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
机器未来
Paddle
提交
8dd18208
P
Paddle
项目概览
机器未来
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
1
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1
列表
看板
标记
里程碑
合并请求
0
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
Issue
1
列表
看板
标记
里程碑
合并请求
0
合并请求
0
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
未验证
提交
8dd18208
编写于
7月 13, 2022
作者:
Z
zhoutianzi666
提交者:
GitHub
7月 13, 2022
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
[Paddle-TRT] fix convtranspose and elementwise in op_teller (#44147)
* fix convtranspose and elementwise
上级
b809be1a
变更
4
显示空白变更内容
内联
并排
Showing
4 changed file
with
293 addition
and
41 deletion
+293
-41
paddle/fluid/inference/tensorrt/convert/elementwise_op.cc
paddle/fluid/inference/tensorrt/convert/elementwise_op.cc
+10
-1
paddle/fluid/inference/tensorrt/op_teller.cc
paddle/fluid/inference/tensorrt/op_teller.cc
+48
-6
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_conv2d_transpose.py
...ittests/ir/inference/test_trt_convert_conv2d_transpose.py
+118
-0
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py
...ts/unittests/ir/inference/test_trt_convert_elementwise.py
+117
-34
未找到文件。
paddle/fluid/inference/tensorrt/convert/elementwise_op.cc
浏览文件 @
8dd18208
...
@@ -62,11 +62,13 @@ class ElementwiseTensorOpConverter : public OpConverter {
...
@@ -62,11 +62,13 @@ class ElementwiseTensorOpConverter : public OpConverter {
}
else
{
}
else
{
Y
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"Y"
).
front
());
Y
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"Y"
).
front
());
}
}
bool
swap_xy
=
false
;
// Swap X and Y
if
(
X
->
getDimensions
().
nbDims
<
Y
->
getDimensions
().
nbDims
)
{
if
(
X
->
getDimensions
().
nbDims
<
Y
->
getDimensions
().
nbDims
)
{
auto
*
tmp
=
X
;
auto
*
tmp
=
X
;
X
=
Y
;
X
=
Y
;
Y
=
tmp
;
Y
=
tmp
;
swap_xy
=
true
;
}
}
nvinfer1
::
Dims
dims_x
=
X
->
getDimensions
();
nvinfer1
::
Dims
dims_x
=
X
->
getDimensions
();
nvinfer1
::
Dims
dims_y
=
Y
->
getDimensions
();
nvinfer1
::
Dims
dims_y
=
Y
->
getDimensions
();
...
@@ -130,6 +132,13 @@ class ElementwiseTensorOpConverter : public OpConverter {
...
@@ -130,6 +132,13 @@ class ElementwiseTensorOpConverter : public OpConverter {
reshape_y_tensor
=
Y
;
reshape_y_tensor
=
Y
;
}
}
// We should swap X and Y back, because some operators do not have symmetry
if
(
swap_xy
)
{
auto
*
tmp
=
reshape_y_tensor
;
reshape_y_tensor
=
X
;
X
=
tmp
;
}
auto
op_pair
=
ops
.
find
(
op_type_
);
auto
op_pair
=
ops
.
find
(
op_type_
);
PADDLE_ENFORCE_NE
(
op_pair
,
PADDLE_ENFORCE_NE
(
op_pair
,
ops
.
end
(),
ops
.
end
(),
...
...
paddle/fluid/inference/tensorrt/op_teller.cc
浏览文件 @
8dd18208
...
@@ -325,6 +325,28 @@ bool OpTeller::Tell(const framework::ir::Node* node,
...
@@ -325,6 +325,28 @@ bool OpTeller::Tell(const framework::ir::Node* node,
#endif
#endif
}
}
// In static shape mode in TRT, we can't allow that op's input is a
// 1D-tensor So we filter it here. Some op like elementwise having "Y" too,
// but that is dealt with in the specified op, here just the common case
if
(
!
with_dynamic_shape
)
{
std
::
string
X_name
;
auto
inputs
=
desc
.
Inputs
();
if
(
inputs
.
count
(
"X"
))
{
X_name
=
desc
.
Input
(
"X"
)[
0
];
}
else
if
(
inputs
.
count
(
"Input"
))
{
X_name
=
desc
.
Input
(
"Input"
)[
0
];
}
auto
*
block
=
desc
.
Block
();
if
(
block
)
{
auto
*
x_var_desc
=
block
->
FindVar
(
X_name
);
// Can't get feed op's TensorDesc
if
(
op_type
!=
"feed"
&&
x_var_desc
&&
!
x_var_desc
->
Persistable
())
{
const
auto
x_shape
=
x_var_desc
->
GetShape
();
if
(
x_shape
.
size
()
==
1
)
return
false
;
}
}
}
if
(
op_type
==
"pool2d"
)
{
if
(
op_type
==
"pool2d"
)
{
std
::
vector
<
int
>
paddings
=
std
::
vector
<
int
>
paddings
=
BOOST_GET_CONST
(
std
::
vector
<
int
>
,
desc
.
