STT-tensorflow/tensorflow/lite/kernels/add_test.cc

/* Copyright 2017 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 <stddef.h>
#include <stdint.h>

#include <vector>

#include <gtest/gtest.h>
#include "flatbuffers/flatbuffers.h"  // from @flatbuffers
#include "tensorflow/lite/kernels/test_util.h"
#include "tensorflow/lite/schema/schema_generated.h"

namespace tflite {
namespace {

using ::testing::ElementsAreArray;

class BaseAddOpModel : public SingleOpModel {
 public:
  BaseAddOpModel(const TensorData& input1, const TensorData& input2,
                 const TensorData& output,
                 ActivationFunctionType activation_type) {
    input1_ = AddInput(input1);
    input2_ = AddInput(input2);
    output_ = AddOutput(output);
    SetBuiltinOp(BuiltinOperator_ADD, BuiltinOptions_AddOptions,
                 CreateAddOptions(builder_, activation_type).Union());
    BuildInterpreter({GetShape(input1_), GetShape(input2_)});
  }

  int input1() { return input1_; }
  int input2() { return input2_; }

 protected:
  int input1_;
  int input2_;
  int output_;
};

class FloatAddOpModel : public BaseAddOpModel {
 public:
  using BaseAddOpModel::BaseAddOpModel;

  std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
};

class IntegerAddOpModel : public BaseAddOpModel {
 public:
  using BaseAddOpModel::BaseAddOpModel;

  std::vector<int32_t> GetOutput() { return ExtractVector<int32_t>(output_); }
};

class QuantizedAddOpModel : public BaseAddOpModel {
 public:
  using BaseAddOpModel::BaseAddOpModel;

  template <typename integer_dtype>
  std::vector<float> GetDequantizedOutput() {
    return Dequantize<integer_dtype>(ExtractVector<integer_dtype>(output_),
                                     GetScale(output_), GetZeroPoint(output_));
  }

  std::vector<float> GetDequantizedOutputInt16() {
    return Dequantize<int16_t>(ExtractVector<int16_t>(output_),
                               GetScale(output_), GetZeroPoint(output_));
  }
};

// for quantized Add, the error shouldn't exceed step
float GetTolerance(float min, float max) {
  float kQuantizedStep = (max - min) / 255.0;
  return kQuantizedStep;
}

float GetToleranceInt16(float min, float max) {
  float kQuantizedStep = (max - min) / 32767.f;
  return kQuantizedStep;
}

TEST(FloatAddOpModel, NoActivation) {
  FloatAddOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}},
                    {TensorType_FLOAT32, {1, 2, 2, 1}},
                    {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
  m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
  m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
  m.Invoke();
  EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.9, 0.4, 1.0, 1.3}));
}

TEST(FloatAddOpModel, ActivationRELU_N1_TO_1) {
  FloatAddOpModel m(
      {TensorType_FLOAT32, {1, 2, 2, 1}}, {TensorType_FLOAT32, {1, 2, 2, 1}},
      {TensorType_FLOAT32, {}}, ActivationFunctionType_RELU_N1_TO_1);
  m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
  m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
  m.Invoke();
  EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.0, 0.4, 1.0, 1.0}));
}

TEST(FloatAddOpModel, VariousInputShapes) {
  std::vector<std::vector<int>> test_shapes = {
      {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
  for (size_t i = 0; i < test_shapes.size(); ++i) {
    FloatAddOpModel m({TensorType_FLOAT32, test_shapes[i]},
                      {TensorType_FLOAT32, test_shapes[i]},
                      {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
    m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
    m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5, 1.1, 0.1});
    m.Invoke();
    EXPECT_THAT(m.GetOutput(),
                ElementsAreArray({-1.9, 0.4, 1.0, 1.3, 2.2, 2.1}))
        << "With shape number " << i;
  }
}

