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
PaddlePaddle
Paddle
提交
cf799a6a
P
Paddle
项目概览
PaddlePaddle
/
Paddle
12 个月 前同步成功
通知
2292
Star
20931
Fork
5422
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1423
列表
看板
标记
里程碑
合并请求
543
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
1,423
Issue
1,423
列表
看板
标记
里程碑
合并请求
543
合并请求
543
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 >>
未验证
提交
cf799a6a
编写于
8月 10, 2018
作者:
S
sneaxiy
提交者:
GitHub
8月 10, 2018
浏览文件
操作
浏览文件
下载
差异文件
Merge pull request #12553 from sneaxiy/refine_softmax_with_cross_entropy
Refine softmax_with_cross_entropy op
上级
772ceee3
1b4515f6
变更
1
显示空白变更内容
内联
并排
Showing
1 changed file
with
209 addition
and
9 deletion
+209
-9
paddle/fluid/operators/softmax_with_cross_entropy_op.cu
paddle/fluid/operators/softmax_with_cross_entropy_op.cu
+209
-9
未找到文件。
paddle/fluid/operators/softmax_with_cross_entropy_op.cu
浏览文件 @
cf799a6a
/* Copyright (c) 201
6
PaddlePaddle Authors. All Rights Reserved.
/* Copyright (c) 201
8
PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
...
...
@@ -14,6 +14,8 @@ limitations under the License. */
#define EIGEN_USE_GPU
#include <cub/cub.cuh>
#include "paddle/fluid/operators/math/cross_entropy.h"
#include "paddle/fluid/operators/softmax_with_cross_entropy_op.h"
namespace
paddle
{
...
...
@@ -53,8 +55,196 @@ __global__ void SoftCrossEntropyGradientKernel(T* logit_grad,
logit_grad
[
ids
]
=
loss_grad
[
row_ids
]
*
(
logit_grad
[
ids
]
-
labels
[
ids
]);
}
}
}
// namespace
static
__device__
__forceinline__
float
real_exp
(
float
x
)
{
return
expf
(
x
);
}
static
__device__
__forceinline__
double
real_exp
(
double
x
)
{
return
exp
(
x
);
}
static
__device__
__forceinline__
float
real_log
(
float
x
)
{
return
math
::
TolerableValue
<
float
>
()(
logf
(
x
));
}
static
__device__
__forceinline__
double
real_log
(
double
x
)
{
return
math
::
TolerableValue
<
double
>
()(
log
(
x
));
}
/** In the following codes, 3 CUDA kernels are implemented to calculate softmax
* and loss **/
/*
Supposing the x is `logits` and y is `labels`, the equations are as
followings:
cross\_entropy_i = \sum_{j}[- y_i_j * log({e^{x_i_j}/\sum_{j}e^{x_i_j}})]
= \sum_{j}[- y_i_j * log({e^{x_i_j - max_i}/\sum_{j}e^{x_i_j-max_i}})]
= \sum_{j}[-y_i_j * (x_i_j - max_i - log\sum_{j}e^{x_i_j - max_i})]
= \sum_{j}[-y_i_j * (x_i_j - max_i - logDiffMaxSum_i)]
= \sum_{j}(-y_i_j * tmp_i_j)
softmax_i_j = e^{tmp_i_j}
where:
max_i = \max_{j}{x_i_j}
logDiffMaxSum_i = log\sum_{j}e^{x_i_j - max_i}
tmp_i_j = x_i_j - max_i - logDiffMaxSum_i
Therefore, the calculation can be separated into 3 steps:
Step 1: row-wise operation to calculate max_i
Step 2: row-wise operation to calculate logDiffMaxSum_i
Step 3: caculate tmp_i_j, and finally get softmax_i_j and cross\_entropy_i
To save memory, we can share memory among max_i, logDiffMaxSum_i and
cross\_entropy_i.
