AveragePool3d.cu

#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
#include <ATen/core/Tensor.h>
#include <ATen/AccumulateType.h>
#include <ATen/ceil_div.h>
#include <ATen/Dispatch.h>
#include <ATen/native/Pool.h>
#include <ATen/cuda/Atomic.cuh>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/detail/TensorInfo.cuh>
#include <ATen/cuda/detail/IndexUtils.cuh>
#include <ATen/cuda/detail/KernelUtils.h>
#include <ATen/native/cuda/KernelUtils.cuh>
#include <c10/macros/Macros.h>

#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/Functions.h>
#include <ATen/NativeFunctions.h>
#else
#include <ATen/ops/avg_pool3d_native.h>
#include <ATen/ops/avg_pool3d_backward_native.h>
#endif


namespace at {
namespace native {
namespace {

__device__ inline int min(int a, int b) {
  return a <= b ? a : b;
}

__device__ inline int max(int a, int b) {
  return a >= b ? a : b;
}

template <typename scalar_t, typename accscalar_t>
__global__ void avg_pool3d_cuda_update_output(
  PackedTensorAccessor64<scalar_t, 4> input,
  PackedTensorAccessor64<scalar_t, 4> output,
  int kT, int kH, int kW,
  int dT, int dH, int dW,
  int padT, int padH, int padW,
  bool count_include_pad,
  int offsetZ, int divisor_override)
{
  int oCol   = blockIdx.x * blockDim.x + threadIdx.x;
  int oRow   = blockIdx.y * blockDim.y + threadIdx.y;
  int oFrame = (blockIdx.z + offsetZ) % output.size(1); // output frame/time
  int slice  = (blockIdx.z + offsetZ) / output.size(1); // output slice/feature

  if (oRow < output.size(2) && oCol < output.size(3))
  {
    accscalar_t sum = 0.0;

    int tstart = oFrame * dT - padT;
    int hstart = oRow   * dH - padH;
    int wstart = oCol   * dW - padW;
    int tend = min(tstart + kT, input.size(1) + padT);
    int hend = min(hstart + kH, input.size(2) + padH);
    int wend = min(wstart + kW, input.size(3) + padW);
    int pool_size = (tend - tstart) * (hend - hstart) * (wend - wstart);
    tstart = max(tstart, 0);
    hstart = max(hstart, 0);
    wstart = max(wstart, 0);
    tend = min(tend, input.size(1));
    hend = min(hend, input.size(2));
    wend = min(wend, input.size(3));

    if (tstart >= tend || hstart >= hend || wstart >= wend) {
      output[slice][oFrame][oRow][oCol] = scalar_t(0);
      return;
    }

    accscalar_t divide_factor;
    if (divisor_override) {
      divide_factor = static_cast<accscalar_t>(divisor_override);
    } else {
      if(count_include_pad) {
        divide_factor = static_cast<accscalar_t>(pool_size);
      } else {
        divide_factor = static_cast<accscalar_t>((tend - tstart) * (hend - hstart) * (wend - wstart));
      }
    }

    int ti, hi, wi;
    for (ti = tstart; ti < tend; ++ti)
    {
      for (hi = hstart; hi < hend; ++hi)
      {
        for (wi = wstart; wi < wend; ++wi)
        {
          scalar_t val = input[slice][ti][hi][wi];
          sum += val;
        }
      }
    }

    output[slice][oFrame][oRow][oCol] = static_cast<scalar_t>(sum / divide_factor);
  }
}

// Inner-most loop size (kW) passed as template parameter for
// performance reasons.
//
template<int KERNEL_WIDTH, typename scalar_t, typename accscalar_t>
__global__ void avg_pool3d_cuda_update_output(
  PackedTensorAccessor64<scalar_t, 4> input,
  PackedTensorAccessor64<scalar_t, 4> output,
  int kT, int kH,
  int dT, int dH, int dW,
  int padT, int padH, int padW,
  bool count_include_pad,
  int offsetZ, int divisor_override)
{
  int oCol   = blockIdx.x * blockDim.x + threadIdx.x;
  int oRow   = blockIdx.y * blockDim.y + threadIdx.y;
  int oFrame = (blockIdx.z + offsetZ) % output.size(1); // output frame/time
  int slice  = (blockIdx.z + offsetZ) / output.size(1); // output slice/feature

