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体验新版 GitCode,发现更多精彩内容 >>
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提交
6ecbf083
编写于
1月 09, 2018
作者:
Q
QI JUN
提交者:
GitHub
1月 09, 2018
浏览文件
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电子邮件补丁
差异文件
add memory optimization transpiler (#7356)
上级
45e77154
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
153 addition
and
1 deletion
+153
-1
python/paddle/v2/fluid/__init__.py
python/paddle/v2/fluid/__init__.py
+2
-1
python/paddle/v2/fluid/framework.py
python/paddle/v2/fluid/framework.py
+3
-0
python/paddle/v2/fluid/memory_optimization_transpiler.py
python/paddle/v2/fluid/memory_optimization_transpiler.py
+115
-0
python/paddle/v2/fluid/tests/test_memory_optimization_transpiler.py
...dle/v2/fluid/tests/test_memory_optimization_transpiler.py
+33
-0
未找到文件。
python/paddle/v2/fluid/__init__.py
浏览文件 @
6ecbf083
...
...
@@ -19,12 +19,13 @@ from data_feeder import DataFeeder
from
core
import
LoDTensor
,
CPUPlace
,
CUDAPlace
from
distribute_transpiler
import
DistributeTranspiler
import
clip
from
memory_optimization_transpiler
import
memory_optimize
Tensor
=
LoDTensor
__all__
=
framework
.
__all__
+
executor
.
__all__
+
[
'io'
,
'initializer'
,
'layers'
,
'nets'
,
'optimizer'
,
'backward'
,
'regularizer'
,
'LoDTensor'
,
'CPUPlace'
,
'CUDAPlace'
,
'Tensor'
,
'ParamAttr'
'DataFeeder'
,
'clip'
,
'DistributeTranspiler'
'DataFeeder'
,
'clip'
,
'DistributeTranspiler'
,
'memory_optimize'
]
...
...
python/paddle/v2/fluid/framework.py
浏览文件 @
6ecbf083
...
...
@@ -773,6 +773,9 @@ class Program(object):
proto
=
framework_pb2
.
ProgramDesc
.
FromString
(
str
(
protostr
))
return
_debug_string_
(
proto
,
throw_on_error
)
def
get_desc
(
self
):
return
self
.
desc
def
clone
(
self
):
p
=
Program
()
p
.
desc
=
core
.
ProgramDesc
(
self
.
desc
)
...
...
python/paddle/v2/fluid/memory_optimization_transpiler.py
0 → 100644
浏览文件 @
6ecbf083
from
collections
import
defaultdict
import
framework
from
framework
import
Program
,
default_main_program
,
Parameter
,
Variable
import
backward
from
backward
import
_rename_arg_
class
ControlFlowGraph
(
object
):
def
__init__
(
self
,
Program
):
self
.
_program
=
Program
self
.
_succesors
=
defaultdict
(
set
)
self
.
_presucessors
=
defaultdict
(
set
)
self
.
_uses
=
defaultdict
(
set
)
self
.
_defs
=
defaultdict
(
set
)
self
.
_live_in
=
defaultdict
(
set
)
self
.
_live_out
=
defaultdict
(
set
)
def
_add_connections
(
self
,
connections
):
for
node1
,
node2
in
connections
:
self
.
_add
(
node1
,
node2
)
def
_add
(
self
,
node1
,
node2
):
self
.
_succesors
[
node1
].
add
(
node2
)
self
.
_presucessors
[
node2
].
add
(
node1
)
def
_build_graph
(
self
):
program_desc
=
self
.
_program
.
get_desc
()
block_size
=
program_desc
.
num_blocks
()
# TODO(qijun) handle Program with if/while operators
self
.
global_block
=
program_desc
.
block
(
0
)
self
.
op_size
=
self
.
global_block
.
op_size
()
op_node_connections
=
[(
i
,
i
+
1
)
for
i
in
range
(
self
.
op_size
-
1
)]
self
.
_add_connections
(
op_node_connections
)
self
.
ops
=
[
self
.
global_block
.
op
(
i
)
for
i
in
range
(
self
.
op_size
)]
for
i
in
range
(
self
.
op_size
):
self
.
_uses
[
i
].
update
(
self
.
ops
[
i
].
input_arg_names
())
self
.
_defs
[
i
].
update
(
self
.
ops
[
i
].
output_arg_names
())
def
_reach_fixed_point
(
self
,
live_in
,
live_out
):
if
len
(
live_in
)
!=
len
(
self
.
_live_in
):
return
False
if
len
(
live_out
)
!=
len
(
self
.
