[onert] Introduce trainable_ops attribute to circle_traininfo schema (#…

…13000)

This commit introduces trainable_ops attribute to circle_traininfo
schema to support Transfer learning.

ONE-DCO-1.0-Signed-off-by: Jiyoung Yun <jy910.yun@samsung.com>

runtime/libs/circle-schema/include/circle_traininfo.fbs

@@ -118,6 +118,7 @@ table ModelTraining {
   epochs: int;
   batch_size: int;
   loss_reduction_type : LossReductionType;
+  trainable_ops : [int];
 }
 
 root_type ModelTraining;

runtime/libs/circle-schema/include/circle_traininfo_generated.h

@@ -13,8 +13,8 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+// command: flatc -c --gen-onefile circle_traininfo.fbs
 // automatically generated by the FlatBuffers compiler, do not modify
-// command: flatc -c circle_traininfo.fbs
 
 #ifndef FLATBUFFERS_GENERATED_CIRCLETRAININFO_CIRCLE_H_
 #define FLATBUFFERS_GENERATED_CIRCLETRAININFO_CIRCLE_H_
@@ -494,7 +494,8 @@ struct ModelTraining FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table
     VT_LOSSFN_OPT = 16,
     VT_EPOCHS = 18,
     VT_BATCH_SIZE = 20,
-    VT_LOSS_REDUCTION_TYPE = 22
+    VT_LOSS_REDUCTION_TYPE = 22,
+    VT_TRAINABLE_OPS = 24
   };
   uint32_t version() const { return GetField<uint32_t>(VT_VERSION, 0); }
   circle::Optimizer optimizer() const
@@ -554,6 +555,10 @@ struct ModelTraining FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table
   {
     return static_cast<circle::LossReductionType>(GetField<int8_t>(VT_LOSS_REDUCTION_TYPE, 0));
   }
+  const ::flatbuffers::Vector<int32_t> *trainable_ops() const
+  {
+    return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_TRAINABLE_OPS);
+  }
   bool Verify(::flatbuffers::Verifier &verifier) const
   {
     return VerifyTableStart(verifier) && VerifyField<uint32_t>(verifier, VT_VERSION, 4) &&
@@ -567,7 +572,9 @@ struct ModelTraining FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table
            VerifyLossFnOptions(verifier, lossfn_opt(), lossfn_opt_type()) &&
            VerifyField<int32_t>(verifier, VT_EPOCHS, 4) &&
            VerifyField<int32_t>(verifier, VT_BATCH_SIZE, 4) &&
-           VerifyField<int8_t>(verifier, VT_LOSS_REDUCTION_TYPE, 1) && verifier.EndTable();
+           VerifyField<int8_t>(verifier, VT_LOSS_REDUCTION_TYPE, 1) &&
+           VerifyOffset(verifier, VT_TRAINABLE_OPS) && verifier.VerifyVector(trainable_ops()) &&
+           verifier.EndTable();
   }
 };
 
@@ -649,6 +656,10 @@ struct ModelTrainingBuilder
     fbb_.AddElement<int8_t>(ModelTraining::VT_LOSS_REDUCTION_TYPE,
                             static_cast<int8_t>(loss_reduction_type), 0);
   }
+  void add_trainable_ops(::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> trainable_ops)
+  {
+    fbb_.AddOffset(ModelTraining::VT_TRAINABLE_OPS, trainable_ops);
+  }
   explicit ModelTrainingBuilder(::flatbuffers::FlatBufferBuilder &_fbb) : fbb_(_fbb)
   {
     start_ = fbb_.StartTable();
@@ -669,9 +680,11 @@ inline ::flatbuffers::Offset<ModelTraining> CreateModelTraining(
   circle::LossFn lossfn = circle::LossFn_SPARSE_CATEGORICAL_CROSSENTROPY,
   circle::LossFnOptions lossfn_opt_type = circle::LossFnOptions_NONE,
   ::flatbuffers::Offset<void> lossfn_opt = 0, int32_t epochs = 0, int32_t batch_size = 0,
-  circle::LossReductionType loss_reduction_type = circle::LossReductionType_SumOverBatchSize)
+  circle::LossReductionType loss_reduction_type = circle::LossReductionType_SumOverBatchSize,
+  ::flatbuffers::Offset<::flatbuffers::Vector<int32_t>> trainable_ops = 0)
 {
   ModelTrainingBuilder builder_(_fbb);
+  builder_.add_trainable_ops(trainable_ops);
   builder_.add_batch_size(batch_size);
   builder_.add_epochs(epochs);
   builder_.add_lossfn_opt(lossfn_opt);
@@ -685,6 +698,23 @@ inline ::flatbuffers::Offset<ModelTraining> CreateModelTraining(
   return builder_.Finish();
 }
 
+inline ::flatbuffers::Offset<ModelTraining> CreateModelTrainingDirect(
+  ::flatbuffers::FlatBufferBuilder &_fbb, uint32_t version = 0,
+  circle::Optimizer optimizer = circle::Optimizer_SGD,
+  circle::OptimizerOptions optimizer_opt_type = circle::OptimizerOptions_NONE,
+  ::flatbuffers::Offset<void> optimizer_opt = 0,
+  circle::LossFn lossfn = circle::LossFn_SPARSE_CATEGORICAL_CROSSENTROPY,
+  circle::LossFnOptions lossfn_opt_type = circle::LossFnOptions_NONE,
+  ::flatbuffers::Offset<void> lossfn_opt = 0, int32_t epochs = 0, int32_t batch_size = 0,
+  circle::LossReductionType loss_reduction_type = circle::LossReductionType_SumOverBatchSize,
+  const std::vector<int32_t> *trainable_ops = nullptr)
+{
+  auto trainable_ops__ = trainable_ops ? _fbb.CreateVector<int32_t>(*trainable_ops) : 0;
+  return circle::CreateModelTraining(_fbb, version, optimizer, optimizer_opt_type, optimizer_opt,
+                                     lossfn, lossfn_opt_type, lossfn_opt, epochs, batch_size,
+                                     loss_reduction_type, trainable_ops__);
+}
+
 inline bool VerifyOptimizerOptions(::flatbuffers::Verifier &verifier, const void *obj,
                                    OptimizerOptions type)
 {