MagicPIG

<div align="center"> <h1><img src="assets/MGPF2.png" height="40px" align="top"/> MagicPIG-v0.2: LSH sampling<br>for Efficient LLM Generation </h1>

Zhuoming Chen¹, Ranajoy Sadhukhan¹, Zihao Ye², Yang Zhou¹, Jianyu Zhang^3,4, Niklas Nolte⁴, <br> Yuandong Tian⁴, Matthijs Douze⁴, Leon Bottou^3,4, Zhihao Jia¹, Beidi Chen¹

¹ Carnegie Mellon University, ²University of Washington, ³New York University, ⁴FAIR

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For exploring the possibility of GPU-CPU system powered by Locality-Sensitive-Hashing.

</div> <div align="center"> [<a href="https://arxiv.org/abs/2410.16179">Paper</a>] | [<a href="https://sites.google.com/view/ magicpig-llm">Blog</a>] </div> <br>

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Latest News 📣

• [2024.12] Use FlashInfer to compute the GPU attention parts.
• [2024.12] More efficient and easy-to-use CPU sparse attention.
• [2024.12] Overlap hash table construction and prefilling to hide CPU overhead.

<table> <tr> <td> <img src="assets/Throughput_Improved.jpg" align="top"/> <td> <img src="assets/batch_size_vs_throughput.png" align="top" width="1200"/> </tr> <caption> MagicPIG improves decoding throughput in various regimes by 1.76-4.99x compared with GPU-only attention. Here, we use L20 GPUs with Intel 8563C to simulate different sizes of VRAM. </caption> </table> <table> <tr> <td> <img src="assets/RULER.png" align="top"/> <td> <img src="assets/Reasoning.png" align="top"/> </tr> <caption> MagicPIG achieves higher downstream accuracy in retrieval and reasoning tasks than Quest, the state-of-the-art baseline with lower computation. K10L150 and K11L300 correspond to 2% computation cost while K10L170 corresponds to 2.5%. </caption> </table>

Installation

Commands:

conda create -n magicpig

conda activate magicpig

bash install.sh

Hardware requirements:

Basic: Intel CPUs supporting AVX512.

BFloat16: Intel CPUs supporting AVX512_BF16, GCC Version $\geq$ 11.

Recommended Python version: 3.9/3.10.

Generation

Commands:

cd examples
numactl -C 0-31,52-83 -m 0,1 \
python generation.py \
--model meta-llama/Meta-Llama-3.1-8B-Instruct \
--M 8192 \ 
--G 256 \
--K 10 \ 
--L 170 \ 
--template meta-llama3 \
--data ../data/story.txt

Explanations:

--model : Name or path for a huggingface model (Only Llamas are supported currently).

--M: Maximum sequence length for pre-allocated the VRAM. It should be larger than context length + generation length.

--G: Generation length.

--K, --L: LSH hyper-parameter (when K=0, we use full attention).

--template: Chat template (only support meta-llama3 and meta-llama2 currently).

--data: Source data for generation (.txt file).

Benchmark

Commands:

cd examples
numactl -C 0-31,52-83 -m 0,1 \
python bench.py \
--model meta-llama/Meta-Llama-3.1-8B-Instruct \
--B 1 \
--P 98000 \ 
--M 98304 \ 
--K 10 \ 
--L 150

Explanations: --model : Name or path for a huggingface model (Only Llamas are supported currently).

--M: Maximum sequence length for pre-allocated the VRAM. It should be larger than --M (by at least 192).

--P: Actual context length for benchmarking.

--B: Batch Size.

--K, --L: LSH hyper-parameter (when K=0, we use full attention).

numactl can improve the throughput. On different CPU platforms, the OpenMP threads need to be manually reset to achieve the best performance (Now we use 64).

library/sparse_attention/sparse_attention.h:Line 10 : ATTENTION_THREADS

library/lsh/lsh.h:Line 12 : LSH_THREADS

The number of threads is recommended to set as (or slightly smaller than) the number of physical CPU cores (not hyper-thread) to achieve the best performance.

Evaluations

Install RULER environments

Commands:

cd evaluations/RULER
pip install -r requirements.txt

Run RULER Benchmark

Commands:

cd evaluations/RULER
python download_nltk.py
bash run.sh llama3-8b-chat-128k synthetic $K $L

replace K and L with the hyper-parameter you want to evaluate.

Currently, we support the following models.

llama3-8b-chat-128k: [meta-llama/Llama-3.1-8B-Instruct], llama3-8b-chat-512k: [princeton-nlp/Llama-3-8B-ProLong-512k-Instruct], mistral-7b-chat-512k: [aws-prototyping/MegaBeam-Mistral-7B-512k], llama3-70b-chat-128k: [meta-llama/Llama-3.1-70B-Instruct],

Notice: This will call the compiled lsh and sparse_attention_cpu to execute the proposed systems in the paper. Require lsh and sparse_attention_cpu are successfully installed.

Not all users/developpers have AVX512 machines. You can still test the accuracy of MagicPIG with the following two mathematically equivalent implementations (Tensor Parallelism & Single GPU with Huggingface) even if you cannot finish the installation. This is not for latency/throughput evaluations but for accuracy evaluations.

Tensor Parallelism (GPU + Mask)

We implement a mathematically equivalent version with tensor parallelism.

Commands:

cd evaluations/RULER
python download_nltk.py
bash run_tensor_parallel.sh llama3-8b-chat-128k synthetic $K $L

replace K and L with the hyper-parameter you want to evaluate.

Single GPU (Huggingface + Mask)

We implement a mathematically equivalent version with huggingface for easy-exporting to other evaluation frameworks (e.g., lm-eval-harness, LongBench).

Commands:

cd evaluations/RULER
python download_nltk.py
bash run_single_gpu.sh llama3-8b-chat-128k synthetic $K $L 4 64 $method 0

replace K and L with the hyper-parameter you want to evaluate.

$method: 0: MagicPIG; 1: Quest; 2: TopK 3: Oracle Sampling

$K: LSH hyper-parameter for MagicPIG and Page Size for Quest

$L: LSH hyper-parameter for MagicPIG and number of selected pages for Quest

Pipeline parallelism can be enabled with Accelerate by adding more GPU ids in Line 26 of run_single_gpu.sh.

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This project was made possible thanks to a collaboration with

<a href="https://www.cmu.edu"><img src="https://upload.wikimedia.org/wikipedia/commons/9/9b/Carnegie_Mellon_wordmark.svg" height="20"></a>     <a href="https://www.washington.edu/"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/f/f7/University_of_Washington_signature.svg/2560px-University_of_Washington_signature.svg.png" height="25"></a>     <a href="https://www.nyu.edu/"><img src="https://upload.wikimedia.org/wikipedia/en/thumb/5/58/NYU_logo.svg/2560px-NYU_logo.svg.png" height="21"></a>     <a href="https://ai.meta.com/research/"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/7/7b/Meta_Platforms_Inc._logo.svg/2560px-Meta_Platforms_Inc._logo.svg.png" height="21"></a>    

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Reference

@misc{chen2024magicpiglshsamplingefficient,
      title={MagicPIG: LSH Sampling for Efficient LLM Generation}, 
      author={Zhuoming Chen and Ranajoy Sadhukhan and Zihao Ye and Yang Zhou and Jianyu Zhang and Niklas Nolte and Yuandong Tian and Matthijs Douze and Leon Bottou and Zhihao Jia and Beidi Chen},
      year={2024},
      eprint={2410.16179},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.16179}, 
}