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03dfc413
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03dfc413
编写于
5月 27, 2020
作者:
X
xjqbest
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix
上级
e8d99992
变更
11
显示空白变更内容
内联
并排
Showing
11 changed file
with
351 addition
and
547 deletion
+351
-547
core/factory.py
core/factory.py
+0
-4
core/model.py
core/model.py
+27
-10
core/reader.py
core/reader.py
+6
-4
core/trainers/single_auc_yamlopt.py
core/trainers/single_auc_yamlopt.py
+0
-214
core/trainers/single_trainer.py
core/trainers/single_trainer.py
+218
-64
core/trainers/single_trainer_yamlopt.py
core/trainers/single_trainer_yamlopt.py
+0
-214
core/trainers/transpiler_trainer.py
core/trainers/transpiler_trainer.py
+4
-0
core/utils/dataloader_instance.py
core/utils/dataloader_instance.py
+91
-1
core/utils/envs.py
core/utils/envs.py
+0
-2
models/rank/dnn/config.yaml
models/rank/dnn/config.yaml
+4
-3
run.py
run.py
+1
-31
未找到文件。
core/factory.py
浏览文件 @
03dfc413
...
...
@@ -36,10 +36,6 @@ def trainer_registry():
"tdm_single_trainer.py"
)
trainers
[
"TDMClusterTrainer"
]
=
os
.
path
.
join
(
trainer_abs
,
"tdm_cluster_trainer.py"
)
trainers
[
"SingleTrainerYamlOpt"
]
=
os
.
path
.
join
(
trainer_abs
,
"single_trainer_yamlopt.py"
)
trainers
[
"SingleAucYamlOpt"
]
=
os
.
path
.
join
(
trainer_abs
,
"single_auc_yamlopt.py"
)
trainer_registry
()
...
...
core/model.py
浏览文件 @
03dfc413
...
...
@@ -39,15 +39,32 @@ class Model(object):
self
.
_platform
=
envs
.
get_platform
()
self
.
_init_hyper_parameters
()
self
.
_env
=
config
self
.
_slot_inited
=
False
def
_init_hyper_parameters
(
self
):
pass
def
_init_slots
(
self
):
sparse_slots
=
envs
.
get_global_env
(
"sparse_slots"
,
None
,
"train.reader"
)
dense_slots
=
envs
.
get_global_env
(
"dense_slots"
,
None
,
"train.reader"
)
def
_init_slots
(
self
,
**
kargs
):
if
self
.
_slot_inited
:
return
self
.
_slot_inited
=
True
dataset
=
{}
model_dict
=
{}
#self._env["executor"]#[kargs["name"]]
for
i
in
self
.
_env
[
"executor"
]:
if
i
[
"name"
]
==
kargs
[
"name"
]:
model_dict
=
i
break
for
i
in
self
.
_env
[
"dataset"
]:
if
i
[
"name"
]
==
model_dict
[
"dataset_name"
]:
dataset
=
i
break
name
=
"dataset."
+
dataset
[
"name"
]
+
"."
sparse_slots
=
envs
.
get_global_env
(
name
+
"sparse_slots"
)
#"sparse_slots", None,
#"train.reader")
dense_slots
=
envs
.
get_global_env
(
name
+
"dense_slots"
)
#"dense_slots", None, "train.reader")
#print(sparse_slots)
#print(dense_slots)
if
sparse_slots
is
not
None
or
dense_slots
is
not
None
:
sparse_slots
=
sparse_slots
.
strip
().
split
(
" "
)
dense_slots
=
dense_slots
.
strip
().
split
(
" "
)
...
...
@@ -70,12 +87,13 @@ class Model(object):
self
.
_data_var
.
append
(
l
)
self
.
_sparse_data_var
.
append
(
l
)
dataset_class
=
envs
.
get_global_env
(
"dataset_class"
,
None
,
"train.reader"
)
if
dataset_class
==
"DataLoader"
:
self
.
_init_dataloader
()
#dataset_class = dataset["type"]#
envs.get_global_env("dataset_class", None,
#
"train.reader")
#
if dataset_class == "DataLoader":
#
self._init_dataloader()
def
_init_dataloader
(
self
):
#print(self._data_var)
self
.
_data_loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
feed_list
=
self
.
_data_var
,
capacity
=
64
,
...
...
@@ -131,7 +149,6 @@ class Model(object):
None
,
self
.
_namespace
)
optimizer
=
envs
.
get_global_env
(
"hyper_parameters.optimizer"
,
None
,
self
.
_namespace
)
print
(
">>>>>>>>>>>.learnig rate: %s"
%
learning_rate
)
return
self
.
_build_optimizer
(
optimizer
,
learning_rate
)
def
input_data
(
self
,
is_infer
=
False
,
**
kwargs
):
...
...
core/reader.py
浏览文件 @
03dfc413
...
...
@@ -35,8 +35,6 @@ class Reader(dg.MultiSlotDataGenerator):
else
:
raise
ValueError
(
"reader config only support yaml"
)
envs
.
set_global_envs
(
_config
)
envs
.
update_workspace
()
@
abc
.
abstractmethod
def
init
(
self
):
...
...
@@ -63,7 +61,11 @@ class SlotReader(dg.MultiSlotDataGenerator):
def
init
(
self
,
sparse_slots
,
dense_slots
,
padding
=
0
):
from
operator
import
mul
self
.
sparse_slots
=
[]
if
sparse_slots
.
strip
()
!=
"#"
:
self
.
sparse_slots
=
sparse_slots
.
strip
().
split
(
" "
)
self
.
dense_slots
=
[]
if
dense_slots
.
strip
()
!=
"#"
:
self
.
dense_slots
=
dense_slots
.
strip
().
split
(
" "
)
self
.
dense_slots_shape
=
[
reduce
(
mul
,
...
...
core/trainers/single_auc_yamlopt.py
已删除
100755 → 0
浏览文件 @
e8d99992
# 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.
"""
Training use fluid with one node only.
"""
from
__future__
import
print_function
import
time
import
logging
import
os
import
paddle.fluid
as
fluid
from
paddlerec.core.trainers.transpiler_trainer
import
TranspileTrainer
from
paddlerec.core.utils
import
envs
from
paddlerec.core.reader
import
SlotReader
logging
.
basicConfig
(
format
=
"%(asctime)s - %(levelname)s - %(message)s"
)
logger
=
logging
.
getLogger
(
"fluid"
)
logger
.
setLevel
(
logging
.
INFO
)
class
SingleAucYamlOpt
(
TranspileTrainer
):
def
__init__
(
self
,
config
=
None
):
super
(
TranspileTrainer
,
self
).
__init__
(
config
)
self
.
