Andrej
77 lines
3.4 KiB
Markdown
77 lines
3.4 KiB
Markdown
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## llama2.c
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Have you ever wanted to inference a baby [Llama 2](https://ai.meta.com/llama/) model in pure C? No? Well, now you can!
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<img src="assets/llama_cute.jpg" width="300" height="300">
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With this code you can train the Llama 2 LLM architecture from scratch in PyTorch, then save the weights to a raw binary file, then load that into one ~simple 500-line C file ([run.c](run.c)) that inferences the model, simply in fp32 for now. On my cloud Linux devbox a dim 288 6-layer 6-head model (~15M params) inferences at ~18 tok/s in fp32, and about the same on my M1 MacBook Air. I was somewhat pleasantly surprised that one can run reasonably sized models (few ten million params) at interactive rates with an approach this simple.
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Please note that this is just a 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](run.c). As such, this is not really meant to be a production-grade library right now.
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Hat tip to [llama.cpp](https://github.com/ggerganov/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.
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## howto
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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.
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First let's download and pretokenize the TinyStories dataset:
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```bash
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python tinystories.py download
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python tinystories.py pretokenize
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```
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Then train our model:
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```bash
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python train.py
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```
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See 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:
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```bash
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wget TODOhoweasiesthmm
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```
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Once we have the model.bin file, we can inference in C. Compile the C code first:
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```bash
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gcc -o run run.c -lm
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```
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You can now run it simply as
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```bash
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./run
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```
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But note that this only emits the SentencePiece tokens. To decode the tokens into text too, run this script through a simple wrapper:
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```bash
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python run_wrap.py
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```
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I hope to delete this script soon though. Anyway, watch the tokens stream by, fun!
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To verify correctness, we can also run the PyTorch inference script:
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```bash
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python sample.py
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```
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Which gives the same results. I'd love to find some time to create actual tests, one day maybe. For now I just manually inspected activations and verified that they match, and that the samples are identical at temperature 0. If someone wishes to help me with tests I welcome PRs.
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## unsorted todos
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- why SentencePiece can't iteratively decode properly?
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- would love to delete run_wrap.py and just directly use C code to string, help welcome
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- todo multiquery support? doesn't seem as useful for smaller models that run on CPU
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- todo support inferencing beyond max_seq_len steps, have to think through the kv cache
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- why is MFU so low (~20%) on my A100 40GB for training?
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- weird errors with torch.compile and wandb when using DDP
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- make tests to decrease yolo
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## License
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MIT
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