from __future__ import annotations import os import gc from tqdm import tqdm import wandb import torch from torch.optim import AdamW from torch.utils.data import DataLoader, Dataset, SequentialSampler from torch.optim.lr_scheduler import LinearLR, SequentialLR from einops import rearrange from accelerate import Accelerator from accelerate.utils import DistributedDataParallelKwargs from ema_pytorch import EMA from model import CFM from model.utils import exists, default from model.dataset import DynamicBatchSampler, collate_fn # trainer class Trainer: def __init__( self, model: CFM, epochs, learning_rate, num_warmup_updates = 20000, save_per_updates = 1000, checkpoint_path = None, batch_size = 32, batch_size_type: str = "sample", max_samples = 32, grad_accumulation_steps = 1, max_grad_norm = 1.0, noise_scheduler: str | None = None, duration_predictor: torch.nn.Module | None = None, wandb_project = "test_e2-tts", wandb_run_name = "test_run", wandb_resume_id: str = None, last_per_steps = None, accelerate_kwargs: dict = dict(), ema_kwargs: dict = dict() ): ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters = True) self.accelerator = Accelerator( log_with = "wandb", kwargs_handlers = [ddp_kwargs], gradient_accumulation_steps = grad_accumulation_steps, **accelerate_kwargs ) if exists(wandb_resume_id): init_kwargs={"wandb": {"resume": "allow", "name": wandb_run_name, 'id': wandb_resume_id}} else: init_kwargs={"wandb": {"resume": "allow", "name": wandb_run_name}} self.accelerator.init_trackers( project_name = wandb_project, init_kwargs=init_kwargs, config={"epochs": epochs, "learning_rate": learning_rate, "num_warmup_updates": num_warmup_updates, "batch_size": batch_size, "batch_size_type": batch_size_type, "max_samples": max_samples, "grad_accumulation_steps": grad_accumulation_steps, "max_grad_norm": max_grad_norm, "gpus": self.accelerator.num_processes, "noise_scheduler": noise_scheduler} ) self.model = model if self.is_main: self.ema_model = EMA( model, include_online_model = False, **ema_kwargs ) self.ema_model.to(self.accelerator.device) self.epochs = epochs self.num_warmup_updates = num_warmup_updates self.save_per_updates = save_per_updates self.last_per_steps = default(last_per_steps, save_per_updates * grad_accumulation_steps) self.checkpoint_path = default(checkpoint_path, 'ckpts/test_e2-tts') self.batch_size = batch_size self.batch_size_type = batch_size_type self.max_samples = max_samples self.grad_accumulation_steps = grad_accumulation_steps self.max_grad_norm = max_grad_norm self.noise_scheduler = noise_scheduler self.duration_predictor = duration_predictor self.optimizer = AdamW(model.parameters(), lr=learning_rate) self.model, self.optimizer = self.accelerator.prepare( self.model, self.optimizer ) @property def is_main(self): return self.accelerator.is_main_process def save_checkpoint(self, step, last=False): self.accelerator.wait_for_everyone() if self.is_main: checkpoint = dict( model_state_dict = self.accelerator.unwrap_model(self.model).state_dict(), optimizer_state_dict = self.accelerator.unwrap_model(self.optimizer).state_dict(), ema_model_state_dict = self.ema_model.state_dict(), scheduler_state_dict = self.scheduler.state_dict(), step = step ) if not os.path.exists(self.checkpoint_path): os.makedirs(self.checkpoint_path) if last == True: self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_last.pt") print(f"Saved last checkpoint at step {step}") else: self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_{step}.pt") def load_checkpoint(self): if not exists(self.checkpoint_path) or not os.path.exists(self.checkpoint_path) or not os.listdir(self.checkpoint_path): return 0 self.accelerator.wait_for_everyone() if "model_last.pt" in os.listdir(self.checkpoint_path): latest_checkpoint = "model_last.pt" else: latest_checkpoint = sorted([f for f in os.listdir(self.checkpoint_path) if f.endswith('.pt')], key=lambda x: int(''.join(filter(str.isdigit, x))))[-1] # checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location=self.accelerator.device) # rather use accelerator.load_state ಥ_ಥ checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location="cpu") if self.is_main: self.ema_model.load_state_dict(checkpoint['ema_model_state_dict']) if 'step' in checkpoint: self.accelerator.unwrap_model(self.model).load_state_dict(checkpoint['model_state_dict']) self.accelerator.unwrap_model(self.optimizer).load_state_dict(checkpoint['optimizer_state_dict']) if self.scheduler: self.scheduler.load_state_dict(checkpoint['scheduler_state_dict']) step = checkpoint['step'] else: checkpoint['model_state_dict'] = {k.replace("ema_model.", ""): v for k, v in checkpoint['ema_model_state_dict'].items() if k not in ["initted", "step"]} self.accelerator.unwrap_model(self.model).load_state_dict(checkpoint['model_state_dict']) step = 0 del checkpoint; gc.collect() return step def train(self, train_dataset: Dataset, num_workers=16, resumable_with_seed: int = None): if exists(resumable_with_seed): generator = torch.Generator() generator.manual_seed(resumable_with_seed) else: generator = None if self.batch_size_type == "sample": train_dataloader = DataLoader(train_dataset, collate_fn=collate_fn, num_workers=num_workers, pin_memory=True, persistent_workers=True, batch_size=self.batch_size, shuffle=True, generator=generator) elif self.batch_size_type == "frame": self.accelerator.even_batches = False sampler = SequentialSampler(train_dataset) batch_sampler = DynamicBatchSampler(sampler, self.batch_size, max_samples=self.max_samples, random_seed=resumable_with_seed, drop_last=False) train_dataloader = DataLoader(train_dataset, collate_fn=collate_fn, num_workers=num_workers, pin_memory=True, persistent_workers=True, batch_sampler=batch_sampler) else: raise ValueError(f"batch_size_type must be either 'sample' or 'frame', but received {self.batch_size_type}") # accelerator.prepare() dispatches batches to devices; # which means the length of dataloader calculated before, should consider the number of devices warmup_steps = self.num_warmup_updates * self.accelerator.num_processes # consider a fixed warmup steps while using accelerate multi-gpu ddp # otherwise by default with split_batches=False, warmup steps change with num_processes total_steps = len(train_dataloader) * self.epochs / self.grad_accumulation_steps decay_steps = total_steps - warmup_steps warmup_scheduler = LinearLR(self.optimizer, start_factor=1e-8, end_factor=1.0, total_iters=warmup_steps) decay_scheduler = LinearLR(self.optimizer, start_factor=1.0, end_factor=1e-8, total_iters=decay_steps) self.scheduler = SequentialLR(self.optimizer, schedulers=[warmup_scheduler, decay_scheduler], milestones=[warmup_steps]) train_dataloader, self.scheduler = self.accelerator.prepare(train_dataloader, self.scheduler) # actual steps = 1 gpu steps / gpus start_step = self.load_checkpoint() global_step = start_step if exists(resumable_with_seed): orig_epoch_step = len(train_dataloader) skipped_epoch = int(start_step // orig_epoch_step) skipped_batch = start_step % orig_epoch_step skipped_dataloader = self.accelerator.skip_first_batches(train_dataloader, num_batches=skipped_batch) else: skipped_epoch = 0 for epoch in range(skipped_epoch, self.epochs): self.model.train() if exists(resumable_with_seed) and epoch == skipped_epoch: progress_bar = tqdm(skipped_dataloader, desc=f"Epoch {epoch+1}/{self.epochs}", unit="step", disable=not self.accelerator.is_local_main_process, initial=skipped_batch, total=orig_epoch_step) else: progress_bar = tqdm(train_dataloader, desc=f"Epoch {epoch+1}/{self.epochs}", unit="step", disable=not self.accelerator.is_local_main_process) for batch in progress_bar: with self.accelerator.accumulate(self.model): text_inputs = batch['text'] mel_spec = rearrange(batch['mel'], 'b d n -> b n d') mel_lengths = batch["mel_lengths"] # TODO. add duration predictor training if self.duration_predictor is not None and self.accelerator.is_local_main_process: dur_loss = self.duration_predictor(mel_spec, lens=batch.get('durations')) self.accelerator.log({"duration loss": dur_loss.item()}, step=global_step) loss, cond, pred = self.model(mel_spec, text=text_inputs, lens=mel_lengths, noise_scheduler=self.noise_scheduler) self.accelerator.backward(loss) if self.max_grad_norm > 0 and self.accelerator.sync_gradients: self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm) self.optimizer.step() self.scheduler.step() self.optimizer.zero_grad() if self.is_main: self.ema_model.update() global_step += 1 if self.accelerator.is_local_main_process: self.accelerator.log({"loss": loss.item(), "lr": self.scheduler.get_last_lr()[0]}, step=global_step) progress_bar.set_postfix(step=str(global_step), loss=loss.item()) if global_step % (self.save_per_updates * self.grad_accumulation_steps) == 0: self.save_checkpoint(global_step) if global_step % self.last_per_steps == 0: self.save_checkpoint(global_step, last=True) self.accelerator.end_training()