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体验新版 GitCode,发现更多精彩内容 >>
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1a0ef45e
编写于
9月 01, 2022
作者:
T
taixiurong
提交者:
GitHub
9月 01, 2022
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差异文件
xpu-paddlepaddle-37 [任务] 迁移lamb到phi (#45520)
test=kunlun
上级
5696f967
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
357 addition
and
168 deletion
+357
-168
paddle/fluid/operators/optimizers/lamb_op_xpu.cc
paddle/fluid/operators/optimizers/lamb_op_xpu.cc
+0
-109
paddle/fluid/platform/device/xpu/xpu2_op_list.h
paddle/fluid/platform/device/xpu/xpu2_op_list.h
+4
-2
paddle/phi/kernels/xpu/lamb_kernel.cc
paddle/phi/kernels/xpu/lamb_kernel.cc
+228
-0
python/paddle/fluid/tests/unittests/xpu/get_test_cover_info.py
...n/paddle/fluid/tests/unittests/xpu/get_test_cover_info.py
+1
-0
python/paddle/fluid/tests/unittests/xpu/test_lamb_op_xpu.py
python/paddle/fluid/tests/unittests/xpu/test_lamb_op_xpu.py
+122
-56
python/paddle/optimizer/lamb.py
python/paddle/optimizer/lamb.py
+2
-1
未找到文件。
paddle/fluid/operators/optimizers/lamb_op_xpu.cc
已删除
100644 → 0
浏览文件 @
5696f967
/* Copyright (c) 2022 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.
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 "gflags/gflags.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device/device_wrapper.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
#ifdef PADDLE_WITH_XPU
template
<
typename
DeviceContext
,
typename
T
>
class
LambOpXPUKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
using
paddle
::
framework
::
LoDTensor
;
const
auto
*
param_var
=
ctx
.
InputVar
(
"Param"
);
PADDLE_ENFORCE_EQ
(
param_var
->
IsType
<
framework
::
LoDTensor
>
(),
true
,
platform
::
errors
::
InvalidArgument
(
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s"
,
ctx
.
InputNames
(
"Param"
).
front
(),
framework
::
ToTypeName
(
param_var
->
Type
())));
using
paddle
::
framework
::
LoDTensor
;
// inputs
T
epsilon
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"epsilon"
));
T
weight_decay
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"weight_decay"
));
T
beta1
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"beta1"
));
T
beta2
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"beta2"
));
auto
&
param
=
GET_DATA_SAFELY
(
ctx
.
Input
<
LoDTensor
>
(
"Param"
),
"Input"
,
"Param"
,
"Lamb"
);
auto
*
grad_var
=
ctx
.
InputVar
(
"Grad"
);
auto
&
mom1
=
GET_DATA_SAFELY
(
ctx
.
Input
<
LoDTensor
>
(
"Moment1"
),
"Input"
,
"Moment1"
,
"Lamb"
);
auto
&
mom2
=
GET_DATA_SAFELY
(
ctx
.
Input
<
LoDTensor
>
(
"Moment2"
),
"Input"
,
"Moment2"
,
"Lamb"
);
auto
&
lr
=
GET_DATA_SAFELY
(
ctx
.
Input
<
LoDTensor
>
(
"LearningRate"
),
"Input"
,
"LearningRate"
,
"Lamb"
);
auto
&
beta1_pow
=
GET_DATA_SAFELY
(
ctx
.
Input
<
LoDTensor
>
(
"Beta1Pow"
),
"Input"
,
"Beta1Pow"
,
"Lamb"
);
auto
&
beta2_pow
=
GET_DATA_SAFELY
(
ctx
.
Input
<
LoDTensor
>
(
"Beta2Pow"
),
"Input"
,
"Beta2Pow"
,
"Lamb"
);
auto
&
param_out
=
GET_DATA_SAFELY
(
ctx
.
Output
<
LoDTensor
>
(
"ParamOut"
),
"Output"
,
"ParamOut"
,
"Lamb"
);
auto
&
mom1_out
=
GET_DATA_SAFELY
(
ctx
.
Output
<
LoDTensor
>
(
"Moment1Out"
),
"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
>();
if
(
grad_var
->
IsType
<
framework
::
LoDTensor
>
())
{
auto
&
grad
=
*
ctx
.
