Datasets:
File size: 2,225 Bytes
7c0b4c1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | #!/usr/bin/env python3
"""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
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