mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-05 22:00:09 +06:00
c0f99b4d2e
* draft * update tokenization limma and conversion script * more udpates * initial commit * style * default pad to None * draft tokenization tests * update test * update tokenization tests * nits * update * versioning test * major fix * fix more testst * finish fixing special masks * last nit * more nits * add encode decode tests * add more * fix token type ids * style
413 lines
18 KiB
Python
413 lines
18 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import shutil
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import tempfile
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import unittest
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from datasets import load_dataset
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from transformers import (
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SPIECE_UNDERLINE,
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AddedToken,
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LlamaTokenizer,
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is_torch_available,
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)
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from transformers.testing_utils import (
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get_tests_dir,
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nested_simplify,
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require_sentencepiece,
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require_tokenizers,
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require_torch,
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slow,
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)
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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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pass
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@require_sentencepiece
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@require_tokenizers
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class LlamaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = LlamaTokenizer
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test_rust_tokenizer = False
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test_sentencepiece = True
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from_pretrained_kwargs = {}
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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 = LlamaTokenizer(SAMPLE_VOCAB, keep_accents=True)
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.save_pretrained(self.tmpdirname)
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def test_full_tokenizer(self):
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tokenizer = LlamaTokenizer(SAMPLE_VOCAB, keep_accents=True)
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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(
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tokenizer.convert_tokens_to_ids(tokens),
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[285, 46, 10, 170, 382],
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)
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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(
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ids,
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[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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)
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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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@unittest.skip("Let's wait for the fast tokenizer!")
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def test_save_pretrained(self):
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self.tokenizers_list += (self.rust_tokenizer_class, "hf-internal-testing/llama-tokenizer", {})
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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tmpdirname2 = tempfile.mkdtemp()
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tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
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tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
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# Checks it save with the same files + the tokenizer.json file for the fast one
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self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
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tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
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self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
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# Checks everything loads correctly in the same way
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tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
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tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
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# Check special tokens are set accordingly on Rust and Python
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for key in tokenizer_pp.special_tokens_map:
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self.assertTrue(hasattr(tokenizer_rp, key))
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shutil.rmtree(tmpdirname2)
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# Save tokenizer rust, legacy_format=True
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tmpdirname2 = tempfile.mkdtemp()
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tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True)
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tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
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# Checks it save with the same files
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self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
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# Checks everything loads correctly in the same way
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tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
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tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
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# Check special tokens are set accordingly on Rust and Python
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for key in tokenizer_pp.special_tokens_map:
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self.assertTrue(hasattr(tokenizer_rp, key))
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shutil.rmtree(tmpdirname2)
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# Save tokenizer rust, legacy_format=False
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tmpdirname2 = tempfile.mkdtemp()
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tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False)
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tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
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# Checks it saved the tokenizer.json file
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self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
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# Checks everything loads correctly in the same way
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tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
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tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
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# Check special tokens are set accordingly on Rust and Python
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for key in tokenizer_pp.special_tokens_map:
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self.assertTrue(hasattr(tokenizer_rp, key))
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shutil.rmtree(tmpdirname2)
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@require_torch
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def test_batch_tokenization(self):
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if not self.test_seq2seq:
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return
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tokenizers = self.get_tokenizers()
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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# Longer text that will definitely require truncation.
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text = [
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" UN Chief Says There Is No Military Solution in Syria",
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" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
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" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
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" will only worsen the violence and misery for millions of people.",
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]
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try:
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batch = tokenizer(
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text=text,
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max_length=3,
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max_target_length=10,
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return_tensors="pt",
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)
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except NotImplementedError:
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return
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self.assertEqual(batch.input_ids.shape[1], 3)
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# max_target_length will default to max_length if not specified
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batch = tokenizer(text, max_length=3, return_tensors="pt")
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self.assertEqual(batch.input_ids.shape[1], 3)
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batch_encoder_only = tokenizer(text=text, max_length=3, max_target_length=10, return_tensors="pt")
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self.assertEqual(batch_encoder_only.input_ids.shape[1], 3)
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self.assertEqual(batch_encoder_only.attention_mask.shape[1], 3)
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self.assertNotIn("decoder_input_ids", batch_encoder_only)
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@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.")
