finetune.py

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

import argparse
import logging
import math
import os
import random
import datasets
import torch
from functools import partial
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
import pdb

import transformers
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
    SchedulerType,
    DataCollatorForSeq2Seq,
    get_scheduler,
    GPTNeoXTokenizerFast,
    GPT2Tokenizer,
    OPTForCausalLM,
)
from peft import LoraConfig, TaskType, get_peft_model

logger = get_logger(__name__)

"""
@misc{wang2023far,
      title={How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources}, 
      author={Yizhong Wang and Hamish Ivison and Pradeep Dasigi and Jack Hessel and Tushar Khot and Khyathi Raghavi Chandu and David Wadden and Kelsey MacMillan and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi},
      year={2023},
      eprint={2306.04751},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""


def parse_args():
    parser = argparse.ArgumentParser(
        description="Finetune a transformers model on a causal language modeling task"
    )
    parser.add_argument(
        "--dataset_name",
        type=str,
        default=None,
        help="The name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--dataset_config_name",
        type=str,
        default=None,
        help="The configuration name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--train_file",
        type=str,
        default=None,
        help="A csv or a json file containing the training data.",
    )
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
        required=False,
    )
    parser.add_argument(
        "--config_name",
        type=str,
        default=None,
        help="Pretrained config name or path if not the same as model_name",
    )
    parser.add_argument(
        "--use_lora",
        action="store_true",
        help="If passed, will use LORA (low-rank parameter-efficient training) to train the model.",
    )
    parser.add_argument(
        "--lora_rank",
        type=int,
        default=64,
        help="The rank of lora.",
    )
    parser.add_argument(
        "--lora_alpha",
        type=float,
        default=16,
        help="The alpha parameter of lora.",
    )
    parser.add_argument(
        "--lora_dropout",
        type=float,
        default=0.1,
        help="The dropout rate of lora modules.",
    )
    parser.add_argument(
        "--save_merged_lora_model",
        action="store_true",
        help="If passed, will merge the lora modules and save the entire model.",
    )
    parser.add_argument(
        "--tokenizer_name",
        type=str,
        default=None,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    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(
        "--max_seq_length",
        type=int,
        default=4096,
        help="The maximum total sequence length (prompt+completion) of each training example.",
    )
    parser.add_argument(
        "--per_device_train_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the training 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(
        "--warmup_ratio",
        type=float,
        default=0,
        help="Ratio of total training steps used for warmup.",
    )
    parser.add_argument(
        "--output_dir", type=str, default=None, help="Where to store the final model."
    )
    parser.add_argument(
        "--seed", type=int, default=None, help="A seed for reproducible training."
    )
    parser.add_argument(
        "--preprocessing_num_workers",
        type=int,
        default=None,
        help="The number of processes to use for the preprocessing.",
    )
    parser.add_argument(
        "--overwrite_cache",
        action="store_true",
        help="Overwrite the cached training and evaluation sets",
    )
    parser.add_argument(
        "--checkpointing_steps",
        type=str,
        default=None,
        help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
    )
    parser.add_argument(
        "--logging_steps",
        type=int,
        default=None,
        help="Log the training loss and learning rate every logging_steps steps.",
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help="If the training should continue from a checkpoint folder.",
    )
    parser.add_argument(
        "--with_tracking",
        action="store_true",
        help="Whether to enable experiment trackers for logging.",
    )
    parser.add_argument(
        "--tracking_group",
        type=str,
        default=None,
        help="Configure tracking group name.",
    )
    parser.add_argument(
        "--tracking_project",
        type=str,
        default="test",
        help="Configure tracking project name.",
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="all",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
            ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.'
            "Only applicable when `--with_tracking` is passed."
        ),
    )
    parser.add_argument(
        "--low_cpu_mem_usage",
        action="store_true",
        help=(
            "It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded."
            "If passed, LLM loading time and RAM consumption will be benefited."
        ),
    )
    parser.add_argument(
        "--mask_observation_compl",
        action="store_true",
    )

    args = parser.parse_args()

    # Sanity checks
    if args.dataset_name is None and args.train_file is None:
        raise ValueError("Need either a dataset name or a training file.")
    else:
        if args.train_file is not None:
            extension = args.train_file.split(".")[-1]
            assert extension in [
                "json",
                "jsonl",
            ], "`train_file` should be a json/jsonl file."