  if (oRow < output.size(2) && oCol < output.size(3))
  {
    accscalar_t sum = 0.0;

    int tstart = oFrame * dT - padT;
    int hstart = oRow   * dH - padH;
    int wstart = oCol   * dW - padW;
    int tend = min(tstart + kT, input.size(1) + padT);
    int hend = min(hstart + kH, input.size(2) + padH);
    int wend = min(wstart + KERNEL_WIDTH, input.size(3) + padW);
    int pool_size = (tend - tstart) * (hend - hstart) * (wend - wstart);
    tstart = max(tstart, 0);
    hstart = max(hstart, 0);
    wstart = max(wstart, 0);
    tend = min(tend, input.size(1));
    hend = min(hend, input.size(2));
    wend = min(wend, input.size(3));

    if (tstart >= tend || hstart >= hend || wstart >= wend) {
      output[slice][oFrame][oRow][oCol] = scalar_t(0);
      return;
    }

    accscalar_t divide_factor;
    if (divisor_override) {
      divide_factor = static_cast<accscalar_t>(divisor_override);
    } else {
      if(count_include_pad) {
        divide_factor = static_cast<accscalar_t>(pool_size);
      } else {
        divide_factor = static_cast<accscalar_t>((tend - tstart) * (hend - hstart) * (wend - wstart));
      }
    }

    int ti, hi, wi;
    for (ti = tstart; ti < tend; ++ti)
    {
      for (hi = hstart; hi < hend; ++hi)
      {
        for (wi = wstart; wi < wend; ++wi)
        {
          scalar_t val = input[slice][ti][hi][wi];
          sum += val;
        }
      }
    }

    output[slice][oFrame][oRow][oCol] = static_cast<scalar_t>(sum / divide_factor);
  }
}

template <typename scalar_t, typename accscalar_t>
__global__ void avg_pool3d_single_backward_out_frame_stride1(
  PackedTensorAccessor64<scalar_t, 4> gradOutput,
  PackedTensorAccessor64<scalar_t, 4> gradInput,
  int kT, int kH, int kW,
  accscalar_t normFactor,
  int offsetZ)
{
  int iCol   = blockIdx.x * blockDim.x + threadIdx.x;
  int iRow   = blockIdx.y * blockDim.y + threadIdx.y;
  int iFrame = (blockIdx.z + offsetZ) % gradInput.size(1); // input frame/time
  int slice  = (blockIdx.z + offsetZ) / gradInput.size(1); // input slice/feature

  // guard against over-tiled threads
  if (iRow < gradInput.size(2) && iCol < gradInput.size(3))
  {
    accscalar_t sum = 0.0;
    scalar_t *gOut = &gradOutput[slice][max(0, iFrame - kT + 1)]
      [max(0, iRow - kH + 1)][max(0, iCol - kW + 1)];
    int frameOffset = 0;
    for (int oFrame  = max(0, iFrame - kT + 1);
         oFrame < min(iFrame + 1, gradOutput.size(1));
         ++oFrame)
    {
      int rowOffset = frameOffset;
      for (int oRow = max(0, iRow - kH + 1);
           oRow < min(iRow + 1, gradOutput.size(2));
           ++oRow)
      {
        int colOffset = rowOffset;
        for (int oCol = max(0, iCol - kW + 1);
             oCol < min(iCol + 1, gradOutput.size(3));
             ++oCol)
        {
          sum += gOut[colOffset];
          ++colOffset;
        }
        rowOffset += gradOutput.size(3);
      }
      frameOffset += gradOutput.size(2) * gradOutput.size(3);
    }
    gradInput[slice][iFrame][iRow][iCol] = static_cast<scalar_t>(sum * normFactor);
  }
}

template <typename scalar_t, typename accscalar_t>
__global__ void avg_pool3d_cuda_update_grad_input_atomic(
  PackedTensorAccessor64<scalar_t, 4> gradOutput,
  PackedTensorAccessor64<scalar_t, 4> gradInput,
  int kT, int kH, int kW,
  int dT, int dH, int dW,
  int padT, int padH, int padW,
  bool count_include_pad,
  int offsetZ, int divisor_override, const int gradInput_numel)
{
  int oCol   = blockIdx.x * blockDim.x + threadIdx.x;
  int oRow   = blockIdx.y * blockDim.y + threadIdx.y;
  int oFrame = (blockIdx.z + offsetZ) % gradOutput.size(1); // gradOutput frame/time
  int slice  = (blockIdx.z + offsetZ) / gradOutput.size(1); // gradOutput slice/feature

