UpSampleMoreKernel.cpp

#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
#include <vector>

#include <ATen/core/Tensor.h>
#include <ATen/Dispatch.h>
#include <ATen/native/UpSample.h>
#include <ATen/Parallel.h>
#include <ATen/TensorIterator.h>
#include <c10/util/irange.h>
#include <ATen/cpu/vec/vec.h>

namespace at::native {
namespace {

using scale_t = std::vector<c10::optional<double>>;

template <typename scalar_t, typename scale_type, nearest_idx_fn_t nearest_idx_fn>
void cpu_upsample_nearest_backward(
    const Tensor& grad_input_,
    const Tensor& grad_output_,
    const scale_type& scales) {
  TORCH_CHECK(grad_input_.dtype() == grad_output_.dtype(), "expected dtype ", grad_output_.dtype(),
              " for `grad_input` but got dtype ", grad_input_.dtype());

  auto grad_output = grad_output_.contiguous();
  auto grad_input = grad_input_.contiguous();

  auto grad_output_data = grad_output.data_ptr<scalar_t>();
  auto grad_input_data = grad_input.data_ptr<scalar_t>();
  auto input_sizes = grad_input.sizes().vec();
  auto output_sizes = grad_output.sizes().vec();
  auto ndim = input_sizes.size();

  // treat nbatch and channels as one dimension
  int64_t channels = input_sizes[0] * input_sizes[1];
  int64_t input_depth = (ndim == 5) ? input_sizes[2] : 1;
  int64_t output_depth = (ndim == 5) ? output_sizes[2] : 1;
  int64_t input_height = (ndim >= 4) ? input_sizes[ndim - 2] : 1;
  int64_t output_height = (ndim >= 4) ? output_sizes[ndim - 2] : 1;
  int64_t input_width = input_sizes[ndim - 1];
  int64_t output_width = output_sizes[ndim - 1];

  int64_t output_slice_size = output_depth * output_height * output_width;
  int64_t input_slice_size = input_depth * input_height * input_width;

  auto loop1d = [&](int64_t begin, int64_t end) {
    for (const auto c : c10::irange(begin, end)) {
      for (const auto ow : c10::irange(output_width)) {
        int64_t iw = nearest_idx_fn(ow, input_width, output_width, scales[0]);
        int64_t output_offset = c * output_slice_size + ow;
        int64_t input_offset = c * input_slice_size + iw;
        grad_input_data[input_offset] += grad_output_data[output_offset];
      }
    }
  };

  auto loop2d = [&](int64_t begin, int64_t end) {
    for (const auto c : c10::irange(begin, end)) {
      for (const auto oh : c10::irange(output_height)) {
        int64_t ih = nearest_idx_fn(oh, input_height, output_height, scales[0]);
        for (const auto ow : c10::irange(output_width)) {
          int64_t iw = nearest_idx_fn(ow, input_width, output_width, scales[1]);
          int64_t output_offset = c * output_slice_size + oh * output_width + ow;
          int64_t input_offset = c * input_slice_size + ih * input_width + iw;
          grad_input_data[input_offset] += grad_output_data[output_offset];
        }
      }
    }
  };

  auto loop3d = [&](int64_t begin, int64_t end) {
    for (const auto c : c10::irange(begin, end)) {
      for (const auto od : c10::irange(output_depth)) {
        int64_t id = nearest_idx_fn(od, input_depth, output_depth, scales[0]);
        for (const auto oh : c10::irange(output_height)) {
          int64_t ih = nearest_idx_fn(oh, input_height, output_height, scales[1]);
          for (const auto ow : c10::irange(output_width)) {
            int64_t iw = nearest_idx_fn(ow, input_width, output_width, scales[2]);
            int64_t output_offset = c * output_slice_size +
                od *  output_height * output_width + oh * output_width + ow;
            int64_t input_offset = c * input_slice_size +
                id * input_height * input_width + ih * input_width + iw;
            grad_input_data[input_offset] += grad_output_data[output_offset];
          }
        }
      }
    }
  };

