On writing good rrule / frule methods

Code Style

Use named local functions for the pullback in an rrule.

# good:
function rrule(::typeof(foo), x)
    Y = foo(x)
    function foo_pullback(Ȳ)
        return NoTangent(), bar(Ȳ)
    return Y, foo_pullback
#== output
julia> rrule(foo, 2)
(4, var"#foo_pullback#11"())

# bad:
function rrule(::typeof(foo), x)
    return foo(x), x̄ -> (NoTangent(), bar(x̄))
#== output:
julia> rrule(foo, 2)
(4, var"##9#10"())

While this is more verbose, it ensures that if an error is thrown during the pullback the gensym name of the local function will include the name you gave it. This makes it a lot simpler to debug from the stacktrace.

Use ZeroTangent() as the return value

The ZeroTangent() object exists as an alternative to directly returning 0 or zeros(n). It allows more optimal computation when chaining pullbacks/pushforwards, to avoid work. They should be used where possible.

However, sometimes for performance reasons this is not ideal. Especially, if it is to replace a scalar, and is in a type-unstable way. It causes problems if mapping over such pullbacks/pushforwards. This woull be solved once JuliaLang/julia#38241 has been addressed.

Use Thunks appropriately

If work is only required for one of the returned differentials, then it should be wrapped in a @thunk (potentially using a begin-end block).

If there are multiple return values, their computation should almost always be wrapped in a @thunk.

Do not wrap variables in a @thunk; wrap the computations that fill those variables in @thunk:

# good:
∂A = @thunk(foo(x))
return ∂A

# bad:
∂A = foo(x)
return @thunk(∂A)

In the bad example foo(x) gets computed eagerly, and all that the thunk is doing is wrapping the already calculated result in a function that returns it.

Do not use @thunk if this would be equal or more work than actually evaluating the expression itself. Examples being:

  • The expression being a constant
  • The expression is merely wrapping something in a struct, such as Adjoint(x) or Diagonal(x)
  • The expression being itself a thunk
  • The expression being from another rrule or frule; it would be @thunked if required by the defining rule already.
  • There is only one derivative being returned, so from the fact that the user called frule/rrule they clearly will want to use that one.

Ensure you remain in the primal's subspace (i.e. use ProjectTo appropriately)

Rules with abstractly-typed arguments may return incorrect answers when called with certain concrete types. A classic example is the matrix-matrix multiplication rule, a naive definition of which follows:

function rrule(::typeof(*), A::AbstractMatrix, B::AbstractMatrix)
    function times_pullback(ȳ)
        dA = ȳ * B'
        dB = A' * ȳ
        return NoTangent(), dA, dB
    return A * B, times_pullback 

When computing *(A, B), where A isa Diagonal and B isa Matrix, the output will be a Matrix. As a result, in the pullback will be a Matrix, and consequently dA for a A isa Diagonal will be a Matrix, which is wrong. Not only is it the wrong type, but it can contain non-zeros off the diagonal, which is not possible, it is outside of the subspace. While a specialised rules can indeed be written for the Diagonal case, there are many other types and we don't want to be forced to write a rule for each of them. Instead, project_A = ProjectTo(A) can be used (outside the pullback) to extract an object that knows how to project onto the type of A (e.g. also knows the size of the array). This object can be called with a tangent ȳ * B', by doing project_A(ȳ * B'), to project it on the tangent space of A. The correct rule then looks like

function rrule(::typeof(*), A::AbstractMatrix, B::AbstractMatrix)
    project_A = ProjectTo(A)
    project_B = ProjectTo(B)
    function times_pullback(ȳ)
        dA = ȳ * B'
        dB = A' * ȳ
        return NoTangent(), project_A(dA), project_B(dB)
    return A * B, times_pullback
It is often good to `@thunk` your projections

The above example is potentially a good place for using a @thunk. This is not required, but can in some cases be more computationally efficient, see Use Thunks appropriately. When combining thunks and projections, @thunk() must be the outermost call.

