path: root/tensorflow/core/util/sparse/sparse_tensor_test.cc

#include "tensorflow/core/util/sparse/sparse_tensor.h"

#include <string>
#include <vector>

#include "tensorflow/core/framework/tensor_types.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/public/tensor.h"
#include <gtest/gtest.h>
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"

namespace tensorflow {
namespace sparse {
namespace {

Eigen::Tensor<int64, 2, Eigen::RowMajor, Eigen::DenseIndex>
GetSimpleIndexTensor(int N, const int NDIM) {
  Eigen::Tensor<int64, 2, Eigen::RowMajor, Eigen::DenseIndex> ix(N, NDIM);
  ix(0, 0) = 0;
  ix(0, 1) = 0;
  ix(0, 2) = 0;

  ix(1, 0) = 3;
  ix(1, 1) = 0;
  ix(1, 2) = 0;

  ix(2, 0) = 2;
  ix(2, 1) = 0;
  ix(2, 2) = 0;

  ix(3, 0) = 0;
  ix(3, 1) = 1;
  ix(3, 2) = 0;

  ix(4, 0) = 0;
  ix(4, 1) = 0;
  ix(4, 2) = 2;
  return ix;
}

TEST(SparseTensorTest, DimComparatorSorts) {
  std::size_t N = 5;
  const int NDIM = 3;
  auto ix = GetSimpleIndexTensor(N, NDIM);
  TTypes<int64>::Matrix map(ix.data(), N, NDIM);

  std::vector<int64> sorting(N);
  for (std::size_t n = 0; n < N; ++n) sorting[n] = n;

  // new order should be: {0, 4, 3, 2, 1}
  std::vector<int64> order{0, 1, 2};
  DimComparator sorter(map, order, NDIM);
  std::sort(sorting.begin(), sorting.end(), sorter);

  EXPECT_EQ(sorting, std::vector<int64>({0, 4, 3, 2, 1}));

  // new order should be: {0, 3, 2, 1, 4}
  std::vector<int64> order1{2, 0, 1};
  DimComparator sorter1(map, order1, NDIM);
  for (std::size_t n = 0; n < N; ++n) sorting[n] = n;
  std::sort(sorting.begin(), sorting.end(), sorter1);

  EXPECT_EQ(sorting, std::vector<int64>({0, 3, 2, 1, 4}));
}

TEST(SparseTensorTest, SparseTensorConstruction) {
  int N = 5;
  const int NDIM = 3;
  auto ix_c = GetSimpleIndexTensor(N, NDIM);
  Eigen::Tensor<string, 1, Eigen::RowMajor> vals_c(N);
  vals_c(0) = "hi0";
  vals_c(1) = "hi1";
  vals_c(2) = "hi2";
  vals_c(3) = "hi3";
  vals_c(4) = "hi4";

  Tensor ix(DT_INT64, TensorShape({N, NDIM}));
  Tensor vals(DT_STRING, TensorShape({N}));

  auto ix_t = ix.matrix<int64>();
  auto vals_t = vals.vec<string>();
  vals_t = vals_c;
  ix_t = ix_c;

  TensorShape shape({10, 10, 10});
  std::vector<int64> order{0, 1, 2};
  SparseTensor st(ix, vals, shape, order);
  EXPECT_FALSE(st.IndicesValid());  // Out of order

  // Regardless of how order is updated; so long as there are no
  // duplicates, the resulting indices are valid.
  st.Reorder<string>({2, 0, 1});
  EXPECT_TRUE(st.IndicesValid());
  EXPECT_EQ(vals_t(0), "hi0");
  EXPECT_EQ(vals_t(1), "hi3");
  EXPECT_EQ(vals_t(2), "hi2");
  EXPECT_EQ(vals_t(3), "hi1");
  EXPECT_EQ(vals_t(4), "hi4");

  ix_t = ix_c;
  vals_t = vals_c;
  st.Reorder<string>({0, 1, 2});
  EXPECT_TRUE(st.IndicesValid());
  EXPECT_EQ(vals_t(0), "hi0");
  EXPECT_EQ(vals_t(1), "hi4");
  EXPECT_EQ(vals_t(2), "hi3");
  EXPECT_EQ(vals_t(3), "hi2");
  EXPECT_EQ(vals_t(4), "hi1");

  ix_t = ix_c;
  vals_t = vals_c;
  st.Reorder<string>({2, 1, 0});
  EXPECT_TRUE(st.IndicesValid());
}

TEST(SparseTensorTest, EmptySparseTensorAllowed) {
  int N = 0;
  const int NDIM = 3;

  Tensor ix(DT_INT64, TensorShape({N, NDIM}));
  Tensor vals(DT_STRING, TensorShape({N}));

