[TRTLLM-10271][test] Add DGX-Spark QA functional test cases for single node #10443
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📝 WalkthroughWalkthroughThis PR expands integration tests by adding numerous new model parametrizations (Llama, Qwen, Phi, Gemma, Nemotron, Eagle3, GPT-OSS variants) with various quantization formats (FP4, FP8, BF16, NVFP4), along with model-specific conditional logic for memory expectations and attention backend configurations. Changes
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🧹 Nitpick comments (1)
tests/integration/defs/test_e2e.py (1)
2096-2122: Consider extracting memory expectations to a mapping.The conditional memory expectation logic works correctly but could be more maintainable if extracted into a dictionary mapping, especially as more models are added:
eagle3_memory_expectations = { "GPT-OSS-120B": [106.71, 0, 0, 0], "default": [25.2, 0, 0, 0] } expected_mem = eagle3_memory_expectations.get( next((k for k in eagle3_memory_expectations if k in model_name), None), eagle3_memory_expectations["default"] )However, with only two cases, the current inline approach is acceptable.
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tests/integration/defs/test_e2e.pytests/integration/test_lists/qa/llm_digits_func.txt
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tests/integration/defs/test_e2e.py
🧠 Learnings (7)
📓 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: 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.
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.
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: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 8063
File: tensorrt_llm/lora_manager.py:1080-1112
Timestamp: 2025-09-29T15:14:28.503Z
Learning: In tensorrt_llm/lora_manager.py, when calculating part_sizes for attn_qkv fused LoRA modules, the sizes are correctly multiplied by tp_size because model_config.num_heads and model_config.num_kv_heads are already divided by tp_size (per-TP-rank values), so multiplication is needed to get the original full concatenated dimension size. The interleave_fused_lora_weights_for_tp function provides proper validation with asserts for total size and TP divisibility.
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 8063
File: tensorrt_llm/lora_manager.py:1080-1112
Timestamp: 2025-09-29T15:14:28.503Z
Learning: In tensorrt_llm/lora_manager.py, when calculating part_sizes for attn_qkv fused LoRA modules, the sizes are correctly multiplied by tp_size because model_config.num_heads and model_config.num_kv_heads are already divided by tp_size (per-TP-rank values), so multiplication is needed to get the original full concatenated dimension size. The interleave_fused_lora_weights_for_tp function provides proper validation.
Learnt from: amitz-nv
Repo: NVIDIA/TensorRT-LLM PR: 8063
File: tensorrt_llm/lora_manager.py:1080-1112
Timestamp: 2025-09-29T15:14:28.503Z
Learning: In tensorrt_llm/lora_manager.py, when calculating part_sizes for attn_qkv fused LoRA modules, the sizes are correctly multiplied by tp_size because model_config.num_heads and model_config.num_kv_heads are already divided by tp_size (per-TP-rank values), so multiplication is needed to get the original full concatenated dimension size. The interleave_fused_lora_weights_for_tp function provides proper validation.
📚 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_digits_func.txttests/integration/defs/test_e2e.py
📚 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_digits_func.txttests/integration/defs/test_e2e.py
📚 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_digits_func.txttests/integration/defs/test_e2e.py
📚 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_digits_func.txttests/integration/defs/test_e2e.py
📚 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_digits_func.txttests/integration/defs/test_e2e.py
📚 Learning: 2025-08-29T14:07:45.863Z
Learnt from: EmmaQiaoCh
Repo: NVIDIA/TensorRT-LLM PR: 7370
File: tests/unittest/trt/model_api/test_model_quantization.py:24-27
Timestamp: 2025-08-29T14:07:45.863Z
Learning: In TensorRT-LLM's CI infrastructure, pytest skip markers (pytest.mark.skip) are properly honored even when test files have __main__ blocks that call test functions directly. The testing system correctly skips tests without requiring modifications to the __main__ block execution pattern.
Applied to files:
tests/integration/defs/test_e2e.py
🪛 Ruff (0.14.10)
tests/integration/defs/test_e2e.py
2671-2672: zip() without an explicit strict= parameter
Add explicit value for parameter strict=
(B905)
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🔇 Additional comments (7)
tests/integration/test_lists/qa/llm_digits_func.txt (1)
1-41: LGTM - Comprehensive test coverage added.The test list properly covers the LLM and VLM models described in the PR objectives with appropriate precision variants (FP8, FP4, NVFP4, BF16) and modalities (image, audio, multimodal). The test naming is consistent and follows established patterns.
tests/integration/defs/test_e2e.py (6)
1905-1936: LGTM - Well-structured parametrize entries.The new model parametrizations are properly configured with appropriate GPU architecture markers (skip_pre_hopper, skip_pre_blackwell) matching the hardware requirements for FP8 and NVFP4 quantization formats.
1940-1947: LGTM - Appropriate model-specific configuration.The logic correctly extends the Nemotron-H-8B handling to include Nemotron-Nano variants, using substring matching to accommodate the full model name "Nemotron-Nano-9B-v2-NVFP4".
1968-1970: LGTM - Consistent configuration for 70B models.Appropriately extends the max_num_tokens limit to Llama3.3-70B, maintaining parity with the Llama3.1-70B configuration.
2554-2566: LGTM - Multimodal test coverage added.The new Qwen2.5-VL and Gemma-3 test entries are properly configured with appropriate GPU architecture markers for the quantized variants.
2626-2672: Excellent validation pattern for expected keywords.The logic properly:
- Uses base model names in the expected_keywords dictionary for easier matching across precision variants
- Implements substring matching to find the appropriate keyword set
- Validates that keywords are defined for every model, preventing silent test failures
The assertion at line 2669-2670 ensures new models won't run without defining expected outputs, which is a good defensive practice.
Note: Static analysis flags the
zip()at line 2671 for missingstrict=parameter. Whilestrict=Truewould be better practice in Python 3.10+, it's not available in Python 3.8 and the current code is safe sinceparsed_outputsandexpected_keywords[matched_key][modality]are guaranteed to have matching lengths by construction.
2655-2658: LGTM - Model-specific backend configuration.Properly sets the FLASHINFER attention backend for Gemma models as required by the VLM architecture. The case-insensitive check handles both naming conventions.
JennyLiu-nv
requested review from
farazkh80,
pamelap-nvidia and
yechank-nvidia
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yechank-nvidia
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thanks for creating the PR for test plan @JennyLiu-nv. I also have a PR for spark L0 tests, and added some gpt-oss 20b accuracy test there #9920. We can add the new ones I have in L0 to the test plan later. |
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I will add this case after #9920 merged. |
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@farazkh80 I added the extra tests/integration/test_lists/qa/llm_digits_core.txt for 1.2 top models, could you help to review if these are enough? many thanks. |
…e node This commit includes: - Add QA functional test cases for LLM and VLM models - Add core test list for DGX-Spark top models - Update test lists based on reviewer feedback Signed-off-by: Jenny Liu <JennyLiu-nv+JennyLiu@users.noreply.github.com>
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JennyLiu-nv
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close the PR try to merge the 10443 and #10497 together |
JennyLiu-nv
removed request for
Shixiaowei02,
bmarimuthu-nv,
hchings,
hypdeb,
kaiyux,
symphonylyh,
yizhang-nv and
yweng0828
5 participants
Add comprehensive test coverage for dgx-spark functional testing:
LLM test cases (test_ptp_quickstart_advanced):
VLM test cases (test_ptp_quickstart_multimodal):
Spec dec testing for GPT-OSS 120b
Files modified:
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