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5822e15c
编写于
1月 31, 2023
作者:
Z
Zhang Jun
提交者:
GitHub
1月 31, 2023
浏览文件
操作
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电子邮件补丁
差异文件
[inference][trt] add elementwise input data type check (#49675)
上级
86a23818
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
335 addition
and
15 deletion
+335
-15
paddle/fluid/inference/tensorrt/op_teller.cc
paddle/fluid/inference/tensorrt/op_teller.cc
+39
-6
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_compare_and_logical.py
...ests/ir/inference/test_trt_convert_compare_and_logical.py
+137
-0
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py
...ts/unittests/ir/inference/test_trt_convert_elementwise.py
+158
-8
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_equal.py
...id/tests/unittests/ir/inference/test_trt_convert_equal.py
+1
-1
未找到文件。
paddle/fluid/inference/tensorrt/op_teller.cc
浏览文件 @
5822e15c
...
...
@@ -1365,16 +1365,26 @@ struct SimpleOpTypeSetTeller : public Teller {
VLOG
(
3
)
<<
"Ops("
<<
op_type
<<
") do not support static shape yet."
;
return
false
;
}
auto
*
block
=
desc
.
Block
();
auto
*
x_var_desc
=
block
->
FindVar
(
desc
.
Input
(
"X"
)[
0
]);
auto
*
y_var_desc
=
block
->
FindVar
(
desc
.
Input
(
"Y"
)[
0
]);
auto
x_dtype
=
x_var_desc
->
GetDataType
();
auto
y_dtype
=
y_var_desc
->
GetDataType
();
if
(
op_type
==
"logical_or"
||
op_type
==
"logical_xor"
||
op_type
==
"logical_and"
)
{
auto
*
block
=
desc
.
Block
();
auto
*
x_var_desc
=
block
->
FindVar
(
desc
.
Input
(
"X"
)[
0
]);
auto
*
y_var_desc
=
block
->
FindVar
(
desc
.
Input
(
"Y"
)[
0
]);
auto
x_dtype
=
x_var_desc
->
GetDataType
();
auto
y_dtype
=
y_var_desc
->
GetDataType
();
if
(
x_dtype
!=
framework
::
proto
::
VarType
::
BOOL
||
y_dtype
!=
framework
::
proto
::
VarType
::
BOOL
)
{
VLOG
(
3
)
<<
"the op only support input of BOOL."
;
VLOG
(
3
)
<<
"the op ("
<<
op_type
<<
") only support input of BOOL."
;
return
false
;
}
}
if
(
op_type
==
"less_than"
||
op_type
==
"greater_than"
||
op_type
==
"less_equal"
)
{
if
(
x_dtype
==
framework
::
proto
::
VarType
::
BOOL
||
y_dtype
==
framework
::
proto
::
VarType
::
BOOL
)
{
VLOG
(
3
)
<<
"ElementWiseOperation::kLESS/ElementWiseOperation::kGREATER "
"do not support boolean datatype."
;
return
false
;
}
}
...
...
@@ -1417,6 +1427,29 @@ struct SimpleOpTypeSetTeller : public Teller {
const
auto
x_shape
=
x_var_desc
->
GetShape
();
const
auto
y_shape
=
y_var_desc
->
GetShape
();
// These operations do not support boolean datatype.
if
(
op_type
==
"elementwise_add"
||
op_type
==
"elementwise_mul"
||
op_type
==
"elementwise_sub"
||
op_type
==
"elementwise_div"
||
op_type
==
"elementwise_pow"
||
op_type
==
"elementwise_min"
||
op_type
==
"elementwise_max"
||
op_type
==
"elementwise_floordiv"
)
{
if
(
x_var_desc
->
GetDataType
()
==
paddle
::
framework
::
proto
::
VarType_Type
::
VarType_Type_BOOL
)
{
VLOG
(
3
)
<<
"These operations "
"(elementwise_add/mul/sub/div/pow/min/max/floordiv) do "
"not support boolean datatype."
;
return
false
;
}
}
// These operations input do not support int32 datatype.
if
(
op_type
==
"elementwise_pow"
)
{
if
(
x_var_desc
->
GetDataType
()
==
paddle
::
framework
::
proto
::
VarType_Type
::
VarType_Type_INT32
)
{
VLOG
(
3
)
<<
"These operations (elementwise_pow) do not support int32 "
"datatype."
;
return
false
;
}
}
// 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
)
{
...
...
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_compare_and_logical.py
浏览文件 @
5822e15c
...
...
