Ensure numpy random seed is within range.

megatron/data/bert_dataset.py

@@ -77,7 +77,8 @@ class BertDataset(torch.utils.data.Dataset):
         sample = [self.indexed_dataset[i] for i in range(start_idx, end_idx)]
         # Note that this rng state should be numpy and not python since
         # python randint is inclusive whereas the numpy one is exclusive.
-        np_rng = np.random.RandomState(seed=(self.seed + idx))
+        # We % 2**32 since numpy requres the seed to be between 0 and 2**32 - 1
+        np_rng = np.random.RandomState(seed=((self.seed + idx) % 2**32))
         return build_training_sample(sample, seq_length,
                                      self.max_seq_length,  # needed for padding
                                      self.vocab_id_list,