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68497e7b
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体验新版 GitCode,发现更多精彩内容 >>
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68497e7b
编写于
3月 24, 2021
作者:
C
Chen Weihang
提交者:
GitHub
3月 24, 2021
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电子邮件补丁
差异文件
change trainable to stop_gradient in optimizer (#31823)
上级
270699e6
变更
3
隐藏空白更改
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并排
Showing
3 changed file
with
12 addition
and
11 deletion
+12
-11
python/paddle/optimizer/adam.py
python/paddle/optimizer/adam.py
+1
-1
python/paddle/optimizer/adamax.py
python/paddle/optimizer/adamax.py
+1
-1
python/paddle/optimizer/optimizer.py
python/paddle/optimizer/optimizer.py
+10
-9
未找到文件。
python/paddle/optimizer/adam.py
浏览文件 @
68497e7b
...
...
@@ -351,7 +351,7 @@ class Adam(Optimizer):
"""
params_grads
=
[]
for
param
in
self
.
_parameter_list
:
if
not
param
.
trainable
:
if
param
.
stop_gradient
:
continue
if
param
.
_grad_ivar
()
is
not
None
:
grad_var
=
param
.
_grad_ivar
()
...
...
python/paddle/optimizer/adamax.py
浏览文件 @
68497e7b
...
...
@@ -184,7 +184,7 @@ class Adamax(Optimizer):
"""
assert
isinstance
(
block
,
framework
.
Block
)
for
param
,
grad
in
parameters_and_grads
:
if
grad
is
None
or
param
.
trainable
is
Fals
e
:
if
grad
is
None
or
param
.
stop_gradient
is
Tru
e
:
continue
with
param
.
block
.
program
.
_optimized_guard
(
[
param
,
grad
]),
name_scope
(
'adamax'
):
...
...
python/paddle/optimizer/optimizer.py
浏览文件 @
68497e7b
...
...
@@ -542,7 +542,7 @@ class Optimizer(object):
def
_update_param_device_map
(
self
,
parameters_and_grads
,
target_block
):
for
param_and_grad
in
parameters_and_grads
:
if
param_and_grad
[
0
].
trainable
is
Tru
e
:
if
param_and_grad
[
0
].
stop_gradient
is
Fals
e
:
param_name
=
param_and_grad
[
0
].
name
ops
=
target_block
.
ops
device_attr_name
=
core
.
op_proto_and_checker_maker
.
kOpDeviceAttrName
(
...
...
@@ -598,14 +598,14 @@ class Optimizer(object):
self
.
_update_param_device_map
(
parameters_and_grads
,
target_block
)
self
.
_create_accumulators
(
target_block
,
[
p
[
0
]
for
p
in
parameters_and_grads
if
p
[
0
].
trainable
])
[
p
[
0
]
for
p
in
parameters_and_grads
if
not
p
[
0
].
stop_gradient
])
self
.
_create_global_learning_rate
()
if
framework
.
in_dygraph_mode
():
for
param_and_grad
in
parameters_and_grads
:
if
param_and_grad
[
1
]
is
None
:
continue
if
param_and_grad
[
0
].
trainable
is
Tru
e
:
if
param_and_grad
[
0
].
stop_gradient
is
Fals
e
:
self
.
_append_optimize_op
(
target_block
,
param_and_grad
)
else
:
for
param_and_grad
in
parameters_and_grads
:
...
...
@@ -613,7 +613,7 @@ class Optimizer(object):
continue
with
param_and_grad
[
0
].
block
.
program
.
_optimized_guard
(
param_and_grad
),
name_scope
(
"optimizer"
):
if
param_and_grad
[
0
].
trainable
is
Tru
e
:
if
param_and_grad
[
0
].
stop_gradient
is
Fals
e
:
device
=
self
.
_get_device_for_param
(
param_and_grad
[
0
]
.
name
)
with
device_guard
(
device
):
...
...
@@ -689,7 +689,7 @@ class Optimizer(object):
params_grads
=
[]
for
param
in
parameter_list
:
if
not
param
.
trainable
:
if
param
.
stop_gradient
:
continue
if
param
.
_grad_ivar
()
is
not
None
:
# create gradient tensor
...
...
@@ -789,8 +789,9 @@ class Optimizer(object):
def
_get_no_grad_set
(
self
,
loss
,
no_grad_set
=
None
):
no_grad_set
=
_get_no_grad_set_name
(
no_grad_set
)
parameters
=
loss
.
block
.
program
.
global_block
().
all_parameters
()
param_no_trainable
=
set
(
[
param
.
name
for
param
in
parameters
if
param
.
trainable
is
False
])
param_no_trainable
=
set
([
param
.
name
for
param
in
parameters
if
param
.
stop_gradient
is
True
])
# If the parameter is no trainable, it should not have a gradient.
no_grad_set
.
update
(
param_no_trainable
)
...
...
@@ -825,7 +826,7 @@ class Optimizer(object):
"""
for
p
in
self
.
_parameter_list
:
if
p
.
trainable
:
if
not
p
.
stop_gradient
:
p
.
clear_gradient
()
@
imperative_base
.
no_grad
...
...
@@ -920,7 +921,7 @@ class Optimizer(object):
"""
params_grads
=
[]
for
param
in
self
.
_parameter_list
:
if
not
param
.
trainable
:
if
param
.
stop_gradient
:
continue
if
param
.
_grad_ivar
()
is
not
None
:
grad_var
=
param
.
_grad_ivar
()
...
...
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