570 lines
22 KiB
Python
570 lines
22 KiB
Python
from __future__ import annotations
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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 scheduler policy"""
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import os
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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 enum import Enum, auto
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from typing import TYPE_CHECKING, Dict, List, Optional, Set, Union
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import torch
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from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
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from sglang.srt.mem_cache.allocator import SWATokenToKVPoolAllocator
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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 RadixCache, TreeNode
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if TYPE_CHECKING:
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from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
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# Clip the estimation of 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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# Note that this only clips the estimation in the scheduler but does not change the stop
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# condition. The request can still generate tokens until it hits the unclipped max_new_tokens.
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CLIP_MAX_NEW_TOKENS_ESTIMATION = int(
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os.environ.get("SGLANG_CLIP_MAX_NEW_TOKENS_ESTIMATION", "4096")
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)
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# Threshold for in-batch prefix cache.
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# If a request has a matched prefix length (against existing cache) less than this value,
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# the scheduler runs the in-batch prefix caching check for this request.
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# If we set it to -1, it means we disable in-batch prefix caching.
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IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD = int(
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os.environ.get("IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD", "32")
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)
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# Threshold for in-batch prefix cache.
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# If a request has a matched prefix length (within the waiting queue) larger than this value,
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# the scheduler deprioritizes this request
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IN_BATCH_PREFIX_CACHING_DEPRIORITIZE_THRESHOLD = int(
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os.environ.get("IN_BATCH_PREFIX_CACHING_DEPRIORITIZE_THRESHOLD", "32")
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)
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IGNORE_EOS_RESERVE_TOKENS = 1
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class CacheAwarePolicy(Enum):
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"""Scheduling policies that are aware of the tree cache."""
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LPM = "lpm" # longest prefix match
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DFS_WEIGHT = "dfs-weight" # depth-first search weighting
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class CacheAgnosticPolicy(Enum):
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"""Scheduling policies that are not aware of the tree cache."""
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FCFS = "fcfs" # first come first serve
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LOF = "lof" # longest output first
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RANDOM = "random"
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class SchedulePolicy:
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Policy = Union[CacheAwarePolicy, CacheAgnosticPolicy]
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def __init__(
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self,
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policy: str,
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tree_cache: BasePrefixCache,
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enable_hierarchical_cache: bool,
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):
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self.policy = self._validate_and_adjust_policy(policy, tree_cache)
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self.tree_cache = tree_cache
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self.enable_hierarchical_cache = enable_hierarchical_cache
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# It is used to find the matching prefix for in-batch prefix caching.
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self.waiting_queue_radix_tree = RadixCache(
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req_to_token_pool=None,
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token_to_kv_pool_allocator=None,
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page_size=1,
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disable=False,
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)
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def calc_priority(self, waiting_queue: List[Req]) -> bool:
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if self.policy == CacheAgnosticPolicy.FCFS:
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# A shortcut for FCFS
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return False
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policy = self._determine_active_policy(waiting_queue)
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prefix_computed = False
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if isinstance(policy, CacheAwarePolicy):
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prefix_computed = True
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temporary_deprioritized = self._compute_prefix_matches(
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waiting_queue, policy
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)
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if policy == CacheAwarePolicy.LPM:
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SchedulePolicy._sort_by_longest_prefix(
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waiting_queue, temporary_deprioritized
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)
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elif policy == CacheAwarePolicy.DFS_WEIGHT:
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SchedulePolicy._sort_by_dfs_weight(waiting_queue, self.tree_cache)
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else:
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raise ValueError(f"Unknown CacheAware Policy: {policy=}")
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else:
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if policy == CacheAgnosticPolicy.FCFS:
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pass
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elif policy == CacheAgnosticPolicy.LOF:
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SchedulePolicy._sort_by_longest_output(waiting_queue)
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elif policy == CacheAgnosticPolicy.RANDOM:
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SchedulePolicy._sort_randomly(waiting_queue)
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else:
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raise ValueError(f"Unknown CacheAgnostic Policy: {policy=}")
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return prefix_computed
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def _determine_active_policy(self, waiting_queue: List[Req]) -> Policy:
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if self.policy == CacheAwarePolicy.LPM and len(waiting_queue) > 128:
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# Turn off the expensive prefix matching and sorting when the #queue is large.
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return CacheAgnosticPolicy.FCFS
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return self.policy
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def _validate_and_adjust_policy(
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self, policy: str, tree_cache: BasePrefixCache
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) -> Policy:
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"""
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Validates the policy and adjusts it if necessary based on tree cache settings.
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"""
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try:
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policy_enum = CacheAwarePolicy(policy)
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if getattr(tree_cache, "disable", True):
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# If tree_cache is disabled, using CacheAgnosticPolicy policy
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return CacheAgnosticPolicy.FCFS
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return policy_enum
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except ValueError:
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try:
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return CacheAgnosticPolicy(policy)
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except ValueError:
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raise ValueError(f"Unknown schedule_policy: {policy=}")
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def _compute_prefix_matches(
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self, waiting_queue: List[Req], policy: CacheAwarePolicy
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) -> Set[int]:
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"""
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Computes and caches the matching prefixes for requests in the waiting queue,
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and handles in-batch prefix caching logic.
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"""
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temporary_deprioritized: Set[int] = set()
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self.waiting_queue_radix_tree.reset()
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for r in waiting_queue:
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prefix_ids = r.adjust_max_prefix_ids()
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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, r.last_host_node, r.host_hit_length = (
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self.tree_cache.match_prefix(rid=r.rid, key=prefix_ids)
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)
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# NOTE(sang): This logic is for in-batch prefix caching;
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# If there are more than 1 request that have small matching prefix from
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# existing cache, but all those requests share the same prefix, we prefer
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# to schedule only one of them so that we can increase the cache hit rate.
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# We prefer to set IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD > 0 because too small
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# threshold means we cannot use in-batch prefix caching for short prefixes.
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# It is kind of common when the engine is long running (e.g., imagine the prefix "the").
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if len(r.prefix_indices) <= IN_BATCH_PREFIX_CACHING_CHECK_THRESHOLD:
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in_batch_matching_prefixes, _, _, _ = (
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self.waiting_queue_radix_tree.match_prefix(
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rid=r.rid, key=prefix_ids
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)
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)
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if (
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len(in_batch_matching_prefixes)
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>= IN_BATCH_PREFIX_CACHING_DEPRIORITIZE_THRESHOLD
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):
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temporary_deprioritized.add(r.rid)
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else:
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# Insert with a dummy key
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self.waiting_queue_radix_tree.insert(
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prefix_ids, torch.empty(len(prefix_ids), dtype=torch.bool)
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)
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return temporary_deprioritized
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@staticmethod
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def _sort_by_longest_prefix(
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waiting_queue: List[Req], temporary_deprioritized: Set[int]
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) -> None:
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"""Sorts the waiting queue based on the longest prefix match."""
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waiting_queue.sort(
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key=lambda r: (
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-len(r.prefix_indices)
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if r.rid not in temporary_deprioritized
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else float("inf")
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)
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)
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@staticmethod
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def _sort_by_dfs_weight(
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waiting_queue: List[Req], tree_cache: BasePrefixCache
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) -> None:
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"""Sorts the waiting queue based on a depth-first search weighting."""
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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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SchedulePolicy._calc_weight(tree_cache.root_node, node_to_weight)
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waiting_queue.clear()
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SchedulePolicy._get_dfs_priority(
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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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@staticmethod
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def _sort_by_longest_output(waiting_queue: List[Req]) -> None:
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"""Sorts the waiting queue based on the longest output (max_new_tokens)."""
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waiting_queue.sort(key=lambda x: -x.sampling_params.max_new_tokens)
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@staticmethod
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def _sort_randomly(waiting_queue: List[Req]) -> None:
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"""Shuffles the waiting queue randomly."""
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random.shuffle(waiting_queue)
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@staticmethod
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def _calc_weight(cur_node: TreeNode, node_to_weight: Dict[TreeNode, int]) -> None:
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for child in cur_node.children.values():
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SchedulePolicy._calc_weight(child, node_to_weight)
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node_to_weight[cur_node] += node_to_weight[child]
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@staticmethod
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def _get_dfs_priority(
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cur_node: TreeNode,
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node_to_priority: Dict[TreeNode, int],
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last_node_to_reqs: Dict[TreeNode, List[Req]],
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q: List,
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) -> None:
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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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SchedulePolicy._get_dfs_priority(
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child, node_to_priority, last_node_to_reqs, q
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)
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q.extend(last_node_to_reqs[cur_node])
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class AddReqResult(Enum):
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CONTINUE = auto() # Continue to add requests
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NO_TOKEN = auto() # No token left
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OTHER = auto() # Other reasons to stop adding requests
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class PrefillAdder:
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def __init__(
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self,
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page_size: int,
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tree_cache: BasePrefixCache,
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token_to_kv_pool_allocator: BaseTokenToKVPoolAllocator,
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running_batch: ScheduleBatch,
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new_token_ratio: float,
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rem_input_tokens: int,
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rem_chunk_tokens: Optional[int],
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mixed_with_decode_tokens: int = 0,
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):
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self.page_size = page_size
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self.tree_cache = tree_cache
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self.token_to_kv_pool_allocator = token_to_kv_pool_allocator
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self.running_batch = running_batch
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self.new_token_ratio = new_token_ratio
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self.rem_input_tokens = rem_input_tokens - mixed_with_decode_tokens
