# Copyright (c) 2023 Amphion. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import json import os import random import shutil import time from abc import abstractmethod from pathlib import Path import accelerate import json5 import numpy as np import torch from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration from torch.utils.data import ConcatDataset, DataLoader from tqdm import tqdm from models.base.base_sampler import build_samplers from optimizer.optimizers import NoamLR class BaseTrainer(object): r"""The base trainer for all tasks. Any trainer should inherit from this class.""" def __init__(self, args=None, cfg=None): super().__init__() self.args = args self.cfg = cfg cfg.exp_name = args.exp_name # init with accelerate self._init_accelerator() self.accelerator.wait_for_everyone() # Use accelerate logger for distributed training with self.accelerator.main_process_first(): self.logger = get_logger(args.exp_name, log_level=args.log_level) # Log some info self.logger.info("=" * 56) self.logger.info("||\t\t" + "New training process started." + "\t\t||") self.logger.info("=" * 56) self.logger.info("\n") self.logger.debug(f"Using {args.log_level.upper()} logging level.") self.logger.info(f"Experiment name: {args.exp_name}") self.logger.info(f"Experiment directory: {self.exp_dir}") self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") if self.accelerator.is_main_process: os.makedirs(self.checkpoint_dir, exist_ok=True) self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") # init counts self.batch_count: int = 0 self.step: int = 0 self.epoch: int = 0 self.max_epoch = ( self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf") ) self.logger.info( "Max epoch: {}".format( self.max_epoch if self.max_epoch < float("inf") else "Unlimited" ) ) # Check values if self.accelerator.is_main_process: self.__check_basic_configs() # Set runtime configs self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride self.checkpoints_path = [ [] for _ in range(len(self.save_checkpoint_stride)) ] self.keep_last = [ i if i > 0 else float("inf") for i in self.cfg.train.keep_last ] self.run_eval = self.cfg.train.run_eval # set random seed with self.accelerator.main_process_first(): start = time.monotonic_ns() self._set_random_seed(self.cfg.train.random_seed) end = time.monotonic_ns() self.logger.debug( f"Setting random seed done in {(end - start) / 1e6:.2f}ms" ) self.logger.debug(f"Random seed: {self.cfg.train.random_seed}") # setup data_loader with self.accelerator.main_process_first(): self.logger.info("Building dataset...") start = time.monotonic_ns() self.train_dataloader, self.valid_dataloader = self._build_dataloader() end = time.monotonic_ns() self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms") # setup model with self.accelerator.main_process_first(): self.logger.info("Building model...") start = time.monotonic_ns() self.model = self._build_model() end = time.monotonic_ns() self.logger.debug(self.model) self.logger.info(f"Building model done in {(end - start) / 1e6:.2f}ms") self.logger.info( f"Model parameters: {self.__count_parameters(self.model)/1e6:.2f}M" ) # optimizer & scheduler with self.accelerator.main_process_first(): self.logger.info("Building optimizer and scheduler...") start = time.monotonic_ns() self.optimizer = self.__build_optimizer() self.scheduler = self.__build_scheduler() end = time.monotonic_ns() self.logger.info( f"Building optimizer and scheduler done in {(end - start) / 1e6:.2f}ms" ) # accelerate prepare self.logger.info("Initializing accelerate...") start = time.monotonic_ns() ( self.train_dataloader, self.valid_dataloader, self.model, self.optimizer, self.scheduler, ) = self.accelerator.prepare( self.train_dataloader, self.valid_dataloader, self.model, self.optimizer, self.scheduler, ) end = time.monotonic_ns() self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.2f}ms") # create criterion with self.accelerator.main_process_first(): self.logger.info("Building criterion...") start = time.monotonic_ns() self.criterion = self._build_criterion() end = time.monotonic_ns() self.logger.info(f"Building criterion done in {(end - start) / 1e6:.2f}ms") # Resume or Finetune with self.accelerator.main_process_first(): if args.resume: ## Automatically resume according to the current exprimental name self.logger.info("Resuming from {}...".format(self.checkpoint_dir)) start = time.monotonic_ns() ckpt_path = self.