Commit message: Fix heap-buffer-overflow issue with `tf.raw_ops.SparseFillEmptyRows`.

PiperOrigin-RevId: 372009178
Change-Id: Ia1a9e9691ecaa072f32fb39a0887b2aabd399210
CVE: CVE-2021-29565
CWE: CWE-703
Vulnerability description: TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference in the implementation of `tf.raw_ops.SparseFillEmptyRows`. This is because of missing validation(https://github.com/tensorflow/tensorflow/blob/fdc82089d206e281c628a93771336bf87863d5e8/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc#L230-L231) that was covered under a `TODO`. If the `dense_shape` tensor is empty, then `dense_shape_t.vec<>()` would cause a null pointer dereference in the implementation of the op. 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: sparse_fill_empty_rows_op.cc

  void ComputeAsync(OpKernelContext* context, DoneCallback done) override {
    SparseFillEmptyRowsOpImpl<GPUDevice, T, Tindex>(context, done);
  }

Project: tensorflow
File: sparse_fill_empty_rows_op.cc

  Status operator()(OpKernelContext* context, const Tensor& default_value_t,
                    const Tensor& indices_t, const Tensor& values_t,
                    const Tensor& dense_shape_t,
                    typename AsyncOpKernel::DoneCallback done) {
    (void)done;  // Unused (only used in GPU implementation)
    const int kOutputIndicesOutput = 0;
    const int kOutputValuesOutput = 1;
    const int kEmptyRowIndicatorOutput = 2;
    const int kReverseIndexMapOutput = 3;

    const T& default_value = default_value_t.scalar<T>()();
    const auto indices = indices_t.matrix<Tindex>();
    const auto values = values_t.vec<T>();
    const auto dense_shape = dense_shape_t.vec<Tindex>();

    const Tindex N = indices_t.shape().dim_size(0);
    const Tindex dense_rows = dense_shape(0);

    bool* empty_row_indicator = nullptr;
    if (context->output_required(kEmptyRowIndicatorOutput)) {
      Tensor* empty_row_indicator_t = nullptr;
      TF_RETURN_IF_ERROR(context->allocate_output(kEmptyRowIndicatorOutput,
                                                  TensorShape({dense_rows}),
                                                  &empty_row_indicator_t));
      empty_row_indicator = empty_row_indicator_t->vec<bool>().data();
    }
    Tindex* reverse_index_map = nullptr;
    if (context->output_required(kReverseIndexMapOutput)) {
      Tensor* reverse_index_map_t = nullptr;
      TF_RETURN_IF_ERROR(context->allocate_output(
          kReverseIndexMapOutput, TensorShape({N}), &reverse_index_map_t));
      reverse_index_map = reverse_index_map_t->vec<Tindex>().data();
    }

    int rank = indices_t.shape().dim_size(1);

    if (dense_rows == 0) {
      if (N != 0) {
        return errors::InvalidArgument(
            "Received SparseTensor with dense_shape[0] = 0 but "
            "indices.shape[0] = ",
            N);
      }
      Tensor* output_indices_t;
      TensorShape output_indices_shape({0, rank});
      TF_RETURN_IF_ERROR(context->allocate_output(
          kOutputIndicesOutput, output_indices_shape, &output_indices_t));
      Tensor* output_values_t;
      TF_RETURN_IF_ERROR(context->allocate_output(
          kOutputValuesOutput, TensorShape({0}), &output_values_t));

      // Exit early, nothing more to do.
      return Status::OK();
    }

    bool rows_are_ordered = true;
    Tindex last_indices_row = 0;
    std::vector<Tindex> csr_offset(dense_rows, 0);
    for (int i = 0; i < N; ++i) {
      const Tindex row = indices(i, 0);
      if (row < 0 || row >= dense_rows) {
        return errors::InvalidArgument("indices(", i, ", 0) is invalid: ", row,
                                       " >= ", dense_rows);
      }
      ++csr_offset[row];
      rows_are_ordered = rows_are_ordered & (row >= last_indices_row);
      last_indices_row = row;
    }
    bool all_rows_full = true;
    for (int row = 0; row < dense_rows; ++row) {
      // csr_offset here describes the number of elements in this dense row
      bool row_empty = (csr_offset[row] == 0);
      if (empty_row_indicator) {
        empty_row_indicator[row] = row_empty;
      }
      all_rows_full = all_rows_full & !row_empty;
      // In filled version, each row has at least one element.
      csr_offset[row] = std::max(csr_offset[row], Tindex{1});
      // Update csr_offset to represent the number of elements up to and
      // including dense_row + 1:
      //  csr_offset(0) == #{elements of row 0}
      //  csr_offset(1) == #{elements of row 1} + #{elements of row 0}
      //  ..
      //  csr_offset(i) == starting index for elements in row i + 1.
      if (row > 0) {
        csr_offset[row] += csr_offset[row - 1];
      }
    }

    if (all_rows_full && rows_are_ordered) {
      context->set_output(kOutputIndicesOutput, indices_t);
      context->set_output(kOutputValuesOutput, values_t);
      if (reverse_index_map) {
        for (Tindex i = 0; i < N; ++i) {
          reverse_index_map[i] = i;
        }
      }
    } else {
      Tensor* output_indices_t;
      const Tindex N_full = csr_offset[dense_rows - 1];
      TensorShape output_indices_shape({N_full, rank});
      TF_RETURN_IF_ERROR(context->allocate_output(
          kOutputIndicesOutput, output_indices_shape, &output_indices_t));
      auto output_indices = output_indices_t->matrix<Tindex>();