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def test_save_slow_from_fast_and_reload_fast(self):
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pass
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def test_special_tokens_initialization(self):
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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added_tokens = [AddedToken("<special>", lstrip=True)]
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tokenizer_r = self.rust_tokenizer_class.from_pretrained(
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pretrained_name, additional_special_tokens=added_tokens, **kwargs
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)
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r_output = tokenizer_r.encode("Hey this is a <special> token")
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special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
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self.assertTrue(special_token_id in r_output)
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if self.test_slow_tokenizer:
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tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
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pretrained_name,
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additional_special_tokens=added_tokens,
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**kwargs, # , from_slow=True <- unfortunately too slow to convert
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)
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tokenizer_p = self.tokenizer_class.from_pretrained(
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pretrained_name, additional_special_tokens=added_tokens, **kwargs
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)
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p_output = tokenizer_p.encode("Hey this is a <special> token")
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cr_output = tokenizer_cr.encode("Hey this is a <special> token")
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self.assertEqual(p_output, r_output)
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self.assertEqual(cr_output, r_output)
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self.assertTrue(special_token_id in p_output)
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self.assertTrue(special_token_id in cr_output)
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@slow
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def test_tokenizer_integration(self):
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# fmt: off
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expected_encoding = {'input_ids': [[1, 4103, 689, 414, 313, 24784, 368, 2998, 408, 282, 3637, 25350, 29899, 9067, 414, 322, 282, 3637, 25350, 29899, 1457, 3018, 1312, 29899, 2151, 29897, 8128, 2498, 29899, 15503, 4220, 6956, 1973, 313, 13635, 29911, 29892, 402, 7982, 29899, 29906, 29892, 1528, 13635, 29911, 29874, 29892, 1060, 26369, 29892, 6652, 309, 29933, 814, 29892, 1060, 29931, 6779, 11410, 363, 18385, 17088, 7634, 11235, 313, 25103, 29965, 29897, 322, 18385, 17088, 28203, 313, 25103, 29954, 29897, 411, 975, 29871, 29941, 29906, 29974, 758, 3018, 1312, 4733, 297, 29871, 29896, 29900, 29900, 29974, 10276, 322, 6483, 1006, 3372, 3097, 1546, 435, 1165, 29892, 10772, 29911, 25350, 322, 323, 6073, 17907, 29889], [1, 350, 20161, 338, 8688, 304, 758, 29899, 14968, 6483, 21000, 8684, 284, 22540, 515, 443, 29880, 24025, 1426, 491, 14002, 368, 4195, 292, 373, 1716, 2175, 322, 1492, 3030, 297, 599, 15359, 29889], [1, 450, 4996, 17354, 1701, 29916, 432, 17204, 975, 278, 17366, 11203, 29889]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
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# fmt: on
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self.tokenizer_integration_test_util(
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expected_encoding=expected_encoding,
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model_name="hf-internal-testing/llama-tokenizer",
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revision="0984d03108b1a041ed679bd253b6519b7e1a4778",
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padding=False,
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)
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@require_torch
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@require_sentencepiece
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@require_tokenizers
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class LlamaIntegrationTest(unittest.TestCase):
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checkpoint_name = "hf-internal-testing/llama-tokenizer"
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@classmethod
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def setUpClass(cls):
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cls.tokenizer: LlamaTokenizer = LlamaTokenizer.from_pretrained(cls.checkpoint_name)
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cls.rust_tokenizer = cls.tokenizer # TODO @narsil replace with the rust one
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cls.pad_token_id = 1
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return cls
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@require_torch
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def integration_tests(self):
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inputs = self.tokenizer(
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["The following string should be properly encoded: Hello.", "But ird and ปี ird ด"],
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return_tensors="pt",
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)
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self.assertEqual(
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nested_simplify(inputs),
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{
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"input_ids": [
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[1, 450, 1494, 1347, 881, 367, 6284, 18511, 29901, 15043, 29889],
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[1, 1205, 29871, 1823, 322, 29871, 31010, 30691, 1678, 1823, 1678, 30718],
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],
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"attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]],
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},
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)
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def test_simple_encode_decode(self):
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pyth_tokenizer = self.tokenizer
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rust_tokenizer = self.rust_tokenizer
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self.assertEqual(pyth_tokenizer.encode("This is a test"), [1, 910, 338, 263, 1243])
