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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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202
vllm/sampling_params.py
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@@ -1,6 +1,7 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Sampling parameters for text generation."""
import copy
import warnings
from dataclasses import field
@@ -50,26 +51,32 @@ class StructuredOutputsParams:
def __post_init__(self):
"""Validate that some fields are mutually exclusive."""
count = sum([
self.json is not None, self.regex is not None, self.choice
is not None, self.grammar is not None, self.json_object is not None
])
count = sum(
[
self.json is not None,
self.regex is not None,
self.choice is not None,
self.grammar is not None,
self.json_object is not None,
]
)
if count > 1:
raise ValueError(
"You can only use one kind of structured outputs constraint "
f"but multiple are specified: {self.__dict__}")
f"but multiple are specified: {self.__dict__}"
)
@dataclass
class GuidedDecodingParams(StructuredOutputsParams):
def __post_init__(self):
warnings.warn(
"GuidedDecodingParams is deprecated. This will be removed in "
"v0.12.0 or v1.0.0, which ever is soonest. Please use "
"StructuredOutputsParams instead.",
DeprecationWarning,
stacklevel=2)
stacklevel=2,
)
return super().__post_init__()
@@ -83,10 +90,11 @@ class RequestOutputKind(Enum):
class SamplingParams(
msgspec.Struct,
omit_defaults=True, # type: ignore[call-arg]
# required for @cached_property.
dict=True): # type: ignore[call-arg]
msgspec.Struct,
omit_defaults=True, # type: ignore[call-arg]
# required for @cached_property.
dict=True,
): # type: ignore[call-arg]
"""Sampling parameters for text generation.
Overall, we follow the sampling parameters from the OpenAI text completion
@@ -178,8 +186,7 @@ class SamplingParams(
optionally prompt tokens as a first argument."""
include_stop_str_in_output: bool = False
"""Whether to include the stop strings in output text."""
truncate_prompt_tokens: Optional[Annotated[int,
msgspec.Meta(ge=-1)]] = None
truncate_prompt_tokens: Optional[Annotated[int, msgspec.Meta(ge=-1)]] = None
"""If set to -1, will use the truncation size supported by the model. If
set to an integer k, will use only the last k tokens from the prompt
(i.e., left truncation). If set to `None`, truncation is disabled."""
@@ -238,9 +245,7 @@ class SamplingParams(
skip_special_tokens: bool = True,
spaces_between_special_tokens: bool = True,
logits_processors: Optional[list[LogitsProcessor]] = None,
truncate_prompt_tokens: Optional[Annotated[int,
msgspec.Meta(
ge=-1)]] = None,
truncate_prompt_tokens: Optional[Annotated[int, msgspec.Meta(ge=-1)]] = None,
output_kind: RequestOutputKind = RequestOutputKind.CUMULATIVE,
structured_outputs: Optional[StructuredOutputsParams] = None,
guided_decoding: Optional[GuidedDecodingParams] = None,
@@ -261,19 +266,19 @@ class SamplingParams(
"v0.12.0 or v1.0.0, which ever is soonest. Please use "
"structured_outputs instead.",
DeprecationWarning,
stacklevel=2)
stacklevel=2,
)
structured_outputs = guided_decoding
guided_decoding = None
return SamplingParams(
n=1 if n is None else n,
best_of=best_of,
presence_penalty=0.0
if presence_penalty is None else presence_penalty,
frequency_penalty=0.0
if frequency_penalty is None else frequency_penalty,
presence_penalty=0.0 if presence_penalty is None else presence_penalty,
frequency_penalty=0.0 if frequency_penalty is None else frequency_penalty,
repetition_penalty=1.0
if repetition_penalty is None else repetition_penalty,
if repetition_penalty is None
else repetition_penalty,
temperature=1.0 if temperature is None else temperature,
top_p=1.0 if top_p is None else top_p,
top_k=top_k,
@@ -311,7 +316,8 @@ class SamplingParams(
if self.best_of < self.n:
raise ValueError(
f"best_of must be greater than or equal to n, "
f"got n={self.n} and best_of={self.best_of}.")
f"got n={self.n} and best_of={self.best_of}."
