February 2026: Delivered FP8 KV_BLOCKSCALE Batch Prefill with Per-Page Descale Parameter for ROCm/aiter. This feature extends quantization capabilities by enabling per-page K/V descale in batch prefill, improving model accuracy and flexibility. Implemented end-to-end changes across Python API, C++ wrappers, and CK kernels, along with a comprehensive test suite and code restructuring for maintainability. The work enables broader FP8 quantization workflows and strengthens production readiness.

January 2026 monthly performance summary for ROCm/aiter focused on batch prefill kernel improvements, API flexibility, and test coverage. Delivered vectorized KV cache layout with vLLM-style block tables and extended kernel API; added page size 16 support; expanded layout support to 3D/5D KV tensors; and introduced profiling for performance measurements. Strengthened validation to ensure correctness across layouts and reduced risk when upgrading FMHA workloads.

Concise monthly summary focusing on key accomplishments for 2025-11 across ROCm/aiter. The main deliverable was enabling robust training with variable-length sequences by adding support for padded sequence lengths in the backward pass of fmha_v3_varlen_bwd, along with API/kernel refinements and test coverage. CI/BUILD reliability was improved by resolving a build issue in unrelated benchmark tests that surfaced during integration.

October 2025: Delivered robust variable-length sequence padding for the FMHA backward pass and unified padding/length handling across forward and backward passes in ROCm/composable_kernel. Implemented query padding support, introduced logical length handling via seqlen_*_ptr/cu_seqlen_*_ptr, and standardized length precedence. Added comprehensive tests for padding scenarios including zero-length sequences and deterministic mode. Refactored FMHA padding code, updated the backward runner, and aligned documentation. Result: more accurate gradients for padded inputs, improved correctness/robustness, and a cleaner, maintainable interface for padding in FMHA.

September 2025 monthly summary for ROCm/aiter focused on delivering scalable attention enhancements for variable-length sequences. Implemented variable-length sequence padding support for the FMHA forward pass via the Composable Kernel (CK) API, enabling efficient attention computation for batches with variable sequence lengths by ignoring padded tokens. Introduced new padding control parameters for both batch and group modes and added tests to validate correctness and performance implications. The change is captured in commit df5ef82745d98107ad1c5330fe95833612227651, establishing traceability from feature work to production-ready code.