“”“
Speculative Decoding Engine — 2026-07-03

热点信号：DeepSeek-V3 MTP / Medusa / EAGLE 推测解码加速
技术落地：草稿模型+目标模型双管线，验证+回退机制
加速比理论：2x-3.5x（取决于草稿模型命中率）

独立运行: python speculative_decoder.py
“”“

import math
import random
import time
from collections import deque
from dataclasses import dataclass
from typing import List, Tuple, Optional

── 模型模拟 ────────────────────────────────────────────

class DraftModel:
“”“草稿模型：轻量快速，质量稍低”““

def __init__(self, vocab_size: int = 100, speed_factor: float = 8.0):
    self.vocab_size = vocab_size
    self.speed_factor = speed_factor
    self._weights = [random.random() for_  in range(vocab_size)]

def generate(self, context: List[int], max_tokens: int) -> List[int]:
    """快速生成（质量较低）"""
    tokens = []
    seed = sum(context) + 7
    for  _in range(max_tokens):
        seed = (seed * 1103515245 + 12345) & 0x7FFFFFFF
        # 偏向于context相关token
        bias_idx = context[-1] if context else 0
        if random.random() < 0.6:
            token = (bias_idx + seed % 5) % self.vocab_size
        else:
            token = seed % self.vocab_size
        tokens.append(token)
        context = context + [token]
    return tokens

def forward_latency(self, n_tokens: int) -> float:
    """模拟延迟 (ms)"""
    return n_tokens / self.speed_factor

class TargetModel:
“”“目标模型：大参数高精度，速度慢”““

def __init__(self, vocab_size: int = 100, speed_factor: float = 1.0):
    self.vocab_size = vocab_size
    self.speed_factor = speed_factor
    self._weights = [[random.random() for_  in range(vocab_size)]
                     for  _in range(vocab_size)]

def verify(self, context: List[int], draft_tokens: List[int]) -> Tuple[List[int], int]:
    """
    验证草稿token序列
    返回：(接受的token序列, 接受长度)
    """
    accepted = []
    ctx = list(context)

    for i, draft_tok in enumerate(draft_tokens):
        # 模拟目标模型概率分布
        seed = sum(ctx) % (self.vocab_size * 7)
        probs = [
            math.exp(-abs((j - draft_tok + seed % 3) % self.vocab_size) / 2)
            for j in range(self.vocab_size)
        ]
        total = sum(probs)
        probs = [p / total for p in probs]

        # 与草稿模型对比（模拟一致概率0.65-0.85之间）
        draft_prob = 0.7 + 0.15  *math.sin(i*  0.5)
        agree = random.random() < draft_prob

        if agree:
            accepted.append(draft_tok)
            ctx.append(draft_tok)
        else:
            # 分歧点：用目标模型采样新token
            r = random.random()
            cum = 0
            for tok, prob in enumerate(probs):
                cum += prob
                if r < cum:
                    accepted.append(tok)
                    break
            return accepted, len(accepted)

    return accepted, len(accepted)

def forward_latency(self, n_tokens: int) -> float:
    return n_tokens / self.speed_factor

── 推测解码引擎 ────────────────────────────────────────

@dataclass
class SpecDecodeConfig:
draft_len: int = 5 # 草稿模型每次生成的token数
max_target_tokens: int = 64 # 总目标token数
enable_tree_verify: bool = True # 树形验证（多个草稿分支）

class SpeculativeDecoder:
“”“
推测解码主引擎

┌──────────┐
│  Context  │
└────┬─────┘
     │
┌────▼──────────────┐      ┌───────────────┐
│  Draft Model      │─────▶│ Draft Tokens  │
│  (fast, cheap)    │      │  (γ = 5)      │
└───────────────────┘      └───────┬───────┘
                                   │
┌───────────────────┐      ┌───────▼───────┐
│  Target Model     │◀─────│  Verify +     │
│  (slow, accurate) │      │  Accept/Reject│
└───────┬───────────┘      └───────────────┘
        │
┌───────▼───────────┐
│  Accepted Tokens  │
└───────────────────┘
"""

def __init__(self, config: Optional[SpecDecodeConfig] = None):
    self.cfg = config or SpecDecodeConfig()
    vocab = 100
    self.draft = DraftModel(vocab_size=vocab, speed_factor=8.0)
    self.target = TargetModel(vocab_size=vocab, speed_factor=1.0)

    self.stats = {
        "total_draft_tokens": 0,
        "total_accepted": 0,
        "iterations": 0,
        "draft_time_ms": 0.0,
        "target_time_ms": 0.0,
    }

def decode(self, prompt: List[int]) -> dict:
    """
    推测解码主循环
    """
    context = list(prompt)
    generated = []
    t0 = time.perf_counter()

    while len(generated) < self.cfg.max_target_tokens:
        # Step 1: 草稿模型生成
        t_draft_start = time.perf_counter()
        draft_tokens = self.draft.generate(context, self.cfg.draft_len)
        self.stats["draft_time_ms"] += (
            self.draft.forward_latency(self.cfg.draft_len)
        )

        # Step 2: 目标模型验证
        t_target_start = time.perf_counter()
        accepted, accept_len = self.target.verify(context, draft_tokens)
        target_tokens_processed = accept_len + 1  # +1 for the sampled token on rejection
        self.stats["target_time_ms"] += (
            self.target.forward_latency(target_tokens_processed)
        )

        # Step 3: 追加接受的token
        context.extend(accepted)
        generated.extend(accepted)

        self.stats["total_draft_tokens"] += len(draft_tokens)
        self.stats["total_accepted"] += accept_len
        self.stats["iterations"] += 1

        # 如果全接受，继续循环
        if accept_len == len(draft_tokens):
            pass  # 全接受，下次生成更多

    elapsed_ms = (time.perf_counter() - t0) * 1000

    # 计算加速比
    baseline_latency = self.target.forward_latency(len(generated))
    accept_rate = (self.stats["total_accepted"] /
                   max(self.stats["total_draft_tokens"], 1))

    return {
        "genera

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