async run graph

mindspore/ccsrc/backend/session/CMakeLists.txt

@@ -3,6 +3,8 @@ file(GLOB_RECURSE _SESSION_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
     "kernel_graph.cc"
     "session_basic.cc"
     "session_factory.cc"
+    "executor.cc"
+    "executor_manager.cc"
     "anf_runtime_algorithm.cc"
 )
 

mindspore/ccsrc/backend/session/ascend_session.cc

@@ -312,7 +312,6 @@ void AscendSession::RunGraph(const GraphId &graph_id, const std::vector<tensor::
   // if none of child graph and no anf output exists
   if (!kernel_graph->executable()) {
     MS_LOG(INFO) << "No child graph has anf output";
-    UpdateOutputs(kernel_graph, outputs, inputs);
     return;
   }
   // load input data from user input
@@ -322,12 +321,9 @@ void AscendSession::RunGraph(const GraphId &graph_id, const std::vector<tensor::
   InitPSParamAndOptim(kernel_graph, inputs);
 #endif
   {
-    py::gil_scoped_release release;
     // run task on device
     ExecTask(kernel_graph);
   }
-  // get result from device
-  UpdateOutputs(kernel_graph, outputs, inputs);
   // summary
   Summary(kernel_graph.get());
 #ifdef ENABLE_DEBUGGER
@@ -396,8 +392,8 @@ void AscendSession::BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph
   MS_LOG(INFO) << "Build op " << op_run_info.op_name << " finish !";
 }
 
-py::tuple AscendSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
-                               const std::vector<tensor::TensorPtr> &input_tensors) {
+void AscendSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
+                          const std::vector<tensor::TensorPtr> &input_tensors, VectorRef *outputs) {
   auto graph = run_op_graphs_[graph_info];
   MS_EXCEPTION_IF_NULL(graph);
   MS_LOG(INFO) << "Run op " << op_run_info.op_name << " start!";
@@ -408,7 +404,6 @@ py::tuple AscendSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &gr
   // run op
   RunOpExecTask(graph);
   // get output
-  VectorRef outputs;
   if (op_run_info.value != nullptr) {
     std::vector<tensor::TensorPtr> pre_output_tensors;
     TensorValueToTensor(op_run_info.value, &pre_output_tensors);
@@ -416,22 +411,13 @@ py::tuple AscendSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &gr
       tensor::TensorPtr tensor = std::make_shared<tensor::Tensor>(pre_output->data_type(), pre_output->shape());
       tensor->set_device_address(pre_output->device_address());
       tensor->set_dirty(false);
-      outputs.emplace_back(tensor);
+      outputs->emplace_back(tensor);
     }
   } else {
-    UpdateOutputs(graph, &outputs, input_tensors);
+    UpdateOutputs(graph, outputs, input_tensors);
   }
-  // trans output to tuple
-  auto output_tensors = TransformBaseRefListToTuple(outputs);
-  if (!utils::isa<PyObjectRef>(output_tensors) ||
-      !py::isinstance<py::tuple>(utils::cast<PyObjectRef>(output_tensors).object_)) {
-    MS_LOG(EXCEPTION) << "The output tensors should be a tuple !";
-  }
-  py::object tuple_obj = utils::cast<PyObjectRef>(output_tensors).object_;
-  py::tuple tuple_tensors = py::cast<py::tuple>(tuple_obj);
   RunOpMemoryClear(graph.get());
   MS_LOG(INFO) << "Run op " << op_run_info.op_name << " finish!";
-  return tuple_tensors;
 }
 
 // compile graph steps

mindspore/ccsrc/backend/session/ascend_session.h

@@ -29,7 +29,7 @@
 #include "backend/kernel_compiler/kernel.h"
 #include "backend/session/session_factory.h"
 #include "backend/session/ascend_control_parser.h"
-#include "runtime/device/ascend/ascend_memory_pool.h"
+#include "runtime/context.h"
 
 namespace mindspore {
 namespace session {
@@ -38,10 +38,17 @@ enum GraphType : int { COMMON_GRAPH = 0, CONDITION_GRAPH = 1, BRANCH_START = 2,
 class AscendSession : public SessionBasic {
  public:
   AscendSession() { final_graph_id_ = kInvalidGraphId; }
-  ~AscendSession() override { mindspore::device::ascend::AscendMemoryPool::GetInstance().ResetIdleMemBuf(); }
+  ~AscendSession() override = default;
   void Init(uint32_t device_id) override {
-    SessionBasic::Init(device_id);
-    context_ = std::make_shared<Context>(kAscendDevice, device_id);
+    InitDevice(kAscendDevice, device_id);
+    auto ret = rtCtxCreate(&rt_context_, 0, device_id);
+    if (ret != RT_ERROR_NONE) {
+      MS_EXCEPTION(DeviceProcessError) << "Call rtCtxCreate, ret[" << static_cast<int>(ret) << "]";
+    }
+    ret = rtCtxSetCurrent(rt_context_);
+    if (ret != RT_ERROR_NONE) {
+      MS_EXCEPTION(DeviceProcessError) << "Call rtCtxSetCurrent, ret[" << ret << "]";
+    }
   }
   GraphId CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) override;
   GraphId CompileGraph(NotNull<FuncGraphPtr> func_graph) override;
@@ -49,8 +56,8 @@ class AscendSession : public SessionBasic {
   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<int> &tensors_mask) override;
-  py::tuple RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
-                  const std::vector<tensor::TensorPtr> &input_tensors) override;
+  void RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
+             const std::vector<tensor::TensorPtr> &input_tensors, VectorRef *outputs) override;
 
   // get graph id in child graphs by ME front anf node pointer
   GraphId GetGraphIdByNode(const AnfNodePtr &front_anf) const override;
@@ -121,6 +128,8 @@ class AscendSession : public SessionBasic {
   std::map<std::pair<GraphId, size_t>, tensor::TensorPtr> initial_tenosrs_;
   // final_graph_id is used in every root graph has it's own session situation
   GraphId final_graph_id_;
+  // ascend runtime context
+  rtContext_t rt_context_{nullptr};
 };
 MS_REG_SESSION(kAscendDevice, AscendSession);
 }  // namespace session

mindspore/ccsrc/backend/session/cpu_session.cc

@@ -83,15 +83,23 @@ GraphId CPUSession::CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList
   return graph_id;
 }
 
+void CPUSession::CreateOutputTensors(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &input_tensors,
+                                     VectorRef *outputs,
+                                     std::map<tensor::TensorPtr, session::KernelWithIndex> *tensor_to_node) {
+  auto kernel_graph = GetGraph(graph_id);
+  MS_EXCEPTION_IF_NULL(kernel_graph);
+  MS_LOG(INFO) << "Bind input output address";
+  runtime_.BindInputOutput(kernel_graph.get(), input_tensors, outputs);
+  return;
+}
+
 void CPUSession::RunGraph(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs, VectorRef *outputs) {
-  auto &kernel_graph = graphs_[graph_id];
+  auto kernel_graph = GetGraph(graph_id);
   MS_EXCEPTION_IF_NULL(kernel_graph);
 #if (ENABLE_CPU && (ENABLE_D || ENABLE_GPU))
   InitPSParamAndOptim(kernel_graph, inputs);
 #endif
-  MS_LOG(INFO) << "Bind input output address";
-  std::vector<tensor::TensorPtr> need_sync_outputs;
-  runtime_.BindInputOutput(kernel_graph.get(), inputs, outputs, &need_sync_outputs);
+
   MS_LOG(INFO) << "Run graph start";
   auto execution_order = kernel_graph->execution_order();
   Reorder(&execution_order);
@@ -114,9 +122,6 @@ void CPUSession::RunGraph(const GraphId &graph_id, const std::vector<tensor::Ten
   if (!ret) {
     MS_LOG(EXCEPTION) << "Run graph failed";
   }
-  for (auto output : need_sync_outputs) {
-    (void)output->data_sync();
-  }
 
   if (enable_summary) {
     Summary(kernel_graph.get());

mindspore/ccsrc/backend/session/cpu_session.h

@@ -17,6 +17,7 @@
 #define MINDSPORE_CCSRC_BACKEND_SESSION_CPU_SESSION_H
 #include <string>
 #include <memory>
+#include <map>
 #include <vector>
 #include "backend/session/session_basic.h"
 #include "backend/session/kernel_graph.h"
@@ -28,13 +29,13 @@ class CPUSession : public SessionBasic {
  public:
   CPUSession() = default;
   ~CPUSession() override = default;
-  void Init(uint32_t device_id) override {
-    SessionBasic::Init(device_id);
-    context_ = std::make_shared<Context>(kCPUDevice, device_id);
-  }
+  void Init(uint32_t device_id) override { InitDevice(kCPUDevice, device_id); }
   GraphId CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) override;
   void RunGraph(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs, VectorRef *outputs) override;
 
