calib_data.py

import torch
import logging
from typing import List, Union
from datasets import load_dataset

def get_calib_dataset(data: Union[str, List[str]] = "pileval",
                      tokenizer=None, n_samples=512, block_size=512,
                      split="train", text_column="text"):
    if isinstance(data, str):
        if data == "pileval":
            dataset = load_dataset("mit-han-lab/pile-val-backup", split="validation")
        else:
            dataset = load_dataset(data, split=split)
        
        dataset = dataset.shuffle(seed=42)

    elif isinstance(data, list):
        dataset = [{text_column: text} for text in data]
    else:
        raise NotImplementedError(
            "Either pass a string to a huggingface dataset or a list"
            "that is preprocessed with one sample of text per element.")
    
    samples = []
    n_run = 0
    for data in dataset:
        line = data[text_column]
        line = line.strip()
        line_encoded = tokenizer.encode(line)
        if len(line_encoded) > 512:
            continue
        sample = torch.tensor([line_encoded])
        if sample.numel() == 0:
            continue
        samples.append(sample)
        n_run += 1
        if n_run == n_samples:
            break
    # now concatenate all samples and split according to block size
    cat_samples = torch.cat(samples, dim=1)
    n_split = cat_samples.shape[1] // block_size
    logging.debug(f" * Split into {n_split} blocks")
    return [cat_samples[:, i*block_size:(i+1)*block_size] for i in range(n_split)]