_env
=
self
.
_config
device
=
envs
.
get_global_env
(
"device"
)
if
device
==
'gpu'
:
self
.
_place
=
fluid
.
CUDAPlace
(
0
)
elif
device
==
'cpu'
:
self
.
_place
=
fluid
.
CPUPlace
()
self
.
_exe
=
fluid
.
Executor
(
self
.
_place
)
self
.
processor_register
()
self
.
_model
=
{}
self
.
_dataset
=
{}
envs
.
set_global_envs
(
self
.
_config
)
envs
.
update_workspace
()
def
processor_register
(
self
):
self
.
regist_context_processor
(
'uninit'
,
self
.
instance
)
self
.
regist_context_processor
(
'init_pass'
,
self
.
init
)
self
.
regist_context_processor
(
'startup_pass'
,
self
.
startup
)
self
.
regist_context_processor
(
'train_pass'
,
self
.
executor_train
)
self
.
regist_context_processor
(
'terminal_pass'
,
self
.
terminal
)
def
instance
(
self
,
context
):
context
[
'status'
]
=
'init_pass'
def
_create_dataset
(
self
,
dataset_name
):
name
=
"dataset."
+
dataset_name
+
"."
sparse_slots
=
envs
.
get_global_env
(
name
+
"sparse_slots"
)
dense_slots
=
envs
.
get_global_env
(
name
+
"dense_slots"
)
thread_num
=
envs
.
get_global_env
(
name
+
"thread_num"
)
batch_size
=
envs
.
get_global_env
(
name
+
"batch_size"
)
reader_type
=
envs
.
get_global_env
(
name
+
"type"
)
if
envs
.
get_platform
()
!=
"LINUX"
:
print
(
"platform "
,
envs
.
get_platform
(),
" change reader to DataLoader"
)
reader_type
=
"DataLoader"
padding
=
0
reader
=
envs
.
path_adapter
(
"paddlerec.core.utils"
)
+
"/dataset_instance.py"
pipe_cmd
=
"python {} {} {} {} {} {} {} {}"
.
format
(
reader
,
"slot"
,
"slot"
,
self
.
_config_yaml
,
"fake"
,
\
sparse_slots
.
replace
(
" "
,
"#"
),
dense_slots
.
replace
(
" "
,
"#"
),
str
(
padding
))
type_name
=
envs
.
get_global_env
(
name
+
"type"
)
if
type_name
==
"QueueDataset"
:
dataset
=
fluid
.
DatasetFactory
().
create_dataset
()
dataset
.
set_batch_size
(
envs
.
get_global_env
(
name
+
"batch_size"
))
dataset
.
set_pipe_command
(
pipe_cmd
)
train_data_path
=
envs
.
get_global_env
(
name
+
"data_path"
)
file_list
=
[
os
.
path
.
join
(
train_data_path
,
x
)
for
x
in
os
.
listdir
(
train_data_path
)
]
dataset
.
set_filelist
(
file_list
)
for
model_dict
in
self
.
_env
[
"executor"
]:
if
model_dict
[
"dataset_name"
]
==
dataset_name
:
model
=
self
.
_model
[
model_dict
[
"name"
]][
3
]
inputs
=
model
.
get_inputs
()
dataset
.
set_use_var
(
inputs
)
break
else
:
pass
return
dataset
def
init
(
self
,
context
):
for
model_dict
in
self
.
_env
[
"executor"
]:
self
.
_model
[
model_dict
[
"name"
]]
=
[
None
]
*
4
train_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
scope
=
fluid
.
Scope
()
opt_name
=
envs
.
get_global_env
(
"hyper_parameters.optimizer.class"
)
opt_lr
=
envs
.
get_global_env
(
"hyper_parameters.optimizer.learning_rate"
)
opt_strategy
=
envs
.
get_global_env
(
"hyper_parameters.optimizer.strategy"
)
with
fluid
.
program_guard
(
train_program
,
startup_program
):
with
fluid
.
unique_name
.
guard
():
model_path
=
model_dict
[
"model"
].
replace
(
"{workspace}"
,
envs
.
path_adapter
(
self
.
_env
[
"workspace"
]))
model
=
envs
.
lazy_instance_by_fliename
(
model_path
,
"Model"
)(
self
.
_env
)
model
.
_data_var
=
model
.
input_data
(
dataset_name
=
model_dict
[
"dataset_name"
])
model
.
net
(
None
,
is_infer
=
True
)
optimizer
=
model
.
_build_optimizer
(
opt_name
,
opt_lr
,
opt_strategy
)
optimizer
.
minimize
(
model
.
_cost
)
self
.
_model
[
model_dict
[
"name"
]][
0
]
=
train_program
self
.
_model
[
model_dict
[
"name"
]][
1
]
=
startup_program
self
.
_model
[
model_dict
[
"name"
]][
2
]
=
scope
self
.
_model
[
model_dict
[
"name"
]][
3
]
=
model
for
dataset
in
self
.
_env
[
"dataset"
]:
self
.
_dataset
[
dataset
[
"name"
]]
=
self
.
_create_dataset
(
dataset
[
"name"
])
context
[
'status'
]
=
'startup_pass'
def
startup
(
self
,
context
):
for
model_dict
in
self
.
_env
[
"executor"
]:
with
fluid
.
scope_guard
(
self
.
_model
[
model_dict
[
"name"
]][
2
]):
self
.
_exe
.
run
(
self
.
_model
[
model_dict
[
"name"
]][
1
])
context
[
'status'
]
=
'train_pass'
def
executor_train
(
self
,
context
):
epochs
=
int
(
self
.
_env
[
"epochs"
])
for
j
in
range
(
epochs
):
for
model_dict
in
self
.
_env
[
"executor"
]:
reader_name
=
model_dict
[
"dataset_name"
]
name
=
"dataset."
+
reader_name
+
"."
begin_time
=
time
.
time
()
if
envs
.
get_global_env
(
name
+
"type"
)
==
"DataLoader"
:
self
.
_executor_dataloader_train
(
model_dict
)
else
:
self
.
_executor_dataset_train
(
model_dict
)
end_time
=
time
.
time
()
seconds
=
end_time
-
begin_time
print
(
"epoch {} done, time elasped: {}"
.
format
(
j
,
seconds
))
context
[
'status'
]
=
"terminal_pass"
def
_executor_dataset_train
(
self
,
model_dict
):
reader_name
=
model_dict
[
"dataset_name"
]
model_name
=
model_dict
[
"name"
]
model_class
=
self
.
_model
[
model_name
][
3
]
fetch_vars
=
[]
fetch_alias
=
[]
fetch_period
=
20
metrics
=
model_class
.
get_metrics
()
if
metrics
:
fetch_vars
=
metrics
.
values
()
fetch_alias
=
metrics
.
keys
()
scope
=
self
.
