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
11e78eba
P
Paddle
项目概览
PaddlePaddle
/
Paddle
大约 1 年 前同步成功
通知
2298
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
11e78eba
编写于
1月 17, 2021
作者:
G
guofei
提交者:
GitHub
1月 17, 2021
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Modify the calculation logic of LambOptimizer (#29313)
* Modify the calculation logic of LambOptimizer
上级
c5ffad12
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
544 addition
and
112 deletion
+544
-112
paddle/fluid/operators/optimizers/lamb_op.cc
paddle/fluid/operators/optimizers/lamb_op.cc
+42
-2
paddle/fluid/operators/optimizers/lamb_op.h
paddle/fluid/operators/optimizers/lamb_op.h
+257
-35
paddle/fluid/pybind/op_function_generator.cc
paddle/fluid/pybind/op_function_generator.cc
+4
-0
python/paddle/fluid/optimizer.py
python/paddle/fluid/optimizer.py
+21
-5
python/paddle/fluid/tests/unittests/test_imperative_optimizer_v2.py
...dle/fluid/tests/unittests/test_imperative_optimizer_v2.py
+4
-4
python/paddle/fluid/tests/unittests/test_lamb_op.py
python/paddle/fluid/tests/unittests/test_lamb_op.py
+42
-21
python/paddle/fluid/tests/unittests/test_lambv2_op.py
python/paddle/fluid/tests/unittests/test_lambv2_op.py
+127
-21
python/paddle/optimizer/lamb.py
python/paddle/optimizer/lamb.py
+47
-24
未找到文件。
paddle/fluid/operators/optimizers/lamb_op.cc
浏览文件 @
11e78eba
...
...
@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/optimizers/lamb_op.h"
#include <string>
#include "paddle/fluid/framework/op_version_registry.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -21,7 +23,7 @@ class LambOp : public framework::OperatorWithKernel {
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE_EQ
(
ctx
->
HasInput
(
"Param"
),
true
,
platform
::
errors
::
NotFound
(
"Input(Param) of LambOp should not be null."
));
...
...
@@ -53,6 +55,12 @@ class LambOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Moment2Out"
),
true
,
platform
::
errors
::
NotFound
(
"Output(Moment2Out) of LambOp should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Beta1PowOut"
),
true
,
platform
::
errors
::
NotFound
(
"Output(Beta1PowOut) of LambOp should not be null."
));
PADDLE_ENFORCE_EQ
(
ctx
->
HasOutput
(
"Beta2PowOut"
),
true
,
platform
::
errors
::
NotFound
(
"Output(Beta2PowOut) of LambOp should not be null."
));
auto
lr_dims
=
ctx
->
GetInputDim
(
"LearningRate"
);
PADDLE_ENFORCE_NE
(
...
...
@@ -108,14 +116,26 @@ class LambOp : public framework::OperatorWithKernel {
ctx
->
SetOutputDim
(
"ParamOut"
,
param_dims
);
ctx
->
SetOutputDim
(
"Moment1Out"
,
param_dims
);
ctx
->
SetOutputDim
(
"Moment2Out"
,
param_dims
);
ctx
->
SetOutputDim
(
"Beta1PowOut"
,
beta1_pow_dims
);
ctx
->
SetOutputDim
(
"Beta2PowOut"
,
beta2_pow_dims
);
}
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
input_data_type
=
OperatorWithKernel
::
IndicateVarDataType
(
ctx
,
"Param"
);
return
framework
::
OpKernelType
(
input_data_type
,
ctx
.
GetPlace
());
}
framework
::
OpKernelType
GetKernelTypeForVar
(
const
std
::
string
&
var_name
,
const
framework
::
Tensor
&
tensor
,
const
framework
::
OpKernelType
&
expected_kernel_type
)
const
{
if
(
var_name
==
"Beta1Pow"
||
var_name
==
"Beta2Pow"
)
{
return
expected_kernel_type
;
}
else
{
return
framework
::
OpKernelType
(
expected_kernel_type
.
data_type_
,
tensor
.
place
(),
tensor
.
layout
());
}
}
};
class
LambOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
...
...
@@ -136,6 +156,10 @@ class LambOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput
(
"ParamOut"
,
"(Tensor) Output parameter."
);
AddOutput
(
"Moment1Out"
,
"(Tensor) Output first moment."
);
AddOutput
(
"Moment2Out"
,
"(Tensor) Output second moment."
);
AddOutput
(
"Beta1PowOut"
,
"(Tensor) Output beta1 power accumulator"
)
.
AsDispensable
();
AddOutput
(
"Beta2PowOut"
,
"(Tensor) Output beta2 power accumulator"
)
.
AsDispensable
();
AddAttr
<
float
>
(
"weight_decay"
,
"(float) Weight decay rate."
);
AddAttr
<
float
>
(
"beta1"
,
"(float, default 0.9) The exponential decay rate for the "
...
...
@@ -164,6 +188,10 @@ m_t &= \beta_1 m_{t - 1}+ (1 - \beta_1)g_t \\
v_t &= \beta_2 v_{t - 1} + (1 - \beta_2)g_t^2 \\
m_t &= \frac{m_t}{\beta_1^t} \\
v_t &= \frac{v_t}{\beta_2^t} \\
r_t &= \frac{m_t}{\sqrt{v_t}+\epsilon} \\
w_t &= w_{t-1} -\eta_t \frac{\left \| w_{t-1}\right \|}{\left \| r_t + \lambda w_{t-1}\right \|} (r_t + \lambda w_{t-1})
...
...
@@ -183,3 +211,15 @@ REGISTER_OP_WITHOUT_GRADIENT(lamb, ops::LambOp, ops::LambOpMaker);
REGISTER_OP_CPU_KERNEL
(
lamb
,
ops
::
LambOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
LambOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
/* ========================== register checkpoint ===========================*/
REGISTER_OP_VERSION
(
lamb
)
.
AddCheckpoint
(
R"ROC(Upgrade lamb, add two new outputs [Beta1PowOut] and [Beta2PowOut].)ROC"
,
paddle
::
framework
::
compatible
::
OpVersionDesc
()
.
NewInput
(
"Beta1PowOut"
,
"The Output beta1 power accumulator. 'Beta1PowOut' is "
"dispensable."
)
.
NewInput
(
"Beta2PowOut"
,
"The Output beta2 power accumulator. 'Beta2PowOut' is "
"dispensable."
));
paddle/fluid/operators/optimizers/lamb_op.h
浏览文件 @
11e78eba
...
...
