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体验新版 GitCode,发现更多精彩内容 >>
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57d434df
编写于
8月 20, 2020
作者:
1
123malin
提交者:
GitHub
8月 20, 2020
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差异文件
add save/load for parameter server (#26235)
* add save/load for parameter server
上级
0cc63cc3
变更
13
隐藏空白更改
内联
并排
Showing
13 changed file
with
562 addition
and
55 deletion
+562
-55
python/paddle/distributed/fleet/__init__.py
python/paddle/distributed/fleet/__init__.py
+2
-0
python/paddle/distributed/fleet/base/fleet_base.py
python/paddle/distributed/fleet/base/fleet_base.py
+34
-4
python/paddle/distributed/fleet/runtime/parameter_server_runtime.py
...dle/distributed/fleet/runtime/parameter_server_runtime.py
+313
-1
python/paddle/distributed/fleet/runtime/runtime_base.py
python/paddle/distributed/fleet/runtime/runtime_base.py
+6
-0
python/paddle/fluid/tests/unittests/CMakeLists.txt
python/paddle/fluid/tests/unittests/CMakeLists.txt
+4
-0
python/paddle/fluid/tests/unittests/dist_fleet_ctr.py
python/paddle/fluid/tests/unittests/dist_fleet_ctr.py
+4
-11
python/paddle/fluid/tests/unittests/dist_fleet_sparse_embedding_ctr.py
.../fluid/tests/unittests/dist_fleet_sparse_embedding_ctr.py
+2
-8
python/paddle/fluid/tests/unittests/test_dist_fleet_base.py
python/paddle/fluid/tests/unittests/test_dist_fleet_base.py
+14
-11
python/paddle/fluid/tests/unittests/test_dist_fleet_ctr.py
python/paddle/fluid/tests/unittests/test_dist_fleet_ctr.py
+2
-2
python/paddle/fluid/tests/unittests/test_fleet_base.py
python/paddle/fluid/tests/unittests/test_fleet_base.py
+4
-18
python/paddle/fluid/tests/unittests/test_fleet_base_2.py
python/paddle/fluid/tests/unittests/test_fleet_base_2.py
+102
-0
python/paddle/fluid/tests/unittests/test_fleet_base_3.py
python/paddle/fluid/tests/unittests/test_fleet_base_3.py
+52
-0
python/paddle/fluid/tests/unittests/test_fleet_runtime.py
python/paddle/fluid/tests/unittests/test_fleet_runtime.py
+23
-0
未找到文件。
python/paddle/distributed/fleet/__init__.py
浏览文件 @
57d434df
...
...
@@ -48,4 +48,6 @@ init_server = fleet.init_server
run_server
=
fleet
.
run_server
stop_worker
=
fleet
.
stop_worker
distributed_optimizer
=
fleet
.
distributed_optimizer
save_inference_model
=
fleet
.
save_inference_model
save_persistables
=
fleet
.
save_persistables
minimize
=
fleet
.
minimize
python/paddle/distributed/fleet/base/fleet_base.py
浏览文件 @
57d434df
...
...
@@ -19,10 +19,26 @@ from .distributed_strategy import DistributedStrategy
from
.meta_optimizer_factory
import
MetaOptimizerFactory
from
.runtime_factory
import
RuntimeFactory
from
.util_factory
import
UtilFactory
from
paddle.fluid.wrapped_decorator
import
wrap_decorator
__all__
=
[
'Fleet'
]
def
_inited_runtime_handler_
(
func
):
def
__impl__
(
*
args
,
**
kwargs
):
cls
=
args
[
0
]
if
cls
.
_runtime_handle
is
None
:
raise
ValueError
(
"Fleet can not find suitable runtime handler"
)
return
func
(
*
args
,
**
kwargs
)
return
__impl__
inited_runtime_handler
=
wrap_decorator
(
_inited_runtime_handler_
)
class
Fleet
(
object
):
"""
Unified API for distributed training of PaddlePaddle
...
...
@@ -182,34 +198,48 @@ class Fleet(object):
"""
self
.
_role_maker
.
barrier_worker
()
@
inited_runtime_handler
def
init_worker
(
self
):
"""
init worker
"""
assert
self
.
_runtime_handle
is
not
None
self
.
_runtime_handle
.
_init_worker
()
@
inited_runtime_handler
def
init_server
(
self
,
*
args
,
**
kwargs
):
"""
init server
"""
assert
self
.
_runtime_handle
is
not
None
self
.
_runtime_handle
.
_init_server
(
*
args
,
**
kwargs
)
@
inited_runtime_handler
def
run_server
(
self
):
"""
run server
"""
assert
self
.
_runtime_handle
is
not
None
self
.
_runtime_handle
.
_run_server
()
@
inited_runtime_handler
def
stop_worker
(
self
):
"""
stop worker
"""
assert
self
.
_runtime_handle
is
not
None
self
.
_runtime_handle
.
_stop_worker
()
def
save_inference_model
(
self
,
executor
,
dirname
,
feeded_var_names
,
target_vars
,
main_program
=
None
,
export_for_deployment
=
True
):
self
.
