A Flexible Framework for Experiencing Cutting-edge LLM Inference Optimizations
🔥 Show Cases | 🚀 Quick Start | 📃 Tutorial | 💬 Discussion🎉 Introduction
KTransformers, pronounced as Quick Transformers, is designed to enhance your 🤗 Transformers experience with advanced kernel optimizations and placement/parallelism strategies.
KTransformers is a flexible, Python-centric framework designed with extensibility at its core.
By implementing and injecting an optimized module with a single line of code, users gain access to a Transformers-compatible
interface, RESTful APIs compliant with OpenAI and Ollama, and even a simplified ChatGPT-like web UI.
Our vision for KTransformers is to serve as a flexible platform for experimenting with innovative LLM inference optimizations. Please let us know if you need any other features.
🔥 Updates
- Aug 28, 2024: Support 1M context under the InternLM2.5-7B-Chat-1M model, utilizing 24GB of VRAM and 150GB of DRAM. The detailed tutorial is here.
- Aug 28, 2024: Decrease DeepseekV2's required VRAM from 21G to 11G.
- Aug 15, 2024: Update detailed TUTORIAL for injection and multi-GPU.
- Aug 14, 2024: Support llamfile as linear backend.
- Aug 12, 2024: Support multiple GPU; Support new model: mixtral 8*7B and 8*22B; Support q2k, q3k, q5k dequant on gpu.
- Aug 9, 2024: Support windows native.
🔥 Show Cases
1M Context Local Inference on a Desktop with Only 24GB VRAM
https://github.com/user-attachments/assets/a865e5e4-bca3-401e-94b8-af3c080e6c12 * **1M Context InternLM 2.5 7B**: Operates at full bf16 precision, utilizing 24GB VRAM and 150GB DRAM, which is feasible on a local desktop setup. It achieves a 92.88% success rate on the 1M "Needle In a Haystack" test and 100% on the 128K NIAH test.
GPT-4-level Local VSCode Copilot on a Desktop with only 24GB VRAM
https://github.com/user-attachments/assets/4c6a8a38-05aa-497d-8eb1-3a5b3918429c
More advanced features will coming soon, so stay tuned!🚀 Quick Start
Preparation
Some preparation: - CUDA 12.1 and above, if you didn't have it yet, you may install from [here](https://developer.nvidia.com/cuda-downloads). ```sh # Adding CUDA to PATH export PATH=/usr/local/cuda/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH export CUDA_PATH=/usr/local/cuda ``` - Linux-x86_64 with gcc, g++ and cmake ```sh sudo apt-get update sudo apt-get install gcc g++ cmake ninja-build ``` - We recommend using [Conda](https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh) to create a virtual environment with Python=3.11 to run our program. ```sh conda create --name ktransformers python=3.11 conda activate ktransformers # you may need to run ‘conda init’ and reopen shell first ``` - Make sure that PyTorch, packaging, ninja is installed ``` pip install torch packaging ninja ```Installation
1. Use a Docker image, see [documentation for Docker](./doc/en/Docker.md) 2. You can install using Pypi (for linux): ``` pip install ktransformers --no-build-isolation ``` for windows we prepare a pre compiled whl package in [ktransformers-0.1.1+cu125torch24avx2-cp311-cp311-win_amd64.whl](https://github.com/kvcache-ai/ktransformers/releases/download/v0.1.1/ktransformers-0.1.1+cu125torch24avx2-cp311-cp311-win_amd64.whl), which require cuda-12.5, torch-2.4, python-3.11, more pre compiled package are being produced. 3. Or you can download source code and compile: - init source code ```sh git clone https://github.com/kvcache-ai/ktransformers.git cd ktransformers git submodule init git submodule update ``` - [Optional] If you want to run with website, please [compile the website](./doc/en/api/server/website.md) before execute ```bash install.sh``` - Compile and install (for Linux) ``` bash install.sh ``` - Compile and install(for Windows) ``` install.bat ``` 4. If you are developer, you can make use of the makefile to compile and format the code.the detailed usage of makefile is [here](./doc/en/makefile_usage.md)