TEST(FloatAddOpModel, WithBroadcast) {
  std::vector<std::vector<int>> test_shapes = {
      {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
  for (size_t i = 0; i < test_shapes.size(); ++i) {
    FloatAddOpModel m({TensorType_FLOAT32, test_shapes[i]},
                      {TensorType_FLOAT32, {}},  // always a scalar
                      {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
    m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
    m.PopulateTensor<float>(m.input2(), {0.1});
    m.Invoke();
    EXPECT_THAT(
        m.GetOutput(),
        ElementsAreArray(ArrayFloatNear({-1.9, 0.3, 0.8, 0.9, 1.2, 2.1})))
        << "With shape number " << i;
  }
}

TEST(FloatAddOpModel, WithBroadcastGeneric) {
  std::vector<int> test_shape1 = {1, 3, 1};
  std::vector<int> test_shape2 = {2, 1, 2};
  FloatAddOpModel m({TensorType_FLOAT32, test_shape1},
                    {TensorType_FLOAT32, test_shape2}, {TensorType_FLOAT32, {}},
                    ActivationFunctionType_NONE);
  m.PopulateTensor<float>(m.input1(), {0.1, 0.2, 0.3});
  m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.4});
  m.Invoke();
  EXPECT_THAT(m.GetOutput(),
              ElementsAreArray(ArrayFloatNear({0.2, 0.3, 0.3, 0.4, 0.4, 0.5,
                                               0.4, 0.5, 0.5, 0.6, 0.6, 0.7})));
}

TEST(FloatAddOpModel, MixedBroadcast) {
  const std::vector<int> base_shape = {2, 3, 1, 2};
  std::vector<std::vector<int>> test_shapes = {
      {1, 1, 3, 2}, {1, 3, 1, 2}, {2, 1, 3, 1}, {2, 3, 1, 1}};
  std::vector<std::vector<float>> test_outputs = {
      {-0.1f, 2.6f,  -0.7f, 2.8f, 0.7f,  3.2f, 1.1f,  0.8f, 0.5f,
       1.0f,  1.9f,  1.4f,  1.0f, -0.8f, 0.4f, -0.6f, 1.8f, -0.2f,
       1.4f,  3.1f,  0.8f,  3.3f, 2.2f,  3.7f, -1.4f, 0.3f, -2.0f,
       0.5f,  -0.6f, 0.9f,  0.9f, -1.9f, 0.3f, -1.7f, 1.7f, -1.3f},
      {-0.1f, 2.6f, 0.5f, 1.0f, 1.8f, -0.2f, 1.4f, 3.1f, -2.0f, 0.5f, 1.7f,
       -1.3f},
      {-0.1f, 2.5f,  0.0f, 2.6f, -0.7f, 1.9f, 1.1f,  0.7f, 1.2f,
       0.8f,  0.5f,  0.1f, 1.0f, -0.9f, 1.1f, -0.8f, 0.4f, -1.5f,
       1.7f,  3.3f,  2.2f, 3.8f, 2.1f,  3.7f, -1.1f, 0.5f, -0.6f,
       1.0f,  -0.7f, 0.9f, 1.2f, -1.7f, 1.7f, -1.2f, 1.6f, -1.3f},
      {-0.1f, 2.5f, 1.2f, 0.8f, 0.4f, -1.5f, 1.7f, 3.3f, -0.6f, 1.0f, 1.6f,
       -1.3f}};
  for (size_t i = 0; i < test_shapes.size(); ++i) {
    FloatAddOpModel model_fixture(
        {TensorType_FLOAT32, base_shape}, {TensorType_FLOAT32, test_shapes[i]},
        {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
    model_fixture.PopulateTensor<float>(
        model_fixture.input1(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
                                 2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
    model_fixture.PopulateTensor<float>(model_fixture.input2(),
                                        {0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
    model_fixture.Invoke();
    EXPECT_THAT(model_fixture.GetOutput(),
                ElementsAreArray(ArrayFloatNear(test_outputs[i], 0.0001f)))
        << "With shape number " << i;
  }
  // Re-run with exchanged inputs.
  for (size_t i = 0; i < test_shapes.size(); ++i) {
    FloatAddOpModel model_fixture(
        {TensorType_FLOAT32, test_shapes[i]}, {TensorType_FLOAT32, base_shape},
        {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
    model_fixture.PopulateTensor<float>(model_fixture.input1(),
                                        {0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
    model_fixture.PopulateTensor<float>(
        model_fixture.input2(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
                                 2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
    model_fixture.Invoke();
    EXPECT_THAT(model_fixture.GetOutput(),
                ElementsAreArray(ArrayFloatNear(test_outputs[i], 0.0001f)))
        << "With shape number " << i;
  }
}

