Commit message: Validate `MatrixDiagV{2,3}` arguments to prevent breakage.

PiperOrigin-RevId: 369056033
Change-Id: Ic2018c297d3dd6f252dc1dd3667f1ed5cb1eaa42
CVE: CVE-2021-29515
CWE: CWE-190
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. The implementation of `MatrixDiag*` operations(https://github.com/tensorflow/tensorflow/blob/4c4f420e68f1cfaf8f4b6e8e3eb857e9e4c3ff33/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L195-L197) does not validate that the tensor arguments are non-empty. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Project: tensorflow
File: matrix_diag_op.cc

  void Compute(OpKernelContext* context) override {
    const Tensor& input = context->input(0);

    // MatrixDiagPart and MatrixDiagPartV2 both use this OpKernel.
    // MatrixDiagPart only has one input, so we have to check the number of
    // inputs before reading additional parameters in MatrixDiagV2.
    int32 lower_diag_index = 0;
    int32 upper_diag_index = 0;
    T padding_value(0);

    // MatrixDiagPartV2-specific.
    if (context->num_inputs() > kNumV1Inputs) {
      auto& diag_index = context->input(1);
      OP_REQUIRES(context,
                  TensorShapeUtils::IsScalar(diag_index.shape()) ||
                      TensorShapeUtils::IsVector(diag_index.shape()),
                  errors::InvalidArgument(
                      "diag_index must be a scalar or vector, received shape: ",
                      diag_index.shape().DebugString()));
      lower_diag_index = diag_index.flat<int32>()(0);
      upper_diag_index = lower_diag_index;
      if (TensorShapeUtils::IsVector(diag_index.shape())) {
        auto diag_index_size = diag_index.dim_size(0);
        OP_REQUIRES(
            context, 0 < diag_index_size && diag_index_size <= 2,
            errors::InvalidArgument(
                "diag_index must have only one or two elements, received ",
                diag_index_size, " elements."));
        if (diag_index_size > 1) {
          upper_diag_index = diag_index.flat<int32>()(1);
        }
      }
      padding_value = context->input(2).flat<T>()(0);
    }
    const TensorShape& input_shape = input.shape();

    // Preliminary validation of sizes.
    OP_REQUIRES(context, TensorShapeUtils::IsMatrixOrHigher(input_shape),
                errors::InvalidArgument(
                    "input must be at least 2-dim, received shape: ",
                    input.shape().DebugString()));

    // Make sure lower_diag_index and upper_diag_index is valid.
    const int rank = input_shape.dims();
    const Eigen::Index num_rows = input_shape.dim_size(rank - 2);
    const Eigen::Index num_cols = input_shape.dim_size(rank - 1);
    OP_REQUIRES(  // Checks lower_diag_index == 0 for when matrix shape = 0.
        context,
        (-num_rows < lower_diag_index && lower_diag_index < num_cols) ||
            lower_diag_index == 0,
        errors::InvalidArgument(
            "lower_diag_index is out of bound: ", lower_diag_index,
            ". It must be between ", -num_rows, " and ", num_cols));
    OP_REQUIRES(context,
                (-num_rows < upper_diag_index && upper_diag_index < num_cols) ||
                    upper_diag_index == 0,
                errors::InvalidArgument(
                    "upper_diag_index is out of bound: ", upper_diag_index,
                    " It must be between ", -num_rows, " and ", num_cols));
    OP_REQUIRES(
        context, lower_diag_index <= upper_diag_index,
        errors::InvalidArgument(
            "lower_diag_index must not be larger than upper_diag_index: ",
            lower_diag_index, " > ", upper_diag_index));

    TensorShape output_shape;
    for (int i = 0; i < rank - 2; ++i) {
      output_shape.AddDim(input_shape.dim_size(i));
    }
    const Eigen::Index num_diags = upper_diag_index - lower_diag_index + 1;
    if (num_diags > 1) output_shape.AddDim(num_diags);
    const int32 max_diag_len =
        std::min(num_rows + std::min(upper_diag_index, 0),
                 num_cols - std::max(lower_diag_index, 0));
    output_shape.AddDim(max_diag_len);

    Tensor* output = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
    auto output_reshaped = output->flat<T>();
    auto input_reshaped = input.flat_inner_dims<T, 3>();
    functor::MatrixDiagPart<Device, T>::Compute(
        context, context->eigen_device<Device>(), input_reshaped,
        output_reshaped, lower_diag_index, upper_diag_index, max_diag_len,
        padding_value, left_align_superdiagonal_, left_align_subdiagonal_);
  }

Project: tensorflow
File: matrix_diag_op.cc

  void Compute(OpKernelContext* context) override {
    const Tensor& diagonal = context->input(0);

    // MatrixDiag and MatrixDiagV2 both use this OpKernel. MatrixDiag only has
    // one input, so we have to check the number of inputs before reading
    // additional parameters in MatrixDiagV2.
    int32 lower_diag_index = 0;
    int32 upper_diag_index = 0;
    int32 num_rows = -1;
    int32 num_cols = -1;
    T padding_value(0);

