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logic.rs
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logic.rs
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use crate::forest::Forest;
use crate::normalize_deep::DeepNormalizer;
use crate::slg::{ResolventOps, SlgContext, SlgContextOps};
use crate::stack::{Stack, StackIndex};
use crate::strand::{CanonicalStrand, SelectedSubgoal, Strand};
use crate::table::{AnswerIndex, Table};
use crate::{
Answer, AnswerMode, CompleteAnswer, ExClause, FlounderedSubgoal, Literal, Minimums, TableIndex,
TimeStamp,
};
use chalk_ir::could_match::CouldMatch;
use chalk_ir::interner::Interner;
use chalk_ir::{
AnswerSubst, Canonical, ConstrainedSubst, Constraints, FallibleOrFloundered, Floundered, Goal,
GoalData, InEnvironment, NoSolution, ProgramClause, Substitution, UCanonical, UniverseMap,
};
use chalk_solve::clauses::program_clauses_that_could_match;
use chalk_solve::coinductive_goal::IsCoinductive;
use chalk_solve::infer::ucanonicalize::UCanonicalized;
use chalk_solve::infer::InferenceTable;
use chalk_solve::solve::truncate;
use tracing::{debug, debug_span, info, instrument};
type RootSearchResult<T> = Result<T, RootSearchFail>;
/// The different ways that a *root* search (which potentially pursues
/// many strands) can fail. A root search is one that begins with an
/// empty stack.
#[derive(Debug)]
pub(super) enum RootSearchFail {
/// The table we were trying to solve cannot succeed.
NoMoreSolutions,
/// The table cannot be solved without more type information.
Floundered,
/// We did not find a solution, but we still have things to try.
/// Repeat the request, and we'll give one of those a spin.
///
/// (In a purely depth-first-based solver, like Prolog, this
/// doesn't appear.)
QuantumExceeded,
/// A negative cycle was found. This is fail-fast, so even if there was
/// possibly a solution (ambiguous or not), it may not have been found.
NegativeCycle,
/// The current answer index is not useful. Currently, this is returned
/// because the current answer needs refining.
InvalidAnswer,
}
/// This is returned when we try to select a subgoal for a strand.
#[derive(PartialEq)]
enum SubGoalSelection {
/// A subgoal was successfully selected. It has already been checked
/// to not be floundering. However, it may have an answer already, be
/// coinductive, or create a cycle.
Selected,
/// This strand has no remaining subgoals, but there may still be
/// floundered subgoals.
NotSelected,
}
/// This is returned `on_no_remaining_subgoals`
enum NoRemainingSubgoalsResult {
/// There is an answer available for the root table
RootAnswerAvailable,
/// There was a `RootSearchFail`
RootSearchFail(RootSearchFail),
// This was a success
Success,
}
impl<I: Interner> Forest<I> {
/// Returns an answer with a given index for the given table. This
/// may require activating a strand and following it. It returns
/// `Ok(answer)` if they answer is available and otherwise a
/// `RootSearchFail` result.
pub(super) fn root_answer(
&mut self,
context: &SlgContextOps<I>,
table: TableIndex,
answer_index: AnswerIndex,
) -> RootSearchResult<CompleteAnswer<I>> {
let stack = Stack::default();
let mut state = SolveState {
forest: self,
context,
stack,
};
match state.ensure_root_answer(table, answer_index) {
Ok(()) => {
assert!(state.stack.is_empty());
let answer = state.forest.answer(table, answer_index);
if !answer.subst.value.delayed_subgoals.is_empty() {
return Err(RootSearchFail::InvalidAnswer);
}
Ok(CompleteAnswer {
subst: Canonical {
binders: answer.subst.binders.clone(),
value: ConstrainedSubst {
subst: answer.subst.value.subst.clone(),
constraints: answer.subst.value.constraints.clone(),
},
},
ambiguous: answer.ambiguous,
})
}
Err(err) => Err(err),
}
}
pub(super) fn any_future_answer(
&self,
table: TableIndex,
mut answer_index: AnswerIndex,
mut test: impl FnMut(&Substitution<I>) -> bool,
) -> bool {
// Check any cached answers, starting at `answer_index`.
