ROCm/aiter
Oct 2025 – Dec 2025
3 Months active
Languages Used
C++Python
Technical Skills
CUDADeep Learning OptimizationFP8 ComputationPerformance BenchmarkingTritonDeep Learning
Feng Shijie
PROFILE
Feng Shijie
Over a three-month period, contributed advanced deep learning optimizations to the ROCm/aiter repository, focusing on FP8 computation and multi-query attention workloads. Developed and integrated Triton kernel enhancements for Deepgemm FP8 paged_mqa_logits, introducing context-split and variable-context optimizations to improve throughput and scalability. Leveraged C++ and Python to implement performance benchmarking, robust scheduling functions, and support for edge-case block sizes such as ChunkK alignment. Enhanced pipeline granularity, introduced scheduling barriers, and improved code maintainability through linting and code review. The work emphasized GPU programming, deep learning optimization, and performance tuning, resulting in more efficient and stable inference paths for critical workloads.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
4,158
Activity Months3
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1750 peopleSame Organization
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Work History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 performance and scheduling enhancements in ROCm/aiter. Delivered MQA logits optimization and scheduling for ChunkK alignment, enabling correct handling when mqa_logits block size is a multiple of ChunkK. Implemented var-context optimization for pa_mqa_logits and introduced a new scheduling function to coordinate these optimizations. Included s_set_prio optimization as part of the changes. Routine lint fixes (ruff) were completed to improve maintainability. These changes improve throughput and stability for workloads using MQA logits, reducing edge-case handling overhead and better aligning execution with scheduling priorities.
1 Commits • 1 Features
Dec 1, 2025December 2025 performance and scheduling enhancements in ROCm/aiter. Delivered MQA logits optimization and scheduling for ChunkK alignment, enabling correct handling when mqa_logits block size is a multiple of ChunkK. Implemented var-context optimization for pa_mqa_logits and introduced a new scheduling function to coordinate these optimizations. Included s_set_prio optimization as part of the changes. Routine lint fixes (ruff) were completed to improve maintainability. These changes improve throughput and stability for workloads using MQA logits, reducing edge-case handling overhead and better aligning execution with scheduling priorities.
December 2025
November 2025
1 Commits • 1 Features
Nov 1, 2025Monthly performance summary for 2025-11 focusing on key accomplishments in ROCm/aiter. Delivered significant pa_mqa_logits performance optimization with Triton 3.5 JIT support, KV preshuffle, and blocksize 16/64. Enhanced pipeline granularity and scheduling barriers. Improved splitkv strategy and added out-of-bounds checks for robustness. Resolved code reviews and stabilized feature, contributing to higher throughput and reduced latency in critical workloads.
November 2025
1 Commits • 1 Features
Nov 1, 2025Monthly performance summary for 2025-11 focusing on key accomplishments in ROCm/aiter. Delivered significant pa_mqa_logits performance optimization with Triton 3.5 JIT support, KV preshuffle, and blocksize 16/64. Enhanced pipeline granularity and scheduling barriers. Improved splitkv strategy and added out-of-bounds checks for robustness. Resolved code reviews and stabilized feature, contributing to higher throughput and reduced latency in critical workloads.
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for ROCm/aiter: Delivered Deepgemm FP8 paged_mqa_logits optimization with Triton kernels, including context-split optimization, tests, and benchmarks, enabling improved performance and scalability for FP8-based attention workloads.
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for ROCm/aiter: Delivered Deepgemm FP8 paged_mqa_logits optimization with Triton kernels, including context-split optimization, tests, and benchmarks, enabling improved performance and scalability for FP8-based attention workloads.
October 2025
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Quality Metrics
Correctness86.6%
Maintainability80.0%
Architecture80.0%
Performance86.6%
AI Usage33.4%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
CUDADeep LearningDeep Learning OptimizationFP8 ComputationGPU ProgrammingPerformance BenchmarkingPerformance OptimizationPyTorchTriton
Repositories Contributed To
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