Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
wk1234123
Paddle
提交
97509b68
P
Paddle
项目概览
wk1234123
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
4
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
97509b68
编写于
9月 26, 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
cross entropy as a functor to avoid duplicated codes.
上级
40aee48a
变更
9
显示空白变更内容
内联
并排
Showing
9 changed file
with
251 addition
and
212 deletion
+251
-212
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+5
-1
paddle/operators/cross_entropy_op.cu
paddle/operators/cross_entropy_op.cu
+6
-77
paddle/operators/cross_entropy_op.h
paddle/operators/cross_entropy_op.h
+4
-34
paddle/operators/math/CMakeLists.txt
paddle/operators/math/CMakeLists.txt
+3
-0
paddle/operators/math/cross_entropy.cc
paddle/operators/math/cross_entropy.cc
+59
-0
paddle/operators/math/cross_entropy.cu
paddle/operators/math/cross_entropy.cu
+111
-0
paddle/operators/math/cross_entropy.h
paddle/operators/math/cross_entropy.h
+48
-0
paddle/operators/softmax_with_cross_entropy_op.cu
paddle/operators/softmax_with_cross_entropy_op.cu
+10
-76
paddle/operators/softmax_with_cross_entropy_op.h
paddle/operators/softmax_with_cross_entropy_op.h
+5
-24
未找到文件。
paddle/operators/CMakeLists.txt
浏览文件 @
97509b68
...
...
@@ -88,10 +88,14 @@ add_subdirectory(math)
set
(
DEPS_OPS
recurrent_op
cond_op
)
cond_op
cross_entropy_op
softmax_with_cross_entropy_op
)
op_library
(
recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS framework_proto tensor net_op
)
op_library
(
cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op
)
op_library
(
cross_entropy_op DEPS cross_entropy_function
)
op_library
(
softmax_with_cross_entropy_op DEPS cross_entropy_function softmax_function
)
list
(
REMOVE_ITEM GENERAL_OPS
${
DEPS_OPS
}
)
foreach
(
src
${
GENERAL_OPS
}
)
...
...
paddle/operators/cross_entropy_op.cu
浏览文件 @
97509b68
...
...
@@ -12,62 +12,12 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/op_registry.h"
#include "paddle/operators/cross_entropy_op.h"
#include "paddle/platform/assert.h"
#include "paddle/platform/hostdevice.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
__global__
void
CrossEntropyKernel
(
T
*
Y
,
const
T
*
X
,
const
int
*
label
,
const
int
N
,
const
int
D
)
{
// TOOD(qingqing) define CUDA_1D_KERNEL_LOOP macro in a common file.
// CUDA_1D_KERNEL_LOOP(i, N) {
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
]
=
-
TolerableValue
<
T
>
()(
log
(
X
[
i
*
D
+
label
[
i
]]));
}
}
template
<
typename
T
>
__device__
__forceinline__
T
sum_single_warp
(
T
val
)
{
val
+=
__shfl_down
(
val
,
16
);
val
+=
__shfl_down
(
val
,
8
);
val
+=
__shfl_down
(
val
,
4
);
val
+=
__shfl_down
(
val
,
2
);
val
+=
__shfl_down
(
val
,
1
);
return
val
;
}
template
<
typename
T
>
__global__
void
SoftCrossEntropyKernel
(
T
*
Y
,
const
T
*
X
,
const
T
*
label
,
const
int
class_num
)
{
int
tid
=
threadIdx
.
x
;
extern
__shared__
T
d_sum
[];
d_sum
[
tid
]
=
0
;
int
cur_idx
=
tid
;
int
next_idx
=
blockIdx
.
x
*
class_num
+
tid
;
while
(
cur_idx
<
class_num
)
{
d_sum
[
tid
]
+=
TolerableValue
<
T
>
()(
std
::
log
(
X
[
next_idx
]))
*
label
[
next_idx
];
next_idx
+=
blockDim
.
x
;
cur_idx
+=
blockDim
.
x
;
}
__syncthreads
();
for
(
unsigned
int
stride
=
blockDim
.
x
>>
1
;
stride
>=
32
;
stride
>>=
1
)
{
if
(
tid
<
stride
)
d_sum
[
tid
]
+=
d_sum
[
tid
+
stride
];
__syncthreads
();
}
T
val
=
d_sum
[
tid
];
val
=
sum_single_warp
<
T
>
(
val
);
if
(
tid
==
0
)
Y
[
blockIdx
.
x
]
=
-
val
;
}
namespace
{
// TODO(qingqing): make zero setting a common function.
template
<
typename
T
>
__global__
void
Zero
(
T
*
X
,
const
int
N
)
{
...