while let Some(answer) = self.tables[table].answer(answer_index) {
info!("answer cached = {:?}", answer);
if test(&answer.subst.value.subst) {
return true;
}
answer_index.increment();
}
// Check any unsolved strands, which may give further answers.
self.tables[table]
.strands()
.any(|strand| test(&strand.value.ex_clause.subst))
}
pub(crate) fn answer(&self, table: TableIndex, answer: AnswerIndex) -> &Answer<I> {
self.tables[table].answer(answer).unwrap()
}
fn canonicalize_strand_from(
context: &SlgContextOps<I>,
infer: &mut InferenceTable<I>,
strand: &Strand<I>,
) -> CanonicalStrand<I> {
infer
.canonicalize(context.program().interner(), strand.clone())
.quantified
}
/// Given a subgoal, converts the literal into u-canonical form
/// and searches for an existing table. If one is found, it is
/// returned, but otherwise a new table is created (and populated
/// with its initial set of strands).
///
/// Returns `None` if the literal cannot be converted into a table
/// -- for example, this can occur when we have selected a
/// negative literal with free existential variables, in which
/// case the execution is said to "flounder".
///
/// In terms of the NFTD paper, creating a new table corresponds
/// to the *New Subgoal* step as well as the *Program Clause
/// Resolution* steps.
#[instrument(level = "debug", skip(self, context, infer))]
fn get_or_create_table_for_subgoal(
&mut self,
context: &SlgContextOps<I>,
infer: &mut InferenceTable<I>,
subgoal: &Literal<I>,
) -> Option<(TableIndex, UniverseMap)> {
// Subgoal abstraction:
let (ucanonical_subgoal, universe_map) = match subgoal {
Literal::Positive(subgoal) => {
Forest::abstract_positive_literal(context, infer, subgoal.clone())?
}
Literal::Negative(subgoal) => {
Forest::abstract_negative_literal(context, infer, subgoal.clone())?
}
};
debug!(?ucanonical_subgoal, ?universe_map);
let table = self.get_or_create_table_for_ucanonical_goal(context, ucanonical_subgoal);
Some((table, universe_map))
}
/// Given a u-canonical goal, searches for an existing table. If
/// one is found, it is returned, but otherwise a new table is
/// created (and populated with its initial set of strands).
///
/// In terms of the NFTD paper, creating a new table corresponds
/// to the *New Subgoal* step as well as the *Program Clause
/// Resolution* steps.
#[instrument(level = "debug", skip(self, context))]
pub(crate) fn get_or_create_table_for_ucanonical_goal(
&mut self,
context: &SlgContextOps<I>,
goal: UCanonical<InEnvironment<Goal<I>>>,
) -> TableIndex {
if let Some(table) = self.tables.index_of(&goal) {
debug!(?table, "found existing table");
return table;
}
info!(
table = ?self.tables.next_index(),
"creating new table with goal = {:#?}",
goal,
);
let table = Self::build_table(context, self.tables.next_index(), goal);
self.tables.insert(table)
}
/// When a table is first created, this function is invoked to
/// create the initial set of strands. If the table represents a
/// domain goal, these strands are created from the program
/// clauses as well as the clauses found in the environment. If
/// the table represents a non-domain goal, such as `for<T> G`
/// etc, then `simplify_goal` is invoked to create a strand
/// that breaks the goal down.
///
/// In terms of the NFTD paper, this corresponds to the *Program
/// Clause Resolution* step being applied eagerly, as many times
/// as possible.