__load_model( checkpoint_dir=self.checkpoint_dir, resume_type=args.resume_type ) end = time.monotonic_ns() self.logger.info( f"Resuming from checkpoint done in {(end - start) / 1e6:.2f}ms" ) self.checkpoints_path = json.load( open(os.path.join(ckpt_path, "ckpts.json"), "r") ) elif args.resume_from_ckpt_path and args.resume_from_ckpt_path != "": ## Resume from the given checkpoint path if not os.path.exists(args.resume_from_ckpt_path): raise ValueError( "[Error] The resumed checkpoint path {} don't exist.".format( args.resume_from_ckpt_path ) ) self.logger.info( "Resuming from {}...".format(args.resume_from_ckpt_path) ) start = time.monotonic_ns() ckpt_path = self.__load_model( checkpoint_path=args.resume_from_ckpt_path, resume_type=args.resume_type, ) end = time.monotonic_ns() self.logger.info( f"Resuming from checkpoint done in {(end - start) / 1e6:.2f}ms" ) # save config file path self.config_save_path = os.path.join(self.exp_dir, "args.json") ### Following are abstract methods that should be implemented in child classes ### @abstractmethod def _build_dataset(self): r"""Build dataset for model training/validating/evaluating.""" pass @staticmethod @abstractmethod def _build_criterion(): r"""Build criterion function for model loss calculation.""" pass @abstractmethod def _build_model(self): r"""Build model for training/validating/evaluating.""" pass @abstractmethod def _forward_step(self, batch): r"""One forward step of the neural network. This abstract method is trying to unify ``_train_step`` and ``_valid_step`` and avoid redundant implementation. However, for special case that using different forward step pattern for training and validating, you could just override this method with ``pass`` and implement ``_train_step`` and ``_valid_step`` separately. """ pass @abstractmethod def _save_auxiliary_states(self): r"""To save some auxiliary states when saving model's ckpt""" pass ### Abstract methods end ### ### THIS IS MAIN ENTRY ### def train_loop(self): r"""Training loop. The public entry of training process.""" # Wait everyone to prepare before we move on self.accelerator.wait_for_everyone() # dump config file if self.accelerator.is_main_process: self.__dump_cfg(self.config_save_path) self.model.train() self.optimizer.zero_grad() # Wait to ensure good to go self.accelerator.wait_for_everyone() while self.epoch < self.max_epoch: self.logger.info("\n") self.logger.info("-" * 32) self.logger.info("Epoch {}: ".format(self.epoch)) ### TODO: change the return values of _train_epoch() to a loss dict, or (total_loss, loss_dict) ### It's inconvenient for the model with multiple losses # Do training & validating epoch train_loss = self._train_epoch() self.logger.info(" |- Train/Loss: {:.6f}".format(train_loss)) valid_loss = self._valid_epoch() self.logger.info(" |- Valid/Loss: {:.6f}".format(valid_loss)) self.accelerator.log( {"Epoch/Train Loss": train_loss, "Epoch/Valid Loss": valid_loss}, step=self.epoch, ) self.accelerator.wait_for_everyone() # TODO: what is scheduler? self.scheduler.step(valid_loss) # FIXME: use epoch track correct? # Check if hit save_checkpoint_stride and run_eval run_eval = False if self.accelerator.is_main_process: save_checkpoint = False hit_dix = [] for i, num in enumerate(self.save_checkpoint_stride): if self.epoch % num == 0: save_checkpoint = True hit_dix.append(i) run_eval |= self.run_eval[i] self.accelerator.wait_for_everyone() if self.accelerator.is_main_process and save_checkpoint: path = os.path.join( self.checkpoint_dir, "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( self.epoch, self.step, train_loss ), ) self.tmp_checkpoint_save_path = path self.accelerator.save_state(path) print(f"save checkpoint in {path}") json.dump( self.checkpoints_path, open(os.path.join(path, "ckpts.json"), "w"), ensure_ascii=False, indent=4, ) self._save_auxiliary_states() # Remove old checkpoints to_remove = [] for idx in hit_dix: self.checkpoints_path[idx].append(path) while len(self.checkpoints_path[idx]) > self.keep_last[idx]: to_remove.append((idx, self.checkpoints_path[idx].pop(0))) # Search conflicts total = set() for i in self.checkpoints_path: total |= set(i) do_remove = set() for idx, path in to_remove[::-1]: if path in total: self.checkpoints_path[idx].insert(0, path) else: do_remove.add(path) # Remove old checkpoints