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revert everything, these changes were a mistake

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Josh-Heaps
2026-06-13 20:30:26 -06:00
parent b99525ae51
commit e5e0c9cc48
23 changed files with 998 additions and 1611 deletions
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native/chess_engine/CMakeLists.txt
-3
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@@ -8,9 +8,6 @@ set(CMAKE_CXX_EXTENSIONS OFF)
# Shared library: chess_engine.dll (Windows) / libchess_engine.so (Linux).
add_library(chess_engine SHARED
src/chess_engine.cpp
src/eval.cpp
src/search.cpp
src/learned_model.cpp
src/bitboard.cpp
src/zobrist.cpp
src/position.cpp
native/chess_engine/chess_engine/chess_engine.vcxproj
-6
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@@ -152,9 +152,6 @@
</ItemDefinitionGroup>
<ItemGroup>
<ClCompile Include="..\src\chess_engine.cpp" />
<ClCompile Include="..\src\eval.cpp" />
<ClCompile Include="..\src\search.cpp" />
<ClCompile Include="..\src\learned_model.cpp" />
<ClCompile Include="..\src\bitboard.cpp" />
<ClCompile Include="..\src\zobrist.cpp" />
<ClCompile Include="..\src\position.cpp" />
@@ -164,9 +161,6 @@
</ItemGroup>
<ItemGroup>
<ClInclude Include="..\include\chess_engine.h" />
<ClInclude Include="..\src\eval.h" />
<ClInclude Include="..\src\search.h" />
<ClInclude Include="..\src\learned_model.h" />
<ClInclude Include="..\src\types.h" />
<ClInclude Include="..\src\bitboard.h" />
<ClInclude Include="..\src\zobrist.h" />
native/chess_engine/src/chess_engine.cpp
+749 -27
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@@ -1,15 +1,15 @@
/* chess_engine.cpp - the DLL boundary (extern "C" ABI).
*
* This file is intentionally thin: it owns only the C ABI surface and the FEN/UCI string
* marshalling at the managed boundary. The real work lives in the modules it delegates to:
* - eval.{h,cpp} : classic + learned evaluation, feature computation
* - search.{h,cpp} : transposition table, move ordering, negamax + iterative deepening
* - learned_model.{h,cpp} : global learned weights (state/persistence) and the trainer
* The managed side crosses this boundary once per move; everything below it stays native.
* The rules layer (board, move generation, make/unmake, hashing, perft) lives in
* the other src/*.cpp files and is ready to use. engine_best_move is intentionally
* left for YOU: that is where your search/evaluation goes. Everything below the
* FEN-in / UCI-out boundary should stay native — the managed side crosses it once
* per move.
*/
#ifndef CHESS_ENGINE_BUILD
#define CHESS_ENGINE_BUILD /* fallback when not building via CMake (which defines it) */
#endif
#pragma once
#include "chess_engine.h"
#include "bitboard.h"
@@ -17,24 +17,126 @@
#include "position.h"
#include "movegen.h"
#include "uci.h"
#include "eval.h"
#include "search.h"
#include "learned_model.h"
#include <algorithm>
#include <atomic>
#include <cmath>
#include <cstdint>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <memory>
#include <fstream>
#include <mutex>
#include <new>
#include <string>
#include <memory>
#include <vector>
/* Internal engine state. One ChessEngine = one game. The transposition table is NOT here:
* it is the shared table owned by search.cpp. */
/* Search score constants. Scores are side-to-move-relative (negamax): positive is
* good for whoever is to move. MATE_BOUND is the threshold above which a score is a
* "mate in N" rather than a positional eval; INF is the window sentinel (kept above
* MATE so negating it can never hit signed-overflow UB the way INT_MIN would). */
static constexpr int MATE = 200000;
static constexpr int MATE_BOUND = MATE - 1000;
static constexpr int INF = 1000000;
/* Bound kind stored in a TT entry. LOWER = a fail-high (true score >= stored),
* UPPER = a fail-low (true score <= stored), EXACT = fully resolved. */
enum class Bound : uint8_t { NONE, EXACT, LOWER, UPPER };
/* One shared, process-wide transposition table backs every game (every engine
* handle), so analysis persists and is reused across games. It is lock-free: each
* slot is two 64-bit words — `data` (the packed payload) and `xorKey` (the Zobrist
* key XOR-ed with `data`). A reader recovers the key as `xorKey ^ data`; if two
* concurrent searches tore the pair, the recovered key won't match and the read is
* treated as a miss — never a wrong-but-trusted entry (Hyatt's lockless hashing). */
struct TTEntry {
std::atomic<uint64_t> xorKey{0};
std::atomic<uint64_t> data{0};
};
struct TranspositionTable {
std::unique_ptr<TTEntry[]> entries;
size_t mask = 0; /* count - 1; count is a power of two */
};
static TranspositionTable g_tt;
static constexpr size_t TT_MEGABYTES = 256;
/* Pack/unpack the 64-bit payload: score(32) | move(16) | depth(8) | bound(8). A stored
* entry always has depth >= 1 and a non-NONE bound, so a real entry never packs to 0 —
* letting data == 0 mean "empty slot". */
static uint64_t tt_pack(int score, chess::Move move, int depth, Bound bound) {
return static_cast<uint64_t>(static_cast<uint32_t>(score))
| (static_cast<uint64_t>(move.data) << 32)
| (static_cast<uint64_t>(static_cast<uint8_t>(depth)) << 48)
| (static_cast<uint64_t>(static_cast<uint8_t>(bound)) << 56);
}
static int tt_score(uint64_t d) { return static_cast<int32_t>(static_cast<uint32_t>(d & 0xFFFFFFFFu)); }
static chess::Move tt_move (uint64_t d) { return chess::Move(static_cast<uint16_t>(d >> 32)); }
static int tt_depth(uint64_t d) { return static_cast<int>(static_cast<uint8_t>(d >> 48)); }
static Bound tt_bound(uint64_t d) { return static_cast<Bound>(static_cast<uint8_t>(d >> 56)); }
/* Eval variant for an engine handle. CLASSIC = the hand-crafted evaluate(); LEARNED =
* material + learned phase-split piece-square tables + learned feature weights. */
enum EvalVariant : int { EVAL_CLASSIC = 0, EVAL_LEARNED = 1 };
/* The learned feature knobs (beyond the piece-square tables). Each has one weight learned
* from game outcomes; its activation is computed by compute_features(). Mobility is per
* piece type. Order is fixed — it is the on-disk and snapshot layout after the two tables. */
enum Feature : int {
FEAT_MOB_N, FEAT_MOB_B, FEAT_MOB_R, FEAT_MOB_Q, /* legal-move counts, per piece type */
FEAT_PASSED, /* passed pawns, endgame-weighted */
FEAT_ISOLATED, /* isolated pawns */
FEAT_DOUBLED, /* doubled pawns */
FEAT_KING, /* king pawn-shelter, midgame-weighted */
FEATURE_NB
};
/* Per-handle eval configuration, snapshotted from the global learned weights at
* engine_create so the search reads a stable copy. The tables are white-relative: a black
* piece indexes the rank-mirrored square (sq ^ 56). `mg`/`eg` are blended by game phase.
* Indexed by chess::PieceType (PAWN..KING). Only consulted when variant == EVAL_LEARNED. */
struct EvalParams {
int variant = EVAL_CLASSIC;
int mg[chess::PIECE_TYPE_NB][64] = {};
int eg[chess::PIECE_TYPE_NB][64] = {};
int featW[FEATURE_NB] = {};
};
/* Internal engine state. One ChessEngine = one game. The transposition table is NOT
* here: it is the shared g_tt above. */
struct ChessEngine {
int skill = 20; /* 1..20 from the UI; controls search depth */
EvalParams eval; /* which evaluation the search uses, plus any learned weights */
};
/* The process-global learned weights: the single source of truth, loaded from disk once and
* updated in place by training. Engine handles snapshot it at creation; the visualization
* snapshots it on demand. Guarded by g_weightsMutex for updates/saves (eval reads its own
* per-handle copy, so it never touches this concurrently). */
struct LearnedWeights {
int mg[chess::PIECE_TYPE_NB][64] = {};
int eg[chess::PIECE_TYPE_NB][64] = {};
int featW[FEATURE_NB] = {};
};
static LearnedWeights g_weights;
static std::mutex g_weightsMutex;
static std::string g_weightsPath;
/* Per-game training accumulator (one per learned CPU-vs-CPU game). Records, per ply, where
* each side's pieces sat (split into midgame/endgame by phase) and each side's feature
* activations; trainer_apply turns the totals into weight nudges. Squares are white-relative
* (black indexes sq ^ 56), so a side's tally lines up with the shared white-relative table. */
struct Trainer {
double mgOcc[chess::COLOR_NB][chess::PIECE_TYPE_NB][64] = {};
double egOcc[chess::COLOR_NB][chess::PIECE_TYPE_NB][64] = {};
double featAcc[chess::COLOR_NB][FEATURE_NB] = {};
int plies = 0;
};
static int copy_out(const char* src, char* out_buf, int out_len) {
if (!out_buf || out_len <= 0) return CHESS_ERR_BUFFER;
const size_t need = std::strlen(src) + 1; /* + NUL */
@@ -69,16 +171,425 @@ static int parse_variant(const char* options) {
return std::strncmp(p + 8, "learned", 7) == 0 ? EVAL_LEARNED : EVAL_CLASSIC;
}
/* On-disk format: 6*64 mg ints (PAWN..KING, squares 0..63), then 6*64 eg ints, then
* FEATURE_NB feature ints, whitespace-separated. A missing file or short read leaves the
* rest neutral (0), so an absent weights file just means "train from a blank slate".
