Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
Paddle
提交
c59c8e4f
P
Paddle
项目概览
PaddlePaddle
/
Paddle
大约 1 年 前同步成功
通知
2298
Star
20931
Fork
5422
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1423
列表
看板
标记
里程碑
合并请求
543
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
1,423
Issue
1,423
列表
看板
标记
里程碑
合并请求
543
合并请求
543
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
c59c8e4f
编写于
9月 17, 2021
作者:
津
津
提交者:
GitHub
9月 17, 2021
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
[inference]add hard_swish dynamic plugin (#35214)
上级
d43f797a
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
313 addition
and
12 deletion
+313
-12
paddle/fluid/inference/tensorrt/convert/hard_swish_op.cc
paddle/fluid/inference/tensorrt/convert/hard_swish_op.cc
+15
-3
paddle/fluid/inference/tensorrt/plugin/hard_swish_op_plugin.cu
...e/fluid/inference/tensorrt/plugin/hard_swish_op_plugin.cu
+74
-9
paddle/fluid/inference/tensorrt/plugin/hard_swish_op_plugin.h
...le/fluid/inference/tensorrt/plugin/hard_swish_op_plugin.h
+107
-0
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_hard_swish.py
...sts/unittests/ir/inference/test_trt_convert_hard_swish.py
+117
-0
未找到文件。
paddle/fluid/inference/tensorrt/convert/hard_swish_op.cc
浏览文件 @
c59c8e4f
...
...
@@ -64,9 +64,21 @@ class HardSwishOpConverter : public OpConverter {
nvinfer1
::
ElementWiseOperation
::
kPROD
);
layer
=
eltwise_layer
;
}
else
{
plugin
::
HardSwishPlugin
*
plugin
=
new
plugin
::
HardSwishPlugin
(
threshold
,
scale
,
offset
);
layer
=
engine_
->
AddPlugin
(
&
input
,
input_num
,
plugin
);
if
(
engine_
->
with_dynamic_shape
())
{
#if IS_TRT_VERSION_GE(6000)
plugin
::
HardSwishPluginDynamic
*
plugin
=
new
plugin
::
HardSwishPluginDynamic
(
threshold
,
scale
,
offset
);
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
{
plugin
::
HardSwishPlugin
*
plugin
=
new
plugin
::
HardSwishPlugin
(
threshold
,
scale
,
offset
);
layer
=
engine_
->
AddPlugin
(
&
input
,
input_num
,
plugin
);
}
}
auto
output_name
=
op_desc
.
Output
(
"Out"
)[
0
];
RreplenishLayerAndOutput
(
layer
,
"hard_swish"
,
{
output_name
},
test_mode
);
...
...
paddle/fluid/inference/tensorrt/plugin/hard_swish_op_plugin.cu
浏览文件 @
c59c8e4f
...
...
@@ -22,10 +22,10 @@ namespace tensorrt {
namespace
plugin
{
nvinfer1
::
Dims
HardSwishPlugin
::
getOutputDimensions
(
int
index
,
const
nvinfer1
::
Dims
*
in_dims
,
int
nb_inputs
)
TRT_NOEXCEPT
{
int
index
,
const
nvinfer1
::
Dims
*
in_dims
,
int
nb_inputs
)
TRT_NOEXCEPT
{
assert
(
nb_inputs
==
1
);
assert
(
index
<
this
->
getNbOutputs
());
nvinfer1
::
Dims
const
&
input_dims
=
in_dims
[
0
];
nvinfer1
::
Dims
const
&
input_dims
=
in_dims
[
0
];
nvinfer1
::
Dims
output_dims
=
input_dims
;
return
output_dims
;
}
...
...
@@ -42,7 +42,7 @@ __device__ T kMin(T a, T b) {
template
<
typename
T
,
unsigned
TPB
>
__global__
void
hard_swish_kernel
(
float
threshold
,
float
scale
,
float
offset
,
int
n
,
const
T
*
input
,
T
*
output
)
{
int
n
,
const
T
*
input
,
T
*
output
)
{
const
int
idx
=
blockIdx
.
x
*
TPB
+
threadIdx
.
x
;
if
(
idx
<
n
)
{
const
T
in
=
input
[
idx
];
...
...
