Method CallEigenVersion

tensorflow/core/kernels/mkl_concat_op.cc:830–870

  }

  void CallEigenVersion(OpKernelContext* context, const OpInputList& values,
                        const OpInputList& input_mins,
                        const OpInputList& input_maxes,
                        const MklDnnShapeList& mkl_input_shapes,
                        bool quantized_input) {
    size_t num_mkl_input_shapes = mkl_input_shapes.size();
    DCHECK_EQ(values.size(), num_mkl_input_shapes);
    std::vector<Tensor> converted_values(num_mkl_input_shapes);
    TensorShapeList tf_input_shapes;
    for (size_t i = 0; i < num_mkl_input_shapes; ++i) {
      if (mkl_input_shapes[i].IsMklTensor()) {
        // do conversion from OneDNN to TF
        OP_REQUIRES_OK(
            context, ConvertMklToTF<T>(context, values[i], mkl_input_shapes[i],
                                       &converted_values[i]));
        tf_input_shapes.push_back(mkl_input_shapes[i].GetTfShape());
      } else {
        // no conversion since it is TF tensor already
        converted_values[i] = values[i];
        tf_input_shapes.push_back(values[i].shape());
      }
    }

    // Call Eigen concat.
    eigen_concat_op_.Compute(context, converted_values, tf_input_shapes,
                             input_mins, input_maxes, quantized_input);

    // Get the number of dims from first input since all input tensors
    // should have same rank.
    size_t dims = values[0].shape().dims();
    MklDnnShape output_data_mkl_shape;
    output_data_mkl_shape.SetMklTensor(false);
    output_data_mkl_shape.SetDimensions(dims);
    AllocateOutputSetMklShape(context, 0, output_data_mkl_shape);
    if (quantized_input) {
      MklDnnShape output_min_max_mkl_shape;
      output_min_max_mkl_shape.SetMklTensor(false);
      AllocateOutputSetMklShape(context, 1, output_min_max_mkl_shape);
      AllocateOutputSetMklShape(context, 2, output_min_max_mkl_shape);
    }
  }

  // This method finds the most common format across all OneDNN inputs
  // Inputs: