# The propagators: pushforward and pullback

*Pushforward* and *pullback* are fancy words that the autodiff community recently adopted from Differential Geometry. They are broadly in agreement with the use of pullback and pushforward in differential geometry. But any geometer will tell you these are the super-boring flat cases. Some will also frown at you. They are also sometimes described in terms of the jacobian: The *pushforward* is *jacobian vector product* (`jvp`

), and *pullback* is *jacobian transpose vector product* (`j'vp`

). Other terms that may be used include for *pullback* the **backpropagator**, and by analogy for *pushforward* the **forwardpropagator**, thus these are the *propagators*. These are also good names because effectively they propagate wiggles and wobbles through them, via the chain rule. (the term **backpropagator** may originate with "Lambda The Ultimate Backpropagator" by Pearlmutter and Siskind, 2008)

### Core Idea

#### Less formally

- The
**pushforward**takes a wiggle in the*input space*, and tells what wobble you would create in the output space, by passing it through the function. - The
**pullback**takes wobbliness information with respect to the function's output, and tells the equivalent wobbliness with respect to the functions input.

#### More formally

The **pushforward** of $f$ takes the *sensitivity* of the input of $f$ to a quantity, and gives the *sensitivity* of the output of $f$ to that quantity.

The **pullback** of $f$ takes the *sensitivity* of a quantity to the output of $f$, and gives the *sensitivity* of that quantity to the input of $f$.

### Math

This is all a bit simplified by talking in 1D.

#### Lighter Math

For a chain of expressions:

```
a = f(x)
b = g(a)
c = h(b)
```

The pullback of `g`

, which incorporates the knowledge of `∂b/∂a`

, applies the chain rule to go from `∂c/∂b`

to `∂c/∂a`

.

The pushforward of `g`

, which also incorporates the knowledge of `∂b/∂a`

, applies the chain rule to go from `∂a/∂x`

to `∂b/∂x`

.

#### Geometric interpretation of reverse and forwards mode AD

Let us think of our types geometrically. In other words, elements of a type form a *manifold*. This document will explain this point of view in some detail.

##### Some terminology/conventions

Let $p$ be an element of type $M$, which is defined by some assignment of numbers $x_1, \dots, x_m$, say $(x_1, \dots, x_m) = (a_1, \dots, a_m)$

A *function* $f:M \to K$ on $M$ is (for simplicity) a polynomial $K[x_1, \dots, x_m]$

The tangent space $T_pM$ of $M$ at point $p$ is the $K$-vector space spanned by derivations $d/dx$. The tangent space acts linearly on the space of functions. They act as usual on functions. Our starting point is that we know how to write down $d/dx(f) = df/dx$.

The collection of tangent spaces ${T_pM}$ for $p\in M$ is called the *tangent bundle* of $M$.

Let $df$ denote the first order information of $f$ at each point. This is called the differential of $f$. If the derivatives of $f$ and $g$ agree at $p$, we say that $df$ and $dg$ represent the same cotangent at $p$. The covectors $dx_1, \dots, dx_m$ form the basis of the cotangent space $T^*_pM$ at $p$. Notice that this vector space is dual to $T_pM$.

The collection of cotangent spaces ${T^*_pM}$ for $p\in M$ is called the *cotangent bundle* of $M$.

##### Push-forwards and pullbacks

Let $N$ be another type, defined by numbers $y_1,...,y_n$, and let $g:M \to N$ be a *map*, that is, an $n$-dimensional vector $(g_1, ..., g_m)$ of functions on $M$.

We define the *push-forward* $g_*:TM \to TN$ between tangent bundles by $g_*(X)(h) = X(g\circ h)$ for any tangent vector $X$ and function $f$. We have $g_*(d/dx_i)(y_j) = dg_j/dx_i$, so the push-forward corresponds to the Jacobian, given a chosen basis.

Similarly, the pullback of the differential $df$ is defined by $g^*(df) = d(f\circ g)$. So for a coordinate differential $dy_j$, we have $g^*(dy_j) = d(g_j)$. Notice that this is a covector, and we could have defined the pullback by its action on vectors by $g^*(dh)(X) = g_*(X)(dh) = X(g\circ h)$ for any function $f$ on $N$ and $X\in TM$. In particular, $g^*(dy_j)(d/dx_i) = d(g_j)/dx_i$. If you work out the action in a basis of the cotangent space, you see that it acts by the adjoint of the Jacobian.

Notice that the pullback of a differential and the pushforward of a vector have a very different meaning, and this should be reflected on how they are used in code.

The information contained in the push-forward map is exactly *what does my function do to tangent vectors*. Pullbacks, acting on differentials of functions, act by taking the total derivative of a function. This works in a coordinate invariant way, and works without the notion of a metric. *Gradients* recall are vectors, yet they should contain the same information of the differential $df$. Assuming we use the standard euclidean metric, we can identify $df$ and $\nabla f$ as vectors. But pulling back gradients still should not be a thing.

If the goal is to evaluate the gradient of a function $f=g\circ h:M \to N \to K$, where $g$ is a map and $h$ is a function, we have two obvious options: First, we may push-forward a basis of $M$ to $TK$ which we identify with K itself. This results in $m$ scalars, representing components of the gradient. Step-by-step in coordinates:

- Compute the push-forward of the basis of $T_pM$, i.e. just the columns of the Jacobian $dg_i/dx_j$.
- Compute the push-forward of the function $h$ (consider it as a map, K is also a manifold!) to get $h_*(g_*T_pM) = \sum_j dh/dy_i (dg_i/dx_j)$

Second, we pull back the differential $dh$:

- compute $dh = dh/dy_1,...,dh/dy_n$ in coordinates.
- pull back by (in coordinates) multiplying with the adjoint of the Jacobian, resulting in $g_*(dh) = \sum_i(dg_i/dx_j)(dh/dy_i)$.

