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a5d2a6d1
编写于
1月 13, 2019
作者:
T
tensor-tang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add fuse pass of sequared mat sub fusion
上级
531f4a15
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
422 addition
and
0 deletion
+422
-0
paddle/fluid/framework/ir/CMakeLists.txt
paddle/fluid/framework/ir/CMakeLists.txt
+1
-0
paddle/fluid/framework/ir/squared_mat_sub_fuse_pass.cc
paddle/fluid/framework/ir/squared_mat_sub_fuse_pass.cc
+379
-0
paddle/fluid/framework/ir/squared_mat_sub_fuse_pass.h
paddle/fluid/framework/ir/squared_mat_sub_fuse_pass.h
+41
-0
paddle/fluid/inference/api/paddle_pass_builder.h
paddle/fluid/inference/api/paddle_pass_builder.h
+1
-0
未找到文件。
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @
a5d2a6d1
...
...
@@ -44,6 +44,7 @@ pass_library(conv_bn_fuse_pass inference)
pass_library
(
seqconv_eltadd_relu_fuse_pass inference
)
pass_library
(
seqpool_concat_fuse_pass inference
)
pass_library
(
repeated_fc_relu_fuse_pass inference
)
pass_library
(
squared_mat_sub_fuse_pass inference
)
pass_library
(
is_test_pass base
)
pass_library
(
conv_elementwise_add_act_fuse_pass inference
)
pass_library
(
conv_elementwise_add2_act_fuse_pass inference
)
...
...
paddle/fluid/framework/ir/squared_mat_sub_fuse_pass.cc
0 → 100644
浏览文件 @
a5d2a6d1
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License. */
#include "paddle/fluid/framework/ir/squared_mat_sub_fuse_pass.h"
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
namespace
paddle
{
namespace
framework
{
namespace
ir
{
PDNode
*
BuildSquaredMatSubPattern
(
PDPattern
*
pattern
,
const
std
::
string
&
name_scope
)
{
auto
var_is_op_input
=
[
=
](
Node
*
x
,
const
std
::
string
&
op_type
,
const
std
::
string
&
arg_name
=
""
)
->
bool
{
if
(
!
(
x
&&
x
->
IsVar
()))
{
return
false
;
}
for
(
auto
*
op
:
x
->
outputs
)
{
if
(
op
&&
op
->
IsOp
()
&&
op
->
Op
()
&&
op
->
Op
()
->
Type
()
==
op_type
)
{
if
(
arg_name
.
empty
())
{
return
true
;
}
for
(
auto
&
name
:
op
->
Op
()
->
Input
(
arg_name
))
{
if
(
name
==
x
->
Name
())
{
return
true
;
}
}
}
}
return
false
;
};
auto
var_is_op_only_output
=
[](
Node
*
x
,
const
std
::
string
&
op_type
)
->
bool
{
return
x
&&
x
->
IsVar
()
&&
x
->
inputs
.
size
()
==
1
&&
x
->
inputs
[
0
]
&&
x
->
inputs
[
0
]
->
IsOp
()
&&
x
->
inputs
[
0
]
->
Op
()
->
Type
()
==
op_type
&&
x
->
inputs
[
0
]
->
outputs
.
size
()
==
1
;
};
auto
next_op
=
[
=
](
Node
*
x
,
const
std
::
string
&
op_type
)
->
Node
*
{
if
(
!
(
x
&&
x
->
IsVar
()))
{
return
false
;
}
for
(
auto
*
op
:
x
->
outputs
)
{
if
(
op
&&
op
->
IsOp
()
&&
op
->
Op
()
&&
op
->
Op
()
->
Type
()
==
op_type
)
{
return
op
;
}
}
return
nullptr
;
};
auto
get_op_input_var
=
[
=
](
Node
*
x
,
const
std
::
string
&
arg_name
)
->
Node
*
{
if
(
!
(
x
&&
x
->
IsOp
()))
{
return
false
;
}
for
(
auto
*
var
:
x
->
inputs
)
{
for
(
auto
name
:
x
->
Op
()
->
Input
(
arg_name
))
{
if
(
var
->
Name
()
==
name
)
{
return
var
;
}
}
}
return
nullptr
;
};
auto
is_fusion_input_var
=
[
=
](
Node
*
x
,
const
std
::
string
&
arg_name
)
{
bool
basic
=
var_is_op_input
(
x
,
"matmul"
,
arg_name
)
&&
var_is_op_input
(
x
,
"square"
,
"X"
);
if
(
!
basic
)
{
return
false
;
}
auto
*
squared_x_op
=
next_op
(
x
,
"square"
);
if
(
!
