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6129b0e2
编写于
9月 02, 2020
作者:
Y
Yang Zhang
提交者:
GitHub
9月 02, 2020
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电子邮件补丁
差异文件
Revert `no_grad` changes and add new implementation (#26826)
上级
d067e66d
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
138 addition
and
39 deletion
+138
-39
python/paddle/__init__.py
python/paddle/__init__.py
+1
-1
python/paddle/fluid/clip.py
python/paddle/fluid/clip.py
+4
-4
python/paddle/fluid/dygraph/base.py
python/paddle/fluid/dygraph/base.py
+78
-4
python/paddle/fluid/dygraph/math_op_patch.py
python/paddle/fluid/dygraph/math_op_patch.py
+1
-1
python/paddle/fluid/dygraph/parallel.py
python/paddle/fluid/dygraph/parallel.py
+1
-1
python/paddle/fluid/optimizer.py
python/paddle/fluid/optimizer.py
+4
-4
python/paddle/fluid/tests/unittests/test_imperative_decorator.py
...paddle/fluid/tests/unittests/test_imperative_decorator.py
+46
-21
python/paddle/optimizer/optimizer.py
python/paddle/optimizer/optimizer.py
+2
-2
python/paddle/optimizer/sgd.py
python/paddle/optimizer/sgd.py
+1
-1
未找到文件。
python/paddle/__init__.py
浏览文件 @
6129b0e2
...
...
@@ -259,7 +259,7 @@ from .device import get_device
from
.fluid.dygraph.base
import
enable_dygraph
as
disable_static
#DEFINE_ALIAS
from
.fluid.dygraph.base
import
disable_dygraph
as
enable_static
#DEFINE_ALIAS
from
.fluid.framework
import
in_dygraph_mode
as
in_dynamic_mode
#DEFINE_ALIAS
from
.fluid.dygraph.base
import
no_grad
#DEFINE_ALIAS
from
.fluid.dygraph.base
import
no_grad
_
as
no_grad
#DEFINE_ALIAS
from
.
import
jit
from
.
import
static
...
...
python/paddle/fluid/clip.py
浏览文件 @
6129b0e2
...
...
@@ -129,7 +129,7 @@ class GradientClipBase(object):
def
__str__
(
self
):
raise
NotImplementedError
()
@
imperative_base
.
no_grad
()
@
imperative_base
.
no_grad
def
_dygraph_clip
(
self
,
params_grads
):
raise
NotImplementedError
...
...
@@ -258,7 +258,7 @@ class GradientClipByValue(GradientClipBase):
def
__str__
(
self
):
return
"Gradient Clip By Value, min = %f, max=%f"
%
(
self
.
min
,
self
.
max
)
@
imperative_base
.
no_grad
()
@
imperative_base
.
no_grad
def
_dygraph_clip
(
self
,
params_grads
):
params_and_grads
=
[]
for
p
,
g
in
params_grads
:
...
...
@@ -413,7 +413,7 @@ class GradientClipByNorm(GradientClipBase):
def
__str__
(
self
):
return
"Gradient Clip By Norm, clip_norm=%f"
%
self
.
clip_norm
@
imperative_base
.
no_grad
()
@
imperative_base
.
no_grad
def
_dygraph_clip
(
self
,
params_grads
):
params_and_grads
=
[]
for
p
,
g
in
params_grads
:
...
...
@@ -565,7 +565,7 @@ class GradientClipByGlobalNorm(GradientClipBase):
def
__str__
(
self
):
return
"Gradient Clip By GlobalNorm, global_norm=%f"
%
(
self
.
clip_norm
)
@
imperative_base
.
no_grad
()
@
imperative_base
.
no_grad
def
_dygraph_clip
(
self
,
params_grads
):
params_and_grads
=
[]
sum_square_list
=
[]
...
...
python/paddle/fluid/dygraph/base.py
浏览文件 @
6129b0e2
...
...
@@ -12,9 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from
..wrapped_decorator
import
signature_safe_contextmanager
,
wrap_decorator
import
inspect
import
decorator
import
contextlib
import
functools
import
inspect
import
sys
import
numpy
as
np
from
paddle.fluid
import
core
...
...
@@ -26,8 +27,8 @@ import objgraph
from
..data_feeder
import
convert_dtype
__all__
=
[
'no_grad'
,
'
grad'
,
'guard'
,
'enable_dygraph'
,
'disable_dygraph'
,
'enabled
'
,
'to_variable'
'no_grad'
,
'
no_grad_'
,
'grad'
,
'guard'
,
'enable_dygraph'
,
'disable_dygraph
'
,
'
enabled'
,
'
to_variable'
]
...
...
