only cast when level is O2

mindspore/ccsrc/operator/composite/unpack_call.h

@@ -35,7 +35,6 @@
 namespace mindspore {
 // namespace to support composite operators definition
 namespace prim {
-
 // Expand the tuple and dict parameters generated when parsing the function call,
 // and generate positional parameters and key-value pairs for function.
 class UnpackCall : public MetaFuncGraph {
@@ -47,7 +46,6 @@ class UnpackCall : public MetaFuncGraph {
   friend bool operator==(const UnpackCall &lhs, const UnpackCall &rhs) { return lhs.name_ == rhs.name_; }
 };
 using UnpackCallPtr = std::shared_ptr<UnpackCall>;
-
 }  // namespace prim
 }  // namespace mindspore
 

mindspore/ccsrc/pipeline/pipeline.cc

@@ -300,6 +300,10 @@ void ExecutorPy::SaveCompiledGraphToPb(const std::string &phase_s) {
   // save the graph to file in protobuf format
   FuncGraphPtr func_graph = info_[phase_s]->resource->func_graph();
   MS_EXCEPTION_IF_NULL(func_graph);
+  if (phase_s.empty()) {
+    MS_LOG(ERROR) << "`phase` is empty '" << phase_s << "'!";
+    return;
+  }
   std::string name_prefix = phase_s.substr(0, phase_s.find("."));
   std::string pb_filename = std::string("ms_output_") + name_prefix + ".pb";
   std::string filename = GetFilePathName(pb_filename);

mindspore/nn/wrap/cell_wrapper.py

@@ -304,15 +304,19 @@ class WithEvalCell(Cell):
         >>> eval_net = nn.WithEvalCell(net, loss_fn)
     """
 
-    def __init__(self, network, loss_fn):
+    def __init__(self, network, loss_fn, add_cast_fp32=False):
         super(WithEvalCell, self).__init__(auto_prefix=False)
         self._network = network
         self._loss_fn = loss_fn
+        self.add_cast_fp32 = add_cast_fp32
+
 
     def construct(self, data, label):
         outputs = self._network(data)
-        label = _mp_cast_helper(mstype.float32, label)
-        loss = self._loss_fn(F.cast(outputs, mstype.float32), label)
+        if self.add_cast_fp32:
+            label = _mp_cast_helper(mstype.float32, label)
+            outputs = F.cast(outputs, mstype.float32)
+        loss = self._loss_fn(outputs, label)
         return loss, outputs, label
 
 

mindspore/train/model.py

@@ -162,7 +162,7 @@ class Model:
         else:
             if self._loss_fn is None:
                 raise ValueError("loss_fn can not be None.")
-            self._eval_network = nn.WithEvalCell(self._network, self._loss_fn)
+            self._eval_network = nn.WithEvalCell(self._network, self._loss_fn, self._amp_level == "O2")
             self._eval_indexes = [0, 1, 2]
 
     def _build_predict_network(self):