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PaddleRec
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019cb085
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体验新版 GitCode,发现更多精彩内容 >>
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019cb085
编写于
5月 28, 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
d7777a77
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
121 addition
and
88 deletion
+121
-88
core/factory.py
core/factory.py
+1
-0
core/trainers/single_trainer.py
core/trainers/single_trainer.py
+33
-57
core/utils/envs.py
core/utils/envs.py
+2
-1
models/rank/dnn/config.yaml
models/rank/dnn/config.yaml
+28
-20
run.py
run.py
+57
-10
未找到文件。
core/factory.py
浏览文件 @
019cb085
...
...
@@ -26,6 +26,7 @@ trainers = {}
def
trainer_registry
():
trainers
[
"SingleTrainer"
]
=
os
.
path
.
join
(
trainer_abs
,
"single_trainer.py"
)
trainers
[
"SingleInfer"
]
=
os
.
path
.
join
(
trainer_abs
,
"single_infer.py"
)
trainers
[
"ClusterTrainer"
]
=
os
.
path
.
join
(
trainer_abs
,
"cluster_trainer.py"
)
trainers
[
"CtrCodingTrainer"
]
=
os
.
path
.
join
(
trainer_abs
,
...
...
core/trainers/single_trainer.py
浏览文件 @
019cb085
...
...
@@ -47,6 +47,7 @@ class SingleTrainer(TranspileTrainer):
self
.
_dataset
=
{}
envs
.
set_global_envs
(
self
.
_config
)
envs
.
update_workspace
()
self
.
_runner_name
=
envs
.
get_global_env
(
"mode"
)
def
processor_register
(
self
):
self
.
regist_context_processor
(
'uninit'
,
self
.
instance
)
...
...
@@ -90,13 +91,10 @@ class SingleTrainer(TranspileTrainer):
for
x
in
os
.
listdir
(
train_data_path
)
]
dataset
.
set_filelist
(
file_list
)
for
model_dict
in
self
.
_env
[
"
executor
"
]:
for
model_dict
in
self
.
_env
[
"
phase
"
]:
if
model_dict
[
"dataset_name"
]
==
dataset_name
:
model
=
self
.
_model
[
model_dict
[
"name"
]][
3
]
if
model_dict
[
"is_infer"
]:
inputs
=
model
.
_infer_data_var
else
:
inputs
=
model
.
_data_var
inputs
=
model
.
_data_var
dataset
.
set_use_var
(
inputs
)
break
return
dataset
...
...
@@ -144,7 +142,7 @@ class SingleTrainer(TranspileTrainer):
return
self
.
_get_dataset
(
dataset_name
)
def
init
(
self
,
context
):
for
model_dict
in
self
.
_env
[
"
executor
"
]:
for
model_dict
in
self
.
_env
[
"
phase
"
]:
self
.
_model
[
model_dict
[
"name"
]]
=
[
None
]
*
5
train_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
...
...
@@ -163,26 +161,17 @@ class SingleTrainer(TranspileTrainer):
envs
.
path_adapter
(
self
.
_env
[
"workspace"
]))
model
=
envs
.
lazy_instance_by_fliename
(
model_path
,
"Model"
)(
self
.
_env
)
is_infer
=
model_dict
.
get
(
"is_infer"
,
False
)
if
is_infer
:
model
.
_infer_data_var
=
model
.
input_data
(
dataset_name
=
model_dict
[
"dataset_name"
])
else
:
model
.
_data_var
=
model
.
input_data
(
dataset_name
=
model_dict
[
"dataset_name"
])
model
.
_data_var
=
model
.
input_data
(
dataset_name
=
model_dict
[
"dataset_name"
])
if
envs
.
get_global_env
(
"dataset."
+
dataset_name
+
".type"
)
==
"DataLoader"
:
model
.
_init_dataloader
(
is_infer
=
is_infer
)
model
.
_init_dataloader
(
is_infer
=
False
)
self
.
_get_dataloader
(
dataset_name
,
model
.
_data_loader
)
if
is_infer
:
model
.
net
(
model
.
