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体验新版 GitCode,发现更多精彩内容 >>
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提交
552ed8d8
编写于
7月 19, 2023
作者:
C
csy0225
提交者:
GitHub
7月 19, 2023
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
Delete repeat ops add gather squeeze unsqueeze (#55371)
上级
bc153701
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
788 addition
and
19 deletion
+788
-19
paddle/fluid/framework/ir/CMakeLists.txt
paddle/fluid/framework/ir/CMakeLists.txt
+1
-0
paddle/fluid/framework/ir/delete_repeated_ops_pass.cc
paddle/fluid/framework/ir/delete_repeated_ops_pass.cc
+59
-18
paddle/fluid/framework/ir/fused_continuous_same_ops_pass.cc
paddle/fluid/framework/ir/fused_continuous_same_ops_pass.cc
+237
-0
paddle/fluid/inference/api/paddle_pass_builder.cc
paddle/fluid/inference/api/paddle_pass_builder.cc
+2
-1
test/ir/inference/test_xpu_delete_repeated_ops_pass.py
test/ir/inference/test_xpu_delete_repeated_ops_pass.py
+347
-0
test/ir/inference/test_xpu_fused_continuous_same_ops_pass.py
test/ir/inference/test_xpu_fused_continuous_same_ops_pass.py
+142
-0
未找到文件。
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @
552ed8d8
...
...
@@ -129,6 +129,7 @@ pass_library(dense_multihead_matmul_to_sparse_pass inference)
pass_library
(
delete_cast_op_pass inference
)
pass_library
(
delete_elementwise_mul_op_pass inference
)
pass_library
(
delete_repeated_ops_pass inference
)
pass_library
(
fused_continuous_same_ops_pass inference
)
pass_library
(
sigmoid_elementmul_fuse_pass inference
)
pass_library
(
generate_pass DEPS pass_desc_proto
)
target_link_libraries
(
generate_pass pass_desc_proto
)
...
...
paddle/fluid/framework/ir/delete_repeated_ops_pass.cc
浏览文件 @
552ed8d8
...
...
@@ -101,18 +101,18 @@ class DeleteRepeatedOpsPass : public FusePassBase {
void
ApplyImpl
(
ir
::
Graph
*
graph
)
const
override
;
private:
void
DeleteRepeatedOps
(
ir
::
Graph
*
graph
,
const
std
::
string
&
op_type
,
std
::
function
<
std
::
string
(
OpDesc
*
)
>
gen_op_key_fn
)
const
;
void
DeleteRepeatedOps
(
ir
::
Graph
*
graph
,
const
std
::
string
&
op_type
,
std
::
function
<
std
::
string
(
Node
*
)
>
gen_op_key_fn
)
const
;
const
std
::
string
name_scope_
{
"delete_repeated_ops_pass"
};
mutable
int
delete_op_count
{
0
};
};
void
DeleteRepeatedOpsPass
::
DeleteRepeatedOps
(
ir
::
Graph
*
graph
,
const
std
::
string
&
op_type
,
std
::
function
<
std
::
string
(
OpDesc
*
)
>
gen_op_key_fn
)
const
{
std
::
function
<
std
::
string
(
Node
*
)
>
gen_op_key_fn
)
const
{
GraphPatternDetector
gpd
;
patterns
::
VarWithRepeatedOpsPattern
pattern
(
gpd
.
mutable_pattern
(),
name_scope_
,
op_type
);
...
...
@@ -140,7 +140,7 @@ void DeleteRepeatedOpsPass::DeleteRepeatedOps(
}
}
if
(
out_op_is_invalid
)
continue
;
auto
attr_key
=
gen_op_key_fn
(
op
->
Op
()
);
auto
attr_key
=
gen_op_key_fn
(
op
);
ops_map
[
attr_key
].
push_back
(
op
);
}
for
(
auto
iter
=
ops_map
.
begin
();
iter
!=
ops_map
.
end
();)
{
...
...
@@ -173,16 +173,18 @@ void DeleteRepeatedOpsPass::DeleteRepeatedOps(
};
gpd
(
graph
,
handler
);
delete_op_count
+=
delete_counts
;
if
(
delete_counts
>
0
)
{
LOG
(
INFO
)
<<
"--- delete "
<<
delete_counts
<<
" repeated "
<<
op_type
<<
" ops"
;
}
}
std
::
string
GenShapeAttrKey
(
OpDesc
*
slice_op_desc
)
{
return
""
;
}
std
::
string
GenShapeAttrKey
(
Node
*
shape_op_node
)
{
return
""
;
}
std
::
string
GenSliceAttrKey
(
OpDesc
*
slice_op_desc
)
{
std
::
string
GenSliceAttrKey
(
Node
*
slice_op_node
)
{
std
::
string
attr_key
;
auto
slice_op_desc
=
slice_op_node
->
Op
();
auto
starts
=
slice_op_desc
->
GetAttrIfExists
<
std
::
vector
<
int
>>
(
"starts"
);
auto
ends
=
slice_op_desc
->
GetAttrIfExists
<
std
::
vector
<
int
>>
(
"ends"
);
auto
axes
=
slice_op_desc
->
GetAttrIfExists
<
std
::
vector
<
int
>>
(
"axes"
);
...
