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体验新版 GitCode,发现更多精彩内容 >>
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5067e3a8
编写于
1月 20, 2021
作者:
A
Aurelius84
提交者:
GitHub
1月 20, 2021
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差异文件
[Dy2Static]Enhance check of TracedLayers out vars (#30576)
上级
d1b25ed9
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
46 addition
and
24 deletion
+46
-24
python/paddle/fluid/dygraph/jit.py
python/paddle/fluid/dygraph/jit.py
+21
-21
python/paddle/fluid/tests/unittests/test_traced_layer_err_msg.py
...paddle/fluid/tests/unittests/test_traced_layer_err_msg.py
+25
-3
未找到文件。
python/paddle/fluid/dygraph/jit.py
浏览文件 @
5067e3a8
...
...
@@ -53,21 +53,21 @@ def create_program_from_desc(program_desc):
return
program
def
_extract_vars
(
inputs
,
result_list
):
def
_extract_vars
(
inputs
,
result_list
,
err_tag
=
'inputs'
):
if
isinstance
(
inputs
,
Variable
):
result_list
.
append
(
inputs
)
elif
isinstance
(
inputs
,
(
list
,
tuple
)):
for
var
in
inputs
:
_extract_vars
(
var
,
result_list
)
_extract_vars
(
var
,
result_list
,
err_tag
)
else
:
raise
TypeError
(
"The type of 'each element of
inputs
' in fluid.dygraph.jit.TracedLayer.trace must be fluid.Variable, but received {}."
.
format
(
type
(
inputs
)))
"The type of 'each element of
{}
' in fluid.dygraph.jit.TracedLayer.trace must be fluid.Variable, but received {}."
.
format
(
err_tag
,
type
(
inputs
)))
def
extract_vars
(
inputs
):
def
extract_vars
(
inputs
,
err_tag
=
'inputs'
):
result_list
=
[]
_extract_vars
(
inputs
,
result_list
)
_extract_vars
(
inputs
,
result_list
,
err_tag
)
return
result_list
...
...
@@ -278,8 +278,8 @@ class _SaveLoadConfig(object):
# NOTE: Users rarely use following configs, so these configs are not open to users,
# reducing user learning costs, but we retain the configuration capabilities
# If True, programs are modified to only support direct inference deployment.
# Otherwise,more information will be stored for flexible optimization and re-training.
# If True, programs are modified to only support direct inference deployment.
# Otherwise,more information will be stored for flexible optimization and re-training.
# Currently, only True is supported
self
.
_export_for_deployment
=
True
...
...
@@ -406,7 +406,7 @@ def _get_input_var_names(inputs, input_spec):
elif
input_spec
is
not
None
and
len
(
input_spec
)
==
len
(
input_var_names
):
# no prune
result_list
=
input_var_names
# if input spec name not in input_var_names, only raise warning
# if input spec name not in input_var_names, only raise warning
for
spec
in
input_spec
:
if
spec
.
name
is
None
:
warnings
.
warn
(
name_none_error
%
spec
)
...
...
@@ -624,7 +624,7 @@ def save(layer, path, input_spec=None, **configs):
# NOTE(chenweihang): If the input layer be wrapped by DataParallel,
# the args and kwargs of forward method will can't be parsed by
# function_spec, so here we save DataParallel._layers instead
# function_spec, so here we save DataParallel._layers instead
# DataParallel it self
# NOTE(chenweihang): using inner_layer, do not change input layer
if
isinstance
(
layer
,
paddle
.
DataParallel
):
...
...
@@ -684,7 +684,7 @@ def save(layer, path, input_spec=None, **configs):
static_forward
=
declarative
(
inner_layer
.
forward
,
input_spec
=
inner_input_spec
)
concrete_program
=
static_forward
.
concrete_program
# the input_spec has been used in declarative, which is equal to
# the input_spec has been used in declarative, which is equal to
# @declarative with input_spec and jit.save without input_spec,
# avoid needless warning
inner_input_spec
=
None
...
...
@@ -704,21 +704,21 @@ def save(layer, path, input_spec=None, **configs):
inner_input_spec
)
# NOTE(chenweihang): [ Get output variables ]
# the rule is like [ Get input variables name ]. For output var,
# we only support VarBase spec, and actually, we only need the
# the rule is like [ Get input variables name ]. For output var,
# we only support VarBase spec, and actually, we only need the
# var name of output, and we don't recommended to use output_spec
output_vars
=
_get_output_vars
(
concrete_program
.
outputs
,
configs
.
output_spec
)
# NOTE(chenweihang): we maintain the mapping of variable name to
# structured name, the buffer variable (non-persistable)
# saved to inference program may not need by dygraph Layer,
# saved to inference program may not need by dygraph Layer,
# we only record the state_dict variable's structured name
state_names_dict
=
dict
()
for
structured_name
,
var
in
six
.
iteritems
(
inner_layer
.
state_dict
()):
state_names_dict
[
var
.
name
]
=
structured_name
# 4. share parameters from Layer to scope & record var info
# 4. share parameters from Layer to scope & record var info
for
param_or_buffer
in
concrete_program
.
parameters
:
# share to scope
param_or_buffer_tensor
=
scope
.
var
(
param_or_buffer
.
name
).
get_tensor
(
...
