We define specializations for pybind11 defined templates
(in particular, PYBIND11_DECLARE_HOLDER_TYPE) and consequently
it is important that these specializations *always* be #include'd
when making use of pybind11 templates whose behavior depends on
these specializations, otherwise we can cause an ODR violation.
The easiest way to ensure that all the specializations are always
loaded is to designate a header (in this case, torch/csrc/util/pybind.h)
that ensures the specializations are defined, and then add a lint
to ensure this header is included whenever pybind11 headers are
included.
The existing grep linter didn't have enough knobs to do this
conveniently, so I added some features. I'm open to suggestions
for how to structure the features better. The main changes:
- Added an --allowlist-pattern flag, which turns off the grep lint
if some other line exists. This is used to stop the grep
lint from complaining about pybind11 includes if the util
include already exists.
- Added --match-first-only flag, which lets grep only match against
the first matching line. This is because, even if there are multiple
includes that are problematic, I only need to fix one of them.
We don't /really/ need this, but when I was running lintrunner -a
to fixup the preexisting codebase it was annoying without this,
as the lintrunner overall driver fails if there are multiple edits
on the same file.
I excluded any files that didn't otherwise have a dependency on
torch/ATen, this was mostly caffe2 and the valgrind wrapper compat
bindings.
Note the grep replacement is kind of crappy, but clang-tidy lint
cleaned it up in most cases.
See also https://github.com/pybind/pybind11/issues/4099
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82552
Approved by: https://github.com/albanD
Summary:
Follow up to https://github.com/pytorch/pytorch/issues/68095
This also changes the files from the ATen folder to include c10's `Export.h` instead since they can't ever be exporting `TORCH_PYTHON_API`.
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse SciPioneer H-Huang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69585
Reviewed By: mrshenli
Differential Revision: D32958594
Pulled By: albanD
fbshipit-source-id: 1ec7ef63764573fa2b486928955e3a1172150061
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 PR suppresses clang-tidy warnings in the codebase (for now) so that we can re-enable clang-tidy checks on master.
I ran this script to add the `NOLINTNEXTLINE` comments (on a devserver):
```bash
python3 setup.py develop
# Uses same script that's run on CI and adds the -j (parallel), -s (add comments), -k (continue if diagnostic errors are found) options
python3 tools/clang_tidy.py \
-j \
-s \
-k \
-v \
--paths torch/csrc/ \
-g"-torch/csrc/jit/passes/onnx/helper.cpp" \
-g"-torch/csrc/jit/passes/onnx/shape_type_inference.cpp" \
-g"-torch/csrc/jit/serialization/onnx.cpp" \
-g"-torch/csrc/jit/serialization/export.cpp" \
-g"-torch/csrc/jit/serialization/import.cpp" \
-g"-torch/csrc/jit/serialization/import_legacy.cpp" \
-g"-torch/csrc/onnx/init.cpp" \
-g"-torch/csrc/cuda/nccl.*" \
-g"-torch/csrc/cuda/python_nccl.cpp" \
-g"-torch/csrc/autograd/FunctionsManual.cpp" \
-g"-torch/csrc/generic/*.cpp" \
-g"-torch/csrc/jit/codegen/cuda/runtime/*" \
-g"-torch/csrc/deploy/interpreter/interpreter.cpp" \
-g"-torch/csrc/deploy/interpreter/interpreter.h" \
-g"-torch/csrc/deploy/interpreter/interpreter_impl.h" \
-g"-torch/csrc/deploy/interpreter/test_main.cpp"
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60649
Test Plan: Verified changes by re-running the script (without the `-s` option) and seeing no warnings/errors.
Reviewed By: walterddr, janeyx99
Differential Revision: D29504258
Pulled By: 1ntEgr8
fbshipit-source-id: 78310b30ee8213b73ddb4771ad874665323e7a4e
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:
This is something which I wrote because it was useful during my debugging sessions, but I think it might be generally useful to other people as well so I took the liberty of proposing an official `pytorch-gdb` extension.
`pytorch-gdb` is a gdb script written in python. Currently, it contains only one command: `torch-tensor-repr`, which prints a human-readable repr of an `at::Tensor` object. Example:
```
Breakpoint 1, at::native::neg (self=...) at [...]/pytorch/aten/src/ATen/native/UnaryOps.cpp:520
520 Tensor neg(const Tensor& self) { return unary_op_impl(self, at::neg_out); }
(gdb) # the default repr of 'self' is not very useful
(gdb) p self
$1 = (const at::Tensor &) 0x7ffff72ed780: {impl_ = {target_ = 0x5555559df6e0}}
(gdb) torch-tensor-repr self
Python-level repr of self:
tensor([1., 2., 3., 4.], dtype=torch.float64)
```
The idea is that by having an official place where to put these things, `pytorch-gdb` will slowly grow other useful features and make the pytorch debugging experience nicer and faster.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54339
Reviewed By: bdhirsh
Differential Revision: D27253674
Pulled By: ezyang
fbshipit-source-id: dba219e126cc2fe66b2d26740f3a8e3b886e56f5
Summary:
Following up on this: https://github.com/pytorch/pytorch/pull/35851 cross dtype storage copy is not being used internally, so I have not included cross dtype copy for complex.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35771
Differential Revision: D21319650
Pulled By: anjali411
fbshipit-source-id: 07c72996ee598eba0cf401ad61534494d6f5b5b3
Summary:
* adds TORCH_API and AT_CUDA_API in places
* refactor code generation Python logic to separate
caffe2/torch outputs
* fix hip and asan
* remove profiler_cuda from hip
* fix gcc warnings for enums
* Fix PythonOp::Kind
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19554
Differential Revision: D15082727
Pulled By: kostmo
fbshipit-source-id: 83a8a99717f025ab44b29608848928d76b3147a4
Summary:
This is the first commit from a series of planned changes in order to add boolean tensors to PyTorch. The whole plan looks like this:
