9112520b15
* fix seq2seq data collator to respect the given padding strategy further added tests for the seq2seq data collator in the style of the `data_collator_for_token_classification` (pt, tf, np) * formatting and change bool equals "==" to "is" * add missed return types in tests * update numpy test as it can handle unequal shapes, not like pt or tf
1140 lines
56 KiB
Python
1140 lines
56 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import os
|
|
import shutil
|
|
import tempfile
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
from transformers import (
|
|
BertTokenizer,
|
|
DataCollatorForLanguageModeling,
|
|
DataCollatorForPermutationLanguageModeling,
|
|
DataCollatorForSeq2Seq,
|
|
DataCollatorForTokenClassification,
|
|
DataCollatorForWholeWordMask,
|
|
DataCollatorWithPadding,
|
|
default_data_collator,
|
|
is_tf_available,
|
|
is_torch_available,
|
|
set_seed,
|
|
)
|
|
from transformers.testing_utils import require_tf, require_torch
|
|
from transformers.utils import PaddingStrategy
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
if is_tf_available():
|
|
import tensorflow as tf
|
|
|
|
|
|
@require_torch
|
|
class DataCollatorIntegrationTest(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)
|
|
self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
|
|
self.assertEqual(batch["labels"].dtype, torch.long)
|
|
self.assertEqual(batch["inputs"].shape, torch.Size([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)
|
|
self.assertTrue(batch["labels"].equal(torch.tensor([[0, 1, 2]] * 8)))
|
|
self.assertEqual(batch["labels"].dtype, torch.long)
|
|
self.assertEqual(batch["inputs"].shape, torch.Size([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)
|
|
self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
|
|
self.assertEqual(batch["labels"].dtype, torch.long)
|
|
self.assertEqual(batch["inputs"].shape, torch.Size([8, 10]))
|
|
|
|
# Labels can already be tensors
|
|
features = [{"label": torch.tensor(i), "inputs": np.random.randint(0, 10, [10])} for i in range(8)]
|
|
batch = default_data_collator(features)
|
|
self.assertEqual(batch["labels"].dtype, torch.long)
|
|
self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8)))))
|
|
self.assertEqual(batch["labels"].dtype, torch.long)
|
|
self.assertEqual(batch["inputs"].shape, torch.Size([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)
|
|
self.assertEqual(batch["labels"].dtype, torch.long)
|
|
|
|
features = [{"input_ids": [0, 1, 2, 3, 4], "label": float(i)} for i in range(4)]
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["labels"].dtype, torch.float)
|
|
|
|
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)
|
|
self.assertTrue("labels" not in batch)
|
|
self.assertEqual(batch["inputs"].shape, torch.Size([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)
|
|
self.assertTrue("labels" not in batch)
|
|
self.assertEqual(batch["inputs"].shape, torch.Size([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)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([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)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 10]))
|
|
|
|
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([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)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
|
|
self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
|
|
self.assertEqual(batch["labels"].shape, torch.Size([2, 6]))
|
|
self.assertEqual(batch["labels"][0].tolist(), [0, 1, 2] + [-100] * 3)
|
|
|
|
data_collator = DataCollatorForTokenClassification(tokenizer, padding="max_length", max_length=10)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 10]))
|
|
self.assertEqual(batch["labels"].shape, torch.Size([2, 10]))
|
|
|
|
data_collator = DataCollatorForTokenClassification(tokenizer, pad_to_multiple_of=8)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 8]))
|
|
self.assertEqual(batch["labels"].shape, torch.Size([2, 8]))
|
|
|
|
data_collator = DataCollatorForTokenClassification(tokenizer, label_pad_token_id=-1)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
|
|
self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
|
|
self.assertEqual(batch["labels"].shape, torch.Size([2, 6]))
|
|
self.assertEqual(batch["labels"][0].tolist(), [0, 1, 2] + [-1] * 3)
|
|
|
|
for feature in features:
|
|
feature.pop("labels")
|
|
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
|
|
self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
|
|
|
|
def test_data_collator_for_token_classification_works_with_pt_tensors(self):
|
|
tokenizer = BertTokenizer(self.vocab_file)
|
|
features = [
|
|
{"input_ids": torch.tensor([0, 1, 2]), "labels": torch.tensor([0, 1, 2])},
|
|
{"input_ids": torch.tensor([0, 1, 2, 3, 4, 5]), "labels": torch.tensor([0, 1, 2, 3, 4, 5])},
|
|
]
|
|
|
|
data_collator = DataCollatorForTokenClassification(tokenizer)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
|
|
self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
|
|
self.assertEqual(batch["labels"].shape, torch.Size([2, 6]))
|
|
self.assertEqual(batch["labels"][0].tolist(), [0, 1, 2] + [-100] * 3)
|
|
|
|
data_collator = DataCollatorForTokenClassification(tokenizer, padding="max_length", max_length=10)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 10]))
|
|
self.assertEqual(batch["labels"].shape, torch.Size([2, 10]))
|
|
|
|
data_collator = DataCollatorForTokenClassification(tokenizer, pad_to_multiple_of=8)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 8]))
|
|
self.assertEqual(batch["labels"].shape, torch.Size([2, 8]))
|
|
|
|
data_collator = DataCollatorForTokenClassification(tokenizer, label_pad_token_id=-1)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
|
|
self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
|
|
self.assertEqual(batch["labels"].shape, torch.Size([2, 6]))
|
|
self.assertEqual(batch["labels"][0].tolist(), [0, 1, 2] + [-1] * 3)
|
|
|
|
for feature in features:
|
|
feature.pop("labels")
|
|
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
|
|
self.assertEqual(batch["input_ids"][0].tolist(), [0, 1, 2] + [tokenizer.pad_token_id] * 3)
|
|
|
|
def _test_data_collator_for_seq2seq(self, to_torch):
|
|
def create_features(to_torch):
|
|
if to_torch:
|
|
features = [
|
|
{"input_ids": torch.tensor(list(range(3))), "labels": torch.tensor(list(range(3)))},
|
|
{"input_ids": torch.tensor(list(range(6))), "labels": torch.tensor(list(range(6)))},
|
|
]
|
|
else:
|
|
features = [
|
|
{"input_ids": list(range(3)), "labels": list(range(3))},
|
|
{"input_ids": list(range(6)), "labels": list(range(6))},
|
|
]
|
|
return features
|
|
|
|
tokenizer = BertTokenizer(self.vocab_file)
|
|
features = create_features(to_torch)
|
|
|
|
data_collator = DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.LONGEST)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
|
|
self.assertEqual(batch["input_ids"][0].tolist(), list(range(3)) + [tokenizer.pad_token_id] * 3)
|
|
self.assertEqual(batch["input_ids"][1].tolist(), list(range(6)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size([2, 6]))
|
|
self.assertEqual(batch["labels"][0].tolist(), list(range(3)) + [-100] * 3)
|
|
self.assertEqual(batch["labels"][1].tolist(), list(range(6)))
|
|
|
|
data_collator = DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.MAX_LENGTH, max_length=7)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 7]))
|
|
self.assertEqual(batch["input_ids"][0].tolist(), list(range(3)) + [tokenizer.pad_token_id] * 4)
|
|
self.assertEqual(batch["input_ids"][1].tolist(), list(range(6)) + [tokenizer.pad_token_id] * 1)
|
|
self.assertEqual(batch["labels"].shape, torch.Size([2, 7]))
|
|
self.assertEqual(batch["labels"][0].tolist(), list(range(3)) + [-100] * 4)
|
|
self.assertEqual(batch["labels"][1].tolist(), list(range(6)) + [-100] * 1)