...
@@ -207,21 +209,24 @@ std::string GenSliceAttrKey(OpDesc* slice_op_desc) {
return
attr_key
;
}
std
::
string
GenCastAttrKey
(
OpDesc
*
cast_op_desc
)
{
std
::
string
GenCastAttrKey
(
Node
*
cast_op_node
)
{
auto
cast_op_desc
=
cast_op_node
->
Op
();
auto
in_dtype
=
cast_op_desc
->
GetAttrIfExists
<
int
>
(
"in_dtype"
);
auto
out_dtype
=
cast_op_desc
->
GetAttrIfExists
<
int
>
(
"out_dtype"
);
return
"in_dtype_"
+
std
::
to_string
(
in_dtype
)
+
"_out_dtype_"
+
std
::
to_string
(
out_dtype
);
}
std
::
string
GenAddAttrKey
(
OpDesc
*
add_op_desc
)
{
std
::
string
GenAddAttrKey
(
Node
*
add_op_node
)
{
auto
add_op_desc
=
add_op_node
->
Op
();
std
::
string
x_name
=
add_op_desc
->
Input
(
"X"
)[
0
];
std
::
string
y_name
=
add_op_desc
->
Input
(
"Y"
)[
0
];
auto
axis
=
add_op_desc
->
GetAttrIfExists
<
int
>
(
"axis"
);
return
x_name
+
"_"
+
y_name
+
"_axis_"
+
std
::
to_string
(
axis
);
}
std
::
string
GenScaleAttrKey
(
OpDesc
*
scale_op_desc
)
{
std
::
string
GenScaleAttrKey
(
Node
*
scale_op_node
)
{
auto
scale_op_desc
=
scale_op_node
->
Op
();
auto
scale
=
scale_op_desc
->
GetAttrIfExists
<
float
>
(
"scale"
);
auto
bias
=
scale_op_desc
->
GetAttrIfExists
<
float
>
(
"bias"
);
auto
bias_after_scale
=
...
...
@@ -230,17 +235,53 @@ std::string GenScaleAttrKey(OpDesc* scale_op_desc) {
"_bias_after_scale_"
+
std
::
to_string
(
bias_after_scale
);
}
std
::
string
GenGatherAttrKey
(
Node
*
gather_op_node
)
{
std
::
string
input_names
{
""
};
for
(
auto
input_var
:
gather_op_node
->
inputs
)
{
input_names
+=
input_var
->
Var
()
->
Name
();
}
auto
gather_op_desc
=
gather_op_node
->
Op
();
auto
axis
=
gather_op_desc
->
GetAttrIfExists
<
int
>
(
"axis"
);
return
"axis_"
+
std
::
to_string
(
axis
)
+
"_input_names_"
+
input_names
;
}
std
::
string
GenSqueeze2AttrKey
(
Node
*
squeeze2_op_node
)
{
auto
squeeze2_op_desc
=
squeeze2_op_node
->
Op
();
auto
axes
=
squeeze2_op_desc
->
GetAttrIfExists
<
std
::
vector
<
int
>>
(
"axes"
);
std
::
string
attr_key
{
""
};
attr_key
+=
"axes_"
;
for
(
auto
axis
:
axes
)
{
attr_key
+=
std
::
to_string
(
axis
)
+
"_"
;
}
return
attr_key
;
}
void
DeleteRepeatedOpsPass
::
ApplyImpl
(
ir
::
Graph
*
graph
)
const
{
PADDLE_ENFORCE_NOT_NULL
(
graph
,
platform
::
errors
::
PreconditionNotMet
(
"graph should not be null."
