""" PentachoraViT: Vision Transformer with Pentachoron Geometric Structure Enhanced with Geometric Attention for improved head cohesion and generalization FIXED: CLS tokens now properly reference and utilize vocabulary embeddings """ import torch import torch.nn as nn import torch.nn.functional as F import numpy as np from einops import rearrange, repeat import math from typing import Optional, Dict, Tuple, List, Any from dataclasses import dataclass import warnings # ============================================ # CONFIGURATION CLASSES # ============================================ @dataclass class PentachoraConfig: """Configuration for PentachoraViT models.""" img_size: int = 32 patch_size: int = 4 num_classes: int = 100 dim: int = 512 vocab_dim: Optional[int] = None # Vocabulary dimension (can differ from model dim) depth: int = 12 heads: int = 8 mlp_ratio: float = 4.0 use_mesh_attention: bool = True preserve_structure_until_layer: int = 6 dropout_rate: float = 0.1 drop_path_rate: float = 0.1 aux_loss_weight: float = 0.3 geo_loss_weight: float = 0.1 vocab: Optional[Any] = None @property def num_patches(self) -> int: return (self.img_size // self.patch_size) ** 2 # ============================================ # GEOMETRIC ATTENTION COMPONENTS # ============================================ def perfect_4simplex(device): """Create perfect 4-simplex (pentachoron) vertices in 4D.""" sqrt5 = math.sqrt(5) vertices = torch.tensor([ [1, 1, 1, -1/sqrt5], [1, -1, -1, -1/sqrt5], [-1, 1, -1, -1/sqrt5], [-1, -1, 1, -1/sqrt5], [0, 0, 0, 4/sqrt5] ], device=device, dtype=torch.float32) return vertices / 2 # Normalize scale def softmin_over_last(distances, tau): """Softmin over last dimension.""" return F.softmax(-distances / tau, dim=-1).sum(dim=-1) @dataclass class GeometricConfig: """Configuration for geometric attention.""" softmin_tau: float = 0.05 fuse_alpha: float = 0.7 phases: Tuple[float, ...] = (0.0, math.pi/2, math.pi, 3*math.pi/2) jitter: float = 0.02 shift: float = 0.25 rotate_cycle: int = 11 use_phase_variance: bool = False geometry_type: str = "pentachoron" class GeometricNavigator(nn.Module): """Maps inputs to geometric regions in 4D space.""" def __init__(self, input_dim: int, num_regions: int, config: GeometricConfig): super().__init__() self.input_dim = input_dim self.num_regions = num_regions self.config = config self.to_nav = nn.Linear(input_dim, 4, bias=False) self.vertex_w = nn.Parameter(torch.zeros(num_regions, 5)) # Initialize geometry after module is created self.register_parameter('D', None) self.register_parameter('S', None) def _lazy_init_geometry(self, device): """Initialize geometry on first forward pass.""" if self.D is not None: return base = perfect_4simplex(device) D = torch.zeros(self.num_regions, 5, 4, device=device) S = torch.zeros(self.num_regions, 5, 4, device=device) for r in range(self.num_regions): D[r] = base + self.config.jitter * torch.randn_like(base) theta = torch.tensor(0.27 + 0.05 * (r % self.config.rotate_cycle), device=device) rot = torch.eye(4, device=device) c, s_val = torch.cos(theta), torch.sin(theta) rot[0, 0] = c; rot[0, 1] = -s_val rot[1, 0] = s_val; rot[1, 1] = c S[r] = (base @ rot) + self.config.shift S[r] += self.config.jitter * torch.randn_like(S[r]) self.D = nn.Parameter(D) self.S = nn.Parameter(S) def navigate(self, x: torch.Tensor) -> Dict[str, torch.Tensor]: """Navigate inputs through geometric space.""" self._lazy_init_geometry(x.device) nav_x = self.to_nav(x) nav_x_exp = nav_x[:, None, None, :] D_exp = self.D[None, :, :, :] d_disp = torch.norm(nav_x_exp - D_exp, dim=-1) s_disp = -softmin_over_last(d_disp, self.config.softmin_tau) w = F.softmax(self.vertex_w, dim=1) phase_scores = [] for phase in self.config.phases: phase_tensor = torch.tensor(phase, device=x.device) ct = torch.cos(phase_tensor) st = torch.sin(phase_tensor) Vt = ct * self.D + st * self.S w_expanded = w.unsqueeze(-1) Vt_mean = Vt.mean(dim=1, keepdim=True) Vt = (1.0 - w_expanded) * Vt + w_expanded * Vt_mean Vt_exp = Vt[None, :, :, :] d_ribbon = torch.norm(nav_x_exp - Vt_exp, dim=-1) s_ribbon = -softmin_over_last(d_ribbon, self.config.softmin_tau) phase_scores.append(s_ribbon) s_ribbon = torch.stack(phase_scores).mean(dim=0) scores = self.config.fuse_alpha * s_ribbon + (1 - self.config.fuse_alpha) * s_disp diagnostics = { 'dispatcher_scores': s_disp.detach(), 'ribbon_scores': s_ribbon.detach() } return {'scores': scores, 'diagnostics': diagnostics} class GeometricAttention(nn.Module): """Multi-head geometric attention with Q-K alignment.""" def __init__(self, dim: int, num_heads: int = 8, num_regions: Optional[int] = None, config: Optional[GeometricConfig] = None, dropout: float = 0.0): super().__init__() self.dim = dim self.num_heads = num_heads self.head_dim = dim // num_heads if num_regions is None: num_regions = min(self.head_dim, 16) if config is None: config = GeometricConfig() self.config = config self.to_qkv = nn.Linear(dim, dim * 3, bias=False) self.q_navigators = nn.ModuleList([ GeometricNavigator(self.head_dim, num_regions, config) for _ in range(num_heads) ]) self.k_navigators = nn.ModuleList([ GeometricNavigator(self.head_dim, num_regions, config) for _ in range(num_heads) ]) self.out_proj = nn.Linear(dim, dim) self.dropout = nn.Dropout(dropout) def forward(self, x: torch.Tensor, mask: Optional[torch.Tensor] = None, return_diagnostics: bool = False) -> Tuple[torch.Tensor, Optional[Dict]]: B, T, D = x.shape qkv = self.to_qkv(x) q, k, v = qkv.chunk(3, dim=-1) q = q.reshape(B, T, self.num_heads, self.head_dim).transpose(1, 2) k = k.reshape(B, T, self.num_heads, self.head_dim).transpose(1, 2) v = v.reshape(B, T, self.num_heads, self.head_dim).transpose(1, 2) outputs = [] all_diagnostics = [] if return_diagnostics else None for h in range(self.num_heads): q_h_flat = q[:, h].reshape(B * T, self.head_dim) k_h_flat = k[:, h].reshape(B * T, self.head_dim) q_nav = self.q_navigators[h].navigate(q_h_flat) k_nav = self.k_navigators[h].navigate(k_h_flat) q_scores = q_nav['scores'].reshape(B, T, -1) k_scores = k_nav['scores'].reshape(B, T, -1) attn = torch.bmm(q_scores, k_scores.transpose(1, 2)) attn = attn / math.sqrt(q_scores.size(-1)) if mask is not None: attn = attn.masked_fill(mask.unsqueeze(1) == 0, -1e9) attn = F.softmax(attn, dim=-1) attn = self.dropout(attn) out = torch.bmm(attn, v[:, h]) outputs.append(out) if return_diagnostics: all_diagnostics.append({'q': q_nav['diagnostics'], 'k': k_nav['diagnostics']}) output = torch.stack(outputs, dim=1).transpose(1, 2).reshape(B, T, D) output = self.out_proj(output) output = self.dropout(output) if return_diagnostics: return output, {'head_diagnostics': all_diagnostics} return output, None # ============================================ # DROP PATH (STOCHASTIC DEPTH) # ============================================ class DropPath(nn.Module): """Drop paths (Stochastic Depth) per sample.""" def __init__(self, drop_prob: float = 0.): super().__init__() self.drop_prob = drop_prob def forward(self, x: torch.Tensor) -> torch.Tensor: if self.drop_prob == 0. or not self.training: return x keep_prob = 1 - self.drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) random_tensor.floor_() output = x.div(keep_prob) * random_tensor return output # ============================================ # HIERARCHICAL CLS WITH PENTACHORA (FIXED) # ============================================ class HierarchicalPentachoronCLS(nn.Module): """ Hierarchical CLS structure with pentachoron geometry. FIXED: Now properly uses vocabulary embeddings for CLS tokens. """ def __init__(self, dim: int, vocab_dim: int, num_classes: int = 100): super().