#include "op_plugin/OpApiInterface.h"
#include "op_plugin/utils/op_api_common.h"
#include "op_plugin/utils/custom_functions/opapi/UpsampleConstants.h"
#include "torch_npu/csrc/framework/utils/UtilForOpAdapter.h"
namespace op_api {
using npu_preparation = at_npu::native::OpPreparation;
bool checkBilinearScales(float realScale_h, float realScale_w)
{
return !(realScale_h > MAX_SUPPORT_SCALE || realScale_w > MAX_SUPPORT_SCALE);
}
bool checkBilinearUseFast(const at::Tensor &self, at::IntArrayRef output_size, bool align_corners, double scales_h,
double scales_w, at::Tensor &result)
{
static const bool is_support_nd_out = (c10_npu::GetSocVersion() >= c10_npu::SocVersion::Ascend910B1 &&
c10_npu::GetSocVersion() < c10_npu::SocVersion::Ascend310B1) ||
(c10_npu::GetSocVersion() > c10_npu::SocVersion::Ascend310B4);
double realScale_h =
op_plugin::utils::compute_scale(self.size(H_INDEX), result.size(H_INDEX), scales_h);
double realScale_w =
op_plugin::utils::compute_scale(self.size(W_INDEX), result.size(W_INDEX), scales_w);
if (!is_support_nd_out || !checkBilinearScales(realScale_h, realScale_w)) {
return false;
}
return true;
}
at::Tensor &upsample_bilinear2d_aa_out_slow(const at::Tensor &self, at::IntArrayRef output_size, bool align_corners,
c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor &result)
{
auto scalar_type = self.scalar_type();
at::Tensor self_slow = self.cpu().to(at::ScalarType::Float);
at::Tensor result_slow = at::_upsample_bilinear2d_aa(self_slow, output_size, align_corners, scales_h, scales_w);
result.copy_(result_slow.to(scalar_type));
return result;
}
at::Tensor upsample_bilinear2d_aa_slow(const at::Tensor &self, at::IntArrayRef output_size, bool align_corners,
c10::optional<double> scales_h, c10::optional<double> scales_w)
{
auto scalar_type = self.scalar_type();
auto outputSize =
op_infer::upsample_bilinear2d_npu_output_size(self, output_size);
at::Tensor result = npu_preparation::apply_tensor_without_format(outputSize, self.options());
at::Tensor self_slow = self.cpu().to(at::ScalarType::Float);
at::Tensor result_slow = at::_upsample_bilinear2d_aa(self_slow, output_size, align_corners, scales_h, scales_w);
result.copy_(result_slow.to(scalar_type));
return result;
}
at::Tensor &_upsample_bilinear2d_aa_out(const at::Tensor &self, at::IntArrayRef output_size, bool align_corners,
c10::optional<double> scales_h, c10::optional<double> scales_w, at::Tensor &result)
{
DO_COMPATIBILITY(aclnnUpsampleBilinear2dAA,
upsample_bilinear2d_aa_out_slow(self, output_size, align_corners, scales_h, scales_w, result));
auto outputSize =
op_infer::upsample_bilinear2d_npu_output_size(self, output_size);
npu_preparation::check_tensor({self}, result, self, outputSize);
double scales_h_attr = scales_h.value_or(0);
double scales_w_attr = scales_w.value_or(0);
if (!checkBilinearUseFast(self, output_size, align_corners, scales_h_attr, scales_w_attr, result)) {
return upsample_bilinear2d_aa_out_slow(self, output_size, align_corners, scales_h, scales_w, result);
}
EXEC_NPU_CMD(aclnnUpsampleBilinear2dAA, self, output_size, align_corners, scales_h_attr, scales_w_attr, result);
return result;
}
at::Tensor _upsample_bilinear2d_aa(const at::Tensor &self, at::IntArrayRef output_size, bool align_corners,
c10::optional<double> scales_h, c10::optional<double> scales_w)
{
DO_COMPATIBILITY(
aclnnUpsampleBilinear2dAA, upsample_bilinear2d_aa_slow(self, output_size, align_corners, scales_h, scales_w));
double scales_h_attr = scales_h.value_or(0);
double scales_w_attr = scales_w.value_or(0);
auto outputSize =
op_infer::upsample_bilinear2d_npu_output_size(self, output_size);
at::Tensor result = npu_preparation::apply_tensor_without_format(outputSize, self.options());
if (!checkBilinearUseFast(self, output_size, align_corners, scales_h_attr, scales_w_attr, result)) {
return upsample_bilinear2d_aa_slow(self, output_size, align_corners, scales_h, scales_w);
}
EXEC_NPU_CMD(aclnnUpsampleBilinear2dAA, self, output_size, align_corners, scales_h_attr, scales_w_attr, result);
return result;
}
}