  // guard against over-tiled threads
  if (oRow < gradOutput.size(2) && oCol < gradOutput.size(3))
  {
    int tstart = oFrame * dT - padT;
    int hstart = oRow   * dH - padH;
    int wstart = oCol   * dW - padW;
    int tend = min(tstart + kT, gradInput.size(1) + padT);
    int hend = min(hstart + kH, gradInput.size(2) + padH);
    int wend = min(wstart + kW, gradInput.size(3) + padW);
    int pool_size = (tend - tstart) * (hend - hstart) * (wend - wstart);
    tstart = max(tstart, 0);
    hstart = max(hstart, 0);
    wstart = max(wstart, 0);
    tend = min(tend, gradInput.size(1));
    hend = min(hend, gradInput.size(2));
    wend = min(wend, gradInput.size(3));

    accscalar_t divide_factor;
    if (divisor_override) {
      divide_factor = static_cast<accscalar_t>(divisor_override);
    } else {
      if(count_include_pad) {
        divide_factor = static_cast<accscalar_t>(pool_size);
      } else {
        divide_factor = static_cast<accscalar_t>((tend - tstart) * (hend - hstart) * (wend - wstart));
      }
    }

    scalar_t val = static_cast<scalar_t>(
      static_cast<accscalar_t>(gradOutput[slice][oFrame][oRow][oCol]) / divide_factor);
    for (int iFrame = tstart; iFrame < tend; ++iFrame)
    {
      for (int iRow = hstart; iRow < hend; ++iRow)
      {
        for (int iCol = wstart; iCol < wend; ++iCol)
        {
          const int index = slice * gradInput.stride(0) + iFrame * gradInput.stride(1) + iRow * gradInput.stride(2) + iCol * gradInput.stride(3);
          fastAtomicAdd(gradInput.data(), index, gradInput_numel, val, true);
        }
      }
    }
  }
}

template <typename scalar_t, typename accscalar_t>
__global__ void avg_pool3d_cuda_update_grad_input(
  PackedTensorAccessor64<scalar_t, 4> gradOutput,
  PackedTensorAccessor64<scalar_t, 4> gradInput,
  int kT, int kH, int kW,
  int dT, int dH, int dW,
  int padT, int padH, int padW,
  bool count_include_pad, int offsetZ, int divisor_override)
{
  int oCol   = blockIdx.x * blockDim.x + threadIdx.x;
  int oRow   = blockIdx.y * blockDim.y + threadIdx.y;
  int oFrame = (blockIdx.z + offsetZ) % gradOutput.size(1); // gradOutput frame/time
  int slice  = (blockIdx.z + offsetZ) / gradOutput.size(1); // gradOutput slice/feature

  // guard against over-tiled threads
  if (oRow < gradOutput.size(2) && oCol < gradOutput.size(3))
  {
    int tstart = oFrame * dT - padT;
    int hstart = oRow   * dH - padH;
    int wstart = oCol   * dW - padW;
    int tend = min(tstart + kT, gradInput.size(1) + padT);
    int hend = min(hstart + kH, gradInput.size(2) + padH);
    int wend = min(wstart + kW, gradInput.size(3) + padW);
    int pool_size = (tend - tstart) * (hend - hstart) * (wend - wstart);
    tstart = max(tstart, 0);
    hstart = max(hstart, 0);
    wstart = max(wstart, 0);
    tend = min(tend, gradInput.size(1));
    hend = min(hend, gradInput.size(2));
    wend = min(wend, gradInput.size(3));

    accscalar_t divide_factor;
    if (divisor_override) {
      divide_factor = static_cast<accscalar_t>(divisor_override);
    } else {
      if(count_include_pad) {
        divide_factor = static_cast<accscalar_t>(pool_size);
      } else {
        divide_factor = static_cast<accscalar_t>((tend - tstart) * (hend - hstart) * (wend - wstart));
      }
    }

    scalar_t val = static_cast<scalar_t>(
      static_cast<accscalar_t>(gradOutput[slice][oFrame][oRow][oCol]) / divide_factor);
    for (int iFrame = tstart; iFrame < tend; ++iFrame)
    {
      for (int iRow = hstart; iRow < hend; ++iRow)
      {
        for (int iCol = wstart; iCol < wend; ++iCol)
        {
          gradInput[slice][iFrame][iRow][iCol] = val;
        }
      }
    }
  }
}

} // anonymous namespace

#define LAUNCH_UPDATE_OUTPUT_KERNEL_WIDTH(KW) case KW:      \
  avg_pool3d_cuda_update_output<KW, scalar_t, accscalar_t>  \
    <<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>( \
       work_input.packed_accessor64<scalar_t, 4>(),         \
       work_output.packed_accessor64<scalar_t, 4>(),        \
       kT, kH,                                              \
       dT, dH, dW,                                          \
       padT, padH, padW,                                    \
       count_include_pad,                                   \
       offsetZ, divisor);                                   \
  C10_CUDA_KERNEL_LAUNCH_CHECK();                           \
  break


TORCH_IMPL_FUNC(avg_pool3d_out_cuda) (
  const Tensor& input,
  IntArrayRef kernel_size,
  IntArrayRef stride,
  IntArrayRef padding,
  bool ceil_mode,
  bool count_include_pad,
  c10::optional<int64_t> divisor_override,
  const Tensor& output
) {
  TensorArg output_arg{ output, "output", 1 };
  TensorArg input_arg{ input, "input", 2 };

  checkAllSameGPU(__func__, {output_arg, input_arg});

  const int kT = safe_downcast<int, int64_t>(kernel_size[0]);
  const int kH = kernel_size.size() == 1 ? kT : safe_downcast<int, int64_t>(kernel_size[1]);
  const int kW = kernel_size.size() == 1 ? kT : safe_downcast<int, int64_t>(kernel_size[2]);

  const int dT = stride.empty() ? kT : safe_downcast<int, int64_t>(stride[0]);
  const int dH = stride.empty() ? kH :
                 stride.size() == 1 ? dT : safe_downcast<int, int64_t>(stride[1]);
  const int dW = stride.empty() ? kW :
                 stride.size() == 1 ? dT : safe_downcast<int, int64_t>(stride[2]);

  const int padT = safe_downcast<int, int64_t>(padding[0]);
  const int padH = padding.size() == 1 ? padT : safe_downcast<int, int64_t>(padding[1]);
  const int padW = padding.size() == 1 ? padT : safe_downcast<int, int64_t>(padding[2]);

  // if divisor==0 then we will ignore it
  int64_t divisor = 0;
  if (divisor_override.has_value()) {
    divisor = divisor_override.value();
  }

  const int64_t nbatch = input.ndimension() == 5 ? input.size(-5) : 1;
  const int64_t nslices = input.size(-4);
  const int64_t itime = input.size(-3);
  const int64_t iheight = input.size(-2);
  const int64_t iwidth = input.size(-1);

  const int64_t otime = pooling_output_shape<int64_t>(itime, kT, padT, dT, 1, ceil_mode);
  const int64_t oheight = pooling_output_shape<int64_t>(iheight, kH, padH, dH, 1, ceil_mode);
  const int64_t owidth = pooling_output_shape<int64_t>(iwidth, kW, padW, dW, 1, ceil_mode);

  Tensor work_input = input.contiguous();
  Tensor work_output = output;
  if (input.ndimension() == 5) {
    // Collapse batch and feature dimensions.
    work_input = work_input.reshape({nbatch * nslices, itime, iheight, iwidth});
    work_output = work_output.reshape({nbatch * nslices, otime, oheight, owidth});
  }

