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Convert formatting to use ruff instead of yapf + isort (#26247)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
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106
tests/samplers/test_no_bad_words.py
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@@ -5,6 +5,7 @@
Run `pytest tests/samplers/test_no_bad_words.py`.
"""
from typing import Optional
import pytest
@@ -16,7 +17,7 @@ from vllm import LLM, SamplingParams
@pytest.fixture(autouse=True)
def v1(monkeypatch):
"""Only run on vLLM v1."""
monkeypatch.setenv('VLLM_USE_V1', '1')
monkeypatch.setenv("VLLM_USE_V1", "1")
def _generate(
@@ -49,25 +50,24 @@ class TestOneTokenBadWord:
TARGET_TOKEN = "you"
def setup_method(self, method):
self.tokenizer = AutoTokenizer.from_pretrained(self.MODEL,
add_prefix_space=True)
self.tokenizer = AutoTokenizer.from_pretrained(
self.MODEL, add_prefix_space=True
)
self.num_prompt_tokens = len(self._encode(self.PROMPT))
self.target_token_id = self._encode(self.TARGET_TOKEN,
add_special_tokens=False)[0]
self.target_token_id = self._encode(
self.TARGET_TOKEN, add_special_tokens=False
)[0]
def test_one_token_bad_word(self, vllm_runner):
with vllm_runner(self.MODEL) as llm:
output_token_ids = self._generate(llm)
assert output_token_ids[0] == self.target_token_id
output_token_ids = self._generate(llm,
bad_words=[self.TARGET_TOKEN])
output_token_ids = self._generate(llm, bad_words=[self.TARGET_TOKEN])
assert self.target_token_id not in output_token_ids
def _generate(self,
llm: LLM,
bad_words: Optional[list[str]] = None) -> list[int]:
def _generate(self, llm: LLM, bad_words: Optional[list[str]] = None) -> list[int]:
return _generate(
llm=llm,
prompt=self.PROMPT,
@@ -75,11 +75,8 @@ class TestOneTokenBadWord:
bad_words=bad_words,
)
def _encode(self,
prompt: str,
add_special_tokens: bool = True) -> list[int]:
return self.tokenizer(prompt,
add_special_tokens=add_special_tokens).input_ids
def _encode(self, prompt: str, add_special_tokens: bool = True) -> list[int]:
return self.tokenizer(prompt, add_special_tokens=add_special_tokens).input_ids
class TestTwoTokenBadWord:
@@ -92,72 +89,80 @@ class TestTwoTokenBadWord:
NEIGHBOUR_TOKEN2 = "older"
def setup_method(self, method):
self.tokenizer = AutoTokenizer.from_pretrained(self.MODEL,
add_prefix_space=True)
self.tokenizer = AutoTokenizer.from_pretrained(
self.MODEL, add_prefix_space=True
)
self.num_prompt_tokens = len(self._encode(self.PROMPT))
self.target_token_id1 = self._encode(self.TARGET_TOKEN1,
add_special_tokens=False)[0]
self.target_token_id2 = self._encode(self.TARGET_TOKEN2,
add_special_tokens=False)[0]
self.neighbour_token_id2 = self._encode(self.NEIGHBOUR_TOKEN2,
add_special_tokens=False)[0]
self.target_token_id1 = self._encode(
self.TARGET_TOKEN1, add_special_tokens=False
)[0]
self.target_token_id2 = self._encode(
self.TARGET_TOKEN2, add_special_tokens=False
)[0]
self.neighbour_token_id2 = self._encode(
self.NEIGHBOUR_TOKEN2, add_special_tokens=False
)[0]
def test_two_token_bad_word(self, vllm_runner):
with vllm_runner(self.MODEL, dtype="half") as llm:
output_token_ids = self._generate(llm)
assert output_token_ids[:2] == [
self.target_token_id1, self.target_token_id2
self.target_token_id1,
self.target_token_id2,
]
output_token_ids = self._generate(llm,
bad_words=[self.TARGET_TOKEN1])
output_token_ids = self._generate(llm, bad_words=[self.TARGET_TOKEN1])
assert self.target_token_id1 not in output_token_ids
output_token_ids = self._generate(llm,
bad_words=[self.TARGET_TOKEN2])
output_token_ids = self._generate(llm, bad_words=[self.TARGET_TOKEN2])
assert output_token_ids[0] == self.target_token_id1
assert self.target_token_id2 not in output_token_ids
output_token_ids = self._generate(
llm, bad_words=[f'{self.TARGET_TOKEN1} {self.TARGET_TOKEN2}'])
llm, bad_words=[f"{self.TARGET_TOKEN1} {self.TARGET_TOKEN2}"]
)
assert output_token_ids[0] == self.target_token_id1
assert output_token_ids[:2] != [
self.target_token_id1, self.target_token_id2
self.target_token_id1,
self.target_token_id2,
]
assert not self._contains(
output_token_ids,
[self.target_token_id1, self.target_token_id2])
output_token_ids, [self.target_token_id1, self.target_token_id2]
)
# Model dependent behaviour
assert output_token_ids[:2] == [
self.target_token_id1, self.neighbour_token_id2
self.target_token_id1,
self.neighbour_token_id2,
]
output_token_ids = self._generate(
llm,
bad_words=[
f'{self.TARGET_TOKEN1} {self.TARGET_TOKEN2}',
f'{self.TARGET_TOKEN1} {self.NEIGHBOUR_TOKEN2}'
])
f"{self.TARGET_TOKEN1} {self.TARGET_TOKEN2}",
f"{self.TARGET_TOKEN1} {self.NEIGHBOUR_TOKEN2}",
],
)
assert output_token_ids[0] == self.target_token_id1
assert output_token_ids[:2] != [
self.target_token_id1, self.target_token_id2
self.target_token_id1,
self.target_token_id2,
]
assert not self._contains(
output_token_ids,
[self.target_token_id1, self.target_token_id2])
output_token_ids, [self.target_token_id1, self.target_token_id2]
)
assert output_token_ids[:2] != [
self.target_token_id1, self.neighbour_token_id2
self.target_token_id1,
self.neighbour_token_id2,
]
assert not self._contains(
output_token_ids,
[self.target_token_id1, self.neighbour_token_id2])
assert ((self.target_token_id2 in output_token_ids)
or (self.neighbour_token_id2 in output_token_ids))
output_token_ids, [self.target_token_id1, self.neighbour_token_id2]
)
assert (self.target_token_id2 in output_token_ids) or (
self.neighbour_token_id2 in output_token_ids
)
def _generate(self,
llm: LLM,
bad_words: Optional[list[str]] = None) -> list[int]:
def _generate(self, llm: LLM, bad_words: Optional[list[str]] = None) -> list[int]:
return _generate(
llm=llm,
prompt=self.PROMPT,
@@ -187,8 +192,5 @@ class TestTwoTokenBadWord:
return False
def _encode(self,
prompt: str,
add_special_tokens: bool = True) -> list[int]:
return self.tokenizer(prompt,
add_special_tokens=add_special_tokens).input_ids
def _encode(self, prompt: str, add_special_tokens: bool = True) -> list[int]:
return self.tokenizer(prompt, add_special_tokens=add_special_tokens).input_ids