m1n1 VS ml-ane-transformers

One of the coolest things (IMO) about the entire Asahi effort, and why I'm not at all surprised that they surpassed Apple, was the dedicated effort to build bespoke developer-friendly Python tooling early in the reverse engineering process.

https://asahilinux.org/2021/08/progress-report-august-2021/

> Since the hypervisor is built on m1n1, it works together with Python code running on a separate host machine. Effectively, the Python host can “puppeteer” the M1 and its guest OS remotely. The hypervisor itself is partially written in Python! This allows us to have a very fast test cycle, and we can even update parts of the hypervisor itself live during guest execution, without a reboot.

> We then started building a Python implementation of this RPC protocol and marshaling system. This implementation serves a triple purpose: it allows us to parse the DCP logs from the hypervisor to understand what macOS does, it allows us to build a prototype DCP driver entirely in Python, and it will in the future be used to automatically generate marshaling code for the Linux kernel DCP driver.

Code here: https://github.com/AsahiLinux/m1n1/blob/main/proxyclient/m1n...

If you watch any of Asahi Lina's streams from the time before they had working drivers, she's able to weave together complex bitflag-manipulating pipelines at the speed of thought with self-documenting code, all in Python running on the host machine, all while joking with viewers via her adorable avatar. I've never seen anything like it before. The whole workflow is a tremendous and unprecedented accomplishment by the entire Asahi team.

I wrote a m1n1 experiment to test IRQ delivery in EL1 and noticed something was weird. I already knew about that IRQ control register (3 masks IRQs entirely and is the default, that whole thing took like a day or two back when I first added M1 Pro/Max support), so I tried other values and 2 fixed it.

They have not dropped hardly any official documentation on iBoot. The best safe documentation is in: https://github.com/AsahiLinux/m1n1/ There's a lot of tainted docs out there because the source to iBoot was illegally leaked a while back, but marcan is known for being a bit of a hardass when it comes to legal reverse engineering (thankfully).

No its uefi boot manager “https://github.com/AsahiLinux/m1n1 “

What makes you say there isn't community participation? The repo for m1n1, at least, has 42 contributors according to Github[1]. There's plenty more reporting bugs and such, and their IRC channel seems relatively active.

1: https://github.com/AsahiLinux/m1n1

I can confirm that’s pretty much exactly what happens. I did some digging with the help of m1n1 a while back, and essentially yeah, this is almost exactly what it does. The only difference is that, rather than tracking which cores are running emulated code, the scheduler keeps track of which processes are running in emulation mode, and prior to returning to userland after a context switch, the kernel sets control register bits for features like TSO (which is what I was interested in looking into at the time; I believe that specifically is controlled somewhere in the actlr_el1 register). Although only the P-cores actually implement the necessary x86 emulation behavior, the scheduler of course wants to ensure that a process is not pinned to any one core, and that native and emulated processes alike can preempt and interleave with each other on the P-cores just as they would if there were no emulation.

That shim/stub is m1n1. It is the bootloader for Asahi Linux, but also makes it possible to talk to the hardware over USB as just described by Lina. marcan even implemented a small hypervisor in m1n1, so it can be used to run MacOS and trace how MacOS is accessing the hardware.

Windows 11 ARM dualboot will come to M1 macs in the near future. See m1n1 for progress.

Article: Deploying Transformers on the Apple Neural Engine Code: Apple Neural Engine (ANE) Transformers

Apple ML team released a paper and repo last year ( Ane_transformers )that shows how to optimize transformer architecture for ANE use prior to converting a PyTorch model to CoreML.

Apple: Transformer architecture optimized for Apple Silicon (https://github.com/apple/ml-ane-transformers)

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Have you seen this?

https://github.com/apple/ml-ane-transformers

I can't wait until people use it for AI and publicize this more, as well as the Neural Engine with its 16 cores and trillions of operations per second.

I'm also looking into converting these models from PyTorch to CoreML format, and seeing how well they run when given access to the GPU and Neural Engine. There's even an optimized library Apple has specifically for this type of model.