[Test refactor 1/5] Per-folder tests reorganization (#15725)
* Per-folder tests reorganization Co-authored-by: sgugger <sylvain.gugger@gmail.com> Co-authored-by: Stas Bekman <stas@stason.org>
This commit is contained in:
Lysandre Debut
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tests/trainer/test_data_collator.py
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864
tests/trainer/test_data_collator.py
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# Copyright 2020 The HuggingFace 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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import unittest
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import numpy as np
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from transformers import (
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BertTokenizer,
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DataCollatorForLanguageModeling,
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DataCollatorForPermutationLanguageModeling,
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DataCollatorForTokenClassification,
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DataCollatorForWholeWordMask,
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DataCollatorWithPadding,
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default_data_collator,
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is_tf_available,
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is_torch_available,
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set_seed,
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)
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from transformers.testing_utils import require_tf, require_torch
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if is_torch_available():
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import torch
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if is_tf_available():
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import tensorflow as tf
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@require_torch
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class DataCollatorIntegrationTest(unittest.TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]
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self.vocab_file = os.path.join(self.tmpdirname, "vocab.txt")
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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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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def test_default_with_dict(self):
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features = [{"label": i, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
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batch = default_data_collator(features)
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self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
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self.assertEqual(batch["labels"].dtype, torch.long)
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self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
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# With label_ids
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features = [{"label_ids": [0, 1, 2], "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
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batch = default_data_collator(features)
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self.assertTrue(batch["labels"].equal(torch.tensor([[0, 1, 2]] * 8)))
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self.assertEqual(batch["labels"].dtype, torch.long)
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self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
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# Features can already be tensors
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features = [{"label": i, "inputs": np.random.randint(0, 10, [10])} for i in range(8)]
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batch = default_data_collator(features)
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self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
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self.assertEqual(batch["labels"].dtype, torch.long)
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self.assertEqual(batch["inputs"].shape, torch.Size([8, 10]))
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# Labels can already be tensors
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features = [{"label": torch.tensor(i), "inputs": np.random.randint(0, 10, [10])} for i in range(8)]
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batch = default_data_collator(features)
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self.assertEqual(batch["labels"].dtype, torch.long)
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self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
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self.assertEqual(batch["labels"].dtype, torch.long)
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self.assertEqual(batch["inputs"].shape, torch.Size([8, 10]))
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def test_default_classification_and_regression(self):
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data_collator = default_data_collator
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features = [{"input_ids": [0, 1, 2, 3, 4], "label": i} for i in range(4)]
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batch = data_collator(features)
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self.assertEqual(batch["labels"].dtype, torch.long)
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features = [{"input_ids": [0, 1, 2, 3, 4], "label": float(i)} for i in range(4)]
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batch = data_collator(features)
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self.assertEqual(batch["labels"].dtype, torch.float)
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def test_default_with_no_labels(self):
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features = [{"label": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
