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Custom Autograd Function

Sometimes, you need to teach autograd new tricks.

Key Insight​

You are not limited to PyTorch's built-in operations. By subclassing torch.autograd.Function, you can define custom forward and backward pass logic. You explicitly save required inputs using ctx.save_for_backward() and provide the exact derivative computation.

Why This Matters​

Custom autograd functions allow you to implement novel research ideas, optimize memory usage, or bypass non-differentiable steps. It is a powerful tool for bridging the gap between theoretical math and practical deep learning implementation.