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[V0 Deprecation] Remove code related to per-request logits processors (#34400)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2026-02-12 20:44:28 +08:00
committed by GitHub
parent f5897613fb
commit fb455ed547
12 changed files with 15 additions and 122 deletions
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1
tests/entrypoints/openai/test_chat_error.py
View File

@@ -45,7 +45,6 @@ class MockModelConfig:
multimodal_config = MultiModalConfig()
hf_config = MockHFConfig()
hf_text_config = MockHFConfig()
logits_processor_pattern = None
logits_processors: list[str] | None = None
diff_sampling_param: dict | None = None
allowed_local_media_path: str = ""

1
tests/entrypoints/openai/test_completion_error.py
View File

@@ -44,7 +44,6 @@ class MockModelConfig:
tokenizer_revision = None
multimodal_config = MultiModalConfig()
hf_config = MockHFConfig()
logits_processor_pattern = None
logits_processors: list[str] | None = None
diff_sampling_param: dict | None = None
allowed_local_media_path: str = ""

1
tests/entrypoints/openai/test_lora_resolvers.py
View File

@@ -45,7 +45,6 @@ class MockModelConfig:
multimodal_config: MultiModalConfig = field(default_factory=MultiModalConfig)
hf_config: MockHFConfig = field(default_factory=MockHFConfig)
logits_processors: list[str] | None = None
logits_processor_pattern: str | None = None
diff_sampling_param: dict | None = None
allowed_local_media_path: str = ""
allowed_media_domains: list[str] | None = None

1
tests/entrypoints/openai/test_serving_chat.py
View File

@@ -521,7 +521,6 @@ class MockModelConfig:
hf_config = MockHFConfig()
hf_text_config = MockHFConfig()
logits_processors: list[str] | None = None
logits_processor_pattern = None
diff_sampling_param: dict | None = None
allowed_local_media_path: str = ""
allowed_media_domains: list[str] | None = None

14
tests/v1/sample/test_sampling_params_e2e.py
View File

@@ -144,20 +144,6 @@ def test_bad_words(llm):
assert not contains_bad_word(new_text, new_tokens, bad_words_2)
def test_logits_processor(llm):
"""Check that we reject logits processor."""
# This sample logits processor gives infinite score to the i-th token,
# where i is the length of the input sequence.
# We therefore expect the output token sequence to be [0, 1, 2, ...]
def pick_ith(token_ids, logits):
logits[len(token_ids)] = float("inf")
return logits
with pytest.raises(ValueError):
_ = llm.generate(PROMPT, SamplingParams(logits_processors=[pick_ith]))
def test_allowed_token_ids(llm):
"""Check that we can use allowed_token_ids."""

5
vllm/config/model.py
View File

@@ -252,10 +252,6 @@ class ModelConfig:
hf_overrides: HfOverrides = field(default_factory=dict)
"""If a dictionary, contains arguments to be forwarded to the Hugging Face
config. If a callable, it is called to update the HuggingFace config."""
logits_processor_pattern: str | None = None
"""Optional regex pattern specifying valid logits processor qualified names
that can be passed with the `logits_processors` extra completion argument.
Defaults to `None`, which allows no processors."""
generation_config: str = "auto"
"""The folder path to the generation config. Defaults to `"auto"`, the
generation config will be loaded from model path. If set to `"vllm"`, no
@@ -342,7 +338,6 @@ class ModelConfig:
"config_format",
"hf_token",
"hf_overrides",
"logits_processor_pattern",
"override_attention_dtype",
"logits_processors",
"io_processor_plugin",

13
vllm/engine/arg_utils.py
View File

@@ -508,8 +508,6 @@ class EngineArgs:
reasoning_parser: str = StructuredOutputsConfig.reasoning_parser
reasoning_parser_plugin: str | None = None
logits_processor_pattern: str | None = ModelConfig.logits_processor_pattern
speculative_config: dict[str, Any] | None = None
show_hidden_metrics_for_version: str | None = (
@@ -710,9 +708,6 @@ class EngineArgs:
)
model_group.add_argument("--hf-overrides", **model_kwargs["hf_overrides"])
model_group.add_argument("--pooler-config", **model_kwargs["pooler_config"])
model_group.add_argument(
"--logits-processor-pattern", **model_kwargs["logits_processor_pattern"]
)
model_group.add_argument(
"--generation-config", **model_kwargs["generation_config"]
)
@@ -1320,7 +1315,6 @@ class EngineArgs:
mm_encoder_tp_mode=self.mm_encoder_tp_mode,
mm_encoder_attn_backend=self.mm_encoder_attn_backend,
pooler_config=self.pooler_config,
logits_processor_pattern=self.logits_processor_pattern,
generation_config=self.generation_config,
override_generation_config=self.override_generation_config,
enable_sleep_mode=self.enable_sleep_mode,
@@ -1429,7 +1423,7 @@ class EngineArgs:
self.model_weights = model_config.model_weights
self.tokenizer = model_config.tokenizer
self._check_feature_supported(model_config)
self._check_feature_supported()
self._set_default_chunked_prefill_and_prefix_caching_args(model_config)
self._set_default_max_num_seqs_and_batched_tokens_args(
usage_context, model_config
@@ -1831,11 +1825,8 @@ class EngineArgs:
return config
def _check_feature_supported(self, model_config: ModelConfig):
def _check_feature_supported(self):
"""Raise an error if the feature is not supported."""
if self.logits_processor_pattern != EngineArgs.logits_processor_pattern:
_raise_unsupported_error(feature_name="--logits-processor-pattern")
# No Concurrent Partial Prefills so far.
if (
self.max_num_partial_prefills != SchedulerConfig.max_num_partial_prefills