)
if not self._real_n:
self._real_n = self.n
self.n = self.best_of
@@ -320,7 +326,10 @@ class SamplingParams(
logger.warning(
"temperature %s is less than %s, which may cause numerical "
"errors nan or inf in tensors. We have maxed it out to %s.",
self.temperature, _MAX_TEMP, _MAX_TEMP)
self.temperature,
_MAX_TEMP,
_MAX_TEMP,
)
self.temperature = max(self.temperature, _MAX_TEMP)
if self.seed == -1:
@@ -366,101 +375,116 @@ class SamplingParams(
"v0.12.0 or v1.0.0, which ever is soonest. Please use "
"structured_outputs instead.",
DeprecationWarning,
stacklevel=2)
stacklevel=2,
)
self.structured_outputs = self.guided_decoding
self.guided_decoding = None
def _verify_args(self) -> None:
if not isinstance(self.n, int):
raise ValueError(f"n must be an int, but is of "
f"type {type(self.n)}")
raise ValueError(f"n must be an int, but is of type {type(self.n)}")
if self.n < 1:
raise ValueError(f"n must be at least 1, got {self.n}.")
if self.best_of is not None:
if not isinstance(self.best_of, int):
raise ValueError(
f"best_of must be an integer, got {type(self.best_of)}")
f"best_of must be an integer, got {type(self.best_of)}"
)
if self.best_of < 1:
raise ValueError(
f"best_of must be at least 1, got {self.best_of}")
raise ValueError(f"best_of must be at least 1, got {self.best_of}")
if self.best_of < self.n:
raise ValueError(
f"best_of must be greater than or equal to n, "
f"got n={self.n} and best_of={self.best_of}.")
f"got n={self.n} and best_of={self.best_of}."
)
if not -2.0 <= self.presence_penalty <= 2.0:
raise ValueError("presence_penalty must be in [-2, 2], got "
f"{self.presence_penalty}.")
raise ValueError(
f"presence_penalty must be in [-2, 2], got {self.presence_penalty}."
)
if not -2.0 <= self.frequency_penalty <= 2.0:
raise ValueError("frequency_penalty must be in [-2, 2], got "
f"{self.frequency_penalty}.")
raise ValueError(
f"frequency_penalty must be in [-2, 2], got {self.frequency_penalty}."
)
if self.repetition_penalty <= 0.0:
raise ValueError(
"repetition_penalty must be greater than zero, got "
f"{self.repetition_penalty}.")
f"{self.repetition_penalty}."
)
if self.temperature < 0.0:
raise ValueError(
f"temperature must be non-negative, got {self.temperature}.")
f"temperature must be non-negative, got {self.temperature}."
)
if not 0.0 < self.top_p <= 1.0:
raise ValueError(f"top_p must be in (0, 1], got {self.top_p}.")
# quietly accept -1 as disabled, but prefer 0
if self.top_k < -1:
raise ValueError(f"top_k must be 0 (disable), or at least 1, "
f"got {self.top_k}.")
raise ValueError(
f"top_k must be 0 (disable), or at least 1, got {self.top_k}."
)
if not isinstance(self.top_k, int):
raise TypeError(
f"top_k must be an integer, got {type(self.top_k).__name__}")
f"top_k must be an integer, got {type(self.top_k).__name__}"
)
if not 0.0 <= self.min_p <= 1.0:
raise ValueError("min_p must be in [0, 1], got "
f"{self.min_p}.")
raise ValueError(f"min_p must be in [0, 1], got {self.min_p}.")
if self.max_tokens is not None and self.max_tokens < 1:
raise ValueError(
f"max_tokens must be at least 1, got {self.max_tokens}.")
raise ValueError(f"max_tokens must be at least 1, got {self.max_tokens}.")
if self.min_tokens < 0:
raise ValueError(f"min_tokens must be greater than or equal to 0, "
f"got {self.min_tokens}.")
raise ValueError(
f"min_tokens must be greater than or equal to 0, got {self.min_tokens}."
)
if self.max_tokens is not None and self.min_tokens > self.max_tokens:
raise ValueError(
f"min_tokens must be less than or equal to "
f"max_tokens={self.max_tokens}, got {self.min_tokens}.")
if (self.logprobs is not None and self.logprobs != -1
and self.logprobs < 0):
f"max_tokens={self.max_tokens}, got {self.min_tokens}."
)
if self.logprobs is not None and self.logprobs != -1 and self.logprobs < 0:
raise ValueError(
f"logprobs must be non-negative or -1, got {self.logprobs}.")
if (self.prompt_logprobs is not None and self.prompt_logprobs != -1
and self.prompt_logprobs < 0):
f"logprobs must be non-negative or -1, got {self.logprobs}."
)
if (
self.prompt_logprobs is not None
and self.prompt_logprobs != -1
and self.prompt_logprobs < 0
):
raise ValueError(
f"prompt_logprobs must be non-negative or -1, got "
f"{self.prompt_logprobs}.")
if (self.truncate_prompt_tokens is not None
and (self.truncate_prompt_tokens == 0
or self.truncate_prompt_tokens < -1)):
f"{self.prompt_logprobs}."
)
if self.truncate_prompt_tokens is not None and (
self.truncate_prompt_tokens == 0 or self.truncate_prompt_tokens < -1
):
raise ValueError(
f"truncate_prompt_tokens must be an integer >= 1 or -1, "
f"got {self.truncate_prompt_tokens}")
f"got {self.truncate_prompt_tokens}"
)
assert isinstance(self.stop_token_ids, list)
if not all(isinstance(st_id, int) for st_id in self.stop_token_ids):
raise ValueError(f"stop_token_ids must contain only integers, "
f"got {self.stop_token_ids}.")
raise ValueError(
f"stop_token_ids must contain only integers, got {self.stop_token_ids}."
)
assert isinstance(self.stop, list)
if any(not stop_str for stop_str in self.stop):
raise ValueError("stop cannot contain an empty string.")
if self.stop and not self.detokenize:
raise ValueError(
"stop strings are only supported when detokenize is True. "
"Set detokenize=True to use stop.")
"Set detokenize=True to use stop."
)
if self.best_of != self._real_n and self.output_kind == (
RequestOutputKind.DELTA):
RequestOutputKind.DELTA
):
raise ValueError("best_of must equal n to use output_kind=DELTA")
def _verify_greedy_sampling(self) -> None:
if self.n > 1:
raise ValueError("n must be 1 when using greedy sampling, "
f"got {self.n}.")
raise ValueError(f"n must be 1 when using greedy sampling, got {self.n}.")
def update_from_generation_config(
self,
generation_config: dict[str, Any],
model_eos_token_id: Optional[int] = None) -> None:
self,
generation_config: dict[str, Any],
model_eos_token_id: Optional[int] = None,
) -> None:
"""Update if there are non-default values from generation_config"""
if model_eos_token_id is not None:
@@ -494,30 +518,33 @@ class SamplingParams(
for add_prefix_space in [False, True]:
prefix = " " if add_prefix_space else ""
prompt = prefix + bad_word.lstrip()
prompt_token_ids = tokenizer.encode(text=prompt,
add_special_tokens=False)
prompt_token_ids = tokenizer.encode(
text=prompt, add_special_tokens=False
)
# If no space at the beginning
# or if prefix space produces a new word token
if (not add_prefix_space) or (
add_prefix_space and prompt_token_ids[0]
!= self._bad_words_token_ids[-1][0]
and len(prompt_token_ids) == len(
self._bad_words_token_ids[-1])):
add_prefix_space
and prompt_token_ids[0] != self._bad_words_token_ids[-1][0]
and len(prompt_token_ids) == len(self._bad_words_token_ids[-1])
):
self._bad_words_token_ids.append(prompt_token_ids)
invalid_token_ids = [
token_id for bad_words_token_ids in self._bad_words_token_ids
token_id
for bad_words_token_ids in self._bad_words_token_ids
for token_id in bad_words_token_ids
if token_id < 0 or token_id > tokenizer.max_token_id
]
if len(invalid_token_ids) > 0:
raise ValueError(
f"The model vocabulary size is {tokenizer.max_token_id+1},"
f"The model vocabulary size is {tokenizer.max_token_id + 1},"
f" but the following tokens"
f" were specified as bad: {invalid_token_ids}."
f" All token id values should be integers satisfying:"
f" 0 <= token_id <= {tokenizer.max_token_id}.")
f" 0 <= token_id <= {tokenizer.max_token_id}."
)
@cached_property
def sampling_type(self) -> SamplingType:
@@ -545,10 +572,14 @@ class SamplingParams(
See https://github.com/vllm-project/vllm/issues/3087
"""
logit_processor_refs = None if self.logits_processors is None else {
id(lp): lp.clone() if hasattr(lp, 'clone') else lp
for lp in self.logits_processors
}
logit_processor_refs = (
None
if self.logits_processors is None
else {
id(lp): lp.clone() if hasattr(lp, "clone") else lp
for lp in self.logits_processors
}
)
return copy.deepcopy(self, memo=logit_processor_refs)
def __repr__(self) -> str:
@@ -576,15 +607,18 @@ class SamplingParams(
f"{self.spaces_between_special_tokens}, "
f"truncate_prompt_tokens={self.truncate_prompt_tokens}, "
f"structured_outputs={self.structured_outputs}, "
f"extra_args={self.extra_args})")
f"extra_args={self.extra_args})"
)
class BeamSearchParams(
msgspec.Struct,
omit_defaults=True, # type: ignore[call-arg]
# required for @cached_property.
dict=True): # type: ignore[call-arg]
msgspec.Struct,
omit_defaults=True, # type: ignore[call-arg]
# required for @cached_property.
dict=True,
): # type: ignore[call-arg]
"""Beam search parameters for text generation."""
beam_width: int
max_tokens: int
ignore_eos: bool = False