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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
显示空白变更内容
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
));
}
}
};
...
...
@@ -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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