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Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation
Mathieu Fourment1, Christiaan J Swanepoel2,3, Jared G Galloway4
1Australian Institute for Microbiology and Infection, University of Technology Sydney, Ultimo, NSW, Australia.
Genome Biology and Evolution
|June 2, 2023
Summary
Automatic differentiation is slower than specialized methods for phylogenetic likelihood gradients. A combined approach using both phylogenetic and machine learning libraries offers the best performance and flexibility.
Area of Science:
- Computational Statistics
- Machine Learning
- Phylogenetics
Background:
- Gradients of probabilistic model likelihoods are crucial for computational statistics and machine learning.
- Automatic differentiation (AD) in libraries like TensorFlow and PyTorch offers general gradient computation.
- The efficiency of AD for phylogenetics-specific tasks compared to specialized code is not well-established.
Purpose of the Study:
- To compare the performance of automatic differentiation with phylogenetics-specific gradient implementations.
- To evaluate gradient calculation speed and scalability in the context of phylogenetic likelihood functions.
- To assess the impact of gradient computation on variational inference procedures in phylogenetics.
Main Methods:
- Implemented and compared six different gradient calculation methods for phylogenetic likelihood functions.
- Evaluated implementations both in isolation and within a variational inference framework.
- Assessed performance based on computational speed and scalability with respect to tree size.
Main Results:
- Automatic differentiation scales approximately linearly with tree size.
- Specialized gradient calculations for tree likelihood and ratio transformations are significantly faster than AD.
- AD's general-purpose nature results in slower performance for these specific phylogenetic operations.
Conclusions:
- A hybrid approach, combining specialized phylogenetic libraries with general machine learning libraries, is recommended.
- This mixed strategy balances computational speed with the flexibility needed for complex phylogenetic modeling.
- Future advancements should leverage the strengths of both specialized and general-purpose tools.
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