# coding=utf-8 # Copyright 2020 The HuggingFace 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. from __future__ import annotations import unittest from transformers import AlbertConfig, is_tf_available from transformers.models.auto import get_values 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 tensorflow as tf from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING from transformers.models.albert.modeling_tf_albert import ( TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFAlbertForMaskedLM, TFAlbertForMultipleChoice, TFAlbertForPreTraining, TFAlbertForQuestionAnswering, TFAlbertForSequenceClassification, TFAlbertForTokenClassification, TFAlbertModel, ) class TFAlbertModelTester: 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, embedding_size=16, 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, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_input_mask = True self.use_token_type_ids = True self.use_labels = True self.vocab_size = 99 self.embedding_size = 16 self.hidden_size = 32 self.num_hidden_layers = 2 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None 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 = AlbertConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, embedding_size=self.embedding_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, ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def create_and_check_albert_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFAlbertModel(config=config) # inputs = {'input_ids': input_ids, # 'attention_mask': input_mask, # 'token_type_ids': token_type_ids} # sequence_output, pooled_output = model(**inputs) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_albert_for_pretraining( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TFAlbertForPreTraining(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) self.parent.assertEqual(result.sop_logits.shape, (self.batch_size, self.num_labels)) def create_and_check_albert_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFAlbertForMaskedLM(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_albert_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TFAlbertForSequenceClassification(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_albert_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFAlbertForQuestionAnswering(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) 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)) def create_and_check_albert_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = TFAlbertForMultipleChoice(config=config) multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1)) multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1)) multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1)) inputs = { "input_ids": multiple_choice_inputs_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } result = model(inputs) self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.num_choices]) def create_and_check_albert_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TFAlbertForTokenClassification(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) self.parent.assertListEqual(list(result["logits"].shape), [self.batch_size, self.seq_length, self.num_labels]) 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, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_tf class TFAlbertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( TFAlbertModel, TFAlbertForPreTraining, TFAlbertForMaskedLM, TFAlbertForSequenceClassification, TFAlbertForQuestionAnswering, TFAlbertForTokenClassification, TFAlbertForMultipleChoice, ) if is_tf_available() else () ) pipeline_model_mapping = ( { "feature-extraction": TFAlbertModel, "fill-mask": TFAlbertForMaskedLM, "question-answering": TFAlbertForQuestionAnswering, "text-classification": TFAlbertForSequenceClassification, "token-classification": TFAlbertForTokenClassification, "zero-shot": TFAlbertForSequenceClassification, } if is_tf_available() else {} ) test_head_masking = False test_onnx = False # special case for ForPreTraining model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class in get_values(TF_MODEL_FOR_PRETRAINING_MAPPING): inputs_dict["sentence_order_label"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) return inputs_dict def setUp(self): self.model_tester = TFAlbertModelTester(self) self.config_tester = ConfigTester(self, config_class=AlbertConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_albert_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_albert_model(*config_and_inputs) def test_for_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_albert_for_pretraining(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_albert_for_masked_lm(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_albert_for_multiple_choice(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_albert_for_sequence_classification(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_albert_for_question_answering(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFAlbertModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_tf class TFAlbertModelIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): model = TFAlbertForPreTraining.from_pretrained("albert-base-v2") input_ids = tf.constant([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] expected_shape = [1, 6, 30000] self.assertEqual(output.shape, expected_shape) expected_slice = tf.constant( [ [ [4.595668, 0.74462754, -1.818147], [4.5954347, 0.7454184, -1.8188258], [4.5954905, 0.7448235, -1.8182316], ] ] ) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)