未验证 提交 e5af9cad 编写于 作者: D Dong Daxiang 提交者: GitHub

Merge pull request #13 from seiriosPlus/delete_unused_trainer

Delete unused trainer
...@@ -40,6 +40,7 @@ class Reader(dg.MultiSlotDataGenerator): ...@@ -40,6 +40,7 @@ class Reader(dg.MultiSlotDataGenerator):
@abc.abstractmethod @abc.abstractmethod
def init(self): def init(self):
"""init"""
pass pass
@abc.abstractmethod @abc.abstractmethod
......
# 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
此差异已折叠。
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