SWA Prefix Cache (#7367)

Co-authored-by: Ying Sheng <sqy1415@gmail.com>

python/sglang/srt/configs/model_config.py

@@ -711,7 +711,6 @@ def get_hybrid_layer_ids(model_architectures: List[str], num_hidden_layers: int)
             i for i in range(num_hidden_layers) if (i + 1) % 4 == 0
         ]
     else:
-        raise ValueError(
-            "get_hybrid_layer_ids is only implemented for Llama4ForConditionalGeneration"
-        )
+        swa_attention_layer_ids = None
+        full_attention_layer_ids = None
     return swa_attention_layer_ids, full_attention_layer_ids

python/sglang/srt/disaggregation/decode.py

@@ -439,7 +439,15 @@ class DecodePreallocQueue:
             else 0
         )
 
-        allocatable_tokens = self.token_to_kv_pool_allocator.available_size() - max(
+        if self.scheduler.model_config.is_hybrid:
+            available_size = min(
+                self.token_to_kv_pool_allocator.full_available_size(),
+                self.token_to_kv_pool_allocator.swa_available_size(),
+            )
+        else:
+            available_size = self.token_to_kv_pool_allocator.available_size()
+
+        allocatable_tokens = available_size - max(
             # preserve some space for future decode
             self.num_reserved_decode_tokens
             * (

python/sglang/srt/layers/attention/flashinfer_backend.py

@@ -26,6 +26,7 @@ from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
 from sglang.srt.layers.attention.utils import create_flashinfer_kv_indices_triton
 from sglang.srt.layers.dp_attention import get_attention_tp_size
 from sglang.srt.layers.utils import is_sm100_supported
+from sglang.srt.mem_cache.allocator import SWATokenToKVPoolAllocator
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
 from sglang.srt.speculative.eagle_utils import EagleDraftInput, EagleVerifyInput
 from sglang.srt.utils import is_flashinfer_available, next_power_of_2
@@ -589,6 +590,7 @@ class FlashInferIndicesUpdaterDecode:
         self.kv_indptr = attn_backend.kv_indptr
         self.kv_last_page_len = attn_backend.kv_last_page_len
         self.req_to_token = model_runner.req_to_token_pool.req_to_token
+        self.token_to_kv_pool_allocator = model_runner.token_to_kv_pool_allocator
 
         # Dispatch the update function
         if self.attn_backend.dispatch_reason == WrapperDispatch.SLIDING_WINDOW:
@@ -655,6 +657,10 @@ class FlashInferIndicesUpdaterDecode:
                 paged_kernel_lens_sum_tmp = seq_lens_sum
                 kv_start_idx_tmp = None
 
+            use_sliding_window_kv_pool = wrapper_id == 0 and isinstance(
+                self.token_to_kv_pool_allocator, SWATokenToKVPoolAllocator
+            )
+
             self.call_begin_forward(
                 decode_wrappers[wrapper_id],
                 req_pool_indices,
@@ -663,6 +669,7 @@ class FlashInferIndicesUpdaterDecode:
                 self.kv_indptr[wrapper_id],
                 kv_start_idx_tmp,
                 spec_info,
+                use_sliding_window_kv_pool=use_sliding_window_kv_pool,
             )
 
     def update_cross_attention(
@@ -704,6 +711,7 @@ class FlashInferIndicesUpdaterDecode:
         kv_indptr: torch.Tensor,
         kv_start_idx: torch.Tensor,
         spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
+        use_sliding_window_kv_pool: bool = False,
     ):
         if spec_info is None:
             bs = len(req_pool_indices)
@@ -731,6 +739,14 @@ class FlashInferIndicesUpdaterDecode:
             kv_indptr, kv_indices = spec_info.kv_indptr, spec_info.kv_indices
             bs = kv_indptr.shape[0] - 1
 
+        if use_sliding_window_kv_pool:
+            kv_last_index = kv_indptr[-1]
+            kv_indices[:kv_last_index] = (
+                self.token_to_kv_pool_allocator.translate_loc_from_full_to_swa(
+                    kv_indices[:kv_last_index]
+                )
+            )
+
         wrapper.begin_forward(
             kv_indptr,
             kv_indices,
@@ -765,6 +781,7 @@ class FlashInferIndicesUpdaterPrefill:
         self.kv_last_page_len = attn_backend.kv_last_page_len
         self.qo_indptr = attn_backend.qo_indptr
         self.req_to_token = model_runner.req_to_token_pool.req_to_token
+        self.token_to_kv_pool_allocator = model_runner.token_to_kv_pool_allocator
         self.prefill_wrapper_ragged = attn_backend.prefill_wrapper_ragged
 
         # Dispatch the update function
@@ -848,6 +865,9 @@ class FlashInferIndicesUpdaterPrefill:
                 paged_kernel_lens_sum = seq_lens_sum
 
             kv_start_idx = seq_lens - paged_kernel_lens
+            use_sliding_window_kv_pool = wrapper_id == 0 and isinstance(
+                self.token_to_kv_pool_allocator, SWATokenToKVPoolAllocator
+            )
 
             self.call_begin_forward(
                 self.prefill_wrapper_ragged,
@@ -862,6 +882,7 @@ class FlashInferIndicesUpdaterPrefill:
                 self.qo_indptr[wrapper_id],
                 use_ragged,
                 spec_info,
+                use_sliding_window_kv_pool=use_sliding_window_kv_pool,
             )
 
     def update_cross_attention(
@@ -916,6 +937,7 @@ class FlashInferIndicesUpdaterPrefill:
         qo_indptr: torch.Tensor,
         use_ragged: bool,
         spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
+        use_sliding_window_kv_pool: bool = False,
     ):
         bs = len(seq_lens)
         if spec_info is None:
@@ -964,6 +986,14 @@ class FlashInferIndicesUpdaterPrefill:
                 q_data_type=self.q_data_type,
             )
 
+        if use_sliding_window_kv_pool:
+            kv_last_index = kv_indptr[-1]
+            kv_indices[:kv_last_index] = (
+                self.token_to_kv_pool_allocator.translate_loc_from_full_to_swa(
+                    kv_indices[:kv_last_index]
+                )
+            )
+
         # cached part
         wrapper_paged.begin_forward(
             qo_indptr,

python/sglang/srt/managers/schedule_batch.py

@@ -52,10 +52,14 @@ from sglang.srt.disaggregation.decode_schedule_batch_mixin import (
     ScheduleBatchDisaggregationDecodeMixin,
 )
 from sglang.srt.distributed.parallel_state import get_tensor_model_parallel_rank
-from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
+from sglang.srt.mem_cache.allocator import (
+    BaseTokenToKVPoolAllocator,
+    SWATokenToKVPoolAllocator,
+)
 from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
 from sglang.srt.mem_cache.chunk_cache import ChunkCache, SWAChunkCache
 from sglang.srt.mem_cache.memory_pool import ReqToTokenPool
+from sglang.srt.mem_cache.swa_radix_cache import SWARadixCache
 from sglang.srt.metrics.collector import TimeStats
 from sglang.srt.model_executor.forward_batch_info import CaptureHiddenMode, ForwardMode
 from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
@@ -527,6 +531,8 @@ class Req:
         self.last_node: Any = None
         self.last_host_node: Any = None
         self.host_hit_length = 0
+        # The node to lock until for swa radix tree lock ref
+        self.swa_uuid_for_lock: Optional[int] = None
 