+  void CreateOutputTensors(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &input_tensors, VectorRef *,
+                           std::map<tensor::TensorPtr, session::KernelWithIndex> *tensor_to_node) override;
+
  protected:
   ParameterPtr CreateNewParameterFromParameter(const AnfNodePtr &anf, bool valid_input, KernelGraph *graph) override;
   void Optimize(const std::shared_ptr<KernelGraph> &kernel_graph);

mindspore/ccsrc/backend/session/executor.cc

@@ -0,0 +1,292 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "backend/session/executor.h"
+#include "runtime/device/kernel_runtime_manager.h"
+#include "backend/session/executor_manager.h"
+
+namespace mindspore {
+namespace session {
+namespace {
+void UpdateOutputTensors(VectorRef *outputs,
+                         const std::map<tensor::TensorPtr, session::KernelWithIndex> &tensor_to_node) {
+  MS_EXCEPTION_IF_NULL(outputs);
+  for (auto item : *outputs) {
+    if (utils::isa<VectorRefPtr>(item)) {
+      auto vector_ref = utils::cast<VectorRef>(item);
+      UpdateOutputTensors(&vector_ref, tensor_to_node);
+    } else if (utils::isa<tensor::TensorPtr>(item)) {
+      auto tensor = utils::cast<tensor::TensorPtr>(item);
+      MS_EXCEPTION_IF_NULL(tensor);
+      tensor->SetNeedWait(false);
+      auto iter = tensor_to_node.find(tensor);
+      if (iter != tensor_to_node.end()) {
+        auto &node = iter->second.first;
+        auto &output_index = iter->second.second;
+        auto address = AnfAlgo::GetMutableOutputAddr(node, output_index);
+        tensor->set_device_address(address);
+      }
+      if (tensor->need_sync()) {
+        tensor->data_sync();
+        tensor->set_need_sync(false);
+      }
+    }
+  }
+}
+
+BaseRef TransformBaseRefListToTuple(const BaseRef &base_ref) {
+  if (utils::isa<VectorRef>(base_ref)) {
+    auto ref_list = utils::cast<VectorRef>(base_ref);
+    py::tuple output_tensors(ref_list.size());
+    for (size_t i = 0; i < ref_list.size(); ++i) {
+      auto output = TransformBaseRefListToTuple(ref_list[i]);  // use pyObjectRef
+      if (utils::isa<tensor::TensorPtr>(output)) {
+        auto tensor_ptr = utils::cast<tensor::TensorPtr>(output);
+        MS_EXCEPTION_IF_NULL(tensor_ptr);
+        output_tensors[i] = tensor_ptr;
+      } else if (utils::isa<PyObjectRef>(output)) {
+        py::object obj = utils::cast<PyObjectRef>(output).object_;
+        py::tuple tensor_tuple = py::cast<py::tuple>(obj);
+        output_tensors[i] = tensor_tuple;
+      } else {
+        MS_LOG(EXCEPTION) << "The output is not a base ref list or a tensor!";
+      }
+    }
+    return output_tensors;  // turn tuple to py::object and store in PyObjectRef
+  } else if (utils::isa<tensor::TensorPtr>(base_ref)) {
+    return base_ref;
+  } else {
+    MS_LOG(EXCEPTION) << "The output is not a base ref list or a tensor!";
+  }
+}
+}  // namespace
+void CompileNodesTask::Run() {
+  MS_EXCEPTION_IF_NULL(session_);
+  graph_id_ = session_->CompileGraph(nodes_, output_nodes_);
+}
+
+void CompileGraphTask::Run() {
+  MS_EXCEPTION_IF_NULL(session_);
+  graph_id_ = session_->CompileGraph(NOT_NULL(func_graph_));
+}
+
+void BuildGraphTask::Run() {
+  MS_EXCEPTION_IF_NULL(session_);
+  session_->BuildGraph(graph_id_);
+}
+
+void RunGraphTask::Run() {
+  MS_EXCEPTION_IF_NULL(session_);
+  session_->RunGraph(graph_id_, input_tensors_, &outputs_);
+  UpdateOutputTensors(&outputs_, tensor_to_node_);
+  ExecutorManager::Instance().OnRunGraphFinished();
+}
+
+void BuildOpTask::Run() {
+  MS_EXCEPTION_IF_NULL(session_);
+  session_->BuildOp(*op_run_info_, graph_info_, input_tensors_, tensors_mask_);
+}
+
+void RunOpTask::Run() {
+  MS_EXCEPTION_IF_NULL(session_);
+  session_->RunOp(*op_run_info_, graph_info_, input_tensors_, &outputs_);
+}
+
+Executor::Executor(const std::string &device_name, uint32_t device_id) {
+  device_name_ = device_name;
+  device_id_ = device_id;
+  worker_ = std::make_shared<std::thread>(&Executor::WorkerLoop, this);
+}
+
+void Executor::WorkerJoin() {
+  StopWorker();
+  worker_->join();
+}
+
+void Executor::WorkerLoop() {
+  while (true) {
+    std::shared_ptr<Task> task;
+    {
+      std::unique_lock<std::mutex> lock(task_mutex_);
+      task_cond_var_.wait(lock, [this] { return !ready_tasks_.empty(); });
+      task = ready_tasks_.front();
+      ready_tasks_.pop();
+    }
+    if (task->type_ == kExit) {
+      OnWorkerExit();
+      return;
+    }
+    task->Run();
+    if (task->type_ == kCompileNodes) {
+      compile_cond_var_.notify_all();
+    } else if (task->type_ == kCompileGraph) {
+      compile_cond_var_.notify_all();
+    } else if (task->type_ == kBuildGraph) {
+      build_cond_var_.notify_all();
+    } else if (task->type_ == kRunGraph) {
+      run_cond_var_.notify_all();
+    } else if (task->type_ == kBuildOp) {
+      build_op_cond_var_.notify_all();
+    } else if (task->type_ == kRunOp) {
+      run_op_cond_var_.notify_all();
+    }
+  }
+}
+
+std::vector<std::shared_ptr<RunGraphTask>> Executor::GetNewReadyTasks() {
+  std::vector<std::shared_ptr<RunGraphTask>> new_ready_tasks;
+  std::unique_lock<std::mutex> lock(pending_task_mutex_);
+  for (auto iter = pending_tasks_.begin(); iter != pending_tasks_.end();) {
+    auto task = *iter;
+    if (IsAllInputsReady(task->input_tensors_)) {
+      new_ready_tasks.emplace_back(task);
+      pending_tasks_.erase(iter++);
+    } else {
+      iter++;
+    }
+  }
+  return new_ready_tasks;
+}
+
+void Executor::OnRunGraphFinished() {
+  auto new_ready_tasks = GetNewReadyTasks();
+  std::unique_lock<std::mutex> lock(task_mutex_);
+  for (auto &task : new_ready_tasks) {
+    ready_tasks_.push(task);
+  }
+  if (new_ready_tasks.size() > 0) {
+    task_cond_var_.notify_all();
+  }
+}
+
+bool Executor::IsAllInputsReady(const std::vector<tensor::TensorPtr> &inputs) {
+  for (auto &input : inputs) {
+    MS_EXCEPTION_IF_NULL(input);
+    if (input->NeedWait()) {
+      return false;
+    }
+  }
+  return true;
+}
+
+GraphId Executor::CompileGraphAsync(const SessionPtr &session, const AnfNodePtrList &lst,
+                                    const AnfNodePtrList &outputs) {
+  std::unique_lock<std::mutex> lock(task_mutex_);
+  auto task = std::make_shared<CompileNodesTask>();
+  task->session_ = session;
+  task->nodes_ = lst;
+  task->output_nodes_ = outputs;
+  ready_tasks_.push(task);
+  task_cond_var_.notify_all();
+  compile_cond_var_.wait(lock);
+  return task->graph_id_;
+}
+
+GraphId Executor::CompileGraphAsync(const SessionPtr &session, NotNull<FuncGraphPtr> func_graph) {
+  std::unique_lock<std::mutex> lock(task_mutex_);
+  auto task = std::make_shared<CompileGraphTask>();
+  task->session_ = session;
+  task->func_graph_ = func_graph;
+  ready_tasks_.push(task);
+  task_cond_var_.notify_all();
+  compile_cond_var_.wait(lock);
+  return task->graph_id_;
+}
+
+void Executor::BuildGraphAsync(const SessionPtr &session, GraphId graphId) {
+  std::unique_lock<std::mutex> lock(task_mutex_);
+  auto task = std::make_shared<BuildGraphTask>();
+  task->session_ = session;
+  task->graph_id_ = graphId;
+  ready_tasks_.push(task);
+  task_cond_var_.notify_all();
+  build_cond_var_.wait(lock);
+}
+
+void Executor::RunGraphAsync(const SessionPtr &session, const GraphId &graph_id,
+                             const std::vector<tensor::TensorPtr> &inputs, VectorRef *outputs) {
+  auto task = std::make_shared<RunGraphTask>();
+  task->session_ = session;
+  task->graph_id_ = graph_id;
+  task->input_tensors_ = inputs;
+  MS_EXCEPTION_IF_NULL(session);
+  session->CreateOutputTensors(graph_id, inputs, outputs, &task->tensor_to_node_);
+  // maintain a copy of output vector
+  task->outputs_ = *outputs;
+
+  bool ready = IsAllInputsReady(inputs);
+  if (!ready) {
+    std::unique_lock<std::mutex> lock(pending_task_mutex_);
+    pending_tasks_.push_back(task);
+    return;
+  }
+  std::unique_lock<std::mutex> lock(task_mutex_);
+  ready_tasks_.push(task);
+  task_cond_var_.notify_all();
+  py::gil_scoped_release release;
+  run_cond_var_.wait(lock);
+}
+
+void Executor::BuildOpAsync(const SessionPtr &session, OpRunInfo *op_run_info, const GraphInfo &graph_info,
+                            const std::vector<tensor::TensorPtr> &input_tensors, const std::vector<int> &tensors_mask) {
+  std::unique_lock<std::mutex> lock(task_mutex_);
+  auto task = std::make_shared<BuildOpTask>();
+  task->session_ = session;
+  task->op_run_info_ = op_run_info;
+  task->graph_info_ = graph_info;
+  task->input_tensors_ = input_tensors;
+  task->tensors_mask_ = tensors_mask;
+  ready_tasks_.push(task);
+  task_cond_var_.notify_all();
+  build_op_cond_var_.wait(lock);
+}
+
+py::tuple Executor::RunOpAsync(const SessionPtr &session, OpRunInfo *op_run_info, const GraphInfo &graph_info,
+                               const std::vector<tensor::TensorPtr> &input_tensors) {
+  std::unique_lock<std::mutex> lock(task_mutex_);
+  auto task = std::make_shared<RunOpTask>();
+  task->session_ = session;
+  task->op_run_info_ = op_run_info;
+  task->graph_info_ = graph_info;
+  task->input_tensors_ = input_tensors;
+  ready_tasks_.push(task);
+  task_cond_var_.notify_all();
+  run_op_cond_var_.wait(lock);
+
+  // Trans output to tuple
+  auto output_tensors = TransformBaseRefListToTuple(task->outputs_);
+  if (!utils::isa<PyObjectRef>(output_tensors) ||
+      !py::isinstance<py::tuple>(utils::cast<PyObjectRef>(output_tensors).object_)) {
+    MS_EXCEPTION(NotSupportError) << "The output tensors should be a tuple !";
+  }
+  py::object tuple_obj = utils::cast<PyObjectRef>(output_tensors).object_;
+  py::tuple tuple_tensors = py::cast<py::tuple>(tuple_obj);
+  return tuple_tensors;
+}
+
+void Executor::StopWorker() {
+  std::unique_lock<std::mutex> lock(task_mutex_);
+  auto task = std::make_shared<ExitTask>();
+  ready_tasks_.push(task);
+  task_cond_var_.notify_all();
+}
+
+void Executor::OnWorkerExit() {
+  if (device_name_ == kAscendDevice) {
+    device::KernelRuntimeManager::Instance().ReleaseKernelRuntime(kAscendDevice, device_id_);
+  }
+}
+}  // namespace session
+}  // namespace mindspore

