bitsandbytes
bitsandbytes-rocm
bitsandbytes | bitsandbytes-rocm | |
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61 | 4 | |
5,447 | 38 | |
- | - | |
9.4 | 8.8 | |
5 days ago | 12 months ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
bitsandbytes
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French AI startup Mistral secures €2B valuation
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
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Lora training with Kohya issue
CUDA SETUP: To manually override the PyTorch CUDA version please see:https://github.com/TimDettmers/bitsandbytes/blob/main/how_to_use_nonpytorch_cuda.md
- FLaNK Stack Weekly for 30 Oct 2023
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A comprehensive guide to running Llama 2 locally
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...
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Bit inference 4.2x faster than 16 bit
Release notes: https://github.com/TimDettmers/bitsandbytes/releases/tag/0.4...
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Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0']
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)!
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Having trouble using the multimodal tools.
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
- [TextGen WebUI] Service terminated error? (Screenshots in post)
- Considering getting a Jetson AGX Orin.. anyone have experience with it?
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How to disable the `bitsandbytes` intro message:
===================================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...
bitsandbytes-rocm
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Any methods to train using AMD?
Install dependencies like hipblas-devel hipsparse-devel hipcub-devel git python3.10 make libstdc++-devel accelerate, rocm and hip git clone https://github.com/bmaltais/kohya_ss && cd kohya_ss python3.10 -m venv venv source venv/bin/activate pip3 install torch==1.13.1 torchvision==0.14.1 torchtext==0.14.1 torchaudio==0.13.1 --index-url https://download.pytorch.org/whl/rocm5.2 # problems on 2.0.0 last I tried, but kohya gotten updates since then pip3 install --upgrade -r requirements.txt pip3 uninstall tensorflow && pip3 install tensorflow-rocm pip uninstall bitsandbytes && git clone https://github.com/broncotc/bitsandbytes-rocm # bitsandbytes not required if not using adam8? cd bitesandbytes-rocm && nano Makefile # Replace all 3 instances of 5.3.0 with 5.4.3 make hip python3 setup.py install
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How to run Pygmalion on 4.5GB of VRAM with full context size.
There are a lot of ROCm versions of bitsandbytes. For example this one: https://github.com/broncotc/bitsandbytes-rocm The problem is compatibility with most of the requirements. Kobold does a better job than ooba in offering a more streamlined approach for AMD users.
- Is it possible to load a model in 8bit precision with an AMD card? (6700xt)
- Have you got running LoRA training on an AMD GPU?
What are some alternatives?
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
sd-scripts
accelerate - 🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
GPTQ-for-LLaMa - 4 bits quantization of LLMs using GPTQ
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
Dreambooth-Stable-Diffusion-cpu - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
LoRA_Easy_Training_Scripts - A UI made in Pyside6 to make training LoRA/LoCon and other LoRA type models in sd-scripts easy
llama.cpp - LLM inference in C/C++
LyCORIS - Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion.
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
qlora - QLoRA: Efficient Finetuning of Quantized LLMs