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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.

from cerebras_pytorch/src/cerebras/pytorch/nn/modules import autogen_loss

@autogen_loss

class CustomLoss(nn.Module):

   def __init__(self, ...):

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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:

loss = MSELoss(..., use_autogen=True)

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

​
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.

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:

loss = MSELoss(..., use_autogen=True)

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