feat: disk cache v1.0

[blueswhen]

[gemini-code-assist[bot]]

Summary of Changes

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This pull request introduces a significant feature by implementing a disk cache layer for the KV cache system. The primary goal is to extend the effective memory capacity for KV cache pages by allowing less frequently used pages to be stored on disk and reloaded on demand. This change aims to improve performance and resource utilization, especially for scenarios with large models or many concurrent requests, by reducing the reliance on expensive GPU or CPU memory for all cached data. The integration includes new worker processes, updated cache management logic, and new metrics to monitor disk cache activity.

Highlights

• Disk Cache Integration: Introduced a new disk-based caching mechanism to offload and retrieve KV cache pages, enhancing overall cache capacity and memory management.
• New DiskCacheWorker: Added a dedicated DiskCacheWorker class to manage the persistence and retrieval of KV cache pages to and from disk using PyLocalCacheService.
• Enhanced CPU Cache Client: Modified the CpuKvCacheClient with new methods for encoding/decoding offload page indexes, improved page lifecycle management (e.g., mark_pages_recyclable, recycle_pages), and support for grouped page offloading.
• Metrics and Logging: Added disk_prompt_cache_len to the request object and integrated its tracking and logging, allowing for better visibility into disk cache hit rates.
• Optimized Cache Matching Logic: Updated the multi_level_kv_cache manager to incorporate disk cache lookups, allowing the system to first check CPU cache, then disk cache for missing pages, and load them back into CPU memory as needed.

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