_model
[
model_name
][
2
]
program
=
self
.
_model
[
model_name
][
0
]
reader
=
self
.
_dataset
[
reader_name
]
with
fluid
.
scope_guard
(
scope
):
self
.
_exe
.
train_from_dataset
(
program
=
program
,
dataset
=
reader
,
fetch_list
=
fetch_vars
,
fetch_info
=
fetch_alias
,
print_period
=
fetch_period
)
def
_executor_dataloader_train
(
self
,
model_dict
):
reader_name
=
model_dict
[
"dataset_name"
]
model_name
=
model_dict
[
"name"
]
model_class
=
self
.
_model
[
model_name
][
3
]
self
.
_model
[
model_name
][
1
]
=
fluid
.
compiler
.
CompiledProgram
(
self
.
_model
[
model_name
][
1
]).
with_data_parallel
(
loss_name
=
model_class
.
get_avg_cost
().
name
)
fetch_vars
=
[]
fetch_alias
=
[]
fetch_period
=
20
metrics
=
model_class
.
get_metrics
()
if
metrics
:
fetch_vars
=
metrics
.
values
()
fetch_alias
=
metrics
.
keys
()
metrics_varnames
=
[]
metrics_format
=
[]
metrics_format
.
append
(
"{}: {{}}"
.
format
(
"epoch"
))
metrics_format
.
append
(
"{}: {{}}"
.
format
(
"batch"
))
for
name
,
var
in
model_class
.
items
():
metrics_varnames
.
append
(
var
.
name
)
metrics_format
.
append
(
"{}: {{}}"
.
format
(
name
))
metrics_format
=
", "
.
join
(
metrics_format
)
reader
=
self
.
_dataset
[
reader_name
]
reader
.
start
()
batch_id
=
0
scope
=
self
.
_model
[
model_name
][
2
]
prorgram
=
self
.
_model
[
model_name
][
0
]
with
fluid
.
scope_guard
(
scope
):
try
:
while
True
:
metrics_rets
=
self
.
_exe
.
run
(
program
=
program
,
fetch_list
=
metrics_varnames
)
metrics
=
[
epoch
,
batch_id
]
metrics
.
extend
(
metrics_rets
)
if
batch_id
%
self
.
fetch_period
==
0
and
batch_id
!=
0
:
print
(
metrics_format
.
format
(
*
metrics
))
batch_id
+=
1
except
fluid
.
core
.
EOFException
:
reader
.
reset
()
def
terminal
(
self
,
context
):
context
[
'is_exit'
]
=
True
core/trainers/single_trainer.py
浏览文件 @
03dfc413
...
...
@@ -19,83 +19,263 @@ from __future__ import print_function
import
time
import
logging
import
os
import
paddle.fluid
as
fluid
from
paddlerec.core.trainers.transpiler_trainer
import
TranspileTrainer
from
paddlerec.core.utils
import
envs
from
paddlerec.core.reader
import
SlotReader
from
paddlerec.core.utils
import
dataloader_instance
logging
.
basicConfig
(
format
=
"%(asctime)s - %(levelname)s - %(message)s"
)
logger
=
logging
.
getLogger
(
"fluid"
)
logger
.
setLevel
(
logging
.
INFO
)
class
SingleTrainer
(
TranspileTrainer
):
class
SingleTrainerYamlOpt
(
TranspileTrainer
):
def
__init__
(
self
,
config
=
None
):
super
(
TranspileTrainer
,
self
).
__init__
(
config
)
self
.
_env
=
self
.
_config
device
=
envs
.
get_global_env
(
"device"
)
if
device
==
'gpu'
:
self
.
_place
=
fluid
.
CUDAPlace
(
0
)
elif
device
==
'cpu'
:
self
.
_place
=
fluid
.
CPUPlace
()
self
.
_exe
=
fluid
.
Executor
(
self
.
_place
)
self
.
processor_register
()
self
.
_model
=
{}
self
.
_dataset
=
{}
envs
.
set_global_envs
(
self
.
_config
)
envs
.
update_workspace
()
def
processor_register
(
self
):
self
.
regist_context_processor
(
'uninit'
,
self
.
instance
)
self
.
regist_context_processor
(
'init_pass'
,
self
.
init
)
self
.
regist_context_processor
(
'startup_pass'
,
self
.
startup
)
self
.
regist_context_processor
(
'train_pass'
,
self
.
executor_train
)
self
.
regist_context_processor
(
'terminal_pass'
,
self
.
terminal
)
def
instance
(
self
,
context
):
context
[
'status'
]
=
'init_pass'
def
_get_dataset
(
self
,
dataset_name
):
name
=
"dataset."
+
dataset_name
+
"."
sparse_slots
=
envs
.
get_global_env
(
name
+
"sparse_slots"
)
dense_slots
=
envs
.
get_global_env
(
name
+
"dense_slots"
)
thread_num
=
envs
.
get_global_env
(
name
+
"thread_num"
)
batch_size
=
envs
.
get_global_env
(
name
+
"batch_size"
)
reader_class
=
envs
.
get_global_env
(
"data_convertor"
)
abs_dir
=
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))
reader
=
os
.
path
.
join
(
abs_dir
,
'../utils'
,
'dataset_instance.py'
)
if
sparse_slots
is
None
and
dense_slots
is
None
:
pipe_cmd
=
"python {} {} {} {}"
.
format
(
reader
,
reader_class
,
"fake"
,
self
.
_config_yaml
)
if
envs
.
get_platform
()
==
"LINUX"
and
envs
.
get_global_env
(
"dataset_class"
,
None
,
"train.reader"
)
!=
"DataLoader"
:
self
.
regist_context_processor
(
'train_pass'
,
self
.
dataset_train
)
else
:
self
.
regist_context_processor
(
'train_pass'
,
self
.
dataloader_train
)
if
sparse_slots
is
None
:
sparse_slots
=
"#"
if
dense_slots
is
None
:
dense_slots
=
"#"
padding
=
envs
.
get_global_env
(
name
+
"padding"
,
0
)
pipe_cmd
=
"python {} {} {} {} {} {} {} {}"
.
format
(
reader
,
"slot"
,
"slot"
,
self
.
_config_yaml
,
"fake"
,
\
sparse_slots
.
replace
(
" "
,
"#"
),
dense_slots
.
replace
(
" "
,
"#"
),
str
(
padding
))
self
.
regist_context_processor
(
'infer_pass'
,
self
.
infer
)
self
.
regist_context_processor
(
'terminal_pass'
,
self
.
terminal
)
dataset
=
fluid
.
