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517c7c9861
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18 Commits

Author SHA1 Message Date
Edward Yang
517c7c9861 Canonicalize all includes in PyTorch. (#14849) 
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
 2018-12-08 19:38:30 -08:00
Peter Goldsborough
d6c53328f9 Large scale fix of python-related files in torch/csrc/ 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14515

Differential Revision: D13247966

Pulled By: goldsborough

fbshipit-source-id: 7a127c508fc576a7a92626dd6b729f660162d628
 2018-12-07 13:04:46 -08:00
Zachary DeVito
d985cf46f1
Add workaround to fix include warnings in Python 2 builds. (#6716) 2018-04-24 12:30:19 -07:00
peterjc123
63af898d46 Fix extension test on Windows (#5548) 
* Change cpp_extensions.py to make it work on Windows

* Fix linting

* Show python paths

* Debug

* Debug 1

* set PYTHONPATH

* Add ATen into library

* expose essential libs and functions, and copy _C.lib

* Specify dir in header

* Update check_abi for MSVC

* Activate cl environment to compile cpp extensions

* change version string

* Redirect stderr to stdout

* Add monkey patch for windows

* Remove unnecessary self

* Fix various issues

* Append necessary flags

* add /MD flag to cuda

* Install ninja

* Use THP_API instead of THP_CLASS

* Beautify the paths

* Revert "Use THP_API instead of THP_CLASS"

This reverts commit dd7e74c44db48e4c5f85bb8e3c698ff9de71ba2d.

* Use THP_API instead of THP_CLASS(new)
 2018-04-02 13:53:25 -04:00
Sam Gross
30ec06c140
Merge Variable and Tensor classes (#5225) 
This replaces the torch.Tensor constructors with factories that produce
Variables. Similarly, functions on the torch module (e.g. torch.randn)
now return Variables.

To keep the PR to a reasonable size, I've left most of the unused tensor
code. Subsequent PRs will remove the dead code, clean-up calls to
torch.autograd.Variable, and rename Variable to Tensor everywhere.

There are some breaking changes because Variable and Tensors had
slightly different semantics. There's a list of those changes here:

 https://github.com/pytorch/pytorch/wiki/Breaking-Changes-from-Variable-and-Tensor-merge
 2018-02-23 18:03:31 -05:00
gchanan
9bb6d33d35
Enable scalars if compiled with WITH_SCALAR environment variable. (#4806) 
* Enable scalars if compiled with WITH_SCALAR environment variable.

We are pretty close to enabling scalars (0-dimensional arrays); this allows turning them on
for development purposes and to be able to write code that works both with and without scalars enabled.

WITH_SCALARS is currently broken with distributions, but should work for test_torch, test_autograd, test_nn.

* Fix unsqueeze.

* Fix wrap dim, wrapping with Scalar.
 2018-01-23 15:44:11 -05:00
Sam Gross
e23acb3b08
Allow Variables in the (legacy) THNN bindings. (#4723) 
The legacy NN bindings currently operate only on Tensors. We are slowly
replacing all uses of Tensor with Variable in Python code so that there
will only be one user-visible class. This changes the NN bindings
accessed through type2backend to accept either Tensors or Variables.

This does not affect the NN bindings that go through ATen.
 2018-01-19 10:56:58 -05:00
gchanan
eb857ec367
Introduce a (non-public) autograd scalar method and improve printing (#4586) 
* Specialize Variable pinting and always print device for GPU tensors/Variables.

* Introduce a (non-public) _scalar_sum() method for autograd scalar testing.
 2018-01-12 14:26:38 -05:00
Sam Gross
d605058212
Replace Variable.volatile with torch.no_grad() (#3970) 
This removes volatile from Variable. The functionality is mostly
replaced by a global (thread-local) flag, which is controlled by
torch.set_grad_enabled() and the context manager torch.no_grad().

In C++, the flag is exposed through GradMode::is_enabled() and GradMode::set_enabled()

Fixes #3627
 2017-12-18 15:46:13 -05:00
Sam Gross
fde355f7d4
Allow in-place operations on views (#3384) 
Allow in-place operations on views

Adds VariableViewImpl, a subclass of VariableImpl which has a pointer to
the base Variable on which it is a view. In-place operations on views
change the grad_fn of the base.

Note that in-place operations only work on views that are the first output of the function that created them. All C++/ATen implemented functions have this behavior, but it's possible to write Python-implemented autograd functions that do not. In-place operations on these view will raise an exception.

Fixes #3313
 2017-11-06 18:19:56 -05:00
Sam Gross
6647475bc2 Lazily create Variable.data PyObject* (#3149) 
Previously, we the Variable.data PyObject* in THPVariable_Wrap. For many
Variables, we don't access their data directly. Instead, they are passed
from one Variable compuatation to another.

This reduces the overhead of ATen-implemented Variable methods by
~200ns.
 2017-10-17 11:54:55 -04:00
Sam Gross
1290e586fb Use at::Tensor based autograd Variable (#2676) 
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.
 2017-09-12 11:36:01 -04:00
Edward Z. Yang
1f3ff5ced2 Miscellaneous documentation around autograd. (#1577) 
* Miscellaneous documentation around autograd.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>
 2017-05-17 19:19:24 -04:00
Adam Paszke
35cf380ed1 Improve output wrapping logic in autograd 2017-05-10 16:43:14 +02:00
Adam Paszke
20aa5b066f Convert some of the functions to new format 
Also, fix a lot of issues that appeared after the previous commits.
 2017-05-01 16:44:56 -04:00
Adam Paszke
702a2e3bc5 Make Variables not subclass Function anymore 
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.
 2017-05-01 16:44:56 -04:00
Adam Paszke
2ca787fcf4 Refactor attribute names in autograd 2017-05-01 16:44:56 -04:00
Sam Gross
bd5303010d Refactor autograd package to separate Python dependencies. (#662) 
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.
 2017-02-13 16:00:16 -08:00