...
@@ -100,6 +50,7 @@ __global__ void SoftCrossEntropyGradientKernel(T* dX, const T* dY, const T* X,
dX
[
ids
]
=
-
label
[
ids
]
*
dY
[
row_ids
]
/
X
[
ids
];
}
}
}
// namespace
template
<
typename
T
>
class
CrossEntropyOpCUDAKernel
:
public
framework
::
OpKernel
{
...
...
@@ -107,36 +58,13 @@ class CrossEntropyOpCUDAKernel : public framework::OpKernel {
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()),
"This kernel only runs on GPU device."
);
const
Tensor
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
const
Tensor
*
label
=
ctx
.
Input
<
Tensor
>
(
"Label"
);
Tensor
*
y
=
ctx
.
Output
<
Tensor
>
(
"Y"
);
y
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
T
*
x_data
=
x
->
data
<
T
>
();
T
*
y_data
=
y
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int
batch_size
=
x
->
dims
()[
0
];
int
class_num
=
x
->
dims
()[
1
];
if
(
ctx
.
Attr
<
bool
>
(
"softLabel"
))
{
auto
*
label_data
=
ctx
.
Input
<
Tensor
>
(
"Label"
)
->
data
<
T
>
();
int
block
=
class_num
>
512
?
512
:
pow
(
2
,
int
(
std
::
log2
(
class_num
)));
SoftCrossEntropyKernel
<
T
><<<
batch_size
,
block
,
block
*
sizeof
(
T
),
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
()
>>>
(
y_data
,
x_data
,
label_data
,
class_num
);
}
else
{
auto
*
label_data
=
ctx
.
Input
<
Tensor
>
(
"Label"
)
->
data
<
int
>
();
int
block
=
512
;
int
grid
=
(
batch_size
+
block
-
1
)
/
block
;
CrossEntropyKernel
<
T
><<<
grid
,
block
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
()
>>>
(
y_data
,
x_data
,
label_data
,
batch_size
,
class_num
);
}
math
::
CrossEntropyFunctor
<
platform
::
GPUPlace
,
T
>
()(
ctx
,
y
,
x
,
label
,
ctx
.
Attr
<
bool
>
(
"softLabel"
));
}
};
...
...
@@ -150,6 +78,7 @@ class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
const
Tensor
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
const
Tensor
*
label
=
ctx
.
Input
<
Tensor
>
(
"Label"
);
Tensor
*
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
T
*
dy_data
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
))
->
data
<
T
>
();
...
...
paddle/operators/cross_entropy_op.h
浏览文件 @
97509b68
...
...
@@ -15,7 +15,7 @@ limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/
platform/hostdevice
.h"
#include "paddle/
operators/math/cross_entropy
.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -25,18 +25,6 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
T
>
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
;
}
};
template
<
typename
T
>
class
CrossEntropyOpKernel
:
public
framework
::
OpKernel
{
public:
...
...
@@ -46,28 +34,10 @@ class CrossEntropyOpKernel : public framework::OpKernel {
const
Tensor
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
const
Tensor
*
labels
=
ctx
.
Input
<
Tensor
>
(
"Label"
);
Tensor
*
y
=
ctx
.
Output
<
Tensor
>
(
"Y"
);
T
*
y_data
=
y
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
int
batch_size
=
x
->
dims
()[
0
];
if
(
ctx
.
Attr
<
bool
>
(
"softLabel"
))
{
auto
prob
=
EigenMatrix
<
T
>::
From
(
*
x
);
auto
lbl_mat
=
EigenMatrix
<
T
>::
From
(
*
labels
);
auto
loss
=
EigenMatrix
<
T
>::
From
(
*
y
);
y
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
loss
.
device
(
ctx
.
