"""PPL Inferencer.""" import os from typing import List, Optional import torch from tqdm import trange from opencompass.models.base import BaseModel from opencompass.registry import ICL_INFERENCERS from ..icl_prompt_template import PromptTemplate from ..icl_retriever import BaseRetriever from ..utils import get_logger from .icl_base_inferencer import BaseInferencer, PPLInferencerOutputHandler logger = get_logger(__name__) @ICL_INFERENCERS.register_module() class PPLInferencer(BaseInferencer): """PPL Inferencer class to evaluate by perplexity. Attributes: model (:obj:`BaseModel`, optional): The module to inference. max_seq_len (:obj:`int`): Maximum number of tokenized words allowed by the LM. batch_size (:obj:`int`, optional): Batch size for the :obj:`DataLoader` output_json_filepath (:obj:`str`, optional): File path for output `JSON` file. output_json_filename (:obj:`str`, optional): File name for output `JSON` file. labels (:obj:`List`, optional): A list of labels for all classes. """ def __init__( self, model: BaseModel, max_seq_len: Optional[int] = None, batch_size: Optional[int] = 1, output_json_filepath: Optional[str] = './icl_inference_output', output_json_filename: Optional[str] = 'predictions', labels: Optional[List] = None, **kwargs) -> None: super().__init__( model=model, max_seq_len=max_seq_len, batch_size=batch_size, output_json_filename=output_json_filename, output_json_filepath=output_json_filepath, **kwargs, ) self.labels = labels def inference(self, retriever: BaseRetriever, ice_template: Optional[PromptTemplate] = None, prompt_template: Optional[PromptTemplate] = None, output_json_filepath: Optional[str] = None, output_json_filename: Optional[str] = None, normalizing_str: Optional[str] = None) -> List: # 1. Preparation for output logs output_handler = PPLInferencerOutputHandler() sub_predictions = [] ppl = [] ice = [] if output_json_filepath is None: output_json_filepath = self.output_json_filepath if output_json_filename is None: output_json_filename = self.output_json_filename # 2. Get results of retrieval process ice_idx_list = retriever.retrieve() # 3. Get labels of all the classes if self.labels is None: labels = retriever.get_labels(ice_template=ice_template, prompt_template=prompt_template) else: labels = self.labels # 4. Generate in-context examples for testing inputs for idx in range(len(ice_idx_list)): ice.append( retriever.generate_ice(ice_idx_list[idx], ice_template=ice_template)) output_handler.save_ice(self.model.parse_template(ice, mode='ppl')) # 5. Calculating PPL for prompts in each label's class for label in labels: index = 0 prompt_list = [] sub_ppl_list = [] token_num_list = [] normalizing_prompt_list = [] context_length_list = [] # 5.1 Generate prompts of current label and truncate # TODO: Refactor for idx in range(len(ice_idx_list)): prompt = retriever.generate_label_prompt( idx, ice[idx], label, ice_template=ice_template, prompt_template=prompt_template, remain_sep=normalizing_str is not None) if self.max_seq_len is not None: prompt_token_num = self.model.get_token_len_from_template( prompt, mode='ppl') while len(ice_idx_list[idx] ) > 0 and prompt_token_num > self.max_seq_len: ice_idx_list[idx] = ice_idx_list[idx][:-1] ice[idx] = retriever.generate_ice( ice_idx_list[idx], ice_template=ice_template) prompt = retriever.generate_label_prompt( idx, ice[idx], label, ice_template=ice_template, prompt_template=prompt_template) prompt_token_num = self.model.get_token_len_from_template( # noqa prompt, mode='ppl') # noqa if normalizing_str is not None: assert isinstance(prompt, str), \ 'Prompt must be a string when normalizing_str is set.' prompt_sep = prompt if prompt_template is not None: sep_token = prompt_template.sep_token else: sep_token = ice_template.sep_token sep_pos = prompt_sep.find(sep_token) context = prompt_sep[0:sep_pos] answer = prompt_sep[sep_pos:].replace(sep_token, '') prompt = context + answer normalizing_prompt = normalizing_str + answer context_length_list.append( self.model.get_token_len_from_template(context, mode='ppl')) normalizing_prompt_list.append(normalizing_prompt) prompt_list.append(prompt) token_num_list.append(prompt_token_num) if normalizing_str is not None: normalizing_str_len = self.model.get_token_len_from_template( normalizing_str, mode='ppl') # 5.2 Get PPL logger.info(f"Calculating PPL for prompts labeled '{label}'") for idx in trange(0, len(prompt_list), self.batch_size, disable=not self.is_main_process): sub_prompt_list = prompt_list[idx:idx + self.batch_size] if normalizing_str is not None: sub_context_length_list = context_length_list[idx:idx + self. batch_size] sub_normalizing_prompt_list = normalizing_prompt_list[ idx:idx + self.batch_size] with torch.no_grad(): if normalizing_str is not None: res1 = self.model.get_ppl_from_template( sub_prompt_list, mask_length=sub_context_length_list) res2 = self.model.get_ppl_from_template( sub_normalizing_prompt_list, mask_length=[ normalizing_str_len for i in range(len(sub_prompt_list)) ]) sub_res = res1 - res2 else: sub_res = self.model.get_ppl_from_template( sub_prompt_list).tolist() for res, prompt in zip( sub_res, self.model.parse_template(sub_prompt_list, mode='ppl')): sub_ppl_list.append(res) ice_str = self.model.parse_template(ice[idx], mode='ppl') output_handler.save_prompt_and_ppl( label, prompt.replace(ice_str, ''), prompt, res, index) output_handler.results_dict[str( index)][f'label: {str(label)}'][ 'BPB'] = res * token_num_list[index] / len( prompt.replace(ice_str, '').encode()) index = index + 1 ppl.append(sub_ppl_list) # 6. Get lowest PPL class as predictions ppl = list(zip(*ppl)) for single_ppl in ppl: sub_predictions.append(labels[single_ppl.index(min(single_ppl))]) output_handler.save_predictions(sub_predictions) # 7. Fetch gold answers if exist ds_reader = retriever.dataset_reader if ds_reader.output_column: golds = ds_reader.dataset['test'][ds_reader.output_column] output_handler.save_golds(golds) # 8. Output if self.is_main_process: os.makedirs(output_json_filepath, exist_ok=True) output_handler.write_to_json(output_json_filepath, output_json_filename) return [ sample['prediction'] for sample in output_handler.results_dict.values() ]