Fix crashing, implement LMR and tempo bonus

Chess/nim.cfg

@@ -4,4 +4,4 @@
 --passL:"-flto -lmimalloc"
 --passC:"-flto -march=native -mtune=native"
 -d:useMalloc
---mm:atomicArc
+--mm:atomicArc

Chess/nimfish/nimfishpkg/board.nim

@@ -98,6 +98,10 @@ func drawnByRepetition*(self: Chessboard): bool =
             inc(count)
             if count == 2:
                 return true
+        if self.positions[i].halfMoveClock == 0:
+            # Position was reached via a pawn move or
+            # capture: cannot repeat beyond this point!
+            return false
         dec(i)
 
 

Chess/nimfish/nimfishpkg/eval.nim

@@ -21,6 +21,7 @@ type
 
 # Stolen from https://www.chessprogramming.org/PeSTO's_Evaluation_Function
 const
+    TEMPO_BONUS = Score(10)
     PAWN_MIDDLEGAME_SCORES: array[Square(0)..Square(63), Score] = [
         0, 0, 0, 0, 0, 0, 0, 0,
         98, 134, 61, 95, 68, 126, 34, -11,
@@ -311,3 +312,5 @@ proc evaluate*(position: Position): Score =
     result = position.evaluateMaterial()
     when defined(evalPawns):
         result += position.evaluatePawnStructure()
+    # Tempo bonus: gains 19.5 +/- 13.7
+    result += TEMPO_BONUS

Chess/nimfish/nimfishpkg/moves.nim

@@ -40,7 +40,7 @@ type
         targetSquare*: Square
         flags*: uint16
 
-    MoveList* = ref object
+    MoveList* = object
         ## A list of moves
         data*: array[218, Move]
         len: int8
@@ -203,6 +203,5 @@ func toAlgebraic*(self: Move): string =
 
 
 proc newMoveList*: MoveList =
-    new(result)
     for i in 0..result.data.high():
         result.data[i] = nullMove()

Chess/nimfish/nimfishpkg/search.nim

@@ -19,6 +19,7 @@ import eval
 import transpositions
 
 
+import std/math
 import std/times
 import std/atomics
 import std/algorithm
@@ -26,38 +27,54 @@ import std/monotimes
 import std/strformat
 
 
-const NUM_KILLERS = 2
+const NUM_KILLERS* = 2
+const MAX_DEPTH* = 255
+
+
+func computeLMRTable: array[MAX_DEPTH, array[218, int]] {.compileTime.} =
+    ## Precomputes the table containing reduction offsets at compile
+    ## time
+    for i in 1..result.high():
+        for j in 1..result[0].high():
+            result[i][j] = round(0.8 + ln(i.float) * ln(j.float) * 0.4).int
+
+
+const LMR_TABLE {.used.} = computeLMRTable()
 
 
 type
     HistoryTable* = array[PieceColor.White..PieceColor.Black, array[Square(0)..Square(63), array[Square(0)..Square(63), Score]]]
-    KillersTable* = seq[array[NUM_KILLERS, Move]]
+    KillersTable* = array[MAX_DEPTH, array[NUM_KILLERS, Move]]
     SearchManager* = object
         ## A simple state storage
         ## for our search
         searchFlag: ptr Atomic[bool]
         stopFlag: ptr Atomic[bool]
         board: Chessboard
-        bestMoveRoot: Move
         bestRootScore: Score
         searchStart: MonoTime
         hardLimit: MonoTime
         softLimit: MonoTime
         nodeCount: uint64
         maxNodes: uint64
-        currentMove: Move
-        currentMoveNumber: int
         searchMoves: seq[Move]
-        previousBestMove: Move
         transpositionTable: ptr TTable
         history: ptr HistoryTable
         killers: ptr KillersTable
+        # We keep one extra entry so we don't need any special casing
+        # inside the search function when constructing pv lines
+        pvMoves: array[MAX_DEPTH + 1, array[MAX_DEPTH + 1, Move]]
+        selectiveDepth: int
 
