legacy/penta_vit_model_v1.py

"""
PentachoraViT: Vision Transformer with Pentachoron Geometric Structure
Enhanced with Geometric Attention for improved head cohesion and generalization
FIXED: All parameters initialized at module creation time (no lazy init)
"""

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 = 0.0
    drop_path_rate: float = 0.0
    aux_loss_weight: float = 0.0
    geo_loss_weight: float = 0.0
    vocab: Optional[Any] = None

    @property
    def num_patches(self) -> int:
        return (self.img_size // self.patch_size) ** 2

# ============================================
# GEOMETRIC ATTENTION COMPONENTS (FIXED INIT)
# ============================================

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 = 0.71
    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 - FIXED with immediate initialization."""

    def __init__(self, input_dim: int, num_regions: int, config: GeometricConfig, num_heads: int = 1, device=None):
        super().__init__()
        self.input_dim = input_dim
        self.num_regions = num_regions
        self.config = config
        self.num_heads = num_heads

        # Use CPU by default if device not specified
        if device is None:
            device = torch.device('cpu')
        
        # Create separate parameters for each head if num_heads > 1
        if num_heads > 1:
            self.to_nav = nn.Parameter(torch.randn(num_heads, input_dim, 4, device=device) * 0.02)
            self.vertex_w = nn.Parameter(torch.zeros(num_heads, num_regions, 5, device=device))
        else:
            self.to_nav = nn.Linear(input_dim, 4, bias=False)
            self.vertex_w = nn.Parameter(torch.zeros(num_regions, 5, device=device))

        # Pre-compute phase tensors for vectorization
        self.register_buffer('phase_cos', torch.cos(torch.tensor(config.phases, dtype=torch.float32, device=device)))
        self.register_buffer('phase_sin', torch.sin(torch.tensor(config.phases, dtype=torch.float32, device=device)))
        
        # Initialize geometry immediately at creation time
        self._init_geometry(device)

    def _init_geometry(self, device):
        """Initialize geometry at module creation time."""
        base = perfect_4simplex(device)

        if self.num_heads > 1:
            D = torch.zeros(self.num_heads, self.num_regions, 5, 4, device=device)
            S = torch.zeros(self.num_heads, self.num_regions, 5, 4, device=device)

            for h in range(self.num_heads):
                for r in range(self.num_regions):
                    D[h, 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[h, r] = (base @ rot) + self.config.shift
                    S[h, r] += self.config.jitter * torch.randn_like(S[h, r])
        else:
            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 - OPTIMIZED with vectorized phase computation."""
        if self.num_heads > 1:
            # Batched navigation for multiple heads
            BT, H, head_dim = x.shape
            
            # Batched transformation
            nav_x = torch.einsum('bhi,hio->bho', x, self.to_nav)  # [BT, H, 4]
            
            # Dispatcher scores
            nav_x_disp = nav_x.view(BT, H, 1, 1, 4)
            D_exp = self.D.unsqueeze(0)  # [1, H, regions, 5, 4]
            d_disp = torch.norm(nav_x_disp - D_exp, dim=-1)
            s_disp = -softmin_over_last(d_disp, self.config.softmin_tau)
            
            # OPTIMIZED: Vectorized phase computation (no loop)
            cos_phases = self.phase_cos.view(-1, 1, 1, 1, 1)
            sin_phases = self.phase_sin.view(-1, 1, 1, 1, 1)
            
            # Compute all phase variants at once [phases, H, regions, 5, 4]
            Vt_all = cos_phases * self.D.unsqueeze(0) + sin_phases * self.S.unsqueeze(0)
            
            # Apply vertex weighting to all phases
            w = F.softmax(self.vertex_w, dim=-1)
            w_exp = w.unsqueeze(0).unsqueeze(-1)  # [1, H, regions, 5, 1]
            Vt_mean = Vt_all.mean(dim=3, keepdim=True)
            Vt_all = (1.0 - w_exp) * Vt_all + w_exp * Vt_mean
            
            # Compute all ribbon distances at once
            nav_x_ribbon = nav_x.view(BT, 1, H, 1, 1, 4)
            Vt_exp = Vt_all.unsqueeze(0)  # [1, phases, H, regions, 5, 4]
            d_ribbon_all = torch.norm(nav_x_ribbon - Vt_exp, dim=-1)
            s_ribbon_all = -softmin_over_last(d_ribbon_all, self.config.softmin_tau)
            s_ribbon = s_ribbon_all.mean(dim=1)  # Average over phases
            
            scores = self.config.fuse_alpha * s_ribbon + (1 - self.config.fuse_alpha) * s_disp
            scores = scores.reshape(BT * H, self.num_regions)
            
        else:
            # Original single-head navigation
            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)
            
            # OPTIMIZED: Vectorized phase computation for single head
            cos_phases = self.phase_cos.view(-1, 1, 1, 1)
            sin_phases = self.phase_sin.view(-1, 1, 1, 1)
            
            Vt_all = cos_phases * self.D.unsqueeze(0) + sin_phases * self.S.unsqueeze(0)
            w_expanded = w.unsqueeze(0).unsqueeze(-1)
            Vt_mean = Vt_all.mean(dim=2, keepdim=True)
            Vt_all = (1.0 - w_expanded) * Vt_all + w_expanded * Vt_mean
            
            nav_x_phase = nav_x[:, None, None, None, :]
            Vt_exp = Vt_all.unsqueeze(0)
            d_ribbon_all = torch.norm(nav_x_phase - Vt_exp, dim=-1)
            s_ribbon_all = -softmin_over_last(d_ribbon_all, self.config.softmin_tau)
            s_ribbon = s_ribbon_all.mean(dim=1)

            scores = self.config.fuse_alpha * s_ribbon + (1 - self.config.fuse_alpha) * s_disp

        diagnostics = {
            'dispatcher_scores': s_disp.detach() if self.num_heads == 1 else s_disp.reshape(BT * H, -1).detach(),
            'ribbon_scores': s_ribbon.detach() if self.num_heads == 1 else s_ribbon.reshape(BT * H, -1).detach()
        }

        return {'scores': scores, 'diagnostics': diagnostics}

class GeometricAttention(nn.Module):
    """Multi-head geometric attention with Q-K alignment - FIXED with proper device handling."""

    def __init__(self, dim: int, num_heads: int = 8, num_regions: Optional[int] = None,
                 config: Optional[GeometricConfig] = None, dropout: float = 0.0, device=None):
        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)

        # Create batched navigators with device
        self.q_navigator = GeometricNavigator(self.head_dim, num_regions, config, num_heads=num_heads, device=device)
        self.k_navigator = GeometricNavigator(self.head_dim, num_regions, config, num_heads=num_heads, device=device)

        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)

        # Prepare for batched navigation
        q_batched = q.transpose(1, 2).reshape(B * T, self.num_heads, self.head_dim)
        k_batched = k.transpose(1, 2).reshape(B * T, self.num_heads, self.head_dim)

        # Navigate all heads at once
        q_nav = self.q_navigator.navigate(q_batched)
        k_nav = self.k_navigator.navigate(k_batched)

        # Reshape scores back
        q_scores = q_nav['scores'].reshape(B, T, self.num_heads, -1).transpose(1, 2)
        k_scores = k_nav['scores'].reshape(B, T, self.num_heads, -1).transpose(1, 2)

        # OPTIMIZED: Compute attention for all heads at once using einsum
        scale = math.sqrt(q_scores.size(-1))
        attn = torch.einsum('bhqr,bhkr->bhqk', q_scores, k_scores) / scale

        if mask is not None:
            mask_expanded = mask.unsqueeze(1).unsqueeze(2)
            attn = attn.masked_fill(mask_expanded == 0, -1e9)

        attn = F.softmax(attn, dim=-1)
        attn = self.dropout(attn)

        # Apply attention to values
        out = torch.einsum('bhqk,bhkd->bhqd', attn, v)
        out = out.transpose(1, 2).reshape(B, T, D)
        
        output = self.out_proj(out)
        output = self.dropout(output)

        if return_diagnostics:
            return output, {'q_diagnostics': q_nav['diagnostics'], 'k_diagnostics': k_nav['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
# ============================================

class HierarchicalPentachoronCLS(nn.Module):
    """
    Hierarchical CLS structure with pentachoron geometry.
    Uses vocabulary embeddings for CLS tokens.
    """
    def __init__(self, dim: int, vocab_dim: int, num_classes: int = 100):
        super().__init__()
        self.dim = dim
        self.vocab_dim = vocab_dim
        self.num_classes = num_classes

        # Class-specific pentachora from vocabulary
        self.register_buffer('class_pentachora', 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
        self.vertex_weights = nn.Parameter(torch.ones(5) / 5)

