[UnifiedTree]: Support HiCache For DeepSeek_V4 (#24691)

Co-authored-by: ispobock <ispobaoke@gmail.com>
Co-authored-by: 晟海 <huangtingwei.htw@antgroup.com>

python/sglang/srt/mem_cache/deepseek_v4_memory_pool.py

@@ -605,7 +605,12 @@ class DeepSeekV4TokenToKVPool(BaseSWAKVPool):
             else:
                 raise ValueError(f"Unsupported compression ratio: {ratio}")
 
+    def wait_layer_transfer(self, layer_id: int) -> None:
+        if self.layer_transfer_counter is not None:
+            self.layer_transfer_counter.wait_until(layer_id - self.start_layer)
+
     def get_attention_compress_states(self, layer_id: int) -> CompressStatePool:
+        self.wait_layer_transfer(layer_id)
         compress_state_pool = self.compress_state_pools[layer_id]
         assert (
             compress_state_pool is not None
@@ -613,6 +618,7 @@ class DeepSeekV4TokenToKVPool(BaseSWAKVPool):
         return compress_state_pool
 
     def get_indexer_compress_states(self, layer_id: int) -> CompressStatePool:
+        self.wait_layer_transfer(layer_id)
         indexer_compress_state_pool = self.indexer_compress_state_pools[layer_id]
         assert (
             indexer_compress_state_pool is not None
@@ -620,6 +626,7 @@ class DeepSeekV4TokenToKVPool(BaseSWAKVPool):
         return indexer_compress_state_pool
 
     def get_swa_key_buffer(self, layer_id: int) -> torch.Tensor:
+        self.wait_layer_transfer(layer_id)
         return self.swa_kv_pool.get_key_buffer(layer_id)
 
     def set_swa_key_buffer(
@@ -635,7 +642,8 @@ class DeepSeekV4TokenToKVPool(BaseSWAKVPool):
         assert compress_kv_pool is not None
         return compress_kv_pool.page_size
 
-    def get_extra_key_buffer(self, layer_id: int) -> torch.Tensor:
+    def get_extra_key_buffer(self, layer_id: int) -> torch.Tensor | None:
+        self.wait_layer_transfer(layer_id)
         _, compress_layer_id, compress_kv_pool = self.layer_mapping[layer_id]
         assert compress_kv_pool is not None
         return compress_kv_pool.get_key_buffer(compress_layer_id)
@@ -656,6 +664,7 @@ class DeepSeekV4TokenToKVPool(BaseSWAKVPool):
         return self.c4_indexer_kv_pool.page_size
 
     def get_index_k_with_scale_buffer(self, layer_id: int) -> torch.Tensor:
+        self.wait_layer_transfer(layer_id)
         compress_ratio, compress_layer_id, _ = self.layer_mapping[layer_id]
         assert compress_ratio == 4, f"only c4 has indexer, got {compress_ratio = }"
         return self.c4_indexer_kv_pool.get_index_k_with_scale_buffer(compress_layer_id)
@@ -666,6 +675,7 @@ class DeepSeekV4TokenToKVPool(BaseSWAKVPool):
         seq_len: int,
         page_indices: torch.Tensor,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
+        self.wait_layer_transfer(layer_id)
         compress_ratio, compress_layer_id, _ = self.layer_mapping[layer_id]
         assert compress_ratio == 4, f"only c4 has indexer, got {compress_ratio = }"
         return self.c4_indexer_kv_pool.get_index_k_scale_buffer(
@@ -709,6 +719,7 @@ class DeepSeekV4TokenToKVPool(BaseSWAKVPool):
         )
 
     def get_swa_key_buffer_radix(self, layer_id: int) -> torch.Tensor:
+        self.wait_layer_transfer(layer_id)
         return self.swa_kv_pool.get_key_buffer(layer_id)
 
     def set_swa_key_buffer_radix_fused(

python/sglang/srt/mem_cache/hicache_storage.py

@@ -1,14 +1,18 @@
+from __future__ import annotations
+
 import logging
 import os
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
 from enum import Enum
-from typing import Any, List, Optional, Set
+from typing import TYPE_CHECKING, Any, List, Optional, Set
 
 import torch
 
 from sglang.srt.environ import envs
-from sglang.srt.mem_cache.memory_pool_host import HostKVCache
+
+if TYPE_CHECKING:
+    from sglang.srt.mem_cache.memory_pool_host import HostKVCache
 
 logger = logging.getLogger(__name__)
 
@@ -52,6 +56,14 @@ class PoolName(str, Enum):
     MAMBA = "mamba"
     SWA = "swa"
     INDEXER = "indexer"
+    # TODO(hzh0425): Current DeepSeek V4 pool naming is verbose; will be normalized to
+    # 'COMPRESSED_KV / COMPRESSED_INDEXER / COMPRESSED_STATE' in the next PR.
+    DEEPSEEK_V4_C4 = "deepseek_v4_c4"
+    DEEPSEEK_V4_C4_INDEXER = "deepseek_v4_c4_indexer"
+    DEEPSEEK_V4_C128 = "deepseek_v4_c128"
+    DEEPSEEK_V4_C4_STATE = "deepseek_v4_c4_state"
+    DEEPSEEK_V4_C4_INDEXER_STATE = "deepseek_v4_c4_indexer_state"
+    DEEPSEEK_V4_C128_STATE = "deepseek_v4_c128_state"
 
     def __str__(self) -> str:
         return self.value
@@ -83,6 +95,16 @@ class PoolTransfer:
     keys: Optional[List[str]] = None
     hit_policy: PoolHitPolicy = PoolHitPolicy.ALL_PAGES
     nodes_to_load: Optional[List[Any]] = None
+    indices_from_pool: Optional[PoolName] = None
+
+
+@dataclass(frozen=True)
+class SidecarPoolSpec:
+    """Pool whose transfer indices are reused from one real source pool."""
+
+    pool_name: PoolName
+    indices_from_pool: PoolName
+    hit_policy: PoolHitPolicy = PoolHitPolicy.ALL_PAGES
 
 
 @dataclass

python/sglang/srt/mem_cache/hiradix_cache.py

@@ -647,6 +647,7 @@ class HiRadixCache(RadixCache):
             pool = PoolTransfer(
                 name=PoolName.INDEXER,
                 hit_policy=PoolHitPolicy.ALL_PAGES,
+                indices_from_pool=PoolName.KV,
             )
             return {"extra_pools": [pool]}
         else:

python/sglang/srt/mem_cache/hybrid_cache/hybrid_cache_controller.py

@@ -23,6 +23,7 @@ from sglang.srt.managers.cache_controller import (
 from sglang.srt.mem_cache.hicache_storage import (
     HiCacheStorageExtraInfo,
     PoolHitPolicy,
+    PoolName,
     PoolTransfer,
     PoolTransferResult,
 )
@@ -50,12 +51,12 @@ class CacheOperation(BaseCacheOperation):
 
     @staticmethod
     def merge_pool_transfers(
-        ops: List["CacheOperation"],
+        ops: List[CacheOperation],
     ) -> Optional[list[PoolTransfer]]:
-        grouped: dict[str, list[PoolTransfer]] = {}
+        grouped: dict[tuple[PoolName, Optional[PoolName]], list[PoolTransfer]] = {}
         for op in ops:
             for t in op.pool_transfers or []:
-                grouped.setdefault(t.name, []).append(t)
+                grouped.setdefault((t.name, t.indices_from_pool), []).append(t)
         if not grouped:
             return None
 
@@ -65,16 +66,18 @@ class CacheOperation(BaseCacheOperation):
 
         return [
             PoolTransfer(
-                name=name,
+                name=ts[0].name,
                 host_indices=cat_or_none(t.host_indices for t in ts),
                 device_indices=cat_or_none(t.device_indices for t in ts),
                 keys=[k for t in ts if t.keys for k in t.keys] or None,
+                hit_policy=ts[0].hit_policy,
+                indices_from_pool=ts[0].indices_from_pool,
             )
-            for name, ts in grouped.items()
+            for ts in grouped.values()
         ]
 
     @staticmethod
-    def merge_ops(ops: List["CacheOperation"]) -> "CacheOperation":
+    def merge_ops(ops: List[CacheOperation]) -> CacheOperation:
         if len(ops) == 1:
             return ops[0]
         host_indices = torch.cat([op.host_indices for op in ops])
@@ -478,6 +481,7 @@ class HybridCacheController(BaseHiCacheController):
                         device_indices=transfer_device_indices,
                         keys=transfer.keys,
                         hit_policy=transfer.hit_policy,
+                        indices_from_pool=transfer.indices_from_pool,
                     )
                 )
         return host_indices, device_indices, resolved_pool_transfers
@@ -485,7 +489,7 @@ class HybridCacheController(BaseHiCacheController):
     def _page_transfer(self, operation):
         # Transfer extra pools
         if operation.pool_transfers and not operation.is_terminated():
-            self._resolve_shared_pool_transfers(operation)
+            self._resolve_sidecar_derived_pool_transfers(operation)
             results = self.storage_backend.batch_get_v2(operation.pool_transfers)
             operation.pool_storage_result.update_extra_pool_hit_pages(results)
 
@@ -495,19 +499,26 @@ class HybridCacheController(BaseHiCacheController):
     def _page_backup(self, operation):
         # Backup extra pools
         if operation.pool_transfers:
-            self._resolve_shared_pool_transfers(operation)
+            self._resolve_sidecar_derived_pool_transfers(operation)
             results = self.storage_backend.batch_set_v2(operation.pool_transfers)
             operation.pool_storage_result.update_extra_pool_hit_pages(results)
 
