# coding=utf-8 # Copyright 2023 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 pickle import shutil import tempfile import unittest from datasets import load_dataset from transformers import ( SPIECE_UNDERLINE, AddedToken, LlamaTokenizer, LlamaTokenizerFast, is_torch_available, ) from transformers.convert_slow_tokenizer import convert_slow_tokenizer from transformers.testing_utils import ( get_tests_dir, nested_simplify, require_jinja, require_sentencepiece, require_tokenizers, require_torch, slow, ) from ...test_tokenization_common import TokenizerTesterMixin SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") if is_torch_available(): pass @require_sentencepiece @require_tokenizers class LlamaTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = LlamaTokenizer rust_tokenizer_class = LlamaTokenizerFast test_rust_tokenizer = False test_sentencepiece = True from_pretrained_kwargs = {} def setUp(self): super().setUp() # We have a SentencePiece fixture for testing tokenizer = LlamaTokenizer(SAMPLE_VOCAB, keep_accents=True) tokenizer.pad_token = tokenizer.eos_token tokenizer.save_pretrained(self.tmpdirname) def get_tokenizers(self, **kwargs): kwargs.update({"pad_token": ""}) return super().get_tokenizers(**kwargs) def test_full_tokenizer(self): tokenizer = LlamaTokenizer(SAMPLE_VOCAB, keep_accents=True) tokens = tokenizer.tokenize("This is a test") self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"]) self.assertListEqual( tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382], ) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") self.assertListEqual( tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", ".", ], ) ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual( ids, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4], ) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual( back_tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "", ".", ], ) @unittest.skip("Let's wait for the fast tokenizer!") def test_save_pretrained(self): self.tokenizers_list += (self.rust_tokenizer_class, "hf-internal-testing/llama-tokenizer", {}) for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) tmpdirname2 = tempfile.mkdtemp() tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2) tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2) # Checks it save with the same files + the tokenizer.json file for the fast one self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files)) tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f) self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files) # Checks everything loads correctly in the same way tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2) tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(tokenizer_rp, key)) shutil.rmtree(tmpdirname2) # Save tokenizer rust, legacy_format=True tmpdirname2 = tempfile.mkdtemp() tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True) tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2) # Checks it save with the same files self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files) # Checks everything loads correctly in the same way tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2) tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(tokenizer_rp, key)) shutil.rmtree(tmpdirname2) # Save tokenizer rust, legacy_format=False tmpdirname2 = tempfile.mkdtemp() tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False) tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2) # Checks it saved the tokenizer.json file self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files)) # Checks everything loads correctly in the same way tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2) tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(tokenizer_rp, key)) shutil.rmtree(tmpdirname2) @require_torch