class MoESpeculativeMetrics:
    def __init__(self):
        self.total_draft_tokens = 0
        self.accepted_tokens = 0
        self.expert_activation_counts = []
        self.memory_bandwidth_usage = []
        self.num_experts = 8  # 默认专家数量
        
    def compute_target_efficiency(self):
        """
        Target Efficiency = (Accepted Tokens / Draft Tokens) 
                          * (Avg Expert Utilization)
                          * (Memory Bandwidth Efficiency)
        """
        acceptance_rate = self.accepted_tokens / max(self.total_draft_tokens, 1)
        avg_expert_util = sum(self.expert_activation_counts) / len(self.expert_activation_counts) / self.num_experts
        
        # 内存带宽效率（实测vs理论峰值）
        avg_mem_bw = sum(self.memory_bandwidth_usage) / len(self.memory_bandwidth_usage)
        peak_bw = self.get_peak_memory_bandwidth()
        mem_bw_efficiency = avg_mem_bw / peak_bw
        
        return acceptance_rate * avg_expert_util * mem_bw_efficiency
    
    def get_peak_memory_bandwidth(self):
        """
        获取当前硬件的理论峰值内存带宽
        这个值可以根据GPU型号进行配置
        """
        # 示例：A100的峰值带宽约为2TB/s
        gpu_model = self.detect_gpu_model()
        if gpu_model == "A100":
            return 2039.0  # GB/s
        elif gpu_model == "H100":
            return 3350.0  # GB/s
        elif gpu_model == "B100":
            return 4000.0  # GB/s
        else:
            return 1500.0  # 默认值
    
    def detect_gpu_model(self):
        """
        检测当前GPU型号
        """
        try:
            import torch
            if torch.cuda.is_available():
                return torch.cuda.get_device_name(0)
        except ImportError:
            pass
        return "unknown"

def benchmark_moe_vs_dense(batch_sizes, model_configs):
    results = {}
    
    for bs in batch_sizes:
        # Dense模型基准测试
        dense_time = benchmark_dense_model(bs, model_configs['dense'])
        
        # MoE模型测试
        moe_time_no_spec = benchmark_moe_model(bs, model_configs['moe'], use_speculation=False)
        moe_time_with_spec = benchmark_moe_model(bs, model_configs['moe'], use_speculation=True)
        
        results[bs] = {
            'dense': dense_time,
            'moe_no_spec': moe_time_no_spec,
            'moe_with_spec': moe_time_with_spec,
            'speedup': moe_time_no_spec / moe_time_with_spec,
            'memory_usage_gb': get_memory_usage(model_configs['moe'], bs),
            'acceptance_rate': get_acceptance_rate(model_configs['moe'], bs)
        }
    
    return results

def get_memory_usage(model_config, batch_size):
    """
    估算MoE模型在给定batch size下的显存使用量
    """
    base_memory = model_config['base_params'] * 2  # 假设FP16，每个参数2字节
    expert_memory = model_config['expert_count'] * model_config['expert_params'] * 2
    kv_cache_memory = batch_size * model_config['max_seq_len'] * model_config['hidden_size'] * 2 * 2  # KV各一份
    
    return (base_memory + expert_memory + kv_cache_memory) / (1024**3)  # 转换为GB

def get_acceptance_rate(model_config, batch_size):
    """
    根据经验公式估算接受率
    """
    # 接受率通常与batch size成反比
    base_acceptance = 0.85  # 小batch size下的基础接受率
    batch_penalty = min(0.3, (batch_size - 1) * 0.02)  # batch size越大，接受率越低
    return max(0.5, base_acceptance - batch_penalty)

class CascadeSpeculator:
    def __init__(self):
        self.k_values = [1, 2, 3, 5, 7]  # 候选推测深度
        self.utility_threshold = 0.8
        self.load_predictor = LoadPredictor()  # 负载预测器
        self.acceptance_model = self._load_acceptance_model()
        
    def _load_acceptance_model(self):
        """
        加载预训练的接受率预测模型
        这个模型基于历史运行数据训练得到
        """
        # 在实际实现中，这里会加载一个轻量级的MLP模型
        # 为了简化示例，我们使用一个简单的规则-based方法
        return SimpleAcceptancePredictor()
        
    def select_k_dynamic(self, batch_size, seq_len, expert_load):
        """根据当前负载动态选择推测深度K"""
        if batch_size > 64:
            # 高batch size场景：推测解码收益降低
            return 1  # 禁用推测
        
        # 计算每个K值的期望utility
        utilities = {}
        for k in self.k_values:
            expected_acceptance = self.predict_acceptance(k, seq_len, expert_load)
            compute_cost = self.estimate_compute_cost(k, expert_load)
            utilities[k] = expected_acceptance / compute_cost
            
