refine downpour sgd API and adapt to pslib proto desc

paddle/fluid/framework/CMakeLists.txt

@@ -180,7 +180,7 @@ cc_library(parallel_executor SRCS parallel_executor.cc DEPS
         graph build_strategy
         fast_threaded_ssa_graph_executor variable_helper)
 
-cc_library(async_executor SRCS async_executor.cc data_feed.cc data_feed_factory.cc executor_thread_worker.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass async_executor_proto variable_helper)
+cc_library(async_executor SRCS async_executor.cc data_feed.cc data_feed_factory.cc executor_thread_worker.cc DEPS op_registry device_context scope framework_proto glog lod_rank_table feed_fetch_method graph_to_program_pass async_executor_proto variable_helper pslib)
 
 cc_test(data_feed_test SRCS data_feed_test.cc DEPS async_executor)
 cc_library(prune SRCS prune.cc DEPS framework_proto)

python/paddle/fluid/distribute_lookup_table.py

@@ -15,6 +15,24 @@
 LOOKUP_TABLE_TYPE = "lookup_table"
 
 
+def find_distributed_lookup_table_inputs(program, table_name):
+    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")])
+    return inputs
+
+def find_distributed_lookup_table_outputs(program, table_name):
+    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")])
+    return outputs
+
 def find_distributed_lookup_table(program):
     """
     Find distribute lookup table in program.

python/paddle/fluid/distributed/downpour.py

@@ -3,6 +3,8 @@ 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 paddle.fluid.distribute_lookup_table import find_distributed_lookup_table_inputs
+from paddle.fluid.distribute_lookup_table import find_distributed_lookup_table_outputs
 from google.protobuf import text_format
 
 class DownpourSGD(object):
@@ -12,21 +14,24 @@ class DownpourSGD(object):
         self.window_ = window
 
     def minimize(self, loss, startup_program=None,
-                 parameter_list=None, no_grad_set=None,
-                 prefetch_slots=None, prefetch_slots_emb=None):
+                 parameter_list=None, no_grad_set=None):
         params_grads = sorted(append_backward(loss), 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()
         worker = DownpourWorker(self.window_)
-        server.add_sparse_table(0, learning_rate,
+        server.add_sparse_table(0, self.learning_rate_,
                                 prefetch_slots, prefetch_slots_emb)
-        server.add_dense_table(1, learning_rate, params, grads)
-        worker.add_sparse_table(0, learning_rate,
+        server.add_dense_table(1, self.learning_rate_, params_grads[0], params_grads[1])
+        worker.add_sparse_table(0, self.learning_rate_,
                                 prefetch_slots, prefetch_slots_emb)
-        worker.add_dense_table(1, learning_rate, params, grads)
+        worker.add_dense_table(1, 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())
         worker_skipped_ops = ["lookup_table", "lookup_table_grad"]
         ps_param_str = text_format.MessageToString(ps_param)
-        return [ps_param_str, worker_skipped_ops]
+        return [ps_param_str, worker_skipped_ops, text_format.MessageToString(worker.get_desc())]

python/paddle/fluid/distributed/node.py

@@ -16,25 +16,26 @@ class DownpourServer(Server):
         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()
+                         slot_key_vars, slot_value_var):
         table = self.server_.downpour_server_param.downpour_table_param.add()
         table.table_id = table_id
-        table.type = PS_SPARSE_TABLE
+        table.type = pslib.PS_SPARSE_TABLE
         table.accessor.accessor_class = "DownpourFeatureValueAccessor"
         table.accessor.dense_sgd_param.adam.learning_rate = learning_rate
-        table.accessor.fea_dim = slot_value_var[0].shape[1]
+        table.accessor.fea_dim = abs(reduce(lambda x, y: x * y, 
+                                            slot_value_var[0].shape, 1))
 
     def add_dense_table(self, table_id, learning_rate, 
                         param_var, grad_var):
-        #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.type = pslib.PS_DENSE_TABLE
         table.accessor.accessor_class = "DownpourDenseValueAccessor"
         table.accessor.sparse_sgd_param.learning_rate = learning_rate
-        table.accessor.fea_dim = 1
-        #table.accessor.fea_dim = reduce(lambda x, y: x.shape, 1 for x in param_var)
+        fea_dim = 0
+        for param in param_var:
+            fea_dim += reduce(lambda x, y: x * y, param.shape, 1)
+        table.accessor.fea_dim = fea_dim
 
     def get_desc(self):
         return self.server_
@@ -43,28 +44,24 @@ class DownpourServer(Server):
 class DownpourWorker(Worker):
     def __init__(self, window):
         self.window = window
-        #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_.downpour_worker_param.sparse_table.add()
+    def add_sparse_table(self, table_id, learning_rate,
+                         slot_key_vars, slot_value_vars):
+        table = self.worker_.sparse_table.add()
         table.table_id = table_id
-        table.slot.extend(slot_keys)
-        self.worker_.extend([grad.name for grad in slot_grad_vars])
+        table.slot_key.extend(
+            [var.name for var in slot_key_vars])
+        table.slot_value.extend(
+            [var.name for var in slot_value_vars])
+        table.slot_gradient.extend(
+            [var.name + "@GRAD" for var in slot_value_vars])
 
-    def add_dense_table(self, table_id, param_vars, grad_vars):
-        #table = self.worker_.dense_table.add()
-        table = self.worker_.downpour_worker_param.dense_table.add()
+    def add_dense_table(self, table_id, learning_rate, 
+                        param_vars, grad_vars):
+        table = self.worker_.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])