for path in do_remove: shutil.rmtree(path, ignore_errors=True) self.logger.debug(f"Remove old checkpoint: {path}") self.accelerator.wait_for_everyone() if run_eval: # TODO: run evaluation pass # Update info for each epoch self.epoch += 1 # Finish training and save final checkpoint self.accelerator.wait_for_everyone() if self.accelerator.is_main_process: self.accelerator.save_state( os.path.join( self.checkpoint_dir, "final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( self.epoch, self.step, valid_loss ), ) ) self._save_auxiliary_states() self.accelerator.end_training() ### Following are methods that can be used directly in child classes ### def _train_epoch(self): r"""Training epoch. Should return average loss of a batch (sample) over one epoch. See ``train_loop`` for usage. """ self.model.train() epoch_sum_loss: float = 0.0 epoch_step: int = 0 for batch in tqdm( self.train_dataloader, desc=f"Training Epoch {self.epoch}", unit="batch", colour="GREEN", leave=False, dynamic_ncols=True, smoothing=0.04, disable=not self.accelerator.is_main_process, ): # Do training step and BP with self.accelerator.accumulate(self.model): loss = self._train_step(batch) self.accelerator.backward(loss) self.optimizer.step() self.optimizer.zero_grad() self.batch_count += 1 # Update info for each step # TODO: step means BP counts or batch counts? if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: epoch_sum_loss += loss self.accelerator.log( { "Step/Train Loss": loss, "Step/Learning Rate": self.optimizer.param_groups[0]["lr"], }, step=self.step, ) self.step += 1 epoch_step += 1 self.accelerator.wait_for_everyone() return ( epoch_sum_loss / len(self.train_dataloader) * self.cfg.train.gradient_accumulation_step ) @torch.inference_mode() def _valid_epoch(self): r"""Testing epoch. Should return average loss of a batch (sample) over one epoch. See ``train_loop`` for usage. """ self.model.eval() epoch_sum_loss = 0.0 for batch in tqdm( self.valid_dataloader, desc=f"Validating Epoch {self.epoch}", unit="batch", colour="GREEN", leave=False, dynamic_ncols=True, smoothing=0.04, disable=not self.accelerator.is_main_process, ): batch_loss = self._valid_step(batch) epoch_sum_loss += batch_loss.item() self.accelerator.wait_for_everyone() return epoch_sum_loss / len(self.valid_dataloader) def _train_step(self, batch): r"""Training forward step. Should return average loss of a sample over one batch. Provoke ``_forward_step`` is recommended except for special case. See ``_train_epoch`` for usage. """ return self._forward_step(batch) @torch.inference_mode() def _valid_step(self, batch): r"""Testing forward step. Should return average loss of a sample over one batch. Provoke ``_forward_step`` is recommended except for special case. See ``_test_epoch`` for usage. """ return self._forward_step(batch) def __load_model( self, checkpoint_dir: str = None, checkpoint_path: str = None, resume_type: str = "", ): r"""Load model from checkpoint. If checkpoint_path is None, it will load the latest checkpoint in checkpoint_dir. If checkpoint_path is not None, it will load the checkpoint specified by checkpoint_path. **Only use this method after** ``accelerator.prepare()``. """ if checkpoint_path is None: ls = [str(i) for i in Path(checkpoint_dir).glob("*")] ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True) checkpoint_path = ls[0] self.logger.info("Resume from {}...".format(checkpoint_path)) if resume_type in ["resume", ""]: # Load all the things, including model weights, optimizer, scheduler, and random states. self.accelerator.load_state(input_dir=checkpoint_path) # set epoch and step self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + 1 self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + 1 elif resume_type == "finetune": # Load only the model weights accelerate.load_checkpoint_and_dispatch( self.accelerator.unwrap_model(self.model), os.path.join(checkpoint_path, "pytorch_model.bin"), ) self.logger.info("Load model weights for finetune...") else: raise ValueError("Resume_type must be `resume` or `finetune`.") return checkpoint_path # TODO: LEGACY CODE def _build_dataloader(self): Dataset, Collator = self._build_dataset() # build dataset instance for each dataset and combine them by ConcatDataset datasets_list = [] for dataset in self.cfg.dataset: subdataset = Dataset(self.cfg, dataset, is_valid=False) datasets_list.append(subdataset) train_dataset = ConcatDataset(datasets_list) train_collate = Collator(self.cfg) _, batch_sampler = build_samplers(train_dataset, self.cfg, self.logger, "train") self.logger.debug(f"train batch_sampler: {list(batch_sampler)}") self.logger.debug(f"length: {train_dataset.cumulative_sizes}") # TODO: use config instead of (sampler, shuffle, drop_last, batch_size) train_loader = DataLoader( train_dataset, collate_fn=train_collate, batch_sampler=batch_sampler, num_workers=self.cfg.train.dataloader.num_worker, pin_memory=self.cfg.train.dataloader.pin_memory, ) # Build valid dataloader datasets_list = [] for dataset in self.cfg.dataset: subdataset = Dataset(self.cfg, dataset, is_valid=True) datasets_list.append(subdataset) valid_dataset = ConcatDataset(datasets_list) valid_collate = Collator(self.cfg) _, batch_sampler = build_samplers(valid_dataset, self.cfg, self.logger, "valid") self.logger.debug(f"valid batch_sampler: {list(batch_sampler)}") self.logger.debug(f"length: {valid_dataset.cumulative_sizes}") valid_loader = DataLoader( valid_dataset, collate_fn=valid_collate, batch_sampler=batch_sampler, num_workers=self.cfg.train.dataloader.num_worker, pin_memory=self.cfg.train.dataloader.pin_memory, ) return train_loader, valid_loader @staticmethod def _set_random_seed(seed): r"""Set random seed for all possible random modules.""" random.seed(seed) np.random.seed(seed) torch.random.manual_seed(seed) def _check_nan(self, loss, y_pred, y_gt): if torch.any(torch.isnan(loss)): self.logger.fatal("Fatal Error: Training is down since loss has Nan!") self.logger.error("loss = {:.6f}".format(loss.item()), in_order=True) if torch.any(torch.isnan(y_pred)): self.logger.error( f"y_pred has Nan: {torch.any(torch.isnan(y_pred))}", in_order=True ) else: self.logger.debug( f"y_pred has Nan: {torch.any(torch.isnan(y_pred))}", in_order=True ) if torch.any(torch.isnan(y_gt)): self.logger.error( f"y_gt has Nan: {torch.any(torch.isnan(y_gt))}", in_order=True ) else: self.logger.debug( f"y_gt has nan: {torch.any(torch.isnan(y_gt))}", in_order=True ) if torch.any(torch.isnan(y_pred)): self.logger.error(f"y_pred: {y_pred}", in_order=True) else: self.logger.debug(f"y_pred: {y_pred}", in_order=True) if torch.any(torch.isnan(y_gt)): self.logger.error(f"y_gt: {y_gt}", in_order=True) else: self.logger.debug(f"y_gt: {y_gt}", in_order=True) # TODO: still OK to save tracking? self.accelerator.end_training() raise RuntimeError("Loss has Nan! See log for more info.") ### Protected methods end ### ## Following are private methods ## ## !!! These are inconvenient for GAN-based model training. It'd be better to move these to svc_trainer.py if needed. def __build_optimizer(self): r"""Build optimizer for model.""" # Make case-insensitive matching if self.cfg.train.optimizer.lower() == "adadelta": optimizer = torch.optim.Adadelta( self.model.parameters(), **self.cfg.train.adadelta ) self.logger.info("Using Adadelta optimizer.") elif self.cfg.train.optimizer.lower() == "adagrad": optimizer = torch.optim.Adagrad( self.model.parameters(), **self.cfg.train.adagrad ) self.logger.info("Using Adagrad optimizer.") elif self.cfg.train.optimizer.lower() == "adam": optimizer = torch.optim.Adam(self.model.parameters(), **self.cfg.train.adam) self.logger.info("Using Adam optimizer.") elif self.cfg.train.optimizer.lower() == "adamw": optimizer = torch.optim.AdamW( self.model.parameters(), **self.cfg.train.adamw ) elif self.cfg.train.optimizer.lower() == "sparseadam": optimizer = torch.optim.SparseAdam( self.model.parameters(), **self.cfg.train.sparseadam ) elif self.cfg.train.optimizer.lower() == "adamax": optimizer = torch.optim.Adamax( self.model.parameters(), **self.cfg.train.adamax ) elif self.cfg.train.optimizer.lower() == "asgd": optimizer = torch.optim.ASGD(self.model.parameters(), **self.cfg.train.asgd) elif self.cfg.train.optimizer.lower() == "lbfgs": optimizer = torch.optim.LBFGS( self.model.parameters(), **self.cfg.train.lbfgs ) elif self.cfg.train.optimizer.lower() == "nadam": optimizer = torch.optim.NAdam( self.model.parameters(), **self.cfg.train.nadam ) elif self.cfg.train.optimizer.lower() == "radam": optimizer = torch.optim.RAdam( self.model.parameters(), **self.cfg.train.radam ) elif self.cfg.train.optimizer.lower() == "rmsprop": optimizer = torch.optim.RMSprop( self.model.parameters(), **self.cfg.train.rmsprop ) elif