        # 选择utility最高的K
        best_k = max(utilities, key=utilities.get)
        return best_k if utilities[best_k] > self.utility_threshold else 1
    
    def predict_acceptance(self, k, seq_len, expert_load):
        """预测给定条件下K步推测的接受率"""
        # 基于历史数据和当前负载的机器学习模型
        features = [k, seq_len, expert_load, self.get_context_diversity()]
        return self.acceptance_model.predict(features)
    
    def estimate_compute_cost(self, k, expert_load):
        """
        估算推测深度k的计算成本
        包括草稿模型和验证阶段的成本
        """
        draft_cost = k * 0.1  # 草稿模型相对便宜
        verify_cost = k * (1.0 + expert_load * 0.5)  # 验证成本与专家负载相关
        return draft_cost + verify_cost
    
    def get_context_diversity(self):
        """
        获取当前上下文的多样性分数
        多样性越高，推测难度越大
        """
        # 在实际实现中，这会分析当前token的分布熵
        return 0.5  # 简化示例

class SimpleAcceptancePredictor:
    def predict(self, features):
        k, seq_len, expert_load, diversity = features
        # 简单的线性模型
        base_acceptance = 0.9
        k_penalty = k * 0.05
        load_penalty = expert_load * 0.1
        diversity_penalty = diversity * 0.2
        return max(0.3, base_acceptance - k_penalty - load_penalty - diversity_penalty)

class SPMoEEngine:
    def __init__(self, model_config):
        self.expert_offload_strategy = "hot_standby"  # 热备策略
        self.speculative_pipeline = True
        self.expert_cache = ExpertCache(config=model_config)
        self.speculation_depth = 5
        self.draft_model = self._create_draft_model(model_config)
        
    def _create_draft_model(self, model_config):
        """
        创建简化的草稿模型
        通常是目标模型的轻量版本
        """
        # 在实际实现中，这里会创建一个层数更少或专家更少的模型
        draft_config = model_config.copy()
        draft_config['num_layers'] = max(1, model_config['num_layers'] // 4)
        draft_config['num_experts'] = max(2, model_config['num_experts'] // 2)
        return MoEModel(draft_config)
        
    def forward_with_speculation(self, input_ids, past_key_values):
        # 步骤1：使用轻量版模型生成候选token
        draft_tokens = self.draft_model.generate(
            input_ids, 
            num_tokens=self.speculation_depth,
            expert_subset="active_only"  # 只激活高频专家
        )
        
        # 步骤2：并行验证（考虑专家并行通信）
        verified_tokens = self.verify_with_expert_parallel(
            draft_tokens,
            pipeline_communication=True  # 计算-通信流水线
        )
        
        return verified_tokens
    
    def verify_with_expert_parallel(self, draft_tokens, pipeline_communication=False):
        """并行验证草稿token"""
        if pipeline_communication:
            # 启动流水线通信
            comm_stream = torch.cuda.Stream()
            compute_stream = torch.cuda.Stream()
            
            with torch.cuda.stream(comm_stream):
                # 异步启动专家并行通信
                self.dispatch_expert_activations_async(draft_tokens)
            
            with torch.cuda.stream(compute_stream):
                # 并行计算验证结果
                verification_results = self.compute_verification(draft_tokens)
            
            # 同步两个流
            torch.cuda.synchronize()
            return verification_results
        else:
            return self.compute_verification(draft_tokens)
    
    def dispatch_expert_activations_async(self, tokens):
        """
        异步分发专家激活请求
        这里会触发专家卸载/加载操作
        """
        required_experts = self.router(tokens)
        for expert_id in required_experts:
            if not self.expert_cache.is_loaded(expert_id):
                # 异步加载专家
                self.expert_cache.load_async(expert_id)
    
    def compute_verification(self, draft_tokens):
        """
        执行实际的验证计算
        """
        # 这里会调用完整的MoE模型进行验证
        logits = self.full_model(draft_tokens)
        # 执行接受/拒绝逻辑
        accepted_tokens = self.apply_speculative_decoding(logits)
        return accepted_tokens

class MoESpecVerifier:
    def __init__(self, expert_capacity_limit=4):
        self.expert_capacity_limit = expert_capacity_limit  # 每层最多激活专家数
        
    def verify_with_budget(self, draft_tokens, full_model):
        """
        在验证阶段强制执行专家容量限制
        实现推测深度与内存成本的解耦
        """
        # 计算每个token的专家路由分数
        router_logits = full_model.router(draft_tokens)
        
        # Top-k选择，但限制每层总激活数
        selected_experts = self.constrained_topk(
            router_logits, 
            k=self.expert_capacity_limit,
            strategy="global_budget"  # 全局预算分配
        )
        
