# coding=utf-8 # Copyright 2022 The HuggingFace Inc. 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. """ Testing suite for the PyTorch REALM model. """ import copy import unittest import numpy as np from transformers import RealmConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( RealmEmbedder, RealmForOpenQA, RealmKnowledgeAugEncoder, RealmReader, RealmRetriever, RealmScorer, RealmTokenizer, ) class RealmModelTester: def __init__( self, parent, batch_size=13, retriever_proj_size=128, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, layer_norm_eps=1e-12, span_hidden_size=50, max_span_width=10, reader_layer_norm_eps=1e-3, reader_beam_size=4, reader_seq_len=288 + 32, num_block_records=13353718, searcher_beam_size=8, searcher_seq_len=64, num_labels=3, num_choices=4, num_candidates=10, scope=None, ): # General config self.parent = parent self.batch_size = batch_size self.retriever_proj_size = retriever_proj_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps # Reader config self.span_hidden_size = span_hidden_size self.max_span_width = max_span_width self.reader_layer_norm_eps = reader_layer_norm_eps self.reader_beam_size = reader_beam_size self.reader_seq_len = reader_seq_len # Searcher config self.num_block_records = num_block_records self.searcher_beam_size = searcher_beam_size self.searcher_seq_len = searcher_seq_len self.num_labels = num_labels self.num_choices = num_choices self.num_candidates = num_candidates self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) candiate_input_ids = ids_tensor([self.batch_size, self.num_candidates, self.seq_length], self.vocab_size) reader_input_ids = ids_tensor([self.reader_beam_size, self.reader_seq_len], self.vocab_size) input_mask = None candiate_input_mask = None reader_input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) candiate_input_mask = random_attention_mask([self.batch_size, self.num_candidates, self.seq_length]) reader_input_mask = random_attention_mask([self.reader_beam_size, self.reader_seq_len]) token_type_ids = None candidate_token_type_ids = None reader_token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) candidate_token_type_ids = ids_tensor( [self.batch_size, self.num_candidates, self.seq_length], self.type_vocab_size ) reader_token_type_ids = ids_tensor([self.reader_beam_size, self.reader_seq_len], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() # inputs with additional num_candidates axis. scorer_encoder_inputs = (candiate_input_ids, candiate_input_mask, candidate_token_type_ids) # reader inputs reader_inputs = (reader_input_ids, reader_input_mask, reader_token_type_ids) return ( config, input_ids, token_type_ids, input_mask, scorer_encoder_inputs, reader_inputs, sequence_labels, token_labels, choice_labels, ) def get_config(self): return RealmConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, retriever_proj_size=self.retriever_proj_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_candidates=self.num_candidates, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, ) def create_and_check_embedder( self, config, input_ids, token_type_ids, input_mask, scorer_encoder_inputs, reader_inputs, sequence_labels, token_labels, choice_labels, ): model = RealmEmbedder(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.projected_score.shape, (self.batch_size, self.retriever_proj_size)) def create_and_check_encoder( self, config, input_ids, token_type_ids, input_mask, scorer_encoder_inputs, reader_inputs, sequence_labels, token_labels, choice_labels, ): model = RealmKnowledgeAugEncoder(config=config) model.to(torch_device) model.eval() relevance_score = floats_tensor([self.batch_size, self.num_candidates]) result = model( scorer_encoder_inputs[0], attention_mask=scorer_encoder_inputs[1], token_type_ids=scorer_encoder_inputs[2], relevance_score=relevance_score, labels=token_labels, ) self.parent.assertEqual( result.logits.shape, (self.batch_size * self.num_candidates, self.seq_length, self.vocab_size) ) def create_and_check_reader( self, config, input_ids, token_type_ids, input_mask, scorer_encoder_inputs, reader_inputs, sequence_labels, token_labels, choice_labels, ): model = RealmReader(config=config) model.to(torch_device) model.eval() relevance_score = floats_tensor([self.reader_beam_size]) result = model( reader_inputs[0], attention_mask=reader_inputs[1], token_type_ids=reader_inputs[2], relevance_score=relevance_score, ) self.parent.assertEqual(result.block_idx.shape, ()) self.parent.assertEqual(result.candidate.shape, ()) self.parent.assertEqual(result.start_pos.shape, ()) self.parent.assertEqual(result.end_pos.shape, ()) def create_and_check_scorer( self, config, input_ids, token_type_ids, input_mask, scorer_encoder_inputs, reader_inputs, sequence_labels, token_labels, choice_labels, ): model = RealmScorer(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, candidate_input_ids=scorer_encoder_inputs[0], candidate_attention_mask=scorer_encoder_inputs[1], candidate_token_type_ids=scorer_encoder_inputs[2], ) self.parent.assertEqual(result.relevance_score.shape, (self.batch_size, self.num_candidates)) self.parent.assertEqual(result.query_score.shape, (self.batch_size, self.retriever_proj_size)) self.parent.assertEqual( result.candidate_score.shape, (self.batch_size, self.num_candidates, self.retriever_proj_size) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, scorer_encoder_inputs, reader_inputs, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class RealmModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( RealmEmbedder, RealmKnowledgeAugEncoder, # RealmScorer is excluded from common tests as it is a container model # consisting of two RealmEmbedders & a simple inner product calculation. # RealmScorer ) if is_torch_available() else () ) all_generative_model_classes = () pipeline_model_mapping = {} if is_torch_available() else {} # disable these tests because there is no base_model in Realm test_save_load_fast_init_from_base = False test_save_load_fast_init_to_base = False def setUp(self): self.test_pruning = False self.model_tester = RealmModelTester(self) self.config_tester = ConfigTester(self, config_class=RealmConfig) def test_config(self): self.config_tester.run_common_tests() def test_embedder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_embedder(*config_and_inputs) def test_encoder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_encoder(*config_and_inputs) def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_embedder(*config_and_inputs) self.model_tester.create_and_check_encoder(*config_and_inputs) def test_scorer(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_scorer(*config_and_inputs) def test_training(self): if not self.model_tester.is_training: return config, *inputs = self.model_tester.prepare_config_and_inputs() input_ids, token_type_ids, input_mask, scorer_encoder_inputs = inputs[0:4] config.return_dict = True tokenizer = RealmTokenizer.from_pretrained("google/realm-orqa-nq-openqa") # RealmKnowledgeAugEncoder training model = RealmKnowledgeAugEncoder(config) model.to(torch_device) model.train() inputs_dict = { "input_ids": scorer_encoder_inputs[0].to(torch_device), "attention_mask": scorer_encoder_inputs[1].to(torch_device), "token_type_ids": scorer_encoder_inputs[2].to(torch_device), "relevance_score": floats_tensor([self.model_tester.batch_size, self.model_tester.num_candidates]), } inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs = inputs_dict loss = model(**inputs).loss loss.backward() # RealmForOpenQA training openqa_config = copy.deepcopy(config) openqa_config.vocab_size = 30522 # the retrieved texts will inevitably have more than 99 vocabs. openqa_config.num_block_records = 5 openqa_config.searcher_beam_size = 2 block_records = np.array( [ b"This is the first record.", b"This is the second record.", b"This is the third record.", b"This is the fourth record.", b"This is the fifth record.", ], dtype=object, ) retriever = RealmRetriever(block_records, tokenizer) model = RealmForOpenQA(openqa_config, retriever) model.to(torch_device) model.train() inputs_dict = { "input_ids": input_ids[:1].to(torch_device), "attention_mask": input_mask[:1].to(torch_device), "token_type_ids": token_type_ids[:1].to(torch_device), "answer_ids": input_ids[:1].tolist(), } inputs = self._prepare_for_class(inputs_dict, RealmForOpenQA) loss = model(**inputs).reader_output.loss loss.backward() # Test model.block_embedding_to device = torch.device("cpu") model.block_embedding_to(device) loss = model(**inputs).reader_output.loss loss.backward() self.assertEqual(model.block_emb.device.type, device.type) @slow def test_embedder_from_pretrained(self): model = RealmEmbedder.from_pretrained("google/realm-cc-news-pretrained-embedder") self.assertIsNotNone(model) @slow