Local Chat
We provide a simple command-line local chat Python script that you can run for testing. > Note that this is a very simple test tool only support one round chat without any memory about last input, if you want to try full ability of the model, you may go to [RESTful API and Web UI](#id_666). We use the DeepSeek-V2-Lite-Chat-GGUF model as an example here. But we also support other models, you can replace it with any other model that you want to test.Run Example
```shell # Begin from root of your cloned repo! # Begin from root of your cloned repo!! # Begin from root of your cloned repo!!! # Download mzwing/DeepSeek-V2-Lite-Chat-GGUF from huggingface mkdir DeepSeek-V2-Lite-Chat-GGUF cd DeepSeek-V2-Lite-Chat-GGUF wget https://huggingface.co/mzwing/DeepSeek-V2-Lite-Chat-GGUF/resolve/main/DeepSeek-V2-Lite-Chat.Q4_K_M.gguf -O DeepSeek-V2-Lite-Chat.Q4_K_M.gguf cd .. # Move to repo's root dir # Start local chat python -m ktransformers.local_chat --model_path deepseek-ai/DeepSeek-V2-Lite-Chat --gguf_path ./DeepSeek-V2-Lite-Chat-GGUF # If you see “OSError: We couldn't connect to 'https://huggingface.co' to load this file”, try： # GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite # python ktransformers.local_chat --model_path ./DeepSeek-V2-Lite --gguf_path ./DeepSeek-V2-Lite-Chat-GGUF ``` It features the following arguments: - `--model_path` (required): Name of the model (such as "deepseek-ai/DeepSeek-V2-Lite-Chat" which will automatically download configs from [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite)). Or if you already got local files you may directly use that path to initialize the model. > Note: .safetensors files are not required in the directory. We only need config files to build model and tokenizer. - `--gguf_path` (required): Path of a directory containing GGUF files which could that can be downloaded from [Hugging Face](https://huggingface.co/mzwing/DeepSeek-V2-Lite-Chat-GGUF/tree/main). - `--optimize_rule_path` (required except for Qwen2Moe and DeepSeek-V2): Path of YAML file containing optimize rules. There are two rule files pre-written in the [ktransformers/optimize/optimize_rules](ktransformers/optimize/optimize_rules) directory for optimizing DeepSeek-V2 and Qwen2-57B-A14, two SOTA MoE models. - `--max_new_tokens`: Int (default=1000). Maximum number of new tokens to generate. - `--cpu_infer`: Int (default=10). The number of CPUs used for inference. Should ideally be set to the (total number of cores - 2).Suggested Model
| Model Name | Model Size | VRAM | Minimum DRAM | Recommended DRAM | | ------------------------------ | ---------- | ----- | --------------- | ----------------- | | DeepSeek-V2-q4_k_m | 133G | 11G | 136G | 192G | | DeepSeek-V2.5-q4_k_m | 133G | 11G | 136G | 192G | | DeepSeek-V2.5-IQ4_XS | 117G | 10G | 107G | 128G | | Qwen2-57B-A14B-Instruct-q4_k_m | 33G | 8G | 34G | 64G | | DeepSeek-V2-Lite-q4_k_m | 9.7G | 3G | 13G | 16G | | Mixtral-8x7B-q4_k_m | 25G | 1.6G | 51G | 64G | | Mixtral-8x22B-q4_k_m | 80G | 4G | 86.1G | 96G | | InternLM2.5-7B-Chat-1M | 15.5G | 15.5G | 8G(32K context) | 150G (1M context) | More will come soon. Please let us know which models you are most interested in. Be aware that you need to be subject to their corresponding model licenses when using [DeepSeek](https://huggingface.co/deepseek-ai/DeepSeek-V2/blob/main/LICENSE) and [QWen](https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE).Click To Show how to run other examples
