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:
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
This PR adds the possibility to build the C++ parts of autograd and jit, with no dependency on Python.
The goal is to allow taking a PyTorch IR representation (a tree s-expr) and running it with provided inputs.
Prerequisite: build PyTorch so that codegen runs once.
Instructions:
cd tools/cpp_build
bash build_all.sh
This will build libtorchjit and torchjit_test in tools/cpp_build/build/torchjit-build. The latter basically runs the code in test_jit.cpp for now.
While writing the PR, it turned out that a few of Python.h includes were redundant. They were removed here (PyTorch tests still pass on my machine, we'll see CI).
* Introduce Python-free builds of autograd and jit
* Remove NO_PYTHON ifdef in functions/special
Implements from_numpy using ATen tensors. Variable.from_numpy is a
convenient placeholder for the variant that returns Variables until we
merge Tensor and Variable.
The behavior is slightly changed:
- from_numpy() on an empty array now returns an empty tensor instead of
throwing an exception. The shape may not be preserved.
- CharTensor(ndarray) used to throw an exception. It now copies the
ndarray. Copying is implemented via ATen toType.
Implements basic and advanced indexing using ATen tensors/variables.
Basic indexing is translated at the Python-binding level
(python_variable_indexing.cpp) to slice/squeeze/unsqueeze/select calls.
Advanced indexing is implemented in ATen in terms of take() and put()
calls.