  if (ndim == 3) {
    // upsample nearest 1d
    at::parallel_for(0, channels, at::internal::GRAIN_SIZE / output_slice_size, loop1d);
  } else if (ndim == 4) {
    // upsample nearest 2d
    at::parallel_for(0, channels, at::internal::GRAIN_SIZE / output_slice_size , loop2d);
  } else {
    // upsample nearest 3d
    TORCH_INTERNAL_ASSERT(ndim == 5);
    at::parallel_for(0, channels, at::internal::GRAIN_SIZE / output_slice_size, loop3d);
  }

  if (!grad_input_.is_contiguous()) {
    grad_input_.copy_(grad_input);
  }
}

template <typename scalar_t, typename scale_type, nearest_idx_fn_t nearest_idx_fn>
void cpu_upsample_nearest_backward_channels_last(
    const Tensor& grad_input_,
    const Tensor& grad_output_,
    const scale_type& scales) {
  TORCH_CHECK(grad_input_.dtype() == grad_output_.dtype(), "expected dtype ", grad_output_.dtype(),
              " for `grad_input` but got dtype ", grad_input_.dtype());

  auto ndim = grad_output_.ndimension();
  TORCH_CHECK(ndim >=4 && ndim <= 5, "Upsample with NHWC format supports tensors with 4 or 5 dims.")

  auto channels_last_memory_format = ndim == 4 ? at::MemoryFormat::ChannelsLast : at::MemoryFormat::ChannelsLast3d;
  auto grad_output = grad_output_.contiguous(channels_last_memory_format);
  auto grad_input = grad_input_.contiguous(channels_last_memory_format);

  auto grad_output_data = grad_output.data_ptr<scalar_t>();
  auto grad_input_data = grad_input.data_ptr<scalar_t>();

  auto input_sizes = grad_input.sizes().vec();
  auto output_sizes = grad_output.sizes().vec();

  int64_t num_batches =  input_sizes[0];
  int64_t channels =  input_sizes[1];
  int64_t input_depth = (ndim == 5) ? input_sizes[2] : 1;
  int64_t output_depth = (ndim == 5) ? output_sizes[2] : 1;
  int64_t input_height = (ndim >= 4) ? input_sizes[ndim - 2] : 1;
  int64_t output_height = (ndim >= 4) ? output_sizes[ndim - 2] : 1;
  int64_t input_width = input_sizes[ndim - 1];
  int64_t output_width = output_sizes[ndim - 1];

  using Vec = vec::Vectorized<scalar_t>;
  auto acc = [](scalar_t* gin, scalar_t* gout, int64_t size) {
    int64_t d = 0;
    for (; d < size - (size % Vec::size()); d += Vec::size()) {
      Vec gin_vec = Vec::loadu(gin + d) + Vec::loadu(gout + d);
      gin_vec.store(gin + d);
    }
    for (; d < size; d++) {
      gin[d] += gout[d];
    }
  };

  auto loop2d = [&](int64_t begin, int64_t end) {
    for (const auto n : c10::irange(begin, end)) {
      for (const auto oh : c10::irange(output_height)) {
        int64_t ih = nearest_idx_fn(oh, input_height, output_height, scales[0]);
        for (const auto ow : c10::irange(output_width)) {
          int64_t iw = nearest_idx_fn(ow, input_width, output_width, scales[1]);
          scalar_t* grad_output_ptr = grad_output_data +
              (n * output_height * output_width + oh * output_width + ow) * channels;
          scalar_t* grad_input_ptr = grad_input_data +
              (n * input_height * input_width + ih * input_width + iw) * channels;
          acc(grad_input_ptr, grad_output_ptr, channels);
        }
      }
    }
  };