@@ -27,14 +27,81 @@ namespace operators {
namespace
scatter
=
paddle
::
operators
::
math
::
scatter
;
template
<
typename
T
>
struct
LambMomentUpdateFunctor
{
struct
LambMomentREGUpdateFunctor
{
T
weight_decay_
;
T
beta1_
;
T
beta2_
;
T
epsilon_
;
T
beta1_pow_
;
T
*
beta1_pow_out_
;
T
beta2_pow_
;
T
*
beta2_pow_out_
;
const
T
*
moment1_
;
T
*
moment1_out_
;
const
T
*
moment2_
;
T
*
moment2_out_
;
const
T
*
grad_
;
const
T
*
param_
;
T
*
trust_ratio_div_
;
LambMomentREGUpdateFunctor
(
T
weight_decay
,
T
beta1
,
T
beta2
,
T
epsilon
,
T
beta1_pow
,
T
*
beta1_pow_out
,
T
beta2_pow
,
T
*
beta2_pow_out
,
const
T
*
mom1
,
T
*
mom1_out
,
const
T
*
mom2
,
T
*
mom2_out
,
const
T
*
grad
,
const
T
*
param
,
T
*
trust_ratio_div
)
:
weight_decay_
(
weight_decay
),
beta1_
(
beta1
),
beta2_
(
beta2
),
epsilon_
(
epsilon
),
beta1_pow_
(
beta1_pow
),
beta1_pow_out_
(
beta1_pow_out
),
beta2_pow_
(
beta2_pow
),
beta2_pow_out_
(
beta2_pow_out
),
moment1_
(
mom1
),
moment1_out_
(
mom1_out
),
moment2_
(
mom2
),
moment2_out_
(
mom2_out
),
grad_
(
grad
),
param_
(
param
),
trust_ratio_div_
(
trust_ratio_div
)
{}
inline
HOSTDEVICE
void
operator
()(
size_t
i
)
const
{
T
g
=
grad_
[
i
];
T
mom1
=
moment1_
[
i
];
T
mom2
=
moment2_
[
i
];
T
beta1_pow
=
beta1_pow_
;
T
beta2_pow
=
beta2_pow_
;
T
p
=
param_
[
i
];
mom1
=
beta1_
*
mom1
+
(
1
-
beta1_
)
*
g
;
mom2
=
beta2_
*
mom2
+
(
1
-
beta2_
)
*
g
*
g
;
moment1_out_
[
i
]
=
mom1
;
moment2_out_
[
i
]
=
mom2
;
T
mom1_unbiased
=
mom1
/
(
1
-
beta1_pow
);
T
mom2_unbiased
=
mom2
/
(
1
-
beta2_pow
);
trust_ratio_div_
[
i
]
=
mom1_unbiased
/
(
sqrt
(
mom2_unbiased
)
+
epsilon_
)
+
weight_decay_
*
p
;
if
(
beta1_pow_out_
&&
beta2_pow_out_
)
{
beta1_pow_out_
[
0
]
=
beta1_pow
*
beta1_
;
beta2_pow_out_
[
0
]
=
beta2_pow
*
beta2_
;
}
}
};
template
<
typename
T
>
struct
LambMomentMENUpdateFunctor
{
T
weight_decay_
;
T
beta1_
;
T
beta2_
;
T
epsilon_
;
const
T
*
beta1_pow_
;
T
*
beta1_pow_out_
;
const
T
*
beta2_pow_
;
T
*
beta2_pow_out_
;
const
T
*
moment1_
;
T
*
moment1_out_
;
const
T
*
moment2_
;
...
...
@@ -43,16 +110,20 @@ struct LambMomentUpdateFunctor {
const
T
*
param_
;
T
*
trust_ratio_div_
;
LambMomentUpdateFunctor
(
T
weight_decay
,
T
beta1
,
T
beta2
,
T
epsilon
,
const
T
*
beta1_pow
,
const
T
*
beta2_pow
,
const
T
*
mom1
,
T
*
mom1_out
,
const
T
*
mom2
,
T
*
mom2_out
,
const
T
*
grad
,
const
T
*
param
,
T
*
trust_ratio_div
)
LambMomentMENUpdateFunctor
(
T
weight_decay
,
T
beta1
,
T
beta2
,
T
epsilon
,
const
T
*
beta1_pow
,
T
*
beta1_pow_out
,
const
T
*
beta2_pow
,
T
*
beta2_pow_out
,
const
T
*
mom1
,
T
*
mom1_out
,
const
T
*
mom2
,
T
*
mom2_out
,
const
T
*
grad
,
const
T
*
param
,
T
*
trust_ratio_div
)
:
weight_decay_
(
weight_decay
),
beta1_
(
beta1
),
beta2_
(
beta2
),
epsilon_
(
epsilon
),
beta1_pow_
(
beta1_pow
),
beta1_pow_out_
(
beta1_pow_out
),
beta2_pow_
(
beta2_pow
),
beta2_pow_out_
(
beta2_pow_out
),
moment1_
(
mom1
),
moment1_out_
(
mom1_out
),
moment2_
(
mom2
),
...
...
@@ -65,6 +136,8 @@ struct LambMomentUpdateFunctor {
T
g
=
grad_
[
i
];
T
mom1
=
moment1_
[
i
];
T
mom2
=
moment2_
[
i
];
T
beta1_pow
=
*
beta1_pow_
;
T
beta2_pow
=
*
beta2_pow_
;
T
p
=
param_
[
i
];
mom1
=
beta1_
*
mom1
+
(
1
-
beta1_
)
*
g
;
...
...
@@ -72,19 +145,110 @@ struct LambMomentUpdateFunctor {
moment1_out_
[
i
]
=
mom1
;
moment2_out_
[
i
]
=
mom2
;
trust_ratio_div_
[
i
]
=
mom1
/
(
sqrt
(
mom2
)
+
epsilon_
)
+
weight_decay_
*
p
;
T
mom1_unbiased
=
mom1
/
(
1
-
beta1_pow
);
T
mom2_unbiased
=
mom2
/
(
1
-
beta2_pow
);
trust_ratio_div_
[
i
]
=
mom1_unbiased
/
(
sqrt
(
mom2_unbiased
)
+
epsilon_
)
+
weight_decay_
*
p
;
if
(
beta1_pow_out_
&&
beta2_pow_out_
)
{
beta1_pow_out_
[
0
]
=
beta1_pow
*
beta1_
;
beta2_pow_out_
[
0
]
=
beta2_pow
*
beta2_
;
}
}
};
template
<
typename
T
>
struct
SparseLambMomentUpdateFunctor
{
struct
SparseLambMomentREGUpdateFunctor
{
T
weight_decay_
;
T
beta1_
;
T
beta2_
;
T
epsilon_
;
T
beta1_pow_
;
T
*
beta1_pow_out_
;
T
beta2_pow_
;
T
*
beta2_pow_out_
;
const
T
*
moment1_
;
T
*
moment1_out_
;
const
T
*
moment2_
;
T
*
moment2_out_
;
const
T
*
grad_
;
const
T
*
param_
;
T
*
trust_ratio_div_
;
const
int64_t
*
rows_
;
int64_t
row_numel_
;
int64_t
row_count_
;
SparseLambMomentREGUpdateFunctor
(
T
weight_decay
,
T
beta1
,
T
beta2
,
T
epsilon
,
T
beta1_pow
,
T
*
beta1_pow_out
,
T
beta2_pow
,
T
*
beta2_pow_out
,
const
T
*
mom1
,
T
*
mom1_out
,
