ROCm/aiter
May 2025 – Jul 2026
11 Months active
Languages Used
C++CudaPythonJSON
Technical Skills
Argument ParsingBackend DevelopmentCUDACode RefactoringDeep LearningGPU Computing
Shao-Chun Lee
PROFILE
Shao-chun Lee
Over 11 months, contributed to ROCm/aiter by engineering high-performance GPU kernels and backend optimizations for large language models and deep learning workloads. Developed and tuned fused GEMM, attention, and quantization paths using Python, C++, and Triton, targeting AMD GFX1250 and GFX12 hardware. Enhanced matrix multiplication, RoPE, and MoE kernels for throughput, stability, and hardware compatibility, while expanding test coverage and configuration management. Addressed bugs in memory access, activation handling, and CI integration, ensuring robust deployment across diverse platforms. The work emphasized performance benchmarking, numerical computing, and backend development, resulting in scalable, production-ready infrastructure for advanced machine learning systems.
Overall Statistics
Feature vs Bugs
78%Features
Repository Contributions
64Total
Bugs
6
Commits
64
Features
21
Lines of code
68,332
Activity Months11
Your Network
1985 peopleSame Organization
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Work History
July 2026
14 Commits • 1 Features
Jul 1, 2026July 2026 monthly summary for ROCm/aiter. Delivered high-performance Triton attention and GEMM kernels for Gluon on GFX12/GFX1250, stabilised API/CI across MI35X and GFX12/GFX1250, and strengthened platform readiness for diverse ML workloads. Key outcomes include throughput and efficiency gains on Gluon workloads, robust Triton integration with kernel-level optimizations, and improved CI reliability and upstream alignment.
14 Commits • 1 Features
Jul 1, 2026July 2026 monthly summary for ROCm/aiter. Delivered high-performance Triton attention and GEMM kernels for Gluon on GFX12/GFX1250, stabilised API/CI across MI35X and GFX12/GFX1250, and strengthened platform readiness for diverse ML workloads. Key outcomes include throughput and efficiency gains on Gluon workloads, robust Triton integration with kernel-level optimizations, and improved CI reliability and upstream alignment.
July 2026
June 2026
16 Commits • 4 Features
Jun 1, 2026June 2026 monthly summary for ROCm/aiter focusing on delivering business value and technical excellence across UA3D/MLA, compiler/back-end tuning, and hardware-specific optimizations. Key features delivered include unified attention (UA3D) and MLA enhancements with FP4 support, SWA for unified attention, improved fused-kernel grid layout, upcast_operand option, shuffled KV cache, and support for large numbers of query heads to boost throughput and efficiency. Representative commits include fc6c9c3fcb05ba8eca463a1d57a94378e262b2b6 (FP4 support for UA3D/MLA and KV cache shuffle), 57de65d19feaf2d8f65a131cee516ee1615a07e8 (UA3D config updates), 501da4e97cc6f15ece48fb6a9994dc3c28c15e73 (new grid layout for fused_qk_rope_cat_and_cache_mla and upcast_operand), 6b237bdbfa5e9f36be55c6793224574e017bce76 (support for shuffled_kv and blocked MLA), b2d9e97e097a6cf061af5b2bdadc65275a4e7808 (MLA large QH handling), and 6d0304e8ce88bddef4ec875bdd844804fd631089 (enable swa). Other notable work includes a GFX12 Triton import fix (c122f489d10da101a77268f3b47c5c9fe9685b44), Batched GEMM and BF16 GEMM performance optimizations with MXFP8 MOE and Split-K (commits 5228838e57482b33b48fb191d2fed02680efbfd2, 6e2052b0bdd0abacb1386bcddef00fb4fab583d5, cdb0e6a53fe9d57a064619237590d1658f0da66d, 54dc7dfb5ce0b5c8c72bfa87b62faa0a961e4354, fe8bd1aed3ec2f231a9a00e7de5d3225d14f3a64), as well as an OOM fix for gather_kv_b_proj on gfx942 (93999c35764565efed02c37744407d9407354201) and DSv4 hardware configuration support (ccd554b1a05159029b8e23e7e6f9cd51b2c231bc). Additionally, gfx1250/GFX12-specific kernels and configurations were introduced to boost throughput and RoPE-related KV cache reshaping performance (f6ec8b6847b4bf77916d6d3ce56d109f6055ef41, e701c22ef5726afb14234db2bc73831d79b4827c). These efforts collectively improve throughput, stability, and hardware coverage, enabling faster inference, more efficient resource usage, and broader deployment options.
