/* 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 <vector>

#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/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/logging.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/test_benchmark.h"

namespace tensorflow {
namespace {

class ScatterUpdateOpTest : public OpsTestBase {
 protected:
  void MakeOp(DataType variable_ref_type, DataType index_type) {
    TF_ASSERT_OK(NodeDefBuilder("myop", "ScatterUpdate")
                     .Input(FakeInput(variable_ref_type))
                     .Input(FakeInput(index_type))
                     .Input(FakeInput(RemoveRefType(variable_ref_type)))
                     .Finalize(node_def()));
    TF_ASSERT_OK(InitOp());
  }
};

TEST_F(ScatterUpdateOpTest, Simple_StringType) {
  MakeOp(DT_STRING_REF, DT_INT32);
  AddInputFromArray<tstring>(TensorShape({1}), {"Brain"});
  AddInputFromArray<int32>(TensorShape({1}), {0});
  AddInputFromArray<tstring>(TensorShape({1}), {"TensorFlow"});
  TF_ASSERT_OK(RunOpKernel());
  // Check the new state of the input
  Tensor params_tensor = *mutable_input(0).tensor;
  Tensor expected(allocator(), DT_STRING, TensorShape({1}));
  test::FillValues<tstring>(&expected, {"TensorFlow"});
  test::ExpectTensorEqual<tstring>(expected, params_tensor);
}

TEST_F(ScatterUpdateOpTest, Simple_BoolType) {
  MakeOp(DT_BOOL_REF, DT_INT32);
  AddInputFromArray<bool>(TensorShape({1}), {false});
  AddInputFromArray<int32>(TensorShape({1}), {0});
  AddInputFromArray<bool>(TensorShape({1}), {true});
  TF_ASSERT_OK(RunOpKernel());
  // Check the new state of the input
  Tensor params_tensor = *mutable_input(0).tensor;
  Tensor expected(allocator(), DT_BOOL, TensorShape({1}));
  test::FillValues<bool>(&expected, {true});
  test::ExpectTensorEqual<bool>(expected, params_tensor);
}

TEST_F(ScatterUpdateOpTest, Simple_TwoD32) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({5, 3}),
                           {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({3}), {0, 4, 2});
  AddInputFromArray<float>(TensorShape({3, 3}),
                           {100, 101, 102, 777, 778, 779, 10000, 10001, 10002});
  TF_ASSERT_OK(RunOpKernel());

  // Check the new state of the input
  Tensor params_tensor = *mutable_input(0).tensor;
  Tensor expected(allocator(), DT_FLOAT, TensorShape({5, 3}));
  test::FillValues<float>(&expected, {100, 101, 102, 0, 0, 0, 10000, 10001,
                                      10002, 0, 0, 0, 777, 778, 779});
  test::ExpectTensorEqual<float>(expected, params_tensor);
}

TEST_F(ScatterUpdateOpTest, Simple_Two64) {
  MakeOp(DT_FLOAT_REF, DT_INT64);

  // Feed and run
  AddInputFromArray<float>(TensorShape({5, 3}),
                           {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
  AddInputFromArray<int64>(TensorShape({3}), {0, 4, 2});
  AddInputFromArray<float>(TensorShape({3, 3}),
                           {100, 101, 102, 777, 778, 779, 10000, 10001, 10002});
  TF_ASSERT_OK(RunOpKernel());

  // Check the new state of the input
  Tensor params_tensor = *mutable_input(0).tensor;
  Tensor expected(allocator(), DT_FLOAT, TensorShape({5, 3}));
  test::FillValues<float>(&expected, {100, 101, 102, 0, 0, 0, 10000, 10001,
                                      10002, 0, 0, 0, 777, 778, 779});
  test::ExpectTensorEqual<float>(expected, params_tensor);
}

TEST_F(ScatterUpdateOpTest, Simple_ZeroD) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({5}), {0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({}), {3});
  AddInputFromArray<float>(TensorShape({}), {101});
  TF_ASSERT_OK(RunOpKernel());

  // Check the new state of the input
  Tensor params_tensor = *mutable_input(0).tensor;
  Tensor expected(allocator(), DT_FLOAT, TensorShape({5}));
  test::FillValues<float>(&expected, {0, 0, 0, 101, 0});
  test::ExpectTensorEqual<float>(expected, params_tensor);
}

TEST_F(ScatterUpdateOpTest, Simple_OneD) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({5}), {0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({3}), {0, 4, 2});
  AddInputFromArray<float>(TensorShape({3}), {100, 101, 102});
  TF_ASSERT_OK(RunOpKernel());

