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9700f6f206da5102c5dfb2414c5e7e13d844fcbf
stockfish/src/search.cpp
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Miloslav MacůrekandJoost VandeVondele 9700f6f206 Fix crash with UCI_LimitStrength and tablebases at the root 
`Skill::pick_best()` assumes the root moves are sorted by score in descending order:

```cpp
Value topScore = rootMoves[0].score;
int   delta    = std::min(topScore - rootMoves[multiPV - 1].score, int(PawnValue));
```

That does not have to be so with Syzygy tablebases at the root, where the moves are ordered by `tbRank` instead. `rootMoves[0]` is then not the highest score and `delta` can go negative. Since `delta * (rng.rand<unsigned>() % int(weakness))` is evaluated as unsigned, a negative `delta` wraps to a large value and the `int(...)` cast overflows, so the `score + push >= maxScore` check never passes and `best` stays `Move::none()`. The caller

```cpp
std::swap(rootMoves[0],
          *std::find(rootMoves.begin(), rootMoves.end(),
                     skill.best ? skill.best : skill.pick_best(rootMoves, multiPV)));
```

then dereferences `end()`. The result is a crash, or sometimes `bestmove (none)` / an illegal move, in tablebase endgames when `UCI_LimitStrength` is set (or `Skill Level` is below 20).

The fix computes the score range over the candidate moves directly, so `delta` stays non-negative and a valid move is always returned.

**To reproduce**:
```
setoption name UCI_LimitStrength value true
setoption name UCI_Elo value 2900
setoption name SyzygyPath value <syzygy tablebases path>
position fen 8/8/8/4k3/8/8/3BKN2/8 b - - 0 1
go wtime 250 btime 250 winc 100 binc 100
```

You need to call the go command a couple of times before crash, after which is ends with

```
bestmove a1e5 ponder (none)
```

and on the next `go wtime 250 btime 250 winc 100 binc 100` it crashes printing:

```
info string Available processors: 0-31
info string Using 1 thread
info string NNUE evaluation using nn-af1339a6dea3.nnue (106MiB, (83248, 1024, 31, 32, 1))
info string Network replica 1: Shared memory.
```

Bench is unchanged, since this code only runs under `UCI_LimitStrength` / `Skill Level`.

closes https://github.com/official-stockfish/Stockfish/pull/6957

No functional change
2026-07-10 20:17:23 +02:00

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/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2026 The Stockfish developers (see AUTHORS file)
Stockfish is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Stockfish is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#include "search.h"
#include <algorithm>
#include <array>
#include <atomic>
#include <cassert>
#include <chrono>
#include <cmath>
#include <cstdlib>
#include <initializer_list>
#include <iostream>
#include <list>
#include <ratio>
#include <string>
#include <utility>
#include "bitboard.h"
#include "evaluate.h"
#include "history.h"
#include "misc.h"
#include "movegen.h"
#include "movepick.h"
#include "nnue/network.h"
#include "nnue/nnue_accumulator.h"
#include "position.h"
#include "syzygy/tbprobe.h"
#include "thread.h"
#include "timeman.h"
#include "tt.h"
#include "types.h"
#include "uci.h"
#include "ucioption.h"
namespace Stockfish {
static constexpr std::array<int, 16> lmrDivisor = {3307, 2930, 2874, 2818, 3215, 3225, 3224, 2782,
2858, 2919, 3088, 3275, 3180, 2868, 3006, 3599};
namespace TB = Tablebases;
void syzygy_extend_pv(const OptionsMap& options,
const Search::LimitsType& limits,
Stockfish::Position& pos,
Stockfish::Search::RootMove& rootMove,
Value& v);
using namespace Search;
namespace {
constexpr u64 NODES_LIMIT_OUTPUT = 10'000'000;
constexpr int SEARCHEDLIST_CAPACITY = 32;
using SearchedList = ValueList<Move, SEARCHEDLIST_CAPACITY>;
// (*Scalers):
// The values with Scaler asterisks have proven non-linear scaling.
// They are optimized to time controls of 180 + 1.8 and longer,
// so changing them or adding conditions that are similar requires
// tests at these types of time controls.
// (*Scaler) All tuned parameters at time controls shorter than
// optimized for require verifications at longer time controls
int correction_value(const Worker& w, const Position& pos, const Stack* const ss) {
const Color us = pos.side_to_move();
const auto m = (ss - 1)->currentMove;
const auto& shared = w.sharedHistory;
const int pcv = shared.pawn_correction_entry(pos)[us].pawn;
const int micv = shared.minor_piece_correction_entry(pos)[us].minor;
const int wnpcv = shared.nonpawn_correction_entry<WHITE>(pos)[us].nonPawnWhite;
const int bnpcv = shared.nonpawn_correction_entry<BLACK>(pos)[us].nonPawnBlack;
const int cntcv =
m.is_ok()
? 8363
* ((*(ss - 2)->continuationCorrectionHistory)[pos.piece_on(m.to_sq())][m.to_sq()]
+ (*(ss - 4)->continuationCorrectionHistory)[pos.piece_on(m.to_sq())][m.to_sq()])
: 64549;
return 13345 * pcv + 9280 * micv + 11840 * (wnpcv + bnpcv) + cntcv;
}
// Add correctionHistory value to raw staticEval and guarantee evaluation
// does not hit the tablebase range.
Value to_corrected_static_eval(const Value v, const int cv) {
return std::clamp(v + cv / 131072, VALUE_TB_LOSS_IN_MAX_PLY + 1, VALUE_TB_WIN_IN_MAX_PLY - 1);
}
void update_correction_history(const Position& pos,
Stack* const ss,
Search::Worker& workerThread,
const int bonus) {
const Move m = (ss - 1)->currentMove;
const Color us = pos.side_to_move();
constexpr int nonPawnWeight = 186;
auto& shared = workerThread.sharedHistory;
shared.pawn_correction_entry(pos)[us].pawn << bonus;
shared.minor_piece_correction_entry(pos)[us].minor << bonus * 152 / 128;
shared.nonpawn_correction_entry<WHITE>(pos)[us].nonPawnWhite << bonus * nonPawnWeight / 128;
shared.nonpawn_correction_entry<BLACK>(pos)[us].nonPawnBlack << bonus * nonPawnWeight / 128;
if (m.is_ok())
{
const Square to = m.to_sq();
const Piece pc = pos.piece_on(to);
(*(ss - 2)->continuationCorrectionHistory)[pc][to] << bonus * 136 / 128;
(*(ss - 4)->continuationCorrectionHistory)[pc][to] << bonus * 68 / 128;
}
}
// Add a small random component to draw evaluations to avoid 3-fold blindness
Value value_draw(usize nodes) { return VALUE_DRAW - 1 + Value(nodes & 0x2); }
Value value_to_tt(Value v, int ply);
Value value_from_tt(Value v, int ply, int r50c);
void update_continuation_histories(Stack* ss, Piece pc, Square to, int bonus);
void update_quiet_histories(
const Position& pos, Stack* ss, Search::Worker& workerThread, Move move, int bonus);
void update_all_stats(const Position& pos,
Stack* ss,
Search::Worker& workerThread,
Move bestMove,
Square prevSq,
SearchedList& quietsSearched,
SearchedList& capturesSearched,
Depth depth,
Move ttMove,
bool PvNode);
// Detect shuffling moves in order to limit search explosions
// Added in #6447 as non-regression, and so its parameters should not be tuned
bool is_shuffling(Move move, Stack* const ss, const Position& pos) {
if (pos.capture_stage(move) || pos.rule50_count() < 10)
return false;
if (pos.state()->pliesFromNull < 6 || ss->ply < 20)
return false;
return move.from_sq() == (ss - 2)->currentMove.to_sq()
&& (ss - 2)->currentMove.from_sq() == (ss - 4)->currentMove.to_sq();
}
} // namespace
Search::Worker::Worker(SharedState& sharedState,
std::unique_ptr<ISearchManager> sm,
usize threadId,
usize numaThreadId,
usize numaTotalThreads,
NumaReplicatedAccessToken token) :
// Unpack the SharedState struct into member variables
sharedHistory(sharedState.sharedHistories.at(token.get_numa_index())),
continuationHistory(sharedHistory.continuationHistory),
threadIdx(threadId),
numaThreadIdx(numaThreadId),
numaTotal(numaTotalThreads),
numaAccessToken(token),
manager(std::move(sm)),
options(sharedState.options),
threads(sharedState.threads),
tt(sharedState.tt),
network(sharedState.network),
refreshTable(network[token]) {
clear();
}
void Search::Worker::ensure_network_replicated() {
// Access once to force lazy initialization.
// We do this because we want to avoid initialization during search.
(void) (network[numaAccessToken]);
}
void Search::Worker::start_searching() {
accumulatorStack.reset();
// Non-main threads go directly to iterative_deepening()
if (!is_mainthread())
{
iterative_deepening();
return;
}
main_manager()->tm.init(limits, rootPos.side_to_move(), rootPos.game_ply(), options,
main_manager()->originalTimeAdjust);
tt.new_search();
if (rootMoves.empty())
{
main_manager()->updates.onUpdateNoMoves(
{0, {rootPos.checkers() ? -VALUE_MATE : VALUE_DRAW, rootPos}});
main_manager()->updates.onBestmove(UCIEngine::move(Move::none()), "");
return;
}
// Main thread starts non-main threads, and begins own search.
threads.start_searching();
bool uciPvSent = iterative_deepening();
// When we reach the maximum depth, we can arrive here without a raise of
// threads.stop. However, if we are pondering or in an infinite search,
// the UCI protocol states that we shouldn't print the best move before the
// GUI sends a "stop" or "ponderhit" command. We therefore simply wait here
// until the GUI sends one of those commands.
while (!threads.stop && (main_manager()->ponder || limits.infinite))
{} // Busy wait for a stop or a ponder reset
// Stop the threads if not already stopped (also raise the stop if
// "ponderhit" just reset threads.ponder)
threads.stop = true;
// Wait until all threads have finished
threads.wait_for_search_finished();
// When playing in 'nodes as time' mode, subtract the searched nodes from
// the available ones before exiting.
if (limits.npmsec)
main_manager()->tm.advance_nodes_time(threads.nodes_searched()
- limits.inc[rootPos.side_to_move()]);
Worker* bestThread = this;
Skill skill =
Skill(options["Skill Level"], options["UCI_LimitStrength"] ? int(options["UCI_Elo"]) : 0);
if (!limits.depth && !skill.enabled())
bestThread = threads.get_best_thread()->worker.get();
main_manager()->bestPreviousScore = bestThread->rootMoves[0].score;
main_manager()->bestPreviousAverageScore = bestThread->rootMoves[0].averageScore;
if (bestThread->rootMoves[0].pv.size() == 1
&& bestThread->rootMoves[0].extract_ponder_from_tt(tt, rootPos))
uciPvSent = false;
// Send PV info if it has changed since last output in iterative_deepening().
if (!uciPvSent || bestThread != this)
main_manager()->output_pv(*bestThread, threads, tt, bestThread->rootDepth);
// In rare cases, output_pv() may change the ponder move through syzygy_extend_pv().
std::string ponder;
if (bestThread->rootMoves[0].pv.size() > 1)
ponder = UCIEngine::move(bestThread->rootMoves[0].pv[1], rootPos.is_chess960());
auto bestmove = UCIEngine::move(bestThread->rootMoves[0].pv[0], rootPos.is_chess960());
main_manager()->updates.onBestmove(bestmove, ponder);
}
// Main iterative deepening loop. It calls search()
// repeatedly with increasing depth until the allocated thinking time has been
// consumed, the user stops the search, or the maximum search depth is reached.
bool Search::Worker::iterative_deepening() {
SearchManager* mainThread = (is_mainthread() ? main_manager() : nullptr);
PVMoves pv;
PVMoves lastBestMovePV;
Depth lastBestMoveDepth = 0;
Value lastBestMoveScore = -VALUE_INFINITE;
Value alpha, beta;
Value bestValue = -VALUE_INFINITE;
Color us = rootPos.side_to_move();
double timeReduction = 1, totBestMoveChanges = 0;
int delta, iterIdx = 0;
// Allocate stack with extra size to allow access from (ss - 7) to (ss + 2):
// (ss - 7) is needed for update_continuation_histories(ss - 1) which accesses (ss - 6),
// (ss + 2) is needed for initialization of cutOffCnt.
Stack stack[MAX_PLY + 10] = {};
Stack* ss = stack + 7;
for (int i = 7; i > 0; --i)
{
(ss - i)->continuationHistory =
&continuationHistory[0][0][NO_PIECE][0]; // Use as a sentinel
(ss - i)->continuationCorrectionHistory = &continuationCorrectionHistory[NO_PIECE][0];
(ss - i)->staticEval = VALUE_NONE;
}
for (int i = 0; i <= MAX_PLY + 2; ++i)
(ss + i)->ply = i;
ss->pv = &pv;
if (mainThread)
{
if (mainThread->bestPreviousScore == VALUE_INFINITE)
mainThread->iterValue.fill(VALUE_ZERO);
else
mainThread->iterValue.fill(mainThread->bestPreviousScore);
}
usize multiPV = usize(options["MultiPV"]);
Skill skill(options["Skill Level"], options["UCI_LimitStrength"] ? int(options["UCI_Elo"]) : 0);
// When playing with strength handicap enable MultiPV search that we will
// use behind-the-scenes to retrieve a set of possible moves.
if (skill.enabled())
multiPV = std::max(multiPV, usize(4));
multiPV = std::min(multiPV, rootMoves.size());
int searchAgainCounter = 0;
bool uciPvSent = false;
lowPlyHistory.fill(100);
for (Color c : {WHITE, BLACK})
for (int i = 0; i < UINT_16_HISTORY_SIZE; i++)
mainHistory[c][i] = mainHistory[c][i] * 789 / 1024;
// Iterative deepening loop until requested to stop or the target depth is reached
while (rootDepth + 1 < MAX_PLY && !threads.stop
&& !(limits.depth && mainThread && rootDepth >= limits.depth))
{
rootDepth++;
// Age out PV variability metric and signal the start of a new iteration.
if (mainThread)
{
totBestMoveChanges /= 2;
uciPvSent = false;
}
// Save the last iteration's scores before the first PV line is searched and
// all the move scores except the (new) PV are set to -VALUE_INFINITE.
for (usize i = 0; i < rootMoves.size(); ++i)
{
rootMoves[i].previousScore = rootMoves[i].score;
rootMoves[i].previousPV = rootMoves[i].pv;
rootMoves[i].previousScoreExact = i < multiPV;
}
usize pvFirst = pvLast = 0;
if (!threads.increaseDepth)
searchAgainCounter++;
// MultiPV loop. We perform a full root search for each PV line
for (pvIdx = 0; pvIdx < multiPV; ++pvIdx)
{
if (pvIdx == pvLast)
{
pvFirst = pvLast;
for (pvLast++; pvLast < rootMoves.size(); pvLast++)
if (rootMoves[pvLast].tbRank != rootMoves[pvFirst].tbRank)
break;
}
lastIterationIdxPV = rootMoves[pvIdx].previousPV;
// Reset UCI info selDepth for each depth and each PV line
selDepth = 0;
// Reset aspiration window starting size
delta = 5 + threadIdx % 8 + std::abs(rootMoves[pvIdx].meanSquaredScore) / 10588;
Value avg = rootMoves[pvIdx].averageScore;
alpha = std::max(avg - delta, -VALUE_INFINITE);
beta = std::min(avg + delta, VALUE_INFINITE);
// Adjust optimism based on root move's averageScore
optimism[us] = 137 * avg / (std::abs(avg) + 81);
optimism[~us] = -optimism[us];
// Start with a small aspiration window and, in the case of a fail
// high/low, re-search with a bigger window until we don't fail
// high/low anymore.
int failedHighCnt = 0;
while (true)
{
// Adjust the effective depth searched, but ensure at least one
// effective increment for every four searchAgain steps (see issue #2717).
Depth adjustedDepth =
std::max(1, rootDepth - failedHighCnt - 3 * (searchAgainCounter + 1) / 4);
rootDelta = beta - alpha;
bestValue = search<Root>(rootPos, ss, alpha, beta, adjustedDepth, false);
// Bring the best move to the front. It is critical that sorting
// is done with a stable algorithm because all the values but the
// first and eventually the new best one is set to -VALUE_INFINITE
// and we want to keep the same order for all the moves except the
// new PV that goes to the front. Note that in the case of MultiPV
// search the already searched PV lines are preserved.
std::stable_sort(rootMoves.begin() + pvIdx, rootMoves.begin() + pvLast);
// If search has been stopped, we break immediately. Sorting is
// safe because RootMoves is still valid, although it refers to
// the previous iteration.
if (threads.stop)
break;
// When failing high/low give some update before a re-search. To avoid
// excessive output that could hang GUIs like Fritz 19, only start
// at nodes > 10M (rather than depth N, which can be reached quickly)
if (mainThread && multiPV == 1 && (bestValue <= alpha || bestValue >= beta)
&& nodes > NODES_LIMIT_OUTPUT)
main_manager()->output_pv(*this, threads, tt, rootDepth);
// In case of failing low/high increase aspiration window and re-search,
// otherwise exit the loop.
if (bestValue <= alpha)
{
beta = alpha;
alpha = std::max(bestValue - delta, -VALUE_INFINITE);
failedHighCnt = 0;
if (mainThread)
mainThread->stopOnPonderhit = false;
}
else if (bestValue >= beta)
{
alpha = std::max(beta - delta, alpha);
beta = std::min(bestValue + delta, VALUE_INFINITE);
++failedHighCnt;
}
else
break;
delta += 44 * delta / 128;
assert(alpha >= -VALUE_INFINITE && beta <= VALUE_INFINITE);
}
if (threads.stop && pvIdx)
{
// In multiPV analysis we do not let aborted searches spoil mated-in/
// TB loss scores from a completed search in an earlier PV line.
// Hence we guard against an aborted pvIdx line overtaking pvIdx - 1
// when pvIdx - 1 is a proven loss.
// Moreover, we do not trust an exact loss score from an aborted search.
if ((is_loss(rootMoves[pvIdx - 1].score) && rootMoves[pvIdx] < rootMoves[pvIdx - 1])
|| rootMoves[pvIdx].score_is_exact_loss())
{
// If previousScore is exact and worse than pvIdx - 1, we can safely use it.
// If it is equal, we make sure it cannot overtake pvIdx - 1.
if (rootMoves[pvIdx].previousScore != -VALUE_INFINITE
&& rootMoves[pvIdx].previousScoreExact
&& rootMoves[pvIdx].previousScore <= rootMoves[pvIdx - 1].score)
{
rootMoves[pvIdx].score = rootMoves[pvIdx].uciScore =
rootMoves[pvIdx].previousScore;
rootMoves[pvIdx].previousScore = -VALUE_INFINITE;
rootMoves[pvIdx].pv = rootMoves[pvIdx].previousPV;
rootMoves[pvIdx].unset_bound_flags();
}
// Otherwise, if we can, we cap the score to the best possible, and mark
// the score as a bound (also a valid excuse for the incomplete PV.)
else
{
if (is_loss(rootMoves[pvIdx - 1].score))
{
rootMoves[pvIdx].score = rootMoves[pvIdx].uciScore =
rootMoves[pvIdx - 1].score;
rootMoves[pvIdx].previousScore = -VALUE_INFINITE;
rootMoves[pvIdx].pv.resize(1);
rootMoves[pvIdx].scoreUpperbound = true;
}
else
rootMoves[pvIdx].scoreUpperbound = false;
rootMoves[pvIdx].scoreLowerbound = !rootMoves[pvIdx].scoreUpperbound;
}
}
// Finally, we mark all loss scores from partially searched moves as a bound.
for (usize i = pvIdx + 1; i < multiPV; ++i)
if (rootMoves[i].score_is_exact_loss())
rootMoves[i].scoreLowerbound = true;
}
// Sort the PV lines searched so far and update the GUI
std::stable_sort(rootMoves.begin() + pvFirst, rootMoves.begin() + pvIdx + 1);
if (mainThread && !threads.stop && (pvIdx + 1 == multiPV || nodes > NODES_LIMIT_OUTPUT))
{
main_manager()->output_pv(*this, threads, tt, rootDepth);
uciPvSent = (pvIdx + 1 == multiPV);
}
if (threads.stop)
break;
}
const bool forgottenMate = lastBestMoveScore != -VALUE_INFINITE
&& is_mate_or_mated(lastBestMoveScore)
&& (std::abs(rootMoves[0].score) < std::abs(lastBestMoveScore)
|| rootMoves[0].score_is_bound());
if (!threads.stop)
{
if (lastBestMovePV.empty() || lastBestMovePV[0] != rootMoves[0].pv[0])
lastBestMoveDepth = rootDepth;
// Do not replace (shorter) mate scores from a previous iteration.
if (!forgottenMate)
{
lastBestMovePV = rootMoves[0].pv;
lastBestMoveScore = rootMoves[0].score;
}
}
const bool abortedLossSearch = threads.stop && !pvIdx && rootMoves[0].score_is_exact_loss();
// An exact mated-in/TB-loss score from an aborted search cannot be trusted: the
// loss could be delayed or refuted upon exploring the remaining root-moves.
// Thus here we roll back to the score from the previous iteration.
