Monads

Building on Functors and Applicatives we can now introduce monads.

A monad is another type of abstract, functional structure. Let's explore what makes it different from the first two structures.

What is a Monad?

A monad is a computational context. It provides a structure that allows you to chain together operations that have some kind of shared state or similar effect. Whereas pure functional code can only operate on explicit input parameters and affect the program through explicit return values, operations in a monad can affect other computations in the chain implicitly through side effects, especially modification of an implicitly shared value.

How are monads represented in Lean?

Like functors and applicatives, monads are represented with a type class in Lean:

class Monad (m : Type u → Type v) extends Applicative m, Bind m where

Just as every applicative is a functor, every monad is also an applicative and there's one more new base type class used here that you need to understand, namely, Bind.

class Bind (f : Type u → Type v) where
  bind : {α β : Type u} → f α → (α → f β) → f β

The bind operator also has infix notation >>= where x >>= g represents the result of executing x to get a value of type f α then unwrapping the value α from that and passing it to function g of type α → f β returning the result of type f β where f is the target structure type (like Option or List)

This bind operation looks similar to the other ones you've seen so far, if you put them all together Monad has the following operations:

class Monad (f : Type u → Type v) extends Applicative f, Bind f where
  pure {α : Type u} : α → f α
  map : {α β : Type u} → (α → β) → f α → f β
  seq : {α β : Type u} → f (α → β) → (Unit → f α) → f β
  bind : {α β : Type u} → f α → (α → f β) → f β
  ...

Notice Monad also contains pure it must also have a "default" way to wrap a value in the structure.

The bind operator is similar to the applicative seq operator in that it chains two operations, with one of them being function related. Notice that bind, seq and map all take a function of some kind. Let's examine those function types:

• map: (α → β)
• seq: f (α → β)
• bind: (α → f β)

So map is a pure function, seq is a pure function wrapped in the structure, and bind takes a pure input but produces an output wrapped in the structure.

Note: we are ignoring the (Unit → f α) function used by seq here since that has a special purpose explained in Applicatives Lazy Evaluation.

Basic Monad Example

Just as Option is a functor and an applicative functor, it is also a monad! Let's start with how Option implements the Monad type class.

instance: Monad Option
instance : 
Monad: (Type u_1 → Type u_1) → Type (u_1 + 1)
Monad 
Option: Type u_1 → Type u_1
Option where
  pure := 
Option.some: {α : Type u_1} → α → Option α
Option.some
  bind := 
Option.bind: {α β : Type u_1} → Option α → (α → Option β) → Option β
Option.bind

where:

def Option.bind : Option α → (α → Option β) → Option β
  | none,   _ => none
  | some a, f => f a

Side note: this function definition is using a special shorthand syntax in Lean where the := match a, b with code can be collapsed away. To make this more clear consider the following simpler example, where Option.bind is using the second form like bar:

def 
foo: Option Nat → Nat → Option Nat
foo (
x: Option Nat
x : 
Option: Type → Type
Option 
Nat: Type
Nat) (
y: Nat
y : 
Nat: Type
Nat) : 
Option: Type → Type
Option 
Nat: Type
Nat :=
  match 
x: Option Nat
x, 
y: Nat
y with
  | 
none: {α : Type ?u.143} → Option α
none, _ => 
none: {α : Type} → Option α
none
  | 
some: {α : Type ?u.161} → α → Option α
some 
x: Nat
x, 
y: Nat
y => 
some: {α : Type} → α → Option α
some (
x: Nat
x + 
y: Nat
y)

def 
bar: Option Nat → Nat → Option Nat
bar : 
Option: Type → Type
Option 
Nat: Type
Nat → 
Nat: Type
Nat → 
Option: Type → Type
Option 
Nat: Type
Nat
  | 
none: {α : Type ?u.309} → Option α
none, _ => 
none: {α : Type} → Option α
none
  | 
some: {α : Type ?u.327} → α → Option α
some 
x: Nat
x, 
y: Nat
y => 
some: {α : Type} → α → Option α
some (
x: Nat
x + 
y: Nat
y)

