Rename kTraining -> training

Author: IvanYashchuk

torch/csrc/jit/codegen/cuda/python_frontend/fusion_record.h

@@ -1353,14 +1353,14 @@ struct BatchNormOpRecord : RecordFunctor {
   BatchNormOpRecord(
       std::vector<State> args,
       std::vector<State> outputs,
-      bool kTraining,
+      bool training,
       bool channels_last)
       : RecordFunctor(
             std::move(args),
             std::move(outputs),
             "ops.batch_norm",
             RecordType::BatchNormOp),
-        kTraining_(kTraining),
+        training_(training),
         channels_last_(channels_last) {}
   virtual ~BatchNormOpRecord() = default;
   virtual RecordFunctor* clone() final {
@@ -1371,15 +1371,15 @@ struct BatchNormOpRecord : RecordFunctor {
     auto result = false;
     if (auto child_ptr = dynamic_cast<const BatchNormOpRecord*>(&other)) {
       result = RecordFunctor::operator==(other);
-      result = result && (kTraining_ == child_ptr->kTraining_);
+      result = result && (training_ == child_ptr->training_);
       result = result && (channels_last_ == child_ptr->channels_last_);
     }
     return result;
   }
 
   virtual size_t hash() const final {
     auto result = RecordFunctor::hash();
-    return result | (static_cast<size_t>(kTraining_) << 28) |
+    return result | (static_cast<size_t>(training_) << 28) |
         (static_cast<size_t>(channels_last_) << 29);
   }
 
@@ -1399,7 +1399,7 @@ struct BatchNormOpRecord : RecordFunctor {
         bias,
         running_mean,
         running_var,
-        kTraining_,
+        training_,
         momentum,
         eps,
         channels_last_);
@@ -1409,7 +1409,7 @@ struct BatchNormOpRecord : RecordFunctor {
   }
 
  private:
-  bool kTraining_;
+  bool training_;
   bool channels_last_;
 };
 

torch/csrc/jit/codegen/cuda/python_frontend/python_bindings.cpp

@@ -1277,7 +1277,7 @@ void initNvFuserPythonBindings(PyObject* module) {
          nvfuser::Tensor bias,
          nvfuser::Tensor running_mean,
          nvfuser::Tensor running_var,
-         bool kTraining,
+         bool training,
          nvfuser::Scalar momentum,
          nvfuser::Scalar eps,
          bool channels_last) -> decltype(auto) {
@@ -1297,7 +1297,7 @@ void initNvFuserPythonBindings(PyObject* module) {
             {fd->recordingState(output()),
              fd->recordingState(mean()),
              fd->recordingState(invstd())},
-            kTraining,
+            training,
             channels_last));
         return std::make_tuple(output, mean, invstd);
       },
@@ -1306,7 +1306,7 @@ void initNvFuserPythonBindings(PyObject* module) {
       py::arg("bias").none(true),
       py::arg("running_mean").none(true),
       py::arg("running_var").none(true),
-      py::arg("kTraining"),
+      py::arg("training"),
       py::arg("momentum"),
       py::arg("eps"),
       py::arg("channels_last") = false,