[None][test] Refactor qa/llm_perf_nim.yml test list #9700
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Signed-off-by: yufeiwu <[email protected]>
Signed-off-by: yufeiwu <[email protected]>
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📝 WalkthroughWalkthroughThe pull request removes a cluster-based LLM performance test configuration file and substantially restructures the main NIM performance test configuration to expand model variants, add granular GPU and hardware conditions, reorganize test groupings by GPU/compute capability, and include new quantization pathways (FP8, FP4). Changes
Estimated code review effort🎯 4 (Complex) | ⏱️ ~50 minutes
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🧹 Nitpick comments (1)
tests/integration/test_lists/qa/llm_perf_nim.yml (1)
275-276: Inconsistent TIMEOUT annotation formatting.Some inline TIMEOUT markers use
TIMEOUT(120)(no space before parenthesis), while others useTIMEOUT (120)(with space). Although likely parsed as-is by the test harness (since they are inline comments in YAML strings), normalize the format for consistency.Apply this diff to standardize all TIMEOUT annotations to remove the space before the parenthesis:
- - perf/test_perf.py::test_perf[mistral_small_v3.1_24b-bench-pytorch-bfloat16-maxbs:1-maxnt:20000-input_output_len:20000,2000-reqs:8-con:1] TIMEOUT (120) + - perf/test_perf.py::test_perf[mistral_small_v3.1_24b-bench-pytorch-bfloat16-maxbs:1-maxnt:20000-input_output_len:20000,2000-reqs:8-con:1] TIMEOUT(120) - - perf/test_perf.py::test_perf[mistral_small_v3.1_24b-bench-pytorch-bfloat16-maxbs:4096-maxnt:20000-input_output_len:20000,2000-reqs:300-con:200] TIMEOUT (120) + - perf/test_perf.py::test_perf[mistral_small_v3.1_24b-bench-pytorch-bfloat16-maxbs:4096-maxnt:20000-input_output_len:20000,2000-reqs:300-con:200] TIMEOUT(120) - - perf/test_perf.py::test_perf[deepseek_r1_nvfp4-bench-pytorch-float4-maxbs:384-maxnt:1536-input_output_len:1000,2000-reqs:49152-con:3072-ep:8-tp:8-gpus:8] TIMEOUT (120) #max throughput test + - perf/test_perf.py::test_perf[deepseek_r1_nvfp4-bench-pytorch-float4-maxbs:384-maxnt:1536-input_output_len:1000,2000-reqs:49152-con:3072-ep:8-tp:8-gpus:8] TIMEOUT(120) #max throughput test - - perf/test_perf.py::test_perf[llama_v4_maverick_17b_128e_instruct-bench-pytorch-streaming-bfloat16-input_output_len:2000,500-ep:8-tp:8-gpus:8] TIMEOUT (40) + - perf/test_perf.py::test_perf[llama_v4_maverick_17b_128e_instruct-bench-pytorch-streaming-bfloat16-input_output_len:2000,500-ep:8-tp:8-gpus:8] TIMEOUT(40) - - perf/test_perf.py::test_perf[llama_v4_maverick_17b_128e_instruct_fp8-bench-pytorch-float8-input_output_len:2000,500-ep:8-tp:8-gpus:8] TIMEOUT (40) + - perf/test_perf.py::test_perf[llama_v4_maverick_17b_128e_instruct_fp8-bench-pytorch-float8-input_output_len:2000,500-ep:8-tp:8-gpus:8] TIMEOUT(40)Also applies to: 287-288, 292-294, 299-299, 304-304, 309-309, 343-343, 346-347, 352-352, 355-355, 362-363, 380-382, 396-397
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📒 Files selected for processing (2)
tests/integration/test_lists/qa/llm_perf_cluster_nim.yml(0 hunks)tests/integration/test_lists/qa/llm_perf_nim.yml(1 hunks)
💤 Files with no reviewable changes (1)
- tests/integration/test_lists/qa/llm_perf_cluster_nim.yml
🧰 Additional context used
🧠 Learnings (11)
📓 Common learnings
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.
