Turboderp exllama gpu split. Recent commits have higher weight than older ones.

Turboderp exllama gpu split 0bpw -- Options: ['gpu_split: auto'] -- Loading ExLlamaV2 是一个用于在现代消费级 GPU 上运行本地大型语言模型（LLMs）的快速推理库。它支持 GPTQ 和 EXL2 量化的模型，能够在本地或远程进行推理，并提供与 OpenAI 兼容的 API。ExLlamaV2 的官方推荐后端服务器是 TabbyAPI，它提供了模型下载、嵌入模型支 Hello I am running a 2x 4090 PC, Windows, with exllama on 7b llama-2. In this tutorial, we will run LLM on the GPU entirely, which will allow us to speed it up significantly. It is a 16k Context length Vicuna 4bit quantized model. model imp turboderp / exllama Public. It's surprising if you can get over 50% utilization per GPU, actually, because that shouldn't be happening. But then the second thing is that ExLlama isn't written with AMD devices in mind. Notifications You must be signed in to change notification settings; Fork 221; Star 2. I am turboderp / exllama Public. Note: Exllama not yet support embedding REST API. By Hey @turboderp, @aljungberg Firstly thank you for the awesome repo!! from model import ExLlama, ExLlamaCache, ExLlamaConfig from tokenizer import ExLlamaTokenizer from generator import ExLlamaGenerator turboderp / exllama Public. I think I achieve that with --gpu_split 0,12 But how would I do the same in inference. #163 The upside is that you'll probably be able to fit a lot more context, because despite the model being larger than 65B, with the GQA configuration Meta chose for 70B, it should reduce the memory requirement for context by a factor of 8. @pineking: The inference speed at least theoretically is 3-4x faster than FP16 once you're bandwidth-limited, since all that ends up mattering is how fast your GPU can read through every parameter of the model once per token. . py -m /nvme/LLMs/turboderp_Smaug-72B-exl2_3. cpp or koboldcpp. You could also try llama. 1. b[perm, :] is, by the way, the state that the weights tensor is in right after the rows have been quantized in descending order of activation. set_auto_map('16,24') config. Purely speculatively, I know turboderp is looking into improved quantization methods for ExLLama v2, so if that pans out, and if LLaMA 2 34B is actually released, 34B BTW， I found exllama lora also support mlp and self_attn in source code. Activity is a relative number indicating how actively a project is being developed. Using the latest commit d3184ec, I was able to make my own 4bpw quant of dbrx-instruct. device_count(), exllama crashes with a cryptic stack trace. The improvement batching gives increases greatly with batch size but then each batch needs to be smaller to fit into memory, its a hard position to be in given that exllama is very optimized for consumer GPUs with somewhat limited vRAM but if you try it out on larger vRAM cards (like the A6000) with batch_size over 6+ you will see bigger differences To partially answer my own question, the modified GPTQ that turboderp's working on for ExLlama v2 is looking really promising even down to 3 bits. 7k. You signed out in another tab or window. turboderp. 00 MiB (GPU 0; 23. 23 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting Hey all! Hoping someone can help me out with better understanding GPU-Split for EXL2. I'm running into OOM when trying to load Turboderp's exl2 quants of Smaug. You also I'm running into OOM when trying to load Turboderp's exl2 quants of Smaug. I'm not aware of anyone releasing sharded GPTQ models, but if you have a link to where you found those files I could probably take a look. Explore the GitHub Discussions forum for turboderp exllama. De-quantizing the weights on the fly is cheap compared to the memory access and should pipeline just fine, with the CUDA cores The cache doesn't require lots of memory due to tensor copies. Code; 65B working on multi-gpu #38. Notifications You must be signed in to change notification settings; Fork 213; Star 2. 24 is what I use. Speed is great, about 15t/s. 0] #Setting this for multi GPU. - llm-jp/FastChat2 You signed in with another tab or window. I'm trying to split it across 4 RX A4000s. Same as other GPTQ stuff or anything that The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives. Language: For best performance, enable Hardware Accelerated GPU Scheduling. 65 bits on a 4090 plus a 3090Ti (also 24 GB). But, everytime I set do_sample=False, the answer is never change when I use exllama v1. For those getting started, the easiest one click installer I've used is Nomic. cpp in being a barebone reimplementation of just the part needed to run inference. Problem is CUDA isn't great for controlling multiple devices in ExLlama expects a single . For multi-gpu, write the numbers separated by spaces, eg --pre_layer 30 60. Factor in GPTQ with its very efficient VRAM usage and suddenly Python becomes the bottleneck. while I'm also trying to focus my attention on ExLlamaV2. 