mindspore/ccsrc/backend/session/executor.h

@@ -0,0 +1,155 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_CCSRC_BACKEND_SESSION_EXECUTOR_H
+#define MINDSPORE_CCSRC_BACKEND_SESSION_EXECUTOR_H
+
+#include <vector>
+#include <string>
+#include <utility>
+#include <memory>
+#include <list>
+#include <queue>
+#include <map>
+#include <thread>
+#include <mutex>
+#include <condition_variable>
+#include "backend/session/session_basic.h"
+#include "ir/anf.h"
+#include "ir/tensor.h"
+#include "utils/any.h"
+#include "utils/contract.h"
+
+namespace mindspore {
+namespace session {
+enum TaskType { kUnKnown, kExit, kCompileNodes, kCompileGraph, kBuildGraph, kBuildOp, kRunGraph, kRunOp };
+
+class Task {
+ public:
+  Task() = default;
+  virtual ~Task() = default;
+  SessionPtr session_{nullptr};
+  TaskType type_{kUnKnown};
+  virtual void Run() {}
+};
+
+class CompileNodesTask : public Task {
+ public:
+  CompileNodesTask() { type_ = kCompileNodes; }
+  ~CompileNodesTask() override = default;
+  void Run() override;
+  AnfNodePtrList nodes_;
+  AnfNodePtrList output_nodes_;
+  GraphId graph_id_{0};
+};
+
+class CompileGraphTask : public Task {
+ public:
+  CompileGraphTask() { type_ = kCompileGraph; }
+  ~CompileGraphTask() override = default;
+  void Run() override;
+  FuncGraphPtr func_graph_{nullptr};
+  GraphId graph_id_{0};
+};
+
+class BuildGraphTask : public Task {
+ public:
+  BuildGraphTask() { type_ = kBuildGraph; }
+  ~BuildGraphTask() override = default;
+  void Run() override;
+  GraphId graph_id_{0};
+};
+
+class RunGraphTask : public Task {
+ public:
+  RunGraphTask() { type_ = kRunGraph; }
+  ~RunGraphTask() override = default;
+  void Run() override;
+  std::vector<tensor::TensorPtr> input_tensors_;
+  VectorRef outputs_;
+  GraphId graph_id_{0};
+  std::map<tensor::TensorPtr, session::KernelWithIndex> tensor_to_node_;
+};
+
+class BuildOpTask : public Task {
+ public:
+  BuildOpTask() { type_ = kBuildOp; }
+  ~BuildOpTask() override = default;
+  void Run() override;
+  OpRunInfo *op_run_info_{nullptr};
+  GraphInfo graph_info_;
+  std::vector<tensor::TensorPtr> input_tensors_;
+  std::vector<int> tensors_mask_;
+};
+
+class RunOpTask : public Task {
+ public:
+  RunOpTask() { type_ = kRunOp; }
+  ~RunOpTask() override = default;
+  void Run() override;
+  OpRunInfo *op_run_info_{nullptr};
+  GraphInfo graph_info_;
+  std::vector<tensor::TensorPtr> input_tensors_;
+  VectorRef outputs_;
+};
+
+class ExitTask : public Task {
+ public:
+  ExitTask() { type_ = kExit; }
+  ~ExitTask() override = default;
+};
+
+class Executor {
+ public:
+  Executor(const std::string &device_name, uint32_t device_id);
+  ~Executor() = default;
+  void WorkerLoop();
+  void WorkerJoin();
+  GraphId CompileGraphAsync(const SessionPtr &session, const AnfNodePtrList &lst, const AnfNodePtrList &outputs);
+  GraphId CompileGraphAsync(const SessionPtr &session, NotNull<FuncGraphPtr> func_graph);
+  void BuildGraphAsync(const SessionPtr &session, GraphId graphId);
+  void RunGraphAsync(const SessionPtr &session, const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs,
+                     VectorRef *outputs);
+  void BuildOpAsync(const SessionPtr &session, OpRunInfo *op_run_info, const GraphInfo &graph_info,
+                    const std::vector<tensor::TensorPtr> &input_tensors, const std::vector<int> &tensors_mask);
+  py::tuple RunOpAsync(const SessionPtr &session, OpRunInfo *op_run_info, const GraphInfo &graph_info,
+                       const std::vector<tensor::TensorPtr> &input_tensors);
+  void OnRunGraphFinished();
+
+ protected:
+  void UpdateOutputTensors(VectorRef *outputs,
+                           const std::map<tensor::TensorPtr, session::KernelWithIndex> &tensor_to_node);
+  std::vector<std::shared_ptr<RunGraphTask>> GetNewReadyTasks();
+  bool IsAllInputsReady(const std::vector<tensor::TensorPtr> &inputs);
+  void StopWorker();
+  void OnWorkerExit();
+
+  uint32_t device_id_;
+  std::string device_name_;
+  std::mutex task_mutex_;
+  std::mutex pending_task_mutex_;
+  std::condition_variable task_cond_var_;
+  std::condition_variable compile_cond_var_;
+  std::condition_variable build_cond_var_;
+  std::condition_variable run_cond_var_;
+  std::condition_variable build_op_cond_var_;
+  std::condition_variable run_op_cond_var_;
+  std::queue<std::shared_ptr<Task>> ready_tasks_;
+  std::list<std::shared_ptr<RunGraphTask>> pending_tasks_;
+  std::shared_ptr<std::thread> worker_;
+};
+}  // namespace session
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_BACKEND_SESSION_EXECUTOR_H

mindspore/ccsrc/backend/session/executor_manager.cc

@@ -0,0 +1,54 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "backend/session/executor_manager.h"
+
+namespace mindspore {
+namespace session {
+std::shared_ptr<Executor> ExecutorManager::GetExecutor(const std::string &device_name, int device_id) {
+  std::string device_key = device_name + "_" + std::to_string(device_id);
+  auto iter = executors_.find(device_key);
+  if (iter != executors_.end()) {
+    return iter->second;
+  }
+  auto executor = std::make_shared<Executor>(device_name, device_id);
+  executors_[device_key] = executor;
+  return executor;
+}
+
+void ExecutorManager::OnRunGraphFinished() {
+  for (auto &item : executors_) {
+    auto &executor = item.second;
+    if (executor != nullptr) {
+      executor->OnRunGraphFinished();
+    }
+  }
+}
+
+void ExecutorManager::JoinExecutorWorkers() {
+  for (auto &item : executors_) {
+    auto &executor = item.second;
+    if (executor != nullptr) {
+      executor->WorkerJoin();
+    }
+  }
+}
+
+void ExecutorManager::Clear() {
+  JoinExecutorWorkers();
+  executors_.clear();
+}
+}  // namespace session
+}  // namespace mindspore

mindspore/ccsrc/backend/session/executor_manager.h

@@ -0,0 +1,45 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_CCSRC_BACKEND_SESSION_EXECUTOR_MANGER_H_
+#define MINDSPORE_CCSRC_BACKEND_SESSION_EXECUTOR_MANGER_H_
+#include <set>
+#include <map>
+#include <string>
+#include <memory>
+#include "backend/session/executor.h"
+namespace mindspore {
+namespace session {
+class Executor;
+class ExecutorManager {
+ public:
+  static ExecutorManager &Instance() {
+    static ExecutorManager instance;
+    return instance;
+  }
+  std::shared_ptr<Executor> GetExecutor(const std::string &device_name, int device_id);
+  void OnRunGraphFinished();
+  void Clear();
+
+ private:
+  ExecutorManager() = default;
+  ~ExecutorManager() = default;
+  DISABLE_COPY_AND_ASSIGN(ExecutorManager)
+  void JoinExecutorWorkers();
+  std::map<std::string, std::shared_ptr<Executor>> executors_;
+};
+}  // namespace session
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_BACKEND_SESSION_EXECUTOR_MANGER_H_

mindspore/ccsrc/backend/session/gpu_session.cc

@@ -260,16 +260,10 @@ void GPUSession::RunGraph(const GraphId &graph_id, const std::vector<tensor::Ten
   InitPSParamAndOptim(kernel_graph, inputs);
 #endif
   MS_EXCEPTION_IF_NULL(kernel_graph);
-  {
-    py::gil_scoped_release gil_release;
-    // Run graph on GPU
-    Execute(kernel_graph);
-  }
+  Execute(kernel_graph);
 #ifdef ENABLE_DEBUGGER
   PostLoadTensor(kernel_graph);
 #endif
-  // Get result from GPU
-  UpdateOutputs(kernel_graph, outputs, inputs);
   // Summary
   auto context_ptr = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(context_ptr);
@@ -298,8 +292,8 @@ void GPUSession::BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_in
   run_op_graphs_[graph_info] = kernel_graph;
 }
 
