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体验新版 GitCode,发现更多精彩内容 >>
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d6aee759
编写于
11月 20, 2020
作者:
A
Aurelius84
提交者:
GitHub
11月 20, 2020
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差异文件
[Dy2Stat]Set buff.persistable=False when it's not initialized (#28749)
上级
1a532d51
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
19 addition
and
11 deletion
+19
-11
python/paddle/fluid/dygraph/base.py
python/paddle/fluid/dygraph/base.py
+5
-1
python/paddle/fluid/tests/unittests/dygraph_to_static/test_lstm.py
...ddle/fluid/tests/unittests/dygraph_to_static/test_lstm.py
+14
-10
未找到文件。
python/paddle/fluid/dygraph/base.py
浏览文件 @
d6aee759
...
...
@@ -79,8 +79,12 @@ def param_guard(parameters):
# `mask` Tensor or `hidden_0` in RNN layers, which is equivalent to a Parameter
# and necessary for inferring. It will be pruned if it's not necessary for inferring.
else
:
# But if its shape is empty while created from `create_variable()`, we consider this buffer
# non-persistable. See case of `drop_state` in lstm api.
is_persistable
=
len
(
var_base
.
shape
)
>
0
new_var
=
var_base
.
_to_static_var
(
to_parameter
=
False
,
persistable
=
Tru
e
)
to_parameter
=
False
,
persistable
=
is_persistabl
e
)
parameters
[
name
]
=
new_var
yield
parameters
.
update
(
origin_parameters
)
...
...
python/paddle/fluid/tests/unittests/dygraph_to_static/test_lstm.py
浏览文件 @
d6aee759
...
...
@@ -61,25 +61,26 @@ class TestLstm(unittest.TestCase):
msg
=
'dygraph_out is {}
\n
static_out is
\n
{}'
.
format
(
dygraph_out
,
static_out
))
def
test_save_in_eval
(
self
):
def
test_save_in_eval
(
self
,
with_training
=
True
):
paddle
.
jit
.
ProgramTranslator
().
enable
(
True
)
net
=
Net
(
12
,
2
)
x
=
paddle
.
randn
((
2
,
10
,
12
))
x
.
stop_gradient
=
False
dygraph_out
=
net
(
x
)
loss
=
paddle
.
mean
(
dygraph_out
)
sgd
=
paddle
.
optimizer
.
SGD
(
learning_rate
=
0.001
,
parameters
=
net
.
parameters
())
loss
.
backward
()
sgd
.
step
()
if
with_training
:
x
.
stop_gradient
=
False
dygraph_out
=
net
(
x
)
loss
=
paddle
.
mean
(
dygraph_out
)
sgd
=
paddle
.
optimizer
.
SGD
(
learning_rate
=
0.001
,
parameters
=
net
.
parameters
())
loss
.
backward
()
sgd
.
step
()
# switch eval mode firstly
net
.
eval
()
x
=
paddle
.
randn
((
2
,
10
,
12
))
dygraph_out
=
net
(
x
)
dropout_out
=
net
(
x
)
net
=
paddle
.
jit
.
to_static
(
net
,
input_spec
=
[
paddle
.
static
.
InputSpec
(
shape
=
[
-
1
,
10
,
12
])])
paddle
.
jit
.
save
(
net
,
'simple_lstm'
)
dygraph_out
=
net
(
x
)
# load saved model
load_net
=
paddle
.
jit
.
load
(
'simple_lstm'
)
...
...
@@ -96,6 +97,9 @@ class TestLstm(unittest.TestCase):
msg
=
'dygraph_out is {}
\n
static_out is
\n
{}'
.
format
(
dygraph_out
,
train_out
))
def
test_save_without_training
(
self
):
self
.
test_save_in_eval
(
with_training
=
False
)
class
LinearNet
(
nn
.
Layer
):
def
__init__
(
self
):
...
...
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