!1138 Pynative fix topk run failed

Merge pull request !1138 from JoyLvliang/pynative-fix-topk-run-failed

mindspore/ccsrc/common/trans.cc

@@ -575,7 +575,7 @@ bool NchwToFracZ(const FormatArgs &args, void *result) {
           auto src_ni = hfi * kCubeSize + col;
           auto src_idx = src_row_offset + chw * col;
           auto dst_idx = gfi * fractal_ele_cnt + col * c0 + row;
-          auto pad_zero = src_ni >= n || src_idx >= nchw || src_ci >= c;
+          auto pad_zero = (src_ni >= n || src_idx >= nchw || src_ci >= c) ? 1 : 0;
           SetDataBysize(size, pad_zero);
         }
       }
@@ -770,9 +770,11 @@ bool NchwToFracNz(const FormatArgs &args, void *result) {
       auto h1h0_head = times_head + h1h0_idx * w0;
       auto src_h_head = src_times_head + h1h0_idx * w;
       for (size_t w1_idx = 0; w1_idx < num_w1; w1_idx++) {
-        size_t dst_idx = h1h0_head + w1_idx * h1h0w0;
-        size_t src_idx = src_h_head + w1_idx * w0;
-        SetDataBysize(size, 0);
+        for (size_t i = 0; i < w0; ++i) {
+          size_t src_idx = src_h_head + w1_idx * w0 + i;
+          size_t dst_idx = h1h0_head + w1_idx * h1h0w0 + i;
+          SetDataBysize(size, 0);
+        }
       }
       auto w1_head = num_w1 * w0;
       for (size_t w0_idx = 0; w1_head + w0_idx < w; w0_idx++) {
@@ -830,9 +832,11 @@ bool FracNzToNchw(const FormatArgs &args, void *result) {
       auto h1h0_head = times_head + h1h0_idx * w0;
       auto src_h_head = src_times_head + h1h0_idx * w;
       for (size_t w1_idx = 0; w1_idx < num_w1; w1_idx++) {
-        size_t src_idx = h1h0_head + w1_idx * h1h0w0;
-        size_t dst_idx = src_h_head + w1_idx * w0;
-        SetDataBysize(size, 0);
+        for (size_t i = 0; i < w0; ++i) {
+          size_t src_idx = h1h0_head + w1_idx * h1h0w0 + i;
+          size_t dst_idx = src_h_head + w1_idx * w0 + i;
+          SetDataBysize(size, 0);
+        }
       }
       auto w1_head = num_w1 * w0;
       for (size_t w0_idx = 0; w1_head + w0_idx < w; w0_idx++) {