_infer_data_var
,
True
)
else
:
model
.
net
(
model
.
_data_var
,
False
)
optimizer
=
model
.
_build_optimizer
(
opt_name
,
opt_lr
,
opt_strategy
)
optimizer
.
minimize
(
model
.
_cost
)
model_dict
[
"is_infer"
]
=
is_infer
model
.
net
(
model
.
_data_var
,
False
)
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
...
...
@@ -197,7 +186,7 @@ class SingleTrainer(TranspileTrainer):
context
[
'status'
]
=
'startup_pass'
def
startup
(
self
,
context
):
for
model_dict
in
self
.
_env
[
"
executor
"
]:
for
model_dict
in
self
.
_env
[
"
phase
"
]:
with
fluid
.
scope_guard
(
self
.
_model
[
model_dict
[
"name"
]][
2
]):
self
.
_exe
.
run
(
self
.
_model
[
model_dict
[
"name"
]][
1
])
context
[
'status'
]
=
'train_pass'
...
...
@@ -205,7 +194,7 @@ class SingleTrainer(TranspileTrainer):
def
executor_train
(
self
,
context
):
epochs
=
int
(
self
.
_env
[
"epochs"
])
for
j
in
range
(
epochs
):
for
model_dict
in
self
.
_env
[
"
executor
"
]:
for
model_dict
in
self
.
_env
[
"
phase
"
]:
if
j
==
0
:
with
fluid
.
scope_guard
(
self
.
_model
[
model_dict
[
"name"
]][
2
]):
train_prog
=
self
.
_model
[
model_dict
[
"name"
]][
0
]
...
...
@@ -236,10 +225,7 @@ class SingleTrainer(TranspileTrainer):
fetch_vars
=
[]
fetch_alias
=
[]
fetch_period
=
20
if
model_dict
[
"is_infer"
]:
metrics
=
model_class
.
get_infer_results
()
else
:
metrics
=
model_class
.
get_metrics
()
metrics
=
model_class
.
get_metrics
()
if
metrics
:
fetch_vars
=
metrics
.
values
()
fetch_alias
=
metrics
.
keys
()
...
...
@@ -247,37 +233,24 @@ class SingleTrainer(TranspileTrainer):
program
=
self
.
_model
[
model_name
][
0
]
reader
=
self
.
_dataset
[
reader_name
]
with
fluid
.
scope_guard
(
scope
):
if
model_dict
[
"is_infer"
]:
self
.
_exe
.
infer_from_dataset
(
program
=
program
,
dataset
=
reader
,
fetch_list
=
fetch_vars
,
fetch_info
=
fetch_alias
,
print_period
=
fetch_period
)
else
:
self
.
_exe
.
train_from_dataset
(
program
=
program
,
dataset
=
reader
,
fetch_list
=
fetch_vars
,
fetch_info
=
fetch_alias
,
print_period
=
fetch_period
)
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
]
program
=
self
.
_model
[
model_name
][
0
].
clone
()
if
not
model_dict
[
"is_infer"
]:
program
=
fluid
.
compiler
.
CompiledProgram
(
program
).
with_data_parallel
(
loss_name
=
model_class
.
get_avg_cost
().
name
)
program
=
fluid
.
compiler
.
CompiledProgram
(
program
).
with_data_parallel
(
loss_name
=
model_class
.
get_avg_cost
().
name
)
fetch_vars
=
[]
fetch_alias
=
[]
fetch_period
=
20
if
model_dict
[
"is_infer"
]:
metrics
=
model_class
.
get_infer_results
()
else
:
metrics
=
model_class
.
get_metrics
()
metrics
=
model_class
.
get_metrics
()
if
metrics
:
fetch_vars
=
metrics
.
values
()
fetch_alias
=
metrics
.
keys
()
...
...
@@ -312,7 +285,8 @@ class SingleTrainer(TranspileTrainer):
context
[
'is_exit'
]
=
True
def
load
(
self
,
is_fleet
=
False
):
dirname
=
envs
.
get_global_env
(
"epoch.init_model_path"
,
None
)
dirname
=
envs
.
get_global_env
(
"runner."