  auto loop3d = [&](int64_t begin, int64_t end) {
    for (const auto n : c10::irange(begin, end)) {
      for (int64_t od = 0; od < output_depth; od++) {
        int64_t id = nearest_idx_fn(od, input_depth, output_depth, scales[0]);
        for (int64_t oh = 0; oh < output_height; oh++) {
          int64_t ih = nearest_idx_fn(oh, input_height, output_height, scales[1]);
          for (int64_t ow = 0; ow < output_width; ow++) {
            int64_t iw = nearest_idx_fn(ow, input_width, output_width, scales[2]);
            scalar_t* grad_output_ptr = grad_output_data +
                (n * output_depth * output_height * output_width +
                 od * output_height * output_width + oh * output_width + ow) * channels;
            scalar_t* grad_input_ptr = grad_input_data +
                (n * input_depth * input_height * input_width +
                 id * input_height * input_width + ih * input_width + iw) * channels;
            acc(grad_input_ptr, grad_output_ptr, channels);
          }
        }
      }
    }
  };

  if (ndim == 4) {
    // upsample nearest 2d
    at::parallel_for(0, num_batches, 0, loop2d);
  } else {
    // upsample nearest 3d
    TORCH_INTERNAL_ASSERT(ndim == 5);
    at::parallel_for(0, num_batches, 0, loop3d);
  }

  if (!grad_input_.is_contiguous(channels_last_memory_format)) {
    grad_input_.copy_(grad_input);
  }
}

void upsample_nearest1d_backward_kernel_impl(
    const Tensor& grad_input,
    const Tensor& grad_output,
    c10::optional<double> scales_w) {
  AT_DISPATCH_FLOATING_TYPES_AND(at::ScalarType::BFloat16, grad_output.scalar_type(), "upsample_nearest1d_backward", [&] {
    cpu_upsample_nearest_backward<scalar_t, scale_t, nearest_idx>(grad_input, grad_output, {scales_w});
  });
}

void _upsample_nearest_exact1d_backward_kernel_impl(
    const Tensor& grad_input,
    const Tensor& grad_output,
    c10::optional<double> scales_w) {
  AT_DISPATCH_FLOATING_TYPES_AND(at::ScalarType::BFloat16, grad_output.scalar_type(), "_upsample_nearest_exact1d_backward", [&] {
    cpu_upsample_nearest_backward<scalar_t, scale_t, nearest_exact_idx>(grad_input, grad_output, {scales_w});
  });
}

void upsample_nearest2d_backward_kernel_impl(
    const Tensor& grad_input,
    const Tensor& grad_output,
    c10::optional<double> scales_h,
    c10::optional<double> scales_w) {
  if (grad_output.is_contiguous(at::MemoryFormat::ChannelsLast)) {
    AT_DISPATCH_FLOATING_TYPES_AND(at::ScalarType::BFloat16, grad_output.scalar_type(), "upsample_nearest2d_backward_cl", [&] {
      cpu_upsample_nearest_backward_channels_last<scalar_t, scale_t, nearest_idx>(grad_input, grad_output, {scales_h, scales_w});
    });
  } else {
    AT_DISPATCH_FLOATING_TYPES_AND(at::ScalarType::BFloat16, grad_output.scalar_type(), "upsample_nearest2d_backward", [&] {
      cpu_upsample_nearest_backward<scalar_t, scale_t, nearest_idx>(grad_input, grad_output, {scales_h, scales_w});
    });
  }
}

void _upsample_nearest_exact2d_backward_kernel_impl(
    const Tensor& grad_input,
    const Tensor& grad_output,
    c10::optional<double> scales_h,
    c10::optional<double> scales_w) {
  if (grad_output.is_contiguous(at::MemoryFormat::ChannelsLast)) {
    AT_DISPATCH_FLOATING_TYPES_AND(at::ScalarType::BFloat16, grad_output.scalar_type(), "_upsample_nearest_exact2d_backward_cl", [&] {
      cpu_upsample_nearest_backward_channels_last<scalar_t, scale_t, nearest_exact_idx>(grad_input, grad_output, {scales_h, scales_w});
    });
  } else {
    AT_DISPATCH_FLOATING_TYPES_AND(at::ScalarType::BFloat16, grad_output.scalar_type(), "_upsample_nearest_exact2d_backward", [&] {
      cpu_upsample_nearest_backward<scalar_t, scale_t, nearest_exact_idx>(grad_input, grad_output, {scales_h, scales_w});
    });
  }
}

