feat: 嵌套张量变长 flash 注意力(--attn varlen)，统一 CONFIG.attn 分发

每用户当独立序列、is_causal 块对角因果，一个 flash 内核处理一 batch 内所有
用户，无稠密mask/无padding浪费/开销远低于FlexAttention。CONFIG.attn∈
{sdpa(默认),flex,varlen}；bench --attn varlen；test_equiv 加 varlen 等价测试。

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

代码/code/tests/test_equiv.py

@@ -64,11 +64,32 @@ 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_varlen_matches_dense_attention():
+    if not torch.cuda.is_available():
+        print("[SKIP] varlen 等价测试（需 CUDA）")
+        return
+    torch.manual_seed(0)
+    dev = "cuda"
+    H, Dh = 8, 64
+    offs = _offsets([10, 25, 7, 40, 18], dev)
+    S = int(offs[-1])
+    q = torch.randn(1, H, S, Dh, device=dev, dtype=torch.float16)
+    k = torch.randn(1, H, S, Dh, device=dev, dtype=torch.float16)
+    v = torch.randn(1, H, S, Dh, device=dev, dtype=torch.float16)
+    with torch.no_grad():
+        dense = infer.scaled_dot_product(q, k, v, {"mask": _dense_causal_mask(offs)[None, None]})
+        varlen = infer.scaled_dot_product(q, k, v, {"varlen_offsets": offs})
+    err = (dense.float() - varlen.float()).abs().max().item()
+    assert torch.allclose(dense.float(), varlen.float(), atol=2e-2, rtol=2e-2), \
+        f"varlen 不等价 max err={err:.3e}"
+    print(f"[PASS] varlen(嵌套张量) == 稠密SDPA (max err={err:.2e})")
+
+
 def test_flex_matches_dense_attention():
     ok = (torch.cuda.is_available() and infer._HAS_FLEX
           and torch.cuda.get_device_capability()[0] >= 8)
     if not ok:
-        print("[SKIP] FlexAttention 等价测试（需 CUDA SM80+，当前环境不满足）")
+        print("[SKIP] FlexAttention 等价测试（需 CUDA SM80+）")
         return
     torch.manual_seed(0)
     dev = "cuda"
@@ -88,5 +109,6 @@ def test_flex_matches_dense_attention():
 
 if __name__ == "__main__":
     test_moe_dense_matches_loop()
+    test_varlen_matches_dense_attention()
     test_flex_matches_dense_attention()
     print("[DONE] 等价测试结束")