/* 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/types.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/graph/testlib.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/lib/strings/str_util.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/test_benchmark.h"

namespace tensorflow {
namespace {

class DynamicPartitionOpTest : public OpsTestBase {
 protected:
  void MakeOp() {
    TF_ASSERT_OK(NodeDefBuilder("myop", "DynamicPartition")
                     .Input(FakeInput(DT_FLOAT))
                     .Input(FakeInput(DT_INT32))
                     .Attr("num_partitions", 4)
                     .Finalize(node_def()));
    TF_ASSERT_OK(InitOp());
  }
};

TEST_F(DynamicPartitionOpTest, Simple_OneD) {
  MakeOp();

  // Similar to how we would use this to split embedding ids to be looked up

  // Feed and run
  AddInputFromArray<float>(TensorShape({6}), {0, 13, 2, 39, 4, 17});
  AddInputFromArray<int32>(TensorShape({6}), {0, 0, 2, 3, 2, 1});
  TF_ASSERT_OK(RunOpKernel());

  // Check the output sizes
  {  // Output 0
    Tensor expected(allocator(), DT_FLOAT, TensorShape({2}));
    test::FillValues<float>(&expected, {0, 13});
    test::ExpectTensorEqual<float>(expected, *GetOutput(0));
  }
  {  // Output 1
    Tensor expected(allocator(), DT_FLOAT, TensorShape({1}));
    test::FillValues<float>(&expected, {17});
    test::ExpectTensorEqual<float>(expected, *GetOutput(1));
  }
  {  // Output 2
    Tensor expected(allocator(), DT_FLOAT, TensorShape({2}));
    test::FillValues<float>(&expected, {2, 4});
    test::ExpectTensorEqual<float>(expected, *GetOutput(2));
  }
  {  // Output 3
    Tensor expected(allocator(), DT_FLOAT, TensorShape({1}));
    test::FillValues<float>(&expected, {39});
    test::ExpectTensorEqual<float>(expected, *GetOutput(3));
  }
}

TEST_F(DynamicPartitionOpTest, Simple_TwoD) {
  MakeOp();

  // Feed and run
  AddInputFromArray<float>(
      TensorShape({6, 3}),
      {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17});
  AddInputFromArray<int32>(TensorShape({6}), {0, 0, 2, 3, 2, 1});
  TF_ASSERT_OK(RunOpKernel());

  // Check the output sizes
  {  // Output 0
    Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 3}));
    test::FillValues<float>(&expected, {0, 1, 2, 3, 4, 5});
    test::ExpectTensorEqual<float>(expected, *GetOutput(0));
  }
  {  // Output 1
    Tensor expected(allocator(), DT_FLOAT, TensorShape({1, 3}));
    test::FillValues<float>(&expected, {15, 16, 17});
    test::ExpectTensorEqual<float>(expected, *GetOutput(1));
  }
  {  // Output 2
    Tensor expected(allocator(), DT_FLOAT, TensorShape({2, 3}));
    test::FillValues<float>(&expected, {6, 7, 8, 12, 13, 14});
    test::ExpectTensorEqual<float>(expected, *GetOutput(2));
  }
  {  // Output 3
    Tensor expected(allocator(), DT_FLOAT, TensorShape({1, 3}));
    test::FillValues<float>(&expected, {9, 10, 11});
    test::ExpectTensorEqual<float>(expected, *GetOutput(3));
  }
}

TEST_F(DynamicPartitionOpTest, SomeOutputsEmpty) {
  MakeOp();

  // Feed and run
  AddInputFromArray<float>(TensorShape({6}), {0, 13, 2, 39, 4, 17});
  AddInputFromArray<int32>(TensorShape({6}), {0, 0, 2, 2, 0, 2});
  TF_ASSERT_OK(RunOpKernel());

  TensorShape empty_one_dim;
  empty_one_dim.AddDim(0);
  Tensor expected_empty(allocator(), DT_FLOAT, empty_one_dim);

