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PaddleRec
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43d49e3f
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43d49e3f
编写于
5月 27, 2020
作者:
X
xjqbest
浏览文件
操作
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电子邮件补丁
差异文件
test
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e4683727
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
309 addition
and
75 deletion
+309
-75
core/model.py
core/model.py
+16
-5
core/reader.py
core/reader.py
+2
-2
core/trainers/single_trainer.py
core/trainers/single_trainer.py
+235
-28
core/utils/envs.py
core/utils/envs.py
+5
-0
models/rank/dnn/config.yaml
models/rank/dnn/config.yaml
+41
-32
models/rank/dnn/model.py
models/rank/dnn/model.py
+10
-8
未找到文件。
core/model.py
浏览文件 @
43d49e3f
...
...
@@ -38,6 +38,7 @@ class Model(object):
self
.
_namespace
=
"train.model"
self
.
_platform
=
envs
.
get_platform
()
self
.
_init_hyper_parameters
()
self
.
_env
=
config
def
_init_hyper_parameters
(
self
):
pass
...
...
@@ -103,7 +104,7 @@ class Model(object):
def
get_fetch_period
(
self
):
return
self
.
_fetch_interval
def
_build_optimizer
(
self
,
name
,
lr
):
def
_build_optimizer
(
self
,
name
,
lr
,
strategy
=
None
):
name
=
name
.
upper
()
optimizers
=
[
"SGD"
,
"ADAM"
,
"ADAGRAD"
]
if
name
not
in
optimizers
:
...
...
@@ -133,10 +134,18 @@ class Model(object):
print
(
">>>>>>>>>>>.learnig rate: %s"
%
learning_rate
)
return
self
.
_build_optimizer
(
optimizer
,
learning_rate
)
def
input_data
(
self
,
is_infer
=
False
):
sparse_slots
=
envs
.
get_global_env
(
"sparse_slots"
,
None
,
"train.reader"
)
dense_slots
=
envs
.
get_global_env
(
"dense_slots"
,
None
,
"train.reader"
)
def
input_data
(
self
,
is_infer
=
False
,
dataset_name
=
None
,
program
=
None
):
dataset
=
{}
for
i
in
self
.
_env
[
"dataset"
]:
if
i
[
"name"
]
==
dataset_name
:
dataset
=
i
break
sparse_slots
=
dataset
.
get
(
"sparse_slots"
,
None
)
#sparse_slots =
#envs.get_global_env("sparse_slots", None,
# "train.reader")
#dense_slots = envs.get_global_env("dense_slots", None, "train.reader")
dense_slots
=
dataset
.
get
(
"dense_slots"
,
None
)
if
sparse_slots
is
not
None
or
dense_slots
is
not
None
:
sparse_slots
=
sparse_slots
.
strip
().
split
(
" "
)
dense_slots
=
dense_slots
.
strip
().
split
(
" "
)
...
...
@@ -159,6 +168,8 @@ class Model(object):
name
=
name
,
shape
=
[
1
],
lod_level
=
1
,
dtype
=
"int64"
)
data_var_
.
append
(
l
)
self
.
_sparse_data_var
.
append
(
l
)
print
(
self
.
_dense_data_var
)
print
(
self
.
_sparse_data_var
)
return
data_var_
else
:
...
...
core/reader.py
浏览文件 @
43d49e3f
...
...
@@ -58,8 +58,8 @@ class SlotReader(dg.MultiSlotDataGenerator):
_config
=
yaml
.
load
(
rb
.
read
(),
Loader
=
yaml
.
FullLoader
)
else
:
raise
ValueError
(
"reader config only support yaml"
)
envs
.
set_global_envs
(
_config
)
envs
.
update_workspace
()
#
envs.set_global_envs(_config)
#
envs.update_workspace()
def
init
(
self
,
sparse_slots
,
dense_slots
,
padding
=
0
):
from
operator
import
mul
...
...
core/trainers/single_trainer.py
浏览文件 @
43d49e3f
...
...
@@ -19,11 +19,12 @@ 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"
)
...
...
@@ -31,47 +32,253 @@ logger.setLevel(logging.INFO)
class
SingleTrainer
(
TranspileTrainer
):
def
__init__
(
self
,
config
=
None
):
super
(
TranspileTrainer
,
self
).
__init__
(
config
)
self
.