 
 proc newSearchManager*(board: Chessboard, transpositions: ptr TTable, stopFlag, searchFlag: ptr Atomic[bool],
                        history: ptr HistoryTable, killers: ptr KillersTable): SearchManager =
-    result = SearchManager(board: board, bestMoveRoot: nullMove(), transpositionTable: transpositions, stopFlag: stopFlag,
+    result = SearchManager(board: board, transpositionTable: transpositions, stopFlag: stopFlag,
                            searchFlag: searchFlag, history: history, killers: killers)
+    for i in 0..MAX_DEPTH:
+        for j in 0..MAX_DEPTH:
+            result.pvMoves[i][j] = nullMove()
+
 
 
 proc isSearching*(self: SearchManager): bool =
@@ -91,14 +108,11 @@ proc getEstimatedMoveScore(self: SearchManager, move: Move, ply: int): Score =
         sideToMove = self.board.position.sideToMove
         nonSideToMove = sideToMove.opposite()
 
-    if self.previousBestMove != nullMove() and move == self.previousBestMove:
-        return highestEval() + 1
-
     let query = self.transpositionTable[].get(self.board.position.zobristKey)
     if query.success and query.entry.bestMove != nullMove() and query.entry.bestMove == move:
-        return highestEval() - 1
+        return highestEval() + 1
 
-    if self.isKillerMove(move, ply):
+    if ply > 0 and self.isKillerMove(move, ply):
         result += self.board.position.getPieceScore(move.startSquare) * 5
 
     if not move.isCapture():
@@ -156,10 +170,14 @@ proc log(self: SearchManager, depth: int) =
     let 
         elapsedMsec = self.elapsedTime().uint64
         nps = 1000 * (self.nodeCount div max(elapsedMsec, 1))
-    var logMsg = &"info depth {depth} time {elapsedMsec} nodes {self.nodeCount} nps {nps}"
+    var logMsg = &"info depth {depth} seldepth {self.selectiveDepth} time {elapsedMsec} nodes {self.nodeCount} nps {nps}"
-    logMsg &= &" hashfull {self.transpositionTable[].getFillEstimate()}"     
+    logMsg &= &" hashfull {self.transpositionTable[].getFillEstimate()} score cp {self.bestRootScore}"
-    if self.bestMoveRoot != nullMove():
+    if self.pvMoves[0][0] != nullMove():
-        logMsg &= &" score cp {self.bestRootScore} pv {self.bestMoveRoot.toAlgebraic()}"
+        logMsg &= " pv "
+        for move in self.pvMoves[0]:
+            if move == nullMove():
+                break
+            logMsg &= &"{move.toAlgebraic()} "
     echo logMsg
 
 
@@ -186,6 +204,23 @@ proc getSearchExtension(self: SearchManager, move: Move): int {.used.} =
         return 1
 
 
+proc getReduction(self: SearchManager, move: Move, depth, ply, moveNumber: int, isPV: bool): int =
+    ## Returns the amount a search depth can be reduced to 
+    
+    # Move reduction gains: 54.1 +/- 25.5
+    if moveNumber > 3 and depth > 2:
+        result = LMR_TABLE[depth][moveNumber]
+        if not isPV:
+            # Reduce non PV nodes more
+            inc(result)
+
+        if self.board.inCheck():
+            # Reduce less when opponent is in check
+            dec(result)
+        # Keep the reduction in the right range
+        result = result.clamp(0, depth - 1)
+
+
 proc qsearch(self: var SearchManager, ply: int, alpha, beta: Score): Score =
     ## Negamax search with a/b pruning that is restricted to
     ## capture moves (commonly called quiescent search). The
@@ -202,7 +237,7 @@ proc qsearch(self: var SearchManager, ply: int, alpha, beta: Score): Score =
     ## exist
     if self.shouldStop():
         return
-    if ply == 127:
+    if ply == MAX_DEPTH:
         return Score(0)
     let score = self.board.position.evaluate()
     if score >= beta:
@@ -234,7 +269,7 @@ proc qsearch(self: var SearchManager, ply: int, alpha, beta: Score): Score =
     return bestScore
 
