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4e553e2b
编写于
8月 06, 2019
作者:
Y
Yibing Liu
提交者:
GitHub
8月 06, 2019
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Use gc, PyReader & compiledprogram for bert (#3035)
上级
80283e6d
变更
7
显示空白变更内容
内联
并排
Showing
7 changed file
with
128 addition
and
121 deletion
+128
-121
PaddleNLP/README.md
PaddleNLP/README.md
+1
-1
PaddleNLP/language_representations_kit/BERT/README.md
PaddleNLP/language_representations_kit/BERT/README.md
+12
-8
PaddleNLP/language_representations_kit/BERT/model/classifier.py
...NLP/language_representations_kit/BERT/model/classifier.py
+20
-15
PaddleNLP/language_representations_kit/BERT/predict_classifier.py
...P/language_representations_kit/BERT/predict_classifier.py
+2
-3
PaddleNLP/language_representations_kit/BERT/run_classifier.py
...leNLP/language_representations_kit/BERT/run_classifier.py
+29
-31
PaddleNLP/language_representations_kit/BERT/run_squad.py
PaddleNLP/language_representations_kit/BERT/run_squad.py
+29
-23
PaddleNLP/language_representations_kit/BERT/train.py
PaddleNLP/language_representations_kit/BERT/train.py
+35
-40
未找到文件。
PaddleNLP/README.md
浏览文件 @
4e553e2b
...
...
@@ -84,7 +84,7 @@ cd models/PaddleNLP/sentiment_classification
-
[
机器翻译
](
https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/neural_machine_translation/transformer
)
### 语义表示与语言模型
-
[
语言表示工具箱
](
https://github.com/PaddlePaddle/
LARK/tree/develop
)
-
[
语言表示工具箱
](
https://github.com/PaddlePaddle/
models/tree/develop/PaddleNLP/language_representations_kit
)
-
[
语言模型
](
https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/language_model
)
### 复杂任务
...
...
PaddleNLP/language_representations_kit/BERT/README.md
浏览文件 @
4e553e2b
...
...
@@ -18,6 +18,8 @@
| Model | Layers | Hidden size | Heads |Parameters |
| :------| :------: | :------: |:------: |:------: |
|
[
BERT-Large, Uncased (Whole Word Masking)
](
https://bert-models.bj.bcebos.com/wwm_uncased_L-24_H-1024_A-16.tar.gz
)
| 24 | 1024 | 16 | 340M |
|
[
BERT-Large, Cased (Whole Word Masking)
](
https://bert-models.bj.bcebos.com/wwm_cased_L-24_H-1024_A-16.tar.gz
)
| 24 | 1024 | 16 | 340M |
|
[
BERT-Base, Uncased
](
https://bert-models.bj.bcebos.com/uncased_L-12_H-768_A-12.tar.gz
)
| 12 | 768 |12 |110M |
|
[
BERT-Large, Uncased
](
https://bert-models.bj.bcebos.com/uncased_L-24_H-1024_A-16.tar.gz
)
| 24 | 1024 |16 |340M |
|
[
BERT-Base, Cased
](
https://bert-models.bj.bcebos.com/cased_L-12_H-768_A-12.tar.gz
)
|12|768|12|110M|
...
...
@@ -46,7 +48,7 @@
-
[
inference 接口调用示例
](
#inference-接口调用示例
)
## 安装
本项目依赖于 Paddle Fluid
**1.
3
.1**
,请参考
[
安装指南
](
http://www.paddlepaddle.org/#quick-start
)
进行安装。如果需要进行 TensorFlow 模型到 Paddle Fluid 参数的转换,则需要同时安装 TensorFlow 1.12。
本项目依赖于 Paddle Fluid
**1.
5
.1**
,请参考
[
安装指南
](
http://www.paddlepaddle.org/#quick-start
)
进行安装。如果需要进行 TensorFlow 模型到 Paddle Fluid 参数的转换,则需要同时安装 TensorFlow 1.12。
## 预训练
...