_env
=
self
.
_config
#envs.get_global_envs()
#device = envs.get_global_env("train.device", "cpu")
device
=
self
.
_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
=
{}
#self.inference_models = []
#self.increment_models = []
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
)
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 envs.get_platform() == "LINUX" and envs.get_global_env(
# "dataset_class", None, "train.reader") != "DataLoader":
self
.
regist_context_processor
(
'train_pass'
,
self
.
executor_train
)
# if envs.get_platform() == "LINUX" and envs.get_global_env(
# ""
# self.regist_context_processor('train_pass', self.dataset_train)
# else:
# self.regist_context_processor('train_pass', self.dataloader_train)
self
.
regist_context_processor
(
'infer_pass'
,
self
.
infer
)
#
self.regist_context_processor('infer_pass', self.infer)
self
.
regist_context_processor
(
'terminal_pass'
,
self
.
terminal
)
def
init
(
self
,
context
):
self
.
model
.
train_net
()
optimizer
=
self
.
model
.
optimizer
()
optimizer
.
minimize
((
self
.
model
.
get_avg_cost
()))
def
instance
(
self
,
context
):
context
[
'status'
]
=
'init_pass'
self
.
fetch_vars
=
[]
self
.
fetch_alias
=
[]
self
.
fetch_period
=
self
.
model
.
get_fetch_period
()
def
dataloader_train
(
self
,
context
):
pass
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
dataset_train
(
self
,
context
):
pass
#def _get_optmizer(self, cost):
# if self._env["hyper_parameters"]["optimizer"]["class"] == "Adam":
def
_create_dataset
(
self
,
dataset_name
):
config_dict
=
None
for
i
in
self
.
_env
[
"dataset"
]:
if
i
[
"name"
]
==
dataset_name
:
config_dict
=
i
break
#reader_ins = SlotReader(self._config_yaml)
sparse_slots
=
config_dict
[
"sparse_slots"
]
dense_slots
=
config_dict
[
"dense_slots"
]
padding
=
0
reader
=
envs
.
path_adapter
(
"paddlerec.core.utils"
)
+
"/dataset_instance.py"
#reader = "{workspace}/paddlerec/core/utils/dataset_instance.py".replace("{workspace}", envs.path_adapter(self._env["workspace"]))
pipe_cmd
=
"python {} {} {} {} {} {} {} {}"
.
format
(
reader
,
"slot"
,
"slot"
,
self
.
_config_yaml
,
"fake"
,
\
sparse_slots
.
replace
(
" "
,
"#"
),
dense_slots
.
replace
(
" "
,
"#"
),
str
(
padding
))
if
config_dict
[
"type"
]
==
"QueueDataset"
:
dataset
=
fluid
.
DatasetFactory
().
create_dataset
(
config_dict
[
"type"
])
dataset
.
set_batch_size
(
config_dict
[
"batch_size"
])
#dataset.set_thread(config_dict["thread_num"])
#dataset.set_hdfs_config(config_dict["data_fs_name"], config_dict["data_fs_ugi"])
dataset
.
set_pipe_command
(
pipe_cmd
)
train_data_path
=
config_dict
[
"data_path"
].
replace
(
"{workspace}"
,
envs
.
path_adapter
(
self
.
_env
[
"workspace"
]))
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
:
context
[
'status'
]
=
'startup_pass'
pass
return
dataset
def
init
(
self
,
context
):
#self.model.train_net()
for
model_dict
in
self
.
_env
[
"executor"
]:
self
.
_model
[
model_dict
[
"name"
]]
=
[
None
]
*
4
# self._model[model_dict["name"]][0] = fluid.Program() #train_program
# self._model[model_dict["name"]][1] = fluid.Program() #startup_program
# self._model[model_dict["name"]][2] = fluid.Scope() #scope
train_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
scope
=
fluid
.
Scope
()
opt_name
=
self
.
_env
[
"hyper_parameters"
][
"optimizer"
][
"class"
]
opt_lr
=
self
.
_env
[
"hyper_parameters"
][
"optimizer"
][
"learning_rate"
]
opt_strategy
=
self
.
_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"
])
# 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'
#else:
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
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"
]
#print(self._dataset)
#print(reader_name)
dataset
=
None
for
i
in
self
.
