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/*
Stockfish, a UCI chess playing engine derived from Glaurung 2.1
Copyright (C) 2004-2021 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
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Stockfish is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#include <cassert>
#include <cmath>
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#include <iostream>
#include <sstream>
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#include <string>
#include "evaluate.h"
#include "movegen.h"
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#include "position.h"
#include "search.h"
#include "thread.h"
Add support for playing in 'nodes as time' mode When running more games in parallel, or simply when running a game with a background process, due to how OS scheduling works, there is no guarantee that the CPU resources allocated evenly between the two players. This introduces noise in the result that leads to unreliable result and in the worst cases can even invalidate the result. For instance in SF test framework we avoid running from clouds virtual machines because are a known source of very unstable CPU speed. To overcome this issue, without requiring changes to the GUI, the idea is to use searched nodes instead of time, and to convert time to available nodes upfront, at the beginning of the game. When nodestime UCI option is set at a given nodes per milliseconds (npmsec), at the beginning of the game (and only once), the engine reads the available time to think, sent by the GUI with 'go wtime x' UCI command. Then it translates time in available nodes (nodes = npmsec * x), then feeds available nodes instead of time to the time management logic and starts the search. During the search the engine checks the searched nodes against the available ones in such a way that all the time management logic still fully applies, and the game mimics a real one played on real time. When the search finishes, before returning best move, the total available nodes are updated, subtracting the real searched nodes. After the first move, the time information sent by the GUI is ignored, and the engine fully relies on the updated total available nodes to feed time management. To avoid time losses, the speed of the engine (npms) must be set to a value lower than real speed so that if the real TC is for instance 30 secs, and npms is half of the real speed, the game will last on average 15 secs, so much less than the TC limit, providing for a safety 'time buffer'. There are 2 main limitations with this mode. 1. Engine speed should be the same for both players, and this limits the approach to mainly parameter tuning patches. 2. Because npms is fixed while, in real engines, the speed increases toward endgame, this introduces an artifact that is equivalent to an altered time management. Namely it is like the time management gives less available time than what should be in standard case. May be the second limitation could be mitigated in a future with a smarter 'dynamic npms' approach. Tests shows that the standard deviation of the results with 'nodestime' is lower than in standard TC, as is expected because now all the introduced noise due the random speed variability of the engines during the game is fully removed. Original NIT idea by Michael Hoffman that shows how to play in NIT mode without requiring changes to the GUI. This implementation goes a bit further, the key difference is that we read TC from GUI only once upfront instead of re-reading after every move as in Michael's implementation. No functional change.
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#include "timeman.h"
#include "tt.h"
#include "uci.h"
#include "syzygy/tbprobe.h"
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using namespace std;
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namespace Stockfish {
extern vector<string> setup_bench(const Position&, istream&);
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namespace {
// FEN string of the initial position, normal chess
const char* StartFEN = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1";
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// position() is called when engine receives the "position" UCI command.
// The function sets up the position described in the given FEN string ("fen")
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// or the starting position ("startpos") and then makes the moves given in the
// following move list ("moves").
