Commit message: Ensure non-empty padding_value input to tf.raw_ops.MatrixDiagPartV2, if a padding_value is input

PiperOrigin-RevId: 388314614
Change-Id: If0b51ad58d5d8543a6be6ce8f42ae4755c80d55f
CVE: CVE-2021-37643
CWE: CWE-125
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. If a user does not provide a valid padding value to `tf.raw_ops.MatrixDiagPartOp`, then the code triggers a null pointer dereference (if input is empty) or produces invalid behavior, ignoring all values after the first. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L89) reads the first value from a tensor buffer without first checking that the tensor has values to read from. We have patched the issue in GitHub commit 482da92095c4d48f8784b1f00dda4f81c28d2988. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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_t lower_diag_index = 0;
    int32_t 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()));
      OP_REQUIRES(context, diag_index.NumElements() > 0,
                  errors::InvalidArgument(
                      "Expected diag_index to have at least 1 element"));
      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_t 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_);
  }