class QuantSpecModel(nn.Module):
    def __init__(self, base_model, quantization_bits=4):
        super().__init__()
        self.target_model = base_model  # 8-bit或FP16精度的目标模型
        self.draft_model = self._create_quantized_version(base_model, quantization_bits)
        
    def _create_quantized_version(self, model, bits):
        """创建4-bit量化版本的草稿模型"""
        quantized_model = copy.deepcopy(model)
        for name, module in quantized_model.named_modules():
            if isinstance(module, (nn.Linear, nn.Conv2d)):
                # 对权重进行4-bit量化
                weight_quantizer = Quantizer(bits=bits)
                module.weight.data = weight_quantizer.quantize(module.weight.data)
        return quantized_model

class HierarchicalQuantizedKVCache:
    def __init__(self, num_layers, num_heads, head_dim, max_seq_len):
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.head_dim = head_dim
        self.max_seq_len = max_seq_len
        
        # 定义不同的量化级别
        self.quant_levels = {
            'recent': {'bits': 8, 'threshold': 64},      # 最近64个token使用8-bit
            'important': {'bits': 6, 'threshold': 512},  # 64-512个token使用6-bit
            'normal': {'bits': 4, 'threshold': float('inf')}  # 其余使用4-bit
        }
        
    def quantize_kvcache(self, k_tensor, v_tensor, position_ids):
        """根据位置重要性进行分层量化"""
        quantized_k, quantized_v = {}, {}
        
        for pos in position_ids:
            level = self._determine_quant_level(pos)
            quantizer = Quantizer(bits=self.quant_levels[level]['bits'])
            
            quantized_k[pos] = quantizer.quantize(k_tensor[:, :, pos, :])
            quantized_v[pos] = quantizer.quantize(v_tensor[:, :, pos, :])
            
        return quantized_k, quantized_v
    
    def _determine_quant_level(self, position):
        """根据位置确定量化级别"""
        current_pos = self.max_seq_len
        distance = current_pos - position
        
        if distance <= self.quant_levels['recent']['threshold']:
            return 'recent'
        elif distance <= self.quant_levels['important']['threshold']:
            return 'important'
        else:
            return 'normal'

def enhanced_importance_scoring(self, token_id, position, attention_weights, context_features):
    """增强的重要性评分"""
    base_score = self._position_based_score(position)
    
    # 注意力权重贡献
    attn_contribution = torch.mean(attention_weights[:, :, position]).item()
    
    # 语义特征贡献
    semantic_score = self._semantic_importance(token_id, context_features)
    
    # 综合评分
    final_score = 0.6 * base_score + 0.3 * attn_contribution + 0.1 * semantic_score
    
    return self._score_to_quant_level(final_score)

def quantspec_decode(self, input_ids, max_new_tokens=100, gamma=5):
    """
    QuantSpec核心解码流程
    gamma: 每轮推测的token数量
    """
    generated = input_ids.clone()
    
    for step in range(max_new_tokens // gamma):
        # 阶段1: 4-bit草稿模型快速生成候选
        draft_input = generated[-gamma:]  # 使用最近的gamma个token作为输入
        draft_candidates = self.draft_model.generate(
            draft_input,
            max_new_tokens=gamma,
            use_quantized_kv=True  # 使用分层量化KV Cache
        )
        
        # 阶段2: 目标模型并行验证
        full_sequence = torch.cat([generated, draft_candidates], dim=-1)
        with torch.no_grad():
            full_logits = self.target_model.forward(full_sequence)
        
        # 阶段3: 接受/拒绝决策
        accepted_tokens = []
        current_pos = len(generated)
        
        for i in range(gamma):
            draft_token = draft_candidates[i].item()
            target_logits = full_logits[current_pos + i]
            
