| Luckily | |
| you can accomplish that easily by activating a special module that will do the detection automatically. | |
| If you're using [Trainer], you just need to add: | |
| --debug underflow_overflow | |
| to the normal command line arguments, or pass debug="underflow_overflow" when creating the | |
| [TrainingArguments] object. | |
| If you're using your own training loop or another Trainer you can accomplish the same with: | |
| thon | |
| from transformers.debug_utils import DebugUnderflowOverflow | |
| debug_overflow = DebugUnderflowOverflow(model) | |
| [~debug_utils.DebugUnderflowOverflow] inserts hooks into the model that immediately after each | |
| forward call will test input and output variables and also the corresponding module's weights. |