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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

app.py

@@ -0,0 +1,18 @@
+import gradio as gr
+import torch
+
+from board import BitBoard
+from data import bitboard_to_tensor
+from model import ChessModel
+
+mdl = torch.load("model.pth", map_location='cpu')
+
+def evaluate_fen(fen):
+    board = BitBoard.from_fen(fen)
+    arr = bitboard_to_tensor(board).to(torch.float32)
+    _embedding, predicted_popularity, _predicted_evaluation, _predicted_board_vec = mdl(arr)
+    return f"Estimated popularity: {predicted_popularity.cpu().item()}"
+
+demo = gr.Interface(fn=evaluate_fen, inputs="text", outputs="text")
+
+demo.launch()

board.py

@@ -0,0 +1,136 @@
+from enum import IntEnum
+
+
+PIECE_TYPES = 14
+
+
+_COLUMN_TO_INT = {
+    c:num for c, num in zip("abcdefgh", range(0, 8))
+}
+
+
+class Player(IntEnum):
+    WHITE = 1
+    BLACK = 2
+
+
+class Piece(IntEnum):
+    PAWN_W = 1
+    PAWN_B = 2
+    ROOK_W = 3
+    ROOK_B = 4
+    KNIGHT_W = 5
+    KNIGHT_B = 6
+    BISHOP_W = 7
+    BISHOP_B = 8
+    QUEEN_W = 9
+    QUEEN_B = 10
+    KING_W = 11
+    KING_B = 12
+    EN_PASSANT_SPACE = 13
+
+    @classmethod
+    def from_fen(cls, letter: chr, default = None):
+        return {
+            'p': Piece.PAWN_B,
+            'P': Piece.PAWN_W,
+            'r': Piece.ROOK_B,
+            'R': Piece.ROOK_W,
+            'n': Piece.KNIGHT_B,
+            'N': Piece.KNIGHT_W,
+            'b': Piece.BISHOP_B,
+            'B': Piece.BISHOP_W,
+            'q': Piece.QUEEN_B,
+            'Q': Piece.QUEEN_W,
+            'k': Piece.KING_B,
+            'K': Piece.KING_W,
+        }.get(letter, default)
+
+
+class BitBoard:
+    def __init__(self):
+        self.boards = [0]*PIECE_TYPES
+        self.next_to_move = Player.WHITE
+        self.castling_status = set()  # Just add pieces for QUEEN_B, etc.
+        self.halfstep_count = 0
+        self.fullstep_count = 0
+
+    def make_move(self, mv: str):
+        # NOTE: This does not handle castling yet and probably screws up clearing en-passant.
+        # Convert the pair into from/to.
+        from_x = _COLUMN_TO_INT[mv[0].lower()]
+        from_y = int(mv[1])-1
+        to_x = _COLUMN_TO_INT[mv[2].lower()]
+        to_y = int(mv[3])-1
+        promote_to = None
+        if len(mv) > 3:
+            promote_to = Piece.from_fen(mv[4])
+
+        # Clear piece at moving spot.
+        moving_piece = self.get_piece(from_x, from_y)
+        self.clear_piece(from_x, from_y)
+        if promote_to is None:
+            self.set_piece(to_x, to_y, moving_piece)
+        else:
+            self.set_piece(to_x, to_y, promote_to)
+
+    @classmethod
+    def from_fen(cls, fen_string: str):
+        # rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1
+        # 4r3/1k6/pp3r2/1b2P2p/3R1p2/P1R2P2/1P4PP/6K1 w - - 0 35
+        new_board = cls()
+        board_layout, current_player, castle_status, en_passant, halfmove_clock, fullmove_clock = fen_string.split(" ")
+        board_rows = board_layout.split("/")
+        for y, row in enumerate(reversed(board_rows)):
+            x = 0
+            while row:
+                if row[0].isdigit():
+                    x += int(row[0])
+                else:
+                    piece = Piece.from_fen(row[0])
+                    new_board.set_piece(x, y, piece)
+                    x += 1
+                row = row[1:]
+        
+        if current_player == 'w':
+            new_board.next_to_move = Player.WHITE
+        elif current_player == 'b':
+            new_board.next_to_move = Player.BLACK
+        else:
+            raise Exception(f"Bad parse.  Starting player unrecognized: {current_player}")
+
+        # Parse castling here.
+        for character in castle_status:
+            new_board.castling_status.add(Piece.from_fen(character))
+
+        # Parse en_passant here
+
+        new_board.halfstep_count = int(halfmove_clock)
+        new_board.fullmove_count = int(fullmove_clock)
+        
+        return new_board
+
+    def get_piece(self, x, y):
+        assert(0 <= x < 8 and 0 <= y < 8)
+        # X and Y should be in the range 0-7 inclusive.
+        # a1 is bottom-left, bit zero.
+        # h8 is the top-right, bit 63.
+        idx = (1 << x+y*8)
+        for i in range(1, PIECE_TYPES):
+            if self.boards[i] & idx:
+                return Piece(i)
+        return None
+    
+    def set_piece(self, x, y, piece: Piece, clear_previous=True):
+        assert(x >= 0 and x < 8 and y >= 0 and y < 8)
+        idx = (1 << x+y*8)
+        if clear_previous:
+            self.clear_piece(x, y)
+        self.boards[int(piece)] |= idx
+
+    def clear_piece(self, x, y):
+        assert (x >= 0 and x < 8 and y >= 0 and y < 8)
+        idx = (1 << x + y * 8)
+        clear_mask = 0xFFFF_FFFF_FFFF_FFFF & ~idx
+        for i in range(0, PIECE_TYPES):
+            self.boards[i] &= clear_mask

