腾讯混元Hy4 Preview深度技术解析：770B MoE、递归自我改进与国产大模型闪电战

import torch
import torch.nn.functional as F

class MoERouter(torch.nn.Module):
    """Hy4 preview 风格的 MoE 路由模块"""
    def __init__(self, hidden_size: int, num_experts: int = 256, top_k: int = 8):
        super().__init__()
        self.num_experts = num_experts
        self.top_k = top_k
        # 路由网络：线性投影 + 可选激活
        self.gate = torch.nn.Linear(hidden_size, num_experts, bias=False)
        
    def forward(self, x: torch.Tensor):
        # x: (batch_size, seq_len, hidden_size)
        # 计算路由分数
        scores = self.gate(x)  # (batch, seq, num_experts)
        
        # 可选：添加负载均衡噪声（训练时）
        if self.training:
            noise = torch.randn_like(scores) * 0.01
            scores = scores + noise
        
        # Top-8 选择
        top_k_weights, top_k_indices = torch.topk(
            F.softmax(scores, dim=-1), 
            k=self.top_k, 
            dim=-1
        )
        
        return top_k_weights, top_k_indices, scores

class MoELayer(torch.nn.Module):
    """Hy4 preview 单层 MoE 实现"""
    def __init__(self, hidden_size: int, moe_intermediate_size: int = 2048,
                 num_experts: int = 256, top_k: int = 8):
        super().__init__()
        self.router = MoERouter(hidden_size, num_experts, top_k)
        # 256 个路由专家
        self.experts = torch.nn.ModuleList([
            ExpertFFN(hidden_size, moe_intermediate_size)
            for _ in range(num_experts)
        ])
        # 1 个共享专家（始终激活）
        self.shared_expert = ExpertFFN(hidden_size, moe_intermediate_size)
        
    def forward(self, x: torch.Tensor):
        batch, seq, hidden = x.shape
        
        # 共享专家输出（始终计算）
        shared_output = self.shared_expert(x)
        
        # 路由专家选择
        top_k_weights, top_k_indices, _ = self.router(x)
        
        # 稀疏激活：仅计算选中的专家
        # 实际实现中会使用并行 dispatch 机制
        routed_output = torch.zeros_like(x)
        for b in range(batch):
            for s in range(seq):
                for k in range(self.router.top_k):
                    expert_idx = top_k_indices[b, s, k]
                    weight = top_k_weights[b, s, k]
                    expert_out = self.experts[expert_idx](x[b:b+1, s:s+1])
                    routed_output[b, s] += weight * expert_out[b, 0]
        
        # 路由专家 + 共享专家输出合并
        return routed_output + shared_output

import torch
import torch.nn.functional as F

class GatedDSA(torch.nn.Module):
    """Gated DeepSeek Sparse Attention 简化实现"""
    def __init__(self, hidden_size: int = 6144, num_heads: int = 64,
                 num_indexer_heads: int = 32, top_k: int = 2048):
        super().__init__()
        self.num_heads = num_heads
        self.num_indexer_heads = num_indexer_heads
        self.top_k = top_k
        self.head_dim = hidden_size // num_heads
        
        # QKV 投影
        self.q_proj = torch.nn.Linear(hidden_size, hidden_size)
        self.k_proj = torch.nn.Linear(hidden_size, hidden_size)
        self.v_proj = torch.nn.Linear(hidden_size, hidden_size)
        
        # 查询压缩 (query → 2048 维)
        self.q_compress = torch.nn.Linear(hidden_size, 2048)
        # KV 压缩 (key/value → 512 维)
        self.kv_compress = torch.nn.Linear(hidden_size, 512)
        
        # Indexer: 32 头, 每头 128 维
        self.indexer = torch.nn.Linear(2048, num_indexer_heads * 128)
        self.indexer_top_k = top_k  # 索引器选择 top-2048
        
        # 门控网络
        self.gate = torch.nn.Sequential(
            torch.nn.Linear(self.head_dim, 1),
            torch.nn.Sigmoid()
        )
        
    def forward(self, x: torch.Tensor, index_cache=None):
        batch, seq, _ = x.shape
        
