diff --git a/mindspore/ccsrc/onnx/onnx_exporter.cc b/mindspore/ccsrc/onnx/onnx_exporter.cc
index f6f4ec2f1f..a0c8de75af 100644
--- a/mindspore/ccsrc/onnx/onnx_exporter.cc
+++ b/mindspore/ccsrc/onnx/onnx_exporter.cc
@@ -249,6 +249,13 @@ OPERATOR_ONNX_CONVERT_DEFINE(
     .Attr("padding", "auto_pad", onnx::AttributeProto_AttributeType_STRING, SetPoolingPadMode)
     .Attr("strides", "strides", onnx::AttributeProto_AttributeType_INTS, SetAttrTupleValueToProto<2>))
 
+OPERATOR_ONNX_CONVERT_DEFINE(GatherV2, Gather, OpNameInfo())
+OPERATOR_ONNX_CONVERT_DEFINE(make_tuple, SequenceConstruct, OpNameInfo())
+OPERATOR_ONNX_CONVERT_DEFINE(Concat, Concat, OpNameInfo())
+OPERATOR_ONNX_CONVERT_DEFINE(RealDiv, Div, OpNameInfo())
+OPERATOR_ONNX_CONVERT_DEFINE(ReduceSum, ReduceSum, OpNameInfo())
+OPERATOR_ONNX_CONVERT_DEFINE(Sub, Sub, OpNameInfo())
+
 #define OP_CONVERT_FUNCTION_NAME(name) GetOpOnnxConvertInfo_##name
 
 void RegisterOpConverters(const std::function<void(OpNameInfo &&)> &fn) {
@@ -269,6 +276,12 @@ void RegisterOpConverters(const std::function<void(OpNameInfo &&)> &fn) {
   fn(OP_CONVERT_FUNCTION_NAME(Squeeze)());
   fn(OP_CONVERT_FUNCTION_NAME(BatchNorm)());
   fn(OP_CONVERT_FUNCTION_NAME(MatMul)());
+
+  fn(OP_CONVERT_FUNCTION_NAME(make_tuple)());
+  fn(OP_CONVERT_FUNCTION_NAME(Concat)());
+  fn(OP_CONVERT_FUNCTION_NAME(RealDiv)());
+  fn(OP_CONVERT_FUNCTION_NAME(BiasAdd)());
+  fn(OP_CONVERT_FUNCTION_NAME(Sub)());
 }
 
 class OpConvertRegistry {
@@ -325,8 +338,8 @@ class OnnxExporter {
 
   void ExportPrimReshape(const FuncGraphPtr &func_graph, const CNodePtr &node,
                          std::map<AnfNodePtr, size_t> *node_map_ptr, onnx::GraphProto *graph_proto);
-  void ExportPrimReduceMean(const FuncGraphPtr &func_graph, const CNodePtr &node,
-                            std::map<AnfNodePtr, size_t> *node_map_ptr, onnx::GraphProto *graph_proto);
+  void ExportPrimReduce(const FuncGraphPtr &func_graph, const CNodePtr &node,
+                        std::map<AnfNodePtr, size_t> *node_map_ptr, onnx::GraphProto *graph_proto);
   void ExportPrimCast(const FuncGraphPtr &func_graph, const CNodePtr &node, std::map<AnfNodePtr, size_t> *node_map_ptr,
                       onnx::GraphProto *graph_proto);
   void ExportPrimPReLU(const FuncGraphPtr &func_graph, const CNodePtr &node, std::map<AnfNodePtr, size_t> *node_map_ptr,
@@ -335,6 +348,12 @@ class OnnxExporter {
                        onnx::GraphProto *graph_proto);
   void ExportPrimDepthwiseConv2d(const FuncGraphPtr &func_graph, const CNodePtr &node,
                                  std::map<AnfNodePtr, size_t> *node_map_ptr, onnx::GraphProto *graph_proto);
+  void ExportPrimTile(const FuncGraphPtr &func_graph, const CNodePtr &node, std::map<AnfNodePtr, size_t> *node_map_ptr,
+                      onnx::GraphProto *graph_proto);
+  void ExportPrimSquare(const FuncGraphPtr &func_graph, const CNodePtr &node,
+                        std::map<AnfNodePtr, size_t> *node_map_ptr, onnx::GraphProto *graph_proto);
+  void ExportPrimGatherV2(const FuncGraphPtr &func_graph, const CNodePtr &node,
+                          std::map<AnfNodePtr, size_t> *node_map_ptr, onnx::GraphProto *graph_proto);
 
   void ExportMergeConv(const FuncGraphPtr &func_graph, const CNodePtr &node, std::map<AnfNodePtr, size_t> *node_map_ptr,
                        onnx::GraphProto *graph_proto);
@@ -628,16 +647,19 @@ void OnnxExporter::ExportPrimReshape(const FuncGraphPtr & /*func_graph*/, const
   node_proto->add_input(name_shape);
 }
 