...
@@ -138,22 +140,24 @@ python -u run_classifier.py --task_name ${TASK_NAME} \
--do_train
true
\
--do_val
true
\
--do_test
true
\
--batch_size
819
2
\
--in_tokens
tru
e
\
--batch_size
3
2
\
--in_tokens
fals
e
\
--init_pretraining_params
${
BERT_BASE_PATH
}
/params
\
--data_dir
${
DATA_PATH
}
\
--vocab_path
${
BERT_BASE_PATH
}
/vocab.txt
\
--checkpoints
${
CKPT_PATH
}
\
--save_steps
1000
\
--weight_decay
0.01
\
--warmup_proportion
0.
0
\
--validation_steps
25
\
--warmup_proportion
0.
1
\
--validation_steps
100
\
--epoch
3
\
--max_seq_len
512
\
--max_seq_len
128
\
--bert_config_path
${
BERT_BASE_PATH
}
/bert_config.json
\
--learning_rate
1e-4
\
--learning_rate
5e-5
\
--skip_steps
10
\
--random_seed
1
--num_iteration_per_drop_scope
10
\
--use_fp16
true
\
--verbose
true
```
这里的
`chinese_L-12_H-768_A-12`
即是转换后的中文预训练模型。需要注意的是,BERT on PaddlePaddle 支持按两种方式构建一个 batch 的数据,
`in_tokens`
参数影响
`batch_size`
参数的意义,如果
`in_tokens`
为
`true`
则按照 token 个数构建 batch, 如不设定则按照 example 个数来构建 batch. 训练过程中会输出训练误差、训练速度等信息,训练结束后会输出如下所示的在验证集上的测试结果:
...
...
PaddleNLP/language_representations_kit/BERT/model/classifier.py
浏览文件 @
4e553e2b
...
...
@@ -22,22 +22,27 @@ import paddle.fluid as fluid
from
model.bert
import
BertModel
def
create_model
(
args
,
pyreader_name
,
bert_config
,
num_labels
,
is_prediction
=
False
):
pyreader
=
fluid
.
layers
.
py_reader
(
capacity
=
50
,
shapes
=
[[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
def
create_model
(
args
,
bert_config
,
num_labels
,
is_prediction
=
False
):
input_fields
=
{
'names'
:
[
'src_ids'
,
'pos_ids'
,
'sent_ids'
,
'input_mask'
,
'labels'
],
'shapes'
:
[[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
1
]],
dtypes
=
[
'int64'
,
'int64'
,
'int64'
,
'float32'
,
'int64'
],
lod_levels
=
[
0
,
0
,
0
,
0
,
0
],
name
=
pyreader_name
,
use_double_buffer
=
True
)
'dtypes'
:
[
'int64'
,
'int64'
,
'int64'
,
'float32'
,
'int64'
],
'lod_levels'
:
[
0
,
0
,
0
,
0
,
0
],
}
(
src_ids
,
pos_ids
,
sent_ids
,
input_mask
,
labels
)
=
fluid
.
layers
.
read_file
(
pyreader
)
inputs
=
[
fluid
.
layers
.
data
(
name
=
input_fields
[
'names'
][
i
],
shape
=
input_fields
[
'shapes'
][
i
],
dtype
=
input_fields
[
'dtypes'
][
i
],
lod_level
=
input_fields
[
'lod_levels'
][
i
])
for
i
in
range
(
len
(
input_fields
[
'names'
]))
]
(
src_ids
,
pos_ids
,
sent_ids
,
input_mask
,
labels
)
=
inputs
pyreader
=
fluid
.
io
.
PyReader
(
feed_list
=
inputs
,
capacity
=
50
,
iterable
=
False
)
bert
=
BertModel
(
src_ids
=
src_ids
,
...
...
PaddleNLP/language_representations_kit/BERT/predict_classifier.py
浏览文件 @
4e553e2b
...
...