_runtime_handle
.
_save_inference_model
(
executor
,
dirname
,
feeded_var_names
,
target_vars
,
main_program
,
export_for_deployment
)
def
save_persistables
(
self
,
executor
,
dirname
,
main_program
=
None
):
self
.
_runtime_handle
.
_save_persistables
(
executor
,
dirname
,
main_program
)
def
distributed_optimizer
(
self
,
optimizer
,
strategy
=
None
):
"""
distirbuted_optimizer
...
...
python/paddle/distributed/fleet/runtime/parameter_server_runtime.py
浏览文件 @
57d434df
...
...
@@ -13,11 +13,14 @@
# limitations under the License.
import
os
import
logging
import
warnings
import
paddle.fluid
as
fluid
from
paddle.fluid
import
core
from
paddle.fluid.framework
import
Program
from
paddle.fluid.compiler
import
CompiledProgram
from
paddle.fluid.executor
import
Executor
from
paddle.fluid.parallel_executor
import
ParallelExecutor
from
.runtime_base
import
RuntimeBase
...
...
@@ -241,3 +244,312 @@ class ParameterServerRuntime(RuntimeBase):
self
.
_communicator
.
stop
()
executor
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
executor
.
close
()
def
_get_optimizer_status
(
self
,
op
,
param_name
):
supported_opts
=
[
"sgd"
,
"adam"
,
"adagrad"
,
"adamax"
,
"momentum"
,
"lars_momentum"
,
"rmsprop"
,
"decayed_adagrad"
,
"ftrl"
]
reshaped_val_map
=
{}
reshaped_val_map
[
"sgd"
]
=
[]
reshaped_val_map
[
"adam"
]
=
[
"moment1_0"
,
"moment2_0"
]
reshaped_val_map
[
"adagrad"
]
=
[
"moment_0"
]
reshaped_val_map
[
"adamax"
]
=
[
"moment_0"
,
"inf_norm_0"
]
reshaped_val_map
[
"momentum"
]
=
[
"velocity_0"
]
reshaped_val_map
[
"lars_momentum"
]
=
[
"velocity_0"
]
reshaped_val_map
[
"rmsprop"
]
=
[
"momentum_0"
,
"mean_square_0"
,
"mean_grad_0"
]
reshaped_val_map
[
"decayed_adagrad"
]
=
[
"moment_0"
]
reshaped_val_map
[
"ftrl"
]
=
[
"squared_0"
,
"linear_0"
]
orishaped_val_map
=
{}
orishaped_val_map
[
"adam"
]
=
[
"beta1_pow_acc_0"
,
"beta2_pow_acc_0"
]
orishaped_val_map
[
"adamax"
]
=
[
"beta1_pow_acc_0"
]
if
op
not
in
supported_opts
:
raise
ValueError
(
"fleet can not support optimizer: {}, only this can be supported: {}"
.
format
(
op
,
supported_opts
))
reshaped_names
=
[
param_name
+
"_"
+
val
for
val
in
reshaped_val_map
[
op
]
]
if
op
not
in
orishaped_val_map
:
origin_names
=
[]
else
:
origin_names
=
[
param_name
+
"_"
+
val
for
val
in
orishaped_val_map
[
op
]
]
return
reshaped_names
,
origin_names
def
_get_optimizer_op
(
self
,
param_name
):
from
paddle.fluid.incubate.fleet.parameter_server.ir.public
import
_get_optimize_ops
opts
=
_get_optimize_ops
(
self
.
origin_main_program
)
for
op
in
opts
:
if
"Param"
in
op
.
input_names
and
\
"LearningRate"
in
op
.
input_names
and
op
.
input
(
"Param"
)[
0
]
==
param_name
:
return
op
def
_save_dense_params
(
self
,
executor
,
dirname
,
context
,
main_program
):
self
.
_communicator
.
recv
()
prog
=
Program
()
block
=
prog
.
global_block
()
local_vars
=
[]
for
name
,
var_ctx
in
context
.
items
():
if
len
(
var_ctx
.
origin_varnames
())
!=
1
:
raise
ValueError
(
"Dense can not support split now."
)
varname
=
var_ctx
.
origin_varnames
()[
0
]
local_vars
.
append
(
varname
)
optimizer
=
self
.
_get_optimizer_op
(
varname
)
reshaped_varnames
,
origin_varnames
=
self
.
