X-Git-Url: https://git.sesse.net/?p=stockfish;a=blobdiff_plain;f=src%2Fsearch.cpp;h=0fa5290616861f0f32cf8dea39d9f79e6ea9296b;hp=5921ae0fd77a093c95b59e3c0c8f3a1b2d02bc04;hb=1036cadcecc43737a1234eec00960a5a81073971;hpb=c71ae794df257e3d6f1e925b6d013434bb2f99ef diff --git a/src/search.cpp b/src/search.cpp index 5921ae0f..0fa52906 100644 --- a/src/search.cpp +++ b/src/search.cpp @@ -52,21 +52,22 @@ namespace { enum NodeType { Root, PV, NonPV, SplitPointPV, SplitPointNonPV }; // RootMove struct is used for moves at the root of the tree. For each root - // move, we store a pv_score, a node count, and a PV (really a refutation - // in the case of moves which fail low). Value pv_score is normally set at + // move, we store a score, a node count, and a PV (really a refutation + // in the case of moves which fail low). Score is normally set at // -VALUE_INFINITE for all non-pv moves. struct RootMove { // RootMove::operator<() is the comparison function used when // sorting the moves. A move m1 is considered to be better - // than a move m2 if it has an higher pv_score - bool operator<(const RootMove& m) const { return pv_score < m.pv_score; } + // than a move m2 if it has an higher score + bool operator<(const RootMove& m) const { return score < m.score; } void extract_pv_from_tt(Position& pos); void insert_pv_in_tt(Position& pos); int64_t nodes; - Value pv_score; + Value score; + Value prevScore; std::vector pv; }; @@ -534,8 +535,10 @@ namespace { // Iterative deepening loop until requested to stop or target depth reached while (!StopRequest && ++depth <= PLY_MAX && (!Limits.maxDepth || depth <= Limits.maxDepth)) { - // Remember best moves and values from previous iteration - RootMoveList prevRml = Rml; + // Save last iteration's scores, this needs to be done now, because in + // the following MultiPV loop Rml moves could be reordered. + for (size_t i = 0; i < Rml.size(); i++) + Rml[i].prevScore = Rml[i].score; Rml.bestMoveChanges = 0; @@ -543,7 +546,7 @@ namespace { for (MultiPVIteration = 0; MultiPVIteration < Min(MultiPV, (int)Rml.size()); MultiPVIteration++) { // Calculate dynamic aspiration window based on previous iterations - if (depth >= 5 && abs(prevRml[MultiPVIteration].pv_score) < VALUE_KNOWN_WIN) + if (depth >= 5 && abs(Rml[MultiPVIteration].prevScore) < VALUE_KNOWN_WIN) { int prevDelta1 = bestValues[depth - 1] - bestValues[depth - 2]; int prevDelta2 = bestValues[depth - 2] - bestValues[depth - 3]; @@ -551,8 +554,8 @@ namespace { aspirationDelta = Min(Max(abs(prevDelta1) + abs(prevDelta2) / 2, 16), 24); aspirationDelta = (aspirationDelta + 7) / 8 * 8; // Round to match grainSize - alpha = Max(prevRml[MultiPVIteration].pv_score - aspirationDelta, -VALUE_INFINITE); - beta = Min(prevRml[MultiPVIteration].pv_score + aspirationDelta, VALUE_INFINITE); + alpha = Max(Rml[MultiPVIteration].prevScore - aspirationDelta, -VALUE_INFINITE); + beta = Min(Rml[MultiPVIteration].prevScore + aspirationDelta, VALUE_INFINITE); } else { @@ -590,21 +593,14 @@ namespace { // Send full PV info to GUI if we are going to leave the loop or // if we have a fail high/low and we are deep in the search. if ((value > alpha && value < beta) || current_search_time() > 2000) - for (int i = 0; i < Min(UCIMultiPV, (int)Rml.size()); i++) + for (int i = 0; i < Min(UCIMultiPV, MultiPVIteration); i++) { - bool updated = (i <= MultiPVIteration); - - if (depth == 1 && !updated) - continue; - - const RootMoveList& rml = (updated ? Rml : prevRml); - cout << "info" - << depth_to_uci((updated ? depth : depth - 1) * ONE_PLY) - << (i == MultiPVIteration ? score_to_uci(rml[i].pv_score, alpha, beta) - : score_to_uci(rml[i].pv_score)) + << depth_to_uci(depth * ONE_PLY) + << (i == MultiPVIteration ? score_to_uci(Rml[i].score, alpha, beta) : + score_to_uci(Rml[i].score)) << speed_to_uci(pos.nodes_searched()) - << pv_to_uci(&rml[i].pv[0], i + 1, pos.is_chess960()) + << pv_to_uci(&Rml[i].pv[0], i + 1, pos.is_chess960()) << endl; } @@ -643,7 +639,7 @@ namespace { LogFile << pretty_pv(pos, depth, value, current_search_time(), &Rml[0].pv[0]) << endl; // Init easyMove after first iteration or drop if differs from the best move - if (depth == 1 && (Rml.size() == 1 || Rml[0].pv_score > Rml[1].pv_score + EasyMoveMargin)) + if (depth == 1 && (Rml.size() == 1 || Rml[0].score > Rml[1].score + EasyMoveMargin)) easyMove = bestMove; else if (bestMove != easyMove) easyMove = MOVE_NONE; @@ -1219,7 +1215,7 @@ split_point_start: // At split points actual search starts from here if (isPvMove || value > alpha) { // Update PV - rm->pv_score = value; + rm->score = value; rm->extract_pv_from_tt(pos); // We record how often the best move has been changed in each @@ -1236,7 +1232,7 @@ split_point_start: // At split points actual search starts from here // All other moves but the PV are set to the lowest value, this // is not a problem when sorting becuase sort is stable and move // position in the list is preserved, just the PV is pushed up. - rm->pv_score = -VALUE_INFINITE; + rm->score = -VALUE_INFINITE; } // RootNode @@ -2016,12 +2012,12 @@ split_point_start: // At split points actual search starts from here static RKISS rk; - // Rml list is already sorted by pv_score in descending order + // Rml list is already sorted by score in descending order int s; int max_s = -VALUE_INFINITE; int size = Min(MultiPV, (int)Rml.size()); - int max = Rml[0].pv_score; - int var = Min(max - Rml[size - 1].pv_score, PawnValueMidgame); + int max = Rml[0].score; + int var = Min(max - Rml[size - 1].score, PawnValueMidgame); int wk = 120 - 2 * SkillLevel; // PRNG sequence should be non deterministic @@ -2033,10 +2029,10 @@ split_point_start: // At split points actual search starts from here // then we choose the move with the resulting highest score. for (int i = 0; i < size; i++) { - s = Rml[i].pv_score; + s = Rml[i].score; // Don't allow crazy blunders even at very low skills - if (i > 0 && Rml[i-1].pv_score > s + EasyMoveMargin) + if (i > 0 && Rml[i-1].score > s + EasyMoveMargin) break; // This is our magical formula @@ -2073,7 +2069,7 @@ split_point_start: // At split points actual search starts from here RootMove rm; rm.pv.push_back(ml.move()); rm.pv.push_back(MOVE_NONE); - rm.pv_score = -VALUE_INFINITE; + rm.score = rm.prevScore = -VALUE_INFINITE; rm.nodes = 0; push_back(rm); }