model.py

from __future__ import annotations

import json
import math
import os
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F

from transformers import PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.utils.hub import cached_file


def _is_square(tok: str) -> bool:
    return len(tok) == 2 and tok[0] in "abcdefgh" and tok[1] in "12345678"


def _resolve_file(name_or_path: str, filename: str) -> str:
    if isinstance(name_or_path, str) and os.path.isdir(name_or_path):
        p = os.path.join(name_or_path, filename)
        if os.path.exists(p):
            return p
    return cached_file(name_or_path, filename)


def _load_vocab(name_or_path: str) -> Tuple[Dict[str, int], Dict[int, str]]:
    vocab_path = _resolve_file(name_or_path, "vocab.json")
    with open(vocab_path, "r", encoding="utf-8") as f:
        tok2id = json.load(f)
    id2tok = {int(i): t for t, i in tok2id.items()}
    return tok2id, id2tok


@dataclass
class TokenScheme:
    W: str
    B: str
    pieces: Dict[str, str]
    sep: Optional[str]
    suffix: Dict[str, str]
    prom: Dict[str, str]
    pad_id: int
    bos_id: int
    eos_id: int
    unk_id: int


def _detect_scheme(tok2id: Dict[str, int], config) -> TokenScheme:
    W = "W" if "W" in tok2id else None
    B = "B" if "B" in tok2id else None
    if W is None or B is None:
        raise ValueError("Cannot find W/B tokens in vocab")

    pieces = {}
    for p in ["P", "N", "B", "R", "Q", "K"]:
        if p in tok2id:
            pieces[p] = p
        else:
            raise ValueError(f"Cannot find piece token {p} in vocab")

    sep = " " if " " in tok2id else None

    suffix = {}
    for k, v in [
        ("cap", "(x)"),
        ("cap_check", "(x*)"),
        ("cap_mate", "(x+*)"),
        ("check", "(+)"),
        ("mate", "(+*)"),
        ("o", "(o)"),
        ("O", "(O)"),
    ]:
        if v in tok2id:
            suffix[k] = v

    prom = {}
    for p, v in [("Q", "(Q)"), ("R", "(R)"), ("B", "(B)"), ("N", "(N)")]:
        if v in tok2id:
            prom[p] = v

    pad_id = int(getattr(config, "pad_token_id", 0))
    bos_id = int(getattr(config, "bos_token_id", 1))
    eos_id = int(getattr(config, "eos_token_id", 2))
    unk_id = int(getattr(config, "unk_token_id", 3))

    return TokenScheme(W=W, B=B, pieces=pieces, sep=sep, suffix=suffix, prom=prom,
                       pad_id=pad_id, bos_id=bos_id, eos_id=eos_id, unk_id=unk_id)


class ChessConfig(PretrainedConfig):
    model_type = "chess_transformer"

    def __init__(
        self,
        vocab_size: int = 85,
        n_embd: int = 128,
        n_layer: int = 5,
        n_head: int = 4,
        n_ctx: int = 256,
        n_inner: Optional[int] = None,
        dropout: float = 0.1,
        layer_norm_epsilon: float = 1e-5,
        tie_weights: bool = False,
        pad_token_id: int = 0,
        bos_token_id: int = 1,
        eos_token_id: int = 2,
        unk_token_id: int = 3,
        **kwargs,
    ):
        self.vocab_size = int(vocab_size)
        self.n_embd = int(n_embd)
        self.n_layer = int(n_layer)
        self.n_head = int(n_head)
        self.n_ctx = int(n_ctx)
        self.n_inner = int(n_inner) if n_inner is not None else 3 * int(n_embd)
        self.dropout = float(dropout)
        self.layer_norm_epsilon = float(layer_norm_epsilon)
        self.tie_weights = bool(tie_weights)

        kwargs["pad_token_id"] = pad_token_id
        kwargs["bos_token_id"] = bos_token_id
        kwargs["eos_token_id"] = eos_token_id
        kwargs["unk_token_id"] = unk_token_id
        super().__init__(**kwargs)


