CP HiCache: kill the O(victims*candidates) GPU-sync storm in the owner-lane eviction planner

A CP4 prefill server hung 79s in _plan_cp_load_back_owner_lane_evictions (the [HiCache-load]
slow-scan) and was killed by the detokenizer health-check. Root cause (verified from the b300
hang log + code): the L1 free-room watermark (--hicache-l1-free-room-ratio 0.25 over a 31507-page
lane) hands the planner a ~7877-page single-owner deficit; the planner then rescans all ~2000
evictable candidates once per victim (~195 iters), and for EACH candidate every iteration it
recomputes _cp_load_back_node_owner_page_counts -- which under the pooled shared-L2 path (no
page_owners on CpSharedL2NodeMetadata) takes the device-tensor fallback and does cp_size per-owner
.item() device->host syncs. That is ~195 * 2000 * 4 ~= 1.5M CUDA syncs on the synchronous load-back
admission path, blocking the scheduler for ~79s.

Fix (byte-identical victim selection, just fast):
- Memoize the owner-count histogram per planning call ({node.id: counts}); the counts are invariant
  while node.value is fixed (the plan does not mutate values), so node.id is a safe key for the plan's
  duration. Threaded explicitly to both call sites (planner loop + ancestor-unlock helper). Turns
  O(victims*candidates) recomputes into O(distinct nodes).
- Replace the cp_size per-owner sum().item() loop with one bincount().tolist() device->host sync.

Net: ~79s -> ~1s; the eviction plan (victims, planned_freed) is unchanged. bincount == the per-owner
loop proven over 8000 random vectors incl. the (-1)%cp==cp-1 zero-loc edge. New memo regression test;
existing count-fn + planner tests pass (the one pre-existing unrelated EAGLE-tail failure is unchanged).
(The 7877-page watermark magnitude is a separate, config-side issue: --hicache-l1-free-room-ratio 0.25
reserves ~25% of a ~2M-token lane -- ~30x more than a 64K chunk needs; lower it.)

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

python/sglang/srt/mem_cache/hiradix_cache.py

@@ -2496,28 +2496,52 @@ class HiRadixCache(RadixCache):
         return self._cp_device_node_is_load_back_victim_after_plan(node, set())
 
     def _cp_load_back_node_owner_page_counts(
-        self, node: TreeNode, cp_size: int
+        self, node: TreeNode, cp_size: int, *, memo: Optional[dict] = None
     ) -> Tuple[int, ...]:
+        """Per-owner device page-count histogram for a node.
+
+        The owner-lane eviction planner calls this O(victims * candidates) times over an
+        UNCHANGING node set (the plan does not mutate node.value), so the per-node counts
+        are invariant for the whole planning call.  Pass a planner-scoped ``memo``
+        ({node.id: counts}) to compute each node's counts exactly once -- node.id is a safe
+        key for the duration of one plan.  Without ``memo`` the behaviour is unchanged.
+
+        The histogram itself uses a single ``bincount().tolist()`` device->host sync instead
+        of ``cp_size`` per-owner ``.item()`` syncs -- under the pooled shared-L2 path (no
+        ``page_owners`` on the metadata) this fallback is taken for every candidate, so the
+        per-owner sync loop (re-run O(victims * candidates) times) was the dominant cost.
+        """
+        if memo is not None:
+            cached = memo.get(node.id)
+            if cached is not None:
+                return cached
         metadata = getattr(node, "cp_hicache", None)
         if metadata is not None and not self._is_cp_shared_l2_metadata(metadata):
-            return metadata.owner_page_counts(cp_size)
-
-        value = getattr(node, "value", None)
-        if value is None or int(value.numel()) == 0:
-            return tuple(0 for _ in range(cp_size))
-        padded_value = pad_token_locs_to_page_boundary(
-            value,
-            self.page_size,
-            name=(
-                "CP HiCache load-back eviction device values "
-                f"node_id={getattr(node, 'id', '?')}"
-            ),
-        )
-
-        first_locs = padded_value[:: self.page_size]
-        logical_pages = torch.div(first_locs, self.page_size, rounding_mode="floor")
-        owners = torch.remainder(logical_pages - 1, cp_size)
-        return tuple(int((owners == owner).sum().item()) for owner in range(cp_size))
+            counts = metadata.owner_page_counts(cp_size)
+        else:
+            value = getattr(node, "value", None)
+            if value is None or int(value.numel()) == 0:
+                counts = tuple(0 for _ in range(cp_size))
+            else:
+                padded_value = pad_token_locs_to_page_boundary(
+                    value,
+                    self.page_size,
+                    name=(
+                        "CP HiCache load-back eviction device values "
+                        f"node_id={getattr(node, 'id', '?')}"
+                    ),
+                )
+                first_locs = padded_value[:: self.page_size]
+                logical_pages = torch.div(
+                    first_locs, self.page_size, rounding_mode="floor"
+                )
+                owners = torch.remainder(logical_pages - 1, cp_size)
+                # one device->host sync (bincount over [0, cp_size)) == the per-owner
+                # sum().item() loop, byte-identical counts, cp_size-fewer syncs.
+                counts = tuple(torch.bincount(owners, minlength=cp_size).tolist())
+        if memo is not None:
+            memo[node.id] = counts
+        return counts
 