@@ -84,7 +84,6 @@ def main(args):
with
fluid
.
unique_name
.
guard
():
predict_pyreader
,
probs
,
feed_target_names
=
create_model
(
args
,
pyreader_name
=
'predict_reader'
,
bert_config
=
bert_config
,
num_labels
=
num_labels
,
is_prediction
=
True
)
...
...
@@ -103,7 +102,7 @@ def main(args):
exe
.
run
(
predict_startup
)
if
args
.
init_checkpoint
:
init_pretraining_params
(
exe
,
args
.
init_checkpoint
,
predict_prog
)
init_pretraining_params
(
exe
,
args
.
init_checkpoint
,
predict_prog
,
args
.
use_fp16
)
else
:
raise
ValueError
(
"args 'init_checkpoint' should be set for prediction!"
)
...
...
@@ -113,7 +112,7 @@ def main(args):
predict_exe
=
fluid
.
ParallelExecutor
(
use_cuda
=
args
.
use_cuda
,
main_program
=
predict_prog
)
predict_pyreader
.
decorate_
tensor_provide
r
(
predict_pyreader
.
decorate_
batch_generato
r
(
processor
.
data_generator
(
batch_size
=
args
.
batch_size
,
phase
=
'test'
,
epoch
=
1
,
shuffle
=
False
))
...
...
PaddleNLP/language_representations_kit/BERT/run_classifier.py
浏览文件 @
4e553e2b
...
...
@@ -193,7 +193,6 @@ def main(args):
with
fluid
.
unique_name
.
guard
():
train_pyreader
,
loss
,
probs
,
accuracy
,
num_seqs
=
create_model
(
args
,
pyreader_name
=
'train_reader'
,
bert_config
=
bert_config
,
num_labels
=
num_labels
)
scheduled_lr
=
optimization
(
...
...
@@ -219,17 +218,41 @@ def main(args):
print
(
"Theoretical memory usage in training: %.3f - %.3f %s"
%
(
lower_mem
,
upper_mem
,
unit
))
if
args
.
do_val
or
args
.
do_test
:
if
args
.
do_val
:
dev_prog
=
fluid
.
Program
()
with
fluid
.
program_guard
(
dev_prog
,
startup_prog
):
with
fluid
.
unique_name
.
guard
():
dev_pyreader
,
loss
,
probs
,
accuracy
,
num_seqs
=
create_model
(
args
,
bert_config
=
bert_config
,
num_labels
=
num_labels
)
dev_prog
=
dev_prog
.
clone
(
for_test
=
True
)
dev_pyreader
.
decorate_batch_generator
(
processor
.
data_generator
(
batch_size
=
args
.
batch_size
,
phase
=
'dev'
,
epoch
=
1
,
dev_count
=
1
,
shuffle
=
False
),
place
)
if
args
.
do_test
:
test_prog
=
fluid
.
Program
()
with
fluid
.
program_guard
(
test_prog
,
startup_prog
):
with
fluid
.
unique_name
.
guard
():
test_pyreader
,
loss
,
probs
,
accuracy
,
num_seqs
=
create_model
(
args
,
pyreader_name
=
'test_reader'
,
bert_config
=
bert_config
,
num_labels
=
num_labels
)
test_prog
=
test_prog
.
clone
(
for_test
=
True
)
test_pyreader
.
decorate_batch_generator
(
processor
.
data_generator
(
batch_size
=
args
.
batch_size
,
phase
=
'test'
,
epoch
=
1
,
dev_count
=
1
,
shuffle
=
False
),
place
)
exe
.
run
(
startup_prog
)
...
...
@@ -276,7 +299,7 @@ def main(args):
train_compiled_program
=
fluid
.
CompiledProgram
(
train_program
).
with_data_parallel
(
loss_name
=
loss
.
name
,
build_strategy
=
build_strategy
)
train_pyreader
.
decorate_
tensor_provider
(
train_data_generator
)
train_pyreader
.
decorate_
batch_generator
(
train_data_generator
,
place
)
if
args
.
do_train
:
...