         # Backup kv pools
         super()._page_backup(operation)
 
-    def _resolve_shared_pool_transfers(self, operation):
+    def _resolve_sidecar_derived_pool_transfers(self, operation):
         for transfer in operation.pool_transfers:
-            entry = self.mem_pool_host.entry_map.get(transfer.name)
+            if transfer.indices_from_pool is None:
-            if entry.share_indices_with_anchor:
+                continue
-                transfer.keys = operation.hash_value
+            if transfer.indices_from_pool != PoolName.KV:
+                # TODO(hzh): Support storage sidecar derived pools from other sources
+                raise AssertionError(
+                    "Storage sidecar derived pool currently only supports KV-shared "
+                    f"indices, got {transfer.name} from {transfer.indices_from_pool}."
+                )
             transfer.host_indices = operation.host_indices
+            if transfer.keys is None:
+                transfer.keys = operation.hash_value
 
     def _sync_trailing_keys(
         self,
@@ -541,14 +552,23 @@ class HybridCacheController(BaseHiCacheController):
             return None
         # (pool, free_fn, indices) for atomic rollback on failure.
         newly_allocated: list[tuple[PoolTransfer, Callable, torch.Tensor]] = []
+        derived_transfers: list[PoolTransfer] = []
+
+        def rollback_allocated() -> None:
+            for prev_pool, prev_free_fn, prev_indices in newly_allocated:
+                prev_free_fn(prev_indices)
+                if alloc_host:
+                    prev_pool.host_indices = None
+                else:
+                    prev_pool.device_indices = None
+
         for pool in extra_pools:
+            if pool.indices_from_pool is not None:
+                derived_transfers.append(pool)
+                continue
             entry = self.mem_pool_host.entry_map.get(pool.name)
             if entry is None:
                 continue
-            if entry.share_indices_with_anchor:
-                pool.device_indices = kv_device_indices
-                pool.host_indices = kv_host_indices
-                continue
             if alloc_host:
                 if pool.host_indices is not None or pool.device_indices is None:
                     continue
@@ -572,16 +592,33 @@ class HybridCacheController(BaseHiCacheController):
                 indices = alloc_fn(size)
             if indices is None:
                 # Atomic rollback: free everything we successfully allocated.
-                for prev_pool, prev_free_fn, prev_indices in newly_allocated:
+                rollback_allocated()
-                    prev_free_fn(prev_indices)
-                    if alloc_host:
-                        prev_pool.host_indices = None
-                    else:
-                        prev_pool.device_indices = None
                 return None
             if alloc_host:
                 pool.host_indices = indices
             else:
                 pool.device_indices = indices
             newly_allocated.append((pool, free_fn, indices))
+
+        # Assign indices to deferred pools from their source.
+        for pool in derived_transfers:
+            if pool.indices_from_pool == PoolName.KV:
+                pool.host_indices = kv_host_indices
+                pool.device_indices = kv_device_indices
+                continue
+
+            source = next(
+                (
+                    transfer
+                    for transfer in extra_pools
+                    if transfer.indices_from_pool is None
+                    and transfer.name == pool.indices_from_pool
+                ),
+                None,
+            )
+            if source is None:
+                rollback_allocated()
+                return None
+            pool.host_indices = source.host_indices
+            pool.device_indices = source.device_indices
         return extra_pools

python/sglang/srt/mem_cache/hybrid_cache/hybrid_pool_assembler.py

@@ -3,12 +3,15 @@ from __future__ import annotations
 import logging
 from typing import TYPE_CHECKING, Any, Callable, Optional
 
-from sglang.srt.mem_cache.hicache_storage import PoolHitPolicy, PoolName
+from sglang.srt.mem_cache.hicache_storage import PoolName, SidecarPoolSpec
 from sglang.srt.mem_cache.hybrid_cache.hybrid_cache_controller import (
     HybridCacheController,
 )
 from sglang.srt.mem_cache.memory_pool_host import (
+    DeepSeekV4PagedHostPool,
+    DeepSeekV4StateHostPool,
     HostPoolGroup,
+    LogicalHostPool,
     MambaPoolHost,
     MHATokenToKVPoolHost,
     MLATokenToKVPoolHost,
@@ -71,7 +74,6 @@ def build_pool_entry(
     layer_mapping: dict[int, int],
     transfer_layer_num: int,
     is_anchor: bool = False,
-    share_indices_with_anchor: bool = False,
     host_evict_fn: Optional[Callable[[int], Any]] = None,
     device_evict_fn: Optional[Callable[[int], Any]] = None,
     device_alloc_fn: Optional[Callable[[int], Any]] = None,
@@ -83,7 +85,6 @@ def build_pool_entry(
         device_pool=device_pool,
         layer_mapper=_make_layer_mapper(layer_mapping, transfer_layer_num),
         is_primary_index_anchor=is_anchor,
-        share_indices_with_anchor=share_indices_with_anchor,
         host_evict_fn=host_evict_fn,
         device_evict_fn=device_evict_fn,
         device_alloc_fn=device_alloc_fn,
@@ -100,8 +101,8 @@ def build_kv_only_stack(
     page_size: int,
     tp_group,
     load_cache_event,
-    attn_cp_group: Optional["torch.distributed.ProcessGroup"] = None,
+    attn_cp_group: Optional[torch.distributed.ProcessGroup] = None,
-    attn_tp_group: Optional["torch.distributed.ProcessGroup"] = None,
+    attn_tp_group: Optional[torch.distributed.ProcessGroup] = None,
     storage_backend: Optional[str],
     use_mla: bool,
     override_kv_cache_dim: Optional[int] = None,
@@ -164,8 +165,8 @@ def build_hybrid_swa_stack(
     page_size: int,
     tp_group,
     load_cache_event,
-    attn_cp_group: Optional["torch.distributed.ProcessGroup"] = None,
+    attn_cp_group: Optional[torch.distributed.ProcessGroup] = None,
-    attn_tp_group: Optional["torch.distributed.ProcessGroup"] = None,
+    attn_tp_group: Optional[torch.distributed.ProcessGroup] = None,
     storage_backend: Optional[str],
     use_mla: bool,
     host_swa_evict_fn: Optional[Callable[[int], Any]] = None,
@@ -237,6 +238,249 @@ def build_hybrid_swa_stack(
     return host_pool_group, cache_controller
 