GetEigenDevice
<
platform
::
CPUPlace
>
())
=
-
((
lbl_mat
*
prob
.
log
().
unaryExpr
(
TolerableValue
<
T
>
()))
.
sum
(
Eigen
::
DSizes
<
int
,
1
>
(
1
))
.
reshape
(
Eigen
::
DSizes
<
int
,
2
>
(
batch_size
,
1
)));
}
else
{
const
int
class_num
=
x
->
dims
()[
1
];
const
T
*
x_data
=
x
->
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
]
=
-
TolerableValue
<
T
>
()(
std
::
log
(
x_data
[
index
]));
}
}
math
::
CrossEntropyFunctor
<
platform
::
CPUPlace
,
T
>
()(
ctx
,
y
,
x
,
labels
,
ctx
.
Attr
<
bool
>
(
"softLabel"
));
}
};
...
...
paddle/operators/math/CMakeLists.txt
浏览文件 @
97509b68
...
...
@@ -3,10 +3,13 @@ if(WITH_GPU)
im2col.cu DEPS cblas device_context operator
)
nv_library
(
softmax_function SRCS softmax.cc softmax.cu
DEPS operator
)
nv_library
(
cross_entropy_function SRCS cross_entropy.cc cross_entropy.cu
DEPS operator
)
else
()
cc_library
(
math_function SRCS math_function.cc im2col.cc
DEPS cblas device_context operator
)
cc_library
(
softmax_function SRCS softmax.cc DEPS operator
)
cc_library
(
cross_entropy_function SRCS cross_entropy.cc DEPS operator
)
endif
()
nv_test
(
math_function_test SRCS math_function_test.cc DEPS math_function tensor
)
...
...
paddle/operators/math/cross_entropy.cc
0 → 100644
浏览文件 @
97509b68
/* 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
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. */
#include "paddle/operators/math/cross_entropy.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
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
>
class
CrossEntropyFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
void
operator
()(
const
framework
::
ExecutionContext
&
ctx
,
framework
::
Tensor
*
out
,
const
framework
::
Tensor
*
prob
,
const
framework
::
Tensor
*
labels
,
const
bool
softLabel
)
{
const
int
batch_size
=
prob
->
dims
()[
0
];
if
(
softLabel
)
{
auto
in
=
EigenMatrix
<
T
>::
From
(
*
prob
);
auto
lbl
=
EigenMatrix
<
T
>::
From
(
*
labels
);
auto
loss
=
EigenMatrix
<
T
>::
From
(
*
out
);
loss
.
device
(
ctx
.
GetEigenDevice
<
platform
::
CPUPlace
>
())
=
-
((
lbl
*
in
.
log
().
unaryExpr
(
math
::
TolerableValue
<
T
>
()))
.
sum
(
Eigen
::
DSizes
<
int
,
1
>
(
1
))
.
reshape
(
Eigen
::
DSizes
<
int
,
2
>
(
batch_size
,
1
)));
}
else
{
const
int
class_num
=
prob
->
dims
()[
1
];
const
T
*
prob_data
=
prob
->
data
<
T
>
();
T
*
loss_data
=
out
->
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
];
loss_data
[
i
]
=
-
math
::
TolerableValue
<
T
>
()(
std
::
log
(
prob_data
[
index
]));
}
}
}
};
template
class
CrossEntropyFunctor
<
platform
::
CPUPlace
,
float
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/cross_entropy.cu
0 → 100644
浏览文件 @
97509b68
/* 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
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. */
#include "paddle/operators/math/cross_entropy.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
namespace
{
template
<
typename
T
>
__global__
void
CrossEntropyKernel
(
T
*
Y
,
const
T
*
X
,
const
int
*
label
,
const
int
N
,
const
int
D
)
{
// TOOD(qingqing) define CUDA_1D_KERNEL_LOOP macro in a common file.
