unitable/src/trainer/train_beit.py

import wandb
from pathlib import Path
from typing import Tuple, List, Union, Dict
from omegaconf import DictConfig
from hydra.utils import instantiate
import logging
import torch
import time
from torch import nn, Tensor, autograd
from torch.utils.data import DataLoader
from torch.nn.parallel import DistributedDataParallel as DDP

from ..utils import printer, compute_grad_norm
from ..trainer.utils import configure_optimizer_weight_decay

SNAPSHOT_KEYS = set(["EPOCH", "STEP", "OPTIMIZER", "LR_SCHEDULER", "MODEL", "LOSS"])


class BeitTrainer:
    def __init__(
        self,
        device: int,
        model: nn.Module,
        model_vqvae: nn.Module,
        log: logging.Logger,
        exp_dir: Path,
        snapshot: Path = None,
        model_weights: Path = None,  # only for testing
    ) -> None:
        self.device = device
        self.log = log
        self.exp_dir = exp_dir
        self.criterion = nn.CrossEntropyLoss()
        assert (
            snapshot is None or model_weights is None
        ), "Snapshot and model weights cannot be set at the same time."

        self.model = model
        if snapshot is not None and snapshot.is_file():
            self.snapshot = self.load_snapshot(snapshot)
            self.model.load_state_dict(self.snapshot["MODEL"])
            self.start_epoch = self.snapshot["EPOCH"]
            self.global_step = self.snapshot["STEP"]
        elif model_weights is not None and model_weights.is_file():
            self.load_model(model_weights)
        else:
            self.snapshot = None
            self.start_epoch = 0
            self.global_step = 0

        self.model = self.model.to(device)
        self.model = DDP(self.model, device_ids=[device])
        self.model_vqvae = model_vqvae.to(device)

        # https://discuss.pytorch.org/t/extra-10gb-memory-on-gpu-0-in-ddp-tutorial/118113
        torch.cuda.set_device(device)  # master gpu takes up extra memory
        torch.cuda.empty_cache()

    def train_epoch(self, epoch: int, grad_clip: float = None):
        start = time.time()
        total_loss = 0.0
        total_samples = 0

        for i, obj in enumerate(self.train_dataloader):
            (trans_image, vqvae_image), bool_mask_pos = obj
            trans_image, vqvae_image, bool_mask_pos = (
                trans_image.to(self.device),
                vqvae_image.to(self.device),
                bool_mask_pos.to(self.device),
            )

            with torch.no_grad():
                input_ids = self.model_vqvae.get_codebook_indices(vqvae_image).flatten(
                    1
                )
                bool_mask_pos = bool_mask_pos.flatten(1).to(torch.bool)
                labels = input_ids[bool_mask_pos]

            with autograd.detect_anomaly():
                outputs = self.model(
                    trans_image, bool_mask_pos, return_all_tokens=False
                )
                loss = self.criterion(outputs, labels)

                self.optimizer.zero_grad()
                loss.backward()
                if grad_clip:
                    nn.utils.clip_grad_norm_(
                        self.model.parameters(), max_norm=grad_clip
                    )
                self.optimizer.step()

            loss = loss.detach().cpu().data
            total_loss += loss * trans_image.shape[0]
            total_samples += trans_image.shape[0]

            self.lr_scheduler.step()
            self.global_step += 1

            if i % 10 == 0:
                grad_norm = compute_grad_norm(self.model)
                lr = self.optimizer.param_groups[0]["lr"]
                elapsed = time.time() - start
                self.log.info(
                    printer(
                        self.device,
                        f"Epoch {epoch} Step {i + 1}/{len(self.train_dataloader)} | Loss {loss:.4f} ({total_loss / total_samples:.4f}) | Grad norm {grad_norm:.3f} | {total_samples / elapsed:4.1f} images/s | lr {lr:5.1e}",
                    )
                )

            if i % 100 == 0 and self.device == 0:
                lr = self.optimizer.param_groups[0]["lr"]
                log_info = {
                    "epoch": epoch,
                    "train_loss": loss,
                    "learning rate": lr,
                    "grad_norm": grad_norm,
                }

                wandb.log(
                    log_info,
                    step=self.global_step,
                )

        return total_loss / total_samples

    def train(
        self,
        train_dataloader: DataLoader,
        valid_dataloader: DataLoader,
        train_cfg: DictConfig,
        valid_cfg: DictConfig,
    ):
        self.train_dataloader = train_dataloader
        self.valid_dataloader = valid_dataloader

