from itertools import chain
from typing import List, Tuple
import pandas as pd
from autorag.nodes.passagereranker.base import BasePassageReranker
from autorag.nodes.passagereranker.tart.modeling_enc_t5 import (
EncT5ForSequenceClassification,
)
from autorag.nodes.passagereranker.tart.tokenization_enc_t5 import EncT5Tokenizer
from autorag.utils.util import (
make_batch,
sort_by_scores,
flatten_apply,
select_top_k,
result_to_dataframe,
empty_cuda_cache,
)
[docs]
class Tart(BasePassageReranker):
def __init__(self, project_dir: str, *args, **kwargs):
super().__init__(project_dir)
try:
import torch
except ImportError:
raise ImportError(
"torch is not installed. Please install torch first to use TART reranker."
)
model_name = "facebook/tart-full-flan-t5-xl"
self.model = EncT5ForSequenceClassification.from_pretrained(model_name)
self.tokenizer = EncT5Tokenizer.from_pretrained(model_name)
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model = self.model.to(self.device)
def __del__(self):
del self.model
del self.tokenizer
empty_cuda_cache()
super().__del__()
[docs]
@result_to_dataframe(["retrieved_contents", "retrieved_ids", "retrieve_scores"])
def pure(self, previous_result: pd.DataFrame, *args, **kwargs):
queries, contents, _, ids = self.cast_to_run(previous_result)
top_k = kwargs.pop("top_k")
instruction = kwargs.pop("instruction", "Find passage to answer given question")
batch = kwargs.pop("batch", 64)
return self._pure(queries, contents, ids, top_k, instruction, batch)
def _pure(
self,
queries: List[str],
contents_list: List[List[str]],
ids_list: List[List[str]],
top_k: int,
instruction: str = "Find passage to answer given question",
batch: int = 64,
) -> Tuple[List[List[str]], List[List[str]], List[List[float]]]:
"""
Rerank a list of contents based on their relevance to a query using Tart.
TART is a reranker based on TART (https://github.com/facebookresearch/tart).
You can rerank the passages with the instruction using TARTReranker.
The default model is facebook/tart-full-flan-t5-xl.
:param queries: The list of queries to use for reranking
:param contents_list: The list of lists of contents to rerank
:param ids_list: The list of lists of ids retrieved from the initial ranking
:param top_k: The number of passages to be retrieved
:param instruction: The instruction for reranking.
Note: default instruction is "Find passage to answer given question"
The default instruction from the TART paper is being used.
If you want to use a different instruction, you can change the instruction through this parameter
:param batch: The number of queries to be processed in a batch
:return: tuple of lists containing the reranked contents, ids, and scores
"""
nested_list = [
[["{} [SEP] {}".format(instruction, query)] for _ in contents]
for query, contents in zip(queries, contents_list)
]
rerank_scores = flatten_apply(
tart_run_model,
nested_list,
model=self.model,
batch_size=batch,
tokenizer=self.tokenizer,
device=self.device,
contents_list=contents_list,
)
df = pd.DataFrame(
{
"contents": contents_list,
"ids": ids_list,
"scores": rerank_scores,
}
)
df[["contents", "ids", "scores"]] = df.apply(
sort_by_scores, axis=1, result_type="expand"
)
results = select_top_k(df, ["contents", "ids", "scores"], top_k)
return (
results["contents"].tolist(),
results["ids"].tolist(),
results["scores"].tolist(),
)
[docs]
def tart_run_model(
input_texts, contents_list, model, batch_size: int, tokenizer, device
):
try:
import torch
import torch.nn.functional as F
except ImportError:
raise ImportError(
"torch is not installed. Please install torch first to use TART reranker."
)
flattened_texts = list(chain.from_iterable(input_texts))
flattened_contents = list(chain.from_iterable(contents_list))
batch_input_texts = make_batch(flattened_texts, batch_size)
batch_contents_list = make_batch(flattened_contents, batch_size)
results = []
for batch_texts, batch_contents in zip(batch_input_texts, batch_contents_list):
feature = tokenizer(
batch_texts,
batch_contents,
padding=True,
truncation=True,
return_tensors="pt",
).to(device)
with torch.no_grad():
pred_scores = model(**feature).logits
normalized_scores = [
float(score[1]) for score in F.softmax(pred_scores, dim=1)
]
results.extend(normalized_scores)
return results