Create engine/search.py

engine/search.py

@@ -0,0 +1,469 @@
+"""
+Synapse-Base Main Search Engine
+State-of-the-art alpha-beta with advanced enhancements
+
+Research Implementation:
+- Alpha-Beta with PVS (Principal Variation Search)
+- Aspiration Windows
+- Null Move Pruning
+- Late Move Reductions (LMR)
+- Quiescence Search with SEE
+- Iterative Deepening
+- Transposition Table with Zobrist
+- Advanced Move Ordering
+"""
+
+import chess
+import time
+import logging
+from typing import Optional, Tuple, List, Dict
+
+from .evaluate import NeuralEvaluator
+from .transposition import TranspositionTable, NodeType
+from .move_ordering import MoveOrderer
+from .time_manager import TimeManager
+from .endgame import EndgameDetector
+
+logger = logging.getLogger(__name__)
+
+
+class SynapseEngine:
+    """
+    State-of-the-art chess engine with neural evaluation
+    """
+    
+    # Search constants (tuned values from research)
+    MATE_SCORE = 100000
+    MAX_PLY = 100
+    
+    # Null move pruning parameters
+    NULL_MOVE_REDUCTION = 2
+    NULL_MOVE_MIN_DEPTH = 3
+    
+    # Late move reduction parameters
+    LMR_MIN_DEPTH = 3
+    LMR_MOVE_THRESHOLD = 4
+    
+    # Aspiration window size
+    ASPIRATION_WINDOW = 50
+    
+    def __init__(self, model_path: str, num_threads: int = 2):
+        """Initialize engine components"""
+        
+        # Core components
+        self.evaluator = NeuralEvaluator(model_path, num_threads)
+        self.tt = TranspositionTable(size_mb=256)  # 256MB TT
+        self.move_orderer = MoveOrderer()
+        self.time_manager = TimeManager()
+        self.endgame_detector = EndgameDetector()
+        
+        # Search statistics
+        self.nodes_evaluated = 0
+        self.depth_reached = 0
+        self.sel_depth = 0  # Selective depth (quiescence)
+        self.principal_variation = []
+        
+        logger.info("🎯 Synapse-Base Engine initialized")
+        logger.info(f"   Model: {self.evaluator.get_model_size_mb():.2f} MB")
+        logger.info(f"   TT Size: 256 MB")
+    
+    def get_best_move(
+        self,
+        fen: str,
+        depth: int = 5,
+        time_limit: int = 5000
+    ) -> Dict:
+        """
+        Main search entry point
+        
+        Args:
+            fen: Position in FEN notation
+            depth: Maximum search depth
+            time_limit: Time limit in milliseconds
+        
+        Returns:
+            Dictionary with best_move, evaluation, stats
+        """
+        board = chess.Board(fen)
+        
+        # Reset statistics
+        self.nodes_evaluated = 0
+        self.depth_reached = 0
+        self.sel_depth = 0
+        self.principal_variation = []
+        
+        # Time management
+        time_limit_sec = time_limit / 1000.0
+        self.time_manager.start_search(time_limit_sec, time_limit_sec)
+        
+        # Age history for new search
+        self.move_orderer.age_history(0.95)
+        self.tt.increment_age()
+        
+        # Special cases
+        legal_moves = list(board.legal_moves)
+        if len(legal_moves) == 0:
+            return self._no_legal_moves_result()
+        
+        if len(legal_moves) == 1:
+            return self._single_move_result(board, legal_moves[0])
+        
+        # Iterative deepening with aspiration windows
+        best_move = legal_moves[0]
+        best_score = float('-inf')
+        alpha = float('-inf')
+        beta = float('inf')
+        
+        for current_depth in range(1, depth + 1):
+            # Time check
+            if self.time_manager.should_stop(current_depth):
+                break
+            
+            # Aspiration window for depth >= 4
+            if current_depth >= 4 and abs(best_score) < self.MATE_SCORE - 1000:
+                alpha = best_score - self.ASPIRATION_WINDOW
+                beta = best_score + self.ASPIRATION_WINDOW
+            else:
+                alpha = float('-inf')
+                beta = float('inf')
+            
+            # Search with aspiration window
+            score, move, pv = self._search_root(
+                board, current_depth, alpha, beta
+            )
+            
+            # Handle aspiration window failures
+            if score <= alpha or score >= beta:
+                # Research with full window
+                score, move, pv = self._search_root(
+                    board, current_depth, float('-inf'), float('inf')
+                )
+            
+            # Update best move
+            if move:
+                best_move = move
+                best_score = score
+                self.depth_reached = current_depth
+                self.principal_variation = pv
+                
+                logger.info(
+                    f"Depth {current_depth}: {move.uci()} "
+                    f"({score:+.2f}) | Nodes: {self.nodes_evaluated} | "
+                    f"Time: {self.time_manager.elapsed():.2f}s"
+                )
+        
+        # Return result
+        return {
+            'best_move': best_move.uci(),
+            'evaluation': round(best_score / 100.0, 2),  # Convert to pawns