...
@@ -350,25 +373,11 @@ def main(args):
throughput
=
[]
# evaluate dev set
if
args
.
do_val
:
test_pyreader
.
decorate_tensor_provider
(
processor
.
data_generator
(
batch_size
=
args
.
batch_size
,
phase
=
'dev'
,
epoch
=
1
,
dev_count
=
1
,
shuffle
=
False
))
evaluate
(
exe
,
test_prog
,
test_pyreader
,
evaluate
(
exe
,
dev_prog
,
dev_pyreader
,
[
loss
.
name
,
accuracy
.
name
,
num_seqs
.
name
],
"dev"
)
# evaluate test set
if
args
.
do_test
:
test_pyreader
.
decorate_tensor_provider
(
processor
.
data_generator
(
batch_size
=
args
.
batch_size
,
phase
=
'test'
,
epoch
=
1
,
dev_count
=
1
,
shuffle
=
False
))
evaluate
(
exe
,
test_prog
,
test_pyreader
,
[
loss
.
name
,
accuracy
.
name
,
num_seqs
.
name
],
"test"
)
...
...
@@ -398,23 +407,12 @@ def main(args):
# final eval on dev set
if
args
.
do_val
:
test_pyreader
.
decorate_tensor_provider
(
processor
.
data_generator
(
batch_size
=
args
.
batch_size
,
phase
=
'dev'
,
epoch
=
1
,
dev_count
=
1
,
shuffle
=
False
))
print
(
"Final validation result:"
)
evaluate
(
exe
,
test_prog
,
test
_pyreader
,
evaluate
(
exe
,
dev_prog
,
dev
_pyreader
,
[
loss
.
name
,
accuracy
.
name
,
num_seqs
.
name
],
"dev"
)
# final eval on test set
if
args
.
do_test
:
test_pyreader
.
decorate_tensor_provider
(
processor
.
data_generator
(
batch_size
=
args
.
batch_size
,
phase
=
'test'
,
epoch
=
1
,
dev_count
=
1
,
shuffle
=
False
))
print
(
"Final test result:"
)
evaluate
(
exe
,
test_prog
,
test_pyreader
,
[
loss
.
name
,
accuracy
.
name
,
num_seqs
.
name
],
"test"
)
...
...
PaddleNLP/language_representations_kit/BERT/run_squad.py
浏览文件 @
4e553e2b
...
...
@@ -92,31 +92,39 @@ run_type_g.add_arg("do_predict", bool, True, "Whether to pe
args
=
parser
.
parse_args
()
# yapf: enable.
def
create_model
(
pyreader_name
,
bert_config
,
is_training
=
False
):
def
create_model
(
bert_config
,
is_training
=
False
):
if
is_training
:
pyreader
=
fluid
.
layers
.
py_reader
(
capacity
=
50
,
shapes
=
[[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
input_fields
=
{
'names'
:
[
'src_ids'
,
'pos_ids'
,
'sent_ids'
,
'input_mask'
,
'start_positions'
,
'end_positions'
]
,
'shapes'
:
[[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
1
],
[
-
1
,
1
]],
dtypes
=
[
'dtypes'
:
[
'int64'
,
'int64'
,
'int64'
,
'float32'
,
'int64'
,
'int64'
],
lod_levels
=
[
0
,
0
,
0
,
0
,
0
,
0
],
name
=
pyreader_name
,
use_double_buffer
=
True
)
(
src_ids
,
pos_ids
,
sent_ids
,
input_mask
,
start_positions
,
end_positions
)
=
fluid
.
layers
.
read_file
(
pyreader
)
'lod_levels'
:
[
0
,
0
,
0
,
0
,
0
,
0
],
}
else
:
pyreader
=
fluid
.
layers
.