         # Whether or not if it is chunked. It increments whenever
         # it is chunked, and decrement whenever chunked request is
@@ -745,6 +751,7 @@ class Req:
     def reset_for_retract(self):
         self.prefix_indices = []
         self.last_node = None
+        self.swa_uuid_for_lock = None
         self.extend_input_len = 0
         self.is_retracted = True
         self.input_token_logprobs = None
@@ -813,6 +820,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
     req_to_token_pool: ReqToTokenPool = None
     token_to_kv_pool_allocator: BaseTokenToKVPoolAllocator = None
     tree_cache: BasePrefixCache = None
+    is_hybrid: bool = False
 
     # Batch configs
     model_config: ModelConfig = None
@@ -918,11 +926,19 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
     ):
         return_logprob = any(req.return_logprob for req in reqs)
 
+        is_hybrid = False
+        if isinstance(token_to_kv_pool_allocator, SWATokenToKVPoolAllocator):
+            assert isinstance(tree_cache, SWARadixCache) or isinstance(
+                tree_cache, SWAChunkCache
+            ), "SWARadixCache or SWAChunkCache is required for SWATokenToKVPoolAllocator"
+            is_hybrid = True
+
         return cls(
             reqs=reqs,
             req_to_token_pool=req_to_token_pool,
             token_to_kv_pool_allocator=token_to_kv_pool_allocator,
             tree_cache=tree_cache,
+            is_hybrid=is_hybrid,
             model_config=model_config,
             enable_overlap=enable_overlap,
             return_logprob=return_logprob,
@@ -953,9 +969,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         return req_pool_indices
 
     def alloc_token_slots(self, num_tokens: int, backup_state: bool = False):
-        if self.token_to_kv_pool_allocator.available_size() < num_tokens:
-            if self.tree_cache is not None:
-                self.tree_cache.evict(num_tokens)
+        self._evict_tree_cache_if_needed(num_tokens)
 
         if backup_state:
             state = self.token_to_kv_pool_allocator.backup_state()
@@ -966,7 +980,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             error_msg = (
                 f"{phase_str} out of memory. Try to lower your batch size.\n"
                 f"Try to allocate {num_tokens} tokens.\n"
-                f"Available tokens: {self.token_to_kv_pool_allocator.available_size() + self.tree_cache.evictable_size()}\n"
+                f"{self._available_and_evictable_str()}"
             )
             logger.error(error_msg)
             if self.tree_cache is not None:
@@ -986,16 +1000,11 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         extend_num_tokens: int,
         backup_state: bool = False,
     ):
-        if (
-            self.token_to_kv_pool_allocator.available_size()
-            < extend_num_tokens
-            + len(seq_lens) * self.token_to_kv_pool_allocator.page_size
-        ):
-            if self.tree_cache is not None:
-                self.tree_cache.evict(
+        num_tokens = (
             extend_num_tokens
-                    + len(seq_lens) * self.token_to_kv_pool_allocator.page_size,
+            + len(seq_lens) * self.token_to_kv_pool_allocator.page_size
         )
+        self._evict_tree_cache_if_needed(num_tokens)
 
         if backup_state:
             state = self.token_to_kv_pool_allocator.backup_state()
@@ -1007,9 +1016,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             error_msg = (
                 f"Prefill out of memory. Try to lower your batch size.\n"
                 f"Try to allocate {extend_num_tokens} tokens.\n"
-                f"Available tokens: {self.token_to_kv_pool_allocator.available_size() + self.tree_cache.evictable_size()}\n"
-                f"{self.token_to_kv_pool_allocator.available_size()=}\n"
-                f"{self.tree_cache.evictable_size()=}\n"
+                f"{self._available_and_evictable_str()}"
             )
             logger.error(error_msg)
             raise RuntimeError(error_msg)
@@ -1025,14 +1032,9 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         last_loc: torch.Tensor,
         backup_state: bool = False,
     ):
-        if self.tree_cache is not None:
-            if (
-                self.token_to_kv_pool_allocator.available_size()
-                < len(seq_lens) * self.token_to_kv_pool_allocator.page_size
-            ):
-                self.tree_cache.evict(
-                    len(seq_lens) * self.token_to_kv_pool_allocator.page_size,
-                )
+        num_tokens = len(seq_lens) * self.token_to_kv_pool_allocator.page_size
+
+        self._evict_tree_cache_if_needed(num_tokens)
 
         if backup_state:
             state = self.token_to_kv_pool_allocator.backup_state()
@@ -1042,9 +1044,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             error_msg = (
                 f"Decode out of memory. Try to lower your batch size.\n"
                 f"Try to allocate {len(seq_lens)} tokens.\n"
-                f"Available tokens: {self.token_to_kv_pool_allocator.available_size() + self.tree_cache.evictable_size()}\n"
-                f"{self.token_to_kv_pool_allocator.available_size()=}\n"
-                f"{self.tree_cache.evictable_size()=}\n"
+                f"{self._available_and_evictable_str()}"
             )
             logger.error(error_msg)
             raise RuntimeError(error_msg)
@@ -1181,7 +1181,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
                     (req.req_pool_idx, slice(0, pre_len)), req.prefix_indices
                 )
                 if isinstance(self.tree_cache, SWAChunkCache):
-                    self.tree_cache.evict(
+                    self.tree_cache.evict_swa(
                         req, pre_len, self.model_config.attention_chunk_size
                     )
 
@@ -1371,17 +1371,14 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         )
 
     def check_decode_mem(self, buf_multiplier=1):
-        tokens_required = (
+        num_tokens = (
             self.new_page_count_next_decode()
             * buf_multiplier
             * self.token_to_kv_pool_allocator.page_size
         )
-        if self.token_to_kv_pool_allocator.available_size() >= tokens_required:
-            return True
-
-        self.tree_cache.evict(tokens_required)
 
-        return self.token_to_kv_pool_allocator.available_size() >= tokens_required
+        self._evict_tree_cache_if_needed(num_tokens)
+        return self._is_available_size_sufficient(num_tokens)
 
     def retract_decode(self, server_args: ServerArgs):
         """Retract the decoding requests when there is not enough memory."""
@@ -1414,19 +1411,38 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
                 num_reqs * global_config.retract_decode_steps + headroom_for_spec_decode
             )
 
