| 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. |
