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✅ No Failures

As of commit 0e08161 with merge base ad67e7a ():
💚 Looks good so far! There are no failures yet. 💚

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@pytorchbot merge -f "Would not hurt"

Merge started

Your change will be merged immediately since you used the force (-f) flag, bypassing any CI checks (ETA: 1-5 minutes). Please use -f as last resort and instead consider -i/--ignore-current to continue the merge ignoring current failures. This will allow currently pending tests to finish and report signal before the merge.

Learn more about merging in the wiki.

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@pytorchbot revert -m "Reverted automatically by pytorch's autorevert, to avoid this behaviour add the tag autorevert: disable" -c autorevert

This PR is attributed to have caused regression in:

• vllm-test: vllm-x-pytorch-test / test (hud)

Please investigate and fix the issues.
@claude Can you please read this revert comment, follow the links and read the errors, to then give a brief diagnostics on the cause of the error? If you judge the error to be legitimate reason for a revert, please provide brief guidance on how the author could fix it.

Claude finished @pytorch-auto-revert[bot]'s task in 1m 50s —— View job

Diagnostics: vllm-test Failure Attribution for PR #177211

• Read revert comment and identify failing CI job
• Fetch CI failure logs and diagnose root cause
• Determine if revert is legitimate and provide guidance

Failing Job

Job: vllm-x-pytorch-test / test (vllm_multi_model_test_28_failure_test, 1, 1, linux.g6.4xlarge.experimental.nvidia.gpu) — View job

Failed Tests (2 failures, 44 passed, 8 skipped)

1. test_reranker_text[half-nvidia/llama-nemotron-rerank-vl-1b-v2]
2. test_reranker_image_doc[half-nvidia/llama-nemotron-rerank-vl-1b-v2]

Root Cause

Both failures have the same root cause — a vLLM model weight loading error for the nvidia/llama-nemotron-rerank-vl-1b-v2 model:

RuntimeError: Cannot find any model weights with
`/mnt/hf_cache/hub/models--nvidia--llama-nemotron-rerank-vl-1b-v2/snapshots/0dff00ba958ae46b0fb9c931fa2a4e3bf308404b`

The vLLM EngineCore fails to start because it cannot locate the cached model weights on disk. This happens in vllm/model_executor/model_loader/default_loader.py:179 (_prepare_weights), deep inside the vLLM model loading stack — not in any PyTorch code.

Is This a Legitimate Revert?

No — this is almost certainly a false attribution. Here's why:

1. PR [CI] Add third shard to pull/linux-jammy-py3.10-gcc11 distributed CPU tests #177211 only changed .github/workflows/pull.yml (adding a third shard to distributed CPU tests). It modified zero PyTorch source code, zero test files, and nothing related to vLLM, model loading, or GPU tests.

2. The failure is in a vllm_multi_model_test_28_failure_test job — note the 28_failure_test suffix, which indicates this is a test suite tracking known failures from a specific vLLM release (2.8). These tests are expected to be flaky or broken.

3. The error is an infrastructure/cache issue: the HuggingFace model cache at /mnt/hf_cache/ doesn't have the expected model snapshot. This is a CI environment caching problem, not a code regression.

4. The test was retried 3 times (via pytest-rerunfailures with --reruns 2) and failed consistently, pointing to a persistent cache/infra issue rather than flakiness introduced by code changes.

Guidance for the Author

@huydhn — No code fix is needed. The revert was a false positive by the autorevert bot. The vLLM nvidia/llama-nemotron-rerank-vl-1b-v2 model weights were not available in the HF cache on the CI machine. This is an infrastructure issue unrelated to adding a third distributed test shard.

To reland the PR:

• Simply re-submit the same change. Consider adding the autorevert: disable tag if this test suite continues to produce false attributions.
• The vLLM cache issue may resolve on its own or may need to be investigated by the CI infra team.

@pytorchbot successfully started a revert job. Check the current status here.
Questions? Feedback? Please reach out to the PyTorch DevX Team

@huydhn your PR has been successfully reverted.

@pytorchbot merge -f "Cause and effect is hard for autorevert bot"

Merge started

Your change will be merged immediately since you used the force (-f) flag, bypassing any CI checks (ETA: 1-5 minutes). Please use -f as last resort and instead consider -i/--ignore-current to continue the merge ignoring current failures. This will allow currently pending tests to finish and report signal before the merge.

Learn more about merging in the wiki.

Questions? Feedback? Please reach out to the PyTorch DevX Team