(
squared_x_op
&&
squared_x_op
->
outputs
.
size
()
==
1
))
{
return
false
;
}
auto
*
squared_x
=
squared_x_op
->
outputs
[
0
];
bool
next_is_matmul_from_arg
=
var_is_op_input
(
squared_x
,
"matmul"
,
arg_name
)
&&
squared_x
->
outputs
.
size
()
==
1
&&
squared_x
->
outputs
[
0
]
->
outputs
.
size
()
==
1
;
if
(
!
next_is_matmul_from_arg
)
{
return
false
;
}
auto
*
sub_x
=
squared_x
->
outputs
[
0
]
->
outputs
[
0
];
return
var_is_op_input
(
sub_x
,
"elementwise_sub"
,
"X"
)
&&
sub_x
->
outputs
[
0
]
->
outputs
.
size
()
==
1
&&
var_is_op_input
(
sub_x
->
outputs
[
0
]
->
outputs
[
0
],
"elementwise_mul"
);
};
auto
is_fusion_first_mul_out
=
[
=
](
Node
*
x
)
->
bool
{
bool
input_is_matmul_op
=
x
&&
x
->
inputs
.
size
()
==
1
&&
x
->
inputs
[
0
]
->
IsOp
()
&&
x
->
inputs
[
0
]
->
Op
()
->
Type
()
==
"matmul"
;
if
(
!
input_is_matmul_op
)
{
return
false
;
}
auto
*
mat_x
=
get_op_input_var
(
x
->
inputs
[
0
],
"X"
);
auto
*
mat_y
=
get_op_input_var
(
x
->
inputs
[
0
],
"Y"
);
bool
input_mul_is_valid
=
mat_x
&&
is_fusion_input_var
(
mat_x
,
"X"
)
&&
mat_y
&&
is_fusion_input_var
(
mat_y
,
"Y"
);
if
(
!
input_mul_is_valid
)
{
return
false
;
}
bool
next_is_square
=
var_is_op_input
(
x
,
"square"
,
"X"
)
&&
x
->
outputs
.
size
()
==
1
&&
x
->
outputs
[
0
]
->
outputs
.
size
()
==
1
;
if
(
!
next_is_square
)
{
return
false
;
}
auto
*
sub_y
=
x
->
outputs
[
0
]
->
outputs
[
0
];
return
var_is_op_input
(
sub_y
,
"elementwise_sub"
,
"Y"
)
&&
sub_y
->
outputs
[
0
]
->
outputs
.
size
()
==
1
&&
var_is_op_input
(
sub_y
->
outputs
[
0
]
->
outputs
[
0
],
"elementwise_mul"
);
};
auto
*
x
=
pattern
->
NewNode
(
[
=
](
Node
*
x
)
{
return
is_fusion_input_var
(
x
,
"X"
);
},
name_scope
+
"/x"
);
auto
*
y
=
pattern
->
NewNode
(
[
=
](
Node
*
x
)
{
return
is_fusion_input_var
(
x
,
"Y"
);
},
name_scope
+
"/y"
);
auto
*
square_x_op
=
pattern
->
NewNode
(
[
=
](
Node
*
x
)
{
return
x
&&
x
->
IsOp
()
&&
x
->
Op
()
->
Type
()
==
"square"
&&
is_fusion_input_var
(
x
->
inputs
[
0
],
"X"
);
},
name_scope
+
"/squared_x_op"
);
auto
*
square_y_op
=
pattern
->
NewNode
(
[
=
](
Node
*
x
)
{
return
x
&&
x
->
IsOp
()
&&
x
->
Op
()
->
Type
()
==
"square"
&&
is_fusion_input_var
(
x
->
inputs
[
0
],
"Y"
);
},
name_scope
+
"/squared_y_op"
);
auto
*
squared_x
=
pattern
->
NewNode
(
[
=
](
Node
*
x
)
{
return
x
&&
x
->
inputs
.
size
()
==
1
&&
x
->
inputs
[
0
]
->
inputs
.
size
()
==
1
&&
is_fusion_input_var
(
x
->
inputs
[
0
]
->
inputs
[
0
],
"X"
);
},
name_scope
+
"/squared_x"
);
auto
*
squared_y
=
pattern
->
NewNode
(
[
=
](
Node
*
x
)
{
return
x
&&
x
->
inputs
.
size
()
==
1
&&
x
->
inputs
[
0
]
->
inputs
.
