transformers/tests/test_tokenization_fast.py

import unittest

import numpy as np

from tests.utils import require_torch
from transformers import (
    BertTokenizer,
    BertTokenizerFast,
    DistilBertTokenizer,
    GPT2Tokenizer,
    GPT2TokenizerFast,
    OpenAIGPTTokenizer,
    PreTrainedTokenizer,
    RobertaTokenizer,
    TransfoXLTokenizer,
    is_torch_available,
)
from transformers.tokenization_distilbert import DistilBertTokenizerFast
from transformers.tokenization_openai import OpenAIGPTTokenizerFast
from transformers.tokenization_roberta import RobertaTokenizerFast
from transformers.tokenization_transfo_xl import TransfoXLTokenizerFast


class FastTokenizerMatchingTest(unittest.TestCase):
    def setUp(self) -> None:
        with open("tests/fixtures/sample_text.txt") as f_data:
            self._data = f_data.read().replace("\n\n", "\n").strip()

    def assert_sequence_almost_equals(self, a, b, threshold):

        # Handle padding
        if len(a) != len(b):
            max_len = max(len(a), len(b))

            # Pad with a negative number as vocab doesnt allow idx < 0
            # if will be tracked as differences
            if len(a) < max_len:
                a += [-1] * (max_len - len(a))

            if len(b) < max_len:
                b += [-1] * (max_len - len(b))

        # Convert to numpy for convenience
        a_, b_ = np.array(a), np.array(b)

        # Compute elementwise difference
        inputs_diffs = a_ - b_
        inputs_diff = np.count_nonzero(inputs_diffs)
        self.assertLessEqual(inputs_diff / a_.shape[0], threshold)

    def assert_tokenization_python_rust_almost_equals(self, tokenizer_p, tokenizer_r, threshold: float):
        # Ensure basic input match
        input_p = tokenizer_p.encode_plus(self._data)
        input_r = tokenizer_r.encode_plus(self._data)

        for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
            self.assert_sequence_almost_equals(input_p[key], input_r[key], threshold)

        input_pairs_p = tokenizer_p.encode_plus(self._data, self._data)
        input_pairs_r = tokenizer_r.encode_plus(self._data, self._data)

        for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
            self.assert_sequence_almost_equals(input_pairs_p[key], input_pairs_r[key], threshold)

        # Ensure truncation match
        input_p = tokenizer_p.encode_plus(self._data, max_length=512)
        input_r = tokenizer_r.encode_plus(self._data, max_length=512)

        for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
            self.assert_sequence_almost_equals(input_p[key], input_r[key], threshold)

        # Ensure truncation with stride match
        input_p = tokenizer_p.encode_plus(self._data, max_length=512, stride=3, return_overflowing_tokens=True)
        input_r = tokenizer_r.encode_plus(self._data, max_length=512, stride=3, return_overflowing_tokens=True)

        for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
            self.assert_sequence_almost_equals(input_p[key], input_r[key], threshold)

    def assert_add_tokens(self, tokenizer_r):
        vocab_size = tokenizer_r.vocab_size
        self.assertEqual(tokenizer_r.add_tokens(""), 0)
        self.assertEqual(tokenizer_r.add_tokens("testoken"), 1)
        self.assertEqual(tokenizer_r.add_tokens(["testoken1", "testtoken2"]), 2)
        self.assertEqual(len(tokenizer_r), vocab_size + 3)

        self.assertEqual(tokenizer_r.add_special_tokens({}), 0)
        self.assertRaises(
            AssertionError, tokenizer_r.add_special_tokens, {"additional_special_tokens": "<testtoken1>"}
        )
        self.assertEqual(tokenizer_r.add_special_tokens({"additional_special_tokens": ["<testtoken2>"]}), 1)
        self.assertEqual(
            tokenizer_r.add_special_tokens({"additional_special_tokens": ["<testtoken3>", "<testtoken4>"]}), 2
        )
        self.assertEqual(len(tokenizer_r), vocab_size + 6)

    def assert_offsets_mapping(self, tokenizer):
        text = "Wonderful no inspiration example with subtoken"
        pair = "Along with an awesome pair"

        # No pair
        tokens_with_offsets = tokenizer.encode_plus(text, return_special_tokens_mask=True, return_offsets_mapping=True)
        added_tokens = tokenizer.num_added_tokens(False)
        offsets = tokens_with_offsets["offset_mapping"]

