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体验新版 GitCode,发现更多精彩内容 >>
提交
4b8ba0ef
编写于
2月 26, 2018
作者:
G
guosheng
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add learning rate scheduling in Transformer
上级
2c00d0f9
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
56 addition
and
4 deletion
+56
-4
fluid/transformer/config.py
fluid/transformer/config.py
+3
-0
fluid/transformer/optim.py
fluid/transformer/optim.py
+40
-0
fluid/transformer/train.py
fluid/transformer/train.py
+13
-4
未找到文件。
fluid/transformer/config.py
浏览文件 @
4b8ba0ef
...
...
@@ -12,6 +12,9 @@ class TrainTaskConfig(object):
beta2
=
0.98
eps
=
1e-9
# the params for learning rate scheduling
warmup_steps
=
4000
class
ModelHyperParams
(
object
):
# Dictionary size for source and target language. This model directly uses
...
...
fluid/transformer/optim.py
0 → 100644
浏览文件 @
4b8ba0ef
import
numpy
as
np
import
paddle.fluid
as
fluid
import
paddle.fluid.layers
as
layers
class
LearningRateScheduler
(
object
):
"""
Wrapper for learning rate scheduling as described in the Transformer paper.
LearningRateScheduler adapts the learning rate externally and the adapted
learning rate will be feeded into the main_program as input data.
"""
def
__init__
(
self
,
d_model
,
warmup_steps
,
place
,
learning_rate
=
0.001
,
current_steps
=
0
,
name
=
"learning_rate"
):
self
.
current_steps
=
current_steps
self
.
warmup_steps
=
warmup_steps
self
.
d_model
=
d_model
self
.
learning_rate
=
layers
.
create_global_var
(
name
=
name
,
shape
=
[
1
],
value
=
float
(
learning_rate
),
dtype
=
"float32"
,
persistable
=
True
)
self
.
place
=
place
def
update_learning_rate
(
self
,
data_input
):
self
.
current_steps
+=
1
lr_value
=
np
.
power
(
self
.
d_model
,
-
0.5
)
*
np
.
min
([
np
.
power
(
self
.
current_steps
,
-
0.5
),
np
.
power
(
self
.
warmup_steps
,
-
1.5
)
*
self
.
current_steps
])
lr_tensor
=
fluid
.
LoDTensor
()
lr_tensor
.
set
(
np
.
array
([
lr_value
],
dtype
=
"float32"
),
self
.
place
)
data_input
[
self
.
learning_rate
.
name
]
=
lr_tensor
fluid/transformer/train.py
浏览文件 @
4b8ba0ef
...
...
@@ -4,6 +4,7 @@ import paddle.v2 as paddle
import
paddle.fluid
as
fluid
from
model
import
transformer
,
position_encoding_init
from
optim
import
LearningRateScheduler
from
config
import
TrainTaskConfig
,
ModelHyperParams
,
\
pos_enc_param_names
,
input_data_names
...
...
@@ -88,6 +89,9 @@ def prepare_batch_input(insts, input_data_names, src_pad_idx, trg_pad_idx,
def
main
():
place
=
fluid
.
CUDAPlace
(
0
)
if
TrainTaskConfig
.
use_gpu
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
cost
=
transformer
(
ModelHyperParams
.
src_vocab_size
+
1
,
ModelHyperParams
.
trg_vocab_size
+
1
,
ModelHyperParams
.
max_length
+
1
,
...
...
@@ -97,8 +101,11 @@ def main():
ModelHyperParams
.
dropout
,
ModelHyperParams
.
src_pad_idx
,
ModelHyperParams
.
trg_pad_idx
,
ModelHyperParams
.
pos_pad_idx
)
lr_scheduler
=
LearningRateScheduler
(
ModelHyperParams
.
d_model
,
TrainTaskConfig
.
warmup_steps
,
place
,
TrainTaskConfig
.
learning_rate
)
optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
TrainTaskConfig
.
learning_rate
,
learning_rate
=
lr_scheduler
.
learning_rate
,
beta1
=
TrainTaskConfig
.
beta1
,
beta2
=
TrainTaskConfig
.
beta2
,
epsilon
=
TrainTaskConfig
.
eps
)
...
...
@@ -111,9 +118,6 @@ def main():
buf_size
=
51200
),
batch_size
=
TrainTaskConfig
.
batch_size
)
place
=
fluid
.
CUDAPlace
(
0
)
if
TrainTaskConfig
.
use_gpu
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
# Initialize the parameters.
exe
.
run
(
fluid
.
framework
.
default_startup_program
())
for
pos_enc_param_name
in
pos_enc_param_names
:
...
...
@@ -125,10 +129,15 @@ def main():
for
pass_id
in
xrange
(
TrainTaskConfig
.
pass_num
):
for
batch_id
,
data
in
enumerate
(
train_data
()):
# The current program desc is coupled with batch_size, thus all
# mini-batches must have the same number of instances currently.
if
len
(
data
)
!=
TrainTaskConfig
.
batch_size
:
continue
data_input
=
prepare_batch_input
(
data
,
input_data_names
,
ModelHyperParams
.
src_pad_idx
,
ModelHyperParams
.
trg_pad_idx
,
ModelHyperParams
.
max_length
,
ModelHyperParams
.
n_head
,
place
)
lr_scheduler
.
update_learning_rate
(
data_input
)
outs
=
exe
.
run
(
fluid
.
framework
.
default_main_program
(),
feed
=
data_input
,
fetch_list
=
[
cost
])
...
...
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