+            'depth_searched': self.depth_reached,
+            'seldepth': self.sel_depth,
+            'nodes_evaluated': self.nodes_evaluated,
+            'time_taken': int(self.time_manager.elapsed() * 1000),
+            'pv': [m.uci() for m in self.principal_variation],
+            'nps': int(self.nodes_evaluated / max(self.time_manager.elapsed(), 0.001)),
+            'tt_stats': self.tt.get_stats(),
+            'move_ordering_stats': self.move_orderer.get_stats()
+        }
+    
+    def _search_root(
+        self,
+        board: chess.Board,
+        depth: int,
+        alpha: float,
+        beta: float
+    ) -> Tuple[float, Optional[chess.Move], List[chess.Move]]:
+        """Root node search with PVS"""
+        
+        legal_moves = list(board.legal_moves)
+        
+        # TT probe
+        zobrist_key = self.tt.compute_zobrist_key(board)
+        tt_result = self.tt.probe(zobrist_key, depth, alpha, beta)
+        tt_move = tt_result[1] if tt_result else None
+        
+        # Order moves
+        ordered_moves = self.move_orderer.order_moves(
+            board, legal_moves, depth, tt_move
+        )
+        
+        best_move = ordered_moves[0]
+        best_score = float('-inf')
+        best_pv = []
+        
+        for i, move in enumerate(ordered_moves):
+            board.push(move)
+            
+            if i == 0:
+                # Full window search for first move (PV node)
+                score, pv = self._pvs(
+                    board, depth - 1, -beta, -alpha, True
+                )
+                score = -score
+            else:
+                # Null window search for remaining moves
+                score, _ = self._pvs(
+                    board, depth - 1, -alpha - 1, -alpha, False
+                )
+                score = -score
+                
+                # Re-search if failed high
+                if alpha < score < beta:
+                    score, pv = self._pvs(
+                        board, depth - 1, -beta, -alpha, True
+                    )
+                    score = -score
+                else:
+                    pv = []
+            
+            board.pop()
+            
+            # Update best
+            if score > best_score:
+                best_score = score
+                best_move = move
+                best_pv = [move] + pv
+            
+            # Update alpha
+            if score > alpha:
+                alpha = score
+            
+            # Time check
+            if self.time_manager.should_stop(depth):
+                break
+        
+        # Store in TT
+        self.tt.store(
+            zobrist_key, depth, best_score,
+            NodeType.EXACT, best_move
+        )
+        
+        return best_score, best_move, best_pv
+    
+    def _pvs(
+        self,
+        board: chess.Board,
+        depth: int,
+        alpha: float,
+        beta: float,
+        do_null: bool
+    ) -> Tuple[float, List[chess.Move]]:
+        """
+        Principal Variation Search (PVS) with alpha-beta
+        
+        Enhanced with:
+        - Null move pruning
+        - Late move reductions
+        - Transposition table
+        """
+        self.sel_depth = max(self.sel_depth, self.MAX_PLY - depth)
+        
+        # Mate distance pruning
+        alpha = max(alpha, -self.MATE_SCORE + (self.MAX_PLY - depth))
+        beta = min(beta, self.MATE_SCORE - (self.MAX_PLY - depth) - 1)
+        if alpha >= beta:
+            return alpha, []
+        
+        # Draw detection
+        if board.is_repetition(2) or board.is_fifty_moves():
+            return 0, []
+        
+        # TT probe
+        zobrist_key = self.tt.compute_zobrist_key(board)
+        tt_result = self.tt.probe(zobrist_key, depth, alpha, beta)
+        
+        if tt_result and tt_result[0] is not None:
+            return tt_result[0], []
+        
+        tt_move = tt_result[1] if tt_result else None
+        
+        # Quiescence search at leaf nodes
+        if depth <= 0:
+            return self._quiescence(board, alpha, beta, 0), []
+        
+        # Null move pruning
+        if (do_null and 
+            depth >= self.NULL_MOVE_MIN_DEPTH and
+            not board.is_check() and
+            self._has_non_pawn_material(board)):
+            
+            board.push(chess.Move.null())
+            score, _ = self._pvs(
+                board, depth - 1 - self.NULL_MOVE_REDUCTION,
+                -beta, -beta + 1, False
+            )
+            score = -score
+            board.pop()
+            
+            if score >= beta:
+                return beta, []
+        
+        # Generate and order moves
+        legal_moves = list(board.legal_moves)
+        if not legal_moves:
+            if board.is_check():
+                return -self.MATE_SCORE + (self.MAX_PLY - depth), []
+            return 0, []  # Stalemate
+        
+        ordered_moves = self.move_orderer.order_moves(
+            board, legal_moves, depth, tt_move
+        )
+        
+        # Main search loop
+        best_score = float('-inf')