  TensorShape shape({10, 10, 10});
  std::vector<int64> order{0, 1, 2};
  SparseTensor st(ix, vals, shape, order);
  EXPECT_TRUE(st.IndicesValid());
  EXPECT_EQ(st.order(), order);

  std::vector<int64> new_order{1, 0, 2};
  st.Reorder<string>(new_order);
  EXPECT_TRUE(st.IndicesValid());
  EXPECT_EQ(st.order(), new_order);
}

TEST(SparseTensorTest, SortingWorksCorrectly) {
  int N = 30;
  const int NDIM = 4;

  Tensor ix(DT_INT64, TensorShape({N, NDIM}));
  Tensor vals(DT_STRING, TensorShape({N}));
  TensorShape shape({1000, 1000, 1000, 1000});
  SparseTensor st(ix, vals, shape);

  auto ix_t = ix.matrix<int64>();

  for (int n = 0; n < 100; ++n) {
    ix_t = ix_t.random(Eigen::internal::UniformRandomGenerator<int64>(n + 1));
    ix_t = ix_t.abs() % 1000;
    st.Reorder<string>({0, 1, 2, 3});
    EXPECT_TRUE(st.IndicesValid());
    st.Reorder<string>({3, 2, 1, 0});
    EXPECT_TRUE(st.IndicesValid());
    st.Reorder<string>({1, 0, 2, 3});
    EXPECT_TRUE(st.IndicesValid());
    st.Reorder<string>({3, 0, 2, 1});
    EXPECT_TRUE(st.IndicesValid());
  }
}

TEST(SparseTensorTest, ValidateIndicesFindsInvalid) {
  int N = 2;
  const int NDIM = 3;

  Tensor ix(DT_INT64, TensorShape({N, NDIM}));
  Tensor vals(DT_STRING, TensorShape({N}));

  Eigen::Tensor<int64, 2, Eigen::RowMajor> ix_orig(N, NDIM);
  ix_orig(0, 0) = 0;
  ix_orig(0, 1) = 0;
  ix_orig(0, 2) = 0;

  ix_orig(1, 0) = 0;
  ix_orig(1, 1) = 0;
  ix_orig(1, 2) = 0;

  auto ix_t = ix.matrix<int64>();
  ix_t = ix_orig;

  TensorShape shape({10, 10, 10});
  std::vector<int64> order{0, 1, 2};
  SparseTensor st(ix, vals, shape, order);

  st.Reorder<string>(order);
  EXPECT_FALSE(st.IndicesValid());  // two indices are identical

  ix_orig(1, 2) = 1;
  ix_t = ix_orig;
  st.Reorder<string>(order);
  EXPECT_TRUE(st.IndicesValid());  // second index now (0, 0, 1)

  ix_orig(0, 2) = 1;
  ix_t = ix_orig;
  st.Reorder<string>(order);
  EXPECT_FALSE(st.IndicesValid());  // first index now (0, 0, 1)
}

TEST(SparseTensorTest, SparseTensorCheckBoundaries) {
  int N = 5;
  const int NDIM = 3;

  Tensor ix(DT_INT64, TensorShape({N, NDIM}));
  Tensor vals(DT_STRING, TensorShape({N}));

  auto ix_t = GetSimpleIndexTensor(N, NDIM);

  ix.matrix<int64>() = ix_t;

  TensorShape shape({10, 10, 10});
  std::vector<int64> order{0, 1, 2};

  SparseTensor st(ix, vals, shape, order);
  EXPECT_FALSE(st.IndicesValid());

  st.Reorder<string>(order);
  EXPECT_TRUE(st.IndicesValid());

  ix_t(0, 0) = 11;
  ix.matrix<int64>() = ix_t;
  st.Reorder<string>(order);
  EXPECT_FALSE(st.IndicesValid());

  ix_t(0, 0) = -1;
  ix.matrix<int64>() = ix_t;
  st.Reorder<string>(order);
  EXPECT_FALSE(st.IndicesValid());

  ix_t(0, 0) = 0;
  ix.matrix<int64>() = ix_t;
  st.Reorder<string>(order);
  EXPECT_TRUE(st.IndicesValid());
}

TEST(SparseTensorTest, SparseTensorToDenseTensor) {
  int N = 5;
  const int NDIM = 3;

  Tensor ix(DT_INT64, TensorShape({N, NDIM}));
  Tensor vals(DT_STRING, TensorShape({N}));

  auto ix_t = GetSimpleIndexTensor(N, NDIM);
  auto vals_t = vals.vec<string>();

  ix.matrix<int64>() = ix_t;

  vals_t(0) = "hi0";
  vals_t(1) = "hi1";
  vals_t(2) = "hi2";
  vals_t(3) = "hi3";
  vals_t(4) = "hi4";

