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体验新版 GitCode,发现更多精彩内容 >>
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a66a83f7
编写于
8月 09, 2018
作者:
G
Guo Sheng
提交者:
GitHub
8月 09, 2018
浏览文件
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差异文件
Merge pull request #1120 from guoshengCS/add-ce-for-transformer
Add ce for transformer
上级
8f61de12
23c55953
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
134 addition
and
10 deletion
+134
-10
fluid/neural_machine_translation/transformer/.run_ce.sh
fluid/neural_machine_translation/transformer/.run_ce.sh
+27
-0
fluid/neural_machine_translation/transformer/_ce.py
fluid/neural_machine_translation/transformer/_ce.py
+60
-0
fluid/neural_machine_translation/transformer/config.py
fluid/neural_machine_translation/transformer/config.py
+2
-0
fluid/neural_machine_translation/transformer/model.py
fluid/neural_machine_translation/transformer/model.py
+11
-3
fluid/neural_machine_translation/transformer/train.py
fluid/neural_machine_translation/transformer/train.py
+34
-7
未找到文件。
fluid/neural_machine_translation/transformer/.run_ce.sh
0 → 100644
浏览文件 @
a66a83f7
#!/bin/bash
DATA_PATH
=
$HOME
/.cache/paddle/dataset/wmt16
if
[
!
-d
$DATA_PATH
]
;
then
python
-c
'import paddle;paddle.dataset.wmt16.train(10000, 10000, "en")'
\
'().next()'
tar
-zxf
$DATA_PATH
/wmt16.tar.gz
-C
$DATA_PATH
fi
train
(){
python
-u
train.py
\
--src_vocab_fpath
$DATA_PATH
/en_10000.dict
\
--trg_vocab_fpath
$DATA_PATH
/de_10000.dict
\
--special_token
'<s>'
'<e>'
'<unk>'
\
--train_file_pattern
$DATA_PATH
/wmt16/train
\
--val_file_pattern
$DATA_PATH
/wmt16/val
\
--use_token_batch
True
\
--batch_size
2048
\
--sort_type
pool
\
--pool_size
10000
\
--enable_ce
True
\
weight_sharing False
\
pass_num 20
\
dropout_seed 10
}
train | python _ce.py
fluid/neural_machine_translation/transformer/_ce.py
0 → 100644
浏览文件 @
a66a83f7
####this file is only used for continuous evaluation test!
import
os
import
sys
sys
.
path
.
append
(
os
.
environ
[
'ceroot'
])
from
kpi
import
CostKpi
,
DurationKpi
,
AccKpi
#### NOTE kpi.py should shared in models in some way!!!!
train_cost_kpi
=
CostKpi
(
'train_cost'
,
0.02
,
0
,
actived
=
True
)
test_cost_kpi
=
CostKpi
(
'test_cost'
,
0.005
,
0
,
actived
=
True
)
train_duration_kpi
=
DurationKpi
(
'train_duration'
,
0.06
,
0
,
actived
=
True
)
tracking_kpis
=
[
train_cost_kpi
,
test_cost_kpi
,
train_duration_kpi
,
]
def
parse_log
(
log
):
'''
This method should be implemented by model developers.
The suggestion:
each line in the log should be key, value, for example:
"
train_cost
\t
1.0
test_cost
\t
1.0
train_cost
\t
1.0
train_cost
\t
1.0
train_acc
\t
1.2
"
'''
for
line
in
log
.
split
(
'
\n
'
):
fs
=
line
.
strip
().
split
(
'
\t
'
)
print
(
fs
)
if
len
(
fs
)
==
3
and
fs
[
0
]
==
'kpis'
:
print
(
"-----%s"
%
fs
)
kpi_name
=
fs
[
1
]
kpi_value
=
float
(
fs
[
2
])
yield
kpi_name
,
kpi_value
def
log_to_ce
(
log
):
kpi_tracker
=
{}
for
kpi
in
tracking_kpis
:
kpi_tracker
[
kpi
.
name
]
=
kpi
for
(
kpi_name
,
kpi_value
)
in
parse_log
(
log
):
print
(
kpi_name
,
kpi_value
)
kpi_tracker
[
kpi_name
].
add_record
(
kpi_value
)
kpi_tracker
[
kpi_name
].
persist
()
if
__name__
==
'__main__'
:
log
=
sys
.
stdin
.
read
()
print
(
"*****"
)
print
(
log
)
print
(
"****"
)
log_to_ce
(
log
)
\ No newline at end of file
fluid/neural_machine_translation/transformer/config.py
浏览文件 @
a66a83f7
...
...
@@ -81,6 +81,8 @@ class ModelHyperParams(object):
n_layer
=
6
# dropout rate used by all dropout layers.
dropout
=
0.1
# random seed used in dropout for CE.
dropout_seed
=
None
# the flag indicating whether to share embedding and softmax weights.
# vocabularies in source and target should be same for weight sharing.
weight_sharing
=
True
...
...
fluid/neural_machine_translation/transformer/model.py
浏览文件 @
a66a83f7
...
...
@@ -111,7 +111,10 @@ def multi_head_attention(queries,
x
=
weights
,
shape
=
product
.
shape
,
actual_shape
=
post_softmax_shape
)
if
dropout_rate
:
weights
=
layers
.
dropout
(
weights
,
dropout_prob
=
dropout_rate
,
is_test
=
False
)
weights
,
dropout_prob
=
dropout_rate
,
seed
=
ModelHyperParams
.
dropout_seed
,
is_test
=
False
)
out
=
layers
.
matmul
(
weights
,
v
)
return
out
...
