Merge pull request #14 from rusty1s/tracing

prepare tracing

.coveragerc

@@ -3,5 +3,6 @@ source=torch_spline_conv
 [report]
 exclude_lines =
     pragma: no cover
-    cuda
-    backward
+    torch.jit.script
+    raise
+    except

.gitignore

@@ -1,5 +1,4 @@
 __pycache__/
-_ext/
 build/
 dist/
 .cache/

.travis.yml

@@ -1,49 +1,90 @@
+language: shell
+
+os:
+  - linux
+  - osx
+  - windows
+
+env:
+  global:
+    - CUDA_HOME=/usr/local/cuda
+  jobs:
+    - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cpu
+    - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu92
+    - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu100
+    - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu101
+    - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cpu
+    - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cu92
+    - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cu100
+    - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cu101
+    - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cpu
+    - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cu92
+    - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cu100
+    - TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cu101
+
 jobs:
-  include:
-    - os: linux
-      language: python
-      python: 3.7
-      addons:
-        apt:
-          sources:
-            - ubuntu-toolchain-r-test
-          packages:
-            - gcc-5
-            - g++-5
-      env:
-        - CC=gcc-5
-        - CXX=g++-5
-    - os: osx
-      language: sh
-      before_cache:
-        - brew cleanup
-      cache:
-        directories:
-          - $HOME/Library/Caches/Homebrew
-          - /usr/local/Homebrew
-      addons:
-        homebrew:
-          packages: python3
-      before_install:
-        - python3 -m pip install --upgrade virtualenv
-        - virtualenv -p python3 --system-site-packages "$HOME/venv"
-        - source "$HOME/venv/bin/activate"
-      env:
-        - CC=clang
-        - CXX=clang++
+  exclude:  # Exclude *all* macOS CUDA jobs and Windows CUDA 9.2/10.0 jobs.
+    - os: osx
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu92
+    - os: osx
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu100
+    - os: osx
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu101
+    - os: osx
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cu92
+    - os: osx
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cu100
+    - os: osx
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cu101
+    - os: osx
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cu92
+    - os: osx
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cu100
+    - os: osx
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cu101
+    - os: windows
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu92
+    - os: windows
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu100
+    - os: windows
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cu92
+    - os: windows
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.7 IDX=cu100
+    - os: windows
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cu92
+    - os: windows
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.6 IDX=cu100
+    - os: windows
+      env: TORCH_VERSION=1.4.0 PYTHON_VERSION=3.8 IDX=cu101
+
 install:
-  - pip install numpy
-  - pip install --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
-  - pip install pycodestyle
-  - pip install flake8
-  - pip install codecov
+  - source script/cuda.sh
+  - source script/conda.sh
+  - conda create --yes -n test python="${PYTHON_VERSION}"
+  - source activate test
+  - conda install pytorch=${TORCH_VERSION} ${TOOLKIT} -c pytorch --yes
+  - source script/torch.sh
+  - pip install flake8 codecov
+  - python setup.py install
 script:
-  - python -c "import torch; print(torch.__version__)"
-  - pycodestyle .
   - flake8 .
-  - python setup.py install
   - python setup.py test
 after_success:
+  - python setup.py bdist_wheel --dist-dir=dist/torch-${TORCH_VERSION}
+  - python script/rename_wheel.py ${IDX}
   - codecov
+deploy:
+  provider: s3
+  region: eu-central-1
+  edge: true
+  access_key_id: ${S3_ACCESS_KEY}
+  secret_access_key: ${S3_SECRET_ACCESS_KEY}
+  bucket: pytorch-geometric.com
+  local_dir: dist/torch-${TORCH_VERSION}
+  upload_dir: whl/torch-${TORCH_VERSION}
+  acl: public_read
+  on:
+    repo: rusty1s/pytorch_spline_conv
+    tags: true
 notifications:
   email: false

LICENSE

@@ -1,4 +1,4 @@
-Copyright (c) 2019 Matthias Fey <matthias.fey@tu-dortmund.de>
+Copyright (c) 2020 Matthias Fey <matthias.fey@tu-dortmund.de>
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

