# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team, Microsoft Corporation. # # 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 MPNetConfig, 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 tensorflow as tf from transformers.models.mpnet.modeling_tf_mpnet import ( TFMPNetForMaskedLM, TFMPNetForMultipleChoice, TFMPNetForQuestionAnswering, TFMPNetForSequenceClassification, TFMPNetForTokenClassification, TFMPNetModel, ) class TFMPNetModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=64, num_hidden_layers=2, num_attention_heads=4, intermediate_size=64, 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 = 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 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]) 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 = MPNetConfig( 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, initializer_range=self.initializer_range, ) return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels def create_and_check_mpnet_model( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFMPNetModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask} result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_mpnet_for_masked_lm( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFMPNetForMaskedLM(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_mpnet_for_question_answering( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFMPNetForQuestionAnswering(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, } 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_mpnet_for_sequence_classification( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TFMPNetForSequenceClassification(config) inputs = {"input_ids": input_ids, "attention_mask": input_mask} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_mpnet_for_multiple_choice( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = TFMPNetForMultipleChoice(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)) inputs = { "input_ids": multiple_choice_inputs_ids, "attention_mask": multiple_choice_input_mask, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def create_and_check_mpnet_for_token_classification( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TFMPNetForTokenClassification(config) inputs = {"input_ids": input_ids, "attention_mask": input_mask} result = model(inputs) self.parent.assertEqual(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, input_mask, sequence_labels, token_labels, choice_labels) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_tf class TFMPNetModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( TFMPNetForMaskedLM, TFMPNetForMultipleChoice, TFMPNetForQuestionAnswering, TFMPNetForSequenceClassification, TFMPNetForTokenClassification, TFMPNetModel, ) if is_tf_available() else () ) pipeline_model_mapping = ( { "feature-extraction": TFMPNetModel, "fill-mask": TFMPNetForMaskedLM, "question-answering": TFMPNetForQuestionAnswering, "text-classification": TFMPNetForSequenceClassification, "token-classification": TFMPNetForTokenClassification, "zero-shot": TFMPNetForSequenceClassification, } if is_tf_available() else {} ) test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFMPNetModelTester(self) self.config_tester = ConfigTester(self, config_class=MPNetConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_mpnet_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mpnet_model(*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_mpnet_for_masked_lm(*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_mpnet_for_question_answering(*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_mpnet_for_sequence_classification(*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_mpnet_for_multiple_choice(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mpnet_for_token_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in ["microsoft/mpnet-base"]: model = TFMPNetModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_tf class TFMPNetModelIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): model = TFMPNetModel.from_pretrained("microsoft/mpnet-base") input_ids = tf.constant([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] expected_shape = [1, 6, 768] self.assertEqual(output.shape, expected_shape) expected_slice = tf.constant( [ [ [-0.1067172, 0.08216473, 0.0024543], [-0.03465879, 0.8354118, -0.03252288], [-0.06569476, -0.12424111, -0.0494436], ] ] ) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)