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batch = default_data_collator(features)
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self.assertTrue("labels" not in batch)
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self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
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# With label_ids
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features = [{"label_ids": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
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batch = default_data_collator(features)
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self.assertTrue("labels" not in batch)
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self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
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def test_data_collator_with_padding(self):
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tokenizer = BertTokenizer(self.vocab_file)
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features = [{"input_ids": [0, 1, 2]}, {"input_ids": [0, 1, 2, 3, 4, 5]}]
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data_collator = DataCollatorWithPadding(tokenizer)
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batch = data_collator(features)
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self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
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self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
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data_collator = DataCollatorWithPadding(tokenizer, padding="max_length", max_length=10)
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batch = data_collator(features)
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self.assertEqual(batch["input_ids"].shape, torch.Size([2, 10]))
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data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
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batch = data_collator(features)
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self.assertEqual(batch["input_ids"].shape, torch.Size([2, 8]))
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def test_data_collator_for_token_classification(self):
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tokenizer = BertTokenizer(self.vocab_file)
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features = [
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{"input_ids": [0, 1, 2], "labels": [0, 1, 2]},
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{"input_ids": [0, 1, 2, 3, 4, 5], "labels": [0, 1, 2, 3, 4, 5]},
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]
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data_collator = DataCollatorForTokenClassification(tokenizer)
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batch = data_collator(features)
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self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
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self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
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self.assertEqual(batch["labels"].shape, torch.Size([2, 6]))
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self.assertEqual(batch["labels"][0].tolist(), [0, 1, 2] + [-100] * 3)
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data_collator = DataCollatorForTokenClassification(tokenizer, padding="max_length", max_length=10)
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batch = data_collator(features)
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self.assertEqual(batch["input_ids"].shape, torch.Size([2, 10]))
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self.assertEqual(batch["labels"].shape, torch.Size([2, 10]))
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data_collator = DataCollatorForTokenClassification(tokenizer, pad_to_multiple_of=8)
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batch = data_collator(features)
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self.assertEqual(batch["input_ids"].shape, torch.Size([2, 8]))
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self.assertEqual(batch["labels"].shape, torch.Size([2, 8]))
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data_collator = DataCollatorForTokenClassification(tokenizer, label_pad_token_id=-1)
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batch = data_collator(features)
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self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
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self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
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self.assertEqual(batch["labels"].shape, torch.Size([2, 6]))
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self.assertEqual(batch["labels"][0].tolist(), [0, 1, 2] + [-1] * 3)
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def _test_no_pad_and_pad(self, no_pad_features, pad_features):
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tokenizer = BertTokenizer(self.vocab_file)
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data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
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batch = data_collator(no_pad_features)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
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batch = data_collator(pad_features)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
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data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False, pad_to_multiple_of=8)
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batch = data_collator(no_pad_features)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 16)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
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batch = data_collator(pad_features)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 16)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
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tokenizer._pad_token = None
