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50a02adf
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体验新版 GitCode,发现更多精彩内容 >>
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50a02adf
编写于
1月 10, 2018
作者:
T
typhoonzero
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
transpile program ok
上级
9c0b1cf1
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
66 addition
and
27 deletion
+66
-27
python/paddle/v2/fluid/distribute_transpiler.py
python/paddle/v2/fluid/distribute_transpiler.py
+66
-27
未找到文件。
python/paddle/v2/fluid/distribute_transpiler.py
浏览文件 @
50a02adf
...
...
@@ -114,12 +114,15 @@ class DistributeTranspiler:
# step3
send_inputs
=
[]
send_outputs
=
[]
for
_
,
splited
in
grad_var_mapping
.
iteritems
():
send_inputs
.
extend
(
splited
)
for
b
in
grad_blocks
:
# append by order
varname
,
block_id
,
_
=
b
.
split
(
":"
)
send_inputs
.
append
(
grad_var_mapping
[
varname
][
int
(
block_id
)])
param_var_mapping
=
self
.
_create_vars_from_blocklist
(
program
,
param_blocks
)
for
_
,
splited
in
param_var_mapping
.
iteritems
():
send_outputs
.
extend
(
splited
)
for
b
in
param_blocks
:
varname
,
block_id
,
_
=
b
.
split
(
":"
)
send_outputs
.
append
(
param_var_mapping
[
varname
][
int
(
block_id
)])
# let send_op know which endpoint to send which var, eplist is of the same
# order of send_inputs.
eplist
=
split_method
(
send_inputs
,
pserver_endpoints
)
...
...
@@ -243,8 +246,37 @@ class DistributeTranspiler:
var_list
.
append
(
var_each
)
return
var_list
def
_append_pserver_ops
(
self
,
opt_op
,
endpoint
):
def
_get_optimizer_input_shape
(
self
,
op_type
,
varkey
,
orig_shape
,
param_shape
):
"""
Returns the shape for optimizer inputs that need to be reshaped when
Param and Grad is splited to multiple servers.
"""
# HACK(typhoonzero): Should use functions of corresponding optimizer in
# optimizer.py to get the shape, do not bind this in the transpiler.
if
op_type
==
"adam"
:
if
varkey
in
[
"Moment1"
,
"Moment2"
]:
return
param_shape
elif
op_type
==
"adagrad"
:
if
varkey
==
"Moment"
:
return
param_shape
elif
op_type
==
"adamax"
:
if
varkey
in
[
"Moment"
,
"InfNorm"
]:
return
param_shape
elif
op_type
==
"momentum"
:
if
varkey
==
"Velocity"
:
return
param_shape
elif
op_type
==
""
:
if
varkey
==
"Moment"
:
return
param_shape
elif
op_type
==
"sgd"
:
pass
return
orig_shape
def
_append_pserver_ops
(
self
,
program
,
opt_op
,
endpoint
):
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
():
if
key
==
"Grad"
:
grad_block
=
None
...
...
@@ -256,7 +288,7 @@ class DistributeTranspiler:
# do not append this op if current endpoint
# is not dealing with this grad block
return
merged_var
=
optimize_sub_
program
.
global_block
().
create_var
(
merged_var
=
program
.
global_block
().
create_var
(
name
=
grad_block
.
name
,
persistable
=
grad_block
.
persistable
,
dtype
=
grad_block
.
dtype
,
...
...
@@ -264,13 +296,12 @@ class DistributeTranspiler:
# append merging ops if trainers > 1
if
self
.
trainers
>
1
:
vars2merge
=
self
.
_create_var_for_trainers
(
optimize_sub_program
.
global_block
(),
grad_block
,
self
.
trainers
)
optimize_sub_program
.
global_block
().
append_op
(
program
.
global_block
(),
grad_block
,
self
.
trainers
)
program
.
global_block
().
append_op
(
type
=
"sum"
,
inputs
=
{
"X"
:
vars2merge
},
outputs
=
{
"Out"
:
merged_var
})
optimize_sub_
program
.
global_block
().
append_op
(
program
.
global_block
().
append_op
(
type
=
"scale"
,
inputs
=
{
"X"
:
merged_var
},
outputs
=
{
"Out"
:
merged_var
},
...
