mc.py

import os
import torch
import torch.nn.functional as F
import numpy as np
import random
import json
import math
import sys
from typing import Iterable
import argparse
import time
import datetime
from util import dist
from torch.utils.data import DataLoader, DistributedSampler
from collections import namedtuple
from functools import reduce

from datasets import build_mc_dataset, mc_collate_fn
from model import build_model, get_tokenizer
from main import get_args_parser
from util.misc import get_mask, adjust_learning_rate, mask_tokens
from util.metrics import MetricLogger


def train_one_epoch(
    model: torch.nn.Module,
    tokenizer,
    data_loader: Iterable,
    optimizer: torch.optim.Optimizer,
    device: torch.device,
    epoch: int,
    args,
    max_norm: float = 0,
):
    model.train()
    metric_logger = MetricLogger(delimiter="  ")
    header = "Epoch: [{}]".format(epoch)
    num_training_steps = int(len(data_loader) * args.epochs)

    for i_batch, batch_dict in enumerate(
        metric_logger.log_every(data_loader, args.print_freq, header)
    ):
        video = batch_dict["video"].to(device)
        video_len = batch_dict["video_len"]
        video_mask = get_mask(video_len, video.size(1)).to(device)

        text = batch_dict["text"]
        logits_list = []
        for aid in range(len(text)):  # one forward per answer candidate id
            encoded = tokenizer(
                text[aid],
                add_special_tokens=True,
                max_length=args.max_tokens,
                padding="longest",
                truncation=True,
                return_tensors="pt",
            )
            # forward
            output = model(
                video=video,
                video_mask=video_mask,
                input_ids=encoded["input_ids"].to(device),
                attention_mask=encoded["attention_mask"].to(device),
            )
            logits = output["logits"]
            # get logits for the mask token
            delay = args.max_feats if args.use_video else 0
            logits = logits[:, delay : encoded["input_ids"].size(1) + delay][
                encoded["input_ids"] == tokenizer.mask_token_id
            ]
            logits = logits.softmax(-1)
            logits_list.append(logits[:, 0])
        logits = torch.stack(logits_list, 1)
        gt = batch_dict["answer_id"].to(device)
        if data_loader.dataset.mc > 1:
            pos_logits = logits[torch.arange(len(logits)), gt]
            neg_mask = torch.ones_like(logits)
            neg_mask.scatter_(1, gt.unsqueeze(-1), 0)
            neg_logits = logits[neg_mask[:, :].bool()].view(
                len(logits), data_loader.dataset.mc - 1
            )
            pos_loss = F.binary_cross_entropy(
                pos_logits, torch.ones(len(pos_logits)).to(device)
            )
            neg_logits = neg_logits.view(-1)
            neg_loss = F.binary_cross_entropy(
                neg_logits,
                torch.zeros(len(neg_logits)).to(device),
            )
            loss = (pos_loss + neg_loss) / 2  # balanced BCE
        else:
            loss = F.binary_cross_entropy(logits.squeeze(1), gt.float())

        loss_dict = {"cls_loss": loss}
        loss_dict_reduced = dist.reduce_dict(loss_dict)
        loss_reduced = sum(loss_dict_reduced.values())
        loss_value = loss_reduced.item()

        if not math.isfinite(loss_value):
            print("Loss is {}, stopping training".format(loss_value))
            print(loss_dict_reduced)
            sys.exit(1)

        optimizer.zero_grad()
        loss.backward()
        if max_norm > 0:
            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
        optimizer.step()

        adjust_learning_rate(
            optimizer,
            curr_step=epoch * len(data_loader) + i_batch,
            num_training_steps=num_training_steps,
            args=args,
        )

        metric_logger.update(loss=loss_value, **loss_dict_reduced)
        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger)
    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}


