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""" |
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Standalone ChessBot Chess Model |
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This file contains all the necessary code to run the ChessBot model |
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without requiring the HFChessRL package installation. |
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Requirements: |
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- torch>=2.0.0 |
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- transformers>=4.30.0 |
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- python-chess>=1.10.0 |
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- numpy>=1.21.0 |
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""" |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import numpy as np |
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import chess |
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from transformers import PreTrainedModel, PretrainedConfig, AutoConfig, AutoModel |
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from transformers.modeling_outputs import BaseModelOutput |
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from typing import Optional, Tuple |
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import math |
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class ChessBotConfig(PretrainedConfig): |
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""" |
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Configuration class for ChessBot model. |
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""" |
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model_type = "chessbot" |
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def __init__( |
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self, |
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num_layers: int = 10, |
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d_model: int = 512, |
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d_ff: int = 736, |
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num_heads: int = 8, |
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vocab_size: int = 1929, |
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max_position_embeddings: int = 64, |
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**kwargs, |
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): |
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super().__init__(**kwargs) |
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self.num_layers = num_layers |
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self.d_model = d_model |
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self.d_ff = d_ff |
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self.num_heads = num_heads |
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self.vocab_size = vocab_size |
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self.max_position_embeddings = max_position_embeddings |
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def fen_to_tensor(fen: str): |
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""" |
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Convert FEN string to tensor representation for the model. |
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""" |
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board = chess.Board(fen) |
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tensor = np.zeros((8, 8, 19), dtype=np.float32) |
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piece_map = { |
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'P': 0, 'N': 1, 'B': 2, 'R': 3, 'Q': 4, 'K': 5, |
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'p': 6, 'n': 7, 'b': 8, 'r': 9, 'q': 10, 'k': 11 |
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} |
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for square in chess.SQUARES: |
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piece = board.piece_at(square) |
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if piece: |
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row = 7 - (square // 8) |
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col = square % 8 |
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tensor[row, col, piece_map[piece.symbol()]] = 1.0 |
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if board.turn == chess.WHITE: |
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tensor[:, :, 12] = 1.0 |
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if board.turn == chess.BLACK: |
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tensor[:, :, 13] = 1.0 |
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if board.has_kingside_castling_rights(chess.WHITE): |
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tensor[:, :, 14] = 1.0 |
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if board.has_queenside_castling_rights(chess.WHITE): |
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tensor[:, :, 15] = 1.0 |
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if board.has_kingside_castling_rights(chess.BLACK): |
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tensor[:, :, 16] = 1.0 |
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if board.has_queenside_castling_rights(chess.BLACK): |
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tensor[:, :, 17] = 1.0 |
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if board.ep_square is not None: |
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ep_row = 7 - (board.ep_square // 8) |
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ep_col = board.ep_square % 8 |
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tensor[ep_row, ep_col, 18] = 1.0 |
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return tensor |
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policy_index = [ |
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"a1b1", "a1c1", "a1d1", "a1e1", "a1f1", "a1g1", "a1h1", "a1a2", "a1b2", |
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"a1c2", "a1a3", "a1b3", "a1c3", "a1a4", "a1d4", "a1a5", "a1e5", "a1a6", |
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"a1f6", "a1a7", "a1g7", "a1a8", "a1h8", "b1a1", "b1c1", "b1d1", "b1e1", |
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"b1f1", "b1g1", "b1h1", "b1a2", "b1b2", "b1c2", "b1d2", "b1a3", "b1b3", |
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"b1c3", "b1d3", "b1b4", "b1e4", "b1b5", "b1f5", "b1b6", "b1g6", "b1b7", |
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"b1h7", "b1b8", "c1a1", "c1b1", "c1d1", "c1e1", "c1f1", "c1g1", "c1h1", |
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"c1a2", "c1b2", "c1c2", "c1d2", "c1e2", "c1a3", "c1b3", "c1c3", "c1d3", |
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"c1e3", "c1c4", "c1f4", "c1c5", "c1g5", "c1c6", "c1h6", "c1c7", "c1c8", |
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"d1a1", "d1b1", "d1c1", "d1e1", "d1f1", "d1g1", "d1h1", "d1b2", "d1c2", |
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"d1d2", "d1e2", "d1f2", "d1b3", "d1c3", "d1d3", "d1e3", "d1f3", "d1a4", |
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"d1d4", "d1g4", "d1d5", "d1h5", "d1d6", "d1d7", "d1d8", "e1a1", "e1b1", |
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"e1c1", "e1d1", "e1f1", "e1g1", "e1h1", "e1c2", "e1d2", "e1e2", "e1f2", |
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"e1g2", "e1c3", "e1d3", "e1e3", "e1f3", "e1g3", "e1b4", "e1e4", "e1h4", |
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"e1a5", "e1e5", "e1e6", "e1e7", "e1e8", "f1a1", "f1b1", "f1c1", "f1d1", |
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"f1e1", "f1g1", "f1h1", "f1d2", "f1e2", "f1f2", "f1g2", "f1h2", "f1d3", |
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"f1e3", "f1f3", "f1g3", "f1h3", "f1c4", "f1f4", "f1b5", "f1f5", "f1a6", |
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"f1f6", "f1f7", "f1f8", "g1a1", "g1b1", "g1c1", "g1d1", "g1e1", "g1f1", |
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"g1h1", "g1e2", "g1f2", "g1g2", "g1h2", "g1e3", "g1f3", "g1g3", "g1h3", |
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"g1d4", "g1g4", "g1c5", "g1g5", "g1b6", "g1g6", "g1a7", "g1g7", "g1g8", |
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"h1a1", "h1b1", "h1c1", "h1d1", "h1e1", "h1f1", "h1g1", "h1f2", "h1g2", |
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"h1h2", "h1f3", "h1g3", "h1h3", "h1e4", "h1h4", "h1d5", "h1h5", "h1c6", |
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"h1h6", "h1b7", "h1h7", "h1a8", "h1h8", "a2a1", "a2b1", "a2c1", "a2b2", |
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"a2c2", "a2d2", "a2e2", "a2f2", "a2g2", "a2h2", "a2a3", "a2b3", "a2c3", |
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"a2a4", "a2b4", "a2c4", "a2a5", "a2d5", "a2a6", "a2e6", "a2a7", "a2f7", |
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"a2a8", "a2g8", "b2a1", "b2b1", "b2c1", "b2d1", "b2a2", "b2c2", "b2d2", |
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"b2e2", "b2f2", "b2g2", "b2h2", "b2a3", "b2b3", "b2c3", "b2d3", "b2a4", |
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"b2b4", "b2c4", "b2d4", "b2b5", "b2e5", "b2b6", "b2f6", "b2b7", "b2g7", |
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"b2b8", "b2h8", "c2a1", "c2b1", "c2c1", "c2d1", "c2e1", "c2a2", "c2b2", |
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"c2d2", "c2e2", "c2f2", "c2g2", "c2h2", "c2a3", "c2b3", "c2c3", "c2d3", |
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"c2e3", "c2a4", "c2b4", "c2c4", "c2d4", "c2e4", "c2c5", "c2f5", "c2c6", |
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"c2g6", "c2c7", "c2h7", "c2c8", "d2b1", "d2c1", "d2d1", "d2e1", "d2f1", |
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"d2a2", "d2b2", "d2c2", "d2e2", "d2f2", "d2g2", "d2h2", "d2b3", "d2c3", |
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"d2d3", "d2e3", "d2f3", "d2b4", "d2c4", "d2d4", "d2e4", "d2f4", "d2a5", |
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"d2d5", "d2g5", "d2d6", "d2h6", "d2d7", "d2d8", "e2c1", "e2d1", "e2e1", |
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"e2f1", "e2g1", "e2a2", "e2b2", "e2c2", "e2d2", "e2f2", "e2g2", "e2h2", |
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"e2c3", "e2d3", "e2e3", "e2f3", "e2g3", "e2c4", "e2d4", "e2e4", "e2f4", |
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"e2g4", "e2b5", "e2e5", "e2h5", "e2a6", "e2e6", "e2e7", "e2e8", "f2d1", |
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"f2e1", "f2f1", "f2g1", "f2h1", "f2a2", "f2b2", "f2c2", "f2d2", "f2e2", |
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"f2g2", "f2h2", "f2d3", "f2e3", "f2f3", "f2g3", "f2h3", "f2d4", "f2e4", |
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"f2f4", "f2g4", "f2h4", "f2c5", "f2f5", "f2b6", "f2f6", "f2a7", "f2f7", |