Input
<
LoDTensor
>
(
"Grad"
);
int
r
=
xpu
::
lamb
(
dev_ctx
.
x_context
(),
grad
.
template
data
<
T
>(),
mom1
.
template
data
<
T
>(),
mom2
.
template
data
<
T
>(),
param
.
template
data
<
T
>(),
beta1_pow
.
template
data
<
T
>(),
beta2_pow
.
template
data
<
T
>(),
mom1_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
mom2_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
param_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
beta1_pow_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
beta2_pow_out
.
template
mutable_data
<
T
>(
ctx
.
GetPlace
()),
beta1
,
beta2
,
epsilon
,
weight_decay
,
lr
.
template
data
<
T
>(),
param
.
numel
());
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"lamb"
);
}
else
{
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
"Variable type not supported by lamb_op. Expect LoDTensor, "
"but got %s"
,
framework
::
ToTypeName
(
param_var
->
Type
())));
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_XPU_KERNEL
(
lamb
,
ops
::
LambOpXPUKernel
<
paddle
::
platform
::
XPUDeviceContext
,
float
>
);
#endif
paddle/fluid/platform/device/xpu/xpu2_op_list.h
浏览文件 @
1a0ef45e
...
...
@@ -306,6 +306,9 @@ XPUOpMap& get_kl2_ops() {
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"label_smooth"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"lamb"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
pOpKernelType
(
vartype
::
FP16
,
XPUPlace
())})},
{
"lars_momentum"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
()),
pOpKernelType
(
vartype
::
FP16
,
XPUPlace
())})},
...
...
@@ -650,8 +653,7 @@ XPUOpMap& get_kl2_ops() {
{
"resnet_basic_block_grad"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
{
"resnet_basic_block"
,
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})},
};
XPUKernelSet
({
pOpKernelType
(
vartype
::
FP32
,
XPUPlace
())})}};
return
s_xpu2_kernels
;
}
...
...
paddle/phi/kernels/xpu/lamb_kernel.cc
0 → 100644
浏览文件 @
1a0ef45e
// Copyright (c) 2022 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.
// 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/phi/kernels/lamb_kernel.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/backends/xpu/xpu_context.h"
#include "paddle/phi/common/amp_type_traits.h"
#include "paddle/phi/common/place.h"
#include "paddle/phi/core/kernel_registry.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
LambKernel
(
const
Context
&
dev_ctx
,
const
DenseTensor
&
param
,
const
DenseTensor
&
grad
,
const
DenseTensor
&
learning_rate
,
const
DenseTensor
&
moment1
,
const
DenseTensor
&
moment2
,
const
DenseTensor
&
beta1_pow
,
const
DenseTensor
&
beta2_pow
,
const
paddle
::
optional
<
DenseTensor
>&
master_param
,
const
paddle
::
optional
<
DenseTensor
>&
skip_update
,
float
weight_decay
,
float
beta1
,
float
beta2
,
float
epsilon
,
bool
multi_precision
,
DenseTensor
*
param_outs
,
DenseTensor
*
moment1_out
,
DenseTensor
*
moment2_out
,
DenseTensor
*
beta1_pow_out
,
DenseTensor
*
beta2_pow_out
,
DenseTensor
*
master_param_outs
)
{
using
XPUType
=
typename
XPUTypeTrait
<
T
>::
Type
;
using
MT
=
typename
phi
::
dtype
::
MPTypeTrait
<
T
>::
Type
;
if
(
!
multi_precision
)
{
constexpr
auto
kIsSameType
=
std
::
is_same
<
T
,
MT
>::
value
;
PADDLE_ENFORCE_EQ
(
kIsSameType
,
true
,
phi
::
errors
::
InvalidArgument
(
"When multi_precision=False, T and MT must be the same type."
));
}
bool
cpu_skip_update
=
false
;
if
(
skip_update
&&
skip_update
->
IsInitialized
())
{
if
(
paddle
::
platform
::
is_cpu_place
(
skip_update
->
place
()))
{
cpu_skip_update
=
*
(
skip_update
->
data
<
bool
>
());
}
else
{
const
bool
*
skip_update_flag
=
skip_update
->
data
<
bool
>
();
paddle
::
memory
::
Copy
(
phi
::
CPUPlace
(),
static_cast
<
void
*>
(
&
cpu_skip_update
),
dev_ctx
.
