test_modeling_openai.py
No OneTemporary
Actions

Subscribers

None

File Metadata

Created: Sun, Jul 6, 21:22

test_modeling_openai.py
View Options

	# 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


	if is_torch_available():
	import torch

	from transformers import (
	OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
	OpenAIGPTConfig,
	OpenAIGPTDoubleHeadsModel,
	OpenAIGPTForSequenceClassification,
	OpenAIGPTLMHeadModel,
	OpenAIGPTModel,
	)


	class OpenAIGPTModelTester:
	def __init__(
	self,
	parent,
	):
	self.parent = parent
	self.batch_size = 13
	self.seq_length = 7
	self.is_training = 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
	self.pad_token_id = self.vocab_size - 1

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

	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 = OpenAIGPTConfig(
	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
	pad_token_id=self.pad_token_id,
	)

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

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

	def create_and_check_openai_gpt_model(self, config, input_ids, head_mask, token_type_ids, *args):
	model = OpenAIGPTModel(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))

	def create_and_check_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
	model = OpenAIGPTLMHeadModel(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_double_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
	model = OpenAIGPTDoubleHeadsModel(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_openai_gpt_for_sequence_classification(
	self, config, input_ids, head_mask, token_type_ids, *args
	):
	config.num_labels = self.num_labels
	model = OpenAIGPTForSequenceClassification(config)
	model.to(torch_device)
	model.eval()
	# print(config.num_labels, sequence_labels.size())
	sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
	result = model(input_ids, 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,
	head_mask,
	token_type_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 OpenAIGPTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):

	all_model_classes = (
	(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
	if is_torch_available()
	else ()
	)
	all_generative_model_classes = (
	(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
	) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly

	# 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__ == "OpenAIGPTDoubleHeadsModel":
	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 = OpenAIGPTModelTester(self)
	self.config_tester = ConfigTester(self, config_class=OpenAIGPTConfig, n_embd=37)

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

	def test_openai_gpt_model(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_openai_gpt_model(*config_and_inputs)

	def test_openai_gpt_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_openai_gpt_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_openai_gpt_classification_model(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*config_and_inputs)

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


	@require_torch
	class OPENAIGPTModelLanguageGenerationTest(unittest.TestCase):
	@slow
	def test_lm_generate_openai_gpt(self):
	model = OpenAIGPTLMHeadModel.from_pretrained("openai-gpt")
	model.to(torch_device)
	input_ids = torch.tensor([[481, 4735, 544]], dtype=torch.long, device=torch_device) # the president is
	expected_output_ids = [
	481,
	4735,
	544,
	246,
	963,
	870,
	762,
	239,
	244,
	40477,
	244,
	249,
	719,
	881,
	487,
	544,
	240,
	244,
	603,
	481,
	] # the president is a very good man. " \n " i\'m sure he is, " said the

	output_ids = model.generate(input_ids, do_sample=False)
	self.assertListEqual(output_ids[0].tolist(), expected_output_ids)

test_modeling_openai.pyNo OneTemporaryActions

File Metadata

test_modeling_openai.pyView Options

Event Timeline

test_modeling_openai.py
No OneTemporary
Actions

test_modeling_openai.py
View Options