egs/librispeech/SSL/hubert/pretrain_ce.py

#!/usr/bin/env python3
# Copyright    2021-2024  Xiaomi Corp.              (authors: Fangjun Kuang,
#                                                             Wei Kang,
#                                                             Mingshuang Luo,
#                                                             Zengwei Yao,
#                                                             Yifan Yang,
#                                                             Daniel Povey)
# Copyright    2024  Shanghai Jiao Tong University  (authors: Jianheng Zhuo)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Usage:

export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"

# For hubert model pretraining:
./hubert/pretrain.py \
  --world-size 8 \
  --num-epochs 400 \
  --start-epoch 1 \
  --use-fp16 1 \
  --exp-dir hubert/exp \
  --full-libri 1 \
  --max-duration 87.5 \
  --accum-grad 4
"""


import argparse
import copy
import logging
import sys
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, Optional, Tuple, Union

import k2
import optim
import torch
import torch.multiprocessing as mp
import torch.nn as nn
from hubert_ce import HubertModel, add_hubert_arguments
from lhotse.cut import Cut
from lhotse.dataset.sampling.base import CutSampler
from lhotse.utils import fix_random_seed
from optim import Eden, ScaledAdam
from ssl_datamodule import LibriSpeechDataModule
from torch import Tensor
from torch.cuda.amp import GradScaler
from torch.nn.functional import pad
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.tensorboard import SummaryWriter

from icefall import diagnostics
from icefall.checkpoint import load_checkpoint, remove_checkpoints
from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
from icefall.checkpoint import (
    save_checkpoint_with_global_batch_idx,
    update_averaged_model,
)
from icefall.dist import cleanup_dist, setup_dist
from icefall.env import get_env_info
from icefall.hooks import register_inf_check_hooks
from icefall.utils import (
    AttributeDict,
    MetricsTracker,
    get_parameter_groups_with_lrs,
    setup_logger,
    str2bool,
    torch_autocast,
)

LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]


def get_adjusted_batch_count(params: AttributeDict) -> float:
    # returns the number of batches we would have used so far if we had used the reference
    # duration.  This is for purposes of set_batch_count().
    return (
        params.batch_idx_train
        * params.accum_grad
        * (params.max_duration * params.world_size)
        / params.ref_duration
    )


def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None:
    if isinstance(model, DDP):
        # get underlying nn.Module
        model = model.module
    for name, module in model.named_modules():
        if hasattr(module, "batch_count"):
            module.batch_count = batch_count
        if hasattr(module, "name"):
            module.name = name


def get_parser():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )

    parser.add_argument(
        "--world-size",
        type=int,
        default=1,
        help="Number of GPUs for DDP training.",
    )

    parser.add_argument(
        "--master-port",
        type=int,
        default=12354,
        help="Master port to use for DDP training.",
    )

    parser.add_argument(
        "--tensorboard",
        type=str2bool,
        default=True,
        help="Should various information be logged in tensorboard.",
    )

    parser.add_argument(
        "--num-epochs",
        type=int,
        default=400,
        help="Number of epochs to train.",
    )

    parser.add_argument(
        "--start-epoch",
        type=int,
        default=1,
        help="""Resume training from this epoch. It should be positive.
        If larger than 1, it will load checkpoint from
        exp-dir/epoch-{start_epoch-1}.pt
        """,
    )

    parser.add_argument(
        "--start-batch",
        type=int,
        default=0,
        help="""If positive, --start-epoch is ignored and
        it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt
        """,
    )

    parser.add_argument(
        "--exp-dir",
        type=str,
        default="hubert/exp",
        help="""The experiment dir.
        It specifies the directory where all training related
        files, e.g., checkpoints, log, etc, are saved
        """,
    )

    parser.add_argument(
        "--base-lr", type=float, default=0.045, help="The base learning rate."
    )

    parser.add_argument(
        "--lr-batches",
        type=float,
        default=7500,
        help="""Number of steps that affects how rapidly the learning rate
        decreases. We suggest not to change this.""",
    )