#eval
some 3 
foo: Option Nat → Nat → Option Nat
foo (
some: {α : Type} → α → Option α
some 
1: Nat
1) 
2: Nat
2  -- some 3
#eval
some 3 
bar: Option Nat → Nat → Option Nat
bar (
some: {α : Type} → α → Option α
some 
1: Nat
1) 
2: Nat
2  -- some 3

What is important is that Option.bind is using a match statement to unwrap the input value Option α, if it is none then it does nothing and returns none, if it has a value of type α then it applies the function in the second argument (α → Option β) to this value, which is the expression f a that you see in the line | some a, f => f a above. The function returns a result of type Option β which then becomes the return value for bind. So there is no structure wrapping required on the return value since the input function already did that.

But let's bring in the definition of a monad. What does it mean to describe Option as a computational context?

The Option monad encapsulates the context of failure. Essentially, the Option monad lets us abort a series of operations whenever one of them fails. This allows future operations to assume that all previous operations have succeeded. Here's some code to motivate this idea:

def 
optionFunc1: String → Option Nat
optionFunc1 : 
String: Type
String -> 
Option: Type → Type
Option 
Nat: Type
Nat
  | 
"": String
"" => 
none: {α : Type} → Option α
none
  | 
str: String
str => 
some: {α : Type} → α → Option α
some 
str: String
str.
length: String → Nat
length

def 
optionFunc2: Nat → Option Float
optionFunc2 (
i: Nat
i : 
Nat: Type
Nat) : 
Option: Type → Type
Option 
Float: Type
Float :=
  if 
i: Nat
i % 
2: Nat
2 == 
0: Nat
0 then 
none: {α : Type} → Option α
none else 
some: {α : Type} → α → Option α
some (
i: Nat
i.
toFloat: Nat → Float
toFloat * 
3.14159: Float
3.14159)

def 
optionFunc3: Float → Option (List Nat)
optionFunc3 (
f: Float
f : 
Float: Type
Float) : 
Option: Type → Type
Option (
List: Type → Type
List 
Nat: Type
Nat) :=
  if 
f: Float
f > 
15.0: Float
15.0 then 
none: {α : Type} → Option α
none else 
some: {α : Type} → α → Option α
some [
f: Float
f.
floor: Float → Float
floor.
toUInt32: Float → UInt32
toUInt32.
toNat: UInt32 → Nat
toNat, 
f: Float
f.
ceil: Float → Float
ceil.
toUInt32: Float → UInt32
toUInt32.
toNat: UInt32 → Nat
toNat]

def 
runOptionFuncs: String → Option (List Nat)
runOptionFuncs (
input: String
input : 
String: Type
String) : 
Option: Type → Type
Option (
List: Type → Type
List 
Nat: Type
Nat) :=
  match 
optionFunc1: String → Option Nat
optionFunc1 
input: String
input with
  | 
none: {α : Type ?u.1335} → Option α
none => 
none: {α : Type} → Option α
none
  | 
some: {α : Type ?u.1346} → α → Option α
some 
i: Nat
i => match 
optionFunc2: Nat → Option Float
optionFunc2 
i: Nat
i with
    | 
none: {α : Type ?u.1362} → Option α
none => 
none: {α : Type} → Option α
none
    | 
some: {α : Type ?u.1372} → α → Option α
some 
f: Float
f => 
optionFunc3: Float → Option (List Nat)
optionFunc3 
f: Float
f

#eval
some [9, 10] 
runOptionFuncs: String → Option (List Nat)
runOptionFuncs 
"big": String
"big" -- some [9, 10]

Here you see three different functions that could fail. These are then combined in runOptionFuncs. But then you have to use nested match expressions to check if the previous result succeeded. It would be very tedious to continue this pattern much longer.

