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Partial Evaluation of Forward-Mode Automatic Differentiation

Talkat LAFI 2023 · on Sunday, 2023-01-15· [compilers-and-language-design, machine-learning]
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A whole-program transformation called PEAD (partial evaluation automatic differentiation) combines compile-time symbolic differentiation with forward-mode AD to generate code that eliminates much dual-number overhead by partially lifting only the differentiation-affected code and then partially evaluating the AD-generated dual operations. The pipeline explicitly handles lifting and differentiation of free variables, uses the type system to keep both normal and lifted versions of functions to avoid blind code explosion, and enforces a user-defined max recursion unroll to pragmatically bound partial-evaluation expansion; produces significantly leaner runtime code for many scalar cases.

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  1. 0:00Partial Evaluation of Forward-Mode Automatic Differentiation
  2. 0:53Research Challenge
  3. 1:21Approach Overview
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