Building a Linux GPU Driver for the M4 Mac Mini in One Month

來源原標題

Building a Linux GPU Driver for the M4 Mac Mini in One Month

作者

ADevWithAnIdea

原始標籤

hackernews

來源訊號

HN points 400　HN 留言 248

原始連結

https://codyho.dev/blog/gpu-driver/

摘要原文

I Came, I Prompted, I Left Part 2: Building a GPU Driver From Scratch in One Month Previous blog post: https://codyho.dev/blog/hypervisor-macbook-neo/ What We Did TL;DR: Niklas and I built a fully OpenGL ES 3.0 compliant GPU driver for the M4 Mac Mini and MacBook Neo in about a month, a process which normally takes years. Here is Chrome and Firefox running WebGL on the M4 Mac Mini with working compositing: Most importantly, the driver is fast enough to run Minecraft at 200fps: Building this driver involved reverse engineering the AGX’s (Apple’s name for the GPU) incredibly complicated firmware ABI and user-space components. This was all done in a transparent, verifiably clean room manner using well established techniques. The code is not yet ready for end users, but we are looking to get it to end users as soon as possible. How We Did It Previously, I built a hypervisor to reverse engineer macOS. Now the goal became to actually do something useful with it, and what better target than writing a GPU driver. The GPU is effectively a requirement for any modern system, otherwise everything needs to be CPU rendered which is orders of magnitude slower and less power efficient. Our goal was to implement conformant OpenGL (and soon, Vulkan) drivers for the M4 Mac Mini and MacBook Neo. Normally, building a GPU driver is an endeavor that takes years; our goal was to do it in days. It turns out that days was overly optimistic, but weeks is still a massive improvement. In those weeks we have: Reverse engineered the M4, A18 Pro, and (mostly) M5 user space using only live probing, discovering hardware-supported features and instructions not emitted by Apple’s driver Built a fully working user-space driver, including a new custom IR/shader compiler, command stream builder, and many more components Reverse engineered, from scratch, the full AGX firmware ABI using traces from the hypervisor I previously built Implemented a full Linux kernel driver for said firmware ABI Throughout this process, we have not looked at any Apple binaries, only hardware traces (from our hypervisor) and shaders we built ourselves. For user-space graphics RE, we were careful to treat any required Apple blobs as opaque objects. We had a friend write documentation on these blobs 1 so we could write a clean room implementation ourselves (which was mostly built by just blindly trying stuff until it worked). We have published all of our experiments so that anyone can verify the provenance of our work (see the twin agx-re repos under Deliverables ). This blog post is divided into two parts, user and kernel space. This mirrors the split in all modern GPU drivers: the kernel is responsible for interfacing with the firmware, allocating buffers, and managing scheduling, while the actual contents of those buffers and what is being scheduled are opaque. User space is responsible for actually understanding how the GPU works and filling those buffers with stuff. Kernel Space On Apple Silicon, the kernel driver does not interface directly with the hardware. Instead, it talks to the GPU firmware running a custom RTOS called RTKit. That means that the first step to a kernel driver is not talking to hardware, it’s figuring out the firmware ABI. The firmware ABI was by far the most annoying part of this project, because rather than doing the sane thing of coming up with a reasonable ABI with nice interfaces, Apple essentially took a regular kernel driver, cut it in half, and then put half of it in the AGX and called it firmware, with the other half of the kernel driver communicating using shared structs in memory. Many of these structs have firmware owned fields (which we must never modify and which we must learn from reverse engineering) interleaved with host controlled fields. For an idea of how complicated the ABI is, this is what the shared memory tree looks like on the M1/M2: Asahi Lina famously figured all of this out over grueling 12-hour days to build the M1/M2 kernel driver, an amazing technical accomplishment. Unfortunately, the A18 Pro firmware ABI (I started my RE work on the MacBook Neo and later pivoted to the M4 Mac Mini) is significantly more complicated than the already very complicated M1 firmware ABI: What the F@!#, Apple. Note how the A18 has: 1.5x as many structs twice as many pointers a significantly more complicated process for submitting work There are many other issues that add friction to the RE process 2 . I did have some documentation on the firmware ABI, but it was highly incomplete and honestly was not very useful 3 . My approach was simple and based on the approach used to successfully reverse engineer the M1/M2 machines: watch what macOS did, replay it, then try to do it ourselves, which is made possible by the hypervisor. When I described this approach to the LLM, it took replay extremely literally: the first thing it did was wait for the first firmware visible event (these are called “kicks”), then saved a copy of the entire GPU memory state . After a reboot, it copied the saved memory state straight back into host memory, performed the kick, and saw the output pages change. It would then try to reconstruct these objects in code, following all the pointers and making sense of the contents. Over successive experiments, Codex would reduce the number of pages it copied until there was no more replayed state and everything was built from source. 4 Amazingly, I noticed Codex had good taste regarding when it should poke the hardware some more and when it should just run the hypervisor and capture the state itself. There were three major issues, and all were caused by our inability to get a clean capture of host work: The first issue was render work submitted after the GPU firmware started. We could prestage work before the firmware started, start the GPU, and that work would be completed as expected, but once the firmware started any work submitted would just be ACKed and retired without actually doing anything. Once the firmware has started, capturing state is much harder because everything becomes dynamic and the firmware becomes a stateful object with state you can’t easily replay. I had to step in at this point and examine Codex’s process. It turns out it was trying to replay a capture very late in the AGX’s lifecycle, where there had already been many previous events. When I told it to choose a capture far earlier in the AGX’s lifecycle, the very first capture after firmware start, Codex was able to almost immediately discover the issue (it was missing a single byte descriptor). This took a few days. The second, and only major blocking, issue was compute. The AGX, broadly, supports two kinds of work: compute and render. In the regular GUI path, compute work is only scheduled after a significant amount of render work was already executed. Thus, it took a long time to get a clean capture of a compute workload, and when Codex finally did it was 336 MB and impossible to replay (it tried, for a long time). It also tried to construct the objects itself by looking at the capture, and spent over a week doing this, but was ultimately unsuccessful. There was just too much nonsense to sift through. This was exacerbated by issues on my side– after getting render working, I expected that submitting compute work would be simpler (the firmware ABI for compute is indeed simpler, so I was correct here), but lost my humility and thought it would be a cakewalk that would only take a few hours. Thus, I didn’t scaffold out the task properly for the LLM. The fix actually was given to me in another Codex session. In essence: Disable the GUI by booting into single-user mode; this means no render work would be done. Install a LaunchDaemon to run at the earliest possible point, the moment Metal (Apple’s proprietary graphics framework) became available. Run a tiny Metal program that we supplied Capture and replay this tiny, pure compute trace. The trace was captured successfully. (400 points, 248 comments on Hacker News)