EVOLUTION-MANAGER

Edit File: gather_functor_gpu.cu.h

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==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_GATHER_FUNCTOR_GPU_CU_H_
#define TENSORFLOW_CORE_KERNELS_GATHER_FUNCTOR_GPU_CU_H_

#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM

#define EIGEN_USE_GPU

#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/kernels/gather_functor.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/util/gpu_kernel_helper.h"

namespace tensorflow {

typedef Eigen::GpuDevice GPUDevice;

template <typename T, typename Index, bool is_axis_zero>
__global__ void GatherOpKernel(const T* __restrict__ params,
                               const Index* __restrict__ indices,
                               T* __restrict__ out, int64 gather_dim_size,
                               int64 indices_size, int64 slice_size,
                               int64 out_size) {
  GPU_1D_KERNEL_LOOP(i, out_size) {
    Index batch_i = 0;
    Index indices_i = 0;
    Index slice_i = 0;
    if (is_axis_zero) {
      indices_i = i / slice_size;
      slice_i = i - indices_i * slice_size;
    } else {
      Index batch_indices_i = i / slice_size;
      // The batch index into params to use for i.
      batch_i = batch_indices_i / indices_size;
      // The index into indices to use for i.
      indices_i = batch_indices_i - batch_i * indices_size;
      // Index into the current slice in params to use for i.
      slice_i = i - batch_indices_i * slice_size;
    }

// Index into the gather axis to use for i.
    Index gather_i = ldg(indices + indices_i);

// Check gather_i is in [0, gather_dim_size).
    if (!FastBoundsCheck(gather_i, gather_dim_size)) {
      // Set indices out of range to zero
      // TODO(fpmc): Log an error for transfer back to host.
      out[i] = T(0);
    } else {
      // params is a [batch_size, gather_dim_size, slice_size] tensor. Read
      // params[batch_i, gather_i, slice_i] and write it to the i'th position in
      // out.
      Index params_i =
          (batch_i * gather_dim_size + gather_i) * slice_size + slice_i;
      out[i] = ldg(params + params_i);
    }
  }
}

namespace functor {
template <typename T, typename Index>
struct GatherFunctor<GPUDevice, T, Index> {
  int64 operator()(OpKernelContext* ctx,
                   typename TTypes<T, 3>::ConstTensor params,
                   typename TTypes<Index>::ConstFlat indices,
                   typename TTypes<T, 3>::Tensor out) {
    const GPUDevice& d = ctx->eigen_gpu_device();
    const int64 out_size = out.size();
    if (out_size == 0) {
      // We need a check here since the CPU version does useful error checking
      // work if there are nonempty indices but empty slices, so the kernel is
      // executed in that case.  In the GPU case we don't know how to do error
      // checking, so we skip the loop entirely.
      return -1;
    }
    const bool is_axis_zero = params.dimension(0) == 1;
    const int64 gather_dim_size = params.dimension(1);
    const int64 indices_size = indices.size();
    const int64 slice_size = params.dimension(2);

if (is_axis_zero) {
      GpuLaunchConfig config = GetGpuLaunchConfig(
          out_size, d, &GatherOpKernel<T, Index, true>,
          /*dynamic_shared_memory_size=*/0, /*block_size_limit=*/0);
      TF_CHECK_OK(GpuLaunchKernel(
          GatherOpKernel<T, Index, true>, config.block_count,
          config.thread_per_block, 0, d.stream(), params.data(), indices.data(),
          out.data(), gather_dim_size, indices_size, slice_size, out_size));
    } else {
      GpuLaunchConfig config = GetGpuLaunchConfig(
          out_size, d, &GatherOpKernel<T, Index, false>,
          /*dynamic_shared_memory_size=*/0, /*block_size_limit=*/0);
      TF_CHECK_OK(GpuLaunchKernel(
          GatherOpKernel<T, Index, false>, config.block_count,
          config.thread_per_block, 0, d.stream(), params.data(), indices.data(),
          out.data(), gather_dim_size, indices_size, slice_size, out_size));
    }
    // TODO(fpmc): enable indices validation on GPU.
    // Right now checking for indices out of bound in the kernel would
    // require copying code between GPU/CPU, and thus slow.
    return -1;
  }
};

}  // namespace functor
}  // namespace tensorflow

#endif  // GOOGLE_CUDA || TENSORFLOW_USE_ROCM

#endif  // TENSORFLOW_CORE_KERNELS_GATHER_FUNCTOR_GPU_CU_H_