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体验新版 GitCode,发现更多精彩内容 >>
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a3202760
编写于
1月 10, 2018
作者:
Y
Yang yaming
提交者:
GitHub
1月 10, 2018
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差异文件
Merge pull request #7176 from pkuyym/fix-7173
Enhence shrink_rnn_memory_op.
上级
4bcc0b64
12ed53c1
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
87 addition
and
29 deletion
+87
-29
paddle/operators/shrink_rnn_memory_op.cc
paddle/operators/shrink_rnn_memory_op.cc
+20
-5
python/paddle/v2/fluid/tests/test_shrink_rnn_memory.py
python/paddle/v2/fluid/tests/test_shrink_rnn_memory.py
+67
-24
未找到文件。
paddle/operators/shrink_rnn_memory_op.cc
浏览文件 @
a3202760
...
...
@@ -12,6 +12,7 @@ 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. */
#include "paddle/framework/lod_rank_table.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/operators/array_operator.h"
#include "paddle/operators/math/math_function.h"
...
...
@@ -46,8 +47,21 @@ class ShrinkRNNMemoryOp : public ArrayOp {
auto
*
out_var
=
scope
.
FindVar
(
Output
(
"Out"
));
PADDLE_ENFORCE
(
out_var
!=
nullptr
,
"Output Out must be set"
);
auto
&
out_tensor
=
*
out_var
->
GetMutable
<
framework
::
LoDTensor
>
();
size_t
height
=
dst_num_rows
;
// do shrink for the top level LoD
if
(
x_tensor
.
lod
().
size
()
>
0
&&
x_tensor
.
lod
()[
0
].
size
()
>
static_cast
<
size_t
>
(
dst_num_rows
))
{
auto
lod_offset
=
framework
::
GetSubLoDAndAbsoluteOffset
(
x_tensor
.
lod
(),
0
,
dst_num_rows
,
0
);
height
=
lod_offset
.
second
.
second
;
auto
out_lod
=
out_tensor
.
mutable_lod
();
framework
::
AppendLoD
(
out_lod
,
lod_offset
.
first
);
}
if
(
dst_num_rows
!=
0
)
{
out_tensor
.
ShareDataWith
(
x_tensor
.
Slice
(
0
,
dst_num_rows
));
out_tensor
.
ShareDataWith
(
x_tensor
.
Slice
(
0
,
height
));
}
}
};
...
...
@@ -64,11 +78,11 @@ class ShrinkRNNMemoryOpProtoMaker : public framework::OpProtoAndCheckerMaker {
AddOutput
(
"Out"
,
"(LoDTensor) The shrinked RNN step memory."
);
AddComment
(
R"DOC(
In dynamic RNN, we are able to handle sequences of different lengths.
Because of the multiple lengths, the size of each step input can be
In dynamic RNN, we are able to handle sequences of different lengths.
Because of the multiple lengths, the size of each step input can be
different, which may lead to a mismatching between the input of
the current step and the memory generated by the previous one. This
operator shrinks memory according to the size of the next step input,
the current step and the memory generated by the previous one. This
operator shrinks memory according to the size of the next step input,
to make sure that they can match each other.
)DOC"
);
}
...
...
@@ -132,6 +146,7 @@ class ShrinkRNNMemoryGradInferShape : public framework::InferShapeBase {
PADDLE_ENFORCE
(
context
->
HasOutput
(
framework
::
GradVarName
(
"X"
)));
context
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
context
->
GetInputDim
(
"X"
));
context
->
ShareLoD
(
"X"
,
framework
::
GradVarName
(
"X"
));
}
};
...
...
python/paddle/v2/fluid/tests/test_shrink_rnn_memory.py
浏览文件 @
a3202760
...
...
@@ -3,43 +3,86 @@ import paddle.v2.fluid.core as core
from
paddle.v2.fluid.executor
import
Executor
import
paddle.v2.fluid.layers
as
layers
from
paddle.v2.fluid.backward
import
append_backward
from
paddle.v2.fluid.framework
import
default_main_program
import
numpy
from
paddle.v2.fluid.framework
import
default_main_program
,
switch_main_program
from
paddle.v2.fluid.framework
import
Program
import
numpy
as
np
main_program
=
default_main_program
()
class
TestShrinkRNNMemory
(
unittest
.
TestCase
):
def
test_shrink_rnn_memory
(
self
):
class
TestShrinkRNNMemoryBase
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
main_program
=
Program
()
switch_main_program
(
self
.
main_program
)
x
=
layers
.
data
(
'x'
,
shape
=
[
100
],
dtype
=
'float32'
)
x
.
stop_gradient
=
False
table
=
layers
.
lod_rank_table
(
x
=
x
)
rank_table_tensor
=
layers
.
data
(
'rank_table_tensor'
,
shape
=
[
1
],
dtype
=
'float32'
,
lod_level
=
1
)
table
=
layers
.
lod_rank_table
(
x
=
rank_table_tensor
)
i
=
layers
.
zeros
(
dtype
=
'int64'
,
shape
=
[
1
])
mem1
=
layers
.
shrink_memory
(
x
=
x
,
i
=
i
,
table
=
table
)
self
.
mem1
=
layers
.
shrink_memory
(
x
=
x
,
i
=
i
,
table
=
table
)
i
=
layers
.
increment
(
x
=
i
)
i
.
stop_gradient
=
True
mem2
=
layers
.
shrink_memory
(
x
=
mem1
,
i
=
i
,
table
=
table
)
self
.
mem2
=
layers
.
shrink_memory
(
x
=
self
.
mem1
,
i
=
i
,
table
=
table
)
i
=
layers
.
increment
(
x
=
i
)
i
.
stop_gradient
=
True
mem3
=
layers
.
shrink_memory
(
x
=
mem2
,
i
=
i
,
table
=
table
)
self
.
mem3
=
layers
.
shrink_memory
(
x
=
self
.
mem2
,
i
=
i
,
table
=
table
)
mem3_mean
=
layers
.
mean
(
x
=
self
.
mem3
)
append_backward
(
loss
=
mem3_mean
)
self
.
x_grad
=
self
.
main_program
.
global_block
().
var
(
'x@GRAD'
)
def
sum_lodtensor
(
self
,
tensor
):
sum_res
=
0.0
for
i
in
xrange
(
np
.
product
(
tensor
.
get_dims
())):
sum_res
+=
tensor
.
get_float_element
(
i
)
return
sum_res
class
TestShrinkRNNMemoryReferLoD
(
TestShrinkRNNMemoryBase
):
def
test_refer_lod
(
self
):
cpu
=
core
.
CPUPlace
()
tensor
=
core
.
LoDTensor
()
tensor
.
set_lod
([[
0
,
2
,
5
,
6
]])
tensor_np
=
numpy
.
random
.
random
(
size
=
(
3
,
100
)).
astype
(
'float32'
)
tensor
.
set
(
tensor_np
,
cpu
)
x_tensor
=
core
.
LoDTensor
()
x_tensor
.
set_lod
([[
0
,
2
,
5
,
6
]])
tensor_np
=
np
.
random
.
random
(
size
=
(
6
,
100
)).
astype
(
'float32'
)
x_tensor
.
set
(
tensor_np
,
cpu
)
rank_table_tensor
=
core
.
LoDTensor
()
rank_table_tensor
.
set_lod
([[
0
,
1
,
3
,
6
]])
rank_table_tensor
.
set
(
np
.
random
.
random
(
size
=
(
6
,
1
)).
astype
(
'float32'
),
cpu
)
exe
=
Executor
(
cpu
)
outs
=
exe
.
run
(
feed
=
{
'x'
:
tensor
},
fetch_list
=
[
mem1
,
mem2
,
mem3
])
self
.
assertTrue
(
numpy
.
allclose
(
tensor_np
[
0
:
3
],
outs
[
0
]))
self
.
assertTrue
(
numpy
.
allclose
(
tensor_np
[
0
:
2
],
outs
[
1
]))
self
.
assertTrue
(
numpy
.
allclose
(
tensor_np
[
0
:
1
],
outs
[
2
]))
outs
=
exe
.
run
(
feed
=
{
'x'
:
x_tensor
,
'rank_table_tensor'
:
rank_table_tensor
},
fetch_list
=
[
self
.
mem1
,
self
.
mem2
,
self
.
mem3
,
self
.
x_grad
],
return_numpy
=
False
)
self
.
assertTrue
(
np
.
allclose
(
tensor_np
[
0
:
6
],
outs
[
0
]))
self
.
assertTrue
(
np
.
allclose
(
tensor_np
[
0
:
5
],
outs
[
1
]))
self
.
assertTrue
(
np
.
allclose
(
tensor_np
[
0
:
2
],
outs
[
2
]))
self
.
assertAlmostEqual
(
1.0
,
self
.
sum_lodtensor
(
outs
[
3
]),
delta
=
0.01
)
mem3_mean
=
layers
.
mean
(
x
=
mem3
)
append_backward
(
loss
=
mem3_mean
)
x_grad
=
exe
.
run
(
feed
=
{
'x'
:
tensor
},
fetch_list
=
[
main_program
.
global_block
().
var
(
'x@GRAD'
)])[
0
]
self
.
assertAlmostEqual
(
1.0
,
x_grad
.
sum
(),
delta
=
0.1
)
class
TestShrinkRNNMemoryNoLoD
(
TestShrinkRNNMemoryBase
):
def
test_no_lod
(
self
):
cpu
=
core
.
CPUPlace
()
x_tensor
=
core
.
LoDTensor
()
tensor_np
=
np
.
random
.
random
(
size
=
(
3
,
100
)).
astype
(
'float32'
)
x_tensor
.
set
(
tensor_np
,
cpu
)
rank_table_tensor
=
core
.
LoDTensor
()
rank_table_tensor
.
set_lod
([[
0
,
1
,
3
,
6
]])
rank_table_tensor
.
set
(
np
.
random
.
random
(
size
=
(
6
,
1
)).
astype
(
'float32'
),
cpu
)
exe
=
Executor
(
cpu
)
outs
=
exe
.
run
(
feed
=
{
'x'
:
x_tensor
,
'rank_table_tensor'
:
rank_table_tensor
},
fetch_list
=
[
self
.
mem1
,
self
.
mem2
,
self
.
mem3
,
self
.
x_grad
],
return_numpy
=
False
)
self
.
assertTrue
(
np
.
allclose
(
tensor_np
[
0
:
3
],
outs
[
0
]))
self
.
assertTrue
(
np
.
allclose
(
tensor_np
[
0
:
2
],
outs
[
1
]))
self
.
assertTrue
(
np
.
allclose
(
tensor_np
[
0
:
1
],
outs
[
2
]))
self
.
assertAlmostEqual
(
1.0
,
self
.
sum_lodtensor
(
outs
[
3
]),
delta
=
0.01
)
if
__name__
==
'__main__'
:
...
...
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