# Copyright ESPnet (https://github.com/espnet/espnet). All Rights Reserved.
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Trainer module."""
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import argparse
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import dataclasses
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import logging
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import os
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import time
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from contextlib import contextmanager
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from dataclasses import is_dataclass
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from distutils.version import LooseVersion
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from io import BytesIO
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from pathlib import Path
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from typing import Dict
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from typing import Iterable
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from typing import List
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from typing import Optional
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from typing import Sequence
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from typing import Tuple
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from typing import Union
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import humanfriendly
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import oss2
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import torch
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import torch.nn
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import torch.optim
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from funasr.iterators.abs_iter_factory import AbsIterFactory
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from funasr.main_funcs.average_nbest_models import average_nbest_models
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from funasr.models.base_model import FunASRModel
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from funasr.schedulers.abs_scheduler import AbsBatchStepScheduler
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from funasr.schedulers.abs_scheduler import AbsEpochStepScheduler
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from funasr.schedulers.abs_scheduler import AbsScheduler
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from funasr.schedulers.abs_scheduler import AbsValEpochStepScheduler
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from funasr.torch_utils.add_gradient_noise import add_gradient_noise
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from funasr.torch_utils.device_funcs import to_device
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from funasr.torch_utils.recursive_op import recursive_average
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from funasr.torch_utils.set_all_random_seed import set_all_random_seed
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from funasr.train.distributed_utils import DistributedOption
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from funasr.train.reporter import Reporter
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from funasr.train.reporter import SubReporter
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from funasr.utils.build_dataclass import build_dataclass
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if torch.distributed.is_available():
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from torch.distributed import ReduceOp
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if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
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from torch.cuda.amp import autocast
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from torch.cuda.amp import GradScaler
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else:
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# Nothing to do if torch<1.6.0
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@contextmanager
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def autocast(enabled=True):
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yield
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GradScaler = None
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try:
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import fairscale
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except ImportError:
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fairscale = None
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@dataclasses.dataclass
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class TrainerOptions:
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ngpu: int
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resume: bool
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use_amp: bool
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train_dtype: str
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grad_noise: bool
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accum_grad: int
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grad_clip: float
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grad_clip_type: float
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log_interval: Optional[int]
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# no_forward_run: bool
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use_tensorboard: bool
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# use_wandb: bool
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output_dir: Union[Path, str]
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max_epoch: int
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max_update: int
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seed: int
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# sharded_ddp: bool
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patience: Optional[int]
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keep_nbest_models: Union[int, List[int]]
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nbest_averaging_interval: int
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early_stopping_criterion: Sequence[str]
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best_model_criterion: Sequence[Sequence[str]]
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val_scheduler_criterion: Sequence[str]
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unused_parameters: bool
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# wandb_model_log_interval: int
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use_pai: bool
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oss_bucket: Union[oss2.Bucket, None]
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class Trainer:
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"""Trainer
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"""
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def __init__(self,
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args,
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model: FunASRModel,
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optimizers: Sequence[torch.optim.Optimizer],
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schedulers: Sequence[Optional[AbsScheduler]],
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train_dataloader: AbsIterFactory,
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valid_dataloader: AbsIterFactory,
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distributed_option: DistributedOption):
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self.trainer_options = self.build_options(args)
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self.model = model
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self.optimizers = optimizers
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self.schedulers = schedulers
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self.train_dataloader = train_dataloader
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self.valid_dataloader = valid_dataloader
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self.distributed_option = distributed_option
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def build_options(self, args: argparse.Namespace) -> TrainerOptions:
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"""Build options consumed by train(), eval()"""
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return build_dataclass(TrainerOptions, args)
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@classmethod
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def add_arguments(cls, parser: argparse.ArgumentParser):
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"""Reserved for future development of another Trainer"""
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pass
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def resume(self,
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checkpoint: Union[str, Path],
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model: torch.nn.Module,
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reporter: Reporter,
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optimizers: Sequence[torch.optim.Optimizer],
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schedulers: Sequence[Optional[AbsScheduler]],
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scaler: Optional[GradScaler],
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ngpu: int = 0,
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):
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states = torch.load(
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checkpoint,
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map_location=f"cuda:{torch.cuda.current_device()}" if ngpu > 0 else "cpu",
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)
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model.load_state_dict(states["model"])
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reporter.load_state_dict(states["reporter"])
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for optimizer, state in zip(optimizers, states["optimizers"]):
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optimizer.load_state_dict(state)
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for scheduler, state in zip(schedulers, states["schedulers"]):
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if scheduler is not None:
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scheduler.load_state_dict(state)
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if scaler is not None:
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if states["scaler"] is None:
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logging.warning("scaler state is not found")
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else:
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scaler.load_state_dict(states["scaler"])
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logging.info(f"The training was resumed using {checkpoint}")
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def run(self) -> None:
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"""Perform training. This method performs the main process of training."""
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# NOTE(kamo): Don't check the type more strictly as far trainer_options
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model = self.model
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optimizers = self.optimizers
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schedulers = self.schedulers
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train_dataloader = self.train_dataloader
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valid_dataloader = self.valid_dataloader
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trainer_options = self.trainer_options
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distributed_option = self.distributed_option
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assert is_dataclass(trainer_options), type(trainer_options)
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assert len(optimizers) == len(schedulers), (len(optimizers), len(schedulers))
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if isinstance(trainer_options.keep_nbest_models, int):
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keep_nbest_models = [trainer_options.keep_nbest_models]
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else:
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if len(trainer_options.keep_nbest_models) == 0:
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logging.warning("No keep_nbest_models is given. Change to [1]")
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trainer_options.keep_nbest_models = [1]
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keep_nbest_models = trainer_options.keep_nbest_models
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output_dir = Path(trainer_options.output_dir)
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reporter = Reporter()
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if trainer_options.use_amp:
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if LooseVersion(torch.__version__) < LooseVersion("1.6.0"):
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raise RuntimeError(
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"Require torch>=1.6.0 for Automatic Mixed Precision"
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)
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# if trainer_options.sharded_ddp:
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# if fairscale is None:
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# raise RuntimeError(
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# "Requiring fairscale. Do 'pip install fairscale'"
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# )
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# scaler = fairscale.optim.grad_scaler.ShardedGradScaler()
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# else:
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scaler = GradScaler()
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else:
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scaler = None
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if trainer_options.resume and (output_dir / "checkpoint.pb").exists():
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self.resume(
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checkpoint=output_dir / "checkpoint.pb",
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model=model,
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optimizers=optimizers,
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schedulers=schedulers,
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reporter=reporter,
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scaler=scaler,
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ngpu=trainer_options.ngpu,
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)
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start_epoch = reporter.get_epoch() + 1
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if start_epoch == trainer_options.max_epoch + 1:
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logging.warning(
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f"The training has already reached at max_epoch: {start_epoch}"
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)
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if distributed_option.distributed:
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dp_model = torch.nn.parallel.DistributedDataParallel(
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model, find_unused_parameters=trainer_options.unused_parameters)
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elif distributed_option.ngpu > 1:
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dp_model = torch.nn.parallel.DataParallel(
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model,
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device_ids=list(range(distributed_option.ngpu)),
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)
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else:
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# NOTE(kamo): DataParallel also should work with ngpu=1,
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# but for debuggability it's better to keep this block.