const
T
*
mom2
,
T
*
mom2_out
,
const
T
*
grad
,
const
T
*
param
,
T
*
trust_ratio_div
,
const
int64_t
*
rows
,
int64_t
row_numel
,
int64_t
row_count
)
:
weight_decay_
(
weight_decay
),
beta1_
(
beta1
),
beta2_
(
beta2
),
epsilon_
(
epsilon
),
beta1_pow_
(
beta1_pow
),
beta1_pow_out_
(
beta1_pow_out
),
beta2_pow_
(
beta2_pow
),
beta2_pow_out_
(
beta2_pow_out
),
moment1_
(
mom1
),
moment1_out_
(
mom1_out
),
moment2_
(
mom2
),
moment2_out_
(
mom2_out
),
grad_
(
grad
),
param_
(
param
),
trust_ratio_div_
(
trust_ratio_div
),
rows_
(
rows
),
row_numel_
(
row_numel
),
row_count_
(
row_count
)
{}
inline
HOSTDEVICE
void
update
(
size_t
i
,
T
g
)
const
{
// The following code is same as dense
T
mom1
=
moment1_
[
i
];
T
mom2
=
moment2_
[
i
];
T
beta1_pow
=
beta1_pow_
;
T
beta2_pow
=
beta2_pow_
;
T
p
=
param_
[
i
];
mom1
=
beta1_
*
mom1
+
(
1
-
beta1_
)
*
g
;
mom2
=
beta2_
*
mom2
+
(
1
-
beta2_
)
*
g
*
g
;
moment1_out_
[
i
]
=
mom1
;
moment2_out_
[
i
]
=
mom2
;
T
mom1_unbiased
=
mom1
/
(
1
-
beta1_pow
);
T
mom2_unbiased
=
mom2
/
(
1
-
beta2_pow
);
trust_ratio_div_
[
i
]
=
mom1_unbiased
/
(
sqrt
(
mom2_unbiased
)
+
epsilon_
)
+
weight_decay_
*
p
;
if
(
beta1_pow_out_
&&
beta1_pow_out_
)
{
beta1_pow_out_
[
0
]
=
beta1_pow
*
beta1_
;
beta2_pow_out_
[
0
]
=
beta2_pow
*
beta2_
;
}
}
inline
HOSTDEVICE
void
operator
()(
size_t
i
)
const
{
auto
row_idx
=
math
::
BinarySearch
<
int64_t
>
(
rows_
,
row_count_
,
i
/
row_numel_
);
T
g
=
row_idx
>=
0
?
grad_
[
row_idx
*
row_numel_
+
i
%
row_numel_
]
:
0
;
update
(
i
,
g
);
}
};
template
<
typename
T
>
struct
SparseLambMomentMENUpdateFunctor
{
T
weight_decay_
;
T
beta1_
;
T
beta2_
;
T
epsilon_
;
const
T
*
beta1_pow_
;
T
*
beta1_pow_out_
;
const
T
*
beta2_pow_
;
T
*
beta2_pow_out_
;
const
T
*
moment1_
;
T
*
moment1_out_
;
const
T
*
moment2_
;
...
...
@@ -97,18 +261,21 @@ struct SparseLambMomentUpdateFunctor {
int64_t
row_numel_
;
int64_t
row_count_
;
SparseLambMomentUpdateFunctor
(
T
weight_decay
,
T
beta1
,
T
beta2
,
T
epsilon
,
const
T
*
beta1_pow
,
const
T
*
beta2_pow
,
const
T
*
mom1
,
T
*
mom1_out
,
const
T
*
mom2
,
T
*
mom2_out
,
const
T
*
grad
,
const
T
*
param
,
T
*
trust_ratio_div
,
const
int64_t
*
rows
,
int64_t
row_numel
,
int64_t
row_count
)
SparseLambMomentMENUpdateFunctor
(
T
weight_decay
,
T
beta1
,
T
beta2
,
T
epsilon
,
const
T
*
beta1_pow
,
T
*
beta1_pow_out
,
const
T
*
beta2_pow
,
T
*
beta2_pow_out
,
const
T
*
mom1
,
T
*
mom1_out
,
const
T
*
mom2
,
T
*
mom2_out
,
const
T
*
grad
,
const
T
*
param
,
T
*
trust_ratio_div
,
const
int64_t
*
rows
,
int64_t
row_numel
,
int64_t
row_count
)
:
weight_decay_
(
weight_decay
),
beta1_
(
beta1
),
beta2_
(
beta2
),
epsilon_
(
epsilon
),
beta1_pow_
(
beta1_pow
),
beta1_pow_out_
(
beta1_pow_out
),
beta2_pow_
(
beta2_pow
),
beta2_pow_out_
(
beta2_pow_out
),
moment1_
(
mom1
),
moment1_out_
(
mom1_out
),
moment2_
(
mom2
),
...
...
@@ -124,6 +291,8 @@ struct SparseLambMomentUpdateFunctor {
// The following code is same as dense
T
mom1
=
moment1_
[
i
];
T
mom2
=
moment2_
[
i
];
T
beta1_pow
=
*
beta1_pow_
;
T
beta2_pow
=
*
beta2_pow_
;
T
p
=
param_
[
i
];
mom1
=
beta1_
*
mom1
+
(
1
-
beta1_
)
*
g
;
...
...
@@ -131,7 +300,15 @@ struct SparseLambMomentUpdateFunctor {
moment1_out_
[
i
]
=
mom1
;
moment2_out_
[
i
]
=
mom2
;
trust_ratio_div_
[
i
]
=
mom1
/
(
sqrt
(
mom2
)
+
epsilon_
)
+
weight_decay_
*
p
;
T
mom1_unbiased
=
mom1
/
(
1
-
beta1_pow
);
T
mom2_unbiased
=
mom2
/
(
1
-
beta2_pow
);
trust_ratio_div_
[
i
]
=
mom1_unbiased
/
(
sqrt
(
mom2_unbiased
)
+
epsilon_
)
+
weight_decay_
*
p
;
if
(
beta1_pow_out_
&&
beta1_pow_out_
)
{
beta1_pow_out_
[
0
]
=
beta1_pow
*
beta1_
;
beta2_pow_out_
[
0
]
=
beta2_pow
*
beta2_
;
}
}
inline
HOSTDEVICE
void
operator
()(
size_t
i
)
const
{
...
...
@@ -211,6 +388,10 @@ class LambOpKernel : public framework::OpKernel<T> {
"Output"
,
"Moment1Out"
,
"Lamb"
);
auto
&
mom2_out
=
GET_DATA_SAFELY
(
ctx
.
Output
<
LoDTensor
>
(
"Moment2Out"
),
"Output"
,
"Moment2Out"
,
"Lamb"
);
auto
&
beta1_pow_out
=
GET_DATA_SAFELY
(
ctx
.
Output
<
LoDTensor
>
(
"Beta1PowOut"
),
"Output"
,
"Beta1PowOut"
,
"Lamb"
);
auto
&
beta2_pow_out
=
GET_DATA_SAFELY
(
ctx
.
Output
<
LoDTensor
>
(
"Beta2PowOut"
),
"Output"
,
"Beta2PowOut"
,
"Lamb"
);
auto
&
dev_ctx
=
ctx
.
template
device_context
<
DeviceContext
>();
platform
::
ForRange
<
DeviceContext
>
for_range
(
dev_ctx
,
param
.
numel
());
...
...
