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[Reformer] Fix example and error message (#4191)

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* fix example reformer

* fix error message and example docstring

* improved error message
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Patrick von Platen 2020-05-07 10:50:11 +02:00 committed by GitHub
parent 96c78396ce
commit 74ffc9ea6b
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1 changed files with 6 additions and 6 deletions
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src/transformers/modeling_reformer.py
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@ -124,8 +124,8 @@ class AxialPositionEmbeddings(nn.Module):
if self.training is True: if self.training is True:
assert ( assert (
reduce(mul, self.axial_pos_shape) == sequence_length reduce(mul, self.axial_pos_shape) == sequence_length
), "Make sure that config.axial_pos_shape factors: {} multiply to sequence length: {}".format( ), "If training, make sure that config.axial_pos_shape factors: {} multiply to sequence length. Got prod({}) != sequence_length: {}. You might want to consider padding your sequence length to {} or changing config.axial_pos_shape.".format(
self.axial_pos_shape, sequence_length self.axial_pos_shape, self.axial_pos_shape, sequence_length, reduce(mul, self.axial_pos_shape)
) )
if self.dropout > 0: if self.dropout > 0:
weights = torch.cat(broadcasted_weights, dim=-1) weights = torch.cat(broadcasted_weights, dim=-1)
@ -1515,11 +1515,11 @@ class ReformerModel(ReformerPreTrainedModel):
Examples:: Examples::
from transformers import ReformerModel, ReformerTokenizer from transformers import ReformerModelWithLMHead, ReformerTokenizer
import torch import torch
tokenizer = ReformerTokenizer.from_pretrained('bert-base-uncased') tokenizer = ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment')
model = ReformerModel.from_pretrained('bert-base-uncased') model = ReformerModelWithLMHead.from_pretrained('google/reformer-crime-and-punishment')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1
outputs = model(input_ids) outputs = model(input_ids)
@ -1562,7 +1562,7 @@ class ReformerModel(ReformerPreTrainedModel):
if self.training is True: if self.training is True:
raise ValueError( raise ValueError(
"If training, sequence Length {} has to be a multiple of least common multiple chunk_length {}. Please consider padding the input to a length of {}.".format( "If training, sequence Length {} has to be a multiple of least common multiple chunk_length {}. Please consider padding the input to a length of {}.".format(
input_shape[-2], least_common_mult_chunk_length, input_shape[-2] + padding_length input_shape[-1], least_common_mult_chunk_length, input_shape[-1] + padding_length
) )
) )