    parser.add_argument(
        "--lr-epochs",
        type=float,
        default=10.5,
        help="""Number of epochs that affects how rapidly the learning rate decreases.
        """,
    )

    parser.add_argument(
        "--warmup-batches",
        type=float,
        default=5000,
        help="Eden warmup steps",
    )

    parser.add_argument(
        "--warmup-start",
        type=float,
        default=0,
        help="Eden warmup start learning rate",
    )

    parser.add_argument(
        "--ref-duration",
        type=float,
        default=80,
        help="Reference batch duration for purposes of adjusting batch counts for setting various "
        "schedules inside the model",
    )

    parser.add_argument(
        "--seed",
        type=int,
        default=42,
        help="The seed for random generators intended for reproducibility",
    )

    parser.add_argument(
        "--print-diagnostics",
        type=str2bool,
        default=False,
        help="Accumulate stats on activations, print them and exit.",
    )

    parser.add_argument(
        "--sanity-check",
        type=str2bool,
        default=False,
        help="Check if any of the batches in epoch 1 would cause OOM.",
    )

    parser.add_argument(
        "--inf-check",
        type=str2bool,
        default=False,
        help="Add hooks to check for infinite module outputs and gradients.",
    )

    parser.add_argument(
        "--save-every-n",
        type=int,
        default=100000,
        help="""Save checkpoint after processing this number of batches"
        periodically. We save checkpoint to exp-dir/ whenever
        params.batch_idx_train % save_every_n == 0. The checkpoint filename
        has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt'
        Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the
        end of each epoch where `xxx` is the epoch number counting from 1.
        """,
    )

    parser.add_argument(
        "--keep-last-k",
        type=int,
        default=30,
        help="""Only keep this number of checkpoints on disk.
        For instance, if it is 3, there are only 3 checkpoints
        in the exp-dir with filenames `checkpoint-xxx.pt`.
        It does not affect checkpoints with name `epoch-xxx.pt`.
        """,
    )

    parser.add_argument(
        "--average-period",
        type=int,
        default=200,
        help="""Update the averaged model, namely `model_avg`, after processing
        this number of batches. `model_avg` is a separate version of model,
        in which each floating-point parameter is the average of all the
        parameters from the start of training. Each time we take the average,
        we do: `model_avg = model * (average_period / batch_idx_train) +
            model_avg * ((batch_idx_train - average_period) / batch_idx_train)`.
        """,
    )

    parser.add_argument(
        "--accum-grad",
        type=int,
        default=4,
        help="""update gradient when batch_idx_train % accum_grad == 0.
        """,
    )

    parser.add_argument(
        "--use-fp16",
        type=str2bool,
        default=False,
        help="Whether to use half precision training.",
    )

    parser.add_argument(
        "--max-keep-size",
        type=int,
        default=sys.maxsize,
        help="exclude sample longer than this.",
    )

    parser.add_argument(
        "--min-keep-size",
        type=float,
        default=32000,
        help="exclude sample longer less than this.",
    )

    parser.add_argument(
        "--max-sample-size",
        type=float,
        default=250000,
        help="max sample size to crop to for batching.",
    )

    add_hubert_arguments(parser)

    return parser


def get_params() -> AttributeDict:
    """Return a dict containing training parameters.

    All training related parameters that are not passed from the commandline
    are saved in the variable `params`.

    Commandline options are merged into `params` after they are parsed, so
    you can also access them via `params`.

    Explanation of options saved in `params`:

        - best_train_loss: Best training loss so far. It is used to select
                           the model that has the lowest training loss. It is
                           updated during the training.

        - best_valid_loss: Best validation loss so far. It is used to select
                           the model that has the lowest validation loss. It is
                           updated during the training.

        - best_train_epoch: It is the epoch that has the best training loss.

        - best_valid_epoch: It is the epoch that has the best validation loss.

        - batch_idx_train: Used to writing statistics to tensorboard. It
                           contains number of updates happen to the model so far across
                           epochs.

        - sub_batch_idx_train: It contains number of batch trained so far across
                           epochs.

