icefall/egs/mgb2/ASR/conformer_ctc/train.py

#!/usr/bin/env python3
# Copyright 2022 Johns Hopkins University  (Amir Hussein)
# Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)


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
from asr_datamodule import MGB2AsrDataModule
from conformer import Conformer
from lhotse.cut import Cut
from lhotse.utils import fix_random_seed
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 transformer import Noam

from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
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.lexicon import Lexicon
from icefall.utils import (
    AttributeDict,
    MetricsTracker,
    encode_supervisions,
    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=50,
        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
        conformer_ctc/exp/epoch-{start_epoch-1}.pt
        """,
    )

    parser.add_argument(
        "--exp-dir",
        type=str,
        default="conformer_ctc/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_bpe_500",
        help="""The lang dir
        It contains language related input files such as
        "lexicon.txt"
        """,
    )

    parser.add_argument(
        "--att-rate",
        type=float,
        default=0.8,
        help="""The attention rate.
        The total loss is (1 -  att_rate) * ctc_loss + att_rate * att_loss
        """,
    )

    parser.add_argument(
        "--num-decoder-layers",
        type=int,
        default=6,
        help="""Number of decoder layer of transformer decoder.
        Setting this to 0 will not create the decoder at all (pure CTC model)
        """,
    )

    parser.add_argument(
        "--lr-factor",
        type=float,
        default=5.0,
        help="The lr_factor for Noam optimizer",
    )

    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

        - valid_interval:  Run validation if batch_idx % valid_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.

        - use_feat_batchnorm: Normalization for the input features, can be a
                              boolean indicating whether to do batch
                              normalization, or a float which means just scaling
                              the input features with this float value.
                              If given a float value, we will remove batchnorm
                              layer in `ConvolutionModule` as well.

        - attention_dim: Hidden dim for multi-head attention model.

        - head: Number of heads of multi-head attention model.

        - num_decoder_layers: Number of decoder layer of transformer decoder.

        - 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

        - weight_decay:  The weight_decay for the optimizer.

        - 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,
            "valid_interval": 3000,
            # parameters for conformer
            "feature_dim": 80,
            "subsampling_factor": 4,
            "use_feat_batchnorm": True,
            "attention_dim": 512,
            "nhead": 8,
            "num_decoder_layers": 6,
            # parameters for loss
            "beam_size": 10,
            "reduction": "sum",
            "use_double_scores": True,
            # parameters for Noam
            "weight_decay": 1e-6,
            "warm_step": 80000,
            "env_info": get_env_info(),
        }
    )

    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: Optional[torch.optim.Optimizer] = None,
    scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = 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.
    """
    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: BpeCtcTrainingGraphCompiler,
    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 Conformer 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)

    supervisions = batch["supervisions"]
    with torch.set_grad_enabled(is_training):
        nnet_output, encoder_memory, memory_mask = model(feature, supervisions)
        # 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`
    supervision_segments, texts = encode_supervisions(
        supervisions, subsampling_factor=params.subsampling_factor
    )

    token_ids = graph_compiler.texts_to_ids(texts)

    decoding_graph = graph_compiler.compile(token_ids)

    dense_fsa_vec = k2.DenseFsaVec(
        nnet_output,
        supervision_segments,
        allow_truncate=params.subsampling_factor - 1,
    )

    ctc_loss = k2.ctc_loss(
        decoding_graph=decoding_graph,
        dense_fsa_vec=dense_fsa_vec,
        output_beam=params.beam_size,
        reduction="none",
        use_double_scores=params.use_double_scores,
    )
    # filter inf from ctc_loss
    ctc_loss = torch.sum(
        torch.where(
            ctc_loss != float("inf"),
            ctc_loss,
            torch.tensor(0, dtype=torch.float32).to(device),
        )
    )

    if params.att_rate != 0.0:
        with torch.set_grad_enabled(is_training):
            mmodel = model.module if hasattr(model, "module") else model
            # Note: We need to generate an unsorted version of token_ids
            # `encode_supervisions()` called above sorts text, but
            # encoder_memory and memory_mask are not sorted, so we
            # use an unsorted version `supervisions["text"]` to regenerate
            # the token_ids
            #
            # See https://github.com/k2-fsa/icefall/issues/97
            # for more details
            unsorted_token_ids = graph_compiler.texts_to_ids(supervisions["text"])

