177 lines
11 KiB
Markdown
177 lines
11 KiB
Markdown
---
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title: Compiling a Functional Language Using C++, Part 1 - Tokenizing
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date: 2019-08-03T01:02:30-07:00
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tags: ["C and C++", "Functional Languages", "Compilers"]
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series: "Compiling a Functional Language using C++"
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description: "In this post, we tackle the first component of our compiler: tokenizing."
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---
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It makes sense to build a compiler bit by bit, following the stages we outlined in
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the first post of the series. This is because these stages are essentially a pipeline,
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with program text coming in one end, and the final program coming out of the other.
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So as we build up our pipeline, we'll be able to push program text further and further,
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until eventually we get something that we can run on our machine.
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This is how most tutorials go about building a compiler, too. The result is that
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there are a __lot__ of tutorials covering tokenizing and parsing.
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Nonetheless, I will cover the steps required to tokenize and parse our little functional
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language. Before we start, it might help to refresh your memory about
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the syntax of the language, which we outlined in the
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[previous post]({{< relref "00_compiler_intro.md" >}}).
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When we first get our program text, it's in a representation difficult for us to make
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sense of. If we look at how it's represented in C++, we see that it's just an array
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of characters (potentially hundreds, thousands, or millions in length). We __could__
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jump straight to parsing the text (which involves creating a tree structure, known
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as an __abstract syntax tree__; more on that later). There's nothing wrong with this approach -
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in fact, in functional languages, tokenizing is frequently skipped. However,
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in our closer-to-metal language (C++), it happens to be more convenient to first break the
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input text into a bunch of distinct segments (tokens).
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For example, consider the string "320+6". If we skip tokenizing and go straight
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into parsing, we'd feed our parser the sequence of characters `['3', '2', '6', '+', '6', '\0']`.
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On the other hand, if we run a tokenizing step on the string first, we'd be feeding our
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parser three tokens, `("320", NUMBER)`, `("+", OPERATOR)`, and `("6", NUMBER)`.
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To us, this is a bit more clear - we've partitioned the string into logical segments.
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Our parser, then, won't have to care about recognizing a number - it will just know
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that a number is next in the string, and do with that information what it needs.
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### The Theory
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How do we go about breaking up a string into tokens? We need to come up with a
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way to compare some characters in a string against a set of rules. But "rules"
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is a very general term - we could, for instance, define a particular
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token that is a fibonacci number - 1, 2, 3, 5, and so on would be marked
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as a "fibonacci number", while the other numbers will be marked as just
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a regular number. To support that, our rules would get pretty complex. And
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equally complex will become our checking of these rules for particular strings.
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Fortunately, we're not insane. We observe that the rules for tokens in practice
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are fairly simple - one or more digits is an integer, a few letters together
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are a variable name. In order to be able to efficiently break text up into
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such tokens, we restrict ourselves to __regular languages__. A language
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is defined as a set of strings (potentially infinite), and a regular
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language is one for which we can write a __regular expression__ to check if
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a string is in the set. Regular expressions are a way of representing
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patterns that a string has to match. We define regular expressions
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as follows:
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* Any character is a regular expression that matches that character. Thus,
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\\(a\\) is a regular expression (from now shortened to regex) that matches
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the character 'a', and nothing else.
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* \\(r_1r_2\\), or the concatenation of \\(r_1\\) and \\(r_2\\), is
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a regular expression that matches anything matched by \\(r_1\\), followed
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by anything that matches \\(r_2\\). For instance, \\(ab\\), matches
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the character 'a' followed by the character 'b' (thus matching "ab").
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* \\(r_1|r_2\\) matches anything that is either matched by \\(r_1\\) or
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\\(r_2\\). Thus, \\(a|b\\) matches the character 'a' or the character 'b'.
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* \\(r_1?\\) matches either an empty string, or anything matched by \\(r_1\\).
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* \\(r_1+\\) matches one or more things matched by \\(r_1\\). So,
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\\(a+\\) matches "a", "aa", "aaa", and so on.
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* \\((r_1)\\) matches anything that matches \\(r_1\\). This is mostly used
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to group things together in more complicated expressions.
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* \\(.\\) matches any character.
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More powerful variations of regex also include an "any of" operator, \\([c_1c_2c_3]\\),
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which is equivalent to \\(c_1|c_2|c_3\\), and a "range" operator, \\([c_1-c_n]\\), which
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matches all characters in the range between \\(c_1\\) and \\(c_n\\), inclusive.
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Let's see some examples. An integer, such as 326, can be represented with \\([0-9]+\\).
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This means, one or more characters between 0 or 9. Some (most) regex implementations
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have a special symbol for \\([0-9]\\), written as \\(\\setminus d\\). A variable,
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starting with a lowercase letter and containing lowercase or uppercase letters after it,
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can be written as \\(\[a-z\]([a-zA-Z]+)?\\). Again, most regex implementations provide
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a special operator for \\((r_1+)?\\), written as \\(r_1*\\).
