perf: searchsorted 构造因果mask，消除最后一个同步点(repeat_interleave张量repeats)

dense MoE 去掉MoE的nonzero同步省了评测20s；embedding融合(无同步)只省1s
->真正的杠杆是消同步点。mask构造的repeat_interleave(lengths张量)是model(batch)
内最后一个同步点，改用searchsorted求doc_id(输出size已知,无同步)。等价测试已加。

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

代码/code/tests/test_equiv.py

@@ -64,6 +64,19 @@ def test_moe_dense_matches_loop():
     print(f"[PASS] MoE 稠密向量化 == 逐expert循环 (max err={err:.2e}, dev={dev})")
 
 
+def test_syncfree_mask_matches():
+    dev = "cuda" if torch.cuda.is_available() else "cpu"
+    rep = infer.RepEncoder(vocab_size=100, emb_dim=8, slot_num=28, d_model=8)
+    seq = infer.TransformerEncoder(d_model=8, n_heads=2, num_layers=1, dim_ff=16)
+    model = infer.CTRModel(rep, seq, d_model=8).to(dev)
+    offs = torch.tensor([0, 10, 35, 42, 60], device=dev)  # 4 个用户，变长
+    S = int(offs[-1])
+    m1 = model.get_sequence_causal_mask(offs)
+    m2 = model.causal_mask_syncfree(offs, S, torch.device(dev))
+    assert torch.equal(m1, m2), "sync-free mask 与原 mask 不一致"
+    print(f"[PASS] searchsorted mask == repeat_interleave mask (dev={dev})")
+
+
 def test_varlen_matches_dense_attention():
     if not torch.cuda.is_available():
         print("[SKIP] varlen 等价测试（需 CUDA）")
@@ -134,6 +147,7 @@ def test_flex_matches_dense_attention():
 if __name__ == "__main__":
     test_moe_dense_matches_loop()
     test_fused_embedding_matches_perslot()
+    test_syncfree_mask_matches()
     test_varlen_matches_dense_attention()
     test_flex_matches_dense_attention()
     print("[DONE] 等价测试结束")