-void OnnxExporter::ExportPrimReduceMean(const FuncGraphPtr & /*func_graph*/, const CNodePtr &node,
-                                        std::map<AnfNodePtr, size_t> *node_map_ptr,
-                                        onnx::GraphProto *const graph_proto) {
+void OnnxExporter::ExportPrimReduce(const FuncGraphPtr & /*func_graph*/, const CNodePtr &node,
+                                    std::map<AnfNodePtr, size_t> *node_map_ptr, onnx::GraphProto *const graph_proto) {
   auto input_data = GetNodeInputName(node->input(1), node_map_ptr, graph_proto);
   auto input_axis = node->input(2);
 
   auto node_idx = AllocateNodeIndex();
   (*node_map_ptr)[node] = node_idx;
   onnx::NodeProto *node_proto = graph_proto->add_node();
-  node_proto->set_op_type(prim::kPrimReduceMean->name());
+  auto name = prim::kPrimReduceMean->name();
+  if (node->IsApply(prim::kPrimReduceSum)) {
+    name = prim::kPrimReduceSum->name();
+  }
+  node_proto->set_op_type(name);
   node_proto->add_output(std::to_string(node_idx));
   node_proto->add_input(input_data);
 
@@ -646,13 +668,18 @@ void OnnxExporter::ExportPrimReduceMean(const FuncGraphPtr & /*func_graph*/, con
     attr_proto->set_name("axes");
     attr_proto->set_type(onnx::AttributeProto_AttributeType_INTS);
     auto axis_value = dyn_cast<ValueNode>(input_axis)->value();
-    auto tuple_ptr = dyn_cast<ValueTuple>(axis_value);
-    MS_EXCEPTION_IF_NULL(tuple_ptr);
-    for (size_t i = 0; i < tuple_ptr->size(); ++i) {
-      attr_proto->add_ints(GetValue<int>((*tuple_ptr)[i]));
+    auto int_ptr = dyn_cast<Int32Imm>(axis_value);
+    if (int_ptr == nullptr) {
+      auto tuple_ptr = dyn_cast<ValueTuple>(axis_value);
+      MS_EXCEPTION_IF_NULL(tuple_ptr);
+      for (size_t i = 0; i < tuple_ptr->size(); ++i) {
+        attr_proto->add_ints(GetValue<int>((*tuple_ptr)[i]));
+      }
+    } else {
+      attr_proto->add_ints(int_ptr->value());
     }
   } else {
-    MS_LOG(EXCEPTION) << "Need to insert op convert variable from tuple to attributes for ReduceMean.";
+    MS_LOG(EXCEPTION) << "Need to insert op convert variable from tuple to attributes for " << name;
   }
 }
 
@@ -826,6 +853,83 @@ void OnnxExporter::ExportPrimDepthwiseConv2d(const FuncGraphPtr & /*func_graph*/
   SetAttrTupleValueToProto<2>(prim->GetAttr("stride"), onnx::AttributeProto_AttributeType_INTS, onnx_attr_proto, prim);
 }
 