        - log_interval:  Print training loss if batch_idx % log_interval` is 0

        - reset_interval: Reset statistics if batch_idx % reset_interval is 0

        - valid_interval:  Run validation if batch_idx % valid_interval is 0
    """
    params = AttributeDict(
        {
            "best_train_loss": float("inf"),
            "best_valid_loss": float("inf"),
            "best_train_epoch": -1,
            "best_valid_epoch": -1,
            "batch_idx_train": 0,
            "sub_batch_idx_train": 0,
            "log_interval": 50,
            "reset_interval": 200,
            "valid_interval": 3000,  # For the 100h subset, use 800
            "env_info": get_env_info(),
        }
    )

    return params


def _to_int_tuple(s: str):
    return tuple(map(int, s.split(",")))


def get_model(params: AttributeDict) -> nn.Module:
    model = HubertModel(params)
    return model


def load_checkpoint_if_available(
    params: AttributeDict,
    model: nn.Module,
    model_avg: nn.Module = None,
    optimizer: Optional[torch.optim.Optimizer] = None,
    scheduler: Optional[LRSchedulerType] = None,
) -> Optional[Dict[str, Any]]:
    """Load checkpoint from file.

    If params.start_batch is positive, it will load the checkpoint from
    `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if
    params.start_epoch is larger than 1, it will load the checkpoint from
    `params.start_epoch - 1`.

    Apart from loading state dict for `model` and `optimizer` it also updates
    `best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
    and `best_valid_loss` in `params`.

    Args:
      params:
        The return value of :func:`get_params`.
      model:
        The training model.
      model_avg:
        The stored model averaged from the start of training.
      optimizer:
        The optimizer that we are using.
      scheduler:
        The scheduler that we are using.
    Returns:
      Return a dict containing previously saved training info.
    """
    if params.start_batch > 0:
        filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt"
    elif params.start_epoch > 1:
        filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
    else:
        return None

    assert filename.is_file(), f"{filename} does not exist!"

    saved_params = load_checkpoint(
        filename,
        model=model,
        model_avg=model_avg,
        optimizer=optimizer,
        scheduler=scheduler,
    )

    keys = [
        "best_train_epoch",
        "best_valid_epoch",
        "batch_idx_train",
        "best_train_loss",
        "best_valid_loss",
    ]
    for k in keys:
        params[k] = saved_params[k]

    if params.start_batch > 0:
        if "cur_epoch" in saved_params:
            params["start_epoch"] = saved_params["cur_epoch"]

    return saved_params


def save_checkpoint(
    params: AttributeDict,
    model: Union[nn.Module, DDP],
    model_avg: Optional[nn.Module] = None,
    optimizer: Optional[torch.optim.Optimizer] = None,
    scheduler: Optional[LRSchedulerType] = None,
    sampler: Optional[CutSampler] = None,
    scaler: Optional[GradScaler] = None,
    rank: int = 0,
) -> None:
    """Save model, optimizer, scheduler and training stats to file.

    Args:
      params:
        It is returned by :func:`get_params`.
      model:
        The training model.
      model_avg:
        The stored model averaged from the start of training.
      optimizer:
        The optimizer used in the training.
      sampler:
       The sampler for the training dataset.
      scaler:
        The scaler used for mix precision training.
    """
    if rank != 0:
        return
    filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
    save_checkpoint_impl(
        filename=filename,
        model=model,
        model_avg=model_avg,
        params=params,
        optimizer=optimizer,
        scheduler=scheduler,
        sampler=sampler,
        scaler=scaler,
        rank=rank,
    )

    if params.best_train_epoch == params.cur_epoch:
        best_train_filename = params.exp_dir / "best-train-loss.pt"
        copyfile(src=filename, dst=best_train_filename)

    if params.best_valid_epoch == params.cur_epoch:
        best_valid_filename = params.exp_dir / "best-valid-loss.pt"
        copyfile(src=filename, dst=best_valid_filename)


def compute_loss(
    params: AttributeDict,
    model: Union[nn.Module, DDP],
    batch: dict,
    is_training: bool,
) -> Tuple[Tensor, MetricsTracker]:
    """
    Compute loss given the model and its inputs.

