[CodeLlama] Add support for CodeLlama (#25740)
* add all * Revert "Delete .github directory" This reverts commit 9b0ff7b052e2b20b629a26fb13606b78a42944d1. * make conversion script backward compatible * fixup * more styling * copy to llama changes * fix repo consistency * nits * document correct classes * updates * more fixes * nits * update auto mappings * add readmes * smallupdates * llama-code replace with llama_code * make fixup * updates to the testsing suite * fix fast nits * more small fixes * fix decode * fix template processing * properly reset the normalizer * nits processor * tokenization tests pass * styling * last tests * additional nits * one test is left * nits Co-authored-by faabian <faabian@users.noreply.github.com> * update failing test * fixup * remove decode infilling users should handle it on their onw after generation, padding can be a problem * update * make test slow and more meaningfull * fixup * doc update * fixup * Apply suggestions from code review * add kwargs doc * tokenizer requires `requires_backend` * type requires_backends * CodeLlama instead of LlamaCode * more name cahnges * nits * make doctests happy * small pipeline nits * last nit * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * update * add codellama to toctree --------- Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Arthur
0
tests/models/code_llama/__init__.py
Normal file
0
tests/models/code_llama/__init__.py
Normal file
605
tests/models/code_llama/test_tokenization_code_llama.py
Normal file
605
tests/models/code_llama/test_tokenization_code_llama.py
Normal file
@@ -0,0 +1,605 @@
|
||||
# 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):
|
||||
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 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", {}),
|
||||
]
|
||||
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):
|
||||
# fmt: off
|
||||
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: on
|
||||
|
||||
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_tokenzier = CodeLlamaTokenizer.from_pretrained(
|
||||
"hf-internal-testing/llama-tokenizer", add_eos_token=True, add_bos_token=False
|
||||
)
|
||||
slow = slow_tokenzier.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])
|
||||
self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [1, 29871, 30486, 31704, 30210, 30848, 235, 179, 158, 30392])
|
||||
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(["<REPR_END>"], special_tokens=True)
|
||||
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
|
||||
)
|
||||
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_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-hf")
|
||||
tokenizer_fast = CodeLlamaTokenizerFast.from_pretrained("codellama/CodeLlama-7b-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))
|
||||
@@ -20,7 +20,7 @@ import unittest
|
||||
from parameterized import parameterized
|
||||
|
||||
from transformers import LlamaConfig, is_torch_available, set_seed
|
||||
from transformers.testing_utils import require_torch, slow, torch_device
|
||||
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
|
||||
|
||||
from ...generation.test_utils import GenerationTesterMixin
|
||||
from ...test_configuration_common import ConfigTester
|
||||
@@ -31,7 +31,13 @@ from ...test_pipeline_mixin import PipelineTesterMixin
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
|
||||
from transformers import (
|
||||
CodeLlamaTokenizer,
|
||||
LlamaForCausalLM,
|
||||
LlamaForSequenceClassification,
|
||||
LlamaModel,
|
||||
LlamaTokenizer,
|
||||
)
|
||||
|
||||
|
||||
class LlamaModelTester:
|
||||
@@ -450,3 +456,85 @@ class LlamaIntegrationTest(unittest.TestCase):
|
||||
generated_ids = model.generate(input_ids, max_new_tokens=64, top_p=None, temperature=1, do_sample=False)
|
||||
text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
|
||||
self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
|
||||
|
||||
|
||||
@require_torch
|
||||
class CodeLlamaIntegrationTest(unittest.TestCase):
|
||||
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
|
||||
""",
|
||||
]
|
||||
|
||||
@require_torch_gpu
|
||||
@slow
|
||||
def test_model_7b_logits(self):
|
||||
model = LlamaForCausalLM.from_pretrained("codellama/CodeLlama-7b-hf").to(torch_device)
|
||||
tokenizer = CodeLlamaTokenizer.from_pretrained("codellama/CodeLlama-7b-hf")
