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[紫薇六大资产·代码资产] speculative_decoding.py

By tat6…lkrp September 2, 2026

“”“
SpeculativeDecoding: Draft-Target Speculative Sampling for 2-4x Inference Speedup

深度学习方向：推测解码 — 打破自回归瓶颈，实现无损加速（2026-07-12）

热点信号来源：

  • 今日intel: “Cheap AI APIs in 2026” — 推理成本优化是2026核心工程挑战
  • 今日intel: GPT-5.6 Sol “durability concept” → 高效推理架构需求
  • 背景: 自回归逐token生成 → O(n)串行步数 → GPU利用率仅~30%
  • 创新: 小模型(草稿)快速生成候选 → 大模型(目标)并行验证 → 2-4x加速

核心原理（五层深度）：
▸ Layer 1 — 基础Speculative: 草稿模型自回归生成γ个候选token
▸ Layer 2 — 并行验证: 目标模型一次forward验证所有γ个候选logits
▸ Layer 3 — 修正拒绝采样: 保证输出分布与目标模型自回归完全相同（无损）
▸ Layer 4 — Tree Drafting: 草稿多条路径 → 树形并行验证（Spector 2024）
▸ Layer 5 — Self-Speculative: 同模型浅层作为草稿 → 零额外模型成本

论文关联技术：

  • Leviathan et al. (2023): “Fast Inference from Transformers via Speculative Decoding”
  • Chen et al. (2023): “Accelerating LLM Inference with Staged Speculative Decoding”
  • Spector (Google 2024): Tree-based parallel draft verification
  • Medusa (Cai et al. 2024): Multiple draft heads for parallel candidates
  • Eagle (Li et al. 2024): Feature-level draft token prediction
  • Self-Speculative (Zhang et al. 2024): Early exit layers as draft model
  • REST (He et al. 2024): Retrieval-based speculative decoding

独立运行: python speculative_decoding.py
测试覆盖: 10个单元测试 + 加速比基准测试 + 端到端生成demo + 质量验证
“”“

import math
import random
import time
from dataclasses import dataclass, field
from typing import List, Tuple, Dict, Optional, Callable, Set
from enum import Enum
from collections import defaultdict, deque
import hashlib

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配置与枚举

══════════════════════════════════════════════════════════════════

class DraftStrategy(Enum):
“”“草稿生成策略”““
STANDARD = “standard” # 单链草稿 (Leviathan et al.)
TREE = “tree” # 树形草稿 (Spector)
SELF_SPECULATIVE = “self_spec” # 自推测 (early exit)
MEDUSA = “medusa” # 多头草稿
STAGED = “staged” # 多级草稿 (Chen et al.)

class VerificationMode(Enum):
“”“验证模式”““
GREEDY = “greedy” # 贪心验证 (确定性)
SAMPLING = “sampling” # 随机采样验证 (无损)
TOPK = “topk” # Top-K验证
TOPP = “topp” # Nucleus验证

@dataclass
class SpecDecodeConfig:
“”“推测解码超参数”““
# 草稿配置
gamma: int = 5 # 每步草稿长度 (推荐3-10)
draft_strategy: DraftStrategy = DraftStrategy.STANDARD
draft_temperature: float = 0.0 # 草稿温度 (0=贪婪)

# 树形草稿配置
tree_width: int = 3                # 树形草稿分支数
tree_depth: int = 4                # 树形草稿深度

# 自推测配置
early_exit_layer: int = 4          # 提前退出层
total_layers: int = 32             # 总层数

# 验证配置
verification_mode: VerificationMode = VerificationMode.SAMPLING
target_temperature: float = 1.0    # 目标模型温度
top_k: int = 50                    # Top-K过滤
top_p: float = 0.9                 # Nucleus阈值

# 预算
max_new_tokens: int = 256          # 最大生成token数
max_draft_steps: int = 100         # 最大草稿步数

# 模型模拟
vocab_size: int = 128              # 模拟词表大小
draft_hidden_dim: int = 64         # 草稿模型隐藏维度
target_hidden_dim: int = 256       # 目标模型隐藏维度
draft_layers: int = 4              # 草稿模型层数
target_layers: int = 32            # 目标模型层数

@dataclass
class DraftStep:
“”“单步草稿结果”““
tokens: List[int] # 候选token序列 (长度γ)
draft_logprobs: List[float] # 草稿模型log概率
draft_time_ms: float = 0.0 # 草稿耗时(ms)

@dataclass
class VerifyResult:
“”“单步验证结果”““
accepted_tokens: List[int] # 被接受的token
rejected_at: int # 第一个被拒绝的位置 (-1=全部接受)
bonus_token: Optional[int] # 拒绝后的补充采样token
target_logprobs: List[List[float]] # 目标模型对每个位置的完整分布
acceptance_rate: float # 接受率
verify_time_ms: float = 0.0 # 验证耗时(ms)

@dataclass
class DecodeStats:
“”“解码统计”““
total_draft_tokens: int = 0 # 草稿总token数
total_accepted_tokens: int = 0 # 接受总token数
total_draft_steps: int = 0 # 草稿步数
total_target_forwards: int = 0 # 目标模型forward次数
avg_acceptance_rate: float = 0.0 # 平均接受率
speedup_ratio: float = 0.0 # 实际加速比
total_time_ms: float = 0.0 # 总耗时
equivalent_autoregressive_ms: float = 0.0 # 等效自回归耗时

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模拟语言模型

══════════════════════════════════════════════════════════════════

class SimulatedLLM:
“”“
模拟Transformer语言模型

使用随机权重矩阵模拟前向传播，保证:
- 相同输入→相同输出（确定性）
- 草稿/目标模型共享相同"知识分布"但精度不同
- 模拟真实的logits分布特征
"""

def __init__(self, name: str, vocab_size: int, hidden_dim: int, 
             num_layers: int, seed: int = 42, is_target: bool = True):
    self.name = name
    self.vocab_size = vocab_size
    self.hidden_dim = hidden_dim
    self.num_layers = num_layers
    self.is_target = is_target
    
    # 初始化模拟权重
    rng = random.Random(seed)
    
    # Token embedding: 用哈希模拟确定性的embedding
    self._embed_seed = seed + 1
    
    # 每层的投影矩阵（模拟）
    self._layer_seeds = [seed + 10 + i * 3 for i in range(num_layers)]
    
    # LM head: 隐藏维度→词表
    self._head_seed = seed + 1000
    
    # Forward计数器
    self.forward_count = 0
    self.total_forward_time_ms = 0.0

def _token_embed(self, token: int) -> List[float]:
    """模拟token嵌入（确定性的）"""
    h = hashlib.sha256(f"{self._embed_seed}:{token}".encode()).digest()
    vec = []
    for i in range(self.hidden_dim):
        byte_val = h[i % len(h)]
        vec.append((byte_val / 255.0) * 2 - 1)
    # 归一化
    norm = math.sqrt(sum(v * v for v in vec))
    if norm > 1e-8:
        vec = [v / norm * math.sqrt(self.hidden_dim) for v in vec]
    return vec

def _layer_forward(self, hidden: List[float], layer_idx: int) -> List[float]:
    """模拟单层Transfor

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