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:
Fixes https://github.com/pytorch/pytorch/issues/41656
For the CPU version, this is a regression introduced in https://github.com/pytorch/pytorch/issues/10980 which vectorized the `grid_sampler_2d` implementation. It uses the AVX2 gather intrinsic which for `float` requires 32-bit indexing to match the number of floats in the AVX register. There is also an `i64gather_ps` variant but this only utilizes half of the vector width so would be expected to give worse performance in the more likely case where 32-bit indexing is acceptable. So, I've left the optimised AVX version as-is and reinstated the old non-vectorized version as a fallback.
For the CUDA version, this operation has never supported 32-bit indexing so this isn't a regression. I've templated the kernel on index type and added 64-bit variants. Although I gather in some places a simple `TORCH_CHECK(canUse32BitIndexMath(...))` is used instead. So, there is a decision to be made here.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41923
Reviewed By: glaringlee
Differential Revision: D22925931
Pulled By: zou3519
fbshipit-source-id: 920816107aae26360c5e7f4e9c729fa9057268bb
