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865a714e
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体验新版 GitCode,发现更多精彩内容 >>
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865a714e
编写于
1月 31, 2018
作者:
Y
Yu Yang
提交者:
GitHub
1月 31, 2018
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差异文件
Merge pull request #7970 from reyoung/feature/test_w2v_parallel.do
Make word2vec uses parallel.do when CI
上级
ed942263
f1e32e24
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
177 addition
and
81 deletion
+177
-81
paddle/operators/sum_op.h
paddle/operators/sum_op.h
+38
-11
python/paddle/v2/fluid/tests/book/test_word2vec.py
python/paddle/v2/fluid/tests/book/test_word2vec.py
+139
-70
未找到文件。
paddle/operators/sum_op.h
浏览文件 @
865a714e
...
...
@@ -68,7 +68,32 @@ class SumKernel : public framework::OpKernel<T> {
}
}
}
else
if
(
out_var
->
IsType
<
framework
::
SelectedRows
>
())
{
PADDLE_ENFORCE
(
!
in_place
,
"SelectedRows not support inplace sum now"
);
std
::
unique_ptr
<
framework
::
SelectedRows
>
in0
;
if
(
in_place
)
{
// If is in_place, we store the input[0] to in0
auto
&
in_sel0
=
in_vars
[
0
]
->
Get
<
SelectedRows
>
();
auto
&
rows
=
in_sel0
.
rows
();
#ifdef PADDLE_WITH_CUDA
std
::
vector
<
int64_t
>
rows_in_cpu
;
rows_in_cpu
.
reserve
(
rows
.
size
());
for
(
auto
item
:
rows
)
{
rows_in_cpu
.
push_back
(
item
);
}
in0
.
reset
(
new
framework
::
SelectedRows
(
rows_in_cpu
,
in_sel0
.
height
()));
#else
in0
.
reset
(
new
framework
::
SelectedRows
(
rows
,
in_sel0
.
height
()));
#endif
in0
->
mutable_value
()
->
ShareDataWith
(
in_sel0
.
value
());
}
auto
get_selected_row
=
[
&
](
size_t
i
)
->
const
SelectedRows
&
{
if
(
i
==
0
&&
in0
)
{
return
*
in0
.
get
();
}
else
{
return
in_vars
[
i
]
->
Get
<
SelectedRows
>
();
}
};
auto
*
out
=
context
.
Output
<
SelectedRows
>
(
"Out"
);
out
->
mutable_rows
()
->
clear
();
auto
*
out_value
=
out
->
mutable_value
();
...
...
@@ -76,24 +101,26 @@ class SumKernel : public framework::OpKernel<T> {
// Runtime InferShape
size_t
first_dim
=
0
;
for
(
int
i
=
0
;
i
<
N
;
i
++
)
{
first_dim
+=
in_vars
[
i
]
->
Get
<
SelectedRows
>
().
rows
().
size
();
auto
&
sel_row
=
get_selected_row
(
i
);
first_dim
+=
sel_row
.
rows
().
size
();
}
auto
in_dim
=
in_vars
[
0
]
->
Get
<
SelectedRows
>
().
value
().
dims
();
auto
in_dim_vec
=
framework
::
vectorize
(
in_dim
);
in_dim
_vec
[
0
]
=
static_cast
<
int64_t
>
(
first_dim
);
auto
in_dim
=
framework
::
vectorize
(
get_selected_row
(
N
-
1
).
value
().
dims
()
);
in_dim
[
0
]
=
static_cast
<
int64_t
>
(
first_dim
);
out_value
->
Resize
(
framework
::
make_ddim
(
in_dim
_vec
));
out_value
->
Resize
(
framework
::
make_ddim
(
in_dim
));
out_value
->
mutable_data
<
T
>
(
context
.
GetPlace
());
math
::
SelectedRowsAddTo
<
DeviceContext
,
T
>
functor
;
int64_t
offset
=
0
;
for
(
int
i
=
0
;
i
<
N
;
i
++
)
{
PADDLE_ENFORCE_EQ
(
out
->
height
(),
in_vars
[
i
]
->
Get
<
SelectedRows
>
().
height
());
functor
(
context
.
template
device_context
<
DeviceContext
>(),
in_vars
[
i
]
->
Get
<
SelectedRows
>
(),
offset
,
out
);
offset
+=
in_vars
[
i
]
->
Get
<
SelectedRows
>
().
value
().
numel
();
auto
&
sel_row
=
get_selected_row
(
i
);
PADDLE_ENFORCE_EQ
(
out
->
height
(),
sel_row
.
height
());
functor
(
context
.
template
device_context
<
DeviceContext
>(),
sel_row
,
offset
,
out
);
offset
+=
sel_row
.
value
().
numel
();
}
}
else
if
(
out_var
->
IsType
<
framework
::
LoDTensorArray
>
())
{
auto
&
out_array
=
*
out_var
->
GetMutable
<
framework
::
LoDTensorArray
>
();
...
