Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into port_python3_syntax

Author: velconia

paddle/fluid/API.spec

@@ -170,6 +170,7 @@ paddle.fluid.layers.mean_iou ArgSpec(args=['input', 'label', 'num_classes'], var
 paddle.fluid.layers.relu ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.log ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
+paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
@@ -201,7 +202,6 @@ paddle.fluid.layers.zeros ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=
 paddle.fluid.layers.reverse ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.While.__init__ ArgSpec(args=['self', 'cond', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.While.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.layers.While.complete ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.Switch.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.Switch.case ArgSpec(args=['self', 'condition'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.Switch.default ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
@@ -225,17 +225,14 @@ paddle.fluid.layers.DynamicRNN.static_input ArgSpec(args=['self', 'x'], varargs=
 paddle.fluid.layers.DynamicRNN.step_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.DynamicRNN.update_memory ArgSpec(args=['self', 'ex_mem', 'new_mem'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.StaticRNN.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
-paddle.fluid.layers.StaticRNN.complete_op ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.StaticRNN.memory ArgSpec(args=['self', 'init', 'shape', 'batch_ref', 'init_value', 'init_batch_dim_idx', 'ref_batch_dim_idx'], varargs=None, keywords=None, defaults=(None, None, None, 0.0, 0, 1))
 paddle.fluid.layers.StaticRNN.output ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None)
-paddle.fluid.layers.StaticRNN.parent_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.StaticRNN.step ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.StaticRNN.step_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.StaticRNN.step_output ArgSpec(args=['self', 'o'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.StaticRNN.update_memory ArgSpec(args=['self', 'mem', 'var'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.reorder_lod_tensor_by_rank ArgSpec(args=['x', 'rank_table'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.ParallelDo.__init__ ArgSpec(args=['self', 'places', 'use_nccl', 'name'], varargs=None, keywords=None, defaults=(False, None))
-paddle.fluid.layers.ParallelDo.complete_op ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.ParallelDo.do ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.ParallelDo.get_parameters ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.ParallelDo.parent_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)

python/paddle/fluid/layers/control_flow.py

@@ -21,6 +21,7 @@
 from ..initializer import force_init_on_cpu
 from .ops import logical_and, logical_not, logical_or
 import numpy
+import warnings
 from functools import reduce
 
 __all__ = [
@@ -276,11 +277,14 @@ class ParallelDo(object):
           avg_cost = fluid.layers.mean(x=cost)
 
     .. warning::
-    
+
        It will be soon deprecated, please use ParallelExecutor instead.
     """
 
     def __init__(self, places, use_nccl=False, name=None):
+        warnings.warn(
+            "API ParallelDo is deprecated since 0.15.0. Please use ParallelExecutor instead.",
+            Warning)
         self.helper = LayerHelper("parallel_do", name=name)
         self.inputs = []
         self.places = places
@@ -339,7 +343,7 @@ def get_parameters(self):
 
         return [parent_block.var(name) for name in params]
 
-    def complete_op(self):
+    def _complete_op(self):
         main_program = self.helper.main_program
         current_block = main_program.current_block()
         parent_block = self.parent_block()
@@ -395,7 +399,7 @@ def __exit__(self, exc_type, exc_val, exc_tb):
         if exc_type is not None:
             return False
         self.rnn.status = StaticRNN.AFTER_RNN_BLOCK
-        self.rnn.complete_op()
+        self.rnn._complete_op()
         return super(BlockGuardWithCompletion, self).__exit__(exc_type, exc_val,
                                                               exc_tb)
 
@@ -471,7 +475,7 @@ def memory(self,
             if shape is None or batch_ref is None:
                 raise ValueError(
                     "if init is None, memory at least need shape and batch_ref")
-            parent_block = self.parent_block()
+            parent_block = self._parent_block()
             var_name = unique_name.generate("@".join(
                 [self.helper.name, "memory_boot"]))
             boot_var = parent_block.create_var(
@@ -528,7 +532,7 @@ def step_output(self, o):
             outputs={'Out': tmp_o},
             attrs={'dtype': o.dtype})
 