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data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
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with self.assertRaises(ValueError):
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# Expect error due to padding token missing
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data_collator(pad_features)
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set_seed(42) # For reproducibility
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tokenizer = BertTokenizer(self.vocab_file)
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data_collator = DataCollatorForLanguageModeling(tokenizer)
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batch = data_collator(no_pad_features)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
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masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
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self.assertTrue(torch.any(masked_tokens))
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self.assertTrue(all(x == -100 for x in batch["labels"][~masked_tokens].tolist()))
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batch = data_collator(pad_features)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
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masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
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self.assertTrue(torch.any(masked_tokens))
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self.assertTrue(all(x == -100 for x in batch["labels"][~masked_tokens].tolist()))
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data_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8)
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batch = data_collator(no_pad_features)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 16)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
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masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
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self.assertTrue(torch.any(masked_tokens))
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self.assertTrue(all(x == -100 for x in batch["labels"][~masked_tokens].tolist()))
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batch = data_collator(pad_features)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 16)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
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masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
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self.assertTrue(torch.any(masked_tokens))
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self.assertTrue(all(x == -100 for x in batch["labels"][~masked_tokens].tolist()))
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def test_data_collator_for_language_modeling(self):
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no_pad_features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
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pad_features = [{"input_ids": list(range(5))}, {"input_ids": list(range(10))}]
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self._test_no_pad_and_pad(no_pad_features, pad_features)
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no_pad_features = [list(range(10)), list(range(10))]
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pad_features = [list(range(5)), list(range(10))]
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self._test_no_pad_and_pad(no_pad_features, pad_features)
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def test_data_collator_for_whole_word_mask(self):
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features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
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tokenizer = BertTokenizer(self.vocab_file)
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data_collator = DataCollatorForWholeWordMask(tokenizer, return_tensors="pt")
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batch = data_collator(features)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
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def test_plm(self):
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tokenizer = BertTokenizer(self.vocab_file)
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no_pad_features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
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pad_features = [{"input_ids": list(range(5))}, {"input_ids": list(range(10))}]
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data_collator = DataCollatorForPermutationLanguageModeling(tokenizer)
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batch = data_collator(pad_features)
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self.assertIsInstance(batch, dict)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
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self.assertEqual(batch["perm_mask"].shape, torch.Size((2, 10, 10)))
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self.assertEqual(batch["target_mapping"].shape, torch.Size((2, 10, 10)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
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batch = data_collator(no_pad_features)
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self.assertIsInstance(batch, dict)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
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self.assertEqual(batch["perm_mask"].shape, torch.Size((2, 10, 10)))
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self.assertEqual(batch["target_mapping"].shape, torch.Size((2, 10, 10)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
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example = [np.random.randint(0, 5, [5])]
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with self.assertRaises(ValueError):
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# Expect error due to odd sequence length
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data_collator(example)
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def test_nsp(self):
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tokenizer = BertTokenizer(self.vocab_file)
