This is post number three in a series on Automata (in the formal languages / regex / parsing sense). This is the promised “implementation-heavy” post, where we go into implementing automata for real and useful things.
As always the programming language is Rust. By now I’ve actually had a bit of practice with the language, so hopefully the code will be less naive. Where in the previous post on Finite Automata we went through examples of direct encodings of specific automata, in this post we’ll look at more reusable code. I hope to publish the code discussed here in a crate eventually.
This is part one out of two three. It’s taking too long to write everything in one post, so I decided to split it up and publish this part first. The full code of this blogpost is tagged on github.
Non-deterministic Finite Automata
Quick recap: The so-called NFA goes from state to state based on the input symbol. Once we’re out of input symbols, if the state is a “final” state, we accept the input. The non-deterministic part means that from any state an input symbol can direct us to zero or more other states, so we can be in multiple states at once.
So let’s look at a general framework for NFAs (don’t panic, explanation below):
use std::collections::HashMap;
use std::collections::HashSet;
struct NFAHashState<Input, StateRef, Payload> {
transitions: HashMap<Input, HashSet<StateRef>>,
payload: Option<Payload>,
}
pub struct NFA<Input, Payload> {
alphabet: Vec<Input>,
states: Vec<NFAHashState<Input, usize, Payload>>,
}
I did say general framework, so generics galore :) Let’s take it apart:
We have a state of an NFA which can take certain Input, uses StateRef to refer to other states, and has a Payload. The Payload refers to data that the automaton returns when it’s in a final state. Inside the struct is the transition map from input to a set of state references, and the Option<Payload> where None means non-final state and Some(payload) means a final state with a payload.
The NFA struct holds the states and the alphabet. It is also generic over Input and Payload. Maybe it should really be generic over the exact state struct rather than the Payload, but I’m not sure as that would require a NFAState trait… Whatever, we’re going with this for now.
NFA execution
For the simplest interaction with an existing NFA, we just supply it some input and see if it “accepts” it. Let’s look into that first:
pub const AUTO_START: StateNumber = 0;
impl<Input: Eq + Hash, Payload: Clone> NFA<Input, Payload> {
pub fn apply<I: AsRef<[Input]>>(&self, input: I) -> Option<Payload> {
let mut cur_states = HashSet::new();
let mut nxt_states = HashSet::new();
cur_states.insert(AUTO_START);
for symbol in input.as_ref() {
for &cur_state in &cur_states {
if let Some(nxts) = self.states[cur_state].transitions.get(symbol) {
nxt_states.extend(nxts);
}
}
cur_states.clear();
mem::swap(&mut cur_states, &mut nxt_states);
}
cur_states.iter().filter_map(|&state| self.states[state].payload.clone()).next()
}
}
The bounds on the generics are because we use HashSets and maps. We also require Payload: Clone so we can give back an Option<Payload>. The apply method uses AsRef to be able to take Vec<Input> directly, or &str if Input=u8.
The implementation creates two sets: current states and next states. We start in AUTO_START, a predefined (constant) start state. For every symbol in the input we go over the current states. We use the symbol and cur_state to find nxts (next states) and add them to the nxt_states set. After going through all current states we clear the cur_states and swap it with the nxt_states. So the nxt_states is empty again and the cur_states are filled for the next symbol. This clear and swap is slightly more memory efficient than doing cur_states = nxt_states; nxt_states = HashSet::new(); because clear doesn’t throw away the already allocated memory. Hurray for premature optimisation! Anyway, after all the input has been processed, we grab the first payload we can find.
We’re not focussing on performance here, but obviously building these sets in the inner loop is kind of terrible. Let’s fix that, by turning the NFA into a DFA, a Deterministic Finite Automaton. Basically we’ll need to pre-compute the sets of states you can be in and make those single states in the DFA. This mean we have an potential combinatorial explosion of states on our hands, which could make things worse.. Eh ¯\_(ツ)_/¯
Powerset Construction (NFA → DFA)
The standard algorithm for NFA to DFA transformation is powerset construction. It’s named after the state-space of the resulting DFA, which is the powerset of the states of the NFA. The DFA struct and DFAHashState are very similar to the NFA one, so I’m not going to show them here to save a bit of space. Basically the only difference is that the DFAHashState transitions don’t map to a HashSet but to a single StateRef. Ah, and here we see that we could make the NFA struct a more general struct that is generic over the states so we can reuse it for the DFA. Note to self: fix that up.
impl<Input: Eq + Hash + Clone, Payload: Clone> NFA<Input, Payload> {
// ...
pub fn powerset_construction<F>(&self, payload_fold: &F) -> DFA<Input, Payload>
where F: Fn(Option<Payload>, &Option<Payload>) -> Option<Payload>
{
type StateRef = usize;
let mut states = vec![DFAHashState::new()];
let mut states_map: HashMap<BTreeSet<StateRef>, StateRef> = HashMap::new();
let cur_states: BTreeSet<StateRef> = iter::once(AUTO_START).collect();
states[AUTO_START].payload = self.states[AUTO_START].payload.clone();
states_map.insert(cur_states.clone(), AUTO_START);
psc_rec_helper(self, &mut states, &mut states_map, cur_states, AUTO_START, payload_fold);
DFA {
alphabet: self.alphabet.clone(),
states: states,
}
}
}
So first off, we need room for the states of the DFA. Then we need to be able to rename the sets of NFA states, so we create a states_map for that. We use a BTreeSet<StateRef> because HashSet doesn’t implement the Hash trait itself.
