test_modeling_rag.py

# coding=utf-8
# Copyright 2020, The RAG Authors and The HuggingFace Inc. team.
#
# 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 gc
import json
import os
import shutil
import tempfile
import unittest
from unittest.mock import patch

import numpy as np

from transformers import BartTokenizer, T5Tokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import (
    get_tests_dir,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    require_torch_non_multi_gpu,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_datasets_available, is_faiss_available, is_torch_available

from ..bart.test_modeling_bart import BartModelTester
from ..dpr.test_modeling_dpr import DPRModelTester
from ..t5.test_modeling_t5 import T5ModelTester


TOLERANCE = 1e-3

T5_SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available() and is_datasets_available() and is_faiss_available():
    import torch
    from datasets import Dataset

    import faiss
    from transformers import (
        AutoConfig,
        AutoModel,
        AutoModelForSeq2SeqLM,
        DPRContextEncoder,
        RagConfig,
        RagModel,
        RagRetriever,
        RagSequenceForGeneration,
        RagTokenForGeneration,
        RagTokenizer,
    )
    from transformers.modeling_outputs import BaseModelOutput


def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
    """If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""
    if a is None and b is None:
        return True
    try:
        if torch.allclose(a, b, atol=atol):
            return True
        raise
    except Exception:
        msg = f"{a} != {b}"
        if prefix:
            msg = prefix + ": " + msg
        raise AssertionError(msg)


def require_retrieval(test_case):
    """
    Decorator marking a test that requires a set of dependencies necessary for pefrorm retrieval with
    [`RagRetriever`].

    These tests are skipped when respective libraries are not installed.

    """
    if not (is_torch_available() and is_datasets_available() and is_faiss_available()):
        test_case = unittest.skip("test requires PyTorch, datasets and faiss")(test_case)
    return test_case


@require_torch
@require_retrieval
@require_sentencepiece
class RagTestMixin:

    all_model_classes = (
        (RagModel, RagTokenForGeneration, RagSequenceForGeneration)
        if is_torch_available() and is_datasets_available() and is_faiss_available()
        else ()
    )

    retrieval_vector_size = 32
    n_docs = 3
    max_combined_length = 16

    def setUp(self):
        self.tmpdirname = tempfile.mkdtemp()

        # 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))

        t5_tokenizer = T5Tokenizer(T5_SAMPLE_VOCAB)
        t5_tokenizer_path = os.path.join(self.tmpdirname, "t5_tokenizer")
        t5_tokenizer.save_pretrained(t5_tokenizer_path)

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

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

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

    @cached_property
    def t5_tokenizer(self) -> BartTokenizer:
        return T5Tokenizer.from_pretrained(os.path.join(self.tmpdirname, "t5_tokenizer"))

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

        # clean-up as much as possible GPU memory occupied by PyTorch
        gc.collect()
        torch.cuda.empty_cache()

    def get_retriever(self, config):
        dataset = Dataset.from_dict(
            {
                "id": ["0", "1", "3"],
                "text": ["foo", "bar", "qux"],
                "title": ["Foo", "Bar", "Qux"],
                "embeddings": [
                    np.ones(self.retrieval_vector_size),
                    2 * np.ones(self.retrieval_vector_size),
                    3 * np.ones(self.retrieval_vector_size),
                ],
            }
        )
        dataset.add_faiss_index("embeddings", string_factory="Flat", metric_type=faiss.METRIC_INNER_PRODUCT)
        tokenizer = self.bart_tokenizer if config.generator.model_type == "bart" else self.t5_tokenizer
        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.dpr_tokenizer,
                generator_tokenizer=tokenizer,
            )
        return retriever

    def check_model_with_retriever(
        self, config, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, **kwargs
    ):
        self.assertIsNotNone(config.question_encoder)
        self.assertIsNotNone(config.generator)

        for model_class in self.all_model_classes:
            model = model_class(config, retriever=self.get_retriever(config)).to(torch_device)
            model.eval()

            self.assertTrue(model.config.is_encoder_decoder)

            outputs = model(
                input_ids=input_ids,
                attention_mask=attention_mask,
                decoder_input_ids=decoder_input_ids,
                decoder_attention_mask=decoder_attention_mask,
            )