@@ -50,14 +50,14 @@ __global__ void hard_swish_kernel(float threshold, float scale, float offset,
}
}
int
HardSwishPlugin
::
enqueue
(
int
batch_size
,
const
void
*
const
*
inputs
,
int
HardSwishPlugin
::
enqueue
(
int
batch_size
,
const
void
*
const
*
inputs
,
#if IS_TRT_VERSION_LT(8000)
void
**
outputs
,
void
*
,
cudaStream_t
stream
)
{
void
**
outputs
,
void
*
,
cudaStream_t
stream
)
{
#else
void
*
const
*
outputs
,
void
*
,
void
*
const
*
outputs
,
void
*
,
cudaStream_t
stream
)
TRT_NOEXCEPT
{
#endif
const
auto
&
input_dims
=
this
->
getInputDims
(
0
);
const
auto
&
input_dims
=
this
->
getInputDims
(
0
);
int
num
=
batch_size
;
for
(
int
i
=
0
;
i
<
input_dims
.
nbDims
;
i
++
)
{
num
*=
input_dims
.
d
[
i
];
...
...
@@ -69,14 +69,79 @@ int HardSwishPlugin::enqueue(int batch_size, const void* const* inputs,
const
int
block_size
=
256
;
const
int
grid_size
=
(
num
+
block_size
-
1
)
/
block_size
;
const
float
*
input
=
static_cast
<
const
float
*>
(
inputs
[
0
]);
float
*
output
=
static_cast
<
float
*>
(
outputs
[
0
]);
const
float
*
input
=
static_cast
<
const
float
*>
(
inputs
[
0
]);
float
*
output
=
static_cast
<
float
*>
(
outputs
[
0
]);
hard_swish_kernel
<
float
,
block_size
><<<
grid_size
,
block_size
,
0
,
stream
>>>
(
threshold
,
scale
,
offset
,
num
,
input
,
output
);
return
cudaGetLastError
()
!=
cudaSuccess
;
}
#if IS_TRT_VERSION_GE(6000)
nvinfer1
::
DimsExprs
HardSwishPluginDynamic
::
getOutputDimensions
(
int
output_index
,
const
nvinfer1
::
DimsExprs
*
inputs
,
int
nb_inputs
,
nvinfer1
::
IExprBuilder
&
expr_builder
)
TRT_NOEXCEPT
{
return
inputs
[
0
];
}
int
HardSwishPluginDynamic
::
enqueue
(
const
nvinfer1
::
PluginTensorDesc
*
input_desc
,
const
nvinfer1
::
PluginTensorDesc
*
output_desc
,
const
void
*
const
*
inputs
,
void
*
const
*
outputs
,
void
*
workspace
,
cudaStream_t
stream
)
TRT_NOEXCEPT
{
auto
input_dims
=
input_desc
[
0
].
dims
;
int
num
=
1
;
for
(
int
i
=
0
;
i
<
input_dims
.
nbDims
;
i
++
)
{
num
*=
input_dims
.
d
[
i
];
}
float
threshold
=
threshold_
;
float
scale
=
scale_
;
float
offset
=
offset_
;
const
int
block_size
=
256
;
const
int
grid_size
=
(
num
+
block_size
-
1
)
/
block_size
;
const
float
*
input
=
static_cast
<
const
float
*>
(
inputs
[
0
]);
float
*
output
=
static_cast
<
float
*>
(
outputs
[
0
]);
hard_swish_kernel
<
float
,
block_size
><<<
grid_size
,
block_size
,
0
,
stream
>>>
(
threshold
,
scale
,
offset
,
num
,
input
,
output
);
return
cudaGetLastError
()
!=
cudaSuccess
;
}
nvinfer1
::
DataType
HardSwishPluginDynamic
::
getOutputDataType
(
int
index
,
const
nvinfer1
::
DataType
*
input_types
,
int
nb_inputs
)
const
TRT_NOEXCEPT
{
PADDLE_ENFORCE_EQ
(
index
,
0
,
platform
::
errors
::
InvalidArgument
(
"The Elementwise Plugin only has one input, so the "
"index value should be 0, but get %d."
,
index
));
return
input_types
[
0
];
}
bool
HardSwishPluginDynamic
::
supportsFormatCombination
(
int
pos
,
const
nvinfer1
::
PluginTensorDesc
*
in_out
,
int
nb_inputs
,
int
nb_outputs
)
TRT_NOEXCEPT
{
PADDLE_ENFORCE_NOT_NULL
(
in_out
,
platform
::
errors
::
InvalidArgument
(
"The input of swish plugin shoule not be nullptr."