|
||||
# Tokenize and prepare for the model a list of sequences or a list of pairs of sequences.
|
||||
# meaning by default this supports passing splitted list of inputs
|
||||
processed_text = tokenizer.batch_decode(tokenizer(self.PROMPTS)["input_ids"], add_special_tokens=False)
|
||||
# fmt: off
|
||||
EXPECTED_TEXT = [
|
||||
'<s> <PRE> def remove_non_ascii(s: str) -> str:\n """ <SUF>\n return result\n <MID>',
|
||||
'<s> <PRE> # Installation instructions:\n ```bash\n <SUF>\n ```\nThis downloads the LLaMA inference code and installs the repository as a local pip package.\n <MID>',
|
||||
'<s> <PRE> class InterfaceManagerFactory(AbstractManagerFactory):\n def __init__( <SUF>\ndef main():\n factory = InterfaceManagerFactory(start=datetime.now())\n managers = []\n for i in range(10):\n managers.append(factory.build(id=i))\n <MID>',
|
||||
'<s> <PRE> /-- A quasi-prefunctoid is 1-connected iff all its etalisations are 1-connected. -/\ntheorem connected_iff_etalisation [C D : precategoroid] (P : quasi_prefunctoid C D) :\nπ₁ P = 0 ↔ <SUF> = 0 :=\nbegin\nsplit,\n{ intros h f,\n rw pi_1_etalisation at h,\n simp [h],\n refl\n},\n{ intro h,\n have := @quasi_adjoint C D P,\n simp [←pi_1_etalisation, this, h],\n refl\n}\nend\n <MID>'
|
||||
]
|
||||
# fmt: on
|
||||
self.assertEqual(processed_text, EXPECTED_TEXT)
|
||||
processed_text_suffix_first = tokenizer.batch_decode(
|
||||
tokenizer(self.PROMPTS, suffix_first=True, add_special_tokens=False)["input_ids"]
|
||||
)
|
||||
|
||||
# fmt: off
|
||||
EXPECTED_TEXT = [
|
||||
'<PRE> <SUF>\n return result\n <MID> def remove_non_ascii(s: str) -> str:\n """ ',
|
||||
'<PRE> <SUF>\n ```\nThis downloads the LLaMA inference code and installs the repository as a local pip package.\n <MID> # Installation instructions:\n ```bash\n',
|
||||
'<PRE> <SUF>\ndef main():\n factory = InterfaceManagerFactory(start=datetime.now())\n managers = []\n for i in range(10):\n managers.append(factory.build(id=i))\n <MID> class InterfaceManagerFactory(AbstractManagerFactory):\n def __init__(',
|
||||
'<PRE> <SUF> = 0 :=\nbegin\nsplit,\n{ intros h f,\n rw pi_1_etalisation at h,\n simp [h],\n refl\n},\n{ intro h,\n have := @quasi_adjoint C D P,\n simp [←pi_1_etalisation, this, h],\n refl\n}\nend\n <MID> /-- A quasi-prefunctoid is 1-connected iff all its etalisations are 1-connected. -/\ntheorem connected_iff_etalisation [C D : precategoroid] (P : quasi_prefunctoid C D) :\nπ₁ P = 0 ↔ '
|
||||
]
|
||||
EXPECTED_IDS = torch.tensor([[ 1, 32007, 822, 3349, 29918, 5464, 29918, 294, 18869, 29898,29879, 29901, 851, 29897, 1599, 851, 29901, 13, 1678, 9995, 29871, 32008, 13, 1678, 736, 1121, 13, 32009, 15941, 1661, 29899, 28599, 2687, 4890, 515, 263, 1347, 29889, 13, 13, 1678, 826, 3174, 29901, 13, 4706, 269, 29901, 450, 1347, 304, 3349, 1661, 29899, 28599, 2687, 4890, 515, 29889, 13, 13, 1678, 16969, 29901, 13, 4706, 450, 1347, 411, 1661, 29899, 28599, 2687, 4890, 6206, 29889, 13, 1678, 9995, 13, 1678, 1121, 353, 5124, 13, 1678, 363, 274, 297, 269, 29901, 13, 4706, 565, 4356, 29898, 29883, 29897, 529, 29871, 29896, 29906, 29947, 29901, 13, 9651, 1121, 4619, 274, 32010, 2]])
|
||||
# fmt: on
|
||||
self.assertEqual(processed_text_suffix_first, EXPECTED_TEXT)
|
||||
input_ids = tokenizer(self.PROMPTS[0], return_tensors="pt")["input_ids"]
|
||||
generated_ids = model.generate(input_ids.to(torch_device), max_new_tokens=128)
|
||||
torch.testing.assert_close(generated_ids, EXPECTED_IDS)
|
||||
|
||||
EXPECTED_INFILLING = [
|
||||
'<s> <PRE> def remove_non_ascii(s: str) -> str:\n """ <SUF>\n return result\n <MID>Remove non-ASCII characters from a string.\n\n Args:\n s: The string to remove non-ASCII characters from.\n\n Returns:\n The string with non-ASCII characters removed.\n """\n result = ""\n for c in s:\n if ord(c) < 128:\n result += c <EOT></s>'
|
||||
]
|
||||
infilling = tokenizer.batch_decode(generated_ids)
|
||||
self.assertEqual(infilling, EXPECTED_INFILLING)
|
||||
|
||||
Reference in New Issue
Block a user