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With the ambition to enable forward-mode and higher-order differentiation support, checking `input.requires_grad` is not sufficient to determine if we will need the relevant arguments in the future.
WARNING: this breaks compatibility with PyTorch < 2.0
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This PR adds support for forward-mode and higher-order differentiation in PyTorch autograd engine. Additionally, it enables integration with
torch.func, which implements JAX-style composable functional transforms in PyTorch, allowing for a flexible and elegant use of different types of automatic differentiation (+vectorizing map functionality also implemented in this PR).Integration with
torch.funcbreaks compatibility with PyTorch < 2.0, however, so we need to estimate if it's safe to drop support for it at this moment.To do: