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3b87080a
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体验新版 GitCode,发现更多精彩内容 >>
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3b87080a
编写于
2月 01, 2018
作者:
Y
Yang Yu
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Make NMT as normal python unittests
上级
270ecbe4
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
85 addition
and
24 deletion
+85
-24
paddle/operators/while_op.cc
paddle/operators/while_op.cc
+2
-0
python/paddle/v2/fluid/layers/tensor.py
python/paddle/v2/fluid/layers/tensor.py
+2
-2
python/paddle/v2/fluid/tests/book/test_machine_translation.py
...on/paddle/v2/fluid/tests/book/test_machine_translation.py
+81
-22
未找到文件。
paddle/operators/while_op.cc
浏览文件 @
3b87080a
...
...
@@ -53,6 +53,8 @@ class WhileOp : public framework::OperatorBase {
auto
step_scopes
=
scope
.
FindVar
(
Output
(
kStepScopes
))
->
GetMutable
<
StepScopeVar
>
();
PADDLE_ENFORCE
(
platform
::
is_cpu_place
(
cond
.
place
()),
"Condition of while op must in CPU memory."
);
while
(
cond
.
data
<
bool
>
()[
0
])
{
auto
&
current_scope
=
scope
.
NewScope
();
step_scopes
->
push_back
(
&
current_scope
);
...
...
python/paddle/v2/fluid/layers/tensor.py
浏览文件 @
3b87080a
...
...
@@ -295,7 +295,7 @@ def fill_constant_batch_size_like(input,
return
out
def
ones
(
shape
,
dtype
):
def
ones
(
shape
,
dtype
,
force_cpu
=
False
):
"""
**ones**
...
...
@@ -319,7 +319,7 @@ def ones(shape, dtype):
return
fill_constant
(
value
=
1.0
,
**
locals
())
def
zeros
(
shape
,
dtype
):
def
zeros
(
shape
,
dtype
,
force_cpu
=
False
):
"""
**zeros**
...
...
python/paddle/v2/fluid/tests/book/test_machine_translation.py
浏览文件 @
3b87080a
...
...
@@ -11,21 +11,20 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
contextlib
import
numpy
as
np
import
paddle.v2
as
paddle
import
paddle.v2.fluid
as
fluid
import
paddle.v2.fluid.core
as
core
import
paddle.v2.fluid.framework
as
framework
import
paddle.v2.fluid.layers
as
pd
from
paddle.v2.fluid.executor
import
Executor
import
unittest
dict_size
=
30000
source_dict_dim
=
target_dict_dim
=
dict_size
src_dict
,
trg_dict
=
paddle
.
dataset
.
wmt14
.
get_dict
(
dict_size
)
hidden_dim
=
32
word_dim
=
16
IS_SPARSE
=
True
batch_size
=
2
max_length
=
8
topk_size
=
50
...
...
@@ -34,10 +33,8 @@ beam_size = 2
decoder_size
=
hidden_dim
place
=
core
.
CPUPlace
()
def
encoder
():
def
encoder
(
is_sparse
):
# encoder
src_word_id
=
pd
.
data
(
name
=
"src_word_id"
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
...
...
@@ -45,7 +42,7 @@ def encoder():
input
=
src_word_id
,
size
=
[
dict_size
,
word_dim
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
is_sparse
=
is_sparse
,
param_attr
=
fluid
.
ParamAttr
(
name
=
'vemb'
))
fc1
=
pd
.
fc
(
input
=
src_embedding
,
size
=
hidden_dim
*
4
,
act
=
'tanh'
)
...
...
@@ -54,7 +51,7 @@ def encoder():
return
encoder_out
def
decoder_train
(
context
):
def
decoder_train
(
context
,
is_sparse
):
# decoder
trg_language_word
=
pd
.
data
(
name
=
"target_language_word"
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
...
...
@@ -62,7 +59,7 @@ def decoder_train(context):
input
=
trg_language_word
,
size
=
[
dict_size
,
word_dim
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
is_sparse
=
is_sparse
,
param_attr
=
fluid
.
ParamAttr
(
name
=
'vemb'
))
rnn
=
pd
.
DynamicRNN
()
...
...
@@ -82,10 +79,10 @@ def decoder_train(context):
return
rnn
()
def
decoder_decode
(
context
):
def
decoder_decode
(
context
,
is_sparse
):
init_state
=
context
array_len
=
pd
.
fill_constant
(
shape
=
[
1
],
dtype
=
'int64'
,
value
=
max_length
)
counter
=
pd
.
zeros
(
shape
=
[
1
],
dtype
=
'int64'
)
counter
=
pd
.
zeros
(
shape
=
[
1
],
dtype
=
'int64'
,
force_cpu
=
True
)
# fill the first element with init_state
state_array
=
pd
.
create_array
(
'float32'
)
...