));
PADDLE_ENFORCE_LT
(
pos
,
nb_inputs
+
nb_outputs
,
platform
::
errors
::
InvalidArgument
(
"The pos(%d) should be less than the "
"num(%d) of the input and the output."
,
pos
,
nb_inputs
+
nb_outputs
));
(
in_out
&&
pos
<
(
nb_inputs
+
nb_outputs
));
const
nvinfer1
::
PluginTensorDesc
&
in
=
in_out
[
pos
];
if
(
pos
==
0
)
{
return
(
in
.
type
==
nvinfer1
::
DataType
::
kFLOAT
)
&&
(
in
.
format
==
nvinfer1
::
TensorFormat
::
kLINEAR
);
}
const
nvinfer1
::
PluginTensorDesc
&
prev
=
in_out
[
pos
-
1
];
// output
return
in
.
type
==
prev
.
type
&&
in
.
format
==
prev
.
format
;
}
#endif
}
// namespace plugin
}
// namespace tensorrt
}
// namespace inference
...
...
paddle/fluid/inference/tensorrt/plugin/hard_swish_op_plugin.h
浏览文件 @
c59c8e4f
...
...
@@ -94,6 +94,113 @@ class HardSwishPluginCreator : public TensorRTPluginCreator {
};
REGISTER_TRT_PLUGIN_V2
(
HardSwishPluginCreator
);
#if IS_TRT_VERSION_GE(6000)
class
HardSwishPluginDynamic
:
public
DynamicPluginTensorRT
{
public:
HardSwishPluginDynamic
(
const
float
threshold
,
const
float
scale
,
const
float
offset
)
:
threshold_
(
threshold
),
scale_
(
scale
),
offset_
(
offset
)
{}
// It was used for tensorrt deserialization.
// It should not be called by users.
HardSwishPluginDynamic
(
void
const
*
serialData
,
size_t
serialLength
)
{
DeserializeValue
(
&
serialData
,
&
serialLength
,
&
threshold_
);
DeserializeValue
(
&
serialData
,
&
serialLength
,
&
scale_
);
DeserializeValue
(
&
serialData
,
&
serialLength
,
&
offset_
);
}
~
HardSwishPluginDynamic
()
{}
nvinfer1
::
IPluginV2DynamicExt
*
clone
()
const
TRT_NOEXCEPT
override
{
return
new
HardSwishPluginDynamic
(
threshold_
,
scale_
,
offset_
);
}
const
char
*
getPluginType
()
const
TRT_NOEXCEPT
override
{
return
"hard_swish_plugin_dynamic"
;
}
int
getNbOutputs
()
const
TRT_NOEXCEPT
override
{
return
1
;
}
int
initialize
()
TRT_NOEXCEPT
override
{
return
0
;
}
nvinfer1
::
DimsExprs
getOutputDimensions
(
int
output_index
,
const
nvinfer1
::
DimsExprs
*
inputs
,
int
nb_inputs
,
nvinfer1
::
IExprBuilder
&
expr_builder
)
TRT_NOEXCEPT
override
;
int
enqueue
(
const
nvinfer1
::
PluginTensorDesc
*
inputDesc
,
const
nvinfer1
::
PluginTensorDesc
*
outputDesc
,
const
void
*
const
*
inputs
,
void
*
const
*
outputs
,
void
*
workspace
,
cudaStream_t
stream
)
TRT_NOEXCEPT
override
;
size_t
getSerializationSize
()
const
TRT_NOEXCEPT
override
{
return
SerializedSize
(
threshold_
)
+
SerializedSize
(
scale_
)
+
SerializedSize
(
offset_
);
}
// TRT will call this func to serialize the configuration of TRT
// It should not be called by users.
