Month: 2026-03 | Repository: ROCm/aiter Overview: This month focused on stabilizing GEMM paths and improving memory-safety across the CKTile blockscale wrapper to support non-standard tensor layouts, enabling more reliable large-model inference on ROCm. Key features delivered: - GEMM stability improvements: fixed use-after-free in GEMM x_scale handling by keeping the transposed x_scale tensor in scope during kernel execution (8-warp and PreshuffleB paths). Commit: 3e4552bcc005911ad438de8b09ec5ecc84c03dc7; message: Fix use-after-free in cktile blockscale GEMM x_scale handling (#2358). - Range of stride handling fixed: CKTile blockscale GEMM now reads leading-dimension strides from tensor metadata instead of assuming dense layouts, addressing non-standard strides as observed in vLLM on ROCm. Commit: f02877b8c29a9c3469af5ee0a400fffc14b805c9; message: Fix CKTile blockscale GEMM to read strides from tensor metadata (#2466). Major bugs fixed: This work eliminates a memory lifetime bug that could trigger use-after-free in asynchronous kernels, and prevents layout-derived incorrect results when input tensors have non-standard strides or after FP8 weight padding adjustments. Overall impact and accomplishments: Restored correctness and stability in the critical GEMM path used by large-model inference, reducing risk of corrupted outputs and production incidents. These changes align with ongoing efforts to support non-standard tensor layouts and FP8 optimization, improving reliability across ROCm deployments. Technologies/skills demonstrated: PyTorch tensor metadata handling, CUDA/RoCm kernel lifecycle management, memory lifetime management, strides and layout awareness, input padding/FP8 handling, code robustness checks (TORCH_CHECK). Business value: More reliable inference for large models on ROCm, lower risk of silent data corruption, and broader compatibility with non-standard tensor layouts and optimization strategies.

December 2025 performance month focused on expanding model support and improving reliability across ROCm kernels. Delivered layout-flexible BQuant GEMM and inter_dim=192 support for CK 2stage MoE with targeted performance tuning, resulting in broader hardware compatibility and better suitability for large-scale models like Qwen3-235B. Stabilized builds and tests around new feature sets to reduce integration risk.

November 2025 monthly summary for ROCm/composable_kernel (CK_TILE): Delivered substantive enhancements to 2D quantized GEMM and CK_TILE tiling performance, coupled with targeted build fixes to improve reliability of the quantization workflow. Key outcomes include enabling 2D block-scale GEMM support for B-matrix quantization with configurable M/N/K quantization groups, refining tile distributions and UniversalGemmBasePolicy to optimize tensor layouts and CK-Tile performance, and ensuring robust CK_TILE builds and example correctness. Also aligned legacy Non-K Major paths with CK-Tile for compatibility and updated documentation and changelog to reflect new capabilities.

Performance-focused monthly summary for 2025-10 covering ROCm/composable_kernel and ROCm/aiter. Delivered key features enabling scalable GEMM workloads, expanded activation options for attention models, and fused operations with tests; business value includes higher throughput, broader applicability, and improved maintainability.

2025-09 Monthly Summary for ROCm/composable_kernel: Delivered substantial quantization and robustness work for CK_TILE GEMM, complemented by code hygiene improvements and architecture-robust fixes. The efforts enhance business value by enabling practical low-precision GEMM paths, improving maintainability, and increasing cross-architecture reliability.

August 2025 monthly summary for StreamHPC/rocm-libraries focused on delivering key capabilities that improve debuggability, execution flexibility, and GEMM versatility, while maintaining reliability through refactors and tests.

June 2025 monthly summary for StreamHPC/rocm-libraries: Delivered two high-impact GEMM improvements that enhance performance, scalability, and maintainability. Implemented a persistent GEMM kernel across tile loops with CK_TILE integration, including updates to gemm_basic.cpp, gemm_utils.hpp, universal_gemm.cpp and tests, with a new persistent argument and proper grid sizing. This work is backed by commits ffb52783d0a6b3afc168dfa6bfb5bd119f48b65b and 1c6f83df6c1d96668feb5ab7fd3f7d9fbc69d264. Also refactored GEMM pipeline tail handling by moving logic into dedicated pipeline classes to reduce duplication and improve maintainability, via commit 7ea1508b59a0e8f89540d8d5f7eb3e7da9a50a62. No explicit major bug fixes are documented for this month in the provided data. Overall impact: higher throughput for repeated GEMM workloads, cleaner architecture, and better test coverage. Technologies/skills demonstrated: C++, GEMM kernel development, CK_TILE integration, pipeline architecture, testing.

May 2025 monthly summary for StreamHPC/rocm-libraries focusing on delivered features, bug fixes, and impact. Highlights include a new persistent kernel mode for grouped GEMM under CK_TILE, plus build configuration cleanup for GEMM tests. The changes emphasize performance, maintainability, and clear CI signals for GEMM workloads.