// We do the same if a search has failed to recover a mate score that was found
// in a previous iteration.
if (abortedLossSearch || (rootMoves[0].score != -VALUE_INFINITE && forgottenMate))
{
// Bring the last best move to the front for best thread selection.
if (!lastBestMovePV.empty())
{
Utility::move_to_front(rootMoves, [&lastPV = std::as_const(lastBestMovePV)](
const auto& rm) { return rm == lastPV[0]; });
rootMoves[0].score = rootMoves[0].uciScore = lastBestMoveScore;
rootMoves[0].pv = lastBestMovePV;
rootMoves[0].unset_bound_flags();
if (mainThread)
uciPvSent = false;
}
// For an aborted d1 search we label the loss score as a lower bound.
else if (abortedLossSearch)
rootMoves[0].scoreLowerbound = true;
}
// Have we found a "mate in x" after a completed iteration?
if (limits.mate && !threads.stop && is_mate_or_mated(rootMoves[0].score)
&& VALUE_MATE - std::abs(rootMoves[0].score) <= 2 * limits.mate)
threads.stop = true;
if (!mainThread)
continue;
// If the skill level is enabled and time is up, pick a sub-optimal best move
if (skill.enabled() && skill.time_to_pick(rootDepth))
skill.pick_best(rootMoves, multiPV);
// Use part of the gained time from a previous stable move for the current move
for (auto&& th : threads)
{
totBestMoveChanges += th->worker->bestMoveChanges;
th->worker->bestMoveChanges = 0;
}
// Do we have time for the next iteration? Can we stop searching now?
if (limits.use_time_management() && !threads.stop && !mainThread->stopOnPonderhit)
{
u64 nodesEffort = rootMoves[0].effort * 100000 / std::max(u64(1), u64(nodes));
double fallingEval = (11.87 + 2.21 * (mainThread->bestPreviousAverageScore - bestValue)
+ 1.0 * (mainThread->iterValue[iterIdx] - bestValue))
/ 100.0;
fallingEval = std::clamp(fallingEval, 0.572, 1.708);
// If the bestMove is stable over several iterations, reduce time accordingly
timeReduction =
std::clamp(interpolate(double(rootDepth - lastBestMoveDepth), 5.0, 18.0, 0.65, 1.55),
0.65, 1.55);
double reduction = (1.48 + mainThread->previousTimeReduction) / (2.157 * timeReduction);
double bestMoveInstability = 1.096 + 2.29 * totBestMoveChanges / threads.size();
double highBestMoveEffort = std::clamp(
interpolate(i64(nodesEffort), i64(79219), i64(101822), 0.924, 0.71), 0.71, 0.924);
double totalTime = mainThread->tm.optimum() * fallingEval * reduction
* bestMoveInstability * highBestMoveEffort;
if (rootMoves.size() == 1)
// Cap used time to 0.5s for a better viewer experience
totalTime = std::min(500.0, totalTime);
auto elapsedTime = elapsed();
// Stop the search if we have exceeded totalTime or maximum time,
// or if we know that there are no better moves in the analysed line(s)
if (elapsedTime > std::min(totalTime, double(mainThread->tm.maximum()))
|| rootMoves[multiPV - 1].score >= mate_in(3) || rootMoves[0].score == mated_in(2))
{
// If we are allowed to ponder do not stop the search now but
// keep pondering until the GUI sends "ponderhit" or "stop".
if (mainThread->ponder)
mainThread->stopOnPonderhit = true;
else
threads.stop = true;
}
else
threads.increaseDepth = mainThread->ponder || elapsedTime <= totalTime * 0.50;
}
mainThread->iterValue[iterIdx] = bestValue;
iterIdx = (iterIdx + 1) & 3;
}
if (!mainThread)
return false;
mainThread->previousTimeReduction = timeReduction;
// If the skill level is enabled, swap the best PV line with the sub-optimal one
if (skill.enabled())
std::swap(rootMoves[0],
*std::find(rootMoves.begin(), rootMoves.end(),
skill.best ? skill.best : skill.pick_best(rootMoves, multiPV)));
return uciPvSent;
}
void Search::Worker::do_move(Position& pos, const Move move, StateInfo& st, Stack* const ss) {
do_move(pos, move, st, pos.gives_check(move), ss);
}
void Search::Worker::do_move(
Position& pos, const Move move, StateInfo& st, const bool givesCheck, Stack* const ss) {
// prefetch_key does not model castling, en passant or promotion keys
// exactly; for rare moves the prefetch lands on an unused line.
prefetch(tt.first_entry(pos.prefetch_key(move)));
bool capture = pos.capture_stage(move);
++nodes;
auto [dirtyPiece, dirtyThreats] = accumulatorStack.push();
pos.do_move(move, st, givesCheck, dirtyPiece, dirtyThreats, &tt, &sharedHistory);
if (ss != nullptr)
{
ss->currentMove = move;
ss->continuationHistory =
&continuationHistory[ss->inCheck][capture][dirtyPiece.pc][move.to_sq()];
ss->continuationCorrectionHistory =
&continuationCorrectionHistory[dirtyPiece.pc][move.to_sq()];
}
}
void Search::Worker::do_null_move(Position& pos, StateInfo& st, Stack* const ss) {
pos.do_null_move(st);
ss->currentMove = Move::null();
ss->continuationHistory = &continuationHistory[0][0][NO_PIECE][0];
ss->continuationCorrectionHistory = &continuationCorrectionHistory[NO_PIECE][0];
}
void Search::Worker::undo_move(Position& pos, const Move move) {
pos.undo_move(move);
accumulatorStack.pop();
}
void Search::Worker::undo_null_move(Position& pos) { pos.undo_null_move(); }
// Reset histories, usually before a new game
void Search::Worker::clear() {
mainHistory.fill(-5);
captureHistory.fill(-699);
// Each thread is responsible for clearing their part of shared history
sharedHistory.correctionHistory.clear_range(-6, numaThreadIdx, numaTotal);
sharedHistory.pawnHistory.clear_range(-1262, numaThreadIdx, numaTotal);
ttMoveHistory = 0;
for (auto& to : continuationCorrectionHistory)
for (auto& h : to)
h.fill(5);
for (bool inCheck : {false, true})
for (StatsType c : {NoCaptures, Captures})
for (auto& to : continuationHistory[inCheck][c])
for (auto& h : to)
h.fill(-552);
for (usize i = 1; i < reductions.size(); ++i)
reductions[i] = int(2834 / 128.0 * std::log(i));
refreshTable.clear(network[numaAccessToken]);
}
// Main search function for both PV and non-PV nodes
template<NodeType nodeType>
Value Search::Worker::search(
Position& pos, Stack* ss, Value alpha, Value beta, Depth depth, const bool cutNode) {
constexpr bool PvNode = nodeType != NonPV;
constexpr bool rootNode = nodeType == Root;
const bool allNode = !(PvNode || cutNode);
// Dive into quiescence search when the depth reaches zero
if (depth <= 0)
return qsearch<PvNode ? PV : NonPV>(pos, ss, alpha, beta);
// Limit the depth if extensions made it too large
depth = std::min(depth, MAX_PLY - 1);
// Check if we have an upcoming move that draws by repetition
if (!rootNode && alpha < VALUE_DRAW && pos.upcoming_repetition(ss->ply))
{
alpha = value_draw(nodes);
if (alpha >= beta)
return alpha;
}
assert(-VALUE_INFINITE <= alpha && alpha < beta && beta <= VALUE_INFINITE);
assert(PvNode || (alpha == beta - 1));
assert(0 < depth && depth < MAX_PLY);
assert(!(PvNode && cutNode));
PVMoves pv;
StateInfo st;
Key posKey;
Move move, excludedMove, bestMove;
Depth extension, newDepth;
Value bestValue, value, eval, maxValue, probCutBeta;
bool givesCheck, improving, priorCapture, opponentWorsening;
bool capture, ttCapture;
int priorReduction;
Piece movedPiece;
SearchedList capturesSearched;
SearchedList quietsSearched;
// Step 1. Initialize node
ss->inCheck = pos.checkers();
priorCapture = pos.captured_piece();
Color us = pos.side_to_move();
ss->moveCount = 0;
bestValue = -VALUE_INFINITE;
maxValue = VALUE_INFINITE;
ss->followPV = rootNode
|| ((ss - 1)->followPV
&& (static_cast<usize>(ss->ply - 1) < lastIterationIdxPV.size()
&& (ss - 1)->currentMove == lastIterationIdxPV[ss->ply - 1]));
// Check for the available remaining time
if (is_mainthread())
main_manager()->check_time(*this);
// Used to send selDepth info to GUI (selDepth counts from 1, ply from 0)
if (PvNode && selDepth < ss->ply + 1)
selDepth = ss->ply + 1;
if (!rootNode)
{
// Step 2. Check for aborted search and immediate draw
if (threads.stop.load(std::memory_order_relaxed) || pos.is_draw(ss->ply)
|| ss->ply >= MAX_PLY)
return (ss->ply >= MAX_PLY && !ss->inCheck) ? evaluate(pos) : value_draw(nodes);
// Step 3. Mate distance pruning. Even if we mate at the next move our score
// would be at best mate_in(ss->ply + 1), but if alpha is already bigger because
// a shorter mate was found upward in the tree then there is no need to search
// because we will never beat the current alpha. Same logic but with reversed
// signs apply also in the opposite condition of being mated instead of giving
// mate. In this case, return a fail-high score.
alpha = std::max(mated_in(ss->ply), alpha);
beta = std::min(mate_in(ss->ply + 1), beta);
if (alpha >= beta)
return alpha;
}
assert(0 <= ss->ply && ss->ply < MAX_PLY);
Square prevSq = ((ss - 1)->currentMove).is_ok() ? ((ss - 1)->currentMove).to_sq() : SQ_NONE;
bestMove = Move::none();
priorReduction = (ss - 1)->reduction;
(ss - 1)->reduction = 0;
ss->statScore = 0;
(ss + 2)->cutoffCnt = 0;
const auto correctionValue = correction_value(*this, pos, ss);
// Step 4. Transposition table lookup
excludedMove = ss->excludedMove;
posKey = pos.key();
auto [ttHit, ttData, ttWriter] = tt.probe(posKey);
// Need further processing of the saved data
ss->ttHit = ttHit;
ttData.move = rootNode ? rootMoves[pvIdx].pv[0] : ttHit ? ttData.move : Move::none();
ttData.value = ttHit ? value_from_tt(ttData.value, ss->ply, pos.rule50_count()) : VALUE_NONE;
ss->ttPv = excludedMove ? ss->ttPv : PvNode || (ttHit && ttData.is_pv);
ttCapture = ttData.move && pos.capture_stage(ttData.move);
// Step 5. Static evaluation of the position
Value unadjustedStaticEval = VALUE_NONE;
// Skip early pruning when in check
if (ss->inCheck)
ss->staticEval = eval = (ss - 2)->staticEval;
else if (excludedMove)
unadjustedStaticEval = eval = ss->staticEval;
else if (ss->ttHit)
{
// Never assume anything about values stored in TT
unadjustedStaticEval = ttData.eval;
if (!is_valid(unadjustedStaticEval))
unadjustedStaticEval = evaluate(pos);
ss->staticEval = eval = to_corrected_static_eval(unadjustedStaticEval, correctionValue);
// ttValue can be used as a better position evaluation
if (is_valid(ttData.value)
&& (ttData.bound & (ttData.value > eval ? BOUND_LOWER : BOUND_UPPER)))
eval = ttData.value;
}
else
{
unadjustedStaticEval = evaluate(pos);
ss->staticEval = eval = to_corrected_static_eval(unadjustedStaticEval, correctionValue);
// Static evaluation is saved as it was before adjustment by correction history
ttWriter.write(posKey, VALUE_NONE, ss->ttPv, BOUND_NONE, DEPTH_UNSEARCHED, Move::none(),
unadjustedStaticEval, tt.generation());
}
// Set up the improving flag, which is true if current static evaluation is
// bigger than the previous static evaluation at our turn (if we were in
// check at our previous move we go back until we weren't in check) and is
// false otherwise. The improving flag is used in various pruning heuristics.
