stable-diffusion-rocm VS bitsandbytes

Re: stable diffusion https://github.com/AshleyYakeley/stable-diffusion-rocm

Very easy actually. This is not officially documented, but with a recent enough kernel you don't have to install anything. You can grab the official rocm container and it'll just work. For example for Stable Diffusion see https://github.com/AshleyYakeley/stable-diffusion-rocm/blob/...

I had good luck with these directions, which let you run inside a docker container:

https://github.com/AshleyYakeley/stable-diffusion-rocm

I had to make the one line change suggested in issue #3 to get it to run under 8GB.

radeontop suggests 4GB might work.

I also had to add this environment variable to make it work on my unsupported radeon 6600xt:

HSA_OVERRIDE_GFX_VERSION=10.3.0

It takes under two minutes per batch of 5 images with the --turbo option.

(Base OS is manjaro; using the distro's version of docker; not the flatpack docker package.)

If you don't have a GPU, paperspace will rent you an appropriate VM.

I have it working on an RX 6800, used the scripts from this repo[0] to build a docker image that has ROCm drivers and PyTorch installed.

I'm running Ubuntu 22.04 LTS as the host OS, didn't have to touch anything beyond the basic Docker install. Next step is build a new Dockerfile that adds in the Stable Diffusion WebUI.[1]

[0] https://github.com/AshleyYakeley/stable-diffusion-rocm

No. Without the inference code, the best we can have are guesses on its implementation, so the benchmark figures we can get could be quite wrong. It does seem better than Llama2-70B in my tests, which rely on the work done by Dmytro Dzhulgakov[0] and DiscoResearch[1].

But the point of releasing on bittorrent is to see the effervescence in hobbyist research and early attempts at MoE quantization, which are already ongoing[2]. They are benefitting from the community.

[0]: https://github.com/dzhulgakov/llama-mistral

[1]: https://huggingface.co/DiscoResearch/mixtral-7b-8expert

[2]: https://github.com/TimDettmers/bitsandbytes/tree/sparse_moe

CUDA SETUP: To manually override the PyTorch CUDA version please see:https://github.com/TimDettmers/bitsandbytes/blob/main/how_to_use_nonpytorch_cuda.md

While on the surface, a 192GB Mac Studio seems like a great deal (it's not much more than a 48GB A6000!), there are several reasons why this might not be a good idea:

* I assume most people have never used llama.cpp Metal w/ large models. It will drop to CPU speeds whenever the context window is full: https://github.com/ggerganov/llama.cpp/issues/1730#issuecomm... - while sure this might be fixed in the future, it's been an issue since Metal support was added, and is a significant problem if you are actually trying to actually use it for inferencing. With 192GB of memory, you could probably run larger models w/o quantization, but I've never seen anyone post benchmarks of their experiences. Note that at that point, the limited memory bandwidth will be a big factor.

* If you are planning on using Apple Silicon for ML/training, I'd also be wary. There are multi-year long open bugs in PyTorch[1], and most major LLM libs like deepspeed, bitsandbytes, etc don't have Apple Silicon support[2][3].

You can see similar patterns w/ Stable Diffusion support [4][5] - support lagging by months, lots of problems and poor performance with inference, much less fine tuning. You can apply this to basically any ML application you want (srt, tts, video, etc)

Macs are fine to poke around with, but if you actually plan to do more than run a small LLM and say "neat", especially for a business, recommending a Mac for anyone getting started w/ ML workloads is a bad take. (In general, for anyone getting started, unless you're just burning budget, renting cloud GPU is going to be the best cost/perf, although on-prem/local obviously has other advantages.)

[1] https://github.com/pytorch/pytorch/issues?q=is%3Aissue+is%3A...

[2] https://github.com/microsoft/DeepSpeed/issues/1580

[3] https://github.com/TimDettmers/bitsandbytes/issues/485

[4] https://github.com/AUTOMATIC1111/stable-diffusion-webui/disc...

[5] https://forums.macrumors.com/threads/ai-generated-art-stable...

Release notes: https://github.com/TimDettmers/bitsandbytes/releases/tag/0.4...

Welcome to bitsandbytes. For bug reports, please run python -m bitsandbytes and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues ================================================================================ bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cpu.so /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32 CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths... ERROR: /usr/bin/python3: undefined symbol: cudaRuntimeGetVersion CUDA SETUP: libcudart.so path is None CUDA SETUP: Is seems that your cuda installation is not in your path. See https://github.com/TimDettmers/bitsandbytes/issues/85 for more information. CUDA SETUP: CUDA version lower than 11 are currently not supported for LLM.int8(). You will be only to use 8-bit optimizers and quantization routines!! CUDA SETUP: Highest compute capability among GPUs detected: 7.5 CUDA SETUP: Detected CUDA version 00 CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cpu.so... /usr/local/lib/python3.10/dist-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable. warn("The installed version of bitsandbytes was compiled without GPU support. " /usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths... warn(msg) /usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')} warn(msg) /usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('http'), PosixPath('//172.28.0.1'), PosixPath('8013')} warn(msg) /usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-s-1b6gsytv7z9le --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true'), PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https')} warn(msg) /usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')} warn(msg) /usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')} warn(msg) /usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: No libcudart.so found! Install CUDA or the cudatoolkit package (anaconda)!

RuntimeError: CUDA Setup failed despite GPU being available. Inspect the CUDA SETUP outputs above to fix your environment! If you cannot find any issues and suspect a bug, please open an issue with detals about your environment: https://github.com/TimDettmers/bitsandbytes/issues

===================================BUG REPORT=================================== Welcome to bitsandbytes. For bug reports, please run python -m bitsandbytes and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues ================================================================================ bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda121.so CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths... CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so CUDA SETUP: Highest compute capability among GPUs detected: 8.9 CUDA SETUP: Detected CUDA version 121 CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda121.so...