ROCm/aiter
Nov 2025 – Jul 2026
5 Months active
Languages Used
PythonC++CUDA
Technical Skills
GPU ProgrammingNumerical ComputingTensor OperationsTritonCUDA programmingDeep Learning
yinfengLiu
PROFILE
Yinfengliu
Worked on the ROCm/aiter repository to deliver advanced GPU-accelerated features for deep learning and numerical computing, focusing on kernel development, quantization, and performance optimization. Developed and optimized CUDA and Triton kernels for tensor operations, including 3D ROPE and normalization, and introduced batch- and head-aware FP8 quantization paths to improve throughput and accuracy in large-scale models. Addressed concurrency and memory safety in rope data structures, enhancing reliability for multi-threaded workloads. Leveraged C++, CUDA, and Python to implement fused attention kernels, benchmarking tools, and robust test coverage, resulting in scalable, maintainable solutions for distributed machine learning and high-performance GPU environments.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
11Total
Bugs
3
Commits
11
Features
6
Lines of code
6,539
Activity Months5
Your Network
1985 peopleSame Organization
@amd.com1733
7b30f3f5e26d48061f873d04cc7e1d1f_amdengMember
GunaShekar, AjayMember
aasbodduMember
Abdul Lateef AttarMember
Acim MaravicMember
Adam DickinMember
Pryor, AdamMember
Pryor, AdamMember
Adel JoharMember
Work History
July 2026
3 Commits • 1 Features
Jul 1, 2026July 2026: Delivered key enhancements to quantized attention in ROCm/aiter, boosting accuracy and throughput for FP8/INT8 paths. Key deliverables: fused Q/K/V quantization with 1-way RoPE on Z-Image (gfx942 guard) plus SageAttention v1 q_smooth with Hadamard rotation; new HIP/Triton kernels, wrappers, benchmarks, and tests (bf16 reference validation). Implemented RoPE memory-safety fix by sizing cos/sin vectors with PAIR_VEC_SIZE to prevent OOB access. Strengthened long-sequence performance with q_smooth delta_s bias integration and optional a3 launch presets for MI308/gfx942. Expanded test coverage and benchmarking for all paths, improving reliability and maintainability. Business impact: higher accuracy, lower error rates, and better deployment scalability for quantized attention workloads.
3 Commits • 1 Features
Jul 1, 2026July 2026: Delivered key enhancements to quantized attention in ROCm/aiter, boosting accuracy and throughput for FP8/INT8 paths. Key deliverables: fused Q/K/V quantization with 1-way RoPE on Z-Image (gfx942 guard) plus SageAttention v1 q_smooth with Hadamard rotation; new HIP/Triton kernels, wrappers, benchmarks, and tests (bf16 reference validation). Implemented RoPE memory-safety fix by sizing cos/sin vectors with PAIR_VEC_SIZE to prevent OOB access. Strengthened long-sequence performance with q_smooth delta_s bias integration and optional a3 launch presets for MI308/gfx942. Expanded test coverage and benchmarking for all paths, improving reliability and maintainability. Business impact: higher accuracy, lower error rates, and better deployment scalability for quantized attention workloads.
July 2026
June 2026
3 Commits • 3 Features
Jun 1, 2026June 2026 focused on performance and efficiency improvements in ROCm/aiter, delivering batch- and head-aware quantization, large-M optimization, and fused runtime paths for Tensor Parallel. Key results include a new per-(batch, head) FP8 quantization path for fused QK norm/rope and V, a large-M hybrid path for mhc_fused_post_pre (M >= 1024) with cache-policy tuning and upgraded benchmarking, and a fused AllReduce + MHC post-processing path for TP with one- and two-stage dispatch and automated path selection. These changes reduce latency and boost throughput for large models, improve scalability across multi-GPU setups, and broaden profiling and test coverage. Technologies demonstrated include FP8 quantization, kernel dispatch orchestration, cross-kernel optimizations, benchmarking/OP tests, and CI/test quality improvements.
June 2026
3 Commits • 3 Features
Jun 1, 2026June 2026 focused on performance and efficiency improvements in ROCm/aiter, delivering batch- and head-aware quantization, large-M optimization, and fused runtime paths for Tensor Parallel. Key results include a new per-(batch, head) FP8 quantization path for fused QK norm/rope and V, a large-M hybrid path for mhc_fused_post_pre (M >= 1024) with cache-policy tuning and upgraded benchmarking, and a fused AllReduce + MHC post-processing path for TP with one- and two-stage dispatch and automated path selection. These changes reduce latency and boost throughput for large models, improve scalability across multi-GPU setups, and broaden profiling and test coverage. Technologies demonstrated include FP8 quantization, kernel dispatch orchestration, cross-kernel optimizations, benchmarking/OP tests, and CI/test quality improvements.
May 2026
1 Commits
May 1, 2026Month: 2026-05. Stability hardening for ROCm/aiter focusing on rope data structures under concurrency. Implemented a concurrency-safety fix that prevents out-of-bounds access in rope operations by introducing a pair-vector-size constant and adjusting the iteration loop. This targeted improvement reduces memory-access violations and enhances reliability in multi-threaded workloads, aligning with the ongoing robustness initiatives.
1 Commits
May 1, 2026Month: 2026-05. Stability hardening for ROCm/aiter focusing on rope data structures under concurrency. Implemented a concurrency-safety fix that prevents out-of-bounds access in rope operations by introducing a pair-vector-size constant and adjusting the iteration loop. This targeted improvement reduces memory-access violations and enhances reliability in multi-threaded workloads, aligning with the ongoing robustness initiatives.
May 2026
December 2025
3 Commits • 1 Features
Dec 1, 2025December 2025 ROCm/aiter monthly summary focusing on delivering performance improvements for normalization kernels and stabilizing group normalization and inductor-mode compilation. The work emphasizes business value through improved throughput for large matrices, broader kernel coverage, and stable functionality across release branches.
December 2025
3 Commits • 1 Features
Dec 1, 2025December 2025 ROCm/aiter monthly summary focusing on delivering performance improvements for normalization kernels and stabilizing group normalization and inductor-mode compilation. The work emphasizes business value through improved throughput for large matrices, broader kernel coverage, and stable functionality across release branches.
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 performance summary for ROCm/aiter focused on delivering a high-impact kernel enhancement and preparing it for downstream integration. Primary work centered on a performance-oriented 3D ROPE kernel for complex-number multiplication in tensor operations, with Triton compatibility and clean merge/refactor discipline.
1 Commits • 1 Features
Nov 1, 2025November 2025 performance summary for ROCm/aiter focused on delivering a high-impact kernel enhancement and preparing it for downstream integration. Primary work centered on a performance-oriented 3D ROPE kernel for complex-number multiplication in tensor operations, with Triton compatibility and clean merge/refactor discipline.
November 2025
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Quality Metrics
Correctness87.2%
Maintainability80.0%
Architecture85.4%
Performance87.2%
AI Usage49.2%
Skills & Technologies
Programming Languages
C++CUDAPython
Technical Skills
BenchmarkingC++CUDACUDA programmingCUDA/HIPDeep LearningDistributed SystemsGPU OptimizationGPU ProgrammingGPU programmingMachine LearningMachine Learning KernelsNumerical ComputingPerformance EngineeringPerformance Optimization
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