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117 lines
4.7 KiB
Markdown
117 lines
4.7 KiB
Markdown
# RLLaMA
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This is my attempt at making the LLaMA language model working on a pure Rust
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CPU implementation. I was inspired by an amazing CPU implementation here:
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https://github.com/ggerganov/ggml that could run GPT-J 6B models.
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With my crappy OpenCL, this will do around ~270ms on my GTX 3090 per token.
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With pure CPU on Ryzen 3950X and OpenCL, I can get around 700ms per token. And
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without any OpenCL, pure Rust code only, with some of my handwritten AVX2
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intrinsics, about 1 second per token. All on LLaMA-7B.
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(Scroll to the bottom to see some benchmarks)
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I've also managed to run LLaMA-13B which just barely fits in my 64-gig machine
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with 32-bit float weights everywhere.
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LLaMA-30B technically runs but my computer does not have enough memory to keep
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all the weights around so generating a token takes minutes.
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I have not tried LLaMA-60B but presumably if all the smaller models work it
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would run given a sufficiently chonky computer.
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It also has a Python unpickler that understands the `.pth` files used by
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PyTorch. Well almost, it doesn't unzip them automatically (see below).
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# How to run
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You will need Rust. Make sure you can run `cargo` from a command line. In
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particular, this is using unstable features so you need nightly rust. Make sure
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that if you write `cargo --version` it shows that it is nightly Rust.
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You will need to download LLaMA-7B weights. Refer to https://github.com/facebookresearch/llama/
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Once you have 7B weights, and the `tokenizer.model` it comes with, you need to
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decompress it.
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```shell
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$ cd LLaMA
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$ cd 7B
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$ unzip consolidated.00.pth
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# For LLaMA-7B, rename consolidated to consolidated.00
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# For the larger models, the number is there already so no need to do this step.
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$ mv consolidated consolidated.00
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```
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You should then be ready to generate some text.
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```shell
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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"
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```
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Right now it seems to use around ~25 gigabytes of memory for 7B and around ~50
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gigabytes for 13B. If you don't use OpenCL, then internally all parameters are
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cast to 32-bit floats.
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You can use `--temperature`, `--top-p` and `--top-k` to adjust token sampler
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settings.
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# Screenshot
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# Notes and future plans
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This is a hobby thing for me so don't expect updates or help.
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* Some other CPU implementations use quantization to reduce the size of weights
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and generally speed up everything a lot.
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* Put some of the operations on the OpenCL GPU/CPU. I've made some initial
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OpenCL code for matrix multiplications but the performance is not competetive
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with frameworks like PyTorch on GPU.
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* I've heard there is some thing called Tensor Cores on nVidia GPUs. Not
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accessible with OpenCL. But might be accessible on Vulkan with a an
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extension.
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* More sophisticated token sampling. I saw on Hackernews some comments how the
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samplers included in Facebook's reference code are kinda garbage and you can
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get much better results with good defaults and things like repetition
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penalty.
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* There is an initial start-up time as the program has to pass through the
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initial prompt. I don't know if this start-up time can be eliminated
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completely but it could be cached on disk. Use cases like having a standard
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prompt to prime the text generation that you reuse many times.
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* Stanford released some instruct-finetuned LLaMA-7B, once I find the weights
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then I'd like to try make a chat-like command-line interface.
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# Benchmarks
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I'm trying to track that I'm making this faster and not slower.
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For 50-length sequence generation:
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```
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cargo run --release --
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--model-path /LLaMA/13B \
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--param-path /LLaMA/13B/params.json \
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--tokenizer-path /LLaMA/tokenizer.model \
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--prompt "Computers are pretty complica" --max-seq-len 50
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# commit c9c861d199bd2d87d7e883e3087661c1e287f6c4 (13 March 2023)
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LLaMA-7B: AMD Ryzen 3950X: 1058ms / token
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LLaMA-13B: AMD Ryzen 3950X: 2005ms / token
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# commit 63d27dba9091823f8ba11a270ab5790d6f597311 (13 March 2023)
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# This one has one part of the transformer moved to GPU as a type of smoke test
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LLaMA-7B: AMD Ryzen 3950X + OpenCL GTX 3090 Ti: 567ms / token
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LLaMA-7B: AMD Ryzen 3950X + OpenCL Ryzen 3950X: 956ms / token
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LLaMA-13B: AMD Ryzen 3950X + OpenCL GTX 3090 Ti: 987ms / token
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LLaMA-13B: AMD Ryzen 3950X + OpenCL Ryzen 3950X: 1706ms / token
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# commit 35b0c372a87192761e17beb421699ea5ad4ac1ce (13 March 2023)
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# I moved some attention stuff to OpenCL too.
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LLaMA-7B: AMD Ryzen 3950X + OpenCL GTX 3090 Ti: 283ms / token
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LLaMA-7B: AMD Ryzen 3950X + OpenCL Ryzen 3950X: 679ms / token
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LLaMA-13B: AMD Ryzen 3950X + OpenCL GTX 3090 Ti: <ran out of GPU memory>
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LLaMA-13B: AMD Ryzen 3950X + OpenCL Ryzen 3950X: 1226ms / token
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```
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