Exllama slow. py”, line 73, in load_model_wrapper shared.

Exllama slow 5 tokens per second. 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 With ExLlama's speed and memory efficiency, I would imagine that a 3-bit 13B model (or 2-bit if really needed) could be quite viable for those of us with less VRAM. These quantized LLMs can also be fast during inference when using a GPU, especially with optimized CUDA kernels and an efficient backend, e. Recently, generating a text with large preexisting context has become very slow when using GPU offloading. The length that you will be able to reach will depend on the model size and your GPU memory. I'm developing AI assistant for fiction writer. 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 AI response speed is quite fast. When testing exllama both GPUs can do 50% at the same time. Hello I am running a 2x 4090 PC, Windows, with exllama on 7b llama-2. This might cause a significant slowdown. Based on the high system RAM usage, In this tutorial, we will run LLM on the GPU entirely, which will allow us to speed it up significantly. But there is one problem. The Generation with exllama was extremely slow and the fix resolved my issue. It is probably because the author has "turbo" in his name. Make sure that exllama is ExLlamaV2 is a library designed to squeeze even more performance out of GPTQ. 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. Exllama doesn't want to play along at all when I try to split the model between two cards. q2_K (2-bit) test with llama. 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. 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. The following is a fairly informal proposal for @turboderp to review:. Sorry Exllama is slow on pascal cards because of the prompt reading, there is a workaround here though: turboderp/exllama#111. py install --user This will install the "JIT version" of the package, i. We can train it to be a general purpose assistant that follows YOUR ethos inserted of OpenAI's. 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. 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. It is activated by default: disable_exllamav2=False in load_quantized_model(). 2 ; anything after that gets slow, x10 slower. ExLlama is a Python/C++/CUDA implementation of the Llama model that is designed for faster inference with 4-bit GPTQ weights (check out these benchmarks). By default it automatically uses the Exllama kernel if it can but its not supported on all GPTQ models. Question | Help I’m not sure what I’m doing wrong. Exllama by itself is very fast when model fits in VRAM completely. For 13B and 30B models: Ooba with exllama, blows everything else out of the water. 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 In that thread, someone asked for tests of speculative decoding for both Exllama v2 and llama. bat with nvidia choice-add model TheBloke/Mistral-7B-Instruct-v0. 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. After starting oobabooga again, it did not work anymore. Can those be installed along side standard Geforce drivers? ExLlama is a smaller project but contributions are being actively merged (I submitted a PR) and the maintainer is super responsive. 0 When I try to load a 70B model ~ 40GB, my system stalls out. cpp and ExLlama using the transformers library like I had been doing for many months for GPTQ-for-LLaMa, transformers, and AutoGPTQ: 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 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. Llama2 i can run 16b gptq (gptq is purely vram) using exllama Llama2 i can run 70B ggml, but it is so slow. Here is a 4-bit GPTQ version that will work with ExLlama, text-generation-webui etc. The tool hasn't changed; it's taken from version control and it hasn't changed for years. g. ) Reply reply Very slow on 3090 24G upvotes ExLlama. ExLlama supports 4bpw GPTQ models, exllamav2 adds support for exl2 which can be quantised to fractional bits per weight. As per discussion in issue #270. exlla For VRAM tests, I loaded ExLlama and llama. 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. Is there any config or something else for a100??? Share Add a Comment. Also tried emb 4 with 2048 and it was still slow. py I added the following: You signed in with another tab or window. I am running an Oobabooga I have an Alienware R15 32G DDR5, i9, RTX4090. nope, old Exllama still ~2. While this may not be a bug, it's something to keep in mind when Open the Model tab, set the loader as ExLlama or ExLlama_HF. Inference is relatively slow going, down from around 12-14 t/s to 2-4 t/s with nearly 6k context. - 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. There is a CUDA and Triton mode, but the biggest selling point is that it can not only inference, but also quantize and fine 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 ExLlama/ExLlama_HF, set max_seq_len to 4096 (or the highest value before you run out of memory). Update 3: the takeaway messages have been updated in light of the latest data. Scan over the pull requests on the exllama repo to see why it is so fast. py. First of all, exllama v2 is a really great module. Here's some quick numbers on a 13B llama model with exllama on a 3060 12GB in Linux: Output generated in 10. 