Upload load_dataset.py with huggingface_hub

load_dataset.py

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+#!/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