        # ensure correct weight decay: https://github.com/karpathy/minGPT/blob/37baab71b9abea1b76ab957409a1cc2fbfba8a26/mingpt/model.py#L215
        optim_params = configure_optimizer_weight_decay(
            self.model.module, weight_decay=train_cfg.optimizer.weight_decay
        )
        self.optimizer = instantiate(train_cfg.optimizer, optim_params)

        self.lr_scheduler = instantiate(
            train_cfg.lr_scheduler, optimizer=self.optimizer
        )

        if self.snapshot is not None:
            self.optimizer.load_state_dict(self.snapshot["OPTIMIZER"])
            self.lr_scheduler.load_state_dict(self.snapshot["LR_SCHEDULER"])

        best_loss = float("inf")
        self.model.train()
        for epoch in range(self.start_epoch, train_cfg.epochs):
            train_dataloader.sampler.set_epoch(epoch)
            train_loss = self.train_epoch(epoch, grad_clip=train_cfg.grad_clip)

            torch.cuda.empty_cache()

            valid_loss = self.valid(valid_cfg)

            if self.device == 0:
                wandb.log(
                    {
                        "train loss (epoch)": train_loss,
                        "valid loss (epoch)": valid_loss,
                    },
                    step=self.global_step,
                )

                if epoch % train_cfg.save_every == 0:
                    self.save_snapshot(epoch, best_loss)
                if valid_loss < best_loss:
                    self.save_model(epoch)
                    best_loss = valid_loss

    def valid(self, cfg: DictConfig):
        total_samples = 0
        total_loss = 0.0

        self.model.eval()
        for i, obj in enumerate(self.valid_dataloader):
            (trans_image, vqvae_image), bool_mask_pos = obj
            trans_image, vqvae_image, bool_mask_pos = (
                trans_image.to(self.device),
                vqvae_image.to(self.device),
                bool_mask_pos.to(self.device),
            )

            with torch.no_grad():
                input_ids = self.model_vqvae.get_codebook_indices(vqvae_image).flatten(
                    1
                )
                bool_mask_pos = bool_mask_pos.flatten(1).to(torch.bool)
                labels = input_ids[bool_mask_pos]

                outputs = self.model(
                    trans_image, bool_mask_pos, return_all_tokens=False
                )
                loss = self.criterion(outputs, labels)

                loss = loss.detach().cpu().data
                total_loss += loss * trans_image.shape[0]
                total_samples += trans_image.shape[0]

            if i % 10 == 0:
                self.log.info(
                    printer(
                        self.device,
                        f"Valid: Step {i + 1}/{len(self.valid_dataloader)} | Loss {loss:.4f} ({total_loss / total_samples:.4f})",
                    )
                )

        return total_loss / total_samples

    def save_model(self, epoch: int):
        filename = Path(self.exp_dir) / "model" / f"epoch{epoch}_model.pt"
        torch.save(self.model.module.state_dict(), filename)
        self.log.info(printer(self.device, f"Saving model to {filename}"))
        filename = Path(self.exp_dir) / "model" / f"best.pt"
        torch.save(self.model.module.state_dict(), filename)

    def load_model(self, path: Union[str, Path]):
        self.model.load_state_dict(torch.load(path, map_location="cpu"))
        self.log.info(printer(self.device, f"Loading model from {path}"))

    def save_snapshot(self, epoch: int, best_loss: float):
        state_info = {
            "EPOCH": epoch + 1,
            "STEP": self.global_step,
            "OPTIMIZER": self.optimizer.state_dict(),
            "LR_SCHEDULER": self.lr_scheduler.state_dict(),
            "MODEL": self.model.module.state_dict(),
            "LOSS": best_loss,
        }

        snapshot_path = Path(self.exp_dir) / "snapshot" / f"epoch{epoch}_snapshot.pt"
        torch.save(state_info, snapshot_path)

        self.log.info(printer(self.device, f"Saving snapshot to {snapshot_path}"))

    def load_snapshot(self, path: Path):
        self.log.info(printer(self.device, f"Loading snapshot from {path}"))
        snapshot = torch.load(path, map_location="cpu")
        assert SNAPSHOT_KEYS.issubset(snapshot.keys())
        return snapshot