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vllm - 💡(How to fix) Fix [Usage]: In the context of pp parallelism, UniformTypeKVCacheSpecs type, the calculation of page size considers all the layers， not only the layerss of one pp rank ? [1 participants]

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vllm-project/vllm#38601•Fetched 2026-04-08 01:59:02
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Fix / Workaround

class UniformTypeKVCacheSpecs(KVCacheSpec): """ A KV cache spec for multiple layers with the same type of attention. Here, same types means always need the same number of token slots. For example, sliding window attentions with different window sizes are not the same type and should not be merged into one UniformTypeKVCacheSpecs. """

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class UniformTypeKVCacheSpecs(KVCacheSpec): """ A KV cache spec for multiple layers with the same type of attention. Here, same types means always need the same number of token slots. For example, sliding window attentions with different window sizes are not the same type and should not be merged into one UniformTypeKVCacheSpecs. """

kv_cache_specs: dict[str, KVCacheSpec]

@property
def page_size_bytes(self) -> int:
    return sum(spec.page_size_bytes for spec in self.kv_cache_specs.values())

How would you like to use vllm

I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.

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To integrate a specific model with vllm for inference, review the documentation and explore existing issues for guidance on model integration.

Guidance

  • Check the vllm documentation for sections on model integration and inference, as it may provide step-by-step instructions or examples.
  • Search existing issues for keywords related to model integration, such as "inference" or "model setup", to see if others have encountered similar challenges.
  • Consider reaching out to the community or support channels mentioned in the documentation for more direct assistance with integrating the specific model.
  • Review the UniformTypeKVCacheSpecs class to understand if it has any relevance to the model integration process, although its direct application is unclear from the provided context.

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The provided information lacks specific details about the model and its requirements, making it challenging to offer a precise solution. Therefore, focusing on documentation and community resources is the safest approach.

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Apply workaround: Review documentation and community resources for model integration guidance, as there is no clear indication of a direct fix or upgrade path from the provided information.

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#generation error#database connection#vector store#embedding generation#cache error

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