June 2026
16 Commits • 4 Features
Jun 1, 2026June 2026 monthly summary for ROCm/aiter focusing on delivering business value and technical excellence across UA3D/MLA, compiler/back-end tuning, and hardware-specific optimizations. Key features delivered include unified attention (UA3D) and MLA enhancements with FP4 support, SWA for unified attention, improved fused-kernel grid layout, upcast_operand option, shuffled KV cache, and support for large numbers of query heads to boost throughput and efficiency. Representative commits include fc6c9c3fcb05ba8eca463a1d57a94378e262b2b6 (FP4 support for UA3D/MLA and KV cache shuffle), 57de65d19feaf2d8f65a131cee516ee1615a07e8 (UA3D config updates), 501da4e97cc6f15ece48fb6a9994dc3c28c15e73 (new grid layout for fused_qk_rope_cat_and_cache_mla and upcast_operand), 6b237bdbfa5e9f36be55c6793224574e017bce76 (support for shuffled_kv and blocked MLA), b2d9e97e097a6cf061af5b2bdadc65275a4e7808 (MLA large QH handling), and 6d0304e8ce88bddef4ec875bdd844804fd631089 (enable swa). Other notable work includes a GFX12 Triton import fix (c122f489d10da101a77268f3b47c5c9fe9685b44), Batched GEMM and BF16 GEMM performance optimizations with MXFP8 MOE and Split-K (commits 5228838e57482b33b48fb191d2fed02680efbfd2, 6e2052b0bdd0abacb1386bcddef00fb4fab583d5, cdb0e6a53fe9d57a064619237590d1658f0da66d, 54dc7dfb5ce0b5c8c72bfa87b62faa0a961e4354, fe8bd1aed3ec2f231a9a00e7de5d3225d14f3a64), as well as an OOM fix for gather_kv_b_proj on gfx942 (93999c35764565efed02c37744407d9407354201) and DSv4 hardware configuration support (ccd554b1a05159029b8e23e7e6f9cd51b2c231bc). Additionally, gfx1250/GFX12-specific kernels and configurations were introduced to boost throughput and RoPE-related KV cache reshaping performance (f6ec8b6847b4bf77916d6d3ce56d109f6055ef41, e701c22ef5726afb14234db2bc73831d79b4827c). These efforts collectively improve throughput, stability, and hardware coverage, enabling faster inference, more efficient resource usage, and broader deployment options.
May 2026
7 Commits • 4 Features
May 1, 2026May 2026 (2026-05) delivered a set of kernel-level and system-level optimizations for ROCm/aiter that improve throughput, reduce latency, and broaden hardware compatibility for large-model workloads. Key work included fused operations in Triton with top-k routing and fused normalization/activation, efficient attention kernels enabling sparse paged-decode and MLA paths, MOE stage 2 efficiency improvements by removing redundant fill(0), and HIP-friendly asymmetric exponential domain support for Sinkhorn-Knopp and mhc_pre.
7 Commits • 4 Features
May 1, 2026May 2026 (2026-05) delivered a set of kernel-level and system-level optimizations for ROCm/aiter that improve throughput, reduce latency, and broaden hardware compatibility for large-model workloads. Key work included fused operations in Triton with top-k routing and fused normalization/activation, efficient attention kernels enabling sparse paged-decode and MLA paths, MOE stage 2 efficiency improvements by removing redundant fill(0), and HIP-friendly asymmetric exponential domain support for Sinkhorn-Knopp and mhc_pre.
May 2026
February 2026
2 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for ROCm/aiter focused on delivering high-value GEMM configuration improvements and a robust Triton GEMM tuning workflow. Key deliverables enabled larger matrix sizes, stronger test coverage, and a scalable path for future GEMM optimizations across workloads.
February 2026
2 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for ROCm/aiter focused on delivering high-value GEMM configuration improvements and a robust Triton GEMM tuning workflow. Key deliverables enabled larger matrix sizes, stronger test coverage, and a scalable path for future GEMM optimizations across workloads.
January 2026
6 Commits • 2 Features
Jan 1, 2026January 2026 performance summary for ROCm/aiter focused on delivering higher-performance GEMM paths, improving GEMM configuration robustness, and fixing configuration handling gaps to enable more efficient FP8-precision workloads and smoother production use.
6 Commits • 2 Features
Jan 1, 2026January 2026 performance summary for ROCm/aiter focused on delivering higher-performance GEMM paths, improving GEMM configuration robustness, and fixing configuration handling gaps to enable more efficient FP8-precision workloads and smoother production use.
