1 files changed, 132 insertions, 0 deletions
diff --git a/compiler/luci-interpreter/src/kernels/FloorMod.cpp b/compiler/luci-interpreter/src/kernels/FloorMod.cpp
new file mode 100644
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+++ b/compiler/luci-interpreter/src/kernels/FloorMod.cpp
@@ -0,0 +1,132 @@
+/*
+ * Copyright (c) 2023 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright 2018 The TensorFlow Authors. 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 "kernels/FloorMod.h"
+#include "kernels/Utils.h"
+
+#include <tensorflow/lite/kernels/internal/reference/binary_function.h>
+#include <cmath>
+
+namespace
+{
+
+template <typename T> T FloorDivFunc(T input1, T input2)
+{
+  struct FloatMod
+  {
+    float operator()(const float lhs, const float rhs) const { return std::fmod(lhs, rhs); }
+  };
+  using ModFunc =
+    typename std::conditional<std::is_integral<T>::value, std::modulus<T>, FloatMod>::type;
+  ModFunc mod_func;
+  T trunc_mod = mod_func(input1, input2);
+  return (trunc_mod != 0) && ((input2 < 0) != (trunc_mod < 0)) ? (trunc_mod + input2) : trunc_mod;
+}
+
+} // namespace
+
+namespace luci_interpreter
+{
+
+namespace kernels
+{
+
+FloorMod::FloorMod(const Tensor *x, const Tensor *y, Tensor *output) : Kernel({x, y}, {output}) {}
+
+void FloorMod::configure()
+{
+  LUCI_INTERPRETER_CHECK(x()->element_type() == output()->element_type());
+  LUCI_INTERPRETER_CHECK(y()->element_type() == output()->element_type());
+
+  output()->resize(calculateShapeForBroadcast(x()->shape(), y()->shape()));
+}
+
+void FloorMod::execute() const
+{
+  switch (x()->element_type())
+  {
+    case DataType::FLOAT32:
+      evalFloat();
+      break;
+    case DataType::S8:
+      evalInteger<int8_t>();
+      break;
+    case DataType::S16:
+      evalInteger<int16_t>();
+      break;
+    case DataType::S32:
+      evalInteger<int32_t>();
+      break;
+    case DataType::S64:
+      evalInteger<int64_t>();
+      break;
+    default:
+      throw std::runtime_error("Unsupported type.");
+  }
+}
+
+void FloorMod::evalFloat() const
+{
+  const auto x_data = getTensorData<float>(x());
+  const auto y_data = getTensorData<float>(y());
+
+  if (x()->shape() != y()->shape())
+  {
+    tflite::reference_ops::BroadcastBinaryFunction4DSlow<float, float, float>(
+      getTensorShape(x()), x_data, getTensorShape(y()), y_data, getTensorShape(output()),
+      getTensorData<float>(output()), FloorDivFunc<float>);
+  }
+  else
+  {
+    tflite::reference_ops::BinaryFunction<float, float, float>(
+      getTensorShape(x()), x_data, getTensorShape(y()), y_data, getTensorShape(output()),
+      getTensorData<float>(output()), FloorDivFunc<float>);
+  }
+}
+
+template <typename T> void FloorMod::evalInteger() const
+{
+  const auto x_data = getTensorData<T>(x());
+  const auto y_data = getTensorData<T>(y());
+
+  // Check the denominator
+  const auto y_data_type = y()->element_type();
+  if (y_data_type == DataType::S8 || y_data_type == DataType::S16 || y_data_type == DataType::S32 ||
+      y_data_type == DataType::S64)
+  {
+    for (int i = 0; i < getTensorShape(y()).FlatSize(); ++i)
+    {
+      LUCI_INTERPRETER_CHECK(y_data[i] != 0);
+    }
+  }
+
+  if (x()->shape() != y()->shape())
+  {
+    tflite::reference_ops::BroadcastBinaryFunction4DSlow<T, T, T>(
+      getTensorShape(x()), x_data, getTensorShape(y()), y_data, getTensorShape(output()),
+      getTensorData<T>(output()), FloorDivFunc<T>);
+  }
+  else
+  {
+    tflite::reference_ops::BinaryFunction<T, T, T>(getTensorShape(x()), x_data, getTensorShape(y()),
+                                                   y_data, getTensorShape(output()),
+                                                   getTensorData<T>(output()), FloorDivFunc<T>);
+  }
+}
+
+} // namespace kernels
+} // namespace luci_interpreter