Pretrain.py

# Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts (https://arxiv.org/abs/2111.08276)
# Github: https://github.com/zengyan-97/X-VLM
# Copyright (c) 2022, ByteDance Inc.
# All rights reserved.

import argparse
import os
import sys

import ruamel.yaml as yaml
import numpy as np
import random
import time
import datetime
import json
import math

import torch
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from torch.optim import Optimizer

from models.model_pretrain import XVLM

import utils
from dataset import create_dataset
from scheduler import create_scheduler
from optim import create_optimizer

from utils.checkpointer import Checkpointer
from utils.hdfs_io import hmkdir, hcopy
from accelerators.apex_ddp_accelerator import ApexDDPAccelerator


def reinit_scheduler_properties_mysched(optimizer: Optimizer, scheduler, cfg) -> None:
    """
    with ApexDDP, do re-init to avoid lr_scheduler warning.
    issue: https://github.com/pytorch/pytorch/issues/27595
    issue: https://github.com/PyTorchLightning/pytorch-lightning/issues/841
    """
    args = cfg

    if scheduler.optimizer == optimizer:
        # from transformers import get_linear_schedule_with_warmup
        def lr_lambda(current_step: int):
            if current_step < args.num_warmup_steps:
                return float(current_step) / float(max(1, args.num_warmup_steps))
            return max(
                0.0, float(args.num_training_steps - current_step) / float(
                    max(1, args.num_training_steps - args.num_warmup_steps))
            )

        scheduler.__init__(optimizer, lr_lambda, last_epoch=-1)


def train(model, general_loader, region_loader, optimizer, epoch_info, device, scheduler, config, accelerator, checkpointer):
    model.train()
    start_epoch, _ = epoch_info
    metric_logger = utils.MetricLogger(delimiter="  ")
    metric_logger.add_meter('loss_itc', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
    metric_logger.add_meter('loss_itm', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
    metric_logger.add_meter('loss_mlm', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
    metric_logger.add_meter('loss_bbox', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
    metric_logger.add_meter('loss_giou', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=50, fmt='{value:.6f}'))
    metric_logger.add_meter('lr_large', utils.SmoothedValue(window_size=50, fmt='{value:.6f}'))

    header = 'Train step: [{}]'.format(start_epoch)
    assert start_epoch == 0
    print_freq = 50

    world_size = utils.get_world_size()
    step_per_epoch = math.ceil(config['train_dataset_size']/(config['batch_size']*world_size))
    assert step_per_epoch > 1
    global_step = 0  # start from 0

    subarea_iter = iter(region_loader)

    for i, batch in enumerate(metric_logger.log_every(general_loader, print_freq, header, step_per_epoch, epoch_info)):

        if random.random() < config['regions']['iter_perc']:
            try:
                region_batch = next(subarea_iter)
            except StopIteration:
                subarea_iter = iter(region_loader)
                region_batch = next(subarea_iter)

            image, region_batch = region_batch[0].to(device, non_blocking=True), [
                t.to(device) if t is not None else None for t in region_batch[1:]]

            idx_to_group_img, text_ids, text_atts, text_ids_masked, masked_pos, masked_ids, \
                image_atts, target_bbox, is_image = region_batch

            if config['calc_image_bbox_loss']:
                is_image = None

            optimizer.zero_grad()

            loss_itc, loss_itm, loss_mlm, loss_bbox, loss_giou = \
                model(image, text_ids, text_atts, text_ids_masked=text_ids_masked, masked_pos=masked_pos, masked_ids=masked_ids,
                      image_atts=image_atts, idx_to_group_img=idx_to_group_img, target_bbox=target_bbox, is_image=is_image, ret_bbox_loss=True)

            loss = loss_itc + loss_itm + loss_mlm + loss_bbox + loss_giou
            accelerator.backward_step(loss, optimizer)

            accelerator_clip_grad_norm = float(config['accelerator']['CLIP_GRAD_NORM'])
            if accelerator_clip_grad_norm > 0:
                accelerator.optimizer_step(optimizer, model, accelerator_clip_grad_norm)
            optimizer.step()

            metric_logger.update(loss_bbox=loss_bbox.item())
            metric_logger.update(loss_giou=loss_giou.item())

        else:
            # fix it
            metric_logger.update(loss_bbox=0.5)
            metric_logger.update(loss_giou=0.5)

        image, batch = batch[0].to(device, non_blocking=True), [t.to(device) if t is not None else None for t in batch[1:]]
        text_ids, text_atts, text_ids_masked, masked_pos, masked_ids = batch

        optimizer.zero_grad()

        loss_itc, loss_itm, loss_mlm = model(image, text_ids, text_atts, text_ids_masked=text_ids_masked,
                                            masked_pos=masked_pos, masked_ids=masked_ids)

        loss = loss_itc + loss_itm + loss_mlm
        accelerator.backward_step(loss, optimizer)

        accelerator_clip_grad_norm = float(config['accelerator']['CLIP_GRAD_NORM'])
        if accelerator_clip_grad_norm > 0:
            accelerator.optimizer_step(optimizer, model, accelerator_clip_grad_norm)
        optimizer.step()
        scheduler.step()

        metric_logger.update(loss_itc=loss_itc.item())
        metric_logger.update(loss_itm=loss_itm.item())
        metric_logger.update(loss_mlm=loss_mlm.item())
        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
        metric_logger.update(lr_large=optimizer.param_groups[2]["lr"])

