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03897f25
编写于
9月 28, 2017
作者:
L
Liu Yiqun
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电子邮件补丁
差异文件
Finish the SequenceSoftmaxGradKernel, using SoftmaxGradFunctor.
上级
05ed8ee8
变更
4
显示空白变更内容
内联
并排
Showing
4 changed file
with
111 addition
and
58 deletion
+111
-58
paddle/operators/mul_op.cc
paddle/operators/mul_op.cc
+17
-15
paddle/operators/sequence_softmax_op.cc
paddle/operators/sequence_softmax_op.cc
+49
-30
paddle/operators/sequence_softmax_op.h
paddle/operators/sequence_softmax_op.h
+41
-12
python/paddle/v2/framework/tests/test_sequence_softmax_op.py
python/paddle/v2/framework/tests/test_sequence_softmax_op.py
+4
-1
未找到文件。
paddle/operators/mul_op.cc
浏览文件 @
03897f25
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
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. */
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
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/operators/mul_op.h"
...
...
@@ -35,12 +35,14 @@ class MulOp : public framework::OperatorWithKernel {
int
x_num_col_dims
=
ctx
->
Attrs
().
Get
<
int
>
(
"x_num_col_dims"
);
int
y_num_col_dims
=
ctx
->
Attrs
().
Get
<
int
>
(
"y_num_col_dims"
);
PADDLE_ENFORCE
(
x_dims
.
size
()
>
x_num_col_dims
,
"The rank of input tensor X should be larger than "
"`mul_op`'s `x_num_col_dims`."
);
PADDLE_ENFORCE
(
y_dims
.
size
()
>
y_num_col_dims
,
"The rank of input tensor Y should be larger than "
"`mul_op`'s `y_num_col_dims`."
);
PADDLE_ENFORCE_GT
(
x_dims
.
size
(),
x_num_col_dims
,
"The input tensor X's rank of MulOp should be larger than "
"x_num_col_dims."
);
PADDLE_ENFORCE_GT
(
y_dims
.
size
(),
y_num_col_dims
,
"The input tensor Y's rank of MulOp should be larger than "
"y_num_col_dims."
);
auto
x_mat_dims
=
framework
::
flatten_to_2d
(
x_dims
,
x_num_col_dims
);
auto
y_mat_dims
=
framework
::
flatten_to_2d
(
y_dims
,
y_num_col_dims
);
...
...
paddle/operators/sequence_softmax_op.cc
浏览文件 @
03897f25
...
...
@@ -22,41 +22,42 @@ class SequenceSoftmaxOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X) of SequenceSoftmaxOp should not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
OutputVar
(
"Out"
),
void
InferShape
(
framework
::
InferShapeContextBase
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SequenceSoftmaxOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SequenceSoftmaxOp should not be null."
);
auto
*
x
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
);
auto
lod
=
x
->
lod
();
auto
dims
=
x
->
dims
();
PADDLE_ENFORCE_GE
(
dims
[
0
],
/* batch_size */
static_cast
<
int64_t
>
(
lod
[
0
].
size
()
-
1
),
"The first dimension of Input(X) should be larger than batch size."
);
const
size_t
level
=
lod
.
size
()
-
1
;
PADDLE_ENFORCE_EQ
(
x
->
numel
(),
static_cast
<
int64_t
>
(
lod
[
level
].
back
()),
"The width of each timestep in Input(X) of "
"SequenceSoftmaxOp should be 1."
);
std
::
cout
<<
DebugString
()
<<
std
::
endl
;
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Out"
)
->
Resize
({
dims
});
ctx
->
SetOutputDim
(
"Out"
,
ctx
->
GetInputDim
(
"X"
));
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
class
SequenceSoftmaxOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SequenceSoftmaxOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
SequenceSoftmaxOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(LoDTensor)"
);
AddOutput
(
"Out"
,
"(LoDTensor)"
);
AddInput
(
"X"
,
"(LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension "
"of length 1."
);
AddOutput
(
"Out"
,
"(LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension "
"of length 1."
);
AddComment
(
R"DOC(
Softmax of Sequence.
SequenceSoftmaxOp computes softmax activation among all time-steps for each
sequences. The dimension of each time-step should be 1. Thus, the shape of
input Tensor can be either [N, 1] or [N], where N is the sum of all sequences'
length.
Equation:
for i-th sequence in mini-batch:
Out(X[lod[i]:lod[i+1]], :) =
exp(X[lod[i]:lod[i+1], :]) / sum(exp(X[lod[i]:lod[i+1], :]))
For example, for a mini-batch of 3 sequences with variable-length,
each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7],
then softmax will be computed among X[0:2, :], X[2:5, :], X[2:7, :]
and N turns out to be 7.
)DOC"
);
}
};
...
...
@@ -66,7 +67,25 @@ class SequenceSoftmaxGradOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{}
void
InferShape
(
framework
::
InferShapeContextBase
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Out"
),
"Input(Out) of SequenceSoftmaxGradOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) of SequenceSoftmaxGradOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SequenceSoftmaxOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"Output(X@GRAD) of SequenceSoftmaxOp should not be null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
GetInputDim
(
"Out"
),
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out) and Input(Out@GRAD) of SequenceSoftmaxGradOp should be of "
"the same shape."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
};
}
// namespace operators
...