+        best_pv = []
+        node_type = NodeType.UPPER_BOUND
+        moves_searched = 0
+        
+        for move in ordered_moves:
+            board.push(move)
+            
+            # Late move reductions
+            reduction = 0
+            if (moves_searched >= self.LMR_MOVE_THRESHOLD and
+                depth >= self.LMR_MIN_DEPTH and
+                not board.is_check() and
+                not board.is_capture(board.peek())):
+                reduction = 1
+            
+            # PVS
+            if moves_searched == 0:
+                score, pv = self._pvs(
+                    board, depth - 1, -beta, -alpha, True
+                )
+                score = -score
+            else:
+                # Reduced search
+                score, _ = self._pvs(
+                    board, depth - 1 - reduction, -alpha - 1, -alpha, True
+                )
+                score = -score
+                
+                # Re-search if necessary
+                if alpha < score < beta:
+                    score, pv = self._pvs(
+                        board, depth - 1, -beta, -alpha, True
+                    )
+                    score = -score
+                else:
+                    pv = []
+            
+            board.pop()
+            moves_searched += 1
+            
+            # Update best
+            if score > best_score:
+                best_score = score
+                best_pv = [move] + pv
+                
+                if score > alpha:
+                    alpha = score
+                    node_type = NodeType.EXACT
+                    
+                    # Update history for good moves
+                    if not board.is_capture(move):
+                        self.move_orderer.update_history(move, depth, True)
+                        self.move_orderer.update_killer_move(move, depth)
+                    
+                    if score >= beta:
+                        node_type = NodeType.LOWER_BOUND
+                        break
+        
+        # Store in TT
+        self.tt.store(zobrist_key, depth, best_score, node_type, best_pv[0] if best_pv else None)
+        
+        return best_score, best_pv
+    
+    def _quiescence(
+        self,
+        board: chess.Board,
+        alpha: float,
+        beta: float,
+        qs_depth: int
+    ) -> float:
+        """
+        Quiescence search to resolve tactical sequences
+        Only searches captures and checks
+        """
+        self.nodes_evaluated += 1
+        
+        # Stand-pat evaluation
+        stand_pat = self.evaluator.evaluate_hybrid(board)
+        stand_pat = self.endgame_detector.adjust_evaluation(board, stand_pat)
+        
+        if stand_pat >= beta:
+            return beta
+        if alpha < stand_pat:
+            alpha = stand_pat
+        
+        # Depth limit for quiescence
+        if qs_depth >= 8:
+            return stand_pat
+        
+        # Generate tactical moves
+        tactical_moves = [
+            move for move in board.legal_moves
+            if board.is_capture(move) or board.gives_check(move)
+        ]
+        
+        if not tactical_moves:
+            return stand_pat
+        
+        # Order tactical moves
+        tactical_moves = self.move_orderer.order_moves(
+            board, tactical_moves, 0
+        )
+        
+        for move in tactical_moves:
+            board.push(move)
+            score = -self._quiescence(board, -beta, -alpha, qs_depth + 1)
+            board.pop()
+            
+            if score >= beta:
+                return beta
+            if score > alpha:
+                alpha = score
+        
+        return alpha
+    
+    def _has_non_pawn_material(self, board: chess.Board) -> bool:
+        """Check if side to move has non-pawn material"""
+        for piece_type in [chess.KNIGHT, chess.BISHOP, chess.ROOK, chess.QUEEN]:
+            if board.pieces(piece_type, board.turn):
+                return True
+        return False
+    
+    def _no_legal_moves_result(self) -> Dict:
+        """Result when no legal moves"""
+        return {
+            'best_move': '0000',
+            'evaluation': 0.0,
+            'depth_searched': 0,
+            'nodes_evaluated': 0,
+            'time_taken': 0
+        }
+    
+    def _single_move_result(self, board: chess.Board, move: chess.Move) -> Dict:
+        """Result when only one legal move"""
+        eval_score = self.evaluator.evaluate_hybrid(board)
+        
+        return {
+            'best_move': move.uci(),
+            'evaluation': round(eval_score / 100.0, 2),
+            'depth_searched': 0,
+            'nodes_evaluated': 1,
+            'time_taken': 0,
+            'pv': [move.uci()]
+        }
+    
+    def validate_fen(self, fen: str) -> bool:
+        """Validate FEN string"""
+        try:
+            chess.Board(fen)
+            return True
+        except:
+            return False
+    
+    def get_model_size(self) -> float:
+        """Get model size in MB"""
+        return self.evaluator.get_model_size_mb()