[None][feat] AutoDeploy: Nemotron-H accuracy test #8133
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📝 WalkthroughWalkthroughAdds a new integration accuracy test class for Nemotron-H-8B-Base-8K, including default kwargs and sampling params, and an end-to-end auto-dtype evaluation covering MMLU and GSM8K. Updates imports to include GSM8K. Applies minor formatting tweaks and small non-functional logic reflow within the same test module. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
participant T as TestNemotronH
participant H as LlmapiAccuracyTestHarness
participant E as Eval Runner
participant D1 as Dataset: MMLU
participant D2 as Dataset: GSM8K
Note over T: test_auto_dtype()
T->>T: get_default_kwargs()
T->>T: get_default_sampling_params()
T->>H: run_eval(model="Nemotron-H-8B-Base-8K", kwargs, sampling)
H->>E: evaluate(model, MMLU, sampling/kwargs)
E->>D1: load/supply items
E-->>H: results (MMLU)
H->>E: evaluate(model, GSM8K, defaults)
E->>D2: load/supply items
E-->>H: results (GSM8K)
H-->>T: aggregated results
Note over T: Assertions complete
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes Pre-merge checks and finishing touches❌ Failed checks (2 warnings)
✅ Passed checks (1 passed)
✨ Finishing touches
🧪 Generate unit tests (beta)
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Actionable comments posted: 0
🧹 Nitpick comments (2)
tests/integration/defs/accuracy/test_llm_api_autodeploy.py (2)
78-81: Consider adding hub ID fallback for consistency.Unlike
TestLlama3_1_8B(line 42),MODEL_PATHlacks a fallback to a HuggingFace hub ID. Ifllm_models_root()returnsNoneor the path doesn't exist, this will fail rather than falling back to the hub.Consider applying this pattern for consistency:
- MODEL_PATH = f"{llm_models_root()}/Nemotron-H-8B-Base-8K" + MODEL_PATH = _hf_model_dir_or_hub_id("Nemotron-H-8B-Base-8K", MODEL_NAME)
86-86: Clarify "PyTorch test" reference.The comment mentions "PyTorch test," but this is an AutoDeploy test. Please clarify whether this refers to a different test suite or if the comment should be updated.
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🧬 Code graph analysis (1)
tests/integration/defs/accuracy/test_llm_api_autodeploy.py (1)
tests/integration/defs/accuracy/accuracy_core.py (6)
GSM8K(332-347)MMLU(315-329)CnnDailymail(248-265)LlmapiAccuracyTestHarness(844-855)evaluate(184-245)evaluate(763-773)
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🔇 Additional comments (6)
tests/integration/defs/accuracy/test_llm_api_autodeploy.py (6)
1-14: LGTM!The copyright header is correctly formatted with the current year (2025) and follows the required NVIDIA Apache-2.0 format.
24-24: LGTM!The GSM8K import is necessary for the new test class and follows the module namespace import guidelines.
27-37: LGTM!The walrus operator refactor is concise and preserves the original logic while remaining compatible with Python 3.8+.
40-76: LGTM!The formatting improvements enhance readability and maintain consistency with the rest of the file. No functional changes detected.
105-113: LGTM!The test structure is correct. MMLU uses custom sampling params (appropriate for multiple-choice tasks), while GSM8K uses defaults (appropriate for longer generative outputs). The memory requirement and model/tokenizer setup follow established patterns.
98-103: Verify Nemotron Model EOS Token ID
The EOS ID is currently hardcoded to-1, matching Llama defaults, but Nemotron-H-8B-Base-8K may use a different value. Confirmtokenizer.eos_token_id(ormodel.config.eos_token_id) for this model equals-1and update the hardcoded value if needed.
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Signed-off-by: Lucas Liebenwein <11156568+lucaslie@users.noreply.github.com>
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