1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645
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
/// it yet, we just enqueue it again to pick it up later. Otherwise, we
/// add the answer from the strand onto the table.
fn on_no_remaining_subgoals(
&mut self,
canonical_strand: CanonicalStrand<I>,
) -> NoRemainingSubgoalsResult {
let ambiguous = canonical_strand.value.ex_clause.ambiguous;
if let AnswerMode::Complete = self.forest.tables[self.stack.top().table].answer_mode {
if ambiguous {
// The strand can only return an ambiguous answer, but we don't
// want that right now, so requeue and we'll deal with it later.
self.forest.tables[self.stack.top().table].enqueue_strand(canonical_strand);
return NoRemainingSubgoalsResult::RootSearchFail(RootSearchFail::QuantumExceeded);
}
}
let floundered = !canonical_strand
.value
.ex_clause
.floundered_subgoals
.is_empty();
if floundered {
debug!("all remaining subgoals floundered for the table");
} else {
debug!("no remaining subgoals for the table");
};
match self.pursue_answer(canonical_strand) {
Some(answer_index) => {
debug!("answer is available");
// We found an answer for this strand, and therefore an
// answer for this table. Now, this table was either a
// subgoal for another strand, or was the root table.
let table = self.stack.top().table;
match self.stack.pop_and_take_caller_strand() {
Some(caller_strand) => {
self.stack.top().active_strand = Some(caller_strand);
NoRemainingSubgoalsResult::Success
}
None => {
// That was the root table, so we are done --
// *well*, unless there were delayed
// subgoals. In that case, we want to evaluate
// those delayed subgoals to completion, so we
// have to create a fresh strand that will
// take them as goals. Note that we *still
// need the original answer in place*, because
// we might have to build on it (see the
// Delayed Trivial Self Cycle, Variant 3
// example).
let answer = self.forest.answer(table, answer_index);
if let Some(strand) = self.create_refinement_strand(table, answer) {
self.forest.tables[table].enqueue_strand(strand);
}
NoRemainingSubgoalsResult::RootAnswerAvailable
}
}
}
None => {
debug!("answer is not available (or not new)");
// This strand led nowhere of interest. There might be *other*
// answers on this table, but we don't care right now, we'll
// try again at another time.
// Now we yield with `QuantumExceeded`
self.unwind_stack();
NoRemainingSubgoalsResult::RootSearchFail(RootSearchFail::QuantumExceeded)
}
}
}
/// A "refinement" strand is used in coinduction. When the root
/// table on the stack publishes an answer has delayed subgoals,
/// we create a new strand that will attempt to prove out those
/// delayed subgoals (the root answer here is not *special* except
/// in so far as that there is nothing above it, and hence we know
/// that the delayed subgoals (which resulted in some cycle) must
/// be referring to a table that now has completed).
///
/// Note that it is important for this to be a *refinement* strand
/// -- meaning that the answer with delayed subgoals has been
/// published. This is necessary because sometimes the strand must
/// build on that very answer that it is refining. See Delayed
/// Trivial Self Cycle, Variant 3.
fn create_refinement_strand(
&self,
table: TableIndex,
answer: &Answer<I>,
) -> Option<CanonicalStrand<I>> {
// If there are no delayed subgoals, then there is no need for
// a refinement strand.
if answer.subst.value.delayed_subgoals.is_empty() {
return None;
}
let num_universes = self.forest.tables[table].table_goal.universes;
let (
mut infer,
_,
AnswerSubst {
subst,
constraints,
delayed_subgoals,
},
) = chalk_solve::infer::InferenceTable::from_canonical(
self.context.program().interner(),
num_universes,
answer.subst.clone(),
);
let delayed_subgoals = delayed_subgoals
.into_iter()
.map(Literal::Positive)
.collect();
let strand = Strand {
ex_clause: ExClause {
subst,
ambiguous: answer.ambiguous,
constraints: constraints
.as_slice(self.context.program().interner())
.to_vec(),
subgoals: delayed_subgoals,
delayed_subgoals: Vec::new(),
answer_time: TimeStamp::default(),
floundered_subgoals: Vec::new(),
},
selected_subgoal: None,
last_pursued_time: TimeStamp::default(),
};
Some(Forest::canonicalize_strand_from(
self.context,
&mut infer,
&strand,
))
}
fn on_no_strands_left(&mut self) -> Result<(), RootSearchFail> {
let table = self.stack.top().table;
debug!("no more strands available (or all cycles) for {:?}", table);
// No more strands left to try! This is either because all
// strands have failed, because all strands encountered a
// cycle, or all strands have would give ambiguous answers.
