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Make PreTrainedModel.from_pretrained pass unused arguments to model

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This commit is contained in:
Anish Moorthy
2019-07-22 17:56:27 -04:00
parent 2f869dc665
commit b8009cb0da
1 changed files with 26 additions and 9 deletions
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29
pytorch_transformers/modeling_utils.py
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@@ -78,7 +78,7 @@ class PretrainedConfig(object):
self.to_json_file(output_config_file) self.to_json_file(output_config_file)
@classmethod @classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *input, **kwargs): def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
r""" Instantiate a PretrainedConfig from a pre-trained model configuration. r""" Instantiate a PretrainedConfig from a pre-trained model configuration.
Params: Params:
@@ -105,6 +105,7 @@ class PretrainedConfig(object):
""" """
cache_dir = kwargs.pop('cache_dir', None) cache_dir = kwargs.pop('cache_dir', None)
return_unused_args = kwargs.pop('return_unused_args', False)
if pretrained_model_name_or_path in cls.pretrained_config_archive_map: if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
config_file = cls.pretrained_config_archive_map[pretrained_model_name_or_path] config_file = cls.pretrained_config_archive_map[pretrained_model_name_or_path]
@@ -148,6 +149,9 @@ class PretrainedConfig(object):
kwargs.pop(key, None) kwargs.pop(key, None)
logger.info("Model config %s", config) logger.info("Model config %s", config)
if return_unused_args:
return config, kwargs
else:
return config return config
@classmethod @classmethod
@@ -305,7 +309,7 @@ class PreTrainedModel(nn.Module):
torch.save(model_to_save.state_dict(), output_model_file) torch.save(model_to_save.state_dict(), output_model_file)
@classmethod @classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs): def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""Instantiate a pretrained pytorch model from a pre-trained model configuration. r"""Instantiate a pretrained pytorch model from a pre-trained model configuration.
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are desactivated) The model is set in evaluation mode by default using `model.eval()` (Dropout modules are desactivated)
@@ -336,9 +340,17 @@ class PreTrainedModel(nn.Module):
configuration should be cached if the standard cache should not be used. configuration should be cached if the standard cache should not be used.
**output_loading_info**: (`optional`) boolean: **output_loading_info**: (`optional`) boolean:
Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages. Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
**model_args**: (`optional`) Sequence:
All positional arguments will be passed to the underlying model's __init__ function
**kwargs**: (`optional`) dict: **kwargs**: (`optional`) dict:
Dictionnary of key, values to update the configuration object after loading. Dictionary of key, values to update the configuration object after loading.
Can be used to override selected configuration parameters. E.g. ``output_attention=True`` Can be used to override selected configuration parameters. E.g. ``output_attention=True``.
If config is None, then **kwargs will be passed to the model.
If said key is *not* present, then kwargs will be used to
override any keys shared with the default configuration for the
given pretrained_model_name_or_path, and only the unshared
key/value pairs will be passed to the model.
Examples:: Examples::
@@ -359,7 +371,12 @@ class PreTrainedModel(nn.Module):
# Load config # Load config
if config is None: if config is None:
config = cls.config_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) config, model_kwargs = cls.config_class.from_pretrained(
pretrained_model_name_or_path, *model_args,
return_unused_args=True, **kwargs
)
else:
model_kwargs = kwargs
# Load model # Load model
if pretrained_model_name_or_path in cls.pretrained_model_archive_map: if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
@@ -400,7 +417,7 @@ class PreTrainedModel(nn.Module):
archive_file, resolved_archive_file)) archive_file, resolved_archive_file))
# Instantiate model. # Instantiate model.
model = cls(config) model = cls(config, *model_args, **model_kwargs)
if state_dict is None and not from_tf: if state_dict is None and not from_tf:
state_dict = torch.load(resolved_archive_file, map_location='cpu') state_dict = torch.load(resolved_archive_file, map_location='cpu')