+        def _get_available_size():
+            if self.is_hybrid:
+                return min(
+                    self.token_to_kv_pool_allocator.full_available_size(),
+                    self.token_to_kv_pool_allocator.swa_available_size(),
+                )
+            else:
+                return self.token_to_kv_pool_allocator.available_size()
+
         retracted_reqs = []
         seq_lens_cpu = self.seq_lens.cpu().numpy()
         first_iter = True
         while (
-            self.token_to_kv_pool_allocator.available_size()
-            < get_required_tokens(len(sorted_indices))
+            _get_available_size() < get_required_tokens(len(sorted_indices))
             or first_iter
         ):
             if len(sorted_indices) == 1:
                 # Corner case: only one request left
+                if self.is_hybrid:
+                    full_available_size = (
+                        self.token_to_kv_pool_allocator.full_available_size()
+                    )
+                    swa_available_size = (
+                        self.token_to_kv_pool_allocator.swa_available_size()
+                    )
+                    assert (
+                        full_available_size > 0 and swa_available_size > 0
+                    ), f"No space left for only one request in SWA mode {full_available_size=}, {swa_available_size=}"
+                else:
                     assert (
                         self.token_to_kv_pool_allocator.available_size() > 0
-                ), "No space left for only one request"
+                    ), f"No space left for only one request, {self.token_to_kv_pool_allocator.available_size()=}"
                 break
 
             first_iter = False
@@ -1458,15 +1474,14 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
                 self.req_to_token_pool.free(req.req_pool_idx)
 
                 # release the last node
+                if self.is_hybrid:
+                    self.tree_cache.dec_lock_ref(req.last_node, req.swa_uuid_for_lock)
+                else:
                     self.tree_cache.dec_lock_ref(req.last_node)
 
                 # NOTE(lsyin): we should use the newly evictable memory instantly.
-                residual_size = (
-                    len(sorted_indices) * global_config.retract_decode_steps
-                    - self.token_to_kv_pool_allocator.available_size()
-                )
-                residual_size = max(0, residual_size)
-                self.tree_cache.evict(residual_size)
+                num_tokens = len(sorted_indices) * global_config.retract_decode_steps
+                self._evict_tree_cache_if_needed(num_tokens)
 
             req.reset_for_retract()
 
@@ -1559,7 +1574,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         # free memory
         if isinstance(self.tree_cache, SWAChunkCache):
             for req in self.reqs:
-                self.tree_cache.evict(
+                self.tree_cache.evict_swa(
                     req, req.seqlen - 1, self.model_config.attention_chunk_size
                 )
 
@@ -1778,6 +1793,53 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
             is_extend_in_batch=self.is_extend_in_batch,
         )
 
+    def _evict_tree_cache_if_needed(
+        self,
+        num_tokens: int,
+    ) -> None:
+        if isinstance(self.tree_cache, SWAChunkCache):
+            return
+
+        if self.is_hybrid:
+            full_available_size = self.token_to_kv_pool_allocator.full_available_size()
+            swa_available_size = self.token_to_kv_pool_allocator.swa_available_size()
+
+            if full_available_size < num_tokens or swa_available_size < num_tokens:
+                if self.tree_cache is not None:
+                    full_num_tokens = max(0, num_tokens - full_available_size)
+                    swa_num_tokens = max(0, num_tokens - swa_available_size)
+                    self.tree_cache.evict(full_num_tokens, swa_num_tokens)
+        else:
+            if self.token_to_kv_pool_allocator.available_size() < num_tokens:
+                if self.tree_cache is not None:
+                    self.tree_cache.evict(num_tokens)
+
+    def _is_available_size_sufficient(self, num_tokens: int) -> bool:
+        if self.is_hybrid:
+            return (
+                self.token_to_kv_pool_allocator.full_available_size() >= num_tokens
+                and self.token_to_kv_pool_allocator.swa_available_size() >= num_tokens
+            )
+        else:
+            return self.token_to_kv_pool_allocator.available_size() >= num_tokens
+
+    def _available_and_evictable_str(self) -> str:
+        if self.is_hybrid:
+            full_available_size = self.token_to_kv_pool_allocator.full_available_size()
+            swa_available_size = self.token_to_kv_pool_allocator.swa_available_size()
+            full_evictable_size = self.tree_cache.full_evictable_size()
+            swa_evictable_size = self.tree_cache.swa_evictable_size()
+            return (
+                f"Available full tokens: {full_available_size + full_evictable_size} ({full_available_size=} + {full_evictable_size=})\n"
+                f"Available swa tokens: {swa_available_size + swa_evictable_size} ({swa_available_size=} + {swa_evictable_size=})\n"
+                f"Full LRU list evictable size: {self.tree_cache.full_lru_list_evictable_size()}\n"
+                f"SWA LRU list evictable size: {self.tree_cache.swa_lru_list_evictable_size()}\n"
+            )
+        else:
+            available_size = self.token_to_kv_pool_allocator.available_size()
+            evictable_size = self.tree_cache.evictable_size()
+            return f"Available tokens: {available_size + evictable_size} ({available_size=} + {evictable_size=})\n"
+
     def __str__(self):
         return (
             f"ScheduleBatch(forward_mode={self.forward_mode.name if self.forward_mode else 'None'}, "

python/sglang/srt/managers/schedule_policy.py

@@ -25,6 +25,7 @@ from typing import TYPE_CHECKING, Dict, List, Optional, Set, Union
 import torch
 
 from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
+from sglang.srt.mem_cache.allocator import SWATokenToKVPoolAllocator
 from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
 from sglang.srt.mem_cache.radix_cache import RadixCache, TreeNode
 
@@ -311,22 +312,44 @@ class PrefillAdder:
                 ]
             )
 
+        self.is_hybrid = isinstance(
+            self.token_to_kv_pool_allocator, SWATokenToKVPoolAllocator
+        )
+
     @property
     def rem_total_tokens(self):
-        return (
+        if self.is_hybrid:
+            available_and_evictable = min(
+                self.token_to_kv_pool_allocator.full_available_size()
+                + self.tree_cache.full_evictable_size(),
+                self.token_to_kv_pool_allocator.swa_available_size()
+                + self.tree_cache.swa_evictable_size(),
+            )
+        else:
+            available_and_evictable = (
                 self.token_to_kv_pool_allocator.available_size()
                 + self.tree_cache.evictable_size()
-            - self.rem_total_token_offset
             )
 
+        return available_and_evictable - self.rem_total_token_offset
+
     @property
     def cur_rem_tokens(self):
-        return (
+        if self.is_hybrid:
+            available_and_evictable = min(
+                self.token_to_kv_pool_allocator.full_available_size()
+                + self.tree_cache.full_evictable_size(),
+                self.token_to_kv_pool_allocator.swa_available_size()
+                + self.tree_cache.swa_evictable_size(),
+            )
+        else:
+            available_and_evictable = (
                 self.token_to_kv_pool_allocator.available_size()
                 + self.tree_cache.evictable_size()
-            - self.cur_rem_token_offset
             )
 
+        return available_and_evictable - self.cur_rem_token_offset
+
     def ceil_paged_tokens(self, tokens: int) -> int:
         return -(-tokens // self.page_size) * self.page_size
 
@@ -376,6 +399,13 @@ class PrefillAdder:
 
     @contextmanager
     def _lock_node(self, last_node: TreeNode):
+        if self.is_hybrid:
+            try:
+                swa_uuid_for_lock = self.tree_cache.inc_lock_ref(last_node)
+                yield None
+            finally:
+                self.tree_cache.dec_lock_ref(last_node, swa_uuid_for_lock)
+        else:
             try:
                 self.tree_cache.inc_lock_ref(last_node)
                 yield None
@@ -422,6 +452,9 @@ class PrefillAdder:
         else:
             add_req_state(req, insert_sort=True)
 
