tzhouam
19220aae82
- Introduced `DiffusionScheduler` to handle scheduling for diffusion models, allowing immediate request completion. - Added `OmniNewRequestData` to encapsulate additional request data, including prompt embeddings and extra information. - Updated `OmniScheduler` to ensure compatibility with new request data structures. - Created `output.py` to define new data classes for enriched request handling. - Enhanced `__init__.py` in the engine module to support new payload structures for prompt embeddings and additional information.
315 lines
15 KiB
Python
315 lines
15 KiB
Python
from vllm.v1.core.sched.scheduler import SchedulerOutput, EngineCoreOutputs, Request, RequestStatus, SpecDecodingStats, defaultdict, Optional
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from vllm.v1.core.sched.request_queue import create_request_queue
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from vllm.v1.engine import EngineCoreEventType
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from vllm.distributed.kv_events import KVEventBatch
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import time
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from vllm_omni.core.sched.scheduler import OmniScheduler
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from vllm_omni.outputs import OmniModelRunnerOutput
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from vllm.v1.engine import EngineCoreOutput
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from vllm_omni.core.sched.output import OmniNewRequestData
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class DiffusionScheduler(OmniScheduler):
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def schedule(self) -> SchedulerOutput:
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"""扩散快速通道：
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- 一次性喂入该请求的全部输入 token（若为 0，则分配 1 个占位 token）。
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- 若无法一次性满足 token 预算，则退回上游 vLLM 的默认调度。
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"""
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# 选出零 prompt 且使用 pooling（扩散结果经 pooler_output 回传）的请求
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token_budget = self.max_num_scheduled_tokens
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capacity = self.max_num_running_reqs - len(self.running)
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scheduled_timestamp = time.monotonic()
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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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req_to_new_block_ids: dict[str, tuple[list[int], ...]] = {}
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num_scheduled_tokens: dict[str, int] = {}
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scheduled_spec_decode_tokens: dict[str, list[int]] = {}
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scheduled_encoder_inputs: dict[str, list[int]] = {}
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structured_output_request_ids: dict[str, int] = {}
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# 临时队列：保持等待队列顺序，不破坏非扩散请求
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skipped_waiting_requests = create_request_queue(self.policy)
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# 快速通道挑选并调度（所有请求都视为扩散请求，不依赖 pooling_params）
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while self.waiting and token_budget > 0 and capacity > 0:
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request = self.waiting.peek_request()
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# 统一按扩散处理。若未来需要条件开关，可接入配置或请求标记。
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is_diffusion = True
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if not is_diffusion:
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# 暂存到跳过队列，稍后归还到等待队列头部
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self.waiting.pop_request()
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skipped_waiting_requests.prepend_request(request)
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continue
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# 一次性为该请求分配全部输入 token（若为 0，则分配 1 个占位 token）
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required_tokens = max(getattr(request, "num_prompt_tokens", 0), 1)
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if required_tokens > token_budget:
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# 无足够预算一次性完成该请求的输入处理，停止快速通道尝试
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break
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num_new_tokens = required_tokens
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new_blocks = self.kv_cache_manager.allocate_slots(
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request,
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num_new_tokens,
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num_lookahead_tokens=self.num_lookahead_tokens,
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)
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if new_blocks is None:
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# 无法分配（显存紧张等），停止快速通道尝试，回退到默认调度
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# 将当前 request 放回等待队列头
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# 注意：此处不改变原队列顺序
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break
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# 正式调度该请求
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request = self.waiting.pop_request()
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self.running.append(request)
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request.status = RequestStatus.RUNNING
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if self.log_stats:
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request.record_event(EngineCoreEventType.SCHEDULED,
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scheduled_timestamp)
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req_to_new_block_ids[request.request_id] = new_blocks.get_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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capacity -= 1
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scheduled_new_reqs.append(request)
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# 归还被跳过的等待请求
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if skipped_waiting_requests:
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self.waiting.prepend_requests(skipped_waiting_requests)
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# 若快速通道未调度任何请求，则回退到原始调度逻辑
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if not num_scheduled_tokens:
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return super().schedule()
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# 计算公共前缀块（与 v1 对齐）
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num_common_prefix_blocks = [0] * len(self.kv_cache_config.kv_cache_groups)
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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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grammar_bitmask = self.structured_output_manager.grammar_bitmask(
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self.requests,
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structured_output_request_ids,
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scheduled_spec_decode_tokens,
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)
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# 组装 SchedulerOutput
