Overcoming the Hidden Performance Traps of Variable-Shaped Tensors: Efficient Data Sampling in PyTorch

Variable-shaped tensors in PyTorch can create hidden performance bottlenecks due to host-device synchronization, limitations on graph compilation, and challenges with data batching. The use of functions like `torch.nonzero` can lead to dynamic tensor shapes, forcing the CPU and GPU to sync and causing drops in GPU utilization. An example data sampling function is analyzed using the PyTorch Profiler to demonstrate this inefficiency. An alternative, GPU-friendly implementation is then proposed, which avoids dynamic shapes by using a combination of static operations like `torch.count_nonzero` and `torch.topk`. This optimized approach circumvents the synchronization overhead, resulting in better runtime performance.