Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[Core] Faster startup for LoRA enabled models #4634

Merged
merged 4 commits into from
May 8, 2024

Conversation

Yard1
Copy link
Collaborator

@Yard1 Yard1 commented May 6, 2024

This PRs makes the startup time for LoRA models much lower by reusing the CPU dummy LoRA used for memory profiling, which creation time is non-trivial. This doesn't impact any GPU operations/profiling and results in the same measurements being taken.


PR Checklist (Click to Expand)

Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.

PR Title and Classification

Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:

  • [Bugfix] for bug fixes.
  • [CI/Build] for build or continuous integration improvements.
  • [Doc] for documentation fixes and improvements.
  • [Model] for adding a new model or improving an existing model. Model name should appear in the title.
  • [Frontend] For changes on the vLLM frontend (e.g., OpenAI API server, LLM class, etc.)
  • [Kernel] for changes affecting CUDA kernels or other compute kernels.
  • [Core] for changes in the core vLLM logic (e.g., LLMEngine, AsyncLLMEngine, Scheduler, etc.)
  • [Hardware][Vendor] for hardware-specific changes. Vendor name should appear in the prefix (e.g., [Hardware][AMD]).
  • [Misc] for PRs that do not fit the above categories. Please use this sparingly.

Note: If the PR spans more than one category, please include all relevant prefixes.

Code Quality

The PR need to meet the following code quality standards:

  • We adhere to Google Python style guide and Google C++ style guide.
  • Pass all linter checks. Please use format.sh to format your code.
  • The code need to be well-documented to ensure future contributors can easily understand the code.
  • Include sufficient tests to ensure the project to stay correct and robust. This includes both unit tests and integration tests.
  • Please add documentation to docs/source/ if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.

Notes for Large Changes

Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with rfc-required and might not go through the PR.

What to Expect for the Reviews

The goal of the vLLM team is to be a transparent reviewing machine. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process:

  • After the PR is submitted, the PR will be assigned to a reviewer. Every reviewer will pick up the PRs based on their expertise and availability.
  • After the PR is assigned, the reviewer will provide status update every 2-3 days. If the PR is not reviewed within 7 days, please feel free to ping the reviewer or the vLLM team.
  • After the review, the reviewer will put an action-required label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.
  • Please respond to all comments within a reasonable time frame. If a comment isn't clear or you disagree with a suggestion, feel free to ask for clarification or discuss the suggestion.

Thank You

Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone!

@Yard1 Yard1 marked this pull request as ready for review May 7, 2024 17:15
@Yard1 Yard1 requested review from rkooo567 and njhill May 7, 2024 18:08
Copy link
Collaborator

@rkooo567 rkooo567 left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

LGTM. So, this improves the performance because you can just share the underlying tensor when you copy dummy lora right?

@@ -174,9 +186,15 @@ def _load_lora(self, lora_request: LoRARequest) -> LoRAModel:
def add_dummy_lora(self, lora_request: LoRARequest, rank: int) -> bool:
if lora_request.lora_int_id in self.list_loras():
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Add assert self._cached_dummy_lora is not False here?

Copy link
Collaborator Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

that would make caching mandatory

Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

oh do we have other use case other than using it for profiling run (which already uses cache)?

Copy link
Collaborator Author

@Yard1 Yard1 May 8, 2024

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

unit testing as well

@Yard1
Copy link
Collaborator Author

Yard1 commented May 8, 2024

@rkooo567 Yes correct, the main issue comes from the fact that creating pinned CPU memory tensors has a large overhead. So we just reuse it.

@Yard1 Yard1 merged commit ad932a2 into vllm-project:main May 8, 2024
59 checks passed
@Yard1 Yard1 deleted the faster_lora_startup branch May 8, 2024 17:33
z103cb pushed a commit to z103cb/opendatahub_vllm that referenced this pull request May 9, 2024
dtrifiro pushed a commit to dtrifiro/vllm that referenced this pull request May 21, 2024
Temirulan pushed a commit to Temirulan/vllm-whisper that referenced this pull request Sep 6, 2024
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

3 participants