fix backward pass

test/composite/test_logsumexp.py

@@ -4,6 +4,7 @@ from torch_scatter import scatter_logsumexp
 
 def test_logsumexp():
     src = torch.tensor([0.5, 0, 0.5, -2.1, 3.2, 7, -1, -100])
+    src.requires_grad_()
     index = torch.tensor([0, 1, 0, 1, 1, 2, 4, 4])
 
     out = scatter_logsumexp(src, index)
@@ -16,3 +17,5 @@ def test_logsumexp():
 
     expected = torch.stack([out0, out1, out2, out3, out4], dim=0)
     assert torch.allclose(out, expected)
+
+    out.backward(torch.randn_like(out))

test/composite/test_softmax.py

@@ -4,6 +4,7 @@ from torch_scatter import scatter_log_softmax, scatter_softmax
 
 def test_softmax():
     src = torch.tensor([0.2, 0, 0.2, -2.1, 3.2, 7, -1, float('-inf')])
+    src.requires_grad_()
     index = torch.tensor([0, 1, 0, 1, 1, 2, 4, 4])
 
     out = scatter_softmax(src, index)
@@ -19,9 +20,12 @@ def test_softmax():
 
     assert torch.allclose(out, expected)
 
+    out.backward(torch.randn_like(out))
+
 
 def test_log_softmax():
     src = torch.tensor([0.2, 0, 0.2, -2.1, 3.2, 7, -1, float('-inf')])
+    src.requires_grad_()
     index = torch.tensor([0, 1, 0, 1, 1, 2, 4, 4])
 
     out = scatter_log_softmax(src, index)
@@ -36,3 +40,5 @@ def test_log_softmax():
     ], dim=0)
 
     assert torch.allclose(out, expected)
+
+    out.backward(torch.randn_like(out))

test/composite/test_std.py

@@ -4,9 +4,12 @@ from torch_scatter import scatter_std
 
 def test_std():
     src = torch.tensor([[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]], dtype=torch.float)
+    src.requires_grad_()
     index = torch.tensor([[0, 0, 0, 0, 0], [1, 1, 1, 1, 1]], dtype=torch.long)
 
     out = scatter_std(src, index, dim=-1, unbiased=True)
     std = src.std(dim=-1, unbiased=True)[0]
     expected = torch.tensor([[std, 0], [0, std]])
     assert torch.allclose(out, expected)
+
+    out.backward(torch.randn_like(out))

torch_scatter/composite/softmax.py

@@ -17,12 +17,12 @@ def scatter_softmax(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
     max_per_src_element = max_value_per_index.gather(dim, index)
 
     recentered_scores = src - max_per_src_element
-    recentered_scores_exp = recentered_scores.exp_()
+    recentered_scores_exp = recentered_scores.exp()
 
     sum_per_index = scatter_sum(recentered_scores_exp, index, dim)
     normalizing_constants = sum_per_index.add_(eps).gather(dim, index)
 
-    return recentered_scores_exp.div_(normalizing_constants)
+    return recentered_scores_exp.div(normalizing_constants)
 
 
 @torch.jit.script

torch_scatter/composite/std.py

@@ -27,14 +27,14 @@ def scatter_std(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
     index = broadcast(index, src, dim)
     tmp = scatter_sum(src, index, dim, dim_size=dim_size)
     count = broadcast(count, tmp, dim).clamp_(1)
-    mean = tmp.div_(count)
+    mean = tmp.div(count)
 
     var = (src - mean.gather(dim, index))
     var = var * var
     out = scatter_sum(var, index, dim, out, dim_size)
 
     if unbiased:
-        count.sub_(1).clamp_(1)
-    out.div_(count).sqrt_()
+        count = count.sub(1).clamp_(1)
+    out = out.div(count).sqrt()
 
     return out