* Use types from typing for better compatibility with older Python versions * Split last double end of line token as per BlinkDL's suggestion * Fix MSVC warnings * Drop Q4_2 support * Update ggml * Bump file format version for quantization changes * Apply suggestions |
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.github/workflows | ||
ggml@00b49ec707 | ||
rwkv | ||
tests | ||
.gitignore | ||
.gitmodules | ||
CMakeLists.txt | ||
CODE_STYLE.md | ||
FILE_FORMAT.md | ||
LICENSE | ||
README.md | ||
rwkv.cpp | ||
rwkv.h |
README.md
rwkv.cpp
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 CPU only.
This project provides a C library rwkv.h and a convinient Python wrapper for it.
RWKV is a novel 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.
Loading LoRA checkpoints in Blealtan's format is supported through merge_lora_into_ggml.py script.
Quality and performance
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.
Below table is for reference only. Measurements were made on 4C/8T x86 CPU with AVX2, 4 threads.
Format | Perplexity (169M) | Latency, ms (1.5B) | File size, GB (1.5B) |
---|---|---|---|
Q4_0 |
17.507 | 76 | 1.53 |
Q4_1 |
17.187 | 72 | 1.68 |
Q5_0 |
16.194 | 78 | 1.60 |
Q5_1 |
15.851 | 81 | 1.68 |
Q8_0 |
15.652 | 89 | 2.13 |
FP16 |
15.623 | 117 | 2.82 |
FP32 |
15.623 | 198 | 5.64 |
How to use
1. Clone the repo
Requirements: git.
git clone --recursive https://github.com/saharNooby/rwkv.cpp.git
cd rwkv.cpp
2. Get the rwkv.cpp library
Option 2.1. Download a pre-compiled library
Windows / Linux / MacOS
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.
Option 2.2. Build the library yourself
This option is recommended for maximum performance, because the library would be built specifically for your CPU and OS.
Windows
Requirements: CMake or CMake from anaconda, MSVC compiler.
cmake .
cmake --build . --config Release
If everything went OK, bin\Release\rwkv.dll
file should appear.
Linux / MacOS
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.
3. Get an RWKV model
Option 3.1. Download pre-quantized Raven model
There are pre-quantized Raven models available on Hugging Face. Check that you are downloading .bin
file, NOT .pth
.
Option 3.2. Convert and quantize PyTorch model
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 rwkv\convert_pytorch_to_ggml.py C:\RWKV-4-Pile-169M-20220807-8023.pth C:\rwkv.cpp-169M.bin float16
# Linux / MacOS
python rwkv/convert_pytorch_to_ggml.py ~/Downloads/RWKV-4-Pile-169M-20220807-8023.pth ~/Downloads/rwkv.cpp-169M.bin float16
Optionally, quantize the model into one of quantized formats from the table above:
# Windows
python rwkv\quantize.py C:\rwkv.cpp-169M.bin C:\rwkv.cpp-169M-Q5_1.bin Q5_1
# Linux / MacOS
python rwkv/quantize.py ~/Downloads/rwkv.cpp-169M.bin ~/Downloads/rwkv.cpp-169M-Q5_1.bin Q5_1
4. Run the model
Requirements: Python 3.x with PyTorch and tokenizers.
Note: change the model path with the non-quantized model for the full weights model.
To generate some text, run:
# Windows
python rwkv\generate_completions.py C:\rwkv.cpp-169M-Q5_1.bin
# Linux / MacOS
python rwkv/generate_completions.py ~/Downloads/rwkv.cpp-169M-Q5_1.bin
To chat with a bot, run:
# Windows
python rwkv\chat_with_bot.py C:\rwkv.cpp-169M-Q5_1.bin
# Linux / MacOS
python rwkv/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.
Example of using rwkv.cpp
in your custom Python script:
import rwkv_cpp_model
import rwkv_cpp_shared_library
# Change to model paths used above (quantized or full weights)
model_path = r'C:\rwkv.cpp-169M.bin'
model = rwkv_cpp_model.RWKVModel(
rwkv_cpp_shared_library.load_rwkv_shared_library(),
model_path
)
logits, state = None, None
for token in [1, 2, 3]:
logits, state = model.eval(token, state)
print(f'Output logits: {logits}')
# Don't forget to free the memory after you've done working with the model
model.free()
Compatibility
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 formatsQ4_3
,Q4_1_O
See also FILE_FORMAT.md for version numbers of rwkv.cpp
model files and their changelog.
Contributing
There is no complete contributor guide yet; but we have CODE_STYLE.md.