|
|
|
|
data_collator = DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.DO_NOT_PAD)
|
|
with self.assertRaises(ValueError):
|
|
# expects an error due to unequal shapes to create tensor
|
|
data_collator(features)
|
|
batch = data_collator([features[0], features[0]])
|
|
input_ids = features[0]["input_ids"] if not to_torch else features[0]["input_ids"].tolist()
|
|
labels = features[0]["labels"] if not to_torch else features[0]["labels"].tolist()
|
|
self.assertEqual(batch["input_ids"][0].tolist(), input_ids)
|
|
self.assertEqual(batch["input_ids"][1].tolist(), input_ids)
|
|
self.assertEqual(batch["labels"][0].tolist(), labels)
|
|
self.assertEqual(batch["labels"][1].tolist(), labels)
|
|
|
|
data_collator = DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.LONGEST, pad_to_multiple_of=8)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 8]))
|
|
self.assertEqual(batch["labels"].shape, torch.Size([2, 8]))
|
|
|
|
# side effects on labels cause mismatch on longest strategy
|
|
features = create_features(to_torch)
|
|
|
|
data_collator = DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.LONGEST, label_pad_token_id=-1)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
|
|
self.assertEqual(batch["input_ids"][0].tolist(), list(range(3)) + [tokenizer.pad_token_id] * 3)
|
|
self.assertEqual(batch["input_ids"][1].tolist(), list(range(6)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size([2, 6]))
|
|
self.assertEqual(batch["labels"][0].tolist(), list(range(3)) + [-1] * 3)
|
|
self.assertEqual(batch["labels"][1].tolist(), list(range(6)))
|
|
|
|
for feature in features:
|
|
feature.pop("labels")
|
|
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size([2, 6]))
|
|
self.assertEqual(batch["input_ids"][0].tolist(), list(range(3)) + [tokenizer.pad_token_id] * 3)
|
|
|
|
def test_data_collator_for_seq2seq_with_lists(self):
|
|
self._test_data_collator_for_seq2seq(to_torch=False)
|
|
|
|
def test_data_collator_for_seq2seq_with_pt(self):
|
|
self._test_data_collator_for_seq2seq(to_torch=True)
|
|
|
|
def _test_no_pad_and_pad(self, no_pad_features, pad_features):
|
|
tokenizer = BertTokenizer(self.vocab_file)
|
|
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
|
|
batch = data_collator(no_pad_features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
|
|
|
|
batch = data_collator(pad_features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
|
|
|
|
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False, pad_to_multiple_of=8)
|
|
batch = data_collator(no_pad_features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 16)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
|
|
|
|
batch = data_collator(pad_features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 16)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
|
|
|
|
tokenizer._pad_token = None
|
|
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
|
|
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)
|
|
batch = data_collator(no_pad_features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
|
|
|
|
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
|
|
self.assertTrue(torch.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, torch.Size((2, 10)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
|
|
|
|
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
|
|
self.assertTrue(torch.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)
|
|
batch = data_collator(no_pad_features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 16)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
|
|
|
|
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
|
|
self.assertTrue(torch.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, torch.Size((2, 16)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 16)))
|
|
|
|
masked_tokens = batch["input_ids"] == tokenizer.mask_token_id
|
|
