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8c7465b2
编写于
7月 09, 2020
作者:
B
Bai Yifan
提交者:
GitHub
7月 09, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Update bert distillation and search code (#376)
上级
2bb6377f
变更
10
隐藏空白更改
内联
并排
Showing
10 changed file
with
619 addition
and
244 deletion
+619
-244
demo/bert/search_bert.py
demo/bert/search_bert.py
+0
-56
demo/bert/train_distill.py
demo/bert/train_distill.py
+204
-0
demo/bert/train_search.py
demo/bert/train_search.py
+232
-0
paddleslim/nas/darts/architect_for_bert.py
paddleslim/nas/darts/architect_for_bert.py
+4
-4
paddleslim/nas/darts/search_space/conv_bert/cls.py
paddleslim/nas/darts/search_space/conv_bert/cls.py
+37
-63
paddleslim/nas/darts/search_space/conv_bert/model/bert.py
paddleslim/nas/darts/search_space/conv_bert/model/bert.py
+12
-41
paddleslim/nas/darts/search_space/conv_bert/model/transformer_encoder.py
...darts/search_space/conv_bert/model/transformer_encoder.py
+67
-71
paddleslim/teachers/bert/cls.py
paddleslim/teachers/bert/cls.py
+3
-2
paddleslim/teachers/bert/model/cls.py
paddleslim/teachers/bert/model/cls.py
+14
-1
paddleslim/teachers/bert/reader/cls.py
paddleslim/teachers/bert/reader/cls.py
+46
-6
未找到文件。
demo/bert/search_bert.py
已删除
100644 → 0
浏览文件 @
2bb6377f
import
paddle.fluid
as
fluid
from
paddleslim.teachers.bert.reader.cls
import
*
from
paddleslim.nas.darts.search_space
import
AdaBERTClassifier
from
paddleslim.nas.darts
import
DARTSearch
def
main
():
place
=
fluid
.
CUDAPlace
(
0
)
BERT_BASE_PATH
=
"./data/pretrained_models/uncased_L-12_H-768_A-12/"
bert_config_path
=
BERT_BASE_PATH
+
"/bert_config.json"
vocab_path
=
BERT_BASE_PATH
+
"/vocab.txt"
data_dir
=
"./data/glue_data/MNLI/"
max_seq_len
=
512
do_lower_case
=
True
batch_size
=
32
epoch
=
30
processor
=
MnliProcessor
(
data_dir
=
data_dir
,
vocab_path
=
vocab_path
,
max_seq_len
=
max_seq_len
,
do_lower_case
=
do_lower_case
,
in_tokens
=
False
)
train_reader
=
processor
.
data_generator
(
batch_size
=
batch_size
,
phase
=
'train'
,
epoch
=
epoch
,
dev_count
=
1
,
shuffle
=
True
)
val_reader
=
processor
.
data_generator
(
batch_size
=
batch_size
,
phase
=
'train'
,
epoch
=
epoch
,
dev_count
=
1
,
shuffle
=
True
)
with
fluid
.
dygraph
.
guard
(
place
):
model
=
AdaBERTClassifier
(
3
,
teacher_model
=
"/work/PaddleSlim/demo/bert_1/checkpoints/steps_23000"
)
searcher
=
DARTSearch
(
model
,
train_reader
,
val_reader
,
batchsize
=
batch_size
,
num_epochs
=
epoch
,
log_freq
=
10
)
searcher
.
train
()
if
__name__
==
'__main__'
:
main
()
demo/bert/train_
cell_base
.py
→
demo/bert/train_
distill
.py
浏览文件 @
8c7465b2
...
@@ -3,192 +3,201 @@ from itertools import izip
...
@@ -3,192 +3,201 @@ from itertools import izip
import
paddle.fluid
as
fluid
import
paddle.fluid
as
fluid
from
paddleslim.teachers.bert.reader.cls
import
*
from
paddleslim.teachers.bert.reader.cls
import
*
from
paddleslim.nas.darts.search_space
import
AdaBERTClassifier
from
paddleslim.nas.darts.search_space
import
AdaBERTClassifier
from
paddleslim.nas.darts.architect_for_bert
import
Architect
from
paddle.fluid.dygraph.base
import
to_variable
from
tqdm
import
tqdm
import
os
import
pickle
import
logging
import
logging
from
paddleslim.common
import
AvgrageMeter
,
get_logger
from
paddleslim.common
import
AvgrageMeter
,
get_logger
logger
=
get_logger
(
__name__
,
level
=
logging
.
INFO
)
logger
=
get_logger
(
__name__
,
level
=
logging
.
INFO
)
def
count_parameters_in_MB
(
all_params
):
def
valid_one_epoch
(
model
,
valid_loader
,
epoch
,
log_freq
):
parameters_number
=
0
accs
=
AvgrageMeter
()
for
param
in
all_params
:
ce_losses
=
AvgrageMeter
()
if
param
.
trainable
:
model
.
student
.
eval
()
parameters_number
+=
np
.
prod
(
param
.
shape
)
return
parameters_number
/
1e6
def
model_loss
(
model
,
data_ids
):
# src_ids = data_ids[0]
# position_ids = data_ids[1]
# sentence_ids = data_ids[2]
# input_mask = data_ids[3]
labels
=
data_ids
[
4
]
labels
.
stop_gradient
=
True
enc_output
=
model
(
data_ids
)
step_id
=
0
for
valid_data
in
valid_loader
():
try
:
loss
,
acc
,
ce_loss
,
_
,
_
=
model
.
_layers
.
loss
(
valid_data
,
epoch
)
except
:
loss
,
acc
,
ce_loss
,
_
,
_
=
model
.
loss
(
valid_data
,
epoch
)
ce_loss
,
probs
=
fluid
.
layers
.
softmax_with_cross_entropy
(
batch_size
=
valid_data
[
0
].
shape
[
0
]
logits
=
enc_output
,
label
=
labels
,
return_softmax
=
True
)
ce_losses
.
update
(
ce_loss
.
numpy
(),
batch_size
)
loss
=
fluid
.
layers
.
mean
(
x
=
ce_loss
)
accs
.
update
(
acc
.
numpy
(),
batch_size
)
num_seqs
=
fluid
.
layers
.
create_tensor
(
dtype
=
'int64'
)
step_id
+=
1
accuracy
=
fluid
.
layers
.
accuracy
(
input
=
probs
,
label
=
labels
,
total
=
num_seqs
)
return
ce_losses
.
avg
[
0
],
accs
.
avg
[
0
]
return
loss
,
accuracy
def
train_one_epoch
(
model
,
architect
,
train_loader
,
valid_loader
,
optimizer
,
def
train_one_epoch
(
model
,
train_loader
,
optimizer
,
epoch
,
use_data_parallel
,
epoch
,
use_data_parallel
,
log_freq
):
log_freq
):
ce
_losses
=
AvgrageMeter
()
total
_losses
=
AvgrageMeter
()
accs
=
AvgrageMeter
()
accs
=
AvgrageMeter
()
model
.
train
()
ce_losses
=
AvgrageMeter
()
kd_losses
=
AvgrageMeter
()
model
.
student
.
train
()
step_id
=
0
step_id
=
0
for
train_data
,
valid_data
in
izip
(
train_loader
(),
valid_loader
):
for
train_data
in
train_loader
():
architect
.
step
(
train_data
,
valid_data
)
batch_size
=
train_data
[
0
].
shape
[
0
]
loss
,
acc
=
model_loss
(
model
,
train_data
)
if
use_data_parallel
:
if
use_data_parallel
:
loss
=
model
.
scale_loss
(
loss
)
total_loss
,
acc
,
ce_loss
,
kd_loss
,
_
=
model
.
_layers
.
loss
(
loss
.
backward
()
train_data
,
epoch
)
model
.
apply_collective_grads
()
else
:
else
:
loss
.
backward
()
total_loss
,
acc
,
ce_loss
,
kd_loss
,
_
=
model
.
loss
(
train_data
,
epoch
)
optimizer
.
minimize
(
loss
)
if
use_data_parallel
:
total_loss
=
model
.
scale_loss
(
total_loss
)
total_loss
.
backward
()
model
.
apply_collective_grads
()
else
:
total_loss
.
backward
()
optimizer
.
minimize
(
total_loss
)
model
.
clear_gradients
()
model
.
clear_gradients
()
total_losses
.
update
(
total_loss
.
numpy
(),
batch_size
)
batch_size
=
train_data
[
0
].
shape
[
0
]
ce_losses
.
update
(
loss
.
numpy
(),
batch_size
)
accs
.
update
(
acc
.
numpy
(),
batch_size
)
accs
.
update
(
acc
.
numpy
(),
batch_size
)
ce_losses
.
update
(
ce_loss
.
numpy
(),
batch_size
)
kd_losses
.
update
(
kd_loss
.
numpy
(),
batch_size
)
if
step_id
%
log_freq
==
0
:
if
step_id
%
log_freq
==
0
:
logger
.
info
(
logger
.
info
(
"Train Epoch {}, Step {}, Lr {:.6f}
loss {:.6f}; acc:
{:.6f};"
.
