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体验新版 GitCode,发现更多精彩内容 >>
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632b320e
编写于
8月 14, 2017
作者:
D
dongzhihong
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
"refine argument with new style "
上级
426d7328
变更
4
显示空白变更内容
内联
并排
Showing
4 changed file
with
66 addition
and
36 deletion
+66
-36
paddle/operators/math/math_function.h
paddle/operators/math/math_function.h
+9
-0
paddle/operators/mul_op.cc
paddle/operators/mul_op.cc
+11
-9
paddle/operators/mul_op.h
paddle/operators/mul_op.h
+34
-26
python/paddle/v2/framework/tests/test_mul_op.py
python/paddle/v2/framework/tests/test_mul_op.py
+12
-1
未找到文件。
paddle/operators/math/math_function.h
浏览文件 @
632b320e
...
...
@@ -77,6 +77,15 @@ void matmul(const framework::Tensor& matrix_a, bool trans_a,
framework
::
Tensor
*
matrix_out
,
T
beta
,
platform
::
DeviceContext
*
context
);
// // matrix multiply with continuous memory
// template <typename Place, typename T>
// void matmul(const framework::Tensor& matrix_a, bool trans_a,
// const framework::Tensor& matrix_b, bool trans_b,
// framework::Tensor* matrix_out,
// platform::DeviceContext* context) {
// matmul(matrix_a, matrix_b, trans_a, trans_b, 1, matrix_out, 0, context);
// }
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/mul_op.cc
浏览文件 @
632b320e
...
...
@@ -18,6 +18,8 @@
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
class
MulOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
...
...
@@ -60,19 +62,19 @@ class MulOpGrad : public framework::OperatorWithKernel {
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_EQ
(
ctx
.
InputSize
(),
3UL
,
"Input of MulOpGrad should be 3, X, Y, Out@GRAD"
);
PADDLE_ENFORCE_EQ
(
ctx
.
OutputSize
(),
2UL
,
"Output of MulOpGrad should be 2, X@GRAD, Y@GRAD"
);
//
PADDLE_ENFORCE_EQ(ctx.InputSize(), 3UL,
//
"Input of MulOpGrad should be 3, X, Y, Out@GRAD");
//
PADDLE_ENFORCE_EQ(ctx.OutputSize(), 2UL,
//
"Output of MulOpGrad should be 2, X@GRAD, Y@GRAD");
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X) should not be null"
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Y"
),
"Input(Y) should not be null"
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null"
);
auto
*
x_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
dim0
=
ctx
.
Input
<
Tensor
>
(
0
)
->
dims
();
auto
dim1
=
ctx
.
Input
<
Tensor
>
(
1
)
->
dims
();
auto
out_dims
=
ctx
.
Input
<
Tensor
>
(
2
)
->
dims
();
auto
*
x_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
dim0
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"X"
)
)
->
dims
();
auto
dim1
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
)
)
->
dims
();
auto
out_dims
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
)
)
->
dims
();
PADDLE_ENFORCE
(
dim0
[
0
]
*
dim1
[
0
]
==
out_dims
[
0
],
"Out@GRAD[0] must equal to X[0] * Y[0]"
);
PADDLE_ENFORCE
(
dim0
[
1
]
*
dim1
[
1
]
==
out_dims
[
1
],
...
...
paddle/operators/mul_op.h
浏览文件 @
632b320e
...
...
@@ -31,18 +31,22 @@ template <typename Place, typename T>
class
MulKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
Eigen
::
array
<
Eigen
::
IndexPair
<
Eigen
::
DenseIndex
>
,
1
>
dim_pair
=
{
{
Eigen
::
IndexPair
<
Eigen
::
DenseIndex
>
(
1
,
0
)}};
auto
*
input0
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
input1
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
*
output
=
context
.
Output
<
Tensor
>
(
"Out"
);
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
X
=
EigenMatrix
<
T
>::
From
(
*
input0
);
auto
Y
=
EigenMatrix
<
T
>::
From
(
*
input1
);
auto
Z
=
EigenMatrix
<
T
>::
From
(
*
output
);
auto
&
place
=
context
.
