test_modeling_mobilebert.py
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test_modeling_mobilebert.py
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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.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 ()
	)

	# 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 MODEL_FOR_PRETRAINING_MAPPING.values():
	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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