chess_eval_main.py

import openai
import chess
import chess.engine
import os
import csv
import random
import time
import platform

# NOTE: LLAMA AND NANOGPT ARE EXPERIMENTAL PLAYERS, if not using them, comment them out
# from llama_module import BaseLlamaPlayer, LocalLlamaPlayer, LocalLoraLlamaPlayer
from nanogpt.nanogpt_module import NanoGptPlayer
from mamba_module import MambaPlayer
import gpt_query
from lczero.backends import Weights, Backend, GameState
import numpy as np

from typing import Optional, Tuple
from dataclasses import dataclass


@dataclass
class LegalMoveResponse:
    move_san: Optional[str] = None
    move_uci: Optional[chess.Move] = None
    attempts: int = 0
    is_resignation: bool = False
    is_illegal_move: bool = False


# Define base Player class
class Player:
    def get_move(self, board: chess.Board, game_state: str, temperature: float) -> str:
        raise NotImplementedError

    def get_config(self) -> dict:
        raise NotImplementedError


class GPTPlayer(Player):
    def __init__(self, model: str):
        with open("gpt_inputs/api_key.txt", "r") as f:
            openai.api_key = f.read().strip()
        self.model = model

    def get_move(
        self, board: chess.Board, game_state: str, temperature: float
    ) -> Optional[str]:
        response = get_gpt_response(game_state, self.model, temperature)
        return get_move_from_gpt_response(response)

    def get_config(self) -> dict:
        return {"model": self.model}
        
        
class LC0PLayer(Player):
    # "11258-32x4-se.pb.gz" = stockfish level 0- = skill 0
    # "11258-48x5-se.pb.gz" = stockfish level 0+ = skill 1
    # "11258-80x7-se.pb.gz" = stockfish level 1 = skill 2
    # "11258-104x9-se.pb.gz" = stockfish level 2 = skill 3
    # "TK-6430 aka 128x10-BPR-64M-6430000.pb.gz" = stockfish level 3 = skill 4
    # "00af53b081e80147172e6f281c01daf5ca19ada173321438914c730370aa4267" = stockfish level 4 = skill 5
    # "b2ec465d0fb5b5eb39d2e1e3f74041a5d2fc92d413b71aa7ea0b6fb082ccba9c" = stockfish level 5+ = skill 6
    def __init__(self, skill):
        self.skill = skill
        network_paths = ["./lc0/build/release/11258-32x4-se.pb.gz", "./lc0/build/release/11258-48x5-se.pb.gz", "./lc0/build/release/11258-80x7-se.pb.gz", "./lc0/build/release/11258-104x9-se.pb.gz", "./lc0/build/release/TK-6430 aka 128x10-BPR-64M-6430000.pb.gz", "./lc0/build/release/00af53b081e80147172e6f281c01daf5ca19ada173321438914c730370aa4267", "./lc0/build/release/b2ec465d0fb5b5eb39d2e1e3f74041a5d2fc92d413b71aa7ea0b6fb082ccba9c"]
        print(f"\n\nLoading lc0 network: {network_paths[skill]}\n\n")
        self.weights = Weights(network_paths[skill])
        self.backend = Backend(weights=self.weights)
        self.gamestate = GameState()

    def get_move(self, board: chess.Board, game_state: str, temperature: float):
        self.gamestate = GameState(fen=board.fen())
        input_planes = self.gamestate.as_input(self.backend)
        result = self.backend.evaluate(input_planes)[0]
        moves = self.gamestate.moves()
        policy_indices = self.gamestate.policy_indices()
        move_probs = np.array(result.p_softmax(*policy_indices))
        best_move_idx = move_probs.argmax()
        best_move = moves[best_move_idx]
        return board.san(chess.Move.from_uci(best_move))
        
    def get_config(self) -> dict:
        return {"network": self.weights, "skill_level": self.skill, "play_time": 0}


class StockfishPlayer(Player):

    @staticmethod
    def get_stockfish_path() -> str:
        """
        Determines the operating system and returns the appropriate path for Stockfish.
        