if self.forest.tables[table].strands_mut().count() == 0 {
// All strands for the table T on the top of the stack
// have **failed**. Hence we can pop it off the stack and
// check what this means for the table T' that was just
// below T on the stack (if any).
debug!("no more strands available");
let caller_strand = match self.stack.pop_and_borrow_caller_strand() {
Some(s) => s,
None => {
// T was the root table, so we are done.
debug!("no more solutions");
return Err(RootSearchFail::NoMoreSolutions);
}
};
// This subgoal selection for the strand is finished, so take it
let caller_selected_subgoal = caller_strand.value.selected_subgoal.take().unwrap();
return match caller_strand.value.ex_clause.subgoals
[caller_selected_subgoal.subgoal_index]
{
// T' wanted an answer from T, but none is
// forthcoming. Therefore, the active strand from T'
// has failed and can be discarded.
Literal::Positive(_) => {
debug!("discarding strand because positive literal");
self.stack.top().active_strand.take();
self.unwind_stack();
Err(RootSearchFail::QuantumExceeded)
}
// T' wanted there to be no answer from T, but none is forthcoming.
Literal::Negative(_) => {
debug!("subgoal was proven because negative literal");
// There is no solution for this strand. But, this
// is what we want, so can remove this subgoal and
// keep going.
caller_strand
.value
.ex_clause
.subgoals
.remove(caller_selected_subgoal.subgoal_index);
// This strand is still active, so continue
Ok(())
}
};
}
// We can't consider this table as part of a cycle unless we've handled
// all strands, not just non-ambiguous ones. See chalk#571.
if let AnswerMode::Complete = self.forest.tables[table].answer_mode {
debug!("Allowing ambiguous answers.");
self.forest.tables[table].answer_mode = AnswerMode::Ambiguous;
return Err(RootSearchFail::QuantumExceeded);
}
let clock = self.stack.top().clock;
let cyclic_minimums = self.stack.top().cyclic_minimums;
if cyclic_minimums.positive >= clock && cyclic_minimums.negative >= clock {
debug!("cycle with no new answers");
if cyclic_minimums.negative < TimeStamp::MAX {
// This is a negative cycle.
self.unwind_stack();
return Err(RootSearchFail::NegativeCycle);
}
// If all the things that we recursively depend on have
// positive dependencies on things below us in the stack,
// then no more answers are forthcoming. We can clear all
// the strands for those things recursively.
let table = self.stack.top().table;
let cyclic_strands = self.forest.tables[table].take_strands();
self.clear_strands_after_cycle(cyclic_strands);
// Now we yield with `QuantumExceeded`
self.unwind_stack();
Err(RootSearchFail::QuantumExceeded)
} else {
debug!("table part of a cycle");
// This table resulted in a positive cycle, so we have
// to check what this means for the subgoal containing
// this strand
let caller_strand = match self.stack.pop_and_borrow_caller_strand() {
Some(s) => s,
None => {
panic!("nothing on the stack but cyclic result");
}
};
// We can't take this because we might need it later to clear the cycle
let caller_selected_subgoal = caller_strand.value.selected_subgoal.as_ref().unwrap();
match caller_strand.value.ex_clause.subgoals[caller_selected_subgoal.subgoal_index] {
Literal::Positive(_) => {
self.stack
.top()
.cyclic_minimums
.take_minimums(&cyclic_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: cyclic_minimums.minimum_of_pos_and_neg(),
};
self.stack.top().cyclic_minimums.take_minimums(&mins);
}
}
// We can't pursue this strand anymore, so push it back onto the table
let active_strand = self.stack.top().active_strand.take().unwrap();
let table = self.stack.top().table;
self.forest.tables[table].enqueue_strand(active_strand);
// The strand isn't active, but the table is, so just continue
Ok(())
}
}
/// Unwinds the entire stack, returning all active strands back to
/// their tables (this time at the end of the queue).
fn unwind_stack(&mut self) {
loop {
match self.stack.pop_and_take_caller_strand() {
Some(active_strand) => {
let table = self.stack.top().table;
self.forest.tables[table].enqueue_strand(active_strand);
}
None => return,
}
}
}
/// Invoked after we have determined that every strand in `table`
/// encounters a cycle; `strands` is the set of strands (which
/// have been moved out of the table). This method then
/// recursively clears the active strands from the tables
/// referenced in `strands`, since all of them must encounter
/// cycles too.
fn clear_strands_after_cycle(&mut self, strands: impl IntoIterator<Item = CanonicalStrand<I>>) {
for strand in strands {
let selected_subgoal = strand.value.selected_subgoal;
let ex_clause = strand.value.ex_clause;
let selected_subgoal = selected_subgoal.unwrap_or_else(|| {
panic!(
"clear_strands_after_cycle invoked on strand in table \
without a selected subgoal: {:?}",
ex_clause,
)
});
let strand_table = selected_subgoal.subgoal_table;
let strands = self.forest.tables[strand_table].take_strands();
self.clear_strands_after_cycle(strands);
}
}
fn select_subgoal(&mut self, canonical_strand: &mut CanonicalStrand<I>) -> SubGoalSelection {
loop {
while canonical_strand.value.selected_subgoal.is_none() {
if canonical_strand.value.ex_clause.subgoals.is_empty() {
if canonical_strand
.value
.ex_clause
.floundered_subgoals
.is_empty()
{
return SubGoalSelection::NotSelected;
}
self.reconsider_floundered_subgoals(&mut canonical_strand.value.ex_clause);
if canonical_strand.value.ex_clause.subgoals.is_empty() {
// All the subgoals of this strand floundered. We may be able
// to get helpful information from this strand still, but it
// will *always* be ambiguous, so mark it as so.
assert!(!canonical_strand
.value
.ex_clause
.floundered_subgoals
.is_empty());
canonical_strand.value.ex_clause.ambiguous = true;
return SubGoalSelection::NotSelected;
}
continue;
}
let subgoal_index =
SlgContext::next_subgoal_index(&canonical_strand.value.ex_clause);
// Get or create table for this subgoal.
let num_universes = self.forest.tables[self.stack.top().table]
.table_goal
.universes;
let (mut infer, _, strand) = chalk_solve::infer::InferenceTable::from_canonical(
self.context.program().interner(),
num_universes,
canonical_strand.clone(),
);
match self.forest.get_or_create_table_for_subgoal(
self.context,
&mut infer,
&strand.ex_clause.subgoals[subgoal_index],
) {
Some((subgoal_table, universe_map)) => {
canonical_strand.value.selected_subgoal = Some(SelectedSubgoal {
subgoal_index,
subgoal_table,
universe_map,
answer_index: AnswerIndex::ZERO,
});
}
None => {
// If we failed to create a table for the subgoal,
// that is because we have a floundered negative
// literal.
self.flounder_subgoal(&mut canonical_strand.value.ex_clause, subgoal_index);
}
}
}
let selected_subgoal_table = canonical_strand
.value
.selected_subgoal
.as_ref()
.unwrap()
.subgoal_table;
if self.forest.tables[selected_subgoal_table].is_floundered() {
if self.propagate_floundered_subgoal(canonical_strand) {
// This strand will never lead anywhere of interest.
return SubGoalSelection::NotSelected;
} else {
// This subgoal has floundered and has been marked.
// We previously would immediately mark the table as
// floundered too, and maybe come back to it. Now, we
// try to see if any other subgoals can be pursued first.
continue;
}
} else {
return SubGoalSelection::Selected;
}
}
}
/// Invoked when a strand represents an **answer**. This means
/// that the strand has no subgoals left. There are two possibilities:
///
/// - the strand may represent an answer we have already found; in
/// that case, we can return `None`, as this
/// strand led nowhere of interest.
