Add optional parameter to scatter_[log_]softmax to indicate the num…

…ber of segments (rusty1s#243)

* Add parameter to `scatter_[log_]softmax`

* Update softmax.py

* Format code.

torch_scatter/composite/softmax.py

@@ -1,43 +1,51 @@
+from typing import Optional
+
 import torch
 
 from torch_scatter import scatter_sum, scatter_max
 from torch_scatter.utils import broadcast
 
 
 def scatter_softmax(src: torch.Tensor, index: torch.Tensor,
-                    dim: int = -1) -> torch.Tensor:
+                    dim: int = -1,
+                    dim_size: Optional[int] = None) -> torch.Tensor:
     if not torch.is_floating_point(src):
         raise ValueError('`scatter_softmax` can only be computed over tensors '
                          'with floating point data types.')
 
     index = broadcast(index, src, dim)
 
-    max_value_per_index = scatter_max(src, index, dim=dim)[0]
+    max_value_per_index = scatter_max(
+        src, index, dim=dim, dim_size=dim_size)[0]
     max_per_src_element = max_value_per_index.gather(dim, index)
 
     recentered_scores = src - max_per_src_element
     recentered_scores_exp = recentered_scores.exp_()
 
-    sum_per_index = scatter_sum(recentered_scores_exp, index, dim)
+    sum_per_index = scatter_sum(
+        recentered_scores_exp, index, dim, dim_size=dim_size)
     normalizing_constants = sum_per_index.gather(dim, index)
 
     return recentered_scores_exp.div(normalizing_constants)
 
 
 def scatter_log_softmax(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
-                        eps: float = 1e-12) -> torch.Tensor:
+                        eps: float = 1e-12,
+                        dim_size: Optional[int] = None) -> torch.Tensor:
     if not torch.is_floating_point(src):
         raise ValueError('`scatter_log_softmax` can only be computed over '
                          'tensors with floating point data types.')
 
     index = broadcast(index, src, dim)
 
-    max_value_per_index = scatter_max(src, index, dim=dim)[0]
+    max_value_per_index = scatter_max(
+        src, index, dim=dim, dim_size=dim_size)[0]
     max_per_src_element = max_value_per_index.gather(dim, index)
 
     recentered_scores = src - max_per_src_element
 
-    sum_per_index = scatter_sum(recentered_scores.exp(), index, dim)
+    sum_per_index = scatter_sum(
+        recentered_scores.exp(), index, dim, dim_size=dim_size)
     normalizing_constants = sum_per_index.add_(eps).log_().gather(dim, index)
 
     return recentered_scores.sub_(normalizing_constants)