> ## Documentation Index
> Fetch the complete documentation index at: https://training-docs.cerebras.ai/llms.txt
> Use this file to discover all available pages before exploring further.

#  Loss Compilation Issues With Autogen 

## Custom loss functions with AutoGen

When creating custom losses, you might encounter compilation failures. To address this, wrap your custom loss class with the @autogen\_loss decorator, which enables AutoGen to handle the compilation of these custom losses efficiently.

```python theme={null}
from cerebras_pytorch/src/cerebras/pytorch/nn/modules import autogen_loss

@autogen_loss

class CustomLoss(nn.Module):

   def __init__(self, ...):
```

## Improving loss function performance

Enable autogen to use fused autogenerated graphs for losses in PyTorch, enhancing performance. Set `use_autogen = True` when defining your loss:

```python theme={null}
loss = MSELoss(..., use_autogen=True)
```

Supported losses include L1Loss, MSELoss, and others. Note that CosineEmbeddingLoss is not supported and will default to primitive kernels.