test_modeling_gpt2.py
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test_modeling_gpt2.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 datetime
	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 (
	GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
	GPT2Config,
	GPT2DoubleHeadsModel,
	GPT2ForSequenceClassification,
	GPT2LMHeadModel,
	GPT2Model,
	GPT2Tokenizer,
	)


	class GPT2ModelTester:
	def __init__(
	self,
	parent,
	batch_size=14,
	seq_length=7,
	is_training=True,
	use_token_type_ids=True,
	use_input_mask=True,
	use_labels=True,
	use_mc_token_ids=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_token_type_ids = use_token_type_ids
	self.use_input_mask = use_input_mask
	self.use_labels = use_labels
	self.use_mc_token_ids = use_mc_token_ids
	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 = None
	self.bos_token_id = vocab_size - 1
	self.eos_token_id = vocab_size - 1
	self.pad_token_id = vocab_size - 1

	def get_large_model_config(self):
	return GPT2Config.from_pretrained("gpt2")

	def prepare_config_and_inputs(self, gradient_checkpointing=False):
	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)

	mc_token_ids = None
	if self.use_mc_token_ids:
	mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)

	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 = GPT2Config(
	vocab_size=self.vocab_size,
	n_embd=self.hidden_size,
	n_layer=self.num_hidden_layers,
	n_head=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,
	n_positions=self.max_position_embeddings,
	n_ctx=self.max_position_embeddings,
	# type_vocab_size=self.type_vocab_size,
	# initializer_range=self.initializer_range,
	use_cache=not gradient_checkpointing,
	bos_token_id=self.bos_token_id,
	eos_token_id=self.eos_token_id,
	pad_token_id=self.pad_token_id,
	gradient_checkpointing=gradient_checkpointing,
	)

	head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)

	return (
	config,
	input_ids,
	input_mask,
	head_mask,
	token_type_ids,
	mc_token_ids,
	sequence_labels,
	token_labels,
	choice_labels,
	)

	def prepare_config_and_inputs_for_decoder(self):
	(
	config,
	input_ids,
	input_mask,
	head_mask,
	token_type_ids,
	mc_token_ids,
	sequence_labels,
	token_labels,
	choice_labels,
	) = self.prepare_config_and_inputs()

	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,
	input_mask,
	head_mask,
	token_type_ids,
	sequence_labels,
	token_labels,
	choice_labels,
	encoder_hidden_states,
	encoder_attention_mask,
	)

	def create_and_check_gpt2_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
	model = GPT2Model(config=config)
	model.to(torch_device)
	model.eval()

	result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
	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(len(result.past_key_values), config.n_layer)

	def create_and_check_gpt2_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
	model = GPT2Model(config=config)
	model.to(torch_device)
	model.eval()

	# first forward pass
	outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True)
	outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids)
	outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False)

	self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
	self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)

	output, past = outputs.to_tuple()

	# create hypothetical next token and extent to next_input_ids
	next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
	next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size)

	# append to next input_ids and token_type_ids
	next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
	next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)

	output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
	output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past)[
	"last_hidden_state"
	]

	# select random slice
	random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
	output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
	output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()

	# 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_gpt2_model_attention_mask_past(
	self, config, input_ids, input_mask, head_mask, token_type_ids, *args
	):
	model = GPT2Model(config=config)
	model.to(torch_device)
	model.eval()

	# create attention mask
	attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
	half_seq_length = self.seq_length // 2
	attn_mask[:, half_seq_length:] = 0

	# first forward pass
	output, past = model(input_ids, attention_mask=attn_mask).to_tuple()

	# create hypothetical next token and extent to next_input_ids
	next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)

	# change a random masked slice from input_ids
	random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
	random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
	input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens

	# append to next input_ids and attn_mask
	next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
	attn_mask = torch.cat(
	[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
	dim=1,
	)

	# get two different outputs
	output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
	output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"]

	# select random slice
	random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
	output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
	output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()

