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c7444501
编写于
2月 07, 2018
作者:
T
typhoonzero
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
refine distribute transpiler
上级
b41205d9
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
89 addition
and
35 deletion
+89
-35
python/paddle/v2/fluid/distribute_transpiler.py
python/paddle/v2/fluid/distribute_transpiler.py
+89
-35
未找到文件。
python/paddle/v2/fluid/distribute_transpiler.py
浏览文件 @
c7444501
...
...
@@ -300,6 +300,9 @@ class DistributeTranspiler:
pass
return
orig_shape
def
_op_input_var
(
self
,
op
,
varname
):
pass
def
_is_op_on_pserver
(
self
,
endpoint
,
all_ops
,
idx
):
"""
Recursively check if the op need to run on current server.
...
...
@@ -309,29 +312,35 @@ class DistributeTranspiler:
p
.
name
for
p
in
self
.
param_grad_ep_mapping
[
endpoint
][
"params"
]
]
op
=
all_ops
[
idx
]
if
op
.
inputs
.
has_key
(
"Param"
):
if
op
.
inputs
[
"Param"
].
name
in
param_names
:
input_names
=
set
(
op
.
input_names
)
# TODO(typhoonzero): using Param and Grad input name to identify
# that the operator is an optimization operator, need a better way.
if
"Param"
in
input_names
:
if
op
.
input
(
"Param"
)[
0
]
in
param_names
:
return
True
else
:
for
n
in
param_names
:
if
same_or_split_var
(
n
,
op
.
input
s
[
"Param"
].
name
)
and
n
!=
op
.
inputs
[
"Param"
].
name
:
if
same_or_split_var
(
n
,
op
.
input
(
"Param"
)[
0
])
\
and
n
!=
op
.
input
(
"Param"
)[
0
]
:
return
True
return
False
else
:
j
=
idx
-
1
while
j
>=
0
:
prev_op
=
all_ops
[
j
]
prev_output_names
=
[
o
.
name
for
o
in
prev_op
.
outputs
.
values
()]
prev_input_names
=
[
o
.
name
for
o
in
prev_op
.
inputs
.
values
()]
# prev_output_names = [o.name for o in prev_op.outputs.values()]
# prev_input_names = [o.name for o in prev_op.inputs.values()]
# NOTE(typhoonzero): consider list input/output
prev_output_names
=
prev_op
.
desc
.
output_arg_names
()
prev_input_names
=
prev_op
.
desc
.
input_arg_names
()
found1
=
False
found2
=
False
for
_
,
v
in
op
.
inputs
.
iteritem
s
():
if
v
.
name
in
prev_output_names
:
for
varname
in
op
.
desc
.
input_arg_name
s
():
if
v
ar
name
in
prev_output_names
:
found1
=
self
.
_is_op_on_pserver
(
endpoint
,
all_ops
,
j
)
# later ops may produce output for prev op's next batch use.
for
_
,
v
in
op
.
outputs
.
iteritem
s
():
if
v
.
name
in
prev_input_names
:
for
varname
in
op
.
desc
.
output_arg_name
s
():
if
v
ar
name
in
prev_input_names
:
found2
=
self
.
_is_op_on_pserver
(
endpoint
,
all_ops
,
j
)
if
found1
or
found2
:
return
True
...
...
@@ -342,11 +351,11 @@ class DistributeTranspiler:
new_inputs
=
dict
()
# update param/grad shape first, then other inputs like
# moment can use the updated shape
for
key
,
var
in
opt_op
.
inputs
.
iteritems
()
:
for
key
in
opt_op
.
input_names
:
if
key
==
"Grad"
:
grad_block
=
None
for
g
in
self
.
param_grad_ep_mapping
[
endpoint
][
"grads"
]:
if
same_or_split_var
(
g
.
name
,
var
.
name
):
if
same_or_split_var
(
g
.
name
,
opt_op
.
input
(
key
)[
0
]
):
grad_block
=
g
break
if
not
grad_block
:
...
...
@@ -376,7 +385,7 @@ class DistributeTranspiler:
# param is already created on global program
param_block
=
None
for
p
in
self
.
param_grad_ep_mapping
[
endpoint
][
"params"
]:
if
same_or_split_var
(
p
.
name
,
var
.
name
):
if
same_or_split_var
(
p
.
name
,
opt_op
.
input
(
key
)
):
param_block
=
p
break
if
not
param_block
:
...
...
@@ -389,11 +398,12 @@ class DistributeTranspiler:
new_inputs
[
key
]
=
tmpvar
for
key
,
var
in
opt_op
.
inputs
.
iteritems
()
:
for
key
in
opt_op
.
input_names
:
if
key
in
[
"Param"
,
"Grad"
]:
continue
# update accumulator variable shape
param_shape
=
new_inputs
[
"Param"
].
shape
var
=
program
.
global_block
().
vars
[
opt_op
.
input
(
key
)]
new_shape
=
self
.
_get_optimizer_input_shape
(
opt_op
.
type
,
key
,
var
.
shape
,
param_shape
)
tmpvar
=
program
.
global_block
().
create_var
(
...
...
@@ -412,30 +422,46 @@ class DistributeTranspiler:
shape
=
new_shape
)
# change output's ParamOut variable
opt_op
.
outputs
[
"ParamOut"
]
=
new_inputs
[
"Param"
]
outputs
=
self
.
