[Core] Adding Strict Priority Scheduling #48

apatke · 2024-06-17T12:36:42Z

FILL IN THE PR DESCRIPTION HERE

Motivation

Currently, vLLM supports first-come-first-served scheduling based on the arrival time of requests. This PR aims to add priorities to requests such that high-priority requests are prioritized better than lower-priority ones in the scheduler. Prioritization has also been mentioned previously in discussion e.g. https://github.com/vllm-project/vllm/issues/3058.  

Implementation

There are two major changes implemented: 

Addition of a new policy SP (strict priority) to the policy factory in addition to FCFS.
Adds a user-defined priority variable to sequence. All requests in the running queue and the waiting queue are sorted first based on this priority. If there is a tie, it falls back to the FCFS policy.
Force preemption of request from the running queue back into the waiting queue.
If there are requests in the running queue whose priority is lower than the requests in the waiting queue, they are forcefully preempted out back into the waiting queue to allow immediate execution of the higher priority request. This force preemption does not use KV cache swapping for now (can be extended in the future).

Evaluation

Evaluation conducted with the benchmark_prioritization.py with Llama-7B and A10 GPU. Requests are randomly assigned low or high priority with equal probability.

Throughput is not impacted significantly in this policy

	Request Throughput	Token Generation
FCFS	1.26 requets/s	650 tokens/s
SP	1.32 requests/s	672 tokens/s

There is no significant difference in throughput between FCFS and SP policy.

Latency for high priority requests is reduced

	High Priority - FCFS	Low Priority - FCFS	High Priority - SP	Low Priority - SP
p50	61s	44s	28s	79s
p75	74s	78s	43s	95s
p90	104s	97s	64s	113s
p99	141s	145s	83s	143s

Head of line blocking time reduction 
Head-of-line blocking time also reduces from waiting for ~100 decode iterations to 1 iteration for the request at the front of the waiting queue. This corresponds to a decrease from 3s to 0.03s.

FIX #xxxx (link existing issues this PR will resolve)

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saurabhjha1 · 2024-06-17T14:17:20Z

@njhill please help us review.

vllm/core/scheduler.py

vllm/engine/llm_engine.py

vllm/entrypoints/llm.py

vllm/core/scheduler.py

maxdebayser

I don't know enough about the swapping to evaluate how complicated it would be to add that but apart from that I think this makes sense. I left some comments with suggestions to make the code more generic to allow for the addition of other policies without changing the scheduler code.

maxdebayser · 2024-06-18T14:35:19Z

Actually, maybe instead of adding the priority argument directly a scheduling_metadata dict could be passed instead so that custom scheduling schemes can be supported in the future.

Signed-off-by: Archit Patke <[email protected]>

apatke · 2024-06-19T18:40:55Z

Thanks for the suggestions. Two changes were made to make the implementation more generalizable: (1) Priority is now under the sched_metadata dict , and (2) Helper functions are added in the policy class (e.g. specifying whether policy supports forced preemption).

Signed-off-by: Archit Patke <[email protected]>

benchmarks/benchmark_prioritization.py

wangchen615 · 2024-06-21T14:54:50Z

vllm/entrypoints/llm.py


    def _add_request(
        self,
        inputs: PromptInputs,
        params: Union[SamplingParams, PoolingParams],
        lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
+        sched_metadata: Optional[Dict[str, Optional[int]]] = None,


This can include both priority score and user ID.

njhill

Thanks @apatke this is great!

I'd suggest to open it as a PR to upstream vLLM for others to review. You could prefix with [RFC] too.

It would be great to also include a link to the preprint if/when it's publicly available.

njhill · 2024-06-23T16:00:37Z

vllm/core/policy.py

+    def forces_preemption(self) -> bool:
+        raise NotImplementedError
+
+    def sort_waiting(self) -> bool:
+        raise NotImplementedError


These could just return False by default

njhill · 2024-06-23T16:07:47Z

vllm/engine/llm_engine.py

+            arrival_time: float,
+            lora_request: Optional[LoRARequest],
+            trace_headers: Optional[Dict[str, str]] = None,
+            sched_metadata: Optional[Dict[str, Optional[int]]] = None) -> None:


I think this should be a dataclass or typed dict, i.e. for now with a single priority: int field.

Also not sure about the name sched_metadata, but can't think of a better one rn :)

vllm/core/scheduler.py

Signed-off-by: Archit Patke <[email protected]>

FerranAgulloLopez · 2024-07-03T18:18:47Z

Hello:) I was reviewing the proposed changes for the new policy, they look very good!

I have some suggestions and questions for future extensions:

It would be nice to have the sched_metadata parameter enter through the openAI API as well
It would be nice to have the policy parameter enter through the CLI (engine/arg_utils.py) as well
In case that a specific policy needs more input parameters for configuration, what would be the right way to proceed in terms of implementation? For example, if implementing a policy based on VTC we would need to configure parameters weight_input_tokens and weight_output_tokens.
In case that a specific policy needs to add more logging through the prometheus endpoint, what would be the right way to proceed in terms of implementation? For example, in VTC, we would like to show the evolution of the user cost counters. I was thinking of adding a new method in the Policy class, i.e., alternative_logging() that is called by the scheduler and llm_engine every time the default logs are executed

What do you think about these suggestions/questions/comments? I can proceed with their implementation if they seem good to you as well.