Create accelerator for apple silicon GPU Acceleration

[NripeshN]

May fix #3364 and fix #1580

[NripeshN]

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[NripeshN]

Hey DeepSpeed team @tjruwase @ShijieZZZZ @jeffra, could you take a look at my code here? I appreciate the feedback.

[tjruwase]

@NripeshN, thanks for contributing this exciting PR. We are reviewing asap.

Out of curiosity, are you already running DeepSpeed on the mps accelerator?

[jeffra]

This is an excellent first step towards supporting apple silicon, thank you so much @NripeshN for initiating this. I've been doing some tests on my own M1 macbook air. I see a handful of other areas that will need fixes outside the abstract accelerator in order to run DeepSpeed training on M1. However, I think we'll need to have some follow-up PRs to address those.

One area I see is even with using a torch.distributed backend of 'gloo' I see errors when using collectives like broadcast or all_reduce. Currently DeepSpeed assumes these are callable even if there's only 1 accelerator being used. We probably need to add guards against calling these collectives in these cases, here's an example:

DeepSpeed/deepspeed/runtime/engine.py

Line 1033 in aa54dba

dist.broadcast(p, groups._get_broadcast_src_rank(), group=self.data_parallel_group)

After I added a few of these guards I was successfully able to get our simple/small model to run with fp32 weights https://github.com/microsoft/DeepSpeed/blob/master/tests/small_model_debugging/test_model.py I did get a seg fault when trying to use fp16 training, but I am not sure how stable that is on M1 even w/o DeepSpeed currently.

[NripeshN]

This is an excellent first step towards supporting apple silicon, thank you so much @NripeshN for initiating this. I've been doing some tests on my own M1 macbook air. I see a handful of other areas that will need fixes outside the abstract accelerator in order to run DeepSpeed training on M1. However, I think we'll need to have some follow-up PRs to address those.

One area I see is even with using a torch.distributed backend of 'gloo' I see errors when using collectives like broadcast or all_reduce. Currently DeepSpeed assumes these are callable even if there's only 1 accelerator being used. We probably need to add guards against calling these collectives in these cases, here's an example:

https://github.com/microsoft/DeepSpeed/blob/aa54dba02532f3f487cc635dfdace8fa95593db0/deepspeed/runtime/engine.py#L1033

After I added a few of these guards I was successfully able to get our simple/small model to run with fp32 weights https://github.com/microsoft/DeepSpeed/blob/master/tests/small_model_debugging/test_model.py I did get a seg fault when trying to use fp16 training, but I am not sure how stable that is on M1 even w/o DeepSpeed currently.

Hi, Thank you soo much!!