Fix logsumexp when out is passed (#445)

* update

* update

* update

test/composite/test_logsumexp.py

@@ -29,3 +29,12 @@ def test_logsumexp():
 
     jit = torch.jit.script(scatter_logsumexp)
     assert jit(inputs, index).tolist() == outputs.tolist()
+
+
+def test_logsumexp_out():
+    src = torch.tensor([-1.0, -50.0])
+    index = torch.tensor([0, 0])
+    out = torch.tensor([-10.0, -10.0])
+
+    scatter_logsumexp(src=src, index=index, out=out)
+    assert out.allclose(torch.tensor([-0.9999, -10.0]), atol=1e-4)

torch_scatter/composite/logsumexp.py

@@ -1,15 +1,18 @@
 from typing import Optional
 
 import torch
-from torch_scatter import scatter_sum, scatter_max
-
+from torch_scatter import scatter_max, scatter_sum
 from torch_scatter.utils import broadcast
 
 
-def scatter_logsumexp(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
-                      out: Optional[torch.Tensor] = None,
-                      dim_size: Optional[int] = None,
-                      eps: float = 1e-12) -> torch.Tensor:
+def scatter_logsumexp(
+    src: torch.Tensor,
+    index: torch.Tensor,
+    dim: int = -1,
+    out: Optional[torch.Tensor] = None,
+    dim_size: Optional[int] = None,
+    eps: float = 1e-12,
+) -> torch.Tensor:
     if not torch.is_floating_point(src):
         raise ValueError('`scatter_logsumexp` can only be computed over '
                          'tensors with floating point data types.')
@@ -24,18 +27,30 @@ def scatter_logsumexp(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
 
     size = list(src.size())
     size[dim] = dim_size
-    max_value_per_index = torch.full(size, float('-inf'), dtype=src.dtype,
-                                     device=src.device)
+    max_value_per_index = torch.full(
+        size,
+        fill_value=float('-inf'),
+        dtype=src.dtype,
+        device=src.device,
+    )
     scatter_max(src, index, dim, max_value_per_index, dim_size=dim_size)[0]
     max_per_src_element = max_value_per_index.gather(dim, index)
     recentered_score = src - max_per_src_element
     recentered_score.masked_fill_(torch.isnan(recentered_score), float('-inf'))
 
+    orig_out: Optional[torch.Tensor] = None
     if out is not None:
+        orig_out = out.clone()
         out = out.sub_(max_value_per_index).exp_()
 
     sum_per_index = scatter_sum(recentered_score.exp_(), index, dim, out,
                                 dim_size)
 
     out = sum_per_index.add_(eps).log_().add_(max_value_per_index)
-    return out.nan_to_num_(neginf=0.0)
+
+    if orig_out is None:
+        return out.nan_to_num_(neginf=0.0)
+
+    mask = ~out.isfinite()
+    out[mask] = orig_out[mask]
+    return out