        # 只计算被选中的专家
        outputs = self.compute_selected_experts(draft_tokens, selected_experts)
        return outputs
    
    def constrained_topk(self, logits, k, strategy="global_budget"):
        """约束Top-K选择"""
        if strategy == "global_budget":
            # 全局预算分配：确保整个批次不超过专家容量
            batch_size, seq_len, n_experts = logits.shape
            flat_logits = logits.view(-1, n_experts)
            
            # 计算全局top-k索引
            topk_values, topk_indices = torch.topk(flat_logits, k, dim=-1)
            
            # 重新整形并去重
            selected_experts = topk_indices.view(batch_size, seq_len, k)
            return torch.unique(selected_experts, sorted=False)
        elif strategy == "layer_wise_budget":
            # 每层独立预算
            batch_size, seq_len, n_experts = logits.shape
            selected_experts = []
            for layer_idx in range(seq_len):
                layer_logits = logits[:, layer_idx, :]
                _, topk_indices = torch.topk(layer_logits, k, dim=-1)
                selected_experts.append(topk_indices)
            return torch.stack(selected_experts, dim=1)
        else:
            raise ValueError(f"Unknown strategy: {strategy}")
    
    def compute_selected_experts(self, tokens, selected_experts):
        """
        只计算被选中的专家，跳过其他专家
        这可以显著减少计算量和内存使用
        """
        outputs = []
        for token_idx, token in enumerate(tokens):
            token_output = 0
            for expert_id in selected_experts[token_idx]:
                expert_output = self.experts[expert_id](token)
                token_output += expert_output
            outputs.append(token_output)
        return torch.stack(outputs, dim=0)

from vllm import LLM, SamplingParams

# 配置MoE模型的推测解码
llm = LLM(
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",  # MoE架构模型
    speculative_model="eagle3-mistral",  # EAGLE-3草稿模型
    num_speculative_tokens=5,  # 推测深度
    speculative_max_model_len=4096,
    tensor_parallel_size=2,
    enable_prefix_caching=True,  # 启用前缀缓存优化
    gpu_memory_utilization=0.9,  # GPU内存利用率
    enforce_eager=False,  # 允许CUDA graphs优化
    # MoE特定配置
    moe_top_k=2,  # 激活专家数量
    moe_renorm_mode="rms_norm",  # 路由归一化方式
    # 推测解码高级配置
    speculative_disable_by_batch_size=64,  # 大batch size时禁用推测
    ngram_prompt_lookup_max=3,  # N-gram提示查找
)

# 采样参数
sampling_params = SamplingParams(
    temperature=0.7, 
    max_tokens=512,
    top_p=0.9,
    presence_penalty=0.6,
    frequency_penalty=0.2,
    stop=["\n\n", "###"]
)

# 生成输出
outputs = llm.generate([
    "Explain the concept of Mixture of Experts in LLMs.",
    "How does speculative decoding accelerate inference?"
], sampling_params)

for output in outputs:
    print(f"Prompt: {output.prompt}")
    print(f"Generated: {output.outputs[0].text}")
    # 打印推测解码统计信息
    if hasattr(output.outputs[0], 'spec_decode_stats'):
        stats = output.outputs[0].spec_decode_stats
        print(f"Acceptance rate: {stats.acceptance_rate:.2%}")
        print(f"Draft tokens: {stats.draft_tokens}")
        print(f"Accepted tokens: {stats.accepted_tokens}")

import sglang as sgl

# 配置SGLang运行时
runtime = sgl.Runtime(
    model_path="mistralai/Mixtral-8x7B-Instruct-v0.1",
    tp_size=2,  # Tensor Parallelism
    mem_fraction_static=0.85,  # 静态内存分配比例
    # FlashInfer MoE配置
    enable_moe=True,
    moe_top_k=2,
    flashinfer_moe=True,  # 启用FlashInfer MoE优化
    # 推测解码配置
    speculative_decoding=True,
    draft_model_path="eagle3-mistral",
    spec_draft_length=5,
)

@sgl.function
def moe_speculative_decode(s, prompt):
    # 使用SGLang的推测解码接口
    with s.moe_expert_dispatch():
        # 草稿模型生成候选token
        draft_output = s.text_to_token_ids(
            prompt, 
            max_new_tokens=5,
            speculative=True,
            expert_subset="active"
        )
        
        # 目标模型并行验证
        final_output = s.verify_speculative_tokens(
            draft_output,
            expert_parallel_comm=True
        )
    
    s += f"Final response: {final_output}"