GetPlace
(),
static_cast
<
const
void
*>
(
skip_update_flag
),
sizeof
(
bool
));
}
}
if
(
cpu_skip_update
)
{
return
;
}
// tensor --> data_ptr
// inputs
const
XPUType
*
param_ptr
=
reinterpret_cast
<
const
XPUType
*>
(
param
.
data
<
T
>
());
const
XPUType
*
grad_ptr
=
reinterpret_cast
<
const
XPUType
*>
(
grad
.
data
<
T
>
());
const
MT
*
learning_rate_ptr
=
learning_rate
.
data
<
MT
>
();
const
MT
*
moment1_ptr
=
moment1
.
data
<
MT
>
();
const
MT
*
moment2_ptr
=
moment2
.
data
<
MT
>
();
const
MT
*
beta1_pow_ptr
=
beta1_pow
.
data
<
MT
>
();
const
MT
*
beta2_pow_ptr
=
beta2_pow
.
data
<
MT
>
();
const
MT
*
master_param_ptr
=
nullptr
;
if
(
multi_precision
)
{
master_param_ptr
=
master_param
.
get_ptr
()
->
data
<
MT
>
();
}
// outputs
XPUType
*
param_outs_ptr
=
reinterpret_cast
<
XPUType
*>
(
dev_ctx
.
template
Alloc
<
T
>(
param_outs
));
MT
*
moment1_out_ptr
=
dev_ctx
.
template
Alloc
<
MT
>(
moment1_out
);
MT
*
moment2_out_ptr
=
dev_ctx
.
template
Alloc
<
MT
>(
moment2_out
);
MT
*
master_param_outs_ptr
=
nullptr
;
if
(
multi_precision
)
{
if
(
master_param_outs
->
numel
()
!=
master_param
.
get_ptr
()
->
numel
())
{
master_param_outs
->
Resize
(
master_param
.
get_ptr
()
->
dims
());
}
master_param_outs_ptr
=
dev_ctx
.
template
Alloc
<
MT
>(
master_param_outs
);
}
MT
*
beta1_pow_out_ptr
=
nullptr
;
MT
*
beta2_pow_out_ptr
=
nullptr
;
MT
*
beta1_pow_xpu_ptr
=
nullptr
;
MT
*
beta2_pow_xpu_ptr
=
nullptr
;
xpu
::
Context
*
xpu_ctx
=
dev_ctx
.
x_context
();
xpu
::
ctx_guard
RAII_GUARD
(
xpu_ctx
);
if
(
beta1_pow
.
place
().
GetType
()
==
phi
::
AllocationType
::
CPU
)
{
int
r
=
xpu_malloc
(
reinterpret_cast
<
void
**>
(
&
beta1_pow_xpu_ptr
),
(
beta1_pow
.
numel
())
*
sizeof
(
MT
));
PADDLE_ENFORCE_XPU_SUCCESS
(
r
);
paddle
::
memory
::
Copy
(
dev_ctx
.
GetPlace
(),
beta1_pow_xpu_ptr
,
beta1_pow
.
place
(),
beta1_pow
.
data
<
MT
>
(),
sizeof
(
MT
)
*
beta1_pow
.
numel
());
beta1_pow_ptr
=
beta1_pow_xpu_ptr
;
beta1_pow_out_ptr
=
RAII_GUARD
.
alloc_l3_or_gm
<
MT
>
(
beta1_pow_out
->
numel
());
PADDLE_ENFORCE_XDNN_NOT_NULL
(
beta1_pow_out_ptr
);
}
else
{
beta1_pow_out_ptr
=
dev_ctx
.
template
Alloc
<
MT
>(
beta1_pow_out
);
}
if
(
beta2_pow
.
place
().
GetType
()
==
phi
::
AllocationType
::
CPU
)
{
int
r
=
xpu_malloc
(
reinterpret_cast
<
void
**>
(
&
beta2_pow_xpu_ptr
),
(
beta2_pow
.
numel
())
*
sizeof
(
MT
));
PADDLE_ENFORCE_XPU_SUCCESS
(
r
);
paddle
::
memory
::
Copy
(
dev_ctx
.
