性能调优

class PagedAttention:
    def __init__(self, block_size=16):
        self.block_size = block_size
        self.physical_blocks = []
        self.block_allocator = BlockAllocator()
    
    def allocate_kv_cache(self, sequence_length):
        """分页式KV缓存分配"""
        num_blocks = math.ceil(sequence_length / self.block_size)
        allocated_blocks = []
        
        for _ in range(num_blocks):
            block = self.block_allocator.allocate()
            allocated_blocks.append(block)
        
        return allocated_blocks
    
    def compute_attention(self, query, key_cache, value_cache, block_tables):
        """高效的分页注意力计算"""
        # 使用CUDA kernel实现高效的分页注意力
        return paged_attention_kernel(
            query, key_cache, value_cache, block_tables
        )

class KVCacheManager:
    def __init__(self, gpu_memory_utilization=0.9):
        self.gpu_memory = torch.cuda.get_device_properties().total_memory
        self.cache_memory = int(self.gpu_memory * gpu_memory_utilization)
        self.memory_pool = create_memory_pool(self.cache_memory)
    
    def preemptive_scheduling(self, requests):
        """抢占式调度释放KV缓存"""
        if self.memory_pool.is_full():
            # 找到优先级最低的请求进行抢占
            victim_request = self.find_preemption_victim(requests)
            self.evict_kv_cache(victim_request)
            
        return self.allocate_cache_blocks()

class KVCacheQuantization:
    def __init__(self, quantization_bits=8):
        self.bits = quantization_bits
        self.scale_factor = 2 ** (quantization_bits - 1) - 1
    
    def quantize_kv_cache(self, key_states, value_states):
        """KV缓存INT8量化"""
        # 计算量化参数
        k_scale = key_states.abs().max() / self.scale_factor
        v_scale = value_states.abs().max() / self.scale_factor
        
        # 执行量化
        k_quantized = torch.clamp(
            key_states / k_scale, -self.scale_factor, self.scale_factor
        ).round().to(torch.int8)
        
        v_quantized = torch.clamp(
            value_states / v_scale, -self.scale_factor, self.scale_factor
        ).round().to(torch.int8)
        
        return k_quantized, v_quantized, k_scale, v_scale
    
    def dequantize_kv_cache(self, k_quantized, v_quantized, k_scale, v_scale):
        """反量化恢复"""
        key_states = k_quantized.float() * k_scale
        value_states = v_quantized.float() * v_scale
        return key_states, value_states

class OmniQuant:
    def __init__(self, model, bits=4):
        self.model = model
        self.bits = bits
        self.learnable_scaling = True
    
    def quantize_weights_and_activations(self):
        """权重和激活值联合量化"""
        for name, module in self.model.named_modules():
            if isinstance(module, nn.Linear):
                # 权重量化
                quantized_weight = self.weight_quantization(module.weight)
                
                # 激活值量化（运行时）
                module.register_forward_hook(self.activation_quantization_hook)
                
                # 更新量化后的权重
                module.weight.data = quantized_weight
    
    def weight_quantization(self, weight):
        """可微分权重量化"""
        # 学习量化参数
        scale = nn.Parameter(torch.ones(weight.shape[0]))
        zero_point = nn.Parameter(torch.zeros(weight.shape[0]))
        
        # STE（直通估计器）实现可微分量化
        quantized = fake_quantize(weight, scale, zero_point, self.bits)
        return quantized

class ChunkedPrefillScheduler:
    def __init__(self, chunk_size=2048):
        self.chunk_size = chunk_size
        self.pending_prefills = []
        self.running_decodes = []
    
    def schedule_batch(self):
        """混合批次调度"""
        batch = []
        
        # 添加解码请求（内存密集型）
        decode_requests = self.get_decode_requests()
        batch.extend(decode_requests)
        
        # 添加分块的预填充请求（计算密集型）
        if self.pending_prefills:
            prefill_chunk = self.create_prefill_chunk()
            batch.append(prefill_chunk)
        
        return self.execute_mixed_batch(batch)
    
    def create_prefill_chunk(self):
        """创建预填充分块"""
        request = self.pending_prefills.pop(0)
        chunk_start = request.processed_tokens
        chunk_end = min(
            chunk_start + self.chunk_size, 
            len(request.input_tokens)
        )
        
        return PrefillChunk(
            request_id=request.id,
            tokens=request.input_tokens[chunk_start:chunk_end],
            chunk_id=request.chunk_count
        )

class PriorityQueueScheduler:
    def __init__(self):
        self.high_priority = PriorityQueue()
        self.normal_priority = PriorityQueue()
        self.batch_size = 32
    
    def schedule_requests(self):
        """多优先级调度避免队头阻塞"""
        batch = []
        
        # 优先处理高优先级请求
        while not self.high_priority.empty() and len(batch) < self.batch_size:
            request = self.high_priority.get()
            if self.can_fit_in_batch(request, batch):
                batch.append(request)
            else:
                self.high_priority.put(request)  # 放回队列
                break
        
