249 lines
9.1 KiB
Python
249 lines
9.1 KiB
Python
# coding=utf-8
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# Copyright 2018 Google T5 Authors and HuggingFace Inc. team.
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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 unittest
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from transformers import SPIECE_UNDERLINE, BatchEncoding, T5Tokenizer, T5TokenizerFast
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from transformers.file_utils import cached_property, is_tf_available, is_torch_available
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from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers
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from .test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
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if is_torch_available():
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FRAMEWORK = "pt"
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elif is_tf_available():
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FRAMEWORK = "tf"
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else:
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FRAMEWORK = "jax"
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@require_sentencepiece
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@require_tokenizers
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class T5TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = T5Tokenizer
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rust_tokenizer_class = T5TokenizerFast
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test_rust_tokenizer = True
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def setUp(self):
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super().setUp()
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# We have a SentencePiece fixture for testing
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tokenizer = T5Tokenizer(SAMPLE_VOCAB)
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tokenizer.save_pretrained(self.tmpdirname)
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def test_full_tokenizer(self):
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tokenizer = T5Tokenizer(SAMPLE_VOCAB)
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tokens = tokenizer.tokenize("This is a test")
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self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
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self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382])
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tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
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self.assertListEqual(
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tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"9",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"é",
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".",
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],
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)
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ids = tokenizer.convert_tokens_to_ids(tokens)
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self.assertListEqual(ids, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4])
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(
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back_tokens,
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[
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SPIECE_UNDERLINE + "I",
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SPIECE_UNDERLINE + "was",
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SPIECE_UNDERLINE + "b",
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"or",
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"n",
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SPIECE_UNDERLINE + "in",
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SPIECE_UNDERLINE + "",
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"<unk>",
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"2",
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"0",
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"0",
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"0",
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",",
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SPIECE_UNDERLINE + "and",
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SPIECE_UNDERLINE + "this",
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SPIECE_UNDERLINE + "is",
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SPIECE_UNDERLINE + "f",
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"al",
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"s",
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"<unk>",
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".",
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],
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)
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@cached_property
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def t5_base_tokenizer(self):
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return T5Tokenizer.from_pretrained("t5-base")
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@cached_property
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def t5_base_tokenizer_fast(self):
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return T5TokenizerFast.from_pretrained("t5-base")
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def get_tokenizer(self, **kwargs) -> T5Tokenizer:
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return self.tokenizer_class.from_pretrained(self.tmpdirname, pad_token=None, **kwargs)
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def get_rust_tokenizer(self, **kwargs) -> T5TokenizerFast:
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return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, pad_token=None, **kwargs)
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def test_rust_and_python_full_tokenizers(self):
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if not self.test_rust_tokenizer:
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return
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tokenizer = self.get_tokenizer()
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rust_tokenizer = self.get_rust_tokenizer()
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sequence = "I was born in 92000, and this is falsé."
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tokens = tokenizer.tokenize(sequence)
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rust_tokens = rust_tokenizer.tokenize(sequence)
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self.assertListEqual(tokens, rust_tokens)
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ids = tokenizer.encode(sequence, add_special_tokens=False)
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rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
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self.assertListEqual(ids, rust_ids)
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rust_tokenizer = self.get_rust_tokenizer()
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ids = tokenizer.encode(sequence)
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rust_ids = rust_tokenizer.encode(sequence)
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self.assertListEqual(ids, rust_ids)
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def test_eos_treatment(self):
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tokenizer = self.t5_base_tokenizer
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batch_with_eos_added = tokenizer(["hi</s>", "I went to the gym</s>", "</s>"])
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batch_without_eos_added = tokenizer(["hi", "I went to the gym", ""])
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self.assertListEqual(batch_with_eos_added["input_ids"], batch_without_eos_added["input_ids"])
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def test_prepare_batch(self):
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tokenizer = self.t5_base_tokenizer
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src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
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expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, tokenizer.eos_token_id]
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batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
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self.assertIsInstance(batch, BatchEncoding)
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if FRAMEWORK != "jax":
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result = list(batch.input_ids.numpy()[0])
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else:
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result = list(batch.input_ids.tolist()[0])
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self.assertListEqual(expected_src_tokens, result)
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self.assertEqual((2, 9), batch.input_ids.shape)
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self.assertEqual((2, 9), batch.attention_mask.shape)
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def test_empty_target_text(self):
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tokenizer = self.t5_base_tokenizer
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src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
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batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
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# check if input_ids are returned and no decoder_input_ids
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self.assertIn("input_ids", batch)
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self.assertIn("attention_mask", batch)
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self.assertNotIn("decoder_input_ids", batch)
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self.assertNotIn("decoder_attention_mask", batch)
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def test_max_length(self):
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tokenizer = self.t5_base_tokenizer
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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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with tokenizer.as_target_tokenizer():
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targets = tokenizer(
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tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors=FRAMEWORK
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)
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self.assertEqual(32, targets["input_ids"].shape[1])
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def test_outputs_not_longer_than_maxlen(self):
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tokenizer = self.t5_base_tokenizer
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batch = tokenizer(
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["I am a small frog" * 1000, "I am a small frog"], padding=True, truncation=True, return_tensors=FRAMEWORK
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)
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self.assertIsInstance(batch, BatchEncoding)
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self.assertEqual(batch.input_ids.shape, (2, 512))
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def test_eos_in_input(self):
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tokenizer = self.t5_base_tokenizer
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src_text = ["A long paragraph for summarization. </s>"]
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tgt_text = ["Summary of the text. </s>"]
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expected_src_tokens = [71, 307, 8986, 21, 4505, 1635, 1707, 5, 1]
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expected_tgt_tokens = [20698, 13, 8, 1499, 5, 1]
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batch = tokenizer(src_text)
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with tokenizer.as_target_tokenizer():
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targets = tokenizer(tgt_text)
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self.assertEqual(expected_src_tokens, batch["input_ids"][0])
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self.assertEqual(expected_tgt_tokens, targets["input_ids"][0])
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def test_token_type_ids(self):
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src_text_1 = ["A first paragraph for summarization."]
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src_text_2 = ["A second paragraph for summarization."]
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fast_token_type_ids = self.t5_base_tokenizer_fast(
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src_text_1, src_text_2, add_special_tokens=True, return_token_type_ids=True
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).token_type_ids
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slow_token_type_ids = self.t5_base_tokenizer(
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src_text_1, src_text_2, add_special_tokens=True, return_token_type_ids=True
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).token_type_ids
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self.assertEqual(slow_token_type_ids, fast_token_type_ids)
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self.assertEqual(len(slow_token_type_ids[0]), 18)
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def test_fast_and_slow_same_result(self):
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src_text = "<pad> Today is <unk> nice day </s>"
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tgt_ids = [0, 1960, 19, 2, 1245, 239, 1]
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tgt_text = "<pad> Today is<unk> nice day</s>"
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fast_ids = self.t5_base_tokenizer_fast(src_text, add_special_tokens=False).input_ids
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slow_ids = self.t5_base_tokenizer(src_text, add_special_tokens=False).input_ids
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self.assertEqual(tgt_ids, fast_ids)
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self.assertEqual(tgt_ids, slow_ids)
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fast_text = self.t5_base_tokenizer_fast.decode(fast_ids)
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slow_text = self.t5_base_tokenizer.decode(fast_ids)
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self.assertEqual(tgt_text, fast_text)
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self.assertEqual(tgt_text, slow_text)
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