The Option monad helps you fix this. Here's what this function looks like using the bind operator.

def 
runOptionFuncsBind: String → Option (List Nat)
runOptionFuncsBind (
input: String
input : 
String: Type
String) : 
Option: Type → Type
Option (
List: Type → Type
List 
Nat: Type
Nat) :=
  
optionFunc1: String → Option Nat
optionFunc1 
input: String
input >>= 
optionFunc2: Nat → Option Float
optionFunc2 >>= 
optionFunc3: Float → Option (List Nat)
optionFunc3

#eval
some [9, 10] 
runOptionFuncsBind: String → Option (List Nat)
runOptionFuncsBind 
"big": String
"big" -- some [9, 10]

It's much cleaner now! You take the first result and pass it into the second and third functions using the bind operation. The monad instance handles all the failure cases so you don't have to!

Let's see why the types work out. The result of optionFunc1 input is simply Option Nat. Then the bind operator allows you to take this Option Nat value and combine it with optionFunc2, whose type is Nat → Option Float The bind operator resolves these to an Option Float. Then you pass this similarly through the bind operator to optionFunc3, resulting in the final type, Option (List Nat).

Your functions will not always combine so cleanly though. This is where do notation comes into play. This notation allows you to write monadic operations one after another, line-by-line. It almost makes your code look like imperative programming. You can rewrite the above as:

def 
runOptionFuncsDo: String → Option (List Nat)
runOptionFuncsDo (
input: String
input : 
String: Type
String) : 
Option: Type → Type
Option (
List: Type → Type
List 
Nat: Type
Nat) := do
  let 
i: Nat
i ← 
optionFunc1: String → Option Nat
optionFunc1 
input: String
input
  let 
f: Float
f ← 
optionFunc2: Nat → Option Float
optionFunc2 
i: Nat
i
  
optionFunc3: Float → Option (List Nat)
optionFunc3 
f: Float
f

#eval
some [9, 10] 
runOptionFuncsDo: String → Option (List Nat)
runOptionFuncsDo 
"big": String
"big" -- some [9, 10]

The ← operator used here is special. It effectively unwraps the value on the right-hand side from the monad. This means the value i has type Nat, even though the result of optionFunc1 is Option Nat. This is done using a bind operation under the hood.

Note you can use <- or the nice unicode symbol ← which you can type into VS code by typing these characters \l . When you type the final space, \l is replaced with ←.

Observe that we do not unwrap the final line of the computation. The function result is Option (List Nat) which matches what optionFunc3 returns. At first glance, this may look more complicated than the bind example. However, it gives you a lot more flexibility, like mixing monadic and non-monadic statements, using if then/else structures with their own local do blocks and so on. It is particularly helpful when one monadic function depends on multiple previous functions.

Example using List

You can easily make List into a monad with the following, since List already provides an implementation of pure and bind.

instance: Monad List
instance : 
Monad: (Type u_1 → Type u_1) → Type (u_1 + 1)
Monad 
List: Type u_1 → Type u_1
List  where
  pure := 
List.singleton: {α : Type u_1} → α → List α
List.singleton
  bind := 
List.flatMap: {α β : Type u_1} → List α → (α → List β) → List β
List.flatMap

Like you saw with the applicative seq operator, the bind operator applies the given function to every element of the list. It is useful to look at the bind implementation for List:

open List
def 
bind: {α : Type u_1} → {β : Type u_2} → List α → (α → List β) → List β
bind (
a: List α
a : 
List: Type u_1 → Type u_1
List 
α: Type u_1
α) (
b: α → List β
b : 
α: Type u_1
α → 
List: Type u_2 → Type u_2
List 
β: Type u_2
β) : 
List: Type u_2 → Type u_2
List 
β: Type u_2
β := 
join: {α : Type u_2} → List (List α) → List α
join
Warning: `List.join` has been deprecated, use `List.flatten` instead (
map: {α : Type u_1} → {β : Type u_2} → (α → β) → List α → List β
map 
b: α → List β
b 
a: List α
a)

So Functor.map is used to apply the function b to every element of a but this would return a whole bunch of little lists, so join is used to turn those back into a single list.