GetPlace
(),
beta2_pow_xpu_ptr
,
beta2_pow
.
place
(),
beta2_pow
.
data
<
MT
>
(),
sizeof
(
MT
)
*
beta2_pow
.
numel
());
beta2_pow_ptr
=
beta2_pow_xpu_ptr
;
beta2_pow_out_ptr
=
RAII_GUARD
.
alloc_l3_or_gm
<
MT
>
(
beta2_pow_out
->
numel
());
PADDLE_ENFORCE_XDNN_NOT_NULL
(
beta2_pow_out_ptr
);
}
else
{
beta2_pow_out_ptr
=
dev_ctx
.
template
Alloc
<
MT
>(
beta2_pow_out
);
}
const
MT
*
param_calc_ptr
=
nullptr
;
const
MT
*
grad_calc_ptr
=
nullptr
;
MT
*
param_outs_calc_ptr
=
nullptr
;
if
(
std
::
is_same
<
T
,
phi
::
dtype
::
float16
>::
value
)
{
MT
*
param_float
=
RAII_GUARD
.
alloc_l3_or_gm
<
MT
>
(
param
.
numel
());
PADDLE_ENFORCE_XDNN_NOT_NULL
(
param_float
);
MT
*
grad_float
=
RAII_GUARD
.
alloc_l3_or_gm
<
MT
>
(
grad
.
numel
());
PADDLE_ENFORCE_XDNN_NOT_NULL
(
grad_float
);
MT
*
param_outs_float
=
RAII_GUARD
.
alloc_l3_or_gm
<
MT
>
(
param_outs
->
numel
());
PADDLE_ENFORCE_XDNN_NOT_NULL
(
param_outs_float
);
int
r
=
xpu
::
cast
<
XPUType
,
MT
>
(
xpu_ctx
,
param_ptr
,
param_float
,
param
.
numel
());
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"cast"
);
r
=
xpu
::
cast
<
XPUType
,
MT
>
(
xpu_ctx
,
grad_ptr
,
grad_float
,
grad
.
numel
());
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"cast"
);
param_calc_ptr
=
param_float
;
grad_calc_ptr
=
grad_float
;
param_outs_calc_ptr
=
param_outs_float
;
}
else
{
param_calc_ptr
=
reinterpret_cast
<
const
MT
*>
(
param_ptr
);
grad_calc_ptr
=
reinterpret_cast
<
const
MT
*>
(
grad_ptr
);
param_outs_calc_ptr
=
reinterpret_cast
<
MT
*>
(
param_outs_ptr
);
}
int
r
=
xpu
::
lamb
<
MT
>
(
xpu_ctx
,
grad_calc_ptr
,
moment1_ptr
,
moment2_ptr
,
(
multi_precision
?
master_param_ptr
:
param_calc_ptr
),
beta1_pow_ptr
,
beta2_pow_ptr
,
moment1_out_ptr
,
moment2_out_ptr
,
(
multi_precision
?
master_param_outs_ptr
:
param_outs_calc_ptr
),
beta1_pow_out_ptr
,
beta2_pow_out_ptr
,
beta1
,
beta2
,
epsilon
,
weight_decay
,
learning_rate_ptr
,
param
.
numel
());
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"lamb"
);
if
(
std
::
is_same
<
T
,
phi
::
dtype
::
float16
>::
value
&&
multi_precision
==
false
)
{
int
r
=
xpu
::
cast
<
MT
,
XPUType
>
(
xpu_ctx
,
param_outs_calc_ptr
,
param_outs_ptr
,
param_outs
->
numel
());
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"cast"
);
}
if
(
beta1_pow
.
place
().
GetType
()
==
phi
::
AllocationType
::
CPU
)
{
// copy beta1_pow_out from xpu to cpu
paddle
::
memory
::
Copy
(
beta1_pow
.
place
(),
dev_ctx
.
template
HostAlloc
<
MT
>(
beta1_pow_out
),
dev_ctx
.
GetPlace
(),
beta1_pow_out_ptr
,
sizeof
(
MT
)
*
beta1_pow_out
->
numel
());
if
(
beta1_pow_xpu_ptr
)
{
xpu_free
(
beta1_pow_xpu_ptr
);
}
}
if
(
beta2_pow
.
place
().
