[V0 Deprecation] Remove pooling model support in V0 (#23434)
Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu> Signed-off-by: Max de Bayser <mbayser@br.ibm.com> Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
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Maximilien de Bayser
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@@ -40,12 +40,11 @@ from vllm.multimodal.cache import processor_only_cache_from_config
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from vllm.multimodal.processing import EncDecMultiModalProcessor
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from vllm.outputs import (PoolingRequestOutput, RequestOutput,
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RequestOutputFactory)
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from vllm.pooling_params import PoolingParams
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from vllm.sampling_params import RequestOutputKind, SamplingParams
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from vllm.sequence import (ExecuteModelRequest, ParallelSampleSequenceGroup,
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PoolingSequenceGroupOutput, Sequence, SequenceGroup,
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SequenceGroupBase, SequenceGroupMetadata,
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SequenceGroupOutput, SequenceStatus)
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Sequence, SequenceGroup, SequenceGroupBase,
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SequenceGroupMetadata, SequenceGroupOutput,
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SequenceStatus)
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from vllm.tracing import (SpanAttributes, SpanKind, extract_trace_context,
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init_tracer)
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from vllm.transformers_utils.detokenizer import Detokenizer
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@@ -93,8 +92,7 @@ class SchedulerContext:
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def __init__(self) -> None:
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self.output_queue: Deque[OutputData] = deque()
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self.request_outputs: List[Union[RequestOutput,
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PoolingRequestOutput]] = []
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self.request_outputs: List[RequestOutput] = []
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self.seq_group_metadata_list: Optional[
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List[SequenceGroupMetadata]] = None
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self.scheduler_outputs: Optional[SchedulerOutputs] = None
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@@ -261,8 +259,7 @@ class LLMEngine:
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self.model_executor = executor_class(vllm_config=vllm_config)
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if self.model_config.runner_type != "pooling":
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self._initialize_kv_caches()
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self._initialize_kv_caches()
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# If usage stat is enabled, collect relevant info.
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if is_usage_stats_enabled():
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@@ -541,7 +538,7 @@ class LLMEngine:
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self,
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request_id: str,
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processed_inputs: ProcessorInputs,
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params: Union[SamplingParams, PoolingParams],
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params: SamplingParams,
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arrival_time: float,
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lora_request: Optional[LoRARequest],
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trace_headers: Optional[Mapping[str, str]] = None,
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@@ -577,7 +574,7 @@ class LLMEngine:
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encoder_seq = (None if encoder_inputs is None else Sequence(
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seq_id, encoder_inputs, block_size, eos_token_id, lora_request))
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# Create a SequenceGroup based on SamplingParams or PoolingParams
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# Create a SequenceGroup based on SamplingParams
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if isinstance(params, SamplingParams):
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seq_group = self._create_sequence_group_with_sampling(
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request_id,
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@@ -588,18 +585,8 @@ class LLMEngine:
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trace_headers=trace_headers,
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encoder_seq=encoder_seq,
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priority=priority)
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elif isinstance(params, PoolingParams):
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seq_group = self._create_sequence_group_with_pooling(
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request_id,
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seq,
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params,
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arrival_time=arrival_time,
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lora_request=lora_request,
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encoder_seq=encoder_seq,
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priority=priority)
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else:
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raise ValueError(
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"Either SamplingParams or PoolingParams must be provided.")
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raise ValueError("SamplingParams must be provided.")
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# Add the sequence group to the scheduler with least unfinished seqs.
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costs = [
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@@ -618,7 +605,7 @@ class LLMEngine:
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self,
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request_id: str,
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prompt: PromptType,
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params: Union[SamplingParams, PoolingParams],
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params: SamplingParams,
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arrival_time: Optional[float] = None,
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lora_request: Optional[LoRARequest] = None,
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tokenization_kwargs: Optional[dict[str, Any]] = None,
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@@ -636,9 +623,8 @@ class LLMEngine:
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prompt: The prompt to the LLM. See
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[PromptType][vllm.inputs.PromptType]
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for more details about the format of each input.
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params: Parameters for sampling or pooling.
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params: Parameters for sampling.
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[SamplingParams][vllm.SamplingParams] for text generation.
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[PoolingParams][vllm.PoolingParams] for pooling.
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arrival_time: The arrival time of the request. If None, we use
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the current monotonic time.
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lora_request: The LoRA request to add.
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@@ -760,29 +746,6 @@ class LLMEngine:
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return seq_group
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def _create_sequence_group_with_pooling(
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self,
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request_id: str,
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seq: Sequence,
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pooling_params: PoolingParams,
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arrival_time: float,
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lora_request: Optional[LoRARequest],
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encoder_seq: Optional[Sequence] = None,
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priority: int = 0,
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) -> SequenceGroup:
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"""Creates a SequenceGroup with PoolingParams."""
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# Defensive copy of PoolingParams, which are used by the pooler
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pooling_params = pooling_params.clone()
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# Create the sequence group.
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seq_group = SequenceGroup(request_id=request_id,
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seqs=[seq],
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arrival_time=arrival_time,
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lora_request=lora_request,
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pooling_params=pooling_params,
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encoder_seq=encoder_seq,
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priority=priority)
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return seq_group
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def abort_request(self, request_id: Union[str, Iterable[str]]) -> None:
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"""Aborts a request(s) with the given ID.
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@@ -856,18 +819,6 @@ class LLMEngine:
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success = success and scheduler.reset_prefix_cache(device)
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return success
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@staticmethod
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def _process_sequence_group_outputs(
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seq_group: SequenceGroup,
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outputs: List[PoolingSequenceGroupOutput],
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) -> None:
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seq_group.pooled_data = outputs[0].data
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for seq in seq_group.get_seqs():
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seq.status = SequenceStatus.FINISHED_STOPPED
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return
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def _process_model_outputs(self,
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ctx: SchedulerContext,
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request_id: Optional[str] = None) -> None:
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@@ -962,13 +913,10 @@ class LLMEngine:
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seq_group.metrics.model_execute_time = (
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o.model_execute_time)
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if self.model_config.runner_type == "pooling":
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self._process_sequence_group_outputs(seq_group, output)
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else:
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self.output_processor.process_prompt_logprob(seq_group, output)
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if seq_group_meta.do_sample:
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self.output_processor.process_outputs(
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seq_group, output, is_async)
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self.output_processor.process_prompt_logprob(seq_group, output)
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if seq_group_meta.do_sample:
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self.output_processor.process_outputs(seq_group, output,
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is_async)
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if seq_group.is_finished():
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finished_now.append(i)
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@@ -1090,7 +1038,7 @@ class LLMEngine:
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seq.append_token_id(sample.output_token, sample.logprobs,
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sample.output_embed)
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def step(self) -> List[Union[RequestOutput, PoolingRequestOutput]]:
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def step(self) -> List[RequestOutput]:
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"""Performs one decoding iteration and returns newly generated results.
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<figure markdown="span">
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