...
python/paddle/v2/fluid/tests/book/test_word2vec.py
浏览文件 @
865a714e
...
...
@@ -12,76 +12,145 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import
numpy
as
np
import
paddle.v2
as
paddle
import
paddle.v2.fluid
as
fluid
import
unittest
import
os
PASS_NUM
=
100
EMBED_SIZE
=
32
HIDDEN_SIZE
=
256
N
=
5
BATCH_SIZE
=
32
IS_SPARSE
=
True
word_dict
=
paddle
.
dataset
.
imikolov
.
build_dict
()
dict_size
=
len
(
word_dict
)
first_word
=
fluid
.
layers
.
data
(
name
=
'firstw'
,
shape
=
[
1
],
dtype
=
'int64'
)
second_word
=
fluid
.
layers
.
data
(
name
=
'secondw'
,
shape
=
[
1
],
dtype
=
'int64'
)
third_word
=
fluid
.
layers
.
data
(
name
=
'thirdw'
,
shape
=
[
1
],
dtype
=
'int64'
)
forth_word
=
fluid
.
layers
.
data
(
name
=
'forthw'
,
shape
=
[
1
],
dtype
=
'int64'
)
next_word
=
fluid
.
layers
.
data
(
name
=
'nextw'
,
shape
=
[
1
],
dtype
=
'int64'
)
embed_first
=
fluid
.
layers
.
embedding
(
input
=
first_word
,
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
'shared_w'
)
embed_second
=
fluid
.
layers
.
embedding
(
input
=
second_word
,
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
'shared_w'
)
embed_third
=
fluid
.
layers
.
embedding
(
input
=
third_word
,
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
'shared_w'
)
embed_forth
=
fluid
.
layers
.
embedding
(
input
=
forth_word
,
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
'shared_w'
)
concat_embed
=
fluid
.
layers
.
concat
(
input
=
[
embed_first
,
embed_second
,
embed_third
,
embed_forth
],
axis
=
1
)
hidden1
=
fluid
.
layers
.
fc
(
input
=
concat_embed
,
size
=
HIDDEN_SIZE
,
act
=
'sigmoid'
)
predict_word
=
fluid
.
layers
.
fc
(
input
=
hidden1
,
size
=
dict_size
,
act
=
'softmax'
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict_word
,
label
=
next_word
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
sgd_optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.001
)
sgd_optimizer
.
minimize
(
avg_cost
)
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
imikolov
.
train
(
word_dict
,
N
),
BATCH_SIZE
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
feeder
=
fluid
.
DataFeeder
(
feed_list
=
[
first_word
,
second_word
,
third_word
,
forth_word
,
next_word
],
place
=
place
)
exe
.
run
(
fluid
.
default_startup_program
())
for
pass_id
in
range
(
PASS_NUM
):
for
data
in
train_reader
():
avg_cost_np
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
])
if
avg_cost_np
[
0
]
<
5.0
:
exit
(
0
)
# if avg cost less than 10.0, we think our code is good.
exit
(
1
)
def
main
(
use_cuda
,
is_sparse
,
parallel
):
if
use_cuda
and
not
fluid
.
core
.
is_compiled_with_cuda
():
return
PASS_NUM
=
100
EMBED_SIZE
=
32
HIDDEN_SIZE
=
256
N
=
5
BATCH_SIZE
=
32
IS_SPARSE
=
is_sparse
def
__network__
(
words
):
embed_first
=
fluid
.
layers
.
embedding
(
input
=
words
[
0
],
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
'shared_w'
)
embed_second
=
fluid
.
layers
.
embedding
(
input
=
words
[
1
],
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
'shared_w'
)
embed_third
=
fluid
.
layers
.
embedding
(
input
=
words
[
2
],
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
'shared_w'
)
embed_forth
=
fluid
.
layers
.