"Train Epoch {}, Step {}, Lr {:.6f}
total_loss {:.6f}; ce_loss {:.6f}, kd_loss {:.6f}, train_acc
{:.6f};"
.
format
(
epoch
,
step_id
,
format
(
epoch
,
step_id
,
optimizer
.
current_step_lr
(),
ce_losses
.
avg
[
0
],
accs
.
avg
[
optimizer
.
current_step_lr
(),
total_losses
.
avg
[
0
],
0
]))
ce_losses
.
avg
[
0
],
kd_losses
.
avg
[
0
],
accs
.
avg
[
0
]))
step_id
+=
1
def
valid_one_epoch
(
model
,
valid_loader
,
epoch
,
log_freq
):
ce_losses
=
AvgrageMeter
()
accs
=
AvgrageMeter
()
model
.
eval
()
step_id
=
0
for
valid_data
in
valid_loader
():
loss
,
acc
=
model_loss
(
model
,
valid_data
)
batch_size
=
valid_data
[
0
].
shape
[
0
]
ce_losses
.
update
(
loss
.
numpy
(),
batch_size
)
accs
.
update
(
acc
.
numpy
(),
batch_size
)
if
step_id
%
log_freq
==
0
:
logger
.
info
(
"Valid Epoch {}, Step {}, loss {:.6f}; acc: {:.6f};"
.
format
(
epoch
,
step_id
,
ce_losses
.
avg
[
0
],
accs
.
avg
[
0
]))
step_id
+=
1
step_id
+=
1
def
main
():
def
main
():
# whether use multi-gpus
use_data_parallel
=
False
use_data_parallel
=
False
place
=
fluid
.
CUDAPlace
(
fluid
.
dygraph
.
parallel
.
Env
(
place
=
fluid
.
CUDAPlace
(
fluid
.
dygraph
.
parallel
.
Env
(
).
dev_id
)
if
use_data_parallel
else
fluid
.
CUDAPlace
(
0
)
).
dev_id
)
if
use_data_parallel
else
fluid
.
CUDAPlace
(
0
)
BERT_BASE_PATH
=
"./data/pretrained_models/uncased_L-12_H-768_A-12"
BERT_BASE_PATH
=
"./data/pretrained_models/uncased_L-12_H-768_A-12"
bert_config_path
=
BERT_BASE_PATH
+
"/bert_config.json"
vocab_path
=
BERT_BASE_PATH
+
"/vocab.txt"
vocab_path
=
BERT_BASE_PATH
+
"/vocab.txt"
data_dir
=
"./data/glue_data/MNLI/"
teacher_model_dir
=
"./teacher_model/steps_23000"
num_samples
=
392702
max_seq_len
=
128
do_lower_case
=
True
do_lower_case
=
True
batch_size
=
128
# augmented dataset nums
# num_samples = 8016987
max_seq_len
=
128
batch_size
=
192
hidden_size
=
768
hidden_size
=
768
emb_size
=
768
emb_size
=
768
max_layer
=
8
epoch
=
80
epoch
=
80
log_freq
=
10
log_freq
=
10
use_fixed_gumbel
=
True
processor
=
MnliProcessor
(
task_name
=
'mnli'
data_dir
=
data_dir
,
vocab_path
=
vocab_path
,
if
task_name
==
'mrpc'
:
max_seq_len
=
max_seq_len
,
data_dir
=
"./data/glue_data/MRPC/"
do_lower_case
=
do_lower_case
,
teacher_model_dir
=
"./data/teacher_model/mrpc"
in_tokens
=
False
)
num_samples
=
3668
max_layer
=
4
train_reader
=
processor
.
data_generator
(
num_labels
=
2
batch_size
=
batch_size
,
processor_func
=
MrpcProcessor
phase
=
'search_train'
,
elif
task_name
==
'mnli'
:
epoch
=
1
,
data_dir
=
"./data/glue_data/MNLI/"
dev_count
=
1
,
teacher_model_dir
=
"./data/teacher_model/steps_23000"
shuffle
=
True
)
num_samples
=
392702
max_layer
=
8
val_reader
=
processor
.
data_generator
(
num_labels
=
3
batch_size
=
batch_size
,
processor_func
=
MnliProcessor
phase
=
'search_valid'
,
epoch
=
1
,
device_num
=
fluid
.
dygraph
.
parallel
.
Env
().
nranks
dev_count
=
1
,
use_fixed_gumbel
=
True
shuffle
=
True
)
train_phase
=
"train"
val_phase
=
"dev"
if
use_data_parallel
:
step_per_epoch
=
int
(
num_samples
/
(
batch_size
*
device_num
))
train_reader
=
fluid
.
contrib
.
reader
.
distributed_batch_reader
(
train_reader
)
valid_reader
=
fluid
.
contrib
.
reader
.
distributed_batch_reader
(
valid_reader
)
with
fluid
.
dygraph
.
guard
(
place
):
with
fluid
.
dygraph
.
guard
(
place
):
if
use_fixed_gumbel
:
# make sure gumbel arch is constant
np
.
random
.
seed
(
1
)
fluid
.
default_main_program
().
random_seed
=
1
model
=
AdaBERTClassifier
(
model
=
AdaBERTClassifier
(
3
,
num_labels
,
n_layer
=
max_layer
,
n_layer
=
max_layer
,
hidden_size
=
hidden_size
,
hidden_size
=
hidden_size
,
task_name
=
task_name
,
emb_size
=
emb_size
,
emb_size
=
emb_size
,
teacher_model
=
teacher_model_dir
,
teacher_model
=
teacher_model_dir
,
data_dir
=
data_dir
,
data_dir
=
data_dir
,
use_fixed_gumbel
=
use_fixed_gumbel
)
use_fixed_gumbel
=
use_fixed_gumbel
)
if
use_data_parallel
:
strategy
=
fluid
.
dygraph
.
parallel
.
prepare_context
()
model
=
fluid
.
dygraph
.
parallel
.
DataParallel
(
model
,
strategy
)
device_num
=
fluid
.
dygraph
.
parallel
.
Env
().
nranks
step_per_epoch
=
int
(
num_samples
/
(
batch_size
*
device_num
))
learning_rate
=
fluid
.
dygraph
.
CosineDecay
(
2e-2
,
step_per_epoch
,
epoch
)
learning_rate
=
fluid
.
dygraph
.
CosineDecay
(
2e-2
,
step_per_epoch
,
epoch
)
model_parameters
=
[
model_parameters
=
[]
p
for
p
in
model
.
parameters
()
for
p
in
model
.
parameters
():
if
p
.
name
not
in
[
a
.
name
for
a
in
model
.
arch_parameters
()]
if
(
p
.
name
not
in
[
a
.
name
for
a
in
model
.
arch_parameters
()]
and
]
p
.
name
not
in
[
a
.
name
for
a
in
model
.
teacher
.
parameters
()]):
model_parameters
.
append
(
p
)
clip
=
fluid
.
clip
.
GradientClipByGlobalNorm
(
clip_norm
=
5.0
)
optimizer
=
fluid
.
optimizer
.
MomentumOptimizer
(
optimizer
=
fluid
.
optimizer
.
MomentumOptimizer
(
learning_rate
,
learning_rate
,
0.9
,
0.9
,
regularization
=
fluid
.
regularizer
.
L2DecayRegularizer
(
3e-4
),
regularization
=
fluid
.
regularizer
.
L2DecayRegularizer
(
3e-4
),
parameter_list
=
model_parameters
,
parameter_list
=
model_parameters
)
grad_clip
=
clip
)
processor
=
processor_func
(
data_dir
=
data_dir
,
vocab_path
=
vocab_path
,
max_seq_len
=
max_seq_len
,
do_lower_case
=
do_lower_case
,
in_tokens
=
False
)
train_reader
=
processor
.
data_generator
(
batch_size
=
batch_size
,
phase
=
train_phase
,
epoch
=
1
,
dev_count
=
1
,
shuffle
=
True
)
dev_reader
=
processor
.
data_generator
(
batch_size
=
batch_size
,
phase
=
val_phase
,
epoch
=
1
,
dev_count
=
1
,
shuffle
=
False
)
if
use_data_parallel
:
train_reader
=
fluid
.
contrib
.
reader
.
distributed_batch_reader
(
train_reader
)
train_loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
train_loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
capacity
=
1
024
,
capacity
=
1
28
,
use_double_buffer
=
True
,
use_double_buffer
=
True
,
iterable
=
True
,
iterable
=
True
,
return_list
=
True
)
return_list
=
True
)
valid
_loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
dev
_loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
capacity
=
1
024
,
capacity
=
1
28
,
use_double_buffer
=
True
,
use_double_buffer
=
True
,
iterable
=
True
,
iterable
=
True
,
return_list
=
True
)
return_list
=
True
)
train_loader
.
set_batch_generator
(
train_reader
,
places
=
place
)
train_loader
.
set_batch_generator
(
train_reader
,
places
=
place
)
valid_loader
.
set_batch_generator
(
val
_reader
,
places
=
place
)
dev_loader
.
set_batch_generator
(
dev
_reader
,
places
=
place
)
architect
=
Architect
(
model
,
learning_rate
,
3e-4
,
place
,
False
)
if
use_data_parallel
:
strategy
=
fluid
.
dygraph
.
parallel
.
prepare_context
()
model
=
fluid
.
dygraph
.
parallel
.
DataParallel
(
model
,
strategy
)
best_valid_acc
=
0
for
epoch_id
in
range
(
epoch
):
for
epoch_id
in
range
(
epoch
):
train_one_epoch
(
model
,
architect
,
train_loader
,
valid_loader
,
train_one_epoch
(
model
,
train_loader
,
optimizer
,
epoch_id
,
optimizer
,
epoch_id
,
use_data_parallel
,
log_freq
)
use_data_parallel
,
log_freq
)
valid_one_epoch
(
model
,
valid_loader
,
epoch_id
,
log_freq
)
loss
,
acc
=
valid_one_epoch
(
model
,
dev_loader
,
epoch_id
,
log_freq
)
print
(
model
.
student
.