DatasetFactory
().
create_dataset
()
dataset
.
set_batch_size
(
envs
.
get_global_env
(
name
+
"batch_size"
))
dataset
.
set_pipe_command
(
pipe_cmd
)
train_data_path
=
envs
.
get_global_env
(
name
+
"data_path"
)
file_list
=
[
os
.
path
.
join
(
train_data_path
,
x
)
for
x
in
os
.
listdir
(
train_data_path
)
]
dataset
.
set_filelist
(
file_list
)
for
model_dict
in
self
.
_env
[
"executor"
]:
if
model_dict
[
"dataset_name"
]
==
dataset_name
:
model
=
self
.
_model
[
model_dict
[
"name"
]][
3
]
inputs
=
model
.
get_inputs
()
dataset
.
set_use_var
(
inputs
)
break
return
dataset
def
init
(
self
,
context
):
self
.
model
.
train_net
()
optimizer
=
self
.
model
.
optimizer
()
optimizer
.
minimize
((
self
.
model
.
get_avg_cost
()))
def
_get_dataloader
(
self
,
dataset_name
):
name
=
"dataset."
+
dataset_name
+
"."
sparse_slots
=
envs
.
get_global_env
(
name
+
"sparse_slots"
)
dense_slots
=
envs
.
get_global_env
(
name
+
"dense_slots"
)
thread_num
=
envs
.
get_global_env
(
name
+
"thread_num"
)
batch_size
=
envs
.
get_global_env
(
name
+
"batch_size"
)
reader_class
=
envs
.
get_global_env
(
"data_convertor"
)
abs_dir
=
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))
#reader = os.path.join(abs_dir, '../utils', 'dataset_instance.py')
if
sparse_slots
is
None
and
dense_slots
is
None
:
#reader_class = envs.get_global_env("class")
reader
=
dataloader_instance
.
dataloader_by_name
(
reader_class
,
dataset_name
,
self
.
_config_yaml
)
reader_class
=
envs
.
lazy_instance_by_fliename
(
reader_class
,
"TrainReader"
)
reader_ins
=
reader_class
(
self
.
_config_yaml
)
else
:
reader
=
dataloader_instance
.
slotdataloader_by_name
(
""
,
dataset_name
,
self
.
_config_yaml
)
reader_ins
=
SlotReader
(
self
.
_config_yaml
)
if
hasattr
(
reader_ins
,
'generate_batch_from_trainfiles'
):
dataloader
.
set_sample_list_generator
(
reader
)
else
:
dataloader
.
set_sample_generator
(
reader
,
batch_size
)
return
dataloader
self
.
fetch_vars
=
[]
self
.
fetch_alias
=
[]
self
.
fetch_period
=
self
.
model
.
get_fetch_period
()
metrics
=
self
.
model
.
get_metrics
()
if
metrics
:
self
.
fetch_vars
=
metrics
.
values
()
self
.
fetch_alias
=
metrics
.
keys
()
evaluate_only
=
envs
.
get_global_env
(
'evaluate_only'
,
False
,
namespace
=
'evaluate'
)
if
evaluate_only
:
context
[
'status'
]
=
'infer_pass'
def
_create_dataset
(
self
,
dataset_name
):
name
=
"dataset."
+
dataset_name
+
"."
sparse_slots
=
envs
.
get_global_env
(
name
+
"sparse_slots"
)
dense_slots
=
envs
.
get_global_env
(
name
+
"dense_slots"
)
thread_num
=
envs
.
get_global_env
(
name
+
"thread_num"
)
batch_size
=
envs
.
get_global_env
(
name
+
"batch_size"
)
type_name
=
envs
.
get_global_env
(
name
+
"type"
)
if
envs
.
get_platform
()
!=
"LINUX"
:
print
(
"platform "
,
envs
.
get_platform
(),
" change reader to DataLoader"
)
type_name
=
"DataLoader"
padding
=
0
if
type_name
==
"DataLoader"
:
return
None
#self._get_dataloader(dataset_name)
else
:
return
self
.
_get_dataset
(
dataset_name
)
reader
=
envs
.
path_adapter
(
"paddlerec.core.utils"
)
+
"/dataset_instance.py"
pipe_cmd
=
"python {} {} {} {} {} {} {} {}"
.
format
(
reader
,
"slot"
,
"slot"
,
self
.
_config_yaml
,
"fake"
,
\
sparse_slots
.
replace
(
" "
,
"#"
),
dense_slots
.
replace
(
" "
,
"#"
),
str
(
padding
))
if
type_name
==
"QueueDataset"
:
dataset
=
fluid
.
DatasetFactory
().
create_dataset
()
dataset
.
set_batch_size
(
envs
.
get_global_env
(
name
+
"batch_size"
))
dataset
.
set_pipe_command
(
pipe_cmd
)
train_data_path
=
envs
.
get_global_env
(
name
+
"data_path"
)
file_list
=
[
os
.
path
.
join
(
train_data_path
,
x
)
for
x
in
os
.
listdir
(
train_data_path
)
]
dataset
.
set_filelist
(
file_list
)
for
model_dict
in
self
.
_env
[
"executor"
]:
if
model_dict
[
"dataset_name"
]
==
dataset_name
:
model
=
self
.
_model
[
model_dict
[
"name"
]][
3
]
inputs
=
model
.
get_inputs
()
dataset
.
set_use_var
(
inputs
)
break
else
:
pass
return
dataset
def
init
(
self
,
context
):
for
model_dict
in
self
.
_env
[
"executor"
]:
self
.
_model
[
model_dict
[
"name"
]]
=
[
None
]
*
4
train_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
scope
=
fluid
.
Scope
()
dataset_name
=
model_dict
[
"dataset_name"
]
opt_name
=
envs
.
get_global_env
(
"hyper_parameters.optimizer.class"
)
opt_lr
=
envs
.
get_global_env
(
"hyper_parameters.optimizer.learning_rate"
)
opt_strategy
=
envs
.
get_global_env
(
"hyper_parameters.optimizer.strategy"
)
with
fluid
.
program_guard
(
train_program
,
startup_program
):
with
fluid
.
unique_name
.
guard
():
model_path
=
model_dict
[
"model"
].
replace
(
"{workspace}"
,
envs
.
path_adapter
(
self
.
_env
[
"workspace"
]))
model
=
envs
.
lazy_instance_by_fliename
(
model_path
,
"Model"
)(
self
.
_env
)
model
.
_data_var
=
model
.
input_data
(
dataset_name
=
model_dict
[
"dataset_name"
])
#model._init_slots(name=model_dict["name"])
if
envs
.
get_global_env
(
"dataset."
+
dataset_name
+
".type"
)
==
"DataLoader"
:
model
.