// CUDA_1D_KERNEL_LOOP(i, N) {
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
]
=
-
math
::
TolerableValue
<
T
>
()(
log
(
X
[
i
*
D
+
label
[
i
]]));
}
}
template
<
typename
T
>
__device__
__forceinline__
T
sum_single_warp
(
T
val
)
{
val
+=
__shfl_down
(
val
,
16
);
val
+=
__shfl_down
(
val
,
8
);
val
+=
__shfl_down
(
val
,
4
);
val
+=
__shfl_down
(
val
,
2
);
val
+=
__shfl_down
(
val
,
1
);
return
val
;
}
template
<
typename
T
>
__global__
void
SoftCrossEntropyKernel
(
T
*
Y
,
const
T
*
X
,
const
T
*
label
,
const
int
class_num
)
{
int
tid
=
threadIdx
.
x
;
extern
__shared__
T
d_sum
[];
d_sum
[
tid
]
=
0
;
int
cur_idx
=
tid
;
int
next_idx
=
blockIdx
.
x
*
class_num
+
tid
;
while
(
cur_idx
<
class_num
)
{
d_sum
[
tid
]
+=
math
::
TolerableValue
<
T
>
()(
std
::
log
(
X
[
next_idx
]))
*
label
[
next_idx
];
next_idx
+=
blockDim
.
x
;
cur_idx
+=
blockDim
.
x
;
}
__syncthreads
();
for
(
unsigned
int
stride
=
blockDim
.
x
>>
1
;
stride
>=
32
;
stride
>>=
1
)
{
if
(
tid
<
stride
)
d_sum
[
tid
]
+=
d_sum
[
tid
+
stride
];
__syncthreads
();
}
T
val
=
d_sum
[
tid
];
val
=
sum_single_warp
<
T
>
(
val
);
if
(
tid
==
0
)
Y
[
blockIdx
.
x
]
=
-
val
;
}
}
// namespace
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
class
CrossEntropyFunctor
<
platform
::
GPUPlace
,
T
>
{
public:
void
operator
()(
const
framework
::
ExecutionContext
&
ctx
,
framework
::
Tensor
*
out
,
const
framework
::
Tensor
*
prob
,
const
framework
::
Tensor
*
labels
,
bool
softLabel
)
{
const
T
*
prob_data
=
prob
->
data
<
T
>
();
T
*
loss_data
=
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
int
batch_size
=
prob
->
dims
()[
0
];
int
class_num
=
prob
->
dims
()[
1
];
if
(
softLabel
)
{
const
T
*
label_data
=
labels
->
data
<
T
>
();
int
block
=
class_num
>
512
?
512
:
pow
(
2
,
int
(
std
::
log2
(
class_num
)));
SoftCrossEntropyKernel
<
T
><<<
batch_size
,
block
,
block
*
sizeof
(
T
),
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
()
>>>
(
loss_data
,
prob_data
,
label_data
,
class_num
);
}
else
{
const
int
*
label_data
=
labels
->
data
<
int
>
();
int
block
=
512
;
int
grid
=
(
batch_size
+
block
-
1
)
/
block
;
CrossEntropyKernel
<
T
><<<
grid
,
block
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
ctx
.
device_context
())
.
stream
()
>>>
(
loss_data
,
prob_data
,
label_data
,
batch_size
,
class_num
);
}
}
};
template
class
CrossEntropyFunctor
<
platform
::
GPUPlace
,
float
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/cross_entropy.h
0 → 100644
浏览文件 @
97509b68
/* 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
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. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/hostdevice.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
template
<
typename
T
>
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
;
}
};
template
<
typename
Place
,
typename
T
>
class
CrossEntropyFunctor
{
public:
// (TODO caoying) it is much better to use DeviceContext as the first
// parameter.
void
operator
()(
const
framework
::
ExecutionContext
&
context
,
framework
::
Tensor
*
out
,
const
framework
::
Tensor
*
prob
,
const
framework
::
Tensor
*
labels
,
const
bool
softLabel
);
};
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/softmax_with_cross_entropy_op.cu
浏览文件 @
97509b68
...
...
