tests / test_modeling_bert.py

add model

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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_torch, slow, torch_device

	from .test_configuration_common import ConfigTester
	from .test_generation_utils import GenerationTesterMixin
	from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


	if is_torch_available():
	import torch

	from transformers import (
	MODEL_FOR_PRETRAINING_MAPPING,
	BertConfig,
	BertForMaskedLM,
	BertForMultipleChoice,
	BertForNextSentencePrediction,
	BertForPreTraining,
	BertForQuestionAnswering,
	BertForSequenceClassification,
	BertForTokenClassification,
	BertLMHeadModel,
	BertModel,
	)
	from transformers.models.bert.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_LIST


	class BertModelTester:
	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 = BertConfig(
	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 = BertModel(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_model_as_decoder(
	self,
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	encoder_hidden_states,
	encoder_attention_mask,
	):
	config.add_cross_attention = True
	model = BertModel(config)
	model.to(torch_device)
	model.eval()
	result = model(
	input_ids,
	attention_mask=input_mask,
	token_type_ids=token_type_ids,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	)
	result = model(
	input_ids,
	attention_mask=input_mask,
	token_type_ids=token_type_ids,
	encoder_hidden_states=encoder_hidden_states,
	)
	result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_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_for_causal_lm(
	self,
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	encoder_hidden_states,
	encoder_attention_mask,
	):
	model = BertLMHeadModel(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_masked_lm(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = BertForMaskedLM(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_model_for_causal_lm_as_decoder(
	self,
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	encoder_hidden_states,
	encoder_attention_mask,
	):
	config.add_cross_attention = True
	model = BertLMHeadModel(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,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	)
	result = model(
	input_ids,
	attention_mask=input_mask,
	token_type_ids=token_type_ids,
	labels=token_labels,
	encoder_hidden_states=encoder_hidden_states,
	)
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

	def create_and_check_decoder_model_past_large_inputs(
	self,
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	encoder_hidden_states,
	encoder_attention_mask,
	):
	config.is_decoder = True
	config.add_cross_attention = True
	model = BertLMHeadModel(config=config).to(torch_device).eval()

	# first forward pass
	outputs = model(
	input_ids,
	attention_mask=input_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	use_cache=True,
	)
	past_key_values = outputs.past_key_values

	# create hypothetical multiple next token and extent to next_input_ids
	next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
	next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)

	# append to next input_ids and
	next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
	next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)

	output_from_no_past = model(
	next_input_ids,
	attention_mask=next_attention_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	output_hidden_states=True,
	)["hidden_states"][0]
	output_from_past = model(
	next_tokens,
	attention_mask=next_attention_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_attention_mask,
	past_key_values=past_key_values,
	output_hidden_states=True,
	)["hidden_states"][0]

	# select random slice
	random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
	output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
	output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()

	self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

	# test that outputs are equal for slice
	self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))

	def create_and_check_for_next_sequence_prediction(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = BertForNextSentencePrediction(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_for_pretraining(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = BertForPreTraining(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_for_question_answering(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = BertForQuestionAnswering(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 = BertForSequenceClassification(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 = BertForTokenClassification(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 = BertForMultipleChoice(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 BertModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):

	all_model_classes = (
	(
	BertModel,
	BertLMHeadModel,
	BertForMaskedLM,
	BertForMultipleChoice,
	BertForNextSentencePrediction,
	BertForPreTraining,
	BertForQuestionAnswering,
	BertForSequenceClassification,
	BertForTokenClassification,
	)
	if is_torch_available()
	else ()
	)
	all_generative_model_classes = (BertLMHeadModel,) 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 = BertModelTester(self)
	self.config_tester = ConfigTester(self, config_class=BertConfig, 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_model_various_embeddings(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	for type in ["absolute", "relative_key", "relative_key_query"]:
	config_and_inputs[0].position_embedding_type = type
	self.model_tester.create_and_check_model(*config_and_inputs)

	def test_model_as_decoder(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
	self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)

	def test_model_as_decoder_with_default_input_mask(self):
	# This regression test was failing with PyTorch < 1.3
	(
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	encoder_hidden_states,
	encoder_attention_mask,
	) = self.model_tester.prepare_config_and_inputs_for_decoder()

	input_mask = None

	self.model_tester.create_and_check_model_as_decoder(
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	encoder_hidden_states,
	encoder_attention_mask,
	)

	def test_for_causal_lm(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
	self.model_tester.create_and_check_for_causal_lm(*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_causal_lm_decoder(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
	self.model_tester.create_and_check_model_for_causal_lm_as_decoder(*config_and_inputs)

	def test_decoder_model_past_with_large_inputs(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
	self.model_tester.create_and_check_decoder_model_past_large_inputs(*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_next_sequence_prediction(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_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_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_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 BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
	model = BertModel.from_pretrained(model_name)
	self.assertIsNotNone(model)


	@require_torch
	class BertModelIntegrationTest(unittest.TestCase):
	@slow
	def test_inference_no_head_absolute_embedding(self):
	model = BertModel.from_pretrained("bert-base-uncased")
	input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
	attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
	output = model(input_ids, attention_mask=attention_mask)[0]
	expected_shape = torch.Size((1, 11, 768))
	self.assertEqual(output.shape, expected_shape)
	expected_slice = torch.tensor([[[0.4249, 0.1008, 0.7531], [0.3771, 0.1188, 0.7467], [0.4152, 0.1098, 0.7108]]])

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

	@slow
	def test_inference_no_head_relative_embedding_key(self):
	model = BertModel.from_pretrained("zhiheng-huang/bert-base-uncased-embedding-relative-key")
	input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
	attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
	output = model(input_ids, attention_mask=attention_mask)[0]
	expected_shape = torch.Size((1, 11, 768))
	self.assertEqual(output.shape, expected_shape)
	expected_slice = torch.tensor(
	[[[0.0756, 0.3142, -0.5128], [0.3761, 0.3462, -0.5477], [0.2052, 0.3760, -0.1240]]]
	)

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

	@slow
	def test_inference_no_head_relative_embedding_key_query(self):
	model = BertModel.from_pretrained("zhiheng-huang/bert-base-uncased-embedding-relative-key-query")
	input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
	attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
	output = model(input_ids, attention_mask=attention_mask)[0]
	expected_shape = torch.Size((1, 11, 768))
	self.assertEqual(output.shape, expected_shape)
	expected_slice = torch.tensor(
	[[[0.6496, 0.3784, 0.8203], [0.8148, 0.5656, 0.2636], [-0.0681, 0.5597, 0.7045]]]
	)

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