Annotation of /trunk/src/ast/types.sml

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Revision 75 - (view) (download)

1 :	jhr	40	(* types.sml
2 :			*
3 :			* COPYRIGHT (c) 2010 The Diderot Project (http://diderot.cs.uchicago.edu)
4 :			* All rights reserved.
5 :			*
6 :	jhr	63	* Internal representation of Diderot types. These are the types produced
7 :			* by the type checker.
8 :	jhr	40	*)
9 :
10 :			structure Types =
11 :			struct
12 :
13 :	jhr	74	(* union type for meta variables and meta-variable bindings *)
14 :			datatype ('ty, 'diff, 'shape, 'dim) kind
15 :			= TYPE of 'ty (* ranges over types *)
16 :			| DIFF of 'diff (* ranges of differentiation levels (0, 1, ...) *)
17 :			| SHAPE of 'shape (* ranges over tensor shapes *)
18 :			| DIM of 'dim (* ranges over dimensions (1, 2, ...) *)
19 :	jhr	40
20 :	jhr	73	(*
21 :	jhr	61	(* raw numeric types as supported by NRRD *)
22 :			datatype raw_ty
23 :			= RT_Int8 | RT_UInt8
24 :			| RT_Int16 | RT_UInt16
25 :			| RT_Int32 | RT_UInt32
26 :			| RT_Int64 | RT_UInt64
27 :			| RT_Float | RT_Double
28 :			| RT_Var of var
29 :	jhr	73	*)
30 :	jhr	61
31 :	jhr	73	datatype ty
32 :	jhr	75	= T_Var of ty_var
33 :	jhr	61	| T_Bool
34 :			| T_Int
35 :	jhr	63	| T_String
36 :			(* convolution kernel; argument is number of levels of differentiation *)
37 :	jhr	74	| T_Kernel of diff
38 :	jhr	63	(* scalars, vectors, matrices, etc.; argument is tensor shape *)
39 :			| T_Tensor of shape
40 :	jhr	61	(* data sets from NRRD *)
41 :			| T_Image of {
42 :	jhr	74	dim : dim, (* 2D or 3D data set *)
43 :	jhr	63	shape : shape (* tensor shape; order is length of list *)
44 :	jhr	61	}
45 :			(* continuous field reconstructed from a data set *)
46 :			| T_Field of {
47 :	jhr	74	diff : diff, (* number of levels of differentiation supported *)
48 :			dim : dim, (* dimension of domain (2D or 3D field) *)
49 :	jhr	63	shape : shape (* shape of tensors in range; order is length of list *)
50 :	jhr	61	}
51 :	jhr	63	| T_Fun of ty list * ty list
52 :	jhr	40
53 :	jhr	74	(* type variables; the kind field restricts their range *)
54 :			and ty_var = TV of {
55 :			id : Stamp.stamp,
56 :			bind : ty option ref
57 :			}
58 :
59 :			(* levels of differentiation *)
60 :			and diff
61 :			= DiffConst of int (* i *)
62 :			| DiffVar of (diff_var * int) (* d + i (i >= 0) *)
63 :
64 :			and diff_var = DfV of {
65 :			id : Stamp.stamp,
66 :			bound : int ref, (* lower bound of differentiation *)
67 :	jhr	75	bind : diff option ref (* unification binding *)
68 :	jhr	74	}
69 :
70 :			(* tensor shapes *)
71 :	jhr	63	and shape
72 :	jhr	74	= Shape of dim list
73 :			| ShapeVar of shape_var
74 :	jhr	75	| ShapeExt of shape * dim (* extension of shape (i.e., for D operator) *)
75 :	jhr	63
76 :	jhr	74	and shape_var = SV of {
77 :			id : Stamp.stamp,
78 :	jhr	75	bind : shape option ref (* unification binding *)
79 :	jhr	74	}
80 :	jhr	40
81 :	jhr	74	(* dimensions *)
82 :			and dim
83 :			= DimConst of int (* i *)
84 :			| DimVar of dim_var
85 :
86 :			and dim_var = DV of {
87 :			id : Stamp.stamp,
88 :	jhr	75	bind : dim option ref (* unification binding *)
89 :	jhr	61	}
90 :	jhr	40
91 :	jhr	74	type meta_var = (ty_var, diff_var, shape_var, dim_var) kind
92 :			type var_bind = (ty, diff, shape, dim) kind
93 :	jhr	63
94 :	jhr	74	type scheme = meta_var list * ty
95 :
96 :	jhr	61	(* useful types *)
97 :	jhr	63	val realTy = T_Tensor(Shape[])
98 :	jhr	75	fun vecTy n = T_Tensor(Shape[DimConst n])
99 :	jhr	70	val vec2Ty = vecTy 2
100 :			val vec3Ty = vecTy 3
101 :			val vec4Ty = vecTy 4
102 :	jhr	40
103 :			end

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