This is a port of BlinkDL/RWKV-LM to ggerganov/ggml.

Besides the usual FP32, it supports FP16, quantized INT4, INT5 and INT8 inference. This project is focused on CPU, but cuBLAS is also supported.

This project provides a C library rwkv.h and a convinient Python wrapper for it.

RWKV is a large language model architecture, with the largest model in the family having 14B parameters. In contrast to Transformer with O(n^2) attention, RWKV requires only state from previous step to calculate logits. This makes RWKV very CPU-friendly on large context lenghts.

RWKV v5 is a major upgrade to RWKV architecture, making it competitive with Transformers in quality. RWKV v5 models are supported.

Loading LoRA checkpoints in Blealtan's format is supported through merge_lora_into_ggml.py script.

If you use rwkv.cpp for anything serious, please test all available formats for perplexity and latency on a representative dataset, and decide which trade-off is best for you.

In general, RWKV v5 models are as fast as RWKV v4 models, with minor differencies in latency and memory consumption, and with having way higher quality than v4. Therefore, it is recommended to use RWKV v5.

Below table is for reference only. Measurements were made on 4C/8T x86 CPU with AVX2, 4 threads. The models are RWKV v4 Pile 169M, RWKV v4 Pile 1.5B.

Measurements were made on Intel i7 13700K & NVIDIA 3060 Ti 8 GB. The model is RWKV-4-Pile-169M, 12 layers were offloaded to GPU.

Latency per token in ms shown.

Note: since cuBLAS is supported only for ggml_mul_mat(), we still need to use few CPU resources to execute remaining operations.

Measurements were made on CPU AMD Ryzen 9 5900X & GPU AMD Radeon RX 7900 XTX. The model is RWKV-novel-4-World-7B-20230810-ctx128k, 32 layers were offloaded to GPU.

Latency per token in ms shown.

Note: same as cuBLAS, hipBLAS only supports ggml_mul_mat(), we still need to use few CPU resources to execute remaining operations.

Requirements: git.

git clone --recursive https://github.com/saharNooby/rwkv.cpp.git
cd rwkv.cpp

Check out Releases, download appropriate ZIP for your OS and CPU, extract rwkv library file into the repository directory.

On Windows: to check whether your CPU supports AVX2 or AVX-512, use CPU-Z.

This option is recommended for maximum performance, because the library would be built specifically for your CPU and OS.

Requirements: CMake or CMake from anaconda, Build Tools for Visual Studio 2019.

cmake .
cmake --build . --config Release

If everything went OK, bin\Release\rwkv.dll file should appear.

Refer to docs/cuBLAS_on_Windows.md for a comprehensive guide.

Refer to docs/hipBLAS_on_Windows.md for a comprehensive guide.

Requirements: CMake (Linux: sudo apt install cmake, MacOS: brew install cmake, anaconoda: cmake package).

cmake .
cmake --build . --config Release

Anaconda & M1 users: please verify that CMAKE_SYSTEM_PROCESSOR: arm64 after running cmake . — if it detects x86_64, edit the CMakeLists.txt file under the # Compile flags to add set(CMAKE_SYSTEM_PROCESSOR "arm64").

If everything went OK, librwkv.so (Linux) or librwkv.dylib (MacOS) file should appear in the base repo folder.

cmake . -DRWKV_CUBLAS=ON
cmake --build . --config Release

If everything went OK, librwkv.so (Linux) or librwkv.dylib (MacOS) file should appear in the base repo folder.

There are pre-quantized Raven models available on Hugging Face. Check that you are downloading .bin file, not .pth.

Requirements: Python 3.x with PyTorch.

This option would require a little more manual work, but you can use it with any RWKV model and any target format.

First, download a model from Hugging Face like this one.

Second, convert it into rwkv.cpp format using following commands:

# Windows
python python\convert_pytorch_to_ggml.py C:\RWKV-4-Pile-169M-20220807-8023.pth C:\rwkv.cpp-169M.bin FP16

# Linux / MacOS
python python/convert_pytorch_to_ggml.py ~/Downloads/RWKV-4-Pile-169M-20220807-8023.pth ~/Downloads/rwkv.cpp-169M.bin FP16

Optionally, quantize the model into one of quantized formats from the table above:

# Windows
python python\quantize.py C:\rwkv.cpp-169M.bin C:\rwkv.cpp-169M-Q5_1.bin Q5_1

# Linux / MacOS
python python/quantize.py ~/Downloads/rwkv.cpp-169M.bin ~/Downloads/rwkv.cpp-169M-Q5_1.bin Q5_1

Requirements: Python 3.x with numpy. If using Pile or Raven models, tokenizers is also required.

To generate some text, run:

# Windows
python python\generate_completions.py C:\rwkv.cpp-169M-Q5_1.bin

# Linux / MacOS
python python/generate_completions.py ~/Downloads/rwkv.cpp-169M-Q5_1.bin

To chat with a bot, run:

# Windows
python python\chat_with_bot.py C:\rwkv.cpp-169M-Q5_1.bin

# Linux / MacOS
python python/chat_with_bot.py ~/Downloads/rwkv.cpp-169M-Q5_1.bin

Edit generate_completions.py or chat_with_bot.py to change prompts and sampling settings.

The short and simple script inference_example.py demostrates the use of rwkv.cpp in Python.

To use rwkv.cpp in C/C++, include the header rwkv.h.

To use rwkv.cpp in any other language, see Bindings section below. If your language is missing, you can try to bind to the C API using the tooling provided by your language.

These projects wrap rwkv.cpp for easier use in other languages/frameworks.

• Golang: seasonjs/rwkv
• Node.js: Atome-FE/llama-node

ggml moves fast, and can occasionally break compatibility with older file formats.

rwkv.cpp will attempt it's best to explain why a model file can't be loaded and what next steps are available to the user.

For reference only, here is a list of latest versions of rwkv.cpp that have supported older formats. No support will be provided for these versions.

• Q4_2, old layout of quantized formats
  • commit 3ca9c7f, release with prebuilt binaries
• Q4_3, Q4_1_O
  • commit c736ef5, release with prebuilt binaries

See also docs/FILE_FORMAT.md for version numbers of rwkv.cpp model files and their changelog.

Please follow the code style described in docs/CODE_STYLE.md.