[DRAFT][runtime] Support RmsNorm operation

compute/cker/include/cker/Types.h

@@ -316,6 +316,11 @@ struct ResizeBilinearParams
   bool half_pixel_centers;
 };
 
+struct RmsNormParams
+{
+  float epsilon;
+};
+
 struct TransposeConvParams
 {
   PaddingType padding_type;

compute/cker/include/cker/operation/RmsNorm.h

@@ -0,0 +1,69 @@
+/*
+ * 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.
+ */
+
+#ifndef __NNFW_CKER_RMS_NORM_H__
+#define __NNFW_CKER_RMS_NORM_H__
+
+#include "cker/Shape.h"
+#include "cker/Types.h"
+#include "cker/Utils.h"
+
+#include <cmath>
+
+namespace nnfw
+{
+namespace cker
+{
+
+inline void RmsNorm(const RmsNormParams &params, const Shape &input_shape, const float *input_data,
+                    const Shape &gamma_shape, const float *gamma_data, const Shape &output_shape,
+                    float *output_data)
+{
+  const int32_t batches = MatchingDim(input_shape, 0, output_shape, 0);
+  const int32_t heights = MatchingDim(input_shape, 1, output_shape, 1);
+  const int32_t widths = MatchingDim(input_shape, 2, output_shape, 2);
+  const int32_t channels = MatchingDim(input_shape, 3, output_shape, 3);
+
+  UNUSED_RELEASE(gamma_shape);
+
+  for (int32_t batch = 0; batch < batches; batch++)
+  {
+    for (int32_t height = 0; height < heights; height++)
+    {
+      for (int32_t width = 0; width < widths; width++)
+      {
+        double square_sum = 0.0f;
+        for (int32_t channel = 0; channel < channels; channel++)
+        {
+          double input_val = input_data[Offset(input_shape, batch, height, width, channel)];
+          square_sum += (input_val * input_val);
+        }
+        double rms = std::sqrt((square_sum / channels) + params.epsilon);
+        for (int32_t channel = 0; channel < channels; channel++)
+        {
+          double gamma = gamma_data[channel];
+          output_data[Offset(output_shape, batch, height, width, channel)] =
+            gamma * (input_data[Offset(input_shape, batch, height, width, channel)] / rms);
+        }
+      }
+    }
+  }
+}
+
+} // namespace cker
+} // namespace nnfw
+
+#endif // __NNFW_CKER_RMS_NORM_H__