feat: fused MoE — baddbmm(cutlass GEMM+bias融合)+跳过推理无用的moe_loss,减kernel

GEMM保留cutlass(triton GEMM难超),融bias epilogue省add kernel;moe_loss仅训练用,
推理跳过省importance/std/mean。延续减kernel方向(embedding_bag/triton已证评测赚)。
默认开,bench --no-moe-baddbmm/--no-skip-moe-loss 对照。AUC无损。

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

代码/code/infer.py

@@ -143,6 +143,8 @@ CONFIG = {
     # synchronize，去掉同步点可能让被计时的 model(batch) 大幅缩短。本地force-sync看不出，
     # 须靠提交验证。AUC中性、MoE仅占2%算力故风险极低。
     "vectorize_moe": True,    # True=稠密向量化MoE(无同步点)；False=原逐expert循环(.nonzero同步)
+    "moe_baddbmm": True,      # MoE FFN 用 baddbmm(cutlass GEMM+bias epilogue融合),省 bias add kernel
+    "skip_moe_loss": True,    # 推理跳过 moe_loss(load-balance,推理无用),省 importance/std/mean kernel
     "fuse_embedding": True,   # True=28个slot的查表+池化融合为1次(减per-batch kernel启动)
     "syncfree_mask": True,    # True=用searchsorted构造因果mask(无同步)；False=repeat_interleave(同步)
     "emb_fp16": True,         # True=Embedding表转FP16(查表带宽减半,实测AUC 0.75932≈无损)
@@ -684,6 +686,9 @@ class SMoE(nn.Module):
         self.register_buffer("b1", torch.stack([e.fc1.bias for e in self.experts]).contiguous())     # [E,F]
         self.register_buffer("W2", torch.stack([e.fc2.weight for e in self.experts]).contiguous())   # [E,D,F]
         self.register_buffer("b2", torch.stack([e.fc2.bias for e in self.experts]).contiguous())      # [E,D]
+        # baddbmm 用的转置权重（[E,D,F] / [E,F,D]），预转 contiguous
+        self.register_buffer("W1t", self.W1.transpose(1, 2).contiguous())  # [E,D,F]
+        self.register_buffer("W2t", self.W2.transpose(1, 2).contiguous())  # [E,F,D]
         self._stacked = True
 
     def forward(self, x):
@@ -698,10 +703,17 @@ class SMoE(nn.Module):
         topk_idx, topk_score, probs = self.gate(x)
 
         xf = x.reshape(-1, D)                                    # [N, D]
-        # 稠密计算所有 expert（GPU 友好、无 Python 循环/同步/gather-scatter）：
-        h = torch.einsum("nd,efd->enf", xf, self.W1) + self.b1.unsqueeze(1)  # [E,N,F]
-        h = F.relu(h)
-        o = torch.einsum("enf,edf->end", h, self.W2) + self.b2.unsqueeze(1)  # [E,N,D]
+        Nt = xf.shape[0]
+        if CONFIG.get("moe_baddbmm", True):
+            # cutlass GEMM + bias epilogue 融合（省 bias add kernel）
+            xe = xf.unsqueeze(0).expand(self.num_experts, -1, -1)            # [E,N,D]
+            h = torch.baddbmm(self.b1.unsqueeze(1), xe, self.W1t)            # [E,N,F]
+            h = F.relu(h)
+            o = torch.baddbmm(self.b2.unsqueeze(1), h, self.W2t)            # [E,N,D]
+        else:
+            h = torch.einsum("nd,efd->enf", xf, self.W1) + self.b1.unsqueeze(1)
+            h = F.relu(h)
+            o = torch.einsum("enf,edf->end", h, self.W2) + self.b2.unsqueeze(1)
 
         # 按每个 token 的 top-k 选取并加权（与逐 expert 循环数学等价）
         o = o.permute(1, 0, 2)                                   # [N, E, D]
@@ -710,8 +722,11 @@ class SMoE(nn.Module):
         sel = torch.gather(o, 1, idx.unsqueeze(-1).expand(-1, -1, D))  # [N, k, D]
         out = (sel * sc.unsqueeze(-1)).sum(dim=1).reshape(B, S, D)
 
-        importance = probs.sum(dim=(0, 1))                       # [E]
-        moe_loss = (importance.std() / (importance.mean() + 1e-6))
+        if CONFIG.get("skip_moe_loss", True):
+            moe_loss = out.new_zeros(())   # 推理无用，跳过 importance/std/mean
+        else:
+            importance = probs.sum(dim=(0, 1))                   # [E]
+            moe_loss = (importance.std() / (importance.mean() + 1e-6))
         return out, moe_loss