class SamplingParams(
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
API (https://platform.openai.com/docs/api-reference/completions/create).
In addition, we support beam search, which is not supported by OpenAI.
Args:
n: Number of output sequences to return for the given prompt.
best_of: Number of output sequences that are generated from the prompt.
From these `best_of` sequences, the top `n` sequences are returned.
`best_of` must be greater than or equal to `n`. By default,
`best_of` is set to `n`. Warning, this is only supported in V0.
presence_penalty: Float that penalizes new tokens based on whether they
appear in the generated text so far. Values > 0 encourage the model
to use new tokens, while values < 0 encourage the model to repeat
tokens.
frequency_penalty: Float that penalizes new tokens based on their
frequency in the generated text so far. Values > 0 encourage the
model to use new tokens, while values < 0 encourage the model to
repeat tokens.
repetition_penalty: Float that penalizes new tokens based on whether
they appear in the prompt and the generated text so far. Values > 1
encourage the model to use new tokens, while values < 1 encourage
the model to repeat tokens.
temperature: Float that controls the randomness of the sampling. Lower
values make the model more deterministic, while higher values make
the model more random. Zero means greedy sampling.
top_p: Float that controls the cumulative probability of the top tokens
to consider. Must be in (0, 1]. Set to 1 to consider all tokens.
top_k: Integer that controls the number of top tokens to consider. Set
to 0 (or -1) to consider all tokens.
min_p: Float that represents the minimum probability for a token to be
considered, relative to the probability of the most likely token.
Must be in [0, 1]. Set to 0 to disable this.
seed: Random seed to use for the generation.
stop: list of strings that stop the generation when they are generated.
The returned output will not contain the stop strings.
stop_token_ids: list of tokens that stop the generation when they are
generated. The returned output will contain the stop tokens unless
the stop tokens are special tokens.
bad_words: list of words that are not allowed to be generated.
More precisely, only the last token of a corresponding
token sequence is not allowed when the next generated token
can complete the sequence.
include_stop_str_in_output: Whether to include the stop strings in
output text. Defaults to False.
ignore_eos: Whether to ignore the EOS token and continue generating
tokens after the EOS token is generated.
max_tokens: Maximum number of tokens to generate per output sequence.
min_tokens: Minimum number of tokens to generate per output sequence
before EOS or stop_token_ids can be generated
logprobs: Number of log probabilities to return per output token.
When set to None, no probability is returned. If set to a non-None
value, the result includes the log probabilities of the specified
number of most likely tokens, as well as the chosen tokens.
Note that the implementation follows the OpenAI API: The API will
always return the log probability of the sampled token, so there
may be up to `logprobs+1` elements in the response.
prompt_logprobs: Number of log probabilities to return per prompt token.
detokenize: Whether to detokenize the output. Defaults to True.
skip_special_tokens: Whether to skip special tokens in the output.
spaces_between_special_tokens: Whether to add spaces between special
tokens in the output. Defaults to True.
logits_processors: list of functions that modify logits based on
previously generated tokens, and optionally prompt tokens as
a first argument.
truncate_prompt_tokens: 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).
Defaults to None (i.e., no truncation).
guided_decoding: If provided, the engine will construct a guided
decoding logits processor from these parameters. Defaults to None.
logit_bias: If provided, the engine will construct a logits processor
that applies these logit biases. Defaults to None.
allowed_token_ids: If provided, the engine will construct a logits
processor which only retains scores for the given token ids.
Defaults to None.
extra_args: Arbitrary additional args, that can be used by custom
sampling implementations, plugins, etc. Not used by any in-tree
sampling implementations.
"""