@@ -220,16 +401,37 @@ class LambOpKernel : public framework::OpKernel<T> {
// Update moments
if
(
grad_var
->
IsType
<
framework
::
LoDTensor
>
())
{
auto
&
grad
=
*
ctx
.
Input
<
LoDTensor
>
(
"Grad"
);
LambMomentUpdateFunctor
<
T
>
moment_update_functor
(
weight_decay
,
beta1
,
beta2
,
epsilon
,
beta1_pow
.
template
data
<
T
>(),
beta2_pow
.
template
data
<
T
>(),
mom1
.
template
data
<
T
>(),
mom1_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
mom2
.
template
data
<
T
>(),
mom2_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
grad
.
template
data
<
T
>(),
param
.
template
data
<
T
>(),
trust_ratio_div
.
template
data
<
T
>());
for_range
(
moment_update_functor
);
if
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
())
&&
beta1_pow
.
place
()
==
platform
::
CPUPlace
()
&&
beta2_pow
.
place
()
==
platform
::
CPUPlace
())
{
LambMomentREGUpdateFunctor
<
T
>
moment_update_functor
(
weight_decay
,
beta1
,
beta2
,
epsilon
,
*
beta1_pow
.
template
data
<
T
>(),
nullptr
,
*
beta2_pow
.
template
data
<
T
>(),
nullptr
,
mom1
.
template
data
<
T
>(),
mom1_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
mom2
.
template
data
<
T
>(),
mom2_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
grad
.
template
data
<
T
>(),
param
.
template
data
<
T
>(),
trust_ratio_div
.
template
data
<
T
>());
for_range
(
moment_update_functor
);
beta1_pow_out
.
template
mutable_data
<
T
>(
platform
::
CPUPlace
())[
0
]
=
beta1
*
beta1_pow
.
template
data
<
T
>()[
0
];
beta2_pow_out
.
template
mutable_data
<
T
>(
platform
::
CPUPlace
())[
0
]
=
beta2
*
beta2_pow
.
template
data
<
T
>()[
0
];
}
else
{
LambMomentMENUpdateFunctor
<
T
>
moment_update_functor
(
weight_decay
,
beta1
,
beta2
,
epsilon
,
beta1_pow
.
template
data
<
T
>(),
beta1_pow_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
beta2_pow
.
template
data
<
T
>(),
beta2_pow_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
mom1
.
template
data
<
T
>(),
mom1_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
mom2
.
template
data
<
T
>(),
mom2_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
grad
.
template
data
<
T
>(),
param
.
template
data
<
T
>(),
trust_ratio_div
.
template
data
<
T
>());
for_range
(
moment_update_functor
);
}
}
else
if
(
grad_var
->
IsType
<
framework
::
SelectedRows
>
())
{
auto
&
grad
=
GET_DATA_SAFELY
(
ctx
.
Input
<
framework
::
SelectedRows
>
(
"Grad"
),
"Input"
,
"Grad"
,
"Lamb"
);
...
...
@@ -264,16 +466,37 @@ class LambOpKernel : public framework::OpKernel<T> {
const
T
*
grad_data
=
grad_tensor
.
template
data
<
T
>();
const
int64_t
*
rows
=
grad_merge
.
rows
().
Data
(
ctx
.
GetPlace
());
auto
row_numel
=
grad_tensor
.
numel
()
/
grad_merge
.
rows
().
size
();
SparseLambMomentUpdateFunctor
<
T
>
moment_update_functor
(
weight_decay
,
beta1
,
beta2
,
epsilon
,
beta1_pow
.
template
data
<
T
>(),
beta2_pow
.
template
data
<
T
>(),
mom1
.
template
data
<
T
>(),
mom1_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
mom2
.
template
data
<
T
>(),
mom2_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
grad_data
,
param
.
template
data
<
T
>(),
trust_ratio_div
.
template
data
<
T
>(),
rows
,
row_numel
,
grad_merge
.
rows
().
size
());
for_range
(
moment_update_functor
);
if
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
())
&&
beta1_pow
.
place
()
==
platform
::
CPUPlace
()
&&
beta2_pow
.
place
()
==
platform
::
CPUPlace
())
{
SparseLambMomentREGUpdateFunctor
<
T
>
moment_update_functor
(
weight_decay
,
beta1
,
beta2
,
epsilon
,
*
beta1_pow
.
template
data
<
T
>(),
nullptr
,
*
beta2_pow
.
template
data
<
T
>(),
nullptr
,
mom1
.
template
data
<
T
>(),
mom1_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
mom2
.
template
data
<
T
>(),
mom2_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
grad_data
,
param
.
template
data
<
T
>(),
trust_ratio_div
.
template
data
<
T
>(),
rows
,
row_numel
,
grad_merge
.
rows
().
size
());
for_range
(
moment_update_functor
);
beta1_pow_out
.
template
mutable_data
<
T
>(
platform
::
CPUPlace
())[
0
]
=
beta1
*
beta1_pow
.
template
data
<
T
>()[
0
];
beta2_pow_out
.
template
mutable_data
<
T
>(
platform
::
CPUPlace
())[
0
]
=
beta2
*
beta2_pow
.
template
data
<
T
>()[
0
];
}
else
{
SparseLambMomentMENUpdateFunctor
<
T
>
moment_update_functor
(
weight_decay
,
beta1
,
beta2
,
epsilon
,
beta1_pow
.
template
data
<
T
>(),
beta1_pow_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
beta2_pow
.
template
data
<
T
>(),
beta2_pow_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
mom1
.
template
data
<
T
>(),
mom1_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
mom2
.
template
data
<
T
>(),
mom2_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
grad_data
,
param
.
template
data
<
T
>(),
trust_ratio_div
.
template
data
<
T
>(),
rows
,
row_numel
,
grad_merge
.
rows
().
size
());
for_range
(
moment_update_functor
);
}
}
else
{
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
"Variable type not supported by lamb_op. Expect LoDTensor or "
...
...
@@ -296,7 +519,6 @@ class LambOpKernel : public framework::OpKernel<T> {
auto
*
place
=
dev_ctx
.
eigen_device
();
p_norm
.
device
(
*
place
)
=
p
.
square
().
sum
().
sqrt
();
trust_ratio_div_norm
.
device
(
*
place
)
=
t
.
square
().
sum
().
sqrt
();
LambParamUpateFunctor
<
T
>
param_update_functor
(
lr
.
template
data
<
T
>(),
param
.
template
data
<
T
>(),
p_norm_t
.
template
data
<
T
>(),
trust_ratio_div
.
template
data
<
T
>(),
...
...
paddle/fluid/pybind/op_function_generator.cc
浏览文件 @
11e78eba
...
...
@@ -89,6 +89,8 @@ std::map<std::string, std::set<std::string>> op_outs_map = {
{
"generate_proposals_v2"
,
{
"RpnRois"
,
"RpnRoiProbs"
,
"RpnRoisNum"
}},
{
"momentum"
,
{
"ParamOut"
,
"VelocityOut"
}},
{
"rnn"
,
{
"DropoutState"
,
"Reserve"
,
"Out"
,
"State"
}},
{
"lamb"
,
{
"ParamOut"
,
"Moment1Out"
,
"Moment2Out"
,
"Beta1PowOut"
,
"Beta2PowOut"
}},
};
// NOTE(zhiqiu): Commonly, the outputs in auto-generated OP function are
...