_get_optimizer_status
(
optimizer
.
type
,
varname
)
for
var_name
in
[
varname
]
+
reshaped_varnames
+
origin_varnames
:
var
=
self
.
origin_main_program
.
global_block
().
vars
[
var_name
]
block
.
append_op
(
type
=
'recv_save'
,
attrs
=
{
"trainer_id"
:
self
.
role_maker
.
worker_index
(),
"shape"
:
var
.
shape
,
"slice_shapes"
:
[
","
.
join
([
str
(
i
)
for
i
in
var
.
shape
])],
"slice_varnames"
:
[
var
.
name
],
"remote_varnames"
:
[
var
.
name
],
"is_sparse"
:
False
,
"endpoints"
:
var_ctx
.
split_endpoints
(),
"file_path"
:
os
.
path
.
join
(
dirname
,
var
.
name
)
})
executor
.
run
(
prog
)
return
local_vars
def
_save_sparse_params
(
self
,
executor
,
dirname
,
context
,
main_program
):
prog
=
Program
()
block
=
prog
.
global_block
()
local_vars
=
[]
for
name
,
var_ctx
in
context
.
items
():
if
len
(
var_ctx
.
origin_varnames
())
!=
1
:
raise
ValueError
(
"Dense can not support split now."
)
varname
=
var_ctx
.
origin_varnames
()[
0
]
local_vars
.
append
(
varname
)
optimizer
=
self
.
_get_optimizer_op
(
varname
)
reshaped_varnames
,
origin_varnames
=
self
.
_get_optimizer_status
(
optimizer
.
type
,
varname
)
var
=
self
.
origin_main_program
.
global_block
().
vars
[
varname
]
slice_shapes
=
[]
dims1
=
","
.
join
([
str
(
i
)
for
i
in
var
.
shape
[
1
:]])
for
section
in
var_ctx
.
sections
():
slice_shapes
.
append
(
str
(
section
)
+
dims1
)
block
.
append_op
(
type
=
'recv_save'
,
attrs
=
{
"trainer_id"
:
self
.
role_maker
.
worker_index
(),
"shape"
:
var
.
shape
,
"slice_shapes"
:
slice_shapes
,
"slice_varnames"
:
var_ctx
.
split_varnames
(),
"remote_varnames"
:
var_ctx
.
split_varnames
(),
"is_sparse"
:
True
,
"endpoints"
:
var_ctx
.
split_endpoints
(),
"pserver_num"
:
len
(
self
.
role_maker
.
get_pserver_endpoints
()),
"file_path"
:
os
.
path
.
join
(
dirname
,
var
.
name
)
})
for
reshaped_varname
in
reshaped_varnames
:
var
=
self
.
origin_main_program
.
global_block
().
vars
[
reshaped_varname
]
slice_varnames
=
[]
remote_varnames
=
[]
for
i
in
range
(
len
(
var_ctx
.
split_varnames
())):
slice_varnames
.
append
(
"{}.block{}"
.
format
(
reshaped_varname
,
i
))
remote_varnames
.
append
(
reshaped_varname
)
block
.
append_op
(
type
=
'recv_save'
,
attrs
=
{
"trainer_id"
:
self
.
role_maker
.
worker_index
(),
"shape"
:
var
.
shape
,
"slice_shapes"
:
slice_shapes
,
"slice_varnames"
:
slice_varnames
,
"remote_varnames"
:
remote_varnames
,
"is_sparse"
:
True
,
"endpoints"
:
var_ctx
.
split_endpoints
(),
"pserver_num"
:
len
(
self
.
role_maker
.
get_pserver_endpoints
()),
"file_path"
:
os
.
path
.
join
(
dirname
,
var
.
name
)
})
for
origin_varname
in
origin_varnames
:
var
=
self
.
origin_main_program
.
global_block
().
vars
[
origin_varname
]
block
.
append_op
(
type
=
'recv_save'
,
attrs
=
{
"trainer_id"
:
self
.
role_maker
.
worker_index
(),
"shape"
:
var
.
shape
,
"slice_shapes"
:
[
","
.
join
([
str
(
i
)
for
i
in
var
.
shape
])],
"slice_varnames"
:
[
origin_varname
],
"remote_varnames"
:
[
origin_varname
],
"is_sparse"
:
False
,
"endpoints"
:
var_ctx
.
split_endpoints
()[:
1
],
"file_path"
:
os
.
path
.
join
(
dirname
,
var
.
name
)
})
executor
.
run
(
prog
)
return
context
.
keys
()
def
_save_distributed_params
(
self
,
executor
,
dirname
,
context
,
main_program
):
prog
=
Program
()
block
=
prog
.
global_block
()
for
name
,
var_ctx
in
context
.
items
():
block
.
append_op
(
type
=
'checkpoint_notify'
,
attrs
=
{
"varname"
:
name
,
"is_slice"
:
True
,
"slice_varnames"
:
var_ctx
.
split_varnames
(),
"remote_varnames"
:
var_ctx
.
split_varnames
(),
"endpoints"
:
var_ctx
.
split_endpoints
(),
"dirname"
:
dirname
})
executor
.
run
(
prog
)
return
context
.
keys
()
def
_save_distributed_persistables
(
self
,
executor
,
dirname
,
main_program
):
dense_ctx
=
self
.
compiled_strategy
.
get_communicator_recv_context
(
recv_type
=
1
)
sparse_ctx
=
self
.
compiled_strategy
.
get_communicator_recv_context
(
recv_type
=
2
)
distributed_ctx
=
self
.
compiled_strategy
.
get_communicator_recv_context
(
recv_type
=
3
)
recv_dense_varnames
=
self
.
_save_dense_params
(
executor
,
dirname
,
dense_ctx
,
main_program
)
recv_sparse_varnames
=
self
.
_save_sparse_params
(
executor
,
dirname
,
sparse_ctx
,
main_program
)
recv_distributed_varnames
=
self
.
_save_distributed_params
(
executor
,
dirname
,
distributed_ctx
,
main_program
)
saved_varnames
=
recv_dense_varnames
+
list
(
recv_sparse_varnames
)
+
list
(
recv_distributed_varnames
)
remaining_vars
=
list
(
filter
(
ParameterServerRuntime
.
__exclude_vars
(
saved_varnames
),
main_program
.
list_vars
()))
fluid
.
io
.
save_vars
(
executor
,
main_program
=
main_program
,
dirname
=
dirname
,
vars
=
remaining_vars
)
def
_ps_inference_save_persistables
(
self
,
executor
,
dirname
,
main_program
=
None
,
**
kwargs
):
"""
This function filters out all variables with `persistable==True` from the
give `main_program` and then saves these variables to the folder `dirname`
or file `filename`.
The `dirname` is used to specify the folder where persistable variables
are going to be saved. If you would like to save variables in separate
files, set `filename` None; if you would like to save all variables in a
single file, use `filename` to specify the file name.
"""
if
isinstance
(
executor
,
ParallelExecutor
):
raise
TypeError
(
"in fleet.save_persistables() function, executor must be as Executor type, ParallelExecutor is not allowed"
)
if
not
isinstance
(
executor
,
Executor
):
raise
TypeError
(
"in fleet.save_persistables() function, executor must be as Executor type"
)
if
main_program
is
None
:
main_program
=
fluid
.
default_main_program
()
if
isinstance
(
main_program
,
CompiledProgram
):
raise
TypeError
(
"in fleet.save_persistables() function, main_program must be as Program type, CompiledProgram is not allowed"
)
self
.
_save_distributed_persistables
(
executor
,
dirname
,
main_program
)
def
_ps_inference_save_inference_model
(
self
,
executor
,
dirname
,
feeded_var_names
,
target_vars
,
main_program
=
None
,
export_for_deployment
=
True
):
"""
Prune the given `main_program` to build a new program especially for inference,
and then save it and all related parameters to given `dirname` by the `executor`.
"""
if
isinstance
(
executor
,
ParallelExecutor
):
raise
TypeError
(
"in fleet.save_inference_model() function, executor must be as Executor type, ParallelExecutor is not allowed"
)
if
not
isinstance
(
executor
,
Executor
):
raise
TypeError
(
"in fleet.save_inference_model() function, executor must be as Executor type"
)
if
main_program
is
not
None
:
if
isinstance
(
main_program
,
CompiledProgram
):
raise
TypeError
(
"in fleet.save_inference_model() function, main_program must be as Program type, CompiledProgram is not allowed"
)
fluid
.
io
.
save_inference_model
(
dirname
,
feeded_var_names
,
target_vars
,
executor
,
main_program
,
None
,
None
,
export_for_deployment
)
else
:
fluid
.
io
.
save_inference_model
(
dirname
,
feeded_var_names
,
target_vars
,
executor
,
self
.
origin_main_program
,
None
,
None
,
export_for_deployment
,
True
)
model_basename
=
"__model__"
model_filename
=
os
.
path
.
join
(
dirname
,
model_basename
)
with
open
(
model_filename
,
"rb"
)
as
f
:
program_desc_str
=
f
.
read
()
program
=
Program
.
parse_from_string
(
program_desc_str
)
program
.
_copy_dist_param_info_from
(
fluid
.
default_main_program
())
self
.
_ps_inference_save_persistables
(
executor
,
dirname
,
program
)
def
_save_inference_model
(
self
,
*
args
,
**
kwargs
):
self
.
_ps_inference_save_inference_model
(
*
args
,
**
kwargs
)
def
_save_persistables
(
self
,
*
args
,
**
kwargs
):
self
.
_ps_inference_save_persistables
(
*
args
,
**
kwargs
)
python/paddle/distributed/fleet/runtime/runtime_base.py
浏览文件 @
57d434df
...
...
@@ -33,3 +33,9 @@ class RuntimeBase(object):
def
_stop_worker
(
self
):
pass
def
_save_inference_model
(
self
,
*
args
,
**
kwargs
):
pass
def
_save_persistables
(
self
,
*
args
,
**
kwargs
):
pass
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @
57d434df
...
...