size
()
==
1
&&
is_fusion_input_var
(
x
->
inputs
[
0
]
->
inputs
[
0
],
"Y"
);
},
name_scope
+
"/squared_y"
);
auto
*
matmuled_xy
=
pattern
->
NewNode
([
=
](
Node
*
x
)
{
return
is_fusion_first_mul_out
(
x
);
},
name_scope
+
"/matmuled_xy"
);
auto
*
matmul_xy_op
=
pattern
->
NewNode
(
[
=
](
Node
*
x
)
{
return
x
&&
x
->
IsOp
()
&&
x
->
Op
()
->
Type
()
==
"matmul"
&&
is_fusion_first_mul_out
(
x
->
outputs
[
0
]);
},
name_scope
+
"/matmul_xy_op"
);
auto
*
square_matmuled_xy_op
=
pattern
->
NewNode
(
[
=
](
Node
*
x
)
{
return
x
&&
x
->
IsOp
()
&&
x
->
Op
()
->
Type
()
==
"square"
&&
is_fusion_first_mul_out
(
x
->
inputs
[
0
]);
},
name_scope
+
"/square_matmuled_xy_op"
);
auto
*
squared_xmuly
=
pattern
->
NewNode
(
[
=
](
Node
*
x
)
{
return
x
&&
x
->
IsVar
()
&&
x
->
inputs
.
size
()
==
1
&&
x
->
inputs
[
0
]
->
IsOp
()
&&
x
->
inputs
[
0
]
->
Op
()
->
Type
()
==
"square"
&&
is_fusion_first_mul_out
(
x
->
inputs
[
0
]
->
inputs
[
0
]);
},
name_scope
+
"/squared_xmuly"
);
auto
is_fusion_mat_squared_x_y_op_out
=
[
=
](
Node
*
x
)
->
bool
{
bool
basic
=
x
&&
x
->
IsVar
()
&&
x
->
inputs
.
size
()
==
1
&&
x
->
inputs
[
0
]
->
IsOp
()
&&
x
->
inputs
[
0
]
->
Op
()
->
Type
()
==
"matmul"
;
if
(
!
basic
)
{
return
false
;
}
auto
*
sqx
=
get_op_input_var
(
x
->
inputs
[
0
],
"X"
);
auto
*
sqy
=
get_op_input_var
(
x
->
inputs
[
0
],
"Y"
);
return
var_is_op_only_output
(
sqx
,
"square"
)
&&
var_is_op_only_output
(
sqy
,
"square"
)
&&
sqx
->
inputs
[
0
]
&&
sqx
->
inputs
[
0
]
->
inputs
.
size
()
==
1
&&
is_fusion_input_var
(
sqx
->
inputs
[
0
]
->
inputs
[
0
],
"X"
)
&&
sqy
->
inputs
[
0
]
&&
sqy
->
inputs
[
0
]
->
inputs
.
size
()
==
1
&&
is_fusion_input_var
(
sqy
->
inputs
[
0
]
->
inputs
[
0
],
"Y"
);
};
auto
*
matmul_squared_x_y_op
=
pattern
->
NewNode
(
[
=
](
Node
*
x
)
{
return
x
&&
x
->
IsOp
()
&&
x
->
Op
()
->
Type
()
==
"matmul"
&&
is_fusion_mat_squared_x_y_op_out
(
x
->
outputs
[
0
]);
},
name_scope
+
"/matmul_squared_x_y_op"
);
auto
*
mat_squared_x_y_op_out
=
pattern
->
NewNode
(
[
=
](
Node
*
x
)
{
return
is_fusion_mat_squared_x_y_op_out
(
x
);
},
name_scope
+
"/mat_squared_x_y_op_out"
);
auto
is_fusion_sub_op
=
[
=
](
Node
*
x
)
->
bool
{
bool
is_sub_op
=
x
&&
x
->
IsOp
()
&&
x
->
Op
()
->
Type
()
==
"elementwise_sub"
;
if
(
!
is_sub_op
)
{
return
false
;
}
auto
*
matmul_sqx_sqy_var
=
get_op_input_var
(
x
,
"X"
);
return
is_fusion_mat_squared_x_y_op_out
(
matmul_sqx_sqy_var
);
};
auto
*
sub_op
=
pattern
->
NewNode
([
=
](
Node
*
x
)
{
return
is_fusion_sub_op
(
x
);
},
name_scope
+
"/sub_op"
);
auto
*
sub_op_out
=
pattern
->
NewNode
(
[
=
](
Node
*
x
)
{
return
x
&&
x
->
IsVar
()
&&
x
->
inputs
.