In this way, the 3 steps should be changed to:
Step 1 (RowReductionForMax): row-wise operation to calculate max_i
Step 2 (RowReductionForDiffMaxSum): calculate immediate result of softmax'_i_j =
x_i_j - max_i, and row-wise operation to calculate logDiffMaxSum_i
Step 3 (RowReductionForSoftmaxAndCrossEntropy): calculate tmp_i_j = softmax'_i_j
- logDiffMaxSum_i, and finally get softmax_i_j and cross\_entropy_i
*/
// There are 3 kinds of reduce algorithms in cub:
// BLOCK_REDUCE_RAKING_COMMUTATIVE_ONLY
// BLOCK_REDUCE_RAKING
// BLOCK_REDUCE_WARP_REDUCTIONS (default)
template
<
typename
T
,
int
BlockDim
>
using
BlockReduce
=
cub
::
BlockReduce
<
T
,
BlockDim
/*, cub::BLOCK_REDUCE_WARP_REDUCTIONS*/
>
;
template
<
typename
T
,
int
BlockDim
>
using
BlockReduceTempStorage
=
typename
BlockReduce
<
T
,
BlockDim
>::
TempStorage
;
// Make sure that BlockDim <= feature_size
// This kernel is used to calculate the max element of each row
template
<
typename
T
,
int
BlockDim
>
__global__
void
RowReductionForMax
(
const
T
*
logits_data
,
T
*
max_data
,
int
feature_size
)
{
__shared__
BlockReduceTempStorage
<
T
,
BlockDim
>
temp_storage
;
auto
beg_idx
=
feature_size
*
blockIdx
.
x
+
threadIdx
.
x
;
auto
end_idx
=
feature_size
*
(
blockIdx
.
x
+
1
);
T
cur_max
=
logits_data
[
beg_idx
];
beg_idx
+=
BlockDim
;
while
(
beg_idx
<
end_idx
)
{
if
(
cur_max
<
logits_data
[
beg_idx
])
{
cur_max
=
logits_data
[
beg_idx
];
}
beg_idx
+=
BlockDim
;
}
cur_max
=
BlockReduce
<
T
,
BlockDim
>
(
temp_storage
).
Reduce
(
cur_max
,
cub
::
Max
());
if
(
threadIdx
.
x
==
0
)
{
max_data
[
blockIdx
.
x
]
=
cur_max
<
-
64
?
-
64
:
cur_max
;
}
}
// Make sure that BlockDim <= feature_size
template
<
typename
T
,
int
BlockDim
>
__global__
void
RowReductionForDiffMaxSum
(
const
T
*
logits_data
,
T
*
max_data
,
T
*
softmax
,
int
feature_size
)
{
__shared__
BlockReduceTempStorage
<
T
,
BlockDim
>
temp_storage
;
auto
beg_idx
=
feature_size
*
blockIdx
.
x
+
threadIdx
.
x
;
auto
end_idx
=
feature_size
*
(
blockIdx
.
x
+
1
);
auto
block_max
=
max_data
[
blockIdx
.
x
];
softmax
[
beg_idx
]
=
logits_data
[
beg_idx
]
-
block_max
;
T
diff_max_sum
=
real_exp
(
softmax
[
beg_idx
]);
beg_idx
+=
BlockDim
;
while
(
beg_idx
<
end_idx
)
{
softmax
[
beg_idx
]
=
logits_data
[
beg_idx
]
-
block_max
;
diff_max_sum
+=
real_exp
(
softmax
[
beg_idx
]);
beg_idx
+=
BlockDim
;
}
diff_max_sum
=
BlockReduce
<
T
,
BlockDim
>
(
temp_storage
).
Reduce
(
diff_max_sum
,
cub
::
Sum
());
if
(
threadIdx
.
x
==
0
)
max_data
[
blockIdx
.
x
]
=
real_log
(
diff_max_sum
);
}
// Make sure that BlockDim <= feature_size
template
<
typename
T
,
int
BlockDim
>
__global__
void
RowReductionForSoftmaxAndCrossEntropy
(
const
T
*
logits_data
,
const
T
*
labels_data
,
T
*
loss_data
,
T
*
softmax
,
int
feature_size
)
{
__shared__
BlockReduceTempStorage
<
T
,
BlockDim
>
temp_storage
;
auto
beg_idx
=
feature_size
*
blockIdx
.
x
+
threadIdx
.
x
;
auto
end_idx
=
feature_size
*
(
blockIdx
.
x
+
1
);
// log_diff_max_sum shares memory with loss
auto
block_log_diff_max_sum
=
loss_data
[
blockIdx
.
x
];
auto
tmp
=
softmax
[
beg_idx
]
-
block_log_diff_max_sum
;
softmax
[
beg_idx
]
=
real_exp
(
tmp
);
auto
loss
=
-
labels_data
[
beg_idx
]
*
tmp
;
beg_idx
+=
BlockDim
;
while
(
beg_idx
<
end_idx
)
{
tmp
=
softmax
[
beg_idx
]
-
block_log_diff_max_sum
;
softmax
[
beg_idx
]
=
real_exp
(
tmp
);
loss
-=
(
labels_data
[
beg_idx
]
*
tmp
);
beg_idx
+=
BlockDim
;
}
loss
=
BlockReduce
<
T
,
BlockDim
>
(
temp_storage
).
