[None][chore] Print memory usage before/after accuracy test in CI

[taylor-yb-lee]

Added print memory status in Nemotron SuperV3 accuracy test to monitor flaky CI failure.

Before and after the test, it prints below info:
============================================================
Memory Usage: Before evaluation
============================================================
  Device 0:
    Allocated:       0.00 GB
    Reserved:        0.00 GB
    Peak Allocated:  0.00 GB
    Peak Reserved:   0.00 GB
    Available:       0.00 GB (in PyTorch cache) | 183.63 GB (on GPU)
    GPU Total:       0.68 / 184.31 GB
  Device 1:
    Allocated:       0.00 GB
    Reserved:        0.00 GB
    Peak Allocated:  0.00 GB
    Peak Reserved:   0.00 GB
    Available:       0.00 GB (in PyTorch cache) | 183.63 GB (on GPU)
    GPU Total:       0.68 / 184.31 GB
  Device 2:
    Allocated:       0.00 GB
    Reserved:        0.00 GB
    Peak Allocated:  0.00 GB
    Peak Reserved:   0.00 GB
    Available:       0.00 GB (in PyTorch cache) | 183.63 GB (on GPU)
    GPU Total:       0.68 / 184.31 GB
  Device 3:
    Allocated:       0.00 GB
    Reserved:        0.00 GB
    Peak Allocated:  0.00 GB
    Peak Reserved:   0.00 GB
    Available:       0.00 GB (in PyTorch cache) | 183.63 GB (on GPU)
    GPU Total:       0.68 / 184.31 GB
============================================================

[01/30/2026-15:41:04] [TRT-LLM] [I] Using LLM with AutoDeploy backend

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Summary by CodeRabbit

• Chores
  • Enhanced internal testing infrastructure with memory profiling capabilities during model evaluations to monitor resource utilization across devices.

_{✏️ Tip: You can customize this high-level summary in your review settings.}

[github-actions]

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[taylor-yb-lee]

/bot run --only-multi-gpu-test

[tensorrt-cicd]

PR_Github #34261 [ run ] triggered by Bot. Commit: c2641b8

[coderabbitai]

📝 Walkthrough

Walkthrough

The changes add memory profiling capabilities to integration tests by introducing a helper function to track CUDA memory usage across devices and strategically placing calls throughout test execution to monitor memory consumption before and after model evaluations.

Changes

Cohort / File(s)	Summary
Memory Profiling tests/integration/defs/accuracy/test_llm_api_autodeploy.py	Added print_memory_usage() helper function to track CUDA memory usage across devices. Integrated memory logging calls before and after model evaluations in test_bf16 flows for Llama3.1-8B and Nemotron SuperV3 test classes.

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~3 minutes

🚥 Pre-merge checks | ✅ 1 | ❌ 2

❌ Failed checks (2 warnings)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 50.00% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.
Description check	⚠️ Warning	PR description lacks required sections: no formal title following template, missing 'Description' and 'Test Coverage' sections with substantive content.	Add PR title in format [ticket][type] Summary, fill 'Description' section explaining issue and solution, and document relevant test coverage.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and specifically describes the main change: adding memory usage printing before/after accuracy tests in CI to monitor issues.

_{✏️ Tip: You can configure your own custom pre-merge checks in the settings.}

✨ Finishing touches

• 📝 Generate docstrings

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment

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[coderabbitai]

Actionable comments posted: 1

Caution

Some comments are outside the diff and can’t be posted inline due to platform limitations.

⚠️ Outside diff range comments (1)

tests/integration/defs/accuracy/test_llm_api_autodeploy.py (1)

1-1: ⚠️ Potential issue | 🟡 Minor

Update the NVIDIA copyright year to 2026.

The file now has 2026 modifications, so the header year should reflect the latest meaningful change.

✍️ Proposed fix

-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.

As per coding guidelines, all TensorRT-LLM source files (.cpp, .h, .cu, .py, and other source files) should contain an NVIDIA copyright header with the year of latest meaningful modification.