-py::tuple GPUSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
-                            const std::vector<tensor::TensorPtr> &input_tensors) {
+void GPUSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
+                       const std::vector<tensor::TensorPtr> &input_tensors, VectorRef *outputs) {
   auto kernel_graph = run_op_graphs_[graph_info];
   MS_EXCEPTION_IF_NULL(kernel_graph);
   // Remove NopOp from execution graph
@@ -307,12 +301,8 @@ py::tuple GPUSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph
   RunOpAllocateMemory(op_run_info.value, input_tensors, kernel_graph.get());
   // Execute the computation
   LoadInputData(kernel_graph, input_tensors);
-  {
-    py::gil_scoped_release gil_release;
-    Execute(kernel_graph);
-  }
+  Execute(kernel_graph);
   // Fetch outputs
-  VectorRef outputs;
   if (op_run_info.value != nullptr) {
     std::vector<tensor::TensorPtr> pre_output_tensors;
     TensorValueToTensor(op_run_info.value, &pre_output_tensors);
@@ -320,21 +310,12 @@ py::tuple GPUSession::RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph
       tensor::TensorPtr tensor = std::make_shared<tensor::Tensor>(pre_output->data_type(), pre_output->shape());
       tensor->set_device_address(pre_output->device_address());
       tensor->set_dirty(false);
-      outputs.emplace_back(tensor);
+      outputs->emplace_back(tensor);
     }
   } else {
-    UpdateOutputs(kernel_graph, &outputs, input_tensors);
-  }
-  // Trans output to tuple
-  auto output_tensors = TransformBaseRefListToTuple(outputs);
-  if (!utils::isa<PyObjectRef>(output_tensors) ||
-      !py::isinstance<py::tuple>(utils::cast<PyObjectRef>(output_tensors).object_)) {
-    MS_EXCEPTION(NotSupportError) << "The output tensors should be a tuple !";
+    UpdateOutputs(kernel_graph, outputs, input_tensors);
   }
-  py::object tuple_obj = utils::cast<PyObjectRef>(output_tensors).object_;
-  py::tuple tuple_tensors = py::cast<py::tuple>(tuple_obj);
   RunOpClearMemory(kernel_graph.get());
-  return tuple_tensors;
 }
 
 #ifdef ENABLE_DEBUGGER

mindspore/ccsrc/backend/session/gpu_session.h

@@ -18,6 +18,7 @@
 
 #include <vector>
 #include <memory>
+#include <algorithm>
 #include "backend/session/session_basic.h"
 #include "backend/session/kernel_graph.h"
 #include "backend/session/session_factory.h"
@@ -31,18 +32,15 @@ class GPUSession : public SessionBasic {
   GPUSession() = default;
   ~GPUSession() override = default;
 
-  void Init(uint32_t device_id) override {
-    SessionBasic::Init(device_id);
-    context_ = std::make_shared<Context>(kGPUDevice, device_id);
-  }
+  void Init(uint32_t device_id) override { InitDevice(kGPUDevice, device_id); }
 
   GraphId CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) override;
 
   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<int> &tensors_mask) override;
-  py::tuple RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
-                  const std::vector<tensor::TensorPtr> &input_tensors) override;
+  void RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
+             const std::vector<tensor::TensorPtr> &input_tensors, VectorRef *outputs) override;
 
  private:
   void SelectKernel(const std::shared_ptr<KernelGraph> &kernel_graph) const;

mindspore/ccsrc/backend/session/infer_session.cc

@@ -318,7 +318,7 @@ void MSInferSession::RegAllOp() {
 Status MSInferSession::CompileGraph(std::shared_ptr<FuncGraph> funcGraphPtr, uint32_t &model_id) {
   MS_ASSERT(session_impl_ != nullptr);
   try {
-    auto graph_id = session_impl_->CompileGraph(NOT_NULL(funcGraphPtr));
+    auto graph_id = session_impl_->CompileGraphAsync(NOT_NULL(funcGraphPtr));
     py::gil_scoped_release gil_release;
     model_id = graph_id;
     return SUCCESS;
@@ -332,7 +332,7 @@ std::vector<tensor::TensorPtr> MSInferSession::RunGraph(uint32_t graph_id,
                                                         const std::vector<tensor::TensorPtr> &inputs) {
   try {
     VectorRef outputs;
-    session_impl_->RunGraph(graph_id, inputs, &outputs);
+    session_impl_->RunGraphAsync(graph_id, inputs, &outputs);
 
     return TransformVectorRefToMultiTensor(outputs);
   } catch (std::exception &e) {
@@ -364,16 +364,16 @@ Status MSInferSession::InitEnv(const std::string &device, uint32_t device_id) {
     return FAILED;
   }
   ms_context->set_device_target(device);
+  if (!context::OpenTsd(ms_context)) {
+    MS_LOG(ERROR) << "Session init OpenTsd failed!";
+    return FAILED;
+  }
   session_impl_ = session::SessionFactory::Get().Create(ajust_device);
   if (session_impl_ == nullptr) {
     MS_LOG(ERROR) << "Session create failed!, please make sure target device:" << device << " is available.";
     return FAILED;
   }
   session_impl_->Init(device_id);
-  if (!context::OpenTsd(ms_context)) {
-    MS_LOG(ERROR) << "Session init OpenTsd failed!";
-    return FAILED;
-  }
   return SUCCESS;
 }
 

mindspore/ccsrc/backend/session/session_basic.cc

@@ -25,8 +25,11 @@
 #include "common/trans.h"
 #include "utils/config_manager.h"
 #include "backend/session/anf_runtime_algorithm.h"
+#include "backend/session/executor.h"
+#include "backend/session/executor_manager.h"
 #include "backend/optimizer/common/common_backend_optimization.h"
 #include "backend/optimizer/common/helper.h"
+#include "runtime/device/kernel_runtime_manager.h"
 #include "utils/ms_utils.h"
 #include "ir/dtype.h"
 #include "ir/anf.h"
@@ -55,8 +58,10 @@ ValuePtr GetParamDefaultValue(const AnfNodePtr &node) {
   return parameter->default_param();
 }
 
-tensor::TensorPtr CreateOutputTensor(const AnfNodePtr &node, size_t output_index, const KernelGraphPtr &graph,
-                                     const DeviceAddressPtr &address) {
+tensor::TensorPtr CreateCNodeOutputTensor(const session::KernelWithIndex &node_output_pair,
+                                          const KernelGraphPtr &graph) {
+  auto &node = node_output_pair.first;
+  auto &output_index = node_output_pair.second;
   MS_EXCEPTION_IF_NULL(node);
   MS_EXCEPTION_IF_NULL(graph);
   TypeId type_id = AnfAlgo::GetOutputDeviceDataType(node, output_index);
@@ -68,9 +73,9 @@ tensor::TensorPtr CreateOutputTensor(const AnfNodePtr &node, size_t output_index
   if (graph->IsUniqueTargetInternalOutput(node, output_index)) {
     temp_shape.emplace_back(1);
     tensor = std::make_shared<tensor::Tensor>(type_id, temp_shape);
-    tensor->set_device_address(address);
     tensor->set_padding_type(AnfAlgo::GetOutputReshapeType(node, output_index));
     tensor->set_dirty(false);
+    tensor->SetNeedWait(true);
     return tensor;
   }
 
@@ -88,23 +93,25 @@ tensor::TensorPtr CreateOutputTensor(const AnfNodePtr &node, size_t output_index
   // if in paynative mode,data only copyed to host when user want to print data
   auto ms_context = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(ms_context);
-  MS_EXCEPTION_IF_NULL(address);
-  if (ms_context->execution_mode() == kPynativeMode || ms_context->device_target() == kGPUDevice) {
-    tensor->set_device_address(address);
-    tensor->set_dirty(false);
-  } else if (!address->SyncDeviceToHost(trans::GetRuntimePaddingShape(node, output_index),
-                                        LongToSize(tensor->data().nbytes()), tensor->data_type(), tensor->data_c())) {
-    MS_LOG(INFO) << "Output sync device to host error!!!";
-    tensor->set_dirty(false);
+  if (ms_context->execution_mode() != kPynativeMode && ms_context->device_target() != kGPUDevice) {
+    tensor->set_need_sync(true);
+  }
+  if (ms_context->execution_mode() != kPynativeMode) {
+    tensor->SetNeedWait(true);
   }
+  tensor->set_dirty(false);
   return tensor;
 }
 
-BaseRef CreateOneTensor(const AnfNodePtr &node, size_t output_index, const KernelGraphPtr &graph,
-                        const std::vector<tensor::TensorPtr> &input_tensors) {
+BaseRef CreateNodeOutputTensor(const session::KernelWithIndex &node_output_pair, const KernelGraphPtr &graph,
+                               const std::vector<tensor::TensorPtr> &input_tensors,
+                               std::map<tensor::TensorPtr, session::KernelWithIndex> *tensor_to_node) {
+  auto &node = node_output_pair.first;
+  auto &output_index = node_output_pair.second;
   MS_EXCEPTION_IF_NULL(node);
   MS_EXCEPTION_IF_NULL(graph);
-  MS_LOG(INFO) << "Create tensor for output[" << node->DebugString() << "] index[" << output_index << "]";
+  MS_EXCEPTION_IF_NULL(tensor_to_node);
+  MS_LOG(INFO) << "Create tensor for output[" << node->DebugString() << "] index[" << node_output_pair.second << "]";
   // if node is a value node, no need sync addr from device to host
   if (node->isa<ValueNode>()) {
     auto value_node = node->cast<ValueNodePtr>();
@@ -124,13 +131,16 @@ BaseRef CreateOneTensor(const AnfNodePtr &node, size_t output_index, const Kerne
       MS_LOG(EXCEPTION) << "Parameter : " << node->DebugString() << " has no output addr";
     }
   }
-  auto address = AnfAlgo::GetMutableOutputAddr(node, output_index);
-  return CreateOutputTensor(node, output_index, graph, address);
+  auto tensor = CreateCNodeOutputTensor(node_output_pair, graph);
+  (*tensor_to_node)[tensor] = node_output_pair;
+  return tensor;
 }
 