py_reader
(
capacity
=
50
,
shapes
=
[[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
input_fields
=
{
'names'
:
[
'src_ids'
,
'pos_ids'
,
'sent_ids'
,
'input_mask'
,
'unique_id'
]
,
'shapes'
:
[[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
1
]],
dtypes
=
[
'int64'
,
'int64'
,
'int64'
,
'float32'
,
'int64'
],
lod_levels
=
[
0
,
0
,
0
,
0
,
0
],
name
=
pyreader_name
,
use_double_buffer
=
True
)
(
src_ids
,
pos_ids
,
sent_ids
,
input_mask
,
unique_id
)
=
fluid
.
layers
.
read_file
(
pyreader
)
'dtypes'
:
[
'int64'
,
'int64'
,
'int64'
,
'float32'
,
'int64'
],
'lod_levels'
:
[
0
,
0
,
0
,
0
,
0
],
}
inputs
=
[
fluid
.
layers
.
data
(
name
=
input_fields
[
'names'
][
i
],
shape
=
input_fields
[
'shapes'
][
i
],
dtype
=
input_fields
[
'dtypes'
][
i
],
lod_level
=
input_fields
[
'lod_levels'
][
i
])
for
i
in
range
(
len
(
input_fields
[
'names'
]))]
pyreader
=
fluid
.
io
.
PyReader
(
feed_list
=
inputs
,
capacity
=
50
,
iterable
=
False
)
if
is_training
:
(
src_ids
,
pos_ids
,
sent_ids
,
input_mask
,
start_positions
,
end_positions
)
=
inputs
else
:
(
src_ids
,
pos_ids
,
sent_ids
,
input_mask
,
unique_id
)
=
inputs
bert
=
BertModel
(
src_ids
=
src_ids
,
...
...
@@ -263,7 +271,6 @@ def train(args):
with
fluid
.
program_guard
(
train_program
,
startup_prog
):
with
fluid
.
unique_name
.
guard
():
train_pyreader
,
loss
,
num_seqs
=
create_model
(
pyreader_name
=
'train_reader'
,
bert_config
=
bert_config
,
is_training
=
True
)
...
...
@@ -296,7 +303,6 @@ def train(args):
with
fluid
.
program_guard
(
test_prog
,
startup_prog
):
with
fluid
.
unique_name
.
guard
():
test_pyreader
,
unique_ids
,
start_logits
,
end_logits
,
num_seqs
=
create_model
(
pyreader_name
=
'test_reader'
,
bert_config
=
bert_config
,
is_training
=
False
)
...
...
@@ -341,7 +347,7 @@ def train(args):
train_compiled_program
=
fluid
.
CompiledProgram
(
train_program
).
with_data_parallel
(
loss_name
=
loss
.
name
,
exec_strategy
=
exec_strategy
)
train_pyreader
.
decorate_
tensor_provider
(
train_data_generator
)
train_pyreader
.
decorate_
batch_generator
(
train_data_generator
,
place
)
train_pyreader
.
start
()
steps
=
0
...
...
@@ -402,14 +408,14 @@ def train(args):
break
if
args
.
do_predict
:
test_pyreader
.
decorate_
tensor_provide
r
(
test_pyreader
.
decorate_
batch_generato
r
(
processor
.
data_generator
(
data_path
=
args
.
predict_file
,
batch_size
=
args
.
batch_size
,
phase
=
'predict'
,
shuffle
=
False
,
dev_count
=
1
,
epoch
=
1
))
epoch
=
1
)
,
place
)
predict
(
exe
,
test_prog
,
test_pyreader
,
[
unique_ids
.
name
,
start_logits
.
name
,
end_logits
.
name
,
num_seqs
.
name
...
...
PaddleNLP/language_representations_kit/BERT/train.py
浏览文件 @
4e553e2b
...
...
