enable autocast

torch_sparse/matmul.py

@@ -11,6 +11,9 @@ def spmm_sum(src: SparseTensor, other: torch.Tensor) -> torch.Tensor:
     csr2csc = src.storage._csr2csc
     colptr = src.storage._colptr
 
+    if value is not None:
+        value = value.to(other.dtype)
+
     if value is not None and value.requires_grad:
         row = src.storage.row()
 
@@ -35,6 +38,9 @@ def spmm_mean(src: SparseTensor, other: torch.Tensor) -> torch.Tensor:
     csr2csc = src.storage._csr2csc
     colptr = src.storage._colptr
 
+    if value is not None:
+        value = value.to(other.dtype)
+
     if value is not None and value.requires_grad:
         row = src.storage.row()
 
@@ -51,12 +57,20 @@ def spmm_mean(src: SparseTensor, other: torch.Tensor) -> torch.Tensor:
 def spmm_min(src: SparseTensor,
              other: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
     rowptr, col, value = src.csr()
+
+    if value is not None:
+        value = value.to(other.dtype)
+
     return torch.ops.torch_sparse.spmm_min(rowptr, col, value, other)
 
 
 def spmm_max(src: SparseTensor,
              other: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
     rowptr, col, value = src.csr()
+
+    if value is not None:
+        value = value.to(other.dtype)
+
     return torch.ops.torch_sparse.spmm_max(rowptr, col, value, other)
 
 
@@ -81,8 +95,8 @@ def spspmm_sum(src: SparseTensor, other: SparseTensor) -> SparseTensor:
     value = valueA
     if valueA is not None and valueA.dtype == torch.half:
         valueA = valueA.to(torch.float)
-    if valueB is not None and valueB.dtype == torch.half:
-        valueB = valueB.to(torch.float)
+    if valueB is not None:
+        valueB = valueB.to(valueA.dtype)
     M, K = src.sparse_size(0), other.sparse_size(1)
     rowptrC, colC, valueC = torch.ops.torch_sparse.spspmm_sum(
         rowptrA, colA, valueA, rowptrB, colB, valueB, K)