_env
[
"dataset"
]:
if
i
[
"name"
]
==
reader_name
:
dataset
=
i
break
if
dataset
[
"type"
]
==
"DataLoader"
:
self
.
_executor_dataloader_train
(
model_dict
)
else
:
self
.
_executor_dataset_train
(
model_dict
)
print
(
"epoch %s done"
%
j
)
# self._model[model_dict["name"]][1] = fluid.compiler.CompiledProgram(
# self._model[model_dict["name"]][1]).with_data_parallel(loss_name=self._model.get_avg_cost().name)
# fetch_vars = []
# fetch_alias = []
# fetch_period = self._model.get_fetch_period()
# metrics = self._model.get_metrics()
# if metrics:
# fetch_vars = metrics.values()
# fetch_alias = metrics.keys()
# metrics_varnames = []
context
[
'status'
]
=
"terminal_pass"
def
_executor_dataset_train
(
self
,
model_dict
):
# dataset = self._get_dataset("TRAIN")
# ins = self._get_dataset_ins()
# epochs = envs.get_global_env("train.epochs")
# for i in range(epochs):
reader_name
=
model_dict
[
"dataset_name"
]
model_name
=
model_dict
[
"name"
]
model_class
=
self
.
_model
[
model_name
][
3
]
fetch_vars
=
[]
fetch_alias
=
[]
fetch_period
=
1
#model_class.get_fetch_period()
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
][
1
]
reader
=
self
.
_dataset
[
reader_name
]
with
fluid
.
scope_guard
(
scope
):
begin_time
=
time
.
time
()
self
.
_exe
.
train_from_dataset
(
program
=
program
,
dataset
=
reader
,
fetch_list
=
fetch_vars
,
fetch_info
=
fetch_alias
,
print_period
=
fetch_period
)
end_time
=
time
.
time
()
times
=
end_time
-
begin_time
#print("epoch {} using time {}".format(i, times))
#print("epoch {} using time {}, speed {:.2f} lines/s".format(
# i, times, ins / times))
def
_executor_dataloader_train
(
self
,
model_dict
):
reader_name
=
model_dict
[
"dataset_name"
]
model_name
=
model_dict
[
"name"
]
model_class
=
self
.
_model
[
model
][
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
=
self
.
_model
.
get_fetch_period
()
metrics
=
self
.
_model
.
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
][
3
]
prorgram
=
self
.
_model
[
model_name
][
1
]
with
fluid
.
scope_guard
(
self
.
_model
[
model_name
][
3
]):
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
dataloader_train
(
self
,
context
):
reader
=
self
.
_get_dataloader
(
"TRAIN"
)
epochs
=
envs
.
get_global_env
(
"train.epochs"
)
exit
(
-
1
)
reader
=
self
.
_get_dataloader
(
self
.
_env
[
"TRAIN"
])
epochs
=
self
.
_env
[
"epochs"
]
program
=
fluid
.
compiler
.
CompiledProgram
(
fluid
.
default_main_program
(
)).
with_data_parallel
(
loss_name
=
self
.
model
.
get_avg_cost
().
name
)
...
...
@@ -130,6 +337,6 @@ class SingleTrainer(TranspileTrainer):
context
[
'status'
]
=
'infer_pass'
def
terminal
(
self
,
context
):
for
model
in
self
.
increment_models
:
print
(
"epoch :{}, dir: {}"
.
format
(
model
[
0
],
model
[
1
]))
#
for model in self.increment_models:
#
print("epoch :{}, dir: {}".format(model[0], model[1]))
context
[
'is_exit'
]
=
True
core/utils/envs.py
浏览文件 @
43d49e3f
...
...
@@ -20,6 +20,7 @@ import sys
global_envs
=
{}
global_envs_raw
=
{}
def
flatten_environs
(
envs
,
separator
=
"."
):
flatten_dict
=
{}
...
...
@@ -62,6 +63,10 @@ def get_trainer():
def
set_global_envs
(
envs
):
assert
isinstance
(
envs
,
dict
)
global_envs_raw
=
envs
return
def
fatten_env_namespace
(
namespace_nests
,
local_envs
):
for
k
,
v
in
local_envs
.
items
():
if
isinstance
(
v
,
dict
):
...
...
models/rank/dnn/config.yaml
浏览文件 @
43d49e3f
...
...
