viable/strict/1788603844: [cuBLAS] Always eagerly allocate cuBLAS(Lt) workspaces (#194311)

This release note describes a prototype change to eagerly allocate cuBLAS(Lt) workspaces as opposed to using a cache-based approach. Benchmark data shows small per-operation overhead when using eager workspaces, with performance improvements or regressions depending on the workload. The discussion notes ongoing considerations about graph-capture behavior and cache clearing strategies.

Release notes summarize a prototype that introduces eager allocation of cuBLAS(Lt) workspaces to replace or augment existing workspace caching. The accompanying benchmarks compare cached vs. eager configurations across several matrix shapes, showing modest increases in per-operation time for eager workspaces.

The notes include a discussion on observed issues such as CUDA_ERROR_ILLEGAL_ADDRESS with cached graphs and the potential need to isolate or clear cached workspaces at graph-capture boundaries. A simpler direction being considered is to retain the cache but manage its scope around graph captures.

Source: Release notes from pytorch — https://github.com/pytorch/pytorch/releases/tag/viable%2Fstrict%2F1788603844