/* 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 "tensorflow/core/common_runtime/kernel_benchmark_testlib.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/kernels/ops_util.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/test_benchmark.h"
#include "tensorflow/core/util/tensor_format.h"

namespace tensorflow {

// Creates a Graph which applies a unary "func" on a 3D tensor of
// type T with "num" elements.
template <typename T>
static Graph* Unary(const string& func, int num, DataType dtype) {
  Graph* g = new Graph(OpRegistry::Global());
  Tensor data(dtype, TensorShape({64, 64, num / (64 * 64)}));
  CHECK_GT(data.NumElements(), 0);
  data.flat<T>().setRandom();
  test::graph::Unary(g, func, test::graph::Constant(g, data), 0);
  return g;
}

static int kRows = 100000;

static int RowsAndColsArg(int r, int c) { return r * kRows + c; }
static int RowsFromArg(int arg) { return (arg / kRows); }
static int ColsFromArg(int arg) { return (arg % kRows); }

#define BM_UNARY(DEVICE, FUNC, T, TYPE)                              \
  static void BM_##DEVICE##_##FUNC##_##TYPE(int iters, int num) {    \
    const int64 tot = static_cast<int64>(iters) * num;               \
    testing::ItemsProcessed(tot);                                    \
    testing::BytesProcessed(tot * sizeof(T));                        \
    test::Benchmark(#DEVICE, Unary<T>(#FUNC, num, TYPE)).Run(iters); \
  }                                                                  \
  BENCHMARK(BM_##DEVICE##_##FUNC##_##TYPE)->Range(4 << 10, 1 << 20);

BM_UNARY(cpu, Floor, float, DT_FLOAT);
BM_UNARY(gpu, Floor, float, DT_FLOAT);
BM_UNARY(cpu, Floor, double, DT_DOUBLE);
BM_UNARY(gpu, Floor, double, DT_DOUBLE);
BM_UNARY(cpu, Conj, std::complex<float>, DT_COMPLEX64);
BM_UNARY(gpu, Conj, std::complex<float>, DT_COMPLEX64);
BM_UNARY(cpu, Conj, std::complex<double>, DT_COMPLEX128);
BM_UNARY(gpu, Conj, std::complex<double>, DT_COMPLEX128);

// data func scalar.
static Graph* BinaryScalar(int num, const string& func) {
  Graph* g = new Graph(OpRegistry::Global());
  Tensor lhs(DT_FLOAT, TensorShape({64, 64, num / (64 * 64)}));
  lhs.flat<float>().setRandom();
  Tensor rhs(DT_FLOAT, TensorShape({}));
  rhs.flat<float>().setRandom();
  test::graph::Binary(g, func, test::graph::Constant(g, lhs),
                      test::graph::Constant(g, rhs));
  return g;
}

#define BM_BINARY_SCALAR(DEVICE, FUNC)                             \
  static void BM_##DEVICE##_##FUNC##_scalar(int iters, int num) {  \
    const int64 tot = static_cast<int64>(iters) * num;             \
    testing::ItemsProcessed(tot);                                  \
    testing::BytesProcessed(tot * sizeof(float));                  \
    test::Benchmark(#DEVICE, BinaryScalar(num, #FUNC)).Run(iters); \
  }                                                                \
  BENCHMARK(BM_##DEVICE##_##FUNC##_scalar)                         \
      ->Arg(4096) /* must >= 4096 */                               \
      ->Arg(32768)                                                 \
      ->Arg(131072)                                                \
      ->Arg(1048576);

BM_BINARY_SCALAR(cpu, Less);
BM_BINARY_SCALAR(gpu, Less);
BM_BINARY_SCALAR(cpu, Add);
BM_BINARY_SCALAR(gpu, Add);
#undef BM_BINARY_SCALAR

template <class T>
static Graph* BiasAdd(int rows, int cols, DataType type) {
  Graph* g = new Graph(OpRegistry::Global());
  Tensor lhs(type, TensorShape({rows, cols}));
  lhs.template flat<T>().setRandom();
  TensorShape rhs_shape;
  rhs_shape = TensorShape({cols});
  Tensor rhs(type, rhs_shape);
  rhs.template flat<T>().setRandom();
  test::graph::Binary(g, "BiasAdd", test::graph::Constant(g, lhs),
                      test::graph::Constant(g, rhs));
  return g;
}