size
()
==
1
&&
is_fusion_sub_op
(
x
->
inputs
[
0
]);
},
name_scope
+
"/sub_op_out"
);
auto
is_fusion_element_op
=
[
=
](
Node
*
x
)
->
bool
{
bool
is_elemul_op
=
x
&&
x
->
IsOp
()
&&
x
->
Op
()
->
Type
()
==
"elementwise_mul"
;
if
(
!
is_elemul_op
)
{
return
false
;
}
for
(
auto
*
in
:
x
->
inputs
)
{
if
(
in
&&
in
->
inputs
[
0
]
&&
is_fusion_sub_op
(
in
->
inputs
[
0
]))
{
return
true
;
}
}
return
false
;
};
auto
*
elementmul_op
=
pattern
->
NewNode
([
=
](
Node
*
x
)
{
return
is_fusion_element_op
(
x
);
},
name_scope
+
"/elementmul_op"
);
auto
*
constant_op
=
pattern
->
NewNode
(
[
=
](
Node
*
x
)
{
return
x
&&
x
->
IsOp
()
&&
x
->
Op
()
->
Type
()
==
"fill_constant"
&&
x
->
outputs
.
size
()
==
1
&&
is_fusion_element_op
(
x
->
outputs
[
0
]
->
outputs
[
0
]);
},
name_scope
+
"/fill_constant_op"
);
auto
*
constant_op_out
=
pattern
->
NewNode
(
[
=
](
Node
*
x
)
{
return
x
&&
x
->
IsVar
()
&&
var_is_op_input
(
x
,
"elementwise_mul"
)
&&
x
->
inputs
[
0
]
&&
x
->
inputs
[
0
]
->
IsOp
()
&&
x
->
inputs
[
0
]
->
Op
()
->
Type
()
==
"fill_constant"
&&
x
->
outputs
[
0
]
&&
is_fusion_element_op
(
x
->
outputs
[
0
]);
},
name_scope
+
"/constant_op_out"
);
auto
*
last_out_var
=
pattern
->
NewNode
(
[
=
](
Node
*
x
)
{
return
var_is_op_only_output
(
x
,
"elementwise_mul"
)
&&
is_fusion_element_op
(
x
->
inputs
[
0
]);
},
name_scope
+
"/out"
);
square_x_op
->
LinksFrom
({
x
}).
LinksTo
({
squared_x
});
square_y_op
->
LinksFrom
({
y
}).
LinksTo
({
squared_y
});
matmul_xy_op
->
LinksFrom
({
x
,
y
}).
LinksTo
({
matmuled_xy
});
matmul_squared_x_y_op
->
LinksFrom
({
squared_x
,
squared_y
})
.
LinksTo
({
mat_squared_x_y_op_out
});
square_matmuled_xy_op
->
LinksFrom
({
matmuled_xy
}).
LinksTo
({
squared_xmuly
});
sub_op
->
LinksFrom
({
mat_squared_x_y_op_out
,
squared_xmuly
})
.
LinksTo
({
sub_op_out
});
constant_op
->
LinksFrom
({}).
LinksTo
({
constant_op_out
});
elementmul_op
->
LinksFrom
({
constant_op_out
,
sub_op_out
})
.
LinksTo
({
last_out_var
});
return
last_out_var
;
}
static
int
BuildFusion
(
Graph
*
graph
,
const
std
::
string
&
name_scope
)
{
GraphPatternDetector
gpd
;
auto
*
pattern
=
gpd
.
mutable_pattern
();
BuildSquaredMatSubPattern
(
pattern
,
name_scope
);
auto
retrieve_node
=
[](
const
std
::
string
&
name
,
const
GraphPatternDetector
::
subgraph_t
&
subgraph
,
const
PDPattern
&
pat
)
->
Node
*
{
PADDLE_ENFORCE
(
subgraph
.
count
(
pat
.
RetrieveNode
(
name
)),
"pattern has no Node called %s"
,
name
.
c_str
());
Node
*
p
=
subgraph
.
at
(
pat
.
RetrieveNode
(
name
));
PADDLE_ENFORCE_NOT_NULL
(
p
,
"subgraph has no node %s"
,
name
.
c_str
());
return
p
;
};
int
fusion_count
{
0
};
auto
handler
=
[
&
](
const
GraphPatternDetector
::
subgraph_t
&
subgraph
,
Graph
*
g
)
{
LOG
(
INFO
)
<<
"handle sqaure mat sub fuse"
;
auto
&
fused_pattern
=
gpd
.