The current states don’t actually need to be mutated after creation, so instead of creating an empty set and mutating it, we can build one from an iterator. In this case we have only one item to put in, so we can build an iterator for it with iter::once. (We could also write a macro like vec! instead, but.. meh, too much trouble[1]).
Our DFA starts with an AUTO_START state. The start state is the same for the NFA and DFA, so we need to copy the payload. Then we insert a ground truth into the states_map: being in only the NFA start state, means we’re in the DFA start state.
Now let’s look at the psc_rec_helper, which stands for PowerSetConstruction-Recursion-Helper:
fn psc_rec_helper<Input, Payload, F>(nfa: &NFA<Input, Payload>,
states: &mut Vec<DFAHashState<Input, usize, Payload>>,
states_map: &mut HashMap<BTreeSet<usize>, usize>,
cur_states: BTreeSet<usize>,
cur_num: usize,
payload_fold: &F)
where Input: Eq + Hash + Clone,
Payload: Clone,
F: Fn(Option<Payload>, &Option<Payload>) -> Option<Payload>
{
for symbol in &nfa.alphabet {
let mut nxt_states = BTreeSet::new();
let mut payload = None;
for &cur_state in &cur_states {
if let Some(states) = nfa.states[cur_state].transitions.get(symbol) {
nxt_states.extend(states);
}
}
if nxt_states.is_empty() {
continue;
}
let nxt_num = states_map.get(&nxt_states).cloned().unwrap_or_else(|| {
let nxt_num = states.len();
let payload = nxt_states.iter()
.map(|&st| &nfa.states[st].payload)
.fold(None, payload_fold);
states.push(DFAHashState::with_payload(payload));
states_map.insert(nxt_states.clone(), nxt_num);
psc_rec_helper(nfa, states, states_map, nxt_states, nxt_num, payload_fold);
nxt_num
});
states[cur_num].transitions.insert(symbol.clone(), nxt_num);
}
}
So we go over every symbol in the alphabet. And for each of them we collect the nxt_states that we can reach with that symbol from the cur_states. Then we look up the DFA state number for nxt_states in states_map. If we find one, we don’t go into the unwrap_or_else and just record that transition from cur_num to nxt_num on the current symbol.
If there isn’t a DFA state number for nxt_states yet, then we haven’t seen it before. We create new DFA states in the order that we find them, so the new nxt_num is the size of the states vector of dnfa. We should add the new state to that vector too, and record it’s payload[2]. I sneaked in a state constructor helper function thingy (how do you call this in Rust anyway?) called with_payload. That one is hopefully self-explanatory.
Now that we’ve discovered a new state we didn’t know about before, we’ll also recursively call psc_rec_helper to record the transitions for it. And that’s it.
Improving the code
Although I spent some time making the code in this post readable, I also found that some steps in between might be worth showing you. So here are some improvements.
A bit of code-reuse
Have you noticed how we keep repeating this loop that computes the next states? It’s fairly easy to pull out, although it requires a bit of extra generics to make it fit for the HashSet in apply and the BTreeSet in psc_rec_helper. This is what it looks like:
impl<Input: Eq + Hash, Payload: Clone> NFA<Input, Payload> {
#[inline]
fn _next_state<'i, 'j, Iter, Ext>(&'j self, states: Iter, symbol: &Input, nxt_states: &mut Ext)
where Iter: IntoIterator<Item = &'i usize>,
Ext: Extend<&'j usize>
{
for &state in states {
if let Some(states) = self.states[state].transitions.get(symbol) {
nxt_states.extend(states);
}
}
}
}
Recursion to worklist
Doesn’t that recursive helper function feel unsatifying? Does it really matter if we discover the transitions of the DFA in depth-first order, switching to new states asap? Not really. So let’s change this recursion into a loop with our own stack of work:
impl NFA {
// ...