void
serialize
(
void
*
buffer
)
const
TRT_NOEXCEPT
override
{
SerializeValue
(
&
buffer
,
threshold_
);
SerializeValue
(
&
buffer
,
scale_
);
SerializeValue
(
&
buffer
,
offset_
);
}
nvinfer1
::
DataType
getOutputDataType
(
int
index
,
const
nvinfer1
::
DataType
*
inputTypes
,
int
nbInputs
)
const
TRT_NOEXCEPT
override
;
bool
supportsFormatCombination
(
int
pos
,
const
nvinfer1
::
PluginTensorDesc
*
inOut
,
int
nbInputs
,
int
nbOutputs
)
TRT_NOEXCEPT
override
;
void
configurePlugin
(
const
nvinfer1
::
DynamicPluginTensorDesc
*
in
,
int
nbInputs
,
const
nvinfer1
::
DynamicPluginTensorDesc
*
out
,
int
nbOutputs
)
TRT_NOEXCEPT
override
{}
void
destroy
()
TRT_NOEXCEPT
override
{
delete
this
;
}
protected:
float
threshold_
;
float
scale_
;
float
offset_
;
};
class
HardSwishPluginDynamicCreator
:
public
nvinfer1
::
IPluginCreator
{
public:
HardSwishPluginDynamicCreator
()
{}
const
char
*
getPluginName
()
const
TRT_NOEXCEPT
override
{
return
"hardswish_plugin_dynamic"
;
}
const
char
*
getPluginVersion
()
const
TRT_NOEXCEPT
override
{
return
"1"
;
}
const
nvinfer1
::
PluginFieldCollection
*
getFieldNames
()
TRT_NOEXCEPT
override
{
return
&
field_collection_
;
}
nvinfer1
::
IPluginV2
*
createPlugin
(
const
char
*
name
,
const
nvinfer1
::
PluginFieldCollection
*
fc
)
TRT_NOEXCEPT
override
{
return
nullptr
;
}
nvinfer1
::
IPluginV2
*
deserializePlugin
(
const
char
*
name
,
const
void
*
serial_data
,
size_t
serial_length
)
TRT_NOEXCEPT
override
{
auto
plugin
=
new
HardSwishPluginDynamic
(
serial_data
,
serial_length
);
return
plugin
;
}
void
setPluginNamespace
(
const
char
*
lib_namespace
)
TRT_NOEXCEPT
override
{
plugin_namespace_
=
lib_namespace
;
}
const
char
*
getPluginNamespace
()
const
TRT_NOEXCEPT
override
{
return
plugin_namespace_
.
c_str
();
}
private:
std
::
string
plugin_namespace_
;
std
::
string
plugin_name_
;
nvinfer1
::
PluginFieldCollection
field_collection_
{
0
,
nullptr
};
std
::
vector
<
nvinfer1
::
PluginField
>
plugin_attributes_
;
};
REGISTER_TRT_PLUGIN_V2
(
HardSwishPluginDynamicCreator
);
#endif
}
// namespace plugin
}
// namespace tensorrt
}
// namespace inference
...
...
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_hard_swish.py
0 → 100644
浏览文件 @
c59c8e4f
# 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
unittest
class
TrtConvertHardSwishTest
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
inputs
=
program_config
.
inputs
weights
=
program_config
.
weights
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
]
if
attrs
[
0
][
'threshold'
]
<=
0
or
attrs
[
0
][
'scale'
]
<=
0
:
return
False
return
True
def
sample_program_configs
(
self
):
def
generate_input1
(
attrs
:
List
[
Dict
[
str
,
Any
]]):
return
np
.
ones
([
1
,
3
,
64
,
64
]).
astype
(
np
.
float32
)
for
threshold
in
[
6.0
,
7.0
,
100.0
,
0.0
,
-
1.0
]:
for
scale
in
[
5.0
,
6.0
,
7.0
,
-
1.0
,
0.0
,
100.0
]:
for
offset
in
[
3.0
,
4.0
,
5.0
,
-
1.0
,
0.0
,
100.0
]:
dics
=
[{
"threshold"
:
threshold
,
"scale"
:
scale
,
"offset"
:
offset
}]
ops_config
=
[{
"op_type"
:
"hard_swish"
,
"op_inputs"
:
{
"X"
:
[
"input_data"
]
},
"op_outputs"
:
{
"Out"
:
[
"hard_swish_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
,
dics
))
},
outputs
=
[
"hard_swish_output_data"
])
yield
program_config
def
sample_predictor_configs
(
self
,
program_config
)
->
(
paddle_infer
.
Config
,
List
[
int
],
float
):
def
generate_dynamic_shape
(
attrs
):
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
):
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-5
,
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
,
1e-5
)
def
test
(
self
):
self
.
run_test
()
if
__name__
==
"__main__"
:
unittest
.
main
()
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录