| |
| """Load the FineWeb-Edu Byte-Level dataset from HuggingFace.""" |
|
|
| import os |
| import numpy as np |
| import torch |
| from torch.utils.data import IterableDataset, DataLoader, get_worker_info |
|
|
|
|
| BOS_ID = 257 |
| EOS_ID = 258 |
| PAD_ID = 256 |
|
|
|
|
| class FineWebEduByteLevelDataset(IterableDataset): |
| def __init__(self, data_dir, seq_len=2048, rank=0, world_size=1): |
| self.seq_len = seq_len |
| self.data_dir = data_dir |
| self.rank = rank |
| self.world_size = world_size |
| self._files = self._discover_files() |
|
|
| def _discover_files(self): |
| import glob as _glob |
| files = sorted(_glob.glob(os.path.join(self.data_dir, "**/*.bin"), recursive=True)) |
| return [f for i, f in enumerate(files) if i % self.world_size == self.rank] |
|
|
| def __iter__(self): |
| worker = get_worker_info() |
| num_workers = worker.num_workers if worker else 1 |
| worker_id = worker.id if worker else 0 |
| files = [f for i, f in enumerate(self._files) if i % num_workers == worker_id] |
| for filepath in files: |
| arr = np.memmap(filepath, dtype=np.uint16, mode='r') |
| pos = 0 |
| while pos + self.seq_len + 1 <= len(arr): |
| chunk = arr[pos:pos + self.seq_len + 1] |
| pos += self.seq_len + 1 |
| x = torch.tensor(chunk[:-1], dtype=torch.long) |
| y = torch.tensor(chunk[1:], dtype=torch.long) |
| y[y == PAD_ID] = -100 |
| yield x, y |
|
|
|
|
| def encode(text: str) -> list: |
| return [BOS_ID] + list(text.encode('utf-8')) + [EOS_ID] |
|
|
|
|
| def decode(ids: list) -> str: |
| return bytes(i for i in ids if 0 <= i <= 255).decode('utf-8', errors='replace') |
|
|
|
|
| if __name__ == "__main__": |
| from huggingface_hub import snapshot_download |
| data_dir = snapshot_download("CLIWorks/Spider-FLEXITOKENS-FP8", repo_type="dataset", allow_patterns=["data/*.bin", "data/metadata.json"]) |
| data_dir = os.path.join(data_dir, "data") |
| ds = FineWebEduByteLevelDataset(data_dir, seq_len=2048) |
| loader = DataLoader(ds, batch_size=4, num_workers=0, pin_memory=True) |
| for i, (x, y) in enumerate(loader): |
| print(f"Batch {i}: x={x.shape}, y={y.shape}") |
| if i >= 2: |
| break |
|
|
