EngineX-Mthreads/enginex-mthreads-vllm
4
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

Sync from v0.13

Browse Source
This commit is contained in:
xiezhongtao
2026-01-19 10:38:50 +08:00
parent b2ef04d792
commit 5aef6c175a
3714 changed files with 854317 additions and 89342 deletions
Download Patch File Download Diff File Expand all files Collapse all files

733
vllm/sampling_params.py
View File

@@ -1,320 +1,560 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Sampling parameters for text generation."""
import copy
from enum import IntEnum
from functools import cached_property
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from pydantic import Field
from typing_extensions import Annotated
import copy
from dataclasses import field
from enum import Enum, IntEnum
from functools import cached_property
from typing import Annotated, Any
import msgspec
from pydantic.dataclasses import dataclass
from vllm.logger import init_logger
from vllm.logits_process import LogitsProcessor
from vllm.tokenizers import TokenizerLike
from vllm.v1.serial_utils import PydanticMsgspecMixin
logger = init_logger(__name__)
_SAMPLING_EPS = 1e-5
_MAX_TEMP = 1e-2
class SamplingType(IntEnum):
GREEDY = 0
RANDOM = 1
RANDOM_SEED = 2
BEAM = 3
LogitsProcessor = Callable[[List[int], torch.Tensor], torch.Tensor]
"""LogitsProcessor is a function that takes a list of previously generated
tokens and a tensor of the logits for the next token, and returns a modified
tensor of logits to sample from."""
# maybe make msgspec?
@dataclass
class StructuredOutputsParams:
# One of these fields will be used to build a logit processor.
json: str | dict | None = None
regex: str | None = None
choice: list[str] | None = None
grammar: str | None = None
json_object: bool | None = None
# These are other options that can be set.
disable_fallback: bool = False
disable_any_whitespace: bool = False
disable_additional_properties: bool = False
whitespace_pattern: str | None = None
structural_tag: str | None = None
_backend: str | None = field(default=None, init=False)
"""CAUTION: Should only be set by Processor._validate_structured_output"""
_backend_was_auto: bool = field(default=False, init=False)
"""CAUTION: Should only be set by Processor._validate_structured_output"""
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,
self.structural_tag 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__}"
)
def all_constraints_none(self) -> bool:
"""
Returns True if all structured-output constraint fields are None.
"""
return all(
getattr(self, field) is None
for field in (
"json",
"regex",
"choice",
"grammar",
"json_object",
"structural_tag",
)
)
def all_non_structural_tag_constraints_none(self) -> bool:
"""
Returns True if all structured-output constraint fields are None.
"""
return all(
getattr(self, field) is None
for field in (
"json",
"regex",
"choice",
"grammar",
"json_object",
)
)
class SamplingParams:
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 RequestOutput
FINAL_ONLY = 2
class SamplingParams(
PydanticMsgspecMixin,
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`. This is treated as
the beam width when `use_beam_search` is True. 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.
use_beam_search: Whether to use beam search instead of sampling.
length_penalty: Float that penalizes sequences based on their length.
Used in beam search.
early_stopping: Controls the stopping condition for beam search. It
accepts the following values: `True`, where the generation stops as
soon as there are `best_of` complete candidates; `False`, where an
heuristic is applied and the generation stops when is it very
unlikely to find better candidates; `"never"`, where the beam search
procedure only stops when there cannot be better candidates
(canonical beam search algorithm).
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.
Note that the implementation follows the OpenAI API: The return
result includes the log probabilities on the `logprobs` most likely
tokens, as well the chosen tokens. 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.
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).
"""
def __init__(
self,
n: int = 1,
best_of: 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,
n: int = 1
"""Number of outputs to return for the given prompt request.
NOTE:
`AsyncLLM` streams outputs by default. When `n > 1`, all `n` outputs
are generated and streamed cumulatively per request. To see all `n`
outputs upon completion, use `output_kind=RequestOutputKind.FINAL_ONLY`
in `SamplingParams`."""
presence_penalty: float = 0.0
"""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 = 0.0
"""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 = 1.0
"""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 = 1.0
"""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 = 1.0
"""Controls the cumulative probability of the top tokens to consider. Must
be in (0, 1]. Set to 1 to consider all tokens."""
top_k: int = 0
"""Controls the number of top tokens to consider. Set to 0 (or -1) to
consider all tokens."""
min_p: float = 0.0
"""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: int | None = None
"""Random seed to use for the generation."""
stop: str | list[str] | None = None
"""String(s) that stop the generation when they are generated. The returned
output will not contain the stop strings."""
stop_token_ids: list[int] | None = None
"""Token IDs that stop the generation when they are generated. The returned
output will contain the stop tokens unless the stop tokens are special
tokens."""
ignore_eos: bool = False
"""Whether to ignore the EOS token and continue generating
tokens after the EOS token is generated."""
max_tokens: int | None = 16
"""Maximum number of tokens to generate per output sequence."""
min_tokens: int = 0
"""Minimum number of tokens to generate per output sequence before EOS or
`stop_token_ids` can be generated"""
logprobs: int | None = None
"""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. When set to -1, return all `vocab_size` log probabilities."""
prompt_logprobs: int | None = None
"""Number of log probabilities to return per prompt token.
