# ezrknn-llm This repo tries to make RKNN LLM usage easier for people who don't want to read through Rockchip's docs ## Requirements Keep in mind this repo is focused for: - High-end Rockchip SoCs, mainly the RK3588 - Linux, not Android - Linux kernels from Rockchip (as of writing 5.10 and 6.1 from Rockchip should work, if your board has one of these it will very likely be Rockchip's kernel) ## Quick Install Run: ```bash curl https://raw.githubusercontent.com/Pelochus/ezrknn-llm/main/install.sh | sudo bash ``` ## Test Run (cd is required): ```bash # TODO ``` ## Converting LLMs for Rockchip's NPUs ### Docker In order to do this, you need a Linux PC x86 (Intel or AMD). Currently, Rockchip does not provide ARM support for converting models, so can't be done on a Orange Pi or similar. Run: `docker run -it pelochus/ezrkllm-toolkit:1.0 bash` Then, inside the Docker container: ```bash apt install -y python3-tk # This needs some configuring from your part cd ezrknn-llm/rkllm-toolkit/examples/huggingface/ ``` Now change the `test.py` with your preferred model. This container provides Qwen-1.8B and LLaMa2 Uncensored. By default, Qwen-1.8B is selected. To convert the model, run: `python3 test.py` I currently cannot convert the models, so I don't know what the output will be. I believe this is Rockchip's fault. Let me know if you could or what error gives you. # Original README starts below
# Description RKLLM software stack can help users to quickly deploy AI models to Rockchip chips. The overall framework is as follows:
In order to use RKNPU, users need to first run the RKLLM-Toolkit tool on the computer, convert the trained model into an RKLLM format model, and then inference on the development board using the RKLLM C API. - RKLLM-Toolkit is a software development kit for users to perform model conversionand quantization on PC. - RKLLM Runtime provides C/C++ programming interfaces for Rockchip NPU platform to help users deploy RKLLM models and accelerate the implementation of LLM applications. - RKNPU kernel driver is responsible for interacting with NPU hardware. It has been open source and can be found in the Rockchip kernel code. # Support Platform - RK3588 Series - RK3576 Series # Support Models - [x] [LLAMA models](https://huggingface.co/meta-llama) - [x] [TinyLLAMA models](https://huggingface.co/TinyLlama) - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen) - [x] [Phi models](https://huggingface.co/models?search=microsoft/phi) - [x] [ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b/tree/103caa40027ebfd8450289ca2f278eac4ff26405) - [x] [Gemma models](https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315) - [x] [InternLM2 models](https://huggingface.co/collections/internlm/internlm2-65b0ce04970888799707893c) - [x] [MiniCPM models](https://huggingface.co/collections/openbmb/minicpm-65d48bf958302b9fd25b698f) # Model Performance Benchmark | model | dtype | seqlen | max_context | new_tokens | TTFT(ms) | Tokens/s | memory(G) | platform | |:-------------- |:---------- |:------:|:-----------:|:----------:|:--------:|:--------:|:---------:|:--------:| | TinyLLAMA-1.1B | w4a16 | 64 | 320 | 256 | 345.00 | 21.10 | 0.77 | RK3576 | | | w4a16_g128 | 64 | 320 | 256 | 410.00 | 18.50 | 0.8 | RK3576 | | | w8a8 | 64 | 320 | 256 | 140.46 | 24.21 | 1.25 | RK3588 | | | w8a8_g512 | 64 | 320 | 256 | 195.00 | 20.08 | 1.29 | RK3588 | | Qwen2-1.5B | w4a16 | 64 | 320 | 256 | 512.00 | 14.40 | 1.75 | RK3576 | | | w4a16_g128 | 64 | 320 | 256 | 550.00 | 12.75 | 1.76 | RK3576 | | | w8a8 | 64 | 320 | 256 | 206.00 | 16.46 | 2.47 | RK3588 | | | w8a8_g128 | 64 | 320 | 256 | 725.00 | 7.00 | 2.65 | RK3588 | | Phi-3-3.8B | w4a16 | 64 | 320 | 256 | 975.00 | 6.60 | 2.16 | RK3576 | | | w4a16_g128 | 64 | 320 | 256 | 1180.00 | 5.85 | 2.23 | RK3576 | | | w8a8 | 64 | 320 | 256 | 516.00 | 7.44 | 3.88 | RK3588 | | | w8a8_g512 | 64 | 320 | 256 | 610.00 | 6.13 | 3.95 | RK3588 | | ChatGLM3-6B | w4a16 | 64 | 320 | 256 | 1168.00 | 4.62 | 3.86 | RK3576 | | | w4a16_g128 | 64 | 320 | 256 | 1582.56 | 3.82 | 3.96 | RK3576 | | | w8a8 | 64 | 320 | 256 | 800.00 | 4.95 | 6.69 | RK3588 | | | w8a8_g128 | 64 | 320 | 256 | 2190.00 | 2.70 | 7.18 | RK3588 | | Gemma2-2B | w4a16 | 64 | 320 | 256 | 628.00 | 8.00 | 3.63 | RK3576 | | | w4a16_g128 | 64 | 320 | 256 | 776.20 | 7.40 | 3.63 | RK3576 | | | w8a8 | 64 | 320 | 256 | 342.29 | 9.67 | 4.84 | RK3588 | | | w8a8_g128 | 64 | 320 | 256 | 1055.00 | 5.49 | 5.14 | RK3588 | | InternLM2-1.8B | w4a16 | 64 | 320 | 256 | 475.00 | 13.30 | 1.59 | RK3576 | | | w4a16_g128 | 64 | 320 | 256 | 572.00 | 11.95 | 1.62 | RK3576 | | | w8a8 | 64 | 320 | 256 | 205.97 | 15.66 | 2.38 | RK3588 | | | w8a8_g512 | 64 | 320 | 256 | 298.00 | 12.66 | 2.45 | RK3588 | | MiniCPM3-4B | w4a16 | 64 | 320 | 256 | 1397.00 | 4.80 | 2.7 | RK3576 | | | w4a16_g128 | 64 | 320 | 256 | 1645.00 | 4.39 | 2.8 | RK3576 | | | w8a8 | 64 | 320 | 256 | 702.18 | 6.15 | 4.65 | RK3588 | | | w8a8_g128 | 64 | 320 | 256 | 1691.00 | 3.42 | 5.06 | RK3588 | | llama3-8B | w4a16 | 64 | 320 | 256 | 1607.98 | 3.60 | 5.63 | RK3576 | | | w4a16_g128 | 64 | 320 | 256 | 2010.00 | 3.00 | 5.76 | RK3576 | | | w8a8 | 64 | 320 | 256 | 1128.00 | 3.79 | 9.21 | RK3588 | | | w8a8_g512 | 64 | 320 | 256 | 1281.35 | 3.05 | 9.45 | RK3588 | - This performance data were collected based on the maximum CPU and NPU frequencies of each platform with version 1.1.0. - The script for setting the frequencies is located in the scripts directory. # Download You can download the latest package, docker image, example, documentation, and platform-tool from [RKLLM_SDK](https://console.zbox.filez.com/l/RJJDmB), fetch code: rkllm # Note - The modifications in version 1.1 are significant, making it incompatible with older version models. Please use the latest toolchain for model conversion and inference. - The supported Python versions are: - Python 3.8 - Python 3.10 - Latest version: [ v1.1.1](https://github.com/airockchip/rknn-llm/releases/tag/release-v1.1.1) # RKNN Toolkit2 If you want to deploy additional AI model, we have introduced a SDK called RKNN-Toolkit2. For details, please refer to: https://github.com/airockchip/rknn-toolkit2 # CHANGELOG ## v1.1.0 - Support group-wise quantization (w4a16 group sizes of 32/64/128, w8a8 group sizes of 128/256/512). - Support joint inference with LoRA model loading - Support storage and preloading of prompt cache. - Support gguf model conversion (currently only support q4_0 and fp16). - Optimize initialization, prefill, and decode time. - Support four input types: prompt, embedding, token, and multimodal. - Add PC-based simulation accuracy testing and inference interface support for rkllm-toolkit. - Add gdq algorithm to improve 4-bit quantization accuracy. - Add mixed quantization algorithm, supporting a combination of grouped and non-grouped quantization based on specified ratios. - Add support for models such as Llama3, Gemma2, and MiniCPM3. - Resolve catastrophic forgetting issue when the number of tokens exceeds max_context. for older version, please refer [CHANGELOG](CHANGELOG.md)