triton
GPTQ-for-LLaMa
triton | GPTQ-for-LLaMa | |
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2 | 75 | |
76 | 2,928 | |
- | - | |
9.7 | 8.6 | |
3 days ago | 10 months ago | |
C++ | Python | |
MIT License | Apache License 2.0 |
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triton
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How to use AMD GPU?
cd .. git clone https://github.com/ROCmSoftwarePlatform/triton.git -b release/pytorch_2.0 cd triton/python pip3 install cmake pip3 install -e .
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Run Stable-Diffusion locally with a AMD GPU (7900XT) on Windows 11
Someone I know returned their 4080 (had a horrible coil whine he said) and yesterday his new 7900XTX came in and he did some testing. Now he can't use xformers and he did not have the sdp optimization on (iow no optimizations) using 5.5.0 beta on docker (that hurts a bit too) he was getting about 16it/s for 512sq and at 768sq he was getting 5.25ish it/s. I had him try with the SDP but optimization but docker is new to him and for some reason I saw no gains, or losses, when it was used (as if docker ignored it). His next test will be for training (which is why he got the card and I will as well). Another thing that hurts is no Triton but here is what he told me yesterday "regarding the 7900 XTX. Inference is fine, around 16 it/s. I couldn't get the training to work, mostly because of what I assume is a bug with the ROCm fork of Triton that's currently in development ( https://github.com/ROCmSoftwarePlatform/triton )."
GPTQ-for-LLaMa
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[P] Early in 2023 I put in a lot of work on a new machine learning project. Now I'm not sure what to do with it.
First I want to make it clear this is not a self promotion post. I hope many machine learning people come at me with questions or comments about this project. A little background about myself. I did work on the 4 bits quantization of LLaMA using GPTQ. (https://github.com/qwopqwop200/GPTQ-for-LLaMa). I've been studying AI in-depth for many years now.
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GPT-4 Details Leaked
Deploying the 60B version is a challenge though and you might need to apply 4-bit quantization with something like https://github.com/PanQiWei/AutoGPTQ or https://github.com/qwopqwop200/GPTQ-for-LLaMa . Then you can improve the inference speed by using https://github.com/turboderp/exllama .
If you prefer to use an "instruct" model à la ChatGPT (i.e. that does not need few-shot learning to output good results) you can use something like this: https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored...
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Rambling
I use gptq-for-llama - from this https://github.com/qwopqwop200/GPTQ-for-LLaMa and Pygmalion 7B.
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Now that ExLlama is out with reduced VRAM usage, are there any GPTQ models bigger than 7b which can fit onto an 8GB card?
exllama is an optimized implementation of GPTQ-for-LLaMa, allowing you to run 4-bit quantized language models with GPU at great speeds.
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GGML – AI at the Edge
With a single NVIDIA 3090 and the fastest inference branch of GPTQ-for-LLAMA https://github.com/qwopqwop200/GPTQ-for-LLaMa/tree/fastest-i..., I get a healthy 10-15 tokens per second on the 30B models. IMO GGML is great (And I totally use it) but it's still not as fast as running the models on GPU for now.
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New quantization method AWQ outperforms GPTQ in 4-bit and 3-bit with 1.45x speedup and works with multimodal LLMs
And exactly what Triton version are they comparing against? I just tried the latest version of this, and on my 4090/12900K I get 77 tokens per second for Llama 7B-128g. My own GPTQ CUDA implementation gets 151 tokens/second on the same model, same hardware. That makes it 96% faster, whereas AWQ is only 79% faster. For 30B-128g I'm currently only getting a 110% speedup over Triton compared to their 178%, but it still seems a little disingenuous to compare against their own CUDA implementation only, when they're trying to present the quantization method as being faster for inference.
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Introducing Basaran: self-hosted open-source alternative to the OpenAI text completion API
Thanks for the explanation. I think some repos, like text generation webui used gptq for llama (I don't know if it's this repo or another one), anyway most repo that I saw use external things (like gptq for llama)
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How to use AMD GPU?
cd ../.. git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b triton cd GPTQ-for-LLaMa pip install -r requirements.txt mkdir -p ../text-generation-webui/repositories ln -s ../../GPTQ-for-LLaMa ../text-generation-webui/repositories/GPTQ-for-LLaMa
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Help needed with installing quant_cuda for the WebUI
cd repositories git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa pip install -r requirements.txt
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The installed version of bitsandbytes was compiled without GPU support
# To use the GPTQ models I need to Install GPTQ-for-LLaMa and the monkey patch mkdir repositories cd repositories git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b triton cd GPTQ-for-LLaMa pip install ninja pip install -r requirements.txt cd cd text-generation-webui # download random model python download-model.py xxx/yyy # try to start the gui python server.py # It returns this warning but it runs bin /home/gm/miniconda3/envs/chat/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so /home/gm/miniconda3/envs/chat/lib/python3.10/site-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. " /home/gm/miniconda3/envs/chat/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32
What are some alternatives?
stable-diffusion-webui-amdgpu - Stable Diffusion web UI
llama.cpp - LLM inference in C/C++
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
stable-diffusion-webui-docker - Easy Docker setup for Stable Diffusion with user-friendly UI
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
alpaca-lora - Instruct-tune LLaMA on consumer hardware
GPTQ-for-LLaMa - 4 bits quantization of LLaMa using GPTQ
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
SHARK - SHARK - High Performance Machine Learning Distribution