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features = [
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{"input_ids": [0, 1, 2, 3, 4], "token_type_ids": [0, 1, 2, 3, 4], "next_sentence_label": i}
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for i in range(2)
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]
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data_collator = DataCollatorForLanguageModeling(tokenizer)
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batch = data_collator(features)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 5)))
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self.assertEqual(batch["token_type_ids"].shape, torch.Size((2, 5)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 5)))
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self.assertEqual(batch["next_sentence_label"].shape, torch.Size((2,)))
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data_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8)
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batch = data_collator(features)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 8)))
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self.assertEqual(batch["token_type_ids"].shape, torch.Size((2, 8)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 8)))
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self.assertEqual(batch["next_sentence_label"].shape, torch.Size((2,)))
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def test_sop(self):
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tokenizer = BertTokenizer(self.vocab_file)
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features = [
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{
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"input_ids": torch.tensor([0, 1, 2, 3, 4]),
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"token_type_ids": torch.tensor([0, 1, 2, 3, 4]),
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"sentence_order_label": i,
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}
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for i in range(2)
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]
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data_collator = DataCollatorForLanguageModeling(tokenizer)
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batch = data_collator(features)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 5)))
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self.assertEqual(batch["token_type_ids"].shape, torch.Size((2, 5)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 5)))
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self.assertEqual(batch["sentence_order_label"].shape, torch.Size((2,)))
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data_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8)
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batch = data_collator(features)
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self.assertEqual(batch["input_ids"].shape, torch.Size((2, 8)))
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self.assertEqual(batch["token_type_ids"].shape, torch.Size((2, 8)))
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self.assertEqual(batch["labels"].shape, torch.Size((2, 8)))
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self.assertEqual(batch["sentence_order_label"].shape, torch.Size((2,)))
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@require_tf
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class TFDataCollatorIntegrationTest(unittest.TestCase):
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def setUp(self):
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super().setUp()
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self.tmpdirname = tempfile.mkdtemp()
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vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]
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self.vocab_file = os.path.join(self.tmpdirname, "vocab.txt")
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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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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def test_default_with_dict(self):
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features = [{"label": i, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
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batch = default_data_collator(features, return_tensors="tf")
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self.assertEqual(batch["labels"].numpy().tolist(), list(range(8)))
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self.assertEqual(batch["labels"].dtype, tf.int64)
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self.assertEqual(batch["inputs"].shape.as_list(), [8, 6])
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# With label_ids
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features = [{"label_ids": [0, 1, 2], "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
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batch = default_data_collator(features, return_tensors="tf")
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self.assertEqual(batch["labels"].numpy().tolist(), ([[0, 1, 2]] * 8))
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self.assertEqual(batch["labels"].dtype, tf.int64)
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self.assertEqual(batch["inputs"].shape.as_list(), [8, 6])
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# Features can already be tensors
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features = [{"label": i, "inputs": np.random.randint(0, 10, [10])} for i in range(8)]
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batch = default_data_collator(features, return_tensors="tf")
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self.assertEqual(batch["labels"].numpy().tolist(), (list(range(8))))
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self.assertEqual(batch["labels"].dtype, tf.int64)
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self.assertEqual(batch["inputs"].shape.as_list(), [8, 10])
