wikitext_benchmark.py

import torch
import torch.nn.functional as F
from datasets import load_dataset
from bit_transformer import text_to_bits, collapse_submodel
from progressive_scaleup import progressive_scale_up_text


def lines_to_bits(lines, max_len=64):
    data = []
    for text in lines:
        bits = text_to_bits(text)[:max_len]
        if len(bits) < max_len:
            bits.extend([0] * (max_len - len(bits)))
        data.append(bits)
    return data


def main():
    ds = load_dataset("wikitext", "wikitext-2-raw-v1", split="train[:1%]")
    val_ds = load_dataset("wikitext", "wikitext-2-raw-v1", split="validation[:1%]")
    train_lines = [item["text"] for item in ds][:256]
    valid_lines = [item["text"] for item in val_ds][:64]

    train_bits = lines_to_bits(train_lines)
    valid_bits = lines_to_bits(valid_lines)

    progressive_scale_up_text(
        eps=0.65,
        steps=4,
        width_mult=2.0,
        max_len=64,
        dataset_size=min(64, len(train_bits)),
    )

    target_params = dict(d_model=16, nhead=4, num_layers=1, dim_feedforward=64, max_seq_len=64)
    model, _ = collapse_submodel(train_bits[:64], target_params, max_rounds=1)

    val_tensor = torch.tensor(valid_bits, dtype=torch.long)
    logits, _ = model(val_tensor)
    pred = logits[:, :-1, :].reshape(-1, 2)
    target = val_tensor[:, 1:].reshape(-1)
    loss = F.cross_entropy(pred, target)
    print("Collapsed model validation loss:", loss.item())


if __name__ == "__main__":
    main()