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#if GOOGLE_CUDA
#define EIGEN_USE_GPU
#include <algorithm>
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/kernels/bias_op.h"
#include "tensorflow/core/util/cuda_kernel_helper.h"
namespace tensorflow {
typedef Eigen::GpuDevice GPUDevice;
// Definition of the GPU implementations declared in bias_op.cc.
namespace functor {
template <typename T>
__global__ void BiasOpCustomKernel(int nthreads, const T* input, const T* bias,
int bias_size, int replicate_count,
T* output) {
CUDA_1D_KERNEL_LOOP(index, nthreads) {
int bias_offset = index % bias_size;
output[index] = input[index] + bias[bias_offset];
}
}
template <typename T, int Dims>
struct Bias<GPUDevice, T, Dims> {
typedef GPUDevice Device;
// 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;
CudaLaunchConfig config = GetCudaLaunchConfig(output.size(), d);
BiasOpCustomKernel<<<config.block_count, config.thread_per_block, 0,
d.stream()>>>(config.virtual_thread_count,
input.data(), bias.data(), bias_size,
rest_size, output.data());
}
};
} // namespace functor
#define DEFINE_GPU_SPECS(T) \
template struct functor::Bias<GPUDevice, T, 2>; \
template struct functor::Bias<GPUDevice, T, 3>; \
template struct functor::Bias<GPUDevice, T, 4>; \
template struct functor::Bias<GPUDevice, T, 5>;
TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS);
} // end namespace tensorflow
#endif // GOOGLE_CUDA
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