test_modeling_xlm.py
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test_modeling_xlm.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, ids_tensor, random_attention_mask


	if is_torch_available():
	import torch

	from transformers import (
	XLMConfig,
	XLMForMultipleChoice,
	XLMForQuestionAnswering,
	XLMForQuestionAnsweringSimple,
	XLMForSequenceClassification,
	XLMForTokenClassification,
	XLMModel,
	XLMWithLMHeadModel,
	)
	from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST


	class XLMModelTester:
	def __init__(
	self,
	parent,
	):
	self.parent = parent
	self.batch_size = 13
	self.seq_length = 7
	self.is_training = True
	self.use_input_lengths = True
	self.use_token_type_ids = True
	self.use_labels = True
	self.gelu_activation = True
	self.sinusoidal_embeddings = False
	self.causal = False
	self.asm = False
	self.n_langs = 2
	self.vocab_size = 99
	self.n_special = 0
	self.hidden_size = 32
	self.num_hidden_layers = 5
	self.num_attention_heads = 4
	self.hidden_dropout_prob = 0.1
	self.attention_probs_dropout_prob = 0.1
	self.max_position_embeddings = 512
	self.type_sequence_label_size = 2
	self.initializer_range = 0.02
	self.num_labels = 2
	self.num_choices = 4
	self.summary_type = "last"
	self.use_proj = True
	self.scope = None
	self.bos_token_id = 0

	def prepare_config_and_inputs(self):
	input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
	input_mask = random_attention_mask([self.batch_size, self.seq_length])

	input_lengths = None
	if self.use_input_lengths:
	input_lengths = (
	ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2
	) # small variation of seq_length

	token_type_ids = None
	if self.use_token_type_ids:
	token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs)

	sequence_labels = None
	token_labels = None
	is_impossible_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)
	is_impossible_labels = ids_tensor([self.batch_size], 2).float()
	choice_labels = ids_tensor([self.batch_size], self.num_choices)

	config = XLMConfig(
	vocab_size=self.vocab_size,
	n_special=self.n_special,
	emb_dim=self.hidden_size,
	n_layers=self.num_hidden_layers,
	n_heads=self.num_attention_heads,
	dropout=self.hidden_dropout_prob,
	attention_dropout=self.attention_probs_dropout_prob,
	gelu_activation=self.gelu_activation,
	sinusoidal_embeddings=self.sinusoidal_embeddings,
	asm=self.asm,
	causal=self.causal,
	n_langs=self.n_langs,
	max_position_embeddings=self.max_position_embeddings,
	initializer_range=self.initializer_range,
	summary_type=self.summary_type,
	use_proj=self.use_proj,
	num_labels=self.num_labels,
	bos_token_id=self.bos_token_id,
	)

	return (
	config,
	input_ids,
	token_type_ids,
	input_lengths,
	sequence_labels,
	token_labels,
	is_impossible_labels,
	choice_labels,
	input_mask,
	)

	def create_and_check_xlm_model(
	self,
	config,
	input_ids,
	token_type_ids,
	input_lengths,
	sequence_labels,
	token_labels,
	is_impossible_labels,
	choice_labels,
	input_mask,
	):
	model = XLMModel(config=config)
	model.to(torch_device)
	model.eval()
	result = model(input_ids, lengths=input_lengths, langs=token_type_ids)
	result = model(input_ids, langs=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_xlm_lm_head(
	self,
	config,
	input_ids,
	token_type_ids,
	input_lengths,
	sequence_labels,
	token_labels,
	is_impossible_labels,
	choice_labels,
	input_mask,
	):
	model = XLMWithLMHeadModel(config)
	model.to(torch_device)
	model.eval()

	result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
	self.parent.assertEqual(result.loss.shape, ())
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

	def create_and_check_xlm_simple_qa(
	self,
	config,
	input_ids,
	token_type_ids,
	input_lengths,
	sequence_labels,
	token_labels,
	is_impossible_labels,
	choice_labels,
	input_mask,
	):
	model = XLMForQuestionAnsweringSimple(config)
	model.to(torch_device)
	model.eval()

	outputs = model(input_ids)

	outputs = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
	result = outputs
	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_xlm_qa(
	self,
	config,
	input_ids,
	token_type_ids,
	input_lengths,
	sequence_labels,
	token_labels,
	is_impossible_labels,
	choice_labels,
	input_mask,
	):
	model = XLMForQuestionAnswering(config)
	model.to(torch_device)
	model.eval()

	result = model(input_ids)

	result_with_labels = model(
	input_ids,
	start_positions=sequence_labels,
	end_positions=sequence_labels,
	cls_index=sequence_labels,
	is_impossible=is_impossible_labels,
	p_mask=input_mask,
	)

	result_with_labels = model(
	input_ids,
	start_positions=sequence_labels,
	end_positions=sequence_labels,
	cls_index=sequence_labels,
	is_impossible=is_impossible_labels,
	)

	(total_loss,) = result_with_labels.to_tuple()

	result_with_labels = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)

