HCha/Stockfish
1
0
Fork 0
mirror of https://github.com/HChaZZY/Stockfish.git synced 2025-12-20 09:06:45 +08:00
Code Issues Packages Projects Releases Wiki Activity

Revert evaluation cache

Browse Source 
And return on using TT as backing store for position
evaluations.

Tests (even on single thread) show eval cache was a regression.
In multi thread result should be even worst because eval cache
is a per-thread struct, while TT is shared.

After 4957 games at 15"+0.05 (single thread)
eval cache vs master 969 - 1093 - 2895  -9 ELO

So previous reported result of +18 ELO was probably due to an
issue in the testing framework (a bug in cutechess-cli) that
has been fixed in the meanwhile.

bench: 5386711
This commit is contained in:
Marco Costalba
2012-12-27 12:13:31 +01:00
parent f78b68b7ff
commit 3cf6471738
6 changed files with 61 additions and 57 deletions
Download Patch File Download Diff File Expand all files Collapse all files

39
src/evaluate.cpp
View File

@@ -244,7 +244,7 @@ namespace {
Score evaluate_pieces_of_color(const Position& pos, EvalInfo& ei, Score& mobility);
template<Color Us, bool Trace>
Score evaluate_king(const Position& pos, EvalInfo& ei, int16_t margins[]);
Score evaluate_king(const Position& pos, EvalInfo& ei, Value margins[]);
template<Color Us>
Score evaluate_threats(const Position& pos, EvalInfo& ei);
@@ -364,27 +364,13 @@ Value do_evaluate(const Position& pos, Value& margin) {
assert(!pos.checkers());
EvalInfo ei;
Value margins[COLOR_NB];
Score score, mobilityWhite, mobilityBlack;
Key key = pos.key();
Thread* th = pos.this_thread();
Eval::Entry* e = th->evalTable[key];
// If e->key matches the position's hash key, it means that we have analysed
// this node before, and we can simply return the information we found the last
// time instead of recomputing it.
if (e->key == key)
{
margin = Value(e->margins[pos.side_to_move()]);
return e->value;
}
// Otherwise we overwrite current content with this node info.
e->key = key;
// margins[] store the uncertainty estimation of position's evaluation
// that typically is used by the search for pruning decisions.
e->margins[WHITE] = e->margins[BLACK] = VALUE_ZERO;
margins[WHITE] = margins[BLACK] = VALUE_ZERO;
// Initialize score by reading the incrementally updated scores included
// in the position object (material + piece square tables) and adding
@@ -400,8 +386,7 @@ Value do_evaluate(const Position& pos, Value& margin) {
if (ei.mi->specialized_eval_exists())
{
margin = VALUE_ZERO;
e->value = ei.mi->evaluate(pos);
return e->value;
return ei.mi->evaluate(pos);
}
// Probe the pawn hash table
@@ -420,8 +405,8 @@ Value do_evaluate(const Position& pos, Value& margin) {
// Evaluate kings after all other pieces because we need complete attack
// information when computing the king safety evaluation.
score += evaluate_king<WHITE, Trace>(pos, ei, e->margins)
- evaluate_king<BLACK, Trace>(pos, ei, e->margins);
score += evaluate_king<WHITE, Trace>(pos, ei, margins)
- evaluate_king<BLACK, Trace>(pos, ei, margins);
// Evaluate tactical threats, we need full attack information including king
score += evaluate_threats<WHITE>(pos, ei)
@@ -467,7 +452,7 @@ Value do_evaluate(const Position& pos, Value& margin) {
sf = ScaleFactor(50);
}
margin = Value(e->margins[pos.side_to_move()]);
margin = margins[pos.side_to_move()];
Value v = interpolate(score, ei.mi->game_phase(), sf);
// In case of tracing add all single evaluation contributions for both white and black
@@ -484,8 +469,8 @@ Value do_evaluate(const Position& pos, Value& margin) {
Score b = make_score(ei.mi->space_weight() * evaluate_space<BLACK>(pos, ei), 0);
trace_add(SPACE, apply_weight(w, Weights[Space]), apply_weight(b, Weights[Space]));
trace_add(TOTAL, score);
TraceStream << "\nUncertainty margin: White: " << to_cp(Value(e->margins[WHITE]))
<< ", Black: " << to_cp(Value(e->margins[BLACK]))
TraceStream << "\nUncertainty margin: White: " << to_cp(margins[WHITE])
<< ", Black: " << to_cp(margins[BLACK])
<< "\nScaling: " << std::noshowpos
<< std::setw(6) << 100.0 * ei.mi->game_phase() / 128.0 << "% MG, "
<< std::setw(6) << 100.0 * (1.0 - ei.mi->game_phase() / 128.0) << "% * "
@@ -493,7 +478,7 @@ Value do_evaluate(const Position& pos, Value& margin) {
<< "Total evaluation: " << to_cp(v);
}
return e->value = pos.side_to_move() == WHITE ? v : -v;
return pos.side_to_move() == WHITE ? v : -v;
}
@@ -768,7 +753,7 @@ Value do_evaluate(const Position& pos, Value& margin) {
// evaluate_king<>() assigns bonuses and penalties to a king of a given color
template<Color Us, bool Trace>
Score evaluate_king(const Position& pos, EvalInfo& ei, int16_t margins[]) {
Score evaluate_king(const Position& pos, EvalInfo& ei, Value margins[]) {
const Color Them = (Us == WHITE ? BLACK : WHITE);
@@ -868,7 +853,7 @@ Value do_evaluate(const Position& pos, Value& margin) {
// be very big, and so capturing a single attacking piece can therefore
// result in a score change far bigger than the value of the captured piece.
score -= KingDangerTable[Us == Search::RootColor][attackUnits];
margins[Us] += int16_t(mg_value(KingDangerTable[Us == Search::RootColor][attackUnits]));
margins[Us] += mg_value(KingDangerTable[Us == Search::RootColor][attackUnits]);
}
if (Trace)