        Returns:
            str: Path to the Stockfish executable based on the operating system.
        """
        if platform.system() == 'Linux':
            return "/usr/games/stockfish"
        elif platform.system() == 'Darwin':  # Darwin is the system name for macOS
            return "stockfish"
        elif platform.system() == 'Windows':
            return r"C:\Users\Haile\Downloads\stockfish\stockfish-windows-x86-64-avx2.exe"
        else:
            raise OSError("Unsupported operating system")
    
    def __init__(self, skill_level: int, play_time: float):
        self._skill_level = skill_level
        self._play_time = play_time
        # If getting started, you need to run brew install stockfish
        stockfish_path = StockfishPlayer.get_stockfish_path()
        self._engine = chess.engine.SimpleEngine.popen_uci(stockfish_path)

    def get_move(
        self, board: chess.Board, game_state: str, temperature: float
    ) -> Optional[str]:
        if self._skill_level == -2:
            legal_moves = list(board.legal_moves)
            random_move = random.choice(legal_moves)
            return board.san(random_move)
        elif self._skill_level < 0:
            self._engine.configure({"Skill Level": 0})
            result = self._engine.play(
                board, chess.engine.Limit(time=1e-8, depth=1, nodes=1)
            )

        else:
            self._engine.configure({"Skill Level": self._skill_level})
            result = self._engine.play(board, chess.engine.Limit(time=self._play_time))
        if result.move is None:
            return None
        return board.san(result.move)

    def get_config(self) -> dict:
        return {"skill_level": self._skill_level, "play_time": self._play_time}

    def close(self):
        self._engine.quit()
        
        
class HumanPlayer(Player):
    def get_move(self, board: chess.Board, game_state: str, temperature: float) -> str:
        # Print board for human player
        print(board)  
        while True:
            move = input("Enter your move (SAN format): ")
            try:
                move_uci = board.parse_san(move) 
                if move_uci in board.legal_moves:  
                    return move
            except:
                print("Illegal move, try again.")

    def get_config(self) -> dict:
        return {"player": "human"}


def get_gpt_response(game_state: str, model: str, temperature: float) -> Optional[str]:
    # trying to prevent what I believe to be rate limit issues
    if model == "gpt-4":
        time.sleep(0.4)
    response = gpt_query.get_gpt_response(game_state, model, temperature)
    return response


def get_move_from_gpt_response(response: Optional[str]) -> Optional[str]:
    if response is None:
        return None

    # Parse the response to get only the first move
    moves = response.split()
    first_move = moves[0] if moves else None

    return first_move


def record_results(
    board: chess.Board,
    player_one: Player,
    player_two: Player,
    game_state: str,
    player_one_illegal_moves: int,
    player_two_illegal_moves: int,
    player_one_legal_moves: int,
    player_two_legal_moves: int,
    total_time: float,
    player_one_resignation: bool,
    player_two_resignation: bool,
    player_one_failed_to_find_legal_move: bool,
    player_two_failed_to_find_legal_move: bool,
    total_moves: int,
    illegal_moves: int,
):
    unique_game_id = generate_unique_game_id()

    (
        player_one_title,
        player_two_title,
        player_one_time,
        player_two_time,
    ) = get_player_titles_and_time(player_one, player_two)

    if player_one_resignation or player_one_failed_to_find_legal_move:
        result = "0-1"
        player_one_score = 0
        player_two_score = 1
    elif player_two_resignation or player_two_failed_to_find_legal_move:
        result = "1-0"
        player_one_score = 1
        player_two_score = 0
    else:
        result = board.result()
        # Hmmm.... debating this one. Annoying if I leave it running and it fails here for some reason, probably involving some
        # resignation / failed move situation I didn't think of
        # -1e10 at least ensures it doesn't fail silently
        if "-" in result:
            player_one_score = result.split("-")[0]
            player_two_score = result.split("-")[1]
        elif result == "*": # Draw due to hitting max moves
            player_one_score = 0#1/2
            player_two_score = 1#1/2
        else:
            player_one_score = -1e10
            player_two_score = -1e10

