[Core] Implementing disaggregated prefilling, and caching KV cache in CPU/disk/database.

[KuntaiDu]

TL; DR: implemented disaggregated prefill with <100 core line change (and most of them are comments)

This PR is a continuation of PR #6170 , with a new design that allows future extension.

Current supported applications:

• Disaggregated prefilling. Check examples/disagg_prefill/disagg_prefill_example.sh for an example, and benchmarks/disagg_prefill for various benchmarks. Benchmarking script are all one-click runnable (after setting HF_TOKEN)
• Connecting to a KV cache storage service LMCache. Examples TBD.

Two roles: KV provider (e.g. prefill vLLM instance) and KV consumer (e.g. decode vLLM instance)

• Provider side: insert: insert a KV cache to a buffer, so that it can be transferred upon request
• Consumer side: drop_select: select a KV cache based on tokens, transfer the selected KV, and drop this KV out from the buffer

Example workflow (the buffer in the following figure is the same as insert)

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @KuntaiDu.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[KuntaiDu]

Now the end-to-end disagg prefill example in the example folder is functional (thanks to Kaichao's suggestion the core change is also much less). The remaining chores are cleaning up the tests, adjust benchmarking files and do some perf benchmark.

• move configuration to vllmconfig
• adjust distributed initialization method
• adjust the abstraction --- now we call anything an "connector", which basically means a connection between P and D instance. The connection can be done via torch.distributed, or by third-party library like LMCache and Mooncake
• update perf benchmark files

We are also aware other bugs and feature requests --- we will fix them in future PRs.

• torch.distributed heartbeat
• Transfer the generated tokens in disagg prefill to bypass token sampling in decode instance
• Implement a disaggregated prefill engine designated for disagg prefill to avoid OpenAI API server reduction
• Pipelining
• XpYd support
• RDMA support

[KuntaiDu]

• Overhead: on llama 8B, the total overhead added by disaggregated prefill is 17.61 ms (measured by benchmarks/disagg_benchmarks/disagg_overhead_benchmark.sh)

Performance benchmark now crashes at high QPS, need some debugging to find out why.