/* Copyright 2015 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 <functional>
#include <memory>

#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h"
#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/framework/fake_input.h"
#include "tensorflow/core/framework/node_def_builder.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/kernels/ops_testutil.h"
#include "tensorflow/core/kernels/ops_util.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/lib/random/simple_philox.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/test_benchmark.h"

namespace tensorflow {

static const float tol_ = 1e-4;

class LRNFloatTest : public OpsTestBase {
 protected:
  LRNFloatTest() : philox_(123, 17), rand_(&philox_) {}

  int GetIntAttr(const string& name) {
    int value;
    TF_CHECK_OK(GetNodeAttr(*node_def(), name, &value));
    return value;
  }

  float GetFloatAttr(const string& name) {
    float value;
    TF_CHECK_OK(GetNodeAttr(*node_def(), name, &value));
    return value;
  }

  bool Compare() {
    const auto& input = GetInput(0);
    const int64 batch_size = input.dim_size(0);
    const int64 rows = input.dim_size(1);
    const int64 cols = input.dim_size(2);
    const int64 depth = input.dim_size(3);
    const int64 rest = cols * rows * batch_size;

    const int64 depth_radius = GetIntAttr("depth_radius");
    const float bias = GetFloatAttr("bias");
    const float alpha = GetFloatAttr("alpha");
    const float beta = GetFloatAttr("beta");

    Eigen::Tensor<float, 4, Eigen::RowMajor> expected(batch_size, rows, cols,
                                                      depth);
    auto out = expected.reshape(Eigen::DSizes<int64, 2>{rest, depth});
    auto in = input.shaped<float, 2>({rest, depth});

    for (int64 i = 0; i < rest; ++i) {
      Eigen::Tensor<float, 1, Eigen::RowMajor> out_col(depth);
      for (int64 d = 0; d < depth; ++d) {
        float denom = 0.0f;
        for (int64 r = std::max(int64{0}, d - depth_radius);
             r < std::min(depth, d + depth_radius + 1); ++r) {
          denom += in(i, r) * in(i, r);
        }
        denom = std::pow(denom * alpha + bias, beta);
        out_col(d) = in(i, d) / denom;
      }
      out.chip<0>(i) = out_col;
    }
    auto actual = GetOutput(0)->tensor<float, 4>();
    Eigen::Tensor<float, 0, Eigen::RowMajor> sum =
        ((expected - actual).abs() > actual.constant(tol_))
            .select(actual.constant(1), actual.constant(0))
            .sum();
    return sum() == 0;
  }

  random::PhiloxRandom philox_;
  random::SimplePhilox rand_;
};

TEST_F(LRNFloatTest, Depth96) {
  TF_ASSERT_OK(NodeDefBuilder("lrn_op", "LRN")
                   .Input(FakeInput())
                   .Attr("depth_radius", 5)
                   .Attr("bias", 1.0f)
                   .Attr("alpha", 0.1f)
                   .Attr("beta", 2.0f)
                   .Finalize(node_def()));
  TF_ASSERT_OK(InitOp());
  AddInput<float>(TensorShape({1, 1, 1, 96}),
                  [](int i) -> float { return i + 1; });
  TF_ASSERT_OK(RunOpKernel());
  auto actual = GetOutput(0)->tensor<float, 4>();

  // Output for Node 0 with Value 1:
  // 1 / (1 + 0.1*(1^2 + 2^2 + 3^2 + 4^2 + 5^2 + 6^2))^2
  EXPECT_NEAR(1. / (10.1 * 10.1), actual(0, 0, 0, 0), tol_);

  // Output for Node 5 with Value 6:
  // 6 / (1 + 0.1*(1^2 + 2^2 + 3^2 + 4^2 + 5^2 + 6^2 ... + 11^2))^2
  EXPECT_NEAR(6. / (51.6 * 51.6), actual(0, 0, 0, 5), tol_);

  // Output for Node 63 with value 64:
  // 64 / (1 + 0.1*(59^2 + 60^2 + 61^2 + 62^2 + 63^2 + 64^2))^2
  EXPECT_NEAR(64. / (2272.1 * 2272.1), actual(0, 0, 0, 63), tol_);

  // Output for Node 64 with value 65:
  // 65 / (1 + 0.1*(65^2 + 66^2 + 67^2 + 68^2 + 69^2 + 70^2))^2
  EXPECT_NEAR(65. / (2736.5 * 2736.5), actual(0, 0, 0, 64), tol_);

  // Output for Node 95 with value 96:
  // 96 / (1 + 0.1*(91^2 + 92^2 + 93^2 + 94^2 + 95^2 + 96^2))^2
  EXPECT_NEAR(96. / (5248.1 * 5248.1), actual(0, 0, 0, 95), tol_);
  EXPECT_TRUE(Compare());
}