    info_dict = {
        "game_id": unique_game_id,
        "transcript": game_state,
        "result": result,
        "player_one": player_one_title,
        "player_two": player_two_title,
        "player_one_time": player_one_time,
        "player_two_time": player_two_time,
        "player_one_score": player_one_score,
        "player_two_score": player_two_score,
        "player_one_illegal_moves": player_one_illegal_moves,
        "player_two_illegal_moves": player_two_illegal_moves,
        "player_one_legal_moves": player_one_legal_moves,
        "player_two_legal_moves": player_two_legal_moves,
        "player_one_resignation": player_one_resignation,
        "player_two_resignation": player_two_resignation,
        "player_one_failed_to_find_legal_move": player_one_failed_to_find_legal_move,
        "player_two_failed_to_find_legal_move": player_two_failed_to_find_legal_move,
        "game_title": f"{player_one_title} vs. {player_two_title}",
        "number_of_moves": board.fullmove_number,
        "time_taken": total_time,
        "total_moves": total_moves,
        "illegal_moves": illegal_moves,
    }

    if RUN_FOR_ANALYSIS:
        csv_file_path = f"logs/{player_one_recording_name}_vs_{player_two_recording_name}"
        csv_file_path = csv_file_path.replace(".", "_") # Because I'm using ckpt filenames for nanogpt models 
        csv_file_path += ".csv"
    else:
        csv_file_path = recording_file



    # Determine if we need to write headers (in case the file doesn't exist yet)
    write_headers = not os.path.exists(csv_file_path)

    # Append the results to the CSV file
    with open(csv_file_path, "a", newline="") as csv_file: # THIS WAS APPEND
        writer = csv.DictWriter(csv_file, fieldnames=info_dict.keys())
        if write_headers:
            writer.writeheader()
        writer.writerow(info_dict)

    with open("game.txt", "w") as f:
        f.write(game_state)


def generate_unique_game_id() -> str:
    timestamp = int(time.time())
    random_num = random.randint(1000, 9999)  # 4-digit random number
    return f"{timestamp}-{random_num}"


def get_player_titles_and_time(
    player_one: Player, player_two: Player
) -> Tuple[str, str, Optional[float], Optional[float]]:
    player_one_config = player_one.get_config()
    player_two_config = player_two.get_config()

    # For player one
    if "model" in player_one_config:
        player_one_title = player_one_config["model"]
        player_one_time = None
    else:
        player_one_title = f"Stockfish {player_one_config['skill_level']}"
        player_one_time = player_one_config["play_time"]

    # For player two
    if "model" in player_two_config:
        player_two_title = player_two_config["model"]
        player_two_time = None
    else:
        player_two_title = f"Stockfish {player_two_config['skill_level']}"
        player_two_time = player_two_config["play_time"]

    return (player_one_title, player_two_title, player_one_time, player_two_time)


used_openings = []
def initialize_game_with_opening(
    game_state: str, board: chess.Board
) -> Tuple[str, chess.Board]:
    global used_openings
    with open("openings.csv", "r") as file:
        lines = file.readlines()[1:]  # Skip header
    moves_string = random.choice(lines)
    while moves_string in used_openings:
        moves_string = random.choice(lines)
    used_openings.append(moves_string)
    if move_num_in_gamestate:
        game_state = moves_string.rstrip() + " "
    else:
        game_state = ' '.join(['.' + m.split(".")[-1] if "." in m else m for m in moves_string.split()])
    # Splitting the moves string on spaces
    tokens = moves_string.split()

    for token in tokens:
        # If the token contains a period, it's a move number + move combination
        if "." in token:
            move = token.split(".")[-1]  # Take the move part after the period
        else:
            move = token

        board.push_san(move)
    return game_state.rstrip(), board


# Return is (move_san, move_uci, attempts, is_resignation, is_illegal_move)
def get_legal_move(
    player: Player,
    board: chess.Board,
    game_state: str,
    player_one: bool,
    max_attempts: int = 5,
) -> LegalMoveResponse:
    """Request a move from the player and ensure it's legal."""
    move_san = None
    move_uci = None

    for attempt in range(max_attempts):
        move_san = player.get_move(
            board, game_state, min(((attempt / max_attempts) * 1) + 0.001, 0.75)
        )

