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c15e53d6
编写于
6月 07, 2023
作者:
周
周周周
提交者:
GitHub
6月 07, 2023
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电子邮件补丁
差异文件
commit (#54339)
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cb2476cf
变更
2
隐藏空白更改
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2 changed file
with
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and
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+343
-0
paddle/fluid/inference/tensorrt/dynamic_shape_infermeta.cc
paddle/fluid/inference/tensorrt/dynamic_shape_infermeta.cc
+340
-0
paddle/fluid/inference/tensorrt/dynamic_shape_infermeta_registry.h
...uid/inference/tensorrt/dynamic_shape_infermeta_registry.h
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paddle/fluid/inference/tensorrt/dynamic_shape_infermeta.cc
浏览文件 @
c15e53d6
...
@@ -21,6 +21,122 @@ namespace paddle {
...
@@ -21,6 +21,122 @@ namespace paddle {
namespace
inference
{
namespace
inference
{
namespace
tensorrt
{
namespace
tensorrt
{
class
ExprWrapper
{
public:
ExprWrapper
()
{}
ExprWrapper
(
const
nvinfer1
::
IDimensionExpr
*
expr
,
nvinfer1
::
IExprBuilder
*
expr_builder
)
{
this
->
expr
=
expr
;
this
->
expr_builder
=
expr_builder
;
}
ExprWrapper
(
int
value
,
nvinfer1
::
IExprBuilder
*
expr_builder
)
{
this
->
expr
=
expr_builder
->
constant
(
value
);
this
->
expr_builder
=
expr_builder
;
}
const
nvinfer1
::
IDimensionExpr
*
extract_expr
()
const
{
return
expr
;
}
public:
friend
ExprWrapper
BinaryOp
(
const
ExprWrapper
&
a
,
const
ExprWrapper
&
b
,
nvinfer1
::
DimensionOperation
op
)
{
ExprWrapper
result
;
if
(
a
.
expr_builder
)
{
result
.
expr_builder
=
a
.
expr_builder
;
}
if
(
b
.
expr_builder
)
{
result
.
expr_builder
=
b
.
expr_builder
;
}
assert
(
result
.
expr
);
result
.
expr
=
result
.
expr_builder
->
operation
(
op
,
*
a
.
expr
,
*
b
.
expr
);
return
result
;
}
friend
ExprWrapper
BinaryOp
(
const
ExprWrapper
&
a
,
int
b_value
,
nvinfer1
::
DimensionOperation
op
)
{
assert
(
a
.
expr_builder
);
ExprWrapper
b
;
b
.
expr_builder
=
a
.
expr_builder
;
b
.
expr
=
b
.
expr_builder
->
constant
(
b_value
);
return
BinaryOp
(
a
,
b
,
op
);
}
friend
ExprWrapper
operator
+
(
const
ExprWrapper
&
a
,
const
ExprWrapper
&
b
)
{
return
BinaryOp
(
a
,
b
,
nvinfer1
::
DimensionOperation
::
kSUM
);
}
friend
ExprWrapper
operator
+
(
const
ExprWrapper
&
a
,
int
b_value
)
{
return
BinaryOp
(
a
,
b_value
,
nvinfer1
::
DimensionOperation
::
kSUM
);
}
friend
ExprWrapper
operator
+
(
int
a_value
,
const
ExprWrapper
&
b
)
{
return
a_value
+
b
;
}
friend
ExprWrapper
operator
-
(
const
ExprWrapper
&
a
,
const
ExprWrapper
&
b
)
{
return
BinaryOp
(
a
,
b
,
nvinfer1
::
DimensionOperation
::
kSUB
);
}
friend
ExprWrapper
operator
-
(
const
ExprWrapper
&
a
,
int
b_value
)
{
return
BinaryOp
(
a
,
b_value
,
nvinfer1
::
DimensionOperation
::
kSUB
);
}
friend
ExprWrapper
operator
*
(
const
ExprWrapper
&
a
,
const
ExprWrapper
&
b
)
{
return
BinaryOp
(
a
,
b
,
nvinfer1
::
DimensionOperation
::
kPROD
);
}
friend
ExprWrapper
operator
*
(
const
ExprWrapper
&
a
,
int
b_value
)
{
return
BinaryOp
(
a
,
b_value
,
nvinfer1
::
DimensionOperation
::
kPROD
);
}
friend
ExprWrapper
operator
*
(
int
a_value
,
const
ExprWrapper
&
b
)
{
return
b
*
a_value
;
}
friend
ExprWrapper
operator
/
(
const
ExprWrapper
&
a
,
const
ExprWrapper
&
b
)
{
return
BinaryOp
(
a
,
b
,
nvinfer1
::
DimensionOperation
::
kFLOOR_DIV
);
}
friend
ExprWrapper
operator
/
(
const
ExprWrapper
&
a
,
int
b_value
)
{
return
BinaryOp
(
a
,
b_value
,
nvinfer1
::
DimensionOperation
::
kFLOOR_DIV
);
}
friend
ExprWrapper
max
(
const
ExprWrapper
&
a
,
const
ExprWrapper
&
b
)
{
return
BinaryOp
(
a
,
b
,
nvinfer1
::
DimensionOperation
::
kMAX
);
}
friend
ExprWrapper
max
(
const
ExprWrapper
&
a
,
int
b_value
)
{
return
BinaryOp
(
a
,
b_value
,
nvinfer1
::
DimensionOperation
::
kMAX
);
}
public:
const
nvinfer1
::
IDimensionExpr
*
expr
;
nvinfer1
::
IExprBuilder
*
expr_builder
;
};
static
std
::
vector
<
ExprWrapper
>
DimsExprs2VecExprWrapper
(
const
nvinfer1
::
DimsExprs
&
x_dims
,
nvinfer1
::
IExprBuilder
&
expr_builder
// NOLINT
)
{
std
::
vector
<
ExprWrapper
>
x_dims_wrap
;
for
(
int
i
=
0
;
i
<
x_dims
.
nbDims
;
i
++
)
{
x_dims_wrap
.
push_back
(
ExprWrapper
(
x_dims
.
d
[
i
],
&
expr_builder
));
}
return
x_dims_wrap
;
}
static
nvinfer1
::
DimsExprs
VecExprWrapper2DimsExprs
(
const
std
::
vector
<
ExprWrapper
>&
output_dims_wrapper
)
{
nvinfer1
::
DimsExprs
output_dims
;
output_dims
.
nbDims
=
output_dims_wrapper
.
size
();
for
(
int
i
=
0
;
i
<
output_dims
.
nbDims
;
i
++
)
{
output_dims
.
d
[
i
]
=
output_dims_wrapper
[
i
].
extract_expr
();
}
return
output_dims
;
}
nvinfer1
::
DimsExprs
GatherNdInferMeta
(
nvinfer1
::
DimsExprs
GatherNdInferMeta
(
int
output_index
,
int
output_index
,
const
nvinfer1
::
DimsExprs
*
inputs
,
const
nvinfer1
::
DimsExprs
*
inputs
,
...
@@ -417,6 +533,148 @@ nvinfer1::DimsExprs GridSamplerInferMeta(
...
@@ -417,6 +533,148 @@ nvinfer1::DimsExprs GridSamplerInferMeta(
return
output
;
return
output
;
}
}
inline
const
void
UpdatePaddingAndDilation
(
std
::
vector
<
ExprWrapper
>*
paddings_wrap
,
std
::
vector
<
int
>*
dilation
,
const
std
::
string
padding_algorithm
,
const
std
::
vector
<
ExprWrapper
>&
hw_dims
,
const
std
::
vector
<
int
>&
strides
,
const
std
::
vector
<
ExprWrapper
>&
k_dims
,
nvinfer1
::
IExprBuilder
&
expr_builder
// NOLINT
)
{
if
(
paddings_wrap
->
size
()
==
hw_dims
.
size
())
{
for
(
size_t
i
=
0
;
i
<
hw_dims
.
size
();
++
i
)
{
auto
copy_pad
=
*
(
paddings_wrap
->
begin
()
+
2
*
i
);
paddings_wrap
->
insert
(
paddings_wrap
->
begin
()
+
2
*
i
+
1
,
copy_pad
);
}
}
else
{
CHECK_EQ
(
hw_dims
.
size
()
==
paddings_wrap
->
size
(),
true
);
}
// when padding_algorithm is "VALID" or "SAME"
if
(
padding_algorithm
==
"SAME"
)
{
for
(
size_t
i
=
0
;
i
<
hw_dims
.
size
();
++
i
)
{
auto
out_size
=
(
hw_dims
[
i
]
+
strides
[
i
]
-
1
)
/
strides
[
i
];
auto
pad_sum
=
max
((
out_size
-
1
)
*
strides
[
i
]
+
k_dims
[
i
]
-
hw_dims
[
i
],
0
);
auto
pad_0
=
pad_sum
/
2
;
auto
pad_1
=
pad_sum
-
pad_0
;
*
(
paddings_wrap
->
begin
()
+
i
*
2
)
=
pad_0
;
*
(
paddings_wrap
->
begin
()
+
i
*
2
+
1
)
=
pad_1
;
// dilation
*
(
dilation
->
begin
()
+
i
)
=
1
;
}
}
else
if
(
padding_algorithm
==
"VALID"
)
{
for
(
auto
it
=
paddings_wrap
->
begin
();
it
!=
paddings_wrap
->
end
();
it
++
)
{
*
it
=
ExprWrapper
(
0
,
&
expr_builder
);
}
}
}
// Here are all examples of using h(height), ok for weight too.
