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3ff0613a
编写于
7月 01, 2020
作者:
B
baiyfbupt
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
refine reader
上级
cc17334b
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
159 addition
and
94 deletion
+159
-94
demo/bert/train_cell_base.py
demo/bert/train_cell_base.py
+107
-41
paddleslim/nas/darts/search_space/conv_bert/cls.py
paddleslim/nas/darts/search_space/conv_bert/cls.py
+5
-5
paddleslim/nas/darts/search_space/conv_bert/model/bert.py
paddleslim/nas/darts/search_space/conv_bert/model/bert.py
+4
-41
paddleslim/nas/darts/search_space/conv_bert/model/transformer_encoder.py
...darts/search_space/conv_bert/model/transformer_encoder.py
+6
-7
paddleslim/teachers/bert/reader/cls.py
paddleslim/teachers/bert/reader/cls.py
+37
-0
未找到文件。
demo/bert/train_cell_base.py
浏览文件 @
3ff0613a
...
...
@@ -3,6 +3,10 @@ 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
...
...
@@ -17,6 +21,35 @@ def count_parameters_in_MB(all_params):
return
parameters_number
/
1e6
def
preprocess_data
(
data_generator
,
data_nums
,
phase
,
cached_data
):
t
=
tqdm
(
total
=
data_nums
)
data_list
=
[]
for
data
in
tqdm
(
data_generator
()):
# data_var = []
# for d in data:
# tmp = fluid.core.LoDTensor()
# tmp.set(d, fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id))
# data_var.append(tmp)
data_list
.
append
(
data
)
t
.
update
(
data
[
0
].
shape
[
0
])
t
.
close
()
logger
.
info
(
"Saving {} data to {}"
.
format
(
phase
,
cached_data
+
phase
))
f
=
open
(
cached_data
+
phase
,
'wb'
)
pickle
.
dump
(
data_list
,
f
)
f
.
close
()
return
data_list
def
generator_reader
(
data_list
):
def
wrapper
():
for
d
in
data_list
:
yield
d
return
wrapper
def
train_one_epoch
(
model
,
train_loader
,
valid_loader
,
optimizer
,
arch_optimizer
,
epoch
,
use_data_parallel
,
log_freq
):
total_losses
=
AvgrageMeter
()
...
...
@@ -31,6 +64,8 @@ def train_one_epoch(model, train_loader, valid_loader, optimizer,
#for train_data in train_loader():
batch_size
=
train_data
[
0
].
shape
[
0
]
# make sure arch on every gpu is same
np
.
random
.
seed
(
step_id
*
2
)
try
:
total_loss
,
acc
,
ce_loss
,
kd_loss
,
_
=
model
.
_layers
.
loss
(
train_data
,
epoch
)
...
...
@@ -51,6 +86,8 @@ def train_one_epoch(model, train_loader, valid_loader, optimizer,
ce_losses
.
update
(
ce_loss
.
numpy
(),
batch_size
)
kd_losses
.
update
(
kd_loss
.
numpy
(),
batch_size
)
# make sure arch on every gpu is same
np
.
random
.
seed
(
step_id
*
2
+
1
)
try
:
arch_loss
,
_
,
_
,
_
,
arch_logits
=
model
.
_layers
.
loss
(
valid_data
,
epoch
)
...
...
@@ -95,29 +132,27 @@ def valid_one_epoch(model, valid_loader, epoch, log_freq):
ce_losses
.
update
(
ce_loss
.
numpy
(),
batch_size
)
accs
.
update
(
acc
.
numpy
(),
batch_size
)
# if step_id % log_freq == 0:
# logger.info("Valid Epoch {}, Step {}, ce_loss {:.6f}; acc: {:.6f};".
# format(epoch, step_id, ce_losses.avg[0], accs.avg[0]))
step_id
+=
1
return
ce_losses
.
avg
[
0
],
accs
.
avg
[
0
]
def
main
():
# whether use multi-gpus
use_data_parallel
=
Tru
e
use_data_parallel
=
Fals
e
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"
bert_config_path
=
BERT_BASE_PATH
+
"/bert_config.json"
vocab_path
=
BERT_BASE_PATH
+
"/vocab.txt"
data_dir
=
"./data/glue_data/MNLI/"
cached_data
=
"./data/glue_data/MNLI/cached_data_"
teacher_model_dir
=
"./data/teacher_model/steps_23000"
do_lower_case
=
True
#num_samples = 392702
num_samples
=
8016987
max_seq_len
=
128
batch_size
=
64
# any modify of vocab/do_lower_case/max_seq_len requires update cached data
batch_size
=
128
hidden_size
=
768
emb_size
=
768
max_layer
=
8
...
...
@@ -157,13 +192,11 @@ def main():
[
a
.
name
for
a
in
model
.
teacher
.
parameters
()]):
model_parameters
.
append
(
p
)
#clip = fluid.clip.GradientClipByGlobalNorm(clip_norm=5.0)
optimizer
=
fluid
.
optimizer
.
MomentumOptimizer
(
learning_rate
,
0.9
,
regularization
=
fluid
.
regularizer
.
L2DecayRegularizer
(
3e-4
),
parameter_list
=
model_parameters
)
# grad_clip=clip)
arch_optimizer
=
fluid
.
optimizer
.
Adam
(
3e-4
,
...
...
@@ -172,29 +205,69 @@ def main():
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
)
print
(
"train_data nums:"
,
processor
.
get_num_examples
(
train_phase
))
print
(
"valid_data nums:"
,
processor
.
get_num_examples
(
val_phase
))
print
(
"dev_data nums:"
,
processor
.
get_num_examples
(
"dev"
))
if
os
.
path
.
exists
(
cached_data
+
"train"
)
and
os
.
path
.
exists
(
cached_data
+
"valid"
)
+
os
.
path
.
exists
(
cached_data
+
"dev"
):
f
=
open
(
cached_data
+
"train"
,
'rb'
)
logger
.
info
(
"loading preprocessed train data from {}"
.
format
(
cached_data
+
"train"
))
train_data_list
=
pickle
.
load
(
f
)
f
.
close
()
f
=
open
(
cached_data
+
"valid"
,
'rb'
)
logger
.
info
(
"loading preprocessed valid data from {}"
.
format
(
cached_data
+
"valid"
))
valid_data_list
=
pickle
.
load
(
f
)
f
.
close
()
f
=
open
(
cached_data
+
"dev"
,
'rb'
)
logger
.
info
(
"loading preprocessed dev data from {}"
.
format
(
cached_data
+
"dev"
))
dev_data_list
=
pickle
.
load
(
f
)
f
.
close
()
else
:
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
)
train_data_nums
=
processor
.
get_num_examples
(
train_phase
)
valid_data_nums
=
processor
.
get_num_examples
(
val_phase
)
dev_data_nums
=
processor
.
get_num_examples
(
"dev"
)
logger
.
info
(
"Preprocessing train data"
)
train_data_list
=
preprocess_data
(
train_reader
,
train_data_nums
,
"train"
,
cached_data
)
logger
.
info
(
"Preprocessing valid data"
)
valid_data_list
=
preprocess_data
(
valid_reader
,
valid_data_nums
,
"valid"
,
cached_data
)
logger
.
info
(
"Preprocessing dev data"
)
dev_data_list
=
preprocess_data
(
dev_reader
,
dev_data_nums
,
"dev"
,
cached_data
)
train_reader
=
generator_reader
(
train_data_list
)
valid_reader
=
generator_reader
(
valid_data_list
)
dev_reader
=
generator_reader
(
dev_data_list
)
if
use_data_parallel
:
train_reader
=
fluid
.
contrib
.
reader
.
distributed_batch_reader
(
...
...
@@ -202,25 +275,18 @@ def main():
valid_reader
=
fluid
.
contrib
.
reader
.
distributed_batch_reader
(
valid_reader
)
dev_reader
=
processor
.
data_generator
(
batch_size
=
batch_size
,
phase
=
"dev"
,
epoch
=
1
,
dev_count
=
1
,
shuffle
=
False
)
train_loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
capacity
=
512
,
capacity
=
128
,
use_double_buffer
=
True
,
iterable
=
True
,
return_list
=
True
)
valid_loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
capacity
=
512
,
capacity
=
128
,
use_double_buffer
=
True
,
iterable
=
True
,
return_list
=
True
)
dev_loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
capacity
=
512
,
capacity
=
128
,
use_double_buffer
=
True
,
iterable
=
True
,
return_list
=
True
)
...