fn build_table(
context: &SlgContextOps<I>,
table_idx: TableIndex,
goal: UCanonical<InEnvironment<Goal<I>>>,
) -> Table<I> {
let coinductive = goal.is_coinductive(context.program());
let mut table = Table::new(goal.clone(), coinductive);
let goal_data = goal.canonical.value.goal.data(context.program().interner());
match goal_data {
GoalData::DomainGoal(domain_goal) => {
let canon_domain_goal = UCanonical {
canonical: Canonical {
binders: goal.canonical.binders,
value: InEnvironment::new(
&goal.canonical.value.environment,
domain_goal.clone(),
),
},
universes: goal.universes,
};
let db = context.program();
let canon_goal = canon_domain_goal.canonical.value.goal.clone();
let could_match = |c: &ProgramClause<I>| {
c.could_match(db.interner(), db.unification_database(), &canon_goal)
};
match program_clauses_that_could_match(db, &canon_domain_goal) {
Ok(mut clauses) => {
clauses.retain(could_match);
clauses.extend(db.custom_clauses().into_iter().filter(could_match));
let (infer, subst, goal) =
chalk_solve::infer::InferenceTable::from_canonical(
context.program().interner(),
canon_domain_goal.universes,
canon_domain_goal.canonical,
);
clauses.extend(
db.program_clauses_for_env(&goal.environment)
.iter(db.interner())
.cloned()
.filter(could_match),
);
let InEnvironment { environment, goal } = goal;
for clause in clauses {
info!("program clause = {:#?}", clause);
let mut infer = infer.clone();
if let Ok(resolvent) = infer.resolvent_clause(
context.unification_database(),
context.program().interner(),
&environment,
&goal,
&subst,
&clause,
) {
info!("pushing initial strand with ex-clause: {:#?}", &resolvent,);
let strand = Strand {
ex_clause: resolvent,
selected_subgoal: None,
last_pursued_time: TimeStamp::default(),
};
let canonical_strand =
Self::canonicalize_strand_from(context, &mut infer, &strand);
table.enqueue_strand(canonical_strand);
}
}
}
Err(Floundered) => {
debug!(
table = ?table_idx,
"Marking table {:?} as floundered! (failed to create program clauses)",
table_idx
);
table.mark_floundered();
}
}
}
_ => {
let (mut infer, subst, InEnvironment { environment, goal }) =
chalk_solve::infer::InferenceTable::from_canonical(
context.program().interner(),
goal.universes,
goal.canonical,
);
// The goal for this table is not a domain goal, so we instead
// simplify it into a series of *literals*, all of which must be
// true. Thus, in EWFS terms, we are effectively creating a
// single child of the `A :- A` goal that is like `A :- B, C, D`
// where B, C, and D are the simplified subgoals. You can think
// of this as applying built-in "meta program clauses" that
// reduce goals into Domain goals.
match Self::simplify_goal(context, &mut infer, subst, environment, goal) {
FallibleOrFloundered::Ok(ex_clause) => {
info!(
ex_clause = ?DeepNormalizer::normalize_deep(
&mut infer,
context.program().interner(),
ex_clause.clone(),
),
"pushing initial strand"
);
let strand = Strand {
ex_clause,
selected_subgoal: None,
last_pursued_time: TimeStamp::default(),
};
let canonical_strand =
Self::canonicalize_strand_from(context, &mut infer, &strand);
table.enqueue_strand(canonical_strand);
}
FallibleOrFloundered::NoSolution => {}
FallibleOrFloundered::Floundered => table.mark_floundered(),
}
}
}
table
}
/// Given a selected positive subgoal, applies the subgoal
/// abstraction function to yield the canonical form that will be
/// used to pick a table. Typically, this abstraction has no
/// effect, and hence we are simply returning the canonical form
/// of `subgoal`; but if the subgoal is getting too big, we return
/// `None`, which causes the subgoal to flounder.
fn abstract_positive_literal(
context: &SlgContextOps<I>,
infer: &mut InferenceTable<I>,
subgoal: InEnvironment<Goal<I>>,
) -> Option<(UCanonical<InEnvironment<Goal<I>>>, UniverseMap)> {
if truncate::needs_truncation(
context.program().interner(),
infer,
context.max_size(),
&subgoal,
) {
None
} else {
let canonicalized_goal = infer
.canonicalize(context.program().interner(), subgoal)
.quantified;
let UCanonicalized {
quantified,
universes,
} = InferenceTable::u_canonicalize(context.program().interner(), &canonicalized_goal);
Some((quantified, universes))
}
}
/// Given a selected negative subgoal, the subgoal is "inverted"
/// (see `InferenceTable<I, C>::invert`) and then potentially truncated
/// (see `abstract_positive_literal`). The result subgoal is
/// canonicalized. In some cases, this may return `None` and hence
/// fail to yield a useful result, for example if free existential
/// variables appear in `subgoal` (in which case the execution is
/// said to "flounder").
fn abstract_negative_literal(
context: &SlgContextOps<I>,
infer: &mut InferenceTable<I>,
subgoal: InEnvironment<Goal<I>>,
) -> Option<(UCanonical<InEnvironment<Goal<I>>>, UniverseMap)> {
// First, we have to check that the selected negative literal
// is ground, and invert any universally quantified variables.