* Caller holds g_weightsMutex. */
static void load_global_weights(const char* path) {
g_weights = LearnedWeights{}; /* reset to neutral before loading */
if (!path || !*path) return;
std::ifstream f(path);
if (!f) return;
for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
for (int sq = 0; sq < 64; ++sq)
if (!(f >> g_weights.mg[pt][sq])) return;
for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
for (int sq = 0; sq < 64; ++sq)
if (!(f >> g_weights.eg[pt][sq])) return;
for (int i = 0; i < FEATURE_NB; ++i)
if (!(f >> g_weights.featW[i])) return;
}
/* Persist g_weights to g_weightsPath in the format load_global_weights reads. Caller holds the lock. */
static void save_global_weights() {
if (g_weightsPath.empty()) return;
std::ofstream f(g_weightsPath);
if (!f) return;
for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
for (int sq = 0; sq < 64; ++sq) f << g_weights.mg[pt][sq] << (sq == 63 ? '\n' : ' ');
for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
for (int sq = 0; sq < 64; ++sq) f << g_weights.eg[pt][sq] << (sq == 63 ? '\n' : ' ');
for (int i = 0; i < FEATURE_NB; ++i) f << g_weights.featW[i] << (i == FEATURE_NB - 1 ? '\n' : ' ');
}
/* Maps the 1..20 difficulty to a search depth. Kept modest: the search has no
* quiescence yet, so deep fixed-depth runs get expensive quickly. */
static int depth_for_skill(int skill) {
return skill; /* skill N -> N plies */
}
static size_t floor_pow2(size_t n) {
size_t p = 1;
while ((p << 1) != 0 && (p << 1) <= n) p <<= 1;
return p;
}
/* Allocate the shared table exactly once, to the largest power-of-two entry count that
* fits in TT_MEGABYTES. Power-of-two count lets indexing use `key & mask`. Thread-safe:
* call_once guards the first concurrent engine_create. Entries start zeroed (empty). */
static void ensure_tt() {
static std::once_flag once;
std::call_once(once, [] {
size_t count = floor_pow2((TT_MEGABYTES << 20) / sizeof(TTEntry));
if (count < 1) count = 1;
g_tt.entries = std::make_unique<TTEntry[]>(count);
g_tt.mask = count - 1;
});
}
/* Positional multiplier in [0.5, 2.0] based on a square's distance from the four
* center squares (d4/e4/d5/e5): 2.0 dead center, 0.5 in a corner, scaling linearly.
* Multiply a piece's base value by this to reward central placement. */
static double center_multiplier(chess::Square s) {
/* |2*coord - 7| is the distance from center in half-squares: 1 (center) .. 7 (edge). */
int fileDist = std::abs(2 * int(chess::file_of(s)) - 7);
int rankDist = std::abs(2 * int(chess::rank_of(s)) - 7);
int dist = fileDist > rankDist ? fileDist : rankDist; /* Chebyshev distance, 1 .. 7 */
return dist * 20; /* 1 -> 2.0, 7 -> 0.5 */
}
static int piece_mobility(const chess::Position& pos, chess::Square s, chess::Piece pc, chess::Color c) {
chess::Bitboard occ = pos.pieces();
chess::Bitboard targets;
switch (chess::type_of(pc)) {
case chess::KNIGHT: targets = chess::KnightAttacks[s]; break;
case chess::BISHOP: targets = chess::bishop_attacks(s, occ); break;
case chess::ROOK: targets = chess::rook_attacks(s, occ); break;
case chess::QUEEN: targets = chess::queen_attacks(s, occ); break;
case chess::KING: targets = chess::KingAttacks[s]; break;
default: return 0; // pawns: mobility usually handled via push/attack separately
}
return chess::popcount(targets & ~pos.pieces(c)); // exclude squares blocked by own pieces
}
static chess::Bitboard front_span(chess::Color c, chess::Square s) {
chess::File f = file_of(s);
chess::Bitboard files = file_bb(f);
if (f > chess::FILE_A) files |= chess::file_bb(chess::File(f - 1));
if (f < chess::FILE_H) files |= chess::file_bb(chess::File(f + 1));
// Pawns never sit on rank 1 or 8, so rank is 1..6 and these shifts
// are always in [8,56] — no shift-by-64 UB to guard against.
chess::Rank r = rank_of(s);
chess::Bitboard ahead = (c == chess::WHITE) ? (~0ULL << (8 * (r + 1))) // ranks > r
: ((1ULL << (8 * r)) - 1); // ranks < r
return files & ahead;
}
static chess::Bitboard front_span_file_only(chess::Color c, chess::Square s) {
chess::File f = file_of(s);
chess::Bitboard files = file_bb(f);
// Pawns never sit on rank 1 or 8, so rank is 1..6 and these shifts
// are always in [8,56] — no shift-by-64 UB to guard against.
chess::Rank r = rank_of(s);
chess::Bitboard ahead = (c == chess::WHITE) ? (~0ULL << (8 * (r + 1))) // ranks > r
: ((1ULL << (8 * r)) - 1); // ranks < r
return files & ahead;
}
static int evaluatePawn(const chess::Position& pos, const chess::Color c, const chess::Square s) {
chess::Bitboard span = front_span(c, s);
chess::Bitboard file_span = front_span_file_only(c, s);
chess::Rank r = rank_of(s);
int squaresToPromotion = (c == chess::WHITE) ? (chess::RANK_8 - r) : (r - chess::RANK_1);;
bool isPassed = !(span & pos.pieces(~c, chess::PAWN));
bool isBlocked = (file_span & pos.pieces(c, chess::PAWN)) | (file_span & pos.pieces(~c, chess::PAWN));
bool isDoubled = (file_span & pos.pieces(c, chess::PAWN));
int score = 100;
if (isPassed && !isBlocked)
score += (6 - squaresToPromotion) * 100; // Bonus for passed pawns, more as they get closer to promotion
if (isDoubled)
score -= 20; // Penalty for doubled pawns
if (isBlocked)
score -= 20; // Penalty for blocked pawns
return score;
}
static int piece_value(chess::PieceType pt) {
switch (pt) {
case chess::PAWN: return 100;
case chess::KNIGHT: return 320;
case chess::BISHOP: return 330;
case chess::ROOK: return 500;
case chess::QUEEN: return 900;
default: return 0;
}
}
static int castleIncentive(const chess::Position& pos, chess::Color c) {
chess::Bitboard pcs = pos.pieces();
int total = 0;
while (pcs) {
chess::Square s = chess::pop_lsb(pcs);
chess::Piece pc = pos.piece_on(s);
chess::Color c = chess::color_of(pc);
total += piece_value(chess::type_of(pc));
}
chess::Square k = pos.king_square(c);
bool castled = (c == chess::WHITE) ? (k == chess::G1 || k == chess::C1)
: (k == chess::G8 || k == chess::C8);
return castled ? (total / 10) : 0;
}
static int evaluatePiece(const chess::Position& pos, const chess::Square& s, const chess::Piece& pc, const chess::Color& c) {
int score = 0;
switch (chess::type_of(pc)) {
case chess::PAWN: score = evaluatePawn(pos, c, s); break;
case chess::KNIGHT: score = 320; break;
case chess::BISHOP: score = 330; break;
case chess::ROOK: score = 500; break;
case chess::QUEEN: score = 900; break;
case chess::KING: score = castleIncentive(pos, c); break;
default: return 0;
}
score += center_multiplier(s);
if (pc != chess::B_PAWN && pc != chess::W_PAWN)
score += piece_mobility(pos, s, pc, c) * 25;
return score;
}
static int evaluate(const chess::Position& pos) {
int score = 0;
chess::Bitboard white = pos.pieces(chess::WHITE);
while (white) {
chess::Square s = chess::pop_lsb(white);
chess::Piece pc = pos.piece_on(s);
chess::Color c = chess::color_of(pc);
score += evaluatePiece(pos, s, pc, c);
}
chess::Bitboard black = pos.pieces(chess::BLACK);
while (black) {
chess::Square s = chess::pop_lsb(black);
chess::Piece pc = pos.piece_on(s);
chess::Color c = chess::color_of(pc);
score -= evaluatePiece(pos, s, pc, c);
}
return score;
}
/* ---- Learned (phase-split tables + feature knobs) evaluation ---------------------------
* The model is a linear combination of features whose weights are learned from outcomes:
* eval = Σ pieces [ material + blend(mg, eg, phase) ] + Σ features featW[i]·activation[i]
* compute_features() is the single source of feature activations, used by BOTH the eval here
* and the trainer, so the two can never disagree. Constants below are the only tunables. */
/* Per-game-outcome learning rates and clamps. Squares accumulate occupancy (plies on a
* square, summed); features accumulate normalized per-ply activation (averaged, divided by a
* nominal scale so high-magnitude mobility doesn't dwarf the small pawn-structure terms). */
static constexpr double SQUARE_LR = 0.5;
static constexpr int SQ_CLAMP = 250;
static constexpr double FEAT_LR = 2.0;
static constexpr int FEAT_CLAMP = 500;
static constexpr double FEAT_SCALE[FEATURE_NB] = { 4, 6, 8, 14, 2, 1, 1, 2 };
/* Game phase in [0,1] from remaining non-pawn material (PeSTO weights N=B=1, R=2, Q=4; max
* 24 for both full sides): 0 = opening, 1 = bare kings. Drives the mg/eg table blend and
* the phase weighting of the passed-pawn (×phase) and king-safety (×(1−phase)) features. */
static double game_phase(const chess::Position& pos) {
int npm = chess::popcount(pos.pieces(chess::KNIGHT)) * 1
+ chess::popcount(pos.pieces(chess::BISHOP)) * 1
+ chess::popcount(pos.pieces(chess::ROOK)) * 2
+ chess::popcount(pos.pieces(chess::QUEEN)) * 4;
constexpr int MAX = 24;
if (npm >= MAX) return 0.0;
return double(MAX - npm) / MAX;
}
/* Blend a midgame and endgame value by phase, rounding per-piece (so training credits a
* square the same way the eval reads it). */
static int blend(int mg, int eg, double phase) {
return int(std::lround((1.0 - phase) * mg + phase * eg));
