# 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 json import os import shutil import tempfile import unittest from functools import cached_property from unittest.mock import patch import numpy as np import requests 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 ( cleanup, get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, require_torch_non_multi_accelerator, slow, torch_device, ) from transformers.utils import 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 faiss import torch from datasets import Dataset, load_dataset 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 aren't 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 Exception 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(reason="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", "", ] 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": ""} 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 cleanup(torch_device) 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.detach().to(device="cpu", dtype=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.detach().to(device="cpu", dtype=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.detach().to(device="cpu", dtype=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.detach().to(device="cpu", dtype=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=1101) 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_accelerator @slow class RagModelIntegrationTests(unittest.TestCase): @classmethod def setUpClass(cls): cls.temp_dir = tempfile.TemporaryDirectory() cls.dataset_path = cls.temp_dir.name cls.index_path = os.path.join(cls.temp_dir.name, "index.faiss") ds = load_dataset("hf-internal-testing/wiki_dpr_dummy")["train"] ds.save_to_disk(cls.dataset_path) url = "https://huggingface.co/datasets/hf-internal-testing/wiki_dpr_dummy/resolve/main/index.faiss" response = requests.get(url, stream=True) with open(cls.index_path, "wb") as fp: fp.write(response.content) @classmethod def tearDownClass(cls): cls.temp_dir.cleanup() def tearDown(self): super().tearDown() # clean-up as much as possible GPU memory occupied by PyTorch cleanup(torch_device, gc_collect=True) @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="custom", passages_path=self.dataset_path, index_path=self.index_path, use_dummy_dataset=True, retrieval_vector_size=768, retrieval_batch_size=8, dataset_revision="b24a417", ) 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) 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) 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" was released through Epic Records in Japan in March 1972. The song was a Top 10 hit in the country. It was the first single to be released by ABBA in the UK. The single was followed by "En Carousel" and "Love Has Its Uses"' EXPECTED_OUTPUT_TEXT_2 = '"She\'s My Kind of Girl" was released through Epic Records in Japan in March 1972. The song was a Top 10 hit in the country. It was the first single to be released by ABBA in the UK. The single was followed by "En Carousel" and "Love Has Its Ways"' self.assertEqual(output_text_1, EXPECTED_OUTPUT_TEXT_1) self.assertEqual(output_text_2, EXPECTED_OUTPUT_TEXT_2) 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 questions_data(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", ] 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="custom", passages_path=self.dataset_path, index_path=self.index_path, ) rag_sequence = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever).to( torch_device ) input_dict = tokenizer( self.questions_data, 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.decode(output_ids, skip_special_tokens=True) # PR #31938 cause the output being changed from `june 22, 2018` to `june 22 , 2018`. EXPECTED_OUTPUTS = [ " albert einstein", " june 22 , 2018", " amplitude modulation", " tim besley ( chairman )", " june 20 , 2018", " 1980", " 7.0", " 8", ] self.assertListEqual(outputs, EXPECTED_OUTPUTS) 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="custom", passages_path=self.dataset_path, index_path=self.index_path, ) rag_sequence = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever).to( torch_device ) input_dict = tokenizer( self.questions_data, 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.detach().cpu().numpy(), question_hidden_states.detach().cpu().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.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) 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="custom", passages_path=self.dataset_path, index_path=self.index_path ) rag_token = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever).to( torch_device ) if torch_device != "cpu": rag_token.half() input_dict = tokenizer( self.questions_data, 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.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): @classmethod def setUpClass(cls): cls.temp_dir = tempfile.TemporaryDirectory() cls.dataset_path = cls.temp_dir.name cls.index_path = os.path.join(cls.temp_dir.name, "index.faiss") ds = load_dataset("hf-internal-testing/wiki_dpr_dummy")["train"] ds.save_to_disk(cls.dataset_path) url = "https://huggingface.co/datasets/hf-internal-testing/wiki_dpr_dummy/resolve/main/index.faiss" response = requests.get(url, stream=True) with open(cls.index_path, "wb") as fp: fp.write(response.content) @classmethod def tearDownClass(cls): cls.temp_dir.cleanup() def tearDown(self): super().tearDown() # clean-up as much as possible GPU memory occupied by PyTorch cleanup(torch_device, gc_collect=True) 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="custom", passages_path=self.dataset_path, index_path=self.index_path, use_dummy_dataset=True, retrieval_vector_size=768, retrieval_batch_size=8, dataset_revision="b24a417", ) @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)