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15ce8e21
编写于
4月 13, 2020
作者:
X
xujiaqi01
提交者:
GitHub
4月 13, 2020
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差异文件
add unit accessor (#23703)
* add unit accessor in fleet, support DownpourUnitAccessor * test=develop
上级
6b4a51ba
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
185 addition
and
4 deletion
+185
-4
python/paddle/fluid/incubate/fleet/parameter_server/pslib/node.py
...addle/fluid/incubate/fleet/parameter_server/pslib/node.py
+89
-3
python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py
...ncubate/fleet/parameter_server/pslib/optimizer_factory.py
+2
-1
python/paddle/fluid/tests/unittests/test_fleet_unitaccessor.py
...n/paddle/fluid/tests/unittests/test_fleet_unitaccessor.py
+94
-0
未找到文件。
python/paddle/fluid/incubate/fleet/parameter_server/pslib/node.py
浏览文件 @
15ce8e21
...
...
@@ -81,7 +81,12 @@ class DownpourServer(Server):
'sparse_delete_after_unseen_days'
,
'sparse_show_click_decay_rate'
,
'sparse_delete_threshold'
,
\
'sparse_converter'
,
'sparse_deconverter'
,
'sparse_enable_cache'
,
'sparse_cache_rate'
,
\
'sparse_cache_file_num'
,
'sparse_beta1_decay_rate'
,
'sparse_beta2_decay_rate'
,
\
'sparse_ada_epsilon'
,
'sparse_optimizer'
,
'sparse_ssd_unseenday_threshold'
]
'sparse_ada_epsilon'
,
'sparse_optimizer'
,
'sparse_ssd_unseenday_threshold'
,
\
'embed_sparse_optimizer'
,
'embed_sparse_learning_rate'
,
'embed_sparse_weight_bounds'
,
\
'embed_sparse_initial_range'
,
'embed_sparse_initial_g2sum'
,
'embed_sparse_beta1_decay_rate'
,
\
'embed_sparse_beta2_decay_rate'
,
'embedx_sparse_optimizer'
,
'embedx_sparse_learning_rate'
,
\
'embedx_sparse_weight_bounds'
,
'embedx_sparse_initial_range'
,
'embedx_sparse_initial_g2sum'
,
\
'embedx_sparse_beta1_decay_rate'
,
'embedx_sparse_beta2_decay_rate'
]
for
key
in
strategy
:
if
key
not
in
support_sparse_key_list
:
...
...
@@ -113,10 +118,12 @@ class DownpourServer(Server):
# DownpourCtrAccessor : for ctr task, has cvm, slot, embedding and sgd info
# DownpourSparseValueAccessor : for general task, has embedding and sgd info
# DownpourCtrDoubleAccessor : for ctr task, which show clk are in double
# DownpourUnitAccessor : for ctr task, has cvm, slot, embedding and sgd info
support_accessor_class
=
[
'DownpourFeatureValueAccessor'
,
'DownpourCtrAccessor'
,
'DownpourSparseValueAccessor'
,
'DownpourCtrDoubleAccessor'
'DownpourSparseValueAccessor'
,
'DownpourCtrDoubleAccessor'
,
'DownpourUnitAccessor'
]
if
strategy
.
get
(
'sparse_accessor_class'
)
is
not
None
:
accessor_class
=
strategy
.
get
(
'sparse_accessor_class'
)
...
...
@@ -130,7 +137,9 @@ class DownpourServer(Server):
table
.
accessor
.
accessor_class
=
accessor_class
if
accessor_class
==
'DownpourFeatureValueAccessor'
or
accessor_class
==
'DownpourCtrAccessor'
or
accessor_class
==
'DownpourCtrDoubleAccessor'
:
if
accessor_class
==
'DownpourFeatureValueAccessor'
\
or
accessor_class
==
'DownpourCtrAccessor'
\
or
accessor_class
==
'DownpourCtrDoubleAccessor'
:
table
.
accessor
.
sparse_sgd_param
.
learning_rate
=
strategy
.
get
(
'sparse_learning_rate'
,
0.05
)
table
.
accessor
.
sparse_sgd_param
.
initial_g2sum
=
strategy
.
get
(
...
...
@@ -245,6 +254,12 @@ class DownpourServer(Server):
table2
.
param
=
2
table2
.
converter
=
converter
table2
.
deconverter
=
deconverter
elif
accessor_class
==
'DownpourUnitAccessor'
:
self
.
add_sparse_table_common_config
(
table
,
strategy
)
self
.
add_sparse_optimizer
(
table
.
accessor
.
embed_sgd_param
,
strategy
,
"embed_"
)
self
.
add_sparse_optimizer
(
table
.
accessor
.
embedx_sgd_param
,
strategy
,
"embedx_"
)
def
add_dense_table
(
self
,
table_id
,
param_var
,
grad_var
,
strategy
,
sparse_table_names
):
...
...