When set to -1, return all `vocab_size` log probabilities."""
flat_logprobs: bool = False
"""Whether to return logprobs in flatten format (i.e. FlatLogprob)
for better performance.
NOTE: GC costs of FlatLogprobs is significantly smaller than
list[dict[int, Logprob]]. After enabled, PromptLogprobs and
SampleLogprobs would populated as FlatLogprobs."""
# 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
"""Whether to detokenize the output."""
skip_special_tokens: bool = True
"""Whether to skip special tokens in the output."""
spaces_between_special_tokens: bool = True
"""Whether to add spaces between special tokens in the output."""
# `list[LogitsProcessor] | None` type. We use Any here because
# `list[LogitsProcessor] | None` type is not supported by msgspec.
logits_processors: Any | None = None
"""Functions that modify logits based on previously generated tokens, and
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: Annotated[int, msgspec.Meta(ge=-1)] | None = 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."""
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
structured_outputs: StructuredOutputsParams | None = None
"""Parameters for configuring structured outputs."""
logit_bias: dict[int, float] | None = None
"""If provided, the engine will construct a logits processor that applies
these logit biases."""
allowed_token_ids: list[int] | None = None
"""If provided, the engine will construct a logits processor which only
retains scores for the given token ids."""
extra_args: dict[str, Any] | None = None
"""Arbitrary additional args, that can be used by custom sampling
implementations, plugins, etc. Not used by any in-tree sampling
implementations."""
# Fields used for bad words
bad_words: list[str] | None = None
"""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."""
_bad_words_token_ids: list[list[int]] | None = None
skip_reading_prefix_cache: bool | None = None
@staticmethod
def from_optional(
n: int | None = 1,
presence_penalty: float | None = 0.0,
frequency_penalty: float | None = 0.0,
repetition_penalty: float | None = 1.0,
temperature: float | None = 1.0,
top_p: float | None = 1.0,
top_k: int = 0,
min_p: float = 0.0,
seed: Optional[int] = None,
use_beam_search: bool = False,
length_penalty: float = 1.0,
early_stopping: Union[bool, str] = False,
stop: Optional[Union[str, List[str]]] = None,
stop_token_ids: Optional[List[int]] = None,
seed: int | None = None,
stop: str | list[str] | None = None,
stop_token_ids: list[int] | None = None,
bad_words: list[str] | None = None,
include_stop_str_in_output: bool = False,
ignore_eos: bool = False,
max_tokens: Optional[int] = 16,
max_tokens: int | None = 16,
min_tokens: int = 0,
logprobs: Optional[int] = None,
prompt_logprobs: Optional[int] = None,
logprobs: int | None = None,
prompt_logprobs: int | None = 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, Field(ge=1)]] = None,
) -> None:
self.n = n
self.best_of = best_of if best_of is not None else n
self.presence_penalty = presence_penalty
self.frequency_penalty = frequency_penalty
self.repetition_penalty = repetition_penalty
self.temperature = temperature
self.top_p = top_p
self.top_k = top_k
self.min_p = min_p
if seed == -1:
logits_processors: list[LogitsProcessor] | None = None,
truncate_prompt_tokens: Annotated[int, msgspec.Meta(ge=-1)] | None = None,
output_kind: RequestOutputKind = RequestOutputKind.CUMULATIVE,
structured_outputs: StructuredOutputsParams | None = None,
logit_bias: dict[int, float] | dict[str, float] | None = None,
allowed_token_ids: list[int] | None = None,
extra_args: dict[str, Any] | None = 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,
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,
structured_outputs=structured_outputs,
logit_bias=logit_bias,
allowed_token_ids=allowed_token_ids,
extra_args=extra_args,
)
def __post_init__(self) -> None:
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 = seed
self.use_beam_search = use_beam_search
self.length_penalty = length_penalty
self.early_stopping = early_stopping
if stop is None:
if self.stop is None:
self.stop = []
elif isinstance(stop, str):
self.stop = [stop]
else:
self.stop = list(stop)
if stop_token_ids is None:
elif isinstance(self.stop, str):
self.stop = [self.stop]
if self.stop_token_ids is None:
self.stop_token_ids = []
else:
self.stop_token_ids = list(stop_token_ids)
