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+# 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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+
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+"""Trainer module."""
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+import argparse
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+from contextlib import contextmanager
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+import dataclasses
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+from dataclasses import is_dataclass
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+from distutils.version import LooseVersion
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+import logging
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+from pathlib import Path
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+import time
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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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+
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+import humanfriendly
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+import oss2
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+from io import BytesIO
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+import os
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+import numpy as np
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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 typeguard import check_argument_types
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+
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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.main_funcs.calculate_all_attentions import calculate_all_attentions
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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.models.base_model import FunASRModel
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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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+
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+if torch.distributed.is_available():
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+ from torch.distributed import ReduceOp
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+
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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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+
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+
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+ GradScaler = None
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+
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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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+
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+
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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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+ batch_interval: int
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+
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+
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+class Trainer:
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+ """Trainer having a optimizer.
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+
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+ If you'd like to use multiple optimizers, then inherit this class
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+ and override the methods if necessary - at least "train_one_epoch()"
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+
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+ >>> class TwoOptimizerTrainer(Trainer):
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+ ... @classmethod
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+ ... def add_arguments(cls, parser):
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+ ... ...
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+ ...
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+ ... @classmethod
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+ ... def train_one_epoch(cls, model, optimizers, ...):
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+ ... loss1 = model.model1(...)
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+ ... loss1.backward()
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+ ... optimizers[0].step()
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+ ...
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+ ... loss2 = model.model2(...)
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+ ... loss2.backward()
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+ ... optimizers[1].step()
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+
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+ """
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+
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+ def __init__(self):
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+ raise RuntimeError("This class can't be instantiated.")
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+
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+ @classmethod
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+ def build_options(cls, args: argparse.Namespace) -> TrainerOptions:
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+ """Build options consumed by train(), eval()"""
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+ assert check_argument_types()
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+ return build_dataclass(TrainerOptions, args)
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+
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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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+
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+ @staticmethod
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+ def resume(
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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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+
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+ logging.info(f"The training was resumed using {checkpoint}")
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+