}
/* Fills `out[FEATURE_NB]` with one color's raw feature activations for a position. The piece-
* square tables handle "where pieces belong"; these capture context a static table can't:
* legal mobility (per piece type, so pins reduce it), passed pawns (endgame-weighted), pawn
* structure, and king shelter (midgame-weighted). Ported nowhere — this is the only copy. */
static void compute_features(chess::Position& pos, chess::Color c, double phase, double out[FEATURE_NB]) {
for (int i = 0; i < FEATURE_NB; ++i) out[i] = 0.0;
/* Mobility: legal moves for color c, bucketed by the moving piece's type. */
chess::MoveList moves;
pos.generate_legal_for(c, moves);
for (int i = 0; i < moves.size(); ++i) {
switch (chess::type_of(pos.piece_on(moves.moves[i].from()))) {
case chess::KNIGHT: out[FEAT_MOB_N] += 1; break;
case chess::BISHOP: out[FEAT_MOB_B] += 1; break;
case chess::ROOK: out[FEAT_MOB_R] += 1; break;
case chess::QUEEN: out[FEAT_MOB_Q] += 1; break;
default: break;
}
}
/* Pawn structure. */
chess::Bitboard pawns = pos.pieces(c, chess::PAWN);
chess::Bitboard bb = pawns;
while (bb) {
chess::Square s = chess::pop_lsb(bb);
if (!(front_span(c, s) & pos.pieces(~c, chess::PAWN))) { /* passed */
chess::Rank r = chess::rank_of(s);
int toPromotion = (c == chess::WHITE) ? (chess::RANK_8 - r) : (r - chess::RANK_1);
out[FEAT_PASSED] += (6 - toPromotion) * phase; /* 0..5 ranks advanced, late-game */
}
if (front_span_file_only(c, s) & pawns) /* doubled (friendly pawn ahead) */
out[FEAT_DOUBLED] += 1;
chess::File f = chess::file_of(s);
chess::Bitboard adjacent = 0;
if (f > chess::FILE_A) adjacent |= chess::file_bb(chess::File(f - 1));
if (f < chess::FILE_H) adjacent |= chess::file_bb(chess::File(f + 1));
if (!(adjacent & pawns)) /* isolated */
out[FEAT_ISOLATED] += 1;
}
/* King safety: friendly pawns sheltering the king (its file + adjacent files, the two
* ranks in front), worth more in the midgame. */
chess::Square k = pos.king_square(c);
chess::File kf = chess::file_of(k);
chess::Rank kr = chess::rank_of(k);
chess::Bitboard kingFiles = chess::file_bb(kf);
if (kf > chess::FILE_A) kingFiles |= chess::file_bb(chess::File(kf - 1));
if (kf < chess::FILE_H) kingFiles |= chess::file_bb(chess::File(kf + 1));
chess::Bitboard shelterRanks = 0;
for (int d = 1; d <= 2; ++d) {
int rr = (c == chess::WHITE) ? (kr + d) : (kr - d);
if (rr >= 0 && rr <= 7) shelterRanks |= (0xFFULL << (8 * rr));
}
out[FEAT_KING] += chess::popcount(kingFiles & shelterRanks & pawns) * (1.0 - phase);
}
/* Learned eval (white-positive/absolute, like evaluate()): material + phase-blended piece-
* square tables + learned feature weights. Black pieces index the rank-mirrored square
* (s ^ 56) so both colors share one white-relative table. Non-const because mobility
* generates legal moves (which the position's move generator does via do/undo). */
static int evaluateLearned(chess::Position& pos, const EvalParams& ep) {
double phase = game_phase(pos);
int score = 0;
chess::Bitboard white = pos.pieces(chess::WHITE);
while (white) {
chess::Square s = chess::pop_lsb(white);
chess::PieceType pt = chess::type_of(pos.piece_on(s));
score += piece_value(pt) + blend(ep.mg[pt][s], ep.eg[pt][s], phase);
}
chess::Bitboard black = pos.pieces(chess::BLACK);
while (black) {
chess::Square s = chess::pop_lsb(black);
chess::PieceType pt = chess::type_of(pos.piece_on(s));
score -= piece_value(pt) + blend(ep.mg[pt][s ^ 56], ep.eg[pt][s ^ 56], phase);
}
double wFeat[FEATURE_NB], bFeat[FEATURE_NB];
compute_features(pos, chess::WHITE, phase, wFeat);
compute_features(pos, chess::BLACK, phase, bFeat);
double feature = 0.0;
for (int i = 0; i < FEATURE_NB; ++i)
feature += ep.featW[i] * (wFeat[i] - bFeat[i]) / FEAT_SCALE[i];
score += int(std::lround(feature));
return score;
}
/* evaluate() is white-positive (absolute). Negamax needs it relative to the side to
* move, so flip the sign when black is to move. */
static int evaluate_stm(chess::Position& pos, bool whiteToMove, const EvalParams& ep) {
int s = (ep.variant == EVAL_LEARNED) ? evaluateLearned(pos, ep) : evaluate(pos);
return whiteToMove ? s : -s;
}
/* Mate scores are "mate in N from THIS node", so they must be re-anchored to the
* probing node's ply when crossing the TT (store adds ply, retrieve subtracts it).
* Non-mate scores pass through untouched. */
static int score_to_tt(int s, int ply) { return s >= MATE_BOUND ? s + ply : s <= -MATE_BOUND ? s - ply : s; }
static int score_from_tt(int s, int ply) { return s >= MATE_BOUND ? s - ply : s <= -MATE_BOUND ? s + ply : s; }
/* Heuristic for searching the most promising moves first, which makes alpha-beta prune far
* more. Bands, highest first: the TT best move, then captures by MVV-LVA (most valuable
* victim, least valuable attacker), then the two killer moves for this ply (quiet moves that
* cut a sibling), then the remaining quiet moves. `killers` points at this ply's two-entry
* slot; `scoreChecks` gates the expensive gives_check term to near-leaf nodes. */
static int order_score(chess::Position& pos, chess::Move m, chess::Move ttMove,
const chess::Move* killers, bool scoreChecks) {
if (m == ttMove)
return 2000000; /* dwarfs any capture/killer/check score below */
int score = 0;
if (scoreChecks && pos.gives_check(m))
score += 1000;
chess::Piece victim = pos.piece_on(m.to());
#ifdef BENCH_DISABLE_KILLERS
/* Benchmark A/B only (defined by bench.ps1): the pre-killer ordering — captures by
* MVV-LVA above quiet moves, no killer band — so the script can time the killer speedup. */
(void)killers;
if (victim != chess::NO_PIECE)
score += 100 + 10 * piece_value(chess::type_of(victim))
- piece_value(chess::type_of(pos.piece_on(m.from())));
else if (m.type() == chess::EN_PASSANT)
score += 100 + 10 * piece_value(chess::PAWN);
#else
if (victim != chess::NO_PIECE)
score += 100000 + 10 * piece_value(chess::type_of(victim))
- piece_value(chess::type_of(pos.piece_on(m.from())));
else if (m.type() == chess::EN_PASSANT)
score += 100000 + 10 * piece_value(chess::PAWN);
else if (m == killers[0])
score += 90000; /* quiet move that beta-cut a sibling at this ply */
else if (m == killers[1])
score += 80000;
#endif
return score;
}
/* Sort the move list in place, best-scoring first. Scores are computed once up
* front so gives_check isn't re-evaluated on every comparison. ttMove may be
* MOVE_NONE, in which case no move matches it and ordering falls back to captures. */
static void order_moves(chess::Position& pos, chess::MoveList& moves, chess::Move ttMove,
const chess::Move* killers, bool scoreChecks) {
struct ScoredMove { int score; chess::Move move; };
ScoredMove scored[256];
for (int i = 0; i < moves.size(); i++)
scored[i] = { order_score(pos, moves.moves[i], ttMove, killers, scoreChecks), moves.moves[i] };
std::sort(scored, scored + moves.size(),
[](const ScoredMove& a, const ScoredMove& b) { return a.score > b.score; });
for (int i = 0; i < moves.size(); i++)
moves.moves[i] = scored[i].move;
}
extern "C" {
CHESS_API EngineHandle CHESS_CALL engine_create(const char* options) {
ensure_initialized();
ensure_tt();
auto* e = new (std::nothrow) ChessEngine();
if (!e) return nullptr;
e->skill = parse_skill(options, e->skill);
e->eval.variant = parse_variant(options);
if (e->eval.variant == EVAL_LEARNED)
learned::copy_weights_to(e->eval); /* stable per-handle copy of the global weights */
if (e->eval.variant == EVAL_LEARNED) {
/* Snapshot the current global weights so the search reads a stable copy (training
* updates the global between games; the weights path is owned by learned_load). */
std::lock_guard<std::mutex> lock(g_weightsMutex);
std::memcpy(e->eval.mg, g_weights.mg, sizeof e->eval.mg);
std::memcpy(e->eval.eg, g_weights.eg, sizeof e->eval.eg);
std::memcpy(e->eval.featW, g_weights.featW, sizeof e->eval.featW);
}
return e;
}
@@ -89,6 +600,113 @@ CHESS_API int CHESS_CALL engine_set_option(EngineHandle engine,
return CHESS_OK; /* TODO: store options */
}
/* Per-search scratch, threaded through the recursion. Kept off global scope so two engine
* handles can search concurrently without sharing node counts or killer tables. killers[ply]
* holds up to two quiet moves that recently caused a beta cutoff at that ply; trying them
* early (right after captures) prunes far more — the quiet-move ordering the search otherwise
* lacks. */
static constexpr int MAX_PLY = 128; /* ply never exceeds maxDepth (<= 20) */
struct SearchContext {
uint64_t nodes = 0;
const EvalParams* eval = nullptr; /* eval config for this search; set by engine_best_move */
chess::Move killers[MAX_PLY][2] = {};/* [ply][slot]; MOVE_NONE until filled */
};
/* Negamax alpha-beta over the shared transposition table. `maxDepth` is the searching
* bot's difficulty (its root depth); `depth` is remaining depth (draft); `ply` is
* distance from the root (mate scoring only). Scores are side-to-move-relative.
* Fail-soft: returns the true best found even outside [alpha, beta]. */
static int negamax(chess::Position& pos, int maxDepth, int depth, int ply,
int alpha, int beta, bool whiteToMove, SearchContext& ctx) {
ctx.nodes++;
/* A draw is 0 even at the search horizon, and the TT key doesn't encode repetition
* history, so this must come before both the leaf eval and any TT probe. */
if (ply > 0 && pos.is_draw())
return 0;
if (depth <= 0)
return evaluate_stm(pos, whiteToMove, *ctx.eval);
const uint64_t key = pos.key();
TTEntry& slot = g_tt.entries[key & g_tt.mask];
const uint64_t data = slot.data.load(std::memory_order_relaxed);
const uint64_t xkey = slot.xorKey.load(std::memory_order_relaxed);
chess::Move ttMove = chess::MOVE_NONE;
if (data != 0 && (xkey ^ data) == key) { /* lockless: XOR check rejects torn reads */
ttMove = tt_move(data); /* always reusable for ordering */
int edepth = tt_depth(data);
Bound b = tt_bound(data);
/* Trust the score only if it was searched deep enough for this node AND no deeper
* than this bot's own strength — so a weak bot can't borrow a stronger game's
* deeper analysis (it still gets the move for ordering, which can't leak strength). */
if (edepth >= depth && edepth <= maxDepth) {
int s = score_from_tt(tt_score(data), ply);
if (b == Bound::EXACT) return s;
if (b == Bound::LOWER && s >= beta) return s;
if (b == Bound::UPPER && s <= alpha) return s;
}
}
chess::MoveList moves;
pos.generate_legal(moves);
if (moves.size() == 0)
return pos.is_draw() ? 0 : -MATE + ply; /* checkmate against side to move */
order_moves(pos, moves, ttMove, ctx.killers[ply], depth <= 2);
const int alphaOrig = alpha;
int best = -INF;
chess::Move bestMove = chess::MOVE_NONE;
for (int i = 0; i < moves.size(); i++) {
chess::Move move = moves.moves[i];
pos.do_move(move);
int score = -negamax(pos, maxDepth, depth - 1, ply + 1, -beta, -alpha, !whiteToMove, ctx);
pos.undo_move(move);
if (score > best) {
best = score;
bestMove = move;
}
if (best > alpha)
alpha = best;
if (best >= beta) {
/* A quiet move good enough to fail high here is a strong candidate in sibling
* lines at this ply — remember it as a killer. pos is back to pre-move state
* after undo_move, so piece_on(to) still flags a capture correctly. */
bool isCapture = pos.piece_on(move.to()) != chess::NO_PIECE
|| move.type() == chess::EN_PASSANT;
if (!isCapture && ply < MAX_PLY && ctx.killers[ply][0] != move) {
ctx.killers[ply][1] = ctx.killers[ply][0];
ctx.killers[ply][0] = move;
}
break; /* fail-high cutoff */
}
}
Bound flag = best <= alphaOrig ? Bound::UPPER
: best >= beta ? Bound::LOWER
: Bound::EXACT;
/* Depth-preferred replacement: keep the deepest analysis of each slot. The stored
* payload is written before the xorKey so any concurrent reader that catches a
* half-update fails the XOR check and treats it as a miss. */
int storedDepth = (data == 0) ? -1 : tt_depth(data);
if (depth >= storedDepth) {
uint64_t packed = tt_pack(score_to_tt(best, ply), bestMove, depth, flag);
slot.data.store(packed, std::memory_order_relaxed);
slot.xorKey.store(key ^ packed, std::memory_order_relaxed);
}
return best;
}
CHESS_API int CHESS_CALL engine_best_move(EngineHandle engine,
const char* fen,
const char* history,
@@ -99,6 +717,7 @@ CHESS_API int CHESS_CALL engine_best_move(EngineHandle engine,
auto held = std::make_unique<chess::Position>(chess::Position::from_fen(fen));
chess::Position& pos = *held;
bool whiteToMove = pos.side_to_move() == chess::WHITE;
/* Seed the prior positions (one FEN per line) so is_draw() sees repetitions and
* the 50-move count that the current FEN alone can't express. */
@@ -117,11 +736,48 @@ CHESS_API int CHESS_CALL engine_best_move(EngineHandle engine,
pos.seed_history(priorKeys.data(), static_cast<int>(priorKeys.size()));
}
chess::Move best = find_best_move(pos, engine->eval, engine->skill);
if (best == chess::MOVE_NONE)
chess::MoveList moves;
pos.generate_legal(moves);
if (moves.size() == 0)
return CHESS_ERR_NO_MOVE;
return copy_out(chess::move_to_uci(best).c_str(), out_buf, out_len);
SearchContext ctx;
ctx.eval = &engine->eval;
int maxDepth = depth_for_skill(engine->skill);
chess::Move bestMove = moves.moves[0]; /* guaranteed-legal fallback */
/* Iterative deepening: each depth seeds the next depth's move ordering (via the
* previous best move and the TT it filled), which makes the deeper search prune
* far harder than searching to maxDepth cold. */
for (int d = 1; d <= maxDepth; d++) {
int alpha = -INF, beta = INF;
chess::Move iterBest = bestMove;
int iterScore = -INF;
order_moves(pos, moves, iterBest, ctx.killers[0], true);
for (int i = 0; i < moves.size(); i++) {
chess::Move move = moves.moves[i];
pos.do_move(move);
int score = -negamax(pos, maxDepth, d - 1, 1, -beta, -alpha, !whiteToMove, ctx);
pos.undo_move(move);
if (score > iterScore) {
iterScore = score;
iterBest = move;
}
if (score > alpha)
alpha = score;
}
bestMove = iterBest; /* commit only a fully completed iteration */
std::fprintf(stderr, "depth %d nodes %llu best %s score %d\n",
d, static_cast<unsigned long long>(ctx.nodes),
chess::move_to_uci(iterBest).c_str(), iterScore);
}
return copy_out(chess::move_to_uci(bestMove).c_str(), out_buf, out_len);
}
CHESS_API int CHESS_CALL engine_version(char* out_buf, int out_len) {
@@ -133,33 +789,99 @@ CHESS_API void CHESS_CALL engine_destroy(EngineHandle engine) {
}
/* ---- Learned-weights / training C ABI --------------------------------------------------
* The managed side orchestrates games but owns no chess logic: it tells the engine where to
* load/save the global weights, records each played position, and applies the result. Each
* export is a thin pass-through to the learned_model module. */
* The managed side orchestrates games but owns no chess logic: it tells the engine where
* to load/save the global weights, records each played position, and applies the result. */
CHESS_API void CHESS_CALL learned_load(const char* path) {
learned::load(path);
std::lock_guard<std::mutex> lock(g_weightsMutex);
g_weightsPath = path ? path : "";
load_global_weights(path);
}
CHESS_API int CHESS_CALL weights_snapshot(int* out, int out_len) {
return learned::snapshot(out, out_len);
const int need = 6 * 64 * 2 + FEATURE_NB; /* mg + eg (PAWN..KING) + features = 776 */
if (!out || out_len < need) return CHESS_ERR_BUFFER;
std::lock_guard<std::mutex> lock(g_weightsMutex);
int n = 0;
for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
for (int sq = 0; sq < 64; ++sq) out[n++] = g_weights.mg[pt][sq];
for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
for (int sq = 0; sq < 64; ++sq) out[n++] = g_weights.eg[pt][sq];
for (int i = 0; i < FEATURE_NB; ++i) out[n++] = g_weights.featW[i];
return n;
}
CHESS_API TrainerHandle CHESS_CALL trainer_create(void) {
return learned::create();
return new (std::nothrow) Trainer();
}
CHESS_API void CHESS_CALL trainer_record(TrainerHandle t, const char* fen) {
ensure_initialized(); /* mobility needs the attack tables */
learned::record(t, fen);
if (!t || !fen || !*fen) return;
ensure_initialized();
chess::Position pos = chess::Position::from_fen(fen);
double phase = game_phase(pos);
/* Per-square occupancy, split into midgame/endgame by phase, white-relative. */
chess::Bitboard occ = pos.pieces();
while (occ) {
chess::Square s = chess::pop_lsb(occ);
chess::Piece pc = pos.piece_on(s);
chess::Color c = chess::color_of(pc);
chess::PieceType pt = chess::type_of(pc);
int relSq = (c == chess::WHITE) ? int(s) : (int(s) ^ 56);
t->mgOcc[c][pt][relSq] += (1.0 - phase);
t->egOcc[c][pt][relSq] += phase;
}
/* Per-side feature activations. */
double w[FEATURE_NB], b[FEATURE_NB];
compute_features(pos, chess::WHITE, phase, w);
compute_features(pos, chess::BLACK, phase, b);
for (int i = 0; i < FEATURE_NB; ++i) {
t->featAcc[chess::WHITE][i] += w[i];
t->featAcc[chess::BLACK][i] += b[i];
}
t->plies++;
}
CHESS_API void CHESS_CALL trainer_apply(TrainerHandle t, int winner, double weight) {
learned::apply(t, winner, weight);
if (!t) return;
std::lock_guard<std::mutex> lock(g_weightsMutex);
/* pass 0 = winner (reward, +1); pass 1 = loser (punish, -1). */
for (int pass = 0; pass < 2; ++pass) {
chess::Color side = chess::Color((pass == 0 ? winner : (winner ^ 1)) & 1);
int sign = pass == 0 ? 1 : -1;
for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
for (int sq = 0; sq < 64; ++sq) {
if (t->mgOcc[side][pt][sq] != 0.0) {
int d = sign * int(std::lround(SQUARE_LR * t->mgOcc[side][pt][sq] * weight));
g_weights.mg[pt][sq] = std::clamp(g_weights.mg[pt][sq] + d, -SQ_CLAMP, SQ_CLAMP);
}
if (t->egOcc[side][pt][sq] != 0.0) {
int d = sign * int(std::lround(SQUARE_LR * t->egOcc[side][pt][sq] * weight));
g_weights.eg[pt][sq] = std::clamp(g_weights.eg[pt][sq] + d, -SQ_CLAMP, SQ_CLAMP);
}
}
if (t->plies > 0)
for (int i = 0; i < FEATURE_NB; ++i) {
double avg = t->featAcc[side][i] / t->plies; /* per-ply average, normalized */
int d = sign * int(std::lround(FEAT_LR * (avg / FEAT_SCALE[i]) * weight));
g_weights.featW[i] = std::clamp(g_weights.featW[i] + d, -FEAT_CLAMP, FEAT_CLAMP);
}
}
save_global_weights();
}
CHESS_API void CHESS_CALL trainer_destroy(TrainerHandle t) {
learned::destroy(t); /* destroy(nullptr) is safe */
delete t; /* delete nullptr is safe */
}
} /* extern "C" */
native/chess_engine/src/eval.cpp
-272
View File
@@ -1,272 +0,0 @@
/* eval.cpp - classic and learned position evaluation, plus feature computation.
