icefall/egs/himia/wuw/ctc_tdnn/train.py

#!/usr/bin/env python3
# Copyright    2023  Xiaomi Corp.        (Author: Liyong Guo)
#
# 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"
  ./ctc_tdnn/train.py \
     --exp-dir ./ctc_tdnn/exp \
     --world-size 1 \
     --max-duration 100 \
     --num-epochs 20
"""

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

import torch
import torch.multiprocessing as mp
import torch.nn as nn
from asr_datamodule import HiMiaWuwDataModule
from tdnn import Tdnn

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 tokenizer import WakeupWordTokenizer
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.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=20,
        help="Number of epochs to train.",
    )

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

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

    parser.add_argument(
        "--lr-factor",
        type=float,
        default=0.001,
        help="The lr_factor for optimizer",
    )

    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
    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.

        - num_class: Number of classes. Each token will have a token id
                     from [0, num_class).
                     In this recipe, 0 is usually kept for blank,
                     and 1 is usually kept for negative words.
        - wakeup_word:  Text of wakeup word, i.e. positive samples.
        - wakeup_word_tokens: A sequence of token ids corresponding wakeup_word.
        - weight_decay:  The weight_decay for the 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": 5,
            "reset_interval": 200,
            "valid_interval": 3000,
            # parameters for model
            "feature_dim": 80,
            "num_class": 9,
            # parameters for tokenizer
            "wakeup_word": "你好米雅",
            "wakeup_word_tokens": [2, 3, 4, 5, 6, 3, 7, 8],
            # parameters for Optimizer
            "weight_decay": 1e-6,
            "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 larger than 1, 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 > 1:
        filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
    else:
        return None

    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,
    tokenizer: WakeupWordTokenizer,
    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.
      tokenizer:
        For positive samples, map their texts to corresponding token index sequence.
        While for negative samples, map their texts to unknown no matter what they are.
      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
    feature = batch["inputs"]
    # at entry, feature is (N, T, C)
    assert feature.ndim == 3
    N, T, C = feature.shape
    feature = feature.to(device)

    supervisions = batch["supervisions"]
    texts = supervisions["text"]
    with torch.set_grad_enabled(is_training):
        # model_output is log_softmax(logit) with shape [N, T, C]
        model_output = model(feature)

    assert torch.all(supervisions["start_frame"] == 0)
    num_frames = supervisions["num_frames"].to(device)

    target, target_lengths, number_positive_samples = tokenizer.texts_to_token_ids(
        texts
    )  # noqa E501
    target = target.to(device)
    target_lengths = target_lengths.to(device)
    ctc_loss = nn.CTCLoss(reduction="sum")
    # [N, T, C] --> [T, N, C]
    model_output = model_output.transpose(0, 1)
    loss = ctc_loss(model_output, target, num_frames, target_lengths)
    loss /= num_frames.sum()

    assert loss.requires_grad == is_training

    info = MetricsTracker()
    info["frames"] = num_frames.sum().item()

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

    # `utt_duration` and `utt_pad_proportion` would be normalized by `utterances`  # noqa
    info["utterances"] = feature.size(0)
    # averaged input duration in frames over utterances
    info["utt_duration"] = supervisions["num_frames"].sum().item()
    # averaged padding proportion over utterances
    info["utt_pad_proportion"] = (
        ((feature.size(1) - supervisions["num_frames"]) / feature.size(1)).sum().item()
    )

    info["number_positive_cuts_ratio"] = (number_positive_samples / N) * info["frames"]

    return loss, info


def compute_validation_loss(
    params: AttributeDict,
    model: nn.Module,
    tokenizer: WakeupWordTokenizer,
    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,
            tokenizer=tokenizer,
            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,
    tokenizer: WakeupWordTokenizer,
    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.
      tokenizer:
        For positive samples, map their texts to corresponding token index sequence.
        While for negative samples, map their texts to unknown no matter what they are.
      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,
            tokenizer=tokenizer,
            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.

        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,
                tokenizer=tokenizer,
                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")
    logging.info(params)

    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)

    tokenizer = WakeupWordTokenizer(
        wakeup_word=params.wakeup_word,
        wakeup_word_tokens=params.wakeup_word_tokens,
    )

    logging.info("About to create model")

    model = Tdnn(params.feature_dim, params.num_class)

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

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

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

    optimizer = torch.optim.Adam(
        model.parameters(),
        lr=params.lr_factor,
        weight_decay=params.weight_decay,
    )

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

    himia = HiMiaWuwDataModule(args)

    train_cuts = himia.train_cuts()

    train_dl = himia.train_dataloaders(train_cuts)

    valid_cuts = himia.dev_cuts()
    valid_dl = himia.valid_dataloaders(valid_cuts)

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

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

        # TODO: Support lr scheduler
        cur_lr = params.lr_factor
        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,
            tokenizer=tokenizer,
            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,
    tokenizer: WakeupWordTokenizer,
    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,
                tokenizer=tokenizer,
                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()
    HiMiaWuwDataModule.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()