@@ -167,7 +168,80 @@ def disable_dygraph():
_functional_dygraph_context_manager
=
None
class
no_grad
:
@
signature_safe_contextmanager
def
_switch_tracer_mode_guard_
(
is_train
=
True
):
tracer
=
framework
.
_dygraph_tracer
()
if
tracer
:
mode
=
tracer
.
_train_mode
tracer
.
_train_mode
=
is_train
try
:
yield
finally
:
tracer
.
_train_mode
=
mode
else
:
yield
def
no_grad
(
func
=
None
):
"""
:api_attr: imperative
Create a context which disables dygraph gradient calculation.
In this mode, the result of every computation will have `stop_gradient=True`.
Also functions as a decorator. (Make sure to instantiate without parenthesis.)
Examples:
.. code-block:: python
import numpy as np
import paddle.fluid as fluid
# use as generator
data = np.array([[2, 3], [4, 5]]).astype('float32')
with fluid.dygraph.guard():
l0 = fluid.Linear(2, 2) # l0.weight.gradient() is None
l1 = fluid.Linear(2, 2)
with fluid.dygraph.no_grad():
# l1.weight.stop_gradient is False
tmp = l1.weight * 2 # tmp.stop_gradient is True
x = fluid.dygraph.to_variable(data)
y = l0(x) + tmp
o = l1(y)
o.backward()
print(tmp.gradient() is None) # True
print(l0.weight.gradient() is None) # False
# use as decorator
@fluid.dygraph.no_grad
def test_layer():
with fluid.dygraph.guard():
inp = np.ones([3, 1024], dtype='float32')
t = fluid.dygraph.base.to_variable(inp)
linear1 = fluid.Linear(1024, 4, bias_attr=False)
linear2 = fluid.Linear(4, 4)
ret = linear1(t)
dy_ret = linear2(ret)
test_layer()
"""
if
func
is
None
:
return
_switch_tracer_mode_guard_
(
is_train
=
False
)
else
:
@
decorator
.
decorator
def
__impl__
(
func
,
*
args
,
**
kwargs
):
with
_switch_tracer_mode_guard_
(
is_train
=
False
):
return
func
(
*
args
,
**
kwargs
)
return
__impl__
(
func
)
class
no_grad_
:
"""
:api_attr: imperative
...
...
python/paddle/fluid/dygraph/math_op_patch.py
浏览文件 @
6129b0e2
...
...
@@ -41,7 +41,7 @@ def monkey_patch_math_varbase():
The difference is, in dygraph mode, use auto-generated op functions for better performance.
"""
@
no_grad
()
@
no_grad
def
create_tensor
(
value
,
dtype
,
shape
):
out
=
_varbase_creator
(
dtype
=
dtype
)
out
=
core
.
ops
.
fill_constant
(
out
,
'dtype'
,
dtype
,
'shape'
,
shape
,
...
...
python/paddle/fluid/dygraph/parallel.py
浏览文件 @
6129b0e2
...
...
@@ -445,7 +445,7 @@ class DataParallel(layers.Layer):
self
.
_reshape_inplace
(
x
=
g_var
,
shape
=
g_shape
)
assert
g_var
.
shape
==
g_shape
@
no_grad
()
@
no_grad
def
apply_collective_grads
(
self
):
"""
AllReduce the Parameters' gradient.
...
...
python/paddle/fluid/optimizer.py
浏览文件 @
6129b0e2
...
...
@@ -61,7 +61,7 @@ class Optimizer(object):
but need to use one of it's implementation.
"""
@
imperative_base
.
no_grad
()
@
imperative_base
.
no_grad
def
__init__
(
self
,
learning_rate
,
parameter_list
=
None
,
...
...
@@ -897,7 +897,7 @@ class Optimizer(object):
if
p
.
trainable
:
p
.
clear_gradient
()
@
imperative_base
.
no_grad
()
@
imperative_base
.
no_grad
def
minimize
(
self
,
loss
,
startup_program
=
None
,
...
...
@@ -1015,7 +1015,7 @@ class SGDOptimizer(Optimizer):
name
=
name
)
self
.
type
=
"sgd"
@
no_grad
()
@
no_grad
def
_append_optimize_op
(
self
,
block
,
param_and_grad
):
lr
=
self
.
_create_param_lr
(
param_and_grad
)
if
framework
.
in_dygraph_mode
():
...
...
@@ -1552,7 +1552,7 @@ class DGCMomentumOptimizer(Optimizer):
dgc_op
.
_set_attr
(
op_maker
.
kOpRoleVarAttrName
(),
[
param_var
.
name
,
grad_var
.
name
])
@
imperative_base
.
no_grad
()
@
imperative_base
.
no_grad
def
apply_gradients
(
self
,
params_grads
):
params_grads
=
sorted
(
params_grads
,
key
=
lambda
x
:
x
[
0
].
name
)
params_grads
,
table_param_and_grad
,
table_optimize_op
=
\
...
...
python/paddle/fluid/tests/unittests/test_imperative_decorator.py
浏览文件 @
6129b0e2
...
...
@@ -28,7 +28,7 @@ class TestTracerMode(unittest.TestCase):
def
get_tracer_mode
(
self
):
assert
fluid
.
in_dygraph_mode
(),
"Dygraph mode must be enabled"
@
paddle
.
no_grad
()
@
fluid
.
dygraph
.
no_grad
def
no_grad_func
(
self
,
a
):
self
.
assertEqual
(
self
.
tracer
.
_train_mode
,
False
)
return
a
...
...
@@ -56,35 +56,17 @@ class TestTracerMode(unittest.TestCase):
def
need_no_grad_func
(
a
,
b
=
1
):
return
a
+
b
decorated_func
=
paddle
.
no_grad
()
(
need_no_grad_func
)
decorated_func
=
fluid
.
dygraph
.
no_grad
(
need_no_grad_func
)
self
.
assertTrue
(
str
(
inspect
.
getargspec
(
decorated_func
))
==
str
(
inspect
.
getargspec
(
need_no_grad_func
)))
self
.
assertEqual
(
self
.
tracer
.
_train_mode
,
self
.
init_mode
)
def
test_gen
():
for
i
in
range
(
3
):
yield
i
a
=
0
for
i
in
test_gen
():
a
+=
i
@
paddle
.
no_grad
()
def
test_wrapped_gen
():
for
i
in
range
(
3
):
yield
i
b
=
0
for
i
in
test_wrapped_gen
():
b
+=
i
self
.
assertEqual
(
a
,
b
)
with
fluid
.
dygraph
.
guard
():
self
.
check_not_support_rlt
(
False
)
paddle
.
enable_static
()
with
new_program_scope
():
self
.
check_not_support_rlt
(
True
)
...
...
@@ -94,5 +76,48 @@ class TestTracerMode2(TestTracerMode):
self
.
init_mode
=
False
class
TestNoGradClass
(
unittest
.
TestCase
):
@
paddle
.
no_grad
()
def
no_grad_func
(
self
,
a
):
self
.
assertEqual
(
self
.
tracer
.
_train_mode
,
False
)
return
a
def
test_main
(
self
):
paddle
.
disable_static
()
self
.
tracer
=
framework
.
_dygraph_tracer
()
self
.
tracer
.
_train_mode
=
True
self
.
assertEqual
(
self
.
no_grad_func
(
1
),
1
)
self
.
assertEqual
(
self
.
no_grad_func
.
__name__
,
"no_grad_func"
)
def
need_no_grad_func
(
a
,
b
=
1
):
return
a
+
b
decorated_func
=
paddle
.
no_grad
()(
need_no_grad_func
)
self
.
assertEqual
(
str
(
inspect
.
getargspec
(
decorated_func
)),
str
(
inspect
.
getargspec
(
need_no_grad_func
)))
def
test_gen
():
for
i
in
range
(
3
):
yield
i
a
=
0
for
i
in
test_gen
():
a
+=
i
@
paddle
.
no_grad
()
def
test_wrapped_gen
():
for
i
in
range
(
3
):
yield
i
b
=
0
for
i
in
test_wrapped_gen
():
b
+=
i
self
.
assertEqual
(
a
,
b
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/optimizer/optimizer.py
浏览文件 @
6129b0e2
...
...
@@ -97,7 +97,7 @@ class Optimizer(object):
"""
@
imperative_base
.
no_grad
()
@
imperative_base
.
no_grad
def
__init__
(
self
,
learning_rate
,
parameters
=
None
,
...
...
@@ -815,7 +815,7 @@ class Optimizer(object):
if
p
.
trainable
:
p
.
clear_gradient
()
@
imperative_base
.
no_grad
()
@
imperative_base
.
no_grad
def
minimize
(
self
,
loss
,
startup_program
=
None
,
...
...
python/paddle/optimizer/sgd.py
浏览文件 @
6129b0e2
...
...
@@ -85,7 +85,7 @@ class SGD(Optimizer):
name
=
name
)
self
.
type
=
"sgd"
@
no_grad
()
@
no_grad
def
_append_optimize_op
(
self
,
block
,
param_and_grad
):
lr
=
self
.
_create_param_lr
(
param_and_grad
)
if
framework
.
in_dygraph_mode
():
...
...
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