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体验新版 GitCode,发现更多精彩内容 >>
提交
e90bfd56
编写于
5月 31, 2018
作者:
M
minqiyang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
1. Make a base unittest class for dist transpiler unittest
2. Merge the develop repo
上级
b31647c6
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
87 addition
and
106 deletion
+87
-106
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
+3
-55
python/paddle/fluid/tests/unittests/test_simple_dist_transpiler.py
...ddle/fluid/tests/unittests/test_simple_dist_transpiler.py
+4
-43
python/paddle/fluid/tests/unittests/transpiler_test.py
python/paddle/fluid/tests/unittests/transpiler_test.py
+73
-0
python/paddle/fluid/transpiler/distribute_transpiler.py
python/paddle/fluid/transpiler/distribute_transpiler.py
+7
-8
未找到文件。
python/paddle/fluid/tests/unittests/test_dist_transpiler.py
浏览文件 @
e90bfd56
...
@@ -12,40 +12,15 @@
...
@@ -12,40 +12,15 @@
# See the License for the specific language governing permissions and
# See the License for the specific language governing permissions and
# limitations under the License.
# limitations under the License.
import
unittest
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
paddle.fluid.layers
as
layers
from
paddle.fluid.transpiler.distribute_transpiler
import
delete_ops
from
paddle.fluid.transpiler.distribute_transpiler
import
delete_ops
import
numpy
from
transpiler_test
import
TranspilerTest
class
TestDistTranspiler
(
unittest
.
TestCase
):
class
TestDistTranspiler
(
TranspilerTest
):
def
setUp
(
self
):
def
setUp
(
self
):
self
.
trainer_id
=
0
self
.
trainers
=
2
self
.
pservers
=
2
self
.
pserver_eps
=
"127.0.0.1:6174,127.0.0.1:6175"
self
.
current_pserver_ep
=
"127.0.0.1:6174"
self
.
current_pserver_ep
=
"127.0.0.1:6174"
def
net_conf
(
self
):
x
=
fluid
.
layers
.
data
(
name
=
'x'
,
shape
=
[
1000
],
dtype
=
'float32'
)
y_predict
=
fluid
.
layers
.
fc
(
input
=
x
,
size
=
1000
,
act
=
None
,
param_attr
=
fluid
.
ParamAttr
(
name
=
'fc_w'
))
y
=
fluid
.
layers
.
data
(
name
=
'y'
,
shape
=
[
1
],
dtype
=
'float32'
)
cost
=
fluid
.
layers
.
square_error_cost
(
input
=
y_predict
,
label
=
y
)
avg_cost
=
fluid
.
layers
.
mean
(
cost
)
sgd_optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.1
)
optimize_ops
,
params_grads
=
sgd_optimizer
.
minimize
(
avg_cost
)
return
optimize_ops
,
params_grads
def
test_transpiler
(
self
):
def
test_transpiler
(
self
):
trainer
=
self
.
get_trainer
()
trainer
=
self
.
get_trainer
()
pserver
,
startup
=
self
.
get_pserver
(
self
.
current_pserver_ep
)
pserver
,
startup
=
self
.
get_pserver
(
self
.
current_pserver_ep
)
...
@@ -70,14 +45,6 @@ class TestDistTranspiler(unittest.TestCase):
...
@@ -70,14 +45,6 @@ class TestDistTranspiler(unittest.TestCase):
fc_w_var
=
startup
.
global_block
().
var
(
"fc_w.block1"
)
fc_w_var
=
startup
.
global_block
().
var
(
"fc_w.block1"
)
self
.
assertEqual
(
fc_w_var
.
shape
,
(
500
,
1000
))
self
.
assertEqual
(
fc_w_var
.
shape
,
(
500
,
1000
))
def
get_main_program
(
self
):
main
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
):
self
.
net_conf
()
return
main
def
get_expect_trainer_ops
(
self
):
def
get_expect_trainer_ops
(
self
):
trainer
=
fluid
.
Program
()
trainer
=
fluid
.
Program
()
...
@@ -92,25 +59,6 @@ class TestDistTranspiler(unittest.TestCase):
...
@@ -92,25 +59,6 @@ class TestDistTranspiler(unittest.TestCase):
ops
.
insert
(
ops
.
index
(
"elementwise_add_grad"
)
+
1
,
"send_vars"
)
ops
.
insert
(
ops
.
index
(
"elementwise_add_grad"
)
+
1
,
"send_vars"
)
return
ops
return
ops
def
get_trainer
(
self
):
return
self
.
_transpiler_instance
().
get_trainer_program
()
def
get_pserver
(
self
,
ep
):
t
=
self
.
_transpiler_instance
()
pserver
=
t
.
get_pserver_program
(
ep
)
startup
=
t
.
get_startup_program
(
ep
,
pserver
)
return
pserver
,
startup
def
_transpiler_instance
(
self
):
main
=
self
.
get_main_program
()
t
=
fluid
.
