[inductor] Add CPU-side profiler event for triton kernels w/ python wrapper (#106351)
This allows you to view the original kernel names (e.g. to reference the triton kernel implementation in the python wrapper code / TORCH_COMPILE_DEBUG logs). `torch._inductor.config.unique_kernel_names=True` does this too, but leaving unique_kernel_names=False will increase triton caching.
Another benefit to this approach is that we can attach additional information to this profiler event in the future. For example, we could attach input shapes/strides (i.e. record_shapes=True for profiler), or possibly paths to the files where the code was dumped.
<img width="435" alt="Screenshot 2023-07-31 at 5 34 25 PM" src="https://github.com/pytorch/pytorch/assets/5067123/839b752f-3907-4f29-9038-9d1822222b45">
^ in the trace above, the pink "triton_poi_fused_add_cos_sin_0" kernel is the new trace event which is added by this PR.
**Performance impact**: [dashboard run](https://hud.pytorch.org/benchmark/compilers?startTime=Thu%2C%2010%20Aug%202023%2000%3A52%3A06%20GMT&stopTime=Thu%2C%2017%20Aug%202023%2000%3A52%3A06%20GMT&granularity=hour&suite=torchbench&mode=inference&dtype=bfloat16&lBranch=gh/davidberard98/216/orig&lCommit=90c4212a7993c3660e7ea53bcd9d21160be31d1a&rBranch=main&rCommit=35cca799ff42182a1b7f1ee4d0225ee879b7c924). There are some regressions, including a 1.72x -> 1.71x on huggingface and 1.30x -> 1.29x on dynamic; however, locally I can't reproduce the results on any of the individual models (differences look like they are within noise). I think the perf impact is likely < 1% overall.
Differential Revision: [D47941809](https://our.internmc.facebook.com/intern/diff/D47941809)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106351
Approved by: https://github.com/eellison, https://github.com/albanD
ghstack dependencies: #107195
