EAGLE cache fix for SWARadixCache

python/sglang/srt/managers/scheduler.py

@@ -777,6 +777,7 @@ def init_memory_pool_and_cache(self):
                     sliding_window_size=self.sliding_window_size,
                     page_size=self.page_size,
                     disable=server_args.disable_radix_cache,
+                    is_eagle=self.spec_algorithm.is_eagle(),
                 )
             elif server_args.enable_lmcache:
                 from sglang.srt.mem_cache.storage.lmcache.lmc_radix_cache import (

python/sglang/srt/mem_cache/allocator.py

@@ -274,10 +274,15 @@ def free_swa(self, free_index: torch.Tensor):
         self.full_to_swa_index_mapping[free_index] = 0
 
     def backup_state(self):
-        raise NotImplementedError
+        return [
+            self.full_attn_allocator.backup_state(),
+            self.swa_attn_allocator.backup_state(),
+        ]
 
     def restore_state(self, state):
-        raise NotImplementedError
+        assert len(state) == 2
+        self.full_attn_allocator.restore_state(state[0])
+        self.swa_attn_allocator.restore_state(state[1])
 
     def clear(self):
         self.swa_attn_allocator.clear()

python/sglang/srt/mem_cache/memory_pool.py

@@ -747,6 +747,7 @@ def __init__(
         self,
         size: int,
         size_swa: int,
+        dtype: torch.dtype,
         swa_attention_layer_ids: List[int],
         full_attention_layer_ids: List[int],
         enable_kvcache_transpose: bool,
@@ -755,6 +756,7 @@ def __init__(
     ):
         self.size = size
         self.size_swa = size_swa
+        self.dtype = dtype
         self.swa_layer_nums = len(swa_attention_layer_ids)
         self.full_layer_nums = len(full_attention_layer_ids)
         kwargs["page_size"] = 1
@@ -764,11 +766,13 @@ def __init__(
 
         self.swa_kv_pool = token_to_kv_pool_class(
             size=size_swa,
+            dtype=dtype,
             layer_num=self.swa_layer_nums,
             **kwargs,
         )
         self.full_kv_pool = token_to_kv_pool_class(
             size=size,
+            dtype=dtype,
             layer_num=self.full_layer_nums,
             **kwargs,
         )

python/sglang/srt/mem_cache/swa_radix_cache.py

@@ -32,6 +32,7 @@
 from sglang.srt.mem_cache.memory_pool import ReqToTokenPool
 from sglang.srt.mem_cache.radix_cache import (
     RadixKey,
+    _convert_to_bigram_key,
     _key_match_page_size1,
     _key_match_paged,
     get_child_key,
@@ -327,12 +328,14 @@ def __init__(
         sliding_window_size: int,
         page_size: int,
         disable: bool = False,
+        is_eagle: bool = False,
     ):
         assert isinstance(token_to_kv_pool_allocator, SWATokenToKVPoolAllocator)
         self.req_to_token_pool = req_to_token_pool
         self.token_to_kv_pool_allocator = token_to_kv_pool_allocator
         self.page_size = page_size
         self.disable = disable
+        self.is_eagle = is_eagle
 
         if self.token_to_kv_pool_allocator:
             self.device = self.token_to_kv_pool_allocator.device
@@ -346,6 +349,11 @@ def __init__(
             self.key_match_fn = partial(_key_match_paged, page_size=page_size)
             self.get_child_key_fn = partial(get_child_key, page_size=page_size)
 
+        if is_eagle:
+            self.key_convert_fn = _convert_to_bigram_key
+        else:
+            self.key_convert_fn = lambda key: key
+
         self.sliding_window_size = sliding_window_size
         self.reset()
 
@@ -376,6 +384,8 @@ def match_prefix(self, key: RadixKey, **kwargs) -> MatchResult:
             The last node create a new child if the prefix is shorter
             than the last node's value.
         """
+        key.token_ids = self.key_convert_fn(key.token_ids)
+
         if self.disable or len(key) == 0:
             return MatchResult(
                 device_indices=torch.empty(
@@ -406,8 +416,15 @@ def insert(self, key: RadixKey, value=None, prev_prefix_len: int = 0) -> int:
         if self.disable:
             return 0
 
+        key.token_ids = self.key_convert_fn(key.token_ids)
+
         if value is None:
             value = torch.tensor([x for x in key.token_ids], dtype=torch.int64)
+
+        if self.is_eagle:
+            # Make sure the value len equal to the EAGLE bigram key len
+            value = value[: len(key)]
+
         return self._insert_helper(self.root_node, key, value, prev_prefix_len)
 
     def cache_finished_req(self, req: Req) -> None:
@@ -422,25 +439,38 @@ def cache_finished_req(self, req: Req) -> None:
             return
 
         token_ids = (req.origin_input_ids + req.output_ids)[:-1]
+        all_token_len = len(token_ids)
+        actual_kv_len = all_token_len - 1 if self.is_eagle else all_token_len
         kv_indices = self.req_to_token_pool.req_to_token[
-            req.req_pool_idx, : len(token_ids)
+            req.req_pool_idx, :all_token_len
         ]
 
         if self.page_size != 1:
-            page_aligned_len = len(kv_indices) // self.page_size * self.page_size
+            page_aligned_len = actual_kv_len // self.page_size * self.page_size
             page_aligned_kv_indices = kv_indices[:page_aligned_len].clone()
             self.token_to_kv_pool_allocator.free(kv_indices[page_aligned_len:])
         else:
-            page_aligned_len = len(kv_indices)
+            page_aligned_len = actual_kv_len
             page_aligned_kv_indices = kv_indices.clone()
+            if self.is_eagle:
+                self.token_to_kv_pool_allocator.free(kv_indices[page_aligned_len:])
+
+        page_aligned_token_len = (
+            page_aligned_len + 1 if self.is_eagle else page_aligned_len
+        )
+
+        old_prefix_len = len(req.prefix_indices)
+        if self.is_eagle and old_prefix_len > req.last_matched_prefix_len:
+            # prefix_indices attached partial part (for page_size > 1) and one unmatched token (for EAGLE)
+            old_prefix_len -= 1
 
         # Radix Cache takes one ref in memory pool
         # insert the token_ids and kv_indices into the radix tree
         # Note: the insert function already frees the overlapped kv_indices
         new_prefix_len = self.insert(
-            RadixKey(token_ids[:page_aligned_len], req.extra_key),
+            RadixKey(token_ids[:page_aligned_token_len], req.extra_key),
             page_aligned_kv_indices,
-            len(req.prefix_indices),
+            old_prefix_len,
         )
 
         # Remove req slot release the cache lock
@@ -459,39 +489,54 @@ def cache_unfinished_req(self, req: Req, chunked=False) -> None:
             return
 
         token_ids = req.fill_ids
+        all_token_len = len(token_ids)
+        # The actual kv len for EAGLE is len(token_ids), since EAGLE uses bigram key
+        actual_kv_len = all_token_len - 1 if self.is_eagle else all_token_len
         kv_indices = self.req_to_token_pool.req_to_token[
-            req.req_pool_idx, : len(token_ids)
+            req.req_pool_idx, :all_token_len
         ]
 
         if self.page_size != 1:
-            page_aligned_len = len(kv_indices) // self.page_size * self.page_size
+            page_aligned_len = actual_kv_len // self.page_size * self.page_size
             page_aligned_kv_indices = kv_indices[:page_aligned_len].clone()
         else:
-            page_aligned_len = len(kv_indices)
+            page_aligned_len = actual_kv_len
             page_aligned_kv_indices = kv_indices.clone()
-        page_aligned_token_ids = token_ids[:page_aligned_len]
+
+        # For EAGLE, the page_aligned_len is for the bigram key, the normal key len should +1
+        page_aligned_token_len = (
+            page_aligned_len + 1 if self.is_eagle else page_aligned_len
+        )
+        page_aligned_token_ids = token_ids[:page_aligned_token_len]
+
+        old_prefix_len = len(req.prefix_indices)
+        if self.is_eagle and old_prefix_len > req.last_matched_prefix_len:
+            # prefix_indices attached partial part (for page_size > 1) and one unmatched token (for EAGLE)
+            old_prefix_len -= 1
 
         # Radix Cache takes one ref in memory pool
         # Note: the insert function already frees the overlapped kv_indices
         new_prefix_len = self.insert(
             RadixKey(page_aligned_token_ids, req.extra_key),
             page_aligned_kv_indices,
-            len(req.prefix_indices),
+            old_prefix_len,
         )
 
         # The prefix indices could be updated, reuse it
         new_indices, new_last_node, _, _ = self.match_prefix(
             RadixKey(page_aligned_token_ids, req.extra_key)
         )
-        assert len(req.prefix_indices) <= len(
+        assert old_prefix_len <= len(
             new_indices
         ), f"{req.prefix_indices=}, {new_indices=}"
         assert new_prefix_len <= len(new_indices), f"{new_prefix_len=}, {new_indices=}"
         self.req_to_token_pool.write(
-            (req.req_pool_idx, slice(len(req.prefix_indices), len(new_indices))),
-            new_indices[len(req.prefix_indices) :],
+            (req.req_pool_idx, slice(old_prefix_len, len(new_indices))),
+            new_indices[old_prefix_len:],
         )
 
+        req.last_matched_prefix_len = len(new_indices)
+
         self.dec_lock_ref(req.last_node, req.swa_uuid_for_lock)
         swa_uuid_for_lock = self.inc_lock_ref(new_last_node)
 
@@ -501,7 +546,13 @@ def cache_unfinished_req(self, req: Req, chunked=False) -> None:
                 [new_indices, kv_indices[len(new_indices) :]]
             )
         else:
-            req.prefix_indices = new_indices
+            if self.is_eagle:
+                # Attach the kv index of the last token for EAGLE, it can be used in chunked prefill
+                req.prefix_indices = torch.cat(
+                    [new_indices, kv_indices[actual_kv_len:]]
+                )
+            else:
+                req.prefix_indices = new_indices
         req.last_node = new_last_node
         req.swa_uuid_for_lock = swa_uuid_for_lock
 

python/sglang/srt/models/utils.py

@@ -27,7 +27,11 @@
 
 def enable_fused_set_kv_buffer(forward_batch: ForwardBatch):
     """Enable fused set_kv_buffer only on CUDA with bfloat16 KV cache."""
-    return _is_cuda and forward_batch.token_to_kv_pool.dtype == torch.bfloat16
+    return (
+        _is_cuda
+        and hasattr(forward_batch.token_to_kv_pool, "dtype")
+        and forward_batch.token_to_kv_pool.dtype == torch.bfloat16
+    )
 
 
 def create_fused_set_kv_buffer_arg(