# Traditional Attention: O(n²) complexity
def standard_attention(Q, K, V):
    scores = torch.matmul(Q, K.transpose(-2, -1))
    return torch.matmul(softmax(scores), V)

# Gemma 4 Hybrid: O(n) + selective O(n²)
def hybrid_attention(Q, K, V, window_size=512):
    # Local sliding window (O(n))
    local_attn = sliding_window_attention(Q, K, V, window_size)
    
    # Global attention for important tokens (selective O(n²))
    importance_scores = compute_importance(Q, K)
    global_tokens = select_top_k(importance_scores, k=64)
    global_attn = full_attention(Q[global_tokens], K, V)
    
    return merge_attention_outputs(local_attn, global_attn)
                        

Training Phases:
  Phase 1: Base pretraining (8T tokens)
    - Web crawl, books, academic papers
    - Code repositories, documentation
    
  Phase 2: Constitutional training (2T tokens)
    - Factual accuracy over confidence
    - Helpful but harmless responses
    - Transparent reasoning chains
    
  Phase 3: Human preference alignment (1T tokens)
    - 500K+ human comparisons
    - Bradley-Terry preference learning
    - PPO with KL divergence constraints
    
  Phase 4: Tool-use fine-tuning (1T tokens)
    - Function calling examples
    - API interaction patterns
    - Safety-constrained execution
                        

# Custom TPU v5 Optimization
class GemmaTPUKernel:
    def __init__(self):
        self.memory_hierarchy = {
            'hbm': 32_000_000_000,      # 32GB HBM
            'vmem': 50_000_000,         # 50MB Vector Memory  
            'smem': 8_000_000           # 8MB Scalar Memory
        }
        
    def optimized_attention(self, q, k, v):
        # Hand-optimized for TPU v5 architecture
        # - Tile-based computation for memory efficiency
        # - Pipelined execution across cores
        # - Mixed precision (BF16/FP32) optimization
        return tpu_fused_attention(q, k, v, 
                                 precision='mixed',
                                 tiling_strategy='optimal')

# Result: 4096 TPU v5 pods coordination
# 2.5x faster training than TPU v4
                        

# Unified Multimodal Architecture
class GemmaMultimodal:
    def __init__(self):
        self.text_encoder = TransformerLayers(52)      # 52 layers
        self.vision_encoder = ViTLarge(24)             # 24 layers
        self.cross_modal = CrossAttention(8)           # 8 fusion layers
        
    def forward(self, text_tokens, image_patches):
        # Unified embedding space
        text_embeds = self.text_encoder(text_tokens)
        vision_embeds = self.vision_encoder(image_patches)
        
        # Adaptive fusion strategy
        if task_complexity == 'simple':
            return early_fusion(text_embeds, vision_embeds)
        elif task_complexity == 'complex':
            return late_fusion(text_embeds, vision_embeds)
        else:
            return adaptive_fusion(text_embeds, vision_embeds)
                        

# Speculative Decoding for 2-4x Speedup
class SpeculativeDecoder:
    def __init__(self, draft_model, target_model):
        self.draft = draft_model      # Gemma 4-1.5B (fast)
        self.target = target_model    # Gemma 4-26B (accurate)
    
    def generate(self, prompt, k=4):
        # Draft model generates k tokens quickly
        draft_tokens = self.draft.generate(prompt, max_tokens=k)
        
        # Target model validates in parallel
        logits = self.target.forward(prompt + draft_tokens)
        accepted_tokens = []
        
        for i, token in enumerate(draft_tokens):
            if self.accept_token(token, logits[i]):
                accepted_tokens.append(token)
            else:
                break  # Reject and resample from target
                
        return accepted_tokens

# Result: 2-4x faster generation with same quality
                        

# Edge Optimization Pipeline
class EdgeOptimizer:
    def optimize_for_edge(self, model, target_device):
        # 1. Model Pruning (50% weight reduction)
        pruned_model = structured_prune(model, sparsity=0.5)
        
        # 2. Knowledge Distillation
        student_model = distill_knowledge(
            teacher=pruned_model,
            student_size='1.5B',
            temperature=3.0
        )
        
        # 3. Quantization (INT4 weights, INT8 activations)
        quantized_model = quantize_model(
            student_model,
            weight_bits=4,
            activation_bits=8
        )
        
        # 4. Operator Fusion (kernel-level optimization)
        fused_model = fuse_operators(quantized_model, target_device)
        
        return fused_model

# Result: 10x smaller, 5x faster on edge devices