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"f2f8", "g2e1", "g2f1", "g2g1", "g2h1", "g2a2", "g2b2", "g2c2", "g2d2", |
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"g2e2", "g2f2", "g2h2", "g2e3", "g2f3", "g2g3", "g2h3", "g2e4", "g2f4", |
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"g2g4", "g2h4", "g2d5", "g2g5", "g2c6", "g2g6", "g2b7", "g2g7", "g2a8", |
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"g2g8", "h2f1", "h2g1", "h2h1", "h2a2", "h2b2", "h2c2", "h2d2", "h2e2", |
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"h2f2", "h2g2", "h2f3", "h2g3", "h2h3", "h2f4", "h2g4", "h2h4", "h2e5", |
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"h2h5", "h2d6", "h2h6", "h2c7", "h2h7", "h2b8", "h2h8", "a3a1", "a3b1", |
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"a3c1", "a3a2", "a3b2", "a3c2", "a3b3", "a3c3", "a3d3", "a3e3", "a3f3", |
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"a3g3", "a3h3", "a3a4", "a3b4", "a3c4", "a3a5", "a3b5", "a3c5", "a3a6", |
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"a3d6", "a3a7", "a3e7", "a3a8", "a3f8", "b3a1", "b3b1", "b3c1", "b3d1", |
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"b3a2", "b3b2", "b3c2", "b3d2", "b3a3", "b3c3", "b3d3", "b3e3", "b3f3", |
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"b3g3", "b3h3", "b3a4", "b3b4", "b3c4", "b3d4", "b3a5", "b3b5", "b3c5", |
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"b3d5", "b3b6", "b3e6", "b3b7", "b3f7", "b3b8", "b3g8", "c3a1", "c3b1", |
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"c3c1", "c3d1", "c3e1", "c3a2", "c3b2", "c3c2", "c3d2", "c3e2", "c3a3", |
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"c3b3", "c3d3", "c3e3", "c3f3", "c3g3", "c3h3", "c3a4", "c3b4", "c3c4", |
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"c3d4", "c3e4", "c3a5", "c3b5", "c3c5", "c3d5", "c3e5", "c3c6", "c3f6", |
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"c3c7", "c3g7", "c3c8", "c3h8", "d3b1", "d3c1", "d3d1", "d3e1", "d3f1", |
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"d3b2", "d3c2", "d3d2", "d3e2", "d3f2", "d3a3", "d3b3", "d3c3", "d3e3", |
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"d3f3", "d3g3", "d3h3", "d3b4", "d3c4", "d3d4", "d3e4", "d3f4", "d3b5", |
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"d3c5", "d3d5", "d3e5", "d3f5", "d3a6", "d3d6", "d3g6", "d3d7", "d3h7", |
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"d3d8", "e3c1", "e3d1", "e3e1", "e3f1", "e3g1", "e3c2", "e3d2", "e3e2", |
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"e3f2", "e3g2", "e3a3", "e3b3", "e3c3", "e3d3", "e3f3", "e3g3", "e3h3", |
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"e3c4", "e3d4", "e3e4", "e3f4", "e3g4", "e3c5", "e3d5", "e3e5", "e3f5", |
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"e3g5", "e3b6", "e3e6", "e3h6", "e3a7", "e3e7", "e3e8", "f3d1", "f3e1", |
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"f3f1", "f3g1", "f3h1", "f3d2", "f3e2", "f3f2", "f3g2", "f3h2", "f3a3", |
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"f3b3", "f3c3", "f3d3", "f3e3", "f3g3", "f3h3", "f3d4", "f3e4", "f3f4", |
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"f3g4", "f3h4", "f3d5", "f3e5", "f3f5", "f3g5", "f3h5", "f3c6", "f3f6", |
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"f3b7", "f3f7", "f3a8", "f3f8", "g3e1", "g3f1", "g3g1", "g3h1", "g3e2", |
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"g3f2", "g3g2", "g3h2", "g3a3", "g3b3", "g3c3", "g3d3", "g3e3", "g3f3", |
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"g3h3", "g3e4", "g3f4", "g3g4", "g3h4", "g3e5", "g3f5", "g3g5", "g3h5", |
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"g3d6", "g3g6", "g3c7", "g3g7", "g3b8", "g3g8", "h3f1", "h3g1", "h3h1", |
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"h3f2", "h3g2", "h3h2", "h3a3", "h3b3", "h3c3", "h3d3", "h3e3", "h3f3", |
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"h3g3", "h3f4", "h3g4", "h3h4", "h3f5", "h3g5", "h3h5", "h3e6", "h3h6", |
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"h3d7", "h3h7", "h3c8", "h3h8", "a4a1", "a4d1", "a4a2", "a4b2", "a4c2", |
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"a4a3", "a4b3", "a4c3", "a4b4", "a4c4", "a4d4", "a4e4", "a4f4", "a4g4", |
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"a4h4", "a4a5", "a4b5", "a4c5", "a4a6", "a4b6", "a4c6", "a4a7", "a4d7", |
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"a4a8", "a4e8", "b4b1", "b4e1", "b4a2", "b4b2", "b4c2", "b4d2", "b4a3", |
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"b4b3", "b4c3", "b4d3", "b4a4", "b4c4", "b4d4", "b4e4", "b4f4", "b4g4", |
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"b4h4", "b4a5", "b4b5", "b4c5", "b4d5", "b4a6", "b4b6", "b4c6", "b4d6", |
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"b4b7", "b4e7", "b4b8", "b4f8", "c4c1", "c4f1", "c4a2", "c4b2", "c4c2", |
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"c4d2", "c4e2", "c4a3", "c4b3", "c4c3", "c4d3", "c4e3", "c4a4", "c4b4", |
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"c4d4", "c4e4", "c4f4", "c4g4", "c4h4", "c4a5", "c4b5", "c4c5", "c4d5", |
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"c4e5", "c4a6", "c4b6", "c4c6", "c4d6", "c4e6", "c4c7", "c4f7", "c4c8", |
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"c4g8", "d4a1", "d4d1", "d4g1", "d4b2", "d4c2", "d4d2", "d4e2", "d4f2", |
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"d4b3", "d4c3", "d4d3", "d4e3", "d4f3", "d4a4", "d4b4", "d4c4", "d4e4", |
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"d4f4", "d4g4", "d4h4", "d4b5", "d4c5", "d4d5", "d4e5", "d4f5", "d4b6", |
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"d4c6", "d4d6", "d4e6", "d4f6", "d4a7", "d4d7", "d4g7", "d4d8", "d4h8", |
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"e4b1", "e4e1", "e4h1", "e4c2", "e4d2", "e4e2", "e4f2", "e4g2", "e4c3", |
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"e4d3", "e4e3", "e4f3", "e4g3", "e4a4", "e4b4", "e4c4", "e4d4", "e4f4", |
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"e4g4", "e4h4", "e4c5", "e4d5", "e4e5", "e4f5", "e4g5", "e4c6", "e4d6", |
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"e4e6", "e4f6", "e4g6", "e4b7", "e4e7", "e4h7", "e4a8", "e4e8", "f4c1", |
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"f4f1", "f4d2", "f4e2", "f4f2", "f4g2", "f4h2", "f4d3", "f4e3", "f4f3", |
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"f4g3", "f4h3", "f4a4", "f4b4", "f4c4", "f4d4", "f4e4", "f4g4", "f4h4", |
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"f4d5", "f4e5", "f4f5", "f4g5", "f4h5", "f4d6", "f4e6", "f4f6", "f4g6", |
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"f4h6", "f4c7", "f4f7", "f4b8", "f4f8", "g4d1", "g4g1", "g4e2", "g4f2", |
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"g4g2", "g4h2", "g4e3", "g4f3", "g4g3", "g4h3", "g4a4", "g4b4", "g4c4", |
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"g4d4", "g4e4", "g4f4", "g4h4", "g4e5", "g4f5", "g4g5", "g4h5", "g4e6", |
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"g4f6", "g4g6", "g4h6", "g4d7", "g4g7", "g4c8", "g4g8", "h4e1", "h4h1", |
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"h4f2", "h4g2", "h4h2", "h4f3", "h4g3", "h4h3", "h4a4", "h4b4", "h4c4", |
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"h4d4", "h4e4", "h4f4", "h4g4", "h4f5", "h4g5", "h4h5", "h4f6", "h4g6", |
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"h4h6", "h4e7", "h4h7", "h4d8", "h4h8", "a5a1", "a5e1", "a5a2", "a5d2", |
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"a5a3", "a5b3", "a5c3", "a5a4", "a5b4", "a5c4", "a5b5", "a5c5", "a5d5", |
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"a5e5", "a5f5", "a5g5", "a5h5", "a5a6", "a5b6", "a5c6", "a5a7", "a5b7", |
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"a5c7", "a5a8", "a5d8", "b5b1", "b5f1", "b5b2", "b5e2", "b5a3", "b5b3", |
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"b5c3", "b5d3", "b5a4", "b5b4", "b5c4", "b5d4", "b5a5", "b5c5", "b5d5", |
|
|
"b5e5", "b5f5", "b5g5", "b5h5", "b5a6", "b5b6", "b5c6", "b5d6", "b5a7", |
|
|
"b5b7", "b5c7", "b5d7", "b5b8", "b5e8", "c5c1", "c5g1", "c5c2", "c5f2", |
|
|
"c5a3", "c5b3", "c5c3", "c5d3", "c5e3", "c5a4", "c5b4", "c5c4", "c5d4", |
|
|
"c5e4", "c5a5", "c5b5", "c5d5", "c5e5", "c5f5", "c5g5", "c5h5", "c5a6", |
|
|
"c5b6", "c5c6", "c5d6", "c5e6", "c5a7", "c5b7", "c5c7", "c5d7", "c5e7", |
|
|
"c5c8", "c5f8", "d5d1", "d5h1", "d5a2", "d5d2", "d5g2", "d5b3", "d5c3", |
|
|
"d5d3", "d5e3", "d5f3", "d5b4", "d5c4", "d5d4", "d5e4", "d5f4", "d5a5", |
|
|
"d5b5", "d5c5", "d5e5", "d5f5", "d5g5", "d5h5", "d5b6", "d5c6", "d5d6", |
|
|
"d5e6", "d5f6", "d5b7", "d5c7", "d5d7", "d5e7", "d5f7", "d5a8", "d5d8", |
|
|
"d5g8", "e5a1", "e5e1", "e5b2", "e5e2", "e5h2", "e5c3", "e5d3", "e5e3", |
|
|
"e5f3", "e5g3", "e5c4", "e5d4", "e5e4", "e5f4", "e5g4", "e5a5", "e5b5", |
|
|
"e5c5", "e5d5", "e5f5", "e5g5", "e5h5", "e5c6", "e5d6", "e5e6", "e5f6", |
|
|
"e5g6", "e5c7", "e5d7", "e5e7", "e5f7", "e5g7", "e5b8", "e5e8", "e5h8", |
|
|
"f5b1", "f5f1", "f5c2", "f5f2", "f5d3", "f5e3", "f5f3", "f5g3", "f5h3", |
|
|
"f5d4", "f5e4", "f5f4", "f5g4", "f5h4", "f5a5", "f5b5", "f5c5", "f5d5", |
|
|
"f5e5", "f5g5", "f5h5", "f5d6", "f5e6", "f5f6", "f5g6", "f5h6", "f5d7", |
|
|
"f5e7", "f5f7", "f5g7", "f5h7", "f5c8", "f5f8", "g5c1", "g5g1", "g5d2", |
|
|
"g5g2", "g5e3", "g5f3", "g5g3", "g5h3", "g5e4", "g5f4", "g5g4", "g5h4", |
|
|
"g5a5", "g5b5", "g5c5", "g5d5", "g5e5", "g5f5", "g5h5", "g5e6", "g5f6", |
|
|
"g5g6", "g5h6", "g5e7", "g5f7", "g5g7", "g5h7", "g5d8", "g5g8", "h5d1", |
|
|
"h5h1", "h5e2", "h5h2", "h5f3", "h5g3", "h5h3", "h5f4", "h5g4", "h5h4", |
|
|
"h5a5", "h5b5", "h5c5", "h5d5", "h5e5", "h5f5", "h5g5", "h5f6", "h5g6", |
|
|
"h5h6", "h5f7", "h5g7", "h5h7", "h5e8", "h5h8", "a6a1", "a6f1", "a6a2", |
|
|
"a6e2", "a6a3", "a6d3", "a6a4", "a6b4", "a6c4", "a6a5", "a6b5", "a6c5", |
|
|
"a6b6", "a6c6", "a6d6", "a6e6", "a6f6", "a6g6", "a6h6", "a6a7", "a6b7", |
|
|
"a6c7", "a6a8", "a6b8", "a6c8", "b6b1", "b6g1", "b6b2", "b6f2", "b6b3", |
|
|
"b6e3", "b6a4", "b6b4", "b6c4", "b6d4", "b6a5", "b6b5", "b6c5", "b6d5", |
|
|
"b6a6", "b6c6", "b6d6", "b6e6", "b6f6", "b6g6", "b6h6", "b6a7", "b6b7", |
|
|
"b6c7", "b6d7", "b6a8", "b6b8", "b6c8", "b6d8", "c6c1", "c6h1", "c6c2", |
|
|
"c6g2", "c6c3", "c6f3", "c6a4", "c6b4", "c6c4", "c6d4", "c6e4", "c6a5", |
|
|
"c6b5", "c6c5", "c6d5", "c6e5", "c6a6", "c6b6", "c6d6", "c6e6", "c6f6", |
|
|
"c6g6", "c6h6", "c6a7", "c6b7", "c6c7", "c6d7", "c6e7", "c6a8", "c6b8", |
|
|
"c6c8", "c6d8", "c6e8", "d6d1", "d6d2", "d6h2", "d6a3", "d6d3", "d6g3", |
|
|
"d6b4", "d6c4", "d6d4", "d6e4", "d6f4", "d6b5", "d6c5", "d6d5", "d6e5", |
|
|
"d6f5", "d6a6", "d6b6", "d6c6", "d6e6", "d6f6", "d6g6", "d6h6", "d6b7", |
|
|
"d6c7", "d6d7", "d6e7", "d6f7", "d6b8", "d6c8", "d6d8", "d6e8", "d6f8", |
|
|
"e6e1", "e6a2", "e6e2", "e6b3", "e6e3", "e6h3", "e6c4", "e6d4", "e6e4", |
|
|
"e6f4", "e6g4", "e6c5", "e6d5", "e6e5", "e6f5", "e6g5", "e6a6", "e6b6", |
