icefall/egs/wenetspeech/ASR/pruned_transducer_stateless2/finetune.py

#!/usr/bin/env python3
# Copyright    2021-2022  Xiaomi Corp.        (authors: Xiaoyu Yang,
#
# 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"

./pruned_transducer_stateless2/finetune.py \
  --world-size 4 \
  --num-epochs 30 \
  --start-epoch 1 \
  --exp-dir pruned_transducer_stateless2/exp \
  --full-libri 1 \
  --do-finetune 1 \
  --max-duration 100

"""


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

import k2
import optim
import torch
import torch.multiprocessing as mp
import torch.nn as nn
from aishell import AishellAsrDataModule
from conformer import Conformer
from decoder import Decoder
from joiner import Joiner
from lhotse.dataset.sampling.base import CutSampler
from lhotse.utils import fix_random_seed
from model import Transducer
from optim import Eden, Eve
from torch import Tensor
from torch.cuda.amp import GradScaler
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.tensorboard import SummaryWriter

from icefall import diagnostics
from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
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
from icefall.dist import cleanup_dist, setup_dist
from icefall.env import get_env_info
from icefall.lexicon import Lexicon
from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool

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


def add_finetune_arguments(parser: argparse.ArgumentParser):
    parser.add_argument("--do-finetune", type=str2bool, default=False)

    parser.add_argument(
        "--init-modules",
        type=str,
        default=None,
        help="""
        Modules to be initialized. It matches all parameters starting with
        a specific key. The keys are given with Comma separated. If None,
        all modules will be initialised. For example, if you only want to
        initialise all parameters staring with "encoder", use "encoder";
        if you want to initialise parameters starting with encoder or decoder,
        use "encoder,joiner".
        """,
    )

    parser.add_argument(
        "--finetune-ckpt",
        type=str,
        default=None,
        help="Fine-tuning from which checkpoint (a path to a .pt file)",
    )


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=30,
        help="Number of epochs to train.",
    )

    parser.add_argument(
        "--start-epoch",
        type=int,
        default=0,
        help="""Resume training from from this epoch.
        If it is positive, it will load checkpoint from
        pruned_transducer_stateless2/exp/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="pruned_transducer_stateless2/exp",
        help="""The experiment dir.
        It specifies the directory where all training related
        files, e.g., checkpoints, log, etc, are saved
        """,
    )

    parser.add_argument(
        "--lang-dir",
        type=str,
        default="data/lang_char",
        help="""The lang dir
        It contains language related input files such as
        "lexicon.txt"
        """,
    )

    parser.add_argument(
        "--initial-lr",
        type=float,
        default=0.0001,
        help="The initial learning rate.  This value should not need to be changed.",
    )

    parser.add_argument(
        "--lr-batches",
        type=float,
        default=100000,
        help="""Number of steps that affects how rapidly the learning rate
        decreases. During fine-tuning, we set this very large so that the
        learning rate slowly decays with number of batches. You may tune
        its value by yourself.
        """,
    )

    parser.add_argument(
        "--lr-epochs",
        type=float,
        default=100,
        help="""Number of epochs that affects how rapidly the learning rate
        decreases. During fine-tuning, we set this very large so that the
        learning rate slowly decays with number of batches. You may tune
        its value by yourself.
        """,
    )

    parser.add_argument(
        "--context-size",
        type=int,
        default=1,
        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
    )

    parser.add_argument(
        "--prune-range",
        type=int,
        default=5,
        help="The prune range for rnnt loss, it means how many symbols(context)"
        "we are using to compute the loss",
    )

    parser.add_argument(
        "--lm-scale",
        type=float,
        default=0.25,
        help="The scale to smooth the loss with lm "
        "(output of prediction network) part.",
    )

    parser.add_argument(
        "--am-scale",
        type=float,
        default=0.0,
        help="The scale to smooth the loss with am (output of encoder network) part.",
    )

    parser.add_argument(
        "--simple-loss-scale",
        type=float,
        default=0.5,
        help="To get pruning ranges, we will calculate a simple version"
        "loss(joiner is just addition), this simple loss also uses for"
        "training (as a regularization item). We will scale the simple loss"
        "with this parameter before adding to the final loss.",
    )

    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(
        "--save-every-n",
        type=int,
        default=2000,
        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 0.
        """,
    )

    parser.add_argument(
        "--keep-last-k",
        type=int,
        default=20,
        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(
        "--use-fp16",
        type=str2bool,
        default=False,
        help="Whether to use half precision training.",
    )

    parser.add_argument(
        "--valid-interval",
        type=int,
        default=3000,
        help="""When training_subset is L, set the valid_interval to 3000.
        When training_subset is M, set the valid_interval to 1000.
        When training_subset is S, set the valid_interval to 400.
        """,
    )

    parser.add_argument(
        "--model-warm-step",
        type=int,
        default=3000,
        help="""When training_subset is L, set the model_warm_step to 3000.
        When training_subset is M, set the model_warm_step to 500.
        When training_subset is S, set the model_warm_step to 100.
        """,
    )

    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 batches 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
        - feature_dim: The model input dim. It has to match the one used
                       in computing features.
        - subsampling_factor:  The subsampling factor for the model.
        - encoder_dim: Hidden dim for multi-head attention model.
        - num_decoder_layers: Number of decoder layer of transformer decoder.
        - warm_step: The warm_step for Noam optimizer.
    """
    params = AttributeDict(
        {
            "best_train_loss": float("inf"),
            "best_valid_loss": float("inf"),
            "best_train_epoch": -1,
            "best_valid_epoch": -1,
            "batch_idx_train": 0,
            "log_interval": 50,
            "reset_interval": 200,
            # parameters for conformer
            "feature_dim": 80,
            "subsampling_factor": 4,
            "encoder_dim": 512,
            "nhead": 8,
            "dim_feedforward": 2048,
            "num_encoder_layers": 12,
            # parameters for decoder
            "decoder_dim": 512,
            # parameters for joiner
            "joiner_dim": 512,
            "env_info": get_env_info(),
        }
    )

    return params


def get_encoder_model(params: AttributeDict) -> nn.Module:
    # TODO: We can add an option to switch between Conformer and Transformer
    encoder = Conformer(
        num_features=params.feature_dim,
        subsampling_factor=params.subsampling_factor,
        d_model=params.encoder_dim,
        nhead=params.nhead,
        dim_feedforward=params.dim_feedforward,
        num_encoder_layers=params.num_encoder_layers,
    )
    return encoder


def get_decoder_model(params: AttributeDict) -> nn.Module:
    decoder = Decoder(
        vocab_size=params.vocab_size,
        decoder_dim=params.decoder_dim,
        blank_id=params.blank_id,
        context_size=params.context_size,
    )
    return decoder


def get_joiner_model(params: AttributeDict) -> nn.Module:
    joiner = Joiner(
        encoder_dim=params.encoder_dim,
        decoder_dim=params.decoder_dim,
        joiner_dim=params.joiner_dim,
        vocab_size=params.vocab_size,
    )
    return joiner


def get_transducer_model(params: AttributeDict) -> nn.Module:
    encoder = get_encoder_model(params)
    decoder = get_decoder_model(params)
    joiner = get_joiner_model(params)

    model = Transducer(
        encoder=encoder,
        decoder=decoder,
        joiner=joiner,
        encoder_dim=params.encoder_dim,
        decoder_dim=params.decoder_dim,
        joiner_dim=params.joiner_dim,
        vocab_size=params.vocab_size,
    )
    return model


def load_checkpoint_if_available(
    params: AttributeDict,
    model: nn.Module,
    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 positive, 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.
      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 > 0:
        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,
        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 load_model_params(
    ckpt: str, model: nn.Module, init_modules: List[str] = None, strict: bool = True
):
    """Load model params from checkpoint

    Args:
        ckpt (str): Path to the checkpoint
        model (nn.Module): model to be loaded

    """
    logging.info(f"Loading checkpoint from {ckpt}")
    checkpoint = torch.load(ckpt, map_location="cpu", weights_only=False)

    # if module list is empty, load the whole model from ckpt
    if not init_modules:
        if next(iter(checkpoint["model"])).startswith("module."):
            logging.info("Loading checkpoint saved by DDP")

            dst_state_dict = model.state_dict()
            src_state_dict = checkpoint["model"]
            for key in dst_state_dict.keys():
                src_key = "{}.{}".format("module", key)
                dst_state_dict[key] = src_state_dict.pop(src_key)
            assert len(src_state_dict) == 0
            model.load_state_dict(dst_state_dict, strict=strict)
        else:
            model.load_state_dict(checkpoint["model"], strict=strict)
    else:
        src_state_dict = checkpoint["model"]
        dst_state_dict = model.state_dict()
        for module in init_modules:
            logging.info(f"Loading parameters starting with prefix {module}")
            src_keys = [
                k for k in src_state_dict.keys() if k.startswith(module.strip() + ".")
            ]
            dst_keys = [
                k for k in dst_state_dict.keys() if k.startswith(module.strip() + ".")
            ]
            assert set(src_keys) == set(dst_keys)  # two sets should match exactly
            for key in src_keys:
                dst_state_dict[key] = src_state_dict.pop(key)

        model.load_state_dict(dst_state_dict, strict=strict)

    return None


def save_checkpoint(
    params: AttributeDict,
    model: nn.Module,
    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.
      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,
        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: nn.Module,
    graph_compiler: CharCtcTrainingGraphCompiler,
    batch: dict,
    is_training: bool,
    warmup: float = 1.0,
) -> Tuple[Tensor, MetricsTracker]:
    """
    Compute transducer 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 `lhotse.dataset.K2SpeechRecognitionDataset()`
        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.
     warmup: a floating point value which increases throughout training;
        values >= 1.0 are fully warmed up and have all modules present.
    """
    device = model.device
    feature = batch["inputs"]
    # at entry, feature is (N, T, C)
    assert feature.ndim == 3
    feature = feature.to(device)

    supervisions = batch["supervisions"]
    feature_lens = supervisions["num_frames"].to(device)

    texts = batch["supervisions"]["text"]

    y = graph_compiler.texts_to_ids(texts)
    if isinstance(y, list):
        y = k2.RaggedTensor(y).to(device)
    else:
        y = y.to(device)

    with torch.set_grad_enabled(is_training):
        simple_loss, pruned_loss = model(
            x=feature,
            x_lens=feature_lens,
            y=y,
            prune_range=params.prune_range,
            am_scale=params.am_scale,
            lm_scale=params.lm_scale,
            warmup=warmup,
        )
        # after the main warmup step, we keep pruned_loss_scale small
        # for the same amount of time (model_warm_step), to avoid
        # overwhelming the simple_loss and causing it to diverge,
        # in case it had not fully learned the alignment yet.
        pruned_loss_scale = (
            0.0 if warmup < 1.0 else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0)
        )
        loss = params.simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
    assert loss.requires_grad == is_training

    info = MetricsTracker()
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        info["frames"] = (feature_lens // params.subsampling_factor).sum().item()

    # Note: We use reduction=sum while computing the loss.
    info["loss"] = loss.detach().cpu().item()
    info["simple_loss"] = simple_loss.detach().cpu().item()
    info["pruned_loss"] = pruned_loss.detach().cpu().item()

    return loss, info


def compute_validation_loss(
    params: AttributeDict,
    model: nn.Module,
    graph_compiler: CharCtcTrainingGraphCompiler,
    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,
            graph_compiler=graph_compiler,
            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: nn.Module,
    optimizer: torch.optim.Optimizer,
    scheduler: LRSchedulerType,
    graph_compiler: CharCtcTrainingGraphCompiler,
    train_dl: torch.utils.data.DataLoader,
    valid_dl: torch.utils.data.DataLoader,
    scaler: GradScaler,
    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.
      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()