...
@@ -117,7 +114,7 @@ def decoder_decode(context):
input
=
pre_ids
,
size
=
[
dict_size
,
word_dim
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
)
is_sparse
=
is_sparse
)
# use rnn unit to update rnn
current_state
=
pd
.
fc
(
input
=
[
pre_ids_emb
,
pre_state_expanded
],
...
...
@@ -150,7 +147,7 @@ def decoder_decode(context):
def
set_init_lod
(
data
,
lod
,
place
):
res
=
core
.
LoDTensor
()
res
=
fluid
.
LoDTensor
()
res
.
set
(
data
,
place
)
res
.
set_lod
(
lod
)
return
res
...
...
@@ -165,15 +162,19 @@ def to_lodtensor(data, place):
lod
.
append
(
cur_len
)
flattened_data
=
np
.
concatenate
(
data
,
axis
=
0
).
astype
(
"int64"
)
flattened_data
=
flattened_data
.
reshape
([
len
(
flattened_data
),
1
])
res
=
core
.
LoDTensor
()
res
=
fluid
.
LoDTensor
()
res
.
set
(
flattened_data
,
place
)
res
.
set_lod
([
lod
])
return
res
def
train_main
():
context
=
encoder
()
rnn_out
=
decoder_train
(
context
)
def
train_main
(
use_cuda
,
is_sparse
):
if
use_cuda
and
not
fluid
.
core
.
is_compiled_with_cuda
():
return
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
context
=
encoder
(
is_sparse
)
rnn_out
=
decoder_train
(
context
,
is_sparse
)
label
=
pd
.
data
(
name
=
"target_language_next_word"
,
shape
=
[
1
],
dtype
=
'int64'
,
lod_level
=
1
)
cost
=
pd
.
cross_entropy
(
input
=
rnn_out
,
label
=
label
)
...
...
@@ -212,9 +213,13 @@ def train_main():
batch_id
+=
1
def
decode_main
():
context
=
encoder
()
translation_ids
,
translation_scores
=
decoder_decode
(
context
)
def
decode_main
(
use_cuda
,
is_sparse
):
if
use_cuda
and
not
fluid
.
core
.
is_compiled_with_cuda
():
return
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
context
=
encoder
(
is_sparse
)
translation_ids
,
translation_scores
=
decoder_decode
(
context
,
is_sparse
)
exe
=
Executor
(
place
)
exe
.
run
(
framework
.
default_startup_program
())
...
...
@@ -250,6 +255,60 @@ def decode_main():
break
class
TestMachineTranslation
(
unittest
.
TestCase
):
pass
@
contextlib
.
contextmanager
def
scope_prog_guard
():
prog
=
fluid
.
Program
()
startup_prog
=
fluid
.
Program
()
scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
scope
):
with
fluid
.
program_guard
(
prog
,
startup_prog
):
yield
def
inject_test_train
(
use_cuda
,
is_sparse
):
f_name
=
'test_{0}_{1}_train'
.
format
(
'cuda'
if
use_cuda
else
'cpu'
,
'sparse'
if
is_sparse
else
'dense'
)
def
f
(
*
args
):
with
scope_prog_guard
():
train_main
(
use_cuda
,
is_sparse
)
setattr
(
TestMachineTranslation
,
f_name
,
f
)
def
inject_test_decode
(
use_cuda
,
is_sparse
,
decorator
=
None
):
f_name
=
'test_{0}_{1}_decode'
.
format
(
'cuda'
if
use_cuda
else
'cpu'
,
'sparse'
if
is_sparse
else
'dense'
)
def
f
(
*
args
):
with
scope_prog_guard
():
decode_main
(
use_cuda
,
is_sparse
)
if
decorator
is
not
None
:
f
=
decorator
(
f
)
setattr
(
TestMachineTranslation
,
f_name
,
f
)
for
_use_cuda_
in
(
False
,
True
):
for
_is_sparse_
in
(
False
,
True
):
inject_test_train
(
_use_cuda_
,
_is_sparse_
)
for
_use_cuda_
in
(
False
,
True
):
for
_is_sparse_
in
(
False
,
True
):
_decorator_
=
None
if
_use_cuda_
:
_decorator_
=
unittest
.
skip
(
reason
=
'Beam Search does not support CUDA!'
)
inject_test_decode
(
is_sparse
=
_is_sparse_
,
use_cuda
=
_use_cuda_
,
decorator
=
_decorator_
)
if
__name__
==
'__main__'
:
# train_main()
decode_main
()
unittest
.
main
()
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