Exllama slow. For 60B models or CPU only: Faraday.

Exllama slow Using both llama. Update 4: added llama-65b. Interested to hear your experience @turboderp. tokenizer = load_model(shared. 23 tokens/second With lama-cpp-python I get the same response in 9. Yes, I place the model in a 5 years old disk, but both my ram and disk are not fully loaded. dev, hands down the best UI out there with awesome dev support, but they only support GGML with GPU Splitting layers between GPUs (the first parameter in the example above) and compute in parallel. It features much lower VRAM usage and much higher speeds due to not relying on unoptimized transformers code. Downsides are that it uses more ram and crashes when it runs out of memory. Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company For some reason the first time is always slower. All reactions. However, in the I have been struggling with llama. Are you finding it slower in exllama v2 than in exllama? I do. I managed to get it to work pretty easily via text generation webui and inference is really fast! ExLlama implementation without an interface? I tried an autoGPTQ implementation of Llama on Huggingface, but it is so slow compared to Like even at 2k context size Exllama seems to be quite a bit slower compared to GGML (q3 variants and below). cache/torch_extensions for subsequent use. 2t/s. Instead of replacing the current rotary embedding calculation. All the models can be found on Huggingface. GPTQ is the standard for running on GPU only, while AWQ is supposed to be OMG, and I'm not bouncing off the VRAM limit when approaching 2K tokens. I'm also really struggling with disk space, but I ordered some more SSDs, which should help I guess. I've been slowly moving some stuff in linux direction too, so far just using WSL and a raspbian bitcoin/ordinals node I set up. By default it automatically uses the Exllama kernel if it can but its not supported on all GPTQ models. Then, when I edit history/context on a really long conversation, it REALLY slows down until it reprocesses. PSA for anyone using those unholy 4x7B Frankenmoes: I'd assumed there were only 8x7B models out there and I didn't account for 4x, so those models fall back on the slower default inference path. But there is one problem. They are much closer if both batch sizes are set to 2048. It's quite slow however. It should be a bit slower I think, since it has to output transformers samplers to exllama itself. Exllama itself, this is the fastest of the bunch. So, using GGML models and the llama_hf loader, I have been able to achieve higher context. In a recent thread it was suggested that with 24g of vram I should use a 70b exl2 with exllama rather than a gguf. QLora is slower during inference. model_name_or_path = "TheBloke/Mistral-7B-Instruct-v0. 1B-1T-OpenOrca-GPTQ. OpenAI’s Python Library Import: LM Studio allows developers to import the OpenAI Python library and point the base URL to a local server (localhost). I'm sure there's probably a better way to be running it but I haven't figured it out yet. EXLlama support added to oobabooga-text-generation-webui Llama-2 has 4096 context length. Exllama does the magic for you. 11T/s speeds. GGUF/llama. Evaluation speed. 7 t/sec with exllama but that isn't compatible with most software. 3-5 T/S is just fine with my rtx3080 on a 13b - its not much slower than oai completion I'm running a 70B GPTQ model with ExLlama_HF on a 4090 and most of the time just deal with the 0. on the Chat Settings tab, choose Instruction template tab and pick Llama-v2 With the above sample Python code, you can reuse an existing OpenAI configuration and modify the base url to point to your localhost. It also takes a considerable context length before attention starts to slow things down noticeably EXLLAMA_NOCOMPILE= python setup. Speaking from personal experience, the current prompt eval speed on llama. I see the system RAM max out at ~30/32GB, which doesn't make a lot of sense. cpp is pretty fast till you get over 4k context, can use all GPU and has a python implementation too. It achieves about a third of the speed of ExLlama, but also running on models that take up three times as much VRAM. Is there any config or something else for a100??? Share Add a Comment. Reload to refresh your session. While this may not be a bug, it's something to keep in mind when Hello I am running a 2x 4090 PC, Windows, with exllama on 7b llama-2. 