+
self
.
_runner_name
+
".init_model_path"
,
None
)
if
dirname
is
None
:
return
print
(
"going to load "
,
dirname
)
...
...
@@ -331,21 +305,22 @@ class SingleTrainer(TranspileTrainer):
return
epoch_id
%
epoch_interval
==
0
def
save_inference_model
():
name
=
"runner."
+
self
.
_runner_name
+
"."
save_interval
=
int
(
envs
.
get_global_env
(
"epoch.
save_inference_interval"
,
-
1
))
envs
.
get_global_env
(
name
+
"
save_inference_interval"
,
-
1
))
if
not
need_save
(
epoch_id
,
save_interval
,
False
):
return
feed_varnames
=
envs
.
get_global_env
(
"epoch.
save_inference_feed_varnames"
,
None
)
name
+
"
save_inference_feed_varnames"
,
None
)
fetch_varnames
=
envs
.
get_global_env
(
"epoch.
save_inference_fetch_varnames"
,
None
)
name
+
"
save_inference_fetch_varnames"
,
None
)
if
feed_varnames
is
None
or
fetch_varnames
is
None
or
feed_varnames
==
""
:
return
fetch_vars
=
[
fluid
.
default_main_program
().
global_block
().
vars
[
varname
]
for
varname
in
fetch_varnames
]
dirname
=
envs
.
get_global_env
(
"epoch.
save_inference_path"
,
None
)
dirname
=
envs
.
get_global_env
(
name
+
"
save_inference_path"
,
None
)
assert
dirname
is
not
None
dirname
=
os
.
path
.
join
(
dirname
,
str
(
epoch_id
))
...
...
@@ -358,11 +333,12 @@ class SingleTrainer(TranspileTrainer):
fetch_vars
,
self
.
_exe
)
def
save_persistables
():
name
=
"runner."
+
self
.
_runner_name
+
"."
save_interval
=
int
(
envs
.
get_global_env
(
"epoch.
save_checkpoint_interval"
,
-
1
))
envs
.
get_global_env
(
name
+
"
save_checkpoint_interval"
,
-
1
))
if
not
need_save
(
epoch_id
,
save_interval
,
False
):
return
dirname
=
envs
.
get_global_env
(
"epoch.
save_checkpoint_path"
,
None
)
dirname
=
envs
.
get_global_env
(
name
+
"
save_checkpoint_path"
,
None
)
if
dirname
is
None
or
dirname
==
""
:
return
dirname
=
os
.
path
.
join
(
dirname
,
str
(
epoch_id
))
...
...
core/utils/envs.py
浏览文件 @
019cb085
...
...
@@ -68,7 +68,8 @@ def set_global_envs(envs):
nests
=
copy
.
deepcopy
(
namespace_nests
)
nests
.
append
(
k
)
fatten_env_namespace
(
nests
,
v
)
elif
(
k
==
"dataset"
or
k
==
"executor"
)
and
isinstance
(
v
,
list
):
elif
(
k
==
"dataset"
or
k
==
"phase"
or
k
==
"runner"
)
and
isinstance
(
v
,
list
):
for
i
in
v
:
if
i
.
get
(
"name"
)
is
None
:
raise
ValueError
(
"name must be in dataset list "
,
v
)
...
...
models/rank/dnn/config.yaml
浏览文件 @
019cb085
...
...
@@ -21,12 +21,18 @@ workspace: "paddlerec.models.rank.dnn"
# dataset列表
dataset
:
-
name
:
dataset_
2
# 名字,用来区分不同的dataset
-
name
:
dataset_
train
# 名字,用来区分不同的dataset
batch_size
:
2
type
:
DataLoader
# 或者QueueDataset
data_path
:
"
{workspace}/data/sample_data/train"
# 数据路径
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"
-
name
:
dataset_infer
# 名字,用来区分不同的dataset
batch_size
:
2
type
:
DataLoader
# 或者QueueDataset
data_path
:
"
{workspace}/data/sample_data/test"
# 数据路径
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"
# 超参数
hyper_parameters
:
...
...
@@ -42,27 +48,29 @@ hyper_parameters:
dense_input_dim
:
13
fc_sizes
:
[
512
,
256
,
128
,
32
]
# executor配置
epoch
:
name
:
trainer_class
:
single
save_checkpoint_interval
:
2
# 保存模型
save_inference_interval
:
4
# 保存预测模型
save_checkpoint_path
:
"
increment"
# 保存模型路径
save_inference_path
:
"
inference"
# 保存预测模型路径
#save_inference_feed_varnames: [] # 预测模型feed vars
#save_inference_fetch_varnames: [] # 预测模型 fetch vars
#init_model_path: "xxxx" # 加载模型
mode
:
runner1
# runner配置
runner
:
-
name
:
runner1
class
:
single_train
save_checkpoint_interval
:
2
# 保存模型
save_inference_interval
:
4
# 保存预测模型
save_checkpoint_path
:
"
increment"
# 保存模型路径
save_inference_path
:
"
inference"
# 保存预测模型路径
#save_inference_feed_varnames: [] # 预测模型feed vars
#save_inference_fetch_varnames: [] # 预测模型 fetch vars
#init_model_path: "xxxx" # 加载模型
-
name
:
runner2
class
:
single_infer
init_model_path
:
"
increment/0"
# 加载模型
# 执行器,每轮要跑的所有
模型
executor
:
-
name
:
train
# 执行器,每轮要跑的所有
阶段
phase
:
-
name
:
phase1
model
:
"
{workspace}/model.py"
# 模型路径
dataset_name
:
dataset_
2
# 名字,用来区分不同的阶段
dataset_name
:
dataset_
train
# 名字,用来区分不同的阶段
thread_num
:
1
# 线程数
is_infer
:
False
# 是否是infer
# - name: infer
# - name: phase2
# model: "{workspace}/model.py" # 模型路径
# dataset_name: dataset_
2
# 名字,用来区分不同的阶段
# dataset_name: dataset_
infer
# 名字,用来区分不同的阶段
# thread_num: 1 # 线程数
# is_infer: True # 是否是infer
run.py
浏览文件 @
019cb085
...
...
@@ -18,7 +18,7 @@ import subprocess
import
argparse
import
tempfile
import
yaml
import
copy
from
paddlerec.core.factory
import
TrainerFactory
from
paddlerec.core.utils
import
envs
from
paddlerec.core.utils
import
util
...
...
@@ -27,8 +27,8 @@ engines = {}
device
=
[
"CPU"
,
"GPU"
]
clusters
=
[
"SINGLE"
,
"LOCAL_CLUSTER"
,
"CLUSTER"
]
engine_choices
=
[
"SINGLE
"
,
"LOCAL_CLUSTER"
,
"CLUSTER"
,
"TDM_SINGLE"
,
"TDM_LOCAL_CLUSTER
"
,
"TDM_
CLUST
ER"
"SINGLE
_TRAIN"
,
"LOCAL_CLUSTER"
,
"CLUSTER"
,
"TDM_SINGLE
"
,
"TDM_
LOCAL_CLUSTER"
,
"TDM_CLUSTER"
,
"SINGLE_INF
ER"
]
custom_model
=
[
'TDM'
]
model_name
=
""
...
...
@@ -38,7 +38,8 @@ def engine_registry():
engines
[
"TRANSPILER"
]
=
{}
engines
[
"PSLIB"
]
=
{}
engines
[
"TRANSPILER"
][
"SINGLE"
]
=
single_engine
engines
[
"TRANSPILER"
][
"SINGLE_TRAIN"
]
=
single_train_engine
engines
[
"TRANSPILER"
][
"SINGLE_INFER"
]
=
single_infer_engine
engines
[
"TRANSPILER"
][
"LOCAL_CLUSTER"
]
=
local_cluster_engine
engines
[
"TRANSPILER"
][
"CLUSTER"
]
=
cluster_engine
engines
[
"PSLIB"
][
"SINGLE"
]
=
local_mpi_engine
...