3. 169K subscribers in the LocalLLaMA community. - Issues Hi folks, I tried running the 7b-chat-hf variant from meta (fp16) with 2*RTX3060 (2*12GB). 4. I am only getting ~70-75t/s during inference (using just 1x 4090), but based on the charts, I should be getting 140+t/s. cuda. It requires lots of memory because it's a big list of tensors. 4-GPTQ --loader exllama --gpu-split 20,20 --listen --api 2023-07-13 10:14:19 INFO:Loading airoboros-65B-gpt4-1. py to split large . 1、exllama通过Python/C++/CUDA 实现，与 4 位 GPTQ 权重一起使用，旨在在现代 GPU 上实现快速且内存高效。 https://github. Code; # This line doesn't work model = ExLlama(config) tokenizer = ExLlamaTokenizer(tokenizer_path) BATCH_SIZE = 16 cache = ExLlamaCache(model, batch_size=BATCH_SIZE) generator Saved searches Use saved searches to filter your results more quickly @pineking: The inference speed at least theoretically is 3-4x faster than FP16 once you're bandwidth-limited, since all that ends up mattering is how fast your GPU can read through every parameter of the model once per token. 0 and 2. set_auto_map("10,24") Which return the following error: Exception ha Splitting across GPUs just involves doing half of the multiplication on each GPU and then copying the results across when you're done. Any way to put model straight onto gpu without saving to cpu lol, using a GPU vm that has 30gb vram but ExLlama really doesn't like P40s, all the heavy math it does is in FP16, and P40s are very very poor at FP16 math. Issues · turboderp/exllama. Installing collected packages: tokenizers Successfully installed tokenizers-0. Code; Issues 59; Pull ~/text-generation-webui$ python server. If you want to call the forward pass directly, you need to specify a But once you're doing token-by-token inference the GPU operations get a lot smaller. You also need some space per device for activations, and sadly it can't work that part out on its own (yet). The memory limits are still merely suggestions. 0-Uncensored-GPTQ). I lower the first limit until the split looks good and the 2nd GPU doesn't OOM during inference. Tried to allocate 112. 8k. As for ExLlama, currently that card will fit 7B or 13B. I was able to load the model shards into both GPUs using "device_map" in AutoModelForCausalLM. Subreddit to discuss about Llama, the large language model created by Meta AI. It also produces an index. Hello @turboderp, Not @mousepixels, but I'm experiencing the same confusion here. Fork: 221 Star: 2823 (更新于 2025-02-17 03:10:50) license: MIT . I've tried both Llama-2-7B-chat-GPTQ and Llama-2-70B-chat-GPTQ with gptq-4bit-128g-actorder_True branch. Notifications You must be signed in to change notification settings; Fork 222; Star 2 apparently because it made parallelism across 8 GPUs more difficult. 5b-instruct-exl2 - 4. 3B, 7B, and 13B models have been unthoroughly tested, but going by early results, each ExLlama is a standalone implementation that doesn't interface with Transformers, but AutoGPTQ ported the kernels over to get some of the performance benefits for Transformers anyway. How to. Running it on a single 4090 works well. Also, exllama has the advantage that it uses a similar philosophy to llama. act-order. 5-72B-Instruct-exl2 - 4. gpu_peer_fix = True model = ExLlama(config) cache = ExLlamaCache(model) tokenizer = ExLlamaTokenizer(tokenizer_model_path) generator = The --gpu_split is bad. I don't mind taking donations, but I'm a little wary about what expectations might come attached to a grant like that. 6k. 56 MiB free; 22. 88 MiB free; 23. It completely fills up the first GPU's VRAM and OOMs. I am trying to run experiments using two gpus and I need to be able to specify the target gpu. Maybe. Notifications You must be signed in to change Are there any plans to add the ability to split the model between VRAM and system RAM like AutoGPTQ does? For example the oobabooga webui, through AutoGPTQ, lets you load even a 65B parameter model on a 8GB VRAM GPU, where only 1GB is loaded in VRAM and the rest Integrated ExllamaV2 customized kernel into Fastchat to provide Faster GPTQ inference speed. Are theses two also linear and trainable by lora? The text was updated successfully, but these errors were encountered: 122 votes, 79 comments. cpp and other inference programs like ExLlama can split the work across multiple GPUs. The recommended software for this used to be auto-gptq, but its generation speed has since then been surpassed by exllama. Solution It would be nice for exllama to fall back to gpu_split = None with a warning to the user. You The --gpu_split is bad. 5. Reload to refresh your session. - exllama/model_init. Both GPTQ and exl2 are GPU only formats meaning inference cannot be split with the CPU and the model must fit entirely in VRAM. It just calls generate_simple with a list of input strings rather than a single string, and then it returns a list of outputs instead of a single output. Recent commits have higher weight than older ones. Hello. model_path = model_path config. The official and recommended backend server for ExLlamaV2 is TabbyAPI, which provides an OpenAI-compatible API for local or remote In the future, if ExLlama gets proper GPU parallelism, you would probably want more than mining risers. The GPUs work in turn and there's only a small amount of data to pass between them at the point where the model is split, which is why PCIe bandwidth doesn't make much of a difference. De-quantizing the weights on the fly is cheap compared to the memory access and should pipeline just fine, with the CUDA cores I've been trying to use exllama with a LoRA, and it works until the following lines are added: config. None of the other 3 GPUs ever get anything loaded on it. max_seq_len = 2048 config. json file for the sharded model, just for completeness, although ExLlama doesn't need it to read the shards. At that point, I'll have a total of 16GB + 24GB = 40GB VRAM available for LLMs. Downloading llama weights from meta I can't get gpu-split to take effect. Alternatively a P100 (or three) would work better given that their FP16 performance is pretty good (over 100x Exploring LLMs using the FastChat repo. - exllama/model. Notifications You must be signed in to change notification settings; Fork 214; Star 2. Using Exllama backend requires all the modules to be on GPU - how? #306 opened Nov 6, 2023 by tigerinus. 23 GiB already allocated; 23. py. It doesn't automatically use multiple GPUs yet, but there is support for it. So, one of the things that makes ExLlama fast If I may answer for turboderp, speculative decoding is planned in some time for exllama v2 I am also interested and would really like to implement it if turboderp has lots of other stuff to do :) reference: #149 (comment) The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives. Leaving the max_dq_size at the higher value doesn't seem to change any of my GPU breakpoints for smaller models, but maybe it does for some people. assigning the Q, K and V matrices to separate GPUs since they are parallel in the model) it's hard to overcome the overhead of moving data between This is not a issue, just reporting that it works great with Guanaco-65B-GPTQ-4bit. Release repo for Vicuna and Chatbot Arena. Code; Issues 60; Pull requests 6; Discussions; Actions; Projects 0; 5 x 20 -- Options: ['attention: pytorch_scaled_dp', 'matmul: quant_only', 'gpu_split: 16,22'] -- Groupsize (inferred): None -- Act-order (inferred): no This b' here means the permutation b[perm, :]. 2. If not how do I convert the safetensors to a format that works with exllamav2? but each . Thanks for this amazing work. 1), and even different model. 65 GiB total capacity; 22. 0bpw it crashes after filling the first 2 gpus and when it should start loading the rest of the model in the third gpu. Notifications You must be signed in to change notification settings; Fork 212; Star 2. ExLlama是一个基于Python/C++/CUDA的独立实现,针对4位GPTQ权重进行了优化,旨在提高现代GPU上的运行速度和内存效率。该项目支持 15. The single head's worth of KV cache can't be split across GPUs, apparently, it has to be duplicated, and if you're going to do that then you may as well use ExLlamaV2是一个专为在现代消费级GPU上本地运行大语言模型(LLM)而设计的高效推理库。它是ExLlama项目的升级版本,旨在提供更快速、更节省内存的LLM推理体验。 主要特点. What's odd is that it's running into OOM even without consuming all --pre_layer: The number of layers to allocate to the GPU. I can set it to auto, or 22,22,22,22, or 10,22,22,22, or even 1,1,1,1, and the result is the same. I'm already spending as much time as I can on ExLlama, and it's tough keeping up with the number of requests, issues, suggestions etc. PCIe bandwidth becomes a bottleneck, except when you're working with a single-row hidden state and then the result that has to be synchronized is also a single row, just a few kB of data. You're probably better off asking over there what's required for Transformers to load a model in a way that's compatible with the way they integrated the ExLlama kernels. Just retrained a 33B lora (had to rent compute since split gpu training was buggy) and it seems to be working I've done some experiments trying to split up the process across multiple GPUs but there's only so much splitting you can do due to the nature of the algorithm, and even where you can split (e. Changing hyper-parameters after initilization without reloading weights from disk. ExLlama only expects the split for the weights. 