39). It uses the GGML and GGUF formated models, with GGUF being the newest format. The speeds will be significantly slower then if you had the model on GPU only, though. By contrast, ExLlama (and I think most if not all other implementations) just let the GPUs work The only way I could use exllama on horde was with Occam's koboldai branch, and he's been busy on other projects, and Henky decided to drop plans to officially support exllama in the united branch. The length that you will be able to reach will depend on the model size and your GPU memory. On llama. It is activated by default. cpp is a C++ refactoring of transformers along with optimizations. Another side-effect is that every application becomes Oobabooga WebUI had a HUGE update adding ExLlama and ExLlama_HF model loaders that use LESS VRAM and have HUGE speed increases, and even 8K tokens to play ar exllama + GPTQ was fastest for me vLLM also very competitive if you want to run without quantization TGI for me was slow even tho it uses exllama kernels. https://github. For multi-gpu models llama. cpp is way slower to ExLlama There is a flag for gptq/torch called use_cuda_fp16 = False that gives a massive speed boost -- is it possible to do something similar in exllama? Well, it would give a massive boost on the P40 because of its really poor FP16 Larger sized model, slower inference and minimal gain of perplexity. 1-GPTQ I create a feature request on the official repo :Exllama integration to run GPTQ models · Issue #8385 · langchain-ai/langchain (github. cpp generation. There may be more performance optimizations in the future, and speeds will vary across GPUs, with slow CPUs still being a potential bottleneck: Converting large models can be somewhat slow, so be warned. According to the project's repository, Exllama can achieve around 40 tokens/sec on a 33b model, surpassing the performance of other options like AutoGPTQ with CUDA. 1. Should work for other 7000 series AMD GPUs such as 7900XTX. See the Anyway, it's never going to be a fair comparison between vLLM and ExLlama because they're not using quantized models and ExLlama uses only quantized models. Here are his words: "I'm working on some benchmarks at the moment, but they're taking a while to run. llama. cpp comparison. Note that you will only be able to overwrite the There's already software that does what you're after, and there's a reason why it's so slow despite having thousands of contributors working on it for years. I don't know if GGML would be faster with some kind AutoGPTQ, depending on the version you are using this does / does not support GPTQ models using an Exllama kernel. Exllama V2 defaults to a prompt processing batch size of 2048, while llama. q2_K (2-bit) test with llama. 1 t/s) than llama. Download the model (and all files) from HF and place it somewhere. TheBloke. Exllama is also banned on kobold horde now and workers spotted running it get put into maintenance. But then the second thing is that ExLlama isn't written with AMD devices in mind. py at master · turboderp/exllama In the past I've been using GPTQ (Exllama) on my main system with the 3090, but this won't work with the P40 due to its lack of FP16 instruction acceleration. The "HF" version is slow as molasses. Preliminary results show the Q4 cache mode is more precise overall than FP8, and comparable to full precision. Usage Configure text-generation-webui to use exllama via the UI or command line: In the "Model" tab, set "Loader" to "exllama" Specify --loader exllama on the command line For merges I find it slower, and painful for juggling storage around between ext3/4 and ntfs for big databases. The actual processing is what takes all of the resources. See translation. However, 15 tokens per second is a bit too slow and exllama v2 should still be very comparable to llama. py install --user This will install the "JIT version" of the package, i. An example is SuperHOT ExLlama is an extremely optimized GPTQ backend for LLaMA models. exllama makes 65b reasoning possible, so I feel very excited. Takes 3secs to load a LoRA. 5x 4090s, 13900K (takes more VRAM than a single 4090) Model: ShiningValiant-2. 44 seconds, 150 tokens, 4. from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline. Maybe it's better optimized for data centers (A100) vs what I have locally (3090) Currently, the two best model backends are llama. Under everything else it was 30%. The A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights. I personally would rather use a more accurate but slower model than the other way around. You signed in with another tab or window. Wish the I think this repo is great, I would really like to be able to do similar work on optimising performance of LLM for my particular use case. And 2 cheap secondhand 3090s' 65b speed is 15 token/s on Exllama. 2 ; anything after that gets slow, x10 slower. Just plugged them both in. P40 needs Tesla specific drivers. In the past exllama v1, there was a slight slowdown when using Lora, but it was approximately 10%. They have all the talent, experience and Cache and state has to reside on the same device as the associated weights. They are marked with (new) Update 2: also added a test for 30b with 128g + desc_act using ExLlama. For 13B and 30B models: Ooba with exllama, blows everything else out of the water. 