__init__() self.dim = dim # Model's internal dimension self.vocab_dim = vocab_dim # Vocabulary's dimension self.num_classes = num_classes # Class-specific pentachora from vocabulary (in vocabulary dimension) self.class_pentachora = nn.Parameter(torch.randn(num_classes, 5, vocab_dim) * 0.02) # Projection from vocabulary dimension to model dimension if vocab_dim != dim: self.vocab_to_model = nn.Linear(vocab_dim, dim) else: self.vocab_to_model = nn.Identity() # Learnable aggregation weights for creating global CLS from vertices self.vertex_weights = nn.Parameter(torch.ones(5) / 5) # Optional learnable offset for global CLS self.global_offset = nn.Parameter(torch.zeros(1, 1, dim)) # Layer norms self.vertex_norm = nn.LayerNorm(dim) self.global_norm = nn.LayerNorm(dim) def forward(self, batch_size: int, class_indices: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]: """ Generate CLS tokens for batch. Args: batch_size: Batch size class_indices: Optional class indices for class-specific initialization Returns: global_cls: [B, 1, D] - Global CLS tokens vertex_cls: [B, 5, D] - Vertex CLS tokens """ if class_indices is not None and class_indices.shape[0] == batch_size: # Use class-specific pentachora when class indices are provided # This would typically be used during training with labels vertex_cls_vocab = self.class_pentachora[class_indices] # [B, 5, vocab_dim] else: # Use mean of all class pentachora when no specific classes provided # This is used during inference or when class is unknown vertex_cls_vocab = self.class_pentachora.mean(dim=0, keepdim=True) # [1, 5, vocab_dim] vertex_cls_vocab = vertex_cls_vocab.expand(batch_size, -1, -1) # [B, 5, vocab_dim] # Project from vocabulary dimension to model dimension vertex_cls = self.vocab_to_model(vertex_cls_vocab) # [B, 5, dim] vertex_cls = self.vertex_norm(vertex_cls) # Create global CLS as weighted combination of vertices weights = F.softmax(self.vertex_weights, dim=0) global_cls = torch.einsum('bvd,v->bd', vertex_cls, weights).unsqueeze(1) # [B, 1, dim] global_cls = global_cls + self.global_offset global_cls = self.global_norm(global_cls) return global_cls, vertex_cls def get_class_prototypes(self) -> torch.Tensor: """ Get class prototypes in model dimension. Returns: prototypes: [num_classes, dim] - Class prototype vectors """ # Project class pentachora to model dimension pentachora_model = self.vocab_to_model(self.class_pentachora) # [C, 5, dim] # Aggregate vertices to get class prototypes weights = F.softmax(self.vertex_weights, dim=0) prototypes = torch.einsum('cvd,v->cd', pentachora_model, weights) # [C, dim] return prototypes # ============================================ # GEOMETRIC PROJECTION LAYER (ENHANCED) # ============================================ class GeometricProjection(nn.Module): """ Project patches onto pentachoron geometry. ENHANCED: Now provides better integration with vocabulary. """ def __init__(self, dim: int, vocab_dim: int, num_classes: int = 100, dropout: float = 0.1): super().__init__() self.dim = dim # Model dimension self.vocab_dim = vocab_dim # Vocabulary dimension self.num_classes = num_classes # Projection from model dim to vocab dim for alignment self.to_vocab_space = nn.Linear(dim, vocab_dim) # Vertex-specific projections for fine-grained alignment self.vertex_projections = nn.ModuleList([ nn.Linear(vocab_dim, vocab_dim, bias=False) for _ in range(5) ]) # Temperature for alignment scores self.temperature = nn.Parameter(torch.ones(1)) self.norm = nn.LayerNorm(dim) self.dropout = nn.Dropout(dropout) def forward(self, patches: torch.Tensor, pentachora: torch.Tensor) -> torch.Tensor: """ Compute alignment between patches and class pentachora. Args: patches: [B, N, D] - patch embeddings in model dimension pentachora: [C, 5, vocab_dim] - class pentachora in vocabulary dimension Returns: [B, N, C] - alignment scores """ B, N, D = patches.shape C = pentachora.shape[0] # Normalize patches patches = self.norm(patches) # Project patches to vocabulary space patches_vocab = self.to_vocab_space(patches) # [B, N, vocab_dim] patches_vocab = F.normalize(patches_vocab, dim=-1) # Compute alignment with each vertex alignments = [] for v in range(5): # Apply vertex-specific transformation patches_v = self.vertex_projections[v](patches_vocab) patches_v = F.normalize(patches_v, dim=-1) # Get vertex v of all classes vertex_v = F.normalize(pentachora[:, v, :], dim=-1) # [C, vocab_dim] # Compute alignment scores alignment = torch.matmul(patches_v, vertex_v.T) / self.temperature # [B, N, C] alignments.append(alignment) # Average alignments across vertices alignments = torch.stack(alignments, dim=-1).mean(dim=-1) # [B, N, C] return self.dropout(alignments) # ============================================ # MLP BLOCK # ============================================ class MLP(nn.Module): """MLP block with GELU activation.""" def __init__(self, in_features: int, hidden_features: Optional[int] = None, out_features: Optional[int] = None, dropout: float = 0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.act = nn.GELU() self.drop1 = nn.Dropout(dropout) self.fc2 = nn.Linear(hidden_features, out_features) self.drop2 = nn.Dropout(dropout) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.fc1(x) x = self.act(x) x = self.drop1(x) x = self.fc2(x) x = self.drop2(x) return x # ============================================ # VIT BLOCK WITH GEOMETRIC ATTENTION # ============================================ class PentachoronViTBlock(nn.Module): """ViT block with geometric attention for structured layers.""" def __init__(self, dim: int, heads: int = 8, mlp_ratio: float = 4.0, use_mesh: bool = True, dropout: float = 0., attn_dropout: float = 0., drop_path: float = 0.): super().__init__() self.norm1 = nn.LayerNorm(dim) # Use GeometricAttention for structured layers, standard for others if use_mesh: self.attn = GeometricAttention( dim=dim, num_heads=heads, num_regions=min(dim // heads, 16), config=GeometricConfig(), dropout=attn_dropout ) else: # Standard multi-head attention for later layers self.attn = nn.MultiheadAttention(dim, heads, dropout=attn_dropout, batch_first=True) self.use_mesh = use_mesh self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity() self.norm2 = nn.LayerNorm(dim) mlp_hidden = int(dim * mlp_ratio) self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden, dropout=dropout) self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity() def forward(self, x: torch.Tensor, preserve_structure: bool = True) -> torch.Tensor: if self.use_mesh: # GeometricAttention attn_out, _ = self.attn(self.norm1(x)) x = x + self.drop_path1(attn_out) else: # Standard attention normalized = self.norm1(x) attn_out, _ = self.attn(normalized, normalized, normalized) x = x + self.drop_path1(attn_out) x = x + self.drop_path2(self.mlp(self.norm2(x))) return x # ============================================ # PATCH EMBEDDING # ============================================ class PatchEmbed(nn.Module): """2D Image to Patch Embedding.""" def __init__(self, img_size: int = 32, patch_size: int = 4, in_chans: int = 3, embed_dim: int = 512): super().__init__() self.img_size = img_size self.patch_size = patch_size self.num_patches = (img_size // patch_size) ** 2 self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) self.norm = nn.LayerNorm(embed_dim) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.proj(x) x = rearrange(x, 'b c h w -> b (h w) c') x = self.norm(x) return x # ============================================ # PENTACHORA VISION TRANSFORMER (FIXED) # ============================================ class PentachoraViT(nn.Module): """ Vision Transformer with pentachoron-based hierarchical CLS tokens and geometric vocabulary integration. FIXED: CLS tokens now properly reference vocabulary embeddings. """ def __init__(self, config: Optional[PentachoraConfig] = None, **kwargs): super().