void upsample_nearest3d_backward_kernel_impl(
    const Tensor& grad_input,
    const Tensor& grad_output,
    c10::optional<double> scales_d,
    c10::optional<double> scales_h,
    c10::optional<double> scales_w) {
  AT_DISPATCH_FLOATING_TYPES_AND(at::ScalarType::BFloat16, grad_output.scalar_type(), "upsample_nearest3d_backward", [&] {
    cpu_upsample_nearest_backward<scalar_t, scale_t, nearest_idx>(grad_input, grad_output, {scales_d, scales_h, scales_w});
  });
}

void _upsample_nearest_exact3d_backward_kernel_impl(
    const Tensor& grad_input,
    const Tensor& grad_output,
    c10::optional<double> scales_d,
    c10::optional<double> scales_h,
    c10::optional<double> scales_w) {
  AT_DISPATCH_FLOATING_TYPES_AND(at::ScalarType::BFloat16, grad_output.scalar_type(), "_upsample_nearest_exact3d_backward", [&] {
    cpu_upsample_nearest_backward<scalar_t, scale_t, nearest_exact_idx>(grad_input, grad_output, {scales_d, scales_h, scales_w});
  });
}

template <typename scalar_t, typename scale_type>
void cpu_upsample_linear_backward(
    const Tensor& grad_input_,
    const Tensor& grad_output_,
    bool align_corners,
    const scale_type& scales) {
  TORCH_CHECK(grad_input_.dtype() == grad_output_.dtype(), "expected dtype ", grad_output_.dtype(),
              " for `grad_input` but got dtype ", grad_input_.dtype());

  auto grad_output = grad_output_.contiguous();
  auto grad_input = grad_input_.contiguous();

  auto grad_output_data = grad_output.data_ptr<scalar_t>();
  auto grad_input_data = grad_input.data_ptr<scalar_t>();
  auto input_sizes = grad_input.sizes().vec();
  auto output_sizes = grad_output.sizes().vec();
  auto ndim = input_sizes.size();

  // treat nbatch and channels as one dimension
  int64_t channels = input_sizes[0] * input_sizes[1];
  int64_t input_depth = (ndim == 5) ? input_sizes[2] : 1;
  int64_t output_depth = (ndim == 5) ? output_sizes[2] : 1;
  int64_t input_height = (ndim >= 4) ? input_sizes[ndim - 2] : 1;
  int64_t output_height = (ndim >= 4) ? output_sizes[ndim - 2] : 1;
  int64_t input_width = input_sizes[ndim - 1];
  int64_t output_width = output_sizes[ndim - 1];

  int64_t output_slice_size = output_depth * output_height * output_width;

  auto loop1d = [&](int64_t begin, int64_t end) {
    const scalar_t width_scale = area_pixel_compute_scale<scalar_t>(
        input_width, output_width, align_corners, scales[0]);

    auto input_indexr = [=](int64_t c, int64_t w) {
      return grad_input_data + c * input_width + w;
    };

    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
    int64_t iw0, iw1;
    scalar_t w0lambda, w1lambda;
    for (const auto c : c10::irange(begin, end)) {
      for (const auto ow : c10::irange(output_width)) {
        compute_source_index_and_lambda(
            iw0, iw1, w0lambda, w1lambda, width_scale, ow, input_width, output_width, align_corners);
        scalar_t grad_output_value = grad_output_data[c * output_slice_size + ow];
        *input_indexr(c, iw0) += w0lambda * grad_output_value; /* i0 */
        *input_indexr(c, iw1) += w1lambda * grad_output_value; /* i1*/
      }
    }
  };

  auto loop2d = [&](int64_t begin, int64_t end) {
    const scalar_t height_scale = area_pixel_compute_scale<scalar_t>(
        input_height, output_height, align_corners, scales[0]);
    const scalar_t width_scale = area_pixel_compute_scale<scalar_t>(
        input_width, output_width, align_corners, scales[1]);

    auto input_indexr = [=](int64_t c, int64_t h, int64_t w){
      return grad_input_data + c * input_height * input_width + h * input_width + w;
    };

