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体验新版 GitCode,发现更多精彩内容 >>
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2b6a5793
编写于
9月 14, 2020
作者:
S
ShenLiang
提交者:
GitHub
9月 14, 2020
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电子邮件补丁
差异文件
remove auto mode from localsgd optimizer (#27237)
* rm auto from localsgd
上级
cc3f4b81
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
29 addition
and
53 deletion
+29
-53
paddle/fluid/framework/distributed_strategy.proto
paddle/fluid/framework/distributed_strategy.proto
+4
-1
python/paddle/distributed/fleet/base/distributed_strategy.py
python/paddle/distributed/fleet/base/distributed_strategy.py
+3
-6
python/paddle/distributed/fleet/meta_optimizers/localsgd_optimizer.py
...e/distributed/fleet/meta_optimizers/localsgd_optimizer.py
+18
-44
python/paddle/fluid/tests/unittests/test_fleet_distributed_strategy.py
.../fluid/tests/unittests/test_fleet_distributed_strategy.py
+3
-2
python/paddle/fluid/tests/unittests/test_fleet_localsgd_meta_optimizer.py
...uid/tests/unittests/test_fleet_localsgd_meta_optimizer.py
+1
-0
未找到文件。
paddle/fluid/framework/distributed_strategy.proto
100755 → 100644
浏览文件 @
2b6a5793
...
@@ -36,7 +36,10 @@ message AMPConfig {
...
@@ -36,7 +36,10 @@ message AMPConfig {
repeated
string
custom_black_varnames
=
9
;
repeated
string
custom_black_varnames
=
9
;
}
}
message
LocalSGDConfig
{
optional
int32
k_steps
=
1
[
default
=
4
];
}
message
LocalSGDConfig
{
optional
int32
k_steps
=
1
[
default
=
1
];
optional
int32
begin_step
=
2
[
default
=
1
];
}
message
GradientMergeConfig
{
message
GradientMergeConfig
{
optional
int32
k_steps
=
1
[
default
=
1
];
optional
int32
k_steps
=
1
[
default
=
1
];
...
...
python/paddle/distributed/fleet/base/distributed_strategy.py
浏览文件 @
2b6a5793
...
@@ -707,11 +707,7 @@ class DistributedStrategy(object):
...
@@ -707,11 +707,7 @@ class DistributedStrategy(object):
**Notes**:
**Notes**:
k_steps(int) The local steps for training before parameter synchronization. Default 1.
k_steps(int) The local steps for training before parameter synchronization. Default 1.
begin_step(int) The step of begining training by localsgd. Default 1.
If strategy.auto is set True, the local steps will be calculated automatically during training.
The algorithm is referenced in this paper:
`Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD <https://arxiv.org/pdf/1810.08313.pdf>`_.
In this case, k_steps indicates the first local steps which is suggested setting to 1.
Examples:
Examples:
.. code-block:: python
.. code-block:: python
...
@@ -719,7 +715,8 @@ class DistributedStrategy(object):
...
@@ -719,7 +715,8 @@ class DistributedStrategy(object):
import paddle.distributed.fleet as fleet
import paddle.distributed.fleet as fleet
strategy = fleet.DistributedStrategy()
strategy = fleet.DistributedStrategy()
strategy.localsgd = True
strategy.localsgd = True
strategy.localsgd_configs = {"k_steps": 4}
strategy.localsgd_configs = {"k_steps": 4,
"begin_step": 30}
"""
"""
return
get_msg_dict
(
self
.
strategy
.
localsgd_configs
)
return
get_msg_dict
(
self
.
strategy
.
localsgd_configs
)
...
...
python/paddle/distributed/fleet/meta_optimizers/localsgd_optimizer.py
浏览文件 @
2b6a5793
...
@@ -49,7 +49,7 @@ class LocalSGDOptimizer(MetaOptimizerBase):
...
@@ -49,7 +49,7 @@ class LocalSGDOptimizer(MetaOptimizerBase):
def
_enable_strategy
(
self
,
dist_strategy
,
context
):
def
_enable_strategy
(
self
,
dist_strategy
,
context
):
dist_strategy
.
localsgd
=
True
dist_strategy
.
localsgd
=
True
dist_strategy
.
localsgd_configs
=
{
"k_steps"
:
1
}
dist_strategy
.
localsgd_configs
=
{
"k_steps"
:
1
,
"begin_step"
:
1
}
def
snapshot_name
(
self
,
param_name
):
def
snapshot_name
(
self
,
param_name
):
return
param_name
+
self
.
snapshot_key
return
param_name
+
self
.
snapshot_key
...