pattern
();
auto
*
matx
=
retrieve_node
(
name_scope
+
"/x"
,
subgraph
,
fused_pattern
);
auto
*
maty
=
retrieve_node
(
name_scope
+
"/y"
,
subgraph
,
fused_pattern
);
auto
*
squaredx
=
retrieve_node
(
name_scope
+
"/squared_x"
,
subgraph
,
fused_pattern
);
auto
*
squaredy
=
retrieve_node
(
name_scope
+
"/squared_y"
,
subgraph
,
fused_pattern
);
auto
*
squaredxy
=
retrieve_node
(
name_scope
+
"/squared_xmuly"
,
subgraph
,
fused_pattern
);
auto
*
last_out_var
=
retrieve_node
(
name_scope
+
"/out"
,
subgraph
,
fused_pattern
);
auto
*
fill_constant_op
=
retrieve_node
(
name_scope
+
"/fill_constant_op"
,
subgraph
,
fused_pattern
);
// Create New OpDesc
OpDesc
op_desc
;
op_desc
.
SetType
(
"fusion_squared_mat_sub"
);
op_desc
.
SetInput
(
"X"
,
{
matx
->
Name
()});
op_desc
.
SetInput
(
"Y"
,
{
maty
->
Name
()});
op_desc
.
SetOutput
(
"SquaredX"
,
{
squaredx
->
Name
()});
op_desc
.
SetOutput
(
"SquaredY"
,
{
squaredy
->
Name
()});
op_desc
.
SetOutput
(
"SquaredXY"
,
{
squaredxy
->
Name
()});
op_desc
.
SetOutput
(
"Out"
,
{
last_out_var
->
Name
()});
op_desc
.
SetAttr
(
"scalar"
,
fill_constant_op
->
Op
()
->
GetAttr
(
"value"
));
auto
*
op
=
graph
->
CreateOpNode
(
&
op_desc
);
IR_NODE_LINK_TO
(
matx
,
op
);
IR_NODE_LINK_TO
(
maty
,
op
);
IR_NODE_LINK_TO
(
op
,
squaredx
);
IR_NODE_LINK_TO
(
op
,
squaredy
);
IR_NODE_LINK_TO
(
op
,
squaredxy
);
IR_NODE_LINK_TO
(
op
,
last_out_var
);
std
::
unordered_set
<
const
Node
*>
marked_nodes
;
for
(
auto
&
item
:
subgraph
)
{
marked_nodes
.
insert
(
item
.
second
);
}
marked_nodes
.
erase
(
matx
);
marked_nodes
.
erase
(
maty
);
marked_nodes
.
erase
(
squaredx
);
marked_nodes
.
erase
(
squaredy
);
marked_nodes
.
erase
(
squaredxy
);
marked_nodes
.
erase
(
last_out_var
);
GraphSafeRemoveNodes
(
graph
,
marked_nodes
);
++
fusion_count
;
};
gpd
(
graph
,
handler
);
return
fusion_count
;
}
std
::
unique_ptr
<
ir
::
Graph
>
SquaredMatSubFusePass
::
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
{
FusePassBase
::
Init
(
name_scope_
,
graph
.
get
());
int
fusion_count
=
BuildFusion
(
graph
.
get
(),
name_scope_
);
AddStatis
(
fusion_count
);
return
graph
;
}
}
// namespace ir
}
// namespace framework
}
// namespace paddle
REGISTER_PASS
(
squared_mat_sub_fuse_pass
,
paddle
::
framework
::
ir
::
SquaredMatSubFusePass
);
paddle/fluid/framework/ir/squared_mat_sub_fuse_pass.h
0 → 100644
浏览文件 @
a5d2a6d1
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License. */
#pragma once
#include <string>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace
paddle
{
namespace
framework
{
namespace
ir
{
/**
* Fuse ( (A.^2 * B.^2) - (A * B).^2 ) .* scalar
*/
class
SquaredMatSubFusePass
:
public
FusePassBase
{
public:
virtual
~
SquaredMatSubFusePass
()
{}
protected:
std
::
unique_ptr
<
ir
::
Graph
>
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
;
const
std
::
string
name_scope_
{
"squared_mat_sub"
};
};
}
// namespace ir
}
// namespace framework
}
// namespace paddle
paddle/fluid/inference/api/paddle_pass_builder.h
浏览文件 @
a5d2a6d1
...
...
@@ -99,6 +99,7 @@ class CpuPassStrategy : public PassStrategy {
"seq_concat_fc_fuse_pass"
,
//
"fc_fuse_pass"
,
//
"repeated_fc_relu_fuse_pass"
,
//
"squared_mat_sub_fuse_pass"
,
//
"conv_bn_fuse_pass"
,
//
"conv_eltwiseadd_bn_fuse_pass"
,
//
"is_test_pass"
,
//
...
...
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