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[V1] AsyncLLMEngine Proposal #9826

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@robertgshaw2-neuralmagic robertgshaw2-neuralmagic commented Oct 30, 2024

SUMMARY:

  • Proposal/prototype for architecture of AsyncLLMEngine in V1
image
  • LLM, required calling del to shutdown
from vllm import LLM

model = LLM("Qwen/Qwen2-0.5B-Instruct", enforce_eager=True)
output = model.generate("Hello my name is")
print(output)
del model

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@robertgshaw2-neuralmagic robertgshaw2-neuralmagic changed the title Rework rs proto [V1] Async Input Processing and Detokenization Oct 31, 2024
@robertgshaw2-neuralmagic robertgshaw2-neuralmagic changed the title [V1] Async Input Processing and Detokenization [V1] Fully Async Input Processing and Detokenization Oct 31, 2024
@mergify mergify bot added the frontend label Oct 31, 2024
@@ -227,13 +228,12 @@ def update_from_output(
self,
scheduler_output: "SchedulerOutput",
model_runner_output: "ModelRunnerOutput",
) -> List[Tuple[Request, int]]:
) -> List[EngineCoreOutput]:
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Im not sure it makes sense for this method to be in scheduler.py

The only item related to setting the scheduler here is updating which self.running

@robertgshaw2-neuralmagic robertgshaw2-neuralmagic changed the title [V1] Fully Async Input Processing and Detokenization [V1] AsyncLLMEngine Proposal Oct 31, 2024

# TODO: should this be wrapped in a class?
if envs.VLLM_USE_V1:
engine_config = engine_args.create_engine_config()
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@njhill - im starting to think this stuff should be wrapped into llmengine

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@robertgshaw2-neuralmagic yeah I was thinking we might have a completely separate LLM class but that may be tricky if we want to be able to switch existing code with the env var.

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