/// - the strand may represent a new answer, in which case it is
/// added to the table and `Some(())` is returned.
fn pursue_answer(&mut self, canonical_strand: CanonicalStrand<I>) -> Option<AnswerIndex> {
let table = self.stack.top().table;
let Canonical {
binders,
value: strand,
} = canonical_strand;
let ExClause {
subst,
constraints,
ambiguous,
subgoals,
delayed_subgoals,
answer_time: _,
floundered_subgoals,
} = strand.ex_clause;
// If there are subgoals left, they should be followed
assert!(subgoals.is_empty());
// We can still try to get an ambiguous answer if there are floundered subgoals
let floundered = !floundered_subgoals.is_empty();
// So let's make sure that it *really* is an ambiguous answer (this should be set previously)
assert!(!floundered || ambiguous);
// FIXME: If there are floundered subgoals, we *could* potentially
// actually check if the partial answers to any of these subgoals
// conflict. But this requires that we think about whether they are
// positive or negative subgoals. This duplicates some of the logic
// in `merge_answer_into_strand`, so a bit of refactoring is needed.
// If the answer gets too large, mark the table as floundered.
// This is the *most conservative* course. There are a few alternatives:
// 1) Replace the answer with a truncated version of it. (This was done
// previously, but turned out to be more complicated than we wanted and
// and a source of multiple bugs.)
// 2) Mark this *strand* as floundered. We don't currently have a mechanism
// for this (only floundered subgoals), so implementing this is more
// difficult because we don't want to just *remove* this strand from the
// table, because that might make the table give `NoMoreSolutions`, which
// is *wrong*.
// 3) Do something fancy with delayed subgoals, effectively delayed the
// truncated bits to a different strand (and a more "refined" answer).
// (This one probably needs more thought, but is here for "completeness")
//
// Ultimately, the current decision to flounder the entire table mostly boils
// down to "it works as we expect for the current tests". And, we likely don't
// even *need* the added complexity just for potentially more answers.
if truncate::needs_truncation(
self.context.program().interner(),
&mut InferenceTable::new(),
self.context.max_size(),
&subst,
) {
self.forest.tables[table].mark_floundered();
return None;
}
let table_goal = &self.forest.tables[table].table_goal;
let filtered_delayed_subgoals = delayed_subgoals
.into_iter()
.filter(|delayed_subgoal| {
let canonicalized = InferenceTable::u_canonicalize(
self.context.program().interner(),
&chalk_ir::Canonical {
binders: binders.clone(),
value: delayed_subgoal.clone(),
},
)
.quantified;
*table_goal != canonicalized
})
.collect();
let subst = Canonical {
binders,
value: AnswerSubst {
subst,
constraints: Constraints::from_iter(self.context.program().interner(), constraints),
delayed_subgoals: filtered_delayed_subgoals,
},
};
debug!(?table, ?subst, ?floundered, "found answer");
let answer = Answer { subst, ambiguous };
// A "trivial" answer is one that is 'just true for all cases'
// -- in other words, it gives no information back to the
// caller. For example, `Vec<u32>: Sized` is "just true".
// Such answers are important because they are the most
// general case, and after we provide a trivial answer, no
// further answers are useful -- therefore we can clear any
// further pending strands (this is a "green cut", in
// Prolog parlance).
//
// This optimization is *crucial* for performance: for
// example, `projection_from_env_slow` fails miserably without
// it. The reason is that we wind up (thanks to implied bounds)
// with a clause like this:
//
// ```ignore
// forall<T> { (<T as SliceExt>::Item: Clone) :- WF(T: SliceExt) }
// ```
//
// we then apply that clause to `!1: Clone`, resulting in the
// table goal `!1: Clone :- <?0 as SliceExt>::Item = !1,
// WF(?0: SliceExt)`. This causes us to **enumerate all types
// `?0` that where `Slice<?0>` normalizes to `!1` -- this is
// an infinite set of types, effectively. Interestingly,
// though, we only need one and we are done, because (if you
// look) our goal (`!1: Clone`) doesn't have any output
// parameters.
//
// This is actually a kind of general case. Due to Rust's rule
// about constrained impl type parameters, generally speaking
// when we have some free inference variable (like `?0`)
// within our clause, it must appear in the head of the
// clause. This means that the values we create for it will
// propagate up to the caller, and they will quickly surmise
// that there is ambiguity and stop requesting more answers.