 
+def _deepseek_v4_num_host_pages(
+    *,
+    params: CacheInitParams,
+    server_args: ServerArgs,
+    kvcache: Any,
+    page_size: int,
+    swa_page_size: int,
+) -> tuple[int, int]:
+    allocator = params.token_to_kv_pool_allocator
+    device_full_size = getattr(allocator, "size_full", kvcache.size)
+    device_full_pages = (device_full_size + page_size - 1) // page_size
+
+    device_swa_pages = (kvcache.swa_size + swa_page_size - 1) // swa_page_size
+
+    if server_args.hicache_size > 0:
+        raise ValueError(
+            "DeepSeek V4 HiCache currently does not support --hicache-size; "
+            "use --hicache-ratio instead."
+        )
+    ratio = server_args.hicache_ratio
+    full_host_pages = max(int(device_full_pages * ratio), device_full_pages + 1)
+    swa_host_pages = max(int(device_swa_pages * ratio), device_swa_pages + 1)
+    return full_host_pages, swa_host_pages
+
+
+def build_deepseek_v4_hicache_stack(
+    *,
+    params: CacheInitParams,
+    server_args: ServerArgs,
+    kvcache: Any,
+    page_size: int,
+    tp_group,
+    load_cache_event,
+    attn_cp_group: Optional[torch.distributed.ProcessGroup] = None,
+    attn_tp_group: Optional[torch.distributed.ProcessGroup] = None,
+    storage_backend: Optional[str],
+    host_swa_evict_fn: Optional[Callable[[int], Any]] = None,
+    device_swa_evict_fn: Optional[Callable[[int], Any]] = None,
+    prefetch_threshold: int = 256,
+    model_name: Optional[str] = None,
+    storage_backend_extra_config: Optional[dict] = None,
+    pp_rank: int = 0,
+    pp_size: int = 1,
+    enable_storage_metrics: bool = False,
+) -> tuple[HostPoolGroup, HybridCacheController]:
+    transfer_layer_num = len(kvcache.compression_ratios)
+    full_layer_mapping = {layer_id: layer_id for layer_id in range(transfer_layer_num)}
+    swa_layer_mapping = {
+        layer_id: layer_id for layer_id in range(len(kvcache.swa_kv_pool.kv_buffer))
+    }
+
+    c4_layer_mapping = {}
+    c128_layer_mapping = {}
+    c4_state_global_layers = []
+    c128_state_global_layers = []
+    for layer_id, layer_item in enumerate(kvcache.layer_mapping):
+        if layer_item.compress_ratio == 4:
+            c4_layer_mapping[layer_id] = layer_item.compress_layer_id
+            c4_state_global_layers.append(layer_id)
+        elif layer_item.compress_ratio == 128:
+            c128_layer_mapping[layer_id] = layer_item.compress_layer_id
+            c128_state_global_layers.append(layer_id)
+
+    c4_state_mapping = {
+        layer_id: local_id for local_id, layer_id in enumerate(c4_state_global_layers)
+    }
+    c128_state_mapping = {
+        layer_id: local_id for local_id, layer_id in enumerate(c128_state_global_layers)
+    }
+    num_host_pages, swa_num_host_pages = _deepseek_v4_num_host_pages(
+        params=params,
+        server_args=server_args,
+        kvcache=kvcache,
+        page_size=page_size,
+        swa_page_size=kvcache.swa_page_size,
+    )
+
+    logical_host_pool = LogicalHostPool(num_host_pages * page_size, page_size)
+    swa_host_pool = DeepSeekV4PagedHostPool(
+        pool_name=str(PoolName.SWA),
+        device_buffers=kvcache.swa_kv_pool.kv_buffer,
+        item_bytes=kvcache.swa_kv_pool.bytes_per_page_padded,
+        num_host_pages=swa_num_host_pages,
+        slot_page_size=kvcache.swa_page_size,
+        allocator_type=server_args.hicache_storage_backend,
+    )
+    swa_attn_allocator = params.token_to_kv_pool_allocator.swa_attn_allocator
+    entries = [
+        build_pool_entry(
+            name=PoolName.KV,
+            host_pool=logical_host_pool,
+            device_pool=kvcache,
+            layer_mapping=full_layer_mapping,
+            transfer_layer_num=transfer_layer_num,
+            is_anchor=True,
+        ),
+        build_pool_entry(
+            name=PoolName.SWA,
+            host_pool=swa_host_pool,
+            device_pool=kvcache.swa_kv_pool,
+            layer_mapping=swa_layer_mapping,
+            transfer_layer_num=transfer_layer_num,
+            host_evict_fn=host_swa_evict_fn,
+            device_evict_fn=device_swa_evict_fn,
+            device_alloc_fn=swa_attn_allocator.alloc,
+            device_free_fn=swa_attn_allocator.free,
+        ),
+    ]
+
+    if c4_layer_mapping:
+        c4_host_pool = DeepSeekV4PagedHostPool(
+            pool_name=str(PoolName.DEEPSEEK_V4_C4),
+            device_buffers=kvcache.c4_kv_pool.kv_buffer,
+            item_bytes=kvcache.c4_kv_pool.bytes_per_page_padded,
+            num_host_pages=num_host_pages,
+            slot_page_size=page_size,
+            allocator_type=server_args.hicache_storage_backend,
+        )
+        c4_indexer_host_pool = DeepSeekV4PagedHostPool(
+            pool_name=str(PoolName.DEEPSEEK_V4_C4_INDEXER),
+            device_buffers=kvcache.c4_indexer_kv_pool.index_k_with_scale_buffer,
+            item_bytes=(
+                kvcache.c4_indexer_kv_pool.index_k_with_scale_buffer[0].shape[1]
+                * kvcache.c4_indexer_kv_pool.index_k_with_scale_buffer[0].element_size()
+            ),
+            num_host_pages=num_host_pages,
+            slot_page_size=page_size,
+            allocator_type=server_args.hicache_storage_backend,
+        )
+        c4_state_host_pool = DeepSeekV4StateHostPool(
+            pool_name=str(PoolName.DEEPSEEK_V4_C4_STATE),
+            state_pools=[
+                kvcache.compress_state_pools[layer_id]
+                for layer_id in c4_state_global_layers
+            ],
+            num_host_pages=swa_num_host_pages,
+            swa_page_size=kvcache.swa_page_size,
+            allocator_type=server_args.hicache_storage_backend,
+        )
+        c4_indexer_state_host_pool = DeepSeekV4StateHostPool(
+            pool_name=str(PoolName.DEEPSEEK_V4_C4_INDEXER_STATE),
+            state_pools=[
+                kvcache.indexer_compress_state_pools[layer_id]
+                for layer_id in c4_state_global_layers
+            ],
+            num_host_pages=swa_num_host_pages,
+            swa_page_size=kvcache.swa_page_size,
+            allocator_type=server_args.hicache_storage_backend,
+        )
+        entries.extend(
+            [
+                build_pool_entry(
+                    name=PoolName.DEEPSEEK_V4_C4,
+                    host_pool=c4_host_pool,
+                    device_pool=kvcache.c4_kv_pool,
+                    layer_mapping=c4_layer_mapping,
+                    transfer_layer_num=transfer_layer_num,
+                ),
+                build_pool_entry(
+                    name=PoolName.DEEPSEEK_V4_C4_INDEXER,
+                    host_pool=c4_indexer_host_pool,
+                    device_pool=kvcache.c4_indexer_kv_pool,
+                    layer_mapping=c4_layer_mapping,
+                    transfer_layer_num=transfer_layer_num,
+                ),
+                build_pool_entry(
+                    name=PoolName.DEEPSEEK_V4_C4_STATE,
+                    host_pool=c4_state_host_pool,
+                    device_pool=None,
+                    layer_mapping=c4_state_mapping,
+                    transfer_layer_num=transfer_layer_num,
+                ),
+                build_pool_entry(
+                    name=PoolName.DEEPSEEK_V4_C4_INDEXER_STATE,
+                    host_pool=c4_indexer_state_host_pool,
+                    device_pool=None,
+                    layer_mapping=c4_state_mapping,
+                    transfer_layer_num=transfer_layer_num,
+                ),
+            ]
+        )
+
+    if c128_layer_mapping:
+        c128_host_pool = DeepSeekV4PagedHostPool(
+            pool_name=str(PoolName.DEEPSEEK_V4_C128),
+            device_buffers=kvcache.c128_kv_pool.kv_buffer,
+            item_bytes=kvcache.c128_kv_pool.bytes_per_page_padded,
+            num_host_pages=num_host_pages,
+            slot_page_size=page_size,
+            allocator_type=server_args.hicache_storage_backend,
+        )
+        c128_state_host_pool = DeepSeekV4StateHostPool(
+            pool_name=str(PoolName.DEEPSEEK_V4_C128_STATE),
+            state_pools=[
+                kvcache.compress_state_pools[layer_id]
+                for layer_id in c128_state_global_layers
+            ],
+            num_host_pages=swa_num_host_pages,
+            swa_page_size=kvcache.swa_page_size,
+            allocator_type=server_args.hicache_storage_backend,
+        )
+        entries.extend(
+            [
+                build_pool_entry(
+                    name=PoolName.DEEPSEEK_V4_C128,
+                    host_pool=c128_host_pool,
+                    device_pool=kvcache.c128_kv_pool,
+                    layer_mapping=c128_layer_mapping,
+                    transfer_layer_num=transfer_layer_num,
+                ),
+                build_pool_entry(
+                    name=PoolName.DEEPSEEK_V4_C128_STATE,
+                    host_pool=c128_state_host_pool,
+                    device_pool=None,
+                    layer_mapping=c128_state_mapping,
+                    transfer_layer_num=transfer_layer_num,
+                ),
+            ]
+        )
+
+    host_pool_group = HostPoolGroup(entries)
+    cache_controller = HybridCacheController(
+        params.token_to_kv_pool_allocator,
+        host_pool_group,
+        page_size,
+        tp_group,
+        load_cache_event=load_cache_event,
+        attn_cp_group=attn_cp_group,
+        attn_tp_group=attn_tp_group,
+        write_policy=server_args.hicache_write_policy,
+        io_backend=server_args.hicache_io_backend,
+        storage_backend=storage_backend,
+        prefetch_threshold=prefetch_threshold,
+        model_name=model_name,
+        storage_backend_extra_config=storage_backend_extra_config,
+        pp_rank=pp_rank,
+        pp_size=pp_size,
+        transfer_layer_num=transfer_layer_num,
+        enable_storage_metrics=enable_storage_metrics,
+    )
+    return host_pool_group, cache_controller
+
+
 def build_hybrid_mamba_stack(
     *,
     params: CacheInitParams,
@@ -248,8 +492,8 @@ def build_hybrid_mamba_stack(
     page_size: int,
     tp_group,
     load_cache_event,
-    attn_cp_group: Optional["torch.distributed.ProcessGroup"] = None,
+    attn_cp_group: Optional[torch.distributed.ProcessGroup] = None,
-    attn_tp_group: Optional["torch.distributed.ProcessGroup"] = None,
+    attn_tp_group: Optional[torch.distributed.ProcessGroup] = None,
     storage_backend: Optional[str],
     use_mla: bool,
     host_mamba_evict_fn: Optional[Callable[[int], Any]] = None,
@@ -317,22 +561,22 @@ def build_hybrid_mamba_stack(
     return host_pool_group, cache_controller
 
 
-def build_shared_anchor_stack(
+def build_anchor_sidecar_stack(
     *,
     params: CacheInitParams,
     server_args: ServerArgs,
     kv_pool: Any,
-    shared_pool_name: PoolName,
+    sidecar_pool_name: PoolName,
     full_layer_mapping: dict[int, int],
     page_size: int,
     tp_group,
     load_cache_event,
-    attn_cp_group: Optional["torch.distributed.ProcessGroup"] = None,
+    attn_cp_group: Optional[torch.distributed.ProcessGroup] = None,
-    attn_tp_group: Optional["torch.distributed.ProcessGroup"] = None,
+    attn_tp_group: Optional[torch.distributed.ProcessGroup] = None,
     storage_backend: Optional[str],
     use_mla: bool,
     override_kv_cache_dim: Optional[int] = None,
-    shared_host_pool_factory: Callable[[Any], Any],
+    sidecar_host_pool_factory: Callable[[Any], Any],
     prefetch_threshold: int = 256,
     model_name: Optional[str] = None,
     storage_backend_extra_config: Optional[dict] = None,
@@ -348,7 +592,7 @@ def build_shared_anchor_stack(
         use_mla=use_mla,
         override_kv_cache_dim=override_kv_cache_dim,
     )
-    shared_host_pool = shared_host_pool_factory(kv_host_pool)
+    sidecar_host_pool = sidecar_host_pool_factory(kv_host_pool)
     entries = [
         build_pool_entry(
             name=PoolName.KV,
@@ -359,12 +603,11 @@ def build_shared_anchor_stack(
             is_anchor=True,
         ),
         build_pool_entry(
-            name=shared_pool_name,
+            name=sidecar_pool_name,
-            host_pool=shared_host_pool,
+            host_pool=sidecar_host_pool,
             device_pool=kv_pool,
             layer_mapping=full_layer_mapping,
             transfer_layer_num=transfer_layer_num,
-            share_indices_with_anchor=True,
         ),
     ]
     host_pool_group = HostPoolGroup(entries)
@@ -396,11 +639,12 @@ def attach_hybrid_pool_to_unified_cache(
     server_args: ServerArgs,
     *,
     load_cache_event,
-    attn_cp_group: Optional["torch.distributed.ProcessGroup"] = None,
+    attn_cp_group: Optional[torch.distributed.ProcessGroup] = None,
-    attn_tp_group: Optional["torch.distributed.ProcessGroup"] = None,
+    attn_tp_group: Optional[torch.distributed.ProcessGroup] = None,
 ) -> None:
     """Attach HostPoolGroup + HybridCacheController to UnifiedRadixCache."""
     from sglang.srt.mem_cache.base_prefix_cache import EvictParams
+    from sglang.srt.mem_cache.deepseek_v4_memory_pool import DeepSeekV4TokenToKVPool
     from sglang.srt.mem_cache.memory_pool import (
         HybridLinearKVPool,
         MLATokenToKVPool,
@@ -414,8 +658,15 @@ def attach_hybrid_pool_to_unified_cache(
         swa_stack = isinstance(kvcache, SWAKVPool)
         mamba_stack = isinstance(kvcache, HybridLinearKVPool)
         nsa_stack = isinstance(kvcache, NSATokenToKVPool)
+        deepseek_v4_stack = isinstance(kvcache, DeepSeekV4TokenToKVPool)
 
-        if mamba_stack:
+        if deepseek_v4_stack:
+            use_mla = False
+            assert set(cache.components.keys()) == {
+                ComponentType.FULL,
+                ComponentType.SWA,
+            }, "DeepSeekV4TokenToKVPool requires FULL + SWA in UnifiedRadixCache."
+        elif mamba_stack:
             full_kv_pool = kvcache.full_kv_pool
             use_mla = kvcache.use_mla
             assert set(cache.components.keys()) == {
@@ -436,7 +687,51 @@ def attach_hybrid_pool_to_unified_cache(
                 ComponentType.FULL
             }, "Non-hybrid KV pool currently only supports FULL-only UnifiedRadixCache."
 
