feat: add page-aligned index validation for CP HiCache direct transfers

Add validate_page_aligned_token_indices utility and apply it across
HiCache write/load paths, NSA indexer transfers, and CUDA direct copy
kernels to reject malformed (partial, misaligned, non-contiguous) page
groups before they reach native transfer code. Also validate supported
CP HiCache backend/layout combinations at server startup.

python/sglang/srt/managers/cache_controller.py

@@ -40,6 +40,7 @@ from sglang.srt.layers.dp_attention import (
 )
 from sglang.srt.mem_cache.cp_shared_kv_layout import CpSharedKVLayout
 from sglang.srt.mem_cache.memory_pool import MLATokenToKVPool, NSATokenToKVPool
+from sglang.srt.mem_cache.page_index_utils import validate_page_aligned_token_indices
 from sglang.srt.utils import get_device_module
 
 logger = logging.getLogger(__name__)
@@ -735,6 +736,16 @@ class HiCacheController:
         event.record()
         self.ack_load_queue.append(HiCacheAck(event, event, [node_id]))
 
+    def _validate_cp_hicache_page_indices(
+        self,
+        host_indices: torch.Tensor,
+        device_indices: torch.Tensor,
+    ) -> None:
+        validate_page_aligned_token_indices(host_indices, self.page_size, "host_indices")
+        validate_page_aligned_token_indices(
+            device_indices, self.page_size, "physical_device_indices"
+        )
+
     def _write_cp(
         self,
         device_indices: torch.Tensor,
@@ -762,6 +773,11 @@ class HiCacheController:
         host_indices = self.mem_pool_host.alloc(len(physical_device_indices))
         if host_indices is None:
             return HiCacheWriteFailure(required_host_slots=len(physical_device_indices))
+        try:
+            self._validate_cp_hicache_page_indices(host_indices, physical_device_indices)
+        except Exception:
+            self.mem_pool_host.free(host_indices)
+            raise
 
         self.write_queue.append(
             CacheOperation(host_indices, physical_device_indices, node_id, priority)
@@ -817,10 +833,17 @@ class HiCacheController:
             self._append_completed_load_ack(node_id)
             return device_indices
 
+        host_indices = torch.cat(host_chunks)
+        physical_device_indices = torch.cat(physical_chunks)
+        try:
+            self._validate_cp_hicache_page_indices(host_indices, physical_device_indices)
+        except Exception:
+            self.mem_pool_device_allocator.free(device_indices)
+            raise
         self.load_queue.append(
             CacheOperation(
-                torch.cat(host_chunks),
-                torch.cat(physical_chunks),
+                host_indices,
+                physical_device_indices,
                 node_id,
             )
         )

python/sglang/srt/mem_cache/memory_pool_host.py

@@ -23,6 +23,7 @@ from sglang.srt.mem_cache.memory_pool import (
     MLATokenToKVPool,
     NSATokenToKVPool,
 )
+from sglang.srt.mem_cache.page_index_utils import validate_page_aligned_token_indices
 from sglang.srt.utils import is_cuda, is_mps, is_npu, is_xpu
 
 _is_cuda = is_cuda()
@@ -1169,10 +1170,10 @@ class NSATokenToKVPoolHost(MLATokenToKVPoolHost):
     def _get_indexer_page_indices(self, host_indices, device_indices):
         if host_indices.numel() == 0:
             return host_indices, device_indices
-        if host_indices.numel() % self.page_size != 0:
-            raise ValueError(
-                "Index buffer transfer expects page-aligned indices for NSA."
-            )
+        validate_page_aligned_token_indices(host_indices, self.page_size, "host_indices")
+        validate_page_aligned_token_indices(
+            device_indices, self.page_size, "device_indices"
+        )
         host_page_indices = (
             host_indices.reshape(-1, self.page_size)[:, 0] // self.page_size
         )

