chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Definition of TrainerFactory."""
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import logging
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import threading
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import time
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import numpy as np
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from paddle.base.log_helper import get_logger
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local_logger = get_logger(
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__name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s'
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)
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from .device_worker import ( # noqa: F401
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DownpourLite,
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DownpourSGD,
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DownpourSGDOPT,
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HeterSection,
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Hogwild,
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Section,
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)
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from .framework import Variable
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from .trainer_desc import (
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MultiTrainer,
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)
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__all__ = []
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class TrainerFactory:
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"""
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Create trainer and device worker.
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If opt_info is not None, it will get configs from opt_info,
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otherwise create MultiTrainer and Hogwild.
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"""
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def __init__(self):
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pass
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def _create_trainer(self, opt_info=None):
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trainer = None
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device_worker = None
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if not opt_info:
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# default is MultiTrainer + Hogwild
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trainer = MultiTrainer()
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device_worker = Hogwild()
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trainer._set_device_worker(device_worker)
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else:
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trainer_class = opt_info.get("trainer", "MultiTrainer")
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device_worker_class = opt_info.get("device_worker", "Hogwild")
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trainer = globals()[trainer_class]()
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device_worker = globals()[device_worker_class]()
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# for debug tools
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if opt_info is not None:
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if opt_info.get("trainers") is not None:
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trainer._set_trainers(opt_info["trainers"])
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if opt_info.get("trainer_id") is not None:
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trainer._set_trainer_id(opt_info["trainer_id"])
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if opt_info.get("dump_slot") is not None:
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trainer._set_dump_slot(opt_info["dump_slot"])
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if opt_info.get("mpi_rank") is not None:
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trainer._set_mpi_rank(opt_info["mpi_rank"])
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if opt_info.get("mpi_size") is not None:
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trainer._set_mpi_size(opt_info["mpi_size"])
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if (
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opt_info.get("dump_fields") is not None
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and len(opt_info.get("dump_fields")) != 0
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):
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trainer._set_dump_fields(opt_info["dump_fields"])
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if (
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opt_info.get("dump_fields_path") is not None
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and len(opt_info.get("dump_fields_path")) != 0
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):
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trainer._set_dump_fields_path(opt_info["dump_fields_path"])
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if opt_info.get("dump_fields_mode") is not None:
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trainer._set_dump_fields_mode(opt_info["dump_fields_mode"])
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if (
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opt_info.get("user_define_dump_filename") is not None
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and len(opt_info.get("user_define_dump_filename")) != 0
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):
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trainer._set_user_define_dump_filename(
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opt_info["user_define_dump_filename"]
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)
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if opt_info.get("dump_file_num") is not None:
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trainer._set_dump_file_num(opt_info["dump_file_num"])
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if opt_info.get("dump_converter") is not None:
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trainer._set_dump_converter(opt_info["dump_converter"])
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if (
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opt_info.get("dump_param") is not None
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and len(opt_info.get("dump_param")) != 0
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):
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trainer._set_dump_param(opt_info["dump_param"])
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if opt_info.get("worker_places") is not None:
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trainer._set_worker_places(opt_info["worker_places"])
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if opt_info.get("use_ps_gpu") is not None:
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trainer._set_use_ps_gpu(opt_info["use_ps_gpu"])
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if opt_info.get("use_gpu_graph") is not None:
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trainer._set_use_gpu_graph(opt_info["use_gpu_graph"])
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if opt_info.get("is_dump_in_simple_mode") is not None:
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trainer._set_is_dump_in_simple_mode(
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opt_info["is_dump_in_simple_mode"]
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)
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if opt_info.get("dump_num_decimals") is not None:
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trainer._set_dump_num_decimals(
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opt_info["dump_num_decimals"]
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)
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if opt_info.get("enable_random_dump") is not None:
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trainer._set_enable_random_dump(
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opt_info["enable_random_dump"]
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)
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if opt_info.get("dump_interval") is not None:
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trainer._set_dump_interval(opt_info["dump_interval"])
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if opt_info.get("random_with_lineid") is not None:
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trainer._set_random_with_lineid(
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opt_info["random_with_lineid"]
