# -*- coding: utf-8 -*- import importlib import multiprocessing import os import platform import sys import time import traceback from argparse import Namespace from dataclasses import dataclass, field from inspect import signature from typing import Callable, Dict, List, Tuple, Union import torch import torch.distributed as dist from coqpit import Coqpit from torch import nn from torch.nn.parallel import DistributedDataParallel as DDP_th from torch.utils.data import DataLoader from TTS.utils.callbacks import TrainerCallback from TTS.utils.distribute import init_distributed from TTS.utils.generic_utils import ( KeepAverage, count_parameters, get_experiment_folder_path, get_git_branch, remove_experiment_folder, set_init_dict, to_cuda, ) from TTS.utils.io import copy_model_files, load_fsspec, save_best_model, save_checkpoint from TTS.utils.logging import ConsoleLogger, TensorboardLogger, WandbLogger, init_dashboard_logger from TTS.utils.trainer_utils import ( get_last_checkpoint, get_optimizer, get_scheduler, is_apex_available, setup_torch_training_env, ) multiprocessing.set_start_method("fork") if platform.system() != "Windows": # https://github.com/pytorch/pytorch/issues/973 import resource rlimit = resource.getrlimit(resource.RLIMIT_NOFILE) resource.setrlimit(resource.RLIMIT_NOFILE, (4096, rlimit[1])) if is_apex_available(): from apex import amp @dataclass class TrainingArgs(Coqpit): """Trainer arguments to be defined externally. It helps integrating the `Trainer` with the higher level APIs and set the values for distributed training.""" continue_path: str = field( default="", metadata={ "help": "Path to a training folder to continue training. Restore the model from the last checkpoint and continue training under the same folder." }, ) restore_path: str = field( default="", metadata={ "help": "Path to a model checkpoit. Restore the model with the given checkpoint and start a new training." }, ) best_path: str = field( default="", metadata={ "help": "Best model file to be used for extracting the best loss. If not specified, the latest best model in continue path is used" }, ) skip_train_epoch: bool = field( default=False, metadata={"help": "Run only evaluation iteration. Useful for debugging."} ) config_path: str = field(default="", metadata={"help": "Path to the configuration file."}) rank: int = field(default=0, metadata={"help": "Process rank in distributed training."}) group_id: str = field(default="", metadata={"help": "Process group id in distributed training."}) use_ddp: bool = field( default=False, metadata={"help": "Use DDP in distributed training. It is to set in `distribute.py`. Do not set manually."}, ) class Trainer: def __init__( # pylint: disable=dangerous-default-value self, args: Union[Coqpit, Namespace], config: Coqpit, output_path: str, c_logger: ConsoleLogger = None, dashboard_logger: Union[TensorboardLogger, WandbLogger] = None, model: nn.Module = None, get_model: Callable = None, get_data_samples: Callable = None, train_samples: List = None, eval_samples: List = None, cudnn_benchmark: bool = False, training_assets: Dict = {}, parse_command_line_args: bool = True, ) -> None: """Simple yet powerful 🐸💬 TTS trainer for PyTorch. It can train all the available `tts` and `vocoder` models or easily be customized. Notes: Supports Automatic Mixed Precision training. If `Apex` is availabe, it automatically picks that, else it uses PyTorch's native `amp` module. `Apex` may provide more stable training in some cases. Args: args (Union[Coqpit, Namespace]): Training arguments parsed either from console by `argparse` or `TrainingArgs` config object. config (Coqpit): Model config object. It includes all the values necessary for initializing, training, evaluating and testing the model. output_path (str): Path to the output training folder. All the files are saved under thi path. c_logger (ConsoleLogger, optional): Console logger for printing training status. If not provided, the default console logger is used. Defaults to None. dashboard_logger Union[TensorboardLogger, WandbLogger]: Dashboard logger. If not provided, the tensorboard logger is used. Defaults to None. model (nn.Module, optional): Initialized and ready-to-train model. If it is not defined, `Trainer` initializes a model from the provided config. Defaults to None. get_model (Callable): A function that returns a model. It is used to initialize the model when `model` is not provided. It either takes the config as the only argument or does not take any argument. Defaults to None get_data_samples (Callable): A function that returns a list of training and evaluation samples. Used if `train_samples` and `eval_samples` are None. Defaults to None. train_samples (List): A list of training