DistributeTranspiler
()
t
.
transpile
(
self
.
trainer_id
,
program
=
main
,
pservers
=
self
.
pserver_eps
,
trainers
=
self
.
trainers
)
return
t
if
__name__
==
"__main__"
:
if
__name__
==
"__main__"
:
unittest
.
main
()
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_simple_dist_transpiler.py
浏览文件 @
e90bfd56
...
@@ -12,40 +12,18 @@
...
@@ -12,40 +12,18 @@
# See the License for the specific language governing permissions and
# See the License for the specific language governing permissions and
# limitations under the License.
# limitations under the License.
import
unittest
import
numpy
as
np
import
paddle.fluid
as
fluid
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
paddle.fluid.layers
as
layers
from
paddle.fluid.transpiler.distribute_transpiler
import
delete_ops
from
paddle.fluid.transpiler.distribute_transpiler
import
delete_ops
import
numpy
as
np
from
transpiler_test
import
TranspilerTest
class
TestSimpleDistTranspiler
(
unittest
.
TestCase
):
class
TestSimpleDistTranspiler
(
TranspilerTest
):
def
setUp
(
self
):
def
setUp
(
self
):
self
.
trainer_id
=
0
self
.
trainers
=
2
self
.
pservers
=
2
self
.
pserver_eps
=
"127.0.0.1:6174,127.0.0.1:6175"
self
.
current_pserver_ep
=
"127.0.0.1:6175"
self
.
current_pserver_ep
=
"127.0.0.1:6175"
def
net_conf
(
self
):
x
=
fluid
.
layers
.
data
(
name
=
'x'
,
shape
=
[
1000
],
dtype
=
'float32'
)
y_predict
=
fluid
.
layers
.
fc
(
input
=
x
,
size
=
1000
,
act
=
None
,
param_attr
=
fluid
.
ParamAttr
(
name
=
'fc_w'
))
y
=
fluid
.
layers
.
data
(
name
=
'y'
,
shape
=
[
1
],
dtype
=
'float32'
)
cost
=
fluid
.
layers
.
square_error_cost
(
input
=
y_predict
,
label
=
y
)
avg_cost
=
fluid
.
layers
.
mean
(
cost
)
sgd_optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.1
)
optimize_ops
,
params_grads
=
sgd_optimizer
.
minimize
(
avg_cost
)
return
optimize_ops
,
params_grads
def
test_simple_transpiler
(
self
):
def
test_simple_transpiler
(
self
):
np
.
random
.
seed
(
1
)
np
.
random
.
seed
(
1
)
...
@@ -73,14 +51,6 @@ class TestSimpleDistTranspiler(unittest.TestCase):
...
@@ -73,14 +51,6 @@ class TestSimpleDistTranspiler(unittest.TestCase):
fc_w_var
=
startup
.
global_block
().
var
(
"fc_w@GRAD.trainer_0"
)
fc_w_var
=
startup
.
global_block
().
var
(
"fc_w@GRAD.trainer_0"
)
self
.
assertEqual
(
fc_w_var
.
shape
,
(
1000
,
1000
))
self
.
assertEqual
(
fc_w_var
.
shape
,
(
1000
,
1000
))
def
get_main_program
(
self
):
main
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
):
self
.
net_conf
()
return
main
def
get_expect_trainer_ops
(
self
):
def
get_expect_trainer_ops
(
self
):
trainer
=
fluid
.
Program
()
trainer
=
fluid
.
Program
()
...
@@ -94,15 +64,6 @@ class TestSimpleDistTranspiler(unittest.TestCase):
...
@@ -94,15 +64,6 @@ class TestSimpleDistTranspiler(unittest.TestCase):
ops
.
insert
(
ops
.
index
(
"elementwise_add_grad"
)
+
1
,
"send_vars"
)
ops
.
insert
(
ops
.
index
(
"elementwise_add_grad"
)
+
1
,
"send_vars"
)
return
ops
return
ops
def
get_trainer
(
self
):
return
self
.
_transpiler_instance
().
get_trainer_program
()
def
get_pserver
(
self
,
ep
):
t
=
self
.
_transpiler_instance
()
pserver
=
t
.
get_pserver_program
(
ep
)
startup
=
t
.
get_startup_program
(
ep
,
pserver
)
return
pserver
,
startup
def
_transpiler_instance
(
self
):
def
_transpiler_instance
(
self
):
main
=
self
.
get_main_program
()
main
=
self
.
get_main_program
()
t
=
fluid
.
DistributeTranspiler
()
t
=
fluid
.
DistributeTranspiler
()
...
...
python/paddle/fluid/tests/unittests/transpiler_test.py
0 → 100644
浏览文件 @
e90bfd56
# 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.
import
unittest
import
numpy
as
np
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
paddle.fluid.layers
as
layers
class
TranspilerTest
(
unittest
.
