Imported Upstream version 1.11.0upstream/1.11.0

1 files changed, 139 insertions, 0 deletions

diff --git a/compiler/luci-interpreter/src/kernels/Sub.cpp b/compiler/luci-interpreter/src/kernels/Sub.cpp
new file mode 100644
index 000000000..dd9c1102f
--- /dev/null
+++ b/compiler/luci-interpreter/src/kernels/Sub.cpp
@@ -0,0 +1,139 @@
+/*
+ * Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright 2019 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/Sub.h"
+#include "kernels/Utils.h"
+
+#include <tensorflow/lite/kernels/internal/optimized/legacy_optimized_ops.h>
+
+#include <stdexcept>
+
+namespace luci_interpreter
+{
+namespace kernels
+{
+
+Sub::Sub(const Tensor *input1, const Tensor *input2, Tensor *output, const SubParams &params)
+    : KernelWithParams<SubParams>({input1, input2}, {output}, params)
+{
+}
+
+void Sub::configure()
+{
+  LUCI_INTERPRETER_CHECK(!(input1()->element_type() != input2()->element_type()))
+  output()->resize(calculateShapeForBroadcast(input1()->shape(), input2()->shape()));
+}
+
+void Sub::execute() const
+{
+  switch (input1()->element_type())
+  {
+    case DataType::FLOAT32:
+      evalFloat();
+      break;
+    case DataType::U8:
+      evalQuantized();
+      break;
+    default:
+      throw std::runtime_error("Unsupported type.");
+  }
+}
+
+void Sub::evalFloat() const
+{
+  float activation_min{};
+  float activation_max{};
+  calculateActivationRange(_params.activation, &activation_min, &activation_max);
+
+  tflite::ArithmeticParams params{};
+  params.float_activation_min = activation_min;
+  params.float_activation_max = activation_max;
+
+  const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes(
+      getTensorShape(input1()), getTensorShape(input2()), &params);
+
+  if (need_broadcast)
+  {
+    tflite::reference_ops::BroadcastSubSlow(
+        params, getTensorShape(input1()), getTensorData<float>(input1()), getTensorShape(input2()),
+        getTensorData<float>(input2()), getTensorShape(output()), getTensorData<float>(output()));
+  }
+  else
+  {
+    tflite::optimized_ops::Sub(params, getTensorShape(input1()), getTensorData<float>(input1()),
+                               getTensorShape(input2()), getTensorData<float>(input2()),
+                               getTensorShape(output()), getTensorData<float>(output()));
+  }
+}
+
+void Sub::evalQuantized() const
+{
+  const auto input1_scale = static_cast<double>(input1()->scale());
+  const auto input2_scale = static_cast<double>(input2()->scale());
+  const auto output_scale = static_cast<double>(output()->scale());
+
+  const int left_shift = 20;
+  const double twice_max_input_scale = 2 * std::max(input1_scale, input2_scale);
+  const double real_input1_multiplier = input1_scale / twice_max_input_scale;
+  const double real_input2_multiplier = input2_scale / twice_max_input_scale;
+  const double real_output_multiplier = twice_max_input_scale / ((1 << left_shift) * output_scale);
+
+  int32_t input1_multiplier{}, input2_multiplier{}, output_multiplier{};
+  int input1_shift{}, input2_shift{}, output_shift{};
+  quantizeMultiplierSmallerThanOneExp(real_input1_multiplier, &input1_multiplier, &input1_shift);
+  quantizeMultiplierSmallerThanOneExp(real_input2_multiplier, &input2_multiplier, &input2_shift);
+  quantizeMultiplierSmallerThanOneExp(real_output_multiplier, &output_multiplier, &output_shift);
+
+  int32_t activation_min{};
+  int32_t activation_max{};
+  calculateActivationRangeQuantized(_params.activation, output(), &activation_min, &activation_max);
+
+  tflite::ArithmeticParams params{};
+  params.left_shift = left_shift;
+  // The kernel expects inputs' zero points to be negated.
+  params.input1_offset = -input1()->zero_point(); // Note the '-'.
+  params.input1_multiplier = input1_multiplier;
+  params.input1_shift = input1_shift;
+  params.input2_offset = -input2()->zero_point(); // Note the '-'.
+  params.input2_multiplier = input2_multiplier;
+  params.input2_shift = input2_shift;
+  params.output_offset = output()->zero_point();
+  params.output_multiplier = output_multiplier;
+  params.output_shift = output_shift;
+  params.quantized_activation_min = activation_min;
+  params.quantized_activation_max = activation_max;
+
+  const bool need_broadcast = tflite::reference_ops::ProcessBroadcastShapes(
+      getTensorShape(input1()), getTensorShape(input2()), &params);
+
+  if (need_broadcast)
+  {
+    tflite::reference_ops::BroadcastSubSlow(
+        params, getTensorShape(input1()), getTensorData<uint8_t>(input1()),
+        getTensorShape(input2()), getTensorData<uint8_t>(input2()), getTensorShape(output()),
+        getTensorData<uint8_t>(output()));
+  }
+  else
+  {
+    tflite::reference_ops::Sub(params, getTensorShape(input1()), getTensorData<uint8_t>(input1()),
+                               getTensorShape(input2()), getTensorData<uint8_t>(input2()),
+                               getTensorShape(output()), getTensorData<uint8_t>(output()));
+  }
+}
+
+} // namespace kernels
+} // namespace luci_interpreter