0. exllama (not hf) has top k, top p Exllama, from its inception, has been made for users with 1-2 commercial graphics cards, lacking in batching and the ability to compute in parallel. I can easily produce the 20+ tokens/sec of output I need when predicting longer outputs, but when I try and exl2 processes most things in FP16, which the 1080ti, being from the Pascal era, is veryyy slow at. cpp. It is probably because the author has "turbo" in his name. Is there an existing issue for this? I have searched the existing issues; Reproduction-git pull latest version-start_window. Open comment sort options Also try on exllama with some exl2 model and try what you downloaded in 8bit and 4bit with bitsandbytes. I'm having a similar experience on an RTX-3090 on Windows 11 / WSL. 93 tokens/s, 256 tokens, context 15, seed 545675865) Output generated in 10. cpp with GPU offload (3 t/s). Slower than OpenAI, but hey, it's self-hosted! It will do whatever you train it to do, all depends on a good dataset. Instead, the extension will be built the first time the library is used, then cached in ~/. 13B 6Bit quantized is acceptable. It has a ton of options made specifically for RP. However, in the case of exllama v2, it is good to support Lora, but when using Lora, the token creation speed slows down by almost 2 times. 35 seconds (24. py. cpp beats exllama on my machine and can use the P40 on Q6 models. Make sure to also set Truncate the prompt up to this length to 4096 under Parameters. Exllama does not run well on it, I get less than 1t/s. 6 seconds, 232 tokens, bash is significantly slower than python to execute (Not even using a bytecode), and if bash slowed our programs by 30%, that would clearly and obviously be a bug, they're both just a tool to more easily call other C++ programs and send short strings back and forth, and we eat that cost in sub-millisecond latency before and after the call, but The issue with P40s really is that because of their older CUDA level, newer loaders like Exllama run terribly slow (lack of fp16 on the P40 i think), so the various SuperHOT models can't achieve full context. This will overwrite the quantization config stored in the config. com - Older xeons are slow and loud and hot - Older AMD Epycs, i really don't know much about and would love some data - Newer AMD Epycs, i don't even know if these exist, and would love some data. Anything that uses the API should basically see zero slow down. cpp It should be still higher. Some initial benchmarks First of all, exllama v2 is a really great module. it will install the Python components without building the C++ extension in the process. This may because you installed auto_gptq using a pre-build wheel on Windows, in which exllama Exllama kernels for faster inference For 4-bit model, you can use the exllama kernels in order to a faster inference speed. This seemed I'm aware that there are GGML versions of those models, but the inference speed is painfully slow compared to GPTQ. That's amazing what can do the latest version of text-generation-webui using the new loader Exllama-HF! I can load a 33B model into 16,95GB of VRAM! 21,112GB of VRAM with AutoGPTQ!20,07GB of VRAM with Exllama. Tried the new llama2-70b-guanaco in ooba with exllama (20,24 for the memory split parameter). . cpp can so MLC gets an advantage over the others for inferencing (since it slows down with longer context), my previous query on how to actually do apples-to I did see that the server now supports setting K and V quant types with -ctk TYPE and -ctv TYPE but the implementation seems off, as #5932 mentions, the efficiencies observed in exllama v2 are much better than we observed in #4312 - seems like some more relevant work is being done on this in #4801 to optimize the matmuls for int8 quants I'm developing AI assistant for fiction writer. Please call the exllama_set_max_input_length function to increase the buffer size. Exllama doesn't want to play along at all when I try to split the model between two cards. Update 3: the takeaway messages have been updated in light of the latest data. You can't do CUDA operations across devices, and while you could store just the cache on a separate device, it would be slower than just swapping it to system RAM, which is still slow enough to be kind of useless. Usage Configure text-generation-webui to use exllama via the UI or command line: In the "Model" tab, set "Loader" to "exllama" Specify --loader exllama on the command line Turboderp, developer of Exllama V2 has made a breakthrough: A 4 bit KV Cache that seemingly performs on par with FP16. -nommq takes EXLLAMA_NOCOMPILE= python setup. You switched accounts on another tab or window. Upload images, audio, and videos by dragging in the text input, pasting, or clicking here. but I can't even find CUDA or exllama_ext. You can do that by setting n_batch and u_batch to 2048 (-b 2048 -ub 2048) FA slows down llama. cpp loader and GGUF (using oobabooga and the same LLM model), no matter how I set the parameters and how many offloaded layers to GPUs, llama. It is so slow. They are way cheaper than Apple Studio with M2 ultra. 4 models work fine and are smart, I used Exllamav2_HF loader (not for speculative tests above) because I haven't worked out the right sampling parameters. It's not that those guys don't know what they're doing. 