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# Labels can already be tensors
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features = [{"label": np.array(i), "inputs": np.random.randint(0, 10, [10])} for i in range(8)]
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batch = default_data_collator(features, return_tensors="tf")
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self.assertEqual(batch["labels"].dtype, tf.int64)
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self.assertEqual(batch["labels"].numpy().tolist(), list(range(8)))
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self.assertEqual(batch["labels"].dtype, tf.int64)
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self.assertEqual(batch["inputs"].shape.as_list(), [8, 10])
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def test_numpy_dtype_preservation(self):
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data_collator = default_data_collator
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# Confirms that numpy inputs are handled correctly even when scalars
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features = [{"input_ids": np.array([0, 1, 2, 3, 4]), "label": np.int64(i)} for i in range(4)]
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batch = data_collator(features, return_tensors="tf")
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self.assertEqual(batch["labels"].dtype, tf.int64)
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def test_default_classification_and_regression(self):
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data_collator = default_data_collator
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features = [{"input_ids": [0, 1, 2, 3, 4], "label": i} for i in range(4)]
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batch = data_collator(features, return_tensors="tf")
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||||
self.assertEqual(batch["labels"].dtype, tf.int64)
|
||||
|
||||
features = [{"input_ids": [0, 1, 2, 3, 4], "label": float(i)} for i in range(4)]
|
||||
batch = data_collator(features, return_tensors="tf")
|
||||
self.assertEqual(batch["labels"].dtype, tf.float32)
|
||||
|
||||
def test_default_with_no_labels(self):
|
||||
features = [{"label": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
|
||||
batch = default_data_collator(features, return_tensors="tf")
|
||||
self.assertTrue("labels" not in batch)
|
||||
self.assertEqual(batch["inputs"].shape.as_list(), [8, 6])
|
||||
|
||||
# With label_ids
|
||||
features = [{"label_ids": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
|
||||
batch = default_data_collator(features, return_tensors="tf")
|
||||
self.assertTrue("labels" not in batch)
|
||||
self.assertEqual(batch["inputs"].shape.as_list(), [8, 6])
|
||||
|
||||
def test_data_collator_with_padding(self):
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
features = [{"input_ids": [0, 1, 2]}, {"input_ids": [0, 1, 2, 3, 4, 5]}]
|
||||
|
||||
data_collator = DataCollatorWithPadding(tokenizer, return_tensors="tf")
|
||||
batch = data_collator(features)
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 6])
|
||||
self.assertEqual(batch["input_ids"][0].numpy().tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
|
||||
|
||||
data_collator = DataCollatorWithPadding(tokenizer, padding="max_length", max_length=10, return_tensors="tf")
|
||||
batch = data_collator(features)
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
|
||||
|
||||
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8, return_tensors="tf")
|
||||
batch = data_collator(features)
|
||||
self.assertEqual(batch["input_ids"].shape, [2, 8])
|
||||
|
||||
def test_data_collator_for_token_classification(self):
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
features = [
|
||||
{"input_ids": [0, 1, 2], "labels": [0, 1, 2]},
|
||||
{"input_ids": [0, 1, 2, 3, 4, 5], "labels": [0, 1, 2, 3, 4, 5]},
|
||||
]
|
||||
|
||||
data_collator = DataCollatorForTokenClassification(tokenizer, return_tensors="tf")
|
||||
batch = data_collator(features)
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 6])
|
||||
self.assertEqual(batch["input_ids"][0].numpy().tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 6])
|
||||
self.assertEqual(batch["labels"][0].numpy().tolist(), [0, 1, 2] + [-100] * 3)
|
||||
|
||||
data_collator = DataCollatorForTokenClassification(
|
||||
tokenizer, padding="max_length", max_length=10, return_tensors="tf"
|
||||
)
|
||||
batch = data_collator(features)
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 10])
|
||||
|
||||
data_collator = DataCollatorForTokenClassification(tokenizer, pad_to_multiple_of=8, return_tensors="tf")
|
||||
batch = data_collator(features)
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 8])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 8])
|
||||
|
||||
data_collator = DataCollatorForTokenClassification(tokenizer, label_pad_token_id=-1, return_tensors="tf")
|
||||
batch = data_collator(features)
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 6])
|
||||
self.assertEqual(batch["input_ids"][0].numpy().tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 6])
|
||||
self.assertEqual(batch["labels"][0].numpy().tolist(), [0, 1, 2] + [-1] * 3)
|
||||
|
||||
def _test_no_pad_and_pad(self, no_pad_features, pad_features):
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False, return_tensors="tf")
|
||||
batch = data_collator(no_pad_features)
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 10])
|
||||
|
||||
batch = data_collator(pad_features)
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 10])
|
||||
|
||||
data_collator = DataCollatorForLanguageModeling(
|
||||
tokenizer, mlm=False, pad_to_multiple_of=8, return_tensors="tf"
|
||||
)
|
||||
batch = data_collator(no_pad_features)
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 16])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 16])
|
||||
|
||||
batch = data_collator(pad_features)
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 16])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 16])
|
||||
|
||||
tokenizer._pad_token = None
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False, return_tensors="tf")
|
||||
with self.assertRaises(ValueError):
