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体验新版 GitCode,发现更多精彩内容 >>
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6fb34e74
编写于
8月 19, 2022
作者:
W
Wang Bojun
提交者:
GitHub
8月 19, 2022
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电子邮件补丁
差异文件
fix layernormTrt meanVar alloc bug (#45255)
* fix layernormTrt meanVar alloc bug
上级
1c4134f6
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
135 addition
and
10 deletion
+135
-10
paddle/fluid/inference/tensorrt/convert/layer_norm_op.cc
paddle/fluid/inference/tensorrt/convert/layer_norm_op.cc
+12
-10
paddle/fluid/inference/tensorrt/plugin/layer_norm_op_plugin.cu
...e/fluid/inference/tensorrt/plugin/layer_norm_op_plugin.cu
+5
-0
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_layer_norm.py
...sts/unittests/ir/inference/test_trt_convert_layer_norm.py
+118
-0
未找到文件。
paddle/fluid/inference/tensorrt/convert/layer_norm_op.cc
浏览文件 @
6fb34e74
...
...
@@ -56,12 +56,14 @@ class LayerNormOpConverter : public OpConverter {
nvinfer1
::
ILayer
*
layernorm_layer
=
nullptr
;
if
(
engine_
->
with_dynamic_shape
())
{
int
input_num
=
1
;
for
(
int
i
=
begin_norm_axis
;
i
<
X
->
getDimensions
().
nbDims
;
i
++
)
{
input_num
*=
X
->
getDimensions
().
d
[
i
];
int
statis_num
=
1
;
// For dynamic shape,
// the batch num will be taken into account in plugin runtime.
for
(
int
i
=
1
;
i
<
begin_norm_axis
;
i
++
)
{
statis_num
*=
X
->
getDimensions
().
d
[
i
];
}
std
::
vector
<
int64_t
>
mean_shape
{
input
_num
};
std
::
vector
<
int64_t
>
variance_shape
{
input
_num
};
std
::
vector
<
int64_t
>
mean_shape
{
statis
_num
};
std
::
vector
<
int64_t
>
variance_shape
{
statis
_num
};
plugin
::
LayerNormPluginDynamic
*
plugin
=
new
plugin
::
LayerNormPluginDynamic
(
static_cast
<
const
float
*>
(
bias_weight
.
get
().
values
),
...
...
@@ -74,12 +76,12 @@ class LayerNormOpConverter : public OpConverter {
variance_shape
);
layernorm_layer
=
engine_
->
AddDynamicPlugin
(
&
X
,
1
,
plugin
);
}
else
{
int
input
_num
=
1
;
for
(
int
i
=
begin_norm_axis
-
1
;
i
<
X
->
getDimensions
().
nbDim
s
;
i
++
)
{
input
_num
*=
X
->
getDimensions
().
d
[
i
];
int
statis
_num
=
1
;
for
(
int
i
=
0
;
i
<
begin_norm_axi
s
;
i
++
)
{
statis
_num
*=
X
->
getDimensions
().
d
[
i
];
}
std
::
vector
<
int64_t
>
mean_shape
{
input
_num
};
std
::
vector
<
int64_t
>
variance_shape
{
input
_num
};
std
::
vector
<
int64_t
>
mean_shape
{
statis
_num
};
std
::
vector
<
int64_t
>
variance_shape
{
statis
_num
};
plugin
::
LayerNormPlugin
*
plugin
=
new
plugin
::
LayerNormPlugin
(
static_cast
<
const
float
*>
(
bias_weight
.
get
().
values
),
bias_weight
.
get
().
count
,
...
...
paddle/fluid/inference/tensorrt/plugin/layer_norm_op_plugin.cu
浏览文件 @
6fb34e74
...
...
@@ -175,6 +175,11 @@ int LayerNormPluginDynamic::enqueue(
for
(
int
i
=
0
;
i
<
input_dims
.
nbDims
;
i
++
)
{
input_shape
.
push_back
(
input_dims
.
d
[
i
]);
}
// in dynamic shape
// the batch num should be involved in mean/variance shape
mean_shape_
[
0
]
*=
input_dims
.
d
[
0
];
variance_shape_
[
0
]
*=
input_dims
.
d
[
0
];
const
auto
input_ddim
=
phi
::
make_ddim
(
input_shape
);
auto
matrix_dim
=
phi
::
flatten_to_2d
(
input_ddim
,
begin_norm_axis
);
int
feature_size
=
static_cast
<
int
>
(
matrix_dim
[
1
]);
...
...
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_layer_norm.py
浏览文件 @
6fb34e74
...
...
@@ -140,5 +140,123 @@ class TrtConvertLayerNormTest(TrtLayerAutoScanTest):
self
.
run_test
()
class
TrtConvertLayerNormTest_2
(
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
][
'epsilon'
]
<
0
or
attrs
[
0
][
'epsilon'
]
>
0.001
:
return
False
if
attrs
[
0
][
'begin_norm_axis'
]
<=
0
or
attrs
[
0
][
'begin_norm_axis'
]
>=
(
len
(
inputs
[
'input_data'
].
shape
)
-
1
):
return
False
return
True
def
sample_program_configs
(
self
):
def
generate_input1
(
attrs
:
List
[
Dict
[
str
,
Any
]],
shape_input
):
return
np
.
ones
(
shape_input
).
astype
(
np
.
float32
)
def
generate_input2
(
attrs
:
List
[
Dict
[
str
,
Any
]],
shape_input
):
begin
=
attrs
[
0
][
"begin_norm_axis"
]
sum
=
1
for
x
in
range
(
begin
,
len
(
shape_input
)):
sum
*=
shape_input
[
x
]
return
np
.
ones
([
sum
]).
astype
(
np
.
float32
)
for
epsilon
in
[
0.0005
,
-
1
,
1
]:
for
begin_norm_axis
in
[
1
,
0
,
-
1
,
2
,
3
]:
dics
=
[{
"epsilon"
:
epsilon
,
"begin_norm_axis"
:
begin_norm_axis
},
{}]
ops_config
=
[{
"op_type"
:
"layer_norm"
,
"op_inputs"
:
{
"X"
:
[
"input_data"
],
"Scale"
:
[
"scale_data"
],
"Bias"
:
[
"bias_data"
]
},
"op_outputs"
:
{
"Y"
:
[
"y_data"
],
"Mean"
:
[
"saved_mean_data"
],
"Variance"
:
[
"saved_variance_data"
]
},
"op_attrs"
:
dics
[
0
]
}]
ops
=
self
.
generate_op_config
(
ops_config
)
shape_input
=
[
2
,
64
,
3
,
3
]
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{
"bias_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input2
,
dics
,
shape_input
)),
"scale_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input2
,
dics
,
shape_input
))
},
inputs
=
{
"input_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
,
dics
,
shape_input
))
},
outputs
=
[
"y_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
,
64
,
3
,
3
]}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
64
,
3
,
3
]}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
2
,
64
,
3
,
3
]}
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
):
inputs
=
program_config
.
inputs
#if not dynamic_shape:
# if attrs[0]["begin_norm_axis"] >= len(inputs["input_data"].shape) - 1:
# print ("iiiiiii")
# 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-2
# 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-2
def
test
(
self
):
self
.
run_test
()
if
__name__
==
"__main__"
:
unittest
.
main
()
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