Hello everyone,
I’m trying to load an encoder that I trained with fastai v1 when creating a text classifier with fastai v2. The strange thing is that I get an error only when I’m using learn.lr_find().
I’m using the vocab.itos from my DataBunch that I created with v1. This part seems to work.
dblock = DataBlock(blocks=(TextBlock.from_df('text', seq_len=sl, vocab=data_lm.vocab.itos), CategoryBlock),
get_x=ColReader('text'),
get_y=ColReader('target'),
splitter=ColSplitter('is_valid'))
dls_cls = dblock.dataloaders(df, bs=bs, seq_len=sl)
Then I’m creating the learner and load the encoder:
learn = text_classifier_learner(dls_cls, AWD_LSTM, drop_mult=0.5, metrics=[accuracy,F1Score(average='macro')],
loss_func=LabelSmoothingCrossEntropy()).to_fp16()
learn.load_encoder('lm_octa_30_enc');
If I now run fit_one_cycle as below everything works fine.
lr, n_epoch, wd = 2e-2, 1, 0.01
learn.fit_one_cycle(n_epoch, lr, wd=wd)
Only when I run learn.lr_find() first and then start fitting I’m getting the following error message:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-80-31c072db3bda> in <module>
1 lr, n_epoch, wd = 2e-2, 1, 0.01
----> 2 learn.fit_one_cycle(n_epoch, lr, wd=wd)
/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai2/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
/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai2/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
195
196 except CancelFitException: self('after_cancel_fit')
--> 197 finally: self('after_fit')
198
199 def validate(self, ds_idx=1, dl=None, cbs=None):
/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai2/learner.py in __call__(self, event_name)
121 def ordered_cbs(self, cb_func): return [cb for cb in sort_by_run(self.cbs) if hasattr(cb, cb_func)]
122
--> 123 def __call__(self, event_name): L(event_name).map(self._call_one)
124 def _call_one(self, event_name):
125 assert hasattr(event, event_name)
/opt/conda/envs/fastai/lib/python3.7/site-packages/fastcore/foundation.py in map(self, f, *args, **kwargs)
360 else f.format if isinstance(f,str)
361 else f.__getitem__)
--> 362 return self._new(map(g, self))
363
364 def filter(self, f, negate=False, **kwargs):
/opt/conda/envs/fastai/lib/python3.7/site-packages/fastcore/foundation.py in _new(self, items, *args, **kwargs)
313 @property
314 def _xtra(self): return None
--> 315 def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
316 def __getitem__(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)
317 def copy(self): return self._new(self.items.copy())
/opt/conda/envs/fastai/lib/python3.7/site-packages/fastcore/foundation.py in __call__(cls, x, *args, **kwargs)
39 return x
40
---> 41 res = super().__call__(*((x,) + args), **kwargs)
42 res._newchk = 0
43 return res
/opt/conda/envs/fastai/lib/python3.7/site-packages/fastcore/foundation.py in __init__(self, items, use_list, match, *rest)
304 if items is None: items = []
305 if (use_list is not None) or not _is_array(items):
--> 306 items = list(items) if use_list else _listify(items)
307 if match is not None:
308 if is_coll(match): match = len(match)
/opt/conda/envs/fastai/lib/python3.7/site-packages/fastcore/foundation.py in _listify(o)
240 if isinstance(o, list): return o
241 if isinstance(o, str) or _is_array(o): return [o]
--> 242 if is_iter(o): return list(o)
243 return [o]
244
/opt/conda/envs/fastai/lib/python3.7/site-packages/fastcore/foundation.py in __call__(self, *args, **kwargs)
206 if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
207 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 208 return self.fn(*fargs, **kwargs)
209
210 # Cell
/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai2/learner.py in _call_one(self, event_name)
124 def _call_one(self, event_name):
125 assert hasattr(event, event_name)
--> 126 [cb(event_name) for cb in sort_by_run(self.cbs)]
127
128 def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)
/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai2/learner.py in <listcomp>(.0)
124 def _call_one(self, event_name):
125 assert hasattr(event, event_name)
--> 126 [cb(event_name) for cb in sort_by_run(self.cbs)]
127
128 def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)
/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai2/callback/core.py in __call__(self, event_name)
21 _run = (event_name not in _inner_loop or (self.run_train and getattr(self, 'training', True)) or
22 (self.run_valid and not getattr(self, 'training', False)))
---> 23 if self.run and _run: getattr(self, event_name, noop)()
24 if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
25
/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai2/callback/fp16.py in after_fit(self)
66 run_before=TrainEvalCallback
67 def begin_fit(self): self.learn.model = convert_network(self.model, dtype=torch.float16)
---> 68 def after_fit(self): self.learn.model = convert_network(self.model, dtype=torch.float32)
69
70 # Cell
/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai2/fp16_utils.py in convert_network(network, dtype)
68 convert_module(module, dtype)
69 if isinstance(module, torch.nn.RNNBase) or isinstance(module, torch.nn.modules.rnn.RNNBase):
---> 70 module.flatten_parameters()
71 return network
72
/opt/conda/envs/fastai/lib/python3.7/site-packages/torch/nn/modules/rnn.py in flatten_parameters(self)
127 all_weights, (4 if self.bias else 2),
128 self.input_size, rnn.get_cudnn_mode(self.mode), self.hidden_size, self.num_layers,
--> 129 self.batch_first, bool(self.bidirectional))
130
131 def _apply(self, fn):
RuntimeError: param_from.type() == param_to.type() INTERNAL ASSERT FAILED at /opt/conda/conda-bld/pytorch_1579022060824/work/aten/src/ATen/native/cudnn/RNN.cpp:541, please report a bug to PyTorch. parameter types mismatch
Any ideas? Loading a pre-trained model from v1 into v2 should be generally working, right?