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## Contributing to PyTorch
If you are interested in contributing to PyTorch, your contributions will fall
into two categories:
1. You want to propose a new Feature and implement it
- post about your intended feature, and we shall discuss the design and
implementation. Once we agree that the plan looks good, go ahead and implement it.
2. You want to implement a feature or bug-fix for an outstanding issue
- Look at the outstanding issues here: https://github.com/pytorch/pytorch/issues
- Especially look at the Low Priority and Medium Priority issues
- Pick an issue and comment on the task that you want to work on this feature
- If you need more context on a particular issue, please ask and we shall provide.
Once you finish implementing a feature or bugfix, please send a Pull Request to
https://github.com/pytorch/pytorch
If you are not familiar with creating a Pull Request, here are some guides:
- http://stackoverflow.com/questions/14680711/how-to-do-a-github-pull-request
- https://help.github.com/articles/creating-a-pull-request/
## Developing locally with PyTorch
To locally develop with PyTorch, here are some tips:
1. Uninstall all existing pytorch installs
```
conda uninstall pytorch
pip uninstall torch
pip uninstall torch # run this command twice
```
2. Locally clone a copy of PyTorch from source:
```
git clone https://github.com/pytorch/pytorch
cd pytorch
```
3. Install PyTorch in `build develop` mode:
A full set of instructions on installing PyTorch from Source are here:
https://github.com/pytorch/pytorch#from-source
The change you have to make is to replace
```
python setup.py install
```
with
```
python setup.py build develop
```
This is especially useful if you are only changing Python files.
This mode will symlink the python files from the current local source tree into the
python install.
Hence, if you modify a python file, you do not need to reinstall pytorch again and again.
For example:
- Install local pytorch in `build develop` mode
- modify your python file `torch/__init__.py` (for example)
- test functionality
- modify your python file `torch/__init__.py`
- test functionality
- modify your python file `torch/__init__.py`
- test functionality
You do not need to repeatedly install after modifying python files.
## Writing documentation
PyTorch uses [Google style](http://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html)
for formatting docstrings. Length of line inside docstrings block must be limited to 80 characters to
fit into Jupyter documentation popups.
## Managing multiple build trees
One downside to using `python setup.py develop` is that your development
version of pytorch will be installed globally on your account (e.g., if
you run `import torch` anywhere else, the development version will be
used.
If you want to manage multiple builds of PyTorch, you can make use of
[conda environments](https://conda.io/docs/using/envs.html) to maintain
separate Python package environments, each of which can be tied to a
specific build of PyTorch. To set one up:
```
conda create -n pytorch-myfeature
source activate pytorch-myfeature
# if you run python now, torch will NOT be installed
python setup.py build develop
```
## C++ Development tips
If you are working on the C++ code, there are a few important things that you
will want to keep in mind:
1. How to rebuild only the code you are working on, and
2. How to make rebuilds in the absence of changes go faster.
### Build only what you need.
`python setup.py build` will build everything, but since our build system is
not very optimized for incremental rebuilds, this will actually be very slow.
Far better is to only request rebuilds of the parts of the project you are
working on:
- Working on `torch/csrc`? Run `python setup.py develop` to rebuild
(NB: no `build` here!)
- Working on `torch/lib/TH`, did not make any cmake changes, and just want to
see if it compiles? Run `(cd torch/lib/build/TH && make install -j$(getconf _NPROCESSORS_ONLN))`. This
applies for any other subdirectory of `torch/lib`. **Warning: Changes you
make here will not be visible from Python.** See below.
- Working on `torch/lib` and want to run your changes / rerun cmake? Run
`python setup.py build_deps`. Note that this will rerun cmake for
every subdirectory in TH; if you are only working on one project,
consider editing `torch/lib/build_all.sh` and commenting out the
`build` lines of libraries you are not working on.
On the initial build, you can also speed things up with the environment
variables `DEBUG` and `NO_CUDA`.
- `DEBUG=1` will enable debug builds (-g -O0)
- `NO_CUDA=1` will disable compiling CUDA (in case you are developing on something not CUDA related), to save compile time.
For example:
```
NO_CUDA=1 DEBUG=1 python setup.py build develop
```
Make sure you continue to pass these flags on subsequent builds.
### Make no-op build fast.
Python `setuptools` is pretty dumb, and always rebuilds every C file in a
project. Using ccache in a situation like this is a real time-saver. However, by
default, ccache does not properly support CUDA stuff, so here are the
instructions for installing a custom `ccache` fork that has CUDA support:
```
# install and export ccache
if ! ls ~/ccache/bin/ccache
then
sudo apt-get update
sudo apt-get install -y automake autoconf
sudo apt-get install -y asciidoc
mkdir -p ~/ccache
pushd /tmp
rm -rf ccache
git clone https://github.com/colesbury/ccache -b ccbin
pushd ccache
./autogen.sh
./configure
make install prefix=~/ccache
popd
popd
mkdir -p ~/ccache/lib
mkdir -p ~/ccache/cuda
ln -s ~/ccache/bin/ccache ~/ccache/lib/cc
ln -s ~/ccache/bin/ccache ~/ccache/lib/c++
ln -s ~/ccache/bin/ccache ~/ccache/lib/gcc
ln -s ~/ccache/bin/ccache ~/ccache/lib/g++
ln -s ~/ccache/bin/ccache ~/ccache/cuda/nvcc
~/ccache/bin/ccache -M 25Gi
fi
export PATH=~/ccache/lib:$PATH
export CUDA_NVCC_EXECUTABLE=~/ccache/cuda/nvcc
```
## CUDA Development tips
If you are working on the CUDA code, here are some useful CUDA debugging tips:
1. `CUDA_DEBUG=1` will enable CUDA debugging symbols (-g -G). This is particularly
helpful in debugging device code. However, it will slow down the build process,
so use wisely.
2. `cuda-gdb` and `cuda-memcheck` are your best CUDA debuging friends. Unlike`gdb`,
`cuda-gdb` can display actual values in a CUDA tensor (rather than all zeros).
Hope this helps, and thanks for considering to contribute.
|
