Make benchmark tool selection explicit in llm-d and RHAIIS - #274
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📝 WalkthroughWalkthroughThe llm_d and rhaiis orchestration code now resolves benchmark tools and records tool selection in test metadata. llm_d dispatches Guidellm benchmarks and applies Guidellm-specific defaults. Resource cleanup no longer assumes or separately removes resources for the default benchmark name. ChangesBenchmark tool handling
Benchmark resource cleanup
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~20 minutes Change: Feature Suggested reviewers: Merge Risk: 🟡 Moderate · up to Selecting aiperf for an executable benchmark predictably fails after consuming cluster work in either project. Reject that selection before deployment unless the delayed failure is explicitly accepted. Security Architecture ReviewSecurity architecture risk: 🔵 Low · up to An explicitly selected tool can cause cluster deployment before either orchestrator reports that it has no runner. Cleanup is normally attempted, and no new direct security exposure was established. Deployment and cleanup behavior was not verified on a cluster. Retained concerns
Security review detailsSecurity Blast Radius
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Actionable comments posted: 2
- 🪄 Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @projects/llm_d/orchestration/test_phase.py:
- Line 733: Check runner availability at the start of do_test, before deploying
the inference service or sending a smoke request, and reject unsupported tools
such as aiperf there. Keep the ValueError in run_benchmark as a safeguard for
direct calls.
Review comments at @projects/rhaiis/orchestration/test_phase.py:
- Around line 449-450: Update _run_test to reject benchmark tools without a
runner before deployment or either workload phase, including GuideLLM warmup and
profiler work. Keep runner-availability validation separate from tool-name
validation so standalone-analysis mode can still accept the configured tool.
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📒 Files selected for processing (8)
projects/llm_d/orchestration/config.d/workloads.yamlprojects/llm_d/orchestration/runtime_config.pyprojects/llm_d/orchestration/test_phase.pyprojects/llm_d/tests/test_profiles.pyprojects/llm_d/toolbox/cleanup_test_resources/main.pyprojects/rhaiis/orchestration/config.yamlprojects/rhaiis/orchestration/runtime_config.pyprojects/rhaiis/orchestration/test_phase.py
💤 Files with no reviewable changes (1)
- projects/llm_d/toolbox/cleanup_test_resources/main.py
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Follow-up AI Perf integration: thameem-abbas/forge#1. It is stacked on this PR's branch for review. After this PR merges, the AI Perf branch will be proposed to |
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Validation done before:
Validation after the CodeRabbit runner-availability fixes (
The new tests verify that an unavailable runner is rejected before cluster work in both projects, while RHAIIS standalone analysis can still use the configured tool name. |
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@albertoperdomo2 This is a preparation PR to make the benchmark runner semantics a bit generic for aiperf to be integrated in a follow-up PR. @kpouget @Harshith-umesh Including either if you since it does involve a couple of files in the RHAIIS project and the core. |
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hey Thameem, looks good overall, |
@kpouget That definitely would be the right way. Was not sure of the change surface that would entail. Let me see what that will involve.
Yes. That will involve it's own. I agree it'd not be wise to try and shove all the metrics / post-processing from different load generators into the same path. |
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@kpouget I tried your suggestion in this prototype diff. PR1 itself is unchanged.
Does this match the structure you had in mind? |
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@thameem-abbas yes that's the idea, these lines (first one in particular) imply a long-life object being configured. Better carry an object an not use singleton configuration and I didn't check if you already covered this aspect, but it can be interesting to consider PSAP-3090 as well for this work, having a common methodology for launching Guidellm in Forge/RHAIIS vs Forge/llm-d vs Forge/. |
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and, important @thameem-abbas , please make sure you locate the files in |
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Summary
Prepare llm-d and RHAIIS benchmark orchestration for more than one tool. Existing profiles use a single configured GuideLLM default; profiles do not need individual edits. The resolver validates explicit tool names, llm-d dispatches through a tool-aware entrypoint, and both projects record
benchmark_toolin test metadata. llm-d cleanup no longer guesses a GuideLLM Job name.This PR is the base for the AI Perf integration in the stacked follow-up PR. GuideLLM remains the only executable benchmark tool in this branch.
Validation
.venv/bin/python -m pytest -q projects/llm_d/tests/test_profiles.pypassed (51 tests).No cluster benchmark was run.
Summary by CodeRabbit