[Optimization] Use Shared CachedRequestData Instance Across All Requests
#20232
Conversation
WoosukKwon
requested review from
robertgshaw2-redhat,
njhill,
ywang96,
comaniac and
alexm-redhat
as code owners
|
👋 Hi! Thank you for contributing to the vLLM project. 💬 Join our developer Slack at https://slack.vllm.ai to discuss your PR in #pr-reviews, coordinate on features in #feat- channels, or join special interest groups in #sig- channels. Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging. To run CI, PR reviewers can either: Add 🚀 |
![gemini-code-assist[bot]](https://avatars.githubusercontent.com/in/956858?s=60&v=4){kind=small}
There was a problem hiding this comment.
Summary of Changes
Hello @WoosukKwon, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request introduces a significant optimization by refactoring how CachedRequestData is handled within the scheduling and worker components. Instead of creating and managing individual CachedRequestData instances for each request, the system now uses a single, shared CachedRequestData instance per batch. This change aims to simplify the codebase by eliminating complex caching logic and improving serialization performance by consolidating data for multiple requests into one object.
Highlights
- Data Structure Refactoring: The
CachedRequestDatadataclass has been redesigned to hold lists of request-specific data (e.g.,req_ids,new_token_ids,new_block_ids) rather than single values, enabling it to represent data for an entire batch of requests. - Scheduler Output Simplification: The
SchedulerOutputnow contains a singleCachedRequestDataobject for all scheduled cached requests, replacing the previous list of individualCachedRequestDatainstances. - Elimination of Request-Level Caching: The complex
_cached_reqs_datadictionary, which previously managed a deque ofCachedRequestDataobjects per request, has been removed from theSchedulerclass, simplifying the scheduling logic and preventing potential memory leaks. - Batch-Oriented Data Preparation: The
_make_cached_request_datamethod in theSchedulerhas been updated to collect and consolidate data for all running and resumed requests into a singleCachedRequestDatainstance, streamlining the data transfer to workers. - Worker-Side Adaptation: The
_update_statesmethods ingpu_model_runner.pyandtpu_model_runner.pyhave been adjusted to correctly process the new batch-orientedCachedRequestDatastructure, iterating through its internal lists to update individual request states.
Using Gemini Code Assist
The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.
Invoking Gemini
You can request assistance from Gemini at any point in your pull request via creating an issue comment (i.e. comment on the pull request page) using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands.
| Feature | Command | Description |
|---|---|---|
| Code Review | /gemini review |
Performs a code review for the current pull request in its current state. |
| Pull Request Summary | /gemini summary |
Provides a summary of the current pull request in its current state. |
| Comment | @gemini-code-assist | Responds in comments when explicitly tagged, both in issue comments and review comments. |
| Help | /gemini help |
Displays a list of available commands. |
Customization
To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.
Limitations & Feedback
Gemini Code Assist is currently in preview and may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments to provide feedback.
You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.
Footnotes
-
Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩
There was a problem hiding this comment.
Code Review
This pull request successfully implements an optimization to use a shared CachedRequestData instance across all requests in a batch. This change simplifies the codebase by removing the previous complex caching logic and improves serialization performance, as demonstrated by the provided metrics. The changes are consistently applied across the scheduler and worker modules.
I've added a couple of suggestions to improve the readability and maintainability of the loops that process the new CachedRequestData structure in the worker modules. Overall, this is a solid improvement.
Signed-off-by: Woosuk Kwon <[email protected]>
Signed-off-by: Woosuk Kwon <[email protected]>
|
Profiled this; adding results here for posterity PR:
Main:
The biggest win though seems to be the lack of large GC runs PR:
Main:
|
…uests (vllm-project#20232) Signed-off-by: Woosuk Kwon <[email protected]>
* Fix hpu_model_runner due to PR (vllm-project#20232) Signed-off-by: Chendi.Xue <[email protected]> * add UT in plugin and will be used by upstream test Signed-off-by: Chendi.Xue <[email protected]> --------- Signed-off-by: Chendi.Xue <[email protected]>
…uests (vllm-project#20232) Signed-off-by: Woosuk Kwon <[email protected]>
…uests (vllm-project#20232) Signed-off-by: Woosuk Kwon <[email protected]> Signed-off-by: avigny <[email protected]>
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.Purpose
This PR updates the
CachedRequestDatadefinition to use a single instance shared across all requests in a batch, instead of creating a new instance per request.This change brings two advantages:
Code simplification: Previously, to avoid the cost of instantiating
CachedRequestDatafor every request, we cached and reused the class, introducing complexity and sometimes even causing a memory leak. With a single shared instance, we can eliminate this caching logic entirely, simplifying the codebase and removing the chance of leak.Faster serialization: Sharing a single instance across the batch speeds up the serialization of
SchedulerOutput. Although the data size remains unchanged, serializing one big object is faster than serializing many (up to 1024) small objects.For sharegpt + llama3 8B,
This PR + uni-process (no serialization): 54.71 reqs/s
This PR + multi-process (serialization): 52.83 reqs/s (96.5% of no-serialization perf)
main + multi-process (serialization): 49.01 reqs/s (89.5% of no-serialization perf)
Test Plan
Test Result
(Optional) Documentation Update