Memory profiler for cuda #1996
Memory profiler for cuda #1996
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/1996
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Pull Request Overview
This PR introduces memory profiling support for CUDA in the microbenchmarks, integrating both model profiling and memory profiling into the benchmarking workflow. Key changes include the addition of utility functions for uploading trace files and generating URLs for Perfetto UI, modifications in the benchmark configuration to enable profiling, and updates to the benchmark runner to execute the new profiling functionalities.
Reviewed Changes
Copilot reviewed 4 out of 4 changed files in this pull request and generated 1 comment.
| File | Description |
|---|---|
| benchmarks/microbenchmarks/utils.py | Added functions to upload trace files, generate model and memory profiles, and generate Perfetto URLs. |
| benchmarks/microbenchmarks/test/benchmark_config.yml | Enabled profiler and memory profile flags for one benchmark configuration. |
| benchmarks/microbenchmarks/benchmark_runner.py | Updated error handling and conditional CSV generation based on collected results. |
| benchmarks/microbenchmarks/benchmark_inference.py | Integrated calls to the newly added profiling functions with proper error logging. |
| DEFAULT_TTL_SEC = 28 * 24 * 60 * 60 | ||
| file_name = os.path.basename(local_path) | ||
| manifold_path = os.path.join( | ||
| MANIFOLD_FOLDER, f"{os.getlogin()}_{str(uuid.uuid4())}_{file_name}" |
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Using os.getlogin() can raise an OSError in non-interactive or service environments; consider using getpass.getuser() for improved robustness.
| MANIFOLD_FOLDER, f"{os.getlogin()}_{str(uuid.uuid4())}_{file_name}" | |
| MANIFOLD_FOLDER, f"{getpass.getuser()}_{str(uuid.uuid4())}_{file_name}" |
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