 
-proc storeKillerMove(self: SearchManager, ply: int, move: Move) =
+proc storeKillerMove(self: SearchManager, ply: int, move: Move) {.used.} =
     ## Stores a killer move into our killers table at the given
     ## ply
 
@@ -249,25 +284,21 @@ proc storeKillerMove(self: SearchManager, ply: int, move: Move) =
         # Shift moves one spot down
         self.killers[][ply][j + 1] = self.killers[][ply][j];
         dec(j)
-    self.killers[][ply][0] = move;
+    self.killers[][ply][0] = move
 
 
-proc shouldReduce(self: SearchManager, move: Move, depth, moveNumber: int): bool =
+proc search(self: var SearchManager, depth, ply: int, alpha, beta: Score, isPV: bool): Score {.discardable.} =
-    ## Returns whether the search should be reduced at the given
-    ## depth and move number
-    return defined(searchLMR) and moveNumber >= 5 and depth > 3 and not move.isCapture()
-
-
-proc search(self: var SearchManager, depth, ply: int, alpha, beta: Score): Score {.discardable.} =
     ## Negamax search with various optimizations and search features
+    
+    # Clear the PV table
+    for i in 0..MAX_DEPTH:
+        self.pvMoves[ply][i] = nullMove()
     if depth > 1 and self.shouldStop():
         # We do not let ourselves get cancelled at depth
         # one because then we wouldn't have a move to return.
         # In practice this should not be a problem
         return
-    when defined(killers):
+    self.selectiveDepth = max(self.selectiveDepth, ply)
-        if self.killers[].high() < ply:
-            self.killers[].add([nullMove(), nullMove()])
     if ply > 0:
         let query = self.transpositionTable[].get(self.board.position.zobristKey, depth.uint8)
         if query.success:
@@ -285,7 +316,7 @@ proc search(self: var SearchManager, depth, ply: int, alpha, beta: Score): Score
     if depth == 0:
         # Quiescent search gain: 264.8 +/- 71.6
         return self.qsearch(0, alpha, beta)
-    var 
+    var
         moves = newMoveList()
         depth = depth
         bestMove = nullMove()
@@ -305,14 +336,14 @@ proc search(self: var SearchManager, depth, ply: int, alpha, beta: Score): Score
     var alpha = alpha    
     let 
         sideToMove = self.board.position.sideToMove
-        nonSideToMove = sideToMove.opposite()
+        nonSideToMove {.used.} = sideToMove.opposite()
     for i, move in moves:
         if ply == 0 and self.searchMoves.len() > 0 and move notin self.searchMoves:
             continue
         self.board.doMove(move)
-        self.currentMove = move
+        let 
-        self.currentMoveNumber = i
+            extension = self.getSearchExtension(move)
-        let extension = self.getSearchExtension(move)
+            reduction = self.getReduction(move, depth, ply, i, isPV)
         inc(self.nodeCount)
         # Find the best move for us (worst move
         # for our opponent, hence the negative sign)
@@ -321,30 +352,30 @@ proc search(self: var SearchManager, depth, ply: int, alpha, beta: Score): Score
         if i == 0:
             # Due to our move ordering scheme, the first move is always the "best", so
             # search it always at full depth with the full search window
-            score = -self.search(depth - 1 + extension, ply + 1, -beta, -alpha)
+            score = -self.search(depth - 1 + extension, ply + 1, -beta, -alpha, isPV)
-        elif extension == 0 and self.shouldReduce(move, depth, i):
+        elif reduction > 0:
             # Late Move Reductions: assume our move orderer did a good job,
             # so it is not worth it to look at all moves at the same depth equally.
             # If this move turns out to be better than we expected, we'll re-search
             # it at full depth
-            const reduction = 1
+
             # We first do a null-window search to see if there's a move that beats alpha
             # (we don't care about the actual value, so we search in the range [alpha, alpha + 1]
             # to increase the number of cutoffs)
-            score = -self.search(depth - 1 - reduction, ply + 1, -alpha - 1, -alpha)
+            score = -self.search(depth - 1 - reduction, ply + 1, -alpha - 1, -alpha, isPV=false)
             # If the null window search beats alpha, we do a full window reduced search to get a
             # better feel for the actual score of the position. If the score turns out to beat alpha
             # (but not beta) again, we'll re-search this at full depth later
             if score > alpha:
-                score = -self.search(depth - 1 - reduction, ply + 1, -beta, -alpha)
+                score = -self.search(depth - 1 - reduction, ply + 1, -beta, -alpha, isPV=false)
         else:
             # Move wasn't reduced, just do a null window search
-            score = -self.search(depth - 1 + extension, ply + 1, -alpha - 1, -alpha)
+            score = -self.search(depth - 1 + extension, ply + 1, -alpha - 1, -alpha, isPV=false)
         if i > 0 and score > alpha and score < beta:
-            # The move failed high (and not low, which would mean it was too good for us and
+            # The position failed high (and not low, which would mean it was too good for us and
             # our opponent wouldn't let us play it) in the null window search, search it
             # again with the full depth and full window
-            score = -self.search(depth - 1 + extension, ply + 1, -beta, -alpha)
+            score = -self.search(depth - 1 + extension, ply + 1, -beta, -alpha, isPV)
         self.board.unmakeMove()
         # When a search is cancelled or times out, we need
         # to make sure the entire call stack unwinds back
@@ -356,7 +387,7 @@ proc search(self: var SearchManager, depth, ply: int, alpha, beta: Score): Score
             if not move.isCapture() and self.history[][sideToMove][move.startSquare][move.targetSquare] < highestEval():
                 # History euristic: keep track of moves that caused a beta cutoff and order
                 # them early in subsequent searches, as they might be really good later. A
-                # quadratic bonus wrt. depth is usually the bonus that is used (though some
+                # quadratic bonus wrt. depth is usually the value that is used (though some
                 # engines, namely Stockfish, use a linear bonus. Maybe we can investigate this)
                 self.history[][sideToMove][move.startSquare][move.targetSquare] += Score(depth * depth)
                 # Killer move heuristic: store moves that caused a beta cutoff according to the distance from
@@ -369,8 +400,17 @@ proc search(self: var SearchManager, depth, ply: int, alpha, beta: Score): Score
             alpha = score
             bestMove = move
             if ply == 0:
-                self.bestMoveRoot = move
+                self.bestRootScore = score
-                self.bestRootScore = bestScore
+            if isPV:
+                # This loop is why pvMoves has one extra move.
+                # We can just do ply + 1 and i + 1 without ever
+                # fearing about buffer overflows
+                for i, pv in self.pvMoves[ply + 1]:
+                    if pv == nullMove():
+                        self.pvMoves[ply][i + 1] = nullMove()
+                        break
+                    self.pvMoves[ply][i + 1] = pv
+                self.pvMoves[ply][0] = move
         # TODO
         # else:
         #     when defined(noScaleHistory):
@@ -397,15 +437,14 @@ proc findBestMove*(self: var SearchManager, timeRemaining, increment: int64, max
     ## nodes. If searchMoves is provided and is not empty, search will
     ## be restricted to the moves in the list. Note that regardless of
     ## any time limitations or explicit cancellations, the search will
-    ## not stop until it  has at least cleared depth one
+    ## not stop until it  has at least cleared depth one. Search depth
+    ## is always constrained to at most MAX_DEPTH ply from the root
     