@@ -86,8 +86,9 @@ class LocalSGDOptimizer(MetaOptimizerBase):
...
@@ -86,8 +86,9 @@ class LocalSGDOptimizer(MetaOptimizerBase):
minimized
=
self
.
inner_opt
.
minimize
(
minimized
=
self
.
inner_opt
.
minimize
(
loss
,
startup_program
=
startup_program
)
loss
,
startup_program
=
startup_program
)
init_k_steps
=
self
.
user_defined_strategy
.
localsgd_configs
[
'k_steps'
]
k_steps_value
=
self
.
user_defined_strategy
.
localsgd_configs
[
'k_steps'
]
auto_steps
=
self
.
user_defined_strategy
.
auto
begin_step_value
=
self
.
user_defined_strategy
.
localsgd_configs
[
'begin_step'
]
if
startup_program
is
None
:
if
startup_program
is
None
:
startup_program
=
default_startup_program
()
startup_program
=
default_startup_program
()
...
@@ -101,45 +102,28 @@ class LocalSGDOptimizer(MetaOptimizerBase):
...
@@ -101,45 +102,28 @@ class LocalSGDOptimizer(MetaOptimizerBase):
p2s
=
self
.
create_snapshot_vars
(
main_block
.
program
)
p2s
=
self
.
create_snapshot_vars
(
main_block
.
program
)
with
program_guard
(
main_block
.
program
,
startup_program
):
with
program_guard
(
main_block
.
program
,
startup_program
):
step
=
layers
.
autoincreased_step_counter
(
begin
=
0
)
step
=
layers
.
autoincreased_step_counter
(
begin
=
1
)
k_steps
=
layers
.
create_global_var
(
k_steps
=
layers
.
create_global_var
(
name
=
"k_steps"
,
name
=
"k_steps"
,
shape
=
[
1
],
shape
=
[
1
],
value
=
init_k_steps
,
value
=
k_steps_value
,
dtype
=
'int64'
,
dtype
=
'int64'
,
persistable
=
True
)
persistable
=
True
)
begin_step
=
layers
.
create_global_var
(
name
=
"begin_step"
,
shape
=
[
1
],
value
=
begin_step_value
,
dtype
=
'int64'
,
persistable
=
True
)
last_step
=
layers
.
create_global_var
(
last_step
=
layers
.
create_global_var
(
name
=
"last_step"
,
name
=
"last_step"
,
shape
=
[
1
],
shape
=
[
1
],
value
=
int
(
0
)
,
value
=
begin_step_value
,
dtype
=
'int64'
,
dtype
=
'int64'
,
persistable
=
True
)
persistable
=
True
)
if
auto_steps
:
avg_loss
=
layers
.
collective
.
_c_allreduce
(
loss
)
/
self
.
role_maker
.
worker_num
()
lr_0
=
layers
.
create_global_var
(
name
=
"lr_0"
,
shape
=
[
1
],
value
=
float
(
0
),
dtype
=
'float32'
,
persistable
=
True
)
loss_0
=
layers
.
create_global_var
(
name
=
"loss_0"
,
shape
=
[
1
],
value
=
float
(
0
),
dtype
=
'float32'
,
persistable
=
True
)
global_lr
=
self
.
inner_opt
.
_global_learning_rate
()
def
initialize
():
layers
.
assign
(
loss
,
loss_0
)
layers
.
assign
(
global_lr
,
lr_0
)
layers
.
cond
(
step
==
0
,
initialize
)
def
communicate
():
def
communicate
():
sub_block
=
default_main_program
().
current_block
()
sub_block
=
default_main_program
().
current_block
()
ring_id
=
-
1
ring_id
=
-
1
...
@@ -195,20 +179,10 @@ class LocalSGDOptimizer(MetaOptimizerBase):
...
@@ -195,20 +179,10 @@ class LocalSGDOptimizer(MetaOptimizerBase):
inputs
=
{
'X'
:
[
param
]},
inputs
=
{
'X'
:
[
param
]},
outputs
=
{
'Out'
:
[
snapshot
]},
outputs
=
{
'Out'
:
[
snapshot
]},
attrs
=
{
OP_ROLE_KEY
:
OpRole
.
