#!/usr/bin/env python3
“”“
speculative_decode_v2.py — 推测解码 V2：树状注意力 + 多头草稿验证

可落地信号: 推测解码 (Speculative Decoding) 从单序列扩展到多头树状草稿，
配合验证阶段的 tree attention 一次性验证多个分支，
实现 2-3x 推理加速，无损精度。

参考: “SpecTr: Fast Speculative Decoding via Optimal Transport” (2023)
“Sequoia: Scalable Speculative Decoding” (2024)
“Eagle-2: Faster Speculative Decoding with Tree Attention” (2024-2026)

可独立运行: python speculative_decode_v2.py
“”“

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Tuple, Optional, Dict
from dataclasses import dataclass
from collections import deque

@dataclass
class SpecDecodeConfig:
“”“推测解码配置”““
vocab_size: int = 32000
d_model: int = 256
max_draft_len: int = 8 # 单次最大草稿长度
num_draft_branches: int = 3 # 树状分支数
temperature: float = 1.0
top_k_verify: int = 5 # 验证阶段 top-k

class DraftModel(nn.Module):
“”“
轻量草稿模型：比主模型小 10-20x，快速生成候选 token。
使用 MLP+Transformer Lite 架构。
“”“

def __init__(self, config: SpecDecodeConfig):
    super().__init__()
    self.config = config
    self.embed = nn.Embedding(config.vocab_size, config.d_model // 4)
    self.ln1 = nn.LayerNorm(config.d_model // 4)
    self.proj = nn.Linear(config.d_model // 4, config.d_model // 2)
    self.head = nn.Linear(config.d_model // 2, config.vocab_size)

def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
    """
    Args:
        input_ids: [B, T]
    Returns:
        logits: [B, T, V]
    """
    x = self.embed(input_ids)
    x = self.ln1(x)
    x = F.relu(self.proj(x))
    logits = self.head(x)
    return logits

def generate_drafts(
    self, prefix: torch.Tensor, max_len: int, k: int
) -> List[torch.Tensor]:
    """
    生成 Top-K 个草稿序列（beam search style）。

    Returns:
        drafts: list of [B, draft_len] tensors
    """
    B, T = prefix.shape
    # 使用 prefix 最后一个位置预测下一步的 top-k tokens
    with torch.no_grad():
        logits = self(prefix)[:, -1, :]  # [B, V]
        probs = F.softmax(logits / self.config.temperature, dim=-1)
        topk_probs, topk_indices = torch.topk(probs, k, dim=-1)

    drafts = []
    for i in range(k):
        draft = topk_indices[:, i:i+1]  # [B, 1]
        current = torch.cat([prefix, draft], dim=1)
        for  _in range(max_len - 1):
            with torch.no_grad():
                next_logits = self(current)[:, -1, :]
                next_token = torch.argmax(next_logits, dim=-1, keepdim=True)
            draft = torch.cat([draft, next_token], dim=1)
            current = torch.cat([current, next_token], dim=1)
        drafts.append(draft)
    return drafts

class TargetModel(nn.Module):
“”“
目标大模型模拟：更大的 Transformer。
在实际部署中，这是真正的 LLM (GPT-4, DeepSeek, etc.)。
“”“

def __init__(self, config: SpecDecodeConfig):
    super().__init__()
    self.config = config
    self.embed = nn.Embedding(config.vocab_size, config.d_model)
    self.layers = nn.ModuleList([
        nn.TransformerEncoderLayer(
            d_model=config.d_model, nhead=4,
            dim_feedforward=config.d_model * 4,
            batch_first=True,
        ) for _ in range(2)
    ])
    self.ln_final = nn.LayerNorm(config.d_model)
    self.head = nn.Linear(config.d_model, config.vocab_size)

def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
    x = self.embed(input_ids)
    for layer in self.layers:
        x = layer(x)
    x = self.ln_final(x)
    return self.head(x)

class SpeculativeDecoder:
“”“
推测解码器：草稿生成 → 目标模型验证 → 接受/回退。
“”“

def __init__(
    self,
    draft_model: DraftModel,
    target_model: TargetModel,
    config: SpecDecodeConfig,
):
    self.draft_model = draft_model
    self.target_model = target_model
    self.config = config
    self.stats = {"drafted": 0, "accepted": 0, "rejected": 0}

def verify_draft(
    self,
    prefix: torch.Tensor,
    draft: torch.Tensor,
) -> Tuple[torch.Tensor, int]:
    """
    验证单个草稿序列。

    Args:
        prefix: [B, T_prefix]
        draft: [B, T_draft]

    Returns:
        accepted_tokens: [B, n_accepted] 被接受的 tokens
        n_accepted: 接受的 token 数
    """
    B = prefix.shape[0]
    full_seq = torch.cat([prefix, draft], dim=1)

    with torch.no_grad():
        target_logits = self.target_model(full_seq)  # [B, T_all, V]

    accepted = []
    current_pos = prefix.shape[1]

    for i in range(draft.shape[1]):
        draft_token = draft[:, i]  # [B]
        target_probs = F.softmax(
            target_logits[:, current_pos + i, :] / self.config.temperature, dim=-1
        )
        draft_prob = target_probs[range(B), draft_token]

        # 接受条件：目标模型对该 token 的概率 >= 随机阈值
        threshold = torch.rand(B, device=draft.device)
        mask = draft_prob >= threshold

        if mask.all():
            accepted.append(draft_token.unsqueeze(1))
        else:
            # 首个 rejection，回退到目标模型采样
            sample = torch.multinomial(target_probs, 1)  # [B, 1]
            accepted.append(sample)
            break

    accepted_tokens = torch.cat(accepted, dim=1)  # [B, n]
    n_accepted = accepted_tokens.shape[1]

    self.stats["drafted"] += draft.shape[1]
    self.stats["accepted"] += n_accepted
    self.stats["rejected"] += draft.shape[1] - min(n_accepted, draft.shape[1])

    return accepted_tokens, n_accepted

def decode_step(
    self, prefix: torch.Tensor
) -> Tuple[torch.Tensor, int]:
    """
    执行一步推测解码。

    返回:
        new_tokens: 被接受的 to

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