    Args:
      params:
        Parameters for training. See :func:`get_params`.
      model:
        The model for training. It is an instance of Zipformer in our case.
      batch:
        A batch of data. See `dataset.HubertDataset()`
        for the content in it.
      is_training:
        True for training. False for validation. When it is True, this
        function enables autograd during computation; when it is False, it
        disables autograd.
    """
    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
    audio = batch["audio"].to(device)
    padding_mask = batch["padding_mask"].to(device)
    kmeans = batch["kmeans"].to(device)

    with torch.set_grad_enabled(is_training):
        loss, num_masked_tokens, logging_output = model(
            source=audio, target_list=[kmeans], padding_mask=padding_mask
        )

    assert loss.requires_grad == is_training

    info = MetricsTracker()
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        info["frames"] = num_masked_tokens
    for item in logging_output:
        info[item] = logging_output[item]
    return loss, info


def compute_validation_loss(
    params: AttributeDict,
    model: Union[nn.Module, DDP],
    valid_dl: torch.utils.data.DataLoader,
    world_size: int = 1,
) -> MetricsTracker:
    """Run the validation process."""
    model.eval()

    tot_loss = MetricsTracker()

    for batch_idx, batch in enumerate(valid_dl):
        loss, loss_info = compute_loss(
            params=params,
            model=model,
            batch=batch,
            is_training=False,
        )
        assert loss.requires_grad is False
        tot_loss = tot_loss + loss_info

    if world_size > 1:
        tot_loss.reduce(loss.device)

    loss_value = tot_loss["loss"] / tot_loss["frames"]
    if loss_value < params.best_valid_loss:
        params.best_valid_epoch = params.cur_epoch
        params.best_valid_loss = loss_value

    return tot_loss


def train_one_epoch(
    params: AttributeDict,
    model: Union[nn.Module, DDP],
    optimizer: torch.optim.Optimizer,
    scheduler: LRSchedulerType,
    train_dl: torch.utils.data.DataLoader,
    valid_dl: torch.utils.data.DataLoader,
    scaler: GradScaler,
    model_avg: Optional[nn.Module] = None,
    tb_writer: Optional[SummaryWriter] = None,
    world_size: int = 1,
    rank: int = 0,
) -> None:
    """Train the model for one epoch.

    The training loss from the mean of all frames is saved in
    `params.train_loss`. It runs the validation process every
    `params.valid_interval` batches.

    Args:
      params:
        It is returned by :func:`get_params`.
      model:
        The model for training.
      optimizer:
        The optimizer we are using.
      scheduler:
        The learning rate scheduler, we call step() every step.
      train_dl:
        Dataloader for the training dataset.
      valid_dl:
        Dataloader for the validation dataset.
      scaler:
        The scaler used for mix precision training.
      model_avg:
        The stored model averaged from the start of training.
      tb_writer:
        Writer to write log messages to tensorboard.
      world_size:
        Number of nodes in DDP training. If it is 1, DDP is disabled.
      rank:
        The rank of the node in DDP training. If no DDP is used, it should
        be set to 0.
    """
    model.train()

    tot_loss = MetricsTracker()

    saved_bad_model = False

    def save_bad_model(suffix: str = ""):
        save_checkpoint_impl(
            filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt",
            model=model,
            model_avg=model_avg,
            params=params,
            optimizer=optimizer,
            scheduler=scheduler,
            sampler=train_dl.sampler,
            scaler=scaler,
            rank=0,
        )

    for sub_batch_idx, batch in enumerate(train_dl):
        params.sub_batch_idx_train += 1
        batch_idx = sub_batch_idx // params.accum_grad

        if batch_idx % 10 == 0:
            set_batch_count(model, get_adjusted_batch_count(params))

        batch_size = batch["kmeans"].shape[0]

        try:
            with torch_autocast(enabled=params.use_fp16):
                loss, loss_info = compute_loss(
                    params=params,
                    model=model,
                    batch=batch,
                    is_training=True,
                )
            # summary stats
            tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info