A more optimized implementation of the matrix-matrix multiplication example would have

times_pullback(ȳ) = NoTangent(), @thunk(project_A(ȳ * B')), @thunk(project_B(A' * ȳ))

within the rrule. This defers both the evaluation of the product rule and the projection until(/if) the tangent gets used.

Structs: constructors and functors

To define an frule or rrule for a function foo we dispatch on the type of foo, which is typeof(foo). For example, the rrule signature would be like:

function rrule(::typeof(foo), args...; kwargs...)
    return y, foo_pullback

For a struct Bar,

struct Bar

(bar::Bar)(x, y) = return bar.a + x + y # functor (i.e. callable object, overloading the call action)

we can define an frule/rrule for the Bar constructor(s), as well as any Bar functors.


To define an rrule for a constructor for a type Bar we need to be careful to dispatch only on Type{Bar}. For example, the rrule signature for a Bar constructor would be like:

function ChainRulesCore.rrule(::Type{Bar}, a)
    Bar_pullback(Δbar) = NoTangent(), Δbar.a
    return Bar(a), Bar_pullback

Use Type{<:Bar} (with the <:) for non-concrete types, such that the rrule is defined for all subtypes. In particular, be careful not to use typeof(Bar) here. Because typeof(Bar) is DataType, using this to define an rrule/frule will define an rrule/frule for all constructors.

You can check which to use with Core.Typeof:

julia> function foo end
foo (generic function with 0 methods)

julia> typeof(foo)

julia> Core.Typeof(foob)

julia> typeof(Bar)

julia> Core.Typeof(Bar)

julia> abstract type AbstractT end

julia> typeof(AbstractT)

julia> Core.Typeof(AbstractT)

Functors (callable objects)

In contrast to defining a rule for a constructor, it is possible to define rules for calling an instance of an object. In that case, use bar::Bar, i.e.

function ChainRulesCore.rrule(bar::Bar, x, y)
    # Notice the first return is not `NoTangent()`
    Bar_pullback(Δy) = Tangent{Bar}(;a=Δy), Δy, Δy
    return bar(x, y), Bar_pullback

to define the rules.

Ensure your pullback can accept the right types

As a rule the number of types you need to accept in a pullback is theoretically unlimitted, but practically highly constrained to be in line with the primal return type. The three kinds of inputs you will practically need to accept one or more of: natural tangents, structural tangents, and thunks. You do not in general have to handle AbstractZeros as the AD system will not call the pullback if the input is a zero, since the output will also be. Some more background information on these types can be found in the design notes. In many cases all these tangents can be treated the same: tangent types overload a bunch of linear-operators, and the majority of functions used inside a pullback are linear operators. If you find linear operators from Base/stdlibs that are not supported, consider opening an issue or a PR on the ChainRulesCore.jl repo.

Natural tangents

Natural tangent types are the types you might feel the tangent should be, to represent a small change in the primal value. For example, if the primal is a Float32, the natural tangent is also a Float32. Slightly more complex, for a ComplexF64 the natural tangent is again also a ComplexF64, we almost never want to use the structural tangent Tangent{ComplexF64}(re=..., im=...) which is defined. For other cases, this gets a little more complicated, see below. These are a purely human notion, they are the types the user wants to use because they make the math easy. There is currently no formal definition of what constitutes a natural tangent, but there are a few heuristics. For example, if a primal type P overloads subtraction (-(::P,::P)) then that generally returns a natural tangent type for P; but this is not required to be defined and sometimes it is defined poorly.

Common cases for types that represent a vector-space (e.g. Float64, Array{Float64}) is that the natural tangent type is the same as the primal type. However, this is not always the case. For example for a PDiagMat a natural tangent is Diagonal since there is no requirement that a positive definite diagonal matrix has a positive definite tangent. Another example is for a DateTime, any Period subtype, such as Millisecond or Nanosecond is a natural differential. There are often many different natural tangent types for a given primal type. However, they are generally closely related and duck-type the same. For example, for most AbstractArray subtypes, most other AbstractArrays (of right size and element type) can be considered as natural tangent types.

Not all types have natural tangent types. For example there is no natural differential for a Tuple. It is not a Tuple since that doesn't have any method for +. Similar is true for many structs. For those cases there is only a structural differential.

