TorchJD: Training with multiple losses in PyTorch [P]

Hi everyone! I wanted to share some recent progress on TorchJD that might be useful to the machine learning community. When training models with multiple losses (multiple tasks, constraints, auxiliary losses, regularization terms, etc.), you typically have two options: Scalarization: Various ways to combine those losses into a single loss (e.g. average them or combine them with trainable weights); then you can do gradient descent on it. Jacobian descent: Compute the Jacobian of the vector of losses (i.e. on