self.ignore_eos = ignore_eos
self.max_tokens = max_tokens
self.min_tokens = min_tokens
self.logprobs = logprobs
self.prompt_logprobs = prompt_logprobs
# 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.
self.detokenize = detokenize
self.skip_special_tokens = skip_special_tokens
self.spaces_between_special_tokens = spaces_between_special_tokens
self.logits_processors = logits_processors
self.include_stop_str_in_output = include_stop_str_in_output
self.truncate_prompt_tokens = truncate_prompt_tokens
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 include_stop_str_in_output:
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
else:
self.output_text_buffer_length = 0
self._verify_args()
if self.use_beam_search:
self._verify_beam_search()
else:
self._verify_non_beam_search()
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()
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 = set(self.stop_token_ids)
self._all_stop_token_ids.update(self.stop_token_ids)
if self.skip_reading_prefix_cache is None:
# If prefix caching is enabled,
# the output of prompt logprobs may less than n_prompt_tokens,
# we need to skip reading cache at this request.
self.skip_reading_prefix_cache = self.prompt_logprobs is not None
def _verify_args(self) -> None:
if not isinstance(self.n, int):
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 < 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}.")
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}.")
if not 0.0 < self.repetition_penalty <= 2.0:
raise ValueError("repetition_penalty must be in (0, 2], got "
f"{self.repetition_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}."
)
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}.")
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 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:
# quietly accept -1 as disabled, but prefer 0
if self.top_k < -1:
raise ValueError(
f"max_tokens must be at least 1, got {self.max_tokens}.")
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__}"
)
if not 0.0 <= self.min_p <= 1.0:
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}.")
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 < 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, 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}")
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
):
raise ValueError(
f"truncate_prompt_tokens must be an integer >= 1 or -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, 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.")
def _verify_beam_search(self) -> None:
if self.best_of == 1:
raise ValueError("best_of must be greater than 1 when using beam "
f"search. Got {self.best_of}.")
if self.temperature > _SAMPLING_EPS:
raise ValueError("temperature must be 0 when using beam search.")
if self.top_p < 1.0 - _SAMPLING_EPS:
raise ValueError("top_p must be 1 when using beam search.")
if self.top_k != -1:
raise ValueError("top_k must be -1 when using beam search.")
if self.early_stopping not in [True, False, "never"]:
raise ValueError(
f"early_stopping must be True, False, or 'never', "
f"got {self.early_stopping}.")
def _verify_non_beam_search(self) -> None:
if self.early_stopping is not False:
raise ValueError("early_stopping is not effective and must be "
"False when not using beam search.")
if (self.length_penalty < 1.0 - _SAMPLING_EPS
or self.length_penalty > 1.0 + _SAMPLING_EPS):
raise ValueError(
"length_penalty is not effective and must be the "
"default value of 1.0 when not using beam search.")
"Set detokenize=True to use stop."
)
def _verify_greedy_sampling(self) -> None:
if self.best_of > 1:
raise ValueError("best_of must be 1 when using greedy sampling."
f"Got {self.best_of}.")
if self.n > 1:
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]) -> None:
self,
generation_config: dict[str, Any],
model_eos_token_id: int | None = 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 (not self.ignore_eos) and (eos_ids :=
generation_config.get("eos_token_id")):
if (eos_ids := generation_config.get("eos_token_id")) is not None:
# it can be either int or list of int
if isinstance(eos_ids, int):
eos_ids = [eos_ids]
original_stop_token_ids = set(self.stop_token_ids)
original_stop_token_ids.update(eos_ids)
self.stop_token_ids = list(original_stop_token_ids)
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: TokenizerLike) -> 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.use_beam_search:
return SamplingType.BEAM
if self.temperature < _SAMPLING_EPS:
return SamplingType.GREEDY
if self.seed is not None:
return SamplingType.RANDOM_SEED
return SamplingType.RANDOM
def clone(self) -> "SamplingParams":
"""Deep copy excluding LogitsProcessor objects.
@property
def all_stop_token_ids(self) -> set[int]:
return self._all_stop_token_ids
LogitsProcessor objects are excluded because they may contain an
arbitrary, nontrivial amount of data.
@property
def bad_words_token_ids(self) -> list[list[int]] | None:
# 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
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:
return (
f"SamplingParams(n={self.n}, "
f"best_of={self.best_of}, "
f"presence_penalty={self.presence_penalty}, "
f"frequency_penalty={self.frequency_penalty}, "
f"repetition_penalty={self.repetition_penalty}, "
@@ -323,11 +563,9 @@ class SamplingParams:
f"top_k={self.top_k}, "
f"min_p={self.min_p}, "
f"seed={self.seed}, "
f"use_beam_search={self.use_beam_search}, "
f"length_penalty={self.length_penalty}, "
f"early_stopping={self.early_stopping}, "
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}, "
@@ -337,4 +575,23 @@ class SamplingParams:
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"truncate_prompt_tokens={self.truncate_prompt_tokens}, "
f"structured_outputs={self.structured_outputs}, "
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]
"""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
include_stop_str_in_output: bool = False