-BaseRef CreateTensorForOutput(const AnfNodePtr &anf, const KernelGraphPtr &graph,
-                              const std::vector<tensor::TensorPtr> &input_tensors) {
+BaseRef CreateNodeOutputTensors(const AnfNodePtr &anf, const KernelGraphPtr &graph,
+                                const std::vector<tensor::TensorPtr> &input_tensors,
+                                std::map<tensor::TensorPtr, session::KernelWithIndex> *tensor_to_node) {
   MS_EXCEPTION_IF_NULL(anf);
+  MS_EXCEPTION_IF_NULL(tensor_to_node);
   MS_LOG(INFO) << "Create tensor for output[" << anf->DebugString() << "]";
   auto item_with_index = AnfAlgo::VisitKernelWithReturnType(anf, 0);
   MS_EXCEPTION_IF_NULL(item_with_index.first);
@@ -141,7 +151,7 @@ BaseRef CreateTensorForOutput(const AnfNodePtr &anf, const KernelGraphPtr &graph
     MS_EXCEPTION_IF_NULL(cnode);
     VectorRef ret;
     for (size_t i = 1; i < cnode->inputs().size(); ++i) {
-      auto out = CreateTensorForOutput(cnode->input(i), graph, input_tensors);
+      auto out = CreateNodeOutputTensors(cnode->input(i), graph, input_tensors, tensor_to_node);
       ret.push_back(out);
     }
     return ret;
@@ -151,7 +161,7 @@ BaseRef CreateTensorForOutput(const AnfNodePtr &anf, const KernelGraphPtr &graph
   if (size == 0) {
     return VectorRef();
   }
-  return CreateOneTensor(item_with_index.first, item_with_index.second, graph, input_tensors);
+  return CreateNodeOutputTensor(item_with_index, graph, input_tensors, tensor_to_node);
 }
 
 ValueNodePtr CreateNewValueNode(const AnfNodePtr &anf, KernelGraph *graph) {
@@ -321,6 +331,12 @@ bool IgnoreCreateParameterForMakeTuple(const AnfNodePtr &node) {
 
 GraphId SessionBasic::graph_sum_ = 0;
 
+void SessionBasic::InitDevice(const std::string &device_name, uint32_t device_id) {
+  device_id_ = device_id;
+  context_ = std::make_shared<Context>(device_name, device_id);
+  executor_ = ExecutorManager::Instance().GetExecutor(device_name, device_id);
+}
+
 KernelGraphPtr SessionBasic::GetGraph(mindspore::GraphId graph_id) const {
   auto it = graphs_.find(graph_id);
   if (it == graphs_.end()) {
@@ -982,11 +998,36 @@ void SessionBasic::UpdateOutputs(const std::shared_ptr<KernelGraph> &kernel_grap
                                  const std::vector<tensor::TensorPtr> &input_tensors) const {
   MS_EXCEPTION_IF_NULL(kernel_graph);
   MS_EXCEPTION_IF_NULL(outputs);
+  std::map<tensor::TensorPtr, session::KernelWithIndex> tensor_to_node;
   auto anf_outputs = kernel_graph->outputs();
   for (auto &item : anf_outputs) {
     MS_EXCEPTION_IF_NULL(item);
     MS_LOG(INFO) << "Update output[" << item->DebugString() << "]";
-    outputs->emplace_back(CreateTensorForOutput(item, kernel_graph, input_tensors));
+    outputs->emplace_back(CreateNodeOutputTensors(item, kernel_graph, input_tensors, &tensor_to_node));
+  }
+
+  for (auto &item : tensor_to_node) {
+    auto &tensor = item.first;
+    auto &node = item.second.first;
+    auto &output_index = item.second.second;
+    auto address = AnfAlgo::GetMutableOutputAddr(node, output_index);
+    tensor->set_device_address(address);
+    tensor->SetNeedWait(false);
+  }
+}
+
+void SessionBasic::CreateOutputTensors(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &input_tensors,
+                                       VectorRef *outputs,
+                                       std::map<tensor::TensorPtr, session::KernelWithIndex> *tensor_to_node) {
+  auto kernel_graph = GetGraph(graph_id);
+  MS_EXCEPTION_IF_NULL(kernel_graph);
+  MS_EXCEPTION_IF_NULL(outputs);
+  MS_EXCEPTION_IF_NULL(tensor_to_node);
+  auto anf_outputs = kernel_graph->outputs();
+  for (auto &item : anf_outputs) {
+    MS_EXCEPTION_IF_NULL(item);
+    MS_LOG(INFO) << "Create node output[" << item->DebugString() << "]";
+    outputs->emplace_back(CreateNodeOutputTensors(item, kernel_graph, input_tensors, tensor_to_node));
   }
 }
 
@@ -1231,32 +1272,6 @@ std::shared_ptr<KernelGraph> SessionBasic::ConstructSingleOpGraph(const OpRunInf
   return graph;
 }
 
-BaseRef SessionBasic::TransformBaseRefListToTuple(const BaseRef &base_ref) {
-  if (utils::isa<VectorRef>(base_ref)) {
-    auto ref_list = utils::cast<VectorRef>(base_ref);
-    py::tuple output_tensors(ref_list.size());
-    for (size_t i = 0; i < ref_list.size(); ++i) {
-      auto output = TransformBaseRefListToTuple(ref_list[i]);  // use pyObjectRef
-      if (utils::isa<tensor::TensorPtr>(output)) {
-        auto tensor_ptr = utils::cast<tensor::TensorPtr>(output);
-        MS_EXCEPTION_IF_NULL(tensor_ptr);
-        output_tensors[i] = tensor_ptr;
-      } else if (utils::isa<PyObjectRef>(output)) {
-        py::object obj = utils::cast<PyObjectRef>(output).object_;
-        py::tuple tensor_tuple = py::cast<py::tuple>(obj);
-        output_tensors[i] = tensor_tuple;
-      } else {
-        MS_LOG(EXCEPTION) << "The output is not a base ref list or a tensor!";
-      }
-    }
-    return output_tensors;  // turn tuple to py::object and store in PyObjectRef
-  } else if (utils::isa<tensor::TensorPtr>(base_ref)) {
-    return base_ref;
-  } else {
-    MS_LOG(EXCEPTION) << "The output is not a base ref list or a tensor!";
-  }
-}
-
 KernelGraphPtr SessionBasic::NewKernelGraph() {
   auto graph = std::make_shared<KernelGraph>();
   graph->set_graph_id(graph_sum_);
@@ -1281,6 +1296,40 @@ AnfNodePtr SessionBasic::FindPullNode(const AnfNodePtr &push_node, const std::ve
   return nullptr;
 }
 
+GraphId SessionBasic::CompileGraphAsync(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) {
+  MS_EXCEPTION_IF_NULL(executor_);
+  return executor_->CompileGraphAsync(shared_from_this(), lst, outputs);
+}
+
+GraphId SessionBasic::CompileGraphAsync(NotNull<FuncGraphPtr> func_graph) {
+  MS_EXCEPTION_IF_NULL(executor_);
+  return executor_->CompileGraphAsync(shared_from_this(), func_graph);
+}
+
+void SessionBasic::BuildGraphAsync(GraphId graph_id) {
+  MS_EXCEPTION_IF_NULL(executor_);
+  executor_->BuildGraphAsync(shared_from_this(), graph_id);
+}
+
+void SessionBasic::BuildOpAsync(OpRunInfo *op_run_info, const GraphInfo &graph_info,
+                                const std::vector<tensor::TensorPtr> &input_tensors,
+                                const std::vector<int> &tensors_mask) {
+  MS_EXCEPTION_IF_NULL(executor_);
+  executor_->BuildOpAsync(shared_from_this(), op_run_info, graph_info, input_tensors, tensors_mask);
+}
+
+py::tuple SessionBasic::RunOpAsync(OpRunInfo *op_run_info, const GraphInfo &graph_info,
+                                   const std::vector<tensor::TensorPtr> &input_tensors) {
+  MS_EXCEPTION_IF_NULL(executor_);
+  return executor_->RunOpAsync(shared_from_this(), op_run_info, graph_info, input_tensors);
+}
+
+void SessionBasic::RunGraphAsync(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs,
+                                 VectorRef *outputs) {
+  MS_EXCEPTION_IF_NULL(executor_);
+  executor_->RunGraphAsync(shared_from_this(), graph_id, inputs, outputs);
+}
+
 #if (ENABLE_CPU && (ENABLE_D || ENABLE_GPU))
 void SessionBasic::AssignParamKey(const KernelGraphPtr &kernel_graph) {
   if (!parallel::ps::Util::IsRoleOfWorker()) {

mindspore/ccsrc/backend/session/session_basic.h

@@ -22,10 +22,10 @@
 #include <utility>
 #include <memory>
 #include <map>
-
 #include "utils/base_ref_extends.h"
 #include "backend/session/session_context.h"
 #include "backend/session/kernel_graph.h"
+#include "backend/session/anf_runtime_algorithm.h"
 #include "ir/anf.h"
 #include "ir/tensor.h"
 #include "utils/any.h"
@@ -49,8 +49,8 @@ using AnyListPtr = std::shared_ptr<AnyList>;
 
 using OpRunInfo = pynative::OpExecInfo;
 using OpRunInfoPtr = std::shared_ptr<OpRunInfo>;
-
-class SessionBasic {
+class Executor;
+class SessionBasic : public std::enable_shared_from_this<SessionBasic> {
  public:
   SessionBasic() : context_(nullptr), summary_callback_(nullptr), device_id_(0) {
 #ifdef ENABLE_DEBUGGER
@@ -60,6 +60,12 @@ class SessionBasic {
 
   virtual void Init(uint32_t device_id) { device_id_ = device_id; }
 
+  void InitDevice(const std::string &device_name, uint32_t device_id);
+
+  virtual void CreateOutputTensors(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &input_tensors,
+                                   VectorRef *outputs,
+                                   std::map<tensor::TensorPtr, session::KernelWithIndex> *tensor_to_node);
+
   virtual ~SessionBasic() { summary_callback_ = nullptr; }
 
   virtual GraphId CompileGraph(const AnfNodePtrList &lst, const AnfNodePtrList &outputs) = 0;
@@ -69,12 +75,19 @@ 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<int> &tensors_mask) {}
+  virtual void BuildOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
+                       const std::vector<tensor::TensorPtr> &input_tensors, const std::vector<int> &tensors_mask) {}
 