22
vllm/entrypoints/openai/chat_completion/protocol.py
View File

@@ -26,13 +26,11 @@ from vllm.entrypoints.openai.engine.protocol import (
FunctionCall,
FunctionDefinition,
LegacyStructuralTagResponseFormat,
LogitsProcessors,
OpenAIBaseModel,
StreamOptions,
StructuralTagResponseFormat,
ToolCall,
UsageInfo,
get_logits_processors,
)
from vllm.exceptions import VLLMValidationError
from vllm.logger import init_logger
@@ -293,19 +291,7 @@ class ChatCompletionRequest(OpenAIBaseModel):
"through out the inference process and return in response."
),
)
logits_processors: LogitsProcessors | None = Field(
default=None,
description=(
"A list of either qualified names of logits processors, or "
"constructor objects, to apply when sampling. A constructor is "
"a JSON object with a required 'qualname' field specifying the "
"qualified name of the processor class/factory, and optional "
"'args' and 'kwargs' fields containing positional and keyword "
"arguments. For example: {'qualname': "
"'my_module.MyLogitsProcessor', 'args': [1, 2], 'kwargs': "
"{'param': 'value'}}."
),
)
return_tokens_as_token_ids: bool | None = Field(
default=None,
description=(
@@ -324,6 +310,7 @@ class ChatCompletionRequest(OpenAIBaseModel):
"need to map generated text back to input tokens."
),
)
cache_salt: str | None = Field(
default=None,
description=(
@@ -335,6 +322,7 @@ class ChatCompletionRequest(OpenAIBaseModel):
"to 256 bit)."
),
)
kv_transfer_params: dict[str, Any] | None = Field(
default=None,
description="KVTransfer parameters used for disaggregated serving.",
@@ -417,7 +405,6 @@ class ChatCompletionRequest(OpenAIBaseModel):
def to_sampling_params(
self,
max_tokens: int,
logits_processor_pattern: str | None,
default_sampling_params: dict,
) -> SamplingParams:
# Default parameters
@@ -502,9 +489,6 @@ class ChatCompletionRequest(OpenAIBaseModel):
min_tokens=self.min_tokens,
skip_special_tokens=self.skip_special_tokens,
spaces_between_special_tokens=self.spaces_between_special_tokens,
logits_processors=get_logits_processors(
self.logits_processors, logits_processor_pattern
),
include_stop_str_in_output=self.include_stop_str_in_output,
truncate_prompt_tokens=self.truncate_prompt_tokens,
output_kind=RequestOutputKind.DELTA

9
vllm/entrypoints/openai/chat_completion/serving.py
View File

@@ -86,7 +86,6 @@ from vllm.tool_parsers import ToolParser
from vllm.tool_parsers.mistral_tool_parser import MistralToolCall
from vllm.tool_parsers.utils import partial_json_loads
from vllm.utils.collection_utils import as_list
from vllm.v1.sample.logits_processor import validate_logits_processors_parameters
logger = init_logger(__name__)
@@ -130,9 +129,6 @@ class OpenAIServingChat(OpenAIServing):
self.enable_log_outputs = enable_log_outputs
self.enable_log_deltas = enable_log_deltas
# set up logits processors
self.logits_processors = self.model_config.logits_processors
# set up reasoning parser
self.reasoning_parser_cls = ParserManager.get_reasoning_parser(
reasoning_parser_name=reasoning_parser
@@ -403,13 +399,8 @@ class OpenAIServingChat(OpenAIServing):
else:
sampling_params = request.to_sampling_params(
max_tokens,
self.model_config.logits_processor_pattern,
self.default_sampling_params,
)
validate_logits_processors_parameters(
self.logits_processors,
sampling_params,
)
self._log_inputs(
sub_request_id,