	# 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_gpt2_model_past_large_inputs(
	self, config, input_ids, input_mask, head_mask, token_type_ids, *args
	):
	model = GPT2Model(config=config)
	model.to(torch_device)
	model.eval()

	# first forward pass
	outputs = model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask, use_cache=True)

	output, past = outputs.to_tuple()

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

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

	output_from_no_past = model(
	next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask
	)["last_hidden_state"]
	output_from_past = model(
	next_tokens, token_type_ids=next_token_types, attention_mask=next_attention_mask, past_key_values=past
	)["last_hidden_state"]
	self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])

	# 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()

	# 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_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
	model = GPT2LMHeadModel(config)
	model.to(torch_device)
	model.eval()

	result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
	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_forward_and_backwards(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
	model = GPT2LMHeadModel(config)
	model.to(torch_device)

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

	def create_and_check_double_lm_head_model(
	self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args
	):
	model = GPT2DoubleHeadsModel(config)
	model.to(torch_device)
	model.eval()

	multiple_choice_inputs_ids = input_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()
	multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()

	inputs = {
	"input_ids": multiple_choice_inputs_ids,
	"mc_token_ids": mc_token_ids,
	"attention_mask": multiple_choice_input_mask,
	"token_type_ids": multiple_choice_token_type_ids,
	"labels": multiple_choice_inputs_ids,
	}

	result = model(**inputs)
	self.parent.assertEqual(result.loss.shape, ())
	self.parent.assertEqual(
	result.logits.shape, (self.batch_size, self.num_choices, self.seq_length, self.vocab_size)
	)
	self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices))

	def create_and_check_gpt2_for_sequence_classification(
	self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
	):
	config.num_labels = self.num_labels
	model = GPT2ForSequenceClassification(config)
	model.to(torch_device)
	model.eval()
	print(config.num_labels, sequence_labels.size())
	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 prepare_config_and_inputs_for_common(self):
	config_and_inputs = self.prepare_config_and_inputs()

	(
	config,
	input_ids,
	input_mask,
	head_mask,
	token_type_ids,
	mc_token_ids,
	sequence_labels,
	token_labels,
	choice_labels,
	) = config_and_inputs

	inputs_dict = {
	"input_ids": input_ids,
	"token_type_ids": token_type_ids,
	"head_mask": head_mask,
	}

	return config, inputs_dict


	@require_torch
	class GPT2ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):

	all_model_classes = (
	(GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel, GPT2ForSequenceClassification)
	if is_torch_available()
	else ()
	)
	all_generative_model_classes = (GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
	all_parallelizable_model_classes = (GPT2LMHeadModel,) if is_torch_available() else ()
	test_missing_keys = False
	test_model_parallel = True

	# special case for DoubleHeads 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.__name__ == "GPT2DoubleHeadsModel":
	inputs_dict["labels"] = torch.zeros(
	(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length),
	dtype=torch.long,
	device=torch_device,
	)
	inputs_dict["input_ids"] = inputs_dict["labels"]
	inputs_dict["token_type_ids"] = inputs_dict["labels"]
	inputs_dict["mc_token_ids"] = torch.zeros(
	(self.model_tester.batch_size, self.model_tester.num_choices),
	dtype=torch.long,
	device=torch_device,
	)
	inputs_dict["mc_labels"] = torch.zeros(
	self.model_tester.batch_size, dtype=torch.long, device=torch_device
	)
	return inputs_dict

	def setUp(self):
	self.model_tester = GPT2ModelTester(self)
	self.config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37)

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

	def test_gpt2_model(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_gpt2_model(*config_and_inputs)

	def test_gpt2_model_past(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_gpt2_model_past(*config_and_inputs)

	def test_gpt2_model_att_mask_past(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_gpt2_model_attention_mask_past(*config_and_inputs)

	def test_gpt2_model_past_large_inputs(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_gpt2_model_past_large_inputs(*config_and_inputs)

	def test_gpt2_lm_head_model(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_lm_head_model(*config_and_inputs)

	def test_gpt2_double_lm_head_model(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs)

	def test_gpt2_sequence_classification_model(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_gpt2_for_sequence_classification(*config_and_inputs)

	def test_gpt2_gradient_checkpointing(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs(gradient_checkpointing=True)
	self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs)

	@slow
	def test_batch_generation(self):
	model = GPT2LMHeadModel.from_pretrained("gpt2")
	model.to(torch_device)
	tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

	tokenizer.padding_side = "left"

	# Define PAD Token = EOS Token = 50256
	tokenizer.pad_token = tokenizer.eos_token
	model.config.pad_token_id = model.config.eos_token_id

	# use different length sentences to test batching
	sentences = [
	"Hello, my dog is a little",
	"Today, I",
	]

	inputs = tokenizer(sentences, return_tensors="pt", padding=True)
	input_ids = inputs["input_ids"].to(torch_device)
	token_type_ids = torch.cat(
	[
	input_ids.new_full((input_ids.shape[0], input_ids.shape[1] - 1), 0),
	input_ids.new_full((input_ids.shape[0], 1), 500),
	],
	dim=-1,
	)

	outputs = model.generate(
	input_ids=input_ids,
	attention_mask=inputs["attention_mask"].to(torch_device),
	)

	outputs_tt = model.generate(
	input_ids=input_ids,
	attention_mask=inputs["attention_mask"].to(torch_device),
	token_type_ids=token_type_ids,
	)

	inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
	output_non_padded = model.generate(input_ids=inputs_non_padded)

	num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
	inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
	output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)

	batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
	batch_out_sentence_tt = tokenizer.batch_decode(outputs_tt, skip_special_tokens=True)
	non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
	padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)

	expected_output_sentence = [
	"Hello, my dog is a little bit of a mess. I'm not sure if he's going",
	"Today, I'm going to be doing a lot of research on this. I",
	]
	self.assertListEqual(expected_output_sentence, batch_out_sentence)
	self.assertTrue(batch_out_sentence_tt != batch_out_sentence) # token_type_ids should change output
	self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])

	@slow
	def test_batch_generation_2heads(self):
	model = GPT2DoubleHeadsModel.from_pretrained("gpt2")
	model.to(torch_device)
	tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

	tokenizer.padding_side = "left"

	# This tokenizer has no pad token, so we have to set it in some way
	# Define PAD Token = EOS Token = 50256
	tokenizer.pad_token = tokenizer.eos_token
	model.config.pad_token_id = model.config.eos_token_id

	# use different length sentences to test batching
	sentences = [
	"Hello, my dog is a little",
	"Today, I",
	]

	inputs = tokenizer(sentences, return_tensors="pt", padding=True)
	input_ids = inputs["input_ids"].to(torch_device)
	token_type_ids = torch.cat(
	[
	input_ids.new_full((input_ids.shape[0], input_ids.shape[1] - 1), 0),
	input_ids.new_full((input_ids.shape[0], 1), 500),
	],
	dim=-1,
	)

	outputs = model.generate(
	input_ids=input_ids,
	attention_mask=inputs["attention_mask"].to(torch_device),
	)

	outputs_tt = model.generate(
	input_ids=input_ids,
	attention_mask=inputs["attention_mask"].to(torch_device),
	token_type_ids=token_type_ids,
	)

	inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
	output_non_padded = model.generate(input_ids=inputs_non_padded)

	num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
	inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
	output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)

	batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
	batch_out_sentence_tt = tokenizer.batch_decode(outputs_tt, skip_special_tokens=True)
	non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
	padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)

	expected_output_sentence = [
	"Hello, my dog is a little bit of a mess. I'm not sure if he's going",
	"Today, I'm going to be doing a lot of research on this. I",
	]
	self.assertListEqual(expected_output_sentence, batch_out_sentence)
	self.assertTrue(batch_out_sentence_tt != batch_out_sentence) # token_type_ids should change output
	self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])