        # 标准 QKV 投影
        q = self.q_proj(x)
        k = self.k_proj(x)
        v = self.v_proj(x)
        
        # 查询压缩用于索引
        q_compressed = self.q_compress(x)  # (batch, seq, 2048)
        
        # 索引器：生成稀疏注意力索引
        indexer_out = self.indexer(q_compressed)
        # 使用 index_cache 或重新计算
        if index_cache is not None:
            # 跨层复用索引
            sparse_indices = index_cache
        else:
            # 计算当前层的稀疏索引
            indexer_scores = torch.matmul(
                indexer_out, 
                indexer_out.transpose(-2, -1)
            )
            _, sparse_indices = torch.topk(
                indexer_scores, 
                k=self.indexer_top_k, 
                dim=-1
            )
        
        # 仅在选中的稀疏位置上计算注意力
        # 实际实现中会使用 gather + flash attention 变体
        k_sparse = torch.gather(
            k.unsqueeze(1).expand(-1, self.num_heads, -1, -1),
            dim=2,
            index=sparse_indices.unsqueeze(-1).expand(-1, -1, -1, self.head_dim)
        )
        v_sparse = torch.gather(
            v.unsqueeze(1).expand(-1, self.num_heads, -1, -1),
            dim=2,
            index=sparse_indices.unsqueeze(-1).expand(-1, -1, -1, self.head_dim)
        )
        
        # 稀疏注意力计算
        attn_scores = torch.matmul(q.view(batch, -1, self.num_heads, self.head_dim).transpose(1, 2),
                                    k_sparse.transpose(-2, -1)) / (self.head_dim ** 0.5)
        attn_weights = F.softmax(attn_scores, dim=-1)
        
        # 门控机制
        gate_values = self.gate(
            attn_weights.mean(dim=-1, keepdim=True)
        )
        attn_weights = attn_weights * gate_values
        
        attn_output = torch.matmul(attn_weights, v_sparse)
        attn_output = attn_output.transpose(1, 2).contiguous().view(batch, seq, -1)
        
        return attn_output, sparse_indices

import torch
import torch.nn.functional as F

class MTPDecoder:
    """Hy4 preview 内置 MTP 投机解码"""
    def __init__(self, main_model, mtp_module, 
                 num_speculative_tokens: int = 3):
        self.main_model = main_model
        self.mtp_module = mtp_module  # 10B 参数 / 0.7B 激活
        self.num_speculative_tokens = num_speculative_tokens
        
    @torch.no_grad()
    def generate(self, input_ids: torch.Tensor, max_new_tokens: int = 1024):
        generated = input_ids.clone()
        prompt_len = input_ids.shape[1]
        
        while generated.shape[1] - prompt_len < max_new_tokens:
            # Step 1: 使用 MTP 模块生成草稿 token
            draft_tokens = self._draft(generated)
            
            # Step 2: 主模型并行验证草稿
            accepted = self._verify(generated, draft_tokens)
            
            # Step 3: 接受通过验证的 token
            generated = torch.cat([generated, accepted], dim=-1)
            
        return generated
    
    def _draft(self, context: torch.Tensor) -> torch.Tensor:
        """MTP 草稿生成：预测后续 num_speculative_tokens 个 token"""
        drafts = []
        current = context
        
        for _ in range(self.num_speculative_tokens):
            # MTP 前向传播（轻量级）
            logits = self.mtp_module(current)
            next_token = self._sample(logits[:, -1, :])
            drafts.append(next_token)
            current = torch.cat([current, next_token], dim=-1)
            
        return torch.cat(drafts, dim=-1)
    
    def _verify(self, context: torch.Tensor, 
                drafts: torch.Tensor) -> torch.Tensor:
        """主模型并行验证草稿 token"""
        # 拼接上下文和草稿
        full_input = torch.cat([context, drafts], dim=-1)
        
        # 主模型单次前向（并行计算所有位置）
        main_logits = self.main_model(full_input)
        
        # 验证每个草稿位置
        accepted = []
        for i in range(drafts.shape[1]):
            draft_token = drafts[:, i:i+1]
            target_logits = main_logits[:, context.shape[1] + i, :]
            target_probs = F.softmax(target_logits, dim=-1)
            