January 2026
December 2025
4 Commits • 1 Features
Dec 1, 2025December 2025 — ROCm/aiter monthly highlights focusing on performance-critical FP4/FP8 fusion paths Key features delivered: - Fused GEMM kernels for FP4 and FP8 with preshuffling, quantization, and tuning utilities. Implemented preshuffle for FP4, fused GEMM with scaling and addition for FP8, and utilities to validate tuning status/configurations. Commit traces include DS FP4 fusions redo, kernel integration in fused_moe, and FP4/GEMM support files (e.g., 63539c21c1459e521bf3c4700509eee761b2851c; a18d6b6607a34d5056dfc410b3abb6bca0f544bd; ffa79a916837bdc935126f73c5698463b21a7e46; 044fcd817ed017e20e529df2a8e9224a6fa1a86c). Major bugs fixed: - Resolved multiple correctness and UT coverage issues in FP4/FP8 fusion paths; fixed config loading and representation issues observed during FP4 FP8 flows; addressed internal bug fixes across fused_gemm and related utilities (notably fixes described as bug fixes and bumps in PR notes). This improved stability of the fused GEMM stack and AOT representations. Overall impact and accomplishments: - Enhanced DL performance for FP4/FP8 workloads by reducing memory bandwidth and compute overhead through fused GEMM, preshuffling, and quantization. Expanded testing and validation lead to more reliable deployments in production inference/training pipelines. Strengthened code maintainability with tuning utilities and config validation checks; enabled smoother integration into fused_moe and downstream components. Technologies/skills demonstrated: - Triton-based fused GEMM development, FP4/FP8 data path optimization, preshuffling, and quantization techniques. - Performance tuning, kernel configuration, and automated validation utilities. - Unit testing coverage expansion, AOT/config management, and collaboration across commits (co-authored work and integration efforts).
December 2025
4 Commits • 1 Features
Dec 1, 2025December 2025 — ROCm/aiter monthly highlights focusing on performance-critical FP4/FP8 fusion paths Key features delivered: - Fused GEMM kernels for FP4 and FP8 with preshuffling, quantization, and tuning utilities. Implemented preshuffle for FP4, fused GEMM with scaling and addition for FP8, and utilities to validate tuning status/configurations. Commit traces include DS FP4 fusions redo, kernel integration in fused_moe, and FP4/GEMM support files (e.g., 63539c21c1459e521bf3c4700509eee761b2851c; a18d6b6607a34d5056dfc410b3abb6bca0f544bd; ffa79a916837bdc935126f73c5698463b21a7e46; 044fcd817ed017e20e529df2a8e9224a6fa1a86c). Major bugs fixed: - Resolved multiple correctness and UT coverage issues in FP4/FP8 fusion paths; fixed config loading and representation issues observed during FP4 FP8 flows; addressed internal bug fixes across fused_gemm and related utilities (notably fixes described as bug fixes and bumps in PR notes). This improved stability of the fused GEMM stack and AOT representations. Overall impact and accomplishments: - Enhanced DL performance for FP4/FP8 workloads by reducing memory bandwidth and compute overhead through fused GEMM, preshuffling, and quantization. Expanded testing and validation lead to more reliable deployments in production inference/training pipelines. Strengthened code maintainability with tuning utilities and config validation checks; enabled smoother integration into fused_moe and downstream components. Technologies/skills demonstrated: - Triton-based fused GEMM development, FP4/FP8 data path optimization, preshuffling, and quantization techniques. - Performance tuning, kernel configuration, and automated validation utilities. - Unit testing coverage expansion, AOT/config management, and collaboration across commits (co-authored work and integration efforts).
November 2025
4 Commits • 1 Features
Nov 1, 2025Month 2025-11 ROCm/aiter: Delivered substantial Triton FP4/FP8 quantization and GEMM enhancements, expanding production-ready quantization, boosting performance and flexibility. Implemented FP4/FP8 quantization optimizations and fused GEMM paths (A16/WFP4) with fused RMS reduction; introduced new tensor shapes, configurations, and activation handling improvements. Updated to rename the BF16 GEMM config for clarity and added broader configuration management. Brought in DS a16w8 GEMM and fused_reduce_rms_fp8_group_quant, plus comprehensive FP4 Triton fusion with new kernels and configs (fused_gemm_afp4wfp4_a16w16.py, gemm_a16wfp4.py). Added MI300 config support, code formatting (black), and multiple bug fixes, particularly addressing unit-test issues and integration gaps.