            # 计算接受概率
            draft_prob = self._get_token_probability(draft_token, target_logits)
            target_prob = self._get_token_probability(draft_token, target_logits)
            
            accept_prob = min(1.0, target_prob / (draft_prob + 1e-10))
            
            if random.random() < accept_prob:
                accepted_tokens.append(draft_token)
            else:
                # 采样替代token
                replacement_token = torch.multinomial(
                    torch.softmax(target_logits, dim=-1), 
                    num_samples=1
                ).item()
                accepted_tokens.append(replacement_token)
                break  # 一旦拒绝，停止后续验证
        
        # 更新生成序列
        accepted_tensor = torch.tensor(accepted_tokens, device=generated.device)
        generated = torch.cat([generated, accepted_tensor], dim=-1)
    
    return generated

class AsymmetricQuantizer:
    def __init__(self, bits=4, group_size=128):
        self.bits = bits
        self.group_size = group_size
        self.scale_factor = 2 ** bits - 1  # 4-bit -> 15 levels
        
    def quantize(self, tensor):
        """非均匀量化实现"""
        # 按组进行量化以适应局部分布差异
        original_shape = tensor.shape
        reshaped_tensor = tensor.view(-1, self.group_size)
        
        # 计算每组的动态范围
        min_val = reshaped_tensor.min(dim=-1, keepdim=True)[0]
        max_val = reshaped_tensor.max(dim=-1, keepdim=True)[0]
        
        # 避免除零错误
        scale = (max_val - min_val) / self.scale_factor
        scale = torch.clamp(scale, min=1e-8)
        
        # 非均匀量化到整数范围
        zero_point = -min_val / scale
        quantized = torch.round(reshaped_tensor / scale + zero_point)
        quantized = torch.clamp(quantized, 0, self.scale_factor)
        
        return {
            'data': quantized.type(torch.uint8),  # 存储为uint8，实际只用4-bit
            'scale': scale.view(original_shape[:-1] + (1,)),
            'zero_point': zero_point.view(original_shape[:-1] + (1,))
        }
    
    def dequantize(self, quantized_dict):
        """反量化恢复原始精度"""
        quantized_data = quantized_dict['data'].float()
        scale = quantized_dict['scale']
        zero_point = quantized_dict['zero_point']
        
        dequantized = (quantized_data - zero_point) * scale
        return dequantized

class HardwareOptimizedQuantSpec:
    def __init__(self):
        # CUDA kernel优化
        self.use_cuda_kernels = True
        # Tensor Core利用
        self.enable_tensor_cores = True
        # 内存访问模式优化
        self.optimize_memory_layout = True
    
    def optimized_quantize_kernel(self, input_tensor):
        """硬件优化的量化kernel"""
        if self.use_cuda_kernels and torch.cuda.is_available():
            # 使用自定义CUDA kernel进行量化
            return self._cuda_quantize(input_tensor)
        else:
            # 回退到PyTorch实现
            return self._pytorch_quantize(input_tensor)
    
    def _cuda_quantize(self, tensor):
        """CUDA优化实现"""
        # 利用warp-level primitives加速
        # 优化内存带宽利用率
        # 减少寄存器压力
        pass

class AcceptanceRateOptimizer:
    def __init__(self, base_temperature=1.0, adaptive_gamma=True):
        self.base_temperature = base_temperature
        self.adaptive_gamma = adaptive_gamma
        
    def dynamic_gamma_adjustment(self, historical_acceptance_rate):
        """动态调整推测长度gamma"""
        if historical_acceptance_rate > 0.95:
            return min(10, gamma + 1)  # 高接受率时增加gamma
        elif historical_acceptance_rate < 0.85:
            return max(2, gamma - 1)   # 低接受率时减少gamma
        else:
            return gamma  # 保持当前gamma
    
    def temperature_scaling(self, logits, position, sequence_length):
        """位置感知的温度调节"""
        # 随着序列长度增加，适当提高温度以平衡探索与利用
        pos_ratio = position / sequence_length
        adjusted_temp = self.base_temperature * (1 + 0.1 * pos_ratio)
        
        return logits / adjusted_temp
    
    def context_aware_verification(self, draft_logits, target_logits, context_similarity):
        """基于上下文相似度的验证策略"""
        # 计算上下文变化程度，调整验证严格度
        context_change = self._compute_context_change(context_similarity)
        