22x longer than ExLlamav2 to process a 3200 tokens prompt. In the Model tab, select "ExLlama_HF" under "Model loader", set max_seq_len to 8192, and set compress_pos_emb to 4. 11 release, so for now you'll have to build from The llama. ExLlama gets around the problem by reordering rows at load-time and discarding the group index. It's slower than the GPU, but it was way cheaper and I can run the 70B model easily. ExLlama supports 4bpw GPTQ models, exllamav2 adds support for exl2 which can be quantised to fractional bits per weight. Tap or paste here to upload images. I edit a lot, which is why I moved from GGUF to EXL2 in the first place. Put this somewhere inside the wsl linux filesystem, not under /mnt/c/somewhere otherwise the model loading will be mega slow regardless of your disk speed; on model. Additionally, only for the web UI: To run on Traceback (most recent call last): File “C:\oobabooga_windows\text-generation-webui\server. On Mac, Won't be nearly as fast as exllama but you could offload a decent amount of layers to 3090 with ggml. Can those be installed along side standard Geforce drivers? In that thread, someone asked for tests of speculative decoding for both Exllama v2 and llama. cpp and exllama, in my opinion. Which model are you using and which loader (llama. , ExLlama for GPTQ. It's neck and neck with exllama for multi card. cpp option was slow, achieving around 0. We can train it to be a general purpose assistant that follows YOUR ethos inserted of OpenAI's. These quantized LLMs can also be fast during inference when using a GPU, especially with optimized CUDA kernels and an efficient backend, e. cpp is way slower to ExLlama (v1&2), not just According to Pinokio/TGI, I am actually getting way better than ~15 tokens/s. I can't even get 2k context fused and barely touch 3k unfused. For me, these were the parameters that worked with 24GB VRAM: VRAM can also fully accommodate 7b q8 models and 13b q4 models, but heavier models will already use CPU RAM, which will slow down the speed a lot. (pip uninstall exllama and modified q4_matmul. Basically, the windows defender is slowing the IDE so adding exclusions to IntelliJ processes and folders helped: Go to Start > Settings -> Update & Security -> Virus & threat protection -> Virus & threat protection; Under Virus & threat protection settings select Manage settings; Under Exclusions, select Add or remove exclusions and add the With the fused attention it is fast like exllama, but without it is slow AF. Is there an existing issue for this? I have searched the existing issues; Reproduction. 4bpw-h6-exl2. Could not manage to get any decent speed with exLlama. The build used to take 4 minutes and now it takes 17. After starting oobabooga again, it did not work anymore. However lora works with transformers but slow af we really need exllama for this. The recommended software for this used to be auto-gptq, but its generation speed has since then been surpassed by exllama. I have heard its slower than full on Exllama. cpp models with a context length of 1. Evaluation. The EXLlama option was significantly faster at around 2. @turboderp would you be able to share some of the process for how you go about speeding up the models? I'm sure there are lots of others out there who also want to learn more too. Apr 26, 2023. Creator of Exllama Uploads Llama-3-70B Fine-Tune New Model An amazing new fine-tune has been uploaded to Turboderp's huggingface account! Fine i1 uses a newer quant method, it might work slower on older hardware though. There could be something keeping the GPU occupied or power limited, or maybe your CPU is very slow? I recently added the --affinity argument which you 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 When using exllama inference, it can reach 20 token/s per second or more. 0 When I try to load a 70B model ~ 40GB, my system stalls out. Still slow + every other model is now also just 10 tokens / sec instead of 40 tokens / sec so I stay with ooba's fork. Test 1 Wizard-Vicuna-30B-Uncensored. (I didn’t have time for this, but if I was going to use exllama for In this tutorial, we will run LLM on the GPU entirely, which will allow us to speed it up significantly. Has anyone here had experience with this setup or similar configurations? I'd love to hear Loading the 13b model take few minutes, which is acceptable, but loading the 30b-4bit is extremely slow, took around 20 minutes. Update to I had the issue mentioned here: oobabooga/text-generation-webui#2949 Generation with exllama was extremely slow and the fix resolved my issue. AutoGPTQ works fine but it's still rather slow to inference. You can see what's happening in Exllama is slow on pascal cards because of the prompt reading, there is a workaround here though: turboderp/exllama#111. It stays full speed forever! I was fine with 7B 4bit models, but with the 13B models, soemewhere close to 2K tokens it would start DRAGGING, because VRAM usage would slowly creep up, but exllama isn't doing that. py I added the following: Exllama kernels for faster inference. With the release of exllamav2 kernels, you can get faster inference speed compared to exllama kernels for 4-bit model. 