python/sglang/srt/mem_cache/page_index_utils.py

@@ -0,0 +1,46 @@
+from __future__ import annotations
+
+import torch
+
+
+def validate_page_aligned_token_indices(
+    indices: torch.Tensor,
+    page_size: int,
+    name: str,
+) -> None:
+    """Validate that token indices are grouped as complete contiguous pages.
+
+    HiCache page-first direct transfers treat every `page_size` entries as one
+    page and derive the page id from the first token. A malformed group can
+    make native direct-copy code address the wrong page, so validate the exact
+    invariant before entering transfer code.
+    """
+
+    if page_size <= 0:
+        raise ValueError(f"page_size must be positive, got {page_size}")
+    if indices.dim() != 1:
+        raise ValueError(
+            f"{name} must be a 1-D tensor, got shape={tuple(indices.shape)}"
+        )
+    if indices.numel() == 0:
+        return
+    if indices.numel() % page_size != 0:
+        raise ValueError(
+            f"{name} must contain whole pages: numel={indices.numel()} page_size={page_size}"
+        )
+
+    page_view = indices.reshape(-1, page_size)
+    starts = page_view[:, 0]
+    if torch.any(torch.remainder(starts, page_size) != 0):
+        raise ValueError(
+            f"{name} page groups must start at page boundaries: page_size={page_size}"
+        )
+
+    expected_offsets = torch.arange(
+        page_size, device=indices.device, dtype=indices.dtype
+    )
+    expected = starts[:, None] + expected_offsets
+    if not torch.equal(page_view, expected):
+        raise ValueError(
+            f"{name} page groups must be contiguous page spans: page_size={page_size}"
+        )

python/sglang/srt/server_args.py

@@ -798,6 +798,7 @@ class ServerArgs:
 
         # Handle Hicache settings.
         self._handle_hicache()
+        self._handle_cp_hicache_layout_validation()
 
         # Handle data parallelism.
         self._handle_data_parallelism()
@@ -917,6 +918,33 @@ class ServerArgs:
             "Disable hicache_storage_backend or disable CP shared KV."
         )
 
+    def _handle_cp_hicache_layout_validation(self):
+        if not (
+            self.enable_nsa_prefill_cp_shared_kv and self.enable_hierarchical_cache
+        ):
+            return
+
+        supported_pairs = {
+            ("kernel", "layer_first"),
+            ("kernel", "page_first"),
+            ("direct", "layer_first"),
+            ("direct", "page_first_direct"),
+        }
+        current_pair = (self.hicache_io_backend, self.hicache_mem_layout)
+        if current_pair in supported_pairs:
+            return
+
+        raise ValueError(
+            "CP shared KV HiCache supports only "
+            "kernel/layer_first, kernel/page_first, direct/layer_first, "
+            "and direct/page_first_direct after HiCache normalization. "
+            f"Got hicache_io_backend={self.hicache_io_backend!r} "
+            f"hicache_mem_layout={self.hicache_mem_layout!r}. "
+            "page_head is MHA/Mooncake-specific, page_first_kv_split is "
+            "kernel_ascend-specific, and kernel_ascend CP shared KV HiCache "
+            "is not implemented in the CUDA NSA host path."
+        )
+
     def _handle_deprecated_args(self):
         # Handle deprecated tool call parsers
         deprecated_tool_call_parsers = {"qwen25": "qwen", "glm45": "glm"}

sgl-kernel/csrc/kvcacheio/transfer.cu

@@ -686,6 +686,38 @@ inline void transfer_page_direct(
           /* non_blocking= */ true);
 }
 
+inline void validate_page_first_direct_indices(
+    const int64_t* indices_ptr, int64_t num_indices, int64_t page_size, const char* name) {
+  if (num_indices == 0) {
+    return;
+  }
+  for (int64_t i = 0; i < num_indices; i += page_size) {
+    const int64_t start = indices_ptr[i];
+    TORCH_CHECK(
+        start % page_size == 0,
+        name,
+        " page groups must start at page boundaries: group=",
+        i / page_size,
+        " start=",
+        start,
+        " page_size=",
+        page_size);
+    for (int64_t offset = 1; offset < page_size; ++offset) {
+      TORCH_CHECK(
+          indices_ptr[i + offset] == start + offset,
+          name,
+          " page groups must be contiguous page spans: group=",
+          i / page_size,
+          " offset=",
+          offset,
+          " expected=",
+          start + offset,
+          " got=",
+          indices_ptr[i + offset]);
+    }
+  }
+}
+
 void transfer_kv_direct(
     const std::vector<at::Tensor>& src_layers,
     std::vector<at::Tensor> dst_layers,
@@ -749,6 +781,8 @@ inline void transfer_kv_page_first_direct_impl(
   const int64_t num_pages = src_indices_cpu.size(0) / page_size;
   int64_t* src_indices_ptr = src_indices_cpu.data_ptr<int64_t>();
   int64_t* dst_indices_ptr = dst_indices_cpu.data_ptr<int64_t>();
+  validate_page_first_direct_indices(src_indices_ptr, src_indices_cpu.numel(), page_size, "src_indices");
+  validate_page_first_direct_indices(dst_indices_ptr, dst_indices_cpu.numel(), page_size, "dst_indices");
 
