test_modeling_deberta.py
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Created: Fri, Jul 4, 21:49

test_modeling_deberta.py
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	# coding=utf-8
	# Copyright 2018 Microsoft Authors and the HuggingFace Inc. team.
	#
	# 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


	if is_torch_available():
	import torch

	from transformers import (
	DebertaConfig,
	DebertaForMaskedLM,
	DebertaForQuestionAnswering,
	DebertaForSequenceClassification,
	DebertaForTokenClassification,
	DebertaModel,
	)
	from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST


	@require_torch
	class DebertaModelTest(ModelTesterMixin, unittest.TestCase):

	all_model_classes = (
	(
	DebertaModel,
	DebertaForMaskedLM,
	DebertaForSequenceClassification,
	DebertaForTokenClassification,
	DebertaForQuestionAnswering,
	)
	if is_torch_available()
	else ()
	)

	test_torchscript = False
	test_pruning = False
	test_head_masking = False
	is_encoder_decoder = False

	class DebertaModelTester(object):
	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,
	relative_attention=False,
	position_biased_input=True,
	pos_att_type="None",
	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.relative_attention = relative_attention
	self.position_biased_input = position_biased_input
	self.pos_att_type = pos_att_type
	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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)

	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 = DebertaConfig(
	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,
	relative_attention=self.relative_attention,
	position_biased_input=self.position_biased_input,
	pos_att_type=self.pos_att_type,
	)

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

	def check_loss_output(self, result):
	self.parent.assertListEqual(list(result.loss.size()), [])

	def create_and_check_deberta_model(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = DebertaModel(config=config)
	model.to(torch_device)
	model.eval()
	sequence_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)[0]
	sequence_output = model(input_ids, token_type_ids=token_type_ids)[0]
	sequence_output = model(input_ids)[0]

	self.parent.assertListEqual(
	list(sequence_output.size()), [self.batch_size, self.seq_length, self.hidden_size]
	)

	def create_and_check_deberta_for_masked_lm(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = DebertaForMaskedLM(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_deberta_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 = DebertaForSequenceClassification(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.assertListEqual(list(result.logits.size()), [self.batch_size, self.num_labels])
	self.check_loss_output(result)

	def create_and_check_deberta_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 = DebertaForTokenClassification(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_deberta_for_question_answering(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = DebertaForQuestionAnswering(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

	def setUp(self):
	self.model_tester = DebertaModelTest.DebertaModelTester(self)
	self.config_tester = ConfigTester(self, config_class=DebertaConfig, hidden_size=37)

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

	def test_deberta_model(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_deberta_model(*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_deberta_for_sequence_classification(*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_deberta_for_masked_lm(*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_deberta_for_question_answering(*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_deberta_for_token_classification(*config_and_inputs)

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


	@require_torch
	@require_sentencepiece
	@require_tokenizers
	class DebertaModelIntegrationTest(unittest.TestCase):
	@unittest.skip(reason="Model not available yet")
	def test_inference_masked_lm(self):
	pass

	@slow
	def test_inference_no_head(self):
	model = DebertaModel.from_pretrained("microsoft/deberta-base")

	input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 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]
	# compare the actual values for a slice.
	expected_slice = torch.tensor(
	[[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]
	)
	self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4), f"{output[:, 1:4, 1:4]}")

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