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void position(Position& pos, istringstream& is, StateListPtr& states) {
Move m;
string token, fen;
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is >> token;
if (token == "startpos")
{
fen = StartFEN;
is >> token; // Consume "moves" token if any
}
else if (token == "fen")
while (is >> token && token != "moves")
fen += token + " ";
else
return;
states = StateListPtr(new std::deque<StateInfo>(1)); // Drop old and create a new one
pos.set(fen, Options["UCI_Chess960"], &states->back(), Threads.main());
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// Parse move list (if any)
while (is >> token && (m = UCI::to_move(pos, token)) != MOVE_NONE)
{
states->emplace_back();
pos.do_move(m, states->back());
}
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}
Add NNUE evaluation This patch ports the efficiently updatable neural network (NNUE) evaluation to Stockfish. Both the NNUE and the classical evaluations are available, and can be used to assign a value to a position that is later used in alpha-beta (PVS) search to find the best move. The classical evaluation computes this value as a function of various chess concepts, handcrafted by experts, tested and tuned using fishtest. The NNUE evaluation computes this value with a neural network based on basic inputs. The network is optimized and trained on the evalutions of millions of positions at moderate search depth. The NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward. It can be evaluated efficiently on CPUs, and exploits the fact that only parts of the neural network need to be updated after a typical chess move. [The nodchip repository](https://github.com/nodchip/Stockfish) provides additional tools to train and develop the NNUE networks. This patch is the result of contributions of various authors, from various communities, including: nodchip, ynasu87, yaneurao (initial port and NNUE authors), domschl, FireFather, rqs, xXH4CKST3RXx, tttak, zz4032, joergoster, mstembera, nguyenpham, erbsenzaehler, dorzechowski, and vondele. This new evaluation needed various changes to fishtest and the corresponding infrastructure, for which tomtor, ppigazzini, noobpwnftw, daylen, and vondele are gratefully acknowledged. The first networks have been provided by gekkehenker and sergiovieri, with the latter net (nn-97f742aaefcd.nnue) being the current default. The evaluation function can be selected at run time with the `Use NNUE` (true/false) UCI option, provided the `EvalFile` option points the the network file (depending on the GUI, with full path). The performance of the NNUE evaluation relative to the classical evaluation depends somewhat on the hardware, and is expected to improve quickly, but is currently on > 80 Elo on fishtest: 60000 @ 10+0.1 th 1 https://tests.stockfishchess.org/tests/view/5f28fe6ea5abc164f05e4c4c ELO: 92.77 +-2.1 (95%) LOS: 100.0% Total: 60000 W: 24193 L: 8543 D: 27264 Ptnml(0-2): 609, 3850, 9708, 10948, 4885 40000 @ 20+0.2 th 8 https://tests.stockfishchess.org/tests/view/5f290229a5abc164f05e4c58 ELO: 89.47 +-2.0 (95%) LOS: 100.0% Total: 40000 W: 12756 L: 2677 D: 24567 Ptnml(0-2): 74, 1583, 8550, 7776, 2017 At the same time, the impact on the classical evaluation remains minimal, causing no significant regression: sprt @ 10+0.1 th 1 https://tests.stockfishchess.org/tests/view/5f2906a2a5abc164f05e4c5b LLR: 2.94 (-2.94,2.94) {-6.00,-4.00} Total: 34936 W: 6502 L: 6825 D: 21609 Ptnml(0-2): 571, 4082, 8434, 3861, 520 sprt @ 60+0.6 th 1 https://tests.stockfishchess.org/tests/view/5f2906cfa5abc164f05e4c5d LLR: 2.93 (-2.94,2.94) {-6.00,-4.00} Total: 10088 W: 1232 L: 1265 D: 7591 Ptnml(0-2): 49, 914, 3170, 843, 68 The needed networks can be found at https://tests.stockfishchess.org/nns It is recommended to use the default one as indicated by the `EvalFile` UCI option. Guidelines for testing new nets can be found at https://github.com/glinscott/fishtest/wiki/Creating-my-first-test#nnue-net-tests Integration has been discussed in various issues: https://github.com/official-stockfish/Stockfish/issues/2823 https://github.com/official-stockfish/Stockfish/issues/2728 The integration branch will be closed after the merge: https://github.com/official-stockfish/Stockfish/pull/2825 https://github.com/official-stockfish/Stockfish/tree/nnue-player-wip closes https://github.com/official-stockfish/Stockfish/pull/2912 This will be an exciting time for computer chess, looking forward to seeing the evolution of this approach. Bench: 4746616
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// trace_eval() prints the evaluation for the current position, consistent with the UCI
// options set so far.