pub fn powerset_construction<F>(&self, payload_fold: &F) -> DFA<Input, Payload>
where F: Fn(Option<Payload>, &Option<Payload>) -> Option<Payload>
{
type StateRef = usize;
let mut states = vec![DFAHashState::new()];
let mut states_map: HashMap<BTreeSet<StateRef>, StateRef> = HashMap::new();
let cur_states: BTreeSet<StateRef> = iter::once(AUTO_START).collect();
states[AUTO_START].payload = self.states[AUTO_START].payload.clone();
states_map.insert(cur_states.clone(), AUTO_START);
let mut worklist = vec![(cur_states, AUTO_START)];
while let Some((cur_states, cur_num)) = worklist.pop() {
for symbol in &self.alphabet {
let mut nxt_states = BTreeSet::new();
self._next_state(&cur_states, symbol, &mut nxt_states);
// Skip the stuck state
if nxt_states.is_empty() {
continue;
}
let nxt_num = states_map.get(&nxt_states).cloned().unwrap_or_else(|| {
let nxt_num = states.len();
let payload = nxt_states.iter()
.map(|&st| &self.states[st].payload)
.fold(None, payload_fold);
states.push(DFAHashState::from_payload(payload));
states_map.insert(nxt_states.clone(), nxt_num);
worklist.push((nxt_states, nxt_num));
nxt_num
});
states[cur_num]
.transitions
.insert(symbol.clone(), nxt_num);
}
}
DFA {
alphabet: self.alphabet.clone(),
states: states,
}
}
}
Note that we now put our newly found states on a stack called worklist instead of doing a recursive call. This is done inside a while-loop that pops work off the worklist again. So as soon as we discover no new states, the stack will decrease and the loop will end.
A general interface
We’re already using AUTO_START and _next_state for these automata. What else can be make them do similarly? I cheated and took a peek at the interface of the aho-corasick Automaton. The basic idea is: have an iterator of intermediate matches. So we don’t just return the payload at the end of the input, but return matches whenever we hit a final state (i.e. with a payload). And so we get the following trait:
pub trait Automaton<Input, Payload> {
type State: Debug;
fn start_state() -> Self::State;
fn next_state(&self, state: &Self::State, input: &Input) -> Self::State;
fn get_match(&self, state: &Self::State, text_offset: usize) -> Option<Match<Payload>>;
fn find<'i, 'a>(&'a self, s: &'i [Input]) -> Matches<'i, 'a, Input, Payload, Self>
where Self: Sized
{
Matches {
aut: self,
input: s,
offset: 0,
state: Self::start_state(),
}
}
}
Why the hassle with a State and start_state() etc? Because NFAs are in a set of states at a time, and DFAs are in only one. Ok, so let’s look into these Match and Matches:
#[derive(Copy, Clone, Debug, Hash, PartialEq, Eq)]
pub struct Match<Payload> {
pub payload: Payload,
pub end: usize,
}
#[derive(Debug)]
pub struct Matches<'i, 'a, Input: 'i, Payload, A: 'a + Automaton<Input, Payload>> {
aut: &'a A,
input: &'i [Input],
offset: usize,
state: A::State,
}
impl<'i, 'a, Input, Payload, A: Automaton<Input, Payload>> Iterator
for Matches<'i, 'a, Input, Payload, A> {
type Item = Match<Payload>;
fn next(&mut self) -> Option<Self::Item> {
let mut offset = self.offset;
while offset < self.input.len() {
self.state = self.aut.next_state(&self.state, &self.input[offset]);
offset += 1;
if let Some(m) = self.aut.get_match(&self.state, 0) {
self.offset = offset;
return Some(m);
}
}
None
}
}
So a Match consists of a payload and the offset into the text where the end of the “match” is. The Matches iterator keeps track of the offset and mostly leans on the trait functions to find the next match. So let’s look at an implementation of the trait:
impl<Input: Eq + Hash, Payload: Clone> Automaton<Input, Payload> for NFA<Input, Payload> {
type State = HashSet<usize>;
#[inline]
fn start_state() -> Self::State {
iter::once(AUTO_START).collect()
}
#[inline]
fn next_state(&self, states: &Self::State, symbol: &Input) -> Self::State {
let mut nxt_states = HashSet::new();
self._next_state(states, symbol, &mut nxt_states);
nxt_states
}
#[inline]
fn get_match(&self, states: &Self::State, text_offset: usize) -> Option<Match<Payload>> {
for &state in states {
if let Some(ref payload) = self.states[state].payload {
return Some(Match {
payload: payload.clone(),
end: text_offset,
});
}
}
None
}
}
That start state set should perhaps be put into a lazy_static, but that’s yet another little thing I’ll sacrifice to get this post published. Note how all the implemented functions are annotated with #[inline], which will hopefully make the inner loop of the Matches iterator a bit faster. The next_state method uses are previously defined _next_state (now you get where that name came from ^^). get_match finds the first state in the set of states which has a payload. I’m not completely sure that this is the right approach, since the DFA version (through powerset construction) will give the combined payload of all states that the NFA would be in. On the other hand, it’s not that easy to get the payload folding function in here. I may or may not change this implementation to return a list of payloads instead just the first one.. Hmm, whatever ¯\_(ツ)_/¯
-
I mean sure, it’s not hard to write a
vec!-like macro forHashSet, but it’s not super easy to reuse in multiple projects. And I’m not volunteering to create and maintain a std-lib-extra-utilities crate. Or.. huh, maybe that’s not a bad idea. Though it might just be better to contribute it to the std library… ↩ -
If you’re trying something like this with a folding function as parameter, make sure you get the type right. It took me quite a while to figure out how to make
foldnot eat myFnMut. As you can see, I got it working by using a borrow of aFninstead (becauseFnMutis implemented for that). ↩