"
,
...
...
@@ -2330,6 +2334,7 @@ struct SimpleOpTypeSetTeller : public Teller {
"shuffle_channel"
,
"swish"
,
"silu"
,
"celu"
,
"split"
,
"instance_norm"
,
"gelu"
,
...
...
@@ -2387,6 +2392,8 @@ struct SimpleOpTypeSetTeller : public Teller {
"unsqueeze2"
,
"fused_token_prune"
,
"layernorm_shift_partition"
,
"tanh_shrink"
,
"logsigmoid"
,
"preln_layernorm_shift_partition"
,
"merge_layernorm"
,
"lookup_table"
,
...
...
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_celu.py
0 → 100644
浏览文件 @
b045fdfb
# 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
TrtConvertCeluTest
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
def
sample_program_configs
(
self
):
def
generate_input1
(
dims
,
attrs
:
List
[
Dict
[
str
,
Any
]]):
if
dims
==
1
:
return
np
.
ones
([
3
]).
astype
(
np
.
float32
)
elif
dims
==
2
:
return
np
.
ones
([
3
,
64
]).
astype
(
np
.
float32
)
elif
dims
==
3
:
return
np
.
ones
([
3
,
64
,
64
]).
astype
(
np
.
float32
)
else
:
return
np
.
ones
([
1
,
3
,
64
,
64
]).
astype
(
np
.
float32
)
for
dims
in
[
1
,
2
,
3
,
4
]:
for
alpha
in
[
1.0
,
2.0
,
3.0
]:
self
.
dims
=
dims
dics
=
[{
"alpha"
:
alpha
}]
ops_config
=
[
{
"op_type"
:
"celu"
,
"op_inputs"
:
{
"X"
:
[
"input_data"
],
},
"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
,
dims
,
dics
)
)
},
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
.
dims
==
1
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
]}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
128
]}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
64
]}
elif
self
.
dims
==
2
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
32
]}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
64
]}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
3
,
64
]}
elif
self
.
dims
==
3
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
32
,
32
]}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
10
,
64
,
64
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
3
,
64
,
64
]}
else
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
3
,
32
,
32
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
3
,
64
,
64
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
1
,
3
,
64
,
64
]
}
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
self
.
dims
==
1
:
return
0
,
3
return
1
,
2
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
(
attrs
,
False
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
attrs
,
False
),
(
1e-3
,
1e-3
)
# 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-3
,
1e-3
)
def
test
(
self
):
self
.
run_test
()
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_logsigmoid.py
0 → 100755
浏览文件 @
b045fdfb
# 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
TrtConvertLogSigmoidTest
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
def
sample_program_configs
(
self
):
def
generate_input1
(
dims
,
attrs
:
List
[
Dict
[
str
,
Any
]]):
if
dims
==
1
:
return
np
.
ones
([
3
]).
astype
(
np
.
float32
)
elif
dims
==
2
:
return
np
.
ones
([
3
,
64
]).
astype
(
np
.
float32
)
elif
dims
==
3
:
return
np
.
ones
([
3
,
64
,
64
]).
astype
(
np
.
float32
)
else
:
return
np
.
ones
([
1
,
3
,
64
,
64
]).
astype
(
np
.
float32
)
for
dims
in
[
1
,
2
,
3
,
4
]:
self
.
dims
=
dims
ops_config
=
[
{
"op_type"
:
"logsigmoid"
,
"op_inputs"
:
{
"X"
:
[
"input_data"
],
},
"op_outputs"
:
{
"Out"
:
[
"output_data"
]},
"op_attrs"
:
{},
}
]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"input_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
,
dims
,
{})
)
},
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
.
dims
==
1
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
]}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
128
]}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
64
]}
elif
self
.
dims
==
2
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
32
]}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
64
]}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
3
,
64
]}
elif
self
.
dims
==
3
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
32
,
32
]}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
10
,
64
,
64
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
3
,
64
,
64
]}
else
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
3
,
32
,
32
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
3
,
64
,
64
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
1
,
3
,
64
,
64
]
}
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
self
.
dims
==
1
:
return
0
,
3
return
1
,
2
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
(
attrs
,
False
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
attrs
,
False
),
(
1e-3
,
1e-3
)
# 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-3
,
1e-3
)
def
test
(
self
):
self
.
run_test
()
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_silu.py
浏览文件 @
b045fdfb
...
...
@@ -21,7 +21,7 @@ from typing import Any, Dict, List
import
unittest
class
TrtConvertS
wish
Test
(
TrtLayerAutoScanTest
):
class
TrtConvertS
ilu
Test
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
...
...
@@ -37,7 +37,6 @@ class TrtConvertSwishTest(TrtLayerAutoScanTest):
return
np
.
ones
([
1
,
3
,
64
,
64
]).
astype
(
np
.
float32
)
for
dims
in
[
1
,
2
,
3
,
4
]:
for
beta
in
[
1.0
,
2.0
,
3.0
]:
self
.
dims
=
dims
ops_config
=
[
...
...
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_tanhshrink.py
0 → 100755
浏览文件 @
b045fdfb
# 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
TrtConvertTanhshrinkTest
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
def
sample_program_configs
(
self
):
def
generate_input1
(
dims
,
attrs
:
List
[
Dict
[
str
,
Any
]]):
if
dims
==
1
:
return
np
.
ones
([
3
]).
astype
(
np
.
float32
)
elif
dims
==
2
:
return
np
.
ones
([
3
,
64
]).
astype
(
np
.
float32
)
elif
dims
==
3
:
return
np
.
ones
([
3
,
64
,
64
]).
astype
(
np
.
float32
)
else
:
return
np
.
ones
([
1
,
3
,
64
,
64
]).
astype
(
np
.
float32
)
for
dims
in
[
1
,
2
,
3
,
4
]:
self
.
dims
=
dims
ops_config
=
[
{
"op_type"
:
"tanh_shrink"
,
"op_inputs"
:
{
"X"
:
[
"input_data"
],
},
"op_outputs"
:
{
"Out"
:
[
"output_data"
]},
"op_attrs"
:
{},
}
]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"input_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
,
dims
,
{})
)
},
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
.
dims
==
1
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
]}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
128
]}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
64
]}
elif
self
.
dims
==
2
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
32
]}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
64
]}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
3
,
64
]}
elif
self
.
dims
==
3
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
32
,
32
]}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
10
,
64
,
64
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
3
,
64
,
64
]}
else
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
3
,
32
,
32
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
3
,
64
,
64
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
1
,
3
,
64
,
64
]
}
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
self
.
dims
==
1
:
return
0
,
3
return
1
,
2
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
(
attrs
,
False
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
attrs
,
False
),
(
1e-3
,
1e-3
)
# 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-3
,
1e-3
)
def
test
(
self
):
self
.
run_test
()
if
__name__
==
"__main__"
:
unittest
.
main
()
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