308 lines
11 KiB
Python
308 lines
11 KiB
Python
# coding=utf-8
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# Copyright 2020 Huggingface
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import tempfile
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import unittest
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from transformers import DPRConfig, is_torch_available
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from transformers.testing_utils import require_torch, slow, torch_device
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
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class DPRModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=False,
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use_input_mask=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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scope=None,
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projection_dim=0,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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self.projection_dim = projection_dim
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config()
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self):
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return DPRConfig(
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projection_dim=self.projection_dim,
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range,
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)
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def create_and_check_context_encoder(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = DPRContextEncoder(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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result = model(input_ids, token_type_ids=token_type_ids)
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result = model(input_ids)
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size))
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def create_and_check_question_encoder(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = DPRQuestionEncoder(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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result = model(input_ids, token_type_ids=token_type_ids)
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result = model(input_ids)
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size))
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def create_and_check_reader(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = DPRReader(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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)
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self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
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self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
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self.parent.assertEqual(result.relevance_logits.shape, (self.batch_size,))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids}
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return config, inputs_dict
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@require_torch
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class DPRModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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DPRContextEncoder,
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DPRQuestionEncoder,
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DPRReader,
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)
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if is_torch_available()
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else ()
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)
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pipeline_model_mapping = {"feature-extraction": DPRQuestionEncoder} if is_torch_available() else {}
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test_resize_embeddings = False
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test_missing_keys = False # why?
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test_pruning = False
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test_head_masking = False
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def setUp(self):
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self.model_tester = DPRModelTester(self)
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self.config_tester = ConfigTester(self, config_class=DPRConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_context_encoder_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_context_encoder(*config_and_inputs)
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def test_question_encoder_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_question_encoder(*config_and_inputs)
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def test_reader_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_reader(*config_and_inputs)
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def test_init_changed_config(self):
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config = self.model_tester.prepare_config_and_inputs()[0]
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model = DPRQuestionEncoder(config=config)
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model.to(torch_device)
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model.eval()
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with tempfile.TemporaryDirectory() as tmp_dirname:
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model.save_pretrained(tmp_dirname)
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model = DPRQuestionEncoder.from_pretrained(tmp_dirname, projection_dim=512)
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self.assertIsNotNone(model)
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@slow
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def test_model_from_pretrained(self):
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model_name = "facebook/dpr-ctx_encoder-single-nq-base"
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model = DPRContextEncoder.from_pretrained(model_name)
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self.assertIsNotNone(model)
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model_name = "facebook/dpr-ctx_encoder-single-nq-base"
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model = DPRContextEncoder.from_pretrained(model_name)
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self.assertIsNotNone(model)
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model_name = "facebook/dpr-ctx_encoder-single-nq-base"
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model = DPRQuestionEncoder.from_pretrained(model_name)
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self.assertIsNotNone(model)
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model_name = "facebook/dpr-ctx_encoder-single-nq-base"
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model = DPRReader.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@require_torch
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class DPRModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_no_head(self):
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model = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base", return_dict=False)
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model.to(torch_device)
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input_ids = torch.tensor(
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[[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]], dtype=torch.long, device=torch_device
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) # [CLS] hello, is my dog cute? [SEP]
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output = model(input_ids)[0] # embedding shape = (1, 768)
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# compare the actual values for a slice.
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expected_slice = torch.tensor(
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[
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[
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0.03236253,
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0.12753335,
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0.16818509,
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0.00279786,
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0.3896933,
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0.24264945,
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0.2178971,
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-0.02335227,
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-0.08481959,
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-0.14324117,
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]
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],
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dtype=torch.float,
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device=torch_device,
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)
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self.assertTrue(torch.allclose(output[:, :10], expected_slice, atol=1e-4))
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@slow
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def test_reader_inference(self):
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tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base")
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model = DPRReader.from_pretrained("facebook/dpr-reader-single-nq-base")
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model.to(torch_device)
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encoded_inputs = tokenizer(
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questions="What is love ?",
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titles="Haddaway",
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texts="What Is Love is a song recorded by the artist Haddaway",
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padding=True,
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return_tensors="pt",
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)
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encoded_inputs.to(torch_device)
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outputs = model(**encoded_inputs)
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# compare the actual values for a slice.
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expected_start_logits = torch.tensor(
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[[-10.3005, -10.7765, -11.4872, -11.6841, -11.9312, -10.3002, -9.8544, -11.7378, -12.0821, -10.2975]],
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dtype=torch.float,
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device=torch_device,
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)
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expected_end_logits = torch.tensor(
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[[-11.0684, -11.7041, -11.5397, -10.3465, -10.8791, -6.8443, -11.9959, -11.0364, -10.0096, -6.8405]],
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dtype=torch.float,
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device=torch_device,
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)
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self.assertTrue(torch.allclose(outputs.start_logits[:, :10], expected_start_logits, atol=1e-4))
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self.assertTrue(torch.allclose(outputs.end_logits[:, :10], expected_end_logits, atol=1e-4))
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