config.py

@@ -0,0 +1,4 @@
+import os
+
+STOCKFISH_EXECUTABLE_PATH = os.environ['STOCKFISH_PATH']  # i.e. "/home/joseph/Applications/stockfish/stockfish-9-64/stockfish-os-2022-x86-64-avx2.app"
+LICHESS_DATASET_PATH = os.environ['LICHESS_DATA_PATH']

data.py

@@ -0,0 +1,146 @@
+import csv
+import gzip
+import math
+
+import numpy
+import torch
+from stockfish import Stockfish, StockfishException
+from torch.utils.data import Dataset
+from tqdm import tqdm
+
+from board import BitBoard, Piece, Player, PIECE_TYPES
+from config import LICHESS_DATASET_PATH, STOCKFISH_EXECUTABLE_PATH
+
+
+BOARD_VECTOR_SIZE = 8*8*PIECE_TYPES+7
+
+
+def _bitboard_to_ndarray(board: BitBoard):
+    """Note that the bitboard does not include a bit-flag for the current player, castling status, etc."""
+    output = numpy.zeros((14, 8, 8), dtype=float)
+    for y in range(0, 8):
+        for x in range(0, 8):
+            ptype = board.get_piece(x, y)
+            if ptype is None:
+                output[0, y, x] = 1.0
+            else:
+                output[int(ptype), y, x] = 1.0
+    return output
+
+
+def _ndarray_to_board(arr):
+    board = BitBoard()
+    for piece_type in range(0, PIECE_TYPES):
+        ptype = Piece(piece_type)
+        for y in range(0, 8):
+            for x in range(0, 8):
+                if arr[piece_type, y, x] > 0.5:
+                    board.set_piece(x, y, ptype)
+    return board
+
+
+def bitboard_to_tensor(board: BitBoard) -> torch.Tensor:
+    arr = _bitboard_to_ndarray(board)
+    flat_array = numpy.reshape(arr, (PIECE_TYPES*8*8,), order='C')
+    # We need to add 7 extra spaces for next to play, castling status (4 bits), half-move count, and full-move count.
+    # En-passant is encoded in the bitboard separately.
+    game_info = numpy.zeros((7,), dtype=float)
+    game_info[0] = 0.0 if board.next_to_move == Player.WHITE else 1.0
+    # KQkq
+    game_info[1] = (Piece.KING_W in board.castling_status) * 1.0
+    game_info[2] = (Piece.QUEEN_W in board.castling_status) * 1.0
+    game_info[3] = (Piece.KING_B in board.castling_status) * 1.0
+    game_info[4] = (Piece.QUEEN_B in board.castling_status) * 1.0
+
+    game_info[5] = board.halfstep_count
+    game_info[6] = board.fullstep_count / 50.0
+    return torch.tensor(numpy.hstack([flat_array, game_info]))
+
+
+def tensor_to_bitboard(arr: torch.Tensor) -> BitBoard:
+    array = arr.detach().numpy()
+    # Cut off the last eight.
+    board = _ndarray_to_board(array[:PIECE_TYPES*8*8].resize(PIECE_TYPES, 8, 8))
+    if array[-7] > 0.5:
+        board.next_to_move = Player.BLACK
+    else:
+        board.next_to_move = Player.WHITE
+    if array[-6] > 0.5:
+        board.castling_status.add(Piece.KING_W)
+    if array[-5] > 0.5:
+        board.castling_status.add(Piece.QUEEN_W)
+    if array[-4] > 0.5:
+        board.castling_status.add(Piece.KING_B)
+    if array[-3] > 0.5:
+        board.castling_status.add(Piece.QUEEN_B)
+    board.halfstep_count = int(array[-2])
+    board.fullstep_count = int(array[-1]*50)
+    return board
+
+
+class LichessPuzzleDataset(Dataset):
+    def __init__(self, cap_data: int = None):
+        # See https://database.lichess.org/#puzzles
+        fin = gzip.open(LICHESS_DATASET_PATH, 'rt')
+        cin = csv.DictReader(fin)
+        cin.fieldnames = ["PuzzleId", "FEN", "Moves", "Rating", "RatingDeviation", "Popularity", "NbPlays", "Themes", "GameUrl", "OpeningFamily", "OpeningVariation"]
