lleaves
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Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.
lleaves 🍃
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.Modelis a subset ofLightGBM.Booster. - Dependencies:
llvmliteandnumpy. 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 expensive recompilation, you can call lleaves.Model.compile() and pass a cache=<filepath> argument.
This will store an ELF (Linux) / Mach-O (macOS) file at the given path when the method is first called.
Subsequent calls of compile(cache=<same filepath>) will skip compilation and load the stored binary file instead.
For more info, see docs.
To eliminate any Python overhead during inference you can link against this generated binary.
For an example of how to do this see benchmarks/c_bench/.
The function signature might 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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