|
||||
# Expect error due to padding token missing
|
||||
data_collator(pad_features)
|
||||
|
||||
set_seed(42) # For reproducibility
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, return_tensors="tf")
|
||||
batch = data_collator(no_pad_features)
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 10])
|
||||
|
||||
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
|
||||
self.assertTrue(tf.reduce_any(masked_tokens))
|
||||
# self.assertTrue(all(x == -100 for x in batch["labels"].numpy()[~masked_tokens.numpy()].tolist()))
|
||||
|
||||
batch = data_collator(pad_features, return_tensors="tf")
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 10])
|
||||
|
||||
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
|
||||
self.assertTrue(tf.reduce_any(masked_tokens))
|
||||
# self.assertTrue(all(x == -100 for x in batch["labels"].numpy()[~masked_tokens.numpy()].tolist()))
|
||||
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8, return_tensors="tf")
|
||||
batch = data_collator(no_pad_features)
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 16])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 16])
|
||||
|
||||
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
|
||||
self.assertTrue(tf.reduce_any(masked_tokens))
|
||||
# self.assertTrue(all(x == -100 for x in batch["labels"].numpy()[~masked_tokens.numpy()].tolist()))
|
||||
|
||||
batch = data_collator(pad_features, return_tensors="tf")
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 16])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 16])
|
||||
|
||||
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
|
||||
self.assertTrue(tf.reduce_any(masked_tokens))
|
||||
# self.assertTrue(all(x == -100 for x in batch["labels"].numpy()[~masked_tokens.numpy()].tolist()))
|
||||
|
||||
def test_data_collator_for_language_modeling(self):
|
||||
no_pad_features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
|
||||
pad_features = [{"input_ids": list(range(5))}, {"input_ids": list(range(10))}]
|
||||
self._test_no_pad_and_pad(no_pad_features, pad_features)
|
||||
|
||||
no_pad_features = [list(range(10)), list(range(10))]
|
||||
pad_features = [list(range(5)), list(range(10))]
|
||||
self._test_no_pad_and_pad(no_pad_features, pad_features)
|
||||
|
||||
def test_data_collator_for_whole_word_mask(self):
|
||||
features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
|
||||
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
data_collator = DataCollatorForWholeWordMask(tokenizer, return_tensors="tf")
|
||||
batch = data_collator(features)
|
||||
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 10])
|
||||
|
||||
def test_plm(self):
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
no_pad_features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
|
||||
pad_features = [{"input_ids": list(range(5))}, {"input_ids": list(range(10))}]
|
||||
|
||||
data_collator = DataCollatorForPermutationLanguageModeling(tokenizer, return_tensors="tf")
|
||||
|
||||
batch = data_collator(pad_features)
|
||||
self.assertIsInstance(batch, dict)
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
|
||||
self.assertEqual(batch["perm_mask"].shape.as_list(), [2, 10, 10])
|
||||
self.assertEqual(batch["target_mapping"].shape.as_list(), [2, 10, 10])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 10])
|
||||
|
||||
batch = data_collator(no_pad_features)
|
||||
self.assertIsInstance(batch, dict)
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
|
||||
self.assertEqual(batch["perm_mask"].shape.as_list(), [2, 10, 10])
|
||||
self.assertEqual(batch["target_mapping"].shape.as_list(), [2, 10, 10])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 10])
|
||||
|
||||
example = [np.random.randint(0, 5, [5])]
|
||||
with self.assertRaises(ValueError):
|
||||
# Expect error due to odd sequence length
|
||||
data_collator(example)
|
||||
|
||||
def test_nsp(self):
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
features = [
|
||||
{"input_ids": [0, 1, 2, 3, 4], "token_type_ids": [0, 1, 2, 3, 4], "next_sentence_label": i}
|
||||
for i in range(2)
|
||||
]
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, return_tensors="tf")
|
||||
batch = data_collator(features)
|
||||
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 5])
|
||||
self.assertEqual(batch["token_type_ids"].shape.as_list(), [2, 5])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 5])
|
||||
self.assertEqual(batch["next_sentence_label"].shape.as_list(), [2])
|
||||
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8, return_tensors="tf")
|
||||
batch = data_collator(features)
|
||||
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 8])
|
||||
self.assertEqual(batch["token_type_ids"].shape.as_list(), [2, 8])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 8])
|
||||
self.assertEqual(batch["next_sentence_label"].shape.as_list(), [2])
|
||||
|
||||
def test_sop(self):
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
features = [
|
||||
{
|
||||
"input_ids": tf.convert_to_tensor([0, 1, 2, 3, 4]),
|
||||
"token_type_ids": tf.convert_to_tensor([0, 1, 2, 3, 4]),
|
||||
"sentence_order_label": i,
|
||||
}
|
||||
for i in range(2)
|
||||
]
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, return_tensors="tf")
|
||||
batch = data_collator(features)
|
||||
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 5])
|
||||
self.assertEqual(batch["token_type_ids"].shape.as_list(), [2, 5])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 5])
|
||||
self.assertEqual(batch["sentence_order_label"].shape.as_list(), [2])
|
||||
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8, return_tensors="tf")
|
||||
batch = data_collator(features)
|
||||
|
||||
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 8])
|
||||
self.assertEqual(batch["token_type_ids"].shape.as_list(), [2, 8])
|
||||
self.assertEqual(batch["labels"].shape.as_list(), [2, 8])
|
||||
self.assertEqual(batch["sentence_order_label"].shape.as_list(), [2])
|
||||
|
||||
|
||||
class NumpyDataCollatorIntegrationTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.tmpdirname = tempfile.mkdtemp()
|
||||
|
||||
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]
|
||||