+        if not self.is_hybrid:
+            # Skip this logic for swa. The SWA has different memory management, and
+            # this mechanism is underestimating the memory usage.
             cur_rem_tokens = self.cur_rem_tokens - len(req.origin_input_ids)
             tokens_freed = 0
             for i, (tokens_left, tokens_occupied) in enumerate(self.req_states):
@@ -499,6 +532,10 @@ class PrefillAdder:
             if self.rem_chunk_tokens is None or input_tokens <= self.rem_chunk_tokens:
                 # Non-chunked prefill
                 self.can_run_list.append(req)
+                if self.is_hybrid:
+                    swa_uuid_for_lock = self.tree_cache.inc_lock_ref(req.last_node)
+                    req.swa_uuid_for_lock = swa_uuid_for_lock
+                else:
                     self.tree_cache.inc_lock_ref(req.last_node)
                 self._update_prefill_budget(
                     prefix_len,
@@ -520,6 +557,10 @@ class PrefillAdder:
 
                 self.can_run_list.append(req)
                 self.new_chunked_req = req
+                if self.is_hybrid:
+                    swa_uuid_for_lock = self.tree_cache.inc_lock_ref(req.last_node)
+                    req.swa_uuid_for_lock = swa_uuid_for_lock
+                else:
                     self.tree_cache.inc_lock_ref(req.last_node)
                 self._update_prefill_budget(prefix_len, trunc_len, 0)
 

python/sglang/srt/managers/scheduler.py

@@ -129,10 +129,10 @@ from sglang.srt.managers.session_controller import Session
 from sglang.srt.managers.tp_worker import TpModelWorker
 from sglang.srt.managers.tp_worker_overlap_thread import TpModelWorkerClient
 from sglang.srt.managers.utils import validate_input_length
-from sglang.srt.mem_cache.allocator import SWATokenToKVPoolAllocator
 from sglang.srt.mem_cache.chunk_cache import ChunkCache, SWAChunkCache
 from sglang.srt.mem_cache.hiradix_cache import HiRadixCache
 from sglang.srt.mem_cache.radix_cache import RadixCache
+from sglang.srt.mem_cache.swa_radix_cache import SWARadixCache
 from sglang.srt.metrics.collector import SchedulerMetricsCollector, SchedulerStats
 from sglang.srt.model_executor.forward_batch_info import ForwardMode, PPProxyTensors
 from sglang.srt.reasoning_parser import ReasoningParser
@@ -390,6 +390,14 @@ class Scheduler(
         global_server_args_dict.update(worker_global_server_args_dict)
         set_random_seed(self.random_seed)
 
+        # Hybrid
+        self.is_hybrid = self.tp_worker.is_hybrid
+        if self.is_hybrid:
+            self.sliding_window_size = self.tp_worker.sliding_window_size
+            self.full_tokens_per_layer, self.swa_tokens_per_layer = (
+                self.tp_worker.get_tokens_per_layer_info()
+            )
+
         # Print debug info
         if tp_rank == 0:
             avail_mem = get_available_gpu_memory(
@@ -570,7 +578,7 @@ class Scheduler(
             server_args.chunked_prefill_size is not None
             and server_args.disable_radix_cache
         ):
-            if self.model_config.is_hybrid:
+            if self.is_hybrid:
                 ChunkCacheClass = SWAChunkCache
             else:
                 ChunkCacheClass = ChunkCache
@@ -603,6 +611,17 @@ class Scheduler(
                 self.tp_worker.register_hicache_layer_transfer_counter(
                     self.tree_cache.cache_controller.layer_done_counter
                 )
+            elif self.is_hybrid:
+                assert (
+                    self.server_args.disaggregation_mode == "null"
+                ), "Hybrid mode does not support disaggregation yet"
+                self.tree_cache = SWARadixCache(
+                    req_to_token_pool=self.req_to_token_pool,
+                    token_to_kv_pool_allocator=self.token_to_kv_pool_allocator,
+                    sliding_window_size=self.sliding_window_size,
+                    page_size=self.page_size,
+                    disable=server_args.disable_radix_cache,
+                )
 
             else:
                 self.tree_cache = RadixCache(
@@ -774,6 +793,7 @@ class Scheduler(
             else:
                 # When the server is idle, do self-check and re-init some states
                 self.check_memory()
+                self.check_tree_cache()
                 self.new_token_ratio = self.init_new_token_ratio
                 self.maybe_sleep_on_idle()
 
@@ -819,6 +839,7 @@ class Scheduler(
             elif batch is None:
                 # When the server is idle, do self-check and re-init some states
                 self.check_memory()
+                self.check_tree_cache()
                 self.new_token_ratio = self.init_new_token_ratio
                 self.maybe_sleep_on_idle()
 
@@ -955,6 +976,7 @@ class Scheduler(
             # When the server is idle, self-check and re-init some states
             if server_is_idle:
                 self.check_memory()
+                self.check_tree_cache()
                 self.new_token_ratio = self.init_new_token_ratio
                 self.maybe_sleep_on_idle()
 
@@ -1306,9 +1328,26 @@ class Scheduler(
         self.last_input_throughput = self.last_prefill_tokens / gap_latency
         self.last_prefill_tokens = adder.log_input_tokens
 
-        usage_msg, num_used = self.token_to_kv_pool_allocator.log_usage(
-            self.tree_cache.evictable_size()
+        if self.is_hybrid:
+            (
+                full_num_used,
+                swa_num_used,
+                full_token_usage,
+                swa_token_usage,
+                _,
+                _,
+                _,
+                _,
+            ) = self._get_swa_token_info()
+            num_used = max(full_num_used, swa_num_used)
+            token_usage = max(full_token_usage, swa_token_usage)
+            token_msg = (
+                f"full token usage: {full_token_usage:.2f}, "
+                f"swa token usage: {swa_token_usage:.2f}, "
             )
+        else:
+            num_used, token_usage, _, _ = self._get_token_info()
+            token_msg = f"token usage: {token_usage:.2f}, "
 
         num_new_seq = len(can_run_list)
         f = (
@@ -1316,7 +1355,7 @@ class Scheduler(
             f"#new-seq: {num_new_seq}, "
             f"#new-token: {adder.log_input_tokens}, "
             f"#cached-token: {adder.log_hit_tokens}, "
-            f"{usage_msg}"
+            f"{token_msg}"
         )
 
         if self.disaggregation_mode == DisaggregationMode.PREFILL:
@@ -1338,7 +1377,7 @@ class Scheduler(
             )
             self.stats.num_running_reqs = running_bs
             self.stats.num_used_tokens = num_used
-            self.stats.token_usage = round(num_used / self.max_total_num_tokens, 2)
+            self.stats.token_usage = round(token_usage, 2)
             self.stats.num_queue_reqs = len(self.waiting_queue)
             self.stats.cache_hit_rate = cache_hit_rate
 
