"""PPL Inferencer.""" import os from typing import List, Optional import mmengine import torch from tqdm import tqdm 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, dump_results_dict logger = get_logger(__name__) @ICL_INFERENCERS.register_module() class MinKPercentInferencer(BaseInferencer): """PPLOnlyInferencer class to calculate PPL and PPL only, no choice is made. This Inferencer is usually used along with AveragePPLEvaluator. 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. save_every (:obj:`int`, optional): Save intermediate results every """ 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', save_every: Optional[int] = 1, **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.save_every = save_every 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) -> List: # 1. Preparation for output logs output_handler = PPLOnlyInferencerOutputHandler() 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. Generate prompts for testing input prompt_list = self.get_generation_prompt_list_from_retriever_indices( ice_idx_list, retriever, max_seq_len=self.max_seq_len, ice_template=ice_template, prompt_template=prompt_template) # 3.1 Fetch and zip prompt & gold answer if output column exists ds_reader = retriever.dataset_reader assert ds_reader.output_column is None, ( 'PPLOnlyInferencer supports `output_column=None` only.') # Create tmp json file for saving intermediate results and future # resuming index = 0 tmp_json_filepath = os.path.join(output_json_filepath, 'tmp_' + output_json_filename) if os.path.exists(tmp_json_filepath): # TODO: move resume to output handler try: tmp_result_dict = mmengine.load(tmp_json_filepath) except Exception: pass else: output_handler.results_dict = tmp_result_dict index = len(tmp_result_dict) # 4. Wrap prompts with Dataloader dataloader = self.get_dataloader(prompt_list[index:], self.batch_size) # 5. Inference for prompts in each batch logger.info('Starting inference process...') for datum in tqdm(dataloader, disable=not self.is_main_process): entry = datum # 5-1. Inference with local model with torch.no_grad(): sub_inputs = self.model.parse_template(entry, mode='ppl') minks = self.model.get_mink_percent(sub_inputs).tolist() parsed_entries = self.model.parse_template(entry, mode='gen') # 5-3. Save current output for prompt, mink, in zip(parsed_entries, minks): output_handler.save_results(prompt, mink, index) index = index + 1 # 5-4. Save intermediate results if (self.save_every is not None and index % self.save_every == 0 and self.is_main_process): output_handler.write_to_json(output_json_filepath, 'tmp_' + output_json_filename) # 6. 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) if os.path.exists(tmp_json_filepath): os.remove(tmp_json_filepath) return [ sample['mink'] for sample in output_handler.results_dict.values() ] def get_generation_prompt_list_from_retriever_indices( self, ice_idx_list: List[List[int]], retriever: BaseRetriever, max_seq_len: Optional[int] = None, ice_template: Optional[PromptTemplate] = None, prompt_template: Optional[PromptTemplate] = None): prompt_list = [] for idx, ice_idx in enumerate(ice_idx_list): ice = retriever.generate_ice(ice_idx, ice_template=ice_template) prompt = retriever.generate_prompt_for_generate_task( idx, ice, ice_template=ice_template, prompt_template=prompt_template) if max_seq_len is not None: prompt_token_num = self.model.get_token_len_from_template( prompt, mode='gen') while len(ice_idx) > 0 and prompt_token_num > max_seq_len: ice_idx = ice_idx[:-1] ice = retriever.generate_ice(ice_idx, ice_template=ice_template) prompt = retriever.generate_prompt_for_generate_task( idx, ice, ice_template=ice_template, prompt_template=prompt_template) prompt_token_num = self.model.get_token_len_from_template( prompt, mode='gen') prompt_list.append(prompt) return prompt_list class PPLOnlyInferencerOutputHandler: origin_prompt_dict = {} output_dict = {} results_dict = {} def __init__(self) -> None: self.results_dict = {} def write_to_json(self, save_dir: str, filename: str): """Dump the result to a json file.""" dump_results_dict(self.results_dict, os.path.join(save_dir, filename)) def save_results(self, origin_prompt, mink, idx): self.results_dict[str(idx)] = { 'origin_prompt': origin_prompt, 'mink': mink, }