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. 11 seconds (25. cu according to turboderp/exllama#111. 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. Also the memory use isn't good. 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. You may have to reduce max_seq_len if you run out of memory while trying to generate text. tokenizer = load_model(shared. model_name_or_path = "TheBloke/Mistral-7B-Instruct-v0. Growth - month over month growth in stars. I have a Jetson Nano 4GB with a 32GB SD card running a vanilla OS install and a 65 watt micro usb power supply. Many people conveniently ignore the prompt evalution speed of Mac. Get up and running with Llama 3. I had the issue mentioned here: oobabooga/text-generation-webui#2949 Generation with exllama was extremely slow and the fix resolved my issue. CyberTimon. Any Pascal card except the P100 will run badly on exllama/exllamav2. This is not an Ooba specific issue but an issue for all WSL The llama. I generally only run models in GPTQ, AWQ or exl2 formats, but was interested in doing the exl2 vs. md at master · turboderp/exllamav2 Sadly, it's much slower. cpp. On a 70b parameter model with ~1024 max_sequence_length, repeated generation starts at ~1 tokens/s, and then will go up to 7. 18. Saved searches Use saved searches to filter your results more quickly Lllama. They are much closer if both batch sizes are set to 2048. Exllama: 9+ t/s, ExllamaV2 1. It also introduces a new quantization format, EXL2, which Thanks for sharing! I have been struggling with llama. Interested to hear your experience @turboderp. Reply reply which ends up being quite slow. 39). The triton version gets 11. 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. So, using GGML models and the llama_hf loader, I have been able to achieve higher context. In the past exllama v1, there was a slight slowdown when using Lora, but it was approximately 10%. Draft model: TinyLlama-1. py at master · turboderp/exllama It is so slow. Download the model (and all files) from HF and place it somewhere. I pretty much tried every step between 2048 and 3584 with emb 2 and they all gave the same 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. I have a fork of GPTQ that supports the act-order models and gets 14. I was able to load 70B GGML model offloading 42 layers onto the GPU using oobabooga. 32 tokens/s, 256 tokens, context 15, seed 1844401441) Output generated in 10. I wonder if that's how it's supposed to be or if Update 1: I added tests with 128g + desc_act using ExLlama. 11 release, so for now you'll have to build from With the fused attention it is fast like exllama, but without it is slow AF. Wish the ExLlama is an extremely optimized GPTQ backend for LLaMA models. Tap or paste here to upload images. Also, exllama has the advantage that it uses a similar philosophy to llama. Downsides are that it uses more ram and crashes when it runs out of memory. Then, when I edit history/context on a really long conversation, it REALLY slows down until it reprocesses. 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. I am loading T5 Flan small and getting OK speeds running . cpp generation. One thing that I think would help is if you ban eos token and just use notebook to I have been struggling with llama. Text generation web ui is slower then using exllama v2 because of all the gradio overhead. Like even at 2k context size Exllama seems to be quite a bit slower compared to GGML (q3 variants and below). P40 can't use newer bitsandbyes. - exllama/model. The quantization of EXL2 itself is more complicated than the other formats so that could also be a factor. ggmlv3. 2t/s, suhsequent text 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 Slower than OpenAI, but hey, it's self-hosted! It will do whatever you train it to do, all depends on a good dataset. cpp/llamacpp_HF, set n_ctx to 4096. It's neck and neck with exllama for multi card. Please call the exllama_set_max_input_length function to increase the buffer size. You will have to stick with ollama VS exllama Compare ollama vs exllama and see what are their differences. https://github. Pick one of the 4, 5, or 6 bit models here if you would like to experiment with offloading. Reply reply Radiant-Practice-270 • 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. Under everything else it was 30%. 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 Llama-2 has 4096 context length. cpp, exllama) Question | Help I have an application that requires < 200ms total inference time. 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. I edit a lot, which is why I moved from GGUF to EXL2 in the first place. Edit Preview. cpp defaults to 512. 