mindspore/ccsrc/device/kernel_runtime.cc

@@ -159,6 +159,11 @@ void KernelRuntime::RunOpAssignInputMemory(const std::vector<tensor::TensorPtr>
                                            const session::KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(graph);
   MS_EXCEPTION_IF_NULL(mem_manager_);
+  if (input_tensors.size() != graph->inputs().size()) {
+    MS_LOG(EXCEPTION) << "Input tensors size " << input_tensors.size()
+                      << " should be equal to graph input parameter size " << graph->inputs().size();
+  }
+
   for (size_t input_index = 0; input_index < graph->inputs().size(); ++input_index) {
     auto item = graph->inputs()[input_index];
     MS_EXCEPTION_IF_NULL(item);
@@ -416,6 +421,8 @@ void KernelRuntime::AssignValueNodeTensor(const ValueNodePtr &value_node, const
   MS_EXCEPTION_IF_NULL(value_node);
   MS_EXCEPTION_IF_NULL(node_value);
   MS_EXCEPTION_IF_NULL(mem_manager_);
+  auto ms_context = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(ms_context);
   auto tensor = node_value->cast<TensorPtr>();
   if (tensor == nullptr) {
     MS_LOG(WARNING) << "Tensor is null";
@@ -423,13 +430,23 @@ void KernelRuntime::AssignValueNodeTensor(const ValueNodePtr &value_node, const
   }
   size_t tensor_size = tensor->data().nbytes();
   auto node_size = CountNodeDeviceMemorySize(value_node, output_idx);
-  auto ptr = mem_manager_->MallocMem(kStaticMem, node_size);
   TypeId output_type_id = AnfAlgo::GetOutputDeviceDataType(value_node, output_idx);
   if (output_type_id == kTypeUnknown) {
     output_type_id = AnfAlgo::GetOutputInferDataType(value_node, output_idx);
   }
-  auto address = CreateDeviceAddress(ptr, node_size, AnfAlgo::GetOutputFormat(value_node, output_idx), output_type_id);
-  MS_EXCEPTION_IF_NULL(address);
+  auto output_format = AnfAlgo::GetOutputFormat(value_node, output_idx);
+  DeviceAddressPtr address = nullptr;
+  if (ms_context->enable_pynative_infer()) {
+    address = CreateDeviceAddress(nullptr, node_size, output_format, output_type_id);
+    MS_EXCEPTION_IF_NULL(address);
+    if (!mem_manager_->MallocMemFromMemPool(address, node_size)) {
+      MS_LOG(EXCEPTION) << "Malloc value node device memory failed !";
+    }
+  } else {
+    auto ptr = mem_manager_->MallocMem(kStaticMem, node_size);
+    address = CreateDeviceAddress(ptr, node_size, output_format, output_type_id);
+    MS_EXCEPTION_IF_NULL(address);
+  }
   AnfAlgo::SetOutputAddr(address, output_idx, value_node.get());
   if (!address->SyncHostToDevice(trans::GetRuntimePaddingShape(value_node, 0), tensor_size, tensor->data_type(),
                                  tensor->data_c(false))) {
@@ -442,6 +459,8 @@ void KernelRuntime::AssignValueNodeTensor(const ValueNodePtr &value_node, const
 void KernelRuntime::AssignStaticMemoryValueNode(session::KernelGraph *graph) {
   MS_EXCEPTION_IF_NULL(graph);
   MS_EXCEPTION_IF_NULL(mem_manager_);
+  auto ms_context = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(ms_context);
   for (auto &value_node : graph->graph_value_nodes()) {
     MS_EXCEPTION_IF_NULL(value_node);
     if (AnfAlgo::OutputAddrExist(value_node, 0)) {
@@ -455,9 +474,18 @@ void KernelRuntime::AssignStaticMemoryValueNode(session::KernelGraph *graph) {
     } else if (node_value->isa<StringImm>()) {
       auto value = GetValue<std::string>(node_value);
       size_t tensor_size = value.size();
-      auto ptr = mem_manager_->MallocMem(kStaticMem, tensor_size);
-      auto address = CreateDeviceAddress(ptr, tensor_size, kOpFormat_DEFAULT, kNumberTypeUInt8);
-      MS_EXCEPTION_IF_NULL(address);
+      DeviceAddressPtr address = nullptr;
+      if (ms_context->enable_pynative_infer()) {
+        address = CreateDeviceAddress(nullptr, tensor_size, kOpFormat_DEFAULT, kNumberTypeUInt8);
+        MS_EXCEPTION_IF_NULL(address);
+        if (!mem_manager_->MallocMemFromMemPool(address, tensor_size)) {
+          MS_LOG(EXCEPTION) << "Malloc value node device memory failed !";
+        }
+      } else {
+        auto ptr = mem_manager_->MallocMem(kStaticMem, tensor_size);
+        address = CreateDeviceAddress(ptr, tensor_size, kOpFormat_DEFAULT, kNumberTypeUInt8);
+        MS_EXCEPTION_IF_NULL(address);
+      }
       AnfAlgo::SetOutputAddr(address, 0, value_node.get());
       std::vector<int> shape = {1, SizeToInt(tensor_size)};
       if (!address->SyncHostToDevice(shape, tensor_size, kNumberTypeUInt8, value.data())) {

mindspore/ccsrc/pynative/pynative_execute.cc

@@ -213,22 +213,22 @@ bool RunOpConvertConstInputToAttr(const py::object &input_object, size_t input_i
 }
 
 void PlantTensorTupleToVector(const py::tuple &tuple_inputs, const PrimitivePtr &op_prim,
-                              std::vector<tensor::TensorPtr> *input_tensor) {
+                              std::vector<tensor::TensorPtr> *input_tensors) {
   MS_EXCEPTION_IF_NULL(op_prim);
-  MS_EXCEPTION_IF_NULL(input_tensor);
+  MS_EXCEPTION_IF_NULL(input_tensors);
   for (const auto &input_object : tuple_inputs) {
     if (!py::isinstance<tensor::Tensor>(input_object)) {
       MS_LOG(EXCEPTION) << "The input object is not a tensor!";
     }
     auto tensor = py::cast<tensor::TensorPtr>(input_object);
     MS_EXCEPTION_IF_NULL(tensor);
-    input_tensor->push_back(tensor);
+    input_tensors->push_back(tensor);
   }
   op_prim->set_attr(kAttrDynInputSizes, MakeValue(std::vector<int>{SizeToInt(tuple_inputs.size())}));
 }
 
