Commit message: Replace faulty overflow check with a builder for `TensorShape`.

Prevents an integer overflow that was not caught before.

PiperOrigin-RevId: 415381595
Change-Id: I76585ddedc912bd9f4a390aeafa8e2ced1a28863
CVE: CVE-2022-23568
CWE: CWE-190
Vulnerability description: Tensorflow is an Open Source Machine Learning Framework. The implementation of `AddManySparseToTensorsMap` is vulnerable to an integer overflow which results in a `CHECK`-fail when building new `TensorShape` objects (so, an assert failure based denial of service). We are missing some validation on the shapes of the input tensors as well as directly constructing a large `TensorShape` with user-provided dimensions. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Project: tensorflow
File: sparse_tensors_map_ops.cc

  void Compute(OpKernelContext* context) override {
    const Tensor* input_indices;
    const Tensor* input_values;
    const Tensor* input_shape;
    SparseTensorsMap* map;

    OP_REQUIRES_OK(context, context->input("sparse_indices", &input_indices));
    OP_REQUIRES_OK(context, context->input("sparse_values", &input_values));
    OP_REQUIRES_OK(context, context->input("sparse_shape", &input_shape));
    OP_REQUIRES_OK(context, GetMap(context, true /* is_writing */, &map));

    OP_REQUIRES(context, TensorShapeUtils::IsMatrix(input_indices->shape()),
                errors::InvalidArgument(
                    "Input indices should be a matrix but received shape ",
                    input_indices->shape().DebugString()));
    OP_REQUIRES(context, TensorShapeUtils::IsVector(input_values->shape()),
                errors::InvalidArgument(
                    "Input values should be a vector but received shape ",
                    input_values->shape().DebugString()));
    OP_REQUIRES(context, TensorShapeUtils::IsVector(input_shape->shape()),
                errors::InvalidArgument(
                    "Input shape should be a vector but received shape ",
                    input_shape->shape().DebugString()));
    OP_REQUIRES(
        context,
        input_values->shape().dim_size(0) == input_indices->shape().dim_size(0),
        errors::InvalidArgument(
            "Number of values must match first dimension of indices. ", "Got ",
            input_values->shape().dim_size(0),
            " values, indices shape: ", input_indices->shape().DebugString()));
    OP_REQUIRES(
        context,
        input_shape->shape().dim_size(0) == input_indices->shape().dim_size(1),
        errors::InvalidArgument(
            "Number of dimensions must match second dimension of indices. ",
            "Got ", input_shape->shape().dim_size(0),
            " dimensions, indices shape: ",
            input_indices->shape().DebugString()));

    int rank = input_shape->NumElements();

    OP_REQUIRES(
        context, rank > 1,
        errors::InvalidArgument(
            "Rank of input SparseTensor should be > 1, but saw rank: ", rank));

    auto input_shape_vec = input_shape->vec<int64_t>();
    int new_num_elements = 1;
    bool overflow_ocurred = false;
    for (int i = 0; i < input_shape_vec.size(); i++) {
      new_num_elements =
          MultiplyWithoutOverflow(new_num_elements, input_shape_vec(i));
      if (new_num_elements < 0) {
        overflow_ocurred = true;
        break;
      }
    }

    OP_REQUIRES(
        context, !overflow_ocurred,
        errors::Internal("Encountered overflow from large input shape."));

    TensorShape tensor_input_shape(input_shape_vec);
    gtl::InlinedVector<int64_t, 8> std_order(rank);
    std::iota(std_order.begin(), std_order.end(), 0);
    SparseTensor input_st;
    OP_REQUIRES_OK(context, SparseTensor::Create(*input_indices, *input_values,
                                                 tensor_input_shape, std_order,
                                                 &input_st));

    const int64_t N = input_shape_vec(0);

    Tensor sparse_handles(DT_INT64, TensorShape({N}));
    auto sparse_handles_t = sparse_handles.vec<int64_t>();

    OP_REQUIRES_OK(context, input_st.IndicesValid());

    // We can generate the output shape proto string now, for all
    // minibatch entries.
    TensorShape output_shape;
    OP_REQUIRES_OK(context, TensorShapeUtils::MakeShape(
                                input_shape_vec.data() + 1,
                                input_shape->NumElements() - 1, &output_shape));

    // Get groups by minibatch dimension
    std::unordered_set<int64_t> visited;
    sparse::GroupIterable minibatch = input_st.group({0});
    for (const auto& subset : minibatch) {
      const int64_t b = subset.group()[0];
      visited.insert(b);
      OP_REQUIRES(
          context, b > -1 && b < N,
          errors::InvalidArgument(
              "Received unexpected column 0 value in input SparseTensor: ", b,
              " < 0 or >= N (= ", N, ")"));

      const auto indices = subset.indices();
      const auto values = subset.values<T>();
      const int64_t num_entries = values.size();

      Tensor output_indices = Tensor(DT_INT64, {num_entries, rank - 1});
      Tensor output_values = Tensor(DataTypeToEnum<T>::value, {num_entries});

      auto output_indices_t = output_indices.matrix<int64_t>();
      auto output_values_t = output_values.vec<T>();

      for (int i = 0; i < num_entries; ++i) {
        for (int d = 1; d < rank; ++d) {
          output_indices_t(i, d - 1) = indices(i, d);
        }
        output_values_t(i) = values(i);
      }

      SparseTensor st_i;
      OP_REQUIRES_OK(context,
                     SparseTensor::Create(output_indices, output_values,
                                          output_shape, &st_i));
      int64_t handle;
      OP_REQUIRES_OK(context, map->AddSparseTensor(context, st_i, &handle));
      sparse_handles_t(b) = handle;
    }

    // Fill in any gaps; we must provide an empty ST for batch entries
    // the grouper didn't find.
    if (visited.size() < N) {
      Tensor empty_indices(DT_INT64, {0, rank - 1});
      Tensor empty_values(DataTypeToEnum<T>::value, {0});
      SparseTensor empty_st;
      OP_REQUIRES_OK(context, SparseTensor::Create(empty_indices, empty_values,
                                                   output_shape, &empty_st));

      for (int64_t b = 0; b < N; ++b) {
        // We skipped this batch entry.
        if (visited.find(b) == visited.end()) {
          int64_t handle;
          OP_REQUIRES_OK(context,
                         map->AddSparseTensor(context, empty_st, &handle));
          sparse_handles_t(b) = handle;
        }
      }
    }

    context->set_output(0, sparse_handles);
  }

Project: tensorflow
File: sparse_tensors_map_ops.cc

  void Compute(OpKernelContext* context) override {
    const Tensor* input_indices;
    const Tensor* input_values;
    const Tensor* input_shape;
    SparseTensorsMap* map;

    OP_REQUIRES_OK(context, context->input("sparse_indices", &input_indices));
    OP_REQUIRES_OK(context, context->input("sparse_values", &input_values));
    OP_REQUIRES_OK(context, context->input("sparse_shape", &input_shape));
    OP_REQUIRES_OK(context, GetMap(context, true /* is_writing */, &map));

    OP_REQUIRES(context, TensorShapeUtils::IsMatrix(input_indices->shape()),
                errors::InvalidArgument(
                    "Input indices should be a matrix but received shape ",
                    input_indices->shape().DebugString()));
    OP_REQUIRES(context, TensorShapeUtils::IsVector(input_values->shape()),
                errors::InvalidArgument(
                    "Input values should be a vector but received shape ",
                    input_values->shape().DebugString()));
    OP_REQUIRES(context, TensorShapeUtils::IsVector(input_shape->shape()),
                errors::InvalidArgument(
                    "Input shape should be a vector but received shape ",
                    input_shape->shape().DebugString()));
    OP_REQUIRES(
        context,
        input_values->shape().dim_size(0) == input_indices->shape().dim_size(0),
        errors::InvalidArgument(
            "Number of values must match first dimension of indices. ", "Got ",
            input_values->shape().dim_size(0),
            " values, indices shape: ", input_indices->shape().DebugString()));
    OP_REQUIRES(
        context,
        input_shape->shape().dim_size(0) == input_indices->shape().dim_size(1),
        errors::InvalidArgument(
            "Number of dimensions must match second dimension of indices. ",
            "Got ", input_shape->shape().dim_size(0),
            " dimensions, indices shape: ",
            input_indices->shape().DebugString()));

    int rank = input_shape->NumElements();

