[WIP] tracebale functions

.coveragerc

@@ -3,10 +3,5 @@ source=torch_scatter
 [report]
 exclude_lines =
     pragma: no cover
-    cuda
-    forward
-    backward
-    apply
+    torch.jit.script
     raise
-    min_value
-    max_value

.travis.yml

@@ -39,6 +39,7 @@ install:
   - pip install codecov
   - pip install sphinx
   - pip install sphinx_rtd_theme
+  - pip install sphinx-autodoc-typehints
 script:
   - python -c "import torch; print(torch.__version__)"
   - pycodestyle .

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

README.md

@@ -22,34 +22,27 @@
 
 **[Documentation](https://pytorch-scatter.readthedocs.io)**
 
-This package consists of a small extension library of highly optimized sparse update (scatter) operations for the use in [PyTorch](http://pytorch.org/), which are missing in the main package.
-Scatter operations can be roughly described as reduce operations based on a given "group-index" tensor.
-The package consists of the following operations:
+This package consists of a small extension library of highly optimized sparse update (scatter and segment) operations for the use in [PyTorch](http://pytorch.org/), which are missing in the main package.
+Scatter and segment operations can be roughly described as reduce operations based on a given "group-index" tensor.
+Segment operations require the "group-index" tensor to be sorted, whereas scatter operations are not subject to these requirements.
 
-* [**Scatter Add**](https://pytorch-scatter.readthedocs.io/en/latest/functions/add.html)
-* [**Scatter Sub**](https://pytorch-scatter.readthedocs.io/en/latest/functions/sub.html)
-* [**Scatter Mul**](https://pytorch-scatter.readthedocs.io/en/latest/functions/mul.html)
-* [**Scatter Div**](https://pytorch-scatter.readthedocs.io/en/latest/functions/div.html)
-* [**Scatter Mean**](https://pytorch-scatter.readthedocs.io/en/latest/functions/mean.html)
-* [**Scatter Std**](https://pytorch-scatter.readthedocs.io/en/latest/functions/std.html)
-* [**Scatter Min**](https://pytorch-scatter.readthedocs.io/en/latest/functions/min.html)
-* [**Scatter Max**](https://pytorch-scatter.readthedocs.io/en/latest/functions/max.html)
-* [**Scatter LogSumExp**](https://pytorch-scatter.readthedocs.io/en/latest/functions/logsumexp.html)
+The package consists of the following operations with reduction types `"sum"|"mean"|"min"|"max"`:
 
-In addition, we provide composite functions which make use of `scatter_*` operations under the hood:
+* [**scatter**](https://pytorch-scatter.readthedocs.io/en/latest/functions/segment.html) based on arbitrary indices
+* [**segment_coo**](https://pytorch-scatter.readthedocs.io/en/latest/functions/segment_coo.html) based on sorted indices
+* [**segment_csr**](https://pytorch-scatter.readthedocs.io/en/latest/functions/segment_csr.html) based on compressed indices via pointers
 
-* [**Scatter Softmax**](https://pytorch-scatter.readthedocs.io/en/latest/composite/softmax.html#torch_scatter.composite.scatter_softmax)
-* [**Scatter LogSoftmax**](https://pytorch-scatter.readthedocs.io/en/latest/composite/softmax.html#torch_scatter.composite.scatter_log_softmax)
+In addition, we provide the following **composite functions** which make use of `scatter_*` operations under the hood: :`scatter_std`, `scatter_logsumexp`, `scatter_softmax` and `scatter_log_softmax`.
 
-All included operations are broadcastable, work on varying data types, and are implemented both for CPU and GPU with corresponding backward implementations.
+All included operations are broadcastable, work on varying data types, are implemented both for CPU and GPU with corresponding backward implementations, and are fully traceable.
 
 ## 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.*:
+Ensure that at least PyTorch 1.3.0 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
+>>> 1.3.0
 
 $ echo $PATH
 >>> /usr/local/cuda/bin:...
@@ -81,17 +74,17 @@ from torch_scatter import scatter_max
 src = torch.tensor([[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]])
 index = torch.tensor([[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]])
 
-out, argmax = scatter_max(src, index, fill_value=0)
+out, argmax = scatter_max(src, index, dim=-1)
 ```
 
