659 lines
29 KiB
Python
659 lines
29 KiB
Python
# coding=utf-8
|
||
# Copyright 2023 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 os
|
||
import pickle
|
||
import shutil
|
||
import tempfile
|
||
import unittest
|
||
|
||
from datasets import load_dataset
|
||
|
||
from transformers import (
|
||
SPIECE_UNDERLINE,
|
||
AddedToken,
|
||
CodeLlamaTokenizer,
|
||
CodeLlamaTokenizerFast,
|
||
is_torch_available,
|
||
)
|
||
from transformers.convert_slow_tokenizer import convert_slow_tokenizer
|
||
from transformers.testing_utils import (
|
||
get_tests_dir,
|
||
nested_simplify,
|
||
require_sentencepiece,
|
||
require_tokenizers,
|
||
require_torch,
|
||
slow,
|
||
)
|
||
|
||
from ...test_tokenization_common import TokenizerTesterMixin
|
||
|
||
|
||
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
|
||
|
||
|
||
if is_torch_available():
|
||
pass
|
||
|
||
|
||
@require_sentencepiece
|
||
@require_tokenizers
|
||
class CodeLlamaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
|
||
from_pretrained_id = "hf-internal-testing/llama-code-tokenizer"
|
||
tokenizer_class = CodeLlamaTokenizer
|
||
rust_tokenizer_class = CodeLlamaTokenizerFast
|
||
test_rust_tokenizer = False
|
||
test_sentencepiece = True
|
||
from_pretrained_kwargs = {}
|
||
|
||
def setUp(self):
|
||
super().setUp()
|
||
|
||
# We have a SentencePiece fixture for testing
|
||
tokenizer = CodeLlamaTokenizer(SAMPLE_VOCAB, keep_accents=True)
|
||
tokenizer.pad_token = tokenizer.eos_token
|
||
tokenizer.save_pretrained(self.tmpdirname)
|
||
|
||
def get_tokenizers(self, **kwargs):
|
||
kwargs.update({"pad_token": "<PAD>"})
|
||
return super().get_tokenizers(**kwargs)
|
||
|
||
def test_no_infilling_init(self):
|
||
tokenizer = CodeLlamaTokenizer(SAMPLE_VOCAB, prefix_token=None, keep_accents=True)
|
||
with self.assertRaises(ValueError):
|
||
tokenizer.tokenize("This is <FILL_ME> prefix")
|
||
|
||
def test_full_tokenizer(self):
|
||
tokenizer = CodeLlamaTokenizer(SAMPLE_VOCAB, keep_accents=True)
|
||
|
||
tokens = tokenizer.tokenize("This is a test")
|
||
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
|
||
|
||
self.assertListEqual(
|
||
tokenizer.convert_tokens_to_ids(tokens),
|
||
[285, 46, 10, 170, 382],
|
||
)
|
||
|
||
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
|
||
self.assertListEqual(
|
||
tokens,
|
||
[
|
||
SPIECE_UNDERLINE + "I",
|
||
SPIECE_UNDERLINE + "was",
|
||
SPIECE_UNDERLINE + "b",
|
||
"or",
|
||
"n",
|
||
SPIECE_UNDERLINE + "in",
|
||
SPIECE_UNDERLINE + "",
|
||
"9",
|
||
"2",
|
||
"0",
|
||
"0",
|
||
"0",
|
||
",",
|
||
SPIECE_UNDERLINE + "and",
|
||
SPIECE_UNDERLINE + "this",
|
||
SPIECE_UNDERLINE + "is",
|
||
SPIECE_UNDERLINE + "f",
|
||
"al",
|
||
"s",
|
||
"é",
|
||
".",
|
||
],
|
||
)
|
||
ids = tokenizer.convert_tokens_to_ids(tokens)
|
||
self.assertListEqual(
|
||
ids,
|
||
[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
|
||
)
|
||
|
||
back_tokens = tokenizer.convert_ids_to_tokens(ids)
|
||
self.assertListEqual(
|
||
back_tokens,
|
||
[
|
||
SPIECE_UNDERLINE + "I",
|
||
SPIECE_UNDERLINE + "was",
|
||
SPIECE_UNDERLINE + "b",
|
||
"or",
|
||
"n",
|
||
SPIECE_UNDERLINE + "in",
|
||
SPIECE_UNDERLINE + "",
|
||
"<unk>",
|
||
"2",
|
||
