InfiniTensor/InfiniCore
May 2025 – Dec 2025
4 Months active
Languages Used
C++CUDALua
Technical Skills
API DevelopmentAlgorithm ImplementationC++CUDACUDA ProgrammingCUDA programming
crapromer
PROFILE
Crapromer
Tiannie Hao contributed to the InfiniTensor/InfiniCore repository by developing cross-accelerator features and improving hardware compatibility, focusing on MACA and Metax device support. She implemented new CUDA and C++ kernels for SwiGLU activation, migrated key operators to support Metax, and integrated the Maca SDK for FP8 workflows. Her work included refining numerical precision in activation functions, enhancing code maintainability, and ensuring CUDA 13.0 compatibility. Tiannie also enabled flexible backend communication by adding CCL support with conditional compilation. Through deep learning frameworks, GPU programming, and low-level optimization, she delivered robust, maintainable solutions that expanded deployment scenarios and improved platform reliability.
Overall Statistics
Feature vs Bugs
63%Features
Repository Contributions
13Total
Bugs
3
Commits
13
Features
5
Lines of code
2,459
Activity Months4
Your Network
29 people
Work History
December 2025
1 Commits • 1 Features
Dec 1, 2025Month 2025-12: Delivered CCL support in MACA using MC API with a selectable backend. Implemented conditional compilation to switch between CCL and HCCL based on macros, enhancing flexibility and compatibility of the InfiniCore communication layer. No critical bugs reported this month; primary focus on feature enablement and groundwork for broader backend deployment. Commit: 6433bf2bcd9112c51d05cf025752ccb358beb8aa (issue/704).
1 Commits • 1 Features
Dec 1, 2025Month 2025-12: Delivered CCL support in MACA using MC API with a selectable backend. Implemented conditional compilation to switch between CCL and HCCL based on macros, enhancing flexibility and compatibility of the InfiniCore communication layer. No critical bugs reported this month; primary focus on feature enablement and groundwork for broader backend deployment. Commit: 6433bf2bcd9112c51d05cf025752ccb358beb8aa (issue/704).
December 2025
November 2025
3 Commits • 1 Features
Nov 1, 2025Month 2025-11 highlights: Delivered critical platform integration and stability improvements in InfiniCore, enabling Metax FP8 workflows and CUDA 13.0 compatibility. Key work included Maca SDK integration for Metax with FP8 support and a CUDA kernel compatibility fix, driving platform readiness, performance, and build reliability for upcoming releases.
November 2025
3 Commits • 1 Features
Nov 1, 2025Month 2025-11 highlights: Delivered critical platform integration and stability improvements in InfiniCore, enabling Metax FP8 workflows and CUDA 13.0 compatibility. Key work included Maca SDK integration for Metax with FP8 support and a CUDA kernel compatibility fix, driving platform readiness, performance, and build reliability for upcoming releases.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly highlights for InfiniCore: key features delivered and bugs fixed with clear business impact. Focused on numerical precision correctness in sigmoid activation and maintainability improvements for rearrange_kernel.h. These changes reinforce numerical stability, reduce risk of precision-related bugs, and improve readability for future work.
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly highlights for InfiniCore: key features delivered and bugs fixed with clear business impact. Focused on numerical precision correctness in sigmoid activation and maintainability improvements for rearrange_kernel.h. These changes reinforce numerical stability, reduce risk of precision-related bugs, and improve readability for future work.
June 2025
May 2025
7 Commits • 2 Features
May 1, 2025Summary for 2025-05: Delivered cross-accelerator enhancements and code quality improvements in InfiniCore. Key features: SwiGLU activation and MACA accelerator support across devices with new kernels, device implementations, and API wrappers for MACA compatibility; Metax device support across multiple ops (causal softmax, random sampling, Rope, rearrange) with CUDA-to-Metax migrations and operator integration. Major bug fixes: Header formatting cleanup ensuring trailing newlines for tool compatibility. Overall impact: expanded hardware support and performance paths, improved maintainability and build reliability, and strengthened cross-device consistency. Technologies/skills demonstrated: kernel development, device abstraction and integration, CUDA-to-Metax migration, API design and testing, code formatting standards. Business value highlights: - Broadened hardware compatibility (MACA/Metax) enabling more deployment scenarios. - Potential performance gains from accelerator-specific optimizations and unified operator support. - Improved code quality and tooling alignment reducing integration friction.
May 2025
7 Commits • 2 Features
May 1, 2025Summary for 2025-05: Delivered cross-accelerator enhancements and code quality improvements in InfiniCore. Key features: SwiGLU activation and MACA accelerator support across devices with new kernels, device implementations, and API wrappers for MACA compatibility; Metax device support across multiple ops (causal softmax, random sampling, Rope, rearrange) with CUDA-to-Metax migrations and operator integration. Major bug fixes: Header formatting cleanup ensuring trailing newlines for tool compatibility. Overall impact: expanded hardware support and performance paths, improved maintainability and build reliability, and strengthened cross-device consistency. Technologies/skills demonstrated: kernel development, device abstraction and integration, CUDA-to-Metax migration, API design and testing, code formatting standards. Business value highlights: - Broadened hardware compatibility (MACA/Metax) enabling more deployment scenarios. - Potential performance gains from accelerator-specific optimizations and unified operator support. - Improved code quality and tooling alignment reducing integration friction.
Activity
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Quality Metrics
Correctness87.6%
Maintainability84.6%
Architecture86.2%
Performance77.8%
AI Usage23.0%
Skills & Technologies
Programming Languages
C++CUDALua
Technical Skills
API DevelopmentAPI integrationAlgorithm ImplementationBuild system configurationC++C++ developmentC++ programmingCUDACUDA ProgrammingCUDA programmingCode FormattingCode formattingDeep learning frameworksDevice AbstractionDevice integration
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