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
19c554f9
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 >>
提交
19c554f9
编写于
1月 19, 2018
作者:
F
fengjiayi
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
update
上级
538f1ad2
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
59 addition
and
67 deletion
+59
-67
python/paddle/v2/fluid/clip.py
python/paddle/v2/fluid/clip.py
+38
-44
python/paddle/v2/fluid/tests/test_gradient_clip.py
python/paddle/v2/fluid/tests/test_gradient_clip.py
+21
-23
未找到文件。
python/paddle/v2/fluid/clip.py
浏览文件 @
19c554f9
...
...
@@ -112,58 +112,52 @@ class GradientClipByNorm(BaseGradientClipAttr):
class
GradientClipByGlobalNorm
(
BaseGradientClipAttr
):
global_norm_var
=
None
local_norm_var
=
None
clip_norm_var
=
None
scale_var
=
None
@
classmethod
def
init
(
cls
,
clip_norm
):
if
not
(
isinstance
(
clip_norm
,
int
)
or
isinstance
(
clip_norm
,
float
)):
raise
TypeError
(
"The 'clip_norm' must be a value of int or float"
)
cls
.
global_norm_var
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
"float32"
,
value
=
0.0
)
cls
.
local_norm_var
=
layers
.
create_tensor
(
dtype
=
"float32"
)
cls
.
clip_norm_var
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
"float32"
,
value
=
clip_norm
)
@
classmethod
def
check_init
(
cls
):
if
not
(
isinstance
(
cls
.
global_norm_var
,
framework
.
Variable
)
and
isinstance
(
cls
.
local_norm_var
,
framework
.
Variable
)
and
isinstance
(
cls
.
clip_norm_var
,
framework
.
Variable
)):
raise
ValueError
(
"Class 'GradientClipByGlobalNorm' has not been properly initialized.
\
Please call GradientClipByGlobalNorm.init() first."
)
def
__init__
(
self
,
clip_norm
,
group_name
=
"default_group"
):
if
not
isinstance
(
group_name
,
basestring
):
raise
TypeError
(
"'group_name' must be a basestring."
)
self
.
clip_norm
=
clip_norm
self
.
group_name
=
group_name
def
process_context
(
self
,
context
,
param
,
grad
):
cls
=
self
.
__class__
cls
.
check_init
()
if
self
.
group_name
not
in
context
:
context
[
self
.
group_name
]
=
[]
context
[
self
.
group_name
+
"_clip_value"
]
=
self
.
clip_norm
context
[
self
.
group_name
+
"_clip"
]
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
"float32"
,
value
=
self
.
clip_norm
)
else
:
if
not
self
.
clip_norm
==
context
[
self
.
group_name
+
"_clip_value"
]:
raise
ValueError
(
"All parameters' 'clip_norm' of a same group should be the same"
)
cls
.
local_norm_var
=
layers
.
reduce_sum
(
input
=
layers
.
pow
(
x
=
grad
,
factor
=
2.0
))
layers
.
sums
(
input
=
[
cls
.
local_norm_var
,
cls
.
global_norm_var
],
out
=
[
cls
.
global_norm_var
])
local_norm_var
=
layers
.
reduce_sum
(
input
=
layers
.
pow
(
x
=
grad
,
factor
=
2.0
))
context
[
self
.
group_name
].
append
(
local_norm_var
)
def
create_operators
(
self
,
param
,
grad
):
cls
=
self
.
__class__
cls
.
check_init
()
self
.
context
=
context
if
cls
.
scale_var
is
None
:
layers
.
sqrt
(
x
=
cls
.
global_norm_var
,
out
=
cls
.
global_norm_var
)
cls
.
scale_var
=
layers
.
elementwise_div
(
x
=
cls
.
clip_norm_var
,
def
create_operators
(
self
,
param
,
grad
):
group_scale_name
=
self
.
group_name
+
"_scale"
if
group_scale_name
not
in
self
.
context
:
group_norm_var
=
layers
.
sums
(
input
=
self
.
context
[
self
.
group_name
])
layers
.
sqrt
(
x
=
group_norm_var
,
out
=
group_norm_var
)
clip_var
=
self
.
context
[
self
.
group_name
+
"_clip"
]
group_scale_var
=
layers
.
elementwise_div
(
x
=
clip_var
,
y
=
layers
.
elementwise_max
(
x
=
cls
.
clip_norm_var
,
y
=
cls
.
global_norm_var
))
assert
cls
.
scale_var
.
shape
==
(
1L
,
)
x
=
clip_var
,
y
=
group_norm_var
))
assert
group_scale_var
.
shape
==
(
1L
,
)
self
.
context
[
group_scale_name
]
=
group_scale_var
new_grad
=
layers
.
elementwise_mul
(
x
=
grad
,
y
=
cls
.
scale_var
)
new_grad
=
layers
.
elementwise_mul
(
x
=
grad
,
y
=
self
.
context
[
group_scale_name
])
return
param
,
new_grad
def
gradient_clip_by_global_norm
(
clip_norm
,
param_list
=
None
,
program
=
None
):
def
gradient_clip_by_global_norm
(
clip_norm
,
param_list
=
None
,
group_name
=
"default_group"
,
program
=
None
):
if
program
is
None
:
program
=
framework
.
default_main_program
()
if
param_list
is
None
:
...