2t/s, suhsequent text generation is about 1. cpp's metal or CPU is extremely slow and practically unusable. AutoGPTQ and GPTQ-for-LLaMA don't have this optimization (yet) so you end up paying a big performance penalty when using both act Exllama: 4096 context possible, 41GB VRAM usage total, 12-15 tokens/s GPTQ for LLaMA and AutoGPTQ: 2500 max context, 48GB VRAM usage, 2 tokens/s It does works with exllama_hf as well, a little slower speed. Reply reply Radiant-Practice-270 • Several times I notice a slight speed increase using direct implementations like llama-cpp-python OAI server. com/turboderp/exllama 👉ⓢⓤⓑⓢ Exllama v2. cpp from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline. Furthermore, if RP is what you're into, consider using SillyTavern as a frontend after loading the model in Ooba. The prompt processing speeds of load_in_4bit and AutoAWQ are not impressive. 11 seconds (25. You may have to reduce max_seq_len if you run out of memory while trying to generate text. ExLlama_HF uses the logits from ExLlama but replaces ExLlama's sampler with the same HF pipeline used by other implementations, so that sampling parameters are interpreted the same and more samplers are Hello everyone,I'm currently running Llama-2 70b on an A6000 GPU using Exllama, and I'm achieving an average inference speed of 10t/s, with peaks up to 13t/s. Weirdly, inference seems to speed up over time. Beta Was this translation helpful? Give Of course, with that you should still be getting 20% more tokens per second on the MI100. On ExLlama/ExLlama_HF, set max_seq_len to 4096 (or the highest value before you run out of memory). 23 tokens/second First of all, exllama v2 is a really great module. Thinking I can't be the only one struggling with this, it seemed a new post would give the question greater visibility for those in a similar Hi, I tried to use exllamv2 with Mistral 7B Instruct instead of my llama-cpp-python test implementation. It is activated by default: disable_exllamav2=False in load_quantized_model(). Only odd man out is AutoGPTQ and now AWQ because they're still using accelerate to split up models for that slow ride. Pinokio is stating ~44 t/s with EXL2-HF, and switching to regular EXL2 brought me up to 56 t/s. 4 t/sec. Yes the models are smaller but once you hit generate, they use more than GGUF or EXL2 or Open the Model tab, set the loader as ExLlama or ExLlama_HF. An the capital of USA. I have a fork of GPTQ that supports the act-order models and gets 14. In order to use these kernels, you need to have the entire model on gpus. Sort by: Best. The command line is stuck on "INFO:Loading Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-GPTQ Upvote for exllama. Then, select the llama-13b-4bit-128g model in the "Model" dropdown to load it. Is there a way I can run it In this tutorial, we will run LLM on the GPU entirely, which will allow us to speed it up significantly. Sadly, prompt ingestion is currently somewhat slower in the TP mode, since In some instances it would be super-useful to be able load separate lora's on top of a GPTQ model loaded with exllama. You may be better off running GGUF models in llama. Is it possible to implement a fix like this for pascal card users? Changing it in the repositories/exllama/ didnt fix it for me. If you are really serious about using exllama, I recommend trying to use it without the text generation UI and look at the exllama repo, specifically at test_benchmark_inference. Also tried emb 4 with 2048 and it was still slow. Reply reply More replies. By uploading the F16 model first, you can save your own time as well the time of other users who might be looking for different quantizations of the models. If your NVIDIA driver supports system RAM swapping, that's a way to run larger models than you could otherwise fit in VRAM, but it's going to be horrendously slow. There's an update now that enables the fused kernels for 4x models as well, but it isn't in the 0. So are there any models bigger than 7B which might fight onto 8GB of ExLlama v1 vs ExLlama v2 GPTQ speed (update) I had originally measured the GPTQ speeds through ExLlama v1 only, but turboderp pointed out that GPTQ is faster on ExLlama v2, so I collected the following additional data for the model Hi, I am working with a Telsa V100 16GB to run Llama-2 7b and 13b, I have used gptq and ggml version. Reply reply which ends up being quite slow. You will have to stick with 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. So keep that in mind. For instance, the latest Nvidia drivers have introduced design choices that slow down the inference process. I'm using exllama manually into ooba (without the wheel). It sort of get's slow at high contexts more than EXL2 or GPTQ does though. Don’t know if that slows it down to the same as naive MP in Exllama. Hope he can update it soon. Its really quite simple, exllama's