140 lines
4.4 KiB
Python
140 lines
4.4 KiB
Python
from itertools import chain
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from typing import List, Tuple
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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.nodes.passagereranker.tart.modeling_enc_t5 import (
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EncT5ForSequenceClassification,
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)
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from autorag.nodes.passagereranker.tart.tokenization_enc_t5 import EncT5Tokenizer
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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 Tart(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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except ImportError:
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raise ImportError(
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"torch is not installed. Please install torch first to use TART reranker."
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)
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model_name = "facebook/tart-full-flan-t5-xl"
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self.model = EncT5ForSequenceClassification.from_pretrained(model_name)
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self.tokenizer = EncT5Tokenizer.from_pretrained(model_name)
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.model = self.model.to(self.device)
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def __del__(self):
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del self.model
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del self.tokenizer
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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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instruction = kwargs.pop("instruction", "Find passage to answer given question")
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batch = kwargs.pop("batch", 64)
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return self._pure(queries, contents, ids, top_k, instruction, 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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instruction: str = "Find passage to answer given question",
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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 Tart.
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TART is a reranker based on TART (https://github.com/facebookresearch/tart).
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You can rerank the passages with the instruction using TARTReranker.
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The default model is facebook/tart-full-flan-t5-xl.
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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 instruction: The instruction for reranking.
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Note: default instruction is "Find passage to answer given question"
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The default instruction from the TART paper is being used.
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If you want to use a different instruction, you can change the instruction through this parameter
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:param batch: The number of queries to be processed in a batch
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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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[["{} [SEP] {}".format(instruction, query)] for _ in contents]
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for query, contents in zip(queries, contents_list)
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]
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rerank_scores = flatten_apply(
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tart_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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contents_list=contents_list,
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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 tart_run_model(
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input_texts, contents_list, model, batch_size: int, tokenizer, device
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):
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try:
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import torch
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import torch.nn.functional as F
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except ImportError:
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raise ImportError(
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"torch is not installed. Please install torch first to use TART reranker."
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)
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flattened_texts = list(chain.from_iterable(input_texts))
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flattened_contents = list(chain.from_iterable(contents_list))
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batch_input_texts = make_batch(flattened_texts, batch_size)
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batch_contents_list = make_batch(flattened_contents, batch_size)
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results = []
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for batch_texts, batch_contents in zip(batch_input_texts, batch_contents_list):
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feature = tokenizer(
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batch_texts,
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batch_contents,
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padding=True,
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truncation=True,
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return_tensors="pt",
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).to(device)
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with torch.no_grad():
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pred_scores = model(**feature).logits
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normalized_scores = [
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float(score[1]) for score in F.softmax(pred_scores, dim=1)
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]
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results.extend(normalized_scores)
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return results
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