2.9 KiB
RLLaMA
This is my attempt at making the LLaMA language model working on a pure Rust CPU implementation. I was inspired by an amazing CPU implementation here: https://github.com/ggerganov/ggml that could run GPT-J 8B models.
As of writing of this, this can run LLaMA-7B at around ~1 token per second, on a Ryzen 3950X using something like 1.5 threads because I haven't yet properly figured out how to multithread this.
I've also managed to run LLaMA-13B which just barely fits in my 64-gig machine with 32-bit float weights everywhere.
LLaMA-30B technically runs but my computer does not have enough memory to keep all the weights around so generating a token takes minutes.
I have not tried LLaMA-60B but presumably if all the smaller models work it would run given a sufficiently chonky computer.
This uses AVX2 intrinsics to speed up itself. Therefore, you need an x86-family CPU to run this.
It also has a Python unpickler that understands the .pth files used by
PyTorch. Well almost, it doesn't unzip them automatically (see below).
How to run
You will need Rust. Make sure you can run cargo from a command line. In
particular, this is using unstable features so you need nightly rust. Make sure
if you write cargo --version it is nightly.
You will need to download LLaMA-7B weights. Refer to https://github.com/facebookresearch/llama/
Once you have 7B weights, and the tokenizer.model it comes with, you need to
decompress it.
$ cd LLaMA
$ cd 7B
$ unzip consolidated.00.pth
# For LLaMA-7B, rename consolidated to consolidated.00
# For the larger models, the number is there already so no need to do this step.
$ mv consolidated consolidated.00
You should then be ready to generate some text.
cargo run --release -- --tokenizer-model /path/to/tokenizer.model --model-path /path/to/LLaMA/7B --param-path /path/to/LLaMA/7B/params.json --prompt "The meaning of life is"
Right now it seems to use around ~25 gigabytes of memory for 7B and around ~50 gigabytes for 13B. Internally all weights are cast to 32-bit floats.
You can use --temperature, --top-p and --top-k to adjust token sampler
settings.
Future plans
This is a hobby thing for me so don't expect updates or help.
- Some other CPU implementations use quantization to reduce the size of weights
- Put some of the operations on the OpenCL GPU/CPU. I've made some initial OpenCL code but it is not used in the transformer loop yet. The CPU OpenCL improves my own AVX2 code by like 100% and massively so on GPU although I am also like 20x slower than equivalent operation on PyTorch on the same GPU.
- I've heard there is some thing called Tensor Cores on nVidia GPUs. Not accessible with OpenCL. But might be accessible on Vulkan with a an extension.
- More sophisticated token sampling. I saw on Hackernews some comments how the samplers are kinda garbage and you can get much better results with good defaults and things like repetition penalty.