refine downpour sgd API with pslib

python/paddle/fluid/distributed/downpour.py

-import paddle.fluid as fluid
-import pslib_pb2 as pslib
 from .node import DownpourServer
 from .node import DownpourWorker
+from ..backward import append_backward
+import ps_pb2 as pslib
 from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table
+from google.protobuf import text_format
 
 class DownpourSGD(object):
-    def __init__(self, optimizer=opt, learning_rate=0.001, window=1):
+    def __init__(self, learning_rate=0.001, window=1):
         # todo(guru4elephant): if optimizer is not None, will warning here
-        self.learning_rate_ = opt.learning_rate
+        self.learning_rate_ = learning_rate
         self.window_ = window
 
     def minimize(self, loss, startup_program=None,
                  parameter_list=None, no_grad_set=None,
                  prefetch_slots=None, prefetch_slots_emb=None):
         params_grads = sorted(append_backward(loss), key=lambda x:x[0].name)
-        table_name = fluid_distributed_lookup_table(loss.block.program)
+        table_name = find_distributed_lookup_table(loss.block.program)
         server = DownpourServer()
-        worker = DownpourWorker()
+        worker = DownpourWorker(self.window_)
         server.add_sparse_table(0, learning_rate,
                                 prefetch_slots, prefetch_slots_emb)
         server.add_dense_table(1, learning_rate, params, grads)
         worker.add_sparse_table(0, learning_rate,
                                 prefetch_slots, prefetch_slots_emb)
         worker.add_dense_table(1, learning_rate, params, grads)
-
         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())
         worker_skipped_ops = ["lookup_table", "lookup_table_grad"]
-        
-        return [solver_desc, parallel_desc]
-        
-            
+        ps_param_str = text_format.MessageToString(ps_param)
+        return [ps_param_str, worker_skipped_ops]

python/paddle/fluid/distributed/node.py

-import paddle.fluid as fluid
-import pslib_pb2 as pslib
+import ps_pb2 as pslib
 
 class Server(object):
     def __init__(self):
@@ -13,11 +12,13 @@ class Worker(object):
 
 class DownpourServer(Server):
     def __init__(self):
-        self.server_ = pslib.ServerParameter().downpour_server_param
+        #self.server_ = pslib.ServerParameter().downpour_server_param
+        self.server_ = pslib.ServerParameter()
 
     def add_sparse_table(self, table_id, learning_rate,
                          slot_key, slot_value_var, slot_grad_var):
-        table = self.server_.downpour_table_param.add()
+        #table = self.server_.downpour_table_param.add()
+        table = self.server_.downpour_server_param.downpour_table_param.add()
         table.table_id = table_id
         table.type = PS_SPARSE_TABLE
         table.accessor.accessor_class = "DownpourFeatureValueAccessor"
@@ -26,12 +27,14 @@ class DownpourServer(Server):
 
     def add_dense_table(self, table_id, learning_rate, 
                         param_var, grad_var):
-        table = self.server_.downpour_table_param.add()
+        #table = self.server_.downpour_table_param.add()
+        table = self.server_.downpour_server_param.downpour_table_param.add()
         table.table_id = table_id
         table.type = PS_DENSE_TABLE
         table.accessor.accessor_class = "DownpourDenseValueAccessor"
         table.accessor.sparse_sgd_param.learning_rate = learning_rate
-        table.accessor.fea_dim = reduce(lambda x, y: x.shape, 1 for x in param_var)
+        table.accessor.fea_dim = 1
+        #table.accessor.fea_dim = reduce(lambda x, y: x.shape, 1 for x in param_var)
 
     def get_desc(self):
         return self.server_
@@ -40,19 +43,28 @@ class DownpourServer(Server):
 class DownpourWorker(Worker):
     def __init__(self, window):
         self.window = window
-        self.worker_ = pslib.WorkerParameter().downpour_worker_param
+        #self.worker_ = pslib.WorkerParameter().downpour_worker_param
+        #self.worker_ = pslib.WorkerParameter()
+        self.worker_ = pslib.DownpourTrainerParameter()
+        #self.worker_.pull_dense_per_batch = window
+        #self.worker_.push_dense_per_batch = window
+        #self.worker_.downpour_worker_param.pull_dense_per_batch = window
+        #self.worker_.downpour_worker_param.push_dense_per_batch = window
         self.worker_.pull_dense_per_batch = window
         self.worker_.push_dense_per_batch = window
+        print(self.worker_)
 
     def add_sparse_table(self, table_id, 
                          slot_keys, slot_value_vars, slot_grad_vars):
-        table = self.worker_.sparse_table.add()
+        #table = self.worker_.sparse_table.add()
+        table = self.worker_.downpour_worker_param.sparse_table.add()
         table.table_id = table_id
         table.slot.extend(slot_keys)
         self.worker_.extend([grad.name for grad in slot_grad_vars])
 
     def add_dense_table(self, table_id, param_vars, grad_vars):
-        table = self.worker_.dense_table.add()
+        #table = self.worker_.dense_table.add()
+        table = self.worker_.downpour_worker_param.dense_table.add()
         table.table_id = table_id
         table.dense_variable_name.extend([p.name for p in param_vars])
         table.dense_gradient_variable_name.extend([g.name for g in grad_vars])