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