            # 拒绝采样：使用草稿分布 vs 目标分布
            draft_prob = target_probs.gather(-1, draft_token)
            accept_prob = torch.min(
                torch.ones_like(draft_prob),
                draft_prob / (draft_prob + 1e-8)
            )
            
            if torch.rand(1) < accept_prob:
                accepted.append(draft_token)
            else:
                # 从修正后的分布重新采样
                adjusted_probs = F.softmax(target_logits, dim=-1)
                # 减去草稿分布中的概率进行修正
                corrected = F.relu(adjusted_probs - draft_prob * adjusted_probs)
                corrected = corrected / corrected.sum(dim=-1, keepdim=True)
                resampled = torch.multinomial(corrected, 1)
                accepted.append(resampled)
                break
                
        return torch.cat(accepted, dim=-1) if accepted else drafts[:, :1]
    
    @staticmethod
    def _sample(logits: torch.Tensor, temperature: float = 0.9):
        probs = F.softmax(logits / temperature, dim=-1)
        return torch.multinomial(probs, 1)

# 官方 vLLM 部署命令（8 卡张量并行）
docker run --gpus all \
  -p 8000:8000 \
  --ipc=host \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai:hy4-preview \
  tencent/Hy4-preview-FP8 \
  --tensor-parallel-size 8 \
  --speculative-config '{"num_speculative_tokens": 3, "method": "mtp"}' \
  --attention-backend FLASHMLA_SPARSE \
  --tool-call-parser hy_v4 \
  --reasoning-parser hy_v4 \
  --enable-auto-tool-choice \
  --port 8000 \
  --served-model-name hy4-preview

# SGLang 部署命令
docker run --gpus all --ipc=host -p 8000:8000 \
  lmsysorg/sglang:hy4-preview \
  python3 -m sglang.launch_server \
  --model tencent/Hy4-preview-FP8 \
  --tp-size 8 \
  --reasoning-parser auto \
  --tool-call-parser auto \
  --speculative-algorithm NEXTN \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --port 8000 \
  --served-model-name hy4-preview

# 抽象描述 Hy4 preview 的推理系统自优化流程
class InferenceOptimizer:
    """Hy4 preview 自主推理优化框架"""
    
    def analyze_bottleneck(self, profile_data: dict) -> str:
        """分析推理 profile 数据，识别瓶颈"""
        # 分析各算子的耗时占比
        op_times = profile_data['op_times']
        # 识别通信开销
        comm_overhead = profile_data['communication_latency']
        # 识别显存瓶颈
        memory_bandwidth = profile_data['memory_bandwidth_util']
        
        if op_times['attention'] > 0.4 * sum(op_times.values()):
            return 'attention_is_bottleneck'
        elif comm_overhead > 0.3 * profile_data['total_time']:
            return 'communication_is_bottleneck'
        elif memory_bandwidth < 0.5:
            return 'memory_bound'
        else:
            return 'compute_bound'
    
    def propose_optimization(self, bottleneck: str) -> dict:
        """根据瓶颈类型提出优化方案"""
        optimizations = {
            'attention_is_bottleneck': {
                'action': 'fuse_attention_ops',
                'target': '将 QKV 投影 + 注意力计算 + 输出投影融合为单个 kernel',
                'expected_gain': '0.15-0.25'
            },
            'communication_is_bottleneck': {
                'action': 'optimize_allreduce',
                'target': '使用 AllReduce 合并 + 异步通信隐藏传输延迟',
                'expected_gain': '0.10-0.20'
            },
            'memory_bound': {
                'action': 'kv_cache_compression',
                'target': '对 KV cache 进行低精度量化 + 稀疏化存储',
                'expected_gain': '0.20-0.35'
            },
            'compute_bound': {
                'action': 'tensor_parallel_resharding',
                'target': '调整张量并行策略，减少计算倾斜',
                'expected_gain': '0.05-0.15'
            }
        }
        return optimizations.get(bottleneck, {})
    
    def run_experiment(self, optimization: dict) -> dict:
        """运行优化实验，返回结果"""
        # 实现优化方案
        # 运行基准测试
        # 收集性能数据
        return {
            'throughput_gain': 0.318,
            'latency_reduction': 0.24,
            'memory_saved_gb': 12.5,
            'stability': 'consistent_across_context_lengths'
        }