vit_zana_v3.py

"""
Baseline Vision Transformer with Frozen Pentachora Embeddings
Clean architecture with geometric semantic anchors
Assumes PentachoronStabilizer is loaded externally
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from einops import rearrange
import math
from typing import Optional, Tuple, Dict, Any


class PentachoraEmbedding(nn.Module):
    """
    A single frozen pentachora embedding (5 vertices in geometric space).
    Accepts pre-computed vertices only. No random initialization.
    """
    
    def __init__(self, vertices: torch.Tensor):
        super().__init__()
        #assert vertices.shape == (5, 128), f"Expected shape (5, 128), got {vertices.shape}"
        
        self.embed_dim = vertices.shape[-1]
        
        # Store provided vertices as frozen buffer
        self.register_buffer('vertices', vertices)
        self.vertices.requires_grad = False
        
        # Precompute normalized versions and centroid
        with torch.no_grad():
            self.register_buffer('vertices_norm', F.normalize(self.vertices, dim=-1))
            self.register_buffer('centroid', self.vertices.mean(dim=0))
            self.register_buffer('centroid_norm', F.normalize(self.centroid, dim=-1))
    
    def get_vertices(self) -> torch.Tensor:
        """Get all 5 vertices."""
        return self.vertices
    
    def get_centroid(self) -> torch.Tensor:
        """Get the centroid of the pentachora."""
        return self.centroid
    
    def compute_rose_score(self, features: torch.Tensor) -> torch.Tensor:
        """
        Compute Rose similarity score with this pentachora.
        Uses external PentachoronStabilizer.rose_score_magnitude
        """
        # Prepare vertices for rose scoring
        verts = self.vertices.unsqueeze(0)  # [1, 5, D]
        if features.dim() == 1:
            features = features.unsqueeze(0)
        
        # Expand vertices to batch size if needed
        B = features.shape[0]
        if B > 1:
            verts = verts.expand(B, -1, -1)
        
        return PentachoronStabilizer.rose_score_magnitude(features, verts)
    
    def compute_similarity(self, features: torch.Tensor, mode: str = 'centroid') -> torch.Tensor:
        """
        Compute similarity between features and this pentachora.
        
        Args:
            features: [batch, dim] or [batch, seq, dim]
            mode: 'centroid', 'max' (max over vertices), or 'rose' (Rose score)
        
        Returns:
            similarities: [batch] or [batch, seq]
        """
        if mode == 'rose':
            return self.compute_rose_score(features)
        
        features_norm = F.normalize(features, dim=-1)
        
        if mode == 'centroid':
            # Dot product with centroid
            return torch.matmul(features_norm, self.centroid_norm)
        else:  # mode == 'max'
            # Max similarity across vertices
            sims = torch.matmul(features_norm, self.vertices_norm.T)
            return sims.max(dim=-1)[0]


class TransformerBlock(nn.Module):
    """Standard transformer block with multi-head attention and MLP."""
    
    def __init__(
        self,
        dim: int,
        num_heads: int = 8,
        mlp_ratio: float = 4.0,
        dropout: float = 0.0,
        attn_dropout: float = 0.0
    ):
        super().__init__()
        
        self.norm1 = nn.LayerNorm(dim)
        self.attn = nn.MultiheadAttention(
            dim, 
            num_heads, 
            dropout=attn_dropout,
            batch_first=True
        )
        
        self.norm2 = nn.LayerNorm(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = nn.Sequential(
            nn.Linear(dim, mlp_hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(mlp_hidden_dim, dim),
            nn.Dropout(dropout)
        )
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # Self-attention
        x_norm = self.norm1(x)
        attn_out, _ = self.attn(x_norm, x_norm, x_norm)
        x = x + attn_out
        
        # MLP
        x = x + self.mlp(self.norm2(x))
        
        return x


class BaselineViT(nn.Module):
    """
    Clean baseline Vision Transformer with frozen pentachora embeddings.
    """
    
    def __init__(
        self,
        pentachora_list: list,  # List of torch.Tensor, each [5, vocab_dim]
        vocab_dim: int = 256,
        img_size: int = 32,
        patch_size: int = 4,
        embed_dim: int = 512,
        depth: int = 12,
        num_heads: int = 8,
        mlp_ratio: float = 4.0,
        dropout: float = 0.0,
        attn_dropout: float = 0.0,
        similarity_mode: str = 'rose'  # 'centroid', 'max', or 'rose'
    ):
        super().__init__()
        
        # Validate pentachora list
        assert isinstance(pentachora_list, list), f"Expected list, got {type(pentachora_list)}"
        assert len(pentachora_list) > 0, "Empty pentachora list"
        
        # Validate each pentachora
        for i, penta in enumerate(pentachora_list):
            assert isinstance(penta, torch.Tensor), f"Item {i} is not a tensor"
        
        self.num_classes = len(pentachora_list)
        self.embed_dim = embed_dim
        self.num_patches = (img_size // patch_size) ** 2
        self.similarity_mode = similarity_mode
        self.pentachora_dim = vocab_dim
        
        # Create individual pentachora embeddings from list
        self.class_pentachora = nn.ModuleList([
            PentachoraEmbedding(vertices=penta)
            for penta in pentachora_list
        ])
        
        # Patch embedding
        self.patch_embed = nn.Conv2d(3, embed_dim, kernel_size=patch_size, stride=patch_size)
        
        # CLS token - learnable
        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        
        # Position embeddings
        self.pos_embed = nn.Parameter(torch.zeros(1, 1 + self.num_patches, embed_dim))
        self.pos_drop = nn.Dropout(dropout)
        
        # Transformer blocks
        self.blocks = nn.ModuleList([
            TransformerBlock(
                dim=embed_dim,
                num_heads=num_heads,
                mlp_ratio=mlp_ratio,
                dropout=dropout,
                attn_dropout=attn_dropout
            )
            for i in range(depth)
        ])
        
        # Final norm
        self.norm = nn.LayerNorm(embed_dim)
        
        # Project to pentachora dimension if needed
        if self.pentachora_dim != embed_dim:
            self.to_pentachora_dim = nn.Linear(embed_dim, self.pentachora_dim)
        else:
            self.to_pentachora_dim = nn.Identity()
        
        # Temperature for similarity-based classification
        self.temperature = nn.Parameter(torch.ones(1) * np.log(1/0.07))
        
        self.register_buffer(
            'all_centroids',
            torch.stack([penta.centroid for penta in self.class_pentachora])
        )
        self.register_buffer(
            'all_centroids_norm',
            F.normalize(self.all_centroids, dim=-1)
        )

        # Initialize weights
        self.init_weights()
    
    def init_weights(self):
        """Initialize model weights."""
        nn.init.trunc_normal_(self.cls_token, std=0.02)
        nn.init.trunc_normal_(self.pos_embed, std=0.02)
        
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.trunc_normal_(m.weight, std=0.02)
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, nn.LayerNorm):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
    
    # Then get_class_centroids becomes:
    def get_class_centroids(self) -> torch.Tensor:
        return self.all_centroids_norm
        
    def compute_pentachora_similarities(self, features: torch.Tensor) -> torch.Tensor:
        """
        Compute similarities between features and all class pentachora (vectorized).
        """
        if self.similarity_mode == 'rose':
            # Stack all vertices into single tensor for batch Rose scoring
            all_vertices = torch.stack([penta.vertices for penta in self.class_pentachora])  # [100, 5, vocab_dim]
            # Expand features for batch computation
            features_exp = features.unsqueeze(1).expand(-1, self.num_classes, -1)  # [B, 100, vocab_dim]
            # Compute Rose scores in parallel
            return PentachoronStabilizer.rose_score_magnitude(features_exp.reshape(-1, self.embed_dim), all_vertices.repeat(features.shape[0], 1, 1)).reshape(features.shape[0], -1)
        else:
            # Stack all centroids
            centroids = torch.stack([penta.centroid_norm for penta in self.class_pentachora])  # [100, vocab_dim]
            features_norm = F.normalize(features, dim=-1)  # [B, vocab_dim]
            return torch.matmul(features_norm, centroids.T)  # [B, 100]

    
    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
        """Extract features from images."""
        B = x.shape[0]
        
        # Patch embedding
        x = self.patch_embed(x)  # [B, embed_dim, H', W']
        x = x.flatten(2).transpose(1, 2)  # [B, num_patches, embed_dim]
        
        # Add CLS token
        cls_tokens = self.cls_token.expand(B, -1, -1)
        x = torch.cat([cls_tokens, x], dim=1)
        
        # Add position embeddings
        x = x + self.pos_embed
        x = self.pos_drop(x)
        
        # Apply transformer blocks
        for block in self.blocks:
            x = block(x)
        
        # Final norm
        x = self.norm(x)
        
        # Return CLS token
        return x[:, 0]
    
    def forward(self, x: torch.Tensor, return_features: bool = False) -> Dict[str, torch.Tensor]:
        """
        Forward pass.
        
        Returns dict with:
            - logits: classification logits
            - features: CLS features (if return_features=True)
            - similarities: raw similarities to pentachora
        """
        features = self.forward_features(x)
        
        output = {}
        
        # Project to pentachora dimension
        features_proj = self.to_pentachora_dim(features)
        
        # Compute similarities based on mode
        if self.similarity_mode == 'rose':
            # Use Rose scoring
            similarities = self.compute_pentachora_similarities(features_proj)
        else:
            # Use centroid or max similarity
            features_norm = F.normalize(features_proj, dim=-1)
            centroids = self.get_class_centroids()
            similarities = torch.matmul(features_norm, centroids.T)
        
        # Scale by temperature
        logits = similarities * self.temperature.exp()
        
        output['logits'] = logits
        output['similarities'] = similarities
        
        if return_features:
            output['features'] = features
        
        return output


# Test - requires external setup
if __name__ == "__main__":
    print("BaselineViT requires:")
    print("  1. PentachoronStabilizer loaded externally")
    print("  2. pentachora_batch tensor [num_classes, 5, vocab_dim]")
    print("\nNo random initialization. No fallbacks.")