Function BM_ScatterHelper

tensorflow/core/kernels/scatter_op_test.cc:241–274

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");