需要的模型： TheBloke/Pygmalion-13B-SuperHOT-8K-fp16 · Hugging Face. Anyway, ExLlama doesn't use multiple devices in parallel. @turboderp GPU utilization still shuttles rapidly between all GPUs during ingestion, at an even tighter timescale than I'm running the following code on 2x4090 and model outputs gibberish. Stars - the number of stars that a project has on GitHub. py --notebook --model airoboros-65B-gpt4-1. I think with some tighter synchronization multi-GPU setups should be able to get a significant speed boost on individual tokens, and I How can I assign GPU allocation? turboderp / exllama Public. It seems to not respect the gpu_split parameter. @turboderp lets say I wanted to extend exllama to support Falcon I need to strip out a bit of unused code and build out the C++ extension a bunch and experiment with splitting matmuls acrooss multiple GPUs. I can split larger models like Yes, dual GPUs are supported. So it's not as Here is my code for Exllamav2_HF import torch, os from contextlib import contextmanager from pathlib import Path from typing import Optional, List, Union, Dict from transformers import AutoConfig, An open platform for training, serving, and evaluating large language models. Closed ortegaalfredo opened this issue Jun 7, 2023 · 1 comment Closed turboderp converted this issue into discussion #39 Jun 7, 2023. Tried to allocate 288. So GPU 1 needs to copy the state from GPU 2 and vice versa, hundreds of times per token. They are equivalent to llama. --exllama-cache-8bit can be used to enable 8-bit caching with exllama and save some VRAM Exllama is amazing! Thank you for all the work! My question is - would it be possible to add to gpu-split functionality ability to offload some part of the model to RAM? Speed of Exllama's generation is more than enough so I wouldn't min @turboderp The weird thing is, I already load my API to many kind of GPU (A5000, 4080, 4090, A6000, A100) and using different cuda version (11 and 12), different pytorch version (2. tensor_parallel: true # I have 3090 + 4090 gpu_split_auto: true gpu_split: Qwen2. q_weight, . First and foremost, I want to express my sincere gratitude for taking the time to address my inquiry. 15. q_scale etc. io/ This runs with a simple GUI on Windows/Mac/Linux, leverages a fork of llama. I don't own any and while HIPifying the code seems to work for the most part, I can't actually test this myself, let alone optimize for a range of AMD GPUs. 通过Python/C++/CUDA 实现，与 4 位 GPTQ 权重一起使用，旨在在现代 GPU 上实现快速且内存高效。 https:// github. Code; Hardware-Accelerated It's my understanding that llama. py at master · turboderp/exllama ExLlama and exllamav2 are inference engines. Its much faster that any other framework on multiple In this use case, you would split a document into chunks that fits well within the context length ExLlama and exllamav2 are inference engines. 0bpw --gpu_split auto -p "Here is a funny joke about linux" -- Model: /nvme/LLMs/turboderp_Smaug-72B-exl2_3. py at master · turboderp/exllama Exllama v2 crashes when starting to load in the third gpu. Copying in-place actually saves a large amount of memory and bandwidth compared to the HF approach which concatenates the cache for every generated token, a much more expensive operation which also tends to cause memory fragmentation. turboderp/exllama . The TP implementation in ExLlama is a little half-baked. Notifications You must be signed in to change notification CUDA out of memory. cpp on the backend and supports GPU acceleration, and LLaMA, Falcon, MPT, and GPT-J There's an example in example_batch. ai's gpt4all: https://gpt4all. If I were looking to future-proof, and had to choose one or the other, PCIe 4. Hi, sorry to open an issue for this. No matter if the order is 3090,3090,A4000 or A4000,3090,3090, when I try to load the turboderp Mistral Large 2407 exl2 3. Here are some benchmarks from my initial testing today using the included benchmarking script (128 tokens, 1920 I am running a GPU server with 16GB of VRAM but could upgrade if needed. safetensors files. You switched accounts on another tab or window. 3 Qwen2. 5-0. py? Thanks @TikkunCreation I'm sort of in two minds about it. Notifications You must be signed in to change notification settings; Fork 222; Star 2. It's hard to say since the code doesn't exist yet. 27 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid Generation speed is the same (about 3x slower than the 70B of the same bpw). You should even be able to go slightly higher bitrate. Growth - month over month growth in stars. Even with 4 cards, only the second one gets usage and the benchmark never It could be split to use all 5 at 2k context, but it would just strictly be a little slower than 4. from exllama. Code; Issues 60; Pull model gets loaded, then the second GPU in sequence gets hit with a 100% load forever, regardless of the model size or GPU split. 64 GiB total capacity; 23. 3 turboderp commented Nov 20, 2024. 2 (exui) x0xxin at llama in ~/exllamav2 on master* $ python test_inference. Notifications You must be signed in to change notification settings; Fork 220; In some instances it would be super-useful to be able load separate lora's on top of a GPTQ model loaded with exllama. Fantastic work! I just started using exllama and the performance is very impressive. Is there an existing issue for this? I have searched the existing issues; turboderp / exllama Public. cpp's GPU offloading thing, that does support GPU parallelism, but the PCI-E bandwidth bottleneck of mining risers might cause performance problems there, and it's not as memory optimized as exllama either. ExLlama supports 4bpw GPTQ models, exllamav2 adds support for exl2 which can be quantised to fractional bits per weight. Contribute to snwagh/llm-train development by creating an account on GitHub. My chat site will get several thousand visitors a day, so there could be 25-50 concurrent chats. from_pretrained() and both GPUs memory is turboderp / exllama Public. 00 GiB already allocated; 104. com/turboderp/ex llama. 4-GPTQ turboderp / exllama Public. I'm trying to load the models split across the gpu's and its not working as intend. gpu_peer_fix = True config. Then you can run nvidia-smi to see how much memory ends up being used, and adjust accordingly. I run 70B at 4. Notifications You must be signed in to change notification settings; config. So what the kernel actually computes is a[:, perm] @ b[perm, :] = a @ b. Suppose I buy a Thunderbolt GPU dock like a TH3P4G3 and put a 3090/4090 with 24GB VRAM in it, then connect it to the laptop via Thunderbolt. Problem When len(gpu_split) > torch. 0 8 x 2 would probably be a safer bet. A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights. Try with --gpu_split 16,16,16,16, or even less. g. I can split larger models like Goliath 3bpw exl2. com/turboderp Of course, with that you should still be getting 20% more tokens per second on the MI100. The split work when loading a model via AUTO-GPT (TheBloke/WizardLM-33B-V1. 2023-08-12: Preliminary, initial and tentative release of The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives. config. How do I implement the multi-GPU inference using Ipython and not the WebUI? At present, I am implementing it this way. You can do the same split across different devices, but the catch is that you have to combine the results after every matmul. exllama makes 65b reasoning possible, so I feel very excited. Sep 13, 2023. For training lora, I am just curious if there is a back propagation module, whether the training speed will be much higher than the traditional The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives. You'll want a GPU split of something like (19,24), but YMMV a bit on that. Maintainer - though, and currently it's implemented the "dumb" way by doing batched linear layers along with split attention. Setting this parameter enables CPU offloading for 4-bit models. q_perm, . I thought about just writing up a pr, A fast inference library for running LLMs locally on modern consumer-class GPUs In fact, I can use 8 cards to train a 65b model based on bnb4bit or gptq, but the inference is too slow, so there is no practical value. But still, it loads the model on just one GPU and goes OOM during Yeah, ExLlama will need grouped-query attention support before 70B or (not-yet-released) 34B will work with it. 0,20. 0bpw from exllama 2. Note that the safetensors dependency was bumped to version 0. Discuss code, ask questions & collaborate with the developer community. So, one of the things that makes ExLlama fast Also there's a little script in util/shard. This is to allow act-order and group size to function at the same time without every row having a new group index. auto_map = [20. safetensors from TheBloke using 2x3090. ExLlama: -gs/--gpu_split: Comma 1、exllama. safetensors file and doesn't currently support sharding. Specifically one of turboderp's HF model repos. How hard would it be to write an inference engine based on exllama that supported tensor parallel, using the existing building blocks? Assume the quantized weight tensors would need to be split across the GPUs (either column or row-wise), and that the non-quantized pieces (hidden tensors) and any KV cache chunks (which could be quantized) are But once you're doing token-by-token inference the GPU operations get a lot smaller. turboderp / exllama Public. 支持4位GPTQ量化模型; 动态批处理与智能提示缓存; K/V缓 turboderp / exllama Public. 2、配 If you're just doing inference, use exllama. Hello @turboderp. weight tensor is split into several smaller tensors labeled . Tested with other exl2 models, and they all accurately adhere to the gpu_split I configure. Clone repo, install dependencies, and run benchmark: ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs. bslgnu ymcpk jlg jwllnr fghoka vbopd kce xdy fbxe csteku hikebta bwmxe zizhbn yuycdc jriw