-        if mamba_stack:
+        if deepseek_v4_stack:
+            host_pool_group, cache_controller = build_deepseek_v4_hicache_stack(
+                params=params,
+                server_args=server_args,
+                kvcache=kvcache,
+                page_size=cache.page_size,
+                tp_group=params.tp_cache_group,
+                load_cache_event=load_cache_event,
+                attn_cp_group=attn_cp_group,
+                attn_tp_group=attn_tp_group,
+                storage_backend=None,
+                host_swa_evict_fn=lambda n: cache.evict_host(n, ComponentType.SWA),
+                device_swa_evict_fn=lambda n: cache.evict(
+                    EvictParams(swa_num_tokens=n)
+                ),
+                pp_rank=params.pp_rank,
+                pp_size=params.pp_size,
+            )
+            cache.full_kv_pool_host = host_pool_group.get_pool(PoolName.KV)
+            cache.host_pool_group = host_pool_group
+            cache.cache_controller = cache_controller
+            cache.components[ComponentType.FULL]._full_kv_pool_host = (
+                cache.full_kv_pool_host
+            )
+            cache.swa_kv_pool_host = host_pool_group.get_pool(PoolName.SWA)
+            cache.components[ComponentType.SWA]._swa_kv_pool_host = (
+                cache.swa_kv_pool_host
+            )
+            for pool_name, indices_from_pool in (
+                (PoolName.DEEPSEEK_V4_C4, PoolName.KV),
+                (PoolName.DEEPSEEK_V4_C4_INDEXER, PoolName.KV),
+                (PoolName.DEEPSEEK_V4_C128, PoolName.KV),
+                (PoolName.DEEPSEEK_V4_C4_STATE, PoolName.SWA),
+                (PoolName.DEEPSEEK_V4_C4_INDEXER_STATE, PoolName.SWA),
+                (PoolName.DEEPSEEK_V4_C128_STATE, PoolName.SWA),
+            ):
+                if pool_name in host_pool_group.entry_map:
+                    cache.register_sidecar_pool(
+                        SidecarPoolSpec(
+                            pool_name=pool_name,
+                            indices_from_pool=indices_from_pool,
+                        )
+                    )
+            transfer_layer_num = len(kvcache.compression_ratios)
+        elif mamba_stack:
             full_layer_mapping = dict(kvcache.full_attention_layer_id_mapping)
             mamba_layer_mapping = dict(params.req_to_token_pool.mamba_map)
             host_pool_group, cache_controller = build_hybrid_mamba_stack(
@@ -519,11 +814,11 @@ def attach_hybrid_pool_to_unified_cache(
             full_layer_mapping = {
                 layer_id: layer_id for layer_id in range(full_kv_pool.layer_num)
             }
-            host_pool_group, cache_controller = build_shared_anchor_stack(
+            host_pool_group, cache_controller = build_anchor_sidecar_stack(
                 params=params,
                 server_args=server_args,
                 kv_pool=full_kv_pool,
-                shared_pool_name=PoolName.INDEXER,
+                sidecar_pool_name=PoolName.INDEXER,
                 full_layer_mapping=full_layer_mapping,
                 page_size=cache.page_size,
                 tp_group=params.tp_cache_group,
@@ -533,7 +828,7 @@ def attach_hybrid_pool_to_unified_cache(
                 storage_backend=None,
                 use_mla=use_mla,
                 override_kv_cache_dim=full_kv_pool.kv_cache_dim,
-                shared_host_pool_factory=lambda kv_host_pool: NSAIndexerPoolHost(
+                sidecar_host_pool_factory=lambda kv_host_pool: NSAIndexerPoolHost(
                     full_kv_pool,
                     kv_host_pool,
                     server_args.hicache_mem_layout,
@@ -545,11 +840,11 @@ def attach_hybrid_pool_to_unified_cache(
             cache.full_kv_pool_host = host_pool_group.get_pool(PoolName.KV)
             cache.host_pool_group = host_pool_group
             cache.cache_controller = cache_controller
-            # Register the NSA indexer pool as sharing anchor-KV indices so
+            cache.register_sidecar_pool(
-            # HiCache backup/load emits its PoolTransfer together with KV.
+                SidecarPoolSpec(
-            cache.register_hicache_anchor_kv_shared_indices_pool(
+                    pool_name=PoolName.INDEXER,
-                PoolName.INDEXER,
+                    indices_from_pool=PoolName.KV,
-                hit_policy=PoolHitPolicy.ALL_PAGES,
+                )
             )
             cache.components[ComponentType.FULL]._full_kv_pool_host = (
                 cache.full_kv_pool_host
@@ -586,7 +881,9 @@ def attach_hybrid_pool_to_unified_cache(
             cache.cache_controller.layer_done_counter
         )
 
-        if mamba_stack:
+        if deepseek_v4_stack:
+            pools_desc = "KV + SWA + DeepSeekV4 sidecars"
+        elif mamba_stack:
             pools_desc = "KV + MAMBA"
         elif swa_stack:
             pools_desc = "KV + SWA"
@@ -613,8 +910,8 @@ def attach_hybrid_nsa_pool_to_hiradix_cache(
     prefetch_threshold: int,
     enable_storage_metrics: bool,
     load_cache_event,
-    attn_cp_group: Optional["torch.distributed.ProcessGroup"] = None,
+    attn_cp_group: Optional[torch.distributed.ProcessGroup] = None,
-    attn_tp_group: Optional["torch.distributed.ProcessGroup"] = None,
+    attn_tp_group: Optional[torch.distributed.ProcessGroup] = None,
 ) -> None:
     """Attach HostPoolGroup (KV + indexer) + HybridCacheController for HiRadixCache.
 
@@ -623,11 +920,11 @@ def attach_hybrid_nsa_pool_to_hiradix_cache(
     try:
         kv = radix_cache.kv_cache
         layer_mapping = {layer_id: layer_id for layer_id in range(kv.layer_num)}
-        host_pool_group, cache_controller = build_shared_anchor_stack(
+        host_pool_group, cache_controller = build_anchor_sidecar_stack(
             params=params,
             server_args=server_args,
             kv_pool=kv,
-            shared_pool_name=PoolName.INDEXER,
+            sidecar_pool_name=PoolName.INDEXER,
             full_layer_mapping=layer_mapping,
             page_size=radix_cache.page_size,
             tp_group=radix_cache.tp_group,
@@ -638,7 +935,7 @@ def attach_hybrid_nsa_pool_to_hiradix_cache(
             use_mla=True,
             override_kv_cache_dim=kv.kv_cache_dim,
             prefetch_threshold=prefetch_threshold,
-            shared_host_pool_factory=lambda kv_host_pool: NSAIndexerPoolHost(
+            sidecar_host_pool_factory=lambda kv_host_pool: NSAIndexerPoolHost(
                 kv,
                 kv_host_pool,
                 server_args.hicache_mem_layout,
@@ -672,8 +969,8 @@ def attach_hybrid_pool_to_mamba_cache(
     prefetch_threshold: int,
     load_cache_event,
     enable_storage_metrics: bool = False,
-    attn_cp_group: Optional["torch.distributed.ProcessGroup"] = None,
+    attn_cp_group: Optional[torch.distributed.ProcessGroup] = None,
-    attn_tp_group: Optional["torch.distributed.ProcessGroup"] = None,
+    attn_tp_group: Optional[torch.distributed.ProcessGroup] = None,
 ) -> None:
     """Attach HostPoolGroup (KV + Mamba) + HybridCacheController for HiMambaRadixCache.
 

python/sglang/srt/mem_cache/memory_pool_host.py

@@ -1656,6 +1656,496 @@ class MambaPoolHost(HostKVCache):
         return ptr_list, element_size_list
 
