Support M1 GPU in FARMReader

Is your feature request related to a problem? Please describe. Since haystack v1.6 we have support for pytorch 1.12 which also means support for the M1 GPU. However, we currently initialize the device to be either cpu or cuda depending on availability and if the user passes in the use_gpu=True parameter. For GPU use on the M1, pytorch actually uses the mps backend. See: https://pytorch.org/docs/stable/notes/mps.html

If we could allow the users to pass in the actual device into the FARMReader then this might support of GPU training and inference on the M1 possible.

Describe the solution you’d like Allow the user to pass in devices=[<device>] into FARMReader.__init__ and use these devices in initialize_device_settings. We could make this non-breaking by making this an optional argument to the reader init and the device initialization.

Describe alternatives you’ve considered A clear and concise description of any alternative solutions or features you’ve considered.

Additional context Add any other context or screenshots about the feature request here.

[sjrl]

That’s great! I would say that anywhere the user passes an option to initialize_device_settings should have the option of passing a list of devices instead. Similar to what is already done in this load function for the Inferencer https://github.com/deepset-ai/haystack/blob/be127e5b61e60f59292a1e5d73676eb34691f668/haystack/modeling/infer.py#L175-L176

where devices is of type https://github.com/deepset-ai/haystack/blob/be127e5b61e60f59292a1e5d73676eb34691f668/haystack/modeling/infer.py#L128

So what is inconsistent at the moment is that the devices option is only supported in some places in Haystack. And I think we should support it everywhere where the user can pass in the use_gpu boolean.

[sjrl]

Yes, it seems to be already used everywhere, but we should make sure that it does get used in addition to making sure we provide devices parameter.

Yes I agree.