* See eval.h for the public surface. Everything else here is file-static. */
#include "eval.h"
#include "bitboard.h"
#include "position.h"
#include <algorithm>
#include <cmath>
/* ---- Shared piece values ------------------------------------------------------------- */
int piece_value(chess::PieceType pt) {
switch (pt) {
case chess::PAWN: return 100;
case chess::KNIGHT: return 320;
case chess::BISHOP: return 330;
case chess::ROOK: return 500;
case chess::QUEEN: return 900;
default: return 0;
}
}
/* ---- Classic (hand-crafted) evaluation ----------------------------------------------- */
/* Positional bonus (centipawns) from a square's Chebyshev distance to the center, added to
* a piece's score by evaluatePiece. Returns 20 (dead center) .. 140 (edge / corner). */
static int center_multiplier(chess::Square s) {
/* |2*coord - 7| is the distance from center in half-squares: 1 (center) .. 7 (edge). */
int fileDist = std::abs(2 * int(chess::file_of(s)) - 7);
int rankDist = std::abs(2 * int(chess::rank_of(s)) - 7);
int dist = fileDist > rankDist ? fileDist : rankDist; /* Chebyshev distance, 1 .. 7 */
return (8-dist) * 20;
}
static int piece_mobility(const chess::Position& pos, chess::Square s, chess::Piece pc, chess::Color c) {
chess::Bitboard occ = pos.pieces();
chess::Bitboard targets;
switch (chess::type_of(pc)) {
case chess::KNIGHT: targets = chess::KnightAttacks[s]; break;
case chess::BISHOP: targets = chess::bishop_attacks(s, occ); break;
case chess::ROOK: targets = chess::rook_attacks(s, occ); break;
case chess::QUEEN: targets = chess::queen_attacks(s, occ); break;
case chess::KING: targets = chess::KingAttacks[s]; break;
default: return 0; // pawns: mobility usually handled via push/attack separately
}
return chess::popcount(targets & ~pos.pieces(c)); // exclude squares blocked by own pieces
}
static chess::Bitboard front_span(chess::Color c, chess::Square s) {
chess::File f = file_of(s);
chess::Bitboard files = file_bb(f);
if (f > chess::FILE_A) files |= chess::file_bb(chess::File(f - 1));
if (f < chess::FILE_H) files |= chess::file_bb(chess::File(f + 1));
// Pawns never sit on rank 1 or 8, so rank is 1..6 and these shifts
// are always in [8,56] — no shift-by-64 UB to guard against.
chess::Rank r = rank_of(s);
chess::Bitboard ahead = (c == chess::WHITE) ? (~0ULL << (8 * (r + 1))) // ranks > r
: ((1ULL << (8 * r)) - 1); // ranks < r
return files & ahead;
}
static chess::Bitboard front_span_file_only(chess::Color c, chess::Square s) {
chess::File f = file_of(s);
chess::Bitboard files = file_bb(f);
// Pawns never sit on rank 1 or 8, so rank is 1..6 and these shifts
// are always in [8,56] — no shift-by-64 UB to guard against.
chess::Rank r = rank_of(s);
chess::Bitboard ahead = (c == chess::WHITE) ? (~0ULL << (8 * (r + 1))) // ranks > r
: ((1ULL << (8 * r)) - 1); // ranks < r
return files & ahead;
}
static int evaluatePawn(const chess::Position& pos, const chess::Color c, const chess::Square s) {
chess::Bitboard span = front_span(c, s);
chess::Bitboard file_span = front_span_file_only(c, s);
chess::Rank r = rank_of(s);
int squaresToPromotion = (c == chess::WHITE) ? (chess::RANK_8 - r) : (r - chess::RANK_1);;
bool isPassed = !(span & pos.pieces(~c, chess::PAWN));
bool isBlocked = (file_span & pos.pieces(c, chess::PAWN)) | (file_span & pos.pieces(~c, chess::PAWN));
bool isDoubled = (file_span & pos.pieces(c, chess::PAWN));
int score = 100;
if (isPassed && !isBlocked)
score += (6 - squaresToPromotion) * 100; // Bonus for passed pawns, more as they get closer to promotion
if (isDoubled)
score -= 20; // Penalty for doubled pawns
if (isBlocked)
score -= 20; // Penalty for blocked pawns
return score;
}
static int castleIncentive(const chess::Position& pos, chess::Color c) {
chess::Bitboard pcs = pos.pieces();
int total = 0;
while (pcs) {
chess::Square s = chess::pop_lsb(pcs);
chess::Piece pc = pos.piece_on(s);
chess::Color c = chess::color_of(pc);
total += piece_value(chess::type_of(pc));
}
chess::Square k = pos.king_square(c);
bool castled = (c == chess::WHITE) ? (k == chess::G1 || k == chess::C1)
: (k == chess::G8 || k == chess::C8);
return castled ? (total / 10) : 0;
}
static int evaluatePiece(const chess::Position& pos, const chess::Square& s, const chess::Piece& pc, const chess::Color& c) {
int score = 0;
switch (chess::type_of(pc)) {
case chess::PAWN: score = evaluatePawn(pos, c, s); break;
case chess::KNIGHT: score = 320; break;
case chess::BISHOP: score = 330; break;
case chess::ROOK: score = 500; break;
case chess::QUEEN: score = 900; break;
case chess::KING: score = castleIncentive(pos, c); break;
default: return 0;
}
score += center_multiplier(s);
if (pc != chess::B_PAWN && pc != chess::W_PAWN)
score += piece_mobility(pos, s, pc, c) * 25;
return score;
}
static int evaluate(const chess::Position& pos) {
int score = 0;
chess::Bitboard white = pos.pieces(chess::WHITE);
while (white) {
chess::Square s = chess::pop_lsb(white);
chess::Piece pc = pos.piece_on(s);
chess::Color c = chess::color_of(pc);
score += evaluatePiece(pos, s, pc, c);
}
chess::Bitboard black = pos.pieces(chess::BLACK);
while (black) {
chess::Square s = chess::pop_lsb(black);
chess::Piece pc = pos.piece_on(s);
chess::Color c = chess::color_of(pc);
score -= evaluatePiece(pos, s, pc, c);
}
return score;
}
/* ---- Learned (phase-split tables + feature knobs) evaluation ---------------------------
* The model is a linear combination of features whose weights are learned from outcomes:
* eval = Σ pieces [ material + blend(mg, eg, phase) ] + Σ features featW[i]·activation[i]
* compute_features() is the single source of feature activations, used by BOTH the eval here
* and the trainer, so the two can never disagree. */
double game_phase(const chess::Position& pos) {
int npm = chess::popcount(pos.pieces(chess::KNIGHT)) * 1
+ chess::popcount(pos.pieces(chess::BISHOP)) * 1
+ chess::popcount(pos.pieces(chess::ROOK)) * 2
+ chess::popcount(pos.pieces(chess::QUEEN)) * 4;
constexpr int MAX = 24;
if (npm >= MAX) return 0.0;
return double(MAX - npm) / MAX;
}
/* Blend a midgame and endgame value by phase, rounding per-piece (so training credits a
* square the same way the eval reads it). */
static int blend(int mg, int eg, double phase) {
return int(std::lround((1.0 - phase) * mg + phase * eg));
}
void compute_features(chess::Position& pos, chess::Color c, double phase, double out[FEATURE_NB]) {
for (int i = 0; i < FEATURE_NB; ++i) out[i] = 0.0;
/* Mobility: legal moves for color c, bucketed by the moving piece's type. */
chess::MoveList moves;
pos.generate_legal_for(c, moves);
for (int i = 0; i < moves.size(); ++i) {
switch (chess::type_of(pos.piece_on(moves.moves[i].from()))) {
case chess::KNIGHT: out[FEAT_MOB_N] += 1; break;
case chess::BISHOP: out[FEAT_MOB_B] += 1; break;
case chess::ROOK: out[FEAT_MOB_R] += 1; break;
case chess::QUEEN: out[FEAT_MOB_Q] += 1; break;
default: break;
}
}
/* Pawn structure. */
chess::Bitboard pawns = pos.pieces(c, chess::PAWN);
chess::Bitboard bb = pawns;
while (bb) {
chess::Square s = chess::pop_lsb(bb);
if (!(front_span(c, s) & pos.pieces(~c, chess::PAWN))) { /* passed */
chess::Rank r = chess::rank_of(s);
int toPromotion = (c == chess::WHITE) ? (chess::RANK_8 - r) : (r - chess::RANK_1);
out[FEAT_PASSED] += (6 - toPromotion) * phase; /* 0..5 ranks advanced, late-game */
}
}
/* Pawn links: friendly pawns that are defended by another friendly pawn (one per
* defended pawn, regardless of how many defenders). */
chess::Bitboard pawnAttacks = 0;
chess::Bitboard pp = pawns;
while (pp) pawnAttacks |= chess::PawnAttacks[c][chess::pop_lsb(pp)];
out[FEAT_PAWN_LINK] += chess::popcount(pawns & pawnAttacks);
/* King safety: friendly pawns sheltering the king (its file + adjacent files, the two
* ranks in front), worth more in the midgame. */
chess::Square k = pos.king_square(c);
chess::File kf = chess::file_of(k);
chess::Rank kr = chess::rank_of(k);
chess::Bitboard kingFiles = chess::file_bb(kf);
if (kf > chess::FILE_A) kingFiles |= chess::file_bb(chess::File(kf - 1));
if (kf < chess::FILE_H) kingFiles |= chess::file_bb(chess::File(kf + 1));
chess::Bitboard shelterRanks = 0;
for (int d = 1; d <= 2; ++d) {
int rr = (c == chess::WHITE) ? (kr + d) : (kr - d);
if (rr >= 0 && rr <= 7) shelterRanks |= (0xFFULL << (8 * rr));
}
out[FEAT_KING] += chess::popcount(kingFiles & shelterRanks & pawns) * (1.0 - phase);
}
/* Learned eval (white-positive/absolute, like evaluate()): material + phase-blended piece-
* square tables + learned feature weights. Black pieces index the rank-mirrored square
* (s ^ 56) so both colors share one white-relative table. Non-const because mobility
* generates legal moves (which the position's move generator does via do/undo). */
static int evaluateLearned(chess::Position& pos, const EvalParams& ep) {
double phase = game_phase(pos);
int score = 0;
chess::Bitboard white = pos.pieces(chess::WHITE);
while (white) {
chess::Square s = chess::pop_lsb(white);
chess::PieceType pt = chess::type_of(pos.piece_on(s));
score += piece_value(pt) + blend(ep.mg[pt][s], ep.eg[pt][s], phase);
}
chess::Bitboard black = pos.pieces(chess::BLACK);
while (black) {
chess::Square s = chess::pop_lsb(black);
chess::PieceType pt = chess::type_of(pos.piece_on(s));
score -= piece_value(pt) + blend(ep.mg[pt][s ^ 56], ep.eg[pt][s ^ 56], phase);
}
double wFeat[FEATURE_NB], bFeat[FEATURE_NB];
compute_features(pos, chess::WHITE, phase, wFeat);
compute_features(pos, chess::BLACK, phase, bFeat);
double feature = 0.0;
for (int i = 0; i < FEATURE_NB; ++i)
feature += ep.featW[i] * (wFeat[i] - bFeat[i]) / FEAT_SCALE[i];
score += int(std::lround(feature));
return score;
}
/* evaluate() is white-positive (absolute). Negamax needs it relative to the side to
* move, so flip the sign when black is to move. */
int evaluate_stm(chess::Position& pos, bool whiteToMove, const EvalParams& ep) {
int s = (ep.variant == EVAL_LEARNED) ? evaluateLearned(pos, ep) : evaluate(pos);
return whiteToMove ? s : -s;
}
native/chess_engine/src/eval.h
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/* eval.h - position evaluation (classic + learned) and feature computation.
