test=develop

python/paddle/fluid/contrib/decoder/beam_search_decoder.py

@@ -22,7 +22,7 @@ This API is still under active development and may change drastically.
 
 from __future__ import print_function
 
-import contextlib
+from ...wrapped_decorator import contextmanager
 import numpy as np
 import six
 
@@ -419,7 +419,7 @@ class TrainingDecoder(object):
         self._state_cell = state_cell
         self._state_cell._enter_decoder(self)
 
-    @contextlib.contextmanager
+    @contextmanager
     def block(self):
         """
         Define the behavior of the decoder for each RNN time step.
@@ -613,7 +613,7 @@ class BeamSearchDecoder(object):
         self._word_dim = word_dim
         self._input_var_dict = input_var_dict
 
-    @contextlib.contextmanager
+    @contextmanager
     def block(self):
         """
         Define the behavior of the decoder for each RNN time step.

python/paddle/fluid/contrib/inferencer.py

@@ -14,7 +14,7 @@
 
 from __future__ import print_function
 
-import contextlib
+from ..wrapped_decorator import contextmanager
 
 from .. import core
 
@@ -105,7 +105,7 @@ class Inferencer(object):
 
         return results
 
-    @contextlib.contextmanager
+    @contextmanager
     def _prog_and_scope_guard(self):
         with framework.program_guard(main_program=self.inference_program):
             with executor.scope_guard(self.scope):

python/paddle/fluid/contrib/trainer.py

@@ -14,7 +14,7 @@
 
 from __future__ import print_function
 
-import contextlib
+from .wrapped_decorator import contextmanager
 import os
 import errno
 import shutil
@@ -453,7 +453,7 @@ class Trainer(object):
             io.save_inference_model(param_path, feeded_var_names, target_vars,
                                     exe)
 
-    @contextlib.contextmanager
+    @contextmanager
     def _prog_and_scope_guard(self):
         with framework.program_guard(
                 main_program=self.train_program,

python/paddle/fluid/executor.py

@@ -17,7 +17,7 @@ from __future__ import print_function
 import os
 import multiprocessing
 import numpy as np
-import contextlib
+from .wrapped_decorator import contextmanager
 import six
 from .framework import Program, default_main_program, Variable
 from . import core
@@ -49,7 +49,7 @@ def _switch_scope(scope):
     return ex
 
 
-@contextlib.contextmanager
+@contextmanager
 def scope_guard(scope):
     """
     Change the global/default scope instance by Python `with` statement. All

python/paddle/fluid/framework.py

@@ -16,7 +16,7 @@ from __future__ import print_function
 
 import collections
 from collections import defaultdict
-import contextlib
+from .wrapped_decorator import contextmanager
 import os
 import re
 import traceback
@@ -111,7 +111,7 @@ class NameScope(object):
 _name_scope = NameScope()
 
 
-@contextlib.contextmanager
+@contextmanager
 def name_scope(prefix=None):
     """
     Generate hierarchical name prefix for the operators.
@@ -1775,7 +1775,7 @@ class Program(object):
     def set_op_role_var(self, var_name):
         self._op_role_var = [var_name]
 
-    @contextlib.contextmanager
+    @contextmanager
     def _optimized_guard(self, param_and_grads):
         """
         A with guard to set :code:`Optimization` :code:`OpRole` and
@@ -1805,7 +1805,7 @@ class Program(object):
         self._op_role_var = tmp_var
         self._current_role = tmp_role
 
-    @contextlib.contextmanager
+    @contextmanager
     def _lr_schedule_guard(self, is_with_opt=False):
         """
         A with guard to set :code:`LRSched` :code:`OpRole` and
@@ -2459,7 +2459,7 @@ def switch_startup_program(program):
     return prev_program
 
 
-@contextlib.contextmanager
+@contextmanager
 def program_guard(main_program, startup_program=None):
     """
     Change the global main program and startup program with `with` statement.
@@ -2524,7 +2524,7 @@ def _get_var(name, program=None):
     return program.global_block().var(name)
 
 
-@contextlib.contextmanager
+@contextmanager
 def _imperative_guard(tracer):
     global _imperative_tracer_
     tmp_trace = _imperative_tracer_
@@ -2535,7 +2535,7 @@ def _imperative_guard(tracer):
     _imperative_tracer_ = tmp_trace
 
