Exllama slow. nope, old Exllama still ~2.

Exllama slow Can those be installed along side standard Geforce drivers? Exllama V2 defaults to a prompt processing batch size of 2048, while llama. It is the moment, your vram is getting full. 22x longer than ExLlamav2 to process a 3200 tokens prompt. ggmlv3. GPTQ, AWQ, and EXLLAMA are quantization methods that only run on the GPU, while GGUF can balance the load between the CPU and GPU. They all unfortunately slowed things down. py. With every hardware. SL-Stone opened this issue Dec 24, 2023 · 5 comments Closed Using a slow tokenizer. I'm aware that there are GGML The bitsandbytes approach makes inference much slower, which others have reported. For 7b and 13b, ExLlama is as accurate as AutoGPTQ (a tiny bit lower actually), confirming that its GPTQ reimplementation has been successful. 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 For instance, the latest Nvidia drivers have introduced design choices that slow down the inference process. it will install the Python components without building the C++ extension in the process. In some instances it would be super-useful to be able load separate lora's on top of a GPTQ model loaded with exllama. Qwen-int4 is supported by autogptq. Text generation web ui is slower then using exllama v2 because of all the gradio overhead. There's an update now that enables the fused kernels for 4x models as well, but it isn't in the 0. Now with exllama and 8K context being all the rage, I'm glad that this new koboldcpp version gives us CPU users more speed and bigger contexts, too. I've run into the same thing when profiling, and it's caused by the fact that . 4bpw-h6-exl2. 7 t/sec with exllama but that isn't compatible with most software. It will pin So far it is topping old exllama by at least 3t/s. exLLaMA recently got some fixes for ROCm, and I don't think theres a better framework for squeezing the most quantization quality out of 24GB of VRAM. from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline. What I also meant to point out is the bus speed of the PCI x16 slot might be slower then what the P40 can output, thus slowing it down. EXL2 is the fastest, followed by GPTQ through ExLlama v1. Exllama does not run well on it, I get less than 1t/s. 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 The llama. There is a CUDA and Triton mode, but the biggest selling point is that it can not only inference, Of course, with that you should still be getting 20% more tokens per second on the MI100. Very good work, but I have a question about the inference speed of different machines, I got 43. 74 tokens/s, 256 tokens, context 15, seed 91871968) In a recent thread it was suggested that with 24g of vram I should use a 70b exl2 with exllama rather than a gguf. cpp defaults to 512. 22 tokens/s speed on A10, but only 51. so if exllama supports model like Qwen-72b-chat-gptq, it shall be so exciting! exl2 processes most things in FP16, which the 1080ti, being from the Pascal era, is veryyy slow at. Still slow + every other model is now also just 10 tokens / sec instead of 40 When using exllama inference, it can reach 20 token/s per second or more. Reply reply Radiant-Practice-270 The bitsandbytes approach makes inference much slower, which others have reported. 1 t/s) than llama. Under everything else it was 30%. The same on a 4090 when interfering with a 33b model an 8k context size with over 4K chat history. So now I just need to wonder if how ExLlama compares to Llama-to-GPTQ. Many people conveniently ignore the prompt evalution speed of Mac. In this tutorial, we will run LLM on the GPU entirely, which will allow us to speed it up significantly. Takes 3secs to load a LoRA. But that might be one cause. 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. The actual processing is what takes all of the resources. Some initial benchmarks - 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, When testing exllama both GPUs can do 50% at the same time. Upload images, audio, and videos by dragging in the text input, pasting, or clicking here. I'm using exllama manually into ooba (without the wheel). See the [BUG] Try using vLLM for Qwen-72B-Chat-Int4, got NameError: name 'exllama_import_exception' is not defined #856. That and getting exllama going. Is it possible to implement a fix like this for pascal card users? Changing it in the repositories/exllama/ didnt fix it for me. Reply reply Gama-Tech 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. as I say, this is a guess. Use exllama for maximum speed. But then the second thing is that ExLlama isn't written with AMD devices in mind. exllamv2 works, but the performance is very slow compared to llama-cpp-python. 0-2. Not even GPTQ works right now. CyberTimon. Consider using a fast tokenizer instead. That's rather slow, which is why I only use 65B for chatting when I don't mind waiting. Reply reply More replies. As seen on Definitely didn't know that, thanks I will try it out. I'm wondering if there's any way to further optimize this setup to increase the inference speed. Reply reply Yeah slow filesystem performance outside of WSL is a known issue. Generation with exllama was extremely slow and the fix resolved my issue. cpp is way slower to ExLlama Some quick tests to compare performance with ExLlama V1. --top_k1 1 also seemed to slow things down. Lm studio does not use gradio, hence it will be a bit faster. 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. cpp It should be still higher. 