));
Init
(
name_scope_
,
graph
);
DeleteRepeatedOps
(
graph
,
"shape"
,
GenShapeAttrKey
);
DeleteRepeatedOps
(
graph
,
"slice"
,
GenSliceAttrKey
);
DeleteRepeatedOps
(
graph
,
"cast"
,
GenCastAttrKey
);
DeleteRepeatedOps
(
graph
,
"elementwise_add"
,
GenAddAttrKey
);
DeleteRepeatedOps
(
graph
,
"scale"
,
GenScaleAttrKey
);
DeleteRepeatedOps
(
graph
,
"cast"
,
GenCastAttrKey
);
int
repeat_time
=
0
;
int
total_delete_op_count
=
0
;
// This pass needs to loop run until there are no nodes in the graph that need
// to be deleted.
while
(
true
)
{
delete_op_count
=
0
;
DeleteRepeatedOps
(
graph
,
"shape"
,
GenShapeAttrKey
);
DeleteRepeatedOps
(
graph
,
"slice"
,
GenSliceAttrKey
);
DeleteRepeatedOps
(
graph
,
"cast"
,
GenCastAttrKey
);
DeleteRepeatedOps
(
graph
,
"elementwise_add"
,
GenAddAttrKey
);
DeleteRepeatedOps
(
graph
,
"scale"
,
GenScaleAttrKey
);
DeleteRepeatedOps
(
graph
,
"gather"
,
GenGatherAttrKey
);
DeleteRepeatedOps
(
graph
,
"squeeze2"
,
GenSqueeze2AttrKey
);
DeleteRepeatedOps
(
graph
,
"unsqueeze2"
,
GenSqueeze2AttrKey
);
LOG
(
INFO
)
<<
"Round "
<<
repeat_time
++
<<
": delete op counts: "
<<
delete_op_count
;
total_delete_op_count
+=
delete_op_count
;
if
(
delete_op_count
==
0
)
{
break
;
// No node need to delete.
}
}
LOG
(
INFO
)
<<
"Total delete op counts: "
<<
total_delete_op_count
;
}
}
// namespace ir
...
...
paddle/fluid/framework/ir/fused_continuous_same_ops_pass.cc
0 → 100644
浏览文件 @
552ed8d8
// Copyright (c) 2023 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 <string>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/fluid/platform/enforce.h"
namespace
phi
{
class
DenseTensor
;
}
// namespace phi
namespace
paddle
{
namespace
framework
{
class
Scope
;
}
// namespace framework
}
// namespace paddle
namespace
paddle
{
namespace
framework
{
namespace
ir
{
namespace
patterns
{
struct
ContinuousSameOpsPattern
:
public
PatternBase
{
ContinuousSameOpsPattern
(
PDPattern
*
pattern
,
const
std
::
string
&
name_scope
,
const
std
::
string
&
op_type
);
PATTERN_DECL_NODE
(
first_in_var_node
);
PATTERN_DECL_NODE
(
first_out_var_node
);
PATTERN_DECL_NODE
(
second_out_var_node
);
// declare op node's name
PATTERN_DECL_NODE
(
first_op_node
);
PATTERN_DECL_NODE
(
second_op_node
);
std
::
string
op_type_
;
};
ContinuousSameOpsPattern
::
ContinuousSameOpsPattern
(
PDPattern
*
pattern
,
const
std
::
string
&
name_scope
,
const
std
::
string
&
op_type
)
:
PatternBase
(
pattern
,
name_scope
,
name_scope
),
op_type_
(
op_type
)
{
auto
*
first_in_var_node
=
pattern
->
NewNode
(
first_in_var_node_repr
())
->
assert_var_not_persistable
()
->
assert_is_op_input
(
op_type_
,
"X"
)
->
AsInput
()
->
assert_more
([
&
](
Node
*
node
)
{
// assert pre op type is not same.
auto
input_nodes
=
node
->
inputs
;
if
(
input_nodes
.
size
()
!=
1
)
return
false
;
if
(
!
input_nodes
.
empty
()
&&
input_nodes
[
0
]
->
IsOp
()
&&
input_nodes
[
0
]
->
Op
()
->
Type
()
==
op_type_
)
{
return
false
;
}
return
true
;
});
auto
*
first_op_node
=
pattern
->
NewNode
(
first_op_node_repr
())
->
assert_is_op
(
op_type_
);
auto
*
first_out_var_node
=
pattern
->
NewNode
(
first_out_var_node_repr
())
->
assert_var_not_persistable
()
->
assert_is_op_output
(
op_type_
,
"Out"
)
->
assert_has_n_outputs
(
1
);
first_op_node
->
LinksFrom
({
first_in_var_node
}).
LinksTo
({
first_out_var_node
});
auto
*
second_op_node
=
pattern
->
NewNode
(
second_op_node_repr
())
->
assert_is_op
(
op_type_
);
auto
*
second_out_var_node
=
pattern
->
NewNode
(
second_out_var_node_repr
())
->
assert_var_not_persistable
()
->
assert_is_op_output
(
op_type_
,
"Out"
)
->
AsOutput
();
second_op_node
->
LinksFrom
({
first_out_var_node
})
.
LinksTo
({
second_out_var_node
});
}
}
// namespace patterns
/*
Fused continuous same ops into one.
