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6c072366
编写于
1月 11, 2018
作者:
H
Helin Wang
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电子邮件补丁
差异文件
Add distributed label semantic role book chapter
上级
020630b7
变更
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1 changed file
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python/paddle/v2/fluid/tests/book_distribute/test_dist_label_semantic_roles.py
...d/tests/book_distribute/test_dist_label_semantic_roles.py
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python/paddle/v2/fluid/tests/book_distribute/test_dist_label_semantic_roles.py
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6c072366
import
math
import
numpy
as
np
import
paddle.v2
as
paddle
import
paddle.v2.dataset.conll05
as
conll05
import
paddle.v2.fluid
as
fluid
import
time
import
os
word_dict
,
verb_dict
,
label_dict
=
conll05
.
get_dict
()
word_dict_len
=
len
(
word_dict
)
label_dict_len
=
len
(
label_dict
)
pred_len
=
len
(
verb_dict
)
mark_dict_len
=
2
word_dim
=
32
mark_dim
=
5
hidden_dim
=
512
depth
=
8
mix_hidden_lr
=
1e-3
IS_SPARSE
=
True
PASS_NUM
=
10
BATCH_SIZE
=
20
embedding_name
=
'emb'
def
load_parameter
(
file_name
,
h
,
w
):
with
open
(
file_name
,
'rb'
)
as
f
:
f
.
read
(
16
)
# skip header.
return
np
.
fromfile
(
f
,
dtype
=
np
.
float32
).
reshape
(
h
,
w
)
def
db_lstm
(
word
,
predicate
,
ctx_n2
,
ctx_n1
,
ctx_0
,
ctx_p1
,
ctx_p2
,
mark
,
**
ignored
):
# 8 features
predicate_embedding
=
fluid
.
layers
.
embedding
(
input
=
predicate
,
size
=
[
pred_len
,
word_dim
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
'vemb'
)
mark_embedding
=
fluid
.
layers
.
embedding
(
input
=
mark
,
size
=
[
mark_dict_len
,
mark_dim
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
)
word_input
=
[
word
,
ctx_n2
,
ctx_n1
,
ctx_0
,
ctx_p1
,
ctx_p2
]
emb_layers
=
[
fluid
.
layers
.
embedding
(
size
=
[
word_dict_len
,
word_dim
],
input
=
x
,
param_attr
=
fluid
.
ParamAttr
(
name
=
embedding_name
,
trainable
=
False
))
for
x
in
word_input
]
emb_layers
.
append
(
predicate_embedding
)
emb_layers
.
append
(
mark_embedding
)
hidden_0_layers
=
[
fluid
.
layers
.
fc
(
input
=
emb
,
size
=
hidden_dim
)
for
emb
in
emb_layers
]
hidden_0
=
fluid
.
layers
.
sums
(
input
=
hidden_0_layers
)
lstm_0
=
fluid
.
layers
.
dynamic_lstm
(
input
=
hidden_0
,
size
=
hidden_dim
,
candidate_activation
=
'relu'
,
gate_activation
=
'sigmoid'
,
cell_activation
=
'sigmoid'
)
# stack L-LSTM and R-LSTM with direct edges
input_tmp
=
[
hidden_0
,
lstm_0
]
for
i
in
range
(
1
,
depth
):
mix_hidden
=
fluid
.
layers
.
sums
(
input
=
[
fluid
.
layers
.
fc
(
input
=
input_tmp
[
0
],
size
=
hidden_dim
),
fluid
.
layers
.
fc
(
input
=
input_tmp
[
1
],
size
=
hidden_dim
)
])
lstm
=
fluid
.
layers
.
dynamic_lstm
(
input
=
mix_hidden
,
size
=
hidden_dim
,
candidate_activation
=
'relu'
,
gate_activation
=
'sigmoid'
,
cell_activation
=
'sigmoid'
,
is_reverse
=
((
i
%
2
)
==
1
))
input_tmp
=
[
mix_hidden
,
lstm
]
feature_out
=
fluid
.
layers
.
sums
(
input
=
[
fluid
.
layers
.
fc
(
input
=
input_tmp
[
0
],
size
=
label_dict_len
),
fluid
.
layers
.
fc
(
input
=
input_tmp
[
1
],
size
=
label_dict_len
)
])
return
feature_out
def
to_lodtensor
(
data
,
place
):
seq_lens
=
[
len
(
seq
)
for
seq
in
data
]
cur_len
=
0
lod
=
[
cur_len
]
for
l
in
seq_lens
:
cur_len
+=
l
lod
.
append
(
cur_len
)
flattened_data
=
np
.
concatenate
(
data
,
axis
=
0
).
astype
(
"int64"
)
flattened_data
=
flattened_data
.
reshape
([
len
(
flattened_data
),
1
])
res
=
fluid
.
