I cannot reproduce this issue inside the training loop:
optimizer = torch.optim.SGD([torch.randn(1, requires_grad=True)], lr=1.)
train_loader = DataLoader(torch.randn(100, 1))

scheduler = optim.lr_scheduler.OneCycleLR(optimizer, max_lr=1.,
                                          steps_per_epoch=int(len(train_loader)),
                                          epochs=1,
                                          anneal_strategy='linear')

for data in train_loader:
    optimizer.step()
    scheduler.step()

scheduler.step() # raises error

Coud epochs be smaller than the hparams['epochs'], which is passed to the epochs argument in the creation of the scheduler?
In my code snippet the error after the training loop is expected, since I used epochs=1.