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ea8013e4
编写于
4月 09, 2017
作者:
Q
qiaolongfei
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
can run test.py for generating
上级
f7be384e
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
236 addition
and
51 deletion
+236
-51
demo/seqToseq/api_train_v2.py
demo/seqToseq/api_train_v2.py
+62
-25
demo/seqToseq/seqToseq_net.py
demo/seqToseq/seqToseq_net.py
+6
-5
demo/seqToseq/translation/train.conf
demo/seqToseq/translation/train.conf
+2
-1
python/paddle/v2/config_base.py
python/paddle/v2/config_base.py
+4
-0
python/paddle/v2/layer.py
python/paddle/v2/layer.py
+29
-20
python/paddle/v2/layers/__init__.py
python/paddle/v2/layers/__init__.py
+1
-0
python/paddle/v2/layers/beam_search.py
python/paddle/v2/layers/beam_search.py
+132
-0
未找到文件。
demo/seqToseq/api_train_v2.py
浏览文件 @
ea8013e4
import
sys
import
sys
import
paddle.v2
as
paddle
import
paddle.v2
as
paddle
import
paddle.v2.layer.beam_search
as
beam_search
def
seqToseq_net
(
source_dict_dim
,
target_dict_dim
):
def
seqToseq_net
(
source_dict_dim
,
target_dict_dim
,
is_generating
):
### Network Architecture
### Network Architecture
word_vector_dim
=
512
# dimension of word vector
word_vector_dim
=
512
# dimension of word vector
decoder_size
=
512
# dimension of hidden unit in GRU Decoder network
decoder_size
=
512
# dimension of hidden unit in GRU Decoder network
encoder_size
=
512
# dimension of hidden unit in GRU Encoder network
encoder_size
=
512
# dimension of hidden unit in GRU Encoder network
beam_size
=
3
max_length
=
250
#### Encoder
#### Encoder
src_word_id
=
paddle
.
layer
.
data
(
src_word_id
=
paddle
.
layer
.
data
(
name
=
'source_language_word'
,
name
=
'source_language_word'
,
...
@@ -67,30 +71,63 @@ def seqToseq_net(source_dict_dim, target_dict_dim):
...
@@ -67,30 +71,63 @@ def seqToseq_net(source_dict_dim, target_dict_dim):
group_input2
=
paddle
.
layer
.
StaticInputV2
(
input
=
encoded_proj
,
is_seq
=
True
)
group_input2
=
paddle
.
layer
.
StaticInputV2
(
input
=
encoded_proj
,
is_seq
=
True
)
group_inputs
=
[
group_input1
,
group_input2
]
group_inputs
=
[
group_input1
,
group_input2
]
trg_embedding
=
paddle
.
layer
.
embedding
(
if
not
is_generating
:
input
=
paddle
.
layer
.
data
(
trg_embedding
=
paddle
.
layer
.
embedding
(
name
=
'target_language_word'
,
input
=
paddle
.
layer
.
data
(
type
=
paddle
.
data_type
.
integer_value_sequence
(
target_dict_dim
)),
name
=
'target_language_word'
,
size
=
word_vector_dim
,
type
=
paddle
.
data_type
.
integer_value_sequence
(
target_dict_dim
)),
param_attr
=
paddle
.
attr
.
ParamAttr
(
name
=
'_target_language_embedding'
))
size
=
word_vector_dim
,
group_inputs
.
append
(
trg_embedding
)
param_attr
=
paddle
.
attr
.
ParamAttr
(
name
=
'_target_language_embedding'
))
group_inputs
.
append
(
trg_embedding
)
# For decoder equipped with attention mechanism, in training,
# target embeding (the groudtruth) is the data input,
# For decoder equipped with attention mechanism, in training,
# while encoded source sequence is accessed to as an unbounded memory.
# target embeding (the groudtruth) is the data input,
# Here, the StaticInput defines a read-only memory
# while encoded source sequence is accessed to as an unbounded memory.
# for the recurrent_group.
# Here, the StaticInput defines a read-only memory
decoder
=
paddle
.
layer
.
recurrent_group
(
# for the recurrent_group.
name
=
decoder_group_name
,
decoder
=
paddle
.
layer
.
recurrent_group
(
step
=
gru_decoder_with_attention
,
name
=
decoder_group_name
,
input
=
group_inputs
)
step
=
gru_decoder_with_attention
,
input
=
group_inputs
)
lbl
=
paddle
.