    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
    int64_t ih0, ih1, iw0, iw1;
    scalar_t h0lambda, h1lambda, w0lambda, w1lambda;
    for (const auto c : c10::irange(begin, end)) {
      for (const auto oh : c10::irange(output_height)) {
        compute_source_index_and_lambda(
            ih0, ih1, h0lambda, h1lambda, height_scale, oh, input_height, output_height, align_corners);
        for (const auto ow : c10::irange(output_width)) {
          compute_source_index_and_lambda(
              iw0, iw1, w0lambda, w1lambda, width_scale, ow, input_width, output_width, align_corners);
          scalar_t grad_output_value = grad_output_data[c * output_slice_size + oh * output_width + ow];
          *input_indexr(c, ih0, iw0) += h0lambda * w0lambda * grad_output_value; /* i00 */
          *input_indexr(c, ih0, iw1) += h0lambda * w1lambda * grad_output_value; /* i01 */
          *input_indexr(c, ih1, iw0) += h1lambda * w0lambda * grad_output_value; /* i10 */
          *input_indexr(c, ih1, iw1) += h1lambda * w1lambda * grad_output_value; /* i11 */
        }
      }
    }
  };

  auto loop3d = [&](int64_t begin, int64_t end) {
    const scalar_t depth_scale = area_pixel_compute_scale<scalar_t>(
        input_depth, output_depth, align_corners, scales[0]);
    const scalar_t height_scale = area_pixel_compute_scale<scalar_t>(
        input_height, output_height, align_corners, scales[1]);
    const scalar_t width_scale = area_pixel_compute_scale<scalar_t>(
        input_width, output_width, align_corners, scales[2]);

    auto input_indexr = [=](int64_t c, int64_t d, int64_t h, int64_t w) {
      return grad_input_data + c * input_depth * input_height * input_width +
          d * input_height * input_width + h * input_width + w;
    };

    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
    int64_t id0, id1, ih0, ih1, iw0, iw1;
    scalar_t d0lambda, d1lambda, h0lambda, h1lambda, w0lambda, w1lambda;
    for (const auto c : c10::irange(begin, end)) {
      for (const auto od : c10::irange(output_depth)) {
        compute_source_index_and_lambda(
            id0, id1, d0lambda, d1lambda, depth_scale, od, input_depth, output_depth, align_corners);
        for (const auto oh : c10::irange(output_height)) {
          compute_source_index_and_lambda(
              ih0, ih1, h0lambda, h1lambda, height_scale, oh, input_height, output_height, align_corners);
          for (const auto ow : c10::irange(output_width)) {
            compute_source_index_and_lambda(
                iw0, iw1, w0lambda, w1lambda, width_scale, ow, input_width, output_width, align_corners);
            scalar_t grad_output_value = grad_output_data[c * output_slice_size +
                od *  output_height * output_width + oh * output_width + ow];
            *input_indexr(c, id0, ih0, iw0) += d0lambda * h0lambda * w0lambda * grad_output_value; /* i000 */
            *input_indexr(c, id0, ih0, iw1) += d0lambda * h0lambda * w1lambda * grad_output_value; /* i001 */
            *input_indexr(c, id0, ih1, iw0) += d0lambda * h1lambda * w0lambda * grad_output_value; /* i010 */
            *input_indexr(c, id0, ih1, iw1) += d0lambda * h1lambda * w1lambda * grad_output_value; /* i011 */
            *input_indexr(c, id1, ih0, iw0) += d1lambda * h0lambda * w0lambda * grad_output_value; /* i100 */
            *input_indexr(c, id1, ih0, iw1) += d1lambda * h0lambda * w1lambda * grad_output_value; /* i101 */
            *input_indexr(c, id1, ih1, iw0) += d1lambda * h1lambda * w0lambda * grad_output_value; /* i110 */
            *input_indexr(c, id1, ih1, iw1) += d1lambda * h1lambda * w1lambda * grad_output_value; /* i111 */
          }
        }
      }
    }
  };