            # NOTE: We use reduction==sum and loss is computed over utterances
            # in the batch and there is no normalization to it so far.
            scaler.scale(loss / params.accum_grad).backward()

            if sub_batch_idx % params.accum_grad == params.accum_grad - 1:
                params.batch_idx_train += 1
                scheduler.step_batch(params.batch_idx_train)

                scaler.step(optimizer)
                scaler.update()
                optimizer.zero_grad()
            else:
                continue

        except:  # noqa
            save_bad_model()
            display_and_save_batch(batch, params=params)
            raise

        if params.print_diagnostics and batch_idx == 5:
            return

        if (
            rank == 0
            and params.batch_idx_train > 0
            and params.batch_idx_train % params.average_period == 0
        ):
            update_averaged_model(
                params=params,
                model_cur=model,
                model_avg=model_avg,
            )

        if (
            params.batch_idx_train > 0
            and params.batch_idx_train % params.save_every_n == 0
        ):
            save_checkpoint_with_global_batch_idx(
                out_dir=params.exp_dir,
                global_batch_idx=params.batch_idx_train,
                model=model,
                model_avg=model_avg,
                params=params,
                optimizer=optimizer,
                scheduler=scheduler,
                sampler=train_dl.sampler,
                scaler=scaler,
                rank=rank,
            )
            remove_checkpoints(
                out_dir=params.exp_dir,
                topk=params.keep_last_k,
                rank=rank,
            )

        if batch_idx % 100 == 0 and params.use_fp16:
            # If the grad scale was less than 1, try increasing it.    The _growth_interval
            # of the grad scaler is configurable, but we can't configure it to have different
            # behavior depending on the current grad scale.
            cur_grad_scale = scaler._scale.item()

            if cur_grad_scale < 8.0 or (cur_grad_scale < 32.0 and batch_idx % 400 == 0):
                scaler.update(cur_grad_scale * 2.0)
            if cur_grad_scale < 0.01:
                if not saved_bad_model:
                    save_bad_model(suffix="-first-warning")
                    saved_bad_model = True
                logging.warning(f"Grad scale is small: {cur_grad_scale}")
            if cur_grad_scale < 1.0e-05:
                save_bad_model()
                raise RuntimeError(
                    f"grad_scale is too small, exiting: {cur_grad_scale}"
                )

        if batch_idx % params.log_interval == 0:
            cur_lr = max(scheduler.get_last_lr())
            cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0

            logging.info(
                f"Epoch {params.cur_epoch}, "
                f"batch {batch_idx}, loss[{loss_info}], "
                f"tot_loss[{tot_loss}], batch size: {batch_size}, "
                f"lr: {cur_lr:.2e}, "
                + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "")
            )

            if tb_writer is not None:
                tb_writer.add_scalar(
                    "train/learning_rate", cur_lr, params.batch_idx_train
                )

                loss_info.write_summary(
                    tb_writer, "train/current_", params.batch_idx_train
                )
                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
                if params.use_fp16:
                    tb_writer.add_scalar(
                        "train/grad_scale",
                        cur_grad_scale,
                        params.batch_idx_train,
                    )

        if batch_idx % params.valid_interval == 0 and not params.print_diagnostics:
            logging.info("Computing validation loss")
            valid_info = compute_validation_loss(
                params=params,
                model=model,
                valid_dl=valid_dl,
                world_size=world_size,
            )
            model.train()
            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
            logging.info(
                f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
            )
            if tb_writer is not None:
                valid_info.write_summary(
                    tb_writer, "train/valid_", params.batch_idx_train
                )

    if sub_batch_idx % params.accum_grad != params.accum_grad - 1:
        optimizer.zero_grad()
    loss_value = tot_loss["loss"] / tot_loss["frames"]
    params.train_loss = loss_value
    if params.train_loss < params.best_train_loss:
        params.best_train_epoch = params.cur_epoch
        params.best_train_loss = params.train_loss


def run(rank, world_size, args):
    """
    Args:
      rank:
        It is a value between 0 and `world_size-1`, which is
        passed automatically by `mp.spawn()` in :func:`main`.
        The node with rank 0 is responsible for saving checkpoint.
      world_size:
        Number of GPUs for DDP training.
      args:
        The return value of get_parser().parse_args()
    """
    params = get_params()
    params.update(vars(args))

