mirror of https://github.com/inclusionAI/AReaL
131 lines
4.4 KiB
Python
131 lines
4.4 KiB
Python
import getpass
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import os
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from typing import Dict
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import torch.distributed as dist
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import wandb
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from tensorboardX import SummaryWrite
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from torchdata.stateful_dataloader import StatefulDataLoader
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from arealite.api.cli_args import TrainerConfig
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from arealite.api.engine_api import InferenceEngine, TrainEngine
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from realhf.api.core.data_api import load_hf_tokenizer
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from realhf.base import logging, timeutil
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class Trainer:
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def __init__(
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self,
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config: TrainerConfig,
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train_dataloader: StatefulDataLoader,
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valid_dataloader: StatefulDataLoader,
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engine: TrainEngine,
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inf_engine: InferenceEngine | None = None,
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):
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self.config = config
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self.train_dataloader = train_dataloader
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self.valid_dataloader = valid_dataloader
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self.engine = engine
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self.inf_engine = inf_engine
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self.tokenizer = load_hf_tokenizer(config.tokenizer_path)
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self.save_ctl = timeutil.EpochStepTimeFreqCtl(
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freq_epoch=config.exp_ctrl.save_freq_epochs,
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freq_step=config.exp_ctrl.save_freq_steps,
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freq_sec=config.exp_ctrl.save_freq_secs,
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)
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self.eval_ctl = timeutil.EpochStepTimeFreqCtl(
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freq_epoch=config.exp_ctrl.eval_freq_epochs,
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freq_step=config.exp_ctrl.eval_freq_steps,
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freq_sec=config.exp_ctrl.eval_freq_steps,
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)
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self.logger = logging.getLogger(self.__class__.__name__)
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self.init_stats_logging()
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def init_stats_logging(self):
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"""
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Initialize wandb and/or tensorboard according to config.
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If torch.distributed is initialized
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Return:
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tensorboard SummaryWriter if self.config.tensorboard.path is not None
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"""
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if dist.is_initialized() and dist.get_rank() != 0:
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return
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# wandb init, connect to remote wandb host
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if self.config.wandb.mode != "disabled":
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wandb.login()
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wandb.init(
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mode=self.config.wandb.mode,
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entity=self.config.wandb.entity,
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project=self.config.wandb.project or self.config.experiment_name,
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name=self.config.wandb.name or self.config.trial_name,
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job_type=self.config.wandb.job_type,
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group=self.config.wandb.group
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or f"{self.config.experiment_name}_{self.config.trial_name}",
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notes=self.config.wandb.notes,
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tags=self.config.wandb.tags,
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config=self.config.wandb.config,
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dir=Trainer.get_log_path(self.config),
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force=True,
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id=f"{self.config.experiment_name}_{self.config.trial_name}_train",
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resume="allow",
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settings=wandb.Settings(start_method="fork"),
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)
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# tensorboard logging
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self.summary_writer = None
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if self.config.tensorboard.path is not None:
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self.summary_writer = SummaryWriter(log_dir=self.config.tensorboard.path)
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def log_wandb_tensorboard(self, step: int, data: Dict):
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if dist.is_initialized() and dist.get_rank() != 0:
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return
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wandb.log(data, step=step)
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if self.summary_writer is not None:
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for key, val in data.items():
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self.summary_writer.add_scalar(f"{key}", val, step)
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def close_wandb_tensorboard(self):
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if dist.is_initialized() and dist.get_rank() != 0:
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return
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wandb.finish()
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if self.summary_writer is not None:
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self.summary_writer.close()
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@staticmethod
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def get_save_checkpoint_path(
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config: TrainerConfig,
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epoch: int,
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step: int,
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globalstep: int,
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name: str = "default",
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):
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path = os.path.join(
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f"{config.fileroot}/checkpoints/{getpass.getuser()}/{config.experiment_name}/{config.trial_name}",
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name,
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f"epoch{epoch}epochstep{step}globalstep{globalstep}",
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)
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os.makedirs(path, exist_ok=True)
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return path
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@staticmethod
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def get_log_path(config: TrainerConfig):
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path = f"{config.fileroot}/logs/{getpass.getuser()}/{config.experiment_name}/{config.trial_name}"
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os.makedirs(path, exist_ok=True)
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return path
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def log(self, msg: str, level="info"):
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if dist.is_initialized() and dist.get_rank() > 0:
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return
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log_fn = getattr(self.logger, level, "info")
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return log_fn(msg)
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def train(self):
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raise NotImplementedError()
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