_encoder
.
alphas
.
numpy
())
if
acc
>
best_valid_acc
:
print
(
"="
*
100
)
best_valid_acc
=
acc
logger
.
info
(
"dev set, ce_loss {:.6f}; acc {:.6f}, best_acc {:.6f};"
.
format
(
loss
,
acc
,
best_valid_acc
))
if
__name__
==
'__main__'
:
if
__name__
==
'__main__'
:
...
...
demo/bert/train_search.py
0 → 100755
浏览文件 @
8c7465b2
import
numpy
as
np
from
itertools
import
izip
import
paddle.fluid
as
fluid
from
paddleslim.teachers.bert.reader.cls
import
*
from
paddleslim.nas.darts.search_space
import
AdaBERTClassifier
from
paddle.fluid.dygraph.base
import
to_variable
from
tqdm
import
tqdm
import
os
import
pickle
import
logging
from
paddleslim.common
import
AvgrageMeter
,
get_logger
logger
=
get_logger
(
__name__
,
level
=
logging
.
INFO
)
def
valid_one_epoch
(
model
,
valid_loader
,
epoch
,
log_freq
):
accs
=
AvgrageMeter
()
ce_losses
=
AvgrageMeter
()
model
.
student
.
eval
()
step_id
=
0
for
valid_data
in
valid_loader
():
try
:
loss
,
acc
,
ce_loss
,
_
,
_
=
model
.
_layers
.
loss
(
valid_data
,
epoch
)
except
:
loss
,
acc
,
ce_loss
,
_
,
_
=
model
.
loss
(
valid_data
,
epoch
)
batch_size
=
valid_data
[
0
].
shape
[
0
]
ce_losses
.
update
(
ce_loss
.
numpy
(),
batch_size
)
accs
.
update
(
acc
.
numpy
(),
batch_size
)
step_id
+=
1
return
ce_losses
.
avg
[
0
],
accs
.
avg
[
0
]
def
train_one_epoch
(
model
,
train_loader
,
valid_loader
,
optimizer
,
arch_optimizer
,
epoch
,
use_data_parallel
,
log_freq
):
total_losses
=
AvgrageMeter
()
accs
=
AvgrageMeter
()
ce_losses
=
AvgrageMeter
()
kd_losses
=
AvgrageMeter
()
val_accs
=
AvgrageMeter
()
model
.
student
.
train
()
step_id
=
0
for
train_data
,
valid_data
in
izip
(
train_loader
(),
valid_loader
()):
batch_size
=
train_data
[
0
].
shape
[
0
]
# make sure arch on every gpu is same, otherwise an error will occurs
np
.
random
.
seed
(
step_id
*
2
*
(
epoch
+
1
))
if
use_data_parallel
:
total_loss
,
acc
,
ce_loss
,
kd_loss
,
_
=
model
.
_layers
.
loss
(
train_data
,
epoch
)
else
:
total_loss
,
acc
,
ce_loss
,
kd_loss
,
_
=
model
.
loss
(
train_data
,
epoch
)
if
use_data_parallel
:
total_loss
=
model
.
scale_loss
(
total_loss
)
total_loss
.
backward
()
model
.
apply_collective_grads
()
else
:
total_loss
.
backward
()
optimizer
.
minimize
(
total_loss
)
model
.
clear_gradients
()
total_losses
.
update
(
total_loss
.
numpy
(),
batch_size
)
accs
.
update
(
acc
.
numpy
(),
batch_size
)
ce_losses
.
update
(
ce_loss
.
numpy
(),
batch_size
)
kd_losses
.
update
(
kd_loss
.
numpy
(),
batch_size
)
# make sure arch on every gpu is same, otherwise an error will occurs
np
.
random
.
seed
(
step_id
*
2
*
(
epoch
+
1
)
+
1
)
if
use_data_parallel
:
arch_loss
,
_
,
_
,
_
,
arch_logits
=
model
.
_layers
.
loss
(
valid_data
,
epoch
)
else
:
arch_loss
,
_
,
_
,
_
,
arch_logits
=
model
.
loss
(
valid_data
,
epoch
)
if
use_data_parallel
:
arch_loss
=
model
.
scale_loss
(
arch_loss
)
arch_loss
.
backward
()
model
.
apply_collective_grads
()
else
:
arch_loss
.
backward
()
arch_optimizer
.
minimize
(
arch_loss
)
model
.
clear_gradients
()
probs
=
fluid
.
layers
.
softmax
(
arch_logits
[
-
1
])
val_acc
=
fluid
.
layers
.
accuracy
(
input
=
probs
,
label
=
valid_data
[
4
])
val_accs
.
update
(
val_acc
.
numpy
(),
batch_size
)
if
step_id
%
log_freq
==
0
:
logger
.
info
(
"Train Epoch {}, Step {}, Lr {:.6f} total_loss {:.6f}; ce_loss {:.6f}, kd_loss {:.6f}, train_acc {:.6f}, search_valid_acc {:.6f};"
.
format
(
epoch
,
step_id
,
optimizer
.
current_step_lr
(),
total_losses
.
avg
[
0
],
ce_losses
.
avg
[
0
],
kd_losses
.
avg
[
0
],
accs
.
avg
[
0
],
val_accs
.
avg
[
0
]))
step_id
+=
1
def
main
():
# whether use multi-gpus
use_data_parallel
=
False
place
=
fluid
.
CUDAPlace
(
fluid
.
dygraph
.
parallel
.
Env
(
).
dev_id
)
if
use_data_parallel
else
fluid
.
CUDAPlace
(
0
)
BERT_BASE_PATH
=
"./data/pretrained_models/uncased_L-12_H-768_A-12"
vocab_path
=
BERT_BASE_PATH
+
"/vocab.txt"
data_dir
=
"./data/glue_data/MNLI/"
teacher_model_dir
=
"./data/teacher_model/steps_23000"
do_lower_case
=
True
num_samples
=
392702
# augmented dataset nums
# num_samples = 8016987
max_seq_len
=
128
batch_size
=
128
hidden_size
=
768
emb_size
=
768
max_layer
=
8
epoch
=
80
log_freq
=
10
device_num
=
fluid
.
dygraph
.
parallel
.
Env
().
nranks
use_fixed_gumbel
=
False
train_phase
=
"search_train"
val_phase
=
"search_valid"
step_per_epoch
=
int
(
num_samples
*
0.5
/
((
batch_size
)
*
device_num
))
with
fluid
.
dygraph
.
guard
(
place
):
model
=
AdaBERTClassifier
(
3
,
n_layer
=
max_layer
,
hidden_size
=
hidden_size
,
emb_size
=
emb_size
,
teacher_model
=
teacher_model_dir
,
data_dir
=
data_dir
,
use_fixed_gumbel
=
use_fixed_gumbel
)
learning_rate
=
fluid
.
dygraph
.
CosineDecay
(
2e-2
,
step_per_epoch
,
epoch
)
model_parameters
=
[]
for
p
in
model
.
parameters
():
if
(
p
.
name
not
in
[
a
.
name
for
a
in
model
.
arch_parameters
()]
and
p
.
name
not
in
[
a
.
name
for
a
in
model
.
teacher
.
parameters
()]):
model_parameters
.
append
(
p
)
optimizer
=
fluid
.
optimizer
.
MomentumOptimizer
(
learning_rate
,
0.9
,
regularization
=
fluid
.
regularizer
.
L2DecayRegularizer
(
3e-4
),
parameter_list
=
model_parameters
)
arch_optimizer
=
fluid
.
optimizer
.
Adam
(
3e-4
,
0.5
,
0.999
,
regularization
=
fluid
.
regularizer
.
L2Decay
(
1e-3
),
parameter_list
=
model
.
arch_parameters
())
processor
=
MnliProcessor
(
data_dir
=
data_dir
,
vocab_path
=
vocab_path
,
max_seq_len
=
max_seq_len
,
do_lower_case
=
do_lower_case
,
in_tokens
=
False
)
train_reader
=
processor
.
data_generator
(
batch_size
=
batch_size
,
phase
=
train_phase
,
epoch
=
1
,
dev_count
=
1
,
shuffle
=
True
)
valid_reader
=
processor
.
data_generator
(
batch_size
=
batch_size
,
phase
=
val_phase
,
epoch
=
1
,
dev_count
=
1
,
shuffle
=
True
)
dev_reader
=
processor
.
data_generator
(
batch_size
=
batch_size
,
phase
=
"dev"
,
epoch
=
1
,
dev_count
=
1
,
shuffle
=
False
)
if
use_data_parallel
:
train_reader
=
fluid
.
contrib
.
reader
.
distributed_batch_reader
(
train_reader
)
valid_reader
=
fluid
.
contrib
.
reader
.
distributed_batch_reader
(
valid_reader
)
train_loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
capacity
=
128
,
use_double_buffer
=
True
,
iterable
=
True
,
return_list
=
True
)
valid_loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
capacity
=
128
,
use_double_buffer
=
True
,
iterable
=
True
,
return_list
=
True
)
dev_loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
capacity
=
128
,
use_double_buffer
=
True
,
iterable
=
True
,
return_list
=
True
)
train_loader
.
set_batch_generator
(
train_reader
,
places
=
place
)
valid_loader
.
set_batch_generator
(
valid_reader
,
places
=
place
)
dev_loader
.
set_batch_generator
(
dev_reader
,
places
=
place
)
if
use_data_parallel
:
strategy
=
fluid
.
dygraph
.
parallel
.
prepare_context
()
model
=
fluid
.
dygraph
.
parallel
.