    fix_random_seed(params.seed)
    if world_size > 1:
        setup_dist(rank, world_size, params.master_port)

    setup_logger(f"{params.exp_dir}/log/log-train")
    logging.info("Training started")

    if args.tensorboard and rank == 0:
        tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
    else:
        tb_writer = None

    device = torch.device("cpu")
    if torch.cuda.is_available():
        device = torch.device("cuda", rank)
    logging.info(f"Device: {device}")
    logging.info(params)

    logging.info("About to create model")
    model = get_model(params)

    num_param = sum([p.numel() for p in model.parameters()])
    logging.info(f"Number of model parameters: {num_param}")

    assert params.save_every_n >= params.average_period
    model_avg: Optional[nn.Module] = None
    if rank == 0:
        # model_avg is only used with rank 0
        model_avg = copy.deepcopy(model).to(torch.float64)

    assert params.start_epoch > 0, params.start_epoch
    checkpoints = load_checkpoint_if_available(
        params=params, model=model, model_avg=model_avg
    )

    model.to(device)
    if world_size > 1:
        logging.info("Using DDP")
        model = DDP(model, device_ids=[rank], find_unused_parameters=True)

    optimizer = ScaledAdam(
        get_parameter_groups_with_lrs(model, lr=params.base_lr, include_names=True),
        lr=params.base_lr,  # should have no effect
        clipping_scale=2.0,
    )

    scheduler = Eden(
        optimizer,
        params.lr_batches,
        params.lr_epochs,
        params.warmup_batches,
        params.warmup_start,
    )

    if checkpoints and "optimizer" in checkpoints:
        logging.info("Loading optimizer state dict")
        optimizer.load_state_dict(checkpoints["optimizer"])

    if (
        checkpoints
        and "scheduler" in checkpoints
        and checkpoints["scheduler"] is not None
    ):
        logging.info("Loading scheduler state dict")
        scheduler.load_state_dict(checkpoints["scheduler"])

    if params.print_diagnostics:
        opts = diagnostics.TensorDiagnosticOptions(
            512
        )  # allow 4 megabytes per sub-module
        diagnostic = diagnostics.attach_diagnostics(model, opts)

    if params.inf_check:
        register_inf_check_hooks(model)

    librispeech = LibriSpeechDataModule(args)

    train_cuts = (
        librispeech.train_all_shuf_cuts()
        if params.full_libri
        else librispeech.train_clean_100_cuts()
    )

    def remove_short_and_long_utt(c: Cut):
        # Keep only utterances with duration between 1 second and 20 seconds
        #
        # Caution: There is a reason to select 20.0 here. Please see
        # ../local/display_manifest_statistics.py
        #
        # You should use ../local/display_manifest_statistics.py to get
        # an utterance duration distribution for your dataset to select
        # the threshold
        if (
            c.duration < params.min_keep_size / params.sample_rate
            or c.duration > params.max_keep_size / params.sample_rate
        ):
            logging.warning(
                f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
            )
            return False

        return True

    train_cuts = train_cuts.filter(remove_short_and_long_utt)

    if params.start_batch > 0 and checkpoints and "sampler" in checkpoints:
        # We only load the sampler's state dict when it loads a checkpoint
        # saved in the middle of an epoch
        sampler_state_dict = checkpoints["sampler"]
    else:
        sampler_state_dict = None

    train_dl = librispeech.train_dataloaders(
        train_cuts,
        max_sample_size=params.max_sample_size,
        sample_rate=params.sample_rate,
        label_rate=params.label_rate,
        random_crop=params.random_crop,
        pad_audio=False,
        num_classes=params.num_classes,
        do_normalize=params.do_normalize,
        sampler_state_dict=sampler_state_dict,
    )

    valid_cuts = librispeech.dev_clean_cuts()
    # valid_cuts += librispeech.dev_other_cuts()
    valid_cuts = valid_cuts.filter(remove_short_and_long_utt)

    valid_dl = librispeech.valid_dataloaders(
        valid_cuts,
        max_sample_size=params.max_sample_size,
        sample_rate=params.sample_rate,
        label_rate=params.label_rate,
        random_crop=params.random_crop,
        pad_audio=False,
        num_classes=params.num_classes,
        do_normalize=params.do_normalize,
    )

    if params.sanity_check and not params.print_diagnostics:
        scan_pessimistic_batches_for_oom(
            model=model,
            train_dl=train_dl,
            optimizer=optimizer,
            params=params,
        )