...
@@ -51,7 +52,6 @@ def get_inters_from_yaml(file, filters):
_envs
=
yaml
.
load
(
rb
.
read
(),
Loader
=
yaml
.
FullLoader
)
flattens
=
envs
.
flatten_environs
(
_envs
)
inters
=
{}
for
k
,
v
in
flattens
.
items
():
for
f
in
filters
:
...
...
@@ -60,15 +60,50 @@ def get_inters_from_yaml(file, filters):
return
inters
def
get_all_inters_from_yaml
(
file
,
filters
):
with
open
(
file
,
'r'
)
as
rb
:
_envs
=
yaml
.
load
(
rb
.
read
(),
Loader
=
yaml
.
FullLoader
)
all_flattens
=
{}
def
fatten_env_namespace
(
namespace_nests
,
local_envs
):
for
k
,
v
in
local_envs
.
items
():
if
isinstance
(
v
,
dict
):
nests
=
copy
.
deepcopy
(
namespace_nests
)
nests
.
append
(
k
)
fatten_env_namespace
(
nests
,
v
)
elif
(
k
==
"dataset"
or
k
==
"phase"
or
k
==
"runner"
)
and
isinstance
(
v
,
list
):
for
i
in
v
:
if
i
.
get
(
"name"
)
is
None
:
raise
ValueError
(
"name must be in dataset list "
,
v
)
nests
=
copy
.
deepcopy
(
namespace_nests
)
nests
.
append
(
k
)
nests
.
append
(
i
[
"name"
])
fatten_env_namespace
(
nests
,
i
)
else
:
global_k
=
"."
.
join
(
namespace_nests
+
[
k
])
all_flattens
[
global_k
]
=
v
fatten_env_namespace
([],
_envs
)
ret
=
{}
for
k
,
v
in
all_flattens
.
items
():
for
f
in
filters
:
if
k
.
startswith
(
f
):
ret
[
k
]
=
v
return
ret
def
get_engine
(
args
):
transpiler
=
get_transpiler
()
run_extras
=
get_inters_from_yaml
(
args
.
model
,
[
"train."
,
"epoch."
])
with
open
(
args
.
model
,
'r'
)
as
rb
:
envs
=
yaml
.
load
(
rb
.
read
(),
Loader
=
yaml
.
FullLoader
)
run_extras
=
get_all_inters_from_yaml
(
args
.
model
,
[
"train."
,
"runner."
])
engine
=
run_extras
.
get
(
"train.engine"
,
None
)
if
engine
is
None
:
engine
=
run_extras
.
get
(
"
epoch.trainer_
class"
,
None
)
engine
=
run_extras
.
get
(
"
runner."
+
envs
[
"mode"
]
+
".
class"
,
None
)
if
engine
is
None
:
engine
=
"single"
engine
=
"single
_train
"
engine
=
engine
.
upper
()
if
engine
not
in
engine_choices
:
raise
ValueError
(
"train.engin can not be chosen in {}"
.
format
(
...
...
@@ -120,15 +155,27 @@ def get_trainer_prefix(args):
return
""
def
single_engine
(
args
):
def
single_
train_
engine
(
args
):
trainer
=
get_trainer_prefix
(
args
)
+
"SingleTrainer"
single_envs
=
{}
single_envs
[
"train.trainer.trainer"
]
=
trainer
single_envs
[
"train.trainer.threads"
]
=
"2"
single_envs
[
"train.trainer.engine"
]
=
"single"
single_envs
[
"train.trainer.engine"
]
=
"single
_train
"
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_infer_engine
(
args
):
trainer
=
get_trainer_prefix
(
args
)
+
"SingleInfer"
single_envs
=
{}
single_envs
[
"train.trainer.trainer"
]
=
trainer
single_envs
[
"train.trainer.threads"
]
=
"2"
single_envs
[
"train.trainer.engine"
]
=
"single_infer"
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
...
...
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