PyTorch profiler guide: optimizing attention layers in transformers

This blog post is the third in a series on PyTorch profiling, following earlier installments on basic math operations and linear layers. It advances naturally to attention, the next fundamental building block in transformer architectures. The post takes a pedagogical approach: it first implements attention naively from primitives (matmul, scaling, masking, softmax), profiles it to show the individual kernels, then identifies inefficiencies (the Memcpy from out-of-place masking) and fixes them with a one-line change to in-place operations.

The larger lesson is that PyTorch abstracts away the complexity of attention through `F.scaled_dot_product_attention()`, which dispatches transparently to multiple highly-optimized backends. However, understanding what is happening under the hood—what kernels are launched, how memory is managed, where redundant operations hide—is essential for engineers tuning models in production. The post emphasizes that small savings per layer compound: attention runs once per transformer block, and modern models have dozens or hundreds of blocks, so eliminating a single Memcpy kernel per attention layer translates to measurable wall-clock speedup and memory savings across the entire model. The provided scripts and profiler traces make the concepts concrete and actionable.