91 lines
4.0 KiB
Python
91 lines
4.0 KiB
Python
import unittest
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from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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from transformers.file_utils import cached_property
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class TestTokenizationBart(unittest.TestCase):
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@cached_property
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def default_tokenizer(self):
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return BartTokenizer.from_pretrained("facebook/bart-large")
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@cached_property
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def default_tokenizer_fast(self):
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return BartTokenizerFast.from_pretrained("facebook/bart-large")
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def test_prepare_seq2seq_batch(self):
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tokenizers = [self.default_tokenizer, self.default_tokenizer_fast]
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src_text = ["A long paragraph for summrization.", "Another paragraph for summrization."]
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tgt_text = [
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"Summary of the text.",
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"Another summary.",
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]
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expected_src_tokens = [0, 250, 251, 17818, 13, 32933, 21645, 1258, 4, 2]
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for tokenizer in tokenizers:
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batch = tokenizer.prepare_seq2seq_batch(
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src_text, tgt_texts=tgt_text, max_length=len(expected_src_tokens), return_tensors="pt"
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)
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self.assertIsInstance(batch, BatchEncoding)
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self.assertEqual((2, 10), batch.input_ids.shape)
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self.assertEqual((2, 10), batch.attention_mask.shape)
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result = batch.input_ids.tolist()[0]
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self.assertListEqual(expected_src_tokens, result)
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# Test that special tokens are reset
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def test_empty_target_text(self):
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tokenizers = [self.default_tokenizer, self.default_tokenizer_fast]
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src_text = ["A long paragraph for summrization.", "Another paragraph for summrization."]
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for tokenizer in tokenizers:
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batch = tokenizer.prepare_seq2seq_batch(src_text, return_tensors="pt")
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# check if input_ids are returned and no labels
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self.assertIn("input_ids", batch)
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self.assertIn("attention_mask", batch)
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self.assertNotIn("labels", batch)
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self.assertNotIn("decoder_attention_mask", batch)
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def test_max_target_length(self):
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tokenizers = [self.default_tokenizer, self.default_tokenizer_fast]
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src_text = ["A long paragraph for summrization.", "Another paragraph for summrization."]
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tgt_text = [
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"Summary of the text.",
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"Another summary.",
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]
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for tokenizer in tokenizers:
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batch = tokenizer.prepare_seq2seq_batch(
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src_text, tgt_texts=tgt_text, max_target_length=32, padding="max_length", return_tensors="pt"
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)
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self.assertEqual(32, batch["labels"].shape[1])
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# test None max_target_length
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batch = tokenizer.prepare_seq2seq_batch(
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src_text, tgt_texts=tgt_text, max_length=32, padding="max_length", return_tensors="pt"
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)
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self.assertEqual(32, batch["labels"].shape[1])
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def test_outputs_not_longer_than_maxlen(self):
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tokenizers = [self.default_tokenizer, self.default_tokenizer_fast]
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for tokenizer in tokenizers:
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batch = tokenizer.prepare_seq2seq_batch(
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["I am a small frog" * 1024, "I am a small frog"], return_tensors="pt"
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)
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self.assertIsInstance(batch, BatchEncoding)
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self.assertEqual(batch.input_ids.shape, (2, 1024))
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def test_special_tokens(self):
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tokenizers = [self.default_tokenizer, self.default_tokenizer_fast]
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src_text = ["A long paragraph for summrization."]
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tgt_text = [
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"Summary of the text.",
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]
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for tokenizer in tokenizers:
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batch = tokenizer.prepare_seq2seq_batch(src_text, tgt_texts=tgt_text, return_tensors="pt")
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input_ids = batch["input_ids"]
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labels = batch["labels"]
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self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
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self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
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self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
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self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())
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