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Fix vLLM x torch.compile config caching #16491

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Apr 15, 2025
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Fix vLLM x torch.compile config caching #16491

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12 changes: 9 additions & 3 deletions vllm/config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -294,12 +294,18 @@ def compute_hash(self) -> str:
factors.append(self.quantization)
factors.append(self.revision)
factors.append(self.code_revision)
factors.append(self.max_model_len)
factors.append(self.max_logprobs)
factors.append(self.disable_sliding_window)
factors.append(self.trust_remote_code)
factors.append(self.mm_processor_kwargs)
factors.append(self.generation_config)
factors.append(self.model_impl)
factors.append(self.override_generation_config)
factors.append(self.rope_scaling)
factors.append(self.rope_theta)
# rope cos/sin cache depends on the max_position_embeddings
factors.append(
getattr(self.hf_config, "max_position_embeddings", "None"))
# hf_config can control how the model looks!
factors.append(self.hf_config.to_json_string())
return hashlib.sha256(str(factors).encode()).hexdigest()
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@youkaichao youkaichao Apr 21, 2025

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sorry for the late feedback, can we add one more assert to make sure str(factors) do not contain object ids? like searching for object at 0x. not sure how stable python object representations are.

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@zou3519 zou3519 Apr 21, 2025

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I can do that. @youkaichao Were you thinking an assert (hard error) or a warning?

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@youkaichao youkaichao Apr 21, 2025

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I think it should be an assert (hard error). we should make sure object id does not occur, and when it occurs, we should be aware of it and let users report so that we can investigate.


def __init__(
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