// Similarly, opponentWorsening is true if our static evaluation is better
// for us than at the last ply.
improving = ss->staticEval > (ss - 2)->staticEval;
opponentWorsening = ss->staticEval > -(ss - 1)->staticEval;
// Hindsight adjustment of reductions based on static evaluation difference.
if (priorReduction >= 3 && !opponentWorsening)
depth++;
if (priorReduction >= 2 && depth >= 2 && ss->staticEval + (ss - 1)->staticEval > 173)
depth--;
// At non-PV nodes we check for an early TT cutoff
if (!PvNode && !excludedMove && ttData.depth > depth - (ttData.value <= beta)
&& is_valid(ttData.value) // Can happen when !ttHit or when access race in probe()
&& (ttData.bound & (ttData.value >= beta ? BOUND_LOWER : BOUND_UPPER))
&& (cutNode == (ttData.value >= beta) || depth > 4))
{
// If ttMove is quiet, update move sorting heuristics on TT hit
if (ttData.move && ttData.value >= beta)
{
// Bonus for a quiet ttMove that fails high
if (!ttCapture)
update_quiet_histories(pos, ss, *this, ttData.move, std::min(114 * depth, 724));
// Extra penalty for early quiet moves of the previous ply
if (prevSq != SQ_NONE && (ss - 1)->moveCount < 4 && !priorCapture)
update_continuation_histories(ss - 1, pos.piece_on(prevSq), prevSq, -2187);
}
// Partial workaround for the graph history interaction problem
// For high rule50 counts don't produce transposition table cutoffs.
if (pos.rule50_count() < 96)
{
if (depth >= 7 && ttData.move && pos.pseudo_legal(ttData.move) && pos.legal(ttData.move)
&& !is_decisive(ttData.value))
{
pos.do_move(ttData.move, st);
Key nextPosKey = pos.key();
auto [ttHitNext, ttDataNext, ttWriterNext] = tt.probe(nextPosKey);
pos.undo_move(ttData.move);
// Check that the ttValue after the tt move would also trigger a cutoff
if (!is_valid(ttDataNext.value))
return ttData.value;
if ((ttData.value >= beta) == (-ttDataNext.value >= beta))
return ttData.value;
}
else
return ttData.value;
}
} // No cutoff, but why? Does the stored inexact value mismatch our aspiration window?
else if (!PvNode && !excludedMove && ttData.depth > depth - (ttData.value <= beta)
&& is_valid(ttData.value) && ttData.bound != BOUND_EXACT
&& ttData.bound & (ttData.value >= beta ? BOUND_UPPER : BOUND_LOWER) && depth > 5)
{ // If a window-bound mismatch is the only reason cutoff failed, penalize the now-useless tte
ttWriter.penalize(1);
}
// Step 6. Tablebases probe
if (!rootNode && !excludedMove && tbConfig.cardinality)
{
int piecesCount = pos.count<ALL_PIECES>();
if (piecesCount <= tbConfig.cardinality
&& (piecesCount < tbConfig.cardinality || depth >= tbConfig.probeDepth)
&& pos.rule50_count() == 0 && !pos.can_castle(ANY_CASTLING))
{
TB::ProbeState err;
TB::WDLScore wdl = TB::probe_wdl(pos, &err);
// Force check of time on the next occasion
if (is_mainthread())
main_manager()->callsCnt = 0;
if (err != TB::ProbeState::FAIL)
{
++tbHits;
int drawScore = tbConfig.useRule50 ? 1 : 0;
Value tbValue = VALUE_TB - ss->ply;
// Use the range VALUE_TB to VALUE_TB_WIN_IN_MAX_PLY to score
value = wdl < -drawScore ? -tbValue
: wdl > drawScore ? tbValue
: VALUE_DRAW + 2 * wdl * drawScore;
Bound b = wdl < -drawScore ? BOUND_UPPER
: wdl > drawScore ? BOUND_LOWER
: BOUND_EXACT;
if (b == BOUND_EXACT || (b == BOUND_LOWER ? value >= beta : value <= alpha))
{
ttWriter.write(posKey, value_to_tt(value, ss->ply), ss->ttPv, b,
std::min(MAX_PLY - 1, depth + 6), Move::none(), VALUE_NONE,
tt.generation());
return value;
}
if (PvNode)
{
if (b == BOUND_LOWER)
bestValue = value, alpha = std::max(alpha, bestValue);
else
maxValue = value;
}
}
}
}
if (ss->inCheck)
goto moves_loop;
// Use static evaluation difference to improve quiet move ordering
if (((ss - 1)->currentMove).is_ok() && !(ss - 1)->inCheck && !priorCapture)
{
int evalDiff = std::clamp(-int((ss - 1)->staticEval + ss->staticEval), -183, 180) + 62;
mainHistory[~us][((ss - 1)->currentMove).raw()] << evalDiff * 10;
if (!ttHit && type_of(pos.piece_on(prevSq)) != PAWN
&& ((ss - 1)->currentMove).type_of() != PROMOTION)
sharedHistory.pawn_entry(pos)[pos.piece_on(prevSq)][prevSq] << evalDiff * 13;
}
// Step 7. Razoring
// If eval is really low, skip search entirely and return the qsearch value.
// For PvNodes, we must have a guard against mates being returned.
if (!PvNode && eval < alpha - 465 - 300 * depth * depth)
return qsearch<NonPV>(pos, ss, alpha, beta);
// Step 8. Futility pruning: child node
// The depth condition is important for mate finding.
if (!ss->ttPv && depth < 17 && eval >= beta && (!ttData.move || ttCapture) && !is_loss(beta)
&& !is_win(eval))
{
Value futilityMult = std::min(40 + depth * 4, 80);
futilityMult -= 20 * !ss->ttHit;
Value futilityMargin = futilityMult * depth
- (2934 * improving + 343 * opponentWorsening) * futilityMult / 1024
+ std::abs(correctionValue) / 182069;
if (eval - futilityMargin >= beta)
return (716 * beta + 308 * eval) / 1024;
}
// Step 9. Null move search with verification search
if (cutNode && ss->staticEval >= beta - 14 * depth - 45 * improving + 374 && !excludedMove
&& pos.non_pawn_material(us) && ss->ply >= nmpMinPly && !is_loss(beta))
{
assert((ss - 1)->currentMove != Move::null());
// Null move dynamic reduction based on depth
Depth R = 7 + depth / 3;
do_null_move(pos, st, ss);
Value nullValue = -search<NonPV>(pos, ss + 1, -beta, -beta + 1, depth - R, false);
undo_null_move(pos);
// Do not return unproven mate or TB scores
if (nullValue >= beta && !is_win(nullValue))
{
if (nmpMinPly || depth < 16)
return nullValue;
assert(!nmpMinPly); // Recursive verification is not allowed
// Do verification search at high depths, with null move pruning disabled
// until ply exceeds nmpMinPly.
nmpMinPly = ss->ply + 3 * (depth - R) / 4;
Value v = search<NonPV>(pos, ss, beta - 1, beta, depth - R, false);
nmpMinPly = 0;
if (v >= beta)
return nullValue;
}
}
improving |= ss->staticEval >= beta;
// Step 10. Internal iterative reductions
// At sufficient depth, reduce depth for PV/Cut nodes without a TTMove.
// (*Scaler) Making IIR more aggressive scales poorly.
if (!ss->followPV && !allNode && depth >= 6 && !ttData.move)
depth--;
// Step 11. ProbCut
// If we have a good enough capture (or queen promotion) and a reduced search
// returns a value much above beta, we can (almost) safely prune the previous move.
probCutBeta = beta + 214 - 59 * improving;
if (depth >= 3
&& !is_decisive(beta)
// If value from transposition table is lower than probCutBeta, don't attempt
// probCut there
&& !(is_valid(ttData.value) && ttData.value < probCutBeta))
{
assert(probCutBeta < VALUE_INFINITE && probCutBeta > beta);
MovePicker mp(pos, ttData.move, probCutBeta - ss->staticEval, &captureHistory);
Depth probCutDepth = depth - 4 - improving;
while ((move = mp.next_move()) != Move::none())
{
assert(move.is_ok());
if (move == excludedMove || !pos.legal(move))
continue;
assert(pos.capture_stage(move));
do_move(pos, move, st, ss);
// Perform a preliminary qsearch to verify that the move holds
value = -qsearch<NonPV>(pos, ss + 1, -probCutBeta, -probCutBeta + 1);
// If the qsearch held, perform the regular search
if (value >= probCutBeta && probCutDepth > 0)
value = -search<NonPV>(pos, ss + 1, -probCutBeta, -probCutBeta + 1, probCutDepth,
!cutNode);
undo_move(pos, move);
if (value >= probCutBeta)
{
// Save ProbCut data into transposition table
ttWriter.write(posKey, value_to_tt(value, ss->ply), ss->ttPv, BOUND_LOWER,
probCutDepth + 1, move, unadjustedStaticEval, tt.generation());
if (!is_decisive(value))
return value - (probCutBeta - beta);
}
}
}
moves_loop: // When in check, search starts here
// Step 12. A small Probcut idea
probCutBeta = beta + 428;
if ((ttData.bound & BOUND_LOWER) && ttData.depth >= depth - 4 && ttData.value >= probCutBeta
&& !is_decisive(beta) && is_valid(ttData.value) && !is_decisive(ttData.value))
return probCutBeta;
const PieceToHistory* contHist[] = {
(ss - 1)->continuationHistory, (ss - 2)->continuationHistory, (ss - 3)->continuationHistory,
(ss - 4)->continuationHistory, (ss - 5)->continuationHistory, (ss - 6)->continuationHistory};
MovePicker mp(pos, ttData.move, depth, &mainHistory, &lowPlyHistory, &captureHistory, contHist,
&sharedHistory, ss->ply);
value = bestValue;
int moveCount = 0;
// Step 13. Loop through all pseudo-legal moves until no moves remain
// or a beta cutoff occurs.
