Commit message: Fix crash in softmax-xent when some input dimensions are 1.

Before, tf.nn.softmax_cross_entropy_with_logits would fail a CHECK if one input tensor had shape (1, 1) and the other did not.

In particular, the call to ToIndexArray<2> here https://github.com/tensorflow/tensorflow/blob/1f3da84a89702d3b4f234ee83762d738caffe098/tensorflow/core/kernels/xent_op.cc#L99 would fail, since the call assumed the array had two dimensions. If both dimensions were 1, BCast would merge the two dimensions into a single dimension. Passing fewer_dims_optimization=false stops this optimization

PiperOrigin-RevId: 384844496
Change-Id: Ifb02dc74964132c3ed3f3bc98b0858dbe4e258b7
CVE: CVE-2021-41206
CWE: CWE-354
Vulnerability description: TensorFlow is an open source platform for machine learning. In affected versions several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or `CHECK`-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible. We have discovered these issues internally via tooling while working on improving/testing GPU op determinism. As such, we don't have reproducers and there will be multiple fixes for these issues. These fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Project: tensorflow
File: xent_op.cc

  void Compute(OpKernelContext* context) override {
    const Tensor& logits_in = context->input(0);
    const Tensor& labels_in = context->input(1);

    TensorShape shape_in = logits_in.shape();

    BCast bcast(BCast::FromShape(logits_in.shape()),
                BCast::FromShape(labels_in.shape()),
                /*fewer_dims_optimization=*/false);
    if (!logits_in.IsSameSize(labels_in)) {
      OP_REQUIRES(context, bcast.IsValid(),
                  errors::InvalidArgument(
                      "logits and labels must be broadcastable: logits_size=",
                      logits_in.shape().DebugString(),
                      " labels_size=", labels_in.shape().DebugString()));
      shape_in = BCast::ToShape(bcast.output_shape());
    }
    OP_REQUIRES(context, TensorShapeUtils::IsMatrix(shape_in),
                errors::InvalidArgument("logits and labels must be either "
                                        "2-dimensional, or broadcasted to be "
                                        "2-dimensional"));

    if (std::is_same<Device, GPUDevice>::value) {
      OP_REQUIRES(context, !OpDeterminismRequired(),
                  errors::Unimplemented(
                      "The GPU implementation of SoftmaxCrossEntropyWithLogits"
                      " that would have been executed is not deterministic."
                      " Note that the Python API uses an alternative,"
                      " deterministic, GPU-accelerated path when determinism is"
                      " enabled."));
    }

    // loss is 1-D (one per example), and size is batch_size.

    Tensor scratch;
    OP_REQUIRES_OK(
        context, context->allocate_temp(DataTypeToEnum<T>::value,
                                        TensorShape({shape_in.dim_size(0), 1}),
                                        &scratch));

    Tensor* loss_out = nullptr;
    OP_REQUIRES_OK(context,
                   context->allocate_output(
                       0, TensorShape({shape_in.dim_size(0)}), &loss_out));
    Tensor* back_out = nullptr;
    // Try to reuse the logits_in buffer for the backprop output.
    OP_REQUIRES_OK(context, context->forward_input_or_allocate_output(
                                {0}, 1, shape_in, &back_out));
    if (shape_in.dim_size(0) > 0) {
      functor::XentFunctor<Device, T> functor;
      functor(context->eigen_device<Device>(), shape_in.AsEigenDSizes<2>(),
              BCast::ToIndexArray<2>(bcast.x_bcast()),
              BCast::ToIndexArray<2>(bcast.y_bcast()),
              logits_in.template shaped<T, 2>(bcast.x_reshape()),
              labels_in.template shaped<T, 2>(bcast.y_reshape()),
              scratch.matrix<T>(), loss_out->vec<T>(), back_out->matrix<T>());
    }
  }

Project: tensorflow
File: xent_op.cc

  void operator()(const Device& d,
                  const Eigen::DSizes<Eigen::DenseIndex, 2>& shape,
                  const Eigen::array<Eigen::DenseIndex, 2>& logits_bcast,
                  const Eigen::array<Eigen::DenseIndex, 2>& labels_bcast,
                  typename TTypes<T>::ConstMatrix logits,
                  typename TTypes<T>::ConstMatrix labels,
                  typename TTypes<T>::Matrix scratch,
                  typename TTypes<T>::Vec loss,
                  typename TTypes<T>::Matrix backprop) {
    XentEigenImpl<Device, T>::Compute(d, shape, logits_bcast, labels_bcast,
                                      logits, labels, scratch, loss, backprop);
  }

Project: tensorflow
File: xent_op.cc

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