def test_batch_tokenization(self): if not self.test_seq2seq: return tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # Longer text that will definitely require truncation. text = [ " UN Chief Says There Is No Military Solution in Syria", " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for" " Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons" " will only worsen the violence and misery for millions of people.", ] try: batch = tokenizer( text=text, max_length=3, max_target_length=10, return_tensors="pt", ) except NotImplementedError: return self.assertEqual(batch.input_ids.shape[1], 3) # max_target_length will default to max_length if not specified batch = tokenizer(text, max_length=3, return_tensors="pt") self.assertEqual(batch.input_ids.shape[1], 3) batch_encoder_only = tokenizer(text=text, max_length=3, max_target_length=10, return_tensors="pt") self.assertEqual(batch_encoder_only.input_ids.shape[1], 3) self.assertEqual(batch_encoder_only.attention_mask.shape[1], 3) self.assertNotIn("decoder_input_ids", batch_encoder_only) @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.") def test_save_slow_from_fast_and_reload_fast(self): pass def test_special_tokens_initialization(self): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): added_tokens = [AddedToken("", lstrip=True)] tokenizer_r = self.rust_tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs ) r_output = tokenizer_r.encode("Hey this is a token") special_token_id = tokenizer_r.encode("", add_special_tokens=False)[0] self.assertTrue(special_token_id in r_output) if self.test_slow_tokenizer: tokenizer_cr = self.rust_tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs, # , from_slow=True <- unfortunately too slow to convert ) tokenizer_p = self.tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs ) p_output = tokenizer_p.encode("Hey this is a token") cr_output = tokenizer_cr.encode("Hey this is a token") self.assertEqual(p_output, r_output) self.assertEqual(cr_output, r_output) self.assertTrue(special_token_id in p_output) self.assertTrue(special_token_id in cr_output) @slow def test_tokenizer_integration(self): # fmt: off 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]]} # fmt: on self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="hf-internal-testing/llama-tokenizer", revision="0984d03108b1a041ed679bd253b6519b7e1a4778", padding=False, ) def test_picklable(self): with tempfile.NamedTemporaryFile() as f: shutil.copyfile(SAMPLE_VOCAB, f.name) tokenizer = LlamaTokenizer(f.name, keep_accents=True) pickled_tokenizer = pickle.dumps(tokenizer) pickle.loads(pickled_tokenizer) @unittest.skip("worker 'gw4' crashed on CI, passing locally.") def test_pickle_subword_regularization_tokenizer(self): pass @unittest.skip("worker 'gw4' crashed on CI, passing locally.") def test_subword_regularization_tokenizer(self): pass @require_torch @require_sentencepiece @require_tokenizers class LlamaIntegrationTest(unittest.TestCase): @classmethod def setUpClass(cls): checkpoint_name = "hf-internal-testing/llama-tokenizer-non-normalized" cls.tokenizer: LlamaTokenizer = LlamaTokenizer.from_pretrained(checkpoint_name) cls.rust_tokenizer = LlamaTokenizerFast.from_pretrained(checkpoint_name) return cls @require_torch def integration_tests(self): inputs = self.tokenizer( ["The following string should be properly encoded: Hello.", "But ird and ปี ird ด"], return_tensors="pt", ) self.assertEqual( nested_simplify(inputs), { "input_ids": [ [1, 450, 1494, 1347, 881, 367, 6284, 18511, 29901, 15043, 29889], [1, 1205, 29871, 1823, 322, 29871, 31010, 30691, 1678, 1823, 1678, 30718], ], "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]], }, ) def test_fast_special_tokens(self): slow_tokenizer = self.tokenizer fast_tokenizer = self.rust_tokenizer slow = slow_tokenizer.encode("A sample test", add_special_tokens=True) assert slow == [1, 319, 4559, 1243] fast_tokenizer.add_eos_token = False fast = fast_tokenizer.encode("A sample test", add_special_tokens=True) assert fast == [1, 319, 4559, 1243] fast_tokenizer.add_eos_token = True fast = fast_tokenizer.encode("A sample test", add_special_tokens=True) assert fast == [1, 319, 4559, 1243, 2] slow_tokenizer.add_eos_token = True slow = slow_tokenizer.encode("A sample test", add_special_tokens=True) assert slow == [1, 319, 4559, 1243, 2] fast_tokenizer = LlamaTokenizerFast.from_pretrained( "hf-internal-testing/llama-tokenizer", add_eos_token=True, add_bos_token=False ) fast = fast_tokenizer.encode("A sample test", add_special_tokens=True) assert fast == [319, 4559, 1243, 2] slow_tokenzier = LlamaTokenizer.from_pretrained( "hf-internal-testing/llama-tokenizer", add_eos_token=True, add_bos_token=False ) slow = slow_tokenzier.encode("A sample test", add_special_tokens=True) assert slow == [319, 4559, 1243, 2] self.tokenizer.add_eos_token = False self.rust_tokenizer.add_eos_token = False @slow def test_conversion(self): # This is excruciatingly slow since it has to recreate the entire merge # list from the original vocabulary in spm self.rust_tokenizer.save_pretrained("./out") with tempfile.TemporaryDirectory() as dirname: self.rust_tokenizer.save_pretrained(dirname) with open(os.path.join(dirname, "tokenizer.json"), "r") as f: old_serialized = f.read() new_tokenizer = convert_slow_tokenizer(self.tokenizer) with tempfile.NamedTemporaryFile() as f: new_tokenizer.save(f.name) # Re-opening since `f` is in bytes. new_serialized = open(f.name, "r").read() with open("out_tokenizer.json", "w") as g: g.write(new_serialized) self.assertEqual(old_serialized, new_serialized) def test_simple_encode_decode(self): pyth_tokenizer = self.tokenizer rust_tokenizer = self.rust_tokenizer self.assertEqual(pyth_tokenizer.encode("This is a test"), [1, 910, 338, 263, 1243]) self.assertEqual(rust_tokenizer.encode("This is a test"), [1, 910, 338, 263, 1243]) self.assertEqual(pyth_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test") self.assertEqual(rust_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test") # bytefallback showcase self.assertEqual(pyth_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392]) self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392]) self.assertEqual( pyth_tokenizer.decode( [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True ), "生活的真谛是", ) self.assertEqual( rust_tokenizer.decode( [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True ), "生活的真谛是", ) # Inner spaces showcase self.assertEqual(pyth_tokenizer.encode("Hi Hello"), [1, 6324, 29871, 15043]) self.assertEqual(rust_tokenizer.encode("Hi Hello"), [1, 6324, 29871, 15043]) self.assertEqual(pyth_tokenizer.decode([1, 6324, 29871, 15043], skip_special_tokens=True), "Hi Hello") self.assertEqual(rust_tokenizer.decode([1, 6324, 29871, 15043], skip_special_tokens=True), "Hi Hello") self.assertEqual(pyth_tokenizer.encode("Hi Hello"), [1, 6324, 259, 15043]) self.assertEqual(rust_tokenizer.encode("Hi Hello"), [1, 6324, 259, 15043]) self.assertEqual(pyth_tokenizer.decode([1, 6324, 259, 15043], skip_special_tokens=True), "Hi Hello") self.assertEqual(rust_tokenizer.decode([1, 6324, 259, 15043], skip_special_tokens=True), "Hi Hello") self.assertEqual(pyth_tokenizer.encode(""), [1]) self.assertEqual(rust_tokenizer.encode(""), [1]) self.assertEqual(pyth_tokenizer.encode(" "), [1, 259]) self.assertEqual(rust_tokenizer.encode(" "), [1, 259]) self.assertEqual(pyth_tokenizer.encode(" "), [1, 1678]) self.assertEqual(rust_tokenizer.encode(" "), [1, 1678]) self.assertEqual(pyth_tokenizer.encode(" Hello"), [1, 29871, 15043]) self.assertEqual(rust_tokenizer.encode(" Hello"), [1, 29871, 15043]) def test_no_differences_showcase(self): pyth_tokenizer = self.tokenizer rust_tokenizer = self.rust_tokenizer self.assertEqual(pyth_tokenizer.encode(""), [1]) self.assertEqual(rust_tokenizer.encode(""), [1]) self.assertEqual(pyth_tokenizer.encode(" "), [1, 259]) self.assertEqual(rust_tokenizer.encode(" "), [1, 259]) self.assertEqual(pyth_tokenizer.encode(" "), [1, 1678]) self.assertEqual(rust_tokenizer.encode(" "), [1, 1678]) self.assertEqual(pyth_tokenizer.encode(" Hello"), [1, 29871, 15043]) self.assertEqual(rust_tokenizer.encode(" Hello"), [1, 29871, 15043]) self.assertEqual(pyth_tokenizer.encode(""), [1, 1]) self.assertEqual(rust_tokenizer.encode(""), [1, 1]) def test_no_differences_decode(self): pyth_tokenizer = self.tokenizer rust_tokenizer = self.rust_tokenizer self.assertEqual(pyth_tokenizer.decode([869]), ".") self.assertEqual(rust_tokenizer.decode([869]), ".") self.assertEqual(pyth_tokenizer.decode([30112, 869]), "ا .") self.assertEqual(rust_tokenizer.decode([30112, 869]), "ا .") def test_no_differences_special_tokens(self): pyth_tokenizer = self.tokenizer rust_tokenizer = self.rust_tokenizer self.assertEqual(pyth_tokenizer.encode(""), [1]) self.assertEqual(rust_tokenizer.encode(""), [1]) self.assertEqual(pyth_tokenizer.encode(""), [1, 1]) self.assertEqual(rust_tokenizer.encode(""), [1, 1]) @unittest.skipIf( os.getenv("RUN_TOKENIZER_INTEGRATION", "0") == "0", "RUN_TOKENIZER_INTEGRATION=1 to run tokenizer integration tests", ) def test_integration_test_xnli(self): import tqdm pyth_tokenizer = self.tokenizer rust_tokenizer = self.rust_tokenizer dataset = load_dataset("code_x_glue_ct_code_to_text", "go") for item in tqdm.tqdm(dataset["validation"]): string = item["code"] encoded1 = pyth_tokenizer.encode(string) encoded2 = rust_tokenizer.encode(string) self.assertEqual(encoded1, encoded2) decoded1 = pyth_tokenizer.decode(encoded1, skip_special_tokens=True) decoded2 = rust_tokenizer.decode(encoded2, skip_special_tokens=True) self.assertEqual(decoded1, decoded2) dataset = load_dataset("xnli", "all_languages") for item in tqdm.tqdm(dataset["train"]): for string in item["premise"].values(): encoded1 = pyth_tokenizer.encode(string) encoded2 = rust_tokenizer.encode(string) self.assertEqual(encoded1, encoded2) decoded1 = pyth_tokenizer.decode(encoded1, skip_special_tokens=True) decoded2 = rust_tokenizer.decode(encoded2, skip_special_tokens=True) self.assertEqual(decoded1, decoded2) def test_special_token_special_word(self): # the word inform should be split as ['in', 'form'] tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", legacy=False) tokenizer.add_tokens([""], special_tokens=False) out1 = tokenizer.decode( tokenizer.encode("inform", add_special_tokens=False), spaces_between_special_tokens=False ) self.assertEqual(out1, "inform") out2 = tokenizer.decode( tokenizer.encode("inform", add_special_tokens=False), spaces_between_special_tokens=True ) # decoding strips the added prefix space. self.assertEqual(out2, " inform") input_ids = tokenizer.encode("inform", add_special_tokens=False) self.assertEqual(input_ids, [29871, 32000, 262, 689]) # 29871 is the spiece underline, '▁' added as it should out2 = tokenizer.decode( tokenizer.encode(" inform", add_special_tokens=False), spaces_between_special_tokens=False ) # TODO @ArthurZ currently we strip left and right, so this will not keep the spaces self.assertEqual(out2, "inform") ### Let's make sure decoding does not add extra spaces here and there # TODO @ArthurZ this should be affected by the lstrip/rstrip/single word /normalize refactoring # Since currently we always strip left and right of the token, results are as such input_ids = tokenizer.encode(" Hellohow", add_special_tokens=False) self.assertEqual(input_ids, [1, 15043, 1, 3525]) tokens = tokenizer.tokenize(" Hellohow", add_special_tokens=False) self.assertEqual(tokens, ["", "▁Hello", "", "how"]) decoded_tokens = tokenizer.decode(input_ids) self.assertEqual(decoded_tokens, " Hellohow") # Let's make sure that if there are any spaces, we don't remove them! input_ids = tokenizer.encode(" Hello how", add_special_tokens=False) self.assertEqual(input_ids, [259, 1, 15043, 1, 920]) tokens = tokenizer.tokenize(" Hello how", add_special_tokens=False) self.assertEqual(tokens, ["▁▁", "", "▁Hello", "", "▁how"]) decoded_tokens = tokenizer.decode(input_ids) self.assertEqual(decoded_tokens, " Hello how") def test_some_edge_cases(self): tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", legacy=False) sp_tokens = tokenizer.sp_model.encode(">", out_type=str) self.assertEqual(sp_tokens, ["<", "s", ">>"]) tokens = tokenizer.tokenize(">") self.assertNotEqual(sp_tokens, tokens) self.assertEqual(tokens, ["", ">"]) tokens = tokenizer.tokenize("") self.assertEqual(tokens, []) self.assertEqual(tokens, tokenizer.sp_model.encode("", out_type=str)) tokens = tokenizer.tokenize(" ") self.assertEqual(tokens, ["▁▁"]) # a dummy prefix space is not added by the sp_model as it was de-activated self.assertEqual(tokens, tokenizer.sp_model.encode(" ", out_type=str)) tokens = tokenizer.tokenize("▁") self.assertEqual(tokens, ["▁▁"]) # a dummy prefix space is not added by the sp_model as it was de-activated self.assertEqual(tokens, tokenizer.sp_model.encode("▁▁", out_type=str)) tokens = tokenizer.tokenize(" ▁") self.assertEqual(tokens, ["▁▁▁"]) # a dummy prefix space is not added by the sp_model as it was de-activated self.assertEqual(tokens, tokenizer.sp_model.encode("▁▁▁", out_type=str)) def test_fast_post_processor(self): tokenizer = LlamaTokenizerFast( SAMPLE_VOCAB, eos_token=None, bos_token=None, add_bos_token=False, add_eos_token=False ) tokenizer.encode(" Hey ") with self.assertRaises(ValueError): tokenizer = LlamaTokenizerFast( SAMPLE_VOCAB, bos_token=None, eos_token="", add_bos_token=True, add_eos_token=False ) with self.assertRaises(ValueError): tokenizer = LlamaTokenizerFast(SAMPLE_VOCAB, eos_token=None, add_bos_token=True, add_eos_token=True) @require_jinja def test_tokenization_for_chat(self): tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", legacy=False) test_chats = [ [{"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}], [ {"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}, {"role": "assistant", "content": "Nice to meet you."}, ], [{"role": "user", "content": "Hello!"}], ] # Matt: The third test case tests the default system message, but if this is ever changed in the # class/repo code then that test will fail, and the case will need to be updated. tokenized_chats = [tokenizer.apply_chat_template(test_chat) for test_chat in test_chats] # fmt: off expected_tokens = [ [1, 29961, 25580, 29962, 3532, 14816, 29903, 6778, 13, 3492, 526, 263, 8444, 13563, 7451, 29889, 13, 29966, 829, 14816, 29903, 6778, 13, 13, 10994, 29991, 518, 29914, 25580, 29962], [1, 29961, 25580, 29962, 3532, 14816, 29903, 6778, 13, 3492, 526, 263, 8444, 13563, 7451, 29889, 13, 29966, 829, 14816, 29903, 6778, 13, 13, 10994, 29991, 518, 29914, 25580, 29962, 20103, 304, 5870, 366, 29889, 29871, 2], [1, 29961, 25580, 29962, 15043, 29991, 518, 29914, 25580, 29962] ] # fmt: on for tokenized_chat, expected_tokens in zip(tokenized_chats, expected_tokens): self.assertListEqual(tokenized_chat, expected_tokens) @require_sentencepiece @require_tokenizers class CommonSpmIntegrationTests(unittest.TestCase): """ A class that regroups important test to make sure that we properly handle the special tokens. """ @classmethod def setUpClass(cls): tokenizer = LlamaTokenizer(SAMPLE_VOCAB, extra_ids=0, add_bos_token=False, legacy=False) tokenizer.add_special_tokens({"additional_special_tokens": [AddedToken("", rstrip=False, lstrip=False)]}) cls.tokenizer = tokenizer return cls def test_add_dummy_prefix(self): # make sure `'▁'` is prepended, and outputs match sp_model's # `sentencepiece.NormalizerSpec.add_dummy_prefix` attribute input_ids = self.tokenizer.encode(". Hello") self.assertEqual(input_ids, [7, 4, 156, 86, 20]) sp_encode = self.tokenizer.sp_model.encode(". Hello") self.assertEqual(input_ids, [7] + sp_encode) tokens = self.tokenizer.tokenize(". Hello") self.assertEqual(tokens, ["▁", ".", "▁He", "ll", "o"]) tokens = self.tokenizer.tokenize("") self.assertEqual(tokens, []) self.assertEqual(tokens, self.tokenizer.sp_model.encode("", out_type=str)) tokens = self.tokenizer.tokenize(" ") self.assertEqual(tokens, []) self.assertEqual(tokens, self.tokenizer.sp_model.encode(" ", out_type=str)) tokens = self.tokenizer.tokenize("▁") self.assertEqual(tokens, []) self.assertEqual(tokens, self.tokenizer.sp_model.encode("▁", out_type=str)) def test_remove_extra_whitespaces(self): # make sure the extra spaces are eaten. Since the sample vocab does not have # `______`. sentencepiece.NormalizerSpec.remove_extra_whitespaces attribute is set to False input_ids = self.tokenizer.encode(" . Hello") self.assertEqual(input_ids, [7, 4, 156, 86, 20]) sp_encode = self.tokenizer.sp_model.encode(" . Hello") self.assertEqual(input_ids, [7] + sp_encode) tokens = self.tokenizer.tokenize(" . Hello") self.assertEqual(tokens, ["▁", ".", "▁He", "ll", "o"]) # `'▁'` is also a whitespace input_ids = self.tokenizer.encode("▁He is not") self.assertEqual(input_ids, [156, 46, 44]) tokens = self.tokenizer.tokenize("▁He is not") sp_encode = [ self.tokenizer.sp_model.piece_to_id("▁He"), self.tokenizer.sp_model.piece_to_id("▁is"), self.tokenizer.sp_model.piece_to_id("▁not"), ] self.assertEqual(input_ids, sp_encode) self.assertEqual(tokens, ["▁He", "▁is", "▁not"]) # no extra space added input_ids = self.tokenizer.encode("▁He is not ▁He") self.assertEqual(input_ids, [156, 46, 44, 1, 156]) tokens = self.tokenizer.tokenize("▁He is not ▁He") self.assertEqual(tokens, ["▁He", "▁is", "▁not", "", "▁He"]) # spaces are eaten by spm + our strip # make sure that the output after the extra id is the same as if # extra_id was not there input_ids = self.tokenizer.encode("▁He is not ▁He") self.assertEqual(input_ids, [156, 46, 44, 156]) tokens = self.tokenizer.tokenize("▁He is not ▁He") self.assertEqual(tokens, ["▁He", "▁is", "▁not", "▁He"]) # spaces are eaten by spm even if not start def test_character_after_special_token(self): # Make sure that `tokenizer.tokenize` is similar to # adding the equivalent special token to the vocab input_ids = self.tokenizer.encode("Hey I") self.assertEqual(input_ids, [156, 30, 1, 100]) sp_encode = self.tokenizer.sp_model.encode("Hey .I") # the last token should be 100 self.assertEqual(input_ids[-1], sp_encode[-1]) tokens = self.tokenizer.tokenize("I") self.assertEqual(tokens, ["", "I"]) input_ids = self.tokenizer.encode("Hello, ,") self.assertEqual(input_ids, [156, 86, 20, 3, 1, 3]) tokens = self.tokenizer.tokenize("Hello, ,") self.assertEqual(tokens, ["▁He", "ll", "o", ",", "", ","]) def test_special_tokens_strip(self): input_ids = self.tokenizer.encode(" ,") self.assertEqual(input_ids, [1, 7, 3]) tokens = self.tokenizer.tokenize(" ,") # spaces are eaten by rstrip / lstrip + spm sp_model.encode(" ") = [] self.assertEqual(tokens, ["", "▁", ","]) input_ids = self.tokenizer.encode("No ▁He") self.assertEqual(input_ids, [284, 1, 156]) tokens = self.tokenizer.tokenize("No ▁He") self.assertEqual(tokens, ["▁No", "", "▁He"]) # spaces are eaten by rstrip / lstrip