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self.rem_chunk_tokens = rem_chunk_tokens
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if self.rem_chunk_tokens is not None:
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self.rem_chunk_tokens -= mixed_with_decode_tokens
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self.rem_total_token_offset = mixed_with_decode_tokens
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self.cur_rem_token_offset = mixed_with_decode_tokens
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self.req_states = None
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self.can_run_list = []
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self.new_chunked_req = None
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self.log_hit_tokens = 0
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# TODO(lsyin): report the real input tokens excluding page alignment
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self.log_input_tokens = 0
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if running_batch is not None:
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self.rem_total_token_offset += 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_ESTIMATION,
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)
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* self.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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self.is_hybrid = isinstance(
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self.token_to_kv_pool_allocator, SWATokenToKVPoolAllocator
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)
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@property
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def rem_total_tokens(self):
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if self.is_hybrid:
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available_and_evictable = min(
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self.token_to_kv_pool_allocator.full_available_size()
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+ self.tree_cache.full_evictable_size(),
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self.token_to_kv_pool_allocator.swa_available_size()
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+ self.tree_cache.swa_evictable_size(),
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)
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else:
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available_and_evictable = (
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self.token_to_kv_pool_allocator.available_size()
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+ self.tree_cache.evictable_size()
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)
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return available_and_evictable - self.rem_total_token_offset
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@property
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def cur_rem_tokens(self):
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if self.is_hybrid:
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available_and_evictable = min(
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self.token_to_kv_pool_allocator.full_available_size()
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+ self.tree_cache.full_evictable_size(),
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self.token_to_kv_pool_allocator.swa_available_size()
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+ self.tree_cache.swa_evictable_size(),
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)
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else:
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available_and_evictable = (
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self.token_to_kv_pool_allocator.available_size()
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+ self.tree_cache.evictable_size()
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)
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return available_and_evictable - self.cur_rem_token_offset
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def ceil_paged_tokens(self, tokens: int) -> int:
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return -(-tokens // self.page_size) * self.page_size
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def budget_state(self):
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if self.rem_total_tokens <= 0 or self.cur_rem_tokens <= 0:
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return AddReqResult.NO_TOKEN
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if self.rem_input_tokens <= 0 or (
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self.rem_chunk_tokens is not None and self.rem_chunk_tokens <= 0
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):
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return AddReqResult.OTHER
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return AddReqResult.CONTINUE
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def _update_prefill_budget(
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self, prefix_len: int, extend_input_len: int, max_new_tokens: int
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):
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# TODO(lsyin): check this workaround logic, which only ensures the prefill will not out of memory, and may be too conservative
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extend_input_len = self.ceil_paged_tokens(extend_input_len)
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self.rem_total_token_offset += extend_input_len + max_new_tokens
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self.cur_rem_token_offset += extend_input_len
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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_chunked_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._update_prefill_budget(
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0,
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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_ESTIMATION)
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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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if self.is_hybrid:
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try:
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swa_uuid_for_lock = self.tree_cache.inc_lock_ref(last_node)
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yield None
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finally:
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self.tree_cache.dec_lock_ref(last_node, swa_uuid_for_lock)
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else:
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try:
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self.tree_cache.inc_lock_ref(last_node)
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yield None
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finally:
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self.tree_cache.dec_lock_ref(last_node)
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def add_one_req_ignore_eos(self, req: Req, has_chunked_req: bool):
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# Early exit if no enough tokens for the input tokens
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if self.ceil_paged_tokens(req.extend_input_len) > min(
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self.cur_rem_tokens, self.rem_total_tokens
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):
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return AddReqResult.NO_TOKEN
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def add_req_state(r, insert_sort=False):
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new_token_ratio = (
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1.0 if r.sampling_params.ignore_eos else self.new_token_ratio
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)
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tokens_left = r.sampling_params.max_new_tokens * new_token_ratio - len(
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r.output_ids
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)
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tokens_occupied = len(r.origin_input_ids) + len(r.output_ids)
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if tokens_left <= 0:
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return
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if not insert_sort:
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self.req_states.append((tokens_left, tokens_occupied))
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else:
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i = 0
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for i in range(len(self.req_states)):
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if tokens_left <= self.req_states[i][0]:
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break
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self.req_states.insert(i, (tokens_left, tokens_occupied))
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if self.req_states is None:
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self.req_states = []
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add_req_state(req)
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if self.running_batch is not None:
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for r in self.running_batch.reqs:
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add_req_state(r)
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for r in self.can_run_list:
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add_req_state(r)
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self.req_states.sort(key=lambda x: x[0])
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else:
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add_req_state(req, insert_sort=True)
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if not self.is_hybrid:
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# Skip this logic for swa. The SWA has different memory management, and
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# this mechanism is underestimating the memory usage.
