# coding=utf-8 # Copyright 2020 Huggingface # # 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 tempfile import unittest from transformers import DPRConfig, 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, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer from transformers.models.dpr.modeling_dpr import ( DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, ) class DPRModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=False, 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, num_labels=3, num_choices=4, scope=None, projection_dim=0, ): self.parent = parent self.batch_size = batch_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.num_labels = num_labels self.num_choices = num_choices self.scope = scope self.projection_dim = projection_dim def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) 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) 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() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return DPRConfig( projection_dim=self.projection_dim, vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, 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_context_encoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DPRContextEncoder(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size)) def create_and_check_question_encoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DPRQuestionEncoder(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size)) def create_and_check_reader( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DPRReader(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.relevance_logits.shape, (self.batch_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, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids} return config, inputs_dict @require_torch class DPRModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( DPRContextEncoder, DPRQuestionEncoder, DPRReader, ) if is_torch_available() else () ) pipeline_model_mapping = {"feature-extraction": DPRQuestionEncoder} if is_torch_available() else {} test_resize_embeddings = False test_missing_keys = False # why? test_pruning = False test_head_masking = False def setUp(self): self.model_tester = DPRModelTester(self) self.config_tester = ConfigTester(self, config_class=DPRConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_context_encoder_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_context_encoder(*config_and_inputs) def test_question_encoder_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_question_encoder(*config_and_inputs) def test_reader_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_reader(*config_and_inputs) def test_init_changed_config(self): config = self.model_tester.prepare_config_and_inputs()[0] model = DPRQuestionEncoder(config=config) model.to(torch_device) model.eval() with tempfile.TemporaryDirectory() as tmp_dirname: model.save_pretrained(tmp_dirname) model = DPRQuestionEncoder.from_pretrained(tmp_dirname, projection_dim=512) self.assertIsNotNone(model) @slow def test_model_from_pretrained(self): for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = DPRContextEncoder.from_pretrained(model_name) self.assertIsNotNone(model) for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = DPRContextEncoder.from_pretrained(model_name) self.assertIsNotNone(model) for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = DPRQuestionEncoder.from_pretrained(model_name) self.assertIsNotNone(model) for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = DPRReader.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class DPRModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head(self): model = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base", return_dict=False) model.to(torch_device) input_ids = torch.tensor( [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]], dtype=torch.long, device=torch_device ) # [CLS] hello, is my dog cute? [SEP] output = model(input_ids)[0] # embedding shape = (1, 768) # compare the actual values for a slice. expected_slice = torch.tensor( [ [ 0.03236253, 0.12753335, 0.16818509, 0.00279786, 0.3896933, 0.24264945, 0.2178971, -0.02335227, -0.08481959, -0.14324117, ] ], dtype=torch.float, device=torch_device, ) self.assertTrue(torch.allclose(output[:, :10], expected_slice, atol=1e-4)) @slow def test_reader_inference(self): tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base") model = DPRReader.from_pretrained("facebook/dpr-reader-single-nq-base") model.to(torch_device) encoded_inputs = tokenizer( questions="What is love ?", titles="Haddaway", texts="What Is Love is a song recorded by the artist Haddaway", padding=True, return_tensors="pt", ) encoded_inputs.to(torch_device) outputs = model(**encoded_inputs) # compare the actual values for a slice. expected_start_logits = torch.tensor( [[-10.3005, -10.7765, -11.4872, -11.6841, -11.9312, -10.3002, -9.8544, -11.7378, -12.0821, -10.2975]], dtype=torch.float, device=torch_device, ) expected_end_logits = torch.tensor( [[-11.0684, -11.7041, -11.5397, -10.3465, -10.8791, -6.8443, -11.9959, -11.0364, -10.0096, -6.8405]], dtype=torch.float, device=torch_device, ) self.assertTrue(torch.allclose(outputs.start_logits[:, :10], expected_start_logits, atol=1e-4)) self.assertTrue(torch.allclose(outputs.end_logits[:, :10], expected_end_logits, atol=1e-4))