@@ -1361,16 +1400,35 @@ class Scheduler(
         self.last_gen_throughput = self.num_generated_tokens / gap_latency
         self.num_generated_tokens = 0
         num_running_reqs = len(batch.reqs)
-        usage_msg, num_used = self.token_to_kv_pool_allocator.log_usage(
-            self.tree_cache.evictable_size()
+        if self.is_hybrid:
+            (
+                full_num_used,
+                swa_num_used,
+                full_token_usage,
+                swa_token_usage,
+                _,
+                _,
+                _,
+                _,
+            ) = self._get_swa_token_info()
+            num_used = max(full_num_used, swa_num_used)
+            token_usage = max(full_token_usage, swa_token_usage)
+            token_msg = (
+                f"#full token: {full_num_used}, "
+                f"full token usage: {full_token_usage:.2f}, "
+                f"#swa token: {swa_num_used}, "
+                f"swa token usage: {swa_token_usage:.2f}, "
             )
+        else:
+            num_used, token_usage, _, _ = self._get_token_info()
+            token_msg = f"#token: {num_used}, " f"token usage: {token_usage:.2f}, "
 
         if RECORD_STEP_TIME:
             self.step_time_dict[num_running_reqs].append(
                 gap_latency / self.server_args.decode_log_interval
             )
 
-        msg = f"Decode batch. " f"#running-req: {num_running_reqs}, " f"{usage_msg}"
+        msg = f"Decode batch. #running-req: {num_running_reqs}, {token_msg}"
 
         if self.spec_algorithm.is_none():
             spec_accept_length = 0
@@ -1398,7 +1456,7 @@ class Scheduler(
         if self.enable_metrics:
             self.stats.num_running_reqs = num_running_reqs
             self.stats.num_used_tokens = num_used
-            self.stats.token_usage = num_used / self.max_total_num_tokens
+            self.stats.token_usage = round(token_usage, 2)
             self.stats.cache_hit_rate = 0.0
             self.stats.gen_throughput = self.last_gen_throughput
             self.stats.num_queue_reqs = len(self.waiting_queue)
@@ -1409,24 +1467,34 @@ class Scheduler(
         self._publish_kv_events()
 
     def check_memory(self):
-        if isinstance(self.token_to_kv_pool_allocator, SWATokenToKVPoolAllocator):
-            available_token_size = self.token_to_kv_pool_allocator.full_available_size()
+        if self.is_hybrid:
+            (
+                full_num_used,
+                swa_num_used,
+                _,
+                _,
+                full_available_size,
+                full_evictable_size,
+                swa_available_size,
+                swa_evictable_size,
+            ) = self._get_swa_token_info()
+            memory_leak = full_num_used != 0 or swa_num_used != 0
+            token_msg = (
+                f"{self.full_tokens_per_layer=}, {full_available_size=}, {full_evictable_size=}, {self.tree_cache.full_protected_size()=}\n"
+                f"{self.swa_tokens_per_layer=}, {swa_available_size=}, {swa_evictable_size=}, {self.tree_cache.swa_protected_size()=}\n"
+            )
         else:
-            available_token_size = self.token_to_kv_pool_allocator.available_size()
-        available_size = available_token_size + self.tree_cache.evictable_size()
+            _, _, available_size, evictable_size = self._get_token_info()
             protected_size = self.tree_cache.protected_size()
-        memory_leak = available_size != (
+            memory_leak = (available_size + evictable_size) != (
                 self.max_total_num_tokens
                 if not self.enable_hierarchical_cache
                 else self.max_total_num_tokens - protected_size
             )
+            token_msg = f"{self.max_total_num_tokens=}, {available_size=}, {evictable_size=}, {protected_size=}\n"
+
         if memory_leak:
-            msg = (
-                "token_to_kv_pool_allocator memory leak detected! "
-                f"{available_size=}, {protected_size=}, {self.max_total_num_tokens=}\n"
-                f"{available_token_size=}\n"
-                f"{self.tree_cache.evictable_size()=}\n"
-            )
+            msg = "token_to_kv_pool_allocator memory leak detected! " f"{token_msg}"
             raise ValueError(msg)
 
         if self.disaggregation_mode == DisaggregationMode.DECODE:
@@ -1450,20 +1518,66 @@ class Scheduler(
             and time.perf_counter() > self.metrics_collector.last_log_time + 30
         ):
             # During idle time, also collect metrics every 30 seconds.
-            num_used = self.max_total_num_tokens - (
-                self.token_to_kv_pool_allocator.available_size()
-                + self.tree_cache.evictable_size()
-            )
+            if self.is_hybrid:
+                (
+                    full_num_used,
+                    swa_num_used,
+                    full_token_usage,
+                    swa_token_usage,
+                    _,
+                    _,
+                    _,
+                    _,
+                ) = self._get_swa_token_info()
+                num_used = max(full_num_used, swa_num_used)
+                token_usage = max(full_token_usage, swa_token_usage)
+            else:
+                num_used, token_usage, _, _ = self._get_token_info()
             num_running_reqs = len(self.running_batch.reqs)
             self.stats.num_running_reqs = num_running_reqs
             self.stats.num_used_tokens = num_used
-            self.stats.token_usage = num_used / self.max_total_num_tokens
+            self.stats.token_usage = round(token_usage, 2)
             self.stats.gen_throughput = 0
             self.stats.num_queue_reqs = len(self.waiting_queue)
             self.stats.num_grammar_queue_reqs = len(self.grammar_queue)
             self.metrics_collector.log_stats(self.stats)
         self._publish_kv_events()
 
+    def check_tree_cache(self):
+        if self.is_hybrid and isinstance(self.tree_cache, SWARadixCache):
+            self.tree_cache.sanity_check()
+
+    def _get_token_info(self):
+        available_size = self.token_to_kv_pool_allocator.available_size()
+        evictable_size = self.tree_cache.evictable_size()
+        num_used = self.max_total_num_tokens - (available_size + evictable_size)
+        token_usage = num_used / self.max_total_num_tokens
+        return num_used, token_usage, available_size, evictable_size
+
+    def _get_swa_token_info(self):
+        full_available_size = self.token_to_kv_pool_allocator.full_available_size()
+        full_evictable_size = self.tree_cache.full_evictable_size()
+        swa_available_size = self.token_to_kv_pool_allocator.swa_available_size()
+        swa_evictable_size = self.tree_cache.swa_evictable_size()
+        full_num_used = self.full_tokens_per_layer - (
+            full_available_size + full_evictable_size
+        )
+        swa_num_used = self.swa_tokens_per_layer - (
+            swa_available_size + swa_evictable_size
+        )
+        full_token_usage = full_num_used / self.full_tokens_per_layer
+        swa_token_usage = swa_num_used / self.swa_tokens_per_layer
+        return (
+            full_num_used,
+            swa_num_used,
+            full_token_usage,
+            swa_token_usage,
+            full_available_size,
+            full_evictable_size,
+            swa_available_size,
+            swa_evictable_size,
+        )
+
     def get_next_batch_to_run(self) -> Optional[ScheduleBatch]:
         # Merge the prefill batch into the running batch
         chunked_req_to_exclude = set()
@@ -2042,11 +2156,30 @@ class Scheduler(
 
