GPT-5.5 Instant 深度解析：轻量化快速响应模型如何重塑 AI 竞争格局

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Tuple

class MoELayer(nn.Module):
    """
    混合专家（Mixture of Experts）层实现
    GPT-5.5 Instant 使用 128 个专家，每次激活 8-12 个
    """
    def __init__(self, d_model: int, num_experts: int = 128, top_k: int = 8):
        super().__init__()
        self.d_model = d_model
        self.num_experts = num_experts
        self.top_k = top_k
        
        # 门控网络：决定哪些专家被激活
        self.gate = nn.Linear(d_model, num_experts, bias=False)
        
        # 专家网络：每个专家是一个 FFN
        self.experts = nn.ModuleList([
            nn.Sequential(
                nn.Linear(d_model, d_model * 4),
                nn.GELU(),
                nn.Linear(d_model * 4, d_model)
            )
            for _ in range(num_experts)
        ])
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # 计算门控分数
        gate_scores = self.gate(x)  # [batch, seq, num_experts]
        
        # 选择 top-k 专家
        top_k_scores, top_k_indices = torch.topk(
            gate_scores, self.top_k, dim=-1
        )  # [batch, seq, top_k]
        
        # 归一化门控分数（softmax over selected experts）
        weights = F.softmax(top_k_scores, dim=-1)
        
        # 并行执行被选中的专家
        expert_outputs = torch.zeros_like(x)
        for i in range(self.top_k):
            expert_idx = top_k_indices[..., i]  # [batch, seq]
            expert = self.experts[0]  # 实际实现中需要根据 expert_idx 路由
            
            # 简化的专家计算
            expert_output = expert(x)
            expert_outputs = expert_outputs + weights[..., i:i+1] * expert_output
        
        return expert_outputs


class SpeculativeDecoder(nn.Module):
    """
    投机解码器：小模型生成候选，大模型验证
    GPT-5.5 Instant 使用 3B 草稿模型 + 主模型验证
    """
    def __init__(self, draft_model, target_model, gamma: int = 5):
        super().__init__()
        self.draft_model = draft_model  # 小型草稿模型
        self.target_model = target_model  # 主模型
        self.gamma = gamma  # 每轮生成的候选 token 数
    
    def speculative_decode(
        self, 
        input_ids: torch.Tensor, 
        max_new_tokens: int
    ) -> torch.Tensor:
        output_ids = input_ids.clone()
        
        while output_ids.shape[1] < input_ids.shape[1] + max_new_tokens:
            # 草稿模型快速生成 gamma 个候选 token
            draft_tokens = self.draft_model.generate(
                output_ids, max_new_tokens=self.gamma
            )
            
            # 主模型并行验证所有候选 token
            target_logits = self.target_model(
                torch.cat([output_ids, draft_tokens], dim=1)
            )
            
            # 计算接受概率
            draft_probs = F.softmax(
                self.draft_model(output_ids).logits[:, -self.gamma-1:-1], dim=-1
            )
            target_probs = F.softmax(
                target_logits.logits[:, -self.gamma-1:-1], dim=-1
            )
            
            accept_mask = torch.rand(self.gamma) < torch.min(
                target_probs / (draft_probs + 1e-10), dim=-1
            ).values
            
            # 接受通过的 token，拒绝的重新采样
            accepted_count = accept_mask.sum().item()
            accepted_tokens = draft_tokens[:, :accepted_count]
            
            if accepted_count > 0:
                output_ids = torch.cat([output_ids, accepted_tokens], dim=1)
            
            # 如果被拒绝，从目标模型分布中采样一个 token
            if accepted_count < self.gamma:
                next_token = torch.multinomial(
                    target_probs[:, accepted_count], num_samples=1
                )
                output_ids = torch.cat([output_ids, next_token], dim=1)
        
        return output_ids

/**
 * KV 缓存优化管理器
 * 模拟 PagedAttention 的分页管理机制
 */
interface KVPage {
  pageId: number;
  blockIds: number[];    // 该页包含的 KV block ID
  data: Float32Array;    // KV 缓存数据
  tokenCount: number;    // 该页存储的 token 数
}

class KVCacheManager {
  private pageSize: number;       // 每页可存储的 token 数
  private totalPages: number;     // 总页数
  private pages: Map<number, KVPage>;
  private freePages: number[];    // 空闲页 ID 池
  private blockTable: Map<number, number>; // blockId -> pageId 映射
  
  constructor(pageSize: number = 16, totalPages: number = 1024) {
    this.pageSize = pageSize;
    this.totalPages = totalPages;
    this.pages = new Map();
    this.freePages = Array.from({ length: totalPages }, (_, i) => i);
    this.blockTable = new Map();
  }
  
  /** 分配一个新页 */
  allocatePage(): KVPage | null {
    if (this.freePages.length === 0) {
      throw new Error('KV Cache 内存耗尽：需要增加 GPU 内存或减少 batch size');
    }
    const pageId = this.freePages.pop()!;
    const page: KVPage = {
      pageId,
      blockIds: [],
      data: new Float32Array(this.pageSize * 128), // 假设每个 token 128 维
      tokenCount: 0
    };
    this.pages.set(pageId, page);
    return page;
  }
  
  /** 追加 token 到 KV 缓存 */
  appendTokens(blockId: number, kvData: Float32Array): void {
    let pageId = this.blockTable.get(blockId);
    let page = pageId !== undefined ? this.pages.get(pageId) : undefined;
    