TEST(IntegerAddOpModel, NoActivation) {
  IntegerAddOpModel m({TensorType_INT32, {1, 2, 2, 1}},
                      {TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}},
                      ActivationFunctionType_NONE);
  m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8});
  m.PopulateTensor<int32_t>(m.input2(), {1, 2, 3, 5});
  m.Invoke();
  EXPECT_THAT(m.GetOutput(), ElementsAreArray({-19, 4, 10, 13}));
}

TEST(IntegerAddOpModel, ActivationRELU_N1_TO_1) {
  IntegerAddOpModel m({TensorType_INT32, {1, 2, 2, 1}},
                      {TensorType_INT32, {1, 2, 2, 1}}, {TensorType_INT32, {}},
                      ActivationFunctionType_RELU_N1_TO_1);
  m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8});
  m.PopulateTensor<int32_t>(m.input2(), {1, 2, 3, 5});
  m.Invoke();
  EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1, 1, 1, 1}));
}

TEST(IntegerAddOpModel, VariousInputShapes) {
  std::vector<std::vector<int>> test_shapes = {
      {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
  for (size_t i = 0; i < test_shapes.size(); ++i) {
    IntegerAddOpModel m({TensorType_INT32, test_shapes[i]},
                        {TensorType_INT32, test_shapes[i]},
                        {TensorType_INT32, {}}, ActivationFunctionType_NONE);
    m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8, 11, 20});
    m.PopulateTensor<int32_t>(m.input2(), {1, 2, 3, 5, 11, 1});
    m.Invoke();
    EXPECT_THAT(m.GetOutput(), ElementsAreArray({-19, 04, 10, 13, 22, 21}))
        << "With shape number " << i;
  }
}

TEST(IntegerAddOpModel, WithBroadcast) {
  std::vector<std::vector<int>> test_shapes = {
      {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
  for (size_t i = 0; i < test_shapes.size(); ++i) {
    IntegerAddOpModel m({TensorType_INT32, test_shapes[i]},
                        {TensorType_INT32, {}},  // always a scalar
                        {TensorType_INT32, {}}, ActivationFunctionType_NONE);
    m.PopulateTensor<int32_t>(m.input1(), {-20, 2, 7, 8, 11, 20});
    m.PopulateTensor<int32_t>(m.input2(), {1});
    m.Invoke();
    EXPECT_THAT(m.GetOutput(),
                ElementsAreArray(ArrayFloatNear({-19, 3, 8, 9, 12, 21})))
        << "With shape number " << i;
  }
}

TEST(IntegerAddOpModel, Int32MultiDimBroadcast) {
  IntegerAddOpModel m({TensorType_INT32, {1, 2}}, {TensorType_INT32, {2, 1}},
                      {TensorType_INT32, {}}, ActivationFunctionType_NONE);
  m.PopulateTensor<int32_t>(m.input1(), {3, 5});
  m.PopulateTensor<int32_t>(m.input2(), {1, 4});
  m.Invoke();
  EXPECT_THAT(m.GetOutput(), ElementsAreArray({4, 6, 7, 9}));
}