n: int = 1
best_of: Optional[int] = None
_real_n: Optional[int] = None
presence_penalty: float = 0.0
frequency_penalty: float = 0.0
repetition_penalty: float = 1.0
temperature: float = 1.0
top_p: float = 1.0
top_k: int = 0
min_p: float = 0.0
seed: Optional[int] = None
stop: Optional[Union[str, list[str]]] = None
stop_token_ids: Optional[list[int]] = None
ignore_eos: bool = False
max_tokens: Optional[int] = 16
min_tokens: int = 0
logprobs: Optional[int] = None
prompt_logprobs: Optional[int] = None
# NOTE: This parameter is only exposed at the engine level for now.
# It is not exposed in the OpenAI API server, as the OpenAI API does
# not support returning only a list of token IDs.
detokenize: bool = True
skip_special_tokens: bool = True
spaces_between_special_tokens: bool = True
# Optional[list[LogitsProcessor]] type. We use Any here because
# Optional[list[LogitsProcessor]] type is not supported by msgspec.
logits_processors: Optional[Any] = None
include_stop_str_in_output: bool = False
truncate_prompt_tokens: Optional[Annotated[int, msgspec.Meta(ge=1)]] = None
output_kind: RequestOutputKind = RequestOutputKind.CUMULATIVE
# The below fields are not supposed to be used as an input.
# They are set in post_init.
output_text_buffer_length: int = 0
_all_stop_token_ids: set[int] = msgspec.field(default_factory=set)
# Fields used to construct logits processors
guided_decoding: Optional[GuidedDecodingParams] = None
logit_bias: Optional[dict[int, float]] = None
allowed_token_ids: Optional[list[int]] = None
extra_args: Optional[dict[str, Any]] = None
# Fields used for bad words
bad_words: Optional[list[str]] = None
_bad_words_token_ids: Optional[list[list[int]]] = None
@staticmethod
def from_optional(
n: Optional[int] = 1,
best_of: Optional[int] = None,
presence_penalty: Optional[float] = 0.0,
frequency_penalty: Optional[float] = 0.0,
repetition_penalty: Optional[float] = 1.0,
temperature: Optional[float] = 1.0,
top_p: Optional[float] = 1.0,
top_k: int = 0,
min_p: float = 0.0,
seed: Optional[int] = None,
stop: Optional[Union[str, list[str]]] = None,
stop_token_ids: Optional[list[int]] = None,
bad_words: Optional[list[str]] = None,
include_stop_str_in_output: bool = False,
ignore_eos: bool = False,
max_tokens: Optional[int] = 16,
min_tokens: int = 0,
logprobs: Optional[int] = None,
prompt_logprobs: Optional[int] = None,
detokenize: bool = True,
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,
output_kind: RequestOutputKind = RequestOutputKind.CUMULATIVE,
guided_decoding: Optional[GuidedDecodingParams] = None,
logit_bias: Optional[Union[dict[int, float], dict[str, float]]] = None,
allowed_token_ids: Optional[list[int]] = None,
extra_args: Optional[dict[str, Any]] = None,
) -> "SamplingParams":
if logit_bias is not None:
# Convert token_id to integer
# Clamp the bias between -100 and 100 per OpenAI API spec
logit_bias = {
int(token): min(100.0, max(-100.0, bias))
for token, bias in logit_bias.items()
}
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,
repetition_penalty=1.0
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,
min_p=min_p,
seed=seed,
stop=stop,
stop_token_ids=stop_token_ids,
bad_words=bad_words,
include_stop_str_in_output=include_stop_str_in_output,
ignore_eos=ignore_eos,
max_tokens=max_tokens,
min_tokens=min_tokens,
logprobs=logprobs,
prompt_logprobs=prompt_logprobs,
detokenize=detokenize,
skip_special_tokens=skip_special_tokens,
spaces_between_special_tokens=spaces_between_special_tokens,
logits_processors=logits_processors,
truncate_prompt_tokens=truncate_prompt_tokens,
output_kind=output_kind,
guided_decoding=guided_decoding,
logit_bias=logit_bias,
allowed_token_ids=allowed_token_ids,
extra_args=extra_args,
)
def __post_init__(self) -> None:
# how we deal with `best_of``:
# if `best_of`` is not set, we default to `n`;
# if `best_of`` is set, we set `n`` to `best_of`,
# and set `_real_n`` to the original `n`.
# when we return the result, we will check
# if we need to return `n` or `_real_n` results
if 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}.")
if not self._real_n:
self._real_n = self.n
self.n = self.best_of
if 0 < self.temperature < _MAX_TEMP:
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(self.temperature, _MAX_TEMP)
if self.seed == -1:
self.seed = None
if self.stop is None:
self.stop = []
elif isinstance(self.stop, str):
self.stop = [self.stop]
if self.stop_token_ids is None:
self.stop_token_ids = []
if self.bad_words is None:
self.bad_words = []
if self.logprobs is True:
self.logprobs = 1
if self.prompt_logprobs is True:
self.prompt_logprobs = 1
# Number of characters to hold back for stop string evaluation
# until sequence is finished.
if self.stop and not self.include_stop_str_in_output:
self.output_text_buffer_length = max(len(s) for s in self.stop) - 1
self._verify_args()
if self.temperature < _SAMPLING_EPS:
# Zero temperature means greedy sampling.
self.top_p = 1.0
self.top_k = 0
self.min_p = 0.0
self._verify_greedy_sampling()
# eos_token_id is added to this by the engine
self._all_stop_token_ids.update(self.stop_token_ids)
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)}")
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)}")
if self.best_of < 1:
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}.")
if not -2.0 <= self.presence_penalty <= 2.0:
raise ValueError("presence_penalty must be in [-2, 2], got "
f"{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}.")
if self.repetition_penalty <= 0.0:
raise ValueError(
"repetition_penalty must be greater than zero, got "
f"{self.repetition_penalty}.")
if self.temperature < 0.0:
raise ValueError(
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}.")
if not isinstance(self.top_k, int):
raise TypeError(
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}.")