while ((move = mp.next_move()) != Move::none())
{
assert(move.is_ok());
if (move == excludedMove)
continue;
// Check for legality
if (!pos.legal(move))
continue;
// At root obey the "searchmoves" option and skip moves not listed in Root
// Move List. In MultiPV mode we also skip PV moves that have been already
// searched and those of lower "TB rank" if we are in a TB root position.
if (rootNode && !std::count(rootMoves.begin() + pvIdx, rootMoves.begin() + pvLast, move))
continue;
ss->moveCount = ++moveCount;
if (rootNode && is_mainthread() && nodes > NODES_LIMIT_OUTPUT)
{
main_manager()->updates.onIter(
{depth, UCIEngine::move(move, pos.is_chess960()), moveCount + pvIdx});
}
if (PvNode)
(ss + 1)->pv = nullptr;
extension = 0;
capture = pos.capture_stage(move);
movedPiece = pos.moved_piece(move);
givesCheck = pos.gives_check(move);
// Calculate new depth for this move
newDepth = depth - 1;
int delta = beta - alpha;
int r = reduction(improving, depth, moveCount, delta);
// Increase reduction for ttPv nodes (*Scaler)
// Larger values scale well
if (ss->ttPv)
r += 1006;
// Step 14. Pruning at shallow depths.
// Depth conditions are important for mate finding.
if (!rootNode && pos.non_pawn_material(us) && !is_loss(bestValue))
{
// Skip quiet moves if movecount exceeds our threshold
if (moveCount >= (3 + depth * depth) / (2 - improving))
mp.skip_quiet_moves();
// Reduced depth of the next LMR search
int lmrDepth = newDepth - r / 1024;
if (capture || givesCheck)
{
Piece capturedPiece = pos.piece_on(move.to_sq());
int captHist = captureHistory[movedPiece][move.to_sq()][type_of(capturedPiece)];
// Futility pruning for captures
if (!givesCheck && lmrDepth < 7)
{
Value futilityValue = ss->staticEval + 231 + 232 * lmrDepth
+ PieceValue[capturedPiece] + 131 * captHist / 1024;
if (futilityValue <= alpha)
continue;
}
// SEE based pruning for captures and checks
// Avoid pruning sacrifices of our last piece for stalemate
int margin = 175 * depth + captHist * 34 / 1024;
if ((alpha >= VALUE_DRAW || pos.non_pawn_material(us) != PieceValue[movedPiece])
&& !pos.see_ge(move, -margin))
continue;
}
else if (!ss->followPV || !PvNode)
{
int dIndex = std::min(int(depth), int(lmrDivisor.size())) - 1;
int history = (*contHist[0])[movedPiece][move.to_sq()]
+ (*contHist[1])[movedPiece][move.to_sq()]
+ sharedHistory.pawn_entry(pos)[movedPiece][move.to_sq()];
// Continuation history based pruning
if (history < -4313 * depth)
continue;
history += 64 * mainHistory[us][move.raw()] / 32;
// (*Scaler): Generally, lower divisors scale well
lmrDepth += history / lmrDivisor[dIndex];
Value futilityValue = ss->staticEval + 40 + 138 * !bestMove + 117 * lmrDepth
+ 90 * (ss->staticEval > alpha);
// Futility pruning: parent node
// (*Scaler): Generally, more frequent futility pruning
// scales well
if (!ss->inCheck && lmrDepth < 12 && futilityValue <= alpha)
{
if (bestValue <= futilityValue && !is_decisive(bestValue)
&& !is_win(futilityValue))
bestValue = futilityValue;
continue;
}
lmrDepth = std::max(lmrDepth, 0);
// Prune moves with negative SEE
if (!pos.see_ge(move, -25 * lmrDepth * lmrDepth))
continue;
}
}
// Step 15. Extensions
// Singular extension search. If all moves but one
// fail low on a search of (alpha-s, beta-s), and just one fails high on
// (alpha, beta), then that move is singular and should be extended. To
// verify this we do a reduced search on the position excluding the ttMove
// and if the result is lower than ttValue minus a margin, then we will
// extend the ttMove. Recursive singular search is avoided.
// (*Scaler) Generally, higher singularBeta (i.e closer to ttValue)
// and lower extension margins scale well.
if (!rootNode && move == ttData.move && !excludedMove && depth >= 6 + ss->ttPv
&& is_valid(ttData.value) && !is_decisive(ttData.value) && (ttData.bound & BOUND_LOWER)
&& ttData.depth >= depth - 3 && !is_shuffling(move, ss, pos))
{
Value singularBeta = ttData.value - (60 + 70 * (ss->ttPv && !PvNode)) * depth / 59;
Depth singularDepth = newDepth / 2;
ss->excludedMove = move;
value = search<NonPV>(pos, ss, singularBeta - 1, singularBeta, singularDepth, cutNode);
ss->excludedMove = Move::none();
if (value < singularBeta)
{
int corrValAdj = std::abs(correctionValue) / 194822;
int doubleMargin = -3 + 201 * PvNode - 157 * !ttCapture - corrValAdj
- 1081 * ttMoveHistory / 117824 - (ss->ply > rootDepth) * 41;
int tripleMargin = 72 + 306 * PvNode - 188 * !ttCapture + 84 * ss->ttPv - corrValAdj
- (ss->ply > rootDepth) * 45;
extension =
1 + (value < singularBeta - doubleMargin) + (value < singularBeta - tripleMargin);
depth++;
}
// Multi-cut pruning
// Our ttMove is assumed to fail high based on the bound of the TT entry,
// and if after excluding the ttMove with a reduced search we fail high
// over the original beta, we assume this expected cut-node is not
// singular (multiple moves fail high), and we can prune the whole
// subtree by returning a softbound.
else if (value >= beta && !is_decisive(value))
{
ttMoveHistory << -442 - 108 * depth;
return value;
}
// Negative extensions
// If other moves failed high over (ttValue - margin) without the
// ttMove on a reduced search, but we cannot do multi-cut because
// (ttValue - margin) is lower than the original beta, we do not know
// if the ttMove is singular or can do a multi-cut, so we reduce the
// ttMove in favor of other moves based on some conditions:
// If the ttMove is assumed to fail high over current beta
else if (ttData.value >= beta)
extension = -3;
// If we are on a cutNode but the ttMove is not assumed to fail high
// over current beta
else if (cutNode)
extension = -2;
}
u64 nodeCount = rootNode ? u64(nodes) : 0;
// Step 16. Make the move
do_move(pos, move, st, givesCheck, ss);
// Add extension to new depth
newDepth += extension;
// Decrease reduction for PvNodes (*Scaler)
if (ss->ttPv)
r -= 2766 + PvNode * 1017 + (ttData.value > alpha) * 838
+ (ttData.depth >= depth) * (923 + cutNode * 955);
r += 714; // Base reduction offset to compensate for other tweaks
r -= moveCount * 62;
r -= std::abs(correctionValue) / 26131;
// Increase reduction for cut nodes
if (cutNode)
r += 3995 + 1059 * !ttData.move;
// Increase reduction if ttMove is a capture
if (ttCapture)
r += 1039;
// Increase reduction if next ply has a lot of fail high
if ((ss + 1)->cutoffCnt > 1)
r += 236 + 1079 * ((ss + 1)->cutoffCnt > 2) + 1143 * allNode;
// For first picked move (ttMove) reduce reduction
else if (move == ttData.move)
r = std::max(0, r - 2016);
if (capture)
ss->statScore = 809 * int(PieceValue[pos.captured_piece()]) / 128
+ captureHistory[movedPiece][move.to_sq()][type_of(pos.captured_piece())];
else
ss->statScore = 2 * mainHistory[us][move.raw()]
+ (*contHist[0])[movedPiece][move.to_sq()]
+ (*contHist[1])[movedPiece][move.to_sq()];
// Decrease/increase reduction for moves with a good/bad history
r -= ss->statScore * 445 / 4096;
// Scale up reductions for expected ALL nodes
if (allNode)
r += r * 272 / (256 * depth + 285);
// Step 17. Late moves reduction / extension (LMR)
if (depth >= 2 && moveCount > 1)
{
// In general we want to cap the LMR depth search at newDepth, but when
// reduction is negative, we allow this move a limited search extension
// beyond the first move depth.
// To prevent problems when the max value is less than the min value,
// std::clamp has been replaced by a more robust implementation.
Depth d = std::max(1, std::min(newDepth - r / 1024, newDepth + 2)) + PvNode;
ss->reduction = newDepth - d;
value = -search<NonPV>(pos, ss + 1, -(alpha + 1), -alpha, d, true);
ss->reduction = 0;
// Do a full-depth search when reduced LMR search fails high
// (*Scaler) Shallower searches here don't scale well
if (value > alpha)
{
// Adjust full-depth search based on LMR results - if the result was
// good enough search deeper, if it was bad enough search shallower.
const bool doDeeperSearch = d < newDepth && value > bestValue + 52;
const bool doShallowerSearch = value < bestValue + 9;
newDepth += doDeeperSearch - doShallowerSearch;
if (newDepth > d)
value = -search<NonPV>(pos, ss + 1, -(alpha + 1), -alpha, newDepth, !cutNode);
// Post LMR continuation history updates
update_continuation_histories(ss, movedPiece, move.to_sq(), 1415);
}
}
// Step 18. Full-depth search when LMR is skipped
else if (!PvNode || moveCount > 1)
{
// Increase reduction if ttMove is not present
if (!ttData.move)
r += 1085;
// Note that if expected reduction is high, we reduce search depth here
value = -search<NonPV>(pos, ss + 1, -(alpha + 1), -alpha,
newDepth - (r > 5039) - (r > 5223 && newDepth > 2), !cutNode);
}
// For PV nodes only, do a full PV search on the first move or after a fail high,
// otherwise let the parent node fail low with value <= alpha and try another move.
if (PvNode && (moveCount == 1 || value > alpha))
{
(ss + 1)->pv = &pv;
(ss + 1)->pv->clear();
// Extend move from transposition table if we are about to dive into qsearch.