      Tensor* output_values_t;
      TF_RETURN_IF_ERROR(context->allocate_output(
          kOutputValuesOutput, TensorShape({N_full}), &output_values_t));
      auto output_values = output_values_t->vec<T>();

      std::vector<Tindex> filled_count(dense_rows, 0);

      // Fill in values for rows that are not missing
      for (Tindex i = 0; i < N; ++i) {
        const Tindex row = indices(i, 0);
        Tindex& offset = filled_count[row];
        const Tindex output_i = ((row == 0) ? 0 : csr_offset[row - 1]) + offset;
        offset++;  // Increment the filled count for this row.
        std::copy_n(&indices(i, 0), rank, &output_indices(output_i, 0));
        output_values(output_i) = values(i);
        // We'll need this reverse index map to backprop correctly.
        if (reverse_index_map) {
          reverse_index_map[i] = output_i;
        }
      }

      // Fill in values for rows that are missing
      for (Tindex row = 0; row < dense_rows; ++row) {
        const Tindex row_count = filled_count[row];
        if (row_count == 0) {  // We haven't filled this row
          const Tindex starting_index = (row == 0) ? 0 : csr_offset[row - 1];
          // Remaining index values were set to zero already.
          // Just need to set the row index in the right location.
          output_indices(starting_index, 0) = row;
          for (Tindex col = 1; col < rank; ++col) {
            output_indices(starting_index, col) = 0;
          }
          output_values(starting_index) = default_value;
        }
      }
    }

    return Status::OK();
  }

Project: tensorflow
File: sparse_fill_empty_rows_op.cc

  Status operator()(OpKernelContext* context,
                    typename TTypes<Tindex>::ConstVec reverse_index_map,
                    typename TTypes<T>::ConstVec grad_values,
                    typename TTypes<T>::Vec d_values,
                    typename TTypes<T>::Scalar d_default_value) {
    const CPUDevice& device = context->eigen_device<CPUDevice>();
    const Tindex N = reverse_index_map.dimension(0);
    const Tindex N_full = grad_values.dimension(0);

    T& d_default_value_scalar = d_default_value();
    d_default_value_scalar = T();

    Tensor visited_t;
    TF_RETURN_IF_ERROR(
        context->allocate_temp(DT_BOOL, TensorShape({N_full}), &visited_t));
    auto visited = visited_t.vec<bool>();
    visited.device(device) = visited.constant(false);

    for (int i = 0; i < N; ++i) {
      // Locate the index of the output of the forward prop associated
      // with this location in the input of the forward prop.  Copy
      // the gradient into it.  Mark it as visited.
      int64 reverse_index = reverse_index_map(i);
      if (reverse_index < 0 || reverse_index >= N_full) {
        return errors::InvalidArgument(
            "Elements in reverse index must be in [0, ", N_full, ") but got ",
            reverse_index);
      }
      d_values(i) = grad_values(reverse_index);
      visited(reverse_index) = true;
    }
    for (int j = 0; j < N_full; ++j) {
      // The default value gradient gets the accumulated remainder of
      // the backprop values (since the default value was used to fill
      // in these slots in the forward calculation).
      if (!visited(j)) {
        d_default_value_scalar += grad_values(j);
      }
    }
    return Status::OK();
  }

Project: tensorflow
File: sparse_fill_empty_rows_op.cc

  explicit SparseFillEmptyRowsGradOp(OpKernelConstruction* context)
      : OpKernel(context) {}

Project: tensorflow
File: sparse_fill_empty_rows_op.cc

  explicit SparseFillEmptyRowsOp(OpKernelConstruction* context)
      : OpKernel(context) {}

Project: tensorflow
File: sparse_fill_empty_rows_op.cc

  void Compute(OpKernelContext* context) override {
    SparseFillEmptyRowsOpImpl<Device, T, Tindex>(context);
  }

Project: tensorflow
File: sparse_fill_empty_rows_op.cc

  explicit SparseFillEmptyRowsGPUOp(OpKernelConstruction* context)
      : AsyncOpKernel(context) {}

Project: tensorflow
File: sparse_fill_empty_rows_op.cc

  void Compute(OpKernelContext* context) override {
    const Tensor* reverse_index_map_t;
    const Tensor* grad_values_t;
    OP_REQUIRES_OK(context,
                   context->input("reverse_index_map", &reverse_index_map_t));
    OP_REQUIRES_OK(context, context->input("grad_values", &grad_values_t));

    OP_REQUIRES(
        context, TensorShapeUtils::IsVector(reverse_index_map_t->shape()),
        errors::InvalidArgument("reverse_index_map must be a vector, saw: ",
                                reverse_index_map_t->shape().DebugString()));
    OP_REQUIRES(context, TensorShapeUtils::IsVector(grad_values_t->shape()),
                errors::InvalidArgument("grad_values must be a vector, saw: ",
                                        grad_values_t->shape().DebugString()));

    const auto reverse_index_map = reverse_index_map_t->vec<Tindex>();
    const auto grad_values = grad_values_t->vec<T>();

    const Tindex N = reverse_index_map_t->shape().dim_size(0);

    Tensor* d_values_t;
    OP_REQUIRES_OK(context, context->allocate_output(
                                "d_values", TensorShape({N}), &d_values_t));
    auto d_values = d_values_t->vec<T>();
    Tensor* d_default_value_t;
    OP_REQUIRES_OK(context,
                   context->allocate_output("d_default_value", TensorShape({}),
                                            &d_default_value_t));
    auto d_default_value = d_default_value_t->scalar<T>();

    OP_REQUIRES_OK(context,
                   functor::SparseFillEmptyRowsGrad<Device, T, Tindex>()(
                       context, reverse_index_map, grad_values, d_values,
                       d_default_value));
  }