class MLP(nn.Module):
    def __init__(self, config: ChessConfig):
        super().__init__()
        self.c_fc = nn.Linear(config.n_embd, config.n_inner)
        self.c_proj = nn.Linear(config.n_inner, config.n_embd)
        self.dropout = nn.Dropout(config.dropout)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.c_fc(x)
        x = F.gelu(x)
        x = self.c_proj(x)
        x = self.dropout(x)
        return x


class MultiHeadAttention(nn.Module):
    def __init__(self, config: ChessConfig):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        self.n_head = config.n_head
        self.head_dim = config.n_embd // config.n_head

        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
        self.dropout = nn.Dropout(config.dropout)

        bias = torch.tril(torch.ones(config.n_ctx, config.n_ctx)).view(1, 1, config.n_ctx, config.n_ctx)
        self.register_buffer("bias", bias, persistent=False)

    def forward(self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
        B, T, C = x.size()
        qkv = self.c_attn(x)
        q, k, v = qkv.split(C, dim=2)

        q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
        k = k.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
        v = v.view(B, T, self.n_head, self.head_dim).transpose(1, 2)

        att = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)
        att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float("-inf"))

        if attention_mask is not None:
            att = att.masked_fill(attention_mask.view(B, 1, 1, T) == 0, float("-inf"))

        att = F.softmax(att, dim=-1)
        att = self.dropout(att)

        y = att @ v
        y = y.transpose(1, 2).contiguous().view(B, T, C)
        y = self.c_proj(y)
        y = self.dropout(y)
        return y


class Block(nn.Module):
    def __init__(self, config: ChessConfig):
        super().__init__()
        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
        self.attn = MultiHeadAttention(config)
        self.ln_2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
        self.mlp = MLP(config)

    def forward(self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
        x = x + self.attn(self.ln_1(x), attention_mask=attention_mask)
        x = x + self.mlp(self.ln_2(x))
        return x


class ChessForCausalLM(PreTrainedModel):
    config_class = ChessConfig
    base_model_prefix = ""

    def __init__(self, config: ChessConfig):
        super().__init__(config)
        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
        self.wpe = nn.Embedding(config.n_ctx, config.n_embd)
        self.drop = nn.Dropout(config.dropout)
        self.h = nn.ModuleList([Block(config) for _ in range(config.n_layer)])
        self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

        if getattr(config, "tie_weights", False):
            self.lm_head.weight = self.wte.weight

        self.post_init()

        self._tok2id = None
        self._id2tok = None
        self._scheme = None

    def _ensure_vocab(self):
        if self._tok2id is None or self._id2tok is None:
            name_or_path = getattr(self.config, "_name_or_path", None) or getattr(self, "name_or_path", None)
            if not name_or_path:
                raise ValueError("Cannot resolve model path to load vocab.json")
            self._tok2id, self._id2tok = _load_vocab(name_or_path)

    def _get_scheme(self) -> TokenScheme:
        if self._scheme is None:
            self._ensure_vocab()
            self._scheme = _detect_scheme(self._tok2id, self.config)
        return self._scheme

    def forward(self, input_ids, attention_mask=None, labels=None, return_dict=True, **kwargs):
        B, T = input_ids.shape
        if T > self.config.n_ctx:
            input_ids = input_ids[:, -self.config.n_ctx :]
            if attention_mask is not None:
                attention_mask = attention_mask[:, -self.config.n_ctx :]
            if labels is not None:
                labels = labels[:, -self.config.n_ctx :]
            B, T = input_ids.shape

        pos = torch.arange(0, T, device=input_ids.device).unsqueeze(0)
        x = self.wte(input_ids) + self.wpe(pos)
        x = self.drop(x)

        for block in self.h:
            x = block(x, attention_mask=attention_mask)

        x = self.ln_f(x)
        logits = self.lm_head(x)

