168 lines
7.4 KiB
Python
168 lines
7.4 KiB
Python
# coding=utf-8
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# Copyright 2022 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 datasets import load_dataset
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from transformers import BloomTokenizerFast
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from transformers.testing_utils import require_jinja, require_tokenizers
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from ...test_tokenization_common import TokenizerTesterMixin
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@require_tokenizers
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class BloomTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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slow_tokenizer_class = None
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rust_tokenizer_class = BloomTokenizerFast
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tokenizer_class = BloomTokenizerFast
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test_rust_tokenizer = True
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test_slow_tokenizer = False
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from_pretrained_vocab_key = "tokenizer_file"
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special_tokens_map = {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
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def setUp(self):
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super().setUp()
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tokenizer = BloomTokenizerFast.from_pretrained("bigscience/tokenizer")
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tokenizer.save_pretrained(self.tmpdirname)
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def get_rust_tokenizer(self, **kwargs):
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kwargs.update(self.special_tokens_map)
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return BloomTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
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@unittest.skip("This needs a slow tokenizer. Bloom does not have one!")
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def test_encode_decode_with_spaces(self):
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return
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def test_encodings_from_sample_data(self):
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"""
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Assert that the created tokens are the same than the hard-coded ones
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"""
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tokenizer = self.get_rust_tokenizer()
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INPUT_SENTENCES = ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
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TARGET_TOKENS = [[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]
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computed_tokens = tokenizer.batch_encode_plus(INPUT_SENTENCES)["input_ids"]
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self.assertListEqual(TARGET_TOKENS, computed_tokens)
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decoded_tokens = tokenizer.batch_decode(computed_tokens)
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self.assertListEqual(decoded_tokens, INPUT_SENTENCES)
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def test_padding(self, max_length=6):
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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_r.pad_token = None # Hotfixing padding = None
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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 1", "This is a simple input 2"]
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p = ("This is a simple input", "This is a pair")
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p2 = [
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("This is a simple input 1", "This is a simple input 2"),
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("This is a simple pair 1", "This is a simple pair 2"),
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]
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# Simple input tests
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try:
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tokenizer_r.encode(s, max_length=max_length)
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tokenizer_r.encode_plus(s, max_length=max_length)
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tokenizer_r.batch_encode_plus(s2, max_length=max_length)
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tokenizer_r.encode(p, max_length=max_length)
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tokenizer_r.batch_encode_plus(p2, max_length=max_length)
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except ValueError:
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self.fail("Bloom Tokenizer should be able to deal with padding")
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tokenizer_r.pad_token = None # Hotfixing padding = None
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self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
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# Simple input
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self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
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# Simple input
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self.assertRaises(
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ValueError,
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tokenizer_r.batch_encode_plus,
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s2,
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max_length=max_length,
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padding="max_length",
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)
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# Pair input
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self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
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# Pair input
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self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
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# Pair input
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self.assertRaises(
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ValueError,
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tokenizer_r.batch_encode_plus,
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p2,
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max_length=max_length,
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padding="max_length",
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)
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def test_encodings_from_xnli_dataset(self):
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"""
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Tests the tokenizer downloaded from here:
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- https://huggingface.co/bigscience/tokenizer/
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"""
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tokenizer = self.get_rust_tokenizer()
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ds = load_dataset("xnli", "all_languages", split="test", streaming=True)
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sample_data = next(iter(ds))["premise"] # pick up one data
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input_text = list(sample_data.values())
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output_tokens = list(map(tokenizer.encode, input_text))
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predicted_text = [tokenizer.decode(x, clean_up_tokenization_spaces=False) for x in output_tokens]
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self.assertListEqual(predicted_text, input_text)
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def test_pretrained_model_lists(self):
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# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
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# any sequence length constraints. This test of the parent class will fail since it relies on the
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# maximum sequence length of the positoonal embeddings.
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self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map), 1)
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self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]), 1)
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@require_jinja
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def test_tokenization_for_chat(self):
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tokenizer = self.get_rust_tokenizer()
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test_chats = [
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[{"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}],
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[
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{"role": "system", "content": "You are a helpful chatbot."},
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{"role": "user", "content": "Hello!"},
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{"role": "assistant", "content": "Nice to meet you."},
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],
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[{"role": "assistant", "content": "Nice to meet you."}, {"role": "user", "content": "Hello!"}],
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]
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tokenized_chats = [tokenizer.apply_chat_template(test_chat) for test_chat in test_chats]
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expected_tokens = [
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[5448, 1306, 267, 66799, 44799, 37143, 17, 2, 59414, 4, 2],
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[5448, 1306, 267, 66799, 44799, 37143, 17, 2, 59414, 4, 2, 229126, 427, 11890, 1152, 17, 2],
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[229126, 427, 11890, 1152, 17, 2, 59414, 4, 2],
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]
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for tokenized_chat, expected_tokens in zip(tokenized_chats, expected_tokens):
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self.assertListEqual(tokenized_chat, expected_tokens)
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def test_add_prefix_space_fast(self):
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tokenizer_w_prefix = self.get_rust_tokenizer(add_prefix_space=True)
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tokenizer_wo_prefix = self.get_rust_tokenizer(add_prefix_space=False)
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tokens_w_prefix = tokenizer_w_prefix.tokenize("Hey")
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tokens_wo_prefix = tokenizer_wo_prefix.tokenize("Hey")
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self.assertNotEqual(tokens_w_prefix, tokens_wo_prefix)
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