void trace_eval(Position& pos) {
StateListPtr states(new std::deque<StateInfo>(1));
Position p;
p.set(pos.fen(), Options["UCI_Chess960"], &states->back(), Threads.main());
Eval::NNUE::verify();
Add NNUE evaluation This patch ports the efficiently updatable neural network (NNUE) evaluation to Stockfish. Both the NNUE and the classical evaluations are available, and can be used to assign a value to a position that is later used in alpha-beta (PVS) search to find the best move. The classical evaluation computes this value as a function of various chess concepts, handcrafted by experts, tested and tuned using fishtest. The NNUE evaluation computes this value with a neural network based on basic inputs. The network is optimized and trained on the evalutions of millions of positions at moderate search depth. The NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward. It can be evaluated efficiently on CPUs, and exploits the fact that only parts of the neural network need to be updated after a typical chess move. [The nodchip repository](https://github.com/nodchip/Stockfish) provides additional tools to train and develop the NNUE networks. This patch is the result of contributions of various authors, from various communities, including: nodchip, ynasu87, yaneurao (initial port and NNUE authors), domschl, FireFather, rqs, xXH4CKST3RXx, tttak, zz4032, joergoster, mstembera, nguyenpham, erbsenzaehler, dorzechowski, and vondele. This new evaluation needed various changes to fishtest and the corresponding infrastructure, for which tomtor, ppigazzini, noobpwnftw, daylen, and vondele are gratefully acknowledged. The first networks have been provided by gekkehenker and sergiovieri, with the latter net (nn-97f742aaefcd.nnue) being the current default. The evaluation function can be selected at run time with the `Use NNUE` (true/false) UCI option, provided the `EvalFile` option points the the network file (depending on the GUI, with full path). The performance of the NNUE evaluation relative to the classical evaluation depends somewhat on the hardware, and is expected to improve quickly, but is currently on > 80 Elo on fishtest: 60000 @ 10+0.1 th 1 https://tests.stockfishchess.org/tests/view/5f28fe6ea5abc164f05e4c4c ELO: 92.77 +-2.1 (95%) LOS: 100.0% Total: 60000 W: 24193 L: 8543 D: 27264 Ptnml(0-2): 609, 3850, 9708, 10948, 4885 40000 @ 20+0.2 th 8 https://tests.stockfishchess.org/tests/view/5f290229a5abc164f05e4c58 ELO: 89.47 +-2.0 (95%) LOS: 100.0% Total: 40000 W: 12756 L: 2677 D: 24567 Ptnml(0-2): 74, 1583, 8550, 7776, 2017 At the same time, the impact on the classical evaluation remains minimal, causing no significant regression: sprt @ 10+0.1 th 1 https://tests.stockfishchess.org/tests/view/5f2906a2a5abc164f05e4c5b LLR: 2.94 (-2.94,2.94) {-6.00,-4.00} Total: 34936 W: 6502 L: 6825 D: 21609 Ptnml(0-2): 571, 4082, 8434, 3861, 520 sprt @ 60+0.6 th 1 https://tests.stockfishchess.org/tests/view/5f2906cfa5abc164f05e4c5d LLR: 2.93 (-2.94,2.94) {-6.00,-4.00} Total: 10088 W: 1232 L: 1265 D: 7591 Ptnml(0-2): 49, 914, 3170, 843, 68 The needed networks can be found at https://tests.stockfishchess.org/nns It is recommended to use the default one as indicated by the `EvalFile` UCI option. Guidelines for testing new nets can be found at https://github.com/glinscott/fishtest/wiki/Creating-my-first-test#nnue-net-tests Integration has been discussed in various issues: https://github.com/official-stockfish/Stockfish/issues/2823 https://github.com/official-stockfish/Stockfish/issues/2728 The integration branch will be closed after the merge: https://github.com/official-stockfish/Stockfish/pull/2825 https://github.com/official-stockfish/Stockfish/tree/nnue-player-wip closes https://github.com/official-stockfish/Stockfish/pull/2912 This will be an exciting time for computer chess, looking forward to seeing the evolution of this approach. Bench: 4746616
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sync_cout << "\n" << Eval::trace(p) << sync_endl;
}
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// setoption() is called when engine receives the "setoption" UCI command. The
// function updates the UCI option ("name") to the given value ("value").
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void setoption(istringstream& is) {
string token, name, value;
is >> token; // Consume "name" token
// Read option name (can contain spaces)
while (is >> token && token != "value")
name += (name.empty() ? "" : " ") + token;
// Read option value (can contain spaces)
while (is >> token)
value += (value.empty() ? "" : " ") + token;
if (Options.count(name))
Options[name] = value;
else
sync_cout << "No such option: " << name << sync_endl;
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}
// go() is called when engine receives the "go" UCI command. The function sets
// the thinking time and other parameters from the input string, then starts
// the search.