 
+# ---- V4 Compressed KV Host Pools ----
+
+
+class LogicalHostPool:
+    """Pure-logical anchor pool for V4 HiCache.
+
+    The pool manages page-aligned token slots but holds no KV tensor. V4
+    compressed side pools use these logical FULL indices as stable page anchors.
+    """
+
+    def __init__(self, size: int, page_size: int):
+        if size % page_size != 0:
+            raise ValueError(
+                "LogicalHostPool size must be page-aligned, "
+                f"got size={size}, page_size={page_size}"
+            )
+        self.size = size
+        self.page_size = page_size
+        self.device = "cpu"
+        self.layout = "layer_first"
+        self.dtype = torch.uint8
+        self.layer_num = 0
+        self.start_layer = 0
+        self.end_layer = 0
+        self.kv_buffer = None
+        self.size_per_token = 0
+        self.allocator = None
+        self.lock = threading.RLock()
+        self.clear()
+
+    @synchronized
+    def clear(self):
+        self.free_slots = torch.arange(self.size, dtype=torch.int64)
+
+    def available_size(self):
+        return len(self.free_slots)
+
+    @synchronized
+    def alloc(self, need_size: int) -> Optional[torch.Tensor]:
+        if need_size % self.page_size != 0:
+            raise ValueError(
+                "LogicalHostPool allocation must be page-aligned, "
+                f"got need_size={need_size}, page_size={self.page_size}"
+            )
+        if need_size > self.available_size():
+            return None
+        select_index = self.free_slots[:need_size]
+        self.free_slots = self.free_slots[need_size:]
+        return select_index
+
+    @synchronized
+    def free(self, indices: torch.Tensor) -> int:
+        if len(indices) % self.page_size != 0:
+            raise ValueError(
+                "LogicalHostPool free must be page-aligned, "
+                f"got len(indices)={len(indices)}, page_size={self.page_size}"
+            )
+        self.free_slots = torch.cat(
+            [self.free_slots, indices.to(dtype=torch.int64, device="cpu").flatten()]
+        )
+        return len(indices)
+
+    def backup_from_device_all_layer(
+        self, device_pool, host_indices, device_indices, io_backend
+    ):
+        pass
+
+    def load_to_device_per_layer(
+        self, device_pool, host_indices, device_indices, layer_id, io_backend
+    ):
+        pass
+
+    def get_data_page(self, index, flat=True):
+        return torch.empty(0, dtype=torch.uint8)
+
+    def get_dummy_flat_data_page(self):
+        return torch.empty(0, dtype=torch.uint8)
+
+    def set_from_flat_data_page(self, index, data_page):
+        pass
+
+    def get_page_buffer_meta(self, indices):
+        return None
+
+    def get_ksize_per_token(self):
+        return 0
+
+
+class DeepSeekV4PagedHostPool(HostKVCache):
+    """Host mirror for a DeepSeek V4 paged KV/indexer sub-pool."""
+
+    def __init__(
+        self,
+        pool_name: str,
+        device_buffers: list[torch.Tensor],
+        item_bytes: int,
+        num_host_pages: int,
+        slot_page_size: int,
+        device: str = "cpu",
+        pin_memory: bool = True,
+        allocator_type: str = "default",
+    ):
+        self.pool_name = pool_name
+        self.layer_num = len(device_buffers)
+        self.item_bytes = item_bytes
+        self.num_host_pages = num_host_pages
+        self.slot_page_size = slot_page_size
+        self.dtype = torch.uint8
+        self.device = device
+        self.pin_memory = pin_memory
+        self.allocator = get_allocator_from_storage(allocator_type)
+        self.page_size = slot_page_size
+        self.size = num_host_pages * slot_page_size
+        self.layout = "layer_first"
+        self.size_per_token = item_bytes
+        self.start_layer = 0
+        self.end_layer = self.layer_num
+        self.lock = threading.RLock()
+
+        self.device_buffers = device_buffers
+        self.gpu_device = device_buffers[0].device if device_buffers else device
+
+        requested_bytes = self.layer_num * num_host_pages * self.item_bytes
+        host_mem = psutil.virtual_memory()
+        available_bytes = host_mem.available - HICACHE_HOST_MEMORY_RESERVE_BYTES
+        if requested_bytes > available_bytes:
+            raise ValueError(
+                f"Not enough host memory for V4 paged pool {pool_name}. "
+                f"Requesting {requested_bytes / 1e9:.2f} GB but only have "
+                f"{available_bytes / 1e9:.2f} GB free."
+            )
+
+        alloc_func = ALLOC_MEMORY_FUNCS[self.gpu_device]
+        self.kv_buffer = [
+            alloc_func(
+                (num_host_pages, self.item_bytes),
+                dtype=self.dtype,
+                device=self.device,
+                pin_memory=self.pin_memory,
+                allocator=self.allocator,
+            )
+            for _ in range(self.layer_num)
+        ]
+        self.data_refs = [self.kv_buffer[i] for i in range(self.layer_num)]
+
+        logger.info(
+            "Allocating %.2f GB host memory for V4 paged pool '%s' "
+            "(layers=%d, pages=%d, item_bytes=%d).",
+            requested_bytes / 1e9,
+            self.pool_name,
+            self.layer_num,
+            num_host_pages,
+            self.item_bytes,
+        )
+        self.clear()
+
+    def _to_page_indices(self, indices: torch.Tensor) -> torch.Tensor:
+        if indices.numel() % self.slot_page_size != 0:
+            raise ValueError(
+                f"{self.pool_name} transfer indices must be page-aligned, "
+                f"got numel={indices.numel()}, slot_page_size={self.slot_page_size}"
+            )
+        return indices.reshape(-1, self.slot_page_size)[:, 0] // self.slot_page_size
+
+    def _check_io_backend(self, io_backend: str) -> None:
+        if io_backend != "direct":
+            raise NotImplementedError(
+                f"{self.pool_name} supports only direct io_backend, got {io_backend}"
+            )
+
+    def get_size_per_token(self):
+        return self.item_bytes
+
+    def get_ksize_per_token(self):
+        return self.item_bytes
+
+    def init_kv_buffer(self):
+        return self.kv_buffer
+
+    def get_hybrid_pool_buffer(self):
+        return self.kv_buffer
+
+    def clear(self):
+        self.free_slots = torch.arange(self.size, dtype=torch.int64)
+
+    def available_size(self):
+        return len(self.free_slots)
+
+    @synchronized
+    def alloc(self, need_size: int) -> Optional[torch.Tensor]:
+        need_size = (
+            (need_size + self.slot_page_size - 1) // self.slot_page_size
+        ) * self.slot_page_size
+        if need_size > self.available_size():
+            return None
+        select_index = self.free_slots[:need_size]
+        self.free_slots = self.free_slots[need_size:]
+        return select_index
+
+    @synchronized
+    def free(self, indices: torch.Tensor) -> int:
+        self.free_slots = torch.cat(
+            [self.free_slots, indices.to(dtype=torch.int64, device="cpu").flatten()]
+        )
+        return len(indices)
+
+    def backup_from_device_all_layer(
+        self, device_pool, host_indices, device_indices, io_backend
+    ):
+        if host_indices is None or device_indices is None:
+            return
+        self._check_io_backend(io_backend)
+        host_rows = self._to_page_indices(host_indices)
+        device_rows = self._to_page_indices(device_indices)
+        transfer_kv_direct(
+            src_layers=self.device_buffers,
+            dst_layers=self.data_refs,
+            src_indices=device_rows,
+            dst_indices=host_rows,
+            page_size=1,
+        )
+
+    def load_to_device_per_layer(
+        self, device_pool, host_indices, device_indices, layer_id, io_backend
+    ):
+        if host_indices is None or device_indices is None:
+            return
+        self._check_io_backend(io_backend)
+        host_rows = self._to_page_indices(host_indices)
+        device_rows = self._to_page_indices(device_indices)
+        transfer_kv_direct(
+            src_layers=[self.kv_buffer[layer_id]],
+            dst_layers=[self.device_buffers[layer_id]],
+            src_indices=host_rows,
+            dst_indices=device_rows,
+            page_size=1,
+        )
+
+    def get_data_page(self, index, flat=True):
+        index = int(index) // self.slot_page_size
+        data_page = torch.stack(
+            [self.kv_buffer[i][index] for i in range(self.layer_num)]
+        )
+        return data_page.flatten() if flat else data_page
+
+    def get_dummy_flat_data_page(self):
+        return torch.zeros(
+            (self.layer_num, self.item_bytes),
+            dtype=self.dtype,
+            device=self.device,
+            pin_memory=self.pin_memory,
+        ).flatten()
+
+    def set_from_flat_data_page(self, index, data_page):
+        index = int(index) // self.slot_page_size
+        data = data_page.view(self.dtype).reshape(self.layer_num, self.item_bytes)
+        for i in range(self.layer_num):
+            self.kv_buffer[i][index].copy_(data[i])
+
+    def get_page_buffer_meta(self, indices):
+        ptr_list = []
+        rows = self._to_page_indices(indices).tolist()
+        for row in rows:
+            for layer_id in range(self.layer_num):
+                ptr = (
+                    self.kv_buffer[layer_id].data_ptr()
+                    + int(row) * self.item_bytes * self.dtype.itemsize
+                )
+                ptr_list.append(ptr)
+        element_size = self.item_bytes * self.dtype.itemsize
+        return ptr_list, [element_size] * len(ptr_list)
+
+
+class DeepSeekV4StateHostPool(HostKVCache):
+    """Host pool for V4 CompressStatePool page rows."""
+
+    def __init__(
+        self,
+        pool_name: str,
+        state_pools: list,
+        num_host_pages: int,
+        swa_page_size: int,
+        device: str = "cpu",
+        pin_memory: bool = True,
+        allocator_type: str = "default",
+    ):
+        if any(pool is None for pool in state_pools):
+            raise ValueError(f"{pool_name} state_pools must not contain None")
+
+        self.pool_name = pool_name
+        self.state_pools = state_pools
+        self.layer_num = len(state_pools)
+        self.num_host_pages = num_host_pages
+        self.swa_page_size = swa_page_size
+        self.dtype = torch.uint8
+        self.device = device
+        self.pin_memory = pin_memory
+        self.allocator = get_allocator_from_storage(allocator_type)
+        self.page_size = swa_page_size
+        self.size = num_host_pages * swa_page_size
+        self.layout = "layer_first"
+        self.start_layer = 0
+        self.end_layer = self.layer_num
+        self.lock = threading.RLock()
+
+        self.ring_size = 0
+        self.state_page_bytes = 0
+        self.device_page_views = []
+        self.gpu_device = device
+        self._init_device_page_views()
+        self.size_per_token = self.state_page_bytes
+
+        requested_bytes = self.layer_num * num_host_pages * self.state_page_bytes
+        host_mem = psutil.virtual_memory()
+        available_bytes = host_mem.available - HICACHE_HOST_MEMORY_RESERVE_BYTES
+        if requested_bytes > available_bytes:
+            raise ValueError(
+                f"Not enough host memory for V4 state pool {pool_name}. "
+                f"Requesting {requested_bytes / 1e9:.2f} GB but only have "
+                f"{available_bytes / 1e9:.2f} GB free."
+            )
+
+        alloc_func = ALLOC_MEMORY_FUNCS[self.gpu_device]
+        self.kv_buffer = [
+            alloc_func(
+                (num_host_pages, self.state_page_bytes),
+                dtype=self.dtype,
+                device=self.device,
+                pin_memory=self.pin_memory,
+                allocator=self.allocator,
+            )
+            for _ in range(self.layer_num)
+        ]
+        self.data_refs = [self.kv_buffer[i] for i in range(self.layer_num)]
+        logger.info(
+            "Allocating %.2f GB host memory for V4 state pool '%s' "
+            "(layers=%d, pages=%d, state_page_bytes=%d).",
+            requested_bytes / 1e9,
+            self.pool_name,
+            self.layer_num,
+            num_host_pages,
+            self.state_page_bytes,
+        )
+
+    def _init_device_page_views(self) -> None:
+        expected_ring_size = None
+        expected_state_page_bytes = None
+        for pool in self.state_pools:
+            state_tensor = pool.kv_score_buffer.kv_score
+            if not state_tensor.is_contiguous():
+                raise ValueError(f"{self.pool_name} state tensor must be contiguous")
+            ring_size = pool.ring_size
+            slot_bytes = state_tensor[0].nbytes
+            state_page_bytes = ring_size * slot_bytes
+            if expected_ring_size is None:
+                expected_ring_size = ring_size
+                expected_state_page_bytes = state_page_bytes
+                self.gpu_device = state_tensor.device
+            elif (
+                expected_ring_size != ring_size
+                or expected_state_page_bytes != state_page_bytes
+            ):
+                raise ValueError(
+                    f"{self.pool_name} state pools must share ring size and slot bytes"
+                )
+
+            state_bytes = state_tensor.view(torch.uint8).reshape(
+                state_tensor.shape[0], -1
+            )
+            usable_slots = (state_tensor.shape[0] // ring_size) * ring_size
+            self.device_page_views.append(
+                state_bytes[:usable_slots].reshape(-1, state_page_bytes)
+            )
+
+        self.ring_size = expected_ring_size or 0
+        self.state_page_bytes = expected_state_page_bytes or 0
+
+    def _to_page_indices(self, indices: torch.Tensor) -> torch.Tensor:
+        if indices.numel() % self.swa_page_size != 0:
+            raise ValueError(
+                f"{self.pool_name} transfer indices must be SWA-page-aligned, "
+                f"got numel={indices.numel()}, swa_page_size={self.swa_page_size}"
+            )
+        return indices.reshape(-1, self.swa_page_size)[:, 0] // self.swa_page_size
+
+    def _check_io_backend(self, io_backend: str) -> None:
+        if io_backend != "direct":
+            raise NotImplementedError(
+                f"{self.pool_name} supports only direct io_backend, got {io_backend}"
+            )
+
+    def get_size_per_token(self):
+        return self.state_page_bytes
+
+    def get_ksize_per_token(self):
+        return self.state_page_bytes
+
+    def init_kv_buffer(self):
+        return self.kv_buffer
+
+    def get_hybrid_pool_buffer(self):
+        return self.kv_buffer
+
+    def clear(self):
+        pass
+
+    def available_size(self):
+        raise NotImplementedError(
+            f"{self.pool_name} reuses SWA transfer indices and has no allocator"
+        )
+
+    @synchronized
+    def alloc(self, need_size: int) -> Optional[torch.Tensor]:
+        raise NotImplementedError(
+            f"{self.pool_name} reuses SWA transfer indices and has no allocator"
+        )
+
+    @synchronized
+    def free(self, indices: torch.Tensor) -> int:
+        raise NotImplementedError(
+            f"{self.pool_name} reuses SWA transfer indices and has no free list"
+        )
+
+    def backup_from_device_all_layer(
+        self, device_pool, host_indices, device_indices, io_backend
+    ):
+        if host_indices is None or device_indices is None:
+            return
+        self._check_io_backend(io_backend)
+        host_rows = self._to_page_indices(host_indices)
+        device_rows = self._to_page_indices(device_indices)
+        transfer_kv_direct(
+            src_layers=self.device_page_views,
+            dst_layers=self.data_refs,
+            src_indices=device_rows,
+            dst_indices=host_rows,
+            page_size=1,
+        )
+
+    def load_to_device_per_layer(
+        self, device_pool, host_indices, device_indices, layer_id, io_backend
+    ):
+        if host_indices is None or device_indices is None:
+            return
+        self._check_io_backend(io_backend)
+        host_rows = self._to_page_indices(host_indices)
+        device_rows = self._to_page_indices(device_indices)
+        transfer_kv_direct(
+            src_layers=[self.kv_buffer[layer_id]],
+            dst_layers=[self.device_page_views[layer_id]],
+            src_indices=host_rows,
+            dst_indices=device_rows,
+            page_size=1,
+        )
+
+    def get_data_page(self, index, flat=True):
+        index = int(index) // self.swa_page_size
+        data_page = torch.stack(
+            [self.kv_buffer[i][index] for i in range(self.layer_num)]
+        )
+        return data_page.flatten() if flat else data_page
+
+    def get_dummy_flat_data_page(self):
+        return torch.zeros(
+            (self.layer_num, self.state_page_bytes),
+            dtype=self.dtype,
+            device=self.device,
+            pin_memory=self.pin_memory,
+        ).flatten()
+
+    def set_from_flat_data_page(self, index, data_page):
+        index = int(index) // self.swa_page_size
+        data = data_page.view(self.dtype).reshape(self.layer_num, self.state_page_bytes)
+        for i in range(self.layer_num):
+            self.kv_buffer[i][index].copy_(data[i])
+
+    def get_page_buffer_meta(self, indices):
+        ptr_list = []
+        rows = self._to_page_indices(indices).tolist()
+        for row in rows:
+            for layer_id in range(self.layer_num):
+                ptr = (
+                    self.kv_buffer[layer_id].data_ptr()
+                    + int(row) * self.state_page_bytes * self.dtype.itemsize
+                )
+                ptr_list.append(ptr)
+        element_size = self.state_page_bytes * self.dtype.itemsize
+        return ptr_list, [element_size] * len(ptr_list)
+
+
 @dataclass
 class PoolEntry:
     name: PoolName
@@ -1663,9 +2153,6 @@ class PoolEntry:
     device_pool: Any
     layer_mapper: Callable[[int], Optional[int]]
     is_primary_index_anchor: bool = False
-    # When True, host_pool uses the same logical slot indices as the anchor pool
-    # (e.g. DSA indexer); HostPoolGroup.free mirrors frees to this pool.
-    share_indices_with_anchor: bool = False
     # Optional eviction callbacks for auto-alloc in HybridCacheController.
     # host_evict_fn(n): evict n slots from the host pool (used by write()).
     # device_evict_fn(n): evict n slots from the device pool (used by load()).