embedding
(
input
=
words
[
3
],
size
=
[
dict_size
,
EMBED_SIZE
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
'shared_w'
)
concat_embed
=
fluid
.
layers
.
concat
(
input
=
[
embed_first
,
embed_second
,
embed_third
,
embed_forth
],
axis
=
1
)
hidden1
=
fluid
.
layers
.
fc
(
input
=
concat_embed
,
size
=
HIDDEN_SIZE
,
act
=
'sigmoid'
)
predict_word
=
fluid
.
layers
.
fc
(
input
=
hidden1
,
size
=
dict_size
,
act
=
'softmax'
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict_word
,
label
=
words
[
4
])
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
return
avg_cost
word_dict
=
paddle
.
dataset
.
imikolov
.
build_dict
()
dict_size
=
len
(
word_dict
)
first_word
=
fluid
.
layers
.
data
(
name
=
'firstw'
,
shape
=
[
1
],
dtype
=
'int64'
)
second_word
=
fluid
.
layers
.
data
(
name
=
'secondw'
,
shape
=
[
1
],
dtype
=
'int64'
)
third_word
=
fluid
.
layers
.
data
(
name
=
'thirdw'
,
shape
=
[
1
],
dtype
=
'int64'
)
forth_word
=
fluid
.
layers
.
data
(
name
=
'forthw'
,
shape
=
[
1
],
dtype
=
'int64'
)
next_word
=
fluid
.
layers
.
data
(
name
=
'nextw'
,
shape
=
[
1
],
dtype
=
'int64'
)
if
not
parallel
:
avg_cost
=
__network__
(
[
first_word
,
second_word
,
third_word
,
forth_word
,
next_word
])
else
:
places
=
fluid
.
layers
.
get_places
()
pd
=
fluid
.
layers
.
ParallelDo
(
places
)
with
pd
.
do
():
avg_cost
=
__network__
(
map
(
pd
.
read_input
,
[
first_word
,
second_word
,
third_word
,
forth_word
,
next_word
]))
pd
.
write_output
(
avg_cost
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
pd
())
sgd_optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.001
)
sgd_optimizer
.
minimize
(
avg_cost
)
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
imikolov
.
train
(
word_dict
,
N
),
BATCH_SIZE
)
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
feeder
=
fluid
.
DataFeeder
(
feed_list
=
[
first_word
,
second_word
,
third_word
,
forth_word
,
next_word
],
place
=
place
)
exe
.
run
(
fluid
.
default_startup_program
())
for
pass_id
in
range
(
PASS_NUM
):
for
data
in
train_reader
():
avg_cost_np
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
])
if
avg_cost_np
[
0
]
<
5.0
:
return
raise
AssertionError
(
"Cost is too large {0:2.2}"
.
format
(
avg_cost_np
[
0
]))
FULL_TEST
=
os
.
getenv
(
'FULL_TEST'
,
'0'
).
lower
()
in
[
'true'
,
'1'
,
't'
,
'y'
,
'yes'
,
'on'
]
SKIP_REASON
=
"Only run minimum number of tests in CI server, to make CI faster"
class
W2VTest
(
unittest
.
TestCase
):
pass
def
inject_test_method
(
use_cuda
,
is_sparse
,
parallel
):
fn_name
=
"test_{0}_{1}_{2}"
.
format
(
"cuda"
if
use_cuda
else
"cpu"
,
"sparse"
if
is_sparse
else
"dense"
,
"parallel"
if
parallel
else
"normal"
)
def
__impl__
(
*
args
,
**
kwargs
):
prog
=
fluid
.
Program
()
startup_prog
=
fluid
.
Program
()
scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
scope
):
with
fluid
.
program_guard
(
prog
,
startup_prog
):
main
(
use_cuda
=
use_cuda
,
is_sparse
=
is_sparse
,
parallel
=
parallel
)
if
use_cuda
and
is_sparse
and
parallel
:
fn
=
__impl__
else
:
# skip the other test when on CI server
fn
=
unittest
.
skipUnless
(
condition
=
FULL_TEST
,
reason
=
SKIP_REASON
)(
__impl__
)
setattr
(
W2VTest
,
fn_name
,
fn
)
for
use_cuda
in
(
False
,
True
):
for
is_sparse
in
(
False
,
True
):
for
parallel
in
(
False
,
True
):
inject_test_method
(
use_cuda
,
is_sparse
,
parallel
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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