finetune_CE.py

#!/usr/bin/env python
# coding=utf-8

# code adapted from https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue_no_trainer.py 
""" Finetuning model for sequence classification with no trainer (fix text encoder)"""

import argparse
import logging
import math
import os
import random
import numpy as np
from pathlib import Path
from typing import (
    Union,
    Any,
    Dict,
)

import torch
import torch.nn.functional as F
import datasets
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from tqdm.auto import tqdm

import transformers
from accelerate import Accelerator
from huggingface_hub import Repository
from transformers import (
    AdamW,
    AutoConfig,
    AutoTokenizer,
    DataCollatorWithPadding,
    PretrainedConfig,
    SchedulerType,
    default_data_collator,
    get_scheduler,
    set_seed,
)
from transformers.file_utils import get_full_repo_name
from transformers.utils.versions import require_version

from models.bert.modeling_bert import BertForSequenceClassification

from utils import FairClassificationMetrics

logger = logging.getLogger(__name__)

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

def parse_args():
    parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
    parser.add_argument(
        "--max_length",
        type=int,
        default=128,
        help=(
            "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
            " sequences shorter will be padded if `--pad_to_max_lengh` is passed."
        ),
    )
    parser.add_argument(
        "--pad_to_max_length",
        action="store_true",
        help="If passed, pad all samples to `max_length`. Otherwise, dynamic padding is used.",
    )
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
        required=True,
    )
    parser.add_argument(
        "--use_slow_tokenizer",
        action="store_true",
        help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
    )
    parser.add_argument(
        "--per_device_train_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the training dataloader.",
    )
    parser.add_argument(
        "--per_device_eval_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the evaluation dataloader.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-5,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=None,
        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--lr_scheduler_type",
        type=SchedulerType,
        default="linear",
        help="The scheduler type to use.",
        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
    )
    parser.add_argument(
        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
    parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.")
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument(
        "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
    )
    parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
    # new arguments comes here
    parser.add_argument(
        "--eval_before_train",
        action="store_true",
        help="evaluation before training",
    )
    parser.add_argument(
        "--save_model",
        action="store_true",
        help="if save model",
    )
    parser.add_argument(
        "--dataset",
        type=str,
        default="jigsaw",
        choices=["biasbios", "jigsaw-race"],
        help="datasets",
    )
    parser.add_argument(
        "--model_type",
        type=str,
        default="bert",
        choices=["bert"],
        help="model type",
    )
    parser.add_argument(
        "--fine_tune_whole_network",
        action="store_true",
        help="whether fine-tune whole network or not",
    )
    args = parser.parse_args()

    # Sanity checks
    if args.push_to_hub:
        assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
    assert args.model_type is not None and args.model_type in ["bert"]
    return args

def main():
    args = parse_args()

    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
    accelerator = Accelerator()

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)
    
    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub:
            if args.hub_model_id is None:
                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
            else:
                repo_name = args.hub_model_id
            repo = Repository(args.output_dir, clone_from=repo_name)
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
    accelerator.wait_for_everyone()

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
        handlers=[
            logging.FileHandler(os.path.join(args.output_dir, f"train_{args.seed}.log")),
            logging.StreamHandler(),
        ],
    )
    logger.info(accelerator.state)

    # Setup logging, we only want one process per machine to log things on the screen.
    # accelerator.is_local_main_process is only True for one process per machine.
    logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    # load datasets and tokenizer, and preprocess datasets
    if args.dataset == 'biasbios':
        from dataset_loading import load_biasbios_for_ce
        
        # load tokenizer and preprocessing the datasets    
        tokenizer = AutoTokenizer.from_pretrained(
            args.model_name_or_path, 
            use_fast=not args.use_slow_tokenizer
        )
        processed_dataset, dataset_info  = load_biasbios_for_ce(tokenizer, args, accelerator)
        
        id_to_label = dataset_info["id_to_label"]
        label_to_id = dataset_info["label_to_id"]
        num_labels = dataset_info["num_labels"]
        
        train_dataset = processed_dataset["train"]
        val_dataset = processed_dataset["val"]
        test_dataset = processed_dataset["test"]
        
    elif args.dataset == 'jigsaw-race':
        from dataset_loading import load_jigsaw_race_for_ce
        
