""" Chess Transformer Model for the Chess Challenge. This module provides a simple GPT-style transformer architecture designed to fit within the 1M parameter constraint. Key components: - ChessConfig: Configuration class for model hyperparameters - ChessForCausalLM: The main model class for next-move prediction """ from __future__ import annotations import math from dataclasses import dataclass 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): """ Configuration class for the Chess Transformer model. This configuration is designed for a ~1M parameter model. Students can adjust these values to explore different architectures. Parameter budget breakdown (with default values): - Embeddings (vocab): 1200 x 128 = 153,600 - Position Embeddings: 256 x 128 = 32,768 - Transformer Layers: 6 x ~120,000 = ~720,000 - LM Head (with weight tying): 0 (shared with embeddings) - Total: ~906,000 parameters Attributes: vocab_size: Size of the vocabulary (number of unique moves). n_embd: Embedding dimension (d_model). n_layer: Number of transformer layers. n_head: Number of attention heads. n_ctx: Maximum sequence length (context window). n_inner: Feed-forward inner dimension (default: 3 * n_embd). dropout: Dropout probability. layer_norm_epsilon: Epsilon for layer normalization. tie_weights: Whether to tie embedding and output weights. """ model_type = "chess_transformer" def __init__( self, vocab_size: int = 1200, n_embd: int = 128, n_layer: int = 6, 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 = True, pad_token_id: int = 0, bos_token_id: int = 1, eos_token_id: int = 2, attn: str = "MHA", num_groups: int = 2, rot_pos_emb=False, rotary_base=10000, **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 = 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 # Reduced from 4x to 3x self.dropout = dropout self.layer_norm_epsilon = layer_norm_epsilon self.tie_weights = tie_weights # Inform HF base class about tying behavior self.tie_word_embeddings = bool(tie_weights) # for GQA self.attn = attn self.num_groups = num_groups # rot_pos_emb self.rot_pos_emb = rot_pos_emb self.rotary_base = rotary_base class MultiHeadAttention(nn.Module): """ Multi-head self-attention module. This is a standard scaled dot-product attention implementation with causal masking for autoregressive generation. """ def __init__(self, config: ChessConfig): super().__init__() assert config.n_embd % config.n_head == 0, \ 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.rot_pos_emb = config.rot_pos_emb if self.rot_pos_emb: self.rotary_emb = RotaryEmbedding( self.head_dim, max_position_embeddings=config.n_ctx, base=getattr(config, 'rotary_base', 10000) ) # Combined QKV projection for efficiency 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) # Causal mask (will be created on first forward pass) 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() # Compute Q, K, V qkv = self.c_attn(x) q, k, v = qkv.split(self.n_embd, dim=2) # Reshape for multi-head attention 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) if self.rot_pos_emb: cos, sin = self.rotary_emb(v, seq_len=seq_len) q, k = apply_rotary_pos_emb(q, k, cos, sin) # Scaled dot-product attention attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim) # Apply causal mask causal_mask = self.bias[:, :, :seq_len, :seq_len] attn_weights = attn_weights.masked_fill(causal_mask == 0, float("-inf")) # Apply attention mask (for padding) if attention_mask is not None: # attention_mask shape: (batch_size, seq_len) -> (batch_size, 1, 1, seq_len) attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) attn_weights = attn_weights.masked_fill(attention_mask == 0, float("-inf")) attn_weights = F.softmax(attn_weights, dim=-1) attn_weights = self.dropout(attn_weights) # Apply attention to values attn_output = torch.matmul(attn_weights, v) # Reshape back attn_output = attn_output.transpose(1, 2).contiguous().view( batch_size, seq_len, self.n_embd ) # Output projection attn_output = self.c_proj(attn_output) return attn_output class FeedForward(nn.Module): """ Feed-forward network (MLP) module. Standard two-layer MLP with GELU activation. """ 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 GroupedQueryAttention(nn.Module): """ Standard Grouped Query Attention """ def __init__(self, config: ChessConfig): super().