//
// DIVERGENCE -- In the RR paper, to ensure completeness, they
// permit non-ground negative literals, but only consider
// them to succeed when the target table has no answers at
// all. This is equivalent inverting those free existentials
// into universals, as discussed in the comments of
// `invert`. This is clearly *sound*, but the completeness is
// a subtle point. In particular, it can cause **us** to reach
// false conclusions, because e.g. given a program like
// (selected left-to-right):
//
// not { ?T: Copy }, ?T = Vec<u32>
//
// we would select `not { ?T: Copy }` first. For this goal to
// succeed we would require that -- effectively -- `forall<T>
// { not { T: Copy } }`, which clearly doesn't hold. (In the
// terms of RR, we would require that the table for `?T: Copy`
// has failed before we can continue.)
//
// In the RR paper, this is acceptable because they assume all
// of their input programs are both **normal** (negative
// literals are selected after positive ones) and **safe**
// (all free variables in negative literals occur in positive
// literals). It is plausible for us to guarantee "normal"
// form, we can reorder clauses as we need. I suspect we can
// guarantee safety too, but I have to think about it.
//
// For now, we opt for the safer route of terming such
// executions as floundering, because I think our use of
// negative goals is sufficiently limited we can get away with
// it. The practical effect is that we will judge more
// executions as floundering than we ought to (i.e., where we
// could instead generate an (imprecise) result). As you can
// see a bit later, we also diverge in some other aspects that
// affect completeness when it comes to subgoal abstraction.
let inverted_subgoal = infer.invert(context.program().interner(), subgoal)?;
if truncate::needs_truncation(
context.program().interner(),
infer,
context.max_size(),
&inverted_subgoal,
) {
None
} else {
let canonicalized_goal = infer
.canonicalize(context.program().interner(), inverted_subgoal)
.quantified;
let UCanonicalized {
quantified,
universes,
} = InferenceTable::u_canonicalize(context.program().interner(), &canonicalized_goal);
Some((quantified, universes))
}
}
}
pub(crate) struct SolveState<'forest, I: Interner> {
forest: &'forest mut Forest<I>,
context: &'forest SlgContextOps<'forest, I>,
stack: Stack<I>,
}
impl<'forest, I: Interner> Drop for SolveState<'forest, I> {
fn drop(&mut self) {
if !self.stack.is_empty() {
if let Some(active_strand) = self.stack.top().active_strand.take() {
let table = self.stack.top().table;
self.forest.tables[table].enqueue_strand(active_strand);
}
self.unwind_stack();
}
}
}
impl<'forest, I: Interner> SolveState<'forest, I> {
/// Ensures that answer with the given index is available from the
/// given table. Returns `Ok` if there is an answer.
///
/// This function first attempts to fetch answer that is cached in
/// the table. If none is found, then it will recursively search
/// to find an answer.
#[instrument(level = "info", skip(self))]
fn ensure_root_answer(
&mut self,
initial_table: TableIndex,
initial_answer: AnswerIndex,
) -> RootSearchResult<()> {
info!(
"table goal = {:#?}",
self.forest.tables[initial_table].table_goal
);
// Check if this table has floundered.
if self.forest.tables[initial_table].is_floundered() {
return Err(RootSearchFail::Floundered);
}
// Check for a tabled answer.
if let Some(answer) = self.forest.tables[initial_table].answer(initial_answer) {
info!("answer cached = {:?}", answer);
return Ok(());
}
// If no tabled answer is present, we ought to be requesting
// the next available index.
assert_eq!(
self.forest.tables[initial_table].next_answer_index(),
initial_answer
);
self.stack
.push(initial_table, Minimums::MAX, self.forest.increment_clock());
loop {
let clock = self.stack.top().clock;
// If we had an active strand, continue to pursue it
let table = self.stack.top().table;
let table_answer_mode = self.forest.tables[table].answer_mode;
// We track when we last pursued each strand. If all the strands have been
// pursued at this depth, then that means they all encountered a cycle.