Structural tangents

Structural tangents are tangent types that shadow the structure of the primal type. They are represented by the Tangent type. They can represent any composite type, such as a tuple, or a structure (or a NamedTuple) etc.

Do I have to support the structural tangents as well?

Technically, you might not actually have to write rules to accept structural tangents; if the AD system never has to decompose down to the level of getfield. This is common for types that don't support user getfield/getproperty access, and that have a lot of rules for the ways they are accessed (such cases include some AbstractArray subtypes). You really should support it just in case; especially if the primal type in question is not restricted to a well-tested concrete type. But if it is causing struggles, then you can leave it off til someone complains.


A thunk (either a Thunk, or a InplaceableThunk), represents a delayed computation. They can be thought of as a wrapper of the value the computation returns. In this sense they wrap either a natural or structural tangent.

You should support AbstractThunk inputs even if you don't use thunks

Unfortunately the AD sytems do not know which rules support thunks and which do not. So all rules have to; at least if they want to play nicely with arbitrary AD systems. Luckily it is not hard: much of the time they will duck-type as the object they wrap. If not, then just add a unthunk after the start of your pullback. (Even when they do duck-type, if they are used multiple times then unthunking at the start will prevent them from being recomputed.) If you are using @thunk and the input is only needed for one of them then the unthunk should be in that one. If not, and you have a bunch of pullbacks you might like to write a little helper unthunking(f) = x̄ -> f(unthunk(x̄)) that you can wrap your pullback function in before returning it from the rrule. Yes, this is a bit of boiler-plate, and it is unfortunate. Sadly, it is needed because if the AD wants to benefit it can't get that benifit unless things are not unthunked unnecessarily. Which eventually allows them in some cases to never be unthunked at all. There are two ways common things are never unthunked. One is if the unthunking happens inside a @thunk which is never unthunked itself because it is the tangent for a primal input that never has it's tangent queried. The second is if they are not unthunked because the rule does not need to know what is inside: consider the pullback for identity: x̄ -> (NoTangent(), x̄).

Use @not_implemented appropriately

You can use @not_implemented to mark missing differentials. This is helpful if the function has multiple inputs or outputs, and you have worked out analytically and implemented some but not all differentials.

It is recommended to include a link to a GitHub issue about the missing differential in the debugging information:

    derivatives of Bessel functions with respect to the order are not implemented:

Do not use @not_implemented if the differential does not exist mathematically (use NoTangent() instead).

Note: ChainRulesTestUtils.jl marks @not_implemented differentials as "test broken".

Use rule definition tools

Rule definition tools can help you write more frules and the rrules with less lines of code.


For non-differentiable functions the @non_differentiable macro can be used. For example, instead of manually defining the frule and the rrule for string concatenation *(String..), the macro call

@non_differentiable *(String...)

defines the following frule and rrule automatically

function ChainRulesCore.frule(var"##_#1600", ::Core.Typeof(*), String::Any...; kwargs...)
    return (*(String...; kwargs...), NoTangent())
function ChainRulesCore.rrule(::Core.Typeof(*), String::Any...; kwargs...)
    return (*(String...; kwargs...), function var"*_pullback"(_)
        (ZeroTangent(), ntuple((_->NoTangent()), 0 + length(String))...)

Note that the types of arguments are propagated to the frule and rrule definitions. This is needed in case the function differentiable for some but not for other types of arguments. For example *(1, 2, 3) is differentiable, and is not defined with the macro call above.


For functions involving only scalars, i.e. subtypes of Number (no structs, Strings...), both the frule and the rrule can be defined using a single @scalar_rule macro call.

Note that the function does not have to be $\mathbb{R} \rightarrow \mathbb{R}$. In fact, any number of scalar arguments is supported, as is returning a tuple of scalars.

See docstrings for the comprehensive usage instructions.