LoDTensor
()
res
.
set
(
flattened_data
,
place
)
res
.
set_lod
([
lod
])
return
res
def
main
():
# define network topology
word
=
fluid
.
layers
.
data
(
name
=
'word_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
predicate
=
fluid
.
layers
.
data
(
name
=
'verb_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
ctx_n2
=
fluid
.
layers
.
data
(
name
=
'ctx_n2_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
ctx_n1
=
fluid
.
layers
.
data
(
name
=
'ctx_n1_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
ctx_0
=
fluid
.
layers
.
data
(
name
=
'ctx_0_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
ctx_p1
=
fluid
.
layers
.
data
(
name
=
'ctx_p1_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
ctx_p2
=
fluid
.
layers
.
data
(
name
=
'ctx_p2_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
mark
=
fluid
.
layers
.
data
(
name
=
'mark_data'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
feature_out
=
db_lstm
(
**
locals
())
target
=
fluid
.
layers
.
data
(
name
=
'target'
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
crf_cost
=
fluid
.
layers
.
linear_chain_crf
(
input
=
feature_out
,
label
=
target
,
param_attr
=
fluid
.
ParamAttr
(
name
=
'crfw'
,
learning_rate
=
mix_hidden_lr
))
avg_cost
=
fluid
.
layers
.
mean
(
x
=
crf_cost
)
# TODO(qiao)
# check other optimizers and check why out will be NAN
sgd_optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.0001
)
optimize_ops
,
params_grads
=
sgd_optimizer
.
minimize
(
avg_cost
)
# TODO(qiao)
# add dependency track and move this config before optimizer
crf_decode
=
fluid
.
layers
.
crf_decoding
(
input
=
feature_out
,
param_attr
=
fluid
.
ParamAttr
(
name
=
'crfw'
))
chunk_evaluator
=
fluid
.
evaluator
.
ChunkEvaluator
(
input
=
crf_decode
,
label
=
target
,
chunk_scheme
=
"IOB"
,
num_chunk_types
=
int
(
math
.
ceil
((
label_dict_len
-
1
)
/
2.0
)))
train_data
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
conll05
.
test
(),
buf_size
=
8192
),
batch_size
=
BATCH_SIZE
)
place
=
fluid
.
CPUPlace
()
feeder
=
fluid
.
DataFeeder
(
feed_list
=
[
word
,
ctx_n2
,
ctx_n1
,
ctx_0
,
ctx_p1
,
ctx_p2
,
predicate
,
mark
,
target
],
place
=
place
)
exe
=
fluid
.
Executor
(
place
)
t
=
fluid
.
DistributeTranspiler
()
pserver_endpoints
=
os
.
getenv
(
"PSERVERS"
)
# server endpoint for current node
current_endpoint
=
os
.
getenv
(
"SERVER_ENDPOINT"
)
# run as trainer or parameter server
training_role
=
os
.
getenv
(
"TRAINING_ROLE"
,
"TRAINER"
)
# get the training role: trainer/pserver
t
.
transpile
(
optimize_ops
,
params_grads
,
pservers
=
pserver_endpoints
,
trainers
=
2
)
if
training_role
==
"PSERVER"
:
if
not
current_endpoint
:
print
(
"need env SERVER_ENDPOINT"
)
exit
(
1
)
pserver_prog
=
t
.
get_pserver_program
(
current_endpoint
,
optimize_ops
)
exe
.
run
(
fluid
.
default_startup_program
())
exe
.
run
(
pserver_prog
)
elif
training_role
==
"TRAINER"
:
trainer_prog
=
t
.
get_trainer_program
()
start_time
=
time
.
time
()
batch_id
=
0
exe
.
run
(
fluid
.
default_startup_program
())
embedding_param
=
fluid
.
global_scope
().
find_var
(
embedding_name
).
get_tensor
()
embedding_param
.
set
(
load_parameter
(
conll05
.
get_embedding
(),
word_dict_len
,
word_dim
),
place
)
for
pass_id
in
xrange
(
PASS_NUM
):
chunk_evaluator
.
reset
(
exe
)
for
data
in
train_data
():
cost
,
precision
,
recall
,
f1_score
=
exe
.
run
(
trainer_prog
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
]
+
chunk_evaluator
.
metrics
)
pass_precision
,
pass_recall
,
pass_f1_score
=
chunk_evaluator
.
eval
(
exe
)
if
batch_id
%
10
==
0
:
print
(
"avg_cost:"
+
str
(
cost
)
+
" precision:"
+
str
(
precision
)
+
" recall:"
+
str
(
recall
)
+
" f1_score:"
+
str
(
f1_score
)
+
" pass_precision:"
+
str
(
pass_precision
)
+
" pass_recall:"
+
str
(
pass_recall
)
+
" pass_f1_score:"
+
str
(
pass_f1_score
))
if
batch_id
!=
0
:
print
(
"second per batch: "
+
str
((
time
.
time
(
)
-
start_time
)
/
batch_id
))
batch_id
=
batch_id
+
1
if
__name__
==
'__main__'
:
main
()
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