        # Assert there is the same number of tokens and offsets
        self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"]))

        # Assert there is online added_tokens special_tokens
        self.assertEqual(sum([0 if x else 1 for x in offsets]), added_tokens)
        self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens)

        # Pairs
        tokens_with_offsets = tokenizer.encode_plus(
            text, pair, return_special_tokens_mask=True, return_offsets_mapping=True
        )
        added_tokens = tokenizer.num_added_tokens(True)
        offsets = tokens_with_offsets["offset_mapping"]

        # Assert there is the same number of tokens and offsets
        self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"]))

        # Assert there is online added_tokens special_tokens
        self.assertEqual(sum([0 if x else 1 for x in offsets]), added_tokens)
        self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens)

    def assert_batch_encode_dynamic_overflowing(self, tokenizer: PreTrainedTokenizer):
        """
        When calling batch_encode with multiple sequence it can returns different number of
        overflowing encoding for each sequence:
        [
          Sequence 1: [Encoding 1, Encoding 2],
          Sequence 2: [Encoding 1],
          Sequence 3: [Encoding 1, Encoding 2, ... Encoding N]
        ]
        This needs to be padded so that it can represented as a tensor
        """
        returned_tensor = "pt" if is_torch_available() else "tf"

        tokens = tokenizer.encode_plus(
            "HuggingFace is solving NLP one commit at a time",
            max_length=6,
            return_tensors=returned_tensor,
            return_overflowing_tokens=True,
        )

        for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()):
            self.assertEqual(len(tokens[key].shape), 2)

        # Mono sample
        tokens = tokenizer.batch_encode_plus(
            ["HuggingFace is solving NLP one commit at a time"],
            max_length=6,
            pad_to_max_len=True,
            return_tensors=returned_tensor,
            return_overflowing_tokens=True,
        )

        for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()):
            self.assertEqual(len(tokens[key].shape), 2)
            self.assertEqual(tokens[key].shape[-1], 6)

        # Multi sample
        tokens = tokenizer.batch_encode_plus(
            ["HuggingFace is solving NLP one commit at a time", "Very tiny input"],
            max_length=6,
            pad_to_max_len=True,
            return_tensors=returned_tensor,
            return_overflowing_tokens=True,
        )

        for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()):
            self.assertEqual(len(tokens[key].shape), 2)
            self.assertEqual(tokens[key].shape[-1], 6)

    def test_bert(self):
        for tokenizer_name in BertTokenizer.pretrained_vocab_files_map["vocab_file"].keys():
            tokenizer_p = BertTokenizer.from_pretrained(tokenizer_name)
            tokenizer_r = BertTokenizerFast.from_pretrained(tokenizer_name)

            # Check we have the same number of added_tokens for both pair and non-pair inputs.
            self.assertEqual(tokenizer_r.num_added_tokens(False), tokenizer_p.num_added_tokens(False))
            self.assertEqual(tokenizer_r.num_added_tokens(True), tokenizer_p.num_added_tokens(True))

            # Check we have the correct max_length for both pair and non-pair inputs.
            self.assertEqual(tokenizer_r.max_len_single_sentence, tokenizer_p.max_len_single_sentence)
            self.assertEqual(tokenizer_r.max_len_sentences_pair, tokenizer_p.max_len_sentences_pair)

            # Assert the set of special tokens match.
            self.assertSequenceEqual(
                tokenizer_p.special_tokens_map.items(),
                tokenizer_r.special_tokens_map.items(),
                "Bert tokenizers doesn't have the same set of special_tokens",
            )

            # Assure tokenization overlap between python and rust impl.
            self.assert_tokenization_python_rust_almost_equals(tokenizer_p, tokenizer_r, 0.0)

            # Ensure add_tokens and add_special_tokens return the correct vocab size
            self.assert_add_tokens(tokenizer_r)

            # Check for offsets mapping
            self.assert_offsets_mapping(tokenizer_r)

            # Check for dynamic encoding sequence handling in batch_encode_plus
            self.assert_batch_encode_dynamic_overflowing(tokenizer_r)

