use scatter add pytorch implementation

benchmark/scatter_segment.py

@@ -217,7 +217,7 @@ if __name__ == '__main__':
     parser.add_argument('--with_backward', action='store_true')
     parser.add_argument('--device', type=str, default='cuda')
     args = parser.parse_args()
-    iters = 1 if args.device == 'cpu' else 50
+    iters = 1 if args.device == 'cpu' else 20
     sizes = [1, 16, 32, 64, 128, 256, 512]
     sizes = sizes[:3] if args.device == 'cpu' else sizes
 

test/test_zero_tensors.py

+import torch
+from torch_scatter import scatter
+
+
+def test_zero_elements():
+    x = torch.randn(0, 16)
+    index = torch.tensor([]).view(0, 16)
+    print(x)
+    print(index)
+
+    scatter(x, index, dim=0, dim_size=0, reduce="add")

torch_scatter/composite/logsumexp.py

@@ -3,7 +3,7 @@ from typing import Optional
 import torch
 from torch_scatter import scatter_sum, scatter_max
 
-from .utils import broadcast
+from torch_scatter.utils import broadcast
 
 
 @torch.jit.script

torch_scatter/composite/softmax.py

 import torch
 
 from torch_scatter import scatter_sum, scatter_max
-
-from .utils import broadcast
+from torch_scatter.utils import broadcast
 
 
 @torch.jit.script

torch_scatter/composite/std.py

@@ -2,8 +2,7 @@ from typing import Optional
 
 import torch
 from torch_scatter import scatter_sum
-
-from .utils import broadcast
+from torch_scatter.utils import broadcast
 
 
 @torch.jit.script

torch_scatter/scatter.py

@@ -4,6 +4,8 @@ from typing import Optional, Tuple
 
 import torch
 
+from .utils import broadcast
+
 try:
     torch.ops.load_library(
         osp.join(osp.dirname(osp.abspath(__file__)), '_scatter.so'))
@@ -23,7 +25,6 @@ except OSError:
         raise ImportError
         return src, index
 
-    torch.ops.torch_scatter.scatter_sum = scatter_placeholder
     torch.ops.torch_scatter.scatter_mean = scatter_placeholder
     torch.ops.torch_scatter.scatter_min = scatter_with_arg_placeholder
     torch.ops.torch_scatter.scatter_max = scatter_with_arg_placeholder
@@ -33,14 +34,24 @@ except OSError:
 def scatter_sum(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
                 out: Optional[torch.Tensor] = None,
                 dim_size: Optional[int] = None) -> torch.Tensor:
-    return torch.ops.torch_scatter.scatter_sum(src, index, dim, out, dim_size)
+    index = broadcast(index, src, dim)
+    if out is None:
+        size = src.size()
+        if dim_size is None:
+            size[dim] = int(index.max()) + 1
+        else:
+            size[dim] = dim_size
+        out = src.new_zeros(size)
+        return out.scatter_add_(dim, index, src)
+    else:
+        return out.scatter_add_(dim, index, src)
 
 
 @torch.jit.script
 def scatter_add(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
                 out: Optional[torch.Tensor] = None,
                 dim_size: Optional[int] = None) -> torch.Tensor:
-    return torch.ops.torch_scatter.scatter_sum(src, index, dim, out, dim_size)
+    return scatter_sum(src, index, dim, out, dim_size)
 
 
 @torch.jit.script