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Output attention takes an s (#6903)

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* Fix output_attention -> output_attentions

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This commit is contained in:
Sylvain Gugger
2020-09-02 08:11:45 -04:00
committed by GitHub
parent 485da7222f
commit 8f2723caf0
11 changed files with 75 additions and 63 deletions
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24
hubconf.py
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@@ -28,10 +28,10 @@ def config(*args, **kwargs):
config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased') # Download configuration from S3 and cache.
config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
config = torch.hub.load('huggingface/transformers', 'config', './test/bert_saved_model/my_configuration.json')
config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False)
assert config.output_attention == True
config, unused_kwargs = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attention=True, foo=False, return_unused_kwargs=True)
assert config.output_attention == True
config = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attentions=True, foo=False)
assert config.output_attentions == True
config, unused_kwargs = torch.hub.load('huggingface/transformers', 'config', 'bert-base-uncased', output_attentions=True, foo=False, return_unused_kwargs=True)
assert config.output_attentions == True
assert unused_kwargs == {'foo': False}
"""
@@ -61,8 +61,8 @@ def model(*args, **kwargs):
model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased') # Download model and configuration from S3 and cache.
model = torch.hub.load('huggingface/transformers', 'model', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True
model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased', output_attentions=True) # Update configuration during loading
assert model.config.output_attentions == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
model = torch.hub.load('huggingface/transformers', 'model', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
@@ -80,8 +80,8 @@ def modelWithLMHead(*args, **kwargs):
model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased') # Download model and configuration from S3 and cache.
model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True
model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased', output_attentions=True) # Update configuration during loading
assert model.config.output_attentions == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
@@ -98,8 +98,8 @@ def modelForSequenceClassification(*args, **kwargs):
model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased') # Download model and configuration from S3 and cache.
model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True
model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', 'bert-base-uncased', output_attentions=True) # Update configuration during loading
assert model.config.output_attentions == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
model = torch.hub.load('huggingface/transformers', 'modelForSequenceClassification', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
@@ -117,8 +117,8 @@ def modelForQuestionAnswering(*args, **kwargs):
model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased') # Download model and configuration from S3 and cache.
model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == True
model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased', output_attentions=True) # Update configuration during loading
assert model.config.output_attentions == True
# Loading from a TF checkpoint file instead of a PyTorch model (slower)
config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)