icefall/egs/yesno/ASR/tdnn/train.py

#!/usr/bin/env python3

import argparse
import logging
from pathlib import Path
from shutil import copyfile
from typing import Optional, Tuple

import k2
import torch
import torch.multiprocessing as mp
import torch.nn as nn
import torch.optim as optim
from asr_datamodule import YesNoAsrDataModule
from lhotse.utils import fix_random_seed
from model import Tdnn
from torch import Tensor
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.nn.utils import clip_grad_norm_
from torch.utils.tensorboard import SummaryWriter

from icefall.checkpoint import load_checkpoint
from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
from icefall.dist import cleanup_dist, setup_dist
from icefall.env import get_env_info
from icefall.graph_compiler import CtcTrainingGraphCompiler
from icefall.lexicon import Lexicon
from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool


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=15,
        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
        tdnn/exp/epoch-{start_epoch-1}.pt
        """,
    )

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

    return parser


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

    All training related parameters that are not passed from the commandline
    is 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`:

        - exp_dir: It specifies the directory where all training related
                   files, e.g., checkpoints, log, etc, are saved

        - lang_dir: It contains language related input files such as
                    "lexicon.txt"

        - lr: It specifies the initial learning rate

        - feature_dim: The model input dim. It has to match the one used
                       in computing features.

        - weight_decay:  The weight_decay for the optimizer.

        - subsampling_factor:  The subsampling factor for the model.

        - start_epoch:  If it is not zero, load checkpoint `start_epoch-1`
                        and continue training from that checkpoint.

        - 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

        - valid_interval:  Run validation if batch_idx % valid_interval` is 0

        - reset_interval: Reset statistics if batch_idx % reset_interval is 0

        - beam_size: It is used in k2.ctc_loss

        - reduction: It is used in k2.ctc_loss

        - use_double_scores: It is used in k2.ctc_loss
    """
    params = AttributeDict(
        {
            "exp_dir": Path("tdnn/exp"),
            "lang_dir": Path("data/lang_phone"),
            "lr": 1e-2,
            "feature_dim": 23,
            "weight_decay": 1e-6,
            "start_epoch": 0,
            "best_train_loss": float("inf"),
            "best_valid_loss": float("inf"),
            "best_train_epoch": -1,
            "best_valid_epoch": -1,
            "batch_idx_train": 0,
            "log_interval": 10,
            "reset_interval": 20,
            "valid_interval": 10,
            "beam_size": 10,
            "reduction": "sum",
            "use_double_scores": True,
        }
    )

    return params


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

    If params.start_epoch is positive, it will load the checkpoint from
    `params.start_epoch - 1`. Otherwise, this function does nothing.

    Apart from loading state dict for `model`, `optimizer` and `scheduler`,
    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 learning rate scheduler we are using.
    Returns:
      Return None.
    """
    if params.start_epoch <= 0:
        return

    filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
    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]

    return saved_params


def save_checkpoint(
    params: AttributeDict,
    model: nn.Module,
    optimizer: torch.optim.Optimizer,
    scheduler: torch.optim.lr_scheduler._LRScheduler,
    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.
    """
    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,
        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,
    batch: dict,
    graph_compiler: CtcTrainingGraphCompiler,
    is_training: bool,
) -> Tuple[Tensor, MetricsTracker]:
    """
    Compute CTC 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 Tdnn in our case.
      batch:
        A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
        for the content in it.
      graph_compiler:
        It is used to build a decoding graph from a ctc topo and training
        transcript. The training transcript is contained in the given `batch`,
        while the ctc topo is built when this compiler is instantiated.
      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 = graph_compiler.device
    feature = batch["inputs"]
    # at entry, feature is (N, T, C)
    assert feature.ndim == 3
    feature = feature.to(device)

    with torch.set_grad_enabled(is_training):
        nnet_output = model(feature)
        # nnet_output is (N, T, C)

    # NOTE: We need `encode_supervisions` to sort sequences with
    # different duration in decreasing order, required by
    # `k2.intersect_dense` called in `k2.ctc_loss`
    supervisions = batch["supervisions"]
    texts = supervisions["text"]

    batch_size = nnet_output.shape[0]
    supervision_segments = torch.tensor(
        [[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
        dtype=torch.int32,
    )

    decoding_graph = graph_compiler.compile(texts)

    dense_fsa_vec = k2.DenseFsaVec(
        nnet_output,
        supervision_segments,
    )

    loss = k2.ctc_loss(
        decoding_graph=decoding_graph,
        dense_fsa_vec=dense_fsa_vec,
        output_beam=params.beam_size,
        reduction=params.reduction,
        use_double_scores=params.use_double_scores,
    )

    assert loss.requires_grad == is_training

    info = MetricsTracker()
    info["frames"] = supervision_segments[:, 2].sum().item()
    info["loss"] = loss.detach().cpu().item()

    return loss, info


def compute_validation_loss(
    params: AttributeDict,
    model: nn.Module,
    graph_compiler: CtcTrainingGraphCompiler,
    valid_dl: torch.utils.data.DataLoader,
    world_size: int = 1,
) -> MetricsTracker:
    """Run the validation process. The validation loss
    is saved in `params.valid_loss`.
    """
    model.eval()

    tot_loss = MetricsTracker()

    for batch_idx, batch in enumerate(valid_dl):
        loss, loss_info = compute_loss(
            params=params,
            model=model,
            batch=batch,
            graph_compiler=graph_compiler,
            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,
    graph_compiler: CtcTrainingGraphCompiler,
    train_dl: torch.utils.data.DataLoader,
    valid_dl: torch.utils.data.DataLoader,
    tb_writer: Optional[SummaryWriter] = None,
    world_size: int = 1,
) -> 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.
      graph_compiler:
        It is used to convert transcripts to FSAs.
      train_dl:
        Dataloader for the training dataset.
      valid_dl:
        Dataloader for the validation dataset.
      tb_writer:
        Writer to write log messages to tensorboard.
      world_size:
        Number of nodes in DDP training. If it is 1, DDP is disabled.
    """
    model.train()

    tot_loss = MetricsTracker()

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

        loss, loss_info = compute_loss(
            params=params,
            model=model,
            batch=batch,
            graph_compiler=graph_compiler,
            is_training=True,
        )
        # summary stats.
        tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info

        optimizer.zero_grad()
        loss.backward()
        clip_grad_norm_(model.parameters(), 5.0, 2.0)
        optimizer.step()

        if batch_idx % params.log_interval == 0:
            logging.info(
                f"Epoch {params.cur_epoch}, "
                f"batch {batch_idx}, loss[{loss_info}], "
                f"tot_loss[{tot_loss}], batch size: {batch_size}"
            )
        if batch_idx % params.log_interval == 0:

            if tb_writer is not None:
                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 > 0 and batch_idx % params.valid_interval == 0:
            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))
    params["env_info"] = get_env_info()

    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")
    logging.info(params)

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

    lexicon = Lexicon(params.lang_dir)
    max_phone_id = max(lexicon.tokens)

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

    graph_compiler = CtcTrainingGraphCompiler(lexicon=lexicon, device=device)

    model = Tdnn(
        num_features=params.feature_dim,
        num_classes=max_phone_id + 1,  # +1 for the blank symbol
    )

    checkpoints = load_checkpoint_if_available(params=params, model=model)

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

    optimizer = optim.SGD(
        model.parameters(),
        lr=params.lr,
        weight_decay=params.weight_decay,
    )

    if checkpoints:
        optimizer.load_state_dict(checkpoints["optimizer"])

    yes_no = YesNoAsrDataModule(args)
    train_dl = yes_no.train_dataloaders()

    # There are only 60 waves: 30 files are used for training
    # and the remaining 30 files are used for testing.
    # We use test data as validation.
    valid_dl = yes_no.test_dataloaders()

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

        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,
            graph_compiler=graph_compiler,
            train_dl=train_dl,
            valid_dl=valid_dl,
            tb_writer=tb_writer,
            world_size=world_size,
        )

        save_checkpoint(
            params=params,
            model=model,
            optimizer=optimizer,
            scheduler=None,
            rank=rank,
        )

    logging.info("Done!")
    if world_size > 1:
        torch.distributed.barrier()
        cleanup_dist()


def main():
    parser = get_parser()
    YesNoAsrDataModule.add_arguments(parser)
    args = parser.parse_args()

    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)


if __name__ == "__main__":
    main()