139 lines
3.8 KiB
Python
139 lines
3.8 KiB
Python
# 250313 reranker module_type 추가 - 김용연
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from typing import List, Tuple
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import numpy as np
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import pandas as pd
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from autorag.nodes.passagereranker.base import BasePassageReranker
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from autorag.utils.util import (
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make_batch,
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sort_by_scores,
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flatten_apply,
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select_top_k,
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result_to_dataframe,
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empty_cuda_cache,
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)
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class DragonKue2(BasePassageReranker):
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def __init__(self, project_dir: str, *args, **kwargs):
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super().__init__(project_dir)
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try:
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import torch
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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except ImportError:
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raise ImportError("For using dragonkue2, please install torch first.")
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model_path = "dragonkue/bge-reranker-v2-m3-ko"
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self.tokenizer = AutoTokenizer.from_pretrained(model_path)
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self.model = AutoModelForSequenceClassification.from_pretrained(model_path)
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self.model.eval()
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# Determine the device to run the model on (GPU if available, otherwise CPU)
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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self.model.to(self.device)
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def __del__(self):
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del self.model
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empty_cuda_cache()
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super().__del__()
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@result_to_dataframe(["retrieved_contents", "retrieved_ids", "retrieve_scores"])
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def pure(self, previous_result: pd.DataFrame, *args, **kwargs):
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queries, contents, _, ids = self.cast_to_run(previous_result)
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top_k = kwargs.pop("top_k")
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batch = kwargs.pop("batch", 64)
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return self._pure(queries, contents, ids, top_k, batch)
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def _pure(
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self,
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queries: List[str],
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contents_list: List[List[str]],
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ids_list: List[List[str]],
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top_k: int,
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batch: int = 64,
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) -> Tuple[List[List[str]], List[List[str]], List[List[float]]]:
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"""
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Rerank a list of contents based on their relevance to a query using ko-reranker.
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bge-reranker-v2-m3-ko is a reranker based on korean (https://huggingface.co/dragonkue/bge-reranker-v2-m3-ko).
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:param queries: The list of queries to use for reranking
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:param contents_list: The list of lists of contents to rerank
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:param ids_list: The list of lists of ids retrieved from the initial ranking
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:param top_k: The number of passages to be retrieved
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:param batch: The number of queries to be processed in a batch
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Default is 64.
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:return: Tuple of lists containing the reranked contents, ids, and scores
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"""
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nested_list = [
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list(map(lambda x: [query, x], content_list))
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for query, content_list in zip(queries, contents_list)
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]
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scores_nps = flatten_apply(
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dragonku2_run_model,
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nested_list,
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model=self.model,
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batch_size=batch,
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tokenizer=self.tokenizer,
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device=self.device,
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)
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rerank_scores = list(
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map(
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lambda scores: exp_normalize(np.array(scores)).astype(float), scores_nps
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)
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)
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df = pd.DataFrame(
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{
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"contents": contents_list,
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"ids": ids_list,
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"scores": rerank_scores,
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}
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)
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df[["contents", "ids", "scores"]] = df.apply(
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sort_by_scores, axis=1, result_type="expand"
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)
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results = select_top_k(df, ["contents", "ids", "scores"], top_k)
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return (
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results["contents"].tolist(),
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results["ids"].tolist(),
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results["scores"].tolist(),
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)
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def dragonku2_run_model(input_texts, model, tokenizer, device, batch_size: int): # 250313 추가 - 김용연
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try:
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import torch
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except ImportError:
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raise ImportError("For using drangonku2, please install torch first.")
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batch_input_texts = make_batch(input_texts, batch_size)
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results = []
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for batch_texts in batch_input_texts:
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inputs = tokenizer(
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batch_texts,
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padding=True,
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truncation=True,
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return_tensors="pt",
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max_length=512,
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)
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inputs = inputs.to(device)
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with torch.no_grad():
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scores = (
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model(**inputs, return_dict=True)
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.logits.view(
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-1,
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)
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.float()
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)
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scores_np = scores.cpu().numpy()
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results.extend(scores_np)
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return results
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def exp_normalize(x):
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b = x.max()
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y = np.exp(x - b)
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return y / y.sum()
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