            # logits
            self.assertEqual(
                outputs.logits.shape,
                (self.n_docs * decoder_input_ids.shape[0], decoder_input_ids.shape[1], config.generator.vocab_size),
            )
            # generator encoder last hidden states
            self.assertEqual(
                outputs.generator_enc_last_hidden_state.shape,
                (self.n_docs * decoder_input_ids.shape[0], self.max_combined_length, config.generator.hidden_size),
            )
            # doc scores
            self.assertEqual(outputs.doc_scores.shape, (input_ids.shape[0], self.n_docs))

    def check_model_with_end2end_retriever(
        self, config, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, **kwargs
    ):
        self.assertIsNotNone(config.question_encoder)
        self.assertIsNotNone(config.generator)

        context_encoder_tokenizer = self.dpr_ctx_encoder_tokenizer
        dpr_context_encoder = DPRContextEncoder(config.question_encoder)  # dpr is a twin tower

        retriever = self.get_retriever(config)
        retriever.set_ctx_encoder_tokenizer(context_encoder_tokenizer)  # setting the ctx_encoder_tokenizer.

        for model_class in [RagTokenForGeneration, RagSequenceForGeneration]:
            model = model_class(config, retriever=retriever)
            model.set_context_encoder_for_training(dpr_context_encoder)  # set the context_encoder for training
            model.to(torch_device)
            model.eval()

            self.assertTrue(model.config.is_encoder_decoder)

            outputs = model(
                input_ids=input_ids,
                attention_mask=attention_mask,
                decoder_input_ids=decoder_input_ids,
                decoder_attention_mask=decoder_attention_mask,
            )

            # logits
            self.assertEqual(
                outputs.logits.shape,
                (self.n_docs * decoder_input_ids.shape[0], decoder_input_ids.shape[1], config.generator.vocab_size),
            )
            # generator encoder last hidden states
            self.assertEqual(
                outputs.generator_enc_last_hidden_state.shape,
                (self.n_docs * decoder_input_ids.shape[0], self.max_combined_length, config.generator.hidden_size),
            )
            # doc scores
            self.assertEqual(outputs.doc_scores.shape, (input_ids.shape[0], self.n_docs))

    def check_model_generate_from_context_input_ids(
        self, config, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, **kwargs
    ):
        self.assertIsNotNone(config.question_encoder)
        self.assertIsNotNone(config.generator)

        retriever = self.get_retriever(config)

        for model_class in self.all_model_classes:
            model = model_class(config).to(torch_device)
            model.eval()
            self.assertTrue(model.config.is_encoder_decoder)

            question_hidden_states = model.question_encoder(input_ids, attention_mask=attention_mask)[0]

            out = retriever(
                input_ids,
                question_hidden_states.cpu().detach().to(torch.float32).numpy(),
                prefix=config.generator.prefix,
                return_tensors="pt",
            )

            context_input_ids, context_attention_mask, retrieved_doc_embeds = (
                out["context_input_ids"],
                out["context_attention_mask"],
                out["retrieved_doc_embeds"],
            )

            # cast
            retrieved_doc_embeds = retrieved_doc_embeds.to(question_hidden_states)
            context_input_ids = context_input_ids.to(input_ids)
            context_attention_mask = context_attention_mask.to(input_ids)

            # compute doc_scores
            doc_scores = torch.bmm(question_hidden_states.unsqueeze(1), retrieved_doc_embeds.transpose(1, 2)).squeeze(
                1
            )

            outputs = model.generate(
                context_input_ids=context_input_ids,
                context_attention_mask=context_attention_mask,
                doc_scores=doc_scores,
                do_deduplication=True,
            )

            self.assertIsNotNone(outputs)

    def check_model_generate(
        self, config, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, **kwargs
    ):
        self.assertIsNotNone(config.question_encoder)
        self.assertIsNotNone(config.generator)

        for model_class in self.all_model_classes[1:]:
            model = model_class(config, retriever=self.get_retriever(config)).to(torch_device)
            model.eval()

            self.assertTrue(model.config.is_encoder_decoder)

            outputs = model.generate(
                input_ids=input_ids,
                num_beams=2,
                num_return_sequences=2,
                decoder_start_token_id=config.generator.eos_token_id,
            )

            self.assertIsNotNone(outputs)

    def check_model_without_retriever(
        self, config, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, **kwargs
    ):
        self.assertIsNotNone(config.question_encoder)
        self.assertIsNotNone(config.generator)

        retriever = self.get_retriever(config)

        for model_class in self.all_model_classes:
            model = model_class(config).to(torch_device)
            model.eval()
            self.assertTrue(model.config.is_encoder_decoder)

            question_hidden_states = model.question_encoder(input_ids, attention_mask=attention_mask)[0]

            out = retriever(
                input_ids,
                question_hidden_states.cpu().detach().to(torch.float32).numpy(),
                prefix=config.generator.prefix,
                return_tensors="pt",
            )

            context_input_ids, context_attention_mask, retrieved_doc_embeds = (
                out["context_input_ids"],
                out["context_attention_mask"],
                out["retrieved_doc_embeds"],
            )

