Introduction

DynamicDiff

DynamicDiff.jl provides compilation-free symbolic differentiation for runtime-generated expressions.

Built for SymbolicRegression.jl, it is also a generic library for computing derivatives.

The Derivative Operator, D

This operator computes symbolic partial derivatives of any AbstractExpression object (from DynamicExpressions.jl).

D(ex::AbstractExpression, feature::Integer)

This works by extending the OperatorEnum contained within ex to include the additional derivative operators (one-time compilation for a given set of operators), and then manipulating the symbolic tree to reference the new operators and compute chain rule compositions.

Evaluation then can simply call the result with standard DynamicExpressions.jl syntax, which uses the fast DynamicExpressions.eval_tree_array function.

This operator can be nested an arbitrary number of times.

Performance

Let's see an example.

First, let's set up some variables with a given set of operators:

using DynamicDiff, DynamicExpressions

operators = OperatorEnum(; binary_operators=(+, *, /, -), unary_operators=(sin, cos));
variable_names = ["x1", "x2", "x3"];
x1, x2, x3 = (Expression(Node{Float64}(feature=i); operators, variable_names) for i in 1:3);

Now, we can generate some symbolic functions and take derivatives:

julia> f = x1 * sin(x2 - 0.5)
x1 * sin(x2 - 0.5)

julia> D(f, 1)
sin(x2 - 0.5)

julia> D(f, 2)
x1 * cos(x2 - 0.5)

These symbolic derivatives are done by simply incrementing integers and arranging a binary tree, so this process is very fast:

julia> using BenchmarkTools

julia> @btime D($f, 2);
  52.865 ns (5 allocations: 240 bytes)

This isn't compiled or cached! To show this, let's randomly generate arbitrary expressions and then take derivatives of them:

julia> @btime D(g, 1) setup=(g = [x1, x2, x3][rand(1:3)] * sin([x1, x2, x3][rand(1:3)] - randn())) evals=100
  60.270 ns (2 allocations: 80 bytes)

These expressions can then be evaluated using DynamicExpressions.jl:

julia> df = D(f, 1);

julia> df([1.0 2.0]')
1-element Vector{Float64}:
 0.9974949866040544

This is also very fast. Let's take the symbolic derivative of an expression and then evaluate it on 32 batches:

julia> @btime D($f, 1)(x) setup=(x = randn(2, 32));
  187.132 ns (4 allocations: 416 bytes)

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