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. Anything that uses the API should basically see zero slow down. Llama. Sort by: Best. EXLLAMA_NOCOMPILE= python setup. 35 seconds (24. Scan over the pull requests on the exllama repo to see why it is so fast. 7 tokens/s after a few times regenerating. Loading the 13b model take few minutes, which is acceptable, but loading the 30b-4bit is extremely slow, took around 20 minutes. 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. Thanks. After starting oobabooga again, it did not work anymore. Are you finding it slower in exllama v2 than in exllama? I do. I tried that with 65B on single 4090 and exllama is much slower (0. cpp is way slower to ExLlama (v1&2), not just 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 could try. py install --user This will install the "JIT version" of the package, i. 5x 4090s, 13900K (takes more VRAM than a single 4090) Model: ShiningValiant-2. Evaluation. ExLlama, ExLlama_HF, ExLlamaV2, ExLlamaV2_HF are the more recent loaders for GPTQ models. This issue is being reopened. 6 seconds, 232 tokens, First of all, exllama v2 is a really great module. The recommended software for this used to be auto-gptq, but its generation speed has since then been surpassed by exllama. cpp option was slow, achieving around 0. 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. Ok, maybe it's the fact I'm trying llama 1 30b. . One thing that I think would help is if you ban eos token and just use notebook to Going from 4 bit 32g actorder true GPTQ on ExLlama to b5 h6 on exl2 I have found a noticeable increase in quality with no speed penalty. 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 AI response speed is quite fast. Beta Was this translation helpful? Give feedback. Set context to 4096 and the slider under it to 2 Edit: use exllamav2 if the option is there, You can try the CuBlas backend first with 43 layers on the GPU, if that is still slow then your probably running out of Hi there, just an small post of appreciation to exllama, which have some speeds I NEVER expected to see. I can't even get 2k context fused and barely touch 3k unfused. So, using GGML models and the llama_hf loader, I have been able to achieve higher context. Exllama is slow on pascal cards because of the prompt reading, there is a workaround here though: turboderp/exllama#111. 5 times faster than ExllamaV2. Reply reply ExLlama is a smaller project but contributions are being actively merged (I submitted a PR) and the maintainer is super responsive. Closed 2 tasks done. They are much closer if both batch sizes are set to 2048. Im guessing you may have been loading your models as "Transformers" which will work for pretty much all models, but is very slow. I've been slowly moving some stuff in linux direction too, so far Just looking over the code it seems to use many of the same tricks as ExLlama. cache/torch_extensions for subsequent use. cpp's metal or CPU is extremely slow and practically unusable. cpp. - exllama/model. You can do that by setting n_batch and u_batch to 2048 (-b 2048 -ub 2048) FA slows down llama. Now, as the rows are processed in-order during inference, you have to constantly reload the quantization parameters, which ends up being quite slow. to("cpu") is a synchronization point. Weirdly, inference seems to speed up over time. GGUF/llama. Should work for other 7000 series AMD GPUs such as 7900XTX. 32 tokens/s, 256 tokens, context 15, seed 1844401441) Output generated in 10. Tested and works on Windows as well. The CUDA kernels look very similar in places, It also takes a considerable context length before attention starts to slow things down noticeably, since every other part of the inference is O(1). 1. And then having another model choose the best one for the query. While this may not be a bug, it's something to keep in mind when considering the performance And 2 cheap secondhand 3090s' 65b speed is 15 token/s on Exllama. Basically the 65B feels like mail exchanges while 33B and 13B work like an actual live chat. Draft model: TinyLlama-1. AutoGPTQ - this engine, while generally slower may be better for older GPU architectures. CUDA extension not installed. ExLlama implementation without an interface? I tried an autoGPTQ implementation of Llama on Huggingface, but it is so slow compared to ExLlama. 4 t/sec. Minor thing, but worth noting. but it will become very slow run in multiple gpus. At this breakpoint, everything gets slow. -nommq takes more VRAM and is slower on base inference. The triton version gets 11. For 13B and 30B models: Ooba with exllama, blows everything else out of the water. The only way to make it practical is with exllama or similar. cpp in being a barebone reimplementation of just the part needed to run inference. Yes, I place the model in a 5 years old disk, but both my ram and disk are not fully loaded. Exllama v2. However, when I switched to exllamav2, I found that the speed dropped to about 7 token/s, which was slowed down. Thinking I can't be the only one struggling with this, 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 not during the call itself. 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. 0. I tried other options found with --help like --parallel and --sequences and they had no effect. This may because you installed auto_gptq using a pre-build wheel on Windows, in which exllama_kernels are not compiled. cpp is the slowest, taking 2. To make gptq work for llama v2 models requires a bunch of work, you have to install Thanks for sharing! I have been struggling with llama. Also, exllama has the advantage that it uses a similar philosophy to llama. You signed in with another tab or window. I am loading only old 70b with varying groups and act order. Any process on exllama todo "Look into improving P40 performance"? env: kernel: 6. 