test_modeling_dpr.py
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test_modeling_dpr.py
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	# coding=utf-8
	# Copyright 2020 Huggingface
	#
	# 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_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


	if is_torch_available():
	import torch

	from transformers import DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
	from transformers.models.dpr.modeling_dpr import (
	DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
	DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
	DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
	)


	class DPRModelTester:
	def __init__(
	self,
	parent,
	batch_size=13,
	seq_length=7,
	is_training=False,
	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,
	num_labels=3,
	num_choices=4,
	scope=None,
	projection_dim=0,
	):
	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.scope = scope
	self.projection_dim = projection_dim

	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 = 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)

	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 = DPRConfig(
	projection_dim=self.projection_dim,
	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,
	)

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

	def create_and_check_context_encoder(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = DPRContextEncoder(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.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size))

	def create_and_check_question_encoder(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = DPRQuestionEncoder(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.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size))

	def create_and_check_reader(
	self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	):
	model = DPRReader(config=config)
	model.to(torch_device)
	model.eval()
	result = model(
	input_ids,
	attention_mask=input_mask,
	)

	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))
	self.parent.assertEqual(result.relevance_logits.shape, (self.batch_size,))

	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}
	return config, inputs_dict


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

	all_model_classes = (
	(
	DPRContextEncoder,
	DPRQuestionEncoder,
	DPRReader,
	)
	if is_torch_available()
	else ()
	)

	test_resize_embeddings = False
	test_missing_keys = False # why?
	test_pruning = False
	test_head_masking = False

	def setUp(self):
	self.model_tester = DPRModelTester(self)
	self.config_tester = ConfigTester(self, config_class=DPRConfig, hidden_size=37)

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

	def test_context_encoder_model(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_context_encoder(*config_and_inputs)

	def test_question_encoder_model(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_question_encoder(*config_and_inputs)

	def test_reader_model(self):
	config_and_inputs = self.model_tester.prepare_config_and_inputs()
	self.model_tester.create_and_check_reader(*config_and_inputs)

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

	for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
	model = DPRContextEncoder.from_pretrained(model_name)
	self.assertIsNotNone(model)

	for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
	model = DPRQuestionEncoder.from_pretrained(model_name)
	self.assertIsNotNone(model)

	for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
	model = DPRReader.from_pretrained(model_name)
	self.assertIsNotNone(model)


	@require_torch
	class DPRModelIntegrationTest(unittest.TestCase):
	@slow
	def test_inference_no_head(self):
	model = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base", return_dict=False)
	model.to(torch_device)

	input_ids = torch.tensor(
	[[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]], dtype=torch.long, device=torch_device
	) # [CLS] hello, is my dog cute? [SEP]
	output = model(input_ids)[0] # embedding shape = (1, 768)
	# compare the actual values for a slice.
	expected_slice = torch.tensor(
	[
	[
	0.03236253,
	0.12753335,
	0.16818509,
	0.00279786,
	0.3896933,
	0.24264945,
	0.2178971,
	-0.02335227,
	-0.08481959,
	-0.14324117,
	]
	],
	dtype=torch.float,
	device=torch_device,
	)
	self.assertTrue(torch.allclose(output[:, :10], expected_slice, atol=1e-4))

	@slow
	def test_reader_inference(self):
	tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base")
	model = DPRReader.from_pretrained("facebook/dpr-reader-single-nq-base")
	model.to(torch_device)

	encoded_inputs = tokenizer(
	questions="What is love ?",
	titles="Haddaway",
	texts="What Is Love is a song recorded by the artist Haddaway",
	padding=True,
	return_tensors="pt",
	)
	encoded_inputs.to(torch_device)

	outputs = model(**encoded_inputs)

	# compare the actual values for a slice.
	expected_start_logits = torch.tensor(
	[[-10.3005, -10.7765, -11.4872, -11.6841, -11.9312, -10.3002, -9.8544, -11.7378, -12.0821, -10.2975]],
	dtype=torch.float,
	device=torch_device,
	)

	expected_end_logits = torch.tensor(
	[[-11.0684, -11.7041, -11.5397, -10.3465, -10.8791, -6.8443, -11.9959, -11.0364, -10.0096, -6.8405]],
	dtype=torch.float,
	device=torch_device,
	)
	self.assertTrue(torch.allclose(outputs.start_logits[:, :10], expected_start_logits, atol=1e-4))
	self.assertTrue(torch.allclose(outputs.end_logits[:, :10], expected_end_logits, atol=1e-4))

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