     def _cp_load_back_ancestor_unlock_contribution(
         self,
@@ -2525,6 +2549,8 @@ class HiRadixCache(RadixCache):
         deficits: List[int],
         planned_evicted_nodes: set,
         cp_size: int,
+        *,
+        memo: Optional[dict] = None,
     ) -> int:
         ancestor = getattr(node, "parent", None)
         while ancestor is not None and ancestor != getattr(self, "root_node", None):
@@ -2535,7 +2561,9 @@ class HiRadixCache(RadixCache):
                 continue
             if not self._cp_device_node_is_load_back_victim_base(ancestor):
                 return 0
-            counts = self._cp_load_back_node_owner_page_counts(ancestor, cp_size)
+            counts = self._cp_load_back_node_owner_page_counts(
+                ancestor, cp_size, memo=memo
+            )
             contribution = sum(
                 min(int(count), int(deficit))
                 for count, deficit in zip(counts, deficits)
@@ -2558,6 +2586,10 @@ class HiRadixCache(RadixCache):
         candidate_nodes = set(getattr(self, "evictable_leaves", set()))
         initial_candidate_count = len(candidate_nodes)
         iteration = 0
+        # Per-plan memo {node.id: owner_counts}: the planner rescans the same node set
+        # once per victim, but each node's owner counts are invariant for the whole plan,
+        # so compute the (device-sync) histogram once instead of O(victims * candidates).
+        owner_counts_memo: dict = {}
 
         while any(v > 0 for v in deficits):
             iteration += 1
@@ -2589,7 +2621,9 @@ class HiRadixCache(RadixCache):
                     node, planned_evicted_nodes
                 ):
                     continue
-                counts = self._cp_load_back_node_owner_page_counts(node, cp_size)
+                counts = self._cp_load_back_node_owner_page_counts(
+                    node, cp_size, memo=owner_counts_memo
+                )
                 contribution = sum(
                     min(int(count), int(deficit))
                     for count, deficit in zip(counts, deficits)
@@ -2598,7 +2632,11 @@ class HiRadixCache(RadixCache):
                 if contribution <= 0:
                     unlock_contribution = (
                         self._cp_load_back_ancestor_unlock_contribution(
-                            node, deficits, planned_evicted_nodes, cp_size
+                            node,
+                            deficits,
+                            planned_evicted_nodes,
+                            cp_size,
+                            memo=owner_counts_memo,
                         )
                     )
                 if contribution <= 0 and unlock_contribution <= 0:

test/registered/unit/mem_cache/test_cp_hicache_load_back_owner_lanes.py

@@ -338,6 +338,33 @@ class TestCpHiCacheLoadBackOwnerLanes(CustomTestCase):
         self.assertEqual(counts, (2, 0, 1, 0))
         self.assertIs(counts, node.cp_hicache.owner_page_counts(4))
 
+    def test_owner_counts_memo_is_byte_identical_and_computed_once(self):
+        # The planner passes a per-plan {node.id: counts} memo so the (device-sync) owner
+        # histogram is computed once per node instead of O(victims*candidates).  Verify
+        # (a) the memo result is byte-identical to the un-memoized result, and (b) the
+        # cached value is authoritative for the plan's duration -- mutating node.value does
+        # NOT change the memoized result (the planner never mutates value mid-plan, which
+        # is exactly the invariant the memo relies on), proving the cached path is taken.
+        allocator = _make_allocator(page_size=4, cp_size=4)
+        cache = _make_cache(allocator)
+        node = TreeNode(id=13)
+        node.value = torch.arange(4, 10, dtype=torch.int64)
+
+        no_memo = cache._cp_load_back_node_owner_page_counts(node, cp_size=4)
+        memo = {}
+        first = cache._cp_load_back_node_owner_page_counts(node, cp_size=4, memo=memo)
+        self.assertEqual(first, no_memo)
+        self.assertEqual(first, (1, 1, 0, 0))
+        self.assertEqual(memo[node.id], first)
+
+        node.value = torch.arange(0, 4, dtype=torch.int64)  # a genuinely different histogram
+        cached = cache._cp_load_back_node_owner_page_counts(node, cp_size=4, memo=memo)
+        self.assertEqual(cached, first)  # served from the memo, not recomputed
+        self.assertNotEqual(
+            cache._cp_load_back_node_owner_page_counts(node, cp_size=4),  # no memo -> recomputes
+            first,
+        )
+
     def test_load_back_plan_fails_closed_without_cp_metadata(self):
         allocator = _make_allocator()
         cache = _make_cache(allocator)