Summary:
As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH`
All changes but the ones to `.clang-tidy` are generated using following script:
```
for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008
Reviewed By: driazati, r-barnes
Differential Revision: D29838584
Pulled By: malfet
fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
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:
And few typos
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34791
Test Plan: CI
Differential Revision: D20524879
Pulled By: malfet
fbshipit-source-id: 58fa03bd6356979e77cd1bffb6370d41a177c409
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32935
Mock away the content of onnxified net with some low cost ops so that we can still mimic the input/output transfer while doing minimal work on the card.
Test Plan:
```
buck run glow/fb/test:sparsenn_test -- --gtest_filter='SparseNNTest.vanillaC2' --onnxifi_debug_mode --onnxifi_loop_test_mode --nocaffe2_predictor_use_memonger
```
Differential Revision: D19631971
fbshipit-source-id: f970c55ccb410702f479255eeb750e01e3f8c2ae
Summary:
The second input (`lengths`) is not supported.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16727
Differential Revision: D14054105
Pulled By: houseroad
fbshipit-source-id: 36b8d00460f9623696439e1bd2a6bc60b7bb263c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9299
Onnx has ReduceL1 and ReduceL2 operators that would facilitate this, so allow pytorch to export those and allow caffe2 to run them.
I only implemented this on CPU so far.
Reviewed By: pjh5
Differential Revision: D8757381
fbshipit-source-id: 68afc9e2f90042a70929b73ace05a499b5c670c7
* Update elementwise ops to support numpy style boradcast
Update elementwise ops to support numpy style boradcast
* Fix sqrt_op
* Fix compare ops
* Fix gradient test
* Fix optimizer legacy broadcast
* Fix legacy broadcast for elementwise ops
* Skip flaky test
* Fix eigen simple binary op
* Fix attention test
* Fix rnn test
* Fix LSTM test
* Fix tan grad
* Fix schema check
* Refactor reduce ops to take flexible input types
* Add DISPATCH_FUNCTION macros in common_gpu.h
* Use macros to reduce switch case in dispatching cuda functions
* Update ReduceMean
* Add reduce mean to math
* Update cuda flag
* Update Eigen::Tensor ctor
* Remove unused variables
* Skip ReduceTensorGPUTest if no gpus
* Add NOMINMAX for windows
* Fix lpnorm_op in windows
* Reduce Sum and Reduce Mean
* Handle reductions with empty 'axes'
* Merge codebase and simplify tesnor reduction logic
* Restructure code and add comments.
* Fix parameter to scale
* Fix parameter to scale