     # Apparently negative remaining time is a thing. Welp
     let 
         maxSearchTime = max(1, (timeRemaining div 10) + (increment div 2))
         softLimit = maxSearchTime div 3
-    echo maxSearchTime
+    result = nullMove()
-    self.bestMoveRoot = nullMove()
-    result = self.bestMoveRoot
     self.maxNodes = maxNodes
     self.searchMoves = searchMoves
     self.searchStart = getMonoTime()
@@ -416,23 +455,18 @@ proc findBestMove*(self: var SearchManager, timeRemaining, increment: int64, max
         maxDepth = 30
     self.searchFlag[].store(true)
     # Iterative deepening loop
-    for i in 1..maxDepth:
+    for i in 1..min(MAX_DEPTH, maxDepth):
-        # Search the previous best move first
+        self.search(i, 0, lowestEval(), highestEval(), true)
-        self.previousBestMove = self.bestMoveRoot
+        if self.pvMoves[0][0] != nullMove():
-        self.search(i, 0, lowestEval(), highestEval())
+            result = self.pvMoves[0][0]
-        # Since we always search the best move from the
+        if self.shouldStop():
-        # previous iteration, we can use partial search
+            self.log(i - 1)
-        # results: the engine will either not have changed
+            break
-        # its mind, or it will have found an even better move
+        self.log(i)
-        # in the meantime, which we should obviously use!
-        result = self.bestMoveRoot
         # Soft time management: don't start a new search iteration
         # if the soft limit has expired, as it is unlikely to complete
         # anyway
-        if self.shouldStop() or getMonoTime() >= self.softLimit:
+        if getMonoTime() >= self.softLimit:
-            self.log(i - 1)
             break
-        else:
-            self.log(i)
     self.searchFlag[].store(false)
     self.stopFlag[].store(false)

