💾 Archived View for runjimmyrunrunyoufuckerrun.com › src › games › chessengines › crafty › iterate.c captured on 2021-12-17 at 13:26:06.
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#include "chess.h"
#include "data.h"
#include "epdglue.h"
/* last modified 05/12/15 */
/*
*******************************************************************************
* *
* Iterate() is the routine used to drive the iterated search. It *
* repeatedly calls search, incrementing the search depth after each call, *
* until either time is exhausted or the maximum set search depth is *
* reached. *
* *
* Crafty has several specialized modes that influence how moves are chosen *
* and when. *
* *
* (1) "mode tournament" is a special way of handling book moves. Here we *
* are dealing with pondering. We play our move, and then we take all of *
* the known book moves for the opponent (moves where we have an instant *
* reply since they are in the book) and eliminate those from the set of *
* root moves to search. We do a short search to figure out which of those *
* non-book moves is best, and then we ponder that move. It will look like *
* we are always out of book, but we are not. We are just looking for one *
* of two cases: (i) The opponent's book runs out and he doesn't play the *
* expected book line (which is normally a mistake), where this will give us *
* a good chance of pondering the move he will actually play (a non-book *
* move) without sitting around and doing nothing until he takes us out of *
* book; (ii) Our book doesn't have a reasonable choice, so we do a search *
* and ponder a better choice so again we are not wasting time. The value *
* of "mode" will be set to "tournament" to enable this. *
* *
* (2) "book random 0" tells the program to enumerate the list of known book *
* moves, but rather than playing one randomly, we do a shortened search and *
* use the normal move selection approach (which will, unfortunately, accept *
* many gambits that a normal book line would bypass as too risky. But this *
* can also find a better book move in many positions, since many book lines *
* are not verified with computer searches. *
* *
* Those modes are handled within Book() and Ponder() but they all use the *
* same iterated search as is used for normal moves. *
* *
*******************************************************************************
*/
int Iterate(int wtm, int search_type, int root_list_done) {
TREE *const tree = block[0];
ROOT_MOVE temp_rm;
int i, alpha, beta, current_rm = 0, force_print = 0;
int value = 0, twtm, correct, correct_count, npc, cpl, max;
unsigned int idle_time;
char buff[32];
#if (CPUS > 1)
pthread_t pt;
#endif
/*
************************************************************
* *
* Initialize draw score. This has to be done here since *
* we don't know whether we are searching for black or *
* white until we get to this point. *
* *
************************************************************
*/
draw_score[black] = (wtm) ? -abs_draw_score : abs_draw_score;
draw_score[white] = (wtm) ? abs_draw_score : -abs_draw_score;
#if defined(NODES)
temp_search_nodes = search_nodes;
#endif
/*
************************************************************
* *
* Initialize statistical counters and such. *
* *
************************************************************
*/
abort_search = 0;
book_move = 0;
program_start_time = ReadClock();
start_time = ReadClock();
root_wtm = wtm;
kibitz_depth = 0;
tree->nodes_searched = 0;
tree->fail_highs = 0;
tree->fail_high_first_move = 0;
parallel_splits = 0;
parallel_splits_wasted = 0;
parallel_aborts = 0;
parallel_joins = 0;
for (i = 0; i < smp_max_threads; i++) {
thread[i].max_blocks = 0;
thread[i].tree = 0;
thread[i].idle = 0;
thread[i].terminate = 0;
}
thread[0].tree = block[0];
correct_count = 0;
burp = 15 * 100;
transposition_age = (transposition_age + 1) & 0x1ff;
next_time_check = nodes_between_time_checks;
tree->evaluations = 0;
tree->egtb_probes = 0;
tree->egtb_hits = 0;
tree->extensions_done = 0;