@torch.no_grad()
def evaluate(
    model: torch.nn.Module,
    tokenizer,
    data_loader,
    device: torch.device,
    dataset_name,
    args,
    split="test",
    type_map={0: "all"},
):
    model.eval()
    metric_logger = MetricLogger(delimiter="  ")
    header = f"{split}:"

    res = {}

    for i_batch, batch_dict in enumerate(
        metric_logger.log_every(data_loader, args.print_freq, header)
    ):
        video = batch_dict["video"].to(device)
        video_len = batch_dict["video_len"]
        video_mask = get_mask(video_len, video.size(1)).to(device)
        text = batch_dict["text"]
        logits_list = []
        for aid in range(len(text)):
            encoded = tokenizer(
                text[aid],
                add_special_tokens=True,
                max_length=args.max_tokens,
                padding="longest",
                truncation=True,
                return_tensors="pt",
            )
            # forward
            output = model(
                video=video,
                video_mask=video_mask,
                input_ids=encoded["input_ids"].to(device),
                attention_mask=encoded["attention_mask"].to(device),
            )
            logits = output["logits"]
            # get logits for the mask token
            delay = args.max_feats if args.use_video else 0
            logits = logits[:, delay : encoded["input_ids"].size(1) + delay][
                encoded["input_ids"] == tokenizer.mask_token_id
            ]
            logits_list.append(logits.softmax(-1)[:, 0])
        logits = torch.stack(logits_list, 1)
        if logits.shape[1] == 1:
            preds = logits.round().long().squeeze(1)
        else:
            preds = logits.max(1).indices
        qids = batch_dict["qid"]
        types = batch_dict["type"]
        if batch_dict["answer_id"][0].item() != -1:
            answer_id = batch_dict["answer_id"].to(device)
            agreeings = preds == answer_id

            for i, (qid, gt, pred, type) in enumerate(
                zip(qids, answer_id, preds, types)
            ):
                res[qid] = (
                    {
                        "pred": pred.cpu().detach().item(),
                        "gt": gt.cpu().detach().item(),
                        "type": int(type),
                    }
                    if type_map is not None and len(type_map) > 1
                    else {
                        "pred": pred.cpu().detach().item(),
                        "gt": gt.cpu().detach().item(),
                    }
                )
                res[qid][f"acc"] = agreeings[i].cpu().detach().item()

            dico = {"acc": agreeings.sum() / len(qids)}
            dico_reduced = dist.reduce_dict(dico)
            acc_value = dico_reduced["acc"].item()
            metric_logger.update(acc=acc_value)
        else:
            for i, (qid, pred, type) in enumerate(zip(qids, preds, types)):
                res[str(qid)] = int(pred.cpu().detach().item())

    all_res = dist.all_gather(res)
    results = reduce(lambda a, b: a.update(b) or a, all_res, {})
    assert len(results) == len(data_loader.dataset)
    if isinstance(next(iter(results.values())), dict):
        acc = sum(int(results[qid][f"acc"]) for qid in results) / len(results)
        if type_map is not None and len(type_map) > 1:
            acc_type = {
                type_map[i]: sum(
                    results[qid][f"acc"] for qid in results if results[qid]["type"] == i
                )
                / len([x for x in results.values() if x["type"] == i])
                for i in type_map
            }
        if dist.is_main_process():
            print(dataset_name)
            print(f"{split} acc: {acc: .2%}")
            if type_map is not None and len(type_map) > 1:
                for x in acc_type:
                    print(f"acc {x}: {acc_type[x]: .2%}")

        return results, acc
    else:
        return results, 0


def main(args):
    # Init distributed mode
    dist.init_distributed_mode(args)

    if dist.is_main_process():
        if args.save_dir and not (os.path.isdir(args.save_dir)):
            os.makedirs(os.path.join(args.save_dir), exist_ok=True)
        print(args)

    device = torch.device(args.device)

    # Fix seeds
    seed = args.seed + dist.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)

    # Build model
    args.n_ans = 2  # Yes and No
    model = build_model(args)
    model.to(device)
    tokenizer = get_tokenizer(args)
    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
    if dist.is_main_process():
        print("number of params:", n_parameters)