            # cast
            retrieved_doc_embeds = retrieved_doc_embeds.to(question_hidden_states)
            context_input_ids = context_input_ids.to(input_ids)
            context_attention_mask = context_attention_mask.to(input_ids)

            # compute doc_scores
            doc_scores = torch.bmm(question_hidden_states.unsqueeze(1), retrieved_doc_embeds.transpose(1, 2)).squeeze(
                1
            )

            outputs = model(
                context_input_ids=context_input_ids,
                context_attention_mask=context_attention_mask,
                doc_scores=doc_scores,
                decoder_input_ids=decoder_input_ids,
                decoder_attention_mask=decoder_attention_mask,
            )

            # logits
            self.assertEqual(
                outputs.logits.shape,
                (self.n_docs * decoder_input_ids.shape[0], decoder_input_ids.shape[1], config.generator.vocab_size),
            )
            # generator encoder last hidden states
            self.assertEqual(
                outputs.generator_enc_last_hidden_state.shape,
                (self.n_docs * decoder_input_ids.shape[0], self.max_combined_length, config.generator.hidden_size),
            )
            # doc scores
            self.assertEqual(outputs.doc_scores.shape, (input_ids.shape[0], self.n_docs))

    def check_model_custom_n_docs(
        self, config, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, n_docs, **kwargs
    ):
        self.assertIsNotNone(config.question_encoder)
        self.assertIsNotNone(config.generator)

        retriever = self.get_retriever(config)

        for model_class in self.all_model_classes:
            model = model_class(config).to(torch_device)
            model.eval()
            self.assertTrue(model.config.is_encoder_decoder)

            question_hidden_states = model.question_encoder(input_ids, attention_mask=attention_mask)[0]

            out = retriever(
                input_ids,
                question_hidden_states.cpu().detach().to(torch.float32).numpy(),
                prefix=config.generator.prefix,
                return_tensors="pt",
                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"],
            )

            # cast
            retrieved_doc_embeds = retrieved_doc_embeds.to(question_hidden_states)
            context_input_ids = context_input_ids.to(input_ids)
            context_attention_mask = context_attention_mask.to(input_ids)

            # compute doc_scores
            doc_scores = torch.bmm(question_hidden_states.unsqueeze(1), retrieved_doc_embeds.transpose(1, 2)).squeeze(
                1
            )

            outputs = model(
                context_input_ids=context_input_ids,
                context_attention_mask=context_attention_mask,
                doc_scores=doc_scores,
                decoder_input_ids=decoder_input_ids,
                decoder_attention_mask=decoder_attention_mask,
                n_docs=n_docs,
            )

            # logits
            self.assertEqual(
                outputs.logits.shape,
                (n_docs * decoder_input_ids.shape[0], decoder_input_ids.shape[1], config.generator.vocab_size),
            )
            # generator encoder last hidden states
            self.assertEqual(
                outputs.generator_enc_last_hidden_state.shape,
                (n_docs * decoder_input_ids.shape[0], self.max_combined_length, config.generator.hidden_size),
            )
            # doc scores
            self.assertEqual(outputs.doc_scores.shape, (input_ids.shape[0], n_docs))

    def check_model_with_mismatch_n_docs_value(
        self,
        config,
        input_ids,
        attention_mask,
        decoder_input_ids,
        decoder_attention_mask,
        retriever_n_docs,
        generator_n_docs,
        **kwargs
    ):
        self.assertIsNotNone(config.question_encoder)
        self.assertIsNotNone(config.generator)

        retriever = self.get_retriever(config)

        for model_class in self.all_model_classes:
            model = model_class(config).to(torch_device)
            model.eval()
            self.assertTrue(model.config.is_encoder_decoder)

            question_hidden_states = model.question_encoder(input_ids, attention_mask=attention_mask)[0]

            out = retriever(
                input_ids,
                question_hidden_states.cpu().detach().to(torch.float32).numpy(),
                prefix=config.generator.prefix,
                return_tensors="pt",
                n_docs=retriever_n_docs,
            )

            context_input_ids, context_attention_mask, retrieved_doc_embeds = (
                out["context_input_ids"],
                out["context_attention_mask"],
                out["retrieved_doc_embeds"],
            )

            # cast
            retrieved_doc_embeds = retrieved_doc_embeds.to(question_hidden_states)
            context_input_ids = context_input_ids.to(input_ids)
            context_attention_mask = context_attention_mask.to(input_ids)

