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modify from_pretrained for OpenAIGPT

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VictorSanh
2019-05-31 00:27:18 -04:00
parent 372a5c1cee
commit 0c5a4fe9c9
1 changed files with 10 additions and 6 deletions
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16
pytorch_pretrained_bert/modeling_openai.py
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@@ -419,9 +419,7 @@ class OpenAIGPTPreTrainedModel(nn.Module):
pass pass
@classmethod @classmethod
def from_pretrained( def from_pretrained(cls, pretrained_model_name_or_path, num_special_tokens=None, *inputs, **kwargs):
cls, pretrained_model_name_or_path, num_special_tokens=None, state_dict=None, cache_dir=None, from_tf=False, *inputs, **kwargs
):
""" """
Instantiate a OpenAIGPTPreTrainedModel from a pre-trained model file or a pytorch state dict. Instantiate a OpenAIGPTPreTrainedModel from a pre-trained model file or a pytorch state dict.
Download and cache the pre-trained model file if needed. Download and cache the pre-trained model file if needed.
@@ -434,14 +432,20 @@ class OpenAIGPTPreTrainedModel(nn.Module):
. `openai_gpt_config.json` a configuration file for the model . `openai_gpt_config.json` a configuration file for the model
. `pytorch_model.bin` a PyTorch dump of a OpenAIGPTModel instance . `pytorch_model.bin` a PyTorch dump of a OpenAIGPTModel instance
- a path or url to a pretrained model archive containing: - a path or url to a pretrained model archive containing:
. `bert_config.json` a configuration file for the model . `openai-gpt-config.json` a configuration file for the model
. a series of NumPy files containing OpenAI TensorFlow trained weights . a series of NumPy files containing OpenAI TensorFlow trained weights
from_tf: should we load the weights from a locally saved TensorFlow checkpoint from_tf: should we load the weights from a locally saved TensorFlow checkpoint
cache_dir: an optional path to a folder in which the pre-trained models will be cached. cache_dir: an optional path to a folder in which the pre-trained models will be cached.
state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of pre-trained models state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of pre-trained models
*inputs, **kwargs: additional input for the specific Bert class *inputs, **kwargs: additional input for the specific OpenAI-GPT class
(ex: num_labels for BertForSequenceClassification)
""" """
state_dict = kwargs.get('state_dict', None)
kwargs.pop('state_dict', None)
cache_dir = kwargs.get('cache_dir', None)
kwargs.pop('cache_dir', None)
from_tf = kwargs.get('from_tf', False)
kwargs.pop('from_tf', None)
if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP: if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path] archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
config_file = PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path] config_file = PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]