        loss = None
        if labels is not None:
            shift_logits = logits[:, :-1].contiguous()
            shift_labels = labels[:, 1:].contiguous()
            loss = F.cross_entropy(
                shift_logits.view(-1, shift_logits.size(-1)),
                shift_labels.view(-1),
                ignore_index=-100,
            )

        if not return_dict:
            return (logits, loss)
        return CausalLMOutputWithPast(logits=logits, loss=loss)

    def _ids_to_tokens(self, ids: List[int]) -> List[str]:
        self._ensure_vocab()
        return [self._id2tok.get(int(i), "[UNK]") for i in ids]

    def _parse_history_to_board(self, input_ids_1d: List[int]):
        import chess
        scheme = self._get_scheme()
        toks = self._ids_to_tokens(input_ids_1d)

        specials = {"[PAD]", "[BOS]", "[EOS]", "[UNK]"}
        toks = [t for t in toks if t not in specials]

        b = chess.Board()
        i = 0
        while i < len(toks):
            while i < len(toks) and toks[i] not in (scheme.W, scheme.B):
                i += 1
            if i >= len(toks):
                break

            i += 1

            while i < len(toks) and scheme.sep is not None and toks[i] == scheme.sep:
                i += 1

            if i >= len(toks) or toks[i] not in scheme.pieces.values():
                break
            i += 1

            while i < len(toks) and scheme.sep is not None and toks[i] == scheme.sep:
                i += 1

            if i >= len(toks) or not _is_square(toks[i]):
                break
            src = toks[i]
            i += 1

            while i < len(toks) and scheme.sep is not None and toks[i] == scheme.sep:
                i += 1

            if i >= len(toks) or not _is_square(toks[i]):
                break
            dst = toks[i]
            i += 1

            suffixes = []
            while i < len(toks) and toks[i] not in (scheme.W, scheme.B):
                if scheme.sep is not None and toks[i] == scheme.sep:
                    i += 1
                    continue
                suffixes.append(toks[i])
                i += 1

            uci = f"{src}{dst}"
            promo = None
            for p, ptok in scheme.prom.items():
                if ptok in suffixes:
                    promo = p.lower()
                    break
            if promo is not None:
                uci += promo

            try:
                mv = chess.Move.from_uci(uci)
                if mv in b.legal_moves:
                    b.push(mv)
                else:
                    break
            except Exception:
                break

        return b

    def _move_to_ids(self, board, move_uci: str) -> List[int]:
        import chess

        scheme = self._get_scheme()
        self._ensure_vocab()
        tok2id = self._tok2id

        mv = chess.Move.from_uci(move_uci)

        color_tok = scheme.W if board.turn == chess.WHITE else scheme.B
        piece = board.piece_at(mv.from_square)
        pl = piece.symbol().upper() if piece is not None else "P"
        if pl not in scheme.pieces:
            pl = "P"

        src = chess.square_name(mv.from_square)
        dst = chess.square_name(mv.to_square)

        toks = [color_tok, pl]
        if scheme.sep is not None:
            toks += [scheme.sep, src, scheme.sep, dst]
        else:
            toks += [src, dst]

        is_capture = board.is_capture(mv)
        board.push(mv)
        is_mate = board.is_checkmate()
        is_check = board.is_check()
        board.pop()

        suffix_tok = None
        if is_capture and is_mate:
            suffix_tok = scheme.suffix.get("cap_mate")
        elif is_capture and is_check:
            suffix_tok = scheme.suffix.get("cap_check")
        elif is_capture:
            suffix_tok = scheme.suffix.get("cap")
        elif is_mate:
            suffix_tok = scheme.suffix.get("mate")
        elif is_check:
            suffix_tok = scheme.suffix.get("check")

        if suffix_tok is not None:
            toks.append(suffix_tok)

        if mv.promotion is not None:
            prom = chess.piece_symbol(mv.promotion).upper()
            if prom in scheme.prom:
                toks.append(scheme.prom[prom])

        if scheme.sep is not None:
            toks.append(scheme.sep)

        return [tok2id.get(t, scheme.unk_id) for t in toks]