        if utils.is_main_process():
            current_epoch = global_step // step_per_epoch
            train_stats = {k: "{:.5f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}

            if (global_step+1) % step_per_epoch == 0:
                log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                             'epoch': current_epoch,
                             }

                with open("log.txt", "a") as f:
                    f.write(json.dumps(log_stats) + "\n")

                if (current_epoch+1) % config['ckpt_frequent'] == 0:
                    model_without_ddp = model
                    if hasattr(model, 'module'):
                        model_without_ddp = model.module

                    save_obj = {
                        'model': model_without_ddp.state_dict(),
                        # 'optimizer': optimizer.state_dict(),
                        # 'lr_scheduler': scheduler.state_dict(),
                        'config': config,
                        # 'epoch': current_epoch,
                    }
                    checkpointer.save_checkpoint(model_state=save_obj,
                                                 epoch=current_epoch,
                                                 training_states=optimizer.state_dict())

            if (global_step+1) % config['ckpt_frequent_step'] == 0:
                    model_without_ddp = model
                    if hasattr(model, 'module'):
                        model_without_ddp = model.module

                    save_obj = {
                        'model': model_without_ddp.state_dict(),
                        # 'optimizer': optimizer.state_dict(),
                        # 'lr_scheduler': scheduler.state_dict(),
                        'config': config,
                        # 'epoch': current_epoch,
                    }

                    checkpointer.save_checkpoint(model_state=save_obj,
                                                 epoch=current_epoch, step=global_step,
                                                 training_states=optimizer.state_dict())

        global_step += 1

    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger.global_avg())     
    return {k: "{:.5f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}
    

def main(args, config):
    utils.init_distributed_mode(args)
    device = torch.device(args.device)

    config['train_file'] = ','.join(config['train_file'])
    config['train_file_regions'] = ','.join(config['train_file_regions'])
    config['batch_size'] = config['images']['batch_size']

    seed = args.seed + utils.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    cudnn.benchmark = True

    print("Creating dataset", flush=True)
    general_dataset, region_dataset = create_dataset('pretrain', config)

    if utils.is_main_process():
        print(f"### train_file: {config['train_file']}", flush=True)
        print(f"### train_file_regions: {config['train_file_regions']}", flush=True)
        print(f"### batch size, {config['batch_size']} x {int(os.environ.get('WORLD_SIZE', 1))}")

    general_loader = torch.utils.data.DataLoader(general_dataset, batch_size=config['images']['batch_size'],
                                               num_workers=config['images']['num_workers'],
                                               pin_memory=True,
                                               drop_last=False,
                                               collate_fn=general_dataset.collate_fn)

    region_loader = torch.utils.data.DataLoader(region_dataset, batch_size=config['regions']['max_images'],  # batch_size = max_images * max_regions
                                               num_workers=config['regions']['num_workers'],
                                               pin_memory=True,
                                               drop_last=False,
                                               collate_fn=region_dataset.collate_fn)

    print("Creating model", flush=True)
    model = XVLM(config=config)
    print(model)
    model = model.to(device)
    print("### Total Params: ", sum(p.numel() for p in model.parameters() if p.requires_grad), flush=True)

    rank = int(os.environ.get('RANK', 0))
    local_rank = int(os.environ.get('LOCAL_RANK', 0))

    arg_opt = utils.AttrDict(config['optimizer'])
    optimizer = create_optimizer(arg_opt, model)

    arg_sche = utils.AttrDict(config['schedular'])
    world_size = int(os.environ.get('WORLD_SIZE', 1))
    arg_sche['step_per_epoch'] = math.ceil(config['train_dataset_size'] / (config['batch_size'] * world_size))
    lr_scheduler = create_scheduler(arg_sche, optimizer)

    arg_acc = utils.AttrDict(config['accelerator'])
    accelerator = ApexDDPAccelerator(arg_acc, logger=None)

    model, optimizer, lr_scheduler = accelerator.set_up(model, optimizer, lr_scheduler, local_rank, world_size, rank)
    reinit_scheduler_properties_mysched(optimizer, lr_scheduler, arg_sche)

    checkpointer = Checkpointer(args.output_dir)

    print("### output_dir, ", args.output_dir, flush=True)
    start_time = time.time()

    start_epoch = 0
    max_epoch = config['schedular']['epochs']
    epoch_info = (start_epoch, max_epoch)

    print("Start training", flush=True)
    train(model, general_loader, region_loader, optimizer, epoch_info, device, lr_scheduler, config,
          accelerator, checkpointer)
    dist.barrier()

    if utils.is_main_process():
        os.system("cat log.txt")
        hcopy('log.txt', args.output_dir)

    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print('Training time {}'.format(total_time_str), flush=True)

    print('### Time {}'.format(total_time_str))

    
if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--config', type=str, required=True)
    parser.add_argument('--output_dir', type=str, default='output/pretrain')
    parser.add_argument('--seed', default=42, type=int)
    parser.add_argument('--device', default='cuda')
    parser.add_argument('--distributed', action='store_false')
    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
    args = parser.parse_args()

    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)

    hmkdir(args.output_dir)

    yaml.dump(config, open('config.yaml', 'w'))
    hcopy('config.yaml', args.output_dir)

    main(args, config)