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:
Switches most of the simple for loops outside of `jit` directories to use `c10::irange`.
Generated with D28874212.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59481
Test Plan: Sandcastle
Reviewed By: ngimel
Differential Revision: D28909681
fbshipit-source-id: ec9ab1bd602933238d9d0f73d4d8d027b75d9d85
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/55799
I'm going to change the implementation of cdata soon so I need to
abstract over cdata access with a function. Additionally, many
users are casting manually casting to THPVariable to access
the member so I can remove these unsafe casts in the client code
(the implementation, of course, is still doing an unsafe cast.)
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D27712130
Pulled By: ezyang
fbshipit-source-id: 95fcc013bf3913d67f2c634068eb5b3aab144cb3
Summary:
Given that pybind11 implements these gil functions, I don't think it makes sense for Pytorch to have its own bespoke versions.
Fixes https://github.com/pytorch/pytorch/issues/29065
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29095
Differential Revision: D18301806
Pulled By: ezyang
fbshipit-source-id: 03da6a26c41ee65aaadf7b67b9f0b14d2def2a5a
Summary:
Follow-up to gh-25483, more of the same fixes for warnings like:
```
../torch/csrc/autograd/python_variable.cpp:503:31: warning: cast between incompatible function types from ‘PyObject* (*)(THPVariable*)’ {aka ‘_object* (*)(THPVariable*)’} to ‘getter’ {aka ‘_object* (*)(_object*, void*)’} [-Wcast-function-type]
503 | {"_backward_hooks", (getter)THPVariable_get_backwards_hooks, (setter)THPVariable_set_backwards_hooks, nullptr, nullptr},
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
```
This takes the build log output for a full rebuild with GCC 9.1 from ~10,000 to ~7,000 lines.
`clang-tidy` is going to complain, no way around that - see discussion at the end of gh-25483.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26104
Differential Revision: D17396831
Pulled By: ezyang
fbshipit-source-id: d71696bfe4dbe25519e4bcb7753151c118bd39f7
Summary:
This PR fixes around 250 places in the codebase where we were making unnecessary copies of objects (some large, some small).
ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15026
Differential Revision: D13458784
Pulled By: goldsborough
fbshipit-source-id: be5148b2ce09493588d70952e6f6d6ff5ec5199b
Summary:
Anywhere we used #include "foo.h", we now say #include <foo.h>
Paths are adjusted to be rooted out of aten/src, torch/lib, or
the root level directory.
I modified CMakeLists.txt by hand to remove TH and THC from
the include paths.
I used the following script to do the canonicalization:
```
import subprocess
import re
import os.path
files = subprocess.check_output(['git', 'ls-files']).decode('utf-8').rstrip().split('\n')
for fn in files:
if not any(fn.endswith(suff) for suff in ['.cu', '.cpp', '.in', '.h', '.hpp', '.cu', '.cuh', '.cc']):
continue
if not any(fn.startswith(pref) for pref in ["aten/", "torch/"]):
continue
with open(fn, 'r') as f:
c = f.read()
def fmt(p):
return "#include <{}>".format(p)
def repl(m):
p = m.group(1)
if p in ["dlfcn.h", "unistd.h", "nvrtc.h", "cuda.h", "cuda_runtime.h", "cstdint", "cudnn.h", "Python.h", "cusparse.h", "cuda_runtime_api.h", "cuda_fp16.h", "cublas_v2.h", "stdint.h", "curand_kernel.h"]:
return fmt(p)
if any(p.startswith(pref) for pref in ["torch/csrc", "c10/", "ATen/", "caffe2/", "TH/", "THC/", "Eigen/", "gtest/", "zdl/", "gloo/", "onnx/", "miopen/"]):
return fmt(p)
for root in ["aten/src", "torch/lib", ""]:
for bad_root in [os.path.dirname(fn), "aten/src/TH", "aten/src/THC", "torch/csrc"]:
new_p = os.path.relpath(os.path.join(bad_root, p), root)
if not new_p.startswith("../") and (os.path.exists(os.path.join(root, new_p)) or os.path.exists(os.path.join(root, new_p + ".in"))):
return fmt(new_p)
print("ERROR: ", fn, p)
return m.group(0)
new_c = re.sub(r'#include "([^"]+)"', repl, c)
if new_c != c:
print(fn)
with open(fn, 'w') as f:
f.write(new_c)
```
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14849
Reviewed By: dzhulgakov
Differential Revision: D13363445
Pulled By: ezyang
fbshipit-source-id: 52361f878a672785f9306c9e9ab2513128092b68
Summary:
This is needed because the JIT declares some custom autograd functions.
colesbury
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11082
Differential Revision: D9580456
Pulled By: apaszke
fbshipit-source-id: 6bf00c1188a20b2ee6ecf60e5a0099f8263ad55a
* Improve Function interface
* Undo tracer changes
* Fix bug in VariableType.set_history
* Rename function_counter and sequence_number to sequence_nr
* Clarify Function documentation
* Replace swap_next_edges with next_edges() getter
* Bring back set_gradient_edge
* Simplify special.cpp
* add_gradient_edge -> create_gradient_edge
* Add mutable getters for pre/post hooks
* Use make_variable with Edge
* Remove remove_gradient_edge in favor of detach_
* Fix documentation and remove create_gradient_edge friend method
* Canonicalize some includes
Variable is now a subclass of at::Tensor backed by a VariableImpl* pImpl. The implementation of the ATen functions is defined in the auto-generated VariableType.h/cpp file.
Currently, only functions which fall through to the base type, such as sizes() and isCuda() are implemented. Differentiable ops like add() and mul() will be added in a subsequent PR.
* add SharedFunctionMaker to create Function shared in the graph
* Clean shared_ptr usage for only function that will be used in the graph
* make Function binding match Varible one
* remove unnecessary changes
* fix comments
* proper weakref implementation
* add call to clear in dealloc
* Fix clang warnings
* Raise errors when unsupported ConvNd configurations are used
* Properly handle Variable indexing with LongTensors
* Support both tensors and variables in Variable.type_as
Because of this Variables can no longer appear in the graph.
Every usage of a leaf Variable will leave an AccumulateGrad
function that has no outputs, but modifies var.grad as a side
effect.
The core autograd Variable, Function, and Engine no longer depend on the
Python API. This let's us implement functions in C++. In the future, we
can also multithread engine and release the GIL for most of the
non-Python backwards.
