mirror of
https://github.com/open-compass/opencompass.git
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7505b3cadf
* add TheoremQA with 5-shot * add huggingface_above_v4_33 classes * use num_worker partitioner in cli * update theoremqa * update TheoremQA * add TheoremQA * rename theoremqa -> TheoremQA * update TheoremQA output path * rewrite many model configs * update huggingface * further update * refine configs * update configs * update configs * add configs/eval_llama3_instruct.py * add summarizer multi faceted * update bbh datasets * update configs/models/hf_llama/lmdeploy_llama3_8b_instruct.py * rename class * update readme * update hf above v4.33
188 lines
8.1 KiB
Python
188 lines
8.1 KiB
Python
# flake8: noqa
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# yapf: disable
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"""PPL Inferencer."""
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import os
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from typing import List, Optional
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import torch
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from tqdm import trange
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from opencompass.models.base import BaseModel
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from opencompass.registry import ICL_INFERENCERS
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from ..icl_prompt_template import PromptTemplate
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from ..icl_retriever import BaseRetriever
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from ..utils import get_logger
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from .icl_base_inferencer import BaseInferencer, PPLInferencerOutputHandler
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logger = get_logger(__name__)
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@ICL_INFERENCERS.register_module()
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class PPLInferencer(BaseInferencer):
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"""PPL Inferencer class to evaluate by perplexity.
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Attributes:
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model (:obj:`BaseModel`, optional): The module to inference.
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max_seq_len (:obj:`int`): Maximum number of tokenized words allowed by
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the LM.
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batch_size (:obj:`int`, optional): Batch size for the :obj:`DataLoader`
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output_json_filepath (:obj:`str`, optional): File path for output
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`JSON` file.
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output_json_filename (:obj:`str`, optional): File name for output
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`JSON` file.
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labels (:obj:`List`, optional): A list of labels for all classes.
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"""
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def __init__(
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self,
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model: BaseModel,
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max_seq_len: Optional[int] = None,
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batch_size: Optional[int] = 1,
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output_json_filepath: Optional[str] = './icl_inference_output',
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output_json_filename: Optional[str] = 'predictions',
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labels: Optional[List] = None,
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**kwargs) -> None:
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super().__init__(
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model=model,
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max_seq_len=max_seq_len,
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batch_size=batch_size,
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output_json_filename=output_json_filename,
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output_json_filepath=output_json_filepath,
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**kwargs,
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)
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self.labels = labels
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def inference(self,
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retriever: BaseRetriever,
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ice_template: Optional[PromptTemplate] = None,
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prompt_template: Optional[PromptTemplate] = None,
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output_json_filepath: Optional[str] = None,
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output_json_filename: Optional[str] = None,
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normalizing_str: Optional[str] = None) -> List:
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# 1. Preparation for output logs
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output_handler = PPLInferencerOutputHandler()
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sub_predictions = []
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ppl = []
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ice = []
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if output_json_filepath is None:
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output_json_filepath = self.output_json_filepath
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if output_json_filename is None:
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output_json_filename = self.output_json_filename
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# 2. Get results of retrieval process
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ice_idx_list = retriever.retrieve()
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# 3. Get labels of all the classes
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if self.labels is None:
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labels = retriever.get_labels(ice_template=ice_template,
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prompt_template=prompt_template)
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else:
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labels = self.labels
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# 4. Generate in-context examples for testing inputs
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for idx in range(len(ice_idx_list)):
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ice.append(retriever.generate_ice(ice_idx_list[idx], ice_template=ice_template))
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output_handler.save_ice(self.model.parse_template(ice, mode='ppl'))
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# 5. Calculating PPL for prompts in each label's class
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for label in labels:
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index = 0
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prompt_list = []
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sub_ppl_list = []
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token_num_list = []
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normalizing_prompt_list = []
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context_length_list = []
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# 5.1 Generate prompts of current label and truncate
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# TODO: Refactor
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for idx in range(len(ice_idx_list)):
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prompt_kwargs = {
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'idx': idx,
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'ice': ice[idx],
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'label': label,
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'ice_template': ice_template,
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'prompt_template': prompt_template,
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'remain_sep': normalizing_str is not None
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}
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prompt = retriever.generate_label_prompt(**prompt_kwargs)
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prompt_token_num = self.model.get_token_len_from_template(prompt, mode='ppl')
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if self.max_seq_len is not None:
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while len(ice_idx_list[idx]) > 0 and prompt_token_num > self.max_seq_len:
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ice_idx_list[idx] = ice_idx_list[idx][:-1]
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ice[idx] = retriever.generate_ice(ice_idx_list[idx], ice_template=ice_template)
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prompt_kwargs['ice'] = ice[idx]
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prompt = retriever.generate_label_prompt(**prompt_kwargs)
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prompt_token_num = self.model.get_token_len_from_template(prompt, mode='ppl')
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if normalizing_str is not None:
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assert isinstance(prompt, str), 'Prompt must be a string when normalizing_str is set.'
