#!/usr/bin/env python3 # This is just at the very beginning ... import argparse import logging from pathlib import Path from shutil import copyfile from typing import Optional import k2 import torch import torch.distributed as dist import torch.multiprocessing as mp import torch.nn as nn import torch.optim as optim from lhotse.utils import fix_random_seed from model import TdnnLstm from torch.nn.parallel import DistributedDataParallel as DDP from torch.nn.utils import clip_grad_value_ from torch.optim.lr_scheduler import StepLR from torch.utils.tensorboard import SummaryWriter from icefall.checkpoint import load_checkpoint from icefall.checkpoint import save_checkpoint as save_checkpoint_impl from icefall.dataset.librispeech import LibriSpeechAsrDataModule from icefall.dist import cleanup_dist, setup_dist from icefall.graph_compiler import CtcTrainingGraphCompiler from icefall.lexicon import Lexicon from icefall.utils import ( AttributeDict, 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.", ) # TODO: add extra arguments and support DDP training. # Currently, only single GPU training is implemented. Will add # DDP training once single GPU training is finished. 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. - num_epochs: Number of epochs to train. - 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 - 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_lstm_ctc/exp"), "lang_dir": Path("data/lang"), "lr": 1e-3, "feature_dim": 80, "weight_decay": 5e-4, "subsampling_factor": 3, "start_epoch": 0, "num_epochs": 10, "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, "valid_interval": 1000, "beam_size": 10, "reduction": "sum", "use_double_scores": True, } ) return params def load_checkpoint_if_available( params: AttributeDict, model: nn.Module, optimizer: torch.optim.Optimizer, scheduler: 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] 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. """ 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, ): """ 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 TdnnLstm 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] feature = feature.permute(0, 2, 1) # now feature is [N, C, T] 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"] supervision_segments, texts = encode_supervisions( supervisions, subsampling_factor=params.subsampling_factor ) decoding_graph = graph_compiler.compile(texts) dense_fsa_vec = k2.DenseFsaVec( nnet_output, supervision_segments, allow_truncate=params.subsampling_factor - 1, ) 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 # train_frames and valid_frames are used for printing. if is_training: params.train_frames = supervision_segments[:, 2].sum().item() else: params.valid_frames = supervision_segments[:, 2].sum().item() return loss def compute_validation_loss( params: AttributeDict, model: nn.Module, graph_compiler: CtcTrainingGraphCompiler, valid_dl: torch.utils.data.DataLoader, world_size: int = 1, ) -> None: """Run the validation process. The validation loss is saved in `params.valid_loss`. """ model.eval() tot_loss = 0.0 tot_frames = 0.0 for batch_idx, batch in enumerate(valid_dl): loss = compute_loss( params=params, model=model, batch=batch, graph_compiler=graph_compiler, is_training=False, ) assert loss.requires_grad is False loss_cpu = loss.detach().cpu().item() tot_loss += loss_cpu tot_frames += params.valid_frames if world_size > 1: s = torch.tensor([tot_loss, tot_frames], device=loss.device) dist.all_reduce(s, op=dist.ReduceOp.SUM) s = s.cpu().tolist() tot_loss = s[0] tot_frames = s[1] params.valid_loss = tot_loss / tot_frames if params.valid_loss < params.best_valid_loss: params.best_valid_epoch = params.cur_epoch params.best_valid_loss = params.valid_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 = 0.0 # sum of losses over all batches tot_frames = 0.0 # sum of frames over all batches for batch_idx, batch in enumerate(train_dl): params.batch_idx_train += 1 batch_size = len(batch["supervisions"]["text"]) loss = compute_loss( params=params, model=model, batch=batch, graph_compiler=graph_compiler, is_training=True, ) # 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_value_(model.parameters(), 5.0) optimizer.step() loss_cpu = loss.detach().cpu().item() tot_frames += params.train_frames tot_loss += loss_cpu tot_avg_loss = tot_loss / tot_frames if batch_idx % params.log_interval == 0: logging.info( f"Epoch {params.cur_epoch}, batch {batch_idx}, " f"batch avg loss {loss_cpu/params.train_frames:.4f}, " f"total avg loss: {tot_avg_loss:.4f}, " f"batch size: {batch_size}" ) if batch_idx > 0 and batch_idx % params.valid_interval == 0: 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}, valid loss {params.valid_loss:.4f}," f" best valid loss: {params.best_valid_loss:.4f} " f"best valid epoch: {params.best_valid_epoch}" ) params.train_loss = tot_loss / tot_frames 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_phone_id = max(lexicon.tokens) device = torch.device("cpu") if torch.cuda.is_available(): device = torch.device("cuda", rank) graph_compiler = CtcTrainingGraphCompiler(lexicon=lexicon, device=device) model = TdnnLstm( num_features=params.feature_dim, num_classes=max_phone_id + 1, # +1 for the blank symbol subsampling_factor=params.subsampling_factor, ) model.to(device) if world_size > 1: model = DDP(model, device_ids=[rank]) optimizer = optim.AdamW( model.parameters(), lr=params.lr, weight_decay=params.weight_decay, ) scheduler = StepLR(optimizer, step_size=8, gamma=0.1) load_checkpoint_if_available( params=params, model=model, optimizer=optimizer ) librispeech = LibriSpeechAsrDataModule(args) train_dl = librispeech.train_dataloaders() valid_dl = librispeech.valid_dataloaders() for epoch in range(params.start_epoch, params.num_epochs): train_dl.sampler.set_epoch(epoch) if epoch > params.start_epoch: logging.info(f"epoch {epoch}, lr: {scheduler.get_last_lr()[0]}") if tb_writer is not None: tb_writer.add_scalar( "train/lr", scheduler.get_last_lr()[0], params.batch_idx_train, ) 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, ) scheduler.step() save_checkpoint( params=params, model=model, optimizer=optimizer, scheduler=scheduler, rank=rank, ) logging.info("Done!") if world_size > 1: torch.distributed.barrier() cleanup_dist() def main(): parser = get_parser() LibriSpeechAsrDataModule.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()