transformers/examples/research_projects/codeparrot/scripts/codeparrot_training.py

import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

import transformers
from accelerate import Accelerator, DistributedType
from arguments import TrainingArguments
from huggingface_hub import Repository
from transformers import AdamW, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed


class ConstantLengthDataset(IterableDataset):
    """
    Iterable dataset that returns constant length chunks of tokens from stream of text files.
        Args:
            tokenizer (Tokenizer): The processor used for proccessing the data.
            dataset (dataset.Dataset): Dataset with text files.
            infinite (bool): If True the iterator is reset after dataset reaches end else stops.
            seq_length (int): Length of token sequences to return.
            num_of_sequences: Number of token sequences to keep in buffer.
            chars_per_token: Number of characters per token used to estimate number of tokens in text buffer.
    """

    def __init__(
        self, tokenizer, dataset, infinite=False, seq_length=1024, num_of_sequences=1024, chars_per_token=3.6
    ):
        self.tokenizer = tokenizer
        self.concat_token_id = tokenizer.bos_token_id
        self.dataset = dataset
        self.seq_length = seq_length
        self.input_characters = seq_length * chars_per_token * num_of_sequences
        self.epoch = 0
        self.infinite = infinite
        self.current_size = 0

    def __iter__(self):
        iterator = iter(self.dataset)
        more_examples = True
        while more_examples:
            buffer, buffer_len = [], 0
            while True:
                if buffer_len >= self.input_characters:
                    break
                try:
                    buffer.append(next(iterator)["content"])
                    buffer_len += len(buffer[-1])
                except StopIteration:
                    if self.infinite:
                        iterator = iter(self.dataset)
                        self.epoch += 1
                        logger.info(f"Dataset epoch: {self.epoch}")
                    else:
                        more_examples = False
                        break
            tokenized_inputs = self.tokenizer(buffer, truncation=False)["input_ids"]
            all_token_ids = []
            for tokenized_input in tokenized_inputs:
                all_token_ids.extend(tokenized_input + [self.concat_token_id])
            for i in range(0, len(all_token_ids), self.seq_length):
                input_ids = all_token_ids[i : i + self.seq_length]
                if len(input_ids) == self.seq_length:
                    self.current_size += 1
                    yield torch.tensor(input_ids)


def setup_logging(args):
    project_name = args.model_ckpt.split("/")[-1]
    logger = logging.getLogger(__name__)
    log_dir = Path(args.save_dir) / "log/"
    log_dir.mkdir(exist_ok=True)
    filename = f"debug_{accelerator.process_index}.log"
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
        handlers=[logging.FileHandler(log_dir / filename), logging.StreamHandler()],
    )
    if accelerator.is_main_process:  # we only want to setup logging once
        accelerator.init_trackers(project_name, vars(args))
        run_name = accelerator.trackers[0].run.name
        logger.setLevel(logging.INFO)
        datasets.utils.logging.set_verbosity_info()
        transformers.utils.logging.set_verbosity_info()
    else:
        run_name = ""
        logger.setLevel(logging.ERROR)
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
    return logger, run_name


def create_dataloaders(args):
    ds_kwargs = {"streaming": True}
    train_data = load_dataset(args.dataset_name_train, split="train", **ds_kwargs)
    train_data = train_data.shuffle(buffer_size=args.shuffle_buffer, seed=args.seed)
    valid_data = load_dataset(args.dataset_name_valid, split="train", **ds_kwargs)
    train_dataset = ConstantLengthDataset(tokenizer, train_data, infinite=True, seq_length=args.seq_length)
    valid_dataset = ConstantLengthDataset(tokenizer, valid_data, infinite=False, seq_length=args.seq_length)
    train_dataloader = DataLoader(train_dataset, batch_size=args.train_batch_size)
    eval_dataloader = DataLoader(valid_dataset, batch_size=args.valid_batch_size)
    return train_dataloader, eval_dataloader


def get_grouped_params(model, args, no_decay=["bias", "LayerNorm.weight"]):
    params_with_wd, params_without_wd = [], []
    for n, p in model.named_parameters():
        if any(nd in n for nd in no_decay):
            params_without_wd.append(p)
        else:
            params_with_wd.append(p)
    return [
        {"params": params_with_wd, "weight_decay": args.weight_decay},
        {"params": params_without_wd, "weight_decay": 0.0},
    ]


