191 lines
6.2 KiB
Python
191 lines
6.2 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.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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pop_params,
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empty_cuda_cache,
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)
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prediction_tokens = {
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"castorini/monot5-base-msmarco": ["▁false", "▁true"],
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"castorini/monot5-base-msmarco-10k": ["▁false", "▁true"],
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"castorini/monot5-large-msmarco": ["▁false", "▁true"],
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"castorini/monot5-large-msmarco-10k": ["▁false", "▁true"],
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"castorini/monot5-base-med-msmarco": ["▁false", "▁true"],
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"castorini/monot5-3b-med-msmarco": ["▁false", "▁true"],
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"castorini/monot5-3b-msmarco-10k": ["▁false", "▁true"],
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"unicamp-dl/mt5-base-en-msmarco": ["▁no", "▁yes"],
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"unicamp-dl/ptt5-base-pt-msmarco-10k-v2": ["▁não", "▁sim"],
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"unicamp-dl/ptt5-base-pt-msmarco-100k-v2": ["▁não", "▁sim"],
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"unicamp-dl/ptt5-base-en-pt-msmarco-100k-v2": ["▁não", "▁sim"],
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"unicamp-dl/mt5-base-en-pt-msmarco-v2": ["▁no", "▁yes"],
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"unicamp-dl/mt5-base-mmarco-v2": ["▁no", "▁yes"],
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"unicamp-dl/mt5-base-en-pt-msmarco-v1": ["▁no", "▁yes"],
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"unicamp-dl/mt5-base-mmarco-v1": ["▁no", "▁yes"],
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"unicamp-dl/ptt5-base-pt-msmarco-10k-v1": ["▁não", "▁sim"],
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"unicamp-dl/ptt5-base-pt-msmarco-100k-v1": ["▁não", "▁sim"],
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"unicamp-dl/ptt5-base-en-pt-msmarco-10k-v1": ["▁não", "▁sim"],
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"unicamp-dl/mt5-3B-mmarco-en-pt": ["▁", "▁true"],
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"unicamp-dl/mt5-13b-mmarco-100k": ["▁", "▁true"],
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}
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class MonoT5(BasePassageReranker):
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def __init__(
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self,
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project_dir: str,
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model_name: str = "castorini/monot5-3b-msmarco-10k",
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*args,
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**kwargs,
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):
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"""
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Initialize the MonoT5 reranker.
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:param project_dir: The project directory
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:param model_name: The name of the MonoT5 model to use for reranking
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Note: default model name is 'castorini/monot5-3b-msmarco-10k'
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If there is a '/' in the model name parameter,
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when we create the file to store the results, the path will be twisted because of the '/'.
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Therefore, it will be received as '_' instead of '/'.
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:param kwargs: The extra arguments for the MonoT5 reranker
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"""
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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 T5Tokenizer, T5ForConditionalGeneration
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except ImportError:
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raise ImportError("For using MonoT5 Reranker, please install torch first.")
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# replace '_' to '/'
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if "_" in model_name:
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model_name = model_name.replace("_", "/")
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# Load the tokenizer and model from the pre-trained MonoT5 model
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self.tokenizer = T5Tokenizer.from_pretrained(model_name)
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model_params = pop_params(T5ForConditionalGeneration.from_pretrained, kwargs)
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self.model = T5ForConditionalGeneration.from_pretrained(
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model_name, **model_params
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).eval()
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# Determine the device to run the model on (GPU if available, otherwise CPU)
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.model.to(self.device)
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token_false, token_true = prediction_tokens[model_name]
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self.token_false_id = self.tokenizer.convert_tokens_to_ids(token_false)
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self.token_true_id = self.tokenizer.convert_tokens_to_ids(token_true)
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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.get("top_k", 3)
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batch = kwargs.get("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 MonoT5.
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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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:return: tuple of lists containing the reranked contents, ids, and scores
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"""
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# Retrieve the tokens used by the model to represent false and true predictions
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nested_list = [
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list(map(lambda x: [f"Query: {query} Document: {x}"], content_list))
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for query, content_list in zip(queries, contents_list)
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]
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rerank_scores = flatten_apply(
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monot5_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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token_false_id=self.token_false_id,
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token_true_id=self.token_true_id,
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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 monot5_run_model(
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input_texts,
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model,
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batch_size: int,
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tokenizer,
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device,
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token_false_id,
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token_true_id,
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):
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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 MonoT5 Reranker, 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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flattened_batch_texts = list(chain.from_iterable(batch_texts))
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input_encodings = tokenizer(
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flattened_batch_texts,
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padding=True,
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truncation=True,
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max_length=512,
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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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outputs = model.generate(
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input_ids=input_encodings["input_ids"],
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attention_mask=input_encodings["attention_mask"],
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output_scores=True,
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return_dict_in_generate=True,
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)
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# Extract logits for the 'false' and 'true' tokens from the model's output
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logits = outputs.scores[-1][:, [token_false_id, token_true_id]]
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# Calculate the softmax probability of the 'true' token
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probs = torch.nn.functional.softmax(logits, dim=-1)[:, 1]
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results.extend(probs.tolist())
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return results
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