wehub-resource-sync
227 lines
8.8 KiB
Python
227 lines
8.8 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
# Copyright 2020 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 json
|
|
import os
|
|
import unittest
|
|
|
|
from paddlenlp.transformers import GPTTokenizer, GPTTokenizerFast
|
|
|
|
from ..test_tokenizer_common import TokenizerTesterMixin
|
|
|
|
VOCAB_FILES_NAMES = {
|
|
"vocab_file": "vocab.json",
|
|
"merges_file": "merges.txt",
|
|
}
|
|
|
|
|
|
class GPTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
|
|
|
|
tokenizer_class = GPTTokenizer
|
|
rust_tokenizer_class = GPTTokenizerFast
|
|
test_rust_tokenizer = True
|
|
from_pretrained_kwargs = {"add_prefix_space": True}
|
|
test_seq2seq = False
|
|
|
|
def setUp(self):
|
|
super().setUp()
|
|
|
|
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
|
|
vocab = [
|
|
"l",
|
|
"o",
|
|
"w",
|
|
"e",
|
|
"r",
|
|
"s",
|
|
"t",
|
|
"i",
|
|
"d",
|
|
"n",
|
|
"\u0120",
|
|
"\u0120l",
|
|
"\u0120n",
|
|
"\u0120lo",
|
|
"\u0120low",
|
|
"er",
|
|
"\u0120lowest",
|
|
"\u0120newer",
|
|
"\u0120wider",
|
|
"<unk>",
|
|
"<|endoftext|>",
|
|
]
|
|
vocab_tokens = dict(zip(vocab, range(len(vocab))))
|
|
merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
|
|
self.special_tokens_map = {"unk_token": "<unk>"}
|
|
|
|
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
|
|
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
|
|
with open(self.vocab_file, "w", encoding="utf-8") as fp:
|
|
fp.write(json.dumps(vocab_tokens) + "\n")
|
|
with open(self.merges_file, "w", encoding="utf-8") as fp:
|
|
fp.write("\n".join(merges))
|
|
|
|
def get_tokenizer(self, **kwargs):
|
|
kwargs.update(self.special_tokens_map)
|
|
return GPTTokenizer.from_pretrained(self.tmpdirname, **kwargs)
|
|
|
|
def get_rust_tokenizer(self, **kwargs):
|
|
kwargs.update(self.special_tokens_map)
|
|
return GPTTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
|
|
|
|
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 = GPTTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
|
|
text = "lower newer"
|
|
bpe_tokens = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
|
|
tokens = tokenizer.tokenize(text, add_prefix_space=True)
|
|
self.assertListEqual(tokens, bpe_tokens)
|
|
|
|
input_tokens = tokens + [tokenizer.unk_token]
|
|
input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19]
|
|
|
|
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
|
|
|
|
def test_rust_and_python_full_tokenizers(self):
|
|
if not self.test_rust_tokenizer:
|
|
self.skipTest(reason="test_rust_tokenizer is set to False")
|
|
|
|
tokenizer = self.get_tokenizer()
|
|
rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True)
|
|
|
|
sequence = "lower newer"
|
|
|
|
# Testing tokenization
|
|
tokens = tokenizer.tokenize(sequence, add_prefix_space=True)
|
|
rust_tokens = rust_tokenizer.tokenize(sequence)
|
|
self.assertListEqual(tokens, rust_tokens)
|
|
|
|
# Testing conversion to ids without special tokens
|
|
ids = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=True)["input_ids"]
|
|
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)["input_ids"]
|
|
self.assertListEqual(ids, rust_ids)
|
|
|
|
# Testing conversion to ids with special tokens
|
|
rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True)
|
|
ids = tokenizer.encode(sequence, add_prefix_space=True)["input_ids"]
|
|
rust_ids = rust_tokenizer.encode(sequence)["input_ids"]
|
|
self.assertListEqual(ids, rust_ids)
|
|
|
|
# Testing the unknown token
|
|
input_tokens = tokens + [rust_tokenizer.unk_token]
|
|
input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19]
|
|
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
|
|
|
|
def test_pretokenized_inputs(self, *args, **kwargs):
|
|
pass
|
|
|
|
def test_padding_if_pad_token_set_slow(self):
|
|
tokenizer = GPTTokenizer.from_pretrained(self.tmpdirname, pad_token="<pad>")
|
|
|
|
# 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")
|
|
p2 = [
|
|
("This is a simple input loooooong", "This is a simple input"),
|
|
("This is a simple pair loooooong", "This is a simple 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)
|
|
out_p2 = tokenizer(p2, padding=True, truncate=True, 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], 33)
|
|
# 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"])
|
|
|
|
# p2
|
|
# test automatic padding pair
|
|
self.assertEqual(out_p2["input_ids"].shape[-1], 52)
|
|
# long slice pair doesn't have padding
|
|
self.assertFalse(pad_token_id in out_p2["input_ids"][0])
|
|
self.assertFalse(0 in out_p2["attention_mask"][0])
|
|
# short slice pair does have padding
|
|
self.assertTrue(pad_token_id in out_p2["input_ids"][1])
|
|
self.assertTrue(0 in out_p2["attention_mask"][1])
|
|
|
|
def test_add_bos_token_slow(self):
|
|
bos_token = "$$$"
|
|
tokenizer = GPTTokenizer.from_pretrained(self.tmpdirname, bos_token=bos_token, add_bos_token=True)
|
|
|
|
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)
|
|
out_s2 = tokenizer(s2)
|
|
|
|
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"]))
|
|
|
|
decode_s = tokenizer.decode(out_s["input_ids"])
|
|
decode_s2 = tokenizer.batch_decode(out_s2["input_ids"])
|
|
|
|
self.assertEqual(decode_s.split()[0], bos_token)
|
|
self.assertTrue(all(d.split()[0] == bos_token for d in decode_s2))
|
|
|
|
# tokenizer has no padding token
|
|
def test_padding_different_model_input_name(self):
|
|
pass
|
|
|
|
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_consecutive_unk_string(self):
|
|
tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
|
|
for tokenizer in tokenizers:
|
|
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)
|