# Automasymbolic Differentiation

Automatic differentiation is one of those things that’s famous for not being as famous as it should be (uh..). It’s useful, it’s convenient, and yet fewer know about it than one would think.

This article (by one of the guys working on Venture) is the single best introduction you’ll probably find to AD, anywhere. It gives a wonderful introduction to the basics, the subtleties, and the gotchas of the subject. You should read it. I’m going to assume you have.

In particular, you should note this part:

[computing gradients] can be done — the method is called reverse mode — but it introduces both code complexity and runtime cost in the form of managing this storage (traditionally called the “tape”). In particular, the space requirements of raw reverse mode are proportional to the runtime of f.

In some applications this can be somewhat inconvenient - picture iterative gradient-based sampling algorithms like Hamiltonian Monte Carlo, its famous auto-tuning version NUTS, and Riemannian manifold variants. Tom noted in this reddit thread that symbolic differentiation - which doesn’t need to deal with tapes and infinitesimal-tracking - can often be orders of magnitude faster than AD for this kind of problem. When running these algos we calculate gradients many, many times, and the additional runtime cost of the reverse-mode AD dance can add up.

An interesting question is whether or not this can this be mitigated at all, and to what degree. In particular: can we use automatic differentiation to implement efficient symbolic differentiation? Here’s a possible attack plan:

• use an existing automatic differentiation implementation to calculate the gradient for some target function at a point
• capture the symbolic expression for the gradient and optimize it by eliminating common subexpressions or whatnot
• reify that expression as a function that we can evaluate for any input

Ed Kmett’s ‘ad’ library is the best automatic differentiation library I know of, in terms of its balance between power and accessibility. It can be used on arbitrary Haskell types that are instances of the Num typeclass and can carry out automatic differentiation via a number of modes, so it’s very easy to get started with. Rather than rolling my own AD implementation to try to do this sort of thing, I’d much rather use his.

Start with a really basic language. In practice we’d be interested in working with more expressive things, but this is sort of the minimal interesting language capable of illustrating the problem:

import Numeric.AD

data Expr a =
Lit a
| Var String
| Add (Expr a) (Expr a)
| Sub (Expr a) (Expr a)
| Mul (Expr a) (Expr a)
deriving (Eq, Show)

instance Num a => Num (Expr a) where
fromInteger = Lit . fromInteger
e0 + e1 = Add e0 e1
e0 - e1 = Sub e0 e1
e0 * e1 = Mul e0 e1


We can already use Kmett’s ad library on these expressions to generate symbolic expressions. We just have to write functions we’re interested in generically (using +, -, and *) and then call diff or grad or whatnot on them with a concretely-typed argument. Some examples:

> :t diff (\x -> 2 * x ^ 2)
diff (\x -> 2 * x ^ 2) :: Num a => a -> a

> diff (\x -> 2 * x ^ 2) (Lit 1)
Mul (Add (Mul (Lit 1) (Lit 1)) (Mul (Lit 1) (Lit 1))) (Lit 2)

> grad (\[x, y] -> 2 * x ^ 2 + 3 * y) [Lit 1, Lit 2]
(Lit 0) (Mul (Lit 1) (Lit 1))))))) (Mul (Lit 1) (Add (Lit 0) (Mul (Lit 2) (Add
(Lit 0) (Mul (Lit 1) (Lit 1)))))))
, Add (Lit 0) (Add (Lit 0) (Mul (Lit 3) (Add (Lit 0) (Mul (Lit 1) (Lit 1)))))
]


It’s really easy to extract a proper derivative/gradient ‘function’ by doing something like this:

> diff (\x -> 2 * x ^ 2) (Var "x")
Mul (Add (Mul (Var "x") (Lit 1)) (Mul (Lit 1) (Var "x"))) (Lit 2)


and then, given that expression, reifying a direct function for the derivative by substituting over the captured variable and evaluating everything. Here’s some initial machinery to handle that:

-- | Close an expression over some variable.
close :: Expr a -> String -> a -> Expr a
close (Add e0 e1) s x = Add (close e0 s x) (close e1 s x)
close (Sub e0 e1) s x = Sub (close e0 s x) (close e1 s x)
close (Mul e0 e1) s x = Mul (close e0 s x) (close e1 s x)

close (Var v) s x
| v == s    = Lit x
| otherwise = Var v

close e _ _ = e

-- | Evaluate a closed expression.
eval :: Num a => Expr a -> a
eval (Lit d) = d
eval (Var _) = error "expression not closed"
eval (Add e0 e1) = eval e0 + eval e1
eval (Sub e0 e1) = eval e0 - eval e1
eval (Mul e0 e1) = eval e0 * eval e1