19
vllm/entrypoints/openai/completion/protocol.py
View File

@@ -15,12 +15,10 @@ from vllm.config import ModelConfig
from vllm.entrypoints.openai.engine.protocol import (
AnyResponseFormat,
LegacyStructuralTagResponseFormat,
LogitsProcessors,
OpenAIBaseModel,
StreamOptions,
StructuralTagResponseFormat,
UsageInfo,
get_logits_processors,
)
from vllm.exceptions import VLLMValidationError
from vllm.logger import init_logger
@@ -117,19 +115,6 @@ class CompletionRequest(OpenAIBaseModel):
"through out the inference process and return in response."
),
)
logits_processors: LogitsProcessors | None = Field(
default=None,
description=(
"A list of either qualified names of logits processors, or "
"constructor objects, to apply when sampling. A constructor is "
"a JSON object with a required 'qualname' field specifying the "
"qualified name of the processor class/factory, and optional "
"'args' and 'kwargs' fields containing positional and keyword "
"arguments. For example: {'qualname': "
"'my_module.MyLogitsProcessor', 'args': [1, 2], 'kwargs': "
"{'param': 'value'}}."
),
)
return_tokens_as_token_ids: bool | None = Field(
default=None,
@@ -221,7 +206,6 @@ class CompletionRequest(OpenAIBaseModel):
def to_sampling_params(
self,
max_tokens: int,
logits_processor_pattern: str | None,
default_sampling_params: dict | None = None,
) -> SamplingParams:
if default_sampling_params is None:
@@ -312,9 +296,6 @@ class CompletionRequest(OpenAIBaseModel):
skip_special_tokens=self.skip_special_tokens,
spaces_between_special_tokens=self.spaces_between_special_tokens,
include_stop_str_in_output=self.include_stop_str_in_output,
logits_processors=get_logits_processors(
self.logits_processors, logits_processor_pattern
),
truncate_prompt_tokens=self.truncate_prompt_tokens,
output_kind=RequestOutputKind.DELTA
if self.stream

9
vllm/entrypoints/openai/completion/serving.py
View File

@@ -42,7 +42,6 @@ from vllm.sampling_params import BeamSearchParams, SamplingParams
from vllm.tokenizers import TokenizerLike
from vllm.utils.async_utils import merge_async_iterators
from vllm.utils.collection_utils import as_list
from vllm.v1.sample.logits_processor import validate_logits_processors_parameters
logger = init_logger(__name__)
@@ -67,9 +66,6 @@ class OpenAIServingCompletion(OpenAIServing):
log_error_stack=log_error_stack,
)
# set up logits processors
self.logits_processors = self.model_config.logits_processors
self.enable_prompt_tokens_details = enable_prompt_tokens_details
self.enable_force_include_usage = enable_force_include_usage
@@ -178,13 +174,8 @@ class OpenAIServingCompletion(OpenAIServing):
else:
sampling_params = request.to_sampling_params(
max_tokens,
self.model_config.logits_processor_pattern,
self.default_sampling_params,
)
validate_logits_processors_parameters(
self.logits_processors,
sampling_params,
)
request_id_item = f"{request_id}-{i}"

42
vllm/sampling_params.py
View File

@@ -15,7 +15,6 @@ from pydantic.dataclasses import dataclass
from vllm.config import ModelConfig, SpeculativeConfig, StructuredOutputsConfig
from vllm.exceptions import VLLMValidationError
from vllm.logger import init_logger
from vllm.logits_process import LogitsProcessor
from vllm.tokenizers import TokenizerLike
from vllm.v1.serial_utils import PydanticMsgspecMixin
@@ -207,11 +206,6 @@ class SamplingParams(
"""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
@@ -277,7 +271,6 @@ class SamplingParams(
detokenize: bool = True,
skip_special_tokens: bool = True,
spaces_between_special_tokens: bool = True,
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,
@@ -318,7 +311,6 @@ class SamplingParams(
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,
@@ -455,11 +447,6 @@ class SamplingParams(
parameter="prompt_logprobs",
value=self.prompt_logprobs,
)
if self.logits_processors:
# TODO: Remove `logits_processors` attribute
raise ValueError(
"vLLM V1 does not support per request user-provided logits processors."
)
if self.truncate_prompt_tokens is not None and (
self.truncate_prompt_tokens == 0 or self.truncate_prompt_tokens < -1
):
@@ -573,28 +560,11 @@ class SamplingParams(
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
If skip_clone is True, uses shallow copy instead of deep copy.
"""
"""If skip_clone is True, uses shallow copy instead of deep copy."""
if self.skip_clone:
return copy.copy(self)
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)
return copy.deepcopy(self)
def verify(
self,
@@ -605,6 +575,7 @@ class SamplingParams(
) -> None:
self._validate_logprobs(model_config)
self._validate_logit_bias(model_config)
self._validate_logits_processors(model_config)
self._validate_allowed_token_ids(tokenizer)
self._validate_spec_decode(speculative_config)
self._validate_structured_outputs(structured_outputs_config, tokenizer)
@@ -658,6 +629,13 @@ class SamplingParams(
value=invalid_token_ids,
)
def _validate_logits_processors(self, model_config: ModelConfig) -> None:
from vllm.v1.sample.logits_processor import (
validate_logits_processors_parameters,
)
validate_logits_processors_parameters(model_config.logits_processors, self)
def _validate_allowed_token_ids(self, tokenizer: TokenizerLike | None) -> None:
allowed_token_ids = self.allowed_token_ids
if allowed_token_ids is None: