[nnc] Per-operator benchmarks (#51093)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51093
Operator level benchmarks comparing eager-mode PyTorch to
NNC-generated fused kernels. We wouldn't normally see these in isolation, but
it points out where NNC is falling short (or doing well).
I threw in a composed hardswish for fun, because it's my favorite activation
function.
Notably, it exposes a bug in our build process that's preventing vectorization
from using `sleef`, so we're using scalar calls to libm with predictably lousy
performance. Fix incoming.
This benchmark is similar to the pure NNC approach in `microbenchmarks.py`, but
will include the overhead of dispatching the fused kernel through TorchScript.
ghstack-source-id: 120403675
Test Plan:
```
op eager nnc speedup
hardswish 0.187 0.051 3.70
hardswish 0.052 0.052 1.00
sigmoid 0.148 1.177 0.13
reciprocal 0.049 0.050 0.98
neg 0.038 0.037 1.02
relu 0.037 0.036 1.03
isnan 0.119 0.020 5.86
log 0.082 1.330 0.06
log10 0.148 1.848 0.08
log1p 0.204 1.413 0.14
log2 0.285 1.167 0.24
exp 0.063 1.123 0.06
expm1 0.402 1.417 0.28
erf 0.167 0.852 0.20
erfc 0.181 1.098 0.16
cos 0.124 0.793 0.16
sin 0.126 0.838 0.15
tan 0.285 1.777 0.16
acos 0.144 1.358 0.11
asin 0.126 1.193 0.11
cosh 0.384 1.761 0.22
sinh 0.390 2.279 0.17
atan 0.240 1.564 0.15
tanh 0.320 2.259 0.14
sqrt 0.043 0.069 0.63
rsqrt 0.118 0.117 1.01
abs 0.038 0.037 1.03
ceil 0.038 0.038 1.01
floor 0.039 0.039 1.00
round 0.039 0.292 0.13
trunc 0.040 0.036 1.12
lgamma 2.045 2.721 0.75
```
Reviewed By: zheng-xq
Differential Revision: D26069791
fbshipit-source-id: 236e7287ba1b3f67fdcb938949a92bbbdfa13dba
