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"""MDL Retriever.""" |
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from typing import List, Optional |
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import numpy as np |
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import torch |
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import tqdm |
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from transformers import AutoModelForCausalLM |
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from opencompass.openicl.icl_prompt_template import PromptTemplate |
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from opencompass.openicl.icl_retriever.icl_topk_retriever import TopkRetriever |
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from opencompass.openicl.utils.logging import get_logger |
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from opencompass.registry import ICL_PROMPT_TEMPLATES, ICL_RETRIEVERS |
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logger = get_logger(__name__) |
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@ICL_RETRIEVERS.register_module() |
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class MDLRetriever(TopkRetriever): |
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"""MDL Retriever, subclass of `TopkRetriever`. MDL is a abbreviation of |
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Minimum Description Length, specially designed for ppl evaluation. You may |
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refer to the paper for more details: https://arxiv.org/pdf/2212.10375.pdf. |
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Args: |
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dataset (`BaseDataset`): Any BaseDataset instances. |
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Attributes of ``reader``, ``train`` and ``test`` will be used. |
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ice_separator (`Optional[str]`): The separator between each in-context |
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example template when origin `PromptTemplate` is provided. Defaults |
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to '\n'. |
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ice_eos_token (`Optional[str]`): The end of sentence token for |
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in-context example template when origin `PromptTemplate` is |
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provided. Defaults to '\n'. |
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ice_num (`Optional[int]`): The number of in-context example template |
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when origin `PromptTemplate` is provided. Defaults to 1. |
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sentence_transformers_model_name (`Optional[str]`): The name of the |
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sentence transformers model. Defaults to 'all-mpnet-base-v2'. |
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tokenizer_name (`Optional[str]`): The name of the tokenizer. Defaults |
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to 'gpt2-xl'. |
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batch_size (`Optional[int]`): The batch size for the dataloader. |
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Defaults to 1. |
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candidate_num (`Optional[int]`): The number of candidates to retrieve |
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for each example. Defaults to 1. |
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ce_model_name (`Optional[str]`): The name of the model for calculating |
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MDL. Defaults to 'gpt2-xl'. |
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select_time (`Optional[int]`): The number of times to select MDL. |
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Defaults to 5. |
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ice_template (`Optional[PromptTemplate]`): The template for in-context |
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example. Defaults to None. |
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prompt_template (`Optional[PromptTemplate]`): The template for prompt. |
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Defaults to None. |
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labels (`Optional[List]`): The labels for calculating MDL. Defaults to |
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None. |
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seed (`Optional[int]`): The seed for random. Defaults to 1. |
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""" |
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metric_model = None |
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def __init__(self, |
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dataset, |
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ice_separator: Optional[str] = '\n', |
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ice_eos_token: Optional[str] = '\n', |
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ice_num: Optional[int] = 1, |
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sentence_transformers_model_name: Optional[ |
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str] = 'all-mpnet-base-v2', |
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tokenizer_name: Optional[str] = 'gpt2-xl', |
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batch_size: Optional[int] = 1, |
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candidate_num: Optional[int] = 1, |
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ce_model_name: Optional[str] = 'gpt2-xl', |
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select_time: Optional[int] = 5, |
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ice_template: Optional[PromptTemplate] = None, |
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prompt_template: Optional[PromptTemplate] = None, |
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labels: Optional[List] = None, |
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seed: Optional[int] = 1) -> None: |
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super().__init__(dataset, ice_separator, ice_eos_token, ice_num, |
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sentence_transformers_model_name, tokenizer_name, |
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batch_size) |
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self.ce_model_name = ce_model_name |
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self.candidate_num = candidate_num |
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self.select_time = select_time |
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self.ice_template = ICL_PROMPT_TEMPLATES.build(ice_template) |
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if prompt_template is not None: |
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self.prompt_template = ICL_PROMPT_TEMPLATES.build(prompt_template) |
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else: |
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self.prompt_template = None |
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self.labels = labels |
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self.seed = seed |
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def topk_search(self): |
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np.random.seed(self.seed) |
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res_list = self.forward(self.dataloader) |
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rtr_idx_list = [[] for _ in range(len(res_list))] |