...
@@ -175,9 +169,9 @@ def gradient_clip_by_global_norm(clip_norm, param_list=None, program=None):
"'param_list' should be a list of Parameter or basestring(parameter's name)."
)
GradientClipByGlobalNorm
.
init
(
clip_norm
)
for
param
in
param_list
:
param
.
gradient_clip_attr
=
GradientClipByGlobalNorm
()
param
.
gradient_clip_attr
=
GradientClipByGlobalNorm
(
clip_norm
,
group_name
)
def
append_gradient_clip_ops
(
param_grad
):
...
...
python/paddle/v2/fluid/tests/test_gradient_clip.py
浏览文件 @
19c554f9
...
...
@@ -15,21 +15,10 @@ import numpy as np
import
paddle.v2
as
paddle
import
paddle.v2.fluid
as
fluid
def
_get_global_param_norm_
(
params_grads
):
res
=
fluid
.
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
"float32"
,
value
=
0.0
)
for
_
,
grad
in
params_grads
:
norm_var
=
fluid
.
layers
.
reduce_sum
(
input
=
fluid
.
layers
.
pow
(
x
=
grad
,
factor
=
2.0
))
fluid
.
layers
.
sums
(
input
=
[
norm_var
,
res
],
out
=
[
res
])
fluid
.
layers
.
sqrt
(
x
=
res
,
out
=
res
)
return
res
BATCH_SIZE
=
128
CLIP
=
0.5
prog
=
fluid
.
framework
.
Program
()
CLIP
=
1
prog
=
fluid
.
framework
.
Program
()
with
fluid
.
program_guard
(
main_program
=
prog
):
image
=
fluid
.
layers
.
data
(
name
=
'x'
,
shape
=
[
784
],
dtype
=
'float32'
)
...
...
@@ -49,13 +38,12 @@ avg_cost_clip = prog_clip.block(0).var(avg_cost.name)
p_g
=
fluid
.
backward
.
append_backward
(
loss
=
avg_cost
)
p_g_clip
=
fluid
.
backward
.
append_backward
(
loss
=
avg_cost_clip
)
with
fluid
.
program_guard
(
main_program
=
prog
):
gloabl_norm
=
_get_global_param_norm_
(
p_g
)
with
fluid
.
program_guard
(
main_program
=
prog_clip
):
fluid
.
clip
.
gradient_clip_by_global_norm
(
clip_norm
=
CLIP
)
p_g_clip
=
fluid
.
clip
.
append_gradient_clip_ops
(
p_g_clip
)
gloabl_norm_clip
=
_get_global_param_norm_
(
p_g_clip
)
grad_list
=
[
elem
[
1
]
for
elem
in
p_g
]
grad_clip_list
=
[
elem
[
1
]
for
elem
in
p_g_clip
]
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
...
...
@@ -72,11 +60,21 @@ for data in train_reader():
count
+=
1
if
count
>
5
:
break
out
,
=
exe
.
run
(
prog
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
gloabl_norm
])
out_clip
,
=
exe
.
run
(
prog_clip
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
gloabl_norm_clip
])
if
not
np
.
allclose
(
out_clip
,
np
.
minimum
(
out
,
np
.
array
([
CLIP
]))):
out
=
exe
.
run
(
prog
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
grad_list
)
out_clip
=
exe
.
run
(
prog_clip
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
grad_clip_list
)
global_norm
=
0
for
v
in
out
[
1
:]:
global_norm
+=
np
.
sum
(
np
.
power
(
v
,
2
))
global_norm
=
np
.
sqrt
(
global_norm
)
global_norm_clip
=
0
for
v
in
out_clip
[
1
:]:
global_norm_clip
+=
np
.
sum
(
np
.
power
(
v
,
2
))
global_norm_clip
=
np
.
sqrt
(
global_norm_clip
)
if
not
np
.
isclose
(
a
=
global_norm_clip
,
b
=
np
.
minimum
(
global_norm
,
CLIP
),
rtol
=
5e-3
):
exit
(
1
)
exit
(
0
)
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