   auto fallback_to_page_copy = [&]() {
     if constexpr (IsLf2Pf) {

sgl-kernel/tests/test_kvcacheio.py

@@ -33,6 +33,15 @@ def ref_copy_with_indices_pf_direct(
             ][layer_id].to(dst_pool.device)
 
 
+def make_page_indices(page_ids, page_size):
+    return torch.cat(
+        [
+            torch.arange(p * page_size, (p + 1) * page_size, dtype=torch.int64)
+            for p in page_ids
+        ]
+    )
+
+
 def ref_copy_with_indices_page_head(
     src_pool,
     dst_pool,
@@ -522,6 +531,54 @@ def test_transfer_kv_pf_direct(
     torch.set_default_dtype(original_dtype)
 
 
+@pytest.mark.skipif(is_hip(), reason="HIP uses the fallback path for this direct validation")
+def test_transfer_kv_all_layer_direct_lf_pf_rejects_misaligned_page_start():
+    page_size = 4
+    item_size = 8
+    num_layers = 1
+    src_pool = torch.randn(num_layers, 32, item_size, device="cuda")
+    dst_pool = torch.zeros(8, num_layers, page_size, item_size).pin_memory()
+    src_indices = torch.tensor([1, 2, 3, 4], dtype=torch.int64)
+    dst_indices = make_page_indices([0], page_size)
+
+    with pytest.raises(RuntimeError, match="page boundaries"):
+        transfer_kv_all_layer_direct_lf_pf(
+            [src_pool[0]], [dst_pool], src_indices, dst_indices, page_size
+        )
+
+
+@pytest.mark.skipif(is_hip(), reason="HIP uses the fallback path for this direct validation")
+def test_transfer_kv_all_layer_direct_lf_pf_rejects_non_contiguous_page_group():
+    page_size = 4
+    item_size = 8
+    num_layers = 1
+    src_pool = torch.randn(num_layers, 32, item_size, device="cuda")
+    dst_pool = torch.zeros(8, num_layers, page_size, item_size).pin_memory()
+    src_indices = torch.tensor([4, 5, 7, 6], dtype=torch.int64)
+    dst_indices = make_page_indices([0], page_size)
+
+    with pytest.raises(RuntimeError, match="contiguous page spans"):
+        transfer_kv_all_layer_direct_lf_pf(
+            [src_pool[0]], [dst_pool], src_indices, dst_indices, page_size
+        )
+
+
+@pytest.mark.skipif(is_hip(), reason="HIP uses the fallback path for this direct validation")
+def test_transfer_kv_per_layer_direct_pf_lf_rejects_misaligned_host_page_start():
+    page_size = 4
+    item_size = 8
+    layer_id = 0
+    src_pool = torch.randn(8, 1, page_size, item_size).pin_memory()
+    dst_pool = torch.zeros(1, 32, item_size, device="cuda")
+    src_indices = torch.tensor([1, 2, 3, 4], dtype=torch.int64)
+    dst_indices = make_page_indices([0], page_size).to("cuda")
+
+    with pytest.raises(RuntimeError, match="page boundaries"):
+        transfer_kv_per_layer_direct_pf_lf(
+            [src_pool], [dst_pool[0]], src_indices, dst_indices, layer_id, page_size
+        )
+
+
 @pytest.mark.skipif(is_hip(), reason="HIP is not supported for this test")
 @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16])
 @pytest.mark.parametrize("num_items_to_transfer", [256, 1024])

test/registered/unit/managers/test_hicache_controller_cp.py

@@ -195,6 +195,32 @@ class TestHiCacheControllerCPWrite(CustomTestCase):
         self.assertEqual(host_pool.alloc_calls, [4])
         self.assertEqual(host_pool.backups[0][1].tolist(), [4, 5, 6, 7])
 
+    def test_cp_write_rejects_incomplete_owned_physical_page(self):
+        host_pool = FakeHostPool(torch.tensor([100, 101, 102, 103], dtype=torch.int64))
+        controller = self.make_controller(host_pool, cp_rank=1)
+        logical_locs = torch.tensor([8, 9, 10], dtype=torch.int64)
+
+        with self.assertRaisesRegex(ValueError, "physical_device_indices.*whole pages"):
+            controller.write(logical_locs, node_id=21)
+
+    def test_cp_write_rejects_non_contiguous_owned_physical_page(self):
+        host_pool = FakeHostPool(torch.tensor([100, 101, 102, 103], dtype=torch.int64))
+        controller = self.make_controller(host_pool, cp_rank=1)
+        logical_locs = torch.tensor([8, 9, 11, 10], dtype=torch.int64)
+
+        with self.assertRaisesRegex(
+            ValueError, "physical_device_indices.*contiguous page spans"
+        ):
+            controller.write(logical_locs, node_id=22)
+
+    def test_cp_write_rejects_non_contiguous_host_page(self):
+        host_pool = FakeHostPool(torch.tensor([100, 101, 103, 102], dtype=torch.int64))
+        controller = self.make_controller(host_pool, cp_rank=1)
+        logical_locs = torch.arange(8, 12, dtype=torch.int64)
+
+        with self.assertRaisesRegex(ValueError, "host_indices.*contiguous page spans"):
+            controller.write(logical_locs, node_id=23)
+
     def test_cp_write_zero_owned_returns_metadata_and_noop_ack(self):
         host_pool = FakeHostPool(torch.tensor([], dtype=torch.int64))
         controller = self.make_controller(host_pool, cp_rank=3)
@@ -315,6 +341,43 @@ class TestHiCacheControllerCPLoad(TestHiCacheControllerCPWrite):
         self.assertEqual(queued_op.host_indices.tolist(), [100, 101, 102, 103])
         self.assertEqual(queued_op.device_indices.tolist(), [20, 21, 22, 23])
 
+    def test_cp_load_rejects_non_contiguous_physical_device_page(self):
+        host_pool = FakeHostPool(torch.tensor([100, 101, 102, 103], dtype=torch.int64))
+        allocator = FakeAllocator(
+            alloc_result=torch.tensor(
+                [64, 65, 66, 67, 68, 69, 71, 70, 72, 73, 74, 75, 76, 77, 78, 79],
+                dtype=torch.int64,
+            )
+        )
+        controller = self.make_controller(host_pool, allocator=allocator, cp_rank=1)
+        node = TreeNode()
+        node.host_len = 16
+        node.cp_hicache = CpHiCacheNodeMetadata(
+            logical_len=16,
+            owned_positions=torch.tensor([4, 5, 6, 7], dtype=torch.int64),
+            host_indices=torch.tensor([100, 101, 102, 103], dtype=torch.int64),
+        )
+
+        with self.assertRaisesRegex(
+            ValueError, "physical_device_indices.*contiguous page spans"
+        ):
+            controller.load_cp([node], node_id=31)
+
+    def test_cp_load_rejects_non_contiguous_host_page(self):
+        host_pool = FakeHostPool(torch.tensor([100, 101, 102, 103], dtype=torch.int64))
+        allocator = FakeAllocator(alloc_result=torch.arange(64, 80, dtype=torch.int64))
+        controller = self.make_controller(host_pool, allocator=allocator, cp_rank=1)
+        node = TreeNode()
+        node.host_len = 16
+        node.cp_hicache = CpHiCacheNodeMetadata(
+            logical_len=16,
+            owned_positions=torch.tensor([4, 5, 6, 7], dtype=torch.int64),
+            host_indices=torch.tensor([100, 101, 103, 102], dtype=torch.int64),
+        )
+
+        with self.assertRaisesRegex(ValueError, "host_indices.*contiguous page spans"):
+            controller.load_cp([node], node_id=32)
+
 