    @torch.no_grad()
    def _score_candidates(self, prefix_ids, cand_ids_list, attention_mask, temperature, batch_size=64):
        device = prefix_ids.device
        T0 = prefix_ids.size(1)
        scores = torch.empty(len(cand_ids_list), device=device, dtype=torch.float32)
        pad_id = int(self.config.pad_token_id)

        for start in range(0, len(cand_ids_list), batch_size):
            batch = cand_ids_list[start : start + batch_size]
            max_c = max(len(c) for c in batch)

            input_ids_list = []
            attn_list = []

            for c in batch:
                c_ids = torch.tensor(c, device=device, dtype=torch.long).unsqueeze(0)
                seq = torch.cat([prefix_ids, c_ids], dim=1)
                pad_len = (T0 + max_c) - seq.size(1)
                if pad_len > 0:
                    pad = torch.full((1, pad_len), pad_id, device=device, dtype=torch.long)
                    seq = torch.cat([seq, pad], dim=1)
                input_ids_list.append(seq)

                if attention_mask is None:
                    a = torch.ones((1, seq.size(1)), device=device, dtype=torch.long)
                else:
                    a = attention_mask
                    if a.size(1) != T0:
                        a = a[:, -T0:]
                    ones = torch.ones((1, len(c)), device=device, dtype=torch.long)
                    zeros = torch.zeros((1, max_c - len(c)), device=device, dtype=torch.long)
                    a = torch.cat([a, ones, zeros], dim=1)
                attn_list.append(a)

            input_ids = torch.cat(input_ids_list, dim=0)
            attn_mask = torch.cat(attn_list, dim=0)

            out = self.forward(input_ids=input_ids, attention_mask=attn_mask, return_dict=True)
            logits = out.logits / float(max(1e-6, temperature))
            logp = torch.log_softmax(logits, dim=-1)

            for bi, c in enumerate(batch):
                lp = 0.0
                for j in range(len(c)):
                    pos = T0 + j - 1
                    if pos < 0:
                        continue
                    tok_id = int(c[j])
                    lp += float(logp[bi, pos, tok_id].item())
                scores[start + bi] = lp

        return scores

    def generate(self, input_ids=None, attention_mask=None, max_new_tokens=16, temperature=1.0, do_sample=False, **kwargs):
        import chess

        if input_ids is None:
            raise ValueError("generate() requires input_ids")
        if input_ids.dim() == 1:
            input_ids = input_ids.unsqueeze(0)

        if input_ids.size(0) != 1:
            return super().generate(
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_new_tokens=max_new_tokens,
                temperature=temperature,
                do_sample=do_sample,
                **kwargs,
            )

        try:
            board = self._parse_history_to_board(input_ids[0].tolist())
        except Exception:
            board = None

        if board is None or board.is_game_over():
            return super().generate(
                input_ids=input_ids,
                attention_mask=attention_mask,
                max_new_tokens=max_new_tokens,
                temperature=temperature,
                do_sample=do_sample,
                **kwargs,
            )

        legal = list(board.legal_moves)
        if not legal:
            return input_ids

        cand_ids_list = [self._move_to_ids(board, mv.uci()) for mv in legal]

        scores = self._score_candidates(
            prefix_ids=input_ids,
            cand_ids_list=cand_ids_list,
            attention_mask=attention_mask,
            temperature=float(temperature),
            batch_size=64,
        )

        best = int(torch.argmax(scores).item())
        best_ids = torch.tensor(cand_ids_list[best], device=input_ids.device, dtype=torch.long).unsqueeze(0)

        if best_ids.size(1) > int(max_new_tokens):
            best_ids = best_ids[:, : int(max_new_tokens)]

        return torch.cat([input_ids, best_ids], dim=1)