-        out_var = self.parent_block().create_var(
+        out_var = self._parent_block().create_var(
             name=tmp_o.name,
             shape=[self.seq_len] + list(tmp_o.shape),
             dtype=tmp_o.dtype)
@@ -544,7 +548,7 @@ def update_memory(self, mem, var):
             raise TypeError("update memory should take variables")
         self.memories[mem.name].mem = var
 
-    def parent_block(self):
+    def _parent_block(self):
         prog = self.helper.main_program
         parent_idx = prog.current_block().parent_idx
         assert parent_idx >= 0
@@ -561,10 +565,10 @@ def __call__(self, *args, **kwargs):
         else:
             return self.outputs
 
-    def complete_op(self):
+    def _complete_op(self):
         main_program = self.helper.main_program
         rnn_block = main_program.current_block()
-        parent_block = self.parent_block()
+        parent_block = self._parent_block()
 
         local_inputs = set()
 
@@ -644,7 +648,7 @@ def __exit__(self, exc_type, exc_val, exc_tb):
         if exc_type is not None:
             return False
         self.while_op.status = While.AFTER_WHILE_BLOCK
-        self.while_op.complete()
+        self.while_op._complete()
         return super(WhileGuard, self).__exit__(exc_type, exc_val, exc_tb)
 
 
@@ -691,7 +695,7 @@ def __init__(self, cond, name=None):
     def block(self):
         return WhileGuard(self)
 
-    def complete(self):
+    def _complete(self):
         main_program = self.helper.main_program
         while_block = main_program.current_block()
         parent_block = main_program.block(main_program.current_block()
@@ -816,21 +820,21 @@ def max_sequence_len(rank_table):
 
 
 def lod_tensor_to_array(x, table):
-    """ 
+    """
     Convert a LoDTensor to a LoDTensorArray.
 
-    This function split a LoDTesnor to a LoDTensorArray according to its LoD 
-    information. LoDTensorArray is an alias of C++ std::vector<LoDTensor> in 
-    PaddlePaddle. The generated LoDTensorArray of this function can be further read 
-    or written by `read_from_array()` and `write_to_array()` operators. However, 
-    this function is generally an internal component of PaddlePaddle `DynamicRNN`. 
+    This function split a LoDTesnor to a LoDTensorArray according to its LoD
+    information. LoDTensorArray is an alias of C++ std::vector<LoDTensor> in
+    PaddlePaddle. The generated LoDTensorArray of this function can be further read
+    or written by `read_from_array()` and `write_to_array()` operators. However,
+    this function is generally an internal component of PaddlePaddle `DynamicRNN`.
     Users should not use it directly.
 
     Args:
         x (Variable|list): The LoDTensor to be converted to a LoDTensorArray.
         table (ParamAttr|list): The variable that stores the level of lod
                                 which is ordered by sequence length in
-                                descending order. It is generally generated 
+                                descending order. It is generally generated
                                 by `layers.lod_rank_table()` API.
 
     Returns:
@@ -1064,9 +1068,9 @@ def array_read(array, i):
         Given:
 
         array = [0.6, 0.1, 0.3, 0.1]
-        
+
         And:
-        
+
         i = 2
 
         Then:
@@ -1173,9 +1177,9 @@ def array_length(array):
 
 class ConditionalBlockGuard(BlockGuard):
     """
-    ConditionalBlockGuard is derived from BlockGuard. It is dedicated for 
-    holding a ConditionalBlock, and helping users entering and exiting the 
-    ConditionalBlock via Python's 'with' keyword. However, ConditionalBlockGuard 
+    ConditionalBlockGuard is derived from BlockGuard. It is dedicated for
+    holding a ConditionalBlock, and helping users entering and exiting the
+    ConditionalBlock via Python's 'with' keyword. However, ConditionalBlockGuard
     is generally an internal component of IfElse, users should not use it directly.
     """
 
@@ -1929,7 +1933,7 @@ def is_empty(x, cond=None, **ignored):
 
     Args:
         x (Variable): The Variable to be tested.
-        cond (Variable|None): Output parameter. Returns the test result 
+        cond (Variable|None): Output parameter. Returns the test result
                               of given 'x'. Default: None
 
     Returns: