Using Enzyme within Clad

Like Clad, Enzyme is also a library for Automatic Differentiation(AD). A major difference is that, Enzyme works at the LLVM Intermediate Representation(IR) level, while Clad works at the Clang Abstract Syntax Tree(AST) level. Clad analyses the Clang AST to generate the derivative of a function, while Enzyme analyses the LLVM IR of a function to generate the derivative of the same.

Languages such as Rust, C++, Julia and Swift output LLVM IR which makes Enzyme more interoperable as it can differentiate functions originally written in multiple languages. However, in Enzyme, interoperability comes at a cost. For example, while basic functions are supported across various languages; containers, classes and other language specific constructs require extra scaffolding to be supported by Enzyme. Clad is more tightly coupled with the Clang frontend and the C++ language. It has access to the high-level program structure and compile-only constructs such as consteval. That allows more coherency when differentiating C++. This includes first class support of Object Oriented Programs written in C++.

The initial integration of Enzyme in Clad is to enable cross validation of the produced code.

Currently, use of Enzyme for Reverse Mode AD is supported within Clad. This section describes how Enzyme can be used within Clad.

Configuring Clad to use Enzyme

To enable the use of enzyme within Clad, one needs to configure Clad to use Enzyme. This can be done by adding the flag -DCLAD_ENABLE_ENZYME_BACKEND=On to cmake while configuring Clad build. Thus the overall cmake command should look something like this

cmake ../clad -DClang_DIR=/usr/lib/llvm-11 -DLLVM_DIR=/usr/lib/llvm-11

This flag instructs the build system to download and build Enzyme. Then it is linked as a static library to Clad.

Asking Clad to generate gradients with Enzyme

The following code snippet shows how one can request Clad to generate gradients with Enzyme:

#include "clad/Differentiator/Differentiator.h"

double array_product(double* arr) { return arr[0] * arr[1]; }

int main(){
    auto grad = clad::gradient<clad::opts::use_enzyme>(array_product);
    double v[2] = {3, 4};
    double g[2] = {0};
    grad.execute(v, g);
    printf("d_x = %.2f, d_y = %.2f\n", g[0], g[1]);

Thus, the calling convention is to use clad::gradient<clad::opts::use_enzyme>(...) instead of the usual calling convention, clad::gradient(...). Calling execute, dump and other functionalities remain same as that of Clad.

Extent of support for Enzyme within Clad

Currently functions that take in arrays, pointers and primitive types(integer and real) as parameters are supported for differentiation with Enzyme within Clad. For more ideas on the type of functions supported, please have a look at test/Enzyme/ReverseMode.C for examples that can be differentiated within Clad.