        # 填充普通优先级请求
        while not self.normal_priority.empty() and len(batch) < self.batch_size:
            request = self.normal_priority.get()
            if self.can_fit_in_batch(request, batch):
                batch.append(request)
            else:
                self.normal_priority.put(request)
                break
        
        return batch

class SpeculativeDecoding:
    def __init__(self, draft_model, target_model, lookahead_steps=4):
        self.draft_model = draft_model  # 小模型，速度快
        self.target_model = target_model  # 大模型，质量高
        self.lookahead_steps = lookahead_steps
    
    def generate_with_speculation(self, input_ids):
        """投机解码生成"""
        generated_tokens = []
        current_ids = input_ids
        
        while len(generated_tokens) < max_length:
            # 步骤1：草稿模型快速生成候选tokens
            draft_tokens = self.draft_model.generate(
                current_ids, 
                max_new_tokens=self.lookahead_steps,
                do_sample=True
            )
            
            # 步骤2：目标模型并行验证
            acceptance_probs = self.target_model.compute_token_probs(
                current_ids, draft_tokens
            )
            
            # 步骤3：接受/拒绝决策
            accepted_tokens = self.accept_reject_tokens(
                draft_tokens, acceptance_probs
            )
            
            generated_tokens.extend(accepted_tokens)
            current_ids = torch.cat([current_ids, accepted_tokens])
            
            # 如果全部接受，获得最大加速
            if len(accepted_tokens) == self.lookahead_steps:
                speedup = self.lookahead_steps / 1  # 理论最大加速比
        
        return generated_tokens
    
    def accept_reject_tokens(self, draft_tokens, target_probs):
        """接受拒绝采样"""
        accepted = []
        
        for i, token in enumerate(draft_tokens):
            draft_prob = self.draft_model.get_token_prob(token)
            target_prob = target_probs[i]
            
            # 接受概率 = min(1, target_prob / draft_prob)
            accept_prob = min(1.0, target_prob / draft_prob)
            
            if random.random() < accept_prob:
                accepted.append(token)
            else:
                # 使用修正分布重新采样
                corrected_prob = (target_prob - draft_prob) / (1 - draft_prob)
                new_token = self.sample_from_corrected_dist(corrected_prob)
                accepted.append(new_token)
                break  # 拒绝后停止
        
        return accepted

# vLLM服务配置
vllm_config:
  model: "mistral-7b-instruct"
  gpu_memory_utilization: 0.9  # GPU内存利用率
  max_model_len: 32768         # 最大序列长度
  tensor_parallel_size: 4      # 张量并行度
  pipeline_parallel_size: 1    # 流水线并行度
  
  # KV缓存优化
  kv_cache_dtype: "fp8"        # KV缓存数据类型
  enable_chunked_prefill: true # 启用分块预填充
  max_num_batched_tokens: 8192 # 批次最大token数
  
  # 量化配置
  quantization: "fp8"          # 权重量化
  kv_cache_quantization: true  # KV缓存量化
  
  # 调度优化
  scheduler_policy: "fcfs"     # 调度策略
  enable_prefix_caching: true  # 前缀缓存

class DistributedLLMService:
    def __init__(self, config):
        self.config = config
        self.setup_distributed_inference()
    
    def setup_distributed_inference(self):
        """分布式推理设置"""
        # 张量并行：层内分割
        self.tensor_parallel_group = create_tensor_parallel_group(
            self.config.tensor_parallel_size
        )
        
        # 流水线并行：层间分割
        self.pipeline_parallel_group = create_pipeline_parallel_group(
            self.config.pipeline_parallel_size
        )
        
        # 负载均衡器
        self.load_balancer = GPULoadBalancer()
    
    def optimized_inference(self, requests):
        """优化推理流程"""
        # 1. 请求路由和负载均衡
        distributed_requests = self.load_balancer.distribute(requests)
        
        # 2. 内存预分配和KV缓存管理
        cache_manager = self.allocate_kv_cache(distributed_requests)
        
        # 3. 分布式推理执行
        results = []
        for gpu_id, gpu_requests in distributed_requests.items():
            gpu_results = self.execute_on_gpu(gpu_id, gpu_requests)
            results.extend(gpu_results)
        
        return results

class LLMPerformanceMonitor:
    def __init__(self):
        self.metrics = {
            'throughput': ThroughputMetric(),
            'latency': LatencyMetric(),
            'gpu_utilization': GPUUtilizationMetric(),
            'memory_usage': MemoryUsageMetric(),
            'cache_hit_rate': CacheHitRateMetric()
        }
    
    def collect_metrics(self):
        """收集性能指标"""
        return {
            # 吞吐量指标
            'tokens_per_second': self.metrics['throughput'].get(),
            'requests_per_second': self.calculate_rps(),
            
            # 延迟指标
            'p50_latency': self.metrics['latency'].percentile(50),
            'p95_latency': self.metrics['latency'].percentile(95),
            'p99_latency': self.metrics['latency'].percentile(99),
            
            # 资源利用率
            'gpu_memory_usage': self.metrics['memory_usage'].gpu_memory(),
            'kv_cache_usage': self.metrics['memory_usage'].kv_cache(),
            'gpu_compute_utilization': self.metrics['gpu_utilization'].get(),
            
            # 缓存效率
            'prefix_cache_hit_rate': self.metrics['cache_hit_rate'].prefix(),
            'kv_cache_efficiency': self.metrics['cache_hit_rate'].kv()
        }

def calculate_cost_efficiency(baseline_cost, optimized_performance):
    """计算成本效益"""
    return {
        'cost_per_token': baseline_cost / optimized_performance['throughput'],
        'latency_improvement': (
            baseline_latency - optimized_performance['latency']
        ) / baseline_latency,
        'hardware_savings': 1 - (optimized_gpus / baseline_gpus),
        'roi_months': calculate_roi_period(
            hardware_savings, optimization_investment
        )
    }