[TRTLLM-12358][chore] Dedup multimodal unit tests on B200#13584
[TRTLLM-12358][chore] Dedup multimodal unit tests on B200#13584QiJune merged 1 commit intoNVIDIA:mainfrom
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Signed-off-by: junq <22017000+QiJune@users.noreply.github.com>
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Inline comments:
In `@tests/integration/test_lists/test-db/l0_b200.yml`:
- Around line 121-123: Add an explicit QA-list impact sentence to the B200
multimodal comment clarifying whether the QA schedule must be updated: state
that the B-tier reduction only removes dense bf16 coverage on B200 because FP8
MoE is run (DEEPGEMM is SM100-only via _get_moe_config_for_blackwell) and that
dense bf16 remains covered on Hopper, so no change to the QA list is required;
if instead you intend a QA change, add a brief note specifying an entry for the
B200 FP8-MoE exclusion in the qa list.
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tests/integration/test_lists/test-db/l0_b200.yml
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Summary
tests/integration/test_lists/test-db/l0_b200.ymlschedules 6 files underunittest/_torch/multimodal/on the DGX_B200 stage. Five of them are pure Python plumbing with no SM-specific code path (test_multimodal_runtime,test_find_num_image_tokens,test_fuse_input_embeds,test_external_embedding,test_share_multiparams) andtest_mm_encoder_standalone.py'smodel_dirfixture has 4 parametrize values, of which onlyqwen3_30b_a3b_fp8exercises a Blackwell-only path (FP8 MoE + DEEPGEMM backend, gated by_get_moe_config_for_blackwell()→get_sm_version() >= 100). The other three (llava_7b,qwen2.5_3b,qwen3_2b) are dense bf16 and run identical kernels on Hopper.Replace the six entries with one line that filters the encoder test to
unittest/_torch/multimodal/test_mm_encoder_standalone.py -k "qwen3_30b_a3b_fp8".Coverage is preserved on Hopper:
l0_h100.yml:44runs the wholeunittest/_torch/multimodaldirectory, so the five dropped files and the dense bf16 parametrize variants are still exercised.Savings on the DGX_B200 single-GPU PyTorch stage (90-day OS averages): before ≈ 40.4 min/run (37.1 + 0.4 + 1.8 + 0.4 + 0.4 + 0.4); after ≈ 5.9 min/run (3 FP8 tests). ~33 min/PR saved on this stage.
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