[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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[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.

[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

[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]

lol something happened when I tried to sign off. I'll fix it tmrw

[KuntaiDu]

Moving the development to PR #10502 in order to fix DCO issue.

[coolkp]

-X POST i had to add this to make it work on my instance

[tanzelin430]

Higher QPS, Longer context can lead to crush

[tanzelin430]

@KuntaiDu Hi, first of all thanks for submitting such valuable pr.However I think this pr is far from being ready to be published. Under high qps and longer context in limited memory(eg.24G) with llama3-8b, your system will crash in a short time. And I am now working on it.
for example in vllm adapter line 400, rebuilt_attn_metadata.block_tables = torch.tensor(
rebuilt_block_tables,
dtype=model_input.attn_metadata.block_tables.dtype).to(device)
the alignment or padding can be a real problem...

[tanzelin430]

@KuntaiDu With all due respect...I mean it, really thank you for your labor...but such distributed system could be very difficult to debug for a stranger...Maybe you can add more test cases for more detailed testing. And I am willing to help...I feel exhausted that I finally found the system has its short cuts since being published, I was thinking if it was my problem orz