Reduce
(
loss
,
cub
::
Sum
());
if
(
threadIdx
.
x
==
0
)
loss_data
[
blockIdx
.
x
]
=
loss
;
}
template
<
typename
T
>
__global__
void
SetSoftmaxToOneWhenFeatureSizeIsOne
(
T
*
out
,
int
batch_size
)
{
auto
idx
=
threadIdx
.
x
+
blockIdx
.
x
*
blockDim
.
x
;
if
(
idx
<
batch_size
)
out
[
idx
]
=
static_cast
<
T
>
(
1
);
}
template
<
typename
T
>
static
void
SoftmaxWithCrossEntropyFusedKernel
(
const
T
*
logits_data
,
const
T
*
labels_data
,
T
*
softmax_data
,
T
*
loss_data
,
int
batch_size
,
int
feature_size
,
cudaStream_t
stream
)
{
constexpr
int
kMaxBlockDim
=
512
;
int
block_dim
=
feature_size
>=
kMaxBlockDim
?
kMaxBlockDim
:
(
1
<<
static_cast
<
int
>
(
std
::
log2
(
feature_size
)));
#define CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(BlockDim) \
case BlockDim: \
RowReductionForMax<T, BlockDim><<<batch_size, BlockDim, 0, stream>>>( \
logits_data, loss_data, feature_size); \
RowReductionForDiffMaxSum<T, \
BlockDim><<<batch_size, BlockDim, 0, stream>>>( \
logits_data, loss_data, softmax_data, feature_size); \
RowReductionForSoftmaxAndCrossEntropy< \
T, BlockDim><<<batch_size, BlockDim, 0, stream>>>( \
logits_data, labels_data, loss_data, softmax_data, feature_size); \
break
switch
(
block_dim
)
{
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
512
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
256
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
128
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
64
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
32
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
16
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
8
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
4
);
CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
(
2
);
case
1
:
SetSoftmaxToOneWhenFeatureSizeIsOne
<<<
(
batch_size
+
kMaxBlockDim
-
1
)
/
kMaxBlockDim
,
kMaxBlockDim
,
0
,
stream
>>>
(
softmax_data
,
batch_size
);
cudaMemsetAsync
(
loss_data
,
0
,
batch_size
,
stream
);
break
;
default:
PADDLE_THROW
(
"BlockDim must be 2^n in softmax_with_cross_entropy_op"
);
break
;
}
#undef CALL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
}
template
<
typename
T
>
class
SoftmaxWithCrossEntropyCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
...
...
@@ -66,14 +256,24 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
Tensor
*
softmax
=
context
.
Output
<
Tensor
>
(
"Softmax"
);
Tensor
*
loss
=
context
.
Output
<
Tensor
>
(
"Loss"
);
softmax
->
mutable_data
<
T
>
(
context
.
GetPlace
());
loss
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
*
softmax_data
=
softmax
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
*
loss_data
=
loss
->
mutable_data
<
T
>
(
context
.
GetPlace
());
math
::
SoftmaxFunctor
<
platform
::
CUDADeviceContext
,
T
>
()(
context
.
cuda_device_context
(),
logits
,
softmax
);
auto
soft_label
=
context
.
Attr
<
bool
>
(
"soft_label"
);
if
(
soft_label
)
{
int
batch_size
=
logits
->
dims
()[
0
];
int
feature_size
=
logits
->
dims
()[
1
];
auto
*
logits_data
=
logits
->
data
<
T
>
();
auto
*
labels_data
=
labels
->
data
<
T
>
();
SoftmaxWithCrossEntropyFusedKernel
(
logits_data
,
labels_data
,
softmax_data
,
loss_data
,
batch_size
,
feature_size
,
context
.
cuda_device_context
().
stream
());
}
else
{
math
::
SoftmaxCUDNNFunctor
<
T
>
()(
context
.
cuda_device_context
(),
logits
,
softmax
);
math
::
CrossEntropyFunctor
<
platform
::
CUDADeviceContext
,
T
>
()(
context
.
cuda_device_context
(),
loss
,
softmax
,
labels
,
context
.
Attr
<
bool
>
(
"soft_label"
));
context
.
cuda_device_context
(),
loss
,
softmax
,
labels
,
false
);
}
}
};
...
...
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