5x 4090s, 13900K (takes more VRAM than a single 4090) Model: ShiningValiant-2. A fast inference library for running LLMs locally on modern consumer-class GPUs - exllamav2/README. Is it possible to implement a fix like this for pascal card users? Changing it in the Anything that uses the API should basically see zero slow down. Q4_K_M is 6% slower than Q4_0 for example, as the model file is 8% larger. Evaluation speed. exllamv2 works, but the performance is very slow compared to llama-cpp-python. Thank you for your post, this is an amazing improvement. But that might be one cause. Update 4: added llama-65b. I can easily produce the 20+ tokens/sec of output I need when predicting longer outputs, but when I try and However lora works with transformers but slow af we really need exllama for this. I also installed jtop to see the GPU bar move when generate an inference. model, shared. 0). That and getting exllama going. ExLlama doesn't support 8-bit 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. For 60B models or CPU 30b running slowly on 4090 . I only need ~ 2 tokens of output and have a large high-quality dataset to fine-tune my model. 4). We can train it to comment, edit or suggest code. 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? I want to use the ExLlama models because it enables me to use the Llama 70b version with my 2 RTX 4090. The "HF" version is slow as molasses. Yes, I place the model in a 5 years old disk, but both my ram and disk are not fully loaded. . 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 For multi-gpu models llama. The ExLlama kernel is activated by default when you create a GPTQConfig object. Effectively a Mixture of Experts. You switched accounts on another tab or window. It uses 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 The RAM speed is the only factor, and 64Gb is slower than 32Gb, but I don't know yet how much in practice. The command line is stuck on "INFO:Loading Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-GPTQ First of all, exllama v2 is a really great module. Beta Was this translation helpful? Give feedback. It achieves about a third of the speed of ExLlama, but also running on models that take up three times as much VRAM. The prompt processing speeds of load_in_4bit and AutoAWQ are not impressive. Maybe it's better optimized for data centers (A100) vs what I have locally (3090) 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). They are marked with (new) Update 2: also added a test for 30b with 128g + desc_act using ExLlama. GPTQ, AWQ, and EXLLAMA are quantization methods that only run on the GPU, while GGUF can balance the load between the CPU and GPU. Furthermore, if RP is what you're into, consider using SillyTavern as a frontend after loading the model in Ooba. AutoGPTQ has much better oddball model support, however and can train. P40 needs Tesla specific drivers. SL-Stone opened this issue Dec 24, 2023 · 5 comments Closed Using a slow tokenizer. It will pin I have very slow results with transformers loader on mbp m1. 23 tokens/second With lama-cpp-python I get the same response in 9. I personally would rather use a more accurate but slower model than the other way around. 1-GPTQ" To use a different branch, change revision EXLLAMA_NOCOMPILE= python setup. cpp's metal or CPU is extremely slow and practically unusable. I have a 4090 and 32Gib of memory running on Ubuntu server with an 11700K. 27 seconds (24. exllama makes 65b reasoning possible, so I feel very excited. Yes the models are smaller but once you hit generate, they use more than GGUF or EXL2 or The AMD GPU model is 6700XT. Also I noticed that autoGPTQ works best if frozen at v0. 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 is a C++ refactoring of transformers along with optimizations. GGUF/llama. They are way cheaper than Apple Studio with M2 ultra. Turboderp, developer of Exllama V2 has made a breakthrough: A 4 bit KV Cache that seemingly performs on par with FP16. Reload to refresh your session. 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 the way the filesystem is mounted. 2) versions of PyTorch (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. Reply reply x6q5g3o7 • Good to know that 32GB isn't as limiting as it Cache and state has to reside on the same device as the associated weights. The recommended software for this used to be auto-gptq, but its generation speed has since then been surpassed by exllama. However, when I switched to exllamav2, I found that the speed dropped to about 7 token/s, which was slowed down. cpp option was slow, achieving around 0. it will install the Python components without building the C++ extension in the process. I have been playing with things and thought it better to ask a question in a new thread. Apr 26, 2023. 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. However, 15 tokens per second is a bit too slow and exllama v2 should still be very comparable to llama. 55bpw would work better with 24gb of VRAM So far it is topping old exllama by at least 3t/s. This makes the models directly comparable to the AWQ and transformers models, for which the cache is not preallocated at load time. cpp is way slower to ExLlama (v1&2), not just First of all, exllama v2 is a really great module. 7 tokens/s after a few times regenerating. ExLlama gets around the problem by reordering rows at load-time and discarding the group index. Exllama itself, this is the fastest of the bunch. I get 17. 23 tokens/second Model slows down greatly after a few chat interactions due to hitting a memory bottleneck. 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. It should be a bit slower I think, since it has to output transformers samplers to exllama itself. 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. Instead of replacing the current rotary embedding calculation. 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. Beta Was this translation helpful? Give Of course, with that you should still be getting 20% more tokens per second on the MI100. 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 EXLLAMA_NOCOMPILE= python setup. On llama. For me, these were the parameters that worked with 24GB VRAM: RuntimeError: The temp_state buffer is too small in the exllama backend. 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 EXL2 is the fastest, followed by GPTQ through ExLlama v1. Exllama does not run well on it, I get less than 1t/s. Takes 3secs to load a LoRA. cpp in being a barebone reimplementation of just the part needed to run inference. Reply reply More replies. The EXLlama option was significantly faster at around 2. 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. Reply reply OpenAI compatible API; Loading/unloading models; HuggingFace model downloading; Embedding model support; JSON schema + Regex + EBNF support; AI Horde support And 2 cheap secondhand 3090s' 65b speed is 15 token/s on Exllama. AutoGPTQ, depending on the version you are using this does / does not support GPTQ models using an Exllama kernel. Closed 2 tasks done. Instead, the extension will be built the first time the library is used, then cached in ~/. In the Model tab, select "ExLlama_HF" under "Model loader", set max_seq_len to 8192, and set compress_pos_emb to 4. You signed out in another tab or window. 3. Or we can simply train it to be a waifu with scary verbal intelligence :D OMG, and I'm not bouncing off the VRAM limit when approaching 2K tokens. After the initial load and first text generation which is extremely slow at ~0. 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 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. Open comment sort options Also try on exllama with some exl2 model and try what you downloaded in 8bit and 4bit with bitsandbytes. Try classification. See translation. The github repo link is: https://github. --top_k1 1 also seemed to slow things down. exllamv2 works, but the performance is very slow compared to llama-cpp-python. cpp comparison. 1-GPTQ" # To use a different branch, change revision Currently, the two best model backends are llama. Make sure to also set Truncate the prompt up to this length to 4096 under Parameters. I don't know if GGML would be faster with some kind from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline. 44 seconds, 150 tokens, 4. llama. 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 ExLlama is an extremely optimized GPTQ backend for LLaMA models. You can do that by setting n_batch and u_batch to 2048 (-b 2048 -ub 2048) FA slows down llama. Are you finding it slower in exllama v2 than in exllama? I do. The build used to take 4 minutes and now it takes 17. 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. The console is stuck on "INFO:Loading The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives. This is because users can convert the F16 model to any other quantization they might need, including SOTA Q-quantized and exllama models. I am loading only old 70b with varying groups and act order. I'm also really struggling with disk space, but I ordered some more SSDs, which should help I guess. Same thing happened with alpaca_lora_4bit, his gradio UI had strange loss of performance. cpp is the slowest, taking 2. ollama. Should work for other 7000 series AMD GPUs such as 7900XTX. 9 [BUG] Try using vLLM for Qwen-72B-Chat-Int4, got NameError: name 'exllama_import_exception' is not defined #856. I tried that with 65B on single 4090 and exllama is much slower (0. With exllamv2 I get my sample response in: 35. The text was updated successfully, but these errors were encountered: which are a good amount slower than exllama. So presumably if they added quantization support the speed would be comparable. Still slow + every other model is now also just 10 tokens / sec instead of 40 tokens / sec so I stay with ooba's fork. You signed in with another tab or window. 7 t/sec with exllama but that isn't compatible with most software. All reactions. 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. It also takes a considerable context length before attention starts to slow things down noticeably It works with Exllama v2 (release: 0. com/turboderp/exllama 👉ⓢⓤⓑⓢ Exllama v2. Exllama is also banned on kobold horde now and workers spotted running it get put into maintenance. I tried llama-cpp-python versions 0. An example is SuperHOT Won't be nearly as fast as exllama but you could offload a decent amount of layers to 3090 with ggml. e. When I select exllama, the slider to select the amount of layers to offload to ram disappears, I use 13b models with a 8gb vram card, so I have to offload some layers, is it possible? it'll just be slower than usual since it will use shared memory when it runs out of dedicated vram. Weirdly, inference seems to speed up over time. Could not manage to get any decent speed with exLlama. TheBloke. 