	(total_loss,) = result_with_labels.to_tuple()

	self.parent.assertEqual(result_with_labels.loss.shape, ())
	self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top))
	self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top))
	self.parent.assertEqual(
	result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
	)
	self.parent.assertEqual(
	result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
	)
	self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,))

	def create_and_check_xlm_sequence_classif(
	self,
	config,
	input_ids,
	token_type_ids,
	input_lengths,
	sequence_labels,
	token_labels,
	is_impossible_labels,
	choice_labels,
	input_mask,
	):
	model = XLMForSequenceClassification(config)
	model.to(torch_device)
	model.eval()

	result = model(input_ids)
	result = model(input_ids, labels=sequence_labels)
	self.parent.assertEqual(result.loss.shape, ())
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))

	def create_and_check_xlm_token_classif(
	self,
	config,
	input_ids,
	token_type_ids,
	input_lengths,
	sequence_labels,
	token_labels,
	is_impossible_labels,
	choice_labels,
	input_mask,
	):
	config.num_labels = self.num_labels
	model = XLMForTokenClassification(config)
	model.to(torch_device)
	model.eval()

	result = model(input_ids, attention_mask=input_mask, labels=token_labels)
	self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))

	def create_and_check_xlm_for_multiple_choice(
	self,
	config,
	input_ids,
	token_type_ids,
	input_lengths,
	sequence_labels,
	token_labels,
	is_impossible_labels,
	choice_labels,
	input_mask,
	):
	config.num_choices = self.num_choices
	model = XLMForMultipleChoice(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_lengths,
	sequence_labels,
	token_labels,
	is_impossible_labels,
	choice_labels,
	input_mask,
	) = config_and_inputs
	inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
	return config, inputs_dict


	@require_torch
	class XLMModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):

	all_model_classes = (
	(
	XLMModel,
	XLMWithLMHeadModel,
	XLMForQuestionAnswering,
	XLMForSequenceClassification,
	XLMForQuestionAnsweringSimple,
	XLMForTokenClassification,
	XLMForMultipleChoice,
	)
	if is_torch_available()
	else ()
	)
	all_generative_model_classes = (
	(XLMWithLMHeadModel,) if is_torch_available() else ()
	) # TODO (PVP): Check other models whether language generation is also applicable

	# XLM has 2 QA models -> need to manually set the correct labels for one of them here
	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.__name__ == "XLMForQuestionAnswering":
	inputs_dict["start_positions"] = torch.zeros(
	self.model_tester.batch_size, dtype=torch.long, device=torch_device
	)
	inputs_dict["end_positions"] = torch.zeros(
	self.model_tester.batch_size, dtype=torch.long, device=torch_device
	)

	return inputs_dict

	def setUp(self):
	self.model_tester = XLMModelTester(self)
	self.config_tester = ConfigTester(self, config_class=XLMConfig, emb_dim=37)

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

	def test_xlm_model(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_xlm_model(*config_and_inputs)

	def test_xlm_lm_head(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_xlm_lm_head(*config_and_inputs)

	def test_xlm_simple_qa(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_xlm_simple_qa(*config_and_inputs)

	def test_xlm_qa(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_xlm_qa(*config_and_inputs)

	def test_xlm_sequence_classif(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_xlm_sequence_classif(*config_and_inputs)

	def test_xlm_token_classif(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_xlm_token_classif(*config_and_inputs)

	def test_xlm_for_multiple_choice(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_xlm_for_multiple_choice(*config_and_inputs)

	def _check_attentions_for_generate(
	self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
	):
	self.assertIsInstance(attentions, tuple)
	self.assertListEqual(
	[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
	)
	self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)

	for idx, iter_attentions in enumerate(attentions):
	# adds PAD dummy token
	tgt_len = min_length + idx + 1
	src_len = min_length + idx + 1

	expected_shape = (
	batch_size * num_beam_groups,
	config.num_attention_heads,
	tgt_len,
	src_len,
	)
	# check attn size
	self.assertListEqual(
	[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
	)

	def _check_hidden_states_for_generate(
	self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
	):
	self.assertIsInstance(hidden_states, tuple)
	self.assertListEqual(
	[isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
	[True] * len(hidden_states),
	)
	self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)

	for idx, iter_hidden_states in enumerate(hidden_states):
	# adds PAD dummy token
	seq_len = min_length + idx + 1
	expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
	# check hidden size
	self.assertListEqual(
	[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
	[expected_shape] * len(iter_hidden_states),
	)
	pass

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


	@require_torch
	class XLMModelLanguageGenerationTest(unittest.TestCase):
	@slow
	def test_lm_generate_xlm_mlm_en_2048(self):
	model = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048")
	model.to(torch_device)
	input_ids = torch.tensor([[14, 447]], dtype=torch.long, device=torch_device) # the president
	expected_output_ids = [
	14,
	447,
	14,
	447,
	14,
	447,
	14,
	447,
	14,
	447,
	14,
	447,
	14,
	447,
	14,
	447,
	14,
	447,
	14,
	447,
	] # the president the president the president the president the president the president the president the president the president the president
	# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
	output_ids = model.generate(input_ids, do_sample=False)
	self.assertListEqual(output_ids[0].cpu().numpy().tolist(), expected_output_ids)

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