NotImplementedError

[xiang-xiang-zhu]

when i run inference code
python3 main.py \ --train_data_file ${ROOT_PATH}/data/${DATA_TYPE}/train \ --dev_data_file ${ROOT_PATH}/data/${DATA_TYPE}/dev \ --test_data_file ${ROOT_PATH}/data/${DATA_TYPE}/test \ --graph_path 2hops_100_directed_triple_filter.json \ --output_dir ${ROOT_PATH}/models/${DATA_TYPE}/grf-${DATA_TYPE} \ --source_length 32 \ --target_length 32 \ --model_type gpt2 \ --model_name_or_path ${ROOT_PATH}/models/gpt2-small \ --do_eval \ --per_gpu_train_batch_size 16 \ --per_gpu_eval_batch_size 16 \ --workers 7 \ --seed 42 \ --evaluate_metrics bleu \ --overwrite_output_dir \ --aggregate_method max \ --gamma 0.5 \

Error
[04/09/2022 21:49:57 - WARNING - root] Process rank: -1, device: cuda, n_gpu: 1, distributed training: False, 16-bits training: False
Setting 'max_len_single_sentence' is now deprecated. This value is automatically set up.
Setting 'max_len_sentences_pair' is now deprecated. This value is automatically set up.
source length: 32
[04/09/2022 21:49:58 - INFO - modeling_gpt2] Tie weights in head!!!!!
[04/09/2022 21:49:58 - INFO - modeling_gpt2] Tie weights in head!!!!!
Traceback (most recent call last):
File "main.py", line 620, in
main()
File "main.py", line 588, in main
model.resize_token_embeddings(len(tokenizer))
File "/home/hx/anaconda3/envs/torch1.4/lib/python3.7/site-packages/transformers/modeling_utils.py", line 724, in resize_token_embeddings
model_embeds = self._resize_token_embeddings(new_num_tokens)
File "/home/hx/anaconda3/envs/torch1.4/lib/python3.7/site-packages/transformers/modeling_utils.py", line 738, in _resize_token_embeddings
old_embeddings = self.get_input_embeddings()
File "/home/hx/anaconda3/envs/torch1.4/lib/python3.7/site-packages/transformers/modeling_utils.py", line 563, in get_input_embeddings
return base_model.get_input_embeddings()
File "/home/hx/anaconda3/envs/torch1.4/lib/python3.7/site-packages/transformers/modeling_utils.py", line 565, in get_input_embeddings
raise NotImplementedError
NotImplementedError

[MoemenGaafar]

Add these two functions to the GPT2Model class in modeling_gpt2.py:

def get_input_embeddings(self):
return self.wte

def set_input_embeddings(self, new_embeddings):
self.wte = new_embeddings