...
@@ -136,6 +138,8 @@ std::map<std::string, std::set<std::string>> op_passing_outs_map = {
{
"update_loss_scaling"
,
{
"Out"
,
"LossScaling"
,
"OutGoodSteps"
,
"OutBadSteps"
}},
{
"moving_average_abs_max_scale"
,
{
"OutScale"
,
"OutAccum"
,
"OutState"
}},
{
"lamb"
,
{
"ParamOut"
,
"Moment1Out"
,
"Moment2Out"
,
"Beta1PowOut"
,
"Beta2PowOut"
}},
{
"rnn"
,
{
"DropoutState"
}},
};
...
...
python/paddle/fluid/optimizer.py
浏览文件 @
11e78eba
...
...
@@ -2983,6 +2983,10 @@ class LambOptimizer(AdamOptimizer):
v_t &= \\beta_2 v_{t - 1} + (1 - \\beta_2)g_t^2
m_t &= \\frac{m_t}{\\beta_1^t}
v_t &= \\frac{v_t}{\\beta_2^t}
r_t &= \\frac{m_t}{\\sqrt{v_t}+\\epsilon}
w_t &= w_{t-1} -\\eta_t \\frac{\\left \| w_{t-1}\\right \|}{\\left \| r_t + \\lambda w_{t-1}\\right \|} (r_t + \\lambda w_{t-1})
...
...
@@ -3010,8 +3014,9 @@ class LambOptimizer(AdamOptimizer):
Default None, meaning there is no regularization.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
( :ref:`api_paddle_fluid_clip_ClipGradByGlobalNorm` , :ref:`api_paddle_fluid_clip_ClipGradByNorm` ,
:ref:`api_paddle_fluid_clip_ClipGradByValue` ). If you want better convergence, it is recommended
to use :ref:`api_paddle_fluid_clip_ClipGradByGlobalNorm` . Default None, meaning there is no gradient clipping.
exclude_from_weight_decay_fn (function|None): Exclude a parameter from weight
decay when **exclude_from_weight_decay_fn(parameter)** returns true.
Default None.
...
...
@@ -3036,7 +3041,6 @@ class LambOptimizer(AdamOptimizer):
"""
_moment1_acc_str
=
"moment1"
_moment2_acc_str
=
"moment2"
# these two not used in op temporarily
_beta1_pow_acc_str
=
"beta1_pow_acc"
_beta2_pow_acc_str
=
"beta2_pow_acc"
...
...
@@ -3087,6 +3091,16 @@ class LambOptimizer(AdamOptimizer):
weight_decay
=
0.0
else
:
weight_decay
=
self
.
_weight_decay
lr
=
self
.
_create_param_lr
(
param_and_grad
)
if
framework
.
in_dygraph_mode
():
_
,
_
,
_
,
_
,
_
=
core
.
ops
.
lamb
(
param_and_grad
[
0
],
param_and_grad
[
1
],
lr
,
moment1
,
moment2
,
beta1_pow_acc
,
beta2_pow_acc
,
param_and_grad
[
0
],
moment1
,
moment2
,
beta1_pow_acc
,
beta2_pow_acc
,
'beta1'
,
self
.
_beta1
,
'beta2'
,
self
.
_beta2
,
'epsilon'
,
self
.
_epsilon
,
'weight_decay'
,
weight_decay
)
return
None
# create the lamb optimize op
lamb_op
=
block
.
append_op
(
...
...
@@ -3094,7 +3108,7 @@ class LambOptimizer(AdamOptimizer):
inputs
=
{
"Param"
:
param_and_grad
[
0
],
"Grad"
:
param_and_grad
[
1
],
"LearningRate"
:
self
.
_create_param_lr
(
param_and_grad
)
,
"LearningRate"
:
lr
,
"Moment1"
:
moment1
,
"Moment2"
:
moment2
,
"Beta1Pow"
:
beta1_pow_acc
,
...
...
@@ -3103,7 +3117,9 @@ class LambOptimizer(AdamOptimizer):
outputs
=
{
"ParamOut"
:
param_and_grad
[
0
],
"Moment1Out"
:
moment1
,
"Moment2Out"
:
moment2
"Moment2Out"
:
moment2
,
"Beta1PowOut"
:
beta1_pow_acc
,
"Beta2PowOut"
:
beta2_pow_acc
},
attrs
=
{
"beta1"
:
self
.
_beta1
,
...
...
python/paddle/fluid/tests/unittests/test_imperative_optimizer_v2.py
浏览文件 @
11e78eba
...
...
@@ -23,7 +23,7 @@ import itertools
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid
import
core
from
paddle.fluid.optimizer
import
MomentumOptimizer
,
LarsMomentumOptimizer
,
AdagradOptimizer
,
AdamaxOptimizer
,
DpsgdOptimizer
,
DecayedAdagradOptimizer
,
AdadeltaOptimizer
,
RMSPropOptimizer
,
FtrlOptimizer
,
LambOptimizer
from
paddle.fluid.optimizer
import
MomentumOptimizer
,
LarsMomentumOptimizer
,
AdagradOptimizer
,
AdamaxOptimizer
,
DpsgdOptimizer
,
DecayedAdagradOptimizer
,
AdadeltaOptimizer
,
RMSPropOptimizer
,
FtrlOptimizer
from
paddle.fluid.optimizer
import
ModelAverage
,
DGCMomentumOptimizer
,
ExponentialMovingAverage
,
PipelineOptimizer
,
LookaheadOptimizer
,
RecomputeOptimizer
from
paddle.fluid.dygraph
import
Linear
from
paddle.fluid.dygraph.base
import
to_variable
...
...
@@ -702,14 +702,14 @@ def exclude_fn(param):
class
TestImperativeLambOptimizer
(
TestImperativeOptimizerBase
):
def
get_optimizer_dygraph
(
self
,
parameter_list
):
optimizer
=
LambOptimizer
(
optimizer
=
paddle
.
optimizer
.
Lamb
(
learning_rate
=
0.002
,
exclude_from_weight_decay_fn
=
exclude_fn
,
parameter
_list
=
parameter_list
)
parameter
s
=
parameter_list
)
return
optimizer
def
get_optimizer
(
self
):
optimizer
=
LambOptimizer
(
optimizer
=
paddle
.
optimizer
.
Lamb
(
learning_rate
=
0.002
,
exclude_from_weight_decay_fn
=
exclude_fn
)
return
optimizer
...
...
python/paddle/fluid/tests/unittests/test_lamb_op.py
浏览文件 @
11e78eba
...
...
@@ -17,9 +17,13 @@ from __future__ import print_function
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid
import
core
from
paddle.fluid.op
import
Operator
paddle
.
enable_static
()
class
TestLambOp1
(
OpTest
):
def
set_attrs
(
self
):
...
...
