When running test.py, RuntimeError: Backward is not reentrant

[qqpann]

Docker from pytorch/pytorch:1.0.1-cuda10.0-cudnn7-devel

Full error:

root@ad6d5fac0fd6:/app/DCN# python test.py
torch.Size([2, 128, 128, 128])
torch.Size([2, 128, 128, 128])
torch.Size([20, 32, 7, 7])
torch.Size([20, 32, 7, 7])
torch.Size([20, 32, 7, 7])
checking
dconv im2col_step forward passed with 0.0
tensor(0., device='cuda:0', grad_fn=<MaxBackward1>)
dconv im2col_step backward passed with 7.450580596923828e-09 = 7.450580596923828e-09+0.0+0.0+0.0
mdconv im2col_step forward passed with 0.0
tensor(0., device='cuda:0', grad_fn=<MaxBackward1>)
mdconv im2col_step backward passed with 3.725290298461914e-09
0.971507, 1.943014
0.971507, 1.943014
tensor(0., device='cuda:0')
dconv zero offset passed with 1.4901161193847656e-07
dconv zero offset identify passed with 0.0
tensor(0., device='cuda:0')
mdconv zero offset passed with 2.384185791015625e-07
mdconv zero offset identify passed with 0.0
check_gradient_conv:  True
Traceback (most recent call last):
  File "test.py", line 624, in <module>
    check_gradient_dconv()
  File "test.py", line 400, in check_gradient_dconv
    eps=1e-3, atol=1e-3, rtol=1e-2, raise_exception=True))
  File "/opt/conda/lib/python3.6/site-packages/torch/autograd/gradcheck.py", line 208, in gradcheck
    return fail_test('Backward is not reentrant, i.e., running backward with same '
  File "/opt/conda/lib/python3.6/site-packages/torch/autograd/gradcheck.py", line 185, in fail_test
    raise RuntimeError(msg)
RuntimeError: Backward is not reentrant, i.e., running backward with same input and grad_output multiple times gives different values, although analytical gradient matches numerical gradient