test_modeling_roberta.py
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test_modeling_roberta.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_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 (
	RobertaConfig,
	RobertaForCausalLM,
	RobertaForMaskedLM,
	RobertaForMultipleChoice,
	RobertaForQuestionAnswering,
	RobertaForSequenceClassification,
	RobertaForTokenClassification,
	RobertaModel,
	)
	from transformers.models.roberta.modeling_roberta import (
	ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
	RobertaEmbeddings,
	create_position_ids_from_input_ids,
	)


	class RobertaModelTester:
	def __init__(
	self,
	parent,
	):
	self.parent = parent
	self.batch_size = 13
	self.seq_length = 7
	self.is_training = True
	self.use_input_mask = True
	self.use_token_type_ids = True
	self.use_labels = True
	self.vocab_size = 99
	self.hidden_size = 32
	self.num_hidden_layers = 5
	self.num_attention_heads = 4
	self.intermediate_size = 37
	self.hidden_act = "gelu"
	self.hidden_dropout_prob = 0.1
	self.attention_probs_dropout_prob = 0.1
	self.max_position_embeddings = 512
	self.type_vocab_size = 16
	self.type_sequence_label_size = 2
	self.initializer_range = 0.02
	self.num_labels = 3
	self.num_choices = 4
	self.scope = None

	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 = RobertaConfig(
	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,
	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 = RobertaModel(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 = RobertaModel(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 = RobertaForCausalLM(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_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 = RobertaForCausalLM(config=config).to(torch_device).eval()

	# make sure that ids don't start with pad token
	mask = input_ids.ne(config.pad_token_id).long()
	input_ids = input_ids * mask

	# 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)

	# make sure that ids don't start with pad token
	mask = next_tokens.ne(config.pad_token_id).long()
	next_tokens = next_tokens * mask
	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_masked_lm(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = RobertaForMaskedLM(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_token_classification(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	config.num_labels = self.num_labels
	model = RobertaForTokenClassification(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 = RobertaForMultipleChoice(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 create_and_check_for_question_answering(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = RobertaForQuestionAnswering(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 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 RobertaModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):

	all_model_classes = (
	(
	RobertaForCausalLM,
	RobertaForMaskedLM,
	RobertaModel,
	RobertaForSequenceClassification,
	RobertaForTokenClassification,
	RobertaForMultipleChoice,
	RobertaForQuestionAnswering,
	)
	if is_torch_available()
	else ()
	)
	all_generative_model_classes = (RobertaForCausalLM,) if is_torch_available() else ()

	def setUp(self):
	self.model_tester = RobertaModelTester(self)
	self.config_tester = ConfigTester(self, config_class=RobertaConfig, 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_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_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_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)

	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)

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

	def test_create_position_ids_respects_padding_index(self):
	"""Ensure that the default position ids only assign a sequential . This is a regression
	test for https://github.com/huggingface/transformers/issues/1761

	The position ids should be masked with the embedding object's padding index. Therefore, the
	first available non-padding position index is RobertaEmbeddings.padding_idx + 1
	"""
	config = self.model_tester.prepare_config_and_inputs()[0]
	model = RobertaEmbeddings(config=config)

	input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
	expected_positions = torch.as_tensor(
	[[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]]
	)

	position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx)
	self.assertEqual(position_ids.shape, expected_positions.shape)
	self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))

	def test_create_position_ids_from_inputs_embeds(self):
	"""Ensure that the default position ids only assign a sequential . This is a regression
	test for https://github.com/huggingface/transformers/issues/1761

	The position ids should be masked with the embedding object's padding index. Therefore, the
	first available non-padding position index is RobertaEmbeddings.padding_idx + 1
	"""
	config = self.model_tester.prepare_config_and_inputs()[0]
	embeddings = RobertaEmbeddings(config=config)

	inputs_embeds = torch.Tensor(2, 4, 30)
	expected_single_positions = [
	0 + embeddings.padding_idx + 1,
	1 + embeddings.padding_idx + 1,
	2 + embeddings.padding_idx + 1,
	3 + embeddings.padding_idx + 1,
	]
	expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
	position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds)
	self.assertEqual(position_ids.shape, expected_positions.shape)
	self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))


	@require_torch
	class RobertaModelIntegrationTest(unittest.TestCase):
	@slow
	def test_inference_masked_lm(self):
	model = RobertaForMaskedLM.from_pretrained("roberta-base")

	input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
	output = model(input_ids)[0]
	expected_shape = torch.Size((1, 11, 50265))
	self.assertEqual(output.shape, expected_shape)
	# compare the actual values for a slice.
	expected_slice = torch.tensor(
	[[[33.8802, -4.3103, 22.7761], [4.6539, -2.8098, 13.6253], [1.8228, -3.6898, 8.8600]]]
	)

	# roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
	# roberta.eval()
	# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

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

	@slow
	def test_inference_no_head(self):
	model = RobertaModel.from_pretrained("roberta-base")

	input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
	output = model(input_ids)[0]
	# compare the actual values for a slice.
	expected_slice = torch.tensor(
	[[[-0.0231, 0.0782, 0.0074], [-0.1854, 0.0540, -0.0175], [0.0548, 0.0799, 0.1687]]]
	)

	# roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
	# roberta.eval()
	# expected_slice = roberta.extract_features(input_ids)[:, :3, :3].detach()

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

	@slow
	def test_inference_classification_head(self):
	model = RobertaForSequenceClassification.from_pretrained("roberta-large-mnli")

	input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
	output = model(input_ids)[0]
	expected_shape = torch.Size((1, 3))
	self.assertEqual(output.shape, expected_shape)
	expected_tensor = torch.tensor([[-0.9469, 0.3913, 0.5118]])

	# roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
	# roberta.eval()
	# expected_tensor = roberta.predict("mnli", input_ids, return_logits=True).detach()

	self.assertTrue(torch.allclose(output, expected_tensor, atol=1e-4))

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