[https://nvbugs/5451028][fix] Lower BS for NemotronUltra tests #7212
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Signed-off-by: Nave Assaf <[email protected]>
📝 WalkthroughWalkthroughIntegration test updates in tests/integration/defs/accuracy/test_llm_api_pytorch.py reduce max_batch_size to 8 and limit evaluations. Nemotron Ultra runs only MMLU; GSM8K and GPQADiamond are removed. Nemotron Ultra FP8 prequantized runs only GSM8K; MMLU and GPQADiamond are removed. Inline comments note a single evaluation due to small batch size. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
participant T as PyTest Runner
participant L as LLM (Nemotron Ultra)
participant E as Evaluator (MMLU)
T->>L: Initialize LLM (max_batch_size=8)
Note over L: Test: test_auto_dtype
T->>E: Run MMLU evaluation
E-->>T: Return results
sequenceDiagram
autonumber
participant T as PyTest Runner
participant L as LLM (Nemotron Ultra FP8 prequantized)
participant E as Evaluator (GSM8K)
T->>L: Initialize LLM (max_batch_size=8)
Note over L: Test: test_fp8_prequantized
T->>E: Run GSM8K evaluation
E-->>T: Return results
Estimated code review effort🎯 2 (Simple) | ⏱️ ~10 minutes Possibly related PRs
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amitz-nv
approved these changes
Aug 25, 2025
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Actionable comments posted: 2
🧹 Nitpick comments (2)
tests/integration/defs/accuracy/test_llm_api_pytorch.py (2)
1971-1971: Nit: clarify the comment wording.Consider being explicit that this is about test runtime and memory, and use the same term as the API ("max_batch_size").
- # Run only one eval as maximal BS is not large + # Run only one eval to keep memory and runtime in check with max_batch_size=8
1993-1993: Nit: unify the comment with the auto-dtype case.Mirror the clearer wording.
- # Run only one eval as maximal BS is not large + # Run only one eval to keep memory and runtime in check with max_batch_size=8
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📒 Files selected for processing (1)
tests/integration/defs/accuracy/test_llm_api_pytorch.py(2 hunks)
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🧠 Learnings (1)
📚 Learning: 2025-07-28T17:06:08.621Z
Learnt from: moraxu
PR: NVIDIA/TensorRT-LLM#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/defs/accuracy/test_llm_api_pytorch.py
🧬 Code graph analysis (1)
tests/integration/defs/accuracy/test_llm_api_pytorch.py (3)
tensorrt_llm/llmapi/llm_args.py (4)
CudaGraphConfig(106-163)KvCacheConfig(944-1039)quant_config(2190-2193)quant_config(2196-2197)tensorrt_llm/models/modeling_utils.py (1)
quant_algo(547-548)tensorrt_llm/quantization/mode.py (1)
QuantAlgo(23-46)
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Summary by CodeRabbit
Description
Lower BS so that NemotronUltra does not go OOM during integration tests
Test Coverage
accuracy.test_llm_api_pytorch.TestNemotronUltra
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