Introduction to rwkv.cpp
The rwkv.cpp project is an innovative approach to language modeling, which adapts the RWKV-LM to use the ggml library. This adaptation brings enhancements in computational efficiency and flexibility, particularly for CPU-focused applications, while still supporting GPU through cuBLAS.
Key Features
One of the standout features of rwkv.cpp is its support for multiple numerical formats beyond the usual FP32, including FP16, and quantized INT4, INT5, and INT8 formats. This flexibility allows users to choose between different trade-offs involving precision, speed, and memory use based on their specific needs.
The project centers around providing a C library (rwkv.h) and a convenient Python wrapper, making it accessible for integration into a variety of projects.
The RWKV Model Architecture
rwkv.cpp supports RWKV, a large language model architecture designed to be particularly efficient on CPUs. Unlike traditional Transformer models which require O(n^2) attention computation, RWKV only requires the state from the previous step to calculate logits, making it CPU-friendly when dealing with large context lengths.
RWKV Version Support
- RWKV v5: This version significantly upgrades the architecture, making it competitive with Transformers in terms of quality and efficiency.
- RWKV v6: A further refinement of the architecture that provides even better quality in language model processing.
Performance and Quality
Users utilizing rwkv.cpp for serious applications are advised to test all available formats for perplexity and latency using a representative dataset. This allows users to decide the best trade-off for their particular needs. Generally, RWKV v5 models deliver higher quality and comparable speed to the RWKV v4 models with slight differences in memory consumption.
Performance measurements indicate varying perplexity, latency, and file size efficiency across formats, with FP16 and FP32 providing the best quality at the cost of higher latency and file size, compared to quantized formats.
Compatibility with cuBLAS and hipBLAS
rwkv.cpp supports cuBLAS and hipBLAS, enabling GPU acceleration. However, note that these libraries only support matrix multiplication (ggml_mul_mat()), so some CPU resources are still needed for other operations.
How to Use rwkv.cpp
Setting Up
- Clone the Repository: Begin by cloning the repository using Git.
- Obtain the Library: Either download a pre-compiled library or build it from source for optimized performance.
- Get an RWKV Model: Download a model from Hugging Face and convert it using the provided Python scripts.
- Running the Model: Use command-line tools provided within the package to generate text or set up a bot.
Integrating in Code
To integrate rwkv.cpp into applications, users can utilize the Python examples or include the C header file for C/C++ applications. Bindings are also available for Golang and Node.js.
Community and Contributions
rwkv.cpp is an open-source project that welcomes contributions. Developers interested in contributing should follow the project's code style guidelines.
Overall, rwkv.cpp offers a highly efficient and flexible platform for language modeling tasks, tailored for scalable performance on both CPUs and GPUs, with robust support for various numerical representations and architecture enhancements in recent RWKV versions.