            # compute doc_scores
            doc_scores = torch.bmm(question_hidden_states.unsqueeze(1), retrieved_doc_embeds.transpose(1, 2)).squeeze(
                1
            )

            self.assertRaises(
                AssertionError,
                model.__call__,
                context_input_ids=context_input_ids,
                context_attention_mask=context_attention_mask,
                doc_scores=doc_scores,
                decoder_input_ids=decoder_input_ids,
                decoder_attention_mask=decoder_attention_mask,
                n_docs=generator_n_docs,
            )

    def check_model_with_encoder_outputs(
        self, config, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, **kwargs
    ):
        self.assertIsNotNone(config.question_encoder)
        self.assertIsNotNone(config.generator)

        for model_class in self.all_model_classes:
            model = model_class(config, retriever=self.get_retriever(config)).to(torch_device)
            model.eval()

            self.assertTrue(model.config.is_encoder_decoder)

            outputs = model(
                input_ids=input_ids,
                attention_mask=attention_mask,
                decoder_input_ids=decoder_input_ids,
                decoder_attention_mask=decoder_attention_mask,
            )

            encoder_outputs = BaseModelOutput(outputs.generator_enc_last_hidden_state)

            # run only generator
            outputs = model(
                encoder_outputs=encoder_outputs,
                doc_scores=outputs.doc_scores,
                decoder_input_ids=decoder_input_ids,
                decoder_attention_mask=decoder_attention_mask,
            )

            # logits
            self.assertEqual(
                outputs.logits.shape,
                (self.n_docs * decoder_input_ids.shape[0], decoder_input_ids.shape[1], config.generator.vocab_size),
            )
            # generator encoder last hidden states
            self.assertEqual(
                outputs.generator_enc_last_hidden_state.shape,
                (self.n_docs * decoder_input_ids.shape[0], self.max_combined_length, config.generator.hidden_size),
            )
            # doc scores
            self.assertEqual(outputs.doc_scores.shape, (input_ids.shape[0], self.n_docs))

    def test_model_with_retriever(self):
        inputs_dict = self.config_and_inputs
        self.check_model_with_retriever(**inputs_dict)

    def test_model_with_end2end_retriever(self):
        inputs_dict = self.config_and_inputs
        self.check_model_with_end2end_retriever(**inputs_dict)

    def test_model_without_retriever(self):
        inputs_dict = self.config_and_inputs
        self.check_model_without_retriever(**inputs_dict)

    def test_model_with_encoder_outputs(self):
        inputs_dict = self.config_and_inputs
        self.check_model_with_encoder_outputs(**inputs_dict)

    def test_model_generate(self):
        inputs_dict = self.config_and_inputs
        self.check_model_generate(**inputs_dict)

    def test_model_with_custom_n_docs(self):
        inputs_dict = self.config_and_inputs
        inputs_dict["n_docs"] = 1
        self.check_model_custom_n_docs(**inputs_dict)

    def test_model_with_mismatch_n_docs_value(self):
        inputs_dict = self.config_and_inputs
        inputs_dict["retriever_n_docs"] = 3
        inputs_dict["generator_n_docs"] = 2
        self.check_model_with_mismatch_n_docs_value(**inputs_dict)


@require_torch
@require_retrieval
class RagDPRBartTest(RagTestMixin, unittest.TestCase):
    @cached_property
    def config_and_inputs(self):
        question_encoder_tester = DPRModelTester(self)
        dpr_config_and_inputs = question_encoder_tester.prepare_config_and_inputs()
        generator_tester = BartModelTester(self)
        bart_config_and_inputs = generator_tester.prepare_config_and_inputs_for_common()

        (question_encoder_config, input_ids, _, input_mask, _, _, _) = dpr_config_and_inputs
        (generator_config, bart_inputs_dict) = bart_config_and_inputs
        decoder_input_ids, decoder_attention_mask = bart_inputs_dict["input_ids"], bart_inputs_dict["attention_mask"]

        config = RagConfig.from_question_encoder_generator_configs(
            question_encoder_config,
            generator_config,
            n_docs=self.n_docs,
            retrieval_vector_size=self.retrieval_vector_size,
            max_combined_length=self.max_combined_length,
        )

        return {
            "config": config,
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "decoder_input_ids": decoder_input_ids,
            "decoder_attention_mask": decoder_attention_mask,
        }