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new_reqs_data = [
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OmniNewRequestData.from_request(req, req_to_new_block_ids[req.request_id])
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for req in scheduled_new_reqs
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]
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cached_reqs_data = self._make_cached_request_data(
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scheduled_running_reqs,
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scheduled_resumed_reqs,
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num_scheduled_tokens,
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scheduled_spec_decode_tokens,
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req_to_new_block_ids,
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)
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total_num_scheduled_tokens = sum(num_scheduled_tokens.values())
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scheduler_output = SchedulerOutput(
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scheduled_new_reqs=new_reqs_data,
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scheduled_cached_reqs=cached_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_spec_decode_tokens=scheduled_spec_decode_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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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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structured_output_request_ids=structured_output_request_ids,
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grammar_bitmask=grammar_bitmask,
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)
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# KVTransfer：封装元信息
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if self.connector is not None:
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meta = self.connector.build_connector_meta(scheduler_output)
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scheduler_output.kv_connector_metadata = meta
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# 发布 KV 事件（与 v1 对齐）
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events = self.kv_cache_manager.take_events()
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if events:
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batch = KVEventBatch(ts=time.time(), events=events)
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self.kv_event_publisher.publish(batch)
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# 更新内部状态（推进 num_computed_tokens，释放 encoder 输入等）
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self._update_after_schedule(scheduler_output)
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return scheduler_output
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"""
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Scheduler for the diffusion model.
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This scheduler is modified to stop the request immediately for the diffusion model.
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This is because the diffusion model can generate the final image/audio in one step.
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Note: This is just a minimal modification to the original scheduler, and there should be some further efforts to optimize the scheduler.
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The original scheduler is still used for the AR model.
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"""
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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: OmniModelRunnerOutput,
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) -> dict[int, EngineCoreOutputs]:
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"""Update the scheduler state based on the model runner output.
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This method is modified to stop the request immediately for the diffusion model.
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"""
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sampled_token_ids = model_runner_output.sampled_token_ids
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spec_token_ids = model_runner_output.spec_token_ids
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logprobs = model_runner_output.logprobs
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prompt_logprobs_dict = model_runner_output.prompt_logprobs_dict
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num_scheduled_tokens = scheduler_output.num_scheduled_tokens
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pooler_outputs = model_runner_output.pooler_output
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num_nans_in_logits = model_runner_output.num_nans_in_logits
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outputs: dict[int, list[EngineCoreOutput]] = defaultdict(list)
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spec_decoding_stats: Optional[SpecDecodingStats] = None
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# NOTE(woosuk): As len(num_scheduled_tokens) can be up to 1K or more,
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# the below loop can be a performance bottleneck. We should do our best
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# to avoid expensive operations inside the loop.
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stopped_running_reqs: set[Request] = set()
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stopped_preempted_reqs: set[Request] = set()
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for req_id, num_tokens_scheduled in num_scheduled_tokens.items():
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assert num_tokens_scheduled > 0
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request = self.requests.get(req_id)
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if request is None:
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# The request is already finished. This can happen if the
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# request is aborted while the model is executing it (e.g.,
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# in pipeline parallelism).
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continue
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req_index = model_runner_output.req_id_to_index[req_id]
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generated_token_ids = sampled_token_ids[
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req_index] if sampled_token_ids else []
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scheduled_spec_token_ids = (
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scheduler_output.scheduled_spec_decode_tokens.get(req_id))
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if scheduled_spec_token_ids:
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# num_computed_tokens represents the number of tokens
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# processed in the current step, considering scheduled
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# tokens and rejections. If some tokens are rejected,
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# num_computed_tokens is decreased by the number of rejected
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# tokens, where is given by:
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# len(scheduled_spec_token_ids) + 1 - len(generated_token_ids).