### The anatomy of pullback and pushforward

For our function `foo(args...; kwargs...) = y`

:

```
function pullback(Δy)
...
return ∂self, ∂args...
end
```

The input to the pullback is often called the *seed*. If the function is `y = f(x)`

often the pullback will be written `s̄elf, x̄ = pullback(ȳ)`

.

The pullback returns one `∂arg`

per `arg`

to the original function, plus one `∂self`

for the fields of the function itself (explained below).

Sometimes *perturbation*, *seed*, and even *sensitivity* will be used interchangeably. They are not generally synonymous, and ChainRules shouldn't mix them up. One must be careful when reading literature. At the end of the day, they are all *wiggles* or *wobbles*.

The pushforward is a part of the `frule`

function. Considered alone it would look like:

```
function pushforward(Δself, Δargs...)
...
return ∂y
end
```

But because it is fused into frule we see it as part of:

```
function frule((Δself, Δargs...), ::typeof(foo), args...; kwargs...)
...
return y, ∂y
end
```

The input to the pushforward is often called the *perturbation*. If the function is `y = f(x)`

often the pushforward will be written `ẏ = last(frule((ṡelf, ẋ), f, x))`

. `ẏ`

is commonly used to represent the perturbation for `y`

.

In the `frule`

/pushforward, there is one `Δarg`

per `arg`

to the original function. The `Δargs`

are similar in type/structure to the corresponding inputs `args`

(`Δself`

is explained below). The `∂y`

are similar in type/structure to the original function's output `Y`

. In particular if that function returned a tuple then `∂y`

will be a tuple of the same size.

### Self derivative `Δself`

, `∂self`

, `s̄elf`

, `ṡelf`

etc

It is the derivatives with respect to the internal fields of the function. To the best of our knowledge there is no standard terminology for this. Other good names might be `Δinternal`

/`∂internal`

.

From the mathematical perspective, one may have been wondering what all this `Δself`

, `∂self`

is. Given that a function with two inputs, say `f(a, b)`

, only has two partial derivatives: $\dfrac{∂f}{∂a}$, $\dfrac{∂f}{∂b}$. Why then does a `pushforward`

take in this extra `Δself`

, and why does a `pullback`

return this extra `∂self`

?

The reason is that in Julia the function `f`

may itself have internal fields. For example a closure has the fields it closes over; a callable object (i.e. a functor) like a `Flux.Dense`

has the fields of that object.

**Thus every function is treated as having the extra implicit argument self, which captures those fields.** So every

`pushforward`

takes in an extra argument, which is ignored unless the original function has fields. It is common to write `function foo_pushforward(_, Δargs...)`

in the case when `foo`

does not have fields. Similarly every `pullback`

returns an extra `∂self`

, which for things without fields is `NoTangent()`

, indicating there are no fields within the function itself.Here's an example showing how to define `∂self`

in an `rrule`

when the primal function has internal fields (implicit arguments):

```
struct Multiplier{T}
x::T
end
(m::Multiplier)(y) = m.x * y
function ChainRulesCore.rrule(m::Multiplier, y)
product = m(y)
function pullback(Δproduct)
∂self = Tangent{Multiplier}(; x = Δproduct * y')
∂y = m.x' * Δproduct
return ∂self, ∂y
end
return product, pullback
end
```

### Pushforward / Pullback summary

**Pullback**- returned by
`rrule`

- takes output space wobbles, gives input space wiggles
- Argument structure matches structure of primal function output
- If primal function returns a tuple, then pullback takes in a tuple of differentials.
- 1 return per original function argument + 1 for the function itself

- returned by
**Pushforward:**- part of
`frule`

- takes input space wiggles, gives output space wobbles
- Argument structure matches primal function argument structure, but passed as a tuple at start of
`frule`

- 1 argument per original function argument + 1 for the function itself
- 1 return per original function return

- part of

### Pullback/Pushforward and Directional Derivative/Gradient

The most trivial use of the `pushforward`

from within `frule`

is to calculate the directional derivative:

If we would like to know the directional derivative of `f`

for an input change of `(1.5, 0.4, -1)`

```
direction = (1.5, 0.4, -1) # (ȧ, ḃ, ċ)
y, ẏ = frule((ZeroTangent(), direction...), f, a, b, c)
```

On the basis directions one gets the partial derivatives of `y`

:

```
y, ∂y_∂a = frule((ZeroTangent(), 1, 0, 0), f, a, b, c)
y, ∂y_∂b = frule((ZeroTangent(), 0, 1, 0), f, a, b, c)
y, ∂y_∂c = frule((ZeroTangent(), 0, 0, 1), f, a, b, c)
```

Similarly, the most trivial use of `rrule`

and returned `pullback`

is to calculate the gradient:

```
y, f_pullback = rrule(f, a, b, c)
∇f = f_pullback(1) # for appropriate `1`-like seed.
s̄elf, ā, b̄, c̄ = ∇f
```

Then we have that `∇f`

is the *gradient* of `f`

at `(a, b, c)`

. And we thus have the partial derivatives $\overline{\mathrm{self}} = \dfrac{∂f}{∂\mathrm{self}}$, $\overline{a} = \dfrac{∂f}{∂a}$, $\overline{b} = \dfrac{∂f}{∂b}$, $\overline{c} = \dfrac{∂f}{∂c}$, including the self-partial derivative, $\overline{\mathrm{self}}$.