TEST(IntegerAddOpModel, Float32MultiDimBroadcast) {
  FloatAddOpModel m({TensorType_FLOAT32, {1, 2}}, {TensorType_FLOAT32, {2, 1}},
                    {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE);
  m.PopulateTensor<float>(m.input1(), {3, 5});
  m.PopulateTensor<float>(m.input2(), {1, 4});
  m.Invoke();
  EXPECT_THAT(m.GetOutput(), ElementsAreArray({4, 6, 7, 9}));
}

template <TensorType tensor_type, typename integer_dtype>
void QuantizedTestsNoActivation() {
  float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
  std::vector<std::vector<float>> inputs1 = {
      {0.1, 0.2, 0.3, 0.4}, {-0.8, 0.2, 0.4, 0.7}, {-0.8, 0.2, 0.7, 0.3}};
  std::vector<std::vector<float>> inputs2 = {
      {0.6, 0.4, 0.3, 0.1}, {0.6, 0.4, 0.5, -0.8}, {0.6, 0.4, -0.8, 0.5}};
  std::vector<std::vector<float>> results = {
      {0.7, 0.6, 0.6, 0.5}, {-0.2, 0.6, 0.9, -0.1}, {-0.2, 0.6, -0.1, 0.8}};
  for (size_t i = 0; i < inputs1.size(); ++i) {
    QuantizedAddOpModel m({tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
                          {tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
                          {tensor_type, {}, -1.0, 1.0},
                          ActivationFunctionType_NONE);
    m.QuantizeAndPopulate<integer_dtype>(m.input1(), inputs1[i]);
    m.QuantizeAndPopulate<integer_dtype>(m.input2(), inputs2[i]);
    m.Invoke();
    EXPECT_THAT(
        m.GetDequantizedOutput<integer_dtype>(),
        ElementsAreArray(ArrayFloatNear(results[i], kQuantizedTolerance)))
        << "With test number " << i;
  }
}

TEST(QuantizedAddOpModel, QuantizedTestsNoActivationUInt8) {
  QuantizedTestsNoActivation<TensorType_UINT8, uint8_t>();
}

TEST(QuantizedAddOpModel, QuantizedTestsNoActivationInt8) {
  QuantizedTestsNoActivation<TensorType_INT8, int8_t>();
}

TEST(QuantizedAddOpModel, QuantizedTestsNoActivationInt16) {
  const float kMin = -1.f;
  const float kMax = 32767.f / 32768.f;
  float kQuantizedTolerance = GetToleranceInt16(kMin, kMax);
  std::vector<std::vector<float>> inputs1 = {{0.1, 0.2, 0.3, 0.4, 0.9, 0.7},
                                             {-0.8, 0.2, 0.4, 0.7, 0.1, 0.0},
                                             {-0.8, 0.2, 0.7, 0.3, 0.9, 0.1}};
  std::vector<std::vector<float>> inputs2 = {{0.6, 0.4, 0.3, 0.1, -0.1, 0.3},
                                             {0.6, 0.4, 0.5, -0.8, 0.0, -1.0},
                                             {0.6, 0.4, -0.8, 0.5, -0.9, 0.1}};
  std::vector<std::vector<float>> results = {{0.7, 0.6, 0.6, 0.5, 0.8, 1.0},
                                             {-0.2, 0.6, 0.9, -0.1, 0.1, -1.0},
                                             {-0.2, 0.6, -0.1, 0.8, 0.0, 0.2}};
  for (size_t i = 0; i < inputs1.size(); ++i) {
    QuantizedAddOpModel m({TensorType_INT16, {1, 2, 3, 1}, kMin, kMax},
                          {TensorType_INT16, {1, 2, 3, 1}, kMin, kMax},
                          {TensorType_INT16, {}, kMin, kMax},
                          ActivationFunctionType_NONE);
    m.QuantizeAndPopulate<int16_t>(m.input1(), inputs1[i]);
    m.QuantizeAndPopulate<int16_t>(m.input2(), inputs2[i]);
    m.Invoke();
    EXPECT_THAT(
        m.GetDequantizedOutputInt16(),
        ElementsAreArray(ArrayFloatNear(results[i], kQuantizedTolerance)))
        << "With test number " << i;
  }
}