        # Sometimes when GPT thinks it's the end of the game, it will just output the result
        # Like "1-0". If so, this really isn't an illegal move, so we'll add a check for that.
        if move_san is not None:
            if move_san == "1-0" or move_san == "0-1" or move_san == "1/2-1/2":
                print(f"{move_san}, player has resigned")
                return LegalMoveResponse(
                    move_san=None,
                    move_uci=None,
                    attempts=attempt,
                    is_resignation=True,
                )

        try:
            move_uci = board.parse_san(move_san)
        except Exception as e:
            print(f"Error parsing move {move_san}: {e}")
            # check if player is gpt-3.5-turbo-instruct
            # only recording errors for gpt-3.5-turbo-instruct because it's errors are so rare
            if player.get_config()["model"] == "gpt-3.5-turbo-instruct":
                with open("gpt-3.5-turbo-instruct-illegal-moves.txt", "a") as f:
                    f.write(f"{game_state}\n{move_san}\n")
            continue

        if move_uci in board.legal_moves:
            if player_one == False:
                if not move_san.startswith(" "):
                    move_san = " " + move_san
            else:
                if move_san.startswith(" "):
                    move_san = move_san[1:]
            return LegalMoveResponse(move_san, move_uci, attempt)
        print(f"Illegal move: {move_san}")

    # If we reach here, the player has made illegal moves for all attempts.
    print(f"{player} provided illegal moves for {max_attempts} attempts.")
    return LegalMoveResponse(
        move_san=None, move_uci=None, attempts=max_attempts, is_illegal_move=True
    )


def play_turn(
    player: Player, board: chess.Board, game_state: str, player_one: bool
) -> Tuple[str, bool, bool, int]:
    result = get_legal_move(player, board, game_state, player_one, 5)
    illegal_moves = result.attempts
    move_san = result.move_san
    move_uci = result.move_uci
    resignation = result.is_resignation
    failed_to_find_legal_move = result.is_illegal_move

    if resignation:
        print(f"{player} resigned with result: {board.result()}")
    elif failed_to_find_legal_move:
        print(f"Game over: 5 consecutive illegal moves from {player}")
    elif move_san is None or move_uci is None:
        print(f"Game over: {player} failed to find a legal move")
    else:
        board.push(move_uci)
        game_state += move_san
        print(move_san, end=" ")

    return game_state, resignation, failed_to_find_legal_move, illegal_moves


def play_game(
    player_one: Player,
    player_two: Player,
    max_games: int = 10,
    random_opening_seed: bool = False,
):
    for z in range(max_games):
        print(f"\nGame {z} of {max_games}\n")
    
        with open("gpt_inputs/prompt.txt", "r") as f:
            game_state = f.read()
        board = chess.Board()

        if random_opening_seed:
            game_state, board = initialize_game_with_opening(game_state, board)
        player_one_illegal_moves = 0
        player_two_illegal_moves = 0
        player_one_legal_moves = 0
        player_two_legal_moves = 0
        player_one_resignation = False
        player_two_resignation = False
        player_one_failed_to_find_legal_move = False
        player_two_failed_to_find_legal_move = False
        start_time = time.time()

        total_moves = 0
        illegal_moves = 0
        print_for_human = isinstance(player_one, HumanPlayer) or isinstance(player_two, HumanPlayer)

        while not board.is_game_over():
            if print_for_human:
                print(board)

            with open("game.txt", "w") as f:
                f.write(game_state)
            current_move_num = f"{board.fullmove_number if move_num_in_gamestate else ''}."
            total_moves += 1
            # I increment legal moves here so player_two isn't penalized for the game ending before its turn
            player_one_legal_moves += 1
            player_two_legal_moves += 1