 if __name__ == "__main__":
     main()

test/registered/unit/mem_cache/test_nsa_pool_host_unit.py

@@ -10,11 +10,12 @@ from sglang.srt.mem_cache.memory_pool_host import (
 )
 from sglang.srt.utils import is_cuda, is_hip, is_npu, is_xpu
 from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.test_utils import CustomTestCase
 
 register_cuda_ci(est_time=3, suite="stage-b-test-1-gpu-small")
 
 
-class TestNSAHiCacheTransfer(unittest.TestCase):
+class TestNSAHiCacheTransfer(CustomTestCase):
     def setUp(self):
         if not torch.cuda.is_available():
             self.skipTest("CUDA is required for NSA host transfer tests.")
@@ -36,7 +37,7 @@ class TestNSAHiCacheTransfer(unittest.TestCase):
         ]
         return torch.cat(parts, dim=0)
 
-    def _run_device_to_host_indexer_copy(self, io_backend: str):
+    def _run_device_to_host_indexer_copy(self, io_backend: str, layout: str):
         page_size = 1 if is_hip() else 64
         layer_num = 2
         size = page_size * 4
@@ -63,7 +64,7 @@ class TestNSAHiCacheTransfer(unittest.TestCase):
                 host_to_device_ratio=2.0,
                 host_size=0,
                 page_size=page_size,
-                layout="layer_first",
+                layout=layout,
                 pin_memory=pin_memory,
                 device="cpu",
             )
@@ -105,26 +106,88 @@ class TestNSAHiCacheTransfer(unittest.TestCase):
             for host_page, device_page in zip(
                 host_pages.tolist(), device_pages.tolist()
             ):
-                got = host_pool.index_k_with_scale_buffer[layer_id][host_page].cpu()
+                if layout == "page_first_direct":
+                    got = host_pool.index_k_with_scale_buffer[host_page, layer_id].cpu()
+                else:
+                    got = host_pool.index_k_with_scale_buffer[layer_id][host_page].cpu()
                 expected = device_pool.index_k_with_scale_buffer[layer_id][
                     device_page
                 ].cpu()
                 self.assertTrue(torch.equal(got, expected))
-                host_start = host_page * page_size
                 device_start = device_page * page_size
-                got_kv = host_pool.kv_buffer[layer_id][
-                    host_start : host_start + page_size
-                ].cpu()
                 expected_kv = device_pool.kv_buffer[layer_id][
                     device_start : device_start + page_size
                 ].cpu()
+                if layout == "page_first_direct":
+                    got_kv = host_pool.kv_buffer[host_page, layer_id].cpu()
+                else:
+                    host_start = host_page * page_size
+                    got_kv = host_pool.kv_buffer[layer_id][
+                        host_start : host_start + page_size
+                    ].cpu()
                 self.assertTrue(torch.equal(got_kv, expected_kv))
 
-    def test_device_to_host_indexer_kernel(self):
-        self._run_device_to_host_indexer_copy(io_backend="kernel")
+    def test_device_to_host_indexer_kernel_layer_first(self):
+        self._run_device_to_host_indexer_copy(
+            io_backend="kernel", layout="layer_first"
+        )
 
