[None][infra] Move some of the tests from b200 to gb200 to avoid load model issue #9492
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📝 WalkthroughWalkthroughTest configuration updates for GB200 multi-node tests and B200 test removal. Node allocation parameters adjusted in Jenkins test configurations and new test entries added to the multi-node test list, including condition header removal from the YAML structure. Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes
Pre-merge checks and finishing touches❌ Failed checks (1 warning)
✅ Passed checks (2 passed)
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🧹 Nitpick comments (2)
jenkins/L0_Test.groovy (2)
2750-2751: Track the temporary disabling of B200-8_GPUs tests.The DGX_B200-8_GPUs-PyTorch-1 test stage has been commented out due to a load model issue. This is a temporary workaround that should be tracked and reverted once the underlying issue is resolved.
Do you want me to create a template for a tracking issue to ensure this temporary change is reverted once the B200 load model issue is fixed?
2793-2796: Clarify comment about resource allocation.The comment "Request 1 more nodes for tests that moved to GB200 from B200" is potentially misleading. Based on the configuration format
[splitId, split_count, gpu_count, node_count], you're actually requesting 1 more split (parallel job), not 1 more physical node. Thenode_countparameter remains 2 for all entries.Consider updating the comment for clarity:
- // Request 1 more nodes for tests that moved to GB200 from B200 + // Request 1 more split (3 total) to distribute additional tests moved from B200 "GB200-8_GPUs-2_Nodes-PyTorch-3": ["gb200-oci-trtllm", "l0_gb200_multi_nodes", 3, 3, 8, 2],
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📒 Files selected for processing (2)
jenkins/L0_Test.groovy(2 hunks)tests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml(1 hunks)
🧰 Additional context used
🧠 Learnings (5)
📓 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*").
📚 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.groovytests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml
📚 Learning: 2025-09-17T02:48:52.732Z
Learnt from: tongyuantongyu
Repo: NVIDIA/TensorRT-LLM PR: 7781
File: tests/integration/test_lists/waives.txt:313-313
Timestamp: 2025-09-17T02:48:52.732Z
Learning: In TensorRT-LLM, `tests/integration/test_lists/waives.txt` is specifically for waiving/skipping tests, while other test list files like those in `test-db/` and `qa/` directories are for different test execution contexts (pre-merge, post-merge, QA tests). The same test appearing in both waives.txt and execution list files is intentional - the test is part of test suites but will be skipped due to the waiver.
Applied to files:
tests/integration/test_lists/test-db/l0_gb200_multi_nodes.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_nodes.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_nodes.yml
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🔇 Additional comments (1)
tests/integration/test_lists/test-db/l0_gb200_multi_nodes.yml (1)
18-18: Based on the learnings provided and the information available, I can now provide a rewritten review comment. The key learning from a previous review of similar code indicates that TensorRT-LLM's test scheduling system is specifically designed to handle wildcard matching and prevent duplicate test execution even when tests appear in multiple YAML files with overlapping GPU wildcards.
The temporary test additions are properly handled by the scheduling system's wildcard deduplication.
The tests have been temporarily added to GB200 to work around a load model issue on B200. The test scheduling system's wildcard matching logic (e.g.,
*b200*and*gb200*) automatically prevents duplicate execution even if these tests remain in both the B200 and GB200 configuration files.However, consider adding a tracking issue or follow-up comment in the configuration to document when this temporary workaround should be reverted once the B200 load model issue is resolved.
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/bot run --stage-list "DGX_B200-4_GPUs-PyTorch-Post-Merge-1,GB200-8_GPUs-2_Nodes-PyTorch-1,GB200-8_GPUs-2_Nodes-PyTorch-2,GB200-8_GPUs-2_Nodes-PyTorch-3" |
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/bot run --stage-list "DGX_B200-4_GPUs-PyTorch-Post-Merge-1,GB200-8_GPUs-2_Nodes-PyTorch-1,GB200-8_GPUs-2_Nodes-PyTorch-2,GB200-8_GPUs-2_Nodes-PyTorch-3" --disable-fail-fast |
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