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num_tokens_rejected = (len(scheduled_spec_token_ids) + 1 -
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len(generated_token_ids))
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request.num_computed_tokens -= num_tokens_rejected
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spec_decoding_stats = self.make_spec_decoding_stats(
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spec_decoding_stats,
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num_draft_tokens=len(scheduled_spec_token_ids),
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num_accepted_tokens=len(generated_token_ids) - 1)
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new_logprobs = None
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new_token_ids = generated_token_ids
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kv_transfer_params = None
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status_before_stop = request.status
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pooler_output = None
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if pooler_outputs:
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pooler_output = pooler_outputs[req_index]
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# 扩散请求：单步完成，直接标记完成并释放资源
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request.status = RequestStatus.FINISHED_STOPPED
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# 可选：标注停止原因，便于前端区分（不影响协议）
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request.stop_reason = request.stop_reason or "diffusion_done"
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kv_transfer_params = self._free_request(request)
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if status_before_stop == RequestStatus.RUNNING:
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stopped_running_reqs.add(request)
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else:
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stopped_preempted_reqs.add(request)
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# Extract sample logprobs if needed.
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if request.sampling_params is not None \
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and request.sampling_params.logprobs is not None and logprobs:
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# NOTE: once we support N tokens per step (spec decode),
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# the outer lists can be of length > 1.
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new_logprobs = logprobs.slice(req_index, req_index + 1)
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if new_token_ids and self.structured_output_manager.should_advance(
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request):
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# NOTE: structured_output_request
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# should not be None if use_structured_output, we have
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# check above, so safe to ignore type warning
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request.structured_output_request.grammar.accept_tokens( # type: ignore[union-attr]
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req_id, new_token_ids)
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# spec_token_ids comes from the model runner output
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if num_nans_in_logits is not None and req_id in num_nans_in_logits:
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request.num_nans_in_logits = num_nans_in_logits[req_id]
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# Add newly generated spec token ids to the request.
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if spec_token_ids is not None:
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if self.structured_output_manager.should_advance(request):
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metadata = request.structured_output_request
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# Needs to happen after new_token_ids are accepted.
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request.spec_token_ids = metadata.grammar.validate_tokens( # type: ignore[union-attr]
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spec_token_ids[req_index])
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else:
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request.spec_token_ids = spec_token_ids[req_index]
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# Get prompt logprobs for this request.
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prompt_logprobs_tensors = prompt_logprobs_dict.get(req_id)
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if new_token_ids or pooler_output is not None \
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or kv_transfer_params:
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# Add EngineCoreOutput for this Request.
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outputs[request.client_index].append(
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EngineCoreOutput(
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request_id=req_id,
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new_token_ids=new_token_ids,
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finish_reason=request.get_finished_reason(),
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new_logprobs=new_logprobs,
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new_prompt_logprobs_tensors=prompt_logprobs_tensors,
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pooling_output=pooler_output,
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stop_reason=request.stop_reason,
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events=request.take_events(),
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kv_transfer_params=kv_transfer_params,
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num_cached_tokens=request.num_cached_tokens,
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))
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else:
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# Invariant: EngineCore returns no partial prefill outputs.
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assert not prompt_logprobs_tensors
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# Remove the stopped requests from the running and waiting queues.
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if stopped_running_reqs:
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self.running = [
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req for req in self.running if req not in stopped_running_reqs
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]
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if stopped_preempted_reqs:
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# This is a rare case and unlikely to impact performance.
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self.waiting.remove_requests(stopped_preempted_reqs)
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# KV Connector: update state for finished KV Transfers.
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if model_runner_output.kv_connector_output:
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self._update_from_kv_xfer_finished(
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model_runner_output.kv_connector_output)
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# Create EngineCoreOutputs for all clients that have requests with
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# outputs in this step.
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engine_core_outputs = {
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client_index: EngineCoreOutputs(outputs=outs)
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for client_index, outs in outputs.items()
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}
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finished_req_ids = self.finished_req_ids_dict
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if finished_req_ids:
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# Include ids of requests that finished since last outputs
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# were sent.
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for client_index, finished_set in finished_req_ids.items():
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# Set finished request set in EngineCoreOutputs for this client.
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if (eco := engine_core_outputs.get(client_index)) is not None:
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eco.finished_requests = finished_set
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else:
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engine_core_outputs[client_index] = EngineCoreOutputs(
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finished_requests=finished_set)
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finished_req_ids.clear()
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if engine_core_outputs:
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# Return stats to only one of the front-ends.
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next(iter(engine_core_outputs.values())).scheduler_stats = (
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self.make_stats(spec_decoding_stats))
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return engine_core_outputs |