        # load tokenizer and preprocessing the datasets    
        tokenizer = AutoTokenizer.from_pretrained(
            args.model_name_or_path, 
            use_fast=not args.use_slow_tokenizer
        )
        
        processed_dataset, dataset_info  = load_jigsaw_race_for_ce(tokenizer, args, accelerator)
        
        id_to_label = dataset_info["id_to_label"]
        label_to_id = dataset_info["label_to_id"]
        num_labels = dataset_info["num_labels"]
        
        train_dataset = processed_dataset["train"]
        val_dataset = processed_dataset["val"]
        test_dataset = processed_dataset["test"]
        
    else:
        raise NotImplementedError

    # Load pretrained model and config based on datasets
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        args.model_name_or_path, 
        num_labels=num_labels
    )
    if args.model_type == 'bert':
        model = BertForSequenceClassification.from_pretrained(
            args.model_name_or_path,
            config=config,
        )
    else:
        raise NotImplementedError
    
    model.config.label2id = label_to_id
    model.config.id2label = id_to_label

    # Log a few random samples from the training set:
    # for index in random.sample(range(len(train_dataset)), 2):
    #     logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
        
    # Log some parameters here!
    logger.info(f"parser args : {vars(args)}")
    
    # DataLoaders creation:
    if args.pad_to_max_length:
        # If padding was already done ot max length, we use the default data collator that will just convert everything
        # to tensors.
        data_collator = default_data_collator
    else:
        # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of
        # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
        # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
        data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None))

    train_dataloader = DataLoader(
        train_dataset, 
        shuffle=True, 
        collate_fn=data_collator, 
        batch_size=args.per_device_train_batch_size,
    )
    eval_dataloader = DataLoader(
        val_dataset, 
        collate_fn=data_collator, 
        batch_size=args.per_device_eval_batch_size,
    )
    test_dataloader = DataLoader(
        test_dataset, 
        collate_fn=data_collator, 
        batch_size=args.per_device_eval_batch_size,
    )

    if args.fine_tune_whole_network:
        logger.info("Fine-tuning the whole network")
        # Optimizer
        # Split weights in two groups, one with weight decay and the other not.
        no_decay = ["bias", "LayerNorm.weight"]
        optimizer_grouped_parameters = [
            {
                "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
                "weight_decay": args.weight_decay,
            },
            {
                "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
                "weight_decay": 0.0,
            },
        ]
        optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
    else:
        logger.info("Fine-tuning the last layers")

        # disable model require_grad (except pooler and classifier)
        for n, p in model.named_parameters():
            if ('pooler' not in n) and ('classifier' not in n):
                p.requires_grad = False

        # Optimizer
        # Split weights in two groups, one with weight decay and the other not.
        # only add model pooler and cls to optimizer_grouped_parameters
        no_decay = ["bias", "LayerNorm.weight"]
        modules_to_optimize = ["pooler", "classifier"]
        # parameter w/ and w/o weight decay (we only finetune pooler and classifier)
        params_w_wd = [p for n, p in model.named_parameters() if (not any(nd in n for nd in no_decay)) and (any(mo in n for mo in modules_to_optimize))] 
        params_wo_wd = [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay) and (any(mo in n for mo in modules_to_optimize))]
        optimizer_grouped_parameters = [
            {
                "params": params_w_wd,
                "weight_decay": args.weight_decay,
            },
            {
                "params": params_wo_wd,
                "weight_decay": 0.0,
            },
        ]
        optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
    
    # Prepare everything with our `accelerator`.
    model, optimizer, train_dataloader, eval_dataloader, test_dataloader = accelerator.prepare(
        model, optimizer, train_dataloader, eval_dataloader, test_dataloader
    )

    # Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be
    # shorter in multiprocess)

    # Scheduler and math around the number of training steps.
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    else:
        args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    lr_scheduler = get_scheduler(
        name=args.lr_scheduler_type,
        optimizer=optimizer,
        num_warmup_steps=args.num_warmup_steps,
        num_training_steps=args.max_train_steps,
    )

    # load metric
    metrics = FairClassificationMetrics()

    # Train!
    total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num Epochs = {args.num_train_epochs}")
    logger.info(f"  Instantaneous batch size per device = {args.per_device_train_batch_size}")
    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
    logger.info(f"  Total optimization steps = {args.max_train_steps}")
    # Only show the progress bar once on each machine.
    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
    completed_steps = 0
    
    if args.eval_before_train:
        model.eval()
        assert len(metrics) == 0
        with torch.no_grad():
            for _, batch in enumerate(test_dataloader):
                outputs = model(**batch)
                predictions = outputs.logits.argmax(dim=-1)
                scores = F.softmax(outputs.logits, dim=-1)
                # add batch here
                metrics.add_batch(
                    predictions=accelerator.gather(predictions),
                    references=accelerator.gather(batch["labels"]),
                    scores=accelerator.gather(scores),
                    sensitive_attributes=accelerator.gather(batch["protected_group_labels"]),
                )
            # compute metrics
            eval_metrics, _ = metrics.compute()
            logger.info(f"Eval before train: {eval_metrics}")
    
    for epoch in range(args.num_train_epochs):
        model.train()
        for step, batch in enumerate(train_dataloader):
            outputs = model(**batch)
            loss = outputs.loss
            loss = loss / args.gradient_accumulation_steps
            accelerator.backward(loss)
            
            if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()
                progress_bar.update(1)
                completed_steps += 1
            
            if completed_steps >= args.max_train_steps:
                break
        
        model.eval()
        assert len(metrics) == 0
        with torch.no_grad():
            for _, batch in enumerate(test_dataloader):
                outputs = model(**batch)
                predictions = outputs.logits.argmax(dim=-1)
                scores = F.softmax(outputs.logits, dim=-1)
                # add batch here
                metrics.add_batch(
                    predictions=accelerator.gather(predictions),
                    references=accelerator.gather(batch["labels"]),
                    scores=accelerator.gather(scores),
                    sensitive_attributes=accelerator.gather(batch["protected_group_labels"]),
                )
            
            eval_metrics, _ = metrics.compute()
            logger.info(f"epoch {epoch}: {eval_metrics}")

        if args.push_to_hub and epoch < args.num_train_epochs - 1 and args.save_model:
            accelerator.wait_for_everyone()
            unwrapped_model = accelerator.unwrap_model(model)
            unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
            if accelerator.is_main_process:
                tokenizer.save_pretrained(args.output_dir)
                repo.push_to_hub(commit_message=f"Training in progress epoch {epoch}", blocking=False)

    # final evaluation on the test set
    model.eval()
    assert len(metrics) == 0
    with torch.no_grad():
        for _, batch in enumerate(test_dataloader):
            outputs = model(**batch)
            predictions = outputs.logits.argmax(dim=-1)
            scores = F.softmax(outputs.logits, dim=-1)
            # add batch here
            metrics.add_batch(
                predictions=accelerator.gather(predictions),
                references=accelerator.gather(batch["labels"]),
                scores=accelerator.gather(scores),
                sensitive_attributes=accelerator.gather(batch["protected_group_labels"]),
            )

        eval_metrics, _ = metrics.compute()
        logger.info(f"Performance on test set after training {epoch} epoch(s): {eval_metrics}")

    if args.output_dir is not None and args.save_model:
        accelerator.wait_for_everyone()
        unwrapped_model = accelerator.unwrap_model(model)
        unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
        if accelerator.is_main_process:
            tokenizer.save_pretrained(args.output_dir)
            if args.push_to_hub:
                repo.push_to_hub(commit_message="End of training")


if __name__ == "__main__":
    main()