@@ -14,26 +14,14 @@
#define EIGEN_USE_GPU
#include "paddle/framework/op_registry.h"
#include "paddle/operators/cross_entropy_op.h"
#include "paddle/operators/math/softmax.h"
#include "paddle/operators/softmax_with_cross_entropy_op.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
__global__
void
CrossEntropy
(
T
*
out
,
const
T
*
softmax_out
,
const
int
*
labels
,
const
int
batch_size
,
const
int
class_num
)
{
int
i
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
i
<
batch_size
)
{
PADDLE_ASSERT
(
labels
[
i
]
>=
0
&&
labels
[
i
]
<
class_num
);
out
[
i
]
=
-
TolerableValue
<
T
>
()(
std
::
log
(
softmax_out
[
i
*
class_num
+
labels
[
i
]]));
}
}
namespace
{
template
<
typename
T
>
__global__
void
CrossEntropyGrad
(
T
*
out_grad
,
const
T
*
in_grad
,
const
int
*
labels
,
const
int
batch_size
,
...
...
@@ -50,42 +38,6 @@ __global__ void CrossEntropyGrad(T* out_grad, const T* in_grad,
}
}
template
<
typename
T
>
__device__
__forceinline__
T
sum_single_warp
(
T
val
)
{
val
+=
__shfl_down
(
val
,
16
);
val
+=
__shfl_down
(
val
,
8
);
val
+=
__shfl_down
(
val
,
4
);
val
+=
__shfl_down
(
val
,
2
);
val
+=
__shfl_down
(
val
,
1
);
return
val
;
}
template
<
typename
T
>
__global__
void
SoftCrossEntropyKernel
(
T
*
Y
,
const
T
*
X
,
const
T
*
label
,
const
int
class_num
)
{
int
tid
=
threadIdx
.
x
;
extern
__shared__
T
d_sum
[];
d_sum
[
tid
]
=
0
;
int
cur_idx
=
tid
;
int
next_idx
=
blockIdx
.
x
*
class_num
+
tid
;
while
(
cur_idx
<
class_num
)
{
d_sum
[
tid
]
+=
TolerableValue
<
T
>
()(
std
::
log
(
X
[
next_idx
]))
*
label
[
next_idx
];
next_idx
+=
blockDim
.
x
;
cur_idx
+=
blockDim
.
x
;
}
__syncthreads
();
for
(
unsigned
int
stride
=
blockDim
.
x
>>
1
;
stride
>=
32
;
stride
>>=
1
)
{
if
(
tid
<
stride
)
d_sum
[
tid
]
+=
d_sum
[
tid
+
stride
];
__syncthreads
();
}
T
val
=
d_sum
[
tid
];
val
=
sum_single_warp
<
T
>
(
val
);
if
(
tid
==
0
)
Y
[
blockIdx
.
x
]
=
-
val
;
}
template
<
typename
T
>
__global__
void
SoftCrossEntropyGradientKernel
(
T
*
logit_grad
,
const
T
*
loss_grad
,
...
...
@@ -98,6 +50,7 @@ __global__ void SoftCrossEntropyGradientKernel(T* logit_grad,
logit_grad
[
ids
]
=
logit_grad
[
ids
]
*
loss_grad
[
row_ids
]
-
labels
[
ids
];
}
}
}
// namespace
template
<
typename
T
>
class
SoftmaxWithCrossEntropyCUDAKernel
:
public
framework
::
OpKernel
{
...
...
@@ -105,36 +58,17 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel {
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
context
.
GetPlace
()),
"This kernel only runs on GPU device."
);
T
*
loss_data
=
context
.
Output
<
Tensor
>
(
"Loss"
)
->
mutable_data
<
T
>
(
context
.
GetPlace
());
const
Tensor
*
logits
=
context
.
Input
<
Tensor
>
(
"Logits"
);
const
Tensor
*
labels
=
context
.
Input
<
Tensor
>
(
"Label"
);
Tensor
*
softmax
=
context
.
Output
<
Tensor
>
(
"Softmax"
);
T
*
softmax_out
=
softmax
->
mutable_data
<
T
>
(
context
.
GetPlace
());
math
::
SoftmaxFunctor
<
platform
::
GPUPlace
,
T
>
()(
context
,
logits
,
softmax
);
const
int
batch_size
=
logits
->
dims
()[
0
];
const
int
class_num
=
logits
->
dims
()[
1
];
int
block
=
512
;
int
grid
=
(
batch_size
+
block
-
1
)
/
block
;
if
(
context
.
