Exllama slow. So keep that in mind.



    • ● Exllama slow It is so slow. The tool hasn't changed; it's taken from version control and it hasn't changed for years. Using 2x 7900 XTX on EndeavourOS + pytorch nightly for ROCm 6. For 60B models or CPU only: Faraday. However, in the I have been struggling with llama. Are you finding it slower in exllama v2 than in exllama? I do. py. Additionally, only for the web UI: To run on Traceback (most recent call last): File “C:\oobabooga_windows\text-generation-webui\server. exllamv2 works, but the performance is very slow compared to llama-cpp-python. 4 t/sec. Please call the exllama_set_max_input_length function to increase the buffer size. Slower than OpenAI, but hey, it's self-hosted! It will do whatever you train it to do, all depends on a good dataset. exllama makes 65b reasoning possible, so I feel very excited. ; 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). 4bpw-h6-exl2. I have a 4090 and 32Gib of memory running on Ubuntu server with an 11700K. Test 1 Wizard-Vicuna-30B-Uncensored. bat with nvidia choice-add model TheBloke/Mistral-7B-Instruct-v0. model_name, loader) File “C:\oobabooga_windows\text Thanks for sharing! I have been struggling with llama. 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. 5 times faster than ExllamaV2. Beta Was this translation helpful? Give Of course, with that you should still be getting 20% more tokens per second on the MI100. You can offload inactive users' caches to system memory (i. I can't even get 2k context fused and barely touch 3k unfused. On ExLlama/ExLlama_HF, set max_seq_len to 4096 (or the highest value before you run out of memory). Thank you for your post, this is an amazing improvement. You can do that by setting n_batch and u_batch to 2048 (-b 2048 -ub 2048) FA slows down llama. Furthermore, if RP is what you're into, consider using SillyTavern as a frontend after loading the model in Ooba. Will look for nans. 39). It has a ton of options made specifically for RP. 2t/s. 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. I only need ~ 2 tokens of output and have a large high-quality dataset to fine-tune my model. 5x 4090s, 13900K (takes more VRAM than a single 4090) Model: ShiningValiant-2. 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. But then the second thing is that ExLlama isn't written with AMD devices in mind. Effectively a Mixture of Experts. q2_K (2-bit) test with llama. Just plugged them both in. 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. Interested to hear your experience @turboderp. The A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights. You signed in with another tab or window. ggmlv3. Speaking from personal experience, the current prompt eval speed on llama. cache/torch_extensions for subsequent use. 32 tokens/s, 256 tokens, context 15, seed 1844401441) Output generated in 10. This makes the models directly comparable to the AWQ and transformers models, for which the cache is not preallocated at load time. Exllama is also banned on kobold horde now and workers spotted running it get put into maintenance. Ok, maybe it's the fact I'm trying llama 1 30b. Then, select the llama-13b-4bit-128g model in the "Model" dropdown to load it. 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. 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. Lllama. But there is one problem. 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. The speeds will be significantly slower then if you had the model on GPU only, though. Is it possible to implement a fix like this for pascal card users? Changing it in the repositories/exllama/ didnt fix it for me. 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. You switched accounts on another tab or window. Then, when I edit history/context on a really long conversation, it REALLY slows down until it reprocesses. 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. It's quite slow however. The triton version gets 11. 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. Exllama doesn't want to play along at all when I try to split the model between two cards. 22x longer than ExLlamav2 to process a 3200 tokens prompt. We can train it to comment, edit or suggest code. For TP, there’d be quite a bit chatter p2p. If it's still slow then this I suppose this must be a GPU-specific issue, and not as I thought OS/installation specific. Anything that uses the API should basically see zero slow down. Pick one of the 4, 5, or 6 bit models here if you would like to experiment with offloading. 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. The command line is stuck on "INFO:Loading Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-GPTQ Upvote for exllama. llama. 3. 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. it will install the Python components without building the C++ extension in the process. Scan over the pull requests on the exllama repo to see why it is so fast. We would like to show you a description here but the site won’t allow us. 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. Exllama does the magic for you. RuntimeError: The temp_state buffer is too small in the exllama backend. 