  if (ndim == 3) {
    // upsample linear 1d
    at::parallel_for(0, channels, at::internal::GRAIN_SIZE / output_slice_size / 2, loop1d);
  } else if (ndim == 4) {
    // upsample bilinear 2d
    at::parallel_for(0, channels, at::internal::GRAIN_SIZE / output_slice_size / 4, loop2d);
  } else {
    // upsample trilinear 3d
    TORCH_INTERNAL_ASSERT(ndim == 5);
    at::parallel_for(0, channels, at::internal::GRAIN_SIZE / output_slice_size / 8, loop3d);
  }

  if (!grad_input_.is_contiguous()) {
    grad_input_.copy_(grad_input);
  }
}

template <typename scalar_t, typename scale_type>
void cpu_upsample_linear_backward_channels_last(
    const Tensor& grad_input_,
    const Tensor& grad_output_,
    bool align_corners,
    const scale_type& scales) {
  TORCH_CHECK(grad_input_.dtype() == grad_output_.dtype(), "expected dtype ", grad_output_.dtype(),
              " for `grad_input` but got dtype ", grad_input_.dtype());

  auto ndim = grad_output_.ndimension();
  TORCH_CHECK(ndim >=4 && ndim <= 5, "Upsample with NHWC format supports tensors with 4 or 5 dims.")

  auto channels_last_memory_format = ndim == 4 ? at::MemoryFormat::ChannelsLast : at::MemoryFormat::ChannelsLast3d;
  auto grad_output = grad_output_.contiguous(channels_last_memory_format);
  auto grad_input = grad_input_.contiguous(channels_last_memory_format);

  auto grad_output_data = grad_output.data_ptr<scalar_t>();
  auto grad_input_data = grad_input.data_ptr<scalar_t>();

  auto input_sizes = grad_input.sizes().vec();
  auto output_sizes = grad_output.sizes().vec();

  int64_t num_batches =  input_sizes[0];
  int64_t channels =  input_sizes[1];
  int64_t input_depth = (ndim == 5) ? input_sizes[2] : 1;
  int64_t output_depth = (ndim == 5) ? output_sizes[2] : 1;
  int64_t input_height = (ndim >= 4) ? input_sizes[ndim - 2] : 1;
  int64_t output_height = (ndim >= 4) ? output_sizes[ndim - 2] : 1;
  int64_t input_width = input_sizes[ndim - 1];
  int64_t output_width = output_sizes[ndim - 1];

  using opmath_t = at::opmath_type<scalar_t>;
  using Vec = vec::Vectorized<scalar_t>;
  auto acc = [](scalar_t* gin, scalar_t* gout, opmath_t w, int64_t size) {
    int64_t d = 0;
    for (; d < size - (size % Vec::size()); d += Vec::size()) {
      Vec gin_vec = Vec::loadu(gin + d) + Vec(w) * Vec::loadu(gout + d);
      gin_vec.store(gin + d);
    }
    for (; d < size; d++) {
      gin[d] += w * gout[d];
    }
  };

  auto loop2d = [&](int64_t begin, int64_t end) {
    const scalar_t height_scale = area_pixel_compute_scale<scalar_t>(
        input_height, output_height, align_corners, scales[0]);
    const scalar_t width_scale = area_pixel_compute_scale<scalar_t>(
        input_width, output_width, align_corners, scales[1]);

    auto input_indexr = [=](int64_t n, int64_t h, int64_t w){
      return grad_input_data + (n * input_height * input_width + h * input_width + w) * channels;
    };