@@ -481,5 +481,142 @@ class TrtConvertLessEqualTest(TrtLayerAutoScanTest):
self
.
run_test
()
class
TrtConvertCompareSkipTest
(
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
.
int32
)
for
shape
in
[[
2
,
16
],
[
2
,
16
,
32
],
[
1
,
32
,
16
,
32
]]:
for
op_type
in
[
"less_than"
,
"greater_than"
]:
for
axis
in
[
-
1
]:
self
.
dims
=
len
(
shape
)
dics
=
[
{
"axis"
:
axis
},
{
"in_dtype"
:
2
,
"out_dtype"
:
0
},
{
"in_dtype"
:
0
,
"out_dtype"
:
2
},
]
ops_config
=
[
{
"op_type"
:
"cast"
,
"op_inputs"
:
{
"X"
:
[
"input_data1"
]},
"op_outputs"
:
{
"Out"
:
[
"cast_output_data1"
]},
"op_attrs"
:
dics
[
1
],
"outputs_dtype"
:
{
"cast_output_data1"
:
np
.
bool_
},
},
{
"op_type"
:
"cast"
,
"op_inputs"
:
{
"X"
:
[
"input_data2"
]},
"op_outputs"
:
{
"Out"
:
[
"cast_output_data2"
]},
"op_attrs"
:
dics
[
1
],
"outputs_dtype"
:
{
"cast_output_data2"
:
np
.
bool_
},
},
{
"op_type"
:
op_type
,
"op_inputs"
:
{
"X"
:
[
"cast_output_data1"
],
"Y"
:
[
"cast_output_data2"
],
},
"op_outputs"
:
{
"Out"
:
[
"cast_output_data0"
]},
"op_attrs"
:
dics
[
0
],
"outputs_dtype"
:
{
"cast_output_data0"
:
np
.
bool_
},
},
{
"op_type"
:
"cast"
,
"op_inputs"
:
{
"X"
:
[
"cast_output_data0"
]},
"op_outputs"
:
{
"Out"
:
[
"output_data"
]},
"op_attrs"
:
dics
[
2
],
"outputs_dtype"
:
{
"output_data"
:
np
.
int32
},
},
]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"input_data1"
:
TensorConfig
(
data_gen
=
partial
(
generate_input
,
shape
)
),
"input_data2"
:
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
):
if
self
.
dims
==
2
:
shape_data
=
[
2
,
16
]
if
self
.
dims
==
3
:
shape_data
=
[
2
,
16
,
32
]
if
self
.
dims
==
4
:
shape_data
=
[
1
,
32
,
16
,
32
]
shape_info
=
{
"input_data1"
:
shape_data
,
"input_data2"
:
shape_data
,
"cast_output_data0"
:
shape_data
,
"cast_output_data1"
:
shape_data
,
"cast_output_data2"
:
shape_data
,
}
self
.
dynamic_shape
.
min_input_shape
=
shape_info
self
.
dynamic_shape
.
max_input_shape
=
shape_info
self
.
dynamic_shape
.
opt_input_shape
=
shape_info
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
):
ver
=
paddle_infer
.
get_trt_compile_version
()
if
ver
[
0
]
*
1000
+
ver
[
1
]
*
100
+
ver
[
2
]
*
10
<
8400
:
return
0
,
7
if
not
dynamic_shape
:
return
0
,
7
return
3
,
4
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
add_skip_trt_case
(
self
):
pass
def
test
(
self
):
self
.
add_skip_trt_case
()
self
.
run_test
()
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py
浏览文件 @
5822e15c
...
...
@@ -25,7 +25,7 @@ import paddle.inference as paddle_infer
# This is the special test case with weight including batch dimension
# I don't want to mess up the code written by others, so I wrote a class specifically
class
TrtConvertElementwiseTest
_one_input_special_c
ase0
(
TrtLayerAutoScanTest
):
class
TrtConvertElementwiseTest
OneInputSpecialC
ase0
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
...
...
@@ -158,7 +158,7 @@ class TrtConvertElementwiseTest_one_input_special_case0(TrtLayerAutoScanTest):
# This is the special test case
class
TrtConvertElementwiseTest
_one_input_special_c
ase1
(
TrtLayerAutoScanTest
):
class
TrtConvertElementwiseTest
OneInputSpecialC
ase1
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
...
...
@@ -279,7 +279,7 @@ class TrtConvertElementwiseTest_one_input_special_case1(TrtLayerAutoScanTest):
self
.
run_test
()
class
TrtConvertElementwiseTest
_one_i
nput
(
TrtLayerAutoScanTest
):
class
TrtConvertElementwiseTest
OneI
nput
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
...
...
@@ -431,9 +431,7 @@ class TrtConvertElementwiseTest_one_input(TrtLayerAutoScanTest):
self
.
run_test
()
class
TrtConvertElementwiseTest_two_input_without_broadcast
(
TrtLayerAutoScanTest
):
class
TrtConvertElementwiseTestTwoInputWithoutBroadcast
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
...
