llama2.rs 🦀

Have you ever wanted to inference a baby Llama 2 model in pure C? No? Well, now you can!

Great news! More stuff you didn't want but now can!

I've forked and ported Karpathy's llama2.c to Rust! 🦀 It's just as minimalistic as the original C code. This is a quick-and-dirty first attempt.

Why? Because it was FUN! Plus, I'm curious to see how the C and Rust versions will evolve differently.

Full Llama2 Support 🚀🚀

We can now run the full llama2-7B!! No memory mapping for now, so all the weights must fit in memory (~26Gb). On my codespaces VM with 16 cores and 64Gb memory, the inference runs at 1.4 tokens per second.

How to run?

1. Grab Karpathy's baby Llama2 (Orig instructions) pretrained on TinyStories dataset

   wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.bin
   wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories42M.bin
   wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories110M.bin

2. Make sure you have the tokenizer binary - tokenizer.bin (if not see tokenizer.py)

3. Compile and run the Rust code

   Single threaded:

   cargo run --release stories42M.bin 0.9  # <model_path> [temperature]

   Multipthreaded (depends on Rayon)

   cargo run --release  -F parallel stories42M.bin 0.9 # <model_path> [temperature]

   You can also run make rust or make rustfast to get run-rs binary

Performance

Hacky tokens/sec measurement on dev VM (16 cores/64G mem).

tok/s	15M	42M	110M	7B
single core	~75	~25	~10	🙅‍♂️
core per head	~310	~110	~65	1.4

Keeping up with the original

I'm pretty sure that llama2.c is going to move fast and get lots of contributions.

So any contribution is welcome here!

Contribution Ideas

• memory map weights
• WASM port?
• fuse QKV matmuls to a single matmul

License

MIT