inline
ExprWrapper
ConvOutputSize
(
ExprWrapper
ih
,
ExprWrapper
kh
,
int
dilation_h
,
ExprWrapper
pad_h0
,
ExprWrapper
pad_h1
,
int
stride_h
)
{
ExprWrapper
oh
=
(
ih
+
pad_h0
+
pad_h1
-
dilation_h
*
(
kh
-
1
)
-
1
)
/
stride_h
+
1
;
return
oh
;
}
nvinfer1
::
DimsExprs
Conv2dFusionInferMeta
(
int
output_index
,
const
nvinfer1
::
DimsExprs
*
inputs
,
int
nb_inputs
,
nvinfer1
::
IExprBuilder
&
expr_builder
,
// NOLINT
const
framework
::
OpDesc
&
op_desc
)
{
// we may update dilations.
std
::
vector
<
int
>
dilations
=
PADDLE_GET_CONST
(
std
::
vector
<
int
>
,
op_desc
.
GetAttr
(
"dilations"
));
const
std
::
vector
<
int
>
strides
=
PADDLE_GET_CONST
(
std
::
vector
<
int
>
,
op_desc
.
GetAttr
(
"strides"
));
std
::
vector
<
int
>
paddings
=
PADDLE_GET_CONST
(
std
::
vector
<
int
>
,
op_desc
.
GetAttr
(
"paddings"
));
std
::
string
padding_algorithm
=
"EXPLICIT"
;
if
(
op_desc
.
HasAttr
(
"padding_algorithm"
))
padding_algorithm
=
PADDLE_GET_CONST
(
std
::
string
,
op_desc
.
GetAttr
(
"padding_algorithm"
));
if
(
padding_algorithm
==
"VALID"
)
{
for
(
size_t
i
=
0
;
i
<
paddings
.
size
();
i
++
)
{
paddings
[
i
]
=
0
;
}
}
// TODO(zhangjun): nhwc support
bool
channel_last
=
false
;
// conv_fusion: input, filter, bias
const
nvinfer1
::
DimsExprs
input_dims
=
inputs
[
0
];
const
nvinfer1
::
DimsExprs
filter_dims
=
inputs
[
1
];
auto
input_dims_wrap
=
DimsExprs2VecExprWrapper
(
input_dims
,
expr_builder
);
auto
filter_dims_wrap
=
DimsExprs2VecExprWrapper
(
filter_dims
,
expr_builder
);
std
::
vector
<
ExprWrapper
>
hw_dims_wrap
;
// d, h, w
if
(
channel_last
)
{
for
(
int
i
=
1
;
i
<
input_dims
.
nbDims
-
1
;
++
i
)
{
hw_dims_wrap
.
emplace_back
(
input_dims_wrap
[
i
]);
}
}
else
{
for
(
int
i
=
2
;
i
<
input_dims
.
nbDims
;
++
i
)
{
hw_dims_wrap
.
emplace_back
(
input_dims_wrap
[
i
]);
}
}
std
::
vector
<
ExprWrapper
>
filter_hw_dims_wrap
;
// filter_h, filter_w
if
(
channel_last
)
{
for
(
int
i
=
1
;
i
<
filter_dims
.
nbDims
-
1
;
++
i
)
{
filter_hw_dims_wrap
.
emplace_back
(
filter_dims_wrap
[
i
]);
}
}
else
{
for
(
int
i
=
2
;
i
<
filter_dims
.
nbDims
;
++
i
)
{
filter_hw_dims_wrap
.
emplace_back
(
filter_dims_wrap
[
i
]);
}
}
std
::
vector
<
ExprWrapper
>
paddings_wrap
;
for
(
size_t
i
=
0
;
i
<
paddings
.
size
();
++
i
)
{
paddings_wrap
.
emplace_back
(
ExprWrapper
(
paddings
[
i
],
&
expr_builder
));
}
UpdatePaddingAndDilation
(
&
paddings_wrap
,
&
dilations
,
padding_algorithm
,
hw_dims_wrap
,
strides
,
filter_hw_dims_wrap
,
expr_builder
);
std
::
vector
<
ExprWrapper
>
output_dims_wrap
(
input_dims
.