GetType
()
==
phi
::
AllocationType
::
CPU
)
{
// copy beta2_pow_out from xpu to cpu
paddle
::
memory
::
Copy
(
beta2_pow
.
place
(),
dev_ctx
.
template
HostAlloc
<
MT
>(
beta2_pow_out
),
dev_ctx
.
GetPlace
(),
beta2_pow_out_ptr
,
sizeof
(
MT
)
*
beta2_pow_out
->
numel
());
if
(
beta2_pow_xpu_ptr
)
{
xpu_free
(
beta2_pow_xpu_ptr
);
}
}
}
}
// namespace phi
PD_REGISTER_KERNEL
(
lamb
,
XPU
,
ALL_LAYOUT
,
phi
::
LambKernel
,
float
,
phi
::
dtype
::
float16
)
{
kernel
->
InputAt
(
5
).
SetBackend
(
phi
::
Backend
::
ALL_BACKEND
);
kernel
->
InputAt
(
6
).
SetBackend
(
phi
::
Backend
::
ALL_BACKEND
);
}
python/paddle/fluid/tests/unittests/xpu/get_test_cover_info.py
浏览文件 @
1a0ef45e
...
...
@@ -88,6 +88,7 @@ xpu_test_op_type_white_list = [
'dropout_float16'
,
'dropout_grad_float16'
,
"grad_add_float32"
,
# no api for grad_add, skip
"lamb_float16"
,
"lars_momentum_float32"
,
"resnet_unit"
,
"resnet_unit_grad"
...
...
python/paddle/fluid/tests/unittests/xpu/test_lamb_op_xpu.py
浏览文件 @
1a0ef45e
...
...
@@ -23,53 +23,8 @@ from paddle.fluid import core
from
paddle.fluid.op
import
Operator
import
paddle.fluid
as
fluid
import
paddle
"""
class TestLambOp1(XPUOpTest):
def set_attrs(self):
self.attrs = {
'epsilon': 1e-6,
'beta1': 0.9,
'beta2': 0.999,
'weight_decay': 0.01
}
def setUp(self):
'''Test Lamb Op with supplied attributes
'''
self.op_type = 'lamb'
param = np.random.uniform(-1, 1, 5000).astype('float32')
grad = np.random.uniform(-1, 1, 5000).astype('float32')
moment1 = np.random.uniform(-1, 1, 5000).astype('float32')
moment2 = np.random.random(5000).astype('float32')
self.set_attrs()
learning_rate = 0.001
beta1_pow = self.attrs['beta1']
beta2_pow = self.attrs['beta2']
self.inputs = {
'Param': param,
'Grad': grad,
'Moment1': moment1,
'Moment2': moment2,
'LearningRate': np.array([learning_rate]).astype('float32'),
'Beta1Pow': np.array([beta1_pow]).astype('float32'),
'Beta2Pow': np.array([beta2_pow]).astype('float32')
}
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,
'Beta1PowOut': beta1_pow_out,
'Beta2PowOut': beta2_pow_out
}
def test_check_output(self):
self.check_output_with_place(paddle.XPUPlace(0))
from
xpu.get_test_cover_info
import
create_test_class
,
get_xpu_op_support_types
,
XPUOpTestWrapper
def
lamb_step
(
inputs
,
attributes
):
...
...
@@ -100,21 +55,132 @@ def lamb_step(inputs, attributes):
moment2_unbiased
=
moment2_out
/
(
1
-
beta2_pow
)
r_1
=
np
.
linalg
.
norm
(
param
)
r_2 = np.linalg.norm(moment1_unbiased / (np.sqrt(moment2_unbiased) + epsilon
) + weight_decay * param)
if r_1 > 0.0 and r_2 > 0.0:
lr_t = lr * r_1 / r_2
else:
lr_t = 1.0
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_unbiased / (
np.sqrt(moment2_unbiased) + epsilon) + weight_decay * param)
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
"""
class
XPUTestLambOp
(
XPUOpTestWrapper
):
def
__init__
(
self
):
self
.
op_name
=
'lamb'
self
.
use_dynamic_create_class
=
False
class
TestLambOp1
(
XPUOpTest
):
def
set_attrs
(
self
):
self
.
attrs
=
{
'epsilon'
:
1e-4
,
'beta1'
:
0.78
,
'beta2'
:
0.836
,
'weight_decay'
:
0.01
}
def
setUp
(
self
):
'''Test Lamb Op with supplied attributes
'''
# self.op_type = self.op_name
self
.