-    def test_device_to_host_indexer_direct(self):
-        self._run_device_to_host_indexer_copy(io_backend="direct")
+    def test_device_to_host_indexer_direct_layer_first(self):
+        self._run_device_to_host_indexer_copy(
+            io_backend="direct", layout="layer_first"
+        )
+
+    def test_device_to_host_indexer_direct_page_first_direct(self):
+        self._run_device_to_host_indexer_copy(
+            io_backend="direct", layout="page_first_direct"
+        )
+
+
+class TestNSAIndexerPageIndices(CustomTestCase):
+    def make_host_pool_stub(self, page_size: int):
+        host_pool = NSATokenToKVPoolHost.__new__(NSATokenToKVPoolHost)
+        host_pool.page_size = page_size
+        return host_pool
+
+    def test_indexer_page_indices_accepts_valid_page_spans(self):
+        host_pool = self.make_host_pool_stub(page_size=4)
+
+        host_pages, device_pages = host_pool._get_indexer_page_indices(
+            torch.tensor([8, 9, 10, 11], dtype=torch.int64),
+            torch.tensor([16, 17, 18, 19], dtype=torch.int64),
+        )
+
+        self.assertEqual(host_pages.tolist(), [2])
+        self.assertEqual(device_pages.tolist(), [4])
+
+    def test_indexer_page_indices_rejects_partial_host_page(self):
+        host_pool = self.make_host_pool_stub(page_size=4)
+
+        with self.assertRaisesRegex(ValueError, "host_indices.*whole pages"):
+            host_pool._get_indexer_page_indices(
+                torch.tensor([8, 9, 10], dtype=torch.int64),
+                torch.tensor([16, 17, 18], dtype=torch.int64),
+            )
+
+    def test_indexer_page_indices_rejects_misaligned_device_page(self):
+        host_pool = self.make_host_pool_stub(page_size=4)
+
+        with self.assertRaisesRegex(
+            ValueError, "device_indices.*start at page boundaries"
+        ):
+            host_pool._get_indexer_page_indices(
+                torch.tensor([8, 9, 10, 11], dtype=torch.int64),
+                torch.tensor([17, 18, 19, 20], dtype=torch.int64),
+            )
+
+    def test_indexer_page_indices_rejects_non_contiguous_host_page(self):
+        host_pool = self.make_host_pool_stub(page_size=4)
+
+        with self.assertRaisesRegex(ValueError, "host_indices.*contiguous page spans"):
+            host_pool._get_indexer_page_indices(
+                torch.tensor([8, 9, 11, 10], dtype=torch.int64),
+                torch.tensor([16, 17, 18, 19], dtype=torch.int64),
+            )
 
 
 if __name__ == "__main__":

test/registered/unit/mem_cache/test_page_index_utils.py

@@ -0,0 +1,64 @@
+import unittest
+
+import torch
+
+from sglang.srt.mem_cache.page_index_utils import (
+    validate_page_aligned_token_indices,
+)
+from sglang.test.ci.ci_register import register_cpu_ci
+from sglang.test.test_utils import CustomTestCase
+
+register_cpu_ci(est_time=1, suite="stage-a-test-cpu")
+
+
+class TestPageIndexUtils(CustomTestCase):
+    def test_valid_empty_indices(self):
+        validate_page_aligned_token_indices(
+            torch.empty((0,), dtype=torch.int64), 4, "indices"
+        )
+
+    def test_valid_single_page(self):
+        validate_page_aligned_token_indices(
+            torch.tensor([8, 9, 10, 11], dtype=torch.int64), 4, "indices"
+        )
+
+    def test_valid_multiple_pages(self):
+        validate_page_aligned_token_indices(
+            torch.tensor([4, 5, 6, 7, 16, 17, 18, 19], dtype=torch.int64),
+            4,
+            "indices",
+        )
+
+    def test_rejects_non_1d_tensor(self):
+        with self.assertRaisesRegex(ValueError, "must be a 1-D tensor"):
+            validate_page_aligned_token_indices(
+                torch.tensor([[0, 1], [2, 3]], dtype=torch.int64), 4, "indices"
+            )
+
+    def test_rejects_non_positive_page_size(self):
+        with self.assertRaisesRegex(ValueError, "page_size must be positive"):
+            validate_page_aligned_token_indices(
+                torch.tensor([0, 1, 2, 3], dtype=torch.int64), 0, "indices"
+            )
+
+    def test_rejects_partial_page(self):
+        with self.assertRaisesRegex(ValueError, "whole pages"):
+            validate_page_aligned_token_indices(
+                torch.tensor([8, 9, 10], dtype=torch.int64), 4, "indices"
+            )
+
+    def test_rejects_non_page_start(self):
+        with self.assertRaisesRegex(ValueError, "start at page boundaries"):
+            validate_page_aligned_token_indices(
+                torch.tensor([9, 10, 11, 12], dtype=torch.int64), 4, "indices"
+            )
+
+    def test_rejects_non_contiguous_group(self):
+        with self.assertRaisesRegex(ValueError, "contiguous page spans"):
+            validate_page_aligned_token_indices(
+                torch.tensor([8, 9, 11, 10], dtype=torch.int64), 4, "indices"
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()

test/registered/unit/server_args/test_server_args.py

@@ -371,6 +371,26 @@ class TestHiCacheArgs(CustomTestCase):
         if expected_decode_backend is not None:
             self.assertEqual(args.decode_attention_backend, expected_decode_backend)
 