-void ConvertValueTupleToTensor(const py::object &input_object, std::vector<tensor::TensorPtr> *input_tensor) {
-  MS_EXCEPTION_IF_NULL(input_tensor);
+void ConvertValueTupleToTensor(const py::object &input_object, std::vector<tensor::TensorPtr> *input_tensors) {
+  MS_EXCEPTION_IF_NULL(input_tensors);
   ValuePtr input_value = parse::data_converter::PyDataToValue(input_object);
   MS_EXCEPTION_IF_NULL(input_value);
   if (!input_value->isa<ValueTuple>()) {
@@ -238,20 +238,23 @@ void ConvertValueTupleToTensor(const py::object &input_object, std::vector<tenso
   MS_EXCEPTION_IF_NULL(value_tuple);
   tensor::TensorPtr tensor_ptr = opt::CreateTupleTensor(value_tuple);
   MS_EXCEPTION_IF_NULL(tensor_ptr);
-  input_tensor->push_back(tensor_ptr);
+  input_tensors->push_back(tensor_ptr);
 }
 
 void ConvertPyObjectToTensor(const py::object &input_object, const PrimitivePtr &op_prim,
-                             std::vector<tensor::TensorPtr> *input_tensor) {
+                             std::vector<tensor::TensorPtr> *input_tensors, int *tensor_mask) {
   MS_EXCEPTION_IF_NULL(op_prim);
-  MS_EXCEPTION_IF_NULL(input_tensor);
+  MS_EXCEPTION_IF_NULL(input_tensors);
+  MS_EXCEPTION_IF_NULL(tensor_mask);
   tensor::TensorPtr tensor_ptr = nullptr;
   if (py::isinstance<tensor::Tensor>(input_object)) {
     tensor_ptr = py::cast<tensor::TensorPtr>(input_object);
   } else if (py::isinstance<py::float_>(input_object)) {
     tensor_ptr = std::make_shared<tensor::Tensor>(py::cast<py::float_>(input_object), kFloat32);
+    *tensor_mask = kValueNodeTensorMask;
   } else if (py::isinstance<py::int_>(input_object)) {
-    tensor_ptr = std::make_shared<tensor::Tensor>(py::cast<py::int_>(input_object), nullptr);
+    tensor_ptr = std::make_shared<tensor::Tensor>(py::cast<py::int_>(input_object), kInt32);
+    *tensor_mask = kValueNodeTensorMask;
   } else if (py::isinstance<py::list>(input_object)) {
     tensor_ptr = std::make_shared<tensor::Tensor>(py::cast<py::list>(input_object), nullptr);
   } else if (py::isinstance<py::array>(input_object)) {
@@ -261,20 +264,22 @@ void ConvertPyObjectToTensor(const py::object &input_object, const PrimitivePtr
   } else if (py::isinstance<py::tuple>(input_object)) {
     auto tuple_inputs = py::cast<py::tuple>(input_object);
     if (py::isinstance<tensor::Tensor>(tuple_inputs[0])) {
-      PlantTensorTupleToVector(tuple_inputs, op_prim, input_tensor);
+      PlantTensorTupleToVector(tuple_inputs, op_prim, input_tensors);
     } else {
-      ConvertValueTupleToTensor(input_object, input_tensor);
+      ConvertValueTupleToTensor(input_object, input_tensors);
+      *tensor_mask = kValueNodeTensorMask;
     }
     return;
   } else {
     MS_LOG(EXCEPTION) << "Run op inputs type is invalid!";
   }
   MS_EXCEPTION_IF_NULL(tensor_ptr);
-  input_tensor->push_back(tensor_ptr);
+  input_tensors->push_back(tensor_ptr);
 }
 
