add doc string for downpour.py and distribute_lookup_table.py

python/paddle/fluid/distribute_lookup_table.py

@@ -16,31 +16,51 @@ LOOKUP_TABLE_TYPE = "lookup_table"
 
 
 def find_distributed_lookup_table_inputs(program, table_name):
+    """
+    Find input variable of distribute lookup table in program.
+    We only support one distribute table now.
+    Args:
+    program(Program): given program, locate distributed lookup table
+    table_name(str): given table name that is found beforehand
+    Returns:
+    inputs
+    """
     local_vars = program.current_block().vars
     inputs = []
     for op in program.global_block().ops:
         if op.type == LOOKUP_TABLE_TYPE:
             if table_name == op.input("W")[0]:
-                inputs.extend(
-                    [local_vars[name] for name in op.input("Ids")])
+                inputs.extend([local_vars[name] for name in op.input("Ids")])
     return inputs
 
+
 def find_distributed_lookup_table_outputs(program, table_name):
+    """
+    Find output variable of distribute lookup table in program.
+    We only support one distribute table now.
+    Args:
+    program(Program): given program, locate distributed lookup table
+    table_name(str): given table name that is found beforehand
+    Returns:
+    outputs
+    """
     local_vars = program.current_block().vars
     outputs = []
     for op in program.global_block().ops:
         if op.type == LOOKUP_TABLE_TYPE:
             if table_name == op.input("W")[0]:
-                outputs.extend(
-                    [local_vars[name] for name in op.output("Out")])
+                outputs.extend([local_vars[name] for name in op.output("Out")])
     return outputs
 
+
 def find_distributed_lookup_table(program):
     """
     Find distribute lookup table in program.
     We only support one distribute table now.
-    :param program:
-    :return: table_name or None
+    Args:
+    program(Program): given program, locate distributed lookup table
+    Returns:
+    table_name or None
     """
     table_name = None
 

python/paddle/fluid/distributed/downpour.py

@@ -20,6 +20,7 @@ from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table_i
 from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table_outputs
 from google.protobuf import text_format
 
+
 class DownpourSGD(object):
     """
     Distributed optimizer of downpour stochastic gradient descent
@@ -35,17 +36,38 @@ class DownpourSGD(object):
              downpour_sgd = fluid.distributed.DownpourSGD(learning_rate=0.2)
              downpour_sgd.minimize(cost)
     """
+
     def __init__(self, learning_rate=0.001, window=1):
         # todo(guru4elephant): add more optimizers here as argument
         # todo(guru4elephant): make learning_rate as a variable
         self.learning_rate_ = learning_rate
         self.window_ = window
         self.type = "downpour"
-    
-    def minimize(self, loss, startup_program=None,
-                 parameter_list=None, no_grad_set=None):
-        params_grads = sorted(append_backward(
-            loss, parameter_list, no_grad_set), key=lambda x:x[0].name)
+
+    def minimize(self,
+                 loss,
+                 startup_program=None,
+                 parameter_list=None,
+                 no_grad_set=None):
+        """
+        DownpounSGD is a distributed optimizer so
+        that user can call minimize to generate backward
+        operators and optimization operators within minmize function
+        Args:
+            loss(Variable): loss variable defined by user
+            startup_program(Program): startup program that defined by user
+            parameter_list(str list): parameter names defined by users
+            no_grad_set(set): a set of variables that is defined by users
+            so that these variables do not need gradient computation
+        Returns:
+            [ps_param, worker_skipped_ops]
+            ps_param: parameter server protobuf desc
+            worker_skipped_ops: operator names that need
+            to be skipped during execution
+        """
+        params_grads = sorted(
+            append_backward(loss, parameter_list, no_grad_set),
+            key=lambda x: x[0].name)
         table_name = find_distributed_lookup_table(loss.block.program)
         prefetch_slots = find_distributed_lookup_table_inputs(
             loss.block.program, table_name)
@@ -67,12 +89,12 @@ class DownpourSGD(object):
             grads.append(i[1])
         server.add_sparse_table(sparse_table_index, self.learning_rate_,
                                 prefetch_slots, prefetch_slots_emb)
-        server.add_dense_table(dense_table_index, self.learning_rate_, 
-                               params, grads)
+        server.add_dense_table(dense_table_index, self.learning_rate_, params,
+                               grads)
         worker.add_sparse_table(sparse_table_index, self.learning_rate_,
                                 prefetch_slots, prefetch_slots_emb)
-        worker.add_dense_table(dense_table_index, self.learning_rate_, 
-                               params, grads)
+        worker.add_dense_table(dense_table_index, self.learning_rate_, params,
+                               grads)
         ps_param = pslib.PSParameter()
         ps_param.server_param.CopyFrom(server.get_desc())
         ps_param.trainer_param.CopyFrom(worker.get_desc())
@@ -80,5 +102,4 @@ class DownpourSGD(object):
         # currently only support lookup_table
         worker_skipped_ops = ["lookup_table", "lookup_table_grad"]
         ps_param.trainer_param.skip_op.extend(worker_skipped_ops)
-        ps_param_str = text_format.MessageToString(ps_param)
         return [ps_param, worker_skipped_ops]