@@ -41,8 +45,8 @@ class TestLambOp1(OpTest):
learning_rate
=
0.001
self
.
set_attrs
()
beta1_pow
=
self
.
attrs
[
'beta1'
]
**
10
beta2_pow
=
self
.
attrs
[
'beta2'
]
**
10
beta1_pow
=
self
.
attrs
[
'beta1'
]
beta2_pow
=
self
.
attrs
[
'beta2'
]
self
.
inputs
=
{
'Param'
:
param
,
...
...
@@ -55,13 +59,15 @@ class TestLambOp1(OpTest):
}
param_out
,
moment1_out
,
\
moment2
_out
=
lamb_step
(
self
.
inputs
,
self
.
attrs
)
param_out
,
moment1_out
,
moment2_out
,
\
beta1_pow_out
,
beta2_pow
_out
=
lamb_step
(
self
.
inputs
,
self
.
attrs
)
self
.
outputs
=
{
'Moment1Out'
:
moment1_out
,
'Moment2Out'
:
moment2_out
,
'ParamOut'
:
param_out
'ParamOut'
:
param_out
,
'Beta1PowOut'
:
beta1_pow_out
,
'Beta2PowOut'
:
beta2_pow_out
}
def
test_check_output
(
self
):
...
...
@@ -89,14 +95,16 @@ class TestLambOpMultipleSteps(TestLambOp1):
self
.
num_steps
=
10
def
test_check_output
(
self
):
for
_
in
range
(
self
.
num_steps
):
param_out
,
moment1_out
,
\
moment2
_out
=
lamb_step
(
self
.
inputs
,
self
.
attrs
)
for
i
in
range
(
self
.
num_steps
):
param_out
,
moment1_out
,
moment2_out
,
\
beta1_pow_out
,
beta2_pow
_out
=
lamb_step
(
self
.
inputs
,
self
.
attrs
)
self
.
outputs
=
{
'Moment1Out'
:
moment1_out
,
'Moment2Out'
:
moment2_out
,
'ParamOut'
:
param_out
'ParamOut'
:
param_out
,
'Beta1PowOut'
:
beta1_pow_out
,
'Beta2PowOut'
:
beta2_pow_out
}
# Verify output for this step
...
...
@@ -108,8 +116,8 @@ class TestLambOpMultipleSteps(TestLambOp1):
self
.
inputs
[
'Moment2'
]
=
moment2_out
# Update powers of Beta1 and Beta2 for next time step
self
.
inputs
[
'Beta1Pow'
]
*=
self
.
attrs
[
'beta1'
]
self
.
inputs
[
'Beta2Pow'
]
*=
self
.
attrs
[
'beta1'
]
self
.
inputs
[
'Beta1Pow'
]
=
beta1_pow_out
self
.
inputs
[
'Beta2Pow'
]
=
beta2_pow_out
# Randomize gradient for next step
self
.
inputs
[
'Grad'
]
=
np
.
random
.
uniform
(
...
...
@@ -140,14 +148,21 @@ def lamb_step(inputs, attributes):
moment1_out
=
beta1
*
moment1
+
(
1
-
beta1
)
*
grad
moment2_out
=
beta2
*
moment2
+
(
1
-
beta2
)
*
np
.
square
(
grad
)
moment1_unbiased
=
moment1_out
/
(
1
-
beta1_pow
)
moment2_unbiased
=
moment2_out
/
(
1
-
beta2_pow
)
r_1
=
np
.
linalg
.
norm
(
param
)
r_2
=
np
.
linalg
.
norm
(
moment1_
out
/
(
np
.
sqrt
(
moment2_out
)
+
epsilon
)
+
weight_decay
*
param
)
r_2
=
np
.
linalg
.
norm
(
moment1_
unbiased
/
(
np
.
sqrt
(
moment2_unbiased
)
+
epsilon
)
+
weight_decay
*
param
)
lr_t
=
lr
*
r_1
/
r_2
param_out
=
param
-
lr_t
*
(
moment1_out
/
(
np
.
sqrt
(
moment2_out
)
+
epsilon
)
+
weight_decay
*
param
)
return
param_out
,
moment1_out
,
moment2_out
param_out
=
param
-
lr_t
*
(
moment1_unbiased
/
(
np
.
sqrt
(
moment2_unbiased
)
+
epsilon
)
+
weight_decay
*
param
)
beta1_pow_out
=
beta1_pow
*
beta1
beta2_pow_out
=
beta2_pow
*
beta2
return
param_out
,
moment1_out
,
moment2_out
,
beta1_pow_out
,
beta2_pow_out
def
lamb_step_sparse
(
inputs
,
attributes
,
height
,
rows
,
row_numel
,
np_grad
):
...
...
@@ -174,6 +189,8 @@ def lamb_step_sparse(inputs, attributes, height, rows, row_numel, np_grad):
moment1_out
=
np
.
zeros
(
shape
=
[
height
,
row_numel
])
moment2_out
=
np
.
zeros
(
shape
=
[
height
,
row_numel
])
param_out
=
np
.
zeros
(
shape
=
[
height
,
row_numel
])
moment1_unbiased
=
np
.
zeros
(
shape
=
[
height
,
row_numel
])
moment2_unbiased
=
np
.
zeros
(
shape
=
[
height
,
row_numel
])
def
update_mom
(
row_id
,
update_value
):
moment1_out
[
row_id
]
=
beta1
*
moment1
[
row_id
]
+
(
1
-
beta1
...
...
@@ -202,8 +219,10 @@ def lamb_step_sparse(inputs, attributes, height, rows, row_numel, np_grad):
update_mom
(
row_id
,
update_value
)
update_param
()
beta1_pow_out
=
beta1_pow
*
beta1
beta2_pow_out
=
beta2_pow
*
beta2
return
param_out
,
moment1_out
,
moment2_out
return
param_out
,
moment1_out
,
moment2_out
,
beta1_pow_out
,
beta2_pow_out
class
TestSparseLambOp
(
unittest
.
TestCase
):
...
...
@@ -221,8 +240,8 @@ class TestSparseLambOp(unittest.TestCase):
"Param"
:
np
.
full
((
height
,
row_numel
),
5.0
).
astype
(
"float32"
),
"Moment1"
:
np
.
full
((
height
,
row_numel
),
5.0
).
astype
(
"float32"
),
"Moment2"
:
np
.
full
((
height
,
row_numel
),
5.0
).
astype
(
"float32"
),
'Beta1Pow'
:
np
.
array
([
beta1
**
10
]).
astype
(
"float32"
),
'Beta2Pow'
:
np
.
array
([
beta2
**
10
]).
astype
(
"float32"
),
'Beta1Pow'
:
np
.
array
([
beta1
]).
astype
(
"float32"
),
'Beta2Pow'
:
np
.
array
([
beta2
]).
astype
(
"float32"
),
"LearningRate"
:
np
.
full
((
1
),
2.0
).
astype
(
"float32"
)
}
self
.
init_output
=
np
.
full
((
height
,
row_numel
),
0.0
).
astype
(
"float32"
)
...
...
@@ -245,12 +264,14 @@ class TestSparseLambOp(unittest.TestCase):
self
.
sparse_inputs
=
[
"Grad"
]
param_out
,
mom1
,
mom2
=
lamb_step_sparse
(
param_out
,
mom1
,
mom2
,
beta1_pow_out
,
beta2_pow_out
=
lamb_step_sparse
(
self
.
dense_inputs
,
self
.
attrs
,
height
,
rows
,
row_numel
,
np_array
)
self
.
outputs
=
{
"ParamOut"
:
param_out
,
"Moment1Out"
:
mom1
,
"Moment2Out"
:
mom2
"Moment2Out"
:
mom2
,
'Beta1PowOut'
:
beta1_pow_out
,
'Beta2PowOut'
:
beta2_pow_out
}
def
check_with_place
(
self
,
place
):
...
...
python/paddle/fluid/tests/unittests/test_lambv2_op.py
浏览文件 @
11e78eba
...
...
@@ -19,34 +19,140 @@ import numpy as np
from
op_test
import
OpTest
from
paddle.fluid
import
core
from
paddle.fluid.op
import
Operator
import
paddle.fluid
as
fluid
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.layers
as
layers
class
LAMBOptimizer
(
paddle
.
optimizer
.
Lamb
):
def
_append_optimize_op
(
self
,
block
,
param_and_grad
):
assert
isinstance
(
block
,
fluid
.
framework
.
Block
)
block
.
program
.
_use_lamb
=
True
m
=
moment1
=
self
.
_get_accumulator
(
self
.
_moment1_acc_str
,
param_and_grad
[
0
])
v
=
self
.
_get_accumulator
(
self
.
_moment2_acc_str
,
param_and_grad
[
0
])
beta_1_pow_acc
=
self
.
_get_accumulator
(
self
.
_beta1_pow_acc_str
,
param_and_grad
[
0
])
beta_2_pow_acc
=
self
.
_get_accumulator
(
self
.
_beta2_pow_acc_str
,
param_and_grad
[
0
])
beta_1
=
layers
.
fill_constant
(
dtype
=
'float32'
,
shape
=
[
1
],
value
=
self
.
_beta1
,
name
=
'lamb_beta_1'
)
beta_2
=
layers
.
fill_constant
(
dtype
=
'float32'
,
shape
=
[
1
],
value
=
self
.
_beta2
,
name
=
'lamb_beta_2'
)
epsilon
=
layers
.
fill_constant
(
dtype
=
'float32'
,
shape
=
[
1
],
value
=
self
.
_epsilon
,
name
=
'epsilon'
)
one
=
paddle
.
ones
(
shape
=
[
1
]).
astype
(
'float32'
)
zero
=
paddle
.
zeros
(
shape
=
[
1
]).
astype
(
'float32'
)
next_m
=
paddle
.
multiply
(
m
,
beta_1
)
+
paddle
.
multiply
(
param_and_grad
[
1
],
one
-
beta_1
)
next_v
=
paddle
.
multiply
(
v
,
beta_2
)
+
paddle
.
multiply
(
paddle
.
pow
(
param_and_grad
[
1
],
2
),
one
-
beta_2
)
beta1_correction
=
one
-
beta_1_pow_acc
beta2_correction
=
one
-
beta_2_pow_acc
next_m_unbiased
=
next_m
/
beta1_correction
next_v_unbiased
=
next_v
/
beta2_correction
update
=
next_m_unbiased
/
(
paddle
.
sqrt
(
next_v_unbiased
)
+
epsilon
)
if
self
.
_exclude_from_weight_decay_fn
is
not
None
and
self
.
_exclude_from_weight_decay_fn
(
param_and_grad
[
0
]):
self
.
_lamb_weight_decay
=
0.0
update
+=
self
.
_lamb_weight_decay
*
param_and_grad
[
0
]
w_norm
=
paddle
.
norm
(
param_and_grad
[
0
],
p
=
2
)
g_norm
=
paddle
.
norm
(
update
,
p
=
2
)
learning_rate
=
self
.
_create_param_lr
(
param_and_grad
)
ratio
=
paddle
.
where
(
paddle
.
greater_than
(
w_norm
,
zero
),
paddle
.
where
(
paddle
.
greater_than
(
g_norm
,
zero
),
(
w_norm
/
g_norm
),
one
),
one
)
update_with_lr
=
ratio
*
learning_rate
*
update
next_param
=
param_and_grad
[
0
]
-
update_with_lr
beta_1_pow_acc
*=
beta_1
beta_2_pow_acc
*=
beta_2
paddle
.
assign
(
next_m
,
m
)
paddle
.
assign
(
next_v
,
v
)
paddle
.
assign
(
next_param
,
param_and_grad
[
0
])
return
None
class
TestLambOpV2
(
unittest
.
TestCase
):
def
test_lamb_op
(
self
):
shape
=
[
2
,
4
,
8
,
8
]
data
=
paddle
.
to_tensor
(
np
.
random
.
random
(
size
=
shape
).
astype
(
"float32"
))
conv
=
paddle
.
nn
.
Conv2D
(
4
,
6
,
(
3
,
3
))
data
=
conv
(
data
)
loss
=
paddle
.
mean
(
data
)
opt
=
paddle
.
optimizer
.
Lamb
(
learning_rate
=
1e-5
,
epsilon
=
1e-8
,
parameters
=
conv
.
parameters
())
loss
.
backward
()
opt
.
minimize
(
loss
)
assert
loss
.
numpy
()
is
not
None
class
TestLambOpWithCombinedOp
(
unittest
.
TestCase
):
def
test_lamb_op_with_multi_steps
(
self
):
paddle
.
enable_static
()
def
_build_static_model
(
main
,
startup
,
seed
=
100
):
with
fluid
.
program_guard
(
main
,
startup
):
main
.
random_seed
=
seed
startup
.
random_seed
=
seed
x
=
fluid
.
layers
.
data
(
name
=
'X'
,
shape
=
[
13
],
dtype
=
'float32'
)
y
=
fluid
.
layers
.
data
(
name
=
'Y'
,
shape
=
[
1
],
dtype
=
'float32'
)
prediction
=
fluid
.
layers
.
fc
(
input
=
x
,
size
=
1
,
act
=
None
)
loss
=
fluid
.
layers
.
square_error_cost
(
input
=
prediction
,
label
=
y
)
avg_loss
=
fluid
.
layers
.
mean
(
loss
)
return
avg_loss
place
=
fluid
.
CPUPlace
()
shape
=
[
2
,
3
,
8
,
8
]
exe
=
fluid
.
Executor
(
place
)
train_prog
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
with
fluid
.
program_guard
(
train_prog
,
startup
):
with
fluid
.
unique_name
.
guard
():
data
=
fluid
.
data
(
name
=
"data"
,
shape
=
shape
)
conv
=
fluid
.
layers
.
conv2d
(
data
,
8
,
3
)
loss
=
fluid
.
layers
.
reduce_mean
(
conv
)
beta1
=
0.85
beta2
=
0.95
betas
=
[
beta1
,
beta2
]
opt
=
paddle
.
optimizer
.