	@slow
	def test_model_from_pretrained(self):
	for model_name in GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
	model = GPT2Model.from_pretrained(model_name)
	self.assertIsNotNone(model)


	@require_torch
	class GPT2ModelLanguageGenerationTest(unittest.TestCase):
	@slow
	def test_lm_generate_gpt2(self):
	for checkpointing in [True, False]:
	model = GPT2LMHeadModel.from_pretrained("gpt2", gradient_checkpointing=checkpointing)
	model.to(torch_device)
	input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device) # The dog
	expected_output_ids = [
	464,
	3290,
	373,
	1043,
	287,
	257,
	2214,
	1474,
	262,
	16246,
	286,
	2688,
	290,
	2688,
	27262,
	13,
	198,
	198,
	464,
	3290,
	] # The dog was found in a field near the intersection of West and West Streets.\n\nThe dog
	output_ids = model.generate(input_ids, do_sample=False)
	self.assertListEqual(output_ids[0].tolist(), expected_output_ids)

	@slow
	def test_gpt2_sample(self):
	tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
	model = GPT2LMHeadModel.from_pretrained("gpt2")
	model.to(torch_device)

	torch.manual_seed(0)
	tokenized = tokenizer("Today is a nice day and", return_tensors="pt", return_token_type_ids=True)
	input_ids = tokenized.input_ids.to(torch_device)
	output_ids = model.generate(input_ids, do_sample=True)
	output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)

	token_type_ids = tokenized.token_type_ids.to(torch_device)
	output_seq = model.generate(input_ids=input_ids, do_sample=True, num_return_sequences=5)
	output_seq_tt = model.generate(
	input_ids=input_ids, token_type_ids=token_type_ids, do_sample=True, num_return_sequences=5
	)
	output_seq_strs = tokenizer.batch_decode(output_seq, skip_special_tokens=True)
	output_seq_tt_strs = tokenizer.batch_decode(output_seq_tt, skip_special_tokens=True)

	EXPECTED_OUTPUT_STR = (
	"Today is a nice day and if you don't know anything about the state of play during your holiday"
	)
	self.assertEqual(output_str, EXPECTED_OUTPUT_STR)
	self.assertTrue(
	all([output_seq_strs[idx] != output_seq_tt_strs[idx] for idx in range(len(output_seq_tt_strs))])
	) # token_type_ids should change output

	@slow
	def test_gpt2_sample_max_time(self):
	tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
	model = GPT2LMHeadModel.from_pretrained("gpt2")
	model.to(torch_device)

	torch.manual_seed(0)
	tokenized = tokenizer("Today is a nice day and", return_tensors="pt", return_token_type_ids=True)
	input_ids = tokenized.input_ids.to(torch_device)

	MAX_TIME = 0.5

	start = datetime.datetime.now()
	model.generate(input_ids, do_sample=True, max_time=MAX_TIME, max_length=256)
	duration = datetime.datetime.now() - start
	self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
	self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))

	start = datetime.datetime.now()
	model.generate(input_ids, do_sample=False, max_time=MAX_TIME, max_length=256)
	duration = datetime.datetime.now() - start
	self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
	self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))

	start = datetime.datetime.now()
	model.generate(input_ids, do_sample=False, num_beams=2, max_time=MAX_TIME, max_length=256)
	duration = datetime.datetime.now() - start
	self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
	self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))

	start = datetime.datetime.now()
	model.generate(input_ids, do_sample=True, num_beams=2, max_time=MAX_TIME, max_length=256)
	duration = datetime.datetime.now() - start
	self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
	self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))

	start = datetime.datetime.now()
	model.generate(input_ids, do_sample=False, max_time=None, max_length=256)
	duration = datetime.datetime.now() - start
	self.assertGreater(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))

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