nbDims
);
int
out_idx
=
0
;
output_dims_wrap
[
out_idx
++
]
=
input_dims_wrap
[
0
];
if
(
!
channel_last
)
{
output_dims_wrap
[
out_idx
++
]
=
filter_dims_wrap
[
0
];
}
for
(
size_t
i
=
0
;
i
<
hw_dims_wrap
.
size
();
++
i
)
{
output_dims_wrap
[
out_idx
++
]
=
ConvOutputSize
(
hw_dims_wrap
[
i
],
filter_hw_dims_wrap
[
i
],
dilations
[
i
],
paddings_wrap
[
2
*
i
],
paddings_wrap
[
2
*
i
+
1
],
strides
[
i
]);
}
if
(
channel_last
)
{
output_dims_wrap
[
out_idx
++
]
=
filter_dims_wrap
[
0
];
}
return
VecExprWrapper2DimsExprs
(
output_dims_wrap
);
}
nvinfer1
::
DimsExprs
LookupTableV2InferMeta
(
nvinfer1
::
DimsExprs
LookupTableV2InferMeta
(
int
output_index
,
int
output_index
,
const
nvinfer1
::
DimsExprs
*
inputs
,
const
nvinfer1
::
DimsExprs
*
inputs
,
...
@@ -435,6 +693,85 @@ nvinfer1::DimsExprs LookupTableV2InferMeta(
...
@@ -435,6 +693,85 @@ nvinfer1::DimsExprs LookupTableV2InferMeta(
return
output
;
return
output
;
}
}
nvinfer1
::
DimsExprs
Conv2dTransposeInferMeta
(
int
output_index
,
const
nvinfer1
::
DimsExprs
*
inputs
,
int
nb_inputs
,
nvinfer1
::
IExprBuilder
&
expr_builder
,
// NOLINT
const
framework
::
OpDesc
&
op_desc
)
{
auto
x_dims
=
inputs
[
0
];
auto
filter_dims
=
inputs
[
1
];
std
::
vector
<
ExprWrapper
>
x_dims_wrap
=
DimsExprs2VecExprWrapper
(
x_dims
,
expr_builder
);
std
::
vector
<
ExprWrapper
>
filter_dims_wrap
=
DimsExprs2VecExprWrapper
(
filter_dims
,
expr_builder
);
const
std
::
vector
<
int
>
dilations
=
PADDLE_GET_CONST
(
std
::
vector
<
int
>
,
op_desc
.
GetAttr
(
"dilations"
));
const
std
::
vector
<
int
>
strides
=
PADDLE_GET_CONST
(
std
::
vector
<
int
>
,
op_desc
.
GetAttr
(
"strides"
));
std
::
vector
<
int
>
paddings
=
PADDLE_GET_CONST
(
std
::
vector
<
int
>
,
op_desc
.
GetAttr
(
"paddings"
));
std
::
vector
<
int
>
output_size
=
PADDLE_GET_CONST
(
std
::
vector
<
int
>
,
op_desc
.
GetAttr
(
"output_size"
));
std
::
vector
<
int
>
output_padding
=
PADDLE_GET_CONST
(
std
::
vector
<
int
>
,
op_desc
.
GetAttr
(
"output_padding"
));
auto
data_format
=
PADDLE_GET_CONST
(
std
::
string
,
op_desc
.
GetAttr
(
"data_format"
));
int
groups
=
PADDLE_GET_CONST
(
int
,
op_desc
.
GetAttr
(
"groups"
));
std
::
string
padding_algorithm
=
"EXPLICIT"
;
if
(
op_desc
.
HasAttr
(
"padding_algorithm"
))
{
padding_algorithm
=
PADDLE_GET_CONST
(
std
::
string
,
op_desc
.
GetAttr
(
"padding_algorithm"
));
}
CHECK_EQ
(
padding_algorithm
==
"EXPLICIT"
,
true
);
CHECK_EQ
(
data_format
==
"NCHW"
,
true
);
CHECK_EQ
(
output_size
.
size
()
==
0
,
true
);
CHECK_EQ
(
paddings
.
size
()
==
2
,
true
);
CHECK_EQ
(
x_dims
.
nbDims
==
4
,
true
);
CHECK_EQ
(
x_dims
.
nbDims
==
filter_dims
.
nbDims
,
true
);
CHECK_EQ
(
output_padding
.
size
()
==
0
,
true
);
int
stride_size
=
strides
.
size
();
for
(
int
i
=
0
;
i
<
stride_size
;
++
i
)
{
CHECK_EQ
(
strides
[
i
]
>
0
,
true
);
}
int
in_sub_stride_size
=
x_dims
.
nbDims
-
stride_size
;
CHECK_EQ
(
in_sub_stride_size
==
2
,
true
);
if
(
output_size
.
size
())
{
CHECK_EQ
(
output_size
.
size
()
==
strides
.
size
(),
true
);
}
if
(
output_padding
.
size
())
{
CHECK_EQ
(
strides
.
size
()
==
output_padding
.
size
(),
true
);
}
std
::
vector
<
ExprWrapper
>
output_dims_wrap
(
x_dims
.
nbDims
);
output_dims_wrap
[
0
]
=
x_dims_wrap
[
0
];
output_dims_wrap
[
1
]
=
filter_dims_wrap
[
1
]
*
groups
;
auto
ih
=
x_dims_wrap
[
2
];
auto
iw
=
x_dims_wrap
[
3
];
auto
kh
=
filter_dims_wrap
[
2
];
auto
kw
=
filter_dims_wrap
[
3
];
int
pad_h0
=
paddings
[
0
];
int
pad_h1
=
paddings
[
0
];
int
pad_w0
=
paddings
[
1
];
int
pad_w1
=
paddings
[
1
];
output_dims_wrap
[
2
]
=
(
ih
-
1
)
*
strides
[
0
]
-
pad_h0
-
pad_h1
+
(
kh
-
1
)
*
dilations
[
0
]
+
1
;
output_dims_wrap
[
3
]
=
(
iw
-
1
)
*
strides
[
1
]
-
pad_w0
-
pad_w1
+
(
kw
-
1
)
*
dilations
[
1
]
+
1
;
return
VecExprWrapper2DimsExprs
(
output_dims_wrap
);
}
PD_REGISTER_DYNAMIC_INFER_META_FN
(
gather_nd
,
GatherNdInferMeta
);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
gather_nd
,
GatherNdInferMeta
);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
yolo_box
,
YoloBoxInferMeta
);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
yolo_box
,
YoloBoxInferMeta
);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
instance_norm
,
InstanceNormInferMeta
);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
instance_norm
,
InstanceNormInferMeta
);
...
@@ -444,6 +781,9 @@ PD_REGISTER_DYNAMIC_INFER_META_FN(inverse, UnchangedInferMeta);
...
@@ -444,6 +781,9 @@ PD_REGISTER_DYNAMIC_INFER_META_FN(inverse, UnchangedInferMeta);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
moe
,
MoeInferMeta
);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
moe
,
MoeInferMeta
);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
pad3d
,
Pad3dInferMeta
);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
pad3d
,
Pad3dInferMeta
);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
grid_sampler
,
GridSamplerInferMeta
);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
grid_sampler
,
GridSamplerInferMeta
);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
conv2d_fusion
,
Conv2dFusionInferMeta
);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
conv2d
,
Conv2dFusionInferMeta
);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
conv2d_transpose
,
Conv2dTransposeInferMeta
);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
p_norm
,
PNormInferMeta
);
PD_REGISTER_DYNAMIC_INFER_META_FN
(
p_norm
,
PNormInferMeta
);
}
// namespace tensorrt
}
// namespace tensorrt
...
...
paddle/fluid/inference/tensorrt/dynamic_shape_infermeta_registry.h
浏览文件 @
c15e53d6
...
@@ -28,6 +28,9 @@ USE_TRT_DYNAMIC_INFER_META_FN(scatter_nd_add);
...
@@ -28,6 +28,9 @@ USE_TRT_DYNAMIC_INFER_META_FN(scatter_nd_add);
USE_TRT_DYNAMIC_INFER_META_FN
(
pad3d
);
USE_TRT_DYNAMIC_INFER_META_FN
(
pad3d
);
USE_TRT_DYNAMIC_INFER_META_FN
(
inverse
);
USE_TRT_DYNAMIC_INFER_META_FN
(
inverse
);
USE_TRT_DYNAMIC_INFER_META_FN
(
grid_sampler
);
USE_TRT_DYNAMIC_INFER_META_FN
(
grid_sampler
);
USE_TRT_DYNAMIC_INFER_META_FN
(
conv2d_fusion
);
USE_TRT_DYNAMIC_INFER_META_FN
(
conv2d
);
USE_TRT_DYNAMIC_INFER_META_FN
(
conv2d_transpose
);
USE_TRT_DYNAMIC_INFER_META_FN
(
p_norm
);
USE_TRT_DYNAMIC_INFER_META_FN
(
p_norm
);
}
// namespace tensorrt
}
// namespace tensorrt
}
// namespace inference
}
// namespace inference
...
...
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