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This enables different Stockfish processes that use the same weights to use the same memory. The approach establishes equivalence by memory content, and is compatible with NUMA replication. The benefit of sharing is reduced memory usage and a speedup thanks to improved (inter-process) caching of the network in the CPUs cache, and thus reduced bandwidth usage to main memory. Even though this change doesn't benefit a user running a single process, this helps on fishtest or e.g. for Lichess, when multiple games run concurrently, or multiple positions are analyzed in parallel. This concept was probably first introduced in the Monty engine (https://github.com/official-monty/Monty/pull/62), after a discussion in https://github.com/official-stockfish/fishtest/issues/2077 on the issue of memory pressure. Measurements based on Torch (https://github.com/user-attachments/files/21386224/verbatim.pdf) further suggested that large gains were possible. Multiple other engines have adopted this 'verbatim' format as well. The implementation here adds the flexibility needed for SF, for example, retains the ability to bundle compressed networks with the binary, to load nets by uci option, and to distribute the shared nets to the proper NUMA region. This flexibility comes with a fair amount of complexity in the implementation, such as OS specific code, and fallback code. For most users this should be transparent. However, for example, those running docker containers should ensure the `--ipc` flag is set correctly, and `--shm-size` is sufficiently large. The benefits of this patch significantly depend on hardware, with systems with many cores and a large (O(150MB), the net size) L3 cache benefitting typically most. On such systems SF speedups (as measured via nps playing games with large concurrency but just 1 thread) can be 38%, which results in master vs. patch Elo which gains about 25 Elo. ``` # PLAYER : RATING ERROR POINTS PLAYED (%) 1 shared_memoryPR : 24.8 1.9 39432.0 73728 53 2 master : 0.0 ---- 34296.0 73728 47 ``` In a multithreaded setup, where weights are already shared, that benefit is smaller, for example on the same HW as above, but with 8t for each side. ``` # PLAYER : RATING ERROR POINTS PLAYED (%) 1 shared_memoryPR : 5.2 3.5 9351.0 18432 51 2 master : 0.0 ---- 9081.0 18432 49 ``` On fishtest with a typical hardware mix of our contributors, the following was measured: STC, 60k games https://tests.stockfishchess.org/tests/view/69074a49ea4b268f1fac236c Elo: 4.69 ± 1.4 (95%) LOS: 100.0% Total: 60000 W: 16085 L: 15275 D: 28640 Ptnml(0-2): 154, 6440, 16053, 7148, 205 nElo: 9.38 ± 2.8 (95%) PairsRatio: 1.12 To verify correctness with a single process on a NUMA architecture, speedtest was used, confirming near equivalence: ``` master: Average (over 10): 296236186 shared_memory: Average (over 10): 295769332 ``` Currently, using large pages for the shared network weights is not always possible, which can lead to a small slowdown (1-2%), in case a single process is run. closes https://github.com/official-stockfish/Stockfish/pull/6173 No functional change Co-authored-by: disservin <disservin.social@gmail.com> Co-authored-by: Joost VandeVondele <Joost.VandeVondele@gmail.com>
125 lines
4.8 KiB
C++
125 lines
4.8 KiB
C++
/*
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Stockfish, a UCI chess playing engine derived from Glaurung 2.1
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Copyright (C) 2004-2025 The Stockfish developers (see AUTHORS file)
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Stockfish is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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Stockfish is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include "evaluate.h"
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#include <algorithm>
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#include <cassert>
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#include <cmath>
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#include <cstdlib>
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#include <iomanip>
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#include <iostream>
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#include <memory>
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#include <sstream>
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#include <tuple>
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#include "nnue/network.h"
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#include "nnue/nnue_misc.h"
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#include "position.h"
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#include "types.h"
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#include "uci.h"
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#include "nnue/nnue_accumulator.h"
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namespace Stockfish {
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// Returns a static, purely materialistic evaluation of the position from
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// the point of view of the side to move. It can be divided by PawnValue to get
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// an approximation of the material advantage on the board in terms of pawns.
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int Eval::simple_eval(const Position& pos) {
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Color c = pos.side_to_move();
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return PawnValue * (pos.count<PAWN>(c) - pos.count<PAWN>(~c))
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+ (pos.non_pawn_material(c) - pos.non_pawn_material(~c));
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}
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bool Eval::use_smallnet(const Position& pos) { return std::abs(simple_eval(pos)) > 962; }
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// Evaluate is the evaluator for the outer world. It returns a static evaluation
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// of the position from the point of view of the side to move.
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Value Eval::evaluate(const Eval::NNUE::Networks& networks,
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const Position& pos,
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Eval::NNUE::AccumulatorStack& accumulators,
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Eval::NNUE::AccumulatorCaches& caches,
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int optimism) {
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assert(!pos.checkers());
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bool smallNet = use_smallnet(pos);
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auto [psqt, positional] = smallNet ? networks.small.evaluate(pos, accumulators, &caches.small)
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: networks.big.evaluate(pos, accumulators, &caches.big);
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Value nnue = (125 * psqt + 131 * positional) / 128;
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// Re-evaluate the position when higher eval accuracy is worth the time spent
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if (smallNet && (std::abs(nnue) < 236))
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{
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std::tie(psqt, positional) = networks.big.evaluate(pos, accumulators, &caches.big);
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nnue = (125 * psqt + 131 * positional) / 128;
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smallNet = false;
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}
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// Blend optimism and eval with nnue complexity
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int nnueComplexity = std::abs(psqt - positional);
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optimism += optimism * nnueComplexity / 468;
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nnue -= nnue * nnueComplexity / 18000;
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int material = 535 * pos.count<PAWN>() + pos.non_pawn_material();
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int v = (nnue * (77777 + material) + optimism * (7777 + material)) / 77777;
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// Damp down the evaluation linearly when shuffling
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v -= v * pos.rule50_count() / 212;
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// Guarantee evaluation does not hit the tablebase range
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v = std::clamp(v, VALUE_TB_LOSS_IN_MAX_PLY + 1, VALUE_TB_WIN_IN_MAX_PLY - 1);
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return v;
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}
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// Like evaluate(), but instead of returning a value, it returns
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// a string (suitable for outputting to stdout) that contains the detailed
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// descriptions and values of each evaluation term. Useful for debugging.
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// Trace scores are from white's point of view
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std::string Eval::trace(Position& pos, const Eval::NNUE::Networks& networks) {
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if (pos.checkers())
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return "Final evaluation: none (in check)";
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auto accumulators = std::make_unique<Eval::NNUE::AccumulatorStack>();
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auto caches = std::make_unique<Eval::NNUE::AccumulatorCaches>(networks);
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std::stringstream ss;
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ss << std::showpoint << std::noshowpos << std::fixed << std::setprecision(2);
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ss << '\n' << NNUE::trace(pos, networks, *caches) << '\n';
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ss << std::showpoint << std::showpos << std::fixed << std::setprecision(2) << std::setw(15);
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auto [psqt, positional] = networks.big.evaluate(pos, *accumulators, &caches->big);
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Value v = psqt + positional;
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v = pos.side_to_move() == WHITE ? v : -v;
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ss << "NNUE evaluation " << 0.01 * UCIEngine::to_cp(v, pos) << " (white side)\n";
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v = evaluate(networks, pos, *accumulators, *caches, VALUE_ZERO);
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v = pos.side_to_move() == WHITE ? v : -v;
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ss << "Final evaluation " << 0.01 * UCIEngine::to_cp(v, pos) << " (white side)";
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ss << " [with scaled NNUE, ...]";
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ss << "\n";
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return ss.str();
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}
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} // namespace Stockfish
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