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- # 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.
-
-
- import unittest
-
- from transformers import is_torch_available
- from transformers.models.auto import get_values
- from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
-
- from .test_configuration_common import ConfigTester
- from .test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
-
-
- if is_torch_available():
- import torch
-
- from transformers import (
- MODEL_FOR_PRETRAINING_MAPPING,
- MobileBertConfig,
- MobileBertForMaskedLM,
- MobileBertForMultipleChoice,
- MobileBertForNextSentencePrediction,
- MobileBertForPreTraining,
- MobileBertForQuestionAnswering,
- MobileBertForSequenceClassification,
- MobileBertForTokenClassification,
- MobileBertModel,
- )
-
-
- class MobileBertModelTester:
- 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=64,
- embedding_size=32,
- num_hidden_layers=5,
- 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 = 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.embedding_size = embedding_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])
-
- 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 = MobileBertConfig(
- 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,
- embedding_size=self.embedding_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,
- )
-
- return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
-
- def create_and_check_mobilebert_model(
- self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
- ):
- model = MobileBertModel(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.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_mobilebert_for_masked_lm(
- self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
- ):
- model = MobileBertForMaskedLM(config=config)
- model.to(torch_device)
- model.eval()
- result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
- self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
-
- def create_and_check_mobilebert_for_next_sequence_prediction(
- self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
- ):
- model = MobileBertForNextSentencePrediction(config=config)
- model.to(torch_device)
- model.eval()
- result = model(
- input_ids,
- attention_mask=input_mask,
- token_type_ids=token_type_ids,
- labels=sequence_labels,
- )
- self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
-
- def create_and_check_mobilebert_for_pretraining(
- self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
- ):
- model = MobileBertForPreTraining(config=config)
- model.to(torch_device)
- model.eval()
- result = model(
- input_ids,
- attention_mask=input_mask,
- token_type_ids=token_type_ids,
- labels=token_labels,
- next_sentence_label=sequence_labels,
- )
- self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
- self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
-
- def create_and_check_mobilebert_for_question_answering(
- self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
- ):
- model = MobileBertForQuestionAnswering(config=config)
- model.to(torch_device)
- model.eval()
- result = model(
- input_ids,
- attention_mask=input_mask,
- token_type_ids=token_type_ids,
- start_positions=sequence_labels,
- end_positions=sequence_labels,
- )
- 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_mobilebert_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 = MobileBertForSequenceClassification(config)
- model.to(torch_device)
- model.eval()
- result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
- self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
-
- def create_and_check_mobilebert_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 = MobileBertForTokenClassification(config=config)
- model.to(torch_device)
- model.eval()
- result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
- self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
-
- def create_and_check_mobilebert_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 = MobileBertForMultipleChoice(config=config)
- model.to(torch_device)
- model.eval()
- multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
- multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
- multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
- result = model(
- multiple_choice_inputs_ids,
- attention_mask=multiple_choice_input_mask,
- token_type_ids=multiple_choice_token_type_ids,
- labels=choice_labels,
- )
- self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
-
- 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_torch
- class MobileBertModelTest(ModelTesterMixin, unittest.TestCase):
-
- all_model_classes = (
- (
- MobileBertModel,
- MobileBertForMaskedLM,
- MobileBertForMultipleChoice,
- MobileBertForNextSentencePrediction,
- MobileBertForPreTraining,
- MobileBertForQuestionAnswering,
- MobileBertForSequenceClassification,
- MobileBertForTokenClassification,
- )
- if is_torch_available()
- else ()
- )
- fx_ready_model_classes = all_model_classes
- test_sequence_classification_problem_types = True
-
- # 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(MODEL_FOR_PRETRAINING_MAPPING):
- inputs_dict["labels"] = torch.zeros(
- (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
- )
- inputs_dict["next_sentence_label"] = torch.zeros(
- self.model_tester.batch_size, dtype=torch.long, device=torch_device
- )
- return inputs_dict
-
- def setUp(self):
- self.model_tester = MobileBertModelTester(self)
- self.config_tester = ConfigTester(self, config_class=MobileBertConfig, hidden_size=37)
-
- def test_config(self):
- self.config_tester.run_common_tests()
-
- def test_mobilebert_model(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_mobilebert_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_mobilebert_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_mobilebert_for_multiple_choice(*config_and_inputs)
-
- def test_for_next_sequence_prediction(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*config_and_inputs)
-
- def test_for_pretraining(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- self.model_tester.create_and_check_mobilebert_for_pretraining(*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_mobilebert_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_mobilebert_for_sequence_classification(*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_mobilebert_for_token_classification(*config_and_inputs)
-
-
- def _long_tensor(tok_lst):
- return torch.tensor(
- tok_lst,
- dtype=torch.long,
- device=torch_device,
- )
-
-
- TOLERANCE = 1e-3
-
-
- @require_torch
- @require_sentencepiece
- @require_tokenizers
- class MobileBertModelIntegrationTests(unittest.TestCase):
- @slow
- def test_inference_no_head(self):
- model = MobileBertModel.from_pretrained("google/mobilebert-uncased").to(torch_device)
- input_ids = _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]])
- with torch.no_grad():
- output = model(input_ids)[0]
- expected_shape = torch.Size((1, 9, 512))
- self.assertEqual(output.shape, expected_shape)
- expected_slice = torch.tensor(
- [
- [
- [-2.4736526e07, 8.2691656e04, 1.6521838e05],
- [-5.7541704e-01, 3.9056022e00, 4.4011507e00],
- [2.6047359e00, 1.5677652e00, -1.7324188e-01],
- ]
- ],
- device=torch_device,
- )
-
- # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
- # ~1 difference, it's therefore not a good idea to measure using addition.
- # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
- # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
- lower_bound = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE)
- upper_bound = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE)
-
- self.assertTrue(lower_bound and upper_bound)
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