class RetrievalModel(torch.nn.Module):
def __init__(self, bert_path_or_config, fc_path=None):
super(RetrievalModel, self).__init__()
if isinstance(bert_path_or_config, str):
self.bert = BertModel.from_pretrained(
pretrained_model_name_or_path=bert_path_or_config)
self.tokenizer = BertTokenizer.from_pretrained(bert_path_or_config)
elif isinstance(bert_path_or_config, BertConfig):
self.bert = BertModel(bert_path_or_config)
self.tokenizer = None
self.fc = torch.nn.Linear(in_features=self.bert.config.hidden_size,
out_features=1,
bias=False)
if fc_path:
self.fc.load_state_dict(torch.load(fc_path))
def forward(self,
input_ids=None,
attention_mask=None,
token_type_ids=None):
_, pooler_output = self.bert(input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids)
scores = self.fc(pooler_output)
return scores
def save(self, save_dir):
self.bert.save_pretrained(save_dir)
if self.tokenizer:
self.tokenizer.save_pretrained(save_dir)
torch.save(self.fc.state_dict(), os.path.join(save_dir,
"fc_weight.bin"))
def run(pretrained_model, out_dir, num_layers=3):
os.makedirs(out_dir, exist_ok=True)
tokenizer = AutoTokenizer.from_pretrained(pretrained_model)
model = BertModel.from_pretrained(pretrained_model, return_dict=True)
small_config = copy.deepcopy(model.config)
small_config.num_hidden_layers = num_layers
small_model = BertModel(small_config)
small_model.load_state_dict(model.state_dict(), strict=False)
tokenizer.save_pretrained(out_dir)
small_model.save_pretrained(out_dir)
def test_export_custom_bert_model(self):
from transformers import BertModel
vocab = ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
with NamedTemporaryFile(mode="w+t") as vocab_file:
vocab_file.write("\n".join(vocab))
vocab_file.flush()
tokenizer = BertTokenizerFast(vocab_file.name)
with TemporaryDirectory() as bert_save_dir:
model = BertModel(BertConfig(vocab_size=len(vocab)))
model.save_pretrained(bert_save_dir)
self._test_export(bert_save_dir, "pt", 12, tokenizer)
