CPU Convolution benchmark harness for some popular models (#56455)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56455
CPU convolution performance is pretty important for inference, so
tracking performance for CNNs often boils down to finding shapes that have
either regressed or need optimization. This diff adds a benchmark harness that
lets you pretty easily add new sets of convolution parameters to benchmark.
I've started with an exhaustive list of layers from MobileNetV3, ResNet-18 and
ResNet-50, which are fairly popular torchvision models. More to come if these
prove useful.
I've also added four backend configurations:
- native: uses at::conv2d, which applies its own backend selection heuristics
- mkldnn_none: uses mkldnn but applies no prepacking; uses the NCHW default
- mkldnn_weight: prepacks weights in an mkldnn-friendly format
- mkldnn_input: also prepacks the inputs in NCHW16c
ghstack-source-id: 127027784
Test Plan: Ran this on my Skylake Xeon
Reviewed By: ngimel
Differential Revision: D27876139
fbshipit-source-id: 950e1dfa09a33cc3acc7efd579f56df8453af1f2