Lamb
(
learning_rate
=
1e-5
,
beta1
=
beta1
,
beta2
=
beta2
,
epsilon
=
1e-8
)
opt
.
minimize
(
loss
)
exe
.
run
(
startup
)
data_np
=
np
.
random
.
random
(
shape
).
astype
(
'float32'
)
rets
=
exe
.
run
(
train_prog
,
feed
=
{
"data"
:
data_np
},
fetch_list
=
[
loss
])
assert
rets
[
0
]
is
not
None
num_steps
=
10
for
i
in
range
(
num_steps
):
feed_x
=
np
.
random
.
random
(
size
=
(
10
,
13
)).
astype
(
'float32'
)
feed_y
=
np
.
random
.
random
(
size
=
(
10
,
1
)).
astype
(
'float32'
)
main_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main_program
,
startup_program
):
avg_loss
=
_build_static_model
(
main_program
,
startup_program
)
lamb_kernel
=
paddle
.
optimizer
.
Lamb
(
learning_rate
=
0.2
)
lamb_kernel
.
minimize
(
avg_loss
)
executor
=
fluid
.
Executor
(
place
)
executor
.
run
(
startup_program
)
output
=
executor
.
run
(
program
=
main_program
,
feed
=
{
'X'
:
feed_x
,
'Y'
:
feed_y
},
fetch_list
=
[
avg_loss
.
name
])
main
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
,
startup
):
loss
=
_build_static_model
(
main
,
startup
)
lamb
=
LAMBOptimizer
(
learning_rate
=
0.2
)
lamb
.
minimize
(
loss
)
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
startup
)
out
=
exe
.
run
(
program
=
main
,
feed
=
{
'X'
:
feed_x
,
'Y'
:
feed_y
},
fetch_list
=
[
loss
.
name
])
self
.
assertTrue
(
np
.
allclose
(
out
,
output
))
if
__name__
==
"__main__"
:
...
...
python/paddle/optimizer/lamb.py
浏览文件 @
11e78eba
...
...
@@ -37,6 +37,10 @@ class Lamb(Optimizer):
v_t &= \\beta_2 v_{t - 1} + (1 - \\beta_2)g_t^2
m_t &= \\frac{m_t}{\\beta_1^t}
v_t &= \\frac{v_t}{\\beta_2^t}
r_t &= \\frac{m_t}{\\sqrt{v_t}+\\epsilon}
w_t &= w_{t-1} -\\eta_t \\frac{\\left \| w_{t-1}\\right \|}{\\left \| r_t + \\lambda w_{t-1}\\right \|} (r_t + \\lambda w_{t-1})
...
...
@@ -59,8 +63,9 @@ class Lamb(Optimizer):
The default value is None in static mode, at this time all parameters will be updated.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
( :ref:`api_paddle_fluid_clip_ClipGradByGlobalNorm` , :ref:`api_paddle_fluid_clip_ClipGradByNorm` ,
:ref:`api_paddle_fluid_clip_ClipGradByValue` ). If you want better convergence, it is recommended
to use :ref:`api_paddle_fluid_clip_ClipGradByGlobalNorm` . Default None, meaning there is no gradient clipping.
name(str|None): For detailed information, please refer to
:ref:`api_guide_Name` . Usually name is no need to set and None by default.
Examples:
...
...
@@ -81,7 +86,6 @@ class Lamb(Optimizer):
"""
_moment1_acc_str
=
"moment1"
_moment2_acc_str
=
"moment2"
# these two not used in op temporarily
_beta1_pow_acc_str
=
"beta1_pow_acc"
_beta2_pow_acc_str
=
"beta2_pow_acc"
...
...
@@ -93,6 +97,7 @@ class Lamb(Optimizer):
epsilon
=
1e-6
,
parameters
=
None
,
grad_clip
=
None
,
exclude_from_weight_decay_fn
=
None
,
name
=
None
):
assert
learning_rate
is
not
None
assert
beta1
is
not
None
...
...
@@ -109,6 +114,7 @@ class Lamb(Optimizer):
self
.
_beta2
=
beta2
self
.
_epsilon
=
epsilon
self
.
_lamb_weight_decay
=
lamb_weight_decay
self
.
_exclude_from_weight_decay_fn
=
exclude_from_weight_decay_fn
def
_create_accumulators
(
self
,
block
,
parameters
):
assert
isinstance
(
block
,
framework
.
Block
)
...
...
@@ -145,34 +151,51 @@ class Lamb(Optimizer):
beta2_pow_acc
=
self
.
_get_accumulator
(
self
.
_beta2_pow_acc_str
,
param_and_grad
[
0
])
if
param_and_grad
[
0
].
need_clip
:
if
self
.
_exclude_from_weight_decay_fn
is
not
None
\
and
self
.
_exclude_from_weight_decay_fn
(
param_and_grad
[
0
]):
weight_decay
=
0.0
else
:
weight_decay
=
self
.
_lamb_weight_decay
lr
=
self
.
_create_param_lr
(
param_and_grad
)
if
framework
.
in_dygraph_mode
():
_
,
_
,
_
,
_
,
_
=
core
.
ops
.
lamb
(
param_and_grad
[
0
],
param_and_grad
[
1
],
lr
,
moment1
,
moment2
,
beta1_pow_acc
,
beta2_pow_acc
,
param_and_grad
[
0
],
moment1
,
moment2
,
beta1_pow_acc
,
beta2_pow_acc
,
'beta1'
,
self
.
_beta1
,
'beta2'
,
self
.
_beta2
,
'epsilon'
,
self
.
_epsilon
,
'weight_decay'
,
weight_decay
)
return
None
# create the lamb optimize op
inputs
=
{
"Param"
:
param_and_grad
[
0
],
"Grad"
:
param_and_grad
[
1
],
"LearningRate"
:
lr
,
"Moment1"
:
moment1
,
"Moment2"
:
moment2
,
"Beta1Pow"
:
beta1_pow_acc
,
"Beta2Pow"
:
beta2_pow_acc
}
outputs
=
{
"ParamOut"
:
param_and_grad
[
0
],
"Moment1Out"
:
moment1
,
"Moment2Out"
:
moment2
,
"Beta1PowOut"
:
beta1_pow_acc
,
"Beta2PowOut"
:
beta2_pow_acc
}
attrs
=
{
"beta1"
:
self
.
_beta1
,
"beta2"
:
self
.
_beta2
,
"epsilon"
:
self
.
_epsilon
,
"weight_decay"
:
weight_decay
}
lamb_op
=
block
.
append_op
(
type
=
self
.
type
,
inputs
=
{
"Param"
:
param_and_grad
[
0
],
"Grad"
:
param_and_grad
[
1
],
"LearningRate"
:
self
.
_create_param_lr
(
param_and_grad
),
"Moment1"
:
moment1
,
"Moment2"
:
moment2
,
"Beta1Pow"
:
beta1_pow_acc
,
"Beta2Pow"
:
beta2_pow_acc
},
outputs
=
{
"ParamOut"
:
param_and_grad
[
0
],
"Moment1Out"
:
moment1
,
"Moment2Out"
:
moment2
},
attrs
=
{
"beta1"
:
self
.
_beta1
,
"beta2"
:
self
.
_beta2
,
"epsilon"
:
self
.
_epsilon
,
"weight_decay"
:
weight_decay
},
inputs
=
inputs
,
outputs
=
outputs
,
attrs
=
attrs
,
stop_gradient
=
True
)
return
lamb_op
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