layer
.
data
(
name
=
'target_language_next_word'
,
lbl
=
paddle
.
layer
.
data
(
type
=
paddle
.
data_type
.
integer_value_sequence
(
target_dict_dim
))
name
=
'target_language_next_word'
,
cost
=
paddle
.
layer
.
classification_cost
(
input
=
decoder
,
label
=
lbl
)
type
=
paddle
.
data_type
.
integer_value_sequence
(
target_dict_dim
))
cost
=
paddle
.
layer
.
classification_cost
(
input
=
decoder
,
label
=
lbl
)
return
cost
return
cost
else
:
# In generation, the decoder predicts a next target word based on
# the encoded source sequence and the last generated target word.
# The encoded source sequence (encoder's output) must be specified by
# StaticInput, which is a read-only memory.
# Embedding of the last generated word is automatically gotten by
# GeneratedInputs, which is initialized by a start mark, such as <s>,
# and must be included in generation.
trg_embedding
=
beam_search
.
GeneratedInputV2
(
size
=
target_dict_dim
,
embedding_name
=
'_target_language_embedding'
,
embedding_size
=
word_vector_dim
)
group_inputs
.
append
(
trg_embedding
)
beam_gen
=
beam_search
.
beam_search
(
name
=
decoder_group_name
,
step
=
gru_decoder_with_attention
,
input
=
group_inputs
,
bos_id
=
0
,
eos_id
=
1
,
beam_size
=
beam_size
,
max_length
=
max_length
)
#
# seqtext_printer_evaluator(
# input=beam_gen,
# id_input=data_layer(
# name="sent_id", size=1),
# dict_file=trg_dict_path,
# result_file=gen_trans_file)
return
beam_gen
def
main
():
def
main
():
...
...
demo/seqToseq/seqToseq_net.py
浏览文件 @
ea8013e4
...
@@ -81,8 +81,10 @@ def gru_encoder_decoder(data_conf,
...
@@ -81,8 +81,10 @@ def gru_encoder_decoder(data_conf,
"""
"""
for
k
,
v
in
data_conf
.
iteritems
():
for
k
,
v
in
data_conf
.
iteritems
():
globals
()[
k
]
=
v
globals
()[
k
]
=
v
source_dict_dim
=
len
(
open
(
src_dict_path
,
"r"
).
readlines
())
#source_dict_dim = len(open(src_dict_path, "r").readlines())
target_dict_dim
=
len
(
open
(
trg_dict_path
,
"r"
).
readlines
())
#target_dict_dim = len(open(trg_dict_path, "r").readlines())
source_dict_dim
=
1000
target_dict_dim
=
2000
gen_trans_file
=
gen_result
gen_trans_file
=
gen_result
src_word_id
=
data_layer
(
name
=
'source_language_word'
,
size
=
source_dict_dim
)
src_word_id
=
data_layer
(
name
=
'source_language_word'
,
size
=
source_dict_dim
)
...
@@ -131,9 +133,8 @@ def gru_encoder_decoder(data_conf,
...
@@ -131,9 +133,8 @@ def gru_encoder_decoder(data_conf,
decoder_group_name
=
"decoder_group"
decoder_group_name
=
"decoder_group"
group_inputs
=
[
group_inputs
=
[
StaticInput
(
StaticInput
(
input
=
encoded_vector
,
is_seq
=
True
),
input
=
encoded_vector
,
is_seq
=
True
),
StaticInput
(
StaticInput
(
input
=
encoded_proj
,
is_seq
=
True
)
input
=
encoded_proj
,
is_seq
=
True
)
]
]
if
not
is_generating
:
if
not
is_generating
:
...
...
demo/seqToseq/translation/train.conf
浏览文件 @
ea8013e4
...
@@ -19,7 +19,8 @@ sys.path.append("..")
...
@@ -19,7 +19,8 @@ sys.path.append("..")
from
seqToseq_net
import
*
from
seqToseq_net
import
*
# whether this config is used for generating
# whether this config is used for generating
is_generating
=
False
#is_generating = False
is_generating
=
True
### Data Definiation
### Data Definiation
data_dir
=
"./data/pre-wmt14"
data_dir
=
"./data/pre-wmt14"
...
...
python/paddle/v2/config_base.py
浏览文件 @
ea8013e4
...
@@ -76,6 +76,10 @@ class Layer(object):
...