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. 93 tokens/s, 256 tokens, context 15, seed 545675865) Output generated in 10. 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. from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline. 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. 27 seconds (24. -nommq takes EXLLAMA_NOCOMPILE= python setup. 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. It's also shit for samplers and when it doesn't re-process the prompt you can get identical re-rolls. I have been playing with things and thought it better to ask a question in a new thread. 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. AutoGPTQ has much better oddball model support, however and can train. cpp's metal or CPU is extremely slow and practically unusable. 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. 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. Let's try with llama 2 13b. 1 t/s) than llama. Here are his words: "I'm working on some benchmarks at the moment, but they're taking a while to run. 74 tokens/s, 256 tokens, context 15, seed 91871968) Generation with exllama was extremely slow and the fix resolved my issue. cpp, exllama) Question | Help I have an application that requires < 200ms total inference time. We can train it to be a general purpose assistant that follows YOUR ethos inserted of OpenAI's. CyberTimon. See translation. cpp comparison. The quantization of EXL2 itself is more complicated than the other formats so that could also be a factor. 13B 6Bit quantized is acceptable. 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. 4). You signed out in another tab or window. Also the memory use isn't good. 11 release, so for now you'll have to build from The llama. 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. Also I noticed that autoGPTQ works best if frozen at v0. , ExLlama for GPTQ. Exllama V2 defaults to a prompt processing batch size of 2048, while llama. py install --user This will install the "JIT version" of the package, i. Both GPTQ and exl2 are GPU only Some quick tests to compare performance with ExLlama V1. 1-GPTQ" To use a different branch, change revision The bitsandbytes approach makes inference much slower, which others have reported. It also takes a considerable context length before attention starts to slow things down noticeably EXLLAMA_NOCOMPILE= python setup. Should work for other 7000 series AMD GPUs such as 7900XTX. They are much closer if both batch sizes are set to 2048. Inference is relatively slow going, down from around 12-14 t/s to 2-4 t/s with nearly 6k context. 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). The AI response speed is quite fast. 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. Reply reply More replies. cpp defaults to 512. You may be better off running GGUF models in llama. 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. cpp in being a barebone reimplementation of just the part needed to run inference. 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. The build used to take 4 minutes and now it takes 17. For instance, the latest Nvidia drivers have introduced design choices that slow down the inference process. cpp beats exllama on my machine and can use the P40 on Q6 models. Downsides are that it uses more ram and crashes when it runs out of memory. 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). QLora is slower during inference. Still slow + every other model is now also just 10 tokens / sec instead of 40 tokens / sec so I stay with ooba's fork. 5 tokens per second. 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. 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. The "HF" version is slow as molasses. It uses Update 1: I added tests with 128g + desc_act using ExLlama. All reactions. (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. Reply reply You signed in with another tab or window. 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. Q4_K_M is 6% slower than Q4_0 for example, as the model file is 8% larger. Only odd man out is AutoGPTQ and now AWQ because they're still using accelerate to split up models for that slow ride. Yes, I place the model in a 5 years old disk, but both my ram and disk are not fully loaded. 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. ExLlama supports 4bpw GPTQ models, exllamav2 adds support for exl2 which can be quantised to fractional bits per weight. 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. 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. cpp, offloading what you can onto the GPU but doing CPU inference for the rest. Under everything else it was 30%. Tap or paste here to upload images. 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. On Mac, Won't be nearly as fast as exllama but you could offload a decent amount of layers to 3090 with ggml. ROCm is also theoretically supported (via HIP) though I currently have no AMD devices to test or optimize on. Lm studio does not use gradio, hence it will be a bit faster. 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. Update 3: the takeaway messages have been updated in light of the latest data. 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. 