[TRTLLM-8274][feat] Check if executor is shutdown in /health entrypoint #9057
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📝 WalkthroughWalkthroughAdded a health check mechanism to the LLM API and OpenAI server. A new Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~12 minutes
Pre-merge checks and finishing touches❌ Failed checks (1 warning)
✅ Passed checks (2 passed)
✨ Finishing touches
🧪 Generate unit tests (beta)
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📒 Files selected for processing (3)
tensorrt_llm/llmapi/llm.py(1 hunks)tensorrt_llm/serve/openai_server.py(2 hunks)tests/unittest/llmapi/apps/_test_openai_metrics.py(1 hunks)
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tensorrt_llm/llmapi/llm.pytensorrt_llm/serve/openai_server.pytests/unittest/llmapi/apps/_test_openai_metrics.py
**/*.{cpp,cxx,cc,h,hpp,hh,hxx,cu,cuh,py}
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🧠 Learnings (1)
📚 Learning: 2025-07-28T17:06:08.621Z
Learnt from: moraxu
Repo: NVIDIA/TensorRT-LLM PR: 6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.
Applied to files:
tests/unittest/llmapi/apps/_test_openai_metrics.py
🧬 Code graph analysis (3)
tensorrt_llm/llmapi/llm.py (1)
tensorrt_llm/executor/executor.py (1)
is_shutdown(294-295)
tensorrt_llm/serve/openai_server.py (1)
tensorrt_llm/llmapi/llm.py (1)
check_health(769-773)
tests/unittest/llmapi/apps/_test_openai_metrics.py (1)
tensorrt_llm/llmapi/llm.py (1)
shutdown(760-767)
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- GitHub Check: Pre-commit Check
🔇 Additional comments (3)
tensorrt_llm/serve/openai_server.py (2)
236-237: LGTM! Simple delegation pattern.The delegation to
llm.check_health()is straightforward. While this could be called directly in thehealth()method, having a separate method provides a clear extension point for adding server-specific health checks in the future (e.g., checking middleware state, connection pools, etc.).
298-302: LGTM! Correct implementation of health check endpoint.The health endpoint correctly returns HTTP 200 when healthy and 503 (Service Unavailable) when the executor is shutdown. The error message appropriately directs users to check server logs.
tests/unittest/llmapi/apps/_test_openai_metrics.py (1)
28-36: LGTM! Simplified test fixture setup.The client fixture correctly depends on the llm fixture and initializes OpenAIServer with appropriate test defaults (
tool_parser=None,server_role=None,metadata_server_cfg=None).
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tests/integration/defs/test_e2e.py
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| test_root = unittest_path() / "llmapi" / "apps" | ||
| llm_venv.run_cmd([ | ||
| "-m", "pytest", | ||
| str(test_root / "_test_openai_metrics.py -k test_health") |
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Please update to :
str(test_root / "_test_openai_metrics.py"), "-k", "test_health"
Signed-off-by: Junyi Xu <219237550+JunyiXu-nv@users.noreply.github.com>
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Summary by CodeRabbit
New Features
Description
Currently the
/healthentrypoint does nothing but return a 200 OK to the client. In this PR, we make it return 503 error when executor is shutdown, which could enable user detect the server status and reboot when the executor crashes.Test Coverage
PR Checklist
Please review the following before submitting your PR:
PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.
PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
Test cases are provided for new code paths (see test instructions)
Any new dependencies have been scanned for license and vulnerabilities
CODEOWNERS updated if ownership changes
Documentation updated as needed
Update tava architecture diagram if there is a significant design change in PR.
The reviewers assigned automatically/manually are appropriate for the PR.
Please check this after reviewing the above items as appropriate for this PR.
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