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. In the past exllama v1, there was a slight slowdown when using Lora, but it was approximately 10%. py at master · turboderp/exllama 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 supported. 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. 169K subscribers in the LocalLLaMA community. I have been playing with things and thought it better to ask a question in a new thread. Effectively a Mixture of Experts. Update 4: added llama-65b. If you use AWQ, there is a 2. There may be more performance optimizations in the future, and speeds will vary across GPUs, with slow CPUs still being a Converting large models can be somewhat slow, so be warned. 4 tokens/s speed on A100, according to my understanding at least should Twice the difference Is there a Like even at 2k context size Exllama seems to be quite a bit slower compared to GGML (q3 variants and below). q2_K (2-bit) test with llama. 5 tokens per second. A number of evaluaion 122 votes, 79 comments. are you using exllama or exllama_hf as the loader? Several times I notice a slight speed increase using direct implementations like llama-cpp-python OAI server. Update 3: the takeaway messages have been updated in light of the latest data. Hopefully more details about how it works As per discussion in issue #270. Is there any config or something else for a100??? Share Add a Comment. dev, No, it can run in 2x3090 with 8-bit or 4-bit quantization using bitsandbytes, but it runs extremely slow. There is a technical reason for it (Which you can find detailed here if you are curious) but the TL;DR is that reading a file outside of WSL will always be significantly slower due to 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 could try. Hi, I tried to use exllamv2 with Mistral 7B Instruct instead of my llama-cpp-python test implementation. See translation. GPTQ is the standard for running on GPU only, while AWQ is supposed to be an improved version of GPTQ, but I don't know much about EXLLAMA since it's still new and I personally use GGUF. Also tried emb 4 with 2048 and it was still slow. AutoGPTQ works fine but it's still rather slow to inference. So far it seems like out of the two you have used ExLlama is faster and more accurate. com/turboderp/exllama 👉ⓢⓤⓑⓢ 2. Tap or paste here to upload images. 53-x64v3-xanmod1 system: "Linux Mint 21. The github repo link is: https://github. Is there an existing issue for this? I have searched the existing issues; Reproduction. I'm having a similar experience on an RTX-3090 on Windows 11 / WSL. I recently switched from exllama to exllama_hf because there's a bug that prevents the stopping_strings param from working via the API, and there's a branch on text-generation-webui that supports stopping_strings if you use exllama. P40 needs Tesla specific drivers. Edit Preview. 4). 11 release, so for now you'll have to build from source to get full speed for those. 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-apples comparisons; This is using the prebuilt CLI llama2 model from, which the docs say is the most optimized version? When I load a 65b in exllama across my two 3090tis, I have to set the first card to 18gb and the second to the full 24gb. e. You signed out in another tab or window. I was hoping to add a third 3090 (or preferably something cheaper/ with more vram) one day when context lengths get really big locally but if you have to keep context on each card that will really start to limit things. Saved searches Use saved searches to filter your results more quickly Qwen is the sota open source llm in China and its 72b-chat model will be released this month. It sort of get's slow at high contexts more than EXL2 or GPTQ does though. Try classification. Speed is little slower vs pure EXLlama, but a lot better than GPTQ. The quantization of EXL2 itself is more complicated than the other formats so that could also be a factor. They are way cheaper than Apple Studio with M2 ultra. This makes the models directly comparable to the AWQ and transformers models, for which the cache is not preallocated at load time. For VRAM tests, I loaded ExLlama and llama. I don't know how MLC to control output like ExLlama or llama. model_name_or_path = "TheBloke/Mistral-7B-Instruct-v0. Other loaders are for different types of models. It is probably because the author has "turbo" in his name. Exllama by itself is very fast when model fits in VRAM completely. I pretty much tried every step between 2048 and 3584 with emb 2 and they all gave the same result. 11 seconds (25. cpp on the other hand is capable of using an FP32 pathway when required for the older cards, Gotta stick with llama. exlla P40 can't use newer bitsandbyes. All reactions. cu according to turboderp/exllama#111. llama. Inference is relatively slow going, down from around 12-14 t/s to 2-4 t/s with nearly 6k context. 