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体验新版 GitCode,发现更多精彩内容 >>
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de992400
编写于
6月 17, 2022
作者:
X
xiaoxiaohehe001
提交者:
GitHub
6月 17, 2022
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电子邮件补丁
差异文件
[Paddle Inference] Support dyanmic_shape of shuffle_channel. (#43488)
* shuffle_channel * shuffle * shuffle * shuffle
上级
35694c2a
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
57 addition
and
17 deletion
+57
-17
paddle/fluid/inference/tensorrt/convert/shuffle_channel_op.cc
...le/fluid/inference/tensorrt/convert/shuffle_channel_op.cc
+50
-15
paddle/fluid/inference/tensorrt/op_teller.cc
paddle/fluid/inference/tensorrt/op_teller.cc
+4
-1
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_shuffle_channel.py
...nittests/ir/inference/test_trt_convert_shuffle_channel.py
+3
-1
未找到文件。
paddle/fluid/inference/tensorrt/convert/shuffle_channel_op.cc
浏览文件 @
de992400
...
...
@@ -39,25 +39,60 @@ class ShuffleChannelOpConverter : public OpConverter {
// Declare inputs
auto
*
input
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"X"
)[
0
]);
auto
input_dims
=
input
->
getDimensions
();
int
c
=
input_dims
.
d
[
0
];
int
h
=
input_dims
.
d
[
1
];
int
w
=
input_dims
.
d
[
2
];
auto
output_name
=
op_desc
.
Output
(
"Out"
)[
0
];
int
group
=
BOOST_GET_CONST
(
int
,
op_desc
.
GetAttr
(
"group"
));
auto
*
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Shuffle
,
*
input
);
nvinfer1
::
Dims4
reshape_dim
(
group
,
c
/
group
,
h
,
w
);
layer
->
setReshapeDimensions
(
reshape_dim
);
layer
->
setSecondTranspose
({
1
,
0
,
2
,
3
});
auto
*
output
=
layer
->
getOutput
(
0
);
#if IS_TRT_VERSION_GE(8000)
if
(
engine_
->
with_dynamic_shape
())
{
auto
*
input_shape_tensor
=
Shape
(
input
);
auto
*
channel_shape_tensor
=
GetEleTensorOfShape
(
input_shape_tensor
,
1
);
auto
*
group_tensor
=
Add1DConstantLayer
(
group
,
output_name
+
"_group_tensor_"
);
auto
*
new_channel_shape_tensor
=
Div
(
channel_shape_tensor
,
group_tensor
);
std
::
vector
<
int32_t
>
shape_dim3
{
0
,
2
,
3
};
auto
*
shape_dim3_tensor
=
Gather
(
input_shape_tensor
,
shape_dim3
);
auto
*
reshape_layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Shuffle
,
*
output
);
nvinfer1
::
Dims3
reshape_dim2
(
c
,
h
,
w
);
reshape_layer
->
setReshapeDimensions
(
reshape_dim2
);
std
::
vector
<
nvinfer1
::
ITensor
*>
itensors
;
itensors
.
push_back
(
shape_dim3_tensor
);
itensors
.
push_back
(
group_tensor
);
itensors
.
push_back
(
new_channel_shape_tensor
);
auto
*
reshape_tensor
=
Concat
(
itensors
);
auto
output_name
=
op_desc
.
Output
(
"Out"
)[
0
];
RreplenishLayerAndOutput
(
reshape_layer
,
"shuffle_channel"
,
{
output_name
},
test_mode
);
auto
*
reshape_layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Shuffle
,
*
reshape_tensor
);
nvinfer1
::
Permutation
transpose_new_input
{
0
,
3
,
4
,
1
,
2
};
reshape_layer
->
setSecondTranspose
(
transpose_new_input
);
auto
*
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Shuffle
,
*
input
);
layer
->
setInput
(
1
,
*
(
reshape_layer
->
getOutput
(
0
)));
nvinfer1
::
Permutation
transpose_embed
{
0
,
2
,
1
,
3
,
4
};
layer
->
setSecondTranspose
(
transpose_embed
);
auto
*
output
=
layer
->
getOutput
(
0
);
auto
*
output_layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Shuffle
,
*
output
);
output_layer
->
setInput
(
1
,
*
input_shape_tensor
);
RreplenishLayerAndOutput
(
output_layer
,
"shuffle_channel"
,
{
output_name
},
test_mode
);
}
#endif
if
(
!
engine_
->
with_dynamic_shape
())
{
int
c
=
input_dims
.
d
[
0
];
int
h
=
input_dims
.
d
[
1
];
int
w
=
input_dims
.
d
[
2
];
auto
*
layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Shuffle
,
*
input
);
nvinfer1
::
Dims4
reshape_dim
(
group
,
c
/
group
,
h
,
w
);
layer
->
setReshapeDimensions
(
reshape_dim
);
layer
->
setSecondTranspose
({
1
,
0
,
2
,
3
});
auto
*
output
=
layer
->
getOutput
(
0
);
auto
*
reshape_layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Shuffle
,
*
output
);
nvinfer1
::
Dims3
reshape_dim2
(
c
,
h
,
w
);
reshape_layer
->
setReshapeDimensions
(
reshape_dim2
);
RreplenishLayerAndOutput
(
reshape_layer
,
"shuffle_channel"
,
{
output_name
},
test_mode
);
}
}
};
...
...
paddle/fluid/inference/tensorrt/op_teller.cc
浏览文件 @
de992400
...
...
@@ -1604,11 +1604,14 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
}
if
(
op_type
==
"shuffle_channel"
)
{
#if !IS_TRT_VERSION_GE(8000)
if
(
with_dynamic_shape
)
{
VLOG
(
3
)
<<
"You are running the TRT Dynamic Shape mode, "
"the shuffle_channel op does not support dynamic shape yet"
;
"the shuffle_channel op does not support dynamic shape "
"trt versions below 8.0 yet"
;
return
false
;
}
#endif
}
if
(
op_type
==
"skip_layernorm"
)
{
...
...
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_shuffle_channel.py
浏览文件 @
de992400
...
...
@@ -77,7 +77,9 @@ class TrtConvertShuffleChannelTest(TrtLayerAutoScanTest):
self
.
dynamic_shape
.
opt_input_shape
=
{}
def
generate_trt_nodes_num
(
attrs
,
dynamic_shape
):
if
dynamic_shape
==
True
:
ver
=
paddle_infer
.
get_trt_compile_version
()
if
ver
[
0
]
*
1000
+
ver
[
1
]
*
100
+
ver
[
2
]
*
10
<
8000
and
dynamic_shape
==
True
:
return
0
,
3
else
:
return
1
,
2
...
...
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