-  virtual py::tuple RunOp(const OpRunInfo &, const GraphInfo &, const std::vector<tensor::TensorPtr> &input_tensors) {
-    return py::tuple();
-  }
+  virtual void RunOp(const OpRunInfo &op_run_info, const GraphInfo &graph_info,
+                     const std::vector<tensor::TensorPtr> &input_tensors, VectorRef *outputs) {}
+
+  GraphId CompileGraphAsync(const AnfNodePtrList &lst, const AnfNodePtrList &outputs);
+  GraphId CompileGraphAsync(NotNull<FuncGraphPtr> func_graph);
+  void BuildGraphAsync(GraphId graphId);
+  void RunGraphAsync(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs, VectorRef *outputs);
+  void BuildOpAsync(OpRunInfo *, const GraphInfo &, const std::vector<tensor::TensorPtr> &input_tensors,
+                    const std::vector<int> &tensors_mask);
+  py::tuple RunOpAsync(OpRunInfo *, const GraphInfo &, const std::vector<tensor::TensorPtr> &input_tensors);
 
   virtual void RegisterSummaryCallBackFunc(const CallBackFunc &callback);
 
@@ -116,9 +129,11 @@ class SessionBasic {
   void CreateCNodeInputs(const CNodePtr &cnode, KernelGraph *graph, std::vector<AnfNodePtr> *cnode_inputs);
 
  protected:
-  virtual void SetSummaryNodes(KernelGraph *graph);
   // Get graph by graph id ,if not exist return null ptr
   KernelGraphPtr GetGraph(GraphId graph_id) const;
+
+  virtual void SetSummaryNodes(KernelGraph *graph);
+
   virtual void LoadInputData(const std::shared_ptr<KernelGraph> &kernel_graph,
                              const std::vector<tensor::TensorPtr> &inputs_const) const;
   void UpdateOutputs(const std::shared_ptr<KernelGraph> &kernel_graph, VectorRef *const outputs,
@@ -132,8 +147,6 @@ class SessionBasic {
   std::shared_ptr<KernelGraph> ConstructSingleOpGraph(const OpRunInfo &op_run_info,
                                                       const std::vector<tensor::TensorPtr> &input_tensors,
                                                       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
   KernelGraphPtr NewKernelGraph();
   std::vector<AnfNodePtr> CreateParameterFromTuple(const AnfNodePtr &node, bool valid_input, KernelGraph *graph);
@@ -152,6 +165,7 @@ class SessionBasic {
   CallBackFunc summary_callback_;
   static GraphId graph_sum_;
   uint32_t device_id_;
+  std::shared_ptr<Executor> executor_;
 #ifdef ENABLE_DEBUGGER
   std::shared_ptr<Debugger> debugger_;
 #endif

mindspore/ccsrc/pipeline/jit/pipeline.cc

@@ -37,6 +37,7 @@
 #include "frontend/parallel/context.h"
 #include "frontend/parallel/graph_util/get_parallel_info.h"
 #include "runtime/device/kernel_runtime_manager.h"
+#include "backend/session/executor_manager.h"
 #include "debug/trace.h"
 #include "pipeline/pynative/pynative_execute.h"
 #include "frontend/optimizer/py_pass_manager.h"
@@ -1023,7 +1024,6 @@ void ClearResAtexit() {
   MS_LOG(DEBUG) << "Pipeline clear all resource";
   pynative::ClearPyNativeSession();
   session::ClearPythonParasMap();
-  device::KernelRuntimeManager::Instance().ClearRuntimeResource();
 #if (ENABLE_CPU && (ENABLE_D || ENABLE_GPU))
   if (mindspore::parallel::ps::Util::IsParamServerMode()) {
     if (parallel::ps::Util::IsRoleOfWorker()) {
@@ -1047,6 +1047,8 @@ void ClearResAtexit() {
 #else
   ConfigManager::GetInstance().ResetIterNum();
 #endif
+  session::ExecutorManager::Instance().Clear();
+  device::KernelRuntimeManager::Instance().ClearRuntimeResource();
   ReleaseGeTsd();
   parse::python_adapter::ResetPythonScope();
 }

mindspore/ccsrc/pipeline/pynative/pynative_execute.cc

@@ -574,9 +574,9 @@ py::object RunOpInMs(const OpExecInfoPtr &op_exec_info, PynativeStatusCode *stat
   ConstructInputTensor(op_exec_info, &tensors_mask, &input_tensors);
   // get graph info for checking it whether existing in the cache
   std::string graph_info = GetSingleOpGraphInfo(op_exec_info, input_tensors);
-  session->BuildOp(*op_exec_info, graph_info, input_tensors, tensors_mask);
+  session->BuildOpAsync(op_exec_info.get(), graph_info, input_tensors, tensors_mask);
   EraseValueNodeTensor(tensors_mask, &input_tensors);
-  py::tuple result = session->RunOp(*op_exec_info, graph_info, input_tensors);
+  py::tuple result = session->RunOpAsync(op_exec_info.get(), graph_info, input_tensors);
   ms_context->set_enable_pynative_infer(false);
   *status = PYNATIVE_SUCCESS;
   MS_LOG(INFO) << "End run op[" << op_exec_info->op_name << "] with backend policy ms";

mindspore/ccsrc/runtime/device/ascend/ascend_kernel_runtime.cc

@@ -94,7 +94,18 @@ std::string GetRankId() {
 
 AscendKernelRuntime::~AscendKernelRuntime() { graph_model_map_.clear(); }
 
+void AscendKernelRuntime::SetContext() {
+  if (rt_context_ == nullptr) {
+    return;
+  }
+  auto ret = rtCtxSetCurrent(rt_context_);
+  if (ret != RT_ERROR_NONE) {
+    MS_EXCEPTION(DeviceProcessError) << "Call rtCtxSetCurrent, ret[" << ret << "]";
+  }
+}
+
 void AscendKernelRuntime::ClearGraphModelMap() {
+  SetContext();
   for (auto &iter : graph_data_dumper_) {
     MS_LOG(INFO) << "[DataDump] Unload data dumper:" << iter.first;
     auto &data_dumper = iter.second;
@@ -118,6 +129,7 @@ void AscendKernelRuntime::ClearGraphModelMap() {
 void AscendKernelRuntime::ClearGraphRuntimeResource(uint32_t graph_id, const std::vector<AnfNodePtr> &,
                                                     const std::unordered_set<ValueNodePtr> &,
                                                     const std::vector<CNodePtr> &) {
+  SetContext();
   MS_LOG(DEBUG) << "Clear graph:" << graph_id << " data dumper";
   if (auto dumper_iter = graph_data_dumper_.find(graph_id); dumper_iter != graph_data_dumper_.end()) {
     MS_LOG(DEBUG) << "Unload dump info " << graph_id;
@@ -156,6 +168,10 @@ bool AscendKernelRuntime::NeedDestroyHccl() {
 
 void AscendKernelRuntime::ReleaseDeviceRes() {
   MS_LOG(INFO) << "Ascend finalize start";
+  if (!initialized_) {
+    return;
+  }
+  SetContext();
   // release ge runtime
   ClearGraphModelMap();
 
@@ -438,6 +454,7 @@ DeviceAddressPtr AscendKernelRuntime::CreateDeviceAddress(void *device_ptr, size
 }
 
 bool AscendKernelRuntime::GenTask(const session::KernelGraph *graph) {
+  SetContext();
   if (graph == nullptr) {
     MS_EXCEPTION(NotExistsError) << "session::KernelGraph is NULL!";
   }
@@ -494,6 +511,7 @@ bool AscendKernelRuntime::GenTask(const session::KernelGraph *graph) {
 }
 
 bool AscendKernelRuntime::LoadTask(const session::KernelGraph *graph) {
+  SetContext();
   if (graph == nullptr) {
     MS_EXCEPTION(NotExistsError) << "Null pointer graph, LoadTask failed. ";
   }
@@ -586,6 +604,7 @@ void AscendKernelRuntime::DebugTaskIdName(GraphId graph_id) {
 }
 
 bool AscendKernelRuntime::RunTask(const session::KernelGraph *graph) {
+  SetContext();
   MS_EXCEPTION_IF_NULL(graph);
   MS_LOG(INFO) << "RunTask start. GraphId:" << graph->graph_id();
 