"0",
|
||
"0",
|
||
"0",
|
||
",",
|
||
SPIECE_UNDERLINE + "and",
|
||
SPIECE_UNDERLINE + "this",
|
||
SPIECE_UNDERLINE + "is",
|
||
SPIECE_UNDERLINE + "f",
|
||
"al",
|
||
"s",
|
||
"<unk>",
|
||
".",
|
||
],
|
||
)
|
||
|
||
def test_save_pretrained(self):
|
||
self.tokenizers_list = [
|
||
(self.rust_tokenizer_class, "hf-internal-testing/llama-code-tokenizer", {}),
|
||
(self.tokenizer_class, "hf-internal-testing/llama-code-tokenizer", {}),
|
||
(self.tokenizer_class, "codellama/CodeLlama-34b-Instruct-hf", {}),
|
||
(self.rust_tokenizer_class, "codellama/CodeLlama-34b-Instruct-hf", {}),
|
||
]
|
||
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)
|
||
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
|
||
|
||
tmpdirname2 = tempfile.mkdtemp()
|
||
|
||
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
|
||
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
|
||
|
||
# Checks it save with the same files + the tokenizer.json file for the fast one
|
||
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
|
||
tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
|
||
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
|
||
|
||
# Checks everything loads correctly in the same way
|
||
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
|
||
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
|
||
|
||
# Check special tokens are set accordingly on Rust and Python
|
||
for key in tokenizer_pp.special_tokens_map:
|
||
self.assertTrue(hasattr(tokenizer_rp, key))
|
||
|
||
shutil.rmtree(tmpdirname2)
|
||
|
||
# Save tokenizer rust, legacy_format=True
|
||
tmpdirname2 = tempfile.mkdtemp()
|
||
|
||
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True)
|
||
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
|
||
|
||
# Checks it save with the same files
|
||
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
|
||
|
||
# Checks everything loads correctly in the same way
|
||
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
|
||
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
|
||
|
||
# Check special tokens are set accordingly on Rust and Python
|
||
for key in tokenizer_pp.special_tokens_map:
|
||
self.assertTrue(hasattr(tokenizer_rp, key))
|
||
|
||
shutil.rmtree(tmpdirname2)
|
||
|
||
# Save tokenizer rust, legacy_format=False
|
||
tmpdirname2 = tempfile.mkdtemp()
|
||
|
||
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False)
|
||
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
|
||
|
||
# Checks it saved the tokenizer.json file
|
||
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
|
||
|
||
# Checks everything loads correctly in the same way
|
||
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
|
||
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
|
||
|
||
# Check special tokens are set accordingly on Rust and Python
|
||
for key in tokenizer_pp.special_tokens_map:
|
||
self.assertTrue(hasattr(tokenizer_rp, key))
|
||
|
||
shutil.rmtree(tmpdirname2)
|
||
|
||
@require_torch
|
||
def test_batch_tokenization(self):
|
||
if not self.test_seq2seq:
|
||
return
|
||
|
||
tokenizers = self.get_tokenizers()
|
||
for tokenizer in tokenizers:
|
||
with self.subTest(f"{tokenizer.__class__.__name__}"):