@@ -33,6 +33,8 @@ list(APPEND MIXED_DIST_TEST_OPS test_fleet_api_input)
list
(
APPEND MIXED_DIST_TEST_OPS test_fleet_checkpoint
)
list
(
APPEND MIXED_DIST_TEST_OPS test_collective_optimizer
)
list
(
APPEND MIXED_DIST_TEST_OPS test_fleet_base
)
list
(
APPEND MIXED_DIST_TEST_OPS test_fleet_base_2
)
list
(
APPEND MIXED_DIST_TEST_OPS test_fleet_base_3
)
list
(
APPEND MIXED_DIST_TEST_OPS test_fleet_recompute_meta_optimizer
)
list
(
APPEND MIXED_DIST_TEST_OPS test_fleet_graph_execution_meta_optimizer
)
list
(
APPEND MIXED_DIST_TEST_OPS test_fleet_pipeline_meta_optimizer
)
...
...
@@ -382,6 +384,8 @@ if(WITH_DISTRIBUTE)
py_test_modules
(
test_collective_optimizer MODULES test_collective_optimizer
)
if
(
NOT APPLE
)
py_test_modules
(
test_fleet_base MODULES test_fleet_base ENVS
${
dist_ENVS
}
)
py_test_modules
(
test_fleet_base_2 MODULES test_fleet_base_2 ENVS
${
dist_ENVS
}
)
py_test_modules
(
test_fleet_base_3 MODULES test_fleet_base_3 ENVS
${
dist_ENVS
}
)
py_test_modules
(
test_fleet_recompute_meta_optimizer MODULES test_fleet_recompute_meta_optimizer ENVS
${
dist_ENVS
}
)
py_test_modules
(
test_fleet_graph_execution_meta_optimizer MODULES test_fleet_graph_execution_meta_optimizer ENVS
${
dist_ENVS
}
)
py_test_modules
(
test_fleet_graph_executor MODULES test_fleet_graph_executor ENVS
${
dist_ENVS
}
)
...
...
python/paddle/fluid/tests/unittests/dist_fleet_ctr.py
浏览文件 @
57d434df
...
...
@@ -162,24 +162,17 @@ class TestDistCTR2x2(FleetDistRunnerBase):
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
fleet
.
init_worker
()
exe
.
run
(
fleet
.
startup_program
)
exe
.
run
(
fluid
.
default_startup_program
())
batch_size
=
4
train_reader
=
paddle
.
batch
(
fake_ctr_reader
(),
batch_size
=
batch_size
)
self
.
reader
.
decorate_sample_list_generator
(
train_reader
)
compiled_prog
=
fluid
.
compiler
.
CompiledProgram
(
fleet
.
main_program
).
with_data_parallel
(
loss_name
=
self
.
avg_cost
.
name
,
build_strategy
=
self
.
strategy
.
get_build_strategy
(),
exec_strategy
=
self
.
strategy
.
get_execute_strategy
())
for
epoch_id
in
range
(
1
):
self
.
reader
.
start
()
try
:
pass_start
=
time
.
time
()
while
True
:
loss_val
=
exe
.
run
(
program
=
compiled_prog
,
loss_val
=
exe
.
run
(
program
=
fluid
.
default_main_program
()
,
fetch_list
=
[
self
.
avg_cost
.
name
])
loss_val
=
np
.
mean
(
loss_val
)
# TODO(randomly fail)
...
...
@@ -209,7 +202,7 @@ class TestDistCTR2x2(FleetDistRunnerBase):
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
fleet
.
init_worker
()
exe
.
run
(
fl
eet
.
startup_program
)
exe
.
run
(
fl
uid
.
default_startup_program
()
)
thread_num
=
2
batch_size
=
128
...
...
@@ -231,7 +224,7 @@ class TestDistCTR2x2(FleetDistRunnerBase):
pass_start
=
time
.
time
()
dataset
.
set_filelist
(
filelist
)
exe
.
train_from_dataset
(
program
=
fl
eet
.
main_program
,
program
=
fl
uid
.
default_main_program
()
,
dataset
=
dataset
,
fetch_list
=
[
self
.
avg_cost
],
fetch_info
=
[
"cost"
],
...
...
python/paddle/fluid/tests/unittests/dist_fleet_sparse_embedding_ctr.py
浏览文件 @
57d434df
...
...
@@ -152,24 +152,18 @@ class TestDistCTR2x2(FleetDistRunnerBase):
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
fleet
.
init_worker
()
exe
.
run
(
fl
eet
.
startup_program
)
exe
.
run
(
fl
uid
.
default_startup_program
()
)
batch_size
=
4
train_reader
=
paddle
.
batch
(
fake_ctr_reader
(),
batch_size
=
batch_size
)
self
.
reader
.
decorate_sample_list_generator
(
train_reader
)
compiled_prog
=
fluid
.
compiler
.
CompiledProgram
(
fleet
.
main_program
).
with_data_parallel
(
loss_name
=
self
.
avg_cost
.
name
,
build_strategy
=
self
.
strategy
.
get_build_strategy
(),
exec_strategy
=
self
.
strategy
.
get_execute_strategy
())
for
epoch_id
in
range
(
1
):
self
.
reader
.
start
()
try
:
while
True
:
loss_val
=
exe
.
run
(
program
=
compiled_prog
,
loss_val
=
exe
.
run
(
program
=
fluid
.
default_main_program
()
,
fetch_list
=
[
self
.
avg_cost
.
name
])
loss_val
=
np
.
mean
(
loss_val
)
print
(
"TRAIN ---> pass: {} loss: {}
\n
"
.
format
(
epoch_id
,
...