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self.assertEqual(rust_tokenizer.encode("This is a test"), [1, 910, 338, 263, 1243])
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self.assertEqual(pyth_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test")
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self.assertEqual(rust_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test")
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# bytefallback showcase
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self.assertEqual(pyth_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392])
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self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392])
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self.assertEqual(
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pyth_tokenizer.decode(
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[1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True
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),
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"生活的真谛是",
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)
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self.assertEqual(
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rust_tokenizer.decode(
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[1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True
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),
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"生活的真谛是",
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)
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# Inner spaces showcase
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self.assertEqual(pyth_tokenizer.encode("Hi Hello"), [1, 6324, 29871, 15043])
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self.assertEqual(rust_tokenizer.encode("Hi Hello"), [1, 6324, 29871, 15043])
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self.assertEqual(pyth_tokenizer.decode([1, 6324, 29871, 15043], skip_special_tokens=True), "Hi Hello")
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self.assertEqual(rust_tokenizer.decode([1, 6324, 29871, 15043], skip_special_tokens=True), "Hi Hello")
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self.assertEqual(pyth_tokenizer.encode("Hi Hello"), [1, 6324, 259, 15043])
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self.assertEqual(rust_tokenizer.encode("Hi Hello"), [1, 6324, 259, 15043])
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self.assertEqual(pyth_tokenizer.decode([1, 6324, 259, 15043], skip_special_tokens=True), "Hi Hello")
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self.assertEqual(rust_tokenizer.decode([1, 6324, 259, 15043], skip_special_tokens=True), "Hi Hello")
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self.assertEqual(pyth_tokenizer.encode(""), [1])
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self.assertEqual(rust_tokenizer.encode(""), [1])
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self.assertEqual(pyth_tokenizer.encode(" "), [1, 259])
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self.assertEqual(rust_tokenizer.encode(" "), [1, 259])
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self.assertEqual(pyth_tokenizer.encode(" "), [1, 1678])
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self.assertEqual(rust_tokenizer.encode(" "), [1, 1678])
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self.assertEqual(pyth_tokenizer.encode(" Hello"), [1, 29871, 15043])
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self.assertEqual(rust_tokenizer.encode(" Hello"), [1, 29871, 15043])
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self.assertEqual(pyth_tokenizer.encode("<s>"), [1, 1])
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self.assertEqual(rust_tokenizer.encode("<s>"), [1, 1])
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self.assertEqual(pyth_tokenizer.encode(""), [1])
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self.assertEqual(rust_tokenizer.encode(""), [1])
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self.assertEqual(pyth_tokenizer.decode([869]), ".")
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self.assertEqual(rust_tokenizer.decode([869]), ".")
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self.assertEqual(pyth_tokenizer.decode([30112, 869]), "ا .")
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self.assertEqual(rust_tokenizer.decode([30112, 869]), "ا .")
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@unittest.skipIf(
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os.getenv("RUN_TOKENIZER_INTEGRATION", "0") == "0",
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"RUN_TOKENIZER_INTEGRATION=1 to run tokenizer integration tests",
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)
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def test_integration_test_xnli(self):
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import tqdm
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pyth_tokenizer = self.tokenizer
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rust_tokenizer = self.rust_tokenizer
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dataset = load_dataset("code_x_glue_ct_code_to_text", "go")
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for item in tqdm.tqdm(dataset["validation"]):
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string = item["code"]
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encoded1 = pyth_tokenizer.encode(string)
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encoded2 = rust_tokenizer.encode(string)
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self.assertEqual(encoded1, encoded2)
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decoded1 = pyth_tokenizer.decode(encoded1)
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decoded2 = rust_tokenizer.decode(encoded2)
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self.assertEqual(decoded1, decoded2)
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dataset = load_dataset("xnli", "all_languages")
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for item in tqdm.tqdm(dataset["train"]):
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for string in item["premise"].values():
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encoded1 = pyth_tokenizer.encode(string)
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encoded2 = rust_tokenizer.encode(string)
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self.assertEqual(encoded1, encoded2)
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decoded1 = pyth_tokenizer.decode(encoded1)
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decoded2 = rust_tokenizer.decode(encoded2)
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self.assertEqual(decoded1, decoded2)
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