model/trainer.py

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 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(),
        bnb_optimizer: bool = False,
    ):
        ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)

        logger = "wandb" if wandb.api.api_key else None
        print(f"Using logger: {logger}")

        self.accelerator = Accelerator(
            log_with=logger,
            kwargs_handlers=[ddp_kwargs],
            gradient_accumulation_steps=grad_accumulation_steps,
            **accelerate_kwargs,
        )

        if logger == "wandb":
            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

        if bnb_optimizer:
            import bitsandbytes as bnb

            self.optimizer = bnb.optim.AdamW8bit(model.parameters(), lr=learning_rate)
        else:
            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:
                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 = batch["mel"].permute(0, 2, 1)
                    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.save_checkpoint(global_step, last=True)

        self.accelerator.end_training()