tree->qchecks_done = 0;
tree->moves_fpruned = 0;
tree->moves_mpruned = 0;
for (i = 0; i < 16; i++) {
tree->LMR_done[i] = 0;
tree->null_done[i] = 0;
}
root_wtm = wtm;
/*
************************************************************
* *
* We do a quick check to see if this position has a known *
* book reply. If not, we drop into the main iterated *
* search, otherwise we skip to the bottom and return the *
* move that Book() returned. *
* *
* Note the "booking" exception below. If you use the *
* "book random 0" you instruct Crafty to enumerate the *
* known set of book moves, and then initiate a normal *
* iterated search, but with just those known book moves *
* included in the root move list. We therefore choose *
* (based on a normal search / evaluation but with a lower *
* time limit) from the book moves given. *
* *
************************************************************
*/
if (!root_list_done)
RootMoveList(wtm);
if (booking || !Book(tree, wtm))
do {
if (abort_search)
break;
#if !defined(NOEGTB)
if (EGTB_draw && !puzzling && swindle_mode)
EGTB_use = 0;
else
EGTB_use = EGTBlimit;
if (EGTBlimit && !EGTB_use)
Print(32, "Drawn at root, trying for swindle.\n");
#endif
/*
************************************************************
* *
* The first action for a real search is to generate the *
* root move list if it has not already been done. For *
* some circumstances, such as a non-random book move *
* search, we are given the root move list, which only *
* contains the known book moves. Those are all we need *
* to search. If there are no legal moves, it is either *
* mate or draw depending on whether the side to move is *
* in check or not (mate or stalemate.) *
* *
* Why would there be already be a root move list? See *
* the two modes described at the top (mode tournament and *
* book random 0) which would have already inserted just *
* the moves that should be searched. *
* *
************************************************************
*/
if (n_root_moves == 0) {
program_end_time = ReadClock();
tree->pv[0].pathl = 0;
tree->pv[0].pathd = 0;
if (Check(wtm))
value = -(MATE - 1);
else
value = DrawScore(wtm);
Print(2, " depth time score variation\n");
Print(2, " (no moves)\n");
tree->nodes_searched = 1;
if (!puzzling)
last_root_value = value;
return value;
}
/*
************************************************************
* *
* Now set the search time and iteratively call Search() *
* to analyze the position deeper and deeper. Note that *
* Search() is called with an alpha/beta window roughly *
* 1/3 of a pawn wide, centered on the score last returned *
* by search. *
* *
************************************************************
*/
TimeSet(search_type);
iteration = 1;
noise_block = 0;
force_print = 0;
if (last_pv.pathd > 1) {
iteration = last_pv.pathd + 1;
value = last_root_value;
tree->pv[0] = last_pv;
root_moves[0].path = tree->pv[0];
noise_block = 1;
force_print = 1;
} else
difficulty = 100;
Print(2, " depth time score variation (%d)\n",
iteration);
abort_search = 0;
/*
************************************************************
* *
* Set the initial search bounds based on the last search *
* or default values. *
* *
************************************************************
*/
tree->pv[0] = last_pv;
if (iteration > 1) {
alpha = Max(-MATE, last_value - 16);
beta = Min(MATE, last_value + 16);
} else {
alpha = -MATE;
beta = MATE;
}
/*
************************************************************
* *
* If we are using multiple threads, and they have not *
* been started yet, then start them now as the search is *
* ready to begin. *
* *
* If we are using CPU affinity, we need to set this up *
* for thread 0 since it could have changed since we *
* initialized everything. *
* *
************************************************************
*/
#if (CPUS > 1)
if (smp_max_threads > smp_threads + 1) {
long proc;
initialized_threads = 1;
Print(32, "starting thread");
for (proc = smp_threads + 1; proc < smp_max_threads; proc++) {
Print(32, " %d", proc);
# if defined(UNIX)
pthread_create(&pt, &attributes, ThreadInit, (void *) proc);
# else
NumaStartThread(ThreadInit, (void *) proc);
# endif
smp_threads++;
}
Print(32, " <done>\n");
}
WaitForAllThreadsInitialized();
ThreadAffinity(smp_affinity);
#endif
if (search_nodes)
nodes_between_time_checks = search_nodes;
/*
************************************************************
* *
* Main iterative-deepening loop starts here. We either *
* start at depth = 1, or if we are pondering and have a *
* PV from the previous search, we use that to set the *
* starting depth. *
* *
* First install the old PV into the hash table so that *
* these moves will be searched first. We do this since *
* the last iteration done could have overwritten the PV *
* as the last few root moves were searched. *
* *
************************************************************
*/
for (; iteration <= MAXPLY - 5; iteration++) {
twtm = wtm;
for (i = 1; i < (int) tree->pv[0].pathl; i++) {
if (!VerifyMove(tree, i, twtm, tree->pv[0].path[i])) {
Print(2048, "ERROR, not installing bogus pv info at ply=%d\n", i);
Print(2048, "not installing from=%d to=%d piece=%d\n",
From(tree->pv[0].path[i]), To(tree->pv[0].path[i]),
Piece(tree->pv[0].path[i]));
Print(2048, "pathlen=%d\n", tree->pv[0].pathl);
break;
}
HashStorePV(tree, twtm, i);
MakeMove(tree, i, twtm, tree->pv[0].path[i]);
twtm = Flip(twtm);
}
for (i--; i > 0; i--) {
twtm = Flip(twtm);
UnmakeMove(tree, i, twtm, tree->pv[0].path[i]);
}
/*
************************************************************
* *
* Now we call Search() and start the next search *
* iteration. We already have solid alpha/beta bounds set *
* up for the aspiration search. When each iteration *
* completes, these aspiration values are recomputed and *
* used for the next iteration. *
* *
* We need to set "nodes_between_time_checks" to a value *
* that will force us to check the time reasonably often *
* without wasting excessive time doing this check. As *
* the target time limit gets shorter, we shorten the *
* interval between time checks to avoid burning time off *
* of the clock unnecessarily. *
* *
************************************************************
*/
if (trace_level) {
Print(32, "==================================\n");
Print(32, "= search iteration %2d =\n", iteration);
Print(32, "==================================\n");
}
if (tree->nodes_searched) {
nodes_between_time_checks =
nodes_per_second / (10 * Max(smp_max_threads, 1));
if (!analyze_mode) {
if (time_limit < 1000)
nodes_between_time_checks /= 10;
if (time_limit < 100)
nodes_between_time_checks /= 10;
} else
nodes_between_time_checks = Min(nodes_per_second, 1000000);
}
if (search_nodes)
nodes_between_time_checks = search_nodes - tree->nodes_searched;
nodes_between_time_checks =
Min(nodes_between_time_checks, MAX_TC_NODES);
next_time_check = nodes_between_time_checks;
/*
************************************************************
* *
* This loop will execute until we either run out of time *
* or complete this iteration. Since we can return to *
* Iterate() multiple times during this iteration, due to *
* multiple fail highs (and perhaps even an initial fail *
* low) we stick in this loop until we have completed all *
* root moves or TimeCheck() tells us it is time to stop. *
* *
************************************************************
*/
failhi_delta = 16;
faillo_delta = 16;
for (i = 0; i < n_root_moves; i++) {
if (i || iteration == 1)
root_moves[i].path.pathv = -99999999;
root_moves[i].status &= 4;
}
while (1) {
if (smp_max_threads > 1)
smp_split = 1;
rep_index--;
value = Search(tree, 1, iteration, wtm, alpha, beta, Check(wtm), 0);
rep_index++;
end_time = ReadClock();
if (abort_search)
break;
for (current_rm = 0; current_rm < n_root_moves; current_rm++)
if (tree->pv[0].path[1] == root_moves[current_rm].move)
break;
/*
************************************************************
* *
* Check for the case where we get a score back that is *
* greater than or equal to beta. This is called a fail *
* high condition and requires a re-search with a better *
* (more optimistic) beta value so that we can discover *
* just how good this move really is. *
* *
* Note that each time we return here, we need to increase *
* the upper search bound (beta). We have a value named *
* failhi_delta that is initially set to 16 on the first *
* fail high of a particular move. We increase beta by *
* this value each time we fail high. However, each time *
* we fail high, we also double this value so that we *
* increase beta at an ever-increasing rate. Small jumps *
* at first let us detect marginal score increases while *
* still allowing cutoffs for branches with excessively *
* high scores. But each re-search sees the margin double *
* which quickly increases the bound as needed. *
* *
* We also use ComputeDifficulty() to adjust the level of *
* difficulty for this move since we might be changing our *
* mind at the root. (If we are failing high on the first *
* root move we skip this update.) *
* *
************************************************************
*/
if (value >= beta) {
beta = Min(beta + failhi_delta, MATE);
failhi_delta *= 2;
if (failhi_delta > 10 * PAWN_VALUE)
failhi_delta = 99999;
root_moves[current_rm].status &= 7;
root_moves[current_rm].bm_age = 4;
if ((root_moves[current_rm].status & 2) == 0)
difficulty = ComputeDifficulty(difficulty, +1);
root_moves[current_rm].status |= 2;
DisplayFail(tree, 1, 5, wtm, end_time - start_time,
root_moves[current_rm].move, value, force_print);
temp_rm = root_moves[current_rm];
for (i = current_rm; i > 0; i--)
root_moves[i] = root_moves[i - 1];
root_moves[0] = temp_rm;
}
/*
************************************************************
* *
* Check for the case where we get a score back that is *
* less than or equal to alpha. This is called a fail *
* low condition and requires a re-search with a better *
* more pessimistic)) alpha value so that we can discover *
* just how bad this move really is. *
* *
* Note that each time we return here, we need to decrease *
* the lower search bound (alpha). We have a value named *
* faillo_delta that is initially set to 16 on the first *
* fail low of a particular move. We decrease alpha by *
* this value each time we fail low. However, each time *
* we fail low, we also double this value so that we *
* decrease alpha at an ever-increasing rate. Small jumps *
* at first let us detect marginal score decreases while *
* still allowing cutoffs for branches with excessively *
* low scores. But each re-search sees the margin double *
* which quickly decreases the bound as needed. *
* *
* We also use ComputeDifficulty() to adjust the level of *
* difficulty for this move since we are failing low on *
* the first move at the root, and we don't want to stop *
* before we have a chance to find a better one. *
* *
************************************************************
*/
else if (value <= alpha) {
alpha = Max(alpha - faillo_delta, -MATE);
faillo_delta *= 2;
if (faillo_delta > 10 * PAWN_VALUE)
faillo_delta = 99999;
root_moves[current_rm].status &= 7;
if ((root_moves[current_rm].status & 1) == 0)
difficulty = ComputeDifficulty(Max(100, difficulty), -1);
root_moves[current_rm].status |= 1;
DisplayFail(tree, 2, 5, wtm, end_time - start_time,
root_moves[current_rm].move, value, force_print);
} else
break;
}
if (value > alpha && value < beta)
last_root_value = value;
/*
************************************************************
* *
* If we are running a test suite, check to see if we can *
* exit the search. This happens when N successive *
* iterations produce the correct solution. N is set by *
* the test command in Option(). *
* *
************************************************************
*/
correct = solution_type;
for (i = 0; i < number_of_solutions; i++) {
if (!solution_type) {
if (solutions[i] == tree->pv[0].path[1])
correct = 1;
} else if (solutions[i] == root_moves[current_rm].move)
correct = 0;
}
if (correct)
correct_count++;
else
correct_count = 0;
/*
************************************************************
* *
* Notice that we don't search moves that were best over *
* the last 3 iterations in parallel, nor do we reduce *
* those since they are potential best moves again. *
* *
************************************************************
*/
for (i = 0; i < n_root_moves; i++) {
root_moves[i].status &= 3;
if (root_moves[i].bm_age)
root_moves[i].bm_age--;
if (root_moves[i].bm_age)
root_moves[i].status |= 4;
}
SortRootMoves();
difficulty = ComputeDifficulty(difficulty, 0);
/*
************************************************************
* *
* If requested, print the ply=1 move list along with the *
* flags for each move. Once we print this (if requested) *
* we can then clear all of the status flags (except the *
* "do not search in parallel or reduce" flag) to prepare *
* for the start of the next iteration, since that is the *
* only flag that needs to be carried forward to the next *
* iteration. *
* *
************************************************************
*/
if (display_options & 64) {
Print(64, " rmove score age S ! ?\n");
for (i = 0; i < n_root_moves; i++) {
Print(64, " %10s ", OutputMove(tree, 1, wtm, root_moves[i].move));
if (root_moves[i].path.pathv > -MATE)
Print(64, "%s", DisplayEvaluation(root_moves[i].path.pathv,
wtm));
else
Print(64, " -----");
Print(64, " %d %d %d %d\n", root_moves[i].bm_age,
(root_moves[i].status & 4) != 0,
(root_moves[i].status & 2) != 0,
(root_moves[i].status & 1) != 0);
}
}
/*
************************************************************
* *
* Here we simply display the PV from the current search *
* iteration, and then set the aspiration for the next *
* iteration to the current score +/- 16. *
* *
************************************************************
*/
if (end_time - start_time > 10)
nodes_per_second =
tree->nodes_searched * 100 / (uint64_t) (end_time - start_time);
else
nodes_per_second = 1000000;
tree->pv[0] = root_moves[0].path;
if (!abort_search && value != -(MATE - 1)) {
if (end_time - start_time >= noise_level) {
DisplayPV(tree, 5, wtm, end_time - start_time, &tree->pv[0],
force_print);
noise_block = 0;
} else
noise_block = 1;
}
alpha = Max(-MATE, value - 16);
beta = Min(MATE, value + 16);
/*
************************************************************
* *
* There are multiple termination criteria that are used. *
* The first and most obvious is that we have exceeded the *
* target time limit. Others include reaching a user-set *
* maximum search depth, finding a mate and we searched so *
* deep there is little chance of another iteration find- *
* ing a shorter mate; the search was aborted due to some *
* sort of user input (usually during pondering); and *
* finally, when running a test suite, we had the correct *
* best move for N successive iterations and the user *
* asked us to stop after that number. *
* *
************************************************************
*/
if (TimeCheck(tree, 0))
break;
if (iteration > 3 && value > 32000 && value >= (MATE - iteration + 3)
&& value > last_mate_score)
break;
if ((iteration >= search_depth) && search_depth)
break;
if (abort_search)
break;
end_time = ReadClock() - start_time;
if (correct_count >= early_exit)
break;
#if !defined(NOEGTB)
if (iteration > EGTB_depth + 10 && TotalAllPieces <= EGTBlimit &&
EGTB_use && EGTBProbe(tree, 1, wtm, &i))
break;
#endif
if (search_nodes && tree->nodes_searched >= search_nodes)
break;
}
/*
************************************************************
* *
* Search done, now display statistics, depending on the *
* display options set (see display command in Option().) *
* *
* Simple kludge here. If the last search was quite deep *
* while we were pondering, we start this iteration at the *
* last depth - 1. Sometimes that will result in a search *
* that is deep enough that we do not produce/print a PV *
* before time runs out. On other occasions, noise_level *
* prevents us from printing anything, leaving us with no *
* output during this PV. We initialize a variable named *
* noise_block to 1. If, during this iteration, we do *
* manage to print a PV, we set it to zero until the next *
* iteration starts. Otherwise this will force us to at *
* display the PV from the last iteration (first two moves *
* were removed in main(), so they are not present) so we *
* have some sort of output for this iteration. *
* *
************************************************************
*/
end_time = ReadClock();
if (end_time > 10)
nodes_per_second =
(uint64_t) tree->nodes_searched * 100 / Max((uint64_t) end_time -
start_time, 1);
if (abort_search != 2 && !puzzling) {
if (noise_block)
DisplayPV(tree, 5, wtm, end_time - start_time, &tree->pv[0], 1);
tree->evaluations = (tree->evaluations) ? tree->evaluations : 1;
tree->fail_highs++;
tree->fail_high_first_move++;
idle_time = 0;
for (i = 0; i < smp_max_threads; i++)
idle_time += thread[i].idle;
busy_percent =
100 - Min(100,
100 * idle_time / (smp_max_threads * (end_time - start_time) +
1));
Print(8, " time=%s(%d%%)",
DisplayTimeKibitz(end_time - start_time), busy_percent);
Print(8, " nodes=%" PRIu64 "(%s)", tree->nodes_searched,
DisplayKMB(tree->nodes_searched, 0));
Print(8, " fh1=%d%%",
tree->fail_high_first_move * 100 / tree->fail_highs);
Print(8, " pred=%d", predicted);
Print(8, " nps=%s\n", DisplayKMB(nodes_per_second, 0));
Print(8, " chk=%s", DisplayKMB(tree->extensions_done, 0));
Print(8, " qchk=%s", DisplayKMB(tree->qchecks_done, 0));
Print(8, " fp=%s", DisplayKMB(tree->moves_fpruned, 0));
Print(8, " mcp=%s", DisplayKMB(tree->moves_mpruned, 0));
Print(8, " 50move=%d", Reversible(0));
if (tree->egtb_hits)
Print(8, " egtb=%s", DisplayKMB(tree->egtb_hits, 0));
Print(8, "\n");
Print(8, " LMReductions:");
npc = 21;
cpl = 75;
for (i = 1; i < 16; i++)
if (tree->LMR_done[i]) {
sprintf(buff, "%d/%s", i, DisplayKMB(tree->LMR_done[i], 0));
if (npc + strlen(buff) > cpl) {
Print(8, "\n ");
npc = 12;
}
Print(8, " %s", buff);
npc += strlen(buff) + 2;
}
if (npc)
Print(8, "\n");
npc = 24;
cpl = 75;
if (tree->null_done[null_depth])
Print(8, " null-move (R):");
for (i = null_depth; i < 16; i++)
if (tree->null_done[i]) {
sprintf(buff, "%d/%s", i, DisplayKMB(tree->null_done[i], 0));
if (npc + strlen(buff) > cpl) {
Print(8, "\n ");
npc = 12;
}
Print(8, " %s", buff);
npc += strlen(buff) + 2;
}
if (npc)
Print(8, "\n");
if (parallel_splits) {
max = 0;
for (i = 0; i < smp_max_threads; i++) {
max = Max(max, PopCnt(thread[i].max_blocks));
game_max_blocks |= thread[i].max_blocks;
}
Print(8, " splits=%s", DisplayKMB(parallel_splits, 0));
Print(8, "(%s)", DisplayKMB(parallel_splits_wasted, 0));
Print(8, " aborts=%s", DisplayKMB(parallel_aborts, 0));
Print(8, " joins=%s", DisplayKMB(parallel_joins, 0));
Print(8, " data=%d%%(%d%%)\n", 100 * max / 64,
100 * PopCnt(game_max_blocks) / 64);
}
}
} while (0);
/*
************************************************************
* *
* If this is a known book position, Book() has already *
* set the PV/best move so we can return without doing the *
* iterated search at all. *
* *
************************************************************
*/
else {
last_root_value = 0;
value = 0;
book_move = 1;
if (analyze_mode)
Kibitz(4, wtm, 0, 0, 0, 0, 0, 0, kibitz_text);
}
/*
************************************************************
* *
* If "smp_nice" is set, and we are not allowed to ponder *
* while waiting on the opponent to move, then terminate *
* the parallel threads so they won't sit in their normal *
* spin-wait loop while waiting for new work which will *
* "burn" smp_max_threads - 1 cpus, penalizing anything *
* else that might be running (such as another chess *
* engine we might be playing in a ponder=off match.) *
* *
************************************************************
*/
if (smp_nice && ponder == 0 && smp_threads) {
int proc;
Print(64, "terminating SMP processes.\n");
for (proc = 1; proc < CPUS; proc++)
thread[proc].terminate = 1;
while (smp_threads);
smp_split = 0;
}
program_end_time = ReadClock();
search_move = 0;
if (quit)
CraftyExit(0);
return last_root_value;
}