MANIFEST.in

@@ -1,3 +1,5 @@
+include README.md
 include LICENSE
-recursive-include cpu *
-recursive-include cuda *
+
+recursive-exclude test *
+recursive-include csrc *

README.md

@@ -21,11 +21,30 @@ The operator works on all floating point data types and is implemented both for
 
 ## Installation
 
-Ensure that at least PyTorch 1.1.0 is installed and verify that `cuda/bin` and `cuda/include` are in your `$PATH` and `$CPATH` respectively, *e.g.*:
+### Binaries
+
+We provide pip wheels for all major OS/PyTorch/CUDA combinations, see [here](https://pytorch-geometric.com/whl).
+To install the binaries for PyTorch 1.4.0, simply run
+
+```
+pip install torch-spline-conv==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.4.0.html
+```
+
+where `${CUDA}` should be replaced by either `cpu`, `cu92`, `cu100` or `cu101` depending on your PyTorch installation.
+
+|             | `cpu` | `cu92` | `cu100` | `cu101` |
+|-------------|-------|--------|---------|---------|
+| **Linux**   | ✅    | ✅     | ✅      | ✅      |
+| **Windows** | ✅    | ❌     | ❌      | ✅      |
+| **macOS**   | ✅    |        |         |         |
+
+### From source
+
+Ensure that at least PyTorch 1.4.0 is installed and verify that `cuda/bin` and `cuda/include` are in your `$PATH` and `$CPATH` respectively, *e.g.*:
 
 ```
 $ python -c "import torch; print(torch.__version__)"
->>> 1.1.0
+>>> 1.4.0
 
 $ echo $PATH
 >>> /usr/local/cuda/bin:...
@@ -40,24 +59,28 @@ Then run:
 pip install torch-spline-conv
 ```
 
-If you are running into any installation problems, please create an [issue](https://github.com/rusty1s/pytorch_spline_conv/issues).
-Be sure to import `torch` first before using this package to resolve symbols the dynamic linker must see.
+When running in a docker container without NVIDIA driver, PyTorch needs to evaluate the compute capabilities and may fail.
+In this case, ensure that the compute capabilities are set via `TORCH_CUDA_ARCH_LIST`, *e.g.*:
+
+```
+export TORCH_CUDA_ARCH_LIST = "6.0 6.1 7.2+PTX 7.5+PTX"
+```
 
 ## Usage
 
 ```python
-from torch_spline_conv import SplineConv
-
-out = SplineConv.apply(x,
-                       edge_index,
-                       pseudo,
-                       weight,
-                       kernel_size,
-                       is_open_spline,
-                       degree=1,
-                       norm=True,
-                       root_weight=None,
-                       bias=None)
+from torch_spline_conv import spline_conv
+
+out = spline_conv(x,
+                  edge_index,
+                  pseudo,
+                  weight,
+                  kernel_size,
+                  is_open_spline,
+                  degree=1,
+                  norm=True,
+                  root_weight=None,
+                  bias=None)
 ```
 
 Applies the spline-based convolution operator
@@ -93,7 +116,7 @@ The kernel function is defined over the weighted B-spline tensor product basis,
 
 ```python
 import torch
-from torch_spline_conv import SplineConv
+from torch_spline_conv import spline_conv
 
 x = torch.rand((4, 2), dtype=torch.float)  # 4 nodes with 2 features each
 edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])  # 6 edges
@@ -106,8 +129,8 @@ norm = True  # Normalize output by node degree.
 root_weight = torch.rand((2, 4), dtype=torch.float)  # separately weight root nodes
 bias = None  # do not apply an additional bias
 
-out = SplineConv.apply(x, edge_index, pseudo, weight, kernel_size,
-                       is_open_spline, degree, norm, root_weight, bias)
+out = spline_conv(x, edge_index, pseudo, weight, kernel_size,
+                  is_open_spline, degree, norm, root_weight, bias)
 
 print(out.size())
 torch.Size([4, 4])  # 4 nodes with 4 features each