0. Storage Implementation (this change)
1. Tensor Creation.
2. Tensor Conversions.
3. Tensor Indexing.
4. Tensor Operations.
5. Back compatibility related changes.
This feature was requested by the community:
https://github.com/pytorch/pytorch/issues/4764https://github.com/pytorch/pytorch/issues/4219https://github.com/pytorch/pytorch/issues/4288
**Change**:
Added boolean type to the Storage class for CPU and CUDA backends.
**Tested via**:
1. unit tests
2. running this:
-> import torch
-> torch.BoolStorage
<class 'torch.BoolStorage'>
-> torch.cuda.BoolStorage
<class 'torch.cuda.BoolStorage'>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16810
Reviewed By: gchanan
Differential Revision: D14087246
Pulled By: izdeby
fbshipit-source-id: 042642ced1cb0fd1bb6bff05f9ca871a5c54ee5e
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14802
The definetion of THPStorage does not depend on any Real, its macro
defintion is unnecessary, refactor the code so that THPStorage is not macro
defined.
Reviewed By: ezyang
Differential Revision: D13340445
fbshipit-source-id: 343393d0a36c868b9a06eea2ad9b80f5e395e947
Summary:
How did we get so many uses of `NULL` again?
ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11047
Differential Revision: D9566799
Pulled By: goldsborough
fbshipit-source-id: 83469f352ac69aa65bdaf1a1a21f922d892e0db3
Summary:
This is enabled by the allocator patch; previously we could not
deduplicate THStorage_free/THCStorage_free; now we can.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9495
Reviewed By: SsnL
Differential Revision: D8875497
Pulled By: ezyang
fbshipit-source-id: 387198dff446eb9f84d2d6187066fae1d595dea7
* Remove remaining TensorTypeUtils functions.
Mostly what's remaining is copy utilities -- these are now provided in THCTensorCopy.hpp and templatized on the ScalarType rather than the TensorType.
* Have a single THTensor / THCTensor type.
As was previously done with Storages, have only a single (dtype-independent) THTensor / THCTensor.
For documentation and backwards compatibility purposes, the old names, e.g. TH(Cuda)LongTensor alias the new TH(C)Tensor type.
* undef GENERATE_SPARSE.
These files don't follow the usual pattern: In general the files torch/csrc/X torch/csrc/cuda/X
both include the generic file torch/csrc/generic/X, where torch/csrc/X includes the cpu implementations and torch/csrc/cuda/X includes the cuda implementations.
(Aside: this is probably not the best structure, the torch/csrc/X fiels should probably be moved to torch/csrc/cpu/X).
utils.cpp combines these so that torch/csrc/utils.cpp has cuda specific code. This makes it impossible to declare a single THTensor and THCTensor template type (i.e. THPPointer<_THTensor>, THPointer<_THCTensor>).
Changelist:
- Move *.c to *.cpp
- Change includes of ".c" to ".cpp"
- A bunch of cmake configuration modifying CMAKE_C_FLAGS changed
to CMAKE_CXX_FLAGS or add_compile_options, because if you do CMAKE_C_FLAGS it only applies when you compile C code
- Explicitly cast void* to T* in a number of places
- Delete extern "C" { ... } blocks; instead, properly apply TH_API to everything that should have it (TH_API handles extern "C")
- Stop using stdatomic.h, instead, use <atomic>. This resulted in a bunch of placement-new/delete to be "totally properly correct"
- Refactor of THLongStorageView to not have static constructor methods (since it no longer has a copy/move constructor)
- Documentation about how the TH C interface (and extern C business) works
- Note that THD master_worker mode is dead
- C++ headers in TH libraries are given .hpp suffix, to make it less likely that you'll confuse them with the C-compatible headers (now suffixed .h)
- New function THCStream_stream and THCStream_device to project out fields of THCStream instead of accessing fields directly
- New function THStorage_(retainIfLive), which is equivalent to a retain but only if the refcount is greater than zero.
- In general, I tried to avoid using hpp headers outside of ATen/TH. However, there were a few places where I gave up and depended on the headers for my own sanity. See Note [TH abstraction violation] for all the sites where this occurred. All other sites were refactored to use functions
- Some extra Werror fixes (char* versus const char*)
* Use ATen infer_size implementation rather than TH.
The only substantitive difference between the two implementations is in how empty sizes are handled;
in ATen these are treated as scalars (i.e., can be expanded to anything), whereas in TH they are treated
as a special case of empty tensors (i.e., can't be expanded to anything). Therefore, this change is
necessary to support scalars (0-dimensional tensors). We could also take a bool parameter for determining
how we treat empty tensors but this seems unnecessary: if one tries to expand an empty tensors (as a result
of an infer_size calculation), the expansion will fail.
* Make changes for review.
* Attempt to fix windows build.
* long -> int.
The Tensor and Variable classes are being merged in Python. This means
that all interfaces to C++ must accept Variables where they previously
accepted Tensors.
Setting torch.utils.backcompat.broadcast.warning.enabled=True
will cause Python warnings in the case where broadcast occurs
but previously 1-d view style pointwise ops occured.