+        little_fish = Stockfish(path=STOCKFISH_EXECUTABLE_PATH, depth=3, parameters={"Hash": 64, "Threads": 1})
+        #big_fish = Stockfish(path=STOCKFISH_EXECUTABLE_PATH, depth=18, parameters={"Hash": 2048, "Threads": 4, "Minimum Thinking Time": 30})
+        self.fen_start = list()
+        self.puzzle_start = list()
+        self.moves = list()
+        self.rating = list()
+        self.rating_stddev = list()
+        self.popularity = list()
+        self.number_of_plays = list()
+        self.themes = list()
+        self.evaluations = list()  # stockfish.get_evaluation() -> {"type":"cp", "value":12} or {"type":"mate", "value":-3}
+
+        for row in tqdm(cin):
+            puzzle_id = row['PuzzleId']
+            fen = row['FEN']
+            moves = row['Moves']
+            rating = row['Rating']
+            rating_stddev = row['RatingDeviation']
+            popularity = row['Popularity']
+            number_of_plays = row['NbPlays']
+            themes = row['Themes']
+            url = row['GameUrl']
+            opening = row['OpeningFamily']
+            opening_var = row['OpeningVariation']
+
+            self.fen_start.append(fen)  # The fen starts with the enemy turn.  We apply the first move.
+            try:
+                little_fish.set_fen_position(fen)
+                little_fish.make_moves_from_current_position([moves.split(" ")[0]])  # sf.make_moves_from_current_position(["g4d7", "a8b8", "f1d1"])
+                self.puzzle_start.append(little_fish.get_fen_position())
+                self.moves.append(moves)  # Moves is a list
+                self.rating.append(int(rating))
+                self.rating_stddev.append(int(rating_stddev))
+                self.popularity.append(float(popularity) / 100.0)
+                self.number_of_plays.append(int(number_of_plays))
+                self.themes.append(themes)
+                stockfish_score = 0.0
+                #big_fish.set_fen_position(little_fish.get_fen_position())
+                #evaluation = big_fish.get_evaluation()
+                #if "type" in evaluation:
+                #    # This is unbounded on both sides.  It's the value in 'centipawns', so we have to pass through a sigmoid.
+                #    stockfish_score = math.tanh(evaluation["value"])
+                #elif "mate" in evaluation:
+                #    stockfish_score = math.copysign(1.0, evaluation["value"])
+                self.evaluations.append(stockfish_score)
+
+                # DEBUG: Chop off dataset after 100 examples so we can get a test flow.
+                if cap_data and len(self.puzzle_start) > cap_data:
+                    break
+            except StockfishException:
+                print(f"Stockfish crashed after trying {fen}.  Reinitializing and skipping.")
+                little_fish = Stockfish(path=STOCKFISH_EXECUTABLE_PATH, depth=3, parameters={"Hash": 64, "Threads": 1})
+                #big_fish = Stockfish(path=STOCKFISH_EXECUTABLE_PATH, depth=18, parameters={"Hash": 2048, "Threads": 4, "Minimum Thinking Time": 30})
+
+    def __len__(self):
+        return len(self.puzzle_start)
+
+    def __getitem__(self, idx):
+        """Produce pairs of three outputs: a board vector of size 8*8*PIECE_TYPES+8, popularity (+1 to -1), and a stockfish evaluation from +1 to -1."""
+        return bitboard_to_tensor(BitBoard.from_fen(self.puzzle_start[idx])), self.popularity[idx], self.evaluations[idx]

