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141b8dbc
编写于
9月 21, 2017
作者:
C
caoying03
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
update the backward kernel.
上级
a3a8a090
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
59 addition
and
47 deletion
+59
-47
paddle/operators/cross_entropy_op.cu
paddle/operators/cross_entropy_op.cu
+20
-16
paddle/operators/cross_entropy_op.h
paddle/operators/cross_entropy_op.h
+39
-31
未找到文件。
paddle/operators/cross_entropy_op.cu
浏览文件 @
141b8dbc
...
...
@@ -28,27 +28,27 @@ __global__ void CrossEntropyKernel(T* Y, const T* X, const int* label,
for
(
int
i
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
i
<
N
;
i
+=
blockDim
.
x
*
gridDim
.
x
)
{
PADDLE_ASSERT
(
label
[
i
]
>=
0
&&
label
[
i
]
<
D
);
Y
[
i
]
=
-
tolerable_value
(
log
(
X
[
i
*
D
+
label
[
i
]]));
Y
[
i
]
=
-
TolerableValue
<
T
>
()
(
log
(
X
[
i
*
D
+
label
[
i
]]));
}
}
template
<
typename
T
,
int
b
lockSize
>
template
<
typename
T
,
int
B
lockSize
>
__global__
void
SoftCrossEntropyKernel
(
T
*
Y
,
const
T
*
X
,
const
T
*
label
,
const
int
N
,
const
int
D
)
{
int
tid
=
threadIdx
.
x
;
__shared__
T
d_sum
[
b
lockSize
];
__shared__
T
d_sum
[
B
lockSize
];
int
next_idx
=
blockIdx
.
x
*
D
+
tid
;
d_sum
[
tid
]
=
0
;
int
cur_idx
=
tid
;
while
(
cur_idx
<
D
)
{
d_sum
[
tid
]
+=
tolerable_value
(
std
::
log
(
X
[
next_idx
]))
*
label
[
next_idx
];
next_idx
+=
b
lockSize
;
cur_idx
+=
b
lockSize
;
d_sum
[
tid
]
+=
TolerableValue
<
T
>
()
(
std
::
log
(
X
[
next_idx
]))
*
label
[
next_idx
];
next_idx
+=
B
lockSize
;
cur_idx
+=
B
lockSize
;
}
__syncthreads
();
for
(
int
stride
=
b
lockSize
>>
1
;
stride
>
0
;
stride
>>=
1
)
{
for
(
int
stride
=
B
lockSize
>>
1
;
stride
>
0
;
stride
>>=
1
)
{
__syncthreads
();
if
(
tid
<
stride
)
{
next_idx
=
tid
+
stride
;
...
...
@@ -88,13 +88,12 @@ template <typename T>
__global__
void
SoftCrossEntropyGradientKernel
(
T
*
dX
,
const
T
*
dY
,
const
T
*
X
,
const
T
*
label
,
const
int
N
,
const
int
D
)
{
// TOOD(qingqing): optimize for this kernel
for
(
int
i
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
i
<
N
;
i
+=
blockDim
.
x
*
gridDim
.
x
)
{
for
(
int
j
=
0
;
j
<
D
;
++
j
)
{
int
idx
=
i
*
D
+
j
;
dX
[
idx
]
=
-
label
[
idx
]
*
dY
[
i
]
/
X
[
idx
];
}
int
row_ids
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
col_ids
=
blockIdx
.
y
*
blockDim
.
y
+
threadIdx
.
y
;
int
ids
=
row_ids
*
D
+
col_ids
;
if
(
ids
<
N
*
D
)
{
dX
[
ids
]
=
-
label
[
ids
]
*
dY
[
row_ids
]
/
X
[
ids
];
}
}
...
...
@@ -103,7 +102,7 @@ class CrossEntropyOpCUDAKernel : public framework::OpKernel {
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()),
"
It must use GPUPla
ce."
);
"
This kernel only runs on GPU devi
ce."
);
auto
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
y
=
ctx
.
Output
<
Tensor
>
(
"Y"
);
...
...
@@ -136,7 +135,7 @@ class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()),
"
It must use GPUPla
ce."
);
"
This kernel only runs on GPU devi
ce."
);
auto
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
...
...
@@ -156,6 +155,11 @@ class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
// TODO(qingqing): launch kernel on specified stream
// base on ExecutionContext.
if
(
ctx
.