TEST_F(LRNFloatTest, Depth16) {
  TF_ASSERT_OK(NodeDefBuilder("lrn_op", "LRN")
                   .Input(FakeInput())
                   .Attr("depth_radius", 5)
                   .Attr("bias", 1.0f)
                   .Attr("alpha", 0.1f)
                   .Attr("beta", 2.0f)
                   .Finalize(node_def()));
  TF_ASSERT_OK(InitOp());
  AddInput<float>(TensorShape({1, 1, 1, 16}),
                  [](int i) -> float { return i + 1; });
  TF_ASSERT_OK(RunOpKernel());
  auto actual = GetOutput(0)->tensor<float, 4>();

  // Output for Node 0 with Value 1:
  // 1 / (1 + 0.1*(1^2 + 2^2 + 3^2 + 4^2 + 5^2 + 6^2))^2
  EXPECT_NEAR(1. / (10.1 * 10.1), actual(0, 0, 0, 0), tol_);

  // Output for Node 5 with Value 6:
  // 6 / (1 + 0.1*(1^2 + 2^2 + 3^2 + 4^2 + 5^2 + 6^2 ... + 11^2))^2
  EXPECT_NEAR(6. / (51.6 * 51.6), actual(0, 0, 0, 5), tol_);

  // Output for Node 15 with value 16:
  // 16 / (1 + 0.1*(11^2 + 12^2 + 13^2 + 14^2 + 15^2 + 16^2))^2
  EXPECT_NEAR(16. / (112.1 * 112.1), actual(0, 0, 0, 15), tol_);
  EXPECT_TRUE(Compare());
}

static double RndGaussian(random::SimplePhilox* rnd) {
  // Box-Muller transformation.
  // See, for example, http://www.taygeta.com/random/gaussian.html
  double x1, x2;
  double r;
  do {
    x1 = 2 * rnd->RandDouble() - 1;
    x2 = 2 * rnd->RandDouble() - 1;
    r = x1 * x1 + x2 * x2;
  } while (r == 0 || r >= 1.0);
  double w = sqrt(-2.0 * log(r) / r);
  return x1 * w;
}

#define TCASE(NAME, DEPTH, BATCH, DEPTH_RADIUS, BIAS, ALPHA, BETA)           \
  TEST_F(LRNFloatTest, NAME) {                                               \
    TF_ASSERT_OK(NodeDefBuilder("lrn_op", "LRN")                             \
                     .Input(FakeInput())                                     \
                     .Attr("depth_radius", (DEPTH_RADIUS))                   \
                     .Attr("bias", (BIAS))                                   \
                     .Attr("alpha", ((ALPHA) / 10))                          \
                     .Attr("beta", (BETA))                                   \
                     .Finalize(node_def()));                                 \
    TF_ASSERT_OK(InitOp());                                                  \
    AddInput<float>(TensorShape({BATCH, 1, 1, DEPTH}),                       \
                    [this](int i) -> float { return RndGaussian(&rand_); }); \
    TF_ASSERT_OK(RunOpKernel());                                             \
    EXPECT_TRUE(Compare());                                                  \
  }

// clang-format off
//        DEPTH  BATCH  DEPTH_RADIUS  BIAS  ALPHA  BETA
TCASE(T0, 4,     2,     2,            1.0f, 1.0f,  2.0f)
TCASE(T1, 16,    1,     5,            1.0f, 1.0f,  2.0f)
TCASE(T2, 16,    32,    2,            1.0f, 2.0f,  1.0f)
TCASE(T3, 128,   4,     3,            2.0f, 1.0f,  1.0f)
// clang-format on

#undef TCASE

static Graph* BM_LRNGrad(int batches, int rows, int cols, int depth,
                         int depth_radius) {
  Graph* g = new Graph(OpRegistry::Global());
  Tensor grads(DT_FLOAT, TensorShape({batches, rows, cols, depth}));
  grads.flat<float>().setRandom();

  Tensor in(DT_FLOAT, TensorShape({batches, rows, cols, depth}));
  in.flat<float>().setRandom();

  Tensor out(DT_FLOAT, TensorShape({batches, rows, cols, depth}));

  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("lrn_grad_op"), "LRNGrad")
                  .Input(test::graph::Constant(g, grads))
                  .Input(test::graph::Constant(g, in))
                  .Input(test::graph::Constant(g, out))
                  .Attr("depth_radius", depth_radius)
                  .Attr("bias", 1.0f)
                  .Attr("alpha", 1.0f / 10)
                  .Attr("beta", 2.0f)
                  .Finalize(g, &ret));
  return g;
}

#define BM_LRNGradDev(DEVICE, B, R, C, D, DR)                                 \
  static void BM_LRNGrad_##DEVICE##_##B##_##R##_##C##_##D##_##DR(int iters) { \
    testing::ItemsProcessed(static_cast<int64>(iters) * B * R * C * D * DR *  \
                            4);                                               \
    test::Benchmark(#DEVICE, BM_LRNGrad(B, R, C, D, DR)).Run(iters);          \
  }                                                                           \
  BENCHMARK(BM_LRNGrad_##DEVICE##_##B##_##R##_##C##_##D##_##DR)

BM_LRNGradDev(cpu, 128, 12, 12, 64, 4);
BM_LRNGradDev(cpu, 128, 56, 56, 64, 2);
BM_LRNGradDev(cpu, 128, 27, 27, 192, 2);

}  // namespace tensorflow