main.py

@@ -0,0 +1,74 @@
+import os
+import sys
+
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+import data
+from model import ChessModel
+
+
+def train():
+    device_string = "cuda" if torch.cuda.is_available() else "cpu"
+    device = torch.device(device_string)
+    model = ChessModel(256).to(torch.float32).to(device)
+    opt = torch.optim.Adam(model.parameters())
+    reconstruction_loss_fn = nn.CrossEntropyLoss().to(torch.float32).to(device)
+    popularity_loss_fn = nn.L1Loss().to(torch.float32).to(device)
+    evaluation_loss_fn = nn.L1Loss().to(torch.float32).to(device)
+    data_loader = DataLoader(data.LichessPuzzleDataset(cap_data=65536), batch_size=64, num_workers=1)  # 1 to avoid threading madness.
+    num_epochs = 100
+
+    for epoch in range(num_epochs):
+        model.train()
+        total_reconstruction_loss = 0.0
+        total_popularity_loss = 0.0
+        total_evaluation_loss = 0.0
+        total_batch_loss = 0.0
+        num_batches = 0
+        for batch_idx, (board_vec, popularity, evaluation) in tqdm(enumerate(data_loader)):
+            board_vec = board_vec.to(torch.float32).to(device)  # [batch_size x 903]
+            popularity = popularity.to(torch.float32).to(device).unsqueeze(1)  # enforce [batch_size, 1]
+            evaluation = evaluation.to(torch.float32).to(device).unsqueeze(1)
+
+            _embedding, predicted_popularity, predicted_evaluation, predicted_board_vec = model(board_vec)
+
+            reconstruction_loss = reconstruction_loss_fn(predicted_board_vec, board_vec)
+            popularity_loss = popularity_loss_fn(predicted_popularity, popularity)
+            evaluation_loss = evaluation_loss_fn(predicted_evaluation, evaluation)
+            total_loss = reconstruction_loss + popularity_loss + evaluation_loss
+
+            opt.zero_grad()
+            total_loss.backward()
+            opt.step()
+
+            total_reconstruction_loss += reconstruction_loss.cpu().item()
+            total_popularity_loss += popularity_loss.cpu().item()
+            total_evaluation_loss += evaluation_loss.cpu().item()
+            total_batch_loss += total_loss.cpu().item()
+            num_batches += 1
+        print(f"Average reconstruction loss: {total_reconstruction_loss/num_batches}")
+        print(f"Average popularity loss: {total_popularity_loss/num_batches}")
+        print(f"Average evaluation loss: {total_evaluation_loss/num_batches}")
+        print(f"Average batch loss: {total_batch_loss/num_batches}")
+
+        torch.save(model, f"checkpoints/epoch_{epoch}.pth")
+
+
+def infer(fen):
+    pass
+
+
+def test():
+    pass
+
+
+if __name__ == "__main__":
+    if len(sys.argv) < 2:
+        print("Usage: python {sys.argv[0]} --train|infer")
+    elif sys.argv[1] == "--train":
+        train()
+    elif sys.argv[2] == "--infer":
+        infer(sys.argv[3])

model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:903a3dc9af8a83bc128b0e6581693a0cf8e74dd2127eb669704420463115e18a
+size 15268009

model.py

@@ -0,0 +1,53 @@
+import torch
+from torch import nn
+
+from data import BOARD_VECTOR_SIZE
+
+
+class ChessModel(nn.Module):
+    def __init__(self, embedding_dims):
+        super().__init__()
+        self.encoder = nn.Sequential(
+            nn.Linear(BOARD_VECTOR_SIZE, 512),
+            nn.SiLU(),
+            nn.Linear(512, 1024),
+            nn.SiLU(),
+            nn.Linear(1024, 1024),
+            nn.SiLU(),
+            nn.Linear(1024, embedding_dims),
+            nn.SiLU(),
+        )
+
+        self.popularity_head = nn.Sequential(
+            nn.Linear(embedding_dims, 512),
+            nn.SiLU(),
+            nn.Linear(512, 1),
+            nn.Tanh(),
+        )
+
+        # Since it will take too long for this to evaluate before the jam is over, just noop it.
+        #self.evaluation_head = nn.Sequential(
+        #    nn.Linear(embedding_dims, 512),
+        #    nn.SiLU(),
+        #    nn.Linear(512, 1),
+        #    nn.Tanh(),
+        #)
+        self.evaluation_head = nn.Sequential(
+            nn.Linear(embedding_dims, 1),
+        )
+
+        self.reconstruction_head = nn.Sequential(
+            nn.Linear(embedding_dims, 512),
+            nn.SiLU(),
+            nn.Linear(512, BOARD_VECTOR_SIZE),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, x):
+        """Return the embedding, popularity, evaluation, and reconstruction."""
+        # Outputs have three heads: one for the board reconstruction, one for the popularity, and one for the eval.
+        embedding = self.encoder(x)
+        popularity = self.popularity_head(embedding)
+        evaluation = self.evaluation_head(embedding)
+        reconstruction = self.reconstruction_head(embedding)
+        return embedding, popularity, evaluation, reconstruction

requirements.txt

@@ -0,0 +1,4 @@
+gradio==2.4.6
+stockfish==3.28.0
+torch==1.12.0
+tqdm==4.46.0