[https://nvbugs/5542867][fix] Fix the non-determinism issue in the mm_encoder test #9756
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…_encoder test (NVIDIA#8033) Signed-off-by: Chang Liu (Enterprise Products) <9713593+chang-l@users.noreply.github.com>
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📝 WalkthroughWalkthroughA previously skipped multi-request batch test is enabled and updated to use the new Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~10 minutes
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✅ Passed checks (2 passed)
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⚠️ Outside diff range comments (1)
tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py (1)
1-5: Add required NVIDIA copyright headerThis file is missing the NVIDIA copyright header that the project requires for all
.pysources. Please add the standard NVIDIA copyright block (with the current year) above the imports to keep legal/compliance metadata consistent across the codebase.As per coding guidelines, all TensorRT-LLM
.pyfiles should include this header.
🧹 Nitpick comments (1)
tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py (1)
200-207: Clarify and verifymax_batch_size=1with a 3-request batchHere the encoder is built with
max_batch_size = 3, while the LLM is now constructed withmax_batch_size=1but still receives 3 inputs in a singlegeneratecall. This assumes the LLM API will internally chunk the 3 requests into micro-batches of size 1 rather than enforcinglen(inputs) <= max_batch_sizeas a hard constraint.If release/1.1 behaves that way, this is a reasonable way to reduce non-determinism in the test; the inline comment helps. Please double‑check that on this branch
LLM.generateindeed supportslen(inputs) > max_batch_sizewithout error. As a small readability improvement, you might also consider renaming the local variable at line 184 to something likeencoder_max_batch_size(or adding a separatellm_max_batch_size_for_test = 1) so the intentional asymmetry between encoder and LLM batch sizes is more explicit.
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tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py(1 hunks)
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**/*.py
📄 CodeRabbit inference engine (CODING_GUIDELINES.md)
**/*.py: The code developed for TensorRT-LLM should conform to Python 3.8+
Indent Python code with 4 spaces; do not use tabs
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Python filenames should use snake_case (e.g.,some_file.py)
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Files:
tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py
**/*.{cpp,h,cu,py}
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All TensorRT-LLM Open Source Software code files should contain an NVIDIA copyright header that includes the current year at the top
Files:
tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py
🧠 Learnings (6)
📓 Common learnings
Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 6768
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:577-579
Timestamp: 2025-08-20T06:56:02.889Z
Learning: In cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, maxSequenceLength is now enforced as a non-optional argument in the BlockManager constructor, so concerns about std::nullopt defaulting to 0 are not applicable. When windowSize > maxSequenceLength, a warning should be added instead of handling optional parameter cases.
Learnt from: dbari
Repo: NVIDIA/TensorRT-LLM PR: 6714
File: tests/integration/defs/triton_server/build_model.sh:724-726
Timestamp: 2025-08-13T08:21:13.006Z
Learning: Mistral Small 3.1 multimodal (pixtral model type) supports any batch size for the multimodal encoder, not just batch size 1. The max_batch_size parameter can be set to values like 2 without conflicts with the runtime batching logic.
📚 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/_torch/multimodal/test_mm_encoder_standalone.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/unittest/_torch/multimodal/test_mm_encoder_standalone.py
📚 Learning: 2025-08-26T06:07:02.166Z
Learnt from: shaharmor98
Repo: NVIDIA/TensorRT-LLM PR: 7231
File: tensorrt_llm/_torch/pyexecutor/_util.py:504-509
Timestamp: 2025-08-26T06:07:02.166Z
Learning: In tensorrt_llm/_torch/pyexecutor/_util.py, when calling model_engine.set_lora_model_config(), pass model_binding_config.mlp_hidden_size directly without multiplying by mapping.tp_size, as the mlp_hidden_size from get_bindings_model_config() is already the per-TP rank value needed for LoRA weight packaging.
Applied to files:
tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py
📚 Learning: 2025-08-20T06:56:02.889Z
Learnt from: eopXD
Repo: NVIDIA/TensorRT-LLM PR: 6768
File: cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp:577-579
Timestamp: 2025-08-20T06:56:02.889Z
Learning: In cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp, maxSequenceLength is now enforced as a non-optional argument in the BlockManager constructor, so concerns about std::nullopt defaulting to 0 are not applicable. When windowSize > maxSequenceLength, a warning should be added instead of handling optional parameter cases.
Applied to files:
tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py
📚 Learning: 2025-08-13T08:21:13.006Z
Learnt from: dbari
Repo: NVIDIA/TensorRT-LLM PR: 6714
File: tests/integration/defs/triton_server/build_model.sh:724-726
Timestamp: 2025-08-13T08:21:13.006Z
Learning: Mistral Small 3.1 multimodal (pixtral model type) supports any batch size for the multimodal encoder, not just batch size 1. The max_batch_size parameter can be set to values like 2 without conflicts with the runtime batching logic.
Applied to files:
tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py
🧬 Code graph analysis (1)
tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py (2)
tensorrt_llm/llmapi/llm.py (1)
LLM(1052-1068)tests/unittest/llmapi/apps/_test_openai_misc.py (1)
max_batch_size(30-31)
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cherrypick from #8033
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