413 lines
16 KiB
Python
413 lines
16 KiB
Python
from collections import deque
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from dataclasses import dataclass
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from typing import Deque, Dict, Iterable, List, Optional, Set, Tuple, Union
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from vllm.config import CacheConfig, LoRAConfig, SchedulerConfig
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from vllm.logger import init_logger
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from vllm.multimodal import MultiModalDataDict
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from vllm.sampling_params import SamplingParams
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from vllm.v1.core.kv_cache_manager import KVCacheManager
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from vllm.v1.outputs import ModelRunnerOutput
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from vllm.v1.request import Request, RequestStatus
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logger = init_logger(__name__)
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class Scheduler:
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def __init__(
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self,
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scheduler_config: SchedulerConfig,
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cache_config: CacheConfig,
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lora_config: Optional[LoRAConfig],
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) -> None:
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self.scheduler_config = scheduler_config
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self.cache_config = cache_config
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self.lora_config = lora_config
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# TODO: Support LoRA.
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assert lora_config is None, "V1 does not support LoRA yet."
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num_gpu_blocks = cache_config.num_gpu_blocks
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assert isinstance(num_gpu_blocks, int) and num_gpu_blocks > 0
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# Create the block space manager.
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self.kv_cache_manager = KVCacheManager(
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block_size=self.cache_config.block_size,
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num_gpu_blocks=num_gpu_blocks,
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sliding_window=self.cache_config.sliding_window,
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enable_caching=True)
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self.block_size = self.cache_config.block_size
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# Scheduling constraints.
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self.max_num_running_reqs = self.scheduler_config.max_num_seqs
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self.max_num_scheduled_tokens = \
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self.scheduler_config.max_num_batched_tokens
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self.max_model_len = self.scheduler_config.max_model_len
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# req_id -> Request
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self.requests: Dict[str, Request] = {}
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# Priority queues for requests.
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self.waiting: Deque[Request] = deque()
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self.running: List[Request] = []
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# The request IDs that are finished in between the previous and the
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# current steps. This is used to notify the workers about the finished
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# requests so that they can free the cached states for those requests.
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# This is flushed at the end of each scheduling step.
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self.finished_req_ids: Set[str] = set()
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# OPTIMIZATION: Cache the RunningRequestData objects to avoid creating
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# them at each scheduling step.
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# Request id -> RunningRequestData
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self.running_reqs_data: Dict[str, RunningRequestData] = {}
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def schedule(self) -> "SchedulerOutput":
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scheduled_new_reqs: List[Request] = []
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scheduled_resumed_reqs: List[Request] = []
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scheduled_running_reqs: List[Request] = []
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preempted_reqs: List[Request] = []
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# NOTE(woosuk) on the scheduling algorithm:
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# There's no "decoding phase" nor "prefill phase" in the scheduler.
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# Each request just has the num_computed_tokens and num_tokens,
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# which is equal to len(prompt_token_ids) + len(output_token_ids).
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# At each step, the scheduler tries to assign tokens to the requests
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# so that each request's num_computed_tokens can catch up its
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# num_tokens. This is general enough to cover chunked prefills,
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# prefix caching, and the "jump forward" optimization in the future.
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req_to_new_block_ids: Dict[str, List[int]] = {}
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num_scheduled_tokens: Dict[str, int] = {}
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token_budget = self.max_num_scheduled_tokens
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# First, schedule the RUNNING requests.
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req_index = 0
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while req_index < len(self.running):
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if token_budget == 0:
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break
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request = self.running[req_index]
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num_new_tokens = request.num_tokens - request.num_computed_tokens
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num_new_tokens = min(num_new_tokens, token_budget)
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assert num_new_tokens > 0
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while True:
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new_block_ids = self.kv_cache_manager.append_slots(
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request, num_new_tokens)
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if new_block_ids is None:
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# The request cannot be scheduled.
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# Preempt the lowest-priority request.
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preempted_req = self.running.pop()
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self.kv_cache_manager.free(preempted_req)
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preempted_req.status = RequestStatus.PREEMPTED
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preempted_req.num_computed_tokens = 0
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self.waiting.appendleft(preempted_req)
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preempted_reqs.append(preempted_req)
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if preempted_req == request:
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# No more request to preempt.
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break
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else:
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# The request can be scheduled.
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scheduled_running_reqs.append(request)
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req_to_new_block_ids[request.request_id] = new_block_ids
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num_scheduled_tokens[request.request_id] = num_new_tokens
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token_budget -= num_new_tokens
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req_index += 1
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break
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# Next, schedule the WAITING requests.
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if not preempted_reqs:
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while self.waiting:
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if len(self.running) == self.max_num_running_reqs:
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break
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if token_budget == 0:
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break
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request = self.waiting[0]
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# Get already-cached tokens.
