class SpecDecodeWorker:
    def __init__(self, thinking_budget_enabled=False, max_thinking_budget=200):
        self.thinking_budget_enabled = thinking_budget_enabled
        self.max_thinking_budget = max_thinking_budget
        self.current_thinking_budget = 0
        self.risk_threshold = 0.7  # 风险阈值
        
    def sample_token_positions(self, hidden_states, sampling_params, draft_tokens=5):
        if self.thinking_budget_enabled:
            # 检查当前是否处于 thinking 状态
            thinking_risk_score = self._evaluate_thinking_risk(hidden_states)
            
            if thinking_risk_score > self.risk_threshold:
                # 高风险情况：保守推测
                effective_draft_tokens = min(2, draft_tokens)
                # 动态调整采样参数
                sampling_params.temperature *= 0.6  # 更大幅度降低温度
                sampling_params.top_p = 0.85  # 更严格的 top-p 限制
            else:
                # 低风险情况：正常推测
                effective_draft_tokens = draft_tokens
                sampling_params.temperature *= 0.9  # 轻微调整
                
            # 更新 thinking budget
            self._update_thinking_budget(thinking_risk_score)
            
            return super().sample_token_positions(
                hidden_states, 
                sampling_params, 
                draft_tokens=effective_draft_tokens
            )
        
        return super().sample_token_positions(hidden_states, sampling_params, draft_tokens=draft_tokens)
    
    def _evaluate_thinking_risk(self, hidden_states):
        """评估当前隐藏状态的 thinking 风险分数"""
        # 综合多种信号计算风险分数
        entropy_score = self._calculate_entropy(hidden_states)
        token_type_score = self._check_high_risk_tokens()
        context_pattern_score = self._analyze_context_pattern()
        
        # 加权平均
        risk_score = (0.4 * entropy_score + 
                     0.3 * token_type_score + 
                     0.3 * context_pattern_score)
        
        return risk_score
    
    def _update_thinking_budget(self, risk_score):
        """根据风险分数更新 thinking budget"""
        # 高风险操作消耗更多 budget
        budget_consumption = risk_score * 10
        self.current_thinking_budget = min(
            self.max_thinking_budget,
            self.current_thinking_budget + budget_consumption
        )
        
    def _calculate_entropy(self, hidden_states):
        """计算隐藏状态的熵值"""
        # 实际实现会更复杂
        pass
        
    def _check_high_risk_tokens(self):
        """检查是否存在高风险 token"""
        # 实际实现会检查 token id
        pass
        
    def _analyze_context_pattern(self):
        """分析上下文模式"""
        # 实际实现会分析最近的 token 序列
        pass

from vllm import LLM, SamplingParams

llm = LLM(
    model="deepseek-ai/DeepSeek-R1",
    speculative_model="microsoft/DialoGPT-medium",  # 草稿模型
    num_speculative_tokens=5,
    speculative_draft_tensor_parallel_size=1,
    thinking_budget=True,  # 启用 Thinking Budget 支持
    thinking_budget_config={
        "max_budget": 300,
        "risk_threshold": 0.65,
        "conservative_draft_tokens": 2,
        "aggressive_draft_tokens": 6
    },
    use_v2_block_manager=True,  # v2 block manager 提供更好的内存管理
    gpu_memory_utilization=0.9,
)

# 测试推理任务
sampling_params = SamplingParams(
    temperature=0.6,
    max_tokens=2048,
    stop_token_ids=[32000],  # 根据具体模型调整
)

outputs = llm.generate([
    "请详细推导勾股定理的证明过程",
    "分析量子力学中的不确定性原理"
], sampling_params)

for output in outputs:
    print(f"Prompt: {output.prompt}")
    print(f"Generated: {output.outputs[0].text}")
    print("---")

import sglang as sgl
from sglang import function, system, user, assistant, gen

@function
def complex_reasoning_task(s, problem_description):
    s += system("你是一个专业的数学和逻辑推理专家，需要详细展示推理过程")
    s += user(problem_description)
    
