This PR introduces a new "operator microbenchmark" CI workflow and GitHub Actions for operator microbenchmarks, updating test scripts and job matrices to support new parameters, and broadening the operator benchmark tests to include more data types, larger shapes, and gradient tests. The benchmark configurations now focus more on different cuda hardware and multiple dtypes (bf16, fp16, fp32), for both compile and eager mode.
**Benchmark Configuration and Coverage:**
* Expanded operator benchmark configurations in `addmm_test.py`, `bmm_test.py`, `matmul_test.py`, and `mm_test.py` to benchmark multiple dtypes on CUDA devices, in eager and compile mode, for forward and backward run. The configs with tag "long" for the above mentioned files are being run in CI.
* The CI benchmarking is running on various hardwares: H100, A100.
* The CI job also uploads the microbenchmarking outputs to a [HUD](https://hud.pytorch.org/benchmark/llms?repoName=pytorch%2Fpytorch&benchmarkName=PyTorch+operator+microbenchmark) dashboard.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162530
Approved by: https://github.com/huydhn
Co-authored-by: Huy Do <huydhn@gmail.com>