|
|
"e6c6", "e6d6", "e6f6", "e6g6", "e6h6", "e6c7", "e6d7", "e6e7", "e6f7", |
|
|
"e6g7", "e6c8", "e6d8", "e6e8", "e6f8", "e6g8", "f6a1", "f6f1", "f6b2", |
|
|
"f6f2", "f6c3", "f6f3", "f6d4", "f6e4", "f6f4", "f6g4", "f6h4", "f6d5", |
|
|
"f6e5", "f6f5", "f6g5", "f6h5", "f6a6", "f6b6", "f6c6", "f6d6", "f6e6", |
|
|
"f6g6", "f6h6", "f6d7", "f6e7", "f6f7", "f6g7", "f6h7", "f6d8", "f6e8", |
|
|
"f6f8", "f6g8", "f6h8", "g6b1", "g6g1", "g6c2", "g6g2", "g6d3", "g6g3", |
|
|
"g6e4", "g6f4", "g6g4", "g6h4", "g6e5", "g6f5", "g6g5", "g6h5", "g6a6", |
|
|
"g6b6", "g6c6", "g6d6", "g6e6", "g6f6", "g6h6", "g6e7", "g6f7", "g6g7", |
|
|
"g6h7", "g6e8", "g6f8", "g6g8", "g6h8", "h6c1", "h6h1", "h6d2", "h6h2", |
|
|
"h6e3", "h6h3", "h6f4", "h6g4", "h6h4", "h6f5", "h6g5", "h6h5", "h6a6", |
|
|
"h6b6", "h6c6", "h6d6", "h6e6", "h6f6", "h6g6", "h6f7", "h6g7", "h6h7", |
|
|
"h6f8", "h6g8", "h6h8", "a7a1", "a7g1", "a7a2", "a7f2", "a7a3", "a7e3", |
|
|
"a7a4", "a7d4", "a7a5", "a7b5", "a7c5", "a7a6", "a7b6", "a7c6", "a7b7", |
|
|
"a7c7", "a7d7", "a7e7", "a7f7", "a7g7", "a7h7", "a7a8", "a7b8", "a7c8", |
|
|
"b7b1", "b7h1", "b7b2", "b7g2", "b7b3", "b7f3", "b7b4", "b7e4", "b7a5", |
|
|
"b7b5", "b7c5", "b7d5", "b7a6", "b7b6", "b7c6", "b7d6", "b7a7", "b7c7", |
|
|
"b7d7", "b7e7", "b7f7", "b7g7", "b7h7", "b7a8", "b7b8", "b7c8", "b7d8", |
|
|
"c7c1", "c7c2", "c7h2", "c7c3", "c7g3", "c7c4", "c7f4", "c7a5", "c7b5", |
|
|
"c7c5", "c7d5", "c7e5", "c7a6", "c7b6", "c7c6", "c7d6", "c7e6", "c7a7", |
|
|
"c7b7", "c7d7", "c7e7", "c7f7", "c7g7", "c7h7", "c7a8", "c7b8", "c7c8", |
|
|
"c7d8", "c7e8", "d7d1", "d7d2", "d7d3", "d7h3", "d7a4", "d7d4", "d7g4", |
|
|
"d7b5", "d7c5", "d7d5", "d7e5", "d7f5", "d7b6", "d7c6", "d7d6", "d7e6", |
|
|
"d7f6", "d7a7", "d7b7", "d7c7", "d7e7", "d7f7", "d7g7", "d7h7", "d7b8", |
|
|
"d7c8", "d7d8", "d7e8", "d7f8", "e7e1", "e7e2", "e7a3", "e7e3", "e7b4", |
|
|
"e7e4", "e7h4", "e7c5", "e7d5", "e7e5", "e7f5", "e7g5", "e7c6", "e7d6", |
|
|
"e7e6", "e7f6", "e7g6", "e7a7", "e7b7", "e7c7", "e7d7", "e7f7", "e7g7", |
|
|
"e7h7", "e7c8", "e7d8", "e7e8", "e7f8", "e7g8", "f7f1", "f7a2", "f7f2", |
|
|
"f7b3", "f7f3", "f7c4", "f7f4", "f7d5", "f7e5", "f7f5", "f7g5", "f7h5", |
|
|
"f7d6", "f7e6", "f7f6", "f7g6", "f7h6", "f7a7", "f7b7", "f7c7", "f7d7", |
|
|
"f7e7", "f7g7", "f7h7", "f7d8", "f7e8", "f7f8", "f7g8", "f7h8", "g7a1", |
|
|
"g7g1", "g7b2", "g7g2", "g7c3", "g7g3", "g7d4", "g7g4", "g7e5", "g7f5", |
|
|
"g7g5", "g7h5", "g7e6", "g7f6", "g7g6", "g7h6", "g7a7", "g7b7", "g7c7", |
|
|
"g7d7", "g7e7", "g7f7", "g7h7", "g7e8", "g7f8", "g7g8", "g7h8", "h7b1", |
|
|
"h7h1", "h7c2", "h7h2", "h7d3", "h7h3", "h7e4", "h7h4", "h7f5", "h7g5", |
|
|
"h7h5", "h7f6", "h7g6", "h7h6", "h7a7", "h7b7", "h7c7", "h7d7", "h7e7", |
|
|
"h7f7", "h7g7", "h7f8", "h7g8", "h7h8", "a8a1", "a8h1", "a8a2", "a8g2", |
|
|
"a8a3", "a8f3", "a8a4", "a8e4", "a8a5", "a8d5", "a8a6", "a8b6", "a8c6", |
|
|
"a8a7", "a8b7", "a8c7", "a8b8", "a8c8", "a8d8", "a8e8", "a8f8", "a8g8", |
|
|
"a8h8", "b8b1", "b8b2", "b8h2", "b8b3", "b8g3", "b8b4", "b8f4", "b8b5", |
|
|
"b8e5", "b8a6", "b8b6", "b8c6", "b8d6", "b8a7", "b8b7", "b8c7", "b8d7", |
|
|
"b8a8", "b8c8", "b8d8", "b8e8", "b8f8", "b8g8", "b8h8", "c8c1", "c8c2", |
|
|
"c8c3", "c8h3", "c8c4", "c8g4", "c8c5", "c8f5", "c8a6", "c8b6", "c8c6", |
|
|
"c8d6", "c8e6", "c8a7", "c8b7", "c8c7", "c8d7", "c8e7", "c8a8", "c8b8", |
|
|
"c8d8", "c8e8", "c8f8", "c8g8", "c8h8", "d8d1", "d8d2", "d8d3", "d8d4", |
|
|
"d8h4", "d8a5", "d8d5", "d8g5", "d8b6", "d8c6", "d8d6", "d8e6", "d8f6", |
|
|
"d8b7", "d8c7", "d8d7", "d8e7", "d8f7", "d8a8", "d8b8", "d8c8", "d8e8", |
|
|
"d8f8", "d8g8", "d8h8", "e8e1", "e8e2", "e8e3", "e8a4", "e8e4", "e8b5", |
|
|
"e8e5", "e8h5", "e8c6", "e8d6", "e8e6", "e8f6", "e8g6", "e8c7", "e8d7", |
|
|
"e8e7", "e8f7", "e8g7", "e8a8", "e8b8", "e8c8", "e8d8", "e8f8", "e8g8", |
|
|
"e8h8", "f8f1", "f8f2", "f8a3", "f8f3", "f8b4", "f8f4", "f8c5", "f8f5", |
|
|
"f8d6", "f8e6", "f8f6", "f8g6", "f8h6", "f8d7", "f8e7", "f8f7", "f8g7", |
|
|
"f8h7", "f8a8", "f8b8", "f8c8", "f8d8", "f8e8", "f8g8", "f8h8", "g8g1", |
|
|
"g8a2", "g8g2", "g8b3", "g8g3", "g8c4", "g8g4", "g8d5", "g8g5", "g8e6", |
|
|
"g8f6", "g8g6", "g8h6", "g8e7", "g8f7", "g8g7", "g8h7", "g8a8", "g8b8", |
|
|
"g8c8", "g8d8", "g8e8", "g8f8", "g8h8", "h8a1", "h8h1", "h8b2", "h8h2", |
|
|
"h8c3", "h8h3", "h8d4", "h8h4", "h8e5", "h8h5", "h8f6", "h8g6", "h8h6", |
|
|
"h8f7", "h8g7", "h8h7", "h8a8", "h8b8", "h8c8", "h8d8", "h8e8", "h8f8", |
|
|
"h8g8", "a7a8q", "a7a8r", "a7a8b", "a7b8q", "a7b8r", "a7b8b", "b7a8q", |
|
|
"b7a8r", "b7a8b", "b7b8q", "b7b8r", "b7b8b", "b7c8q", "b7c8r", "b7c8b", |
|
|
"c7b8q", "c7b8r", "c7b8b", "c7c8q", "c7c8r", "c7c8b", "c7d8q", "c7d8r", |
|
|
"c7d8b", "d7c8q", "d7c8r", "d7c8b", "d7d8q", "d7d8r", "d7d8b", "d7e8q", |
|
|
"d7e8r", "d7e8b", "e7d8q", "e7d8r", "e7d8b", "e7e8q", "e7e8r", "e7e8b", |
|
|
"e7f8q", "e7f8r", "e7f8b", "f7e8q", "f7e8r", "f7e8b", "f7f8q", "f7f8r", |
|
|
"f7f8b", "f7g8q", "f7g8r", "f7g8b", "g7f8q", "g7f8r", "g7f8b", "g7g8q", |
|
|
"g7g8r", "g7g8b", "g7h8q", "g7h8r", "g7h8b", "h7g8q", "h7g8r", "h7g8b", |
|
|
"h7h8q", "h7h8r", "h7h8b", |
|
|
"a2a1q","a2a1r","a2a1b","a2b1q","a2b1r","a2b1b", |
|
|
"b2a1q","b2a1r","b2a1b","b2b1q","b2b1r","b2b1b","b2c1q","b2c1r","b2c1b", |
|
|
"c2b1q","c2b1r","c2b1b","c2c1q","c2c1r","c2c1b","c2d1q","c2d1r","c2d1b", |
|
|
"d2c1q","d2c1r","d2c1b","d2d1q","d2d1r","d2d1b","d2e1q","d2e1r","d2e1b", |
|
|
"e2d1q","e2d1r","e2d1b","e2e1q","e2e1r","e2e1b","e2f1q","e2f1r","e2f1b", |
|
|