    # 使用 EAGLE-3 进行推测解码
    reasoning_result = gen(
        "reasoning",
        max_tokens=1024,
        temperature=0.7,
        top_p=0.9,
        # SGLang 自动启用 EAGLE-3 当检测到推理模型
        stop=["最终答案：", "所以答案是"]
    )
    
    s += assistant(reasoning_result)

# 批量处理多个推理任务
problems = [
    "证明费马小定理：如果 p 是质数，a 是整数且 p 不整除 a，则 a^(p-1) ≡ 1 (mod p)",
    "分析递归函数 T(n) = 2T(n/2) + n 的时间复杂度",
    "推导贝叶斯定理并解释其在机器学习中的应用"
]

# 并行执行
states = complex_reasoning_task.run_batch(
    problems,
    temperature=0.6,
    max_new_tokens=1536,
    num_threads=4  # 并行线程数
)

for i, state in enumerate(states):
    print(f"Problem {i+1}: {problems[i]}")
    print(f"Solution: {state['reasoning']}")
    print("=" * 50)

# 启动服务器时的完整配置
"""
python -m sglang.launch_server \
    --model-path deepseek-ai/DeepSeek-R1 \
    --speculative-algorithm eagle3 \
    --num-draft-tokens 8 \
    --enable-overlap-scheduling \
    --mem-fraction-static 0.85 \
    --max-running-requests 32 \
    --tp-size 2 \
    --port 30000
"""

import torch
import torch.nn.functional as F

class EAGLE3DraftModel:
    def __init__(self, target_model, draft_config):
        self.target_model = target_model
        self.draft_config = draft_config
        self.fusion_weights = torch.tensor([0.5, 0.3, 0.2])  # α, β, γ
        
    def forward(self, input_ids, past_key_values=None):
        with torch.no_grad():
            # 获取目标模型的多层隐藏状态
            target_outputs = self.target_model(
                input_ids,
                past_key_values=past_key_values,
                output_hidden_states=True,
                use_cache=True
            )
            
            hidden_states = target_outputs.hidden_states
            
            # 三层特征融合
            # Layer 1: Hidden State from previous token
            h1 = hidden_states[-1][:, -1, :]  # 最后一层，最后一个token
            
            # Layer 2: Attention-weighted fusion from multiple layers
            attention_weights = self._compute_attention_weights(hidden_states)
            h2 = torch.sum(attention_weights.unsqueeze(-1) * 
                          torch.stack(hidden_states[-3:], dim=1), dim=1)
            
            # Layer 3: Prediction-based features
            logits = target_outputs.logits[:, -1, :]
            h3 = self._prediction_to_hidden(logits)
            
            # 融合三层特征
            fused_hidden = (
                self.fusion_weights[0] * h1 +
                self.fusion_weights[1] * h2 +
                self.fusion_weights[2] * h3
            )
            
            # 通过草稿模型头生成下一个token
            draft_logits = self.draft_head(fused_hidden)
            
            return draft_logits, target_outputs.past_key_values
    
    def _compute_attention_weights(self, hidden_states):
        """计算多层注意力权重"""
        # 简化的实现，实际会更复杂
        layer_scores = []
        for i, hs in enumerate(hidden_states[-3:]):
            score = torch.norm(hs[:, -1, :], dim=-1)
            layer_scores.append(score)
        
        weights = F.softmax(torch.stack(layer_scores, dim=1), dim=1)
        return weights
    
    def _prediction_to_hidden(self, logits):
        """将预测 logits 转换为隐藏状态"""
        # 使用 softmax 和嵌入矩阵的逆操作
        probs = F.softmax(logits, dim=-1)
        return probs @ self.target_model.get_input_embeddings().weight

class DFLASHSpeculativeDecoder:
    def __init__(self, num_paths=4, noise_schedule=None):
        self.num_paths = num_paths
        self.noise_schedule = noise_schedule or self._default_noise_schedule()
        
    def generate_speculative_tokens(self, context, target_model, draft_model, k):
        # 初始化多条路径
        paths = []
        path_probs = []
        
        for path_id in range(self.num_paths):
            # 为每条路径添加不同的噪声
            noise_level = self.noise_schedule[path_id % len(self.noise_schedule)]
            
