I’m trying to get feature vectors from the encoder model using pre-trained ALBERT v2 weights. i have a nvidia 1650ti gpu (4 GB) , and plenty RAM but for some reason I’m getting Runtime error saying -
RuntimeError: [enforce fail at …\c10\core\CPUAllocator.cpp:75] data. DefaultCPUAllocator: not enough memory: you tried to allocate 491520000 bytes. Buy new RAM!
I’m really new to pytorch and deep learning in general. Can anyone please tell me what is wrong?
My entire code -
encoded_test_data = tokenized_test_values[‘input_ids’]
encoded_test_masks = tokenized_test_values[‘attention_mask’]
encoded_train_data = torch.from_numpy(encoded_train_data).to(device)
encoded_masks = torch.from_numpy(encoded_masks).to(device)
encoded_test_data = torch.from_numpy(encoded_test_data).to(device)
encoded_test_masks = torch.from_numpy(encoded_test_masks).to(device)
config  = EncoderDecoderConfig.from_encoder_decoder_configs(BertConfig(),BertConfig())
EnD_model = EncoderDecoderModel.from_pretrained(‘albert-base-v2’,config=config)
feature_extractor = EnD_model.get_encoder()
feature_vector = feature_extractor.forward(input_ids=encoded_train_data,attention_mask = encoded_masks)
feature_test_vector = feature_extractor.forward(input_ids = encoded_test_data, attention_mask = encoded_test_masks)
Also 491520000 bytes is about 490 MB which should not be a problem.