[Numpy-discussion] Proposal: add `force=` or `copy=` kwarg to `__array__` interface
jni at fastmail.com
Wed Apr 29 06:26:28 EDT 2020
Hi everyone, and thank you Ralf for carrying the flag in my absence. =D
Sebastian, the *primary* motivation behind avoiding detach() in PyTorch is listed in original post of the PyTorch issue:
> People not very familiar with `requires_grad` and cpu/gpu Tensors might go back and forth with numpy. For example doing pytorch -> numpy -> pytorch and backward on the last Tensor. This will backward without issue but not all the way to the first part of the code and won’t raise any error.
The PyTorch team are concerned that they will be overwhelmed with help requests if np.array() silently succeeds on a tensor with gradients. I definitely get that.
Avoiding .gpu() is more straightforwardly about avoiding implicit expensive computation.
> while others do not choose to teach about it. There seems very little
> or even no "promise" attached to either `force=True` or `force=False`.
NumPy can set a precedent through policy. The *only* reason client libraries would implement `__array__` is to play well with NumPy, so if NumPy documents that `force=True` should *always* succeed, we can expect client libraries to follow suit. At least the PyTorch devs have indicated that they would be open to this.
> E.g. Napari wants to use it, but do the array-providers want Napari to use it?
As Ralf pointed out, the PyTorch devs have already agreed to it.
>From the napari perspective, we'd be ok with leaving the decision on warnings to client libraries. We may or may not suppress them depending on user requests. ;) But the point is to have a way of saying "give me a NumPy array DAMMIT" without having to know about all the possible array libraries. Which are numerous and getting numerouser.
Ralf, you said you don't want warnings — even for sparse arrays? That was an area of concern for you on the PyTorch discussion side.
> And if the conversion still gives warnings for some array-objects, have we actually gained much?
> I would advocate for a `force=` kwarg but personally I don't think it's explicit enough, but probably as explicit as can be given NumPy's API.
Yeah, I agree that force is kind of vague, which is why I was looking for things like `allow_copy`. But it is hard to be general enough here: sparse requires an expensive instantiation, cupy requires copying from gpu to cpu, dask requires arbitrary computation, xarray requires information loss... I'm inclined to agree with Ralf that force= is the only generic-enough term, but I'm happy to entertain other options!
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