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dp_model = model
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if trainer_options.use_tensorboard and (
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not distributed_option.distributed or distributed_option.dist_rank == 0
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):
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from torch.utils.tensorboard import SummaryWriter
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if trainer_options.use_pai:
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train_summary_writer = SummaryWriter(
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os.path.join(trainer_options.output_dir, "tensorboard/train")
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)
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valid_summary_writer = SummaryWriter(
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os.path.join(trainer_options.output_dir, "tensorboard/valid")
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)
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else:
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train_summary_writer = SummaryWriter(
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str(output_dir / "tensorboard" / "train")
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)
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valid_summary_writer = SummaryWriter(
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str(output_dir / "tensorboard" / "valid")
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)
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else:
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train_summary_writer = None
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start_time = time.perf_counter()
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for iepoch in range(start_epoch, trainer_options.max_epoch + 1):
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if iepoch != start_epoch:
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logging.info(
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"{}/{}epoch started. Estimated time to finish: {} hours".format(
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iepoch,
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trainer_options.max_epoch,
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(time.perf_counter() - start_time) / 3600.0 / (iepoch - start_epoch) * (
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trainer_options.max_epoch - iepoch + 1),
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)
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)
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else:
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logging.info(f"{iepoch}/{trainer_options.max_epoch}epoch started")
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set_all_random_seed(trainer_options.seed + iepoch)
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reporter.set_epoch(iepoch)
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# 1. Train and validation for one-epoch
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with reporter.observe("train") as sub_reporter:
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all_steps_are_invalid, max_update_stop = self.train_one_epoch(
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model=dp_model,
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optimizers=optimizers,
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schedulers=schedulers,
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iterator=train_dataloader.build_iter(iepoch),
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reporter=sub_reporter,
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scaler=scaler,
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summary_writer=train_summary_writer,
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options=trainer_options,
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distributed_option=distributed_option,
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)
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with reporter.observe("valid") as sub_reporter:
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self.validate_one_epoch(
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model=dp_model,
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iterator=valid_dataloader.build_iter(iepoch),
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reporter=sub_reporter,
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options=trainer_options,
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distributed_option=distributed_option,
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)
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# 2. LR Scheduler step
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for scheduler in schedulers:
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if isinstance(scheduler, AbsValEpochStepScheduler):
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scheduler.step(
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reporter.get_value(*trainer_options.val_scheduler_criterion)
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)
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elif isinstance(scheduler, AbsEpochStepScheduler):
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scheduler.step()
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# if trainer_options.sharded_ddp:
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# for optimizer in optimizers:
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# if isinstance(optimizer, fairscale.optim.oss.OSS):
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# optimizer.consolidate_state_dict()
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if not distributed_option.distributed or distributed_option.dist_rank == 0:
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# 3. Report the results
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logging.info(reporter.log_message())
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if train_summary_writer is not None:
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reporter.tensorboard_add_scalar(train_summary_writer, key1="train")
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reporter.tensorboard_add_scalar(valid_summary_writer, key1="valid")
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# if trainer_options.use_wandb:
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# reporter.wandb_log()
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# save tensorboard on oss
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if trainer_options.use_pai and train_summary_writer is not None:
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def write_tensorboard_summary(summary_writer_path, oss_bucket):
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file_list = []
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for root, dirs, files in os.walk(summary_writer_path, topdown=False):