    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
    if checkpoints and "grad_scaler" in checkpoints:
        logging.info("Loading grad scaler state dict")
        scaler.load_state_dict(checkpoints["grad_scaler"])

    for epoch in range(params.start_epoch, params.num_epochs + 1):
        scheduler.step_epoch(epoch - 1)
        fix_random_seed(params.seed + epoch - 1)
        train_dl.sampler.set_epoch(epoch - 1)

        if tb_writer is not None:
            tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)

        params.cur_epoch = epoch

        train_one_epoch(
            params=params,
            model=model,
            model_avg=model_avg,
            optimizer=optimizer,
            scheduler=scheduler,
            train_dl=train_dl,
            valid_dl=valid_dl,
            scaler=scaler,
            tb_writer=tb_writer,
            world_size=world_size,
            rank=rank,
        )

        if params.print_diagnostics:
            diagnostic.print_diagnostics()
            break

        save_checkpoint(
            params=params,
            model=model,
            model_avg=model_avg,
            optimizer=optimizer,
            scheduler=scheduler,
            sampler=train_dl.sampler,
            scaler=scaler,
            rank=rank,
        )

    logging.info("Done!")

    if world_size > 1:
        torch.distributed.barrier()
        cleanup_dist()


def display_and_save_batch(
    batch: dict,
    params: AttributeDict,
) -> None:
    """Display the batch statistics and save the batch into disk.

    Args:
      batch:
        A batch of data. See `dataset.HubertDataset()`
        for the content in it.
      params:
        Parameters for training. See :func:`get_params`.
      sp:
        The BPE model.
    """
    from lhotse.utils import uuid4

    filename = f"{params.exp_dir}/batch-{uuid4()}.pt"
    logging.info(f"Saving batch to {filename}")
    torch.save(batch, filename)

    audio = batch["audio"]
    logging.info(f"audio shape: {audio.shape}")


def scan_pessimistic_batches_for_oom(
    model: Union[nn.Module, DDP],
    train_dl: torch.utils.data.DataLoader,
    optimizer: torch.optim.Optimizer,
    params: AttributeDict,
):
    from lhotse.dataset import find_pessimistic_batches

    logging.info(
        "Sanity check -- see if any of the batches in epoch 1 would cause OOM."
    )
    batches, crit_values = find_pessimistic_batches(train_dl.sampler)
    for criterion, cuts in batches.items():
        batch = train_dl.dataset[cuts]
        try:
            with torch_autocast(enabled=params.use_fp16):
                loss, _ = compute_loss(
                    params=params,
                    model=model,
                    batch=batch,
                    is_training=True,
                )
            loss.backward()
            optimizer.zero_grad()
        except Exception as e:
            if "CUDA out of memory" in str(e):
                logging.error(
                    "Your GPU ran out of memory with the current "
                    "max_duration setting. We recommend decreasing "
                    "max_duration and trying again.\n"
                    f"Failing criterion: {criterion} "
                    f"(={crit_values[criterion]}) ..."
                )
            display_and_save_batch(batch, params=params)
            raise
        logging.info(
            f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
        )


def main():
    parser = get_parser()
    LibriSpeechDataModule.add_arguments(parser)
    args = parser.parse_args()
    args.exp_dir = Path(args.exp_dir)

    world_size = args.world_size
    assert world_size >= 1
    if world_size > 1:
        mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
    else:
        run(rank=0, world_size=1, args=args)


torch.set_num_threads(1)
torch.set_num_interop_threads(1)

if __name__ == "__main__":
    main()