test_modeling_funnel.py
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test_modeling_funnel.py
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	# coding=utf-8
	# Copyright 2020 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 FunnelTokenizer, 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 (
	MODEL_FOR_PRETRAINING_MAPPING,
	FunnelBaseModel,
	FunnelConfig,
	FunnelForMaskedLM,
	FunnelForMultipleChoice,
	FunnelForPreTraining,
	FunnelForQuestionAnswering,
	FunnelForSequenceClassification,
	FunnelForTokenClassification,
	FunnelModel,
	)


	class FunnelModelTester:
	"""You can also import this e.g, from .test_modeling_funnel import FunnelModelTester """

	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,
	block_sizes=[1, 1, 2],
	num_decoder_layers=1,
	d_model=32,
	n_head=4,
	d_head=8,
	d_inner=37,
	hidden_act="gelu_new",
	hidden_dropout=0.1,
	attention_dropout=0.1,
	activation_dropout=0.0,
	max_position_embeddings=512,
	type_vocab_size=3,
	num_labels=3,
	num_choices=4,
	scope=None,
	base=False,
	):
	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.block_sizes = block_sizes
	self.num_decoder_layers = num_decoder_layers
	self.d_model = d_model
	self.n_head = n_head
	self.d_head = d_head
	self.d_inner = d_inner
	self.hidden_act = hidden_act
	self.hidden_dropout = hidden_dropout
	self.attention_dropout = attention_dropout
	self.activation_dropout = activation_dropout
	self.max_position_embeddings = max_position_embeddings
	self.type_vocab_size = type_vocab_size
	self.type_sequence_label_size = 2
	self.num_labels = num_labels
	self.num_choices = num_choices
	self.scope = scope

	# Used in the tests to check the size of the first attention layer
	self.num_attention_heads = n_head
	# Used in the tests to check the size of the first hidden state
	self.hidden_size = self.d_model
	# Used in the tests to check the number of output hidden states/attentions
	self.num_hidden_layers = sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
	# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
	# the last hidden state of the first block (which is the first hidden state of the decoder).
	if not base:
	self.expected_num_hidden_layers = self.num_hidden_layers + 2

	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)
	fake_token_labels = ids_tensor([self.batch_size, self.seq_length], 1)

	config = FunnelConfig(
	vocab_size=self.vocab_size,
	block_sizes=self.block_sizes,
	num_decoder_layers=self.num_decoder_layers,
	d_model=self.d_model,
	n_head=self.n_head,
	d_head=self.d_head,
	d_inner=self.d_inner,
	hidden_act=self.hidden_act,
	hidden_dropout=self.hidden_dropout,
	attention_dropout=self.attention_dropout,
	activation_dropout=self.activation_dropout,
	max_position_embeddings=self.max_position_embeddings,
	type_vocab_size=self.type_vocab_size,
	)

	return (
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	fake_token_labels,
	)

	def create_and_check_model(
	self,
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	fake_token_labels,
	):
	model = FunnelModel(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.d_model))

	model.config.truncate_seq = False
	result = model(input_ids)
	self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))

	model.config.separate_cls = False
	result = model(input_ids)
	self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))

	def create_and_check_base_model(
	self,
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	fake_token_labels,
	):
	model = FunnelBaseModel(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, 2, self.d_model))

	model.config.truncate_seq = False
	result = model(input_ids)
	self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model))

	model.config.separate_cls = False
	result = model(input_ids)
	self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))

	def create_and_check_for_pretraining(
	self,
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	fake_token_labels,
	):
	config.num_labels = self.num_labels
	model = FunnelForPreTraining(config=config)
	model.to(torch_device)
	model.eval()
	result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=fake_token_labels)
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))

	def create_and_check_for_masked_lm(
	self,
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	fake_token_labels,
	):
	model = FunnelForMaskedLM(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_sequence_classification(
	self,
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	fake_token_labels,
	):
	config.num_labels = self.num_labels
	model = FunnelForSequenceClassification(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_multiple_choice(
	self,
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	fake_token_labels,
	):
	config.num_choices = self.num_choices
	model = FunnelForMultipleChoice(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_token_classification(
	self,
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	fake_token_labels,
	):
	config.num_labels = self.num_labels
	model = FunnelForTokenClassification(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_question_answering(
	self,
	config,
	input_ids,
	token_type_ids,
	input_mask,
	sequence_labels,
	token_labels,
	choice_labels,
	fake_token_labels,
	):
	model = FunnelForQuestionAnswering(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,
	fake_token_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 FunnelModelTest(ModelTesterMixin, unittest.TestCase):
	test_head_masking = False
	test_pruning = False
	all_model_classes = (
	(
	FunnelModel,
	FunnelForMaskedLM,
	FunnelForPreTraining,
	FunnelForQuestionAnswering,
	FunnelForTokenClassification,
	)
	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
	)
	return inputs_dict

	def setUp(self):
	self.model_tester = FunnelModelTester(self)
	self.config_tester = ConfigTester(self, config_class=FunnelConfig)

	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_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_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_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)


	@require_torch
	class FunnelBaseModelTest(ModelTesterMixin, unittest.TestCase):
	test_head_masking = False
	test_pruning = False
	all_model_classes = (
	(FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
	)

	def setUp(self):
	self.model_tester = FunnelModelTester(self, base=True)
	self.config_tester = ConfigTester(self, config_class=FunnelConfig)

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

	def test_base_model(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_base_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_for_sequence_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)

	# overwrite from test_modeling_common
	def test_training(self):
	config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
	config.return_dict = True

	for model_class in self.all_model_classes:
	if model_class.__name__ == "FunnelBaseModel":
	continue
	model = model_class(config)
	model.to(torch_device)
	model.train()
	inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
	loss = model(**inputs).loss
	loss.backward()


	@require_torch
	@require_sentencepiece
	@require_tokenizers
	class FunnelModelIntegrationTest(unittest.TestCase):
	def test_inference_tiny_model(self):
	batch_size = 13
	sequence_length = 7
	input_ids = torch.arange(0, batch_size * sequence_length).long().reshape(batch_size, sequence_length)
	lengths = [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
	token_type_ids = torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths])

	model = FunnelModel.from_pretrained("sgugger/funnel-random-tiny")
	output = model(input_ids, token_type_ids=token_type_ids)[0].abs()

	expected_output_sum = torch.tensor(2344.8352)
	expected_output_mean = torch.tensor(0.8052)
	self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
	self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))

	attention_mask = torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]])
	output = model(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)[0].abs()

	expected_output_sum = torch.tensor(2343.8425)
	expected_output_mean = torch.tensor(0.8049)
	self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
	self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))

	@slow
	def test_inference_model(self):
	tokenizer = FunnelTokenizer.from_pretrained("huggingface/funnel-small")
	model = FunnelModel.from_pretrained("huggingface/funnel-small")
	inputs = tokenizer("Hello! I am the Funnel Transformer model.", return_tensors="pt")
	output = model(**inputs)[0]

	expected_output_sum = torch.tensor(235.7246)
	expected_output_mean = torch.tensor(0.0256)
	self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
	self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))

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