Avoid using skip() in hf_datasets

We found out that skip() may not behave as expected. This is a workaround solution while we are investiging the root cause.

ghstack-source-id: 412810bc4a83de39df3fdfe62bc249d80c7deb19
Pull Request resolved: #835

torchtitan/datasets/hf_datasets.py

@@ -9,16 +9,16 @@
 from typing import Any, Callable, Dict, List, Optional
 
 import torch
+
+from datasets import Dataset, load_dataset
+from datasets.distributed import split_dataset_by_node
 from torch.distributed.checkpoint.stateful import Stateful
 from torch.utils.data import IterableDataset
 from torchdata.stateful_dataloader import StatefulDataLoader
 
 from torchtitan.datasets.tokenizer import Tokenizer
 from torchtitan.logging import logger
 
-from datasets import Dataset, load_dataset
-from datasets.distributed import split_dataset_by_node
-
 
 def _load_c4_dataset(dataset_path: str):
     """Load C4 dataset with default configuration."""
@@ -99,13 +99,15 @@ def __init__(
         self._all_tokens: List[int] = []
 
     def _get_data_iter(self):
-        if self._sample_idx == 0:
-            return iter(self._data)
-
         if isinstance(self._data, Dataset) and self._sample_idx == len(self._data):
             return iter([])
 
-        return iter(self._data.skip(self._sample_idx))
+        it = iter(self._data)
+
+        for _ in range(self._sample_idx):
+            next(self._data.next())
+
+        return it
 
     def __iter__(self):
         max_buffer_token_len = 1 + self.seq_len