319 lines
16 KiB
Python
319 lines
16 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 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 re
|
|
import unittest
|
|
|
|
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
|
|
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
|
|
from transformers.testing_utils import require_tokenizers, slow
|
|
|
|
from ...test_tokenization_common import TokenizerTesterMixin
|
|
|
|
|
|
@require_tokenizers
|
|
class CodeGenTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
|
|
from_pretrained_id = "Salesforce/codegen-350M-mono"
|
|
tokenizer_class = CodeGenTokenizer
|
|
rust_tokenizer_class = CodeGenTokenizerFast
|
|
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 CodeGenTokenizer.from_pretrained(self.tmpdirname, **kwargs)
|
|
|
|
def get_rust_tokenizer(self, **kwargs):
|
|
kwargs.update(self.special_tokens_map)
|
|
return CodeGenTokenizerFast.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 = CodeGenTokenizer(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:
|
|
return
|
|
|
|
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)
|
|
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
|
|
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)
|
|
rust_ids = rust_tokenizer.encode(sequence)
|
|
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):
|
|
# It's very difficult to mix/test pretokenization with byte-level
|
|
# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
|
|
pass
|
|
|
|
def test_padding(self, max_length=15):
|
|
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
|
|
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
|
|
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
|
|
|
|
# Simple input
|
|
s = "This is a simple input"
|
|
s2 = ["This is a simple input 1", "This is a simple input 2"]
|
|
p = ("This is a simple input", "This is a pair")
|
|
p2 = [
|
|
("This is a simple input 1", "This is a simple input 2"),
|
|
("This is a simple pair 1", "This is a simple pair 2"),
|
|
]
|
|
|
|
# Simple input tests
|
|
self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
|
|
|
|
# Simple input
|
|
self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
|
|
|
|
# Simple input
|
|
self.assertRaises(
|
|
ValueError,
|
|
tokenizer_r.batch_encode_plus,
|
|
s2,
|
|
max_length=max_length,
|
|
padding="max_length",
|
|
)
|
|
|
|
# Pair input
|
|
self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
|
|
|
|
# Pair input
|
|
self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
|
|
|
|
# Pair input
|
|
self.assertRaises(
|
|
ValueError,
|
|
tokenizer_r.batch_encode_plus,
|
|
p2,
|
|
max_length=max_length,
|
|
padding="max_length",
|
|
)
|
|
|
|
def test_padding_if_pad_token_set_slow(self):
|
|
tokenizer = CodeGenTokenizer.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")
|
|
out_s2 = tokenizer(s2, padding=True, truncate=True, return_tensors="np")
|
|
out_p = tokenizer(*p, padding="max_length", max_length=60, return_tensors="np")
|
|
out_p2 = tokenizer(p2, padding=True, truncate=True, return_tensors="np")
|
|
|
|
# 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 = CodeGenTokenizer.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.assertTrue(decode_s.startswith(bos_token))
|
|
self.assertTrue(all(d.startswith(bos_token) for d in decode_s2))
|
|
|
|
@slow
|
|
def test_truncation(self):
|
|
tokenizer = CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono")
|
|
|
|
text = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
|
|
expected_trucated_text = "\nif len_a > len_b: result = a\nelse: result = b"
|
|
|
|
input_ids = tokenizer.encode(text)
|
|
truncation_pattern = ["^#", re.escape("<|endoftext|>"), "^'''", '^"""', "\n\n\n"]
|
|
decoded_text = tokenizer.decode(input_ids, truncate_before_pattern=truncation_pattern)
|
|
self.assertEqual(decoded_text, expected_trucated_text)
|
|
# TODO @ArthurZ outputs of the fast tokenizer are different in this case, un-related to the PR
|
|
|
|
# tokenizer has no padding token
|
|
def test_padding_different_model_input_name(self):
|
|
pass
|
|
|
|
@slow
|
|
def test_tokenizer_integration(self):