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for name in files:
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file_full_path = os.path.join(root, name)
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file_list.append(file_full_path)
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for file_full_path in file_list:
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with open(file_full_path, "rb") as f:
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oss_bucket.put_object(file_full_path, f)
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write_tensorboard_summary(os.path.join(trainer_options.output_dir, "tensorboard/train"),
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trainer_options.oss_bucket)
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write_tensorboard_summary(os.path.join(trainer_options.output_dir, "tensorboard/valid"),
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trainer_options.oss_bucket)
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# 4. Save/Update the checkpoint
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if trainer_options.use_pai:
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buffer = BytesIO()
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torch.save(
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{
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"model": model.state_dict(),
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"reporter": reporter.state_dict(),
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"optimizers": [o.state_dict() for o in optimizers],
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"schedulers": [
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s.state_dict() if s is not None else None
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for s in schedulers
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],
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"scaler": scaler.state_dict() if scaler is not None else None,
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"ema_model": model.encoder.ema.model.state_dict()
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if hasattr(model.encoder, "ema") and model.encoder.ema is not None else None,
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},
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buffer,
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)
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trainer_options.oss_bucket.put_object(os.path.join(trainer_options.output_dir, "checkpoint.pb"),
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buffer.getvalue())
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else:
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torch.save(
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{
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"model": model.state_dict(),
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"reporter": reporter.state_dict(),
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"optimizers": [o.state_dict() for o in optimizers],
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"schedulers": [
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s.state_dict() if s is not None else None
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for s in schedulers
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],
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"scaler": scaler.state_dict() if scaler is not None else None,
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},
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output_dir / "checkpoint.pb",
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)
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# 5. Save and log the model and update the link to the best model
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if trainer_options.use_pai:
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buffer = BytesIO()
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torch.save(model.state_dict(), buffer)
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trainer_options.oss_bucket.put_object(os.path.join(trainer_options.output_dir,
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f"{iepoch}epoch.pb"), buffer.getvalue())
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else:
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torch.save(model.state_dict(), output_dir / f"{iepoch}epoch.pb")
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# Creates a sym link latest.pb -> {iepoch}epoch.pb
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if trainer_options.use_pai:
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p = os.path.join(trainer_options.output_dir, "latest.pb")
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if trainer_options.oss_bucket.object_exists(p):
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trainer_options.oss_bucket.delete_object(p)
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trainer_options.oss_bucket.copy_object(trainer_options.oss_bucket.bucket_name,
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os.path.join(trainer_options.output_dir,
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f"{iepoch}epoch.pb"), p)
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else:
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p = output_dir / "latest.pb"
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if p.is_symlink() or p.exists():
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p.unlink()
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p.symlink_to(f"{iepoch}epoch.pb")
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_improved = []
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for _phase, k, _mode in trainer_options.best_model_criterion:
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# e.g. _phase, k, _mode = "train", "loss", "min"
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if reporter.has(_phase, k):
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best_epoch = reporter.get_best_epoch(_phase, k, _mode)
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# Creates sym links if it's the best result
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if best_epoch == iepoch:
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if trainer_options.use_pai:
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p = os.path.join(trainer_options.output_dir, f"{_phase}.{k}.best.pb")
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if trainer_options.oss_bucket.object_exists(p):
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trainer_options.oss_bucket.delete_object(p)
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trainer_options.oss_bucket.copy_object(trainer_options.oss_bucket.bucket_name,
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os.path.join(trainer_options.output_dir,
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f"{iepoch}epoch.pb"), p)
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else:
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p = output_dir / f"{_phase}.{k}.best.pb"