template <enum TensorType tensor_type, typename integer_dtype>
void QuantizedTestsActivationRELU_N1_TO_1() {
  float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
  std::vector<std::vector<float>> inputs1 = {{-0.8, 0.2, 0.9, 0.7},
                                             {-0.8, 0.2, 0.7, 0.3}};
  std::vector<std::vector<float>> inputs2 = {{0.6, 0.4, 0.9, -0.8},
                                             {0.6, 0.4, -0.8, 0.5}};
  std::vector<std::vector<float>> results = {{-0.2, 0.6, 1.0, -0.1},
                                             {-0.2, 0.6, -0.1, 0.8}};
  for (size_t i = 0; i < inputs1.size(); ++i) {
    QuantizedAddOpModel m({tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
                          {tensor_type, {1, 2, 2, 1}, -1.0, 1.0},
                          {tensor_type, {}, -1.0, 1.0},
                          ActivationFunctionType_RELU_N1_TO_1);
    m.QuantizeAndPopulate<integer_dtype>(m.input1(), inputs1[i]);
    m.QuantizeAndPopulate<integer_dtype>(m.input2(), inputs2[i]);
    m.Invoke();
    EXPECT_THAT(
        m.GetDequantizedOutput<integer_dtype>(),
        ElementsAreArray(ArrayFloatNear(results[i], kQuantizedTolerance)))
        << "With test number " << i;
  }
}

TEST(QuantizedAddOpModel, QuantizedTestsActivationRELU_N1_TO_1UInt8) {
  QuantizedTestsActivationRELU_N1_TO_1<TensorType_UINT8, uint8_t>();
}

TEST(QuantizedAddOpModel, QuantizedTestsActivationRELU_N1_TO_1Int8) {
  QuantizedTestsActivationRELU_N1_TO_1<TensorType_INT8, int8_t>();
}

template <enum TensorType tensor_type, typename integer_dtype>
void QuantizedVariousInputShapes() {
  float kQuantizedTolerance = GetTolerance(-3.0, 3.0);
  std::vector<std::vector<int>> test_shapes = {
      {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
  for (size_t i = 0; i < test_shapes.size(); ++i) {
    QuantizedAddOpModel m({tensor_type, test_shapes[i], -3.0, 3.0},
                          {tensor_type, test_shapes[i], -3.0, 3.0},
                          {tensor_type, {}, -3.0, 3.0},
                          ActivationFunctionType_NONE);
    m.QuantizeAndPopulate<integer_dtype>(m.input1(),
                                         {-2.0, 0.2, 0.7, 0.8, 1.1, 2.0});
    m.QuantizeAndPopulate<integer_dtype>(m.input2(),
                                         {0.1, 0.3, 0.3, 0.5, 1.1, 0.1});
    m.Invoke();
    EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
                ElementsAreArray(ArrayFloatNear({-1.9, 0.5, 1.0, 1.3, 2.2, 2.1},
                                                kQuantizedTolerance)))
        << "With shape number " << i;
  }
}

TEST(QuantizedAddOpModel, QuantizedVariousInputShapesUInt8) {
  QuantizedVariousInputShapes<TensorType_UINT8, uint8_t>();
}

TEST(QuantizedAddOpModel, QuantizedVariousInputShapesInt8) {
  QuantizedVariousInputShapes<TensorType_INT8, int8_t>();
}

