Exllama multi gpu github cpp has doubled in the past week. Reload to refresh your session. Jun 12, 2023 · And next on the list is multi-GPU matmuls that might give a big boost to 65B models on dual GPUs (fingers crossed). Jul 17, 2023 · If it doesn't already fit, it would require either a smaller quantization method (and support for that quantization method by ExLlama), or a more memory efficient attention mechanism (conversion of LLaMA from multi-head attention to grouped-query or multi-query attention, plus ExLlama support), or an actually useful sparsity/pruning method, with your proposed hardware. Load exllama_hf on webui. Get all the model loaded in GPU 0; For the second issue: Apply the PR Fix Multi-GPU not working on exllama_hf #2803 to fix loading in just 1 GPU. 0,20. 43. config. from exllama. The official and recommended backend server for ExLlamaV2 is TabbyAPI, which provides an OpenAI-compatible API for local or remote inference, with extended features like HF model downloading, embedding model support and support for HF Jinja2 chat templates. ExLlama only expects the split for the weights. It's a new UI made specifically for exllama by turboderp, the developer of exllama and exllamav2. The folder is there. The latest code on my 4090 is now capable of generating 36 token/s for a 33b q4_km model. Running it on a single 4090 works well. You just have to set the allocation manually. 2 · Optimum version: 1. -- -,- ason, rang Aug 30, 2023 · I'm running the following code on 2x4090 and model outputs gibberish. Sep 9, 2023 · pha golden Riv. Try to do inference. 40 w/ Hardware Acceleration -- Generating 128 tokens, 1920 token prompt I think adding this as an example makes the most sense, this is a relatively complete example of a conversation model setup using Exllama and langchain. I'd like to get to 30 tokens/second at least. GPU 0 has a total capacty of 10. Thanks for this amazing work. Running a model on just any on NOTE: by default, the service inside the docker container is run by a non-root user. The inference speed of LLama. safetensors from TheBloke using 2x3090. cache/torch_extensions/ which could take a little while. I see there's even a colab notebook so I might add it later. My graphics card is an Nvidia RTX 4070 with 12 gigabytes of video memory. cpp using a GGML model, or EXLlama using a GPTQ version. Jul 28, 2023 · Can't assign model to multi gpu #205. 44 MiB is free. auto_map = [20. -- -,- ason, rang Jun 21, 2023 · Load exllama_hf on webui. It's a very old CPU and motherboard, still performance is quite good. https://github. Dec 15, 2024 · GPTQModel started out as a major refractor (fork) of AutoGPTQ but has now morphed into a full-stand-in replacement with cleaner api, up-to-date model support, faster inference, faster quantization, higher quality quants and a pledge that ModelCloud, together with the open-source ML community, will take every effort to bring the library up-to-date with latest advancements and model support. You signed in with another tab or window. Load a model shared between 2 GPUs. 00 MiB. I recently upgraded my PC with an additional 32 gigabytes of system RAM, bringing the total to 48 gigabytes. 👀 1 Si13x reacted with eyes emoji Aug 16, 2023 · The potential or current problems are that they don't support Multi-GPU, they use a different quantization formats and I couldn't see perplexity results from it. Try to load a model which can't be used on same GPU, but in more than 1 GPUs. It is a 16k Context length Vicuna 4bit quantized model. One thing though, for faster inference you can use EXUI instead of ooba. You signed out in another tab or window. The official and recommended backend server for ExLlamaV2 is TabbyAPI, which provides an OpenAI-compatible API for local or remote inference, with extended features like HF model downloading, embedding model support Jul 24, 2023 · I'm new to exllama, are there any tutorials on how to use this? I'm trying this with the llama-2 70b model. They also lack integrations, they are not a lot of models directly available in their format and popular UI like ooba are not yet compatible with it. It will be compiled on the first run and cached to ~/. Clone repo, install dependencies, and run benchmark: The CUDA extension is loaded at runtime so there's no need to install it separately. Could I ask what CPU you're using? Yes, E5-2680 v4 @ 2. 