@@ -82,21 +82,24 @@ args = parser.parse_args()
# yapf: enable.
def
create_model
(
pyreader_name
,
bert_config
):
pyreader
=
fluid
.
layers
.
py_reader
(
capacity
=
70
,
shapes
=
[[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
def
create_model
(
bert_config
):
input_fields
=
{
'names'
:
[
'src_ids'
,
'pos_ids'
,
'sent_ids'
,
'input_mask'
,
'mask_label'
,
'mask_pos'
,
'labels'
]
,
'shapes'
:
[[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
1
],
[
-
1
,
1
],
[
-
1
,
1
]],
dtypes
=
[
'int64'
,
'int64'
,
'int64'
,
'float32'
,
'int64'
,
'int64'
,
'int64'
],
lod_levels
=
[
0
,
0
,
0
,
0
,
0
,
0
,
0
],
name
=
pyreader_name
,
use_double_buffer
=
True
)
[
-
1
,
args
.
max_seq_len
,
1
],
[
-
1
,
1
],
[
-
1
,
1
],
[
-
1
,
1
]],
'dtypes'
:
[
'int64'
,
'int64'
,
'int64'
,
'float32'
,
'int64'
,
'int64'
,
'int64'
],
'lod_levels'
:
[
0
,
0
,
0
,
0
,
0
,
0
,
0
],
}
(
src_ids
,
pos_ids
,
sent_ids
,
input_mask
,
mask_label
,
mask_pos
,
labels
)
=
fluid
.
layers
.
read_file
(
pyreader
)
inputs
=
[
fluid
.
layers
.
data
(
name
=
input_fields
[
'names'
][
i
],
shape
=
input_fields
[
'shapes'
][
i
],
dtype
=
input_fields
[
'dtypes'
][
i
],
lod_level
=
input_fields
[
'lod_levels'
][
i
])
for
i
in
range
(
len
(
input_fields
[
'names'
]))]
(
src_ids
,
pos_ids
,
sent_ids
,
input_mask
,
mask_label
,
mask_pos
,
labels
)
=
inputs
pyreader
=
fluid
.
io
.
PyReader
(
feed_list
=
inputs
,
capacity
=
50
,
iterable
=
False
)
bert
=
BertModel
(
src_ids
=
src_ids
,
...
...
@@ -143,7 +146,7 @@ def predict_wrapper(args,
def
predict
(
exe
=
exe
,
pyreader
=
pyreader
):
pyreader
.
decorate_
tensor_provide
r
(
data_reader
.
data_generator
())
pyreader
.
decorate_
batch_generato
r
(
data_reader
.
data_generator
())
pyreader
.
start
()
cost
=
0
...
...
@@ -181,7 +184,7 @@ def test(args):
with
fluid
.
program_guard
(
test_prog
,
test_startup
):
with
fluid
.
unique_name
.
guard
():
test_pyreader
,
next_sent_acc
,
mask_lm_loss
,
total_loss
=
create_model
(
pyreader_name
=
'test_reader'
,
bert_config
=
bert_config
)
bert_config
=
bert_config
)
test_prog
=
test_prog
.
clone
(
for_test
=
True
)
...
...
@@ -216,7 +219,7 @@ def train(args):
with
fluid
.
program_guard
(
train_program
,
startup_prog
):
with
fluid
.
unique_name
.
guard
():
train_pyreader
,
next_sent_acc
,
mask_lm_loss
,
total_loss
=
create_model
(
pyreader_name
=
'train_reader'
,
bert_config
=
bert_config
)
bert_config
=
bert_config
)
scheduled_lr
=
optimization
(
loss
=
total_loss
,
warmup_steps
=
args
.
warmup_steps
,
...
...
@@ -229,17 +232,11 @@ def train(args):
use_fp16
=
args
.
use_fp16
,
loss_scaling
=
args
.
loss_scaling
)
fluid
.
memory_optimize
(
input_program
=
train_program
,
skip_opt_set
=
[
next_sent_acc
.
name
,
mask_lm_loss
.
name
,
total_loss
.
name
])
test_prog
=
fluid
.