  // Check the new state of the input
  Tensor params_tensor = *mutable_input(0).tensor;
  Tensor expected(allocator(), DT_FLOAT, TensorShape({5}));
  test::FillValues<float>(&expected, {100, 0, 102, 0, 101});
  test::ExpectTensorEqual<float>(expected, params_tensor);
}

TEST_F(ScatterUpdateOpTest, HigherRank) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({8}), {0, 0, 0, 0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({2, 3}), {0, 4, 2, 1, 3, 6});
  AddInputFromArray<float>(TensorShape({2, 3}), {10, 20, 30, 40, 50, 60});
  TF_ASSERT_OK(RunOpKernel());

  // Check the new state of the input
  Tensor params_tensor = *mutable_input(0).tensor;
  Tensor expected(allocator(), DT_FLOAT, TensorShape({8}));
  test::FillValues<float>(&expected, {10, 40, 30, 50, 20, 0, 60, 0});
  test::ExpectTensorEqual<float>(expected, params_tensor);
}

TEST_F(ScatterUpdateOpTest, Error_IndexOutOfRange) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({5, 3}),
                           {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({3}), {0, 4, 99});
  AddInputFromArray<float>(TensorShape({3, 3}),
                           {100, 101, 102, 777, 778, 779, 10000, 10001, 10002});
  Status s = RunOpKernel();
  EXPECT_TRUE(
      absl::StrContains(s.ToString(), "indices[2] = 99 is not in [0, 5)"))
      << s;
}

TEST_F(ScatterUpdateOpTest, Error_WrongDimsIndices) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({2, 3}), {0, 0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({1, 3}), {0, 4, 99});
  AddInputFromArray<float>(TensorShape({3, 3}),
                           {100, 101, 102, 777, 778, 779, 10000, 10001, 10002});
  Status s = RunOpKernel();
  EXPECT_TRUE(absl::StrContains(s.ToString(),
                                "Must have updates.shape = indices.shape + "
                                "params.shape[1:] or updates.shape = [], got "))
      << s;
}

TEST_F(ScatterUpdateOpTest, Error_MismatchedParamsAndUpdateDimensions) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({5, 3}),
                           {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({3}), {0, 4, 2});
  AddInputFromArray<float>(
      TensorShape({3, 4}),
      {100, 101, 102, 103, 777, 778, 779, 780, 10000, 10001, 10002, 10004});
  Status s = RunOpKernel();
  EXPECT_TRUE(absl::StrContains(s.ToString(),
                                "Must have updates.shape = indices.shape + "
                                "params.shape[1:] or updates.shape = [], got "))

      << s;
}

TEST_F(ScatterUpdateOpTest, Error_MismatchedIndicesAndUpdateDimensions) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({5, 3}),
                           {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({3}), {0, 4, 2});
  AddInputFromArray<float>(TensorShape({2, 3}),
                           {100, 101, 102, 10000, 10001, 10002});
  Status s = RunOpKernel();
  EXPECT_TRUE(absl::StrContains(s.ToString(),
                                "Must have updates.shape = indices.shape + "
                                "params.shape[1:] or updates.shape = [], got "))
      << s;
}

class ScatterUpdateBM : public ScatterUpdateOpTest {
 public:
  void TestBody() override {}
  void MakeBenchmarkOp(const char* op, DataType index_type) {
    TF_ASSERT_OK(NodeDefBuilder("myop", op)
                     .Input(FakeInput(DT_FLOAT_REF))
                     .Input(FakeInput(index_type))
                     .Input(FakeInput(DT_FLOAT))
                     .Finalize(node_def()));
    TF_CHECK_OK(InitOp());
  }
};

template <typename Index>
static void BM_ScatterHelper(int iters, int embedding_size, const char* op) {
  testing::StopTiming();
  const int kRows = 10000000 / embedding_size;
  std::vector<float> values;
  values.reserve(kRows);
  for (int i = 0; i < kRows * embedding_size; i++) {
    values.push_back(i);
  }
  const int kNumUpdates = 1000;
  random::PhiloxRandom philox(301, 17);
  random::SimplePhilox rnd(&philox);
  std::vector<Index> indices;
  std::vector<float> updates;
  for (int i = 0; i < kNumUpdates; i++) {
    indices.push_back(rnd.Uniform(kRows));
    for (int j = 0; j < embedding_size; j++) {
      updates.push_back(i * 10 + j);
    }
  }

  ScatterUpdateBM bm;
  bm.MakeBenchmarkOp(op, DataTypeToEnum<Index>::v());
  bm.AddInputFromArray<float>(TensorShape({kRows, embedding_size}), values);
  bm.AddInputFromArray<Index>(TensorShape({kNumUpdates}), indices);
  bm.AddInputFromArray<float>(TensorShape({kNumUpdates, embedding_size}),
                              updates);
  testing::ItemsProcessed((static_cast<int64>(kNumUpdates) * embedding_size) *
                          iters);
  testing::StartTiming();
  while (iters-- > 0) {
    Status s = bm.RunOpKernel();
  }
  testing::StopTiming();
}

static void BM_ScatterUpdateInt32(int iters, int embedding_size) {
  BM_ScatterHelper<int32>(iters, embedding_size, "ScatterUpdate");
}
static void BM_ScatterUpdateInt64(int iters, int embedding_size) {
  BM_ScatterHelper<int64>(iters, embedding_size, "ScatterUpdate");
}

static void BM_ScatterAddInt32(int iters, int embedding_size) {
  BM_ScatterHelper<int32>(iters, embedding_size, "ScatterAdd");
}
static void BM_ScatterAddInt64(int iters, int embedding_size) {
  BM_ScatterHelper<int64>(iters, embedding_size, "ScatterAdd");
}

static void BM_ScatterMulInt32(int iters, int embedding_size) {
  BM_ScatterHelper<int32>(iters, embedding_size, "ScatterMul");
}
static void BM_ScatterMulInt64(int iters, int embedding_size) {
  BM_ScatterHelper<int64>(iters, embedding_size, "ScatterMul");
}

static void BM_ScatterDivInt32(int iters, int embedding_size) {
  BM_ScatterHelper<int32>(iters, embedding_size, "ScatterDiv");
}
static void BM_ScatterDivInt64(int iters, int embedding_size) {
  BM_ScatterHelper<int64>(iters, embedding_size, "ScatterDiv");
}

static void BM_ScatterMinInt32(int iters, int embedding_size) {
  BM_ScatterHelper<int32>(iters, embedding_size, "ScatterMin");
}
static void BM_ScatterMinInt64(int iters, int embedding_size) {
  BM_ScatterHelper<int64>(iters, embedding_size, "ScatterMin");
}

static void BM_ScatterMaxInt32(int iters, int embedding_size) {
  BM_ScatterHelper<int32>(iters, embedding_size, "ScatterMax");
}
static void BM_ScatterMaxInt64(int iters, int embedding_size) {
  BM_ScatterHelper<int64>(iters, embedding_size, "ScatterMax");
}

BENCHMARK(BM_ScatterUpdateInt32)
    ->Arg(1)
    ->Arg(10)
    ->Arg(32)
    ->Arg(50)
    ->Arg(64)
    ->Arg(80)
    ->Arg(96)
    ->Arg(112)
    ->Arg(192)
    ->Arg(256)
    ->Arg(1024)
    ->Arg(10000)
    ->Arg(100000)
    ->Arg(1000000);
BENCHMARK(BM_ScatterUpdateInt64)
    ->Arg(1)
    ->Arg(10)
    ->Arg(64)
    ->Arg(256)
    ->Arg(1024)
    ->Arg(100000);

BENCHMARK(BM_ScatterAddInt32)->Arg(1)->Arg(10)->Arg(64)->Arg(256)->Arg(1024);
BENCHMARK(BM_ScatterAddInt64)->Arg(1)->Arg(10)->Arg(64)->Arg(256)->Arg(1024);

BENCHMARK(BM_ScatterMulInt32)->Arg(1)->Arg(10)->Arg(64)->Arg(256)->Arg(1024);
BENCHMARK(BM_ScatterMulInt64)->Arg(1)->Arg(10)->Arg(64)->Arg(256)->Arg(1024);

BENCHMARK(BM_ScatterDivInt32)->Arg(1)->Arg(10)->Arg(64)->Arg(256)->Arg(1024);
BENCHMARK(BM_ScatterDivInt64)->Arg(1)->Arg(10)->Arg(64)->Arg(256)->Arg(1024);

BENCHMARK(BM_ScatterMinInt32)->Arg(1)->Arg(10)->Arg(64)->Arg(256)->Arg(1024);
BENCHMARK(BM_ScatterMinInt64)->Arg(1)->Arg(10)->Arg(64)->Arg(256)->Arg(1024);

BENCHMARK(BM_ScatterMaxInt32)->Arg(1)->Arg(10)->Arg(64)->Arg(256)->Arg(1024);
BENCHMARK(BM_ScatterMaxInt64)->Arg(1)->Arg(10)->Arg(64)->Arg(256)->Arg(1024);

}  // namespace
}  // namespace tensorflow