__init__() assert config.n_embd % config.n_head == 0, \ f"n_embd ({config.n_embd}) must be divisible by n_head ({config.n_head})" self.n_head = config.n_head # number of query heads self.n_embd = config.n_embd self.head_dim = config.n_embd // config.n_head self.num_groups = config.num_groups self.group_size = config.n_head // config.num_groups self.q_proj = nn.Linear(self.n_embd, self.n_head * self.head_dim) self.k_proj = nn.Linear(self.n_embd, self.num_groups * self.head_dim) self.v_proj = nn.Linear(self.n_embd, self.num_groups * self.head_dim) self.out_proj = nn.Linear(self.n_head * self.head_dim, self.n_embd) # Causal mask (will be created on first forward pass) 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() # Project queries, keys, and values q = self.q_proj(x).view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2) k = self.k_proj(x).view(batch_size, seq_len, self.num_groups, self.head_dim).transpose(1, 2) v = self.v_proj(x).view(batch_size, seq_len, self.num_groups, self.head_dim).transpose(1, 2) # Expand k and v to match the number of query heads k = k.repeat_interleave(self.group_size, dim=1) v = v.repeat_interleave(self.group_size, dim=1) # Compute attention attn_weights attn_weights = torch.matmul(q, k.transpose(2, 3)) / math.sqrt(self.head_dim) # Apply causal mask causal_mask = self.bias[:, :, :seq_len, :seq_len] attn_weights = attn_weights.masked_fill(causal_mask == 0, float("-inf")) # Apply attention mask (for padding) if attention_mask is not None: # attention_mask shape: (batch_size, seq_len) -> (batch_size, 1, 1, seq_len) attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) attn_weights = attn_weights.masked_fill(attention_mask == 0, float("-inf")) attn_weights = F.softmax(attn_weights, dim=-1) attn_weights = F.dropout(attn_weights, p=0.1) # Apply attention weights to values context = torch.matmul(attn_weights, v).transpose(1, 2).contiguous() context = context.view(batch_size, seq_len, self.n_embd) return self.out_proj(context) class TransformerBlock(nn.Module): """ A single transformer block with attention and feed-forward layers. Uses pre-normalization (LayerNorm before attention/FFN) for better training stability. """ def __init__(self, config: ChessConfig): super().__init__() self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) if config.attn == "MHA": self.attn = MultiHeadAttention(config) elif config.attn == "GQA": self.attn = GroupedQueryAttention(config) else: raise ValueError(f"config.attn expected either MHA or GQA, got {config.attn}") self.ln_2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) self.mlp = FeedForward(config) def forward( self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, ) -> torch.Tensor: # Pre-norm attention x = x + self.attn(self.ln_1(x), attention_mask=attention_mask) # Pre-norm FFN x = x + self.mlp(self.ln_2(x)) return x class ChessForCausalLM(PreTrainedModel): """ Chess Transformer for Causal Language Modeling (next-move prediction). This model is designed to predict the next chess move given a sequence of previous moves. It uses a GPT-style architecture with: - Token embeddings for chess moves - Learned positional embeddings - Stacked transformer blocks - Linear head for next-token prediction The model supports weight tying between the embedding layer and the output projection to save parameters. Example: >>> config = ChessConfig(vocab_size=1200, n_embd=128, n_layer=6) >>> model = ChessForCausalLM(config) >>> inputs = {"input_ids": torch.tensor([[1, 42, 87]])} >>> outputs = model(**inputs) >>> next_move_logits = outputs.logits[:, -1, :] """ config_class = ChessConfig base_model_prefix = "transformer" supports_gradient_checkpointing = True # Suppress missing-key warning for tied lm_head when loading keys_to_ignore_on_load_missing = ["lm_head.weight"] def __init__(self, config: ChessConfig): super().__init__(config) # Token and position embeddings self.wte = nn.Embedding(config.vocab_size, config.n_embd) if not config.rot_pos_emb: self.wpe = nn.Embedding(config.n_ctx, config.n_embd) self.rot_pos_emb = config.rot_pos_emb self.drop = nn.Dropout(config.dropout) # Transformer blocks self.h = nn.ModuleList([ TransformerBlock(config) for _ in range(config.n_layer) ]) # Final layer norm self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) # Output head self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) # Declare tied weights for proper serialization if config.tie_weights: self._tied_weights_keys = ["lm_head.weight"] # Initialize weights self.post_init() # Tie weights if configured 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): # Use HF helper to tie or clone depending on config 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): """Initialize weights following GPT-2 style.""" 