    # Set up optimizer
    params_for_optimization = list(p for p in model.parameters() if p.requires_grad)
    optimizer = torch.optim.Adam(
        params_for_optimization,
        lr=args.lr,
        betas=(args.beta1, args.beta2),
        weight_decay=args.weight_decay,
    )

    nt = namedtuple(
        typename="data",
        field_names=[
            "dataset_name",
            "dataloader_test",
            "dataloader_val",
            "dataloader_train",
        ],
    )

    tuples = []
    for dset_name in args.combine_datasets_val:
        if args.n_ans:
            tok_yes = torch.tensor(
                tokenizer(
                    "Yes",
                    add_special_tokens=False,
                    max_length=1,
                    truncation=True,
                    padding="max_length",
                )["input_ids"],
                dtype=torch.long,
            )
            tok_no = torch.tensor(
                tokenizer(
                    "No",
                    add_special_tokens=False,
                    max_length=1,
                    truncation=True,
                    padding="max_length",
                )["input_ids"],
                dtype=torch.long,
            )
            a2tok = torch.stack([tok_yes, tok_no])
            model.set_answer_embeddings(
                a2tok.to(model.device), freeze_last=args.freeze_last
            )  # initialize answer embedding module
        dataset_test = build_mc_dataset(
            dset_name,
            "val" if (args.eval and not args.test) else "test",
            args,
            tokenizer,
        )
        sampler_test = (
            DistributedSampler(dataset_test, shuffle=False)
            if args.distributed
            else torch.utils.data.SequentialSampler(dataset_test)
        )
        dataloader_test = DataLoader(
            dataset_test,
            batch_size=args.batch_size_val,
            sampler=sampler_test,
            collate_fn=mc_collate_fn,
            num_workers=args.num_workers,
        )

        dataset_val = build_mc_dataset(dset_name, "val", args, tokenizer)
        sampler_val = (
            DistributedSampler(dataset_val, shuffle=False)
            if args.distributed
            else torch.utils.data.SequentialSampler(dataset_val)
        )
        dataloader_val = DataLoader(
            dataset_val,
            batch_size=args.batch_size_val,
            sampler=sampler_val,
            collate_fn=mc_collate_fn,
            num_workers=args.num_workers,
        )

        if not args.eval:
            dataset_train = build_mc_dataset(dset_name, "train", args, tokenizer)
            sampler_train = (
                DistributedSampler(dataset_train)
                if args.distributed
                else torch.utils.data.RandomSampler(dataset_train)
            )
            dataloader_train = DataLoader(
                dataset_train,
                batch_size=args.batch_size,
                sampler=sampler_train,
                collate_fn=mc_collate_fn,
                num_workers=args.num_workers,
            )
        else:
            dataloader_train = None

        tuples.append(
            nt(
                dataset_name=dset_name,
                dataloader_test=dataloader_test,
                dataloader_val=dataloader_val,
                dataloader_train=dataloader_train,
            )
        )

    # Load pretrained checkpoint
    if args.load:
        if dist.is_main_process():
            print("loading from", args.load)
        checkpoint = torch.load(args.load, map_location="cpu")
        model.load_state_dict(checkpoint["model"], strict=False)
        if args.resume and not args.eval:
            optimizer.load_state_dict(checkpoint["optimizer"])
            args.start_epoch = checkpoint["epoch"] + 1

    for i, item in enumerate(tuples):
        if not args.eval:
            if dist.is_main_process():
                print("Start training")
            start_time = time.time()
            best_epoch = args.start_epoch
            best_acc = 0
            for epoch in range(args.start_epoch, args.epochs):
                if dist.is_main_process():
                    print(f"Starting epoch {epoch}")
                if args.distributed:
                    sampler_train.set_epoch(epoch)
                train_stats = train_one_epoch(
                    model=model,
                    tokenizer=tokenizer,
                    data_loader=item.dataloader_train,
                    optimizer=optimizer,
                    device=device,
                    epoch=epoch,
                    args=args,
                    max_norm=args.clip_max_norm,
                )

                if (epoch + 1) % args.eval_skip == 0:
                    val_stats = {}
                    for i, item in enumerate(tuples):
                        print(f"Validating {item.dataset_name}")