  TensorShape shape({4, 4, 5});
  std::vector<int64> order{0, 1, 2};
  SparseTensor st(ix, vals, shape, order);

  Tensor dense(DT_STRING, TensorShape({4, 4, 5}));
  st.ToDense<string>(&dense);

  auto dense_t = dense.tensor<string, 3>();
  Eigen::array<Eigen::DenseIndex, NDIM> ix_n;
  for (int n = 0; n < N; ++n) {
    for (int d = 0; d < NDIM; ++d) ix_n[d] = ix_t(n, d);
    EXPECT_EQ(dense_t(ix_n), vals_t(n));
  }

  // Spot checks on the others
  EXPECT_EQ(dense_t(0, 0, 1), "");
  EXPECT_EQ(dense_t(0, 0, 3), "");
  EXPECT_EQ(dense_t(3, 3, 3), "");
  EXPECT_EQ(dense_t(3, 3, 4), "");
}

TEST(SparseTensorTest, SparseTensorToLargerDenseTensor) {
  int N = 5;
  const int NDIM = 3;

  Tensor ix(DT_INT64, TensorShape({N, NDIM}));
  Tensor vals(DT_STRING, TensorShape({N}));

  auto ix_t = GetSimpleIndexTensor(N, NDIM);
  auto vals_t = vals.vec<string>();

  ix.matrix<int64>() = ix_t;

  vals_t(0) = "hi0";
  vals_t(1) = "hi1";
  vals_t(2) = "hi2";
  vals_t(3) = "hi3";
  vals_t(4) = "hi4";

  TensorShape shape({4, 4, 5});
  std::vector<int64> order{0, 1, 2};
  SparseTensor st(ix, vals, shape, order);

  Tensor dense(DT_STRING, TensorShape({10, 10, 10}));
  st.ToDense<string>(&dense);

  auto dense_t = dense.tensor<string, 3>();
  Eigen::array<Eigen::DenseIndex, NDIM> ix_n;
  for (int n = 0; n < N; ++n) {
    for (int d = 0; d < NDIM; ++d) ix_n[d] = ix_t(n, d);
    EXPECT_EQ(dense_t(ix_n), vals_t(n));
  }

  // Spot checks on the others
  EXPECT_EQ(dense_t(0, 0, 1), "");
  EXPECT_EQ(dense_t(0, 0, 3), "");
  EXPECT_EQ(dense_t(3, 3, 3), "");
  EXPECT_EQ(dense_t(3, 3, 4), "");
  EXPECT_EQ(dense_t(9, 0, 0), "");
  EXPECT_EQ(dense_t(9, 0, 9), "");
  EXPECT_EQ(dense_t(9, 9, 9), "");
}

TEST(SparseTensorTest, SparseTensorGroup) {
  int N = 5;
  const int NDIM = 3;

  Tensor ix(DT_INT64, TensorShape({N, NDIM}));
  Tensor vals(DT_INT32, TensorShape({N}));

  auto ix_t = ix.matrix<int64>();
  auto vals_t = vals.vec<int32>();

  ix_t = GetSimpleIndexTensor(N, NDIM);

  vals_t(0) = 1;  // associated with ix (000)
  vals_t(1) = 2;  // associated with ix (300)
  vals_t(2) = 3;  // associated with ix (200)
  vals_t(3) = 4;  // associated with ix (010)
  vals_t(4) = 5;  // associated with ix (002)

  TensorShape shape({10, 10, 10});
  std::vector<int64> order{0, 1, 2};

  SparseTensor st(ix, vals, shape, order);
  st.Reorder<int32>(order);

  std::vector<std::vector<int64> > groups;
  std::vector<TTypes<int64>::UnalignedConstMatrix> grouped_indices;
  std::vector<TTypes<int32>::UnalignedVec> grouped_values;

  // Group by index 0
  auto gi = st.group({0});

  // All the hard work is right here!
  for (const auto& g : gi) {
    groups.push_back(g.group());
    VLOG(1) << "Group: " << str_util::Join(g.group(), ",");
    VLOG(1) << "Indices: " << g.indices();
    VLOG(1) << "Values: " << g.values<int32>();

    grouped_indices.push_back(g.indices());
    grouped_values.push_back(g.values<int32>());
  }

  // Group by dimension 0, we have groups: 0--, 2--, 3--
  EXPECT_EQ(groups.size(), 3);
  EXPECT_EQ(groups[0], std::vector<int64>({0}));
  EXPECT_EQ(groups[1], std::vector<int64>({2}));
  EXPECT_EQ(groups[2], std::vector<int64>({3}));

  std::vector<Eigen::Tensor<int64, 2, Eigen::RowMajor> > expected_indices;
  std::vector<Eigen::Tensor<int32, 1, Eigen::RowMajor> > expected_vals;

  // First group: 000, 002, 010
  expected_indices.emplace_back(3, NDIM);  // 3 x 3 tensor
  expected_vals.emplace_back(3);           // 3 x 5 x 1 x 1 tensor
  expected_indices[0].setZero();
  expected_indices[0](1, 2) = 2;  // 002
  expected_indices[0](2, 1) = 1;  // 010
  expected_vals[0].setConstant(-1);
  expected_vals[0](0) = 1;  // val associated with ix 000
  expected_vals[0](1) = 5;  // val associated with ix 002
  expected_vals[0](2) = 4;  // val associated with ix 010

  // Second group: 200
  expected_indices.emplace_back(1, NDIM);
  expected_vals.emplace_back(1);
  expected_indices[1].setZero();
  expected_indices[1](0, 0) = 2;  // 200
  expected_vals[1](0) = 3;        // val associated with ix 200

  // Third group: 300
  expected_indices.emplace_back(1, NDIM);
  expected_vals.emplace_back(1);
  expected_indices[2].setZero();
  expected_indices[2](0, 0) = 3;  // 300
  expected_vals[2](0) = 2;        // val associated with ix 300

  for (std::size_t gix = 0; gix < groups.size(); ++gix) {
    // Compare indices
    auto gi_t = grouped_indices[gix];
    Eigen::Tensor<bool, 0, Eigen::RowMajor> eval =
        (gi_t == expected_indices[gix]).all();
    EXPECT_TRUE(eval()) << gix << " indices: " << gi_t << " vs. "
                        << expected_indices[gix];

    // Compare values
    auto gv_t = grouped_values[gix];
    eval = (gv_t == expected_vals[gix]).all();
    EXPECT_TRUE(eval()) << gix << " values: " << gv_t << " vs. "
                        << expected_vals[gix];
  }
}

TEST(SparseTensorTest, Concat) {
  int N = 5;
  const int NDIM = 3;

  Tensor ix(DT_INT64, TensorShape({N, NDIM}));
  Tensor vals(DT_STRING, TensorShape({N}));

  auto ix_c = GetSimpleIndexTensor(N, NDIM);

  auto ix_t = ix.matrix<int64>();
  auto vals_t = vals.vec<string>();

  ix_t = ix_c;

  TensorShape shape({10, 10, 10});
  std::vector<int64> order{0, 1, 2};

  SparseTensor st(ix, vals, shape, order);
  EXPECT_FALSE(st.IndicesValid());
  st.Reorder<string>(order);
  EXPECT_TRUE(st.IndicesValid());

  SparseTensor concatted = SparseTensor::Concat<string>({st, st, st, st});
  EXPECT_EQ(concatted.order(), st.order());
  TensorShape expected_shape({40, 10, 10});
  EXPECT_EQ(concatted.shape(), expected_shape);
  EXPECT_EQ(concatted.num_entries(), 4 * N);
  EXPECT_TRUE(concatted.IndicesValid());

  auto conc_ix_t = concatted.indices().matrix<int64>();
  auto conc_vals_t = concatted.values().vec<string>();

  for (int n = 0; n < 4; ++n) {
    for (int i = 0; i < N; ++i) {
      // Dimensions match except the primary dim, which is offset by
      // shape[order[0]]
      EXPECT_EQ(conc_ix_t(n * N + i, 0), 10 * n + ix_t(i, 0));
      EXPECT_EQ(conc_ix_t(n * N + i, 1), ix_t(i, 1));
      EXPECT_EQ(conc_ix_t(n * N + i, 1), ix_t(i, 1));

      // Values match
      EXPECT_EQ(conc_vals_t(n * N + i), vals_t(i));
    }
  }

  // Concat works if non-primary ix is out of order, but output order
  // is not defined
  SparseTensor st_ooo(ix, vals, shape, {0, 2, 1});  // non-primary ix OOO
  SparseTensor conc_ooo = SparseTensor::Concat<string>({st, st, st, st_ooo});
  std::vector<int64> expected_ooo{-1, -1, -1};
  EXPECT_EQ(conc_ooo.order(), expected_ooo);
  EXPECT_EQ(conc_ooo.shape(), expected_shape);
  EXPECT_EQ(conc_ooo.num_entries(), 4 * N);
}

// TODO(ebrevdo): ReduceToDense(R={dim1,dim2,...}, reduce_fn, &output)
// reduce_fn sees slices of resorted values based on generator (dim: DDIMS), and
// slices of resorted indices on generator.

}  // namespace
}  // namespace sparse
}  // namespace tensorflow