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LLVM-based compiler for LightGBM models

Project description

lleaves 🍃

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A LLVM-based compiler for LightGBM decision trees.

lleaves converts trained LightGBM models to optimized machine code, speeding-up prediction by ≥10x.

Example

lgbm_model = lightgbm.Booster(model_file="NYC_taxi/model.txt")
%timeit lgbm_model.predict(df)
# 12.77s

llvm_model = lleaves.Model(model_file="NYC_taxi/model.txt")
llvm_model.compile()
%timeit llvm_model.predict(df)
# 0.90s 

Why lleaves?

  • Speed: Both low-latency single-row prediction and high-throughput batch-prediction.
  • Drop-in replacement: The interface of lleaves.Model is a subset of LightGBM.Booster.
  • Dependencies: llvmlite and numpy. LLVM comes statically linked.

Installation

conda install -c conda-forge lleaves or pip install lleaves (Linux and MacOS only).

Benchmarks

Ran on a dedicated Intel i7-4770 Haswell, 4 cores. Stated runtime is the minimum over 20.000 runs.

Dataset: NYC-taxi

mostly numerical features. |batchsize | 1 | 10| 100 | |---|---:|---:|---:| |LightGBM | 52.31μs | 84.46μs | 441.15μs | |ONNX Runtime| 11.00μs | 36.74μs | 190.87μs | |Treelite | 28.03μs | 40.81μs | 94.14μs | |lleaves | 9.61μs | 14.06μs | 31.88μs |

Dataset: MTPL2

mix of categorical and numerical features. |batchsize | 10,000 | 100,000 | 678,000 | |---|---:|---:|---:| |LightGBM | 95.14ms | 992.47ms | 7034.65ms | |ONNX Runtime | 38.83ms | 381.40ms | 2849.42ms | |Treelite | 38.15ms | 414.15ms | 2854.10ms | |lleaves | 5.90ms | 56.96ms | 388.88ms |

Advanced usage

To avoid any Python overhead during prediction you can link directly against the generated binary. See benchmarks/c_bench/ for an example of how to do this. The function signature can change between major versions.

Development

conda env create
conda activate lleaves
pip install -e .
pre-commit install
./benchmarks/data/setup_data.sh
pytest

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