4 Commits • 1 Features
Nov 1, 2025Month 2025-11 ROCm/aiter: Delivered substantial Triton FP4/FP8 quantization and GEMM enhancements, expanding production-ready quantization, boosting performance and flexibility. Implemented FP4/FP8 quantization optimizations and fused GEMM paths (A16/WFP4) with fused RMS reduction; introduced new tensor shapes, configurations, and activation handling improvements. Updated to rename the BF16 GEMM config for clarity and added broader configuration management. Brought in DS a16w8 GEMM and fused_reduce_rms_fp8_group_quant, plus comprehensive FP4 Triton fusion with new kernels and configs (fused_gemm_afp4wfp4_a16w16.py, gemm_a16wfp4.py). Added MI300 config support, code formatting (black), and multiple bug fixes, particularly addressing unit-test issues and integration gaps.
November 2025
October 2025
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Monthly delivery focused on performance optimization for large language model inference on ROCm. Delivered a fused RoPE KV-cache kernel integration in ROCm/aiter, enabling Rotary Positional Embeddings to be applied directly within the Key-Value cache operations in Triton. This reduces redundant RoPE computations, improves throughput, and lowers latency for LLM workloads on ROCm platforms. The work includes new Triton kernels, Python bindings, and tests, with alignment to llama.cpp KV-cache path.
October 2025
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Monthly delivery focused on performance optimization for large language model inference on ROCm. Delivered a fused RoPE KV-cache kernel integration in ROCm/aiter, enabling Rotary Positional Embeddings to be applied directly within the Key-Value cache operations in Triton. This reduces redundant RoPE computations, improves throughput, and lowers latency for LLM workloads on ROCm platforms. The work includes new Triton kernels, Python bindings, and tests, with alignment to llama.cpp KV-cache path.
August 2025
2 Commits • 1 Features
Aug 1, 2025ROCm/aiter – August 2025 monthly summary. Focused on validating the FP8 BMM kernel and stabilizing Triton MoE paths through targeted bug fixes and expanded test coverage. The work improves correctness, reliability, and cross-framework validation between PyTorch and Triton, positioning FP8 kernels for production readiness.
2 Commits • 1 Features
Aug 1, 2025ROCm/aiter – August 2025 monthly summary. Focused on validating the FP8 BMM kernel and stabilizing Triton MoE paths through targeted bug fixes and expanded test coverage. The work improves correctness, reliability, and cross-framework validation between PyTorch and Triton, positioning FP8 kernels for production readiness.
August 2025
July 2025
4 Commits • 3 Features
Jul 1, 2025Monthly work summary for 2025-07 focused on ROCm/aiter backend optimization and performance enhancements. Delivered significant kernel and backend optimizations in the Triton-backed AITer workflow, including RoPE optimization, fused Triton operations, and large-matrix GEMM improvements. These changes improve transformer workloads and large-scale training/inference pipelines by increasing throughput, reducing kernel launch overhead, and enhancing scalability. All work included updated tests, benchmarks, and configuration loading to align with refactored kernels.
July 2025
4 Commits • 3 Features
Jul 1, 2025Monthly work summary for 2025-07 focused on ROCm/aiter backend optimization and performance enhancements. Delivered significant kernel and backend optimizations in the Triton-backed AITer workflow, including RoPE optimization, fused Triton operations, and large-matrix GEMM improvements. These changes improve transformer workloads and large-scale training/inference pipelines by increasing throughput, reducing kernel launch overhead, and enhancing scalability. All work included updated tests, benchmarks, and configuration loading to align with refactored kernels.
May 2025
4 Commits • 2 Features
May 1, 2025In May 2025, ROCm/aiter delivered key RoPE-related performance and stability improvements, including kernel enhancements, memory access bug fixes, and benchmarking tooling improvements. These changes enhance throughput and flexibility for large language models while improving reliability and developer productivity.
4 Commits • 2 Features
May 1, 2025In May 2025, ROCm/aiter delivered key RoPE-related performance and stability improvements, including kernel enhancements, memory access bug fixes, and benchmarking tooling improvements. These changes enhance throughput and flexibility for large language models while improving reliability and developer productivity.
May 2025
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Quality Metrics
Correctness85.8%
Maintainability80.6%
Architecture85.4%
Performance88.2%
AI Usage50.0%
Skills & Technologies
Programming Languages
C++CudaJSONPython
Technical Skills
AMD GFX1250AMD ROCmArgument ParsingAttention MechanismsBackend DevelopmentC++CUDACode RefactoringCompute KernelsCustom KernelsDeep LearningDeep Learning OptimizationEmbedded SystemsFP8 KernelsFP8 Quantization
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