# 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. from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import numpy import tensorflow as tf from transformers import ( TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, BertConfig, DPRConfig, TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) class TFDPRModelTester: def __init__( self, parent, batch_size=13, 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, 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: # follow test_modeling_tf_ctrl.py 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 = BertConfig( 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, is_decoder=False, initializer_range=self.initializer_range, ) config = DPRConfig(projection_dim=self.projection_dim, **config.to_dict()) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def create_and_check_dpr_context_encoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFDPRContextEncoder(config=config) 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_dpr_question_encoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFDPRQuestionEncoder(config=config) 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_dpr_reader( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFDPRReader(config=config) 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_tf class TFDPRModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) if is_tf_available() else () ) pipeline_model_mapping = {"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {} test_resize_embeddings = False test_missing_keys = False test_pruning = False test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFDPRModelTester(self) self.config_tester = ConfigTester(self, config_class=DPRConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_dpr_context_encoder_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_context_encoder(*config_and_inputs) def test_dpr_question_encoder_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_question_encoder(*config_and_inputs) def test_dpr_reader_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_reader(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFDPRContextEncoder.from_pretrained(model_name) self.assertIsNotNone(model) for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFDPRContextEncoder.from_pretrained(model_name) self.assertIsNotNone(model) for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFDPRQuestionEncoder.from_pretrained(model_name) self.assertIsNotNone(model) for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFDPRReader.from_pretrained(model_name) self.assertIsNotNone(model) @require_tf class TFDPRModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head(self): model = TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base") input_ids = tf.constant( [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]] ) # [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 = tf.constant( [ [ 0.03236253, 0.12753335, 0.16818509, 0.00279786, 0.3896933, 0.24264945, 0.2178971, -0.02335227, -0.08481959, -0.14324117, ] ] ) self.assertTrue(numpy.allclose(output[:, :10].numpy(), expected_slice.numpy(), atol=1e-4))