__init__() # Use config or kwargs if config is not None: cfg = config else: cfg = PentachoraConfig(**kwargs) self.config = cfg self.num_classes = cfg.num_classes self.dim = cfg.dim self.depth = cfg.depth self.preserve_structure_until_layer = cfg.preserve_structure_until_layer # Set vocabulary dimension if cfg.vocab_dim is not None: self.vocab_dim = cfg.vocab_dim elif 'vocab_dim' in kwargs: self.vocab_dim = kwargs['vocab_dim'] else: self.vocab_dim = cfg.dim # Patch embedding self.patch_embed = PatchEmbed( cfg.img_size, cfg.patch_size, 3, cfg.dim ) num_patches = self.patch_embed.num_patches # Positional embedding self.pos_embed = nn.Parameter(torch.randn(1, num_patches, cfg.dim) * 0.02) self.pos_drop = nn.Dropout(cfg.dropout_rate) # CLS tokens with pentachoron structure self.cls_tokens = HierarchicalPentachoronCLS(cfg.dim, self.vocab_dim, cfg.num_classes) # Geometric projection layer self.geometric_proj = GeometricProjection(cfg.dim, self.vocab_dim, cfg.num_classes, cfg.dropout_rate) # Initialize from vocabulary if provided if cfg.vocab is not None: self._init_from_vocab(cfg.vocab) # Stochastic depth decay rule dpr = [x.item() for x in torch.linspace(0, cfg.drop_path_rate, cfg.depth)] # Transformer blocks with geometric attention self.blocks = nn.ModuleList([ PentachoronViTBlock( dim=cfg.dim, heads=cfg.heads, mlp_ratio=cfg.mlp_ratio, use_mesh=(cfg.use_mesh_attention and i < cfg.preserve_structure_until_layer), dropout=cfg.dropout_rate, attn_dropout=cfg.dropout_rate, drop_path=dpr[i] ) for i in range(cfg.depth) ]) # Final norm self.norm = nn.LayerNorm(cfg.dim) # Classification heads # Primary head uses prototypes for classification self.use_prototype_classifier = True if self.use_prototype_classifier: # No learnable parameters - uses class prototypes directly self.head = None else: # Traditional linear head self.head = nn.Linear(cfg.dim, cfg.num_classes) # Auxiliary head for vertex tokens self.head_aux = nn.Linear(cfg.dim * 5, cfg.num_classes) # Initialize weights self.apply(self._init_weights) def _init_weights(self, m: nn.Module): """Initialize model weights.""" if isinstance(m, nn.Linear): nn.init.trunc_normal_(m.weight, std=0.02) if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) elif isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) def _init_from_vocab(self, vocab): """Initialize class pentachora from geometric vocabulary.""" try: print("Initializing pentachora from vocabulary...") if not hasattr(vocab, 'encode_batch'): print("Vocabulary provided but encode_batch method not found, using random initialization") return # Get CIFAR-100 class names class_names = self._get_cifar100_classes() # Generate pentachora for all classes pentachora_list = vocab.encode_batch(class_names[:self.num_classes], generate=True) pentachora = np.stack(pentachora_list, axis=0) # Get actual dimensions from the encoded data actual_vocab_dim = pentachora.shape[-1] print(f"Encoded pentachora shape: {pentachora.shape}") print(f"Detected vocabulary dimension: {actual_vocab_dim}") # Validate basic shape requirements if pentachora.shape[0] != self.num_classes or pentachora.shape[1] != 5: print(f"Invalid shape: expected ({self.num_classes}, 5, ?), got {pentachora.shape}") print("Using random initialization") return # Update vocabulary dimension self.vocab_dim = actual_vocab_dim self.cls_tokens.vocab_dim = actual_vocab_dim self.geometric_proj.vocab_dim = actual_vocab_dim # Replace class_pentachora with the loaded vocabulary self.cls_tokens.class_pentachora = nn.Parameter( torch.tensor(pentachora, dtype=torch.float32) ) # Update/create projection layer if dimensions differ if actual_vocab_dim != self.dim: self.cls_tokens.vocab_to_model = nn.Linear(actual_vocab_dim, self.dim) else: self.cls_tokens.vocab_to_model = nn.Identity() # Rebuild