        # Optional learnable offset
        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."""
        # Get class-specific pentachora
        class_pentachora = self.class_pentachora  # This is now a computed property
        
        if class_indices is not None and class_indices.shape[0] == batch_size:
            vertex_cls_vocab = class_pentachora[class_indices]
        else:
            vertex_cls_vocab = class_pentachora.mean(dim=0, keepdim=True)
            vertex_cls_vocab = vertex_cls_vocab.expand(batch_size, -1, -1)

        # Project from vocabulary dimension to model dimension
        vertex_cls = self.vocab_to_model(vertex_cls_vocab)
        vertex_cls = self.vertex_norm(vertex_cls)

        # Create global CLS as weighted combination
        weights = F.softmax(self.vertex_weights, dim=0)
        global_cls = torch.einsum('bvd,v->bd', vertex_cls, weights).unsqueeze(1)
        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."""
        class_pentachora = self.class_pentachora  # Get computed pentachora
        pentachora_model = self.vocab_to_model(class_pentachora)
        weights = F.softmax(self.vertex_weights, dim=0)
        prototypes = torch.einsum('cvd,v->cd', pentachora_model, weights)
        return prototypes

# ============================================
# GEOMETRIC PROJECTION LAYER
# ============================================

class GeometricProjection(nn.Module):
    """Project patches onto pentachoron geometry."""
    def __init__(self, dim: int, vocab_dim: int, num_classes: int = 100, dropout: float = 0.1):
        super().__init__()
        self.dim = dim
        self.vocab_dim = vocab_dim
        self.num_classes = num_classes

        # Projection from model dim to vocab dim
        self.to_vocab_space = nn.Linear(dim, vocab_dim)

        # Vertex-specific projections
        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."""
        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)
        patches_vocab = F.normalize(patches_vocab, dim=-1)

        # Compute alignment with each vertex
        alignments = []
        for v in range(5):
            patches_v = self.vertex_projections[v](patches_vocab)
            patches_v = F.normalize(patches_v, dim=-1)
            vertex_v = F.normalize(pentachora[:, v, :], dim=-1)
            alignment = torch.matmul(patches_v, vertex_v.T) / self.temperature
            alignments.append(alignment)

        # Average alignments across vertices
        alignments = torch.stack(alignments, dim=-1).mean(dim=-1)

        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., device=None):
        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,
                device=device
            )
        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
# ============================================

class PentachoraViT(nn.Module):
    """
    Vision Transformer with pentachoron-based hierarchical CLS tokens
    and geometric vocabulary integration.
    """
    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],
                device=torch.device('cpu')  # Initialize on CPU, will be moved later
            )
            for i in range(cfg.depth)
        ])

        # Final norm
        self.norm = nn.LayerNorm(cfg.dim)

        # Classification heads
        self.use_prototype_classifier = True
        if self.use_prototype_classifier:
            self.head = None
        else:
            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 = 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}")

        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."""
        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
        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."""
        # 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:
            prototypes = self.cls_tokens.get_class_prototypes()
            prototypes = F.normalize(prototypes, dim=-1)
            global_cls_norm = F.normalize(features['global_cls'], dim=-1)
            logits = torch.matmul(global_cls_norm, prototypes.T) * 20.0
        else:
            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:
            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_xs': PentachoraConfig(
        dim=100, depth=2, heads=10, mlp_ratio=4.0,
        preserve_structure_until_layer=1,
        dropout_rate=0.0, drop_path_rate=0.0
    ),
    '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_shock': PentachoraConfig(
        dim=100, depth=10, heads=5, mlp_ratio=4.0,
        patch_size=5, preserve_structure_until_layer=5,
        dropout_rate=0.0, drop_path_rate=0.0
    ),
    'pentachora_shock_xs_32d': PentachoraConfig(
        dim=32, depth=2, heads=8, mlp_ratio=4.0,
        preserve_structure_until_layer=4,
        dropout_rate=0.0, drop_path_rate=0.0
    ),
    'pentachora_shock_xs_64d': PentachoraConfig(
        dim=64, depth=2, heads=8, mlp_ratio=1.0,
        preserve_structure_until_layer=4,
        dropout_rate=0.0, drop_path_rate=0.0
    ),
    'pentachora_shock_xs_128d': PentachoraConfig(
        dim=128, depth=2, heads=8, mlp_ratio=2.0,
        preserve_structure_until_layer=4,
        vocab_dim=256,
        dropout_rate=0.0, drop_path_rate=0.0
    ),
    'vit_pixie_256_patch4': PentachoraConfig(
        dim=256, depth=10, heads=16, mlp_ratio=1.0,
        preserve_structure_until_layer=10,
        vocab_dim=256, patch_size=4,
        dropout_rate=0.0, drop_path_rate=0.0
    ),    
    'vit_pixie_256_patch2': PentachoraConfig(
        dim=256, depth=10, heads=16, mlp_ratio=1.0,
        preserve_structure_until_layer=10,
        vocab_dim=256, patch_size=2,
        dropout_rate=0.0, drop_path_rate=0.0
    ),
    'pentachora_shock_xs_256d': PentachoraConfig(
        dim=256, depth=2, heads=8, mlp_ratio=4.0,
        preserve_structure_until_layer=4, 
        vocab_dim=128,
        dropout_rate=0.0, drop_path_rate=0.0
    ),
    'pentachora_shock_xs_512d': PentachoraConfig(
        dim=512, depth=2, heads=8, mlp_ratio=4.0,
        preserve_structure_until_layer=4,
        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."""
    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_tiny(pretrained: bool = False, **kwargs) -> PentachoraViT:
    """Create spark variant (smallest)."""
    return create_pentachora_vit('pentachora_spark_xs', pretrained=pretrained, **kwargs)

def pentachora_shock_xs_64d(pretrained: bool = False, **kwargs) -> PentachoraViT:
    """Create shock xs 64d variant."""
    return create_pentachora_vit('pentachora_shock_xs_64d', pretrained=pretrained, **kwargs)

def pentachora_vit_spark(pretrained: bool = False, **kwargs) -> PentachoraViT:
    """Create spark variant."""
    return create_pentachora_vit('pentachora_spark', pretrained=pretrained, **kwargs)

def pentachora_shock_xs_32d(pretrained: bool = False, **kwargs) -> PentachoraViT:
    """Create shock xs 32d variant."""
    return create_pentachora_vit('pentachora_shock_xs_32d', pretrained=pretrained, **kwargs)

def pentachora_shock_xs_256d(pretrained: bool = False, **kwargs) -> PentachoraViT:
    """Create shock xs 256d variant."""
    return create_pentachora_vit('pentachora_shock_xs_256d', pretrained=pretrained, **kwargs)
    
def pentachora_shock_xs_512d(pretrained: bool = False, **kwargs) -> PentachoraViT:
    """Create shock xs 512d variant."""
    return create_pentachora_vit('pentachora_shock_xs_512d', pretrained=pretrained, **kwargs)

def pentachora_vit_shock(pretrained: bool = False, **kwargs) -> PentachoraViT:
    """Create shock variant."""
    return create_pentachora_vit('pentachora_shock', 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."""
    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."""
    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 Fixed PentachoraViT Model")
    print("=" * 50)

    # Test different variants
    variants = ['pentachora_spark', 'pentachora_shock_xs_256d', '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
        )

        # 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)
        
        # Time the forward pass
        if torch.cuda.is_available():
            model = model.cuda()
            x = x.cuda()
            torch.cuda.synchronize()
            
        import time
        start = time.time()
        outputs = model(x)
        if torch.cuda.is_available():
            torch.cuda.synchronize()
        end = time.time()

        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}")
        print(f"  Forward pass time: {(end - start)*1000:.2f}ms")

        # Test loss computation
        loss_fn = PentachoraLoss()
        targets = torch.randint(0, 100, (2,))
        if torch.cuda.is_available():
            targets = targets.cuda()
        loss = loss_fn(outputs, targets)
        print(f"  Loss: {loss.item():.4f}")

    print("\n" + "=" * 50)
    print("All tests passed!")

if __name__ == "__main__":
    # Run tests
    test_model()
    
    # Example: Create model for training
    print("\nExample: Creating model with proper initialization")
    model = pentachora_shock_xs_256d(
        img_size=32,
        num_classes=100,
        vocab_dim=100,
        dropout_rate=0.0,
        drop_path_rate=0.0
    )
    
    # All parameters are initialized immediately
    print(f"Model has {count_parameters(model)['total']:,} parameters")
    print("All geometric parameters initialized at creation time")
    
    # Move model to CUDA if available
    if torch.cuda.is_available():
        model = model.cuda()
        print("Model moved to CUDA")
    
    # Now torch.compile should work without issues
    if hasattr(torch, 'compile'):
        print("Compiling model with torch.compile...")
        try:
            model = torch.compile(model)
            print("✓ Model compiled successfully")
        except Exception as e:
            print(f"Compilation warning: {e}")
            print("Continuing without compilation")
    
    print("\nModel ready for training with all parameters properly initialized!")