kernels do all calculations on half floats, Pascal gpus other than GP100 (p100) are very slow in fp16 because only a tiny fraction of the devices shaders can do fp16 (1/64th of fp32). I was able to load 70B GGML model offloading 42 layers onto the GPU using oobabooga. Also the memory use isn't good. model, shared. A Text generation web ui is slower then using exllama v2 because of all the gradio overhead. Despite the fact that the CPU "isn't doing anything" during inference, Python is still really slow, and then Torch's underlying C++ libraries add a little overhead as well. ExLlama is an extremely optimized GPTQ backend for LLaMA models. Thank you for your post, this is an amazing improvement. Appreciate your time Reply reply sshan • I’ve been tinkering in this stuff for a while and I As per discussion in issue #270. Check out airoboros 7b maybe The Pascal is usable and works very well, but you do have to fiddle around with drivers versions, cuda versions and bits and bytes versions (0. Scan over the pull requests on the exllama repo to see why it is so fast. bat with nvidia choice-add model TheBloke/Mistral-7B-Instruct-v0. Q4_K_M is 6% slower than Q4_0 for example, as the model file is 8% larger. Effectively a Mixture of Experts. Ok, maybe it's the fact I'm trying llama 1 30b. But that might be one cause. 5 times faster than ExllamaV2. CyberTimon. I get about 700 ms/T with 65b on 16gb vram and an i9 It's much slower splitting across my 4090 and 3xa4000 at around 3tokens/s Reply reply More replies More replies. But that's not a problem anyway, EXL2 First of all, exllama v2 is a really great module. Let's try with llama 2 13b. When testing exllama both GPUs can do 50% at the same time. I want to use the ExLlama models because it enables me to use the Llama 70b version with my 2 RTX 4090. 9 For VRAM tests, I loaded ExLlama and llama. Has anyone else noticed similar issues? I want to believe it's just some EXL2 setting I messed up, but I tried everything I could think of. So presumably if they added quantization support the speed would be comparable. So I suppose this issue is no longer ExLlama is a smaller project but contributions are being actively merged (I submitted a PR) and the maintainer is super responsive. 3. AutoGPTQ - this engine, while generally slower may be better for older GPU architectures. The AI response speed is quite fast. 1-GPTQ" # To use a different branch, change revision GPTQ, AWQ, and EXLLAMA are quantization methods that only run on the GPU, while GGUF can balance the load between the CPU and GPU. Any Pascal card except the P100 will run badly on exllama/exllamav2. 74 tokens/s, 256 tokens, context 15, seed 91871968) Generation with exllama was extremely slow and the fix resolved my issue. AutoGPTQ has much better oddball model support, however and can train. The text generation speed when using 14 or 15 cores as initially suggested can be increased by about 10% when using 3 to 4 cores from each CCD instead, so 6 to 8 cores in total. 5 tokens per second. cpp, exllama, transformers etc)? Ik assuming you will bring using llama cpp with a gguf model here, so open task manager or some system resource monitor and go and see how much vram is being used when the model is loaded and for best performance you want it to be a little bit under the max. Many people conveniently ignore the prompt evalution speed of Mac. The github repo link is: https://github. Or we can simply train it to be a waifu with scary verbal intelligence :D This tool is now slowing down the build. This makes the models directly comparable to the AWQ and transformers models, for which the cache is not preallocated at load time. AWQ and smoothquant are both noticeably slower than fp16 in vllm so far, you definitely take a hit to throughput with those in exchange for lower VRAM For the 34b, I suggest you choose Exllama 2 quants, 20b and 13b you can use other formats and they should still fit in the 24gb of VRAM. cpp/llamacpp_HF, set n_ctx to 4096. cpp on the other hand is capable of using an FP32 pathway when required for the older cards, that's why it's quicker on those cards. I'm wondering if there's any way to further optimize this setup to increase the inference speed. As openai API gets pretty expensive with all the inference tricks needed, I'm looking for a good local alternative for most of inference, saving gpt4 just for polishing final results. Llama. The AMD GPU model is 6700XT. e. I generally only run models in GPTQ, AWQ or exl2 formats, but was interested in doing the exl2 vs. We would like to show you a description here but the site won’t allow us. the generation very slow it takes 25s and 32s respectively. The following is a fairly informal proposal for @turboderp to review:. The console is stuck on "INFO:Loading I got ooba working locally on a 380 16gb card but it runs slow as ass. There is no built-in way, no. Exllama by itself is very fast when model fits in VRAM completely. EXL2 is the fastest, followed by GPTQ through ExLlama v1. Edit