GetEigenDevice
<
Place
>
();
Z
.
device
(
place
)
=
X
.
contract
(
Y
,
dim_pair
);
// Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> dim_pair = {
// {Eigen::IndexPair<Eigen::DenseIndex>(1, 0)}};
auto
*
X
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
Y
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
*
Z
=
context
.
Output
<
Tensor
>
(
"Out"
);
Z
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
*
device_context
=
const_cast
<
platform
::
DeviceContext
*>
(
context
.
device_context_
);
math
::
matmul
<
Place
,
T
>
(
*
X
,
false
,
*
Y
,
false
,
1
,
Z
,
0
,
device_context
);
// auto X = EigenMatrix<T>::From(*input0);
// auto Y = EigenMatrix<T>::From(*input1);
// auto Z = EigenMatrix<T>::From(*output);
// auto& place = context.GetEigenDevice<Place>();
// Z.device(place) = X.contract(Y, dim_pair);
}
};
...
...
@@ -50,27 +54,31 @@ template <typename Place, typename T>
class
MulGradKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input0
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
input1
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
auto
*
input2
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
X
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
Y
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
auto
*
dOut
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
output0
=
ctx
.
Output
<
Tensor
>
(
0
);
auto
*
output1
=
ctx
.
Output
<
Tensor
>
(
1
);
output0
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
output1
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
*
dX
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dY
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
// auto* dXdata = dX->template mutable_data<T>(ctx.GetPlace());
// auto* dYdata = dY->template mutable_data<T>(ctx.GetPlace());
auto
*
device_context
=
const_cast
<
platform
::
DeviceContext
*>
(
ctx
.
device_context_
);
math
::
matmul
<
Place
,
T
>
(
*
dOut
,
false
,
*
Y
,
true
,
1
,
dX
,
0
,
device_context
);
math
::
matmul
<
Place
,
T
>
(
*
X
,
true
,
*
dOut
,
false
,
1
,
dY
,
0
,
device_context
);
auto
X
=
EigenMatrix
<
T
>::
From
(
*
input0
);
auto
Y
=
EigenMatrix
<
T
>::
From
(
*
input1
);
auto
dOut
=
EigenMatrix
<
T
>::
From
(
*
input2
);
auto
dX
=
EigenMatrix
<
T
>::
From
(
*
output0
);
auto
dY
=
EigenMatrix
<
T
>::
From
(
*
output1
);
//
auto X = EigenMatrix<T>::From(*input0);
//
auto Y = EigenMatrix<T>::From(*input1);
//
auto dOut = EigenMatrix<T>::From(*input2);
//
auto dX = EigenMatrix<T>::From(*output0);
//
auto dY = EigenMatrix<T>::From(*output1);
// dX = Out@G * Y'
// dY = X' * Out@G
auto
place
=
ctx
.
GetEigenDevice
<
Place
>
();
//
auto place = ctx.GetEigenDevice<Place>();
// TODO(dzh,qijun) : need transpose feature of blas library
// Eigen Tensor does not support it very well
// dX.device(place) =
dOut.contract(dOut, transpose
)
// dX.device(place) =
matmul(input2,
)
}
};
...
...
python/paddle/v2/framework/tests/test_mul_op.py
浏览文件 @
632b320e
import
unittest
from
op_test_util
import
OpTestMeta
import
numpy
as
np
from
gradient_checker
import
GradientChecker
,
create_op
from
op_test_util
import
OpTestMeta
class
TestMulOp
(
unittest
.
TestCase
):
...
...
@@ -15,6 +16,16 @@ class TestMulOp(unittest.TestCase):
self
.
outputs
=
{
'Out'
:
np
.
dot
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
class
MulGradOpTest
(
GradientChecker
):
def
test_mul
(
self
):
op
=
create_op
(
"mul"
)
inputs
=
{
'X'
:
np
.
random
.
random
((
32
,
84
)).
astype
(
"float32"
),
'Y'
:
np
.
random
.
random
((
84
,
100
)).
astype
(
"float32"
)
}
self
.
check_grad
(
op
,
inputs
,
set
([
"X"
,
"Y"
]),
"Out"
)
# TODO(dzh,qijun) : mulgrad test case need transpose feature of blas library
if
__name__
==
'__main__'
:
...
...
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