@@ -364,6 +379,77 @@ class DownpourServer(Server):
'datanorm_decay_rate'
,
0.999999
)
table
.
accessor
.
fea_dim
=
fea_dim
def
add_sparse_optimizer
(
self
,
sgd
,
strategy
,
prefix
):
optimizer_name
=
strategy
.
get
(
prefix
+
"sparse_optimizer"
,
"adam"
)
sgd
.
name
=
optimizer_name
if
optimizer_name
==
"naive"
:
sgd
.
naive
.
learning_rate
=
\
strategy
.
get
(
prefix
+
'sparse_learning_rate'
,
0.05
)
sgd
.
naive
.
initial_range
=
\
strategy
.
get
(
prefix
+
'sparse_initial_range'
,
1e-4
)
bounds
=
strategy
.
get
(
prefix
+
'sparse_weight_bounds'
,
[
-
10
,
10
])
sgd
.
naive
.
weight_bounds
.
extend
(
bounds
)
elif
optimizer_name
==
"adagrad"
:
sgd
.
adagrad
.
learning_rate
=
\
strategy
.
get
(
prefix
+
'sparse_learning_rate'
,
0.05
)
sgd
.
adagrad
.
initial_range
=
\
strategy
.
get
(
prefix
+
'sparse_initial_range'
,
1e-4
)
sgd
.
adagrad
.
initial_g2sum
=
strategy
.
get
(
prefix
+
'sparse_initial_g2sum'
,
3
)
bounds
=
strategy
.
get
(
prefix
+
'sparse_weight_bounds'
,
[
-
10
,
10
])
sgd
.
adagrad
.
weight_bounds
.
extend
(
bounds
)
elif
optimizer_name
==
"adam"
:
sgd
.
adam
.
learning_rate
=
\
strategy
.
get
(
prefix
+
'sparse_learning_rate'
,
0.001
)
sgd
.
adam
.
initial_range
=
\
strategy
.
get
(
prefix
+
'sparse_initial_range'
,
1e-4
)
sgd
.
adam
.
beta1_decay_rate
=
strategy
.
get
(
prefix
+
'sparse_beta1_decay_rate'
,
0.9
)
sgd
.
adam
.
beta2_decay_rate
=
strategy
.
get
(
prefix
+
'sparse_beta2_decay_rate'
,
0.999
)
sgd
.
adam
.
ada_epsilon
=
strategy
.
get
(
prefix
+
'sparse_ada_epsilon'
,
1e-8
)
bounds
=
strategy
.
get
(
prefix
+
'sparse_weight_bounds'
,
[
-
10
,
10
])
sgd
.
adam
.
weight_bounds
.
extend
(
bounds
)
def
add_sparse_table_common_config
(
self
,
table
,
strategy
):
table
.
accessor
.
embedx_dim
=
strategy
.
get
(
'sparse_embedx_dim'
,
8
)
table
.
accessor
.
embedx_threshold
=
strategy
.
get
(
'sparse_embedx_threshold'
,
10
)
table
.
accessor
.
fea_dim
=
int
(
table
.
accessor
.
embedx_dim
)
+
3
table
.
accessor
.
downpour_accessor_param
.
nonclk_coeff
=
strategy
.
get
(
'sparse_nonclk_coeff'
,
0.1
)
table
.
accessor
.
downpour_accessor_param
.
click_coeff
=
strategy
.
get
(
'sparse_click_coeff'
,
1
)
table
.
accessor
.
downpour_accessor_param
.
base_threshold
=
strategy
.
get
(
'sparse_base_threshold'
,
1.5
)
table
.
accessor
.
downpour_accessor_param
.
delta_threshold
=
strategy
.
get
(
'sparse_delta_threshold'
,
0.25
)
table
.
accessor
.
downpour_accessor_param
.
delta_keep_days
=
strategy
.
get
(
'sparse_delta_keep_days'
,
16
)
table
.
accessor
.
downpour_accessor_param
.
delete_after_unseen_days
=
strategy
.
get
(
'sparse_delete_after_unseen_days'
,
30
)
table
.
accessor
.
downpour_accessor_param
.
show_click_decay_rate
=
strategy
.
get
(
'sparse_show_click_decay_rate'
,
0.98
)
table
.
accessor
.
downpour_accessor_param
.
delete_threshold
=
strategy
.
get
(
'sparse_delete_threshold'
,
0.8
)
converter
=
strategy
.
get
(
'sparse_converter'
,
"(scripts/xbox_compressor_mf.py | bin/xbox_pb_converter)"
)
deconverter
=
strategy
.
get
(
'sparse_deconverter'
,
"(bin/xbox_pb_deconverter | scripts/xbox_decompressor_mf.awk)"
)
table1
=
table
.
accessor
.
table_accessor_save_param
.
add
()
table1
.
param
=
1
table1
.
converter
=
converter
table1
.
deconverter
=
deconverter
table2
=
table
.
accessor
.
table_accessor_save_param
.
add
()
table2
.
param
=
2
table2
.
converter
=
converter
table2
.
deconverter
=
deconverter
def
get_desc
(
self
):
"""
Return downpour server program_desc
...
...
python/paddle/fluid/incubate/fleet/parameter_server/pslib/optimizer_factory.py
浏览文件 @
15ce8e21
...
...
@@ -531,7 +531,8 @@ class DistributedAdam(DistributedOptimizerImplBase):
opt_info
[
"dump_param"
]
=
strategy
.
get
(
"dump_param"
,
[])
if
server
.
_server
.
downpour_server_param
.
downpour_table_param
[
0
].
accessor
.
accessor_class
in
[
"DownpourCtrAccessor"
,
"DownpourCtrDoubleAccessor"
"DownpourCtrAccessor"
,
"DownpourCtrDoubleAccessor"
,
"DownpourUnitAccessor"
]:
opt_info
[
"dump_slot"
]
=
True
elif
server
.
_server
.
downpour_server_param
.
downpour_table_param
[
...
...
python/paddle/fluid/tests/unittests/test_fleet_unitaccessor.py
0 → 100644
浏览文件 @
15ce8e21
# 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.
"""Test fleet."""
from
__future__
import
print_function
import
os
import
unittest
import
paddle.fluid.incubate.fleet.base.role_maker
as
role_maker
class
TestFleet1
(
unittest
.
TestCase
):
"""
Test cases for fleet minimize.
"""
def
setUp
(
self
):
"""Set up, set envs."""
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_pslib_1
(
self
):
"""Test cases for pslib."""
import
paddle.fluid
as
fluid
from
paddle.fluid.incubate.fleet.parameter_server.pslib
import
fleet
from
paddle.fluid.incubate.fleet.parameter_server.pslib
import
PSLib
from
paddle.fluid.incubate.fleet.base.role_maker
import
GeneralRoleMaker
try
:
import
netifaces
except
:
print
(
"warning: no netifaces, skip test_pslib_1"
)
return
os
.
environ
[
"POD_IP"
]
=
"127.0.0.1"
os
.
environ
[
"PADDLE_PORT"
]
=
"36001"
os
.
environ
[
"TRAINING_ROLE"
]
=
"TRAINER"
os
.
environ
[
"PADDLE_TRAINER_ENDPOINTS"
]
=
"127.0.0.1:36001"
os
.
environ
[
"PADDLE_PSERVERS_IP_PORT_LIST"
]
=
"127.0.0.1:36002"
os
.
environ
[
"PADDLE_TRAINER_ID"
]
=
"0"
role_maker
=
GeneralRoleMaker
()
role_maker
.
generate_role
()
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
fleet
.
init
(
role_maker
)
train_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
scope
=
fluid
.
Scope
()
with
fluid
.
program_guard
(
train_program
,
startup_program
):
show
=
fluid
.
layers
.
data
(
name
=
"show"
,
shape
=
[
-
1
,
1
],
\
dtype
=
"int64"
,
lod_level
=
1
,
append_batch_size
=
False
)
emb
=
fluid
.
layers
.
embedding
(
input
=
show
,
size
=
[
1
,
1
],
\
is_sparse
=
True
,
is_distributed
=
True
,
\
param_attr
=
fluid
.
ParamAttr
(
name
=
"embedding"
))
fc
=
fluid
.
layers
.
fc
(
input
=
emb
,
size
=
1
,
act
=
None
)
label
=
fluid
.
layers
.
data
(
name
=
"click"
,
shape
=
[
-
1
,
1
],
\
dtype
=
"int64"
,
lod_level
=
1
,
append_batch_size
=
False
)
label_cast
=
fluid
.
layers
.
cast
(
label
,
dtype
=
'float32'
)
cost
=
fluid
.
layers
.
log_loss
(
fc
,
label_cast
)
strategy
=
{}
strategy
[
"embedding"
]
=
{}
strategy
[
"embedding"
][
"sparse_accessor_class"
]
=
"DownpourUnitAccessor"
strategy
[
"embedding"
][
"embed_sparse_optimizer"
]
=
"naive"
try
:
adam1
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.000005
)
adam1
=
fleet
.
distributed_optimizer
(
adam1
,
strategy
=
strategy
)
adam1
.
minimize
([
cost
],
[
scope
])
strategy
[
"embedding"
][
"embed_sparse_optimizer"
]
=
"adagrad"
adam2
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.000005
)
adam2
=
fleet
.
distributed_optimizer
(
adam2
,
strategy
=
strategy
)
adam2
.
minimize
([
cost
],
[
scope
])
strategy
[
"embedding"
][
"embed_sparse_optimizer"
]
=
"adam"
adam3
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.000005
)
adam3
=
fleet
.
distributed_optimizer
(
adam3
,
strategy
=
strategy
)
adam3
.
minimize
([
cost
],
[
scope
])
except
:
print
(
"do not support pslib test, skip"
)
return
if
__name__
==
"__main__"
:
unittest
.
main
()
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