| | """ |
| | Optimized Chess Transformer |
| | Strategy: Deep & Narrow. |
| | Max parameters dedicated to reasoning (Layers), minimal for Vocab. |
| | """ |
| | 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=80, |
| | n_embd=128, |
| | n_layer=10, |
| | n_head=8, |
| | n_ctx=256, |
| | n_inner=None, |
| | dropout=0.0, |
| | bias=False, |
| | 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 4 * n_embd |
| | self.dropout = dropout |
| | self.bias = bias |
| | self.tie_weights = tie_weights |
| | super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, unk_token_id=unk_token_id, **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, bias=config.bias) |
| | self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias) |
| | self.dropout = nn.Dropout(config.dropout) |
| | self.register_buffer("bias_mask", 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(self.head_dim)) |
| | att = att.masked_fill(self.bias_mask[:,:,: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, bias=config.bias) |
| | self.c_proj = nn.Linear(config.n_inner, config.n_embd, bias=config.bias) |
| | 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) |
| | self.attn = MultiHeadAttention(config) |
| | self.ln_2 = nn.LayerNorm(config.n_embd) |
| | 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) |
| | self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) |
| | if config.tie_weights: self.lm_head.weight = self.wte.weight |
| | self.post_init() |
| |
|
| | def get_input_embeddings(self): return self.wte |
| | def set_input_embeddings(self, new): self.wte = new |
| | def get_output_embeddings(self): return self.lm_head |
| | def set_output_embeddings(self, new): self.lm_head = new |
| |
|
| | 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: |
| | logits[:, :, [self.config.pad_token_id, self.config.bos_token_id, self.config.unk_token_id]] = float("-inf") |
| |
|
| | loss = None |
| | if labels is not None: |
| | shift_logits = logits[..., :-1, :].contiguous() |
| | shift_labels = labels[..., 1:].contiguous() |
| | loss_fct = nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id) |
| | loss = loss_fct(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) |