-void ConstructInputTensor(const OpExecInfoPtr &op_run_info, std::vector<bool> *tensors_mask,
+void ConstructInputTensor(const OpExecInfoPtr &op_run_info, std::vector<int> *tensors_mask,
                           std::vector<tensor::TensorPtr> *input_tensors) {
+  MS_EXCEPTION_IF_NULL(op_run_info);
   MS_EXCEPTION_IF_NULL(tensors_mask);
   MS_EXCEPTION_IF_NULL(input_tensors);
   PrimitivePtr op_prim = op_run_info->py_primitive;
@@ -295,14 +300,29 @@ void ConstructInputTensor(const OpExecInfoPtr &op_run_info, std::vector<bool> *t
       continue;
     }
     // convert const and tuple input to tensor
-    ConvertPyObjectToTensor(op_run_info->op_inputs[index], op_prim, input_tensors);
-    // make tensors, weight : 1, data : 0
-    std::vector<bool> new_mask(input_tensors->size() - tensors_mask->size(),
-                               py::cast<bool>(op_run_info->inputs_mask[index]));
+    int tensor_mask = py::cast<int>(op_run_info->inputs_mask[index]);
+    ConvertPyObjectToTensor(op_run_info->op_inputs[index], op_prim, input_tensors, &tensor_mask);
+    // mark tensors, data : 0, weight : 1, valuenode: 2
+    std::vector<int> new_mask(input_tensors->size() - tensors_mask->size(), tensor_mask);
     tensors_mask->insert(tensors_mask->end(), new_mask.begin(), new_mask.end());
   }
 }
 
+void EraseValueNodeTensor(const std::vector<int> &tensors_mask, std::vector<tensor::TensorPtr> *input_tensors) {
+  MS_EXCEPTION_IF_NULL(input_tensors);
+  if (input_tensors->size() != tensors_mask.size()) {
+    MS_LOG(EXCEPTION) << "Input tensors size " << input_tensors->size() << " should be equal to tensors mask size "
+                      << tensors_mask.size();
+  }
+  std::vector<tensor::TensorPtr> new_input_tensors;
+  for (size_t index = 0; index < tensors_mask.size(); ++index) {
+    if (tensors_mask[index] != kValueNodeTensorMask) {
+      new_input_tensors.push_back(input_tensors->at(index));
+    }
+  }
+  *input_tensors = new_input_tensors;
+}
+
 py::object RunOpInMs(const OpExecInfoPtr &op_exec_info, PynativeStatusCode *status) {
   MS_EXCEPTION_IF_NULL(op_exec_info);
   MS_LOG(INFO) << "Start run op[" << op_exec_info->op_name << "] with backend policy ms";
@@ -323,9 +343,10 @@ py::object RunOpInMs(const OpExecInfoPtr &op_exec_info, PynativeStatusCode *stat
 
   std::string graph_info = GetSingleOpGraphInfo(op_exec_info);
   std::vector<tensor::TensorPtr> input_tensors;
-  std::vector<bool> tensors_mask;
+  std::vector<int> tensors_mask;
   ConstructInputTensor(op_exec_info, &tensors_mask, &input_tensors);
   session->BuildOp(*op_exec_info, graph_info, input_tensors, tensors_mask);
+  EraseValueNodeTensor(tensors_mask, &input_tensors);
   py::tuple result = session->RunOp(*op_exec_info, graph_info, input_tensors);
   ms_context->set_enable_pynative_infer(false);
   *status = PYNATIVE_SUCCESS;

