test_modeling_dpr.py

# 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 DPRConfig, 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 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 = self.get_config()

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

    def get_config(self):
        return 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,
        )

    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))