// We also know that if the first strand has been pursued at this depth,
// then all have. Otherwise, an answer to any strand would have provided an
// answer for the table.
let forest = &mut self.forest;
let next_strand = self.stack.top().active_strand.take().or_else(|| {
forest.tables[table].dequeue_next_strand_that(|strand| {
let time_eligble = strand.value.last_pursued_time < clock;
let mode_eligble = match (table_answer_mode, strand.value.ex_clause.ambiguous) {
(AnswerMode::Complete, false) => true,
(AnswerMode::Complete, true) => false,
(AnswerMode::Ambiguous, _) => true,
};
time_eligble && mode_eligble
})
});
match next_strand {
Some(mut canonical_strand) => {
debug!("starting next strand = {:#?}", canonical_strand);
canonical_strand.value.last_pursued_time = clock;
match self.select_subgoal(&mut canonical_strand) {
SubGoalSelection::Selected => {
// A subgoal has been selected. We now check this subgoal
// table for an existing answer or if it's in a cycle.
// If neither of those are the case, a strand is selected
// and the next loop iteration happens.
self.on_subgoal_selected(canonical_strand)?;
continue;
}
SubGoalSelection::NotSelected => {
match self.on_no_remaining_subgoals(canonical_strand) {
NoRemainingSubgoalsResult::RootAnswerAvailable => return Ok(()),
NoRemainingSubgoalsResult::RootSearchFail(e) => return Err(e),
NoRemainingSubgoalsResult::Success => continue,
};
}
}
}
None => {
self.on_no_strands_left()?;
continue;
}
}
}
}
/// This is called when an answer is available for the selected subgoal
/// of the strand. First, if the selected subgoal is a `Positive` subgoal,
/// it first clones the strand pursuing the next answer. Then, it merges the
/// answer into the provided `Strand`.
/// On success, `Ok` is returned and the `Strand` can be continued to process
/// On failure, `Err` is returned and the `Strand` should be discarded
fn merge_answer_into_strand(
&mut self,
infer: &mut InferenceTable<I>,
strand: &mut Strand<I>,
) -> RootSearchResult<()> {
// At this point, we know we have an answer for
// the selected subgoal of the strand.
// Now, we have to unify that answer onto the strand.
// If this answer is ambiguous and we don't want ambiguous answers
// yet, then we act like this is a floundered subgoal.
let ambiguous = {
let selected_subgoal = strand.selected_subgoal.as_ref().unwrap();
let answer = self.forest.answer(
selected_subgoal.subgoal_table,
selected_subgoal.answer_index,
);
answer.ambiguous
};
if let AnswerMode::Complete = self.forest.tables[self.stack.top().table].answer_mode {
if ambiguous {
// FIXME: we could try to be a little bit smarter here. This can
// really be split into cases:
// 1) Cases where no amount of solving will cause this ambiguity to change.
// (e.g. `CannnotProve`)
// 2) Cases where we may be able to get a better answer if we
// solve other subgoals first.
// (e.g. the `non_enumerable_traits_reorder` test)
// We really only need to delay merging an ambiguous answer for
// case 2. Do note, though, that even if we *do* merge the answer
// case 1, we should stop solving this strand when in
// `AnswerMode::Complete` since we wouldn't use this answer yet
// *anyways*.
// The selected subgoal returned an ambiguous answer, but we don't want that.
// So, we treat this subgoal as floundered.
let selected_subgoal = strand.selected_subgoal.take().unwrap();
self.flounder_subgoal(&mut strand.ex_clause, selected_subgoal.subgoal_index);
return Ok(());
}
}
// If this subgoal was a `Positive` one, whichever way this
// particular answer turns out, there may yet be *more* answers,
// if this isn't a trivial substitution.
// Enqueue that alternative for later.