python/sglang/srt/mem_cache/unified_radix_cache.py

@@ -22,7 +22,11 @@ from sglang.srt.mem_cache.base_prefix_cache import (
     MatchPrefixParams,
     MatchResult,
 )
-from sglang.srt.mem_cache.hicache_storage import PoolHitPolicy, PoolName, PoolTransfer
+from sglang.srt.mem_cache.hicache_storage import (
+    PoolName,
+    PoolTransfer,
+    SidecarPoolSpec,
+)
 from sglang.srt.mem_cache.radix_cache import RadixKey
 from sglang.srt.mem_cache.unified_cache_components import (
     _NUM_COMPONENT_TYPES,
@@ -221,9 +225,7 @@ class UnifiedRadixCache(BasePrefixCache):
         self._components_tuple: tuple[TreeComponent, ...] = tuple(
             self.components.values()
         )
-        self.hicache_anchor_kv_shared_indices_pools: list[
+        self.sidecar_pool_specs: list[SidecarPoolSpec] = []
-            tuple[PoolName, PoolHitPolicy]
-        ] = []
 
         # Streaming session: embedded StreamingSession with self as inner.
         # Always on -- zero overhead when no streaming session is open (the
@@ -309,7 +311,7 @@ class UnifiedRadixCache(BasePrefixCache):
                 )
 
         self.load_cache_event = threading.Event()
-        self.hicache_anchor_kv_shared_indices_pools.clear()
+        self.sidecar_pool_specs.clear()
         attach_hybrid_pool_to_unified_cache(
             self,
             params,
@@ -333,12 +335,8 @@ class UnifiedRadixCache(BasePrefixCache):
             f"transfer_layer_num={self.cache_controller.layer_num}"
         )
 
-    def register_hicache_anchor_kv_shared_indices_pool(
+    def register_sidecar_pool(self, spec: SidecarPoolSpec) -> None:
-        self,
+        self.sidecar_pool_specs.append(spec)
-        pool_name: PoolName,
-        hit_policy: PoolHitPolicy = PoolHitPolicy.ALL_PAGES,
-    ) -> None:
-        self.hicache_anchor_kv_shared_indices_pools.append((pool_name, hit_policy))
 
     def match_prefix(self, params: MatchPrefixParams) -> MatchResult:
         result = self.session.try_match_prefix(params)
@@ -1164,7 +1162,10 @@ class UnifiedRadixCache(BasePrefixCache):
         ):
             return 0
 
-        # Build aux transfers, keyed per component
+        device_value = node.component_data[BASE_COMPONENT_TYPE].value
+        kv_xfer = PoolTransfer(name=PoolName.KV, device_indices=device_value)
+
+        # Build aux transfers, keyed per component.
         comp_xfers: dict[ComponentType, list] = {}
         for comp in self._components_tuple:
             if comp.component_type == BASE_COMPONENT_TYPE:
@@ -1172,13 +1173,11 @@ class UnifiedRadixCache(BasePrefixCache):
             t = comp.build_hicache_transfers(node, CacheTransferPhase.BACKUP_HOST)
             if t:
                 comp_xfers[comp.component_type] = t
-        anchor_kv_shared_indices_xfers = [
+        sidecar_xfers = self._build_sidecar_transfers(
-            PoolTransfer(name=pool_name, hit_policy=hit_policy)
+            CacheTransferPhase.BACKUP_HOST, kv_xfer, comp_xfers
-            for pool_name, hit_policy in self.hicache_anchor_kv_shared_indices_pools
+        )
-        ]
 
         # Pre-evict host if insufficient
-        device_value = node.component_data[BASE_COMPONENT_TYPE].value
         kv_tokens = len(device_value)
         host_avail = self.cache_controller.mem_pool_host.available_size()
         if host_avail < kv_tokens:
@@ -1188,7 +1187,7 @@ class UnifiedRadixCache(BasePrefixCache):
                 return 0
 
         aux_xfers = [x for xfers in comp_xfers.values() for x in xfers]
-        aux_xfers.extend(anchor_kv_shared_indices_xfers)
+        aux_xfers.extend(sidecar_xfers)
         host_indices = self.cache_controller.write(
             device_value, node_id=node.id, extra_pools=aux_xfers or None
         )
@@ -1245,10 +1244,9 @@ class UnifiedRadixCache(BasePrefixCache):
             )
             if t:
                 comp_xfers[comp.component_type] = t
-        anchor_kv_shared_indices_xfers = [
+        sidecar_xfers = self._build_sidecar_transfers(
-            PoolTransfer(name=pool_name, hit_policy=hit_policy)
+            CacheTransferPhase.LOAD_BACK, kv_xfer, comp_xfers
-            for pool_name, hit_policy in self.hicache_anchor_kv_shared_indices_pools
+        )
-        ]
 
         # Skip if there is nothing to load, or if the Full-KV transfer is too
         # small / exceeds memory quota. Aux transfers should still run even
@@ -1269,7 +1267,7 @@ class UnifiedRadixCache(BasePrefixCache):
 
         # Load H→D
         aux_xfers = [x for xfers in comp_xfers.values() for x in xfers]
-        aux_xfers.extend(anchor_kv_shared_indices_xfers)
+        aux_xfers.extend(sidecar_xfers)
         device_indices = self.cache_controller.load(
             host_indices=kv_xfer.host_indices,
             node_id=best_match_node.id,
@@ -1301,6 +1299,52 @@ class UnifiedRadixCache(BasePrefixCache):
         )
         return device_indices
 
+    def _build_sidecar_transfers(
+        self,
+        phase: CacheTransferPhase,
+        kv_xfer: PoolTransfer,
+        comp_xfers: dict[ComponentType, list[PoolTransfer]],
+    ) -> list[PoolTransfer]:
+        transfers: list[PoolTransfer] = []
+        for spec in self.sidecar_pool_specs:
+            if spec.indices_from_pool == PoolName.KV:
+                indices_source = kv_xfer
+            else:
+                source_component = {
+                    PoolName.SWA: ComponentType.SWA,
+                    PoolName.MAMBA: ComponentType.MAMBA,
+                }.get(spec.indices_from_pool)
+                if source_component is None:
+                    raise AssertionError(
+                        f"Unsupported sidecar indices source pool "
+                        f"{spec.indices_from_pool}."
+                    )
+                matching_sources = comp_xfers.get(source_component, ())
+                if not matching_sources:
+                    continue
+                indices_source = matching_sources[0]
+                if indices_source.name != spec.indices_from_pool:
+                    raise AssertionError(
+                        f"Sidecar indices source pool {spec.indices_from_pool} "
+                        f"resolved to {indices_source.name} during {phase}."
+                    )
+
+            indices = (
+                indices_source.device_indices
+                if phase == CacheTransferPhase.BACKUP_HOST
+                else indices_source.host_indices
+            )
+            if indices is None or len(indices) == 0:
+                continue
+            transfers.append(
+                PoolTransfer(
+                    name=spec.pool_name,
+                    hit_policy=spec.hit_policy,
+                    indices_from_pool=spec.indices_from_pool,
+                )
+            )
+        return transfers
+
     def _inc_hit_count(self, node: UnifiedTreeNode, chunked: bool = False) -> None:
         """Increment hit count; trigger write_backup when threshold reached."""
         if self.cache_controller is None:

python/sglang/test/kl_multiturn_utils.py

@@ -104,6 +104,7 @@ def _replay_and_compare_kl(
     output_logprobs: list[list[float]],
     label: str,
     batch_size: int = 1,
+    sampling_temperature: float = 1,
 ):
     """Flush cache, run replay prefill in batches, compare KL divergence."""
     all_input_logprobs = []
@@ -114,6 +115,7 @@ def _replay_and_compare_kl(
                 base_url,
                 replay_input_ids[start:end],
                 output_logprobs[start:end],
+                temperature=sampling_temperature,
             )
         )
     acc = {model_name: {"kl_div": kl_threshold}}
@@ -142,7 +144,9 @@ def _interleave_order(n: int, branches_per_group: int) -> list[int] | None:
     return order
 
 
-def _generate_maybe_interleaved(base_url, inputs, max_new_tokens, order=None):
+def _generate_maybe_interleaved(
+    base_url, inputs, max_new_tokens, order=None, sampling_temperature: float = 1
+):
     """Generate with optional interleaved submission order.
 
     Submits inputs reordered by ``order``, then maps results back to the
@@ -150,9 +154,21 @@ def _generate_maybe_interleaved(base_url, inputs, max_new_tokens, order=None):
     inputs[i].
     """
     if order is None:
-        return _generate(base_url, inputs, max_new_tokens, return_logprob=True)
+        return _generate(
+            base_url,
+            inputs,
+            max_new_tokens,
+            return_logprob=True,
+            temperature=sampling_temperature,
+        )
     ordered = [inputs[i] for i in order]
-    results = _generate(base_url, ordered, max_new_tokens, return_logprob=True)
+    results = _generate(
+        base_url,
+        ordered,
+        max_new_tokens,
+        return_logprob=True,
+        temperature=sampling_temperature,
+    )
     unordered = [None] * len(results)
     for idx, orig in enumerate(order):
         unordered[orig] = results[idx]
@@ -178,6 +194,7 @@ def test_input_output_logprobs_match_helper(
     # --- Cache assertion (for turns > 0) ---
     assert_decode_cached_tokens: Callable | None = None,
     replay_batch_size: int = 1,
+    sampling_temperature: float = 1,
 ):
     """Verify decode logprobs match prefill replay.
 
@@ -213,7 +230,11 @@ def test_input_output_logprobs_match_helper(
             ]
 
         results = _generate(
-            base_url, current_input, max_new_tokens, return_logprob=True
+            base_url,
+            current_input,
+            max_new_tokens,
+            return_logprob=True,
+            temperature=sampling_temperature,
         )
         assert len(results) == n
 
@@ -242,6 +263,7 @@ def test_input_output_logprobs_match_helper(
         output_lps,
         label=label,
         batch_size=replay_batch_size,
+        sampling_temperature=sampling_temperature,
     )
 
 
@@ -269,6 +291,7 @@ def test_input_output_logprobs_match_prefill_cache_hit_helper(
     # --- Interleaving for branch stress ---
     branches_per_group: int = 0,
     replay_batch_size: int = 1,
+    sampling_temperature: float = 1,
 ):
     """Verify logprobs when prefill cache is hit.
 
@@ -305,10 +328,21 @@ def test_input_output_logprobs_match_prefill_cache_hit_helper(
 
     # Seed cache with prefixes
     _flush_cache(base_url)
-    _generate(base_url, prefix_input_ids, max_new_tokens=0)
+    _generate(
+        base_url,
+        prefix_input_ids,
+        max_new_tokens=0,
+        temperature=sampling_temperature,
+    )
 
     # Turn 0: prefill cache hit (NOT interleaved, matching original behavior)
-    results = _generate(base_url, full_input_ids, max_new_tokens, return_logprob=True)
+    results = _generate(
+        base_url,
+        full_input_ids,
+        max_new_tokens,
+        return_logprob=True,
+        temperature=sampling_temperature,
+    )
     assert len(results) == n
 
     for i, result in enumerate(results):
@@ -331,7 +365,11 @@ def test_input_output_logprobs_match_prefill_cache_hit_helper(
                 current_input[i] + last_outputs[i] + suffixes[i] for i in range(n)
             ]
             results = _generate_maybe_interleaved(
-                base_url, current_input, max_new_tokens, order
+                base_url,
+                current_input,
+                max_new_tokens,
+                order,
+                sampling_temperature=sampling_temperature,
             )
             assert len(results) == n
 
@@ -359,6 +397,7 @@ def test_input_output_logprobs_match_prefill_cache_hit_helper(
         output_lps,
         label=label,
         batch_size=replay_batch_size,
+        sampling_temperature=sampling_temperature,
     )
 
 
@@ -383,6 +422,7 @@ def test_input_output_logprobs_match_decode_cache_hit_helper(
     # --- Interleaving ---
     branches_per_group: int = 0,
     replay_batch_size: int = 1,
+    sampling_temperature: float = 1,
 ):
     """Verify logprobs when decode cache is hit.
 
@@ -414,7 +454,11 @@ def test_input_output_logprobs_match_decode_cache_hit_helper(
     # Turn 1: populate cache, no assertion, no interleaving
     _flush_cache(base_url)
     results = _generate(
-        base_url, first_turn_input_ids, max_new_tokens, return_logprob=True
+        base_url,
+        first_turn_input_ids,
+        max_new_tokens,
+        return_logprob=True,
+        temperature=sampling_temperature,
     )
     assert len(results) == n
 
@@ -429,7 +473,11 @@ def test_input_output_logprobs_match_decode_cache_hit_helper(
             current_input[i] + last_outputs[i] + suffixes[i] for i in range(n)
         ]
         results = _generate_maybe_interleaved(
-            base_url, current_input, max_new_tokens, order
+            base_url,
+            current_input,
+            max_new_tokens,
+            order,
+            sampling_temperature=sampling_temperature,
         )
         assert len(results) == n
 
@@ -457,4 +505,5 @@ def test_input_output_logprobs_match_decode_cache_hit_helper(
         output_lps,
         label=label,
         batch_size=replay_batch_size,
+        sampling_temperature=sampling_temperature,
     )

python/sglang/test/kl_test_utils.py

@@ -128,13 +128,18 @@ def _flush_cache(base_url, timeout_s=30):
 
 
 def _generate(
-    base_url, input_ids, max_new_tokens, return_logprob=False, logprob_start_len=-1
+    base_url,
+    input_ids,
+    max_new_tokens,
+    return_logprob=False,
+    logprob_start_len=-1,
+    temperature=1,
 ):
     """Send generate request and return results."""
     json_data = {
         "input_ids": input_ids,
         "sampling_params": {
-            "temperature": 1,
+            "temperature": temperature,
             "max_new_tokens": max_new_tokens,
             "ignore_eos": True,
         },
@@ -151,7 +156,7 @@ def _generate(
     return response.json()
 
 
-def _get_input_logprobs(base_url, new_input_ids, output_logprobs):
+def _get_input_logprobs(base_url, new_input_ids, output_logprobs, temperature=1):
     """Run prefill to get input logprobs matching output logprobs."""
     _flush_cache(base_url)
     results = _generate(
@@ -160,6 +165,7 @@ def _get_input_logprobs(base_url, new_input_ids, output_logprobs):
         max_new_tokens=0,
         return_logprob=True,
         logprob_start_len=0,
+        temperature=temperature,
     )
     assert len(results) == len(new_input_ids)
 

test/registered/radix_cache/test_unified_radix_cache_kl.py

@@ -48,6 +48,7 @@ class UnifiedRadixTreeTestMixin:
     prefix_len: int = 512
     prefill_cache_assert = None
     decode_cache_assert = None
+    sampling_temperature: float = 1
 
     gsm8k_threshold: float = 0.93
     mmlu_threshold: float = 0.8
@@ -106,6 +107,7 @@ class UnifiedRadixTreeTestMixin:
             turn_suffixes=[t2, t3],
             assert_decode_cached_tokens=self.decode_cache_assert,
             max_new_tokens=self.max_new_tokens,
+            sampling_temperature=self.sampling_temperature,
         )
 
     def test_multiturn_prefill_cache_hit_branching(self):
@@ -135,6 +137,7 @@ class UnifiedRadixTreeTestMixin:
             assert_decode_cached_tokens=self.decode_cache_assert,
             branches_per_group=branches,
             max_new_tokens=self.max_new_tokens,
+            sampling_temperature=self.sampling_temperature,
         )
 
     def test_multiturn_decode_cache_hit_branching(self):
@@ -159,6 +162,7 @@ class UnifiedRadixTreeTestMixin:
             assert_decode_cached_tokens=self.decode_cache_assert,
             branches_per_group=branches,
             max_new_tokens=self.max_new_tokens,
+            sampling_temperature=self.sampling_temperature,
         )
 
 
@@ -267,58 +271,5 @@ class TestUnifiedSWARadixCache(UnifiedRadixTreeTestMixin, CustomTestCase):
         kill_process_tree(cls.process.pid)
 
 
-class TestUnifiedMambaRadixCacheWithHiCache(UnifiedRadixTreeTestMixin, CustomTestCase):
-    """Mamba hybrid + HiCache + UnifiedRadixCache."""
-
-    kl_threshold = 0.003
-    prefill_cache_assert = staticmethod(
-        make_mamba_prefill_assert(chunk_size=MAMBA_CHUNK_SIZE)
-    )
-    decode_cache_assert = staticmethod(
-        make_mamba_decode_assert(track_interval=MAMBA_TRACK_INTERVAL)
-    )
-
-    @classmethod
-    def setUpClass(cls):
-        cls.model = MAMBA_MODEL
-        cls.base_url = DEFAULT_URL_FOR_TEST
-        cls.process = popen_launch_server(
-            cls.model,
-            cls.base_url,
-            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
-            other_args=[
-                "--tp-size",
-                "4",
-                "--chunked-prefill-size",
-                "2048",
-                "--mem-fraction-static",
-                "0.85",
-                "--mamba-scheduler-strategy",
-                "extra_buffer",
-                "--mamba-track-interval",
-                str(MAMBA_TRACK_INTERVAL),
-                "--enable-hierarchical-cache",
-                "--hicache-ratio",
-                "4",
-                "--hicache-write-policy",
-                "write_through",
-                "--hicache-io-backend",
-                "direct",
-                "--hicache-mem-layout",
-                "page_first_direct",
-                "--max-total-tokens",
-                "12000",
-                "--max-running-requests",
-                "4",
-            ],
-            env={"SGLANG_ENABLE_UNIFIED_RADIX_TREE": "1"},
-        )
-        cls.input_ids = get_input_ids(cls.model, num_samples=18)
-
-    @classmethod
-    def tearDownClass(cls):
-        kill_process_tree(cls.process.pid)
-
-
 if __name__ == "__main__":
     unittest.main()

test/registered/radix_cache/test_unified_radix_hicache_kl.py

@@ -0,0 +1,162 @@
+"""UnifiedRadixTree + HiCache KL divergence tests.
+
+Tests Mamba hybrid, DeepSeek V4 Flash, and GLM-5 models with HiCache L2
+offloading under UnifiedRadixTree, verifying multi-turn cache correctness
+via KL divergence.
+"""
+
+import unittest
+
+from test_unified_radix_cache_kl import UnifiedRadixTreeTestMixin
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.kl_multiturn_utils import (
+    get_input_ids,
+    make_mamba_decode_assert,
+    make_mamba_prefill_assert,
+)
+from sglang.test.test_utils import (
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    popen_launch_server,
+)
+
+MAMBA_MODEL = "Qwen/Qwen3-Next-80B-A3B-Instruct-FP8"
+MAMBA_CHUNK_SIZE = 64
+MAMBA_TRACK_INTERVAL = 128
+
+DSV4_FLASH_MODEL = "sgl-project/DeepSeek-V4-Flash-FP8"
+DSV4_FLASH_LAUNCH_TIMEOUT = 3600
+
+DSV32_MODEL = "deepseek-ai/DeepSeek-V3.2"
+DSV32_LAUNCH_TIMEOUT = 3600
+
+register_cuda_ci(est_time=900, suite="nightly-8-gpu-h200", nightly=True)
+
+
+class TestUnifiedMambaHiCache(UnifiedRadixTreeTestMixin, CustomTestCase):
+    """Mamba hybrid + HiCache + UnifiedRadixCache."""
+
+    kl_threshold = 0.003
+    prefill_cache_assert = staticmethod(
+        make_mamba_prefill_assert(chunk_size=MAMBA_CHUNK_SIZE)
+    )
+    decode_cache_assert = staticmethod(
+        make_mamba_decode_assert(track_interval=MAMBA_TRACK_INTERVAL)
+    )
+
+    @classmethod
+    def setUpClass(cls):
+        cls.model = MAMBA_MODEL
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+            other_args=[
+                "--tp-size",
+                "4",
+                "--chunked-prefill-size",
+                "2048",
+                "--mem-fraction-static",
+                "0.85",
+                "--mamba-scheduler-strategy",
+                "extra_buffer",
+                "--mamba-track-interval",
+                str(MAMBA_TRACK_INTERVAL),
+                "--enable-hierarchical-cache",
+                "--hicache-ratio",
+                "4",
+                "--hicache-write-policy",
+                "write_through",
+                "--hicache-io-backend",
+                "direct",
+                "--hicache-mem-layout",
+                "page_first_direct",
+                "--max-total-tokens",
+                "12000",
+                "--max-running-requests",
+                "4",
+            ],
+            env={"SGLANG_ENABLE_UNIFIED_RADIX_TREE": "1"},
+        )
+        cls.input_ids = get_input_ids(cls.model, num_samples=18)
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+
+def _assert_dsv4_decode_cached_tokens(result, history_len, output_len, label):
+    expected = history_len + output_len
+    actual = result["meta_info"]["cached_tokens"]
+    lower = max(0, expected - 256)
+    assert actual >= lower, f"{label}: expected cached_tokens>={lower}, got {actual}"
+
+
+class TestUnifiedDeepSeekV4FlashHiCache(UnifiedRadixTreeTestMixin, CustomTestCase):
+    """DeepSeek V4 Flash FP8 + HiCache + UnifiedRadixCache."""
+
+    kl_threshold = 0.0035
+    sampling_temperature = 0
+    decode_cache_assert = staticmethod(_assert_dsv4_decode_cached_tokens)
+    gsm8k_threshold = 0.90
+    num_gsm8k_questions = 100
+
+    @unittest.skip("no stable.")
+    def test_multiturn_logprobs_match(self):
+        pass
+
+    @classmethod
+    def setUpClass(cls):
+        cls.model = DSV4_FLASH_MODEL
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.process = popen_launch_server(
+            cls.model,
+            cls.base_url,
+            timeout=DSV4_FLASH_LAUNCH_TIMEOUT,
+            other_args=[
+                "--trust-remote-code",
+                "--tp-size",
+                "4",
+                "--attention-backend",
+                "compressed",
+                "--page-size",
+                "256",
+                "--chunked-prefill-size",
+                "8192",
+                "--mem-fraction-static",
+                "0.9",
+                "--disable-shared-experts-fusion",
+                "--enable-hierarchical-cache",
+                "--hicache-ratio",
+                "4",
+                "--hicache-write-policy",
+                "write_through",
+                "--hicache-io-backend",
+                "direct",
+                "--hicache-mem-layout",
+                "page_first_direct",
+                "--swa-full-tokens-ratio",
+                "0.25",
+                "--max-total-tokens",
+                "20000",
+                "--max-running-requests",
+                "4",
+            ],
+            env={
+                "SGLANG_DSV4_FP4_EXPERTS": "0",
+                "SGLANG_ENABLE_UNIFIED_RADIX_TREE": "1",
+            },
+        )
+        cls.input_ids = get_input_ids(cls.model, num_samples=18)
+
+    @classmethod
+    def tearDownClass(cls):
+        kill_process_tree(cls.process.pid)
+
+
+if __name__ == "__main__":
+    unittest.main()