self.vocab_file = os.path.join(self.tmpdirname, "vocab.txt")
|
||||
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
|
||||
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
|
||||
|
||||
def tearDown(self):
|
||||
shutil.rmtree(self.tmpdirname)
|
||||
|
||||
def test_default_with_dict(self):
|
||||
features = [{"label": i, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
|
||||
batch = default_data_collator(features, return_tensors="np")
|
||||
self.assertEqual(batch["labels"].tolist(), list(range(8)))
|
||||
self.assertEqual(batch["labels"].dtype, np.int64)
|
||||
self.assertEqual(batch["inputs"].shape, (8, 6))
|
||||
|
||||
# With label_ids
|
||||
features = [{"label_ids": [0, 1, 2], "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
|
||||
batch = default_data_collator(features, return_tensors="np")
|
||||
self.assertEqual(batch["labels"].tolist(), [[0, 1, 2]] * 8)
|
||||
self.assertEqual(batch["labels"].dtype, np.int64)
|
||||
self.assertEqual(batch["inputs"].shape, (8, 6))
|
||||
|
||||
# Features can already be tensors
|
||||
features = [{"label": i, "inputs": np.random.randint(0, 10, [10])} for i in range(8)]
|
||||
batch = default_data_collator(features, return_tensors="np")
|
||||
self.assertEqual(batch["labels"].tolist(), list(range(8)))
|
||||
self.assertEqual(batch["labels"].dtype, np.int64)
|
||||
self.assertEqual(batch["inputs"].shape, (8, 10))
|
||||
|
||||
# Labels can already be tensors
|
||||
features = [{"label": np.array(i), "inputs": np.random.randint(0, 10, [10])} for i in range(8)]
|
||||
batch = default_data_collator(features, return_tensors="np")
|
||||
self.assertEqual(batch["labels"].dtype, np.int64)
|
||||
self.assertEqual(batch["labels"].tolist(), (list(range(8))))
|
||||
self.assertEqual(batch["labels"].dtype, np.int64)
|
||||
self.assertEqual(batch["inputs"].shape, (8, 10))
|
||||
|
||||
def test_default_classification_and_regression(self):
|
||||
data_collator = default_data_collator
|
||||
|
||||
features = [{"input_ids": [0, 1, 2, 3, 4], "label": i} for i in range(4)]
|
||||
batch = data_collator(features, return_tensors="np")
|
||||
self.assertEqual(batch["labels"].dtype, np.int64)
|
||||
|
||||
features = [{"input_ids": [0, 1, 2, 3, 4], "label": float(i)} for i in range(4)]
|
||||
batch = data_collator(features, return_tensors="np")
|
||||
self.assertEqual(batch["labels"].dtype, np.float32)
|
||||
|
||||
def test_default_with_no_labels(self):
|
||||
features = [{"label": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
|
||||
batch = default_data_collator(features, return_tensors="np")
|
||||
self.assertTrue("labels" not in batch)
|
||||
self.assertEqual(batch["inputs"].shape, (8, 6))
|
||||
|
||||
# With label_ids
|
||||
features = [{"label_ids": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)]
|
||||
batch = default_data_collator(features, return_tensors="np")
|
||||
self.assertTrue("labels" not in batch)
|
||||
self.assertEqual(batch["inputs"].shape, (8, 6))
|
||||
|
||||
def test_data_collator_with_padding(self):
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
features = [{"input_ids": [0, 1, 2]}, {"input_ids": [0, 1, 2, 3, 4, 5]}]
|
||||
|
||||
data_collator = DataCollatorWithPadding(tokenizer, return_tensors="np")
|
||||
batch = data_collator(features)
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 6))
|
||||
self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
|
||||
|
||||
data_collator = DataCollatorWithPadding(tokenizer, padding="max_length", max_length=10, return_tensors="np")
|
||||
batch = data_collator(features)
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 10))
|
||||
|
||||
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8, return_tensors="np")
|
||||
batch = data_collator(features)
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 8))
|
||||
|
||||
def test_data_collator_for_token_classification(self):
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
features = [
|
||||
{"input_ids": [0, 1, 2], "labels": [0, 1, 2]},
|
||||
{"input_ids": [0, 1, 2, 3, 4, 5], "labels": [0, 1, 2, 3, 4, 5]},
|
||||
]
|
||||
|
||||
data_collator = DataCollatorForTokenClassification(tokenizer, return_tensors="np")
|
||||
batch = data_collator(features)
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 6))
|
||||
self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
|
||||
self.assertEqual(batch["labels"].shape, (2, 6))
|
||||
self.assertEqual(batch["labels"][0].tolist(), [0, 1, 2] + [-100] * 3)
|
||||
|
||||
data_collator = DataCollatorForTokenClassification(
|
||||
tokenizer, padding="max_length", max_length=10, return_tensors="np"
|
||||
)
|
||||
batch = data_collator(features)
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 10))
|
||||
self.assertEqual(batch["labels"].shape, (2, 10))
|
||||
|
||||
data_collator = DataCollatorForTokenClassification(tokenizer, pad_to_multiple_of=8, return_tensors="np")
|
||||
batch = data_collator(features)
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 8))
|
||||
self.assertEqual(batch["labels"].shape, (2, 8))
|
||||
|
||||
data_collator = DataCollatorForTokenClassification(tokenizer, label_pad_token_id=-1, return_tensors="np")
|
||||
batch = data_collator(features)
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 6))
|
||||
self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
|
||||
self.assertEqual(batch["labels"].shape, (2, 6))
|
||||
self.assertEqual(batch["labels"][0].tolist(), [0, 1, 2] + [-1] * 3)
|
||||
|
||||
def _test_no_pad_and_pad(self, no_pad_features, pad_features):
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False, return_tensors="np")
|
||||
batch = data_collator(no_pad_features)
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 10))
|
||||
self.assertEqual(batch["labels"].shape, (2, 10))
|
||||
|
||||
batch = data_collator(pad_features, return_tensors="np")
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 10))
|
||||
self.assertEqual(batch["labels"].shape, (2, 10))
|
||||
|
||||
data_collator = DataCollatorForLanguageModeling(
|
||||
tokenizer, mlm=False, pad_to_multiple_of=8, return_tensors="np"
|
||||
)
|
||||
batch = data_collator(no_pad_features)
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 16))
|
||||
self.assertEqual(batch["labels"].shape, (2, 16))
|
||||
|
||||