         if not disable_request_logging():
             # Print batch size and memory pool info to check whether there are de-sync issues.
+            if self.is_hybrid:
+                (
+                    _,
+                    _,
+                    _,
+                    _,
+                    full_available_size,
+                    full_evictable_size,
+                    swa_available_size,
+                    swa_evictable_size,
+                ) = self._get_swa_token_info()
+                info_msg = (
+                    f"{full_available_size=}, "
+                    f"{full_evictable_size=}, "
+                    f"{swa_available_size=}, "
+                    f"{swa_evictable_size=}, "
+                )
+            else:
+                _, _, available_size, evictable_size = self._get_token_info()
+                info_msg = f"{available_size=}, " f"{evictable_size=}, "
             logger.error(
                 f"{self.cur_batch.batch_size()=}, "
                 f"{self.cur_batch.reqs=}, "
-                f"{self.token_to_kv_pool_allocator.available_size()=}, "
-                f"{self.tree_cache.evictable_size()=}, "
+                f"{info_msg}"
             )
 
         pyspy_dump_schedulers()
@@ -2101,6 +2234,19 @@ class Scheduler(
 
     def get_load(self):
         # TODO(lsyin): use dynamically maintained num_waiting_tokens
+        if self.is_hybrid:
+            load_full = (
+                self.full_tokens_per_layer
+                - self.token_to_kv_pool_allocator.full_available_size()
+                - self.tree_cache.full_evictable_size()
+            )
+            load_swa = (
+                self.swa_tokens_per_layer
+                - self.token_to_kv_pool_allocator.swa_available_size()
+                - self.tree_cache.swa_evictable_size()
+            )
+            load = max(load_full, load_swa)
+        else:
             load = (
                 self.max_total_num_tokens
                 - self.token_to_kv_pool_allocator.available_size()

python/sglang/srt/managers/tp_worker.py

@@ -174,6 +174,20 @@ class TpModelWorker:
             self.model_runner.token_to_kv_pool.size,
         )
 
+    @property
+    def sliding_window_size(self) -> Optional[int]:
+        return self.model_runner.sliding_window_size
+
+    @property
+    def is_hybrid(self) -> bool:
+        return self.model_runner.is_hybrid is not None
+
+    def get_tokens_per_layer_info(self):
+        return (
+            self.model_runner.full_max_total_num_tokens,
+            self.model_runner.swa_max_total_num_tokens,
+        )
+
     def get_pad_input_ids_func(self):
         return getattr(self.model_runner.model, "pad_input_ids", None)
 

python/sglang/srt/managers/tp_worker_overlap_thread.py

@@ -102,6 +102,17 @@ class TpModelWorkerClient:
     def get_worker_info(self):
         return self.worker.get_worker_info()
 
+    def get_tokens_per_layer_info(self):
+        return self.worker.get_tokens_per_layer_info()
+
+    @property
+    def sliding_window_size(self) -> Optional[int]:
+        return self.worker.sliding_window_size
+
+    @property
+    def is_hybrid(self) -> bool:
+        return self.worker.is_hybrid
+
     def get_pad_input_ids_func(self):
         return self.worker.get_pad_input_ids_func()
 

python/sglang/srt/mem_cache/allocator.py

@@ -57,11 +57,6 @@ class BaseTokenToKVPoolAllocator(abc.ABC):
     def debug_print(self) -> str:
         return ""
 
-    def log_usage(self, evictable_size: int = 0):
-        num_used = self.size - (self.available_size() + evictable_size)
-        msg = f"#token: {num_used}, token usage: {num_used / self.size:.2f}, "
-        return msg, num_used
-
     def available_size(self):
         return len(self.free_pages) * self.page_size
 
@@ -190,7 +185,7 @@ class SWATokenToKVPoolAllocator(BaseTokenToKVPoolAllocator):
         self._kvcache.full_to_swa_index_mapping = self.full_to_swa_index_mapping
 
     def available_size(self):
-        return min(self.full_available_size(), self.swa_available_size())
+        raise NotImplementedError()
 
     def full_available_size(self):
         return self.full_attn_allocator.available_size()
@@ -214,16 +209,6 @@ class SWATokenToKVPoolAllocator(BaseTokenToKVPoolAllocator):
         )
         return msg
 
-    def log_usage(self, swa_evictable_size: int = 0, full_evictable_size: int = 0):
-        used_full = self.size_full - (self.full_available_size() + full_evictable_size)
-        used_swa = self.size_swa - (self.swa_available_size() + swa_evictable_size)
-        msg = (
-            f"#token: full={used_full}, swa={used_swa}, "
-            f"token usage: full={used_full / self.size_full:.2f}, "
-            f"swa={used_swa / self.size_swa:.2f}, "
-        )
-        return msg, used_full
-
     def get_kvcache(self):
         return self._kvcache
 

python/sglang/srt/mem_cache/base_prefix_cache.py

 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any, List, NamedTuple, Tuple
+from typing import TYPE_CHECKING, Any, List, NamedTuple, Optional, Tuple
 
 import torch
 
@@ -56,15 +56,27 @@ class BasePrefixCache(ABC):
         pass
 
     @abstractmethod
-    def dec_lock_ref(self, node: Any):
+    def dec_lock_ref(self, node: Any, swa_uuid_for_lock: Optional[str] = None):
         pass
 
     def evictable_size(self):
         return 0
 
+    def full_evictable_size(self):
+        return 0
+
+    def swa_evictable_size(self):
+        return 0
+
     def protected_size(self):
         return 0
 
+    def full_protected_size(self):
+        return 0
+
+    def swa_protected_size(self):
+        return 0
+
     def total_size(self):
         raise NotImplementedError()
 

python/sglang/srt/mem_cache/chunk_cache.py

@@ -61,7 +61,7 @@ class ChunkCache(BasePrefixCache):
     def inc_lock_ref(self, node: Any):
         return 0
 
-    def dec_lock_ref(self, node: Any):
+    def dec_lock_ref(self, node: Any, swa_uuid_for_lock: Optional[str] = None):
         return 0
 
     def pretty_print(self):
@@ -80,7 +80,7 @@ class SWAChunkCache(ChunkCache):
         super().__init__(req_to_token_pool, token_to_kv_pool_allocator, page_size)
         assert isinstance(token_to_kv_pool_allocator, SWATokenToKVPoolAllocator)
 
-    def evict(
+    def evict_swa(
         self,
         req: Req,
         prelen: int,
@@ -95,3 +95,6 @@ class SWAChunkCache(ChunkCache):
             ]
             self.token_to_kv_pool_allocator.free_swa(free_slots)
             req.evicted_seqlen_local = new_evicted_seqlen_local
+
+    def evict(self, num_tokens: int):
+        pass

python/sglang/srt/model_executor/model_runner.py

@@ -275,6 +275,15 @@ class ModelRunner:
         self.sampler = Sampler()
         self.load_model()
 
+        if (
+            not self.server_args.disable_hybrid_swa_memory
+            and self.sliding_window_size is not None
+            and self.sliding_window_size > 0
+        ):
+            architectures = self.model_config.hf_config.architectures
+            if architectures and not any("Llama4" in arch for arch in architectures):
+                self.is_hybrid = self.model_config.is_hybrid = True
+
         self.start_layer = getattr(self.model, "start_layer", 0)
         self.end_layer = getattr(
             self.model, "end_layer", self.model_config.num_hidden_layers
@@ -471,10 +480,6 @@ class ModelRunner:
             if self.model_config.context_len > 8192:
                 self.mem_fraction_static *= 0.85
 
-        if self.is_hybrid and not server_args.disable_radix_cache:
-            logger.info("Automatically disable radix cache for hybrid cache.")
-            server_args.disable_radix_cache = True
-
     def init_torch_distributed(self):
         logger.info("Init torch distributed begin.")
 