...
@@ -81,4 +100,4 @@ REGISTER_OP_CPU_KERNEL(
ops
::
SequenceSoftmaxKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
sequence_softmax_grad
,
ops
::
SequenceSoftmaxGradKernel
<
paddle
::
platform
::
G
PUPlace
,
float
>
);
ops
::
SequenceSoftmaxGradKernel
<
paddle
::
platform
::
C
PUPlace
,
float
>
);
paddle/operators/sequence_softmax_op.h
浏览文件 @
03897f25
...
...
@@ -16,19 +16,13 @@ limitations under the License. */
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/softmax
_function
.h"
#include "paddle/operators/math/softmax.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
LoDTensor
=
framework
::
LoDTensor
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenVector
=
framework
::
EigenVector
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
Place
,
typename
T
>
class
SequenceSoftmaxKernel
:
public
framework
::
OpKernel
{
...
...
@@ -38,7 +32,17 @@ class SequenceSoftmaxKernel : public framework::OpKernel {
auto
*
out
=
ctx
.
Output
<
LoDTensor
>
(
"Out"
);
auto
lod
=
x
->
lod
();
auto
dims
=
x
->
dims
();
PADDLE_ENFORCE_GE
(
dims
[
0
],
/* batch_size */
static_cast
<
int64_t
>
(
lod
[
0
].
size
()
-
1
),
"The first dimension of Input(X) should be larger than batch size."
);
const
size_t
level
=
lod
.
size
()
-
1
;
PADDLE_ENFORCE_EQ
(
x
->
numel
(),
static_cast
<
int64_t
>
(
lod
[
level
].
back
()),
"The width of each timestep in Input(X) of "
"SequenceSoftmaxOp should be 1."
);
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
lod
[
level
].
size
())
-
1
;
++
i
)
{
...
...
@@ -48,10 +52,10 @@ class SequenceSoftmaxKernel : public framework::OpKernel {
Tensor
out_i
=
out
->
Slice
<
T
>
(
start_pos
,
end_pos
);
// Reshape from (end_pos - start_pos) x 1UL to 1UL x (end_pos - start_pos)
framework
::
DDim
dims
=
framework
::
make_ddim
({
1UL
,
end_pos
-
start_pos
});
x_i
.
Resize
(
dims
);
out_i
.
Resize
(
dims
);
math
::
SoftmaxFunctor
<
Place
,
T
>
()(
&
x_i
,
&
out_i
,
ctx
);
framework
::
DDim
dims
_i
=
framework
::
make_ddim
({
1UL
,
end_pos
-
start_pos
});
x_i
.
Resize
(
dims
_i
);
out_i
.
Resize
(
dims
_i
);
math
::
SoftmaxFunctor
<
Place
,
T
>
()(
ctx
,
&
x_i
,
&
out_i
);
}
}
};
...
...
@@ -59,7 +63,32 @@ class SequenceSoftmaxKernel : public framework::OpKernel {
template
<
typename
Place
,
typename
T
>
class
SequenceSoftmaxGradKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{}
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
out
=
ctx
.
Input
<
LoDTensor
>
(
"Out"
);
auto
*
out_grad
=
ctx
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
x
=
ctx
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
x_grad
=
ctx
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
lod
=
x
->
lod
();
const
size_t
level
=
lod
.
size
()
-
1
;
x_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
lod
[
level
].
size
())
-
1
;
++
i
)
{
int
start_pos
=
static_cast
<
int
>
(
lod
[
level
][
i
]);
int
end_pos
=
static_cast
<
int
>
(
lod
[
level
][
i
+
1
]);
Tensor
out_i
=
out
->
Slice
<
T
>
(
start_pos
,
end_pos
);
Tensor
out_grad_i
=
out_grad
->
Slice
<
T
>
(
start_pos
,
end_pos
);
Tensor
x_grad_i
=
x_grad
->
Slice
<
T
>
(
start_pos
,
end_pos
);
// Reshape from (end_pos - start_pos) x 1UL to 1UL x (end_pos - start_pos)
framework
::
DDim
dims_i
=
framework
::
make_ddim
({
1UL
,
end_pos
-
start_pos
});
out_i
.
Resize
(
dims_i
);
out_grad_i
.
Resize
(
dims_i
);
x_grad_i
.
Resize
(
dims_i
);
math
::
SoftmaxGradFunctor
<
Place
,
T
>
()(
ctx
,
&
out_i
,
&
out_grad_i
,
&
x_grad_i
);
}
}
};
}
// namespace operators
...
...
python/paddle/v2/framework/tests/test_sequence_softmax_op.py
浏览文件 @
03897f25
...
...
@@ -5,7 +5,7 @@ from op_test import OpTest
def
stable_softmax
(
x
):
"""Compute the softmax of vector x in a numerically stable way."""
shiftx
=
x
-
np
.
max
(
x
)
shiftx
=
x
-
np
.
max
(
x
)
.
clip
(
-
64.
)
exps
=
np
.
exp
(
shiftx
)
return
exps
/
np
.
sum
(
exps
)
...
...
@@ -30,6 +30,9 @@ class TestSequenceSoftmaxOp(OpTest):
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
"X"
],
"Out"
,
max_relative_error
=
0.01
)
if
__name__
==
"__main__"
:
unittest
.
main
()
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