Attr
<
bool
>
(
"softLabel"
))
{
const
T
*
label_data
=
context
.
Input
<
Tensor
>
(
"Label"
)
->
data
<
T
>
(
);
block
=
class_num
>
512
?
512
:
pow
(
2
,
int
(
std
::
log2
(
class_num
)
));
Tensor
*
loss
=
context
.
Output
<
Tensor
>
(
"Loss"
);
softmax
->
mutable_data
<
T
>
(
context
.
GetPlace
()
);
loss
->
mutable_data
<
T
>
(
context
.
GetPlace
(
));
SoftCrossEntropyKernel
<
T
><<<
batch_size
,
block
,
block
*
sizeof
(
T
),
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
.
device_context
())
.
stream
()
>>>
(
loss_data
,
softmax_out
,
label_data
,
class_num
);
}
else
{
const
int
*
label_data
=
context
.
Input
<
Tensor
>
(
"Label"
)
->
data
<
int
>
();
CrossEntropy
<
T
><<<
grid
,
block
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
.
device_context
())
.
stream
()
>>>
(
loss_data
,
softmax_out
,
label_data
,
batch_size
,
class_num
);
}
math
::
SoftmaxFunctor
<
platform
::
GPUPlace
,
T
>
()(
context
,
logits
,
softmax
);
math
::
CrossEntropyFunctor
<
platform
::
GPUPlace
,
T
>
()(
context
,
loss
,
softmax
,
labels
,
context
.
Attr
<
bool
>
(
"softLabel"
));
}
};
...
...
paddle/operators/softmax_with_cross_entropy_op.h
浏览文件 @
97509b68
...
...
@@ -15,7 +15,7 @@
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/
cross_entropy_op
.h"
#include "paddle/operators/
math/cross_entropy
.h"
#include "paddle/operators/math/softmax.h"
namespace
paddle
{
...
...
@@ -37,31 +37,12 @@ class SoftmaxWithCrossEntropyKernel : public framework::OpKernel {
Tensor
*
softmax
=
context
.
Output
<
Tensor
>
(
"Softmax"
);
Tensor
*
loss
=
context
.
Output
<
Tensor
>
(
"Loss"
);
T
*
softmax_data
=
softmax
->
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
loss_data
=
loss
->
mutable_data
<
T
>
(
context
.
GetPlace
());
softmax
->
mutable_data
<
T
>
(
context
.
GetPlace
());
loss
->
mutable_data
<
T
>
(
context
.
GetPlace
());
math
::
SoftmaxFunctor
<
platform
::
CPUPlace
,
T
>
()(
context
,
logits
,
softmax
);
const
int
batch_size
=
logits
->
dims
()[
0
];
if
(
context
.
Attr
<
bool
>
(
"softLabel"
))
{
// (TODO caoying) the forward implementation can be further optimized.
// Current implementation is exactly cross entropy after softmax.
auto
prob
=
EigenMatrix
<
T
>::
From
(
*
softmax
);
auto
lbl_mat
=
EigenMatrix
<
T
>::
From
(
*
labels
);
auto
loss_mat
=
EigenMatrix
<
T
>::
From
(
*
loss
);
loss_mat
.
device
(
context
.
GetEigenDevice
<
platform
::
CPUPlace
>
())
=
-
((
lbl_mat
*
prob
.
log
().
unaryExpr
(
TolerableValue
<
T
>
()))
.
sum
(
Eigen
::
DSizes
<
int
,
1
>
(
1
))
.
reshape
(
Eigen
::
DSizes
<
int
,
2
>
(
batch_size
,
1
)));
}
else
{
const
int
*
label_data
=
labels
->
data
<
int
>
();
const
int
class_num
=
logits
->
dims
()[
1
];
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
loss_data
[
i
]
=
-
TolerableValue
<
T
>
()(
std
::
log
(
softmax_data
[
i
*
class_num
+
label_data
[
i
]]));
}
math
::
CrossEntropyFunctor
<
platform
::
CPUPlace
,
T
>
()(
context
,
loss
,
softmax
,
labels
,
context
.
Attr
<
bool
>
(
"softLabel"
));
}
};
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录