_init_dataloader
()
model
.
net
(
model
.
_data_var
)
optimizer
=
model
.
_build_optimizer
(
opt_name
,
opt_lr
,
opt_strategy
)
optimizer
.
minimize
(
model
.
_cost
)
self
.
_model
[
model_dict
[
"name"
]][
0
]
=
train_program
self
.
_model
[
model_dict
[
"name"
]][
1
]
=
startup_program
self
.
_model
[
model_dict
[
"name"
]][
2
]
=
scope
self
.
_model
[
model_dict
[
"name"
]][
3
]
=
model
for
dataset
in
self
.
_env
[
"dataset"
]:
if
dataset
[
"type"
]
!=
"DataLoader"
:
self
.
_dataset
[
dataset
[
"name"
]]
=
self
.
_create_dataset
(
dataset
[
"name"
])
context
[
'status'
]
=
'startup_pass'
def
startup
(
self
,
context
):
self
.
_exe
.
run
(
fluid
.
default_startup_program
())
for
model_dict
in
self
.
_env
[
"executor"
]:
with
fluid
.
scope_guard
(
self
.
_model
[
model_dict
[
"name"
]][
2
]):
self
.
_exe
.
run
(
self
.
_model
[
model_dict
[
"name"
]][
1
])
context
[
'status'
]
=
'train_pass'
def
dataloader_train
(
self
,
context
):
reader
=
self
.
_get_dataloader
(
"TRAIN"
)
epochs
=
envs
.
get_global_env
(
"train.epochs"
)
def
executor_train
(
self
,
context
):
epochs
=
int
(
self
.
_env
[
"epochs"
])
for
j
in
range
(
epochs
):
for
model_dict
in
self
.
_env
[
"executor"
]:
reader_name
=
model_dict
[
"dataset_name"
]
name
=
"dataset."
+
reader_name
+
"."
begin_time
=
time
.
time
()
if
envs
.
get_global_env
(
name
+
"type"
)
==
"DataLoader"
:
self
.
_executor_dataloader_train
(
model_dict
)
else
:
self
.
_executor_dataset_train
(
model_dict
)
end_time
=
time
.
time
()
seconds
=
end_time
-
begin_time
print
(
"epoch {} done, time elasped: {}"
.
format
(
j
,
seconds
))
context
[
'status'
]
=
"terminal_pass"
def
_executor_dataset_train
(
self
,
model_dict
):
reader_name
=
model_dict
[
"dataset_name"
]
model_name
=
model_dict
[
"name"
]
model_class
=
self
.
_model
[
model_name
][
3
]
fetch_vars
=
[]
fetch_alias
=
[]
fetch_period
=
20
metrics
=
model_class
.
get_metrics
()
if
metrics
:
fetch_vars
=
metrics
.
values
()
fetch_alias
=
metrics
.
keys
()
scope
=
self
.
_model
[
model_name
][
2
]
program
=
self
.
_model
[
model_name
][
0
]
reader
=
self
.
_dataset
[
reader_name
]
with
fluid
.
scope_guard
(
scope
):
self
.
_exe
.
train_from_dataset
(
program
=
program
,
dataset
=
reader
,
fetch_list
=
fetch_vars
,
fetch_info
=
fetch_alias
,
print_period
=
fetch_period
)
program
=
fluid
.
compiler
.
CompiledProgram
(
fluid
.
default_main_program
(
)).
with_data_parallel
(
loss_name
=
self
.
model
.
get_avg_cost
().
name
)
def
_executor_dataloader_train
(
self
,
model_dict
):
reader_name
=
model_dict
[
"dataset_name"
]
model_name
=
model_dict
[
"name"
]
model_class
=
self
.
_model
[
model_name
][
3
]
self
.
_model
[
model_name
][
0
]
=
fluid
.
compiler
.
CompiledProgram
(
self
.
_model
[
model_name
][
0
]).
with_data_parallel
(
loss_name
=
model_class
.
get_avg_cost
().
name
)
fetch_vars
=
[]
fetch_alias
=
[]
fetch_period
=
20
metrics
=
model_class
.
get_metrics
()
if
metrics
:
fetch_vars
=
metrics
.
values
()
fetch_alias
=
metrics
.
keys
()
metrics_varnames
=
[]
metrics_format
=
[]
metrics_format
.
append
(
"{}: {{}}"
.
format
(
"epoch"
))
metrics_format
.
append
(
"{}: {{}}"
.
format
(
"batch"
))
for
name
,
var
in
self
.
model
.
get_metrics
().
items
():
for
name
,
var
in
model_class
.
get_metrics
().
items
():
metrics_varnames
.
append
(
var
.
name
)
metrics_format
.
append
(
"{}: {{}}"
.
format
(
name
))
metrics_format
=
", "
.
join
(
metrics_format
)
for
epoch
in
range
(
epochs
):
reader
=
self
.
_model
[
model_name
][
3
].
_data_loader
reader
.
start
()
batch_id
=
0
scope
=
self
.
_model
[
model_name
][
2
]
program
=
self
.
_model
[
model_name
][
0
]
#print(metrics_varnames)
with
fluid
.
scope_guard
(
scope
):
try
:
while
True
:
metrics_rets
=
self
.
_exe
.
run
(
program
=
program
,
fetch_list
=
metrics_varnames
)
metrics
=
[
epoch
,
batch_id
]
metrics
.
extend
(
metrics_rets
)
...
...
@@ -104,32 +284,6 @@ class SingleTrainer(TranspileTrainer):
batch_id
+=
1
except
fluid
.
core
.
EOFException
:
reader
.
reset
()
self
.
save
(
epoch
,
"train"
,
is_fleet
=
False
)
context
[
'status'
]
=
'infer_pass'
def
dataset_train
(
self
,
context
):
dataset
=
self
.
_get_dataset
(
"TRAIN"
)
ins
=
self
.
_get_dataset_ins
()
epochs
=
envs
.
get_global_env
(
"train.epochs"
)
for
i
in
range
(
epochs
):
begin_time
=
time
.
time
()
self
.
_exe
.
train_from_dataset
(
program
=
fluid
.
default_main_program
(),
dataset
=
dataset
,
fetch_list
=
self
.
fetch_vars
,
fetch_info
=
self
.
fetch_alias
,
print_period
=
self
.
fetch_period
)
end_time
=
time
.
time
()
times
=
end_time
-
begin_time
print
(
"epoch {} using time {}, speed {:.2f} lines/s"
.
format
(
i
,
times
,
ins
/
times
))
self
.
save
(
i
,
"train"
,
is_fleet
=
False
)
context
[
'status'
]
=
'infer_pass'
def
terminal
(
self
,
context
):
for
model
in
self
.
increment_models
:
print
(
"epoch :{}, dir: {}"
.
format
(
model
[
0
],
model
[
1
]))
context
[
'is_exit'
]
=
True
core/trainers/single_trainer_yamlopt.py
已删除
100755 → 0
浏览文件 @
e8d99992
# 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.
"""
Training use fluid with one node only.