geometric projection components self.geometric_proj.to_vocab_space = nn.Linear(self.dim, actual_vocab_dim) self.geometric_proj.vertex_projections = nn.ModuleList([ nn.Linear(actual_vocab_dim, actual_vocab_dim, bias=False) for _ in range(5) ]) # Re-initialize the new layers nn.init.xavier_uniform_(self.geometric_proj.to_vocab_space.weight) for proj in self.geometric_proj.vertex_projections: nn.init.xavier_uniform_(proj.weight) if actual_vocab_dim != self.dim: nn.init.xavier_uniform_(self.cls_tokens.vocab_to_model.weight) print(f"✓ Successfully initialized {self.num_classes} class pentachora from vocabulary") print(f" Vocabulary dimension: {actual_vocab_dim}") print(f" Model internal dimension: {self.dim}") print(f" CLS tokens now reference vocabulary embeddings") except Exception as e: print(f"Error initializing from vocabulary: {e}") print("Using random initialization") def _get_cifar100_classes(self): """Get CIFAR-100 class names.""" return [ 'apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel', 'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock', 'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup', 'dinosaur', 'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster', 'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion', 'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse', 'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear', 'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table', 'tank', 'telephone', 'television', 'tiger', 'tractor', 'train', 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman', 'worm' ] def forward_features(self, x: torch.Tensor, class_indices: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]: """ Extract features from input. Args: x: Input images [B, 3, H, W] class_indices: Optional class indices for class-aware CLS tokens [B] """ B = x.shape[0] # Patch embedding x = self.patch_embed(x) x = x + self.pos_embed x = self.pos_drop(x) # Get hierarchical CLS tokens (now properly using vocabulary) global_cls, vertex_cls = self.cls_tokens(B, class_indices) # Concatenate CLS tokens with patches x = torch.cat([global_cls, vertex_cls, x], dim=1) # Apply transformer blocks for i, block in enumerate(self.blocks): preserve = i < self.preserve_structure_until_layer x = block(x, preserve_structure=preserve) # Apply final norm x = self.norm(x) # Split tokens global_cls = x[:, 0] vertex_cls = x[:, 1:6] patches = x[:, 6:] return { 'global_cls': global_cls, 'vertex_cls': vertex_cls, 'patches': patches } def forward(self, x: torch.Tensor, targets: Optional[torch.Tensor] = None) -> Dict[str, torch.Tensor]: """ Forward pass through the model. Args: x: Input images [B, 3, H, W] targets: Optional target labels for class-aware processing [B] """ # During training, use target labels for class-specific CLS initialization class_indices = targets if self.training and targets is not None else None features = self.forward_features(x, class_indices) # Primary classification using prototype matching if self.use_prototype_classifier: # Get class prototypes from vocabulary prototypes = self.cls_tokens.get_class_prototypes() # [C, D] prototypes = F.normalize(prototypes, dim=-1) # Normalize global CLS tokens global_cls_norm = F.normalize(features['global_cls'], dim=-1) # [B, D] # Compute similarity to prototypes logits = torch.matmul(global_cls_norm, prototypes.T) * 20.0 # Scale for better gradients else: # Traditional linear classification logits = self.head(features['global_cls']) # Auxiliary classification using vertex tokens B = features['vertex_cls'].shape[0] vertex_flat = features['vertex_cls'].reshape(B, -1) aux_logits = self.head_aux(vertex_flat) # Geometric alignment scores geometric_alignments = self.geometric_proj( features['patches'], self.cls_tokens.class_pentachora ) return { 'logits': logits, 'aux_logits': aux_logits, 'geometric_alignments': geometric_alignments, 'vertex_cls': features['vertex_cls'], 'global_cls': features['global_cls'], 'patches': features['patches'] } # ============================================ # LOSS FUNCTIONS # ============================================ class PentachoraLoss(nn.Module): """Combined loss for PentachoraViT training.""" def __init__(self, aux_weight: float = 0.3, geo_weight: float = 0.1, smoothing: float = 0.0): super().