"f2e1q","f2e1r","f2e1b","f2f1q","f2f1r","f2f1b","f2g1q","f2g1r","f2g1b", |
|
|
"g2f1q","g2f1r","g2f1b","g2g1q","g2g1r","g2g1b","g2h1q","g2h1r","g2h1b", |
|
|
"h2g1q","h2g1r","h2g1b","h2h1q","h2h1r","h2h1b", |
|
|
"<thinking>","</thinking>","end_variation","end","padding_token" |
|
|
] |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class RelativeMultiHeadAttention2(nn.Module): |
|
|
def __init__(self, d_model: int = 512, num_heads: int = 16, dropout_p: float = 0.1): |
|
|
super().__init__() |
|
|
assert d_model % num_heads == 0 |
|
|
self.d_model = d_model |
|
|
self.num_heads = num_heads |
|
|
self.d_head = d_model // num_heads |
|
|
self.sqrt_dim = math.sqrt(d_model) |
|
|
|
|
|
self.query_proj = nn.Linear(d_model, d_model) |
|
|
self.key_proj = nn.Linear(d_model, d_model) |
|
|
self.value_proj = nn.Linear(d_model, d_model) |
|
|
self.pos_proj = nn.Linear(d_model, d_model) |
|
|
self.out_proj = nn.Linear(d_model, d_model) |
|
|
|
|
|
self.u_bias = nn.Parameter(torch.Tensor(self.num_heads, self.d_head)) |
|
|
self.v_bias = nn.Parameter(torch.Tensor(self.num_heads, self.d_head)) |
|
|
torch.nn.init.xavier_uniform_(self.u_bias) |
|
|
torch.nn.init.xavier_uniform_(self.v_bias) |
|
|
self.dropout = nn.Dropout(dropout_p) |
|
|
|
|
|
def forward(self, query, key, value, pos_embedding, mask=None): |
|
|
batch_size = value.size(0) |
|
|
|
|
|
query = self.query_proj(query).view(batch_size, -1, self.num_heads, self.d_head) |
|
|
key = self.key_proj(key).view(batch_size, -1, self.num_heads, self.d_head).permute(0, 2, 1, 3) |
|
|
value = self.value_proj(value).view(batch_size, -1, self.num_heads, self.d_head).permute(0, 2, 1, 3) |
|
|
|
|
|
pos_embedding = self.pos_proj(pos_embedding).view(batch_size, -1, self.num_heads, self.d_head) |
|
|
|
|
|
content_score = torch.matmul((query + self.u_bias).transpose(1, 2), key.transpose(2, 3)) |
|
|
pos_score = torch.matmul((query + self.v_bias).transpose(1, 2), pos_embedding.permute(0, 2, 3, 1)) |
|
|
pos_score = self._compute_relative_positional_encoding(pos_score) |
|
|
|
|
|
score = (content_score + pos_score) / self.sqrt_dim |
|
|
|
|
|
if mask is not None: |
|
|
mask = mask.unsqueeze(1) |
|
|
score.masked_fill_(mask, -1e9) |
|
|
|
|
|
attn = F.softmax(score, -1) |
|
|
attn = self.dropout(attn) |
|
|
|
|
|
context = torch.matmul(attn, value).transpose(1, 2) |
|
|
context = context.contiguous().view(batch_size, -1, self.d_model) |
|
|
|
|
|
return self.out_proj(context) |
|
|
|
|
|
def _compute_relative_positional_encoding(self, pos_score): |
|
|
batch_size, num_heads, seq_length1, seq_length2 = pos_score.size() |
|
|
zeros = pos_score.new_zeros(batch_size, num_heads, seq_length1, 1) |
|
|
padded_pos_score = torch.cat([zeros, pos_score], dim=-1) |
|
|
|
|
|
padded_pos_score = padded_pos_score.view(batch_size, num_heads, seq_length2 + 1, seq_length1) |
|
|
pos_score = padded_pos_score[:, :, 1:].view_as(pos_score) |
|
|
|
|
|
return pos_score |
|
|
|
|
|
|
|
|
|
|
|
class MaGating(nn.Module): |
|
|
def __init__(self, d_model): |
|
|
super().__init__() |
|
|
self.a = nn.Parameter(torch.zeros(64, d_model)) |
|
|
self.b = nn.Parameter(torch.ones(64, d_model)) |
|
|
|
|
|
def forward(self, x): |
|
|
return x * torch.exp(self.a) + self.b |
|
|
|
|
|
|
|
|
class EncoderLayer(nn.Module): |
|
|
def __init__(self, d_model, d_ff, num_heads): |
|
|
super().__init__() |
|
|
self.attention = RelativeMultiHeadAttention2(d_model, num_heads, 0) |
|
|
self.norm1 = nn.LayerNorm(d_model) |
|
|
self.norm2 = nn.LayerNorm(d_model) |
|
|
self.ff1 = nn.Linear(d_model, d_ff) |
|
|
self.ff2 = nn.Linear(d_ff, d_model) |
|
|
self.gelu = nn.GELU() |
|
|
|
|
|
def forward(self, x, pos_enc): |
|
|
attn_out = self.attention(x, x, x, pos_enc) |
|
|
x = attn_out + x |
|
|
x = self.norm1(x) |
|
|
|
|
|
y = self.ff1(x) |
|
|
y = self.ff2(y) |
|
|
y = self.gelu(y) |
|
|
y = y + x |
|
|
y = self.norm2(y) |
|
|
|
|
|
return y |
|
|
|
|
|
|
|
|
class AbsolutePositionalEncoder(nn.Module): |
|
|
def __init__(self, d_model): |
|
|
super().__init__() |
|
|
self.position = torch.arange(64).unsqueeze(1) |
|
|
self.positional_encoding = torch.zeros(1, 64, d_model) |
|
|
_2i = torch.arange(0, d_model, step=2).float() |
|
|
self.positional_encoding[:, :, 0::2] = torch.sin(self.position / (10000 ** (_2i / d_model))) |
|
|
self.positional_encoding[:, :, 1::2] = torch.cos(self.position / (10000 ** (_2i / d_model))) |
|
|
|
|
|
|
|
|
self.register_buffer('pos_encoding', self.positional_encoding) |
|
|
|
|
|
def forward(self, x): |
|
|
batch_size, _, _ = x.size() |
|
|
return self.pos_encoding.expand(batch_size, -1, -1) |
|
|
|
|
|
|
|
|
class ValueHead(nn.Module): |
|
|
def __init__(self, d_model): |
|
|
super().__init__() |
|
|
self.dense1 = nn.Linear(d_model, 128) |
|
|
self.dense2 = nn.Linear(128*64, 128) |
|
|
self.dense3 = nn.Linear(128, 3) |
|
|
|
|
|
def forward(self, x): |
|
|
b, _, _ = x.size() |
|
|
x = self.dense1(x) |
|
|
x = F.gelu(x) |
|
|
x = x.view(b, -1) |
|
|
x = self.dense2(x) |
|
|
x = F.gelu(x) |
|
|
x = self.dense3(x) |
|
|
return x |
|
|
|
|
|
|
|
|
class ValueHeadQ(nn.Module): |
|
|
def __init__(self, d_model): |
|
|
super().__init__() |
|
|
self.dense1 = nn.Linear(d_model, 128) |
|
|
self.dense2 = nn.Linear(128*64, 128) |
|
|
self.dense3 = nn.Linear(128, 3) |
|
|
|
|
|
def forward(self, x): |
|
|
b, _, _ = x.size() |
|
|
x = self.dense1(x) |
|
|
x = F.gelu(x) |
|
|
x = x.view(b, -1) |
|
|
x = self.dense2(x) |
|
|
x = F.gelu(x) |
|
|
x = self.dense3(x) |
|
|
return x |
|
|
|
|
|
|
|
|
|
|
|
class ChessBotPreTrainedModel(PreTrainedModel): |
