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fix(cache): improve hybrid attention snapshot eviction #1551

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fix(cache): improve hybrid attention snapshot eviction #1551

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60 changes: 42 additions & 18 deletions lightllm/server/router/dynamic_prompt/linear_att_radix_cache.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,8 @@ def __init__(self, hash_page_size: int, big_page_num: int):

self.ref_counter = 0
self.time_id = time_gen.generate_time_id()
# 快照单独记录使用时间，避免匹配更长前缀时，沿途未使用的历史快照也被刷新为热门。
self.state_time_id = self.time_id

self.node_value_len = 0
self.node_prefix_total_len = 0
Expand Down Expand Up @@ -54,7 +56,7 @@ def get_compare_key_for_buffer_idx(self):
assert self.is_big_page_node() is False
# 对于有 buffer_id 的节点的回收处理比较器
assert self.small_page_buffer_idx is not None
return (self.time_id,)
return (self.state_time_id,)

def add_and_return_new_child(
self,
Expand Down Expand Up @@ -172,6 +174,14 @@ def _add_node(self, node: LinearAttPagedTreeNode):
self._evict_tree_set_for_linear_att.add(node)
return

def mark_small_page_state_used(self, node: LinearAttPagedTreeNode):
"""只有实际恢复了该节点的线性状态，才刷新快照 LRU；仅遍历到节点不算使用。"""
assert node.small_page_buffer_idx is not None
# SortedSet 的排序键发生变化时，必须先移除节点，再更新时间并重新加入。
self._evict_tree_set_for_linear_att.discard(node)
node.state_time_id = time_gen.generate_time_id()
self._evict_tree_set_for_linear_att.add(node)

def insert(
self,
key: torch.Tensor,
Expand Down Expand Up @@ -302,6 +312,8 @@ def _insert_helper(
# 将这个buffer id 移交给这个存在的节点。
self._discard_node(child)
child.small_page_buffer_idx = block_linear_idxs[0]
# 原快照已被淘汰，补入的新快照应从当前时间开始参与 LRU 排序。
child.state_time_id = time_gen.generate_time_id()
self._add_node(child)
else:
# 说明节点已经存在了，直接提前移除掉这个节点占用的线性缓存，外部不用处理这个细节了
Expand Down Expand Up @@ -619,22 +631,34 @@ def _evict(self, need_remove_tokens, evict_callback):
num_evicted = 0
while num_evicted < need_remove_tokens:
node: LinearAttPagedTreeNode = self._evict_tree_set.pop(0)
self._discard_node(node)

assert (
node.ref_counter == 0 and len(node.children) == 0 and node is not self.root_node
), "error evict tree node state"
num_evicted += len(node.token_mem_index_value)

if node.is_big_page_node():
assert node.big_page_buffer_idx is not None
self.linear_att_big_page_buffers.free_state_cache([node.big_page_buffer_idx])

evict_callback(node.token_mem_index_value, node.small_page_buffer_idx)
self.tree_total_tokens_num.arr[0] -= len(node.token_mem_index_value)
parent_node: LinearAttPagedTreeNode = node.parent
parent_node.remove_child(node)

self._add_node(parent_node)
while True:
self._discard_node(node)

assert (
node.ref_counter == 0 and len(node.children) == 0 and node is not self.root_node
), "error evict tree node state"
num_evicted += len(node.token_mem_index_value)

if node.is_big_page_node():
assert node.big_page_buffer_idx is not None
self.linear_att_big_page_buffers.free_state_cache([node.big_page_buffer_idx])

evict_callback(node.token_mem_index_value, node.small_page_buffer_idx)
self.tree_total_tokens_num.arr[0] -= len(node.token_mem_index_value)
parent_node: LinearAttPagedTreeNode = node.parent
parent_node.remove_child(node)

self._add_node(parent_node)
# 末尾恢复点被删除后，向上连续的无快照叶节点已无法复用，一并回收，
# 即使释放量已达标也继续清理；遇到引用、其他分支或有效快照就停止。
if (
parent_node is self.root_node
or parent_node.ref_counter != 0
or not parent_node.is_leaf()
or parent_node.is_big_page_node()
or parent_node.small_page_buffer_idx is not None
):
break
node = parent_node

return
3 changes: 3 additions & 0 deletions lightllm/server/router/model_infer/infer_batch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -734,6 +734,7 @@ def _linear_match_radix_cache(self):
req=self,
linear_att_small_page_buffers=g_infer_context.radix_cache.linear_att_small_page_buffers,
)
g_infer_context.radix_cache.mark_small_page_state_used(share_node)
else:
# 如果 大页本质是被启用的，则需要使用小页的匹配结果, 将小页的kv 复制到的新申请的kv位置，同时释放
# 对应的小页对应的节点，递归找到对应最近的大叶节点进行返回,然后赋值到req.shared_node 对象上
Expand Down Expand Up @@ -767,6 +768,8 @@ def _linear_match_radix_cache(self):
req=self,
linear_att_small_page_buffers=g_infer_context.radix_cache.linear_att_small_page_buffers,
)
# 仅在真正使用小页状态时刷新；内存不足而退回大页的分支不刷新。
radix_cache.mark_small_page_state_used(share_node)
self.shared_kv_node = None

big_page_shared_node = radix_cache.deref_to_first_big_page_node(node=share_node)
Expand Down
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