6294677f8acc6bc040baf922910473e1c82995ba,python/dgl/ops/spmm.py,,gspmm,#Any#Any#Any#Any#Any#,11

Before Change

        deg_shape = (ret_shape[0],) + (1,) * (len(ret_shape) - 1)
        return ret / F.reshape(deg, deg_shape)
    else:
        return gspmm_internal(g._graph, op, reduce_op, lhs_data, rhs_data)


def _gen_spmm_func(binary_op, reduce_op):
    name = "u_{}_e_{}".format(binary_op, reduce_op)

After Change

    deg = g.in_degrees()
    min_deg = F.as_scalar(F.min(deg, dim=0))
    if min_deg == 0:
        non_zero_nids = F.nonzero_1d(deg == 0)
        if backend_name == "pytorch":
            ret[non_zero_nids] = 0.
        else:
            dtype = F.dtype(ret)
            ctx = F.context(ret)
            ret = F.scatter_row(ret, non_zero_nids,
                                F.zeros((len(non_zero_nids),) + F.shape(ret)[1:], dtype, ctx))

    // divide in degrees for mean reducer.
    if reduce_op == "mean":
        ret_shape = F.shape(ret)
        if min_deg == 0:
            dgl_warning("Zero-degree nodes encountered in mean reducer. Setting the mean to 0.")
        deg = F.astype(F.clamp(deg, 1, g.number_of_edges()), F.dtype(ret))
        deg_shape = (ret_shape[0],) + (1,) * (len(ret_shape) - 1)
        return ret / F.reshape(deg, deg_shape)
    else:
        return ret


def _gen_spmm_func(binary_op, reduce_op):
    name = "u_{}_e_{}".format(binary_op, reduce_op)

Instances