pytorch/pytorch
Mar 2026 – Mar 2026
1 Month active
Languages Used
C++Python
Technical Skills
C++ developmentCUDA programmingGPU programmingMatrix multiplicationPerformance optimizationTesting
Milica Stankovic
PROFILE
Milica Stankovic
Milica Stankovic contributed targeted reliability improvements to the pytorch/pytorch repository, focusing on GPU programming and Windows development using C++ and CUDA. She addressed two critical bugs: one involved implementing a GPU-specific compile mode for FP8 operations on AMD RDNA4 GPUs, which stabilized small-dimension matrix multiplications and reduced NaN propagation in test results. The other fix resolved a Windows DLL linkage issue in MIOpen CTC loss dispatch, preventing access violations when using CUDA tensors by ensuring proper header inclusion. Her work demonstrated depth in performance optimization and testing, directly enhancing the stability of ROCm and Windows CUDA workflows in PyTorch.
Overall Statistics
Feature vs Bugs
0%Features
Repository Contributions
2Total
Bugs
2
Commits
2
Features
0
Lines of code
27
Activity Months1
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Work History
March 2026
2 Commits
Mar 1, 2026March 2026 (Month: 2026-03) — Delivered two high-impact fixes in the pytorch/pytorch repository that improve reliability and stability across ROCm and Windows CUDA workflows. Key outcomes include GPU-specific handling for FP8 operations on RDNA4 GPUs to stabilize small-dimension matrix multiplications, and a Windows DLL linkage fix for MIOpen CTC loss dispatch to prevent crashes when using CUDA tensors. These fixes reduce NaN propagation in FP8 tests, normalize eager vs compiled results, and remove a critical Windows crash vector in CI.
2 Commits
Mar 1, 2026March 2026 (Month: 2026-03) — Delivered two high-impact fixes in the pytorch/pytorch repository that improve reliability and stability across ROCm and Windows CUDA workflows. Key outcomes include GPU-specific handling for FP8 operations on RDNA4 GPUs to stabilize small-dimension matrix multiplications, and a Windows DLL linkage fix for MIOpen CTC loss dispatch to prevent crashes when using CUDA tensors. These fixes reduce NaN propagation in FP8 tests, normalize eager vs compiled results, and remove a critical Windows crash vector in CI.
March 2026
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Quality Metrics
Correctness100.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++ developmentCUDA programmingGPU programmingMatrix multiplicationPerformance optimizationTestingWindows development
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