Program
()
with
fluid
.
program_guard
(
test_prog
,
startup_prog
):
with
fluid
.
unique_name
.
guard
():
test_pyreader
,
next_sent_acc
,
mask_lm_loss
,
total_loss
=
create_model
(
pyreader_name
=
'test_reader'
,
bert_config
=
bert_config
)
bert_config
=
bert_config
)
test_prog
=
test_prog
.
clone
(
for_test
=
True
)
...
...
@@ -313,18 +310,16 @@ def train(args):
exec_strategy
.
num_threads
=
dev_count
exec_strategy
.
num_iteration_per_drop_scope
=
args
.
num_iteration_per_drop_scope
build_strategy
=
fluid
.
BuildStrategy
()
build_strategy
.
num_trainers
=
nccl2_num_trainers
build_strategy
.
trainer_id
=
nccl2_trainer_id
# use_ngraph is for CPU only, please refer to README_ngraph.md for details
use_ngraph
=
os
.
getenv
(
'FLAGS_use_ngraph'
)
if
not
use_ngraph
:
train_exe
=
fluid
.
ParallelExecutor
(
use_cuda
=
args
.
use_cuda
,
train_compiled_program
=
fluid
.
CompiledProgram
(
train_program
).
with_data_parallel
(
loss_name
=
total_loss
.
name
,
exec_strategy
=
exec_strategy
,
main_program
=
train_program
,
num_trainers
=
nccl2_num_trainers
,
trainer_id
=
nccl2_trainer_id
)
else
:
train_exe
=
exe
build_strategy
=
build_strategy
)
if
args
.
validation_set_dir
and
args
.
validation_set_dir
!=
""
:
predict
=
predict_wrapper
(
...
...
@@ -337,7 +332,7 @@ def train(args):
next_sent_acc
.
name
,
mask_lm_loss
.
name
,
total_loss
.
name
])
train_pyreader
.
decorate_
tensor_provide
r
(
data_reader
.
data_generator
())
train_pyreader
.
decorate_
batch_generato
r
(
data_reader
.
data_generator
())
train_pyreader
.
start
()
steps
=
0
cost
=
[]
...
...
@@ -351,28 +346,28 @@ def train(args):
if
nccl2_trainer_id
!=
0
:
if
use_ngraph
:
train_
exe
.
run
(
fetch_list
=
[],
program
=
train_program
)
exe
.
run
(
fetch_list
=
[],
program
=
train_program
)
else
:
train_exe
.
run
(
fetch_list
=
[]
)
exe
.
run
(
fetch_list
=
[],
program
=
train_compiled_program
)
continue
if
steps
%
skip_steps
!=
0
:
if
use_ngraph
:
train_
exe
.
run
(
fetch_list
=
[],
program
=
train_program
)
exe
.
run
(
fetch_list
=
[],
program
=
train_program
)
else
:
train_exe
.
run
(
fetch_list
=
[]
)
exe
.
run
(
fetch_list
=
[],
program
=
train_compiled_program
)
else
:
if
use_ngraph
:
each_next_acc
,
each_mask_lm_cost
,
each_total_cost
,
np_lr
=
train_
exe
.
run
(
each_next_acc
,
each_mask_lm_cost
,
each_total_cost
,
np_lr
=
exe
.
run
(
fetch_list
=
[
next_sent_acc
.
name
,
mask_lm_loss
.
name
,
total_loss
.
name
,
scheduled_lr
.
name
],
program
=
train_program
)
else
:
each_next_acc
,
each_mask_lm_cost
,
each_total_cost
,
np_lr
=
train_
exe
.
run
(
each_next_acc
,
each_mask_lm_cost
,
each_total_cost
,
np_lr
=
exe
.
run
(
fetch_list
=
[
next_sent_acc
.
name
,
mask_lm_loss
.
name
,
total_loss
.
name
,
scheduled_lr
.
name
])
scheduled_lr
.
name
]
,
program
=
train_compiled_program
)
acc
.
extend
(
each_next_acc
)
lm_cost
.
extend
(
each_mask_lm_cost
)
...
...
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