  // Check the output sizes
  {  // Output 0
    Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
    test::FillValues<float>(&expected, {0, 13, 4});
    test::ExpectTensorEqual<float>(expected, *GetOutput(0));
  }
  {  // Output 1
    test::ExpectTensorEqual<float>(expected_empty, *GetOutput(1));
  }
  {  // Output 2
    Tensor expected(allocator(), DT_FLOAT, TensorShape({3}));
    test::FillValues<float>(&expected, {2, 39, 17});
    test::ExpectTensorEqual<float>(expected, *GetOutput(2));
  }
  {  // Output 3
    test::ExpectTensorEqual<float>(expected_empty, *GetOutput(3));
  }
}

TEST_F(DynamicPartitionOpTest, Error_IndexOutOfRange) {
  MakeOp();

  // Feed and run
  AddInputFromArray<float>(TensorShape({5, 3}),
                           {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14});
  AddInputFromArray<int32>(TensorShape({5}), {0, 2, 99, 2, 2});
  Status s = RunOpKernel();
  EXPECT_TRUE(
      absl::StrContains(s.ToString(), "partitions[2] = 99 is not in [0, 4)"))
      << s;
}

Node* DynamicPartitionNode(Graph* g, Node* in0, Node* in1, int num_partitions) {
  Node* ret;
  TF_CHECK_OK(NodeBuilder(g->NewName("n"), "DynamicPartition")
                  .Input(in0)
                  .Input(in1)
                  .Attr("num_partitions", num_partitions)
                  .Finalize(g, &ret));
  return ret;
}

template <typename T>
static Graph* DynamicPartition(int num_partitions, int dim) {
  Graph* g = new Graph(OpRegistry::Global());
  // Always use a 128MB buffer.
  const int kRows = ((128 << 20) / sizeof(T)) / dim;
  Tensor data(DataTypeToEnum<T>::value, TensorShape({kRows, dim}));
  data.flat<T>().setRandom();

  random::PhiloxRandom philox(301, 17);
  random::SimplePhilox rnd(&philox);
  Tensor partitions(DT_INT32, TensorShape({kRows}));
  for (int i = 0; i < kRows; i++) {
    partitions.flat<int32>()(i) = rnd.Uniform(num_partitions);
  }
  DynamicPartitionNode(g, test::graph::Constant(g, data),
                       test::graph::Constant(g, partitions), num_partitions);
  return g;
}

#define BM_DYNAMIC_PARTITION(DEVICE, T, num)                            \
  static void BM_##DEVICE##_dynpart_##T##_##num(int iters, int dim) {   \
    const int64 items = ((128 << 20) / sizeof(T));                      \
    const int64 tot = static_cast<int64>(iters) * items;                \
    testing::ItemsProcessed(tot);                                       \
    testing::UseRealTime();                                             \
    test::Benchmark(#DEVICE, DynamicPartition<T>(num, dim)).Run(iters); \
  }                                                                     \
  BENCHMARK(BM_##DEVICE##_dynpart_##T##_##num)->Arg(1)->Arg(256)

BM_DYNAMIC_PARTITION(cpu, float, 2);
BM_DYNAMIC_PARTITION(cpu, float, 100);
BM_DYNAMIC_PARTITION(cpu, double, 2);
BM_DYNAMIC_PARTITION(cpu, double, 100);
BM_DYNAMIC_PARTITION(cpu, complex64, 2);
BM_DYNAMIC_PARTITION(cpu, complex64, 100);

BM_DYNAMIC_PARTITION(gpu, float, 2);
BM_DYNAMIC_PARTITION(gpu, float, 100);
BM_DYNAMIC_PARTITION(gpu, double, 2);
BM_DYNAMIC_PARTITION(gpu, double, 100);
BM_DYNAMIC_PARTITION(gpu, complex64, 2);
BM_DYNAMIC_PARTITION(gpu, complex64, 100);

}  // namespace
}  // namespace tensorflow
