feat: F.embedding_bag 融合查表+池化(单kernel,免[M,512]中间) — 攻最大块(dedup index25%+segment11%=36%)

triton版profile:attention已优化出top,新大头=embedding池化36%+MoE22%+add18%。
embedding_bag一个kernel做查表+按段求和。等价测试+bench --emb-bag。默认关待验证。

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

代码/code/tests/test_equiv.py

@@ -86,6 +86,30 @@ def test_chunked_matches_dense_attention():
     print(f"[PASS] chunked SDPA == 稠密SDPA (max err={err:.2e}, dev={dev})")
 
 
+def test_embedding_bag_matches():
+    torch.manual_seed(0)
+    dev = "cuda" if torch.cuda.is_available() else "cpu"
+    slot_num, emb_dim, d_model = 28, 512, 512
+    enc = infer.RepEncoder(vocab_size=200, emb_dim=emb_dim, slot_num=slot_num,
+                           d_model=d_model).to(dev).eval()
+    N = 6
+    batch = {}
+    for s in range(1, slot_num + 1):
+        counts = torch.randint(0, 8, (N,))
+        vals = torch.randint(0, 200, (int(counts.sum()),), device=dev)
+        offs = torch.cat([torch.zeros(1, dtype=torch.long), counts.cumsum(0)]).to(dev)
+        batch[s] = (vals, offs)
+    with torch.no_grad():
+        infer.CONFIG["use_embedding_bag"] = False
+        ref = enc(batch)
+        infer.CONFIG["use_embedding_bag"] = True
+        new = enc(batch)
+    infer.CONFIG["use_embedding_bag"] = False
+    err = (ref - new).abs().max().item()
+    assert torch.allclose(ref, new, atol=1e-3, rtol=1e-3), f"embedding_bag 不等价 max err={err:.3e}"
+    print(f"[PASS] embedding_bag == segment_reduce (max err={err:.2e}, dev={dev})")
+
+
 def test_sparse_pool_matches():
     torch.manual_seed(0)
     dev = "cuda" if torch.cuda.is_available() else "cpu"
@@ -217,6 +241,7 @@ def test_flex_matches_dense_attention():
 if __name__ == "__main__":
     test_moe_dense_matches_loop()
     test_fused_embedding_matches_perslot()
+    test_embedding_bag_matches()
     test_sparse_pool_matches()
     test_syncfree_mask_matches()
     test_triton_varlen_matches_dense()