feat(aiperf): make AIPerf the primary benchmark path

[thangquang09]

Summary

• Integrate AIPerf as the primary benchmark client path for agentic replay workloads.
• Add coding benchmark scenario references for MiniMax and GLM smoke runs.
• Cover both hackathon trace datasets and public InferenceX/Weka datasets.

Squash commit

• eb6b1388 feat(aiperf): make AIPerf primary benchmark path

Testing

• git diff --check origin/main..HEAD
• uv run --with pytest --with pydantic --with pyyaml python -m pytest utils/matrix_logic/ utils/bench_serving/test_aiperf_adapter.py -v
  • 197 passed

[github-actions]

Thanks for the contribution! For vLLM & SGLang, please ensure that your recipes is similar to the official vLLM recipes and/or the SGLang cookbook

• https://docs.sglang.io/cookbook/intro
• https://recipes.vllm.ai/

If it is not, please create a PR first before we can merge your single node PR into the master branch. Let's ensure that the documentation is first class such that the entire ML community can benefit from your hard work! Thank you

• https://github.com/vllm-project/recipes
• https://github.com/sgl-project/sglang/tree/main/docs_new

PR authors are responsible for ensuring that after merging, all GitHub Action jobs fully pass. A lot of the time, failures are just flakes and simply re-running the failed jobs will fix it. If re-running failed jobs is attempted, PR authors are responsible for ensuring it passes. See GitHub's docs on re-running failed jobs: https://docs.github.com/en/actions/how-tos/manage-workflow-runs/re-run-workflows-and-jobs#re-running-failed-jobs-in-a-workflow

As a rule of thumb, generally, PR authors should request a review & get a PR approval from the respective companies' CODEOWNERS before requesting a review from core maintainers.

If additional help is needed, PR authors can reach out to core maintainers over Slack.