Merge branch 'master' into fix1

core/reader.py

@@ -40,6 +40,7 @@ class Reader(dg.MultiSlotDataGenerator):
 
     @abc.abstractmethod
     def init(self):
+        """init"""
         pass
 
     @abc.abstractmethod

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 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 paddlerec.core.utils import envs
-from paddlerec.core.trainer import Trainer
-
-
-class CtrTrainer(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', 'dataset_instance.py')
-        pipe_cmd = "python {} {} {} {}".format(reader, reader_class, "TRAIN",
-                                               self._config_yaml)
-        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_by_fliename(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_avg_cost())
-
-        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()
-        fleet.stop_worker()
-        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