samples used by the model's `get_data_loader` to init the `dataset` and the `data_loader`. Defaults to None. eval_samples (List): A list of evaluation samples used by the model's `get_data_loader` to init the `dataset` and the `data_loader`. Defaults to None. cudnn_benchmark (bool): enable/disable PyTorch cudnn benchmarking. It is better to disable if the model input length is changing batch to batch along the training. training_assets (Dict): A dictionary of assets to be used at training and passed to the model's ```train_log(), eval_log(), get_data_loader()``` during training. It can include `AudioProcessor` or/and `Tokenizer`. Defaults to {}. parse_command_line_args (bool): If true, parse command-line arguments and update `TrainingArgs` and model `config` values. Set it to false if you parse the arguments yourself. Defaults to True. Examples: Running trainer with HifiGAN model. >>> args = TrainingArgs(...) >>> config = HifiganConfig(...) >>> model = GANModel(config) >>> ap = AudioProcessor(**config.audio) >>> assets = {"audio_processor": ap} >>> trainer = Trainer(args, config, output_path, model=model, training_assets=assets) >>> trainer.fit() TODO: - Wrap model for not calling .module in DDP. - Accumulate gradients b/w batches. - Deepspeed integration - Profiler integration. - Overfitting to a batch. - TPU training - NOTE: Consider moving `training_assets` to the model implementation. """ if parse_command_line_args: # parse command-line arguments for TrainingArgs() args, coqpit_overrides = self.parse_argv(args) # get ready for training and parse command-line arguments for the model config config = self.init_training(args, coqpit_overrides, config) # define the experiment path and create the folder output_path = get_experiment_folder_path(config.output_path, config.run_name) os.makedirs(output_path, exist_ok=True) # copy training assets to the output folder copy_model_files(config, output_path, new_fields=None) # init class members self.args = args self.config = config self.output_path = output_path self.config.output_log_path = output_path self.training_assets = training_assets # setup logging log_file = os.path.join(self.output_path, f"trainer_{args.rank}_log.txt") self._setup_logger_config(log_file) time.sleep(1.0) # wait for the logger to be ready # set and initialize Pytorch runtime self.use_cuda, self.num_gpus = setup_torch_training_env(True, cudnn_benchmark, args.use_ddp) # init loggers self.c_logger = ConsoleLogger() if c_logger is None else c_logger self.dashboard_logger = dashboard_logger # only allow dashboard logging for the main process in DDP mode if self.dashboard_logger is None and args.rank == 0: self.dashboard_logger = init_dashboard_logger(config) if not self.config.log_model_step: self.config.log_model_step = self.config.save_step self.total_steps_done = 0 self.epochs_done = 0 self.restore_step = 0 self.best_loss = float("inf") self.train_loader = None self.eval_loader = None self.keep_avg_train = None self.keep_avg_eval = None self.use_apex = self._is_apex_available() self.use_amp_scaler = self.config.mixed_precision and self.use_cuda # load data samples if train_samples is None and get_data_samples is None: raise ValueError("[!] `train_samples` and `get_data_samples` cannot both be None.") if train_samples is not None: self.train_samples = train_samples self.eval_samples = eval_samples else: self.train_samples, self.eval_samples = self.run_get_data_samples(config, get_data_samples) # init TTS model if model is None and get_model is None: raise ValueError("[!] `model` and `get_model` cannot both be None.") if model is not None: self.model = model else: self.run_get_model(self.config, get_model) # TODO: out! # init multispeaker settings of the model if hasattr(self.model, "init_multispeaker"): self.model.init_multispeaker(self.config, self.train_samples + self.eval_samples) # setup criterion self.criterion = self.get_criterion(self.model) # DISTRUBUTED if self.num_gpus > 1: init_distributed( args.rank, self.num_gpus, args.group_id, self.config.distributed_backend, self.config.distributed_url, ) if self.use_cuda: self.model.cuda() if isinstance(self.criterion, list): self.criterion = [x.cuda() for x in self.criterion] else: self.criterion.cuda() # setup optimizer self.optimizer = self.get_optimizer(self.model, self.config) # CALLBACK self.callbacks = TrainerCallback(self) self.callbacks.on_init_start() # init AMP if self.use_amp_scaler: if self.use_apex: self.scaler = None self.model, self.optimizer = amp.initialize(self.model, self.optimizer, opt_level="O1") # if isinstance(self.optimizer, list): # self.scaler = [torch.cuda.amp.GradScaler()] * len(self.optimizer) # else: self.scaler = torch.cuda.amp.GradScaler() else: self.scaler = None if self.args.restore_path: self.model, self.optimizer, self.scaler, self.restore_step = self.restore_model( self.config, args.restore_path, self.model, self.optimizer, self.scaler ) # setup scheduler self.scheduler = self.get_scheduler(self.model, self.config, self.optimizer) if self.scheduler is not None: if self.args.continue_path: if isinstance(self.scheduler, list): for scheduler in self.scheduler: if scheduler is not None: scheduler.last_epoch = self.restore_step else: self.scheduler.last_epoch = self.restore_step # DISTRIBUTED if self.num_gpus > 1: self.model = DDP_th(self.model, device_ids=[args.rank], output_device=args.rank) # count model size num_params = count_parameters(self.model) print("\n > Model has {} parameters".format(num_params)) self.callbacks.on_init_end() @staticmethod def parse_argv(args: Union[Coqpit, List]): """Parse command line arguments to init or override `TrainingArgs()`.""" if isinstance(args, Coqpit): parser = args.init_argparse(arg_prefix="") else: train_config = TrainingArgs() parser = train_config.init_argparse(arg_prefix="") training_args, coqpit_overrides = parser.parse_known_args() args.parse_args(training_args) return args, coqpit_overrides def init_training( self, args: TrainingArgs, coqpit_overrides: Dict, config: Coqpit = None ): # pylint: disable=no-self-use """Initialize training and update model configs from command line arguments. Args: args (argparse.Namespace or dict like): Parsed input arguments. config_overrides (argparse.Namespace or dict like): Parsed config overriding arguments. config (Coqpit): Model config. If none, it is generated from `args`. Defaults to None. Returns: c (TTS.utils.io.AttrDict): Config paramaters. """ # set arguments for continuing training if args.continue_path: experiment_path = args.continue_path args.config_path = os.path.join(args.continue_path, "config.json") args.restore_path, best_model = get_last_checkpoint(args.continue_path) if not args.best_path: args.best_path = best_model # override config values from command-line args # TODO: Maybe it is better to do it outside if len(coqpit_overrides) > 0: config.parse_known_args(coqpit_overrides, relaxed_parser=True) experiment_path = args.continue_path # update the config.json fields and copy it to the output folder if args.rank == 0: new_fields = {} if args.restore_path: new_fields["restore_path"] = args.restore_path new_fields["github_branch"] = get_git_branch() copy_model_files(config, experiment_path, new_fields) return config @staticmethod def run_get_model(config: Coqpit, get_model: Callable) -> nn.Module: """Run the `get_model` function and return the model. Args: config (Coqpit): Model config. Returns: nn.Module: initialized model. """ if len(signature(get_model).sig.parameters) == 1: model = get_model(config) else: model = get_model() return model @staticmethod def run_get_data_samples(config: Coqpit, get_data_samples: Callable) -> nn.Module: if callable(get_data_samples): if len(signature(get_data_samples).sig.parameters) == 1: train_samples, eval_samples = get_data_samples(config) else: train_samples, eval_samples = get_data_samples() return train_samples, eval_samples return None, None def restore_model( self, config: Coqpit, restore_path: str, model: nn.Module, optimizer: torch.optim.Optimizer, scaler: torch.cuda.amp.GradScaler = None, ) -> Tuple[nn.Module, torch.optim.Optimizer, torch.cuda.amp.GradScaler, int]: """Restore training from an old run. It restores model, optimizer, AMP scaler and training stats. Args: config (Coqpit): Model config. restore_path (str): Path to the restored training run. model (nn.Module): Model to restored. optimizer (torch.optim.Optimizer): Optimizer to restore. scaler (torch.cuda.amp.GradScaler, optional): AMP scaler to restore. Defaults to None. Returns: Tuple[nn.Module, torch.optim.Optimizer, torch.cuda.amp.GradScaler, int]: [description] """ def _restore_list_objs(states, obj): if isinstance(obj, list): for idx, state in enumerate(states): obj[idx].load_state_dict(state) else: obj.load_state_dict(states) return obj print(" > Restoring from %s ..." % os.path.basename(restore_path)) checkpoint = load_fsspec(restore_path, map_location="cpu") try: print(" > Restoring Model...") model.load_state_dict(checkpoint["model"]) print(" > Restoring Optimizer...") optimizer = _restore_list_objs(checkpoint["optimizer"], optimizer) if "scaler" in checkpoint and self.use_amp_scaler and checkpoint["scaler"]: print(" > Restoring Scaler...") scaler = _restore_list_objs(checkpoint["scaler"], scaler) except (KeyError, RuntimeError): print(" > Partial model initialization...") model_dict = model.state_dict() model_dict = set_init_dict(model_dict, checkpoint["model"], config) model.load_state_dict(model_dict) del model_dict if isinstance(self.optimizer, list): for idx, optim in enumerate(optimizer): for group in optim.param_groups: group["lr"] = self.get_lr(model, config)[idx] else: for group in optimizer.param_groups: group["lr"] = self.get_lr(model, config) print( " > Model restored from step %d" % checkpoint["step"], ) restore_step = checkpoint["step"] torch.cuda.empty_cache() return model, optimizer, scaler, restore_step ######################### # DATA LOADING FUNCTIONS ######################### def _get_loader( self, model: nn.Module, config: Coqpit, assets: Dict, is_eval: bool, data_items: List, verbose: bool, num_gpus: int, ) -> DataLoader: if num_gpus > 1: if hasattr(model.module, "get_data_loader"): loader = model.module.get_data_loader( config, assets, is_eval, data_items, verbose, num_gpus, self.args.rank ) else: if hasattr(model, "get_data_loader"): loader = model.get_data_loader(config, assets, is_eval, data_items, verbose, num_gpus) return loader def get_train_dataloader(self, training_assets: Dict, data_items: List, verbose: bool) -> DataLoader: """Initialize and return a training data loader. Args: ap (AudioProcessor): Audio processor. data_items (List): Data samples used for training. verbose (bool): enable/disable printing loader stats at initialization. Returns: DataLoader: Initialized training data loader. """ return self._get_loader(self.model, self.config, training_assets, False, data_items, verbose, self.num_gpus) def get_eval_dataloader(self, training_assets: Dict, data_items: List, verbose: bool) -> DataLoader: return self._get_loader(self.model, self.config, training_assets, True, data_items, verbose, self.num_gpus) def format_batch(self, batch: List) -> Dict: """Format the dataloader output and return a batch. Args: batch (List): Batch returned by the dataloader. Returns: Dict: Formatted batch. """ if self.num_gpus > 1: batch = self.model.module.format_batch(batch) else: batch = self.model.format_batch(batch) if self.use_cuda: for k, v in batch.items(): batch[k] = to_cuda(v) return batch ###################### # TRAIN FUNCTIONS ###################### @staticmethod def master_params(optimizer: torch.optim.Optimizer): """Generator over parameters owned by the optimizer. Used to select parameters used by the optimizer for gradient clipping. Args: optimizer: Target optimizer. """ for group in optimizer.param_groups: for p in group["params"]: yield p @staticmethod def _model_train_step( batch: Dict, model: nn.Module, criterion: nn.Module, optimizer_idx: int = None ) -> Tuple[Dict, Dict]: """ Perform a trainig forward step. Compute model outputs and losses. Args: batch (Dict): [description] model (nn.Module): [description] criterion (nn.Module): [description] optimizer_idx (int, optional): [description]. Defaults to None. Returns: Tuple[Dict, Dict]: [description] """ input_args = [batch, criterion] if optimizer_idx is not None: input_args.append(optimizer_idx) # unwrap model in DDP training if hasattr(model, "module"): return model.module.train_step(*input_args) return model.train_step(*input_args) def _optimize( self, batch: Dict, model: nn.Module, optimizer: Union[torch.optim.Optimizer, List], scaler: "AMPScaler", criterion: nn.Module, scheduler: Union[torch.optim.lr_scheduler._LRScheduler, List], # pylint: disable=protected-access config: Coqpit, optimizer_idx: int = None, ) -> Tuple[Dict, Dict, int]: """Perform a forward - backward pass and run the optimizer. Args: batch (Dict): Input batch. If model (nn.Module): Model for training. Defaults to None. optimizer (Union[nn.optim.Optimizer, List]): Model's optimizer. If it is a list then, `optimizer_idx` must be defined to indicate the optimizer in use. scaler (AMPScaler): AMP scaler. criterion (nn.Module): Model's criterion. scheduler (torch.optim.lr_scheduler._LRScheduler): LR scheduler used by the optimizer. config (Coqpit): Model config. optimizer_idx (int, optional): Target optimizer being used. Defaults to None. Raises: RuntimeError: When the loss is NaN. Returns: Tuple[Dict, Dict, int, torch.Tensor]: model outputs, losses, step time and gradient norm. """ step_start_time = time.time() # zero-out optimizer optimizer.zero_grad() # forward pass and loss computation with torch.cuda.amp.autocast(enabled=config.mixed_precision): if optimizer_idx is not None: outputs, loss_dict = self._model_train_step(batch, model, criterion, optimizer_idx=optimizer_idx) else: outputs, loss_dict = self._model_train_step(batch, model, criterion) # skip the rest if outputs is None: step_time = time.time() - step_start_time return None, {}, step_time # # check nan loss # if torch.isnan(loss_dict["loss"]).any(): # raise RuntimeError(f" > NaN loss detected - {loss_dict}") # set