mindspore/ccsrc/session/ascend_session.cc

@@ -127,6 +127,34 @@ std::vector<BaseRef> GetRealArgs(const KernelGraphPtr graph, const VectorRef &ar
   }
   return real_args;
 }
+
+void ClearRunOpMemoryResource(const KernelGraphPtr &kernel_graph) {
+  MS_EXCEPTION_IF_NULL(kernel_graph);
+  // clear input parameter memory resource
+  for (const auto &input_node : kernel_graph->inputs()) {
+    MS_EXCEPTION_IF_NULL(input_node);
+    AnfAlgo::SetOutputAddr(nullptr, 0, input_node.get());
+  }
+  // clear input value node memory resource
+  for (const auto &value_node : kernel_graph->graph_value_nodes()) {
+    MS_EXCEPTION_IF_NULL(value_node);
+    AnfAlgo::SetOutputAddr(nullptr, 0, value_node.get());
+  }
+  for (const auto &cnode : kernel_graph->execution_order()) {
+    MS_EXCEPTION_IF_NULL(cnode);
+    // clear output memory resource
+    for (size_t index = 0; index < AnfAlgo::GetOutputTensorNum(cnode); ++index) {
+      AnfAlgo::SetOutputAddr(nullptr, index, cnode.get());
+    }
+    // clear workspace memory resource
+    auto kernel_mod = AnfAlgo::GetKernelMod(cnode);
+    MS_EXCEPTION_IF_NULL(kernel_mod);
+    auto workspace_lists = kernel_mod->GetWorkspaceSizeList();
+    for (size_t index = 0; index < workspace_lists.size(); ++index) {
+      AnfAlgo::SetWorkspaceAddr(nullptr, index, cnode.get());
+    }
+  }
+}
 }  // namespace
 
 GraphId AscendSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) {
@@ -308,8 +336,7 @@ bool AscendSession::GraphCacheExist(const GraphInfo &graph_info) const {
 }
 
 void AscendSession::BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
-                            const std::vector<tensor::TensorPtr> &input_tensors,
-                            const std::vector<bool> &tensors_mask) {
+                            const std::vector<tensor::TensorPtr> &input_tensors, const std::vector<int> &tensors_mask) {
   MS_LOG(INFO) << "Build op " << op_run_info.op_name << " start !";
   if (GraphCacheExist(graph_info)) {
     MS_LOG(INFO) << "Build op " << op_run_info.op_name << " finish !";
@@ -355,6 +382,7 @@ py::tuple AscendSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &gr
   }
   py::object tuple_obj = utils::cast<PyObjectRef>(output_tensors).object_;
   py::tuple tuple_tensors = py::cast<py::tuple>(tuple_obj);
+  ClearRunOpMemoryResource(graph);
   MS_LOG(INFO) << "Run op " << op_run_info.op_name << " finish!";
   return tuple_tensors;
 }

mindspore/ccsrc/session/ascend_session.h

@@ -46,7 +46,7 @@ class AscendSession : public SessionBasic {
   void RunGraph(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs, VectorRef *outputs) override;
   void BuildGraph(GraphId) override;
   void BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
-               const std::vector<tensor::TensorPtr> &input_tensors, const std::vector<bool> &tensors_mask) override;
+               const std::vector<tensor::TensorPtr> &input_tensors, const std::vector<int> &tensors_mask) override;
   py::tuple RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
                   const std::vector<tensor::TensorPtr> &input_tensors) override;
 

mindspore/ccsrc/session/gpu_session.cc

@@ -133,7 +133,7 @@ void GPUSession::RunGraph(const GraphId &graph_id, const std::vector<tensor::Ten
 }
 
 void GPUSession::BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
-                         const std::vector<tensor::TensorPtr> &input_tensors, const std::vector<bool> &tensors_mask) {
+                         const std::vector<tensor::TensorPtr> &input_tensors, const std::vector<int> &tensors_mask) {
   // Prepare the graph
   auto kernel_graph = ConstructSingleOpGraph(op_run_info, input_tensors, tensors_mask);
   MS_EXCEPTION_IF_NULL(kernel_graph);

mindspore/ccsrc/session/gpu_session.h

@@ -40,7 +40,7 @@ class GPUSession : public SessionBasic {
 
   void RunGraph(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs, VectorRef *outputs) override;
   void BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
-               const std::vector<tensor::TensorPtr> &input_tensors, const std::vector<bool> &tensors_mask) override;
+               const std::vector<tensor::TensorPtr> &input_tensors, const std::vector<int> &tensors_mask) override;
   py::tuple RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
                   const std::vector<tensor::TensorPtr> &input_tensors) override;
 

mindspore/ccsrc/session/session_basic.cc

@@ -312,11 +312,22 @@ size_t LoadCtrlInputTensor(const std::shared_ptr<Context> &context, std::vector<
   return inputs_params->size();
 }
 
+ValueNodePtr ConstructRunOpValueNode(const std::shared_ptr<KernelGraph> &graph, const tensor::TensorPtr &input_tensor) {
+  MS_EXCEPTION_IF_NULL(graph);
+  MS_EXCEPTION_IF_NULL(input_tensor);
+  auto abstract = std::make_shared<abstract::AbstractTensor>(input_tensor);
+  auto value_node = std::make_shared<ValueNode>(input_tensor);
+  value_node->set_abstract(abstract);
+  auto input_value_node = graph->NewValueNode(value_node);
+  graph->AddValueNodeToGraph(input_value_node);
+  return input_value_node;
+}
+
 ParameterPtr ConstructRunOpParameter(const std::shared_ptr<KernelGraph> &graph, const tensor::TensorPtr &input_tensor,
-                                     bool is_weight) {
+                                     int tensor_mask) {
   auto param = graph->NewParameter();
   MS_EXCEPTION_IF_NULL(param);
-  if (is_weight) {
+  if (tensor_mask == 1) {
     py::object obj;
     param->set_default_param(obj);
   }
@@ -675,7 +686,7 @@ void SessionBasic::CreateOutputNode(const CNodePtr &cnode, const std::shared_ptr
 
 std::shared_ptr<KernelGraph> SessionBasic::ConstructSingleOpGraph(const OpRunInfo &op_run_info,
                                                                   const std::vector<tensor::TensorPtr> &input_tensors,
-                                                                  const std::vector<bool> &tensors_mask) {
+                                                                  const std::vector<int> &tensors_mask) {
   auto graph = std::make_shared<KernelGraph>();
   std::vector<AnfNodePtr> inputs;
   // set input[0]
@@ -689,7 +700,12 @@ std::shared_ptr<KernelGraph> SessionBasic::ConstructSingleOpGraph(const OpRunInf
                       << tensors_mask.size();
   }
   for (size_t i = 0; i < input_tensors.size(); ++i) {
-    auto parameter = ConstructRunOpParameter(graph, input_tensors.at(i), tensors_mask[i]);
+    if (tensors_mask[i] == kValueNodeTensorMask) {
+      auto value_node = ConstructRunOpValueNode(graph, input_tensors[i]);
+      inputs.push_back(value_node);
+      continue;
+    }
+    auto parameter = ConstructRunOpParameter(graph, input_tensors[i], tensors_mask[i]);
     inputs.push_back(parameter);
     graph->MutableInputs()->push_back(parameter);
   }

mindspore/ccsrc/session/session_basic.h

@@ -65,7 +65,7 @@ class SessionBasic {
   virtual void RunGraph(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs, VectorRef *outputs) = 0;
 
   virtual void BuildOp(const OpRunInfo &, const GraphInfo &, const std::vector<tensor::TensorPtr> &input_tensors,
-                       const std::vector<bool> &tensors_mask) {}
+                       const std::vector<int> &tensors_mask) {}
 
   virtual py::tuple RunOp(const OpRunInfo &, const GraphInfo &, const std::vector<tensor::TensorPtr> &input_tensors) {
     return py::tuple();
@@ -105,7 +105,7 @@ class SessionBasic {
   // create a single run op graph
   std::shared_ptr<KernelGraph> ConstructSingleOpGraph(const OpRunInfo &op_run_info,
                                                       const std::vector<tensor::TensorPtr> &input_tensors,
-                                                      const std::vector<bool> &tensors_mask);
+                                                      const std::vector<int> &tensors_mask);
   // trans BaseRef list to py::tuple
   BaseRef TransformBaseRefListToTuple(const BaseRef &base_ref);
   // create a new kernel graph and update the graph sum

mindspore/ccsrc/utils/utils.h

@@ -193,6 +193,7 @@ const size_t kShape4dDims = 4;
 const size_t kShape5dDims = 5;
 const size_t kCubeSize = 16;
 const size_t kMemAlignSize = 512;
+const int kValueNodeTensorMask = 2;
 
 // define special index in special node
 constexpr auto kAnfPrimitiveIndex = 0;

mindspore/common/parameter.py

@@ -172,7 +172,8 @@ class Parameter:
 
     def __mul__(self, other):
         res = deepcopy(self)
-        res.default_input = res.default_input * other
+        default_input = res.default_input * other
+        res.default_input = Tensor(default_input.asnumpy().copy())
         return res
 
     def __truediv__(self, other):