test_modeling_convbert.py
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test_modeling_convbert.py
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	# coding=utf-8
	# Copyright 2021 The HuggingFace Inc. 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.
	""" Testing suite for the PyTorch ConvBERT model. """


	import unittest

	from tests.test_modeling_common import floats_tensor
	from transformers import is_torch_available
	from transformers.testing_utils import 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_QUESTION_ANSWERING_MAPPING,
	ConvBertConfig,
	ConvBertForMaskedLM,
	ConvBertForMultipleChoice,
	ConvBertForQuestionAnswering,
	ConvBertForSequenceClassification,
	ConvBertForTokenClassification,
	ConvBertModel,
	)
	from transformers.models.convbert.modeling_convbert import CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST


	class ConvBertModelTester:
	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=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.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 = ConvBertConfig(
	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,
	)

	return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

	def prepare_config_and_inputs_for_decoder(self):
	(
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	) = self.prepare_config_and_inputs()

	config.is_decoder = True
	encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
	encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)

	return (
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	encoder_hidden_states,
	encoder_attention_mask,
	)

	def create_and_check_model(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = ConvBertModel(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))

	def create_and_check_for_masked_lm(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = ConvBertForMaskedLM(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_for_question_answering(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = ConvBertForQuestionAnswering(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_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 = ConvBertForSequenceClassification(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_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 = ConvBertForTokenClassification(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_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 = ConvBertForMultipleChoice(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 ConvBertModelTest(ModelTesterMixin, unittest.TestCase):

	all_model_classes = (
	(
	ConvBertModel,
	ConvBertForMaskedLM,
	ConvBertForMultipleChoice,
	ConvBertForQuestionAnswering,
	ConvBertForSequenceClassification,
	ConvBertForTokenClassification,
	)
	if is_torch_available()
	else ()
	)
	test_pruning = False
	test_head_masking = False

	def setUp(self):
	self.model_tester = ConvBertModelTester(self)
	self.config_tester = ConfigTester(self, config_class=ConvBertConfig, hidden_size=37)

	def test_config(self):
	self.config_tester.run_common_tests()

	def test_model(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_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_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_for_multiple_choice(*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_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_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_for_token_classification(*config_and_inputs)

	@slow
	def test_model_from_pretrained(self):
	for model_name in CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
	model = ConvBertModel.from_pretrained(model_name)
	self.assertIsNotNone(model)

	def test_attention_outputs(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
	config.return_dict = True

	seq_len = getattr(self.model_tester, "seq_length", None)
	decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
	encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
	decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
	encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
	chunk_length = getattr(self.model_tester, "chunk_length", None)
	if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
	encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes

	for model_class in self.all_model_classes:
	inputs_dict["output_attentions"] = True
	inputs_dict["output_hidden_states"] = False
	config.return_dict = True
	model = model_class(config)
	model.to(torch_device)
	model.eval()
	with torch.no_grad():
	outputs = model(**self._prepare_for_class(inputs_dict, model_class))
	attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
	self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

	# check that output_attentions also work using config
	del inputs_dict["output_attentions"]
	config.output_attentions = True
	model = model_class(config)
	model.to(torch_device)
	model.eval()
	with torch.no_grad():
	outputs = model(**self._prepare_for_class(inputs_dict, model_class))
	attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
	self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

	if chunk_length is not None:
	self.assertListEqual(
	list(attentions[0].shape[-4:]),
	[self.model_tester.num_attention_heads / 2, encoder_seq_length, chunk_length, encoder_key_length],
	)
	else:
	self.assertListEqual(
	list(attentions[0].shape[-3:]),
	[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length],
	)
	out_len = len(outputs)

	if self.is_encoder_decoder:
	correct_outlen = 5

	# loss is at first position
	if "labels" in inputs_dict:
	correct_outlen += 1 # loss is added to beginning
	# Question Answering model returns start_logits and end_logits
	if model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
	correct_outlen += 1 # start_logits and end_logits instead of only 1 output
	if "past_key_values" in outputs:
	correct_outlen += 1 # past_key_values have been returned

	self.assertEqual(out_len, correct_outlen)

	# decoder attentions
	decoder_attentions = outputs.decoder_attentions
	self.assertIsInstance(decoder_attentions, (list, tuple))
	self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
	self.assertListEqual(
	list(decoder_attentions[0].shape[-3:]),
	[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
	)

	# cross attentions
	cross_attentions = outputs.cross_attentions
	self.assertIsInstance(cross_attentions, (list, tuple))
	self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
	self.assertListEqual(
	list(cross_attentions[0].shape[-3:]),
	[
	self.model_tester.num_attention_heads,
	decoder_seq_length,
	encoder_key_length,
	],
	)

	# Check attention is always last and order is fine
	inputs_dict["output_attentions"] = True
	inputs_dict["output_hidden_states"] = True
	model = model_class(config)
	model.to(torch_device)
	model.eval()
	with torch.no_grad():
	outputs = model(**self._prepare_for_class(inputs_dict, model_class))

	if hasattr(self.model_tester, "num_hidden_states_types"):
	added_hidden_states = self.model_tester.num_hidden_states_types
	elif self.is_encoder_decoder:
	added_hidden_states = 2
	else:
	added_hidden_states = 1
	self.assertEqual(out_len + added_hidden_states, len(outputs))

	self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

	self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
	if chunk_length is not None:
	self.assertListEqual(
	list(self_attentions[0].shape[-4:]),
	[self.model_tester.num_attention_heads / 2, encoder_seq_length, chunk_length, encoder_key_length],
	)
	else:
	self.assertListEqual(
	list(self_attentions[0].shape[-3:]),
	[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length],
	)


	@require_torch
	class ConvBertModelIntegrationTest(unittest.TestCase):
	@slow
	def test_inference_no_head(self):
	model = ConvBertModel.from_pretrained("YituTech/conv-bert-base")
	input_ids = torch.tensor([[1, 2, 3, 4, 5, 6]])
	output = model(input_ids)[0]

	expected_shape = torch.Size((1, 6, 768))
	self.assertEqual(output.shape, expected_shape)

	expected_slice = torch.tensor(
	[[[-0.0864, -0.4898, -0.3677], [0.1434, -0.2952, -0.7640], [-0.0112, -0.4432, -0.5432]]]
	)

	self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))

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