            att_loss = mmodel.decoder_forward(
                encoder_memory,
                memory_mask,
                token_ids=unsorted_token_ids,
                sos_id=graph_compiler.sos_id,
                eos_id=graph_compiler.eos_id,
            )
        loss = (1.0 - params.att_rate) * ctc_loss + params.att_rate * att_loss
    else:
        loss = ctc_loss
        att_loss = torch.tensor([0])

    assert loss.requires_grad == is_training

    info = MetricsTracker()
    info["frames"] = supervision_segments[:, 2].sum().item()
    info["ctc_loss"] = ctc_loss.detach().cpu().item()
    if params.att_rate != 0.0:
        info["att_loss"] = att_loss.detach().cpu().item()

    info["loss"] = loss.detach().cpu().item()

    return loss, info


def compute_validation_loss(
    params: AttributeDict,
    model: nn.Module,
    graph_compiler: BpeCtcTrainingGraphCompiler,
    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,
            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: BpeCtcTrainingGraphCompiler,
    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):
        if batch["inputs"].shape[0] == len(batch["supervisions"]["text"]):
            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
            # if tot_loss is None:
            #     logging.warning("Batch mismatch. Skipping ...")
            #     del batch
            #     del tot_loss
            #     continue;
            # elif tot_loss.isinf() or tot_loss.isnan():
            #     logging.warning("NaN or Inf loss. Skipping ...")
            #     del batch
            #     del tot_loss
            #     continue;
            # NOTE: We use reduction==sum and loss is computed over utterances
            # in the batch and there is no normalization to it so far.

            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:
                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
                    )
        else:
            logging.warning(
                f"Batch {batch_idx} mismatch in dimentions between the input and the output. Skipping ..."
            )
            continue
    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(42)
    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_token_id = max(lexicon.tokens)
    num_classes = max_token_id + 1  # +1 for the blank

    device = torch.device("cpu")
    if torch.cuda.is_available():
        device = torch.device("cuda", rank)

    graph_compiler = BpeCtcTrainingGraphCompiler(
        params.lang_dir,
        device=device,
        sos_token="<sos/eos>",
        eos_token="<sos/eos>",
    )

    logging.info("About to create model")
    model = Conformer(
        num_features=params.feature_dim,
        nhead=params.nhead,
        d_model=params.attention_dim,
        num_classes=num_classes,
        subsampling_factor=params.subsampling_factor,
        num_decoder_layers=params.num_decoder_layers,
        vgg_frontend=False,
        use_feat_batchnorm=params.use_feat_batchnorm,
    )

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

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

    optimizer = Noam(
        model.parameters(),
        model_size=params.attention_dim,
        factor=params.lr_factor,
        warm_step=params.warm_step,
        weight_decay=params.weight_decay,
    )

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

    MGB2 = MGB2AsrDataModule(args)

    train_cuts = MGB2.train_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
        return 0.5 <= c.duration <= 30.0

    train_cuts = train_cuts.filter(remove_short_and_long_utt)
    train_dl = MGB2.train_dataloaders(train_cuts)

    valid_cuts = MGB2.dev_cuts()
    valid_dl = MGB2.test_dataloaders(valid_cuts)

    scan_pessimistic_batches_for_oom(
        model=model,
        train_dl=train_dl,
        optimizer=optimizer,
        graph_compiler=graph_compiler,
        params=params,
    )

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

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

        if rank == 0:
            logging.info("epoch {}, learning rate {}".format(epoch, cur_lr))

        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,
            rank=rank,
        )

    logging.info("Done!")

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


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

    logging.info(
        "Sanity check -- see if any of the batches in epoch 0 would cause OOM."
    )
    batches, crit_values = find_pessimistic_batches(train_dl.sampler)
    for criterion, cuts in batches.items():
        batch = train_dl.dataset[cuts]
        try:
            optimizer.zero_grad()
            loss, _ = compute_loss(
                params=params,
                model=model,
                batch=batch,
                graph_compiler=graph_compiler,
                is_training=True,
            )
            loss.backward()
            clip_grad_norm_(model.parameters(), 5.0, 2.0)
            optimizer.step()
        except RuntimeError 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]}) ..."
                )
            raise


def main():
    parser = get_parser()
    MGB2AsrDataModule.add_arguments(parser)
    args = parser.parse_args()
    args.exp_dir = Path(args.exp_dir)
    args.lang_dir = Path(args.lang_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()