torch-scatter=2.0 support

README.md

@@ -28,11 +28,11 @@ Note that only `value` comes with autograd support, as `index` is discrete and t
 
 ## 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 1.4.0 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:...
@@ -53,7 +53,7 @@ Be sure to import `torch` first before using this package to resolve symbols the
 ## Coalesce
 
 ```
-torch_sparse.coalesce(index, value, m, n, op="add", fill_value=0) -> (torch.LongTensor, torch.Tensor)
+torch_sparse.coalesce(index, value, m, n, op="add") -> (torch.LongTensor, torch.Tensor)
 ```
 
 Row-wise sorts `index` and removes duplicate entries.
@@ -67,7 +67,6 @@ For scattering, any operation of [`torch_scatter`](https://github.com/rusty1s/py
 * **m** *(int)* - The first dimension of corresponding dense matrix.
 * **n** *(int)* - The second dimension of corresponding dense matrix.
 * **op** *(string, optional)* - The scatter operation to use. (default: `"add"`)
-* **fill_value** *(int, optional)* - The initial fill value of scatter operation. (default: `0`)
 
 ### Returns
 

setup.py

@@ -39,7 +39,7 @@ if CUDA_HOME is not None and GPU:
                       extra_compile_args=extra_compile_args),
     ]
 
-__version__ = '0.4.3'
+__version__ = '0.4.4'
 url = 'https://github.com/rusty1s/pytorch_sparse'
 
 install_requires = ['scipy']

torch_sparse/__init__.py

@@ -5,7 +5,7 @@ from .eye import eye
 from .spmm import spmm
 from .spspmm import spspmm
 
-__version__ = '0.4.3'
+__version__ = '0.4.4'
 
 __all__ = [
     '__version__',

torch_sparse/coalesce.py

@@ -4,7 +4,7 @@ import torch_scatter
 from .utils.unique import unique
 
 
-def coalesce(index, value, m, n, op='add', fill_value=0):
+def coalesce(index, value, m, n, op='add'):
     """Row-wise sorts :obj:`value` and removes duplicate entries. Duplicate
     entries are removed by scattering them together. For scattering, any
     operation of `"torch_scatter"<https://github.com/rusty1s/pytorch_scatter>`_
@@ -17,8 +17,6 @@ def coalesce(index, value, m, n, op='add', fill_value=0):
         n (int): The second dimension of corresponding dense matrix.
         op (string, optional): The scatter operation to use. (default:
             :obj:`"add"`)
-        fill_value (int, optional): The initial fill value of scatter
-            operation. (default: :obj:`0`)
 
     :rtype: (:class:`LongTensor`, :class:`Tensor`)
     """
@@ -37,8 +35,7 @@ def coalesce(index, value, m, n, op='add', fill_value=0):
     index = torch.stack([row[perm], col[perm]], dim=0)
 
     op = getattr(torch_scatter, 'scatter_{}'.format(op))
-    value = op(value, inv, 0, None, perm.size(0), fill_value)
-    if isinstance(value, tuple):
-        value = value[0]
+    value = op(value, inv, 0, None, perm.size(0))
+    value = value[0] if isinstance(value, tuple) else value
 
     return index, value