4bpw-h6-exl2. On Mac, 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. Maybe a slightly lower than 2. cpp (with GPU offloading. 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. With the release of exllamav2 kernels, you can get faster inference speed compared to exllama kernels for 4-bit model. In some instances it would be super-useful to be able load separate lora's on top of a GPTQ model loaded with exllama. Here are his words: "I'm working on some benchmarks at the moment, but they're taking a while to run. Exllama does the magic for you. cpp is pretty fast till you get over 4k context, can use all GPU and has a python implementation too. 22x longer than ExLlamav2 to process a 3200 tokens prompt. cpp It should be still higher. It's quite slow however. 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. AutoGPTQ and GPTQ-for-LLaMA don't have this optimization (yet) so you end up paying a big performance penalty when using both act 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. I'm using exllama It's kinda slow to iterate on since quantizing a 70B model still takes 40 minutes or so. I'm sure there's probably a better way to be running it but I haven't figured it out yet. 1. Sort by: Best. A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights. 0. It sort of get's slow at high contexts more than EXL2 or GPTQ does though. (pip uninstall exllama and modified q4_matmul. Lm studio does not use gradio, hence it will be a bit faster. 1B-1T-OpenOrca-GPTQ. Appreciate your time Reply reply sshan • I’ve been tinkering in this stuff for a 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. Shrug. 3, Mistral, Gemma 2, and other large language models. AutoGPTQ works fine but it's still rather slow to inference. I'm having a similar experience on an RTX-3090 on Windows 11 / WSL. 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. Splitting layers between GPUs (the first parameter in the example above) and compute in parallel. Some initial benchmarks This makes running 13b in 8-bit precision the best option for those with 24GB GPUs. I see the system RAM max out at ~30/32GB, which doesn't make a lot of sense. cache/torch_extensions for subsequent use. This issue is being reopened. Consider using a fast tokenizer instead. Upvote for exllama. I can't even get 2k context fused and barely touch 3k unfused. Ok, maybe it's the fact I'm trying llama 1 30b. However, in the 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 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. Another side-effect is that every application becomes 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. (at least for multiGPU) There's also bitsandbytes, but in that Exllama V2 defaults to a prompt processing batch size of 2048, while llama. To boost inference speed even further, use the ExLlamaV2 kernels by configuring the exllama_config I got ooba working locally on a 380 16gb card but it runs slow as ass. Thanks to new kernels, it’s optimized for (blazingly) fast inference. You should probably start with This tool is now slowing down the build. Comment 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). Speaking from personal experience, the current prompt eval speed on llama. exllama (not hf) has top k, top p Hi, I tried to use exllamv2 with Mistral 7B Instruct instead of my llama-cpp-python test implementation. And then having another model choose the best one for the query. Then, select the llama-13b-4bit-128g model in the "Model" dropdown to load it. Marked as answer Yeah slow filesystem performance outside of WSL is a known issue. 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 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. compress_pos_emb is for models/loras trained with RoPE scaling. But then the second thing is that ExLlama isn't written with AMD devices in mind. Both GPTQ and exl2 are GPU only You signed in with another tab or window. So I suppose this issue is no longer 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. Stars - the number of stars that a project has on GitHub. 1-GPTQ 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. AutoGPTQ - this engine, while generally slower may be better for older GPU architectures. model_name, loader) File “C:\oobabooga_windows\text 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. 13B 6Bit quantized is acceptable. Example: from auto_gptq import exllama_set_max_input_length model = Exllama kernels for faster inference. 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. com When using exllama inference, it can reach 20 token/s per second or more. Upload images, audio, and videos by dragging in the text input, pasting, or clicking here. It features much lower VRAM usage and much higher speeds due to not relying on unoptimized transformers code. This is the speed at which oobabooga initially used exllama, and the speed was like a rocket. There's an update now that enables the fused kernels for 4x models as well, but it isn't in the 0. 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 If it's still slow then this I suppose this must be a GPU-specific issue, and not as I thought OS/installation specific. 4 RAM sticks will be slower than 2 RAM sticks too. For instance, the latest Nvidia drivers have introduced design choices that slow down the inference process. All the models can be found on Huggingface. 5 times faster than ExllamaV2. Only odd man out is AutoGPTQ and now AWQ because they're still using accelerate to split up models for that slow ride. 57 - I get the same behavior. Activity is a relative number indicating how actively a project is being developed. cpp models with a context length of 1. But that's not a problem anyway, EXL2 We would like to show you a description here but the site won’t allow us. I'm experimenting with some and getting 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. The bitsandbytes approach makes inference much slower, which others have reported. 10. It uses the GGML and GGUF formated models, with GGUF being the newest format. -nommq takes more VRAM and is slower on base inference. cpp beats exllama on my machine and can use the P40 on Q6 models. The speeds will be significantly slower then if you had the model on GPU only, though. 61 and 0. exl2 processes most things in FP16, which the 1080ti, being from the Pascal era, is veryyy slow at. Using both llama. I installed CUDA (10. Several times I notice a slight speed increase using direct implementations like llama-cpp-python OAI server. By uploading the F16 model first, you can save your own time as well the time With the above sample Python code, you can reuse an existing OpenAI configuration and modify the base url to point to your localhost. , ExLlama for GPTQ. Set max_seq_len to a number greater than 2048. Some people use ollama, but I didn't Decrease cold-start speed on inference (llama. cpp and exllama, in my opinion. py”, line 73, in load_model_wrapper shared. 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. Let's try with llama 2 13b. I have heard its slower than full on Exllama. (by ollama) the second one uses Mac resources better (checked through macmon), but new models come out a bit slower on it. It is capable of mixed inference with GPU and CPU working together without fuss. I'm wondering if there's any way to further optimize this setup to increase the inference speed. (I didn’t have time for this, but if I was going to use exllama for from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline. 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 Using 2x 7900 XTX on EndeavourOS + pytorch nightly for ROCm 6. 35 seconds (24. The actual processing is what takes all of the resources. 0a0+git36449ea) and transformers (4. Larger sized model, slower inference and minimal gain of perplexity. cpp is way slower to ExLlama Traceback (most recent call last): File “C:\oobabooga_windows\text-generation-webui\server. Preliminary results show the Q4 cache mode is more precise overall than FP8, and comparable to full precision. Transformers has the load_in_8bit option, but it's very slow and unoptimized in comparison to load_in_4bit. I don't know how MLC to control output like ExLlama or llama. @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. but I can't even find CUDA or exllama_ext. cpp with GPU offload (3 t/s). 1 t/s) than llama. cpp 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. But other larger context models are appearing every other day now, since Llama 2 dropped. Recent commits have higher weight than older ones. In order to use these kernels, you need to have the entire model on gpus. Is there an existing issue for this? I have searched the existing issues; Reproduction-git pull latest version-start_window. 1. 3 and 2. By contrast, ExLlama (and I think most if not all other implementations) just let the GPUs work I create a feature request on the official repo :Exllama integration to run GPTQ models · Issue #8385 · langchain-ai/langchain (github. com)I will try to use the fork provided in the comments edit: typo 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. Llama. I'll see if maybe I can't get a 7B model to load, though, and compare it anyway. When I change to different model there is a error like ERROR:Could not find repositories/exllama/. 11T/s speeds. 4 t/sec. OpenAI’s Python Library Import: LM Studio allows developers to import the OpenAI For merges I find it slower, and painful for juggling storage around between ext3/4 and ntfs for big databases. 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. Alternatively, here is the GGML version which you could use with llama. CUDA extension not installed. It has a ton of options made specifically for RP. It's also shit for samplers and when it doesn't re-process the prompt you can get identical re-rolls. To test it in a way that would please me, I wrote the code to evaluate llama. 93 tokens/s, 256 tokens, context 15, seed 545675865) Output generated in 10. pmrhpt arnkxv nnplf pqfvqq fnotw yunc cst eiyc ncwsrc khkll