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computed_block_ids = self.kv_cache_manager.get_computed_blocks(
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request)
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# NOTE(woosuk): Since incomplete blocks are not eligible for
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# sharing, `num_computed_tokens` is always a multiple of
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# `block_size`.
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num_computed_tokens = len(computed_block_ids) * self.block_size
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# Number of tokens to be scheduled.
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# We use `request.num_tokens` instead of
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# `request.num_prompt_tokens` to consider the resumed requests,
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# which have output tokens.
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num_new_tokens = request.num_tokens - num_computed_tokens
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num_new_tokens = min(num_new_tokens, token_budget)
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assert num_new_tokens > 0
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new_block_ids = self.kv_cache_manager.allocate_slots(
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request, num_new_tokens, computed_block_ids)
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if new_block_ids is None:
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# The request cannot be scheduled.
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break
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request.num_computed_tokens = num_computed_tokens
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self.waiting.popleft()
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self.running.append(request)
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if request.status == RequestStatus.WAITING:
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scheduled_new_reqs.append(request)
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elif request.status == RequestStatus.PREEMPTED:
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scheduled_resumed_reqs.append(request)
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else:
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raise RuntimeError(
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f"Invalid request status: {request.status}")
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req_to_new_block_ids[request.request_id] = (
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computed_block_ids + new_block_ids)
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num_scheduled_tokens[request.request_id] = num_new_tokens
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token_budget -= num_new_tokens
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request.status = RequestStatus.RUNNING
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# Check if the scheduling constraints are satisfied.
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total_num_scheduled_tokens = sum(num_scheduled_tokens.values())
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assert total_num_scheduled_tokens <= self.max_num_scheduled_tokens
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assert token_budget >= 0
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assert len(self.running) <= self.max_num_running_reqs
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assert (len(scheduled_new_reqs) + len(scheduled_resumed_reqs) +
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len(scheduled_running_reqs) == len(self.running))
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# Construct the scheduler output.
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new_reqs_data = [
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NewRequestData.from_request(req,
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req_to_new_block_ids[req.request_id],
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req.num_computed_tokens)
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for req in scheduled_new_reqs
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]
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resumed_reqs_data = [
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ResumedRequestData.from_request(
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req, req_to_new_block_ids[req.request_id],
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req.num_computed_tokens) for req in scheduled_resumed_reqs
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]
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running_reqs_data = [
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self._make_running_request_data(
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req, req_to_new_block_ids[req.request_id],
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req.num_computed_tokens) for req in scheduled_running_reqs
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]
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preempted_req_ids = {req.request_id for req in preempted_reqs}
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scheduler_output = SchedulerOutput(
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scheduled_new_reqs=new_reqs_data,
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scheduled_resumed_reqs=resumed_reqs_data,
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scheduled_running_reqs=running_reqs_data,
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num_scheduled_tokens=num_scheduled_tokens,
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total_num_scheduled_tokens=total_num_scheduled_tokens,
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preempted_req_ids=preempted_req_ids,
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# finished_req_ids is an existing state in the scheduler,
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# instead of being newly scheduled in this step.
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# It contains the request IDs that are finished in between
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# the previous and the current steps.
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finished_req_ids=self.finished_req_ids,
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)
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self.finished_req_ids = set()
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return scheduler_output
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def _make_running_request_data(
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self,
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request: Request,
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new_block_ids: List[int],
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num_computed_tokens: int,
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) -> "RunningRequestData":
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# OPTIMIZATION: Cache the RunningRequestData objects to avoid creating
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# them at each scheduling step.
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if request.request_id in self.running_reqs_data:
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req_data = self.running_reqs_data[request.request_id]
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req_data.new_block_ids = new_block_ids
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req_data.num_computed_tokens = num_computed_tokens
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else:
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req_data = RunningRequestData.from_request(request, new_block_ids,
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num_computed_tokens)
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self.running_reqs_data[request.request_id] = req_data
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return req_data
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def update_from_output(
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self,
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scheduler_output: "SchedulerOutput",
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model_runner_output: "ModelRunnerOutput",
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) -> List[Tuple[Request, int]]:
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# NOTE(woosuk): This method doesn't consider speculative decoding.
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sampled_token_ids = model_runner_output.sampled_token_ids_cpu.tolist()
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num_scheduled_tokens = scheduler_output.num_scheduled_tokens
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new_running: List[Request] = []
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# (request, num_sampled_tokens)
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sampled: List[Tuple[Request, int]] = []
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for request in self.running:
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req_id = request.request_id
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request.num_computed_tokens += num_scheduled_tokens[req_id]
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# When the request's num_computed_tokens catches up its num_tokens,
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# the request generates output tokens. Otherwise, we ignore the
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# sampler output for the request.
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assert request.num_computed_tokens <= request.num_tokens
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if request.num_computed_tokens == request.num_tokens:
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req_index = model_runner_output.req_id_to_index[req_id]
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# NOTE(woosuk): Currently, we assume that each request
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# generates at most one token at each step.
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token_id = sampled_token_ids[req_index]
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request.output_token_ids.append(token_id)
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sampled.append((request, 1))
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# TODO: Update the KV cache manager for prefix caching.
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# Check if the request is finished.
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stopped = self._check_stop(request)
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if stopped:
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continue
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new_running.append(request)
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self.running = new_running
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return sampled
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def _check_stop(self, request: Request) -> bool:
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if (request.num_tokens >= self.max_model_len
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or request.num_output_tokens >= request.max_tokens):
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request.status = RequestStatus.FINISHED_LENGTH_CAPPED
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self._free_request(request)
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return True
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sampling_params = request.sampling_params
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last_token_id = request.output_token_ids[-1]
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if (not sampling_params.ignore_eos
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and last_token_id == request.eos_token_id):
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request.status = RequestStatus.FINISHED_STOPPED
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self._free_request(request)
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return True
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if last_token_id in (sampling_params.stop_token_ids or ()):
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request.status = RequestStatus.FINISHED_STOPPED
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request.stop_reason = last_token_id
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self._free_request(request)
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return True
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return False
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def add_request(self, request: Request) -> None:
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self.waiting.append(request)
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self.requests[request.request_id] = request
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def finish_requests(
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self,
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request_ids: Union[str, Iterable[str]],
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finished_status: RequestStatus,
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) -> None:
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"""Handles the finish signal from outside the scheduler.
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For example, the API server can abort a request when the client
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disconnects.
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"""
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assert RequestStatus.is_finished(finished_status)
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if isinstance(request_ids, str):
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request_ids = (request_ids, )
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request_ids = set(request_ids)
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for req_id in request_ids:
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request = self.requests.get(req_id)
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if request is None:
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# Invalid request ID.
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continue
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if request.status == RequestStatus.RUNNING:
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self.running.remove(request)
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else:
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self.waiting.remove(request)
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request.status = finished_status
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self._free_request(request)
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def _free_request(self, request: Request) -> None:
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assert request.is_finished()
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self.kv_cache_manager.free(request)
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self.running_reqs_data.pop(request.request_id, None)
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del self.requests[request.request_id]
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self.finished_req_ids.add(request.request_id)
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def get_num_unfinished_requests(self) -> int:
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return len(self.waiting) + len(self.running)
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def has_unfinished_requests(self) -> bool:
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return self.get_num_unfinished_requests() > 0
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@dataclass
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class NewRequestData:
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req_id: str
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prompt_token_ids: List[int]
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prompt: Optional[str]
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multi_modal_data: Optional[MultiModalDataDict]
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sampling_params: SamplingParams
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block_ids: List[int]
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num_computed_tokens: int
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@classmethod
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def from_request(
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cls,
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request: Request,
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block_ids: List[int],
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num_computed_tokens: int,
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) -> "NewRequestData":
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return cls(
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req_id=request.request_id,
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prompt_token_ids=request.inputs["prompt_token_ids"],
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prompt=request.inputs.get("prompt"),
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multi_modal_data=request.inputs.get("multi_modal_data"),
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sampling_params=request.sampling_params,
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block_ids=block_ids,
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num_computed_tokens=num_computed_tokens,
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)
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@dataclass
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class ResumedRequestData:
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req_id: str
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block_ids: List[int]
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num_computed_tokens: int
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@classmethod
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def from_request(
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cls,
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request: Request,
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block_ids: List[int],
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num_computed_tokens: int,
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) -> "ResumedRequestData":
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return cls(
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req_id=request.request_id,
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block_ids=block_ids,
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num_computed_tokens=num_computed_tokens,
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)
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@dataclass
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class RunningRequestData:
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req_id: str
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new_block_ids: List[int]
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num_computed_tokens: int
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@classmethod
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def from_request(
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cls,
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request: Request,
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new_block_ids: List[int],
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num_computed_tokens: int,
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) -> "RunningRequestData":
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return cls(
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req_id=request.request_id,
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new_block_ids=new_block_ids,
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num_computed_tokens=num_computed_tokens,
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)
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@dataclass
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class SchedulerOutput:
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scheduled_new_reqs: List[NewRequestData]
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scheduled_resumed_reqs: List[ResumedRequestData]
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scheduled_running_reqs: List[RunningRequestData]
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num_scheduled_tokens: Dict[str, int]
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total_num_scheduled_tokens: int
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preempted_req_ids: Set[str]
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finished_req_ids: Set[str]
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