+    def _make_cp_hicache_args(self, **overrides) -> ServerArgs:
+        args = self._make_args(
+            enable_hierarchical_cache=True,
+            enable_nsa_prefill_context_parallel=True,
+            enable_nsa_prefill_cp_shared_kv=True,
+            nsa_prefill_cp_mode="in-seq-split",
+            disaggregation_mode="prefill",
+            page_size=64,
+            enable_hisparse=False,
+        )
+        for key, value in overrides.items():
+            setattr(args, key, value)
+        return args
+
+    def _normalize_and_validate_cp_hicache_args(self, **overrides) -> ServerArgs:
+        args = self._make_cp_hicache_args(**overrides)
+        args._handle_hicache()
+        args._handle_cp_hicache_layout_validation()
+        return args
+
     def test_cp_shared_kv_rejects_hicache_storage_backend(self):
         with (
             self.assertRaisesRegex(
@@ -468,6 +488,61 @@ class TestHiCacheArgs(CustomTestCase):
 
         self.assertEqual(args.decode_attention_backend, "triton")
 
+    def test_cp_hicache_accepts_supported_backend_layout_pairs(self):
+        cases = [
+            ("kernel", "layer_first"),
+            ("kernel", "page_first"),
+            ("direct", "layer_first"),
+            ("direct", "page_first_direct"),
+        ]
+
+        for io_backend, mem_layout in cases:
+            with self.subTest(io_backend=io_backend, mem_layout=mem_layout):
+                args = self._normalize_and_validate_cp_hicache_args(
+                    hicache_io_backend=io_backend,
+                    hicache_mem_layout=mem_layout,
+                )
+                self.assertEqual(args.hicache_io_backend, io_backend)
+                self.assertEqual(args.hicache_mem_layout, mem_layout)
+
+    def test_cp_hicache_normalizes_supported_alias_pairs(self):
+        args = self._normalize_and_validate_cp_hicache_args(
+            hicache_io_backend="kernel",
+            hicache_mem_layout="page_first_direct",
+        )
+        self.assertEqual(args.hicache_io_backend, "direct")
+        self.assertEqual(args.hicache_mem_layout, "page_first_direct")
+
+        args = self._normalize_and_validate_cp_hicache_args(
+            hicache_io_backend="direct",
+            hicache_mem_layout="page_first",
+        )
+        self.assertEqual(args.hicache_io_backend, "direct")
+        self.assertEqual(args.hicache_mem_layout, "page_first_direct")
+
+    def test_cp_hicache_rejects_page_head_layout(self):
+        with self.assertRaisesRegex(ValueError, "CP shared KV HiCache.*page_head"):
+            self._normalize_and_validate_cp_hicache_args(
+                hicache_io_backend="kernel",
+                hicache_mem_layout="page_head",
+            )
+
+    def test_cp_hicache_rejects_cuda_kv_split_layout(self):
+        with self.assertRaisesRegex(
+            ValueError, "CP shared KV HiCache.*page_first_kv_split"
+        ):
+            self._normalize_and_validate_cp_hicache_args(
+                hicache_io_backend="kernel",
+                hicache_mem_layout="page_first_kv_split",
+            )
+
+    def test_cp_hicache_rejects_kernel_ascend_backend(self):
+        with self.assertRaisesRegex(ValueError, "CP shared KV HiCache.*kernel_ascend"):
+            self._normalize_and_validate_cp_hicache_args(
+                hicache_io_backend="kernel_ascend",
+                hicache_mem_layout="page_first_kv_split",
+            )
+
 
 if __name__ == "__main__":
     unittest.main()