@require_torch
@require_retrieval
class RagDPRT5Test(RagTestMixin, unittest.TestCase):
    @cached_property
    def config_and_inputs(self):
        question_encoder_tester = DPRModelTester(self)
        dpr_config_and_inputs = question_encoder_tester.prepare_config_and_inputs()
        generator_tester = T5ModelTester(self, vocab_size=1100)
        t5_config_and_inputs = generator_tester.prepare_config_and_inputs()

        (question_encoder_config, input_ids, _, input_mask, _, _, _) = dpr_config_and_inputs
        (generator_config, _, decoder_input_ids, _, decoder_attention_mask, _) = t5_config_and_inputs
        config = RagConfig.from_question_encoder_generator_configs(
            question_encoder_config,
            generator_config,
            n_docs=self.n_docs,
            retrieval_vector_size=self.retrieval_vector_size,
            max_combined_length=self.max_combined_length,
        )

        return {
            "config": config,
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "decoder_input_ids": decoder_input_ids,
            "decoder_attention_mask": decoder_attention_mask,
        }


@require_torch
@require_retrieval
@require_sentencepiece
@require_tokenizers
@require_torch_non_multi_gpu
class RagModelIntegrationTests(unittest.TestCase):
    def tearDown(self):
        super().tearDown()
        # clean-up as much as possible GPU memory occupied by PyTorch
        gc.collect()
        torch.cuda.empty_cache()

    @cached_property
    def sequence_model(self):
        return (
            RagSequenceForGeneration.from_pretrained_question_encoder_generator(
                "facebook/dpr-question_encoder-single-nq-base", "facebook/bart-large-cnn"
            )
            .to(torch_device)
            .eval()
        )

    @cached_property
    def token_model(self):
        return (
            RagTokenForGeneration.from_pretrained_question_encoder_generator(
                "facebook/dpr-question_encoder-single-nq-base", "facebook/bart-large-cnn"
            )
            .to(torch_device)
            .eval()
        )

    def get_rag_config(self):
        question_encoder_config = AutoConfig.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
        generator_config = AutoConfig.from_pretrained("facebook/bart-large-cnn")
        return RagConfig.from_question_encoder_generator_configs(
            question_encoder_config,
            generator_config,
            bos_token_id=0,
            decoder_start_token_id=2,
            eos_token_id=2,
            is_encoder_decoder=True,
            pad_token_id=1,
            vocab_size=50264,
            title_sep=" / ",
            doc_sep=" // ",
            n_docs=5,
            max_combined_length=300,
            dataset="wiki_dpr",
            dataset_split="train",
            index_name="exact",
            index_path=None,
            use_dummy_dataset=True,
            retrieval_vector_size=768,
            retrieval_batch_size=8,
        )

    @slow
    def test_rag_sequence_inference(self):
        rag_config = self.get_rag_config()
        rag_decoder_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
        rag_question_encoder_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained(
            "facebook/dpr-question_encoder-single-nq-base"
        )
        rag_retriever = RagRetriever(
            rag_config,
            question_encoder_tokenizer=rag_question_encoder_tokenizer,
            generator_tokenizer=rag_decoder_tokenizer,
        )

        rag_sequence = self.sequence_model
        rag_sequence.set_retriever(rag_retriever)

        input_ids = rag_question_encoder_tokenizer(
            "who sings does he love me with reba", return_tensors="pt"
        ).input_ids
        decoder_input_ids = rag_decoder_tokenizer("Linda Davis", return_tensors="pt").input_ids

        input_ids = input_ids.to(torch_device)
        decoder_input_ids = decoder_input_ids.to(torch_device)

        with torch.no_grad():
            output = rag_sequence(
                input_ids,
                labels=decoder_input_ids,
            )

        expected_shape = torch.Size([5, 5, 50264])
        self.assertEqual(output.logits.shape, expected_shape)

        expected_doc_scores = torch.tensor([[75.0286, 74.4998, 74.0804, 74.0306, 73.9504]]).to(torch_device)
        _assert_tensors_equal(expected_doc_scores, output.doc_scores, atol=TOLERANCE)

        expected_loss = torch.tensor([36.7368]).to(torch_device)
        _assert_tensors_equal(expected_loss, output.loss, atol=TOLERANCE)