*
* This is the shared hub of the engine's "scoring" logic. The learned model's
* feature activations (compute_features) and game phase are used by BOTH the eval
* here and the trainer (learned_model.cpp), so they live in one place and can never
* diverge. The search (search.cpp) consumes evaluate_stm and piece_value. */
#pragma once
#include "types.h"
#include "position.h"
/* Eval variant for an engine handle. CLASSIC = the hand-crafted evaluate(); LEARNED =
* material + learned phase-split piece-square tables + learned feature weights. */
enum EvalVariant : int { EVAL_CLASSIC = 0, EVAL_LEARNED = 1 };
/* The learned feature knobs (beyond the piece-square tables). Each has one weight learned
* from game outcomes; its activation is computed by compute_features(). Mobility is per
* piece type. Order is fixed — it is the on-disk and snapshot layout after the two tables. */
enum Feature : int {
FEAT_MOB_N, FEAT_MOB_B, FEAT_MOB_R, FEAT_MOB_Q, /* legal-move counts, per piece type */
FEAT_PASSED, /* passed pawns, endgame-weighted */
FEAT_PAWN_LINK, /* pawns defended by a friendly pawn */
FEAT_KING, /* king pawn-shelter, midgame-weighted */
FEATURE_NB
};
/* Per-feature nominal scale: feature activations are divided by this before being weighted,
* so high-magnitude mobility doesn't dwarf the small pawn-structure terms. Used by both the
* learned eval (to combine) and the trainer (to normalize activations), so it lives here. */
inline constexpr double FEAT_SCALE[FEATURE_NB] = { 4, 6, 8, 14, 2, 3, 2 };
/* Per-handle eval configuration, snapshotted from the global learned weights at
* engine_create so the search reads a stable copy. The tables are white-relative: a black
* piece indexes the rank-mirrored square (sq ^ 56). `mg`/`eg` are blended by game phase.
* Indexed by chess::PieceType (PAWN..KING). Only consulted when variant == EVAL_LEARNED. */
struct EvalParams {
int variant = EVAL_CLASSIC;
int mg[chess::PIECE_TYPE_NB][64] = {};
int eg[chess::PIECE_TYPE_NB][64] = {};
int featW[FEATURE_NB] = {};
};
/* Centipawn material value of a piece type (0 for king / none). Shared with the search's
* MVV-LVA move ordering. */
int piece_value(chess::PieceType pt);
/* Game phase in [0,1] from remaining non-pawn material: 0 = opening, 1 = bare kings. Drives
* the mg/eg table blend and the phase weighting of the passed-pawn / king-safety features. */
double game_phase(const chess::Position& pos);
/* Fills `out[FEATURE_NB]` with one color's raw feature activations for a position (mobility,
* pawn structure, king shelter). The single source of feature activations, shared by the
* learned eval and the trainer. Non-const because mobility generates legal moves. */
void compute_features(chess::Position& pos, chess::Color c, double phase, double out[FEATURE_NB]);
/* Side-to-move-relative evaluation for negamax (positive = good for whoever is to move).
* Dispatches to the classic or learned eval per ep.variant. Non-const because the learned
* eval computes mobility via the move generator. */
int evaluate_stm(chess::Position& pos, bool whiteToMove, const EvalParams& ep);
native/chess_engine/src/learned_model.cpp
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/* learned_model.cpp - global learned weights and the per-game trainer.
*
* The model is trained by WIN RATE, not by additive nudges. For every (piece, phase,
* square) we keep two running totals across all games: `win` (turns the piece spent there in
* games that side won) and `total` (turns spent there in any game). The stored weight is
* derived: weight = (2·win/total − 1)·scale, i.e. win-rate 0→−scale, 0.5→0, 1→+scale. Same
* for each feature, totalling its activation per turn. This focuses training on "how much
* time on this square correlates with winning" and is far less volatile than per-game nudges.
*
* The counters are the persistent source of truth (saved to / loaded from disk); the integer
* weight tables in `g_weights` are recomputed from them. See learned_model.h for the public
* surface; feature/phase math is shared from eval.cpp. */
#include "learned_model.h"
#include "chess_engine.h" /* CHESS_ERR_BUFFER */
#include "eval.h"
#include "position.h"
#include <cmath>
#include <cstring>
#include <fstream>
#include <mutex>
#include <new>
#include <string>
/* Win-rate → weight scale. A 100%-win square/feature reaches +scale, a 0%-win one −scale,
* matching the ranges the additive trainer used to clamp at (squares ±250, features ±500). */
static constexpr double SQ_WEIGHT_SCALE = 250.0;
static constexpr double FEAT_WEIGHT_SCALE = 500.0;
/* On-disk format version, stored as the file's first token. On load, a missing or mismatched
* version means the file is stale (old layout / different feature set): its contents are
* wiped (the file itself is kept) and training restarts from neutral. Bump this whenever the
* counter layout or feature set changes — it replaces having to delete the file by hand. */
static constexpr int LEARNED_VERSION = 1;
/* Derived integer weight tables, read by eval (snapshotted per engine handle) and the viz.
* Recomputed from g_counts whenever the counters change. White-relative (black indexes
* sq ^ 56); mg/eg blended by game phase. */
struct LearnedWeights {
int mg[chess::PIECE_TYPE_NB][64] = {};
int eg[chess::PIECE_TYPE_NB][64] = {};
int featW[FEATURE_NB] = {};
};
/* The persistent training counters: the single source of truth. `win` is credited only to
* the winning side; `total` to both sides (scaled by the outcome weight). */
struct WinCounters {
double winMg[chess::PIECE_TYPE_NB][64] = {};
double totMg[chess::PIECE_TYPE_NB][64] = {};
double winEg[chess::PIECE_TYPE_NB][64] = {};
double totEg[chess::PIECE_TYPE_NB][64] = {};
double winFeat[FEATURE_NB] = {};
double totFeat[FEATURE_NB] = {};
};
static LearnedWeights g_weights;
static WinCounters g_counts;
static std::mutex g_weightsMutex;
static std::string g_weightsPath;
/* win/total → stored weight: win-rate 0 → −scale, 0.5 → 0, 1 → +scale. An untouched
* (total == 0) square/feature is neutral. */
static int derive(double win, double total, double scale) {
if (total <= 0.0) return 0;
double rate = win / total;
return int(std::lround((2.0 * rate - 1.0) * scale));
}
/* Recompute every derived weight from the counters. Caller holds g_weightsMutex. */
static void recompute_weights() {
for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
for (int sq = 0; sq < 64; ++sq) {
g_weights.mg[pt][sq] = derive(g_counts.winMg[pt][sq], g_counts.totMg[pt][sq], SQ_WEIGHT_SCALE);
g_weights.eg[pt][sq] = derive(g_counts.winEg[pt][sq], g_counts.totEg[pt][sq], SQ_WEIGHT_SCALE);
}
for (int i = 0; i < FEATURE_NB; ++i)
g_weights.featW[i] = derive(g_counts.winFeat[i], g_counts.totFeat[i], FEAT_WEIGHT_SCALE);
}
static void save_global_weights(); /* defined below; load rewrites stale files via it */
/* On-disk format: LEARNED_VERSION as the first token, then the counters as whitespace doubles
* in this order — winMg, totMg, winEg, totEg (each 6*64, PAWN..KING, squares 0..63), then
* winFeat, totFeat (each FEATURE_NB). If the version is missing/wrong or the file is short
* (old format, corrupt, or absent), the counters are left neutral and the file is rewritten
* blank-but-versioned — clearing stale contents while keeping the file. Caller holds the lock. */
static void load_global_weights(const char* path) {
WinCounters loaded{};
bool ok = false;
bool fileExisted = false;
if (path && *path) {
std::ifstream f(path);
if (f) {
fileExisted = true;
int version = 0;
if ((f >> version) && version == LEARNED_VERSION) {
auto readTable = [&](double t[chess::PIECE_TYPE_NB][64]) -> bool {
for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
for (int sq = 0; sq < 64; ++sq)
if (!(f >> t[pt][sq])) return false;
return true;
};
ok = readTable(loaded.winMg) && readTable(loaded.totMg)
&& readTable(loaded.winEg) && readTable(loaded.totEg);
for (int i = 0; ok && i < FEATURE_NB; ++i) if (!(f >> loaded.winFeat[i])) ok = false;
for (int i = 0; ok && i < FEATURE_NB; ++i) if (!(f >> loaded.totFeat[i])) ok = false;
}
}
}
g_counts = ok ? loaded : WinCounters{};
recompute_weights();
/* Only create a fresh file when none exists yet (first run): write it blank-but-versioned
* so there's a valid target to persist into. If a file IS present but couldn't be parsed
* (old format, corrupt, or a partial write), leave its bytes untouched — never destroy
* accumulated training data on startup. We just play from neutral weights this session;
* the next training apply() overwrites the file with a clean, current-format save. */
if (!ok && !fileExisted)
save_global_weights();
}
/* Persist g_counts to g_weightsPath in the format load_global_weights reads. Caller holds the lock. */
static void save_global_weights() {
if (g_weightsPath.empty()) return;
std::ofstream f(g_weightsPath);
if (!f) return;
f << LEARNED_VERSION << '\n';
auto writeTable = [&](const double t[chess::PIECE_TYPE_NB][64]) {
for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
for (int sq = 0; sq < 64; ++sq) f << t[pt][sq] << (sq == 63 ? '\n' : ' ');
};
writeTable(g_counts.winMg); writeTable(g_counts.totMg);
writeTable(g_counts.winEg); writeTable(g_counts.totEg);
for (int i = 0; i < FEATURE_NB; ++i) f << g_counts.winFeat[i] << (i == FEATURE_NB - 1 ? '\n' : ' ');
