Fix Dockerfile build issue

autorag/nodes/passagereranker/tart/tart.py

@@ -0,0 +1,139 @@
+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,
+)
+
+
+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__()
+
+	@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(),
+		)
+
+
+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