[https://nvbugs/5451296][bug] Fix a thread leak in test_llm_args.py #7017
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Signed-off-by: Iman Tabrizian <[email protected]>
📝 WalkthroughWalkthroughConverted a unit test to use TorchLLM as a context manager, moving assertions inside the with-block to align with enter/exit lifecycle, without changing the asserted values or public APIs. Changes
Estimated code review effort🎯 1 (Trivial) | ⏱️ ~3 minutes Suggested reviewers
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🧹 Nitpick comments (1)
tests/unittest/llmapi/test_llm_args.py (1)
434-443: DRY the assertions and avoid relying on a private attributeYou can reduce duplication and prefer a public accessor if available, falling back to the private field to keep the test resilient.
- assert llm.args.max_beam_width == 1 - assert llm.args.max_num_tokens == 256 - assert llm.args.max_seq_len == 128 - assert llm.args.max_batch_size == 8 - - assert llm._executor_config.max_beam_width == 1 - assert llm._executor_config.max_num_tokens == 256 - assert llm._executor_config.max_seq_len == 128 - assert llm._executor_config.max_batch_size == 8 + expected = { + "max_beam_width": 1, + "max_num_tokens": 256, + "max_seq_len": 128, + "max_batch_size": 8, + } + for k, v in expected.items(): + assert getattr(llm.args, k) == v + exec_cfg = getattr(llm, "executor_config", getattr(llm, "_executor_config")) + for k, v in expected.items(): + assert getattr(exec_cfg, k) == v
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tests/unittest/llmapi/test_llm_args.py(1 hunks)
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tests/unittest/llmapi/test_llm_args.py
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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.
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🔇 Additional comments (1)
tests/unittest/llmapi/test_llm_args.py (1)
429-433: Good switch to context manager for TorchLLMUsing TorchLLM as a context manager ensures deterministic teardown and resource cleanup even if assertions fail. Moving the assertions inside the with-block is the right call.
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…VIDIA#7017) Signed-off-by: Iman Tabrizian <[email protected]>
…ch (#7043) Signed-off-by: Iman Tabrizian <[email protected]> Co-authored-by: Iman Tabrizian <[email protected]>
…7017) Signed-off-by: Iman Tabrizian <[email protected]>
…VIDIA#7017) Signed-off-by: Iman Tabrizian <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…VIDIA#7017) Signed-off-by: Iman Tabrizian <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…VIDIA#7017) Signed-off-by: Iman Tabrizian <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…VIDIA#7017) Signed-off-by: Iman Tabrizian <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
…VIDIA#7017) Signed-off-by: Iman Tabrizian <[email protected]> Signed-off-by: Wangshanshan <[email protected]>
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