|
|
""" |
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. |
|
|
""" |
|
|
|
|
|
config_class = ChessBotConfig |
|
|
base_model_prefix = "chessbot" |
|
|
supports_gradient_checkpointing = True |
|
|
|
|
|
def _init_weights(self, module): |
|
|
"""Initialize the weights""" |
|
|
if isinstance(module, nn.Linear): |
|
|
module.weight.data.normal_(mean=0.0, std=0.02) |
|
|
if module.bias is not None: |
|
|
module.bias.data.zero_() |
|
|
elif isinstance(module, nn.Embedding): |
|
|
module.weight.data.normal_(mean=0.0, std=0.02) |
|
|
elif isinstance(module, nn.LayerNorm): |
|
|
module.bias.data.zero_() |
|
|
module.weight.data.fill_(1.0) |
|
|
|
|
|
|
|
|
class ChessBotModel(ChessBotPreTrainedModel): |
|
|
""" |
|
|
HuggingFace compatible ChessBot Chess model with ALL original functionality |
|
|
""" |
|
|
|
|
|
def __init__(self, config): |
|
|
super().__init__(config) |
|
|
self.config = config |
|
|
|
|
|
|
|
|
self.is_thinking_model = False |
|
|
self.d_model = config.d_model |
|
|
self.num_layers = config.num_layers |
|
|
|
|
|
|
|
|
self.layers = nn.ModuleList([ |
|
|
EncoderLayer(config.d_model, config.d_ff, config.num_heads) |
|
|
for _ in range(config.num_layers) |
|
|
]) |
|
|
|
|
|
self.linear1 = nn.Linear(19, config.d_model) |
|
|
self.layernorm1 = nn.LayerNorm(config.d_model) |
|
|
self.policy_tokens_lin = nn.Linear(config.d_model, config.d_model) |
|
|
self.queries_pol = nn.Linear(config.d_model, config.d_model) |
|
|
self.keys_pol = nn.Linear(config.d_model, config.d_model) |
|
|
self.positional = AbsolutePositionalEncoder(config.d_model) |
|
|
self.ma_gating = MaGating(config.d_model) |
|
|
self.policy_head = nn.Linear(64*64, config.vocab_size, bias=False) |
|
|
self.value_head = ValueHead(config.d_model) |
|
|
self.value_head_q = ValueHeadQ(config.d_model) |
|
|
|
|
|
|
|
|
self.post_init() |
|
|
|
|
|
def forward(self, input_ids, attention_mask=None, compute_loss=False): |
|
|
""" |
|
|
Forward pass compatible with both HuggingFace interface and original interface |
|
|
""" |
|
|
|
|
|
if isinstance(input_ids, tuple): |
|
|
inp = input_ids |
|
|
x = inp[0] |
|
|
compute_loss = compute_loss or len(inp) > 1 |
|
|
else: |
|
|
x = input_ids |
|
|
inp = (x,) |
|
|
|
|
|
b, seq_len, _, _, emb = x.size() |
|
|
x = x.view(b * seq_len, 64, emb) |
|
|
|
|
|
x = self.linear1(x) |
|
|
x = F.gelu(x) |
|
|
x = self.layernorm1(x) |
|
|
x = self.ma_gating(x) |
|
|
|
|
|
pos_enc = self.positional(x) |
|
|
for i in range(self.num_layers): |
|
|
x = self.layers[i](x, pos_enc) |
|
|
|
|
|
value_h = self.value_head(x) |
|
|
value_h = value_h.view(b, seq_len, 3) |
|
|
value_h_q = self.value_head_q(x) |
|
|
value_h_q = value_h_q.view(b, seq_len, 3) |
|
|
|
|
|
policy_tokens = self.policy_tokens_lin(x) |
|
|
policy_tokens = F.gelu(policy_tokens) |
|
|
policy_tokens = policy_tokens + pos_enc |
|
|
|
|
|
queries = self.queries_pol(policy_tokens) |
|
|
keys = self.keys_pol(policy_tokens) |
|
|
|
|
|
matmul_qk = torch.matmul(queries, torch.transpose(keys, -2, -1)) |
|
|
dk = torch.sqrt(torch.tensor(self.d_model, dtype=torch.float32)) |
|
|
|
|
|
policy_attn_logits = matmul_qk / dk |
|
|
policy_attn_logits = policy_attn_logits.view(b, seq_len, 64*64) |
|
|
|
|
|
policy = self.policy_head(policy_attn_logits) |
|
|
|
|
|
if compute_loss: |
|
|
targets = inp[1] |
|
|
true_values = inp[3] |
|
|
q_values = inp[4] |
|
|
loss_policy = F.cross_entropy(policy.view(-1, policy.size(-1)), targets.view(-1), ignore_index=1928) |
|
|
z = torch.argmax(true_values, dim=-1) |
|
|
loss_value = F.cross_entropy(value_h.view(-1, value_h.size(-1)), z.view(-1), ignore_index=3) |
|
|
value_h_q = torch.softmax(value_h_q, dim=-1) |
|
|
loss_q = F.mse_loss(value_h_q.view(-1, value_h_q.size(-1)), q_values.view(-1, 3)) |
|
|
return policy, value_h, loss_policy, loss_value, loss_q, targets, z |
|
|
|
|
|
return BaseModelOutput( |
|
|
last_hidden_state=x, |
|
|
hidden_states=None, |
|
|
attentions=None, |
|
|
), policy, value_h, value_h_q |
|
|
|
|
|
def get_move_from_fen_no_thinking(self, fen, T=1, device="cuda", force_legal=True, return_probs=False): |
|
|
""" |
|
|
Get a move from FEN string without thinking |
|
|
""" |
|
|
board = chess.Board(fen) |
|
|
legal_moves = [move.uci() if move.uci() in policy_index else move.uci()[:-1] for move in board.legal_moves] |
|
|
if not legal_moves: |
|
|
return None |
|
|
|
|
|
|
|
|
fen_tensor = fen_to_tensor(fen) |
|
|
fen_tensor = torch.from_numpy(fen_tensor).float().to(device) |
|
|
fen_tensor = fen_tensor.unsqueeze(0).unsqueeze(0) |
|
|
|
|
|
|
|
|
with torch.no_grad(): |
|
|
_, policy, _, _ = self.forward(fen_tensor) |
|
|
policy = policy.squeeze(0).squeeze(0) |
|
|
|
|
|
if T == 0: |
|
|
if force_legal: |
|
|
|
|
|
legal_moves_mask = - torch.ones_like(policy) * 999 |
|
|
for move in legal_moves: |
|
|
legal_moves_mask[policy_index.index(move)] = 0 |
|
|
policy = legal_moves_mask + policy |
|
|
return policy_index[torch.argmax(policy).item()] |
|
|
else: |
|
|
max_policy_index = torch.argmax(policy).item() |
|
|
max_policy_move = policy_index[max_policy_index] |
|
|
return max_policy_move |
|
|
|
|
|
|
|
|
if T > 0: |
|
|
policy = policy / T |
|
|
|
|
|
|
|
|
probs = F.softmax(policy, dim=-1) |
|
|
|
|
|
|
|
|
legal_move_probs = {} |
|
|
for move in legal_moves: |
|
|
idx = policy_index.index(move) |
|
|
legal_move_probs[move] = probs[idx].item() |
|
|
|
|
|
|
|
|
if return_probs: |
|
|
return legal_move_probs |
|
|
|
|
|
if force_legal: |
|
|
|
|
|
moves = list(legal_move_probs.keys()) |