...
python/paddle/fluid/tests/unittests/test_dist_fleet_base.py
浏览文件 @
57d434df
...
...
@@ -31,10 +31,11 @@ import time
import
tempfile
import
unittest
import
paddle
import
paddle.fluid
as
fluid
import
paddle.distributed.fleet.base.role_maker
as
role_maker
from
paddle.distributed.fleet.base.util_factory
import
fleet_util
from
paddle.
fluid.incubate.fleet.parameter_server.distribute_transpiler
import
fleet
from
paddle.
distributed.fleet
import
fleet
from
paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy
import
StrategyFactory
__all__
=
[
'FleetDistRunnerBase'
,
'TestFleetBase'
,
'runtime_main'
]
...
...
@@ -75,21 +76,23 @@ class FleetDistRunnerBase(object):
return
role
def
build_strategy
(
self
,
args
):
self
.
strategy
=
None
self
.
strategy
=
paddle
.
distributed
.
fleet
.
DistributedStrategy
()
self
.
strategy
.
a_sync
=
False
if
args
.
mode
==
"async"
:
self
.
strategy
=
StrategyFactory
.
create_async_strategy
()
elif
args
.
mode
==
"sync"
:
self
.
strategy
=
StrategyFactory
.
create_sync_strategy
()
elif
args
.
mode
==
"half_async"
:
self
.
strategy
=
StrategyFactory
.
create_half_async_strategy
()
self
.
strategy
=
paddle
.
distributed
.
fleet
.
DistributedStrategy
()
self
.
strategy
.
a_sync
=
True
elif
args
.
mode
==
"geo"
:
self
.
strategy
=
StrategyFactory
.
create_geo_strategy
(
args
.
geo_sgd_need_push_nums
)
self
.
strategy
=
paddle
.
distributed
.
fleet
.
DistributedStrategy
()
self
.
strategy
.
a_sync
=
True
self
.
strategy
.
a_sync_configs
=
{
"k_steps"
:
args
.
geo_sgd_need_push_nums
}
self
.
dump_param
=
os
.
getenv
(
"dump_param"
,
""
).
split
(
","
)
self
.
dump_fields
=
os
.
getenv
(
"dump_fields"
,
""
).
split
(
","
)
self
.
dump_fields_path
=
os
.
getenv
(
"dump_fields_path"
,
""
)
debug
=
int
(
os
.
getenv
(
"Debug"
,
"0"
))
if
debug
:
# TODO(update strategy to support dump params)
if
False
:
#debug:
self
.
strategy
.
set_debug_opt
({
"dump_param"
:
self
.
dump_param
,
"dump_fields"
:
self
.
dump_fields
,
...
...
@@ -122,7 +125,7 @@ class FleetDistRunnerBase(object):
staircase
=
True
))
else
:
optimizer
=
fluid
.
optimizer
.
SGD
(
LEARNING_RATE
)
optimizer
=
fleet
.
distributed_optimizer
(
optimizer
,
strategy
)
optimizer
=
fleet
.
distributed_optimizer
(
optimizer
,
strategy
=
strategy
)
optimizer
.
minimize
(
avg_cost
)
def
run_pserver
(
self
,
args
):
...
...
python/paddle/fluid/tests/unittests/test_dist_fleet_ctr.py
浏览文件 @
57d434df
...
...
@@ -22,7 +22,7 @@ from test_dist_fleet_base import TestFleetBase
class
TestDistMnistSync2x2
(
TestFleetBase
):
def
_setup_config
(
self
):
self
.
_mode
=
"
a
sync"
self
.
_mode
=
"sync"
self
.
_reader
=
"pyreader"
def
check_with_place
(
self
,
...
...
@@ -123,7 +123,7 @@ class TestDistMnistAsyncDataset2x2(TestFleetBase):
class
TestDistCtrHalfAsync2x2
(
TestFleetBase
):
def
_setup_config
(
self
):
self
.
_mode
=
"
half_
async"
self
.
_mode
=
"async"
self
.
_reader
=
"pyreader"
def
check_with_place
(
self
,
...
...
python/paddle/fluid/tests/unittests/test_fleet_base.py
浏览文件 @
57d434df
...
...
@@ -17,6 +17,7 @@ import paddle
import
paddle.distributed.fleet
as
fleet
import
paddle.distributed.fleet.base.role_maker
as
role_maker
import
os
import
paddle.fluid
as
fluid
class
TestFleetBase
(
unittest
.
TestCase
):
...
...
@@ -119,24 +120,9 @@ class TestFleetBase(unittest.TestCase):
optimizer
=
paddle
.
optimizer
.
