[Fix] Enable mm_processor_cache with vision LoRA

[prashanth058]

Purpose

Enables multi-modal processor cache when enable_tower_connector_lora is active. #26674 prefixes identifier with LoRA hash to avoid incorrect encoder cache hits(#26674 (comment)) but processor cache should be shared across LoRAs.

Solution:

• Add mm_hash field to MultiModalFeatureSpec to store the base hash (without LoRA prefix)
• Processor cache uses mm_hash for cache lookups (shared across LoRAs)
• Encoder cache continues using identifier (LoRA-prefixed, separate per LoRA)

[gemini-code-assist]

Code Review

This pull request enables the multi-modal processor cache for vision LoRA, which was previously unsupported. The changes introduce a mm_hash field to MultiModalFeatureSpec to allow sharing the processor cache across different LoRAs by using a base hash for the multi-modal data, independent of the LoRA. The encoder cache remains LoRA-specific by using a prefixed identifier. Additionally, the LoRA prefix for the identifier is updated for robustness.

The overall approach is sound and the new test case correctly validates the cache sharing logic. However, I've identified a critical issue with the new method of generating the LoRA prefix for cache identifiers. The use of Python's built-in hash() function is not deterministic across processes, which can lead to cache inconsistencies in a distributed environment. I've provided a suggestion to use a deterministic encoding method instead.

[Isotr0py]

LGTM