+void OnnxExporter::ExportPrimTile(const FuncGraphPtr &func_graph, const CNodePtr &node,
+                                  std::map<AnfNodePtr, size_t> *node_map_ptr, onnx::GraphProto *const graph_proto) {
+  auto name_x = GetNodeInputName(node->input(1), node_map_ptr, graph_proto);
+  auto multiples = node->input(2);
+  std::string name_multiples;
+  if (multiples->isa<ValueNode>()) {
+    auto const_node_idx = AllocateNodeIndex();
+    (*node_map_ptr)[multiples] = const_node_idx;
+    onnx::NodeProto *node_proto = graph_proto->add_node();
+    name_multiples = std::to_string(const_node_idx);
+    node_proto->add_output(name_multiples);
+
+    node_proto->set_op_type("Constant");
+    onnx::AttributeProto *attr_proto = node_proto->add_attribute();
+    attr_proto->set_name("repeat");
+
+    attr_proto->set_type(onnx::AttributeProto_AttributeType_TENSOR);
+    ConvertTupleToTensor(dyn_cast<ValueNode>(multiples)->value(), attr_proto->mutable_t());
+  } else {
+    name_multiples = GetNodeInputName(multiples, node_map_ptr, graph_proto);
+    MS_LOG(EXCEPTION) << "Need to insert op convert variable from tuple to tensor for Tile.";
+  }
+
+  auto node_idx = AllocateNodeIndex();
+  (*node_map_ptr)[node] = node_idx;
+  onnx::NodeProto *node_proto = graph_proto->add_node();
+  node_proto->set_op_type("Tile");
+  node_proto->add_output(std::to_string(node_idx));
+  node_proto->add_input(name_x);
+  node_proto->add_input(name_multiples);
+}
+
+void OnnxExporter::ExportPrimSquare(const FuncGraphPtr &func_graph, const CNodePtr &node,
+                                    std::map<AnfNodePtr, size_t> *node_map_ptr, onnx::GraphProto *const graph_proto) {
+  auto name_x = GetNodeInputName(node->input(1), node_map_ptr, graph_proto);
+  std::string name_exponent;
+  auto const_node_idx = AllocateNodeIndex();
+  onnx::NodeProto *node_proto_exp = graph_proto->add_node();
+  name_exponent = std::to_string(const_node_idx);
+  node_proto_exp->add_output(name_exponent);
+
+  node_proto_exp->set_op_type("Constant");
+  onnx::AttributeProto *attr_proto = node_proto_exp->add_attribute();
+  attr_proto->set_type(onnx::AttributeProto_AttributeType_TENSOR);
+  onnx::TensorProto *tensor_proto = attr_proto->mutable_t();
+  tensor_proto->set_name("exponent");
+  tensor_proto->add_dims(static_cast<::google::protobuf::int64>(1));
+  tensor_proto->set_data_type(onnx::TensorProto_DataType_INT64);
+  tensor_proto->add_int64_data(2);
+
+  auto node_idx = AllocateNodeIndex();
+  (*node_map_ptr)[node] = node_idx;
+  onnx::NodeProto *node_proto = graph_proto->add_node();
+  node_proto->set_op_type("Pow");
+  node_proto->add_output(std::to_string(node_idx));
+  node_proto->add_input(name_x);
+  node_proto->add_input(name_exponent);
+}
+
+void OnnxExporter::ExportPrimGatherV2(const FuncGraphPtr &func_graph, const CNodePtr &node,
+                                      std::map<AnfNodePtr, size_t> *node_map_ptr, onnx::GraphProto *const graph_proto) {
+  auto name_x = GetNodeInputName(node->input(1), node_map_ptr, graph_proto);
+  auto name_indices = GetNodeInputName(node->input(2), node_map_ptr, graph_proto);
+  auto axis = node->input(3)->cast<ValueNodePtr>()->value();
+
+  auto node_idx = AllocateNodeIndex();
+  (*node_map_ptr)[node] = node_idx;
+  onnx::NodeProto *node_proto = graph_proto->add_node();
+  node_proto->set_op_type("Gather");
+  node_proto->add_output(std::to_string(node_idx));
+  node_proto->add_input(name_x);
+  node_proto->add_input(name_indices);
+  onnx::AttributeProto *attr_proto = node_proto->add_attribute();
+  attr_proto->set_type(onnx::AttributeProto_AttributeType_INT);
+  attr_proto->set_i(static_cast<::google::protobuf::int64>(dyn_cast<Int32Imm>(axis)->value()));
+}
+
 void OnnxExporter::ExportCNode(const FuncGraphPtr &func_graph, const CNodePtr &node,
                                std::map<AnfNodePtr, size_t> *node_map_ptr, onnx::GraphProto *const graph_proto) {
   // Type of the 2nd input of 'Reshape' of MindSpore is tuple, but ONNX's is tensor, need to do some convert
@@ -833,8 +937,8 @@ void OnnxExporter::ExportCNode(const FuncGraphPtr &func_graph, const CNodePtr &n
     return ExportPrimReshape(func_graph, node, node_map_ptr, graph_proto);
   }
 
-  if (node->IsApply(prim::kPrimReduceMean)) {
-    return ExportPrimReduceMean(func_graph, node, node_map_ptr, graph_proto);
+  if (node->IsApply(prim::kPrimReduceMean) || node->IsApply(prim::kPrimReduceSum)) {
+    return ExportPrimReduce(func_graph, node, node_map_ptr, graph_proto);
   }
 
   // MindSpore Cast(x, T) --> ONNX Cast[to=T](x)
@@ -857,6 +961,21 @@ void OnnxExporter::ExportCNode(const FuncGraphPtr &func_graph, const CNodePtr &n
     return ExportPrimDepthwiseConv2d(func_graph, node, node_map_ptr, graph_proto);
   }
 
