add fl scheduler

1) a fl scheduler will be started before training
2) fl workers can connect fl scheduler
3) scheduling algorithms can be implemented in fl scheduler

paddle_fl/core/__init__.py

@@ -17,3 +17,5 @@ from .master.fl_job import FLRunTimeJob
 from .master.job_generator import JobGenerator
 from .strategy.fl_strategy_base import DPSGDStrategy
 from .strategy.fl_strategy_base import FedAvgStrategy
+from .scheduler.agent_master import FLAgent
+from .scheduler.agent_master import FLScheduler

paddle_fl/core/scheduler/__init__.py

+#   Copyright (c) 2019 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.

paddle_fl/core/scheduler/agent_master.py

+import zmq
+import time
+import random
+
+
+def recv_and_parse_kv(socket):
+    message = socket.recv()
+    socket.send("alive")
+    group = message.split("\t")
+    print(group)
+    assert len(group) == 2
+    return group[0], group[1]
+
+WORKER_EP = "WORKER_EP"
+SERVER_EP = "SERVER_EP"
+
+class FLAgent(object):
+    def __init__(self, scheduler_ep, current_ep):
+        self.scheduler_ep = scheduler_ep
+        self.context = zmq.Context()
+        self.socket = self.context.socket(zmq.REQ)
+        self.current_ep = current_ep
+
+    def connect_scheduler(self):
+        self.socket.send("WORKER_EP\t{}".format(self.current_ep))
+        self.socket.recv()
+
+    def can_join_training(self):
+        self.socket.send("JOIN\t{}".format(self.current_ep))
+        self.socket.recv()
+
+
+class FLScheduler(object):
+    def __init__(self, worker_num, server_num, port=9091):
+        self.context = zmq.Context()
+        self.socket = self.context.socket(zmq.REP)
+        self.socket.bind("tcp://*:{}".format(port))
+        self.worker_num = worker_num
+        self.server_num = server_num
+        self.sample_worker_num = 0
+        self.fl_workers = []
+        self.fl_servers = []
+
+    def set_sample_worker_num(self, sample_worker_num=0):
+        if sample_worker_num == 0:
+            self.sample_worker_num = int(self.worker_num * 0.1)
+        else:
+            self.sample_worker_num = sample_worker_num
+
+    def init_env(self):
+        ready = False
+        while not ready:
+            key, value = recv_and_parse_kv(self.socket)
+            if key == WORKER_EP:
+                self.fl_workers.append(value)
+            if key == SERVER_EP:
+                self.fl_servers.append(value)
+            if len(self.fl_workers) == self.worker_num and \
+               len(self.fl_servers) == self.server_num:
+                ready = True
+
+        print("FL training environment started")
+        print("fl workers endpoints")
+        print(self.fl_workers)
+        print("fl servers endpoints")
+        print(self.fl_servers)
+
+    def start_fl_step(self):
+        # random select some fl_workers here
+        random.shuffle(self.workers)
+        worker_dict = {}
+        for worker in self.workers[:self.sample_worker_num]:
+            worker_dict[worker] = 0
+        ready = False
+        ready_workers = []
+        while not ready:
+            key, value = recv_and_parse_kv(self.socket)
+            if key == "JOIN":
+                if value in worker_dict:
+                    if worker_dict[value] == 0:
+                        ready_workers.append(value)
+                        worker_dict[value] = 1
+            if len(ready_workers) == len(worker_dict):
+                ready = True
+        start_workers = []
+        while len(start_workers) != len(ready_workers):
+            key, value = recv_and_parse_kv(self.socket)
+            if key == "REQUEST_START":
+                if value in ready_workers:
+                    start_workers.append(value)
+                    socket.send("ACCEPT_START")
+                    continue
+            else:
+                socket.send("alive")
+

paddle_fl/core/strategy/__init__.py

+#   Copyright (c) 2019 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.

paddle_fl/core/trainer/fl_trainer.py

@@ -44,10 +44,14 @@ class FLTrainer(object):
         self._step = job._strategy._inner_step
         self._feed_names = job._feed_names
         self._target_names = job._target_names
+        self._scheduler_ep = job._scheduler_ep
 
     def start(self):
+        current_ep = "to be added"
+        self.agent = FLAgent(self._scheduler_ep, current_ep)
         self.exe = fluid.Executor(fluid.CPUPlace())
         self.exe.run(self._startup_program)
+        self.agent.connect_scheduler()
 
     def run(self, feed, fetch):
         self._logger.debug("begin to run")
@@ -73,6 +77,8 @@ class FLTrainer(object):
         # ask for termination with master endpoint
         # currently not open sourced, will release the code later
         # TODO(guru4elephant): add connection with master
+        while not self.agent.can_join_training():
+            
         return False
 
 class FedAvgTrainer(FLTrainer):
@@ -108,7 +114,4 @@ class FedAvgTrainer(FLTrainer):
             self.exe.run(self._send_program)
         self.cur_step += 1
         return loss
-
-    def stop(self):
-        return False
         

paddle_fl/examples/gru4rec_demo/fl_trainer.py

@@ -27,6 +27,7 @@ while not trainer.stop():
     print("batch %d start train" % (step_i))
     for data in train_reader():
         #print(np.array(data['src_wordseq']))
+
         ret_avg_cost = trainer.run(feed=data,
                     fetch=["mean_0.tmp_0"])
         avg_ppl = np.exp(ret_avg_cost[0])