                        curr_val_stats, acc = evaluate(
                            model=model,
                            tokenizer=tokenizer,
                            data_loader=item.dataloader_val,
                            device=device,
                            dataset_name=item.dataset_name,
                            args=args,
                            split="val",
                            type_map=item.dataloader_val.dataset.type_map,
                        )
                        val_stats[item.dataset_name + "_acc"] = acc
                        if acc > best_acc:
                            best_epoch = epoch
                            best_acc = acc

                            if args.save_dir and dist.is_main_process():
                                checkpoint_path = os.path.join(
                                    args.save_dir, f"best_model.pth"
                                )
                                dist.save_on_master(
                                    {
                                        "model": model.state_dict(),
                                        "optimizer": optimizer.state_dict(),
                                        "epoch": epoch,
                                        "args": args,
                                    },
                                    checkpoint_path,
                                )
                                json.dump(
                                    curr_val_stats,
                                    open(
                                        os.path.join(
                                            args.save_dir,
                                            item.dataset_name + "_val.json",
                                        ),
                                        "w",
                                    ),
                                )
                                json.dump(
                                    {"acc": acc, "ep": epoch},
                                    open(
                                        os.path.join(
                                            args.save_dir,
                                            item.dataset_name + "acc_val.json",
                                        ),
                                        "w",
                                    ),
                                )
                else:
                    val_stats = {}

                log_stats = {
                    **{f"train_{k}": v for k, v in train_stats.items()},
                    **{f"val_{k}": v for k, v in val_stats.items()},
                    "epoch": epoch,
                    "n_parameters": n_parameters,
                }

                if args.save_dir and dist.is_main_process():
                    with open(os.path.join(args.save_dir, "log.txt"), "a") as f:
                        f.write(json.dumps(log_stats) + "\n")
                    checkpoint_path = os.path.join(args.save_dir, f"ckpt.pth")
                    dist.save_on_master(
                        {
                            "model": model.state_dict(),
                            "optimizer": optimizer.state_dict(),
                            "epoch": epoch,
                            "args": args,
                        },
                        checkpoint_path,
                    )

            total_time = time.time() - start_time
            total_time_str = str(datetime.timedelta(seconds=int(total_time)))
            print("Training time {}".format(total_time_str))
            # load best ckpt
            if dist.is_main_process() and args.save_dir:
                print(f"loading best checkpoint from epoch {best_epoch}")
            if args.save_dir:
                torch.distributed.barrier()  # wait all processes
                checkpoint = torch.load(
                    os.path.join(args.save_dir, f"best_model.pth"),
                    map_location="cpu",
                )
                model.load_state_dict(checkpoint["model"], strict=False)

        results, acc = evaluate(
            model=model,
            tokenizer=tokenizer,
            data_loader=item.dataloader_test,
            device=device,
            dataset_name=item.dataset_name,
            args=args,
            type_map=item.dataloader_test.dataset.type_map,
            split="val" if (args.eval and not args.test) else "test",
        )

        if args.save_dir and dist.is_main_process():
            json.dump(
                results,
                open(
                    os.path.join(
                        args.save_dir,
                        item.dataset_name + "_val.json"
                        if (args.eval and not args.test)
                        else item.dataset_name + "_test.json",
                    ),
                    "w",
                ),
            )
            json.dump(
                {"acc": float(acc)},
                open(
                    os.path.join(
                        args.save_dir,
                        item.dataset_name + "acc_val.json"
                        if (args.eval and not args.test)
                        else item.dataset_name + "acc_test.json",
                    ),
                    "w",
                ),
            )


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        "Frozen training and evaluation script", parents=[get_args_parser()]
    )
    args = parser.parse_args()
    if args.save_dir:
        args.save_dir = os.path.join(args.presave_dir, args.save_dir)
    args.model_name = os.path.join(os.environ["TRANSFORMERS_CACHE"], args.model_name)
    main(args)