#define BM_BIAS_ADD(DEVICE, C_TYPE, TF_TYPE, R, C)                             \
  static void BM_##DEVICE##_##C_TYPE##_BiasAdd_R##R##_C##C(int iters,          \
                                                           int arg) {          \
    const int rows = RowsFromArg(arg);                                         \
    const int cols = ColsFromArg(arg);                                         \
    const int64 tot = static_cast<int64>(iters) * rows * cols;                 \
    testing::ItemsProcessed(tot);                                              \
    testing::BytesProcessed(tot * sizeof(C_TYPE));                             \
    test::Benchmark(#DEVICE, BiasAdd<C_TYPE>(rows, cols, TF_TYPE)).Run(iters); \
  }                                                                            \
  BENCHMARK(BM_##DEVICE##_##C_TYPE##_BiasAdd_R##R##_C##C)                      \
      ->Arg(RowsAndColsArg(R, C));

#define BM_BIAS_ADD_ALL(DEVICE, C_TYPE, TF_TYPE)   \
  BM_BIAS_ADD(DEVICE, C_TYPE, TF_TYPE, 512, 2048); \
  BM_BIAS_ADD(DEVICE, C_TYPE, TF_TYPE, 512, 4096); \
  BM_BIAS_ADD(DEVICE, C_TYPE, TF_TYPE, 2048, 512); \
  BM_BIAS_ADD(DEVICE, C_TYPE, TF_TYPE, 4096, 512);

using Eigen::half;
BM_BIAS_ADD_ALL(cpu, float, DT_FLOAT);
BM_BIAS_ADD_ALL(gpu, float, DT_FLOAT);
BM_BIAS_ADD_ALL(cpu, half, DT_HALF);
BM_BIAS_ADD_ALL(gpu, half, DT_HALF);
#undef BM_BIAS_ADD_ALL
#undef BM_BIAS_ADD

template <class T>
static Graph* BiasAddGrad(int rows, int cols, int channels, DataType type,
                          TensorFormat format) {
  Graph* g = new Graph(OpRegistry::Global());
  TensorShape lhs_shape;
  if (format == FORMAT_NCHW) {
    lhs_shape = TensorShape({channels, rows, cols});
  } else {
    lhs_shape = TensorShape({rows, cols, channels});
  }
  Tensor lhs(type, lhs_shape);
  lhs.template flat<T>().setRandom();
  Node* n;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "BiasAddGrad")
                  .Attr("data_format", ToString(format))
                  .Input(test::graph::Constant(g, lhs), /*index=*/0)
                  .Finalize(g, &n));
  return g;
}

#define BM_BIAS_ADD_GRAD(DEVICE, FMT, C_TYPE, TF_TYPE, R, C, CH)               \
  static void                                                                  \
      BM_##DEVICE##_##FMT##_##C_TYPE##_BiasAddGrad_R##R##_C##C##_CH##CH(       \
          int iters, int arg, int channels) {                                  \
    const int rows = RowsFromArg(arg);                                         \
    const int cols = ColsFromArg(arg);                                         \
    const int64 tot = static_cast<int64>(iters) * rows * cols * channels;      \
    testing::ItemsProcessed(tot);                                              \
    testing::BytesProcessed(tot * sizeof(C_TYPE));                             \
    test::Benchmark(#DEVICE, BiasAddGrad<C_TYPE>(rows, cols, channels,         \
                                                 TF_TYPE, FORMAT_##FMT))       \
        .Run(iters);                                                           \
  }                                                                            \
  BENCHMARK(BM_##DEVICE##_##FMT##_##C_TYPE##_BiasAddGrad_R##R##_C##C##_CH##CH) \
      ->ArgPair(RowsAndColsArg(R, C), CH);

#define BM_BIAS_ADD_GRAD_ALL(DEVICE, FORMAT, C_TYPE, TF_TYPE)       \
  BM_BIAS_ADD_GRAD(DEVICE, FORMAT, C_TYPE, TF_TYPE, 64, 64, 64);    \
  BM_BIAS_ADD_GRAD(DEVICE, FORMAT, C_TYPE, TF_TYPE, 512, 512, 4);   \
  BM_BIAS_ADD_GRAD(DEVICE, FORMAT, C_TYPE, TF_TYPE, 512, 512, 1);   \
  BM_BIAS_ADD_GRAD(DEVICE, FORMAT, C_TYPE, TF_TYPE, 4096, 4096, 4); \
  BM_BIAS_ADD_GRAD(DEVICE, FORMAT, C_TYPE, TF_TYPE, 4096, 4096, 1);

