| """ | |
| TRM-style model for the Chess Challenge. | |
| This implements a weight-shared recurrent transformer (Tiny Recursive Model style) | |
| for causal language modeling under the 1M parameter constraint. | |
| """ | |
| 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 AutoConfig, AutoModelForCausalLM, PretrainedConfig, PreTrainedModel | |
| from transformers.modeling_outputs import CausalLMOutputWithPast | |
| class ChessTRMConfig(PretrainedConfig): | |
| model_type = "chess_trm" | |
| def __init__( | |
| self, | |
| vocab_size: int = 1200, | |
| n_embd: int = 128, | |
| n_layer: int = 2, | |
| n_head: int = 4, | |
| n_ctx: int = 256, | |
| n_inner: Optional[int] = None, | |
| n_cycles: int = 8, | |
| dropout: float = 0.1, | |
| layer_norm_epsilon: float = 1e-5, | |
| tie_weights: bool = True, | |
| pad_token_id: int = 0, | |
| bos_token_id: int = 1, | |
| eos_token_id: int = 2, | |
| **kwargs, | |
| ): | |
| super().__init__( | |
| pad_token_id=pad_token_id, | |
| bos_token_id=bos_token_id, | |
| eos_token_id=eos_token_id, | |
| **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 int(3 * n_embd) | |
| self.n_cycles = int(n_cycles) | |
| self.dropout = float(dropout) | |
| self.layer_norm_epsilon = float(layer_norm_epsilon) | |
| self.tie_weights = bool(tie_weights) | |
| self.tie_word_embeddings = bool(tie_weights) | |
| class _TRMMultiHeadAttention(nn.Module): | |
| def __init__(self, config: ChessTRMConfig): | |
| super().__init__() | |
| if config.n_embd % config.n_head != 0: | |
| raise ValueError(f"n_embd ({config.n_embd}) must be divisible by n_head ({config.n_head})") | |
| 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: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: | |
| batch_size, seq_len, _ = x.size() | |
| qkv = self.c_attn(x) | |
| q, k, v = qkv.split(self.n_embd, dim=2) | |
| q = q.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2) | |
| k = k.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2) | |
| v = v.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2) | |
| attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim) | |
| causal_mask = self.bias[:, :, :seq_len, :seq_len] | |
| attn_weights = attn_weights.masked_fill(causal_mask == 0, float("-inf")) | |
| if attention_mask is not None: | |
| expanded = attention_mask.unsqueeze(1).unsqueeze(2) | |
| attn_weights = attn_weights.masked_fill(expanded == 0, float("-inf")) | |
| attn_weights = F.softmax(attn_weights, dim=-1) | |
| attn_weights = self.dropout(attn_weights) | |
| attn_output = torch.matmul(attn_weights, v) | |
| attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, seq_len, self.n_embd) | |
| attn_output = self.c_proj(attn_output) | |
| return attn_output | |
| class _TRMFeedForward(nn.Module): | |
| def __init__(self, config: ChessTRMConfig): | |
| 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 _TRMBlock(nn.Module): | |
| def __init__(self, config: ChessTRMConfig): | |
| super().__init__() | |
| self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) | |
| self.attn = _TRMMultiHeadAttention(config) | |
| self.ln_2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) | |
| self.mlp = _TRMFeedForward(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 ChessTRMForCausalLM(PreTrainedModel): | |
| config_class = ChessTRMConfig | |
| base_model_prefix = "trm" | |
| supports_gradient_checkpointing = True | |
| keys_to_ignore_on_load_missing = ["lm_head.weight"] | |
| def __init__(self, config: ChessTRMConfig): | |
| 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.blocks = nn.ModuleList([_TRMBlock(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._tied_weights_keys = ["lm_head.weight"] | |
| self.post_init() | |
| if config.tie_weights: | |
| self.tie_weights() | |
| def get_input_embeddings(self) -> nn.Module: | |
| return self.wte | |
| def set_input_embeddings(self, new_embeddings: nn.Module): | |
| self.wte = new_embeddings | |
| if getattr(self.config, "tie_weights", False): | |
| self.tie_weights() | |
| def get_output_embeddings(self) -> nn.Module: | |
| return self.lm_head | |
| def set_output_embeddings(self, new_embeddings: nn.Module): | |
| self.lm_head = new_embeddings | |
| def tie_weights(self): | |
| if getattr(self.config, "tie_weights", False) or getattr(self.config, "tie_word_embeddings", False): | |
| self._tie_or_clone_weights(self.lm_head, self.wte) | |
| def _init_weights(self, module: nn.Module): | |
| if isinstance(module, nn.Linear): | |
| torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) | |
| if module.bias is not None: | |
| torch.nn.init.zeros_(module.bias) | |
| elif isinstance(module, nn.Embedding): | |
| torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) | |
| elif isinstance(module, nn.LayerNorm): | |
| torch.nn.init.ones_(module.weight) | |
| torch.nn.init.zeros_(module.bias) | |
| def forward( | |
| self, | |
| input_ids: torch.LongTensor, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| position_ids: Optional[torch.LongTensor] = None, | |
| labels: Optional[torch.LongTensor] = None, | |
| return_dict: Optional[bool] = None, | |
| **kwargs, | |
| ) -> Union[Tuple, CausalLMOutputWithPast]: | |
| return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
| batch_size, seq_len = input_ids.size() | |
| device = input_ids.device | |
| if seq_len > self.config.n_ctx: | |
| raise ValueError(f"seq_len ({seq_len}) exceeds n_ctx ({self.config.n_ctx})") | |
| if position_ids is None: | |
| position_ids = torch.arange(seq_len, device=device).unsqueeze(0).expand(batch_size, -1) | |
| token_embeds = self.wte(input_ids) | |
| pos_embeds = self.wpe(position_ids) | |
| input_injection = token_embeds + pos_embeds | |
| hidden_states = self.drop(input_injection) | |
| for _ in range(self.config.n_cycles): | |
| hidden_states = hidden_states + input_injection | |
| for block in self.blocks: | |
| hidden_states = block(hidden_states, attention_mask=attention_mask) | |
| hidden_states = self.ln_f(hidden_states) | |
| logits = self.lm_head(hidden_states) | |
| loss = None | |
| if labels is not None: | |
| shift_logits = logits[..., :-1, :].contiguous() | |
| shift_labels = labels[..., 1:].contiguous() | |
| loss_fct = nn.CrossEntropyLoss(ignore_index=-100) | |
| loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) | |
| if not return_dict: | |
| output = (logits,) | |
| return ((loss,) + output) if loss is not None else output | |
| return CausalLMOutputWithPast( | |
| loss=loss, | |
| logits=logits, | |
| past_key_values=None, | |
| hidden_states=None, | |
| attentions=None, | |
| ) | |
| AutoConfig.register("chess_trm", ChessTRMConfig) | |
| AutoModelForCausalLM.register(ChessTRMConfig, ChessTRMForCausalLM) | |