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Applying differentiable programming techniques and machine learningalgorithms to foreign programs requires developers to either rewrite their codein a machine learning framework, or otherwise provide derivatives of theforeign code. This paper presents Enzyme, a high-performance automaticdifferentiation (AD) compiler plugin for the LLVM compiler framework capable ofsynthesizing gradients of statically analyzable programs expressed in the LLVMintermediate representation (IR). Enzyme synthesizes gradients for programswritten in any language whose compiler targets LLVM IR including C, C++,Fortran, Julia, Rust, Swift, MLIR, etc., thereby providing native ADcapabilities in these languages. Unlike traditional source-to-source andoperator-overloading tools, Enzyme performs AD on optimized IR. On amachine-learning focused benchmark suite including Microsoft's ADBench, AD onoptimized IR achieves a geometric mean speedup of 4.5x over AD on IR beforeoptimization allowing Enzyme to achieve state-of-the-art performance. PackagingEnzyme for PyTorch and TensorFlow provides convenient access to gradients offoreign code with state-of-the art performance, enabling foreign code to bedirectly incorporated into existing machine learning workflows.

Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients