2025-12 Monthly Summary: Implemented GPU-based reference kernels for CK Tile grouped convolution and integrated them into CK Builder via a unified API, enabling direct verification of optimized kernels against a robust, readable reference path. Expanded coverage to 1D/2D/3D convolutions across FP16, BF16, FP32, FP8, and BF8 data types, with forward, backward data, and backward weight paths. Introduced ConvDims-based kernel interfaces and modern C++17 patterns (std::array, get_block_id/get_thread_id helpers, #pragma once) to improve safety and maintainability. Built a comprehensive verification suite (50 tests) with CPU-GPU result equality; achieved 42/42 successes and demonstrated 50-100x performance improvements for large tensors. Strengthened CI with HIP_CHECK_ERROR, v=3 verification, and consistent output formatting. Delivered business value by accelerating kernel verification, reducing debugging time, and increasing confidence in performance-critical paths.

November 2025 performance summary for ROCm/composable_kernel Key features delivered - Split-Image Convolution for Large Tensors (Unified 1D/2D/3D): introduced memory-aware split-image approach for grouped convolution, including a unified multi-dimensional structure, safety checks, and a recursive splitting workflow to enable processing tensors beyond memory limits; 1D/2D/3D support with validated padding, offset handling, and test coverage. - 1D split-image for N=1: implemented a working 1D path for grouped conv with W split; left/right kernel launches; comprehensive test results (N=1) with 94/94 tests passing; confirmed across channels, padding, stride, dilation, and odd dimensions. - Split-N and Split-Image integration improvements: corrected batch stride handling and per-batch spatial offsets; stored spatial offsets in kernel arguments and synchronized with original dimensions to ensure correctness for N>1; all N=1/N>1 tests validated. - Safety and threshold enhancements: added is_possible_to_split checks to gracefully fallback when splitting is not feasible; unified threshold management (TwoGB-based) for Split-N and Split-Image using CalculateSplitImage logic. Major bugs fixed - Split-N + Split-Image compatibility: resolved critical offset/memory-offset mismatch that caused large accuracy deviations when both splits were active; introduced per-piece offset calculation and per-batch stride corrections. - Per-piece offset handling and batch-stride alignment: fixed memory addressing so each batch sees correct input/output slices. - Robust test coverage stabilization: ensured 1D/2D/3D tests pass under N=1 and various N values, including odd dimensions and large tensors. Overall impact and accomplishments - Enhanced ability to train/run on memory-constrained hardware by enabling large-tensor grouped convolutions without memory overflows. - Improved reliability and maintainability through unified split logic, safer fallbacks, and comprehensive tests across 1D/2D/3D. - Brought CK Builder forward convolution device support closer to production readiness with extensive DL-descriptor configurability and a suite of tests passing. Technologies/skills demonstrated - Advanced C++ design (template-driven, concepts), GPU kernel integration, and memory-aware convolution strategies. - Recursive/binary-split algorithms, offset arithmetic, and robust safety checks. - Build-system and developer tooling improvements (CMake, clang-format) and test automation across CPU/GPU paths.

September 2025: Delivered Split-N support for grouped convolution forward in ROCm/composable_kernel enabling large-tensor processing (>2GB) by automatically splitting the batch dimension and leveraging 3D grid parallelism. Implemented 64-bit indexing for batch offsets (long_index_t), removed redundant GemmM initialization, and refactored initialization paths to improve stability. Validated across multiple tensor sizes with tile_example_grouped_conv_fwd (up to 40 splits) to ensure correctness and scalability. This work unlocks larger-scale workloads, improves throughput for grouped conv forward, reduces crash risk on large tensors, and establishes groundwork for future scale-out in CK.

In Aug 2025, delivered scalable, CSV-driven convolution testing pipelines across two ROCm repos, enabling automated, dataset-driven validation with improved CI integration and configurable test modes. The work tightens testing coverage for convolution operations, improves reliability of ROCm-PyTorch interop, and reduces total test time, delivering measurable business value in quality and release readiness.