Here's an example where you use bind to convert a list of strings into a combined list of chars:

#eval
['a', 'p', 'p', 'l', 'e'] 
"apple": String
"apple".
toList: String → List Char
toList  -- ['a', 'p', 'p', 'l', 'e']

#eval
['a', 'p', 'p', 'l', 'e', 'o', 'r', 'a', 'n', 'g', 'e'] [
"apple": String
"apple", 
"orange": String
"orange"] >>= 
String.toList: String → List Char
String.toList
-- ['a', 'p', 'p', 'l', 'e', 'o', 'r', 'a', 'n', 'g', 'e']

The IO Monad

The IO Monad is perhaps the most important monad in Lean. It is also one of the hardest monads to understand starting out. Its actual implementation is too intricate to discuss when first learning monads. So it is best to learn by example.

What is the computational context that describes the IO monad? IO operations can read information from or write information to the terminal, file system, operating system, and/or network. They interact with systems outside of your program. If you want to get user input, print a message to the user, read information from a file, or make a network call, you'll need to do so within the IO Monad.

The state of the world outside your program can change at virtually any moment, and so this IO context is particularly special. So these IO operations are "side effects" which means you cannot perform them from "pure" Lean functions.

Now, the most important job of pretty much any computer program is precisely to perform this interaction with the outside world. For this reason, the root of all executable Lean code is a function called main, with the type IO Unit. So every program starts in the IO monad!

When your function is IO monadic, you can get any input you need, call into "pure" code with the inputs, and then output the result in some way. The reverse does not work. You cannot call into IO code from pure code like you can call into a function that takes Option as input. Another way to say this is you cannot invent an IO context out of thin air, it has to be given to you in your main function.

Let's look at a simple program showing a few of the basic IO functions. It also uses do notation to make the code read nicely:

def 
main: IO Unit
main : 
IO: Type → Type
IO 
Unit: Type
Unit := do
  
IO.println: {α : Type} → [inst : ToString α] → α → IO Unit
IO.println 
"enter a line of text:": String
"enter a line of text:"
  let 
stdin: IO.FS.Stream
stdin ← 
IO.getStdin: BaseIO IO.FS.Stream
IO.getStdin            -- IO IO.FS.Stream (monadic)
  let 
input: String
input ← 
stdin: IO.FS.Stream
stdin.
getLine: IO.FS.Stream → IO String
getLine          -- IO.FS.Stream → IO String (monadic)
  let 
uppercased: String
uppercased := 
input: String
input.
toUpper: String → String
toUpper    -- String → String (pure)
  
IO.println: {α : Type} → [inst : ToString α] → α → IO Unit
IO.println 
uppercased: String
uppercased              -- IO Unit (monadic)

So, once again you can see that the do notation lets you chain a series of monadic actions. IO.getStdin is of type IO IO.FS.Stream and stdin.getLine is of type IO String and IO.println is of type IO Unit.

In between you see a non-monadic expression let uppercased := input.toUpper which is fine too. A let statement can occur in any monad. Just as you could unwrap i from Option Nat to get the inner Nat, you can use ← to unwrap the result of getLine to get a String. You can then manipulate this value using normal pure string functions like toUpper, and then you can pass the result to the IO.println function.

This is a simple echo program. It reads a line from the terminal, and then prints the line back out capitalized to the terminal. Hopefully it gives you a basic understanding of how IO works.

You can test this program using lean --run as follows:

> lean --run Main.lean
enter a line of text:
the quick brown fox
THE QUICK BROWN FOX

Here the user entered the string the quick brown fox and got back the uppercase result.

What separates Monads from Applicatives?

The key that separates these is context. You cannot really determine the structure of "future" operations without knowing the results of "past" operations, because the past can alter the context in which the future operations work. With applicatives, you can't get the final function result without evaluating everything, but you can determine the structure of how the operation will take place. This allows some degree of parallelism with applicatives that is not generally possible with monads.

Conclusion

Hopefully you now have a basic level understanding of what a monad is. But perhaps some more examples of what a "computational context" means would be useful to you. The Reader, State and Except monads each provide a concrete and easily understood context that can be compared easily to function parameters. You can learn more about those in Reader monads, State monads, and the Except monad.