    // MatrixDiagOpV2-specific.
    if (context->num_inputs() > kNumV1Inputs) {
      auto& diag_index = context->input(1);
      OP_REQUIRES(context,
                  TensorShapeUtils::IsScalar(diag_index.shape()) ||
                      TensorShapeUtils::IsVector(diag_index.shape()),
                  errors::InvalidArgument(
                      "diag_index must be a scalar or vector, received shape: ",
                      diag_index.shape().DebugString()));
      lower_diag_index = diag_index.flat<int32>()(0);
      upper_diag_index = lower_diag_index;
      if (TensorShapeUtils::IsVector(diag_index.shape())) {
        auto diag_index_size = diag_index.dim_size(0);
        OP_REQUIRES(
            context, 0 < diag_index_size && diag_index_size <= 2,
            errors::InvalidArgument(
                "diag_index must have only one or two elements, received ",
                diag_index_size, " elements."));
        if (diag_index_size > 1) {
          upper_diag_index = diag_index.flat<int32>()(1);
        }
      }

      auto& num_rows_tensor = context->input(2);
      OP_REQUIRES(context, TensorShapeUtils::IsScalar(num_rows_tensor.shape()),
                  errors::InvalidArgument("num_rows must be a scalar"));
      num_rows = num_rows_tensor.flat<int32>()(0);

      auto& num_cols_tensor = context->input(3);
      OP_REQUIRES(context, TensorShapeUtils::IsScalar(num_cols_tensor.shape()),
                  errors::InvalidArgument("num_cols must be a scalar"));
      num_cols = num_cols_tensor.flat<int32>()(0);

      auto& padding_value_tensor = context->input(4);
      OP_REQUIRES(context,
                  TensorShapeUtils::IsScalar(padding_value_tensor.shape()),
                  errors::InvalidArgument("padding_value must be a scalar"));
      padding_value = padding_value_tensor.flat<T>()(0);
    }

    // Size validations.
    const TensorShape& diagonal_shape = diagonal.shape();
    const int diag_rank = diagonal_shape.dims();
    const Eigen::Index num_diags = upper_diag_index - lower_diag_index + 1;
    OP_REQUIRES(context, TensorShapeUtils::IsVectorOrHigher(diagonal_shape),
                errors::InvalidArgument(
                    "diagonal must be at least 1-dim, received shape: ",
                    diagonal.shape().DebugString()));
    OP_REQUIRES(
        context, lower_diag_index <= upper_diag_index,
        errors::InvalidArgument(
            "lower_diag_index must not be larger than upper_diag_index: ",
            lower_diag_index, " > ", upper_diag_index));
    OP_REQUIRES(context,
                lower_diag_index == upper_diag_index ||
                    diagonal_shape.dim_size(diag_rank - 2) == num_diags,
                errors::InvalidArgument(
                    "The number of diagonals provided in the input does not "
                    "match the lower_diag_index and upper_diag_index range."));

    const Eigen::Index max_diag_len = diagonal_shape.dim_size(diag_rank - 1);
    const int32 min_num_rows = max_diag_len - std::min(upper_diag_index, 0);
    const int32 min_num_cols = max_diag_len + std::max(lower_diag_index, 0);
    OP_REQUIRES(context, num_rows == -1 || num_rows >= min_num_rows,
                errors::InvalidArgument("The number of rows is too small."));
    OP_REQUIRES(context, num_cols == -1 || num_cols >= min_num_cols,
                errors::InvalidArgument("The number of columns is too small."));

    // If both num_rows and num_cols are unknown, assume that output is square.
    // Otherwise, use smallest possible values.
    if (num_rows == -1 && num_cols == -1) {
      num_rows = std::max(min_num_rows, min_num_cols);
      num_cols = num_rows;
    } else if (num_rows == -1) {
      num_rows = min_num_rows;
    } else if (num_cols == -1) {
      num_cols = min_num_cols;
    }
    OP_REQUIRES(context, num_rows == min_num_rows || num_cols == min_num_cols,
                errors::InvalidArgument(
                    "The number of rows or columns is not consistent with "
                    "the specified d_lower, d_upper, and diagonal."));

    TensorShape output_shape = diagonal_shape;
    if (num_diags == 1) {  // Output has rank `rank+1`.
      output_shape.set_dim(diag_rank - 1, num_rows);
      output_shape.AddDim(num_cols);
    } else {  // Output has rank `rank`.
      output_shape.set_dim(diag_rank - 2, num_rows);
      output_shape.set_dim(diag_rank - 1, num_cols);
    }

    Tensor* output = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
    auto output_reshaped = output->flat_inner_dims<T, 3>();
    auto diag_reshaped = diagonal.flat<T>();
    functor::MatrixDiag<Device, T>::Compute(
        context, context->eigen_device<Device>(), diag_reshaped,
        output_reshaped, lower_diag_index, upper_diag_index, max_diag_len,
        padding_value, left_align_superdiagonal_, left_align_subdiagonal_);
  }