// decisive score handling improves mate finding and retrograde analysis.
if (move == ttData.move
&& ((is_valid(ttData.value) && is_decisive(ttData.value) && ttData.depth > 0)
|| ttData.depth > 1))
newDepth = std::max(newDepth, 1);
value = -search<PV>(pos, ss + 1, -beta, -alpha, newDepth, false);
}
// Step 19. Undo move
undo_move(pos, move);
assert(value > -VALUE_INFINITE && value < VALUE_INFINITE);
// Step 20. Check for a new best move
// Finished searching the move. If a stop occurred, the return value of
// the search cannot be trusted, and we return immediately without updating
// best move, principal variation nor transposition table.
if (threads.stop.load(std::memory_order_relaxed))
return VALUE_ZERO;
if (rootNode)
{
RootMove& rm = *std::find(rootMoves.begin(), rootMoves.end(), move);
rm.effort += nodes - nodeCount;
rm.averageScore =
rm.averageScore != -VALUE_INFINITE ? (value + rm.averageScore) / 2 : value;
rm.meanSquaredScore = rm.meanSquaredScore != -VALUE_INFINITE * VALUE_INFINITE
? (value * std::abs(value) + rm.meanSquaredScore) / 2
: value * std::abs(value);
// PV move or new best move?
if (moveCount == 1 || value > alpha)
{
rm.score = rm.uciScore = value;
rm.selDepth = selDepth;
rm.unset_bound_flags();
if (value >= beta)
{
rm.scoreLowerbound = true;
rm.uciScore = beta;
}
else if (value <= alpha)
{
rm.scoreUpperbound = true;
rm.uciScore = alpha;
}
rm.pv.resize(1);
assert((ss + 1)->pv);
for (Move pvMove : *(ss + 1)->pv)
rm.pv.push_back(pvMove);
// We record how often the best move has been changed in each iteration.
// This information is used for time management. In MultiPV mode,
// we must take care to only do this for the first PV line.
if (moveCount > 1 && !pvIdx)
++bestMoveChanges;
}
else
// All other moves but the PV, are set to the lowest value: this
// is not a problem when sorting because the sort is stable and the
// move position in the list is preserved - just the PV is pushed up.
rm.score = -VALUE_INFINITE;
}
// In case we have an alternative move equal in eval to the current bestmove,
// promote it to bestmove by pretending it just exceeds alpha (but not beta).
int inc = (value == bestValue && ss->ply + 2 >= rootDepth && (int(nodes) & 14) == 0
&& !is_win(std::abs(value) + 1));
if (value + inc > bestValue)
{
bestValue = value;
if (value + inc > alpha)
{
bestMove = move;
if (PvNode && !rootNode) // Update pv even in fail-high case
ss->pv->update(move, (ss + 1)->pv);
if (value >= beta)
{
// (*Scaler) Infrequent and small updates scale well
ss->cutoffCnt += (extension < 2) || PvNode;
assert(value >= beta); // Fail high
break;
}
// Reduce other moves if we have found at least one score improvement
if (depth > 2 && depth < 13 && !is_decisive(value))
depth -= 2;
assert(depth > 0);
alpha = value; // Update alpha! Always alpha < beta
}
}
// If the move is worse than some previously searched move,
// remember it, to update its stats later.
if (move != bestMove && moveCount <= SEARCHEDLIST_CAPACITY)
{
if (capture)
capturesSearched.push_back(move);
else
quietsSearched.push_back(move);
}
}
// Step 21. Check for mate and stalemate
// All legal moves have been searched and if there are no legal moves, it
// must be a mate or a stalemate. If we are in a singular extension search then
// return a fail low score.
assert(moveCount || !ss->inCheck || excludedMove || !MoveList<LEGAL>(pos).size());
// Adjust best value for fail high cases
if (bestValue >= beta && !is_decisive(bestValue) && !is_decisive(alpha))
bestValue = (bestValue * depth + beta) / (depth + 1);
if (!moveCount)
bestValue = excludedMove ? alpha : ss->inCheck ? mated_in(ss->ply) : VALUE_DRAW;
// If there is a move that produces search value greater than alpha,
// we update the stats of searched moves.
else if (bestMove)
{
update_all_stats(pos, ss, *this, bestMove, prevSq, quietsSearched, capturesSearched, depth,
ttData.move, PvNode);
if (!PvNode)
ttMoveHistory << (bestMove == ttData.move ? 792 : -779);
}
// Bonus for prior quiet countermove that caused the fail low
else if (!priorCapture && prevSq != SQ_NONE)
{
int bonusScale = -245;
bonusScale -= (ss - 1)->statScore / 98;
bonusScale += std::min(59 * depth, 430);
bonusScale += 191 * ((ss - 1)->moveCount > 8);
bonusScale += 143 * (!ss->inCheck && bestValue <= ss->staticEval - 103);
bonusScale += 151 * (!(ss - 1)->inCheck && bestValue <= -(ss - 1)->staticEval - 78);
bonusScale = std::max(bonusScale, 0);
// scaledBonus ranges from 0 to roughly 2.3M, overflows happen for multipliers larger than 900
const int scaledBonus = std::min(141 * depth - 82, 1472) * bonusScale;
update_continuation_histories(ss - 1, pos.piece_on(prevSq), prevSq,
scaledBonus * 236 / 16384);
mainHistory[~us][((ss - 1)->currentMove).raw()] << scaledBonus * 234 / 32768;
if (type_of(pos.piece_on(prevSq)) != PAWN && ((ss - 1)->currentMove).type_of() != PROMOTION)
sharedHistory.pawn_entry(pos)[pos.piece_on(prevSq)][prevSq] << scaledBonus * 322 / 8192;
}
// Bonus for prior capture countermove that caused the fail low
else if (priorCapture && prevSq != SQ_NONE)
{
Piece capturedPiece = pos.captured_piece();
assert(capturedPiece != NO_PIECE);
captureHistory[pos.piece_on(prevSq)][prevSq][type_of(capturedPiece)] << 901;
}
if (PvNode)
bestValue = std::min(bestValue, maxValue);
// If no good move is found and the previous position was ttPv, then the previous
// opponent move is probably good and the new position is added to the search tree.
if (bestValue <= alpha)
ss->ttPv = ss->ttPv || (ss - 1)->ttPv;
// Write gathered information in transposition table. Note that the
// static evaluation is saved as it was before correction history.
if (!excludedMove && !(rootNode && pvIdx))
ttWriter.write(posKey, value_to_tt(bestValue, ss->ply), ss->ttPv,
bestValue >= beta ? BOUND_LOWER
: PvNode && bestMove ? BOUND_EXACT
: BOUND_UPPER,
moveCount != 0 ? depth : std::min(MAX_PLY - 1, depth + 6), bestMove,
unadjustedStaticEval, tt.generation());
// Adjust correction history if the best move is not a capture
// and the error direction matches whether we are above/below bounds.
if (!ss->inCheck && !(bestMove && pos.capture(bestMove))
&& (bestValue > ss->staticEval) == bool(bestMove))
{
auto bonus =
std::clamp(int(bestValue - ss->staticEval) * depth * (bestMove ? 12 : 18) / 128,
-CORRECTION_HISTORY_LIMIT / 4, CORRECTION_HISTORY_LIMIT / 4);
update_correction_history(pos, ss, *this, 1114 * bonus / 1024);
}
assert(bestValue > -VALUE_INFINITE && bestValue < VALUE_INFINITE);
return bestValue;
}
// Quiescence search function, which is called by the main search function with
// depth zero, or recursively with further decreasing depth. With depth <= 0, we
// "should" be using static eval only, but tactical moves may confuse the static eval.
// To fight this horizon effect, we implement this qsearch of tactical moves.
// See https://www.chessprogramming.org/Horizon_Effect
// and https://www.chessprogramming.org/Quiescence_Search
template<NodeType nodeType>
Value Search::Worker::qsearch(Position& pos, Stack* ss, Value alpha, Value beta) {
static_assert(nodeType != Root);
constexpr bool PvNode = nodeType == PV;
assert(alpha >= -VALUE_INFINITE && alpha < beta && beta <= VALUE_INFINITE);
assert(PvNode || (alpha == beta - 1));
// Check if we have an upcoming move that draws by repetition
if (alpha < VALUE_DRAW && pos.upcoming_repetition(ss->ply))
{
alpha = value_draw(nodes);
if (alpha >= beta)
return alpha;
}
PVMoves pv;
StateInfo st;
Key posKey;
Move move, bestMove;
Value bestValue, value, futilityBase;
bool pvHit, givesCheck, capture;
int moveCount;
// Step 1. Initialize node
if (PvNode)
{
(ss + 1)->pv = &pv;
ss->pv->clear();
}
bestMove = Move::none();
ss->inCheck = pos.checkers();
moveCount = 0;
// Used to send selDepth info to GUI (selDepth counts from 1, ply from 0)
if (PvNode && selDepth < ss->ply + 1)
selDepth = ss->ply + 1;
// Step 2. Check for an immediate draw or maximum ply reached
if (pos.is_draw(ss->ply) || ss->ply >= MAX_PLY)
return (ss->ply >= MAX_PLY && !ss->inCheck) ? evaluate(pos) : VALUE_DRAW;
assert(0 <= ss->ply && ss->ply < MAX_PLY);
// Step 3. Transposition table lookup
posKey = pos.key();
auto [ttHit, ttData, ttWriter] = tt.probe(posKey);
// Need further processing of the saved data
ss->ttHit = ttHit;
ttData.move = ttHit ? ttData.move : Move::none();
ttData.value = ttHit ? value_from_tt(ttData.value, ss->ply, pos.rule50_count()) : VALUE_NONE;
pvHit = ttHit && ttData.is_pv;
// At non-PV nodes we check for an early TT cutoff
if (!PvNode && ttData.depth >= DEPTH_QS
&& is_valid(ttData.value) // Can happen when !ttHit or when access race in probe()
&& (ttData.bound & (ttData.value >= beta ? BOUND_LOWER : BOUND_UPPER)))
return ttData.value;
// Step 4. Static evaluation of the position
Value unadjustedStaticEval = VALUE_NONE;
if (ss->inCheck)
bestValue = futilityBase = -VALUE_INFINITE;
else
{
const auto correctionValue = correction_value(*this, pos, ss);
if (ss->ttHit)
{
// Never assume anything about values stored in TT
unadjustedStaticEval = ttData.eval;
if (!is_valid(unadjustedStaticEval))
unadjustedStaticEval = evaluate(pos);
ss->staticEval = bestValue =
to_corrected_static_eval(unadjustedStaticEval, correctionValue);
// ttValue can be used as a better position evaluation
if (is_valid(ttData.value) && !is_decisive(ttData.value)
&& (ttData.bound & (ttData.value > bestValue ? BOUND_LOWER : BOUND_UPPER)))
bestValue = ttData.value;
}
else
{
unadjustedStaticEval = evaluate(pos);
ss->staticEval = bestValue =
to_corrected_static_eval(unadjustedStaticEval, correctionValue);
}
// Stand pat. Return immediately if static value is at least beta
if (bestValue >= beta)
{
if (!is_decisive(bestValue))
bestValue = (467 * bestValue + 557 * beta) / 1024;
if (!ss->ttHit)
ttWriter.write(posKey, VALUE_NONE, false, BOUND_LOWER, DEPTH_UNSEARCHED,
Move::none(), unadjustedStaticEval, tt.generation());
return bestValue;
}
if (bestValue > alpha)
alpha = bestValue;
futilityBase = ss->staticEval + 335;
}
const PieceToHistory* contHist[] = {(ss - 1)->continuationHistory};
Square prevSq = ((ss - 1)->currentMove).is_ok() ? ((ss - 1)->currentMove).to_sq() : SQ_NONE;
// Initialize a MovePicker object for the current position, and prepare to search
// the moves. We presently use two stages of move generator in quiescence search:
// captures, or evasions only when in check.
MovePicker mp(pos, ttData.move, DEPTH_QS, &mainHistory, &lowPlyHistory, &captureHistory,
contHist, &sharedHistory, ss->ply);
// Step 5. Loop through all pseudo-legal moves until no moves remain or a beta
// cutoff occurs.
while ((move = mp.next_move()) != Move::none())
{
assert(move.is_ok());
if (!pos.legal(move))
continue;
givesCheck = pos.gives_check(move);
capture = pos.capture_stage(move);
moveCount++;
// Step 6. Pruning
if (!is_loss(bestValue))
{
// Futility pruning and moveCount pruning
if (!givesCheck && move.to_sq() != prevSq && !is_loss(futilityBase)
&& move.type_of() != PROMOTION)
{
if (moveCount > 2)
continue;
Value futilityValue = futilityBase + PieceValue[pos.piece_on(move.to_sq())];
// If static eval + value of piece we are going to capture is
// much lower than alpha, we can prune this move.
if (futilityValue <= alpha)
{
bestValue = std::max(bestValue, futilityValue);
continue;
}
// If static exchange evaluation is low enough
// we can prune this move.
if (!pos.see_ge(move, alpha - futilityBase))
{
bestValue = std::max(bestValue, std::min(alpha, futilityBase));
continue;
}
}
// Skip non-captures
if (!capture)
continue;
// Do not search moves with bad enough SEE values
if (!pos.see_ge(move, -74))
continue;
}
// Step 7. Make and search the move
do_move(pos, move, st, givesCheck, ss);
value = -qsearch<nodeType>(pos, ss + 1, -beta, -alpha);
undo_move(pos, move);
assert(value > -VALUE_INFINITE && value < VALUE_INFINITE);
// Step 8. Check for a new best move
if (value > bestValue)
{
bestValue = value;
if (value > alpha)
{
bestMove = move;
if (PvNode) // Update pv even in fail-high case
ss->pv->update(move, (ss + 1)->pv);
if (value < beta) // Update alpha here!
alpha = value;
else
break; // Fail high
}
}
}
// Step 9. Check for mate and stalemate
// All legal moves have been searched. A special case: if we are
// in check and no legal moves were found, it is checkmate.
if (!moveCount)
{
if (ss->inCheck) // Checkmate!
{
assert(!MoveList<LEGAL>(pos).size());
return mated_in(ss->ply); // Plies to mate from the root
}
// Only check for stalemate under specific conditions
Color us = pos.side_to_move();
if (!(pawn_single_push_bb(us, pos.pieces(us, PAWN)) & ~pos.pieces())
&& !pos.non_pawn_material(us) && type_of(pos.captured_piece()) >= KNIGHT
&& !MoveList<LEGAL>(pos).size())
bestValue = VALUE_DRAW;
}
if (!is_decisive(bestValue) && bestValue > beta)
bestValue = (481 * bestValue + 543 * beta) / 1024;
// Save gathered info in transposition table. The static evaluation
// is saved as it was before adjustment by correction history.
ttWriter.write(posKey, value_to_tt(bestValue, ss->ply), pvHit,
bestValue >= beta ? BOUND_LOWER : BOUND_UPPER, DEPTH_QS, bestMove,
unadjustedStaticEval, tt.generation());
assert(bestValue > -VALUE_INFINITE && bestValue < VALUE_INFINITE);
return bestValue;
}
int Search::Worker::reduction(bool i, Depth d, int mn, int delta) const {
int reductionScale = reductions[d] * reductions[mn];
return reductionScale - delta * 617 / rootDelta + !i * reductionScale * 194 / 512 + 1027;
}
// elapsed() returns the time elapsed since the search started. If the
// 'nodestime' option is enabled, it will return the count of nodes searched
// instead. This function is called to check whether the search should be
// stopped based on predefined thresholds like time limits or nodes searched.
TimePoint Search::Worker::elapsed() const {
return main_manager()->tm.elapsed([this]() { return threads.nodes_searched(); });
}
Value Search::Worker::evaluate(const Position& pos) {
return Eval::evaluate(network[numaAccessToken], pos, accumulatorStack, refreshTable,
optimism[pos.side_to_move()]);
}
namespace {
// Adjusts a mate or TB score from "plies to mate from the root" to
// "plies to mate from the current position". Standard scores are unchanged.
// The function is called before storing a value in the transposition table.
Value value_to_tt(Value v, int ply) { return is_win(v) ? v + ply : is_loss(v) ? v - ply : v; }
// Inverse of value_to_tt(): it adjusts a mate or TB score from the transposition
// table (which refers to the plies to mate/be mated from current position) to
// "plies to mate/be mated (TB win/loss) from the root". However, to avoid
// potentially false mate or TB scores related to the 50 moves rule and the
// graph history interaction, we return the highest non-TB score instead.
Value value_from_tt(Value v, int ply, int r50c) {
if (!is_valid(v))
return VALUE_NONE;
// handle TB win or better
if (is_win(v))
{
// Downgrade a potentially false mate score
if (is_mate(v) && VALUE_MATE - v > 100 - r50c)
return VALUE_TB_WIN_IN_MAX_PLY - 1;
// Downgrade a potentially false TB score.
if (VALUE_TB - v > 100 - r50c)
return VALUE_TB_WIN_IN_MAX_PLY - 1;
return v - ply;
}
// handle TB loss or worse
if (is_loss(v))
{
// Downgrade a potentially false mate score.
if (is_mated(v) && VALUE_MATE + v > 100 - r50c)
return VALUE_TB_LOSS_IN_MAX_PLY + 1;
// Downgrade a potentially false TB score.
if (VALUE_TB + v > 100 - r50c)
return VALUE_TB_LOSS_IN_MAX_PLY + 1;
return v + ply;
}
return v;
}
// Updates stats at the end of search() when a bestMove is found
void update_all_stats(const Position& pos,
Stack* ss,
Search::Worker& workerThread,
Move bestMove,
Square prevSq,
SearchedList& quietsSearched,
SearchedList& capturesSearched,
Depth depth,
Move ttMove,
bool PvNode) {
CapturePieceToHistory& captureHistory = workerThread.captureHistory;
Piece movedPiece = pos.moved_piece(bestMove);
PieceType capturedPiece;
int bonus =
std::min(134 * depth - 79, 1572) + 382 * (bestMove == ttMove) + (ss - 1)->statScore / 30;
int malus = std::min(1005 * depth - 205, 2218);
if (!PvNode)
// Important: don't remove the cast to a 64-bit number else the multiplication
// can overflow on 32-bit platforms which would change the bench signature
bonus += int(bonus * u64(quietsSearched.size() + capturesSearched.size()) / 256);
if (!pos.capture_stage(bestMove))
{
update_quiet_histories(pos, ss, workerThread, bestMove, bonus * 824 / 1024);
int actualMalus = malus * 1136 / 1024;
// Decrease stats for all non-best quiet moves
for (Move move : quietsSearched)
{
actualMalus = actualMalus * 956 / 1024;
update_quiet_histories(pos, ss, workerThread, move, -actualMalus);
}
}
else
{
// Increase stats for the best move in case it was a capture move
capturedPiece = type_of(pos.piece_on(bestMove.to_sq()));
captureHistory[movedPiece][bestMove.to_sq()][capturedPiece] << bonus * 1366 / 1024;
}
// Extra penalty for a quiet early move that was not a TT move in
// previous ply when it gets refuted.
if (prevSq != SQ_NONE && ((ss - 1)->moveCount == 1 + (ss - 1)->ttHit) && !pos.captured_piece())
update_continuation_histories(ss - 1, pos.piece_on(prevSq), prevSq, -malus * 683 / 1024);
// Decrease stats for all non-best capture moves
for (Move move : capturesSearched)
{
movedPiece = pos.moved_piece(move);
capturedPiece = type_of(pos.piece_on(move.to_sq()));
captureHistory[movedPiece][move.to_sq()][capturedPiece] << -malus * 1518 / 1024;
}
}
// Updates the continuation histories for the move pairs formed by
// the current move and the moves played in previous plies.
void update_continuation_histories(Stack* ss, Piece pc, Square to, int bonus) {
static constexpr std::array<ConthistBonus, 6> conthist_bonuses = {
{{1, 1040}, {2, 780}, {3, 300}, {4, 537}, {5, 129}, {6, 423}}};
// Multipliers for positive history consistency
constexpr int CMHCMultipliers[] = {96, 113, 101, 105, 127, 121, 126};
int positiveCount = 0;
for (const auto [i, weight] : conthist_bonuses)
{
// Only update the first 2 continuation histories if we are in check
if (ss->inCheck && i > 2)
break;
if (((ss - i)->currentMove).is_ok())
{
auto& historyEntry = (*(ss - i)->continuationHistory)[pc][to];
if (historyEntry > 0)
positiveCount++;
int multiplier = CMHCMultipliers[positiveCount];
historyEntry << (bonus * weight * multiplier / 131072) + 71 * (i < 2);
}
}
}
// Updates move sorting heuristics
void update_quiet_histories(
const Position& pos, Stack* ss, Search::Worker& workerThread, Move move, int bonus) {
Color us = pos.side_to_move();
workerThread.mainHistory[us][move.raw()] << bonus; // Untuned to prevent duplicate effort
if (ss->ply < LOW_PLY_HISTORY_SIZE)
workerThread.lowPlyHistory[ss->ply][move.raw()] << bonus * 663 / 1024;
update_continuation_histories(ss, pos.moved_piece(move), move.to_sq(), bonus * 820 / 1024);
workerThread.sharedHistory.pawn_entry(pos)[pos.moved_piece(move)][move.to_sq()]
<< bonus * (bonus > -7 ? 1038 : 525) / 1024;
}
}
// When playing with strength handicap, choose the best move among a set of
// RootMoves using a statistical rule dependent on 'level'. Idea by Heinz van Saanen.
