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体验新版 GitCode,发现更多精彩内容 >>
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0a6262d5
编写于
11月 13, 2017
作者:
P
peterzhang2029
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
fix warning
上级
665eb015
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
27 addition
and
25 deletion
+27
-25
paddle/operators/bilinear_tensor_product_op.h
paddle/operators/bilinear_tensor_product_op.h
+27
-25
未找到文件。
paddle/operators/bilinear_tensor_product_op.h
浏览文件 @
0a6262d5
...
...
@@ -43,24 +43,26 @@ class BilinearTensorProductKernel : public framework::OpKernel<T> {
auto
batch_size
=
x
->
dims
()[
0
];
auto
weight_dims
=
weight
->
dims
();
int
Out_dim
=
weight_dims
[
0
];
int
X_dim
=
weight_dims
[
1
];
int
Y_dim
=
weight_dims
[
2
];
auto
place
=
ctx
.
GetEigenDevice
<
Place
>
();
// Create the intermediate variable to caculate the result of
// Input(X) multiplied by Input(Weight_i), the formula is:
// left_mul = X Weight_i.
Tensor
left_mul
;
left_mul
.
mutable_data
<
T
>
(
framework
::
make_ddim
({
batch_size
,
weight_dims
[
2
]
}),
left_mul
.
mutable_data
<
T
>
(
framework
::
make_ddim
({
batch_size
,
Y_dim
}),
ctx
.
GetPlace
());
auto
left_mul_mat
=
EigenMatrix
<
T
>::
From
(
left_mul
);
for
(
size_t
i
=
0
;
i
<
weight_dims
[
0
]
;
++
i
)
{
for
(
int
i
=
0
;
i
<
Out_dim
;
++
i
)
{
auto
output_col_vec
=
output_mat
.
chip
(
i
,
1
);
Tensor
weight_mat
=
weight
->
Slice
(
i
,
i
+
1
).
Resize
(
framework
::
make_ddim
({
weight_dims
[
1
],
weight_dims
[
2
]
}));
Tensor
weight_mat
=
weight
->
Slice
(
i
,
i
+
1
).
Resize
(
framework
::
make_ddim
({
X_dim
,
Y_dim
}));
math
::
gemm
<
Place
,
T
>
(
ctx
.
device_context
(),
CblasNoTrans
,
CblasNoTrans
,
batch_size
,
weight_dims
[
2
],
weight_dims
[
1
],
1
,
x
->
data
<
T
>
(),
weight_mat
.
data
<
T
>
(),
0
,
left_mul
.
data
<
T
>
());
batch_size
,
Y_dim
,
X_dim
,
1
,
x
->
data
<
T
>
(),
weight_mat
.
data
<
T
>
(),
0
,
left_mul
.
data
<
T
>
());
output_col_vec
.
device
(
place
)
=
(
left_mul_mat
*
y_mat
).
sum
(
Eigen
::
DSizes
<
int
,
1
>
(
1
));
}
...
...
@@ -87,6 +89,9 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
auto
batch_size
=
x
->
dims
()[
0
];
auto
weight_dims
=
weight
->
dims
();
int
Out_dim
=
weight_dims
[
0
];
int
X_dim
=
weight_dims
[
1
];
int
Y_dim
=
weight_dims
[
2
];
auto
x_mat
=
EigenMatrix
<
T
>::
From
(
*
x
);
auto
y_mat
=
EigenMatrix
<
T
>::
From
(
*
y
);
...
...
@@ -95,13 +100,13 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
// Create the intermediate variable to caculate the Output(Y@Grad).
Tensor
x_scale
;
x_scale
.
mutable_data
<
T
>
(
framework
::
make_ddim
({
batch_size
,
weight_dims
[
1
]
}),
x_scale
.
mutable_data
<
T
>
(
framework
::
make_ddim
({
batch_size
,
X_dim
}),
ctx
.
GetPlace
());
auto
x_scale_mat
=
EigenMatrix
<
T
>::
From
(
x_scale
);
// Create the intermediate variable to caculate the Output(X@Grad).
Tensor
y_scale
;
y_scale
.
mutable_data
<
T
>
(
framework
::
make_ddim
({
batch_size
,
weight_dims
[
2
]
}),
y_scale
.
mutable_data
<
T
>
(
framework
::
make_ddim
({
batch_size
,
Y_dim
}),
ctx
.
GetPlace
());
auto
y_scale_mat
=
EigenMatrix
<
T
>::
From
(
y_scale
);
...
...