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So how does one go about checking if a regular expression matches a string? An efficient way is to
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first construct a [state machine](https://en.wikipedia.org/wiki/Finite-state_machine). A type of state machine can be constructed from a regular expression
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by literally translating each part of it to a series of states, one-to-one. This machine is called
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a __Nondeterministic Finite Automaton__, or NFA for short. The "Finite" means that the number of
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states in the state machine is, well, finite. For us, this means that we can store such
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a machine on disk. The "Nondeterministic" part, though, is more complex: given a particular character
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and a particular state, it's possible that an NFA has the option of transitioning into more
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than one other state. Well, which state __should__ it pick? No easy way to tell. Each time
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we can transition to more than one state, we exponentially increase the number of possible
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states that we can be in. This isn't good - we were going for efficiency, remember?
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What we can do is convert our NFA into another kind of state machine, in which for every character,
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only one possible state transition is possible. This machine is called a __Deterministic Finite Automaton__,
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or DFA for short. There's an algorithm to convert an NFA into a DFA, which I won't explain here.
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Since both the conversion of a regex into an NFA and a conversion of an NFA into a DFA is done
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by following an algorithm, we're always going to get the same DFA for the same regex we put in.
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If we come up with the rules for our tokens once, we don't want to be building a DFA each time
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our tokenizer is run - the result will always be the same! Even worse, translating a regular
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expression all the way into a DFA is the inefficient part of the whole process. The solution is to
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generate a state machine, and convert it into code to simulate that state machine. Then, we include
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that code as part of our compiler. This way, we have a state machine "hardcoded" into our tokenizer,
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and no conversion of regex to DFAs needs to be done at runtime.
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### The Practice
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Creating an NFA, and then a DFA, and then generating C++ code are all cumbersome. If we had to
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write code to do this every time we made a compiler, it would get very repetitive, very fast.
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Fortunately, there exists a tool that does exactly this for us - it's called `flex`. Flex
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takes regular expressions, and generates code that matches a string against those regular expressions.
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It does one more thing in addition to that - for each regular expression it matches, flex
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runs a user-defined action (which we write in C++). We can use this to convert strings that
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represent numbers directly into numbers, and do other small tasks.
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So, what tokens do we have? From our arithmetic definition, we see that we have integers.
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Let's use the regex `[0-9]+` for those. We also have the operators `+`, `-`, `*`, and `/`.
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The regex for `-` is simple enough: it's just `-`. However, we need to
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preface our `/`, `+` and `*` with a backslash, since they happen to also be modifiers
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in flex's regular expressions: `\/`, `\+`, `\*`.
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Let's also represent some reserved keywords. We'll say that `defn`, `data`, `case`, and `of`
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are reserved. Their regular expressions are just their names. We also want to tokenize
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`=`, `->`, `{`, `}`, `,`, `(` and `)`. Finally, we want to represent identifiers, like `f`,
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`x`, `Nil`, and `Cons`. We will actually make a distinction between lowercase identifiers
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and uppercase identifiers, as we will follow Haskell's convention of representing
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data type constructors with uppercase letters, and functions and variables with lowercase ones.
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So, our two regular expressions will be `[a-z][a-zA-Z]*` for the lowercase variables, and
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`[A-Z][a-zA-Z]*` for uppercase variables. Let's make a tokenizer in flex with all this. To do
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this, we create a new file, `scanner.l`, in which we write a mix of regular expressions
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and C++ code. Here's the whole thing:
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{{< rawblock "compiler/01/scanner.l" >}}
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A flex file starts with options. I set the `noyywrap` option, which disables a particular
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feature of flex that we won't use, and which causes linker errors. Next up,
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flex allows us to put some C++ code that we want at the top of our generated code.
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I simply include `iostream`, so that we can use `cout` to print out our tokens.
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Next, `%%`, and after that, the meat of our tokenizer: regular expressions, followed by
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C++ code that should be executed when the regular expression is matched.
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The first token: whitespace. This includes the space character,
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and the newline character. We ignore it, so its rule is empty. After that,
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we have the regular expressions for the tokens we've talked about. For each, I just
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print a description of the token that matched. This will change when we hook this up to
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a parser, but for now, this works fine. Notice that the variable `yytext` contains
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the string matched by our regular expression. This variable is set by the code flex
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generates, and we can use it to get the extract text that matched a regex. This is
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useful, for instance, to print the variable name that we matched. After
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all of our tokens, another `%%`, and more C++ code. For this simple example,
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I declare a `main` function, which just calls `yylex`, a function flex
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generates for us. Let's generate the C++ code, and compile it:
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```
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flex -o scanner.cpp scanner.l
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g++ -o scanner scanner.cpp
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```
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Now, let's feed it an expression:
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```
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echo "3+2*6" | ./scanner
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```
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We get the output:
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```
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NUMBER: 3
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PLUS
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NUMBER: 2
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TIMES
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NUMBER: 6
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```
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Hooray! We have tokenizing.
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With our text neatly divided into meaningful chunks, we
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can continue on to [Part 2 - Parsing]({{< relref "02_compiler_parsing.md" >}}).
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