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c59c8e4f
编写于
9月 17, 2021
作者:
津
津
提交者:
GitHub
9月 17, 2021
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差异文件
[inference]add hard_swish dynamic plugin (#35214)
上级
d43f797a
变更
4
显示空白变更内容
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
...
@@ -63,11 +63,23 @@ class HardSwishOpConverter : public OpConverter {
...
@@ -63,11 +63,23 @@ class HardSwishOpConverter : public OpConverter {
engine_
,
ElementWise
,
*
input
,
*
(
hsig_layer
->
getOutput
(
0
)),
engine_
,
ElementWise
,
*
input
,
*
(
hsig_layer
->
getOutput
(
0
)),
nvinfer1
::
ElementWiseOperation
::
kPROD
);
nvinfer1
::
ElementWiseOperation
::
kPROD
);
layer
=
eltwise_layer
;
layer
=
eltwise_layer
;
}
else
{
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
{
}
else
{
plugin
::
HardSwishPlugin
*
plugin
=
plugin
::
HardSwishPlugin
*
plugin
=
new
plugin
::
HardSwishPlugin
(
threshold
,
scale
,
offset
);
new
plugin
::
HardSwishPlugin
(
threshold
,
scale
,
offset
);
layer
=
engine_
->
AddPlugin
(
&
input
,
input_num
,
plugin
);
layer
=
engine_
->
AddPlugin
(
&
input
,
input_num
,
plugin
);
}
}
}
auto
output_name
=
op_desc
.
Output
(
"Out"
)[
0
];
auto
output_name
=
op_desc
.
Output
(
"Out"
)[
0
];
RreplenishLayerAndOutput
(
layer
,
"hard_swish"
,
{
output_name
},
test_mode
);
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 {
...
@@ -22,10 +22,10 @@ namespace tensorrt {
namespace
plugin
{
namespace
plugin
{
nvinfer1
::
Dims
HardSwishPlugin
::
getOutputDimensions
(
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
(
nb_inputs
==
1
);
assert
(
index
<
this
->
getNbOutputs
());
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
;
nvinfer1
::
Dims
output_dims
=
input_dims
;
return
output_dims
;
return
output_dims
;
}
}
...
@@ -42,7 +42,7 @@ __device__ T kMin(T a, T b) {
...
@@ -42,7 +42,7 @@ __device__ T kMin(T a, T b) {
template
<
typename
T
,
unsigned
TPB
>
template
<
typename
T
,
unsigned
TPB
>
__global__
void
hard_swish_kernel
(
float
threshold
,
float
scale
,
float
offset
,
__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
;
const
int
idx
=
blockIdx
.
x
*
TPB
+
threadIdx
.
x
;
if
(
idx
<
n
)
{
if
(
idx
<
n
)
{
const
T
in
=
input
[
idx
];
const
T
in
=
input
[
idx
];
...
@@ -50,14 +50,14 @@ __global__ void hard_swish_kernel(float threshold, float scale, float offset,
...
@@ -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)
#if IS_TRT_VERSION_LT(8000)
void
**
outputs
,
void
*
,
cudaStream_t
stream
)
{
void
**
outputs
,
void
*
,
cudaStream_t
stream
)
{
#else
#else
void
*
const
*
outputs
,
void
*
,
void
*
const
*
outputs
,
void
*
,
cudaStream_t
stream
)
TRT_NOEXCEPT
{
cudaStream_t
stream
)
TRT_NOEXCEPT
{
#endif
#endif
const
auto
&
input_dims
=
this
->
getInputDims
(
0
);
const
auto
&
input_dims
=
this
->
getInputDims
(
0
);
int
num
=
batch_size
;
int
num
=
batch_size
;
for
(
int
i
=
0
;
i
<
input_dims
.
nbDims
;
i
++
)
{
for
(
int
i
=
0
;
i
<
input_dims
.
nbDims
;
i
++
)
{
num
*=
input_dims
.
d
[
i
];
num
*=
input_dims
.
d
[
i
];
...
@@ -69,14 +69,79 @@ int HardSwishPlugin::enqueue(int batch_size, const void* const* inputs,
...
@@ -69,14 +69,79 @@ int HardSwishPlugin::enqueue(int batch_size, const void* const* inputs,
const
int
block_size
=
256
;
const
int
block_size
=
256
;
const
int
grid_size
=
(
num
+
block_size
-
1
)
/
block_size
;
const
int
grid_size
=
(
num
+
block_size
-
1
)
/
block_size
;
const
float
*
input
=
static_cast
<
const
float
*>
(
inputs
[
0
]);
const
float
*
input
=
static_cast
<
const
float
*>
(
inputs
[
0
]);
float
*
output
=
static_cast
<
float
*>
(
outputs
[
0
]);
float
*
output
=
static_cast
<
float
*>
(
outputs
[
0
]);
hard_swish_kernel
<
float
,
block_size
><<<
grid_size
,
block_size
,
0
,
stream
>>>
(
hard_swish_kernel
<
float
,
block_size
><<<
grid_size
,
block_size
,
0
,
stream
>>>
(
threshold
,
scale
,
offset
,
num
,
input
,
output
);
threshold
,
scale
,
offset
,
num
,
input
,
output
);
return
cudaGetLastError
()
!=
cudaSuccess
;
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 plugin
}
// namespace tensorrt
}
// namespace tensorrt
}
// namespace inference
}
// 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 {
...
@@ -94,6 +94,113 @@ class HardSwishPluginCreator : public TensorRTPluginCreator {
};
};
REGISTER_TRT_PLUGIN_V2
(
HardSwishPluginCreator
);
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 plugin
}
// namespace tensorrt
}
// namespace tensorrt
}
// namespace inference
}
// 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
()
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