188 lines
6.8 KiB
Python
188 lines
6.8 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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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 os
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import shutil
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import tempfile
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from unittest import TestCase
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from unittest.mock import patch
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import numpy as np
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from datasets import Dataset
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from transformers.models.realm.configuration_realm import RealmConfig
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from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
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from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
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class RealmRetrieverTest(TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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self.num_block_records = 5
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# Realm tok
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vocab_tokens = [
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"[UNK]",
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"[CLS]",
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"[SEP]",
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"[PAD]",
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"[MASK]",
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"test",
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"question",
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"this",
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"is",
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"the",
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"first",
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"second",
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"third",
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"fourth",
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"fifth",
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"record",
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"want",
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"##want",
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"##ed",
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"wa",
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"un",
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"runn",
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"##ing",
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",",
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"low",
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"lowest",
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]
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realm_tokenizer_path = os.path.join(self.tmpdirname, "realm_tokenizer")
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os.makedirs(realm_tokenizer_path, exist_ok=True)
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self.vocab_file = os.path.join(realm_tokenizer_path, VOCAB_FILES_NAMES["vocab_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
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vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
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realm_block_records_path = os.path.join(self.tmpdirname, "realm_block_records")
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os.makedirs(realm_block_records_path, exist_ok=True)
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def get_tokenizer(self) -> RealmTokenizer:
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return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname, "realm_tokenizer"))
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def get_config(self):
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config = RealmConfig(num_block_records=self.num_block_records)
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return config
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def get_dummy_dataset(self):
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dataset = Dataset.from_dict(
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{
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"id": ["0", "1"],
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"question": ["foo", "bar"],
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"answers": [["Foo", "Bar"], ["Bar"]],
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}
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)
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return dataset
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def get_dummy_block_records(self):
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block_records = np.array(
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[
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b"This is the first record",
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b"This is the second record",
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b"This is the third record",
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b"This is the fourth record",
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b"This is the fifth record",
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b"This is a longer longer longer record",
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],
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dtype=object,
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)
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return block_records
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def get_dummy_retriever(self):
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retriever = RealmRetriever(
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block_records=self.get_dummy_block_records(),
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tokenizer=self.get_tokenizer(),
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)
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return retriever
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def test_retrieve(self):
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config = self.get_config()
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retriever = self.get_dummy_retriever()
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tokenizer = retriever.tokenizer
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retrieved_block_ids = np.array([0, 3], dtype="long")
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question_input_ids = tokenizer(["Test question"]).input_ids
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answer_ids = tokenizer(
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["the fourth"],
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add_special_tokens=False,
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return_token_type_ids=False,
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return_attention_mask=False,
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).input_ids
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max_length = config.reader_seq_len
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has_answers, start_pos, end_pos, concat_inputs = retriever(
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retrieved_block_ids, question_input_ids, answer_ids=answer_ids, max_length=max_length, return_tensors="np"
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)
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self.assertEqual(len(has_answers), 2)
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self.assertEqual(len(start_pos), 2)
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self.assertEqual(len(end_pos), 2)
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self.assertEqual(concat_inputs.input_ids.shape, (2, 10))
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self.assertEqual(concat_inputs.attention_mask.shape, (2, 10))
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self.assertEqual(concat_inputs.token_type_ids.shape, (2, 10))
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self.assertEqual(concat_inputs.special_tokens_mask.shape, (2, 10))
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self.assertEqual(
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tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]),
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["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "first", "record", "[SEP]"],
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)
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self.assertEqual(
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tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]),
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["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "fourth", "record", "[SEP]"],
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)
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def test_block_has_answer(self):
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config = self.get_config()
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retriever = self.get_dummy_retriever()
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tokenizer = retriever.tokenizer
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retrieved_block_ids = np.array([0, 3, 5], dtype="long")
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question_input_ids = tokenizer(["Test question"]).input_ids
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answer_ids = tokenizer(
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["the fourth", "longer longer"],
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add_special_tokens=False,
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return_token_type_ids=False,
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return_attention_mask=False,
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).input_ids
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max_length = config.reader_seq_len
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has_answers, start_pos, end_pos, _ = retriever(
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retrieved_block_ids, question_input_ids, answer_ids=answer_ids, max_length=max_length, return_tensors="np"
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)
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self.assertEqual([False, True, True], has_answers)
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self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]], start_pos)
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self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]], end_pos)
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def test_save_load_pretrained(self):
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retriever = self.get_dummy_retriever()
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retriever.save_pretrained(os.path.join(self.tmpdirname, "realm_block_records"))
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# Test local path
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retriever = retriever.from_pretrained(os.path.join(self.tmpdirname, "realm_block_records"))
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self.assertEqual(retriever.block_records[0], b"This is the first record")
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# Test mocked remote path
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with patch("transformers.models.realm.retrieval_realm.hf_hub_download") as mock_hf_hub_download:
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mock_hf_hub_download.return_value = os.path.join(
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os.path.join(self.tmpdirname, "realm_block_records"), _REALM_BLOCK_RECORDS_FILENAME
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)
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retriever = RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa")
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self.assertEqual(retriever.block_records[0], b"This is the first record")
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