Code (decreased tensor size slightly so that it runs without error):
import torch

torch.backends.cudnn.benchmark = True

x = torch.rand(1, 128, 8, 180, 180)
conv = torch.nn.ConvTranspose3d(128, 64, kernel_size=5, stride=2, padding=2, output_padding=1)

x = x.to('cuda:0')
conv = conv.to('cuda:0')

with torch.autograd.profiler.profile(enabled=True, use_cuda=True) as prof:
    y = conv(x)

print(prof)

Result:
---------------------------------  ---------------  ---------------  ---------------  ---------------  ---------------
Name                                      CPU time        CUDA time            Calls        CPU total       CUDA total
---------------------------------  ---------------  ---------------  ---------------  ---------------  ---------------
conv_transpose3d                      404852.197us     588765.916us                1     404852.197us     588765.916us
convolution                           404835.016us     588751.855us                1     404835.016us     588751.855us
_convolution                          404819.021us     588739.553us                1     404819.021us     588739.553us
contiguous                                 5.308us          5.984us                1          5.308us          5.984us
empty                                      4.051us          4.096us                1          4.051us          4.096us
_convolution_nogroup                  404767.199us     588701.587us                1     404767.199us     588701.587us
thnn_conv_transpose3d                 404753.928us     588692.262us                1     404753.928us     588692.262us
thnn_conv_transpose3d_forward         404734.862us     588680.168us                1     404734.862us     588680.168us