@@ -613,6 +632,7 @@ bool AscendKernelRuntime::RunTask(const session::KernelGraph *graph) {
 }
 
 bool AscendKernelRuntime::SyncStream() {
+  SetContext();
   if (RT_ERROR_NONE != rtStreamSynchronize(stream_)) {  // o for switch stream
     MS_LOG(ERROR) << "Call runtime rtStreamSynchronize error.";
     return false;
@@ -649,12 +669,7 @@ bool AscendKernelRuntime::InitDevice() {
   if (ret != RT_ERROR_NONE) {
     MS_EXCEPTION(DeviceProcessError) << "Call rtCtxCreate, ret[" << static_cast<int>(ret) << "]";
   }
-
-  ret = rtCtxSetCurrent(rt_context_);
-  if (ret != RT_ERROR_NONE) {
-    MS_EXCEPTION(DeviceProcessError) << "Call rtCtxSetCurrent, ret[" << ret << "]";
-  }
-
+  SetContext();
   ret = rtStreamCreate(&stream_, 0);
   if (ret != RT_ERROR_NONE) {
     MS_LOG(EXCEPTION) << "Call rtStreamCreate, ret[" << ret << "]";
@@ -664,14 +679,9 @@ bool AscendKernelRuntime::InitDevice() {
 }
 
 bool AscendKernelRuntime::ResetDevice() {
-  auto ret = rtCtxSetCurrent(rt_context_);
-  if (ret != RT_ERROR_NONE) {
-    MS_LOG(ERROR) << "Call rtCtxSetCurrent failed";
-    return false;
-  }
-
+  SetContext();
   if (stream_ != nullptr) {
-    ret = rtStreamDestroy(stream_);
+    auto ret = rtStreamDestroy(stream_);
     if (ret != RT_ERROR_NONE) {
       MS_LOG(EXCEPTION) << "Call rtStreamDestroy, ret[" << ret << "]";
     }
@@ -679,7 +689,7 @@ bool AscendKernelRuntime::ResetDevice() {
   }
 
   if (rt_context_ != nullptr) {
-    ret = rtCtxDestroy(rt_context_);
+    auto ret = rtCtxDestroy(rt_context_);
     if (ret != RT_ERROR_NONE) {
       MS_EXCEPTION(DeviceProcessError) << "Call rtCtxDestroy, ret[" << ret << "]";
     }

mindspore/ccsrc/runtime/device/ascend/ascend_kernel_runtime.h

@@ -60,6 +60,7 @@ class AscendKernelRuntime : public KernelRuntime {
   bool HcclInit();
   bool NeedDestroyHccl();
   bool DestroyHccl();
+  void SetContext();
 
   void ClearGraphModelMap();
   void ReleaseDeviceRes() override;

mindspore/ccsrc/runtime/device/cpu/cpu_kernel_runtime.cc

@@ -19,6 +19,7 @@
 #include <memory>
 #include <numeric>
 #include <utility>
+#include <functional>
 #include "backend/kernel_compiler/kernel.h"
 #include "runtime/device/cpu/cpu_device_address.h"
 #include "utils/ms_context.h"
@@ -137,10 +138,8 @@ DeviceAddressPtr CPUKernelRuntime::CreateDeviceAddress(void *device_ptr, size_t
 }
 
 tensor::TensorPtr CPUKernelRuntime::CreatTensorForOutput(session::KernelGraph *kernel_graph, const CNodePtr &node,
-                                                         size_t index,
-                                                         std::vector<tensor::TensorPtr> *need_sync_outputs) {
+                                                         size_t index) {
   MS_EXCEPTION_IF_NULL(node);
-  MS_EXCEPTION_IF_NULL(need_sync_outputs);
   size_t output_size = AnfAlgo::GetOutputTensorNum(node);
   if (index >= output_size) {
     MS_LOG(EXCEPTION) << "Invalid input index " << index;
@@ -163,16 +162,15 @@ tensor::TensorPtr CPUKernelRuntime::CreatTensorForOutput(session::KernelGraph *k
   }
   if (bound_addresses_.find(address) != bound_addresses_.end()) {
     tensor->set_device_address(address);
-    need_sync_outputs->emplace_back(tensor);
+    tensor->set_need_sync(true);
   } else {
     if (infer_type_id != device_type_id) {
       size_t type_size = GetTypeByte(TypeIdToType(device_type_id));
       ShapeVector data_shape = tensor->shape();
       size_t tensor_size = std::accumulate(data_shape.begin(), data_shape.end(), type_size, std::multiplies<size_t>());
       address->ptr_ = resource_manager_.MemMalloc(tensor_size);
-      need_sync_outputs->emplace_back(tensor);
       tensor->set_device_address(address);
-      need_sync_outputs->emplace_back(tensor);
+      tensor->set_need_sync(true);
     } else {
       tensor->set_device_address(nullptr);
       address->ptr_ = tensor->data_c();
@@ -185,8 +183,7 @@ tensor::TensorPtr CPUKernelRuntime::CreatTensorForOutput(session::KernelGraph *k
 }
 
 BaseRef CPUKernelRuntime::CreatTensorForOutput(session::KernelGraph *kernel_graph,
-                                               const session::KernelWithIndex &kernel_with_index,
-                                               std::vector<tensor::TensorPtr> *need_sync_outputs) {
+                                               const session::KernelWithIndex &kernel_with_index) {
   auto &input_node = kernel_with_index.first;
   auto index = kernel_with_index.second;
   MS_EXCEPTION_IF_NULL(input_node);
@@ -197,12 +194,12 @@ BaseRef CPUKernelRuntime::CreatTensorForOutput(session::KernelGraph *kernel_grap
       VectorRef ret;
       for (size_t i = 1; i < node->inputs().size(); i++) {
         auto item_with_index = AnfAlgo::VisitKernelWithReturnType(node->input(i), 0);
-        auto out = CreatTensorForOutput(kernel_graph, item_with_index, need_sync_outputs);
+        auto out = CreatTensorForOutput(kernel_graph, item_with_index);
         ret.push_back(out);
       }
       return ret;
     }
-    return CreatTensorForOutput(kernel_graph, node, index, need_sync_outputs);
+    return CreatTensorForOutput(kernel_graph, node, index);
   } else if (input_node->isa<Parameter>()) {
     auto iter = input_param_tensor_map_.find(input_node);
     if (iter != input_param_tensor_map_.end()) {
@@ -216,7 +213,7 @@ BaseRef CPUKernelRuntime::CreatTensorForOutput(session::KernelGraph *kernel_grap
   return BaseRef();
 }
 void CPUKernelRuntime::BindInputOutput(session::KernelGraph *kernel_graph, const std::vector<tensor::TensorPtr> &inputs,
-                                       VectorRef *outputs, std::vector<tensor::TensorPtr> *need_sync_outputs) {
+                                       VectorRef *outputs) {
   MS_EXCEPTION_IF_NULL(kernel_graph);
   MS_EXCEPTION_IF_NULL(outputs);
   // bind input ptr
@@ -262,7 +259,7 @@ void CPUKernelRuntime::BindInputOutput(session::KernelGraph *kernel_graph, const
   auto output_nodes = kernel_graph->outputs();
   for (const auto &item : output_nodes) {
     auto item_with_index = AnfAlgo::VisitKernelWithReturnType(item, 0, true);
-    auto out = CreatTensorForOutput(kernel_graph, item_with_index, need_sync_outputs);
+    auto out = CreatTensorForOutput(kernel_graph, item_with_index);
     outputs->push_back(std::move(out));
   }
 }

mindspore/ccsrc/runtime/device/cpu/cpu_kernel_runtime.h

@@ -39,7 +39,7 @@ class CPUKernelRuntime : public KernelRuntime {
   bool Run(session::KernelGraph *graph, Debugger *debugger = nullptr) override;
   void AssignKernelAddress(session::KernelGraph *kernel_graph);
   void BindInputOutput(session::KernelGraph *kernel_graph, const std::vector<tensor::TensorPtr> &inputs,
-                       VectorRef *outputs, std::vector<tensor::TensorPtr> *need_sync_outputs);
+                       VectorRef *outputs);
   void IncreaseSummaryRefCount(const session::NamedSummaryOutputs &summary_outputs);
   void DecreaseSummaryRefCount(const session::NamedSummaryOutputs &summary_outputs);
 
@@ -49,11 +49,9 @@ class CPUKernelRuntime : public KernelRuntime {
                                        TypeId type_id) override;
 
  private:
-  tensor::TensorPtr CreatTensorForOutput(session::KernelGraph *kernel_graph, const CNodePtr &node, size_t index,
-                                         std::vector<tensor::TensorPtr> *need_sync_outputs);
+  tensor::TensorPtr CreatTensorForOutput(session::KernelGraph *kernel_graph, const CNodePtr &node, size_t index);
 
-  BaseRef CreatTensorForOutput(session::KernelGraph *kernel_graph, const session::KernelWithIndex &kernel_with_index,
-                               std::vector<tensor::TensorPtr> *need_sync_outputs);
+  BaseRef CreatTensorForOutput(session::KernelGraph *kernel_graph, const session::KernelWithIndex &kernel_with_index);
   void AssignValueNodeAddress(session::KernelGraph *kernel_graph);
   void AssignInputNodeAddress(const session::KernelGraph *kernel_graph);
   void AssignKernelOutputAddress(const session::KernelGraph *kernel_graph);

mindspore/ccsrc/runtime/device/kernel_runtime_manager.cc

@@ -49,8 +49,13 @@ void KernelRuntimeManager::Register(const std::string &device_name, KernelRuntim
   }
 }
 
+std::string KernelRuntimeManager::GetDeviceKey(const std::string &device_name, uint32_t device_id) {
+  std::string device_key = device_name + "_" + std::to_string(device_id);
+  return device_key;
+}
+
 KernelRuntime *KernelRuntimeManager::GetSingleKernelRuntime(const std::string &device_name, uint32_t device_id) {
-  std::string runtime_key = device_name + "_" + std::to_string(device_id);
+  auto runtime_key = GetDeviceKey(device_name, device_id);
   auto runtime_iter = runtime_map_.find(runtime_key);
   if (runtime_iter != runtime_map_.end()) {
     return runtime_iter->second.get();
@@ -72,8 +77,8 @@ KernelRuntime *KernelRuntimeManager::GetSingleKernelRuntime(const std::string &d
 }
 
 KernelRuntime *KernelRuntimeManager::GetKernelRuntime(const std::string &device_name, uint32_t device_id) {
+  std::string runtime_key = GetDeviceKey(device_name, device_id);
   std::lock_guard<std::mutex> guard(lock_);
-  std::string runtime_key = device_name + "_" + std::to_string(device_id);
   auto runtime_iter = runtime_map_.find(runtime_key);
   if (runtime_iter != runtime_map_.end()) {
     return runtime_iter->second.get();
@@ -92,5 +97,20 @@ KernelRuntime *KernelRuntimeManager::GetKernelRuntime(const std::string &device_
 
   return kernel_runtime.get();
 }
+
+void KernelRuntimeManager::ReleaseKernelRuntime(const std::string &device_name, uint32_t device_id) {
+  std::string runtime_key = GetDeviceKey(device_name, device_id);
+  std::lock_guard<std::mutex> guard(lock_);
+  auto runtime_iter = runtime_map_.find(runtime_key);
+  if (runtime_iter == runtime_map_.end()) {
+    return;
+  }
+  auto runtime = runtime_iter->second.get();
+  if (runtime == nullptr) {
+    return;
+  }
+  runtime->ReleaseDeviceRes();
+  runtime_map_.erase(runtime_iter);
+}
 }  // namespace device
 }  // namespace mindspore

mindspore/ccsrc/runtime/device/kernel_runtime_manager.h

@@ -39,6 +39,7 @@ class KernelRuntimeManager {
   void Register(const std::string &device_name, KernelRuntimeCreator &&runtime_creator);
   KernelRuntime *GetKernelRuntime(const std::string &device_name, uint32_t device_id);
   KernelRuntime *GetSingleKernelRuntime(const std::string &device_name, uint32_t device_id);
+  void ReleaseKernelRuntime(const std::string &device_name, uint32_t device_id);
   void ClearRuntimeResource();
   void ClearGraphResource(uint32_t graph_id, const std::vector<AnfNodePtr> &inputs,
                           const std::unordered_set<ValueNodePtr> &value_nodes,
@@ -48,6 +49,7 @@ class KernelRuntimeManager {
   KernelRuntimeManager() = default;
   ~KernelRuntimeManager() = default;
   DISABLE_COPY_AND_ASSIGN(KernelRuntimeManager);
+  std::string GetDeviceKey(const std::string &device_name, uint32_t device_id);
   std::map<std::string, std::shared_ptr<KernelRuntime> > runtime_map_;
   std::map<std::string, KernelRuntimeCreator> runtime_creators_;
   std::mutex lock_;

mindspore/ccsrc/vm/backend.cc

@@ -54,9 +54,9 @@ LinConvertResult MsBackend::MsConvert(const AnfNodePtrList &lst, const std::stri
   GraphId graph_id = kInvalidGraphId;
   if (target != target_device_ && !target.empty()) {
     CreateOtherSession(target);
-    graph_id = other_sess_->CompileGraph(lst, outputs);
+    graph_id = other_sess_->CompileGraphAsync(lst, outputs);
   } else {
-    graph_id = target_sess_->CompileGraph(lst, outputs);
+    graph_id = target_sess_->CompileGraphAsync(lst, outputs);
   }
 
   if (MsContext::GetInstance()->precompile_only()) {
@@ -64,9 +64,9 @@ LinConvertResult MsBackend::MsConvert(const AnfNodePtrList &lst, const std::stri
     return result;
   }
   if (target != target_device_ && !target.empty()) {
-    other_sess_->BuildGraph(graph_id);
+    other_sess_->BuildGraphAsync(graph_id);
   } else if (!is_multi_graph_sink_) {
-    target_sess_->BuildGraph(graph_id);
+    target_sess_->BuildGraphAsync(graph_id);
   }
   result.run = std::make_shared<RunFunc>(
     [graph_id, target, this](const VectorRef &args) -> VectorRef { return MsRunGraph(graph_id, args, target); });
@@ -137,9 +137,9 @@ VectorRef MsBackend::MsRunGraph(const GraphId &g, const VectorRef &args, const s
   VectorRef outputs;
   // call ms rungraph (graphId, input ,output)
   if (target != target_device_ && !target.empty()) {
-    other_sess_->RunGraph(g, inputs, &outputs);
+    other_sess_->RunGraphAsync(g, inputs, &outputs);
   } else {
-    target_sess_->RunGraph(g, inputs, &outputs);
+    target_sess_->RunGraphAsync(g, inputs, &outputs);
   }
 
   MS_LOG(DEBUG) << "RunGraph finished:" << outputs.size();
@@ -150,7 +150,7 @@ void MsBackend::Link(GraphId graph_id) {
   if (graph_id == kInvalidGraphId) {
     graph_id = target_sess_->GetFinalRunGraph();
   }
-  target_sess_->BuildGraph(graph_id);
+  target_sess_->BuildGraphAsync(graph_id);
 }
 
 Backend::Backend(const std::string &name) : name_(name) {
@@ -186,7 +186,7 @@ void MsBackend::CreateOtherSession(const std::string &target) {
   other_device_ = target;
 }
 
-GraphId MsBackend::CompileGraph(NotNull<FuncGraphPtr> fg) { return target_sess_->CompileGraph(fg); }
+GraphId MsBackend::CompileGraph(NotNull<FuncGraphPtr> fg) { return target_sess_->CompileGraphAsync(fg); }
 
 VectorRef MsBackend::RunGraph(GraphId graph_id, const VectorRef &args) { return MsRunGraph(graph_id, args); }
 

mindspore/core/ir/tensor.cc

@@ -418,6 +418,8 @@ Tensor::Tensor(const Tensor &tensor)
       data_(tensor.data_),
       dirty_(tensor.dirty_),
       id_(tensor.id_),
+      event_(tensor.event_),
+      need_sync_(tensor.need_sync_),
       device_sync_(tensor.device_sync_),
       padding_type_(tensor.padding_type()) {}
 
@@ -427,6 +429,8 @@ Tensor::Tensor(const Tensor &tensor, TypeId data_type)
       data_(MakeTensorData(data_type, tensor.shape_, tensor.data_->data(), tensor.data_type_)),
       dirty_(tensor.dirty_),
       id_(tensor.id_),
+      event_(tensor.event_),
+      need_sync_(tensor.need_sync_),
       device_sync_(tensor.device_sync_),
       padding_type_(tensor.padding_type()) {}
 
@@ -477,6 +481,8 @@ Tensor &Tensor::AssignValue(const Tensor &tensor) {
     device_sync_ = tensor.device_sync_;
     data_ = tensor.data_;
     id_ = tensor.id_;
+    event_ = tensor.event_;
+    need_sync_ = tensor.need_sync_;
     padding_type_ = tensor.padding_type_;
   }
   return *this;
@@ -541,6 +547,7 @@ std::string Tensor::ToStringRepr() const {
 }
 
 void Tensor::data_sync() const {
+  const_cast<Tensor *>(this)->Wait();
   if (device_sync_ != nullptr) {
     if (!device_sync_->SyncDeviceToHost(shape(), static_cast<size_t>(data().nbytes()), data_type(), data_c())) {
       MS_LOG(EXCEPTION) << "SyncDeviceToHost failed.";

mindspore/core/ir/tensor.h

@@ -21,6 +21,8 @@
 #include <string>
 #include <vector>
 #include <numeric>
+#include <mutex>
+#include <condition_variable>
 
 #include "ir/device_sync.h"
 #include "ir/meta_tensor.h"
@@ -58,6 +60,30 @@ class TensorData {
 
 using TensorDataPtr = std::shared_ptr<TensorData>;
 
+struct WaitEvent {
+  bool need_wait_{false};
+  std::mutex mutex_;
+  std::condition_variable cond_var_;
+
+  void Wait() {
+    std::unique_lock<std::mutex> lock(mutex_);
+    if (!need_wait_) {
+      return;
+    }
+    cond_var_.wait(lock, [this] { return !need_wait_; });
+  }
+
+  void set_need_wait(bool need_wait) {
+    std::unique_lock<std::mutex> lock(mutex_);
+    need_wait_ = need_wait;
+    if (!need_wait_) {
+      cond_var_.notify_all();
+    }
+  }
+
+  bool need_wait() const { return need_wait_; }
+};
+
 // Tensor entity class
 class Tensor : public MetaTensor {
  public:
@@ -229,11 +255,40 @@ class Tensor : public MetaTensor {
 
   std::string id() const { return id_; }
 
+  void SetNeedWait(bool need_wait) {
+    if (event_ != nullptr) {
+      event_->set_need_wait(need_wait);
+    } else if (need_wait) {
+      event_ = std::make_shared<WaitEvent>();
+      event_->set_need_wait(need_wait);
+    }
+  }
+
+  bool NeedWait() const {
+    if (event_ != nullptr) {
+      return event_->need_wait();
+    }
+    return false;
+  }
+
+  void Wait() {
+    if (event_ != nullptr) {
+      event_->Wait();
+    }
+    event_ == nullptr;
+  }
+
+  void set_need_sync(bool need_sync) { need_sync_ = need_sync; }
+
+  bool need_sync() const { return need_sync_; }
+
  private:
   bool init_flag_{false};
   TensorDataPtr data_{nullptr};
   bool dirty_{true};
   std::string id_{""};
+  std::shared_ptr<WaitEvent> event_{nullptr};
+  bool need_sync_{false};
   DeviceSyncPtr device_sync_{nullptr};
   std::vector<Axis> padding_type_;
 };

tests/ut/cpp/CMakeLists.txt

@@ -84,6 +84,8 @@ file(GLOB_RECURSE MINDSPORE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         "../../../mindspore/ccsrc/backend/session/ascend_control_parser.cc"
         "../../../mindspore/ccsrc/backend/session/kernel_graph.cc"
         "../../../mindspore/ccsrc/backend/session/session_basic.cc"
+        "../../../mindspore/ccsrc/backend/session/executor.cc"
+        "../../../mindspore/ccsrc/backend/session/executor_manager.cc"
         "../../../mindspore/ccsrc/backend/session/session_factory.cc"
         "../../../mindspore/ccsrc/backend/session/kernel_build_client.cc"
         "../../../mindspore/ccsrc/vm/*.cc"