| """ |
| Data pipeline: streams and tokenizes OpenWebText for pretraining. |
| Packs sequences to max_seq_len for efficiency (no padding waste). |
| """ |
|
|
| import os |
| import torch |
| from torch.utils.data import IterableDataset, DataLoader |
| from datasets import load_dataset |
| from transformers import AutoTokenizer |
|
|
|
|
| def get_tokenizer(name: str = "mistralai/Mistral-7B-v0.1"): |
| """Use Mistral's tokenizer — 32k vocab, BPE, well-trained on diverse data.""" |
| tok = AutoTokenizer.from_pretrained(name, use_fast=True) |
| if tok.pad_token is None: |
| tok.pad_token = tok.eos_token |
| return tok |
|
|
|
|
| class PackedPretrainDataset(IterableDataset): |
| """ |
| Streams text from HuggingFace dataset, tokenizes on the fly, |
| and packs into fixed-length sequences for maximum GPU utilization. |
| """ |
|
|
| def __init__(self, tokenizer, max_seq_len: int, split: str = "train", cache_dir: str = None, seed: int = 42): |
| self.tokenizer = tokenizer |
| self.max_seq_len = max_seq_len |
| self.split = split |
| self.cache_dir = cache_dir |
| self.seed = seed |
| self.eos_id = tokenizer.eos_token_id |
|
|
| def _token_stream(self): |
| ds = load_dataset( |
| "HuggingFaceFW/fineweb-edu", |
| name="sample-10BT", |
| split=self.split, |
| streaming=True, |
| cache_dir=self.cache_dir, |
| ) |
| ds = ds.shuffle(seed=self.seed, buffer_size=10_000) |
|
|
| for example in ds: |
| text = example.get("text", "") |
| if len(text.strip()) < 50: |
| continue |
| token_ids = self.tokenizer.encode(text, add_special_tokens=False) |
| yield from token_ids |
| yield self.eos_id |
|
|
| def __iter__(self): |
| buffer = [] |
| for token_id in self._token_stream(): |
| buffer.append(token_id) |
| if len(buffer) == self.max_seq_len + 1: |
| input_ids = torch.tensor(buffer[:-1], dtype=torch.long) |
| labels = torch.tensor(buffer[1:], dtype=torch.long) |
| yield input_ids, labels |
| buffer = [] |
|
|
|
|
| def create_dataloader(tokenizer, config, rank: int = 0, world_size: int = 1, seed_override: int = None): |
| seed = seed_override if seed_override is not None else config.seed |
| dataset = PackedPretrainDataset( |
| tokenizer=tokenizer, |
| max_seq_len=config.max_seq_len, |
| split="train", |
| cache_dir=config.data_cache_dir, |
| seed=seed + rank, |
| ) |
| return DataLoader( |
| dataset, |
| batch_size=config.batch_size_per_gpu, |
| num_workers=config.num_workers, |
| pin_memory=True, |
| prefetch_factor=4, |
| ) |
|
|
