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dataset.py — loads wikitext-2 or tinystories and chunks into fixed-length blocks.
"""
from __future__ import annotations
from typing import Dict
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader, Dataset
class BlockDataset(Dataset):
"""Chunks a flat token sequence into non-overlapping blocks of seq_len."""
def __init__(self, tokens: torch.Tensor, seq_len: int) -> None:
self.seq_len = seq_len
n_blocks = len(tokens) // (seq_len + 1)
# +1 so we can shift for next-token targets
self.data = tokens[: n_blocks * (seq_len + 1)].reshape(n_blocks, seq_len + 1)
def __len__(self) -> int:
return len(self.data)
def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
chunk = self.data[idx]
return {"input_ids": chunk}
def load_text_dataset(
name: str = "wikitext-2",
seq_len: int = 256,
batch_size: int = 16,
tokenizer_name: str = "gpt2",
) -> Dict[str, DataLoader]:
"""
Returns {"train": DataLoader, "validation": DataLoader}.
Supported names: "wikitext-2", "tinystories".
"""
from transformers import AutoTokenizer
tok = AutoTokenizer.from_pretrained(tokenizer_name)
if tok.pad_token is None:
tok.add_special_tokens({"pad_token": "[PAD]"})
if name == "wikitext-2":
raw = load_dataset("wikitext", "wikitext-2-raw-v1")
elif name == "tinystories":
raw = load_dataset("roneneldan/TinyStories")
else:
raise ValueError(f"Unknown dataset: {name}")
def tokenize(examples):
return tok(examples["text"], truncation=False, return_attention_mask=False)
tokenized = raw.map(tokenize, batched=True, remove_columns=raw["train"].column_names)
loaders = {}
for split in ("train", "validation"):
if split not in tokenized:
continue
all_ids = []
for sample in tokenized[split]["input_ids"]:
all_ids.extend(sample)
flat = torch.tensor(all_ids, dtype=torch.long)
ds = BlockDataset(flat, seq_len)
loaders[split] = DataLoader(
ds,
batch_size=batch_size,
shuffle=(split == "train"),
num_workers=2,
pin_memory=True,
)
return loaders
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