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[Core] Faster startup for LoRA enabled models #4634
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LGTM. So, this improves the performance because you can just share the underlying tensor when you copy dummy lora right?
@@ -174,9 +186,15 @@ def _load_lora(self, lora_request: LoRARequest) -> LoRAModel: | |||
def add_dummy_lora(self, lora_request: LoRARequest, rank: int) -> bool: | |||
if lora_request.lora_int_id in self.list_loras(): |
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Add assert self._cached_dummy_lora is not False here?
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that would make caching mandatory
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oh do we have other use case other than using it for profiling run (which already uses cache)?
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unit testing as well
@rkooo567 Yes correct, the main issue comes from the fact that creating pinned CPU memory tensors has a large overhead. So we just reuse it. |
This PRs makes the startup time for LoRA models much lower by reusing the CPU dummy LoRA used for memory profiling, which creation time is non-trivial. This doesn't impact any GPU operations/profiling and results in the same measurements being taken.
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