@@ -76,6 +76,10 @@ class Layer(object):
"""
"""
function to set proto attribute
function to set proto attribute
"""
"""
print
"======"
# print self.name
print
self
.
__parent_layers__
# print self.__context__
self
.
__context__
=
context
self
.
__context__
=
context
# short cut if myself is parsed before.
# short cut if myself is parsed before.
...
...
python/paddle/v2/layer.py
浏览文件 @
ea8013e4
...
@@ -135,6 +135,10 @@ class WithExtraParent(Layer):
...
@@ -135,6 +135,10 @@ class WithExtraParent(Layer):
"""
"""
function to set proto attribute
function to set proto attribute
"""
"""
print
"*************"
# print context
print
self
.
name
print
self
.
__extra_parent__
kwargs
=
dict
()
kwargs
=
dict
()
for
p
in
self
.
__extra_parent__
:
for
p
in
self
.
__extra_parent__
:
p
.
to_proto
(
context
=
context
)
p
.
to_proto
(
context
=
context
)
...
@@ -162,11 +166,12 @@ class WithExtraParent(Layer):
...
@@ -162,11 +166,12 @@ class WithExtraParent(Layer):
class
MemoryV2
(
WithExtraParent
):
class
MemoryV2
(
WithExtraParent
):
def
__init__
(
self
,
name
,
**
kwargs
):
def
__init__
(
self
,
name
,
extra_input
=
None
,
**
kwargs
):
self
.
name
=
name
self
.
name
=
name
super
(
MemoryV2
,
self
).
__init__
(
name
=
name
,
parent_layers
=
dict
())
super
(
MemoryV2
,
self
).
__init__
(
name
=
name
,
parent_layers
=
dict
())
self
.
__kwargs__
=
kwargs
self
.
__kwargs__
=
kwargs
self
.
__boot_layer_name__
=
None
self
.
__boot_layer_name__
=
None
if
'boot_layer'
in
kwargs
:
if
'boot_layer'
in
kwargs
:
begin_of_current_rnn
=
[]
begin_of_current_rnn
=
[]
# TODO(yuyang18): Fix inspect, it could be wrong when user invoke a
# TODO(yuyang18): Fix inspect, it could be wrong when user invoke a
...
@@ -223,22 +228,6 @@ class MemoryV2(WithExtraParent):
...
@@ -223,22 +228,6 @@ class MemoryV2(WithExtraParent):
return
True
return
True
class
LayerOutputV2
(
Layer
):
"""
LayerOutputV2 is used to store the result of LayerOutput in v1 api.
It will not store it's parents because layer_output has been parsed already.
"""
def
__init__
(
self
,
layer_output
):
assert
isinstance
(
layer_output
,
conf_helps
.
LayerOutput
)
self
.
layer_output
=
layer_output
super
(
LayerOutputV2
,
self
).
__init__
(
name
=
layer_output
.
name
,
parent_layers
=
dict
())
def
to_proto_impl
(
self
):
return
self
.
layer_output
class
StaticInputV2
(
object
):
class
StaticInputV2
(
object
):
def
__init__
(
self
,
input
,
is_seq
=
False
,
size
=
None
):
def
__init__
(
self
,
input
,
is_seq
=
False
,
size
=
None
):
assert
isinstance
(
input
,
LayerV2
)
assert
isinstance
(
input
,
LayerV2
)
...
@@ -330,10 +319,15 @@ def mixed(size=0,
...
@@ -330,10 +319,15 @@ def mixed(size=0,
class
RecurrentLayerInput
(
WithExtraParent
):
class
RecurrentLayerInput
(
WithExtraParent
):
def
__init__
(
self
,
recurrent_name
,
index
,
parent_layers
):
def
__init__
(
self
,
recurrent_name
,
index
,
parent_layers
):
assert
len
(
parent_layers
)
==
1
parents_len
=
len
(
parent_layers
)
self
.
__parents__
=
parent_layers
.
values
()[
0
]
assert
parents_len
<=
1
if
parents_len
==
0
:
self
.
__parents__
=
[]
else
:
self
.
__parents__
=
parent_layers
.
values
()[
0
]
name
=
self
.
__parents__
[
index
].
name
if
index
>=
0
else
None
super
(
RecurrentLayerInput
,
self
).
__init__
(
super
(
RecurrentLayerInput
,
self
).
__init__
(
name
=
self
.