// NOTE: this is separate from the match below because we `take` the selected_subgoal
// below, but here we keep it for the new `Strand`.
let selected_subgoal = strand.selected_subgoal.as_ref().unwrap();
if let Literal::Positive(_) = strand.ex_clause.subgoals[selected_subgoal.subgoal_index] {
let answer = self.forest.answer(
selected_subgoal.subgoal_table,
selected_subgoal.answer_index,
);
if !self.forest.tables[selected_subgoal.subgoal_table]
.table_goal
.is_trivial_substitution(self.context.program().interner(), &answer.subst)
{
let mut next_subgoal = selected_subgoal.clone();
next_subgoal.answer_index.increment();
let next_strand = Strand {
ex_clause: strand.ex_clause.clone(),
selected_subgoal: Some(next_subgoal),
last_pursued_time: strand.last_pursued_time,
};
let table = self.stack.top().table;
let canonical_next_strand =
Forest::canonicalize_strand_from(self.context, infer, &next_strand);
self.forest.tables[table].enqueue_strand(canonical_next_strand);
}
}
// Deselect and remove the selected subgoal, now that we have an answer for it.
let selected_subgoal = strand.selected_subgoal.take().unwrap();
let subgoal = strand
.ex_clause
.subgoals
.remove(selected_subgoal.subgoal_index);
match subgoal {
Literal::Positive(subgoal) => {
let SelectedSubgoal {
subgoal_index: _,
subgoal_table,
answer_index,
ref universe_map,
} = selected_subgoal;
use chalk_solve::infer::ucanonicalize::UniverseMapExt;
let table_goal = universe_map.map_from_canonical(
self.context.program().interner(),
&self.forest.tables[subgoal_table].table_goal.canonical,
);
let answer_subst = universe_map.map_from_canonical(
self.context.program().interner(),
&self.forest.answer(subgoal_table, answer_index).subst,
);
match infer.apply_answer_subst(
self.context.program().interner(),
self.context.unification_database(),
&mut strand.ex_clause,
&subgoal,
&table_goal,
answer_subst,
) {
Ok(()) => {
let ex_clause = &mut strand.ex_clause;
// If the answer had was ambiguous, we have to
// ensure that `ex_clause` is also ambiguous. This is
// the SLG FACTOR operation, though NFTD just makes it
// part of computing the SLG resolvent.
if self.forest.answer(subgoal_table, answer_index).ambiguous {
debug!("Marking Strand as ambiguous because answer to (positive) subgoal was ambiguous");
ex_clause.ambiguous = true;
}
// Increment the answer time for the `ex_clause`. Floundered
// subgoals may be eligble to be pursued again.
ex_clause.answer_time.increment();
// Ok, we've applied the answer to this Strand.
Ok(())
}
// This answer led nowhere. Give up for now, but of course
// there may still be other strands to pursue, so return
// `QuantumExceeded`.
Err(NoSolution) => {
info!("answer not unifiable -> NoSolution");
// This strand as no solution. It is no longer active,
// so it dropped at the end of this scope.
// Now we want to propogate back to the up with `QuantumExceeded`
self.unwind_stack();
Err(RootSearchFail::QuantumExceeded)
}
}
}
Literal::Negative(_) => {
let SelectedSubgoal {
subgoal_index: _,
subgoal_table,
answer_index,
universe_map: _,
} = selected_subgoal;
// We got back an answer. This is bad, because we want
// to disprove the subgoal, but it may be
// "conditional" (maybe true, maybe not).
let answer = self.forest.answer(subgoal_table, answer_index);
// By construction, we do not expect negative subgoals
// to have delayed subgoals. This is because we do not
// need to permit `not { L }` where `L` is a
// coinductive goal. We could improve this if needed,
// but it keeps things simple.
if !answer.subst.value.delayed_subgoals.is_empty() {
panic!("Negative subgoal had delayed_subgoals");
}
if !answer.ambiguous {
// We want to disproval the subgoal, but we
// have an unconditional answer for the subgoal,
// therefore we have failed to disprove it.
info!("found unconditional answer to neg literal -> NoSolution");
// This strand as no solution. By returning an Err,
// the caller should discard this `Strand`.