for (int i = 0; i < FEATURE_NB; ++i) f << g_counts.totFeat[i] << (i == FEATURE_NB - 1 ? '\n' : ' ');
}
/* ---- Per-game training accumulator ----------------------------------------------------
* Records, per ply, where each side's pieces sat (split into midgame/endgame by phase) and
* each side's feature activations. learned::apply folds these per-side totals into the global
* win/total counters. Squares are white-relative (black indexes sq ^ 56), so a side's tally
* lines up with the shared white-relative table. */
struct Trainer {
double mgOcc[chess::COLOR_NB][chess::PIECE_TYPE_NB][64] = {};
double egOcc[chess::COLOR_NB][chess::PIECE_TYPE_NB][64] = {};
double featAcc[chess::COLOR_NB][FEATURE_NB] = {};
};
namespace learned {
void load(const char* path) {
std::lock_guard<std::mutex> lock(g_weightsMutex);
g_weightsPath = path ? path : "";
load_global_weights(path);
}
int snapshot(int* out, int out_len) {
const int need = 6 * 64 * 2 + FEATURE_NB; /* mg + eg (PAWN..KING) + features */
if (!out || out_len < need) return CHESS_ERR_BUFFER;
std::lock_guard<std::mutex> lock(g_weightsMutex);
int n = 0;
for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
for (int sq = 0; sq < 64; ++sq) out[n++] = g_weights.mg[pt][sq];
for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
for (int sq = 0; sq < 64; ++sq) out[n++] = g_weights.eg[pt][sq];
for (int i = 0; i < FEATURE_NB; ++i) out[n++] = g_weights.featW[i];
return n;
}
void copy_weights_to(EvalParams& ep) {
std::lock_guard<std::mutex> lock(g_weightsMutex);
std::memcpy(ep.mg, g_weights.mg, sizeof ep.mg);
std::memcpy(ep.eg, g_weights.eg, sizeof ep.eg);
std::memcpy(ep.featW, g_weights.featW, sizeof ep.featW);
}
Trainer* create() {
return new (std::nothrow) Trainer();
}
void destroy(Trainer* t) {
delete t; /* delete nullptr is safe */
}
void record(Trainer* t, const char* fen) {
if (!t || !fen || !*fen) return;
chess::Position pos = chess::Position::from_fen(fen);
double phase = game_phase(pos);
/* Per-square occupancy, split into midgame/endgame by phase, white-relative. */
chess::Bitboard occ = pos.pieces();
while (occ) {
chess::Square s = chess::pop_lsb(occ);
chess::Piece pc = pos.piece_on(s);
chess::Color c = chess::color_of(pc);
chess::PieceType pt = chess::type_of(pc);
int relSq = (c == chess::WHITE) ? int(s) : (int(s) ^ 56);
t->mgOcc[c][pt][relSq] += (1.0 - phase);
t->egOcc[c][pt][relSq] += phase;
}
/* Per-side feature activations. */
double w[FEATURE_NB], b[FEATURE_NB];
compute_features(pos, chess::WHITE, phase, w);
compute_features(pos, chess::BLACK, phase, b);
for (int i = 0; i < FEATURE_NB; ++i) {
t->featAcc[chess::WHITE][i] += w[i];
t->featAcc[chess::BLACK][i] += b[i];
}
}
void apply(Trainer* t, int winner, double weight) {
if (!t) return;
std::lock_guard<std::mutex> lock(g_weightsMutex);
/* Fold each side's per-game tallies into the global counters: both sides credit `total`,
* only the winner credits `win`, each scaled by the outcome weight (1.0 for a decisive
* game, 0.5 for a material-imbalance draw). */
for (int s = 0; s < chess::COLOR_NB; ++s) {
bool isWinner = (s == winner);
for (int pt = chess::PAWN; pt <= chess::KING; ++pt)
for (int sq = 0; sq < 64; ++sq) {
double mg = weight * t->mgOcc[s][pt][sq];
double eg = weight * t->egOcc[s][pt][sq];
g_counts.totMg[pt][sq] += mg;
g_counts.totEg[pt][sq] += eg;
if (isWinner) {
g_counts.winMg[pt][sq] += mg;
g_counts.winEg[pt][sq] += eg;
}
}
for (int i = 0; i < FEATURE_NB; ++i) {
double f = weight * t->featAcc[s][i];
g_counts.totFeat[i] += f;
if (isWinner) g_counts.winFeat[i] += f;
}
}
recompute_weights();
save_global_weights();
}
} // namespace learned
native/chess_engine/src/learned_model.h
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/* learned_model.h - the learned engine's process-global weights and training.
*
* Owns the single source of truth for the learned weights (loaded from / saved to disk),
* the read-only snapshot for visualization, and the per-game training accumulator that
* turns played positions + a result into weight nudges. The DLL ABI (chess_engine.cpp)
* is a thin pass-through to the functions here; feature/phase math is shared from eval.h. */
#pragma once
#include "eval.h"
/* Per-game training accumulator. Global-namespace `Trainer` so it matches the opaque
* `typedef struct Trainer* TrainerHandle` in the public ABI header. Defined in the .cpp. */
struct Trainer;
namespace learned {
/* Set the global weights file path and load from it (idempotent; a missing/short file
* leaves the weights neutral). */
void load(const char* path);
/* Copy the global weights out for visualization: 6*64 midgame + 6*64 endgame + features.
* Returns the count written, or CHESS_ERR_BUFFER if out_len is too small (needs >= 776). */
int snapshot(int* out, int out_len);
/* Snapshot the current global weights into a fresh engine handle's eval config so the
* search reads a stable copy (training updates the global between games). */
void copy_weights_to(EvalParams& ep);
/* Per-game training lifecycle. */
Trainer* create();
void destroy(Trainer* t);
/* Record one played position (post-move FEN) into the accumulator. */
void record(Trainer* t, const char* fen);
/* Apply a finished game's outcome to the global weights and persist: rewards the winner's
* occupied squares / features, punishes the loser's, scaled by `weight`. winner: 0=W, 1=B. */
void apply(Trainer* t, int winner, double weight);
} // namespace learned
native/chess_engine/src/search.cpp
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/* search.cpp - negamax alpha-beta over a shared transposition table, driven by
* iterative deepening. See search.h for the (single-function) public surface. */
#include "search.h"
#include "eval.h"
#include "position.h"
#include "movegen.h"
#include "uci.h"
#include <algorithm>
#include <atomic>
#include <cstdint>
#include <cstdio>
#include <memory>
#include <mutex>
/* Search score constants. Scores are side-to-move-relative (negamax): positive is
* good for whoever is to move. MATE_BOUND is the threshold above which a score is a
* "mate in N" rather than a positional eval; INF is the window sentinel (kept above
* MATE so negating it can never hit signed-overflow UB the way INT_MIN would). */
static constexpr int MATE = 200000;
static constexpr int MATE_BOUND = MATE - 1000;
static constexpr int INF = 1000000;
/* Bound kind stored in a TT entry. LOWER = a fail-high (true score >= stored),
* UPPER = a fail-low (true score <= stored), EXACT = fully resolved. */
enum class Bound : uint8_t { NONE, EXACT, LOWER, UPPER };
/* One shared, process-wide transposition table backs every game (every engine
* handle), so analysis persists and is reused across games. It is lock-free: each
* slot is two 64-bit words — `data` (the packed payload) and `xorKey` (the Zobrist
* key XOR-ed with `data`). A reader recovers the key as `xorKey ^ data`; if two
* concurrent searches tore the pair, the recovered key won't match and the read is
* treated as a miss — never a wrong-but-trusted entry (Hyatt's lockless hashing). */
struct TTEntry {
std::atomic<uint64_t> xorKey{0};
std::atomic<uint64_t> data{0};
};
struct TranspositionTable {
std::unique_ptr<TTEntry[]> entries;
size_t mask = 0; /* count - 1; count is a power of two */
};
static TranspositionTable g_tt;
static constexpr size_t TT_MEGABYTES = 256;
/* Pack/unpack the 64-bit payload: score(32) | move(16) | depth(8) | bound(8). A stored
* entry always has depth >= 1 and a non-NONE bound, so a real entry never packs to 0 —
* letting data == 0 mean "empty slot". */
static uint64_t tt_pack(int score, chess::Move move, int depth, Bound bound) {
return static_cast<uint64_t>(static_cast<uint32_t>(score))
| (static_cast<uint64_t>(move.data) << 32)
| (static_cast<uint64_t>(static_cast<uint8_t>(depth)) << 48)
| (static_cast<uint64_t>(static_cast<uint8_t>(bound)) << 56);
}
static int tt_score(uint64_t d) { return static_cast<int32_t>(static_cast<uint32_t>(d & 0xFFFFFFFFu)); }
static chess::Move tt_move (uint64_t d) { return chess::Move(static_cast<uint16_t>(d >> 32)); }
static int tt_depth(uint64_t d) { return static_cast<int>(static_cast<uint8_t>(d >> 48)); }
static Bound tt_bound(uint64_t d) { return static_cast<Bound>(static_cast<uint8_t>(d >> 56)); }
static size_t floor_pow2(size_t n) {
size_t p = 1;
while ((p << 1) != 0 && (p << 1) <= n) p <<= 1;
return p;
}
/* Allocate the shared table exactly once, to the largest power-of-two entry count that
* fits in TT_MEGABYTES. Power-of-two count lets indexing use `key & mask`. Thread-safe:
* call_once guards the first concurrent search. Entries start zeroed (empty). */
static void ensure_tt() {
static std::once_flag once;
std::call_once(once, [] {
size_t count = floor_pow2((TT_MEGABYTES << 20) / sizeof(TTEntry));
if (count < 1) count = 1;
g_tt.entries = std::make_unique<TTEntry[]>(count);
g_tt.mask = count - 1;
});
}
/* Maps the 1..20 difficulty to a search depth. Kept modest: the search has no
* quiescence yet, so deep fixed-depth runs get expensive quickly. */
static int depth_for_skill(int skill) {
return skill; /* skill N -> N plies */
}
/* Mate scores are "mate in N from THIS node", so they must be re-anchored to the
* probing node's ply when crossing the TT (store adds ply, retrieve subtracts it).