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cur_rem_tokens = self.cur_rem_tokens - self.ceil_paged_tokens(
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req.extend_input_len
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)
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tokens_freed = 0
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for i, (tokens_left, tokens_occupied) in enumerate(self.req_states):
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# tokens_left gives a reservative calculation as the last token is not stored
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bs = len(self.req_states) - i
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min_free_tokens = cur_rem_tokens + tokens_freed - tokens_left * bs
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# reserve tokens for corner cases
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if min_free_tokens <= IGNORE_EOS_RESERVE_TOKENS * bs:
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return AddReqResult.NO_TOKEN
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tokens_freed += tokens_occupied
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if (
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self.rem_chunk_tokens is None # chunked prefill is disabled
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or req.extend_input_len <= self.rem_chunk_tokens # it is the last chunk
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):
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# Non-chunked prefill
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self.can_run_list.append(req)
|
|
self._update_prefill_budget(
|
|
0,
|
|
req.extend_input_len,
|
|
min(req.sampling_params.max_new_tokens, CLIP_MAX_NEW_TOKENS_ESTIMATION),
|
|
)
|
|
else:
|
|
if self.rem_chunk_tokens == 0:
|
|
return AddReqResult.OTHER
|
|
|
|
# Chunked prefill
|
|
trunc_len = self.rem_chunk_tokens
|
|
|
|
req.extend_input_len = trunc_len
|
|
req.fill_ids = req.fill_ids[:trunc_len]
|
|
self.can_run_list.append(req)
|
|
self.new_chunked_req = req
|
|
self._update_prefill_budget(0, trunc_len, 0)
|
|
|
|
return self.budget_state()
|
|
|
|
def add_one_req(self, req: Req, has_chunked_req: bool):
|
|
if req.sampling_params.ignore_eos and getattr(self.tree_cache, "disable", True):
|
|
return self.add_one_req_ignore_eos(req, has_chunked_req)
|
|
|
|
total_tokens = req.extend_input_len + min(
|
|
req.sampling_params.max_new_tokens, CLIP_MAX_NEW_TOKENS_ESTIMATION
|
|
)
|
|
|
|
# adjusting the input_tokens based on host_hit_length and page_size
|
|
real_input_tokens = req.extend_input_len - req.host_hit_length
|
|
real_input_tokens = self.ceil_paged_tokens(real_input_tokens)
|
|
prefix_len = len(req.prefix_indices)
|
|
|
|
if total_tokens >= self.rem_total_tokens:
|
|
return AddReqResult.NO_TOKEN
|
|
|
|
if real_input_tokens >= self.rem_input_tokens and len(self.can_run_list) != 0:
|
|
return AddReqResult.OTHER
|
|
|
|
with self._lock_node(req.last_node):
|
|
# self.rem_total_tokens may decrease after the lock acquisition
|
|
if total_tokens >= self.rem_total_tokens:
|
|
return AddReqResult.NO_TOKEN
|
|
|
|
if req.host_hit_length > 0:
|
|
new_indices, req.last_node = self.tree_cache.init_load_back(
|
|
req.last_host_node, req.host_hit_length
|
|
)
|
|
req.prefix_indices = torch.cat([req.prefix_indices, new_indices])
|
|
req.extend_input_len = len(req.fill_ids) - len(req.prefix_indices)
|
|
prefix_len = len(req.prefix_indices)
|
|
|
|
input_tokens = self.ceil_paged_tokens(req.extend_input_len)
|
|
|
|
if input_tokens >= self.rem_input_tokens and len(self.can_run_list) != 0:
|
|
return AddReqResult.OTHER
|
|
|
|
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,
|
|
input_tokens,
|
|
min(
|
|
req.sampling_params.max_new_tokens,
|
|
CLIP_MAX_NEW_TOKENS_ESTIMATION,
|
|
),
|
|
)
|
|
else:
|
|
# Make sure at least one page is available
|
|
trunc_len = self.rem_chunk_tokens - self.page_size + 1
|
|
if trunc_len <= 0:
|
|
return AddReqResult.OTHER
|
|
|
|
# Chunked prefill
|
|
req.extend_input_len = trunc_len
|
|
req.fill_ids = req.fill_ids[: len(req.prefix_indices) + trunc_len]
|
|
|
|
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)
|
|
|
|
return self.budget_state()
|