@@ -645,11 +650,15 @@ class ModelRunner:
                 )
 
         # Parse other args
-        self.sliding_window_size = (
-            self.model.get_attention_sliding_window_size()
-            if hasattr(self.model, "get_attention_sliding_window_size")
-            else None
+        self.sliding_window_size = None
+        if hasattr(self.model, "get_attention_sliding_window_size"):
+            self.sliding_window_size = self.model.get_attention_sliding_window_size()
+        elif self.model_config.attention_chunk_size is not None:
+            self.sliding_window_size = self.model_config.attention_chunk_size
+            print(
+                f"Setting sliding_window_size to be attention_chunk_size: {self.sliding_window_size}"
             )
+
         self.dtype = self.model_config.dtype
 
         after_avail_memory = get_available_gpu_memory(self.device, self.gpu_id)
@@ -992,8 +1001,53 @@ class ModelRunner:
             )
             self.max_total_num_tokens = self.full_max_total_num_tokens
         else:
-            raise ValueError(
-                f"Unsupported model for hybrid cache: {self.model_config.hf_config.architectures}."
+            assert self.sliding_window_size is not None and self.sliding_window_size > 0
+            full_attention_layer_ids = []
+            swa_attention_layer_ids = []
+
+            try:
+                layers = self.model.model.layers
+            except:
+                try:
+                    layers = self.model.language_model.model.layers
+                except:
+                    self.is_hybrid = False
+                    return
+
+            for layer in layers:
+                if (
+                    layer.self_attn.attn.sliding_window_size is None
+                    or layer.self_attn.attn.sliding_window_size == -1
+                ):
+                    full_attention_layer_ids.append(layer.layer_id)
+                else:
+                    swa_attention_layer_ids.append(layer.layer_id)
+            self.model_config.swa_attention_layer_ids = swa_attention_layer_ids
+            self.model_config.full_attention_layer_ids = full_attention_layer_ids
+
+            # Algorithm:
+            # Existing max_total_num_tokens is per layer and assume all layers have the same number of tokens.
+            # - Find total # of tokens available across layers.
+            # - Calculate full_max_total_num_tokens and swa_max_total_num_tokens based on the given swa_full_tokens_ratio.
+            total_tokens = (
+                self.max_total_num_tokens * self.model_config.num_hidden_layers
+            )
+            full_layers_num = len(full_attention_layer_ids)
+            swa_layers_num = len(swa_attention_layer_ids)
+            swa_full_tokens_ratio = self.server_args.swa_full_tokens_ratio
+
+            # Solve the equations:
+            # 1. swa_max_total_num_tokens * swa_layers_num + full_max_total_num_tokens * full_layers_num == total_tokens
+            # 2. full_max_total_num_tokens * swa_full_tokens_ratio == swa_max_total_num_tokens
+            denominator = swa_full_tokens_ratio * swa_layers_num + full_layers_num
+            self.full_max_total_num_tokens = int(total_tokens / denominator)
+            self.swa_max_total_num_tokens = int(
+                self.full_max_total_num_tokens * swa_full_tokens_ratio
+            )
+            self.max_total_num_tokens = self.full_max_total_num_tokens
+
+            logger.info(
+                f"Use Sliding window memory pool. full_layer_tokens={self.full_max_total_num_tokens}, swa_layer_tokens={self.swa_max_total_num_tokens}"
             )
 
     def init_memory_pool(
@@ -1072,7 +1126,6 @@ class ModelRunner:
             // self.server_args.page_size
             * self.server_args.page_size
         )
-
         # create token size for hybrid cache
         if self.is_hybrid:
             self.set_num_token_hybrid()

python/sglang/srt/models/gemma2.py

@@ -190,6 +190,7 @@ class Gemma2DecoderLayer(nn.Module):
         prefix: str = "",
     ) -> None:
         super().__init__()
+        self.layer_id = layer_id
         self.hidden_size = config.hidden_size
         self.self_attn = Gemma2Attention(
             layer_id=layer_id,

python/sglang/srt/server_args.py

@@ -63,6 +63,7 @@ class ServerArgs:
     enable_multimodal: Optional[bool] = None
     revision: Optional[str] = None
     hybrid_kvcache_ratio: Optional[float] = None
+    swa_full_tokens_ratio: float = 0.8
     impl: str = "auto"
 
     # Port for the HTTP server
@@ -225,6 +226,7 @@ class ServerArgs:
     enable_return_hidden_states: bool = False
     enable_triton_kernel_moe: bool = False
     warmups: Optional[str] = None
+    disable_hybrid_swa_memory: bool = False
 
     # Debug tensor dumps
     debug_tensor_dump_output_folder: Optional[str] = None
@@ -481,14 +483,22 @@ class ServerArgs:
 
             model_arch = get_model_arch(self)
 
-            # Auto set draft_model_path DeepSeek-V3/R1
             if model_arch == "DeepseekV3ForCausalLM":
+                # Auto set draft_model_path DeepSeek-V3/R1
                 if self.speculative_draft_model_path is None:
                     self.speculative_draft_model_path = self.model_path
                 else:
                     logger.warning(
                         "DeepSeek MTP does not require setting speculative_draft_model_path."
                     )
+            elif "Llama4" in model_arch:
+                # TODO: remove this after Llama4 supports in other backends
+                if self.attention_backend != "fa3":
+                    self.attention_backend = "fa3"
+                    logger.warning(
+                        "Llama4 requires using fa3 attention backend. "
+                        "Attention backend is automatically set to fa3."
+                    )
 
             # Auto choose parameters
             if self.speculative_num_steps is None:
@@ -852,6 +862,18 @@ class ServerArgs:
                 "(1.0 = pure hybrid: swa_size / full_size = local_attention_size / context_length)"
             ),
         )
+        parser.add_argument(
+            "--swa-full-tokens-ratio",
+            type=float,
+            default=ServerArgs.swa_full_tokens_ratio,
+            help="The ratio of SWA layer KV tokens / full layer KV tokens, regardless of the number of swa:full layers. It should be between 0 and 1. "
+            "E.g. 0.5 means if each swa layer has 50 tokens, then each full layer has 100 tokens.",
+        )
+        parser.add_argument(
+            "--disable-hybrid-swa-memory",
+            action="store_true",
+            help="Disable the hybrid SWA memory.",
+        )
 
         # Other runtime options
         parser.add_argument(
@@ -1730,10 +1752,6 @@ class ServerArgs:
                 else:
                     self.lora_paths[lora_path] = lora_path
 