Attr
<
int
>
(
"soft_label"
)
==
1
)
{
int
block_x
=
32
;
int
block_y
=
32
;
dim3
block
(
block_x
,
block_y
);
dim3
grid
((
n
+
block_x
-
1
)
/
block_x
,
(
d
+
block_y
-
1
)
/
block_y
);
auto
*
label_data
=
label
->
data
<
T
>
();
SoftCrossEntropyGradientKernel
<
T
><<<
grid
,
block
>>>
(
dx_data
,
dy_data
,
x_data
,
label_data
,
n
,
d
);
...
...
paddle/operators/cross_entropy_op.h
浏览文件 @
141b8dbc
...
...
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/platform/hostdevice.h"
...
...
@@ -20,19 +21,25 @@ namespace paddle {
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
T
>
HOSTDEVICE
T
tolerable_value
(
const
T
x
)
{
PADDLE_ASSERT
(
std
::
is_floating_point
<
T
>::
value
);
const
T
kApproInf
=
1e20
;
if
(
x
==
INFINITY
)
{
return
kApproInf
;
}
if
(
x
==
-
INFINITY
)
{
return
-
kApproInf
;
struct
TolerableValue
{
HOSTDEVICE
T
operator
()(
const
T
&
x
)
const
{
PADDLE_ASSERT
(
std
::
is_floating_point
<
T
>::
value
);
const
T
kApproInf
=
1e20
;
if
(
x
==
INFINITY
)
{
return
kApproInf
;
}
if
(
x
==
-
INFINITY
)
{
return
-
kApproInf
;
}
return
x
;
}
return
x
;
}
};
template
<
typename
T
>
class
CrossEntropyOpKernel
:
public
framework
::
OpKernel
{
...
...
@@ -40,33 +47,34 @@ class CrossEntropyOpKernel : public framework::OpKernel {
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
platform
::
is_cpu_place
(
ctx
.
GetPlace
()),
"It must use CPUPlace."
);
auto
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
y
=
ctx
.
Output
<
Tensor
>
(
"Y"
);
auto
*
x_data
=
x
->
data
<
T
>
();
const
Tensor
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
const
Tensor
*
labels
=
ctx
.
Input
<
Tensor
>
(
"Label"
);
Tensor
*
y
=
ctx
.
Output
<
Tensor
>
(
"Y"
);
y
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
*
y_data
=
y
->
data
<
T
>
();
int
batch_size
=
x
->
dims
()[
0
];
int
class_num
=
x
->
dims
()[
1
];
const
int
batch_size
=
x
->
dims
()[
0
];
if
(
ctx
.
Attr
<
int
>
(
"soft_label"
)
==
1
)
{
auto
*
label_data
=
ctx
.
Input
<
Tensor
>
(
"Label"
)
->
data
<
T
>
();
int
index
=
0
;
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
T
sum
=
static_cast
<
T
>
(
0
);
for
(
int
j
=
0
;
j
<
class_num
;
++
j
)
{
sum
+=
label_data
[
index
]
*
tolerable_value
(
std
::
log
(
x_data
[
index
]));
y_data
[
i
]
=
-
sum
;
index
++
;
}
}
auto
prob
=
EigenMatrix
<
T
>::
From
(
*
x
);
auto
lbl_mat
=
EigenMatrix
<
T
>::
From
(
*
labels
);
auto
loss
=
EigenMatrix
<
T
>::
From
(
*
y
);
// loss.device(ctx.GetEigenDevice<platform::CPUPlace>()) =
// prob.log().unaryExpr(TolerableValue<T>());
loss
.
device
(
ctx
.
GetEigenDevice
<
platform
::
CPUPlace
>
())
=
-
((
lbl_mat
*
prob
.
log
())
.
sum
(
Eigen
::
DSizes
<
int
,
1
>
(
1
))
.
reshape
(
Eigen
::
DSizes
<
int
,
2
>
(
batch_size
,
1
)));
}
else
{
auto
*
label_data
=
ctx
.
Input
<
Tensor
>
(
"Label"
)
->
data
<
int
>
();
const
int
class_num
=
x
->
dims
()[
1
];
const
T
*
x_data
=
x
->
data
<
T
>
();
T
*
y_data
=
y
->
data
<
T
>
();
const
int
*
label_data
=
labels
->
data
<
int
>
();
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
int
index
=
i
*
class_num
+
label_data
[
i
];
y_data
[
i
]
=
-
tolerable_value
(
std
::
log
(
x_data
[
index
]));
y_data
[
i
]
=
-
TolerableValue
<
T
>
()
(
std
::
log
(
x_data
[
index
]));
}
}
}
...
...
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