GetAttr
(
"paddings"
));
BOOST_GET_CONST
(
std
::
vector
<
int
>
,
desc
.
GetAttr
(
"paddings"
));
...
@@ -1309,14 +1331,19 @@ bool OpTeller::Tell(const framework::ir::Node* node,
...
@@ -1309,14 +1331,19 @@ bool OpTeller::Tell(const framework::ir::Node* node,
auto
*
y_var_desc
=
block
->
FindVar
(
desc
.
Input
(
"Y"
)[
0
]);
auto
*
y_var_desc
=
block
->
FindVar
(
desc
.
Input
(
"Y"
)[
0
]);
const
auto
x_shape
=
x_var_desc
->
GetShape
();
const
auto
x_shape
=
x_var_desc
->
GetShape
();
const
auto
y_shape
=
y_var_desc
->
GetShape
();
const
auto
y_shape
=
y_var_desc
->
GetShape
();
if
(
x_shape
.
size
()
==
1
&&
y_shape
.
size
()
==
1
)
{
VLOG
(
3
)
<<
"Now trt may not support two 1d tensor elementwise op."
;
// The case when x_shape.size() == 1 is dealt with in common case
if
(
!
with_dynamic_shape
&&
(
!
y_var_desc
->
Persistable
())
&&
y_shape
.
size
()
==
1
)
{
VLOG
(
3
)
<<
"Static shape in trt not support y is a 1D intermediate "
"tensor in "
"elementwise op."
;
return
false
;
return
false
;
}
}
if
(
x_var_desc
->
Persistable
())
{
if
(
x_var_desc
->
Persistable
()
&&
!
with_dynamic_shape
)
{
VLOG
(
3
)
<<
"Input X is a parameter which is not supported for "
VLOG
(
3
)
"elementwise_add/elementwise_mul in tensorrt, swap x and
"
<<
"Input X is a parameter which is not supported for
"
"
y will work"
;
"elementwise in tensorrt's static shape, swap x and
y will work"
;
return
false
;
return
false
;
}
}
}
}
...
@@ -1912,6 +1939,21 @@ bool OpTeller::Tell(const framework::ir::Node* node,
...
@@ -1912,6 +1939,21 @@ bool OpTeller::Tell(const framework::ir::Node* node,
}
}
#endif
#endif
// conv2d_transpose, conv3d_transpose, depthwise_conv2d_transpose
if
(
op_type
.
find
(
"d_transpose"
)
>
0
)
{
// trt doen't support output_padding,
// output_padding is set when stride > 1
if
(
desc
.
HasAttr
(
"output_padding"
))
{
const
std
::
vector
<
int
>
output_padding
=
BOOST_GET_CONST
(
std
::
vector
<
int
>
,
desc
.
GetAttr
(
"output_padding"
));
if
(
output_padding
.
size
()
>
0
)
{
int
max_padding
=
*
std
::
max_element
(
output_padding
.
begin
(),
output_padding
.
end
());
if
(
max_padding
>
0
)
return
false
;
}
}
}
if
(
op_type
==
"conv3d"
||
op_type
==
"conv3d_transpose"
)
{
if
(
op_type
==
"conv3d"
||
op_type
==
"conv3d_transpose"
)
{
if
(
desc
.
HasAttr
(
"padding_algorithm"
))
{
if
(
desc
.
HasAttr
(
"padding_algorithm"
))
{
std
::
string
padding_algorithm
=
std
::
string
padding_algorithm
=
...
...
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_conv2d_transpose.py
浏览文件 @
8dd18208
...
@@ -219,5 +219,123 @@ class TrtConvertConv2dTransposeTest(TrtLayerAutoScanTest):
...
@@ -219,5 +219,123 @@ class TrtConvertConv2dTransposeTest(TrtLayerAutoScanTest):
self
.
run_test
(
quant
=
True
)
self
.
run_test
(
quant
=
True
)
# Special case
class
TrtConvertConv2dTransposeTest2
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
ver
=
paddle_infer
.
get_trt_compile_version
()
if
ver
[
0
]
*
1000
+
ver
[
1
]
*
100
+
ver
[
2
]
*
10
<
7000
:
return
False
return
True
def
sample_program_configs
(
self
):
self
.
trt_param
.
workspace_size
=
1073741824
def
generate_input1
(
batch
,
num_channels
,
attrs
:
List
[
Dict
[
str
,
Any
]]):
return
np
.