    @slow
    def test_rag_token_inference(self):
        rag_config = self.get_rag_config()
        rag_decoder_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
        rag_question_encoder_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained(
            "facebook/dpr-question_encoder-single-nq-base"
        )
        rag_retriever = RagRetriever(
            rag_config,
            question_encoder_tokenizer=rag_question_encoder_tokenizer,
            generator_tokenizer=rag_decoder_tokenizer,
        )

        rag_token = self.token_model
        rag_token.set_retriever(rag_retriever)

        input_ids = rag_question_encoder_tokenizer(
            "who sings does he love me with reba", return_tensors="pt"
        ).input_ids
        decoder_input_ids = rag_decoder_tokenizer("Linda Davis", return_tensors="pt").input_ids

        input_ids = input_ids.to(torch_device)
        decoder_input_ids = decoder_input_ids.to(torch_device)

        with torch.no_grad():
            output = rag_token(
                input_ids,
                labels=decoder_input_ids,
            )

        expected_shape = torch.Size([5, 5, 50264])
        self.assertEqual(output.logits.shape, expected_shape)

        expected_doc_scores = torch.tensor([[75.0286, 74.4998, 74.0804, 74.0306, 73.9504]]).to(torch_device)
        _assert_tensors_equal(expected_doc_scores, output.doc_scores, atol=TOLERANCE)

        expected_loss = torch.tensor([36.3557]).to(torch_device)
        _assert_tensors_equal(expected_loss, output.loss, atol=TOLERANCE)

    @slow
    def test_rag_token_generate_beam(self):
        rag_config = self.get_rag_config()
        rag_decoder_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
        rag_question_encoder_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained(
            "facebook/dpr-question_encoder-single-nq-base"
        )
        rag_retriever = RagRetriever(
            rag_config,
            question_encoder_tokenizer=rag_question_encoder_tokenizer,
            generator_tokenizer=rag_decoder_tokenizer,
        )

        rag_token = self.token_model
        rag_token.set_retriever(rag_retriever)

        input_ids = rag_question_encoder_tokenizer(
            "who sings does he love me with reba", return_tensors="pt"
        ).input_ids

        input_ids = input_ids.to(torch_device)

        output_ids = rag_token.generate(
            input_ids,
            decoder_start_token_id=rag_token.generator.config.decoder_start_token_id,
            num_beams=2,
            num_return_sequences=2,
        )
        # sequence generate test
        output_text_1 = rag_decoder_tokenizer.decode(output_ids[0], skip_special_tokens=True)
        output_text_2 = rag_decoder_tokenizer.decode(output_ids[1], skip_special_tokens=True)

        # Expected outputs as given by model at integration time.
        EXPECTED_OUTPUT_TEXT_1 = "\"She's My Kind of Girl"
        EXPECTED_OUTPUT_TEXT_2 = "\"She's My Kind of Love"

        self.assertEqual(output_text_1, EXPECTED_OUTPUT_TEXT_1)
        self.assertEqual(output_text_2, EXPECTED_OUTPUT_TEXT_2)

    @slow
    def test_rag_sequence_generate_beam(self):
        rag_config = self.get_rag_config()
        rag_decoder_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
        rag_question_encoder_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained(
            "facebook/dpr-question_encoder-single-nq-base"
        )
        rag_retriever = RagRetriever(
            rag_config,
            question_encoder_tokenizer=rag_question_encoder_tokenizer,
            generator_tokenizer=rag_decoder_tokenizer,
        )

        rag_sequence = self.sequence_model
        rag_sequence.set_retriever(rag_retriever)

        input_ids = rag_question_encoder_tokenizer(
            "who sings does he love me with reba", return_tensors="pt"
        ).input_ids

        input_ids = input_ids.to(torch_device)

        output_ids = rag_sequence.generate(
            input_ids,
            decoder_start_token_id=rag_sequence.generator.config.decoder_start_token_id,
            num_beams=2,
            num_return_sequences=2,
        )
        # sequence generate test
        output_text_1 = rag_decoder_tokenizer.decode(output_ids[0], skip_special_tokens=True)
        output_text_2 = rag_decoder_tokenizer.decode(output_ids[1], skip_special_tokens=True)

        # Expected outputs as given by model at integration time.
        EXPECTED_OUTPUT_TEXT_1 = """\"She's My Kind of Girl\" was released through Epic Records in Japan in March 1972, giving the duo a Top 10 hit. Two more singles were released in Japan, \"En Carousel\" and \"Love Has Its Ways\" Ulvaeus and Andersson persevered with their songwriting and experimented with new sounds and vocal arrangements."""
        EXPECTED_OUTPUT_TEXT_2 = """In September 2018, Björn Ulvaeus revealed that the two new songs, \"I Still Have Faith In You\" and \"Don't Shut Me Down\", would be released no earlier than March 2019. The two new tracks will feature in a TV special set to air later in the year."""

        self.assertEqual(output_text_1, EXPECTED_OUTPUT_TEXT_1)
        self.assertEqual(output_text_2, EXPECTED_OUTPUT_TEXT_2)