    return args


def encode_with_prompt_completion_format(example, tokenizer, max_seq_length):
    """
    Here we assume each example has 'prompt' and 'completion' fields.
    We concatenate prompt and completion and tokenize them together because otherwise prompt will be padded/trancated
    and it doesn't make sense to follow directly with the completion.
    """
    # if prompt doesn't end with space and completion doesn't start with space, add space
    if not example["prompt"].endswith((" ", "\n", "\t")) and not example[
        "completion"
    ].startswith((" ", "\n", "\t")):
        example_text = example["prompt"] + " " + example["completion"]
    else:
        example_text = example["prompt"] + example["completion"]
    example_text = example_text + tokenizer.eos_token
    tokenized_example = tokenizer(
        example_text, return_tensors="pt", max_length=max_seq_length, truncation=True
    )
    input_ids = tokenized_example.input_ids
    labels = input_ids.clone()
    tokenized_prompt = tokenizer(
        example["prompt"],
        return_tensors="pt",
        max_length=max_seq_length,
        truncation=True,
    )
    # mask the prompt part for avoiding loss
    labels[:, : tokenized_prompt.input_ids.shape[1]] = -100
    attention_mask = torch.ones_like(input_ids)
    return {
        "input_ids": input_ids.flatten(),
        "labels": labels.flatten(),
        "attention_mask": attention_mask.flatten(),
    }


def encode_with_prompt_completion_format_observation_mask(
    example,
    tokenizer,
    max_seq_length,
    observation_token="<|observation|>",
    action_token="<|action|>",
):
    """
    Here we assume each example has 'prompt' and 'completion' fields.
    We concatenate prompt and completion and tokenize them together because otherwise prompt will be padded/trancated
    and it doesn't make sense to follow directly with the completion.
    """
    # if prompt doesn't end with space and completion doesn't start with space, add space
    if not example["prompt"].endswith((" ", "\n", "\t")) and not example[
        "completion"
    ].startswith((" ", "\n", "\t")):
        example_text = example["prompt"] + " " + example["completion"]
    else:
        example_text = example["prompt"] + example["completion"]
    example_text = example_text + tokenizer.eos_token
    tokenized_example = tokenizer(
        example_text, return_tensors="pt", max_length=max_seq_length, truncation=True
    )
    input_ids = tokenized_example.input_ids
    labels = input_ids.clone()
    tokenized_prompt = tokenizer(
        example["prompt"],
        return_tensors="pt",
        max_length=max_seq_length,
        truncation=True,
    )

    # unpack reserved token ids
    observation_id = tokenizer(observation_token)["input_ids"][0]
    action_id = tokenizer(action_token)["input_ids"][0]

    mask = torch.zeros_like(labels)
    starts = (
        (
            (input_ids[0, :] == observation_id).int()
            + (input_ids[0, :] == action_id).int()
        )
        .nonzero()
        .squeeze()
    )

    if len(starts.shape) == 0:
        # mask the prompt part for avoiding loss
        labels[:, : tokenized_prompt.input_ids.shape[1]] = -100
        attention_mask = torch.ones_like(input_ids)
        return {
            "input_ids": input_ids.flatten(),
            "labels": labels.flatten(),
            "attention_mask": attention_mask.flatten(),
        }

    # mask the prompt + observation parts for avoiding loss
    for i, s in enumerate(starts):
        if i % 2 == 1:
            mask[:, s + 1 :] += 1
        else:
            mask[:, s:] += 1

    evens_mask = mask % 2 == 0
    labels[evens_mask] = -100
    labels[:, : tokenized_prompt.input_ids.shape[1]] = -100

    attention_mask = torch.ones_like(input_ids)

    return {
        "input_ids": input_ids.flatten(),
        "labels": labels.flatten(),
        "attention_mask": attention_mask.flatten(),
    }


def main():
    args = parse_args()
    import datetime

    torch.distributed.init_process_group(
        init_method=None,
        timeout=datetime.timedelta(seconds=100000),
        world_size=-1,
        rank=-1,
        store=None,
        group_name="",
        pg_options=None,
        backend="nccl",
    )

    accelerator_log_kwargs = {}

    if args.with_tracking:
        accelerator_log_kwargs["log_with"] = args.report_to
        accelerator_log_kwargs["project_dir"] = args.output_dir

    accelerator = Accelerator(
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        **accelerator_log_kwargs,
    )

    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )

    logger.info(accelerator.state, main_process_only=False)
    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()

    if args.seed is not None:
        set_seed(args.seed)

    if accelerator.is_main_process:
        if args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)

    accelerator.wait_for_everyone()

    data_files = {}
    dataset_args = {}
    if args.train_file is not None:
        data_files["train"] = args.train_file

    raw_datasets = load_dataset(
        "json",
        data_files=data_files,
        **dataset_args,
    )

    if args.config_name:
        config = AutoConfig.from_pretrained(args.config_name)
    elif args.model_name_or_path:
        config = AutoConfig.from_pretrained(args.model_name_or_path)
    else:
        raise ValueError(
            "You are instantiating a new config instance from scratch. This is not supported by this script."
        )