// Now we want to propogate back to the up with `QuantumExceeded`
self.unwind_stack();
return Err(RootSearchFail::QuantumExceeded);
}
// Otherwise, the answer is ambiguous. We can keep going,
// but we have to mark our strand, too, as ambiguous.
//
// We want to disproval the subgoal, but we
// have an unconditional answer for the subgoal,
// therefore we have failed to disprove it.
debug!(?strand, "Marking Strand as ambiguous because answer to (negative) subgoal was ambiguous");
strand.ex_clause.ambiguous = true;
// Strand is ambigious.
Ok(())
}
}
}
/// This is called when the selected subgoal for a strand has floundered.
/// We have to decide what this means for the strand.
/// - If the strand was positively dependent on the subgoal, we flounder,
/// the subgoal, then return `false`. This strand may be able to be
/// retried later.
/// - If the strand was negatively dependent on the subgoal, then strand
/// has led nowhere of interest and we return `true`. This strand should
/// be discarded.
///
/// In other words, we return whether this strand flounders.
fn propagate_floundered_subgoal(&mut self, strand: &mut CanonicalStrand<I>) -> bool {
// This subgoal selection for the strand is finished, so take it
let selected_subgoal = strand.value.selected_subgoal.take().unwrap();
match strand.value.ex_clause.subgoals[selected_subgoal.subgoal_index] {
Literal::Positive(_) => {
// If this strand depends on this positively, then we can
// come back to it later. So, we mark that subgoal as
// floundered and yield `QuantumExceeded` up the stack
// If this subgoal floundered, push it onto the
// floundered list, along with the time that it
// floundered. We'll try to solve some other subgoals
// and maybe come back to it.
self.flounder_subgoal(&mut strand.value.ex_clause, selected_subgoal.subgoal_index);
false
}
Literal::Negative(_) => {
// Floundering on a negative literal isn't like a
// positive search: we only pursue negative literals
// when we already know precisely the type we are
// looking for. So there's no point waiting for other
// subgoals, we'll never recover more information.
//
// In fact, floundering on negative searches shouldn't
// normally happen, since there are no uninferred
// variables in the goal, but it can with forall
// goals:
//
// forall<T> { not { T: Debug } }
//
// Here, the table we will be searching for answers is
// `?T: Debug`, so it could well flounder.
// This strand has no solution. It is no longer active,
// so it dropped at the end of this scope.
true
}
}
}
/// This is called if the selected subgoal for a `Strand` is
/// a coinductive cycle.
fn on_coinductive_subgoal(
&mut self,
mut canonical_strand: CanonicalStrand<I>,
) -> Result<(), RootSearchFail> {
// This is a co-inductive cycle. That is, this table
// appears somewhere higher on the stack, and has now
// recursively requested an answer for itself. This
// means that we have to delay this subgoal until we
// reach a trivial self-cycle.
// This subgoal selection for the strand is finished, so take it
let selected_subgoal = canonical_strand.value.selected_subgoal.take().unwrap();
match canonical_strand
.value
.ex_clause
.subgoals
.remove(selected_subgoal.subgoal_index)
{
Literal::Positive(subgoal) => {
// We delay this subgoal
let table = self.stack.top().table;
assert!(
self.forest.tables[table].coinductive_goal
&& self.forest.tables[selected_subgoal.subgoal_table].coinductive_goal
);
canonical_strand
.value
.ex_clause
.delayed_subgoals
.push(subgoal);
self.stack.top().active_strand = Some(canonical_strand);
Ok(())
}
Literal::Negative(_) => {
// We don't allow coinduction for negative literals
info!("found coinductive answer to negative literal");
panic!("Coinductive cycle with negative literal");
}
}
}
/// This is called if the selected subgoal for `strand` is
/// a positive, non-coinductive cycle.