// Indeed, the only exception to this rule about constrained
// type parameters if with associated type projections, as in
// the case above!
//
// (Actually, because of the trivial answer cut off rule, we
// never even get to the point of asking the query above in
// `projection_from_env_slow`.)
//
// However, there is one fly in the ointment: answers include
// region constraints, and you might imagine that we could
// find future answers that are also trivial but with distinct
// sets of region constraints. **For this reason, we only
// apply this green cut rule if the set of generated
// constraints is empty.**
//
// The limitation on region constraints is quite a drag! We
// can probably do better, though: for example, coherence
// guarantees that, for any given set of types, only a single
// impl ought to be applicable, and that impl can only impose
// one set of region constraints. However, it's not quite that
// simple, thanks to specialization as well as the possibility
// of proving things from the environment (though the latter
// is a *bit* suspect; e.g., those things in the environment
// must be backed by an impl *eventually*).
let is_trivial_answer = {
self.forest.tables[table]
.table_goal
.is_trivial_substitution(self.context.program().interner(), &answer.subst)
&& answer
.subst
.value
.constraints
.is_empty(self.context.program().interner())
};
if let Some(answer_index) = self.forest.tables[table].push_answer(answer) {
// See above, if we have a *complete* and trivial answer, we don't
// want to follow any more strands
if !ambiguous && is_trivial_answer {
self.forest.tables[table].take_strands();
}
Some(answer_index)
} else {
info!("answer: not a new answer, returning None");
None
}
}
fn reconsider_floundered_subgoals(&mut self, ex_clause: &mut ExClause<I>) {
info!("reconsider_floundered_subgoals(ex_clause={:#?})", ex_clause,);
let ExClause {
answer_time,
subgoals,
floundered_subgoals,
..
} = ex_clause;
for i in (0..floundered_subgoals.len()).rev() {
if floundered_subgoals[i].floundered_time < *answer_time {
let floundered_subgoal = floundered_subgoals.swap_remove(i);
subgoals.push(floundered_subgoal.floundered_literal);
}
}
}
/// Removes the subgoal at `subgoal_index` from the strand's
/// subgoal list and adds it to the strand's floundered subgoal
/// list.
fn flounder_subgoal(&self, ex_clause: &mut ExClause<I>, subgoal_index: usize) {
let _s = debug_span!(
"flounder_subgoal",
answer_time = ?ex_clause.answer_time,
subgoal = ?ex_clause.subgoals[subgoal_index],
);
let _s = _s.enter();
let floundered_time = ex_clause.answer_time;
let floundered_literal = ex_clause.subgoals.remove(subgoal_index);
ex_clause.floundered_subgoals.push(FlounderedSubgoal {
floundered_literal,
floundered_time,
});
debug!(?ex_clause);
}
/// True if all the tables on the stack starting from `depth` and
/// continuing until the top of the stack are coinductive.
///
/// Example: Given a program like:
///
/// ```notrust
/// struct Foo { a: Option<Box<Bar>> }
/// struct Bar { a: Option<Box<Foo>> }
/// trait XXX { }
/// impl<T: Send> XXX for T { }
/// ```
///
/// and then a goal of `Foo: XXX`, we would eventually wind up
/// with a stack like this:
///
/// | StackIndex | Table Goal |
/// | ---------- | ----------- |
/// | 0 | `Foo: XXX` |
/// | 1 | `Foo: Send` |
/// | 2 | `Bar: Send` |
///
/// Here, the top of the stack is `Bar: Send`. And now we are
/// asking `top_of_stack_is_coinductive_from(1)` -- the answer
/// would be true, since `Send` is an auto trait, which yields a
/// coinductive goal. But `top_of_stack_is_coinductive_from(0)` is
/// false, since `XXX` is not an auto trait.
pub(super) fn top_of_stack_is_coinductive_from(&self, depth: StackIndex) -> bool {
StackIndex::iterate_range(self.stack.top_of_stack_from(depth)).all(|d| {
let table = self.stack[d].table;
self.forest.tables[table].coinductive_goal
})
}
}