Optimize
})
attrs
=
{
OP_ROLE_KEY
:
OpRole
.
Optimize
})
if
auto_steps
:
next_local_steps
=
layers
.
cast
(
layers
.
ceil
(
layers
.
sqrt
(
lr_0
*
loss
/
(
global_lr
*
loss_0
)
*
float
(
init_k_steps
))),
dtype
=
'int64'
)
max_local_steps
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'int64'
,
value
=
16
)
next_local_steps
=
layers
.
elementwise_min
(
next_local_steps
,
max_local_steps
)
layers
.
assign
(
next_local_steps
,
k_steps
)
layers
.
assign
(
step
,
last_step
)
layers
.
assign
(
step
,
last_step
)
layers
.
cond
(
step
-
last_step
==
k_steps
,
communicate
)
def
begin_localsgd
():
layers
.
cond
(
step
-
last_step
==
k_steps
,
communicate
)
layers
.
cond
(
step
>
begin_step
,
begin_localsgd
,
communicate
)
return
minimized
return
minimized
python/paddle/fluid/tests/unittests/test_fleet_distributed_strategy.py
浏览文件 @
2b6a5793
...
@@ -81,9 +81,10 @@ class TestStrategyConfig(unittest.TestCase):
...
@@ -81,9 +81,10 @@ class TestStrategyConfig(unittest.TestCase):
def
test_localsgd_configs
(
self
):
def
test_localsgd_configs
(
self
):
strategy
=
paddle
.
distributed
.
fleet
.
DistributedStrategy
()
strategy
=
paddle
.
distributed
.
fleet
.
DistributedStrategy
()
configs
=
{
"k_steps"
:
4
}
configs
=
{
"k_steps"
:
4
,
"begin_step"
:
120
}
strategy
.
localsgd_configs
=
configs
strategy
.
localsgd_configs
=
configs
self
.
assertEqual
(
strategy
.
localsgd_configs
[
"k_steps"
],
4
)
self
.
assertEqual
(
strategy
.
localsgd_configs
[
"k_steps"
],
4
)
self
.
assertEqual
(
strategy
.
localsgd_configs
[
"begin_step"
],
120
)
def
test_dgc
(
self
):
def
test_dgc
(
self
):
strategy
=
paddle
.
distributed
.
fleet
.
DistributedStrategy
()
strategy
=
paddle
.
distributed
.
fleet
.
DistributedStrategy
()
...
@@ -230,7 +231,7 @@ class TestStrategyConfig(unittest.TestCase):
...
@@ -230,7 +231,7 @@ class TestStrategyConfig(unittest.TestCase):
strategy
.
a_sync
=
True
strategy
.
a_sync
=
True
strategy
.
localsgd
=
True
strategy
.
localsgd
=
True
strategy
.
dgc
=
True
strategy
.
dgc
=
True
localsgd_configs
=
{
"k_steps"
:
5
}
localsgd_configs
=
{
"k_steps"
:
5
,
"begin_step"
:
1
}
strategy
.
localsgd_configs
=
localsgd_configs
strategy
.
localsgd_configs
=
localsgd_configs
build_strategy
=
paddle
.
fluid
.
BuildStrategy
()
build_strategy
=
paddle
.
fluid
.
BuildStrategy
()
build_strategy
.
enable_sequential_execution
=
True
build_strategy
.
enable_sequential_execution
=
True
...
...
python/paddle/fluid/tests/unittests/test_fleet_localsgd_meta_optimizer.py
浏览文件 @
2b6a5793
...
@@ -44,6 +44,7 @@ class TestFleetLocalSGDMetaOptimizer(unittest.TestCase):
...
@@ -44,6 +44,7 @@ class TestFleetLocalSGDMetaOptimizer(unittest.TestCase):
strategy
.
auto
=
True
strategy
.
auto
=
True
config
=
strategy
.
localsgd_configs
config
=
strategy
.
localsgd_configs
config
[
'k_steps'
]
=
1
config
[
'k_steps'
]
=
1
config
[
'begin_step'
]
=
1
strategy
.
localsgd_configs
=
config
strategy
.
localsgd_configs
=
config
optimizer
=
paddle
.
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.01
)
optimizer
=
paddle
.
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.01
)
...
...
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