...
@@ -171,7 +174,10 @@ def pre_post_process_layer(prev_out, out, process_cmd, dropout_rate=0.):
elif
cmd
==
"d"
:
# add dropout
if
dropout_rate
:
out
=
layers
.
dropout
(
out
,
dropout_prob
=
dropout_rate
,
is_test
=
False
)
out
,
dropout_prob
=
dropout_rate
,
seed
=
ModelHyperParams
.
dropout_seed
,
is_test
=
False
)
return
out
...
...
@@ -211,7 +217,9 @@ def prepare_encoder(src_word,
shape
=
[
batch_size
,
seq_len
,
src_emb_dim
],
actual_shape
=
src_data_shape
)
return
layers
.
dropout
(
enc_input
,
dropout_prob
=
dropout_rate
,
enc_input
,
dropout_prob
=
dropout_rate
,
seed
=
ModelHyperParams
.
dropout_seed
,
is_test
=
False
)
if
dropout_rate
else
enc_input
...
...
fluid/neural_machine_translation/transformer/train.py
浏览文件 @
a66a83f7
...
...
@@ -103,6 +103,12 @@ def parse_args():
help
=
"The device type."
)
parser
.
add_argument
(
'--sync'
,
type
=
ast
.
literal_eval
,
default
=
True
,
help
=
"sync mode."
)
parser
.
add_argument
(
"--enable_ce"
,
type
=
ast
.
literal_eval
,
default
=
True
,
help
=
"The flag indicating whether to run the task "
"for continuous evaluation."
)
args
=
parser
.
parse_args
()
# Append args related to dict
...
...
@@ -382,6 +388,12 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
data_input_names
,
util_input_names
,
sum_cost
,
token_num
)
# the best cross-entropy value with label smoothing
loss_normalizer
=
-
((
1.
-
TrainTaskConfig
.
label_smooth_eps
)
*
np
.
log
(
(
1.
-
TrainTaskConfig
.
label_smooth_eps
))
+
TrainTaskConfig
.
label_smooth_eps
*
np
.
log
(
TrainTaskConfig
.
label_smooth_eps
/
(
ModelHyperParams
.
trg_vocab_size
-
1
)
+
1e-20
))
init
=
False
for
pass_id
in
xrange
(
TrainTaskConfig
.
pass_num
):
pass_start_time
=
time
.
time
()
...
...
@@ -421,19 +433,27 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
)
# sum the cost from multi-devices
total_token_num
=
token_num_val
.
sum
()
total_avg_cost
=
total_sum_cost
/
total_token_num
print
(
"epoch: %d, batch: %d, sum loss: %f, avg loss: %f, ppl: %f"
%
(
pass_id
,
batch_id
,
total_sum_cost
,
total_avg_cost
,
np
.
exp
([
min
(
total_avg_cost
,
100
)])))
print
(
"epoch: %d, batch: %d, avg loss: %f, normalized loss: %f,"
" ppl: %f"
%
(
pass_id
,
batch_id
,
total_avg_cost
,
total_avg_cost
-
loss_normalizer
,
np
.
exp
([
min
(
total_avg_cost
,
100
)])))
if
batch_id
>
0
and
batch_id
%
1000
==
0
:
fluid
.
io
.
save_persistables
(
exe
,
os
.
path
.
join
(
TrainTaskConfig
.
ckpt_dir
,
"latest.checkpoint"
))
init
=
True
time_consumed
=
time
.
time
()
-
pass_start_time
# Validate and save the model for inference.
print
(
"epoch: %d, "
%
pass_id
+
(
"val avg loss: %f, val ppl: %f, "
%
test
()
if
args
.
val_file_pattern
is
not
None
else
""
)
+
"consumed %fs"
%
(
time
.
time
()
-
pass_start_time
))
if
args
.
val_file_pattern
is
not
None
:
val_avg_cost
,
val_ppl
=
test
()
print
(
"epoch: %d, val avg loss: %f, val normalized loss: %f, val ppl: %f,"
" consumed %fs"
%
(
pass_id
,
val_avg_cost
,
val_avg_cost
-
loss_normalizer
,
val_ppl
,
time_consumed
))
else
:
print
(
"epoch: %d, consumed %fs"
%
(
pass_id
,
time_consumed
))
fluid
.
io
.
save_persistables
(
exe
,
os
.
path
.
join
(
TrainTaskConfig
.
ckpt_dir
,
...
...
@@ -442,6 +462,10 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
os
.
path
.
join
(
TrainTaskConfig
.
model_dir
,
"pass_"
+
str
(
pass_id
)
+
".infer.model"
),
data_input_names
[:
-
2
]
+
util_input_names
,
[
predict
],
exe
)
if
args
.
enable_ce
:
# For CE
print
(
"kpis
\t
train_cost
\t
%f"
%
total_avg_cost
)
print
(
"kpis
\t
test_cost
\t
%f"
%
val_avg_cost
)
print
(
"kpis
\t
train_duration
\t
%f"
%
time_consumed
)
def
train
(
args
):
...
...
@@ -465,6 +489,9 @@ def train(args):
exe
=
fluid
.
Executor
(
place
)
if
args
.
enable_ce
:
fluid
.
default_startup_program
().
random_seed
=
1000
sum_cost
,
avg_cost
,
predict
,
token_num
=
transformer
(
ModelHyperParams
.
src_vocab_size
,
ModelHyperParams
.
trg_vocab_size
,
ModelHyperParams
.
max_length
+
1
,
ModelHyperParams
.
n_layer
,
...
...
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