def create_and_check_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels): model = YosoForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def convert_yoso_checkpoint(checkpoint_path, yoso_config_file, pytorch_dump_path): orig_state_dict = torch.load(checkpoint_path, map_location="cpu")["model_state_dict"] config = YosoConfig.from_json_file(yoso_config_file) model = YosoForMaskedLM(config) new_state_dict = convert_checkpoint_helper(config.max_position_embeddings, orig_state_dict) print(model.load_state_dict(new_state_dict)) model.eval() model.save_pretrained(pytorch_dump_path) print( f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}" )