huggingface--peft/examples/beft_finetuning/beft_finetuning.py

# This script is based on the example shown in docs/source/task_guides/ia3.md
import torch
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 BeftConfig, get_peft_model


ds = load_dataset("gtfintechlab/financial_phrasebank_sentences_allagree", "5768")
ds = ds["train"].train_test_split(test_size=0.1)
ds["validation"] = ds["test"]
del ds["test"]

classes = ["negative", "neutral", "positive"]
# Keep map in-process; num_proc=1 still uses multiprocessing and can trigger dill issues on some Python versions.
ds = ds.map(
    lambda x: {"text_label": [classes[label] for label in x["label"]]},
    batched=True,
)

text_column = "sentence"
label_column = "text_label"
max_length = 128

tokenizer = AutoTokenizer.from_pretrained("bigscience/mt0-small")


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=3, 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


processed_ds = ds.map(
    preprocess_function,
    batched=True,
    remove_columns=ds["train"].column_names,
    load_from_cache_file=False,
    desc="Running tokenizer on dataset",
)

# low-data regimes: select a subset of the training data, i.e., 500 examples for training
train_ds = processed_ds["train"].select(range(500))
eval_ds = processed_ds["validation"]

batch_size = 8

train_dataloader = DataLoader(
    train_ds, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True
)
eval_dataloader = DataLoader(eval_ds, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)


model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/mt0-small")

# you can try target_modules=["v"], target_modules=["q"], target_modules=["k"]
peft_config = BeftConfig(task_type="SEQ_2_SEQ_LM", target_modules=["v"])
model = get_peft_model(model, peft_config)
print(model.print_trainable_parameters())

lr = 8e-3
num_epochs = 1

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),
)

device = (
    torch.accelerator.current_accelerator().type
    if hasattr(torch, "accelerator")
    else "cuda"
    if torch.cuda.is_available()
    else "cpu"
)
model = model.to(device)

for epoch in range(num_epochs):
    model.train()
    total_loss = 0
    for step, batch in enumerate(tqdm(train_dataloader)):
        batch = {k: v.to(device) for k, v in batch.items()}
        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)):
        batch = {k: v.to(device) for k, v in batch.items()}
        with torch.no_grad():
            outputs = model(**batch)
        loss = outputs.loss
        eval_loss += loss.detach().float()
        eval_preds.extend(
            tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), 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)
    print(f"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}")