Be careful about pullback closures calling other methods of themselves

Due to JuliaLang/Julia#40990, a closure calling another (or the same) method of itself often comes out uninferable (and thus effectively type-unstable). This can be avoided by moving the pullback definition outside the function, so that it is no longer a closure. For example:

double_it(x::AbstractArray) = 2 .* x

function ChainRulesCore.rrule(::typeof(double_it), x)
    double_it_pullback(ȳ::AbstractArray) = (NoTangent(), 2 .* ȳ)
    double_it_pullback(ȳ::AbstractThunk) = double_it_pullback(unthunk(ȳ))
    return double_it(x), double_it_pullback

Ends up infering a return type of Any

julia> _, pullback = rrule(double_it, [2.0, 3.0])
([4.0, 6.0], var"#double_it_pullback#8"(Core.Box(var"#double_it_pullback#8"(#= circular reference @-2 =#))))

julia> @code_warntype pullback(@thunk([10.0, 10.0]))

1 ─ %1 = Core.getfield(#self#, :double_it_pullback)::Core.Box
│   %2 = Core.isdefined(%1, :contents)::Bool
└──      goto #3 if not %2
2 ─      goto #4
3 ─      Core.NewvarNode(:(double_it_pullback))
└──      double_it_pullback
4 ┄ %7 = Core.getfield(%1, :contents)::Any
│   %8 = Main.unthunk(ȳ)::Vector{Float64}
│   %9 = (%7)(%8)::Any
└──      return %9

This can be solved by moving the pullbacks outside the function so they are not closures, and thus to not run into this upstream issue. In this case that is fairly simple, since this example doesn't close over anything (if it did then would need a closure calling an outside function that calls itself. See this example.).

_double_it_pullback(ȳ::AbstractArray) = (NoTangent(), 2 .* ȳ)
_double_it_pullback(ȳ::AbstractThunk) = _double_it_pullback(unthunk(ȳ))

function ChainRulesCore.rrule(::typeof(double_it), x)
    return double_it(x), _double_it_pullback

This infers just fine:

julia> _, pullback = rrule(double_it, [2.0, 3.0])
([4.0, 6.0], _double_it_pullback)

julia> @code_warntype pullback(@thunk([10.0, 10.0]))

Body::Tuple{NoTangent, Vector{Float64}}
1 ─ %1 = Main.unthunk(ȳ)::Vector{Float64}
│   %2 = Main._double_it_pullback(%1)::Core.PartialStruct(Tuple{NoTangent, Vector{Float64}}, Any[Core.Const(NoTangent()), Vector{Float64}])
└──      return %2

Though in this particular case, it can also be solved by taking advantage of duck-typing and just writing one method. Thus avoiding the call that confuses the compiler. Thunks duck-type as the type they wrap in most cases: including broadcast multiplication.

function ChainRulesCore.rrule(::typeof(double_it), x)
    double_it_pullback(ȳ) = (NoTangent(), 2 .* ȳ)
    return double_it(x), double_it_pullback

This infers perfectly.

Write tests

ChainRulesTestUtils.jl provides tools for writing tests based on FiniteDifferences.jl. Take a look at the documentation or the existing ChainRules.jl tests to see how to write the tests.


Don't use analytical derivations for derivatives in the tests. Those are what you use to define the rules, and so cannot be confidently used in the test. If you misread/misunderstood them, then your tests/implementation will have the same mistake. Use finite differencing methods instead, as they are based on the primal computation.

CAS systems are your friends.

It is very easy to check gradients or derivatives with a computer algebra system (CAS) like WolframAlpha.

Which functions need rules?

In principle, a perfect AD system only needs rules for basic operations and can infer the rules for more complicated functions automatically. In practice, performance needs to be considered as well.

Some functions use ccall internally, for example ^. These functions cannot be differentiated through by AD systems, and need custom rules.

Other functions can in principle be differentiated through by an AD system, but there exists a mathematical insight that can dramatically improve the computation of the derivative. An example is numerical integration, where writing a rule implementing the fundamental theorem of calculus removes the need to perform AD through numerical integration.

Furthermore, AD systems make different trade-offs in performance due to their design. This means that a certain rule will help one AD system, but not improve (and also not harm) another. Below, we list some patterns relevant for the Zygote.jl AD system.

