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194 lines
6.9 KiB
Markdown
194 lines
6.9 KiB
Markdown
# RLLaMA
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RLLaMA is a pure Rust implementation of [LLaMA large language model inference.](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/).
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## Supported features
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* Uses either `f16` and `f32` weights.
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* LLaMA-7B, LLaMA-13B, LLaMA-30B, LLaMA-65B all confirmed working
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* Hand-optimized AVX2 implementation
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* OpenCL support for GPU inference.
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## Performance
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The current performance is as follows:
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```
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Pure Rust implementations:
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LLaMA-7B: AMD Ryzen 3950X: 552ms / token f16 (pure Rust)
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LLaMA-7B: AMD Ryzen 3950X: 1008ms / token f32 (pure Rust)
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LLaMA-13B: AMD Ryzen 3950X: 1029ms / token f16 (pure Rust)
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LLaMA-13B: AMD Ryzen 3950X: 1930ms / token f32 (pure Rust)
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LLaMA-30B: AMD Ryzen 5950X: 2112ms / token f16 (pure Rust)
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LLaMA-65B: AMD Ryzen 5950X: 4186ms / token f16 (pure Rust)
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OpenCL (all use f16):
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LLaMA-7B: AMD Ryzen 3950X + OpenCL GTX 3090 Ti: 247ms / token (OpenCL on GPU)
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LLaMA-7B: AMD Ryzen 3950X + OpenCL Ryzen 3950X: 680ms / token (OpenCL on CPU)
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LLaMA-13B: AMD Ryzen 3950X + OpenCL GTX 3090 Ti: <I ran out of GPU memory :(>
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LLaMA-13B: AMD Ryzen 3950X + OpenCL Ryzen 3950X: 1232ms / token (OpenCL on CPU)
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LLaMA-30B: AMD Ryzen 5950X + OpenCL Ryzen 5950X: 4098ms / token (OpenCL on CPU)
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```
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Scroll to the bottom of this README.md to see benchmarks over time.
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## Screenshot
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## Install
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You can install with `cargo` tool. RLLaMA uses intrinsics extensively and you
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likely need to enable them to install the executable.
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```
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RUSTFLAGS="-C target-feature=+sse2,+avx,+fma,+avx2" cargo install rllama
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```
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There is a `.cargo/config.toml` inside this repository that will enable these
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features if you install manually from this Git repository instead.
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## LLaMA weights
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Refer to https://github.com/facebookresearch/llama/ As of now, you need to be
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approved to get weights.
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For LLaMA-7B make sure, you got these files:
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```shell
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* 7B/consolidated.00.pth
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* 7B/params.json
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* tokenizer.model
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```
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The `consolidated.00.pth` is actually a zip file. You need to unzip it:
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```shell
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$ cd 7B
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$ unzip consolidated.00.pth
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$ mv consolidated consolidated.00
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```
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If you are using a larger model like LLaMA-13B, then you can skip the last step
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of renaming the `consolidated` directory.
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You should now be ready to generate some text.
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## Example
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Run LLaMA-7B with some weights casted to 16-bit floats:
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```shell
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rllama --tokenizer-model /path/to/tokenizer.model \
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--model-path /path/to/LLaMA/7B \
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--param-path /path/to/LLaMA/7B/params.json \
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--f16 \
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--prompt "The meaning of life is"
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```
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Use `rllama --help` to see all the options.
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## How to turn on OpenCL
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Use `opencl` Cargo feature.
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```
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RUSTFLAGS="-C target-feature=+sse2,+avx,+fma,+avx2" cargo install rllama --features opencl
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```
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```
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rllama --tokenizer-model /path/to/tokenizer.model \
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--model-path /path/to/LLaMA/7B \
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--param-path /path/to/LLaMA/7B/params.json \
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--opencl-device 0 \
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--prompt "The meaning of life is"
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```
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With `opencl` feature, there is also another argument, `--opencl-device` that
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takes a number. That number selects Nth OpenCL device found on the system. You
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can see the devices in the output when you run the program (e.g. see the
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screenshot below).
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Weights are always cast to 16-bit floats for OpenCL.
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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. `rllama` does not have this.
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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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# commit de5dd592777b3a4f5a9e8c93c8aeef25b9294364 (15 March 2023)
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# The matrix multiplication on GPU is now much faster. It didn't have that much
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# effect overall though, but I got modest improvement on LLaMA-7B GPU.
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LLaMA-7B: AMD Ryzen 3950X + OpenCL GTX 3090 Ti: 247ms / token
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LLaMA-7B: AMD Ryzen 3950X + OpenCL Ryzen 3950X: 680ms / 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: 1232ms / token
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LLaMA-30B: AMD Ryzen 5950X + OpenCL Ryzen 5950X: 4098ms / token
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# commit 3d0afcf24309f28ec540ed7645c35400a865ad6f (17 March 2023)
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# I've been focusing on making the ordinary non-OpenCL CPU implementation
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# faster and I got some gains, most importantly from multithreading.
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# There is Float16 support now, so I've added f16/f32 to these tables:
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#
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# I also managed to run LLaMA-65B for the first time.
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LLaMA-7B: AMD Ryzen 3950X: 552ms / token f16
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LLaMA-7B: AMD Ryzen 3950X: 1008ms / token f32
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LLaMA-13B: AMD Ryzen 3950X: 1029ms / token f16
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LLaMA-13B: AMD Ryzen 3950X: 1930ms / token f32
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LLaMA-30B: AMD Ryzen 5950X: 2112ms / token f16
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LLaMA-65B: AMD Ryzen 5950X: 4186ms / token f16
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```
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