add dist_multi_trainer for distributed training, add trainer_factory and device_worker_factory so that we can easily extend new training mode, add pull dense worker which is a singleton for parameter fetching

python/paddle/fluid/trainer_desc.py

@@ -0,0 +1,63 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from paddle.fluid.proto import trainer_desc_pb2
+from google.protobuf import text_format
+
+__all__ = ['TrainerDesc', 'MultiTrainer', 'DistMultiTrainer']
+
+
+# can be initialized from train_desc, 
+class TrainerDesc(object):
+    def __init__(self):
+        '''
+        self.proto_desc = data_feed_pb2.DataFeedDesc()
+        with open(proto_file, 'r') as f:
+            text_format.Parse(f.read(), self.proto_desc)
+        '''
+        self.proto_desc = trainer_desc_pb2.TrainerDesc()
+
+    def set_thread(self, thread_num):
+        self.proto_desc.thread_num = thread_num
+
+    def set_filelist(self, filelist):
+        self.proto_desc.filelist.extend(filelist)
+
+    def set_data_feed(self, datafeed):
+        self.proto_desc.data_desc.CopyFrom(datafeed.proto_desc)
+
+    def _desc(self):
+        return text_format.MessageToString(self.proto_desc)
+
+
+class MultiTrainer(TrainerDesc):
+    def __init__(self, worker="Hogwild"):
+        super(MultiTrainer, self).__init__()
+        if worker == "Hogwild":
+            self.proto_desc.device_worker_name = worker + "Worker"
+            self.proto_desc.class_name = "MultiTrainer"
+        else:
+            raise ValueError('ValueError: DeviceWorker %s '
+                             'is not supported in MultiTrainer' % worker)
+
+
+class DistMultiTrainer(TrainerDesc):
+    def __init__(self, worker='Downpour'):
+        super(DistMultiTrainer, self).__init__()
+        if worker == "Downpour":
+            self.proto_desc.device_worker_name = worker + "Worker"
+            self.proto_desc.class_name = "DistMultiTrainer"
+        else:
+            raise ValueError('ValueError: DeviceWorker %s '
+                             'is not supported in DistMultiTrainer' % worker)