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[None][ci] move b200 with ray orchestrator test stage to gb200 #8992

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@QiJune QiJune commented Nov 7, 2025 •
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Summary by CodeRabbit

  • Chores
    • Relocated PyTorch GPU test configurations from x86 to SBSA Slurm setup
    • Updated test database entries for GPU configurations

Description

Test Coverage

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@QiJune QiJune requested review from a team as code owners November 7, 2025 02:25
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QiJune commented Nov 7, 2025

/bot run --stage-list "GB200-4_GPUs-PyTorch-Ray-1"

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coderabbitai bot commented Nov 7, 2025

📝 Walkthrough

Walkthrough

The changes relocate a Ray-based PyTorch test configuration from x86 tests to SBSA Slurm tests, remove an 8-GPU test block from the DGX B200 configuration, and restructure test condition organization in the GB200 multi-GPU configuration while preserving the test set.

Changes

Cohort / File(s) Change Summary
Test Configuration Migration
jenkins/L0_Test.groovy		 Removed DGX_B200-4_GPUs-PyTorch-Ray-1 from x86TestConfigs; added GB200-4_GPUs-PyTorch-Ray-1 to SBSASlurmTestConfigs
Test List Removals
tests/integration/test_lists/test-db/l0_dgx_b200.yml		 Removed nested condition block containing pre_merge/8-gpu test entries and associated multi-block test configurations
Test List Restructuring
tests/integration/test_lists/test-db/l0_gb200_multi_gpus.yml		 Reorganized condition block structure; preserved test set and filtering criteria with relocated placement

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

  • Verify that the removal of DGX_B200-4_GPUs-PyTorch-Ray-1 from x86 tests and its absence from other configurations is intentional
  • Confirm the new SBSA Slurm test configuration is correctly placed and properly formatted in SBSASlurmTestConfigs
  • Validate that the removed 8-GPU test block from l0_dgx_b200.yml aligns with the overall test strategy change
  • Ensure the restructured conditions in l0_gb200_multi_gpus.yml produce equivalent test coverage

Pre-merge checks and finishing touches

❌ Failed checks (1 warning)
Check name Status Explanation Resolution
Description check ⚠️ Warning The PR description is incomplete. It contains only the template structure with all sections empty (Description, Test Coverage are blank) and lacks substantive information about the changes, rationale, and test coverage despite the PR making meaningful infrastructure modifications. Fill in the Description section explaining why the test is being moved and the Test Coverage section listing affected test configurations (l0_dgx_b200.yml, l0_gb200_multi_gpus.yml, and L0_Test.groovy).
✅ Passed checks (2 passed)
Check name Status Explanation
Title check ✅ Passed The title clearly summarizes the main change: moving a B200 with Ray orchestrator test stage to GB200, which aligns with the file changes that remove a DGX B200 Ray test from x86 and add a GB200 Ray test to SBSA Slurm configs.
Docstring Coverage ✅ Passed No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
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📥 Commits

Reviewing files that changed from the base of the PR and between b53961e and fef50d9.

📒 Files selected for processing (3)
  • jenkins/L0_Test.groovy (1 hunks)
  • tests/integration/test_lists/test-db/l0_dgx_b200.yml (0 hunks)
  • tests/integration/test_lists/test-db/l0_gb200_multi_gpus.yml (1 hunks)
💤 Files with no reviewable changes (1)
  • tests/integration/test_lists/test-db/l0_dgx_b200.yml
🧰 Additional context used
🧠 Learnings (4)
📓 Common learnings 
Learnt from: pengbowang-nv
Repo: NVIDIA/TensorRT-LLM PR: 7192
File: tests/integration/test_lists/test-db/l0_dgx_b200.yml:56-72
Timestamp: 2025-08-26T09:49:04.956Z
Learning: In TensorRT-LLM test configuration files, the test scheduling system handles wildcard matching with special rules that prevent duplicate test execution even when the same tests appear in multiple yaml files with overlapping GPU wildcards (e.g., "*b200*" and "*gb200*").

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: tests/unittest/_torch/multi_gpu/test_nccl_device.py:138-149
Timestamp: 2025-10-13T19:45:03.518Z
Learning: In test_nccl_device.py, the NCCL device AllReduce implementation compares the entire residual tensor on each rank, unlike the UB implementation which compares per-rank chunks. The residual chunking calculations in the test are intentionally overridden to reflect this design difference.
📚 Learning: 2025-08-26T09:49:04.956Z 
Learnt from: pengbowang-nv
Repo: NVIDIA/TensorRT-LLM PR: 7192
File: tests/integration/test_lists/test-db/l0_dgx_b200.yml:56-72
Timestamp: 2025-08-26T09:49:04.956Z
Learning: In TensorRT-LLM test configuration files, the test scheduling system handles wildcard matching with special rules that prevent duplicate test execution even when the same tests appear in multiple yaml files with overlapping GPU wildcards (e.g., "*b200*" and "*gb200*").

Applied to files:

  • jenkins/L0_Test.groovy
  • tests/integration/test_lists/test-db/l0_gb200_multi_gpus.yml
📚 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/integration/test_lists/test-db/l0_gb200_multi_gpus.yml
📚 Learning: 2025-09-09T09:40:45.658Z 
Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 7645
File: tests/integration/test_lists/qa/llm_function_core.txt:648-648
Timestamp: 2025-09-09T09:40:45.658Z
Learning: In TensorRT-LLM test lists, it's common and intentional for the same test to appear in multiple test list files when they serve different purposes (e.g., llm_function_core.txt for comprehensive core functionality testing and llm_function_core_sanity.txt for quick sanity checks). This duplication allows tests to be run in different testing contexts.

Applied to files:

  • tests/integration/test_lists/test-db/l0_gb200_multi_gpus.yml
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)
  • GitHub Check: Pre-commit Check
🔇 Additional comments (2)
tests/integration/test_lists/test-db/l0_gb200_multi_gpus.yml (1)

43-66: LGTM! Proper separation of Ray orchestrator tests.

The new condition block correctly isolates Ray orchestrator tests from the standard test configuration. The orchestrator: ray term will be properly matched by the Jenkins pipeline's getMakoArgsFromStageName function when the stage name contains "-Ray-".

The test selection is appropriate, including Ray-specific tests (examples/test_ray.py, disaggregated tests) and relevant accuracy tests with compatible parameters.

Based on learnings

jenkins/L0_Test.groovy (1)

2719-2719: LGTM! Correct stage mapping for GB200 Ray orchestrator tests.

The new stage configuration properly maps to the Ray orchestrator condition block added in l0_gb200_multi_gpus.yml. The stage name "GB200-4_GPUs-PyTorch-Ray-1" follows the required format and will correctly trigger Ray-specific test execution via the -Ray- keyword detection in getMakoArgsFromStageName (line 1684).

Platform "gb200-trtllm" is appropriate for SBSA Slurm execution.

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tensorrt-cicd commented Nov 7, 2025

PR_Github #23792 [ run ] triggered by Bot. Commit: fef50d9

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tensorrt-cicd commented Nov 7, 2025

PR_Github #23792 [ run ] completed with state SUCCESS. Commit: fef50d9
/LLM/main/L0_MergeRequest_PR pipeline #17910 (Partly Tested) completed with status: 'FAILURE'

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QiJune commented Nov 7, 2025

/bot run --stage-list "GB200-4_GPUs-PyTorch-Ray-1"

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tensorrt-cicd commented Nov 7, 2025

PR_Github #23812 [ run ] triggered by Bot. Commit: fef50d9

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