"""
from
__future__
import
print_function
import
time
import
logging
import
os
import
paddle.fluid
as
fluid
from
paddlerec.core.trainers.transpiler_trainer
import
TranspileTrainer
from
paddlerec.core.utils
import
envs
from
paddlerec.core.reader
import
SlotReader
logging
.
basicConfig
(
format
=
"%(asctime)s - %(levelname)s - %(message)s"
)
logger
=
logging
.
getLogger
(
"fluid"
)
logger
.
setLevel
(
logging
.
INFO
)
class
SingleTrainerYamlOpt
(
TranspileTrainer
):
def
__init__
(
self
,
config
=
None
):
super
(
TranspileTrainer
,
self
).
__init__
(
config
)
self
.
_env
=
self
.
_config
device
=
envs
.
get_global_env
(
"device"
)
if
device
==
'gpu'
:
self
.
_place
=
fluid
.
CUDAPlace
(
0
)
elif
device
==
'cpu'
:
self
.
_place
=
fluid
.
CPUPlace
()
self
.
_exe
=
fluid
.
Executor
(
self
.
_place
)
self
.
processor_register
()
self
.
_model
=
{}
self
.
_dataset
=
{}
envs
.
set_global_envs
(
self
.
_config
)
envs
.
update_workspace
()
def
processor_register
(
self
):
self
.
regist_context_processor
(
'uninit'
,
self
.
instance
)
self
.
regist_context_processor
(
'init_pass'
,
self
.
init
)
self
.
regist_context_processor
(
'startup_pass'
,
self
.
startup
)
self
.
regist_context_processor
(
'train_pass'
,
self
.
executor_train
)
self
.
regist_context_processor
(
'terminal_pass'
,
self
.
terminal
)
def
instance
(
self
,
context
):
context
[
'status'
]
=
'init_pass'
def
_create_dataset
(
self
,
dataset_name
):
name
=
"dataset."
+
dataset_name
+
"."
sparse_slots
=
envs
.
get_global_env
(
name
+
"sparse_slots"
)
dense_slots
=
envs
.
get_global_env
(
name
+
"dense_slots"
)
thread_num
=
envs
.
get_global_env
(
name
+
"thread_num"
)
batch_size
=
envs
.
get_global_env
(
name
+
"batch_size"
)
reader_type
=
envs
.
get_global_env
(
name
+
"type"
)
if
envs
.
get_platform
()
!=
"LINUX"
:
print
(
"platform "
,
envs
.
get_platform
(),
" change reader to DataLoader"
)
reader_type
=
"DataLoader"
padding
=
0
reader
=
envs
.
path_adapter
(
"paddlerec.core.utils"
)
+
"/dataset_instance.py"
pipe_cmd
=
"python {} {} {} {} {} {} {} {}"
.
format
(
reader
,
"slot"
,
"slot"
,
self
.
_config_yaml
,
"fake"
,
\
sparse_slots
.
replace
(
" "
,
"#"
),
dense_slots
.
replace
(
" "
,
"#"
),
str
(
padding
))
type_name
=
envs
.
get_global_env
(
name
+
"type"
)
if
type_name
==
"QueueDataset"
:
dataset
=
fluid
.
DatasetFactory
().
create_dataset
()
dataset
.
set_batch_size
(
envs
.
get_global_env
(
name
+
"batch_size"
))
dataset
.
set_pipe_command
(
pipe_cmd
)
train_data_path
=
envs
.
get_global_env
(
name
+
"data_path"
)
file_list
=
[
os
.
path
.
join
(
train_data_path
,
x
)
for
x
in
os
.
listdir
(
train_data_path
)
]
dataset
.
set_filelist
(
file_list
)
for
model_dict
in
self
.
_env
[
"executor"
]:
if
model_dict
[
"dataset_name"
]
==
dataset_name
:
model
=
self
.
_model
[
model_dict
[
"name"
]][
3
]
inputs
=
model
.
get_inputs
()
dataset
.
set_use_var
(
inputs
)
break
else
:
pass
return
dataset
def
init
(
self
,
context
):
for
model_dict
in
self
.
_env
[
"executor"
]:
self
.
_model
[
model_dict
[
"name"
]]
=
[
None
]
*
4
train_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
scope
=
fluid
.
Scope
()
opt_name
=
envs
.
get_global_env
(
"hyper_parameters.optimizer.class"
)
opt_lr
=
envs
.
get_global_env
(
"hyper_parameters.optimizer.learning_rate"
)
opt_strategy
=
envs
.
get_global_env
(
"hyper_parameters.optimizer.strategy"
)
with
fluid
.
program_guard
(
train_program
,
startup_program
):
with
fluid
.
unique_name
.
guard
():
model_path
=
model_dict
[
"model"
].
replace
(
"{workspace}"
,
envs
.
path_adapter
(
self
.
_env
[
"workspace"
]))
model
=
envs
.
lazy_instance_by_fliename
(
model_path
,
"Model"
)(
self
.
_env
)
model
.
_data_var
=
model
.
input_data
(
dataset_name
=
model_dict
[
"dataset_name"
])
model
.
net
(
None
)
optimizer
=
model
.
_build_optimizer
(
opt_name
,
opt_lr
,
opt_strategy
)
optimizer
.
minimize
(
model
.
_cost
)
self
.
_model
[
model_dict
[
"name"
]][
0
]
=
train_program
self
.
_model
[
model_dict
[
"name"
]][
1
]
=
startup_program
self
.
_model
[
model_dict
[
"name"
]][
2
]
=
scope
self
.
_model
[
model_dict
[
"name"
]][
3
]
=
model
for
dataset
in
self
.
_env
[
"dataset"
]:
self
.
_dataset
[
dataset
[
"name"
]]
=
self
.
_create_dataset
(
dataset
[
"name"
])
context
[
'status'
]
=
'startup_pass'
def
startup
(
self
,
context
):
for
model_dict
in
self
.
_env
[
"executor"
]:
with
fluid
.
scope_guard
(
self
.
_model
[
model_dict
[
"name"
]][
2
]):
self
.
_exe
.
run
(
self
.
_model
[
model_dict
[
"name"
]][
1
])
context
[
'status'
]
=
'train_pass'
def
executor_train
(
self
,
context
):
epochs
=
int
(
self
.
_env
[
"epochs"
])
for
j
in
range
(
epochs
):
for
model_dict
in
self
.
_env
[
"executor"
]:
reader_name
=
model_dict
[
"dataset_name"
]
name
=
"dataset."
+
reader_name
+
"."
begin_time
=
time
.
time
()
if
envs
.
get_global_env
(
name
+
"type"
)
==
"DataLoader"
:
self
.
_executor_dataloader_train
(
model_dict
)
else
:
self
.
_executor_dataset_train
(
model_dict
)
end_time
=
time
.
time
()
seconds
=
end_time
-
begin_time
print
(
"epoch {} done, time elasped: {}"
.
format
(
j
,
seconds
))
context
[
'status'
]
=
"terminal_pass"
def
_executor_dataset_train
(
self
,
model_dict
):
reader_name
=
model_dict
[
"dataset_name"
]
model_name
=
model_dict
[
"name"
]
model_class
=
self
.
_model
[
model_name
][
3
]
fetch_vars
=
[]
fetch_alias
=
[]
fetch_period
=
20
metrics
=
model_class
.
get_metrics
()
if
metrics
:
fetch_vars
=
metrics
.
values
()
fetch_alias
=
metrics
.
keys
()
scope
=
self
.
_model
[
model_name
][
2
]
program
=
self
.
_model
[
model_name
][
0
]
reader
=
self
.
_dataset
[
reader_name
]
with
fluid
.
scope_guard
(
scope
):
self
.
_exe
.
train_from_dataset
(
program
=
program
,
dataset
=
reader
,
fetch_list
=
fetch_vars
,
fetch_info
=
fetch_alias
,
print_period
=
fetch_period
)
def
_executor_dataloader_train
(
self
,
model_dict
):
reader_name
=
model_dict
[
"dataset_name"
]
model_name
=
model_dict
[
"name"
]
model_class
=
self
.
_model
[
model_name
][
3
]
self
.
_model
[
model_name
][
1
]
=
fluid
.
compiler
.
CompiledProgram
(
self
.
_model
[
model_name
][
1
]).
with_data_parallel
(
loss_name
=
model_class
.
get_avg_cost
().
name
)
fetch_vars
=
[]
fetch_alias
=
[]
fetch_period
=
20
metrics
=
model_class
.
get_metrics
()
if
metrics
:
fetch_vars
=
metrics
.
values
()
fetch_alias
=
metrics
.
keys
()
metrics_varnames
=
[]
metrics_format
=
[]
metrics_format
.
append
(
"{}: {{}}"
.
format
(
"epoch"
))
metrics_format
.
append
(
"{}: {{}}"
.
format
(
"batch"
))
for
name
,
var
in
model_class
.
items
():
metrics_varnames
.
append
(
var
.
name
)
metrics_format
.
append
(
"{}: {{}}"
.
format
(
name
))
metrics_format
=
", "
.
join
(
metrics_format
)
reader
=
self
.
_dataset
[
reader_name
]
reader
.
start
()
batch_id
=
0
scope
=
self
.
_model
[
model_name
][
2
]
prorgram
=
self
.
_model
[
model_name
][
0
]
with
fluid
.
scope_guard
(
scope
):
try
:
while
True
:
metrics_rets
=
self
.
_exe
.
run
(
program
=
program
,
fetch_list
=
metrics_varnames
)
metrics
=
[
epoch
,
batch_id
]
metrics
.
extend
(
metrics_rets
)
if
batch_id
%
self
.
fetch_period
==
0
and
batch_id
!=
0
:
print
(
metrics_format
.
format
(
*
metrics
))
batch_id
+=
1
except
fluid
.
core
.
EOFException
:
reader
.
reset
()
def
terminal
(
self
,
context
):
context
[
'is_exit'
]
=
True
core/trainers/transpiler_trainer.py
浏览文件 @
03dfc413
...
...
@@ -119,6 +119,10 @@ class TranspileTrainer(Trainer):
pipe_cmd
=
"python {} {} {} {}"
.
format
(
reader
,
reader_class
,
state
,
self
.
_config_yaml
)
else
:
if
sparse_slots
is
None
:
sparse_slots
=
"#"
if
dense_slots
is
None
:
dense_slots
=
"#"
padding
=
envs
.
get_global_env
(
"padding"
,
0
,
namespace
)
pipe_cmd
=
"python {} {} {} {} {} {} {} {}"
.
format
(
reader
,
"slot"
,
"slot"
,
self
.
_config_yaml
,
namespace
,
\
...
...
core/utils/dataloader_instance.py
浏览文件 @
03dfc413
...
...
@@ -14,12 +14,102 @@
from
__future__
import
print_function
import
os
from
paddlerec.core.utils.envs
import
lazy_instance_by_fliename
from
paddlerec.core.utils.envs
import
get_global_env
from
paddlerec.core.utils.envs
import
get_runtime_environ
from
paddlerec.core.reader
import
SlotReader
def
dataloader_by_name
(
readerclass
,
dataset_name
,
yaml_file
):
reader_class
=
lazy_instance_by_fliename
(
readerclass
,
"TrainReader"
)
name
=
"dataset."
+
dataset_name
+
"."
data_path
=
get_global_env
(
name
+
"data_path"
)
#else:
# reader_name = "SlotReader"
# namespace = "evaluate.reader"
# data_path = get_global_env("test_data_path", None, namespace)
if
data_path
.
startswith
(
"paddlerec::"
):
package_base
=
get_runtime_environ
(
"PACKAGE_BASE"
)
assert
package_base
is
not
None
data_path
=
os
.
path
.
join
(
package_base
,
data_path
.
split
(
"::"
)[
1
])
files
=
[
str
(
data_path
)
+
"/%s"
%
x
for
x
in
os
.
listdir
(
data_path
)]
reader
=
reader_class
(
yaml_file
)
reader
.
init
()
def
gen_reader
():
for
file
in
files
:
with
open
(
file
,
'r'
)
as
f
:
for
line
in
f
:
line
=
line
.
rstrip
(
'
\n
'
)
iter
=
reader
.
generate_sample
(
line
)
for
parsed_line
in
iter
():
if
parsed_line
is
None
:
continue
else
:
values
=
[]
for
pased
in
parsed_line
:
values
.
append
(
pased
[
1
])
yield
values
def
gen_batch_reader
():
return
reader
.
generate_batch_from_trainfiles
(
files
)
if
hasattr
(
reader
,
'generate_batch_from_trainfiles'
):
return
gen_batch_reader
()
return
gen_reader
def
slotdataloader_by_name
(
readerclass
,
dataset_name
,
yaml_file
):
name
=
"dataset."
+
dataset_name
+
"."
