[None][fix] Fix on-disk cache and revise logger/statistics for AutoTuner.

[hyukn]

• Add a specific environment variable to control the logger level of AutoTuner.
• Add statistics to track the total profiling time for each op. This will help determine the dominating op during warm-up tuning.
• Always enabled tuning w/. or w/o. loading serialized cache.
• Fixed fallback issues when using serialization/deserialization cache.

Summary by CodeRabbit

Release Notes

• New Features

  • Autotuner now tracks and displays per-operation timing costs within statistics output
  • Debug logging enhanced with environment variable configuration for improved diagnostics

• Chores

  • Added statistics output emission following autotuner warmup profiling

Description

Test Coverage

PR Checklist

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[coderabbitai[bot]]

📝 Walkthrough

Walkthrough

The PR adds timing instrumentation and enhanced logging to the autotuner module. It introduces per-operation time cost tracking via a new tuned_op_time_cost field, a configurable debug logger driven by an environment variable, updated statistics reporting, and a public print_statistics() method. A new statistics output call is added to the model engine's autotuner warmup sequence.

Changes

Cohort / File(s)	Summary
Autotuner Core Enhancements tensorrt_llm/_torch/autotuner.py	Added tuned_op_time_cost field to track per-operation durations; introduced _debug_logger with TLLM_AUTOTUNER_LOG_LEVEL_DEBUG_TO_INFO env var configuration; added tuning_start_time and tuning_end_time tracking in profiling flows; refactored logging calls to use new debug logger and conditional info routing; updated AutoTunerStatistics.__str__ to display time costs in milliseconds; added print_statistics() public method; enhanced cache existence check with rank-specific logic.
Runtime Instrumentation tensorrt_llm/_torch/pyexecutor/model_engine.py	Added call to AutoTuner.get().print_statistics() following profiling cache printout in autotuner warmup sequence.
Test Updates tests/unittest/_torch/misc/test_autotuner.py	Added calls to AutoTuner.get().print_profiling_cache() and AutoTuner.get().print_statistics() in test_autotuner_cache_basic to exercise statistics and cache printing functionality.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

• Environment variable interaction: Verify that TLLM_AUTOTUNER_LOG_LEVEL_DEBUG_TO_INFO behavior integrates correctly with existing logging configuration and doesn't cause conflicts.
• Timing accumulation logic: Confirm that tuning_start_time and tuning_end_time are correctly captured and accumulated across profiling flows.
• Rank-specific cache check: Ensure the new file-existence-based decision logic for enabling tuning is functionally equivalent and preserves expected behavior for multi-rank scenarios.

Pre-merge checks and finishing touches

❌ Failed checks (2 warnings, 1 inconclusive)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 56.25% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.
Title check	⚠️ Warning	The title mentions 'fix on-disk cache' but the changes focus on logger/statistics revisions and profiling instrumentation, not cache fixes.	Align the title with the actual changes. Consider revising to: '[None][chore] Revise logger and statistics for AutoTuner' to match the PR objectives and actual implementation.
Description check	❓ Inconclusive	The PR description is incomplete and vague; it lists features but lacks implementation details, rationale, and comprehensive test coverage documentation.	Expand the description section to explain the 'why' behind changes, provide implementation details for each feature point, clearly document all test cases covering new functionality, and explicitly explain what fallback issues were fixed.

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[hyukn]

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