Tensor: fix constructor selection logic

[merlinND]

Hi!

This is a small fix for the Tensor constructor selection logic. Currently, the following could happen, which is very confusing:

import mitsuba as mi

color = mi.Color3f(2.)
dr.enable_grad(color)

tensor = mi.TensorXf(color)
assert dr.grad_enabled(tensor)  # <-- fails

The issue is that Tensor should not be constructible from Color3f in the first place, but because the constructor selection logic does not realize that Color3f is a kind of DrJit Array, it falls back to the __array_interface__ constructor.

I changed the logic to use the IsDrJit attribute, which is the criterion used by dr.is_array_v().

[njroussel]

Hi @merlinND :smile:

We've ran into this at least a few times already, without ever properly solving it.

Rather than returning an error, and potentially breaking someone's existing code that doesn't rely on AD. I'd prefer to figure out the generic set of scatter operations we'd need to handle these castings into TensorXf objects without breaking gradient tracking. Does this seem reasonable to you? I can take a look into this in a few days.

[merlinND]

Agreed, actually being able to construct the TensorXf from those higher-order arrays would be even cooler! IMO the really important part is that we never fallback to __array_interface__ or similar for DrJit arrays.

Would dr.ravel() do the trick?

[njroussel]

Would dr.ravel() do the trick?

Yes! It already figures out the necessary scatters with proper gradient tracking.