Chess/nimfish/nimfishpkg/tui.nim

@@ -388,6 +388,7 @@ proc commandLoop*: int =
                 of "skip":
                     board.position.sideToMove = board.position.sideToMove.opposite()
                     board.position.updateChecksAndPins()
+                    board.position.hash()
                 of "go":
                     handleGoCommand(board, cmd)
                 of "position", "pos":

Chess/nimfish/nimfishpkg/uci.nim

@@ -351,6 +351,10 @@ proc startUCISession* =
     session.transpositionTable[] = newTranspositionTable(session.hashTableSize * 1024 * 1024)
     session.historyTable = cast[ptr HistoryTable](alloc0(sizeof(HistoryTable)))
     session.killerMoves = cast[ptr KillersTable](alloc0(sizeof(KillersTable)))
+    # Initialize killer move array
+    for i in 0..<MAX_DEPTH:
+        for j in 0..<NUM_KILLERS:
+            session.killerMoves[i][j] = nullMove()
     # Fun fact, nim doesn't collect the memory of thread vars. Another stupid fucking design pitfall
     # of nim's AWESOME threading model. Someone is getting a pipebomb in their mailbox about this, mark
     # my fucking words. (for legal purposes THAT IS A JOKE). See https://github.com/nim-lang/Nim/issues/23165
@@ -384,16 +388,20 @@ proc startUCISession* =
                     session.position = startpos()
                     session.history = @[]
                 of Go:
-                    when not defined(noScaleHistory):
+                    # when not defined(noScaleHistory):
-                        # Scale our history coefficients
+                    # Scale our history coefficients
-                        for color in PieceColor.White..PieceColor.Black:
+                    for color in PieceColor.White..PieceColor.Black:
-                            for source in Square(0)..Square(63):
+                        for source in Square(0)..Square(63):
-                                for target in Square(0)..Square(63):
+                            for target in Square(0)..Square(63):
-                                    session.historyTable[][color][source][target] = session.historyTable[][color][source][target] div 2
+                                session.historyTable[][color][source][target] = session.historyTable[][color][source][target] div 2
+                    if searchThread.running:
+                        joinThread(searchThread)
                     createThread(searchThread, bestMove, (session, cmd))
                     if session.debug:
                         echo "info string search started"
                 of Stop:
+                    if not session.searchFlag[].load():
+                        continue
                     session.stopFlag[].store(true)
                     joinThread(searchThread)
                     if session.debug:

Chess/tests/suite.py

@@ -55,7 +55,8 @@ def main(args: Namespace) -> int:
             stop = timeit.default_timer()
             pool.shutdown(cancel_futures=True)
             print(f"\r[S] Interrupted\033[K")
-            print(f"[S] Ran {i} tests at depth {args.ply} in {stop - start:.2f} seconds ({len(successful)} successful, {len(failed)} failed)")
+            total = len(successful) + len(failed)
+            print(f"[S] Ran {total} tests at depth {args.ply} in {stop - start:.2f} seconds ({len(successful)} successful, {len(failed)} failed)")
             if failed and args.show_failures:
                 print("[S] The following FENs failed to pass the test:\n\t", end="")
                 print("\n\t".join(failed))