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}.")
if self.min_tokens < 0:
raise ValueError(f"min_tokens must be greater than or equal to 0, "
f"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 < 0:
raise ValueError(
f"logprobs must be non-negative, got {self.logprobs}.")
if self.prompt_logprobs is not None and self.prompt_logprobs < 0:
raise ValueError(f"prompt_logprobs must be non-negative, got "
f"{self.prompt_logprobs}.")
if (self.truncate_prompt_tokens is not None
and self.truncate_prompt_tokens < 1):
raise ValueError(f"truncate_prompt_tokens must be >= 1, "
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}.")
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.")
if self.best_of != self._real_n and self.output_kind == (
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}.")
def update_from_generation_config(
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:
# Add the eos token id into the sampling_params to support
# min_tokens processing.
self._all_stop_token_ids.add(model_eos_token_id)
# Update eos_token_id for generation
if (eos_ids := generation_config.get("eos_token_id")) is not None:
# it can be either int or list of int
eos_ids = {eos_ids} if isinstance(eos_ids, int) else set(eos_ids)
if model_eos_token_id is not None:
# We don't need to include the primary eos_token_id in
# stop_token_ids since it's handled separately for stopping
# purposes.
eos_ids.discard(model_eos_token_id)
if eos_ids:
self._all_stop_token_ids.update(eos_ids)
if not self.ignore_eos:
eos_ids.update(self.stop_token_ids)
self.stop_token_ids = list(eos_ids)
def update_from_tokenizer(self, tokenizer: AnyTokenizer) -> None:
if not self.bad_words:
return
self._bad_words_token_ids = []
for bad_word in self.bad_words:
# To prohibit words both at the beginning
# and in the middle of text
# (related to add_prefix_space tokenizer parameter)
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)
# 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])):
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
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" 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}.")
@cached_property
def sampling_type(self) -> SamplingType:
if self.temperature < _SAMPLING_EPS:
return SamplingType.GREEDY
if self.seed is not None:
return SamplingType.RANDOM_SEED
return SamplingType.RANDOM
@property
def all_stop_token_ids(self) -> set[int]:
return self._all_stop_token_ids
@property
def bad_words_token_ids(self) -> Optional[list[list[int]]]:
# For internal use only. Backward compatibility not guaranteed
return self._bad_words_token_ids
def clone(self) -> "SamplingParams":
"""Deep copy, but maybe not the LogitsProcessor objects.
LogitsProcessor objects may contain an arbitrary, nontrivial amount of
data that is expensive to copy. However, if not copied, the processor
needs to support parallel decoding for multiple sequences
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
}
return copy.deepcopy(self, memo=logit_processor_refs)
def __repr__(self) -> str:
return (
f"SamplingParams(n={self.n}, "
f"presence_penalty={self.presence_penalty}, "
f"frequency_penalty={self.frequency_penalty}, "
f"repetition_penalty={self.repetition_penalty}, "
f"temperature={self.temperature}, "
f"top_p={self.top_p}, "
f"top_k={self.top_k}, "
f"min_p={self.min_p}, "
f"seed={self.seed}, "
f"stop={self.stop}, "
f"stop_token_ids={self.stop_token_ids}, "
f"bad_words={self.bad_words}, "
f"include_stop_str_in_output={self.include_stop_str_in_output}, "
f"ignore_eos={self.ignore_eos}, "
f"max_tokens={self.max_tokens}, "
f"min_tokens={self.min_tokens}, "
f"logprobs={self.logprobs}, "
f"prompt_logprobs={self.prompt_logprobs}, "
f"skip_special_tokens={self.skip_special_tokens}, "
"spaces_between_special_tokens="
f"{self.spaces_between_special_tokens}, "
f"truncate_prompt_tokens={self.truncate_prompt_tokens}, "
f"guided_decoding={self.guided_decoding}, "
f"extra_args={self.extra_args})")
