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
Shijie Feng developed a series of deep learning performance optimizations for the ROCm/aiter repository, focusing on FP8 multi-query attention workloads. Over three months, Shijie delivered new Triton kernel features for Deepgemm FP8 paged_mqa_logits, implemented context-split and variable-context optimizations, and introduced scheduling enhancements for ChunkK alignment. The work involved extensive use of CUDA, Python, and C++, with careful attention to performance benchmarking and code maintainability. By addressing edge-case block sizes, improving pipeline granularity, and adding robust safety checks, Shijie’s contributions enhanced throughput, scalability, and stability for GPU-based inference, demonstrating depth in both algorithmic design and system integration.
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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1604 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
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