gradient clipping threshold if "grad_clip" in config and config.grad_clip is not None: if optimizer_idx is not None: grad_clip = config.grad_clip[optimizer_idx] else: grad_clip = config.grad_clip else: grad_clip = 0.0 # meaning no gradient clipping if grad_clip <= 0: grad_norm = 0 # optimizer step update_lr_scheduler = True if self.use_amp_scaler: if self.use_apex: # TODO: verify AMP use for GAN training in TTS # https://nvidia.github.io/apex/advanced.html?highlight=accumulate#backward-passes-with-multiple-optimizers with amp.scale_loss(loss_dict["loss"], optimizer) as scaled_loss: scaled_loss.backward() grad_norm = torch.nn.utils.clip_grad_norm_( amp.master_params(optimizer), grad_clip, error_if_nonfinite=False ) else: # model optimizer step in mixed precision mode scaler.scale(loss_dict["loss"]).backward() if grad_clip > 0: scaler.unscale_(optimizer) grad_norm = torch.nn.utils.clip_grad_norm_( self.master_params(optimizer), grad_clip, error_if_nonfinite=False ) # pytorch skips the step when the norm is 0. So ignore the norm value when it is NaN if torch.isnan(grad_norm) or torch.isinf(grad_norm): grad_norm = 0 scale_prev = scaler.get_scale() scaler.step(optimizer) scaler.update() update_lr_scheduler = scale_prev <= scaler.get_scale() else: # main model optimizer step loss_dict["loss"].backward() if grad_clip > 0: grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip, error_if_nonfinite=False) optimizer.step() step_time = time.time() - step_start_time # setup lr if scheduler is not None and update_lr_scheduler and not self.config.scheduler_after_epoch: scheduler.step() # detach losses loss_dict = self._detach_loss_dict(loss_dict) if optimizer_idx is not None: loss_dict[f"loss_{optimizer_idx}"] = loss_dict.pop("loss") loss_dict[f"grad_norm_{optimizer_idx}"] = grad_norm else: loss_dict["grad_norm"] = grad_norm return outputs, loss_dict, step_time def train_step(self, batch: Dict, batch_n_steps: int, step: int, loader_start_time: float) -> Tuple[Dict, Dict]: """Perform a training step on a batch of inputs and log the process. Args: batch (Dict): Input batch. batch_n_steps (int): Number of steps needed to complete an epoch. Needed for logging. step (int): Current step number in this epoch. loader_start_time (float): The time when the data loading is started. Needed for logging. Returns: Tuple[Dict, Dict]: Model outputs and losses. """ self.callbacks.on_train_step_start() # format data batch = self.format_batch(batch) loader_time = time.time() - loader_start_time # conteainers to hold model outputs and losses for each optimizer. outputs_per_optimizer = None loss_dict = {} if not isinstance(self.optimizer, list): # training with a single optimizer outputs, loss_dict_new, step_time = self._optimize( batch, self.model, self.optimizer, self.scaler, self.criterion, self.scheduler, self.config ) loss_dict.update(loss_dict_new) else: # training with multiple optimizers (e.g. GAN) outputs_per_optimizer = [None] * len(self.optimizer) total_step_time = 0 for idx, optimizer in enumerate(self.optimizer): criterion = self.criterion # scaler = self.scaler[idx] if self.use_amp_scaler else None scaler = self.scaler scheduler = self.scheduler[idx] outputs, loss_dict_new, step_time = self._optimize( batch, self.model, optimizer, scaler, criterion, scheduler, self.config, idx ) # skip the rest if the model returns None total_step_time += step_time outputs_per_optimizer[idx] = outputs # merge loss_dicts from each optimizer # rename duplicates with the optimizer idx # if None, model skipped this optimizer if loss_dict_new is not None: for k, v in loss_dict_new.items(): if k in loss_dict: loss_dict[f"{k}-{idx}"] = v else: loss_dict[k] = v step_time = total_step_time outputs = outputs_per_optimizer # update avg runtime stats keep_avg_update = {} keep_avg_update["avg_loader_time"] = loader_time keep_avg_update["avg_step_time"] = step_time self.keep_avg_train.update_values(keep_avg_update) # update avg loss stats update_eval_values = {} for key, value in loss_dict.items(): update_eval_values["avg_" + key] = value self.keep_avg_train.update_values(update_eval_values) # print training progress if self.total_steps_done % self.config.print_step == 0: # log learning rates lrs = {} if isinstance(self.optimizer, list): for idx, optimizer in enumerate(self.optimizer): current_lr = self.optimizer[idx].param_groups[0]["lr"] lrs.update({f"current_lr_{idx}": current_lr}) else: current_lr = self.optimizer.param_groups[0]["lr"] lrs = {"current_lr": current_lr} # log run-time stats loss_dict.update(lrs) loss_dict.update( { "step_time": round(step_time, 4), "loader_time": round(loader_time, 4), } ) self.c_logger.print_train_step( batch_n_steps, step, self.total_steps_done, loss_dict, self.keep_avg_train.avg_values ) if self.args.rank == 0: # Plot Training Iter Stats # reduce TB load and don't log every step if self.total_steps_done % self.config.plot_step == 0: self.dashboard_logger.train_step_stats(self.total_steps_done, loss_dict) if self.total_steps_done % self.config.save_step == 0 and self.total_steps_done != 0: if self.config.checkpoint: # checkpoint the model target_avg_loss = self._pick_target_avg_loss(self.keep_avg_train) save_checkpoint( self.config, self.model, self.optimizer, self.scaler if self.use_amp_scaler else None, self.total_steps_done, self.epochs_done, self.output_path, model_loss=target_avg_loss, ) if self.total_steps_done % self.config.log_model_step == 0: # log checkpoint as artifact aliases = [f"epoch-{self.epochs_done}", f"step-{self.total_steps_done}"] self.dashboard_logger.log_artifact(self.output_path, "checkpoint", "model", aliases) # training visualizations if hasattr(self.model, "module") and hasattr(self.model.module, "train_log"): self.model.module.train_log( batch, outputs, self.dashboard_logger, self.training_assets, self.total_steps_done ) elif hasattr(self.model, "train_log"): self.model.train_log( batch, outputs, self.dashboard_logger, self.training_assets, self.total_steps_done ) self.dashboard_logger.flush() self.total_steps_done += 1 self.callbacks.on_train_step_end() return outputs, loss_dict def train_epoch(self) -> None: """Main entry point for the training loop. Run training on the all training samples.""" # initialize the data loader self.train_loader = self.get_train_dataloader( self.training_assets, self.train_samples, verbose=True, ) # set model to training mode if self.num_gpus > 1: self.model.module.train() else: self.model.train() epoch_start_time = time.time() if self.use_cuda: batch_num_steps = int(len(self.train_loader.dataset) / (self.config.batch_size * self.num_gpus)) else: batch_num_steps = int(len(self.train_loader.dataset) / self.config.batch_size) self.c_logger.print_train_start() loader_start_time = time.time() # iterate over the training samples for cur_step, batch in enumerate(self.train_loader): _, _ = self.train_step(batch, batch_num_steps, cur_step, loader_start_time) loader_start_time = time.time() epoch_time = time.time() - epoch_start_time # plot self.epochs_done Stats if self.args.rank == 0: epoch_stats = {"epoch_time": epoch_time} epoch_stats.update(self.keep_avg_train.avg_values) self.dashboard_logger.train_epoch_stats(self.total_steps_done, epoch_stats) if self.config.model_param_stats: self.logger.model_weights(self.model, self.total_steps_done) # scheduler step after the epoch if self.scheduler is not None and self.config.scheduler_after_epoch: if isinstance(self.scheduler, list): for scheduler in self.scheduler: if scheduler is not None: scheduler.step() else: self.scheduler.step() ####################### # EVAL FUNCTIONS ####################### @staticmethod def _model_eval_step( batch: Dict, model: nn.Module, criterion: nn.Module, optimizer_idx: int = None ) -> Tuple[Dict, Dict]: """ Perform a evaluation forward pass. Compute model outputs and losses with no gradients. Args: batch (Dict): IBatch of inputs. model (nn.Module): Model to call evaluation. criterion (nn.Module): Model criterion. optimizer_idx (int, optional): Optimizer ID to define the closure in multi-optimizer training. Defaults to None. Returns: Tuple[Dict, Dict]: model outputs and losses. """ input_args = [batch, criterion] if optimizer_idx is not None: input_args.append(optimizer_idx) if hasattr(model, "module"): return model.module.eval_step(*input_args) return model.eval_step(*input_args) def eval_step(self, batch: Dict, step: int) -> Tuple[Dict, Dict]: """Perform a evaluation step on a batch of inputs and log the process. Args: batch (Dict): Input batch. step (int): Current step number in this epoch. Returns: Tuple[Dict, Dict]: Model outputs and losses. """ with torch.no_grad(): outputs = [] loss_dict = {} if not isinstance(self.optimizer, list): outputs, loss_dict = self._model_eval_step(batch, self.model, self.criterion) else: outputs = [None] * len(self.optimizer) for idx, _ in enumerate(self.optimizer): criterion = self.criterion outputs_, loss_dict_new = self._model_eval_step(batch, self.model, criterion, idx) outputs[idx] = outputs_ if loss_dict_new is not None: loss_dict_new[f"loss_{idx}"] = loss_dict_new.pop("loss") loss_dict.update(loss_dict_new) loss_dict = self._detach_loss_dict(loss_dict) # update avg stats update_eval_values = {} for key, value in loss_dict.items(): update_eval_values["avg_" + key] = value self.keep_avg_eval.update_values(update_eval_values) if self.config.print_eval: self.c_logger.print_eval_step(step, loss_dict, self.keep_avg_eval.avg_values) return outputs, loss_dict def eval_epoch(self) -> None: """Main entry point for the evaluation loop. Run evaluation on the all validation samples.""" self.eval_loader = ( self.get_eval_dataloader( self.training_assets, self.eval_samples, verbose=True, ) if self.config.run_eval else None ) self.model.eval() self.c_logger.print_eval_start() loader_start_time = time.time() batch = None for cur_step, batch in enumerate(self.eval_loader): # format data batch = self.format_batch(batch) loader_time = time.time() - loader_start_time self.keep_avg_eval.update_values({"avg_loader_time": loader_time}) outputs, _ = self.eval_step(batch, cur_step) loader_start_time = time.time() # plot epoch stats, artifacts and figures if self.args.rank == 0: if hasattr(self.model, "module") and hasattr(self.model.module, "eval_log"): self.model.module.eval_log( batch, outputs, self.dashboard_logger, self.training_assets, self.total_steps_done ) elif hasattr(self.model, "eval_log"): self.model.eval_log(batch, outputs, self.dashboard_logger, self.training_assets, self.total_steps_done) self.dashboard_logger.eval_stats(self.total_steps_done, self.keep_avg_eval.avg_values) def test_run(self) -> None: """Run test and log the results. Test run must be defined by the model. Model must return figures and audios to be logged by the Tensorboard.""" if hasattr(self.model, "test_run") or (self.num_gpus > 1 and hasattr(self.model.module, "test_run")): if self.eval_loader is None: self.eval_loader = self.get_eval_dataloader( self.training_assets, self.eval_samples, verbose=True, ) if hasattr(self.eval_loader.dataset, "load_test_samples"): samples = self.eval_loader.dataset.load_test_samples(1) if self.num_gpus > 1: figures, audios = self.model.module.test_run(self.training_assets, samples, None) else: figures, audios = self.model.test_run(self.training_assets, samples, None) else: if self.num_gpus > 1: figures, audios = self.model.module.test_run(self.training_assets) else: figures, audios = self.model.test_run(self.training_assets) self.dashboard_logger.test_audios(self.total_steps_done, audios, self.config.audio["sample_rate"]) self.dashboard_logger.test_figures(self.total_steps_done, figures) def _restore_best_loss(self): """Restore the best loss from the args.best_path if provided else from the model (`args.restore_path` or `args.continue_path`) used for resuming the training""" if self.restore_step != 0 or self.args.best_path: print(f" > Restoring best loss from {os.path.basename(self.args.best_path)} ...") ch = load_fsspec(self.args.restore_path, map_location="cpu") if "model_loss" in ch: self.best_loss = ch["model_loss"] print(f" > Starting with loaded last best loss {self.best_loss}.") ################################### # FIT FUNCTIONS ################################### def _fit(self) -> None: """🏃 train -> evaluate -> test for the number of epochs.""" self._restore_best_loss() self.total_steps_done = self.restore_step for epoch in range(0, self.config.epochs): if self.num_gpus > 1: # let all processes sync up before starting with a new epoch of training dist.barrier() self.callbacks.on_epoch_start() self.keep_avg_train = KeepAverage() self.keep_avg_eval = KeepAverage() if self.config.run_eval else None self.epochs_done = epoch self.c_logger.print_epoch_start(epoch, self.config.epochs, self.output_path) if not self.args.skip_train_epoch: self.train_epoch() if self.config.run_eval: self.eval_epoch() if epoch >= self.config.test_delay_epochs and self.args.rank <= 0: self.test_run() self.c_logger.print_epoch_end( epoch, self.keep_avg_eval.avg_values if self.config.run_eval else self.keep_avg_train.avg_values ) if self.args.rank in [None, 0]: self.save_best_model() self.callbacks.on_epoch_end() def fit(self) -> None: """Where the ✨️magic✨️ happens...""" try: self._fit() if self.args.rank == 0: self.dashboard_logger.finish() except KeyboardInterrupt: self.callbacks.on_keyboard_interrupt() # if the output folder is empty remove the run. remove_experiment_folder(self.output_path) # clear the DDP processes if self.num_gpus > 1: dist.destroy_process_group() # finish the wandb run and sync data if self.args.rank == 0: self.dashboard_logger.finish() # stop without error signal try: sys.exit(0) except SystemExit: os._exit(0) # pylint: disable=protected-access except BaseException: # pylint: disable=broad-except remove_experiment_folder(self.output_path) traceback.print_exc() sys.exit(1) def save_best_model(self) -> None: """Save the best model. It only saves if the current target loss is smaller then the previous.""" # set the target loss to choose the best model target_loss_dict = self._pick_target_avg_loss(self.keep_avg_eval if self.keep_avg_eval else self.keep_avg_train) # save the model and update