[performance] FeatGraph TVM kernels support

python/dgl/heterograph_index.py

@@ -704,6 +704,18 @@ def adjacency_matrix_scipy(self, etype, transpose, fmt, return_edge_ids=None):
             return scipy.sparse.coo_matrix((data, (row, col)), shape=(nrows, ncols))
         else:
             raise Exception("unknown format")
+
+    def get_coo_dlpack(self, etype):
+        rst = _CAPI_DGLHeteroGetCOOMatrix(self, int(etype))
+        return rst(0), rst(1), rst(2)
+
+    def get_csr_dlpack(self, etype):
+        rst = _CAPI_DGLHeteroGetCSRMatrix(self, int(etype))
+        return rst(0), rst(1), rst(2)
+
+    def get_csc_dlpack(self, etype):
+        rst = _CAPI_DGLHeteroGetCSCMatrix(self, int(etype))
+        return rst(0), rst(1), rst(2)
 
     def incidence_matrix(self, etype, typestr, ctx):
         """Return the incidence matrix representation of this graph.

python/dgl/ops/spmm.py

@@ -64,7 +64,8 @@ def gspmm(g, op, reduce_op, lhs_data, rhs_data):
                          lhs_data, rhs_data)
     # Replace infinity with zero for isolated nodes when reducer is min/max
     if reduce_op in ['min', 'max']:
-        ret = F.replace_inf_with_zero(ret)
+        #ret = F.replace_inf_with_zero(ret)
+        ret[g.in_degrees() == 0] = 0.
 
     # divide in degrees for mean reducer.
     if reduce_op == 'mean':