226 lines
7.8 KiB
Python
226 lines
7.8 KiB
Python
"""
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Copyright 2023-2024 SGLang Team
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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"""Request policy scheduler"""
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import random
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from collections import defaultdict
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from contextlib import contextmanager
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from typing import Dict, List, Optional
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from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
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from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
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from sglang.srt.mem_cache.radix_cache import TreeNode
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# Clip the max new tokens for the request whose max_new_tokens is very large.
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# This can prevent the server from being too conservative.
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CLIP_MAX_NEW_TOKENS = 4096
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class PolicyScheduler:
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def __init__(self, policy: str, tree_cache: BasePrefixCache):
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if tree_cache.disable and policy in ["lpm", "dfs-weight"]:
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# LPM and DFS-weight is meaningless when the tree cache is disabled.
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policy = "fcfs"
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self.policy = policy
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self.tree_cache = tree_cache
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def calc_priority(self, waiting_queue: List[Req]):
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# Compute matched prefix length
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for r in waiting_queue:
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# NOTE: the prefix_indices must always be aligned with last_node
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r.prefix_indices, r.last_node = self.tree_cache.match_prefix(
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rid=r.rid, key=r.adjust_max_prefix_ids()
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)
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if self.policy == "lpm":
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# Longest Prefix Match
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waiting_queue.sort(key=lambda x: -len(x.prefix_indices))
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elif self.policy == "fcfs":
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# first come first serve
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pass
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elif self.policy == "lof":
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# longest output first
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waiting_queue.sort(key=lambda x: -x.sampling_params.max_new_tokens)
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elif self.policy == "random":
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random.shuffle(waiting_queue)
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elif self.policy == "dfs-weight":
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last_node_to_reqs = defaultdict(list)
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for req in waiting_queue:
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last_node_to_reqs[req.last_node].append(req)
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node_to_weight = defaultdict(int)
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for node in last_node_to_reqs:
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node_to_weight[node] = len(last_node_to_reqs[node])
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self.calc_weight(self.tree_cache.root_node, node_to_weight)
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waiting_queue.clear()
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self.get_dfs_priority(
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self.tree_cache.root_node,
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node_to_weight,
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last_node_to_reqs,
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waiting_queue,
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)
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else:
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raise ValueError(f"Unknown schedule_policy: {self.policy}")
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def calc_weight(self, cur_node: TreeNode, node_to_weight: Dict):
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for child in cur_node.children.values():
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self.calc_weight(child, node_to_weight)
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node_to_weight[cur_node] += node_to_weight[child]
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def get_dfs_priority(
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self,
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cur_node: TreeNode,
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node_to_priority: Dict,
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last_node_to_reqs: Dict,
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q: List,
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):
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childs = [child for child in cur_node.children.values()]
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childs.sort(key=lambda x: -node_to_priority[x])
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for child in childs:
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self.get_dfs_priority(child, node_to_priority, last_node_to_reqs, q)
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q.extend(last_node_to_reqs[cur_node])
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class PrefillAdder:
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def __init__(
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self,
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tree_cache: BasePrefixCache,
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rem_total_tokens: int,
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rem_input_tokens: int,
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rem_chunk_tokens: Optional[int],
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):
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self.tree_cache = tree_cache
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self.rem_total_tokens = rem_total_tokens
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self.rem_input_tokens = rem_input_tokens
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self.rem_chunk_tokens = rem_chunk_tokens
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self.can_run_list = []
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self.new_inflight_req = None
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self.log_hit_tokens = 0
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self.log_input_tokens = 0
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def no_remaining_tokens(self):
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return (
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self.rem_total_tokens <= 0
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or self.rem_input_tokens <= 0
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or (
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self.rem_chunk_tokens <= 0
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if self.rem_chunk_tokens is not None
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else False
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)
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)
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def remove_running_tokens(
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self, running_batch: ScheduleBatch, new_token_ratio: float
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):
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self.rem_total_tokens -= sum(
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[
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min(
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(r.sampling_params.max_new_tokens - len(r.output_ids)),
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CLIP_MAX_NEW_TOKENS,
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)
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* new_token_ratio
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for r in running_batch.reqs
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]
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)
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def _prefill_one_req(
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self, prefix_len: int, extend_input_len: int, max_new_tokens: int
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):
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self.rem_total_tokens -= extend_input_len + max_new_tokens
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self.rem_input_tokens -= extend_input_len
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if self.rem_chunk_tokens is not None:
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self.rem_chunk_tokens -= extend_input_len
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self.log_hit_tokens += prefix_len
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self.log_input_tokens += extend_input_len
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def add_inflight_req(self, req: Req):
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truncated = req.extend_input_len > self.rem_chunk_tokens
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req.extend_input_len = min(req.extend_input_len, self.rem_chunk_tokens)
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req.fill_ids = req.fill_ids[: len(req.prefix_indices) + req.extend_input_len]
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self.can_run_list.append(req)
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self._prefill_one_req(
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len(req.prefix_indices),
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req.extend_input_len,
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(
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min(req.sampling_params.max_new_tokens, CLIP_MAX_NEW_TOKENS)
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if not truncated
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else 0
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),
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)
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# Return if chunked prefill not finished
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return req if truncated else None
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@contextmanager
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def _lock_node(self, last_node: TreeNode):
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try:
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delta = self.tree_cache.inc_lock_ref(last_node)
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self.rem_total_tokens += delta
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yield None
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finally:
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delta = self.tree_cache.dec_lock_ref(last_node)
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self.rem_total_tokens += delta
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def add_one_req(self, req: Req):
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total_tokens = req.extend_input_len + min(
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req.sampling_params.max_new_tokens, CLIP_MAX_NEW_TOKENS
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)
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input_tokens = req.extend_input_len
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prefix_len = len(req.prefix_indices)
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if total_tokens >= self.rem_total_tokens:
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return False
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if input_tokens > self.rem_input_tokens and len(self.can_run_list) != 0:
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return False
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with self._lock_node(req.last_node):
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if total_tokens > self.rem_total_tokens:
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return False
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if (
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self.rem_chunk_tokens is None
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or input_tokens <= self.rem_chunk_tokens
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or (req.return_logprob and req.normalized_prompt_logprob is None)
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):
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# Non-chunked prefill
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self.can_run_list.append(req)
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self.tree_cache.inc_lock_ref(req.last_node)
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self._prefill_one_req(
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prefix_len,
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input_tokens,
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min(req.sampling_params.max_new_tokens, CLIP_MAX_NEW_TOKENS),
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)
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else:
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# Chunked prefill
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trunc_len = self.rem_chunk_tokens
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if trunc_len == 0:
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return False
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req.extend_input_len = trunc_len
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req.fill_ids = req.fill_ids[: len(req.prefix_indices) + trunc_len]
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self.can_run_list.append(req)
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self.new_inflight_req = req
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self.tree_cache.inc_lock_ref(req.last_node)
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self._prefill_one_req(prefix_len, trunc_len, 0)
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return True
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