@@ -121,11 +126,11 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
// Caculate the Output(X@Grad) and Output(Y@Grad).
if
(
d_x
||
d_y
)
{
Eigen
::
DSizes
<
int
,
2
>
bcast_for_x
(
1
,
weight_dims
[
2
]
);
Eigen
::
DSizes
<
int
,
2
>
bcast_for_y
(
1
,
weight_dims
[
1
]
);
for
(
int
i
=
0
;
i
<
weight_dims
[
0
]
;
++
i
)
{
Eigen
::
DSizes
<
int
,
2
>
bcast_for_x
(
1
,
Y_dim
);
Eigen
::
DSizes
<
int
,
2
>
bcast_for_y
(
1
,
X_dim
);
for
(
int
i
=
0
;
i
<
Out_dim
;
++
i
)
{
Tensor
weight_i
=
weight
->
Slice
(
i
,
i
+
1
).
Resize
(
framework
::
make_ddim
({
weight_dims
[
1
],
weight_dims
[
2
]
}));
framework
::
make_ddim
({
X_dim
,
Y_dim
}));
auto
output_vec
=
d_out_mat
.
chip
(
i
,
1
);
if
(
d_x
)
{
y_scale_mat
.
device
(
place
)
=
...
...
@@ -133,9 +138,8 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
.
broadcast
(
bcast_for_x
)
*
y_mat
;
math
::
gemm
<
Place
,
T
>
(
ctx
.
device_context
(),
CblasNoTrans
,
CblasTrans
,
batch_size
,
weight_dims
[
1
],
weight_dims
[
2
],
1
,
y_scale
.
data
<
T
>
(),
weight_i
.
data
<
T
>
(),
1
,
d_x
->
data
<
T
>
());
batch_size
,
X_dim
,
Y_dim
,
1
,
y_scale
.
data
<
T
>
(),
weight_i
.
data
<
T
>
(),
1
,
d_x
->
data
<
T
>
());
}
if
(
d_y
)
{
x_scale_mat
.
device
(
place
)
=
...
...
@@ -143,9 +147,8 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
.
broadcast
(
bcast_for_y
)
*
x_mat
;
math
::
gemm
<
Place
,
T
>
(
ctx
.
device_context
(),
CblasNoTrans
,
CblasNoTrans
,
batch_size
,
weight_dims
[
2
],
weight_dims
[
1
],
1
,
x_scale
.
data
<
T
>
(),
weight_i
.
data
<
T
>
(),
1
,
d_y
->
data
<
T
>
());
batch_size
,
Y_dim
,
X_dim
,
1
,
x_scale
.
data
<
T
>
(),
weight_i
.
data
<
T
>
(),
1
,
d_y
->
data
<
T
>
());
}
}
}
...
...
@@ -153,19 +156,18 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
// Caculate the gradient of Input(Weight).
if
(
d_weight
)
{
d_weight
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
Eigen
::
DSizes
<
int
,
2
>
bcast_for_weight
(
1
,
weight_dims
[
1
]
);
for
(
int
i
=
0
;
i
<
weight_dims
[
0
]
;
++
i
)
{
Eigen
::
DSizes
<
int
,
2
>
bcast_for_weight
(
1
,
X_dim
);
for
(
int
i
=
0
;
i
<
Out_dim
;
++
i
)
{
Tensor
d_weight_i
=
d_weight
->
Slice
(
i
,
i
+
1
).
Resize
(
framework
::
make_ddim
({
weight_dims
[
1
],
weight_dims
[
2
]
}));
framework
::
make_ddim
({
X_dim
,
Y_dim
}));
auto
output_vec
=
d_out_mat
.
chip
(
i
,
1
);
x_scale_mat
.
device
(
place
)
=
output_vec
.
reshape
(
Eigen
::
DSizes
<
int
,
2
>
(
batch_size
,
1
))
.
broadcast
(
bcast_for_weight
)
*
x_mat
;
math
::
gemm
<
Place
,
T
>
(
ctx
.
device_context
(),
CblasTrans
,
CblasNoTrans
,
weight_dims
[
1
],
weight_dims
[
2
],
batch_size
,
1
,
x_scale
.
data
<
T
>
(),
y
->
data
<
T
>
(),
0
,
d_weight_i
.
data
<
T
>
());
X_dim
,
Y_dim
,
batch_size
,
1
,
x_scale
.
data
<
T
>
(),
y
->
data
<
T
>
(),
0
,
d_weight_i
.
data
<
T
>
());
}
}
...
...
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