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vllm/vllm/sampling_params.py

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"""Sampling parameters for text generation."""
import copy
from dataclasses import dataclass
from enum import Enum, IntEnum
from functools import cached_property
from typing import Any, Callable, Dict, List, Optional, Set, Union
import msgspec
import torch
from pydantic import BaseModel
from typing_extensions import Annotated
from vllm.logger import init_logger
logger = init_logger(__name__)
_SAMPLING_EPS = 1e-5
_MAX_TEMP = 1e-2
class SamplingType(IntEnum):
GREEDY = 0
RANDOM = 1
RANDOM_SEED = 2
LogitsProcessor = Union[Callable[[List[int], torch.Tensor], torch.Tensor],
Callable[[List[int], List[int], torch.Tensor],
torch.Tensor]]
"""LogitsProcessor is a function that takes a list
of previously generated tokens, the logits tensor
for the next token and, optionally, prompt tokens as a
first argument, and returns a modified tensor of logits
to sample from."""
# maybe make msgspec?
@dataclass
class GuidedDecodingParams:
"""One of these fields will be used to build a logit processor."""
json: Optional[Union[str, Dict]] = None
regex: Optional[str] = None
choice: Optional[List[str]] = None
grammar: Optional[str] = None
json_object: Optional[bool] = None
"""These are other options that can be set"""
backend: Optional[str] = None
whitespace_pattern: Optional[str] = None
@staticmethod
def from_optional(
json: Optional[Union[Dict, BaseModel, str]] = None,
regex: Optional[str] = None,
choice: Optional[List[str]] = None,
grammar: Optional[str] = None,
json_object: Optional[bool] = None,
backend: Optional[str] = None,
whitespace_pattern: Optional[str] = None,
) -> Optional["GuidedDecodingParams"]:
if all(arg is None
for arg in (json, regex, choice, grammar, json_object)):
return None
# Extract json schemas from pydantic models
if isinstance(json, (BaseModel, type(BaseModel))):
json = json.model_json_schema()
return GuidedDecodingParams(
json=json,
regex=regex,
choice=choice,
grammar=grammar,
json_object=json_object,
backend=backend,
whitespace_pattern=whitespace_pattern,
)
def __post_init__(self):
"""Validate that some fields are mutually exclusive."""
guide_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 guide_count > 1:
raise ValueError(
"You can only use one kind of guided decoding but multiple are "
f"specified: {self.__dict__}")
class RequestOutputKind(Enum):
# Return entire output so far in every RequestOutput
CUMULATIVE = 0
# Return only deltas in each RequestOutput
DELTA = 1
# Do not return intermediate RequestOuputs
FINAL_ONLY = 2
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`.
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 -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.
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 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.
"""
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 = -1
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
@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 = -1,
min_p: float = 0.0,
seed: Optional[int] = None,
stop: Optional[Union[str, List[str]]] = None,
stop_token_ids: Optional[List[int]] = 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,
) -> "SamplingParams":
if logit_bias is not None:
logit_bias = {
int(token): 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,
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,
)
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}.")
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
else:
self.seed = self.seed
if self.stop is None:
self.stop = []
elif isinstance(self.stop, str):
self.stop = [self.stop]
else:
self.stop = list(self.stop)
if self.stop_token_ids is None:
self.stop_token_ids = []
else:
self.stop_token_ids = list(self.stop_token_ids)
self.logprobs = 1 if self.logprobs is True else self.logprobs
self.prompt_logprobs = (1 if self.prompt_logprobs is True else
self.prompt_logprobs)
# 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 = -1
self.min_p = 0.0
self._verify_greedy_sampling()
# eos_token_id is added to this by the engine
self._all_stop_token_ids = set(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 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 not 0.0 < self.repetition_penalty <= 2.0:
raise ValueError("repetition_penalty must be in (0, 2], 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}.")
if self.top_k < -1 or self.top_k == 0:
raise ValueError(f"top_k must be -1 (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, 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)
@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
def clone(self) -> "SamplingParams":
"""Deep copy excluding LogitsProcessor objects.
LogitsProcessor objects are excluded because they may contain an
arbitrary, nontrivial amount of data.
See https://github.com/vllm-project/vllm/issues/3087
"""
logit_processor_refs = None if self.logits_processors is None else {
id(lp): 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"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}")
class BeamSearchParams(
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
temperature: float = 0.0
length_penalty: float = 1.0
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