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1 (* Chapter 2: Type-Level Computation *)
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2
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3 (* begin hide *)
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4 val show_string = mkShow (fn s => "\"" ^ s ^ "\"")
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5 (* end *)
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6
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7 (* This tutorial by <a href="http://adam.chlipala.net/">Adam Chlipala</a> is licensed under a <a href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 Unported License</a>. *)
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8
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9 (* The last chapter reviewed some Ur features imported from ML and Haskell. This chapter explores uncharted territory, introducing the features that make Ur unique. *)
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10
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11 (* * Names and Records *)
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12
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13 (* Last chapter, we met Ur's basic record features, including record construction and field projection. *)
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14
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15 val r = { A = 0, B = 1.2, C = "hi"}
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16
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17 (* begin eval *)
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18 r.B
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19 (* end *)
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20
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21 (* Our first taste of Ur's novel expressive power is with the following function, which implements record field projection in a completely generic way. *)
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22
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23 fun project [nm :: Name] [t ::: Type] [ts ::: {Type}] [[nm] ~ ts] (r : $([nm = t] ++ ts)) : t = r.nm
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24
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25 (* begin eval *)
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26 project [#B] r
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27 (* end *)
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28
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29 (* This function introduces a slew of essential features. First, we see type parameters with explicit kind annotations. Formal parameter syntax like <tt>[a :: K]</tt> declares an <b>explicit</b> parameter <tt>a</tt> of kind <tt>K</tt>. Explicit parameters must be passed explicitly at call sites. In contrast, implicit parameters, declared like <tt>[a ::: K]</tt>, are inferred in the usual way.<br>
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30 <br>
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31 Two new kinds appear in this example. We met the basic kind <tt>Type</tt> in a previous example. Here we meet <tt>Name</tt>, the kind of record field names; and <tt>{Type}</tt> the type of finite maps from field names to types, where we'll generally refer to this notion of "finite map" by the name <b>record</b>, as it will be clear from context whether we're discussing type-level or value-level records. That is, in this case, we are referring to names and records <b>at the level of types</b> that <b>exist only at compile time</b>! By the way, the kind <tt>{Type}</tt> is one example of the general <tt>{K}</tt> kind form, which refers to records with fields of kind <tt>K</tt>.<br>
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32 <br>
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33 The English description of <tt>project</tt> is that it projects a field with name <tt>nm</tt> and type <tt>t</tt> out of a record <tt>r</tt> whose other fields are described by type-level record <tt>ts</tt>. We make all this formal by assigning <tt>r</tt> a type that first builds the singleton record <tt>[nm = t]</tt> that maps <tt>nm</tt> to <tt>t</tt>, and then concatenating this record with the remaining field information in <tt>ts</tt>. The <tt>$</tt> operator translates a type-level record (of kind <tt>{Type}</tt>) into a record type (of kind <tt>Type</tt>).<br>
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34 <br>
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35 The type annotation on <tt>r</tt> uses the record concatenation operator <tt>++</tt>. Ur enforces that any concatenation happens between records that share no field names. Otherwise, we'd need to resolve field name ambiguity in some predictable way, which would force us to treat <tt>++</tt> as non-commutative, if we are to maintain the nice modularity properties of polymorphism. However, treating <tt>++</tt> as commutative, and treating records as equal up to field permutation in general, are very convenient for type inference and general programmer experience. Thus, we enforce disjointness to keep things simple.<br>
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36 <br>
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37 For a polymorphic function like <tt>project</tt>, the compiler doesn't know which fields a type-level record variable like <tt>ts</tt> contains. To enable self-contained type-checking, we need to declare some constraints about field disjointness. That's exactly the meaning of syntax like <tt>[r1 ~ r2]</tt>, which asserts disjointness of two type-level records. The disjointness clause for <tt>project</tt> asserts that the name <tt>nm</tt> is not used by <tt>ts</tt>. The syntax <tt>[nm]</tt> is shorthand for <tt>[nm = ()]</tt>, which defines a singleton record of kind <tt>{Unit}</tt>, where <tt>Unit</tt> is the degenerate kind inhabited only by the constructor <tt>()</tt>.<br>
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38 <br>
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39 The last piece of this puzzle is the easiest. In the example call to <tt>project</tt>, we see that the only parameters passed are the one explicit constructor parameter <tt>nm</tt> and the value-level parameter <tt>r</tt>. The rest are inferred, and the disjointness proof obligation is discharged automatically. The syntax <tt>#A</tt> denotes the constructor standing for first-class field name <tt>A</tt>, and we pass all constructor parameters to value-level functions within square brackets (which bear no formal relation to the syntax for type-level record literals <tt>[A = c, ..., A = c]</tt>). *)
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40
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41
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42 (* * Basic Type-Level Programming *)
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43
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44 (* To help us express more interesting operations over records, we will need to do some type-level programming. Ur makes that fairly congenial, since Ur's constructor level includes an embedded copy of the simply-typed lambda calculus. Here are a few examples. *)
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45
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46 con id = fn t :: Type => t
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47
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48 val x : id int = 0
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49 val x : id float = 1.2
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50
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51 con pair = fn t :: Type => t * t
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52
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53 val x : pair int = (0, 1)
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54 val x : pair float = (1.2, 2.3)
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55
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56 con compose = fn (f :: Type -> Type) (g :: Type -> Type) (t :: Type) => f (g t)
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57
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58 val x : compose pair pair int = ((0, 1), (2, 3))
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59
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60 con fst = fn t :: (Type * Type) => t.1
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61 con snd = fn t :: (Type * Type) => t.2
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62
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63 con p = (int, float)
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64 val x : fst p = 0
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65 val x : snd p = 1.2
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66
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67 con mp = fn (f :: Type -> Type) (t1 :: Type, t2 :: Type) => (f t1, f t2)
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68
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69 val x : fst (mp pair p) = (1, 2)
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70
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71 (* Actually, Ur's constructor level goes further than merely including a copy of the simply-typed lambda calculus with tuples. We also effectively import classic <b>let-polymorphism</b>, via <b>kind polymorphism</b>, which we can use to make some of the definitions above more generic. *)
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72
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73 con fst = K1 ==> K2 ==> fn t :: (K1 * K2) => t.1
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74 con snd = K1 ==> K2 ==> fn t :: (K1 * K2) => t.2
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75
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76 con twoFuncs :: ((Type -> Type) * (Type -> Type)) = (id, compose pair pair)
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77
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78 val x : fst twoFuncs int = 0
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79 val x : snd twoFuncs int = ((1, 2), (3, 4))
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80
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81
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82 (* * Type-Level Map *)
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83
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84 (* The examples from the same section may seem cute but not especially useful. In this section, we meet <tt>map</tt>, the real workhorse of Ur's type-level computation. We'll use it to type some useful operations over value-level records. A few more pieces will be necessary before getting there, so we'll start just by showing how interesting type-level operations on records may be built from <tt>map</tt>. *)
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85
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86 con r = [A = int, B = float, C = string]
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87
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88 con optionify = map option
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89 val x : $(optionify r) = {A = Some 1, B = None, C = Some "hi"}
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90
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91 con pairify = map pair
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92 val x : $(pairify r) = {A = (1, 2), B = (3.0, 4.0), C = ("5", "6")}
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93
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94 con stringify = map (fn _ => string)
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95 val x : $(stringify r) = {A = "1", B = "2", C = "3"}
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96
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97 (* We'll also give our first hint at the cleverness within Ur's type inference engine. The following definition type-checks, despite the fact that doing so requires applying several algebraic identities about <tt>map</tt> and <tt>++</tt>. This is the first point where we see a clear advantage of Ur over the type-level computation facilities that have become popular in GHC Haskell. *)
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98
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99 fun concat [f :: Type -> Type] [r1 :: {Type}] [r2 :: {Type}] [r1 ~ r2]
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100 (r1 : $(map f r1)) (r2 : $(map f r2)) : $(map f (r1 ++ r2)) = r1 ++ r2
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101
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102
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103 (* * First-Class Polymorphism *)
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104
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105 (* The idea of <b>first-class polymorphism</b> or <b>impredicative polymorphism</b> has also become popular in GHC Haskell. This feature, which has a long history in type theory, is also central to Ur's metaprogramming facilities. First-class polymorphism goes beyond Hindley-Milner's let-polymorphism to allow arguments to functions to themselves be polymorphic. Among other things, this enables the classic example of Church encodings, as for the natural numbers in this example. *)
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106
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107 type nat = t :: Type -> t -> (t -> t) -> t
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108 val zero : nat = fn [t :: Type] (z : t) (s : t -> t) => z
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109 fun succ (n : nat) : nat = fn [t :: Type] (z : t) (s : t -> t) => s (n [t] z s)
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110
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111 val one = succ zero
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112 val two = succ one
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113 val three = succ two
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114
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115 (* begin eval *)
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116 three [int] 0 (plus 1)
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117 (* end *)
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118
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119 (* begin eval *)
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120 three [string] "" (strcat "!")
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121 (* end *)
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122
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123
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124 (* * Folders *)
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125
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126 (* We're almost ready to implement some more polymorphic operations on records. The key missing piece is <b>folders</b>; more specifically, the type family <tt>folder</tt> that allows iteration over the fields of type-level records. The Ur standard library exposes <tt>folder</tt> abstractly, along with the following key operation over it. Don't mind the clutter at the end of this definition as we rebind the function <tt>fold</tt> from the default-open module <tt>Top</tt>, as we must include an explicit kind-polymorphic binder to appease the associated let-polymorphism. *)
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127
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128 val fold : K --> tf :: ({K} -> Type)
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129 -> (nm :: Name -> v :: K -> r :: {K} -> [[nm] ~ r] =>
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130 tf r -> tf ([nm = v] ++ r))
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131 -> tf []
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132 -> r ::: {K} -> folder r -> tf r
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133 = K ==> Top.fold
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134
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135 (* The type is a bit of a mouthful. We can describe the function arguments in order. First, <tt>K</tt> is the kind of data associated with fields in the record we will be iterating over. Next, <tt>tf</tt> describes the type of an accumulator, much as for standard "fold" operations over lists. The difference here is that the accumulator description is not a mere type, but rather a <b>type-level function</b> that returns a type given a properly kinded record. When we begin iterating over a record <tt>r</tt>, the accumulator has type <tt>tf []</tt> (where <tt>[]</tt> is the empty record), and when we finish iterating, the accumulator has type <tt>tf r</tt>. As we step through the fields of the record, we add each one to the argument we keep passing to <tt>tf</tt> to determine accumulator types.<br>
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136 <br>
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137 The next arguments of <tt>fold</tt> are much like for normal list fold functions: a step function and an initial value. The latter has type <tt>tf []</tt>, just as we expect from the explanation in the last paragraph. The final arguments are <tt>r</tt>, the record we fold over; and a <tt>folder</tt> for it. The function return type follows last paragraph's explanation of accmulator typing.<br>
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138 <br>
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139 We've left a big unexplained piece: the type of the step function. In order, its arguments are <tt>nm</tt>, the current field being processed; <tt>v</tt>, the data associated with that field; <tt>r</tt>, the portion of the input record that we had already stepped through before this point; a proof that the name <tt>nm</tt> didn't already occur in <tt>r</tt>; and the accumulator, typed to show that the set of fields we've already visited is exactly <tt>r</tt>. The return type of the step function is another accumulator type, extended to show that now we've visited <tt>nm</tt>, too.<br>
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140 <br>
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141 Here's a simple example usage, where we write a function to count the number of fields in a type-level record of types. *)
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142
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143 fun countFields [ts :: {Type}] (fl : folder ts) : int =
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144 @fold [fn _ => int] (fn [nm ::_] [v ::_] [r ::_] [[nm] ~ r] n => n + 1) 0 fl
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145
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146 (* We preface <tt>fold</tt> with <tt>@</tt>, to disable inference of folders, since we have one we'd like to pass explicitly. The accumulator type family we use is a simple one that ignores its argument and always returns <tt>int</tt>; at every stage of our traversal of input record <tt>ts</tt>, we keep an integer as our sole state, and the type of this state doesn't depend on which record fields we've visited. The step function binds each type parameter with the notation <tt>::_</tt>, for an explicit parameter whose kind should be inferred.<br>
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147 <br>
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148 The function <tt>countFields</tt> is a lot easier to use than it is to define! Here's an example invocation, where we see that the appropriate folder is inferred. *)
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149
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150 (* begin eval *)
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151 countFields [[A = int, B = float, C = string]]
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152 (* end *)
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153
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154 (* If folders are generally inferred, why bother requiring that they be passed around? The answer has to do with Ur's general rule that type-level records are considered equivalent modulo permutation. As a result, there is no unique traversal order for a record, in general. The programmer has freedom in constructing folders that embody different permutations, using the functions exposed from the module <tt>Folder</tt> (see the top of <tt>lib/ur/top.urs</tt> in the Ur/Web distribution). Still, in most cases, the order in which fields are written in the source code provides an unambiguous clue about desired ordering. Thus, by default, folder parameters are implicit, and they are inferred to follow the order of fields in program text.<br>
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155 <br>
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156 Let's implement a more ambitious traversal. We will take in a record whose fields all contain <tt>option</tt> types, and we will determine if every field contains a <tt>Some</tt>. If so, we return <tt>Some</tt> of a "de-optioned" version; if not, we return <tt>None</tt>. *)
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157
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158 fun join [ts ::: {Type}] (fl : folder ts) (r : $(map option ts)) : option $ts =
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159 @fold [fn ts => $(map option ts) -> option $ts]
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160 (fn [nm ::_] [v ::_] [r ::_] [[nm] ~ r] (f : $(map option r) -> option $r) =>
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161 fn r : $(map option ([nm = v] ++ r)) =>
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162 case r.nm of
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163 None => None
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164 | Some v =>
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165 case f (r -- nm) of
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166 None => None
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167 | Some vs => Some ({nm = v} ++ vs))
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168 (fn _ : $(map option []) => Some {}) fl r
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169
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170 (* Rather than take in an arbitrary record type and add some sort of constraint requiring that it contain only <tt>option</tt> types, the Ur way is to <b>construct</b> a record type with computation over some more primitive inputs, such that the process (A) is guaranteed to construct only records satisfying the constraint and (B) is capable, given the proper inputs, of constructing any record satisfying the original constraint.<br>
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171 <br>
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172 This use of folding involves an accumulator type that <i>is</i> record-dependent. In particular, as we traverse the record, we are building up a "de-optioning" function. To implement the step function, we rely on the record projection form <tt>r.nm</tt> and the record field removal form <tt>r -- nm</tt>, both of which work fine with variables standing for unknown field names. To extend the output record with a new mapping for field <tt>nm</tt>, we use concatenation <tt>++</tt> with a singleton record literal.<br>
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173 <br>
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174 Like for the last example, <tt>join</tt> is much easier to use than to implement! The simple invocations below use Ur's <b>reverse-engineering unification</b> to deduce the value of parameter <tt>ts</tt> from the type of parameter <tt>r</tt>. Also, as before, the folder argument is inferred. *)
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175
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176 (* begin hide *)
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177 fun show_option [t] (_ : show t) : show (option t) =
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178 mkShow (fn x => case x of
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179 None => "None"
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180 | Some x => "Some(" ^ show x ^ ")")
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181
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182 val show_r = mkShow (fn r : {A : int, B : string} =>
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183 "{A = " ^ show r.A ^ ", B = " ^ show r.B ^ "}")
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184 (* end *)
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185
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186 (* begin eval *)
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187 join {A = Some 1, B = Some "X"}
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188 (* end *)
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189
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190 (* begin eval *)
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191 join {A = Some 1, B = None : option string}
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192 (* end *)
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193
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194 (* The Ur/Web standard library includes many variations on <tt>fold</tt> that encapsulate common traversal patterns. For instance, <tt>foldR</tt> captures the idea of folding over a value-level record, and we can use it to simplify the definition of <tt>join</tt>: *)
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195
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196 fun join [ts ::: {Type}] (fl : folder ts) (r : $(map option ts)) : option $ts =
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197 @foldR [option] [fn ts => option $ts]
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198 (fn [nm ::_] [v ::_] [r ::_] [[nm] ~ r] v vs =>
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199 case (v, vs) of
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200 (Some v, Some vs) => Some ({nm = v} ++ vs)
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201 | _ => None)
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202 (Some {}) fl r
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203
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204 (* See <tt>lib/ur/top.urs</tt> for the types of <tt>foldR</tt> and some other handy folding functions. *)
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