    if args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(
            args.tokenizer_name, use_fast=not args.use_slow_tokenizer
        )
    elif args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            args.model_name_or_path, use_fast=not args.use_slow_tokenizer
        )
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    if args.model_name_or_path:
        model = AutoModelForCausalLM.from_pretrained(
            args.model_name_or_path,
            from_tf=bool(".ckpt" in args.model_name_or_path),
            config=config,
            low_cpu_mem_usage=args.low_cpu_mem_usage,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelForCausalLM.from_config(config)

    model = model.cuda()
    num_added_tokens = tokenizer.add_special_tokens(
        {
            "bos_token": "<s>",
            "eos_token": "</s>",
            "unk_token": "<unk>",
            "pad_token": "<pad>",
        }
    )

    from action_textmap import special_tokens_interaction_history

    num_added_tokens += tokenizer.add_tokens(
        [v for v in special_tokens_interaction_history.values()]
    )
    observation_token = special_tokens_interaction_history["observation"]
    action_token = special_tokens_interaction_history["action"]

    print(f"Adding {num_added_tokens} to tokenizer")

    embedding_size = model.get_input_embeddings().weight.shape[0]
    if len(tokenizer) > embedding_size:
        model.resize_token_embeddings(len(tokenizer))

    if args.use_lora:
        logger.info("Initializing LORA model...")
        peft_config = LoraConfig(
            task_type=TaskType.CAUSAL_LM,
            inference_mode=False,
            r=args.lora_rank,
            lora_alpha=args.lora_alpha,
            lora_dropout=args.lora_dropout,
        )
        model = get_peft_model(model, peft_config)
        model.print_trainable_parameters()

    if (
        "prompt" in raw_datasets["train"].column_names
        and "completion" in raw_datasets["train"].column_names
    ):
        if args.mask_observation_compl:
            encode_function = partial(
                encode_with_prompt_completion_format_observation_mask,
                tokenizer=tokenizer,
                max_seq_length=args.max_seq_length,
                observation_token=observation_token,
                action_token=action_token,
            )
        else:
            encode_function = partial(
                encode_with_prompt_completion_format,
                tokenizer=tokenizer,
                max_seq_length=args.max_seq_length,
            )
    else:
        raise ValueError(
            "You need to have either 'prompt'&'completion in your column names."
        )

    with accelerator.main_process_first():
        lm_datasets = raw_datasets.map(
            encode_function,
            batched=False,
            num_proc=args.preprocessing_num_workers,
            load_from_cache_file=not args.overwrite_cache,
            remove_columns=[
                name
                for name in raw_datasets["train"].column_names
                if name not in ["input_ids", "labels", "attention_mask"]
            ],
            desc="Tokenizing and reformatting instruction data",
        )
        lm_datasets.set_format(type="pt")
        lm_datasets = lm_datasets.filter(
            lambda example: (example["labels"] != -100).any()
        )

    train_dataset = lm_datasets["train"]

    for index in random.sample(range(len(train_dataset)), 3):
        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")

    # DataLoaders creation:
    train_dataloader = DataLoader(
        train_dataset,
        shuffle=True,
        collate_fn=DataCollatorForSeq2Seq(
            tokenizer=tokenizer, model=model, padding="longest"
        ),
        batch_size=args.per_device_train_batch_size,
    )

    no_decay = ["bias", "layer_norm.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 = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)

    overrode_max_train_steps = False
    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
        overrode_max_train_steps = True

    num_training_steps_for_scheduler = (
        args.max_train_steps
        if overrode_max_train_steps
        else args.max_train_steps * accelerator.num_processes
    )
    lr_scheduler = get_scheduler(
        name=args.lr_scheduler_type,
        optimizer=optimizer,
        num_training_steps=num_training_steps_for_scheduler,
        num_warmup_steps=int(num_training_steps_for_scheduler * args.warmup_ratio),
    )

    # Prepare everything with `accelerator`.
    model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        model, optimizer, train_dataloader, lr_scheduler
    )

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = math.ceil(
        len(train_dataloader) / args.gradient_accumulation_steps
    )
    if overrode_max_train_steps:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    # Figure out how many steps we should save the Accelerator states
    checkpointing_steps = args.checkpointing_steps
    if checkpointing_steps is not None and checkpointing_steps.isdigit():
        checkpointing_steps = int(checkpointing_steps)

    # We need to initialize the trackers we use, and also store our configuration.
    # The trackers initializes automatically on the main process.
    if args.with_tracking:
        experiment_config = vars(args)
        # TensorBoard cannot log Enums, need the raw value
        experiment_config["lr_scheduler_type"] = experiment_config[
            "lr_scheduler_type"
        ].value
        accelerator.init_trackers(
            args.tracking_project,
            experiment_config,
            init_kwargs={"wandb": {"group": args.tracking_group}},
        )

