[https://nvbugs/5747938][fix] Use local tokenizer #10230
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Signed-off-by: Pengyun Lin <[email protected]>
📝 WalkthroughWalkthroughThe changes enable parameterization of the tokenizer path through environment variables. A shell script now accepts an optional Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~10 minutes Pre-merge checks and finishing touches❌ Failed checks (2 warnings)
✅ Passed checks (1 passed)
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🧹 Nitpick comments (1)
tests/unittest/llmapi/apps/_test_trtllm_serve_example.py (1)
35-36: Setting environment variable in fixture may affect other tests.The
TOKENIZER_PATHenvironment variable is set in a module-scoped fixture but is never cleaned up. While the module scope limits the impact, this variable will persist for the lifetime of the test module and could potentially affect other tests if they read this environment variable.Consider adding cleanup in the fixture's teardown phase if isolation is important.
🔎 Optional refactor to add cleanup
@pytest.fixture(scope="module") def server(model_name: str, temp_extra_llm_api_options_file: str): model_path = get_model_path(model_name) + original_tokenizer_path = os.environ.get("TOKENIZER_PATH") os.environ["TOKENIZER_PATH"] = model_path # fix port to facilitate concise trtllm-serve examples args = ["--extra_llm_api_options", temp_extra_llm_api_options_file] - with RemoteOpenAIServer(model_path, args, port=8000) as remote_server: - yield remote_server + try: + with RemoteOpenAIServer(model_path, args, port=8000) as remote_server: + yield remote_server + finally: + if original_tokenizer_path is None: + os.environ.pop("TOKENIZER_PATH", None) + else: + os.environ["TOKENIZER_PATH"] = original_tokenizer_path
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examples/serve/aiperf_client.shtests/unittest/llmapi/apps/_test_trtllm_serve_example.py
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📓 Path-based instructions (2)
**/*.py
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**/*.py: Code developed for TensorRT-LLM should conform to Python 3.8+
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Files:
tests/unittest/llmapi/apps/_test_trtllm_serve_example.py
**/*.{cpp,h,cu,cuh,py}
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
All TensorRT-LLM Open Source Software code should contain an NVIDIA copyright header that includes the year of its latest meaningful modification
Files:
tests/unittest/llmapi/apps/_test_trtllm_serve_example.py
🧠 Learnings (3)
📓 Common learnings
Learnt from: thorjohnsen
Repo: NVIDIA/TensorRT-LLM PR: 6910
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:0-0
Timestamp: 2025-08-14T21:04:50.248Z
Learning: In KV cache onboarding logic during prefill in cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, when calculating which blocks fall within the attention window, use getTokensPerBlock() to advance token indices rather than block->getUniqueTokens().size(), because the calculation needs to consider the post-prefill state where blocks will be filled to capacity, not their current token count.
Learnt from: samuellees
Repo: NVIDIA/TensorRT-LLM PR: 6974
File: tensorrt_llm/serve/scripts/benchmark_dataset.py:558-566
Timestamp: 2025-08-18T08:42:02.640Z
Learning: In TensorRT-LLM's RandomDataset (tensorrt_llm/serve/scripts/benchmark_dataset.py), when using --random-token-ids option, sequence length accuracy is prioritized over semantic correctness for benchmarking purposes. The encode/decode operations should use skip_special_tokens=True and add_special_tokens=False to ensure exact target token lengths.
📚 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/unittest/llmapi/apps/_test_trtllm_serve_example.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/unittest/llmapi/apps/_test_trtllm_serve_example.py
🧬 Code graph analysis (1)
tests/unittest/llmapi/apps/_test_trtllm_serve_example.py (1)
tests/integration/defs/stress_test/stress_test.py (1)
get_model_path(310-312)
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- GitHub Check: Pre-commit Check
🔇 Additional comments (2)
examples/serve/aiperf_client.sh (1)
5-5: LGTM! Environment variable parameterization is well-implemented.The bash parameter expansion with fallback default is correct and maintains backward compatibility.
tests/unittest/llmapi/apps/_test_trtllm_serve_example.py (1)
9-9: The import path is correct. Thetest_llmmodule exists attests/unittest/llmapi/test_llm.py, and the relative importfrom ..test_llm import get_model_pathfromtests/unittest/llmapi/apps/_test_trtllm_serve_example.pyproperly resolves to this module, which contains theget_model_pathfunction at line 70.
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Description
This also fixes https://nvbugs/5546197
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