| """ | |
| Chess Transformer Model - The "Nuclear Patch" Edition | |
| """ | |
| from __future__ import annotations | |
| import math | |
| from typing import Optional, Tuple, Union | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from transformers import PretrainedConfig, PreTrainedModel | |
| from transformers.modeling_outputs import CausalLMOutputWithPast | |
| class ChessConfig(PretrainedConfig): | |
| model_type = "chess_transformer" | |
| def __init__( | |
| self, | |
| vocab_size=1200, | |
| n_embd=128, | |
| n_layer=6, | |
| n_head=4, | |
| n_ctx=256, | |
| n_inner=None, | |
| dropout=0.1, | |
| layer_norm_epsilon=1e-5, | |
| tie_weights=True, | |
| pad_token_id=0, | |
| bos_token_id=1, | |
| eos_token_id=2, | |
| unk_token_id=3, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.n_embd = n_embd | |
| self.n_layer = n_layer | |
| self.n_head = n_head | |
| self.n_ctx = n_ctx | |
| self.n_inner = n_inner if n_inner is not None else 3 * n_embd | |
| self.dropout = dropout | |
| self.layer_norm_epsilon = layer_norm_epsilon | |
| self.tie_weights = 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 MultiHeadAttention(nn.Module): | |
| def __init__(self, config: ChessConfig): | |
| super().__init__() | |
| self.n_head = config.n_head | |
| self.n_embd = config.n_embd | |
| 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) | |
| self.register_buffer("bias", torch.tril(torch.ones(config.n_ctx, config.n_ctx)).view(1, 1, config.n_ctx, config.n_ctx), persistent=False) | |
| def forward(self, x, attention_mask=None): | |
| B, T, C = x.size() | |
| qkv = self.c_attn(x) | |
| q, k, v = qkv.split(self.n_embd, 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)) * (1.0 / math.sqrt(k.size(-1))) | |
| 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) | |
| return self.c_proj(y) | |
| class FeedForward(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): | |
| return self.dropout(self.c_proj(F.gelu(self.c_fc(x)))) | |
| class TransformerBlock(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 = FeedForward(config) | |
| def forward(self, x, attention_mask=None): | |
| x = x + self.attn(self.ln_1(x), attention_mask) | |
| x = x + self.mlp(self.ln_2(x)) | |
| return x | |
| class ChessForCausalLM(PreTrainedModel): | |
| config_class = ChessConfig | |
| base_model_prefix = "transformer" | |
| 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([TransformerBlock(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 config.tie_weights: self.post_init() | |
| def get_input_embeddings(self): return self.wte | |
| def set_input_embeddings(self, new_embeddings): self.wte = new_embeddings | |
| def get_output_embeddings(self): return self.lm_head | |
| def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings | |
| def forward(self, input_ids, attention_mask=None, position_ids=None, labels=None, return_dict=None, **kwargs): | |
| return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
| if return_dict is None: return_dict = True | |
| device = input_ids.device | |
| b, t = input_ids.size() | |
| if position_ids is None: position_ids = torch.arange(t, device=device).unsqueeze(0) | |
| x = self.wte(input_ids) + self.wpe(position_ids) | |
| x = self.drop(x) | |
| for block in self.h: x = block(x, attention_mask) | |
| x = self.ln_f(x) | |
| logits = self.lm_head(x) | |
| if labels is None: | |
| nuclear_bad_ids = [0, 1, 2, 3] | |
| logits[:, :, nuclear_bad_ids] = float("-inf") | |
| loss = None | |
| if labels is not None: | |
| shift_logits = logits[..., :-1, :].contiguous() | |
| shift_labels = labels[..., 1:].contiguous() | |
| loss = nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) | |
| if not return_dict: | |
| return ((loss,) + (logits,)) if loss is not None else (logits,) | |
| return CausalLMOutputWithPast(loss=loss, logits=logits) | |
| from transformers import AutoConfig, AutoModelForCausalLM | |
| AutoConfig.register("chess_transformer", ChessConfig) | |
| AutoModelForCausalLM.register(ChessConfig, ChessForCausalLM) |