modify set_dataset_mode_config api param

example/yolov3_coco2017/train.py

@@ -67,7 +67,7 @@ if __name__ == '__main__':
     parser.add_argument("--distribute", type=bool, default=False, help="Run distribute, default is false.")
     parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
     parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.")
-    parser.add_argument("--mode", type=str, default="graph", help="Run graph mode or feed mode, default is graph")
+    parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or non-sink mode, default is sink")
     parser.add_argument("--epoch_size", type=int, default=10, help="Epoch size, default is 10")
     parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
     parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path")
@@ -150,8 +150,8 @@ if __name__ == '__main__':
 
         model = Model(net)
         dataset_sink_mode = False
-        if args_opt.mode == "graph":
-            print("In graph mode, one epoch return a loss.")
+        if args_opt.mode == "sink":
+            print("In sink mode, one epoch return a loss.")
             dataset_sink_mode = True
         print("Start train YOLOv3, the first epoch will be slower because of the graph compilation.")
         model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode)

mindspore/ccsrc/pipeline/pipeline_ge.cc

@@ -116,7 +116,7 @@ bool InitExecDatasetGe(const std::string& queue_name, int64_t size, int64_t batc
     return transform::TransformUtil::ConvertDataType(i->type_id());
   });
 
-  ConfigManager::GetInstance().set_dataset_mode(DatasetMode::DS_GRAPH_MODE);
+  ConfigManager::GetInstance().set_dataset_mode(DatasetMode::DS_SINK_MODE);
   ConfigManager::GetInstance().set_iter_num(size);
   ConfigManager::GetInstance().set_dataset_phase(phase);
 
@@ -453,8 +453,8 @@ void ProcessGeArg(const std::map<std::string, ExecutorInfoPtr>& info, const py::
   }
 
   // process the first args of tensor
-  // only in Dataset Feed Mode, fp_bp graph need input tensors
-  if (ConfigManager::GetInstance().dataset_mode() == DS_FEED_MODE) {
+  // only in Dataset non-sink Mode, fp_bp graph need input tensors
+  if (ConfigManager::GetInstance().dataset_mode() == DS_NORMAL_MODE) {
     for (std::size_t i = 0; i < size; i++) {
       ValuePtr converted = nullptr;
       bool succ = parse::ConvertData(args[i], &converted);

mindspore/ccsrc/transform/convert.cc

@@ -440,10 +440,10 @@ void DfGraphConvertor::InitLoopVar(std::vector<ge::Operator> *init_input) {
 
     int64_t value = 0;
     auto const_iter_num = std::make_shared<Constant>("const/npu_runconfig/iterations_per_loop");
-    if (ConfigManager::GetInstance().dataset_mode() == DS_GRAPH_MODE) {
+    if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) {
       value = ConfigManager::GetInstance().iter_num();
     } else {
-      MS_LOG(INFO) << "Run with feed mode, the iterator number will always be 1";
+      MS_LOG(INFO) << "Run with non-sink mode, the iterator number will always be 1";
       value = 1;
       ConfigManager::GetInstance().set_iter_num(value);
     }
@@ -574,7 +574,7 @@ void DfGraphConvertor::SetupParamInitSubGraph(const TensorOrderMap &tensors, std
 
 void DfGraphConvertor::MakeDatasetHandler(const std::string &name, const size_t &input_idx, const AnfNodePtr &it) {
   MS_LOG(INFO) << "The " << name << " is the " << input_idx << "(st/nd/th) input";
-  if (ConfigManager::GetInstance().dataset_mode() == DS_GRAPH_MODE) {
+  if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) {
     auto getnext_idx = static_cast<int64_t>(input_idx);
     DatasetGraphParam param = ConfigManager::GetInstance().dataset_param();
     if (!param.input_indexes().empty() && input_idx <= param.input_indexes().size()) {
@@ -866,7 +866,7 @@ DfGraphConvertor &DfGraphConvertor::ConvertAllNode() {
   }
 
   // Create dataset iterator and iterator_getnext node
-  if (ConfigManager::GetInstance().dataset_mode() == DS_GRAPH_MODE) {
+  if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) {
     DatasetGraphParam param = ConfigManager::GetInstance().dataset_param();
     MS_LOG(INFO) << "Dataset param is " << param.ToString() << ".";
     // GetNext
@@ -975,7 +975,7 @@ void DfGraphConvertor::TraceOutputFromParameter(const AnfNodePtr &anf_out) {
 }
 
 void SetupDatasetIterGetNextNode(const OperatorPtr &op) {
-  if (ConfigManager::GetInstance().dataset_mode() == DS_GRAPH_MODE) {
+  if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) {
     DatasetGraphParam param = ConfigManager::GetInstance().dataset_param();
     size_t output_num = param.ge_types().size();
     MS_LOG(INFO) << "Set iterator_getnext op's output num = " << output_num << ".";
@@ -1034,7 +1034,7 @@ DfGraphConvertor &DfGraphConvertor::BuildGraph() {
 
   // set graph input according to the order from anf graph
   std::vector<Operator> inputs;
-  if (ConfigManager::GetInstance().dataset_mode() == DS_GRAPH_MODE) {
+  if (ConfigManager::GetInstance().dataset_mode() == DS_SINK_MODE) {
     inputs.push_back(*dataset_iter_getnext_);
   } else {
     auto params = anf_graph_->parameters();

mindspore/ccsrc/utils/config_manager.cc

@@ -28,7 +28,7 @@ ConfigManager& ConfigManager::GetInstance() noexcept {
 }
 
 void ConfigManager::SetDatasetModeConfig(const std::string& mode) {
-  static const std::map<std::string, DatasetMode> mode_map = {{"feed", DS_FEED_MODE}, {"graph", DS_GRAPH_MODE}};
+  static const std::map<std::string, DatasetMode> mode_map = {{"normal", DS_NORMAL_MODE}, {"sink", DS_SINK_MODE}};
   if (mode_map.find(mode) == mode_map.end()) {
     MS_LOG(ERROR) << "Invalid dataset mode:" << mode;
     return;
@@ -38,7 +38,7 @@ void ConfigManager::SetDatasetModeConfig(const std::string& mode) {
 
 void ConfigManager::ResetConfig() noexcept {
   parallel_strategy_ = ONE_DEVICE;
-  dataset_mode_ = DS_FEED_MODE;
+  dataset_mode_ = DS_NORMAL_MODE;
   dataset_param_ = DatasetGraphParam("", 0, 0, {}, {}, {});
   iter_num_ = 1;
 }

mindspore/ccsrc/utils/config_manager.h

@@ -33,7 +33,7 @@ enum ParallelStrategy {
   DISTRIBUTION,
 };
 
-enum DatasetMode { DS_FEED_MODE = 0, DS_GRAPH_MODE };
+enum DatasetMode { DS_NORMAL_MODE = 0, DS_SINK_MODE };
 
 class DatasetGraphParam {
  public:
@@ -106,7 +106,7 @@ class ConfigManager {
   ~ConfigManager() = default;
 
   ParallelStrategy parallel_strategy_{ONE_DEVICE};
-  DatasetMode dataset_mode_{DS_FEED_MODE};
+  DatasetMode dataset_mode_{DS_NORMAL_MODE};
   DatasetGraphParam dataset_param_{"", 0, 0, {}, {}, {}};
   int64_t iter_num_{1};
   std::string dataset_phase_{""};

mindspore/common/api.py

@@ -381,9 +381,9 @@ class _Executor:
         if enable_ge:
             # decide whether to sink based on whether the inputs is virtual or not
             if args_list and isinstance(args_list[0], Tensor) and args_list[0].virtual_flag:
-                _set_dataset_mode_config('graph')
+                _set_dataset_mode_config('sink')
             else:
-                _set_dataset_mode_config('feed')
+                _set_dataset_mode_config('normal')
 
             self._build_data_graph(obj, params, phase)
 

mindspore/nn/wrap/loss_scale.py

@@ -43,7 +43,7 @@ class DynamicLossScaleUpdateCell(Cell):
     In every training step, the loss scaling value  will be updated by loss scaling value/`scale_factor`
     when there is overflow. And it will be increased by loss scaling value * `scale_factor` if there is no
     overflow for a continuous `scale_window` steps. This cell is used for Graph mode training in which all
-    logic will be executed on device side(Another training mode is feed mode in which some logic will be
+    logic will be executed on device side(Another training mode is non-sink mode in which some logic will be
     executed on host).
 
     Args:

tests/ut/python/utils/test_callback.py

@@ -24,11 +24,12 @@ from mindspore import context
 from mindspore.common.tensor import Tensor
 from mindspore.nn.optim import Momentum
 from mindspore.nn import TrainOneStepCell, WithLossCell
-from mindspore.train.callback import ModelCheckpoint, _check_file_name_prefix, RunContext,_checkpoint_cb_for_save_op,\
-                                      LossMonitor, _InternalCallbackParam, _chg_ckpt_file_name_if_same_exist,\
-                                      _build_callbacks, CheckpointConfig, _set_cur_net
+from mindspore.train.callback import ModelCheckpoint, _check_file_name_prefix, RunContext, _checkpoint_cb_for_save_op, \
+    LossMonitor, _InternalCallbackParam, _chg_ckpt_file_name_if_same_exist, \
+    _build_callbacks, CheckpointConfig, _set_cur_net
 from mindspore.common.api import ms_function
 
+
 class Net(nn.Cell):
     """Net definition."""
 
@@ -52,6 +53,7 @@ class Net(nn.Cell):
 
 class LossNet(nn.Cell):
     """ LossNet definition """
+
     def __init__(self):
         super(LossNet, self).__init__()
         self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal', pad_mode='valid')
@@ -110,8 +112,8 @@ def test_save_checkpoint():
         os.remove('./test_files/test_ckpt-model.pkl')
 
 
-def test_loss_monitor_graph_model():
-    """Test lossmonitor Graph model."""
+def test_loss_monitor_sink_model():
+    """Test loss monitor sink model."""
     cb_params = _InternalCallbackParam()
     cb_params.cur_epoch_num = 4
     cb_params.cur_step_num = 2
@@ -129,8 +131,8 @@ def test_loss_monitor_graph_model():
     callbacklist.end(run_context)
 
 
-def test_Loss_Monitor_feed_feed_model():
-    """Test Loss Monitor feed feed mode."""
+def test_loss_monitor_feed_model():
+    """Test loss monitor non-sink mode."""
     cb_params = _InternalCallbackParam()
     run_context = RunContext(cb_params)
     loss_cb = LossMonitor(1)