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if p.is_symlink() or p.exists():
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p.unlink()
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p.symlink_to(f"{iepoch}epoch.pb")
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_improved.append(f"{_phase}.{k}")
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if len(_improved) == 0:
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logging.info("There are no improvements in this epoch")
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else:
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logging.info(
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"The best model has been updated: " + ", ".join(_improved)
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)
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# log_model = (
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# trainer_options.wandb_model_log_interval > 0
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# and iepoch % trainer_options.wandb_model_log_interval == 0
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# )
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# if log_model and trainer_options.use_wandb:
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# import wandb
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#
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# logging.info("Logging Model on this epoch :::::")
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# artifact = wandb.Artifact(
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# name=f"model_{wandb.run.id}",
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# type="model",
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# metadata={"improved": _improved},
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# )
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# artifact.add_file(str(output_dir / f"{iepoch}epoch.pb"))
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# aliases = [
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# f"epoch-{iepoch}",
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# "best" if best_epoch == iepoch else "",
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# ]
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# wandb.log_artifact(artifact, aliases=aliases)
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# 6. Remove the model files excluding n-best epoch and latest epoch
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_removed = []
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# Get the union set of the n-best among multiple criterion
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nbests = set().union(
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*[
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set(reporter.sort_epochs(ph, k, m)[: max(keep_nbest_models)])
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for ph, k, m in trainer_options.best_model_criterion
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if reporter.has(ph, k)
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]
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)
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# Generated n-best averaged model
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if (
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trainer_options.nbest_averaging_interval > 0
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and iepoch % trainer_options.nbest_averaging_interval == 0
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):
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average_nbest_models(
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reporter=reporter,
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output_dir=output_dir,
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best_model_criterion=trainer_options.best_model_criterion,
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nbest=keep_nbest_models,
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suffix=f"till{iepoch}epoch",
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oss_bucket=trainer_options.oss_bucket,
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pai_output_dir=trainer_options.output_dir,
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)
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for e in range(1, iepoch):
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if trainer_options.use_pai:
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p = os.path.join(trainer_options.output_dir, f"{e}epoch.pb")
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if trainer_options.oss_bucket.object_exists(p) and e not in nbests:
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trainer_options.oss_bucket.delete_object(p)
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_removed.append(str(p))
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else:
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p = output_dir / f"{e}epoch.pb"
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if p.exists() and e not in nbests:
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p.unlink()
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_removed.append(str(p))
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if len(_removed) != 0:
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logging.info("The model files were removed: " + ", ".join(_removed))
|
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# 7. If any updating haven't happened, stops the training
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if all_steps_are_invalid:
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logging.warning(
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f"The gradients at all steps are invalid in this epoch. "
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f"Something seems wrong. This training was stopped at {iepoch}epoch"
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)
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break
|
|
if max_update_stop:
|
logging.info(
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f"Stopping training due to "
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f"num_updates: {trainer_options.num_updates} >= max_update: {trainer_options.max_update}"
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)
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break
|
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# 8. Check early stopping
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if trainer_options.patience is not None:
|
if reporter.check_early_stopping(
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trainer_options.patience, *trainer_options.early_stopping_criterion
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):
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break
|
|
else:
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logging.info(
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f"The training was finished at {trainer_options.max_epoch} epochs "
|
)
|
|