  AT_DISPATCH_FLOATING_TYPES_AND2(kHalf, kBFloat16,
    input.scalar_type(),
    "avg_pool3d_out_cuda",
    [&] {
      using accscalar_t = acc_type<scalar_t, true>;
      int64_t totalZ = otime * nslices * nbatch;
      int64_t offsetZ = 0;
      dim3 block(32, 8);

      while (totalZ > 0) {
        dim3 grid(ceil_div(owidth, static_cast<int64_t>(block.x)),
                  ceil_div(oheight, static_cast<int64_t>(block.y)),
                  totalZ > 65535 ? 65535 : totalZ);

        switch (kW) {
          LAUNCH_UPDATE_OUTPUT_KERNEL_WIDTH(1);
          LAUNCH_UPDATE_OUTPUT_KERNEL_WIDTH(2);
          LAUNCH_UPDATE_OUTPUT_KERNEL_WIDTH(3);
          LAUNCH_UPDATE_OUTPUT_KERNEL_WIDTH(4);
          LAUNCH_UPDATE_OUTPUT_KERNEL_WIDTH(5);
          LAUNCH_UPDATE_OUTPUT_KERNEL_WIDTH(6);
          LAUNCH_UPDATE_OUTPUT_KERNEL_WIDTH(7);
        default:
          avg_pool3d_cuda_update_output<scalar_t, accscalar_t>
            <<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(
                work_input.packed_accessor64<scalar_t, 4>(),
                work_output.packed_accessor64<scalar_t, 4>(),
                kT, kH, kW,
                dT, dH, dW,
                padT, padH, padW,
                count_include_pad,
                offsetZ, divisor);
          C10_CUDA_KERNEL_LAUNCH_CHECK();
          break;
        }

        totalZ -= 65535;
        offsetZ += 65535;
      }
    }
  );
}

#undef LAUNCH_UPDATE_OUTPUT_KERNEL_WIDTH


TORCH_IMPL_FUNC(avg_pool3d_backward_out_cuda) (
  const Tensor& gradOutput,
  const Tensor& input,
  IntArrayRef kernel_size,
  IntArrayRef stride,
  IntArrayRef padding,
  bool ceil_mode,
  bool count_include_pad,
  c10::optional<int64_t> divisor_override,
  const Tensor& gradInput
) {
  // See Note [Writing Nondeterministic Operations]
  // Nondeterministic because of atomicAdd usage
  globalContext().alertNotDeterministic("avg_pool3d_backward_cuda");

  TensorArg gradInput_arg{ gradInput, "gradInput", 1 };
  TensorArg gradOutput_arg{ gradOutput, "gradOutput", 2 };
  TensorArg input_arg{ input, "input", 3 };

  checkAllSameGPU(__func__,
                  {gradInput_arg, gradOutput_arg, input_arg});

  const int kT = safe_downcast<int, int64_t>(kernel_size[0]);
  const int kH = kernel_size.size() == 1 ? kT : safe_downcast<int, int64_t>(kernel_size[1]);
  const int kW = kernel_size.size() == 1 ? kT : safe_downcast<int, int64_t>(kernel_size[2]);

  const int dT = stride.empty() ? kT : safe_downcast<int, int64_t>(stride[0]);
  const int dH = stride.empty() ? kH :
                 stride.size() == 1 ? dT : safe_downcast<int, int64_t>(stride[1]);
  const int dW = stride.empty() ? kW :
                 stride.size() == 1 ? dT : safe_downcast<int, int64_t>(stride[2]);

  const int padT = safe_downcast<int, int64_t>(padding[0]);
  const int padH = padding.size() == 1 ? padT : safe_downcast<int, int64_t>(padding[1]);
  const int padW = padding.size() == 1 ? padT : safe_downcast<int, int64_t>(padding[2]);

  TORCH_CHECK((gradOutput.ndimension() == 4 || gradOutput.ndimension() == 5),
    "non-empty 4D or 5D (batch mode) tensor expected for gradOutput");

  // if divisor==0 then we will ignore it
  int64_t divisor = 0;
  if (divisor_override.has_value()) {
    divisor = divisor_override.value();
  }

  gradInput.zero_();

  const int64_t nbatch = input.ndimension() == 5 ? input.size(-5) : 1;
  const int64_t nslices = input.size(-4);
  const int64_t itime = input.size(-3);
  const int64_t iheight = input.size(-2);
  const int64_t iwidth = input.size(-1);

  const int64_t otime = gradOutput.size(-3);
  const int64_t oheight = gradOutput.size(-2);
  const int64_t owidth = gradOutput.size(-1);

