wehub-resource-sync
209 lines
8.0 KiB
Python
209 lines
8.0 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2020 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 json
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import os
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import unittest
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from paddlenlp.transformers import BloomTokenizer, BloomTokenizerFast
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from ..test_tokenizer_common import TokenizerTesterMixin
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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.json",
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"merges_file": "merges.txt",
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}
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class BloomTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = BloomTokenizer
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rust_tokenizer_class = BloomTokenizerFast
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from_pretrained_kwargs = {"add_prefix_space": True}
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test_decode_token = True
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test_seq2seq = False
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def setUp(self):
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super().setUp()
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# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
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vocab = [
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"l",
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"o",
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"w",
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"e",
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"r",
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"s",
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"t",
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"i",
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"d",
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"n",
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"\u0120",
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"\u0120l",
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"\u0120n",
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"\u0120lo",
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"\u0120low",
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"er",
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"\u0120lowest",
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"\u0120newer",
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"\u0120wider",
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"<unk>",
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"<|endoftext|>",
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]
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
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self.special_tokens_map = {"unk_token": "<unk>"}
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as fp:
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fp.write(json.dumps(vocab_tokens) + "\n")
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with open(self.merges_file, "w", encoding="utf-8") as fp:
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fp.write("\n".join(merges))
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def get_tokenizer(self, **kwargs):
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kwargs.update(self.special_tokens_map)
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return BloomTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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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 = BloomTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
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text = "lower newer"
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bpe_tokens = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
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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 = [14, 15, 10, 9, 3, 2, 15, 19]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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# test encode_plus
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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.rust_tokenizer_class.from_pretrained("bigscience/bloom-560m")
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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(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_pretokenized_inputs(self, *args, **kwargs):
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pass
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def test_padding_if_pad_token_set_slow(self):
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tokenizer = BloomTokenizer.from_pretrained(self.tmpdirname, pad_token="<pad>")
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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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p2 = [
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("This is a simple input loooooong", "This is a simple input"),
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("This is a simple pair loooooong", "This is a simple pair"),
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]
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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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out_p2 = tokenizer(p2, padding=True, truncate=True, 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], 33)
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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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# p2
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# test automatic padding pair
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self.assertEqual(out_p2["input_ids"].shape[-1], 52)
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# long slice pair doesn't have padding
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self.assertFalse(pad_token_id in out_p2["input_ids"][0])
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self.assertFalse(0 in out_p2["attention_mask"][0])
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# short slice pair does have padding
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self.assertTrue(pad_token_id in out_p2["input_ids"][1])
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self.assertTrue(0 in out_p2["attention_mask"][1])
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def test_add_bos_token_slow(self):
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bos_token = "$$$"
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tokenizer = BloomTokenizer.from_pretrained(self.tmpdirname, bos_token=bos_token, add_bos_token=True)
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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)
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out_s2 = tokenizer(s2)
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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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decode_s = tokenizer.decode(out_s["input_ids"])
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decode_s2 = tokenizer.batch_decode(out_s2["input_ids"])
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self.assertEqual(decode_s.split()[0], bos_token)
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self.assertTrue(all(d.split()[0] == bos_token for d in decode_s2))
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# tokenizer has no padding token
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def test_padding_different_model_input_name(self):
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pass
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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_consecutive_unk_string(self):
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tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
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for tokenizer in tokenizers:
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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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