    @require_torch
    def test_transfoxl(self):
        for tokenizer_name in TransfoXLTokenizer.pretrained_vocab_files_map["pretrained_vocab_file"].keys():
            tokenizer_p = TransfoXLTokenizer.from_pretrained(tokenizer_name)
            tokenizer_r = TransfoXLTokenizerFast.from_pretrained(tokenizer_name)

            # Check we have the same number of added_tokens for both pair and non-pair inputs.
            self.assertEqual(tokenizer_r.num_added_tokens(False), tokenizer_p.num_added_tokens(False))
            self.assertEqual(tokenizer_r.num_added_tokens(True), tokenizer_p.num_added_tokens(True))

            # Check we have the correct max_length for both pair and non-pair inputs.
            self.assertEqual(tokenizer_r.max_len_single_sentence, tokenizer_p.max_len_single_sentence)
            self.assertEqual(tokenizer_r.max_len_sentences_pair, tokenizer_p.max_len_sentences_pair)

            # Assert the set of special tokens match.
            self.assertSequenceEqual(
                tokenizer_p.special_tokens_map.items(),
                tokenizer_r.special_tokens_map.items(),
                "TransfoXL tokenizers doesn't have the same set of special_tokens",
            )

            # Assure tokenization overlap between python and rust impl.
            self.assert_tokenization_python_rust_almost_equals(tokenizer_p, tokenizer_r, 0.0)

            # Ensure add_tokens and add_special_tokens return the correct vocab size
            self.assert_add_tokens(tokenizer_r)

            # Check for offsets mapping
            self.assert_offsets_mapping(tokenizer_r)

            # Check for dynamic encoding sequence handling in batch_encode_plus
            self.assertRaises(ValueError, self.assert_batch_encode_dynamic_overflowing, tokenizer_r)

    def test_distilbert(self):
        for tokenizer_name in DistilBertTokenizer.pretrained_vocab_files_map["vocab_file"].keys():
            tokenizer_p = DistilBertTokenizer.from_pretrained(tokenizer_name)
            tokenizer_r = DistilBertTokenizerFast.from_pretrained(tokenizer_name)

            # Check we have the same number of added_tokens for both pair and non-pair inputs.
            self.assertEqual(tokenizer_r.num_added_tokens(False), tokenizer_p.num_added_tokens(False))
            self.assertEqual(tokenizer_r.num_added_tokens(True), tokenizer_p.num_added_tokens(True))

            # Check we have the correct max_length for both pair and non-pair inputs.
            self.assertEqual(tokenizer_r.max_len_single_sentence, tokenizer_p.max_len_single_sentence)
            self.assertEqual(tokenizer_r.max_len_sentences_pair, tokenizer_p.max_len_sentences_pair)

            # DistilBert should match 100%
            # Assert the set of special tokens match.
            self.assertSequenceEqual(
                tokenizer_p.special_tokens_map.items(),
                tokenizer_r.special_tokens_map.items(),
                "DistilBert tokenizers doesn't have the same set of special_tokens",
            )

            # Assure tokenization overlap between python and rust impl.
            self.assert_tokenization_python_rust_almost_equals(tokenizer_p, tokenizer_r, 0.0)

            # Ensure add_tokens and add_special_tokens return the correct vocab size
            self.assert_add_tokens(tokenizer_r)

            # Check for offsets mapping
            self.assert_offsets_mapping(tokenizer_r)

            # Check for dynamic encoding sequence handling in batch_encode_plus
            self.assert_batch_encode_dynamic_overflowing(tokenizer_r)

    def test_gpt2(self):
        for tokenizer_name in GPT2Tokenizer.pretrained_vocab_files_map["vocab_file"].keys():
            tokenizer_p = GPT2Tokenizer.from_pretrained(tokenizer_name)
            tokenizer_r = GPT2TokenizerFast.from_pretrained(tokenizer_name)

            # Check we have the same number of added_tokens for both pair and non-pair inputs.
            self.assertEqual(tokenizer_r.num_added_tokens(False), tokenizer_p.num_added_tokens(False))
            self.assertEqual(tokenizer_r.num_added_tokens(True), tokenizer_p.num_added_tokens(True))

            # Check we have the correct max_length for both pair and non-pair inputs.
            self.assertEqual(tokenizer_r.max_len_single_sentence, tokenizer_p.max_len_single_sentence)
            self.assertEqual(tokenizer_r.max_len_sentences_pair, tokenizer_p.max_len_sentences_pair)

            # Assert the set of special tokens match.
            self.assertSequenceEqual(
                tokenizer_p.special_tokens_map.items(),
                tokenizer_r.special_tokens_map.items(),
                "GPT2 tokenizers doesn't have the same set of special_tokens",
            )

            # Assure tokenization overlap between python and rust impl.
            self.assert_tokenization_python_rust_almost_equals(tokenizer_p, tokenizer_r, 0.0)

            # Ensure add_tokens and add_special_tokens return the correct vocab size
            self.assert_add_tokens(tokenizer_r)

            # Check for offsets mapping
            self.assert_offsets_mapping(tokenizer_r)

            # Check for dynamic encoding sequence handling in batch_encode_plus
            self.assertRaises(ValueError, self.assert_batch_encode_dynamic_overflowing, tokenizer_r)

    def test_roberta(self):
        for tokenizer_name in RobertaTokenizer.pretrained_vocab_files_map["vocab_file"].keys():
            tokenizer_p = RobertaTokenizer.from_pretrained(tokenizer_name)
            tokenizer_r = RobertaTokenizerFast.from_pretrained(tokenizer_name)

            # Check we have the same number of added_tokens for both pair and non-pair inputs.
            self.assertEqual(tokenizer_r.num_added_tokens(False), tokenizer_p.num_added_tokens(False))
            self.assertEqual(tokenizer_r.num_added_tokens(True), tokenizer_p.num_added_tokens(True))

            # Check we have the correct max_length for both pair and non-pair inputs.
            self.assertEqual(tokenizer_r.max_len_single_sentence, tokenizer_p.max_len_single_sentence)
            self.assertEqual(tokenizer_r.max_len_sentences_pair, tokenizer_p.max_len_sentences_pair)

            # Assert the set of special tokens match.
            self.assertSequenceEqual(
                tokenizer_p.special_tokens_map.items(),
                tokenizer_r.special_tokens_map.items(),
                "Roberta tokenizers doesn't have the same set of special_tokens",
            )

            # Assure tokenization overlap between python and rust impl.
            self.assert_tokenization_python_rust_almost_equals(tokenizer_p, tokenizer_r, 0.01)

            # Ensure add_tokens and add_special_tokens return the correct vocab size
            self.assert_add_tokens(tokenizer_r)

            # Check for offsets mapping
            self.assert_offsets_mapping(tokenizer_r)

            # Check for dynamic encoding sequence handling in batch_encode_plus
            self.assert_batch_encode_dynamic_overflowing(tokenizer_r)

    def test_openai(self):
        for tokenizer_name in OpenAIGPTTokenizer.pretrained_vocab_files_map["vocab_file"].keys():
            tokenizer_p = OpenAIGPTTokenizer.from_pretrained(tokenizer_name)
            tokenizer_r = OpenAIGPTTokenizerFast.from_pretrained(tokenizer_name)

            # Check we have the same number of added_tokens for both pair and non-pair inputs.
            self.assertEqual(tokenizer_r.num_added_tokens(False), tokenizer_p.num_added_tokens(False))
            self.assertEqual(tokenizer_r.num_added_tokens(True), tokenizer_p.num_added_tokens(True))

            # Check we have the correct max_length for both pair and non-pair inputs.
            self.assertEqual(tokenizer_r.max_len_single_sentence, tokenizer_p.max_len_single_sentence)
            self.assertEqual(tokenizer_r.max_len_sentences_pair, tokenizer_p.max_len_sentences_pair)

            # Assert the set of special tokens match.
            self.assertSequenceEqual(
                tokenizer_p.special_tokens_map.items(),
                tokenizer_r.special_tokens_map.items(),
                "GPT tokenizers doesn't have the same set of special_tokens",
            )

            # Assure tokenization overlap between python and rust impl.
            self.assert_tokenization_python_rust_almost_equals(tokenizer_p, tokenizer_r, 0.0)

            # Ensure add_tokens and add_special_tokens return the correct vocab size
            self.assert_add_tokens(tokenizer_r)

            # Check for offsets mapping
            self.assert_offsets_mapping(tokenizer_r)

            # Check for dynamic encoding sequence handling in batch_encode_plus
            self.assertRaises(ValueError, self.assert_batch_encode_dynamic_overflowing, tokenizer_r)


if __name__ == "__main__":
    unittest.main()