EnzymeAD/Enzyme

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1679 stars · 184 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Enzyme is a high-performance automatic differentiation plugin for LLVM and MLIR that computes derivatives and gradients of arbitrary existing code at the IR level. It works across many source languages (C, C++, Rust, Julia, Fortran, Swift, TensorFlow, PyTorch) by differentiating optimized code, often exceeding the performance of source-transformation AD tools.

Use cases

• compute gradients of C/C++ code without rewriting it
• differentiate GPU kernels for machine learning
• generate fast reverse-mode gradients for scientific simulation
• add automatic differentiation to a custom compiler via LLVM/MLIR
• differentiate Rust or Julia programs with one tool
• speed up gradient computation compared to source-transformation AD

When to avoid

• you only need Python-level autodiff for a pure PyTorch/JAX workflow where native autodiff suffices
• your code is not compiled to LLVM IR or uses dynamically unanalyzable constructs
• you want a pure-Python installable package with no LLVM toolchain dependency

Facets

library · maturity active

compiler machine-learning gpu-computing developer-tools machine-learning compilers deep-learning performance cpp cross-platform python rust automatic-differentiation llvm-plugin mlir gradients differentiable-programming high-performance-computing reverse-mode-ad scientific-computing llvm gpu

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markdown · JSON · MCP: product_card(name="EnzymeAD/Enzyme")