1. COS 320 Compilers David Walker

2. Outline Last Week –Introduction to ML Today: –Lexical Analysis –Reading: Chapter 2 of Appel

3. The Front End Lexical Analysis: Create sequence of tokens from characters Syntax Analysis: Create abstract syntax tree from sequence of tokens Type Checking: Check program for well- formedness constraints LexerParser stream of characters stream of tokens abstract syntax Type Checker

4. Lexical Analysis Lexical Analysis: Breaks stream of ASCII characters (source) into tokens Token: An atomic unit of program syntax –i.e., a word as opposed to a sentence Tokens and their types: Type: ID REAL SEMI LPAREN NUM IF Characters Recognized: foo, x, listcount 10.45, 3.14, -2.1 ; ( 50, 100 if Token: ID(foo), ID(x),... REAL(10.45), REAL(3.14),... SEMI LPAREN NUM(50), NUM(100) IF

5. Lexical Analysis Example x = ( y + 4.0 ) ;

6. Lexical Analysis Example x = ( y + 4.0 ) ; ID(x) Lexical Analysis

7. Lexical Analysis Example x = ( y + 4.0 ) ; ID(x) ASSIGN Lexical Analysis

8. Lexical Analysis Example x = ( y + 4.0 ) ; ID(x) ASSIGN LPAREN ID(y) PLUS REAL(4.0) RPAREN SEMI Lexical Analysis

9. Lexer Implementation Implementation Options: 1.Write a Lexer from scratch –Boring, error-prone and too much work 2.Use a Lexer Generator –Quick and easy. Good for lazy compiler writers. Lexer Specification

10. Lexer Implementation Implementation Options: 1.Write a Lexer from scratch –Boring, error-prone and too much work 2.Use a Lexer Generator –Quick and easy. Good for lazy compiler writers. Lexer Specification lexer generator Lexer

11. Lexer Implementation Implementation Options: 1.Write a Lexer from scratch –Boring, error-prone and too much work 2.Use a Lexer Generator –Quick and easy. Good for lazy compiler writers. Lexer Specification lexer generator Lexer stream of characters stream of tokens

12. How do we specify the lexer? –Develop another language –We’ll use a language involving regular expressions to specify tokens What is a lexer generator? –Another compiler....

13. Some Definitions We will want to define the language of legal tokens our lexer can recognize –Alphabet – a collection of symbols (ASCII is an alphabet) –String – a finite sequence of symbols taken from our alphabet –Language of legal tokens – a set of strings Language of ML keywords – set of all strings which are ML keywords (FINITE) Language of ML tokens – set of all strings which map to ML tokens (INFINITE) Some people use the word “language” to mean more general sets: –eg: ML Language – set of all strings representing correct ML programs (INFINITE).

14. Regular Expressions: Construction Base Cases: –For each symbol a in alphabet, a is a RE denoting the set {a} –Epsilon (e) denotes { } Inductive Cases (M and N are REs) –Alternation (M | N) denotes strings in M or N (a | b) == {a, b} –Concatenation (M N) denotes strings in M concatenated with strings in N (a | b) (a | c) == { aa, ac, ba, bc } –Kleene closure (M*) denotes strings formed by any number of repetitions of strings in M (a | b )* == {e, a, b, aa, ab, ba, bb,...}

15. Regular Expressions Integers begin with an optional minus sign, continue with a sequence of digits Regular Expression: (- | e) (0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9)*

16. Regular Expressions Integers begin with an optional minus sign, continue with a sequence of digits Regular Expression: (- | e) (0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9)* So writing (0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9) and even worse (a | b | c |...) gets tedious...

17. Regular Expressions common abbreviations: –[a-c] == (a | b | c) –. == any character except \n –\n == new line character –a+ == one or more –a? == zero or one all abbreviations can be defined in terms of the “standard” regular expressions

18. Ambiguous Token Rule Sets A single expression is a completely unambiguous specification of a token. Sometimes, when we put together a set of regular expressions to specify all of the tokens in a language, ambiguities arise: –i.e., two regular expression match the same string

19. Ambiguous Token Rule Sets Example: –Identifier tokens: a-z* (a-z | 0-9)* –Sample keyword tokens: if, then,... How do we tokenize: –foobar==> ID(foobar) or ID(foo) ID(bar) –if==> ID(if) or IF

20. Ambiguous Token Rule Sets We resolve ambiguities using two rules: –Longest match: The regular expression that matches the longest string takes precedence. –Rule Priority: The regular expressions identifying tokens are written down in sequence. If two regular expressions match the same (longest) string, the first regular expression in the sequence takes precedence.

