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Apostolos Kokolis

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Apostolos Kokolis

Alex Kokolis developed targeted enhancements for PyTorch’s benchmarking and profiling workflows, focusing on reliability and configurability. In the ROCm/pytorch repository, Alex introduced new command-line options in Python to control benchmark iterations and repetitions, enabling reproducible and automated performance validation across environments. Later, in the pytorch/pytorch repository, Alex addressed a kernel profiling fragility by ensuring int64_t declarations for kernel numel variables are consistently emitted during profiling, using CUDA and kernel profiling expertise. This fix eliminated scope-related crashes and improved trace accuracy, reflecting a thoughtful approach to stability and testing in performance-critical code paths. The work demonstrated technical depth and cross-team collaboration.

Overall Statistics

Feature vs Bugs

50%Features

Repository Contributions

2Total
Bugs
1
Commits
2
Features
1
Lines of code
73
Activity Months2

Work History

March 2026

1 Commits

Mar 1, 2026

Month: 2026-03 — Focused on stabilizing kernel profiling in PyTorch Inductor. Delivered a critical codegen fix that ensures int64_t declarations for kernel numel variables are emitted whenever kernel profiling is enabled, preventing scope-related crashes when the same kernel is invoked multiple times under profiling. This work addressed a key fragility exposed when TORCHINDUCTOR_CPP_ENABLE_KERNEL_PROFILE=1 and was implemented in the PyTorch repository (commit c722f2a0b33ced20554e48e9e9667db3aceaff21) as part of PR 176922. The fix eliminates undeclared identifier errors in profiling mode, improves trace accuracy, and enhances the reliability of performance measurements in profiling sessions.

July 2025

1 Commits • 1 Features

Jul 1, 2025

July 2025: Delivered a new command-line interface for the ROCm/pytorch benchmarking module by adding --times and --repeat options to control iterations and repetitions. This enables more reproducible, configurable benchmarks, facilitating faster validation and CI integration. No major bugs fixed this month; primary focus was feature delivery and CI-friendly benchmarking improvements. Demonstrated strong collaboration with the Inductor and benchmarking teams and enhanced the project's testing and performance validation capabilities.

Activity

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Quality Metrics

Correctness100.0%
Maintainability90.0%
Architecture100.0%
Performance90.0%
AI Usage20.0%

Skills & Technologies

Programming Languages

Python

Technical Skills

CUDAKernel ProfilingPythonUnit Testingbenchmarkingcommand line interface development

Repositories Contributed To

2 repos

Overview of all repositories you've contributed to across your timeline

ROCm/pytorch

Jul 2025 Jul 2025
1 Month active

Languages Used

Python

Technical Skills

Pythonbenchmarkingcommand line interface development

pytorch/pytorch

Mar 2026 Mar 2026
1 Month active

Languages Used

Python

Technical Skills

CUDAKernel ProfilingPythonUnit Testing