Move Skill::pick_best(const RootMoves& rootMoves, usize multiPV) {
static PRNG rng(now()); // PRNG sequence should be non-deterministic
// With tablebases at the root, rootMoves are ordered by tbRank rather than by
// score, so compute the score range explicitly to keep 'delta' non-negative.
Value topScore = rootMoves[0].score;
Value minScore = rootMoves[0].score;
for (usize i = 1; i < multiPV; ++i)
{
topScore = std::max(topScore, rootMoves[i].score);
minScore = std::min(minScore, rootMoves[i].score);
}
int delta = std::min(topScore - minScore, int(PawnValue));
int maxScore = -VALUE_INFINITE;
double weakness = 120 - 2 * level;
// Choose best move. For each move score we add two terms, both dependent on
// weakness. One is deterministic and bigger for weaker levels, and one is
// random. Then we choose the move with the resulting highest score.
for (usize i = 0; i < multiPV; ++i)
{
// This is our magic formula
int push = int(weakness * int(topScore - rootMoves[i].score)
+ delta * (rng.rand<unsigned>() % int(weakness)))
/ 128;
if (rootMoves[i].score + push >= maxScore)
{
maxScore = rootMoves[i].score + push;
best = rootMoves[i].pv[0];
}
}
return best;
}
// Used to print debug info and, more importantly, to detect
// when we are out of available time and thus stop the search.
void SearchManager::check_time(Search::Worker& worker) {
if (--callsCnt > 0)
return;
// When using nodes, ensure checking rate is not lower than 0.1% of nodes
callsCnt = worker.limits.nodes ? std::min(512, int(worker.limits.nodes / 1024)) : 512;
static TimePoint lastInfoTime = now();
TimePoint elapsed = tm.elapsed([&worker]() { return worker.threads.nodes_searched(); });
TimePoint tick = worker.limits.startTime + elapsed;
if (tick - lastInfoTime >= 1000)
{
lastInfoTime = tick;
dbg_print();
}
// We should not stop pondering until told so by the GUI
if (ponder)
return;
if ((worker.limits.use_time_management() && (elapsed > tm.maximum() || stopOnPonderhit))
|| (worker.limits.movetime && elapsed >= worker.limits.movetime)
|| (worker.limits.nodes && worker.threads.nodes_searched() >= worker.limits.nodes))
worker.threads.stop = true;
}
// Used to correct and extend PVs for moves that have a TB (but not a mate) score.
// Keeps the search based PV for as long as it is verified to maintain the game
// outcome, truncates afterwards. Finally, extends to mate the PV, providing a
// possible continuation (but not a proven mating line).
void syzygy_extend_pv(const OptionsMap& options,
const Search::LimitsType& limits,
Position& pos,
RootMove& rootMove,
Value& v) {
auto t_start = std::chrono::steady_clock::now();
int moveOverhead = int(options["Move Overhead"]);
bool rule50 = bool(options["Syzygy50MoveRule"]);
// Do not use more than moveOverhead / 2 time, if time management is active
auto time_abort = [&t_start, &moveOverhead, &limits]() -> bool {
auto t_end = std::chrono::steady_clock::now();
return limits.use_time_management()
&& 2 * std::chrono::duration<double, std::milli>(t_end - t_start).count()
> moveOverhead;
};
std::list<StateInfo> sts;
// Step 0, do the rootMove, no correction allowed, as needed for MultiPV in TB.
auto& stRoot = sts.emplace_back();
pos.do_move(rootMove.pv[0], stRoot);
int ply = 1;
// Step 1, walk the PV to the last position in TB with correct decisive score
while (usize(ply) < rootMove.pv.size())
{
Move& pvMove = rootMove.pv[ply];
RootMoves legalMoves;
for (const auto& m : MoveList<LEGAL>(pos))
legalMoves.emplace_back(m);
TB::Config config = TB::rank_root_moves(options, pos, legalMoves, false, time_abort);
RootMove& rm = *std::find(legalMoves.begin(), legalMoves.end(), pvMove);
if (legalMoves[0].tbRank != rm.tbRank)
break;
ply++;
auto& st = sts.emplace_back();
pos.do_move(pvMove, st);
// Do not allow for repetitions or drawing moves along the PV in TB regime
if (config.rootInTB && ((rule50 && pos.is_draw(ply)) || pos.is_repetition(ply)))
{
pos.undo_move(pvMove);
ply--;
break;
}
// Full PV shown will thus be validated and end in TB.
// If we cannot validate the full PV in time, we do not show it.
if (config.rootInTB && time_abort())
break;
}
// Resize the PV to the correct part
rootMove.pv.resize(ply);
// Step 2, now extend the PV to mate, as if the user explored syzygy-tables.info
// using top ranked moves (minimal DTZ), which gives optimal mates only for simple
// endgames e.g. KRvK.
while (!(rule50 && pos.is_draw(0)))
{
if (time_abort())
break;
RootMoves legalMoves;
for (const auto& m : MoveList<LEGAL>(pos))
{
auto& rm = legalMoves.emplace_back(m);
StateInfo tmpSI;
pos.do_move(m, tmpSI);
// Give a score of each move to break DTZ ties restricting opponent mobility,
// but not giving the opponent a capture.
for (const auto& mOpp : MoveList<LEGAL>(pos))
rm.tbRank -= pos.capture(mOpp) ? 100 : 1;
pos.undo_move(m);
}
// Mate found
if (legalMoves.size() == 0)
break;
// Sort moves according to their above assigned rank.
// This will break ties for moves with equal DTZ in rank_root_moves.
std::stable_sort(
legalMoves.begin(), legalMoves.end(),
[](const Search::RootMove& a, const Search::RootMove& b) { return a.tbRank > b.tbRank; });
// The winning side tries to minimize DTZ, the losing side maximizes it
TB::Config config = TB::rank_root_moves(options, pos, legalMoves, true, time_abort);
// If DTZ is not available we might not find a mate, so we bail out
if (!config.rootInTB || config.cardinality > 0)
break;
ply++;
Move& pvMove = legalMoves[0].pv[0];
rootMove.pv.push_back(pvMove);
auto& st = sts.emplace_back();
pos.do_move(pvMove, st);
}
// Finding a draw in this function is an exceptional case, that cannot happen when rule50 is false or
// during engine game play, since we have a winning score, and play correctly
// with TB support. However, it can be that a position is draw due to the 50 move
// rule if it has been reached on the board with a non-optimal 50 move counter
// (e.g. 8/8/6k1/3B4/3K4/4N3/8/8 w - - 54 106 ) which TB with dtz counter rounding
// cannot always correctly rank. See also
// https://github.com/official-stockfish/Stockfish/issues/5175#issuecomment-2058893495
// We adjust the score to match the found PV. Note that a TB loss score can be
// displayed if the engine did not find a drawing move yet, but eventually search
// will figure it out (e.g. 1kq5/q2r4/5K2/8/8/8/8/7Q w - - 96 1 )
if (pos.is_draw(0))
v = VALUE_DRAW;
// Undo the PV moves
for (usize i = rootMove.pv.size(); i > 0; --i)
pos.undo_move(rootMove.pv[i - 1]);
// Inform if we couldn't get a full extension in time
if (time_abort())
sync_cout
<< "info string Syzygy based PV extension requires more time, increase Move Overhead as needed."
<< sync_endl;
}
void SearchManager::output_pv(Search::Worker& worker,
const ThreadPool& threads,
const TranspositionTable& tt,
Depth depth) {
const auto nodes = threads.nodes_searched();
auto& rootMoves = worker.rootMoves;
auto& pos = worker.rootPos;
usize multiPV = std::min(usize(worker.options["MultiPV"]), rootMoves.size());
u64 tbHits = threads.tb_hits() + (worker.tbConfig.rootInTB ? rootMoves.size() : 0);
for (usize i = 0; i < multiPV; ++i)
{
bool usePreviousScore = rootMoves[i].score == -VALUE_INFINITE;
if (depth == 1 && usePreviousScore && i > 0)
continue;
Depth d = usePreviousScore ? std::max(1, depth - 1) : depth;
Value v = usePreviousScore ? rootMoves[i].previousScore : rootMoves[i].uciScore;
if (v == -VALUE_INFINITE)
v = VALUE_ZERO;
bool isTBScore = worker.tbConfig.rootInTB && !is_mate_or_mated(v);
v = isTBScore ? rootMoves[i].tbScore : v;
// Potentially correct and extend the PV, and in exceptional cases v.
// Previous PVs have already been extended. Bound flags indicate an unreliable PV.
if (is_decisive(v) && !is_mate_or_mated(v) && !usePreviousScore
&& (!rootMoves[i].score_is_bound() || isTBScore))
syzygy_extend_pv(worker.options, worker.limits, pos, rootMoves[i], v);
std::string pv;
for (Move m : usePreviousScore ? rootMoves[i].previousPV : rootMoves[i].pv)
pv += UCIEngine::move(m, pos.is_chess960()) + " ";
// Remove last whitespace
if (!pv.empty())
pv.pop_back();
auto wdl = worker.options["UCI_ShowWDL"] ? UCIEngine::wdl(v, pos) : "";
auto bound = rootMoves[i].scoreLowerbound
? "lowerbound"
: (rootMoves[i].scoreUpperbound ? "upperbound" : "");
InfoFull info;
info.depth = d;
info.selDepth = rootMoves[i].selDepth;
info.multiPV = i + 1;
info.score = {v, pos};
info.wdl = wdl;
// TB and previous scores are exact, even though their bound flags may say otherwise.
if (!(isTBScore || usePreviousScore))
info.bound = bound;
TimePoint time = std::max(TimePoint(1), tm.elapsed_time());
info.timeMs = time;
info.nodes = nodes;
info.nps = nodes * 1000 / time;
info.tbHits = tbHits;
info.pv = pv;
info.hashfull = tt.hashfull();
updates.onUpdateFull(info);
}
}
// Called in case we have no ponder move before exiting the search,
// for instance, in case we stop the search during a fail high at root.
// We try hard to have a ponder move to return to the GUI,
// otherwise in case of 'ponder on' we have nothing to think about.
bool RootMove::extract_ponder_from_tt(const TranspositionTable& tt, Position& pos) {
assert(pv.size() == 1 && pv[0] != Move::none());
StateInfo st;
pos.do_move(pv[0], st, &tt);
if (!pos.is_draw(1))
{
auto [ttHit, ttData, ttWriter] = tt.probe(pos.key());
if (ttHit && MoveList<LEGAL>(pos).contains(ttData.move))
pv.push_back(ttData.move);
}
pos.undo_move(pv[0]);
return pv.size() > 1;
}
} // namespace Stockfish
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