|
||
# Longer text that will definitely require truncation.
|
||
text = [
|
||
" UN Chief Says There Is No Military Solution in Syria",
|
||
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
|
||
" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
|
||
" will only worsen the violence and misery for millions of people.",
|
||
]
|
||
try:
|
||
batch = tokenizer(
|
||
text=text,
|
||
max_length=3,
|
||
max_target_length=10,
|
||
return_tensors="pt",
|
||
)
|
||
except NotImplementedError:
|
||
return
|
||
self.assertEqual(batch.input_ids.shape[1], 3)
|
||
# max_target_length will default to max_length if not specified
|
||
batch = tokenizer(text, max_length=3, return_tensors="pt")
|
||
self.assertEqual(batch.input_ids.shape[1], 3)
|
||
|
||
batch_encoder_only = tokenizer(text=text, max_length=3, max_target_length=10, return_tensors="pt")
|
||
self.assertEqual(batch_encoder_only.input_ids.shape[1], 3)
|
||
self.assertEqual(batch_encoder_only.attention_mask.shape[1], 3)
|
||
self.assertNotIn("decoder_input_ids", batch_encoder_only)
|
||
|
||
@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.")
|
||
def test_save_slow_from_fast_and_reload_fast(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("<special>", 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 <special> token")
|
||
|
||
special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
|
||
|
||
self.assertTrue(special_token_id in r_output)
|
||
|
||
if self.test_slow_tokenizer:
|
||
tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
|
||
pretrained_name,
|
||
additional_special_tokens=added_tokens,
|
||
**kwargs, # , from_slow=True <- unfortunately too slow to convert
|
||
)
|
||
tokenizer_p = self.tokenizer_class.from_pretrained(
|
||
pretrained_name, additional_special_tokens=added_tokens, **kwargs
|
||
)
|
||
|
||
p_output = tokenizer_p.encode("Hey this is a <special> token")
|
||
|
||
cr_output = tokenizer_cr.encode("Hey this is a <special> token")
|
||
|
||
self.assertEqual(p_output, r_output)
|
||
self.assertEqual(cr_output, r_output)
|
||
self.assertTrue(special_token_id in p_output)
|
||
self.assertTrue(special_token_id in cr_output)
|
||
|
||
@slow
|
||
def test_tokenizer_integration(self):
|
||
expected_encoding = {'input_ids': [[1, 4103, 689, 414, 313, 24784, 368, 2998, 408, 282, 3637, 25350, 29899, 9067, 414, 322, 282, 3637, 25350, 29899, 1457, 3018, 1312, 29899, 2151, 29897, 8128, 2498, 29899, 15503, 4220, 6956, 1973, 313, 13635, 29911, 29892, 402, 7982, 29899, 29906, 29892, 1528, 13635, 29911, 29874, 29892, 1060, 26369, 29892, 6652, 309, 29933, 814, 29892, 1060, 29931, 6779, 11410, 363, 18385, 17088, 7634, 11235, 313, 25103, 29965, 29897, 322, 18385, 17088, 28203, 313, 25103, 29954, 29897, 411, 975, 29871, 29941, 29906, 29974, 758, 3018, 1312, 4733, 297, 29871, 29896, 29900, 29900, 29974, 10276, 322, 6483, 1006, 3372, 3097, 1546, 435, 1165, 29892, 10772, 29911, 25350, 322, 323, 6073, 17907, 29889], [1, 350, 20161, 338, 8688, 304, 758, 29899, 14968, 6483, 21000, 8684, 284, 22540, 515, 443, 29880, 24025, 1426, 491, 14002, 368, 4195, 292, 373, 1716, 2175, 322, 1492, 3030, 297, 599, 15359, 29889], [1, 450, 4996, 17354, 1701, 29916, 432, 17204, 975, 278, 17366, 11203, 29889]], '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, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # fmt: skip
|
||
|
||
self.tokenizer_integration_test_util(
|
||
expected_encoding=expected_encoding,
|
||
model_name="hf-internal-testing/llama-code-tokenizer",
|
||
revision="6eb30c03ab6a9e2cdef4d523024909ec815ddb75",
|
||
padding=False,
|
||
)
|
||
|
||
def test_picklable(self):
|
||
with tempfile.NamedTemporaryFile() as f:
|
||
shutil.copyfile(SAMPLE_VOCAB, f.name)
|
||
tokenizer = CodeLlamaTokenizer(f.name, keep_accents=True)
|
||
pickled_tokenizer = pickle.dumps(tokenizer)
|
||
pickle.loads(pickled_tokenizer)
|
||
|
||
@unittest.skip("worker 'gw4' crashed on CI, passing locally.")