DataParallel
(
model
,
strategy
)
for
epoch_id
in
range
(
epoch
):
train_one_epoch
(
model
,
train_loader
,
valid_loader
,
optimizer
,
arch_optimizer
,
epoch_id
,
use_data_parallel
,
log_freq
)
loss
,
acc
=
valid_one_epoch
(
model
,
dev_loader
,
epoch_id
,
log_freq
)
logger
.
info
(
"dev set, ce_loss {:.6f}; acc: {:.6f};"
.
format
(
loss
,
acc
))
if
use_data_parallel
:
print
(
model
.
_layers
.
student
.
_encoder
.
alphas
.
numpy
())
else
:
print
(
model
.
student
.
_encoder
.
alphas
.
numpy
())
print
(
"="
*
100
)
if
__name__
==
'__main__'
:
main
()
paddleslim/nas/darts/architect_for_bert.py
浏览文件 @
8c7465b2
...
@@ -49,17 +49,17 @@ class Architect(object):
...
@@ -49,17 +49,17 @@ class Architect(object):
self
.
network_weight_decay
),
self
.
network_weight_decay
),
parameter_list
=
self
.
unrolled_model_params
)
parameter_list
=
self
.
unrolled_model_params
)
def
step
(
self
,
train_data
,
valid_data
):
def
step
(
self
,
train_data
,
valid_data
,
epoch
):
if
self
.
unrolled
:
if
self
.
unrolled
:
params_grads
=
self
.
_backward_step_unrolled
(
train_data
,
valid_data
)
params_grads
=
self
.
_backward_step_unrolled
(
train_data
,
valid_data
)
self
.
optimizer
.
apply_gradients
(
params_grads
)
self
.
optimizer
.
apply_gradients
(
params_grads
)
else
:
else
:
loss
=
self
.
_backward_step
(
valid_data
)
loss
=
self
.
_backward_step
(
valid_data
,
epoch
)
self
.
optimizer
.
minimize
(
loss
)
self
.
optimizer
.
minimize
(
loss
)
self
.
optimizer
.
clear_gradients
()
self
.
optimizer
.
clear_gradients
()
def
_backward_step
(
self
,
valid_data
):
def
_backward_step
(
self
,
valid_data
,
epoch
):
loss
=
self
.
model
.
loss
(
valid_data
)
loss
=
self
.
model
.
loss
(
valid_data
,
epoch
)
loss
[
0
].
backward
()
loss
[
0
].
backward
()
return
loss
[
0
]
return
loss
[
0
]
...
...
paddleslim/nas/darts/search_space/conv_bert/cls.py
浏览文件 @
8c7465b2
...
@@ -31,6 +31,7 @@ import multiprocessing
...
@@ -31,6 +31,7 @@ import multiprocessing
import
paddle
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid
as
fluid
from
paddle.fluid.dygraph
import
to_variable
,
Layer
,
Linear
from
paddle.fluid.dygraph
import
to_variable
,
Layer
,
Linear
from
paddle.fluid.dygraph.base
import
to_variable
from
.reader.cls
import
*
from
.reader.cls
import
*
from
.model.bert
import
BertModelLayer
from
.model.bert
import
BertModelLayer
from
.optimization
import
Optimizer
from
.optimization
import
Optimizer
...
@@ -48,6 +49,7 @@ class AdaBERTClassifier(Layer):
...
@@ -48,6 +49,7 @@ class AdaBERTClassifier(Layer):
hidden_size
=
768
,
hidden_size
=
768
,
gamma
=
0.8
,
gamma
=
0.8
,
beta
=
4
,
beta
=
4
,
task_name
=
'mnli'
,
conv_type
=
"conv_bn"
,
conv_type
=
"conv_bn"
,
search_layer
=
False
,
search_layer
=
False
,
teacher_model
=
None
,
teacher_model
=
None
,
...
@@ -68,17 +70,21 @@ class AdaBERTClassifier(Layer):
...
@@ -68,17 +70,21 @@ class AdaBERTClassifier(Layer):
self
.
_teacher_model
=
teacher_model
self
.
_teacher_model
=
teacher_model
self
.
_data_dir
=
data_dir
self
.
_data_dir
=
data_dir
self
.
use_fixed_gumbel
=
use_fixed_gumbel
self
.
use_fixed_gumbel
=
use_fixed_gumbel
self
.
T
=
t
self
.
T
=
t
print
(
print
(
"----------------------load teacher model and test----------------------------------------"
"----------------------load teacher model and test----------------------------------------"
)
)
self
.
teacher
=
BERTClassifier
(
self
.
teacher
=
BERTClassifier
(
num_labels
,
model_path
=
self
.
_teacher_model
)
num_labels
,
task_name
=
task_name
,
model_path
=
self
.
_teacher_model
)
# global setting, will be overwritten when training(about 1% acc loss)
self
.
teacher
.
eval
()
self
.
teacher
.
test
(
self
.
_data_dir
)
self
.
teacher
.
test
(
self
.
_data_dir
)
print
(
print
(
"----------------------finish load teacher model and test----------------------------------------"
"----------------------finish load teacher model and test----------------------------------------"
)
)
self
.
student
=
BertModelLayer
(
self
.
student
=
BertModelLayer
(
num_labels
=
num_labels
,
n_layer
=
self
.
_n_layer
,
n_layer
=
self
.
_n_layer
,
emb_size
=
self
.
_emb_size
,
emb_size
=
self
.
_emb_size
,
hidden_size
=
self
.
_hidden_size
,
hidden_size
=
self
.
_hidden_size
,
...
@@ -87,6 +93,7 @@ class AdaBERTClassifier(Layer):
...
@@ -87,6 +93,7 @@ class AdaBERTClassifier(Layer):
use_fixed_gumbel
=
self
.
use_fixed_gumbel
,
use_fixed_gumbel
=
self
.
use_fixed_gumbel
,
gumbel_alphas
=
gumbel_alphas
)
gumbel_alphas
=
gumbel_alphas
)
fix_emb
=
False
for
s_emb
,
t_emb
in
zip
(
self
.
student
.
emb_names
(),
for
s_emb
,
t_emb
in
zip
(
self
.
student
.
emb_names
(),
self
.
teacher
.
emb_names
()):
self
.
teacher
.
emb_names
()):
t_emb
.
stop_gradient
=
True
t_emb
.
stop_gradient
=
True
...
@@ -100,91 +107,58 @@ class AdaBERTClassifier(Layer):
...
@@ -100,91 +107,58 @@ class AdaBERTClassifier(Layer):
"Assigned embedding[{}] from teacher to embedding[{}] in student."
.
"Assigned embedding[{}] from teacher to embedding[{}] in student."
.
format
(
t_emb
.
name
,
s_emb
.
name
))
format
(
t_emb
.
name
,
s_emb
.
name
))
self
.
cls_fc
=
list
()
def
forward
(
self
,
data_ids
,
epoch
):
for
i
in
range
(
self
.
_n_layer
):
return
self
.
student
(
data_ids
,
epoch
)
fc
=
Linear
(
input_dim
=
self
.
_hidden_size
,
output_dim
=
self
.
_num_labels
,
param_attr
=
fluid
.
ParamAttr
(
name
=
"s_cls_out_%d_w"
%
i
,
initializer
=
fluid
.
initializer
.
TruncatedNormal
(
scale
=
0.02
)),
bias_attr
=
fluid
.
ParamAttr
(
name
=
"s_cls_out_%d_b"
%
i
,
initializer
=
fluid
.
initializer
.
Constant
(
0.
)))
fc
=
self
.
add_sublayer
(
"cls_fc_%d"
%
i
,
fc
)
self
.
cls_fc
.
append
(
fc
)
def
forward
(
self
,
data_ids
):
src_ids
=
data_ids
[
0
]
position_ids
=
data_ids
[
1
]
sentence_ids
=
data_ids
[
2
]
return
self
.
student
(
src_ids
,
position_ids
,
sentence_ids
)
def
arch_parameters
(
self
):
def
arch_parameters
(
self
):
return
self
.
student
.
arch_parameters
()
return
self
.
student
.
arch_parameters
()
def
genotype
(
self
):
def
loss
(
self
,
data_ids
,
epoch
):
return
self
.
arch_parameters
()
def
ce
(
self
,
logits
):
logits
=
np
.
exp
(
logits
-
np
.
max
(
logits
))
logits
=
logits
/
logits
.
sum
(
axis
=
0
)
return
logits
def
loss
(
self
,
data_ids
):
src_ids
=
data_ids
[
0
]
position_ids
=
data_ids
[
1
]
sentence_ids
=
data_ids
[
2
]
input_mask
=
data_ids
[
3
]
labels
=
data_ids
[
4
]
labels
=
data_ids
[
4
]
s_logits
=
self
.
student
(
s_logits
=
self
.
student
(
data_ids
,
epoch
)
src_ids
,
position_ids
,
sentence_ids
,
flops
=
[],
model_size
=
[])
self
.
teacher
.
eval
()
t_enc_outputs
,
t_logits
,
t_losses
,
t_accs
,
_
=
self
.
teacher
(
data_ids
)
total_loss
,
t_logits
,
t_losses
,
accuracys
,
num_seqs
=
self
.
teacher
(
data_ids
)
# define kd loss
kd_losses
=
[]
#define kd loss
kd_weights
=
[]
kd_weights
=
[]
for
i
in
range
(
len
(
s_logits
)):
for
i
in
range
(
len
(
s_logits
)):
j
=
int
(
np
.
ceil
(
i
*
(
float
(
len
(
t_logits
))
/
len
(
s_logits
))))
j
=
int
(
np
.
ceil
(
i
*
(
float
(
len
(
t_logits
))
/
len
(
s_logits
))))
kd_weights
.
append
(
t_losses
[
j
].
numpy
())
kd_weights
.
append
(
t_losses
[
j
].
numpy
())
kd_weights
=
np
.
array
(
kd_weights
)
kd_weights
=
np
.
array
(
kd_weights
)
kd_weights
=
self
.
ce
(
-
kd_weights
)
kd_weights
=
np
.
squeeze
(
kd_weights
)
s_probs
=
None
kd_weights
=
to_variable
(
kd_weights
)
kd_weights
=
fluid
.
layers
.
softmax
(
-
kd_weights
)
kd_losses
=
[]
for
i
in
range
(
len
(
s_logits
)):
for
i
in
range
(
len
(
s_logits
)):
j
=
int
(
np
.
ceil
(
i
*
(
float
(
len
(
t_logits
))
/
len
(
s_logits
))))
j
=
int
(
np
.
ceil
(
i
*
(
float
(
len
(
t_logits
))
/
len
(
s_logits
))))
t_logit
=
t_logits
[
j
]
t_logit
=
t_logits
[
j
]
s_logit
=
s_logits
[
i
]
s_logit
=
s_logits
[
i
]
t_logit
.
stop_gradient
=
True
t_logit
.
stop_gradient
=
True
t_probs
=
fluid
.
layers
.
softmax
(
t_logit
/
self
.
