[#10696][fix] AutoDeploy prevent torch.export from specializing batch dimension when max_batch_size=1

[MrGeva]

When max_batch_size=1, the example sequence used during export had batch_size=1. This caused torch.export to specialize the batch dimension to a static value of 1 instead of keeping it dynamic, resulting in:

  ValueError: Found the following conflicts between user-specified ranges
  and inferred ranges from model tracing:
  - Received user-specified dim hint Dim.DYNAMIC(min=None, max=None), but export 0/1 specialized due to hint of 1 for dimension inputs['input_ids'].shape[0].
  - Received user-specified dim hint Dim.DYNAMIC(min=None, max=None), but export 0/1 specialized due to hint of 1 for dimension inputs['position_ids'].shape[0].

The fix ensures the example batch size is always >= 2 during export, even when max_batch_size=1, to prevent torch.export from specializing the batch dimension.

Added tests to verify the fix.

Summary by CodeRabbit

• Improvements

  • Optimized default batch size generation during model export to ensure minimum batch size of 2, improving compatibility with minimal batch configurations.

• Tests

  • Added test coverage validating batch size behavior during model export operations across different maximum batch size configurations.

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Description

Test Coverage

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[MrGeva]

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📝 Walkthrough

Walkthrough

The change modifies the default batch size calculation in set_example_sequence to ensure a minimum batch size of 2, even when max_batch_size is 1. Corresponding test cases validate this new behavior.

Changes

Cohort / File(s)	Summary
Batch size calculation fix tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py	Modified batch size computation from min(2, max_batch_size) to max(2, min(2, max_batch_size)), guaranteeing a minimum of 2 for default input generation during export
Test suite for batch size validation tests/unittest/_torch/auto_deploy/unit/singlegpu/shim/test_llm_config.py	Added TestSequenceInfoExampleBatchSize with two test cases: one validating batch size ≥ 2 when max_batch_size=1, and another validating batch size = 2 for max_batch_size=32

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~12 minutes

🚥 Pre-merge checks | ✅ 3

✅ Passed checks (3 passed)

Check name	Status	Explanation
Title check	✅ Passed	The PR title accurately describes the main change: preventing torch.export from specializing the batch dimension when max_batch_size=1 in AutoDeploy.
Description check	✅ Passed	The description explains the issue, solution, and test coverage. However, the PR description template sections (Description and Test Coverage) are not explicitly filled out as separate sections in the provided description.
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.

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✨ Finishing touches

• 📝 Generate docstrings

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🧹 Recent nitpick comments

tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py (1)

728-730: Simplify the batch size expression.

The expression max(2, min(2, self.max_batch_size)) always evaluates to 2 for any positive max_batch_size:

• When max_batch_size=1: min(2,1)=1, then max(2,1)=2
• When max_batch_size>=2: min(2,x)=2, then max(2,2)=2

The fix correctly prevents torch.export from specializing the batch dimension. Consider simplifying for clarity:

♻️ Proposed simplification

         if input_ids is None:
             # Use batch_size >= 2 for export to prevent torch.export from specializing
             # the batch dimension when max_batch_size=1 (dimension value 1 triggers static optimization)
-            bs, seq_len = max(2, min(2, self.max_batch_size)), min(4, self.max_seq_len)
+            bs, seq_len = 2, min(4, self.max_seq_len)
             input_ids = torch.ones(bs, seq_len, dtype=torch.int).tolist()

---

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📥 Commits

Reviewing files that changed from the base of the PR and between cd55fb4 and 83a7198.

📒 Files selected for processing (2)

• tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py
• tests/unittest/_torch/auto_deploy/unit/singlegpu/shim/test_llm_config.py

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Files:

• tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py
• tests/unittest/_torch/auto_deploy/unit/singlegpu/shim/test_llm_config.py

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Files:

• tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py
• tests/unittest/_torch/auto_deploy/unit/singlegpu/shim/test_llm_config.py

🧠 Learnings (3)

📓 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.

📚 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/auto_deploy/unit/singlegpu/shim/test_llm_config.py

📚 Learning: 2025-09-09T09:40:45.658Z

Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 7645
File: tests/integration/test_lists/qa/llm_function_core.txt:648-648
Timestamp: 2025-09-09T09:40:45.658Z
Learning: In TensorRT-LLM test lists, it's common and intentional for the same test to appear in multiple test list files when they serve different purposes (e.g., llm_function_core.txt for comprehensive core functionality testing and llm_function_core_sanity.txt for quick sanity checks). This duplication allows tests to be run in different testing contexts.

Applied to files:

• tests/unittest/_torch/auto_deploy/unit/singlegpu/shim/test_llm_config.py

🧬 Code graph analysis (2)

tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py (1)

tests/unittest/llmapi/apps/_test_openai_misc.py (1)

• max_batch_size (30-31)

tests/unittest/_torch/auto_deploy/unit/singlegpu/shim/test_llm_config.py (1)

tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py (2)

• SequenceInfo (322-1049)
• set_example_sequence (719-751)

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• GitHub Check: Pre-commit Check

🔇 Additional comments (1)

tests/unittest/_torch/auto_deploy/unit/singlegpu/shim/test_llm_config.py (1)

235-278: LGTM!

The tests are well-structured and properly validate the fix:

• Test 1 correctly verifies that example batch size is at least 2 when max_batch_size=1, which is the critical edge case that was causing torch.export to specialize the batch dimension.
• Test 2 ensures the behavior is consistent with larger max_batch_size values.

The docstrings clearly explain the rationale for each test case.

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