template <enum TensorType tensor_type, typename integer_dtype>
void QuantizedWithScalarBroadcast() {
  float kQuantizedTolerance = GetTolerance(-3.f, 3.f);
  std::vector<std::vector<int>> test_shapes = {
      {6}, {2, 3}, {2, 1, 3}, {1, 3, 1, 2}};
  for (size_t i = 0; i < test_shapes.size(); ++i) {
    QuantizedAddOpModel model_fixture(
        {tensor_type, test_shapes[i], -3.f, 3.f}, {tensor_type, {}, -3.f, 3.f},
        {tensor_type, {}, -3.f, 3.f}, ActivationFunctionType_NONE);
    model_fixture.QuantizeAndPopulate<integer_dtype>(
        model_fixture.input1(), {-2.0f, 0.2f, 0.7f, 0.8f, 1.1f, 2.0f});
    model_fixture.QuantizeAndPopulate<integer_dtype>(model_fixture.input2(),
                                                     {0.1f});
    model_fixture.Invoke();
    EXPECT_THAT(
        model_fixture.GetDequantizedOutput<integer_dtype>(),
        ElementsAreArray(ArrayFloatNear({-1.9f, 0.3f, 0.8f, 0.9f, 1.2f, 2.1f},
                                        kQuantizedTolerance)))
        << "With shape number " << i;
  }
  // Re-run with exchanged inputs.
  for (size_t i = 0; i < test_shapes.size(); ++i) {
    QuantizedAddOpModel model_fixture(
        {tensor_type, {}, -3.f, 3.f}, {tensor_type, test_shapes[i], -3.f, 3.f},
        {tensor_type, {}, -3.f, 3.f}, ActivationFunctionType_NONE);
    model_fixture.QuantizeAndPopulate<integer_dtype>(model_fixture.input1(),
                                                     {0.1f});
    model_fixture.QuantizeAndPopulate<integer_dtype>(
        model_fixture.input2(), {-2.0f, 0.2f, 0.7f, 0.8f, 1.1f, 2.0f});
    model_fixture.Invoke();
    EXPECT_THAT(
        model_fixture.GetDequantizedOutput<integer_dtype>(),
        ElementsAreArray(ArrayFloatNear({-1.9f, 0.3f, 0.8f, 0.9f, 1.2f, 2.1f},
                                        kQuantizedTolerance)))
        << "With shape number " << i;
  }
}

TEST(QuantizedAddOpModel, QuantizedWithScalarBroadcastUInt8) {
  QuantizedWithScalarBroadcast<TensorType_UINT8, uint8_t>();
}

TEST(QuantizedAddOpModel, QuantizedWithScalarBroadcastInt8) {
  QuantizedWithScalarBroadcast<TensorType_INT8, int8_t>();
}

TEST(QuantizedAddOpModel, QuantizedWithScalarBroadcastInt16) {
  QuantizedWithScalarBroadcast<TensorType_INT16, int16_t>();
}

template <enum TensorType tensor_type, typename integer_dtype>
void QuantizedWithMixedBroadcast() {
  float kQuantizedTolerance = GetTolerance(-3.f, 3.f);
  const std::vector<int> base_shape = {2, 3, 1, 2};
  std::vector<std::vector<int>> test_shapes = {
      {1, 1, 3, 2}, {1, 3, 1, 2}, {2, 1, 3, 1}, {2, 3, 1, 1}};
  std::vector<std::vector<float>> test_outputs = {
      {-0.1f, 2.6f,  -0.7f, 2.8f, 0.7f,  3.0f, 1.1f,  0.8f, 0.5f,
       1.0f,  1.9f,  1.4f,  1.0f, -0.8f, 0.4f, -0.6f, 1.8f, -0.2f,
       1.4f,  3.0f,  0.8f,  3.0f, 2.2f,  3.0f, -1.4f, 0.3f, -2.0f,
       0.5f,  -0.6f, 0.9f,  0.9f, -1.9f, 0.3f, -1.7f, 1.7f, -1.3f},
      {-0.1f, 2.6f, 0.5f, 1.0f, 1.8f, -0.2f, 1.4f, 3.0f, -2.0f, 0.5f, 1.7f,
       -1.3f},
      {-0.1f, 2.5f,  0.0f, 2.6f, -0.7f, 1.9f, 1.1f,  0.7f, 1.2f,
       0.8f,  0.5f,  0.1f, 1.0f, -0.9f, 1.1f, -0.8f, 0.4f, -1.5f,
       1.7f,  3.0f,  2.2f, 3.0f, 2.1f,  3.0f, -1.1f, 0.5f, -0.6f,
       1.0f,  -0.7f, 0.9f, 1.2f, -1.7f, 1.7f, -1.2f, 1.6f, -1.3f},
      {-0.1f, 2.5f, 1.2f, 0.8f, 0.4f, -1.5f, 1.7f, 3.0f, -0.6f, 1.0f, 1.6f,
       -1.3f}};
  for (size_t i = 0; i < test_shapes.size(); ++i) {
    QuantizedAddOpModel model_fixture({tensor_type, base_shape, -3.f, 3.f},
                                      {tensor_type, test_shapes[i], -3.f, 3.f},
                                      {tensor_type, {}, -3.f, 3.f},
                                      ActivationFunctionType_NONE);
    model_fixture.QuantizeAndPopulate<integer_dtype>(
        model_fixture.input1(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
                                 2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
    model_fixture.QuantizeAndPopulate<integer_dtype>(
        model_fixture.input2(), {0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
    model_fixture.Invoke();
    EXPECT_THAT(
        model_fixture.GetDequantizedOutput<integer_dtype>(),
        ElementsAreArray(ArrayFloatNear(test_outputs[i], kQuantizedTolerance)))
        << "With shape number " << i;
  }
  // Re-run with exchanged inputs.
  for (size_t i = 0; i < test_shapes.size(); ++i) {
    QuantizedAddOpModel model_fixture({tensor_type, test_shapes[i], -3.f, 3.f},
                                      {tensor_type, base_shape, -3.f, 3.f},
                                      {tensor_type, {}, -3.f, 3.f},
                                      ActivationFunctionType_NONE);
    model_fixture.QuantizeAndPopulate<integer_dtype>(
        model_fixture.input1(), {0.2f, 0.3f, -0.4f, 0.5f, 1.0f, 0.9f});
    model_fixture.QuantizeAndPopulate<integer_dtype>(
        model_fixture.input2(), {-0.3f, 2.3f, 0.9f, 0.5f, 0.8f, -1.1f, 1.2f,
                                 2.8f, -1.6f, 0.0f, 0.7f, -2.2f});
    model_fixture.Invoke();
    EXPECT_THAT(
        model_fixture.GetDequantizedOutput<integer_dtype>(),
        ElementsAreArray(ArrayFloatNear(test_outputs[i], kQuantizedTolerance)))
        << "With shape number " << i;
  }
}