            # this if statement may be overkill, just trying to get format to exactly match PGN notation
            if board.fullmove_number != 1:
                game_state += " "
            game_state += current_move_num
            print(f"{current_move_num}", end=" ")

            (
                game_state,
                player_one_resignation,
                player_one_failed_to_find_legal_move,
                illegal_moves_one,
            ) = play_turn(player_one, board, game_state, player_one=True)
            player_one_illegal_moves += illegal_moves_one
            if illegal_moves_one != 0:
                player_one_legal_moves -= 1
            if (
                board.is_game_over()
                or player_one_resignation
                or player_one_failed_to_find_legal_move
            ):
                break

            (
                game_state,
                player_two_resignation,
                player_two_failed_to_find_legal_move,
                illegal_moves_two,
            ) = play_turn(player_two, board, game_state, player_one=False)
            player_two_illegal_moves += illegal_moves_two
            if illegal_moves_two != 0:
                player_two_legal_moves -= 1
            if (
                board.is_game_over()
                or player_two_resignation
                or player_two_failed_to_find_legal_move
            ):
                break

            print("\n", end="")

            if total_moves > MAX_MOVES:
                break

        end_time = time.time()
        total_time = end_time - start_time
        print(f"\nGame over. Total time: {total_time} seconds")
        print(f"Result: {board.result()}")
        print(board)
        print()
        record_results(
            board,
            player_one,
            player_two,
            game_state,
            player_one_illegal_moves,
            player_two_illegal_moves,
            player_one_legal_moves,
            player_two_legal_moves,
            total_time,
            player_one_resignation,
            player_two_resignation,
            player_one_failed_to_find_legal_move,
            player_two_failed_to_find_legal_move,
            total_moves,
            illegal_moves,
        )
    if isinstance(player_one, StockfishPlayer):
        player_one.close()
    if isinstance(player_two, StockfishPlayer):
        player_two.close()

        # print(game_state)


RUN_FOR_ANALYSIS = True
MAX_MOVES = 999 # Due to nanogpt max input length of 1024
recording_file = "logs/determine.csv" # default recording file. Because we are using list [player_ones], recording_file is overwritten
# player_one_recording_name = "ckpt_8.pt"
#player_ones = ["ckpt_iter_20000.pt","ckpt_iter_40000.pt","ckpt_iter_60000.pt","ckpt_iter_80000.pt"] #["ckpt.pt"]
player_ones = ["anneal/anneal_complete.pt"]
player_two_recording_name = "stockfish_sweep" #"stockfish_sweep"
move_num_in_gamestate = True
if __name__ == "__main__":
    for nanogpt_player in player_ones:
        player_one_recording_name = nanogpt_player
        for i in range(2): #range(11):
            num_games = 235 #265 instead of 250 for duplicates (for lc0, stockfish doesn't need it)
            # player_one = GPTPlayer(model="gpt-3.5-turbo-instruct")
            # player_one = LocalLlamaPlayer(model_name="meta-llama/Llama-2-7b-hf")
            # player_one = LocalLoraLlamaPlayer("meta-llama/Llama-2-7b-hf", "/workspace/axolotl/lora2-out")
            # player_one = GPTPlayer(model="gpt-4")
            # player_one = StockfishPlayer(skill_level=-1, play_time=0.1)
            # player_one = NanoGptPlayer(model_name=player_one_recording_name)
            
            player_one = MambaPlayer(model_name=player_one_recording_name)
            player_two = StockfishPlayer(skill_level=i, play_time=0.1)
            #player_two = LC0PLayer(skill=i)
            
            # player_two = GPTPlayer(model="gpt-4")
            # player_two = GPTPlayer(model="gpt-3.5-turbo-instruct")
            
            print(f"\n\nSTARTING GAMES AGAINST STOCKFISH LEVEL {i}\n\n")
            #print(f"\n\nSTARTING GAMES AGAINST LC0 LEVEL {i}\n\n")

            play_game(player_one, player_two, num_games, random_opening_seed=True)
            
print("\n\n\n********\nDONE!\n********\n\n\n")