Create engine/search.py

engine/search.py

@@ -0,0 +1,305 @@
+"""
+Nexus-Nano Search Engine
+Fast alpha-beta with minimal overhead
+
+Focus: Speed > Depth
+Target: Sub-second responses
+"""
+
+import chess
+import logging
+from typing import Optional, Tuple, List, Dict
+
+from .evaluate import NexusNanoEvaluator
+from .transposition import TranspositionTable, NodeType
+from .move_ordering import MoveOrderer
+from .time_manager import TimeManager
+from .endgame import EndgameDetector
+
+logger = logging.getLogger(__name__)
+
+
+class NexusNanoEngine:
+    """Ultra-fast 2.8M parameter chess engine"""
+    
+    MATE_SCORE = 100000
+    MAX_PLY = 100
+    
+    def __init__(self, model_path: str, num_threads: int = 1):
+        """Initialize with single-threaded config"""
+        
+        self.evaluator = NexusNanoEvaluator(model_path, num_threads)
+        self.tt = TranspositionTable(size_mb=64)  # 64MB TT
+        self.move_orderer = MoveOrderer()
+        self.time_manager = TimeManager()
+        self.endgame_detector = EndgameDetector()
+        
+        self.nodes_evaluated = 0
+        self.depth_reached = 0
+        self.sel_depth = 0
+        self.principal_variation = []
+        
+        logger.info("⚡ Nexus-Nano Engine initialized")
+        logger.info(f"   Model: {self.evaluator.get_model_size_mb():.2f} MB")
+        logger.info(f"   TT: 64 MB")
+    
+    def get_best_move(
+        self,
+        fen: str,
+        depth: int = 4,
+        time_limit: int = 2000
+    ) -> Dict:
+        """
+        Fast move search
+        
+        Args:
+            fen: Position
+            depth: Max depth (1-6 recommended)
+            time_limit: Time in ms
+        """
+        
+        board = chess.Board(fen)
+        
+        # Reset
+        self.nodes_evaluated = 0
+        self.depth_reached = 0
+        self.sel_depth = 0
+        self.principal_variation = []
+        
+        # Time setup
+        time_limit_sec = time_limit / 1000.0
+        self.time_manager.start_search(time_limit_sec, time_limit_sec)
+        
+        # Clear old data
+        self.move_orderer.clear()
+        self.tt.increment_age()
+        
+        # Special cases
+        legal_moves = list(board.legal_moves)
+        
+        if len(legal_moves) == 0:
+            return self._no_legal_moves()
+        
+        if len(legal_moves) == 1:
+            return self._single_move(board, legal_moves[0])
+        
+        # Iterative deepening (fast)
+        best_move = legal_moves[0]
+        best_score = float('-inf')
+        
+        for current_depth in range(1, depth + 1):
+            if self.time_manager.should_stop(current_depth):
+                break
+            
+            score, move, pv = self._search_root(
+                board, current_depth, float('-inf'), float('inf')
+            )
+            
+            if move:
+                best_move = move
+                best_score = score
+                self.depth_reached = current_depth
+                self.principal_variation = pv
+        
+        return {
+            'best_move': best_move.uci(),
+            'evaluation': round(best_score / 100.0, 2),
+            '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 search"""
+        
+        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 move in ordered_moves:
+            board.push(move)
+            score, pv = self._alpha_beta(board, depth - 1, -beta, -alpha)
+            score = -score
+            board.pop()
+            
+            if score > best_score:
+                best_score = score
+                best_move = move
+                best_pv = [move] + pv
+            
+            if score > alpha:
+                alpha = score
+            
+            if self.time_manager.should_stop(depth):
+                break
+        
+        self.tt.store(zobrist_key, depth, best_score, NodeType.EXACT, best_move)
+        
+        return best_score, best_move, best_pv
+    
+    def _alpha_beta(
+        self,
+        board: chess.Board,
+        depth: int,
+        alpha: float,
+        beta: float
+    ) -> Tuple[float, List[chess.Move]]:
+        """Fast alpha-beta search"""
+        
+        self.sel_depth = max(self.sel_depth, self.MAX_PLY - depth)
+        
+        # Draw check
+        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
+        if depth <= 0:
+            return self._quiescence(board, alpha, beta, 0), []
+        
+        # Legal 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, []
+        
+        ordered_moves = self.move_orderer.order_moves(
+            board, legal_moves, depth, tt_move
+        )
+        
+        # Search
+        best_score = float('-inf')
+        best_pv = []
+        node_type = NodeType.UPPER_BOUND
+        
+        for move in ordered_moves:
+            board.push(move)
+            score, pv = self._alpha_beta(board, depth - 1, -beta, -alpha)
+            score = -score
+            board.pop()
+            
+            if score > best_score:
+                best_score = score
+                best_pv = [move] + pv
+                
+                if score > alpha:
+                    alpha = score
+                    node_type = NodeType.EXACT
+                    
+                    if not board.is_capture(move):
+                        self.move_orderer.update_killer_move(move, depth)
+                    
+                    if score >= beta:
+                        node_type = NodeType.LOWER_BOUND
+                        break
+        
+        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:
+        """Fast quiescence (captures only)"""
+        
+        self.nodes_evaluated += 1
+        
+        # Stand-pat
+        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
+        if qs_depth >= 6:
+            return stand_pat
+        
+        # Captures only (no checks for speed)
+        captures = [m for m in board.legal_moves if board.is_capture(m)]
+        
+        if not captures:
+            return stand_pat
+        
+        captures = self.move_orderer.order_moves(board, captures, 0)
+        
+        for move in captures:
+            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 _no_legal_moves(self) -> Dict:
+        return {
+            'best_move': '0000',
+            'evaluation': 0.0,
+            'depth_searched': 0,
+            'nodes_evaluated': 0,
+            'time_taken': 0
+        }
+    
+    def _single_move(self, board: chess.Board, move: chess.Move) -> Dict:
+        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:
+        try:
+            chess.Board(fen)
+            return True
+        except:
+            return False
+    
+    def get_model_size(self) -> float:
+        return self.evaluator.get_model_size_mb()