...
@@ -592,7 +590,7 @@ class TrtConvertElementwiseTest_two_input_without_broadcast(
self
.
run_test
()
class
TrtConvertElementwiseTest
_two_input_with_b
roadcast
(
TrtLayerAutoScanTest
):
class
TrtConvertElementwiseTest
TwoInputWithB
roadcast
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
inputs
=
program_config
.
inputs
if
len
(
inputs
[
'input_data1'
].
shape
)
!=
len
(
inputs
[
'input_data2'
].
shape
):
...
...
@@ -754,7 +752,7 @@ class TrtConvertElementwiseTest_two_input_with_broadcast(TrtLayerAutoScanTest):
self
.
run_test
()
class
TrtConvertElementwiseTest
_one_input_corner_c
ase
(
TrtLayerAutoScanTest
):
class
TrtConvertElementwiseTest
OneInputCornerC
ase
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
...
...
@@ -896,5 +894,157 @@ class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest):
self
.
run_test
()
class
TrtConvertElementwiseTestTwoInputSkipCase
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
# if program_config.ops[0].type in "round":
return
True
def
sample_program_configs
(
self
):
def
generate_input
(
shape
,
op_type
):
if
op_type
==
"elementwise_pow"
:
return
np
.
random
.
randint
(
low
=
1
,
high
=
10000
,
size
=
shape
,
dtype
=
np
.
int32
)
# Paddle mul support bool and TensorRT not
if
op_type
==
"elementwise_mul"
:
return
np
.
random
.
random
(
shape
).
astype
(
np
.
bool
)
for
shape
in
[[
4
],
[
4
,
32
],
[
2
,
32
,
16
],
[
1
,
8
,
16
,
32
]]:
for
op_type
in
[
"elementwise_pow"
,
"elementwise_mul"
,
]:
for
axis
in
[
0
,
-
1
]:
self
.
dims
=
len
(
shape
)
dics
=
[{
"axis"
:
axis
}]
ops_config
=
[
{
"op_type"
:
op_type
,
"op_inputs"
:
{
"X"
:
[
"input_data1"
],
"Y"
:
[
"input_data2"
],
},
"op_outputs"
:
{
"Out"
:
[
"output_data"
]},
"op_attrs"
:
dics
[
0
],
"outputs_dtype"
:
{
"output_data"
:
np
.
int32
if
op_type
==
"elementwise_pow"
else
np
.
bool_
},
}
]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"input_data1"
:
TensorConfig
(
data_gen
=
partial
(
generate_input
,
shape
,
op_type
)
),
"input_data2"
:
TensorConfig
(
data_gen
=
partial
(
generate_input
,
shape
,
op_type
)
),
},
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_data1"
:
[
1
],
"input_data2"
:
[
1
],
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data1"
:
[
128
],
"input_data2"
:
[
128
],
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data1"
:
[
32
],
"input_data2"
:
[
32
],
}
elif
self
.
dims
==
2
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data1"
:
[
1
,
4
],
"input_data2"
:
[
1
,
4
],
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data1"
:
[
128
,
256
],
"input_data2"
:
[
128
,
256
],
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data1"
:
[
32
,
64
],
"input_data2"
:
[
32
,
64
],
}
elif
self
.
dims
==
3
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data1"
:
[
1
,
4
,
4
],
"input_data2"
:
[
1
,
4
,
4
],
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data1"
:
[
128
,
128
,
256
],
"input_data2"
:
[
128
,
128
,
256
],
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data1"
:
[
2
,
32
,
16
],
"input_data2"
:
[
2
,
32
,
16
],
}
elif
self
.
dims
==
4
:
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data1"
:
[
1
,
4
,
4
,
4
],
"input_data2"
:
[
1
,
4
,
4
,
4
],
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data1"
:
[
8
,
128
,
64
,
128
],
"input_data2"
:
[
8
,
128
,
64
,
128
],
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data1"
:
[
2
,
64
,
32
,
32
],
"input_data2"
:
[
2
,
64
,
32
,
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
):
return
0
,
4
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
,
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
(),
(
0
,
4
),
(
1e-5
,
1e-5
)
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
0
,
4
),
(
1e-3
,
1e-3
)
def
add_skip_trt_case
(
self
):
pass
def
test
(
self
):
self
.
add_skip_trt_case
()
self
.
run_test
()
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_equal.py
浏览文件 @
5822e15c
...
...
@@ -23,7 +23,7 @@ from trt_layer_auto_scan_test import TrtLayerAutoScanTest
import
paddle.inference
as
paddle_infer
class
TrtConvertE
lementwiseTest_one_input_corner_c
ase
(
TrtLayerAutoScanTest
):
class
TrtConvertE
qualOneInputCornerC
ase
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
...
...
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