    @property
    def test_data_questions(self):
        return [
            "who got the first nobel prize in physics",
            "when is the next deadpool movie being released",
            "which mode is used for short wave broadcast service",
            "who is the owner of reading football club",
            "when is the next scandal episode coming out",
            "when is the last time the philadelphia won the superbowl",
            "what is the most current adobe flash player version",
            "how many episodes are there in dragon ball z",
        ]

    @slow
    def test_rag_sequence_generate_batch(self):
        tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
        retriever = RagRetriever.from_pretrained(
            "facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True
        )
        rag_sequence = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever).to(
            torch_device
        )

        input_dict = tokenizer(
            self.test_data_questions,
            return_tensors="pt",
            padding=True,
            truncation=True,
        )

        input_ids = input_dict.input_ids.to(torch_device)
        attention_mask = input_dict.attention_mask.to(torch_device)

        output_ids = rag_sequence.generate(
            input_ids,
            attention_mask=attention_mask,
        )

        outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)

        EXPECTED_OUTPUTS = [
            " albert einstein",
            " june 22, 2018",
            " amplitude modulation",
            " tim besley ( chairman )",
            " june 20, 2018",
            " 1980",
            " 7.0",
            " 8",
        ]
        self.assertListEqual(outputs, EXPECTED_OUTPUTS)

    @slow
    def test_rag_sequence_generate_batch_from_context_input_ids(self):
        tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
        retriever = RagRetriever.from_pretrained(
            "facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True
        )
        rag_sequence = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever).to(
            torch_device
        )

        input_dict = tokenizer(
            self.test_data_questions,
            return_tensors="pt",
            padding=True,
            truncation=True,
        )

        input_ids = input_dict.input_ids.to(torch_device)
        attention_mask = input_dict.attention_mask.to(torch_device)

        question_hidden_states = rag_sequence.question_encoder(input_ids, attention_mask=attention_mask)[0]
        docs_dict = retriever(
            input_ids.cpu().detach().numpy(), question_hidden_states.cpu().detach().numpy(), return_tensors="pt"
        )
        doc_scores = torch.bmm(
            question_hidden_states.unsqueeze(1),
            docs_dict["retrieved_doc_embeds"].to(torch_device).float().transpose(1, 2),
        ).squeeze(1)

        output_ids = rag_sequence.generate(
            context_input_ids=docs_dict["context_input_ids"].to(torch_device),
            context_attention_mask=docs_dict["context_attention_mask"].to(torch_device),
            doc_scores=doc_scores.to(torch_device),
            do_deduplication=True,
        )

        outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)

        EXPECTED_OUTPUTS = [
            " albert einstein",
            " june 22, 2018",
            " amplitude modulation",
            " tim besley ( chairman )",
            " june 20, 2018",
            " 1980",
            " 7.0",
            " 8",
        ]
        self.assertListEqual(outputs, EXPECTED_OUTPUTS)

    @slow
    def test_rag_token_generate_batch(self):
        tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
        retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
        rag_token = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever).to(
            torch_device
        )

        if torch_device == "cuda":
            rag_token.half()

        input_dict = tokenizer(
            self.test_data_questions,
            return_tensors="pt",
            padding=True,
            truncation=True,
        )

        input_ids = input_dict.input_ids.to(torch_device)
        attention_mask = input_dict.attention_mask.to(torch_device)

        output_ids = rag_token.generate(
            input_ids,
            attention_mask=attention_mask,
        )

        outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)

        EXPECTED_OUTPUTS = [
            " albert einstein",
            " september 22, 2017",
            " amplitude modulation",
            " stefan persson",
            " april 20, 2018",
            " the 1970s",
            " 7.1. 2",
            " 13",
        ]
        self.assertListEqual(outputs, EXPECTED_OUTPUTS)


@require_torch
@require_retrieval
class RagModelSaveLoadTests(unittest.TestCase):
    def tearDown(self):
        super().tearDown()
        # clean-up as much as possible GPU memory occupied by PyTorch
        gc.collect()
        torch.cuda.empty_cache()

    def get_rag_config(self):
        question_encoder_config = AutoConfig.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
        generator_config = AutoConfig.from_pretrained("facebook/bart-large-cnn")
        return RagConfig.from_question_encoder_generator_configs(
            question_encoder_config,
            generator_config,
            bos_token_id=0,
            decoder_start_token_id=2,
            eos_token_id=2,
            is_encoder_decoder=True,
            pad_token_id=1,
            vocab_size=50264,
            title_sep=" / ",
            doc_sep=" // ",
            n_docs=5,
            max_combined_length=300,
            dataset="wiki_dpr",
            dataset_split="train",
            index_name="exact",
            index_path=None,
            use_dummy_dataset=True,
            retrieval_vector_size=768,
            retrieval_batch_size=8,
        )

