So, I’m attempting to use huggingface transformer models that sometimes require None to be passed in. For example, the forward() function to the XLMForQuestionAnswering models looks like this:
input_ids=None, attention_mask=None, langs=None, token_type_ids=None, position_ids=None, lengths=None, cache=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None, is_impossible=None, cls_index=None, p_mask=None
I need to be able to pass in None to everything but input_ids, attention_mask, token_type_ids, cls_index, and p_mask. I can do this and everything trains fine, BUT …
… when I call learn.predict('I really like everything') I get the following error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/development/projects/blurr/_libs/fastai2/fastai2/torch_core.py in to_concat(xs, dim)
216 # in this case we return a big list
--> 217 try: return retain_type(torch.cat(xs, dim=dim), xs[0])
218 except: return sum([L(retain_type(o_.index_select(dim, tensor(i)).squeeze(dim), xs[0])
TypeError: expected Tensor as element 0 in argument 0, but got NoneType
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-24-986299fba05f> in <module>
----> 1 learn.predict('I really liked the movie')
~/development/projects/blurr/_libs/fastai2/fastai2/learner.py in predict(self, item, rm_type_tfms, with_input)
240 def predict(self, item, rm_type_tfms=None, with_input=False):
241 dl = self.dls.test_dl([item], rm_type_tfms=rm_type_tfms, num_workers=0)
--> 242 inp,preds,_,dec_preds = self.get_preds(dl=dl, with_input=True, with_decoded=True)
243 i = getattr(self.dls, 'n_inp', -1)
244 inp = (inp,) if i==1 else tuplify(inp)
~/development/projects/blurr/_libs/fastai2/fastai2/learner.py in get_preds(self, ds_idx, dl, with_input, with_decoded, with_loss, act, inner, reorder, **kwargs)
227 for mgr in ctx_mgrs: stack.enter_context(mgr)
228 self(event.begin_epoch if inner else _before_epoch)
--> 229 self._do_epoch_validate(dl=dl)
230 self(event.after_epoch if inner else _after_epoch)
231 if act is None: act = getattr(self.loss_func, 'activation', noop)
~/development/projects/blurr/_libs/fastai2/fastai2/learner.py in _do_epoch_validate(self, ds_idx, dl)
183 with torch.no_grad(): self.all_batches()
184 except CancelValidException: self('after_cancel_validate')
--> 185 finally: self('after_validate')
186
187 @log_args(but='cbs')
~/development/projects/blurr/_libs/fastai2/fastai2/learner.py in __call__(self, event_name)
132 def ordered_cbs(self, event): return [cb for cb in sort_by_run(self.cbs) if hasattr(cb, event)]
133
--> 134 def __call__(self, event_name): L(event_name).map(self._call_one)
135 def _call_one(self, event_name):
136 assert hasattr(event, event_name)
~/development/projects/blurr/_libs/fastcore/fastcore/foundation.py in map(self, f, *args, **kwargs)
374 else f.format if isinstance(f,str)
375 else f.__getitem__)
--> 376 return self._new(map(g, self))
377
378 def filter(self, f, negate=False, **kwargs):
~/development/projects/blurr/_libs/fastcore/fastcore/foundation.py in _new(self, items, *args, **kwargs)
325 @property
326 def _xtra(self): return None
--> 327 def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
328 def __getitem__(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)
329 def copy(self): return self._new(self.items.copy())
~/development/projects/blurr/_libs/fastcore/fastcore/foundation.py in __call__(cls, x, *args, **kwargs)
45 return x
46
---> 47 res = super().__call__(*((x,) + args), **kwargs)
48 res._newchk = 0
49 return res
~/development/projects/blurr/_libs/fastcore/fastcore/foundation.py in __init__(self, items, use_list, match, *rest)
316 if items is None: items = []
317 if (use_list is not None) or not _is_array(items):
--> 318 items = list(items) if use_list else _listify(items)
319 if match is not None:
320 if is_coll(match): match = len(match)
~/development/projects/blurr/_libs/fastcore/fastcore/foundation.py in _listify(o)