batch = data_collator(pad_features, return_tensors="np")
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 16))
|
||||
self.assertEqual(batch["labels"].shape, (2, 16))
|
||||
|
||||
tokenizer._pad_token = None
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False, return_tensors="np")
|
||||
with self.assertRaises(ValueError):
|
||||
# Expect error due to padding token missing
|
||||
data_collator(pad_features)
|
||||
|
||||
set_seed(42) # For reproducibility
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, return_tensors="np")
|
||||
batch = data_collator(no_pad_features)
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 10))
|
||||
self.assertEqual(batch["labels"].shape, (2, 10))
|
||||
|
||||
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
|
||||
self.assertTrue(np.any(masked_tokens))
|
||||
# self.assertTrue(all(x == -100 for x in batch["labels"][~masked_tokens].tolist()))
|
||||
|
||||
batch = data_collator(pad_features)
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 10))
|
||||
self.assertEqual(batch["labels"].shape, (2, 10))
|
||||
|
||||
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
|
||||
self.assertTrue(np.any(masked_tokens))
|
||||
# self.assertTrue(all(x == -100 for x in batch["labels"][~masked_tokens].tolist()))
|
||||
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8, return_tensors="np")
|
||||
batch = data_collator(no_pad_features)
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 16))
|
||||
self.assertEqual(batch["labels"].shape, (2, 16))
|
||||
|
||||
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
|
||||
self.assertTrue(np.any(masked_tokens))
|
||||
# self.assertTrue(all(x == -100 for x in batch["labels"][~masked_tokens].tolist()))
|
||||
|
||||
batch = data_collator(pad_features)
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 16))
|
||||
self.assertEqual(batch["labels"].shape, (2, 16))
|
||||
|
||||
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
|
||||
self.assertTrue(np.any(masked_tokens))
|
||||
# self.assertTrue(all(x == -100 for x in batch["labels"][~masked_tokens].tolist()))
|
||||
|
||||
def test_data_collator_for_language_modeling(self):
|
||||
no_pad_features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
|
||||
pad_features = [{"input_ids": list(range(5))}, {"input_ids": list(range(10))}]
|
||||
self._test_no_pad_and_pad(no_pad_features, pad_features)
|
||||
|
||||
no_pad_features = [list(range(10)), list(range(10))]
|
||||
pad_features = [list(range(5)), list(range(10))]
|
||||
self._test_no_pad_and_pad(no_pad_features, pad_features)
|
||||
|
||||
def test_data_collator_for_whole_word_mask(self):
|
||||
features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
|
||||
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
data_collator = DataCollatorForWholeWordMask(tokenizer, return_tensors="np")
|
||||
batch = data_collator(features)
|
||||
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 10))
|
||||
self.assertEqual(batch["labels"].shape, (2, 10))
|
||||
|
||||
def test_plm(self):
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
no_pad_features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
|
||||
pad_features = [{"input_ids": list(range(5))}, {"input_ids": list(range(10))}]
|
||||
|
||||
data_collator = DataCollatorForPermutationLanguageModeling(tokenizer, return_tensors="np")
|
||||
|
||||
batch = data_collator(pad_features)
|
||||
self.assertIsInstance(batch, dict)
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 10))
|
||||
self.assertEqual(batch["perm_mask"].shape, (2, 10, 10))
|
||||
self.assertEqual(batch["target_mapping"].shape, (2, 10, 10))
|
||||
self.assertEqual(batch["labels"].shape, (2, 10))
|
||||
|
||||
batch = data_collator(no_pad_features)
|
||||
self.assertIsInstance(batch, dict)
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 10))
|
||||
self.assertEqual(batch["perm_mask"].shape, (2, 10, 10))
|
||||
self.assertEqual(batch["target_mapping"].shape, (2, 10, 10))
|
||||
self.assertEqual(batch["labels"].shape, (2, 10))
|
||||
|
||||
example = [np.random.randint(0, 5, [5])]
|
||||
with self.assertRaises(ValueError):
|
||||
# Expect error due to odd sequence length
|
||||
data_collator(example)
|
||||
|
||||
def test_nsp(self):
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
features = [
|
||||
{"input_ids": [0, 1, 2, 3, 4], "token_type_ids": [0, 1, 2, 3, 4], "next_sentence_label": i}
|
||||
for i in range(2)
|
||||
]
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, return_tensors="np")
|
||||
batch = data_collator(features)
|
||||
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 5))
|
||||
self.assertEqual(batch["token_type_ids"].shape, (2, 5))
|
||||
self.assertEqual(batch["labels"].shape, (2, 5))
|
||||
self.assertEqual(batch["next_sentence_label"].shape, (2,))
|
||||
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8, return_tensors="np")
|
||||
batch = data_collator(features)
|
||||
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 8))
|
||||
self.assertEqual(batch["token_type_ids"].shape, (2, 8))
|
||||
self.assertEqual(batch["labels"].shape, (2, 8))
|
||||
self.assertEqual(batch["next_sentence_label"].shape, (2,))
|
||||
|
||||
def test_sop(self):
|
||||
tokenizer = BertTokenizer(self.vocab_file)
|
||||
features = [
|
||||
{
|
||||
"input_ids": np.array([0, 1, 2, 3, 4]),
|
||||
"token_type_ids": np.array([0, 1, 2, 3, 4]),
|
||||
"sentence_order_label": i,
|
||||
}
|
||||
for i in range(2)
|
||||
]
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, return_tensors="np")
|
||||
batch = data_collator(features)
|
||||
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 5))
|
||||
self.assertEqual(batch["token_type_ids"].shape, (2, 5))
|
||||
self.assertEqual(batch["labels"].shape, (2, 5))
|
||||
self.assertEqual(batch["sentence_order_label"].shape, (2,))
|
||||
|
||||
data_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8, return_tensors="np")
|
||||
batch = data_collator(features)
|
||||
|
||||
self.assertEqual(batch["input_ids"].shape, (2, 8))
|
||||
self.assertEqual(batch["token_type_ids"].shape, (2, 8))
|
||||
self.assertEqual(batch["labels"].shape, (2, 8))
|
||||
self.assertEqual(batch["sentence_order_label"].shape, (2,))
|
||||
Reference in New Issue
Block a user