the best_loss self.best_loss = save_best_model( target_loss_dict, self.best_loss, self.config, self.model, self.optimizer, self.scaler if self.use_amp_scaler else None, self.total_steps_done, self.epochs_done, self.output_path, keep_all_best=self.config.keep_all_best, keep_after=self.config.keep_after, ) ##################### # GET FUNCTIONS ##################### @staticmethod def get_optimizer(model: nn.Module, config: Coqpit) -> Union[torch.optim.Optimizer, List]: """Receive the optimizer from the model if model implements `get_optimizer()` else check the optimizer parameters in the config and try initiating the optimizer. Args: model (nn.Module): Training model. config (Coqpit): Training configuration. Returns: Union[torch.optim.Optimizer, List]: A optimizer or a list of optimizers. GAN models define a list. """ if hasattr(model, "get_optimizer"): optimizer = model.get_optimizer() if optimizer is None: optimizer_name = config.optimizer optimizer_params = config.optimizer_params return get_optimizer(optimizer_name, optimizer_params, config.lr, model) return optimizer @staticmethod def get_lr(model: nn.Module, config: Coqpit) -> Union[float, List[float]]: """Set the initial learning rate by the model if model implements `get_lr()` else try setting the learning rate fromthe config. Args: model (nn.Module): Training model. config (Coqpit): Training configuration. Returns: Union[float, List[float]]: A single learning rate or a list of learning rates, one for each optimzier. """ lr = None if hasattr(model, "get_lr"): lr = model.get_lr() if lr is None: lr = config.lr return lr @staticmethod def get_scheduler( model: nn.Module, config: Coqpit, optimizer: Union[torch.optim.Optimizer, List] ) -> Union[torch.optim.lr_scheduler._LRScheduler, List]: # pylint: disable=protected-access """Receive the scheduler from the model if model implements `get_scheduler()` else check the config and try initiating the scheduler. Args: model (nn.Module): Training model. config (Coqpit): Training configuration. Returns: Union[torch.optim.Optimizer, List]: A scheduler or a list of schedulers, one for each optimizer. """ scheduler = None if hasattr(model, "get_scheduler"): scheduler = model.get_scheduler(optimizer) if scheduler is None: lr_scheduler = config.lr_scheduler lr_scheduler_params = config.lr_scheduler_params return get_scheduler(lr_scheduler, lr_scheduler_params, optimizer) return scheduler @staticmethod def get_criterion(model: nn.Module) -> nn.Module: """Receive the criterion from the model. Model must implement `get_criterion()`. Args: model (nn.Module): Training model. Returns: nn.Module: Criterion layer. """ criterion = None criterion = model.get_criterion() return criterion #################### # HELPER FUNCTIONS #################### @staticmethod def _detach_loss_dict(loss_dict: Dict) -> Dict: """Detach loss values from autograp. Args: loss_dict (Dict): losses. Returns: Dict: losses detached from autograph. """ loss_dict_detached = {} for key, value in loss_dict.items(): if isinstance(value, (int, float)): loss_dict_detached[key] = value else: loss_dict_detached[key] = value.detach() return loss_dict_detached def _pick_target_avg_loss(self, keep_avg_target: KeepAverage) -> Dict: """Pick the target loss to compare models""" target_avg_loss = None # return if target loss defined in the model config if "target_loss" in self.config and self.config.target_loss: return keep_avg_target[f"avg_{self.config.target_loss}"] # take the average of loss_{optimizer_idx} as the target loss when there are multiple optimizers if isinstance(self.optimizer, list): target_avg_loss = 0 for idx in range(len(self.optimizer)): target_avg_loss += keep_avg_target[f"avg_loss_{idx}"] target_avg_loss /= len(self.optimizer) else: target_avg_loss = keep_avg_target["avg_loss"] return target_avg_loss def _setup_logger_config(self, log_file: str) -> None: """Write log strings to a file and print logs to the terminal. TODO: Causes formatting issues in pdb debugging.""" class Logger(object): def __init__(self, print_to_terminal=True): self.print_to_terminal = print_to_terminal self.terminal = sys.stdout self.log_file = log_file def write(self, message): if self.print_to_terminal: self.terminal.write(message) with open(self.log_file, "a", encoding="utf-8") as f: f.write(message) def flush(self): # this flush method is needed for python 3 compatibility. # this handles the flush command by doing nothing. # you might want to specify some extra behavior here. pass # don't let processes rank > 0 write to the terminal sys.stdout = Logger(self.args.rank == 0) @staticmethod def _is_apex_available() -> bool: """Check if Nvidia's APEX is available.""" return importlib.util.find_spec("apex") is not None