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]: """ Forward pass of the model. Args: input_ids: Token IDs of shape (batch_size, seq_len). attention_mask: Attention mask of shape (batch_size, seq_len). position_ids: Position IDs of shape (batch_size, seq_len). labels: Labels for language modeling loss. return_dict: Whether to return a ModelOutput object. Returns: CausalLMOutputWithPast containing loss (if labels provided) and logits. """ 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 # Create position IDs if not provided if position_ids is None: position_ids = torch.arange(seq_len, device=device).unsqueeze(0).expand(batch_size, -1) # Get embeddings token_embeds = self.wte(input_ids) if not self.rot_pos_emb: position_embeds = self.wpe(position_ids) hidden_states = self.drop(token_embeds + position_embeds) else: hidden_states = self.drop(token_embeds) # Pass through transformer blocks for block in self.h: hidden_states = block(hidden_states, attention_mask=attention_mask) # Final layer norm hidden_states = self.ln_f(hidden_states) # Get logits logits = self.lm_head(hidden_states) # Compute loss if labels are provided loss = None if labels is not None: # Shift logits and labels for next-token prediction shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten for cross-entropy loss_fct = nn.CrossEntropyLoss(ignore_index=-100) # 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: 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, ) @torch.no_grad() def generate_move( self, input_ids: torch.LongTensor, temperature: float = 1.0, top_k: Optional[int] = None, top_p: Optional[float] = None, ) -> int: """ Generate the next move given a sequence of moves. Args: input_ids: Token IDs of shape (1, seq_len). temperature: Sampling temperature (1.0 = no change). top_k: If set, only sample from top k tokens. top_p: If set, use nucleus sampling with this threshold. Returns: The token ID of the predicted next move. """ self.eval() # Get logits for the last position outputs = self(input_ids) logits = outputs.logits[:, -1, :] / temperature # Apply top-k filtering if top_k is not None: indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits[indices_to_remove] = float("-inf") # Apply top-p (nucleus) filtering if top_p is not None: sorted_logits, sorted_indices = torch.sort(logits, descending=True) cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) # Remove tokens with cumulative probability above the threshold sorted_indices_to_remove = cumulative_probs > top_p sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 indices_to_remove = sorted_indices_to_remove.scatter( dim=-1, index=sorted_indices, src=sorted_indices_to_remove ) logits[indices_to_remove] = float("-inf") # Sample from the distribution probs = F.softmax(logits, dim=-1) next_token = torch.multinomial(probs, num_samples=1) return next_token.item() class RotaryEmbedding(nn.Module): def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / (self.base ** (torch.arange(0, dim, 2).float().to(device) / dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `forward` cleaner self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() ) def _set_cos_sin_cache(self, seq_len, device, dtype): self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) freqs = torch.outer(t, self.inv_freq) # Different implementations use polar form; here we use the LLaMA style expansion emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) def forward(self, x, seq_len=None): # x: [batch, seq_len, head_dim] if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( self.cos_cached[:seq_len].to(dtype=x.dtype), self.sin_cached[:seq_len].to(dtype=x.dtype), ) def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None): # q, k: [batch, seq_len, heads, head_dim] -> transpose to [batch, heads, seq_len, head_dim] for math # But your model uses [batch, seq_len, heads, head_dim] internally until transpose. # Let's align with the shape inside your attention: # Your code computes: q = q.view(batch, seq, heads, dim).transpose(1, 2) -> [batch, heads, seq, dim] # We assume inputs q, k are [batch, heads, seq_len, head_dim] # cos, sin are [seq_len, head_dim] -> unsqueeze to [1, 1, seq_len, head_dim] cos = cos.unsqueeze(0).unsqueeze(0) # [1, 1, seq_len, head_dim] sin = sin.unsqueeze(0).unsqueeze(0) # If we have custom position_ids (not strictly necessary for causal LM unless doing cache), handle here. # For simple causal LM, we assume standard 0..T indexing. q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed # Register the model with Auto classes for easy loading from transformers import AutoConfig, AutoModelForCausalLM AutoConfig.register("chess_transformer", ChessConfig) AutoModelForCausalLM.register(ChessConfig, ChessForCausalLM)