__parents__
[
index
].
name
,
parent_layers
=
parent_layers
)
name
=
name
,
parent_layers
=
parent_layers
)
self
.
__recurrent_name__
=
recurrent_name
self
.
__recurrent_name__
=
recurrent_name
def
context_name
(
self
):
def
context_name
(
self
):
...
@@ -346,6 +340,10 @@ class RecurrentLayerInput(WithExtraParent):
...
@@ -346,6 +340,10 @@ class RecurrentLayerInput(WithExtraParent):
in_links
=
map
(
lambda
x
:
x
.
name
,
self
.
__parents__
))
in_links
=
map
(
lambda
x
:
x
.
name
,
self
.
__parents__
))
return
self
return
self
def
use_context_name
(
self
):
return
True
class
RecurrentLayerOutput
(
Layer
):
class
RecurrentLayerOutput
(
Layer
):
def
__init__
(
self
,
recurrent_name
,
index
,
parent_layers
):
def
__init__
(
self
,
recurrent_name
,
index
,
parent_layers
):
...
@@ -428,6 +426,9 @@ def recurrent_group(step, input, name=None):
...
@@ -428,6 +426,9 @@ def recurrent_group(step, input, name=None):
non_static_inputs
=
filter
(
lambda
x
:
not
isinstance
(
x
,
StaticInputV2
),
non_static_inputs
=
filter
(
lambda
x
:
not
isinstance
(
x
,
StaticInputV2
),
input
)
input
)
static_inputs
=
filter
(
lambda
x
:
isinstance
(
x
,
StaticInputV2
),
input
)
static_inputs
=
[
static_input
.
input
for
static_input
in
static_inputs
]
actual_input
=
[
actual_input
=
[
RecurrentLayerInput
(
RecurrentLayerInput
(
recurrent_name
=
name
,
recurrent_name
=
name
,
...
@@ -436,6 +437,13 @@ def recurrent_group(step, input, name=None):
...
@@ -436,6 +437,13 @@ def recurrent_group(step, input, name=None):
for
i
in
xrange
(
len
(
non_static_inputs
))
for
i
in
xrange
(
len
(
non_static_inputs
))
]
]
extra_input
=
None
if
len
(
non_static_inputs
)
==
0
:
extra_input
=
RecurrentLayerInput
(
recurrent_name
=
name
,
index
=-
1
,
parent_layers
=
{})
def
__real_step__
(
*
args
):
def
__real_step__
(
*
args
):
rnn_input
=
list
(
args
)
rnn_input
=
list
(
args
)
static_inputs
=
filter
(
lambda
x
:
isinstance
(
x
,
StaticInputV2
),
input
)
static_inputs
=
filter
(
lambda
x
:
isinstance
(
x
,
StaticInputV2
),
input
)
...
@@ -443,6 +451,7 @@ def recurrent_group(step, input, name=None):
...
@@ -443,6 +451,7 @@ def recurrent_group(step, input, name=None):
mem_name
=
"__%s_memory__"
%
static_input
.
input
.
name
mem_name
=
"__%s_memory__"
%
static_input
.
input
.
name
mem
=
memory
(
mem
=
memory
(
name
=
mem_name
,
name
=
mem_name
,
extra_input
=
extra_input
,
is_seq
=
static_input
.
is_seq
,
is_seq
=
static_input
.
is_seq
,
size
=
static_input
.
input
.
calculate_size
,
size
=
static_input
.
input
.
calculate_size
,
boot_layer
=
static_input
.
input
)
boot_layer
=
static_input
.
input
)
...
...
python/paddle/v2/layers/__init__.py
0 → 100644
浏览文件 @
ea8013e4
import
beam_search
\ No newline at end of file
python/paddle/v2/layers/beam_search.py
0 → 100644
浏览文件 @
ea8013e4
import
paddle.v2
as
paddle
from
paddle.v2.config_base
import
Layer
from
paddle.trainer_config_helpers.default_decorators
import
wrap_name_default
from
paddle.trainer_config_helpers.layers
import
RecurrentLayerGroupSetGenerator
,
Generator
class
BaseGeneratedInputV2
(
object
):
def
__init__
(
self
):
self
.
bos_id
=
None
self
.
eos_id
=
None
def
before_real_step
(
self
):
raise
NotImplementedError
()
def
after_real_step
(
self
,
*
args
):
raise
NotImplementedError
()
class
GeneratedInputV2
(
BaseGeneratedInputV2
):
def
__init__
(
self
,
size
,
embedding_name
,
embedding_size
):
super
(
GeneratedInputV2
,
self
).