21. Ambiguous Token Rule Sets Example: –Identifier tokens: a-z (a-z | 0-9)* –Sample keyword tokens: if, then,... How do we tokenize: –foobar==> ID(foobar) or ID(foo) ID(bar) –if==> ID(if) or IF

22. Ambiguous Token Rule Sets Example: –Identifier tokens: a-z* (a-z | 0-9)* –Sample keyword tokens: if, then,... How do we tokenize: –foobar==> ID(foobar) or ID(foo) ID(bar) –if==> ID(if) or IF

23. Lexer Implementation Implementation Options: 1.Write Lexer from scratch –Boring and error-prone 2.Use Lexical Analyzer Generator –Quick and easy ml-lex is a lexical analyzer generator for ML. lex and flex are lexical analyzer generators for C.

24. ML-Lex Specification Lexical specification consists of 3 parts: User Declarations % ML-LEX Definitions % Rules

25. User Declarations User Declarations: –User can define various values that are available to the action fragments. –Two values must be defined in this section: type lexresult –type of the value returned by each rule action. fun eof () –called by lexer when end of input stream is reached.

26. ML-LEX Definitions ML-LEX Definitions: –User can define regular expression abbreviations: –Define multiple lexers to work together. Each is given a unique name. DIGITS = [0-9] +; LETTER = [a-zA-Z]; %s LEX1 LEX2 LEX3;

27. Rules Rules: A rule consists of a pattern and an action: –Pattern in a regular expression. –Action is a fragment of ordinary ML code. Rules may be prefixed with the list of lexers that are allowed to use this rule. regular_expression => (action.code) ;

28. Rules Rules: A rule consists of a pattern and an action: –Pattern in a regular expression. –Action is a fragment of ordinary ML code. –Longest match & rule priority used for disambiguation Rules may be prefixed with the list of lexers that are allowed to use this rule. regular_expression => (action.code) ;

29. Rules Rule actions can use any value defined in the User Declarations section, including –type lexresult type of value returned by each rule action –val eof : unit -> lexresult called by lexer when end of input stream reached special variables: –yytext: input substring matched by regular expression –yypos: file position of the beginning of matched string –continue (): used to recursively called lexer

30. A Simple Lexer datatype token = Num of int | Id of string | IF | THEN | ELSE | EOF type lexresult = token (* mandatory *) fun eof () = EOF (* mandatory *) fun itos s = case Int.fromString s of SOME x => x | NONE => raise fail % NUM = [1-9][0-9]* ID = [a-zA-Z] ([a-zA-Z] | NUM)* % if => (IF); then => (THEN); else => (ELSE); {NUM}=> (Num (itos yytext)); {ID}=> (Id yytext);

31. Using Multiple Lexers Rules prefixed with a lexer name are matched only when that lexer is executing Enter new lexer using command YYBEGIN Initial lexer is called INITIAL

32. Using Multiple Lexers type lexresult = unit (* mandatory *) fun eof () = () (* mandatory *) % %s COMMENT % if => (); [a-z]+ => (); “(*” => (YYBEGIN COMMENT; continue ()); “*)” => (YYBEGIN INITIAL; continue ()); “\n” |. => (continue ());

33. A (Marginally) More Exciting Lexer type lexresult = string (* mandatory *) fun eof () = (print “End of file\n”; “EOF”) (* mandatory *) % %s COMMENT INT = [1-9] [0-9]*; % if => (“IF”); then=> (“THEN”); {INT} => ( “INT(“ ^ yytext ^ “)” ); “(*” => (YYBEGIN COMMENT; continue ()); “*)” => (YYBEGIN INITIAL; continue ()); “\n” |. => (continue ());

34. Implementing Lexers By compiling, of course: –convert REs into non-deterministic finite automata –convert non-deterministic finite automata into deterministic finite automata –convert deterministic finite automata into a blazingly fast table-driven algorithm you did everything but possibly the last step in your favorite algorithms class

35. Table-driven algorithm DFA: Table: Remember start position in character stream Keep reading characters and moving from state to state until no transitions apply An auxiliary table maps final states to the token type identified; yystring = input from start to current 13 24 a b + = = 2 2 34 4 1 2 3 4 a b c = +

36. DFA: Detail: how to deal with longest match? –when reading “iffy” should recognize “iffy” as ID, not “if” as keyword and then “fy” as ID a-z Table-driven algorithm 12 a-z

37. DFA: Detail: how to deal with longest match? –save most recent final state seen and position in character string –when no more transition can be made, revert to last saved legal final state –see Appel 2.4 for more details a-z Table-driven algorithm 12 a-z

38. Summary A Lexer: –input: stream of characters –output: stream of tokens Writing lexers by hand is boring, so we use a lexer generator: ml-lex –lexer generators work by converting REs through automata theory to efficient table- driven algorithms. –theory wins again.