So, using this on the example above yields

> let testExpr = diff (\x -> 2 * x ^ 2) (Var "x")
> eval . close testExpr "x" $1 2  and it looks like a basic toDerivative function for expressions could be implemented as follows: -- | Do some roundabout AD. toDerivative expr = eval . close diffExpr "x" where diffExpr = diff expr (Var "x")  But that’s a no go. ‘ad’ throws a type error, as presumably we’d be at risk of perturbation confusion: Couldn't match expected type ‘AD s (Numeric.AD.Internal.Forward.Forward (Expr c)) -> AD s (Numeric.AD.Internal.Forward.Forward (Expr c))’ with actual type ‘t’ because type variable ‘s’ would escape its scope This (rigid, skolem) type variable is bound by a type expected by the context: AD s (Numeric.AD.Internal.Forward.Forward (Expr c)) -> AD s (Numeric.AD.Internal.Forward.Forward (Expr c)) at ParamBasic.hs:174:14-32 Relevant bindings include diffExpr :: Expr c (bound at ParamBasic.hs:174:3) expr :: t (bound at ParamBasic.hs:173:14) toDerivative :: t -> c -> c (bound at ParamBasic.hs:173:1) In the first argument of ‘diff’, namely ‘expr’ In the expression: diff expr (Var "x")  Instead, we can use ad’s auto combinator to write an alternate eval function: autoEval :: Mode a => String -> Expr (Scalar a) -> a -> a autoEval x expr = (go expr) where go _ (Lit d) = auto d go v (Var s) | s == x = v | otherwise = error "expression not closed" go v (Add e0 e1) = go v e0 + go v e1 go v (Sub e0 e1) = go v e0 - go v e1 go v (Mul e0 e1) = go v e0 * go v e1  and using that, implement a working toDerivative: toDerivative :: Num a => String -> Expr (Expr a) -> Expr a toDerivative v expr = diff (autoEval v expr)  which, though it has a weird-looking type, typechecks and does the trick: > let d = toDerivative "x" (Mul (Lit 2) (Mul (Var "x") (Var "x"))) (Var "x") Mul (Add (Mul (Var "x") (Lit 1)) (Mul (Lit 1) (Var "x"))) (Lit 2) > eval . close "x" 1$ d
4


So now we have access to a reified AST for a derivative (or gradient), which can be tweaked and optimized as needed. Cool.

The available optimizations depend heavily on the underlying language. For starters, there’s easy and universal stuff like this:

-- | Reduce superfluous expressions.
elimIdent :: (Num a, Eq a) => Expr a -> Expr a
elimIdent (Add (Lit 0) e) = elimIdent e
elimIdent (Add e (Lit 0)) = elimIdent e

elimIdent (Sub (Lit 0) e) = elimIdent e
elimIdent (Sub e (Lit 0)) = elimIdent e
elimIdent (Sub e0 e1)     = Sub (elimIdent e0) (elimIdent e1)

elimIdent (Mul (Lit 1) e) = elimIdent e
elimIdent (Mul e (Lit 1)) = elimIdent e
elimIdent (Mul e0 e1)     = Mul (elimIdent e0) (elimIdent e1)

elimIdent e = e


Which lets us do some work up-front:

> let e = 2 * Var "x" ^ 2 + Var "x" ^ 4
Add (Mul (Lit 2) (Mul (Var "x") (Var "x"))) (Mul (Mul (Var "x") (Var "x"))
(Mul (Var "x") (Var "x")))

> let ge = toDerivative "x" e
Add (Mul (Add (Mul (Var "x") (Lit 1)) (Mul (Lit 1) (Var "x"))) (Lit 2))
(Add (Mul (Mul (Var "x") (Var "x")) (Add (Mul (Var "x") (Lit 1)) (Mul
(Lit 1) (Var "x")))) (Mul (Add (Mul (Var "x") (Lit 1)) (Mul (Lit 1)
(Var "x"))) (Mul (Var "x") (Var "x"))))

> let geOptim = elimIdent ge
(Var "x")) (Add (Var "x") (Var "x"))) (Mul (Add (Var "x") (Var "x")) (Mul
(Var "x") (Var "x"))))

> eval . close "x" 1 $geOptim 8  But there are also some more involved optimizations that can be useful for some languages. The basic language I’ve been using above, for example, has no explicit support for sharing common subexpressions. You’ll recall from one of my previous posts that we have a variety of methods to do that in Haskell EDSLs, including some that allow sharing to be observed without modifying the underlying language. We can use data-reify, for example, to observe any implicit sharing in expressions: > reifyGraph$ geOptim
(7,MulF 8 9),(9,AddF 4 4),(8,MulF 4 4),(2,MulF 3 5),(5,LitF 2),(3,AddF 4 4),
(4,VarF "x")] in 1


And even make use of a handy library found on Hackage for performing common subexpression elimination on graphs returned by reifyGraph:

> cse <\$> reifyGraph geOptim