ones
([
batch
,
num_channels
,
20
,
30
]).
astype
(
np
.
float32
)
def
generate_weight1
(
num_channels
,
attrs
:
List
[
Dict
[
str
,
Any
]]):
return
np
.
random
.
random
([
num_channels
,
64
,
3
,
3
]).
astype
(
np
.
float32
)
num_channels
=
128
batch
=
1
self
.
num_channels
=
num_channels
dics
=
[{
"data_fromat"
:
'NCHW'
,
"dilations"
:
[
1
,
1
],
"padding_algorithm"
:
'EXPLICIT'
,
"groups"
:
1
,
"paddings"
:
[
1
,
1
],
"strides"
:
[
2
,
2
],
"output_padding"
:
[
1
,
1
],
"output_size"
:
[],
}]
ops_config
=
[{
"op_type"
:
"conv2d_transpose"
,
"op_inputs"
:
{
"Input"
:
[
"input_data"
],
"Filter"
:
[
"conv2d_weight"
]
},
"op_outputs"
:
{
"Output"
:
[
"output_data"
]
},
"op_attrs"
:
dics
[
0
]
}]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{
"conv2d_weight"
:
TensorConfig
(
data_gen
=
partial
(
generate_weight1
,
num_channels
,
dics
))
},
inputs
=
{
"input_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
,
batch
,
num_channels
,
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
):
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
128
,
20
,
30
],
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
1
,
128
,
20
,
30
],
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
1
,
128
,
20
,
30
],
}
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
0
,
3
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-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-5
,
1e-3
)
def
add_skip_trt_case
(
self
):
pass
def
test
(
self
):
self
.
add_skip_trt_case
()
self
.
run_test
()
def
test_quant
(
self
):
self
.
add_skip_trt_case
()
self
.
run_test
(
quant
=
True
)
if
__name__
==
"__main__"
:
if
__name__
==
"__main__"
:
unittest
.
main
()
unittest
.
main
()
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py
浏览文件 @
8dd18208
...
@@ -124,6 +124,101 @@ class TrtConvertElementwiseTest_one_input_special_case0(TrtLayerAutoScanTest):
...
@@ -124,6 +124,101 @@ class TrtConvertElementwiseTest_one_input_special_case0(TrtLayerAutoScanTest):
self
.
run_test
()
self
.
run_test
()
# This is the special test case
class
TrtConvertElementwiseTest_one_input_special_case1
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
def
sample_program_configs
(
self
):
def
generate_input
(
shape
):
return
np
.
random
.
random
(
shape
).
astype
(
np
.
float32
)
def
generate_weight
():
return
np
.
random
.
randn
(
1
).
astype
(
np
.
float32
)
for
shape
in
[[
32
]]:
for
op_type
in
[
"elementwise_add"
,
"elementwise_mul"
]:
for
axis
in
[
-
1
]:
self
.
dims
=
len
(
shape
)
dics
=
[{
"axis"
:
axis
}]
ops_config
=
[{
"op_type"
:
op_type
,
"op_inputs"
:
{
"X"
:
[
"input_data"
],
"Y"
:
[
"weight"
]
},
"op_outputs"
:
{
"Out"
:
[
"output_data"
]
},
"op_attrs"
:
dics
[
0
]
}]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{
"weight"
:
TensorConfig
(
data_gen
=
partial
(
generate_weight
))
},
inputs
=
{
"input_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input
,
shape
)),
},
outputs
=
[
"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"
:
[
32
]}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
64
]}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
32
]}
def
clear_dynamic_shape
():
self
.
dynamic_shape
.
max_input_shape
=
{}
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
def
generate_trt_nodes_num
(
attrs
,
dynamic_shape
):
if
not
dynamic_shape
:
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-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
TrtConvertElementwiseTest_one_input
(
TrtLayerAutoScanTest
):
class
TrtConvertElementwiseTest_one_input
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
...
@@ -206,7 +301,7 @@ class TrtConvertElementwiseTest_one_input(TrtLayerAutoScanTest):
...
@@ -206,7 +301,7 @@ class TrtConvertElementwiseTest_one_input(TrtLayerAutoScanTest):
self
.
dynamic_shape
.
opt_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
def
generate_trt_nodes_num
(
attrs
,
dynamic_shape
):
def
generate_trt_nodes_num
(
attrs
,
dynamic_shape
):
if
self
.
dims
==
1
:
if
self
.
dims
==
1
and
not
dynamic_shape
:
return
0
,
3
return
0
,
3
return
1
,
2
return
1
,
2
...
@@ -244,10 +339,6 @@ class TrtConvertElementwiseTest_two_input_without_broadcast(
...