|
||
def test_pickle_subword_regularization_tokenizer(self):
|
||
pass
|
||
|
||
@unittest.skip("worker 'gw4' crashed on CI, passing locally.")
|
||
def test_subword_regularization_tokenizer(self):
|
||
pass
|
||
|
||
|
||
@require_torch
|
||
@require_sentencepiece
|
||
@require_tokenizers
|
||
class LlamaIntegrationTest(unittest.TestCase):
|
||
@classmethod
|
||
def setUpClass(cls):
|
||
checkpoint_name = "hf-internal-testing/llama-code-tokenizer"
|
||
cls.tokenizer: CodeLlamaTokenizer = CodeLlamaTokenizer.from_pretrained(checkpoint_name)
|
||
cls.rust_tokenizer = CodeLlamaTokenizerFast.from_pretrained(checkpoint_name)
|
||
return cls
|
||
|
||
@require_torch
|
||
def integration_tests(self):
|
||
inputs = self.tokenizer(
|
||
["The following string should be properly encoded: Hello.", "But ird and ปี ird ด"],
|
||
return_tensors="pt",
|
||
)
|
||
|
||
self.assertEqual(
|
||
nested_simplify(inputs),
|
||
{
|
||
"input_ids": [
|
||
[1, 450, 1494, 1347, 881, 367, 6284, 18511, 29901, 15043, 29889],
|
||
[1, 1205, 29871, 1823, 322, 29871, 31010, 30691, 1678, 1823, 1678, 30718],
|
||
],
|
||
"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]],
|
||
},
|
||
)
|
||
|
||
def test_fast_special_tokens(self):
|
||
slow_tokenizer = self.tokenizer
|
||
fast_tokenizer = self.rust_tokenizer
|
||
slow = slow_tokenizer.encode("A sample test", add_special_tokens=True)
|
||
assert slow == [1, 319, 4559, 1243]
|
||
|
||
fast_tokenizer.add_eos_token = False
|
||
fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)
|
||
assert fast == [1, 319, 4559, 1243]
|
||
|
||
fast_tokenizer.add_eos_token = True
|
||
fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)
|
||
assert fast == [1, 319, 4559, 1243, 2]
|
||
|
||
slow_tokenizer.add_eos_token = True
|
||
slow = slow_tokenizer.encode("A sample test", add_special_tokens=True)
|
||
assert slow == [1, 319, 4559, 1243, 2]
|
||
|
||
fast_tokenizer = CodeLlamaTokenizerFast.from_pretrained(
|
||
"hf-internal-testing/llama-tokenizer", add_eos_token=True, add_bos_token=False
|
||
)
|
||
fast = fast_tokenizer.encode("A sample test", add_special_tokens=True)
|
||
assert fast == [319, 4559, 1243, 2]
|
||
|
||
slow_tokenizer = CodeLlamaTokenizer.from_pretrained(
|
||
"hf-internal-testing/llama-tokenizer", add_eos_token=True, add_bos_token=False
|
||
)
|
||
slow = slow_tokenizer.encode("A sample test", add_special_tokens=True)
|
||
assert slow == [319, 4559, 1243, 2]
|
||
|
||
self.tokenizer.add_eos_token = False
|
||
self.rust_tokenizer.add_eos_token = False
|
||
|
||
@slow
|
||
def test_conversion(self):
|
||
# This is excruciatingly slow since it has to recreate the entire merge
|
||
# list from the original vocabulary in spm
|
||
self.rust_tokenizer.save_pretrained("./out")
|
||
with tempfile.TemporaryDirectory() as dirname:
|
||
self.rust_tokenizer.save_pretrained(dirname)
|
||
|
||
with open(os.path.join(dirname, "tokenizer.json"), "r") as f:
|
||
old_serialized = f.read()
|
||
|
||
new_tokenizer = convert_slow_tokenizer(self.tokenizer)
|
||
with tempfile.NamedTemporaryFile() as f:
|
||
new_tokenizer.save(f.name)
