Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
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
