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体验新版 GitCode,发现更多精彩内容 >>
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07a8f0ef
编写于
4月 10, 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
refine code, remove beam_search.py
上级
bf6fd470
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
168 addition
and
162 deletion
+168
-162
demo/seqToseq/api_train_v2.py
demo/seqToseq/api_train_v2.py
+3
-3
python/paddle/v2/config_base.py
python/paddle/v2/config_base.py
+11
-11
python/paddle/v2/layer.py
python/paddle/v2/layer.py
+153
-13
python/paddle/v2/layers/__init__.py
python/paddle/v2/layers/__init__.py
+0
-1
python/paddle/v2/layers/beam_search.py
python/paddle/v2/layers/beam_search.py
+0
-132
python/setup.py.in
python/setup.py.in
+1
-2
未找到文件。
demo/seqToseq/api_train_v2.py
浏览文件 @
07a8f0ef
import
sys
import
paddle.v2
as
paddle
import
paddle.v2.layers.beam_search
as
beam_search
def
seqToseq_net
(
source_dict_dim
,
target_dict_dim
,
is_generating
):
...
...
@@ -106,13 +106,13 @@ def seqToseq_net(source_dict_dim, target_dict_dim, is_generating):
# GeneratedInputs, which is initialized by a start mark, such as <s>,
# and must be included in generation.
trg_embedding
=
beam_search
.
GeneratedInputV2
(
trg_embedding
=
paddle
.
layer
.
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
(
beam_gen
=
paddle
.
layer
.
beam_search
(
name
=
decoder_group_name
,
step
=
gru_decoder_with_attention
,
input
=
group_inputs
,
...
...
python/paddle/v2/config_base.py
浏览文件 @
07a8f0ef
...
...
@@ -87,19 +87,19 @@ class Layer(object):
"""
self
.
__context__
=
context
#
1.
short cut if this layer is parsed before.
#
STEP:
short cut if this layer is parsed before.
if
self
.
context_name
()
in
context
:
if
self
.
use_context_name
():
return
context
[
self
.
context_name
()]
else
:
return
context
[
self
.
name
]
#
2.
parse extra_parent that is not used by this layer but must
#
STEP:
parse extra_parent that is not used by this layer but must
# be parsed before this layer.
for
p
in
self
.
__extra_parent__
:
p
.
to_proto
(
context
=
context
)
#
3.
parse parent that is used by this layer, get the result and
#
STEP:
parse parent that is used by this layer, get the result and
# insert into kwargs of the next layer's to_proto_impl method.
kwargs
=
dict
()
for
layer_name
in
self
.
__parent_layers__
:
...
...
@@ -112,13 +112,13 @@ class Layer(object):
self
.
__parent_layers__
[
layer_name
])
kwargs
[
layer_name
]
=
v1_layer
#
4.
parse myself and add myself into context.
ret_val
=
self
.
to_proto_impl
(
context
=
context
,
**
kwargs
)
if
self
.
context_name
()
is
not
None
and
self
.
context_name
(
)
not
in
context
:
#
STEP:
parse myself and add myself into context.
ret_val
=
self
.
to_proto_impl
(
**
kwargs
)
if
self
.
context_name
()
is
not
None
\
and
self
.
context_name
(
)
not
in
context
:
context
[
self
.
context_name
()]
=
ret_val
#
5.
parse children that should be pased after this layer.
#
STEP:
parse children that should be pased after this layer.
for
layer
,
pnames
in
self
.
__children_layers__
:
drop
=
False
...
...
@@ -131,7 +131,7 @@ class Layer(object):
continue
layer
.
to_proto
(
context
=
context
)
#
6. return v1 layer result.g
#
STEP: return v1 layer result
if
self
.
context_name
()
is
None
:
return
ret_val
elif
self
.
use_context_name
():
...
...
@@ -139,7 +139,7 @@ class Layer(object):
else
:
return
context
[
self
.
name
]
def
to_proto_impl
(
self
,
context
=
None
,
**
kwargs
):
def
to_proto_impl
(
self
,
**
kwargs
):
raise
NotImplementedError
()
def
context_name
(
self
):
...
...