///
/// # Parameters
///
/// * `strand` the strand from the top of the stack we are pursuing
/// * `minimums` is the collected minimum clock times
fn on_positive_cycle(
&mut self,
canonical_strand: CanonicalStrand<I>,
minimums: Minimums,
) -> Result<(), RootSearchFail> {
// We can't take this because we might need it later to clear the cycle
let selected_subgoal = canonical_strand.value.selected_subgoal.as_ref().unwrap();
match canonical_strand.value.ex_clause.subgoals[selected_subgoal.subgoal_index] {
Literal::Positive(_) => {
self.stack.top().cyclic_minimums.take_minimums(&minimums);
}
Literal::Negative(_) => {
// We depend on `not(subgoal)`. For us to continue,
// `subgoal` must be completely evaluated. Therefore,
// we depend (negatively) on the minimum link of
// `subgoal` as a whole -- it doesn't matter whether
// it's pos or neg.
let mins = Minimums {
positive: self.stack.top().clock,
negative: minimums.minimum_of_pos_and_neg(),
};
self.stack.top().cyclic_minimums.take_minimums(&mins);
}
}
// Ok, we've taken the minimums from this cycle above. Now,
// we just return the strand to the table. The table only
// pulls strands if they have not been checked at this
// depth.
//
// We also can't mark these and return early from this
// because the stack above us might change.
let table = self.stack.top().table;
self.forest.tables[table].enqueue_strand(canonical_strand);
// The strand isn't active, but the table is, so just continue
Ok(())
}
/// Invoked after we've selected a (new) subgoal for the top-most
/// strand. Attempts to pursue this selected subgoal.
///
/// Returns:
///
/// * `Ok` if we should keep searching.
/// * `Err` if the subgoal failed in some way such that the strand can be abandoned.
fn on_subgoal_selected(
&mut self,
mut canonical_strand: CanonicalStrand<I>,
) -> Result<(), RootSearchFail> {
// This may be a newly selected subgoal or an existing selected subgoal.
let SelectedSubgoal {
subgoal_index: _,
subgoal_table,
answer_index,
universe_map: _,
} = *canonical_strand.value.selected_subgoal.as_ref().unwrap();
debug!(
?subgoal_table,
goal = ?self.forest.tables[subgoal_table].table_goal,
"table selection {:?} with goal: {:?}",
subgoal_table, self.forest.tables[subgoal_table].table_goal
);
// This is checked inside select_subgoal
assert!(!self.forest.tables[subgoal_table].is_floundered());
// Check for a tabled answer.
if let Some(answer) = self.forest.tables[subgoal_table].answer(answer_index) {
info!("answer cached = {:?}", answer);
// There was a previous answer available for this table
// We need to check if we can merge it into the current `Strand`.
let num_universes = self.forest.tables[self.stack.top().table]
.table_goal
.universes;
let (mut infer, _, mut strand) = chalk_solve::infer::InferenceTable::from_canonical(
self.context.program().interner(),
num_universes,
canonical_strand.clone(),
);
match self.merge_answer_into_strand(&mut infer, &mut strand) {
Err(e) => {
debug!(?strand, "could not merge into current strand");
drop(strand);
return Err(e);
}
Ok(_) => {
debug!(?strand, "merged answer into current strand");
canonical_strand =
Forest::canonicalize_strand_from(self.context, &mut infer, &strand);
self.stack.top().active_strand = Some(canonical_strand);
return Ok(());
}
}
}
// If no tabled answer is present, we ought to be requesting
// the next available index.
assert_eq!(
self.forest.tables[subgoal_table].next_answer_index(),
answer_index
);
// Next, check if the table is already active. If so, then we
// have a recursive attempt.
if let Some(cyclic_depth) = self.stack.is_active(subgoal_table) {
info!("cycle detected at depth {:?}", cyclic_depth);
let minimums = Minimums {
positive: self.stack[cyclic_depth].clock,
negative: TimeStamp::MAX,
};
if self.top_of_stack_is_coinductive_from(cyclic_depth) {
debug!("table is coinductive");
return self.on_coinductive_subgoal(canonical_strand);
}
debug!("table encountered a positive cycle");
return self.on_positive_cycle(canonical_strand, minimums);
}
// We don't know anything about the selected subgoal table.
// Set this strand as active and push it onto the stack.
self.stack.top().active_strand = Some(canonical_strand);
let cyclic_minimums = Minimums::MAX;
self.stack.push(
subgoal_table,
cyclic_minimums,
self.forest.increment_clock(),
);
Ok(())
}
/// This is called when there are no remaining subgoals for a strand, so
/// it represents an answer. If the strand is ambiguous and we don't want