Re-evaluating colbert-ai Index Artifacts Between PyTorch Versions with Precision-Based torch.allclose Tolerances

Background I recently learned that it’s best practice to use different torch.allclose tolerances based on the precision of the floating point value. As a reminder, torch.allclose uses absolute and relative tolerances as follows: ∣input_i − other_i∣ ≤ atol + rtol × ∣other_i∣ bitsandbytes uses the following heuristic: if dtype == torch.float32: atol, rtol = 1e-6 , 1e-5 elif dtype == torch.bfloat16:…