|
||
# Re-opening since `f` is in bytes.
|
||
new_serialized = open(f.name, "r").read()
|
||
with open("out_tokenizer.json", "w") as g:
|
||
g.write(new_serialized)
|
||
|
||
self.assertEqual(old_serialized, new_serialized)
|
||
|
||
def test_simple_encode_decode(self):
|
||
pyth_tokenizer = self.tokenizer
|
||
rust_tokenizer = self.rust_tokenizer
|
||
|
||
self.assertEqual(pyth_tokenizer.encode("This is a test"), [1, 910, 338, 263, 1243])
|
||
self.assertEqual(rust_tokenizer.encode("This is a test"), [1, 910, 338, 263, 1243])
|
||
self.assertEqual(pyth_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test")
|
||
self.assertEqual(rust_tokenizer.decode([1, 910, 338, 263, 1243], skip_special_tokens=True), "This is a test")
|
||
|
||
# bytefallback showcase
|
||
self.assertEqual(pyth_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392]) # fmt: skip
|
||
self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392]) # fmt: skip
|
||
self.assertEqual(
|
||
pyth_tokenizer.decode(
|
||
[1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True
|
||
),
|
||
"生活的真谛是",
|
||
)
|
||
self.assertEqual(
|
||
rust_tokenizer.decode(
|
||
[1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392], skip_special_tokens=True
|
||
),
|
||
"生活的真谛是",
|
||
)
|
||
|
||
# Inner spaces showcase
|
||
self.assertEqual(pyth_tokenizer.encode("Hi Hello"), [1, 6324, 29871, 15043])
|
||
self.assertEqual(rust_tokenizer.encode("Hi Hello"), [1, 6324, 29871, 15043])
|
||
self.assertEqual(pyth_tokenizer.decode([1, 6324, 29871, 15043], skip_special_tokens=True), "Hi Hello")
|
||
self.assertEqual(rust_tokenizer.decode([1, 6324, 29871, 15043], skip_special_tokens=True), "Hi Hello")
|
||
|
||
self.assertEqual(pyth_tokenizer.encode("Hi Hello"), [1, 6324, 259, 15043])
|
||
self.assertEqual(rust_tokenizer.encode("Hi Hello"), [1, 6324, 259, 15043])
|
||
self.assertEqual(pyth_tokenizer.decode([1, 6324, 259, 15043], skip_special_tokens=True), "Hi Hello")
|
||
self.assertEqual(rust_tokenizer.decode([1, 6324, 259, 15043], skip_special_tokens=True), "Hi Hello")
|
||
|
||
self.assertEqual(pyth_tokenizer.encode(""), [1])
|
||
self.assertEqual(rust_tokenizer.encode(""), [1])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode(" "), [1, 259])
|
||
self.assertEqual(rust_tokenizer.encode(" "), [1, 259])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode(" "), [1, 1678])
|
||
self.assertEqual(rust_tokenizer.encode(" "), [1, 1678])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode(" Hello"), [1, 29871, 15043])
|
||
self.assertEqual(rust_tokenizer.encode(" Hello"), [1, 29871, 15043])
|
||
|
||
def test_no_differences_showcase(self):
|
||
pyth_tokenizer = self.tokenizer
|
||
rust_tokenizer = self.rust_tokenizer
|
||
self.assertEqual(pyth_tokenizer.encode(""), [1])
|
||
self.assertEqual(rust_tokenizer.encode(""), [1])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode(" "), [1, 259])
|
||
self.assertEqual(rust_tokenizer.encode(" "), [1, 259])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode(" "), [1, 1678])
|
||
self.assertEqual(rust_tokenizer.encode(" "), [1, 1678])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode(" Hello"), [1, 29871, 15043])
|
||
self.assertEqual(rust_tokenizer.encode(" Hello"), [1, 29871, 15043])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode("<s>"), [1, 1])
|
||
self.assertEqual(rust_tokenizer.encode("<s>"), [1, 1])
|
||
|
||
def test_no_differences_decode(self):
|
||
pyth_tokenizer = self.tokenizer
|
||
rust_tokenizer = self.rust_tokenizer
|
||
|
||
self.assertEqual(pyth_tokenizer.decode([869]), ".")