    // 如果当前页已满，分配新页
    if (!page || page.tokenCount >= this.pageSize) {
      page = this.allocatePage()!;
      this.blockTable.set(blockId, page.pageId);
    }
    
    // 将 KV 数据写入页
    const offset = page.tokenCount * 128;
    page.data.set(kvData, offset);
    page.tokenCount += kvData.length / 128;
    page.blockIds.push(blockId);
  }
  
  /** 获取完整的 KV 缓存（用于注意力计算） */
  getKVCache(blockId: number): Float32Array | null {
    const pageId = this.blockTable.get(blockId);
    if (pageId === undefined) return null;
    
    const page = this.pages.get(pageId)!;
    return page.data.slice(0, page.tokenCount * 128);
  }
  
  /** 释放页（序列结束时） */
  releasePages(blockId: number): void {
    const pageId = this.blockTable.get(blockId);
    if (pageId !== undefined) {
      this.pages.delete(pageId);
      this.freePages.push(pageId);
      this.blockTable.delete(blockId);
    }
  }
  
  /** 获取内存利用率统计 */
  getStats(): { used: number; total: number; utilization: number } {
    const used = this.totalPages - this.freePages.length;
    return {
      used,
      total: this.totalPages,
      utilization: used / this.totalPages
    };
  }
}

"""
多模型基准测试框架
对比不同轻量化模型在你的实际业务场景中的表现
"""
import time
import asyncio
from dataclasses import dataclass, field
from typing import List, Dict, Any, Callable
import json
from concurrent.futures import ThreadPoolExecutor

@dataclass
class BenchmarkResult:
    model_name: str
    input_sample: str
    output: str
    latency_ms: float
    token_count: int
    cost_usd: float
    quality_score: float = 0.0  # 由人工评分填充

@dataclass
class ModelConfig:
    name: str
    api_endpoint: str
    api_key: str
    max_tokens: int = 4096
    temperature: float = 0.7

class ModelBenchmark:
    """多模型基准测试工具"""
    
    def __init__(self, models: List[ModelConfig]):
        self.models = models
        self.results: Dict[str, List[BenchmarkResult]] = {m.name: [] for m in models}
    
    async def benchmark_single(
        self, 
        model: ModelConfig, 
        sample: str
    ) -> BenchmarkResult:
        """对单个样本运行基准测试"""
        start = time.time()
        
        # 这里调用实际的模型 API
        # output = await call_model_api(model, sample)
        # 模拟 API 调用
        await asyncio.sleep(0.5 + hash(sample) % 100 / 1000)
        output = f"Model {model.name} response to: {sample[:50]}..."
        
        latency = (time.time() - start) * 1000
        token_count = len(output) // 4  # 粗略估算
        cost = token_count * 0.0000005  # 示例定价
        
        return BenchmarkResult(
            model_name=model.name,
            input_sample=sample,
            output=output,
            latency_ms=latency,
            token_count=token_count,
            cost_usd=cost
        )
    
    async def benchmark_all(
        self, 
        samples: List[str],
        max_concurrent: int = 10
    ) -> Dict[str, List[BenchmarkResult]]:
        """对所有模型和所有样本运行基准测试"""
        semaphore = asyncio.Semaphore(max_concurrent)
        
        async def run_with_limit(model, sample):
            async with semaphore:
                return await self.benchmark_single(model, sample)
        
        tasks = []
        for model in self.models:
            for sample in samples:
                tasks.append(run_with_limit(model, sample))
        
        all_results = await asyncio.gather(*tasks)
        
        # 按模型分组结果
        for result in all_results:
            self.results[result.model_name].append(result)
        
        return self.results
    
    def generate_report(self) -> str:
        """生成基准测试报告"""
        report = []
        report.append("## 基准测试报告\n")
        
        for model_name, results in self.results.items():
            if not results:
                continue
            
            avg_latency = sum(r.latency_ms for r in results) / len(results)
            p95_latency = sorted(r.latency_ms for r in results)[int(len(results) * 0.95)]
            total_cost = sum(r.cost_usd for r in results)
            avg_tokens = sum(r.token_count for r in results) / len(results)
            
            report.append(f"### {model_name}")
            report.append(f"- 平均延迟: {avg_latency:.0f}ms")
            report.append(f"- P95 延迟: {p95_latency:.0f}ms")
            report.append(f"- 平均 Token 数: {avg_tokens:.0f}")
            report.append(f"- 总成本: " + f"{total_cost:.4f}" + " 美元")
            report.append("")
        
        return "\n".join(report)


# 使用示例
async def main():
    models = [
        ModelConfig("gpt-5.5-instant", "https://api.openai.com/v1/chat", "key1"),
        ModelConfig("claude-haiku-2", "https://api.anthropic.com/v1/messages", "key2"),
        ModelConfig("gemini-flash-2.5", "https://generativelanguage.googleapis.com", "key3"),
    ]
    
    # 加载你的真实业务样本
    samples = [
        "分析这段代码的潜在安全问题：...",
        "将以下中文翻译成英文：...",
        "解释这个数学公式的含义：...",
        # 添加更多样本...
    ]
    
    benchmark = ModelBenchmark(models)
    results = await benchmark.benchmark_all(samples)
    print(benchmark.generate_report())

# asyncio.run(main())