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#ifndef TENSORFLOW_KERNELS_BIAS_OP_H_
#define TENSORFLOW_KERNELS_BIAS_OP_H_
// Functor definition for BiasOp, must be compilable by nvcc.
#include "tensorflow/core/framework/tensor_types.h"
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
namespace tensorflow {
namespace functor {
// Functor used by BiasOp to do the computations.
template <typename Device, typename T, int Dims>
struct Bias {
// Add "bias" to "input", broadcasting it on all dimensions but the last one.
void operator()(const Device& d, typename TTypes<T, Dims>::ConstTensor input,
typename TTypes<T>::ConstVec bias,
typename TTypes<T, Dims>::Tensor output) {
const int bias_size = bias.dimension(0);
const int rest_size = input.size() / bias_size;
Eigen::DSizes<int, 2> rest_by_bias(rest_size, bias_size);
#if !defined(EIGEN_HAS_INDEX_LIST)
Eigen::DSizes<int, 2> rest_by_one(rest_size, 1);
Eigen::DSizes<int, 2> one_by_bias(1, bias_size);
#else
Eigen::IndexList<int, Eigen::type2index<1> > rest_by_one;
rest_by_one.set(0, rest_size);
Eigen::IndexList<Eigen::type2index<1>, int> one_by_bias;
one_by_bias.set(1, bias_size);
#endif
output.reshape(rest_by_bias).device(d) =
input.reshape(rest_by_bias) +
bias.reshape(one_by_bias).broadcast(rest_by_one);
}
};
} // namespace functor
} // namespace tensorflow
#endif // TENSORFLOW_KERNELS_BIAS_OP_H_
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