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.
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: venkywonka
Repo: NVIDIA/TensorRT-LLM PR: 6029
File: .github/pull_request_template.md:45-53
Timestamp: 2025-08-27T17:50:13.264Z
Learning: For PR templates in TensorRT-LLM, avoid suggesting changes that would increase developer overhead, such as converting plain bullets to mandatory checkboxes. The team prefers guidance-style bullets that don't require explicit interaction to reduce friction in the PR creation process.
Learnt from: achartier
Repo: NVIDIA/TensorRT-LLM PR: 6763
File: tests/integration/defs/triton_server/conftest.py:16-22
Timestamp: 2025-08-11T20:09:24.389Z
Learning: In the TensorRT-LLM test infrastructure, the team prefers simple, direct solutions (like hard-coding directory traversal counts) over more complex but robust approaches when dealing with stable directory structures. They accept the maintenance cost of updating tests if the layout changes.
📚 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:
tests/integration/test_lists/qa/llm_perf_nim.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/qa/llm_perf_nim.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/qa/llm_perf_nim.yml
📚 Learning: 2025-08-06T13:58:07.506Z
Learnt from: galagam
Repo: NVIDIA/TensorRT-LLM PR: 6487
File: tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.py:1-12
Timestamp: 2025-08-06T13:58:07.506Z
Learning: In TensorRT-LLM, test files (files under tests/ directories) do not require NVIDIA copyright headers, unlike production source code files. Test files typically start directly with imports, docstrings, or code.
Applied to files:
tests/integration/test_lists/qa/llm_perf_nim.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/qa/llm_perf_nim.yml
📚 Learning: 2025-10-22T06:53:47.017Z
Learnt from: xinhe-nv
Repo: NVIDIA/TensorRT-LLM PR: 8534
File: scripts/format_test_list.py:1-6
Timestamp: 2025-10-22T06:53:47.017Z
Learning: The file `scripts/format_test_list.py` in the TensorRT-LLM repository does not require the NVIDIA Apache-2.0 copyright header.
Applied to files:
tests/integration/test_lists/qa/llm_perf_nim.yml
📚 Learning: 2025-11-27T09:23:18.742Z
Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 9511
File: tests/integration/defs/examples/serve/test_serve.py:136-186
Timestamp: 2025-11-27T09:23:18.742Z
Learning: In TensorRT-LLM testing, when adding test cases based on RCCA commands, the command format should be copied exactly as it appears in the RCCA case, even if it differs from existing tests. For example, some RCCA commands for trtllm-serve may omit the "serve" subcommand while others include it.
Applied to files:
tests/integration/test_lists/qa/llm_perf_nim.yml
📚 Learning: 2025-08-13T11:07:11.772Z
Learnt from: Funatiq
Repo: NVIDIA/TensorRT-LLM PR: 6754
File: tests/integration/test_lists/test-db/l0_a30.yml:41-47
Timestamp: 2025-08-13T11:07:11.772Z
Learning: In TensorRT-LLM test configuration files like tests/integration/test_lists/test-db/l0_a30.yml, TIMEOUT values are specified in minutes, not seconds.
Applied to files:
tests/integration/test_lists/qa/llm_perf_nim.yml
📚 Learning: 2025-11-14T11:22:03.729Z
Learnt from: nzmora-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 9163
File: tensorrt_llm/_torch/auto_deploy/custom_ops/quant.py:107-113
Timestamp: 2025-11-14T11:22:03.729Z
Learning: In TensorRT-LLM AutoDeploy custom ops, when adding hardware capability checks to select between kernel implementations (e.g., cuBLAS vs. CUDA kernel), use descriptive variable names that identify the specific GPU architectures or families being targeted (e.g., `is_blackwell_geforce_or_ada`) rather than generic names like `enable_cuda_core`. This makes it clear that the code is selecting an implementation path based on hardware capabilities, not enabling/disabling hardware features.
Applied to files:
tests/integration/test_lists/qa/llm_perf_nim.yml
📚 Learning: 2025-08-11T20:09:24.389Z
Learnt from: achartier
Repo: NVIDIA/TensorRT-LLM PR: 6763
File: tests/integration/defs/triton_server/conftest.py:16-22
Timestamp: 2025-08-11T20:09:24.389Z
Learning: In the TensorRT-LLM test infrastructure, the team prefers simple, direct solutions (like hard-coding directory traversal counts) over more complex but robust approaches when dealing with stable directory structures. They accept the maintenance cost of updating tests if the layout changes.
Applied to files:
tests/integration/test_lists/qa/llm_perf_nim.yml
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🔇 Additional comments (3)
tests/integration/test_lists/qa/llm_perf_nim.yml (3)
1-20: Well-organized condition index; clear GPU tier documentation.The added legend (lines 3-20) effectively documents the 14 GPU/compute-capability tiers. This improves maintainability and helps developers understand the targeting strategy.
32-38: Compute capability ranges are well-designed for GPU targeting.The conditions use appropriate compute-capability boundaries to segregate GPU families (A100/L20/L40S at 8.x, H-series at 9.0, Blackwell at 10.0+), enabling precise test distribution. Overlapping conditions with added constraints (e.g., gpu_memory) create intentional test-specialization tiers without duplicating individual test cases.
Also applies to: 56-61, 113-117, 143-144, 191-192, 237-238, 252-254, 318-320, 374-376, 391-393
322-323: Verify precision mismatch for FP8 model variant on Line 323.Line 323 uses the
llama_v3.3_nemotron_super_49b_fp8model variant (FP8) but specifiespytorch-bfloat16precision. Nearby lines 129–135 consistently pair_fp8model variants withfloat8precision, suggesting this may be a copy-paste error. Confirm whether this is intentional (e.g., testing interop between FP8 models and lower precision) or should be corrected topytorch-float8.If this should be
float8, apply this diff:- - perf/test_perf.py::test_perf[llama_v3.3_nemotron_super_49b_fp8-bench-pytorch-bfloat16-input_output_len:500,2000-con:250-gpus:8] + - perf/test_perf.py::test_perf[llama_v3.3_nemotron_super_49b_fp8-bench-pytorch-float8-input_output_len:500,2000-con:250-gpus:8]
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PR_Github #26925 [ run ] triggered by Bot. Commit: |
Signed-off-by: yufeiwu <[email protected]>
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PR_Github #26925 [ run ] completed with state |
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/bot run |
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PR_Github #26995 [ run ] triggered by Bot. Commit: |
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PR_Github #26995 [ run ] completed with state |
Signed-off-by: yufeiwu <[email protected]>
Signed-off-by: yufeiwu <[email protected]>
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/bot skip --comment "only rebase" |
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/bot run |
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/bot skip --comment "only rebase" |
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why not merge this PR into #9714 ? Looks like many changes are identical between the two PRs. |
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We split these changes into separate PRs to allow for convenient rollback should issues occur. It helps isolate risks and keeps reverts granular. |
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/bot skip --comment "only modify test list" |
Signed-off-by: yufeiwu-nv <[email protected]>
StanleySun639
approved these changes
Dec 8, 2025
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/bot skip --comment "only modify test list" |
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PR_Github #27425 [ skip ] triggered by Bot. Commit: |
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PR_Github #27425 [ skip ] completed with state |
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@coderabbitai summary
Description
Issues: use wildcards to filter devices causes unknown device can't recognize, duplicated test cases, etc
Replace device wildcards with compute compactify to organize test cases and add index for user-friendly.
Before: 540 lines
After: 350 lines
Test cases diff:
Common tests means the exact test cases between modification.
Only Add 1 test case to A100 and add some new test cases for RTX6000 while keep others unchanged.
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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