    # 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
    starting_epoch = 0

    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}")
            model.load_checkpoint(args.resume_from_checkpoint)
            path = os.path.basename(args.resume_from_checkpoint)
        else:
            # Get the most recent checkpoint
            dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
            dirs.sort(key=os.path.getctime)
            path = dirs[
                -1
            ]  # Sorts folders by date modified, most recent checkpoint is the last

        # Extract `epoch_{i}` or `step_{i}`
        training_difference = os.path.splitext(path)[0]

        if "epoch" in training_difference:
            starting_epoch = int(training_difference.replace("epoch_", "")) + 1
            resume_step = None
        else:
            # need to multiply `gradient_accumulation_steps` to reflect real steps
            resume_step = (
                int(training_difference.replace("step_", ""))
                * args.gradient_accumulation_steps
            )
            starting_epoch = resume_step // len(train_dataloader)
            resume_step -= starting_epoch * len(train_dataloader)

    # update the progress_bar if load from checkpoint
    progress_bar.update(starting_epoch * num_update_steps_per_epoch)
    completed_steps = starting_epoch * num_update_steps_per_epoch

    for epoch in range(starting_epoch, args.num_train_epochs):
        model.train()
        total_loss = 0
        for step, batch in enumerate(train_dataloader):
            # We need to skip steps until we reach the resumed step
            if args.resume_from_checkpoint and epoch == starting_epoch:
                if resume_step is not None and completed_steps < resume_step:
                    if step % args.gradient_accumulation_steps == 0:
                        progress_bar.update(1)
                        completed_steps += 1
                    continue

            with accelerator.accumulate(model):
                outputs = model(**batch, use_cache=False)
                loss = outputs.loss
                # We keep track of the loss at each logged step
                total_loss += loss.detach().float()
                accelerator.backward(loss)
                optimizer.step()
                optimizer.zero_grad()
                lr_scheduler.step()

            # # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                progress_bar.update(1)
                completed_steps += 1

                if args.logging_steps and completed_steps % args.logging_steps == 0:
                    avg_loss = (
                        accelerator.gather(total_loss).mean().item()
                        / args.gradient_accumulation_steps
                        / args.logging_steps
                    )
                    logger.info(
                        f"  Step: {completed_steps}, LR: {lr_scheduler.get_last_lr()[0]}, Loss: {avg_loss}"
                    )
                    if args.with_tracking:
                        accelerator.log(
                            {
                                "learning_rate": lr_scheduler.get_last_lr()[0],
                                "train_loss": avg_loss,
                            },
                            step=completed_steps,
                        )
                    total_loss = 0

                if isinstance(checkpointing_steps, int):
                    if completed_steps % checkpointing_steps == 0:
                        output_dir = f"step_{completed_steps}"
                        if args.output_dir is not None:
                            output_dir = os.path.join(args.output_dir, output_dir)
                        unwrapped_model = accelerator.unwrap_model(model)
                        state_dict = accelerator.get_state_dict(model)
                        unwrapped_model.save_pretrained(
                            output_dir,
                            is_main_process=accelerator.is_main_process,
                            save_function=accelerator.save,
                            state_dict=state_dict,
                        )
                        tokenizer.save_pretrained(output_dir)
                if completed_steps >= args.max_train_steps:
                    break

        if args.checkpointing_steps == "epoch":
            output_dir = f"epoch_{epoch}"
            if args.output_dir is not None:
                output_dir = os.path.join(args.output_dir, output_dir)
            unwrapped_model = accelerator.unwrap_model(model)
            state_dict = accelerator.get_state_dict(model)
            unwrapped_model.save_pretrained(
                output_dir,
                is_main_process=accelerator.is_main_process,
                save_function=accelerator.save,
                state_dict=state_dict,
            )
            tokenizer.save_pretrained(output_dir)

    if args.with_tracking:
        accelerator.end_training()

    if args.output_dir is not None:
        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            tokenizer.save_pretrained(args.output_dir)
        unwrapped_model = accelerator.unwrap_model(model)
        # When doing multi-gpu training, we need to use accelerator.get_state_dict(model) to get the state_dict.
        # Otherwise, sometimes the model will be saved with only part of the parameters.
        # Also, accelerator needs to use the wrapped model to get the state_dict.
        state_dict = accelerator.get_state_dict(model)
        if args.use_lora:
            # When using lora, the unwrapped model is a PeftModel, which doesn't support the is_main_process
            # and has its own save_pretrained function for only saving lora modules.
            # We have to mannually specify the is_main_process outside the save_pretrained function.
            if accelerator.is_main_process:
                unwrapped_model.save_pretrained(args.output_dir, state_dict=state_dict)
        else:
            unwrapped_model.save_pretrained(
                args.output_dir,
                is_main_process=accelerator.is_main_process,
                save_function=accelerator.save,
                state_dict=state_dict,
            )


if __name__ == "__main__":
    main()