test_retrieval_rag.py

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
    import faiss


@require_faiss
class RagRetrieverTest(TestCase):
    def setUp(self):
        self.tmpdirname = tempfile.mkdtemp()
        self.retrieval_vector_size = 8

        # DPR tok
        vocab_tokens = [
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        dpr_tokenizer_path = os.path.join(self.tmpdirname, "dpr_tokenizer")
        os.makedirs(dpr_tokenizer_path, exist_ok=True)
        self.vocab_file = os.path.join(dpr_tokenizer_path, DPR_VOCAB_FILES_NAMES["vocab_file"])
        with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

        # BART tok
        vocab = [
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "<unk>",
        ]
        vocab_tokens = dict(zip(vocab, range(len(vocab))))
        merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        self.special_tokens_map = {"unk_token": "<unk>"}

        bart_tokenizer_path = os.path.join(self.tmpdirname, "bart_tokenizer")
        os.makedirs(bart_tokenizer_path, exist_ok=True)
        self.vocab_file = os.path.join(bart_tokenizer_path, BART_VOCAB_FILES_NAMES["vocab_file"])
        self.merges_file = os.path.join(bart_tokenizer_path, BART_VOCAB_FILES_NAMES["merges_file"])
        with open(self.vocab_file, "w", encoding="utf-8") as fp:
            fp.write(json.dumps(vocab_tokens) + "\n")
        with open(self.merges_file, "w", encoding="utf-8") as fp:
            fp.write("\n".join(merges))

    def get_dpr_tokenizer(self) -> DPRQuestionEncoderTokenizer:
        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname, "dpr_tokenizer"))

    def get_dpr_ctx_encoder_tokenizer(self) -> DPRContextEncoderTokenizer:
        return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname, "dpr_tokenizer"))

    def get_bart_tokenizer(self) -> BartTokenizer:
        return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname, "bart_tokenizer"))

    def tearDown(self):
        shutil.rmtree(self.tmpdirname)

    def get_dummy_dataset(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)
        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(),
            generator=BartConfig().to_dict(),
        )
        with patch("transformers.models.rag.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_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 test_canonical_hf_index_retriever_retrieve(self):
        n_docs = 1
        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
        )
        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_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.models.rag.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)

    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def test_hf_index_retriever_call(self):
        import torch

        n_docs = 1
        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
        )
        out = retriever(question_input_ids, hidden_states, prefix=retriever.config.generator.prefix, n_docs=n_docs)
        context_input_ids, context_attention_mask, retrieved_doc_embeds = (
            out["context_input_ids"],
            out["context_attention_mask"],
            out["retrieved_doc_embeds"],
        )
        self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size))
        self.assertIsInstance(context_input_ids, list)
        self.assertIsInstance(context_attention_mask, list)
        self.assertIsInstance(retrieved_doc_embeds, np.ndarray)

        out = retriever(
            question_input_ids,
            hidden_states,
            prefix=retriever.config.generator.prefix,
            n_docs=n_docs,
            return_tensors="pt",
        )
        context_input_ids, context_attention_mask, retrieved_doc_embeds, doc_ids = (  # noqa: F841
            out["context_input_ids"],
            out["context_attention_mask"],
            out["retrieved_doc_embeds"],
            out["doc_ids"],
        )
        self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size))
        self.assertIsInstance(context_input_ids, torch.Tensor)
        self.assertIsInstance(context_attention_mask, torch.Tensor)
        self.assertIsInstance(retrieved_doc_embeds, torch.Tensor)

    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def test_custom_hf_index_end2end_retriever_call(self):
        context_encoder_tokenizer = self.get_dpr_ctx_encoder_tokenizer()
        n_docs = 1
        retriever = self.get_dummy_custom_hf_index_retriever(from_disk=False)
        retriever.set_ctx_encoder_tokenizer(context_encoder_tokenizer)

        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
        )
        out = retriever(question_input_ids, hidden_states, prefix=retriever.config.generator.prefix, n_docs=n_docs)

        self.assertEqual(
            len(out), 6
        )  # check whether the retriever output consist of 6 attributes including tokenized docs
        self.assertEqual(
            all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask")), True
        )  # check for doc token related keys in dictionary.