|
||
self.assertEqual(rust_tokenizer.decode([869]), ".")
|
||
|
||
self.assertEqual(pyth_tokenizer.decode([30112, 869]), "ا .")
|
||
self.assertEqual(rust_tokenizer.decode([30112, 869]), "ا .")
|
||
|
||
def test_no_differences_special_tokens(self):
|
||
pyth_tokenizer = self.tokenizer
|
||
rust_tokenizer = self.rust_tokenizer
|
||
self.assertEqual(pyth_tokenizer.encode(""), [1])
|
||
self.assertEqual(rust_tokenizer.encode(""), [1])
|
||
|
||
self.assertEqual(pyth_tokenizer.encode("<s>"), [1, 1])
|
||
self.assertEqual(rust_tokenizer.encode("<s>"), [1, 1])
|
||
|
||
@unittest.skipIf(
|
||
os.getenv("RUN_TOKENIZER_INTEGRATION", "0") == "0",
|
||
"RUN_TOKENIZER_INTEGRATION=1 to run tokenizer integration tests",
|
||
)
|
||
def test_integration_test_xnli(self):
|
||
import tqdm
|
||
|
||
pyth_tokenizer = self.tokenizer
|
||
rust_tokenizer = self.rust_tokenizer
|
||
|
||
dataset = load_dataset("code_x_glue_ct_code_to_text", "go")
|
||
for item in tqdm.tqdm(dataset["validation"]):
|
||
string = item["code"]
|
||
encoded1 = pyth_tokenizer.encode(string)
|
||
encoded2 = rust_tokenizer.encode(string)
|
||
|
||
self.assertEqual(encoded1, encoded2)
|
||
|
||
decoded1 = pyth_tokenizer.decode(encoded1, skip_special_tokens=True)
|
||
decoded2 = rust_tokenizer.decode(encoded2, skip_special_tokens=True)
|
||
|
||
self.assertEqual(decoded1, decoded2)
|
||
|
||
dataset = load_dataset("xnli", "all_languages")
|
||
|
||
for item in tqdm.tqdm(dataset["train"]):
|
||
for string in item["premise"].values():
|
||
encoded1 = pyth_tokenizer.encode(string)
|
||
encoded2 = rust_tokenizer.encode(string)
|
||
|
||
self.assertEqual(encoded1, encoded2)
|
||
|
||
decoded1 = pyth_tokenizer.decode(encoded1, skip_special_tokens=True)
|
||
decoded2 = rust_tokenizer.decode(encoded2, skip_special_tokens=True)
|
||
|
||
self.assertEqual(decoded1, decoded2)
|
||
|
||
def test_special_token_special_word(self):
|
||
# the word inform should be split as ['in', 'form']
|
||
tokenizer = CodeLlamaTokenizer.from_pretrained("codellama/CodeLlama-7b-hf", legacy=False)
|
||
tokenizer.add_tokens([AddedToken("<REPR_END>", rstrip=True, lstrip=True)], special_tokens=False)
|
||
out1 = tokenizer.decode(
|
||
tokenizer.encode("<REPR_END>inform", add_special_tokens=False), spaces_between_special_tokens=False
|
||
)
|
||
self.assertEqual(out1, "<REPR_END>inform")
|
||
out2 = tokenizer.decode(
|
||
tokenizer.encode("<REPR_END>inform", add_special_tokens=False), spaces_between_special_tokens=True
|
||
)
|
||
# the added prefix token should not be decoded
|
||
self.assertEqual(out2, "<REPR_END> inform")
|
||
input_ids = tokenizer.encode("<REPR_END>inform", add_special_tokens=False)
|
||
self.assertEqual(input_ids, [29871, 32016, 262, 689]) # 29871 is the spiece underline, '▁'
|
||
|
||
out2 = tokenizer.decode(
|
||
tokenizer.encode(" <REPR_END> inform", add_special_tokens=False), spaces_between_special_tokens=False
|
||
)
|
||
# TODO @ArthurZ currently we strip left and right, so this will not keep the spaces
|