    for batch_idx, batch in enumerate(train_dl):
        params.batch_idx_train += 1
        batch_size = len(batch["supervisions"]["text"])

        try:
            with torch.cuda.amp.autocast(enabled=params.use_fp16):
                loss, loss_info = compute_loss(
                    params=params,
                    model=model,
                    graph_compiler=graph_compiler,
                    batch=batch,
                    is_training=True,
                    warmup=(params.batch_idx_train / params.model_warm_step),
                )
            # 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).backward()
            scheduler.step_batch(params.batch_idx_train)
            scaler.step(optimizer)
            scaler.update()
            optimizer.zero_grad()
        except:  # noqa
            display_and_save_batch(batch, params=params, graph_compiler=graph_compiler)
            raise

        if params.print_diagnostics and batch_idx == 5:
            return

        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,
                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 % params.log_interval == 0:
            cur_lr = scheduler.get_last_lr()[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}"
            )

            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 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,
                graph_compiler=graph_compiler,
                valid_dl=valid_dl,
                world_size=world_size,
            )
            model.train()
            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
            if tb_writer is not None:
                valid_info.write_summary(
                    tb_writer, "train/valid_", params.batch_idx_train
                )

    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}")

    lexicon = Lexicon(params.lang_dir)
    graph_compiler = CharCtcTrainingGraphCompiler(
        lexicon=lexicon,
        device=device,
    )

    params.blank_id = lexicon.token_table["<blk>"]
    params.vocab_size = max(lexicon.tokens) + 1

    logging.info(params)

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

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

    # load model parameters for model fine-tuning
    if params.do_finetune:
        modules = params.init_modules.split(",") if params.init_modules else None
        checkpoints = load_model_params(
            ckpt=params.finetune_ckpt, model=model, init_modules=modules
        )
    else:
        assert params.start_epoch > 0, params.start_epoch
        checkpoints = load_checkpoint_if_available(params=params, model=model)

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

    optimizer = Eve(model.parameters(), lr=params.initial_lr)

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

    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)

    aishell = AishellAsrDataModule(args)
    train_dl = aishell.train_dataloaders(aishell.train_cuts())
    valid_dl = aishell.valid_dataloaders(aishell.valid_cuts())

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

    scaler = GradScaler(enabled=params.use_fp16)
    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,
            optimizer=optimizer,
            scheduler=scheduler,
            graph_compiler=graph_compiler,
            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,
            optimizer=optimizer,
            scheduler=scheduler,
            sampler=train_dl.sampler,
            rank=rank,
        )

    logging.info("Done!")

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


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

    Args:
      batch:
        A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
        for the content in it.
      params:
        Parameters for training. See :func:`get_params`.
    """
    from lhotse.utils import uuid4

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

    supervisions = batch["supervisions"]
    features = batch["inputs"]

    logging.info(f"features shape: {features.shape}")

    y = graph_compiler.texts_to_ids(supervisions["text"])
    num_tokens = sum(len(i) for i in y)
    logging.info(f"num tokens: {num_tokens}")


def scan_pessimistic_batches_for_oom(
    model: Union[nn.Module, DDP],
    train_dl: torch.utils.data.DataLoader,
    optimizer: torch.optim.Optimizer,
    graph_compiler: CharCtcTrainingGraphCompiler,
    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:
            # warmup = 0.0 is so that the derivs for the pruned loss stay zero
            # (i.e. are not remembered by the decaying-average in adam), because
            # we want to avoid these params being subject to shrinkage in adam.
            with torch.cuda.amp.autocast(enabled=params.use_fp16):
                loss, _ = compute_loss(
                    params=params,
                    model=model,
                    graph_compiler=graph_compiler,
                    batch=batch,
                    is_training=True,
                    warmup=0.0 if params.start_epoch == 1 else 1.0,
                )
            loss.backward()
            optimizer.step()
            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, graph_compiler=graph_compiler)
            raise


def main():
    parser = get_parser()
    AishellAsrDataModule.add_arguments(
        parser
    )  # you may replace this with your own dataset
    add_finetune_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()