use math.ceil instead of round /cc #498

src/axolotl/utils/trainer.py

@@ -588,7 +588,9 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
         "padding": True,  # True/"longest" is the default
     }
     if cfg.pad_to_sequence_len:
-        data_collator_kwargs["pad_to_multiple_of"] = 64 * round(cfg.sequence_len / 64)
+        data_collator_kwargs["pad_to_multiple_of"] = 64 * math.ceil(
+            cfg.sequence_len / 64
+        )
     else:
         # A100 is best at 64, while others at 8. Let's use the larger so we don't have to check
         # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html