||
self.assertEqual(out2, "<REPR_END>inform")
|
||
|
||
### Let's make sure decoding does not add extra spaces here and there
|
||
# TODO @ArthurZ this should be affected by the lstrip/rstrip/single word /normalize refactoring
|
||
# Since currently we always strip left and right of the token, results are as such
|
||
input_ids = tokenizer.encode("<s> Hello<s>how", add_special_tokens=False)
|
||
self.assertEqual(input_ids, [1, 15043, 1, 3525])
|
||
tokens = tokenizer.tokenize("<s> Hello<s>how", add_special_tokens=False)
|
||
self.assertEqual(tokens, ["<s>", "▁Hello", "<s>", "how"])
|
||
decoded_tokens = tokenizer.decode(input_ids)
|
||
self.assertEqual(decoded_tokens, "<s> Hello<s>how")
|
||
|
||
# Let's make sure that if there are any spaces, we don't remove them!
|
||
input_ids = tokenizer.encode(" <s> Hello<s> how", add_special_tokens=False)
|
||
self.assertEqual(input_ids, [259, 1, 15043, 1, 920])
|
||
tokens = tokenizer.tokenize(" <s> Hello<s> how", add_special_tokens=False)
|
||
self.assertEqual(tokens, ["▁▁", "<s>", "▁Hello", "<s>", "▁how"])
|
||
decoded_tokens = tokenizer.decode(input_ids)
|
||
self.assertEqual(decoded_tokens, " <s> Hello<s> how")
|
||
|
||
def test_fill_token(self):
|
||
tokenizer = CodeLlamaTokenizerFast.from_pretrained(
|
||
"codellama/CodeLlama-7b-hf", fill_token=None, prefix_token=None, suffix_token=None, middle_token=None
|
||
)
|
||
tokenizer.encode_plus("Hey how are you").input_ids
|
||
tokenizer.fill_token = "<FILL_ME>"
|
||
with self.assertRaises(ValueError):
|
||
tokenizer.encode("Hey how <FILL_ME> are you")
|
||
tokenizer.encode_plus("Hey how <FILL_ME> are you", "mne too")
|
||
tokenizer.tokenize("Hey how are you", "mne too")
|
||
|
||
tokenizer = CodeLlamaTokenizerFast.from_pretrained(
|
||
"codellama/CodeLlama-7b-hf", revision="3773f63b4511b9e47a9a7ffc765eed7eb0169486"
|
||
)
|
||
tokenizer.encode("Hey how <FILL_ME> are you")
|
||
tokenizer.encode_plus("Hey how <FILL_ME> are you", "mne too")
|
||
tokenizer.tokenize("Hey how are you", "mne too")
|
||
|
||
def test_spm_edge_cases(self):
|
||
# the word inform should be split as ['in', 'form']
|
||
tokenizer = CodeLlamaTokenizer.from_pretrained("codellama/CodeLlama-7b-hf", legacy=False)
|
||
tokens = tokenizer.tokenize("[INST] How are you doing?<s>[/INST]")
|
||
self.assertEqual(
|
||
tokens, ["▁[", "INST", "]", "▁How", "▁are", "▁you", "▁doing", "?", "<s>", "[", "/", "INST", "]"]
|
||
)
|
||
inputs_ids = tokenizer.encode("[INST] How are you doing?<s>[/INST]")
|
||
self.assertEqual(
|
||
inputs_ids, [1, 518, 25580, 29962, 1128, 526, 366, 2599, 29973, 1, 29961, 29914, 25580, 29962]
|
||
)
|
||
|
||
def test_infilling_tokenization(self):
|
||
PROMPTS = [
|
||
'''def remove_non_ascii(s: str) -> str:
|
||
""" <FILL_ME>
|
||
return result
|
||
''',
|
||
"""# Installation instructions:
|
||
```bash
|
||
<FILL_ME>
|
||
```
|
||
This downloads the LLaMA inference code and installs the repository as a local pip package.