Origin graph:
input
|
|
unsqueeze2
|
|
unsqueeze2
|
|
unsqueeze2
|
|
out
After:
input
|
|
unsqueeze2
|
|
out
*/
class
FusedContinuousSameOpsPass
:
public
FusePassBase
{
protected:
void
ApplyImpl
(
ir
::
Graph
*
graph
)
const
override
;
private:
void
FusedReshapeOps
(
ir
::
Graph
*
graph
)
const
;
void
FusedUnsqueezeOps
(
ir
::
Graph
*
graph
)
const
;
const
std
::
string
name_scope_
{
"fused_continuous_same_ops_pass"
};
mutable
int
delete_op_count
{
0
};
};
void
FusedContinuousSameOpsPass
::
FusedReshapeOps
(
ir
::
Graph
*
graph
)
const
{
GraphPatternDetector
gpd
;
patterns
::
ContinuousSameOpsPattern
pattern
(
gpd
.
mutable_pattern
(),
name_scope_
,
"reshape2"
);
int
delete_counts
=
0
;
auto
handler
=
[
&
](
const
GraphPatternDetector
::
subgraph_t
&
subgraph
,
Graph
*
graph
)
{
VLOG
(
4
)
<<
"handle fused continuous reshape ops."
;
GET_IR_NODE_FROM_SUBGRAPH
(
first_in_var_node
,
first_in_var_node
,
pattern
);
GET_IR_NODE_FROM_SUBGRAPH
(
first_out_var_node
,
first_out_var_node
,
pattern
);
GET_IR_NODE_FROM_SUBGRAPH
(
second_out_var_node
,
second_out_var_node
,
pattern
);
GET_IR_NODE_FROM_SUBGRAPH
(
first_op_node
,
first_op_node
,
pattern
);
GET_IR_NODE_FROM_SUBGRAPH
(
second_op_node
,
second_op_node
,
pattern
);
auto
first_node_attr_shape
=
first_op_node
->
Op
()
->
GetAttrIfExists
<
std
::
vector
<
int
>>
(
"shape"
);
if
(
first_node_attr_shape
.
empty
())
return
;
auto
second_node_attr_shape
=
second_op_node
->
Op
()
->
GetAttrIfExists
<
std
::
vector
<
int
>>
(
"shape"
);
if
(
second_node_attr_shape
.
empty
())
return
;
second_op_node
->
Op
()
->
RenameInput
(
first_out_var_node
->
Name
(),
first_in_var_node
->
Name
());
IR_NODE_LINK_TO
(
first_in_var_node
,
second_op_node
);
GraphSafeRemoveNodes
(
graph
,
{
first_op_node
,
first_out_var_node
});
delete_counts
++
;
};
gpd
(
graph
,
handler
);
delete_op_count
+=
delete_counts
;
if
(
delete_counts
>
0
)
{
LOG
(
INFO
)
<<
"--- delete "
<<
delete_counts
<<
" repeated "
<<
"reshape2"
<<
" ops"
;
}
}
void
FusedContinuousSameOpsPass
::
FusedUnsqueezeOps
(
ir
::
Graph
*
graph
)
const
{
GraphPatternDetector
gpd
;
patterns
::
ContinuousSameOpsPattern
pattern
(
gpd
.
mutable_pattern
(),
name_scope_
,
"unsqueeze2"
);
int
delete_counts
=
0
;
auto
handler
=
[
&
](
const
GraphPatternDetector
::
subgraph_t
&
subgraph
,
Graph
*
graph
)
{
VLOG
(
4
)
<<
"handle fused continuous unsqueeze ops."
;
GET_IR_NODE_FROM_SUBGRAPH
(
first_in_var_node
,
first_in_var_node
,
pattern
);
GET_IR_NODE_FROM_SUBGRAPH
(
first_out_var_node
,
first_out_var_node
,
pattern
);
GET_IR_NODE_FROM_SUBGRAPH
(
second_out_var_node
,
second_out_var_node
,
pattern
);
GET_IR_NODE_FROM_SUBGRAPH
(
first_op_node
,
first_op_node
,
pattern
);
GET_IR_NODE_FROM_SUBGRAPH
(
second_op_node
,
second_op_node
,
pattern
);
auto
first_node_attr_axes
=
first_op_node
->
Op
()
->
GetAttrIfExists
<
std
::
vector
<
int
>>
(
"axes"
);
if
(
first_node_attr_axes
.
empty
())
return
;
auto
second_node_attr_axes
=
second_op_node
->
Op
()
->
GetAttrIfExists
<
std
::
vector
<
int
>>
(
"axes"
);
if
(
second_node_attr_axes
.
empty
())
return
;
second_op_node
->
Op
()
->
RenameInput
(
first_out_var_node
->
Name
(),
first_in_var_node
->
Name
());
second_node_attr_axes
.