@@ -12,39 +12,48 @@
# See the License for the specific language governing permissions and
# limitations under the License.
train
:
epochs
:
10
engine
:
single
workspace
:
"
paddlerec.models.rank.dnn"
debug
:
false
cold_start
:
true
epochs
:
10
device
:
cpu
workspace
:
"
paddlerec.models.rank.dnn"
trainer
:
# for cluster training
strategy
:
"
async"
dataset
:
#- name: dataset_1
# batch_size: 2
# 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"
reader
:
batch_size
:
2
train_data_path
:
"
{workspace}/data/sample_data/train"
reader_debug_mode
:
False
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_2
batch_size
:
2
type
:
QueueDataset
data_path
:
"
{workspace}/data/sample_data/train"
# 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"
model
:
models
:
"
{workspace}/model.py"
hyper_parameters
:
sparse_inputs_slots
:
27
sparse_feature_number
:
1000001
sparse_feature_dim
:
9
dense_input_dim
:
13
fc_sizes
:
[
512
,
256
,
128
,
32
]
learning_rate
:
0.001
optimizer
:
adam
hyper_parameters
:
optimizer
:
class
:
Adam
learning_rate
:
0.001
strategy
:
async
sparse_inputs_slots
:
27
sparse_feature_number
:
1000001
sparse_feature_dim
:
9
dense_input_dim
:
13
fc_sizes
:
[
512
,
256
,
128
,
32
]
save
:
increment
:
dirname
:
"
increment"
epoch_interval
:
2
save_last
:
True
inference
:
dirname
:
"
inference"
epoch_interval
:
4
save_last
:
True
epoch
:
trainer_class
:
Single
save_checkpoint_interval
:
2
save_inference_interval
:
4
save_checkpoint_path
:
"
increment"
save_inference_path
:
"
inference"
executor
:
-
name
:
train
model
:
"
{workspace}/model.py"
dataset_name
:
dataset_2
thread_num
:
1
models/rank/dnn/model.py
浏览文件 @
43d49e3f
...
...
@@ -27,12 +27,12 @@ class Model(ModelBase):
def
_init_hyper_parameters
(
self
):
self
.
is_distributed
=
True
if
envs
.
get_trainer
(
)
==
"CtrTrainer"
else
False
self
.
sparse_feature_number
=
envs
.
get_global_env
(
"hyper_parameters.sparse_feature_number"
,
None
,
self
.
_namespace
)
self
.
sparse_feature_dim
=
envs
.
get_global_env
(
"hyper_parameters.sparse_feature_dim"
,
None
,
self
.
_namespace
)
self
.
learning_rate
=
envs
.
get_global_env
(
"hyper_parameters.learning_rate"
,
None
,
self
.
_namespace
)
self
.
sparse_feature_number
=
1000001
#
envs.get_global_env(
#
"hyper_parameters.sparse_feature_number", None, self._namespace)
self
.
sparse_feature_dim
=
9
#
envs.get_global_env(
#
"hyper_parameters.sparse_feature_dim", None, self._namespace)
self
.
learning_rate
=
0.001
#
envs.get_global_env(
#
"hyper_parameters.learning_rate", None, self._namespace)
def
net
(
self
,
input
,
is_infer
=
False
):
self
.
sparse_inputs
=
self
.
_sparse_data_var
[
1
:]
...
...
@@ -56,8 +56,8 @@ class Model(ModelBase):
sparse_embed_seq
+
[
self
.
dense_input
],
axis
=
1
)
fcs
=
[
concated
]
hidden_layers
=
envs
.
get_global_env
(
"hyper_parameters.fc_sizes"
,
None
,
self
.
_namespace
)
hidden_layers
=
[
512
,
256
,
128
,
32
]
#
envs.get_global_env("hyper_parameters.fc_sizes", None,
#
self._namespace)
for
size
in
hidden_layers
:
output
=
fluid
.
layers
.
fc
(
...
...
@@ -82,6 +82,8 @@ class Model(ModelBase):
input
=
self
.
predict
,
label
=
self
.
label_input
)
avg_cost
=
fluid
.
layers
.
reduce_mean
(
cost
)
self
.
_cost
=
avg_cost
auc
,
batch_auc
,
_
=
fluid
.
layers
.
auc
(
input
=
self
.
predict
,
label
=
self
.
label_input
,
num_thresholds
=
2
**
12
,
...
...
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