[onert] Add unit tests for training Softmax op

This commit adds some tests to validate training of Softmax op.

ONE-DCO-1.0-Signed-off-by: ragmani <[email protected]>

tests/nnfw_api/src/GenModelTests/nontrainable_op_trains/Softmax.test.cc

@@ -0,0 +1,99 @@
+/*
+ * Copyright (c) 2024 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * 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 "GenModelTrain.h"
+
+TEST_F(GenModelTrain, NonTrainableOps_FC_Softmax)
+{
+  // (( Input 0 )) -> [ FC ] -> [ Softmax ] -> (( Output 0 ))
+  {
+    CirclePlusGen cgen;
+
+    uint32_t weight_buf = cgen.addBuffer(std::vector<float>(8 * 2, 0.f));
+    uint32_t bias_buf = cgen.addBuffer(std::vector<float>(8, 0.f));
+    int input = cgen.addTensor({{1, 2}, circle::TensorType::TensorType_FLOAT32});
+    int weight = cgen.addTensor({{8, 2}, circle::TensorType::TensorType_FLOAT32, weight_buf});
+    int bias = cgen.addTensor({{8}, circle::TensorType::TensorType_FLOAT32, bias_buf});
+    int fc_output = cgen.addTensor({{1, 8}, circle::TensorType::TensorType_FLOAT32});
+    int output = cgen.addTensor({{1, 8}, circle::TensorType::TensorType_FLOAT32});
+    const float beta = 1.0f;
+    cgen.addOperatorFullyConnected({{input, weight, bias}, {fc_output}});
+    cgen.addOperatorSoftmax({{fc_output}, {output}}, beta);
+    cgen.setInputsAndOutputs({input}, {output});
+
+    float learning_rate = 0.01f;
+    int32_t batch_size = 1;
+    cgen.addTrainInfo({circle::Optimizer::Optimizer_SGD, learning_rate,
+                       circle::LossFn::LossFn_MEAN_SQUARED_ERROR,
+                       circle::LossReductionType::LossReductionType_SumOverBatchSize, batch_size});
+
+    _context = std::make_unique<GenModelTrainContext>(cgen.finish());
+    _context->addTrainCase(
+      uniformTCD<float>({{{1, 3}}, {{2, 1}}},                                     // inputs
+                        {{{0, 1, 0, 0, 0, 0, 0, 0}}, {{0, 0, 0, 0, 0, 1, 0, 0}}}, // expected
+                        {0.1092f}                                                 // loss
+                        ));
+
+    _context->setBackends({"train"});
+    _context->setEpoch(4);
+
+    SUCCEED();
+  }
+}
+
+TEST_F(GenModelTrain, neg_NonTrainableOps_Softmax_InvalidShape)
+{
+  CirclePlusGen cgen;
+
+  int input = cgen.addTensor({{2, 1}, circle::TensorType::TensorType_FLOAT32});
+  // Invalid shape: output shape should be equal to input shape
+  int output = cgen.addTensor({{2, 2}, circle::TensorType::TensorType_FLOAT32});
+  const float beta = 1.0f;
+  cgen.addOperatorSoftmax({{input}, {output}}, beta);
+  cgen.setInputsAndOutputs({input}, {output});
+
+  float learning_rate = 0.01f;
+  int32_t batch_size = 1;
+  cgen.addTrainInfo({circle::Optimizer::Optimizer_SGD, learning_rate,
+                     circle::LossFn::LossFn_MEAN_SQUARED_ERROR,
+                     circle::LossReductionType::LossReductionType_SumOverBatchSize, batch_size});
+
+  _context = std::make_unique<GenModelTrainContext>(cgen.finish());
+  _context->setBackends({"train"});
+  _context->expectFailCompile();
+}
+
+TEST_F(GenModelTrain, neg_NonTrainableOps_Softmax_InvalidType)
+{
+  CirclePlusGen cgen;
+
+  // Invalid type: input tensor type should be FLOAT32
+  int input = cgen.addTensor({{2, 2}, circle::TensorType::TensorType_INT32});
+  int output = cgen.addTensor({{2, 2}, circle::TensorType::TensorType_FLOAT32});
+  const float beta = 1.0f;
+  cgen.addOperatorSoftmax({{input}, {output}}, beta);
+  cgen.setInputsAndOutputs({input}, {output});
+
+  float learning_rate = 0.01f;
+  int32_t batch_size = 1;
+  cgen.addTrainInfo({circle::Optimizer::Optimizer_SGD, learning_rate,
+                     circle::LossFn::LossFn_MEAN_SQUARED_ERROR,
+                     circle::LossReductionType::LossReductionType_SumOverBatchSize, batch_size});
+
+  _context = std::make_unique<GenModelTrainContext>(cgen.finish());
+  _context->setBackends({"train"});
+  _context->expectFailModelLoad();
+}