Performance Profiling on AMD GPUs - Part 7: AI/PyTorch® Edition

This blog, like the previous articles in the profiling guide series (Part 1, Part 2, Part 3, Part 4, Part 5, and Part 6), applies a systematic profiling workflow to a GPU-accelerated application. Those posts establish the core loop on HIP and Fortran applications: baseline timing, kernel hotspots with rocprofv3, rooflines and hardware counters with rocprof-compute, and whole-system timelines with rocprof-sys. Here we apply that loop to a PyTorch training workload, where one logical operator can lower to many GPU kernels and optimization often means changing how the framework dispatches work rather than rewriting a kernel.