-        model_arch = get_model_arch(self)
-        if "Llama4" in model_arch and self.hybrid_kvcache_ratio is not None:
-            assert self.attention_backend == "fa3"
-
 
 def prepare_server_args(argv: List[str]) -> ServerArgs:
     """

test/srt/test_swa_unittest.py

+import unittest
+
+import torch
+
+from sglang.srt.mem_cache.allocator import SWAKVPool, SWATokenToKVPoolAllocator
+from sglang.srt.mem_cache.memory_pool import ReqToTokenPool
+from sglang.srt.mem_cache.radix_cache import SWARadixCache
+
+
+class TestSWA(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        pass
+
+    @classmethod
+    def tearDownClass(cls):
+        pass
+
+    def test_swa_memory_pool(self):
+        size = 16
+        size_swa = 16
+        num_head = 8
+        head_dim = 128
+        num_layers = 48
+        global_interval = 4
+        dtype = torch.bfloat16
+        device = "cuda"
+        full_attention_layer_ids = [i for i in range(0, num_layers, global_interval)]
+        full_attention_layer_ids_set = set(full_attention_layer_ids)
+        swa_attention_layer_ids = [
+            i for i in range(num_layers) if i not in full_attention_layer_ids_set
+        ]
+        pool = SWAKVPool(
+            size,
+            size_swa,
+            dtype,
+            num_head,
+            head_dim,
+            swa_attention_layer_ids,
+            full_attention_layer_ids,
+            device,
+        )
+        alloc = SWATokenToKVPoolAllocator(size, size_swa, dtype, device, pool)
+        assert alloc.available_size() == size + size_swa
+        index = alloc.alloc(1)
+        assert alloc.available_size() == size_swa + size_swa - 2
+        alloc.free_swa(index)
+        result = alloc.translate_loc_from_full_to_swa(index)
+        print(result)
+
+    def test_swa_radix_cache_1(self):
+        # args
+        req_size = 10
+        max_context_len = 128
+        kv_size = 128
+        kv_size_swa = 64
+        sliding_window_size = 4
+        num_head = 8
+        head_dim = 128
+        num_layers = 48
+        global_interval = 4
+        dtype = torch.bfloat16
+        device = "cuda"
+        full_attention_layer_ids = [i for i in range(0, num_layers, global_interval)]
+        full_attention_layer_ids_set = set(full_attention_layer_ids)
+        swa_attention_layer_ids = [
+            i for i in range(num_layers) if i not in full_attention_layer_ids_set
+        ]
+        # setup req to token pool
+        req_to_token_pool = ReqToTokenPool(
+            size=req_size,
+            max_context_len=max_context_len,
+            device=device,
+            enable_memory_saver=False,
+        )
+        # setup kv pool
+        kv_pool = SWAKVPool(
+            kv_size,
+            kv_size_swa,
+            dtype,
+            num_head,
+            head_dim,
+            swa_attention_layer_ids,
+            full_attention_layer_ids,
+            device,
+        )
+        # setup token to kv pool allocator
+        allocator = SWATokenToKVPoolAllocator(
+            kv_size, kv_size_swa, dtype, device, kv_pool
+        )
+        # setup radix cache
+        tree = SWARadixCache(
+            req_to_token_pool=req_to_token_pool,
+            token_to_kv_pool_allocator=allocator,
+            sliding_window_size=sliding_window_size,
+            page_size=1,
+            disable=False,
+        )
+
+        # test
+        print(
+            f"[Start] allocator swa available size: {allocator.swa_available_size()}, full available size: {allocator.full_available_size()}"
+        )
+        req1_token_ids, req1_kv_indices = [1, 2, 3], allocator.alloc(3)
+        assert len(req1_token_ids) == len(req1_kv_indices)
+        print(
+            f"req1: inserting, req1_token_ids: {req1_token_ids}, req1_kv_indices: {req1_kv_indices}"
+        )
+        prefix_len = tree.insert(req1_token_ids, req1_kv_indices)
+        print(
+            f"req1: prefix_len: {prefix_len}, allocator swa available size: {allocator.swa_available_size()}, full available size: {allocator.full_available_size()}"
+        )
+        req2_token_ids, req2_kv_indices = [1, 2, 3, 4, 5, 6, 7], allocator.alloc(7)
+        assert len(req2_token_ids) == len(req2_kv_indices)
+        print(
+            f"req2: inserting, req2_token_ids: {req2_token_ids}, req2_kv_indices: {req2_kv_indices}"
+        )
+        prefix_len = tree.insert(req2_token_ids, req2_kv_indices)
+        print(
+            f"req2: prefix_len: {prefix_len}, allocator swa available size: {allocator.swa_available_size()}, full available size: {allocator.full_available_size()}"
+        )
+        req3_token_ids, req3_kv_indices = [10, 11, 12], allocator.alloc(3)
+        assert len(req3_token_ids) == len(req3_kv_indices)
+        print(
+            f"req3: inserting, req3_token_ids: {req3_token_ids}, req3_kv_indices: {req3_kv_indices}"
+        )
+        prefix_len = tree.insert(req3_token_ids, req3_kv_indices)
+        print(
+            f"req3: prefix_len: {prefix_len}, allocator swa available size: {allocator.swa_available_size()}, full available size: {allocator.full_available_size()}"
+        )
+        req4_token_ids, req4_kv_indices = [1, 2, 3, 4, 5, 60, 70], allocator.alloc(7)
+        assert len(req4_token_ids) == len(req4_kv_indices)
+        print(
+            f"req4: inserting, req4_token_ids: {req4_token_ids}, req4_kv_indices: {req4_kv_indices}"
+        )
+        prefix_len = tree.insert(req4_token_ids, req4_kv_indices)
+        print(
+            f"req4: prefix_len: {prefix_len}, allocator swa available size: {allocator.swa_available_size()}, full available size: {allocator.full_available_size()}"
+        )
+
+        tree.pretty_print()
+        full_num_tokens, swa_num_tokens = 1, 0
+        print(f"evicting {full_num_tokens} full token and {swa_num_tokens} swa token")
+        tree.evict(full_num_tokens=full_num_tokens, swa_num_tokens=swa_num_tokens)
+        tree.pretty_print()
+
+        full_num_tokens, swa_num_tokens = 0, 1
+        print(f"evicting {full_num_tokens} full token and {swa_num_tokens} swa token")
+        tree.evict(full_num_tokens=full_num_tokens, swa_num_tokens=swa_num_tokens)
+        tree.pretty_print()
+
+        full_num_tokens, swa_num_tokens = 1, 2
+        print(f"evicting {full_num_tokens} full token and {swa_num_tokens} swa token")
+        tree.evict(full_num_tokens=full_num_tokens, swa_num_tokens=swa_num_tokens)
+        tree.pretty_print()
+
+        req5_token_ids = [1, 2, 3, 4, 5]
+        kv_indices, last_node = tree.match_prefix(req5_token_ids)
+        print(
+            f"req5: token_ids: {req5_token_ids}, matched kv_indices: {kv_indices}, last_node.key: {last_node.key}"
+        )
+        assert len(kv_indices) == 0
+
+        req6_token_ids = [1, 2, 3, 4, 5, 60, 70]
+        kv_indices, last_node = tree.match_prefix(req6_token_ids)
+        print(
+            f"req6: token_ids: {req6_token_ids}, matched kv_indices: {kv_indices}, last_node.key: {last_node.key}"
+        )
+        assert len(kv_indices) == 7
+        assert len(last_node.key) == 2
+        assert last_node.key[0] == 60
+        assert last_node.key[1] == 70
+
+
+if __name__ == "__main__":
+    unittest.main()