Move epsilon to an argument.

torch_scatter/logsumexp.py

@@ -2,9 +2,8 @@ import torch
 
 from . import scatter_add, scatter_max
 
-EPSILON = 1e-16
 
-def _scatter_logsumexp(src, index, dim=-1, out=None, dim_size=None, fill_value=None):
+def _scatter_logsumexp(src, index, dim=-1, out=None, dim_size=None, fill_value=None, epsilon=1e-16):
     if not torch.is_floating_point(src):
         raise ValueError('logsumexp can be computed over tensors floating point data types.')
 
@@ -25,9 +24,10 @@ def _scatter_logsumexp(src, index, dim=-1, out=None, dim_size=None, fill_value=N
         dim_size=dim_size,
         fill_value=fill_value,
     )
-    return torch.log(sum_per_index + EPSILON) + max_value_per_index, recentered_scores
+    return torch.log(sum_per_index + epsilon) + max_value_per_index, recentered_scores
 
-def scatter_logsumexp(src, index, dim=-1, out=None, dim_size=None, fill_value=None):
+
+def scatter_logsumexp(src, index, dim=-1, out=None, dim_size=None, fill_value=None, epsilon=1e-16):
     r"""
     Numerically safe logsumexp of all values from the :attr:`src` tensor into :attr:`out` at the
     indices specified in the :attr:`index` tensor along a given axis
@@ -63,4 +63,4 @@ def scatter_logsumexp(src, index, dim=-1, out=None, dim_size=None, fill_value=No
 
     :rtype: :class:`Tensor`
     """
-    return _scatter_logsumexp(src,index, dim, out, dim_size, fill_value)[0]
+    return _scatter_logsumexp(src,index, dim, out, dim_size, fill_value, epsilon=epsilon)[0]