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+ @classmethod
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+ def run(
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+ cls,
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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_iter_factory: AbsIterFactory,
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+ valid_iter_factory: AbsIterFactory,
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+ trainer_options,
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+ distributed_option: DistributedOption,
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+ ) -> None:
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+ """Perform training. This method performs the main process of training."""
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+ assert check_argument_types()
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+ # NOTE(kamo): Don't check the type more strictly as far trainer_options
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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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+
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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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+
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+ # assert batch_interval is set and >0
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+ assert trainer_options.batch_interval > 0
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+
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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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+
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+ if trainer_options.resume and (output_dir / "checkpoint.pb").exists():
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+ cls.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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+
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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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+
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+ if distributed_option.distributed:
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+ if trainer_options.sharded_ddp:
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+ dp_model = fairscale.nn.data_parallel.ShardedDataParallel(
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+ module=model,
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+ sharded_optimizer=optimizers,
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+ )
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+ else:
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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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+
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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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+
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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: {}".format(
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+ iepoch,
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+ trainer_options.max_epoch,
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+ humanfriendly.format_timespan(
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+ (time.perf_counter() - start_time)
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+ / (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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+ )
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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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+
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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 = cls.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_iter_factory.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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+
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+ with reporter.observe("valid") as sub_reporter:
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+ cls.validate_one_epoch(
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+ model=dp_model,
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+ iterator=valid_iter_factory.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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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|
|
+ # Creates a sym link latest.pb -> {iepoch}epoch.pb
|
|
|
+ if trainer_options.use_pai:
|
|
|
+ p = os.path.join(trainer_options.output_dir, "latest.pb")
|
|
|
+ if trainer_options.oss_bucket.object_exists(p):
|
|
|
+ trainer_options.oss_bucket.delete_object(p)
|
|
|
+ trainer_options.oss_bucket.copy_object(trainer_options.oss_bucket.bucket_name,
|
|
|
+ os.path.join(trainer_options.output_dir,
|
|
|
+ f"{iepoch}epoch.pb"), p)
|
|
|
+ else:
|
|
|
+ p = output_dir / "latest.pb"
|
|
|
+ if p.is_symlink() or p.exists():
|
|
|
+ p.unlink()
|
|
|
+ p.symlink_to(f"{iepoch}epoch.pb")
|
|
|
+
|
|
|
+ _improved = []
|
|
|
+ for _phase, k, _mode in trainer_options.best_model_criterion:
|
|
|
+ # e.g. _phase, k, _mode = "train", "loss", "min"
|
|
|
+ if reporter.has(_phase, k):
|
|
|
+ best_epoch = reporter.get_best_epoch(_phase, k, _mode)
|
|
|
+ # Creates sym links if it's the best result
|
|
|
+ if best_epoch == iepoch:
|
|
|
+ if trainer_options.use_pai:
|
|
|
+ p = os.path.join(trainer_options.output_dir, f"{_phase}.{k}.best.pb")
|
|
|
+ if trainer_options.oss_bucket.object_exists(p):
|
|
|
+ trainer_options.oss_bucket.delete_object(p)
|
|
|
+ trainer_options.oss_bucket.copy_object(trainer_options.oss_bucket.bucket_name,
|
|
|
+ os.path.join(trainer_options.output_dir,
|
|
|
+ f"{iepoch}epoch.pb"), p)
|
|
|
+ else:
|
|
|
+ p = output_dir / f"{_phase}.{k}.best.pb"
|
|
|
+ if p.is_symlink() or p.exists():
|
|
|
+ p.unlink()
|
|
|
+ p.symlink_to(f"{iepoch}epoch.pb")
|
|
|
+ _improved.append(f"{_phase}.{k}")
|
|
|
+ if len(_improved) == 0:
|
|
|
+ logging.info("There are no improvements in this epoch")
|
|
|
+ else:
|
|
|
+ logging.info(
|
|
|
+ "The best model has been updated: " + ", ".join(_improved)
|
|
|
+ )
|
|
|
+
