pytorch/pytorch
Feb 2026 – Apr 2026
3 Months active
Languages Used
PythonC++
Technical Skills
GPU ProgrammingPerformance OptimizationSoftware DevelopmentPyTorchbackend developmentperformance optimization
Bao Phan
PROFILE
Bao Phan
Bao Phan contributed to the pytorch/pytorch repository by developing and refining GPU performance optimization features using C++ and Python. Over three months, Bao enhanced the AMD HIP autotuning pipeline by enforcing persistent block size constraints, which reduced invalid configurations and improved autotuning reliability. He addressed ROCm compilation bottlenecks by broadening reduction configuration filtering, resulting in faster and more stable builds for large data sizes. Additionally, Bao introduced a Graph Profiling Benchmark Utility that captures per-node input sizes in GraphExecutorBase, extending profiling metrics for deeper performance analysis. His work demonstrated strong backend development and benchmarking skills with a focus on reproducibility.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
4Total
Bugs
2
Commits
4
Features
2
Lines of code
82
Activity Months3
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3475 peopleSame Organization
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Aaryaman SagarMember
Aashay GaikwadMember
Ajanthan AsogamoorthyMember
Work History
April 2026
1 Commits • 1 Features
Apr 1, 2026This month (2026-04) delivered a focused enhancement to PyTorch profiling by introducing a Graph Profiling Benchmark Utility that captures input element counts for each node in GraphExecutorBase, strengthening profiling visibility and performance diagnostics. The work extends ProfileMetrics to include input size, enabling more precise benchmarking and resource analysis. The primary delivery is tied to PR 178434 (commit b7aca017a74beb063ccea127b243839ef63d3432), with a dedicated review cycle to ensure quality and readiness for broader adoption.
1 Commits • 1 Features
Apr 1, 2026This month (2026-04) delivered a focused enhancement to PyTorch profiling by introducing a Graph Profiling Benchmark Utility that captures input element counts for each node in GraphExecutorBase, strengthening profiling visibility and performance diagnostics. The work extends ProfileMetrics to include input size, enabling more precise benchmarking and resource analysis. The primary delivery is tied to PR 178434 (commit b7aca017a74beb063ccea127b243839ef63d3432), with a dedicated review cycle to ensure quality and readiness for broader adoption.
April 2026
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026 focused on performance, stability, and observability in pytorch/pytorch. Delivered two targeted items: (1) AMD ROCm Reduction Configuration Filtering Performance Bug Fix addressing pathological ROCm compilation times for large reductions by broadening filtering of reduction configurations when a persistent sub-kernel is involved on AMD HIP, improving compile times and stability for large data sizes; (2) Triton Kernel Performance Artifacts Saving, packaging Triton kernel metadata into the Lowering output torch package to enable performance tracking, reproducibility, and optimization workflows.
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026 focused on performance, stability, and observability in pytorch/pytorch. Delivered two targeted items: (1) AMD ROCm Reduction Configuration Filtering Performance Bug Fix addressing pathological ROCm compilation times for large reductions by broadening filtering of reduction configurations when a persistent sub-kernel is involved on AMD HIP, improving compile times and stability for large data sizes; (2) Triton Kernel Performance Artifacts Saving, packaging Triton kernel metadata into the Lowering output torch package to enable performance tracking, reproducibility, and optimization workflows.
February 2026
1 Commits
Feb 1, 2026February 2026: Focused on stabilizing the AMD HIP autotuning path by preventing oversized XBLOCK configurations in combo kernels with persistent sub-kernels. Implemented propagation of the maximum persistent block size from the combo kernel to the config generator, reducing invalid configurations, speeding up autotuning, and improving reliability and reproducibility of performance results on AMD GPUs. This work enhances the stability of the autotuning pipeline and reduces wasted compute during hardware exploration.
1 Commits
Feb 1, 2026February 2026: Focused on stabilizing the AMD HIP autotuning path by preventing oversized XBLOCK configurations in combo kernels with persistent sub-kernels. Implemented propagation of the maximum persistent block size from the combo kernel to the config generator, reducing invalid configurations, speeding up autotuning, and improving reliability and reproducibility of performance results on AMD GPUs. This work enhances the stability of the autotuning pipeline and reduces wasted compute during hardware exploration.
February 2026
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Quality Metrics
Correctness95.0%
Maintainability80.0%
Architecture85.0%
Performance85.0%
AI Usage25.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++ developmentGPU ProgrammingPerformance OptimizationPyTorchSoftware Developmentbackend developmentbenchmarkingperformance optimizationperformance profiling
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