# Generated n-best averaged model
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if not distributed_option.distributed or distributed_option.dist_rank == 0:
|
average_nbest_models(
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reporter=reporter,
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output_dir=output_dir,
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best_model_criterion=trainer_options.best_model_criterion,
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nbest=keep_nbest_models,
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oss_bucket=trainer_options.oss_bucket,
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pai_output_dir=trainer_options.output_dir,
|
)
|
|
def train_one_epoch(
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self,
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model: torch.nn.Module,
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iterator: Iterable[Tuple[List[str], Dict[str, torch.Tensor]]],
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optimizers: Sequence[torch.optim.Optimizer],
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schedulers: Sequence[Optional[AbsScheduler]],
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scaler: Optional[GradScaler],
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reporter: SubReporter,
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summary_writer,
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options: TrainerOptions,
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distributed_option: DistributedOption,
|
) -> Tuple[bool, bool]:
|
|
grad_noise = options.grad_noise
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accum_grad = options.accum_grad
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grad_clip = options.grad_clip
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grad_clip_type = options.grad_clip_type
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log_interval = options.log_interval
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# no_forward_run = options.no_forward_run
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ngpu = options.ngpu
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# use_wandb = options.use_wandb
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distributed = distributed_option.distributed
|
|
if log_interval is None:
|
try:
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log_interval = max(len(iterator) // 20, 10)
|
except TypeError:
|
log_interval = 100
|
|
model.train()
|
all_steps_are_invalid = True
|
max_update_stop = False
|
# [For distributed] Because iteration counts are not always equals between
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# processes, send stop-flag to the other processes if iterator is finished
|
iterator_stop = torch.tensor(0).to("cuda" if ngpu > 0 else "cpu")
|
|
start_time = time.perf_counter()
|
for iiter, (_, batch) in enumerate(
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reporter.measure_iter_time(iterator, "iter_time"), 1
|
):
|
assert isinstance(batch, dict), type(batch)
|
|
if distributed:
|
torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
|
if iterator_stop > 0:
|
break
|
|
batch = to_device(batch, "cuda" if ngpu > 0 else "cpu")
|
# if no_forward_run:
|
# all_steps_are_invalid = False
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# continue
|
|
with autocast(scaler is not None):
|
with reporter.measure_time("forward_time"):
|
retval = model(**batch)
|
|
# Note(kamo):
|
# Supporting two patterns for the returned value from the model
|
# a. dict type
|
if isinstance(retval, dict):
|
loss = retval["loss"]
|
stats = retval["stats"]
|
weight = retval["weight"]
|
optim_idx = retval.get("optim_idx")
|
if optim_idx is not None and not isinstance(optim_idx, int):
|
if not isinstance(optim_idx, torch.Tensor):
|
raise RuntimeError(
|
"optim_idx must be int or 1dim torch.Tensor, "
|
f"but got {type(optim_idx)}"
|
)
|
if optim_idx.dim() >= 2:
|
raise RuntimeError(
|
"optim_idx must be int or 1dim torch.Tensor, "
|
f"but got {optim_idx.dim()}dim tensor"
|
)
|
if optim_idx.dim() == 1:
|
for v in optim_idx:
|
if v != optim_idx[0]:
|
raise RuntimeError(
|
"optim_idx must be 1dim tensor "
|
"having same values for all entries"
|
)
|
optim_idx = optim_idx[0].item()
|
else:
|
optim_idx = optim_idx.item()
|
|
# b. tuple or list type
|
else:
|
loss, stats, weight = retval
|
optim_idx = None
|
|
stats = {k: v for k, v in stats.items() if v is not None}
|
if ngpu > 1 or distributed:
|
# Apply weighted averaging for loss and stats
|
loss = (loss * weight.type(loss.dtype)).sum()
|
|
# if distributed, this method can also apply all_reduce()
|
stats, weight = recursive_average(stats, weight, distributed)
|
|
# Now weight is summation over all workers
|
loss /= weight
|
if distributed:
|
# NOTE(kamo): Multiply world_size because DistributedDataParallel
|
# automatically normalizes the gradient by world_size.
|
loss *= torch.distributed.get_world_size()
|
|
loss /= accum_grad
|
|
reporter.register(stats, weight)
|
|
with reporter.measure_time("backward_time"):
|
if scaler is not None:
|
# Scales loss. Calls backward() on scaled loss
|
# to create scaled gradients.
|
# Backward passes under autocast are not recommended.
|
# Backward ops run in the same dtype autocast chose
|
# for corresponding forward ops.
|
scaler.scale(loss).backward()
|
else:
|
loss.backward()
|
|
if iiter % accum_grad == 0:
|
if scaler is not None:
|
# Unscales the gradients of optimizer's assigned params in-place
|
for iopt, optimizer in enumerate(optimizers):
|
if optim_idx is not None and iopt != optim_idx:
|
continue
|
scaler.unscale_(optimizer)
|
|
# gradient noise injection
|
if grad_noise:
|
add_gradient_noise(
|
model,
|
reporter.get_total_count(),
|
duration=100,
|
eta=1.0,
|
scale_factor=0.55,
|
)
|
|
# compute the gradient norm to check if it is normal or not
|
grad_norm = torch.nn.utils.clip_grad_norm_(
|
model.parameters(),
|
max_norm=grad_clip,
|
norm_type=grad_clip_type,
|
)
|
# PyTorch<=1.4, clip_grad_norm_ returns float value
|
if not isinstance(grad_norm, torch.Tensor):
|
grad_norm = torch.tensor(grad_norm)
|
|
if not torch.isfinite(grad_norm):
|
logging.warning(
|
f"The grad norm is {grad_norm}. Skipping updating the model."
|
)
|
|
# Must invoke scaler.update() if unscale_() is used in the iteration
|
# to avoid the following error:
|
# RuntimeError: unscale_() has already been called
|
# on this optimizer since the last update().
|
# Note that if the gradient has inf/nan values,
|
# scaler.step skips optimizer.step().
|
if scaler is not None:
|
for iopt, optimizer in enumerate(optimizers):
|
if optim_idx is not None and iopt != optim_idx:
|
continue
|
scaler.step(optimizer)
|
scaler.update()
|
|
else:
|
all_steps_are_invalid = False
|
with reporter.measure_time("optim_step_time"):
|
for iopt, (optimizer, scheduler) in enumerate(
|
zip(optimizers, schedulers)
|
):
|
if optim_idx is not None and iopt != optim_idx:
|
continue
|
if scaler is not None:
|
# scaler.step() first unscales the gradients of
|
# the optimizer's assigned params.
|
scaler.step(optimizer)
