import logging import torch from tqdm.auto import tqdm from transformers import AutoModelForSeq2SeqLM, AutoTokenizer def load_model_and_tokenizer(model_name): """ load_model_and_tokenizer - a function that loads a model and tokenizer from huggingface Args: model_name (str): the name of the model to load Returns: AutoModelForSeq2SeqLM: the model AutoTokenizer: the tokenizer """ model = AutoModelForSeq2SeqLM.from_pretrained( model_name, low_cpu_mem_usage=True, use_cache=False, ) tokenizer = AutoTokenizer.from_pretrained(model_name) model = model.to("cuda") if torch.cuda.is_available() else model return model, tokenizer def summarize_and_score(ids, mask, model, tokenizer, **kwargs): """ summarize_and_score - given a batch of ids and a mask, return a summary and a score for the summary Args: ids (): the batch of ids mask (): the attention mask for the batch model (): the model to use for summarization tokenizer (): the tokenizer to use for summarization Returns: str: the summary of the batch """ ids = ids[None, :] mask = mask[None, :] input_ids = ids.to("cuda") if torch.cuda.is_available() else ids attention_mask = mask.to("cuda") if torch.cuda.is_available() else mask attention_mask = mask.to("cuda") global_attention_mask = torch.zeros_like(attention_mask) # put global attention on token global_attention_mask[:, 0] = 1 summary_pred_ids = model.generate( input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask, output_scores=True, return_dict_in_generate=True, **kwargs, ) summary = tokenizer.batch_decode( summary_pred_ids.sequences, skip_special_tokens=True, remove_invalid_values=True, ) score = round(summary_pred_ids.sequences_scores.cpu().numpy()[0], 4) return summary, score def summarize_via_tokenbatches( input_text: str, model, tokenizer, batch_length=2048, batch_stride=16, **kwargs, ): """ summarize_via_tokenbatches - a function that takes a string and returns a summary Args: input_text (str): the text to summarize model (): the model to use for summarization tokenizer (): the tokenizer to use for summarization batch_length (int, optional): the length of each batch. Defaults to 2048. batch_stride (int, optional): the stride of each batch. Defaults to 16. The stride is the number of tokens that overlap between batches. Returns: str: the summary """ # log all input parameters logging.info(f"input parameters: {kwargs}, batch_length={batch_length}, batch_stride={batch_stride}") encoded_input = tokenizer( input_text, padding="max_length", truncation=True, max_length=batch_length, stride=batch_stride, return_overflowing_tokens=True, add_special_tokens=False, return_tensors="pt", ) in_id_arr, att_arr = encoded_input.input_ids, encoded_input.attention_mask gen_summaries = [] pbar = tqdm(total=len(in_id_arr)) for _id, _mask in zip(in_id_arr, att_arr): result, score = summarize_and_score( ids=_id, mask=_mask, model=model, tokenizer=tokenizer, **kwargs, ) score = round(float(score), 4) _sum = { "input_tokens": _id, "summary": result, "summary_score": score, } gen_summaries.append(_sum) print(f"\t{result[0]}\nScore:\t{score}") pbar.update() pbar.close() return gen_summaries