|
||
""",
|
||
"""class InterfaceManagerFactory(AbstractManagerFactory):
|
||
def __init__(<FILL_ME>
|
||
def main():
|
||
factory = InterfaceManagerFactory(start=datetime.now())
|
||
managers = []
|
||
for i in range(10):
|
||
managers.append(factory.build(id=i))
|
||
""",
|
||
"""/-- A quasi-prefunctoid is 1-connected iff all its etalisations are 1-connected. -/
|
||
theorem connected_iff_etalisation [C D : precategoroid] (P : quasi_prefunctoid C D) :
|
||
π₁ P = 0 ↔ <FILL_ME> = 0 :=
|
||
begin
|
||
split,
|
||
{ intros h f,
|
||
rw pi_1_etalisation at h,
|
||
simp [h],
|
||
refl
|
||
},
|
||
{ intro h,
|
||
have := @quasi_adjoint C D P,
|
||
simp [←pi_1_etalisation, this, h],
|
||
refl
|
||
}
|
||
end
|
||
""",
|
||
]
|
||
tokenizer = CodeLlamaTokenizer.from_pretrained("codellama/CodeLlama-7b-Instruct-hf")
|
||
tokenizer_fast = CodeLlamaTokenizerFast.from_pretrained("codellama/CodeLlama-7b-Instruct-hf")
|
||
|
||
formatted_prompt = tokenizer.tokenize(PROMPTS[0])
|
||
self.assertEqual(formatted_prompt, tokenizer_fast.tokenize(PROMPTS[0]))
|
||
prefix, suffix = PROMPTS[0].split("<FILL_ME>")
|
||
self.assertEqual(formatted_prompt, tokenizer.tokenize(prefix, suffix))
|
||
self.assertEqual(formatted_prompt, tokenizer_fast.tokenize(prefix, suffix))
|
||
|
||
input_ids = tokenizer.encode(PROMPTS[0], add_special_tokens=False)
|
||
self.assertEqual(input_ids, tokenizer_fast.encode(PROMPTS[0], add_special_tokens=False))
|
||
|
||
prefix, suffix = PROMPTS[0].split("<FILL_ME>")
|
||
input_ids = tokenizer.encode(PROMPTS[0])
|
||
self.assertEqual(input_ids, tokenizer.encode(prefix, suffix=suffix))
|
||
self.assertEqual(tokenizer.encode(prefix, suffix=suffix), tokenizer_fast.encode(prefix, suffix=suffix))
|
||
|
||
# Adding suffix_first check for infilling tasks
|
||
suffix_first_formatted_prompt = tokenizer.tokenize(PROMPTS[0], suffix_first=True)
|
||
self.assertEqual(suffix_first_formatted_prompt, tokenizer_fast.tokenize(PROMPTS[0], suffix_first=True))
|
||
prefix, suffix = PROMPTS[0].split("<FILL_ME>")
|
||
self.assertEqual(suffix_first_formatted_prompt, tokenizer.tokenize(prefix, suffix, suffix_first=True))
|
||
self.assertEqual(suffix_first_formatted_prompt, tokenizer_fast.tokenize(prefix, suffix, suffix_first=True))
|
||
|
||
prefix, suffix = PROMPTS[0].split("<FILL_ME>")
|
||
suffix_first_input_ids = tokenizer.encode(PROMPTS[0], suffix_first=True)
|
||
self.assertEqual(suffix_first_input_ids, tokenizer.encode(prefix, suffix=suffix, suffix_first=True))
|
||
self.assertEqual(suffix_first_input_ids, tokenizer_fast.encode(prefix, suffix=suffix, suffix_first=True))
|