Delete model/trainer.py

model/trainer.py

@@ -1,250 +0,0 @@
-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}", weights_only=True, 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()