_get_output_map_from_op
(
program
.
global_block
(),
opt_op
)
outputs
[
"ParamOut"
]
=
new_inputs
[
"Param"
]
program
.
global_block
().
append_op
(
type
=
opt_op
.
type
,
inputs
=
new_inputs
,
outputs
=
o
pt_op
.
o
utputs
,
outputs
=
outputs
,
attrs
=
opt_op
.
attrs
)
def
_append_pserver_non_opt_ops
(
self
,
program
,
pserver_program
,
opt_op
):
# Append the ops for parameters that do not need to be optimized/updated
for
_
,
var
in
opt_op
.
inputs
.
iteritems
():
program
.
global_block
().
create_var
(
name
=
var
.
name
,
persistable
=
var
.
persistable
,
dtype
=
var
.
dtype
,
shape
=
var
.
shape
)
pserver_program
.
global_block
().
create_var
(
name
=
var
.
name
,
persistable
=
var
.
persistable
,
dtype
=
var
.
dtype
,
shape
=
var
.
shape
)
inputs
=
self
.
_get_input_map_from_op
(
self
.
program
.
global_block
().
vars
,
opt_op
)
for
var
in
inputs
.
itervalues
():
if
type
(
var
)
==
list
:
varlist
=
var
else
:
varlist
=
[
var
]
for
var
in
varlist
:
program
.
global_block
().
create_var
(
name
=
var
.
name
,
persistable
=
var
.
persistable
,
dtype
=
var
.
dtype
,
shape
=
var
.
shape
)
try
:
pserver_program
.
global_block
().
create_var
(
name
=
var
.
name
,
persistable
=
var
.
persistable
,
dtype
=
var
.
dtype
,
shape
=
var
.
shape
)
except
ValueError
:
# create var if not created yet.
pass
outputs
=
self
.
_get_output_map_from_op
(
self
.
program
.
global_block
().
vars
,
opt_op
)
program
.
global_block
().
append_op
(
type
=
opt_op
.
type
,
inputs
=
opt_op
.
inputs
,
outputs
=
o
pt_op
.
o
utputs
,
inputs
=
inputs
,
outputs
=
outputs
,
attrs
=
opt_op
.
attrs
)
def
get_pserver_program
(
self
,
endpoint
):
...
...
@@ -472,7 +498,7 @@ class DistributeTranspiler:
self
.
optimize_ops
,
idx
)
if
not
is_op_on_pserver
:
continue
if
opt_op
.
inputs
.
has_key
(
"Grad"
):
if
"Grad"
in
opt_op
.
desc
.
input_arg_names
(
):
self
.
_append_pserver_ops
(
optimize_sub_program
,
pserver_program
,
opt_op
,
endpoint
)
else
:
...
...
@@ -499,6 +525,30 @@ class DistributeTranspiler:
pserver_program
.
sync_with_cpp
()
return
pserver_program
def
_get_input_map_from_op
(
self
,
varmap
,
op
):
iomap
=
dict
()
for
key
in
op
.
input_names
:
vars
=
[]
for
varname
in
op
.
input
(
key
):
vars
.
append
(
varmap
[
varname
])
if
len
(
vars
)
==
1
:
iomap
[
key
]
=
vars
[
0
]
else
:
iomap
[
key
]
=
vars
return
iomap
def
_get_output_map_from_op
(
self
,
varmap
,
op
):
iomap
=
dict
()
for
key
in
op
.
output_names
:
vars
=
[]
for
varname
in
op
.
output
(
key
):
vars
.
append
(
varmap
[
varname
])
if
len
(
vars
)
==
1
:
iomap
[
key
]
=
vars
[
0
]
else
:
iomap
[
key
]
=
vars
return
iomap
def
get_startup_program
(
self
,
endpoint
,
pserver_program
):
"""
Get startup program for current parameter server.
...
...
@@ -529,17 +579,21 @@ class DistributeTranspiler:
# 2. rename op outputs
for
op
in
orig_s_prog
.
global_block
().
ops
:
new_inputs
=
dict
()
new_outputs
=
dict
()
# do not append startup op if var is not on this pserver
op_on_pserver
=
False
for
key
,
var
in
op
.
outputs
.
iteritems
()
:
newname
,
_
=
_get_splited_name_and_shape
(
var
.
name
)
for
key
in
op
.
output_names
:
newname
,
_
=
_get_splited_name_and_shape
(
op
.
output
(
key
)[
0
]
)
if
newname
:
op_on_pserver
=
True
new_outputs
[
key
]
=
created_var_map
[
newname
]
elif
var
.
name
in
pserver_vars
:
elif
op
.
output
(
key
)[
0
]
in
pserver_vars
:
op_on_pserver
=
True
new_outputs
[
key
]
=
pserver_vars
[
var
.
name
]
new_outputs
[
key
]
=
pserver_vars
[
op
.
output
(
key
)[
0
]]
# most startup program ops have no inputs
new_inputs
=
self
.
_get_input_map_from_op
(
pserver_vars
,
op
)
if
op_on_pserver
:
if
op
.
type
in
[
...
...
@@ -548,7 +602,7 @@ class DistributeTranspiler:
op
.
attrs
[
"shape"
]
=
new_outputs
[
"Out"
].
shape
s_prog
.
global_block
().
append_op
(
type
=
op
.
type
,
inputs
=
op
.
inputs
,
inputs
=
new_
inputs
,
outputs
=
new_outputs
,
attrs
=
op
.
attrs
)
return
s_prog
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