add hadoop helper function for distributed training

python/paddle/fluid/async_executor.py

@@ -150,8 +150,13 @@ class AsyncExecutor(object):
                                      data_feed.desc(), filelist, thread_num,
                                      fetch_var_names, debug)
 
-    def config_ps(self, dist_desc, host_sign_list, node_num, index):
-        self.executor.config_pslib(dist_desc, host_sign_list, node_num, index)
+    def config_distributed_nodes(self, dist_opt):
+        # get total rank
+        # get rank index
+        # get iplists
+        # get hadoop info
+        return
+
 
     def start_server(self):
         self.executor.start_server()

python/paddle/fluid/distribute_lookup_table.py

@@ -21,7 +21,8 @@ def find_distributed_lookup_table_inputs(program, table_name):
     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):
@@ -30,7 +31,8 @@ def find_distributed_lookup_table_outputs(program, table_name):
     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):

python/paddle/fluid/distributed/downpour.py

@@ -8,30 +8,57 @@ from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table_o
 from google.protobuf import text_format
 
 class DownpourSGD(object):
+    """
+    Distributed optimizer of downpour stochastic gradient descent
+    Standard implementation of Google's Downpour SGD
+    in Large Scale Distributed Deep Networks
+
+    Args:
+        learning_rate (float): the learning rate used to update parameters. \
+        Can be a float value
+    Examples:
+        .. code-block:: python
+    
+             downpour_sgd = fluid.distributed.DownpourSGD(learning_rate=0.2)
+             downpour_sgd.minimize(cost)
+    """
     def __init__(self, learning_rate=0.001, window=1):
-        # todo(guru4elephant): if optimizer is not None, will warning here
+        # 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), key=lambda x:x[0].name)
+        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)
         prefetch_slots_emb = find_distributed_lookup_table_outputs(
             loss.block.program, table_name)
         server = DownpourServer()
+        # window is communication strategy
         worker = DownpourWorker(self.window_)
-        server.add_sparse_table(0, self.learning_rate_,
+        # Todo(guru4elephant): support multiple tables definitions
+        # currently support one big sparse table
+        sparse_table_index = 0
+        # currently merge all dense parameters into one dense table
+        dense_table_index = 1
+        server.add_sparse_table(sparse_table_index, self.learning_rate_,
                                 prefetch_slots, prefetch_slots_emb)
-        server.add_dense_table(1, self.learning_rate_, params_grads[0], params_grads[1])
-        worker.add_sparse_table(0, self.learning_rate_,
+        server.add_dense_table(dense_table_index, self.learning_rate_, 
+                               params_grads[0], params_grads[1])
+        worker.add_sparse_table(sparse_table_index, self.learning_rate_,
                                 prefetch_slots, prefetch_slots_emb)
-        worker.add_dense_table(1, self.learning_rate_, params_grads[0], params_grads[1])
+        worker.add_dense_table(dense_table_index, self.learning_rate_, 
+                               params_grads[0], params_grads[1])
         ps_param = pslib.PSParameter()
         ps_param.server_param.CopyFrom(server.get_desc())
-        #ps_param.worker_param.CopyFrom(worker.get_desc())
+        ps_param.worker_param.CopyFrom(worker.get_desc())
+        # Todo(guru4elephant): figure out how to support more sparse parameters
+        # currently only support lookup_table
         worker_skipped_ops = ["lookup_table", "lookup_table_grad"]
         ps_param_str = text_format.MessageToString(ps_param)
-        return [ps_param_str, worker_skipped_ops, text_format.MessageToString(worker.get_desc())]
+        return [ps_param_str, worker_skipped_ops]

python/paddle/fluid/distributed/helper.py

 from mpi4py import MPI
 
+class FileSystem(object):
+    def __init__(self, fs_type="afs",
+                 uri="afs://tianqi.afs.baidu.com:9902",
+                 user=None,
+                 passwd=None,
+                 hadoop_bin="",
+                 afs_conf=None):
+        assert user not None
+        assert passwd not None
+        assert hadoop_bin not None
+        fs_client = pslib.FsClientParameter()
+        if fs_type == "afs":
+            fs_client.fs_type = pslib.FsApiType.AFS
+        else:
+            fs_client.fs_type = pslib.FsApiType.HDFS
+        fs_client.uri = uri
+        fs_client.user = user
+        fs_client.passwd = passwd
+        fs_client.buffer_size = 0
+        fs_client.afs_conf = afs_conf if not afs_conf else ""
+
+
 class MPIHelper(object):
     def __init__(self):
         self.comm = MPI.COMM_WORLD
@@ -18,3 +40,5 @@ class MPIHelper(object):
     def get_hostname(self):
         import socket
         return socket.gethostname()
+    
+        

python/paddle/fluid/distributed/node.py

@@ -12,7 +12,6 @@ class Worker(object):
 
 class DownpourServer(Server):
     def __init__(self):
-        #self.server_ = pslib.ServerParameter().downpour_server_param
         self.server_ = pslib.ServerParameter()
 
     def add_sparse_table(self, table_id, learning_rate,