* Qwen2-57B ```sh pip install flash_attn # For Qwen2 mkdir Qwen2-57B-GGUF && cd Qwen2-57B-GGUF wget https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct-GGUF/resolve/main/qwen2-57b-a14b-instruct-q4_k_m.gguf?download=true -O qwen2-57b-a14b-instruct-q4_k_m.gguf cd .. python -m ktransformers.local_chat --model_name Qwen/Qwen2-57B-A14B-Instruct --gguf_path ./Qwen2-57B-GGUF # If you see “OSError: We couldn't connect to 'https://huggingface.co' to load this file”, try： # GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct # python ktransformers/local_chat.py --model_path ./Qwen2-57B-A14B-Instruct --gguf_path ./DeepSeek-V2-Lite-Chat-GGUF ``` * DeepseekV2 ```sh mkdir DeepSeek-V2-Chat-0628-GGUF && cd DeepSeek-V2-Chat-0628-GGUF # Download weights wget https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF/resolve/main/DeepSeek-V2-Chat-0628-Q4_K_M/DeepSeek-V2-Chat-0628-Q4_K_M-00001-of-00004.gguf -o DeepSeek-V2-Chat-0628-Q4_K_M-00001-of-00004.gguf wget https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF/resolve/main/DeepSeek-V2-Chat-0628-Q4_K_M/DeepSeek-V2-Chat-0628-Q4_K_M-00002-of-00004.gguf -o DeepSeek-V2-Chat-0628-Q4_K_M-00002-of-00004.gguf wget https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF/resolve/main/DeepSeek-V2-Chat-0628-Q4_K_M/DeepSeek-V2-Chat-0628-Q4_K_M-00003-of-00004.gguf -o DeepSeek-V2-Chat-0628-Q4_K_M-00003-of-00004.gguf wget https://huggingface.co/bartowski/DeepSeek-V2-Chat-0628-GGUF/resolve/main/DeepSeek-V2-Chat-0628-Q4_K_M/DeepSeek-V2-Chat-0628-Q4_K_M-00004-of-00004.gguf -o DeepSeek-V2-Chat-0628-Q4_K_M-00004-of-00004.gguf cd .. python -m ktransformers.local_chat --model_name deepseek-ai/DeepSeek-V2-Chat-0628 --gguf_path ./DeepSeek-V2-Chat-0628-GGUF # If you see “OSError: We couldn't connect to 'https://huggingface.co' to load this file”, try： # GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat-0628 # python -m ktransformers.local_chat --model_path ./DeepSeek-V2-Chat-0628 --gguf_path ./DeepSeek-V2-Chat-0628-GGUF ``` | model name | weights download link | |----------|----------| | Qwen2-57B | [Qwen2-57B-A14B-gguf-Q4K-M](https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct-GGUF/tree/main) | | DeepseekV2-coder |[DeepSeek-Coder-V2-Instruct-gguf-Q4K-M](https://huggingface.co/LoneStriker/DeepSeek-Coder-V2-Instruct-GGUF/tree/main) | | DeepseekV2-chat |[DeepSeek-V2-Chat-gguf-Q4K-M](https://huggingface.co/bullerwins/DeepSeek-V2-Chat-0628-GGUF/tree/main) | | DeepseekV2-lite | [DeepSeek-V2-Lite-Chat-GGUF-Q4K-M](https://huggingface.co/mzwing/DeepSeek-V2-Lite-Chat-GGUF/tree/main) |RESTful API and Web UI
Start without website: ```sh ktransformers --model_path deepseek-ai/DeepSeek-V2-Lite-Chat --gguf_path /path/to/DeepSeek-V2-Lite-Chat-GGUF --port 10002 ``` Start with website: ```sh ktransformers --model_path deepseek-ai/DeepSeek-V2-Lite-Chat --gguf_path /path/to/DeepSeek-V2-Lite-Chat-GGUF --port 10002 --web True ``` Or you want to start server with transformers, the model_path should include safetensors ```bash ktransformers --type transformers --model_path /mnt/data/model/Qwen2-0.5B-Instruct --port 10002 --web True ``` Access website with url [http://localhost:10002/web/index.html#/chat](http://localhost:10002/web/index.html#/chat) :
📃 Brief Injection Tutorial
At the heart of KTransformers is a user-friendly, template-based injection framework. This allows researchers to easily replace original torch modules with optimized variants. It also simplifies the process of combining multiple optimizations, allowing the exploration of their synergistic effects.