@@ -203,7 +203,7 @@ def __convert_to_v2__(method_name,
if
wrapper
is
not
None
:
__init__
=
wrapper
(
__init__
)
def
to_proto_impl
(
self
,
context
=
None
,
**
kwargs
):
def
to_proto_impl
(
self
,
**
kwargs
):
args
=
dict
()
for
each
in
kwargs
:
args
[
each
]
=
kwargs
[
each
]
...
...
python/paddle/v2/layer.py
浏览文件 @
07a8f0ef
...
...
@@ -33,22 +33,25 @@ The primary usage shows below.
import
collections
import
inspect
from
config_base
import
Layer
,
__convert_to_v2__
import
re
import
paddle.trainer_config_helpers
as
conf_helps
from
paddle.trainer.config_parser
import
\
RecurrentLayerGroupWithoutOutLinksBegin
,
RecurrentLayerGroupSetOutLink
,
\
RecurrentLayerGroupEnd
,
model_type
from
paddle.trainer_config_helpers.config_parser_utils
import
\
parse_network_config
as
__parse__
from
paddle.trainer_config_helpers.default_decorators
import
wrap_act_default
from
paddle.trainer_config_helpers.default_decorators
import
\
wrap_bias_attr_default
from
paddle.trainer_config_helpers.default_decorators
import
wrap_name_default
from
paddle.trainer_config_helpers.layers
import
RecurrentLayerGroupSetGenerator
,
Generator
from
paddle.trainer_config_helpers.layers
import
layer_support
from
paddle.trainer.config_parser
import
\
RecurrentLayerGroupWithoutOutLinksBegin
,
RecurrentLayerGroupSetOutLink
,
\
RecurrentLayerGroupEnd
,
model_type
import
activation
import
re
import
attr
import
data_type
from
config_base
import
Layer
,
__convert_to_v2__
__all__
=
[
'parse_network'
,
'data'
]
...
...
@@ -111,7 +114,7 @@ class DataLayerV2(Layer):
super
(
DataLayerV2
,
self
).
__init__
(
name
=
name
,
parent_layers
=
dict
())
def
to_proto_impl
(
self
,
context
=
None
,
**
kwargs
):
def
to_proto_impl
(
self
,
**
kwargs
):
args
=
dict
()
args
[
'size'
]
=
self
.
type
.
dim
for
each
in
kwargs
:
...
...
@@ -134,6 +137,16 @@ class DataLayerV2(Layer):
class
MemoryV2
(
Layer
):
def
__init__
(
self
,
name
,
extra_input
=
None
,
**
kwargs
):
"""
Init memory object, if memory is inited inside recurrent_group step
function, it may depend on a boot_layer that should be initialized
outside recurrent_group, so we:
1. add RecurrentLayerInput to extra_parent of self.
2. add boot_layer to the extra_parent of RecurrentLayerInput.
:param extra_input: list of RecurrentLayerInput
:type extra_input: [RecurrentLayerInput]
"""
self
.
name
=
name
super
(
MemoryV2
,
self
).
__init__
(
name
=
name
,
parent_layers
=
dict
())
self
.
__kwargs__
=
kwargs
...
...
@@ -164,7 +177,7 @@ class MemoryV2(Layer):
extra
.
append_extra_parent
(
kwargs
[
'boot_layer'
])
self
.
__boot_layer_name__
=
kwargs
[
'boot_layer'
].
name
def
to_proto_impl
(
self
,
context
=
None
,
**
kwargs
):
def
to_proto_impl
(
self
,
**
kwargs
):
args
=
dict
()
for
each
in
kwargs
:
args
[
each
]
=
kwargs
[
each
]
...
...
@@ -172,7 +185,7 @@ class MemoryV2(Layer):
args
[
each
]
=
self
.
__kwargs__
[
each
]
if
self
.
__boot_layer_name__
is
not
None
:
args
[
'boot_layer'
]
=
context
[
self
.
__boot_layer_name__
]
args
[
'boot_layer'
]
=
self
.
__context__
[
self
.
__boot_layer_name__
]
size
=
args
.
get
(
'size'
,
None
)
if
size
is
not
None
:
...
...