@@ -244,10 +339,6 @@ class TrtConvertElementwiseTest_two_input_without_broadcast(
TrtLayerAutoScanTest
):
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
inputs
=
program_config
.
inputs
if
len
(
inputs
[
'input_data1'
].
shape
)
==
1
:
return
False
return
True
return
True
def
sample_program_configs
(
self
):
def
sample_program_configs
(
self
):
...
@@ -353,6 +444,11 @@ class TrtConvertElementwiseTest_two_input_without_broadcast(
...
@@ -353,6 +444,11 @@ class TrtConvertElementwiseTest_two_input_without_broadcast(
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
def
generate_trt_nodes_num
(
attrs
,
dynamic_shape
):
if
self
.
dims
==
1
and
not
dynamic_shape
:
return
0
,
4
return
1
,
3
attrs
=
[
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
]
]
...
@@ -360,9 +456,11 @@ class TrtConvertElementwiseTest_two_input_without_broadcast(
...
@@ -360,9 +456,11 @@ class TrtConvertElementwiseTest_two_input_without_broadcast(
# for static_shape
# for static_shape
clear_dynamic_shape
()
clear_dynamic_shape
()
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
1
,
3
),
1e-5
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
attrs
,
False
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
1
,
3
),
1e-5
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
attrs
,
False
),
1e-5
# for dynamic_shape
# for dynamic_shape
generate_dynamic_shape
(
attrs
)
generate_dynamic_shape
(
attrs
)
...
@@ -519,15 +617,19 @@ class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest):
...
@@ -519,15 +617,19 @@ class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest):
def
generate_input
(
shape
):
def
generate_input
(
shape
):
return
np
.
random
.
random
(
shape
).
astype
(
np
.
float32
)
return
np
.
random
.
random
(
shape
).
astype
(
np
.
float32
)
# use rand not randn to avoiding pow producing `NAN`
def
generate_weight
():
def
generate_weight
():
return
np
.
random
.
rand
n
(
32
).
astype
(
np
.
float32
)
return
np
.
random
.
rand
(
32
).
astype
(
np
.
float32
)
for
batch
in
[
1
,
2
,
4
]:
for
batch
in
[
1
,
2
,
4
]:
for
shape
in
[[
32
],
[
batch
,
32
],
[
batch
,
32
,
32
],
for
shape
in
[[
32
],
[
batch
,
32
],
[
batch
,
32
,
32
],
[
batch
,
32
,
16
,
32
]]:
[
batch
,
32
,
16
,
32
]]:
for
op_type
in
[
for
op_type
in
[
"elementwise_add"
,
"elementwise_mul"
,
"elementwise_sub"
,
"elementwise_add"
,
"elementwise_div"
,
"elementwise_pow"
"elementwise_mul"
,
"elementwise_sub"
,
"elementwise_div"
,
"elementwise_pow"
,
]:
]:
for
axis
in
[
-
1
if
len
(
shape
)
==
1
else
1
]:
for
axis
in
[
-
1
if
len
(
shape
)
==
1
else
1
]:
self
.
dims
=
len
(
shape
)
self
.
dims
=
len
(
shape
)
...
@@ -595,11 +697,6 @@ class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest):
...
@@ -595,11 +697,6 @@ class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest):
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
opt_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
=
[
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
]
]
...
@@ -607,33 +704,19 @@ class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest):
...
@@ -607,33 +704,19 @@ class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest):
# for static_shape
# for static_shape
clear_dynamic_shape
()
clear_dynamic_shape
()
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
yield
self
.
create_inference_config
(),
(
0
,
3
),
1e-5
attrs
,
False
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
yield
self
.
create_inference_config
(),
(
0
,
3
),
1e-5
attrs
,
False
),
1e-5
# for dynamic_shape
# for dynamic_shape
generate_dynamic_shape
(
attrs
)
generate_dynamic_shape
(
attrs
)
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
yield
self
.
create_inference_config
(),
(
1
,
2
),
1e-5
attrs
,
True
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
yield
self
.
create_inference_config
(),
(
1
,
2
),
1e-5
attrs
,
True
),
1e-5
def
add_skip_trt_case
(
self
):
def
add_skip_trt_case
(
self
):
pass
def
teller1
(
program_config
,
predictor_config
):
input_x_names
=
program_config
.
ops
[
0
].
inputs
[
"X"
]
for
weight_name
in
program_config
.
weights
:
if
weight_name
in
input_x_names
:
return
True
return
False
self
.
add_skip_case
(
teller1
,
SkipReasons
.
TRT_NOT_SUPPORT
,
"Input X should not be parameters in elementwise op."
)
def
test
(
self
):
def
test
(
self
):
self
.
add_skip_trt_case
()
self
.
add_skip_trt_case
()
...
...
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