__class__
.
op_type
=
'lamb'
self
.
dtype
=
self
.
in_type
param
=
np
.
random
.
uniform
(
-
1
,
1
,
(
102
,
105
)).
astype
(
self
.
dtype
)
grad
=
np
.
random
.
uniform
(
-
1
,
1
,
(
102
,
105
)).
astype
(
self
.
dtype
)
moment1
=
np
.
random
.
uniform
(
-
1
,
1
,
(
102
,
105
)).
astype
(
"float32"
)
moment2
=
np
.
random
.
random
((
102
,
105
)).
astype
(
"float32"
)
learning_rate
=
0.001
self
.
set_attrs
()
beta1_pow
=
self
.
attrs
[
'beta1'
]
beta2_pow
=
self
.
attrs
[
'beta2'
]
self
.
inputs
=
{
'Param'
:
param
,
'Grad'
:
grad
,
'Moment1'
:
moment1
,
'Moment2'
:
moment2
,
'LearningRate'
:
np
.
array
([
learning_rate
]).
astype
(
"float32"
),
'Beta1Pow'
:
np
.
array
([
beta1_pow
]).
astype
(
"float32"
),
'Beta2Pow'
:
np
.
array
([
beta2_pow
]).
astype
(
"float32"
)
}
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
,
'Beta1PowOut'
:
beta1_pow_out
,
'Beta2PowOut'
:
beta2_pow_out
}
def
test_check_output
(
self
):
self
.
check_output_with_place
(
paddle
.
XPUPlace
(
0
))
class
TestLambOp2
(
TestLambOp1
):
def
set_attrs
(
self
):
self
.
attrs
=
{
'epsilon'
:
1e-8
,
'beta1'
:
0.9
,
'beta2'
:
0.999
,
'weight_decay'
:
0.01
}
class
TestLambOpMultipleSteps
(
TestLambOp1
):
def
set_attrs
(
self
):
self
.
attrs
=
{
'epsilon'
:
1e-8
,
'beta1'
:
0.9
,
'beta2'
:
0.999
,
'weight_decay'
:
0.01
}
self
.
num_steps
=
10
def
test_check_output
(
self
):
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
,
'Beta1PowOut'
:
beta1_pow_out
,
'Beta2PowOut'
:
beta2_pow_out
}
# Verify output for this step
self
.
check_output
()
# Output of this step becomes input for next step
self
.
inputs
[
'Param'
]
=
param_out
self
.
inputs
[
'Moment1'
]
=
moment1_out
self
.
inputs
[
'Moment2'
]
=
moment2_out
# Update powers of Beta1 and Beta2 for next time step
self
.
inputs
[
'Beta1Pow'
]
=
beta1_pow_out
self
.
inputs
[
'Beta2Pow'
]
=
beta2_pow_out
# Randomize gradient for next step
self
.
inputs
[
'Grad'
]
=
np
.
random
.
uniform
(
-
1
,
1
,
(
102
,
105
)).
astype
(
"float32"
)
support_types
=
get_xpu_op_support_types
(
'lamb'
)
for
stype
in
support_types
:
create_test_class
(
globals
(),
XPUTestLambOp
,
stype
)
if
__name__
==
"__main__"
:
paddle
.
enable_static
()
...
...
python/paddle/optimizer/lamb.py
浏览文件 @
1a0ef45e
...
...
@@ -108,6 +108,7 @@ class Lamb(Optimizer):
parameters
=
None
,
grad_clip
=
None
,
exclude_from_weight_decay_fn
=
None
,
multi_precision
=
False
,
name
=
None
):
assert
learning_rate
is
not
None
assert
beta1
is
not
None
...
...
@@ -134,7 +135,7 @@ class Lamb(Optimizer):
self
.
_master_weights
=
{}
self
.
_used_master_weights
=
{}
# TODO(zengjinle): expose API as soon as possible
self
.
_multi_precision
=
False
self
.
_multi_precision
=
multi_precision
def
_get_parameter
(
self
,
name
,
scope
=
None
):
if
scope
is
None
:
...
...
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