    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
    int64_t ih0, ih1, iw0, iw1;
    scalar_t h0lambda, h1lambda, w0lambda, w1lambda;
    for (const auto n : c10::irange(begin, end)) {
      for (const auto oh : c10::irange(output_height)) {
        compute_source_index_and_lambda(
            ih0, ih1, h0lambda, h1lambda, height_scale, oh, input_height, output_height, align_corners);
        for (const auto ow : c10::irange(output_width)) {
          compute_source_index_and_lambda(
              iw0, iw1, w0lambda, w1lambda, width_scale, ow, input_width, output_width, align_corners);
          scalar_t* grad_output_ptr = grad_output_data +
              (n * output_height * output_width + oh * output_width + ow) * channels;
          acc(input_indexr(n, ih0, iw0), grad_output_ptr, h0lambda * w0lambda, channels); /* i00 */
          acc(input_indexr(n, ih0, iw1), grad_output_ptr, h0lambda * w1lambda, channels); /* i01 */
          acc(input_indexr(n, ih1, iw0), grad_output_ptr, h1lambda * w0lambda, channels); /* i10 */
          acc(input_indexr(n, ih1, iw1), grad_output_ptr, h1lambda * w1lambda, channels); /* i11 */
        }
      }
    }
  };

  auto loop3d = [&](int64_t begin, int64_t end) {
    const scalar_t depth_scale = area_pixel_compute_scale<scalar_t>(
        input_depth, output_depth, align_corners, scales[0]);
    const scalar_t height_scale = area_pixel_compute_scale<scalar_t>(
        input_height, output_height, align_corners, scales[1]);
    const scalar_t width_scale = area_pixel_compute_scale<scalar_t>(
        input_width, output_width, align_corners, scales[2]);

    auto input_indexr = [=](int64_t n, int64_t d, int64_t h, int64_t w) {
      return grad_input_data + (n * input_depth * input_height * input_width +
          d * input_height * input_width + h * input_width + w) * channels;
    };

    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
    int64_t id0, id1, ih0, ih1, iw0, iw1;
    scalar_t d0lambda, d1lambda, h0lambda, h1lambda, w0lambda, w1lambda;
    for (const auto n : c10::irange(begin, end)) {
      for (const auto od : c10::irange(output_depth)) {
        compute_source_index_and_lambda(
            id0, id1, d0lambda, d1lambda, depth_scale, od, input_depth, output_depth, align_corners);
        for (const auto oh : c10::irange(output_height)) {
          compute_source_index_and_lambda(
              ih0, ih1, h0lambda, h1lambda, height_scale, oh, input_height, output_height, align_corners);
          for (const auto ow : c10::irange(output_width)) {
            compute_source_index_and_lambda(
                iw0, iw1, w0lambda, w1lambda, width_scale, ow, input_width, output_width, align_corners);
            scalar_t* grad_output_ptr = grad_output_data + (n * output_depth * output_height * output_width +
                od *  output_height * output_width + oh * output_width + ow) * channels;
            acc(input_indexr(n, id0, ih0, iw0), grad_output_ptr, d0lambda * h0lambda * w0lambda, channels); /* i000 */
            acc(input_indexr(n, id0, ih0, iw1), grad_output_ptr, d0lambda * h0lambda * w1lambda, channels); /* i001 */
            acc(input_indexr(n, id0, ih1, iw0), grad_output_ptr, d0lambda * h1lambda * w0lambda, channels); /* i010 */
            acc(input_indexr(n, id0, ih1, iw1), grad_output_ptr, d0lambda * h1lambda * w1lambda, channels); /* i011 */
            acc(input_indexr(n, id1, ih0, iw0), grad_output_ptr, d1lambda * h0lambda * w0lambda, channels); /* i100 */
            acc(input_indexr(n, id1, ih0, iw1), grad_output_ptr, d1lambda * h0lambda * w1lambda, channels); /* i101 */
            acc(input_indexr(n, id1, ih1, iw0), grad_output_ptr, d1lambda * h1lambda * w0lambda, channels); /* i110 */
            acc(input_indexr(n, id1, ih1, iw1), grad_output_ptr, d1lambda * h1lambda * w1lambda, channels); /* i111 */
          }
        }
      }
    }
  };

  if (ndim == 4) {
    // upsample bilinear 2d
    at::parallel_for(0, num_batches, 0, loop2d);
  } else {
    // upsample trilinear 3d
    TORCH_INTERNAL_ASSERT(ndim == 5);
    at::parallel_for(0, num_batches, 0, loop3d);
  }