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logger.info('Retrieving data for test set...') |
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for entry in tqdm.tqdm(res_list, disable=not self.is_main_process): |
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idx = entry['metadata']['id'] |
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embed = np.expand_dims(entry['embed'], axis=0) |
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near_ids = self.index.search( |
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embed, min(self.candidate_num, |
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len(self.index_ds)))[1][0].tolist() |
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candidates = [] |
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mdl_scores = [] |
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for j in range(self.select_time): |
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if j == 0: |
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rand_idx_list = near_ids[:self.ice_num] |
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else: |
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rand_idx_list = np.random.choice(near_ids, |
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self.ice_num, |
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replace=False) |
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rand_idx_list = [int(i) for i in rand_idx_list] |
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candidates.append(rand_idx_list) |
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ice = self.generate_ice(rand_idx_list, |
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ice_template=self.ice_template) |
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ice = str(ice) |
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mask_length = len( |
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self.tokenizer(ice + self.ice_eos_token, |
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verbose=False)['input_ids']) |
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if self.labels is None: |
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labels = self.get_labels(self.ice_template, |
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self.prompt_template) |
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else: |
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labels = self.labels |
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prompt_list = [] |
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for label in labels: |
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prompt = self.generate_label_prompt( |
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idx, ice, label, self.ice_template, |
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self.prompt_template) |
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prompt = str(prompt) |
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prompt_list.append(prompt) |
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loss_list = self.cal_ce(prompt_list, mask_length=mask_length) |
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probs = np.exp(-np.array(loss_list)) |
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normalized_probs = probs / probs.sum(0, keepdims=True) |
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neg_entropy = -entropy(normalized_probs, label_dim=0) |
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mdl_scores.append(neg_entropy) |
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rtr_idx_list[idx] = candidates[mdl_scores.index(max(mdl_scores))] |
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rtr_idx_list[idx] = [int(i) for i in rtr_idx_list[idx]] |
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return rtr_idx_list |
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def retrieve(self): |
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"""Retrieve the in-context example index for each test example.""" |
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return self.topk_search() |
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@torch.no_grad() |
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def cal_ce(self, input_texts: List[str], mask_length=None): |
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if self.metric_model is None: |
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logger.info( |
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f'Load model {self.ce_model_name} for calculating MDL...') |
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self.metric_model = AutoModelForCausalLM.from_pretrained( |
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self.ce_model_name) |
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self.metric_model.to(self.device) |
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inputs = self.tokenizer(input_texts, |
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padding=True, |
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return_tensors='pt', |
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truncation=True) |
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inputs = {k: v.to(self.device) for k, v in inputs.items()} |
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outputs = self.metric_model(**inputs) |
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shift_logits = outputs.logits[..., :-1, :].contiguous() |
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shift_labels = inputs['input_ids'][..., 1:].contiguous() |
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loss_fct = torch.nn.CrossEntropyLoss( |
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reduction='none', ignore_index=self.tokenizer.pad_token_id) |
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shift_logits = shift_logits.view(-1, shift_logits.size(-1)) |
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loss = loss_fct(shift_logits, |
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shift_labels.view(-1)).view(shift_labels.size()) |
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if mask_length is not None: |
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mask = torch.cat([ |
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torch.zeros([loss.shape[0], mask_length], dtype=torch.float), |
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torch.ones([loss.shape[0], loss.shape[-1] - mask_length], |
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dtype=torch.float) |
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], -1) |
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mask = mask.to(self.device) |
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loss = torch.mul(mask, loss) |
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lens = (inputs['input_ids'] != |
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self.tokenizer.pad_token_id).sum(-1).cpu().numpy() |
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if mask_length is not None: |
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lens -= mask_length |
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ce_loss = loss.sum(-1).cpu().detach().numpy() / lens |
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return ce_loss |
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def entropy(probs: np.array, label_dim: int = 0, mask=None): |
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if mask is None: |
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return -(probs * np.log(probs)).sum(label_dim) |
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return -(mask * probs * np.log(probs)).sum(label_dim) |
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