use_own_knowledge_dataset.py
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Created: Fri, May 3, 08:54

use_own_knowledge_dataset.py
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	import logging
	import os
	from dataclasses import dataclass, field
	from functools import partial
	from pathlib import Path
	from tempfile import TemporaryDirectory
	from typing import List, Optional

	import torch
	from datasets import Features, Sequence, Value, load_dataset

	import faiss
	from transformers import (
	DPRContextEncoder,
	DPRContextEncoderTokenizerFast,
	HfArgumentParser,
	RagRetriever,
	RagSequenceForGeneration,
	RagTokenizer,
	)


	logger = logging.getLogger(__name__)
	torch.set_grad_enabled(False)
	device = "cuda" if torch.cuda.is_available() else "cpu"


	def split_text(text: str, n=100, character=" ") -> List[str]:
	"""Split the text every ``n``-th occurrence of ``character``"""
	text = text.split(character)
	return [character.join(text[i : i + n]).strip() for i in range(0, len(text), n)]


	def split_documents(documents: dict) -> dict:
	"""Split documents into passages"""
	titles, texts = [], []
	for title, text in zip(documents["title"], documents["text"]):
	if text is not None:
	for passage in split_text(text):
	titles.append(title if title is not None else "")
	texts.append(passage)
	return {"title": titles, "text": texts}


	def embed(documents: dict, ctx_encoder: DPRContextEncoder, ctx_tokenizer: DPRContextEncoderTokenizerFast) -> dict:
	"""Compute the DPR embeddings of document passages"""
	input_ids = ctx_tokenizer(
	documents["title"], documents["text"], truncation=True, padding="longest", return_tensors="pt"
	)["input_ids"]
	embeddings = ctx_encoder(input_ids.to(device=device), return_dict=True).pooler_output
	return {"embeddings": embeddings.detach().cpu().numpy()}


	def main(
	rag_example_args: "RagExampleArguments",
	processing_args: "ProcessingArguments",
	index_hnsw_args: "IndexHnswArguments",
	):

	######################################
	logger.info("Step 1 - Create the dataset")
	######################################

	# The dataset needed for RAG must have three columns:
	# - title (string): title of the document
	# - text (string): text of a passage of the document
	# - embeddings (array of dimension d): DPR representation of the passage

	# Let's say you have documents in tab-separated csv files with columns "title" and "text"
	assert os.path.isfile(rag_example_args.csv_path), "Please provide a valid path to a csv file"

	# You can load a Dataset object this way
	dataset = load_dataset(
	"csv", data_files=[rag_example_args.csv_path], split="train", delimiter="\t", column_names=["title", "text"]
	)

	# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

	# Then split the documents into passages of 100 words
	dataset = dataset.map(split_documents, batched=True, num_proc=processing_args.num_proc)

	# And compute the embeddings
	ctx_encoder = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name).to(device=device)
	ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name)
	new_features = Features(
	{"text": Value("string"), "title": Value("string"), "embeddings": Sequence(Value("float32"))}
	) # optional, save as float32 instead of float64 to save space
	dataset = dataset.map(
	partial(embed, ctx_encoder=ctx_encoder, ctx_tokenizer=ctx_tokenizer),
	batched=True,
	batch_size=processing_args.batch_size,
	features=new_features,
	)

	# And finally save your dataset
	passages_path = os.path.join(rag_example_args.output_dir, "my_knowledge_dataset")
	dataset.save_to_disk(passages_path)
	# from datasets import load_from_disk
	# dataset = load_from_disk(passages_path) # to reload the dataset

	######################################
	logger.info("Step 2 - Index the dataset")
	######################################

	# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
	index = faiss.IndexHNSWFlat(index_hnsw_args.d, index_hnsw_args.m, faiss.METRIC_INNER_PRODUCT)
	dataset.add_faiss_index("embeddings", custom_index=index)

	# And save the index
	index_path = os.path.join(rag_example_args.output_dir, "my_knowledge_dataset_hnsw_index.faiss")
	dataset.get_index("embeddings").save(index_path)
	# dataset.load_faiss_index("embeddings", index_path) # to reload the index

	######################################
	logger.info("Step 3 - Load RAG")
	######################################

	# Easy way to load the model
	retriever = RagRetriever.from_pretrained(
	rag_example_args.rag_model_name, index_name="custom", indexed_dataset=dataset
	)
	model = RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name, retriever=retriever)
	tokenizer = RagTokenizer.from_pretrained(rag_example_args.rag_model_name)

	# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
	# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

	######################################
	logger.info("Step 4 - Have fun")
	######################################

	question = rag_example_args.question or "What does Moses' rod turn into ?"
	input_ids = tokenizer.question_encoder(question, return_tensors="pt")["input_ids"]
	generated = model.generate(input_ids)
	generated_string = tokenizer.batch_decode(generated, skip_special_tokens=True)[0]
	logger.info("Q: " + question)
	logger.info("A: " + generated_string)


	@dataclass
	class RagExampleArguments:
	csv_path: str = field(
	default=str(Path(__file__).parent / "test_data" / "my_knowledge_dataset.csv"),
	metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"},
	)
	question: Optional[str] = field(
	default=None,
	metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."},
	)
	rag_model_name: str = field(
	default="facebook/rag-sequence-nq",
	metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"},
	)
	dpr_ctx_encoder_model_name: str = field(
	default="facebook/dpr-ctx_encoder-multiset-base",
	metadata={
	"help": "The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or 'facebook/dpr-ctx_encoder-multiset-base'"
	},
	)
	output_dir: Optional[str] = field(
	default=None,
	metadata={"help": "Path to a directory where the dataset passages and the index will be saved"},
	)


	@dataclass
	class ProcessingArguments:
	num_proc: Optional[int] = field(
	default=None,
	metadata={
	"help": "The number of processes to use to split the documents into passages. Default is single process."
	},
	)
	batch_size: int = field(
	default=16,
	metadata={
	"help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
	},
	)


	@dataclass
	class IndexHnswArguments:
	d: int = field(
	default=768,
	metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."},
	)
	m: int = field(
	default=128,
	metadata={
	"help": "The number of bi-directional links created for every new element during the HNSW index construction."
	},
	)


	if __name__ == "__main__":
	logging.basicConfig(level=logging.WARNING)
	logger.setLevel(logging.INFO)

	parser = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
	rag_example_args, processing_args, index_hnsw_args = parser.parse_args_into_dataclasses()
	with TemporaryDirectory() as tmp_dir:
	rag_example_args.output_dir = rag_example_args.output_dir or tmp_dir
	main(rag_example_args, processing_args, index_hnsw_args)

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