__init__
()
self
.
size
=
size
self
.
embedding_name
=
embedding_name
self
.
embedding_size
=
embedding_size
def
after_real_step
(
self
,
input
):
return
paddle
.
layer
.
max_id
(
input
=
input
,
name
=
'__beam_search_predict__'
)
def
before_real_step
(
self
):
predict_id
=
paddle
.
layer
.
memory
(
name
=
'__beam_search_predict__'
,
size
=
self
.
size
,
boot_with_const_id
=
self
.
bos_id
)
trg_emb
=
paddle
.
layer
.
embedding
(
input
=
predict_id
,
size
=
self
.
embedding_size
,
param_attr
=
paddle
.
attr
.
ParamAttr
(
name
=
self
.
embedding_name
))
return
trg_emb
class
RecurrentLayerGroupSetGeneratorV2
(
Layer
):
def
__init__
(
self
,
eos_name
,
max_length
,
beam_size
,
num_results_per_sample
):
self
.
eos_name
=
eos_name
self
.
max_length
=
max_length
self
.
beam_size
=
beam_size
self
.
num_results_per_sample
=
num_results_per_sample
super
(
RecurrentLayerGroupSetGeneratorV2
,
self
).
__init__
(
name
=
eos_name
,
parent_layers
=
{})
def
to_proto_impl
(
self
,
**
kwargs
):
RecurrentLayerGroupSetGenerator
(
Generator
(
eos_layer_name
=
self
.
eos_name
,
max_num_frames
=
self
.
max_length
,
beam_size
=
self
.
beam_size
,
num_results_per_sample
=
self
.
num_results_per_sample
))
return
self
def
context_name
(
self
):
return
self
.
eos_name
+
".fake"
def
use_context_name
(
self
):
return
True
@
wrap_name_default
()
def
beam_search
(
step
,
input
,
bos_id
,
eos_id
,
beam_size
,
max_length
=
500
,
name
=
None
,
num_results_per_sample
=
None
):
if
num_results_per_sample
is
None
:
num_results_per_sample
=
beam_size
assert
num_results_per_sample
<=
beam_size
# logger.warning("num_results_per_sample should be less than beam_size")
if
isinstance
(
input
,
paddle
.
layer
.
StaticInputV2
)
or
isinstance
(
input
,
BaseGeneratedInputV2
):
input
=
[
input
]
generated_input_index
=
-
1
real_input
=
[]
for
i
,
each_input
in
enumerate
(
input
):
assert
isinstance
(
each_input
,
paddle
.
layer
.
StaticInputV2
)
or
isinstance
(
each_input
,
BaseGeneratedInputV2
)
if
isinstance
(
each_input
,
BaseGeneratedInputV2
):
assert
generated_input_index
==
-
1
generated_input_index
=
i
else
:
real_input
.
append
(
each_input
)
assert
generated_input_index
!=
-
1
gipt
=
input
[
generated_input_index
]
assert
isinstance
(
gipt
,
BaseGeneratedInputV2
)
gipt
.
bos_id
=
bos_id
gipt
.
eos_id
=
eos_id
def
__real_step__
(
*
args
):
eos_name
=
"__%s_eos_layer__"
%
name
generator
=
RecurrentLayerGroupSetGeneratorV2
(
eos_name
,
max_length
,
beam_size
,
num_results_per_sample
)
args
=
list
(
args
)
before_step_layer
=
gipt
.
before_real_step
()
before_step_layer
.
append_child
(
layer
=
generator
,
parent_names
=
[
before_step_layer
.
name
])
args
.
insert
(
generated_input_index
,
before_step_layer
)
predict
=
gipt
.
after_real_step
(
step
(
*
args
))
eos
=
paddle
.
layer
.
eos
(
input
=
predict
,
eos_id
=
eos_id
,
name
=
eos_name
)
predict
.
append_child
(
layer
=
eos
,
parent_names
=
[
predict
.
name
])
return
predict
# tmp = paddle.layer.recurrent_group(
# step=__real_step__,
# input=real_input,
# reverse=False,
# name=name,
# is_generating=True)
tmp
=
paddle
.
layer
.
recurrent_group
(
step
=
__real_step__
,
input
=
real_input
,
name
=
name
)
return
tmp
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