[Bugfix] Fix PixtralHFImagePixelInputs dynamic shape check

[Isotr0py]

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

FIX #22837

Purpose

• Fix failing pixtral test because of incorrect shape check: https://buildkite.com/vllm/ci/builds/26788/steps/canvas?sid=0198a196-b2d8-4850-8b10-bcf6480dc259
• We need to update tests/models/multimodal/test_tensor_schema.py to cover arbitrary image size then... 😅

Test Plan

pytest -s -v tests/models/multimodal/generation/test_pixtral.py

Test Result

Test should pass now

(Optional) Documentation Update

[github-actions]

👋 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 fastcheck CI which starts running only a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of those by going to your fastcheck build on Buildkite UI (linked in the PR checks section) and unblock them. If you do not have permission to unblock, ping simon-mo or khluu to add you in our Buildkite org.

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 ready label to the PR or enable auto-merge.

🚀

[gemini-code-assist]

Code Review

This pull request addresses a bug in the dynamic shape validation for PixtralHFImagePixelInputs. The change correctly marks the height (h) and width (w) dimensions as dynamic in the TensorShape definition. This allows vLLM to process batches of images with varying sizes for Pixtral models, which aligns with the model's capabilities and fixes the failing tests mentioned in the description. The fix is concise and directly targets the issue. The code change looks good to me. As you noted in the description, updating tests/models/multimodal/test_tensor_schema.py to include tests with arbitrary image sizes would be a great follow-up to ensure this functionality is well-covered by tests going forward.

[DarkLight1337]

Why was this not caught by test_model_tensor_schema?

[Isotr0py]

Because that test is using same image size for multi-image inputs, I just realized that I forgot to resize mm_input when implemented that test. 😅

[DarkLight1337]

Can you update the test to use images with different sizes?

[DarkLight1337]

Once #22841 is merged, please also merge from main, so that the test can still pass after updating it to be more comprehensive

[robertgshaw2-redhat]

CONFIRM THIS PASSES:

• MultiModalTests - ExtendedGeneration

[robertgshaw2-redhat]

This broke Multi Modal Models Tests Standard

[robertgshaw2-redhat]

broke other tests

[Isotr0py]

Separated MM test update to #22867. Let's make CI green first.

[robertgshaw2-redhat]

approved, but should wait for CI to pass

[Isotr0py]

Seems MM standard test is broken by #22813

[DarkLight1337]

Oh, didn't realize even tgt_sizes has different shape...

[Isotr0py]

Oh, NVM, #22841 should have fixed this. Let's try again with main branch update.

[DarkLight1337]

Passes now