252 if isinstance(o, list): return o
253 if isinstance(o, str) or _is_array(o): return [o]
--> 254 if is_iter(o): return list(o)
255 return [o]
256
~/development/projects/blurr/_libs/fastcore/fastcore/foundation.py in __call__(self, *args, **kwargs)
218 if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
219 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 220 return self.fn(*fargs, **kwargs)
221
222 # Cell
~/development/projects/blurr/_libs/fastai2/fastai2/learner.py in _call_one(self, event_name)
135 def _call_one(self, event_name):
136 assert hasattr(event, event_name)
--> 137 [cb(event_name) for cb in sort_by_run(self.cbs)]
138
139 def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)
~/development/projects/blurr/_libs/fastai2/fastai2/learner.py in <listcomp>(.0)
135 def _call_one(self, event_name):
136 assert hasattr(event, event_name)
--> 137 [cb(event_name) for cb in sort_by_run(self.cbs)]
138
139 def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)
~/development/projects/blurr/_libs/fastai2/fastai2/callback/core.py in __call__(self, event_name)
22 _run = (event_name not in _inner_loop or (self.run_train and getattr(self, 'training', True)) or
23 (self.run_valid and not getattr(self, 'training', False)))
---> 24 if self.run and _run: getattr(self, event_name, noop)()
25 if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
26
~/development/projects/blurr/_libs/fastai2/fastai2/callback/core.py in after_validate(self)
93 def after_validate(self):
94 "Concatenate all recorded tensors"
---> 95 if self.with_input: self.inputs = detuplify(to_concat(self.inputs, dim=self.concat_dim))
96 if not self.save_preds: self.preds = detuplify(to_concat(self.preds, dim=self.concat_dim))
97 if not self.save_targs: self.targets = detuplify(to_concat(self.targets, dim=self.concat_dim))
~/development/projects/blurr/_libs/fastai2/fastai2/torch_core.py in to_concat(xs, dim)
211 def to_concat(xs, dim=0):
212 "Concat the element in `xs` (recursively if they are tuples/lists of tensors)"
--> 213 if is_listy(xs[0]): return type(xs[0])([to_concat([x[i] for x in xs], dim=dim) for i in range_of(xs[0])])
214 if isinstance(xs[0],dict): return {k: to_concat([x[k] for x in xs], dim=dim) for k in xs[0].keys()}
215 #We may receives xs that are not concatenatable (inputs of a text classifier for instance),
~/development/projects/blurr/_libs/fastai2/fastai2/torch_core.py in <listcomp>(.0)
211 def to_concat(xs, dim=0):
212 "Concat the element in `xs` (recursively if they are tuples/lists of tensors)"
--> 213 if is_listy(xs[0]): return type(xs[0])([to_concat([x[i] for x in xs], dim=dim) for i in range_of(xs[0])])
214 if isinstance(xs[0],dict): return {k: to_concat([x[k] for x in xs], dim=dim) for k in xs[0].keys()}
215 #We may receives xs that are not concatenatable (inputs of a text classifier for instance),
~/development/projects/blurr/_libs/fastai2/fastai2/torch_core.py in to_concat(xs, dim)
217 try: return retain_type(torch.cat(xs, dim=dim), xs[0])
218 except: return sum([L(retain_type(o_.index_select(dim, tensor(i)).squeeze(dim), xs[0])
--> 219 for i in range_of(o_)) for o_ in xs], L())
220
221 # Cell
~/development/projects/blurr/_libs/fastai2/fastai2/torch_core.py in <listcomp>(.0)
217 try: return retain_type(torch.cat(xs, dim=dim), xs[0])
218 except: return sum([L(retain_type(o_.index_select(dim, tensor(i)).squeeze(dim), xs[0])
--> 219 for i in range_of(o_)) for o_ in xs], L())
220
221 # Cell
~/development/projects/blurr/_libs/fastcore/fastcore/utils.py in range_of(x)
170 def range_of(x):
171 "All indices of collection `x` (i.e. `list(range(len(x)))`)"
--> 172 return list(range(len(x)))
173
174 # Cell
TypeError: object of type 'NoneType' has no len()
Is there something I need to do to make this work? Or does something need to be updated with the library to support this use case?
Thanks - wg