|
|
move_probs = list(legal_move_probs.values()) |
|
|
|
|
|
|
|
|
total_prob = sum(move_probs) |
|
|
move_probs = [p / total_prob for p in move_probs] |
|
|
selected_move = np.random.choice(moves, p=move_probs) |
|
|
else: |
|
|
|
|
|
selected_move = policy_index[torch.multinomial(probs, 1).item()] |
|
|
|
|
|
return selected_move |
|
|
|
|
|
def get_position_value(self, fen, device="cuda"): |
|
|
""" |
|
|
Get the value evaluation for a given FEN position. |
|
|
Returns the value vector [black_win_prob, draw_prob, white_win_prob] |
|
|
""" |
|
|
x = torch.from_numpy(fen_to_tensor(fen)).to(device).to(torch.float32) |
|
|
x = x.view(1, 1, 8, 8, 19) |
|
|
|
|
|
|
|
|
with torch.no_grad(): |
|
|
|
|
|
b, seq_len, _, _, emb = x.size() |
|
|
x_processed = x.view(b * seq_len, 64, emb) |
|
|
x_processed = self.linear1(x_processed) |
|
|
x_processed = F.gelu(x_processed) |
|
|
x_processed = self.layernorm1(x_processed) |
|
|
x_processed = self.ma_gating(x_processed) |
|
|
|
|
|
pos_enc = self.positional(x_processed) |
|
|
for i in range(self.num_layers): |
|
|
x_processed = self.layers[i](x_processed, pos_enc) |
|
|
|
|
|
value_logits = self.value_head_q(x_processed) |
|
|
value_logits = value_logits.view(b, seq_len, 3) |
|
|
value_logits = torch.softmax(value_logits, dim=-1) |
|
|
|
|
|
return value_logits.squeeze() |
|
|
|
|
|
def get_batch_position_values(self, fens, device="cuda"): |
|
|
""" |
|
|
Get the value evaluation for a batch of FEN positions efficiently. |
|
|
Args: |
|
|
fens: List of FEN strings |
|
|
device: Device to run computations on |
|
|
Returns: |
|
|
value_probs: Tensor of shape [batch_size, 3] with [black_win_prob, draw_prob, white_win_prob] for each position |
|
|
""" |
|
|
if len(fens) == 0: |
|
|
return torch.empty(0, 3, device=device) |
|
|
|
|
|
|
|
|
position_tensors = [] |
|
|
for fen in fens: |
|
|
x = torch.from_numpy(fen_to_tensor(fen)).to(device).to(torch.float32) |
|
|
position_tensors.append(x) |
|
|
|
|
|
|
|
|
batch_x = torch.stack(position_tensors, dim=0) |
|
|
|
|
|
batch_x = batch_x.unsqueeze(1) |
|
|
|
|
|
|
|
|
with torch.no_grad(): |
|
|
b, seq_len, _, _, emb = batch_x.size() |
|
|
x_processed = batch_x.view(b * seq_len, 64, emb) |
|
|
x_processed = self.linear1(x_processed) |
|
|
x_processed = F.gelu(x_processed) |
|
|
x_processed = self.layernorm1(x_processed) |
|
|
x_processed = self.ma_gating(x_processed) |
|
|
|
|
|
pos_enc = self.positional(x_processed) |
|
|
for i in range(self.num_layers): |
|
|
x_processed = self.layers[i](x_processed, pos_enc) |
|
|
|
|
|
value_logits = self.value_head_q(x_processed) |
|
|
value_logits = value_logits.view(b, seq_len, 3) |
|
|
value_logits = torch.softmax(value_logits, dim=-1) |
|
|
return value_logits.squeeze(1) |
|
|
|
|
|
def calculate_move_values(self, fen, device="cuda"): |
|
|
""" |
|
|
Calculate the value for each legal move from the given position efficiently using batching. |
|
|
For white to move, value = white_win_prob - black_win_prob |
|
|
For black to move, value = black_win_prob - white_win_prob |
|
|
""" |
|
|
board = chess.Board() |
|
|
board.set_fen(fen) |
|
|
|
|
|
|
|
|
is_white_turn = board.turn == chess.WHITE |
|
|
|
|
|
legal_moves = list(board.legal_moves) |
|
|
if len(legal_moves) == 0: |
|
|
return [], torch.empty(0, device=device) |
|
|
|
|
|
|
|
|
resulting_fens = [] |
|
|
for move in legal_moves: |
|
|
board.push(move) |
|
|
resulting_fens.append(board.fen()) |
|
|
board.pop() |
|
|
|
|
|
|
|
|
batch_value_q = self.get_batch_position_values(resulting_fens, device) |
|
|
|
|
|
|
|
|
|
|
|
batch_value_q = batch_value_q[:,2]-batch_value_q[:,0] |
|
|
if is_white_turn: |
|
|
|
|
|
player_values = batch_value_q |
|
|
else: |
|
|
|
|
|
player_values = -batch_value_q |
|
|
|
|
|
return legal_moves, player_values |
|
|
|
|
|
def get_best_move_value(self, fen, T=1, device="cuda", return_probs=False): |
|
|
""" |
|
|
Determine the best move based on the value of resulting positions using efficient batching. |
|
|
|
|
|
Args: |
|
|
fen: FEN string of the position (works for both white and black to move) |
|
|
T: Temperature for sampling (T=0 for greedy, T>0 for stochastic) |
|
|
device: Device to run computations on |
|
|
return_probs: Whether to return the probability distribution |
|
|
|
|
|
Returns: |
|
|
move: UCI string of the selected move |
|
|
probs (optional): probability distribution over moves if return_probs=True |
|
|
""" |
|
|
legal_moves, move_values = self.calculate_move_values(fen, device) |
|
|
|
|
|
if len(legal_moves) == 0: |
|
|
raise ValueError("No legal moves available") |
|
|
|
|
|
if T == 0: |
|
|
|
|
|
best_idx = torch.argmax(move_values) |
|
|
selected_move = legal_moves[best_idx] |
|
|
else: |
|
|
|
|
|
|
|
|
probs = F.softmax(move_values / T, dim=0) |
|
|
|
|
|
|
|
|
sampled_idx = torch.multinomial(probs, num_samples=1) |
|
|
selected_move = legal_moves[sampled_idx.item()] |
|
|
|
|
|
|
|
|
move_uci = selected_move.uci() |
|
|
|
|
|
if return_probs: |
|
|
if T == 0: |
|
|
|
|
|
probs = torch.zeros_like(move_values) |
|
|
probs[best_idx] = 1.0 |
|
|
else: |
|
|
probs = F.softmax(move_values / T, dim=0) |
|
|
|
|
|
|
|
|
move_dict = {} |
|
|
for i, move in enumerate(legal_moves): |
|
|
move_dict[move.uci()] = probs[i].item() |
|
|
return move_uci, move_dict |
|
|
|
|
|
return move_uci |
|
|
|
|
|
|
|
|
|
|
|
AutoConfig.register("chessbot", ChessBotConfig) |
|
|
AutoModel.register(ChessBotConfig, ChessBotModel) |
|
|
|
|
|
|
|
|
ChessBot = ChessBotModel |
|
|
BT4Model = ChessBotModel |
|
|
|
