peft/examples/conditional_generation/peft_lora_seq2seq_accelerate_fsdp.py

import os

import torch
from accelerate import Accelerator
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, default_data_collator, get_linear_schedule_with_warmup

from peft import LoraConfig, TaskType, get_peft_model
from peft.utils.other import fsdp_auto_wrap_policy


def main():
    accelerator = Accelerator()
    model_name_or_path = "t5-base"
    batch_size = 8
    text_column = "sentence"
    label_column = "label"
    max_length = 64
    lr = 1e-3
    num_epochs = 1
    base_path = "temp/data/FinancialPhraseBank-v1.0"

    peft_config = LoraConfig(
        task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1
    )
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)
    model = get_peft_model(model, peft_config)
    accelerator.print(model.print_trainable_parameters())

    dataset = load_dataset(
        "json",
        data_files={
            "train": os.path.join(base_path, "financial_phrase_bank_train.jsonl"),
            "validation": os.path.join(base_path, "financial_phrase_bank_val.jsonl"),
        },
    )

    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)

    def preprocess_function(examples):
        inputs = examples[text_column]
        targets = examples[label_column]
        model_inputs = tokenizer(
            inputs, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt"
        )
        labels = tokenizer(targets, max_length=2, padding="max_length", truncation=True, return_tensors="pt")
        labels = labels["input_ids"]
        labels[labels == tokenizer.pad_token_id] = -100
        model_inputs["labels"] = labels
        return model_inputs

    with accelerator.main_process_first():
        processed_datasets = dataset.map(
            preprocess_function,
            batched=True,
            num_proc=1,
            remove_columns=dataset["train"].column_names,
            load_from_cache_file=False,
            desc="Running tokenizer on dataset",
        )

    train_dataset = processed_datasets["train"]
    eval_dataset = processed_datasets["validation"]

    train_dataloader = DataLoader(
        train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True
    )
    eval_dataloader = DataLoader(
        eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True
    )

    optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
    lr_scheduler = get_linear_schedule_with_warmup(
        optimizer=optimizer,
        num_warmup_steps=0,
        num_training_steps=(len(train_dataloader) * num_epochs),
    )

    if getattr(accelerator.state, "fsdp_plugin", None) is not None:
        accelerator.state.fsdp_plugin.auto_wrap_policy = fsdp_auto_wrap_policy(model)

    model, train_dataloader, eval_dataloader, optimizer, lr_scheduler = accelerator.prepare(
        model, train_dataloader, eval_dataloader, optimizer, lr_scheduler
    )
    accelerator.print(model)

    for epoch in range(num_epochs):
        model.train()
        total_loss = 0
        for step, batch in enumerate(tqdm(train_dataloader)):
            outputs = model(**batch)
            loss = outputs.loss
            total_loss += loss.detach().float()
            loss.backward()
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

        model.eval()
        eval_loss = 0
        eval_preds = []
        for step, batch in enumerate(tqdm(eval_dataloader)):
            with torch.no_grad():
                outputs = model(**batch)
            loss = outputs.loss
            eval_loss += loss.detach().float()
            preds = accelerator.gather_for_metrics(torch.argmax(outputs.logits, -1)).detach().cpu().numpy()
            eval_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True))
        eval_epoch_loss = eval_loss / len(eval_dataloader)
        eval_ppl = torch.exp(eval_epoch_loss)
        train_epoch_loss = total_loss / len(train_dataloader)
        train_ppl = torch.exp(train_epoch_loss)
        accelerator.print(f"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}")

        correct = 0
        total = 0
        for pred, true in zip(eval_preds, dataset["validation"][label_column]):
            if pred.strip() == true.strip():
                correct += 1
            total += 1
        accuracy = correct / total * 100
        accelerator.print(f"{accuracy=}")
        accelerator.print(f"{eval_preds[:10]=}")
        accelerator.print(f"{dataset['validation'][label_column][:10]=}")
        accelerator.wait_for_everyone()
        # Option1: Pushing the model to Hugging Face Hub
        # model.push_to_hub(
        #     f"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}".replace("/", "_"),
        #     token = "hf_..."
        # )
        # token (`bool` or `str`, *optional*):
        #     `token` is to be used for HTTP Bearer authorization when accessing remote files. If `True`, will use the token generated
        #     when running `huggingface-cli login` (stored in `~/.huggingface`). Will default to `True` if `repo_url`
        #     is not specified.
        #     Or you can get your token from https://huggingface.co/settings/token
        # Option2: Saving the model locally
        peft_model_id = f"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}".replace("/", "_")
        model.save_pretrained(peft_model_id)
        accelerator.wait_for_everyone()


if __name__ == "__main__":
    main()