insert
(
second_node_attr_axes
.
begin
(),
first_node_attr_axes
.
begin
(),
first_node_attr_axes
.
end
());
second_op_node
->
Op
()
->
SetAttr
(
"axes"
,
second_node_attr_axes
);
IR_NODE_LINK_TO
(
first_in_var_node
,
second_op_node
);
GraphSafeRemoveNodes
(
graph
,
{
first_op_node
,
first_out_var_node
});
delete_counts
++
;
};
gpd
(
graph
,
handler
);
delete_op_count
+=
delete_counts
;
if
(
delete_counts
>
0
)
{
LOG
(
INFO
)
<<
"--- delete "
<<
delete_counts
<<
" repeated "
<<
"unsqueeze2"
<<
" ops"
;
}
}
void
FusedContinuousSameOpsPass
::
ApplyImpl
(
ir
::
Graph
*
graph
)
const
{
PADDLE_ENFORCE_NOT_NULL
(
graph
,
platform
::
errors
::
PreconditionNotMet
(
"graph should not be null."
));
Init
(
name_scope_
,
graph
);
int
repeat_time
=
0
;
int
total_delete_op_count
=
0
;
// This pass needs to loop run until there are no nodes in the graph that need
// to be deleted.
while
(
true
)
{
delete_op_count
=
0
;
FusedReshapeOps
(
graph
);
FusedUnsqueezeOps
(
graph
);
LOG
(
INFO
)
<<
"Round "
<<
repeat_time
++
<<
": delete op counts: "
<<
delete_op_count
;
total_delete_op_count
+=
delete_op_count
;
if
(
delete_op_count
==
0
)
{
LOG
(
INFO
)
<<
"--- no nodes need to delete --- break"
;
break
;
// No node need to delete.
}
}
LOG
(
INFO
)
<<
"Total delete op counts: "
<<
total_delete_op_count
;
}
}
// namespace ir
}
// namespace framework
}
// namespace paddle
REGISTER_PASS
(
fused_continuous_same_ops_pass
,
paddle
::
framework
::
ir
::
FusedContinuousSameOpsPass
);
REGISTER_PASS_CAPABILITY
(
fused_continuous_same_ops_pass
)
.
AddCombination
(
paddle
::
framework
::
compatible
::
OpVersionComparatorCombination
().
EQ
(
"reshape2"
,
0
))
.
AddCombination
(
paddle
::
framework
::
compatible
::
OpVersionComparatorCombination
().
EQ
(
"unsqueeze2"
,
0
));
paddle/fluid/inference/api/paddle_pass_builder.cc
浏览文件 @
552ed8d8
...
...
@@ -507,8 +507,9 @@ XpuPassStrategy::XpuPassStrategy() : PassStrategy({}) {
"delete_assign_op_pass"
,
"delete_dropout_op_pass"
,
"delete_concat_op_pass"
,
"identity_op_clean_pass"
,
"delete_repeated_ops_pass"
,
"identity_op_clean_pass"
,
"fused_continuous_same_ops_pass"
,
"reshape_unstack_concat_fuse_pass"
,
"delete_op_device_pass"
,
"constant_folding_pass"
,
...
...
test/ir/inference/test_xpu_delete_repeated_ops_pass.py
浏览文件 @
552ed8d8
...
...
@@ -13,8 +13,10 @@
# limitations under the License.
import
unittest
from
functools
import
partial
import
hypothesis.strategies
as
st
import
numpy
as
np
from
auto_scan_test
import
PassAutoScanTest
from
program_config
import
OpConfig
,
ProgramConfig
,
TensorConfig
...
...
@@ -380,5 +382,350 @@ class TestDeleteRepeatedScalePass(PassAutoScanTest):
)
class
TestDeleteRepeatedSqueezePass
(
PassAutoScanTest
):
def
sample_predictor_configs
(
self
,
program_config
):
config
=
self
.
create_inference_config
(
use_xpu
=
True
)
yield
config
,
[
'scale'
,
'squeeze2'
,
'relu'
,
'relu'
,
'relu'
],
(
1e-5
,
1e-5
,
)
def
sample_program_config
(
self
,
draw
):
scale_x
=
draw
(
st
.
lists
(
st
.
integers
(
min_value
=
1
,
max_value
=
20
),
min_size
=
2
,
max_size
=
4
)
)
scale_x
[
0
]
=
1
axis
=
0
scale_op0
=
OpConfig
(
"scale"
,
inputs
=
{
"X"
:
[
"scale_x"
],
},
scale
=
2.0
,
bias
=
1.0
,
bias_after_scale
=
True
,
outputs
=
{
"Out"
:
[
"scale0_out"
]},
)
squeeze_op0
=
OpConfig
(
"squeeze2"
,
inputs
=
{
"X"
:
[
"scale0_out"
],
},
axes
=
[
axis
],
outputs
=
{
"Out"
:
[
"squeeze0_out"
]},
)
relu_op0
=
OpConfig
(
"relu"
,
inputs
=
{
"X"
:
[
"squeeze0_out"
],
},
outputs
=
{
"Out"
:
[
"relu0_out"
]},
)
scale_op1
=
OpConfig
(
"scale"
,
inputs
=
{
"X"
:
[
"scale_x"
],
},
scale
=
2.0
,
bias
=
1.0
,
bias_after_scale
=
True
,
outputs
=
{
"Out"
:
[
"scale1_out"
]},
)
squeeze_op1
=
OpConfig
(
"squeeze2"
,
inputs
=
{
"X"
:
[
"scale1_out"
],
},
axes
=
[
axis
],
outputs
=
{
"Out"
:
[
"squeeze1_out"
]},
)
relu_op1
=
OpConfig
(
"relu"
,
inputs
=
{
"X"
:
[
"squeeze1_out"
],
},
outputs
=
{
"Out"
:
[
"relu1_out"
]},
)
scale_op2
=
OpConfig
(
"scale"
,
inputs
=
{
"X"
:
[
"scale_x"
],
},
scale
=
2.0
,
bias
=
1.0
,
bias_after_scale
=
True
,
outputs
=
{
"Out"
:
[
"scale2_out"
]},
)
squeeze_op2
=
OpConfig
(
"squeeze2"
,
inputs
=
{
"X"
:
[
"scale2_out"
],
},
axes
=
[
axis
],
outputs
=
{
"Out"
:
[
"squeeze2_out"
]},
)
relu_op2
=
OpConfig
(
"relu"
,
inputs
=
{
"X"
:
[
"squeeze2_out"
],
},
outputs
=
{
"Out"
:
[
"relu2_out"
]},
)
ops
=
[
scale_op0
,
squeeze_op0
,
relu_op0
,
scale_op1
,
squeeze_op1
,
relu_op1
,
scale_op2
,
squeeze_op2
,
relu_op2
,
]
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"scale_x"
:
TensorConfig
(
shape
=
scale_x
),
},
outputs
=
[
"relu0_out"
,
"relu1_out"
,
"relu2_out"
],
)
return
program_config
class
TestDeleteRepeatedUnSqueezePass
(
PassAutoScanTest
):
def
sample_predictor_configs
(
self
,
program_config
):
config
=
self
.
create_inference_config
(
use_xpu
=
True
)
yield
config
,
[
'scale'
,
'unsqueeze2'
,
'relu'
,
'relu'
,
'relu'
],
(
1e-5
,
1e-5
,
)
def
sample_program_config
(
self
,
draw
):
scale_x
=
draw
(
st
.
lists
(
st
.
integers
(
min_value
=
1
,
max_value
=
20
),
min_size
=
2
,
max_size
=
4
)
)
axis
=
0
scale_op0
=
OpConfig
(
"scale"
,
inputs
=
{
"X"
:
[
"scale_x"
],
},
scale
=
2.0
,
bias
=
1.0
,
bias_after_scale
=
True
,
outputs
=
{
"Out"
:
[
"scale0_out"
]},
)
unsqueeze_op0
=
OpConfig
(
"unsqueeze2"
,
inputs
=
{
"X"
:
[
"scale0_out"
],
},
axes
=
[
axis
],
outputs
=
{
"Out"
:
[
"unsqueeze0_out"
]},
)
relu_op0
=
OpConfig
(
"relu"
,
inputs
=
{
"X"
:
[
"unsqueeze0_out"
],
},
outputs
=
{
"Out"
:
[
"relu0_out"
]},
)
scale_op1
=
OpConfig
(
"scale"
,
inputs
=
{
"X"
:
[
"scale_x"
],
},
scale
=
2.0
,
bias
=
1.0
,
bias_after_scale
=
True
,
outputs
=
{
"Out"
:
[
"scale1_out"
]},
)
unsqueeze_op1
=
OpConfig
(
"unsqueeze2"
,
inputs
=
{
"X"
:
[
"scale1_out"
],
},
axes
=
[
axis
],
outputs
=
{
"Out"
:
[
"unsqueeze1_out"
]},
)
relu_op1
=
OpConfig
(
"relu"
,
inputs
=
{
"X"
:
[
"unsqueeze1_out"
],
},
outputs
=
{
"Out"
:
[
"relu1_out"
]},
)
scale_op2
=
OpConfig
(
"scale"
,
inputs
=
{
"X"
:
[
"scale_x"
],
},
scale
=
2.0
,
bias
=
1.0
,
bias_after_scale
=
True
,
outputs
=
{
"Out"
:
[
"scale2_out"
]},
)
unsqueeze_op2
=
OpConfig
(
"unsqueeze2"
,
inputs
=
{
"X"
:
[
"scale2_out"
],
},
axes
=
[
axis
],
outputs
=
{
"Out"
:
[
"unsqueeze2_out"
]},
)
relu_op2
=
OpConfig
(
"relu"
,
inputs
=
{
"X"
:
[
"unsqueeze2_out"
],
},
outputs
=
{
"Out"
:
[
"relu2_out"
]},
)
ops
=
[
scale_op0
,
unsqueeze_op0
,
relu_op0
,
scale_op1
,
unsqueeze_op1
,
relu_op1
,
scale_op2
,
unsqueeze_op2
,
relu_op2
,
]
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"scale_x"
:
TensorConfig
(
shape
=
scale_x
),
},
outputs
=
[
"relu0_out"
,
"relu1_out"
,
"relu2_out"
],
)
return
program_config
class
TestDeleteRepeatedGatherPass
(
PassAutoScanTest
):
def
sample_predictor_configs
(
self
,
program_config
):
config
=
self
.
create_inference_config
(
use_xpu
=
True
)
yield
config
,
[
'scale'
,
'gather'
,
'relu'
,
'relu'
,
'relu'
],
(
1e-5
,
1e-5
)
def
sample_program_config
(
self
,
draw
):
scale_x
=
draw
(
st
.
lists
(
st
.
integers
(
min_value
=
3
,
max_value
=
20
),
min_size
=
2
,
max_size
=
4
)
)
axis
=
0
def
generate_index
(
*
args
,
**
kwargs
):
return
np
.
array
([
0
]).
astype
(
np
.
int64
)
gather_index
=
np
.
array
([
0
]).
astype
(
np
.
int64
)
scale_op0
=
OpConfig
(
"scale"
,
inputs
=
{
"X"
:
[
"scale_x"
],
},
scale
=
2.0
,
bias
=
1.0
,
bias_after_scale
=
True
,
outputs
=
{
"Out"
:
[
"scale0_out"
]},
)
gather_op0
=
OpConfig
(
"gather"
,
inputs
=
{
"X"
:
[
"scale0_out"
],
"Index"
:
[
"gather_index"
]},
axis
=
axis
,
outputs
=
{
"Out"
:
[
"gather0_out"
]},
)
relu_op0
=
OpConfig
(
"relu"
,
inputs
=
{
"X"
:
[
"gather0_out"
],
},
outputs
=
{
"Out"
:
[
"relu0_out"
]},
)
scale_op1
=
OpConfig
(
"scale"
,
inputs
=
{
"X"
:
[
"scale_x"
],
},
scale
=
2.0
,
bias
=
1.0
,
bias_after_scale
=
True
,
outputs
=
{
"Out"
:
[
"scale1_out"
]},
)
gather_op1
=
OpConfig
(
"gather"
,
inputs
=
{
"X"
:
[
"scale1_out"
],
"Index"
:
[
"gather_index"
]},
axis
=
axis
,
outputs
=
{
"Out"
:
[
"gather1_out"
]},
)
relu_op1
=
OpConfig
(
"relu"
,
inputs
=
{
"X"
:
[
"gather1_out"
],
},
outputs
=
{
"Out"
:
[
"relu1_out"
]},
)
scale_op2
=
OpConfig
(
"scale"
,
inputs
=
{
"X"
:
[
"scale_x"
],
},
scale
=
2.0
,
bias
=
1.0
,
bias_after_scale
=
True
,
outputs
=
{
"Out"
:
[
"scale2_out"
]},
)
gather_op2
=
OpConfig
(
"gather"
,
inputs
=
{
"X"
:
[
"scale2_out"
],
"Index"
:
[
"gather_index"
]},
axis
=
axis
,
outputs
=
{
"Out"
:
[
"gather2_out"
]},
)
relu_op2
=
OpConfig
(
"relu"
,
inputs
=
{
"X"
:
[
"gather2_out"
],
},
outputs
=
{
"Out"
:
[
"relu2_out"
]},
)
ops
=
[
scale_op0
,
gather_op0
,
relu_op0
,
scale_op1
,
gather_op1
,
relu_op1
,
scale_op2
,
gather_op2
,
relu_op2
,
]
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"scale_x"
:
TensorConfig
(
shape
=
scale_x
),
"gather_index"
:
TensorConfig
(
data_gen
=
partial
(
generate_index
)),
},
outputs
=
[
"relu0_out"
,
"relu1_out"
,
"relu2_out"
],
)
return
program_config
def
test
(
self
):
self
.