 
-@contextlib.contextmanager
+@contextmanager
 def _imperative_place_guard(place):
     global _imperative_current_expected_place_
     tmp_place = _imperative_current_expected_place_

python/paddle/fluid/imperative/base.py

@@ -11,7 +11,7 @@
 # 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.
-import contextlib
+from ..wrapped_decorator import contextmanager
 import numpy as np
 
 from paddle.fluid import core
@@ -24,7 +24,7 @@ def enabled():
     return framework._in_imperative_mode()
 
 
-@contextlib.contextmanager
+@contextmanager
 def guard(place=None):
     train = framework.Program()
     startup = framework.Program()

python/paddle/fluid/initializer.py

@@ -16,7 +16,7 @@ from __future__ import print_function
 
 from . import framework
 import numpy as np
-import contextlib
+from .wrapped_decorator import contextmanager
 from .core import VarDesc
 from . import unique_name
 
@@ -49,7 +49,7 @@ def force_init_on_cpu():
     return _force_init_on_cpu_
 
 
-@contextlib.contextmanager
+@contextmanager
 def init_on_cpu():
     """
     Force the variable to be inited on CPU.

python/paddle/fluid/layers/control_flow.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from __future__ import print_function
-import contextlib
+from ..wrapped_decorator import contextmanager
 
 from .layer_function_generator import autodoc, templatedoc
 from .tensor import assign, fill_constant
@@ -1532,7 +1532,7 @@ class DynamicRNN(object):
             outputs={'Out': [x_reordered]})
         return shrink_memory(x_reordered, self.step_idx, self.lod_rank_table)
 
-    @contextlib.contextmanager
+    @contextmanager
     def block(self):
         """
         The block for user to define operators in RNN. See the class docstring

python/paddle/fluid/layers/io.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from __future__ import print_function
-import contextlib
+from ..wrapped_decorator import contextmanager
 import multiprocessing
 import os
 import six
@@ -1116,7 +1116,7 @@ class Preprocessor(object):
     def _is_completed(self):
         return self.sub_block and self.source_var_names and self.sink_var_names
 
-    @contextlib.contextmanager
+    @contextmanager
     def block(self):
         self.status = Preprocessor.IN_SUB_BLOCK
         self.sub_block = self.main_prog._create_block()

python/paddle/fluid/optimizer.py

@@ -15,7 +15,7 @@
 from __future__ import print_function
 
 from collections import defaultdict
-from contextlib import contextmanager
+from .wrapped_decorator import contextmanager
 
 from paddle.fluid.framework import Program, Variable, name_scope, default_main_program
 from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table

python/paddle/fluid/profiler.py

@@ -15,7 +15,7 @@
 from __future__ import print_function
 
 from . import core
-from contextlib import contextmanager
+from .wrapped_decorator import contextmanager
 import os
 import six
 

python/paddle/fluid/recordio_writer.py

@@ -15,14 +15,14 @@
 from __future__ import print_function
 
 import os
-import contextlib
+from .wrapped_decorator import contextmanager
 from . import core
 __all__ = [
     'convert_reader_to_recordio_file', 'convert_reader_to_recordio_files'
 ]
 
 
-@contextlib.contextmanager
+@contextmanager
 def create_recordio_writer(filename,
                            compressor=core.RecordIOWriter.Compressor.Snappy,
                            max_num_records=1000):

python/paddle/fluid/unique_name.py

@@ -15,7 +15,7 @@
 from __future__ import print_function
 
 import collections
-import contextlib
+from .wrapped_decorator import contextmanager
 import six
 import sys
 
@@ -68,7 +68,7 @@ def switch(new_generator=None):
     return old
 
 
-@contextlib.contextmanager
+@contextmanager
 def guard(new_generator=None):
     if isinstance(new_generator, six.string_types):
         new_generator = UniqueNameGenerator(new_generator)

python/paddle/fluid/wrapped_decorator.py

+# Copyright (c) 2019 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.
+
+import decorator
+import contextlib
+
+__all__ = ['wrap_decorator', 'contextmanager']
+
+
+def wrap_decorator(decorator_func):
+    @decorator.decorator
+    def __impl__(func, *args, **kwargs):
+        wrapped_func = decorator_func(func)
+        return wrapped_func(*args, **kwargs)
+
+    return __impl__
+
+
+contextmanager = wrap_decorator(contextlib.contextmanager)