zlacker

>>mfigui+(OP)
I'm really rooting for AMD to break the CUDA monopoly. To this end, I genuinely don't know whether a translation layer is a good thing or not. On the upside it makes the hardware much more viable instantly and will boost adoption, on the downside you run the risk that devs will never support ROCm, because you can just use the translation layer.

I think this is essentially the same situation as Proton+DXVK for Linux gaming. I think that that is a net positive for Linux, but I'm less sure about this. Getting good performance out of GPU compute requires much more tuning to the concrete architecture, which I'm afraid devs just won't do for AMD GPUs through this layer, always leaving them behind their Nvidia counterparts.

However, AMD desperately needs to do something. Story time:

On the weekend I wanted to play around with Stable Diffusion. Why pay for cloud compute, when I have a powerful GPU at home, I thought. Said GPU is a 7900 XTX, i.e. the most powerful consumer card from AMD at this time. Only very few AMD GPUs are supported by ROCm at this time, but mine is, thankfully.

So, how hard could it possibly to get Stable Diffusion running on my GPU? Hard. I don't think my problems were actually caused by AMD: I had ROCm installed and my card recognized by rocminfo in a matter of minutes. But the whole ML world is so focused on Nvidia that it took me ages to get a working installation of pytorch and friends. The InvokeAI installer, for example, asks if you want to use CUDA or ROCm, but then always installs the CUDA variant whatever you answer. Ultimately, I did get a model to load, but the software crashed my graphical session before generating a single image.

The whole experience left me frustrated and wanting to buy an Nvidia GPU again...

>>Cu3PO4+0r
> Proton+DXVK for Linux gaming

"Building the DirectX shader compiler better than Microsoft?" (2024) >>39324800

E.g. llama.cpp already supports hipBLAS; is there an advantage to this ROCm CUDA-compatibility layer - ZLUDA on Radeon (and not yet Intel OneAPI) - instead or in addition? https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#hi... >>38588573

What can't WebGPU abstract away from CUDA unportability? >>38527552

>>westur+Zc1
BLAS will only get you so far. About the highest level operation it has is matmul, which you can use to build convolution (im2col, matmul, col2im), but that won't be as performant as a hand optimized cuDNN convolution kernel. Same goes for any other high level neural net building blocks - trying to build them on top of BLAS will not get you remotely close to performance of a custom kernel.

What's nice about BLAS is that there are optimized implementations for CPUs (Intel MKL) as well as NVIDIA (cuBLAS) and AMD (hipBLAS), so while it's very much limited in what it can do, you can at least write portable code around it.