self.assertTrue(torch.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):
|
|
tokenizer = BertTokenizer(self.vocab_file)
|
|
data_collator = DataCollatorForWholeWordMask(tokenizer, return_tensors="pt")
|
|
|
|
features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
|
|
|
|
# Features can already be tensors
|
|
features = [{"input_ids": np.arange(10)}, {"input_ids": np.arange(10)}]
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((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)
|
|
|
|
batch = data_collator(pad_features)
|
|
self.assertIsInstance(batch, dict)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
|
|
self.assertEqual(batch["perm_mask"].shape, torch.Size((2, 10, 10)))
|
|
self.assertEqual(batch["target_mapping"].shape, torch.Size((2, 10, 10)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 10)))
|
|
|
|
batch = data_collator(no_pad_features)
|
|
self.assertIsInstance(batch, dict)
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 10)))
|
|
self.assertEqual(batch["perm_mask"].shape, torch.Size((2, 10, 10)))
|
|
self.assertEqual(batch["target_mapping"].shape, torch.Size((2, 10, 10)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((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)
|
|
batch = data_collator(features)
|
|
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 5)))
|
|
self.assertEqual(batch["token_type_ids"].shape, torch.Size((2, 5)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 5)))
|
|
self.assertEqual(batch["next_sentence_label"].shape, torch.Size((2,)))
|
|
|
|
data_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8)
|
|
batch = data_collator(features)
|
|
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 8)))
|
|
self.assertEqual(batch["token_type_ids"].shape, torch.Size((2, 8)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 8)))
|
|
self.assertEqual(batch["next_sentence_label"].shape, torch.Size((2,)))
|
|
|
|
def test_sop(self):
|
|
tokenizer = BertTokenizer(self.vocab_file)
|
|
features = [
|
|
{
|
|
"input_ids": torch.tensor([0, 1, 2, 3, 4]),
|
|
"token_type_ids": torch.tensor([0, 1, 2, 3, 4]),
|
|
"sentence_order_label": i,
|
|
}
|
|
for i in range(2)
|
|
]
|
|
data_collator = DataCollatorForLanguageModeling(tokenizer)
|
|
batch = data_collator(features)
|
|
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 5)))
|
|
self.assertEqual(batch["token_type_ids"].shape, torch.Size((2, 5)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 5)))
|
|
self.assertEqual(batch["sentence_order_label"].shape, torch.Size((2,)))
|
|
|
|
data_collator = DataCollatorForLanguageModeling(tokenizer, pad_to_multiple_of=8)
|
|
batch = data_collator(features)
|
|
|
|
self.assertEqual(batch["input_ids"].shape, torch.Size((2, 8)))
|
|
self.assertEqual(batch["token_type_ids"].shape, torch.Size((2, 8)))
|
|
self.assertEqual(batch["labels"].shape, torch.Size((2, 8)))
|
|
self.assertEqual(batch["sentence_order_label"].shape, torch.Size((2,)))
|
|
|
|
|
|
@require_tf
|
|
class TFDataCollatorIntegrationTest(unittest.TestCase):
|
|
def setUp(self):
|
|
super().setUp()
|
|
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="tf")
|
|
self.assertEqual(batch["labels"].numpy().tolist(), list(range(8)))
|
|
self.assertEqual(batch["labels"].dtype, tf.int64)
|
|
self.assertEqual(batch["inputs"].shape.as_list(), [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="tf")
|
|
self.assertEqual(batch["labels"].numpy().tolist(), ([[0, 1, 2]] * 8))
|
|
self.assertEqual(batch["labels"].dtype, tf.int64)
|
|
self.assertEqual(batch["inputs"].shape.as_list(), [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="tf")
|
|
self.assertEqual(batch["labels"].numpy().tolist(), (list(range(8))))
|
|
self.assertEqual(batch["labels"].dtype, tf.int64)
|
|
self.assertEqual(batch["inputs"].shape.as_list(), [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="tf")
|
|
self.assertEqual(batch["labels"].dtype, tf.int64)
|
|
self.assertEqual(batch["labels"].numpy().tolist(), list(range(8)))
|
|
self.assertEqual(batch["labels"].dtype, tf.int64)
|
|
self.assertEqual(batch["inputs"].shape.as_list(), [8, 10])
|
|
|
|
def test_numpy_dtype_preservation(self):
|
|
data_collator = default_data_collator
|
|
|
|
# Confirms that numpy inputs are handled correctly even when scalars
|
|
features = [{"input_ids": np.array([0, 1, 2, 3, 4]), "label": np.int64(i)} for i in range(4)]
|
|
batch = data_collator(features, return_tensors="tf")
|
|
self.assertEqual(batch["labels"].dtype, tf.int64)
|
|
|
|
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="tf")
|
|
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_data_collator_for_seq2seq(self):
|
|
def create_features():
|
|
return [
|
|
{"input_ids": list(range(3)), "labels": list(range(3))},
|
|
{"input_ids": list(range(6)), "labels": list(range(6))},
|
|
]
|
|
|
|
tokenizer = BertTokenizer(self.vocab_file)
|
|
features = create_features()
|
|
|
|
data_collator = DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.LONGEST, 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(), list(range(3)) + [tokenizer.pad_token_id] * 3)
|
|
self.assertEqual(batch["input_ids"][1].numpy().tolist(), list(range(6)))
|
|
self.assertEqual(batch["labels"].shape.as_list(), [2, 6])
|
|
self.assertEqual(batch["labels"][0].numpy().tolist(), list(range(3)) + [-100] * 3)
|
|
self.assertEqual(batch["labels"][1].numpy().tolist(), list(range(6)))
|
|
|
|
data_collator = DataCollatorForSeq2Seq(
|
|
tokenizer, padding=PaddingStrategy.MAX_LENGTH, max_length=7, return_tensors="tf"
|
|
)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 7])
|
|
self.assertEqual(batch["input_ids"][0].numpy().tolist(), list(range(3)) + [tokenizer.pad_token_id] * 4)
|
|
self.assertEqual(batch["input_ids"][1].numpy().tolist(), list(range(6)) + [tokenizer.pad_token_id] * 1)
|
|
self.assertEqual(batch["labels"].shape.as_list(), [2, 7])
|
|
self.assertEqual(batch["labels"][0].numpy().tolist(), list(range(3)) + [-100] * 4)
|
|
self.assertEqual(batch["labels"][1].numpy().tolist(), list(range(6)) + [-100] * 1)
|
|
|
|
data_collator = DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.DO_NOT_PAD, return_tensors="tf")
|
|
with self.assertRaises(ValueError):
|
|
# expects an error due to unequal shapes to create tensor
|
|
data_collator(features)
|
|
batch = data_collator([features[0], features[0]])
|
|
self.assertEqual(batch["input_ids"][0].numpy().tolist(), features[0]["input_ids"])
|
|
self.assertEqual(batch["input_ids"][1].numpy().tolist(), features[0]["input_ids"])
|
|
self.assertEqual(batch["labels"][0].numpy().tolist(), features[0]["labels"])
|
|
self.assertEqual(batch["labels"][1].numpy().tolist(), features[0]["labels"])
|
|
|
|
data_collator = DataCollatorForSeq2Seq(
|
|
tokenizer, padding=PaddingStrategy.LONGEST, 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])
|
|
|
|
# side effects on labels cause mismatch on longest strategy
|
|
features = create_features()
|
|
|
|
data_collator = DataCollatorForSeq2Seq(
|
|
tokenizer, padding=PaddingStrategy.LONGEST, 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(), list(range(3)) + [tokenizer.pad_token_id] * 3)
|
|
self.assertEqual(batch["input_ids"][1].numpy().tolist(), list(range(6)))
|
|
self.assertEqual(batch["labels"].shape.as_list(), [2, 6])
|
|
self.assertEqual(batch["labels"][0].numpy().tolist(), list(range(3)) + [-1] * 3)
|
|
self.assertEqual(batch["labels"][1].numpy().tolist(), list(range(6)))
|
|
|
|
for feature in features:
|
|
feature.pop("labels")
|
|
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 6])
|
|
self.assertEqual(batch["input_ids"][0].numpy().tolist(), list(range(3)) + [tokenizer.pad_token_id] * 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):
|
|
tokenizer = BertTokenizer(self.vocab_file)
|
|
data_collator = DataCollatorForWholeWordMask(tokenizer, return_tensors="tf")
|
|
|
|
features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape.as_list(), [2, 10])
|
|
self.assertEqual(batch["labels"].shape.as_list(), [2, 10])
|
|
|
|
# Features can already be tensors
|
|
features = [{"input_ids": np.arange(10)}, {"input_ids": np.arange(10)}]
|
|
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_data_collator_for_seq2seq(self):
|
|
def create_features():
|
|
return [
|
|
{"input_ids": list(range(3)), "labels": list(range(3))},
|
|
{"input_ids": list(range(6)), "labels": list(range(6))},
|
|
]
|
|
|
|
tokenizer = BertTokenizer(self.vocab_file)
|
|
features = create_features()
|
|
|
|
data_collator = DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.LONGEST, return_tensors="np")
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, (2, 6))
|
|
self.assertEqual(batch["input_ids"][0].tolist(), list(range(3)) + [tokenizer.pad_token_id] * 3)
|
|
self.assertEqual(batch["input_ids"][1].tolist(), list(range(6)))
|
|
self.assertEqual(batch["labels"].shape, (2, 6))
|
|
self.assertEqual(batch["labels"][0].tolist(), list(range(3)) + [-100] * 3)
|
|
self.assertEqual(batch["labels"][1].tolist(), list(range(6)))
|
|
|
|
data_collator = DataCollatorForSeq2Seq(
|
|
tokenizer, padding=PaddingStrategy.MAX_LENGTH, max_length=7, return_tensors="np"
|
|
)
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, (2, 7))
|
|
self.assertEqual(batch["input_ids"][0].tolist(), list(range(3)) + [tokenizer.pad_token_id] * 4)
|
|
self.assertEqual(batch["input_ids"][1].tolist(), list(range(6)) + [tokenizer.pad_token_id] * 1)
|
|
self.assertEqual(batch["labels"].shape, (2, 7))
|
|
self.assertEqual(batch["labels"][0].tolist(), list(range(3)) + [-100] * 4)
|
|
self.assertEqual(batch["labels"][1].tolist(), list(range(6)) + [-100] * 1)
|
|
|
|
data_collator = DataCollatorForSeq2Seq(tokenizer, padding=PaddingStrategy.DO_NOT_PAD, return_tensors="np")
|
|
# numpy doesn't have issues handling unequal shapes via `dtype=object`
|
|
# with self.assertRaises(ValueError):
|
|
# data_collator(features)
|
|
batch = data_collator([features[0], features[0]])
|
|
self.assertEqual(batch["input_ids"][0].tolist(), features[0]["input_ids"])
|
|
self.assertEqual(batch["input_ids"][1].tolist(), features[0]["input_ids"])
|
|
self.assertEqual(batch["labels"][0].tolist(), features[0]["labels"])
|
|
self.assertEqual(batch["labels"][1].tolist(), features[0]["labels"])
|
|
|
|
data_collator = DataCollatorForSeq2Seq(
|
|
tokenizer, padding=PaddingStrategy.LONGEST, 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))
|
|
|
|
# side effects on labels cause mismatch on longest strategy
|
|
features = create_features()
|
|
|
|
data_collator = DataCollatorForSeq2Seq(
|
|
tokenizer, padding=PaddingStrategy.LONGEST, 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(), list(range(3)) + [tokenizer.pad_token_id] * 3)
|
|
self.assertEqual(batch["input_ids"][1].tolist(), list(range(6)))
|
|
self.assertEqual(batch["labels"].shape, (2, 6))
|
|
self.assertEqual(batch["labels"][0].tolist(), list(range(3)) + [-1] * 3)
|
|
self.assertEqual(batch["labels"][1].tolist(), list(range(6)))
|
|
|
|
for feature in features:
|
|
feature.pop("labels")
|
|
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, (2, 6))
|
|
self.assertEqual(batch["input_ids"][0].tolist(), list(range(3)) + [tokenizer.pad_token_id] * 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):
|
|
tokenizer = BertTokenizer(self.vocab_file)
|
|
data_collator = DataCollatorForWholeWordMask(tokenizer, return_tensors="np")
|
|
|
|
features = [{"input_ids": list(range(10))}, {"input_ids": list(range(10))}]
|
|
batch = data_collator(features)
|
|
self.assertEqual(batch["input_ids"].shape, (2, 10))
|
|
self.assertEqual(batch["labels"].shape, (2, 10))
|
|
|
|
# Features can already be tensors
|
|
features = [{"input_ids": np.arange(10)}, {"input_ids": np.arange(10)}]
|
|
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,))
|