 ```
 print(out)
-tensor([[ 0,  0,  4,  3,  2,  0],
-        [ 2,  4,  3,  0,  0,  0]])
+tensor([[0, 0, 4, 3, 2, 0],
+        [2, 4, 3, 0, 0, 0]])
 
 print(argmax)
-tensor([[-1, -1,  3,  4,  0,  1]
-        [ 1,  4,  3, -1, -1, -1]])
+tensor([[5, 5, 3, 4, 0, 1]
+        [1, 4, 3, 5, 5, 5]])
 ```
 
 ## Running tests

benchmark/scatter_segment.py

@@ -7,9 +7,7 @@
 import torch
 from scipy.io import loadmat
 
-import torch_scatter
-from torch_scatter import scatter_add, scatter_mean, scatter_min, scatter_max
-from torch_scatter import segment_coo, segment_csr
+from torch_scatter import scatter, segment_coo, segment_csr
 
 short_rows = [
     ('DIMACS10', 'citationCiteseer'),
@@ -47,34 +45,30 @@ def correctness(dataset):
             x = torch.randn((row.size(0), size), device=args.device)
             x = x.squeeze(-1) if size == 1 else x
 
-            out1 = scatter_add(x, row, dim=0, dim_size=dim_size)
+            out1 = scatter(x, row, dim=0, dim_size=dim_size, reduce='add')
             out2 = segment_coo(x, row, dim_size=dim_size, reduce='add')
             out3 = segment_csr(x, rowptr, reduce='add')
 
             assert torch.allclose(out1, out2, atol=1e-4)
             assert torch.allclose(out1, out3, atol=1e-4)
 
-            out1 = scatter_mean(x, row, dim=0, dim_size=dim_size)
+            out1 = scatter(x, row, dim=0, dim_size=dim_size, reduce='mean')
             out2 = segment_coo(x, row, dim_size=dim_size, reduce='mean')
             out3 = segment_csr(x, rowptr, reduce='mean')
 
             assert torch.allclose(out1, out2, atol=1e-4)
             assert torch.allclose(out1, out3, atol=1e-4)
 
-            x = x.abs_().mul_(-1)
-
-            out1, _ = scatter_min(x, row, 0, torch.zeros_like(out1))
-            out2, _ = segment_coo(x, row, reduce='min')
-            out3, _ = segment_csr(x, rowptr, reduce='min')
+            out1 = scatter(x, row, dim=0, dim_size=dim_size, reduce='min')
+            out2 = segment_coo(x, row, reduce='min')
+            out3 = segment_csr(x, rowptr, reduce='min')
 
             assert torch.allclose(out1, out2, atol=1e-4)
             assert torch.allclose(out1, out3, atol=1e-4)
 
-            x = x.abs_()
-
-            out1, _ = scatter_max(x, row, 0, torch.zeros_like(out1))
-            out2, _ = segment_coo(x, row, reduce='max')
-            out3, _ = segment_csr(x, rowptr, reduce='max')
+            out1 = scatter(x, row, dim=0, dim_size=dim_size, reduce='max')
+            out2 = segment_coo(x, row, reduce='max')
+            out3 = segment_csr(x, rowptr, reduce='max')
 
             assert torch.allclose(out1, out2, atol=1e-4)
             assert torch.allclose(out1, out3, atol=1e-4)
@@ -117,17 +111,15 @@ def timing(dataset):
     mat = loadmat(f'{name}.mat')['Problem'][0][0][2].tocsr()
     rowptr = torch.from_numpy(mat.indptr).to(args.device, torch.long)
     row = torch.from_numpy(mat.tocoo().row).to(args.device, torch.long)
-    row_perm = row[torch.randperm(row.size(0))]
+    row2 = row[torch.randperm(row.size(0))]
     dim_size = rowptr.size(0) - 1
     avg_row_len = row.size(0) / dim_size
 
     def sca_row(x):
-        op = getattr(torch_scatter, f'scatter_{args.scatter_reduce}')
-        return op(x, row, dim=0, dim_size=dim_size)
+        return scatter(x, row, dim=0, dim_size=dim_size, reduce=args.reduce)
 
     def sca_col(x):
-        op = getattr(torch_scatter, f'scatter_{args.scatter_reduce}')
-        return op(x, row_perm, dim=0, dim_size=dim_size)
+        return scatter(x, row2, dim=0, dim_size=dim_size, reduce=args.reduce)
 
     def seg_coo(x):
         return segment_coo(x, row, reduce=args.reduce)
@@ -205,11 +197,10 @@ def dense2(x):
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--reduce', type=str, required=True,
-                        choices=['sum', 'mean', 'min', 'max'])
+                        choices=['sum', 'add', 'mean', 'min', 'max'])
     parser.add_argument('--with_backward', action='store_true')
     parser.add_argument('--device', type=str, default='cuda')
     args = parser.parse_args()
-    args.scatter_reduce = 'add' if args.reduce == 'sum' else args.reduce
     iters = 1 if args.device == 'cpu' else 20
     sizes = [1, 16, 32, 64, 128, 256, 512]
     sizes = sizes[:3] if args.device == 'cpu' else sizes