Backends

List

We support the following dense backend choices from ADTypes.jl:

Note

DifferentiationInterface.jl itself is compatible with Julia 1.6, the Long Term Support (LTS) version of the language. However, we were only able to test the following backends on Julia 1.6:

AutoFiniteDifferences
AutoForwardDiff
AutoReverseDiff
AutoTracker
AutoZygote

We strongly recommend that users upgrade to Julia 1.10 or above, where all backends are tested.

Features

Given a backend object, you can use:

check_available to know whether the required AD package is loaded
check_inplace to know whether the backend supports in-place functions (all backends support out-of-place functions)

In theory, all we need from each backend is either a pushforward or a pullback: we can deduce every other operator from these two. In practice, many AD backends have custom implementations for high-level operators like gradient or jacobian, which we reuse whenever possible.

Details

In the rough summary table below,

✅ means that we reuse the custom implementation from the backend;
❌ means that a custom implementation doesn't exist, so we use our default fallbacks;
🔀 means it's complicated or not done yet.

	`pf`	`pb`	`der`	`grad`	`jac`	`hess`	`hvp`	`der2`
`AutoChainRules`	❌	✅	❌	❌	❌	❌	❌	❌
`AutoDiffractor`	✅	❌	❌	❌	❌	❌	❌	❌
`AutoEnzyme` (forward)	✅	❌	❌	✅	✅	❌	❌	❌
`AutoEnzyme` (reverse)	❌	✅	❌	✅	✅	❌	🔀	❌
`AutoFastDifferentiation`	✅	✅	✅	✅	✅	✅	✅	✅
`AutoFiniteDiff`	🔀	❌	✅	✅	✅	✅	❌	❌
`AutoFiniteDifferences`	🔀	❌	❌	✅	✅	❌	❌	❌
`AutoForwardDiff`	✅	❌	✅	✅	✅	✅	✅	✅
`AutoMooncake`	❌	✅	❌	❌	❌	❌	❌	❌
`AutoPolyesterForwardDiff`	🔀	❌	🔀	✅	✅	🔀	🔀	🔀
`AutoReverseDiff`	❌	🔀	❌	✅	✅	✅	❌	❌
`AutoSymbolics`	✅	❌	✅	✅	✅	✅	❌	❌
`AutoTracker`	❌	✅	❌	✅	❌	❌	❌	❌
`AutoZygote`	❌	✅	❌	✅	✅	✅	🔀	❌

Moreover, each context type is supported by a specific subset of backends:

	`Constant`
`AutoChainRules`	✅
`AutoDiffractor`	❌
`AutoEnzyme` (forward)	✅
`AutoEnzyme` (reverse)	✅
`AutoFastDifferentiation`	❌
`AutoFiniteDiff`	✅
`AutoFiniteDifferences`	✅
`AutoForwardDiff`	✅
`AutoMooncake`	✅
`AutoPolyesterForwardDiff`	✅
`AutoReverseDiff`	✅
`AutoSymbolics`	❌
`AutoTracker`	✅
`AutoZygote`	✅

Second order

For second-order operators like second_derivative, hessian and hvp, there are two main options. You can either use a single backend, or combine two of them within the SecondOrder struct:

backend = SecondOrder(outer_backend, inner_backend)

The inner backend will be called first, and the outer backend will differentiate the generated code. In general, using a forward outer backend over a reverse inner backend will yield the best performance.

Danger

Second-order AD is tricky, and many backend combinations will fail (even if you combine a backend with itself). Be ready to experiment and open issues if necessary.

Backend switch

The wrapper DifferentiateWith allows you to switch between backends. It takes a function f and specifies that f should be differentiated with the substitute backend of your choice, instead of whatever true backend the surrounding code is trying to use. In other words, when someone tries to differentiate dw = DifferentiateWith(f, substitute_backend) with true_backend, then substitute_backend steps in and true_backend does not dive into the function f itself. At the moment, DifferentiateWith only works when true_backend is either ForwardDiff.jl or a ChainRules.jl-compatible backend.

Implementations

What follows is a list of implementation details from the package extensions of DifferentiationInterface.jl It is not part of the public API or protected by semantic versioning, and it may become outdated. When in doubt, refer to the code itself.

ChainRulesCore

We only implement pullback, using the RuleConfig mechanism to call back into AD. Same-point preparation runs the forward sweep and returns the pullback closure.

Diffractor

We only implement pushforward.

Danger

The latest releases of Diffractor broke DifferentiationInterface.

Enzyme

Depending on the mode attribute inside AutoEnzyme, we implement either pushforward or pullback based on Enzyme.autodiff. When necessary, preparation chooses a number of chunks (for gradient and jacobian in forward mode, for jacobian only in reverse mode).

FastDifferentiation

For every operator, preparation generates an executable function from the symbolic expression of the differentiated function.

Warning

Preparation can be very slow for symbolic AD.

FiniteDiff

Whenever possible, preparation creates a cache object. Pushforward is implemented rather slowly using a closure.

FiniteDifferences

Nothing specific to mention.

ForwardDiff

We implement pushforward directly using Dual numbers, and preparation allocates the necessary space. For higher level operators, preparation creates a config object, which can be type-unstable.

PolyesterForwardDiff

Most operators fall back on AutoForwardDiff.

ReverseDiff

Wherever possible, preparation records a tape of the function's execution. This tape is computed from the arguments x and contexts... provided at preparation time. It is control-flow dependent, so only one branch is recorded at each if statement.

Danger

If your function has value-specific control flow (like if x[1] > 0 or if c == 1), you may get silently wrong results whenever it takes new branches that were not taken during preparation. You must make sure to run preparation with an input and contexts whose values trigger the correct control flow for future executions.

Symbolics

For all operators, preparation generates an executable function from the symbolic expression of the differentiated function.

Warning

Preparation can be very slow for symbolic AD.