  /* XXX shape check behavior from TH */
  const int64_t otime_for_shape_check = pooling_output_shape<int64_t>(itime, kT, padT, dT, 1, ceil_mode);
  const int64_t oheight_for_shape_check = pooling_output_shape<int64_t>(iheight, kH, padH, dH, 1, ceil_mode);
  const int64_t owidth_for_chape_check = pooling_output_shape<int64_t>(iwidth, kW, padW, dW, 1, ceil_mode);

  const bool kernelsOverlap = (dT < kT) || (dH < kH) || (dW < kW);

  Tensor work_grad_input = gradInput;
  Tensor work_grad_output = gradOutput.contiguous();

  if (input.ndimension() == 5) {
    // Collapse batch and feature dimensions.
    work_grad_input = work_grad_input.reshape({nbatch * nslices, itime, iheight, iwidth});
    work_grad_output = work_grad_output.reshape({nbatch * nslices, otime, oheight, owidth});
  }


  // Optimizing for stride 1 is probably only of limited value, but this
  // specialization yields 3x speedup over the gpuAtomicAdd implementation.
  // Padding must be 0, otherwise, pool size may change.
  if (dT == 1 && dH == 1 && dW == 1 && padT == 0 && padH == 0 && padW == 0) {
    AT_DISPATCH_FLOATING_TYPES_AND2(kHalf, kBFloat16, input.scalar_type(),
      "avg_pool3d_backward_out_frame_stride1",
      [&] {
        using accscalar_t = acc_type<scalar_t, true>;
        int64_t totalZ = itime * nslices * nbatch;
        int64_t offsetZ = 0;
        dim3 block(32, 8);

        accscalar_t divide_factor;
        if (divisor) {
          divide_factor = static_cast<accscalar_t>(divisor);
        } else {
          divide_factor = static_cast<accscalar_t>(kT * kH * kW);
        }

        while (totalZ > 0) {
          dim3 grid(ceil_div(iwidth, static_cast<int64_t>(block.x)),
                    ceil_div(iheight, static_cast<int64_t>(block.y)),
                    totalZ > 65535 ? 65535 : totalZ);

          avg_pool3d_single_backward_out_frame_stride1<scalar_t, accscalar_t>
            <<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(
              work_grad_output.packed_accessor64<scalar_t, 4>(),
              work_grad_input.packed_accessor64<scalar_t, 4>(),
              kT, kH, kW,
              1.0f/divide_factor,
              offsetZ);
          C10_CUDA_KERNEL_LAUNCH_CHECK();

          totalZ -= 65535;
          offsetZ += 65535;
        }
      }
    );
  }
  else {
    AT_DISPATCH_FLOATING_TYPES_AND2(kHalf, kBFloat16, input.scalar_type(),
      "avg_pool3d_backward_out_frame",
      [&] {
        using accscalar_t = acc_type<scalar_t, true>;
        int64_t totalZ = otime * nslices * nbatch;
        int64_t offsetZ = 0;
        dim3 block(32, 8);

        while (totalZ > 0) {
          dim3 grid(ceil_div(owidth, static_cast<int64_t>(block.x)),
                    ceil_div(oheight, static_cast<int64_t>(block.y)),
                    totalZ > 65535 ? 65535 : totalZ);

          if (kernelsOverlap) {
            avg_pool3d_cuda_update_grad_input_atomic<scalar_t, accscalar_t>
              <<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(
                  work_grad_output.packed_accessor64<scalar_t, 4>(),
                  work_grad_input.packed_accessor64<scalar_t, 4>(),
                  kT, kH, kW,
                  dT, dH, dW,
                  padT, padH, padW,
                  count_include_pad,
                  offsetZ, divisor, work_grad_input.numel());
            C10_CUDA_KERNEL_LAUNCH_CHECK();
          }
          else {
            avg_pool3d_cuda_update_grad_input<scalar_t, accscalar_t>
              <<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(
                  work_grad_output.packed_accessor64<scalar_t, 4>(),
                  work_grad_input.packed_accessor64<scalar_t, 4>(),
                  kT, kH, kW,
                  dT, dH, dW,
                  padT, padH, padW,
                  count_include_pad,
                  offsetZ, divisor);
            C10_CUDA_KERNEL_LAUNCH_CHECK();
          }

          totalZ -= 65535;
          offsetZ += 65535;
        }
      }
    );
  }
}

} // at::native
} // at