|
|
# Custom test since this tokenizer takes return_token_type_ids as an init argument for backward compatibility.
|
|
|
|
sequences = [
|
|
"Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
|
|
"general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
|
|
"Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained "
|
|
"models in 100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
|
|
"BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
|
|
"conditioning on both left and right context in all layers.",
|
|
"The quick brown fox jumps over the lazy dog.",
|
|
]
|
|
|
|
tokenizer_classes = [self.tokenizer_class]
|
|
if self.test_rust_tokenizer:
|
|
tokenizer_classes.append(self.rust_tokenizer_class)
|
|
|
|
# Test default case. i.e. return_token_type_ids is False.
|
|
for tokenizer_class in tokenizer_classes:
|
|
tokenizer = tokenizer_class.from_pretrained("Salesforce/codegen-350M-mono")
|
|
|
|
encoding = tokenizer(sequences)
|
|
decoded_sequences = [tokenizer.decode(seq, skip_special_tokens=True) for seq in encoding["input_ids"]]
|
|
|
|
# fmt: off
|
|
expected_encoding = {'input_ids': [[41762, 364, 357, 36234, 1900, 355, 12972, 13165, 354, 12, 35636, 364, 290, 12972, 13165, 354, 12, 5310, 13363, 12, 4835, 8, 3769, 2276, 12, 29983, 45619, 357, 13246, 51, 11, 402, 11571, 12, 17, 11, 5564, 13246, 38586, 11, 16276, 44, 11, 4307, 346, 33, 861, 11, 16276, 7934, 23029, 329, 12068, 15417, 28491, 357, 32572, 52, 8, 290, 12068, 15417, 16588, 357, 32572, 38, 8, 351, 625, 3933, 10, 2181, 13363, 4981, 287, 1802, 10, 8950, 290, 2769, 48817, 1799, 1022, 449, 897, 11, 9485, 15884, 354, 290, 309, 22854, 37535, 13], [13246, 51, 318, 3562, 284, 662, 12, 27432, 2769, 8406, 4154, 282, 24612, 422, 9642, 9608, 276, 2420, 416, 26913, 21143, 319, 1111, 1364, 290, 826, 4732, 287, 477, 11685, 13], [464, 2068, 7586, 21831, 18045, 625, 262, 16931, 3290, 13]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
|
|
# fmt: on
|
|
|
|
encoding_data = encoding.data
|
|
self.assertDictEqual(encoding_data, expected_encoding)
|
|
|
|
for expected, decoded in zip(sequences, decoded_sequences):
|
|
self.assertEqual(expected, decoded)
|
|
|
|
# Test return_token_type_ids is True case.
|
|
for tokenizer_class in tokenizer_classes:
|
|
tokenizer = tokenizer_class.from_pretrained("Salesforce/codegen-350M-mono", return_token_type_ids=True)
|
|
|
|
encoding = tokenizer(sequences)
|
|
decoded_sequences = [tokenizer.decode(seq, skip_special_tokens=True) for seq in encoding["input_ids"]]
|
|
|
|
# fmt: off
|
|
expected_encoding = {'input_ids': [[41762, 364, 357, 36234, 1900, 355, 12972, 13165, 354, 12, 35636, 364, 290, 12972, 13165, 354, 12, 5310, 13363, 12, 4835, 8, 3769, 2276, 12, 29983, 45619, 357, 13246, 51, 11, 402, 11571, 12, 17, 11, 5564, 13246, 38586, 11, 16276, 44, 11, 4307, 346, 33, 861, 11, 16276, 7934, 23029, 329, 12068, 15417, 28491, 357, 32572, 52, 8, 290, 12068, 15417, 16588, 357, 32572, 38, 8, 351, 625, 3933, 10, 2181, 13363, 4981, 287, 1802, 10, 8950, 290, 2769, 48817, 1799, 1022, 449, 897, 11, 9485, 15884, 354, 290, 309, 22854, 37535, 13], [13246, 51, 318, 3562, 284, 662, 12, 27432, 2769, 8406, 4154, 282, 24612, 422, 9642, 9608, 276, 2420, 416, 26913, 21143, 319, 1111, 1364, 290, 826, 4732, 287, 477, 11685, 13], [464, 2068, 7586, 21831, 18045, 625, 262, 16931, 3290, 13]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
|
|
# fmt: on
|
|
|
|
encoding_data = encoding.data
|
|
self.assertDictEqual(encoding_data, expected_encoding)
|
|
|
|
for expected, decoded in zip(sequences, decoded_sequences):
|
|
self.assertEqual(expected, decoded)
|