rickachiu/llama2.mojo

Inference Llama 2 in one file of pure 🔥

llama2.🔥

why this port?

This repository serves as a port that provides a Mojo-based implementation of llama2.c.

With the release of Mojo, I was inspired to take my Python port of llama2.py and transition it to Mojo. The result? A version that leverages Mojo's SIMD & vectorization primitives, boosting the Python performance by nearly 250x. Impressively, the Mojo version now outperforms the original llama2.c compiled in runfast mode out of the box by 15-20%. This showcases the potential of hardware-level optimizations through Mojo's advanced features. I think this also can help us to see how far can we go with the original llama2.c hardware optimizations.

performance

Since there were some debates was this comparison legit or not I did some research and found that in runfast mode llama2.c includes multiple optimizations like aggressive vectorization, which makes comparison fair with Mojo SIMD vectorization.

Further researches of both solutions in parallelized mode compilation showed that llama2.c is faster by ~20% I'm still investigating in this direction since not all the possible optimizations were applied to the Mojo version so far.

comparison

OS/HW specs

OS:         Ubuntu 20.04
CPU(s):     6
Model name: Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz
CPU MHz:    3191.998

Model	llama2.py	llama2.c	llama2.c (runfast)	llama2.c (OMP/parallelized)	llama2.mojo	llama2.mojo (parallelized)	llama2.mojo (naive matmul)
stories15M.bin	1.3 tok/s	75.73 tok/s	237 tok/s	450 tok/s	260 tok/s	390 tok/s	67.26 tok/s
stories110M.bin	-	9 tok/s	30 tok/s	64 tok/s	40 tok/s	57 tok/s	9.20 tok/s

prerequisites

Make sure you have installed and configured mojo on your environment

Or you can use mojo playground to run this model.

feel the 🔥 magic

First, navigate to the folder when you keep your projects and clone this repository to this folder:

git clone https://github.com/tairov/llama2.mojo.git

Then, open the repository folder:

cd llama2.mojo

Now, let's download the model

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

Then, just run the Mojo

mojo llama2.mojo stories15M.bin -s 100 -n 256 -t 0.5 -i "Llama is an animal"

Example output

num hardware threads:  6
SIMD vector width:  16
checkpoint size:  60816028
Llama is an animal was walking down the street. She stopped and looked up with a big smile on her face. She had a puppy in her arms. She was so excited to have a new friend.
The puppy ran up to her and said, "Hi! I'm here to be your friend!"
Mandy smiled and said, "Hi! I'm Mandy. Can I play with you?"
The puppy barked and wagged his tail. Mandy was so happy! She gave the puppy a big hug and they played with the puppy all afternoon.
When it was time to go home, Mandy said, "I have to go now. Goodbye!"
The puppy barked and said, "Goodbye Mandy! See you tomorrow!"
Mandy waved goodbye and then she went back home. She was so happy to have a new friend.
<s>
Once upon a time, there was a little girl named Lily. She loved to play outside and explore the world around her. One day, she went for a walk in the park with her mommy. They saw a big tree with lots of leaves.
Lily said,
achieved tok/s:  359.66149506346966

Running via Docker

docker build -t llama2.mojo .
docker run -it llama2.mojo

With Gradio UI:

# uncomment the last line in Dockerfile CMD ["python", "gradio_app.py"]
docker run -it -p 0.0.0.0:7860:7860 llama2.mojo

License

MIT