Preview. Unless you have nvlink/switch, you’d be p2p pcie bandwidth bottlenecked on non-datacenter gpus. Exllama: 9+ t/s, ExllamaV2 1. Will look for nans. It is capable of mixed inference with GPU and CPU working together without fuss. It's obviously a work in progress but it's a fantastic project and wicked fast 👍 Because the user-oriented side is straight python is much easier to script and you can just read the code to understand what's going on. lhl on July 26, 2023 ExLlama_HF loader gpu split 20,22, context size 2048. cpp, offloading what you can onto the GPU but doing CPU inference for the rest. You signed out in another tab or window. g. Unfortunately i can't recommend other GPUs, anything stronger than the 3060 is very different in price (I am estimating this, but its usually close to the exllama speed and the speed of other This is because users can convert the F16 model to any other quantization they might need, including SOTA Q-quantized and exllama models. That and getting exllama going. There is a CUDA and Triton mode, but the biggest selling point is that it can not only inference, but also quantize and fine P40 can't use newer bitsandbyes. Reply reply You signed in with another tab or window. q5_0 CPU With GPU Accelerate What is the capital of Canada. 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 I have an Alienware R15 32G DDR5, i9, RTX4090. It's also shit for samplers and when it doesn't re-process the prompt you can get identical re-rolls. For 60B models or CPU only: Faraday. You can offload inactive users' caches to system memory (i. Try classification. I wonder if that's how it's supposed to be or if Here's some quick numbers on a 13B llama model with exllama on a 3060 12GB in Linux: Output generated in 10. This is the speed at which oobabooga initially used exllama, and the speed was like a rocket. If it's still slow then this I suppose this must be a GPU-specific issue, and not as I thought OS/installation specific. The EPYC is very slow, though, less than half the single-threaded performance of the 12900K, so that's probably what you're running into. I have a 4090 and 32Gib of memory running on Ubuntu server with an 11700K. If you only need to serve 10 of those 50 users at once, then yes, you can allocate entries in the batch to a queue of incoming requests. The quantization of EXL2 itself is more complicated than the other formats so that could also be a factor. 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. For TP, there’d be quite a bit chatter p2p. This has all been changed in recent updates, which allow you to utilize many GPUs at once without any cost to speed. nope, old Exllama still ~2. I have been playing with things and thought it better to ask a question in a new thread. Llama2 i can run 16b gptq (gptq is purely vram) using exllama Llama2 i can run 70B ggml, but it is so slow. exllamv2 works, but the performance is very slow compared to llama-cpp-python. 32 tokens/s, 256 tokens, context 15, seed 1844401441) Output generated in 10. RuntimeError: The temp_state buffer is too small in the exllama backend. Shrug. exllamv2 works, but the performance is very slow compared to llama-cpp-python. Also I noticed that autoGPTQ works best if frozen at v0. com)I will try to use the fork provided in the comments edit: typo Unless you've got extremely slow cores or extremely fast VRAM, the operation ends up being entirely bandwidth-limited, and with even a naively written kernel the multiplication will be done in however long you can read in both matrices from RAM. Come back with questions, I'd be glad to help. exlla exllama is very optimized for consumer GPU architecture so hence enterprise GPUs might not perform or scale as well, point, which should have been more or less dealt with, but in my experience some of these GPU cloud instances have very slow CPU cores, so that could also be part of the explanation. Small caveat: This requires the context to be present on both GPUs (AFAIK, please correct me if this not true), which introduces a sizeable bit of overhead, as the context size expands/grows. After the initial load and first text generation which is extremely slow at ~0. - exllama/model. 4). Pick one of the 4, 5, or 6 bit models here if you would like to experiment with offloading. Using 2x 7900 XTX on EndeavourOS + pytorch nightly for ROCm 6. You can change that behavior by passing disable_exllama in GPTQConfig. Check the alpaca_lora_4bit github repo, it's very easy to setup and has example commands. With exllamv2 I get my sample response in: 35. Same thing happened with alpaca_lora_4bit, his gradio UI had strange loss of performance. 1-GPTQ" To use a different branch, change revision The bitsandbytes approach makes inference much slower, which others have reported. 