SGD
(
learning_rate
=
0.001
)
optimizer
=
fleet
.
distributed_optimizer
(
optimizer
)
def
test_minimize
(
self
):
input_x
=
paddle
.
fluid
.
layers
.
data
(
name
=
"x"
,
shape
=
[
32
],
dtype
=
'float32'
)
input_y
=
paddle
.
fluid
.
layers
.
data
(
name
=
"y"
,
shape
=
[
1
],
dtype
=
'int64'
)
fc_1
=
paddle
.
fluid
.
layers
.
fc
(
input
=
input_x
,
size
=
64
,
act
=
'tanh'
)
fc_2
=
paddle
.
fluid
.
layers
.
fc
(
input
=
fc_1
,
size
=
64
,
act
=
'tanh'
)
prediction
=
paddle
.
fluid
.
layers
.
fc
(
input
=
[
fc_2
],
size
=
2
,
act
=
'softmax'
)
cost
=
paddle
.
fluid
.
layers
.
cross_entropy
(
input
=
prediction
,
label
=
input_y
)
avg_cost
=
paddle
.
fluid
.
layers
.
mean
(
x
=
cost
)
role
=
role_maker
.
PaddleCloudRoleMaker
(
is_collective
=
True
)
fleet
.
init
(
role
)
strategy
=
fleet
.
DistributedStrategy
()
optimizer
=
paddle
.
optimizer
.
SGD
(
learning_rate
=
0.001
)
optimizer
=
fleet
.
distributed_optimizer
(
optimizer
,
strategy
=
strategy
)
optimizer
.
minimize
(
avg_cost
)
def
test_exception
(
self
):
import
paddle.distributed.fleet
as
fleet
self
.
assertRaises
(
Exception
,
fleet
.
init_worker
)
if
__name__
==
"__main__"
:
...
...
python/paddle/fluid/tests/unittests/test_fleet_base_2.py
0 → 100644
浏览文件 @
57d434df
# Copyright (c) 2020 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
paddle
import
os
import
paddle.fluid
as
fluid
class
TestFleetBase
(
unittest
.
TestCase
):
def
setUp
(
self
):
os
.
environ
[
"POD_IP"
]
=
"127.0.0.1"
os
.
environ
[
"PADDLE_TRAINER_ENDPOINTS"
]
=
"127.0.0.1:36001"
os
.
environ
[
"PADDLE_TRAINERS_NUM"
]
=
"2"
os
.
environ
[
"PADDLE_PSERVERS_IP_PORT_LIST"
]
=
\
"127.0.0.1:36001,127.0.0.2:36001"
def
test_ps_minimize
(
self
):
import
paddle
import
paddle.distributed.fleet
as
fleet
import
paddle.fluid.incubate.fleet.base.role_maker
as
role_maker
os
.
environ
[
"TRAINING_ROLE"
]
=
"PSERVER"
os
.
environ
[
"POD_IP"
]
=
"127.0.0.1"
os
.
environ
[
"PADDLE_PORT"
]
=
"36001"
input_x
=
paddle
.
fluid
.
layers
.
data
(
name
=
"x"
,
shape
=
[
32
],
dtype
=
'float32'
)
input_y
=
paddle
.
fluid
.
layers
.
data
(
name
=
"y"
,
shape
=
[
1
],
dtype
=
'int64'
)
fc_1
=
paddle
.
fluid
.
layers
.
fc
(
input
=
input_x
,
size
=
64
,
act
=
'tanh'
)
fc_2
=
paddle
.
fluid
.
layers
.
fc
(
input
=
fc_1
,
size
=
64
,
act
=
'tanh'
)
prediction
=
paddle
.
fluid
.
layers
.
fc
(
input
=
[
fc_2
],
size
=
2
,
act
=
'softmax'
)
cost
=
paddle
.
fluid
.
layers
.
cross_entropy
(
input
=
prediction
,
label
=
input_y
)
avg_cost
=
paddle
.
fluid
.
layers
.
mean
(
x
=
cost
)
role
=
role_maker
.
PaddleCloudRoleMaker
(
is_collective
=
False
)
fleet
.
init
(
role
)
strategy
=
paddle
.
distributed
.
fleet
.
DistributedStrategy
()
strategy
.
a_sync
=
False
optimizer
=
paddle
.
optimizer
.
SGD
(
learning_rate
=
0.001
)
optimizer
=
fleet
.
distributed_optimizer
(
optimizer
,
strategy
=
strategy
)
optimizer
.
minimize
(
avg_cost
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
pe
=
fluid
.
ParallelExecutor
(
use_cuda
=
False
,
loss_name
=
avg_cost
.
name
)
compiled_prog
=
fluid
.
compiler
.