def log_metrics(step, metrics):
    logger.info(f"Step {step}: {metrics}")
    if accelerator.is_main_process:
        accelerator.log(metrics, step)


def compute_tflops(elapsed_time, accelerator, args):
    # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
    config_model = accelerator.unwrap_model(model).config
    checkpoint_factor = 4 if args.gradient_checkpointing else 3
    batch_size = args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
    factor = 24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
    flops_per_iteration = factor * (
        1.0
        + (args.seq_length / (6.0 * config_model.n_embd))
        + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
    )
    tflops = flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
    return tflops


def evaluate(args):
    model.eval()
    losses = []
    for step, batch in enumerate(eval_dataloader):
        with torch.no_grad():
            outputs = model(batch, labels=batch)
        loss = outputs.loss.repeat(args.valid_batch_size)
        losses.append(accelerator.gather(loss))
        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
            break
    losses = torch.cat(losses)
    loss = losses[: eval_dataloader.dataset.current_size].mean()
    try:
        perplexity = torch.exp(loss)
    except OverflowError:
        perplexity = float("inf")
    return loss.item(), perplexity.item()


# Accelerator
accelerator = Accelerator(log_with=["wandb", "tensorboard"])
acc_state = {str(k): str(v) for k, v in accelerator.state.__dict__.items()}

# Settings
parser = HfArgumentParser(TrainingArguments)
args = parser.parse_args()

args = Namespace(**vars(args), **acc_state)
samples_per_step = accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
    hf_repo = Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
logger, run_name = setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
    hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
    model.gradient_checkpointing_enable()
tokenizer = AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
train_dataloader, eval_dataloader = create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
optimizer = AdamW(get_grouped_params(model, args), lr=args.learning_rate)
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,
)
accelerator.register_for_checkpointing(lr_scheduler)


def get_lr():
    return optimizer.param_groups[0]["lr"]


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

# load in the weights and states from a previous save
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}")
        accelerator.load_state(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(args.save_dir) if f.is_dir() and "step" in str(f)]
        dirs.sort(key=os.path.getctime)
        path = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract the step of the checkpoint to continue from there
    training_difference = os.path.splitext(path)[0]
    resume_step = int(training_difference.replace("step_", ""))

# Train model
model.train()
completed_steps = 0
t_start = time.time()
for step, batch in enumerate(train_dataloader, start=1):
    if args.resume_from_checkpoint and step < resume_step:
        continue  # we need to skip steps until we reach the resumed step
    loss = model(batch, labels=batch, use_cache=False).loss
    log_metrics(
        step, {"lr": get_lr(), "samples": step * samples_per_step, "steps": completed_steps, "loss/train": loss.item()}
    )
    loss = loss / args.gradient_accumulation_steps
    if step % args.gradient_accumulation_steps != 0:
        # Prevent backward from doing gradient all_reduce in every step
        if accelerator.distributed_type == DistributedType.MULTI_GPU:
            with model.no_sync():
                accelerator.backward(loss)
        else:
            accelerator.backward(loss)
    else:
        accelerator.backward(loss)
        accelerator.clip_grad_norm_(model.parameters(), 1.0)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
        completed_steps += 1
        elapsed_time = time.time() - t_start
        tflops = compute_tflops(elapsed_time, accelerator, args)
        log_metrics(step, {"steps": completed_steps, "tflops": tflops, "time_per_iteration": elapsed_time})
        t_start = time.time()
    if step % args.save_checkpoint_steps == 0:
        logger.info("Evaluating and saving model checkpoint")
        eval_loss, perplexity = evaluate(args)
        log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
        accelerator.wait_for_everyone()
        save_dir = os.path.join(args.save_dir, f"step_{step}")
        accelerator.save_state(save_dir)
        if accelerator.is_main_process:
            hf_repo.push_to_hub(commit_message=f"step {step}")
        model.train()
    if completed_steps >= args.max_train_steps:
        break

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
eval_loss, perplexity = evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
save_dir = os.path.join(args.save_dir, f"step_{step}")
accelerator.save_state(save_dir)
if accelerator.is_main_process:
    hf_repo.push_to_hub(commit_message="final model")