automatic-differentiation

FITFLOP

In PyTorch, when using loss.backward() to compute gradients, how can I prevent it from overriding the gradients I've manually computed?

Preserving Manual Gradients in Py Torch Understanding loss backward and its Impact In Py Torch the loss backward function is crucial for calculating gradients a

In PyTorch, when using loss.backward() to compute gradients, how can I prevent it from overriding the gradients I've manually computed?

JAX custom_jvp with 'None' output leads to TypeError

JAX Custom JVP with None Output A Common Pitfall and How to Fix It JAXs custom jvp function offers a powerful way to customize the forward and backward passes o

JAX custom_jvp with 'None' output leads to TypeError