CompiledProgram
(
fluid
.
default_main_program
())
self
.
assertRaises
(
Exception
,
fleet
.
save_inference_model
,
dirname
=
'/tmp/'
,
feeded_var_names
=
[
'x'
,
'y'
],
target_vars
=
[
avg_cost
],
executor
=
pe
)
self
.
assertRaises
(
Exception
,
fleet
.
save_inference_model
,
dirname
=
'/tmp/'
,
feeded_var_names
=
[
'x'
,
'y'
],
target_vars
=
[
avg_cost
],
executor
=
"exe"
)
self
.
assertRaises
(
Exception
,
fleet
.
save_inference_model
,
dirname
=
'/tmp/'
,
feeded_var_names
=
[
'x'
,
'y'
],
target_vars
=
[
avg_cost
],
executor
=
exe
,
main_program
=
compiled_prog
)
self
.
assertRaises
(
Exception
,
fleet
.
save_persistables
,
executor
=
pe
,
dirname
=
'/tmp/'
)
self
.
assertRaises
(
Exception
,
fleet
.
save_persistables
,
executor
=
"exe"
,
dirname
=
'/tmp/'
)
self
.
assertRaises
(
Exception
,
fleet
.
save_persistables
,
executor
=
exe
,
dirname
=
'/tmp/'
,
main_program
=
compiled_prog
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_fleet_base_3.py
0 → 100644
浏览文件 @
57d434df
# Copyright (c) 2020 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
os
import
paddle
import
paddle.distributed.fleet
as
fleet
import
paddle.distributed.fleet.base.role_maker
as
role_maker
import
paddle.fluid
as
fluid
class
TestFleetBase
(
unittest
.
TestCase
):
def
setUp
(
self
):
os
.
environ
[
"POD_IP"
]
=
"127.0.0.1"
os
.
environ
[
"PADDLE_TRAINER_ENDPOINTS"
]
=
"127.0.0.1:36001"
os
.
environ
[
"PADDLE_TRAINERS_NUM"
]
=
"2"
os
.
environ
[
"PADDLE_PSERVERS_IP_PORT_LIST"
]
=
\
"127.0.0.1:36001,127.0.0.2:36001"
def
test_collective_minimize
(
self
):
input_x
=
paddle
.
fluid
.
layers
.
data
(
name
=
"x"
,
shape
=
[
32
],
dtype
=
'float32'
)
input_y
=
paddle
.
fluid
.
layers
.
data
(
name
=
"y"
,
shape
=
[
1
],
dtype
=
'int64'
)
fc_1
=
paddle
.
fluid
.
layers
.
fc
(
input
=
input_x
,
size
=
64
,
act
=
'tanh'
)
fc_2
=
paddle
.
fluid
.
layers
.
fc
(
input
=
fc_1
,
size
=
64
,
act
=
'tanh'
)
prediction
=
paddle
.
fluid
.
layers
.
fc
(
input
=
[
fc_2
],
size
=
2
,
act
=
'softmax'
)
cost
=
paddle
.
fluid
.
layers
.
cross_entropy
(
input
=
prediction
,
label
=
input_y
)
avg_cost
=
paddle
.
fluid
.
layers
.
mean
(
x
=
cost
)
role
=
role_maker
.
PaddleCloudRoleMaker
(
is_collective
=
True
)
fleet
.
init
(
role
)
strategy
=
fleet
.
DistributedStrategy
()
optimizer
=
paddle
.
optimizer
.
SGD
(
learning_rate
=
0.001
)
optimizer
=
fleet
.
distributed_optimizer
(
optimizer
,
strategy
=
strategy
)
optimizer
.
minimize
(
avg_cost
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_fleet_runtime.py
浏览文件 @
57d434df
...
...
@@ -25,6 +25,8 @@ class TestFleetRuntime(unittest.TestCase):
base
.
_init_server
()
base
.
_run_server
()
base
.
_stop_worker
()
base
.
_save_inference_model
()
base
.
_save_persistables
()
def
test_fleet_collective_runtime
(
self
):
import
paddle.distributed.fleet.runtime
...
...
@@ -35,6 +37,27 @@ class TestFleetRuntime(unittest.TestCase):
collective_runtime
.
_init_worker
()
collective_runtime
.
_run_server
()
collective_runtime
.
_stop_worker
()
collective_runtime
.
_save_inference_model
()
collective_runtime
.
_save_persistables
()
def
test_fleet_ps_runtime
(
self
):
ps_runtime
=
paddle
.
distributed
.
fleet
.
runtime
.
ParameterServerRuntime
()
self
.
assertRaises
(
Exception
,
ps_runtime
.
_get_optimizer_status
,
"test_op"
,
None
)
reshaped_names
,
origin_names
=
ps_runtime
.
_get_optimizer_status
(
"adam"
,
"param"
)
self
.
assertTrue
(
len
(
reshaped_names
)
==
2
and
reshaped_names
[
0
]
==
'param_moment1_0'
and
reshaped_names
[
1
]
==
'param_moment2_0'
)
self
.
assertTrue
(
len
(
origin_names
)
==
2
and
origin_names
[
0
]
==
'param_beta1_pow_acc_0'
and
origin_names
[
1
]
==
'param_beta2_pow_acc_0'
)
reshaped_names
,
origin_names
=
ps_runtime
.
_get_optimizer_status
(
"sgd"
,
"param"
)
self
.
assertTrue
(
len
(
reshaped_names
)
==
0
and
len
(
origin_names
)
==
0
)
if
__name__
==
"__main__"
:
...
...
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