|
|
|
+ log_model = (
|
|
|
+ trainer_options.wandb_model_log_interval > 0
|
|
|
+ and iepoch % trainer_options.wandb_model_log_interval == 0
|
|
|
+ )
|
|
|
+ if log_model and trainer_options.use_wandb:
|
|
|
+ import wandb
|
|
|
+
|
|
|
+ logging.info("Logging Model on this epoch :::::")
|
|
|
+ artifact = wandb.Artifact(
|
|
|
+ name=f"model_{wandb.run.id}",
|
|
|
+ type="model",
|
|
|
+ metadata={"improved": _improved},
|
|
|
+ )
|
|
|
+ artifact.add_file(str(output_dir / f"{iepoch}epoch.pb"))
|
|
|
+ aliases = [
|
|
|
+ f"epoch-{iepoch}",
|
|
|
+ "best" if best_epoch == iepoch else "",
|
|
|
+ ]
|
|
|
+ wandb.log_artifact(artifact, aliases=aliases)
|
|
|
+
|
|
|
+ # 6. Remove the model files excluding n-best epoch and latest epoch
|
|
|
+ _removed = []
|
|
|
+ # Get the union set of the n-best among multiple criterion
|
|
|
+ nbests = set().union(
|
|
|
+ *[
|
|
|
+ set(reporter.sort_epochs(ph, k, m)[: max(keep_nbest_models)])
|
|
|
+ for ph, k, m in trainer_options.best_model_criterion
|
|
|
+ if reporter.has(ph, k)
|
|
|
+ ]
|
|
|
+ )
|
|
|
+
|
|
|
+ # Generated n-best averaged model
|
|
|
+ if (
|
|
|
+ trainer_options.nbest_averaging_interval > 0
|
|
|
+ and iepoch % trainer_options.nbest_averaging_interval == 0
|
|
|
+ ):
|
|
|
+ average_nbest_models(
|
|
|
+ reporter=reporter,
|
|
|
+ output_dir=output_dir,
|
|
|
+ best_model_criterion=trainer_options.best_model_criterion,
|
|
|
+ nbest=keep_nbest_models,
|
|
|
+ suffix=f"till{iepoch}epoch",
|
|
|
+ oss_bucket=trainer_options.oss_bucket,
|
|
|
+ pai_output_dir=trainer_options.output_dir,
|
|
|
+ )
|
|
|
+
|
|
|
+ for e in range(1, iepoch):
|
|
|
+ if trainer_options.use_pai:
|
|
|
+ p = os.path.join(trainer_options.output_dir, f"{e}epoch.pb")
|
|
|
+ if trainer_options.oss_bucket.object_exists(p) and e not in nbests:
|
|
|
+ trainer_options.oss_bucket.delete_object(p)
|
|
|
+ _removed.append(str(p))
|
|
|
+ else:
|
|
|
+ p = output_dir / f"{e}epoch.pb"
|
|
|
+ if p.exists() and e not in nbests:
|
|
|
+ p.unlink()
|
|
|
+ _removed.append(str(p))
|
|
|
+ if len(_removed) != 0:
|
|
|
+ logging.info("The model files were removed: " + ", ".join(_removed))
|
|
|
+
|
|
|
+ # 7. If any updating haven't happened, stops the training
|
|
|
+ if all_steps_are_invalid:
|
|
|
+ logging.warning(
|
|
|
+ f"The gradients at all steps are invalid in this epoch. "
|
|
|
+ f"Something seems wrong. This training was stopped at {iepoch}epoch"
|
|
|
+ )
|
|
|
+ break
|
|
|
+
|
|
|
+ if max_update_stop:
|
|
|
+ logging.info(
|
|
|
+ f"Stopping training due to "
|
|
|
+ f"num_updates: {trainer_options.num_updates} >= max_update: {trainer_options.max_update}"
|
|
|
+ )
|
|
|
+ break
|
|
|
+
|
|
|
+ # 8. Check early stopping
|
|
|
+ if trainer_options.patience is not None:
|
|
|
+ if reporter.check_early_stopping(
|
|
|
+ trainer_options.patience, *trainer_options.early_stopping_criterion
|
|
|
+ ):
|
|
|
+ break
|
|
|
+
|
|
|
+ else:
|
|
|
+ logging.info(
|
|
|
+ f"The training was finished at {trainer_options.max_epoch} epochs "
|
|
|
+ )
|
|
|
+
|
|
|
+ # Generated n-best averaged model
|
|
|
+ if not distributed_option.distributed or distributed_option.dist_rank == 0:
|
|
|
+ average_nbest_models(
|
|
|
+ reporter=reporter,
|
|
|
+ output_dir=output_dir,
|
|
|
+ best_model_criterion=trainer_options.best_model_criterion,
|
|
|
+ nbest=keep_nbest_models,
|
|
|
+ oss_bucket=trainer_options.oss_bucket,
|
|
|
+ pai_output_dir=trainer_options.output_dir,
|
|
|
+ )
|
|
|
+
|
|
|
+ @classmethod
|
|
|
+ def train_one_epoch(
|
|
|
+ cls,
|
|
|
+ model: torch.nn.Module,
|
|
|
+ iterator: Iterable[Tuple[List[str], Dict[str, torch.Tensor]]],
|
|
|
+ optimizers: Sequence[torch.optim.Optimizer],
|
|
|
+ schedulers: Sequence[Optional[AbsScheduler]],
|
|
|
+ scaler: Optional[GradScaler],
|
|
|
+ reporter: SubReporter,
|
|
|
+ summary_writer,
|
|
|
+ options: TrainerOptions,
|
|
|
+ distributed_option: DistributedOption,
|
|
|
+ ) -> Tuple[bool, bool]:
|
|
|
+ assert check_argument_types()
|
|
|
+
|
|
|
+ grad_noise = options.grad_noise
|
|
|
+ accum_grad = options.accum_grad
|
|
|
+ grad_clip = options.grad_clip
|
|
|
+ grad_clip_type = options.grad_clip_type
|
|
|
+ log_interval = options.log_interval
|
|
|
+ no_forward_run = options.no_forward_run
|
|
|
+ ngpu = options.ngpu
|
|
|
+ use_wandb = options.use_wandb
|
|
|
+ distributed = distributed_option.distributed
|
|
|
+
|
|
|
+ if log_interval is None:
|
|
|
+ try:
|
|
|
+ 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
|
|
|
+ # processes, send stop-flag to the other processes if iterator is finished
|
|
|
+ iterator_stop = torch.tensor(0).to("cuda" if ngpu > 0 else "cpu")
|
|
|
+
|
|
|
+ # get the rank
|
|
|
+ rank = distributed_option.dist_rank
|
|
|
+ # get the num batch updates
|
|
|
+ num_batch_updates = 0
|
|
|
+ # ouput dir
|
|
|
+ output_dir = Path(options.output_dir)
|
|
|
+ # batch interval
|
|
|
+ batch_interval = options.batch_interval
|
|
|
+ assert batch_interval > 0
|
|
|
+
|
|
|
+ start_time = time.perf_counter()
|
|
|
+ for iiter, (_, batch) in enumerate(
|
|
|
+ reporter.measure_iter_time(iterator, "iter_time"), 1
|
|
|
+ ):
|
|
|
+ assert isinstance(batch, dict), type(batch)
|
|
|
+
|
|
|
+ if rank == 0:
|
|
|
+ if hasattr(model, "num_updates") or (hasattr(model, "module") and hasattr(model.module, "num_updates")):
|
|
|
+ num_batch_updates = model.get_num_updates() if hasattr(model,
|
|
|
+ "num_updates") else model.module.get_num_updates()
|
|
|
+ if (num_batch_updates % batch_interval == 0) and (options.oss_bucket is not None) and options.use_pai:
|
|
|
+ buffer = BytesIO()
|
|
|
+ torch.save(model.state_dict(), buffer)
|
|
|
+ options.oss_bucket.put_object(os.path.join(output_dir, f"{num_batch_updates}batch.pth"),
|
|
|
+ buffer.getvalue())
|
|
|
+
|
|
|
+ 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
|
|
|
+ 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
|
|
|
+
|
|
|
+ @classmethod
|
|
|
+ @torch.no_grad()
|
|
|
+ def validate_one_epoch(
|
|
|
+ cls,
|
|
|
+ model: torch.nn.Module,
|
|
|
+ iterator: Iterable[Dict[str, torch.Tensor]],
|
|
|
+ reporter: SubReporter,
|
|
|
+ options: TrainerOptions,
|
|
|
+ distributed_option: DistributedOption,
|
|
|
+ ) -> None:
|
|
|
+ assert check_argument_types()
|
|
|
+ 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)
|
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