[V1] Solve potential deadlock issue in v1 engine core client internally

[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.

Purpose

• Following PR for [CI/Build][Bugfix] Fix deadlock on v1 engine test CI #19872 (comment) to solve the deadlock issue internally.

Test Plan

pytest -v -s v1/engine

Test Result

• Test should still pass after removing with set_default_torch_num_threads(1)

(Optional) Documentation Update

[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @Isotr0py, 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 addresses a specific deadlock scenario identified in the v1 engine's core client. The fix involves carefully managing PyTorch's internal threading during the initialization of multimodal models when using fork-based multiprocessing, ensuring that blocking operations do not lead to deadlocks. This is achieved by conditionally disabling OpenMP during the critical initialization phase.

Highlights

• Deadlock Prevention: I've implemented a conditional mechanism to prevent a potential deadlock during engine initialization. This specifically targets multimodal models when the worker multiprocessing method is set to 'fork', by temporarily limiting PyTorch's internal threading (OpenMP) to a single thread during the _init_engines_direct call.
• Utility Function Enhancement: The set_default_torch_num_threads context manager in vllm/utils.py has been updated to support a 'no-op' mode. If num_threads is passed as -1, the context manager will now skip modifying PyTorch's thread settings, allowing for more flexible conditional usage.

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[gemini-code-assist]

Code Review

This pull request effectively addresses a potential deadlock issue in the v1 engine by conditionally adjusting PyTorch's thread settings during initialization for multimodal models when using a 'fork' multiprocessing method. The changes are well-targeted and improve the robustness of the engine. A minor documentation improvement is suggested for clarity.

[gemini-code-assist]

The set_default_torch_num_threads context manager now has a special behavior when num_threads is -1. This should be documented in the function's docstring to clearly communicate its contract to users.

def set_default_torch_num_threads(num_threads: int):
    """Sets the default number of threads for PyTorch to the given value.
    If `num_threads` is -1, no change is made to PyTorch's thread settings.
    """