pytorch/pytorch
Oct 2025 – Dec 2025
2 Months active
Languages Used
C++Python
Technical Skills
Complex NumbersGPU ComputingIntel XPULinear AlgebraPyTorchPython
Pawel Swider
PROFILE
Pawel Swider
Pawel Swider developed complex data type support for matrix multiplication operations on Intel XPU within the pytorch/pytorch repository, integrating with torch-xpu-ops and adding comprehensive tests to ensure reliability. He addressed linalg.eig test failures by refining complex matmul interactions, which improved workflow stability and hardware coverage for PyTorch users. In a separate effort, Pawel enabled cross-device execution for the Triton Fx Graph test by introducing dynamic device selection and simplifying skip logic, enhancing test robustness across GPUs and XPU devices. His work leveraged C++, Python, and device management skills, demonstrating depth in cross-repository collaboration and end-to-end testing.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
77
Activity Months2
Your Network
2800 peopleSame Organization
@intel.com2035
Work History
December 2025
1 Commits • 1 Features
Dec 1, 2025In December 2025, delivered cross-device support for the Triton Fx Graph test in PyTorch, expanding hardware coverage and improving test robustness. By replacing hardcoded device parameters with a dynamic device selector and simplifying skip logic, the test suite becomes more flexible and reliable across GPUs and XPU devices.
1 Commits • 1 Features
Dec 1, 2025In December 2025, delivered cross-device support for the Triton Fx Graph test in PyTorch, expanding hardware coverage and improving test robustness. By replacing hardcoded device parameters with a dynamic device selector and simplifying skip logic, the test suite becomes more flexible and reliable across GPUs and XPU devices.
December 2025
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025: Delivered complex data type support for matrix multiplication on Intel XPU in PyTorch, enabling complex mm, bmm, addmm, and baddbmm; integrated with external libraries (torch-xpu-ops) and added robust tests. Fixed linalg.eig test failures related to complex matmul interactions, improving test reliability and XPU workflow stability. This work broadens hardware coverage, enhances numerical capabilities for researchers and production workloads, and demonstrates end-to-end cross-repo collaboration with Intel XPU teams.
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025: Delivered complex data type support for matrix multiplication on Intel XPU in PyTorch, enabling complex mm, bmm, addmm, and baddbmm; integrated with external libraries (torch-xpu-ops) and added robust tests. Fixed linalg.eig test failures related to complex matmul interactions, improving test reliability and XPU workflow stability. This work broadens hardware coverage, enhances numerical capabilities for researchers and production workloads, and demonstrates end-to-end cross-repo collaboration with Intel XPU teams.
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Complex NumbersGPU ComputingIntel XPULinear AlgebraPyTorchPythondevice managementtesting
Repositories Contributed To
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