__init__() self.aux_weight = aux_weight self.geo_weight = geo_weight self.criterion = nn.CrossEntropyLoss(label_smoothing=smoothing) def forward(self, outputs: Dict[str, torch.Tensor], targets: torch.Tensor) -> torch.Tensor: """Compute combined loss.""" # Primary classification loss loss = self.criterion(outputs['logits'], targets) # Auxiliary loss from vertex tokens if 'aux_logits' in outputs and self.aux_weight > 0: aux_loss = self.criterion(outputs['aux_logits'], targets) loss = loss + self.aux_weight * aux_loss # Geometric alignment loss if 'geometric_alignments' in outputs and self.geo_weight > 0: # Average over patches geo_logits = outputs['geometric_alignments'].mean(dim=1) geo_loss = self.criterion(geo_logits, targets) loss = loss + self.geo_weight * geo_loss return loss # ============================================ # MODEL REGISTRY AND BUILDERS # ============================================ MODEL_CONFIGS = { 'pentachora_spark': PentachoraConfig( dim=100, depth=5, heads=4, mlp_ratio=4.0, preserve_structure_until_layer=1, dropout_rate=0.0, drop_path_rate=0.0 ), 'pentachora_tiny': PentachoraConfig( dim=384, depth=12, heads=6, mlp_ratio=4.0, preserve_structure_until_layer=6, dropout_rate=0.1, drop_path_rate=0.1 ), 'pentachora_small': PentachoraConfig( dim=512, depth=12, heads=8, mlp_ratio=4.0, preserve_structure_until_layer=6, dropout_rate=0.1, drop_path_rate=0.1 ), 'pentachora_base': PentachoraConfig( dim=768, depth=12, heads=12, mlp_ratio=4.0, preserve_structure_until_layer=8, dropout_rate=0.1, drop_path_rate=0.2 ), 'pentachora_large': PentachoraConfig( dim=1024, depth=24, heads=16, mlp_ratio=4.0, preserve_structure_until_layer=12, dropout_rate=0.1, drop_path_rate=0.3 ), } def create_pentachora_vit(variant: str = 'pentachora_small', pretrained: bool = False, **kwargs) -> PentachoraViT: """ Create PentachoraViT model. Args: variant: Model variant name pretrained: Whether to load pretrained weights **kwargs: Override config parameters (including vocab_dim) Returns: PentachoraViT model """ if variant not in MODEL_CONFIGS: raise ValueError(f"Unknown variant: {variant}. Choose from {list(MODEL_CONFIGS.keys())}") config = MODEL_CONFIGS[variant] # Override config with kwargs for key, value in kwargs.items(): setattr(config, key, value) model = PentachoraViT(config) if pretrained: warnings.warn("Pretrained weights not available yet") return model # Convenience functions for each variant def pentachora_vit_spark(pretrained: bool = False, **kwargs) -> PentachoraViT: """Create spark variant (smallest).""" return create_pentachora_vit('pentachora_spark', pretrained=pretrained, **kwargs) def pentachora_vit_tiny(pretrained: bool = False, **kwargs) -> PentachoraViT: """Create tiny variant.""" return create_pentachora_vit('pentachora_tiny', pretrained=pretrained, **kwargs) def pentachora_vit_small(pretrained: bool = False, **kwargs) -> PentachoraViT: """Create small variant.""" return create_pentachora_vit('pentachora_small', pretrained=pretrained, **kwargs) def pentachora_vit_base(pretrained: bool = False, **kwargs) -> PentachoraViT: """Create base variant.""" return create_pentachora_vit('pentachora_base', pretrained=pretrained, **kwargs) def pentachora_vit_large(pretrained: bool = False, **kwargs) -> PentachoraViT: """Create large variant.""" return create_pentachora_vit('pentachora_large', pretrained=pretrained, **kwargs) # ============================================ # TRAINING UTILITIES # ============================================ def get_parameter_groups(model: PentachoraViT, weight_decay: float = 0.05) -> List[Dict[str, Any]]: """ Get parameter groups for optimizer with weight decay handling. Args: model: PentachoraViT model weight_decay: Weight decay value Returns: List of parameter group dictionaries """ no_decay = ['bias', 'norm', 'LayerNorm'] decay_params = [] no_decay_params = [] for name, param in model.named_parameters(): if not param.requires_grad: continue if any(nd in name for nd in no_decay): no_decay_params.append(param) else: decay_params.append(param) return [ {'params': decay_params, 'weight_decay': weight_decay}, {'params': no_decay_params, 'weight_decay': 0.0} ] def count_parameters(model: nn.Module) -> Dict[str, int]: """Count model parameters.""" total = sum(p.numel() for p in model.parameters()) trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) return { 'total': total, 'trainable': trainable, 'non_trainable': total - trainable } # ============================================ # INFERENCE UTILITIES # ============================================ @torch.no_grad() def extract_features(model: PentachoraViT, images: torch.Tensor, feature_type: str = 'global_cls') -> torch.Tensor: """ Extract features from images using the model. Args: model: PentachoraViT model images: Input images [B, 3, H, W] feature_type: Type of features to extract - 'global_cls': Global CLS token - 'vertex_cls': Vertex CLS tokens - 'patches': Patch embeddings Returns: Extracted features """ model.eval() features = model.forward_features(images) return features.get(feature_type, features['global_cls']) # ============================================ # EXAMPLE USAGE AND TESTING # ============================================ def test_model(): """Test model creation and forward pass.""" print("Testing PentachoraViT Model with Geometric Attention") print("=" * 50) # Test different variants variants = ['pentachora_spark', 'pentachora_tiny', 'pentachora_small'] for variant in variants: print(f"\nTesting {variant}:") # Create model with vocab_dim model = create_pentachora_vit( variant=variant, img_size=32, patch_size=4, num_classes=100, vocab_dim=64 # Test with 64-dim vocabulary ) # Count parameters params = count_parameters(model) print(f" Total parameters: {params['total']:,}") print(f" Trainable parameters: {params['trainable']:,}") # Test forward pass x = torch.randn(2, 3, 32, 32) outputs = model(x) print(f" Output shapes:") print(f" Logits: {outputs['logits'].shape}") print(f" Aux logits: {outputs['aux_logits'].shape}") print(f" Geometric alignments: {outputs['geometric_alignments'].shape}") # Test loss computation loss_fn = PentachoraLoss() targets = torch.randint(0, 100, (2,)) loss = loss_fn(outputs, targets) print(f" Loss: {loss.item():.4f}") # Test feature extraction features = extract_features(model, x, 'global_cls') print(f" Extracted features shape: {features.shape}") print("\n" + "=" * 50) print("All tests passed!") if __name__ == "__main__": # Run tests test_model() # Example: Create model for training with vocabulary print("\nExample: Creating model for training with 64-dim vocabulary") model = pentachora_vit_spark( img_size=32, patch_size=4, num_classes=100, vocab_dim=64, # Specify vocabulary dimension dropout_rate=0.0, drop_path_rate=0.0 ) # Get parameter groups for optimizer param_groups = get_parameter_groups(model, weight_decay=0.05) print(f"Number of parameter groups: {len(param_groups)}") # Example batch images = torch.randn(4, 3, 32, 32) targets = torch.randint(0, 100, (4,)) # Forward pass outputs = model(images) # Compute loss criterion = PentachoraLoss(aux_weight=0.3, geo_weight=0.1) loss = criterion(outputs, targets) print(f"Training loss: {loss.item():.4f}") print("\nModel ready for training with geometric attention!")