* Non-mate scores pass through untouched. */
static int score_to_tt(int s, int ply) { return s >= MATE_BOUND ? s + ply : s <= -MATE_BOUND ? s - ply : s; }
static int score_from_tt(int s, int ply) { return s >= MATE_BOUND ? s - ply : s <= -MATE_BOUND ? s + ply : s; }
/* Heuristic for searching the most promising moves first, which makes alpha-beta prune far
* more. Bands, highest first: the TT best move, then captures by MVV-LVA (most valuable
* victim, least valuable attacker), then the two killer moves for this ply (quiet moves that
* cut a sibling), then the remaining quiet moves. `killers` points at this ply's two-entry
* slot; `scoreChecks` gates the expensive gives_check term to near-leaf nodes. */
static int order_score(chess::Position& pos, chess::Move m, chess::Move ttMove,
const chess::Move* killers, bool scoreChecks) {
if (m == ttMove)
return 2000000; /* dwarfs any capture/killer/check score below */
int score = 0;
if (scoreChecks && pos.gives_check(m))
score += 1000;
chess::Piece victim = pos.piece_on(m.to());
#ifdef BENCH_DISABLE_KILLERS
/* Benchmark A/B only (defined by bench.ps1): the pre-killer ordering — captures by
* MVV-LVA above quiet moves, no killer band — so the script can time the killer speedup. */
(void)killers;
if (victim != chess::NO_PIECE)
score += 100 + 10 * piece_value(chess::type_of(victim))
- piece_value(chess::type_of(pos.piece_on(m.from())));
else if (m.type() == chess::EN_PASSANT)
score += 100 + 10 * piece_value(chess::PAWN);
#else
if (victim != chess::NO_PIECE)
score += 100000 + 10 * piece_value(chess::type_of(victim))
- piece_value(chess::type_of(pos.piece_on(m.from())));
else if (m.type() == chess::EN_PASSANT)
score += 100000 + 10 * piece_value(chess::PAWN);
else if (m == killers[0])
score += 90000; /* quiet move that beta-cut a sibling at this ply */
else if (m == killers[1])
score += 80000;
#endif
return score;
}
/* Sort the move list in place, best-scoring first. Scores are computed once up
* front so gives_check isn't re-evaluated on every comparison. ttMove may be
* MOVE_NONE, in which case no move matches it and ordering falls back to captures. */
static void order_moves(chess::Position& pos, chess::MoveList& moves, chess::Move ttMove,
const chess::Move* killers, bool scoreChecks) {
struct ScoredMove { int score = 0; chess::Move move{}; };
ScoredMove scored[256];
for (int i = 0; i < moves.size(); i++)
scored[i] = { order_score(pos, moves.moves[i], ttMove, killers, scoreChecks), moves.moves[i] };
std::sort(scored, scored + moves.size(),
[](const ScoredMove& a, const ScoredMove& b) { return a.score > b.score; });
for (int i = 0; i < moves.size(); i++)
moves.moves[i] = scored[i].move;
}
/* Per-search scratch, threaded through the recursion. Kept off global scope so two engine
* handles can search concurrently without sharing node counts or killer tables. killers[ply]
* holds up to two quiet moves that recently caused a beta cutoff at that ply; trying them
* early (right after captures) prunes far more — the quiet-move ordering the search otherwise
* lacks. */
static constexpr int MAX_PLY = 128; /* ply never exceeds maxDepth (<= 20) */
struct SearchContext {
uint64_t nodes = 0;
const EvalParams* eval = nullptr; /* eval config for this search; set by find_best_move */
chess::Move killers[MAX_PLY][2] = {};/* [ply][slot]; MOVE_NONE until filled */
};
/* Negamax alpha-beta over the shared transposition table. `maxDepth` is the searching
* bot's difficulty (its root depth); `depth` is remaining depth (draft); `ply` is
* distance from the root (mate scoring only). Scores are side-to-move-relative.
* Fail-soft: returns the true best found even outside [alpha, beta]. */
static int negamax(chess::Position& pos, int maxDepth, int depth, int ply,
int alpha, int beta, bool whiteToMove, SearchContext& ctx) {
ctx.nodes++;
/* A draw is 0 even at the search horizon, and the TT key doesn't encode repetition
* history, so this must come before both the leaf eval and any TT probe. */
if (ply > 0 && pos.is_draw())
return 0;
if (depth <= 0)
return evaluate_stm(pos, whiteToMove, *ctx.eval);
const uint64_t key = pos.key();
TTEntry& slot = g_tt.entries[key & g_tt.mask];
const uint64_t data = slot.data.load(std::memory_order_relaxed);
const uint64_t xkey = slot.xorKey.load(std::memory_order_relaxed);
chess::Move ttMove = chess::MOVE_NONE;
if (data != 0 && (xkey ^ data) == key) { /* lockless: XOR check rejects torn reads */
ttMove = tt_move(data); /* always reusable for ordering */
int edepth = tt_depth(data);
Bound b = tt_bound(data);
/* Trust the score only if it was searched deep enough for this node AND no deeper
* than this bot's own strength — so a weak bot can't borrow a stronger game's
* deeper analysis (it still gets the move for ordering, which can't leak strength). */
if (edepth >= depth && edepth <= maxDepth) {
int s = score_from_tt(tt_score(data), ply);
if (b == Bound::EXACT) return s;
if (b == Bound::LOWER && s >= beta) return s;
if (b == Bound::UPPER && s <= alpha) return s;
}
}
chess::MoveList moves;
pos.generate_legal(moves);
if (moves.size() == 0)
return pos.is_draw() ? 0 : -MATE + ply; /* checkmate against side to move */
order_moves(pos, moves, ttMove, ctx.killers[ply], depth <= 2);
const int alphaOrig = alpha;
int best = -INF;
chess::Move bestMove = chess::MOVE_NONE;
for (int i = 0; i < moves.size(); i++) {
chess::Move move = moves.moves[i];
pos.do_move(move);
int score = -negamax(pos, maxDepth, depth - 1, ply + 1, -beta, -alpha, !whiteToMove, ctx);
pos.undo_move(move);
if (score > best) {
best = score;
bestMove = move;
}
if (best > alpha)
alpha = best;
if (best >= beta) {
/* A quiet move good enough to fail high here is a strong candidate in sibling
* lines at this ply — remember it as a killer. pos is back to pre-move state
* after undo_move, so piece_on(to) still flags a capture correctly. */
bool isCapture = pos.piece_on(move.to()) != chess::NO_PIECE
|| move.type() == chess::EN_PASSANT;
if (!isCapture && ply < MAX_PLY && ctx.killers[ply][0] != move) {
ctx.killers[ply][1] = ctx.killers[ply][0];
ctx.killers[ply][0] = move;
}
break; /* fail-high cutoff */
}
}
Bound flag = best <= alphaOrig ? Bound::UPPER
: best >= beta ? Bound::LOWER
: Bound::EXACT;
/* Depth-preferred replacement: keep the deepest analysis of each slot. The stored
* payload is written before the xorKey so any concurrent reader that catches a
* half-update fails the XOR check and treats it as a miss. */
int storedDepth = (data == 0) ? -1 : tt_depth(data);
if (depth >= storedDepth) {
uint64_t packed = tt_pack(score_to_tt(best, ply), bestMove, depth, flag);
slot.data.store(packed, std::memory_order_relaxed);
slot.xorKey.store(key ^ packed, std::memory_order_relaxed);
}
return best;
}
chess::Move find_best_move(chess::Position& pos, const EvalParams& ep, int skill) {
ensure_tt();
bool whiteToMove = pos.side_to_move() == chess::WHITE;
chess::MoveList moves;
pos.generate_legal(moves);
if (moves.size() == 0)
return chess::MOVE_NONE;
SearchContext ctx;
ctx.eval = &ep;
int maxDepth = depth_for_skill(skill);
chess::Move bestMove = moves.moves[0]; /* guaranteed-legal fallback */
/* Iterative deepening: each depth seeds the next depth's move ordering (via the
* previous best move and the TT it filled), which makes the deeper search prune
* far harder than searching to maxDepth cold. */
for (int d = 1; d <= maxDepth; d++) {
int alpha = -INF, beta = INF;
chess::Move iterBest = bestMove;
int iterScore = -INF;
order_moves(pos, moves, iterBest, ctx.killers[0], true);
for (int i = 0; i < moves.size(); i++) {
chess::Move move = moves.moves[i];
pos.do_move(move);
int score = -negamax(pos, maxDepth, d - 1, 1, -beta, -alpha, !whiteToMove, ctx);
pos.undo_move(move);
if (score > iterScore) {
iterScore = score;
iterBest = move;
}
if (score > alpha)
alpha = score;
}
bestMove = iterBest; /* commit only a fully completed iteration */
std::fprintf(stderr, "depth %d nodes %llu best %s score %d\n",
d, static_cast<unsigned long long>(ctx.nodes),
chess::move_to_uci(iterBest).c_str(), iterScore);
}
return bestMove;
}
native/chess_engine/src/search.h
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@@ -1,14 +0,0 @@
/* search.h - the engine's search: a single entry point.
*
* Everything else (the shared transposition table, move ordering, negamax, and the
* iterative-deepening driver) is an implementation detail of search.cpp. */
#pragma once
#include "position.h"
#include "eval.h"
/* Best move for `pos` using evaluation `ep`, searched to the depth implied by `skill`
* (1..20). Seeds, allocates, and reuses the process-wide transposition table on first
* call. Returns chess::MOVE_NONE when there is no legal move (mate/stalemate). The
* position's repetition/50-move history should already be seeded by the caller. */
chess::Move find_best_move(chess::Position& pos, const EvalParams& ep, int skill);