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void go(Position& pos, istringstream& is, StateListPtr& states) {
Search::LimitsType limits;
string token;
bool ponderMode = false;
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limits.startTime = now(); // As early as possible!
while (is >> token)
if (token == "searchmoves") // Needs to be the last command on the line
while (is >> token)
limits.searchmoves.push_back(UCI::to_move(pos, token));
else if (token == "wtime") is >> limits.time[WHITE];
else if (token == "btime") is >> limits.time[BLACK];
else if (token == "winc") is >> limits.inc[WHITE];
else if (token == "binc") is >> limits.inc[BLACK];
else if (token == "movestogo") is >> limits.movestogo;
else if (token == "depth") is >> limits.depth;
else if (token == "nodes") is >> limits.nodes;
else if (token == "movetime") is >> limits.movetime;
else if (token == "mate") is >> limits.mate;
else if (token == "perft") is >> limits.perft;
else if (token == "infinite") limits.infinite = 1;
else if (token == "ponder") ponderMode = true;
Threads.start_thinking(pos, states, limits, ponderMode);
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}
// bench() is called when engine receives the "bench" command. Firstly
// a list of UCI commands is setup according to bench parameters, then
// it is run one by one printing a summary at the end.
void bench(Position& pos, istream& args, StateListPtr& states) {
string token;
uint64_t num, nodes = 0, cnt = 1;
vector<string> list = setup_bench(pos, args);
num = count_if(list.begin(), list.end(), [](string s) { return s.find("go ") == 0 || s.find("eval") == 0; });
TimePoint elapsed = now();
for (const auto& cmd : list)
{
istringstream is(cmd);
is >> skipws >> token;
if (token == "go" || token == "eval")
{
cerr << "\nPosition: " << cnt++ << '/' << num << " (" << pos.fen() << ")" << endl;
if (token == "go")
{
go(pos, is, states);
Threads.main()->wait_for_search_finished();
nodes += Threads.nodes_searched();
}
else
Add NNUE evaluation This patch ports the efficiently updatable neural network (NNUE) evaluation to Stockfish. Both the NNUE and the classical evaluations are available, and can be used to assign a value to a position that is later used in alpha-beta (PVS) search to find the best move. The classical evaluation computes this value as a function of various chess concepts, handcrafted by experts, tested and tuned using fishtest. The NNUE evaluation computes this value with a neural network based on basic inputs. The network is optimized and trained on the evalutions of millions of positions at moderate search depth. The NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward. It can be evaluated efficiently on CPUs, and exploits the fact that only parts of the neural network need to be updated after a typical chess move. [The nodchip repository](https://github.com/nodchip/Stockfish) provides additional tools to train and develop the NNUE networks. This patch is the result of contributions of various authors, from various communities, including: nodchip, ynasu87, yaneurao (initial port and NNUE authors), domschl, FireFather, rqs, xXH4CKST3RXx, tttak, zz4032, joergoster, mstembera, nguyenpham, erbsenzaehler, dorzechowski, and vondele. This new evaluation needed various changes to fishtest and the corresponding infrastructure, for which tomtor, ppigazzini, noobpwnftw, daylen, and vondele are gratefully acknowledged. The first networks have been provided by gekkehenker and sergiovieri, with the latter net (nn-97f742aaefcd.nnue) being the current default. The evaluation function can be selected at run time with the `Use NNUE` (true/false) UCI option, provided the `EvalFile` option points the the network file (depending on the GUI, with full path). The performance of the NNUE evaluation relative to the classical evaluation depends somewhat on the hardware, and is expected to improve quickly, but is currently on > 80 Elo on fishtest: 60000 @ 10+0.1 th 1 https://tests.stockfishchess.org/tests/view/5f28fe6ea5abc164f05e4c4c ELO: 92.77 +-2.1 (95%) LOS: 100.0% Total: 60000 W: 24193 L: 8543 D: 27264 Ptnml(0-2): 609, 3850, 9708, 10948, 4885 40000 @ 20+0.2 th 8 https://tests.stockfishchess.org/tests/view/5f290229a5abc164f05e4c58 ELO: 89.47 +-2.0 (95%) LOS: 100.0% Total: 40000 W: 12756 L: 2677 D: 24567 Ptnml(0-2): 74, 1583, 8550, 7776, 2017 At the same time, the impact on the classical evaluation remains minimal, causing no significant regression: sprt @ 10+0.1 th 1 https://tests.stockfishchess.org/tests/view/5f2906a2a5abc164f05e4c5b LLR: 2.94 (-2.94,2.94) {-6.00,-4.00} Total: 34936 W: 6502 L: 6825 D: 21609 Ptnml(0-2): 571, 4082, 8434, 3861, 520 sprt @ 60+0.6 th 1 https://tests.stockfishchess.org/tests/view/5f2906cfa5abc164f05e4c5d LLR: 2.93 (-2.94,2.94) {-6.00,-4.00} Total: 10088 W: 1232 L: 1265 D: 7591 Ptnml(0-2): 49, 914, 3170, 843, 68 The needed networks can be found at https://tests.stockfishchess.org/nns It is recommended to use the default one as indicated by the `EvalFile` UCI option. Guidelines for testing new nets can be found at https://github.com/glinscott/fishtest/wiki/Creating-my-first-test#nnue-net-tests Integration has been discussed in various issues: https://github.com/official-stockfish/Stockfish/issues/2823 https://github.com/official-stockfish/Stockfish/issues/2728 The integration branch will be closed after the merge: https://github.com/official-stockfish/Stockfish/pull/2825 https://github.com/official-stockfish/Stockfish/tree/nnue-player-wip closes https://github.com/official-stockfish/Stockfish/pull/2912 This will be an exciting time for computer chess, looking forward to seeing the evolution of this approach. Bench: 4746616
2020-08-05 09:11:15 -06:00
trace_eval(pos);
}
else if (token == "setoption") setoption(is);
else if (token == "position") position(pos, is, states);
else if (token == "ucinewgame") { Search::clear(); elapsed = now(); } // Search::clear() may take some while
}
elapsed = now() - elapsed + 1; // Ensure positivity to avoid a 'divide by zero'
dbg_print(); // Just before exiting
cerr << "\n==========================="
<< "\nTotal time (ms) : " << elapsed
<< "\nNodes searched : " << nodes
<< "\nNodes/second : " << 1000 * nodes / elapsed << endl;
}
// The win rate model returns the probability (per mille) of winning given an eval
// and a game-ply. The model fits rather accurately the LTC fishtest statistics.
int win_rate_model(Value v, int ply) {
// The model captures only up to 240 plies, so limit input (and rescale)
double m = std::min(240, ply) / 64.0;
// Coefficients of a 3rd order polynomial fit based on fishtest data
// for two parameters needed to transform eval to the argument of a
// logistic function.
double as[] = {-8.24404295, 64.23892342, -95.73056462, 153.86478679};
double bs[] = {-3.37154371, 28.44489198, -56.67657741, 72.05858751};
double a = (((as[0] * m + as[1]) * m + as[2]) * m) + as[3];
double b = (((bs[0] * m + bs[1]) * m + bs[2]) * m) + bs[3];
// Transform eval to centipawns with limited range
double x = std::clamp(double(100 * v) / PawnValueEg, -1000.0, 1000.0);
// Return win rate in per mille (rounded to nearest)
return int(0.5 + 1000 / (1 + std::exp((a - x) / b)));
}
} // namespace
/// UCI::loop() waits for a command from stdin, parses it and calls the appropriate
/// function. Also intercepts EOF from stdin to ensure gracefully exiting if the
/// GUI dies unexpectedly. When called with some command line arguments, e.g. to
/// run 'bench', once the command is executed the function returns immediately.
