[Core] Get KV from Block, add KV to Block

[KrishnaM251]

🚧 WIP (currently not functioning) 🚧

Background

Adding functionality to begin implementing SSD offloading.

A nice beginning step for SSD offloading is to extend this part of the code so that it supports 2 APIs:

• get_kv_cache_from_block(block: Block), a function that can return the corresponding KV cache of the given block
• put_kv_cache_into_block(block: Block, kvcache: List[Tensor]), a function that can put the KV cache into the corresponding block.

We can then start to implement SSD offloading from these two APIs.

Change Log

[v0.3] - simple core only implementation

[v0.3.1]

• implemented get_kv_from_block at the block_manager_v2.py level
• get_kv_from_block simply calls lower level methods from within core to obtain the kv_cache without going to the worker
  • used the prepare_worker_input from worker.py as inspiration

[v0.2] - kv_from_block

Summary

• This version focuses on getting a simple working implementation of get_kv_cache_from_block, a function that obtains the KV cache of a single working block
  • Note that in this version, rather than having a get_kv_cache_from_block function, we arbitrarily mark a prempted block as the block we want to extract the kv cache from (see below for details).
• I added a new option for the --preemption-mode flag called "offload" which enables requests to piggyback off of _preempt's existing implementation.
  • This is because eventually, we will want certain preempted sequences to be offloaded to the SSD
• During one decoding iteration (step_async), we arbitrarily choose one block to get the KV cache from
• The relevant Block and KV is stored in an object called kv_from_block, which gets passed between the scheduler and the worker during the execution of step_async
  • kv_from_block is a Dict[int, int] so that in the future we may easily extend to multiple block-to-kv mappings (instead of just one)
• The KV cache can be found in the

Concerns

• This implementation is initiated at the API Server level, and results get returned to API Server level
  • We want to confine the execution / results of get_kv_cache_from_block to the core level (i.e. scheduler.py) and below
• I have not tested this yet (will begin after pushing this).
• line 285 of worker_base.py is incorrect, need to find proper way to add kv_from_block to SamplerOutputs

[v0.1] - buffer

I noticed that step_async in async_llm_engine.py is at the intersection of the scheduler and the worker/cache engine (see code). Since scheduling occurs before worker execution, I reasoned that we must somehow indicate at the scheduler level what blocks / KV caches we want to deal with. On top of this, to facilitate communication between the worker and the schedule, I opted to pass a buffer from the scheduler output to the cache engine. The initial PR is merely scaffolding, and is non-functional. Please let me know of any thoughts or concerns by answering the questions in the Questions section below.

Questions

n/a

CC List

@KuntaiDu @comaniac

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🚀

[KuntaiDu]

Thank you @KrishnaM251 for the thoughts you put into this! As an initial step, let's just focus on implementing the get_kv_cache_from_block and put_kv_cache_into_block for just one block as a way to get you more familiarized with the code base (and these 2 functions are definitely useful for future implementation). No need to implement the buffer for now.