self.cfg.train.optimizer.lower() == "rprop": optimizer = torch.optim.Rprop( self.model.parameters(), **self.cfg.train.rprop ) elif self.cfg.train.optimizer.lower() == "sgd": optimizer = torch.optim.SGD(self.model.parameters(), **self.cfg.train.sgd) else: raise NotImplementedError( f"Optimizer {self.cfg.train.optimizer} not supported yet!" ) return optimizer def __build_scheduler(self): r"""Build scheduler for optimizer.""" # Make case-insensitive matching if self.cfg.train.scheduler.lower() == "lambdalr": scheduler = torch.optim.lr_scheduler.LambdaLR( self.optimizer, **self.cfg.train.lambdalr ) elif self.cfg.train.scheduler.lower() == "multiplicativelr": scheduler = torch.optim.lr_scheduler.MultiplicativeLR( self.optimizer, **self.cfg.train.multiplicativelr ) elif self.cfg.train.scheduler.lower() == "steplr": scheduler = torch.optim.lr_scheduler.StepLR( self.optimizer, **self.cfg.train.steplr ) elif self.cfg.train.scheduler.lower() == "multisteplr": scheduler = torch.optim.lr_scheduler.MultiStepLR( self.optimizer, **self.cfg.train.multisteplr ) elif self.cfg.train.scheduler.lower() == "constantlr": scheduler = torch.optim.lr_scheduler.ConstantLR( self.optimizer, **self.cfg.train.constantlr ) elif self.cfg.train.scheduler.lower() == "linearlr": scheduler = torch.optim.lr_scheduler.LinearLR( self.optimizer, **self.cfg.train.linearlr ) elif self.cfg.train.scheduler.lower() == "exponentiallr": scheduler = torch.optim.lr_scheduler.ExponentialLR( self.optimizer, **self.cfg.train.exponentiallr ) elif self.cfg.train.scheduler.lower() == "polynomiallr": scheduler = torch.optim.lr_scheduler.PolynomialLR( self.optimizer, **self.cfg.train.polynomiallr ) elif self.cfg.train.scheduler.lower() == "cosineannealinglr": scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( self.optimizer, **self.cfg.train.cosineannealinglr ) elif self.cfg.train.scheduler.lower() == "sequentiallr": scheduler = torch.optim.lr_scheduler.SequentialLR( self.optimizer, **self.cfg.train.sequentiallr ) elif self.cfg.train.scheduler.lower() == "reducelronplateau": scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( self.optimizer, **self.cfg.train.reducelronplateau ) elif self.cfg.train.scheduler.lower() == "cycliclr": scheduler = torch.optim.lr_scheduler.CyclicLR( self.optimizer, **self.cfg.train.cycliclr ) elif self.cfg.train.scheduler.lower() == "onecyclelr": scheduler = torch.optim.lr_scheduler.OneCycleLR( self.optimizer, **self.cfg.train.onecyclelr ) elif self.cfg.train.scheduler.lower() == "cosineannearingwarmrestarts": scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( self.optimizer, **self.cfg.train.cosineannearingwarmrestarts ) elif self.cfg.train.scheduler.lower() == "noamlr": scheduler = NoamLR(self.optimizer, **self.cfg.train.lr_scheduler) else: raise NotImplementedError( f"Scheduler {self.cfg.train.scheduler} not supported yet!" ) return scheduler def _init_accelerator(self): self.exp_dir = os.path.join( os.path.abspath(self.cfg.log_dir), self.args.exp_name ) project_config = ProjectConfiguration( project_dir=self.exp_dir, logging_dir=os.path.join(self.exp_dir, "log"), ) self.accelerator = accelerate.Accelerator( gradient_accumulation_steps=self.cfg.train.gradient_accumulation_step, log_with=self.cfg.train.tracker, project_config=project_config, ) if self.accelerator.is_main_process: os.makedirs(project_config.project_dir, exist_ok=True) os.makedirs(project_config.logging_dir, exist_ok=True) with self.accelerator.main_process_first(): self.accelerator.init_trackers(self.args.exp_name) def __check_basic_configs(self): if self.cfg.train.gradient_accumulation_step <= 0: self.logger.fatal("Invalid gradient_accumulation_step value!") self.logger.error( f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." ) self.accelerator.end_training() raise ValueError( f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." ) # TODO: check other values @staticmethod def __count_parameters(model): model_param = 0.0 if isinstance(model, dict): for key, value in model.items(): model_param += sum(p.numel() for p in model[key].parameters()) else: model_param = sum(p.numel() for p in model.parameters()) return model_param def __dump_cfg(self, path): os.makedirs(os.path.dirname(path), exist_ok=True) json5.dump( self.cfg, open(path, "w"), indent=4, sort_keys=True, ensure_ascii=False, quote_keys=True, ) ### Private methods end ###