|
# Updates the scale for next iteration.
|
scaler.update()
|
else:
|
optimizer.step()
|
if isinstance(scheduler, AbsBatchStepScheduler):
|
scheduler.step()
|
for iopt, optimizer in enumerate(optimizers):
|
if optim_idx is not None and iopt != optim_idx:
|
continue
|
optimizer.zero_grad()
|
|
# Register lr and train/load time[sec/step],
|
# where step refers to accum_grad * mini-batch
|
reporter.register(
|
dict(
|
{
|
f"optim{i}_lr{j}": pg["lr"]
|
for i, optimizer in enumerate(optimizers)
|
for j, pg in enumerate(optimizer.param_groups)
|
if "lr" in pg
|
},
|
train_time=time.perf_counter() - start_time,
|
),
|
)
|
start_time = time.perf_counter()
|
|
# update num_updates
|
if distributed:
|
if hasattr(model.module, "num_updates"):
|
model.module.set_num_updates(model.module.get_num_updates() + 1)
|
options.num_updates = model.module.get_num_updates()
|
if model.module.get_num_updates() >= options.max_update:
|
max_update_stop = True
|
else:
|
if hasattr(model, "num_updates"):
|
model.set_num_updates(model.get_num_updates() + 1)
|
options.num_updates = model.get_num_updates()
|
if model.get_num_updates() >= options.max_update:
|
max_update_stop = True
|
|
# NOTE(kamo): Call log_message() after next()
|
reporter.next()
|
if iiter % log_interval == 0:
|
num_updates = options.num_updates if hasattr(options, "num_updates") else None
|
logging.info(reporter.log_message(-log_interval, num_updates=num_updates))
|
if summary_writer is not None:
|
reporter.tensorboard_add_scalar(summary_writer, -log_interval)
|
# if use_wandb:
|
# reporter.wandb_log()
|
|
if max_update_stop:
|
break
|
|
else:
|
if distributed:
|
iterator_stop.fill_(1)
|
torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
|
return all_steps_are_invalid, max_update_stop
|
|
@torch.no_grad()
|
def validate_one_epoch(
|
self,
|
model: torch.nn.Module,
|
iterator: Iterable[Dict[str, torch.Tensor]],
|
reporter: SubReporter,
|
options: TrainerOptions,
|
distributed_option: DistributedOption,
|
) -> None:
|
ngpu = options.ngpu
|
# no_forward_run = options.no_forward_run
|
distributed = distributed_option.distributed
|
|
model.eval()
|
|
# [For distributed] Because iteration counts are not always equals between
|
# processes, send stop-flag to the other processes if iterator is finished
|
iterator_stop = torch.tensor(0).to("cuda" if ngpu > 0 else "cpu")
|
for (_, batch) in iterator:
|
assert isinstance(batch, dict), type(batch)
|
if distributed:
|
torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
|
if iterator_stop > 0:
|
break
|
|
batch = to_device(batch, "cuda" if ngpu > 0 else "cpu")
|
# if no_forward_run:
|
# continue
|
|
retval = model(**batch)
|
if isinstance(retval, dict):
|
stats = retval["stats"]
|
weight = retval["weight"]
|
else:
|
_, stats, weight = retval
|
if ngpu > 1 or distributed:
|
# Apply weighted averaging for stats.
|
# if distributed, this method can also apply all_reduce()
|
stats, weight = recursive_average(stats, weight, distributed)
|
|
reporter.register(stats, weight)
|
reporter.next()
|
|
else:
|
if distributed:
|
iterator_stop.fill_(1)
|
torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
|
|
|
def build_trainer(
|
args,
|
model: FunASRModel,
|
optimizers: Sequence[torch.optim.Optimizer],
|
schedulers: Sequence[Optional[AbsScheduler]],
|
train_dataloader: AbsIterFactory,
|
valid_dataloader: AbsIterFactory,
|
distributed_option: DistributedOption
|
):
|
trainer = Trainer(
|
args=args,
|
model=model,
|
optimizers=optimizers,
|
schedulers=schedulers,
|
train_dataloader=train_dataloader,
|
valid_dataloader=valid_dataloader,
|
distributed_option=distributed_option
|
)
|
return trainer
|
