fix import

fleetrec/core/engine/local_mpi_engine.py

@@ -34,8 +34,8 @@ class LocalMPIEngine(Engine):
         log_fns = []
 
         factory = "fleetrec.core.factory"
-        mpi_cmd = "mpirun -npernode 2 -timestamp-output -tag-output".split(" ")
-        cmd = mpi_cmd.extend([sys.executable, "-u", "-m", factory, self.trainer])
+        cmd = "mpirun -npernode 2 -timestamp-output -tag-output".split(" ")
+        cmd.extend([sys.executable, "-u", "-m", factory, self.trainer])
 
         if logs_dir is not None:
             os.system("mkdir -p {}".format(logs_dir))
@@ -49,7 +49,7 @@ class LocalMPIEngine(Engine):
         for i in range(len(procs)):
             if len(log_fns) > 0:
                 log_fns[i].close()
-            procs[i].terminate()
+            procs[i].wait()
         print("all workers and parameter servers already completed", file=sys.stderr)
 
     def run(self):

fleetrec/core/trainers/ctr_coding_trainer.py

+# Copyright (c) 2020 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.
+import os
+import sys
+import time
+import json
+import datetime
+import numpy as np
+
+import paddle.fluid as fluid
+from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
+from paddle.fluid.incubate.fleet.base.role_maker import MPISymetricRoleMaker
+
+from fleetrec.core.utils import envs
+from fleetrec.core.trainer import Trainer
+
+
+class CtrPaddleTrainer(Trainer):
+    """R
+    """
+
+    def __init__(self, config):
+        """R
+        """
+        Trainer.__init__(self, config)
+
+        self.global_config = config
+        self._metrics = {}
+        self.processor_register()
+
+    def processor_register(self):
+        role = MPISymetricRoleMaker()
+        fleet.init(role)
+
+        if fleet.is_server():
+            self.regist_context_processor('uninit', self.instance)
+            self.regist_context_processor('init_pass', self.init)
+            self.regist_context_processor('server_pass', self.server)
+        else:
+            self.regist_context_processor('uninit', self.instance)
+            self.regist_context_processor('init_pass', self.init)
+            self.regist_context_processor('train_pass', self.train)
+            self.regist_context_processor('terminal_pass', self.terminal)
+
+    def _get_dataset(self):
+        namespace = "train.reader"
+
+        inputs = self.model.get_inputs()
+        threads = envs.get_global_env("train.threads", None)
+        batch_size = envs.get_global_env("batch_size", None, namespace)
+        reader_class = envs.get_global_env("class", None, namespace)
+        abs_dir = os.path.dirname(os.path.abspath(__file__))
+        reader = os.path.join(abs_dir, '../utils', 'reader_instance.py')
+        pipe_cmd = "python {} {} {} {}".format(reader, reader_class, "TRAIN", self._config)
+        train_data_path = envs.get_global_env("train_data_path", None, namespace)
+
+        dataset = fluid.DatasetFactory().create_dataset()
+        dataset.set_use_var(inputs)
+        dataset.set_pipe_command(pipe_cmd)
+        dataset.set_batch_size(batch_size)
+        dataset.set_thread(threads)
+        file_list = [
+            os.path.join(train_data_path, x)
+            for x in os.listdir(train_data_path)
+        ]
+
+        dataset.set_filelist(file_list)
+        return dataset
+
+    def instance(self, context):
+        models = envs.get_global_env("train.model.models")
+        model_class = envs.lazy_instance(models, "Model")
+        self.model = model_class(None)
+        context['status'] = 'init_pass'
+
+    def init(self, context):
+        """R
+        """
+        self.model.train_net()
+        optimizer = self.model.optimizer()
+
+        optimizer = fleet.distributed_optimizer(optimizer, strategy={"use_cvm": False})
+        optimizer.minimize(self.model.get_cost_op())
+
+        if fleet.is_server():
+            context['status'] = 'server_pass'
+        else:
+            self.fetch_vars = []
+            self.fetch_alias = []
+            self.fetch_period = self.model.get_fetch_period()
+
+            metrics = self.model.get_metrics()
+            if metrics:
+                self.fetch_vars = metrics.values()
+                self.fetch_alias = metrics.keys()
+            context['status'] = 'train_pass'
+
+    def server(self, context):
+        fleet.run_server()
+        context['is_exit'] = True
+
+    def train(self, context):
+        self._exe.run(fluid.default_startup_program())
+        fleet.init_worker()
+
+        dataset = self._get_dataset()
+
+        shuf = np.array([fleet.worker_index()])
+        gs = shuf * 0
+        fleet._role_maker._node_type_comm.Allreduce(shuf, gs)
+
+        print("trainer id: {}, trainers: {}, gs: {}".format(fleet.worker_index(), fleet.worker_num(), gs))
+
+        epochs = envs.get_global_env("train.epochs")
+
+        for i in range(epochs):
+            self._exe.train_from_dataset(program=fluid.default_main_program(),
+                                         dataset=dataset,
+                                         fetch_list=self.fetch_vars,
+                                         fetch_info=self.fetch_alias,
+                                         print_period=self.fetch_period)
+
+        context['status'] = 'terminal_pass'
+        fleet.stop_worker()
+
+    def terminal(self, context):
+        print("terminal ended.")
+        context['is_exit'] = True