_live_out
):
return
False
for
i
in
range
(
self
.
op_size
):
if
live_in
[
i
]
!=
self
.
_live_in
[
i
]:
return
False
for
i
in
range
(
self
.
op_size
):
if
live_out
[
i
]
!=
self
.
_live_out
[
i
]:
return
False
return
True
def
_dataflow_analyze
(
self
):
self
.
_build_graph
()
live_in
=
defaultdict
(
set
)
live_out
=
defaultdict
(
set
)
while
True
:
for
i
in
range
(
self
.
op_size
):
live_in
[
i
]
=
set
(
self
.
_live_in
[
i
])
live_out
[
i
]
=
set
(
self
.
_live_out
[
i
])
self
.
_live_in
[
i
]
=
self
.
_uses
[
i
]
|
(
self
.
_live_out
[
i
]
-
self
.
_defs
[
i
])
for
s
in
self
.
_succesors
[
i
]:
self
.
_live_out
[
i
]
|=
self
.
_live_in
[
s
]
if
self
.
_reach_fixed_point
(
live_in
,
live_out
):
break
def
_get_diff
(
self
,
a
,
b
):
u
=
a
&
b
return
a
-
u
,
b
-
u
def
memory_optimize
(
self
):
self
.
_build_graph
()
self
.
_dataflow_analyze
()
self
.
pool
=
[]
for
i
in
range
(
self
.
op_size
):
if
self
.
pool
:
out_pair
=
[(
x
,
self
.
global_block
.
var
(
str
(
x
)).
shape
())
for
x
in
self
.
_defs
[
i
]]
for
x
,
x_shape
in
out_pair
:
for
index
,
cache_pair
in
enumerate
(
self
.
pool
):
cache_var
=
cache_pair
[
0
]
cache_shape
=
cache_pair
[
1
]
if
x_shape
==
cache_shape
:
print
(
"Hit Cache !!!! cache pool index is %d, var name is %s, cached var name is %s, var shape is %s "
%
(
index
,
x
,
cache_var
,
str
(
cache_shape
)))
self
.
pool
.
pop
(
index
)
_rename_arg_
(
self
.
ops
,
x
,
cache_var
,
begin_idx
=
i
)
self
.
_dataflow_analyze
()
break
in_diff
,
out_diff
=
self
.
_get_diff
(
self
.
_live_in
[
i
],
self
.
_live_out
[
i
])
can_optimize
=
filter
(
lambda
x
:
not
self
.
global_block
.
var
(
str
(
x
)).
persistable
(),
in_diff
)
if
can_optimize
:
for
var_name
in
can_optimize
:
self
.
pool
.
append
((
var_name
,
self
.
global_block
.
var
(
str
(
var_name
)).
shape
()))
def
get_program
(
self
):
return
self
.
_program
def
memory_optimize
(
input_program
):
graph
=
ControlFlowGraph
(
input_program
)
graph
.
memory_optimize
()
result_program
=
graph
.
get_program
()
return
result_program
python/paddle/v2/fluid/tests/test_memory_optimization_transpiler.py
0 → 100644
浏览文件 @
6ecbf083
from
__future__
import
print_function
import
unittest
import
paddle.v2.fluid.layers
as
layers
import
paddle.v2.fluid.optimizer
as
optimizer
from
paddle.v2.fluid.framework
import
Program
,
program_guard
from
paddle.v2.fluid.memory_optimization_transpiler
import
memory_optimize
class
TestControlFlowGraph
(
unittest
.
TestCase
):
def
setUp
(
self
):
program
=
Program
()
with
program_guard
(
program
,
startup_program
=
Program
()):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
13
],
dtype
=
'float32'
)
y_predict
=
layers
.
fc
(
input
=
x
,
size
=
1
,
act
=
None
)
y
=
layers
.
data
(
name
=
'y'
,
shape
=
[
1
],
dtype
=
'float32'
)
cost
=
layers
.
square_error_cost
(
input
=
y_predict
,
label
=
y
)
avg_cost
=
layers
.
mean
(
x
=
cost
)
opt
=
optimizer
.
SGD
(
learning_rate
=
0.001
)
opt
=
opt
.
minimize
(
avg_cost
)
self
.
program
=
program
def
test_control_flow_graph
(
self
):
print
(
"before optimization"
)
print
(
str
(
self
.
program
))
result_program
=
memory_optimize
(
self
.
program
)
print
(
"after optimization"
)
print
(
str
(
result_program
))
if
__name__
==
"__main__"
:
unittest
.
main
()
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