7 tokens/s after a few times regenerating. ROCm is also theoretically supported (via HIP) though I currently have no AMD devices to test or optimize on. I noticed SSD activities (likely due to low system RAM) on the first text generation. compress_pos_emb is for models/loras trained with RoPE scaling. Also, exllama has the advantage that it uses a similar philosophy to llama. It's kinda slow to iterate on since quantizing a 70B model still takes 40 minutes or so. Comment exllama is very optimized for consumer GPU architecture so hence enterprise GPUs might not perform or scale as well, im sure @turboderp has the details of why (fp16 math and what not) Or will the slow CPU cores on cloud instances always be a bottleneck? Thank you. However, when I switched to exllamav2, I found that the speed dropped to about 7 token/s, which was slowed down. Set max_seq_len to a number greater than 2048. cpp, exllama) Question | Help I have an application that requires < 200ms total inference time. cpp is the slowest, taking 2. On a 70b parameter model with ~1024 max_sequence_length, repeated generation starts at ~1 tokens/s, and then will go up to 7. 3 and 2. ; Multi-model Session: Use a single prompt and select multiple models As mentioned before, when a model fits into the GPU, exllama is significantly faster (as a reference, with 8 bit quants of llama-3b I get ~64 t/s llamacpp vs ~90 t/s exllama on a 4090). For the first time ever, this means GGML can now outperform AutoGPTQ and GPTQ-for-LLaMa inference (though it still loses to exllama) Note: if you test this, be aware that you should now use --threads 1 as it's no longer beneficial to use multiple threads; in fact it slows down performance a lot. Example: from auto_gptq import exllama_set_max_input_length model = Sadly, it's much slower. Based on the high system RAM usage, Use Exllama (does anyone know why it speeds things up?) Use 4 bit quantization so that I can run more jobs in parallel Exllama is GPTQ 4-bit only, so you kill two birds with one stone here. Lm studio does not use gradio, hence it will be a bit faster. cpp defaults to 512. Lllama. The tool hasn't changed; it's taken from version control and it hasn't changed for years. I get 17. For inference, native Windows is slightly faster now too, with flash attn in Windows, so there is an incentive to keep everything in a Windows drive and avoid the overhead. while they're still reading the last reply or typing), and you can also use dynamic batching to make better use of VRAM, since not all users will need the full context all the time. But other larger context models are appearing every other day now, since Llama 2 dropped. The triton version gets 11. Sorry 30b running slowly on 4090 . We can train it to comment, edit or suggest code. cu according to turboderp/exllama#111. I only need ~ 2 tokens of output and have a large high-quality dataset to fine-tune my model. It uses Update 1: I added tests with 128g + desc_act using ExLlama. And all experiments I've run so far trying to run at extended context lengths immediately OOM on me :/ I'm totally down to settle for slow performance as a tradeoff for 70b, even at 4096 context. 27 seconds (24. cpp in being a barebone reimplementation of just the part needed to run inference. 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. Inference is relatively slow going, down from around 12-14 t/s to 2-4 t/s with nearly 6k context. Both GPTQ and exl2 are GPU only Some quick tests to compare performance with ExLlama V1. model_name, loader) File “C:\oobabooga_windows\text Thanks for sharing! I have been struggling with llama. Decrease cold-start speed on inference (llama. Question | Help I’m not sure what I’m doing wrong. Draft model: TinyLlama-1. I'll see if maybe I can't get a 7B model to load, though, and compare it anyway. ggmlv3. The recommended software for this used to be auto-gptq, but its generation speed has since AutoGPTQ or GPTQ-for-LLaMa are better options at the moment for older GPUs. I pretty much tried every step between 2048 and 3584 with emb 2 and they all gave the same OpenAI compatible API; Loading/unloading models; HuggingFace model downloading; Embedding model support; JSON schema + Regex + EBNF support; AI Horde support 2. Though it still would take me more than 6 minutes to generate a response to near full 4k context with GGML when using I don't know how MLC to control output like ExLlama or llama. This issue is being reopened. None, 'quantize_config': None, 'use_cuda_fp16': True, 'disable_exllama': False} 2023-09-21 10:53:11 WARNING:Exllama kernel is not installed, reset disable_exllama to True. I tried that with 65B on single 4090 and exllama is much slower (0. I'm experimenting with some and getting It works with Exllama v2 (release: 0. py”, line 73, in load_model_wrapper shared. And then having another model choose the best one for the query. You should probably start with smaller models first because the P40 is a very slow card compared to modern cards. The conversion script and its options are explained in detail here. 25 t/s (ran more than once to make sure it's not a fluke) Ok, maybe it's the max_seq_len or alpha_value, so here's a test with the default llama 1 context of 2k. ewjhqu ehxid idoqjw mixx lscz vkcn vdx ajzeur fungwj ntbvl