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Showing content from https://github.com/0cc4m/exllama below:

0cc4m/exllama: A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.

A standalone Python/C++/CUDA implementation of Llama for use with 4-bit GPTQ weights, designed to be fast and memory-efficient on modern GPUs.

Disclaimer: The project is coming along, but it's still a work in progress!

I am developing on an RTX 4090 and an RTX 3090-Ti. 30-series and later NVIDIA GPUs should be well supported, but anything Pascal or older with poor FP16 support isn't going to perform well. AutoGPTQ or GPTQ-for-LLaMa are better options at the moment for older GPUs. ROCm is also theoretically supported (via HIP) though I currently have no AMD devices to test or optimize on.

Additionally, only for the web UI:

pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu118

To run on Windows (without WSL):

  1. Install MSVC 2022. You can choose to install the whole Visual Studio 2022 IDE, or alternatively just the Build Tools for Visual Studio 2022 package (make sure Desktop development with C++ is ticked in the installer), it doesn't really matter which.
  2. Install the appropriate version of PyTorch, choosing one of the CUDA versions. I am developing on the nightly build, but the stable version (2.0.1) should also work.
  3. Install CUDA Toolkit, (11.7 and 11.8 both seem to work, just make sure to match PyTorch's Compute Platform version).
  4. For best performance, enable Hardware Accelerated GPU Scheduling.

Clone repo, install dependencies, and run benchmark:

git clone https://github.com/turboderp/exllama
cd exllama

pip install -r requirements.txt

python test_benchmark_inference.py -d <path_to_model_files> -p -ppl

The CUDA extension is loaded at runtime so there's no need to install it separately. It will be compiled on the first run and cached to ~/.cache/torch_extensions/ which could take a little while. If nothing happens at first, give it a minute to compile.

Chatbot example:

python example_chatbot.py -d <path_to_model_files> -un "Jeff" -p prompt_chatbort.txt

jllllll currently maintains an installable Python module here which may be more suitable for integrating ExLlama with other projects

I also made a simple web UI for it. Don't look at the JavaScript, it was mostly written by ChatGPT and it will haunt your dreams. But it sort of works, and it's kinda fun, especially multibot mode:

To run it:

pip install -r requirements-web.txt

python webui/app.py -d <path_to_model_files>

Note that sessions are stored in ~/exllama_sessions/ by default. You can change that location with -sd if you want.

For security benefits and easier deployment, it is also possible to run the web UI in an isolated docker container. Note: the docker image currently only supports NVIDIA GPUs.

It is recommended to run docker in rootless mode.

The easiest way to build the docker image is using docker compose. First, set the MODEL_PATH and SESSIONS_PATH variables in the .env file to the actual directories on the host. Then run:

It is also possible to manually build the image:

docker build -t exllama-web .

NOTE: by default, the service inside the docker container is run by a non-root user. Hence, the ownership of bind-mounted directories (/data/model and /data/exllama_sessions in the default docker-compose.yml file) is changed to this non-root user in the container entrypoint (entrypoint.sh). To disable this, set RUN_UID=0 in the .env file if using docker compose, or the following command if you manually build the image:

docker build -t exllama-web --build-arg RUN_UID=0 .

Using docker compose:

The web UI can now be accessed on the host at http://localhost:5000.

The configuration can be viewed in docker-compose.yml and changed by creating a docker-compose.override.yml file.

Run manually:

docker run --gpus all -p 5000:5000 -v <path_to_model_dir>:/data/model/ -v <path_to_session_dir>:/data/exllama_sessions --rm -it exllama-web --host 0.0.0.0:5000
Model Size grpsz act Seq. len. VRAM Prompt Best Worst Ppl Llama 7B 128 no 2,048 t 5,194 MB 13,918 t/s 173 t/s 140 t/s 6.45 Llama 13B 128 no 2,048 t 9,127 MB 7,507 t/s 102 t/s 86 t/s 5.60 Llama 33B 128 no 2,048 t 20,795 MB 2,959 t/s 47 t/s 40 t/s 4.60 Llama 33B 128 yes 2,048 t 20,795 MB 2,784 t/s 45 t/s 37 t/s 4.55 Llama 33B 32 yes 1,550 t 1 21,486 MB 2,636 t/s 41 t/s 37 t/s 4.52 Koala 13B 128 yes 2,048 t 9,127 MB 5,529 t/s 93 t/s 79 t/s 6.73 WizardLM 33B - yes 2,048 t 20,199 MB 2,313 t/s 47 t/s 40 t/s 5.75 OpenLlama 3B 128 yes 2,048 t 3,128 MB 16,419 t/s 226 t/s 170 t/s 7.81

1 Can not achieve full sequence length without OoM

All tests done on stock RTX 4090 / 12900K, running with a desktop environment, with a few other apps also using VRAM.

"Prompt" speed is inference over the sequence length listed minus 128 tokens. "Worst" is the average speed for the last 128 tokens of the full context (worst case) and "Best" lists the speed for the first 128 tokens in an empty sequence (best case.)

VRAM usage is as reported by PyTorch and does not include PyTorch's own overhead (CUDA kernels, internal buffers etc.) This is somewhat unpredictable anyway. Best bet is to just optimize VRAM usage by the model, probably aiming for 20 GB on a 24 GB GPU to ensure there is room for a desktop environment and all of Torch's internals.

Perplexity is measured only to verify that the models are working. The dataset used is a particular, small sample from WikiText, so scores are not comparable to other Llama benchmarks and only useful for comparing the different Llama models to one another.

The following benchmarks are from a 4090 + 3090-Ti with -gs 17.2,24:

Model Size groupsize act Seq. len. VRAM Prompt Best Worst Ppl Llama 65B 128 yes 2,048 t 39,804 MB 1,109 t/s 20 t/s 18 t/s 4.20 Llama 65B 32 yes 2,048 t 43,424 MB 1,037 t/s 17 t/s 16 t/s 4.11 Llama-2 70B 128 yes 2,048 t 40,680 MB 914 t/s 17 t/s 14 t/s 4.15 Llama-2 70B 32 yes 2,048 t 36,815 MB 874 t/s 15 t/s 12 t/s 4.10

Note that perplexity scores may not be strictly apples-to-apples between Llama and Llama 2 due to their different pretraining datasets.

Moved the todo list here.

Here is a list of models confirmed to be working right now.

2023-01-09: Added rope_theta parameter for (at least partial) CodeLlama support. If you were using alpha = 97 or similar, you would no longer need that for CodeLlama models. Still stuff to sort out regarding the extended vocabulary.

2023-08-09: Added support for sharded models. config.model_path now accepts either a filename or a list of filenames. model_init() will detect multiple .safetensors files if given a model directory. Note the change in the various examples: model_path = glob.glob(st_pattern)[0] becomes simply model_path = glob.glob(st_pattern). Also there's a little script in util/shard.py to split large .safetensors files. It also produces an index.json file for the sharded model, just for completeness, although ExLlama doesn't need it to read the shards. Note that the safetensors dependency was bumped to version 0.3.2.

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