+  // MindSpore Tile(x) --> ONNX Tile(x, repeat)
+  if (node->IsApply(prim::kPrimTile)) {
+    return ExportPrimTile(func_graph, node, node_map_ptr, graph_proto);
+  }
+
+  // MindSpore Square(x) --> ONNX Pow(x, 2)
+  if (node->IsApply(prim::kPrimSquare)) {
+    return ExportPrimSquare(func_graph, node, node_map_ptr, graph_proto);
+  }
+
+  // MindSpore GatherV2(x, indices, axis) --> ONNX Pow(x, indices)
+  if (node->IsApply(prim::kPrimGatherV2)) {
+    return ExportPrimGatherV2(func_graph, node, node_map_ptr, graph_proto);
+  }
+
   auto inputs = node->inputs();
   if (inputs.size() < 1) {
     MS_LOG(EXCEPTION) << "Inputs of apply node is empty";
@@ -1054,7 +1173,30 @@ void OnnxExporter::SetNodeAttribute(const ValuePtr &value, onnx::NodeProto *cons
   node_proto->set_op_type("Constant");
   onnx::AttributeProto *attr_proto = node_proto->add_attribute();
   attr_proto->set_name("value");
-  MS_LOG(EXCEPTION) << "Need to set value " << value->ToString() << " attribute for Constant node";
+  if (value->isa<Int32Imm>()) {
+    attr_proto->set_type(onnx::AttributeProto_AttributeType_INT);
+    auto casted_value = dyn_cast<Int32Imm>(value);
+    if (casted_value == nullptr) {
+      MS_LOG(EXCEPTION) << "Cast value " << value->ToString() << " to type T failed.";
+    }
+    auto attr_value = casted_value->value();
+    attr_proto->set_i(static_cast<::google::protobuf::int64>(attr_value));
+    attr_proto->set_type(onnx::AttributeProto_AttributeType_INT);
+  } else if (value->isa<tensor::Tensor>()) {
+    attr_proto->set_type(onnx::AttributeProto_AttributeType_TENSOR);
+    onnx::TensorProto *tensor_proto = attr_proto->mutable_t();
+    auto data = dyn_cast<tensor::Tensor>(value);
+    tensor_proto->set_raw_data(data->data().request(true).ptr, static_cast<size_t>(data->data().nbytes()));
+    auto dtype = data->data_type();
+    auto shape = data->shape_c();
+
+    tensor_proto->set_data_type(GetOnnxDataType(dtype));
+    for (const auto &dim : shape) {
+      tensor_proto->add_dims(dim);
+    }
+  } else {
+    MS_LOG(EXCEPTION) << "Need to set value " << value->ToString() << " attribute for Constant node";
+  }
 }
 
 std::string GetOnnxProtoString(const FuncGraphPtr &func_graph) {
diff --git a/tests/ut/python/onnx/test_onnx.py b/tests/ut/python/onnx/test_onnx.py
index ca877548de..e7d04fcfc7 100644
--- a/tests/ut/python/onnx/test_onnx.py
+++ b/tests/ut/python/onnx/test_onnx.py
@@ -142,6 +142,20 @@ class DepthwiseConv2dAndReLU6(nn.Cell):
         x = self.relu6(x)
         return x
 
+class DeepFMOpNet(nn.Cell):
+    """Net definition with Gatherv2 and Tile and Square."""
+
+    def __init__(self):
+        super(DeepFMOpNet, self).__init__()
+        self.gather = P.GatherV2()
+        self.square = P.Square()
+        self.tile = P.Tile()
+
+    def construct(self, x, y):
+        x = self.tile(x, (1000, 1))
+        x = self.square(x)
+        x = self.gather(x, y, 0)
+        return x
 
 # generate mindspore Tensor by shape and numpy datatype
 def gen_tensor(shape, dtype=np.float32):
@@ -153,6 +167,7 @@ net_cfgs = [
     ('lenet', LeNet5(), gen_tensor([1, 1, 32, 32])),
     ('maxpoolwithargmax', DefinedNet(), gen_tensor([1, 3, 224, 224])),
     ('depthwiseconv_relu6', DepthwiseConv2dAndReLU6(3, kernel_size=3), gen_tensor([1, 3, 32, 32])),
+    ('deepfm_ops', DeepFMOpNet(), (gen_tensor([1, 1]), gen_tensor([1000, 1], dtype=np.int32)))
 ]
 
 
@@ -164,7 +179,10 @@ def get_id(cfg):
 @pytest.mark.parametrize('name, net, inp', net_cfgs, ids=get_id(net_cfgs))
 def test_onnx_export(name, net, inp):
     onnx_file = name + ".onnx"
-    export(net, inp, file_name=onnx_file, file_format='ONNX')
+    if isinstance(inp, (tuple, list)):
+        export(net, *inp, file_name=onnx_file, file_format='ONNX')
+    else:
+        export(net, inp, file_name=onnx_file, file_format='ONNX')
 
     # check existence of exported onnx file and delete it
     assert os.path.exists(onnx_file)