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prompt_sep = prompt
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if prompt_template is not None:
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sep_token = prompt_template.sep_token
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else:
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sep_token = ice_template.sep_token
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sep_pos = prompt_sep.find(sep_token)
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context = prompt_sep[0:sep_pos]
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answer = prompt_sep[sep_pos:].replace(sep_token, '')
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prompt = context + answer
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normalizing_prompt = normalizing_str + answer
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context_length_list.append(self.model.get_token_len_from_template(context, mode='ppl'))
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normalizing_prompt_list.append(normalizing_prompt)
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prompt_list.append(prompt)
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token_num_list.append(prompt_token_num)
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if normalizing_str is not None:
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normalizing_str_len = self.model.get_token_len_from_template(
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normalizing_str, mode='ppl')
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# 5.2 Get PPL
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logger.info(f"Calculating PPL for prompts labeled '{label}'")
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for idx in trange(0, len(prompt_list), self.batch_size, disable=not self.is_main_process):
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sub_prompt_list = prompt_list[idx:idx + self.batch_size]
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with torch.no_grad():
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if normalizing_str is not None:
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sub_context_length_list = context_length_list[idx:idx + self.batch_size]
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sub_normalizing_prompt_list = normalizing_prompt_list[idx:idx + self.batch_size]
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res1 = self.model.get_ppl_from_template(sub_prompt_list, mask_length=sub_context_length_list)
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sub_normalizing_context_length_list = [normalizing_str_len for _ in range(len(sub_prompt_list))]
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res2 = self.model.get_ppl_from_template(sub_normalizing_prompt_list, mask_length=sub_normalizing_context_length_list)
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sub_res = res1 - res2
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else:
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sub_res = self.model.get_ppl_from_template(sub_prompt_list).tolist()
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for res, prompt in zip(sub_res, self.model.parse_template(sub_prompt_list, mode='ppl')):
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sub_ppl_list.append(res)
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ice_str = self.model.parse_template(ice[idx], mode='ppl')
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prompt_wo_ice = prompt.replace(ice_str, '')
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output_handler.save_prompt_and_ppl(label, prompt_wo_ice, prompt, res, index)
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output_handler.results_dict[str(index)][f'label: {str(label)}']['BPB'] = res * token_num_list[index] / len(prompt_wo_ice.encode())
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index = index + 1
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ppl.append(sub_ppl_list)
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# 6. Get lowest PPL class as predictions
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ppl = list(zip(*ppl))
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for single_ppl in ppl:
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sub_predictions.append(labels[single_ppl.index(min(single_ppl))])
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output_handler.save_predictions(sub_predictions)
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# 7. Fetch gold answers if exist
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ds_reader = retriever.dataset_reader
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if ds_reader.output_column:
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golds = ds_reader.dataset['test'][ds_reader.output_column]
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output_handler.save_golds(golds)
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# 8. Output
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if self.is_main_process:
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os.makedirs(output_json_filepath, exist_ok=True)
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output_handler.write_to_json(output_json_filepath, output_json_filename)
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return [sample['prediction'] for sample in output_handler.results_dict.values()]
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