/// In addition to the UCI ones, also some additional debug commands are supported.
void UCI::loop(int argc, char* argv[]) {
Position pos;
string token, cmd;
StateListPtr states(new std::deque<StateInfo>(1));
pos.set(StartFEN, false, &states->back(), Threads.main());
for (int i = 1; i < argc; ++i)
cmd += std::string(argv[i]) + " ";
do {
if (argc == 1 && !getline(cin, cmd)) // Block here waiting for input or EOF
cmd = "quit";
istringstream is(cmd);
token.clear(); // Avoid a stale if getline() returns empty or blank line
is >> skipws >> token;
if ( token == "quit"
|| token == "stop")
Threads.stop = true;
// The GUI sends 'ponderhit' to tell us the user has played the expected move.
// So 'ponderhit' will be sent if we were told to ponder on the same move the
// user has played. We should continue searching but switch from pondering to
// normal search.
else if (token == "ponderhit")
Threads.main()->ponder = false; // Switch to normal search
else if (token == "uci")
sync_cout << "id name " << engine_info(true)
<< "\n" << Options
<< "\nuciok" << sync_endl;
else if (token == "setoption") setoption(is);
else if (token == "go") go(pos, is, states);
else if (token == "position") position(pos, is, states);
else if (token == "ucinewgame") Search::clear();
else if (token == "isready") sync_cout << "readyok" << sync_endl;
// Additional custom non-UCI commands, mainly for debugging.
// Do not use these commands during a search!
else if (token == "flip") pos.flip();
else if (token == "bench") bench(pos, is, states);
else if (token == "d") sync_cout << pos << sync_endl;
Add NNUE evaluation This patch ports the efficiently updatable neural network (NNUE) evaluation to Stockfish. Both the NNUE and the classical evaluations are available, and can be used to assign a value to a position that is later used in alpha-beta (PVS) search to find the best move. The classical evaluation computes this value as a function of various chess concepts, handcrafted by experts, tested and tuned using fishtest. The NNUE evaluation computes this value with a neural network based on basic inputs. The network is optimized and trained on the evalutions of millions of positions at moderate search depth. The NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward. It can be evaluated efficiently on CPUs, and exploits the fact that only parts of the neural network need to be updated after a typical chess move. [The nodchip repository](https://github.com/nodchip/Stockfish) provides additional tools to train and develop the NNUE networks. This patch is the result of contributions of various authors, from various communities, including: nodchip, ynasu87, yaneurao (initial port and NNUE authors), domschl, FireFather, rqs, xXH4CKST3RXx, tttak, zz4032, joergoster, mstembera, nguyenpham, erbsenzaehler, dorzechowski, and vondele. This new evaluation needed various changes to fishtest and the corresponding infrastructure, for which tomtor, ppigazzini, noobpwnftw, daylen, and vondele are gratefully acknowledged. The first networks have been provided by gekkehenker and sergiovieri, with the latter net (nn-97f742aaefcd.nnue) being the current default. The evaluation function can be selected at run time with the `Use NNUE` (true/false) UCI option, provided the `EvalFile` option points the the network file (depending on the GUI, with full path). The performance of the NNUE evaluation relative to the classical evaluation depends somewhat on the hardware, and is expected to improve quickly, but is currently on > 80 Elo on fishtest: 60000 @ 10+0.1 th 1 https://tests.stockfishchess.org/tests/view/5f28fe6ea5abc164f05e4c4c ELO: 92.77 +-2.1 (95%) LOS: 100.0% Total: 60000 W: 24193 L: 8543 D: 27264 Ptnml(0-2): 609, 3850, 9708, 10948, 4885 40000 @ 20+0.2 th 8 https://tests.stockfishchess.org/tests/view/5f290229a5abc164f05e4c58 ELO: 89.47 +-2.0 (95%) LOS: 100.0% Total: 40000 W: 12756 L: 2677 D: 24567 