...
@@ -285,37 +316,45 @@ class DistributeTranspiler:
break
if
not
param_block
:
return
tmpvar
=
optimize_sub_
program
.
global_block
().
create_var
(
tmpvar
=
program
.
global_block
().
create_var
(
name
=
param_block
.
name
,
persistable
=
param_block
.
persistable
,
dtype
=
param_block
.
dtype
,
shape
=
param_block
.
shape
)
new_inputs
[
key
]
=
tmpvar
else
:
tmpvar
=
optimize_sub_program
.
global_block
().
create_var
(
name
=
var
.
name
,
persistable
=
var
.
persistable
,
dtype
=
var
.
dtype
,
shape
=
var
.
shape
)
new_inputs
[
key
]
=
tmpvar
for
key
,
var
in
opt_op
.
inputs
.
iteritems
():
if
key
in
[
"Param"
,
"Grad"
]:
continue
# update accumulator variable shape
param_shape
=
new_inputs
[
"Param"
].
shape
new_shape
=
self
.
_get_optimizer_input_shape
(
opt_op
.
type
,
key
,
var
.
shape
,
param_shape
)
print
(
"var, new shape"
,
key
,
var
.
name
,
new_shape
)
tmpvar
=
program
.
global_block
().
create_var
(
name
=
var
.
name
,
persistable
=
var
.
persistable
,
dtype
=
var
.
dtype
,
shape
=
new_shape
)
new_inputs
[
key
]
=
tmpvar
# FIXME: change outputs ParamOut
optimize_sub_
program
.
global_block
().
append_op
(
program
.
global_block
().
append_op
(
type
=
opt_op
.
type
,
inputs
=
new_inputs
,
outputs
=
opt_op
.
outputs
,
attrs
=
opt_op
.
attrs
)
def
_append_pserver_non_opt_ops
(
self
,
opt_op
):
def
_append_pserver_non_opt_ops
(
self
,
program
,
opt_op
):
for
_
,
var
in
opt_op
.
inputs
.
iteritems
():
optimize_sub_
program
.
global_block
().
create_var
(
program
.
global_block
().
create_var
(
name
=
var
.
name
,
persistable
=
var
.
persistable
,
dtype
=
var
.
dtype
,
shape
=
var
.
shape
)
optimize_sub_
program
.
global_block
().
append_op
(
program
.
global_block
().
append_op
(
type
=
opt_op
.
type
,
inputs
=
new_
inputs
,
inputs
=
opt_op
.
inputs
,
outputs
=
opt_op
.
outputs
,
attrs
=
opt_op
.
attrs
)
...
...
@@ -331,15 +370,15 @@ class DistributeTranspiler:
# step5
pserver_program
=
Program
()
for
v
in
self
.
param_grad_ep_mapping
[
endpoint
][
"params"
]:
self
.
_clone_
param
(
pserver_program
.
global_block
(),
v
)
self
.
_clone_
var
(
pserver_program
.
global_block
(),
v
)
# step6
optimize_sub_program
=
Program
()
for
opt_op
in
optimize_ops
:
if
opt_op
s
.
inputs
.
has_key
(
"Grad"
):
if
opt_op
.
inputs
.
has_key
(
"Grad"
):
# append optimize_op
self
.
_append_pserver_ops
(
opt_op
,
endpoint
)
self
.
_append_pserver_ops
(
opt
imize_sub_program
,
opt
_op
,
endpoint
)
else
:
self
.
_append_pserver_non_opt_ops
(
opt_op
)
self
.
_append_pserver_non_opt_ops
(
opt
imize_sub_program
,
opt
_op
)
pserver_program
.
global_block
().
append_op
(
type
=
"recv"
,
...
...
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