I updated to torch 1.7.1 and got this error

Traceback (most recent call last):
File “train.py”, line 90, in 
scalerG.scale(errG_mse).backward()
File “/usr/local/lib/python3.6/dist-packages/torch/tensor.py”, line 221, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File “/usr/local/lib/python3.6/dist-packages/torch/autograd/init.py”, line 132, in backward
allow_unreachable=True)  # allow_unreachable flag
RuntimeError: cuDNN error: CUDNN_STATUS_BAD_PARAM
You can try to repro this exception using the following code snippet. If that doesn’t trigger the error, please include your original repro script when reporting this issue.
import torch
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.allow_tf32 = True
data = torch.randn([10, 80, 128, 128], dtype=torch.float, device=‘cuda’, requires_grad=True)
net = torch.nn.Conv2d(80, 80, kernel_size=[3, 3], padding=[1, 1], stride=[1, 1], dilation=[1, 1], groups=1)
net = net.cuda().float()
out = net(data)
out.backward(torch.randn_like(out))
torch.cuda.synchronize()
ConvolutionParams
data_type = CUDNN_DATA_FLOAT
padding = [1, 1, 0]
stride = [1, 1, 0]
dilation = [1, 1, 0]
groups = 1
deterministic = false
allow_tf32 = true
input: TensorDescriptor 0x7fd7080399b0
type = CUDNN_DATA_FLOAT
nbDims = 4
dimA = 10, 80, 128, 128,
strideA = 1310720, 16384, 128, 1,
output: TensorDescriptor 0x7fd70804b250
type = CUDNN_DATA_FLOAT
nbDims = 4
dimA = 10, 80, 128, 128,
strideA = 1310720, 16384, 128, 1,
weight: FilterDescriptor 0x7fd70808a2f0
type = CUDNN_DATA_FLOAT
tensor_format = CUDNN_TENSOR_NCHW
nbDims = 4
dimA = 80, 80, 3, 3,
Pointer addresses:
input: 0x7fd600000000
output: 0x7fd3fd800000
weight: 0x7fd79b68b800
Additional pointer addresses:
grad_output: 0x7fd3fd800000
grad_weight: 0x7fd79b68b800
Backward filter algorithm: 1