@@ -205,6 +218,66 @@ class StaticInputV2(object):
# assert input.size is not None or size is not None
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
max_id
(
input
=
input
,
name
=
'__beam_search_predict__'
)
def
before_real_step
(
self
):
predict_id
=
memory
(
name
=
'__beam_search_predict__'
,
size
=
self
.
size
,
boot_with_const_id
=
self
.
bos_id
)
trg_emb
=
embedding
(
input
=
predict_id
,
size
=
self
.
embedding_size
,
param_attr
=
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
class
MixedLayerV2
(
Layer
):
"""
This class is use to support `with` grammar. If not, the following code
...
...
@@ -254,7 +327,7 @@ class MixedLayerV2(Layer):
def
__exit__
(
self
,
*
args
,
**
kwargs
):
self
.
finalized
=
True
def
to_proto_impl
(
self
,
context
=
None
,
**
kwargs
):
def
to_proto_impl
(
self
,
**
kwargs
):
args
=
dict
()
for
each
in
kwargs
:
args
[
each
]
=
kwargs
[
each
]
...
...
@@ -300,7 +373,7 @@ class RecurrentLayerInput(Layer):
def
context_name
(
self
):
return
self
.
__recurrent_name__
+
".begin"
def
to_proto_impl
(
self
,
context
=
None
,
**
kwargs
):
def
to_proto_impl
(
self
,
**
kwargs
):
model_type
(
'recurrent_nn'
)
RecurrentLayerGroupWithoutOutLinksBegin
(
name
=
self
.
__recurrent_name__
,
...
...
@@ -319,7 +392,7 @@ class RecurrentLayerOutput(Layer):
def
context_name
(
self
):
return
self
.
__recurrent_name__
+
".end"
def
to_proto_impl
(
self
,
context
=
None
,
**
kwargs
):
def
to_proto_impl
(
self
,
**
kwargs
):
for
l
in
self
.
__parents__
:
RecurrentLayerGroupSetOutLink
(
l
.
name
)
RecurrentLayerGroupEnd
(
name
=
self
.
__recurrent_name__
)
...
...
@@ -418,8 +491,7 @@ def recurrent_group(step, input, name=None):
size
=
static_input
.
input
.
calculate_size
,
act
=
activation
.
Identity
())
as
mix
:
mix
+=
identity_projection
(
input
=
mem
)
mem
.
append_child
(
layer
=
mix
,
parent_names
=
[
mem
.
context_name
()])
rnn_input
.
insert
(
input
.
index
(
static_input
),
mem
)
rnn_input
.
insert
(
input
.
index
(
static_input
),
mix
)
return
step
(
*
rnn_input
)
actual_output
=
__real_step__
(
*
actual_input
)
...
...
@@ -440,6 +512,74 @@ def recurrent_group(step, input, name=None):
return
retv
@
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
,
StaticInputV2
)
or
isinstance
(
input
,
BaseGeneratedInputV2
):
input
=
[
input
]
generated_input_index
=
-
1
real_input
=
[]
for
i
,
each_input
in
enumerate
(
input
):
assert
isinstance
(
each_input
,
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_layer
=
eos
(
input
=
predict
,
eos_id
=
eos_id
,
name
=
eos_name
)
predict
.
append_child
(
layer
=
eos_layer
,
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
=
recurrent_group
(
step
=
__real_step__
,
input
=
real_input
,
name
=
name
)
return
tmp
__projection_names__
=
filter
(
lambda
x
:
x
.
endswith
(
'_projection'
),
dir
(
conf_helps
))
...
...
python/paddle/v2/layers/__init__.py
已删除
100644 → 0
浏览文件 @
bf6fd470
import
beam_search
python/paddle/v2/layers/beam_search.py
已删除
100644 → 0
浏览文件 @
bf6fd470
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
,
context
=
None
,
**
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
python/setup.py.in
浏览文件 @
07a8f0ef
...
...
@@ -8,8 +8,7 @@ packages=['paddle',
'paddle.v2',
'paddle.v2.dataset',
'paddle.v2.reader',
'paddle.v2.plot',
'paddle.v2.layers']
'paddle.v2.plot']
setup(name='paddle',
version='${PADDLE_VERSION}',
...
...
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