        # 上下文变化大时，降低验证阈值以避免过度拒绝
        adjusted_threshold = 0.9 - 0.1 * context_change
        
        return self._verify_tokens(draft_logits, target_logits, threshold=adjusted_threshold)

import time
import torch

def benchmark_methods(model_configs, test_sequences, methods=['baseline', 'speculative', 'quantspec']):
    """性能基准测试"""
    results = {}
    
    for seq_len in [4096, 16384, 32768, 65536]:
        print(f"\n=== Context Length: {seq_len} ===")
        test_prompt = generate_test_prompt(seq_len)
        
        for method in methods:
            torch.cuda.reset_peak_memory_stats()
            start_time = time.time()
            
            if method == 'baseline':
                output = standard_generation(model_configs['baseline'], test_prompt)
            elif method == 'speculative':
                output = speculative_generation(model_configs['speculative'], test_prompt)
            elif method == 'quantspec':
                output = quantspec_generation(model_configs['quantspec'], test_prompt)
            
            end_time = time.time()
            
            tokens_per_second = len(output) / (end_time - start_time)
            peak_memory = torch.cuda.max_memory_allocated() / 1e9
            
            results[f"{method}_{seq_len}"] = {
                'tps': tokens_per_second,
                'memory_gb': peak_memory,
                'speedup': results.get(f"baseline_{seq_len}")['tps'] / tokens_per_second if f"baseline_{seq_len}" in results else 1.0
            }
            
            print(f"{method}: {tokens_per_second:.2f} tok/s, {peak_memory:.2f} GB")
    
    return results

# 测试结果示例（基于论文数据）
test_results = {
    'baseline_32768': {'tps': 15.2, 'memory_gb': 24.8},
    'speculative_32768': {'tps': 28.7, 'memory_gb': 31.2},  # 传统推测解码
    'quantspec_32768': {'tps': 37.9, 'memory_gb': 19.1},   # QuantSpec
}

def evaluate_quality_metrics(generated_texts, reference_texts):
    """质量评估指标"""
    metrics = {}
    
    # BLEU分数
    bleu_scores = []
    for gen, ref in zip(generated_texts, reference_texts):
        bleu = calculate_bleu(gen, ref)
        bleu_scores.append(bleu)
    metrics['bleu'] = sum(bleu_scores) / len(bleu_scores)
    
    # Perplexity
    perplexities = []
    for gen in generated_texts:
        ppl = calculate_perplexity(gen)
        perplexities.append(ppl)
    metrics['perplexity'] = sum(perplexities) / len(perplexities)
    
    # 语义相似度
    similarities = []
    for gen, ref in zip(generated_texts, reference_texts):
        sim = calculate_semantic_similarity(gen, ref)
        similarities.append(sim)
    metrics['semantic_similarity'] = sum(similarities) / len(similarities)
    
    # 事实一致性
    fact_consistency = []
    for gen, ref in zip(generated_texts, reference_texts):
        consistency = calculate_fact_consistency(gen, ref)
        fact_consistency.append(consistency)
    metrics['fact_consistency'] = sum(fact_consistency) / len(fact_consistency)
    
    # 人类评估
    human_scores = []
    for gen in generated_texts:
        score = conduct_human_evaluation(gen)
        human_scores.append(score)
    metrics['human_score'] = sum(human_scores) / len(human_scores)
    
    return metrics

# QuantSpec质量评估结果
quality_results = {
    'baseline': {
        'bleu': 0.285, 
        'perplexity': 12.4, 
        'similarity': 0.892,
        'fact_consistency': 0.923,
        'human_score': 4.2
    },
    'quantspec': {
        'bleu': 0.282, 
        'perplexity': 12.7, 
        'similarity': 0.888,
        'fact_consistency': 0.918,
        'human_score': 4.1
    },
    'quality_drop': {
        'bleu': -1.05%, 
        'perplexity': +2.4%, 
        'similarity': -0.45%,
        'fact_consistency': -0.54%,
        'human_score': -2.4%
    }
}

class QuantSpecDeploymentConfig:
    def __init__(self):
        # 量化配置
        self.quantization_config = {
            'bits': 4,
            'group_size': 128,
            'symmetric': False,
            'per_tensor_scale': False
        }
        