#if train == "TRAIN":
reader_name
=
"SlotReader"
# namespace = "train.reader"
print
(
name
)
data_path
=
get_global_env
(
name
+
"data_path"
)
#else:
# reader_name = "SlotReader"
# namespace = "evaluate.reader"
# data_path = get_global_env("test_data_path", None, namespace)
if
data_path
.
startswith
(
"paddlerec::"
):
package_base
=
get_runtime_environ
(
"PACKAGE_BASE"
)
assert
package_base
is
not
None
data_path
=
os
.
path
.
join
(
package_base
,
data_path
.
split
(
"::"
)[
1
])
files
=
[
str
(
data_path
)
+
"/%s"
%
x
for
x
in
os
.
listdir
(
data_path
)]
#sparse = get_global_env("sparse_slots", None, namespace)
#dense = get_global_env("dense_slots", None, namespace)
#padding = get_global_env("padding", 0, namespace)
#name = "dataset." + dataset_name + "."
sparse
=
get_global_env
(
name
+
"sparse_slots"
)
dense
=
get_global_env
(
name
+
"dense_slots"
)
padding
=
get_global_env
(
name
+
"padding"
,
0
)
reader
=
SlotReader
(
yaml_file
)
reader
.
init
(
sparse
,
dense
,
int
(
padding
))
def
gen_reader
():
for
file
in
files
:
with
open
(
file
,
'r'
)
as
f
:
for
line
in
f
:
line
=
line
.
rstrip
(
'
\n
'
)
iter
=
reader
.
generate_sample
(
line
)
for
parsed_line
in
iter
():
if
parsed_line
is
None
:
continue
else
:
values
=
[]
for
pased
in
parsed_line
:
values
.
append
(
pased
[
1
])
yield
values
def
gen_batch_reader
():
return
reader
.
generate_batch_from_trainfiles
(
files
)
if
hasattr
(
reader
,
'generate_batch_from_trainfiles'
):
return
gen_batch_reader
()
return
gen_reader
def
dataloader
(
readerclass
,
train
,
yaml_file
):
if
train
==
"TRAIN"
:
...
...
core/utils/envs.py
浏览文件 @
03dfc413
...
...
@@ -80,8 +80,6 @@ def set_global_envs(envs):
global_envs
[
global_k
]
=
v
fatten_env_namespace
([],
envs
)
for
i
in
global_envs
:
print
i
,
":"
,
global_envs
[
i
]
def
get_global_env
(
env_name
,
default_value
=
None
,
namespace
=
None
):
"""
...
...
models/rank/dnn/config.yaml
浏览文件 @
03dfc413
...
...
@@ -21,7 +21,8 @@ workspace: "paddlerec.models.rank.dnn"
dataset
:
-
name
:
dataset_2
batch_size
:
2
type
:
QueueDataset
#type: QueueDataset
type
:
DataLoader
data_path
:
"
{workspace}/data/sample_data/train"
sparse_slots
:
"
click
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26"
dense_slots
:
"
dense_var:13"
...
...
@@ -38,8 +39,8 @@ hyper_parameters:
fc_sizes
:
[
512
,
256
,
128
,
32
]
epoch
:
trainer_class
:
single_yamlopt
#trainer_class: single_auc_yamlopt
name
:
trainer_class
:
single
save_checkpoint_interval
:
2
save_inference_interval
:
4
save_checkpoint_path
:
"
increment"
...
...
run.py
浏览文件 @
03dfc413
...
...
@@ -28,7 +28,7 @@ device = ["CPU", "GPU"]
clusters
=
[
"SINGLE"
,
"LOCAL_CLUSTER"
,
"CLUSTER"
]
engine_choices
=
[
"SINGLE"
,
"LOCAL_CLUSTER"
,
"CLUSTER"
,
"TDM_SINGLE"
,
"TDM_LOCAL_CLUSTER"
,
"TDM_CLUSTER"
,
"SINGLE_YAMLOPT"
,
"SINGLE_AUC_YAMLOPT"
"TDM_CLUSTER"
]
custom_model
=
[
'TDM'
]
model_name
=
""
...
...
@@ -41,10 +41,6 @@ def engine_registry():
engines
[
"TRANSPILER"
][
"SINGLE"
]
=
single_engine
engines
[
"TRANSPILER"
][
"LOCAL_CLUSTER"
]
=
local_cluster_engine
engines
[
"TRANSPILER"
][
"CLUSTER"
]
=
cluster_engine
engines
[
"TRANSPILER"
][
"SINGLE_YAMLOPT"
]
=
single_yamlopt_engine
engines
[
"TRANSPILER"
][
"SINGLE_AUC_YAMLOPT"
]
=
single_auc_yamlopt_engine
engines
[
"PSLIB"
][
"SINGLE"
]
=
local_mpi_engine
engines
[
"PSLIB"
][
"LOCAL_CLUSTER"
]
=
local_mpi_engine
engines
[
"PSLIB"
][
"CLUSTER"
]
=
cluster_mpi_engine
...
...
@@ -139,32 +135,6 @@ def single_engine(args):
trainer
=
TrainerFactory
.
create
(
args
.
model
)
return
trainer
def
single_yamlopt_engine
(
args
):
trainer
=
get_trainer_prefix
(
args
)
+
"SingleTrainerYamlOpt"
single_envs
=
{}
single_envs
[
"train.trainer.trainer"
]
=
trainer
single_envs
[
"train.trainer.threads"
]
=
"2"
single_envs
[
"train.trainer.engine"
]
=
"single_yamlopt"
single_envs
[
"train.trainer.platform"
]
=
envs
.
get_platform
()
print
(
"use {} engine to run model: {}"
.
format
(
trainer
,
args
.
model
))
set_runtime_envs
(
single_envs
,
args
.
model
)
trainer
=
TrainerFactory
.
create
(
args
.
model
)
return
trainer
def
single_auc_yamlopt_engine
(
args
):
trainer
=
get_trainer_prefix
(
args
)
+
"SingleAucYamlOpt"
single_envs
=
{}
single_envs
[
"train.trainer.trainer"
]
=
trainer
single_envs
[
"train.trainer.threads"
]
=
"2"
single_envs
[
"train.trainer.engine"
]
=
"single_yamlopt"
single_envs
[
"train.trainer.platform"
]
=
envs
.
get_platform
()
print
(
"use {} engine to run model: {}"
.
format
(
trainer
,
args
.
model
))
set_runtime_envs
(
single_envs
,
args
.
model
)
trainer
=
TrainerFactory
.
create
(
args
.
model
)
return
trainer
def
cluster_engine
(
args
):
def
update_workspace
(
cluster_envs
):
workspace
=
cluster_envs
.
get
(
"engine_workspace"
,
None
)
...
...
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