TestCase
):
@
classmethod
def
setUpClass
(
self
):
self
.
trainer_id
=
0
self
.
trainers
=
2
self
.
pservers
=
2
self
.
pserver_eps
=
"127.0.0.1:6174,127.0.0.1:6175"
def
net_conf
(
self
):
x
=
fluid
.
layers
.
data
(
name
=
'x'
,
shape
=
[
1000
],
dtype
=
'float32'
)
y_predict
=
fluid
.
layers
.
fc
(
input
=
x
,
size
=
1000
,
act
=
None
,
param_attr
=
fluid
.
ParamAttr
(
name
=
'fc_w'
))
y
=
fluid
.
layers
.
data
(
name
=
'y'
,
shape
=
[
1
],
dtype
=
'float32'
)
cost
=
fluid
.
layers
.
square_error_cost
(
input
=
y_predict
,
label
=
y
)
avg_cost
=
fluid
.
layers
.
mean
(
cost
)
sgd_optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.1
)
optimize_ops
,
params_grads
=
sgd_optimizer
.
minimize
(
avg_cost
)
return
optimize_ops
,
params_grads
def
get_main_program
(
self
):
main
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
):
self
.
net_conf
()
return
main
def
get_trainer
(
self
):
return
self
.
_transpiler_instance
().
get_trainer_program
()
def
get_pserver
(
self
,
ep
):
t
=
self
.
_transpiler_instance
()
pserver
=
t
.
get_pserver_program
(
ep
)
startup
=
t
.
get_startup_program
(
ep
,
pserver
)
return
pserver
,
startup
def
_transpiler_instance
(
self
):
main
=
self
.
get_main_program
()
t
=
fluid
.
DistributeTranspiler
()
t
.
transpile
(
self
.
trainer_id
,
program
=
main
,
pservers
=
self
.
pserver_eps
,
trainers
=
self
.
trainers
)
return
t
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @
e90bfd56
...
@@ -178,7 +178,7 @@ class DistributeTranspiler:
...
@@ -178,7 +178,7 @@ class DistributeTranspiler:
for
index
in
range
(
len
(
self
.
pserver_endpoints
))
for
index
in
range
(
len
(
self
.
pserver_endpoints
))
]
]
def
_init_splited_vars
(
self
,
split_method
):
def
_init_splited_vars
(
self
,
split_method
,
align_var_to_block
=
True
):
# update these mappings for further transpile:
# update these mappings for further transpile:
# 1. param_var_mapping: param var name -> [splited params vars]
# 1. param_var_mapping: param var name -> [splited params vars]
# 2. grad_var_mapping: grad var name -> [splited grads vars]
# 2. grad_var_mapping: grad var name -> [splited grads vars]
...
@@ -198,15 +198,14 @@ class DistributeTranspiler:
...
@@ -198,15 +198,14 @@ class DistributeTranspiler:
self
.
params_grads
)
self
.
params_grads
)
if
align_var_to_block
:
if
align_var_to_block
:
grad_blocks
=
split_dense_variable
(
grad_list
,
grad_blocks
=
split_variable
(
grad_list
,
len
(
self
.
pserver_endpoints
))
len
(
pserver_endpoints
))
param_blocks
=
split_variable
(
param_list
,
param_blocks
=
split_dense_variable
(
param_list
,
len
(
self
.
pserver_endpoints
))
len
(
pserver_endpoints
))
else
:
else
:
# when we do NOT align var to block, we will always split params
# when we do NOT align var to block, we will always split params
# grads into one block.
# grads into one block.
grad_blocks
=
split_
dense_
variable
(
grad_list
,
1
)
grad_blocks
=
split_variable
(
grad_list
,
1
)
param_blocks
=
split_
dense_
variable
(
param_list
,
1
)
param_blocks
=
split_variable
(
param_list
,
1
)
assert
(
len
(
grad_blocks
)
==
len
(
param_blocks
))
assert
(
len
(
grad_blocks
)
==
len
(
param_blocks
))
# origin_varname -> [splited_var]
# origin_varname -> [splited_var]
self
.
param_var_mapping
=
self
.
_create_vars_from_blocklist
(
self
.
param_var_mapping
=
self
.
_create_vars_from_blocklist
(
...
@@ -272,7 +271,7 @@ class DistributeTranspiler:
...
@@ -272,7 +271,7 @@ class DistributeTranspiler:
self
.
has_distributed_lookup_table
=
self
.
_has_distributed_lookup_table
()
self
.
has_distributed_lookup_table
=
self
.
_has_distributed_lookup_table
()
# split and create vars, then put splited vars in dicts for later use.
# split and create vars, then put splited vars in dicts for later use.
self
.
_init_splited_vars
(
split_method
)
self
.
_init_splited_vars
(
split_method
,
align_var_to_block
)
# step 3.1: insert send op to send gradient vars to parameter servers
# step 3.1: insert send op to send gradient vars to parameter servers
ps_dispatcher
.
reset
()
ps_dispatcher
.
reset
()
...
...
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