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)
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if "fleet_desc" in opt_info:
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device_worker._set_fleet_desc(opt_info["fleet_desc"])
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trainer._set_fleet_desc(opt_info["fleet_desc"])
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if opt_info.get("use_cvm") is not None:
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trainer._set_use_cvm(opt_info["use_cvm"])
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if opt_info.get("no_cvm") is not None:
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trainer._set_no_cvm(opt_info["no_cvm"])
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if (
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opt_info.get("scale_sparse_gradient_with_batch_size")
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is not None
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):
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trainer._set_scale_sparse_grad_with_batch_size(
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opt_info["scale_sparse_gradient_with_batch_size"]
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)
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if opt_info.get("scale_datanorm") is not None:
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trainer._set_scale_datanorm(opt_info["scale_datanorm"])
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if opt_info.get("adjust_ins_weight") is not None:
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trainer._set_adjust_ins_weight(
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opt_info["adjust_ins_weight"]
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)
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if opt_info.get("copy_table") is not None:
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trainer._set_copy_table_config(opt_info["copy_table"])
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if opt_info.get("check_nan_var_names") is not None:
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trainer._set_check_nan_var_names(
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opt_info["check_nan_var_names"]
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)
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if opt_info.get("loss_names") is not None:
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trainer._set_loss_names(opt_info["loss_names"])
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trainer._set_device_worker(device_worker)
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return trainer
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class FetchHandlerMonitor:
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"""
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Definition of FetchHandlerMonitor class,
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it's for fetch handler.
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"""
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def __init__(self, scope, handler):
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self.fetch_instance = handler
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self.fetch_thread = threading.Thread(
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target=self.handler_launch_func, args=(scope, self.fetch_instance)
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)
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self.running_lock = threading.Lock()
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self.running = False
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def handler_launch_func(self, scope, handler):
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fetch_instance = handler
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period_secs = fetch_instance.period_secs
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var_name_to_key = {}
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for key in fetch_instance.var_dict:
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if isinstance(fetch_instance.var_dict[key], Variable):
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var_name_to_key[fetch_instance.var_dict[key].name] = key
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else:
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local_logger.warning(f"the value of {key} is not a Variable")
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var_name_to_key["None.var"] = key
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elapsed_secs = 0
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while True:
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self.running_lock.acquire()
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if self.running is False:
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break
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if elapsed_secs < period_secs:
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# TODO(guru4elephant): needs customized condition
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time.sleep(1)
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elapsed_secs += 1
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else:
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elapsed_secs = 0
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fetch_dict = {}
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for key in var_name_to_key:
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var = scope.find_var(key)
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fetch_dict[key] = var
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if var is None:
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local_logger.warning(
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f"{var_name_to_key[key]} value currently not available"
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)
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res_dict = {}
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for key in fetch_dict:
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user_name = var_name_to_key[key]
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if fetch_dict[key] is None:
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res_dict[user_name] = None
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continue
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else:
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res_dict[user_name] = fetch_dict[key].get_tensor()
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lod = res_dict[user_name].lod()
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if len(lod) > 0:
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raise RuntimeError(
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"Some of your fetched tensors \
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hold LoD information. \
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They can not be completely cast \
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to Python ndarray. We can \
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not return DenseTensor itself directly, \
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please choose another targets"
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)
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if res_dict[user_name]._is_initialized():
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res_dict[user_name] = np.array(res_dict[user_name])
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else:
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res_dict[user_name] = None
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fetch_instance.handler(res_dict)
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self.running_lock.release()
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def start(self):
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"""
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start monitor,
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it will start a monitor thread.
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"""
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self.running_lock.acquire()
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self.running = True
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self.running_lock.release()
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self.fetch_thread.setDaemon(True)
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self.fetch_thread.start()
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def stop(self):
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self.running_lock.acquire()
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self.running = False
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self.running_lock.release()
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