Have you ever wanted to inference a baby Llama 2 model in pure C? No? Well, now you can!
With the code in this repo you can train the Llama 2 LLM architecture from scratch in PyTorch, then export the weights to a binary file, and load that into one ~simple 500-line C file (run.c) that inferences the model. Hence, this repo is a "fullstack" solution to custom, small LLMs. You might think that you need many billion parameter LLMs to do anything useful, but in fact very small LLMs can have surprisingly strong performance if you make the domain narrow enough. I recommend looking at the TinyStories paper for inspiration.
Please note that this started as just a fun weekend project: I took nanoGPT, tuned it to implement the Llama-2 architecture instead of GPT-2, and the meat of it was writing the C inference engine in run.c. As such, this is not really meant to be a production-grade library right now.
Hat tip to the awesome llama.cpp for inspiring this project. I wanted something super minimal so I chose to hard-code the llama-2 architecture, stick to fp32, and just roll one inference file of pure C with no dependencies.
Let's just run a baby Llama 2 model in C. You need a model checkpoint. Download this 15M parameter model I trained on the TinyStories dataset (~58MB download) and place it into the default checkpoint directory out:
wget https://karpathy.ai/llama2c/model.bin -P out(if that doesn't work try google drive). Compile and run the C code:
gcc -O3 -o run run.c -lm
./run out/model.binYou'll see the text stream a sample. On my M1 MacBook Air this runs at ~110 tokens/s. See performance or the Makefile for compile flags that can significantly speed this up. Sample output:
Once upon a time, there was a boy named Timmy. Timmy loved to play sports with his friends. He was very good at throwing and catching balls. One day, Timmy's mom gave him a new shirt to wear to a party. Timmy thought it was impressive and asked his mom to explain what a shirt could be for. "A shirt is like a special suit for a basketball game," his mom said. Timmy was happy to hear that and put on his new shirt. He felt like a soldier going to the army and shouting. From that day on, Timmy wore his new shirt every time he played sports with his friends at the party. Once upon a time, there was a little girl named Lily. She loved to play outside with her friends. One day, Lily and her friend Emma were playing with a ball. Emma threw the ball too hard and it hit Lily's face. Lily felt embarrassed and didn't want to play anymore. Emma asked Lily what was wrong, and Lily told her about her memory. Emma told Lily that she was embarrassed because she had thrown the ball too hard. Lily felt bad achieved tok/s: 129.146172
Update: I've now also uploaded a bigger checkpoint. This one is dim 512, 8 layers, 8 heads and context length 1024, a ~44M param Transformer. It trained for 200K iterations batch size 32 on 4XA100 40GB GPUs in ~8 hours. You can use this bigger and more powerful checkpoint like so:
wget https://karpathy.ai/llama2c/model44m.bin -P out44m
./run out44m/model44m.binThis still runs at interactive rates and samples more coherent and diverse stories:
Once upon a time, there was a little girl named Lily. She loved playing with her toys on top of her bed. One day, she decided to have a tea party with her stuffed animals. She poured some tea into a tiny teapot and put it on top of the teapot. Suddenly, her little brother Max came into the room and wanted to join the tea party too. Lily didn't want to share her tea and she told Max to go away. Max started to cry and Lily felt bad. She decided to yield her tea party to Max and they both shared the teapot. But then, something unexpected happened. The teapot started to shake and wiggle. Lily and Max were scared and didn't know what to do. Suddenly, the teapot started to fly towards the ceiling and landed on the top of the bed. Lily and Max were amazed and they hugged each other. They realized that sharing was much more fun than being selfish. From that day on, they always shared their tea parties and toys.
It looks like I will have multiple models that I will train on TinyStories, I will catalogue them here.
| model | dim | n_layers | n_heads | max context length | parameters | download |
|---|---|---|---|---|---|---|
| OG | 288 | 6 | 6 | 256 | 15M | model.bin |
| 44M | 512 | 8 | 8 | 1024 | 44M | model44m.bin |
| 120M | 768 | 12 | 12 | 1024 | 120M | training... |
You'll notice that the 120M model is roughly equivalent to GPT-1 in size. Alternatively, this is also the smallest model in the GPT-2 series (GPT-2 small), except the max context length is only 1024 instead of 2048. The only notable changes from GPT-1/2 architecture is that Llama uses RoPE relatively positional embeddings instead of absolute/learned positional embeddings, a bit more fancy SwiGLU non-linearity in the MLP, RMSNorm instead of LayerNorm, bias=False on all Linear layers, and is optionally multiquery (but this is not yet supported in llama2.c).
It should be possible to load the weights released by Meta but I haven't tried because the inference speed, even of the 7B model, would probably be not great with this baby single-threaded C program. So in this repo we focus on more narrow applications, and train the same architecture but from scratch, in this case on the TinyStories dataset for fun.
First let's download and pretokenize some source dataset, e.g. I like TinyStories so this is the only example currently available in this repo. But it should be very easy to add datasets, see the code.
python tinystories.py download
python tinystories.py pretokenizeThen train our model:
python train.pySee the train.py script for more exotic launches and hyperparameter overrides. I didn't tune the hyperparameters, I expect simple hyperparameter exploration should give better models. Totally understand if you want to skip model training, for simple demo just download my pretrained model and save it into the directory out:
wget https://karpathy.ai/llama2c/model.bin -P outOnce we have the model.bin file, we can inference in C. Compile the C code first:
gcc -O3 -o run run.c -lmYou can now run it simply as
./run out/model.binWatch the tokens stream by, fun! We can also run the PyTorch inference script for comparison (to run, add model.ckpt to /out if you haven't already):
python sample.pyWhich gives the same results. More detailed testing will be done in test_all.py, run as:
$ pytestCurrently you will need two files to test or sample: the model.bin file and the model.ckpt file from PyTorch training I ran earlier. I have to think through running the tests without having to download 200MB of data.
(NOTE: this guide is not great because I personally spend a lot of my time in Python land and don't have an amazing understanding of a lot of these features and flags. If someone does and is willing to help document and briefly describe some of these and their tradeoffs, I'd welcome a PR)
There are many ways to potentially speed up this code depending on your system. Here we document a few together with a high-level guide on what they do. Here's again the default way to compile, but using -O3:
gcc -O3 -o run run.c -lm-O3 includes optimizations that are expensive in terms of compile time and memory usage. Including vectorization, loop unrolling, and predicting branches. Here's a few more to try.
-Ofast Run additional optimizations which may break compliance with the C/IEEE specifications, in addition to -O3. See the GCC docs for more information.
-ffast-math breaks IEEE compliance, e.g. allowing reordering of operations, disables a bunch of checks for e.g. NaNs (assuming they don't happen), enables reciprocal approximations, disables signed zero, etc. However, there is a good reason to be suspicious of this setting, one good writeup is here: "Beware of fast-math".
-funsafe-math-optimizations a more limited form of -ffast-math, that still breaks IEEE compliance but doesn't have all of the numeric/error handling changes from -ffasth-math. See the GCC docs for more information.
-march=native Compile the program to use the architecture of the machine you're compiling on rather than a more generic CPU. This may enable additional optimizations and hardware-specific tuning such as improved vector instructions/width.
Putting a few of these together, the fastest throughput I saw so far on my MacBook Air (M1) is with:
gcc -Ofast -o run run.c -lmAlso, I saw someone report higher throughput replacing gcc with clang.
OpenMP Big improvements can also be achieved by compiling with OpenMP, which "activates" the #pragma omp parallel for inside the matmul. You can compile e.g. like so:
clang -Ofast -fopenmp -march=native run.c -lm -o run(I believe you can swap clang/gcc, and may try to leave out -march=native). Then when you run inference, make sure to use OpenMP flags to set the number of threads, e.g.:
OMP_NUM_THREADS=4 ./run out/model.binDepending on your system resources you may want to tweak these hyperparameters. (TODO: I am not intimitely familiar with OpenMP and its configuration, if someone would like to flesh out this section I would welcome a PR).
- why is there a leading space in C sampling code when we
./run? - todo multiquery support? doesn't seem as useful for smaller models that run on CPU (?)
- todo support inferencing beyond max_seq_len steps, have to think through the kv cache
- why is MFU so low (~10%) on my A100 40GB for training?
- weird errors with torch.compile and wandb when using DDP
- make more better tests to decrease yolo
I trained the llama2.c storyteller models on a 4X A100 40GB box graciously provided by the excellent Lambda labs, thank you.
Figured it's possible to reuse my existing discord channel (that I use for my zero to hero youtube series), see #llama2c channel on discord, for any quick questions, related discussions, etc.
MIT