        # 推测配置
        self.speculative_config = {
            'gamma': 5,  # 默认推测长度
            'temperature': 0.7,
            'top_p': 0.9
        }
        
        # 内存管理
        self.memory_config = {
            'kv_cache_strategy': 'hierarchical',
            'offload_enabled': True,  # 支持CPU-GPU混合存储
            'cache_compression': True
        }
        
    def optimize_for_hardware(self, device_type):
        """针对不同硬件优化配置"""
        if device_type == 'gpu_a100':
            # A100优化：利用Tensor Cores
            self.quantization_config.update({
                'enable_tensor_cores': True,
                'optimal_batch_size': 8
            })
        elif device_type == 'edge_gpu':
            # 边缘设备：最大化内存效率
            self.quantization_config.update({
                'aggressive_quantization': True,
                'dynamic_batching': True
            })
        elif device_type == 'mobile':
            # 移动端：最小化功耗
            self.speculative_config.update({
                'gamma': 3,  # 减少推测长度以降低功耗
                'early_exit': True
            })

# vLLM集成示例
from vllm import LLM, SamplingParams

class QuantSpecLLM(LLM):
    def __init__(self, model_path, enable_quantspec=True, **kwargs):
        super().__init__(model_path, **kwargs)
        self.enable_quantspec = enable_quantspec
        if enable_quantspec:
            self.setup_quant_spec()
    
    def setup_quant_spec(self):
        """设置QuantSpec相关组件"""
        self.quantizer = HierarchicalQuantizer()
        self.spec_decoder = SelfSpeculativeDecoder(
            draft_quant_bits=4,
            target_quant_bits=8
        )
    
    def generate(self, prompts, sampling_params=None, **kwargs):
        if self.enable_quantspec:
            return self._quantspec_generate(prompts, sampling_params, **kwargs)
        else:
            return super().generate(prompts, sampling_params, **kwargs)
    
    def _quantspec_generate(self, prompts, sampling_params, **kwargs):
        """QuantSpec生成方法"""
        # 应用分层量化和自推测解码
        results = []
        for prompt in prompts:
            result = self.spec_decoder.decode(
                prompt,
                quantizer=self.quantizer,
                **sampling_params
            )
            results.append(result)
        return results

# SGLang集成
import sglang as sgl

@sgl.function
def quantspec_generate(s, prompt, max_tokens=100):
    # 启用QuantSpec优化
    s.set_default_backend(sgl.QuantSpecBackend())
    
    # 标准生成流程
    s += prompt
    s += sgl.gen("answer", max_tokens=max_tokens)
    
    return s["answer"]

# 使用示例
backend = sgl.QuantSpecBackend(
    model_path="meta-llama/Llama-3-8b",
    quant_config={"bits": 4, "group_size": 128},
    spec_config={"gamma": 5}
)

response = quantspec_generate.run(
    prompt="Explain quantum computing in simple terms.",
    backend=backend
)

class QuantSpecChallenges:
    def __init__(self):
        self.challenges = {
            'numerical_stability': {
                'description': '4-bit量化可能导致数值不稳定',
                'mitigation': '使用混合精度训练和推理'
            },
            'hardware_support': {
                'description': '部分硬件对4-bit运算支持有限',
                'mitigation': '开发硬件无关的量化实现'
            },
            'model_specific_optimization': {
                'description': '不同模型架构的量化敏感性差异',
                'mitigation': '模型特定的量化策略优化'
            }
        }