April 2026 highlights focused on reintroducing and stabilizing the Composable Kernel (CK) backend for variable-length attention on ROCm in PyTorch. Delivered CK backend integration for varlen attention with fixes to dropout handling and softmax shape adjustments, and added a new API to check CK backend availability to improve attention performance and functionality. Consolidated testing and cross-team collaboration, pulling in a single, consolidated change (commit 4f6f0da0a9e99a25d1fcefa82be1f4385d4ec45f) that relands the core work and aligns with PRs 178322/178729 and the D98693429 differential.

March 2026 saw targeted enhancements to PyTorch profiling and ROCm integration, delivering more reliable observability and broader platform support. Key features include unconditional emission of Input Strides metadata in profiler traces and the relanding of expandable segments for ROCm with HIP API integration, ensuring consistent memory allocation behavior across CUDA and ROCm. Tests were added to validate stride data is captured even when concrete inputs aren’t recorded, and to cover ROCm-specific paths. These changes improve performance analysis accuracy, reduce debugging time for profiling issues, and broaden platform compatibility for performance-sensitive workloads.

August 2025 monthly summary: Stabilized cross-version compatibility for the MVAI package by addressing FAISS build issues under ROCm 7. Implemented compile-time checks to select appropriate hipBLAS APIs based on ROCm version, ensuring compatibility with both older and newer ROCm releases. No new user-facing features this month; primary focus was reliability, platform compatibility, and maintainable code changes for FAISS integration.

July 2025 monthly wrap-up for pytorch/FBGEMM: Delivered a correctness-focused fix in the Sparse Permute Kernel to properly handle non-contiguous input tensors, introduced a regression test, and tightened test coverage around sparse permutation paths. These changes reduce risk of silent data corruption in production models and improve reliability of sparse math paths.

June 2025: Focused on AMD GPU training performance optimization in pytorch/FBGEMM. Replaced frequent gpuAtomicIncrement calls inside training loops with a local counter and relaxed atomic adds to reduce atomic operations. This aligns performance with experiments that disable bounds check warnings and improves throughput on AMD GPUs.