🤖 Fix all issues with AI agents

In `@tests/integration/defs/accuracy/test_llm_api_autodeploy.py`:
- Around line 29-54: The print_memory_usage function uses a one-line docstring
and unguarded torch.cuda calls which will fail on CPU-only systems; update
print_memory_usage to use a Google-style docstring describing Args and behavior,
and add a CUDA availability guard at the start (e.g., check
torch.cuda.is_available() and return or log a message when False) before calling
torch.cuda.device_count(), torch.cuda.memory_allocated,
torch.cuda.memory_reserved, torch.cuda.max_memory_allocated,
torch.cuda.max_memory_reserved, and torch.cuda.mem_get_info; ensure all
references to CUDA APIs are inside the guarded block so the function is safe to
run on non‑CUDA environments.

🧹 Nitpick comments (1)

tests/integration/defs/accuracy/test_llm_api_autodeploy.py (1)

342-351: Consider printing “after” while the model is still loaded.

Currently the “After GSM8K evaluation” print runs after the with AutoDeployLLM(...) block exits, so it may capture post-unload memory. If you want in-load memory after evaluation, move it inside the with block (or rename the label to clarify).

💡 Proposed move

         with AutoDeployLLM(model=self.MODEL_PATH_BF16,
                            tokenizer=self.MODEL_PATH_BF16,
                            world_size=4,
                            **kwargs) as llm:
             task = MMLU(self.MODEL_NAME)
             task.evaluate(llm, sampling_params=sampling_params)
             task = GSM8K(self.MODEL_NAME)
             task.evaluate(llm)
-        print_memory_usage("After GSM8K evaluation")
+            print_memory_usage("After GSM8K evaluation")

[tensorrt-cicd]

PR_Github #34261 [ run ] completed with state SUCCESS. Commit: c2641b8
/LLM/main/L0_MergeRequest_PR pipeline #26423 (Partly Tested) completed with status: 'FAILURE'

⚠️ Action Required:

• Please check the failed tests and fix your PR
• If you cannot view the failures, ask the CI triggerer to share details
• Once fixed, request an NVIDIA team member to trigger CI again

[taylor-yb-lee]

/bot run --only-multi-gpu-test

[tensorrt-cicd]

PR_Github #34284 [ run ] triggered by Bot. Commit: 31e0364

[tensorrt-cicd]

PR_Github #34284 [ run ] completed with state SUCCESS. Commit: 31e0364
/LLM/main/L0_MergeRequest_PR pipeline #26440 (Partly Tested) completed with status: 'SUCCESS'
Pipeline has performance regression cases. Check the performance regression report for details.

[taylor-yb-lee]

/bot run

[tensorrt-cicd]

PR_Github #34497 [ run ] triggered by Bot. Commit: 31e0364

[tensorrt-cicd]

PR_Github #34497 [ run ] completed with state FAILURE. Commit: 31e0364
/LLM/main/L0_MergeRequest_PR pipeline #26616 completed with status: 'FAILURE'

⚠️ Action Required:

• Please check the failed tests and fix your PR
• If you cannot view the failures, ask the CI triggerer to share details
• Once fixed, request an NVIDIA team member to trigger CI again

[taylor-yb-lee]

/bot run

[tensorrt-cicd]

PR_Github #34501 [ run ] triggered by Bot. Commit: 31e0364

[tensorrt-cicd]

PR_Github #34501 [ run ] completed with state SUCCESS. Commit: 31e0364
/LLM/main/L0_MergeRequest_PR pipeline #26619 completed with status: 'FAILURE'

⚠️ Action Required:

• Please check the failed tests and fix your PR
• If you cannot view the failures, ask the CI triggerer to share details
• Once fixed, request an NVIDIA team member to trigger CI again

[taylor-yb-lee]

/bot run

[tensorrt-cicd]

PR_Github #34535 [ run ] triggered by Bot. Commit: 31e0364

[tensorrt-cicd]

PR_Github #34535 [ run ] completed with state SUCCESS. Commit: 31e0364
/LLM/main/L0_MergeRequest_PR pipeline #26646 completed with status: 'SUCCESS'

[galagam]

@taylor-yb-lee we need to remove the test waive if we want this to run in CI