    @slow
    def test_rag_sequence_from_pretrained(self):
        rag_config = self.get_rag_config()
        rag_decoder_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
        rag_question_encoder_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained(
            "facebook/dpr-question_encoder-single-nq-base"
        )
        rag_retriever = RagRetriever(
            rag_config,
            question_encoder_tokenizer=rag_question_encoder_tokenizer,
            generator_tokenizer=rag_decoder_tokenizer,
        )

        input_ids = rag_question_encoder_tokenizer(
            "who sings does he love me with reba", return_tensors="pt"
        ).input_ids
        decoder_input_ids = rag_decoder_tokenizer("Linda Davis", return_tensors="pt").input_ids

        input_ids = input_ids.to(torch_device)
        decoder_input_ids = decoder_input_ids.to(torch_device)

        with tempfile.TemporaryDirectory() as tmp_dirname:
            rag_sequence = RagSequenceForGeneration.from_pretrained_question_encoder_generator(
                "facebook/dpr-question_encoder-single-nq-base",
                "facebook/bart-large-cnn",
                retriever=rag_retriever,
                config=rag_config,
            ).to(torch_device)
            # check that the from pretrained methods work
            rag_sequence.save_pretrained(tmp_dirname)
            rag_sequence.from_pretrained(tmp_dirname, retriever=rag_retriever)
            rag_sequence.to(torch_device)

            with torch.no_grad():
                output = rag_sequence(
                    input_ids,
                    labels=decoder_input_ids,
                )

            loss_pretrained = output.loss
            del rag_sequence

        question_encoder = AutoModel.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
        generator = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")
        rag_sequence = RagSequenceForGeneration(
            config=rag_config, question_encoder=question_encoder, generator=generator, retriever=rag_retriever
        )
        rag_sequence.to(torch_device)

        with torch.no_grad():
            output = rag_sequence(
                input_ids,
                labels=decoder_input_ids,
            )

        loss_init = output.loss

        self.assertAlmostEqual(loss_pretrained.item(), loss_init.item(), places=4)

    @slow
    def test_rag_token_from_pretrained(self):
        rag_config = self.get_rag_config()
        rag_decoder_tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
        rag_question_encoder_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained(
            "facebook/dpr-question_encoder-single-nq-base"
        )
        rag_retriever = RagRetriever(
            rag_config,
            question_encoder_tokenizer=rag_question_encoder_tokenizer,
            generator_tokenizer=rag_decoder_tokenizer,
        )

        input_ids = rag_question_encoder_tokenizer(
            "who sings does he love me with reba", return_tensors="pt"
        ).input_ids
        decoder_input_ids = rag_decoder_tokenizer("Linda Davis", return_tensors="pt").input_ids

        input_ids = input_ids.to(torch_device)
        decoder_input_ids = decoder_input_ids.to(torch_device)

        with tempfile.TemporaryDirectory() as tmp_dirname:
            rag_token = RagTokenForGeneration.from_pretrained_question_encoder_generator(
                "facebook/dpr-question_encoder-single-nq-base",
                "facebook/bart-large-cnn",
                retriever=rag_retriever,
                config=rag_config,
                question_encoder_max_length=200,
                generator_max_length=200,
            ).to(torch_device)
            # check that the from pretrained methods work
            rag_token.save_pretrained(tmp_dirname)
            rag_token.from_pretrained(tmp_dirname, retriever=rag_retriever)
            rag_token.to(torch_device)

            self.assertTrue(rag_token.question_encoder.config.max_length == 200)
            self.assertTrue(rag_token.generator.config.max_length == 200)

            with torch.no_grad():
                output = rag_token(
                    input_ids,
                    labels=decoder_input_ids,
                )

            loss_pretrained = output.loss
            del rag_token

        question_encoder = AutoModel.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
        generator = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")
        rag_token = RagTokenForGeneration(
            config=rag_config, question_encoder=question_encoder, generator=generator, retriever=rag_retriever
        )
        rag_token.to(torch_device)

        with torch.no_grad():
            output = rag_token(
                input_ids,
                labels=decoder_input_ids,
            )

        loss_init = output.loss

        self.assertAlmostEqual(loss_pretrained.item(), loss_init.item(), places=4)