Ptnml(0-2): 74, 1583, 8550, 7776, 2017 At the same time, the impact on the classical evaluation remains minimal, causing no significant regression: sprt @ 10+0.1 th 1 https://tests.stockfishchess.org/tests/view/5f2906a2a5abc164f05e4c5b LLR: 2.94 (-2.94,2.94) {-6.00,-4.00} Total: 34936 W: 6502 L: 6825 D: 21609 Ptnml(0-2): 571, 4082, 8434, 3861, 520 sprt @ 60+0.6 th 1 https://tests.stockfishchess.org/tests/view/5f2906cfa5abc164f05e4c5d LLR: 2.93 (-2.94,2.94) {-6.00,-4.00} Total: 10088 W: 1232 L: 1265 D: 7591 Ptnml(0-2): 49, 914, 3170, 843, 68 The needed networks can be found at https://tests.stockfishchess.org/nns It is recommended to use the default one as indicated by the `EvalFile` UCI option. Guidelines for testing new nets can be found at https://github.com/glinscott/fishtest/wiki/Creating-my-first-test#nnue-net-tests Integration has been discussed in various issues: https://github.com/official-stockfish/Stockfish/issues/2823 https://github.com/official-stockfish/Stockfish/issues/2728 The integration branch will be closed after the merge: https://github.com/official-stockfish/Stockfish/pull/2825 https://github.com/official-stockfish/Stockfish/tree/nnue-player-wip closes https://github.com/official-stockfish/Stockfish/pull/2912 This will be an exciting time for computer chess, looking forward to seeing the evolution of this approach. Bench: 4746616
2020-08-05 09:11:15 -06:00
else if (token == "eval") trace_eval(pos);
else if (token == "compiler") sync_cout << compiler_info() << sync_endl;
else
sync_cout << "Unknown command: " << cmd << sync_endl;
} while (token != "quit" && argc == 1); // Command line args are one-shot
2008-08-31 23:59:13 -06:00
}
/// UCI::value() converts a Value to a string suitable for use with the UCI
/// protocol specification:
///
/// cp <x> The score from the engine's point of view in centipawns.
/// mate <y> Mate in y moves, not plies. If the engine is getting mated
/// use negative values for y.
string UCI::value(Value v) {
assert(-VALUE_INFINITE < v && v < VALUE_INFINITE);
stringstream ss;
if (abs(v) < VALUE_MATE_IN_MAX_PLY)
ss << "cp " << v * 100 / PawnValueEg;
else
ss << "mate " << (v > 0 ? VALUE_MATE - v + 1 : -VALUE_MATE - v) / 2;
return ss.str();
}
/// UCI::wdl() report WDL statistics given an evaluation and a game ply, based on
/// data gathered for fishtest LTC games.
string UCI::wdl(Value v, int ply) {
stringstream ss;
int wdl_w = win_rate_model( v, ply);
int wdl_l = win_rate_model(-v, ply);
int wdl_d = 1000 - wdl_w - wdl_l;
ss << " wdl " << wdl_w << " " << wdl_d << " " << wdl_l;
return ss.str();
}
/// UCI::square() converts a Square to a string in algebraic notation (g1, a7, etc.)
std::string UCI::square(Square s) {
return std::string{ char('a' + file_of(s)), char('1' + rank_of(s)) };
}
/// UCI::move() converts a Move to a string in coordinate notation (g1f3, a7a8q).
/// The only special case is castling, where we print in the e1g1 notation in
/// normal chess mode, and in e1h1 notation in chess960 mode. Internally all
/// castling moves are always encoded as 'king captures rook'.
string UCI::move(Move m, bool chess960) {
Square from = from_sq(m);
Square to = to_sq(m);
if (m == MOVE_NONE)
return "(none)";
if (m == MOVE_NULL)
return "0000";
if (type_of(m) == CASTLING && !chess960)
to = make_square(to > from ? FILE_G : FILE_C, rank_of(from));
string move = UCI::square(from) + UCI::square(to);
if (type_of(m) == PROMOTION)
move += " pnbrqk"[promotion_type(m)];
return move;
}
/// UCI::to_move() converts a string representing a move in coordinate notation
/// (g1f3, a7a8q) to the corresponding legal Move, if any.
Move UCI::to_move(const Position& pos, string& str) {
if (str.length() == 5) // Junior could send promotion piece in uppercase
str[4] = char(tolower(str[4]));
for (const auto& m : MoveList<LEGAL>(pos))
if (str == UCI::move(m, pos.is_chess960()))
return m;
return MOVE_NONE;
}
} // namespace Stockfish