# 运行推测解码
state = moe_speculative_decode.run(
    prompt="Explain how MoE and speculative decoding work together",
    temperature=0.7
)
print(state.text())

# 关闭运行时
runtime.shutdown()

def optimize_moe_speculative_params(model_config, hardware_config):
    """MoE推测解码参数优化"""
    optimal_params = {}
    
    # 根据显存大小调整batch size
    available_memory = hardware_config.gpu_memory * 0.8  # 预留20%缓冲
    if available_memory > 40 * 1024:  # >40GB
        optimal_params['batch_size'] = 32
        optimal_params['speculative_tokens'] = 7
    elif available_memory > 20 * 1024:  # >20GB
        optimal_params['batch_size'] = 16
        optimal_params['speculative_tokens'] = 5
    else:
        optimal_params['batch_size'] = 8
        optimal_params['speculative_tokens'] = 3
    
    # 专家激活策略
    if model_config.expert_count <= 8:
        optimal_params['expert_selection'] = 'top2'
    else:
        optimal_params['expert_selection'] = 'constrained_topk'
        optimal_params['expert_budget'] = min(4, model_config.expert_count // 2)
    
    # 通信优化
    if hardware_config.tensor_parallel_size > 1:
        optimal_params['pipeline_parallel_overlap'] = True
        optimal_params['enable_flashinfer'] = True
    
    # 根据GPU架构调整其他参数
    if hardware_config.gpu_architecture == "hopper":
        optimal_params['use_tensor_cores'] = True
        optimal_params['enable_fp8'] = True
    elif hardware_config.gpu_architecture == "ampere":
        optimal_params['use_tensor_cores'] = True
        optimal_params['enable_fp8'] = False
    
    # 根据网络带宽调整通信策略
    if hardware_config.nvlink_bandwidth > 200:  # GB/s
        optimal_params['expert_parallel_degree'] = min(8, hardware_config.gpu_count)
    else:
        optimal_params['expert_parallel_degree'] = 1
        optimal_params['tensor_parallel_degree'] = hardware_config.gpu_count
    
    return optimal_params

class HardwareConfig:
    def __init__(self, gpu_memory_gb, gpu_count, gpu_architecture, nvlink_bandwidth_gb_s):
        self.gpu_memory = gpu_memory_gb * 1024  # 转换为MB
        self.gpu_count = gpu_count
        self.gpu_architecture = gpu_architecture
        self.nvlink_bandwidth = nvlink_bandwidth_gb_s
        self.tensor_parallel_size = gpu_count

class ModelConfig:
    def __init__(self, expert_count, hidden_size, num_layers):
        self.expert_count = expert_count
        self.hidden_size = hidden_size
        self.num_layers = num_layers

class MoESpeculativeMonitor:
    def __init__(self):
        self.metrics = {
            'acceptance_rate': [],
            'throughput': [],
            'memory_usage': [],
            'expert_utilization': [],
            'latency_breakdown': {}
        }
    
    def collect_metrics(self, engine):
        """收集MoE推测解码性能指标"""
        metrics = {}
        
        # 接受率
        metrics['acceptance_rate'] = engine.get_acceptance_rate()
        
        # 吞吐量
        metrics['tokens_per_second'] = engine.get_throughput()
        
        # 专家利用率
        expert_stats = engine.get_expert_utilization()
        metrics['avg_expert_utilization'] = sum(expert_stats.values()) / len(expert_stats)
        metrics['expert_imbalance'] = max(expert_stats.values()) - min(expert_stats.values())
        
        # 内存使用
        metrics['gpu_memory_utilization'] = engine.get_gpu_memory_usage()
        
        # 延迟分解
        latency_breakdown = engine.get_latency_breakdown()
        metrics['draft_generation_time'] = latency_breakdown['draft']
        metrics['verification_time'] = latency_breakdown['verify']
        metrics['communication_time'] = latency_breakdown['comm']
        
        return metrics

class DSASpeculativeAttention:
    def __init__(self, config):
        self.dense_heads = config.dense_heads
        self.sparse_heads = config.sparse_heads
        self.speculative_strategy = "adaptive_sparse"
    
    def forward(self, hidden_states, attention_mask=None):
        # 密集注意力：处理局部依赖
        dense_out = self.dense_attention(hidden_states, attention_mask)
        
        # 稀疏注意力：处理长距离依赖
        sparse_out = self.sparse_attention(hidden_states, attention_mask)
        
        # 推测解码优化：在稀疏部分使用推测
        if self.speculative_strategy == "adaptive_sparse":
            # 对稀疏部分进行推测解码
            speculative_sparse = self.speculate_sparse_attention(hidden_states)
            verified_sparse = self.verify_sparse_attention(speculative_sparse)
            
        return dense_out + verified_sparse