3 and 2. It works with Exllama v2 (release: 0. cpp with GPU offload (3 t/s). However, 15 tokens per second is a bit too slow and exllama v2 should still be very comparable to llama. This might cause a significant slowdown. Instead of replacing the current rotary embedding calculation. It can work in the older PCI slots, but it'll be slower then what it's designed for. 1B-1T-OpenOrca-GPTQ. The conversion script and its options are explained in detail here. I don't have experience with using docker containers yet but that seems like next on the list. 2 Victoria" cuda: 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). To work around this, Update 1: I added tests with 128g + desc_act using ExLlama. For 60B models or CPU only: Faraday. I'm also really struggling with disk space, but I ordered some more SSDs, ExLlama supports 4bpw GPTQ models, exllamav2 adds support for exl2 which can 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. I'm developing AI assistant for fiction writer. 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. But there is one problem. cpp generation. cpp is the most popular framework, but I find that its particularly slow on OpenCL and not nearly as VRAM efficient as exLlama anyway. (updated) For GPTQ, you should be using So I switched the loader to ExLlama_HF and I was able to successfully load the model. It will pin the process to the listed cores, just in case Windows tries A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights. But other larger context models are appearing every other day now, since Llama 2 CPU profiling is a little tricky with this. 1-GPTQ" To use a different branch, change revision It is so slow. I get 17. Exllama: 9+ t/s, ExllamaV2 1. 44 seconds, 150 tokens, 4. 9 t/sec. GPTQ can give good perplexity if you use it with reordering but then the speed can be slow. The following is a fairly informal proposal for @turboderp to review:. 27 seconds (24. 5% decrease in perplexity when quantizing to INT4 and can run at 70-80 I want to use the ExLlama models because it enables me to use the Llama 70b version with my 2 RTX 4090. Also the memory use isn't good. I believe they are specifically GPU based only. 23 tokens/second; I have been struggling with llama. Okay interesting. Reply reply 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. To use exllama_kernels to further speedup 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. (pip uninstall exllama and modified q4_matmul. The AMD GPU model is 6700XT. The prompt processing speeds of load_in_4bit and AutoAWQ are not impressive. ExLlama gets around the problem by reordering rows at load-time and discarding the group index. Reload to refresh your session. cpp unless someone re-writes exllama to upcast. Instead, the extension will be built the first time the library is used, then cached in ~/. But upon sending a message it gets CUDA out of memory again. If the same is true for ExLlama over Llama-to-GPTQ than that is absolutely amazing! Here's some quick numbers on a 13B llama model with exllama on a 3060 12GB in Linux: Output generated in 10. 23 tokens/second With lama-cpp-python I get the same response in 9. If it's still slow then this I suppose this must be a GPU-specific issue, and not as I thought OS/installation specific. Best example is having a A100 80GB card. You switched accounts on another tab or window. It's quite slow however. nope, old Exllama still ~2. 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. Has anyone here had experience with this setup or similar configurations? Just use exllama hf like it says. The OpenAI compatible API; Loading/unloading models; HuggingFace model downloading; Embedding model support; JSON schema + Regex + EBNF support; AI Horde support WARNING:Exllama kernel is not installed, reset disable_exllama to True. Evaluation speed. cpp models with a context length of 1. Set max_seq_len to a number greater than 2048. A post about exllama_hf would be interesting. Open comment sort Also try on exllama with some exl2 model and try what you downloaded in 8bit and 4bit with bitsandbytes. The Exllama v2 format is relatively new and people EXL2 is the fastest, followed by GPTQ through ExLlama v1. Speaking from personal experience, the current prompt eval speed on llama. Open the Model tab, set the loader as ExLlama or ExLlama_HF. 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. to() operation takes like a microsecond or whatever. Same thing happened with alpaca_lora_4bit, his gradio UI had strange loss of performance. The best balance at the moment is to use 4Bit models like autogptq with exllama or 4Bit ggml with a group size of 128. Though it still would take me more than 6 minutes to generate a response to near full 4k context with GGML when using q4_K_S, but with q3_K_S it took about 2 minutes and subsequent regenerations took 40-50 seconds each for 128 tokens. With exllamv2 I get my sample response in: 35. 93 tokens/s, 256 tokens, context 15, seed 545675865) Output generated in 10. We would like to show you a description here but the site won’t allow us. With every model. Let's try with llama 2 13b. I have a fork of GPTQ that supports the act-order models and gets 14. Interested to hear your experience @turboderp. Yes the models are smaller but once you hit generate, they use more than GGUF or EXL2 or GPTQ. It's kinda slow to iterate on since quantizing a 70B model still takes 40 minutes or so. Subreddit to discuss about Llama, the large language model created by Meta AI. exllamv2 works, but the performance is very slow compared to llama-cpp-python. Apr 26, 2023. On Mac, Thanks for the suggestions. 1. PyTorch basically just waits in a busy loop for the CUDA stream to finish all pending operations before it can move the final GPU tensor across, and then the actual . They are marked with (new) Update 2: also added a test for 30b with 128g + desc_act using ExLlama. zxhu wbvwo wxdito zfuwh lpqtce hpup fcfz nyhjx dumydb ehrqlgyvu