unsupported operand type(s) for +=: 'TensorCategory' and 'TensorText' when using AWD_LSTM for text classification

[MariiaS]

Please confirm you have the latest versions of fastai, fastcore, fastscript, and nbdev prior to reporting a bug (delete one): YES / NO

Describe the bug
I'm following the 01_intro.ipynb from fastai course on the Google Colab environment, copy of the official fastai notebook: https://colab.research.google.com/github/fastai/fastbook/blob/master/01_intro.ipynb. I don't make any changes to the code, just executing the code line by line.

On the line with llearn.fine_tune(4, 1e-2) for the block of code

dls = TextDataLoaders.from_folder(untar_data(URLs.IMDB), valid='test')
learn = text_classifier_learner(dls, AWD_LSTM, drop_mult=0.5, metrics=accuracy)
learn.fine_tune(4, 1e-2)

the error unsupported operand type(s) for +=: 'TensorCategory' and 'TensorText' appears.

To Reproduce
Steps to reproduce the behavior:

1. Copy https://colab.research.google.com/github/fastai/fastbook/blob/master/01_intro.ipynb in Google Colab
2. Execute the notebook

Expected behavior
Expected that the learner for text classification is working as expected, no error appears.

Error with full stack trace

TypeError                                 Traceback (most recent call last)
<ipython-input-9-5ab79cd5e866> in <module>()
      3 dls = TextDataLoaders.from_folder(untar_data(URLs.IMDB), valid='test')
      4 learn = text_classifier_learner(dls, AWD_LSTM, drop_mult=0.5, metrics=accuracy)
----> 5 learn.fine_tune(4, 1e-2)

20 frames
/usr/local/lib/python3.6/dist-packages/fastai/callback/schedule.py in fine_tune(self, epochs, base_lr, freeze_epochs, lr_mult, pct_start, div, **kwargs)
    155     "Fine tune with `freeze` for `freeze_epochs` then with `unfreeze` from `epochs` using discriminative LR"
    156     self.freeze()
--> 157     self.fit_one_cycle(freeze_epochs, slice(base_lr), pct_start=0.99, **kwargs)
    158     base_lr /= 2
    159     self.unfreeze()

/usr/local/lib/python3.6/dist-packages/fastai/callback/schedule.py in fit_one_cycle(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt)
    110     scheds = {'lr': combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),
    111               'mom': combined_cos(pct_start, *(self.moms if moms is None else moms))}
--> 112     self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
    113 
    114 # Cell

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
    203             self.opt.set_hypers(lr=self.lr if lr is None else lr)
    204             self.n_epoch = n_epoch
--> 205             self._with_events(self._do_fit, 'fit', CancelFitException, self._end_cleanup)
    206 
    207     def _end_cleanup(self): self.dl,self.xb,self.yb,self.pred,self.loss = None,(None,),(None,),None,None

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    152 
    153     def _with_events(self, f, event_type, ex, final=noop):
--> 154         try:       self(f'before_{event_type}')       ;f()
    155         except ex: self(f'after_cancel_{event_type}')
    156         finally:   self(f'after_{event_type}')        ;final()

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _do_fit(self)
    194         for epoch in range(self.n_epoch):
    195             self.epoch=epoch
--> 196             self._with_events(self._do_epoch, 'epoch', CancelEpochException)
    197 
    198     def fit(self, n_epoch, lr=None, wd=None, cbs=None, reset_opt=False):

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    152 
    153     def _with_events(self, f, event_type, ex, final=noop):
--> 154         try:       self(f'before_{event_type}')       ;f()
    155         except ex: self(f'after_cancel_{event_type}')
    156         finally:   self(f'after_{event_type}')        ;final()

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _do_epoch(self)
    188 
    189     def _do_epoch(self):
--> 190         self._do_epoch_train()
    191         self._do_epoch_validate()
    192 

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _do_epoch_train(self)
    180     def _do_epoch_train(self):
    181         self.dl = self.dls.train
--> 182         self._with_events(self.all_batches, 'train', CancelTrainException)
    183 
    184     def _do_epoch_validate(self, ds_idx=1, dl=None):

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    152 
    153     def _with_events(self, f, event_type, ex, final=noop):
--> 154         try:       self(f'before_{event_type}')       ;f()
    155         except ex: self(f'after_cancel_{event_type}')
    156         finally:   self(f'after_{event_type}')        ;final()