def test_encoder_from_pretrained(self): model = RealmKnowledgeAugEncoder.from_pretrained("google/realm-cc-news-pretrained-encoder") self.assertIsNotNone(model) @slow def test_open_qa_from_pretrained(self): model = RealmForOpenQA.from_pretrained("google/realm-orqa-nq-openqa") self.assertIsNotNone(model) @slow def test_reader_from_pretrained(self): model = RealmReader.from_pretrained("google/realm-orqa-nq-reader") self.assertIsNotNone(model) @slow def test_scorer_from_pretrained(self): model = RealmScorer.from_pretrained("google/realm-cc-news-pretrained-scorer") self.assertIsNotNone(model) @require_torch class RealmModelIntegrationTest(unittest.TestCase): @slow def test_inference_embedder(self): retriever_projected_size = 128 model = RealmEmbedder.from_pretrained("google/realm-cc-news-pretrained-embedder") input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] expected_shape = torch.Size((1, retriever_projected_size)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor([[-0.0714, -0.0837, -0.1314]]) self.assertTrue(torch.allclose(output[:, :3], expected_slice, atol=1e-4)) @slow def test_inference_encoder(self): num_candidates = 2 vocab_size = 30522 model = RealmKnowledgeAugEncoder.from_pretrained( "google/realm-cc-news-pretrained-encoder", num_candidates=num_candidates ) input_ids = torch.tensor([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]) relevance_score = torch.tensor([[0.3, 0.7]], dtype=torch.float32) output = model(input_ids, relevance_score=relevance_score)[0] expected_shape = torch.Size((2, 6, vocab_size)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor([[[-11.0888, -11.2544], [-10.2170, -10.3874]]]) self.assertTrue(torch.allclose(output[1, :2, :2], expected_slice, atol=1e-4)) @slow def test_inference_open_qa(self): from transformers.models.realm.retrieval_realm import RealmRetriever tokenizer = RealmTokenizer.from_pretrained("google/realm-orqa-nq-openqa") retriever = RealmRetriever.from_pretrained("google/realm-orqa-nq-openqa") model = RealmForOpenQA.from_pretrained( "google/realm-orqa-nq-openqa", retriever=retriever, ) question = "Who is the pioneer in modern computer science?" question = tokenizer( [question], padding=True, truncation=True, max_length=model.config.searcher_seq_len, return_tensors="pt", ).to(model.device) predicted_answer_ids = model(**question).predicted_answer_ids predicted_answer = tokenizer.decode(predicted_answer_ids) self.assertEqual(predicted_answer, "alan mathison turing") @slow def test_inference_reader(self): config = RealmConfig(reader_beam_size=2, max_span_width=3) model = RealmReader.from_pretrained("google/realm-orqa-nq-reader", config=config) concat_input_ids = torch.arange(10).view((2, 5)) concat_token_type_ids = torch.tensor([[0, 0, 1, 1, 1], [0, 0, 1, 1, 1]], dtype=torch.int64) concat_block_mask = torch.tensor([[0, 0, 1, 1, 0], [0, 0, 1, 1, 0]], dtype=torch.int64) relevance_score = torch.tensor([0.3, 0.7], dtype=torch.float32) output = model( concat_input_ids, token_type_ids=concat_token_type_ids, relevance_score=relevance_score, block_mask=concat_block_mask, return_dict=True, ) block_idx_expected_shape = torch.Size(()) start_pos_expected_shape = torch.Size((1,)) end_pos_expected_shape = torch.Size((1,)) self.assertEqual(output.block_idx.shape, block_idx_expected_shape) self.assertEqual(output.start_pos.shape, start_pos_expected_shape) self.assertEqual(output.end_pos.shape, end_pos_expected_shape) expected_block_idx = torch.tensor(1) expected_start_pos = torch.tensor(3) expected_end_pos = torch.tensor(3) self.assertTrue(torch.allclose(output.block_idx, expected_block_idx, atol=1e-4)) self.assertTrue(torch.allclose(output.start_pos, expected_start_pos, atol=1e-4)) self.assertTrue(torch.allclose(output.end_pos, expected_end_pos, atol=1e-4)) @slow def test_inference_scorer(self): num_candidates = 2 model = RealmScorer.from_pretrained("google/realm-cc-news-pretrained-scorer", num_candidates=num_candidates) input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]]) candidate_input_ids = torch.tensor([[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]]) output = model(input_ids, candidate_input_ids=candidate_input_ids)[0] expected_shape = torch.Size((1, 2)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor([[0.7410, 0.7170]]) self.assertTrue(torch.allclose(output, expected_slice, atol=1e-4))