  if (!grad_input_.is_contiguous(channels_last_memory_format)) {
    grad_input_.copy_(grad_input);
  }
}

void upsample_linear1d_backward_kernel_impl(
    const Tensor& grad_input,
    const Tensor& grad_output,
    bool align_corners,
    c10::optional<double> scales_w) {
  AT_DISPATCH_FLOATING_TYPES_AND(at::ScalarType::BFloat16, grad_output.scalar_type(), "upsample_linear1d_backward", [&] {
    cpu_upsample_linear_backward<scalar_t, scale_t>(grad_input, grad_output, align_corners, {scales_w});
  });
}

void upsample_bilinear2d_backward_kernel_impl(
    const Tensor& grad_input,
    const Tensor& grad_output,
    bool align_corners,
    c10::optional<double> scales_h,
    c10::optional<double> scales_w) {
  if (grad_output.is_contiguous(at::MemoryFormat::ChannelsLast)) {
    AT_DISPATCH_FLOATING_TYPES_AND(at::ScalarType::BFloat16, grad_output.scalar_type(), "upsample_bilinear2d_backward_channels_last", [&] {
      cpu_upsample_linear_backward_channels_last<scalar_t, scale_t>(grad_input, grad_output, align_corners, {scales_h, scales_w});
    });
  } else {
    AT_DISPATCH_FLOATING_TYPES_AND(at::ScalarType::BFloat16, grad_output.scalar_type(), "upsample_bilinear2d_backward", [&] {
      cpu_upsample_linear_backward<scalar_t, scale_t>(grad_input, grad_output, align_corners, {scales_h, scales_w});
    });
  }
}

void upsample_trilinear3d_backward_kernel_impl(
    const Tensor& grad_input,
    const Tensor& grad_output,
    bool align_corners,
    c10::optional<double> scales_d,
    c10::optional<double> scales_h,
    c10::optional<double> scales_w) {
  if (grad_output.is_contiguous(at::MemoryFormat::ChannelsLast3d)) {
    AT_DISPATCH_FLOATING_TYPES_AND(at::ScalarType::BFloat16, grad_output.scalar_type(), "upsample_trilinear3d_backward_channels_last", [&] {
      cpu_upsample_linear_backward_channels_last<scalar_t, scale_t>(grad_input, grad_output, align_corners, {scales_d, scales_h, scales_w});
    });
  } else {
    AT_DISPATCH_FLOATING_TYPES_AND(at::ScalarType::BFloat16, grad_output.scalar_type(), "upsample_trilinear3d_backward", [&] {
      cpu_upsample_linear_backward<scalar_t, scale_t>(grad_input, grad_output, align_corners, {scales_d, scales_h, scales_w});
    });
  }
}

} // anonymous namespace

REGISTER_DISPATCH(upsample_nearest1d_backward_kernel, &upsample_nearest1d_backward_kernel_impl);
REGISTER_DISPATCH(_upsample_nearest_exact1d_backward_kernel, &_upsample_nearest_exact1d_backward_kernel_impl);
REGISTER_DISPATCH(upsample_nearest2d_backward_kernel, &upsample_nearest2d_backward_kernel_impl);
REGISTER_DISPATCH(_upsample_nearest_exact2d_backward_kernel, &_upsample_nearest_exact2d_backward_kernel_impl);
REGISTER_DISPATCH(upsample_nearest3d_backward_kernel, &upsample_nearest3d_backward_kernel_impl);
REGISTER_DISPATCH(_upsample_nearest_exact3d_backward_kernel, &_upsample_nearest_exact3d_backward_kernel_impl);

REGISTER_DISPATCH(upsample_linear1d_backward_kernel, &upsample_linear1d_backward_kernel_impl);
REGISTER_DISPATCH(upsample_bilinear2d_backward_kernel, &upsample_bilinear2d_backward_kernel_impl);
REGISTER_DISPATCH(upsample_trilinear3d_backward_kernel, &upsample_trilinear3d_backward_kernel_impl);

} // namespace at::native