[P/D] Move FakeNixlWrapper to test dir

[ruisearch42]

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

Move FakeNixlWrapper to test dir so it is not included in vllm package

Follow up for https://github.com/vllm-project/vllm/pull/21072/files#r2216855130

Test Plan

Rerun test_nixl_connector.py

Test Result

passed

(Optional) Documentation Update

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[ruisearch42]

Hi @njhill @kouroshHakha please take a look, thanks

[gemini-code-assist]

Code Review

This pull request correctly moves the FakeNixlWrapper from the main source tree to the test directory, which is a good practice for separating test code from the library package. The approach of dynamically creating a mock package for Ray workers using inspect.getsource is clever. I've identified a couple of areas for improvement in the test utility to enhance robustness and prevent potential issues in CI environments, mainly concerning resource cleanup and maintainability of the dynamic module generation.

[kouroshHakha]

LGTM

[kouroshHakha]

nit: you can dedup this part of the code.

[njhill]

you could do

llm_kwargs = dict(
    model=model_name,
    enforce_eager=True,
    gpu_memory_utilization=0.5,
    kv_transfer_config=kv_transfer_config,
    distributed_executor_backend=distributed_executor_backend)
    
    # ...
    
    llm = LLM(**llm_kwargs)

[ruisearch42]

thanks, refactored

[njhill]

Thanks @ruisearch42. Unlikely that the CI failures are related but we should make sure of that before merging.

[njhill]

you could do

llm_kwargs = dict(
    model=model_name,
    enforce_eager=True,
    gpu_memory_utilization=0.5,
    kv_transfer_config=kv_transfer_config,
    distributed_executor_backend=distributed_executor_backend)
    
    # ...
    
    llm = LLM(**llm_kwargs)