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kwargs are passed to both model and configuration in AutoModels (#2998)

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Lysandre Debut 2020-02-24 14:19:39 -05:00 committed by GitHub
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src/transformers/modeling_auto.py
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@ -359,16 +359,12 @@ class AutoModel(object):
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
kwargs: (`optional`) Remaining dictionary of keyword arguments:
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
These arguments will be passed to the configuration and the model.
Examples::
model = AutoModel.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = AutoModel.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = AutoModel.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == 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')
@ -503,24 +499,12 @@ class AutoModelForPreTraining(object):
output_loading_info: (`optional`) boolean:
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
kwargs: (`optional`) Remaining dictionary of keyword arguments:
Can be used to update the configuration object (after it being loaded) and initiate the model.
(e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or
automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the
underlying model's ``__init__`` method (we assume all relevant updates to the configuration have
already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class
initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of
``kwargs`` that corresponds to a configuration attribute will be used to override said attribute
with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration
attribute will be passed to the underlying model's ``__init__`` function.
These arguments will be passed to the configuration and the model.
Examples::
model = AutoModelForPreTraining.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = AutoModelForPreTraining.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = AutoModelForPreTraining.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == 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')
@ -657,24 +641,12 @@ class AutoModelWithLMHead(object):
output_loading_info: (`optional`) boolean:
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
kwargs: (`optional`) Remaining dictionary of keyword arguments:
Can be used to update the configuration object (after it being loaded) and initiate the model.
(e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or
automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the
underlying model's ``__init__`` method (we assume all relevant updates to the configuration have
already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class
initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of
``kwargs`` that corresponds to a configuration attribute will be used to override said attribute
with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration
attribute will be passed to the underlying model's ``__init__`` function.
These arguments will be passed to the configuration and the model.
Examples::
model = AutoModelWithLMHead.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = AutoModelWithLMHead.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = AutoModelWithLMHead.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == 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')
@ -814,16 +786,12 @@ class AutoModelForSequenceClassification(object):
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
kwargs: (`optional`) Remaining dictionary of keyword arguments:
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
These arguments will be passed to the configuration and the model.
Examples::
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = AutoModelForSequenceClassification.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == 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')
@ -957,16 +925,12 @@ class AutoModelForQuestionAnswering(object):
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
kwargs: (`optional`) Remaining dictionary of keyword arguments:
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
These arguments will be passed to the configuration and the model.
Examples::
model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = AutoModelForQuestionAnswering.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == 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')
@ -1101,16 +1065,12 @@ class AutoModelForTokenClassification:
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
kwargs: (`optional`) Remaining dictionary of keyword arguments:
Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
- If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
- If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
These arguments will be passed to the configuration and the model.
Examples::
model = AutoModelForTokenClassification.from_pretrained('bert-base-uncased') # Download model and configuration from S3 and cache.
model = AutoModelForTokenClassification.from_pretrained('./test/bert_model/') # E.g. model was saved using `save_pretrained('./test/saved_model/')`
model = AutoModelForTokenClassification.from_pretrained('bert-base-uncased', output_attention=True) # Update configuration during loading
assert model.config.output_attention == 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')