611 lines
25 KiB
Python
611 lines
25 KiB
Python
from collections import deque
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from dataclasses import dataclass
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from typing import (TYPE_CHECKING, Deque, Dict, Iterable, List, Optional, Set,
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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 MultiModalKwargs
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from vllm.multimodal.base import PlaceholderRange
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from vllm.sampling_params import SamplingParams
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from vllm.v1.core.encoder_cache_manager import EncoderCacheManager
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from vllm.v1.core.kv_cache_manager import KVCacheManager
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from vllm.v1.engine import EngineCoreOutput
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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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if TYPE_CHECKING:
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from vllm.multimodal import MultiModalKwargs
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from vllm.multimodal.base import PlaceholderRange
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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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# 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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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 KV cache 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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max_model_len=self.max_model_len,
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sliding_window=self.cache_config.sliding_window,
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enable_caching=self.cache_config.enable_prefix_caching)
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self.block_size = self.cache_config.block_size
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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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# Encoder-related.
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# NOTE(woosuk): Here, "encoder" includes the vision encoder (and
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# projector if needed). Currently, we assume that the encoder also
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# has the Transformer architecture (e.g., ViT).
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self.max_num_encoder_input_tokens = self.scheduler_config.max_num_encoder_input_tokens #noqa: E501
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# NOTE(woosuk): For the models without encoder (e.g., text-only models),
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# the encoder cache will not be initialized and used, regardless of
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# the cache size. This is because the memory space for the encoder cache
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# is preallocated in the profiling run.
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self.encoder_cache_manager = EncoderCacheManager(
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cache_size=self.scheduler_config.encoder_cache_size)
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def schedule(self) -> "SchedulerOutput":
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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 decoding" optimization in the future.
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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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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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# Encoder-related.
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scheduled_encoder_inputs: Dict[str, List[int]] = {}
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encoder_budget = self.max_num_encoder_input_tokens
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# First, schedule the RUNNING requests.
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# NOTE(woosuk): At most 1 request in the RUNNING queue is allowed to be
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# in the "partial" state, where the request has some tokens computed
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# but not all. The constraint is due to the persistent batch in the
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# V1 model runner.
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# TODO(woosuk): Remove this constraint after refactoring model runner.
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has_partial_request = False
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req_index = 0
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while req_index < len(self.running):
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# Only the last request in the RUNNING queue can be "partial".
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assert not has_partial_request
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assert token_budget > 0
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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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# Schedule encoder inputs.
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encoder_inputs_to_schedule, num_new_tokens, new_encoder_budget = (
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self._try_schedule_encoder_inputs(request,
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request.num_computed_tokens,
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num_new_tokens,
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encoder_budget))
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assert num_new_tokens > 0
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while True:
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new_blocks = self.kv_cache_manager.append_slots(
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request, num_new_tokens)
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if new_blocks 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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can_schedule = False
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break
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else:
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# The request can be scheduled.
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can_schedule = True
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break
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if not can_schedule:
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break
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assert new_blocks is not None
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# Schedule the request.
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scheduled_running_reqs.append(request)
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req_to_new_block_ids[request.request_id] = [
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b.block_id for b in new_blocks
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]
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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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has_partial_request = (request.num_computed_tokens + num_new_tokens
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< request.num_tokens)
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# Encoder-related.
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if encoder_inputs_to_schedule:
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scheduled_encoder_inputs[request.request_id] = (
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encoder_inputs_to_schedule)
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# Allocate the encoder cache.
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for i in encoder_inputs_to_schedule:
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self.encoder_cache_manager.allocate(request, i)
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encoder_budget = new_encoder_budget
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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 has_partial_request:
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break
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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_blocks = 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_blocks) * 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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if num_new_tokens == 0:
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# The happens when prompt length is divisible by the block
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# size and all blocks are cached. Now we force to recompute
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# the last block. Note that we have to re-compute an entire
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# block because allocate_slots() assumes num_computed_tokens
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# is always a multiple of the block size. This limitation
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# can potentially be removed in the future to slightly
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# improve the performance.
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num_computed_tokens -= self.block_size
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num_new_tokens = self.block_size
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computed_blocks.pop()
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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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# Schedule encoder inputs.
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(encoder_inputs_to_schedule, num_new_tokens,
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new_encoder_budget) = self._try_schedule_encoder_inputs(
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request, num_computed_tokens, num_new_tokens,
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encoder_budget)
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if num_new_tokens == 0:
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# The request cannot be scheduled.
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break
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new_blocks = self.kv_cache_manager.allocate_slots(
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request, num_new_tokens, computed_blocks)
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if new_blocks is None:
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# The request cannot be scheduled.
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break
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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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b.block_id for b in computed_blocks + new_blocks
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]
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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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request.num_computed_tokens = num_computed_tokens
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has_partial_request = (num_computed_tokens + num_new_tokens <
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request.num_tokens)
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# Encoder-related.
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if encoder_inputs_to_schedule:
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scheduled_encoder_inputs[request.request_id] = (
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encoder_inputs_to_schedule)
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# Allocate the encoder cache.
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for i in encoder_inputs_to_schedule:
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self.encoder_cache_manager.allocate(request, i)
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encoder_budget = new_encoder_budget
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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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# Get the longest common prefix among all requests in the running queue.
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# This can be potentially used for cascade attention.
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if self.running:
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any_request = self.running[0]
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num_common_prefix_blocks = (
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self.kv_cache_manager.get_num_common_prefix_blocks(
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any_request, 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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scheduled_encoder_inputs=scheduled_encoder_inputs,
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num_common_prefix_blocks=num_common_prefix_blocks,
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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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free_encoder_input_ids=self.encoder_cache_manager.get_freed_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 _try_schedule_encoder_inputs(
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self,
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request: Request,
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num_computed_tokens: int,
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num_new_tokens: int,
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encoder_budget: int,
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) -> Tuple[List[int], int, int]:
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"""
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Determine which encoder inputs need to be scheduled in the current step,
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and update `num_new_tokens` and encoder token budget accordingly.
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An encoder input will be scheduled if:
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- Its output tokens overlap with the range of tokens being computed
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in this step, i.e.,
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[num_computed_tokens, num_computed_tokens + num_new_tokens).
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- It is not already computed and stored in the encoder cache.
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- There is sufficient encoder token budget to process it.
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- The encoder cache has space to store it.
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If an encoder input cannot be scheduled due to cache or budget
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limitations, the method adjusts `num_new_tokens` to schedule only the
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decoder tokens up to just before the unschedulable encoder input.
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"""
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if not request.has_encoder_inputs():
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return [], num_new_tokens, encoder_budget
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encoder_inputs_to_schedule: List[int] = []
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mm_positions = request.mm_positions
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assert mm_positions is not None
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assert len(mm_positions) > 0
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for i, pos_info in enumerate(mm_positions):
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start_pos = pos_info["offset"]
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num_encoder_tokens = pos_info["length"]
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# The encoder output is needed if the two ranges overlap:
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# [num_computed_tokens, num_computed_tokens + num_new_tokens) and
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# [start_pos, start_pos + num_encoder_tokens)
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if start_pos >= num_computed_tokens + num_new_tokens:
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# The encoder input is not needed in this step.
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break
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if start_pos + num_encoder_tokens <= num_computed_tokens:
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# The encoder input is already computed and stored
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# in the decoder's KV cache.
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continue
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if self.encoder_cache_manager.has_cache(request, i):
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# The encoder input is already computed and cached.
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continue
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if not self.encoder_cache_manager.can_allocate(request, i):
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# The encoder cache is full. We can only schedule the decoder
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# tokens just before the encoder input.
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num_new_tokens = start_pos - num_computed_tokens
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break
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if num_encoder_tokens > encoder_budget:
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# The encoder budget is exhausted. We can only schedule the
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# decoder tokens up until the encoder input.
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# NOTE(woosuk): We assume that the encoder tokens should be
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# processed altogether, as the encoder usually uses
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# bidirectional attention.
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num_new_tokens = start_pos - num_computed_tokens
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break
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encoder_budget -= num_encoder_tokens
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encoder_inputs_to_schedule.append(i)
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return encoder_inputs_to_schedule, num_new_tokens, encoder_budget
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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[EngineCoreOutput]:
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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
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num_scheduled_tokens = scheduler_output.num_scheduled_tokens
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new_running: List[Request] = []
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engine_core_outputs: List[EngineCoreOutput] = []
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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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cached_encoder_input_ids = (
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self.encoder_cache_manager.get_cached_input_ids(request))
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for input_id in list(cached_encoder_input_ids):
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start_pos = request.mm_positions[input_id]["offset"]
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num_tokens = request.mm_positions[input_id]["length"]
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if start_pos + num_tokens <= request.num_computed_tokens:
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# The encoder output is already processed and stored
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# in the decoder's KV cache.
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self.encoder_cache_manager.free(request, input_id)
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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.append_output_token_ids(token_id)
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num_new_tokens = 1
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# TODO: Update the KV cache manager for prefix caching.
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# Check for stop and update request state.
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# This must be called before me make the EngineCoreOutput.
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stopped = self._check_stop(request)
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# Add EngineCoreOutput for this Request.
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output = EngineCoreOutput(
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request_id=req_id,
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new_token_ids=request.output_token_ids[-num_new_tokens:],
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finished=request.is_finished(),
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finish_reason=request.get_finished_reason(),
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stop_reason=request.stop_reason)
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engine_core_outputs.append(output)
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# Breakout of the loop.
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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 engine_core_outputs
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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):
|
|
request.status = RequestStatus.FINISHED_LENGTH_CAPPED
|
|
self._free_request(request)
|
|
return True
|
|
|
|
sampling_params = request.sampling_params
|
|
last_token_id = request.output_token_ids[-1]
|
|
if (not sampling_params.ignore_eos
|
|
and last_token_id == request.eos_token_id):
|
|
request.status = RequestStatus.FINISHED_STOPPED
|
|
self._free_request(request)
|
|
return True
|
|
|
|
if last_token_id in (sampling_params.stop_token_ids or ()):
|
|
request.status = RequestStatus.FINISHED_STOPPED
|
|
request.stop_reason = last_token_id
|
|
self._free_request(request)
|
|
return True
|
|
return False
|
|
|
|
def add_request(self, request: Request) -> None:
|
|
self.waiting.append(request)
|
|
self.requests[request.request_id] = request
|
|
|
|
def finish_requests(
|
|
self,
|
|
request_ids: Union[str, Iterable[str]],
|
|
finished_status: RequestStatus,
|
|
) -> None:
|
|
"""Handles the finish signal from outside the scheduler.
|
|
|
|
For example, the API server can abort a request when the client
|
|
disconnects.
|
|
"""
|
|
assert RequestStatus.is_finished(finished_status)
|
|
if isinstance(request_ids, str):
|
|
request_ids = (request_ids, )
|
|
request_ids = set(request_ids)
|
|
|
|
for req_id in request_ids:
|
|
request = self.requests.get(req_id)
|
|
if request is None:
|
|
# Invalid request ID.
|
|
continue
|
|
|
|
if request.status == RequestStatus.RUNNING:
|
|
self.running.remove(request)
|
|
else:
|
|
self.waiting.remove(request)
|
|
request.status = finished_status
|
|
self._free_request(request)
|
|
|
|
def _free_request(self, request: Request) -> None:
|
|
assert request.is_finished()
|
|
self.kv_cache_manager.free(request)
|
|
self.running_reqs_data.pop(request.request_id, None)
|
|
del self.requests[request.request_id]
|
|
self.finished_req_ids.add(request.request_id)
|
|
|
|
def get_num_unfinished_requests(self) -> int:
|
|
return len(self.waiting) + len(self.running)
|
|
|
|
def has_unfinished_requests(self) -> bool:
|
|
return self.get_num_unfinished_requests() > 0
|
|
|
|
|
|
@dataclass
|
|
class NewRequestData:
|
|
|
|
req_id: str
|
|
prompt_token_ids: List[int]
|
|
prompt: Optional[str]
|
|
mm_inputs: List["MultiModalKwargs"]
|
|
mm_hashes: List[str]
|
|
mm_positions: List["PlaceholderRange"]
|
|
sampling_params: SamplingParams
|
|
block_ids: List[int]
|
|
num_computed_tokens: int
|
|
|
|
@classmethod
|
|
def from_request(
|
|
cls,
|
|
request: Request,
|
|
block_ids: List[int],
|
|
num_computed_tokens: int,
|
|
) -> "NewRequestData":
|
|
return cls(
|
|
req_id=request.request_id,
|
|
prompt_token_ids=request.prompt_token_ids,
|
|
prompt=request.prompt,
|
|
mm_inputs=request.mm_inputs,
|
|
mm_hashes=request.mm_hashes,
|
|
mm_positions=request.mm_positions,
|
|
sampling_params=request.sampling_params,
|
|
block_ids=block_ids,
|
|
num_computed_tokens=num_computed_tokens,
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class ResumedRequestData:
|
|
|
|
req_id: str
|
|
block_ids: List[int]
|
|
num_computed_tokens: int
|
|
|
|
@classmethod
|
|
def from_request(
|
|
cls,
|
|
request: Request,
|
|
block_ids: List[int],
|
|
num_computed_tokens: int,
|
|
) -> "ResumedRequestData":
|
|
return cls(
|
|
req_id=request.request_id,
|
|
block_ids=block_ids,
|
|
num_computed_tokens=num_computed_tokens,
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class RunningRequestData:
|
|
|
|
req_id: str
|
|
new_block_ids: List[int]
|
|
num_computed_tokens: int
|
|
|
|
@classmethod
|
|
def from_request(
|
|
cls,
|
|
request: Request,
|
|
new_block_ids: List[int],
|
|
num_computed_tokens: int,
|
|
) -> "RunningRequestData":
|
|
return cls(
|
|
req_id=request.request_id,
|
|
new_block_ids=new_block_ids,
|
|
num_computed_tokens=num_computed_tokens,
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class SchedulerOutput:
|
|
|
|
scheduled_new_reqs: List[NewRequestData]
|
|
scheduled_resumed_reqs: List[ResumedRequestData]
|
|
scheduled_running_reqs: List[RunningRequestData]
|
|
|
|
num_scheduled_tokens: Dict[str, int]
|
|
total_num_scheduled_tokens: int
|
|
scheduled_encoder_inputs: Dict[str, List[int]]
|
|
num_common_prefix_blocks: int
|
|
|
|
preempted_req_ids: Set[str]
|
|
finished_req_ids: Set[str]
|
|
free_encoder_input_ids: List[Tuple[str, int]]
|