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in all_batches(self)
    158     def all_batches(self):
    159         self.n_iter = len(self.dl)
--> 160         for o in enumerate(self.dl): self.one_batch(*o)
    161 
    162     def _do_one_batch(self):

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in one_batch(self, i, b)
    176         self.iter = i
    177         self._split(b)
--> 178         self._with_events(self._do_one_batch, 'batch', CancelBatchException)
    179 
    180     def _do_epoch_train(self):

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    152 
    153     def _with_events(self, f, event_type, ex, final=noop):
--> 154         try:       self(f'before_{event_type}')       ;f()
    155         except ex: self(f'after_cancel_{event_type}')
    156         finally:   self(f'after_{event_type}')        ;final()

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _do_one_batch(self)
    164         self('after_pred')
    165         if len(self.yb): self.loss = self.loss_func(self.pred, *self.yb)
--> 166         self('after_loss')
    167         if not self.training or not len(self.yb): return
    168         self('before_backward')

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in __call__(self, event_name)
    130     def ordered_cbs(self, event): return [cb for cb in sort_by_run(self.cbs) if hasattr(cb, event)]
    131 
--> 132     def __call__(self, event_name): L(event_name).map(self._call_one)
    133 
    134     def _call_one(self, event_name):

/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in map(self, f, gen, *args, **kwargs)
    177     def range(cls, a, b=None, step=None): return cls(range_of(a, b=b, step=step))
    178 
--> 179     def map(self, f, *args, gen=False, **kwargs): return self._new(map_ex(self, f, *args, gen=gen, **kwargs))
    180     def argwhere(self, f, negate=False, **kwargs): return self._new(argwhere(self, f, negate, **kwargs))
    181     def filter(self, f=noop, negate=False, gen=False, **kwargs):

/usr/local/lib/python3.6/dist-packages/fastcore/basics.py in map_ex(iterable, f, gen, *args, **kwargs)
    604     res = map(g, iterable)
    605     if gen: return res
--> 606     return list(res)
    607 
    608 # Cell

/usr/local/lib/python3.6/dist-packages/fastcore/basics.py in __call__(self, *args, **kwargs)
    594             if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
    595         fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 596         return self.func(*fargs, **kwargs)
    597 
    598 # Cell

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _call_one(self, event_name)
    134     def _call_one(self, event_name):
    135         assert hasattr(event, event_name), event_name
--> 136         [cb(event_name) for cb in sort_by_run(self.cbs)]
    137 
    138     def _bn_bias_state(self, with_bias): return norm_bias_params(self.model, with_bias).map(self.opt.state)

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in <listcomp>(.0)
    134     def _call_one(self, event_name):
    135         assert hasattr(event, event_name), event_name
--> 136         [cb(event_name) for cb in sort_by_run(self.cbs)]
    137 
    138     def _bn_bias_state(self, with_bias): return norm_bias_params(self.model, with_bias).map(self.opt.state)

/usr/local/lib/python3.6/dist-packages/fastai/callback/core.py in __call__(self, event_name)
     42                (self.run_valid and not getattr(self, 'training', False)))
     43         res = None
---> 44         if self.run and _run: res = getattr(self, event_name, noop)()
     45         if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
     46         return res

/usr/local/lib/python3.6/dist-packages/fastai/callback/rnn.py in after_loss(self)
     31     def after_loss(self):
     32         if not self.training: return
---> 33         if self.alpha != 0.:  self.learn.loss += self.alpha * self.out.float().pow(2).mean()
     34         if self.beta != 0.:
     35             h = self.raw_out

TypeError: unsupported operand type(s) for +=: 'TensorCategory' and 'TensorText'

Additional context
I saw the same problem faced by others on the forum, but there was no solution. The error appeared just recently, as just a week ago I was able to execute the same notebook. https://forums.fast.ai/t/tensorcategory-and-tensortext-when-using-awd-lstm-for-text-classification/82428/4

[muellerzr]

Looks to be related to #3022

[Hevia]

I have an additional example of this bug at the top of my post on the forums: https://forums.fast.ai/t/tensorcategory-and-tensortext-when-using-awd-lstm-for-text-classification/82428