Rules for functions which mutate its arguments, e.g. sort!, should not be written at the moment. While technically they are supported, they would break Zygote.jl such that it would sometimes quietly return the wrong answer. This may be resolved in the future by allowing AD systems to opt-in or opt-out of certain types of rules.

Patterns that need rules in Zygote.jl

There are a few classes of functions that Zygote cannot differentiate through. Custom rules will need to be written for these to make AD work.

Other patterns can be AD'ed through, but the backward pass performance can be greatly improved by writing a rule.

Functions which mutate arrays

For example,

function addone!(array)
    array .+= 1
    return sum(array)

complains that

julia> using Zygote
julia> gradient(addone!, a)
ERROR: Mutating arrays is not supported

However, upon adding the rrule (restart the REPL after calling gradient)

function ChainRules.rrule(::typeof(addone!), a)
    y = addone!(a)
    function addone!_pullback(ȳ)
        return NoTangent(), ones(length(a))
    return y, addone!_pullback

the gradient can be evaluated:

julia> gradient(addone!, a)
([1.0, 1.0, 1.0],)
Why restarting REPL after calling `gradient`?

When gradient is called in Zygote for a function with no rrule defined, a backward pass for the function call is generated and cached. When gradient is called for the second time on the same function signature, the backward pass is reused without checking whether an an rrule has been defined between the two calls to gradient.

If an rrule is defined before the first call to gradient it should register the rule and use it, but that prevents comparing what happens before and after the rrule is defined. To compare both versions with and without an rrule in the REPL simultaneously, define a function f(x) = <body> (no rrule), another function f_cr(x) = f(x), and an rrule for f_cr.

Exception handling

Zygote does not support differentiating through try/catch statements. For example, differentiating through

function exception(x)
        return x^2
    catch e
        println("could not square input")

does not work

julia> gradient(exception, 3.0)
ERROR: Compiling Tuple{typeof(exception),Int64}: try/catch is not supported.

without an rrule defined (restart the REPL after calling gradient)

function ChainRulesCore.rrule(::typeof(exception), x)
    y = exception(x)
    function exception_pullback(ȳ)
        return NoTangent(), 2*x
    return y, exception_pullback
julia> gradient(exception, 3.0)


Julia runs loops fast. Unfortunately Zygote differentiates through loops slowly. So, for example, computing the mean squared error by using a loop

function mse(y, ŷ)
    N = length(y)
    s = 0.0
    for i in 1:N
        s +=  (y[i] - ŷ[i])^2.0
    return s/N

takes a lot longer to AD through

julia> y = rand(30)
julia> ŷ = rand(30)
julia> @btime gradient(mse, $y, $ŷ)
  38.180 μs (993 allocations: 65.00 KiB)

than if we supply an rrule, (restart the REPL after calling gradient)

function ChainRules.rrule(::typeof(mse), x, x̂)
    output = mse(x, x̂)
    function mse_pullback(ȳ)
        N = length(x)
        g = (2 ./ N) .* (x .- x̂) .* ȳ
        return NoTangent(), g, -g
    return output, mse_pullback

which is much faster

julia> @btime gradient(mse, $y, $ŷ)
  143.697 ns (2 allocations: 672 bytes)

Inplace accumulation

Inplace accumulation of gradients is slow in Zygote. The issue, demonstrated in the folowing example, is that the gradient of getindex allocates an array of zeros with a single non-zero element.

function sum3(array)
    x = array[1]
    y = array[2]
    z = array[3]
    return x+y+z
julia> @btime gradient(sum3, rand(30))
  424.510 ns (9 allocations: 2.06 KiB)

Computing the gradient with only a single array allocation using an rrule (restart the REPL after calling gradient)

function ChainRulesCore.rrule(::typeof(sum3), a)
    y = sum3(a)
    function sum3_pullback(ȳ)
        grad = zeros(length(a))
        grad[1:3] .+= ȳ
        return NoTangent(), grad
    return y, sum3_pullback

turns out to be significantly faster

julia> @btime gradient(sum3, rand(30))
  192.818 ns (3 allocations: 784 bytes)