using Eigen::half;
BM_BIAS_ADD_GRAD_ALL(gpu, NCHW, float, DT_FLOAT);
BM_BIAS_ADD_GRAD_ALL(gpu, NCHW, half, DT_HALF);
BM_BIAS_ADD_GRAD_ALL(cpu, NHWC, float, DT_FLOAT);
BM_BIAS_ADD_GRAD_ALL(gpu, NHWC, float, DT_FLOAT);
BM_BIAS_ADD_GRAD_ALL(cpu, NHWC, half, DT_HALF);
BM_BIAS_ADD_GRAD_ALL(gpu, NHWC, half, DT_HALF);
#undef BM_BIAS_ADD_GRAD_ALL
#undef BM_BIAS_ADD_GRAD

static Graph* BcastAdd(int rows, int cols, int dim) {
  Graph* g = new Graph(OpRegistry::Global());
  Tensor lhs(DT_FLOAT, TensorShape({rows, cols}));
  lhs.flat<float>().setRandom();
  TensorShape rhs_shape;
  if (dim == 0) {
    rhs_shape = TensorShape({rows, 1});
  } else {
    rhs_shape = TensorShape({cols});
  }
  Tensor rhs(DT_FLOAT, rhs_shape);
  rhs.flat<float>().setRandom();
  test::graph::Binary(g, "Add", test::graph::Constant(g, lhs),
                      test::graph::Constant(g, rhs));
  return g;
}

#define BM_BCAST_ADD_ROW(DEVICE, R, C)                                    \
  static void BM_##DEVICE##_BcastAddRow_R##R##_C##C(int iters, int arg) { \
    const int rows = RowsFromArg(arg);                                    \
    const int cols = ColsFromArg(arg);                                    \
    const int64 tot = static_cast<int64>(iters) * rows * cols;            \
    testing::ItemsProcessed(tot);                                         \
    testing::BytesProcessed(tot * sizeof(float));                         \
    test::Benchmark(#DEVICE, BcastAdd(rows, cols, 0)).Run(iters);         \
  }                                                                       \
  BENCHMARK(BM_##DEVICE##_BcastAddRow_R##R##_C##C)->Arg(RowsAndColsArg(R, C));

#define BM_BCAST_ADD_ROW_ALL(DEVICE)   \
  BM_BCAST_ADD_ROW(DEVICE, 512, 2048); \
  BM_BCAST_ADD_ROW(DEVICE, 512, 4096); \
  BM_BCAST_ADD_ROW(DEVICE, 2048, 512); \
  BM_BCAST_ADD_ROW(DEVICE, 4096, 512);
BM_BCAST_ADD_ROW_ALL(cpu);
BM_BCAST_ADD_ROW_ALL(gpu);
#undef BM_BCAST_ADD_ROW_ALL
#undef BM_BCAST_ADD_ROW

#define BM_BCAST_ADD_COL(DEVICE, R, C)                                    \
  static void BM_##DEVICE##_BcastAddCol_R##R##_C##C(int iters, int arg) { \
    const int rows = RowsFromArg(arg);                                    \
    const int cols = ColsFromArg(arg);                                    \
    const int64 tot = static_cast<int64>(iters) * rows * cols;            \
    testing::ItemsProcessed(tot);                                         \
    testing::BytesProcessed(tot * sizeof(float));                         \
    test::Benchmark(#DEVICE, BcastAdd(rows, cols, 1)).Run(iters);         \
  }                                                                       \
  BENCHMARK(BM_##DEVICE##_BcastAddCol_R##R##_C##C)->Arg(RowsAndColsArg(R, C));

#define BM_BCAST_ADD_COL_ALL(DEVICE)   \
  BM_BCAST_ADD_COL(DEVICE, 512, 2048); \
  BM_BCAST_ADD_COL(DEVICE, 512, 4096); \
  BM_BCAST_ADD_COL(DEVICE, 2048, 512); \
  BM_BCAST_ADD_COL(DEVICE, 4096, 512);
BM_BCAST_ADD_COL_ALL(cpu);
BM_BCAST_ADD_COL_ALL(gpu);
#undef BM_BCAST_ADD_COL_ALL
#undef BM_BCAST_ADD_COL

}  // end namespace tensorflow