...
@@ -742,7 +742,7 @@ def save(layer, path, input_spec=None, **configs):
# construct new save_inference_model arguments
model_path
=
dirname
# NOTE(chenweihang): because prefix contains model and params filename,
# so we don't support set model_filename & params_filename
# so we don't support set model_filename & params_filename
if
'forward'
==
attr_func
:
model_filename
=
file_prefix
+
INFER_MODEL_SUFFIX
params_filename
=
file_prefix
+
INFER_PARAMS_SUFFIX
...
...
@@ -769,12 +769,12 @@ def save(layer, path, input_spec=None, **configs):
# - Which persistent variable are parameter and which are not
# - Parameter.trainable information
#
# The lost information cannot be recovered when it is loaded again,
# so if we want to perform fine-tune after loading, we may need to
# The lost information cannot be recovered when it is loaded again,
# so if we want to perform fine-tune after loading, we may need to
# configure redundant information to proceed.
#
# Due to compatibility issues, we cannot change the original storage structure,
# but we can save these information in `jit.save` without changing the original
# Due to compatibility issues, we cannot change the original storage structure,
# but we can save these information in `jit.save` without changing the original
# storage to improve user experience. So we save extra information into
# file `***.pdiparams.info`
with
scope_guard
(
scope
):
...
...
@@ -1032,7 +1032,7 @@ def _trace(layer,
outputs
=
[
original_outputs
]
else
:
outputs
=
original_outputs
out_vars
=
[
var
for
var
in
outputs
]
out_vars
=
extract_vars
(
outputs
,
err_tag
=
'outputs'
)
program_desc
,
feed_names
,
fetch_names
,
parameters
=
tracer
.
create_program_desc
(
var_list
,
feed_prefix
,
out_vars
,
fetch_prefix
,
tmp_prefix
)
...
...
python/paddle/fluid/tests/unittests/test_traced_layer_err_msg.py
浏览文件 @
5067e3a8
...
...
@@ -13,16 +13,18 @@
# limitations under the License.
import
numpy
as
np
import
paddle
import
paddle.fluid
as
fluid
import
six
import
unittest
import
paddle.nn
as
nn
class
SimpleFCLayer
(
fluid
.
dygraph
.
Layer
):
class
SimpleFCLayer
(
nn
.
Layer
):
def
__init__
(
self
,
feature_size
,
batch_size
,
fc_size
):
super
(
SimpleFCLayer
,
self
).
__init__
()
self
.
_linear
=
fluid
.
dygraph
.
Linear
(
feature_size
,
fc_size
)
self
.
_offset
=
fluid
.
dygraph
.
to_variable
(
self
.
_linear
=
nn
.
Linear
(
feature_size
,
fc_size
)
self
.
_offset
=
paddle
.
to_tensor
(
np
.
random
.
random
((
batch_size
,
fc_size
)).
astype
(
'float32'
))
def
forward
(
self
,
x
):
...
...
@@ -30,6 +32,17 @@ class SimpleFCLayer(fluid.dygraph.Layer):
return
fc
+
self
.
_offset
class
LinearNetWithNone
(
nn
.
Layer
):
def
__init__
(
self
,
feature_size
,
fc_size
):
super
(
LinearNetWithNone
,
self
).
__init__
()
self
.
_linear
=
nn
.
Linear
(
feature_size
,
fc_size
)
def
forward
(
self
,
x
):
fc
=
self
.
_linear
(
x
)
return
[
fc
,
[
None
,
2
]]
class
TestTracedLayerErrMsg
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
batch_size
=
4
...
...
@@ -152,5 +165,14 @@ class TestTracedLayerErrMsg(unittest.TestCase):
return
layer
class
TestOutVarWithNoneErrMsg
(
unittest
.
TestCase
):
def
test_linear_net_with_none
(
self
):
model
=
LinearNetWithNone
(
100
,
16
)
in_x
=
paddle
.
to_tensor
(
np
.
random
.
random
((
4
,
100
)).
astype
(
'float32'
))
with
self
.
assertRaises
(
TypeError
):
dygraph_out
,
traced_layer
=
fluid
.
dygraph
.
TracedLayer
.
trace
(
model
,
[
in_x
])
if
__name__
==
'__main__'
:
unittest
.
main
()
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