T
)
t_probs
=
fluid
.
layers
.
softmax
(
t_logit
)
# P_j^T
s_probs
=
fluid
.
layers
.
softmax
(
s_logit
)
s_probs
=
fluid
.
layers
.
softmax
(
s_logit
/
self
.
T
)
#P_j^S
#kd_loss = -t_probs * fluid.layers.log(s_probs)
#kd_loss = -t_probs * fluid.layers.log(s_probs)
kd_loss
=
fluid
.
layers
.
cross_entropy
(
kd_loss
=
fluid
.
layers
.
cross_entropy
(
input
=
s_probs
,
label
=
t_probs
,
soft_label
=
True
)
input
=
s_probs
,
label
=
t_probs
,
soft_label
=
True
)
kd_loss
=
fluid
.
layers
.
reduce_sum
(
kd_loss
,
dim
=
1
)
kd_loss
=
fluid
.
layers
.
reduce_mean
(
kd_loss
)
kd_loss
=
fluid
.
layers
.
mean
(
kd_loss
)
kd_loss
=
fluid
.
layers
.
scale
(
kd_loss
,
scale
=
kd_weights
[
i
])
# print("kd_loss[{}] = {}; kd_weights[{}] = {}".format(i, kd_loss.numpy(), i, kd_weights[i]))
kd_losses
.
append
(
kd_loss
)
# tmp = kd_loss * kd_weights[i]
tmp
=
fluid
.
layers
.
scale
(
kd_loss
,
scale
=
kd_weights
[
i
])
# print("kd_loss[{}] = {}".format(i, tmp.numpy()))
kd_losses
.
append
(
tmp
)
kd_loss
=
fluid
.
layers
.
sum
(
kd_losses
)
kd_loss
=
fluid
.
layers
.
sum
(
kd_losses
)
# print("kd_loss = {}".format(kd_loss.numpy()))
losses
=
[]
for
logit
in
s_logits
:
ce_loss
,
probs
=
fluid
.
layers
.
softmax_with_cross_entropy
(
logits
=
logit
,
label
=
labels
,
return_softmax
=
True
)
loss
=
fluid
.
layers
.
mean
(
x
=
ce_loss
)
losses
.
append
(
loss
)
num_seqs
=
fluid
.
layers
.
create_tensor
(
dtype
=
'int64'
)
accuracy
=
fluid
.
layers
.
accuracy
(
input
=
probs
,
label
=
labels
,
total
=
num_seqs
)
ce_loss
=
fluid
.
layers
.
sum
(
losses
)
ce_loss
,
probs
=
fluid
.
layers
.
softmax_with_cross_entropy
(
total_loss
=
(
1
-
self
.
_gamma
)
*
ce_loss
+
self
.
_gamma
*
kd_loss
logits
=
s_logits
[
-
1
],
label
=
labels
,
return_softmax
=
True
)
ce_loss
=
fluid
.
layers
.
mean
(
x
=
ce_loss
)
num_seqs
=
fluid
.
layers
.
create_tensor
(
dtype
=
'int64'
)
accuracy
=
fluid
.
layers
.
accuracy
(
input
=
probs
,
label
=
labels
,
total
=
num_seqs
)
loss
=
(
1
-
self
.
_gamma
)
*
ce_loss
+
self
.
_gamma
*
kd_loss
return
total_loss
,
accuracy
,
ce_loss
,
kd_loss
,
s_logits
# return ce_loss, accuracy, None, None
return
loss
,
accuracy
,
ce_loss
,
kd_loss
paddleslim/nas/darts/search_space/conv_bert/model/bert.py
浏览文件 @
8c7465b2
...
@@ -32,6 +32,7 @@ from .transformer_encoder import EncoderLayer
...
@@ -32,6 +32,7 @@ from .transformer_encoder import EncoderLayer
class
BertModelLayer
(
Layer
):
class
BertModelLayer
(
Layer
):
def
__init__
(
self
,
def
__init__
(
self
,
num_labels
,
emb_size
=
128
,
emb_size
=
128
,
hidden_size
=
768
,
hidden_size
=
768
,
n_layer
=
12
,
n_layer
=
12
,
...
@@ -91,6 +92,7 @@ class BertModelLayer(Layer):
...
@@ -91,6 +92,7 @@ class BertModelLayer(Layer):
param_attr
=
fluid
.
ParamAttr
(
name
=
"s_emb_factorization"
))
param_attr
=
fluid
.
ParamAttr
(
name
=
"s_emb_factorization"
))
self
.
_encoder
=
EncoderLayer
(
self
.
_encoder
=
EncoderLayer
(
num_labels
=
num_labels
,
n_layer
=
self
.
_n_layer
,
n_layer
=
self
.
_n_layer
,
hidden_size
=
self
.
_hidden_size
,
hidden_size
=
self
.
_hidden_size
,
search_layer
=
self
.
_search_layer
,
search_layer
=
self
.
_search_layer
,
...
@@ -101,6 +103,10 @@ class BertModelLayer(Layer):
...
@@ -101,6 +103,10 @@ class BertModelLayer(Layer):
return
self
.
_src_emb
.
parameters
()
+
self
.
_pos_emb
.
parameters
(
return
self
.
_src_emb
.
parameters
()
+
self
.
_pos_emb
.
parameters
(
)
+
self
.
_sent_emb
.
parameters
()
)
+
self
.
_sent_emb
.
parameters
()
def
emb_names
(
self
):
return
self
.
_src_emb
.
parameters
()
+
self
.
_pos_emb
.
parameters
(
)
+
self
.
_sent_emb
.
parameters
()
def
max_flops
(
self
):
def
max_flops
(
self
):
return
self
.
_encoder
.
max_flops
return
self
.
_encoder
.
max_flops
...
@@ -110,54 +116,19 @@ class BertModelLayer(Layer):
...
@@ -110,54 +116,19 @@ class BertModelLayer(Layer):
def
arch_parameters
(
self
):
def
arch_parameters
(
self
):
return
[
self
.
_encoder
.
alphas
]
#, self._encoder.k]
return
[
self
.
_encoder
.
alphas
]
#, self._encoder.k]
def
forward
(
self
,
def
forward
(
self
,
data_ids
,
epoch
):
src_ids
,
position_ids
,
sentence_ids
,
flops
=
[],
model_size
=
[]):
"""
"""
forward
forward
"""
"""
ids
=
np
.
squeeze
(
src_ids
.
numpy
())
ids0
=
data_ids
[
5
]
sids
=
np
.
squeeze
(
sentence_ids
.
numpy
())
ids1
=
data_ids
[
6
]
batchsize
=
ids
.
shape
[
0
]
ids_0
=
ids
[((
sids
==
0
)
&
(
ids
!=
0
))]
seqlen_0
=
((
sids
==
0
)
&
(
ids
!=
0
)).
astype
(
np
.
int64
).
sum
(
1
)
y_0
=
np
.
concatenate
([
np
.
arange
(
s
)
for
s
in
seqlen_0
])
x_0
=
np
.
concatenate
([
np
.
ones
(
[
s
],
dtype
=
np
.
int64
)
*
i
for
i
,
s
in
enumerate
(
seqlen_0
)
])
ids0
=
np
.
zeros
([
batchsize
,
seqlen_0
.
max
()],
dtype
=
np
.
int64
)
ids0
[(
x_0
,
y_0
)]
=
ids_0
ids_1
=
ids
[(
sids
==
1
)
&
(
ids
!=
0
)]
seqlen_1
=
((
sids
==
1
)
&
(
ids
!=
0
)).
astype
(
np
.
int64
).
sum
(
1
)
y_1
=
np
.
concatenate
([
np
.
arange
(
s
)
for
s
in
seqlen_1
])
x_1
=
np
.
concatenate
([
np
.
ones
(
[
s
],
dtype
=
np
.
int64
)
*
i
for
i
,
s
in
enumerate
(
seqlen_1
)
])
ids1
=
np
.
zeros
([
batchsize
,
seqlen_1
.
max
()],
dtype
=
np
.
int64
)
ids1
[(
x_1
,
y_1
)]
=
ids_1
msl
=
max
(
seqlen_0
.
max
(),
seqlen_1
.
max
())
ids0
=
np
.
pad
(
ids0
,
[[
0
,
0
],
[
0
,
msl
-
seqlen_0
.
max
()]],
mode
=
'constant'
)
ids1
=
np
.
pad
(
ids1
,
[[
0
,
0
],
[
0
,
msl
-
seqlen_1
.
max
()]],
mode
=
'constant'
)
ids0
=
fluid
.
dygraph
.
to_variable
(
ids0
)
ids1
=
fluid
.
dygraph
.
to_variable
(
ids1
)
src_emb_0
=
self
.
