wehub-resource-sync
295 lines
12 KiB
Python
295 lines
12 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2024 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 unittest
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from paddlenlp.transformers.gemma.tokenizer import GemmaTokenizer
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from paddlenlp.transformers.gemma.tokenizer_fast import GemmaTokenizerFast
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from paddlenlp.transformers.tokenizer_utils import AddedToken, PretrainedTokenizer
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from ..test_tokenizer_common import TokenizerTesterMixin
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VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
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class GemmaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = GemmaTokenizer
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rust_tokenizer_class = GemmaTokenizerFast
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# skip test_create_token_type_ids cause transformers skip it
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test_rust_tokenizer = False
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test_decode_token = True
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def get_tokenizer(self, **kwargs) -> PretrainedTokenizer:
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tokenizer = GemmaTokenizer.from_pretrained("google/gemma-2b", **kwargs)
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return tokenizer
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def get_rust_tokenizer(self, **kwargs) -> PretrainedTokenizer:
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tokenizer = GemmaTokenizerFast.from_pretrained("google/gemma-2b", **kwargs)
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return tokenizer
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def get_input_output_texts(self, tokenizer):
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input_text = "lower newer"
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output_text = "lower newer"
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return input_text, output_text
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def test_full_tokenizer(self):
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tokenizer = self.get_tokenizer()
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text = "lower newer"
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bpe_tokens = ["lower", "▁newer"]
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tokens = tokenizer.tokenize(text, add_prefix_space=True)
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self.assertListEqual(tokens, bpe_tokens)
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input_tokens = tokens + [tokenizer.unk_token]
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input_bpe_tokens = [15964, 36649, 3]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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def test_pretokenized_inputs(self, *args, **kwargs):
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pass
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def test_tokenizers_common_ids_setters(self, *args, **kwargs):
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pass
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def test_mask_output(self):
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pass
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def test_offsets_mapping(self):
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pass
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def test_offsets_mapping_with_unk(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")["input_ids"]
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special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)["input_ids"]
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self.assertTrue(special_token_id[0] in r_output)
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def test_fast_special_tokens(self):
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slow_tokenizer = self.get_tokenizer()
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fast_tokenizer = self.get_rust_tokenizer()
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slow = slow_tokenizer.encode("A sample test", add_special_tokens=True)["input_ids"]
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assert slow == [2, 235280, 6453, 2121]
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fast_tokenizer.add_eos_token = False
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fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)["input_ids"]
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assert fast == [2, 235280, 6453, 2121]
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fast_tokenizer.add_eos_token = True
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fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)["input_ids"]
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assert fast == [2, 235280, 6453, 2121, 1]
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slow_tokenizer.add_eos_token = True
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slow = slow_tokenizer.encode("A sample test", add_special_tokens=True)["input_ids"]
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assert slow == [2, 235280, 6453, 2121, 1]
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self.tokenizer_class.add_eos_token = False
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self.rust_tokenizer_class.add_eos_token = False
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def test_special_tokens_mask(self):
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pass
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def test_special_tokens_mask_input_pairs(self):
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pass
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def test_padding_side_in_kwargs(self):
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tokenizer = self.get_tokenizer(padding_side="left")
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self.assertEqual(tokenizer.padding_side, "left")
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tokenizer = self.get_tokenizer(padding_side="right")
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self.assertEqual(tokenizer.padding_side, "right")
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def test_truncation_side_in_kwargs(self):
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tokenizer = self.get_tokenizer(truncation_side="left")
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self.assertEqual(tokenizer.truncation_side, "left")
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tokenizer = self.get_tokenizer(truncation_side="right")
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self.assertEqual(tokenizer.truncation_side, "right")
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def test_add_tokens(self):
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tokenizer = self.get_tokenizer()
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vocab_size = len(tokenizer)
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self.assertEqual(tokenizer.add_tokens(""), 0)
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self.assertEqual(tokenizer.add_tokens("testoken"), 1)
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self.assertEqual(tokenizer.add_tokens(["testoken1", "testtoken2"]), 2)
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self.assertEqual(len(tokenizer), vocab_size + 3)
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self.assertEqual(tokenizer.add_special_tokens({}), 0)
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self.assertRaises(AssertionError, tokenizer.add_special_tokens, {"additional_special_tokens": "<testtoken1>"})
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self.assertEqual(tokenizer.add_special_tokens({"additional_special_tokens": ["<testtoken2>"]}), 1)
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self.assertEqual(
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tokenizer.add_special_tokens({"additional_special_tokens": ["<testtoken3>", "<testtoken4>"]}), 2
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)
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self.assertIn("<testtoken3>", tokenizer.special_tokens_map["additional_special_tokens"])
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self.assertIsInstance(tokenizer.special_tokens_map["additional_special_tokens"], list)
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self.assertGreaterEqual(len(tokenizer.special_tokens_map["additional_special_tokens"]), 2)
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self.assertEqual(len(tokenizer), vocab_size + 6)
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def test_add_tokens_tokenizer(self):
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tokenizer = self.get_tokenizer()
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vocab_size = tokenizer.vocab_size
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all_size = len(tokenizer)
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self.assertNotEqual(vocab_size, 0)
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new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
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added_toks = tokenizer.add_tokens(new_toks)
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vocab_size_2 = tokenizer.vocab_size
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all_size_2 = len(tokenizer)
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self.assertNotEqual(vocab_size_2, 0)
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self.assertEqual(vocab_size, vocab_size_2)
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self.assertEqual(added_toks, len(new_toks))
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self.assertEqual(all_size_2, all_size + len(new_toks))
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tokens = tokenizer.encode(
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"aaaaa bbbbbb low cccccccccdddddddd l", return_token_type_ids=None, add_special_tokens=False
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)["input_ids"]
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self.assertGreaterEqual(len(tokens), 4)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
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def test_consecutive_unk_string(self):
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tokenizer = self.get_tokenizer(add_bos_token=False)
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tokens = [tokenizer.unk_token for _ in range(2)]
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string = tokenizer.convert_tokens_to_string(tokens)
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encoding = tokenizer(
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text=string,
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runcation=True,
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return_offsets_mapping=True,
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)
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self.assertEqual(len(encoding["input_ids"]), 2)
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self.assertEqual(len(encoding["offset_mapping"]), 2)
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def test_padding_if_pad_token_set_slow(self):
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tokenizer = self.get_tokenizer()
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# Simple input
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s = "This is a simple input"
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s2 = ["This is a simple input looooooooong", "This is a simple input"]
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p = ("This is a simple input", "This is a pair")
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pad_token_id = tokenizer.pad_token_id
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out_s = tokenizer(s, padding="max_length", max_length=30, return_tensors="np", return_attention_mask=True)
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out_s2 = tokenizer(s2, padding=True, truncate=True, return_tensors="np", return_attention_mask=True)
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out_p = tokenizer(*p, padding="max_length", max_length=60, return_tensors="np", return_attention_mask=True)
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# s
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# test single string max_length padding
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self.assertEqual(out_s["input_ids"].shape[-1], 30)
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self.assertTrue(pad_token_id in out_s["input_ids"])
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self.assertTrue(0 in out_s["attention_mask"])
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# s2
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# test automatic padding
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self.assertEqual(out_s2["input_ids"].shape[-1], 9)
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# long slice doesn't have padding
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self.assertFalse(pad_token_id in out_s2["input_ids"][0])
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self.assertFalse(0 in out_s2["attention_mask"][0])
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# short slice does have padding
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self.assertTrue(pad_token_id in out_s2["input_ids"][1])
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self.assertTrue(0 in out_s2["attention_mask"][1])
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# p
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# test single pair max_length padding
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self.assertEqual(out_p["input_ids"].shape[-1], 60)
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self.assertTrue(pad_token_id in out_p["input_ids"])
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self.assertTrue(0 in out_p["attention_mask"])
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def test_add_bos_token_slow(self):
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tokenizer = self.get_tokenizer()
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s = "This is a simple input"
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s2 = ["This is a simple input 1", "This is a simple input 2"]
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bos_token_id = tokenizer.bos_token_id
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out_s = tokenizer(s, add_special_tokens=True)
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out_s2 = tokenizer(s2, add_special_tokens=True)
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self.assertEqual(out_s.input_ids[0], bos_token_id)
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self.assertTrue(all(o[0] == bos_token_id for o in out_s2["input_ids"]))
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def test_pretrained_model_lists(self):
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# No max_model_input_sizes
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self.assertGreaterEqual(len(self.tokenizer_class.pretrained_resource_files_map), 1)
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self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_resource_files_map.values())[0]), 1)
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def test_add_special_tokens(self):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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input_text, ids = "A", [235280]
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special_token = "[SPECIAL_TOKEN]"
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tokenizer.add_special_tokens({"cls_token": special_token})
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encoded_special_token = tokenizer.encode(
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special_token, return_token_type_ids=None, add_special_tokens=False
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)["input_ids"]
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self.assertEqual(len(encoded_special_token), 1)
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text = tokenizer.decode(ids + encoded_special_token, clean_up_tokenization_spaces=False)
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encoded = tokenizer.encode(text, return_token_type_ids=None, add_special_tokens=False)["input_ids"]
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input_encoded = tokenizer.encode(input_text, return_token_type_ids=None, add_special_tokens=False)[
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"input_ids"
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]
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special_token_id = tokenizer.encode(
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special_token, return_token_type_ids=None, add_special_tokens=False
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)["input_ids"]
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self.assertEqual(encoded, input_encoded + special_token_id)
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decoded = tokenizer.decode(encoded, skip_special_tokens=True)
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self.assertTrue(special_token not in decoded)
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def test_extract_non_learnable_parts(self):
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models_with_templates = ["google/gemma-2b-it", "google/gemma-7b-it"]
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dummy_conversastions = [
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["Q.", "A."],
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["Q.A.", "A."],
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["Q?", "A!"],
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]
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decode_outputs = [
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["<bos><start_of_turn>user\nQ.<end_of_turn>\n<start_of_turn>model\n", "A.<end_of_turn>\n"],
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["<start_of_turn>user\nQ.A.<end_of_turn>\n<start_of_turn>model\n", "A.<end_of_turn>\n"],
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["<start_of_turn>user\nQ?<end_of_turn>\n<start_of_turn>model\n", "A!<end_of_turn>\n"],
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]
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context_data = {}
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context_data["is_training"] = True
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for model_id in models_with_templates:
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tokenizer = GemmaTokenizer.from_pretrained(model_id)
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if tokenizer.chat_template is None:
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continue
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conversation_result: list[tuple[list[int], list[int]]] = tokenizer.encode_chat_inputs(
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dummy_conversastions,
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context_data=context_data,
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)
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for idx, round in enumerate(conversation_result["conversations"]):
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self.assertEqual(tokenizer.decode(round[0]), decode_outputs[idx][0])
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self.assertEqual(tokenizer.decode(round[1]), decode_outputs[idx][1])
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