[pull] main from vllm-project:main #19
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
![pull[bot]](https://avatars.githubusercontent.com/in/12910?s=60&v=4){kind=small}
Co-authored-by: Lei Wen <[email protected]>
Signed-off-by: Prashant Gupta <[email protected]> Co-authored-by: Roger Wang <[email protected]>
Co-authored-by: Philipp Moritz <[email protected]> Co-authored-by: Woosuk Kwon <[email protected]> Co-authored-by: mgoin <[email protected]> Co-authored-by: Tyler Michael Smith <[email protected]> Co-authored-by: Cody Yu <[email protected]>
…int (#3467) Co-authored-by: Lily Liu <[email protected]> Co-authored-by: Cyrus Leung <[email protected]>
…#4494) Co-authored-by: Simon Mo <[email protected]>
… obtain the CUDA version. (#4173) Signed-off-by: AnyISalIn <[email protected]>
Signed-off-by: Travis Johnson <[email protected]>
Co-authored-by: Lei Wen <[email protected]>
Co-authored-by: Lei Wen <[email protected]> Co-authored-by: Sage Moore <[email protected]>
This PR updates the tuning script for the fused_moe kernel to support FP8 and also adds configurations for TP4. Note that for the configuration I removed num_warps and num_stages for small batch sizes since that improved performance and brought the benchmarks on par with the numbers before in that regime to make sure this is a strict improvement over the status quo. All the numbers below are for mistralai/Mixtral-8x7B-Instruct-v0.1, 1000 input and 50 output tokens. Before this PR (with static activation scaling): qps = 1: 9.8 ms ITL, 0.49s e2e latency qps = 2: 9.7 ms ITL, 0.49s e2e latency qps = 4: 10.1 ms ITL, 0.52s e2e latency qps = 6: 11.9 ms ITL, 0.59s e2e latency qps = 8: 14.0 ms ITL, 0.70s e2e latency qps = 10: 15.7 ms ITL, 0.79s e2e latency After this PR (with static activation scaling): qps = 1: 9.8 ms ITL, 0.49s e2e latency qps = 2: 9.7 ms ITL, 0.49s e2e latency qps = 4: 10.2 ms ITL, 0.53s e2e latency qps = 6: 11.9 ms ITL, 0.59s e2e latency qps = 8: 11.9 ms ITL, 0.59s e2e latency qps = 10: 12.1 ms ITL, 0.61s e2e latency
Remove the device="cuda" declarations in mixtral as promised in #4343
…n is not 1 and max_tokens is large & Add tests for preemption (#4451)
Co-authored-by: Cade Daniel <[email protected]>
Co-authored-by: Michael Goin <[email protected]>
|
PR needs rebase. Instructions for interacting with me using PR comments are available here. If you have questions or suggestions related to my behavior, please file an issue against the kubernetes-sigs/prow repository. |
|
Hi @pull[bot]. Thanks for your PR. I'm waiting for a opendatahub-io member to verify that this patch is reasonable to test. If it is, they should reply with Once the patch is verified, the new status will be reflected by the I understand the commands that are listed here. Instructions for interacting with me using PR comments are available here. If you have questions or suggestions related to my behavior, please file an issue against the kubernetes-sigs/prow repository. |
|
/ok-to-test |
|
/lgtm |
|
[APPROVALNOTIFIER] This PR is APPROVED This pull-request has been approved by: pull[bot], z103cb The full list of commands accepted by this bot can be found here. The pull request process is described here
Needs approval from an approver in each of these files:
Approvers can indicate their approval by writing |
Merged
dtrifiro
pushed a commit
that referenced
this pull request
May 23, 2024
Update dockerfile.ubi to build vllm using wheels! I had to update some `init` files since we need those packages to be picked up when building the wheel for vllm. ### Integration tests https://v3.travis.ibm.com/github/ai-foundation/fmaas-inference-server/builds/17962397 Image pushed to quay for testing: ``` quay.io/wxpe/tgis-vllm:release-vllm-wheel.eec7a7b ``` <img width="1020" alt="Screenshot 2024-04-23 at 12 18 00" src="https://github.com/IBM/vllm/assets/9909241/f261bc38-d1f9-4d1a-a5d6-9db14aa362a6"> Useful command to build the above tests: ``` env: global: - REMOTE_INTEGRATION_TESTS=true - REMOTE_INTEGRATION_TEST_IMAGE=quay.io/wxpe/tgis-vllm:release-vllm-wheel.eec7a7b - REMOTE_INTEGRATION_TEST_CONFIG=product.vllm ``` --- <details> <!-- inside this <details> section, markdown rendering does not work, so we use raw html here. --> <summary><b> PR Checklist (Click to Expand) </b></summary> <p>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.</p> <h3>PR Title and Classification</h3> <p>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:</p> <ul> <li><code>[Bugfix]</code> for bug fixes.</li> <li><code>[CI/Build]</code> for build or continuous integration improvements.</li> <li><code>[Doc]</code> for documentation fixes and improvements.</li> <li><code>[Model]</code> for adding a new model or improving an existing model. Model name should appear in the title.</li> <li><code>[Frontend]</code> For changes on the vLLM frontend (e.g., OpenAI API server, <code>LLM</code> class, etc.) </li> <li><code>[Kernel]</code> for changes affecting CUDA kernels or other compute kernels.</li> <li><code>[Core]</code> for changes in the core vLLM logic (e.g., <code>LLMEngine</code>, <code>AsyncLLMEngine</code>, <code>Scheduler</code>, etc.)</li> <li><code>[Hardware][Vendor]</code> for hardware-specific changes. Vendor name should appear in the prefix (e.g., <code>[Hardware][AMD]</code>).</li> <li><code>[Misc]</code> for PRs that do not fit the above categories. Please use this sparingly.</li> </ul> <p><strong>Note:</strong> If the PR spans more than one category, please include all relevant prefixes.</p> <h3>Code Quality</h3> <p>The PR need to meet the following code quality standards:</p> <ul> <li>We adhere to <a href="https://google.github.io/styleguide/pyguide.html">Google Python style guide</a> and <a href="https://google.github.io/styleguide/cppguide.html">Google C++ style guide</a>.</li> <li>Pass all linter checks. Please use <a href="https://github.com/vllm-project/vllm/blob/main/format.sh"><code>format.sh</code></a> to format your code.</li> <li>The code need to be well-documented to ensure future contributors can easily understand the code.</li> <li>Include sufficient tests to ensure the project to stay correct and robust. This includes both unit tests and integration tests.</li> <li>Please add documentation to <code>docs/source/</code> if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.</li> </ul> <h3>Notes for Large Changes</h3> <p>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 <code>rfc-required</code> and might not go through the PR.</p> <h3>What to Expect for the Reviews</h3> <p>The goal of the vLLM team is to be a <i>transparent reviewing machine</i>. 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: </p> <ul> <li> 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.</li> <li> 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.</li> <li> After the review, the reviewer will put an <code> action-required</code> label on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.</li> <li> 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. </li> </ul> <h3>Thank You</h3> <p> 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! </p> </details> --------- Signed-off-by: Prashant Gupta <[email protected]>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
See Commits and Changes for more details.
Created by
pull[bot]
Can you help keep this open source service alive? 💖 Please sponsor : )