_src_emb
(
ids0
)
src_emb_0
=
self
.
_src_emb
(
ids0
)
src_emb_1
=
self
.
_src_emb
(
ids1
)
src_emb_1
=
self
.
_src_emb
(
ids1
)
emb_out_0
=
self
.
_emb_fac
(
src_emb_0
)
emb_out_0
=
self
.
_emb_fac
(
src_emb_0
)
emb_out_1
=
self
.
_emb_fac
(
src_emb_1
)
emb_out_1
=
self
.
_emb_fac
(
src_emb_1
)
# (bs, seq_len, 768)
# (bs, seq_len, hidden_size)
enc_outputs
=
self
.
_encoder
(
emb_out_0
,
emb_out_1
,
epoch
)
enc_outputs
=
self
.
_encoder
(
emb_out
,
flops
=
flops
,
model_size
=
model_size
)
return
enc_outputs
return
enc_outputs
paddleslim/nas/darts/search_space/conv_bert/model/transformer_encoder.py
浏览文件 @
8c7465b2
...
@@ -23,14 +23,15 @@ from collections import Iterable
...
@@ -23,14 +23,15 @@ from collections import Iterable
import
paddle
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid
as
fluid
from
paddle.fluid.dygraph
import
Embedding
,
LayerNorm
,
Linear
,
Layer
,
Conv2D
,
BatchNorm
,
Pool2D
,
to_variable
from
paddle.fluid.dygraph
import
Embedding
,
LayerNorm
,
Linear
,
Layer
,
Conv2D
,
BatchNorm
,
Pool2D
,
to_variable
from
paddle.fluid.dygraph
import
to_variable
from
paddle.fluid.initializer
import
NormalInitializer
from
paddle.fluid.initializer
import
NormalInitializer
from
paddle.fluid
import
ParamAttr
from
paddle.fluid
import
ParamAttr
from
paddle.fluid.initializer
import
MSRA
,
ConstantInitializer
from
paddle.fluid.initializer
import
MSRA
,
ConstantInitializer
ConvBN_PRIMITIVES
=
[
ConvBN_PRIMITIVES
=
[
'std_conv_bn_3'
,
'std_conv_bn_5'
,
'std_conv_bn_7'
,
'dil_conv_bn_3'
,
'std_conv_bn_3'
,
'std_conv_bn_5'
,
'std_conv_bn_7'
,
'dil_conv_bn_3'
,
'dil_conv_bn_5'
,
'dil_conv_bn_7'
,
'avg_pool_3'
,
'max_pool_3'
,
'none'
,
'dil_conv_bn_5'
,
'dil_conv_bn_7'
,
'avg_pool_3'
,
'max_pool_3'
,
'skip_connect'
'skip_connect'
,
'none'
]
]
...
@@ -53,11 +54,6 @@ class MixedOp(fluid.dygraph.Layer):
...
@@ -53,11 +54,6 @@ class MixedOp(fluid.dygraph.Layer):
def
__init__
(
self
,
n_channel
,
name
=
None
):
def
__init__
(
self
,
n_channel
,
name
=
None
):
super
(
MixedOp
,
self
).
__init__
()
super
(
MixedOp
,
self
).
__init__
()
PRIMITIVES
=
ConvBN_PRIMITIVES
PRIMITIVES
=
ConvBN_PRIMITIVES
# ops = [
# OPS[primitive](n_channel, name
# if name is None else name + "/" + primitive)
# for primitive in PRIMITIVES
# ]
ops
=
[]
ops
=
[]
for
primitive
in
PRIMITIVES
:
for
primitive
in
PRIMITIVES
:
op
=
OPS
[
primitive
](
n_channel
,
name
op
=
OPS
[
primitive
](
n_channel
,
name
...
@@ -76,26 +72,17 @@ class MixedOp(fluid.dygraph.Layer):
...
@@ -76,26 +72,17 @@ class MixedOp(fluid.dygraph.Layer):
self
.
_ops
=
fluid
.
dygraph
.
LayerList
(
ops
)
self
.
_ops
=
fluid
.
dygraph
.
LayerList
(
ops
)
def
forward
(
self
,
x
,
weights
):
def
forward
(
self
,
x
,
weights
):
#out = weights[0] * self._ops[0](x)
# out = fluid.layers.sums(
# out = fluid.layers.sums(
# [weights[i] * op(x) for i, op in enumerate(self._ops)])
#
[weights[i] * op(x) for i, op in enumerate(self._ops)])
# return out
# return out
for
i
in
range
(
len
(
self
.
_ops
)):
for
i
in
range
(
len
(
weights
.
numpy
())):
if
weights
[
i
].
numpy
()
!=
0
:
if
isinstance
(
weights
,
Iterable
):
weights_i
=
weights
[
i
]
else
:
weights_i
=
weights
[
i
].
numpy
()
if
weights_i
!=
0
:
return
self
.
_ops
[
i
](
x
)
*
weights
[
i
]
return
self
.
_ops
[
i
](
x
)
*
weights
[
i
]
def
gumbel_softmax
(
logits
,
temperature
=
1
,
hard
=
True
,
eps
=
1e-10
):
def
gumbel_softmax
(
logits
,
epoch
,
temperature
=
1.0
,
hard
=
True
,
eps
=
1e-10
):
#U = np.random.uniform(0, 1, logits.shape)
temperature
=
temperature
*
(
0.98
**
epoch
)
#U = - to_variable(
# np.log(-np.log(U + eps) + eps).astype("float32"))
U
=
np
.
random
.
gumbel
(
0
,
1
,
logits
.
shape
).
astype
(
"float32"
)
U
=
np
.
random
.
gumbel
(
0
,
1
,
logits
.
shape
).
astype
(
"float32"
)
logits
=
logits
+
to_variable
(
U
)
logits
=
logits
+
to_variable
(
U
)
...
@@ -105,12 +92,12 @@ def gumbel_softmax(logits, temperature=1, hard=True, eps=1e-10):
...
@@ -105,12 +92,12 @@ def gumbel_softmax(logits, temperature=1, hard=True, eps=1e-10):
if
hard
:
if
hard
:
maxes
=
fluid
.
layers
.
reduce_max
(
logits
,
dim
=
1
,
keep_dim
=
True
)
maxes
=
fluid
.
layers
.
reduce_max
(
logits
,
dim
=
1
,
keep_dim
=
True
)
hard
=
fluid
.
layers
.
cast
((
logits
==
maxes
),
logits
.
dtype
)
hard
=
fluid
.
layers
.
cast
((
logits
==
maxes
),
logits
.
dtype
)
# out = hard - logits.detach() + logits
out
=
hard
-
logits
.
detach
()
+
logits
tmp
=
hard
-
logits
# tmp.stop_gradient = True
tmp
.
stop_gradient
=
True
# out = tmp + logits
out
=
tmp
+
logits
else
:
else
:
out
=
logits
out
=
logits
return
out
return
out
...
@@ -142,8 +129,6 @@ class ReluConvBN(fluid.dygraph.Layer):
...
@@ -142,8 +129,6 @@ class ReluConvBN(fluid.dygraph.Layer):
use_cudnn
=
True
,
use_cudnn
=
True
,
name
=
None
):
name
=
None
):
super
(
ReluConvBN
,
self
).
__init__
()
super
(
ReluConvBN
,
self
).
__init__
()
#conv_std = (2.0 /
# (filter_size[0] * filter_size[1] * out_c * in_c))**0.5
conv_param
=
fluid
.
ParamAttr
(
conv_param
=
fluid
.
ParamAttr
(
name
=
name
if
name
is
None
else
(
name
+
"_conv.weights"
),
name
=
name
if
name
is
None
else
(
name
+
"_conv.weights"
),
initializer
=
fluid
.
initializer
.
MSRA
())
initializer
=
fluid
.
initializer
.
MSRA
())
...
@@ -215,6 +200,7 @@ class EncoderLayer(Layer):
...
@@ -215,6 +200,7 @@ class EncoderLayer(Layer):
"""
"""
def
__init__
(
self
,
def
__init__
(
self
,
num_labels
,
n_layer
,
n_layer
,
hidden_size
=
768
,
hidden_size
=
768
,
name
=
"encoder"
,
name
=
"encoder"
,
...
@@ -224,12 +210,27 @@ class EncoderLayer(Layer):
...
@@ -224,12 +210,27 @@ class EncoderLayer(Layer):
super
(
EncoderLayer
,
self
).
__init__
()
super
(
EncoderLayer
,
self
).
__init__
()
self
.
_n_layer
=
n_layer
self
.
_n_layer
=
n_layer
self
.
_hidden_size
=
hidden_size
self
.
_hidden_size
=
hidden_size
self
.
_n_channel
=
256
self
.
_n_channel
=
128
self
.
_steps
=
3
self
.
_steps
=
3
self
.
_n_ops
=
len
(
ConvBN_PRIMITIVES
)
self
.
_n_ops
=
len
(
ConvBN_PRIMITIVES
)
self
.
use_fixed_gumbel
=
use_fixed_gumbel
self
.
use_fixed_gumbel
=
use_fixed_gumbel
self
.
stem
=
fluid
.
dygraph
.
Sequential
(
self
.
stem0
=
fluid
.
dygraph
.
Sequential
(
Conv2D
(
num_channels
=
1
,
num_filters
=
self
.
_n_channel
,
filter_size
=
[
3
,
self
.
_hidden_size
],
padding
=
[
1
,
0
],
param_attr
=
fluid
.
ParamAttr
(
initializer
=
MSRA
()),
bias_attr
=
False
),
BatchNorm
(
num_channels
=
self
.
_n_channel
,
param_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
(
value
=
1
)),
bias_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
(
value
=
0
))))
self
.
stem1
=
fluid
.
dygraph
.
Sequential
(
Conv2D
(
Conv2D
(
num_channels
=
1
,
num_channels
=
1
,
num_filters
=
self
.
_n_channel
,
num_filters
=
self
.
_n_channel
,
...
@@ -262,16 +263,10 @@ class EncoderLayer(Layer):
...
@@ -262,16 +263,10 @@ class EncoderLayer(Layer):
default_initializer
=
NormalInitializer
(
default_initializer
=
NormalInitializer
(
loc
=
0.0
,
scale
=
1e-3
))
loc
=
0.0
,
scale
=
1e-3
))
# self.k = fluid.layers.create_parameter(
# shape=[1, self._n_layer],
# dtype="float32",
# default_initializer=NormalInitializer(
# loc=0.0, scale=1e-3))
self
.
pool2d_avg
=
Pool2D
(
pool_type
=
'avg'
,
global_pooling
=
True
)
self
.
pool2d_avg
=
Pool2D
(
pool_type
=
'avg'
,
global_pooling
=
True
)
self
.
bns
=
[]
self
.
bns
=
[]
self
.
outs
=
[]
self
.
outs
=
[]
for
i
in
range
(
self
.
_n_layer
):
for
i
in
range
(
self
.
_n_layer
):
bn
=
BatchNorm
(
bn
=
BatchNorm
(
num_channels
=
self
.
_n_channel
,
num_channels
=
self
.
_n_channel
,
param_attr
=
fluid
.
ParamAttr
(
param_attr
=
fluid
.
ParamAttr
(
...
@@ -280,52 +275,53 @@ class EncoderLayer(Layer):
...
@@ -280,52 +275,53 @@ class EncoderLayer(Layer):
bias_attr
=
fluid
.
ParamAttr
(
bias_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
(
value
=
0
),
initializer
=
fluid
.
initializer
.
Constant
(
value
=
0
),
trainable
=
False
))
trainable
=
False
))
self
.
bns
.
append
(
bn
)
out
=
Linear
(
out
=
Linear
(
self
.
_n_channel
,
self
.
_n_channel
,
3
,
num_labels
,
param_attr
=
ParamAttr
(
initializer
=
MSRA
()),
param_attr
=
ParamAttr
(
initializer
=
MSRA
()),
bias_attr
=
ParamAttr
(
initializer
=
MSRA
()))
bias_attr
=
ParamAttr
(
initializer
=
MSRA
()))
self
.
bns
.
append
(
bn
)
self
.
outs
.
append
(
out
)
self
.
outs
.
append
(
out
)
self
.
_bns
=
fluid
.
dygraph
.
LayerList
(
self
.
bns
)
self
.
_outs
=
fluid
.
dygraph
.
LayerList
(
self
.
outs
)
self
.
use_fixed_gumbel
=
use_fixed_gumbel
self
.
use_fixed_gumbel
=
use_fixed_gumbel
self
.
gumbel_alphas
=
gumbel_softmax
(
self
.
alphas
)
#self.gumbel_alphas = gumbel_softmax(self.alphas, 0).detach()
if
gumbel_alphas
is
not
None
:
self
.
gumbel_alphas
=
np
.
array
(
gumbel_alphas
).
reshape
(
mrpc_arch
=
[
self
.
alphas
.
shape
)
[
0
,
0
,
1
,
0
,
0
,
0
,
0
,
0
,
0
,
0
],
# std_conv7 0 # node 0
else
:
[
0
,
0
,
0
,
0
,
1
,
0
,
0
,
0
,
0
,
0
],
# dil_conv5 1
self
.
gumbel_alphas
=
gumbel_softmax
(
self
.
alphas
)
[
0
,
0
,
1
,
0
,
0
,
0
,
0
,
0
,
0
,
0
],
# std_conv7 0 # node 1
self
.
gumbel_alphas
.
stop_gradient
=
True
[
0
,
0
,
0
,
0
,
1
,
0
,
0
,
0
,
0
,
0
],
# dil_conv5 1
[
0
,
0
,
0
,
0
,
0
,
0
,
0
,
0
,
0
,
1
],
# zero 2
[
0
,
0
,
0
,
0
,
0
,
0
,
0
,
0
,
0
,
1
],
# zero 0 # node2
[
1
,
0
,
0
,
0
,
0
,
0
,
0
,
0
,
0
,
0
],
# std_conv3 1
[
0
,
0
,
0
,
0
,
0
,
0
,
0
,
0
,
0
,
1
],
# zero 2
[
0
,
0
,
0
,
1
,
0
,
0
,
0
,
0
,
0
,
0
]
# dil_conv3 3
]
self
.
gumbel_alphas
=
to_variable
(
np
.
array
(
mrpc_arch
).
astype
(
np
.
float32
))
self
.
gumbel_alphas
.
stop_gradient
=
True
print
(
"gumbel_alphas:
\n
"
,
self
.
gumbel_alphas
.
numpy
())
def
forward
(
self
,
enc_input_0
,
enc_input_1
,
epoch
,
flops
=
[],
model_size
=
[]):
alphas
=
self
.
gumbel_alphas
if
self
.
use_fixed_gumbel
else
gumbel_softmax
(
self
.
alphas
,
epoch
)
print
(
"gumbel_alphas: {}"
.
format
(
self
.
gumbel_alphas
))
s0
=
fluid
.
layers
.
unsqueeze
(
enc_input_0
,
[
1
])
s1
=
fluid
.
layers
.
unsqueeze
(
enc_input_1
,
[
1
])
s0
=
self
.
stem0
(
s0
)
s1
=
self
.
stem1
(
s1
)
def
forward
(
self
,
enc_input_0
,
enc_input_1
,
flops
=
[],
model_size
=
[]):
enc_outputs
=
[]
alphas
=
self
.
gumbel_alphas
if
self
.
use_fixed_gumbel
else
gumbel_softmax
(
self
.
alphas
)
s0
=
fluid
.
layers
.
reshape
(
enc_input_0
,
[
-
1
,
1
,
enc_input_0
.
shape
[
1
],
enc_input_0
.
shape
[
2
]])
s1
=
fluid
.
layers
.
reshape
(
enc_input_1
,
[
-
1
,
1
,
enc_input_1
.
shape
[
1
],
enc_input_1
.
shape
[
2
]])
# (bs, 1, seq_len, hidden_size)
s0
=
self
.
stem
(
s0
)
s1
=
self
.
stem
(
s1
)
# (bs, n_channel, seq_len, 1)
if
self
.
use_fixed_gumbel
:
alphas
=
self
.
gumbel_alphas
else
:
alphas
=
gumbel_softmax
(
self
.
alphas
)
s0
=
s1
=
tmp
outputs
=
[]
for
i
in
range
(
self
.
_n_layer
):
for
i
in
range
(
self
.
_n_layer
):
s0
,
s1
=
s1
,
self
.
_cells
[
i
](
s0
,
s1
,
alphas
)
s0
,
s1
=
s1
,
self
.
_cells
[
i
](
s0
,
s1
,
alphas
)
tmp
=
self
.
bns
[
i
](
s1
)
tmp
=
self
.
pool2d_avg
(
tmp
)
# (bs, n_channel, seq_len, 1)
# (bs, n_channel, seq_len, 1)
tmp
=
self
.
_bns
[
i
](
s1
)
tmp
=
self
.
pool2d_avg
(
tmp
)
tmp
=
fluid
.
layers
.
reshape
(
tmp
,
shape
=
[
-
1
,
0
])
tmp
=
fluid
.
layers
.
reshape
(
tmp
,
shape
=
[
-
1
,
0
])
tmp
=
self
.
outs
[
i
](
tmp
)
tmp
=
self
.
_outs
[
i
](
tmp
)
outputs
.
append
(
tmp
)
enc_outputs
.
append
(
tmp
)
return
outputs
return
enc_outputs
paddleslim/teachers/bert/cls.py
浏览文件 @
8c7465b2
...
@@ -58,7 +58,8 @@ class BERTClassifier(Layer):
...
@@ -58,7 +58,8 @@ class BERTClassifier(Layer):
num_labels
,
num_labels
,
task_name
=
"mnli"
,
task_name
=
"mnli"
,
model_path
=
None
,
model_path
=
None
,
use_cuda
=
True
):
use_cuda
=
True
,
return_pooled_out
=
True
):
super
(
BERTClassifier
,
self
).
__init__
()
super
(
BERTClassifier
,
self
).
__init__
()
self
.
task_name
=
task_name
.
lower
()
self
.
task_name
=
task_name
.
lower
()
BERT_BASE_PATH
=
"./data/pretrained_models/uncased_L-12_H-768_A-12/"
BERT_BASE_PATH
=
"./data/pretrained_models/uncased_L-12_H-768_A-12/"
...
@@ -84,7 +85,7 @@ class BERTClassifier(Layer):
...