run_and_statis
(
quant
=
False
,
max_examples
=
25
,
passes
=
[
"delete_repeated_ops_pass"
],
)
if
__name__
==
"__main__"
:
unittest
.
main
()
test/ir/inference/test_xpu_fused_continuous_same_ops_pass.py
0 → 100644
浏览文件 @
552ed8d8
# Copyright (c) 2023 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.
import
unittest
import
hypothesis.strategies
as
st
from
auto_scan_test
import
PassAutoScanTest
from
program_config
import
OpConfig
,
ProgramConfig
,
TensorConfig
class
TestFusedSameUnSqueezePass
(
PassAutoScanTest
):
def
sample_predictor_configs
(
self
,
program_config
):
config
=
self
.
create_inference_config
(
use_xpu
=
True
)
yield
config
,
[
'scale'
,
'unsqueeze2'
],
(
1e-5
,
1e-5
)
def
sample_program_config
(
self
,
draw
):
scale_x
=
draw
(
st
.
lists
(
st
.
integers
(
min_value
=
1
,
max_value
=
20
),
min_size
=
1
,
max_size
=
3
)
)
first_unsqueeze_axis
=
0
second_unsqueeze_axis
=
1
third_unsqueeze_axis
=
2
scale_op0
=
OpConfig
(
"scale"
,
inputs
=
{
"X"
:
[
"scale_x"
],
},
scale
=
2.0
,
bias
=
1.0
,
bias_after_scale
=
True
,
outputs
=
{
"Out"
:
[
"scale0_out"
]},
)
unsqueeze_op0
=
OpConfig
(
"unsqueeze2"
,
inputs
=
{
"X"
:
[
"scale0_out"
],
},
axes
=
[
first_unsqueeze_axis
],
outputs
=
{
"Out"
:
[
"unsqueeze0_out"
]},
)
unsqueeze_op1
=
OpConfig
(
"unsqueeze2"
,
inputs
=
{
"X"
:
[
"unsqueeze0_out"
],
},
axes
=
[
second_unsqueeze_axis
],
outputs
=
{
"Out"
:
[
"unsqueeze1_out"
]},
)
unsqueeze_op2
=
OpConfig
(
"unsqueeze2"
,
inputs
=
{
"X"
:
[
"unsqueeze1_out"
],
},
axes
=
[
third_unsqueeze_axis
],
outputs
=
{
"Out"
:
[
"unsqueeze2_out"
]},
)
ops
=
[
scale_op0
,
unsqueeze_op0
,
unsqueeze_op1
,
unsqueeze_op2
]
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"scale_x"
:
TensorConfig
(
shape
=
scale_x
),
},
outputs
=
[
"unsqueeze2_out"
],
)
return
program_config
class
TestFusedSameReshapePass
(
PassAutoScanTest
):
def
sample_predictor_configs
(
self
,
program_config
):
config
=
self
.
create_inference_config
(
use_xpu
=
True
)
yield
config
,
[
'scale'
,
'reshape2'
],
(
1e-5
,
1e-5
)
def
sample_program_config
(
self
,
draw
):
scale_x
=
draw
(
st
.
sampled_from
([[
8
,
16
],
[
16
,
32
],
[
64
,
16
],
[
16
,
8
],
[
16
,
16
]])
)
first_reshape_shape
=
[
-
1
,
16
,
4
]
second_reshape_shape
=
[
-
1
,
8
]
scale_op0
=
OpConfig
(
"scale"
,
inputs
=
{
"X"
:
[
"scale_x"
],
},
scale
=
2.0
,
bias
=
1.0
,
bias_after_scale
=
True
,
outputs
=
{
"Out"
:
[
"scale0_out"
]},
)
reshape_op0
=
OpConfig
(
"reshape2"
,
inputs
=
{
"X"
:
[
"scale0_out"
],
},
shape
=
first_reshape_shape
,
outputs
=
{
"Out"
:
[
"reshape0_out"
]},
)
reshape_op1
=
OpConfig
(
"reshape2"
,
inputs
=
{
"X"
:
[
"reshape0_out"
],
},
shape
=
second_reshape_shape
,
outputs
=
{
"Out"
:
[
"reshape1_out"
]},
)
ops
=
[
scale_op0
,
reshape_op0
,
reshape_op1
]
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"scale_x"
:
TensorConfig
(
shape
=
scale_x
),
},
outputs
=
[
"reshape1_out"
],
)
return
program_config
def
test
(
self
):
self
.
run_and_statis
(
quant
=
False
,
max_examples
=
25
,
min_success_num
=
5
,
passes
=
[
"fused_continuous_same_ops_pass"
],
)
if
__name__
==
"__main__"
:
unittest
.
main
()
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