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Allow Custom Dataset in RAG Retriever (#7763)

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* add CustomHFIndex

* typo in config

* update tests

* add custom dataset example

* clean script

* update test data

* minor in test

* docs

* docs

* style

* fix imports

* allow to pass the indexed dataset directly

* update tests

* use multiset DPR

* address thom and patrick's comments

* style

* update dpr tokenizer

* add output_dir flag in use_own_knowledge_dataset.py

* allow custom datasets in examples/rag/finetune.py

* add test for custom dataset in distributed rag retriever
This commit is contained in:
Quentin Lhoest
2020-10-19 19:42:45 +02:00
committed by GitHub
parent a09fe140c1
commit 033f29c625
13 changed files with 663 additions and 98 deletions
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123
tests/test_retrieval_rag.py
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@@ -13,7 +13,7 @@ import faiss
from transformers.configuration_bart import BartConfig
from transformers.configuration_dpr import DPRConfig
from transformers.configuration_rag import RagConfig
from transformers.retrieval_rag import RagRetriever
from transformers.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.testing_utils import (
require_datasets,
require_faiss,
@@ -103,7 +103,7 @@ class RagRetrieverTest(TestCase):
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def get_dummy_hf_index_retriever(self):
def get_dummy_dataset(self):
dataset = Dataset.from_dict(
{
"id": ["0", "1"],
@@ -113,6 +113,10 @@ class RagRetrieverTest(TestCase):
}
)
dataset.add_faiss_index("embeddings", string_factory="Flat", metric_type=faiss.METRIC_INNER_PRODUCT)
return dataset
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(),
@@ -127,6 +131,35 @@ class RagRetrieverTest(TestCase):
)
return retriever
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_legacy_index_retriever(self):
dataset = Dataset.from_dict(
{
@@ -152,16 +185,15 @@ class RagRetrieverTest(TestCase):
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
def test_hf_index_retriever_retrieve(self):
def test_canonical_hf_index_retriever_retrieve(self):
n_docs = 1
retriever = self.get_dummy_hf_index_retriever()
retriever = self.get_dummy_canonical_hf_index_retriever()
hidden_states = np.array(
[np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
)
@@ -174,10 +206,73 @@ class RagRetrieverTest(TestCase):
self.assertEqual(doc_dicts[1]["id"][0], "0") # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist(), [[1], [0]])
def test_save_and_from_pretrained(self):
retriever = self.get_dummy_hf_index_retriever()
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 test_custom_hf_index_retriever_retrieve(self):
n_docs = 1
retriever = self.get_dummy_custom_hf_index_retriever(from_disk=False)
hidden_states = np.array(
[np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
)
retrieved_doc_embeds, doc_ids, doc_dicts = retriever.retrieve(hidden_states, n_docs=n_docs)
self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size))
self.assertEqual(len(doc_dicts), 2)
self.assertEqual(sorted(doc_dicts[0]), ["embeddings", "id", "text", "title"])
self.assertEqual(len(doc_dicts[0]["id"]), n_docs)
self.assertEqual(doc_dicts[0]["id"][0], "1") # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0], "0") # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist(), [[1], [0]])
def test_custom_hf_index_retriever_save_and_from_pretrained(self):
retriever = self.get_dummy_custom_hf_index_retriever(from_disk=False)
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_custom_hf_index_retriever_retrieve_from_disk(self):
n_docs = 1
retriever = self.get_dummy_custom_hf_index_retriever(from_disk=True)
hidden_states = np.array(
[np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
)
retrieved_doc_embeds, doc_ids, doc_dicts = retriever.retrieve(hidden_states, n_docs=n_docs)
self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size))
self.assertEqual(len(doc_dicts), 2)
self.assertEqual(sorted(doc_dicts[0]), ["embeddings", "id", "text", "title"])
self.assertEqual(len(doc_dicts[0]["id"]), n_docs)
self.assertEqual(doc_dicts[0]["id"][0], "1") # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0], "0") # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist(), [[1], [0]])
def test_custom_hf_index_retriever_save_and_from_pretrained_from_disk(self):
retriever = self.get_dummy_custom_hf_index_retriever(from_disk=True)
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_legacy_index_retriever_retrieve(self):
n_docs = 1
@@ -194,6 +289,18 @@ class RagRetrieverTest(TestCase):
self.assertEqual(doc_dicts[1]["text"][0], "foo") # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist(), [[1], [0]])
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)
@require_torch
@require_tokenizers
@require_sentencepiece
@@ -201,7 +308,7 @@ class RagRetrieverTest(TestCase):
import torch
n_docs = 1
retriever = self.get_dummy_hf_index_retriever()
retriever = self.get_dummy_canonical_hf_index_retriever()
question_input_ids = [[5, 7], [10, 11]]
hidden_states = np.array(
[np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32