In the below code i am converting the input data into embedings using a pre-trained BERT model. Since I only require the pooler output which is a vector of length 768 and performing a loop throughout the data and concatenate it into the passage_vectors tensor. But this causes the memory to run out after 15-20 data points. I tried running the program after commenting out the torch.cat() statement, which worked completely fine.
passage_vectors = torch.tensor([])
for i in range(len(dicts)):
    preprocessed = preprocessor.process(dicts[i])
    for j in range(len(preprocessed)):
      passage = preprocessed[j]['text']
      passages.append(preprocessed[j]['text'])
      ctx_tokens = context_tokenizer(passage, return_tensors = "pt", padding='max_length',truncation=True)
      ctx_vector = context_model(input_ids = ctx_tokens.input_ids, attention_mask = ctx_tokens.attention_mask)['pooler_output']
      #passage_vectors = torch.cat((passage_vectors,ctx_vector), 0)

There are around 2000 input data. So the final output tensor would be of dimensions 2000x768 which shouldn’t take up that much memory. I am running this in Google Colab which has 12gb RAM. Is there something is can do to make it more memory efficient.