def get_dummy_custom_hf_index_retriever(self, from_disk: bool):
dataset = self.get_dummy_dataset()
config = RagConfig(
retrieval_vector_size=self.retrieval_vector_size,
question_encoder=DPRConfig().to_dict(),
generator=BartConfig().to_dict(),
index_name="custom",
)
if from_disk:
config.passages_path = os.path.join(self.tmpdirname, "dataset")
config.index_path = os.path.join(self.tmpdirname, "index.faiss")
dataset.get_index("embeddings").save(
os.path.join(self.tmpdirname, "index.faiss"))
dataset.drop_index("embeddings")
dataset.save_to_disk(os.path.join(self.tmpdirname, "dataset"))
del dataset
retriever = RagRetriever(
config,
question_encoder_tokenizer=self.get_dpr_tokenizer(),
generator_tokenizer=self.get_bart_tokenizer(),
)
else:
retriever = RagRetriever(
config,
question_encoder_tokenizer=self.get_dpr_tokenizer(),
generator_tokenizer=self.get_bart_tokenizer(),
index=CustomHFIndex(config.retrieval_vector_size, dataset),
)
return retriever
def get_dummy_hf_index_retriever(self):
dataset = Dataset.from_dict({
"id": ["0", "1"],
"text": ["foo", "bar"],
"title": ["Foo", "Bar"],
"embeddings": [
np.ones(self.retrieval_vector_size),
2 * np.ones(self.retrieval_vector_size)
],
})
dataset.add_faiss_index("embeddings",
string_factory="Flat",
metric_type=faiss.METRIC_INNER_PRODUCT)
config = RagConfig(
retrieval_vector_size=self.retrieval_vector_size,
question_encoder=DPRConfig().to_dict(),
generator=BartConfig().to_dict(),
)
with patch("transformers.retrieval_rag.load_dataset"
) as mock_load_dataset:
mock_load_dataset.return_value = dataset
retriever = RagRetriever(
config,
question_encoder_tokenizer=self.get_dpr_tokenizer(),
generator_tokenizer=self.get_bart_tokenizer(),
)
return retriever
def test_legacy_hf_index_retriever_save_and_from_pretrained(self):
retriever = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(tmp_dirname)
retriever = RagRetriever.from_pretrained(tmp_dirname)
self.assertIsInstance(retriever, RagRetriever)
hidden_states = np.array([
np.ones(self.retrieval_vector_size),
-np.ones(self.retrieval_vector_size)
],
dtype=np.float32)
out = retriever.retrieve(hidden_states, n_docs=1)
self.assertTrue(out is not None)
def test_canonical_hf_index_retriever_save_and_from_pretrained(self):
retriever = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch("transformers.retrieval_rag.load_dataset"
) as mock_load_dataset:
mock_load_dataset.return_value = self.get_dummy_dataset()
retriever.save_pretrained(tmp_dirname)
retriever = RagRetriever.from_pretrained(tmp_dirname)
self.assertIsInstance(retriever, RagRetriever)
hidden_states = np.array([
np.ones(self.retrieval_vector_size),
-np.ones(self.retrieval_vector_size)
],
dtype=np.float32)
out = retriever.retrieve(hidden_states, n_docs=1)
self.assertTrue(out is not None)
def get_dummy_canonical_hf_index_retriever(self):
dataset = self.get_dummy_dataset()
config = RagConfig(
retrieval_vector_size=self.retrieval_vector_size,
question_encoder=DPRConfig().to_dict(),
generator=BartConfig().to_dict(),
)
with patch("transformers.retrieval_rag.load_dataset"
) as mock_load_dataset:
mock_load_dataset.return_value = dataset
retriever = RagRetriever(
config,
question_encoder_tokenizer=self.get_dpr_tokenizer(),
generator_tokenizer=self.get_bart_tokenizer(),
)
return retriever
def get_dummy_legacy_index_retriever(self):
dataset = Dataset.from_dict({
"id": ["0", "1"],
"text": ["foo", "bar"],
"title": ["Foo", "Bar"],
"embeddings": [
np.ones(self.retrieval_vector_size + 1),
2 * np.ones(self.retrieval_vector_size + 1)
],
})
dataset.add_faiss_index("embeddings",
string_factory="Flat",
metric_type=faiss.METRIC_INNER_PRODUCT)
index_file_name = os.path.join(
self.tmpdirname, "hf_bert_base.hnswSQ8_correct_phi_128.c_index")
dataset.save_faiss_index("embeddings", index_file_name + ".index.dpr")
pickle.dump(dataset["id"],
open(index_file_name + ".index_meta.dpr", "wb"))
passages_file_name = os.path.join(self.tmpdirname, "psgs_w100.tsv.pkl")
passages = {
sample["id"]: [sample["text"], sample["title"]]
for sample in dataset
}
pickle.dump(passages, open(passages_file_name, "wb"))
config = RagConfig(
retrieval_vector_size=self.retrieval_vector_size,
question_encoder=DPRConfig().to_dict(),
generator=BartConfig().to_dict(),
index_name="legacy",
index_path=self.tmpdirname,
passages_path=self.tmpdirname,
)
retriever = RagRetriever(
config,
question_encoder_tokenizer=self.get_dpr_tokenizer(),
generator_tokenizer=self.get_bart_tokenizer())
return retriever