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. Check the alpaca_lora_4bit github repo, it's very easy to setup and has example commands. Same thing happened with alpaca_lora_4bit, his gradio UI had strange loss of performance. However lora works with transformers but slow af we really need exllama for this. I was able to load 70B GGML model offloading 42 layers onto the GPU using oobabooga. Is there an existing issue for this? I have searched the existing issues; Reproduction-git pull latest version-start_window. com/turboderp/exllama 👉ⓢⓤⓑⓢ Exllama v2. This issue is being reopened. 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. 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. All the models can be found on Huggingface. 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. Maybe it's better optimized for data centers (A100) vs what I have locally (3090) Currently, the two best model backends are llama. The recommended software for this used to be auto-gptq, but its generation speed has since then been surpassed by exllama. cpp from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline. 2t/s, suhsequent text generation is about 1. cpp with GPU offload (3 t/s). cpp and exllama, in my opinion. I get 17. 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. cpp It should be still higher. There is no built-in way, no. Reply reply Radiant-Practice-270 • Several times I notice a slight speed increase using direct implementations like llama-cpp-python OAI server. But that might be one cause. I'm wondering if there's any way to further optimize this setup to increase the inference speed. Takes 3secs to load a LoRA. Sort by: Best. This is the speed at which oobabooga initially used exllama, and the speed was like a rocket. 23 tokens/second First of all, exllama v2 is a really great module. You may have to reduce max_seq_len if you run out of memory while trying to generate text. 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. The AMD GPU model is 6700XT. 0. 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. For 13B and 30B models: Ooba with exllama, blows everything else out of the water. cpp is pretty fast till you get over 4k context, can use all GPU and has a python implementation too. Update 4: added llama-65b. So presumably if they added quantization support the speed would be comparable. Is there any config or something else for a100??? Share Add a Comment. Is there an existing issue for this? I have searched the existing issues; Reproduction. 0 When I try to load a 70B model ~ 40GB, my system stalls out. Hope he can update it soon. The prompt processing speeds of load_in_4bit and AutoAWQ are not impressive. Also, exllama has the advantage that it uses a similar philosophy to llama. Shrug. Many people conveniently ignore the prompt evalution speed of Mac. 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. The conversion script and its options are explained in detail here. But that's not a problem anyway, EXL2 First of all, exllama v2 is a really great module. You can change that behavior by passing disable_exllama in GPTQConfig. By default it automatically uses the Exllama kernel if it can but its not supported on all GPTQ models. 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. Appreciate your time Reply reply sshan • I’ve been tinkering in this stuff for a while and I As per discussion in issue #270. but I can't even find CUDA or exllama_ext. Sorry 30b running slowly on 4090 . Preliminary results show the Q4 cache mode is more precise overall than FP8, and comparable to full precision. 7 tokens/s after a few times regenerating. 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. 1B-1T-OpenOrca-GPTQ. 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. . The length that you will be able to reach will depend on the model size and your GPU memory. An the capital of USA. Set max_seq_len to a number greater than 2048. cpp is a C++ refactoring of transformers along with optimizations. 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. cpp option was slow, achieving around 0. 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. They have all the talent, experience and Cache and state has to reside on the same device as the associated weights. Exllama does not run well on it, I get less than 1t/s. After the initial load and first text generation which is extremely slow at ~0. I'm also really struggling with disk space, but I ordered some more SSDs, which should help I guess. (pip uninstall exllama and modified q4_matmul. 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. 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. In order to use these kernels, you need to have the entire model on gpus. An example is SuperHOT ExLlama is an extremely optimized GPTQ backend for LLaMA models. 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. Unless you have nvlink/switch, you’d be p2p pcie bandwidth bottlenecked on non-datacenter gpus. 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). Instead, the extension will be built the first time the library is used, then cached in ~/. However, 15 tokens per second is a bit too slow and exllama v2 should still be very comparable to llama. 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. It should be a bit slower I think, since it has to output transformers samplers to exllama itself. With the release of exllamav2 kernels, you can get faster inference speed compared to exllama kernels for 4-bit model. 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. 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. Exllama by itself is very fast when model fits in VRAM completely. Download the model (and all files) from HF and place it somewhere. With exllamv2 I get my sample response in: 35. And then having another model choose the best one for the query. On a 70b parameter model with ~1024 max_sequence_length, repeated generation starts at ~1 tokens/s, and then will go up to 7. The actual processing is what takes all of the resources. The github repo link is: https://github. 11T/s speeds. It is activated by default: disable_exllamav2=False in load_quantized_model(). 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. 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. But other larger context models are appearing every other day now, since Llama 2 dropped. Apr 26, 2023. I noticed SSD activities (likely due to low system RAM) on the first text generation. 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. cpp is the slowest, taking 2. You should probably start with smaller models first because the P40 is a very slow card compared to modern cards. Draft model: TinyLlama-1. P40 needs Tesla specific drivers. model, shared. Reload to refresh your session. Example: from auto_gptq import exllama_set_max_input_length model = Sadly, it's much slower. Which model are you using and which loader (llama. 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. However, when I switched to exllamav2, I found that the speed dropped to about 7 token/s, which was slowed down. 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. Also tried emb 4 with 2048 and it was still slow. 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. 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. nope, old Exllama still ~2. I tried that with 65B on single 4090 and exllama is much slower (0. Try classification. q5_0 CPU With GPU Accelerate What is the capital of Canada. 1. 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. AutoGPTQ - this engine, while generally slower may be better for older GPU architectures. It achieves about a third of the speed of ExLlama, but also running on models that take up three times as much VRAM. These quantized LLMs can also be fast during inference when using a GPU, especially with optimized CUDA kernels and an efficient backend, e. When testing exllama both GPUs can do 50% at the same time. In a recent thread it was suggested that with 24g of vram I should use a 70b exl2 with exllama rather than a gguf. It is capable of mixed inference with GPU and CPU working together without fuss. e. 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's neck and neck with exllama for multi card. Come back with questions, I'd be glad to help. 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, using GGML models and the llama_hf loader, I have been able to achieve higher context. Edit Preview. 7 t/sec with exllama but that isn't compatible with most software. It features much lower VRAM usage and much higher speeds due to not relying on unoptimized transformers code. And 2 cheap secondhand 3090s' 65b speed is 15 token/s on Exllama. Upload images, audio, and videos by dragging in the text input, pasting, or clicking here. I'm experimenting with some and getting It works with Exllama v2 (release: 0. cpp/llamacpp_HF, set n_ctx to 4096. 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. Any Pascal card except the P100 will run badly on exllama/exllamav2. TheBloke. 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. exllamv2 works, but the performance is very slow compared to llama-cpp-python. 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. 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. AutoGPTQ works fine but it's still rather slow to inference. 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. Exllama itself, this is the fastest of the bunch. Reply reply which ends up being quite slow. model_name_or_path = "TheBloke/Mistral-7B-Instruct-v0. 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. 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. 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. the generation very slow it takes 25s and 32s respectively. cu according to turboderp/exllama#111. Or we can simply train it to be a waifu with scary verbal intelligence :D This tool is now slowing down the build. This seemed I'm aware that there are GGML versions of those models, but the inference speed is painfully slow compared to GPTQ. It's kinda slow to iterate on since quantizing a 70B model still takes 40 minutes or so. 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. The console is stuck on "INFO:Loading I got ooba working locally on a 380 16gb card but it runs slow as ass. Weirdly, inference seems to speed up over time. Evaluation. I have a fork of GPTQ that supports the act-order models and gets 14. I'm having a similar experience on an RTX-3090 on Windows 11 / WSL. It's slower than the GPU, but it was way cheaper and I can run the 70B model easily. compress_pos_emb is for models/loras trained with RoPE scaling. EXLlama support added to oobabooga-text-generation-webui Llama-2 has 4096 context length. lhl on July 26, 2023 ExLlama_HF loader gpu split 20,22, context size 2048. cpp generation. 44 seconds, 150 tokens, 4. Could not manage to get any decent speed with exLlama. 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. 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. py I added the following: Exllama kernels for faster inference. Llama. For multi-gpu models llama. This has all been changed in recent updates, which allow you to utilize many GPUs at once without any cost to speed. The EXLlama option was significantly faster at around 2. Llama2 i can run 16b gptq (gptq is purely vram) using exllama Llama2 i can run 70B ggml, but it is so slow. I want to use the ExLlama models because it enables me to use the Llama 70b version with my 2 RTX 4090. 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. Make sure to also set Truncate the prompt up to this length to 4096 under Parameters. Pinokio is stating ~44 t/s with EXL2-HF, and switching to regular EXL2 brought me up to 56 t/s. g. It is probably because the author has "turbo" in his name. In the past exllama v1, there was a slight slowdown when using Lora, but it was approximately 10%. I personally would rather use a more accurate but slower model than the other way around. cpp is way slower to ExLlama (v1&2), not just According to Pinokio/TGI, I am actually getting way better than ~15 tokens/s. ExLlama gets around the problem by reordering rows at load-time and discarding the group index. py”, line 73, in load_model_wrapper shared. 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. Evaluation speed. There's an update now that enables the fused kernels for 4x models as well, but it isn't in the 0. tokenizer = load_model(shared. 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. They are marked with (new) Update 2: also added a test for 30b with 128g + desc_act using ExLlama. Open comment sort options Also try on exllama with some exl2 model and try what you downloaded in 8bit and 4bit with bitsandbytes. In the Model tab, select "ExLlama_HF" under "Model loader", set max_seq_len to 8192, and set compress_pos_emb to 4. 1-GPTQ I create a feature request on the official repo :Exllama integration to run GPTQ models · Issue #8385 · langchain-ai/langchain (github. Some initial benchmarks First of all, exllama v2 is a really great module. It's not that those guys don't know what they're doing. EXL2 is the fastest, followed by GPTQ through ExLlama v1. A Text generation web ui is slower then using exllama v2 because of all the gradio overhead. 35 seconds (24. I'm sure there's probably a better way to be running it but I haven't figured it out yet. @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. I have heard its slower than full on Exllama. 9 For VRAM tests, I loaded ExLlama and llama. I'll see if maybe I can't get a 7B model to load, though, and compare it anyway. 11 seconds (25. 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. 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. Exllama: 9+ t/s, ExllamaV2 1. 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. 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. Using both llama. It uses the GGML and GGUF formated models, with GGUF being the newest format. cpp. I'm using exllama manually into ooba (without the wheel). That and getting exllama going. 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. I see the system RAM max out at ~30/32GB, which doesn't make a lot of sense. Question | Help I’m not sure what I’m doing wrong. Don’t know if that slows it down to the same as naive MP in Exllama. 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. - exllama/model. 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. GGUF/llama. I edit a lot, which is why I moved from GGUF to EXL2 in the first place. It sort of get's slow at high contexts more than EXL2 or GPTQ does though. ExLlama is an extremely optimized GPTQ backend for LLaMA models. The following is a fairly informal proposal for @turboderp to review:. Decrease cold-start speed on inference (llama. 3 and 2. https://github. cpp models with a context length of 1. On llama. So keep that in mind. Instead of replacing the current rotary embedding calculation. After starting oobabooga again, it did not work anymore. This will overwrite the quantization config stored in the config. It is activated by default. 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. 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. Tried the new llama2-70b-guanaco in ooba with exllama (20,24 for the memory split parameter). They are way cheaper than Apple Studio with M2 ultra. 2 ; anything after that gets slow, x10 slower. I generally only run models in GPTQ, AWQ or exl2 formats, but was interested in doing the exl2 vs. 23 tokens/second With lama-cpp-python I get the same response in 9. fwze dzrw pftwxho zvzrbbj nyxk ejkj qwmsb zegvuc axaiwl nbi