intel-extension-for-pytorch VS ZLUDA

OK I found it. Looks like they use SYCL (which for some reason they've rebranded to DPC++): https://github.com/intel/intel-extension-for-pytorch/tree/v2...

Just to point out it does, kind of: https://github.com/intel/intel-extension-for-pytorch

I've asked before if they'll merge it back into PyTorch main and include it in the CI, not sure if they've done that yet.

Intel already has a working GPGPU stack, using oneAPI/SYCL.

They also have arguably pretty good OpenCL support, as well as downstream support for PyTorch and Tensorflow using their custom extensions https://github.com/intel/intel-extension-for-tensorflow and https://github.com/intel/intel-extension-for-pytorch which are actively developed and just recently brought up-to-date with upstream releases.

https://github.com/intel/intel-extension-for-pytorch :

> Intel® Extension for PyTorch extends PyTorch* with up-to-date features optimizations for an extra performance boost on Intel hardware. Optimizations take advantage of AVX-512 Vector Neural Network Instructions (AVX512 VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, through PyTorch* xpu device, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs with PyTorch*

https://pytorch.org/blog/celebrate-pytorch-2.0/ :

> As part of the PyTorch 2.0 compilation stack, TorchInductor CPU backend optimization brings notable performance improvements via graph compilation over the PyTorch eager mode.

The TorchInductor CPU backend is sped up by leveraging the technologies from the Intel® Extension for PyTorch for Conv/GEMM ops with post-op fusion and weight prepacking, and PyTorch ATen CPU kernels for memory-bound ops with explicit vectorization on top of OpenMP-based thread parallelization*

DLRS Deep Learning Reference Stack: https://intel.github.io/stacks/dlrs/index.html

Install intel extensions for pytorch using docker. https://github.com/intel/intel-extension-for-pytorch

There is: https://github.com/intel/intel-extension-for-pytorch for intel cards on GPUs, but I would assume this doesn't extend to integraded graphics

Nonetheless, this issue might be relevant for your case.

I'm not sure where the disdain for ROCm is coming from, but tensorflow-rocm and the rocm pytorch container were fairly easy to setup and use from scratch once I got the correct Linux kernel installed along with the rest of the necessary ROCm components needed to use tensorflow and pytorch for rocm. TBF Intel Extension for Tensorflow wasn't too bad to setup either (except for the lack of float16 mixed precision training support, that was definitely a pain point to not be able to have), but Intel Extension for Pytorch for Intel GPUs (a.k.a. IPEX-GPU) however, has been a PITA to use for my i5 11400H iGPU NOT because the iGPU itself is slow, BUT because the current i915 driver in the mainline linux kernel simply doesn't work with IPEX-GPU (every script that I've ran ends up freezing when using even the i915 drivers as recent as Kernel version 6), and when I ended up installing drivers that were meant for the Arc GPUs that finally got IPEX-GPUs to work, I ended up with even more issues such as sh*tty FP64 emulation support that basically meant I had to do some really janky workarounds for things to not break while FP64 emulation was enabled (disabling was simply not an option for me, long story short). And yea unlike Intel, both Nvidia AND AMD actually do support FP64 instructions AND FLOAT16 mixed precision training natively on their GPUs so that one doesn't have to worry about running into "unsupported FP64 instructions" and "unsupported training modes" no matter what software they're running on those GPUs.

It now supports AMD GPUs since 3 weeks ago, check the latest commit at the repo:

https://github.com/vosen/ZLUDA

The article also mentions exactly this fact.

Looks like nvidia is trying to keep the lynchpin of their entire business model from crumbling underneath them. ZLUDA lets you run unmodified CUDA applications with near-native performance on AMD GPUs.

https://github.com/vosen/ZLUDA

With Triton looking to eclipse CUDA entirely, im not sure this prohibition does anything more than placate casual shareholders.

>Dark API functions are reverse-engineered and implemented by ZLUDA on a case-by-case basis once we observe an application making use of it.

https://github.com/vosen/ZLUDA/blob/master/ARCHITECTURE.md

> I know AMD have their competition, but their GPU software division keeps tripping over itself.

They are actively stepping on every rake there is. Eg they just stopped supporting the drop-in-cuda project everyone is waiting for, due to there being "no business-case for CUDA on AMD GPUs" [0].

[0] https://github.com/vosen/ZLUDA?tab=readme-ov-file#faq

https://github.com/vosen/ZLUDA

They still funded it and it was created.

From the same repo, I found this excellent, well-written architecture document: https://github.com/vosen/ZLUDA/blob/master/ARCHITECTURE.md

I love the direct, "no bullshit" style of writing.

Some gems:

> Anyone familiar with C++ will instantly understand that compiling it is a complicated affair.

> Additionally CUDA allows, to a large degree, mixing CPU code and GPU code. What does all this complexity mean for ZLUDA? Absolutely nothing

> Since an application can dynamically link to either Driver API or Runtime API, it would seem that ZLUDA needs to provide both. In reality very few applications dynamically link to Runtime API. For the vast majority of applications it's sufficient to provide Driver API for dynamic (runtime) linking.

CUDA is huge and nvidia spent a ton in a lot of "dead end" use cases optimizing it. There have been experiments with CUDA translation layers with decent performance[1]. There are two things that most projects hit:

1. The CUDA API is huge; I'm sure Intel/AMD will focus on what they need to implement pytorch and ignore every other use case ensuring that CUDA always has the leg up in any new frontier

2. Nvidia actually cares about developer experience. The most prominent example is Geohotz with tinygrad - where AMD examples didn't even work or had glaring compiler bugs. You will find nvidia engineer in github issues for CUDA projects. Intel/AMD hasn't made that level of investment and thats important because GPUs tend to be more fickle than CPUs.

[1] https://github.com/vosen/ZLUDA

> I don't think you understand just how insanely difficult it is to break into that market.

You're right, I have no clue nor have I ever tried myself.

> Even with apple money or something like that, it's a losing prospect because in the time it'll take you to get up and off the ground (which is FOREVER) your competition will crush you.

This I find hard to believe, do you have a source or reference for that claim? Companies with that amount of cash are hardly going to be crushed by competition be it direct or indirect. Anyway, I'm talking more about the Intels and AMDs of this world.

We have very lacklustre efforts from players I won't name with their Zluda library (https://github.com/vosen/ZLUDA) which I got REALLY excited about, until I read the README.txt. Four contributors, last commit early 2021.

Why, oh why, is it this bad?