    OP_REQUIRES(
        context, rank > 1,
        errors::InvalidArgument(
            "Rank of input SparseTensor should be > 1, but saw rank: ", rank));

    auto input_shape_vec = input_shape->vec<int64_t>();

    TensorShape tensor_input_shape;
    OP_REQUIRES_OK(context, TensorShape::BuildTensorShape(input_shape_vec,
                                                          &tensor_input_shape));
    gtl::InlinedVector<int64_t, 8> std_order(rank);
    std::iota(std_order.begin(), std_order.end(), 0);
    SparseTensor input_st;
    OP_REQUIRES_OK(context, SparseTensor::Create(*input_indices, *input_values,
                                                 tensor_input_shape, std_order,
                                                 &input_st));

    const int64_t N = input_shape_vec(0);

    Tensor sparse_handles(DT_INT64, TensorShape({N}));
    auto sparse_handles_t = sparse_handles.vec<int64_t>();

    OP_REQUIRES_OK(context, input_st.IndicesValid());

    // We can generate the output shape proto string now, for all
    // minibatch entries.
    TensorShape output_shape;
    OP_REQUIRES_OK(context, TensorShapeUtils::MakeShape(
                                input_shape_vec.data() + 1,
                                input_shape->NumElements() - 1, &output_shape));

    // Get groups by minibatch dimension
    std::unordered_set<int64_t> visited;
    sparse::GroupIterable minibatch = input_st.group({0});
    for (const auto& subset : minibatch) {
      const int64_t b = subset.group()[0];
      visited.insert(b);
      OP_REQUIRES(
          context, b > -1 && b < N,
          errors::InvalidArgument(
              "Received unexpected column 0 value in input SparseTensor: ", b,
              " < 0 or >= N (= ", N, ")"));

      const auto indices = subset.indices();
      const auto values = subset.values<T>();
      const int64_t num_entries = values.size();

      Tensor output_indices = Tensor(DT_INT64, {num_entries, rank - 1});
      Tensor output_values = Tensor(DataTypeToEnum<T>::value, {num_entries});

      auto output_indices_t = output_indices.matrix<int64_t>();
      auto output_values_t = output_values.vec<T>();

      for (int i = 0; i < num_entries; ++i) {
        for (int d = 1; d < rank; ++d) {
          output_indices_t(i, d - 1) = indices(i, d);
        }
        output_values_t(i) = values(i);
      }

      SparseTensor st_i;
      OP_REQUIRES_OK(context,
                     SparseTensor::Create(output_indices, output_values,
                                          output_shape, &st_i));
      int64_t handle;
      OP_REQUIRES_OK(context, map->AddSparseTensor(context, st_i, &handle));
      sparse_handles_t(b) = handle;
    }

    // Fill in any gaps; we must provide an empty ST for batch entries
    // the grouper didn't find.
    if (visited.size() < N) {
      Tensor empty_indices(DT_INT64, {0, rank - 1});
      Tensor empty_values(DataTypeToEnum<T>::value, {0});
      SparseTensor empty_st;
      OP_REQUIRES_OK(context, SparseTensor::Create(empty_indices, empty_values,
                                                   output_shape, &empty_st));

      for (int64_t b = 0; b < N; ++b) {
        // We skipped this batch entry.
        if (visited.find(b) == visited.end()) {
          int64_t handle;
          OP_REQUIRES_OK(context,
                         map->AddSparseTensor(context, empty_st, &handle));
          sparse_handles_t(b) = handle;
        }
      }
    }

    context->set_output(0, sparse_handles);
  }