2 · GPU model and memory: 11GiB * 2 · CUDA version: 12. yml file) is changed to this non-root user in the container entrypoint (entrypoint. I've probably made some dumb mistakes as I'm not extremely familiar with the inner workings of Exllama, but this is a working example. Sep 15, 2023 · Hello. sh). But still, it loads the model on just one GPU and goes OOM during Jun 22, 2023 · The --gpu_split is bad. Hence, the ownership of bind-mounted directories (/data/model and /data/exllama_sessions in the default docker-compose. Then you can run nvidia-smi to see how much memory ends up being used, and adjust accordingly. That's the main reason I use your repo right now BTW, so very much looking forward to it! All other inference methods I've seen so far suck when you start splitting. 34B and 70B use Grouped-Query Attention, which cuts the size of the KV cache by some factor. Closed # This line doesn't work model = ExLlama(config) tokenizer = ExLlamaTokenizer(tokenizer_path) BATCH_SIZE = 16 cache ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs. How do I implement the multi-GPU inference using Ipython and not the WebUI? At present, I am implementing it this way. Jun 7, 2023 · Especially that you can usable performance from multiple GPUs. I tried loading a 33b model (Guanaco is great), with these two options: llama. 90 GiB of which 770. ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs. May 23, 2023 · exllama is significantly faster for me than Ooba with multi-gpu layering on 33b, testing a 'chat' and allowing some context to build up exllama is about twice as fast. Jcatred (ProcSN proc Dre -:// Mindly means for the and in a Nich říct Forest Rav Rav fran fran fran gaz Agrcastle castleasiacliordinate advers Mem advers Basibenkooor paste Singapore refugeermeanny intellectualsafe Shakespe contempor Mallmanual Quantmousektr Ge Mil shadownehfdzekADmobile Und Euenf Next Dominbuchcock Infoengo‭ Hann NAT ]] Ferr' -. Screenshot. Its seems like it is possible to run these models on two GPUs based on the "Dual GPU Results" table in the README. You switched accounts on another tab or window. Started webui with: CMD_FLAGS = '--chat --model-menu --loader exllama --gpu-split 16,21' Choose the Guanaco 33B and it loaded fine but only on one GPU. 310-330W power consumption on GPU and ~50W on CPU, GPU usage 85-90%. 0] #Setting this for multi GPU. 40GHz with a X99 montherboard. com/turboderp/exui. model imp Aug 7, 2023 · Interesting note here: With hardware accel back on, 70b multi-gpu inference takes a big hit, back down to ~11tok/s from 16 Driver version 536. - GitHub - qwopqwop200/AutoAWQ-exllama: AutoAWQ implements the AWQ . Would it be possible to add an example script to illustrate multi-GPU inference to the repo? Jul 20, 2023 · Splitting a model between two AMD GPUs (Rx 7900XTX and Radeon VII) results in garbage output (gibberish). Excellent! I haven't used it yet but I'll give it a try. Jun 7, 2023 · This is not a issue, just reporting that it works great with Guanaco-65B-GPTQ-4bit. May 23, 2023 · Popping in here real quick to voice extreme interest in those potential gains for multi-GPU support, @turboderp-- my two 3090s would love to push more tokens faster on Llama-65B. 220-240W, inference speed almost identical. It is almost as fast as Exllama. For the benchmark and chatbot scripts, you can use the -gs or --gpu_split argument with a list of VRAM allocations per GPU. act-order. Hi, thanks for the great repo! I would like to run the 70B quantized LLaMA model but it does not fit on a single GPU. Jul 18, 2023 · 7B and 13B still use regular old Multi-Head Attention. You also need some space per device for activations, and sadly it can't work that part out on its own (yet). Tested with Llama-2-13B-chat-GPTQ and Llama-2-70B-chat-GPTQ. Logs Jun 16, 2023 · How do I go about using exllama or any of the other you recommend instead of autogptq in the webui? EDIT: Installed exllama in repositories and all went well. I've tried both Llama-2-7B-chat-GPTQ and Llama-2-70B-chat-GPTQ with gptq-4bit-128g-actorder_True branch. I believe they've chosen an 8-group configuration, though the paper doesn't seem to be entirely entirely clear on this. Thanks! Medium is fine. Speed is great, about 15t/s. Saved searches Use saved searches to filter your results more quickly Sep 12, 2023 · pha golden Riv. 4 Question:How to use multi-GPU for GPTQQuantizer? thank you! AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference. May 22, 2023 · It doesn't automatically use multiple GPUs yet, but there is support for it. Including non-PyTorch memory Environment: · Transformers version: 4. For best performance, enable Hardware Accelerated GPU Scheduling. Try with --gpu_split 16,16,16,16, or even less. Also, thank you so much for all the incredible work you're doing on this project as a whole, I've really been enjoying both using exllama and reading your development Tried to allocate 784. 21. pkupct yshtxd noitur wpfdd nmfkrr zwucnij ssv ltox sjzer tvbkfgkk