...
paddleslim/nas/darts/search_space/conv_bert/cls.py
浏览文件 @
3ff0613a
...
...
@@ -114,13 +114,13 @@ class AdaBERTClassifier(Layer):
return
logits
def
loss
(
self
,
data_ids
,
epoch
):
src_ids
=
data_ids
[
0
]
position_ids
=
data_ids
[
1
]
sentence_ids
=
data_ids
[
2
]
input_mask
=
data_ids
[
3
]
#
src_ids = data_ids[0]
#
position_ids = data_ids[1]
#
sentence_ids = data_ids[2]
#
input_mask = data_ids[3]
labels
=
data_ids
[
4
]
s_logits
=
self
.
student
(
src_ids
,
position_ids
,
sentence
_ids
,
epoch
)
s_logits
=
self
.
student
(
data
_ids
,
epoch
)
t_enc_outputs
,
t_logits
,
t_losses
,
t_accs
,
_
=
self
.
teacher
(
data_ids
)
...
...
paddleslim/nas/darts/search_space/conv_bert/model/bert.py
浏览文件 @
3ff0613a
...
...
@@ -108,48 +108,12 @@ class BertModelLayer(Layer):
def
arch_parameters
(
self
):
return
[
self
.
_encoder
.
alphas
]
#, self._encoder.k]
def
forward
(
self
,
src_ids
,
position_ids
,
sentence_ids
,
epoch
,
flops
=
[],
model_size
=
[]):
def
forward
(
self
,
data_ids
,
epoch
):
"""
forward
"""
ids
=
np
.
squeeze
(
src_ids
.
numpy
())
sids
=
np
.
squeeze
(
sentence_ids
.
numpy
())
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
)
ids0
=
data_ids
[
5
]
ids1
=
data_ids
[
6
]
src_emb_0
=
self
.
_src_emb
(
ids0
)
src_emb_1
=
self
.
_src_emb
(
ids1
)
...
...
@@ -157,7 +121,6 @@ class BertModelLayer(Layer):
emb_out_1
=
self
.
_emb_fac
(
src_emb_1
)
# (bs, seq_len, hidden_size)
enc_outputs
=
self
.
_encoder
(
emb_out_0
,
emb_out_1
,
epoch
,
flops
=
flops
,
model_size
=
model_size
)
enc_outputs
=
self
.
_encoder
(
emb_out_0
,
emb_out_1
,
epoch
)
return
enc_outputs
paddleslim/nas/darts/search_space/conv_bert/model/transformer_encoder.py
浏览文件 @
3ff0613a
...
...
@@ -70,14 +70,13 @@ class MixedOp(fluid.dygraph.Layer):
self
.
_ops
=
fluid
.
dygraph
.
LayerList
(
ops
)
def
forward
(
self
,
x
,
weights
,
index
):
out
=
fluid
.
layers
.
sums
(
[
weights
[
i
]
*
op
(
x
)
for
i
,
op
in
enumerate
(
self
.
_ops
)])
return
out
#
out = fluid.layers.sums(
#
[weights[i] * op(x) for i, op in enumerate(self._ops)])
#
return out
# causebug in multi-gpus
#for i in range(len(self._ops)):
# if weights[i].numpy() != 0:
# return self._ops[i](x) * weights[i]
for
i
in
range
(
len
(
self
.
_ops
)):
if
weights
[
i
].
numpy
()
!=
0
:
return
self
.
_ops
[
i
](
x
)
*
weights
[
i
]
def
gumbel_softmax
(
logits
,
epoch
,
temperature
=
1.0
,
hard
=
True
,
eps
=
1e-10
):
...
...
paddleslim/teachers/bert/reader/cls.py
浏览文件 @
3ff0613a
...
...
@@ -209,16 +209,53 @@ class DataProcessor(object):
return_input_mask
=
True
,
return_max_len
=
False
,
return_num_token
=
False
)
if
len
(
all_dev_batches
)
<
dev_count
:
all_dev_batches
.
append
(
batch_data
)
if
len
(
all_dev_batches
)
==
dev_count
:
for
batch
in
all_dev_batches
:
batch
=
self
.
split_seq_pair
(
batch
)
yield
batch
all_dev_batches
=
[]
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
):
"""A single training/test example for simple sequence classification."""
...
...
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