1 files changed, 113 insertions, 0 deletions
diff --git a/compiler/luci-interpreter/src/kernels/Equal.cpp b/compiler/luci-interpreter/src/kernels/Equal.cpp
new file mode 100644
index 000000000..f58de1250
--- /dev/null
+++ b/compiler/luci-interpreter/src/kernels/Equal.cpp
@@ -0,0 +1,113 @@
+/*
+ * Copyright (c) 2020 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 "kernels/Equal.h"
+#include "kernels/Utils.h"
+
+#include <tensorflow/lite/kernels/internal/reference/comparisons.h>
+
+#include <stdexcept>
+
+namespace luci_interpreter
+{
+
+namespace kernels
+{
+
+Equal::Equal(const Tensor *x, const Tensor *y, Tensor *output) : Kernel({x, y}, {output}) {}
+
+void Equal::configure()
+{
+  LUCI_INTERPRETER_CHECK(x()->element_type() == y()->element_type());
+  LUCI_INTERPRETER_CHECK(output()->element_type() == DataType::BOOL);
+
+  if (x()->element_type() == DataType::U8)
+  {
+    quantizeMultiplierSmallerThanOneExp(x()->scale(), &_x_multiplier, &_x_shift);
+    quantizeMultiplierSmallerThanOneExp(y()->scale(), &_y_multiplier, &_y_shift);
+  }
+  output()->resize(calculateShapeForBroadcast(x()->shape(), y()->shape()));
+}
+
+void Equal::execute() const
+{
+  switch (x()->element_type())
+  {
+    case DataType::FLOAT32:
+      evalFloat();
+      break;
+    case DataType::U8:
+      evalQuantized();
+      break;
+    default:
+      throw std::runtime_error("Unsupported type.");
+  }
+}
+
+void Equal::evalFloat() const
+{
+  const auto x_data = getTensorData<float>(x());
+  const auto y_data = getTensorData<float>(y());
+  auto output_data = getTensorData<bool>(output());
+
+  tflite::ComparisonParams op_params;
+  op_params.is_broadcast = x()->shape() != y()->shape();
+
+  if (op_params.is_broadcast)
+  {
+    tflite::reference_ops::Broadcast4DSlowEqual(op_params, getTensorShape(x()), x_data,
+                                                getTensorShape(y()), y_data,
+                                                getTensorShape(output()), output_data);
+  }
+  else
+  {
+    tflite::reference_ops::Equal(op_params, getTensorShape(x()), x_data, getTensorShape(y()),
+                                 y_data, getTensorShape(output()), output_data);
+  }
+}
+
+void Equal::evalQuantized() const
+{
+  const auto x_data = getTensorData<uint8_t>(x());
+  const auto y_data = getTensorData<uint8_t>(y());
+  auto output_data = getTensorData<bool>(output());
+
+  tflite::ComparisonParams op_params;
+  op_params.left_shift = 8;
+  op_params.input1_offset = -x()->zero_point(); // Note the '-'
+  op_params.input1_shift = _x_shift;
+  op_params.input1_multiplier = _x_multiplier;
+  op_params.input2_offset = -y()->zero_point(); // Note the '-'
+  op_params.input2_shift = _y_shift;
+  op_params.input2_multiplier = _y_multiplier;
+  op_params.is_broadcast = x()->shape() != y()->shape();
+
+  if (op_params.is_broadcast)
+  {
+    tflite::reference_ops::Broadcast4DSlowEqualWithScaling(op_params, getTensorShape(x()), x_data,
+                                                           getTensorShape(y()), y_data,
+                                                           getTensorShape(output()), output_data);
+  }
+  else
+  {
+    tflite::reference_ops::EqualWithScaling(op_params, getTensorShape(x()), x_data,
+                                            getTensorShape(y()), y_data, getTensorShape(output()),
+                                            output_data);
+  }
+}
+
+} // namespace kernels
+} // namespace luci_interpreter