            # 生成路径
            path_tokens, path_logprobs = self._generate_path(
                context, 
                draft_model, 
                k, 
                noise_level
            )
            
            paths.append(path_tokens)
            path_probs.append(torch.exp(path_logprobs).mean().item())
        
        # 路径选择和融合
        selected_path_idx = self._select_best_path(paths, path_probs)
        
        return paths[selected_path_idx], {
            'all_paths': paths,
            'path_probs': path_probs,
            'selected_idx': selected_path_idx
        }
    
    def _generate_path(self, context, draft_model, k, noise_level):
        tokens = []
        logprobs = []
        current_context = context.clone()
        
        for step in range(k):
            logits = draft_model(current_context).logits[:, -1, :]
            
            # 添加噪声
            if noise_level > 0:
                noise = torch.randn_like(logits) * noise_level
                logits = logits + noise
            
            # 采样
            probs = F.softmax(logits, dim=-1)
            token_id = torch.multinomial(probs, 1)
            
            tokens.append(token_id.item())
            logprobs.append(torch.log(probs[0, token_id.item()]).item())
            
            # 更新上下文
            current_context = torch.cat([current_context, token_id.unsqueeze(0)], dim=1)
        
        return tokens, logprobs
    
    def _select_best_path(self, paths, path_probs):
        # 基于概率和多样性选择最佳路径
        diversity_scores = self._compute_diversity_scores(paths)
        combined_scores = [
            prob + 0.1 * diversity  # 平衡质量和多样性
            for prob, diversity in zip(path_probs, diversity_scores)
        ]
        return combined_scores.index(max(combined_scores))
    
    def _compute_diversity_scores(self, paths):
        # 计算路径间的多样性分数
        scores = []
        for i, path in enumerate(paths):
            diversity = 0
            for j, other_path in enumerate(paths):
                if i != j:
                    # 计算路径差异度
                    diff = sum(1 for a, b in zip(path, other_path) if a != b)
                    diversity += diff / len(path)
            scores.append(diversity / (len(paths) - 1))
        return scores
    
    def _default_noise_schedule(self):
        return [0.1, 0.3, 0.5, 0.7]  # 不同路径的不同噪声水平

# vLLM 中的 MoE 推测解码配置
llm = LLM(
    model="deepseek-ai/DeepSeek-V3",
    speculative_model="deepseek-ai/DeepSeek-V3-Draft",  # MoE 草稿模型
    num_speculative_tokens=6,
    draft_model_tp_size=2,  # 草稿模型的 tensor parallelism
    enforce_eager=False,    # 允许 CUDA graph 优化
    moe_config={
        "expert_budget": 0.8,      # 专家预算比例
        "router_logits_scale": 2.0 # 路由器缩放因子
    }
)

import flashinfer

# MoE 推测解码的融合 kernel
def fused_moe_spec_decode(
    hidden_states,
    expert_weights,
    top_k,
    activation_fn="silu"
):
    """
    融合的 MoE 推测解码 kernel
    - hidden_states: 输入隐藏状态
    - expert_weights: 专家权重矩阵
    - top_k: 选择的专家数量
    - activation_fn: 激活函数
    """
    return flashinfer.moe_kernel.fused_spec_decode(
        hidden_states,
        expert_weights,
        top_k=top_k,
        activation=activation_fn
    )

# 推测解码性能监控
import time
from collections import defaultdict

class SpecDecodeMonitor:
    def __init__(self):
        self.metrics = defaultdict(list)
        
    def record_batch(self, batch_info):
        self.metrics['acceptance_rate'].append(batch_info.acceptance_rate)
        self.metrics['avg_accepted_tokens'].append(batch_info.avg_accepted_tokens)
        self.metrics['thinking_token_ratio'].append(batch_info.thinking_token_ratio)
        self.metrics['speedup'].append(batch_info.speedup)
        
    def get_performance_summary(self):
        return {
            'avg_acceptance_rate': sum(self.metrics['acceptance_rate']) / len(self.metrics['acceptance_rate']),
            'avg_speedup': sum(self.metrics['speedup']) / len(self.metrics['speedup']),
            'avg_thinking_ratio': sum(self.metrics['thinking_token_ratio']) / len(self.metrics['thinking_token_ratio'])
        }

# 使用示例
monitor = SpecDecodeMonitor()

# 在推理过程中记录指标
start_time = time.time()
outputs = llm.generate(prompts, sampling_params)
end_time = time.time()

batch_time = end_time - start_time
baseline_time = len(outputs) * avg_token_time  # 自回归解码的估计时间
speedup = baseline_time / batch_time

print(f"Batch processing time: {batch_time:.2f}s")
print(f"Estimated speedup: {speedup:.2f}x")

class AdaptiveSpecConfig:
    def __init__(self):
        self.configs = {
            'high_sla': {
                'num_speculative_tokens': 3,  # 保守推测
                'temperature': 0.5,           # 降低随机性
                'max_thinking_budget': 100    # 限制思考预算
            },
            'standard': {
                'num_speculative_tokens': 5,
                'temperature': 0.7,
                'max_thinking_budget': 200
            },
            'batch_processing': {
                'num_speculative_tokens': 8,  # 激进推测
                'temperature': 0.8,
                'max_thinking_budget': 500
            }
        }
    
    def get_config_for_request(self, request_type, complexity_score):
        """根据请求类型和复杂度返回合适的配置"""
        base_config = self.configs[request_type]
        
        # 根据复杂度进一步调整
        if complexity_score > 0.8:  # 高复杂度推理
            base_config['num_speculative_tokens'] = max(2, base_config['num_speculative_tokens'] // 2)
        
        return base_config

# 使用示例
adaptive_config = AdaptiveSpecConfig()
config = adaptive_config.get_config_for_request('standard', complexity_score=0.6)

llm = LLM(
    model="deepseek-ai/DeepSeek-R1",
    speculative_model="eagle3",
    num_speculative_tokens=config['num_speculative_tokens'],
    temperature=config['temperature']
)

llm = LLM(
    model="deepseek-ai/DeepSeek-R1",
    speculative_model="deepseek-ai/DeepSeek-R1-Draft",
    num_speculative_tokens=4,  # 保守推测长度
    gpu_memory_utilization=0.82,  # 控制内存使用
    max_model_len=4096,  # 限制最大序列长度
    enable_chunked_prefill=True,  # 启用分块预填充
    max_num_batched_tokens=2048,  # 控制批处理token数
)

# 金融风控专用配置
llm = LLM(
    model="deepseek-ai/DeepSeek-R1",
    speculative_model="finetuned/DeepSeek-R1-Finance-Draft",
    num_speculative_tokens=5,
    thinking_budget=True,
    thinking_budget_config={
        "max_budget": 250,
        "risk_threshold": 0.6,
        "high_risk_tokens": ["利率", "本金", "违约", "概率", "+", "-", "*", "/", "=", "%"],
        "conservative_draft_tokens": 2
    },
    gpu_memory_utilization=0.85,
    enable_chunked_prefill=True,
    max_num_batched_tokens=1536,
)

# 自适应调度器
class FinancialRiskScheduler:
    def __init__(self):
        self.acceptance_history = deque(maxlen=100)
        
    def adjust_spec_params(self, current_acceptance_rate):
        self.acceptance_history.append(current_acceptance_rate)
        avg_acceptance = sum(self.acceptance_history) / len(self.acceptance_history)
        
        if avg_acceptance < 0.7:
            return {"num_speculative_tokens": 3, "temperature": 0.5}
        elif avg_acceptance > 0.85:
            return {"num_speculative_tokens": 6, "temperature": 0.7}
        else:
            return {"num_speculative_tokens": 5, "temperature": 0.6}