@@ -84,7 +85,7 @@ class BERTClassifier(Layer):
}
}
self
.
cls_model
=
ClsModelLayer
(
self
.
cls_model
=
ClsModelLayer
(
self
.
bert_config
,
num_labels
,
return_pooled_out
=
True
)
self
.
bert_config
,
num_labels
,
return_pooled_out
=
return_pooled_out
)
if
model_path
is
not
None
:
if
model_path
is
not
None
:
#restore the model
#restore the model
...
...
paddleslim/teachers/bert/model/cls.py
浏览文件 @
8c7465b2
...
@@ -46,6 +46,7 @@ class ClsModelLayer(Layer):
...
@@ -46,6 +46,7 @@ class ClsModelLayer(Layer):
self
.
use_fp16
=
use_fp16
self
.
use_fp16
=
use_fp16
self
.
loss_scaling
=
loss_scaling
self
.
loss_scaling
=
loss_scaling
self
.
n_layers
=
config
[
'num_hidden_layers'
]
self
.
n_layers
=
config
[
'num_hidden_layers'
]
self
.
return_pooled_out
=
return_pooled_out
self
.
bert_layer
=
BertModelLayer
(
self
.
bert_layer
=
BertModelLayer
(
config
=
self
.
config
,
return_pooled_out
=
True
,
use_fp16
=
self
.
use_fp16
)
config
=
self
.
config
,
return_pooled_out
=
True
,
use_fp16
=
self
.
use_fp16
)
...
@@ -79,11 +80,23 @@ class ClsModelLayer(Layer):
...
@@ -79,11 +80,23 @@ class ClsModelLayer(Layer):
enc_outputs
,
next_sent_feats
=
self
.
bert_layer
(
enc_outputs
,
next_sent_feats
=
self
.
bert_layer
(
src_ids
,
position_ids
,
sentence_ids
,
input_mask
)
src_ids
,
position_ids
,
sentence_ids
,
input_mask
)
if
not
self
.
return_pooled_out
:
cls_feat
=
fluid
.
layers
.
dropout
(
x
=
next_sent_feats
[
-
1
],
dropout_prob
=
0.1
,
dropout_implementation
=
"upscale_in_train"
)
logits
=
self
.
cls_fc
[
-
1
](
cls_feat
)
probs
=
fluid
.
layers
.
softmax
(
logits
)
num_seqs
=
fluid
.
layers
.
create_tensor
(
dtype
=
'int64'
)
accuracy
=
fluid
.
layers
.
accuracy
(
input
=
probs
,
label
=
labels
,
total
=
num_seqs
)
return
enc_outputs
,
logits
,
accuracy
,
num_seqs
logits
=
[]
logits
=
[]
losses
=
[]
losses
=
[]
accuracys
=
[]
accuracys
=
[]
for
next_sent_feat
,
fc
in
zip
(
next_sent_feats
,
self
.
cls_fc
):
for
next_sent_feat
,
fc
in
zip
(
next_sent_feats
,
self
.
cls_fc
):
cls_feat
=
fluid
.
layers
.
dropout
(
cls_feat
=
fluid
.
layers
.
dropout
(
x
=
next_sent_feat
,
x
=
next_sent_feat
,
dropout_prob
=
0.1
,
dropout_prob
=
0.1
,
...
...
paddleslim/teachers/bert/reader/cls.py
浏览文件 @
8c7465b2
...
@@ -16,6 +16,7 @@ import io
...
@@ -16,6 +16,7 @@ import io
import
os
import
os
import
types
import
types
import
csv
import
csv
import
random
import
numpy
as
np
import
numpy
as
np
from
.
import
tokenization
from
.
import
tokenization
from
.batching
import
prepare_batch_data
from
.batching
import
prepare_batch_data
...
@@ -139,6 +140,8 @@ class DataProcessor(object):
...
@@ -139,6 +140,8 @@ class DataProcessor(object):
epoch: int. Total epoches to generate data.
epoch: int. Total epoches to generate data.
shuffle: bool. Whether to shuffle examples.
shuffle: bool. Whether to shuffle examples.
"""
"""
search_examples
=
self
.
get_train_examples
(
self
.
data_dir
)
random
.
shuffle
(
search_examples
)
if
phase
==
'train'
:
if
phase
==
'train'
:
examples
=
self
.
get_train_examples
(
self
.
data_dir
)
examples
=
self
.
get_train_examples
(
self
.
data_dir
)
self
.
num_examples
[
'train'
]
=
len
(
examples
)
self
.
num_examples
[
'train'
]
=
len
(
examples
)
...
@@ -152,13 +155,13 @@ class DataProcessor(object):
...
@@ -152,13 +155,13 @@ class DataProcessor(object):
examples
=
self
.
get_test_examples
(
self
.
data_dir
)
examples
=
self
.
get_test_examples
(
self
.
data_dir
)
self
.
num_examples
[
'test'
]
=
len
(
examples
)
self
.
num_examples
[
'test'
]
=
len
(
examples
)
elif
phase
==
'search_train'
:
elif
phase
==
'search_train'
:
examples
=
self
.
get_train_examples
(
self
.
data_dir
)
#
examples = self.get_train_examples(self.data_dir)
self
.
num_examples
[
'search_train'
]
=
len
(
examples
)
/
2
self
.
num_examples
[
'search_train'
]
=
len
(
search_
examples
)
/
2
examples
=
examples
[:
self
.
num_examples
[
'search_train'
]]
examples
=
search_
examples
[:
self
.
num_examples
[
'search_train'
]]
elif
phase
==
'search_valid'
:
elif
phase
==
'search_valid'
:
examples
=
self
.
get_train_examples
(
self
.
data_dir
)
#
examples = self.get_train_examples(self.data_dir)
self
.
num_examples
[
'search_valid'
]
=
len
(
examples
)
/
2
self
.
num_examples
[
'search_valid'
]
=
len
(
search_
examples
)
/
2
examples
=
examples
[
self
.
num_examples
[
'search_train
'
]:]
examples
=
search_examples
[
self
.
num_examples
[
'search_valid
'
]:]
else
:
else
:
raise
ValueError
(
raise
ValueError
(
"Unknown phase, which should be in ['train', 'dev', 'test']."
)
"Unknown phase, which should be in ['train', 'dev', 'test']."
)
...
@@ -213,16 +216,53 @@ class DataProcessor(object):
...
@@ -213,16 +216,53 @@ class DataProcessor(object):
return_input_mask
=
True
,
return_input_mask
=
True
,
return_max_len
=
False
,
return_max_len
=
False
,
return_num_token
=
False
)
return_num_token
=
False
)
if
len
(
all_dev_batches
)
<
dev_count
:
if
len
(
all_dev_batches
)
<
dev_count
:
all_dev_batches
.
append
(
batch_data
)
all_dev_batches
.
append
(
batch_data
)
if
len
(
all_dev_batches
)
==
dev_count
:
if
len
(
all_dev_batches
)
==
dev_count
:
for
batch
in
all_dev_batches
:
for
batch
in
all_dev_batches
:
batch
=
self
.
split_seq_pair
(
batch
)
yield
batch
yield
batch
all_dev_batches
=
[]
all_dev_batches
=
[]
return
wrapper
return
wrapper
def
split_seq_pair
(
self
,
data_ids
):
src_ids
=
data_ids
[
0
]
sentence_ids
=
data_ids
[
2
]
ids
=
np
.
squeeze
(
src_ids
)
sids
=
np
.
squeeze
(
sentence_ids
)
batchsize
=
ids
.
shape
[
0
]
ids_0
=
ids
[((
sids
==
0
)
&
(
ids
!=
0
))]
seqlen_0
=
((
sids
==
0
)
&
(
ids
!=
0
)).
astype
(
np
.
int64
).
sum
(
1
)
y_0
=
np
.
concatenate
([
np
.
arange
(
s
)
for
s
in
seqlen_0
])
x_0
=
np
.
concatenate
([
np
.
ones
(
[
s
],
dtype
=
np
.
int64
)
*
i
for
i
,
s
in
enumerate
(
seqlen_0
)
])
ids0
=
np
.
zeros
([
batchsize
,
seqlen_0
.
max
()],
dtype
=
np
.
int64
)
ids0
[(
x_0
,
y_0
)]
=
ids_0
ids_1
=
ids
[(
sids
==
1
)
&
(
ids
!=
0
)]
seqlen_1
=
((
sids
==
1
)
&
(
ids
!=
0
)).
astype
(
np
.
int64
).
sum
(
1
)
y_1
=
np
.
concatenate
([
np
.
arange
(
s
)
for
s
in
seqlen_1
])
x_1
=
np
.
concatenate
([
np
.
ones
(
[
s
],
dtype
=
np
.
int64
)
*
i
for
i
,
s
in
enumerate
(
seqlen_1
)
])
ids1
=
np
.
zeros
([
batchsize
,
seqlen_1
.
max
()],
dtype
=
np
.
int64
)
ids1
[(
x_1
,
y_1
)]
=
ids_1
msl
=
max
(
seqlen_0
.
max
(),
seqlen_1
.
max
())
ids0
=
np
.
pad
(
ids0
,
[[
0
,
0
],
[
0
,
msl
-
seqlen_0
.
max
()]],
mode
=
'constant'
)
ids1
=
np
.
pad
(
ids1
,
[[
0
,
0
],
[
0
,
msl
-
seqlen_1
.
max
()]],
mode
=
'constant'
)
return
data_ids
+
[
ids0
,
ids1
]
class
InputExample
(
object
):
class
InputExample
(
object
):
"""A single training/test example for simple sequence classification."""
"""A single training/test example for simple sequence classification."""
...
...
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