TEST(QuantizedAddOpModel, QuantizedWithMixedBroadcastUInt8) {
  QuantizedWithMixedBroadcast<TensorType_UINT8, uint8_t>();
}

TEST(QuantizedAddOpModel, QuantizedWithMixedBroadcastInt8) {
  QuantizedWithMixedBroadcast<TensorType_INT8, int8_t>();
}

TEST(QuantizedAddOpModel, QuantizedWithMixedBroadcastInt16) {
  QuantizedWithMixedBroadcast<TensorType_INT16, int16_t>();
}

template <enum TensorType tensor_type, typename integer_dtype>
void QuantizedWithGenericBroadcast() {
  float kQuantizedTolerance = GetTolerance(-1.0, 1.0);
  std::vector<int> test_shape1 = {1, 3, 1};
  std::vector<int> test_shape2 = {2, 1, 2};
  QuantizedAddOpModel m({tensor_type, test_shape1, -1.0, 1.0},
                        {tensor_type, test_shape2, -1.0, 1.0},
                        {tensor_type, {}, -1.0, 1.0},
                        ActivationFunctionType_NONE);
  m.QuantizeAndPopulate<integer_dtype>(m.input1(), {0.1, 0.2, 0.3});
  m.QuantizeAndPopulate<integer_dtype>(m.input2(), {0.1, -0.2, 0.3, -0.4});
  m.Invoke();
  EXPECT_THAT(m.GetDequantizedOutput<integer_dtype>(),
              ElementsAreArray(ArrayFloatNear({0.2, -0.1, 0.3, 0., 0.4, 0.1,
                                               0.4, -0.3, 0.5, -0.2, 0.6, -0.1},
                                              kQuantizedTolerance)));
}

TEST(QuantizedAddOpModel, QuantizedWithGenericBroadcastUInt8) {
  QuantizedWithGenericBroadcast<TensorType_UINT8, uint8_t>();
}

TEST(QuantizedAddOpModel, QuantizedWithGenericdBroadcastInt8) {
  QuantizedWithGenericBroadcast<TensorType_INT8, int8_t>();
}

TEST(QuantizedAddOpModel, QuantizedWithGenericdBroadcastInt16) {
  QuantizedWithGenericBroadcast<TensorType_INT16, int16_t>();
}

}  // namespace
}  // namespace tflite