pytorch/FBGEMM
Aug 2025 – Aug 2025
1 Month active
Languages Used
Python
Technical Skills
GPU ComputingPerformance OptimizationQuantizationTriton Kernels
Víctor Pérez Carrasco
PROFILE
Víctor Pérez Carrasco
Victor Perez developed MTIA Triton kernel support for MX4 quantization and dequantization in the pytorch/FBGEMM repository, focusing on optimizing tensor throughput for MTIA hardware. He implemented conditional routing to MTIA-specific Triton kernels, ensuring that MX4 operations leverage hardware acceleration where available. Using Python and leveraging expertise in GPU computing and performance optimization, Victor introduced explicit exception handling for unsupported features such as stochastic casting, preventing silent failures and improving reliability. His work included comprehensive unit-level validation and robust test coverage, resulting in a maintainable codebase that enhances performance and safety for MTIA-enabled deployments without introducing user-facing bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
32
Activity Months1
Your Network
2876 peopleSame Organization
@meta.com2691
Peter RongMember
Zain RizviMember
Aahan AggarwalMember
Aliaksei AndreyeuMember
Arjun ChaturvediMember
Aaron PollackMember
Aaryaman SagarMember
Aashay GaikwadMember
Ajanthan AsogamoorthyMember
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025For 2025-08, delivered MTIA Triton kernel support for MX4 quantization/dequantization in pytorch/FBGEMM, enabling conditional routing to MTIA-specific Triton kernels to improve MX4 tensor throughput on MTIA hardware. Implemented explicit handling for unsupported features (e.g., stochastic_casting) by raising clear exceptions to avoid silent failures. No major user-facing bugs reported this month; changes include unit-level validations and a targeted commit tied to (#4619). Overall impact: performance uplift for MTIA-enabled deployments with safer feature gating and clearer failure modes, contributing to higher reliability and maintainability. Technologies demonstrated: Triton integration, MTIA-aware kernel routing, MX4 quantization/dequantization, exception design patterns, and code changes with strong test coverage across kernel paths.
1 Commits • 1 Features
Aug 1, 2025For 2025-08, delivered MTIA Triton kernel support for MX4 quantization/dequantization in pytorch/FBGEMM, enabling conditional routing to MTIA-specific Triton kernels to improve MX4 tensor throughput on MTIA hardware. Implemented explicit handling for unsupported features (e.g., stochastic_casting) by raising clear exceptions to avoid silent failures. No major user-facing bugs reported this month; changes include unit-level validations and a targeted commit tied to (#4619). Overall impact: performance uplift for MTIA-enabled deployments with safer feature gating and clearer failure modes, contributing to higher reliability and maintainability. Technologies demonstrated: Triton integration, MTIA-aware kernel routing, MX4 quantization/dequantization, exception design patterns, and code changes with strong test coverage across kernel paths.
August 2025
Activity
Loading activity data...
Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
GPU ComputingPerformance OptimizationQuantizationTriton Kernels
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline